diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_refs/fft.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_refs/fft.py new file mode 100644 index 0000000000000000000000000000000000000000..e4e300bee62aa5c5eeb8130853882f2dc674d935 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_refs/fft.py @@ -0,0 +1,593 @@ +import math +from collections.abc import Iterable, Sequence +from typing import Literal, NamedTuple, Optional, Union + +import torch +import torch._prims as prims +import torch._prims_common as utils +from torch._decomp import register_decomposition +from torch._prims_common import DimsType, ShapeType, TensorLikeType +from torch._prims_common.wrappers import _maybe_convert_to_dtype, out_wrapper + + +__all__ = [ + # Transforms + "fft", + "fft2", + "fftn", + "hfft", + "hfft2", + "hfftn", + "rfft", + "rfft2", + "rfftn", + "ifft", + "ifft2", + "ifftn", + "ihfft", + "ihfft2", + "ihfftn", + "irfft", + "irfft2", + "irfftn", + # Helpers + "fftshift", + "ifftshift", +] + +NormType = Union[None, Literal["forward", "backward", "ortho"]] +_NORM_VALUES = {None, "forward", "backward", "ortho"} +aten = torch._ops.ops.aten + + +def _apply_norm( + x: TensorLikeType, norm: NormType, signal_numel: int, forward: bool +) -> TensorLikeType: + """Apply normalization to the un-normalized FFT result""" + torch._check(norm in _NORM_VALUES, lambda: f"Invalid normalization mode: {norm}") + + if norm == "ortho": + return x * (1 / math.sqrt(signal_numel)) + + normalize = (not forward and (norm is None or norm == "backward")) or ( + forward and norm == "forward" + ) + return x * (1 / signal_numel) if normalize else x + + +def _promote_type_fft( + dtype: torch.dtype, require_complex: bool, device: torch.device +) -> torch.dtype: + """Helper to promote a dtype to one supported by the FFT primitives""" + if dtype.is_complex: + return dtype + + # Promote integral to default float type + if not dtype.is_floating_point: + dtype = torch.get_default_dtype() + + allowed_types = [torch.float32, torch.float64] + maybe_support_half = device.type in ["cuda", "meta"] + + if maybe_support_half: + allowed_types.append(torch.float16) + torch._check(dtype in allowed_types, lambda: f"Unsupported dtype {dtype}") + + if require_complex: + dtype = utils.corresponding_complex_dtype(dtype) + + return dtype + + +def _maybe_promote_tensor_fft( + t: TensorLikeType, require_complex: bool = False +) -> TensorLikeType: + """Helper to promote a tensor to a dtype supported by the FFT primitives""" + cur_type = t.dtype + new_type = _promote_type_fft(cur_type, require_complex, t.device) + return _maybe_convert_to_dtype(t, new_type) # type: ignore[return-value] + + +def _resize_fft_input( + x: TensorLikeType, dims: tuple[int, ...], sizes: tuple[int, ...] +) -> TensorLikeType: + """ + Fixes the shape of x such that x.size(dims[i]) == sizes[i], + either by zero-padding, or by slicing x starting from 0. + """ + assert len(dims) == len(sizes) + must_copy = False + x_sizes = x.shape + pad_amount = [0] * len(x_sizes) * 2 + for i in range(len(dims)): + if sizes[i] == -1: + continue + + if x_sizes[dims[i]] < sizes[i]: + must_copy = True + pad_idx = len(pad_amount) - 2 * dims[i] - 1 + + pad_amount[pad_idx] = sizes[i] - x_sizes[dims[i]] + + if x_sizes[dims[i]] > sizes[i]: + x = x.narrow(dims[i], 0, sizes[i]) + + return torch.constant_pad_nd(x, pad_amount) if must_copy else x + + +def _fft_c2r( + func_name: str, + input: TensorLikeType, + n: Optional[int], + dim: int, + norm: NormType, + forward: bool, +) -> TensorLikeType: + """Common code for performing any complex to real FFT (irfft or hfft)""" + input = _maybe_promote_tensor_fft(input, require_complex=True) + dims = (utils.canonicalize_dim(input.ndim, dim, wrap_scalar=False),) + last_dim_size = n if n is not None else 2 * (input.shape[dim] - 1) + torch._check( + last_dim_size >= 1, + lambda: f"Invalid number of data points ({last_dim_size}) specified", + ) + + if n is not None: + input = _resize_fft_input(input, dims=dims, sizes=(last_dim_size // 2 + 1,)) + + if forward: + input = torch.conj(input) + + output = prims.fft_c2r(input, dim=dims, last_dim_size=last_dim_size) + return _apply_norm(output, norm=norm, signal_numel=last_dim_size, forward=forward) + + +def _fft_r2c( + func_name: str, + input: TensorLikeType, + n: Optional[int], + dim: int, + norm: NormType, + forward: bool, + onesided: bool, +) -> TensorLikeType: + """Common code for performing any real to complex FFT (rfft or ihfft)""" + torch._check( + not input.dtype.is_complex, + lambda: f"{func_name} expects a floating point input tensor, but got {input.dtype}", + ) + input = _maybe_promote_tensor_fft(input) + dims = (utils.canonicalize_dim(input.ndim, dim, wrap_scalar=False),) + dim_size = n if n is not None else input.shape[dim] + torch._check( + dim_size >= 1, lambda: f"Invalid number of data points ({dim_size}) specified" + ) + + if n is not None: + input = _resize_fft_input(input, dims, (n,)) + + ret = prims.fft_r2c(input, dim=dims, onesided=onesided) + ret = _apply_norm(ret, norm, dim_size, forward) + return ret if forward else torch.conj(ret) + + +def _fft_c2c( + func_name: str, + input: TensorLikeType, + n: Optional[int], + dim: int, + norm: NormType, + forward: bool, +) -> TensorLikeType: + """Common code for performing any complex to complex FFT (fft or ifft)""" + torch._check( + input.dtype.is_complex, + lambda: f"{func_name} expects a complex input tensor, but got {input.dtype}", + ) + dims = (utils.canonicalize_dim(input.ndim, dim, wrap_scalar=False),) + dim_size = n if n is not None else input.shape[dim] + torch._check( + dim_size >= 1, lambda: f"Invalid number of data points ({dim_size}) specified" + ) + + if n is not None: + input = _resize_fft_input(input, dims, (n,)) + + ret = prims.fft_c2c(input, dim=dims, forward=forward) + return _apply_norm(ret, norm, dim_size, forward) + + +@register_decomposition(aten.fft_fft) +@out_wrapper() +def fft( + input: TensorLikeType, + n: Optional[int] = None, + dim: int = -1, + norm: NormType = None, +) -> TensorLikeType: + if input.dtype.is_complex: + return _fft_c2c("fft", input, n, dim, norm, forward=True) + else: + return _fft_r2c("fft", input, n, dim, norm, forward=True, onesided=False) + + +@register_decomposition(aten.fft_ifft) +@out_wrapper() +def ifft( + input: TensorLikeType, + n: Optional[int] = None, + dim: int = -1, + norm: NormType = None, +) -> TensorLikeType: + if input.dtype.is_complex: + return _fft_c2c("ifft", input, n, dim, norm, forward=False) + else: + return _fft_r2c("ifft", input, n, dim, norm, forward=False, onesided=False) + + +@register_decomposition(aten.fft_rfft) +@out_wrapper() +def rfft( + input: TensorLikeType, + n: Optional[int] = None, + dim: int = -1, + norm: NormType = None, +) -> TensorLikeType: + return _fft_r2c("rfft", input, n, dim, norm, forward=True, onesided=True) + + +@register_decomposition(aten.fft_irfft) +@out_wrapper() +def irfft( + input: TensorLikeType, + n: Optional[int] = None, + dim: int = -1, + norm: NormType = None, +) -> TensorLikeType: + return _fft_c2r("irfft", input, n, dim, norm, forward=False) + + +@register_decomposition(aten.fft_hfft) +@out_wrapper() +def hfft( + input: TensorLikeType, + n: Optional[int] = None, + dim: int = -1, + norm: NormType = None, +) -> TensorLikeType: + return _fft_c2r("hfft", input, n, dim, norm, forward=True) + + +@register_decomposition(aten.fft_ihfft) +@out_wrapper() +def ihfft( + input: TensorLikeType, + n: Optional[int] = None, + dim: int = -1, + norm: NormType = None, +) -> TensorLikeType: + return _fft_r2c("ihfft", input, n, dim, norm, forward=False, onesided=True) + + +class _ShapeAndDims(NamedTuple): + shape: tuple[int, ...] + dims: tuple[int, ...] + + +def _canonicalize_fft_shape_and_dim_args( + input: TensorLikeType, shape: Optional[ShapeType], dim: Optional[DimsType] +) -> _ShapeAndDims: + """Convert the shape and dim arguments into a canonical form where neither are optional""" + input_dim = input.ndim + input_sizes = input.shape + + if dim is not None: + if not isinstance(dim, Sequence): + dim = (dim,) + ret_dims = utils.canonicalize_dims(input_dim, dim, wrap_scalar=False) + + # Check dims are unique + torch._check( + len(set(ret_dims)) == len(ret_dims), lambda: "FFT dims must be unique" + ) + + if shape is not None: + if not isinstance(shape, Sequence): + shape = (shape,) + + # Has shape, might have dim + torch._check( + dim is None or len(dim) == len(shape), + lambda: "When given, dim and shape arguments must have the same length", + ) + transform_ndim = len(shape) + + torch._check( + transform_ndim <= input_dim, + lambda: f"Got shape with {transform_ndim} values but input tensor " + f"only has {input_dim} dimensions.", + ) + + # If shape is given, dims defaults to the last len(shape) dimensions + if dim is None: + ret_dims = tuple(range(input_dim - transform_ndim, input_dim)) + + # Translate any -1 values in shape to the default length + ret_shape = tuple( + s if s != -1 else input_sizes[d] + for (s, d) in zip(shape, ret_dims) # type: ignore[possibly-undefined] + ) + elif dim is None: + # No shape, no dim + ret_dims = tuple(range(input_dim)) + ret_shape = tuple(input_sizes) + else: + # No shape, has dim + ret_shape = tuple(input_sizes[d] for d in ret_dims) # type: ignore[possibly-undefined] + + for n in ret_shape: + torch._check(n > 0, lambda: f"Invalid number of data points ({n}) specified") + + return _ShapeAndDims(shape=ret_shape, dims=ret_dims) # type: ignore[possibly-undefined] + + +def _prod(xs: Iterable[int]) -> int: + """Compute product of a list""" + prod = 1 + for x in xs: + prod *= x + return prod + + +def _fftn_c2c( + function_name: str, + input: TensorLikeType, + shape: tuple[int, ...], + dim: tuple[int, ...], + norm: NormType, + forward: bool, +) -> TensorLikeType: + """Common code for n-dimensional complex to complex FFTs (fftn or ifftn)""" + torch._check( + input.dtype.is_complex, + lambda: f"{function_name} expects a complex input tensor, " + f"but got {input.dtype}", + ) + x = _resize_fft_input(input, dim, shape) + output = prims.fft_c2c(x, dim=dim, forward=forward) + return _apply_norm(output, norm=norm, signal_numel=_prod(shape), forward=forward) + + +@register_decomposition(aten.fft_fftn) +@out_wrapper() +def fftn( + input: TensorLikeType, + s: Optional[ShapeType] = None, + dim: Optional[DimsType] = None, + norm: NormType = None, +) -> TensorLikeType: + (shape, dim) = _canonicalize_fft_shape_and_dim_args(input, s, dim) + x = _maybe_promote_tensor_fft(input, require_complex=True) + return _fftn_c2c("fftn", x, shape, dim, norm, forward=True) + + +@register_decomposition(aten.fft_ifftn) +@out_wrapper() +def ifftn( + input: TensorLikeType, + s: Optional[ShapeType] = None, + dim: Optional[DimsType] = None, + norm: NormType = None, +) -> TensorLikeType: + (shape, dim) = _canonicalize_fft_shape_and_dim_args(input, s, dim) + x = _maybe_promote_tensor_fft(input, require_complex=True) + return _fftn_c2c("ifftn", x, shape, dim, norm, forward=False) + + +@register_decomposition(aten.fft_rfftn) +@out_wrapper() +def rfftn( + input: TensorLikeType, + s: Optional[ShapeType] = None, + dim: Optional[DimsType] = None, + norm: NormType = None, +) -> TensorLikeType: + torch._check( + not input.dtype.is_complex, + lambda: f"rfftn expects a real-valued input tensor, but got {input.dtype}", + ) + shape, dim = _canonicalize_fft_shape_and_dim_args(input, s, dim) + input = _maybe_promote_tensor_fft(input, require_complex=False) + input = _resize_fft_input(input, dim, shape) + out = prims.fft_r2c(input, dim=dim, onesided=True) + return _apply_norm(out, norm=norm, signal_numel=_prod(shape), forward=True) + + +@register_decomposition(aten.fft_ihfftn) +@out_wrapper() +def ihfftn( + input: TensorLikeType, + s: Optional[ShapeType] = None, + dim: Optional[DimsType] = None, + norm: NormType = None, +) -> TensorLikeType: + torch._check( + not input.dtype.is_complex, + lambda: f"ihfftn expects a real-valued input tensor, but got {input.dtype}", + ) + shape, dim = _canonicalize_fft_shape_and_dim_args(input, s, dim) + torch._check(len(shape) > 0, lambda: "ihfftn must transform at least one axis") + input = _maybe_promote_tensor_fft(input, require_complex=False) + input = _resize_fft_input(input, dim, shape) + + tmp = prims.fft_r2c(input, dim=dim[-1:], onesided=True) + + if len(dim) == 1: + tmp = _apply_norm(tmp, norm=norm, signal_numel=shape[0], forward=False) + return prims.conj(tmp) + + tmp = prims.conj_physical(tmp) + tmp = prims.fft_c2c(tmp, dim=dim[:-1], forward=False) + return _apply_norm(tmp, norm=norm, signal_numel=_prod(shape), forward=False) + + +class _CanonicalizeC2rReturn(NamedTuple): + shape: tuple[int, ...] + dim: tuple[int, ...] + last_dim_size: int + + +def _canonicalize_fft_c2r_shape_and_dim_args( + fname: str, + input: TensorLikeType, + s: Optional[ShapeType], + dim: Optional[DimsType], +) -> _CanonicalizeC2rReturn: + """Canonicalize shape and dim arguments for n-dimensional c2r transforms, + as well as calculating the last_dim_size which is shape[dim[-1]] for the output""" + (shape, dim) = _canonicalize_fft_shape_and_dim_args(input, s, dim) + torch._check(len(shape) > 0, lambda: f"{fname} must transform at least one axis") + + if s is None or s[-1] == -1: + last_dim_size = 2 * (input.shape[dim[-1]] - 1) + else: + last_dim_size = shape[-1] + + torch._check( + last_dim_size >= 1, + lambda: f"Invalid number of data points ({last_dim_size}) specified", + ) + + shape_list = list(shape) + shape_list[-1] = last_dim_size // 2 + 1 + return _CanonicalizeC2rReturn( + shape=tuple(shape_list), dim=dim, last_dim_size=last_dim_size + ) + + +@register_decomposition(aten.fft_irfftn) +@out_wrapper() +def irfftn( + input: TensorLikeType, + s: Optional[ShapeType] = None, + dim: Optional[DimsType] = None, + norm: NormType = None, +) -> TensorLikeType: + shape, dim, last_dim_size = _canonicalize_fft_c2r_shape_and_dim_args( + "irfftn", input, s, dim + ) + input = _maybe_promote_tensor_fft(input, require_complex=True) + input = _resize_fft_input(input, dim, shape) + out = prims.fft_c2r(input, dim=dim, last_dim_size=last_dim_size) + return _apply_norm(out, norm, _prod(out.shape[d] for d in dim), forward=False) + + +@register_decomposition(aten.fft_hfftn) +@out_wrapper() +def hfftn( + input: TensorLikeType, + s: Optional[ShapeType] = None, + dim: Optional[DimsType] = None, + norm: NormType = None, +) -> TensorLikeType: + shape, dim, last_dim_size = _canonicalize_fft_c2r_shape_and_dim_args( + "hfftn", input, s, dim + ) + input = _maybe_promote_tensor_fft(input, require_complex=True) + input = _resize_fft_input(input, dim, shape) + + tmp = prims.fft_c2c(input, dim=dim[:-1], forward=True) if len(dim) > 1 else input + tmp = _apply_norm(tmp, norm, _prod(shape[:-1]), forward=True) + tmp = prims.conj_physical(tmp) + out = prims.fft_c2r(tmp, dim=dim[-1:], last_dim_size=last_dim_size) + return _apply_norm(out, norm, last_dim_size, forward=True) + + +@register_decomposition(aten.fft_fft2) +@out_wrapper() +def fft2( + input: TensorLikeType, + s: Optional[ShapeType] = None, + dim: Optional[DimsType] = (-2, -1), + norm: NormType = None, +) -> TensorLikeType: + return torch.fft.fftn(input, s=s, dim=dim, norm=norm) + + +@register_decomposition(aten.fft_ifft2) +@out_wrapper() +def ifft2( + input: TensorLikeType, + s: Optional[ShapeType] = None, + dim: Optional[DimsType] = (-2, -1), + norm: NormType = None, +) -> TensorLikeType: + return torch.fft.ifftn(input, s=s, dim=dim, norm=norm) + + +@register_decomposition(aten.fft_rfft2) +@out_wrapper() +def rfft2( + input: TensorLikeType, + s: Optional[ShapeType] = None, + dim: Optional[DimsType] = (-2, -1), + norm: NormType = None, +) -> TensorLikeType: + return torch.fft.rfftn(input, s=s, dim=dim, norm=norm) + + +@register_decomposition(aten.fft_irfft2) +@out_wrapper() +def irfft2( + input: TensorLikeType, + s: Optional[ShapeType] = None, + dim: Optional[DimsType] = (-2, -1), + norm: NormType = None, +) -> TensorLikeType: + return torch.fft.irfftn(input, s=s, dim=dim, norm=norm) + + +@register_decomposition(aten.fft_hfft2) +@out_wrapper() +def hfft2( + input: TensorLikeType, + s: Optional[ShapeType] = None, + dim: Optional[DimsType] = (-2, -1), + norm: NormType = None, +) -> TensorLikeType: + return torch.fft.hfftn(input, s=s, dim=dim, norm=norm) + + +@register_decomposition(aten.fft_ihfft2) +@out_wrapper() +def ihfft2( + input: TensorLikeType, + s: Optional[ShapeType] = None, + dim: Optional[DimsType] = (-2, -1), + norm: NormType = None, +) -> TensorLikeType: + return torch.fft.ihfftn(input, s=s, dim=dim, norm=norm) + + +def _default_alldims(dim: Optional[DimsType], x: TensorLikeType) -> list[int]: + """Convert Optional[DimsType] to a simple list, defaulting to all dimensions""" + if dim is None: + return list(range(x.ndim)) + elif not isinstance(dim, Sequence): + return [dim] + else: + return list(dim) + + +@register_decomposition(aten.fft_fftshift) +def fftshift(input: TensorLikeType, dim: Optional[DimsType] = None) -> TensorLikeType: + dims = _default_alldims(dim, input) + shift = [input.shape[d] // 2 for d in dims] + return torch.roll(input, shift, dims) + + +@register_decomposition(aten.fft_ifftshift) +def ifftshift(input: TensorLikeType, dim: Optional[DimsType] = None) -> TensorLikeType: + dims = _default_alldims(dim, input) + shift = [(input.shape[d] + 1) // 2 for d in dims] + return torch.roll(input, shift, dims) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_refs/linalg/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_refs/linalg/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..393e42b06d15cf4736c23a03e87d05468ee0ab35 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_refs/linalg/__init__.py @@ -0,0 +1,435 @@ +# mypy: allow-untyped-defs +import math +from functools import partial +from typing import Optional, Union + +import torch +import torch._prims as prims +import torch._prims_common as utils +import torch._refs as refs +import torch._refs.linalg as linalg +from torch import Tensor +from torch._prims_common import ( + check_fp_or_complex, + check_is_matrix, + Dim, + DimsType, + ELEMENTWISE_TYPE_PROMOTION_KIND, + IntLike, + TensorLikeType, +) +from torch._prims_common.wrappers import ( + _maybe_convert_to_dtype, + elementwise_type_promotion_wrapper, + out_wrapper, +) + + +__all__ = [ + "diagonal", + "matrix_norm", + "norm", + "svd", + "svdvals", + "vector_norm", + "vecdot", + "cross", +] + + +def _check_norm_dtype(dtype: Optional[torch.dtype], x_dtype: torch.dtype, fn_name: str): + """ + Checks related to the dtype kwarg in `linalg.*norm` functions + """ + if dtype is not None: + torch._check( + utils.is_float_dtype(dtype) or utils.is_complex_dtype(dtype), + lambda: f"{fn_name}: dtype should be floating point or complex. Got {dtype}", + ) + torch._check( + utils.is_complex_dtype(dtype) == utils.is_complex_dtype(x_dtype), + lambda: "{fn_name}: dtype should be {d} for {d} inputs. Got {dtype}".format( + fn_name=fn_name, + d="complex" if utils.is_complex_dtype(x_dtype) else "real", + dtype=dtype, + ), + ) + torch._check( + utils.get_higher_dtype(dtype, x_dtype) == dtype, + lambda: f"{fn_name}: the dtype of the input ({x_dtype}) should be convertible " + f"without narrowing to the specified dtype ({dtype})", + ) + + +import operator + +# Utilities should come BEFORE this import +from torch._decomp import register_decomposition +from torch._decomp.decompositions import pw_cast_for_opmath + + +@register_decomposition(torch._ops.ops.aten.linalg_cross) +@out_wrapper() +@pw_cast_for_opmath +def cross(a: Tensor, b: Tensor, dim: int = -1): + torch._check( + a.ndim == b.ndim, + lambda: "linalg.cross: inputs must have the same number of dimensions.", + ) + torch._check( + a.size(dim) == 3 and b.size(dim) == 3, + lambda: f"linalg.cross: inputs dim {dim} must have length 3, got {a.size(dim)} and {b.size(dim)}", + ) + a, b = torch.broadcast_tensors(a, b) + dim = utils.canonicalize_dim(a.ndim, dim) + idx = torch.arange(3, device=a.device) + return a.index_select(dim, (idx + 1) % 3) * b.index_select( + dim, (idx + 2) % 3 + ) - a.index_select(dim, (idx + 2) % 3) * b.index_select(dim, (idx + 1) % 3) + + +def diagonal( + input: TensorLikeType, + *, + offset: int = 0, + dim1: int = -2, + dim2: int = -1, +) -> TensorLikeType: + return torch.diagonal(input, offset=offset, dim1=dim1, dim2=dim2) + + +def _check_vector_norm_args( + x: TensorLikeType, ord: Union[float, int] = 2, dim: Optional[DimsType] = None +): + from torch.fx.experimental.symbolic_shapes import sym_or + + if not (ord < 0.0 or ord == float("inf")): + return + + torch._check( + sym_or( + x.numel() != 0, + not isinstance(dim, IntLike) and dim is not None and len(dim) != 0, + ), + lambda: f"linalg.vector_norm cannot compute the {ord} norm on an empty tensor " + "because the operation does not have an identity", + ) + + shape = x.shape + if dim is not None and not isinstance(dim, IntLike): + for d in dim: + torch._check( + sym_or(x.numel() != 0, d < len(shape) and d >= 0 and shape[d] != 0), + lambda: f"linalg.vector_norm cannot compute the {ord} norm on the " + f"dimension {d} because this dimension is empty and the " + "operation does not have an identity", + ) + + +@register_decomposition(torch._ops.ops.aten.linalg_vector_norm) +@out_wrapper(exact_dtype=True) +def vector_norm( + x: TensorLikeType, + ord: Union[float, int] = 2, + dim: Optional[DimsType] = None, + keepdim: bool = False, + *, + dtype: Optional[torch.dtype] = None, +) -> Tensor: + from torch.fx.experimental.symbolic_shapes import guard_or_false + + check_fp_or_complex(x.dtype, "linalg.vector_norm") + + if isinstance(dim, Dim): + dim = [dim] # type: ignore[assignment] + + _check_vector_norm_args(x, ord, dim) + + _check_norm_dtype(dtype, x.dtype, "linalg.vector_norm") + + computation_dtype, result_dtype = utils.reduction_dtypes( + x, utils.REDUCTION_OUTPUT_TYPE_KIND.COMPLEX_TO_FLOAT, dtype + ) + + to_result_dtype = partial(_maybe_convert_to_dtype, dtype=result_dtype) + + # Implementation + if ord == 0.0: + return torch.sum(torch.ne(x, 0.0), dim=dim, keepdim=keepdim, dtype=result_dtype) + elif ord == float("inf"): + return to_result_dtype(torch.amax(torch.abs(x), dim=dim, keepdim=keepdim)) # type: ignore[return-value,arg-type] + elif ord == float("-inf"): + return to_result_dtype(torch.amin(torch.abs(x), dim=dim, keepdim=keepdim)) # type: ignore[return-value,arg-type] + else: + # From here on the computation dtype is important as the reduction is non-trivial + x = _maybe_convert_to_dtype(x, computation_dtype) # type: ignore[assignment] + reduce_sum = partial(torch.sum, dim=dim, keepdim=keepdim) + + is_ord_even = ord % 2 == 0 if isinstance(ord, IntLike) else ord % 2.0 == 0.0 + if dim == []: + dim = None + + if (dim is None and x.numel() == 1) or ( + dim is not None + and (x.ndim > 0 and all(guard_or_false(x.shape[d] == 1) for d in dim)) + ): + if x.ndim > 64: + raise RuntimeError( + f"Received a tensor with {x.ndim} dimensions, but only tensors with up to 64 dims are supported!" + ) + x = torch.abs(x) + if keepdim or x.ndim == 0: + return to_result_dtype(x).contiguous() + elif dim is None: + return to_result_dtype(x).flatten()[0] + else: + new_shape = [s for d, s in enumerate(x.shape) if d not in dim] + return to_result_dtype(x.view(new_shape)).contiguous() + + if not (is_ord_even and utils.is_float_dtype(x.dtype)): + x = torch.abs(x) + return to_result_dtype(torch.pow(reduce_sum(torch.pow(x, ord)), 1.0 / ord)) # type: ignore[return-value] + + +def _backshift_permutation(dim0, dim1, ndim): + # Auxiliary function for matrix_norm + # Computes the permutation that moves the two given dimensions to the back + ret = [i for i in range(ndim) if i != dim0 and i != dim1] + ret.extend((dim0, dim1)) + return ret + + +def _inverse_permutation(perm): + # Given a permutation, returns its inverse. It's equivalent to argsort on an array + return [i for i, j in sorted(enumerate(perm), key=operator.itemgetter(1))] + + +# CompositeImplicitAutograd +@out_wrapper(exact_dtype=True) +def matrix_norm( + A: TensorLikeType, + ord: Union[float, str] = "fro", + dim: DimsType = (-2, -1), + keepdim: bool = False, + *, + dtype: Optional[torch.dtype] = None, +) -> TensorLikeType: + # shape + check_is_matrix(A, "linalg.matrix_norm") + # dim + + dim = utils.canonicalize_dims(A.ndim, dim) + if isinstance(dim, Dim): + dim = (dim,) # type: ignore[assignment] + torch._check( + len(dim) == 2, lambda: f"linalg.matrix_norm: dim must be a 2-tuple. Got {dim}" + ) + torch._check( + # pyrefly: ignore [index-error] + dim[0] != dim[1], + # pyrefly: ignore [index-error] + lambda: f"linalg.matrix_norm: dims must be different. Got ({dim[0]}, {dim[1]})", + ) + # dtype arg + _check_norm_dtype(dtype, A.dtype, "linalg.matrix_norm") + + if isinstance(ord, str): + # ord + torch._check( + ord in ("fro", "nuc"), + lambda: f"linalg.matrix_norm: Order {ord} not supported.", + ) + # dtype + check_fp_or_complex( + A.dtype, "linalg.matrix_norm", allow_low_precision_dtypes=ord != "nuc" + ) + + if ord == "fro": + return vector_norm(A, 2, dim, keepdim, dtype=dtype) + else: # ord == "nuc" + if dtype is not None: + A = _maybe_convert_to_dtype(A, dtype) # type: ignore[assignment] + # pyrefly: ignore [index-error] + perm = _backshift_permutation(dim[0], dim[1], A.ndim) + result = torch.sum(svdvals(prims.transpose(A, perm)), -1, keepdim) + if keepdim: + inv_perm = _inverse_permutation(perm) + result = prims.transpose(torch.unsqueeze(result, -1), inv_perm) + return result + else: + # ord + abs_ord = abs(ord) + torch._check( + abs_ord in (2, 1, float("inf")), + lambda: f"linalg.matrix_norm: Order {ord} not supported.", + ) + # dtype + check_fp_or_complex( + A.dtype, "linalg.matrix_norm", allow_low_precision_dtypes=ord != 2 + ) + + max_min = partial(torch.amax if ord > 0.0 else torch.amin, keepdim=keepdim) + + def _max_min_wrapper(A, dim): + # pyrefly: ignore [unsupported-operation] + if A.size(dim) == 0 and ord > 0.0: + new_size = list(A.size()) + if keepdim: + new_size[dim] = 1 + else: + del new_size[dim] + return torch.zeros(new_size, dtype=A.dtype, device=A.device) + else: + return max_min(A, dim) + + if abs_ord == 2.0: + if dtype is not None: + A = _maybe_convert_to_dtype(A, dtype) # type: ignore[assignment] + # pyrefly: ignore [index-error] + perm = _backshift_permutation(dim[0], dim[1], A.ndim) + result = _max_min_wrapper(svdvals(prims.transpose(A, perm)), dim=-1) + if keepdim: + inv_perm = _inverse_permutation(perm) + result = prims.transpose(torch.unsqueeze(result, -1), inv_perm) + return result + else: # 1, -1, inf, -inf + # pyrefly: ignore [bad-unpacking] + dim0, dim1 = dim + if abs_ord == float("inf"): + dim0, dim1 = dim1, dim0 + if not keepdim and (dim0 < dim1): + dim1 -= 1 + return _max_min_wrapper( + vector_norm(A, 1.0, dim=dim0, keepdim=keepdim, dtype=dtype), dim1 + ) + + +# CompositeImplicitAutograd +@out_wrapper(exact_dtype=True) +def norm( + A: TensorLikeType, + ord: Optional[Union[float, str]] = None, + dim: Optional[DimsType] = None, + keepdim: bool = False, + *, + dtype: Optional[torch.dtype] = None, +) -> TensorLikeType: + if dim is not None: + if isinstance(dim, Dim): + dim = (dim,) # type: ignore[assignment] + torch._check( + len(dim) in (1, 2), + lambda: f"linalg.norm: If dim is specified, it must be of length 1 or 2. Got {dim}", + ) + elif ord is not None: + torch._check( + A.ndim in (1, 2), + lambda: f"linalg.norm: If dim is not specified but ord is, the input must be 1D or 2D. Got {A.ndim}D", + ) + + if ord is not None and ( + (dim is not None and len(dim) == 2) or (dim is None and A.ndim == 2) + ): + if dim is None: + dim = (0, 1) + return matrix_norm(A, ord, dim, keepdim, dtype=dtype) + else: + if ord is None: + ord = 2.0 + return vector_norm(A, ord, dim, keepdim, dtype=dtype) # type: ignore[arg-type] + + +# CompositeImplicitAutograd +@out_wrapper("U", "S", "Vh", exact_dtype=True) +def svd(A: TensorLikeType, full_matrices: bool = True) -> tuple[Tensor, Tensor, Tensor]: + return prims.svd(A, full_matrices=full_matrices) + + +# CompositeImplicitAutograd +@out_wrapper(exact_dtype=True) +def svdvals(A: TensorLikeType) -> Tensor: + return svd(A, full_matrices=False)[1] + + +# CompositeImplicitAutograd +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("x", "y"), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def vecdot(x: Tensor, y: Tensor, dim: int = -1) -> Tensor: + check_fp_or_complex(x.dtype, "linalg.vecdot") + return (x.conj() * y).sum(dim=dim) + + +def _pivots_to_permutation(pivots, shape, *, inverse=False): + perm = torch.empty(shape, dtype=torch.int32, device=pivots.device) + perm[..., :] = torch.arange(shape[-1], dtype=torch.int32, device=pivots.device) + indices = range(shape[-1]) + if inverse: + indices = reversed(indices) + + if len(shape) > 1: + for i in indices: + j_s = pivots[..., i] + perm_i = perm[..., i].clone() + j_idx = torch.meshgrid( + *[torch.arange(s, device=perm.device) for s in j_s.shape], indexing="ij" + ) + (j_s,) + perm_j = perm[j_idx] + perm.index_put_(j_idx, perm_i) + perm[..., i].copy_(perm_j) + + else: + for i in indices: + j = pivots[i] + perm_i = perm[i].clone() + perm_j = perm[j].clone() + perm[i].copy_(perm_j) + perm[j].copy_(perm_i) + + return perm + + +def _apply_pivots(a, pivots, shape, *, inverse=False): + perm = _pivots_to_permutation(pivots - 1, shape, inverse=inverse) + + if len(shape) == 1: + return a[perm, :] + else: + idx = torch.meshgrid( + *[torch.arange(s, device=a.device) for s in perm.shape], indexing="ij" + )[:-1] + (perm, slice(None)) + return a[idx] + + +def linalg_lu_solve_out_mps(LU, pivots, B, *, left=True, adjoint=False, out): + if out.numel() == 0: + return + + if not left: + adjoint = not adjoint + B = B.mH + + if adjoint: + lu_ = LU.mH + x = torch.linalg.solve_triangular(lu_, B, left=True, upper=False) + x = torch.linalg.solve_triangular( + lu_, x, left=True, upper=True, unitriangular=True + ) + x = _apply_pivots(x, pivots, LU.shape[:-1], inverse=True) + else: + x = _apply_pivots(B, pivots, LU.shape[:-1]) + x = torch.linalg.solve_triangular( + LU, x, left=True, upper=False, unitriangular=True + ) + x = torch.linalg.solve_triangular(LU, x, left=True, upper=True) + + if not left: + x = x.mH + + out.copy_(x) + + +mps_lib = torch.library.Library("aten", "IMPL", "MPS") # noqa: TOR901 +mps_lib.impl("aten::linalg_lu_solve.out", linalg_lu_solve_out_mps) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_refs/nn/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_refs/nn/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c9c2ef67bd9d44a21f9d3673ba631c0840740ced --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_refs/nn/__init__.py @@ -0,0 +1 @@ +__all__: list[str] = [] diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_refs/nn/functional/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_refs/nn/functional/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..135788a439de5cf7882f659133ce63649d8308e2 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_refs/nn/functional/__init__.py @@ -0,0 +1,1293 @@ +# mypy: allow-untyped-decorators +# mypy: allow-untyped-defs +import math +from collections.abc import Callable +from functools import wraps +from typing import Concatenate, Optional, TypeVar, Union +from typing_extensions import ParamSpec + +import torch +import torch._prims as prims +import torch._prims_common as utils +import torch._refs as refs +from torch._decomp import register_decomposition +from torch._prims_common import ( + ELEMENTWISE_TYPE_PROMOTION_KIND, + NumberType, + ShapeType, + TensorLike, + TensorLikeType, +) +from torch._prims_common.wrappers import ( + elementwise_type_promotion_wrapper, + elementwise_unary_scalar_wrapper, + out_wrapper, +) +from torch._refs import _make_inplace + + +__all__ = [ + "alpha_dropout", + "celu", + "celu_", + "channel_shuffle", + "dropout", + "elu", + "elu_", + "gelu", + "glu", + "group_norm", + "hardshrink", + "hardtanh", + "hinge_embedding_loss", + "huber_loss", + "l1_loss", + "layer_norm", + "leaky_relu", + "log_softmax", + "margin_ranking_loss", + "mish", + "mish_", + "mse_loss", + "nll_loss", + "pairwise_distance", + "pdist", + "poisson_nll_loss", + "prelu", + "relu", + "relu6", + "selu", + "selu_", + "smooth_l1_loss", + "softmax", + "softmin", + "softplus", + "softshrink", + "tanhshrink", + "threshold", + "threshold_", + "triplet_margin_loss", +] + +_T = TypeVar("_T") +_P = ParamSpec("_P") + +Tensor = torch.Tensor +aten = torch._ops.ops.aten +DispatchKey = torch._C.DispatchKey # type: ignore[attr-defined] + + +def _dropout_helper( + self: TensorLikeType, + val: float, +) -> TensorLikeType: + """ + Helper function for all dropout-type operators. During training, + some of the elements of the input tensor are randomly masked. + + Returns the masked tensor of the boolean values. + + """ + + return ( + refs._uniform_helper( + self.shape, low=0.0, high=1.0, dtype=torch.float32, device=self.device + ) + < val + ) + + +@register_decomposition(aten.alpha_dropout) +def alpha_dropout( + self: TensorLikeType, p: float = 0.5, training: bool = False, inplace: bool = False +) -> TensorLikeType: + if inplace: + raise NotImplementedError + + if not training: + return self + + torch._check( + p <= 1 and p >= 0, + lambda: f"dropout probability has to be between 0 and 1, but got, {p}", + ) + + if p == 1: + return torch.zeros_like(self) + + if p == 0: + return self + + dropout_mask = _dropout_helper(self, 1 - p) + + # From paper: Self-Normalizing Neural Networks (https://arxiv.org/pdf/1706.02515.pdf) + # alpha = - SELU.alpha * SELU.scale, here + # SELU.alpha = 1.6732632423543772848170429916717 and + # SELU.scale = 1.0507009873554804934193349852946 + alpha = -1.7580993408473766 + + a = 1.0 / math.sqrt((alpha * alpha * p + 1) * (1 - p)) + b = torch.logical_not(dropout_mask) + b = b * (alpha * a) + alpha * a * p + dropout_mask = a * dropout_mask + + return self * dropout_mask + b + + +def _inplace_wrapper(fn: Callable[_P, _T]) -> Callable[_P, _T]: + """ + Given a nn.functional non-linearity, implements its `inplace: bool` argument + """ + + # nb. We use the name of the first argument used in the unary references + @wraps(fn) + def _fn(*args: _P.args, **kwargs: _P.kwargs) -> _T: + # pyrefly: ignore [unsupported-operation] + a = args[0] + if "inplace" not in kwargs: + kwargs["inplace"] = False + # pyrefly: ignore [unsupported-operation] + if kwargs["inplace"]: + torch._check( + "out" not in kwargs, + lambda: "Cannot set inplace=True and pass out= at the same time", + ) + kwargs["inplace"] = False + kwargs["out"] = a + return fn(*args, **kwargs) + else: + return fn(*args, **kwargs) + + return _fn + + +# celu is implemented specially because it has an alpha argument +# celu is very similar to elu +@register_decomposition(aten.celu) +@_inplace_wrapper +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def celu( + a: TensorLikeType, alpha: Optional[NumberType] = None, inplace: bool = False +) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.celu + """ + + if inplace: + raise NotImplementedError + + rhs: TensorLikeType + if alpha is not None: + python_type = utils.dtype_to_type(a.dtype) + if not utils.is_weakly_lesser_type(type(alpha), python_type): + msg = f"alpha argument of type {type(alpha)} cannot be safely cast to type {python_type}!" + raise ValueError(msg) + rhs = alpha * torch.expm1(torch.true_divide(a, alpha)) # type: ignore[arg-type] + else: + rhs = torch.expm1(a) + + return torch.where(a > 0, a, rhs) + + +@_inplace_wrapper +@out_wrapper() +def dropout( + a: TensorLikeType, p: float = 0.5, training: bool = True, inplace: bool = False +) -> TensorLikeType: + if inplace: + raise NotImplementedError + + if not training: + return a + + torch._check( + p <= 1 and p >= 0, + lambda: f"dropout probability has to be between 0 and 1, but got, {p}", + ) + + if p == 1: + return torch.zeros_like(a) + + if p == 0: + return a + + scale = 1 / (1 - p) + dropout_mask = _dropout_helper(a, 1 - p) + + return a * dropout_mask * scale + + +@register_decomposition(aten.elu) +@_inplace_wrapper +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def elu( + a: TensorLikeType, + alpha: NumberType = 1.0, + scale: NumberType = 1.0, + input_scale: NumberType = 1.0, + inplace: bool = False, +) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.elu + """ + if inplace: + raise NotImplementedError + + # nb. This should be factored out into a can_cast aux function + python_type = utils.dtype_to_type(a.dtype) + torch._check( + utils.is_weakly_lesser_type(type(input_scale), python_type), + lambda: f"input_scale argument of type {type(input_scale)} cannot be safely cast to type {python_type}!", + ) + torch._check( + utils.is_weakly_lesser_type(type(scale), python_type), + lambda: f"scale argument of type {type(scale)} cannot be safely cast to type {python_type}!", + ) + torch._check( + utils.is_weakly_lesser_type(type(alpha), python_type), + lambda: f"alpha argument of type {type(alpha)} cannot be safely cast to type {python_type}!", + ) + + return torch.where(a > 0, scale * a, (alpha * scale) * torch.expm1(a * input_scale)) + + +@register_decomposition(aten.relu) +@_inplace_wrapper +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def relu(a: TensorLikeType, inplace: bool = False) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.relu + """ + + if inplace: + raise NotImplementedError + + return torch.where(torch.le(a, 0), 0, a) + + +@register_decomposition(aten.channel_shuffle) +@out_wrapper() +def channel_shuffle(input: TensorLikeType, groups: int) -> TensorLikeType: + """ + Reference implementation of :func:`torch.nn.functional.channel_shuffle`. + """ + from torch._meta_registrations import device_hint + + torch._check( + input.dim() > 2, + lambda: f"channel_shuffle expects input with > 2 dims, but got input with sizes {list(input.size())}", + ) + c = input.shape[1] + torch._check( + groups > 0, + lambda: f"Number of groups to divide channels in must be positive. Value of groups:{groups}", + ) + torch._check( + (c % groups) == 0, + lambda: f"Number of channels must be divisible by groups. Got {c} channels and {groups} groups.", + ) + n = input.shape[0] + cg = c // groups + dhw = input.shape[2:] + + if input.numel() == 0 or ( + device_hint(input) == "cuda" and (groups == 1 or groups == c) + ): + return input.view(input.shape) + + return ( + input.reshape(n, groups, cg, *dhw) + .transpose(1, 2) + .reshape(input.shape) + .contiguous() + ) + + +def group_norm( + input: Tensor, + num_groups: int, + weight: Optional[Tensor] = None, + bias: Optional[Tensor] = None, + eps: float = 1e-5, +) -> Tensor: + """ + Reference implementation of :func:`torch.nn.functional.group_norm`. + """ + torch._check( + input.ndim >= 2, + lambda: f"Expected at least 2 dimensions for input tensor but received {input.ndim}", + ) + + batch_size = input.shape[0] + num_channels = input.shape[1] + torch._check( + num_channels % num_groups == 0, + lambda: "Expected number of channels in input to be divisible by num_groups, " + + f"but got input of shape {input.shape} and num_groups = {num_groups}", + ) + + # input shape is (N, C, *), so we flatten all inner dimensions except (N, C) + flattened_inner_size = 1 + for dim_length in input.shape[2:]: + flattened_inner_size *= dim_length + + return torch.native_group_norm( + input, + weight, + bias, + batch_size, + num_channels, + flattened_inner_size, + num_groups, + eps, + )[0] + + +def layer_norm( + input: Tensor, + normalized_shape: ShapeType, + weight: Optional[Tensor] = None, + bias: Optional[Tensor] = None, + eps: float = 1e-5, +) -> Tensor: + """ + Reference implementation of :func:`torch.nn.functional.layer_norm`. + """ + return torch.native_layer_norm(input, normalized_shape, weight, bias, eps)[0] + + +@register_decomposition(aten.leaky_relu) +@_inplace_wrapper +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def leaky_relu( + a: TensorLikeType, negative_slope: float = 0.01, inplace: bool = False +) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.leaky_relu + """ + + if inplace: + raise NotImplementedError + + python_type = utils.dtype_to_type(a.dtype) + if not utils.is_weakly_lesser_type(type(negative_slope), python_type): + msg = f"negative_slope argument of type {type(negative_slope)} cannot be safely cast to type {python_type}!" + raise ValueError(msg) + return torch.where(torch.gt(a, 0), a, torch.mul(a, negative_slope)) + + +@register_decomposition(aten.mish) +@_inplace_wrapper +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def mish(a: TensorLikeType, inplace: bool = False) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.mish + """ + + if inplace: + raise NotImplementedError + return a * torch.tanh(torch.nn.functional.softplus(a)) + + +@register_decomposition(aten.selu) +@_inplace_wrapper +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def selu(a: TensorLikeType, inplace: bool = False) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.selu + """ + if inplace: + raise NotImplementedError + + alpha = 1.6732632423543772848170429916717 + scale = 1.0507009873554804934193349852946 + + rhs = alpha * torch.expm1(a) + + return scale * torch.where(a > 0, a, rhs) + + +# Forwarding alias: the functional variant doesn't support the out kwarg +# CompositeImplicitAutograd - don't register decomp +def softmax( + a: TensorLikeType, + dim: Optional[int] = None, + _stacklevel: int = 3, # for compat when using TorchRefsMode(strict=True) + dtype: Optional[torch.dtype] = None, +) -> TensorLikeType: + # The error is for compat with regular PyTorch, which has this behavior + # deprecated. For PrimTorch, it's fine to drop support for deprecated + # behavior because it requires explicit opt in. This error is to inform + # users how to update their calls. + torch._check(dim is not None, lambda: "implicit dim not supported, use dim=X") + return torch.softmax(a=a, dim=dim, dtype=dtype) # type: ignore[call-overload] + + +# CompositeImplicitAutograd - don't register decomp +def softmin( + a: TensorLikeType, + dim: Optional[int] = None, + _stacklevel: int = 3, # for compat when using TorchRefsMode(strict=True) + dtype: Optional[torch.dtype] = None, +) -> TensorLikeType: + # The error is for compat with regular PyTorch, which has this behavior + # deprecated. For PrimTorch, it's fine to drop support for deprecated + # behavior because it requires explicit opt in. This error is to inform + # users how to update their calls. + torch._check(dim is not None, lambda: "implicit dim not supported, use dim=X") + return torch.softmax(a=-a, dim=dim, dtype=dtype) # type: ignore[call-overload] + + +# softplus is implemented specially because it has beta and threshold arguments +@register_decomposition(aten.softplus) +@_inplace_wrapper +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def softplus( + a: TensorLikeType, + beta: Optional[NumberType] = None, + threshold: NumberType = 20, + inplace: bool = False, +) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.softplus + """ + + if inplace: + raise NotImplementedError + + rhs: TensorLikeType + if beta is not None: + python_type = utils.dtype_to_type(a.dtype) + if not utils.is_weakly_lesser_type(type(beta), python_type): + msg = f"beta argument of type {type(beta)} cannot be safely cast to type {python_type}!" + raise ValueError(msg) + scaled_input = a * beta + rhs = torch.true_divide(torch.log1p(torch.exp(scaled_input)), beta) # type: ignore[arg-type] + + else: + scaled_input = a + rhs = torch.log1p(torch.exp(scaled_input)) + + return torch.where(scaled_input > threshold, a, rhs) + + +@aten.hardshrink.default.py_impl(DispatchKey.Autograd) +@register_decomposition(aten.hardshrink) +@out_wrapper() +def hardshrink(a: TensorLikeType, lambd: float = 0.5): + # Formula for reference, + # hardshrink(x) = x if x > lambd + # = x if x < -lambd + # = 0 otherwise + return torch.where(torch.abs(a) <= lambd, 0, a) + + +@aten.softshrink.default.py_impl(DispatchKey.Autograd) +@register_decomposition(aten.softshrink) +@out_wrapper() +def softshrink(a: TensorLikeType, lambd: float = 0.5): + # Formula for reference, + # softshrink(x) = x - lambd if x > lambd + # = x + lambd if x < -lambd + # = 0 otherwise + torch._check( + 0 <= lambd <= torch.finfo(a.dtype).max, + lambda: f"lambda must be in range [0, {torch.finfo(a.dtype).max}] for input dtype {a.dtype}, but found {lambd}", + ) + # We implement this in one torch.where to generate better code in the backward + # see https://github.com/pytorch/pytorch/pull/107052#discussion_r1293748211 + # We multiply by 0 for dealing with nans + return torch.where(torch.abs(a) > lambd, a - torch.sign(a) * lambd, a * 0) + + +# Losses +def _reduction_int_to_str(reduction: int) -> str: + from torch._decomp.decompositions import Reduction + + if reduction == Reduction.NONE.value: + return "none" + elif reduction == Reduction.MEAN.value: + return "mean" + elif reduction == Reduction.SUM.value: + return "sum" + else: + raise ValueError(f"{reduction} is not a valid value for reduction") + + +def _apply_loss_reduction(loss: TensorLikeType, reduction: str) -> TensorLikeType: + if reduction == "sum": + return torch.sum(loss) + elif reduction == "mean": + return torch.mean(loss) + else: # reduction == "none" + return loss + + +def _check_reduction_value(reduction: str): + if reduction not in ("mean", "sum", "none"): + raise ValueError(f"{reduction} is not a valid value for reduction") + + +# This helper function maps depreciated arguments, "size_average" and "reduce" +# to their corresponding "reduction" string argument +def _get_string_reduction_arg( + *, size_average: Optional[bool], reduce: Optional[bool] +) -> str: + if size_average is None: + size_average = True + if reduce is None: + reduce = True + if size_average and reduce: + ret = "mean" + elif reduce: + ret = "sum" + else: + ret = "none" + return ret + + +# CompositeImplicitAutograd - don't register decomp +@elementwise_type_promotion_wrapper( + type_promoting_args=("input", "target"), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.COMPLEX_TO_FLOAT, +) +def l1_loss( + input: TensorLikeType, + target: TensorLikeType, + size_average: Optional[bool] = None, + reduce: Optional[bool] = None, + reduction: str = "mean", +) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.l1_loss + """ + if size_average is not None or reduce is not None: + # TODO: Raise exception instead of converting value. This is only for + # primTorch since it can drop support for deprecated arguments. + # msg = "size_average and reduce args are deprecated, please use reduction argument." + reduction = _get_string_reduction_arg(size_average=size_average, reduce=reduce) + _check_reduction_value(reduction) + loss = torch.abs(input - target) + return _apply_loss_reduction(loss, reduction) + + +@elementwise_type_promotion_wrapper( + type_promoting_args=("input", "target"), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.COMPLEX_TO_FLOAT, +) +def smooth_l1_loss( + input: TensorLikeType, + target: TensorLikeType, + size_average: Optional[bool] = None, + reduce: Optional[bool] = None, + reduction: str = "mean", + beta: float = 1.0, +) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.smooth_l1_loss + """ + if size_average is not None or reduce is not None: + # TODO: Raise exception instead of converting value. This is only for + # primTorch since it can drop support for deprecated arguments. + # msg = "size_average and reduce args are deprecated, please use reduction argument." + reduction = _get_string_reduction_arg(size_average=size_average, reduce=reduce) + _check_reduction_value(reduction) + + if beta == 0.0: + return torch.nn.functional.l1_loss( + input, target, size_average=size_average, reduce=reduce, reduction=reduction + ) + else: + loss = torch.abs(input - target) + # pyrefly: ignore [unsupported-operation] + loss = torch.where(loss < beta, 0.5 * loss**2 / beta, loss - 0.5 * beta) + return _apply_loss_reduction(loss, reduction) + + +# Forwarding alias: the functional variant doesn't support the out kwarg +# CompositeImplicitAutograd - don't register decomp +def log_softmax( + a: TensorLikeType, + dim: Optional[int] = None, + _stacklevel: int = 3, # for compat when using TorchRefsMode(strict=True) + dtype: Optional[torch.dtype] = None, +) -> TensorLikeType: + # The error is for compat with regular PyTorch, which has this behavior + # deprecated. For PrimTorch, it's fine to drop support for deprecated + # behavior because it requires explicit opt in. This error is to inform + # users how to update their calls. + torch._check(dim is not None, lambda: "implicit dim not supported, use dim=X") + return torch.log_softmax(a=a, dim=dim, dtype=dtype) # type: ignore[call-overload] + + +@register_decomposition(aten.margin_ranking_loss) +def margin_ranking_loss( + input1: TensorLikeType, + input2: TensorLikeType, + target: TensorLikeType, + margin: float = 0.0, + reduction: str = "mean", +) -> TensorLikeType: + # loss_without_reduction = max(0, -target * (input1 - input2) + margin) + if input1.ndim != input2.ndim or input1.ndim != target.ndim: + raise RuntimeError( + "margin_ranking_loss : All input tensors should have same dimension but got sizes: " + f"input1: {input1.shape}, input2: {input2.shape}, target: {target.shape} " + ) + _check_reduction_value(reduction) + loss = torch.clamp_min(-target * (input1 - input2) + margin, 0) + return _apply_loss_reduction(loss, reduction) + + +@elementwise_type_promotion_wrapper( + type_promoting_args=("input", "target"), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.COMPLEX_TO_FLOAT, +) +def mse_loss( + input: TensorLikeType, + target: TensorLikeType, + size_average: Optional[bool] = None, + reduce: Optional[bool] = None, + reduction: str = "mean", +) -> TensorLikeType: + if size_average is not None or reduce is not None: + # TODO: Raise exception instead of converting value. This is only for + # primTorch since it can drop support for deprecated arguments. + # msg = "size_average and reduce args are deprecated, please use reduction argument." + reduction = _get_string_reduction_arg(size_average=size_average, reduce=reduce) + _check_reduction_value(reduction) + loss = torch.pow(input - target, 2) + return _apply_loss_reduction(loss, reduction) + + +@register_decomposition(aten.hinge_embedding_loss) +def hinge_embedding_loss( + input: TensorLikeType, + target: TensorLikeType, + margin: float = 1.0, + reduction: str = "mean", +) -> TensorLikeType: + # loss_without_reduction = input if y == 1 + # = max(0, margin - input) if y == -1 + _check_reduction_value(reduction) + margin_clamp = torch.clamp_min(margin - input, 0) + output_margin = torch.where(target != 1, margin_clamp, 0) + output_self = torch.where(target != -1, input, 0) + loss = output_margin + output_self + return _apply_loss_reduction(loss, reduction) + + +def _nll_loss_nd( + input: TensorLikeType, + target: TensorLikeType, + weight: Optional[TensorLikeType], + reduction: str, + ignore_index: int, +) -> TensorLikeType: + torch._check( + input.ndim > 0 and input.ndim <= 3, + lambda: f"Expected input dimension to be either [1, 2, 3] but received {input.ndim}.", + ) + + torch._check( + (input.ndim == 1) or (input.shape[0] == target.shape[0]), + lambda: f"Expected input batch size {input.shape[0]} to match target batch size {target.shape[0]}.", + ) + + _check_reduction_value(reduction) + + flat_target = torch.flatten(target) + ignore_classes_mask = torch.eq(flat_target, ignore_index) + + # TODO: Enable data-dependent checks with debug mode + # TODO: This check does not work with FakeTensor inputs; See Issue #85834 + # Explicit cast for class_check to bool; See Issue #78071 + """ + from torch._subclasses.fake_tensor import FakeTensor + num_classes = input.shape[1] if input.ndim > 1 else input.shape[0] + valid_classes_mask = torch.logical_and( + (flat_target >= 0), (flat_target < num_classes) + ) + class_check = torch.all(torch.logical_or(ignore_classes_mask, valid_classes_mask)) + torch._check( + isinstance(target, FakeTensor) or bool(class_check.item()), + lambda: "A target class is out-of-bounds and not the ignore index.", + ) + """ + + ignore_class_weight = torch.scalar_tensor(0, dtype=input.dtype, device=input.device) + class_weight = ( + torch.scalar_tensor(1, dtype=input.dtype, device=input.device) + if weight is None + else weight[flat_target] + ) + current_weight = torch.where( + ignore_classes_mask, + ignore_class_weight, + class_weight, + ) + + if input.ndim == 1: + # implicit batch size = 1 + # input (1 batch size, C classes) + loss = -input[target] * current_weight + elif input.ndim == 2: + # input (N batch size, C classes) + batch_size = input.shape[0] + loss = -input[torch.arange(batch_size), target] * current_weight + else: + # 3D case (N batch size, C classes, K dimensions) + # input (N batch size, C classes, K) + batch_size = input.shape[0] + extent = input.shape[2] + numel = batch_size * extent + indices = torch.arange(numel) + bdx = indices // extent + kdx = indices % extent + loss = -input[bdx, flat_target, kdx] * current_weight + loss = torch.reshape(loss, target.shape) + + if reduction == "none": + return loss + elif reduction == "sum": + return torch.sum(loss) + else: + # calculate weighted mean of the loss function + return torch.sum(loss) / torch.sum(current_weight) + + +@register_decomposition(aten.nll_loss) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("input",), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def nll_loss( + input: TensorLikeType, + target: TensorLikeType, + weight: Optional[TensorLikeType] = None, + size_average: Optional[bool] = None, + ignore_index: int = -100, + reduce: Optional[bool] = None, + reduction: str = "mean", +) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.nll_loss + """ + torch._check( + input.ndim > 0, + lambda: f"Expected input tensor to have 1 or more dimensions (got {input.ndim})", + ) + + # TODO: raise exception instead of converting value + # msg = "size_average and reduce args are deprecated, please use reduction argument." + # Convert these options for consistency with the eager mode + if size_average is not None or reduce is not None: + reduction = _get_string_reduction_arg(size_average=size_average, reduce=reduce) + + # The expected behavior when the target and input have zero elements: + # reduction = 'none' --- tensor([]) + # reduction = 'sum' --- tensor(0.) + # reduction = 'mean' --- tensor(nan) + # Mean reduction on empty tensors produces NaN. See the discussion in + # https://github.com/pytorch/pytorch/pull/64572#issuecomment-926504162 + if input.numel() == 0 and target.numel() == 0: + if reduction == "none": + return torch.zeros_like(target) + elif reduction == "sum": + return torch.empty_like(target) + else: + return torch.full_like(target, float("nan")) + + # The _nll_loss_nd helper function handles the most common cases. + # ndim == 1 (Single Example) + # => Batch Size: 1, Input: (C), Target: () + # ndim == 2 (k = 1) + # => Batch Size: N, Input: (N, C), Target: (N) + # ndim == 3 (k > 1) + # => Batch Size: N, Input: (N, C, K), Target: (N, K) + if input.ndim <= 3: + return _nll_loss_nd(input, target, weight, reduction, ignore_index) + + # For ndim > 3, we reshape the input and target to 3-D case. + # Input (N batch-size, C classes, k-dimensions) + # Target (N batch-size, k-dimensions) + torch._check( + input.ndim > 0 and target.ndim > 0 and target.shape[1:] == input.shape[2:], + lambda: ( + "Expected input and target to both have ndim > 0 and " + "target.shape[1:] == input.shape[2:], but got " + f"target.shape {target.shape} and input.shape {input.shape}" + ), + ) + + batch_size = input.shape[0] + num_classes = input.shape[1] + out_size = [batch_size] + list(target.shape[1:]) + + input = torch.reshape(input, [batch_size, num_classes, -1]) + target = torch.reshape(target, [batch_size, -1]) + if reduction != "none": + return _nll_loss_nd(input, target, weight, reduction, ignore_index) + else: + result = _nll_loss_nd(input, target, weight, reduction, ignore_index) + # reshape flattened inner-dim to original k-dimensions + return torch.reshape(result, out_size) + + +# TODO: This ref supports int reduction and out kwarg to be compatible with ATen: +# https://github.com/pytorch/pytorch/issues/83931 +# TODO: Could be rewritten to support complex: +# https://github.com/pytorch/pytorch/pull/85041 +@register_decomposition(aten.huber_loss) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("input", "target"), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def huber_loss( + input: TensorLikeType, + target: TensorLikeType, + reduction: Union[str, int] = "mean", + delta: float = 1.0, +) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.huber_loss + """ + if type(reduction) is int: + reduction = _reduction_int_to_str(reduction) + _check_reduction_value(reduction) # type: ignore[arg-type] + torch._check( + delta > 0, + lambda: "huber_loss does not support non-positive values for delta.", + ) + z = (input - target).abs() + loss = torch.where(z < delta, 0.5 * z * z, delta * (z - 0.5 * delta)) + return _apply_loss_reduction(loss, reduction) # type: ignore[arg-type] + + +# tanhshrink does not use _make_elementwise_unary_reference because it does not support out +@elementwise_unary_scalar_wrapper +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def tanhshrink(a: TensorLikeType) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.tanhshrink + """ + if not isinstance(a, TensorLike): + raise RuntimeError( + "Expected a tensor input for an elementwise unary operation!" + ) + return a - torch.tanh(a) + + +@register_decomposition(aten.threshold) +@_inplace_wrapper +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def threshold( + a: TensorLikeType, + threshold: NumberType, + value: Union[bool, int, float], + inplace: bool = False, +) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.threshold + """ + + if inplace: + raise NotImplementedError + + return torch.where(a <= threshold, value, a) + + +# CompositeImplicitAutograd - don't register decomp +# No elementwise type promotion - core op doesn't explicitly type promote +def triplet_margin_loss( + anchor: TensorLikeType, + positive: TensorLikeType, + negative: TensorLikeType, + margin: float = 1.0, + p: float = 2, + eps: float = 1e-6, + swap: bool = False, + size_average: Optional[bool] = None, + reduce: Optional[bool] = None, + reduction: str = "mean", +) -> TensorLikeType: + if size_average is not None or reduce is not None: + # TODO: Raise exception instead of converting value. This is only for + # primTorch since it can drop support for deprecated arguments. + # msg = "size_average and reduce args are deprecated, please use reduction argument." + reduction = _get_string_reduction_arg(size_average=size_average, reduce=reduce) + + if margin <= 0: + raise ValueError(f"margin must be greater than 0, got {margin}") + + # torch.nn.functional.triplet_margin_with_distance_loss has no ref defined + # since it's a pure Python implementation. Use this helper instead. + return _triplet_margin_with_distance_loss( + anchor=anchor, + positive=positive, + negative=negative, + distance_function=lambda x, y: torch.pairwise_distance(x, y, p, eps), + margin=margin, + swap=swap, + reduction=reduction, + ) + + +# Pure Python impl - don't register decomp and don't add a ref. Defined as a +# helper here since triplet_margin_loss can be nicely implemented with it. +def _triplet_margin_with_distance_loss( + anchor: TensorLikeType, + positive: TensorLikeType, + negative: TensorLikeType, + *, + distance_function: Optional[ + Callable[[TensorLikeType, TensorLikeType], TensorLikeType] + ] = None, + margin: float = 1.0, + swap: bool = False, + reduction: str = "mean", +) -> TensorLikeType: + _check_reduction_value(reduction) + + a_dim = anchor.ndim + p_dim = positive.ndim + n_dim = negative.ndim + torch._check( + a_dim == p_dim and p_dim == n_dim, + lambda: ( + f"The anchor, positive, and negative tensors are expected to have " + f"the same number of dimensions, but got: anchor {a_dim}D, " + f"positive {p_dim}D, and negative {n_dim}D inputs" + ), + ) + + if distance_function is None: + distance_function = torch.pairwise_distance + + dist_pos = distance_function(anchor, positive) + dist_neg = distance_function(anchor, negative) + # The distance swap is described in the paper "Learning shallow + # convolutional feature descriptors with triplet losses" by V. Balntas, E. + # Riba et al. If True, and if the positive example is closer to the + # negative example than the anchor is, swaps the positive example and the + # anchor in the loss computation. + if swap: + dist_swap = distance_function(positive, negative) + dist_neg = torch.minimum(dist_neg, dist_swap) + loss = torch.clamp_min(margin + dist_pos - dist_neg, 0) + return _apply_loss_reduction(loss, reduction) + + +@register_decomposition(aten.hardtanh) +@_inplace_wrapper +@out_wrapper() +@elementwise_unary_scalar_wrapper +@elementwise_type_promotion_wrapper( + type_promoting_args=("a"), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def hardtanh( + a: TensorLikeType, + min_val: NumberType = -1, + max_val: NumberType = 1, + inplace: bool = False, +) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.hardtanh + """ + if inplace: + raise NotImplementedError + if utils.is_boolean_dtype(a.dtype): + raise RuntimeError("Bool inputs not supported for hardtanh") + + # preserve legacy behavior of boundaries not causing type promotion + if utils.is_integer_dtype(a.dtype): + min_val = int(min_val) # type: ignore[arg-type] + max_val = int(max_val) # type: ignore[arg-type] + if not (a.dtype != torch.uint8 or (min_val >= 0 and max_val >= 0)): + raise RuntimeError( + "Cannot do hardtanh on an unsigned type with negative limits" + ) + + if min_val > max_val: # type: ignore[operator] + raise ValueError("min_val cannot be greater than max_val") + + return torch.clamp(a, min_val, max_val) # type: ignore[arg-type] + + +@register_decomposition(aten.gelu) +@out_wrapper() +@elementwise_unary_scalar_wrapper +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def gelu(a: TensorLikeType, approximate: str = "none") -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.gelu + """ + if not isinstance(a, TensorLike): + raise RuntimeError( + "Expected a tensor input for an elementwise unary operation!" + ) + M_SQRT2 = 1.41421356237309504880 + M_SQRT1_2 = 0.70710678118654752440 + M_2_SQRTPI = 1.12837916709551257390 + if approximate == "tanh": + kBeta = M_SQRT2 * M_2_SQRTPI * 0.5 + kKappa = 0.044715 + a_cube = a * a * a + inner = kBeta * (a + kKappa * a_cube) + return 0.5 * a * (1 + torch.tanh(inner)) + elif approximate == "none": + kAlpha = M_SQRT1_2 + return a * 0.5 * (1 + torch.erf(a * kAlpha)) + else: + raise RuntimeError("approximate argument must be either none or tanh.") + + +# CompositeImplicitAutograd - don't register decomp +@elementwise_type_promotion_wrapper( + type_promoting_args=("input", "target"), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def poisson_nll_loss( + input: TensorLikeType, + target: TensorLikeType, + log_input: bool = True, + full: bool = False, + size_average: Optional[bool] = None, + eps: float = 1e-8, + reduce: Optional[bool] = None, + reduction: str = "mean", +) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.poisson_nll_loss + """ + if size_average is not None or reduce is not None: + # TODO: Raise exception instead of converting value. This is only for + # primTorch since it can drop support for deprecated arguments. + # msg = "size_average and reduce args are deprecated, please use reduction argument." + reduction = _get_string_reduction_arg(size_average=size_average, reduce=reduce) + _check_reduction_value(reduction) + if log_input: + loss = torch.exp(input) - target * input + else: + loss = input - target * torch.log(input + eps) + + if full: + stirling_term = ( + target * torch.log(target) - target + 0.5 * torch.log(2 * torch.pi * target) + ) + # avoid inplace add + loss = loss + stirling_term.masked_fill(target <= 1, 0) + return _apply_loss_reduction(loss, reduction) + + +@register_decomposition(aten.prelu) +@elementwise_type_promotion_wrapper( + type_promoting_args=("a", "weight"), + type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def prelu(a: TensorLikeType, weight: TensorLikeType) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.prelu + """ + torch._check( + isinstance(a, TensorLike), + lambda: f"prelu: Expected `a` to be tensor, but got: {type(a)}", + ) + torch._check( + isinstance(weight, TensorLike), + lambda: f"prelu: Expected `weight` to be tensor, but got: {type(weight)}", + ) + + if weight.numel() != 1: + torch._check(a.ndim > 0, lambda: "Not allow zero-dim input tensor.") + channel_size = a.shape[1] if a.ndim >= 2 else 1 + torch._check( + weight.numel() == channel_size, + lambda: f"Mismatch of parameter numbers and input channel size. Found parameter numbers =" + f" {weight.numel()} and channel size = {channel_size}.", + ) + + torch._check( + weight.ndim == 0 or weight.ndim == 1, + lambda: f"prelu: Expected `weight` to be a scalar or 1D tensor, but got: " + f"ndim = {weight.ndim}", + ) + if a.ndim == 0: + weight = weight[0] if weight.ndim == 1 else weight + else: + weight = prims.broadcast_in_dim( + weight, a.shape, () if weight.ndim == 0 else (0 if a.ndim == 1 else 1,) + ) + + return torch.where(a > 0, a, a * weight) + + +@register_decomposition(aten.relu6) +@_inplace_wrapper +@out_wrapper() +def relu6(a: TensorLikeType, inplace: bool = False) -> TensorLikeType: + """ + Reference implementation of torch.nn.functional.relu6 + """ + if inplace: + raise NotImplementedError + + # See https://github.com/pytorch/pytorch/pull/81142#discussion_r918220126 + # It may be better to use clamp here, but we use hardtanh to replicate + # the behavior of the existing implementation + return torch.nn.functional.hardtanh(a, 0, 6) + + +@register_decomposition(aten.glu) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def glu(a: TensorLikeType, dim: int = -1) -> TensorLikeType: + dim = utils.canonicalize_dims(a.ndim, dim) + torch._check( + a.shape[dim] % 2 == 0, + lambda: f"Halving dimension must be even, but dimension {dim} is size {a.shape[dim]}", + ) + b, c = torch.tensor_split(a, 2, dim) + + return b * torch.sigmoid(c) + + +@register_decomposition(aten.pairwise_distance) +@out_wrapper() +def pairwise_distance( + x1: TensorLikeType, + x2: TensorLikeType, + p: NumberType = 2.0, + eps: NumberType = 1e-6, + keepdim=False, +) -> TensorLikeType: + return torch.linalg.vector_norm(x1 - x2 + eps, ord=p, dim=-1, keepdim=keepdim) + + +@register_decomposition(aten.pdist) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT, +) +def pdist(a: TensorLikeType, p: float = 2) -> TensorLikeType: + torch._check(a.ndim == 2, lambda: f"pdist only supports 2D tensors, got: {a.ndim}D") + torch._check(p >= 0, lambda: "pdist only supports non-negative p values") + # For p == 2 we can use an efficient implementation, but other values of p + # require creating a much bigger tensor for an intermediate step + if p == 2: + aTa = torch.mm(a, a.T) + aTa_diag = torch.diag(aTa) + t = torch.sqrt(torch.clamp(aTa_diag + aTa_diag.unsqueeze(-1) - 2 * aTa, min=0)) + else: + t = torch.linalg.vector_norm(a.unsqueeze(1) - a, ord=p, dim=2) + i = torch.triu_indices(t.shape[0], t.shape[1], offset=1, device=a.device) + return t.flatten().index_select(0, i[0] * t.shape[0] + i[1]) + + +@register_decomposition(aten.pixel_shuffle) +@out_wrapper() +def pixel_shuffle(self: Tensor, upscale_factor: int): + torch._check( + self.dim() >= 3, + lambda: f"pixel_shuffle expects input to have at least 3 dimensions, but got input with {self.dim} dimension(s)", + ) + batch = self.shape[:-3] + C_out = self.shape[-3] // upscale_factor**2 + HW_out = (self.shape[-2] * upscale_factor, self.shape[-1] * upscale_factor) + n = len(batch) + B_dims = range(n) + C_dim, r1_dim, r2_dim, H_dim, W_dim = range(n, n + 5) + return ( + self.view( + *batch, + C_out, + upscale_factor, + upscale_factor, + self.shape[-2], + self.shape[-1], + ) + .permute(*B_dims, C_dim, H_dim, r1_dim, W_dim, r2_dim) + .reshape(*batch, C_out, *HW_out) + .clone(memory_format=utils.suggest_memory_format(self)) + ) + + +@register_decomposition(aten.pixel_unshuffle) +@out_wrapper() +def pixel_unshuffle(self: Tensor, downscale_factor: int): + torch._check( + self.dim() >= 3, + lambda: f"pixel_unshuffle expects input to have at least 3 dimensions, but got input with {self.dim} dimension(s)", + ) + batch = self.shape[:-3] + C_out = self.shape[-3] * downscale_factor**2 + HW_out = (self.shape[-2] // downscale_factor, self.shape[-1] // downscale_factor) + n = len(batch) + B_dims = range(n) + C_dim, H_dim, r1_dim, W_dim, r2_dim = range(n, n + 5) + return ( + self.view( + *batch, + self.shape[-3], + HW_out[0], + downscale_factor, + HW_out[1], + downscale_factor, + ) + .permute(*B_dims, C_dim, r1_dim, r2_dim, H_dim, W_dim) + .reshape(*batch, C_out, *HW_out) + .clone(memory_format=utils.suggest_memory_format(self)) + ) + + +# Needed as aten.{celu_,elu_...} exist (even if they don't have the in-place kwarg) +celu_ = _make_inplace(celu) +elu_ = _make_inplace(elu) +mish_ = _make_inplace(mish) +selu_ = _make_inplace(selu) +threshold_ = _make_inplace(threshold) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_refs/special/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_refs/special/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a7351fb8f10cad27819c4ee7cf17805a3f3d37bc --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_refs/special/__init__.py @@ -0,0 +1,238 @@ +# mypy: allow-untyped-defs +import math +from typing import Optional, Union + +import torch +import torch._prims as prims +import torch._prims_common as utils +import torch._refs as refs +from torch import Tensor +from torch._decomp import register_decomposition +from torch._prims_common import ( + ELEMENTWISE_TYPE_PROMOTION_KIND, + Number, + NumberType, + TensorLike, + TensorLikeType, +) +from torch._prims_common.wrappers import elementwise_type_promotion_wrapper, out_wrapper +from torch._refs import ( + _make_alias, + _make_elementwise_binary_reference, + _make_elementwise_unary_reference, +) + + +__all__ = [ + "bessel_j0", + "bessel_j1", + "entr", + "erfcx", + "expit", + "i0e", + "i1", + "i1e", + "log_ndtr", + "logit", + "log_softmax", + "multigammaln", + "ndtr", + "ndtri", + "softmax", + "spherical_bessel_j0", + "xlog1py", + "zeta", +] +aten = torch._ops.ops.aten + + +@_make_elementwise_unary_reference( + ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def bessel_j0(a: TensorLikeType) -> TensorLikeType: + return prims.bessel_j0(a) + + +@_make_elementwise_unary_reference( + ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def bessel_j1(a: TensorLikeType) -> TensorLikeType: + return prims.bessel_j1(a) + + +@register_decomposition(aten.special_entr) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def entr(a: TensorLikeType) -> TensorLikeType: + return torch.where( + torch.isnan(a), + a, + torch.where(a > 0, -a * torch.log(a), torch.where(a == 0, 0, -torch.inf)), + ) + + +@register_decomposition(aten.special_erfcx) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def erfcx(a: TensorLikeType) -> TensorLikeType: + return prims.erfcx(a) + + +# alias for sigmoid +expit = _make_alias(torch.sigmoid, "expit") + + +@_make_elementwise_unary_reference( + ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def i0e(a: TensorLikeType) -> TensorLikeType: + return prims.bessel_i0e(a) + + +@_make_elementwise_unary_reference( + ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def i1(a: TensorLikeType) -> TensorLikeType: + return prims.bessel_i1(a) + + +@_make_elementwise_unary_reference( + ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def i1e(a: TensorLikeType) -> TensorLikeType: + return prims.bessel_i1e(a) + + +@register_decomposition(aten.special_log_ndtr) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def log_ndtr(a: TensorLikeType) -> TensorLikeType: + # Note: M_SQRT1_2 is the value of 1 / sqrt(2) + M_SQRT1_2 = 0.707106781186547524400844362104849039 + t = a * M_SQRT1_2 + return torch.where( + a < 1.0, + torch.log(torch.special.erfcx(-t) / 2) - t * t, + torch.log1p(-torch.erfc(t) / 2), + ) + + +@register_decomposition(aten.logit) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("self",), + type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def logit(self: TensorLikeType, eps: Optional[float] = None) -> TensorLikeType: + if eps is None: + eps = -1.0 + lo = eps + hi = 1 - eps + self = torch.where(self < lo, lo, torch.where(self > hi, hi, self)) + return torch.log(torch.true_divide(self, torch.sub(1, self))) + + +@register_decomposition(aten.special_xlog1py) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a", "b"), + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def xlog1py(a: Union[TensorLikeType, NumberType], b: Union[TensorLikeType, NumberType]): + torch._check( + isinstance(a, TensorLike) or isinstance(b, TensorLike), + lambda: 'Expected either argument a or b to be a Tensor"', + ) + + # Operations like eq and log do not handle scalar values, so we convert them to scalar_tensors. + if isinstance(a, TensorLike) and isinstance(b, Number): + # pyrefly: ignore [bad-argument-type] + b = refs.scalar_tensor(b, dtype=a.dtype, device=a.device) + elif isinstance(b, TensorLike) and isinstance(a, Number): + # pyrefly: ignore [bad-argument-type] + a = refs.scalar_tensor(a, dtype=b.dtype, device=b.device) + + # mypy: expected "Tensor" + assert isinstance(a, TensorLike) + assert isinstance(b, TensorLike) + rhs = torch.where(torch.eq(a, 0), 0, torch.mul(a, torch.log1p(b))) + return torch.where(torch.isnan(b), float("nan"), rhs) + + +@register_decomposition(aten.mvlgamma) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def multigammaln(a: TensorLikeType, p: int) -> TensorLikeType: + c = 0.25 * p * (p - 1) * math.log(math.pi) + b = 0.5 * torch.arange(start=(1 - p), end=1, step=1, dtype=a.dtype, device=a.device) + return torch.sum(torch.lgamma(a.unsqueeze(-1) + b), dim=-1) + c + + +@register_decomposition(aten.special_ndtr) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def ndtr(a: TensorLikeType) -> TensorLikeType: + # Note: M_SQRT1_2 is the value of 1 / sqrt(2) + M_SQRT1_2 = 0.707106781186547524400844362104849039 + a_sqrt_2 = a * M_SQRT1_2 + return (1 + torch.erf(a_sqrt_2)) * 0.5 + + +@register_decomposition(aten.special_ndtri) +@out_wrapper() +@elementwise_type_promotion_wrapper( + type_promoting_args=("a",), + type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def ndtri(a: TensorLikeType) -> TensorLikeType: + return prims.ndtri(a) + + +# Forwarding alias: the special variant doesn't support the out kwarg +# CompositeImplicitAutograd - don't register decomp +def log_softmax( + a: TensorLikeType, + dim: int, + dtype: Optional[torch.dtype] = None, +) -> TensorLikeType: + return torch.log_softmax(a=a, dim=dim, dtype=dtype) # type: ignore[call-overload] + + +# Forwarding alias: the special variant doesn't support the out kwarg +# CompositeImplicitAutograd - don't register decomp +def softmax( + a: TensorLikeType, + dim: int, + dtype: Optional[torch.dtype] = None, +) -> TensorLikeType: + return torch.softmax(a=a, dim=dim, dtype=dtype) # type: ignore[call-overload] + + +@_make_elementwise_unary_reference( + ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def spherical_bessel_j0(a: TensorLikeType) -> TensorLikeType: + return prims.spherical_bessel_j0(a) + + +# TODO: add docstring +@_make_elementwise_binary_reference( + type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, +) +def zeta(a: TensorLikeType, b: TensorLikeType) -> TensorLikeType: + return prims.zeta(a, b) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_strobelight/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_strobelight/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_strobelight/cli_function_profiler.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_strobelight/cli_function_profiler.py new file mode 100644 index 0000000000000000000000000000000000000000..a63a49c3938a1d4a3cda3f1a6b9b029a90a8a77e --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_strobelight/cli_function_profiler.py @@ -0,0 +1,322 @@ +# mypy: disallow-untyped-defs + +import functools +import logging +import os +import re +import subprocess +import time +from collections.abc import Callable, Sequence +from threading import Lock +from timeit import default_timer as timer +from typing import Any, Optional, TypeVar +from typing_extensions import ParamSpec + + +logger = logging.getLogger("strobelight_function_profiler") + +console_handler = logging.StreamHandler() +formatter = logging.Formatter( + "%(name)s, line %(lineno)d, %(asctime)s, %(levelname)s: %(message)s" +) +console_handler.setFormatter(formatter) + +logger.addHandler(console_handler) +logger.setLevel(logging.INFO) +logger.propagate = False + +_P = ParamSpec("_P") +_R = TypeVar("_R") + + +class StrobelightCLIProfilerError(Exception): + """ + Raised when an error happens during strobelight profiling + """ + + +def _pid_namespace_link(pid: Optional[int] = None) -> str: + """Returns the link to the process's namespace, example: pid:[4026531836]""" + PID_NAMESPACE_PATH = "/proc/{}/ns/pid" + pid = pid or os.getpid() + return os.readlink(PID_NAMESPACE_PATH.format(pid)) + + +def _pid_namespace(pid: Optional[int] = None) -> int: + """Returns the process's namespace id""" + pid = pid or os.getpid() + link = _pid_namespace_link(pid) + return int(link[link.find("[") + 1 : -1]) + + +def _command_to_string(command: Sequence[str]) -> str: + return " ".join(command) + + +class StrobelightCLIFunctionProfiler: + """ + Note: this is a Meta only tool. + + StrobelightCLIFunctionProfiler can be used to profile a python function and + generate a strobelight link with the results. It works on meta servers but + does not requires an fbcode target. + When stop_at_error is false(default), error during profiling does not prevent + the work function from running. + + Check function_profiler_example.py for an example. + """ + + # This lock is used to make sure only one thread is running the profiler at any point. + _lock = Lock() + + def __init__( + self, + *, + stop_at_error: bool = False, + max_profile_duration_sec: int = 60 * 10, + sample_each: float = 1e7, # sample each sample_each cycles. + run_user_name: str = "pytorch-strobelight-ondemand", + timeout_wait_for_running_sec: int = 60, + timeout_wait_for_finished_sec: int = 60, + recorded_env_variables: Optional[list[str]] = None, + sample_tags: Optional[list[str]] = None, + stack_max_len: int = 127, + async_stack_max_len: int = 127, + ): + self.stop_at_error = stop_at_error + self.max_profile_duration_sec = max_profile_duration_sec + self.sample_each = sample_each + self.run_user_name = run_user_name + self.timeout_wait_for_running_sec = timeout_wait_for_running_sec + self.timeout_wait_for_finished_sec = timeout_wait_for_finished_sec + # Results of the most recent run. + # Tracks the strobelight run id of the most recent run + self.current_run_id: Optional[int] = None + self.profile_result: Optional[list[str]] = None + self.sample_tags = sample_tags + + def _run_async(self) -> None: + processId = os.getpid() + namespace = _pid_namespace(processId) + command = [ + "strobeclient", + "run", + "--profiler", + "pyperf", + "--event", + "cycles", + "--async", + "--sample-interval", + f"{int(self.sample_each)}", + "--duration-ms", + f"{int(self.max_profile_duration_sec * 1000)}", + "--pid", + f"{namespace}:{processId}", + ] + + if self.sample_tags: + command.append("--sample-tags") + command.append(",".join(self.sample_tags)) + + logger.debug("running command: %s", _command_to_string(command)) + result = subprocess.run(command, capture_output=True) + output = result.stderr.decode("utf-8") + logger.debug("output:\n{%s}", output) + + if result.returncode != 0: + raise StrobelightCLIProfilerError( + f"failed to start strobelight profiling, error in run_async:{output}" + ) + + if match := re.search(r"INFO Run Id: (-?\d+)", output): + self.current_run_id = int(match.group(1)) + return + + raise StrobelightCLIProfilerError( + f"failed to start strobelight profiling, unexpected result {output}" + ) + + def _wait_for_running(self, counter: int = 0) -> None: + if counter > 20: + raise StrobelightCLIProfilerError( + "wait_for_running called more than 20 times" + ) + + command = ["strobeclient", "getRunStatus", "--run-id", f"{self.current_run_id}"] + logger.debug("running command: %s", _command_to_string(command)) + result = subprocess.run(command, capture_output=True) + output = result.stderr.decode("utf-8") + logger.debug("output:\n{%s}", output) + + if result.returncode != 0: + raise StrobelightCLIProfilerError( + f"failed to start strobelight profiling, error in wait_for_running:{output}" + ) + + if match := re.search("Profile run status: (.*)", output): + current_status = match.group(1) + if current_status == "RUNNING": + return + elif current_status == "PREPARING": + time.sleep(10) + self._wait_for_running(counter + 1) + return + else: + raise StrobelightCLIProfilerError(f"unexpected {current_status} phase") + + raise StrobelightCLIProfilerError(f"unexpected output\n: {output} ") + + def _stop_run(self) -> None: + command = ["strobeclient", "stopRun", "--run-id", str(self.current_run_id)] + logger.debug("running command: %s", _command_to_string(command)) + result = subprocess.run(command, capture_output=True) + output = result.stderr.decode("utf-8") + logger.debug("output:\n{%s}", output) + + if result.returncode != 0: + raise StrobelightCLIProfilerError( + f"failed to stop strobelight profiling, return code is not 0 :{output}" + ) + + if match := re.search("INFO ::1:(.*)", output): + current_status = match.group(1) + if current_status.__contains__("Success!"): + return + else: + raise StrobelightCLIProfilerError( + f"failed to stop strobelight profiling, got {current_status} result" + ) + + raise StrobelightCLIProfilerError(f"unexpected output\n: {output} ") + + def _get_results(self) -> None: + command = ["strobeclient", "getRunStatus", "--run-id", str(self.current_run_id)] + logger.debug("running command: %s", _command_to_string(command)) + result = subprocess.run(command, capture_output=True) + output = result.stderr.decode("utf-8") + logger.debug("output:\n{%s}", output) + + if result.returncode != 0: + raise StrobelightCLIProfilerError( + f"failed to extract profiling results, return code is not 0 : {output}" + ) + + if match := re.search("INFO ::1:(.*)", output): + current_status = match.group(1) + if current_status.__contains__("Profile run status: PROCESSING"): + time.sleep(10) + self._get_results() + return + elif not current_status.__contains__("Profile run finished with SUCCESS"): + raise StrobelightCLIProfilerError( + f"failed to extract profiling results, unexpected response {output}" + ) + + self.profile_result = [] + for item in re.findall( + r"(Total samples(.*)|GraphProfiler(.*)|Icicle view \(python stack\)(.*))", + output, + ): + self.profile_result += item[0] + logger.info(item[0]) + + def _stop_strobelight_no_throw( + self, + collect_results: bool, + ) -> None: + try: + # call stop run + self._stop_run() + logger.info("strobelight profiling stopped") + + logger.debug("collection stopped") + + if not collect_results: + return + + self._get_results() + except Exception: + logger.warning("error during stop_strobelight", exc_info=True) + + # Return true if strobelight started and is running. Never throw. + def _start_strobelight(self) -> bool: + strobelight_started = False + try: + self._run_async() + strobelight_started = True + logger.info("strobelight run id is: %s", self.current_run_id) + self._wait_for_running() + logger.info("strobelight profiling running") + return True + + except Exception: + logger.warning("error during start_strobelight:", exc_info=True) + if strobelight_started: + self._stop_strobelight_no_throw(collect_results=False) + return False + + def profile( + self, work_function: Callable[_P, _R], *args: _P.args, **kwargs: _P.kwargs + ) -> Optional[_R]: + self.current_run_id = None + self.profile_result = None + + if locked := StrobelightCLIFunctionProfiler._lock.acquire(False): + if not locked: + if self.stop_at_error: + raise StrobelightCLIProfilerError("concurrent runs not supported") + + logger.warning("concurrent runs not supported") + return work_function(*args, **kwargs) + + started = self._start_strobelight() + if not started: + if self.stop_at_error: + StrobelightCLIFunctionProfiler._lock.release() + raise StrobelightCLIProfilerError( + "failed to start strobelight profiling" + ) + result = work_function(*args, **kwargs) + StrobelightCLIFunctionProfiler._lock.release() + return result + + try: + logger.debug("collection started") + start = timer() + result = work_function(*args, **kwargs) + end = timer() + total_time = end - start # Time in seconds, e.g. 5.38091952400282 + logger.info("work function took %s seconds", total_time) + self._stop_strobelight_no_throw(collect_results=True) + StrobelightCLIFunctionProfiler._lock.release() + return result + except Exception as error: + logger.warning("work function throw exception", exc_info=True) + self._stop_strobelight_no_throw(collect_results=False) + StrobelightCLIFunctionProfiler._lock.release() + raise error + return None + + +# A function decorator that wraps profile, if no profiler is provided one with +# default args is created. A function can be annotated as: +# @strobelight() +# @strobelight(profiler = StrobelightFunctionProfiler(stop_at_error=True,..)) +# @strobelight(stop_at_error=True,...) +def strobelight( + profiler: Optional[StrobelightCLIFunctionProfiler] = None, **kwargs: Any +) -> Callable[[Callable[_P, _R]], Callable[_P, Optional[_R]]]: + if not profiler: + profiler = StrobelightCLIFunctionProfiler(**kwargs) + + def strobelight_inner( + work_function: Callable[_P, _R], + ) -> Callable[_P, Optional[_R]]: + @functools.wraps(work_function) + def wrapper_function(*args: _P.args, **kwargs: _P.kwargs) -> Optional[_R]: + # pyrefly: ignore [bad-argument-type] + return profiler.profile(work_function, *args, **kwargs) + + return wrapper_function + + return strobelight_inner diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_strobelight/compile_time_profiler.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_strobelight/compile_time_profiler.py new file mode 100644 index 0000000000000000000000000000000000000000..89b44632e27872704b6f6c3e6ea21c9c19416610 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_strobelight/compile_time_profiler.py @@ -0,0 +1,224 @@ +# mypy: disallow-untyped-defs + +import json +import logging +import os +import re +import subprocess +from datetime import datetime +from socket import gethostname +from typing import Any, Optional + +from torch._strobelight.cli_function_profiler import StrobelightCLIFunctionProfiler + + +logger = logging.getLogger("strobelight_compile_time_profiler") + +console_handler = logging.StreamHandler() +formatter = logging.Formatter( + "%(name)s, line %(lineno)d, %(asctime)s, %(levelname)s: %(message)s" +) +console_handler.setFormatter(formatter) + +logger.addHandler(console_handler) +logger.setLevel(logging.INFO) +logger.propagate = False + + +def get_fburl(url: str) -> str: + short_url = url + # Attempt to shorten the URL + try: + result = subprocess.run( + ["fburl", url], capture_output=True, stdin=subprocess.DEVNULL + ) + if result.returncode == 0: + short_url = result.stdout.decode("utf-8") + except Exception as e: + logger.warning("URL shortening failed: %s, using long URL", repr(e)) + return short_url + + +def get_strobelight_url(identifier: str) -> str: + scuba_json = { + "aggregateList": [], + "aggregation_field": "async_stack_complete", + "b_constraints": [[]], + "c_constraints": [[]], + "cols": ["namespace_id", "namespace_process_id"], + "compare": "none", + "constraints": [ + [{"column": "sample_tags", "op": "all", "value": [f'["{identifier}"]']}] + ], + "derivedCols": [], + "end": "now", + "enumCols": [], + "filterMode": "DEFAULT", + "hideEmptyColumns": "false", + "ignoreGroupByInComparison": "false", + "is_timeseries": "false", + "mappedCols": [], + "metric": "count", + "modifiers": [], + "order": "weight", + "order_desc": "true", + "param_dimensions": [ + {"dim": "py_async_stack", "op": "edge", "param": "0", "anchor": "0"} + ], + "purposes": [], + "return_remainder": "false", + "samplingRatio": "1", + "should_pivot": "false", + "start": "-30 days", + "timezone": "America/Los_Angeles", + "top": 10000, + } + scuba_url_prefix = "https://www.internalfb.com/intern/scuba/query/?dataset=pyperf_experimental/on_demand&drillstate=" + scuba_url_suff = "&view=GraphProfilerView&&normalized=1726332703&pool=uber" + long_url = scuba_url_prefix + json.dumps(scuba_json) + scuba_url_suff + return get_fburl(long_url) + + +class StrobelightCompileTimeProfiler: + success_profile_count: int = 0 + failed_profile_count: int = 0 + ignored_profile_runs: int = 0 + inside_profile_compile_time: bool = False + enabled: bool = False + + # A regex that can be used to filter out what frames to profile. ex: "1/.*" + frame_id_filter: Optional[str] = os.environ.get("COMPILE_STROBELIGHT_FRAME_FILTER") + + # A unique identifier that is used as the run_user_name in the strobelight profile to + # associate all compile time profiles together. + identifier: Optional[str] = None + + current_phase: Optional[str] = None + + profiler: Optional[Any] = None + + max_stack_length: int = int( + os.environ.get("COMPILE_STROBELIGHT_MAX_STACK_LENGTH", 500) + ) + max_profile_time: int = int( + os.environ.get("COMPILE_STROBELIGHT_MAX_PROFILE_TIME", 60 * 30) + ) + # Collect sample each x cycles. + sample_each: int = int( + float(os.environ.get("COMPILE_STROBELIGHT_SAMPLE_RATE", 1e7)) + ) + + @classmethod + def get_frame(cls) -> str: + from torch._guards import CompileContext + + return (str)(CompileContext.current_trace_id()) + + @classmethod + def enable(cls, profiler_class: Any = StrobelightCLIFunctionProfiler) -> None: + if cls.enabled: + logger.info("compile time strobelight profiling already enabled") + return + + logger.info("compile time strobelight profiling enabled") + + if profiler_class is StrobelightCLIFunctionProfiler: + import shutil + + if not shutil.which("strobeclient"): + logger.info( + "strobeclient not found, can't enable compile time strobelight profiling, seems" + "like you are not on a FB machine." + ) + return + + cls.enabled = True + cls._cls_init() + # profiler_class should have public API similar to that of StrobelightCLIFunctionProfiler. + # we have pass different functionProfilerClass for meta-internal fbcode targets. + # NB: the actual implementation in Meta is at + # fbcode/caffe2/fb/strobelight/function_profiler.py + cls.profiler = profiler_class( + sample_each=cls.sample_each, + max_profile_duration_sec=cls.max_profile_time, + stack_max_len=cls.max_stack_length, + async_stack_max_len=cls.max_stack_length, + run_user_name="pt2-profiler/" + + os.environ.get("USER", os.environ.get("USERNAME", "")), + sample_tags={cls.identifier}, # pyrefly: ignore # bad-argument-type + ) + + @classmethod + def _cls_init(cls) -> None: + cls.identifier = "{date}{pid}{hostname}".format( + date=datetime.now().strftime("%Y-%m-%d-%H:%M:%S"), + pid=os.getpid(), + hostname=gethostname(), + ) + + logger.info("Unique sample tag for this run is: %s", cls.identifier) + logger.info( + "URL to access the strobelight profile at the end of the run: %s", + get_strobelight_url(cls.identifier), + ) + + @classmethod + def _log_stats(cls) -> None: + logger.info( + "%s strobelight success runs out of %s non-recursive compilation events.", + cls.success_profile_count, + cls.success_profile_count + cls.failed_profile_count, + ) + + # TODO use threadlevel meta data to tags to record phases. + @classmethod + def profile_compile_time( + cls, func: Any, phase_name: str, *args: Any, **kwargs: Any + ) -> Any: + def skip() -> Any: + return func(*args, **kwargs) + + if not cls.enabled: + return skip() + + if cls.profiler is None: + logger.error("profiler is not set") + return + + frame_id = cls.get_frame() + + if cls.inside_profile_compile_time: + cls.ignored_profile_runs += 1 + logger.info( + "profile_compile_time is requested for phase: %s, frame %s, while already in running phase: %s," + "frame %s, recursive call ignored", + phase_name, + frame_id, + cls.current_phase, + frame_id, + ) + return skip() + + if cls.frame_id_filter is not None: + should_run = re.match(cls.frame_id_filter, frame_id) is not None + if not should_run: + logger.info( + "profiling frame %s is skipped due to frame_id_filter %s", + frame_id, + cls.frame_id_filter, + ) + return skip() + + cls.inside_profile_compile_time = True + cls.current_phase = phase_name + logger.info("profiling frame %s", frame_id) + work_result = cls.profiler.profile(func, *args, **kwargs) + + if cls.profiler.profile_result is not None: + cls.success_profile_count += 1 + else: + cls.failed_profile_count += 1 + + cls._log_stats() + cls.inside_profile_compile_time = False + return work_result diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..cdc42f39cbddaf5bdc919cef88d5f049fdba2634 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/__init__.py @@ -0,0 +1,17 @@ +import torch +from torch._subclasses.fake_tensor import ( + DynamicOutputShapeException, + FakeTensor, + FakeTensorMode, + UnsupportedFakeTensorException, +) +from torch._subclasses.fake_utils import CrossRefFakeMode + + +__all__ = [ + "FakeTensor", + "FakeTensorMode", + "UnsupportedFakeTensorException", + "DynamicOutputShapeException", + "CrossRefFakeMode", +] diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/_fake_tensor_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/_fake_tensor_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..cffa4a2216532deac32fa51a3a770236f4040f68 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/_fake_tensor_utils.py @@ -0,0 +1,263 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import Optional, TYPE_CHECKING, Union + +import torch +from torch import SymInt +from torch.fx.experimental.sym_node import SymNode +from torch.types import py_sym_types, PySymType + + +if TYPE_CHECKING: + import sympy + + from torch.fx.experimental.symbolic_shapes import ShapeEnv + + from .fake_tensor import _DispatchCacheKey, _MetadataIntLike + + +@dataclass(frozen=True, slots=True) +class _DeconstructedSymNode: + """ + Represents a SymNode without the associated ShapeEnv + """ + + # n.b. keep the same protocol as SymNode + _expr: sympy.Expr + pytype: type + _hint: Optional[Union[int, float, bool]] + constant: Optional[Union[int, float, bool]] + fx_node: torch.fx.Node + + @staticmethod + def from_node(node: SymNode) -> _DeconstructedSymNode: + return _DeconstructedSymNode( + node._expr, + node.pytype, + node._hint, + node.constant, + # pyrefly: ignore [bad-argument-type] + node.fx_node, + ) + + def extract(self, shape_env: ShapeEnv) -> SymNode: + return SymNode( + self._expr, shape_env, self.pytype, self._hint, self.constant, self.fx_node + ) + + def __str__(self) -> str: + return str(self._expr) + + def __repr__(self) -> str: + return f"_DeconstructedSymNode{{{self._expr!r}, {self.pytype!r}, {self._hint!r}, {self.constant!r}, {self.fx_node!r}}}" + + def __eq__(self, other: object) -> bool: + raise NotImplementedError + + def __hash__(self) -> int: + raise NotImplementedError + + # _value_eq to match SymNode + def _value_eq(self, other: object) -> bool: + if isinstance(other, (SymNode, _DeconstructedSymNode)): + return ( + self._expr == other._expr + and self.pytype == other.pytype + and self._hint == other._hint + and self.constant == other.constant + and self.fx_node == other.fx_node + ) + else: + return False + + # _value_hash to match SymNode + def _value_hash(self) -> int: + return hash((self._expr, self.pytype, self._hint, self.constant, self.fx_node)) + + +@dataclass(frozen=True, slots=True) +class _DeconstructedSymType: + """ + Represents a SymInt, SymFloat, SymBool without the associated ShapeEnv + """ + + ty: type[PySymType] + node: _DeconstructedSymNode + + @staticmethod + def from_sym_type(value: PySymType) -> _DeconstructedSymType: + return _DeconstructedSymType(type(value), value.node) + + def extract(self, shape_env: ShapeEnv) -> PySymType: + return self.ty(self.node.extract(shape_env)) + + def __str__(self) -> str: + return f"{self.ty}({self.node})" + + def __repr__(self) -> str: + return f"_DeconstructedSymType({self.ty}, {self.node!r})" + + def __eq__(self, other: object) -> bool: + return NotImplemented + + def __hash__(self) -> int: + return NotImplemented + + +@dataclass(frozen=True, slots=True) +class _InputBackref: + value: int + + +@dataclass(slots=True) +class _PySymInputStub: + """ + Represents a SymInt in the cached key. Needed because SymInt doesn't + support __eq__ or __hash__ directly. + """ + + # value can be: + # PySymType: This is the 'normal' SymInt value, wrapped so we can use + # hash/eq as value hash/eq (normally SymInt does object + # hash/eq). + # _DeconstructedSymType: This is used when storing the _PySymInputStub in + # the cache to avoid cyclic ShapeEnv references. + # _InputBackref: This is a back-reference to a previous _PySymInputStub in + # the key. + value: Union[PySymType, _DeconstructedSymType, _InputBackref] + + def __init__( + self, value: Union[PySymType, _DeconstructedSymType, _InputBackref] + ) -> None: + # For inputs (values in the `key`) we need to keep the PySymType intact + # - this way if we need to reuse it as an output we can properly copy + # the original value. + self.value = value + + def strip_shape_env(self) -> None: + if isinstance(self.value, py_sym_types): + self.value = _DeconstructedSymType.from_sym_type(self.value) + + def extract(self, shape_env: ShapeEnv) -> PySymType: + if isinstance(self.value, _DeconstructedSymType): + return self.value.extract(shape_env) + else: + # We should never see an _InputBackref here - anyone extracting a + # value should be pulling from the original entry (the one this + # backref points at). + assert not isinstance(self.value, _InputBackref) + return self.value + + def __str__(self) -> str: + return str(self.value) + + def __repr__(self) -> str: + return f"_PySymInputStub({self.value!r})" + + def __eq__(self, other: object) -> bool: + if not isinstance(other, _PySymInputStub): + return False + elif isinstance(self.value, _InputBackref) or isinstance( + other.value, _InputBackref + ): + return self.value == other.value + else: + return self.value.node._value_eq(other.value.node) + + def __hash__(self) -> int: + if isinstance(self.value, _InputBackref): + return hash(self.value) + else: + return self.value.node._value_hash() + + +@dataclass(slots=True) +class _SymIntOutputStub: + """ + Represents a SymInt in the cached output. + """ + + # This is either an `int` which represents the index in the key to copy the + # SymNode from or it's the deconstructed SymNode itself. + value: Union[int, _DeconstructedSymNode] + + def __init__(self, value: SymInt, key_path: Optional[int]) -> None: + if key_path is None: + self.value = _DeconstructedSymNode.from_node(value.node) + else: + self.value = key_path + + def extract(self, key: _DispatchCacheKey, shape_env: ShapeEnv) -> SymInt: + if isinstance(self.value, _DeconstructedSymNode): + return SymInt(self.value.extract(shape_env)) + else: + src = key.key[self.value] + assert isinstance(src, _PySymInputStub) and isinstance(src.value, SymInt) + return src.value + + def __repr__(self) -> str: + return f"_SymIntOutputStub({self.value!r})" + + def __eq__(self, other: object) -> bool: + raise NotImplementedError + + def __hash__(self) -> int: + raise NotImplementedError + + +@dataclass(slots=True) +class _CacheKeyState: + """ + State used while building our cache key. + """ + + # We track the SymNodes so when we get the output we can see if it exactly + # matches one of the inputs so we can uncache it properly. + sym_node_lookup: dict[int, int] # id(SymNode) -> index + + # This is a list of all seen input sympy.Symbols. We use it when building + # the cache entry to see if the output value has any symbols that we didn't + # see on input. See _has_unrepresented_symbols(). + known_symbols: set[sympy.Symbol] + + # There are cases where we're asked to perform an op when we have no + # ShapeEnv on the FakeTensorMode - but for SymNodes we MUST have a + # ShapeEnv. So as we scan if we see a SymNode (with a ShapeEnv) we record it + # here. + shape_env: Optional[ShapeEnv] + + def __init__(self, shape_env: Optional[ShapeEnv] = None) -> None: + self.sym_node_lookup = {} + self.known_symbols = set() + self.shape_env = shape_env + + def cache_on_shape_env(self) -> bool: + """ + Returns true if the CacheKey needs to be cached on the ShapeEnv + rather than the global cache. + + If our inputs contain a SymNode then we can't cache this operation on + the global cache because the cached output will implicitly depend on + guard values which might not be true on some other ShapeEnv. So unless + we're also going to cache the guards we need to cache this operation on + the ShapeEnv instead of globally. + """ + return bool(self.sym_node_lookup) + + def convert_sym_int(self, result: list[object], arg: SymInt) -> None: + node_id = id(arg.node) + if node_id in self.sym_node_lookup: + result.append(_InputBackref(self.sym_node_lookup[node_id])) + else: + self.sym_node_lookup[node_id] = len(result) + self.known_symbols.update(arg.node.expr.free_symbols) + if self.shape_env is None: + self.shape_env = arg.node.shape_env + result.append(_PySymInputStub(arg)) + + def convert_output(self, arg: _MetadataIntLike) -> _MetadataIntLike: + if isinstance(arg, SymInt): + return _SymIntOutputStub(arg, self.sym_node_lookup.get(id(arg.node), None)) + else: + return arg diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/complex_tensor/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/complex_tensor/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1ab4a816261dc088cb740c6dd85575de8a36a0f5 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/complex_tensor/__init__.py @@ -0,0 +1,9 @@ +from ._core import ComplexTensor +from ._ops import ComplexTensorMode, is_complex_tensor + + +__all__ = ["ComplexTensor", "ComplexTensorMode", "is_complex_tensor"] + +ComplexTensor.__module__ = __name__ +ComplexTensorMode.__module__ = __name__ +is_complex_tensor.__module__ = __name__ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/complex_tensor/_core.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/complex_tensor/_core.py new file mode 100644 index 0000000000000000000000000000000000000000..edd7568b2ef06dc3f3e6e9e2f67a586aa15c984f --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/complex_tensor/_core.py @@ -0,0 +1,151 @@ +from __future__ import annotations + +from typing import Any, TYPE_CHECKING +from typing_extensions import Self + +import torch +from torch import Tensor +from torch.autograd import Function + + +if TYPE_CHECKING: + from torch._ops import OpOverload + from torch._prims_common import DeviceLikeType + from torch.autograd.function import FunctionCtx + + +class ComplexTensor(Tensor): + """A class that decomposes all ops on complex Tensors into their real and imaginary parts.""" + + _re: Tensor + _im: Tensor + + def __new__(cls, real: Tensor, imag: Tensor) -> Self: + """Initialize a ComplexTensor from its real and imaginary parts.""" + from ._ops.common import REAL_TO_COMPLEX + + shape = real.shape + device = real.device + + # TODO (hameerabbasi): `torch.compile` sometimes fails here without making these + # contiguous. Why? + real = real.contiguous() + imag = imag.contiguous() + + # TODO (hameerabbasi): + # What should we do with dtype? + # We could convert to the complex type (float32 -> complex64), but we + # can't use that model for say `bfloat16` which does not have a + # corresponding complex dtype. + # If we want to support this complex rep using any float type (see + # https://github.com/pytorch/pytorch/issues/95100) + # We either need to: + # 1) add the complex types for say `complexbf32`, knowing they can't really be used anywhere + # else. + # 2) We use the real float dtype here, and it is up to the user to know + # that dtype=float here really means complex<2xSize> with dtype + # matching that of re/im parts alone + # I'm going with 1 for now, so that I can make gradcheck and some complex + # ops work properly, but might want to discuss this in the RFP. + dtype = REAL_TO_COMPLEX.get(real.dtype) + if dtype is None: + raise TypeError( + "Unsupported dtype for constituent tensors. Supported dtypes are: " + f"{set(REAL_TO_COMPLEX.keys())!r}." + ) + storage_offset = real.storage_offset() + strides = real.stride() + layout = real.layout + pin_memory = real.is_pinned() + + assert shape == imag.shape, f"Expected imag shape {shape}, got {imag.shape}" + assert device == imag.device, ( + f"Expected imag device {device}, got {imag.device}" + ) + assert real.dtype == imag.dtype, ( + f"Expected imag dtype {real.dtype}, got {imag.dtype}" + ) + assert pin_memory == imag.is_pinned(), ( + f"Expected imag pinning {pin_memory}, got {imag.is_pinned()}" + ) + + res = Tensor._make_wrapper_subclass( # type: ignore[attr-defined] + cls, + shape, + device=device, + dtype=dtype, + storage_offset=storage_offset, + strides=strides, + pin_memory=pin_memory, + layout=layout, + requires_grad=False, + ) + res._re = real.clone().detach() + res._im = imag.clone().detach() + + return res + + @property + def re(self) -> Tensor: + return self._re + + @property + def im(self) -> Tensor: + return self._im + + @classmethod + def __torch_dispatch__( + cls, + func: OpOverload, + types: tuple[type, ...], + args: tuple = (), + kwargs: dict | None = None, + ): + from ._ops.common import lookup_complex + + kwargs = {} if kwargs is None else kwargs + + impl = lookup_complex(func, *args, **kwargs) + if impl is None: + return NotImplemented + + return impl(*args, **kwargs) + + @staticmethod + def from_interleaved(t: Tensor) -> ComplexTensor: + t_real = torch.real(t) + t_imag = torch.imag(t) if t.dtype.is_complex else torch.zeros_like(t_real) + return Complex.apply(t_real, t_imag) + + def as_interleaved(self) -> Tensor: + return torch.complex(self.real, self.imag) + + @staticmethod + def __tensor_unflatten__( + inner_tensors: dict[str, Tensor], + meta: Any, + outer_size: tuple[int, ...], + outer_stride: tuple[int, ...], + ) -> ComplexTensor: + assert meta is None + re, im = inner_tensors["re"], inner_tensors["im"] + return ComplexTensor(re, im) + + def __tensor_flatten__(self) -> tuple[list[str], Any]: + return ["re", "im"], None + + def __repr__(self, *, tensor_contents=None) -> str: + return f"ComplexTensor(real={self.re!r}, imag={self.im!r})" + + def is_pinned(self, device: DeviceLikeType | None = None) -> bool: + return self.re.is_pinned(device) + + +class Complex(Function): + @staticmethod + def forward(ctx: FunctionCtx, real: Tensor, imag: Tensor) -> ComplexTensor: # type: ignore[bad-override] + return ComplexTensor(real, imag) + + @staticmethod + def backward(ctx: FunctionCtx, grad_output: ComplexTensor) -> tuple[Tensor, Tensor]: # type: ignore[bad-override] + return grad_output.real, grad_output.imag diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/complex_tensor/_ops/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/complex_tensor/_ops/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c07bdf6099b65d477e45bc7e18078eb53201dc4e --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/complex_tensor/_ops/__init__.py @@ -0,0 +1,5 @@ +from . import aten, prims +from .common import ComplexTensorMode, is_complex_tensor + + +__all__ = ["ComplexTensorMode", "is_complex_tensor", "aten", "prims"] diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/complex_tensor/_ops/aten.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/complex_tensor/_ops/aten.py new file mode 100644 index 0000000000000000000000000000000000000000..e638e5413c2cdc4878756c7878fb700d4901c551 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/complex_tensor/_ops/aten.py @@ -0,0 +1,934 @@ +from __future__ import annotations + +import warnings +from typing import TYPE_CHECKING + +import torch + +from .._core import ComplexTensor +from .common import ( + _get_func_name, + COMPLEX_TO_REAL, + complex_to_real_dtype, + is_complex, + OpType, + promote_tensors, + register_binary_nonlinear, + register_complex, + register_error, + register_force_test, + register_simple, + split_complex_arg, + split_complex_tensor, +) + + +if TYPE_CHECKING: + from collections.abc import Callable, Sequence + from typing import Any + +aten = torch.ops.aten + + +def register_binary_linear(op: OpType): + def impl_with_alpha( + lhs: ComplexTensor, rhs: ComplexTensor, *args, alpha, **kwargs + ) -> ComplexTensor: + return op(lhs, aten.mul(rhs, alpha, *args, **kwargs), *args, **kwargs) + + def impl(lhs: ComplexTensor, rhs: ComplexTensor, *args, **kwargs) -> ComplexTensor: + alpha = kwargs.pop("alpha", None) + if alpha is not None: + return impl_with_alpha(lhs, rhs, *args, alpha=alpha, **kwargs) + a_r, a_i = split_complex_arg(lhs) + b_r, b_i = split_complex_arg(rhs) + out_dt, (a_r, a_i, b_r, b_i) = promote_tensors(a_r, a_i, b_r, b_i) + u = op(a_r, b_r, *args, **kwargs) + v = op(a_i, b_i, *args, **kwargs) + return ComplexTensor(u.to(out_dt), v.to(out_dt)) + + return register_complex(op, impl) + + +@register_complex(aten.real) +def real_impl(self: ComplexTensor) -> torch.Tensor: + re, _ = split_complex_tensor(self) + return re + + +@register_complex(aten.imag) +def imag_impl(self: ComplexTensor) -> torch.Tensor: + _, im = split_complex_tensor(self) + return im + + +@register_complex(aten.is_pinned) +def is_pinned_impl(self: ComplexTensor, device: torch.device | None = None) -> bool: + return self.is_pinned(device) + + +SIMPLE_OPS_LIST = [ + aten.slice, + aten.flatten, + aten.view, + aten.diagonal, + aten.expand, + aten.unsqueeze, + aten.unsqueeze_, + aten.mean, + aten.sum, + aten.clone, + aten.neg, + aten.flip, + aten.permute, + aten.repeat, + aten.index_select, + aten.split, + aten.split_with_sizes, + aten.cumsum, + aten.detach, + aten.select, + aten.squeeze, + aten.zero_, + aten.transpose, + aten.t, + aten.gather, +] + +for simple_op in SIMPLE_OPS_LIST: + globals()[_get_func_name(simple_op)] = register_simple(simple_op) + +# TODO (hameerabbasi): Not being tested +SIMPLE_FORCE_TESTED_OPS = [ + aten.copy, + aten.col2im, + aten.alias, + aten.lift_fresh, + aten._unsafe_view, + aten.index, + aten._neg_view, + aten.avg_pool2d, + aten.avg_pool3d, + aten.avg_pool2d_backward, + aten.avg_pool3d_backward, + aten.masked_scatter_backward, + aten.select_backward, + aten.slice_backward, + aten.embedding, +] + +for simple_op in SIMPLE_FORCE_TESTED_OPS: + globals()[_get_func_name(simple_op)] = register_force_test( + simple_op, register_simple(simple_op) + ) + +del simple_op + +# some binary ops which we can stamp out +mul_impl = register_binary_nonlinear(aten.mul) +mul__impl = register_binary_nonlinear(aten.mul_) +mm_impl = register_binary_nonlinear(aten.mm) +dot_impl = register_binary_nonlinear(aten.dot) +bmm_impl = register_binary_nonlinear(aten.bmm) + +# TODO (hameerabbasi): Not being tested +convolution_impl = register_force_test( + aten.convolution, register_binary_nonlinear(aten.convolution) +) + +slice_scatter_impl = register_force_test( + aten.slice_scatter, register_binary_linear(aten.slice_scatter) +) +select_scatter_impl = register_force_test( + aten.select_scatter, register_binary_linear(aten.select_scatter) +) + +add_impl = register_binary_linear(aten.add) +add__impl = register_binary_linear(aten.add_) +sub_impl = register_binary_linear(aten.sub) +sub__impl = register_binary_linear(aten.sub_) +diagonal_scatter_impl = register_binary_linear(aten.diagonal_scatter) +fill__impl = register_binary_linear(aten.fill_) + + +@register_complex(aten.rsub) +def rsub_impl(lhs: ComplexTensor, rhs: ComplexTensor, alpha=None) -> ComplexTensor: + if alpha is None: + return torch.sub(rhs, lhs) # type: ignore[bad-return] + return torch.sub(rhs, lhs, alpha=alpha) # type: ignore[bad-return] + + +@register_complex(aten.div) +@register_complex(aten.true_divide) +def div_impl(lhs: ComplexTensor, rhs: ComplexTensor, *, rounding_mode=None): + if rounding_mode is not None: + raise NotImplementedError( + "`rounding_mode` other than `None` not implemented for`ComplexTensor`." + ) + a_r, a_i = split_complex_arg(lhs) + if not is_complex(rhs): + return ComplexTensor(a_r / rhs, a_i / rhs) + b_r, b_i = split_complex_arg(rhs) + out_dt, (a_r, a_i, b_r, b_i) = promote_tensors(a_r, a_i, b_r, b_i) + num_r = a_r * b_r + a_i * b_i + num_i = a_i * b_r - a_r * b_i + den = b_r * b_r + b_i * b_i + return ComplexTensor( + (num_r / den).to(out_dt), + (num_i / den).to(out_dt), + ) + + +@register_complex(aten.reciprocal) +def reciprocal_impl(self: ComplexTensor): + self_r, self_i = split_complex_tensor(self) + out_dt, (self_r, self_i) = promote_tensors(self_r, self_i) + den = self_r * self_r + self_i * self_i + return ComplexTensor( + aten.div(self_r, den).to(out_dt), + aten.div(-self_i, den).to(out_dt), + ) + + +# reductions +@register_complex(aten.prod) +def prod_impl(self: ComplexTensor, *args, **kwargs) -> ComplexTensor: + out_dt, (self,) = promote_tensors(self) + dtype = kwargs.pop("dtype", out_dt) + kwargs["dtype"] = complex_to_real_dtype(self.dtype) + + prod_r = torch.prod(torch.abs(self), *args, **kwargs) + sum_phi = torch.sum(torch.angle(self), *args, **kwargs) + u = prod_r * torch.cos(sum_phi) + v = prod_r * torch.sin(sum_phi) + return ComplexTensor(u, v).to(dtype) # type: ignore[bad-return] + + +@register_complex(aten.pow) +def pow_impl(self: ComplexTensor, exponent: ComplexTensor) -> ComplexTensor: + out_dt, (self, exponent) = promote_tensors(self, exponent) + return torch.exp(exponent * torch.log(self)).to(out_dt) # type: ignore[bad-return] + + +@register_complex(aten.cumprod) +def cumprod_impl(self: ComplexTensor, *args, **kwargs) -> ComplexTensor: + dtype = kwargs.pop("dtype", self.dtype) + kwargs["dtype"] = complex_to_real_dtype(dtype) + + prod_r = torch.cumprod(torch.abs(self), *args, **kwargs) + sum_phi = torch.cumsum(torch.angle(self), *args, **kwargs) + u = prod_r * torch.cos(sum_phi) + v = prod_r * torch.sin(sum_phi) + return ComplexTensor(u, v) + + +# unary funcs, +# most of these are simple or require some kind of identity +@register_complex(aten.abs) +def abs_impl(self: ComplexTensor) -> torch.Tensor: + x, y = split_complex_tensor(self) + out_dt, (x, y) = promote_tensors(x, y) + result = torch.hypot(x, y) + return result.to(out_dt) + + +@register_complex(aten.angle) +def angle_impl(self: ComplexTensor) -> torch.Tensor: + x, y = split_complex_tensor(self) + return torch.atan2(y, x) + + +@register_complex(aten.acos) +def acos_impl(self: ComplexTensor) -> ComplexTensor: + _, y = split_complex_tensor(self) + acosh_z = torch.acosh(self) + assert isinstance(acosh_z, ComplexTensor) + acosh_z_re, acosh_z_im = split_complex_tensor(acosh_z) + sign_im = 2 * torch.signbit(y) - 1 + return ComplexTensor(torch.abs(acosh_z_im), sign_im * torch.abs(acosh_z_re)) + + +@register_complex(aten.asin) +def asin_impl(self: ComplexTensor) -> ComplexTensor: + x, y = split_complex_tensor(self) + asinh_iz = torch.asinh(ComplexTensor(-y, x)) + assert isinstance(asinh_iz, ComplexTensor) + asinh_iz_re, asinh_iz_im = split_complex_tensor(asinh_iz) + return ComplexTensor(asinh_iz_im, -asinh_iz_re) + + +@register_complex(aten.atan) +def atan_impl(self: ComplexTensor) -> ComplexTensor: + x, y = split_complex_tensor(self) + tanh_iz = torch.atanh(ComplexTensor(-y, x)) + assert isinstance(tanh_iz, ComplexTensor) + tanh_iz_re, tanh_iz_im = split_complex_tensor(tanh_iz) + return ComplexTensor(tanh_iz_im, -tanh_iz_re) + + +@register_complex(aten.asinh) +def asinh_impl(self: ComplexTensor) -> ComplexTensor: + out_dt, (self,) = promote_tensors(self) + return torch.log(self + torch.sqrt(self * self + 1)).to(out_dt) # type: ignore[bad-return] + + +@register_complex(aten.acosh) +def acosh_impl(self: ComplexTensor) -> ComplexTensor: + out_dt, (self,) = promote_tensors(self) + return torch.log(self + torch.sqrt(self * self - 1)).to(out_dt) # type: ignore[bad-return] + + +@register_complex(aten.atanh) +def atanh_impl(self: ComplexTensor) -> ComplexTensor: + x, y = split_complex_tensor(self) + out_dt, (x, y) = promote_tensors(x, y) + + ret = 0.5 * ( + torch.log(ComplexTensor(1 + x, y)) - torch.log(ComplexTensor(1 - x, -y)) + ) + assert isinstance(ret, ComplexTensor) + ret_re, ret_im = split_complex_tensor(ret) + + return ComplexTensor(ret_re.to(out_dt), ret_im.to(out_dt)) + + +@register_complex(aten.cos) +def cos_impl(self: ComplexTensor) -> ComplexTensor: + x, y = split_complex_tensor(self) + return torch.cosh(ComplexTensor(-y, x)) # type: ignore[bad-return] + + +@register_complex(aten.cosh) +def cosh_impl(self: ComplexTensor) -> ComplexTensor: + x, y = split_complex_tensor(self) + out_dt, (x, y) = promote_tensors(x, y) + u = torch.cosh(x) * torch.cos(y) + v = torch.sinh(x) * torch.sin(y) + return ComplexTensor(u.to(out_dt), v.to(out_dt)) + + +@register_complex(aten.sin) +def sin_impl(self: ComplexTensor) -> ComplexTensor: + x, y = split_complex_tensor(self) + sinh_iz = torch.sinh(ComplexTensor(-y, x)) + assert isinstance(sinh_iz, ComplexTensor) + sinh_iz_re, sinh_iz_im = split_complex_tensor(sinh_iz) + return ComplexTensor(sinh_iz_im, -sinh_iz_re) + + +@register_complex(aten.sinh) +def sinh_impl(self: ComplexTensor) -> ComplexTensor: + x, y = split_complex_tensor(self) + out_dt, (x, y) = promote_tensors(x, y) + u = torch.sinh(x) * torch.cos(y) + v = torch.cosh(x) * torch.sin(y) + return ComplexTensor(u.to(out_dt), v.to(out_dt)) + + +@register_complex(aten.tan) +def tan_impl(self: ComplexTensor) -> ComplexTensor: + x, y = split_complex_tensor(self) + tanh_iz = torch.tanh(ComplexTensor(-y, x)) + assert isinstance(tanh_iz, ComplexTensor) + tanh_iz_re, tanh_iz_im = split_complex_tensor(tanh_iz) + return ComplexTensor(tanh_iz_im, -tanh_iz_re) + + +@register_complex(aten.tanh) +def tanh_impl(self: ComplexTensor) -> ComplexTensor: + x, y = split_complex_tensor(self) + out_dt, (x, y) = promote_tensors(x, y) + + _2x = 2 * x + _2y = 2 * y + _d = torch.cosh(_2x) + torch.cos(_2y) + _2xsh = torch.sinh(_2x) + + out_re = _2xsh / _d + out_im = torch.sin(_2y) / _d + + return ComplexTensor(out_re.to(out_dt), out_im.to(out_dt)) + + +@register_complex(aten.exp) +def exp_impl(self: ComplexTensor) -> ComplexTensor: + x, y = split_complex_tensor(self) + out_dt, (x, y) = promote_tensors(x, y) + ex = torch.exp(x) + u = ex * torch.cos(y) + v = ex * torch.sin(y) + return ComplexTensor(u.to(out_dt), v.to(out_dt)) + + +@register_complex(aten.expm1) +def expm1_impl(self: ComplexTensor) -> ComplexTensor: + x, y = split_complex_tensor(self) + out_dt, (x, y) = promote_tensors(x, y) + # TODO (hameerabbasi): The two lines below may have numerical issues + ex = torch.exp(x) + u = ex * torch.cos(y) - 1 + v = ex * torch.sin(y) + return ComplexTensor(u.to(out_dt), v.to(out_dt)) + + +@register_complex(aten.log) +def log_impl(self: ComplexTensor) -> ComplexTensor: + out_dt, (self,) = promote_tensors(self) + re = torch.log(torch.abs(self)) + im = torch.angle(self) + return ComplexTensor(re, im).to(out_dt) # type: ignore[bad-return] + + +@register_complex(aten.log1p) +def log1p_impl(self: ComplexTensor) -> ComplexTensor: + x, y = split_complex_tensor(self) + # TODO (hameerabbasi): The line below may have numerical issues + return torch.log(ComplexTensor(x + 1, y)) # type: ignore[bad-return] + + +@register_complex(aten.any) +def any_impl(self: ComplexTensor, *args, **kwargs) -> torch.Tensor: + x, y = split_complex_tensor(self) + return torch.any(x, *args, **kwargs) | torch.any(y, *args, **kwargs) + + +@register_complex(aten.all) +def all_impl(self: ComplexTensor, *args, **kwargs) -> torch.Tensor: + x, y = split_complex_tensor(self) + return torch.any(x, *args, **kwargs) & torch.any(y, *args, **kwargs) + + +@register_complex(aten.eq) +def eq_impl(self: ComplexTensor, rhs: ComplexTensor, *args, **kwargs) -> torch.Tensor: + a_r, a_i = split_complex_arg(self) + b_r, b_i = split_complex_arg(rhs) + return torch.eq(a_r, b_r, *args, **kwargs) & torch.eq(a_i, b_i, *args, **kwargs) + + +@register_complex(aten.ne) +def ne_impl(self: ComplexTensor, rhs: ComplexTensor, *args, **kwargs) -> torch.Tensor: + a_r, a_i = split_complex_tensor(self) + b_r, b_i = split_complex_arg(rhs) + return torch.ne(a_r, b_r, *args, **kwargs) | torch.ne(a_i, b_i, *args, **kwargs) + + +@register_complex(aten.isnan) +def isnan_impl(self: ComplexTensor) -> torch.Tensor: + re, im = split_complex_tensor(self) + return torch.isnan(re) | torch.isnan(im) + + +@register_complex(aten.isinf) +def isinf_impl(self: ComplexTensor) -> torch.Tensor: + re, im = split_complex_tensor(self) + return torch.isinf(re) | torch.isinf(im) + + +@register_complex(aten.isfinite) +def isfinite_impl(self: ComplexTensor) -> torch.Tensor: + re, im = split_complex_tensor(self) + return torch.isfinite(re) & torch.isfinite(im) + + +@register_complex(aten.isclose) +def isclose_impl( + self: ComplexTensor, + rhs: ComplexTensor, + rtol=1e-5, + atol=1e-8, + equal_nan: bool = False, +) -> torch.Tensor: + abs_diff = torch.abs(self - rhs) + abs_other = torch.abs(rhs) + basic_condition = abs_diff <= (rtol * abs_other + atol) + + # This is the nontrivial part + if equal_nan: + a_r, a_i = split_complex_tensor(self) + b_r, b_i = split_complex_arg(rhs) + + a_r_nan = torch.isnan(a_r) + b_r_nan = torch.isnan(b_r) + a_i_nan = torch.isnan(a_i) + b_i_nan = torch.isnan(b_i) + a_nan = a_r_nan | a_i_nan + + # This logical expression makes sure that the isnan of both the real and imaginary parts + # matches (so 1 + nan*i doesn't equal nan + 1*i) + equal_nan_condition = ((a_r_nan == b_r_nan) & (a_i_nan == b_i_nan)) & a_nan + return basic_condition | equal_nan_condition + + return basic_condition + + +ERROR_OPS_LIST = [ + aten.lt, + aten.le, + aten.gt, + aten.ge, + aten.amin, + aten.amax, + aten.clamp, + aten.ceil, + aten.floor, + aten.minimum, + aten.maximum, + aten.trunc, + aten.sign, + aten.argmax, + aten.argmin, + aten.sort, + aten.topk, + aten.round, + aten.fmod, +] + + +ERROR_TYPES = { + aten.minimum: RuntimeError, + aten.maximum: RuntimeError, + aten.argmax: RuntimeError, + aten.argmin: RuntimeError, + aten.sort: RuntimeError, + aten.topk: RuntimeError, +} + + +for err_op in ERROR_OPS_LIST: + globals()[_get_func_name(err_op)] = register_error( + err_op, ERROR_TYPES.get(err_op, NotImplementedError) + ) + +del err_op + + +@register_complex(aten.masked_scatter) +def masked_scatter_impl( + self: ComplexTensor, mask: torch.Tensor, source: ComplexTensor +) -> ComplexTensor: + self_r, self_i = split_complex_tensor(self) + source_r, source_i = split_complex_arg(source) + ret_r = torch.masked_scatter(self_r, mask, source_r) + ret_i = torch.masked_scatter(self_i, mask, source_i) + + return ComplexTensor(ret_r, ret_i) + + +@register_complex(aten.where) +def where_impl(mask: torch.Tensor, x: ComplexTensor, y: ComplexTensor) -> ComplexTensor: + x_r, x_i = split_complex_arg(x) + y_r, y_i = split_complex_arg(y) + + ret_r = torch.where(mask, x_r, y_r) + ret_i = torch.where(mask, x_i, y_i) + + return ComplexTensor(ret_r, ret_i) + + +@register_complex(aten.full_like) +def full_like_impl( + input: ComplexTensor, + fill_value: complex, + *args, + dtype: torch.dtype | None = None, + **kwargs, +) -> torch.Tensor | ComplexTensor: + # Note: Cannot be merged with the cases below due to the `fill_value` argument + input_r, input_i = split_complex_tensor(input) + if dtype is not None and dtype not in COMPLEX_TO_REAL: + return torch.full_like(input_r, fill_value, *args, dtype=dtype, **kwargs) + + if dtype is not None: + kwargs["dtype"] = COMPLEX_TO_REAL[dtype] + + fv_r, fv_i = split_complex_arg(fill_value) + ret_r = torch.full_like(input_r, fv_r, *args, **kwargs) + ret_i = torch.full_like(input_i, fv_i, *args, **kwargs) + + return ComplexTensor(ret_r, ret_i) + + +def register_like(op: OpType) -> Callable[..., torch.Tensor | ComplexTensor]: + def impl( + self: ComplexTensor, *args, dtype: torch.dtype | None = None, **kwargs + ) -> torch.Tensor | ComplexTensor: + self_re, self_im = split_complex_tensor(self) + + if dtype is not None and dtype not in COMPLEX_TO_REAL: + return op(self_re, *args, dtype=dtype, **kwargs) + + if dtype is not None: + kwargs["dtype"] = COMPLEX_TO_REAL[dtype] + + ret_re = op(self_re, *args, **kwargs) + ret_im = op(self_im, *args, **kwargs) + + return ComplexTensor(ret_re, ret_im) + + func_name = _get_func_name(op) + impl.__name__ = func_name + impl.__qualname__ = func_name + + return register_complex(op, impl) + + +LIKE_OPS_LIST = [ + aten.empty_like, + aten.zeros_like, + aten.randn_like, + aten.new_zeros, +] + +for like_op in LIKE_OPS_LIST: + globals()[_get_func_name(like_op)] = register_like(like_op) + +del like_op + + +@register_complex(aten.cat) +def cat_impl(tensors: Sequence[ComplexTensor], dim: int = 0) -> ComplexTensor: + tensors_r = [] + tensors_i = [] + + for t in tensors: + t_r, t_i = split_complex_arg(t) + tensors_r.append(t_r) + tensors_i.append(t_i) + + ret_r = torch.cat(tensors_r, dim=dim) + ret_i = torch.cat(tensors_i, dim=dim) + + return ComplexTensor(ret_r, ret_i) + + +@register_complex(aten.sgn) +def sgn_impl(self: ComplexTensor) -> ComplexTensor: + self_r, self_i = split_complex_tensor(self) + out_dt, (self_r, self_i) = promote_tensors(self_r, self_i) + abs_self = torch.abs(ComplexTensor(self_r, self_i)) + mask = (self_r != 0) | (self_i != 0) + masked_sgn = ComplexTensor( + (self_r / abs_self).to(out_dt), (self_i / abs_self).to(out_dt) + ) + return torch.where(mask, masked_sgn, 0) # type: ignore[bad-return] + + +@register_complex(aten.sqrt) +def sqrt_impl(self: ComplexTensor) -> ComplexTensor: + self_r, self_i = split_complex_tensor(self) + out_dt, (self_r, self_i) = promote_tensors(self_r, self_i) + self = ComplexTensor(self_r, self_i) + self_abs_sqrt = torch.sqrt(torch.abs(self)) + self_half_angle = 0.5 * torch.angle(self) + + ret_r = self_abs_sqrt * torch.cos(self_half_angle) + ret_i = self_abs_sqrt * torch.sin(self_half_angle) + + return ComplexTensor(ret_r.to(out_dt), ret_i.to(out_dt)) + + +@register_complex(aten.rsqrt) +def rsqrt_impl(self: ComplexTensor) -> ComplexTensor: + self_r, self_i = split_complex_tensor(self) + out_dt, (self_r, self_i) = promote_tensors(self_r, self_i) + self = ComplexTensor(self_r, self_i) + self_abs_rsqrt = torch.rsqrt(torch.abs(self)) + self_neg_half_angle = -0.5 * torch.angle(self) + + ret_r = self_abs_rsqrt * torch.cos(self_neg_half_angle) + ret_i = self_abs_rsqrt * torch.sin(self_neg_half_angle) + + return ComplexTensor(ret_r.to(out_dt), ret_i.to(out_dt)) + + +@register_complex(aten.addmm) +def addmm_impl( + input: ComplexTensor, + mat1: ComplexTensor, + mat2: ComplexTensor, + out_dtype: torch.dtype | None = None, + beta: complex = 1, + alpha: complex = 1, +) -> ComplexTensor: + ret = beta * input + alpha * torch.mm(mat1, mat2) + assert isinstance(ret, ComplexTensor) + ret_r, ret_i = split_complex_tensor(ret) + if out_dtype is not None: + out_dtype = COMPLEX_TO_REAL[out_dtype] + ret_r, ret_i = ret_r.to(out_dtype), ret_i.to(out_dtype) + return ComplexTensor(ret_r, ret_i) + + +def elemwise_nonzero(self: ComplexTensor) -> torch.Tensor: + re, im = split_complex_tensor(self) + return (re != 0) | (im != 0) + + +def register_nonzero_impl(op: OpType): + def nonzero_impl( + self: ComplexTensor, other: ComplexTensor, *args, **kwargs + ) -> torch.Tensor: + return op(elemwise_nonzero(self), elemwise_nonzero(other), *args, **kwargs) + + func_name = _get_func_name(op) + nonzero_impl.__name__ = func_name + nonzero_impl.__qualname__ = func_name + + return register_complex(op, nonzero_impl) + + +logical_and_impl = register_nonzero_impl(aten.logical_and) +logical_or_impl = register_nonzero_impl(aten.logical_or) +logical_xor_impl = register_nonzero_impl(aten.logical_xor) + + +@register_complex(aten.logical_not) +def logical_not_impl(self: ComplexTensor, *args, **kwargs) -> torch.Tensor: + return torch.logical_not(elemwise_nonzero(self), *args, **kwargs) + + +@register_complex(aten.view_as_real) +def view_as_real_impl(self: ComplexTensor) -> torch.Tensor: + re, im = split_complex_tensor(self) + return torch.stack([re, im], dim=-1) + + +@register_complex(aten.linalg_vector_norm) +def linalg_vector_norm_impl(self: ComplexTensor, *args, **kwargs) -> torch.Tensor: + return torch.linalg.vector_norm(torch.abs(self), *args, **kwargs) + + +@register_force_test(aten.copy_) +def copy__impl( + self: ComplexTensor | torch.Tensor, + src: ComplexTensor | torch.Tensor, + *args, + **kwargs, +) -> ComplexTensor | torch.Tensor: + if not self.dtype.is_complex: + warnings.warn( + "Casting complex values to real discards the imaginary part", UserWarning + ) + src_re, src_im = split_complex_arg(src) + return self.copy_(src_re) + + self_re, self_im = split_complex_arg(self) + src_re, src_im = split_complex_arg(src) + + ret_re = self_re.copy_(src_re, *args, **kwargs) + ret_im = self_im.copy_(src_im, *args, **kwargs) + + return ComplexTensor(ret_re, ret_im) + + +@register_complex(aten._local_scalar_dense) +def _local_scalar_dense_impl(self: ComplexTensor, *args, **kwargs) -> complex: + x, y = split_complex_tensor(self) + u = aten._local_scalar_dense(x, *args, **kwargs) + v = aten._local_scalar_dense(y, *args, **kwargs) + return complex(u, v) + + +@register_complex(aten.allclose) +def allclose_impl( + input: torch.Tensor, + other: torch.Tensor, + rtol: float = 1e-05, + atol: float = 1e-08, + equal_nan: bool = False, +) -> bool: + return torch.all( + torch.isclose(input, other, rtol=rtol, atol=atol, equal_nan=equal_nan) + ).item() # type: ignore[bad-return] + + +@register_complex(aten.stack) +def stack_impl(self: list[ComplexTensor], *args, **kwargs) -> ComplexTensor: + re_im_tuples = [split_complex_arg(self_i) for self_i in self] + u = torch.stack([c[0] for c in re_im_tuples], *args, **kwargs) + v = torch.stack([c[1] for c in re_im_tuples], *args, **kwargs) + return ComplexTensor(u, v) + + +# TODO (hameerabbasi): Not being tested +@register_complex(aten._conj_physical) +@register_complex(aten.conj_physical) +def conj_physical_impl(self: ComplexTensor) -> ComplexTensor: + re, im = split_complex_tensor(self) + return ComplexTensor(re, -im) + + +# TODO (hameerabbasi): Not being tested +@register_complex(aten._conj) +def _conj_impl(self: ComplexTensor) -> ComplexTensor: + re, im = split_complex_tensor(self) + return ComplexTensor(re, torch._neg_view(im)) + + +@register_complex(aten.index_add) +def index_add_impl( + self: ComplexTensor, dim: int, index: torch.Tensor, source: ComplexTensor, **kwargs +) -> ComplexTensor: + alpha = kwargs.pop("alpha", None) + if alpha is not None: + source = source * alpha + self_re, self_im = split_complex_arg(self) + source_re, source_im = split_complex_arg(source) + + ret_re = self_re.index_add(dim, index, source_re) + ret_im = self_im.index_add(dim, index, source_im) + + return ComplexTensor(ret_re, ret_im) + + +# TODO (hameerabbasi): Not being tested +@register_complex(aten.index_add_) +def index_add__impl( + self: ComplexTensor, dim: int, index: torch.Tensor, source: ComplexTensor, **kwargs +) -> ComplexTensor: + alpha = kwargs.pop("alpha", None) + if alpha is not None: + source = source * alpha + + self_re, self_im = split_complex_arg(self) + source_re, source_im = split_complex_arg(source) + + ret_re = self_re.index_add_(dim, index, source_re) + ret_im = self_im.index_add_(dim, index, source_im) + + return ComplexTensor(ret_re, ret_im) + + +@register_complex(aten.masked_fill) +def masked_fill_impl( + self: ComplexTensor, mask: torch.Tensor, value: complex +) -> ComplexTensor: + self_re, self_im = split_complex_arg(self) + value_re, value_im = split_complex_arg(value) + + ret_re = self_re.masked_fill(mask, value_re) + ret_im = self_im.masked_fill(mask, value_im) + + return ComplexTensor(ret_re, ret_im) + + +# TODO (hameerabbasi): Not being tested +@register_complex(aten.masked_fill_) +def masked_fill__impl( + self: ComplexTensor, mask: torch.Tensor, value: complex +) -> ComplexTensor: + self_re, self_im = split_complex_arg(self) + value_re, value_im = split_complex_arg(value) + + ret_re = self_re.masked_fill_(mask, value_re) + ret_im = self_im.masked_fill_(mask, value_im) + + return ComplexTensor(ret_re, ret_im) + + +@register_complex(aten.constant_pad_nd) +def constant_pad_nd_impl( + self: ComplexTensor, pad, value: complex | None = None +) -> ComplexTensor: + self_re, self_im = split_complex_tensor(self) + if value is None: + ret_re = aten.constant_pad_nd(self_re, pad) + ret_im = aten.constant_pad_nd(self_im, pad) + else: + value_re, value_im = split_complex_arg(value) + ret_re = aten.constant_pad_nd(self_re, pad, value_re) + ret_im = aten.constant_pad_nd(self_im, pad, value_im) + + return ComplexTensor(ret_re, ret_im) + + +@register_complex(aten.var) +def var_impl(self: ComplexTensor, *args, **kwargs) -> torch.Tensor: + self_re, self_im = split_complex_tensor(self) + return torch.var(self_re, *args, **kwargs) + torch.var(self_im, *args, **kwargs) + + +@register_complex(aten.scatter_add) +def scatter_add_impl( + self: ComplexTensor, dim, index, src: ComplexTensor +) -> ComplexTensor: + self_re, self_im = split_complex_arg(self) + src_re, src_im = split_complex_arg(src) + + ret_re = torch.scatter_add(self_re, dim, index, src_re) + ret_im = torch.scatter_add(self_im, dim, index, src_im) + + return ComplexTensor(ret_re, ret_im) + + +@register_complex(aten.scatter_add_) +def scatter_add__impl( + self: ComplexTensor, dim, index, src: ComplexTensor +) -> ComplexTensor: + self_re, self_im = split_complex_arg(self) + src_re, src_im = split_complex_arg(src) + + out_re = self_re.scatter_add_(dim, index, src_re) + out_im = self_im.scatter_add_(dim, index, src_im) + + return ComplexTensor(out_re, out_im) + + +@register_complex(aten.index_put_) +def index_put__impl( + self: ComplexTensor, + indices: tuple[torch.Tensor, ...], + values: ComplexTensor, + accumulate: bool = False, +) -> ComplexTensor: + self_re, self_im = split_complex_arg(self) + values_re, values_im = split_complex_arg(values) + + out_re = self_re.index_put_(indices, values_re, accumulate=accumulate) + out_im = self_im.index_put_(indices, values_im, accumulate=accumulate) + + return ComplexTensor(out_re, out_im) + + +@register_complex(aten.tanh_backward) +def tanh_backward(out_grad: torch.Tensor, y: torch.Tensor): + return out_grad * (1.0 - y * y).conj_physical() + + +@register_complex(aten.diagonal_backward) +def diagonal_backward( + grad_output: torch.Tensor, input_sizes: list[int], offset: int, dim1: int, dim2: int +): + grad_input = grad_output.new_zeros(input_sizes) + return torch.diagonal_scatter(grad_input, grad_output, offset, dim1, dim2) + + +def _dt_to_real(dt: torch.dtype | Any) -> torch.dtype | Any: + if not isinstance(dt, torch.dtype): + return dt + + return COMPLEX_TO_REAL[dt] + + +def register_to_impl(op: OpType): + """Register an op similar to `aten.to`, but may have different signatures.""" + + def impl(self: ComplexTensor, *args, **kwargs) -> torch.Tensor | ComplexTensor: + x, y = split_complex_tensor(self) + try: + args = tuple(_dt_to_real(a) for a in args) + kwargs = {k: _dt_to_real(v) for k, v in kwargs.items()} + except KeyError: + return op(x, *args, **kwargs) + + return ComplexTensor(op(x, *args, **kwargs), op(y, *args, **kwargs)) + + func_name = _get_func_name(op) + impl.__name__ = func_name + impl.__qualname__ = func_name + + return register_complex(op, impl) + + +to_impl = register_to_impl(aten.to) +_to_copy_impl = register_to_impl(aten._to_copy) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/complex_tensor/_ops/common.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/complex_tensor/_ops/common.py new file mode 100644 index 0000000000000000000000000000000000000000..88532efe224bba013b221000a988b594ea01b2cf --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/complex_tensor/_ops/common.py @@ -0,0 +1,317 @@ +from collections.abc import Callable +from typing import Any, overload, TypeAlias +from typing_extensions import TypeIs + +import torch +from torch import Tensor +from torch._decomp import get_decompositions +from torch._ops import OpOverload, OpOverloadPacket +from torch._refs import is_complex as _is_complex +from torch.types import Number +from torch.utils._python_dispatch import TorchDispatchMode +from torch.utils._pytree import tree_flatten, tree_map, tree_unflatten + +from .._core import ComplexTensor + + +OpType: TypeAlias = OpOverloadPacket | OpOverload + +TableType: TypeAlias = dict[OpType, Callable] + +# Mapping from ops to implementations +COMPLEX_OPS_TABLE: TableType = {} + +COMPLEX_TO_REAL = { + torch.complex128: torch.float64, + torch.complex64: torch.float32, + torch.complex32: torch.float16, +} + +REAL_TO_COMPLEX = {v: k for k, v in COMPLEX_TO_REAL.items()} + +# Used to promote dtypes in `promote_real_cpu_tensors` +PROMOTE_TYPES = { + torch.float16: torch.float32, + torch.bfloat16: torch.float32, + torch.complex32: torch.complex64, +} + + +def is_complex_tensor(obj: Any, /) -> TypeIs[ComplexTensor]: + r"""Returns True if the input is a ComplexTensor, else False + + Args: + a: any input + + Examples: + + >>> # xdoctest: +SKIP + >>> from torch.complex import ComplexTensor + >>> data = torch.zeros((3, 2), dtype=torch.complex64) + >>> ct = ComplexTensor.from_interleaved(data) + >>> is_complex_tensor(ct) + True + """ + return isinstance(obj, ComplexTensor) + + +@overload +def promote_tensors( + *tensors: ComplexTensor, +) -> tuple[torch.dtype, tuple[ComplexTensor, ...]]: ... + + +@overload +def promote_tensors( + *tensors: Tensor, +) -> tuple[torch.dtype, tuple[Tensor, ...]]: ... + + +def promote_tensors( + *tensors: Tensor | ComplexTensor, +) -> tuple[torch.dtype, tuple[Tensor | ComplexTensor, ...]]: + """ + Promotes all tensors to a common dtype. + Additionally promotes CPU tensors to at least `float32`. + """ + tensor = next(t for t in tensors if isinstance(t, Tensor)) + out_dt = tensor.dtype + for t in tensors: + if isinstance(t, Tensor): + out_dt = torch.promote_types(out_dt, t.dtype) + + prom_dt = PROMOTE_TYPES.get(out_dt, out_dt) + return out_dt, tuple( + t.to(prom_dt) if isinstance(t, Tensor) else torch.asarray(t, dtype=prom_dt) + for t in tensors + ) + + +def register_complex( + op: OpType, + func_impl: Callable | None = None, +): + """Decorator to register an implementation for some ops in some dispatch tables""" + + def inner(func): + if COMPLEX_OPS_TABLE.get(op, func) is not func: + raise RuntimeError(f"Attempted to register multiple functions for {op}") + COMPLEX_OPS_TABLE[op] = func + return func + + if func_impl is None: + return inner + + return inner(func_impl) + + +FORCE_TEST_LIST: list[OpType] = [] + + +def register_force_test(op: OpType, *args, **kwargs): + """Will attempt to test these ops even if they err on "normal" inputs""" + FORCE_TEST_LIST.append(op) + return register_complex(op, *args, **kwargs) + + +DECOMPOSITIONS = get_decompositions(list(torch.ops.aten)) # type: ignore[no-matching-overload] + + +def lookup_complex(func: OpOverload, *args, **kwargs) -> Callable | None: + """ + Lookup an impl from the table. + + Try the particular overload first, then the overload packet. + + If nothing is found, try the decompositions with both. + """ + return COMPLEX_OPS_TABLE.get( + func, + COMPLEX_OPS_TABLE.get( + func.overloadpacket, + DECOMPOSITIONS.get(func, DECOMPOSITIONS.get(func.overloadpacket)), + ), + ) + + +def is_complex(x: Any, /) -> bool: + """Utility to detect if a given object is (known) to be complex.""" + return (isinstance(x, Tensor) and _is_complex(x)) or isinstance(x, complex) + + +@overload +def split_complex_arg( + arg: Tensor | ComplexTensor, +) -> tuple[Tensor, Tensor]: ... + + +@overload +def split_complex_arg( + arg: complex | Number, +) -> tuple[Number, Number]: ... + + +def split_complex_arg( + arg: Tensor | ComplexTensor | complex | Number, +) -> tuple[Tensor, Tensor] | tuple[Number, Number]: + """ + Split a complex argument into a real/imaginary component. + + If real, use zero for the imaginary part. + """ + if isinstance(arg, ComplexTensor): + return split_complex_tensor(arg) + if isinstance(arg, Tensor): + if is_complex(arg): + return arg.real, arg.imag + return arg, torch.zeros_like(arg) + # TODO (hameerabbasi): Should there be a `torch.SymComplex`? + if isinstance(arg, complex): + return arg.real, arg.imag + if isinstance(arg, float | torch.SymFloat): + return arg, 0.0 + if isinstance(arg, int | torch.SymInt): + return arg, 0 + if isinstance(arg, bool | torch.SymBool): + return arg, False + raise TypeError(f"Expected tensor or number got, {type(arg)}") + + +def split_complex_tensor(complex_tensor: ComplexTensor) -> tuple[Tensor, Tensor]: + """Split a ComplexTensor into its real and imaginary parts.""" + return complex_tensor.re, complex_tensor.im + + +def complex_to_real_dtype(dtype: torch.dtype) -> torch.dtype: + """Convert a complex dtype to the dtype of its real part. Return other dtypes as-is.""" + return COMPLEX_TO_REAL.get(dtype, dtype) + + +def _get_op_name(op: OpType) -> str: + """Get the op name from the op.""" + if isinstance(op, OpOverload): + op = op.overloadpacket + return str(op).split(".", 1)[1] + + +def _get_func_name(op: OpType) -> str: + """Get the name of the implementation function from the op.""" + return f"{_get_op_name(op)}_impl" + + +def register_error(op: OpType, exc_type: type[Exception] = NotImplementedError): + msg = f"`aten.{_get_op_name(op)}` not implemented for `{ComplexTensor.__name__}`." + + def ordered_impl(*args, **kwargs): + raise exc_type(msg) + + func_name = _get_func_name(op) + ordered_impl.__name__ = func_name + ordered_impl.__qualname__ = func_name + + return register_force_test(op, ordered_impl) + + +def register_binary_nonlinear(op: OpType) -> Callable: + """Register a "multiplication-style" op, e.g. aten.mul, aten.mm, ...""" + + def impl(lhs: ComplexTensor, rhs: ComplexTensor, *args, **kwargs) -> ComplexTensor: + a_r, a_i = split_complex_arg(lhs) + b_r, b_i = split_complex_arg(rhs) + out_dt, (a_r, a_i, b_r, b_i) = promote_tensors(a_r, a_i, b_r, b_i) + real = op(a_r, b_r, *args, **kwargs) - op(a_i, b_i, *args, **kwargs) + imag = op(a_r, b_i, *args, **kwargs) + op(a_i, b_r, *args, **kwargs) + return ComplexTensor(real.to(out_dt), imag.to(out_dt)) + + func_name = _get_func_name(op) + impl.__name__ = func_name + impl.__qualname__ = func_name + + return register_complex(op, impl) + + +def register_simple(op: OpType): + """Register an op which can be applied independently to the real and complex parts to get the result.""" + + def impl( + self: ComplexTensor, *args, dtype: torch.dtype | None = None, **kwargs + ) -> ComplexTensor: + x, y = split_complex_tensor(self) + if dtype is not None and dtype not in COMPLEX_TO_REAL: + raise RuntimeError( + "Non-complex `dtype` specified, please write custom impl." + ) + + if dtype in COMPLEX_TO_REAL: + assert dtype is not None + kwargs["dtype"] = COMPLEX_TO_REAL[dtype] + + u = op(x, *args, **kwargs) + v = op(y, *args, **kwargs) + + u_flat, u_spec = tree_flatten(u) + v_flat, v_spec = tree_flatten(v) + assert u_spec == v_spec + out_flat = [ + ComplexTensor(ui, vi) for ui, vi in zip(u_flat, v_flat, strict=False) + ] + return tree_unflatten(out_flat, u_spec) + + func_name = _get_func_name(op) + impl.__name__ = func_name + impl.__qualname__ = func_name + + return register_complex(op, impl) + + +def _as_complex_tensor(arg: Tensor | Any) -> Tensor | ComplexTensor | Any: + """Convert a Tensor with complex dtypes to a ComplexTensor. Pass along other args as-is.""" + if ( + not isinstance(arg, ComplexTensor) + and isinstance(arg, Tensor) + and arg.dtype in COMPLEX_TO_REAL + ): + return ComplexTensor.from_interleaved(arg) + return arg + + +def _as_interleaved(arg: ComplexTensor | Any) -> Tensor | Any: + """Convert a ComplexTensor to a Tensor with a complex dtype. Pass other arguments as-is.""" + if isinstance(arg, ComplexTensor): + return arg.as_interleaved() + return arg + + +class ComplexTensorMode(TorchDispatchMode): + _compile: bool + + """ A TorchDispatchMode to replace any Tensor that has a complex dtype with a ComplexTensor for the computation. """ + + def __init__(self, _dispatch_key=None, *, _compile: bool = False): + """Initialize a ComplexTensorMode. + + Args: + _dispatch_key: passed on to TorchDispatchMode + _compile: Compile the op before the computation + """ + super().__init__(_dispatch_key) + self._compile = _compile + + def __torch_dispatch__( + self, + func: OpOverload, + types: tuple[type], + args: tuple = (), + kwargs: dict[str, Any] | None = None, + ): + if kwargs is None: + kwargs = {} + + # TODO (hameerabbasi): Test perf with `_compile` set to `True` + if self._compile: + func = torch.compile(func) # type: ignore[bad-assignment] + + args = tree_map(_as_complex_tensor, args) + kwargs = tree_map(_as_complex_tensor, kwargs) + + return tree_map(_as_interleaved, func(*args, **kwargs)) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/complex_tensor/_ops/prims.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/complex_tensor/_ops/prims.py new file mode 100644 index 0000000000000000000000000000000000000000..9a237b32d99042a649632a432290919ea4db9c46 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/complex_tensor/_ops/prims.py @@ -0,0 +1,34 @@ +import torch + +from .._core import ComplexTensor +from .common import ( + complex_to_real_dtype, + register_complex, + register_force_test, + split_complex_tensor, +) + + +prims = torch.ops.prims +aten = torch.ops.aten + + +# TODO (hameerabbasi): Not being tested +@register_force_test(prims.convert_element_type) +def convert_element_type_impl(x: ComplexTensor, dtype: torch.dtype) -> ComplexTensor: + dtype = complex_to_real_dtype(dtype) + u, v = split_complex_tensor(x) + u_out = prims.convert_element_type(u, dtype) + v_out = prims.convert_element_type(v, dtype) + + return ComplexTensor(u_out, v_out) + + +@register_complex(prims.conj_physical) +def conj_physical_impl(self: ComplexTensor) -> ComplexTensor: + return aten._conj_physical(self) + + +@register_complex(prims.conj) +def conj_impl(self: ComplexTensor) -> ComplexTensor: + return aten._conj(self) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/fake_impls.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/fake_impls.py new file mode 100644 index 0000000000000000000000000000000000000000..ff309af8a29e0e2afa4f94cae67fef775bf874bc --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/fake_impls.py @@ -0,0 +1,1465 @@ +# mypy: ignore-errors + +import functools +import itertools +import math +import operator +import sys +from collections.abc import Callable +from functools import reduce +from typing import Optional, Union + +import torch +import torch._custom_op +import torch._logging +import torch._prims_common as utils +from torch._dispatch.python import no_python_dispatcher +from torch._ops import OpOverload +from torch._prims_common import ( + canonicalize_dim, + elementwise_dtypes, + ELEMENTWISE_TYPE_PROMOTION_KIND, + is_boolean_dtype, + is_contiguous, + is_contiguous_for_memory_format_or_false, + is_contiguous_or_false, + is_float_dtype, + is_integer_dtype, + make_contiguous_strides_for, +) +from torch._subclasses.fake_tensor import ( + DataDependentOutputException, + DynamicOutputShapeException, + FakeTensor, + in_kernel_invocation_manager, + run_fallback_kernel, + UnsupportedOperatorException, +) +from torch.fx.operator_schemas import normalize_function +from torch.utils._stats import count_label + + +pytree = torch.utils._pytree + +__all__ = [ + "op_implementations_checks", + "get_fast_op_impls", + "stride_incorrect_op", + "has_meta", +] + +op_implementations_dict = {} +op_implementations_checks = [] + + +aten = torch._ops.ops.aten + + +def ordered_set(*items): + return dict.fromkeys(items, True) + + +# This function indicates if the backend device +# supports non-contiguous tensors +def is_noncontiguous_supported(device): + return device.type != "hpu" + + +_like_tensor_constructors = ordered_set( + aten.empty_like.default, + aten.empty_like.out, + aten.full_like.default, + aten.full_like.out, + aten.ones_like.default, + aten.ones_like.out, + aten.rand_like.default, + aten.rand_like.generator, + aten.rand_like.out, + aten.rand_like.generator_out, + aten.randn_like.default, + aten.randn_like.generator, + aten.randn_like.out, + aten.randn_like.generator_out, + aten.randint_like.default, + aten.randint_like.generator, + aten.randint_like.Tensor, + aten.randint_like.Tensor_generator, + aten.randint_like.Tensor_out, + aten.randint_like.Tensor_generator_out, + aten.randint_like.out, + aten.randint_like.generator_out, + aten.randint_like.low_dtype, + aten.randint_like.low_generator_dtype, + aten.randint_like.low_dtype_out, + aten.randint_like.low_generator_dtype_out, + aten.zeros_like.default, + aten.zeros_like.out, + aten.new_empty.default, + aten.new_empty.out, + aten.new_empty_strided.default, + aten.new_empty_strided.out, + aten.new_full.default, + aten.new_full.out, + aten.new_zeros.default, + aten.new_zeros.out, + aten.new_ones.default, + aten.new_ones.out, +) + + +_device_not_kwarg_ops = ordered_set( + aten._resize_output_.default, + aten._nested_tensor_from_tensor_list.default, + aten._nested_tensor_from_tensor_list.out, + aten.pin_memory.default, + aten.to.device, + aten.to.prim_Device, + aten.is_pinned.default, + aten._pin_memory.default, + aten._pin_memory.out, + aten._resize_output.default, + aten._resize_output.out, +) + +# this op is never actually used +_non_kwarg_device_constructors = (aten._list_to_tensor,) + + +def contains_tensor_types(type): + tensor_type = torch._C.TensorType.get() + return type.isSubtypeOf(tensor_type) or any( + contains_tensor_types(e) for e in type.containedTypes() + ) + + +@functools.cache +def _is_tensor_constructor(func: OpOverload): + assert isinstance(func, OpOverload) + schema = func._schema + if any(contains_tensor_types(arg.type) for arg in schema.arguments): + return False + # TODO: no real reason to restrict multiple outputs + return ( + len(schema.returns) == 1 and schema.returns[0].type is torch._C.TensorType.get() + ) + + +def register_op_impl(run_impl_check: Union[Callable[[OpOverload], bool], OpOverload]): + def impl_decorator(op_impl): + if isinstance(run_impl_check, OpOverload): + assert run_impl_check not in op_implementations_dict, ( + f"duplicate registration: {run_impl_check}" + ) + op_implementations_dict[run_impl_check] = op_impl + elif isinstance(run_impl_check, (list, tuple)): + for op in run_impl_check: + register_op_impl(op)(op_impl) + else: + assert callable(run_impl_check) + op_implementations_checks.append((run_impl_check, op_impl)) + + return op_impl + + return impl_decorator + + +def _is_op_registered_to_fake_rule(op): + return op in op_implementations_dict + + +def _deregister_op_impl(op): + op_implementations_dict.pop(op, None) + for check, impl in op_implementations_checks: + if check is op: + op_implementations_checks.remove((check, impl)) + break + + +@register_op_impl(op_implementations_dict.__contains__) +def dispatch_to_op_implementations_dict(fake_mode, func, *args, **kwargs): + return op_implementations_dict[func](fake_mode, func, *args, **kwargs) + + +@register_op_impl(_is_tensor_constructor) +@register_op_impl([*_like_tensor_constructors]) +def constructors(fake_mode, func, *args, **kwargs): + assert func not in _non_kwarg_device_constructors + _, new_kwargs = normalize_function( + func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True + ) + if "names" in kwargs: + raise UnsupportedOperatorException( + "torch.compile doesn't support named tensors" + ) + + if func in _like_tensor_constructors: + default_device = new_kwargs["input"].device + # TODO: file issue + args = (new_kwargs.pop("input"),) + else: + # cpu is default device if none is specified + default_device = torch.device("cpu") + args = () + out_device = new_kwargs.pop("device", None) + out_device = out_device if out_device is not None else default_device + new_kwargs["device"] = torch.device("meta") + # _like constructors have fake tensor inputs (maybe this causes the non-like + # to fail? hmmm) + with in_kernel_invocation_manager(fake_mode): + r = func(*args, **new_kwargs) + return FakeTensor(fake_mode, r, out_device) + + +@register_op_impl(aten.is_pinned.default) +def non_kwarg_is_pinned(fake_mode, func, *args, **kwargs): + _, new_kwargs = normalize_function( + func, args, kwargs, normalize_to_only_use_kwargs=True + ) + inp = new_kwargs.pop("input") + # we'll ignore device argument because it is deprecated and not + # actually used by is_pinned. + with in_kernel_invocation_manager(fake_mode): + r = func(inp) + return r + + +@register_op_impl(aten.to.prim_Device) +@register_op_impl(aten.to.device) +def non_kwarg_to(fake_mode, func, *args, **kwargs): + _, new_kwargs = normalize_function( + func, args, kwargs, normalize_to_only_use_kwargs=True + ) + input_device = new_kwargs["device"] + out_device = input_device if input_device else new_kwargs["input"].device + new_kwargs["device"] = torch.device("meta") + inp = new_kwargs.pop("input") + with in_kernel_invocation_manager(fake_mode): + r = func(inp, **new_kwargs) + # TODO: I think this does the wrong thing if r is inp + return fake_mode.fake_tensor_converter.from_meta_and_device( + fake_mode, r, out_device + ) + + +def stride_incorrect_op(op): + return False + + +# These operators have meta implementations with incorrect strides +@register_op_impl(stride_incorrect_op) +def wordaround_stride_incorrect_op(fake_mode, func, *args, **kwargs): + # This is a workaround for meta implementations with incorrect strides + + def is_symbolic(x): + if isinstance(x, FakeTensor): + return x._has_symbolic_sizes_strides + if isinstance(x, (torch.SymInt, torch.SymFloat, torch.SymBool)): + return True + return False + + # For static shapes, we can fall back to eager for the real strides + if fake_mode.allow_fallback_kernels: + require_dynamic = any( + is_symbolic(x) for x in itertools.chain(args, kwargs.values()) + ) + if not require_dynamic: + flat_args, args_spec = pytree.tree_flatten((args, kwargs)) + return run_fallback_kernel(fake_mode, func, flat_args, args_spec, None) + + raise UnsupportedOperatorException(func) + + +# Dont default to default device handling, +# since the device of `the_template` is ignored +@register_op_impl(aten.resize_as_.default) +def resize_as_(fake_mode, func, *args, **kwargs): + with in_kernel_invocation_manager(fake_mode): + return func(*args, **kwargs) + + +@register_op_impl(aten._sparse_coo_tensor_with_dims_and_tensors.default) +def _sparse_coo_tensor_with_dims_and_tensors(fake_mode, func, *args, **kwargs): + # TODO: remove me + return constructors(fake_mode, func, *args, **kwargs) + + +# index.Tensor data-dependent in only some conditions +@register_op_impl( + lambda func: torch.Tag.dynamic_output_shape in func.tags + and func + not in [aten.index.Tensor, aten.nonzero.default, aten.repeat_interleave.Tensor] +) +def dyn_shape(fake_mode, func, *args, **kwargs): + raise DynamicOutputShapeException(func) + + +def _unique( + fake_mode, + func, + arg, + dim, + sorted=True, + return_inverse=False, + return_counts=False, + *, + unique_consecutive=False, +): + if ( + fake_mode.shape_env is None + or not fake_mode.shape_env.allow_dynamic_output_shape_ops + ): + # Without symints/symfloats, cannot handle this + raise DynamicOutputShapeException(func) + + nnz = arg.unique_consecutive_memo if unique_consecutive else arg.unique_memo + + # Do not use a memo for unique_dim + if dim is not None or nnz is None: + # Avoid importing sympy at a module level + from torch.fx.experimental.symbolic_shapes import ( + _constrain_range_for_size, + has_free_symbols, + ) + + if not has_free_symbols(arg.numel()) and arg.numel() == 0: + # If numel is zero, then the output size must be zero. + # In this case, we must not allocate an unbacked SymInt, + # because if we do, it will immediately get refined to + # zero, but this will be inconsistent with size oblivious + # tests (which will continue to claim that the unbacked + # symint cannot equal zero). We could also unconditionally + # allocate an unbacked SymInt and not refine its range, + # but this seems more precise. + nnz = 0 + else: + nnz = fake_mode.shape_env.create_unbacked_symint() + + maxval = sys.maxsize - 1 + + numel = arg.numel() if dim is None else arg.size(dim) + if not has_free_symbols(numel): + maxval = int(numel) + + _constrain_range_for_size(nnz, max=maxval) + + if dim is None: + if unique_consecutive: + arg.unique_consecutive_memo = nnz + else: + arg.unique_memo = nnz + + if dim is None: + ret = [arg.new_empty((nnz,))] + else: + ret = [arg.new_empty(*arg.shape[:dim], nnz, *arg.shape[dim + 1 :])] + + return_if_dim_and_cpu = dim is not None and arg.fake_device == torch.device("cpu") + if return_inverse or return_if_dim_and_cpu: + inverse = arg.new_empty(arg.shape if dim is None else (arg.shape[dim],)) + else: + inverse = arg.new_empty(0) + ret.append(inverse) + + if return_counts or return_if_dim_and_cpu: + counts = arg.new_empty(ret[0].shape if dim is None else (ret[0].shape[dim],)) + else: + counts = arg.new_empty(0) + ret.append(counts) + + return tuple(ret) + + +@register_op_impl(aten._unique2.default) +def unique2( + fake_mode, func, arg, sorted=True, return_inverse=False, return_counts=False +): + return _unique(fake_mode, func, arg, None, sorted, return_inverse, return_counts) + + +@register_op_impl(aten.select.int) +def meta_select(fake_mode, func, self, dim, index): + from torch.fx.experimental.symbolic_shapes import guard_or_false + + if self.is_sparse: + return NotImplemented + + ndim = self.dim() + torch._check_index( + ndim != 0, + lambda: "select() cannot be applied to a 0-dim tensor.", + ) + + dim = dim if dim >= 0 else dim + ndim + size = self.size(dim) + + new_size = list(self.size()) + new_stride = list(self.stride()) + + new_storage_offset = None + if guard_or_false(index >= 0): + new_storage_offset = self.storage_offset() + index * new_stride[dim] + elif guard_or_false(index < 0): + new_storage_offset = self.storage_offset() + (index + size) * new_stride[dim] + + if new_storage_offset is None: + if fake_mode.shape_env is None or ( + not fake_mode.shape_env.allow_scalar_outputs + and not fake_mode.allow_scalar_outputs + ): + raise DataDependentOutputException(func) + + # index is data-dependent, we do not know which index we are accessing it could be index or index+size! + # we assign a new data-dependent symbol for the storage offset. + new_storage_offset = fake_mode.shape_env.create_unbacked_symint() + + del new_size[dim] + del new_stride[dim] + assert new_storage_offset is not None + return self.as_strided(new_size, new_stride, new_storage_offset) + + +@register_op_impl(aten.unique_dim.default) +def unique_dim( + fake_mode, func, arg, dim, sorted=True, return_inverse=False, return_counts=False +): + return _unique( + fake_mode, + func, + arg, + # normalize dim to be non-negative + dim if dim >= 0 else dim % max(arg.ndim, 1), + sorted, + return_inverse, + return_counts, + ) + + +@register_op_impl(aten.unique_consecutive.default) +def _(fake_mode, func, arg, return_inverse=False, return_counts=False, dim=None): + return _unique( + fake_mode, + func, + arg, + dim, + False, + return_inverse, + return_counts, + unique_consecutive=True, + ) + + +# This function is python match of computeStride_impl in TensorUtils.cpp +def _compute_stride(old_shape, old_stride, new_shape, size_oblivious=False): + from torch.fx.experimental.symbolic_shapes import ( + guard_or_false, + guard_or_true, + sym_eq, + ) + + def maybe_guard_or_false(x): + if size_oblivious: + return guard_or_false(x) + + return x + + def maybe_guard_or_true(x): + if size_oblivious: + return guard_or_true(x) + + return x + + if len(old_shape) == 0: + return [1] * len(new_shape) + + numel = reduce(operator.mul, old_shape, 1) + zero_numel = maybe_guard_or_false(numel == 0) + if zero_numel and maybe_guard_or_false(sym_eq(old_shape, new_shape)): + return old_stride + + new_stride = [0] * len(new_shape) + + if zero_numel: + for view_d in range(len(new_shape) - 1, -1, -1): + if view_d == len(new_shape) - 1: + new_stride[view_d] = 1 + else: + new_stride[view_d] = ( + max(new_shape[view_d + 1], 1) * new_stride[view_d + 1] + ) + return new_stride + + view_d = len(new_shape) - 1 + chunk_base_stride = old_stride[-1] + tensor_numel = 1 + view_numel = 1 + + for tensor_d in range(len(old_shape) - 1, -1, -1): + tensor_numel *= old_shape[tensor_d] + + if tensor_d == 0 or ( + maybe_guard_or_true(old_shape[tensor_d - 1] != 1) + and maybe_guard_or_true( + old_stride[tensor_d - 1] != tensor_numel * chunk_base_stride + ) + ): + while view_d >= 0 and ( + maybe_guard_or_true(view_numel < tensor_numel) + or maybe_guard_or_false(new_shape[view_d] == 1) + ): + new_stride[view_d] = view_numel * chunk_base_stride + view_numel *= new_shape[view_d] + view_d -= 1 + + if maybe_guard_or_true(view_numel != tensor_numel): + return None + + if tensor_d > 0: + chunk_base_stride = old_stride[tensor_d - 1] + tensor_numel = 1 + view_numel = 1 + if view_d != -1: + return None + return new_stride + + +def _view_has_unbacked_input(a, shape): + from torch.fx.experimental.symbolic_shapes import has_hint + + shape = utils.extract_shape_from_varargs(shape, validate=False) + + return ( + any(not has_hint(s) for s in a.size()) + or any(not has_hint(s) for s in a.stride()) + or any(not has_hint(s) for s in shape) + ) + + +def _view_unbacked_meta(a, shape, size_oblivious_enabled=True): + from torch._prims import view_of + from torch.fx.experimental.symbolic_shapes import guard_or_false, sym_eq + + # Creates a valid shape + shape = utils.extract_shape_from_varargs(shape, validate=False) + + # Reshape may be given a shape with a -1 length + # This indicates that the dimension's length should be inferred + shape = utils.infer_size(shape, a.numel()) + + # Special-cases reshaping zero dim tensors + if a.ndim == 0: + _a = a + for length in shape: + torch._check(length == 1) + _a = torch._refs.unsqueeze(_a, -1) + if _a is a: + return view_of(a) + else: + return _a + + # Special-cases reshaping to zero dim tensors + if len(shape) == 0: + _a = a + for length in a.shape: + torch._check(length == 1) + _a = torch._refs.squeeze(_a, -1) + if _a is a: + return view_of(a) + else: + return _a + + shape_numel = reduce(operator.mul, shape, 1) + + torch._check( + a.numel() == shape_numel, + lambda: f"Could not reshape a tensor with shape {a.shape} as a tensor with shape {shape}!", + ) + + if len(shape) == len(a.shape) and guard_or_false(sym_eq(shape, a.shape)): + return view_of(a) + + if is_contiguous_or_false(a) if size_oblivious_enabled else is_contiguous(a): + strides = make_contiguous_strides_for(shape) + return a.as_strided(shape, strides) + + new_strides = _compute_stride( + a.size(), a.stride(), shape, size_oblivious=size_oblivious_enabled + ) + + if new_strides is not None: + return a.as_strided(shape, new_strides) + + # If we fail to do size oblivious view, and backed_size_oblivious was on, + # then we redo everything by looking at hints and guarding instead of failing. + # Also if the expression has unbacked symbols, then we run again with size_oblivious_enabled=False + # to throw a data dependent error. + + if size_oblivious_enabled and ( + torch.fx.experimental._config.backed_size_oblivious + or _view_has_unbacked_input(a, shape) + ): + return _view_unbacked_meta(a, shape, size_oblivious_enabled=False) + + msg = f"Cannot view a tensor with shape {a.shape} and strides {a.stride()} as a tensor with shape {shape}!" + raise ValueError(msg) + + +@register_op_impl(aten._reshape_copy.default) +def _reshape_copy(fake_mode, func, a, *shape): + if a.is_sparse or a.is_mkldnn: + return NotImplemented + + shape = utils.infer_size(*shape, a.numel()) + if is_contiguous_or_false(a): + view = _view_meta(fake_mode, func, a, *shape) + return view.clone(memory_format=torch.contiguous_format) + else: + return _view_meta( + fake_mode, func, a.clone(memory_format=torch.contiguous_format), *shape + ) + + +@register_op_impl(aten.view.default) +@register_op_impl(aten._unsafe_view.default) +def _view_meta(fake_mode, func, a, *shape): + if torch.fx.experimental._config.backed_size_oblivious or _view_has_unbacked_input( + a, shape + ): + return _view_unbacked_meta(a, shape) + else: + return torch._refs._reshape_view_helper(a, *shape, allow_copy=False) + + +@register_op_impl(aten.view_copy.default) +def _view_meta_copy(fake_mode, func, a, *shape, out=None): + result = _view_meta(fake_mode, func, a, *shape) + if out is not None: + return result + + return pytree.tree_map( + lambda x: x.clone(memory_format=torch.contiguous_format), + result, + ) + + +@register_op_impl(aten.repeat_interleave.Tensor) +def repeat_interleave_tensor(fake_mode, func, repeats, output_size=None): + if output_size is None: + if ( + fake_mode.shape_env is None + or not fake_mode.shape_env.allow_dynamic_output_shape_ops + ): + raise DynamicOutputShapeException(func) + + output_size = fake_mode.shape_env.create_unbacked_symint() + + # Avoid importing sympy at a module level + from torch.fx.experimental.symbolic_shapes import _constrain_range_for_size + + _constrain_range_for_size(output_size) + # TODO: consider a memo + return repeats.new_empty(output_size) + + +@register_op_impl(torch.ops.aten.item.default) +@register_op_impl(torch.ops.aten._local_scalar_dense.default) +def local_scalar_dense(fake_mode, func, arg): + if (r := arg.item_memo) is not None: + return r + if fake_mode.shape_env is None or ( + not fake_mode.shape_env.allow_scalar_outputs + and not fake_mode.allow_scalar_outputs + ): + # Without symints/symfloats, cannot handle this + raise DataDependentOutputException(func) + if is_float_dtype(arg.dtype): + r = fake_mode.shape_env.create_unbacked_symfloat() + elif is_integer_dtype(arg.dtype): + r = fake_mode.shape_env.create_unbacked_symint() + elif is_boolean_dtype(arg.dtype): + r = fake_mode.shape_env.create_unbacked_symbool() + else: + raise NotImplementedError(f"local_scalar_dense/item NYI for {arg.dtype}") + arg.item_memo = r + return r + + +@register_op_impl(torch.ops.aten.nonzero_numpy.default) +def nonzero_numpy(fake_mode, func, arg): + return torch.ops.aten.nonzero.default(arg).unbind(1) + + +@register_op_impl(torch.ops.aten.nonzero.default) +def nonzero(fake_mode, func, arg): + if ( + fake_mode.shape_env is None + or not fake_mode.shape_env.allow_dynamic_output_shape_ops + ): + # Without symints/symfloats, cannot handle this + raise DynamicOutputShapeException(func) + + if (nnz := arg.nonzero_memo) is None: + # Avoid importing sympy at a module level + from torch.fx.experimental.symbolic_shapes import ( + _constrain_range_for_size, + has_free_symbols, + ) + from torch.utils._sympy.numbers import IntInfinity + from torch.utils._sympy.value_ranges import bound_sympy + + if not has_free_symbols(arg.numel()) and arg.numel() == 0: + # If numel is zero, then the output size must be zero. + # In this case, we must not allocate an unbacked SymInt, + # because if we do, it will immediately get refined to + # zero, but this will be inconsistent with size oblivious + # tests (which will continue to claim that the unbacked + # symint cannot equal zero). We could also unconditionally + # allocate an unbacked SymInt and not refine its range, + # but this seems more precise. + nnz = 0 + else: + nnz = fake_mode.shape_env.create_unbacked_symint() + + maxval = sys.maxsize - 1 + + if not has_free_symbols(arg.numel()): + maxval = int(arg.numel()) + else: + prod_node = math.prod(arg.shape).node + prod_range = bound_sympy( + prod_node.expr, prod_node.shape_env.var_to_range + ) + if isinstance(prod_range.upper, IntInfinity): + maxval = sys.maxsize - 1 + else: + maxval = prod_range.upper + + _constrain_range_for_size(nnz, max=maxval) + + arg.nonzero_memo = nnz + + return arg.new_empty_strided((nnz, arg.dim()), (1, nnz), dtype=torch.int64) + + +@register_op_impl(torch.ops.aten._padded_dense_to_jagged_forward.default) +def _padded_dense_to_jagged_forward(fake_mode, func, padded, offsets, total_L=None): + # only one jagged dim is supported for now + assert len(offsets) == 1 + + if not total_L: + if ( + fake_mode.shape_env is None + or not fake_mode.shape_env.allow_dynamic_output_shape_ops + ): + # Without symints/symfloats, cannot handle this + raise DynamicOutputShapeException(func) + + total_L = fake_mode.shape_env.create_unbacked_symint() + + maxval = sys.maxsize - 1 + + # Avoid importing sympy at a module level + from torch.fx.experimental.symbolic_shapes import ( + _constrain_range_for_size, + has_free_symbols, + ) + + if not has_free_symbols(padded.numel()): + maxval = int(padded.numel()) + + _constrain_range_for_size(total_L, min=0, max=maxval) + + output_shape = (total_L, *padded.shape[2:]) + return padded.new_empty(output_shape) + + +def _compute_slice_index(size, index): + from torch.fx.experimental.symbolic_shapes import guard_or_false, sym_and + + if guard_or_false(sym_and(index >= 0, index <= size)): + return index + elif guard_or_false(sym_and(index < 0, index >= -size)): + return index + size + elif guard_or_false(index < -size): + return 0 + elif guard_or_false(index > size): + return size + return None + + +@register_op_impl(torch.ops.aten.slice.Tensor) +def slice_forward( + fake_mode, + func, + self, + dim: int = 0, + start: Optional[int] = None, + end: Optional[int] = None, + step: int = 1, +): + from torch.fx.experimental.symbolic_shapes import ( + guard_or_false, + statically_known_true, + ) + + shape_env = fake_mode.shape_env + + ndim = self.dim() + if ndim == 0: + raise RuntimeError("slice() cannot be applied to a 0-dim tensor.") + dim = canonicalize_dim(self.dim(), dim) + sizes = list(self.size()) + strides = list(self.stride()) + + if step <= 0: + raise RuntimeError("slice step must be positive") + + # start, end + start_index = 0 if start is None else _compute_slice_index(sizes[dim], start) + end_index = ( + sizes[dim] + if statically_known_true(end == sys.maxsize) or end is None + else _compute_slice_index(sizes[dim], end) + ) + + # size + new_size = None + if start_index is not None and end_index is not None: + if guard_or_false(end_index >= start_index): + new_size = (end_index - start_index + step - 1) // step + elif guard_or_false(start_index >= end_index): + new_size = 0 + + # create unbacked if case unknown + if new_size is None: + new_size = shape_env.create_unbacked_symint() + torch._check(new_size >= 0) + torch._check(new_size <= sizes[dim]) + + # stride + new_stride = strides[dim] * step + + # storage offset + if start_index is not None: + storage_offset = self.storage_offset() + start_index * strides[dim] + else: + storage_offset = shape_env.create_unbacked_symint() + torch._check(storage_offset >= 0) + + sizes[dim] = new_size + strides[dim] = new_stride + if self.is_quantized: + raise NotImplementedError( + "Slice decomposition for quantized tensors aren't implemented" + ) + else: + return self.as_strided(sizes, strides, storage_offset) + + +@register_op_impl(torch.ops.aten.masked_select.default) +def masked_select(fake_mode, func, self, mask): + if ( + fake_mode.shape_env is None + or not fake_mode.shape_env.allow_dynamic_output_shape_ops + ): + # Without symints/symfloats, cannot handle this + raise DynamicOutputShapeException(func) + + nnz = fake_mode.shape_env.create_unbacked_symint() + + # see nonzero for commentary + maxval = sys.maxsize - 1 + + # Avoid importing sympy at a module level + from torch.fx.experimental.symbolic_shapes import ( + _constrain_range_for_size, + has_free_symbols, + ) + from torch.utils._sympy.numbers import IntInfinity + from torch.utils._sympy.value_ranges import bound_sympy + + # If num elements is expressed symbolically, calculate + # the concrete value based on upper bounds. Otherwise, + # we can set max val directly. + if not has_free_symbols(self.numel()): + num_elements = int(self.numel()) + else: + prod_node = math.prod(self.shape).node + prod_range = bound_sympy(prod_node.expr, prod_node.shape_env.var_to_range) + if isinstance(prod_range.upper, IntInfinity): + num_elements = sys.maxsize - 1 + else: + num_elements = prod_range.upper + if num_elements > 2: + maxval = num_elements + + _constrain_range_for_size(nnz, max=maxval) + + return self.new_empty((nnz,)) + + +@register_op_impl(torch.ops.aten._assert_tensor_metadata.default) +def assert_tensor_metadata( + fake_mode, + func, + t, + sizes=None, + strides=None, + dtype=None, + *, + device=None, + layout=None, +) -> None: + if sizes is not None: + assert t.size() == sizes, ( + f"Tensor sizes mismatch! Expected: {sizes}, Got: {t.size()}" + ) + if strides is not None: + assert t.stride() == strides, ( + f"Tensor strides mismatch! Expected: {strides}, Got: {t.stride()}" + ) + if dtype is not None: + assert t.dtype == dtype, ( + f"Tensor dtype mismatch! Expected: {dtype}, Got: {t.dtype}" + ) + if layout is not None: + assert t.layout == layout, ( + f"Tensor layout mismatch! Expected: {layout}, Got: {t.layout()}" + ) + if device is not None: + assert t.device == device, ( + f"Tensor device mismatch! Expected: {device}, Got: {t.device}" + ) + + +# NB: this must be ordered after local_scalar_dense +@register_op_impl(lambda func: torch.Tag.data_dependent_output in func.tags) +def data_dep(fake_mode, func, *args, **kwargs): + raise DataDependentOutputException(func) + + +# Bool Indices get Expanded as Masks +# See: IndexingUtils.h:expandTensors +def check_no_bool_index_tensors(func, self, indices): + for index in indices: + if index is not None and index.dtype in (torch.bool, torch.uint8): + raise DynamicOutputShapeException(func) + + +def run_and_return_new_tensor_of_input_device(fake_mode, func, args, kwargs): + _, new_kwargs = normalize_function( + func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True + ) + + out_device = new_kwargs["input"].device + with in_kernel_invocation_manager(fake_mode): + out = func(*args, **kwargs) + if not is_noncontiguous_supported(out_device): + out = out.new_empty(out.shape) + + if out is new_kwargs["input"]: + return out # copy_ + return FakeTensor(fake_mode, out, out_device) + + +_is_builtin_namespaces = ordered_set("aten", "prims", "prim") + + +def is_builtin(op): + return op.namespace in _is_builtin_namespaces + + +def has_meta(func): + return torch._C._dispatch_has_computed_kernel_for_dispatch_key(func.name(), "Meta") + + +# These are for the `torch._foreach_...` ops like `torch._foreach_add`. +@register_op_impl( + lambda func: is_builtin(func) + and func.name().startswith("aten::_foreach_") + and has_meta(func) +) +def foreach_run_and_map_input_device(fake_mode, func, *args, **kwargs): + tensor_lists = [ + arg + for arg in itertools.chain(args, kwargs.values()) + if isinstance(arg, (list, tuple)) + and len(arg) + and isinstance(arg[0], torch.Tensor) + ] + + try: + with in_kernel_invocation_manager(fake_mode): + out_meta = func(*args, **kwargs) + except NotImplementedError: + return NotImplemented + + if not out_meta: + return out_meta + + assert tensor_lists + out_fake = [] + + for i, meta_t in enumerate(out_meta): + device, _ = FakeTensor._find_common_device(func, [tl[i] for tl in tensor_lists]) + out_fake.append( + fake_mode.fake_tensor_converter.from_meta_and_device( + fake_mode, meta_t, device + ) + ) + + return out_fake + + +# Dont default to default device handling, +# Since op can take in non-zero sized cpu +# index tensors with cuda self +@register_op_impl(aten.index.Tensor) +def index_tensor(fake_mode, func, *args, **kwargs): + from torch._meta_registrations import meta_index_Tensor + + _, new_kwargs = normalize_function( + func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True + ) + + out_device = new_kwargs["input"].device + # ensure nonzero call goes to fake tensor + with fake_mode: + out = meta_index_Tensor(*args, **kwargs) + return out.to(out_device) + + +# Can take mixed meta/non-meta arguments; the meta registration +# will roughly do the right thing even when given real devices +@register_op_impl(aten._embedding_bag.default) +def embedding_bag(fake_mode, func, *args, **kwargs): + from torch._meta_registrations import meta_embedding_bag + + with fake_mode: + return meta_embedding_bag(*args, **kwargs) + + +# takes in multiple-devices, dont default to default device handling +@register_op_impl(aten._unsafe_index_put.default) +@register_op_impl(aten.copy.default) +@register_op_impl(aten.copy_.default) +@register_op_impl(aten.slice_scatter.default) +def multi_device_op_default(fake_mode, func, *args, **kwargs): + return run_and_return_new_tensor_of_input_device(fake_mode, func, args, kwargs) + + +# same with multi_device_op_default, but return the input +@register_op_impl(aten.copy.out) +@register_op_impl(aten.slice_scatter.out) +def multi_device_op_out(fake_mode, func, *args, **kwargs): + with in_kernel_invocation_manager(fake_mode): + func(*args, **kwargs) + + _, new_kwargs = normalize_function( + func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True + ) + + return new_kwargs["input"] + + +@register_op_impl(aten.index_put.default) +@register_op_impl(aten.index_put_.default) +def index_put_impl(fake_mode, func, *args, **kwargs): + _, new_kwargs = normalize_function( + func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True + ) + + values = new_kwargs["values"] + self_device = new_kwargs["input"].fake_device + torch._check( + self_device == values.fake_device or (values.ndim == 0 and values.numel() == 1), + lambda: f"Mismatching {func} device between self ({self_device}) and values ({values.device})", + ) + + out = run_and_return_new_tensor_of_input_device(fake_mode, func, args, kwargs) + if func is aten.index_put_.default: + return new_kwargs["input"] + else: + return out + + +@register_op_impl(aten._nested_tensor_from_tensor_list.default) +@register_op_impl(aten._nested_tensor_from_tensor_list.out) +@register_op_impl(aten._nested_view_from_buffer.default) +@register_op_impl(aten._nested_view_from_buffer_copy.default) +def nested_tensors_unsupported(fake_mode, func, *args, **kwargs): + raise UnsupportedOperatorException( + "torch.compile does not support strided NestedTensor" + ) + + +@register_op_impl( + [ + x + for x in _device_not_kwarg_ops + if x + not in ( + # these are already registered elsewhere + aten.is_pinned.default, + aten.to.device, + aten.to.prim_Device, + aten._nested_tensor_from_tensor_list.default, + aten._nested_tensor_from_tensor_list.out, + ) + ] +) +def nyi(fake_mode, func, *args, **kwargs): + assert func not in _device_not_kwarg_ops, f"NYI: {func}" + + +@register_op_impl([aten.convolution.default, aten.convolution_backward.default]) +def conv(fake_mode, func, *args, **kwargs): + _, kwargs = normalize_function( + func, args=args, kwargs=kwargs, normalize_to_only_use_kwargs=True + ) + device = kwargs["input"].fake_device + # need to re-enable mode so the tensors report fake device + with fake_mode: + # if the input is unsqueezed is done in Convolution.cpp we get segfault + k = kwargs["weight"].ndim + batch = kwargs["input"].shape[0] + + # Avoid importing sympy at a module level + from torch.fx.experimental.symbolic_shapes import has_hint + + if not has_hint(batch): + # TODO: We can make this a little more faithful with best effort + # channels last detection (but only if it's statically obvious!) + mem_fmt = None + else: + if func is aten.convolution.default: + conv_backend = torch._C._select_conv_backend(**kwargs) + else: + conv_backend = torch._C._select_conv_backend( + kwargs["input"], + kwargs["weight"], + bias=None, + stride=kwargs["stride"], + padding=kwargs["padding"], + dilation=kwargs["dilation"], + transposed=kwargs["transposed"], + output_padding=kwargs["output_padding"], + groups=kwargs["groups"], + bias_sizes=kwargs["bias_sizes"], + ) + # Expand 1d -> 2d. + # Note: Avoid expanding before calling _select_conv_backend, + # as the function handles 2D expansion internally. + if k == 3 and not kwargs["input"].is_mkldnn and not kwargs["input"].is_xpu: + # Note: Using input.to(memory_format=contiguous) does not work. + kwargs["input"] = kwargs["input"].contiguous().unsqueeze(2) + kwargs["weight"] = kwargs["weight"].unsqueeze(2) + if len(kwargs["stride"]) == 1: + kwargs["stride"].insert(0, 1) + kwargs["padding"].insert(0, 0) + kwargs["dilation"].insert(0, 1) + kwargs["output_padding"].insert(0, 0) + mem_fmt = torch._C._conv_determine_backend_memory_format( + kwargs["input"], kwargs["weight"], conv_backend + ) + # revert 2d -> 1d + if k == 3 and not kwargs["input"].is_mkldnn and not kwargs["input"].is_xpu: + kwargs["input"] = kwargs["input"].squeeze(2) + kwargs["weight"] = kwargs["weight"].squeeze(2) + if len(kwargs["stride"]) == 2: + kwargs["stride"].pop(0) + kwargs["padding"].pop(0) + kwargs["dilation"].pop(0) + kwargs["output_padding"].pop(0) + + def convert(t, mem_fmt): + if t is None: + return t + if mem_fmt is not None: + # channels last only support 4d, try to expand dim then convert it back later. + if t.dim() == 3 and mem_fmt == torch.channels_last: + t = t.unsqueeze(2).to(memory_format=mem_fmt).squeeze(2) + else: + t = t.to(memory_format=mem_fmt) + return FakeTensor(fake_mode, t, device) + + with in_kernel_invocation_manager(fake_mode): + out = func(**kwargs) + + if func is aten.convolution.default: + return convert(out, mem_fmt) + else: + return ( + convert(out[0], mem_fmt), + convert(out[1], mem_fmt), + convert(out[2], None), + ) + + +@register_op_impl(torch.ops.aten.bincount.default) +def bincount(fake_mode, func, inputs, weights=None, minlength=0): + if ( + fake_mode.shape_env is None + or not fake_mode.shape_env.allow_dynamic_output_shape_ops + ): + # Without symints/symfloats, cannot handle this + raise DynamicOutputShapeException(func) + + new_size = fake_mode.shape_env.create_unbacked_symint() + + from torch.fx.experimental.symbolic_shapes import _constrain_range_for_size + + _constrain_range_for_size(new_size) + torch._check(new_size >= minlength) + return inputs.new_empty(new_size) + + +@register_op_impl(torch.ops.aten._pack_padded_sequence.default) +def _pack_padded_sequence(fake_mode, func, inputs, lengths, batch_first): + if ( + fake_mode.shape_env is None + or not fake_mode.shape_env.allow_dynamic_output_shape_ops + ): + # Without symints/symfloats, cannot handle this + raise DynamicOutputShapeException(func) + + new_batch_size = fake_mode.shape_env.create_unbacked_symint() + + from torch.fx.experimental.symbolic_shapes import _constrain_range_for_size + + _constrain_range_for_size(new_batch_size) + + if not batch_first: + # Inputs should have shape (batch_size, seq_len, *) + inputs = inputs.transpose(0, 1) + + res_size = inputs.shape[1:] + packed_data = inputs.new_empty(res_size) + batch_size = inputs.new_empty((new_batch_size,)) + return (packed_data, batch_size) + + +FAST_OP_IMPLEMENTATIONS = {} + + +# Unlike register_op_impl, these don't do the slow iteration for +# run_impl_check, and these run BEFORE decompositions +def register_fast_op_impl(func: OpOverload): + def impl_decorator(op_impl): + FAST_OP_IMPLEMENTATIONS[func] = op_impl + return op_impl + + return impl_decorator + + +# infer_size_impl in ExpandUtils +def infer_size(a, b): + from torch.fx.experimental.symbolic_shapes import guard_or_false + + dimsA = len(a) + dimsB = len(b) + ndim = max(dimsA, dimsB) + expandedSizes = [0] * ndim + for i in range(ndim - 1, -1, -1): + offset = ndim - 1 - i + dimA = dimsA - 1 - offset + dimB = dimsB - 1 - offset + sizeA = a[dimA] if dimA >= 0 else 1 + sizeB = b[dimB] if dimB >= 0 else 1 + + # NB: It is very important to test for broadcasting, before testing + # sizeA == sizeB. This is because the broadcasting tests are likely + # to be statically known (in particular, if sizeA/sizeB is unbacked + # but size-like, we will unsoundly assume they never equal 1), but + # the sizeA == sizeB test may not be statically known. However, once + # we have established that no broadcasting is happening, the + # sizeA == sizeB is now expect_true and we can defer it as a runtime + # assert (this works because Python will return the terminal + # expression of an or statement as-is, without bool()'ing it; if this + # were not the case, we'd need to write this using torch.sym_or() or + # something like that). + torch._check( + guard_or_false(sizeA == 1) or guard_or_false(sizeB == 1) or sizeA == sizeB, + lambda: f"The size of tensor a ({sizeA}) " + f"must match the size of tensor b ({sizeB}) " + f"at non-singleton dimension {i})", + ) + expandedSizes[i] = sizeB if guard_or_false(sizeA == 1) else sizeA + return tuple(expandedSizes) + + +def make_fast_binary_impl( + slow_ref, type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT +): + def fast_binary_impl(mode, *args, **kwargs): + def slow(msg): + count_label(f"slow {msg}") + with mode: + return slow_ref(*args, **kwargs) + + count_label("attempt fast") + + # Fast path (based off of TensorIterator fast path). + # Unfortunately, there is no way to easily deduplicate + # this with either the TensorIterator C++ implementation + # (which we don't want to SymIntify, and also the algorithm + # here is slightly different from TensorIterator to allow + # for broadcasting), nor the PrimTorch implementation + # (which does not actually implement a fast path.) + + operands = args + + # compute_shape + final_shape = None + for op in operands: + shape = op.shape if isinstance(op, torch.Tensor) else () + if final_shape is None: + final_shape = shape + # TODO: Minor optimization: track if the shapes + # were equal so you can skip the equality check + # below if unnecessary + final_shape = infer_size(final_shape, shape) + assert final_shape is not None + + from torch.fx.experimental.symbolic_shapes import guard_or_false, sym_eq + + # Do some extra safety checks to see if the output + # stride is obvious + for op in operands: + if ( + isinstance(op, torch.Tensor) + and len(op.shape) == len(final_shape) + # take the slow path if result is not determined. + and guard_or_false(sym_eq(op.shape, final_shape)) + ): + break + else: + # if we never break in the for loop above we take the slow path. + return slow("both tensors nontrivially broadcast") + + # compute_types + cpu = torch.device("cpu") + common_device = cpu + common_dtype = None + has_different_input_dtypes = False + for op in operands: + if not isinstance(op, torch.Tensor): + # Use elementwise_dtypes for the tricky case + has_different_input_dtypes = True + continue + if common_device == cpu and op.device.type != "cpu": + common_device = op.device + if common_dtype is None: + if type_promotion_kind != ELEMENTWISE_TYPE_PROMOTION_KIND.DEFAULT: + has_different_input_dtypes = True + else: + common_dtype = op.dtype + elif common_dtype != op.dtype: + has_different_input_dtypes = True + + if has_different_input_dtypes: + # compute promotion + # TODO: we don't need the compute type + _, common_dtype = elementwise_dtypes( + *operands, type_promotion_kind=type_promotion_kind + ) + + # check all tensors on same device + # cpu scalars are assumed allow + current_cpu_scalars_on_non_cpu = 0 + max_cpu_scalars_on_non_cpu = 1 # hard coded atm + for op in operands: + if not isinstance(op, torch.Tensor): + continue + if common_device != cpu and op.dim() == 0 and op.device == cpu: + if current_cpu_scalars_on_non_cpu >= max_cpu_scalars_on_non_cpu: + return slow("error") + current_cpu_scalars_on_non_cpu += 1 + elif op.device != common_device: + return slow("error") + + # compute_fast_setup_type + definitely_contiguous = True + definitely_channels_last = True + + # TODO: is_non-overlapping_and_dense not bound from Python + # no inplace, no out, everything defined + + if is_noncontiguous_supported(common_device): + for op in operands: + if not isinstance(op, torch.Tensor): + continue + definitely_contiguous = ( + definitely_contiguous + and is_contiguous_for_memory_format_or_false( + op, memory_format=torch.contiguous_format + ) + ) + definitely_channels_last = ( + definitely_channels_last + and is_contiguous_for_memory_format_or_false( + op, memory_format=torch.channels_last + ) + ) + if definitely_contiguous: + # do contiguous + count_label("fast is_contiguous") + return FakeTensor( + mode, + torch.empty( + final_shape, + dtype=common_dtype, + device="meta", + memory_format=torch.contiguous_format, + ), + device=common_device, + ) + if definitely_channels_last: + count_label("fast channels_last") + # do channels last + return FakeTensor( + mode, + torch.empty( + final_shape, + dtype=common_dtype, + device="meta", + memory_format=torch.channels_last, + ), + device=common_device, + ) + + return slow("no contiguity match") + + return fast_binary_impl + + +# disable the python dispatcher to avoid decomposing detach() further +# (proxy_mode should still decompose detach() though) +def fast_detach(fake_mode, x, include_real=False): + with no_python_dispatcher(), in_kernel_invocation_manager(fake_mode): + out = torch.ops.aten.detach.default(x) + if include_real: + return FakeTensor(fake_mode, out, x.device, real_tensor=x.real_tensor) + return FakeTensor(fake_mode, out, x.device) + + +@functools.cache +def get_fast_op_impls(): + import torch._refs + + register_fast_op_impl(torch.ops.aten.add.Tensor)( + make_fast_binary_impl(torch._refs.add) + ) + register_fast_op_impl(torch.ops.aten.sub.Tensor)( + make_fast_binary_impl(torch._refs.sub) + ) + register_fast_op_impl(torch.ops.aten.mul.Tensor)( + make_fast_binary_impl(torch._refs.mul) + ) # type: ignore[has-type] + register_fast_op_impl(torch.ops.aten.div.Tensor)( + make_fast_binary_impl( + torch._refs.div, + type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT, + ) + ) + register_fast_op_impl(torch.ops.aten.detach.default)(fast_detach) + return FAST_OP_IMPLEMENTATIONS diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/fake_tensor.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/fake_tensor.py new file mode 100644 index 0000000000000000000000000000000000000000..23d222c5165e4e66775d117163c4b896144b32b9 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/fake_tensor.py @@ -0,0 +1,3422 @@ +# mypy: allow-untyped-decorators +from __future__ import annotations + +import atexit +import contextlib +import dataclasses +import functools +import logging +import math +import os +import threading +import traceback +import types +import typing +import weakref +from collections import defaultdict +from dataclasses import dataclass +from typing import ( + Any, + cast, + Literal, + Optional, + TYPE_CHECKING, + TypeGuard, + TypeVar, + Union, +) +from typing_extensions import Self +from weakref import ReferenceType + +import torch +import torch._library.utils as library_utils +from torch import SymBool, SymFloat, SymInt, Tensor +from torch._C._functorch import is_functorch_wrapped_tensor, is_legacy_batchedtensor +from torch._library.fake_class_registry import FakeScriptObject +from torch._library.fake_profile import MissingOpProfile +from torch._logging import dtrace_structured +from torch._prims_common import suggest_memory_format +from torch._subclasses.meta_utils import ( + assert_eq, + assert_metadata_eq, + is_sparse_any, + is_sparse_compressed, + MetaConverter, +) +from torch._utils import render_call +from torch.fx.immutable_collections import immutable_dict +from torch.fx.operator_schemas import normalize_function +from torch.multiprocessing.reductions import StorageWeakRef +from torch.overrides import TorchFunctionMode +from torch.types import IntLikeType, py_sym_types +from torch.utils._mode_utils import no_dispatch +from torch.utils._python_dispatch import ( + is_traceable_wrapper_subclass, + TorchDispatchMode, +) +from torch.utils._pytree import KeyPath, keystr, PyTree, tree_map, tree_map_, TreeSpec +from torch.utils._stats import count +from torch.utils._traceback import CapturedTraceback + +from ._fake_tensor_utils import _CacheKeyState, _PySymInputStub, _SymIntOutputStub + + +if TYPE_CHECKING: + from collections.abc import Callable, Generator, Iterable, Mapping, Sequence + from types import TracebackType + + from torch._guards import Source + from torch._ops import OpOverload + from torch.fx.experimental.symbolic_shapes import ShapeEnv, SymbolicContext + +log = logging.getLogger(__name__) +hc_log = torch._logging.getArtifactLogger(__name__, "hierarchical_compile") + +# TODO: Hack to unblock https://github.com/pytorch/pytorch/pull/108186 +# Proper fix tracked by https://github.com/pytorch/pytorch/issues/120105 +try: + not_implemented_log = torch._logging.getArtifactLogger(__name__, "not_implemented") +except ValueError as e: + if "'not_implemented' not registered" in str(e): + not_implemented_log = logging.getLogger(__name__ + ".not_implemented") + else: + raise e + + +DimList = list + +pytree = torch.utils._pytree +T = TypeVar("T") + +aten = torch._ops.ops.aten + +CONSTANT_NUMEL_LIMIT = 1 + +RECURSION_COUNT = 0 + + +# Small helper that increments recursion count, and +# resets it when the object goes out of scope. Useful +# if you don't want to increase indentation which is +# what a context manager would do. +class IncrementRecursionCount: + def __init__(self) -> None: + global RECURSION_COUNT + RECURSION_COUNT += 1 + + def __del__(self) -> None: + global RECURSION_COUNT + RECURSION_COUNT -= 1 + + +@dataclass +class UnsupportedFakeTensorException(RuntimeError): + reason: str + + +@dataclass +class DynamicOutputShapeException(RuntimeError): + func: OpOverload + + +@dataclass +class DataDependentOutputException(RuntimeError): + func: OpOverload + + +@dataclass +class UnsupportedOperatorException(RuntimeError): + func: OpOverload + + +@dataclass +class UnsupportedMutationAliasingException(RuntimeError): + reason: str + + +@dataclass +class MetadataMismatchError(RuntimeError): + reason: str + + +class FakeTensorTLS(threading.local): + # Default to None, otherwise it'll be used to override _all_ + # `FakeTensorMode.allow_non_fake_inputs` in this thread. + allow_non_fake_inputs_override: Optional[bool] + non_strict_export_fake_tensor_tracker: weakref.WeakSet + + def __init__(self) -> None: + self.allow_non_fake_inputs_override = None + self.non_strict_export_fake_tensor_tracker = weakref.WeakSet() + + +fake_tensor_tls = FakeTensorTLS() + + +def ordered_set(*items: T) -> dict[T, Literal[True]]: + return dict.fromkeys(items, True) + + +@contextlib.contextmanager +def unset_fake_temporarily() -> Generator[Optional[TorchDispatchMode], None, None]: + old = torch._C._unset_dispatch_mode(torch._C._TorchDispatchModeKey.FAKE) + try: + yield old + finally: + if old is not None: + torch._C._set_dispatch_mode(old) + + +@contextlib.contextmanager +def disable_fake_tensor_cache(fake_mode: FakeTensorMode) -> Generator[None, None, None]: + old_value: bool = fake_mode.cache_enabled + try: + fake_mode.cache_enabled = False + yield + finally: + fake_mode.cache_enabled = old_value + + +def get_plain_tensors( + subclass: Tensor, *, out: list[Union[Tensor, int, SymInt]] +) -> list[Union[Tensor, int, SymInt]]: + # This function is used in Runtime, do not add redundant asserts + todo = [subclass] + while todo: + curr = todo.pop() + if not is_traceable_wrapper_subclass(curr): + out.append(curr) + continue + + inner_keys, _ = curr.__tensor_flatten__() + todo.extend(getattr(curr, key) for key in reversed(inner_keys)) + + return out + + +def is_fake(x: object) -> TypeGuard[Tensor]: + from torch._subclasses.functional_tensor import FunctionalTensor + + if isinstance(x, FakeTensor): + return True + if is_traceable_wrapper_subclass(x): + attrs, _ = type(x).__tensor_flatten__(x) + flattened_tensors = [getattr(x, attr) for attr in attrs] + all_fake = all(is_fake(x) for x in flattened_tensors) + any_fake = any(is_fake(x) for x in flattened_tensors) + assert all_fake == any_fake, "got mixed fake and real tensors!" + return all_fake + elif isinstance(x, FunctionalTensor): + return is_fake(x.elem) + elif isinstance(x, Tensor) and torch._is_functional_tensor(x): + reapply_views = torch._C._functionalization_reapply_views_tls() + unwrapped = torch._C._functorch._unwrap_functional_tensor(x, reapply_views) + return is_fake(unwrapped) + elif isinstance(x, Tensor) and is_functorch_wrapped_tensor(x): + unwrapped = torch._C._functorch.get_unwrapped(x) + return is_fake(unwrapped) + return False + + +def maybe_get_fake_mode(t: object) -> Optional[FakeTensorMode]: + from torch._subclasses.functional_tensor import FunctionalTensor + + if isinstance(t, FakeTensor): + return t.fake_mode + if is_traceable_wrapper_subclass(t): + inner_tensor_names, _ = t.__tensor_flatten__() + modes = [ + maybe_get_fake_mode(getattr(t, t_name)) for t_name in inner_tensor_names + ] + m = modes[0] + assert all(m is x for x in modes) + return m + elif isinstance(t, FunctionalTensor): + return maybe_get_fake_mode(t.elem) + elif isinstance(t, Tensor) and torch._is_functional_tensor(t): + reapply_views = torch._C._functionalization_reapply_views_tls() + unwrapped = torch._C._functorch._unwrap_functional_tensor(t, reapply_views) + return maybe_get_fake_mode(unwrapped) + elif isinstance(t, Tensor) and is_functorch_wrapped_tensor(t): + unwrapped = torch._C._functorch.get_unwrapped(t) + return maybe_get_fake_mode(unwrapped) + return None + + +@functools.cache +def get_schema_info(func: OpOverload) -> torch._C._SchemaInfo: + return torch._C._SchemaInfo(func._schema) + + +# many of the decompositions registered to torch/_prims do not at the moment model +# aliasing or strides, so as an incremental step, just enable the decompositions in +# torch/_decomp/decompositions.py. +# decomps are used for aot autograd tracing so we would like to unify on their +# implementation and add additional testing to them +@functools.cache +def torch_decomp_decompositions(func: OpOverload) -> bool: + from torch._decomp import decomposition_table + + decompositions = torch._decomp.decompositions + # Note that the function in the decomposition table might be + # different from the one in the module because of the difference + # in out handling in aten API and torch public API + return decomposition_table[func].__module__.startswith( + "torch._decomp" + ) and decomposition_table[func].__name__ in dir(decompositions) + + +def tree_flatten_only(ty: type[T], tree: PyTree) -> list[T]: + flat_vals = pytree.tree_leaves(tree) + return [elem for elem in flat_vals if isinstance(elem, ty)] + + +def _is_plain_tensor(t: object) -> bool: + return ( + type(t) is Tensor + and t.layout == torch.strided + and not ( + t.is_sparse + or t.is_nested + or is_functorch_wrapped_tensor(t) + or is_legacy_batchedtensor(t) + or torch._is_functional_tensor(t) + ) + ) + + +# Similar to `MetaConverter`, this is a class for converting +# multiple tensors into fake tensors which share the same view/storage +# structure. Like `MetaConverter`, it uses `WeakIdRef` to +# hold a weak reference for all memoized tensors. +class FakeTensorConverter: + @property + def tensor_memo( + self, + ) -> weakref.WeakValueDictionary: + # not valid until py3.10 + # weakref.WeakValueDictionary["torch._subclasses.meta_utils.MetaTensorId", Optional["FakeTensor"]] + return self.meta_converter.tensor_memo + + meta_converter: MetaConverter + constant_storage_mapping: dict[StorageWeakRef, list[ReferenceType]] + export: bool + + def __init__(self, *, copy_data: bool = False, export: bool = False) -> None: + self.meta_converter = MetaConverter(copy_data=copy_data) + self.export = export + + # map from to storage to corresponding constant tensors + self.constant_storage_mapping = {} + + def add_constant_storage_mapping(self, fake_tensor: FakeTensor) -> None: + # when you have a constant, aliased tensor: + # const_tensor.add_(torch.rand([1])) + # all aliases of it must become no longer const + assert isinstance(fake_tensor, FakeTensor) and fake_tensor.constant is not None + weak_st = StorageWeakRef(fake_tensor.constant._typed_storage()) + + # we need a map from a weak storage to all of its corresponding + # constant tensors. python doesn't have the weak value equivalent + # of defaultdict(list), so we are using a WeakValueDictionary as one + if weak_st not in self.constant_storage_mapping: + self.constant_storage_mapping[weak_st] = [] + self.constant_storage_mapping[weak_st].append(weakref.ref(fake_tensor)) + + def invalidate_constant_aliases(self, tensor: Tensor) -> None: + assert not isinstance(tensor, FakeTensor) + + weak_st = StorageWeakRef(tensor._typed_storage()) + if weak_st not in self.constant_storage_mapping: + return + + for weak_tensor_ref in self.constant_storage_mapping[weak_st]: + ten = weak_tensor_ref() + if ten is not None: + ten._fix_weakref() + ten.constant = None + + del self.constant_storage_mapping[weak_st] + + def _get_memo(self, t: Tensor) -> Optional[FakeTensor]: + tid = self.meta_converter.describer.lookup_tensor.get(t) + if tid is None: + return None + return self.tensor_memo.get(tid) + + def set_tensor_memo(self, t: Tensor, v: FakeTensor) -> None: + tid = self.meta_converter.describer.get_tensor_id(t) + self.meta_converter.tensor_memo[tid] = v + + # You can have a real tensor that you need to convert into a fake tensor. + # If you have a meta tensor already, call from_meta_and_device. + # + # You're allowed to pass a meta tensor to be turned into a fake + # tensor; although an odd thing to do, this can occur if you're doing + # cross ref testing and the inner test is already operating on meta tensors. + def from_real_tensor( + self, + fake_mode: FakeTensorMode, + t: Tensor, + make_constant: bool = False, + shape_env: Optional[ShapeEnv] = None, + *, + source: Optional[Source] = None, + symbolic_context: Optional[SymbolicContext] = None, + trace: bool = True, + ) -> FakeTensor: + # see note [Tensor Fakification and Symbol Caching] + if not symbolic_context and not source and shape_env: + if tracing_context := torch._guards.TracingContext.try_get(): + if t in tracing_context.tensor_to_context: + symbolic_context = tracing_context.tensor_to_context[t] + from torch.fx.experimental.symbolic_shapes import ( + StatefulSymbolicContext, + ) + + assert isinstance(symbolic_context, StatefulSymbolicContext) + source = symbolic_context.tensor_source + + maybe_memo = self._get_memo(t) + if maybe_memo is not None: + return maybe_memo + # not yet supported in metatensors + if t.is_quantized: + raise UnsupportedFakeTensorException("quantized nyi in meta tensors") + if type(t) is torch.nn.Parameter: + assert not make_constant + + constant = t if make_constant else None + + # This callback is used by both subclass and inner tensors. Require the + # caller to explicitly specify the device in case outer and inner tensors + # have different devices. + def mk_fake_tensor( + make_meta_t: Callable[[], object], device: Union[torch.device, str] + ) -> FakeTensor: + # NB: don't use in_kernel_invocation_manager. to + # ensure FakeTensor can internally do constant computation + # as necessary. Invocation manager is "more correct" as + # it works for more operators in make_meta_t, but + # invariant is that make_meta_t only calls factories + # for which it is not strictly necessary to use the + # invocation manager (I think!) + with no_dispatch(): + return FakeTensor( + fake_mode, + # pyrefly: ignore [bad-argument-type] + make_meta_t(), + # pyrefly: ignore [bad-argument-type] + device, + # TODO: callback might be used in recursive contexts, in + # which case using t is wrong! BUG! + constant=constant, + ) + + out = self.meta_converter( + t, + shape_env=shape_env, + callback=mk_fake_tensor, + source=source, + symbolic_context=symbolic_context, + trace=trace, + ) + if out is NotImplemented: + raise UnsupportedFakeTensorException("meta converter nyi") + + from torch._dynamo.source import RandomValueSource + + value = None + if ( + not self.export + and _is_plain_tensor(t) # mostly, we want to know if item() works + and t.dim() == 0 + and t.device.type == "cpu" + # All integer types are fair game, because signed overflow is UB + # (and even int64 can overflow, since integers in Python are + # arbitrary precision). But only float64 is OK for float, because + # switching between float32 and float64 changes semantics in an + # observable way without hitting UB. + and t.dtype + in [torch.int64, torch.int32, torch.int16, torch.int8, torch.float64] + and source is not None + # Impede setting up item() on things coming from random. These + # are not "real" item() calls, instead UnspecializedPythonVariable + # is unsafely pretending an int is a tensor, which can sometimes + # implicitly cause an item call. The problem is this is pretty + # unsound: there's no reason substituting an int with a Tensor is + # going to give the same results. Today, you mostly get around + # this by typically not having capture_scalar_outputs on and graph + # breaking when someone tries to use the unspec variable in an + # int-y context. But allowing it through here would break that. + # So don't. + # + # Once random values are setup to be represented as + # SymNodeVariable, this condition can be removed. To check if + # you've done it right, this is a good test: + # + # PYTORCH_TEST_WITH_DYNAMO=1 python test/test_reductions.py -k + # TestReductionsCPU.test_dim_reduction_fns_fn_name_amax_cpu_bfloat16 + and not isinstance(source, RandomValueSource) + # In Dynamo, shape_env is never none (even with static shapes). + # However, FakeTensorMode can be used by hand and in some cases + # ShapeEnv is not allocated. + and shape_env is not None + ): + from torch._dynamo.source import CallMethodItemSource, FloatTensorSource + from torch.fx.experimental.symbolic_shapes import DimDynamic + + with no_dispatch(): + value = t.item() + if not math.isnan(value) and not math.isinf(value): + # Peephole strip out unnecessary torch.as_tensor(x).item() + if isinstance(source, FloatTensorSource): + item_source = source.base + else: + item_source = CallMethodItemSource(source) + symbol = shape_env.create_unspecified_symbol( + value, + source=item_source, + dynamic_dim=DimDynamic.DYNAMIC, + symbolic_context=symbolic_context, + ) + # NB: reusing item_memo here ensures that we invalidate on + # mutation + if t.dtype == torch.int64: + out.item_memo = shape_env.create_symintnode( + symbol, + hint=value, + source=item_source, + ) + elif t.dtype == torch.float64: + out.item_memo = shape_env.create_symfloatnode( + symbol, + hint=value, + source=item_source, + ) + if make_constant: + self.add_constant_storage_mapping(out) + # NB: meta_converter set the memo + return out + + # If you specify the device, it MUST be a meta tensor. + def from_meta_and_device( + self, + fake_mode: FakeTensorMode, + t: Tensor, + device: torch.device, + pytype: Optional[type[torch.Tensor]] = None, + dispatch_keys: Optional[torch.DispatchKeySet] = None, + ) -> FakeTensor: + assert t.device.type == "meta", ( + f"tensor's device must be `meta`, got {t.device.type} instead" + ) + # This is a bit abusive (this is not the "real" tensor) but whatever, + # the meta tensor should be fresh so there's no way to get it wrong + maybe_memo = self._get_memo(t) + if maybe_memo is not None: + return maybe_memo + out = FakeTensor( + fake_mode, t, device, pytype=pytype, dispatch_keys=dispatch_keys + ) + self.set_tensor_memo(t, out) + return out + + +@functools.cache +def init_gpu_context(device: torch.device) -> None: + # Backward will error with cuda Fake Tensors if no cuda tensors have been initialized first + if torch.cuda.is_available() or torch.xpu.is_available(): + ( + torch.empty(1, device=device) + if torch.version.hip is None + else torch.zeros(1, device=device) + ) + + +@contextlib.contextmanager +def in_kernel_invocation_manager( + fake_mode: FakeTensorMode, +) -> Generator[None, None, None]: + # See: note [Fake Tensor Dispatch Keys] + prev_in_kernel = fake_mode.in_kernel_invocation + meta_in_tls = torch._C._meta_in_tls_dispatch_include() + assert meta_in_tls == prev_in_kernel, f"{meta_in_tls}, {prev_in_kernel}" + + with torch._C._DisableTorchDispatch(): + fake_mode.in_kernel_invocation = True + # Unfortunately _set_meta_in_tls_dispatch_include(False) can leave + # `Dense` turned on (because it's implied by `Meta`) + with torch._C._PreserveDispatchKeyGuard(): + torch._C._set_meta_in_tls_dispatch_include(True) + try: + yield + finally: + fake_mode.in_kernel_invocation = prev_in_kernel + # torch._C._set_meta_in_tls_dispatch_include(prev_in_kernel) + + +# Return if the function allows Python numbers to bind to Tensors +def should_allow_numbers_as_tensors(func: OpOverload) -> bool: + return torch._C._should_allow_numbers_as_tensors( + func.name().split("::")[-1].split(".")[0] + ) + + +class FakeTensorConfig: + debug = os.environ.get("TORCH_FAKE_TENSOR_DEBUG", "0") == "1" + + +# This memorizes unbacked SymInt or SymFloats representing quantities like the +# number of nonzero elements in this tensor or learning rate. There is one +# instance of the descriptor per particular quantity to memoize. +# +# Memoization is helpful if you do something like x[mask] and y[mask]; +# mask.nonzero() gets repeatedly called and should give a consistent unbacked +# SymInt. It needs to be invalidated in the same way constant is. +# +# Making this a descriptor may seem overly fancy, but actually it's the most +# convenient way to ensure access to FakeTensor during access, which is +# required for testing version counter and epoch validity. +class SymNumberMemoDescriptor: + _name: str + + # By default, SymInts in this memo are invalidated across versions/epochs. + # nested_ints however are preserved across epochs and across versions. + # Preserving across versions is okay for nested int since the association + # of a nested int is agnostic to the underlying data and nested ints are not + # shared across multiple distinct tensors. + _is_nested_int: bool + + def __init__(self, *, is_nested_int: bool = False) -> None: + self._is_nested_int = is_nested_int + + def __set_name__(self, owner: str, name: str) -> None: + self._name = name + + def _memo(self, obj: FakeTensor) -> str: + return f"_{self._name}" + + def _memo_vc(self, obj: FakeTensor) -> str: + return f"_{self._name}_vc" + + # When we retrace, we need to invalidate all the memos so that we can + # accurately identify the first time unbacked SymInts are allocated. + # This is only relevant for inputs; for intermediates, they will get fresh + # fake tensors so you won't have a memo anyway + def _memo_epoch(self, obj: FakeTensor) -> str: + return f"_{self._name}_epoch" + + def __get__( + self, obj: FakeTensor, objtype: Optional[type[FakeTensor]] = None + ) -> Optional[Union[torch.SymInt, torch.SymFloat]]: + if (r := getattr(obj, self._memo(obj))) is None: + return None + + # If backed, it's ok to preserve memo since we know it won't renumber. + if isinstance(r, torch.SymFloat) and r.node.hint is not None: + return r + + # Version counter based tracking isn't 100% sound but it's close + # enough + if ( + not self._is_nested_int and getattr(obj, self._memo_vc(obj)) != obj._version + ) or ( + not self._is_nested_int + and getattr(obj, self._memo_epoch(obj)) != obj.fake_mode.epoch + ): + setattr(obj, self._memo(obj), None) + return None + return r + + def __set__( + self, obj: FakeTensor, value: Optional[Union[torch.SymInt, torch.SymFloat]] + ) -> None: + if value is None: + setattr(obj, self._memo(obj), None) + setattr(obj, self._memo_vc(obj), None) + setattr(obj, self._memo_epoch(obj), None) + elif not obj.is_inference() or self._is_nested_int: + setattr(obj, self._memo(obj), value) + if not self._is_nested_int: + setattr(obj, self._memo_vc(obj), obj._version) + setattr(obj, self._memo_epoch(obj), obj.fake_mode.epoch) + + +class FakeTensor(Tensor): + """ + Meta tensors give you the ability to run PyTorch code without having to + actually do computation through tensors allocated on a `meta` device. + Because the device is `meta`, meta tensors do not model device propagation. + FakeTensor extends MetaTensors to also carry an additional `fake_device` + which tracks devices that would have been used. + """ + + fake_device: torch.device + fake_mode: FakeTensorMode + constant: Optional[Tensor] + real_tensor: Optional[Tensor] + + # TODO: Generalize this as needed, e.g., into a trie of memos, if + # you do something like x[0].item() (x[0] is fresh each time, so + # memo mechanism here won't work) + nonzero_memo = SymNumberMemoDescriptor() + item_memo = SymNumberMemoDescriptor() + unique_memo = SymNumberMemoDescriptor() + unique_consecutive_memo = SymNumberMemoDescriptor() + + # We expect nested_int_memo to be None when an offsets is a graph + # intermediate, or an input that has never been associated with a + # nested int. + nested_int_memo = SymNumberMemoDescriptor(is_nested_int=True) + + # FakeTensor doesn't fully emulate the original tensor's Python type + # and dispatch key set, therefore sometimes we want to track them + # separately. + pytype: Optional[type[Tensor]] + dispatch_keys: Optional[torch.DispatchKeySet] + + # Indicates to our torch_dispatch dispatching infra that + # this is an "infra" mode with lower dispatching precedence. + _mode_key = torch._C._TorchDispatchModeKey.FAKE + + @property + # pyrefly: ignore [bad-override] + def device(self) -> torch.device: + if self.fake_mode.in_kernel_invocation: + return torch.device("meta") + else: + return self.fake_device + + @device.setter + def device(self, _: torch.device) -> None: + raise NotImplementedError + + # Note: [Fake Tensor Dispatch Keys] + # In order to model the behavior of device-specific autocast + # and autograd logic, we update the dispatch keys of FakeTensors + # to reflect their fake device. This includes the BackendComponent + # (DispatchKey::Meta -> DispatchKey::CUDA), and also the BackendComponent + # related Autocast and Autograd keys. __torch_dispatch__ sits below + # Autocast and Autograd, and is only invoked when we are at the + # kernel for the BackendComponent. Then, we add Meta to the + # thread-local dispatch include set to hit the meta kernel + # instead of the kernel of the BackendComponent for the fake device. + # The `device_for_backend_keys` does that below + # NOTE: this probably will not do the right thing for backends + # that have dispatch keys which are higher than the "meta" key: + # https://github.com/pytorch/pytorch/blob/main/c10/core/DispatchKey.h#L189 + + # We don't support named tensors; graph break + @property + # pyrefly: ignore [bad-override] + def names(self) -> list[str]: + raise UnsupportedFakeTensorException( + "torch.compile doesn't support named tensors" + ) + + @names.setter + def names(self, _: list[str]) -> None: + raise NotImplementedError + + @staticmethod + def __new__( + cls, + fake_mode: FakeTensorMode, + elem: Tensor, + device: torch.device, + constant: Optional[Tensor] = None, + real_tensor: Optional[Tensor] = None, + pytype: Optional[type[Tensor]] = None, + dispatch_keys: Optional[torch.DispatchKeySet] = None, + ) -> Self: + self = Tensor._make_subclass( + cls, + elem, + elem.requires_grad, + dispatch_device=True, + device_for_backend_keys=device, + ) + if not fake_mode._allow_unsafe_data_ptr_access: + torch._C._set_throw_on_mutable_data_ptr(self) + else: + torch._C._set_warn_deprecated_on_mutable_data_ptr(self) + + assert elem.device.type == "meta", elem.device.type + device = device if isinstance(device, torch.device) else torch.device(device) + # NB: it is fine, if a little confusing, for device to be meta + # (we are faking a meta tensor in that case). However, it often + # indicates some sort of confusion (e.g., you accidentally passed + # in a meta tensor when you should have passed in the real tensor). + # So by default we disallow meta, and if you are working in a situation + # where it is helpful (e.g., crossref testing) you can turn it back + # on + if not fake_mode.allow_meta: + assert device.type != "meta" + # normalize device. + if device.type in ["cuda", "xpu"]: + init_gpu_context(device) + + if ( + device.type + in [ + "cuda", + "hpu", + "xpu", + "mps", + "mtia", + torch._C._get_privateuse1_backend_name(), + ] + and device.index is None + ): + if device.type != "mps" and getattr(torch, device.type).is_initialized(): + device = torch.device( + f"{device.type}:{getattr(torch, device.type).current_device()}" + ) + else: + device = torch.device(f"{device.type}:0") + # pyrefly: ignore [read-only] + self.fake_device = device + self.fake_mode = fake_mode + self.constant = constant + self.pytype = pytype + self.dispatch_keys = dispatch_keys + assert not isinstance(real_tensor, FakeTensor) + self.real_tensor = real_tensor + self.nonzero_memo = None + self.item_memo = None + self.unique_memo = None + self.unique_consecutive_memo = None + self.nested_int_memo = None + + if FakeTensorConfig.debug: + self._debug_trace = CapturedTraceback.extract() # type: ignore[attr-defined] + return self + + # In some circumstances, a conventional Tensor constructor + # will get rewritten to call into FakeTensor. We must provide an + # __init__ method that can accept the Python interpreters initialization + # in such a situation; we must also be able to handle direct fake + # tensor construction via FakeTensor(). + # + # In particular, the __init__ call will look funny in the following case: + # + # with FakeTensorMode(): + # x = Tensor([1, 2, 3]) + # + # this desugars into: + # + # with FakeTensorMode(): + # x = Tensor.__new__([1, 2, 3]) + # # NB: x is a fake tensor, because of the mode! + # x.__init__([1, 2, 3]) # not the normal fake tensor args! + # + def __init__(self, *args: object, **kwargs: object) -> None: + super().__init__() + if ( + torch.compiler.is_exporting() + and torch._export.config.detect_non_strict_fake_tensor_leaks + ): + fake_tensor_tls.non_strict_export_fake_tensor_tracker.add(self) + + @staticmethod + def from_tensor(t: Tensor, fake_mode: FakeTensorMode) -> FakeTensor: + return fake_mode.from_tensor(t) + + @classmethod + @count + def __torch_dispatch__( # type: ignore[override] # TODO + cls, + func: OpOverload, + types: Sequence[type], + args: Sequence[object] = (), + kwargs: Mapping[str, object] = immutable_dict(), + ) -> object: + # need to handle here to avoid infinite recursion + # see [in_kernel_invocation] + if func is torch.ops.prim.device.default: + assert len(args) == 1 and isinstance(args[0], FakeTensor) + if args[0].fake_mode.in_kernel_invocation: + return torch.device("meta") + else: + return args[0].fake_device + + # this handler must be done inside FakeTensor subclass, not mode, because + # we can end up dispatching here when we have a fake tensor with + # symbolic sizes running under in_kernel_invocation_manager. + # The subclass is asked to handle this query because size (not + # sym_size) was called, but we are unable to serve it directly because + # there are symbolic sizes in the class. The use of + # in_kernel_invocation_manager means it's incorrect to activate a + # mode to actually handle this (this caused + # https://github.com/pytorch/pytorch/issues/122772). + if handler := _DISPATCH_META_HANDLERS.get(func): + return handler(args) + + # Because fake mode can return NotImplemented (if it sees a subclass + # it doesn't know how to deal with), this test here is important + # because the next dispatch after a fake mode will attempt to use + # subclasses of tensors to dispatch, and any FakeTensor arguments + # will be considered eligible. + unrecognized_types = [ + t for t in types if not issubclass(t, FakeTensor) and t is not Tensor + ] + if unrecognized_types: + not_implemented_log.debug( + "FakeTensor unrecognized subclass(es): %s", unrecognized_types + ) + return NotImplemented + + fake_mode = None + for arg in pytree.arg_tree_leaves(*args, **kwargs): + if isinstance(arg, FakeTensor): + fake_mode = arg.fake_mode + break + + assert fake_mode is not None + + # If the fake mode is already active, don't try to reapply it! + # NotImplemented is the right thing to return here, because the + # typical situation this can occur is if ProxyTensorMode returned a + # NotImplemented because of a not implemented subclass; we may have + # unluckily attempted to hit FakeTensor's dispatch first, + # NotImplemented lets us keep chaining until we find the actual + # subclass + maybe_cur_fake_mode = torch._C._get_dispatch_mode( + torch._C._TorchDispatchModeKey.FAKE + ) + if maybe_cur_fake_mode: + not_implemented_log.debug( + "FakeTensor mode already active: %s in %s", + fake_mode, + maybe_cur_fake_mode, + ) + return NotImplemented + + assert not fake_mode.in_kernel_invocation + + with fake_mode: + return func(*args, **kwargs) + + @staticmethod + def _find_common_device( + func: OpOverload, flat_args: Sequence[object] + ) -> tuple[torch.device, bool]: + # Returns: (common_device, has_scalar_only_inputs) + + # cpu - zero-dim tensors can be called in cuda kernels, + # so overwrite the common_device if it the only existing + # device comes from a cpu zero-dim tensor + common_device = None + has_scalar_only_inputs = False + is_cpu_zero_dim = None + + # list of ops which can have args(tensor/tensorList) in mixed device + mixed_device_fns = ordered_set( + aten._foreach_copy.default, + ) + + # list of ops not using zero dim cpu tensor logic to align with the eager mode. + bypass_zero_dim_cpu_tensor_check_ops = ordered_set( + aten.nextafter.default, + ) + + def check_cpu_device(device: torch.device) -> bool: + return device.type == "cpu" + + def cpu_zero_dim(t: Tensor) -> bool: + return check_cpu_device(t.device) and t.dim() == 0 + + def merge_devices(t: object) -> None: + nonlocal common_device + nonlocal is_cpu_zero_dim + if not isinstance(t, FakeTensor): + return + + if common_device is None: + common_device = t.device + is_cpu_zero_dim = cpu_zero_dim(t) + return + + t_is_cpu_zero_dim = cpu_zero_dim(t) + if t.device == common_device: + if is_cpu_zero_dim: + is_cpu_zero_dim = t_is_cpu_zero_dim + return + + is_bypass_zero_dim_cpu_tensor_check_op = ( + func in bypass_zero_dim_cpu_tensor_check_ops + ) + + # mismatching devices ! + # if current tensor is cpu 0 dim, defer to existing device + if t_is_cpu_zero_dim and not is_bypass_zero_dim_cpu_tensor_check_op: + return + + # current device is from cpu 0 dim tensor, overwrite + if is_cpu_zero_dim and not is_bypass_zero_dim_cpu_tensor_check_op: + common_device = t.device + is_cpu_zero_dim = t_is_cpu_zero_dim + return + + # if still device mismatches we will check ops which can work + # on different devices for ex. _foreach_copy, and one of the + # device must be cpu in this case we will return from here without + # throwing an error + if func in mixed_device_fns: + if any(map(check_cpu_device, (common_device, t.device))): + return + + # if prefer_device_type is set, prefer that device type over others + prefer_device_type = torch._functorch.config.fake_tensor_prefer_device_type + if prefer_device_type is not None: + common_has_preferred = prefer_device_type in common_device.type + t_has_preferred = prefer_device_type in t.device.type + + if not common_has_preferred and t_has_preferred: + # Switch to the preferred device type + common_device = t.device + is_cpu_zero_dim = t_is_cpu_zero_dim + return + elif common_has_preferred and not t_has_preferred: + # Keep the existing preferred device type + return + + # mismatching devices of non-zero dim tensors, throw + # This might be valid behavior and need to be explicitly modeled, e.g. reshape_as + raise RuntimeError( + f"Unhandled FakeTensor Device Propagation for {func}, found two different devices {common_device}, {t.device}" + ) + + for arg in flat_args: + merge_devices(arg) + + # some functions that allow Python numbers to bind to Tensors + # if we have failed to find a device, and we're running one of these operators, + # we must have scalar only inputs + if should_allow_numbers_as_tensors(func) and common_device is None: + # ops with scalar only inputs always have result on cpu + has_scalar_only_inputs = True + common_device = torch.device("cpu") + + assert common_device is not None, f"Could not find common device for {func}" + + return common_device, has_scalar_only_inputs + + def get_nested_int( + self, + *, + coeff: Union[int, torch.SymInt] = 1, + ) -> torch.SymInt: + if self.nested_int_memo is None: + self.nested_int_memo = self.fake_mode.create_symbolic_nested_int( + nt_tensor_id=None + ) + assert isinstance(self.nested_int_memo, torch.SymInt) + return self.nested_int_memo * coeff + + # Similar to FunctionalTensor.tolist + def tolist(self) -> Any: + if self.dim() == 0: + return self.item() + elif self.dim() == 1: + return [elem.item() for elem in self] + else: + return [elem.tolist() for elem in self] + + +_MetadataIntLike = Union[IntLikeType, "_PySymInputStub", "_SymIntOutputStub"] + + +@dataclass(slots=True) +class TensorMetadata: + """ + The Tensor metadata relevant to hashing FakeTensors when caching. + """ + + dtype: torch.dtype + shape: tuple[_MetadataIntLike, ...] + stride: tuple[_MetadataIntLike, ...] + device: torch.device + layout: torch.layout + memory_format: Optional[torch.memory_format] + storage_offset: _MetadataIntLike + storage_bytes: Optional[_MetadataIntLike] + requires_grad: bool + is_quantized: bool + is_conj: bool + is_neg: bool + is_inference: bool + is_sparse: bool # read: is sparse COO + is_coalesced: Optional[bool] + dense_dim: Optional[int] + sparse_dim: Optional[int] + + def _flatten_into( + self, + result: list[object], + mode: FakeTensorMode, + state: _CacheKeyState, + ) -> None: + # Flatten the TensorMetadata out into `result`. Make sure to call + # state.convert_sym_int() on any SymInts. + for field in dataclasses.fields(self): + value = getattr(self, field.name) + if isinstance(value, (tuple, list, torch.Size)): + # This will recursively flatten the iterable, calling + # convert_sym_int() as necessary. + id_hashed_objects: list[object] = [] + mode._prep_args_for_hash(result, value, state, id_hashed_objects) + id_hashed_objects.clear() + elif isinstance(value, SymInt): + state.convert_sym_int(result, value) + else: + result.append(value) + + +def extract_tensor_metadata(t: Tensor) -> TensorMetadata: + """ + Extract the TensorMetadata of a tensor. + """ + memory_format = suggest_memory_format(t) + # Don't call is_contiguous() on a Tensor which has symbolic sizes or things + # will go badly (guards will be messed up?) + if ( + t._has_symbolic_sizes_strides + or is_sparse_any(t) + or not t.is_contiguous(memory_format=memory_format) + ): + memory_format = None # type: ignore[assignment] + + storage_offset = t.storage_offset() + + return TensorMetadata( + t.dtype, + t.shape, + t.stride() if t.layout == torch.strided else (), + t.device, + t.layout, + memory_format, + storage_offset, + # Only set storage_bytes for tensors that have storage (not sparse) + t.untyped_storage().nbytes() if not is_sparse_any(t) else None, + t.requires_grad, + t.is_quantized, + t.is_conj(), + t.is_neg(), + t.is_inference(), + t.is_sparse, + t.is_coalesced() if t.is_sparse else None, + t.dense_dim() if is_sparse_any(t) else None, + t.sparse_dim() if is_sparse_any(t) else None, + ) + + +@dataclass(slots=True) +class _DispatchCacheKey: + """ + Key for the FakeTensor dispatch cache. + """ + + key: tuple[object, ...] + hashvalue: int + + def __init__(self, tup: tuple[object, ...]) -> None: + self.key = tup + self.hashvalue = hash(tup) + + def __eq__(self, other: object) -> bool: + return isinstance(other, _DispatchCacheKey) and self.key == other.key + + def __hash__(self) -> int: + return self.hashvalue + + def strip_shape_env(self) -> None: + # We need to strip the ShapeEnv from any values before we store in the + # cache so the cache doesn't keep our ShapeEnvs alive. + for v in self.key: + if isinstance(v, _PySymInputStub): + v.strip_shape_env() + + +# Default value for constant_value in _DispatchCacheEntryOutputInfo. This is +# only for checking and differentiates from None. +class SingletonConstant: + pass + + +@dataclass(frozen=True, slots=True) +class _DispatchCacheEntryOutputInfo: + """ + Entry type for the FakeTensor dispatch cache for an output. Accounts for three + possibilities: + 1) The op is inplace, and a hit means we need to alias the argument at a + given index. + 2) We need to synthesize a new FakeTensor given tensor metadata. For view + ops, we further capture the index of the arg to alias. + 3) if the tensor related fields are None, then it is a constant value (e.g. + None or integer) + """ + + inplace_idx: Optional[int] + metadata: Optional[TensorMetadata] + view_idx: Optional[int] + constant_value: Optional[Any] = SingletonConstant + + +@dataclass(frozen=True, slots=True) +class _DispatchCacheValidEntry: + """ + Entry type for the FakeTensor dispatch cache. It supports two types of outputs + 1) tensor + 2) tuple of tensors + + is_output_tuple flag helps in differentiating the return type + """ + + output_infos: tuple[_DispatchCacheEntryOutputInfo] + is_output_tuple: bool = False + + +@dataclass(frozen=True, slots=True) +class _DispatchCacheBypassEntry: + """ + Entry type for a negative cache entry. + """ + + reason: str + + +if TYPE_CHECKING: + _DispatchCacheEntry = Union[_DispatchCacheValidEntry, _DispatchCacheBypassEntry] + + +@dataclass(frozen=True, slots=True) +class _BypassDispatchCache(Exception): + """ + Signals cases that should skip FakeTensor caching. + """ + + reason: str + + +@dataclass(frozen=True, slots=True) +class DispatchCacheInfo: + """ + Information about the state of the FakeTensor dispatch cache. + """ + + hits: int + misses: int + bypasses: dict[str, int] + size: int + + +# We keep one instantiation of `fake_tensor_converter` active +# for the duration of `with FakeTensorMode()`. +# This allows accurate storage aliasing across invocation of +# different operators. While this will keep all freshly allocated +# tensors alive during `FakeTensorMode`, there will be no +# new allocations of Tensors which have non-meta storage so +# memory should not significantly increase. + + +class FakeTensorMode(TorchDispatchMode): + cache: dict[_DispatchCacheKey, _DispatchCacheEntry] = {} + cache_hits: int = 0 + cache_misses: int = 0 + cache_bypasses: dict[str, int] = defaultdict(int) + # Every time you retrace using the same fake tensor mode, you should + # advance the epoch so we don't reuse unbacked memos + epoch: int = 0 + in_kernel_invocation: bool = False + static_shapes: bool + shape_env: Optional[ShapeEnv] + _stack: Optional[str] + allow_meta: bool + + # NestedTensor uses a tensor_id_counter to uniquely identify offsets. + # This counter is incremented when an offsets is used to create an NJT + # for the first time. To avoid mutating eager state if we construct NJT + # during tracing, we maintain a separate counter on the FakeTensorMode. + # The initial count is set to the current eager tensor_id_counter value + # upon initialization, and every time you retrace using the same fake tensor + # mode, you should reset the counter to the initial count. + nt_tensor_id_counter: int = -1 + nt_tensor_id_initial_count: int = -1 + + def __init__( + self, + *, + allow_fallback_kernels: bool = True, + allow_non_fake_inputs: bool = False, + shape_env: Optional[ShapeEnv] = None, + static_shapes: Optional[bool] = None, + # TODO: This is a temporary measure, see + # https://github.com/pytorch/pytorch/pull/126245#discussion_r1604185748 + # We're currently solely using this to impede population of + # item_memo for 0d scalar tensor inputs when export, because this + # causes things that used to be deferred runtime asserts to turn into + # guards, and then the guards are just lost. We can potentially fix + # this by ensuring guards also get put in the graph, but this is + # pending a rework of how deferred runtime asserts in export. Once + # that's done, we can remove this. + export: bool = False, + ) -> None: + log.debug("create_mode 0x%x", id(self)) + super().__init__() + self.allow_fallback_kernels = allow_fallback_kernels + + import torch._dynamo.config + import torch._functorch.config + + self.propagate_real_tensors = ( + torch._functorch.config.fake_tensor_propagate_real_tensors + ) + self.fake_tensor_converter = FakeTensorConverter( + copy_data=self.propagate_real_tensors, + export=export, + ) + + if static_shapes is not None: + self.static_shapes = static_shapes + else: + self.static_shapes = shape_env is None + + # This is temporarily patched to True in Dynamo to grandfather in some + # places where we unconditionally allow scalar outputs, TO BE REMOVED + self.allow_scalar_outputs = False + + self._allow_unsafe_data_ptr_access = ( + torch._functorch.config.fake_tensor_allow_unsafe_data_ptr_access + ) + self.allow_meta = torch._functorch.config.fake_tensor_allow_meta + self.cache_enabled: bool = ( + torch._dynamo.config.fake_tensor_cache_enabled + and not self.propagate_real_tensors + ) + self.cache_crosscheck_enabled = ( + torch._dynamo.config.fake_tensor_cache_crosscheck_enabled + ) + + # A flag that controls, whether we want to invoke ops on mix of + # real weights/global variables and fake inputs + self.allow_non_fake_inputs = allow_non_fake_inputs + + # [in_kernel_invocation] + # when FakeTensor is invoked in user code, .device should return + # the fake_device of the tensor so that code such as as `if x.is_cuda` + # or torch.zeros([10, 10], device=x.device) continues to execute as if + # the FakeTensor were real. However, within kernel execution, we return + # the `Meta` device because all computation within the kernels should + # behave as if the Tensors are on meta devices. Kernels should allocate + # new tensors on meta devices, and checks like `is_meta` should return true. + # within python refs, we always return the real device by defining + # the device property + self.in_kernel_invocation = False + + # True if we enter'ed and actually enabled fake tensor mode, + # false if it was a no-op. Not thread safe but neither is + # in_kernel_invocation + # If another fake mode was already active when we enter, we also stash it here. + # That way when we exit, we know to re-enable the previous fake mode. + self.enter_stack: list[ + tuple[bool, Optional[TorchDispatchMode], Optional[bool]] + ] = [] + + self.shape_env = shape_env + + self._stack_trace = traceback.extract_stack() + self._stack = None + + # Indicates to our torch_dispatch dispatching infra that + # this is an "infra" mode with lower dispatching precedence. + self._mode_key = torch._C._TorchDispatchModeKey.FAKE + + import torch.nested._internal.nested_tensor + + self.nt_tensor_id_initial_count = ( + torch.nested._internal.nested_tensor._tensor_id_counter + ) + self.nt_tensor_id_counter = self.nt_tensor_id_initial_count + + def reset_nt_tensor_id_counter(self) -> None: + self.nt_tensor_id_counter = self.nt_tensor_id_initial_count + + # Typically, there is only one fake tensor mode and you test for it by + # doing an isinstance test. However, in some situations, there might be + # TWO fake tensor modes. The canonical example of this is exporting + # a fake model: there is an outer fake mode created by the user, and + # an inner fake mode created by Dynamo. The two phase process is required + # because the outer fake mode typically won't have a ShapeEnv, even if + # the user is interested in exporting with dynamic shapes (so the inner + # fake mode will actually have a ShapeEnv and swap in symbolic sizes.) + # + # In this case, it's insufficient to test only one FakeTensor: you need + # to distinguish between our fake tensor and other fake tensors. That's + # what this function does. + def is_our_fake(self, t: object) -> TypeGuard[FakeTensor]: + return isinstance(t, FakeTensor) and t.fake_mode is self + + # If we should avoid device init. This changes the behavior of various APIs: + # - We avoid constant-prop on Tensors with ops that move them to another device + # - We change the torch.tensor ctor contract to never materialize + # tensors on device + # (see NOTE: [torch.tensor, lift_fresh, and device movement]) + @property + def avoid_device_init(self) -> bool: + if torch.xpu._is_compiled(): + assert not torch.cuda._is_compiled() + return not torch.xpu.is_available() + + return not ( + torch.cuda.is_available() + or (hasattr(torch, "hpu") and torch.hpu.is_available()) + ) + + @property + def stack(self) -> str: + if self._stack is None: + self._stack = "".join(traceback.format_list(self._stack_trace)) + return self._stack + + @count + # pyrefly: ignore [bad-override] + def __torch_dispatch__( + self, + func: OpOverload, + types: Sequence[type], + args: Sequence[object] = (), + kwargs: Mapping[str, object] = immutable_dict(), + ) -> object: + # FakeTensorMode should not be set when we're inside of it. + assert ( + torch._C._get_dispatch_mode(torch._C._TorchDispatchModeKey.FAKE) is None + ), func + try: + return self.dispatch(func, types, args, kwargs) + except TypeError: + log.exception("fake tensor raised TypeError") + raise + + # No-op if FakeTensorMode is already in use + def __enter__(self) -> Self: + import torch.nested._internal.nested_tensor + + prev_only_lift_cpu_tensors = None + if self.avoid_device_init: + # See NOTE: [torch.tensor, lift_fresh, and device movement] + prev_only_lift_cpu_tensors = torch._C._only_lift_cpu_tensors() + torch._C._set_only_lift_cpu_tensors(True) + + # In the case of CPU-only build or cuda device unavailable, + # we patch the cuda device guard to use NoOpDeviceGuardImpl. + # This enables us to trace over cuda kernels under FakeTensorMode. + torch._C._ensureCUDADeviceGuardSet() + + maybe_prev_fake_mode = torch._C._unset_dispatch_mode(self._mode_key) + if self is not maybe_prev_fake_mode: + self.enter_stack.append( + (True, maybe_prev_fake_mode, prev_only_lift_cpu_tensors) + ) + return super().__enter__() + else: + # no-op (still need to re-set the fake mode though since we unset it) + torch._C._set_dispatch_mode(self) + self.enter_stack.append((False, None, prev_only_lift_cpu_tensors)) + + return self + + def __exit__( + self, + a: Optional[type[BaseException]], + b: Optional[BaseException], + c: Optional[TracebackType], + ) -> None: + ( + live, + maybe_prev_fake_mode, + maybe_prev_only_lift_cpu_tensors, + ) = self.enter_stack.pop() + if live: + super().__exit__(a, b, c) + + # Re-enable the previous fake mode, if there was one. + if maybe_prev_fake_mode is not None: + torch._C._set_dispatch_mode(maybe_prev_fake_mode) + if maybe_prev_only_lift_cpu_tensors is not None: + torch._C._set_only_lift_cpu_tensors(maybe_prev_only_lift_cpu_tensors) + + @classmethod + def is_infra_mode(cls) -> bool: + return True + + @classmethod + def cache_info(cls) -> DispatchCacheInfo: + """ + Query the state of the dispatch cache. + """ + return DispatchCacheInfo( + FakeTensorMode.cache_hits, + FakeTensorMode.cache_misses, + dict(FakeTensorMode.cache_bypasses), + len(FakeTensorMode.cache), + ) + + @classmethod + def cache_clear(cls) -> None: + """ + Clear the dispatch cache. + """ + cls.cache_hits = 0 + cls.cache_misses = 0 + cls.cache_bypasses.clear() + cls.cache.clear() + + def _cached_dispatch_impl( + self, + func: OpOverload, + types: Sequence[type], + args: Sequence[object], + kwargs: Mapping[str, object], + ) -> object: + """ + Lookup a cache entry for the given arguments. If none exists, dispatch + and cache the result (if the result is eligible for caching). + """ + state = None + key = None + try: + state = _CacheKeyState(self.shape_env) + key = self._cache_key(state, func, args, kwargs) + except _BypassDispatchCache as e: + # We couldn't create the cache key at all + if ( + isinstance(func, torch._ops.HigherOrderOperator) + and func.name() == "invoke_subgraph" + ): + hc_log.debug( + "Fake tensor cache failed: identifier = %s, reason = %s", + args[1], + e.reason, + ) + FakeTensorMode.cache_bypasses[e.reason] += 1 + + if key is None: + # Do this dispatch outside the above except handler so if it + # generates its own exception there won't be a __context__ caused by + # the caching mechanism. + # pyrefly: ignore [bad-argument-type] + return self._dispatch_impl(func, types, args, kwargs) + + assert state is not None + if state.cache_on_shape_env(): + assert state.shape_env is not None + cache = state.shape_env.fake_tensor_cache + set_cache_key = _set_cache_key_for_shape_env + else: + cache = FakeTensorMode.cache + set_cache_key = _set_cache_key + entry = cache.get(key, None) + + if entry is not None: + if isinstance(entry, _DispatchCacheBypassEntry): + # This represents a negative cache entry - we already saw that the + # output is uncachable. Compute it from first principals. + FakeTensorMode.cache_bypasses[entry.reason] += 1 + # pyrefly: ignore [bad-argument-type] + return self._dispatch_impl(func, types, args, kwargs) + + # We have a cache entry. + # pyrefly: ignore [bad-argument-type] + output = self._output_from_cache_entry(state, entry, key, func, args) + FakeTensorMode.cache_hits += 1 + if self.cache_crosscheck_enabled: + # For debugging / testing: Validate that the output synthesized + # from the cache matches the output created by normal dispatch. + with disable_fake_tensor_cache(self): + # pyrefly: ignore [bad-argument-type] + self._crosscheck_cache_output(output, func, types, args, kwargs) + return output + + # We don't have a cache entry. + # pyrefly: ignore [bad-argument-type] + output = self._dispatch_impl(func, types, args, kwargs) + + try: + # pyrefly: ignore [bad-argument-type] + entry = self._make_cache_entry(state, key, func, args, kwargs, output) + except _BypassDispatchCache as e: + # We ran "extra" checks on the cache key and determined that it's no + # good. Record the reason and mark it so we don't bother validating + # again. + if ( + isinstance(func, torch._ops.HigherOrderOperator) + and func.name() == "invoke_subgraph" + ): + hc_log.debug( + "Fake tensor cache failed: identifier = %s, reason = %s", + args[1], + e.reason, + ) + FakeTensorMode.cache_bypasses[e.reason] += 1 + set_cache_key(cache, key, _DispatchCacheBypassEntry(e.reason)) + return output + + set_cache_key(cache, key, entry) + FakeTensorMode.cache_misses += 1 + return output + + def _cache_key( + self, + state: _CacheKeyState, + func: OpOverload, + args: Sequence[object], + kwargs: Mapping[str, object], + ) -> _DispatchCacheKey: + """ + Create a cache key given the dispatch args. Raises _BypassDispatchCache + for any situation that precludes caching. + """ + is_tracing = torch.fx.experimental.proxy_tensor.get_proxy_mode() is not None + key_values = [ + func, + # Capture the default_dtype mode since that can affect the output tensor, + # e.g., when operating on constant float values. + torch.get_default_dtype(), + # Capture the current device to support, e.g., cache tensor creation, + # where there isn't necessarily a tensor to take the device from. + torch._C._get_default_device(), + # We want to create tensors from cached metadata only when the inference + # mode is the same. + torch.is_inference_mode_enabled(), + # Shape env settings could affect behavior. One example seen in the wild: + # Disallowing dynamic shapes can introduce a DynamicOutputShapeException + # where it wasn't seen on a previous instance of the same op. + self.shape_env.settings if self.shape_env else None, + # ProxyTorchDispatchMode needs to track how SymNodes are constructed + # so we need to handle things a little different depending on + # whether we're tracing or not. + is_tracing, + ] + if state.known_symbols: + # If there are symbols then include the epoch - this is really more + # of a Shape env var which lives on the FakeTensorMode. + # pyrefly: ignore [bad-argument-type] + key_values.append(self.epoch) + # Collect the id_hashed objects to attach a weakref finalize later + id_hashed_objects: list[object] = [] + # Translate any FakeTensor args to metadata. + if args: + # pyrefly: ignore [bad-argument-type] + self._prep_args_for_hash(key_values, args, state, id_hashed_objects) + if kwargs: + # pyrefly: ignore [bad-argument-type] + self._prep_args_for_hash(key_values, kwargs, state, id_hashed_objects) + key = _DispatchCacheKey(tuple(key_values)) + + for id_hashed_obj in id_hashed_objects: + weakref.finalize( + id_hashed_obj, functools.partial(evict_fake_tensor_cache_key, key=key) + ) + id_hashed_objects.clear() + return key + + def _validate_cache_key( + self, + func: OpOverload, + args: Sequence[object], + kwargs: Mapping[str, object], + ) -> None: + """ + Validate that the cache key generated by _cache_key will be + reasonable. + """ + from torch._higher_order_ops.utils import registered_hop_fake_fns + + # For hops, we perform the validity check in _make_cache_entry because we + # need to have the output tensor. + if ( + isinstance(func, torch._ops.HigherOrderOperator) + and func in registered_hop_fake_fns + ): + return + + # Avoid caching for any ops that would require a more sophisticated + # caching implementation, e.g., data dependent ops or ops that modify + # the inputs. + if torch.Tag.data_dependent_output in func.tags: + raise _BypassDispatchCache("data dependent output") + + if torch.Tag.dynamic_output_shape in func.tags: + if func is aten.index.Tensor: + _, new_kwargs = normalize_function( # type: ignore[misc] + func, + args=args, # type: ignore[arg-type] + kwargs=kwargs, # type: ignore[arg-type] + normalize_to_only_use_kwargs=True, + ) + for index in new_kwargs["indices"]: + # index calls nonzero for bool or int8 tensors, and + # therefore has a dynamic shape output. For other dtypes, + # the output shape depends on the input shape (and not data) + if isinstance(index, torch.Tensor) and index.dtype in ( + torch.bool, + torch.int8, + ): + raise _BypassDispatchCache("dynamic output shape") + return + + raise _BypassDispatchCache("dynamic output shape") + + if torch.Tag.inplace_view in func.tags: + raise _BypassDispatchCache("inplace view") + + if func is aten._unsafe_view.default: + raise _BypassDispatchCache("unsafe view") + + if func in self.lift_fns: + raise _BypassDispatchCache("lift") + + if func.name() == "inductor::resize_storage_bytes_": + raise _BypassDispatchCache("inductor::resize_storage_bytes_") + + if not torch._library.utils.is_builtin(func): + raise _BypassDispatchCache("non-builtin") + + # In order to handle storage aliasing, we need to establish the alias + # for any view op on a cache hit. But CompositeImplicitAutograd ops may + # or may not alias the input, so just punt on caching these. + if func.is_view and torch._C._dispatch_has_kernel_for_dispatch_key( + func.name(), torch._C.DispatchKey.CompositeImplicitAutograd + ): + raise _BypassDispatchCache("CompositeImplicitAutograd") + + def _prep_args_for_hash( + self, + result: list[object], + args: Union[Mapping[str, object], Sequence[object], Iterable[object]], + state: _CacheKeyState, + id_hashed_objects: list[object], + ) -> None: + """ + Translate the provided args into a form suitable for caching at FakeTensor + dispatch, i.e., convert unhashable types like lists & dicts into tuples and + convert FakeTensors into metadata. Raises _BypassDispatchCache to signal + unsupported cases that should bypass caching. + """ + from torch._higher_order_ops.auto_functionalize import ( + FunctionalCallableWithEpilogue, + ) + from torch._higher_order_ops.utils import FunctionalizeCtxWrapper + + if isinstance(args, (list, tuple, dict)): + result.append(type(args)) + result.append(f"length_{len(args)}") + + if isinstance(args, dict): + self._prep_args_for_hash(result, args.keys(), state, id_hashed_objects) + self._prep_args_for_hash(result, args.values(), state, id_hashed_objects) + return + + for arg in args: + if isinstance(arg, FakeTensor): + if not self.is_our_fake(arg): + raise _BypassDispatchCache("not our fake") + if arg.constant is not None: + raise _BypassDispatchCache("constant attribute") + if is_sparse_any(arg): + raise _BypassDispatchCache(f"{arg.layout} tensor") + metadata = extract_tensor_metadata(arg) + metadata._flatten_into(result, self, state) + elif isinstance(arg, Tensor): + raise _BypassDispatchCache("non-fake tensor") + elif isinstance(arg, SymInt): + state.convert_sym_int(result, arg) + elif isinstance(arg, (SymBool, SymFloat)): + raise _BypassDispatchCache("symbolic shape") + elif isinstance(arg, (list, tuple, dict)): + self._prep_args_for_hash(result, arg, state, id_hashed_objects) + elif isinstance(arg, types.FunctionType): + raise _BypassDispatchCache("function argument") + elif isinstance(arg, torch.fx.GraphModule): + # This is used for invoke_subgraph where id(graph_module) allows + # us to cache fake outputs + result.append(type(arg)) + result.append(id(arg)) + id_hashed_objects.append(arg) + elif isinstance(arg, FunctionalizeCtxWrapper): + # Special case for AOT Dispatcher first pass, where the fake + # tensor is called on the functional wrapper of the subgraph. + result.append(hash(arg)) + # functional wrapper is destroyed after fake tensor prop. We + # need to put the finalizer on the subgraph. + id_hashed_objects.append(arg.subgraph) + elif isinstance(arg, FunctionalCallableWithEpilogue): + result.append(type(arg)) + result.append(hash(arg)) + id_hashed_objects.append(arg.orig_callable) + else: + # It's important to capture the type of the arg since, e.g., 1 and 1.0 + # hash to the same value, but can produce different dtypes for the + # output tensor. + result.append(type(arg)) + result.append(arg) + + def _validate_output_for_cache_entry( + self, + state: _CacheKeyState, + key: _DispatchCacheKey, + func: OpOverload, + args: Sequence[object], + kwargs: Mapping[str, object], + output: Optional[FakeTensor], + ) -> None: + # Is this even possible? According to the signature this can be None but + # not `int`. So either the signature is a lie or (part of) this line is + # unnecessary... + if isinstance(output, (int, type(None))): + return + + # Check for symbolic content that should bypass caching - raises + # _BypassDispatchCache if necessary. + _validate_symbolic_output_for_caching(state, output) + + # Some ops return tuples of Tensors, but it's rare, so avoid + # the complexity of caching other types. + if not isinstance(output, FakeTensor): + raise _BypassDispatchCache("non-FakeTensor output") + + # Avoid caching FakeTensors with constants attached since those + # can be invalidated. + if output.constant is not None: + raise _BypassDispatchCache("constant attribute") + + # TODO: support caching sparse outputs? + if output.is_sparse: + raise _BypassDispatchCache("sparse output") + + if is_sparse_compressed(output): + raise _BypassDispatchCache("sparse compressed output") + + # Can an in-place op really reference a kwarg? If so, then we need + # to extend the implementation to handle it. + for kval in kwargs.values(): + if id(kval) == id(output): + raise _BypassDispatchCache("kwarg aliases output") + + def _get_output_info_for_cache_entry( + self, + state: _CacheKeyState, + key: _DispatchCacheKey, + func: OpOverload, + args: Sequence[object], + kwargs: Mapping[str, object], + output: FakeTensor, + ) -> _DispatchCacheEntryOutputInfo: + if isinstance(output, (int, torch.SymInt, type(None))): + return _DispatchCacheEntryOutputInfo( + inplace_idx=None, metadata=None, view_idx=None, constant_value=output + ) + + # If this is an in-place op, the entry records which input arg is aliased. + for idx in range(len(args)): + if id(args[idx]) == id(output): + return _DispatchCacheEntryOutputInfo( + inplace_idx=idx, metadata=None, view_idx=None + ) + + # Otherwise, create an entry that records the output tensor's metadata. + view_idx = None + if isinstance(func, torch._ops.OpOverload) and func.is_view: + idxs = [i for i, t in enumerate(args) if isinstance(t, Tensor)] + assert len(idxs) == 1 + view_idx = idxs[0] + + metadata = extract_tensor_metadata(output) + metadata.shape = tuple(state.convert_output(v) for v in metadata.shape) + metadata.stride = tuple(state.convert_output(v) for v in metadata.stride) + metadata.storage_offset = state.convert_output(metadata.storage_offset) + metadata.storage_bytes = ( + None + if metadata.storage_bytes is None + else state.convert_output(metadata.storage_bytes) + ) + + entry = _DispatchCacheEntryOutputInfo( + inplace_idx=None, + metadata=metadata, + view_idx=view_idx, + ) + + # N.B.: Some checks for bypassing the cache would be performed on the + # output tensor synthesized from the cached metadata. As an optimization, + # we can synthesize a tensor here and do the checks on that instance. + # This approach keeps the (more frequent) cache-hit path as lightweight + # as possible. + entry_for_synth_output = _DispatchCacheValidEntry( + output_infos=(entry,), is_output_tuple=False + ) + from torch.fx.experimental.symbolic_shapes import GuardOnDataDependentSymNode + + try: + synth_output = self._output_from_cache_entry( + state, entry_for_synth_output, key, func, args + ) + except GuardOnDataDependentSymNode: + # This should probably never really happen. If it does it means that + # although the original call didn't get a data-dependent error when + # we tried to reconstruct the output we did - that's almost + # certainly a bug. + raise _BypassDispatchCache("data dependent symnode") from None + + # Make sure the dispatch_key_set from the synthesized output tensor will + # be the same. + synth_key_set = torch._C._dispatch_key_set(synth_output) + key_set = torch._C._dispatch_key_set(output) + if synth_key_set != key_set: + raise _BypassDispatchCache("dispatch_key_set mismatch") + + return entry + + def _make_cache_entry( + self, + state: _CacheKeyState, + key: _DispatchCacheKey, + func: OpOverload, + args: Sequence[object], + kwargs: Mapping[str, object], + output: Optional[FakeTensor], + ) -> _DispatchCacheValidEntry: + """ + Make a cache entry object for the given 'output' Tensor. Raises + _BypassDispatchCache if the output tensor has characteristics that + prevent caching it. + """ + from torch._higher_order_ops.utils import registered_hop_fake_fns + from torch.fx.experimental.symbolic_shapes import has_free_unbacked_symbols + + self._validate_cache_key(func, args, kwargs) + + # For hops, lets look at the output tensor to find any unbacked symints. + # If there are none, then we rely on the existing checks to validate + # caching. + # NB: Note that the HOPs that sta alive till FakeTensor are functional, + # once they support mutations, we will have to revisit this logic. + if ( + isinstance(func, torch._ops.HigherOrderOperator) + and func in registered_hop_fake_fns + ): + assert isinstance(output, tuple) + non_cacheable = any( + isinstance(o, (torch.Tensor, torch.SymInt)) + and has_free_unbacked_symbols(o) + for o in output + ) + if non_cacheable: + raise _BypassDispatchCache(f"unbacked symbol in HOP {func} output") + + if isinstance(output, (int, torch.SymInt, type(None))): + output_info = _DispatchCacheEntryOutputInfo( + inplace_idx=None, metadata=None, view_idx=None, constant_value=output + ) + return _DispatchCacheValidEntry( + output_infos=(output_info,), is_output_tuple=False + ) + + if isinstance(output, tuple): + for out_element in output: + self._validate_output_for_cache_entry( + state, + key, + # pyrefly: ignore [bad-argument-type] + func, + args, + kwargs, + out_element, + ) + else: + self._validate_output_for_cache_entry( + state, + key, + # pyrefly: ignore [bad-argument-type] + func, + args, + kwargs, + output, + ) + + if isinstance(output, tuple): + output_infos = [ + self._get_output_info_for_cache_entry( + state, + key, + # pyrefly: ignore [bad-argument-type] + func, + args, + kwargs, + out_elem, + ) + for out_elem in output + ] + return _DispatchCacheValidEntry( + # pyrefly: ignore [bad-argument-type] + output_infos=tuple(output_infos), + is_output_tuple=True, + ) + + else: + output_info = self._get_output_info_for_cache_entry( + state, + key, + # pyrefly: ignore [bad-argument-type] + func, + args, + kwargs, + output, + ) + return _DispatchCacheValidEntry( + output_infos=(output_info,), is_output_tuple=False + ) + + def _get_output_tensor_from_cache_entry( + self, + state: _CacheKeyState, + entry: _DispatchCacheEntryOutputInfo, + key: _DispatchCacheKey, + func: OpOverload, + args: Sequence[object], + ) -> Optional[FakeTensor]: + if ( + entry.inplace_idx is None + and entry.metadata is None + and entry.view_idx is None + ): + assert entry.constant_value is not SingletonConstant + return entry.constant_value + if entry.inplace_idx is not None: + # This is an in-place op; return the aliased arg. + inplace_arg = args[entry.inplace_idx] + assert isinstance(inplace_arg, FakeTensor) + return inplace_arg + + # Synthesize a new FakeTensor with the cached metadata. + metadata = entry.metadata + if metadata is None: + return None + + assert not is_sparse_any(metadata) + + def check_value( + value: _MetadataIntLike, state: _CacheKeyState + ) -> Union[IntLikeType]: + if isinstance(value, _SymIntOutputStub): + assert state.shape_env is not None + return value.extract(key, state.shape_env) + else: + assert not isinstance(value, _PySymInputStub) + return value + + shape = tuple(check_value(v, state) for v in metadata.shape) + stride = tuple(check_value(v, state) for v in metadata.stride) + storage_offset = check_value(metadata.storage_offset, state) + if metadata.storage_bytes is not None: + check_value(metadata.storage_bytes, state) + + maybe_suppress: Callable[[], typing.ContextManager] = contextlib.nullcontext + if self.shape_env is not None: + maybe_suppress = self.shape_env.suppress_guards + + with in_kernel_invocation_manager(self), maybe_suppress(): + empty = torch.empty_strided( + shape, + stride, + dtype=metadata.dtype, + layout=metadata.layout, + device="meta", + requires_grad=metadata.requires_grad, + ) + + if metadata.is_conj: + torch._C._set_conj(empty, True) + if metadata.is_neg: + torch._C._set_neg(empty, True) + + if isinstance(func, torch._ops.OpOverload) and func.is_view: + # For view ops, the storage should be the same as the tensor input. + view_arg = args[cast(int, entry.view_idx)] + assert isinstance(view_arg, FakeTensor) + storage = view_arg.untyped_storage() + with in_kernel_invocation_manager(self), maybe_suppress(): + empty.set_(storage, storage_offset, shape, stride) + + return FakeTensor(self, empty, metadata.device) + + def _output_from_cache_entry( + self, + state: _CacheKeyState, + entry: _DispatchCacheValidEntry, + key: _DispatchCacheKey, + func: OpOverload, + args: Sequence[object], + ) -> Union[Optional[FakeTensor], tuple[Optional[FakeTensor], ...]]: + """ + Create a new FakeTensor from the cache entry. + """ + + if entry.is_output_tuple: + outputs = [ + self._get_output_tensor_from_cache_entry( + state, output_info, key, func, args + ) + for output_info in entry.output_infos + ] + return tuple(outputs) + else: + return self._get_output_tensor_from_cache_entry( + state, entry.output_infos[0], key, func, args + ) + + def _crosscheck_cache_output( + self, + output: Union[Optional[FakeTensor], tuple[Optional[FakeTensor], ...]], + func: OpOverload, + types: Sequence[type], + args: Sequence[object], + kwargs: Mapping[str, object], + ) -> None: + """ + Helper to validate that the output synthesized from the cache matches + the output created by normal dispatch. + """ + + def assert_helper(a: Any, b: Any) -> None: + if isinstance(a, tuple): + assert isinstance(b, tuple) + assert len(a) == len(b) + for l, r in zip(a, b): + assert_helper(l, r) + elif isinstance(a, int): + assert isinstance(b, int) and a == b + elif a is None: + assert b is None + elif isinstance(a, py_sym_types): + assert type(a) is type(b) and a.node is b.node + elif isinstance(a, torch.Tensor): + assert isinstance(b, torch.Tensor) + assert_metadata_eq(assert_eq, a, b) + else: + raise RuntimeError(f"Unsupported type {type(a)}") + + try: + true_output = self._dispatch_impl(func, types, args, kwargs) + except Exception as e: + raise RuntimeError( + f"FakeTensor cache crosscheck failure: func={func}, " + f"args={args}, kwargs={kwargs}: Dispatch raised={e}" + ) from e + try: + assert_helper(true_output, output) + except Exception as e: + raise RuntimeError( + f"FakeTensor cache crosscheck failure: func={func}, " + f"args={args}, kwargs={kwargs}" + ) from e + + def dispatch( + self, + func: OpOverload, + types: Sequence[type], + args: Sequence[object] = (), + kwargs: Mapping[str, object] = immutable_dict(), + ) -> object: + kwargs = kwargs or {} + with no_dispatch(): + log.debug("%s %s %s", func, args, kwargs) + + if func in _DISPATCH_META_HANDLERS: + return _DISPATCH_META_HANDLERS[func](args) + + if log.getEffectiveLevel() <= logging.DEBUG: + log.debug( + "%sFakeTensorMode.__torch_dispatch__: %s", " " * RECURSION_COUNT, func + ) + # NOTE: incr is intentionally unused for a RAII pattern + incr = IncrementRecursionCount() # noqa: F841 + + # Some attribute queries that can be serviced directly + # See Note [is_coalesced is dispatched] + if func in _DISPATCH_HANDLE_DIRECTLY: + # NB: no_dispatch is ok here too, this func is very simple + with in_kernel_invocation_manager(self): + return func(*args, **kwargs) + + if self.cache_enabled: + return self._cached_dispatch_impl(func, types, args, kwargs) + else: + return self._dispatch_impl(func, types, args, kwargs) + + def _maybe_infer_fake( + self, func: OpOverload, path: KeyPath, fake: object, real: object + ) -> tuple[Optional[object], bool]: + """ + Helper to cross-check fake/real output properties & values, + and create new fake vals if mismatched. + Returns tuple of object & boolean, for whether or not it was overwrriten + """ + import sympy + + from torch._subclasses.fake_utils import _check_fake_real_tensors + + def _check_fake_real_vals(fake: Any, real: Any) -> None: + # use real values + ShapeEnv to check mismatches between potentially symbolic values + if isinstance(fake, (SymInt, SymFloat)): + # symbolic expression, ask ShapeEnv to substitute known backed/unbacked values + assert self.shape_env is not None + if ( + not fake.node.expr.free_symbols + - self.shape_env.var_to_val.keys() + - self.shape_env.unbacked_var_to_val.keys() + ): + if ( + self.shape_env._maybe_evaluate_static( + sympy.Eq(fake.node.expr, real), compute_hint=True + ) + is not sympy.S.true + ): + raise MetadataMismatchError( + f"mismatch between fake value {fake} and real value {real} " + ) + elif isinstance( + fake, (int, float, bool) + ): # concrete value, check direct equality + if fake != real: + raise MetadataMismatchError( + f"mismatch between fake value {fake} and real value {real} " + ) + + if isinstance(fake, torch.Tensor): + try: + _check_fake_real_tensors( + real, # type: ignore[arg-type] + fake, # type: ignore[arg-type] + context="Real tensor propagation found", + sizes=False, # manual check below + strides=False, # skip strides + storage_offset=True, + requires_grad=False, # issues with FakeTensorConverter preserving requires_grad + ) + except MetadataMismatchError as exc: + if torch._functorch.config.generate_fake_kernels_from_real_mismatches: + dtrace_structured( + "mismatched_fake_kernel", + metadata_fn=lambda: { + "op": str(func), + "reason": exc.reason, # noqa: F821 + }, + ) + return _infer_fake_from_real_tensor(self, func, real), True # type: ignore[arg-type] + raise MetadataMismatchError( + f"Real tensor propagation found a metadata mismatch between " + f"fake tensor {fake} and real tensor {real}, " + f" at output{keystr(path)}, for func: {func}" + ) from exc + + for j, (s_fake, s_real) in enumerate(zip(fake.size(), real.size())): # type: ignore[attr-defined] + try: + _check_fake_real_vals(s_fake, s_real) + except MetadataMismatchError as exc: + if torch._functorch.config.generate_fake_kernels_from_real_mismatches: + dtrace_structured( + "mismatched_fake_kernel", + metadata_fn=lambda: { + "op": str(func), + "reason": exc.reason, # noqa: F821 + }, + ) + return _infer_fake_from_real_tensor(self, func, real), True # type: ignore[arg-type] + raise MetadataMismatchError( + f"Real tensor propagation found an output size mismatch between " + f"fake shape {s_fake} and real shape {s_real}, " + f"at output{keystr(path)}.size({j}), for func: {func}" + ) from exc + elif fake is None and real is not None: + if torch._functorch.config.generate_fake_kernels_from_real_mismatches: + dtrace_structured( + "mismatched_fake_kernel", + metadata_fn=lambda: { + "op": str(func), + "reason": f"mismatch between fake value {fake} and real value {real}", # noqa: F821 + }, + ) + return _infer_fake_from_real_tensor(self, func, real), True # type: ignore[arg-type] + raise MetadataMismatchError( + f"Real tensor propagation found a metadata mismatch between " + f"fake tensor {fake} and real tensor {real}, " + f" at output{keystr(path)}, for func: {func}" + ) + else: + try: + _check_fake_real_vals(fake, real) + except MetadataMismatchError as exc: + raise MetadataMismatchError( + f"Real tensor propagation found an output value mismatch between " + f"fake output value {fake} and real output value {real}, " + f"at output{keystr(path)}, for func: {func}" + ) from exc + return fake, False + + def _maybe_infer_fake_kernel_from_pytree_out( + self, + func: OpOverload, + fake_in: object, + real_in: object, + fake_out: object, + real_out: object, + ) -> Optional[object]: + """ + Helper to cross-check fake/real output properties & values, + and create new fake vals if mismatched, but at the kernel level. + Means this handles pytree outputs & checks aliasing. + """ + from torch._subclasses.fake_utils import _check_alias_info + + # we might have to clear pending unbacked symbols, if we override the kernel + pending_unbacked = None + if self.shape_env: + pending_unbacked = list(self.shape_env.pending_fresh_unbacked_symbols) + + def _clear_pending_unbacked() -> None: + self.shape_env.pending_fresh_unbacked_symbols = list( # type: ignore[union-attr] + set(self.shape_env.pending_fresh_unbacked_symbols).difference( # type: ignore[union-attr] + pending_unbacked # type: ignore[arg-type] + ) + ) + + fake_paths_leaves, fake_spec = pytree.tree_flatten_with_path(fake_out) + real_leaves, _ = pytree.tree_flatten(real_out) + try: + # catch aliasing mismatches between fake/real tensors + _check_alias_info( + "Real tensor propagation found", real_out, real_in, fake_out, fake_in + ) + except MetadataMismatchError as exc: + # if mismatch found, optionally infer fake kernel + if torch._functorch.config.generate_fake_kernels_from_real_mismatches: + dtrace_structured( + "mismatched_fake_kernel", + metadata_fn=lambda: { + "op": str(func), + "reason": ( + f"Mismatched aliasing spec between fake kernel and real kernel: {exc.reason}" # noqa: F821 + ), + }, + ) + # if aliasing mismatches are found, it's likely that the fake tensor impl + # is incorrectly aliasing, since we don't support aliasing custom ops. + # in this case we can default to inferring non-aliasing fake kernels from the real outputs. + _clear_pending_unbacked() + return tree_map( + lambda x: _infer_fake_from_real_tensor(self, func, x), real_out + ) + else: + raise MetadataMismatchError( + f"Real tensor propagation found an aliasing mismatch between " + f"fake output {fake_out} and real output {real_out}, " + f" for func: {func}" + ) from exc + + # if no errors raised, run cross checks on fake/real tensors, + # optionally overriding individual fake tensors, if individual meta kernel output is incorrect. + fake_leaves, overrides = zip( + *[ + self._maybe_infer_fake(func, _fake_path, _fake_out, _real_out) + for (_fake_path, _fake_out), _real_out in zip( + fake_paths_leaves, real_leaves + ) + ] + ) + if ( + any(overrides) and pending_unbacked + ): # only keep new pending unbacked symbols + _clear_pending_unbacked() + return pytree.tree_unflatten(fake_leaves, fake_spec) + + def _dispatch_impl( + self, + func: OpOverload, + types: Sequence[type], + args: Sequence[object], + kwargs: Mapping[str, object], + ) -> Optional[FakeTensor]: + from torch._higher_order_ops.utils import registered_hop_fake_fns + + flat_args, args_spec = pytree.tree_flatten((args, kwargs)) + + # DO NOT PUT LOGIC BEFORE UNRECOGNIZED TYPE CHECKING + # We must throw NotImplemented in case of unrecognized types to handle subclasses. + # Throwing the exception will pass the control to the next __torch_dispatch__. + # See [subclass inputs] below + # NB: If you're seeing a mysterious infinite loop involving fake + # tensor, it might be related to this line. Though I'm not sure + # how you'll know to read this comment, as this line won't show up + # in the stack trace. + has_unrecognized_types = _check_for_subclass(flat_args) + if has_unrecognized_types: + unrecognized_types = [ + type(x) for x in flat_args if _check_for_subclass_arg(x) + ] + not_implemented_log.debug( + "FakeTensorMode unrecognized subclass(es): %s", unrecognized_types + ) + return NotImplemented + + flat_arg_fake_tensors = [t for t in flat_args if self.is_our_fake(t)] + has_symbolic_sizes = any( + i._has_symbolic_sizes_strides for i in flat_arg_fake_tensors + ) or any(isinstance(a, SymInt) for a in flat_args) + + converter = self.fake_tensor_converter + + is_lift_func = func in self.lift_fns + + # If we are trying to avoid device init, then we need to avoid constant + # prop on constant tensors for ops that change devices. + avoiding_device_init = False + if self.avoid_device_init: + if ( + func is torch.ops.aten._to_copy.default + and "device" in kwargs + and kwargs["device"].type != "cpu" # type: ignore[attr-defined] + ): + avoiding_device_init = True + if func is torch.ops.prims.device_put.default: + avoiding_device_init = True + + # skip const prop for aten._to_copy if + # 1. input tensor is on "meta" device + # 2. destination device is unavailable, captured by `avoiding_device_init` + device_conversion_skip_const_prop = ( + func is torch.ops.aten._to_copy.default + and isinstance(args[0], torch.Tensor) + and args[0].device.type == "meta" + ) or avoiding_device_init + + # To constant propagate through these functions: + # 1, If this is a lift due to a torch.tensor call, + # the input tensor is guaranteed to be a + # constant, so we keep a copy of the original argument along so + # we can query it if we're asked to item() it at some later point. + # (Note that you can always call a lift fn manually, so we do + # have to check if there are any fake tensors!) + # 2, Some functions that allow Python numbers to bind to Tensors, e.g, torch.div + if (is_lift_func and not flat_arg_fake_tensors) or ( + should_allow_numbers_as_tensors(func) + and not has_symbolic_sizes + and not flat_arg_fake_tensors + and not device_conversion_skip_const_prop + ): + assert all(t.constant is not None for t in flat_arg_fake_tensors), ( + f"{func} should not have fake inputs without constants" + ) + const_flat_args = [ + a.constant if self.is_our_fake(a) else a for a in flat_args + ] + const_args, const_kwargs = pytree.tree_unflatten(const_flat_args, args_spec) + out = func(*const_args, **const_kwargs) + if type(out) is Tensor and self.may_turn_const(out): + # NB: not in_kernel_invocation_manager because we're doing real + # compute here + # NB: no_dispatch() here is VERY DANGEROUS (like, segfault + # dangerous) if this is actually a wrapper subclass tensor, + # therefore the exact type test above + with no_dispatch(): + out = out.clone() + return converter.from_real_tensor(self, out, make_constant=True) + + # if we are in the dispatch mode, we will enter this function even if the inputs + # are not FakeTensors. For now, throw if any non-Fake Tensor inputs + # and just support constructors. + + # this is generated from torch.tensor(), which does not use the + # dispatcher, to allow wrapper subclasses to wrap the new tensor + if is_lift_func: + assert len(kwargs) == 0 and len(args) == 1, f"{args} {kwargs}" + + if type(args[0]) is Tensor: + return converter.from_real_tensor(self, args[0]) + + # Recompute flat_arg_fake_tensors here again in case some of the inputs + # were real tensors and fakified in validate_and_convert_non_fake_tensors + (flat_args, flat_arg_fake_tensors) = self.validate_and_convert_non_fake_tensors( + func, converter, flat_args, args_spec + ) + del args, kwargs # Invalidated + + # The current constant handling only support tracing systems + # (aot autograd, torchdynamo) where each operation is run consecutively. + # Because each operation is run in order, we can trace out and support + # sequences like: x = torch.tensor(0.); y = x.add_(1) + # Whenever a constant is written to but with inputs that cannot be evaluated + # statically, such as random_(), we invalidate all constants that alias the input + # We will rely on functionalization for use of fake tensors constants as persistent + # objects on an FX Graph. + + # We dispatch size/stride/numel on the FakeTensor not its constant, so bail on inplace_view + all_constant = all(e.constant is not None for e in flat_arg_fake_tensors) + if ( + isinstance(func, torch._ops.OpOverload) + and torch.Tag.nondeterministic_seeded not in func.tags + and torch.Tag.inplace_view not in func.tags + and all_constant + and len(flat_arg_fake_tensors) != 0 + and not has_symbolic_sizes + and not avoiding_device_init + and func is not aten._nested_tensor_from_tensor_list.default + ): + const_flat_args = [ + a.constant if self.is_our_fake(a) else a for a in flat_args + ] + const_args, const_kwargs = pytree.tree_unflatten(const_flat_args, args_spec) + + # NB: not in_kernel_invocation_manager(self) as we want to do REAL + # compute + with no_dispatch(): + out = func(*const_args, **const_kwargs) + + flat_out = pytree.tree_leaves(out) + flat_out_tensors = [t for t in flat_out if isinstance(t, Tensor)] + all_constant = all(self.may_turn_const(t) for t in flat_out_tensors) + + if all_constant: + return pytree.tree_map_only( + Tensor, + lambda t: converter.from_real_tensor(self, t, make_constant=True), + out, + ) + + # we weren't able to turn outputs to constants, + # so invalidate all constants that might be aliases of the outputs + for ten in flat_out_tensors: + converter.invalidate_constant_aliases(ten) + + # we are falling through to running non constant tensors, any input constant that + # is written to must be invalidated + args, kwargs = pytree.tree_unflatten(flat_args, args_spec) + + if ( + isinstance(func, torch._ops.HigherOrderOperator) + and func in registered_hop_fake_fns + ): + # Reenable the fake tensor mode for the registered fake function + maybe_ignore_fresh_unbacked_symbols = ( + contextlib.nullcontext + if self.shape_env is None + else self.shape_env.ignore_fresh_unbacked_symbols + ) + + with self, maybe_ignore_fresh_unbacked_symbols(): + # pyrefly: ignore [index-error] + return registered_hop_fake_fns[func](*args, **kwargs) + + self.invalidate_written_to_constants(func, flat_arg_fake_tensors, args, kwargs) + + def maybe_to_real_tensor( + t: T, + ) -> Optional[Union[T, Tensor, torch._C.ScriptObject]]: + if isinstance(t, FakeTensor): + return t.real_tensor + elif isinstance(t, py_sym_types): + assert self.shape_env is not None + return t.node.pytype( + t.node.expr.xreplace(self.shape_env.var_to_val).xreplace( + self.shape_env.unbacked_var_to_val + ) + ) + elif isinstance(t, FakeScriptObject): + return t.real_obj + else: + return t + + from torch.fx.experimental.symbolic_shapes import ( + compute_unbacked_bindings, + free_unbacked_symbols, + ) + + nil = object() + + real_out = nil + if ( + self.propagate_real_tensors + and all(e.real_tensor is not None for e in flat_arg_fake_tensors) + and not any( + ( + isinstance(a, py_sym_types) + and (syms := free_unbacked_symbols(a)) + and self.shape_env is not None + and any(s not in self.shape_env.unbacked_var_to_val for s in syms) + ) + for a in flat_args + ) + ): + log.debug("propagate_real_tensors %s", func) + real_flat_args = [maybe_to_real_tensor(a) for a in flat_args] + real_args, real_kwargs = pytree.tree_unflatten(real_flat_args, args_spec) + + is_builtin = library_utils.is_builtin(func) + if not is_builtin: + mutation_checker = library_utils.MutationChecker( + func, real_flat_args, args_spec + ) + + try: + real_out = func(*real_args, **real_kwargs) + except ZeroDivisionError as exc: + # we shouldn't broadly catch all errors here; + # some come from real-kernel mutation/aliasing checks we want to run. + # add more exception types as needed. + log.debug( # noqa: G200 + "real-tensor fallback failed for %s: %s; silently ignoring", + func, + exc, + ) + + if not is_builtin: + mutation_checker.check() # type: ignore[possibly-undefined] + library_utils.check_aliasing_constraint(func._name, flat_args, real_out) + + elif self.propagate_real_tensors: + # This can happen occasionally legitimately, specifically when you + # are inside the meta of a data dependent operation and you create + # a tensor on an unbacked SymInt; at this point in time we don't + # know what the unbacked SymInt is, but we will know later. + # However, if there's a bug in the condition above, this condition + # will also trigger. + log.debug( + "SKIPPED propagate_real_tensors %s(%s, %s) %s", + func, + flat_arg_fake_tensors, + flat_args, + self.shape_env.unbacked_var_to_val if self.shape_env else None, + ) + + def maybe_propagate_real_tensors(fake_out: T) -> T: + import sympy + + log.debug("maybe_propagate_real_tensors %s", func) + + def go(t: object, real_t: Tensor) -> None: + if isinstance(t, FakeTensor): + # NB: unconditionally overwrite + log.debug( + "maybe_propagate_real_tensors %s -> %s", id(t), id(real_t) + ) + t.real_tensor = real_t + for s, real_s in zip(t.size(), real_t.size()): + go(s, real_s) # type: ignore[arg-type] + for s, real_s in zip(t.stride(), real_t.stride()): + go(s, real_s) # type: ignore[arg-type] + go(t.storage_offset(), real_t.storage_offset()) # type: ignore[arg-type] + elif isinstance(t, py_sym_types) and free_unbacked_symbols(t): + if isinstance(t.node.expr, sympy.Symbol): + assert self.shape_env is not None + self.shape_env.set_unbacked_var_to_val(t.node.expr, real_t) + elif ( + isinstance(s := t.node.expr, sympy.Eq) + and isinstance(s.lhs, sympy.Symbol) + and s.rhs == 1 + ): + assert self.shape_env is not None + + self.shape_env.set_unbacked_var_to_val(s, int(real_t)) + + if real_out is not nil: + # cross check fake/real outputs, and optionally override fake kernel mismatches + if not torch._functorch.config.generate_fake_kernels_from_real_mismatches: + self._maybe_infer_fake_kernel_from_pytree_out( + func, + (args, kwargs), + (real_args, real_kwargs), + fake_out, + real_out, + ) + else: + # this can override the output only when the flag is True + fake_out = self._maybe_infer_fake_kernel_from_pytree_out( # type: ignore[assignment] + func, + (args, kwargs), + (real_args, real_kwargs), + fake_out, + real_out, + ) + + # populate unbacked_var_to_val + if ( + not isinstance(fake_out, Tensor) + and not isinstance(real_out, Tensor) + and type(fake_out) is not type(real_out) + ): + # This can happen when decompositions have different return types, + # e.g. namedtuple vs. tuple vs. list. + tree_map_( + go, + tuple(pytree.tree_flatten(fake_out)), + tuple(pytree.tree_flatten(real_out)), + ) + else: + tree_map_(go, fake_out, real_out) + + # If a data-dependent op is used in a decomposition, we + # may need to get the unbacked settings "early" + # TODO: Is this really needed? + compute_unbacked_bindings(self.shape_env, fake_out, peek=True) + + # pyrefly: ignore [bad-return] + return fake_out + + # Try for fastpath + if has_symbolic_sizes: + fast_impl = get_fast_op_impls().get(func) + if fast_impl is not None: + return maybe_propagate_real_tensors(fast_impl(self, *args, **kwargs)) + + # If there's a Python meta, prefer that over the decomposition + from torch._decomp import meta_table + + if ( + func not in meta_table + and not self.cpp_meta_supports_symint(func) + and not ( + has_symbolic_sizes and func in self._unbacked_special_fake_handling_ops + ) + ): + from torch._decomp import decomposition_table + + # Prefer Python decompositions over C++ ones + if func in decomposition_table and ( + has_symbolic_sizes + or ( + # TODO: Remove these exclusions, so that we can remove + # this leg entirely + torch_decomp_decompositions(func) + and all(not is_sparse_any(e) for e in flat_arg_fake_tensors) + ) + ): + with self: + return maybe_propagate_real_tensors( + decomposition_table[func](*args, **kwargs) + ) + + with self: + # Decomposes CompositeImplicitAutograd ops + r = func.decompose(*args, **kwargs) + if r is not NotImplemented: + return maybe_propagate_real_tensors(r) + + # prims already wrap FakeTensor inputs to FakeTensor outputs + # and do device logic, we dont need do anything but run them + # and ensure that Meta kernels are dispatched to (see) + # Fake Tensor Dispatch Keys + # TODO - we should be use the prim aten impl + # TODO - fix prims complex ops + if ( + "prims::" in func._schema.name + and hasattr(func, "prim_meta_impl") + and not stride_incorrect_op(func) + ): + with self: + return maybe_propagate_real_tensors( + func.prim_meta_impl(*args, **kwargs) + ) + + profiles = torch._dynamo.config._custom_ops_profile + if profiles is not None: + if func in profiles.data: + return profiles.generic_fake_kernel(func, self, *args, **kwargs) + + if ( + self.propagate_real_tensors + and real_out is not nil + and not library_utils.is_builtin(func) + and self.shape_env is not None + ): + # Automatically infer a Fake kernel if there isn't one. + if not library_utils.has_fake_kernel(func): + result = inferred_fake_kernel_from_real_out(self, func, real_out) + + dtrace_structured( + "missing_fake_kernel", + metadata_fn=lambda: { + "op": str(func), + }, + ) + return maybe_propagate_real_tensors(result) + + # Users can register FakeTensor rules for custom operators + # Call them if they exist. + maybe_fake_impl = torch._library.simple_registry.singleton.find( + func.name() + ).fake_impl.kernel + if maybe_fake_impl: + try: + ctx = torch._library.fake_impl.FakeImplCtx(self, func) + with torch._library.fake_impl.set_ctx_getter(lambda: ctx), self: + result = maybe_fake_impl(*args, **kwargs) + return maybe_propagate_real_tensors(result) + + except MissingOpProfile as e: + # If we have a fake kernel registered generated from OpProfiles + # but there doesn't exist a profile for the existing inputs, and we are in + if ( + self.propagate_real_tensors + and real_out is not nil + and not library_utils.is_builtin(func) + and self.shape_env is not None + ): + result = inferred_fake_kernel_from_real_out(self, func, real_out) + + dtrace_structured( + "missing_fake_kernel", + metadata_fn=lambda: { + "op": str(func), + }, + ) + return maybe_propagate_real_tensors(result) + else: + raise e + + # special handling for funcs registered through `register_op_impl`, + # e.g., manipulating args on constructor calls to construct meta tensors + # and then afterwards wrapping them to a FakeTensor + for run_impl_check, op_impl in op_implementations_checks: + if run_impl_check(func): + op_impl_out = op_impl(self, func, *args, **kwargs) + if op_impl_out is not NotImplemented: + return maybe_propagate_real_tensors(op_impl_out) + + def maybe_run_unsafe_fallback( + error: Optional[RuntimeError] = None, + ) -> Optional[FakeTensor]: + # We infer the meta of a custom ops that return None to just + # return None. custom ops are not allowed to mutate metadata + # of their inputs, so this is safe. + if torch._library.utils.can_generate_trivial_fake_impl(func): + return None + # no meta kernel registered, fallback to kernel for the device + if has_symbolic_sizes or not self.can_run_unsafe_fallback(func): + raise UnsupportedOperatorException(func) + if error is None: + error = UnsupportedOperatorException(func) + return run_fallback_kernel(self, func, flat_args, args_spec, error) + + # Optimization: If there is no Meta kernel, it takes a surprisingly long + # amount of time to catch the NotImplementedError, so we check it here. + if not has_meta(func): + fallback = maybe_run_unsafe_fallback() + return maybe_propagate_real_tensors(fallback) + + # run kernel registered to meta for func, which include + # python meta registrations, prims, decomps, and c++ meta fns (structured kernels) + # It's possible that the kernel will return NotImplementedError + try: + with in_kernel_invocation_manager(self): + r = func(*args, **kwargs) + except NotImplementedError as not_implemented_error: + return maybe_run_unsafe_fallback(not_implemented_error) + except Exception: + log.exception("failed while attempting to run meta for %s", func) + raise + + return maybe_propagate_real_tensors( + self.wrap_meta_outputs_with_default_device_logic( + r, func, flat_args, device=kwargs.get("device") + ) + ) + + # WARNING: DO NOT add any additional namespaces/operators here if they refer to operators + # outside of the pytorch/pytorch library! Any pre-existing things here + # are either in the pytorch/pytorch library or have been grandfathered in. + # The fallback does not always work and MAY CRASH and emit unreadable error messages + # so it should not be allowed by default. + _can_run_unsafe_fallback_allowed_namespaces = ordered_set( + "debugprims", + "prims", + "aten", + "xla", + "vision", + "torchtext", + "torchaudio", + "quantized", + ) + + def can_run_unsafe_fallback(self, func: OpOverload) -> bool: + if not self.allow_fallback_kernels: + return False + # It's OK to try the fallback for built-in ops (e.g. aten, prims) + # because we control and test these but the fallback leads to unexpected behavior + # in user-defined custom ops + return ( + func.namespace in self._can_run_unsafe_fallback_allowed_namespaces + or func.name() == "fbgemm::gmm" + ) + + def validate_and_convert_non_fake_tensors( + self, + func: OpOverload, + converter: FakeTensorConverter, + flat_args: Sequence[object], + args_spec: TreeSpec, + ) -> tuple[list[object], list[FakeTensor]]: + """ + Checks if the list of tensors are fake tensors. + If not, try to convert them to fake tensors. + Returns the original args, kwargs, and a flattened list of (args, kwargs) that are fake tensors. + """ + flat_arg_fake_tensors: list[FakeTensor] = [] + + def validate(x: T) -> Union[T, FakeTensor]: + if not isinstance(x, Tensor): + return x + + nonlocal flat_arg_fake_tensors + if not self.is_our_fake(x): + if hasattr(func, "tags") and torch.Tag.inplace_view in func.tags: + args, kwargs = pytree.tree_unflatten(flat_args, args_spec) + raise AssertionError( + f"Can't call metadata mutating ops on non-Fake Tensor inputs. Found in {render_call(func, args, kwargs)}" + ) + allow_non_fake_inputs = ( + self.allow_non_fake_inputs + if fake_tensor_tls.allow_non_fake_inputs_override is None + else fake_tensor_tls.allow_non_fake_inputs_override + ) + if not allow_non_fake_inputs: + if isinstance(x, FakeTensor) and x.fake_mode is not self: + raise AssertionError("Mixing fake modes NYI") + args, kwargs = pytree.tree_unflatten(flat_args, args_spec) + raise AssertionError( + f"Please convert all Tensors to FakeTensors first or instantiate FakeTensorMode " + f"with 'allow_non_fake_inputs'. Found in {render_call(func, args, kwargs)}" + ) + + out = converter.from_real_tensor(self, x) + else: + out = x + + flat_arg_fake_tensors.append(out) + return out + + validated_args = [validate(a) for a in flat_args] + return validated_args, flat_arg_fake_tensors + + def wrap_meta_outputs_with_default_device_logic( + self, + r: object, + func: OpOverload, + flat_args: Sequence[object], + device: torch.device, + ) -> PyTree: + converter = self.fake_tensor_converter + + # Lazily initialized, in case there are no tensor returns + common_device = None + has_scalar_only_inputs = False + + def wrap(e: T) -> Union[T, FakeTensor]: + nonlocal common_device + nonlocal has_scalar_only_inputs + + if not isinstance(e, Tensor): + return e + + if common_device is None: + ( + common_device, + has_scalar_only_inputs, + ) = FakeTensor._find_common_device(func, flat_args) + + is_our_fake = self.is_our_fake(e) + if is_our_fake: + torch._check( + e.device == common_device, + lambda: f"FakeTensor is wrapped to wrong device, found {e.device}, expected {common_device}", + ) + return cast(T, e) + elif converter is not None: + if has_scalar_only_inputs: + # Under FakeTensorMode, op accepts scalar only inputs, such as aten.add/sub/mul/div, + # returns a real scalar tensor on CPU. See TensorMeta() in _prims/__init__.py for details. + # We thus directly convert real tensor to fake tensor. + return converter.from_real_tensor(self, e) + else: + return converter.from_meta_and_device( + self, e, device or common_device + ) + else: + # pyrefly: ignore [bad-return] + return e + + return tree_map(wrap, r) + + def create_symbolic_nested_int( + self, *, nt_tensor_id: Optional[int] = None + ) -> torch.SymInt: + # See Note: [Creating symbolic nested int] + # Returned nested int always has coeff=1; multiply the result by coeff if needed + import torch.nested._internal.nested_tensor + from torch.nested._internal.nested_int import NestedIntNode + + if nt_tensor_id is None: + nt_tensor_id = self.nt_tensor_id_counter + assert self.enter_stack, "should only called while FakeTensorMode is active" + self.nt_tensor_id_counter += 1 + hint = torch.SymInt(NestedIntNode(nt_tensor_id, 1)) + + src = torch._dynamo.source.EphemeralSource("intermediate_offsets_or_lengths") + assert self.shape_env is not None + ret = self.shape_env.create_symintnode( + sym=self.shape_env.create_symbol( + val=hint, + source=src, + ), + hint=hint, + source=src, + ) + return ret + + _cpp_meta_supports_symint = ordered_set( + aten.empty.memory_format, + aten.empty_strided.default, + aten.as_strided_scatter.default, + aten.as_strided.default, + aten.as_strided_.default, + aten.zeros.default, + aten.detach.default, + aten.view_as_real.default, + aten.view_as_complex.default, + aten.set_.source_Storage_storage_offset, + aten._sparse_coo_tensor_with_dims_and_tensors.default, + ) + + _unbacked_special_fake_handling_ops = ordered_set( + aten.view.default, + aten._unsafe_view.default, + aten.slice.Tensor, + ) + + def cpp_meta_supports_symint(self, func: OpOverload) -> bool: + if torch.Tag.view_copy in func.tags: + return True + return func in self._cpp_meta_supports_symint + + lift_fns = ordered_set(aten.lift_fresh.default, aten.lift_fresh_copy.default) + + def may_turn_const(self, t: Tensor) -> bool: + return ( + t.numel() <= CONSTANT_NUMEL_LIMIT + and not is_sparse_any(t) + and not self.is_our_fake(t) + and t.device.type != "meta" + ) + + def invalidate_written_to_constants( + self, + func: OpOverload, + flat_arg_fake_tensors: Sequence[FakeTensor], + args: Sequence[object], + kwargs: Mapping[str, object], + ) -> None: + any_constant = any(e.constant is not None for e in flat_arg_fake_tensors) + schema_info = get_schema_info(func) + if any_constant and schema_info.is_mutable(): + _, new_kwargs = normalize_function( # type: ignore[misc] + func, + args=args, # type: ignore[arg-type] + kwargs=kwargs, # type: ignore[arg-type] + normalize_to_only_use_kwargs=True, + ) + for k, v in new_kwargs.items(): + k = k if (k != "input" or schema_info.has_argument(k)) else "self" + if ( + self.is_our_fake(v) + and schema_info.is_mutable(k) + and v.constant is not None + ): + self.fake_tensor_converter.invalidate_constant_aliases(v.constant) + + def from_tensor( + self, + tensor: Tensor, + *, + static_shapes: Optional[bool] = None, + source: Optional[Source] = None, + symbolic_context: Optional[SymbolicContext] = None, + trace: bool = True, + ) -> FakeTensor: + shape_env: Optional[ShapeEnv] = self.shape_env + if static_shapes is None: + static_shapes = self.static_shapes + if static_shapes: + assert symbolic_context is None, ( + "cannot set both static_shapes and symbolic_context" + ) + shape_env = None + return self.fake_tensor_converter.from_real_tensor( + self, + tensor, + shape_env=shape_env, + source=source, + symbolic_context=symbolic_context, + trace=trace, + ) + + +_StoragePointer = object + + +def _validate_symbolic_output_for_caching( + state: _CacheKeyState, output: FakeTensor +) -> None: + """ + Validate symbolic content in output and raise _BypassDispatchCache if + caching should be bypassed. + + Args: + state: Cache key state containing known symbols + output: Output to validate + proxy_mode_active: Whether PROXY dispatch mode is currently active + + Raises: _BypassDispatchCache: If output contains symbolic content that + prevents caching + + Details: + + If our output contains any symbols that didn't appear in the input then we + need to bypass. Usually this will be unbacked symbols which can't be + properly reconstructed but there could be "weird" cases where backed symbols + spontaneously appear (from non-input state)? + + If we're proxy (symbol) tracing and the output contains ANY symbols then we + need to bypass. The problem is that ProxyTorchDispatchMode relies on SymNode + object identity and being able to see the construction of SymNodes. + + We could improve the proxy tracing case in a few ways: + + 1. If the output SymNodes are directly copied from inputs then this is + actually fine - they're already tracked. This would probably be the + biggest bang/buck. + + 2. If the output (tensors) are all direct copies of the inputs then this is + also fine - since they're inputs they must be tracked. We already compute + this we just don't plumb it around enough. + + 3. If the output SymNodes are already tracked by the proxy then this is also + actually fine - they're properly tracked. This probably wouldn't be + common since for most outputs we use torch.empty_strided() and recompute + strides. + + 4. We could use the proxy to track "how" the SymNodes were computed and when + using the cache we could "replay" them properly to teach the proxy how to + build them. + """ + from torch.fx.experimental.symbolic_shapes import _iterate_exprs, _iterate_nodes + + is_tracing = torch.fx.experimental.proxy_tensor.get_proxy_mode() is not None + if is_tracing: + # Check for SymNode types in PROXY mode - this should bypass caching + # regardless of whether symbols are known or not + for _ in _iterate_nodes(output): + raise _BypassDispatchCache("Proxy mode with SymNode output") + else: + # Check for unrepresented symbols in tensor expressions + for s in _iterate_exprs(output): + for symbol in s.free_symbols: + if symbol not in state.known_symbols: + raise _BypassDispatchCache("unrepresented symbol in output") + + +# NB: returns fake tensors +def run_fallback_kernel( + fake_mode: FakeTensorMode, + func: OpOverload, + flat_args: Sequence[object], + args_spec: PyTree, + orig_not_implemented_exception: RuntimeError, +) -> FakeTensor: + # these should all be supported, just to be safe + # avoid fallback for operators which inplace modify metadata + # because the input fake tensors would be umodified + if torch.Tag.inplace_view in func.tags: + raise orig_not_implemented_exception + + inp_impls = {} + + # Don't use in_kernel_invocation_manager(fake_mode) as we want to do + # REAL compute (not with meta device) + with no_dispatch(): + + def to_real_tensor(e: T) -> Union[T, Tensor]: + if fake_mode.is_our_fake(e): + out = torch.zeros_like(e, device=e.fake_device) + if e.is_sparse: + out._coalesced_(e.is_coalesced()) + inp_impls[id(out)] = e + return out + return e + + flat_args = [to_real_tensor(a) for a in flat_args] + args, kwargs = pytree.tree_unflatten(flat_args, args_spec) + + r = func(*args, **kwargs) + + storages: set[_StoragePointer] = set() + + for e in flat_args: + if isinstance(e, Tensor): + if not is_sparse_any(e): + storages.add(e._typed_storage()._cdata) + + # TODO: also check metadata change on inputs + # proper aliasing/metadata relationship between outputs and inputs will + # not be set up, bc of conversion to device, unless we can reuse an + # input impl + + def map_out(e: T) -> Union[T, FakeTensor]: + if id(e) not in inp_impls and ( + isinstance(e, Tensor) + and not is_sparse_any(e) + and e._typed_storage()._cdata in storages + ): + raise orig_not_implemented_exception + + if isinstance(e, Tensor): + if id(e) in inp_impls: + return inp_impls[id(e)] + else: + return fake_mode.fake_tensor_converter.from_real_tensor(fake_mode, e) + else: + return e + + return pytree.tree_map(map_out, r) + + +def _set_cache_key_for_shape_env( + cache: dict[_DispatchCacheKey, _DispatchCacheEntry], + key: _DispatchCacheKey, + entry: _DispatchCacheEntry, +) -> None: + key.strip_shape_env() + cache[key] = entry + + +def _set_cache_key( + cache: dict[_DispatchCacheKey, _DispatchCacheEntry], + key: _DispatchCacheKey, + entry: _DispatchCacheEntry, +) -> None: + cache[key] = entry + + +# Just for use to allow copying a module to fake tensors, +# does not apply elsewhere +class FakeCopyMode(TorchFunctionMode): + def __init__(self, fake_mode: FakeTensorMode) -> None: + self.fake_mode = fake_mode + + def __torch_function__( + self, + func: OpOverload, + types: Sequence[type], + args: Sequence[object] = (), + kwargs: Optional[Mapping[str, object]] = None, + ) -> FakeTensor: + kwargs = kwargs if kwargs else {} + + # clone will get called in Parameter deepcopy + if func is torch._C.TensorBase.clone: + assert isinstance(args[0], Tensor) + return func( + self.fake_mode.from_tensor(args[0], static_shapes=True), **kwargs + ) + elif func is Tensor.__deepcopy__: + assert len(args) == 2 and len(kwargs) == 0 + tensor = cast(Tensor, args[0]) + memo = cast(dict[int, FakeTensor], args[1]) + + if id(tensor) in memo: + return memo[id(tensor)] + + out = self.fake_mode.from_tensor(tensor, static_shapes=True) + memo[id(tensor)] = out + return out + else: + with torch._C.DisableTorchFunctionSubclass(): + return func(*args, **kwargs) + + +def _device_handler(args: Sequence[object]) -> torch.device: + # NB: Don't use is_our_fake, just serve the fake information + # as is. Notice we don't use 'self'; we use args[0].fake_mode + # because they may not be the same. It would also be possible + # to return NotImplemented here, in which case the FakeTensor + # handler on args[0] would handle it, but we're being nice and + # short-circuiting quickly. + assert len(args) == 1 and isinstance(args[0], FakeTensor) + if args[0].fake_mode.in_kernel_invocation: + return torch.device("meta") + else: + return args[0].fake_device + + +# [subclass inputs] +# Suppose we enable fake tensor mode. This means that fake tensor +# mode will run first. But what if we do an operation that +# involves a tensor subclass that will desugar into normal tensor +# operations? Without returning NotImplemented, fake tensor mode will run first, +# decide that a conversion was made (since there was a non fake +# tensor argument), and report an error that converting non +# fake tensor is not supported. What we actually wanted to happen +# was to give the subclass a chance to figure out what it wants to +# before erroring out. Returning NotImplemented here allows this. +def _check_for_subclass(flat_args: Sequence[object]) -> bool: + return any(_check_for_subclass_arg(x) for x in flat_args) + + +def _check_for_subclass_arg(x: object) -> bool: + return ( + not isinstance(x, FakeTensor) + and isinstance(x, Tensor) + and type(x) is not Tensor + and type(x) is not torch.nn.Parameter + ) + + +_DISPATCH_META_HANDLERS = { + torch.ops.prim.device.default: _device_handler, + torch.ops.aten.size.default: lambda args: tuple( + int(s) for s in cast(Tensor, args[0]).size() + ), + torch.ops.aten.stride.default: lambda args: tuple( + int(s) for s in cast(Tensor, args[0]).stride() + ), + torch.ops.aten.storage_offset.default: lambda args: int( + cast(Tensor, args[0]).storage_offset() + ), +} + +_DISPATCH_HANDLE_DIRECTLY = ordered_set( + torch.ops.aten.is_coalesced.default, + torch.ops.aten.dense_dim.default, + torch.ops.aten.sparse_dim.default, + # _RecordFunction doesn't support __eq__ so make sure not to attempt to + # cache it. + torch.ops.profiler._record_function_exit._RecordFunction, +) + +from torch._subclasses.fake_impls import ( # noqa: F401 + _device_not_kwarg_ops, + _is_tensor_constructor, + _like_tensor_constructors, + contains_tensor_types, + get_fast_op_impls, + has_meta, + op_implementations_checks, + stride_incorrect_op, +) + + +def evict_fake_tensor_cache_key(key: _DispatchCacheKey) -> None: + if key in FakeTensorMode.cache: + FakeTensorMode.cache.pop(key) + + +@atexit.register +def dump_cache_stats() -> None: + log.info("FakeTensor cache stats:") + log.info(" cache_hits: %s", FakeTensorMode.cache_hits) + log.info(" cache_misses: %s", FakeTensorMode.cache_misses) + bypasses = FakeTensorMode.cache_bypasses + if bypasses: + log.info(" cache_bypasses:") + width = max(len(k) for k in bypasses) + for k, v in sorted(bypasses.items(), key=lambda i: -i[1]): + log.info(" %-*s %s", width + 1, f"{k}:", v) + + +def _infer_fake_from_real_tensor( + mode: FakeTensorMode, op: torch._ops.OpOverload, real_out: torch.Tensor +) -> torch.Tensor: + def unsupported(reason: str) -> None: + raise RuntimeError( + f"propagate_real_tensors: we cannot infer a Fake kernel " + f"(meta kernel) for operator {op._name} because {reason}. " + f"Please use torch.library.register_fake to add a Fake kernel." + ) + + if real_out.storage_offset() != 0: + unsupported( + f"a return has a non-zero storage offset {real_out.storage_offset()}" + ) + + # Since PT2 is rank specialized, there's no such thing as a symbolic + # output rank. So we can assume the fake tensor has the same number of + # dimensions as the real tensor output. + # + # We shouldn't assume the Fake sizes/strides are exactly what we see on + # the real tensor output (perhaps we should give users a lever to toggle + # this). This is because there's a good amount of operators that return + # outputs with data-dependent output shape. + # So we infer the output sizes to all be unbacked symints + fake_shape = [ + torch._library.fake_impl.allocate_size(mode.shape_env) + for _ in range(real_out.dim()) + ] + + # We infer what the strides are. We had a couple of options for this: + # - assume the strides are computable from the sizes + # - use new fresh unbacked symints in the strides + # This doesn't work that well (PT2 doesn't support unbacked symint strides well) + # - use the real strides + # This can only be used if we assume the strides are static. + # We went with the first option. + fake_strides = [-1] * real_out.dim() + strides = [(s, idx) for idx, s in enumerate(real_out.stride())] + strides.sort(key=lambda x: (x[0], -x[1])) + expected = 1 + fake_stride = expected + for s, idx in strides: + if s != expected: + unsupported( + f"a return was not dense in memory (sizes {real_out.shape} strides {real_out.stride()})" + ) + fake_strides[idx] = fake_stride + expected = expected * real_out.shape[idx] + fake_stride = fake_stride * fake_shape[idx] + + with mode: + return torch.empty_strided( + fake_shape, + fake_strides, + device=real_out.device, + dtype=real_out.dtype, + layout=real_out.layout, + ) + + +def inferred_fake_kernel_from_real_out( + mode: FakeTensorMode, op: torch._ops.OpOverload, real_out: Any +) -> Any: + assert mode.shape_env is not None + + # Only support operators that have all Tensor outputs + # This is a general limitation on custom ops that we impose for PT2 + # to avoid baking non-symbolic float/int outputs into the graph. + real_flat_out, spec = pytree.tree_flatten(real_out) + if not all(isinstance(t, torch.Tensor) for t in real_flat_out): + raise RuntimeError( + f"propagate_real_tensors: we don't support operators that return " + f"non-Tensors. Got {op._schema}" + ) + + fake_flat_out = [_infer_fake_from_real_tensor(mode, op, t) for t in real_flat_out] + return pytree.tree_unflatten(fake_flat_out, spec) diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/fake_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/fake_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..1212168b090498284e10f11d7c017a3ef8ba94c3 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/fake_utils.py @@ -0,0 +1,305 @@ +# mypy: ignore-errors + +import functools +import warnings +from collections.abc import Callable +from typing import Any, Union + +import torch +import torch.utils._pytree as pytree +from torch._ops import OpOverload +from torch._subclasses.fake_tensor import ( + FakeTensor, + FakeTensorMode, + MetadataMismatchError, + tree_flatten_only, + UnsupportedFakeTensorException, +) +from torch.utils._python_dispatch import TorchDispatchMode + + +aten = torch._ops.ops.aten + + +def outputs_alias_inputs(outputs, inputs): + input_storages = { + inp._typed_storage()._cdata + for inp in tree_flatten_only(torch.Tensor, inputs) + if torch._C._has_storage(inp) + } + return any( + torch._C._has_storage(out) and out._typed_storage()._cdata in input_storages + for out in tree_flatten_only(torch.Tensor, outputs) + ) + + +def outputs_are_inputs(outputs, inputs): + input_ids = {id(inp) for inp in tree_flatten_only(torch.Tensor, inputs)} + return any(id(out) in input_ids for out in tree_flatten_only(torch.Tensor, outputs)) + + +def output_alias_each_other(outputs): + storages = set() + for out in tree_flatten_only(torch.Tensor, outputs): + if not torch._C._has_storage(out): + continue + stor = out._typed_storage()._cdata + if stor in storages: + return True + storages.add(stor) + return False + + +def _check_alias_info(context, real_out, real_in, fake_out, fake_in): + r_aliasing = outputs_alias_inputs(real_out, real_in) + f_aliasing = outputs_alias_inputs(fake_out, fake_in) + if r_aliasing != f_aliasing: + raise MetadataMismatchError( + f"{context} mismatch in outputs_alias_inputs check {f_aliasing} != {r_aliasing}" + ) + + r_identity_eq = outputs_are_inputs(real_out, real_in) + f_identity_eq = outputs_are_inputs(fake_out, fake_in) + if r_identity_eq != f_identity_eq: + raise MetadataMismatchError( + f"{context} mismatch in outputs_are_inputs check {f_identity_eq} != {r_identity_eq}" + ) + + r_output_alias_each_other = output_alias_each_other(real_out) + f_output_alias_each_other = output_alias_each_other(fake_out) + if r_output_alias_each_other != f_output_alias_each_other: + raise MetadataMismatchError( + f"{context} mismatch in outputs_alias_each_other check " + f"{f_output_alias_each_other} != {r_output_alias_each_other}" + ) + + +def is_sdpa_error(func, idx, e): + if ( + ( + func is aten._scaled_dot_product_flash_attention.default + or func is aten._flash_attention_forward.default + ) + and idx in (6, 7) + and "Devices" in repr(e) + ): + return True + if ( + ( + func is aten._scaled_dot_product_efficient_attention.default + or func is aten._efficient_attention_forward.default + ) + and idx in (2, 3) + and "Devices" in repr(e) + ): + return True + if ( + func is aten._scaled_dot_product_cudnn_attention.default + and idx in (6, 7) + and "Devices" in repr(e) + ): + return True + return False + + +def try_convert_fake_to_real( + ten_list: list[Union[FakeTensor, Any]], +) -> list[Union[FakeTensor, torch.Tensor, Any]]: + """ + Attempt to convert fake tensors to a corresponding real tensor with the correct underlying storage by looking up + the FakeTensorMode meta to real storage mapping. On failure to find the storage mapping, the FakeTensor will + remain in the list. + + Note: this is not currently optimized (makes copies of the meta converter internal dictionaries) + """ + + fake_tensor = next( + (item for item in ten_list if isinstance(item, FakeTensor)), None + ) + if fake_tensor is None: + return ten_list + + fake_mode = fake_tensor.fake_mode + meta_converter = fake_mode.fake_tensor_converter.meta_converter + desc = meta_converter.describer + + storage_to_key = {v: k for k, v in meta_converter.storage_memo.items()} + key_to_real_storage = {v: k for k, v in desc.lookup_storage.items()} + out = [] + for t in ten_list: + if not isinstance(t, FakeTensor) or t.layout != torch.strided: + out.append(t) + continue + + key = storage_to_key.get(t.untyped_storage()) + real_storage = None if key is None else key_to_real_storage.get(key) + if real_storage is None: + out.append(t) + continue + + unhinted = False + + def map_symint(s): + nonlocal unhinted + if not isinstance(s, torch.SymInt): + return s + unhinted = unhinted if not unhinted else s.node.has_hint() + return s.node.hint + + stor_offset = map_symint(t.storage_offset()) + size = [map_symint(s) for s in t.shape] + stride = [map_symint(s) for s in t.stride()] + + if unhinted: + out.append(t) + continue + + new_tensor = torch.empty( + [], + dtype=t.dtype, + device=t.device, + ) + new_tensor.set_( + real_storage, + storage_offset=stor_offset, + size=size, + stride=stride, + ) + out.append(new_tensor.clone()) + + return out + + +def _check_fake_real_tensors( + real_out: torch.Tensor, + fake_out: FakeTensor, + context="", + sizes=True, + strides=False, + storage_offset=True, + requires_grad=True, +): + if requires_grad: + if real_out.requires_grad != fake_out.requires_grad: + raise MetadataMismatchError( + f"{context} mismatched requires_grad-ness of outputs. " + f"This usually means that you have added autograd support " + f"for your operator at a dispatch key other than Autograd, " + f"which will lead to problems" + ) + + if torch._C._has_storage(real_out): + r_offset = real_out.storage_offset() + f_offset = fake_out.storage_offset() + if r_offset != f_offset: + raise MetadataMismatchError(f"{context} mismatched storage offset") + + torch._prims.utils.compare_tensor_meta( + real_out, + fake_out, + check_sizes=sizes, + check_strides=strides, + allow_rhs_unbacked=True, + ) + + +class CrossRefFakeMode(TorchDispatchMode): + def __init__( + self, + ignore_op_fn: Union[Callable[[OpOverload], bool], None] = None, + *, + check_strides=True, + check_aliasing=True, + only_check_ops_with_meta=True, + ): + super().__init__() + self.ignore_op_fn = ( + ignore_op_fn if ignore_op_fn is not None else lambda fn: False + ) + self.check_strides = check_strides + self.check_aliasing = check_aliasing + self.only_check_ops_with_meta = only_check_ops_with_meta + + def __torch_dispatch__(self, func, types, args=(), kwargs=None): + kwargs = kwargs or {} + + fake_r = None + + # empty_like excluded for now due to sparse complex + # aten._to_dense.default this one is getting called with csc + if ( + func + not in ( + aten.lift_fresh.default, + aten.lift_fresh_copy.default, + aten.set_.source_Storage_storage_offset, + ) + and not self.ignore_op_fn(func) + and ( + not self.only_check_ops_with_meta + or torch._subclasses.fake_impls.has_meta(func) + ) + and torch.Tag.dynamic_output_shape not in func.tags + and torch.Tag.inplace_view not in func.tags + and torch.Tag.data_dependent_output not in func.tags + ): + # Do not import symbolic_shapes at the top of the module as it imports sympy and that's slow + from torch.fx.experimental.symbolic_shapes import ShapeEnv + + try: + # TODO: enable_python_dispatcher() here + with FakeTensorMode(shape_env=ShapeEnv()) as fake_mode: + fake_args, fake_kwargs = pytree.tree_map_only( + torch.Tensor, + functools.partial(fake_mode.from_tensor, static_shapes=True), + (args, kwargs), + ) + with warnings.catch_warnings(): + fake_r = func(*fake_args, **fake_kwargs) + except UnsupportedFakeTensorException: + pass + + context = ( + f"When comparing the output of {func} on FakeTensor and concrete Tensors, " + f"found" + ) + r = func(*args, **kwargs) + if fake_r is not None: + r_flat = pytree.tree_leaves(r) + f_flat = pytree.tree_leaves(fake_r) + assert len(f_flat) == len(r_flat), ( + f"{context} mismatch in number of returns {len(f_flat)} != {len(r_flat)}" + ) + + if self.check_aliasing: + _check_alias_info( + context, r, (args, kwargs), fake_r, (fake_args, fake_kwargs) + ) + + for idx, (r_out, f_out) in enumerate( + zip(pytree.tree_leaves(r), pytree.tree_leaves(fake_r)) + ): + r_is_ten = isinstance(r_out, torch.Tensor) + assert r_is_ten == isinstance(f_out, torch.Tensor), ( + f"{context} mismatched number of tensor outputs" + ) + if r_is_ten: + try: + _check_fake_real_tensors( + r_out, + f_out, + sizes=True, + strides=self.check_strides, + storage_offset=True, + requires_grad=True, + ) + except Exception as e: + if is_sdpa_error(func, idx, e): + continue + error_message = ( + f"{context} mismatched tensor metadata: {e}" + if len(r_flat) == 1 + else f"{context} mismatched tensor metadata for output[{idx}]: {e}" + ) + raise MetadataMismatchError(error_message) from e + return r diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/functional_tensor.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/functional_tensor.py new file mode 100644 index 0000000000000000000000000000000000000000..b0aa1977b1093948223b9d56cbd43ba7a29416fa --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/functional_tensor.py @@ -0,0 +1,837 @@ +# mypy: allow-untyped-defs +import contextlib +import warnings +import weakref +from abc import ABC, abstractmethod +from collections.abc import Callable +from contextlib import AbstractContextManager +from typing import Any, Optional, Union + +import torch +import torch.fx.traceback as fx_traceback +import torch.utils._pytree as pytree +from torch._C import _functionalization_reapply_views_tls as _reapply_views +from torch._ops import _get_dispatch_mode_pre_dispatch, TorchBindOpOverload +from torch._subclasses.meta_utils import is_sparse_any +from torch.utils._python_dispatch import ( + _detect_infra_mode, + _disable_infra_mode, + autograd_would_have_decomposed, + return_and_correct_aliasing, + TorchDispatchMode, +) + + +not_implemented_log = torch._logging.getArtifactLogger(__name__, "not_implemented") + + +# NOTE Some special handling for tensor conversion during export is needed. +# Normally, when tracing through the model with tensor.to(), the maybe-aliasing +# relationship between input and output tensors will be baked into the graph. +# For example, if we got a tensor with device cpu and call tensor.to("cpu"), +# it will become a no-op in the graph. For a whole graph capture, this is not +# sound so we need to do something different. Instead, in export we will try to +# preserve the tensor conversion by forcing a non-semantic-breaking aten::_to_copy +# operator to be traced in the graph, and subsequently banning mutations on all +# such converted tensors. +# In addition to patching .to() method call in functionalization, we will have to +# patch other similar methods like float() and cpu(), because they intentionally +# don't fall back to .to() methods, but have the same behavior as .to() according to +# pytorch document. https://pytorch.org/docs/stable/generated/torch.Tensor.float.html +# thus we simply force them to go through .to() call. +def _conversion_method_template(**extra_kwargs): + def _(self, *args, **kwargs): + return self.to(*args, **{**kwargs, **extra_kwargs}) + + return _ + + +class FunctionalTensor(torch.Tensor): + """ + Functional tensors represent tensors that will remove mutations + from a program. If you perform a mutable operation on a functional tensor, + it will re-dispatch to the functional variant of that operation. + + Historically, functionalization is implemented in C++ in the dispatcher. + This class is a lightweight python shim around the C++ functionalization logic. + + FunctionalTensor is required to be used with a corresponding + FunctionalTensormode active, because it relies + on using the mode for dispatch (which can properly handle factory functions). + """ + + elem: torch.Tensor + # Indicates to our torch_dispatch dispatching infra that + # this is an "infra" mode with lower dispatching precedence. + _mode_key = torch._C._TorchDispatchModeKey.FUNCTIONAL + + # Note: The reason we add these extra keys to our FunctionalTensor subclass + # is to mirror the behavior of C++ functionalization (we can choose to change this + # later, as long as it doesn't break anything). + # FunctionalTensorWrapper copies **all** dispatch keys from the inner tensor + # to the wrapper, excluding functorch and python dispatch keys. + # Here I'm trying to reuse the keyset the functorch wrapper subclasses copy, + # except that they don't include ZeroTensor so I'm manually adding it in. + _extra_dispatch_keys = torch._C._additional_keys_to_prop_for_wrapper_tensors.add( + torch._C.DispatchKey.ZeroTensor + ) + + # These are all aten ops that correspond to metadata queries. + # We want FunctionalTensor to be able to handle them directly. + metadata_fns = [ + torch.ops.aten.is_contiguous.default, # type: ignore[has-type] + torch.ops.aten.is_contiguous.memory_format, # type: ignore[has-type] + torch.ops.aten.is_strides_like_format.default, # type: ignore[has-type] + torch.ops.aten.is_non_overlapping_and_dense.default, # type: ignore[has-type] + torch.ops.aten.size.default, # type: ignore[has-type] + torch.ops.aten.sym_size.default, # type: ignore[has-type] + torch.ops.aten.stride.default, # type: ignore[has-type] + torch.ops.aten.sym_stride.default, # type: ignore[has-type] + torch.ops.aten.storage_offset.default, # type: ignore[has-type] + torch.ops.aten.sym_storage_offset.default, # type: ignore[has-type] + torch.ops.aten.numel.default, # type: ignore[has-type] + torch.ops.aten.sym_numel.default, # type: ignore[has-type] + torch.ops.aten.dim.default, # type: ignore[has-type] + torch.ops.prim.device.default, # type: ignore[has-type] + ] + + # Used by auto_functionalize to determine base of tensors during inference mode. + _inference_mode_base: Optional["FunctionalTensor"] = None + + def __new__(cls, elem, mode): + assert torch._is_functional_tensor(elem) + + # In general, we'd like our functional tensor subclass to only be in charge of functionalization, + # and defer to the inner subclass for all other functionality. + # Example: If our inner tensor is a ZeroTensor, we would want to defer running the ZeroTensor fallback + # until after we redispatch to our inner ZeroTensor. + # However, there are a few keys that we need to mirror between the inner and outer tensors. + # Conjugate + # Negative + # Why? These keys are used to test metadata queries, like `.is_conj()` and `.is_neg()`. + # We **need** calls to is_conj() to return the same thing on the outer and inner tensors, + # Because user code / framework code that branches like so needs to do the same thing + # when it sees the outer FunctionalTensor: + # if (x.is_conj()) { + # return at::view_as_real(x.resolve_conj()); + # } else { + # return at::view_as_real(x); + # } + extra_dispatch_keys = ( + FunctionalTensor._extra_dispatch_keys & torch._C._dispatch_keys(elem) + ) + + out = torch.Tensor._make_wrapper_subclass( + # TODO: right now, _make_wrapper_subclass's dynamic shape interaction is not great. + # Calling the overload that has kwargs causes us to go down the first overload path, + # which will **always** specialize sizes. + # We should probably eventually fix this so that the first overload can just handle dynamic shapes. + cls, + elem.shape, # sizes + elem.stride() if not is_sparse_any(elem) else None, # strides + ( + elem.storage_offset() if not is_sparse_any(elem) else None + ), # storage_offset + None, # memory_format + elem.dtype, # dtype + elem.layout, # layout + elem.device, # device + False, # pin_memory + elem.requires_grad, # requires_grad + None, # dispatch_sizes_strides_policy + False, # dispatch_device + False, # dispatch_layout + extra_dispatch_keys, # _extra_dispatch_keys + ) + torch._C._set_throw_on_mutable_data_ptr(out) + out.elem = elem + + if ( + torch._export.config.enable_auto_functionalized_v2_for_export + and torch.is_inference_mode_enabled() + and torch._inductor.config.enable_auto_functionalized_v2 + ): + if out.is_base_tensor(): + out._inference_mode_base = None + # This assumes that the FunctionalTensor.elem does not change its storage after this point. + # Otherwise this would be invalid. + mode._storage_to_base[out.elem.untyped_storage()] = out + else: + out._inference_mode_base = mode._storage_to_base[ + out.elem.untyped_storage() + ] + assert out._inference_mode_base is not None + return out + + def __torch_dispatch__(self, func, types, args=(), kwargs=None): # type: ignore[override] + unrecognized_types = [ + t + for t in types + if t not in [torch.Tensor, torch._subclasses.FakeTensor, FunctionalTensor] + ] + if unrecognized_types: + not_implemented_log.debug( + "FunctionalTensor unrecognized subclass(es): %s", unrecognized_types + ) + return NotImplemented + + if kwargs is None: + kwargs = {} + + # FunctionalTensor needs to plumb all metadata requests to the inner tensor. + # In theory we don't have to do this - but if we want to service metadata requests here, + # we need to carefully make sure all metadata is accurate (including metadata mutations) + if func in FunctionalTensor.metadata_fns: + # All metadata accesses should be plumbed to the inner tensor, that way we don't have to worry + # about the problem of keeping metadata in sync between the wrapper and inner tensor. + # This also alleviates us from having to manually handle metadata mutations on the wrapper. + assert len(kwargs) == 0 + if func in [ + torch.ops.aten.is_strides_like_format.default, + torch.ops.aten.is_contiguous.memory_format, + ]: + assert len(args) == 2 and isinstance(args[0], FunctionalTensor) + return func(torch._from_functional_tensor(args[0].elem), args[1]) + assert len(args) == 1 and isinstance(args[0], FunctionalTensor) + + return func(torch._from_functional_tensor(args[0].elem)) + # Originally I tried to implement my subclass without giving it a torch_dispatch, but I gave up: + # - _make_wrapper_subclass requires a __torch_dispatch__ + # - If we want to use _make_subclass(), we have a problem: the subclass will share a TensorImpl with the inner tensor, + # which is of type FunctionalTensorWrapper! We explicitly do not want our wrapper to be a FunctionalTensorWrapper. + # - If we use the default tensor.__new__(), we have another problem: it returns inner_tensor.alias(), + # which causes every subclass created above autograd to have autograd view metadata + # (in addition to also being a FunctionalTensorWrapper). + raise RuntimeError( + "Attempting to use FunctionalTensor on its own. Instead, please use it with a corresponding FunctionalTensorMode()" + ) + + def __repr__(self) -> str: # type: ignore[override] + return f"FunctionalTensor({repr(self.elem)})" + + @staticmethod + def to_functional(x): + # We will do the wrapping for the user. + + assert not torch._is_functional_tensor(x) + # The only autograd metadata we care about on the FunctionalTensor is: + # - requires_grad (so autograd runs) + # - is_leaf (so that mutations on graph inputs that are not leaves are allowed by the autograd engine) + # this is handled by FunctionalTensor.to_functional + x_functional = torch._to_functional_tensor(x) + # Technically the FunctionalTensormode here is unnecessary, + # but it avoids spurious NotImplemented logs during `ProxyTorchDispatchMode` tracing. + # _mirror_autograd_meta_to queries tensor sizes, + # and otherwise the sym_size() call will go to the proxy mode before hitting + # FunctionalTensor.__torch_dispatch__ + + functional_mode = _detect_infra_mode(torch._C._TorchDispatchModeKey.FUNCTIONAL) + assert functional_mode is not None + + with functional_mode: + torch._mirror_autograd_meta_to(x, x_functional) # type: ignore[attr-defined] + out = FunctionalTensor(x_functional, functional_mode) + torch._mirror_autograd_meta_to(x_functional, out) # type: ignore[attr-defined] + return out + + def from_functional(self): + torch._sync(self) + return torch._from_functional_tensor(self.elem) + + def is_base_tensor(self) -> bool: + return torch._is_functional_tensor_base(self.elem) + + def replace_(self, output) -> None: + torch._functionalize_replace(self.elem, output) + + def commit_update(self) -> None: + torch._functionalize_commit_update(self.elem) + + def sync(self) -> None: + torch._functionalize_sync(self.elem) + + def mark_mutation_hidden_from_autograd(self) -> None: + torch._functionalize_mark_mutation_hidden_from_autograd(self.elem) + + def tolist(self) -> Any: + if self.elem.dim() == 0: + return self.elem.item() + elif self.elem.dim() == 1: + return [elem.item() for elem in self.elem] + else: + return [elem.tolist() for elem in self.elem] + + def to(self, *args, **kwargs): + if _detect_infra_mode(torch._C._TorchDispatchModeKey.FUNCTIONAL).export: + torch.ops.aten._assert_tensor_metadata( + self, + dtype=self.dtype, + device=self.device, + layout=self.layout, + ) + # pyrefly: ignore [not-iterable] + return super().to(*args, **kwargs) + + def cuda(self, device=None, *args, **kwargs): + device = device or torch.cuda.current_device() + if len(args) > 0: + return self.to(device, *args, **kwargs) + else: + return self.to(device=device, **kwargs) + + char = _conversion_method_template(dtype=torch.int8) + cpu = _conversion_method_template(device=torch.device("cpu")) + bfloat16 = _conversion_method_template(dtype=torch.bfloat16) + byte = _conversion_method_template(dtype=torch.uint8) + double = _conversion_method_template(dtype=torch.float64) + float = _conversion_method_template(dtype=torch.float32) + bool = _conversion_method_template(dtype=torch.bool) + half = _conversion_method_template(dtype=torch.float16) + int = _conversion_method_template(dtype=torch.int32) + long = _conversion_method_template(dtype=torch.int64) + + # TODO(sparse-team): fixes #133174 but can we do without the relay? + def to_dense(self): # type: ignore[override] + return self.elem.to_dense() + + @property + def layout(self): # type: ignore[override] + return self.elem.layout + + def __bool__(self): + return bool(self.item()) + + +class FunctionalTensorMode(TorchDispatchMode): + def __init__(self, pre_dispatch=False, export=False, _allow_token_discovery=False): + super().__init__() + self.export = export + self.is_on_stack = False + self.enter_stack = [] + # Indicates to our torch_dispatch dispatching infra that + # this is an "infra" mode with lower dispatching precedence. + self._mode_key = torch._C._TorchDispatchModeKey.FUNCTIONAL + self.pre_dispatch = pre_dispatch + # This will be turned off later for pre-dispatch functionalization + self._dispatch_key = torch._C.DispatchKey.PreDispatch if pre_dispatch else None # type: ignore[attr-defined] + # Map of effect type (ex. _EffectType.ORDERED) to a token. The tokens help keep + # track of the ordering between side effectful operations. + self._tokens: dict[Any, torch.Tensor] = {} + + # Filled after forward tracing. + self._tokens_forward_output: dict[Any, torch.Tensor] = {} + + # Functionalization runs twice in AOTAutograd, once in + # `run_functionalized_fw_and_collect_metadata` to collect metadata to + # see which tensors need to be functionalized and discover how many + # tokens we need, and another time in `make_fx` which does the actual + # tracing to replace ops with their functional variants and handling + # side-effectful ops. In the second stage there should be no token + # discovery. This flag distinguishes between the two stages. + self._allow_token_discovery = _allow_token_discovery + + self._storage_to_base: weakref.WeakKeyDictionary[ + torch.storage.UntypedStorage, Optional[FunctionalTensor] + ] = weakref.WeakKeyDictionary() + + # No-op if FunctionalTensorMode is already in use + def __enter__(self): + def _get_prev_mode(): + if self._dispatch_key == torch._C.DispatchKey.PreDispatch: + return _get_dispatch_mode_pre_dispatch( + torch._C._TorchDispatchModeKey.FUNCTIONAL + ) + return torch._C._get_dispatch_mode( + torch._C._TorchDispatchModeKey.FUNCTIONAL + ) + + if _get_prev_mode() is None: + self.enter_stack.append(True) + return super().__enter__() + else: + self.enter_stack.append(False) + return self + + def __exit__(self, a, b, c): + is_on_stack = self.enter_stack.pop() + if is_on_stack: + super().__exit__(a, b, c) + + def __torch_dispatch__(self, func, types, args=(), kwargs=None): + if kwargs is None: + kwargs = {} + + unrecognized_types = [ + t + for t in types + if not issubclass(t, torch._subclasses.FakeTensor) + and t not in [torch.Tensor, FunctionalTensor] + ] + + if unrecognized_types: + not_implemented_log.debug( + "FunctionalTensor unrecognized subclass(es): %s", unrecognized_types + ) + return NotImplemented + + def _can_decompose(func): + # See https://github.com/pytorch/pytorch/pull/115258#issuecomment-1900755832 + # Never decompose dropout in export + if self.export and func is torch.ops.aten.dropout.default: + return False + + # We unconditionally decompose ops that are maybe aliasing or mutating ops + from torch._decomp import _should_decompose_because_unsafe_op + + if _should_decompose_because_unsafe_op(func): + return True + + # (1) we unconditionally decompose maybe-aliasing or maybe-mutating ops, + # because we must know statically of an op mutates or aliasing in order to functionalize it properly + # (2) for mutating ops that have CompositeImplicit decomps, we choose to decompose them today. + # In theory, we could walk this back and avoid decomposing them later if we need to. + alias_info_present = any(arg.alias_info for arg in func._schema.arguments) + if alias_info_present or func._schema.is_mutable: + return True + + # If we are here, it means we are seeing functional composite op. + # For pre-dispatch IR, we don't want to decompose this op + # For post-dispatch IR, we do want to decompose this op. it is fine + # to decompose here even if you want to preserve a CIA in post-dispatch export + # because we already override decompose behaviour so it will do the + # right thing. + if self.export: + if self.pre_dispatch: + # If it is CIA custom op, we warn that we are assuming this op is indeed functional. + if func.namespace not in ["aten", "prim"] and func._can_decompose(): + warnings.warn( + f"At pre-dispatch tracing, we assume that any custom op marked with " + f"CompositeImplicitAutograd and have functional schema are safe to not decompose. " + f"Found {func} to be one such op.", + stacklevel=2, + ) + return False + return True + + # in normal torch.compile IR, we only decompose an op if autograd + # would have decomposed it (NB: autograd may have been skipped if + # we are in inference mode) + # TODO: the flatten here can potentially be deduped with the + # unwrapping pytree_map later + flat_args_kwargs, _ = pytree.tree_flatten((args, kwargs)) + return autograd_would_have_decomposed(func, flat_args_kwargs) + + if ( + func not in FunctionalTensor.metadata_fns + and _can_decompose(func) + # Not all funcs from __torch_dispatch__ are actual dispatcher ops, + # e.g. prim.device + and torch._C._dispatch_has_kernel(func.name()) + ): + with self: + r = func.decompose(*args, **kwargs) + if r is not NotImplemented: + return r + + def wrap(x): + # Only wrap our outputs in subclasses if the inner functionalization call + # also wrapped outputs into FunctionalTensorWrappers. + # When can this happen? e.g. `torch.div(2, 2)` + assert not isinstance(x, FunctionalTensor) + if isinstance(x, torch.Tensor) and torch._is_functional_tensor(x): + return FunctionalTensor(x, self) + return x + + def unwrap(x): + return x.elem + + from torch._higher_order_ops.auto_functionalize import ( + can_auto_functionalize, + do_auto_functionalize, + do_auto_functionalize_v2, + ) + + if can_auto_functionalize( + func + ) and not torch._C._dispatch_has_kernel_for_dispatch_key( + func.name(), torch._C.DispatchKey.Functionalize + ): + import torch._export.config as export_config + import torch._inductor.config as inductor_config + + if torch.compiler.is_exporting(): + if export_config.enable_auto_functionalized_v2_for_export: + return do_auto_functionalize_v2(self, func, args, kwargs) + + return do_auto_functionalize(self, func, args, kwargs) + + if inductor_config.enable_auto_functionalized_v2: + return do_auto_functionalize_v2(self, func, args, kwargs) + return do_auto_functionalize(self, func, args, kwargs) + + from torch._higher_order_ops.effects import handle_effects, has_effects + + if has_effects(func): + assert not torch._C._dispatch_has_kernel_for_dispatch_key( + func.name(), torch._C.DispatchKey.Functionalize + ) + return handle_effects( + self._allow_token_discovery, self._tokens, func, args, kwargs + ) + + args_unwrapped, kwargs_unwrapped = pytree.tree_map_only( + FunctionalTensor, unwrap, (args, kwargs) + ) + + # Expectation: functionalization should not **already** be enabled above our mode. + # Why would that be bad? when we return a FunctionalTensor here, we don't want functionalization + # to run above this mode and further wrap that output in **another** C++ FunctionalTensorWrapper. + is_included = torch._C._dispatch_tls_is_dispatch_key_included( + torch._C.DispatchKey.Functionalize + ) + is_excluded = torch._C._dispatch_tls_is_dispatch_key_excluded( + torch._C.DispatchKey.Functionalize + ) + assert is_excluded or not is_included + include_to_set = ( + torch._C._dispatch_tls_local_include_set() + | torch._C.DispatchKeySet(torch._C.DispatchKey.Functionalize) + ) + exclude_to_set = ( + torch._C._dispatch_tls_local_exclude_set().remove( + torch._C.DispatchKey.Functionalize + ) + - FunctionalTensor._extra_dispatch_keys + ) + + if isinstance(func, TorchBindOpOverload): + # When the function is a TorchBindOpOverload, meaning some of the + # inputs are FakeScriptObjects, we need to skip c++ dispatcher and + # dispatch in python because C++ dispatcher will check the schema + # and cannot recognize FakeScriptObject. + ctx = PythonFunctionalizeAPI() + fully_unwrapped_args = ctx.unwrap_tensors(args) + fully_unwrapped_kwargs = ctx.unwrap_tensors( + kwargs # pyrefly: ignore[bad-argument-type] + ) + outs_unwrapped = func( + *fully_unwrapped_args, + **fully_unwrapped_kwargs, + ) + outs_wrapped = ctx.wrap_tensors(outs_unwrapped) + else: + # All we want to do here is reuse the existing C++ functionalization logic. + # This requires swizzling our TLS dispatch keys so that the Functionalize key is active. + with torch._C._ForceDispatchKeyGuard(include_to_set, exclude_to_set): + try: + # By default for python functionalization (for AOTAutograd), we reapply views. + old_apply_views = torch._functionalize_enable_reapply_views(True) # type: ignore[attr-defined] + + # Sometimes these functions cannot be directly dispatched to functionalize key + # because args are sometimes not functional tensors for some reason? + if func in FunctionalTensor.metadata_fns: + outs_unwrapped = func(*args_unwrapped, **kwargs_unwrapped) + outs_wrapped = pytree.tree_map_only( + torch.Tensor, wrap, outs_unwrapped + ) + else: + # Note: [Functionalization View Replay Annotation] + # When functionalization encounters a mutation, it handles aliases by lazily regenerating the aliases + # at the first time they are next used. + # This is a problem when plumbing user annotations during tracing. We want the view ops from view replay + # to have the same annotation that the user specified on the original views. But view replay in + # functionalization happens the next time the alias is used (e.g. second_op(alias_with_pending_mutation)), + # so when we regenerate views before calling into second_op, those views will end up getting the metadata + # for second_op! + # + # Instead, we need to remember the node metadata from the original views, and ensure that this node metadata + # is globally set when we lazily perform view replay. + # The globally set metadata will be used to populate the fx node created for the replayed operation. + if m := torch._C._get_dispatch_mode( + torch._C._TorchDispatchModeKey.PROXY + ): + for a in pytree.tree_leaves([args, kwargs]): + if not isinstance(a, FunctionalTensor): + continue + curr_node = m.tracer.tensor_tracker[ + torch._from_functional_tensor(a.elem) + ].proxy.node + with fx_traceback.set_current_replay_node(curr_node): + torch._sync(a) + + # When we dispatch to the C++ functionalization kernel, we might need to jump back to the + # PreDispatch mode stack afterwards, to handle any other PreDispatch modes underneath + # FunctionalTensorMode. If we call func() directly, we would need to exclude PreDispatch + # from the TLS in order to avoid infinite looping, but this would prevent us from coming + # back to PreDispatch later + outs_unwrapped = func._op_dk( + torch._C.DispatchKey.Functionalize, + *args_unwrapped, + **kwargs_unwrapped, + ) + + if self.export: + if func is torch.ops.aten.dropout.default: + torch._freeze_functional_tensor(outs_unwrapped) # type: ignore[attr-defined] + outs_wrapped = pytree.tree_map_only( + torch.Tensor, wrap, outs_unwrapped + ) + finally: + torch._disable_functionalization() + torch._functionalize_enable_reapply_views(old_apply_views) # type: ignore[attr-defined] + + is_included = torch._C._dispatch_tls_is_dispatch_key_included( + torch._C.DispatchKey.Functionalize + ) + is_excluded = torch._C._dispatch_tls_is_dispatch_key_excluded( + torch._C.DispatchKey.Functionalize + ) + assert is_excluded or not is_included + + if ( + # If no outputs are our functional subclass, then don't try to fix up aliasing + not any( + isinstance(x, FunctionalTensor) + for x in pytree.tree_leaves(outs_wrapped) + ) + # Since lift_fresh lifts its argument into a functional tensor, we can skip the + # aliasing correction step. Otherwise, we would be setting the storage of a + # lifted tensor to that of an unlifted tensor. + # Ref: https://github.com/pytorch/pytorch/issues/111506 + or func is torch.ops.aten.lift_fresh.default + ): + return outs_wrapped + # for metadata mutations, need to manually mutate the metadata of the FunctionalTensor wrapper + if ( + torch.Tag.inplace_view in func.tags + and func is not torch.ops.aten.set_.source_Tensor + ): + with torch.utils._mode_utils.no_dispatch(): + func(*args, **kwargs) + # Wrapper tensor subclasses do not have correct aliasing info! Use this util to manually correct the output aliasing. + # inplace ops like `aten.add_()` are expected to return inputs **directly**, instead of creating fresh tensor objects. + # Use this util to figure out the right thing to return. + # If none of our inputs were wrapped, then we have no FunctionalTensor outputs that we need to fix up storages for. + return return_and_correct_aliasing(func, args, kwargs, outs_wrapped) + + @classmethod + def is_infra_mode(cls) -> bool: + return True + + +@contextlib.contextmanager +def disable_functional_mode(): + return _disable_infra_mode(torch._C._TorchDispatchModeKey.FUNCTIONAL) + + +# This is similar to torch.func.functionalize, but: +# - It uses FunctionalTensorMode, and FunctionalTensor (a python subclass). +# One important advantage to using this mode is that it will let us +# run functionalization underneath __torch_dispatch__, +# which we need in AOTAutograd. +# - Doing so means that it does not automatically compose with other +# functorch transforms, since these transforms always run above __torch_dispatch__. +# That's why this util lives here, and not in functorch. +def dispatch_functionalize(func, mode: FunctionalTensorMode = FunctionalTensorMode()): + # TODO: pull these from aot autograd + def to_fun(t): + if isinstance(t, torch.Tensor): + return FunctionalTensor.to_functional(t) + return t + + def from_fun(t): + if not isinstance(t, FunctionalTensor): + # quick sanity assert + if isinstance(t, torch.Tensor): + assert not torch._is_functional_tensor(t) + return t + torch._sync(t) + return torch._from_functional_tensor(t.elem) + + def inner(*args, **kwargs): + disable_above = torch._C._ExcludeDispatchKeyGuard( + torch._C.DispatchKeySet(torch._C.DispatchKey.Functionalize) + ) + with disable_above, mode: + func_args = pytree.tree_map_only(torch.Tensor, to_fun, args) + func_kwargs = pytree.tree_map_only(torch.Tensor, to_fun, kwargs) + func_outputs = func(*func_args, **func_kwargs) + outputs = pytree.tree_map_only(FunctionalTensor, from_fun, func_outputs) + + return outputs + + return inner + + +class BaseFunctionalizeAPI(ABC): + @abstractmethod + def wrap_tensors(self, args: tuple[Any]) -> tuple[Any]: + pass + + @abstractmethod + def unwrap_tensors( + self, args: Union[torch.Tensor, tuple[torch.Tensor, ...]] + ) -> Any: + pass + + @abstractmethod + def functionalize(self, inner_f: Callable) -> Callable: + pass + + @abstractmethod + def redispatch_to_next(self) -> AbstractContextManager: + pass + + @abstractmethod + def replace(self, input_tensor, output_tensor) -> None: + pass + + @abstractmethod + def commit_update(self, tensor) -> None: + pass + + @abstractmethod + def sync(self, tensor) -> None: + pass + + @abstractmethod + def mark_mutation_hidden_from_autograd(self, tensor) -> None: + pass + + +class PythonFunctionalizeAPI(BaseFunctionalizeAPI): + def __init__( + self, mode: Optional[FunctionalTensorMode] = None, pre_dispatch: bool = False + ) -> None: + super().__init__() + self.mode = mode if mode else FunctionalTensorMode() + self.pre_dispatch = pre_dispatch + + def wrap_tensors(self, args: tuple[Any]) -> tuple[Any]: + with self.mode: + return torch.utils._pytree.tree_map_only( + torch.Tensor, FunctionalTensor.to_functional, args + ) + + def unwrap_tensors( + self, args: Union[torch.Tensor, tuple[torch.Tensor, ...], list[torch.Tensor]] + ) -> Any: + return torch.utils._pytree.tree_map_only( + FunctionalTensor, FunctionalTensor.from_functional, args + ) + + def functionalize(self, inner_f: Callable) -> Callable: + return dispatch_functionalize(inner_f, self.mode) + + def redispatch_to_next(self) -> AbstractContextManager: + # [NOTE] We don't do anything here because at the time + # we exercise this path, we would have already popped the + # FunctionalTensorMode from mode stack. Since FunctionalTensorMode + # is now stateful, it is better to explicitly pass in correct mode + # directly instead of globally setting it. + return contextlib.nullcontext() + + def replace(self, input_tensor, output_tensor) -> None: + assert isinstance(input_tensor, FunctionalTensor) + assert not isinstance(output_tensor, FunctionalTensor) + input_tensor.replace_(output_tensor) + + def commit_update(self, tensor) -> None: + assert isinstance(tensor, FunctionalTensor) + tensor.commit_update() + + def sync(self, tensor) -> None: + assert isinstance(tensor, FunctionalTensor) + tensor.sync() + + def mark_mutation_hidden_from_autograd(self, tensor) -> None: + assert isinstance(tensor, FunctionalTensor) + tensor.mark_mutation_hidden_from_autograd() + + +class CppFunctionalizeAPI(BaseFunctionalizeAPI): + def wrap_tensors(self, args: tuple[Any]) -> tuple[Any]: + from torch._functorch.eager_transforms import _wrap_all_tensors_to_functional + + return _wrap_all_tensors_to_functional(args, level=0) + + def unwrap_tensors( + self, args: Union[torch.Tensor, tuple[torch.Tensor, ...]] + ) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]: + from torch._functorch.eager_transforms import ( + _unwrap_all_tensors_from_functional, + ) + + return _unwrap_all_tensors_from_functional(args, reapply_views=_reapply_views()) + + def functionalize(self, inner_f: Callable) -> Callable: + return torch.func.functionalize(inner_f) + + def redispatch_to_next(self) -> AbstractContextManager: + return torch._C._ExcludeDispatchKeyGuard( + torch._C.DispatchKeySet(torch._C.DispatchKey.Functionalize) + ) + + def replace(self, input_tensor, output_tensor) -> None: + torch._functionalize_replace(input_tensor, output_tensor) + + def commit_update(self, tensor) -> None: + torch._functionalize_commit_update(tensor) + + def sync(self, tensor) -> None: + torch._functionalize_sync(tensor) + + def mark_mutation_hidden_from_autograd(self, tensor) -> None: + torch._functionalize_mark_mutation_hidden_from_autograd(tensor) + + +class FunctorchFunctionalizeAPI(BaseFunctionalizeAPI): + def __init__(self, interpreter): + self.interpreter = interpreter + + def wrap_tensors(self, args: tuple[Any]) -> tuple[Any]: + from torch._functorch.eager_transforms import _wrap_all_tensors_to_functional + + return _wrap_all_tensors_to_functional(args, level=self.interpreter.level()) + + def unwrap_tensors( + self, args: Union[torch.Tensor, tuple[torch.Tensor, ...]] + ) -> Union[torch.Tensor, tuple[torch.Tensor, ...]]: + from torch._functorch.eager_transforms import ( + _unwrap_all_tensors_from_functional, + ) + + return _unwrap_all_tensors_from_functional( + args, reapply_views=self.interpreter.functionalize_add_back_views() + ) + + def functionalize(self, inner_f: Callable) -> Callable: + return torch.func.functionalize( + inner_f, + remove=( + "mutations_and_views" + if self.interpreter.functionalize_add_back_views() + else "mutations" + ), + ) + + def redispatch_to_next(self) -> AbstractContextManager: + return self.interpreter.lower() + + def replace(self, input_tensor, output_tensor) -> None: + torch._functionalize_replace(input_tensor, output_tensor) + + def commit_update(self, tensor) -> None: + torch._functionalize_commit_update(tensor) + + def sync(self, tensor) -> None: + torch._functionalize_sync(tensor) + + def mark_mutation_hidden_from_autograd(self, tensor) -> None: + torch._functionalize_mark_mutation_hidden_from_autograd(tensor) + + +def mb_unwrap_functional_tensor(tensor: torch.Tensor): + if isinstance(tensor, FunctionalTensor): + return torch._from_functional_tensor(tensor.elem) + return tensor diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/meta_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/meta_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..1db028fdbe2eef67f79cf3a547c4930a23647b56 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/meta_utils.py @@ -0,0 +1,1972 @@ +from __future__ import annotations + +import contextlib +import dataclasses +import functools +import threading +import typing +import weakref +from abc import abstractmethod +from contextlib import AbstractContextManager, contextmanager +from dataclasses import dataclass +from typing import ( + Any, + ClassVar, + Generic, + NewType, + Optional, + Protocol, + TYPE_CHECKING, + TypeGuard, + TypeVar, + Union, +) +from typing_extensions import override, TypedDict, TypeIs, Unpack + +import torch +from torch._C._autograd import CreationMeta +from torch._C._functorch import ( + _add_batch_dim, + _unwrap_functional_tensor, + _wrap_functional_tensor, + get_unwrapped, + is_batchedtensor, + is_functorch_wrapped_tensor, + is_gradtrackingtensor, + is_legacy_batchedtensor, + maybe_get_bdim, + maybe_get_level, + peek_interpreter_stack, +) +from torch._dispatch.python import enable_python_dispatcher +from torch._logging import trace_structured +from torch.utils._mode_utils import no_dispatch +from torch.utils._python_dispatch import is_traceable_wrapper_subclass +from torch.utils.weak import WeakIdKeyDictionary + + +if TYPE_CHECKING: + from collections.abc import Callable, Generator + + from torch._C._functorch import CInterpreter + from torch._guards import Source + from torch._subclasses.fake_tensor import FakeTensor, FakeTensorMode + + # Import here to avoid cycle + # Import the following modules during type checking to enable code intelligence features, + # Do not import unconditionally, as they import sympy and importing sympy is very slow + from torch.fx.experimental.symbolic_shapes import ShapeEnv, SymbolicContext + + +def _is_fake_tensor(t: object) -> TypeIs[FakeTensor]: + from torch._subclasses.fake_tensor import FakeTensor + + return isinstance(t, FakeTensor) + + +DimList = list +_TensorLikeT = TypeVar("_TensorLikeT", "MetaTensorDesc", torch.Tensor) +_T = TypeVar("_T") +_TensorT = TypeVar("_TensorT", bound=torch.Tensor) +_TensorT_cov = TypeVar("_TensorT_cov", bound=torch.Tensor, covariant=True) + + +def safe_is_leaf(t: Union[MetaTensorDesc, torch.Tensor]) -> bool: + try: + return t.is_leaf + except RuntimeError: + # inference mode can trigger this + return False + + +def safe_grad(t: _TensorLikeT) -> Optional[_TensorLikeT]: + with torch._logging.hide_warnings(torch._logging._internal.safe_grad_filter): + # pyrefly: ignore [bad-return] + return t.grad + + +def _expect_safe_grad(t: _TensorLikeT) -> _TensorLikeT: + grad = safe_grad(t) + assert grad is not None + return grad + + +def assert_eq(a: _T, b: _T) -> None: + assert a == b, f"{a} != {b}" + + +tls = threading.local() +# Turns off inference mode for fake tensor propagation. This is turned to True +# only for `torch.compile`. Also look at +# _dynamo.config.fake_tensor_disable_inference_mode +tls.disable_inference_mode = False + + +@contextmanager +def disable_inference_mode_for_fake_prop() -> Generator[None, None, None]: + prior = getattr(tls, "disable_inference_mode", False) + tls.disable_inference_mode = True + try: + yield + finally: + tls.disable_inference_mode = prior + + +def assert_metadata_eq( + assert_eq: Callable[[object, object], None], + m1: Union[MetaTensorDesc, torch.Tensor], + m2: torch.Tensor, + *, + skip_symbolic: bool = False, + skip_leaf: bool = False, +) -> None: + m1 = ( + MetaTensorDescriber().describe_tensor(m1) + if isinstance(m1, torch.Tensor) + else m1 + ) + + def go(m1: MetaTensorDesc, m2: torch.Tensor) -> None: + assert_eq(m1.dtype, m2.dtype) + if not skip_symbolic: + assert_eq(m1.shape, m2.shape) + assert_eq(m1.requires_grad, m2.requires_grad) + if not skip_leaf: + assert_eq(m1.is_leaf, m2.is_leaf) + # MetaTensorDesc doesn't store grad_fn; inferred from leaf + # assert_eq(m1.grad_fn is None, m2.grad_fn is None) + assert_eq(m1.is_sparse, m2.is_sparse) + if not getattr(tls, "disable_inference_mode", False): + assert_eq(m1.is_inference, m2.is_inference()) + else: + assert_eq(m1.is_inference, False) + assert_eq(m1.is_conj, m2.is_conj()) + assert_eq(m1.is_neg, m2.is_neg()) + assert_eq(m1.grad is not None, safe_grad(m2) is not None) + if m1.grad is not None: + go(m1.grad, _expect_safe_grad(m2)) + # TODO: move "assert_eq(m1.layout, m2.layout)" out of sparse + # branches (but not ready for prime time yet)... + if m1.is_sparse: + assert_eq(m1.layout, m2.layout) + assert_eq(m1.dense_dim, m2.dense_dim()) + assert_eq(m1.sparse_dim, m2.sparse_dim()) + assert_eq(m1.is_coalesced, m2.is_coalesced()) + elif is_sparse_compressed(m1): + assert_eq(m1.layout, m2.layout) + assert_eq(m1.dense_dim, m2.dense_dim()) + assert_eq(m1.sparse_dim, m2.sparse_dim()) + else: + if not skip_symbolic: + assert_eq(m1.stride, m2.stride()) + assert_eq(m1.storage_offset, m2.storage_offset()) + assert_eq(m1.is_view, m2._is_view()) + if m1.is_view: + assert m1.base is not None + assert m2._base is not None + go(m1.base, m2._base) + # TODO: test if is resizable (no direct query for this atm) + # TODO: audit AutogradMeta to see if it matches + # TODO: test forward AD + + return go(m1, m2) + + +# TypeGuard (not TypeIs): False does not imply !torch.Tensor +def is_sparse_coo(t: object) -> TypeGuard[torch.Tensor]: + return isinstance(t, torch.Tensor) and t.layout is torch.sparse_coo + + +def is_sparse_compressed_layout(layout: torch.layout) -> bool: + return layout in { + torch.sparse_csr, + torch.sparse_csc, + torch.sparse_bsr, + torch.sparse_bsc, + } + + +# TypeGuard (not TypeIs): False does not imply !torch.Tensor +def is_sparse_compressed(t: object) -> TypeGuard[torch.Tensor]: + return isinstance(t, torch.Tensor) and is_sparse_compressed_layout(t.layout) + + +# TypeGuard (not TypeIs): False does not imply !torch.Tensor +def is_sparse_any(t: object) -> TypeGuard[torch.Tensor]: + return is_sparse_coo(t) or is_sparse_compressed(t) + + +def _checked_cast(ty: type[_T], obj: object) -> _T: + assert isinstance(obj, ty), f"expected {ty} but got {type(obj)}" + return obj + + +def _get_real_storage(base: torch.UntypedStorage) -> torch.UntypedStorage: + return base.real_storage # type: ignore[attr-defined] + + +def _set_real_storage( + base: torch.UntypedStorage, real_storage: torch.UntypedStorage +) -> None: + base.real_storage = real_storage # type: ignore[attr-defined] + + +# Don't use id() directly, because those can get reallocated over time. +MetaStorageId = NewType("MetaStorageId", int) +MetaTensorId = NewType("MetaTensorId", int) + + +_DescriberId = NewType("_DescriberId", int) +DESCRIBER_NEXT_ID = _DescriberId(0) + + +class MetaTensorDescriber: + """ + Given a Tensor/Storage, generate a MetaTensorDesc/MetaStorageDesc + for it, which is enough information to reconstruct a meta tensor/fake tensor + corresponding to a Tensor as faithfully as possible. + + This is a stateful conversion object because we keep track of the IDs + of the tensors/storages passed to us, so we can consistently give + the same ID when we see the same tensor/storage. + """ + + def __init__(self, *, copy_data: bool = False) -> None: + global DESCRIBER_NEXT_ID + self.id = DESCRIBER_NEXT_ID + DESCRIBER_NEXT_ID = _DescriberId(DESCRIBER_NEXT_ID + 1) + self.next_tensor_id: MetaTensorId = MetaTensorId(0) + self.next_storage_id: MetaStorageId = MetaStorageId(0) + # Tensor -> int + self.lookup_tensor = WeakIdKeyDictionary() + # Storage -> int + self.lookup_storage = WeakIdKeyDictionary() + self.copy_data = copy_data + self.traced_tensors: set[int] = set() + self.traced_storages: set[int] = set() + + def get_tensor_id(self, t: torch.Tensor) -> MetaTensorId: + if t not in self.lookup_tensor: + self.lookup_tensor[t] = self.next_tensor_id + self.next_tensor_id = MetaTensorId(self.next_tensor_id + 1) + return self.lookup_tensor[t] + + def get_storage_id(self, s: torch.UntypedStorage) -> MetaStorageId: + if s not in self.lookup_storage: + self.lookup_storage[s] = self.next_storage_id + self.next_storage_id = MetaStorageId(self.next_storage_id + 1) + return self.lookup_storage[s] + + def describe_storage( + self, s: torch.UntypedStorage, *, trace: bool = False + ) -> MetaStorageDesc: + r = MetaStorageDesc( + id=self.get_storage_id(s), + size=s.size(), + # NB: We don't do the copy yet; copy happens when we start + # creating the new storages + data=s if self.copy_data else None, + ) + if trace and r.id not in self.traced_storages: + trace_structured( + "describe_storage", + metadata_fn=lambda: r.as_json(self.id), + ) + self.traced_storages.add(r.id) + return r + + def describe_tensor( + self, t: torch.Tensor, *, recurse: bool = True, trace: bool = False + ) -> MetaTensorDesc: + is_leaf = safe_is_leaf(t) + is_view = t._is_view() + is_sparse = t.is_sparse + layout = t.layout + is_nested = t.is_nested + is_traceable_wrapper_subclass_v = is_traceable_wrapper_subclass(t) + is_functorch_wrapped = is_functorch_wrapped_tensor(t) + is_mkldnn = t.is_mkldnn + is_batchedtensor_v = is_batchedtensor(t) + is_legacy_batchedtensor_v = is_legacy_batchedtensor(t) + is_gradtrackingtensor_v = is_gradtrackingtensor(t) + is_functional = torch._is_functional_tensor(t) + + storage = None + # NB: For compatibility, I default this to zero, as sometimes people + # still have stuffed zero into storage offset even though the tensor + # doesn't meaningfully have an offset + storage_offset = 0 + if not ( + is_sparse + or is_sparse_compressed_layout(layout) + or (is_nested and not is_traceable_wrapper_subclass_v) + or is_mkldnn + # TODO: TBH, functorch wrapped tensors probably should have + # storage associated with them + or is_functorch_wrapped + or is_legacy_batchedtensor_v + ): + # NB: We actually don't use storage to do views, but might as well + # put it in for accuracy + storage = self.describe_storage(t.untyped_storage(), trace=trace) + storage_offset = t.storage_offset() # type: ignore[assignment] + + stride = None + if not ( + is_sparse + or is_sparse_compressed_layout(layout) + or (is_nested and not is_traceable_wrapper_subclass_v) + ): + # stride/storage_offset are called from is_functorch_wrapped, + # view_from_base, empty_create_subclass, + # sym_sizes_strides_storage_offset (empty_create) + stride = t.stride() + + # NB: this technically should refer to functorch unwrapped tensor, but + # I am (perhaps abusively) using it to store both the functorch and + # non-functorch functional tensor + unwrapped = None + autograd_meta_from = None + current_level = None + if is_batchedtensor_v or is_gradtrackingtensor_v: + unwrapped = self.describe_tensor(get_unwrapped(t), trace=trace) + # xla and lazy tensors present as functional tensors, but we want them + # to be handled specially + elif is_functional and t.device.type not in ("xla", "lazy"): + if t._is_view(): + raise RuntimeError( + "Cannot safely fakify a view because this process drops the view information right now." + ) + if not is_functorch_wrapped: + torch._sync(t) + unwrapped = self.describe_tensor( + torch._from_functional_tensor(t), trace=trace + ) + autograd_meta_from = t + else: + reapply_views = torch._C._functionalization_reapply_views_tls() + # NB: has side effects! + unwrapped = self.describe_tensor( + _unwrap_functional_tensor(t, reapply_views), trace=trace + ) + # TODO: It's pretty suspicious that functional tensors don't have + # valid level and thus we just grab whatever the current level + # is + current_level = torch._C._functorch.current_level() + + maybe_functorch_stack = None + if is_functorch_wrapped: + with ( + torch._functorch.pyfunctorch.temporarily_clear_interpreter_stack() + ) as maybe_functorch_stack: + pass + + attrs = None + ctx = None + type_v = None + if is_traceable_wrapper_subclass_v: + assert hasattr(t, "__tensor_flatten__") + raw_attrs, ctx = t.__tensor_flatten__() + attrs = { + attr: self.describe_tensor(getattr(t, attr), trace=trace) + for attr in raw_attrs + } + type_v = type(t) + + from torch.nested._internal.nested_tensor import _tensor_symint_registry + + view_func = ViewFunc.from_tensor(t) + + # TODO: Is it important to enable torch.inference_mode before querying + # these values? + is_inference_mode_disabled = getattr(tls, "disable_inference_mode", False) + r: MetaTensorDesc = MetaTensorDesc( + id=self.get_tensor_id(t), + storage=storage, + is_inference=False if is_inference_mode_disabled else t.is_inference(), + is_leaf=is_leaf, + requires_grad=t.requires_grad, + # NB: ndim should be OK too but there is a disaster at + # python test/dynamo/test_subclasses.py -k test_user_overridden_property_unsupported + # Actually, this means that we have a little bit of a problem + # here, which is that there is some sensitivity to how exactly an + # access is done if you have a __torch_function__ subclass. Maybe + # should disable torch function before doing accesses? + ndim=t.dim(), + dtype=t.dtype, + is_sparse=is_sparse, + is_mkldnn=is_mkldnn, + is_functorch_wrapped=is_functorch_wrapped, + is_batchedtensor=is_batchedtensor_v, + is_legacy_batchedtensor=is_legacy_batchedtensor_v, + is_gradtrackingtensor=is_gradtrackingtensor_v, + is_view=is_view, + is_conj=t.is_conj(), + is_neg=t.is_neg(), + is_parameter=isinstance(t, torch.nn.Parameter), + is_traceable_wrapper_subclass=is_traceable_wrapper_subclass_v, + is_nested=is_nested, + nested_int=( + _tensor_symint_registry[t].node.nested_int() + if t in _tensor_symint_registry + else None + ), + is_functional=is_functional, + layout=layout, + device=t.device, + size=t.size(), + stride=stride, + # pyrefly: ignore [bad-argument-type] + storage_offset=storage_offset, + dynamo_dynamic_indices=list(getattr(t, "_dynamo_dynamic_indices", set())), + dynamo_hint_overrides=getattr(t, "_dynamo_hint_overrides", {}), + sparse_dim=( + t.sparse_dim() if t.is_sparse or is_sparse_compressed(t) else None + ), + dense_dim=t.dense_dim() if t.is_sparse or is_sparse_compressed(t) else None, + is_coalesced=t.is_coalesced() if t.is_sparse else None, + # TODO: I actually think recursing here is correct, but we have at + # least an infinite cycle from base -> values -> base + # https://github.com/pytorch/pytorch/issues/122089 + crow_indices=( + self.describe_tensor(t.crow_indices(), recurse=False, trace=trace) + if recurse and t.layout in {torch.sparse_csr, torch.sparse_bsr} + else None + ), + col_indices=( + self.describe_tensor(t.col_indices(), recurse=False, trace=trace) + if recurse and t.layout in {torch.sparse_csr, torch.sparse_bsr} + else None + ), + ccol_indices=( + self.describe_tensor(t.ccol_indices(), recurse=False, trace=trace) + if recurse and t.layout in {torch.sparse_csc, torch.sparse_bsc} + else None + ), + row_indices=( + self.describe_tensor(t.row_indices(), recurse=False, trace=trace) + if recurse and t.layout in {torch.sparse_csc, torch.sparse_bsc} + else None + ), + values=( + self.describe_tensor(t.values(), recurse=False, trace=trace) + if recurse and is_sparse_compressed(t) + else None + ), + grad=( + self.describe_tensor(grad, trace=trace) + if (grad := safe_grad(t)) is not None + else None + ), + creation_meta=( + torch._C._autograd._get_creation_meta(t) if t._is_view() else None + ), + unwrapped=unwrapped, + level=( + maybe_get_level(t) + if is_batchedtensor_v or is_gradtrackingtensor_v + else None + ), + bdim=maybe_get_bdim(t) if is_batchedtensor_v else None, + base=( + self.describe_tensor(t._base, trace=trace) + if recurse and t._is_view() and t._base is not None + else None + ), + fake_mode=torch._subclasses.fake_tensor.maybe_get_fake_mode(t), + view_func=view_func, + attrs=attrs, + ctx=ctx, + type=type_v, + # NB: even if functorch is enabled, don't actually save the + # interpreter stack here unless we are actually functorch wrapped; + # it's irrelevant for non-functorch stuff + functorch_stack=maybe_functorch_stack, + autograd_meta_from=autograd_meta_from, + current_level=current_level, + data=t if self.copy_data else None, + ) + if trace and r.id not in self.traced_tensors: + trace_structured( + "describe_tensor", + metadata_fn=lambda: r.as_json(self.id), + ) + self.traced_tensors.add(r.id) + return r + + +@dataclass(frozen=True) +class MetaStorageDesc: + id: MetaStorageId + size: int + # NB: this is only populated with copy_data True, it is not directly + # serializable in JSON, you want to do something special here anyway + data: Optional[torch.UntypedStorage] + + def as_json(self, describer_id: _DescriberId) -> dict[str, object]: + return { + "id": self.id, + "describer_id": describer_id, + "size": self.size if isinstance(self.size, int) else repr(self.size), + } + + +@dataclass(frozen=True) +class ViewFunc(Generic[_TensorT]): + @abstractmethod + def apply( + self, + t: _TensorT, + new_base: _TensorT, + symint_visitor_fn: Optional[Callable[[int], int]] = None, + tensor_visitor_fn: Optional[Callable[[torch.Tensor], _TensorT]] = None, + ) -> _TensorT: ... + + @staticmethod + def from_tensor(t: torch.Tensor) -> ViewFunc: + if _is_fake_tensor(t): + return _FakeTensorViewFunc() + else: + return _CustomViewFunc(t._view_func_unsafe) + + +@dataclass(frozen=True) +class _FakeTensorViewFunc(ViewFunc["FakeTensor"]): + @override + def apply( + self, + t: torch.Tensor, + new_base: torch.Tensor, + symint_visitor_fn: Optional[Callable[[int], int]] = None, + tensor_visitor_fn: Optional[Callable[[torch.Tensor], FakeTensor]] = None, + ) -> FakeTensor: + return torch._subclasses.fake_tensor.FakeTensor._view_func_unsafe( + # pyrefly: ignore [bad-argument-type] + t, + new_base, + symint_visitor_fn, + tensor_visitor_fn, + ) + + +@dataclass(frozen=True) +class _CustomViewFunc(ViewFunc[_TensorT], Generic[_TensorT]): + func: Callable[ + [ + torch.Tensor, + Optional[Callable[[int], int]], + Optional[Callable[[torch.Tensor], _TensorT]], + ], + _TensorT, + ] + + @override + def apply( + self, + t: torch.Tensor, + new_base: torch.Tensor, + symint_visitor_fn: Optional[Callable[[int], int]] = None, + tensor_visitor_fn: Optional[Callable[[torch.Tensor], _TensorT]] = None, + ) -> _TensorT: + # ignore `t` + return self.func(new_base, symint_visitor_fn, tensor_visitor_fn) + + +# A callback where the device is either optional or required. +# All of these satisfy this protocol: +# def mk(arg: Callable[[], torch.Tensor], device: Union[torch.device, str]) +# def mk(arg: Callable[[], torch.Tensor], device: Union[torch.device, str] = "meta") +# def mk(arg: Callable[[], torch.Tensor], device: Optional[Union[torch.device, str]] = None) +class _MetaTensorCallback(Protocol, Generic[_TensorT_cov]): + def __call__( + self, arg: Callable[[], torch.Tensor], /, *, device: Union[torch.device, str] + ) -> _TensorT_cov: ... + + +class _MetaTensorCallbackKwargs(TypedDict, total=False): + device: Union[torch.device, str] + + +# A callback where the device may not be provided (is optional). +# All of these satisfy this protocol: +# def mk(arg: Callable[[], torch.Tensor], device: Union[torch.device, str] = "meta") +# def mk(arg: Callable[[], torch.Tensor], device: Optional[Union[torch.device, str]] = None) +class _MetaTensorCallbackOptDevice(Protocol, Generic[_TensorT_cov]): + def __call__( + self, + arg: Callable[[], torch.Tensor], + /, + **kwargs: Unpack[_MetaTensorCallbackKwargs], + ) -> _TensorT_cov: ... + + +@dataclass(frozen=True) +class MetaTensorDesc(Generic[_TensorT]): + id: MetaTensorId + ndim: int + dtype: torch.dtype + device: torch.device + + # NB: Sometimes, size, stride and storage_offset contain SymInt, in which + # case this is NOT serializable. That only happens when you're + # re-fakeifying a fake tensor with an existing ShapeEnv... maybe we + # can get rid of this use case entirely. Notably, even if we are + # fakeifying a real tensor into a fake tensor with symbolic shapes, the + # size here is NOT dynamic + # NB: These also contain SymInt because wrap_meta_outputs_with_default_device_logic + # goes through this codepath. But it really should not LOL. + # NB: size could potentially be None as you can override it and make it + # throw an error, but we don't currently have any subclasses that do this + # except C++ nested tensor but we're going to have nested int to make this + # defined on NJT + size: tuple[int, ...] + dynamo_dynamic_indices: list[int] + dynamo_hint_overrides: dict[int, int] + + layout: torch.layout = torch.strided + is_inference: bool = False + is_leaf: bool = False + requires_grad: bool = False + is_sparse: bool = False + is_mkldnn: bool = False + is_functorch_wrapped: bool = False + is_batchedtensor: bool = False + is_legacy_batchedtensor: bool = False + is_gradtrackingtensor: bool = False + is_view: bool = False + is_nested: bool = False + # We eagerly symbolicize the associated nested int for e.g. offsets / lengths + # metadata if that offsets is already associated with a nested int. + # See test_construct_from_jagged_with_input_offsets_mixed_case. + nested_int: Optional[int] = None + is_traceable_wrapper_subclass: bool = False + is_functional: bool = False + is_conj: bool = False + is_neg: bool = False + is_parameter: bool = False + stride: Optional[tuple[int, ...]] = None + storage_offset: int = 0 + # NB: We have a choice whether or not to store the id or a direct pointer + # to the data structure. For ease of use, we store the data structure, + # but this means that when we serialize, we have to swizzle these pointers + # back into ids (so we have accurate aliasing relationships) + storage: Optional[MetaStorageDesc] = None + sparse_dim: Optional[int] = None # is_sparse, is_sparse_compressed + dense_dim: Optional[int] = None # is_sparse, is_sparse_compressed + is_coalesced: Optional[bool] = None # is_sparse + crow_indices: Optional[MetaTensorDesc] = None # is_sparse_compressed + col_indices: Optional[MetaTensorDesc] = None # is_sparse_compressed + ccol_indices: Optional[MetaTensorDesc] = None # is_sparse_compressed + row_indices: Optional[MetaTensorDesc] = None # is_sparse_compressed + values: Optional[MetaTensorDesc] = None # is_sparse_compressed + unwrapped: Optional[MetaTensorDesc] = None # is_functorch_wrapped + bdim: Optional[int] = None # is_functorch_wrapped + base: Optional[MetaTensorDesc] = None # is_view + attrs: Optional[dict[str, MetaTensorDesc]] = None # is_traceable_wrapper_subclass + creation_meta: Optional[CreationMeta] = None + grad: Optional[MetaTensorDesc] = None + + # Everything below is NOT serializable, need some more work + + _UNSERIALIZABLE: ClassVar[set[str]] = { + "ctx", + "type", + "fake_mode", + # view_func isn't serializable when it's a _CustomViewFunc + "view_func", + "level", + "current_level", + "functorch_stack", + "autograd_meta_from", + "data", + "nested_int", + } + + ctx: Optional[object] = None # is_traceable_wrapper_subclass + type: Optional[type] = None # is_traceable_wrapper_subclass + fake_mode: Optional[FakeTensorMode] = None + view_func: Optional[ViewFunc] = None + # level looks serializable, but actually it is meaningless without + # the functorch_stack below + level: Optional[int] = None # is_functorch_wrapped + current_level: Optional[int] = None + functorch_stack: Optional[list[CInterpreter]] = None + autograd_meta_from: Optional[torch.Tensor] = None + + # This is only populated on copy_data, and typically is not used at all, + # except for some of our meta-ification paths that don't properly use + # storage (pro-tip: you should use storage) + data: Optional[torch.Tensor] = None + + # Faithfully serializing functorch tensors will not be too difficult. + # We only need to consider grad/vmap interpreters, and their internal + # state is only bools (mostly what the grad enabled/disabled state + # should be in the lower layer). Beyond that, tensors just need to + # precisely indicate which particular interpreter they correspond + # to (we then replace level with a pointer to the interpreter stack.) + # However, this use of functorch is very "non-lexical" so it's not + # entirely clear how to make it all lexical again, so we haven't done + # it for now. + + # NB: This will reference numeric IDs, and it is assumed that you've + # already serialized everything this recursively references + def as_json(self, describer_id: _DescriberId) -> dict[str, object]: + def json(k: str, v: object) -> object: + # Some best-effort debugging serialization for unserializable + # fields (feel free to add other special cases as appropriate) + if k in ["data", "autograd_meta_from"]: + return None # never repr these + if k in MetaTensorDesc._UNSERIALIZABLE: + return repr(v) + if isinstance(v, (torch.device, torch.dtype, torch.layout)): + return repr(v) + if isinstance(v, torch.SymInt): + return repr(v) + if isinstance(v, (tuple, list)): + return [json(k, v1) for v1 in v] + if isinstance(v, (MetaStorageDesc, MetaTensorDesc)): + return v.id + if isinstance(v, CreationMeta): + return str(v) + if k == "attrs" and isinstance(v, dict): + return {k1: v1.id for k1, v1 in v.items()} + return v + + r = { + field.name: json(field.name, getattr(self, field.name)) + for field in dataclasses.fields(self) + if not ( + getattr(self, field.name) is field.default + or ( + field.name == "dynamo_dynamic_indices" + and not getattr(self, field.name) + ) + ) + } + r.update({"describer_id": describer_id}) + return r + + @property + def shape(self) -> tuple[int, ...]: + return self.size + + +# A more faithful reproduction would do a copy on the entire +# storage, but this needs to be done carefully because the +# underlying storage could have larger extent than is implied +# by size/stride. The real fix is to properly call +# meta_storage recursively here. +# +# These "safe" functions are intended to be used under no_dispatch() mode. +# The no_dispatch() here is intended to prevent ambient fake tensor mode from +# fakeifying the operation. But if we are given an honest to goodness +# FakeTensor as src, we MUST NOT run the copy/clone operation. A better way +# to do this would be to not use no_dispatch and instead just disable fake +# tensor mode only (allowing for subclass dispatch to occur) +def _safe_copy(dst: torch.Tensor, src: Optional[torch.Tensor]) -> None: + if type(src) is not torch.Tensor: + return + dst.copy_(src) + + +def _safe_clone(src: torch.Tensor) -> Optional[torch.Tensor]: + if type(src) is not torch.Tensor: + return None + return src.clone() + + +# This is a class for converting multiple tensors into meta tensors which +# share the same view/storage structure. The operation model is you allocate +# one of these, and then call it repeatedly on all the tensors you want to +# convert. It's important to use the same object for tensors you want to +# share storage because this is how we correlate shared storages to the same +# meta storages. This class will hold weak references to cached tenosrs +# and tensor storages. +class MetaConverter(Generic[_TensorT]): + def __init__(self, *, copy_data: bool = False) -> None: + # Maps MetaStorageId to UntypedStorage + self.storage_memo: weakref.WeakValueDictionary[ + MetaStorageId, torch.UntypedStorage + ] = weakref.WeakValueDictionary() + # Maps MetaTensorId to torch.Tensor (typically a meta tensor or + # FakeTensor) + self.tensor_memo: weakref.WeakValueDictionary[MetaTensorId, _TensorT] = ( + weakref.WeakValueDictionary() + ) + self.hit = 0 + self.miss = 0 + self.del_hook = None + self.arg_cnt = 0 + # Ensures real_storage/real_tensor are populated on the resulting + # metaified storage/tensor. The naming of this attribute is load + # bearing: FakeTensor relies on real tensor being set to exactly this + # value + self.copy_data = copy_data + self.describer = MetaTensorDescriber(copy_data=copy_data) + + def successful(self) -> bool: + return self.hit > 0 and self.miss == 0 + + def get_tensor_memo(self, t: MetaTensorDesc) -> Optional[torch.Tensor]: + return self.tensor_memo.get(t.id, None) + + def _checked_get_tensor_memo(self, t: MetaTensorDesc) -> _TensorT: + r = self.tensor_memo.get(t.id, None) + assert r is not None + return r + + def set_tensor_memo(self, t: MetaTensorDesc, v: _TensorT) -> None: + self.tensor_memo[t.id] = v + + def get_storage_memo(self, s: MetaStorageDesc) -> Optional[torch.UntypedStorage]: + return self.storage_memo.get(s.id, None) + + def set_storage_memo(self, s: MetaStorageDesc, v: torch.UntypedStorage) -> None: + self.storage_memo[s.id] = v + + def meta_storage( + self, + s: MetaStorageDesc, + callback: Callable[[Callable[[], torch.Tensor]], _TensorT], + ) -> torch.UntypedStorage: + # If we are fakeifying a tensor that has a secretly-zero-sized storage, + # Need to make sure to resize the meta storage too. + if (memo := self.get_storage_memo(s)) is None: + r_s = callback( + lambda: torch.empty(s.size, dtype=torch.uint8, device="meta"), + ).untyped_storage() + if self.copy_data: + # NB: no_dispatch is needed because internally storage copy is + # implemented as Tensor operations + with torch.no_grad(), no_dispatch(): + assert s.data is not None + _set_real_storage(r_s, s.data.clone()) + self.set_storage_memo(s, r_s) + return r_s + else: + return memo + + @classmethod + def _checked_cast_tensor_t(cls, t: torch.Tensor) -> _TensorT: + # TODO: how to check _TensorT? + return typing.cast(_TensorT, t) + + @classmethod + def _identity_callable( + cls, + t: Callable[[], torch.Tensor], + device: Optional[Union[torch.device, str]] = None, + ) -> _TensorT: + return cls._checked_cast_tensor_t(t()) + + @classmethod + def _backward_error(cls, t: _TensorT) -> _TensorT: + errfn = torch._C._functions.DelayedError( + "Internal error: Tried to backward() through example input", + 1, + ) + err = errfn(t) + return typing.cast(_TensorT, err) + + # This function assumes that it's possible to do the conversion + # NB: name here is used in a conventional way by Dynamo; it corresponds + # precisely to the Source.name of the tensor we're fakeifying and + # corresponds to a valid Python expression. When we construct sub-names + # as part of this process, we will maintain this invariant! (Even though + # other users of this may not need it this property to be upheld.) + def meta_tensor( + self, + t: MetaTensorDesc, + shape_env: Optional[ShapeEnv], + callback_: _MetaTensorCallback[_TensorT], + source: Optional[Source], + symbolic_context: Optional[SymbolicContext], + ) -> _TensorT: + callback: _MetaTensorCallbackOptDevice = functools.partial( + callback_, device=t.device + ) + if source is None: + from torch._dynamo.source import ConstantSource + + # TODO: make a dedicated UnknownSource for this? + source = ConstantSource( + f"__meta_utils_unknown_tensor{len(self.tensor_memo)}" + ) + + msg = ( + " This indicates you set no_dispatch() before calling into this" + " function. This is an error: we may be creating fake tensors and" + " will perform operations on them which need fake tensor mode to" + " be active. You will segfault if you are in a no_dispatch() block." + ) + assert not torch._C._dispatch_tls_local_exclude_set().has( + torch._C.DispatchKey.Python + ), msg + self.arg_cnt += 1 + + # When we make as_strided calls, we end up generating a guard + # that the new as_strided tensor is in bounds for the old storage + # for the base (since as_strided calls can "bust" out of their + # bounding box.) This guard is unnecessary: if a user is able + # to provide us a tensor with the view base setup this way, we + # don't need to produce a guard, because the fact that they + # were able to produce the view base means its in bounds. + # + # Now, ordinarily, this guard would be harmless. However, the + # generated guard refers to variables bound on the base variable. + # At the moment, Dynamo doesn't actually guard on x._base, because + # according to Voz this results in a lot of spurious invalidations, + # and also if the user doesn't directly make use of _base, its + # pointless anyway (because programs should be parametric over + # whether or not the input tensor is a view or not--unless you're + # mutating the input, but that's a whole 'nother ballgame). So + # for expediency, we suppress these guards so we don't have to + # deal with this (yet, anyway.) + # + # NB: An old version of this code suppressed guards for ALL operations + # happening during meta conversion, not just as_strided calls. + # This is too aggressive: we do duck sizing and 0/1 simplification + # as we allocate variables, and we do need to register guards for + # these cases. + maybe_suppress: Callable[[], Any] = contextlib.nullcontext + if shape_env is not None: + maybe_suppress = shape_env.suppress_guards + + def sym_sizes_strides_storage_offset( + t: MetaTensorDesc, + src: torch._guards.Source, + symbolic_context: Optional[ + torch.fx.experimental.symbolic_shapes.SymbolicContext + ] = symbolic_context, + ) -> tuple[tuple[int, ...], tuple[int, ...], int]: + assert t.stride is not None + if shape_env is not None: + fake_mode = t.fake_mode + if fake_mode is not None and fake_mode.shape_env is shape_env: + # Don't reallocate the sizes; the shape envs are the same, + # so reuse the old sizes/strides/etc + return (t.size, t.stride, t.storage_offset) + else: + # TODO: deduplicate this + t_size = tuple( + shape_env._maybe_specialize_sym_int_with_hint(sz) + for sz in t.size + ) + t_stride = tuple( + shape_env._maybe_specialize_sym_int_with_hint(sd) + for sd in t.stride + ) + t_storage_offset = shape_env._maybe_specialize_sym_int_with_hint( + t.storage_offset + ) + return shape_env._create_symbolic_sizes_strides_storage_offset( + t_size, + t_stride, + t_storage_offset, + [d in t.dynamo_dynamic_indices for d in range(t.ndim)], + src, + symbolic_context=symbolic_context, + hint_overrides=t.dynamo_hint_overrides, + ) + else: + return (t.size, t.stride, t.storage_offset) + + def empty_create( + inner_t: MetaTensorDesc, + inner_src: torch._guards.Source, + symbolic_context: Optional[ + torch.fx.experimental.symbolic_shapes.SymbolicContext + ] = symbolic_context, + ) -> torch.Tensor: + ( + inner_sizes, + inner_strides, + _inner_storage_offset, + ) = sym_sizes_strides_storage_offset(inner_t, inner_src, symbolic_context) + return torch.empty_strided( + inner_sizes, + inner_strides, + dtype=inner_t.dtype, + device="meta", + ) + + # Creates a subclass instance with empty inner tensors according to the specified + # symbolic context. + def empty_create_subclass( + t: MetaTensorDesc, + outer_size: tuple[int, ...], + outer_stride: tuple[int, ...], + symbolic_context: Optional[ + torch.fx.experimental.symbolic_shapes.SymbolicContext + ] = symbolic_context, + source: Optional[torch._guards.Source] = source, + ) -> _TensorT: + from torch._dynamo.source import AttrSource + from torch.fx.experimental.symbolic_shapes import SubclassSymbolicContext + + assert t.attrs is not None + assert t.type is not None + # NB: t.ctx could be None if the subclass in question has no + # meaningful context + + # Note: transform_subclass will use __tensor_unflatten__ to generate + # a fresh subclass wrapper with outer sizes / strides according to the + # outer symbolic context (passed in to this function). Inner size / stride + # / storage offset symbols are allocated according to the appropriate inner + # symbolic contexts, after which the checks in transform_subclass() will + # relate them to the outer metadata as possible. + # + # Morally, the code here is same as transform_subclass, but we've + # written it from scratch to read EmptyCreateSubclass + outer_size = outer_size if outer_size is not None else t.size + # pyrefly: ignore [bad-assignment] + outer_stride = outer_stride if outer_stride is not None else t.stride + + assert symbolic_context is None or isinstance( + symbolic_context, SubclassSymbolicContext + ) + + def _empty_create_subclass( + t: MetaTensorDesc, + outer_size: Optional[tuple[int, ...]], + outer_stride: Optional[tuple[int, ...]], + symbolic_context: Optional[ + torch.fx.experimental.symbolic_shapes.SymbolicContext + ], + callback: _MetaTensorCallbackOptDevice[_TensorT], + source: torch._guards.Source, + ) -> _TensorT: + # We are hitting plain meta_desc tensor so actually + # create a tensor here. + if t.attrs is None: + return self.meta_tensor( + t, + shape_env, + callback, + source, + symbolic_context, + ) + + inner_tensors = {} + for attr, meta_tensor_desc in t.attrs.items(): + current_context = None + if symbolic_context is not None: + assert isinstance(symbolic_context, SubclassSymbolicContext) + if ( + current_context_ := symbolic_context.inner_contexts[attr] + ) is not None: + current_context = _checked_cast( + torch.fx.experimental.symbolic_shapes.SymbolicContext, + current_context_, + ) + + current_source = AttrSource(source, attr) + inner_callback = functools.partial( + callback, device=meta_tensor_desc.device + ) + new_empty_tensor = _empty_create_subclass( + meta_tensor_desc, + meta_tensor_desc.size, + meta_tensor_desc.stride, + current_context, + inner_callback, + current_source, + ) + inner_tensors[attr] = new_empty_tensor + + assert t.type is not None + return t.type.__tensor_unflatten__( # type: ignore[attr-defined] + inner_tensors, t.ctx, outer_size, outer_stride + ) + + assert source is not None + sub = _empty_create_subclass( + t, outer_size, outer_stride, symbolic_context, callback, source + ) + + # NB: Purposefully guard here to simplify the inner / outer symbols. + # Using sym_eq() for symbolic comparison can result in an expression that's too + # difficult to guard on, so we use == here. + assert sub.shape == outer_size, ( + f"Expected return value from {t.type}__tensor_unflatten__() to have " + f"shape equal to {outer_size}, but got: {sub.shape}" + ) + assert sub.stride() == outer_stride, ( + f"Expected return value from {t.type}__tensor_unflatten__() to have " + f"stride equal to {outer_stride}, but got: {sub.stride()}" + ) + + return sub + + # Returns an all-dynamic symbolic context used for metafying the given tensor with + # fully dynamic dims. This is useful when fake-ifying intermediate tensors in + # closed-over ViewFunc state, as we don't have symbolic contexts for them, but we + # don't want to over-specialize during view replay. + def all_dynamic_symbolic_context( + t: MetaTensorDesc, + source: torch._guards.Source, + shape_env: Optional[torch.fx.experimental.symbolic_shapes.ShapeEnv], + callback: _MetaTensorCallback[_TensorT], + ) -> torch.fx.experimental.symbolic_shapes.SymbolicContext: + from torch._dynamo.source import AttrSource + from torch.fx.experimental.symbolic_shapes import ( + DimDynamic, + StatelessSymbolicContext, + SubclassSymbolicContext, + ) + + view_base_context: Optional[ + torch.fx.experimental.symbolic_shapes.SymbolicContext + ] = None + if t.is_view: + assert t.base is not None + view_base_context = all_dynamic_symbolic_context( + t.base, AttrSource(source, "_base"), shape_env, callback + ) + + t_symbolic_context: torch.fx.experimental.symbolic_shapes.SymbolicContext + t_dynamic_sizes = [DimDynamic.DYNAMIC] * t.ndim + if t.is_traceable_wrapper_subclass: + assert t.attrs is not None + inner_contexts: dict[ + str, torch.fx.experimental.symbolic_shapes.SymbolicContext + ] = {} + for attr, inner in t.attrs.items(): + assert isinstance(attr, str) + inner_contexts[attr] = all_dynamic_symbolic_context( + inner, AttrSource(source, attr), shape_env, callback + ) + t_symbolic_context = SubclassSymbolicContext( + dynamic_sizes=t_dynamic_sizes, + constraint_sizes=[None] * t.ndim, + inner_contexts=inner_contexts, # type: ignore[arg-type] + tensor_source=source, + view_base_context=view_base_context, + ) + else: + t_symbolic_context = StatelessSymbolicContext( + dynamic_sizes=t_dynamic_sizes, + constraint_sizes=[None] * t.ndim, + view_base_context=view_base_context, + ) + + return t_symbolic_context + + # Returns a fake-ified version of an input view tensor t, given an already fake-ified + # base. At a high level, we want two things: + # 1. fake_t should have the same view relationship to the given fake base as the + # input t has to its _base. + # 2. fake_t should have symbolic sizes / strides / storage offset according to the + # appropriate symbolic context (i.e. from the automatic dynamic algorithm). + # + # We currently take different strategies across view types: + # * For dense -> dense views, accomplish both (1) and (2) simultaneously via an + # as_strided() call on the fake-ified base, passing symbolic metadata. + # * For views involving subclasses, perform view replay using view funcs to + # achieve (1). It's necessary for (2) to swap out any closed-over state in + # the view funcs with symbolicized SymInts and fake-ified tensors. Doing this + # avoids specialization (and thus over-eager simplification of symbols) that + # could occur during view replay on the fake-ified base. + # + # Examples: + # * t.unsqueeze(-1) with dense t is a dense -> dense view. It can be modeled + # with an as_strided() call on the fake base passing symbolic metadata. + # * sub.select(dim=0, index=3) is a subclass -> subclass view. The index arg + # is made symbolic to avoid invalid specialization and view replay is then + # done to reconstruct the view. + # * _nested_from_jagged(values, offsets) is a dense -> subclass view + # that returns a subclass instance from a dense values tensor. The offsets + # tensor is closed over in the view func, as it can be considered view metadata. + # First, the offsets tensor is fake-ified according to the inner symbolic + # context and with the correct relationship to the outer size / stride metadata. + # Then view replay is done, swapping in the fake offsets so the view replay output + # is fully fake with no invalid specialization. + def view_from_base( + base: _TensorT, + t: MetaTensorDesc, + shape_env: Optional[ + torch.fx.experimental.symbolic_shapes.ShapeEnv + ] = shape_env, + ) -> _TensorT: + with enable_python_dispatcher(): + # fake-ify t's metadata according to the outer symbolic context + (sizes, strides, storage_offset) = sym_sizes_strides_storage_offset( + t, source + ) + if ( + not t.is_traceable_wrapper_subclass + and not is_traceable_wrapper_subclass(base) + ): + # Dense -> Dense view case uses as_strided() to construct view relationship. + # TODO: Change this logic to use view replay for consistency? + # It's likely there is no view func available. + with maybe_suppress(): + return self._checked_cast_tensor_t( + base.as_strided(sizes, strides, storage_offset) + ) + + from torch._dynamo.source import EphemeralSource + from torch.fx.experimental.symbolic_shapes import ( + StatelessSymbolicContext, + sym_eq, + ) + + def symint_visitor_fn(s: int) -> int: + nonlocal symbolic_context + from torch.fx.experimental.symbolic_shapes import DimDynamic + + all_static_sizes = ( + symbolic_context is not None + and isinstance(symbolic_context, StatelessSymbolicContext) + and all( + x is DimDynamic.STATIC + for x in symbolic_context.dynamic_sizes + ) + ) + # Can't just rely on shape env being None - dynamo always initializes it + if all_static_sizes or shape_env is None: + return s + + # NB: The symbol here is expected to be simplified out because we a priori + # allocate inner and outer symbols according to the appropriate symbolic + # contexts and prefer those over this symbol during symbol simplification + # (via usage of EphemeralSource below). This -shouldn't- happen, but if + # this symbol somehow leaks out beyond the view tensor's shape metadata, our + # assumption of it being simplified out will fail and it may be guarded on, + # which will hard error. + sym_source = EphemeralSource("symint_visitor_fn") + + symbol = shape_env.create_symbol(s, sym_source, positive=None) + return shape_env.create_symintnode( + symbol, hint=s, source=sym_source + ) + + real_to_fake_mapping = {} + if t.is_traceable_wrapper_subclass: + assert t.attrs is not None + # NB: t.ctx could be None if the subclass in question has no + # meaningful context + assert t.type is not None + + # Fake-ify t naively here; this is only done so we can get fake-ified inner + # tensors with the correct relationships to the outer sizes / strides for use + # in view replay. It's done beforehand here because it's not easy to do when + # visiting tensors one-by-one during view replay. + # + # Example: + # Consider a Dense -> NJT view. NJT has (values, offsets) components and we + # want a view of values with the offsets closed over. As the offsets component + # is needed to describe the output view, it's important that it's fakeified + # correctly. + fake_t: _TensorT = empty_create_subclass( + t, outer_size=sizes, outer_stride=strides + ) + attrs, _ = fake_t.__tensor_flatten__() # type: ignore[attr-defined] + for attr in attrs: + real_to_fake_mapping[t.attrs[attr].id] = getattr(fake_t, attr) + + def tensor_visitor_fn( + visited_t: torch.Tensor, + # These arguments are never passed, we just use them to close + # over these relevant values + shape_env: Optional[ + torch.fx.experimental.symbolic_shapes.ShapeEnv + ] = shape_env, + callback: _MetaTensorCallbackOptDevice[_TensorT] = callback, + ) -> torch.Tensor: + # It's possible to close over an undefined tensor (e.g. NJT's lengths). + if visited_t is None: + # pyrefly: ignore [bad-return] + return None + + # NB: visited_t being a Tensor here is very naughty! Should + # have already been described + + # Fake inner tensors of view subclasses will come from the mapping built above. + visited_id = self.describer.get_tensor_id(visited_t) + fake_visited_t = real_to_fake_mapping.get(visited_id) + if fake_visited_t is not None: + return fake_visited_t + + visited_desc = self.describer.describe_tensor(visited_t) + + # For other closed-over tensor state, fake-ify it as all dynamic with an + # ephemeral source. This avoids invalid specialization during view replay. + # If we find that in practice the usage of ephemeral sources isn't enough + # to guarantee that we don't have guards on these symbols, we may need to + # explicitly suppress guards (as is done for _base in the dense -> dense + # view case). + temp_source = EphemeralSource("tensor_visitor_fn") + return self.meta_tensor( + visited_desc, + shape_env, + callback, + temp_source, + all_dynamic_symbolic_context( + visited_desc, temp_source, shape_env, callback + ), + ) + + # Replay the view, swapping out any non-symbolic SymInts or real tensors + # for symbolic SymInts or fake tensors. + assert t.view_func is not None + # NB: we do NOT suppress guards here, we need to remove ephemeral + # sources + fake_t = t.view_func.apply( + t, base, symint_visitor_fn, tensor_visitor_fn + ) + + # Ensure the output has symbolic shapes according to the outer symbolic context. + # These checks should simplify out any symbols created for closed-over view func + # SymInts. + torch._check(sym_eq(fake_t.size(), sizes)) + torch._check(sym_eq(fake_t.stride(), strides)) + torch._check(sym_eq(fake_t.storage_offset(), storage_offset)) + return fake_t + + if self.get_tensor_memo(t) is None: + GRAD_TENSOR_SENTINEL_VALUE = -2 + + with torch.inference_mode(t.is_inference): + if t.is_sparse: + is_leaf = t.is_leaf + + # The lambda function below is similar to + # `t.to(device='meta')` except the latter + # preserves nnz value + r = callback( + lambda: torch.ops.aten._sparse_coo_tensor_with_dims( + t.sparse_dim, + t.dense_dim, + t.size, + dtype=t.dtype, + layout=torch.sparse_coo, + device="meta", + ) + ) + if self.copy_data: + # Pray that sparse clone doesn't lose information + assert t.data is not None + with torch.no_grad(), no_dispatch(): + assert _is_fake_tensor(r) + r.real_tensor = _safe_clone(t.data) + assert safe_is_leaf(r), "the callback you passed in doesn't detach" + # Note [is_coalesced is dispatched] + # Strangely enough, is_coalesced() is a dispatched operator, + # which means that it will get caught by fake tensor mode. + # Ordinarily this would error, but there's some logic in + # fake tensor ensure this doesn't happen. + r._coalesced_(bool(t.is_coalesced)) + if t.requires_grad: + r.requires_grad = True + if t.requires_grad and not is_leaf: + # This should probably use DelayedError, + # but clone is fine for now for sparse tensors. + # (DelayedError does not work for sparse because it causes + # the Fake sparse tensor to "lose" its fakeness) + r = self._checked_cast_tensor_t(r.clone()) + with torch.enable_grad(): + r._coalesced_(bool(t.is_coalesced)) + elif is_sparse_compressed_layout(t.layout): + is_leaf = t.is_leaf + + if t.layout in {torch.sparse_bsr, torch.sparse_bsc}: + assert t.sparse_dim is not None + assert t.dense_dim is not None + assert t.values is not None + batch_dim = t.ndim - t.sparse_dim - t.dense_dim + blocksize = t.values.shape[batch_dim + 1 : batch_dim + 3] + else: + blocksize = () + if t.layout in {torch.sparse_csr, torch.sparse_bsr}: + assert t.crow_indices is not None + index_dtype = t.crow_indices.dtype + else: + assert t.ccol_indices is not None + index_dtype = t.ccol_indices.dtype + + r = callback( + lambda: torch.ops.aten._sparse_compressed_tensor_with_dims( + 0, + t.dense_dim, + t.shape, + blocksize, + index_dtype, + layout=t.layout, + dtype=t.dtype, + device="meta", + ) + ) + if self.copy_data: + # Pray sparse clone doesn't lose information + assert t.data is not None + with torch.no_grad(), no_dispatch(): + assert _is_fake_tensor(r) + r.real_tensor = _safe_clone(t.data) + assert safe_is_leaf(r), "the callback you passed in doesn't detach" + if t.requires_grad: + r.requires_grad = True + if t.requires_grad and not is_leaf: + # pyrefly: ignore [bad-argument-type] + r = self._backward_error(r) + elif t.is_nested and not t.is_traceable_wrapper_subclass: + # TODO: Handle this better in Dynamo? + # There are checks there now, but this can still be triggered by a dense + # tensor graph input that is a view of a strided NT. + from torch._dynamo.exc import unimplemented + + # NOTE this graph break will NOT be present in Dynamo's graph break registry + unimplemented( + gb_type="attempted to apply meta conversion to strided nested tensor", + context=str(t), + explanation="This is not supported.", + hints=[], + ) + elif t.is_mkldnn: + is_leaf = t.is_leaf + ( + sizes, + strides, + _storage_offset, + ) = sym_sizes_strides_storage_offset(t, source) + # TODO: This doesn't seem right, where's the MKLDNN'ness + # lol + r = callback( + lambda: torch.empty_strided( + sizes, strides, dtype=t.dtype, device="meta" + ) + ) + if self.copy_data: + with torch.no_grad(), no_dispatch(): + assert t.size is not None + assert t.stride is not None + assert _is_fake_tensor(r) + r.real_tensor = torch.empty_strided( + t.size, t.stride, dtype=t.dtype, device=t.device + ) + assert t.data is not None + _safe_copy(r.real_tensor, t.data) + assert safe_is_leaf(r), "the callback you passed in doesn't detach" + if t.requires_grad: + r.requires_grad = True + if t.requires_grad and not is_leaf: + # pyrefly: ignore [bad-argument-type] + r = self._backward_error(r) + elif t.is_functorch_wrapped: + if t.is_view: + from torch._dynamo.exc import unimplemented + + unimplemented( + gb_type="attempted to apply meta conversion to view functorch tensor", + context=str(t), + explanation="This is not supported.", + hints=[], + ) + + # Wraps a functorch tensor class (BatchedTensor, GradTrackingTensor) + # in a FakeTensor + def _to_fake_tensor(t: MetaTensorDesc) -> _TensorT: + # TODO: why aren't the recursive calls going to + # meta_tensor + r: _TensorT + if t.is_batchedtensor: + assert t.unwrapped is not None + assert t.level is not None + assert t.bdim is not None + ft = _to_fake_tensor(t.unwrapped) + lvl = t.level + bdim = t.bdim + # You cannot create functorch tensors without + # having the ambient funtorch interpreter stack + # available, as the level refers to things in the + # stack + with torch._functorch.pyfunctorch.temporarily_restore_interpreter_stack( + t.functorch_stack + ): + r = self._checked_cast_tensor_t( + _add_batch_dim(ft, bdim, lvl) + ) + elif t.is_gradtrackingtensor: + assert t.unwrapped is not None + assert t.level is not None + disable_functorch = torch._C._DisableFuncTorch + with disable_functorch(): + ft = _to_fake_tensor(t.unwrapped) + lvl = t.level + if lvl == GRAD_TENSOR_SENTINEL_VALUE: + r = ft + else: + with torch._functorch.pyfunctorch.temporarily_restore_interpreter_stack( + t.functorch_stack + ): + r = self._checked_cast_tensor_t( + torch._C._functorch._wrap_for_grad(ft, lvl), + ) + + is_leaf = t.is_leaf + if t.requires_grad and safe_is_leaf(r): + r.requires_grad = True + elif t.requires_grad and not is_leaf: + r = self._backward_error(r) + elif t.is_functional: + assert t.unwrapped is not None + assert t.current_level is not None + ft = self.meta_tensor( + t.unwrapped, + shape_env, + callback, + # NB: reuse these exactly, we treat the + # functional tensor as "invisible". + # TODO: Actually this all probably doesn't + # work, take a closer look. + source, + symbolic_context, + ) + r = self._checked_cast_tensor_t( + _wrap_functional_tensor(ft, t.current_level), + ) + # TODO: is_leaf/requires_grad? + else: + assert t.stride is not None + + sizes = t.size + strides = t.stride + r = callback( + lambda: torch.empty_strided( + sizes, + strides, + dtype=t.dtype, + device="meta", + ), + # device="meta", + ) + if self.copy_data: + with torch.no_grad(), no_dispatch(): + r.real_tensor = torch.empty_strided( # type: ignore[attr-defined] + t.size, + t.stride, + dtype=t.dtype, + device=t.device, + ) + assert t.data is not None + _safe_copy(r.real_tensor, t.data) # type: ignore[attr-defined] + # pyrefly: ignore [bad-return] + return r + + r = _to_fake_tensor(t) + + elif t.is_functional and t.device.type not in ["xla", "lazy"]: + assert t.unwrapped is not None + assert not t.is_functorch_wrapped # handled above + unwrapped = self.meta_tensor( + t.unwrapped, + shape_env, + callback, + source, + symbolic_context, + ) + r = self._checked_cast_tensor_t( + torch._to_functional_tensor(unwrapped) + ) + torch._mirror_autograd_meta_to(t.autograd_meta_from, r) # type: ignore[attr-defined] + + elif t.is_view: + # Construct views in two steps: recursively meta-fy their + # base, and then create view(s) off that. NB: doing it + # directly from storage is WRONG because this won't cause + # version counters to get shared. + + assert t.base is not None + + base_symbolic_context = None + if shape_env and symbolic_context is not None: + from torch.fx.experimental.symbolic_shapes import ( + StatelessSymbolicContext, + ) + + assert isinstance(symbolic_context, StatelessSymbolicContext) + # NB: This should generally be set when the input is a view, + # but the exception right now is for fake-ifying grads, which is + # a work in progress. + if symbolic_context.view_base_context is not None: + base_symbolic_context = symbolic_context.view_base_context + + base = self.meta_tensor( + t.base, + shape_env, + callback, + torch._dynamo.source.AttrSource(source, "_base"), + base_symbolic_context, + ) + + def is_c_of_r( + complex_dtype: torch.dtype, real_dtype: torch.dtype + ) -> bool: + return ( + utils.is_complex_dtype(complex_dtype) + and utils.corresponding_real_dtype(complex_dtype) + == real_dtype + ) + + # In some situations, MetaConverter may be called in a + # context where autograd is disabled. For the _is_view + # assert to pass, we have to setup the autograd view + # metadata anyway. Do this by reenabling the + # ADInplaceOrView key. This is kind of a hack. + old_exclude = torch._C._dispatch_tls_is_dispatch_key_excluded( + torch._C.DispatchKey.ADInplaceOrView + ) + torch._C._dispatch_tls_set_dispatch_key_excluded( + torch._C.DispatchKey.ADInplaceOrView, False + ) + try: + if base.dtype == t.dtype: + pass + elif is_c_of_r(base.dtype, t.dtype): + base = self._checked_cast_tensor_t(torch.view_as_real(base)) + elif is_c_of_r(t.dtype, base.dtype): + base = self._checked_cast_tensor_t( + torch.view_as_complex(base) + ) + else: + # This is not guaranteed to succeed. If it fails, it + # means there is another dtype-converting view function + # that hasn't been handled here + base = self._checked_cast_tensor_t(base.view(t.dtype)) + + # This is very tricky. Naively, you might expect this + # to hold: + # + # if t.requires_grad and not safe_is_leaf(t) + # assert t._base.requires_grad + # + # But it's not true! As you can see in the following + # program: + # + # x = torch.zeros(4) + # y = x.view(1, 4) + # y.requires_grad = True + # z = y.view(1, 1, 4) + # assert z._base is x + # + # So we may have to do *two* views out of the base to + # recreate this situation. + if t.is_leaf: + # Leaf views that track view metadata are created by + # creating a view inside a no_grad block + with torch.no_grad(): + r = view_from_base(base, t) + # As it's a leaf, we can directly assign requires_grad + r.requires_grad = t.requires_grad + else: + if t.base.requires_grad == t.requires_grad: + # Easy case, just run the view op + with torch.enable_grad(): + r = view_from_base(base, t) + + # NB: We don't actually faithfully replicate + # autograd connectivity, but that doesn't matter + # today. See following for more info: + # https://gist.github.com/soulitzer/e03f015b314c3f5fcf80888c69390913 + else: + # Obscure case. Create a leaf view and give it the + # correct requires_grad, then do the final view. + # NB: Can't have a non-leaf without requiring grad! + assert t.requires_grad + with torch.no_grad(), enable_python_dispatcher(): + mid = self._checked_cast_tensor_t( + base.view(base.shape) + ) + mid.requires_grad = t.requires_grad + with torch.enable_grad(): + r = view_from_base(mid, t) + # The CreationMeta influences whether or not inplace + # mutation is an error or not. So we need to make + # sure we properly propagate this as well. + assert t.creation_meta is not None + torch._C._autograd._set_creation_meta(r, t.creation_meta) + finally: + torch._C._dispatch_tls_set_dispatch_key_excluded( + torch._C.DispatchKey.ADInplaceOrView, old_exclude + ) + + r.fake_device = t.device # type: ignore[attr-defined] + + else: + is_leaf = t.is_leaf + + # Graph-Break for wrapped tensors + if ( + not (t.is_batchedtensor or t.is_gradtrackingtensor) + and t.is_functorch_wrapped + ) or t.is_legacy_batchedtensor: + # pyrefly: ignore [bad-return] + return NotImplemented + + ( + sizes, + strides, + storage_offset, + ) = sym_sizes_strides_storage_offset(t, source, symbolic_context) + + # If we have a subclass that desugars into dense tensors, + # perform our callback on each inner tensor. + if t.is_traceable_wrapper_subclass: + r = empty_create_subclass( + t, outer_size=sizes, outer_stride=strides + ) + else: + r = callback( + lambda: torch.empty_strided( + sizes, + strides, + dtype=t.dtype, + device="meta", + ) + ) + if self.copy_data: + with torch.no_grad(), no_dispatch(): + assert t.size is not None + assert t.stride is not None + assert _is_fake_tensor(r) + r.real_tensor = torch.empty_strided( + t.size, t.stride, dtype=t.dtype, device=t.device + ) + _safe_copy(r.real_tensor, t.data) + + assert safe_is_leaf(r), "the callback you passed in doesn't detach" + if t.requires_grad: + r.requires_grad = t.requires_grad + if not is_leaf: + # Fake up some autograd history. + # Note: we *used* to call .clone() here to mock up some autograd history. + # This is bad for subclasses. + # Consider the case where you have a wrapper subclass that is contiguous, + # but its inner tensor is noncontiguous(). + # .clone() (or other ops) will have the side effect of changing + # the metadata of the inner tensor. + # So instead, we now have a dedicated fn to set autograd history, + # without inadvertently changing other metadata. + # pyrefly: ignore [bad-argument-type] + r = self._backward_error(r) + + s = t.storage + assert s is not None + if s.id not in self.storage_memo and ( + r.is_nested + or ( + r.stride() == strides + and r.storage_offset() == storage_offset + ) + ): + # You're normal and happy, install the fresh storage into the memo + self.set_storage_memo(s, r.untyped_storage()) + if self.copy_data: + assert _is_fake_tensor(r) + assert r.real_tensor is not None + _set_real_storage( + r.untyped_storage(), r.real_tensor.untyped_storage() + ) + else: + # You're in crazy town; somehow you gave us a tensor + # that wasn't a view, but had nonzero storage offset, + # nontrivial strides (such that clone() couldn't + # preserve them), or already aliases with another + # tensor's storage. The most typical way to end + # up here is with set_. So use set_ to bludgeon this + # in. + r_s = self.meta_storage(s, callback=callback) + # NB: In principle, this should always work, but there + # is some subtle difference in the autograd metadata + # that means we will backprop the set_ call, even if + # r is declared as an input to grad. + # See https://github.com/pytorch/pytorch/issues/87956 + # for the reproducer. + # NB: The in_kernel_invocation_manager here is necessary + # for fake tensor. If we run the set_ call with fake + # tensor on, r will improperly report that it is NOT a + # meta tensor but a cpu tensor, and then the set_ call + # will fail due to device mismatch. no_dispatch() is + # not enough, because the fake tensor will still claim + # to be a CPU tensor and you'll end up in the CPU + # kernel. Arguably this is a hack; a cleaner way to + # solve this is to have a FakeStorage concept which + # would report it's CPU device--no problem now! But + # this is difficult to do because we don't have storage + # subclasses. Relevant test is + # DynamicShapesFunctionTests::test_add_dynamic_shapes in + # test/dynamo/test_dynamic_shapes.py + maybe_fake_mgr: AbstractContextManager[None] = ( + contextlib.nullcontext() + ) + from torch._subclasses.fake_tensor import ( + in_kernel_invocation_manager, + maybe_get_fake_mode, + ) + + mb_fake_mode = maybe_get_fake_mode(r) + if mb_fake_mode is not None: + maybe_fake_mgr = in_kernel_invocation_manager(mb_fake_mode) + with torch.no_grad(), maybe_suppress(): + with maybe_fake_mgr: + r.set_(r_s, storage_offset, sizes, strides) + if self.copy_data: + with torch.no_grad(), no_dispatch(): + assert _is_fake_tensor(r) + assert r.real_tensor is not None + assert t.stride is not None + r.real_tensor.set_( + _get_real_storage(r_s), + t.storage_offset, + t.size, + t.stride, + ) + + if t.grad is not None: + from torch._dynamo.source import AttrSource + + # TODO: Use a valid grad-specific symbolic context instead of recycling + # the one from t. This isn't correct if e.g. t._is_view() != t.grad._is_view(). + # pyrefly: ignore [unbound-name] + r.grad = self.meta_tensor( + t.grad, + shape_env, + callback, + AttrSource(source, "grad"), + symbolic_context, + ) + # pyrefly: ignore [unbound-name] + torch._C._set_conj(r, t.is_conj) + # pyrefly: ignore [unbound-name] + torch._C._set_neg(r, t.is_neg) + # This can be skipped if necessary for performance reasons + skip_leaf = ( + t.is_gradtrackingtensor and t.level == GRAD_TENSOR_SENTINEL_VALUE + ) + # pyrefly: ignore [unbound-name] + assert_metadata_eq(assert_eq, t, r, skip_symbolic=True, skip_leaf=skip_leaf) + # Thanks to storage resizing, it's possible to end up with a tensor + # that advertises a real size, but has a storage that actually has zero bytes. + # Need to reflect this in the generated FakeTensor. + from torch.fx.experimental.symbolic_shapes import guard_or_false + + if t.storage is not None and guard_or_false(t.storage.size == 0): + # pyrefly: ignore [unbound-name] + r.untyped_storage().resize_(0) + + if t.is_parameter: + # pyrefly: ignore [unbound-name] + r._is_param = True + + # See Note: [Creating symbolic nested int] + if t.nested_int is not None: + # pyrefly: ignore [unbound-name] + assert _is_fake_tensor(r) + # pyrefly: ignore [unbound-name] + r.nested_int_memo = r.fake_mode.create_symbolic_nested_int( + nt_tensor_id=t.nested_int + ) + + # pyrefly: ignore [bad-argument-type, unbound-name] + self.set_tensor_memo(t, r) + + return self._checked_get_tensor_memo(t) + + def __call__( + self, + t: torch.Tensor, + shape_env: Optional[ShapeEnv] = None, + *, + callback: Optional[_MetaTensorCallback[_TensorT]] = None, + source: Optional[Source] = None, + symbolic_context: Optional[SymbolicContext] = None, + # Controls whether or not we should dump the tensor metadata to structured logs + # when source is not None. Because we refakify after Dynamo is done, + # we don't want to dump info again from AOTAutograd, it is redundant. + trace: bool = True, + ) -> _TensorT: + callback_: _MetaTensorCallback[_TensorT] + if callback is None: + callback_ = self._identity_callable + else: + callback_ = callback + # TODO: zero tensors? We appear to have eliminated them by + # excluding complex for now + + # Filter out cases we don't support + # TODO: This can probably be simplified quite a bit + if isinstance(t, torch.Tensor): + if ( + # Lazy tensors are not supported. Note that XLA is + # implemented on top of lazy tensor, not excluded here; we + # have some special handling for it; this is for XLA Dynamo + # integration + t.device.type == "lazy" + or + # Quantization is not supported + t.is_quantized + or + # Views out of sparse tensors not currently supported (plain + # sparse is supported htough) + (t._is_view() and t._base is not None and t._base.is_sparse) + ): + self.miss += 1 + # pyrefly: ignore [bad-return] + return NotImplemented + else: + self.hit += 1 + elif torch.overrides.is_tensor_like(t): + self.miss += 1 + # pyrefly: ignore [bad-return] + return NotImplemented + else: + # non-Tensor types don't count as hit or miss + return t + + if source is None: + trace = False + + # Describe the tensor. NB: do NOT disable ambient modes, we may need + # to query them when figuring out what to put in here + t_desc = self.describer.describe_tensor(t, trace=trace) + + if trace: + assert source is not None + trace_structured( + "describe_source", + metadata_fn=lambda: { + "describer_id": self.describer.id, + "id": t_desc.id, + "source": source.name, + }, + ) + + # Do the meta-fication. Here, we disable all the ambient modes, to + # better simulate what would be like to re-fakeify from a fresh + # process + with contextlib.ExitStack() as exit_stack: + exit_stack.enter_context(torch._dispatch.python.suspend_functionalization()) + st = peek_interpreter_stack() + if st is not None: + exit_stack.enter_context( + torch._functorch.pyfunctorch.temporarily_clear_interpreter_stack() + ) + + r = self.meta_tensor( + t_desc, + shape_env, + callback_, + source, + symbolic_context, + ) + + if type(t) is torch.nn.Parameter: + # NB: Cannot directly use Parameter constructor + # because that would force a detach, not desirable + r._is_param = True + + # TODO: return the description for later + return r + + +import torch._prims_common as utils diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/schema_check_mode.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/schema_check_mode.py new file mode 100644 index 0000000000000000000000000000000000000000..28bbb8f335ec0c98d2fb6688425309da45e718c9 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_subclasses/schema_check_mode.py @@ -0,0 +1,230 @@ +# mypy: ignore-errors + +from collections import namedtuple +from copy import deepcopy +from itertools import combinations + +import torch +from torch.fx.operator_schemas import normalize_function +from torch.utils import _pytree as pytree +from torch.utils._python_dispatch import TorchDispatchMode +from torch.utils._pytree import tree_map + + +# Named Tuples used within SchemaCheckMode +Mutation = namedtuple("Mutation", ["op_name", "arg_name"]) +Aliasing = namedtuple("Aliasing", ["op_name", "arg_name", "output_number"]) + +# Simplified naming for C++ classes +SchemaArgument = torch._C._SchemaArgument +SchemaArgType = torch._C._SchemaArgType +SchemaInfo = torch._C._SchemaInfo + +# This TorchDispatchMode Subclass is used to verify op schemas +# This TorchDispatchMode Scubclass currently: +# - Records the called ops +# - Checks for mutations on all inputs +# - Checks for aliasing on all inputs + + +# move these 2 functions here to avoid numpy dependency in testing/_internal/common_utils.py + + +def is_iterable_of_tensors(iterable): + # Tensor itself is iterable so we check this first + if isinstance(iterable, torch.Tensor): + return False + try: + if len(iterable) == 0: + return False + for t in iter(iterable): + if not isinstance(t, torch.Tensor): + return False + except TypeError: + return False + return True + + +def clone_inputs(args): + inputs = [] + + for arg in args: + if isinstance(arg, torch.Tensor): + inputs.append(arg.detach().clone()) + elif is_iterable_of_tensors(arg): + inputs.append([t.detach().clone() for t in arg]) + else: + inputs.append(arg) + + return inputs + + +class SchemaCheckMode(TorchDispatchMode): + def __init__(self) -> None: + # Information recorded for testing purposes. For example: + # - incorrect schemas + # - overly conservative schemas + self.ops = [] + self.mutated = [] + self.aliasing = [] + + def reset_cache(self): + self.ops.clear() + self.mutated.clear() + self.aliasing.clear() + + def display_ops(self): + print(*self.ops, sep=",") + + def __torch_dispatch__(self, func, types, args=(), kwargs=None): + def bitwise_equal(lhs, rhs): + if lhs.is_quantized: + # TODO: This is only OK if can't have NaN quantized; idk if + # this is actually true + return torch.equal(lhs, rhs) + else: + return torch.allclose(lhs, rhs, equal_nan=True) + + def has_mutated(before, after, md): + are_tensors = type(before) is torch.Tensor and type(after) is torch.Tensor + if ( + are_tensors + and before.layout != torch.sparse_csr + and after.layout != torch.sparse_csr + ): + return not ( + before.size() == after.size() + and bitwise_equal(before, after) + and md[0] == after.stride() + and md[1] == after._typed_storage()._cdata + ) + return False + + def has_aliased(lhs, rhs): + try: + return torch._C._overlaps(lhs, rhs) + except Exception as exception: + if str(exception).startswith("Cannot inspect value of type "): + return False + else: + raise exception + + def standardize_name(name): + return name if name != "self" else "input" + + def unwrap(e): + if isinstance(e, torch.Tensor) and type(e) is not torch.Tensor: + try: + return e.elem + except AttributeError: + return e + return e + + def parse_metadata(e): + if isinstance(e, torch.Tensor): + if type(e) is not torch.Tensor: + try: + current = e.elem + return ( + deepcopy(current.stride()), + current._typed_storage()._cdata, + ) + except AttributeError: + return None + # Sparse CSR tensors do not have strides or storage + elif e.layout != torch.sparse_csr: + return (deepcopy(e.stride()), e._typed_storage()._cdata) + return None + + self.ops.append(func._schema.name) + + # Clone and process arguments and outputs + pre_arguments = normalize_function( + func, args, kwargs, normalize_to_only_use_kwargs=True + ).kwargs + + c_p_args = dict(zip(pre_arguments.keys(), clone_inputs(pre_arguments.values()))) + cloned_arguments = { + name: tree_map(unwrap, c_p_args.get(name)) for name in c_p_args + } + cloned_metadata = { + name: [ + parse_metadata(a) for a in pytree.tree_leaves(pre_arguments.get(name)) + ] + for name in pre_arguments + } + + out = func(*args, **kwargs) + arguments = { + name: tree_map(unwrap, pre_arguments.get(name)) for name in pre_arguments + } + tuple_out = out if isinstance(out, tuple) else (out,) + tuple_out = tree_map(unwrap, tuple_out) + + schema_info = SchemaInfo(func._schema) + schema_info.add_argument_values(pre_arguments) + + # Process arguments with outputs + for i in range(len(func._schema.arguments)): + arg = func._schema.arguments[i] + name = standardize_name(arg.name) + if arguments.get(name) is not None: + before = cloned_arguments.get(name) + md = cloned_metadata.get(name) + after = arguments.get(name) + for j in range(len(tuple_out)): + # aten::_unsafe_view is intended to have incorrect aliasing notation (hence unsafe) + unsafe_ops = ("aten::_unsafe_view", "aten::unsafe_split") + if ( + has_aliased(tuple_out[j], after) + and func._schema.name not in unsafe_ops + ): + if not schema_info.may_contain_alias( + SchemaArgument(SchemaArgType.output, j), + SchemaArgument(SchemaArgType.input, i), + ): + raise RuntimeError( + f"Argument {name} is not defined to alias output but was aliasing" + ) + else: + self.aliasing.append( + Aliasing(func._schema.name, name, f"output_{j}") + ) + if after is tuple_out[j] and isinstance(after, torch.Tensor): + # Only mutable ops e.g. (add_, add.out) are allowed to directly return inputs. + if not schema_info.is_mutable( + SchemaArgument(SchemaArgType.input, i) + ) and func not in [ + torch.ops.aten.lift.default, + torch.ops.aten.lift_fresh.default, + ]: + raise RuntimeError( + f"""\ +Dispatcher operators below autograd are not allowed to directly return inputs. +However, we found that `outputs[{str(j)}] is {name}""" + ) + if any( + has_mutated(a, b, c) + for a, b, c in zip( + pytree.tree_leaves(before), pytree.tree_leaves(after), md + ) + ): + if not schema_info.is_mutable( + SchemaArgument(SchemaArgType.input, i) + ): + raise RuntimeError( + f"Argument {name} is not defined as mutable but was mutated" + ) + else: + self.mutated.append(Mutation(func._schema.name, name)) + + # Aliasing between outputs + for i, j in combinations(range(len(func._schema.returns)), 2): + if has_aliased(tuple_out[i], tuple_out[j]): + if not schema_info.may_contain_alias( + SchemaArgument(SchemaArgType.output, i), + SchemaArgument(SchemaArgType.output, j), + ): + raise RuntimeError(f"Outputs {i} and {j} alias unexpectedly") + + return out diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_vendor/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_vendor/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_vendor/packaging/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_vendor/packaging/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..22809cfd5dc25792d77070c269fc8d111a12eed0 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_vendor/packaging/__init__.py @@ -0,0 +1,15 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +__title__ = "packaging" +__summary__ = "Core utilities for Python packages" +__uri__ = "https://github.com/pypa/packaging" + +__version__ = "23.2" + +__author__ = "Donald Stufft and individual contributors" +__email__ = "donald@stufft.io" + +__license__ = "BSD-2-Clause or Apache-2.0" +__copyright__ = "2014 %s" % __author__ diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_vendor/packaging/_structures.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_vendor/packaging/_structures.py new file mode 100644 index 0000000000000000000000000000000000000000..90a6465f9682c886363eea5327dac64bf623a6ff --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_vendor/packaging/_structures.py @@ -0,0 +1,61 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + + +class InfinityType: + def __repr__(self) -> str: + return "Infinity" + + def __hash__(self) -> int: + return hash(repr(self)) + + def __lt__(self, other: object) -> bool: + return False + + def __le__(self, other: object) -> bool: + return False + + def __eq__(self, other: object) -> bool: + return isinstance(other, self.__class__) + + def __gt__(self, other: object) -> bool: + return True + + def __ge__(self, other: object) -> bool: + return True + + def __neg__(self: object) -> "NegativeInfinityType": + return NegativeInfinity + + +Infinity = InfinityType() + + +class NegativeInfinityType: + def __repr__(self) -> str: + return "-Infinity" + + def __hash__(self) -> int: + return hash(repr(self)) + + def __lt__(self, other: object) -> bool: + return True + + def __le__(self, other: object) -> bool: + return True + + def __eq__(self, other: object) -> bool: + return isinstance(other, self.__class__) + + def __gt__(self, other: object) -> bool: + return False + + def __ge__(self, other: object) -> bool: + return False + + def __neg__(self: object) -> InfinityType: + return Infinity + + +NegativeInfinity = NegativeInfinityType() diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_vendor/packaging/version.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_vendor/packaging/version.py new file mode 100644 index 0000000000000000000000000000000000000000..5faab9bd0dcf28847960162b2b4f13a8a556ef20 --- /dev/null +++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/_vendor/packaging/version.py @@ -0,0 +1,563 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. +""" +.. testsetup:: + + from packaging.version import parse, Version +""" + +import itertools +import re +from typing import Any, Callable, NamedTuple, Optional, SupportsInt, Tuple, Union + +from ._structures import Infinity, InfinityType, NegativeInfinity, NegativeInfinityType + +__all__ = ["VERSION_PATTERN", "parse", "Version", "InvalidVersion"] + +LocalType = Tuple[Union[int, str], ...] + +CmpPrePostDevType = Union[InfinityType, NegativeInfinityType, Tuple[str, int]] +CmpLocalType = Union[ + NegativeInfinityType, + Tuple[Union[Tuple[int, str], Tuple[NegativeInfinityType, Union[int, str]]], ...], +] +CmpKey = Tuple[ + int, + Tuple[int, ...], + CmpPrePostDevType, + CmpPrePostDevType, + CmpPrePostDevType, + CmpLocalType, +] +VersionComparisonMethod = Callable[[CmpKey, CmpKey], bool] + + +class _Version(NamedTuple): + epoch: int + release: Tuple[int, ...] + dev: Optional[Tuple[str, int]] + pre: Optional[Tuple[str, int]] + post: Optional[Tuple[str, int]] + local: Optional[LocalType] + + +def parse(version: str) -> "Version": + """Parse the given version string. + + >>> parse('1.0.dev1') + + + :param version: The version string to parse. + :raises InvalidVersion: When the version string is not a valid version. + """ + return Version(version) + + +class InvalidVersion(ValueError): + """Raised when a version string is not a valid version. + + >>> Version("invalid") + Traceback (most recent call last): + ... + packaging.version.InvalidVersion: Invalid version: 'invalid' + """ + + +class _BaseVersion: + _key: Tuple[Any, ...] + + def __hash__(self) -> int: + return hash(self._key) + + # Please keep the duplicated `isinstance` check + # in the six comparisons hereunder + # unless you find a way to avoid adding overhead function calls. + def __lt__(self, other: "_BaseVersion") -> bool: + if not isinstance(other, _BaseVersion): + return NotImplemented + + return self._key < other._key + + def __le__(self, other: "_BaseVersion") -> bool: + if not isinstance(other, _BaseVersion): + return NotImplemented + + return self._key <= other._key + + def __eq__(self, other: object) -> bool: + if not isinstance(other, _BaseVersion): + return NotImplemented + + return self._key == other._key + + def __ge__(self, other: "_BaseVersion") -> bool: + if not isinstance(other, _BaseVersion): + return NotImplemented + + return self._key >= other._key + + def __gt__(self, other: "_BaseVersion") -> bool: + if not isinstance(other, _BaseVersion): + return NotImplemented + + return self._key > other._key + + def __ne__(self, other: object) -> bool: + if not isinstance(other, _BaseVersion): + return NotImplemented + + return self._key != other._key + + +# Deliberately not anchored to the start and end of the string, to make it +# easier for 3rd party code to reuse +_VERSION_PATTERN = r""" + v? + (?: + (?:(?P[0-9]+)!)? # epoch + (?P[0-9]+(?:\.[0-9]+)*) # release segment + (?P
                                          # pre-release
+            [-_\.]?
+            (?Palpha|a|beta|b|preview|pre|c|rc)
+            [-_\.]?
+            (?P[0-9]+)?
+        )?
+        (?P                                         # post release
+            (?:-(?P[0-9]+))
+            |
+            (?:
+                [-_\.]?
+                (?Ppost|rev|r)
+                [-_\.]?
+                (?P[0-9]+)?
+            )
+        )?
+        (?P                                          # dev release
+            [-_\.]?
+            (?Pdev)
+            [-_\.]?
+            (?P[0-9]+)?
+        )?
+    )
+    (?:\+(?P[a-z0-9]+(?:[-_\.][a-z0-9]+)*))?       # local version
+"""
+
+VERSION_PATTERN = _VERSION_PATTERN
+"""
+A string containing the regular expression used to match a valid version.
+
+The pattern is not anchored at either end, and is intended for embedding in larger
+expressions (for example, matching a version number as part of a file name). The
+regular expression should be compiled with the ``re.VERBOSE`` and ``re.IGNORECASE``
+flags set.
+
+:meta hide-value:
+"""
+
+
+class Version(_BaseVersion):
+    """This class abstracts handling of a project's versions.
+
+    A :class:`Version` instance is comparison aware and can be compared and
+    sorted using the standard Python interfaces.
+
+    >>> v1 = Version("1.0a5")
+    >>> v2 = Version("1.0")
+    >>> v1
+    
+    >>> v2
+    
+    >>> v1 < v2
+    True
+    >>> v1 == v2
+    False
+    >>> v1 > v2
+    False
+    >>> v1 >= v2
+    False
+    >>> v1 <= v2
+    True
+    """
+
+    _regex = re.compile(r"^\s*" + VERSION_PATTERN + r"\s*$", re.VERBOSE | re.IGNORECASE)
+    _key: CmpKey
+
+    def __init__(self, version: str) -> None:
+        """Initialize a Version object.
+
+        :param version:
+            The string representation of a version which will be parsed and normalized
+            before use.
+        :raises InvalidVersion:
+            If the ``version`` does not conform to PEP 440 in any way then this
+            exception will be raised.
+        """
+
+        # Validate the version and parse it into pieces
+        match = self._regex.search(version)
+        if not match:
+            raise InvalidVersion(f"Invalid version: '{version}'")
+
+        # Store the parsed out pieces of the version
+        self._version = _Version(
+            epoch=int(match.group("epoch")) if match.group("epoch") else 0,
+            release=tuple(int(i) for i in match.group("release").split(".")),
+            pre=_parse_letter_version(match.group("pre_l"), match.group("pre_n")),
+            post=_parse_letter_version(
+                match.group("post_l"), match.group("post_n1") or match.group("post_n2")
+            ),
+            dev=_parse_letter_version(match.group("dev_l"), match.group("dev_n")),
+            local=_parse_local_version(match.group("local")),
+        )
+
+        # Generate a key which will be used for sorting
+        self._key = _cmpkey(
+            self._version.epoch,
+            self._version.release,
+            self._version.pre,
+            self._version.post,
+            self._version.dev,
+            self._version.local,
+        )
+
+    def __repr__(self) -> str:
+        """A representation of the Version that shows all internal state.
+
+        >>> Version('1.0.0')
+        
+        """
+        return f""
+
+    def __str__(self) -> str:
+        """A string representation of the version that can be rounded-tripped.
+
+        >>> str(Version("1.0a5"))
+        '1.0a5'
+        """
+        parts = []
+
+        # Epoch
+        if self.epoch != 0:
+            parts.append(f"{self.epoch}!")
+
+        # Release segment
+        parts.append(".".join(str(x) for x in self.release))
+
+        # Pre-release
+        if self.pre is not None:
+            parts.append("".join(str(x) for x in self.pre))
+
+        # Post-release
+        if self.post is not None:
+            parts.append(f".post{self.post}")
+
+        # Development release
+        if self.dev is not None:
+            parts.append(f".dev{self.dev}")
+
+        # Local version segment
+        if self.local is not None:
+            parts.append(f"+{self.local}")
+
+        return "".join(parts)
+
+    @property
+    def epoch(self) -> int:
+        """The epoch of the version.
+
+        >>> Version("2.0.0").epoch
+        0
+        >>> Version("1!2.0.0").epoch
+        1
+        """
+        return self._version.epoch
+
+    @property
+    def release(self) -> Tuple[int, ...]:
+        """The components of the "release" segment of the version.
+
+        >>> Version("1.2.3").release
+        (1, 2, 3)
+        >>> Version("2.0.0").release
+        (2, 0, 0)
+        >>> Version("1!2.0.0.post0").release
+        (2, 0, 0)
+
+        Includes trailing zeroes but not the epoch or any pre-release / development /
+        post-release suffixes.
+        """
+        return self._version.release
+
+    @property
+    def pre(self) -> Optional[Tuple[str, int]]:
+        """The pre-release segment of the version.
+
+        >>> print(Version("1.2.3").pre)
+        None
+        >>> Version("1.2.3a1").pre
+        ('a', 1)
+        >>> Version("1.2.3b1").pre
+        ('b', 1)
+        >>> Version("1.2.3rc1").pre
+        ('rc', 1)
+        """
+        return self._version.pre
+
+    @property
+    def post(self) -> Optional[int]:
+        """The post-release number of the version.
+
+        >>> print(Version("1.2.3").post)
+        None
+        >>> Version("1.2.3.post1").post
+        1
+        """
+        return self._version.post[1] if self._version.post else None
+
+    @property
+    def dev(self) -> Optional[int]:
+        """The development number of the version.
+
+        >>> print(Version("1.2.3").dev)
+        None
+        >>> Version("1.2.3.dev1").dev
+        1
+        """
+        return self._version.dev[1] if self._version.dev else None
+
+    @property
+    def local(self) -> Optional[str]:
+        """The local version segment of the version.
+
+        >>> print(Version("1.2.3").local)
+        None
+        >>> Version("1.2.3+abc").local
+        'abc'
+        """
+        if self._version.local:
+            return ".".join(str(x) for x in self._version.local)
+        else:
+            return None
+
+    @property
+    def public(self) -> str:
+        """The public portion of the version.
+
+        >>> Version("1.2.3").public
+        '1.2.3'
+        >>> Version("1.2.3+abc").public
+        '1.2.3'
+        >>> Version("1.2.3+abc.dev1").public
+        '1.2.3'
+        """
+        return str(self).split("+", 1)[0]
+
+    @property
+    def base_version(self) -> str:
+        """The "base version" of the version.
+
+        >>> Version("1.2.3").base_version
+        '1.2.3'
+        >>> Version("1.2.3+abc").base_version
+        '1.2.3'
+        >>> Version("1!1.2.3+abc.dev1").base_version
+        '1!1.2.3'
+
+        The "base version" is the public version of the project without any pre or post
+        release markers.
+        """
+        parts = []
+
+        # Epoch
+        if self.epoch != 0:
+            parts.append(f"{self.epoch}!")
+
+        # Release segment
+        parts.append(".".join(str(x) for x in self.release))
+
+        return "".join(parts)
+
+    @property
+    def is_prerelease(self) -> bool:
+        """Whether this version is a pre-release.
+
+        >>> Version("1.2.3").is_prerelease
+        False
+        >>> Version("1.2.3a1").is_prerelease
+        True
+        >>> Version("1.2.3b1").is_prerelease
+        True
+        >>> Version("1.2.3rc1").is_prerelease
+        True
+        >>> Version("1.2.3dev1").is_prerelease
+        True
+        """
+        return self.dev is not None or self.pre is not None
+
+    @property
+    def is_postrelease(self) -> bool:
+        """Whether this version is a post-release.
+
+        >>> Version("1.2.3").is_postrelease
+        False
+        >>> Version("1.2.3.post1").is_postrelease
+        True
+        """
+        return self.post is not None
+
+    @property
+    def is_devrelease(self) -> bool:
+        """Whether this version is a development release.
+
+        >>> Version("1.2.3").is_devrelease
+        False
+        >>> Version("1.2.3.dev1").is_devrelease
+        True
+        """
+        return self.dev is not None
+
+    @property
+    def major(self) -> int:
+        """The first item of :attr:`release` or ``0`` if unavailable.
+
+        >>> Version("1.2.3").major
+        1
+        """
+        return self.release[0] if len(self.release) >= 1 else 0
+
+    @property
+    def minor(self) -> int:
+        """The second item of :attr:`release` or ``0`` if unavailable.
+
+        >>> Version("1.2.3").minor
+        2
+        >>> Version("1").minor
+        0
+        """
+        return self.release[1] if len(self.release) >= 2 else 0
+
+    @property
+    def micro(self) -> int:
+        """The third item of :attr:`release` or ``0`` if unavailable.
+
+        >>> Version("1.2.3").micro
+        3
+        >>> Version("1").micro
+        0
+        """
+        return self.release[2] if len(self.release) >= 3 else 0
+
+
+def _parse_letter_version(
+    letter: Optional[str], number: Union[str, bytes, SupportsInt, None]
+) -> Optional[Tuple[str, int]]:
+
+    if letter:
+        # We consider there to be an implicit 0 in a pre-release if there is
+        # not a numeral associated with it.
+        if number is None:
+            number = 0
+
+        # We normalize any letters to their lower case form
+        letter = letter.lower()
+
+        # We consider some words to be alternate spellings of other words and
+        # in those cases we want to normalize the spellings to our preferred
+        # spelling.
+        if letter == "alpha":
+            letter = "a"
+        elif letter == "beta":
+            letter = "b"
+        elif letter in ["c", "pre", "preview"]:
+            letter = "rc"
+        elif letter in ["rev", "r"]:
+            letter = "post"
+
+        return letter, int(number)
+    if not letter and number:
+        # We assume if we are given a number, but we are not given a letter
+        # then this is using the implicit post release syntax (e.g. 1.0-1)
+        letter = "post"
+
+        return letter, int(number)
+
+    return None
+
+
+_local_version_separators = re.compile(r"[\._-]")
+
+
+def _parse_local_version(local: Optional[str]) -> Optional[LocalType]:
+    """
+    Takes a string like abc.1.twelve and turns it into ("abc", 1, "twelve").
+    """
+    if local is not None:
+        return tuple(
+            part.lower() if not part.isdigit() else int(part)
+            for part in _local_version_separators.split(local)
+        )
+    return None
+
+
+def _cmpkey(
+    epoch: int,
+    release: Tuple[int, ...],
+    pre: Optional[Tuple[str, int]],
+    post: Optional[Tuple[str, int]],
+    dev: Optional[Tuple[str, int]],
+    local: Optional[LocalType],
+) -> CmpKey:
+
+    # When we compare a release version, we want to compare it with all of the
+    # trailing zeros removed. So we'll use a reverse the list, drop all the now
+    # leading zeros until we come to something non zero, then take the rest
+    # re-reverse it back into the correct order and make it a tuple and use
+    # that for our sorting key.
+    _release = tuple(
+        reversed(list(itertools.dropwhile(lambda x: x == 0, reversed(release))))
+    )
+
+    # We need to "trick" the sorting algorithm to put 1.0.dev0 before 1.0a0.
+    # We'll do this by abusing the pre segment, but we _only_ want to do this
+    # if there is not a pre or a post segment. If we have one of those then
+    # the normal sorting rules will handle this case correctly.
+    if pre is None and post is None and dev is not None:
+        _pre: CmpPrePostDevType = NegativeInfinity
+    # Versions without a pre-release (except as noted above) should sort after
+    # those with one.
+    elif pre is None:
+        _pre = Infinity
+    else:
+        _pre = pre
+
+    # Versions without a post segment should sort before those with one.
+    if post is None:
+        _post: CmpPrePostDevType = NegativeInfinity
+
+    else:
+        _post = post
+
+    # Versions without a development segment should sort after those with one.
+    if dev is None:
+        _dev: CmpPrePostDevType = Infinity
+
+    else:
+        _dev = dev
+
+    if local is None:
+        # Versions without a local segment should sort before those with one.
+        _local: CmpLocalType = NegativeInfinity
+    else:
+        # Versions with a local segment need that segment parsed to implement
+        # the sorting rules in PEP440.
+        # - Alpha numeric segments sort before numeric segments
+        # - Alpha numeric segments sort lexicographically
+        # - Numeric segments sort numerically
+        # - Shorter versions sort before longer versions when the prefixes
+        #   match exactly
+        _local = tuple(
+            (i, "") if isinstance(i, int) else (NegativeInfinity, i) for i in local
+        )
+
+    return epoch, _release, _pre, _post, _dev, _local
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/accelerator/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/accelerator/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0dfbe400bfbcb1d0eed42b2ab16fdfa1f42ada6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/accelerator/__init__.py
@@ -0,0 +1,299 @@
+r"""
+This package introduces support for the current :ref:`accelerator` in python.
+"""
+
+from functools import cache
+from typing import Any
+from typing_extensions import deprecated
+
+import torch
+
+from ._utils import _device_t, _get_device_index
+from .memory import (
+    empty_cache,
+    get_memory_info,
+    max_memory_allocated,
+    max_memory_reserved,
+    memory_allocated,
+    memory_reserved,
+    memory_stats,
+    reset_accumulated_memory_stats,
+    reset_peak_memory_stats,
+)
+
+
+__all__ = [
+    "current_accelerator",
+    "current_device_idx",  # deprecated
+    "current_device_index",
+    "get_device_capability",
+    "current_stream",
+    "device_count",
+    "device_index",
+    "empty_cache",
+    "get_memory_info",
+    "is_available",
+    "max_memory_allocated",
+    "max_memory_reserved",
+    "memory_allocated",
+    "memory_reserved",
+    "memory_stats",
+    "reset_accumulated_memory_stats",
+    "reset_peak_memory_stats",
+    "set_device_idx",  # deprecated
+    "set_device_index",
+    "set_stream",
+    "synchronize",
+]
+
+
+def device_count() -> int:
+    r"""Return the number of current :ref:`accelerator` available.
+
+    Returns:
+        int: the number of the current :ref:`accelerator` available.
+            If there is no available accelerators, return 0.
+
+    .. note:: This API delegates to the device-specific version of `device_count`.
+        On CUDA, this API will NOT poison fork if NVML discovery succeeds.
+        Otherwise, it will. For more details, see :ref:`multiprocessing-poison-fork-note`.
+    """
+    acc = current_accelerator()
+    if acc is None:
+        return 0
+
+    mod = torch.get_device_module(acc)
+    return mod.device_count()
+
+
+def is_available() -> bool:
+    r"""Check if the current accelerator is available at runtime: it was build, all the
+    required drivers are available and at least one device is visible.
+    See :ref:`accelerator` for details.
+
+    Returns:
+        bool: A boolean indicating if there is an available :ref:`accelerator`.
+
+    .. note:: This API delegates to the device-specific version of `is_available`.
+        On CUDA, when the environment variable ``PYTORCH_NVML_BASED_CUDA_CHECK=1`` is set,
+        this function will NOT poison fork. Otherwise, it will. For more details, see
+        :ref:`multiprocessing-poison-fork-note`.
+
+    Example::
+
+        >>> assert torch.accelerator.is_available() "No available accelerators detected."
+    """
+    # Why not just check "device_count() > 0" like other is_available call?
+    # Because device like CUDA have a python implementation of is_available that is
+    # non-poisoning and some features like Dataloader rely on it.
+    # So we are careful to delegate to the Python version of the accelerator here
+    acc = current_accelerator()
+    if acc is None:
+        return False
+
+    mod = torch.get_device_module(acc)
+    return mod.is_available()
+
+
+def current_accelerator(check_available: bool = False) -> torch.device | None:
+    r"""Return the device of the accelerator available at compilation time.
+    If no accelerator were available at compilation time, returns None.
+    See :ref:`accelerator` for details.
+
+    Args:
+        check_available (bool, optional): if True, will also do a runtime check to see
+            if the device :func:`torch.accelerator.is_available` on top of the compile-time
+            check.
+            Default: ``False``
+
+    Returns:
+        torch.device: return the current accelerator as :class:`torch.device`.
+
+    .. note:: The index of the returned :class:`torch.device` will be ``None``, please use
+        :func:`torch.accelerator.current_device_index` to know the current index being used.
+        This API does NOT poison fork. For more details, see :ref:`multiprocessing-poison-fork-note`.
+
+    Example::
+
+        >>> # xdoctest:
+        >>> # If an accelerator is available, sent the model to it
+        >>> model = torch.nn.Linear(2, 2)
+        >>> if (current_device := current_accelerator(check_available=True)) is not None:
+        >>>     model.to(current_device)
+    """
+    if (acc := torch._C._accelerator_getAccelerator()) is not None:
+        if (not check_available) or (check_available and is_available()):
+            return acc
+    return None
+
+
+def current_device_index() -> int:
+    r"""Return the index of a currently selected device for the current :ref:`accelerator`.
+
+    Returns:
+        int: the index of a currently selected device.
+    """
+    return torch._C._accelerator_getDeviceIndex()
+
+
+current_device_idx = deprecated(
+    "Use `current_device_index` instead.",
+    category=FutureWarning,
+)(current_device_index)
+
+current_device_idx.__doc__ = r"""
+    (Deprecated) Return the index of a currently selected device for the current :ref:`accelerator`.
+
+    Returns:
+        int: the index of a currently selected device.
+
+    .. warning::
+
+        :func:`torch.accelerator.current_device_idx` is deprecated in favor of :func:`torch.accelerator.current_device_index`
+        and will be removed in a future PyTorch release.
+    """
+
+
+@cache
+def get_device_capability(device: _device_t = None, /) -> dict[str, Any]:
+    r"""Return the capability of the currently selected device.
+
+    Args:
+        device (:class:`torch.device`, str, int, optional): The device to query capabilities for
+            :ref:`accelerator` device type. If not given,
+            use :func:`torch.accelerator.current_device_index` by default.
+
+    Returns:
+        dict[str, Any]: A dictionary containing device capability information. The dictionary includes:
+            - ``supported_dtypes`` (set(torch.dtype)): Set of PyTorch data types supported by the device
+
+    Examples:
+        >>> # xdoctest: +SKIP("requires cuda")
+        >>> # Query capabilities for current device
+        >>> capabilities = torch.accelerator.get_device_capability("cuda:0")
+        >>> print("Supported dtypes:", capabilities["supported_dtypes"])
+    """
+    device_index = _get_device_index(device, optional=True)
+    return torch._C._accelerator_getDeviceCapability(device_index)
+
+
+def set_device_index(device: _device_t, /) -> None:
+    r"""Set the current device index to a given device.
+
+    Args:
+        device (:class:`torch.device`, str, int): a given device that must match the current
+            :ref:`accelerator` device type.
+
+    .. note:: This function is a no-op if this device index is negative.
+    """
+    device_index = _get_device_index(device, optional=False)
+    torch._C._accelerator_setDeviceIndex(device_index)
+
+
+set_device_idx = deprecated(
+    "Use `set_device_index` instead.",
+    category=FutureWarning,
+)(set_device_index)
+
+set_device_idx.__doc__ = r"""
+    (Deprecated) Set the current device index to a given device.
+
+    Args:
+        device (:class:`torch.device`, str, int): a given device that must match the current
+            :ref:`accelerator` device type.
+
+    .. warning::
+
+        :func:`torch.accelerator.set_device_idx` is deprecated in favor of :func:`torch.accelerator.set_device_index`
+        and will be removed in a future PyTorch release.
+    """
+
+
+def current_stream(device: _device_t = None, /) -> torch.Stream:
+    r"""Return the currently selected stream for a given device.
+
+    Args:
+        device (:class:`torch.device`, str, int, optional): a given device that must match the current
+            :ref:`accelerator` device type. If not given,
+            use :func:`torch.accelerator.current_device_index` by default.
+
+    Returns:
+        torch.Stream: the currently selected stream for a given device.
+    """
+    device_index = _get_device_index(device, optional=True)
+    return torch._C._accelerator_getStream(device_index)
+
+
+def set_stream(stream: torch.Stream) -> None:
+    r"""Set the current stream to a given stream.
+
+    Args:
+        stream (torch.Stream): a given stream that must match the current :ref:`accelerator` device type.
+
+    .. note:: This function will set the current device index to the device index of the given stream.
+    """
+    torch._C._accelerator_setStream(stream)
+
+
+def synchronize(device: _device_t = None, /) -> None:
+    r"""Wait for all kernels in all streams on the given device to complete.
+
+    Args:
+        device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match
+            the current :ref:`accelerator` device type. If not given,
+            use :func:`torch.accelerator.current_device_index` by default.
+
+    .. note:: This function is a no-op if the current :ref:`accelerator` is not initialized.
+
+    Example::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
+        >>> assert torch.accelerator.is_available() "No available accelerators detected."
+        >>> start_event = torch.Event(enable_timing=True)
+        >>> end_event = torch.Event(enable_timing=True)
+        >>> start_event.record()
+        >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator())
+        >>> sum = torch.sum(tensor)
+        >>> end_event.record()
+        >>> torch.accelerator.synchronize()
+        >>> elapsed_time_ms = start_event.elapsed_time(end_event)
+    """
+    device_index = _get_device_index(device, optional=True)
+    torch._C._accelerator_synchronizeDevice(device_index)
+
+
+class device_index:
+    r"""Context manager to set the current device index for the current :ref:`accelerator`.
+    Temporarily changes the current device index to the specified value for the duration
+    of the context, and automatically restores the previous device index when exiting
+    the context.
+
+    Args:
+        device (Optional[int]): a given device index to temporarily set. If None,
+            no device index switching occurs.
+
+    Examples:
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
+        >>> # Set device 0 as the current device temporarily
+        >>> with torch.accelerator.device_index(0):
+        ...     # Code here runs with device 0 as the current device
+        ...     pass
+        >>> # Original device is now restored
+        >>> # No-op when None is passed
+        >>> with torch.accelerator.device_index(None):
+        ...     # No device switching occurs
+        ...     pass
+    """
+
+    def __init__(self, device: int | None, /) -> None:
+        self.idx = device
+        self.prev_idx = -1
+
+    def __enter__(self) -> None:
+        if self.idx is not None:
+            self.prev_idx = torch._C._accelerator_exchangeDevice(self.idx)
+
+    def __exit__(self, *exc_info: object) -> None:
+        if self.idx is not None:
+            torch._C._accelerator_maybeExchangeDevice(self.prev_idx)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/accelerator/_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/accelerator/_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef62e298de1c820380f09391e0d8dba257a90d8b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/accelerator/_utils.py
@@ -0,0 +1,26 @@
+import torch
+from torch.types import Device as _device_t
+
+
+def _get_device_index(device: _device_t, optional: bool = False) -> int:
+    if isinstance(device, int):
+        return device
+    if isinstance(device, str):
+        device = torch.device(device)
+    device_index: int | None = None
+    if isinstance(device, torch.device):
+        acc = torch.accelerator.current_accelerator()
+        if acc is None:
+            raise RuntimeError("Accelerator expected")
+        if acc.type != device.type:
+            raise ValueError(
+                f"{device.type} doesn't match the current accelerator {acc}."
+            )
+        device_index = device.index
+    if device_index is None:
+        if not optional:
+            raise ValueError(
+                f"Expected a torch.device with a specified index or an integer, but got:{device}"
+            )
+        return torch.accelerator.current_device_index()
+    return device_index
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/accelerator/memory.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/accelerator/memory.py
new file mode 100644
index 0000000000000000000000000000000000000000..513e497f3883c0af572d365ba89424e8b7a2abd7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/accelerator/memory.py
@@ -0,0 +1,236 @@
+from collections import OrderedDict
+from typing import Any
+
+import torch
+
+from ._utils import _device_t, _get_device_index
+
+
+__all__ = [
+    "empty_cache",
+    "get_memory_info",
+    "max_memory_allocated",
+    "max_memory_reserved",
+    "memory_allocated",
+    "memory_reserved",
+    "memory_stats",
+    "reset_accumulated_memory_stats",
+    "reset_peak_memory_stats",
+]
+
+
+def empty_cache() -> None:
+    r"""Release all unoccupied cached memory currently held by the caching
+    allocator so that those can be used in other application.
+
+    .. note:: This function is a no-op if the memory allocator for the current
+        :ref:`accelerator ` has not been initialized.
+    """
+    if not torch._C._accelerator_isAllocatorInitialized():
+        return
+    torch._C._accelerator_emptyCache()
+
+
+def memory_stats(device_index: _device_t = None, /) -> OrderedDict[str, Any]:
+    r"""Return a dictionary of accelerator device memory allocator statistics for a given device index.
+
+    The return value of this function is a dictionary of statistics, each of
+    which is a non-negative integer.
+
+    Core statistics:
+
+    - ``"allocated.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
+      number of allocation requests received by the memory allocator.
+    - ``"allocated_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
+      amount of allocated memory.
+    - ``"segment.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
+      number of reserved segments from device memory allocation.
+    - ``"reserved_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
+      amount of reserved memory.
+    - ``"active.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
+      number of active memory blocks.
+    - ``"active_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
+      amount of active memory.
+    - ``"inactive_split.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
+      number of inactive, non-releasable memory blocks.
+    - ``"inactive_split_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
+      amount of inactive, non-releasable memory.
+
+    For these core statistics, values are broken down as follows.
+
+    Pool type:
+
+    - ``all``: combined statistics across all memory pools.
+    - ``large_pool``: statistics for the large allocation pool
+      (as of June 2025, for size >= 1MB allocations).
+    - ``small_pool``: statistics for the small allocation pool
+      (as of June 2025, for size < 1MB allocations).
+
+    Metric type:
+
+    - ``current``: current value of this metric.
+    - ``peak``: maximum value of this metric.
+    - ``allocated``: historical total increase in this metric.
+    - ``freed``: historical total decrease in this metric.
+
+    In addition to the core statistics, we also provide some simple event
+    counters:
+
+    - ``"num_alloc_retries"``: number of failed device memory allocation calls that
+      result in a cache flush and retry.
+    - ``"num_ooms"``: number of out-of-memory errors thrown.
+    - ``"num_sync_all_streams"``: number of ``synchronize_and_free_events`` calls.
+    - ``"num_device_alloc"``: number of device memory allocation calls.
+    - ``"num_device_free"``: number of device memory free calls.
+
+    Args:
+        device_index (:class:`torch.device`, str, int, optional): the index of the device to target.
+            If not given, use :func:`torch.accelerator.current_device_index` by default.
+            If a :class:`torch.device` or str is provided, its type must match the current
+            :ref:`accelerator` device type.
+
+    Returns:
+        OrderedDict[str, Any]: an ordered dictionary mapping statistic names to their values.
+    """
+    if not torch._C._accelerator_isAllocatorInitialized():
+        return OrderedDict()
+    device_index = _get_device_index(device_index, optional=True)
+    stats = torch._C._accelerator_getDeviceStats(device_index)
+    flat_stats = []
+
+    def flatten(prefix: str, value: Any) -> None:
+        if isinstance(value, dict):
+            for k, v in value.items():
+                nested_prefix = f"{prefix}.{k}" if prefix else k
+                flatten(nested_prefix, v)
+        else:
+            flat_stats.append((prefix, value))
+
+    flatten("", stats)
+    flat_stats.sort()
+    # pyrefly: ignore [no-matching-overload]
+    return OrderedDict(flat_stats)
+
+
+def memory_allocated(device_index: _device_t = None, /) -> int:
+    r"""Return the current :ref:`accelerator` device memory occupied by tensors
+    in bytes for a given device index.
+
+    Args:
+        device_index (:class:`torch.device`, str, int, optional): the index of the device to target.
+            If not given, use :func:`torch.accelerator.current_device_index` by default.
+            If a :class:`torch.device` or str is provided, its type must match the current
+            :ref:`accelerator` device type.
+
+    Returns:
+        int: the current memory occupied by live tensors (in bytes) within the current process.
+    """
+    return memory_stats(device_index).get("allocated_bytes.all.current", 0)
+
+
+def max_memory_allocated(device_index: _device_t = None, /) -> int:
+    r"""Return the current :ref:`accelerator` maximum device memory occupied by tensors
+    in bytes for a given device index.
+
+    By default, this returns the peak allocated memory since the beginning of
+    this program. :func:`~torch.accelerator.reset_peak_memory_stats` can be used to
+    reset the starting point in tracking this metric.
+
+    Args:
+        device_index (:class:`torch.device`, str, int, optional): the index of the device to target.
+            If not given, use :func:`torch.accelerator.current_device_index` by default.
+            If a :class:`torch.device` or str is provided, its type must match the current
+            :ref:`accelerator` device type.
+
+    Returns:
+        int: the peak memory occupied by live tensors (in bytes) within the current process.
+    """
+    return memory_stats(device_index).get("allocated_bytes.all.peak", 0)
+
+
+def memory_reserved(device_index: _device_t = None, /) -> int:
+    r"""Return the current :ref:`accelerator` device memory managed by the caching allocator
+    in bytes for a given device index.
+
+    Args:
+        device_index (:class:`torch.device`, str, int, optional): the index of the device to target.
+            If not given, use :func:`torch.accelerator.current_device_index` by default.
+            If a :class:`torch.device` or str is provided, its type must match the current
+            :ref:`accelerator` device type.
+
+    Returns:
+        int: the current memory reserved by PyTorch (in bytes) within the current process.
+    """
+    return memory_stats(device_index).get("reserved_bytes.all.current", 0)
+
+
+def max_memory_reserved(device_index: _device_t = None, /) -> int:
+    r"""Return the current :ref:`accelerator` maximum device memory managed by the caching allocator
+    in bytes for a given device index.
+
+    By default, this returns the peak cached memory since the beginning of this
+    program. :func:`~torch.accelerator.reset_peak_memory_stats` can be used to reset
+    the starting point in tracking this metric.
+
+    Args:
+        device_index (:class:`torch.device`, str, int, optional): the index of the device to target.
+            If not given, use :func:`torch.accelerator.current_device_index` by default.
+            If a :class:`torch.device` or str is provided, its type must match the current
+            :ref:`accelerator` device type.
+
+    Returns:
+        int: the peak memory reserved by PyTorch (in bytes) within the current process.
+    """
+    return memory_stats(device_index).get("reserved_bytes.all.peak", 0)
+
+
+def reset_accumulated_memory_stats(device_index: _device_t = None, /) -> None:
+    r"""Reset the "accumulated" (historical) stats tracked by the current :ref:`accelerator`
+    memory allocator for a given device index.
+
+    Args:
+        device_index (:class:`torch.device`, str, int, optional): the index of the device to target.
+            If not given, use :func:`torch.accelerator.current_device_index` by default.
+            If a :class:`torch.device` or str is provided, its type must match the current
+            :ref:`accelerator` device type.
+
+    .. note:: This function is a no-op if the memory allocator for the current
+        :ref:`accelerator ` has not been initialized.
+    """
+    device_index = _get_device_index(device_index, optional=True)
+    return torch._C._accelerator_resetAccumulatedStats(device_index)
+
+
+def reset_peak_memory_stats(device_index: _device_t = None, /) -> None:
+    r"""Reset the "peak" stats tracked by the current :ref:`accelerator`
+    memory allocator for a given device index.
+
+    Args:
+        device_index (:class:`torch.device`, str, int, optional): the index of the device to target.
+            If not given, use :func:`torch.accelerator.current_device_index` by default.
+            If a :class:`torch.device` or str is provided, its type must match the current
+            :ref:`accelerator` device type.
+
+    .. note:: This function is a no-op if the memory allocator for the current
+        :ref:`accelerator ` has not been initialized.
+    """
+    device_index = _get_device_index(device_index, optional=True)
+    return torch._C._accelerator_resetPeakStats(device_index)
+
+
+def get_memory_info(device_index: _device_t = None, /) -> tuple[int, int]:
+    r"""Return the current device memory information for a given device index.
+
+    Args:
+        device_index (:class:`torch.device`, str, int, optional): the index of the device to target.
+            If not given, use :func:`torch.accelerator.current_device_index` by default.
+            If a :class:`torch.device` or str is provided, its type must match the current
+            :ref:`accelerator` device type.
+
+    Returns:
+        tuple[int, int]: a tuple of two integers (free_memory, total_memory) in bytes.
+            The first value is the free memory on the device (available across all processes and applications),
+            The second value is the device's total hardware memory capacity.
+    """
+    device_index = _get_device_index(device_index, optional=True)
+    return torch._C._accelerator_getMemoryInfo(device_index)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/amp/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/amp/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..263908eff68bab7c7d9a123bd77a5f5c048b8c5a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/amp/__init__.py
@@ -0,0 +1,9 @@
+from .autocast_mode import (
+    _enter_autocast,
+    _exit_autocast,
+    autocast,
+    custom_bwd,
+    custom_fwd,
+    is_autocast_available,
+)
+from .grad_scaler import GradScaler
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/amp/autocast_mode.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/amp/autocast_mode.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd9039ce9f0e503d67d29f10a13d33b5af1b3ead
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/amp/autocast_mode.py
@@ -0,0 +1,525 @@
+# mypy: allow-untyped-defs
+import collections
+import functools
+import warnings
+from typing import Any, Optional
+
+import torch
+from torch.types import _dtype
+
+
+try:
+    import numpy as np
+
+    HAS_NUMPY = True
+except ModuleNotFoundError:
+    HAS_NUMPY = False
+    np = None  # type: ignore[assignment]
+
+__all__ = [
+    "autocast_decorator",
+    "autocast",
+    "is_autocast_available",
+    "custom_fwd",
+    "custom_bwd",
+]
+
+
+def is_autocast_available(device_type: str) -> bool:
+    r"""
+    Return a bool indicating if autocast is available on :attr:`device_type`.
+
+    Args:
+        device_type(str):  Device type to use. Possible values are: 'cuda', 'cpu', 'mtia', 'maia', 'xpu', and so on.
+            The type is the same as the `type` attribute of a :class:`torch.device`.
+            Thus, you may obtain the device type of a tensor using `Tensor.device.type`.
+    """
+    return torch._C._is_autocast_available(device_type)
+
+
+def autocast_decorator(autocast_instance, func):
+    @functools.wraps(func)
+    def decorate_autocast(*args, **kwargs):
+        with autocast_instance:
+            return func(*args, **kwargs)
+
+    decorate_autocast.__script_unsupported = (  # type: ignore[attr-defined]
+        "@autocast() decorator is not supported in script mode"
+    )
+    return decorate_autocast
+
+
+class autocast:
+    r"""
+    Instances of :class:`autocast` serve as context managers or decorators that
+    allow regions of your script to run in mixed precision.
+
+    In these regions, ops run in an op-specific dtype chosen by autocast
+    to improve performance while maintaining accuracy.
+    See the :ref:`Autocast Op Reference` for details.
+
+    When entering an autocast-enabled region, Tensors may be any type.
+    You should not call ``half()`` or ``bfloat16()`` on your model(s) or inputs when using autocasting.
+
+    :class:`autocast` should wrap only the forward pass(es) of your network, including the loss
+    computation(s).  Backward passes under autocast are not recommended.
+    Backward ops run in the same type that autocast used for corresponding forward ops.
+
+    Example for CUDA Devices::
+
+        # Creates model and optimizer in default precision
+        model = Net().cuda()
+        optimizer = optim.SGD(model.parameters(), ...)
+
+        for input, target in data:
+            optimizer.zero_grad()
+
+            # Enables autocasting for the forward pass (model + loss)
+            with torch.autocast(device_type="cuda"):
+                output = model(input)
+                loss = loss_fn(output, target)
+
+            # Exits the context manager before backward()
+            loss.backward()
+            optimizer.step()
+
+    See the :ref:`Automatic Mixed Precision examples` for usage (along with gradient scaling)
+    in more complex scenarios (e.g., gradient penalty, multiple models/losses, custom autograd functions).
+
+    :class:`autocast` can also be used as a decorator, e.g., on the ``forward`` method of your model::
+
+        class AutocastModel(nn.Module):
+            ...
+
+            @torch.autocast(device_type="cuda")
+            def forward(self, input): ...
+
+    Floating-point Tensors produced in an autocast-enabled region may be ``float16``.
+    After returning to an autocast-disabled region, using them with floating-point
+    Tensors of different dtypes may cause type mismatch errors.  If so, cast the Tensor(s)
+    produced in the autocast region back to ``float32`` (or other dtype if desired).
+    If a Tensor from the autocast region is already ``float32``, the cast is a no-op,
+    and incurs no additional overhead.
+    CUDA Example::
+
+        # Creates some tensors in default dtype (here assumed to be float32)
+        a_float32 = torch.rand((8, 8), device="cuda")
+        b_float32 = torch.rand((8, 8), device="cuda")
+        c_float32 = torch.rand((8, 8), device="cuda")
+        d_float32 = torch.rand((8, 8), device="cuda")
+
+        with torch.autocast(device_type="cuda"):
+            # torch.mm is on autocast's list of ops that should run in float16.
+            # Inputs are float32, but the op runs in float16 and produces float16 output.
+            # No manual casts are required.
+            e_float16 = torch.mm(a_float32, b_float32)
+            # Also handles mixed input types
+            f_float16 = torch.mm(d_float32, e_float16)
+
+        # After exiting autocast, calls f_float16.float() to use with d_float32
+        g_float32 = torch.mm(d_float32, f_float16.float())
+
+    CPU Training Example::
+
+        # Creates model and optimizer in default precision
+        model = Net()
+        optimizer = optim.SGD(model.parameters(), ...)
+
+        for epoch in epochs:
+            for input, target in data:
+                optimizer.zero_grad()
+
+                # Runs the forward pass with autocasting.
+                with torch.autocast(device_type="cpu", dtype=torch.bfloat16):
+                    output = model(input)
+                    loss = loss_fn(output, target)
+
+                loss.backward()
+                optimizer.step()
+
+
+    CPU Inference Example::
+
+        # Creates model in default precision
+        model = Net().eval()
+
+        with torch.autocast(device_type="cpu", dtype=torch.bfloat16):
+            for input in data:
+                # Runs the forward pass with autocasting.
+                output = model(input)
+
+    CPU Inference Example with Jit Trace::
+
+        class TestModel(nn.Module):
+            def __init__(self, input_size, num_classes):
+                super().__init__()
+                self.fc1 = nn.Linear(input_size, num_classes)
+
+            def forward(self, x):
+                return self.fc1(x)
+
+
+        input_size = 2
+        num_classes = 2
+        model = TestModel(input_size, num_classes).eval()
+
+        # For now, we suggest to disable the Jit Autocast Pass,
+        # As the issue: https://github.com/pytorch/pytorch/issues/75956
+        torch._C._jit_set_autocast_mode(False)
+
+        with torch.cpu.amp.autocast(cache_enabled=False):
+            model = torch.jit.trace(model, torch.randn(1, input_size))
+        model = torch.jit.freeze(model)
+        # Models Run
+        for _ in range(3):
+            model(torch.randn(1, input_size))
+
+    Type mismatch errors *in* an autocast-enabled region are a bug; if this is what you observe,
+    please file an issue.
+
+    ``autocast(enabled=False)`` subregions can be nested in autocast-enabled regions.
+    Locally disabling autocast can be useful, for example, if you want to force a subregion
+    to run in a particular ``dtype``.  Disabling autocast gives you explicit control over
+    the execution type.  In the subregion, inputs from the surrounding region
+    should be cast to ``dtype`` before use::
+
+        # Creates some tensors in default dtype (here assumed to be float32)
+        a_float32 = torch.rand((8, 8), device="cuda")
+        b_float32 = torch.rand((8, 8), device="cuda")
+        c_float32 = torch.rand((8, 8), device="cuda")
+        d_float32 = torch.rand((8, 8), device="cuda")
+
+        with torch.autocast(device_type="cuda"):
+            e_float16 = torch.mm(a_float32, b_float32)
+            with torch.autocast(device_type="cuda", enabled=False):
+                # Calls e_float16.float() to ensure float32 execution
+                # (necessary because e_float16 was created in an autocasted region)
+                f_float32 = torch.mm(c_float32, e_float16.float())
+
+            # No manual casts are required when re-entering the autocast-enabled region.
+            # torch.mm again runs in float16 and produces float16 output, regardless of input types.
+            g_float16 = torch.mm(d_float32, f_float32)
+
+    The autocast state is thread-local.  If you want it enabled in a new thread, the context manager or decorator
+    must be invoked in that thread.  This affects :class:`torch.nn.DataParallel` and
+    :class:`torch.nn.parallel.DistributedDataParallel` when used with more than one GPU per process
+    (see :ref:`Working with Multiple GPUs`).
+
+    Args:
+        device_type(str, required):  Device type to use. Possible values are: 'cuda', 'cpu', 'mtia', 'maia', 'xpu', and 'hpu'.
+                                     The type is the same as the `type` attribute of a :class:`torch.device`.
+                                     Thus, you may obtain the device type of a tensor using `Tensor.device.type`.
+        enabled(bool, optional):  Whether autocasting should be enabled in the region.
+            Default: ``True``
+        dtype(torch_dtype, optional):  Data type for ops run in autocast. It uses the default value
+            (``torch.float16`` for CUDA and ``torch.bfloat16`` for CPU), given by
+            :func:`~torch.get_autocast_dtype`, if :attr:`dtype` is ``None``.
+            Default: ``None``
+        cache_enabled(bool, optional):  Whether the weight cache inside autocast should be enabled.
+            Default: ``True``
+    """
+
+    def __init__(
+        self,
+        device_type: str,
+        dtype: Optional[_dtype] = None,
+        enabled: bool = True,
+        cache_enabled: Optional[bool] = None,
+    ):
+        if not isinstance(device_type, str):
+            raise ValueError(
+                f"Expected `device_type` of type `str`, got: `{type(device_type)}`"
+            )
+        self.fast_dtype = (
+            torch.get_autocast_dtype(device_type) if dtype is None else dtype
+        )
+        if torch._jit_internal.is_scripting():
+            self._enabled = enabled
+            self.device = device_type
+            assert self.fast_dtype is not None
+            return
+        self.device = device_type
+        if not is_autocast_available(self.device):
+            raise RuntimeError(
+                f"User specified an unsupported autocast device_type '{self.device}'"
+            )
+
+        device_supported_dtypes = [torch.bfloat16, torch.float16]
+
+        self.custom_backend_name = torch._C._get_privateuse1_backend_name()
+        if self.device == self.custom_backend_name:
+            necessary_funcs = [
+                "get_amp_supported_dtype",
+            ]
+            message = f"Tried to use AMP with the `{self.custom_backend_name}` backend, but the backend has not "
+            message += "registered a module or  the module miss some necessary funcs. The backend should register "
+            message += "a module by `torch._register_device_module`, and the module must have these funcs: \n"
+            message += "`get_amp_supported_dtype() -> List[torch.dtype]`. \n"
+
+            assert hasattr(torch, self.custom_backend_name), message
+            self.custom_device_mod = getattr(torch, self.custom_backend_name)
+            for func in necessary_funcs:
+                assert hasattr(self.custom_device_mod, func), (
+                    message + f"But the func `{func}` is missing. \n"
+                )
+            device_supported_dtypes = self.custom_device_mod.get_amp_supported_dtype()
+
+        self._cache_enabled = (
+            torch.is_autocast_cache_enabled()
+            if cache_enabled is None
+            else cache_enabled
+        )
+
+        device_name = (
+            self.device
+            if self.device == self.custom_backend_name
+            else self.device.upper()
+        )
+        if enabled:
+            # Special case for CUDA AMP and bfloat16 support
+            if self.device == "cuda":
+                if torch.cuda.amp.common.amp_definitely_not_available():
+                    warnings.warn(
+                        "CUDA is not available or torch_xla is imported. Disabling autocast.",
+                        stacklevel=2,
+                    )
+                    enabled = False
+                elif (
+                    self.fast_dtype == torch.bfloat16
+                    and not torch.cuda.is_bf16_supported()
+                ):
+                    raise RuntimeError(
+                        "Current CUDA Device does not support bfloat16. Please switch dtype to float16."
+                    )
+            elif self.fast_dtype not in device_supported_dtypes:
+                error_message = (
+                    f"In {device_name} autocast, but the target dtype is not supported. Disabling autocast.\n"
+                    f"{device_name} Autocast only supports dtypes of "
+                    + ", ".join(map(str, device_supported_dtypes))
+                    + " currently."
+                )
+                warnings.warn(error_message, stacklevel=2)
+                enabled = False
+                # Special case for MPS bfloat16 support on macOS < 14
+                if (
+                    self.device == "mps"
+                    and self.fast_dtype == torch.bfloat16
+                    and not torch.backends.mps.is_macos_or_newer(14, 0)
+                ):
+                    error_message = (
+                        "In MPS autocast, but the target dtype torch.bfloat16 is not supported "
+                        "on macOS versions below 14. Disabling autocast."
+                    )
+                    warnings.warn(error_message, stacklevel=2)
+                    enabled = False
+        self._enabled = enabled
+
+    def __enter__(self):
+        if torch._jit_internal.is_scripting():
+            assert self.fast_dtype is not None
+            return self
+
+        self.prev_cache_enabled = torch.is_autocast_cache_enabled()
+        self.prev = torch.is_autocast_enabled(self.device)
+        self.prev_fastdtype = torch.get_autocast_dtype(self.device)
+        torch.set_autocast_enabled(self.device, self._enabled)
+        torch.set_autocast_dtype(self.device, self.fast_dtype)  # type: ignore[arg-type]
+        torch.autocast_increment_nesting()
+        torch.set_autocast_cache_enabled(self._cache_enabled)
+
+        # only dispatch to PreDispatchTorchFunctionMode to avoid exposing this
+        # API to other functional modes. We only expose to PreDispatchTorchFunctionMode
+        # for preserving autocast in torch.export.export.
+        if torch._C._is_torch_function_mode_enabled():
+            stacks = torch.overrides._get_current_function_mode_stack()
+            for mode in stacks:
+                if isinstance(
+                    mode,
+                    torch.fx.experimental.proxy_tensor.PreDispatchTorchFunctionMode,
+                ):
+                    args = (
+                        self.device,
+                        self.fast_dtype,
+                        self._enabled,
+                        self._cache_enabled,
+                    )
+                    mode.__torch_function__(torch.amp._enter_autocast, (), args)
+                    return self
+
+        return self
+
+    def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any):  # type: ignore[override]
+        if torch._jit_internal.is_scripting():
+            return
+
+        # Drop the cache when we exit to a nesting level that's outside any instance of autocast.
+        if torch.autocast_decrement_nesting() == 0:
+            torch.clear_autocast_cache()
+        torch.set_autocast_enabled(self.device, self.prev)
+        torch.set_autocast_dtype(self.device, self.prev_fastdtype)
+        torch.set_autocast_cache_enabled(self.prev_cache_enabled)
+
+        # only dispatch to PreDispatchTorchFunctionMode to avoid exposing this
+        # API to other functional modes. We only expose to PreDispatchTorchFunctionMode
+        # for preserving autocast in torch.export.export.
+        if torch._C._is_torch_function_mode_enabled():
+            stacks = torch.overrides._get_current_function_mode_stack()
+            for mode in stacks:
+                if isinstance(
+                    mode,
+                    torch.fx.experimental.proxy_tensor.PreDispatchTorchFunctionMode,
+                ):
+                    mode.__torch_function__(torch.amp._exit_autocast, (), ())
+                    # This is very important because the above line actually doesn't
+                    # run exit code so it end up swallowing exceptions.
+                    return False
+        return False
+
+    def __call__(self, func):
+        if torch._jit_internal.is_scripting():
+            return func
+        return autocast_decorator(self, func)
+
+
+# These functions aren't meant for public usage.
+# They are what we trace into a graph during pre_dispatch tracing
+# when we encounter an autocast context manager.
+def _enter_autocast(*vals):
+    # For pre-dispatch tracing, if a TorchFunction mode is active, we'll want to trace this into a graph.
+    if torch._C._is_torch_function_mode_enabled():
+        return torch.overrides.handle_torch_function(
+            torch.amp._enter_autocast, [], *vals
+        )
+    mode = torch.amp.autocast(*vals)
+    mode.__enter__()
+    return mode
+
+
+def _exit_autocast(mode):
+    if torch._C._is_torch_function_mode_enabled():
+        return torch.overrides.handle_torch_function(torch.amp._exit_autocast, [], mode)
+    mode.__exit__(None, None, None)
+
+
+# Casts Tensors and containers of Tensors.  Special-cases passthroughs for strings and np.ndarrays, which
+# may be falsely detected as "Iterables."
+def _cast(value, device_type: str, dtype: _dtype):
+    if isinstance(value, torch.Tensor):
+        is_eligible = (
+            value.is_floating_point()
+            and value.device.type == device_type
+            and (value.dtype is not torch.float64)
+        )
+        return value.to(dtype) if is_eligible else value
+    elif isinstance(value, (str, bytes)):
+        return value
+    elif HAS_NUMPY and isinstance(
+        value,
+        # pyrefly: ignore [missing-attribute]
+        np.ndarray,
+    ):
+        return value
+    elif isinstance(value, collections.abc.Mapping):
+        return {
+            _cast(k, device_type, dtype): _cast(v, device_type, dtype)
+            for k, v in value.items()
+        }
+    elif isinstance(value, collections.abc.Iterable):
+        iterable = (_cast(v, device_type, dtype) for v in value)
+        if isinstance(value, (list, tuple)):
+            return type(value)(iterable)
+        else:
+            return iterable
+    else:
+        return value
+
+
+def custom_fwd(
+    fwd=None,
+    *,
+    device_type: str,
+    cast_inputs: Optional[_dtype] = None,
+):
+    """
+    Create a helper decorator for ``forward`` methods of custom autograd functions.
+
+    Autograd functions are subclasses of :class:`torch.autograd.Function`.
+    See the :ref:`example page` for more detail.
+
+    Args:
+        device_type(str):  Device type to use. 'cuda', 'cpu', 'mtia', 'maia', 'xpu' and so on.
+            The type is the same as the `type` attribute of a :class:`torch.device`.
+            Thus, you may obtain the device type of a tensor using `Tensor.device.type`.
+        cast_inputs (:class:`torch.dtype` or None, optional, default=None):  If not ``None``,
+            when ``forward`` runs in an autocast-enabled region, casts incoming
+            floating-point Tensors to the target dtype (non-floating-point Tensors are not affected),
+            then executes ``forward`` with autocast disabled.
+            If ``None``, ``forward``'s internal ops execute with the current autocast state.
+
+    .. note::
+        If the decorated ``forward`` is called outside an autocast-enabled region,
+        :func:`custom_fwd` is a no-op and ``cast_inputs`` has no effect.
+    """
+    if not isinstance(device_type, str):
+        raise ValueError(
+            f"Expected `device_type` of type `str`, got: `{type(device_type)}`"
+        )
+    if fwd is None:
+        return functools.partial(
+            custom_fwd, device_type=device_type, cast_inputs=cast_inputs
+        )
+
+    @functools.wraps(fwd)
+    def decorate_fwd(*args, **kwargs):
+        args[0]._dtype = torch.get_autocast_dtype(device_type)
+        if cast_inputs is None:
+            args[0]._fwd_used_autocast = torch.is_autocast_enabled(device_type)
+            return fwd(*args, **kwargs)  # pyrefly: ignore [not-callable]
+        else:
+            autocast_context = torch.is_autocast_enabled(device_type)
+            args[0]._fwd_used_autocast = False
+            if autocast_context:
+                with autocast(device_type=device_type, enabled=False):
+                    return fwd(  # pyrefly: ignore  # not-callable
+                        *_cast(args, device_type, cast_inputs),
+                        **_cast(kwargs, device_type, cast_inputs),
+                    )
+            else:
+                return fwd(*args, **kwargs)  # pyrefly: ignore [not-callable]
+
+    return decorate_fwd
+
+
+# Autograd ensures incoming gradients are the same type as forward outputs.  Allowing a separate
+# cast_inputs argument on custom_bwd is unnecessary and could cause errors if it doesn't match
+# cast_inputs supplied to custom_fwd.
+def custom_bwd(bwd=None, *, device_type: str):
+    """Create a helper decorator for backward methods of custom autograd functions.
+
+    Autograd functions are subclasses of :class:`torch.autograd.Function`.
+    Ensures that ``backward`` executes with the same autocast state as ``forward``.
+    See the :ref:`example page` for more detail.
+
+    Args:
+        device_type(str):  Device type to use. 'cuda', 'cpu', 'mtia', 'maia', 'xpu' and so on.
+            The type is the same as the `type` attribute of a :class:`torch.device`.
+            Thus, you may obtain the device type of a tensor using `Tensor.device.type`.
+    """
+
+    if not isinstance(device_type, str):
+        raise ValueError(
+            f"Expected `device_type` of type `str`, got: `{type(device_type)}`"
+        )
+    if bwd is None:
+        return functools.partial(custom_bwd, device_type=device_type)
+
+    @functools.wraps(bwd)
+    def decorate_bwd(*args, **kwargs):
+        with autocast(
+            device_type=device_type,
+            enabled=args[0]._fwd_used_autocast,
+            dtype=args[0]._dtype,
+        ):
+            return bwd(*args, **kwargs)  # pyrefly: ignore [not-callable]
+
+    return decorate_bwd
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/amp/grad_scaler.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/amp/grad_scaler.py
new file mode 100644
index 0000000000000000000000000000000000000000..506c2cf901600e6d78cbb6bd5831bc7a28c09837
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/amp/grad_scaler.py
@@ -0,0 +1,693 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import inspect
+import warnings
+from collections import abc, defaultdict
+from enum import Enum
+from typing import Any, cast, Optional, overload, TYPE_CHECKING, Union
+
+import torch
+
+
+if TYPE_CHECKING:
+    from collections.abc import Iterable
+
+
+__all__ = ["OptState", "GradScaler"]
+
+
+class _MultiDeviceReplicator:
+    """Lazily serves copies of a tensor to requested devices.
+
+    Copies are cached per-device.
+    """
+
+    def __init__(self, master_tensor: torch.Tensor) -> None:
+        self.master = master_tensor
+        self._per_device_tensors: dict[torch.device, torch.Tensor] = {}
+
+    def get(self, device: torch.device) -> torch.Tensor:
+        retval = self._per_device_tensors.get(device, None)
+        if retval is None:
+            retval = self.master.to(device=device, non_blocking=True, copy=True)
+            self._per_device_tensors[device] = retval
+        return retval
+
+
+# Defines default_factory for GradScaler's _per_optimizer_states defaultdict,
+# as well as associated "enum" values.  Prefers defining these at top level because
+# - Lambdas can't be pickled, so we don't want to supply a lambda as the factory.
+# - Defining READY, UNSCALED, STEPPED and _refresh_per_optimizer_state within GradScaler
+#   causes a circular reference, which we'd rather avoid.
+class OptState(Enum):
+    READY = 0
+    UNSCALED = 1
+    STEPPED = 2
+
+
+def _refresh_per_optimizer_state() -> dict[str, Any]:
+    return {"stage": OptState.READY, "found_inf_per_device": {}}
+
+
+class GradScaler:
+    """An instance ``scaler`` of :class:`GradScaler`.
+
+    Helps perform the steps of gradient scaling
+    conveniently.
+
+    * ``scaler.scale(loss)`` multiplies a given loss by ``scaler``'s current scale factor.
+    * ``scaler.step(optimizer)`` safely unscales gradients and calls ``optimizer.step()``.
+    * ``scaler.update()`` updates ``scaler``'s scale factor.
+
+    Example::
+
+        # Creates a GradScaler once at the beginning of training.
+        scaler = GradScaler()
+
+        for epoch in epochs:
+            for input, target in data:
+                optimizer.zero_grad()
+                output = model(input)
+                loss = loss_fn(output, target)
+
+                # Scales loss.  Calls backward() on scaled loss to create scaled gradients.
+                scaler.scale(loss).backward()
+
+                # scaler.step() first unscales gradients of the optimizer's params.
+                # If gradients don't contain infs/NaNs, optimizer.step() is then called,
+                # otherwise, optimizer.step() is skipped.
+                scaler.step(optimizer)
+
+                # Updates the scale for next iteration.
+                scaler.update()
+
+    See the :ref:`Automatic Mixed Precision examples` for usage
+    (along with autocasting) in more complex cases like gradient clipping, gradient accumulation, gradient penalty,
+    and multiple losses/optimizers.
+
+    ``scaler`` dynamically estimates the scale factor each iteration.  To minimize gradient underflow,
+    a large scale factor should be used.  However, ``float16`` values can "overflow" (become inf or NaN) if
+    the scale factor is too large.  Therefore, the optimal scale factor is the largest factor that can be used
+    without incurring inf or NaN gradient values.
+    ``scaler`` approximates the optimal scale factor over time by checking the gradients for infs and NaNs during every
+    ``scaler.step(optimizer)`` (or optional separate ``scaler.unscale_(optimizer)``, see :meth:`unscale_`).
+
+    * If infs/NaNs are found, ``scaler.step(optimizer)`` skips the underlying ``optimizer.step()`` (so the params
+      themselves remain uncorrupted) and ``update()`` multiplies the scale by ``backoff_factor``.
+
+    * If no infs/NaNs are found, ``scaler.step(optimizer)`` runs the underlying ``optimizer.step()`` as usual.
+      If ``growth_interval`` unskipped iterations occur consecutively, ``update()`` multiplies the scale by
+      ``growth_factor``.
+
+    The scale factor often causes infs/NaNs to appear in gradients for the first few iterations as its
+    value calibrates.  ``scaler.step`` will skip the underlying ``optimizer.step()`` for these
+    iterations.  After that, step skipping should occur rarely (once every few hundred or thousand iterations).
+
+    Args:
+        device (str, optional, default="cuda"): Device type to use. Possible values are: 'cuda' and 'cpu'.
+            The type is the same as the `type` attribute of a :class:`torch.device`.
+            Thus, you may obtain the device type of a tensor using `Tensor.device.type`.
+        init_scale (float, optional, default=2.**16):  Initial scale factor.
+        growth_factor (float, optional, default=2.0):  Factor by which the scale is multiplied during
+            :meth:`update` if no inf/NaN gradients occur for ``growth_interval`` consecutive iterations.
+        backoff_factor (float, optional, default=0.5):  Factor by which the scale is multiplied during
+            :meth:`update` if inf/NaN gradients occur in an iteration.
+        growth_interval (int, optional, default=2000):  Number of consecutive iterations without inf/NaN gradients
+            that must occur for the scale to be multiplied by ``growth_factor``.
+        enabled (bool, optional):  If ``False``, disables gradient scaling. :meth:`step` simply
+            invokes the underlying ``optimizer.step()``, and other methods become no-ops.
+            Default: ``True``
+    """
+
+    def __init__(
+        self,
+        device: str = "cuda",
+        init_scale: float = 2.0**16,
+        growth_factor: float = 2.0,
+        backoff_factor: float = 0.5,
+        growth_interval: int = 2000,
+        enabled: bool = True,
+    ) -> None:
+        self._device = device
+        self._enabled = enabled
+        if self._device == "cuda":
+            if enabled and torch.cuda.amp.common.amp_definitely_not_available():
+                warnings.warn(
+                    "torch.cuda.amp.GradScaler is enabled, but CUDA is not available.  Disabling.",
+                    stacklevel=2,
+                )
+                self._enabled = False
+
+        if self._enabled:
+            assert growth_factor > 1.0, "The growth factor must be > 1.0."
+            assert backoff_factor < 1.0, "The backoff factor must be < 1.0."
+
+            self._init_scale = init_scale
+            # self._scale will be lazily initialized during the first call to scale()
+            self._scale: Optional[torch.Tensor] = None
+            self._growth_factor = growth_factor
+            self._backoff_factor = backoff_factor
+            self._growth_interval = growth_interval
+            self._init_growth_tracker = 0
+            # self._growth_tracker will be lazily initialized during the first call to scale()
+            self._growth_tracker: Optional[torch.Tensor] = None
+            self._per_optimizer_states: dict[int, dict[str, Any]] = defaultdict(
+                _refresh_per_optimizer_state
+            )
+
+    def _check_scale_growth_tracker(
+        self, funcname: str
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        fix = "This may indicate your script did not use scaler.scale(loss or outputs) earlier in the iteration."
+        assert self._scale is not None, (
+            f"Attempted {funcname} but _scale is None.  " + fix
+        )
+        assert self._growth_tracker is not None, (
+            f"Attempted {funcname} but _growth_tracker is None.  " + fix
+        )
+        return (self._scale, self._growth_tracker)
+
+    def _lazy_init_scale_growth_tracker(self, dev: torch.device) -> None:
+        assert self._growth_tracker is None, "_growth_tracker initialized before _scale"
+        self._scale = torch.full((), self._init_scale, dtype=torch.float32, device=dev)
+        self._growth_tracker = torch.full(
+            (), self._init_growth_tracker, dtype=torch.int32, device=dev
+        )
+
+    @overload
+    def scale(self, outputs: torch.Tensor) -> torch.Tensor: ...
+
+    @overload
+    def scale(self, outputs: list[torch.Tensor]) -> list[torch.Tensor]: ...
+
+    @overload
+    def scale(self, outputs: tuple[torch.Tensor, ...]) -> tuple[torch.Tensor, ...]: ...
+
+    @overload
+    def scale(self, outputs: Iterable[torch.Tensor]) -> Iterable[torch.Tensor]: ...
+
+    def scale(
+        self,
+        outputs: Union[torch.Tensor, Iterable[torch.Tensor]],
+    ) -> Union[torch.Tensor, Iterable[torch.Tensor]]:
+        """
+        Multiplies ('scales') a tensor or list of tensors by the scale factor.
+
+        Returns scaled outputs.  If this instance of :class:`GradScaler` is not enabled, outputs are returned
+        unmodified.
+
+        Args:
+            outputs (Tensor or iterable of Tensors):  Outputs to scale.
+        """
+        if not self._enabled:
+            return outputs
+
+        # Short-circuit for the common case.
+        if isinstance(outputs, torch.Tensor):
+            if self._scale is None:
+                self._lazy_init_scale_growth_tracker(outputs.device)
+            assert self._scale is not None
+            return outputs * self._scale.to(device=outputs.device, non_blocking=True)
+
+        # Invoke the more complex machinery only if we're treating multiple outputs.
+        stash: list[
+            _MultiDeviceReplicator
+        ] = []  # holds a reference that can be overwritten by apply_scale
+
+        def apply_scale(val: Union[torch.Tensor, Iterable[torch.Tensor]]):
+            if isinstance(val, torch.Tensor):
+                if len(stash) == 0:
+                    if self._scale is None:
+                        self._lazy_init_scale_growth_tracker(val.device)
+                    assert self._scale is not None
+                    stash.append(_MultiDeviceReplicator(self._scale))
+                return val * stash[0].get(val.device)
+            if isinstance(val, abc.Iterable):
+                iterable = map(apply_scale, val)
+                if isinstance(val, (list, tuple)):
+                    return type(val)(iterable)
+                return iterable
+            raise ValueError("outputs must be a Tensor or an iterable of Tensors")
+
+        return apply_scale(outputs)
+
+    def _unscale_grads_(
+        self,
+        optimizer: torch.optim.Optimizer,
+        inv_scale: torch.Tensor,
+        found_inf: torch.Tensor,
+        allow_fp16: bool,
+    ) -> dict[torch.device, torch.Tensor]:
+        per_device_inv_scale = _MultiDeviceReplicator(inv_scale)
+        per_device_found_inf = _MultiDeviceReplicator(found_inf)
+
+        # To set up _amp_foreach_non_finite_check_and_unscale_, split grads by device and dtype.
+        # There could be hundreds of grads, so we'd like to iterate through them just once.
+        # However, we don't know their devices or dtypes in advance.
+
+        # https://stackoverflow.com/questions/5029934/defaultdict-of-defaultdict
+        # Google says mypy struggles with defaultdicts type annotations.
+        per_device_and_dtype_grads: dict[
+            torch.device, dict[torch.dtype, list[torch.Tensor]]
+        ] = defaultdict(lambda: defaultdict(list))
+        with torch.no_grad():
+            for group in optimizer.param_groups:
+                for param in group["params"]:
+                    assert isinstance(param, torch.Tensor)
+                    if param.grad is None:
+                        continue
+                    if (not allow_fp16) and param.grad.dtype == torch.float16:
+                        raise ValueError("Attempting to unscale FP16 gradients.")
+                    if param.grad.is_sparse:
+                        # is_coalesced() == False means the sparse grad has values with duplicate indices.
+                        # coalesce() deduplicates indices and adds all values that have the same index.
+                        # For scaled fp16 values, there's a good chance coalescing will cause overflow,
+                        # so we should check the coalesced _values().
+                        if param.grad.dtype is torch.float16:
+                            param.grad = param.grad.coalesce()
+                        to_unscale = param.grad._values()
+                    else:
+                        to_unscale = param.grad
+
+                    # TODO: is there a way to split by device and dtype without appending in the inner loop?
+                    per_device_and_dtype_grads[to_unscale.device][
+                        to_unscale.dtype
+                    ].append(to_unscale)
+
+            for device, per_dtype_grads in per_device_and_dtype_grads.items():
+                for grads in per_dtype_grads.values():
+                    torch._amp_foreach_non_finite_check_and_unscale_(
+                        grads,
+                        per_device_found_inf.get(device),
+                        per_device_inv_scale.get(device),
+                    )
+
+        return per_device_found_inf._per_device_tensors
+
+    def unscale_(self, optimizer: torch.optim.Optimizer) -> None:
+        """
+        Divides ("unscales") the optimizer's gradient tensors by the scale factor.
+
+        :meth:`unscale_` is optional, serving cases where you need to
+        :ref:`modify or inspect gradients`
+        between the backward pass(es) and :meth:`step`.
+        If :meth:`unscale_` is not called explicitly,  gradients will be unscaled  automatically during :meth:`step`.
+
+        Simple example, using :meth:`unscale_` to enable clipping of unscaled gradients::
+
+            ...
+            scaler.scale(loss).backward()
+            scaler.unscale_(optimizer)
+            torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
+            scaler.step(optimizer)
+            scaler.update()
+
+        Args:
+            optimizer (torch.optim.Optimizer):  Optimizer that owns the gradients to be unscaled.
+
+        .. note::
+            :meth:`unscale_` does not incur a CPU-GPU sync.
+
+        .. warning::
+            :meth:`unscale_` should only be called once per optimizer per :meth:`step` call,
+            and only after all gradients for that optimizer's assigned parameters have been accumulated.
+            Calling :meth:`unscale_` twice for a given optimizer between each :meth:`step` triggers a RuntimeError.
+
+        .. warning::
+            :meth:`unscale_` may unscale sparse gradients out of place, replacing the ``.grad`` attribute.
+        """
+        if not self._enabled:
+            return
+
+        self._check_scale_growth_tracker("unscale_")
+
+        optimizer_state = self._per_optimizer_states[id(optimizer)]
+
+        if optimizer_state["stage"] is OptState.UNSCALED:
+            raise RuntimeError(
+                "unscale_() has already been called on this optimizer since the last update()."
+            )
+        elif optimizer_state["stage"] is OptState.STEPPED:
+            raise RuntimeError("unscale_() is being called after step().")
+
+        # FP32 division can be imprecise for certain compile options, so we carry out the reciprocal in FP64.
+        assert self._scale is not None
+        inv_scale = (
+            self._scale.double().reciprocal().float()
+            if self._scale.device != torch.device("mps:0")
+            else self._scale.reciprocal()
+        )
+        found_inf = torch.full((), 0.0, dtype=torch.float32, device=self._scale.device)
+
+        optimizer_state["found_inf_per_device"] = self._unscale_grads_(
+            optimizer, inv_scale, found_inf, False
+        )
+        optimizer_state["stage"] = OptState.UNSCALED
+
+    def _maybe_opt_step(
+        self,
+        optimizer: torch.optim.Optimizer,
+        optimizer_state: dict[str, Any],
+        *args: Any,
+        **kwargs: Any,
+    ) -> Optional[float]:
+        retval: Optional[float] = None
+        if not sum(v.item() for v in optimizer_state["found_inf_per_device"].values()):
+            retval = optimizer.step(*args, **kwargs)
+        return retval
+
+    def step(
+        self, optimizer: torch.optim.Optimizer, *args: Any, **kwargs: Any
+    ) -> Optional[float]:
+        """Invoke ``unscale_(optimizer)`` followed by parameter update, if gradients are not infs/NaN.
+
+        :meth:`step` carries out the following two operations:
+
+        1.  Internally invokes ``unscale_(optimizer)`` (unless :meth:`unscale_` was explicitly called for ``optimizer``
+            earlier in the iteration).  As part of the :meth:`unscale_`, gradients are checked for infs/NaNs.
+        2.  If no inf/NaN gradients are found, invokes ``optimizer.step()`` using the unscaled
+            gradients.  Otherwise, ``optimizer.step()`` is skipped to avoid corrupting the params.
+
+        ``*args`` and ``**kwargs`` are forwarded to ``optimizer.step()``.
+
+        Returns the return value of ``optimizer.step(*args, **kwargs)``.
+
+        Args:
+            optimizer (torch.optim.Optimizer):  Optimizer that applies the gradients.
+            args:  Any arguments.
+            kwargs:  Any keyword arguments.
+
+        .. warning::
+            Closure use is not currently supported.
+        """
+        if not self._enabled:
+            return optimizer.step(*args, **kwargs)
+
+        if "closure" in kwargs:
+            raise RuntimeError(
+                "Closure use is not currently supported if GradScaler is enabled."
+            )
+
+        self._check_scale_growth_tracker("step")
+
+        optimizer_state = self._per_optimizer_states[id(optimizer)]
+
+        if optimizer_state["stage"] is OptState.STEPPED:
+            raise RuntimeError(
+                "step() has already been called since the last update()."
+            )
+
+        retval: Optional[float] = None
+
+        if getattr(optimizer, "_step_supports_amp_scaling", False):
+            # This optimizer has customized scale-handling logic, so we can call optimizer.step() directly.
+            # The contract with custom optimizers is that their step() should accept an additional,
+            # optional grad_scaler kwarg.  We append self to the kwargs so the custom optimizer has full information:
+            # it can query its own state, invoke unscale_ on itself, etc
+            # The contract above is being deprecated to avoid introducing `grad_scaler: GradScaler` argument
+            # to `Optimizer.step`. The new behavior is going to add two Tensor attributes of `grad_scale`
+            # and `found_inf` to the passed optimizer so that the optimizer can utilize those
+            # to skip the parameter updates or unscale gradients before updating parameters in
+            # the fused kernel, e.g. `FusedAdamMathFunctor`.
+            # In this behavior, `GradScaler._check_inf_per_device` is called if `OptState.READY`,
+            # while the method is expected to be called by users side, i.e. their optimizers.
+            kwargs_ = kwargs
+            has_grad_scaler_kwarg = (
+                "grad_scaler" in inspect.signature(optimizer.step).parameters
+            )
+            if has_grad_scaler_kwarg:
+                warnings.warn(
+                    "GradScaler is going to stop passing itself as a keyword argument to the passed "
+                    "optimizer. In the near future GradScaler registers `grad_scale: Tensor` and "
+                    "`found_inf: Tensor` to the passed optimizer and let the optimizer use them directly.",
+                    FutureWarning,
+                    stacklevel=2,
+                )
+                kwargs_.update({"grad_scaler": self})
+            else:
+                if optimizer_state["stage"] is OptState.READY:
+                    self._check_inf_per_device(optimizer)
+                scaler = self._get_scale_async()
+                assert scaler is not None
+                found_inf = cast(
+                    torch.Tensor,
+                    sum(
+                        [  # noqa: C419
+                            t.to(scaler.device, non_blocking=True)
+                            for t in optimizer_state["found_inf_per_device"].values()
+                        ]
+                    ),
+                )
+                # Take the product of the scales, if the user has already set `optimizer.grad_scale`.
+                optimizer.grad_scale = (  # type: ignore[attr-defined]
+                    getattr(optimizer, "grad_scale", None)
+                    if optimizer_state["stage"] == OptState.UNSCALED
+                    else scaler * getattr(optimizer, "grad_scale", 1)
+                )
+                optimizer.found_inf = found_inf  # type: ignore[attr-defined]
+            retval = optimizer.step(*args, **kwargs_)
+            optimizer_state["stage"] = OptState.STEPPED
+            if not has_grad_scaler_kwarg:
+                del optimizer.grad_scale  # type: ignore[attr-defined]
+                del optimizer.found_inf  # type: ignore[attr-defined]
+            return retval
+
+        if optimizer_state["stage"] is OptState.READY:
+            self.unscale_(optimizer)
+
+        assert len(optimizer_state["found_inf_per_device"]) > 0, (
+            "No inf checks were recorded for this optimizer."
+        )
+
+        retval = self._maybe_opt_step(optimizer, optimizer_state, *args, **kwargs)
+
+        optimizer_state["stage"] = OptState.STEPPED
+
+        return retval
+
+    def update(self, new_scale: Optional[Union[float, torch.Tensor]] = None) -> None:
+        """Update the scale factor.
+
+        If any optimizer steps were skipped the scale is multiplied by ``backoff_factor``
+        to reduce it. If ``growth_interval`` unskipped iterations occurred consecutively,
+        the scale is multiplied by ``growth_factor`` to increase it.
+
+        Passing ``new_scale`` sets the new scale value manually. (``new_scale`` is not
+        used directly, it's used to fill GradScaler's internal scale tensor. So if
+        ``new_scale`` was a tensor, later in-place changes to that tensor will not further
+        affect the scale GradScaler uses internally.)
+
+        Args:
+            new_scale (float or :class:`torch.Tensor`, optional, default=None):  New scale factor.
+
+        .. warning::
+            :meth:`update` should only be called at the end of the iteration, after ``scaler.step(optimizer)`` has
+            been invoked for all optimizers used this iteration.
+
+        .. warning::
+            For performance reasons, we do not check the scale factor value to avoid synchronizations,
+            so the scale factor is not guaranteed to be above 1. If the scale falls below 1 and/or
+            you are seeing NaNs in your gradients or loss, something is likely wrong. For example,
+            bf16-pretrained models are often incompatible with AMP/fp16 due to differing dynamic ranges.
+        """
+        if not self._enabled:
+            return
+
+        _scale, _growth_tracker = self._check_scale_growth_tracker("update")
+
+        if new_scale is not None:
+            assert self._scale is not None
+            # Accept a new user-defined scale.
+            if isinstance(new_scale, float):
+                self._scale.fill_(new_scale)
+            else:
+                reason = (
+                    "new_scale should be a float or a 1-element torch.cuda.FloatTensor or "
+                    "torch.FloatTensor with requires_grad=False."
+                )
+                assert new_scale.device.type == self._device, reason
+                assert new_scale.numel() == 1, reason
+                assert new_scale.requires_grad is False, reason
+                self._scale.copy_(new_scale)
+        else:
+            # Consume shared inf/nan data collected from optimizers to update the scale.
+            # If all found_inf tensors are on the same device as self._scale, this operation is asynchronous.
+            found_infs = [
+                found_inf.to(device=_scale.device, non_blocking=True)
+                for state in self._per_optimizer_states.values()
+                for found_inf in state["found_inf_per_device"].values()
+            ]
+
+            assert len(found_infs) > 0, "No inf checks were recorded prior to update."
+
+            found_inf_combined = found_infs[0]
+            if len(found_infs) > 1:
+                for i in range(1, len(found_infs)):
+                    found_inf_combined += found_infs[i]
+
+            torch._amp_update_scale_(
+                _scale,
+                _growth_tracker,
+                found_inf_combined,
+                self._growth_factor,
+                self._backoff_factor,
+                self._growth_interval,
+            )
+
+        # To prepare for next iteration, clear the data collected from optimizers this iteration.
+        self._per_optimizer_states = defaultdict(_refresh_per_optimizer_state)
+
+    def _get_scale_async(self) -> Optional[torch.Tensor]:
+        return self._scale
+
+    def get_scale(self) -> float:
+        """Return a Python float containing the current scale, or 1.0 if scaling is disabled.
+
+        .. warning::
+            :meth:`get_scale` incurs a CPU-GPU sync.
+        """
+        if self._enabled:
+            return (
+                self._init_scale
+                if (scale := self._get_scale_async()) is None
+                else cast(float, scale.item())
+            )
+        return 1.0
+
+    def get_growth_factor(self) -> float:
+        r"""Return a Python float containing the scale growth factor."""
+        return self._growth_factor
+
+    def set_growth_factor(self, new_factor: float) -> None:
+        r"""Set a new scale growth factor.
+
+        Args:
+            new_scale (float):  Value to use as the new scale growth factor.
+        """
+        self._growth_factor = new_factor
+
+    def get_backoff_factor(self) -> float:
+        r"""Return a Python float containing the scale backoff factor."""
+        return self._backoff_factor
+
+    def set_backoff_factor(self, new_factor: float) -> None:
+        r"""Set a new scale backoff factor.
+
+        Args:
+            new_scale (float):  Value to use as the new scale backoff factor.
+        """
+        self._backoff_factor = new_factor
+
+    def get_growth_interval(self) -> int:
+        r"""Return a Python int containing the growth interval."""
+        return self._growth_interval
+
+    def set_growth_interval(self, new_interval: int) -> None:
+        r"""Set a new growth interval.
+
+        Args:
+            new_interval (int):  Value to use as the new growth interval.
+        """
+        self._growth_interval = new_interval
+
+    def _get_growth_tracker(self) -> int:
+        if self._enabled:
+            return (
+                self._init_growth_tracker
+                if self._growth_tracker is None
+                else cast(int, self._growth_tracker.item())
+            )
+        return 0
+
+    def is_enabled(self) -> bool:
+        r"""Return a bool indicating whether this instance is enabled."""
+        return self._enabled
+
+    def state_dict(self) -> dict[str, Any]:
+        r"""Return the state of the scaler as a :class:`dict`.
+
+        It contains five entries:
+
+        * ``"scale"`` - a Python float containing the current scale
+        * ``"growth_factor"`` - a Python float containing the current growth factor
+        * ``"backoff_factor"`` - a Python float containing the current backoff factor
+        * ``"growth_interval"`` - a Python int containing the current growth interval
+        * ``"_growth_tracker"`` - a Python int containing the number of recent consecutive unskipped steps.
+
+        If this instance is not enabled, returns an empty dict.
+
+        .. note::
+           If you wish to checkpoint the scaler's state after a particular iteration, :meth:`state_dict`
+           should be called after :meth:`update`.
+        """
+        if self._enabled:
+            return {
+                "scale": self.get_scale(),
+                "growth_factor": self._growth_factor,
+                "backoff_factor": self._backoff_factor,
+                "growth_interval": self._growth_interval,
+                "_growth_tracker": self._get_growth_tracker(),
+            }
+        return {}
+
+    def load_state_dict(self, state_dict: dict[str, Any]) -> None:
+        r"""Load the scaler state.
+
+        If this instance is disabled, :meth:`load_state_dict` is a no-op.
+
+        Args:
+           state_dict(dict): scaler state.  Should be an object returned from a call to :meth:`state_dict`.
+        """
+        if not self._enabled:
+            return
+
+        if len(state_dict) == 0:
+            raise RuntimeError(
+                "The source state dict is empty, possibly because it was saved "
+                "from a disabled instance of GradScaler."
+            )
+
+        self._init_scale = cast(float, state_dict["scale"])
+        if self._scale is not None:
+            self._scale.fill_(state_dict["scale"])
+        self._growth_factor = cast(float, state_dict["growth_factor"])
+        self._backoff_factor = cast(float, state_dict["backoff_factor"])
+        self._growth_interval = cast(int, state_dict["growth_interval"])
+        self._init_growth_tracker = cast(int, state_dict["_growth_tracker"])
+        if self._growth_tracker is not None:
+            self._growth_tracker.fill_(state_dict["_growth_tracker"])
+
+    def __getstate__(self) -> dict[str, Any]:
+        state = self.__dict__.copy()
+        if self._enabled:
+            assert len(self._per_optimizer_states) == 0, (
+                "A GradScaler instance may only be pickled at the beginning "
+                "of an iteration, or at the end after scaler.update()."
+            )
+            # Pickling _scale and _growth_tracker Tensors directly triggers
+            # "warnings.warn("pickle support for Storage will be removed in 1.5..."
+            # so instead, we set the unpickled instance up to reinitialize them lazily.
+            state["_init_scale"] = self.get_scale()
+            state["_init_growth_tracker"] = self._get_growth_tracker()
+            state["_scale"] = None
+            state["_growth_tracker"] = None
+        return state
+
+    def __setstate__(self, state: dict[str, Any]) -> None:
+        self.__dict__.update(state)
+
+    def _check_inf_per_device(self, optimizer: torch.optim.Optimizer) -> dict[str, Any]:
+        _scale, _ = self._check_scale_growth_tracker("_check_inf_per_device")
+
+        dummy_inv_scale = torch.full((), 1.0, dtype=torch.float32, device=_scale.device)
+        found_inf = torch.full((), 0.0, dtype=torch.float32, device=_scale.device)
+
+        self._per_optimizer_states[id(optimizer)]["found_inf_per_device"] = (
+            self._unscale_grads_(optimizer, dummy_inv_scale, found_inf, True)
+        )
+
+        return self._per_optimizer_states[id(optimizer)]["found_inf_per_device"]
+
+    def _found_inf_per_device(self, optimizer: torch.optim.Optimizer) -> dict[str, Any]:
+        return self._per_optimizer_states[id(optimizer)]["found_inf_per_device"]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac866b5073deb7909c5a18ed38dcd0d81ca473a8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/__init__.py
@@ -0,0 +1,31 @@
+# torch.ao is a package with a lot of interdependencies.
+# We will use lazy import to avoid cyclic dependencies here.
+
+from typing import TYPE_CHECKING as _TYPE_CHECKING
+
+
+if _TYPE_CHECKING:
+    from types import ModuleType
+
+    from torch.ao import (  # noqa: TC004
+        nn as nn,
+        ns as ns,
+        pruning as pruning,
+        quantization as quantization,
+    )
+
+
+__all__ = [
+    "nn",
+    "ns",
+    "pruning",
+    "quantization",
+]
+
+
+def __getattr__(name: str) -> "ModuleType":
+    if name in __all__:
+        import importlib
+
+        return importlib.import_module("." + name, __name__)
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7439c22d66882d058e617edb85bc4407cfd742a9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/__init__.py
@@ -0,0 +1,35 @@
+# We are exposing all subpackages to the end-user.
+# Because of possible inter-dependency, we want to avoid
+# the cyclic imports, thus implementing lazy version
+# as per https://peps.python.org/pep-0562/
+
+from typing import TYPE_CHECKING as _TYPE_CHECKING
+
+
+if _TYPE_CHECKING:
+    from types import ModuleType
+
+    from torch.ao.nn import (  # noqa: TC004
+        intrinsic as intrinsic,
+        qat as qat,
+        quantizable as quantizable,
+        quantized as quantized,
+        sparse as sparse,
+    )
+
+
+__all__ = [
+    "intrinsic",
+    "qat",
+    "quantizable",
+    "quantized",
+    "sparse",
+]
+
+
+def __getattr__(name: str) -> "ModuleType":
+    if name in __all__:
+        import importlib
+
+        return importlib.import_module("." + name, __name__)
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..80ba84a84251db6229c38b5f2c48b233fe594fbb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/__init__.py
@@ -0,0 +1,41 @@
+import types
+
+from .modules import *  # noqa: F403
+from .modules.fused import _FusedModule  # noqa: F403
+
+
+# # Subpackages
+# from . import qat  # noqa: F403
+# from . import quantized  # noqa: F403
+
+__all__ = [
+    "ConvBn1d",
+    "ConvBn2d",
+    "ConvBn3d",
+    "ConvBnReLU1d",
+    "ConvBnReLU2d",
+    "ConvBnReLU3d",
+    "ConvReLU1d",
+    "ConvReLU2d",
+    "ConvReLU3d",
+    "LinearReLU",
+    "BNReLU2d",
+    "BNReLU3d",
+    "LinearBn1d",
+    "LinearLeakyReLU",
+    "LinearTanh",
+    "ConvAdd2d",
+    "ConvAddReLU2d",
+]
+
+
+# We are exposing all subpackages to the end-user.
+# Because of possible inter-dependency, we want to avoid
+# the cyclic imports, thus implementing lazy version
+# as per https://peps.python.org/pep-0562/
+def __getattr__(name: str) -> types.ModuleType:
+    if name in __all__:
+        import importlib
+
+        return importlib.import_module("." + name, __name__)
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/modules/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..132137b7357378fe29ef9a63310a554725aea86a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/modules/__init__.py
@@ -0,0 +1,41 @@
+from .fused import (  # noqa: F401
+    _FusedModule,
+    BNReLU2d,
+    BNReLU3d,
+    ConvAdd2d,
+    ConvAddReLU2d,
+    ConvBn1d,
+    ConvBn2d,
+    ConvBn3d,
+    ConvBnReLU1d,
+    ConvBnReLU2d,
+    ConvBnReLU3d,
+    ConvReLU1d,
+    ConvReLU2d,
+    ConvReLU3d,
+    LinearBn1d,
+    LinearLeakyReLU,
+    LinearReLU,
+    LinearTanh,
+)
+
+
+__all__ = [
+    "ConvBn1d",
+    "ConvBn2d",
+    "ConvBn3d",
+    "ConvBnReLU1d",
+    "ConvBnReLU2d",
+    "ConvBnReLU3d",
+    "ConvReLU1d",
+    "ConvReLU2d",
+    "ConvReLU3d",
+    "LinearReLU",
+    "BNReLU2d",
+    "BNReLU3d",
+    "LinearBn1d",
+    "LinearLeakyReLU",
+    "LinearTanh",
+    "ConvAdd2d",
+    "ConvAddReLU2d",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/modules/fused.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/modules/fused.py
new file mode 100644
index 0000000000000000000000000000000000000000..d189e3d92447da930ba487034b58c623e2e7a4ce
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/modules/fused.py
@@ -0,0 +1,289 @@
+# mypy: allow-untyped-defs
+import torch
+from torch.nn import (
+    BatchNorm1d,
+    BatchNorm2d,
+    BatchNorm3d,
+    Conv1d,
+    Conv2d,
+    Conv3d,
+    Linear,
+    ReLU,
+)
+from torch.nn.utils.parametrize import type_before_parametrizations
+
+
+__all__ = [
+    "ConvReLU1d",
+    "ConvReLU2d",
+    "ConvReLU3d",
+    "LinearReLU",
+    "ConvBn1d",
+    "ConvBn2d",
+    "ConvBnReLU1d",
+    "ConvBnReLU2d",
+    "ConvBn3d",
+    "ConvBnReLU3d",
+    "BNReLU2d",
+    "BNReLU3d",
+    "LinearBn1d",
+    "LinearLeakyReLU",
+    "LinearTanh",
+    "ConvAdd2d",
+    "ConvAddReLU2d",
+]
+
+
+# Used for identifying intrinsic modules used in quantization
+class _FusedModule(torch.nn.Sequential):
+    pass
+
+
+class ConvReLU1d(_FusedModule):
+    r"""This is a sequential container which calls the Conv1d and ReLU modules.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, conv, relu):
+        assert (
+            type_before_parametrizations(conv) == Conv1d
+            and type_before_parametrizations(relu) == ReLU
+        ), (
+            f"Incorrect types for input modules{type_before_parametrizations(conv)}"
+            f"{type_before_parametrizations(relu)}"
+        )
+        super().__init__(conv, relu)
+
+
+class ConvReLU2d(_FusedModule):
+    r"""This is a sequential container which calls the Conv2d and ReLU modules.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, conv, relu):
+        assert (
+            type_before_parametrizations(conv) == Conv2d
+            and type_before_parametrizations(relu) == ReLU
+        ), (
+            f"Incorrect types for input modules{type_before_parametrizations(conv)}"
+            f"{type_before_parametrizations(relu)}"
+        )
+        super().__init__(conv, relu)
+
+
+class ConvReLU3d(_FusedModule):
+    r"""This is a sequential container which calls the Conv3d and ReLU modules.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, conv, relu):
+        assert (
+            type_before_parametrizations(conv) == Conv3d
+            and type_before_parametrizations(relu) == ReLU
+        ), (
+            f"Incorrect types for input modules{type_before_parametrizations(conv)}"
+            f"{type_before_parametrizations(relu)}"
+        )
+        super().__init__(conv, relu)
+
+
+class LinearReLU(_FusedModule):
+    r"""This is a sequential container which calls the Linear and ReLU modules.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, linear, relu):
+        assert (
+            type_before_parametrizations(linear) == Linear
+            and type_before_parametrizations(relu) == ReLU
+        ), (
+            f"Incorrect types for input modules{type_before_parametrizations(linear)}"
+            f"{type_before_parametrizations(relu)}"
+        )
+        super().__init__(linear, relu)
+
+
+class ConvBn1d(_FusedModule):
+    r"""This is a sequential container which calls the Conv 1d and Batch Norm 1d modules.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, conv, bn):
+        assert (
+            type_before_parametrizations(conv) == Conv1d
+            and type_before_parametrizations(bn) == BatchNorm1d
+        ), (
+            f"Incorrect types for input modules{type_before_parametrizations(conv)}"
+            f"{type_before_parametrizations(bn)}"
+        )
+        super().__init__(conv, bn)
+
+
+class ConvBn2d(_FusedModule):
+    r"""This is a sequential container which calls the Conv 2d and Batch Norm 2d modules.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, conv, bn):
+        assert (
+            type_before_parametrizations(conv) == Conv2d
+            and type_before_parametrizations(bn) == BatchNorm2d
+        ), (
+            f"Incorrect types for input modules{type_before_parametrizations(conv)}"
+            f"{type_before_parametrizations(bn)}"
+        )
+        super().__init__(conv, bn)
+
+
+class ConvBnReLU1d(_FusedModule):
+    r"""This is a sequential container which calls the Conv 1d, Batch Norm 1d, and ReLU modules.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, conv, bn, relu):
+        assert (
+            type_before_parametrizations(conv) == Conv1d
+            and type_before_parametrizations(bn) == BatchNorm1d
+            and type_before_parametrizations(relu) == ReLU
+        ), (
+            f"Incorrect types for input modules{type_before_parametrizations(conv)}"
+            f"{type_before_parametrizations(bn)}"
+            f"{type_before_parametrizations(relu)}"
+        )
+        super().__init__(conv, bn, relu)
+
+
+class ConvBnReLU2d(_FusedModule):
+    r"""This is a sequential container which calls the Conv 2d, Batch Norm 2d, and ReLU modules.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, conv, bn, relu):
+        assert (
+            type_before_parametrizations(conv) == Conv2d
+            and type_before_parametrizations(bn) == BatchNorm2d
+            and type_before_parametrizations(relu) == ReLU
+        ), (
+            f"Incorrect types for input modules{type_before_parametrizations(conv)}"
+            f"{type_before_parametrizations(bn)}"
+            f"{type_before_parametrizations(relu)}"
+        )
+        super().__init__(conv, bn, relu)
+
+
+class ConvBn3d(_FusedModule):
+    r"""This is a sequential container which calls the Conv 3d and Batch Norm 3d modules.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, conv, bn):
+        assert (
+            type_before_parametrizations(conv) == Conv3d
+            and type_before_parametrizations(bn) == BatchNorm3d
+        ), (
+            f"Incorrect types for input modules{type_before_parametrizations(conv)}"
+            f"{type_before_parametrizations(bn)}"
+        )
+        super().__init__(conv, bn)
+
+
+class ConvBnReLU3d(_FusedModule):
+    r"""This is a sequential container which calls the Conv 3d, Batch Norm 3d, and ReLU modules.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, conv, bn, relu):
+        assert (
+            type_before_parametrizations(conv) == Conv3d
+            and type_before_parametrizations(bn) == BatchNorm3d
+            and type_before_parametrizations(relu) == ReLU
+        ), (
+            f"Incorrect types for input modules{type_before_parametrizations(conv)}"
+            f"{type_before_parametrizations(bn)}"
+            f"{type_before_parametrizations(relu)}"
+        )
+        super().__init__(conv, bn, relu)
+
+
+class BNReLU2d(_FusedModule):
+    r"""This is a sequential container which calls the BatchNorm 2d and ReLU modules.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, batch_norm, relu):
+        assert (
+            type_before_parametrizations(batch_norm) == BatchNorm2d
+            and type_before_parametrizations(relu) == ReLU
+        ), (
+            f"Incorrect types for input modules{type_before_parametrizations(batch_norm)}"
+            f"{type_before_parametrizations(relu)}"
+        )
+        super().__init__(batch_norm, relu)
+
+
+class BNReLU3d(_FusedModule):
+    r"""This is a sequential container which calls the BatchNorm 3d and ReLU modules.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, batch_norm, relu):
+        assert (
+            type_before_parametrizations(batch_norm) == BatchNorm3d
+            and type_before_parametrizations(relu) == ReLU
+        ), (
+            f"Incorrect types for input modules{type_before_parametrizations(batch_norm)}"
+            f"{type_before_parametrizations(relu)}"
+        )
+        super().__init__(batch_norm, relu)
+
+
+class LinearBn1d(_FusedModule):
+    r"""This is a sequential container which calls the Linear and BatchNorm1d modules.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, linear, bn):
+        assert (
+            type_before_parametrizations(linear) == Linear
+            and type_before_parametrizations(bn) == BatchNorm1d
+        ), (
+            f"Incorrect types for input modules{type_before_parametrizations(linear)}"
+            f"{type_before_parametrizations(bn)}"
+        )
+        super().__init__(linear, bn)
+
+
+class LinearLeakyReLU(_FusedModule):
+    r"""This is a sequential container which calls the Linear and LeakyReLU modules.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, linear, leaky_relu):
+        assert type(linear) is Linear and type(leaky_relu) is torch.nn.LeakyReLU, (
+            f"Incorrect types for input modules{type(linear)}{type(leaky_relu)}"
+        )
+        super().__init__(linear, leaky_relu)
+
+
+class LinearTanh(_FusedModule):
+    r"""This is a sequential container which calls the Linear and Tanh modules.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, linear, tanh):
+        assert type(linear) is Linear and type(tanh) is torch.nn.Tanh, (
+            f"Incorrect types for input modules{type(linear)}{type(tanh)}"
+        )
+        super().__init__(linear, tanh)
+
+
+class ConvAdd2d(_FusedModule):
+    r"""This is a sequential container which calls the Conv2d modules with extra Add.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, conv, add):
+        super().__init__(conv)
+        self.add = add
+
+    def forward(self, x1, x2):  # type: ignore[override]
+        r"""Applies convolution to x1 and adds the result to x2."""
+        return self.add(self[0](x1), x2)
+
+
+class ConvAddReLU2d(_FusedModule):
+    r"""This is a sequential container which calls the Conv2d, add, Relu.
+    During quantization this will be replaced with the corresponding fused module."""
+
+    def __init__(self, conv, add, relu):
+        super().__init__(conv)
+        self.add = add
+        self.relu = relu
+
+    def forward(self, x1, x2):  # type: ignore[override]
+        r"""Applies convolution to x1, adds the result to x2, and applies ReLU."""
+        return self.relu(self.add(self[0](x1), x2))
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/qat/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/qat/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d79bdbfe83209f18b17cc8c7b245f322871d6c0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/qat/__init__.py
@@ -0,0 +1 @@
+from .modules import *  # noqa: F403
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/qat/modules/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/qat/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..18534bbc588e7480ac6529c6648c5976eadaea3a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/qat/modules/__init__.py
@@ -0,0 +1,32 @@
+from .conv_fused import (
+    ConvBn1d,
+    ConvBn2d,
+    ConvBn3d,
+    ConvBnReLU1d,
+    ConvBnReLU2d,
+    ConvBnReLU3d,
+    ConvReLU1d,
+    ConvReLU2d,
+    ConvReLU3d,
+    freeze_bn_stats,
+    update_bn_stats,
+)
+from .linear_fused import LinearBn1d
+from .linear_relu import LinearReLU
+
+
+__all__ = [
+    "LinearReLU",
+    "LinearBn1d",
+    "ConvReLU1d",
+    "ConvReLU2d",
+    "ConvReLU3d",
+    "ConvBn1d",
+    "ConvBn2d",
+    "ConvBn3d",
+    "ConvBnReLU1d",
+    "ConvBnReLU2d",
+    "ConvBnReLU3d",
+    "update_bn_stats",
+    "freeze_bn_stats",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/qat/modules/conv_fused.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/qat/modules/conv_fused.py
new file mode 100644
index 0000000000000000000000000000000000000000..10f67764d8f05143e4bcc15ad1196f801015370a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/qat/modules/conv_fused.py
@@ -0,0 +1,958 @@
+# mypy: allow-untyped-defs
+import math
+from typing import ClassVar
+
+import torch
+import torch.ao.nn.intrinsic as nni
+import torch.ao.nn.qat as nnqat
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn import init
+from torch.nn.modules.utils import _pair, _single, _triple
+from torch.nn.parameter import Parameter
+from torch.nn.utils import fuse_conv_bn_weights
+
+
+__all__ = [
+    "ConvBn1d",
+    "ConvBnReLU1d",
+    "ConvReLU1d",
+    "ConvBn2d",
+    "ConvBnReLU2d",
+    "ConvReLU2d",
+    "ConvBn3d",
+    "ConvBnReLU3d",
+    "ConvReLU3d",
+    "update_bn_stats",
+    "freeze_bn_stats",
+]
+_BN_CLASS_MAP = {
+    1: nn.BatchNorm1d,
+    2: nn.BatchNorm2d,
+    3: nn.BatchNorm3d,
+}
+
+
+class _ConvBnNd(nn.modules.conv._ConvNd, nni._FusedModule):
+    _version = 2
+    _FLOAT_MODULE: ClassVar[type[nn.modules.conv._ConvNd]]
+
+    def __init__(
+        self,
+        # ConvNd args
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride,
+        padding,
+        dilation,
+        transposed,
+        output_padding,
+        groups,
+        bias,
+        padding_mode,
+        # BatchNormNd args
+        # num_features: out_channels
+        eps=1e-05,
+        momentum=0.1,
+        # affine: True
+        # track_running_stats: True
+        # Args for this module
+        freeze_bn=False,
+        qconfig=None,
+        dim=2,
+    ):
+        nn.modules.conv._ConvNd.__init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            transposed,
+            output_padding,
+            groups,
+            False,
+            padding_mode,
+        )
+        assert qconfig, "qconfig must be provided for QAT module"
+        self.qconfig = qconfig
+        self.freeze_bn = freeze_bn if self.training else True
+        self.bn = _BN_CLASS_MAP[dim](out_channels, eps, momentum, True, True)
+        self.weight_fake_quant = self.qconfig.weight()
+        if bias:
+            self.bias = Parameter(torch.empty(out_channels))
+        else:
+            self.register_parameter("bias", None)
+        self.reset_bn_parameters()
+
+        # this needs to be called after reset_bn_parameters,
+        # as they modify the same state
+        if self.training:
+            if freeze_bn:
+                self.freeze_bn_stats()
+            else:
+                self.update_bn_stats()
+        else:
+            self.freeze_bn_stats()
+
+        self._enable_slow_path_for_better_numerical_stability = False
+
+    def reset_running_stats(self):
+        self.bn.reset_running_stats()
+
+    def reset_bn_parameters(self):
+        self.bn.reset_running_stats()
+        init.uniform_(self.bn.weight)
+        init.zeros_(self.bn.bias)
+        # note: below is actually for conv, not BN
+        if self.bias is not None:
+            fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
+            bound = 1 / math.sqrt(fan_in)
+            init.uniform_(self.bias, -bound, bound)
+
+    def update_bn_stats(self):
+        self.freeze_bn = False
+        self.bn.training = True
+        return self
+
+    def freeze_bn_stats(self):
+        self.freeze_bn = True
+        self.bn.training = False
+        return self
+
+    def _forward(self, input):
+        if self._enable_slow_path_for_better_numerical_stability:
+            return self._forward_slow(input)
+        return self._forward_approximate(input)
+
+    def _forward_approximate(self, input):
+        """Approximated method to fuse conv and bn. It requires only one forward pass.
+        conv_orig = conv / scale_factor where scale_factor = bn.weight / running_std
+        """
+        assert self.bn.running_var is not None
+        running_std = torch.sqrt(self.bn.running_var + self.bn.eps)
+        scale_factor = self.bn.weight / running_std
+        weight_shape = [1] * len(self.weight.shape)
+        weight_shape[0] = -1
+        bias_shape = [1] * len(self.weight.shape)
+        bias_shape[1] = -1
+        scaled_weight = self.weight_fake_quant(
+            self.weight * scale_factor.reshape(weight_shape)
+        )
+        # using zero bias here since the bias for original conv
+        # will be added later
+        if self.bias is not None:
+            zero_bias = torch.zeros_like(self.bias, dtype=input.dtype)
+        else:
+            zero_bias = torch.zeros(
+                self.out_channels, device=scaled_weight.device, dtype=input.dtype
+            )
+        conv = self._conv_forward(input, scaled_weight, zero_bias)
+        conv_orig = conv / scale_factor.reshape(bias_shape)
+        if self.bias is not None:
+            conv_orig = conv_orig + self.bias.reshape(bias_shape)
+        conv = self.bn(conv_orig)
+        return conv
+
+    def _forward_slow(self, input):
+        """
+        A more accurate but slow method to compute conv bn fusion, following https://arxiv.org/pdf/1806.08342.pdf
+        It requires two forward passes but handles the case bn.weight == 0
+
+        Conv: Y = WX + B_c
+        Conv without bias: Y0 = WX = Y - B_c, Y = Y0 + B_c
+
+        Batch statistics:
+          mean_Y = Y.mean()
+                 = Y0.mean() + B_c
+          var_Y = (Y - mean_Y)^2.mean()
+                = (Y0 - Y0.mean())^2.mean()
+        BN (r: bn.weight, beta: bn.bias):
+          Z = r * (Y - mean_Y) / sqrt(var_Y + eps) + beta
+            = r * (Y0 - Y0.mean()) / sqrt(var_Y + eps) + beta
+
+        Fused Conv BN training (std_Y = sqrt(var_Y + eps)):
+          Z = (r * W / std_Y) * X + r * (B_c - mean_Y) / std_Y + beta
+            = (r * W / std_Y) * X - r * Y0.mean() / std_Y + beta
+
+        Fused Conv BN inference (running_std = sqrt(running_var + eps)):
+          Z = (r * W / running_std) * X - r * (running_mean - B_c) / running_std + beta
+
+        QAT with fused conv bn:
+          Z_train = fake_quant(r * W / running_std) * X * (running_std / std_Y) - r * Y0.mean() / std_Y + beta
+                  = conv(X, fake_quant(r * W / running_std)) * (running_std / std_Y) - r * Y0.mean() / std_Y + beta
+          Z_inference = conv(X, fake_quant(r * W / running_std)) - r * (running_mean - B_c) / running_std + beta
+        """
+
+        assert self.bn.running_var is not None
+        assert self.bn.running_mean is not None
+
+        # using zero bias here since the bias for original conv
+        # will be added later
+        zero_bias = torch.zeros(
+            self.out_channels, device=self.weight.device, dtype=input.dtype
+        )
+
+        weight_shape = [1] * len(self.weight.shape)
+        weight_shape[0] = -1
+        bias_shape = [1] * len(self.weight.shape)
+        bias_shape[1] = -1
+
+        if self.bn.training:
+            # needed to compute batch mean/std
+            conv_out = self._conv_forward(input, self.weight, zero_bias)
+            # update bn statistics
+            with torch.no_grad():
+                conv_out_bias = (
+                    conv_out
+                    if self.bias is None
+                    else conv_out + self.bias.reshape(bias_shape)
+                )
+                self.bn(conv_out_bias)
+
+            # fused conv + bn without bias using bn running statistics
+            running_std = torch.sqrt(self.bn.running_var + self.bn.eps)
+            scale_factor = self.bn.weight / running_std
+            scaled_weight = self.weight_fake_quant(
+                self.weight * scale_factor.reshape(weight_shape)
+            )
+            # fused conv without bias for inference: (r * W / running_std) * X
+            conv_bn = self._conv_forward(input, scaled_weight, zero_bias)
+
+            avg_dims = [0] + list(range(2, len(self.weight.shape)))
+            batch_mean = conv_out.mean(avg_dims)
+            batch_var = torch.square(conv_out - batch_mean.reshape(bias_shape)).mean(
+                avg_dims
+            )
+            batch_std = torch.sqrt(batch_var + self.bn.eps)
+
+            # scale to use batch std in training mode
+            # conv(X, r * W / std_Y) = conv(X, r * W / running_std) * (running_std / std_Y)
+            unscale_factor = running_std / batch_std
+            conv_bn *= unscale_factor.reshape(bias_shape)
+
+            fused_mean = batch_mean
+            fused_std = batch_std
+        else:
+            # fused conv + bn without bias using bn running statistics
+            running_std = torch.sqrt(self.bn.running_var + self.bn.eps)
+            scale_factor = self.bn.weight / running_std
+            scaled_weight = self.weight_fake_quant(
+                self.weight * scale_factor.reshape(weight_shape)
+            )
+            # fused conv without bias for inference: (r * W / running_std) * X
+            conv_bn = self._conv_forward(input, scaled_weight, zero_bias)
+
+            fused_mean = self.bn.running_mean - (
+                self.bias if self.bias is not None else 0
+            )
+            fused_std = running_std
+
+        # fused bias = beta - r * mean / std
+        fused_bias = self.bn.bias - self.bn.weight * fused_mean / fused_std
+        conv_bn += fused_bias.reshape(bias_shape)
+
+        # HACK to let conv bias participate in loss to avoid DDP error (parameters
+        #   were not used in producing loss)
+        if self.bias is not None:
+            conv_bn += (self.bias - self.bias).reshape(bias_shape)
+
+        return conv_bn
+
+    def forward(self, input):
+        return self._forward(input)
+
+    def train(self, mode=True):
+        """
+        Batchnorm's training behavior is using the self.training flag. Prevent
+        changing it if BN is frozen. This makes sure that calling `model.train()`
+        on a model with a frozen BN will behave properly.
+        """
+        self.training = mode
+        if not self.freeze_bn:
+            for module in self.children():
+                module.train(mode)
+        return self
+
+    # ===== Serialization version history =====
+    #
+    # Version 1/None
+    #   self
+    #   |--- weight : Tensor
+    #   |--- bias : Tensor
+    #   |--- gamma : Tensor
+    #   |--- beta : Tensor
+    #   |--- running_mean : Tensor
+    #   |--- running_var : Tensor
+    #   |--- num_batches_tracked : Tensor
+    #
+    # Version 2
+    #   self
+    #   |--- weight : Tensor
+    #   |--- bias : Tensor
+    #   |--- bn : Module
+    #        |--- weight : Tensor (moved from v1.self.gamma)
+    #        |--- bias : Tensor (moved from v1.self.beta)
+    #        |--- running_mean : Tensor (moved from v1.self.running_mean)
+    #        |--- running_var : Tensor (moved from v1.self.running_var)
+    #        |--- num_batches_tracked : Tensor (moved from v1.self.num_batches_tracked)
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        version = local_metadata.get("version", None)
+        if version is None or version == 1:
+            # BN related parameters and buffers were moved into the BN module for v2
+            v2_to_v1_names = {
+                "bn.weight": "gamma",
+                "bn.bias": "beta",
+                "bn.running_mean": "running_mean",
+                "bn.running_var": "running_var",
+                "bn.num_batches_tracked": "num_batches_tracked",
+            }
+            for v2_name, v1_name in v2_to_v1_names.items():
+                if prefix + v1_name in state_dict:
+                    state_dict[prefix + v2_name] = state_dict[prefix + v1_name]
+                    state_dict.pop(prefix + v1_name)
+                elif prefix + v2_name in state_dict:
+                    # there was a brief period where forward compatibility
+                    # for this module was broken (between
+                    # https://github.com/pytorch/pytorch/pull/38478
+                    # and https://github.com/pytorch/pytorch/pull/38820)
+                    # and modules emitted the v2 state_dict format while
+                    # specifying that version == 1. This patches the forward
+                    # compatibility issue by allowing the v2 style entries to
+                    # be used.
+                    pass
+                elif strict:
+                    missing_keys.append(prefix + v2_name)
+
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            strict,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        r"""Create a qat module from a float module or qparams_dict
+
+        Args: `mod` a float module, either produced by torch.ao.quantization utilities
+        or directly from user
+        """
+        # The ignore is because _FLOAT_MODULE is a TypeVar here where the bound
+        # has no __name__ (code is fine though)
+        assert type(mod) is cls._FLOAT_MODULE, (
+            "qat."
+            + cls.__name__
+            + ".from_float only works for "
+            + cls._FLOAT_MODULE.__name__
+        )
+        assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
+        assert mod.qconfig, "Input float module must have a valid qconfig"
+        qconfig = mod.qconfig
+        conv, bn = mod[0], mod[1]  # type: ignore[index]
+        qat_convbn = cls(
+            conv.in_channels,
+            conv.out_channels,
+            conv.kernel_size,
+            conv.stride,
+            conv.padding,
+            conv.dilation,
+            conv.groups,
+            conv.bias is not None,
+            conv.padding_mode,
+            bn.eps,
+            bn.momentum,
+            False,
+            qconfig,
+        )
+        qat_convbn.weight = conv.weight
+        qat_convbn.bias = conv.bias
+        qat_convbn.bn.weight = bn.weight
+        qat_convbn.bn.bias = bn.bias
+        qat_convbn.bn.running_mean = bn.running_mean
+        qat_convbn.bn.running_var = bn.running_var
+        # mypy error: Cannot determine type of 'num_batches_tracked'
+        qat_convbn.bn.num_batches_tracked = bn.num_batches_tracked
+        return qat_convbn
+
+    def to_float(self):
+        cls = type(self)
+        conv = cls._FLOAT_CONV_MODULE(  # type: ignore[attr-defined]
+            self.in_channels,
+            self.out_channels,
+            self.kernel_size,
+            self.stride,
+            self.padding,
+            self.dilation,
+            self.groups,
+            self.bias is not None,
+            self.padding_mode,
+        )
+        conv.weight = torch.nn.Parameter(self.weight.detach())
+        if self.bias is not None:
+            conv.bias = torch.nn.Parameter(self.bias.detach())
+
+        if cls._FLOAT_BN_MODULE:  # type: ignore[attr-defined]
+            # fuse bn into conv
+            assert self.bn.running_var is not None and self.bn.running_mean is not None
+            conv.weight, conv.bias = fuse_conv_bn_weights(
+                conv.weight,
+                conv.bias,
+                self.bn.running_mean,
+                self.bn.running_var,
+                self.bn.eps,
+                self.bn.weight,
+                self.bn.bias,
+            )
+
+        if cls._FLOAT_RELU_MODULE:  # type: ignore[attr-defined]
+            modules = []
+            modules.append(conv)
+            relu = cls._FLOAT_RELU_MODULE()  # type: ignore[attr-defined]
+            modules.append(relu)
+            conv_relu = cls._FUSED_FLOAT_MODULE(*modules)  # type: ignore[attr-defined]
+            conv_relu.train(self.training)
+            return conv_relu
+        else:
+            conv.train(self.training)
+            return conv
+
+
+class ConvBn1d(_ConvBnNd, nn.Conv1d):
+    r"""
+    A ConvBn1d module is a module fused from Conv1d and BatchNorm1d,
+    attached with FakeQuantize modules for weight,
+    used in quantization aware training.
+
+    We combined the interface of :class:`torch.nn.Conv1d` and
+    :class:`torch.nn.BatchNorm1d`.
+
+    Similar to :class:`torch.nn.Conv1d`, with FakeQuantize modules initialized
+    to default.
+
+    Attributes:
+        freeze_bn:
+        weight_fake_quant: fake quant module for weight
+
+    """
+
+    _FLOAT_BN_MODULE: ClassVar[type[nn.BatchNorm1d]] = nn.BatchNorm1d
+    _FLOAT_RELU_MODULE: ClassVar[type[nn.Module] | None] = None
+    _FLOAT_MODULE: ClassVar[type[nn.Module]] = nni.ConvBn1d  # type: ignore[assignment]
+    _FLOAT_CONV_MODULE: ClassVar[type[nn.Conv1d]] = nn.Conv1d
+
+    def __init__(
+        self,
+        # Conv1d args
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=None,
+        padding_mode="zeros",
+        # BatchNorm1d args
+        # num_features: out_channels
+        eps=1e-05,
+        momentum=0.1,
+        # affine: True
+        # track_running_stats: True
+        # Args for this module
+        freeze_bn=False,
+        qconfig=None,
+    ):
+        kernel_size = _single(kernel_size)
+        stride = _single(stride)
+        padding = _single(padding)
+        dilation = _single(dilation)
+        _ConvBnNd.__init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            False,
+            _single(0),
+            groups,
+            bias,
+            padding_mode,
+            eps,
+            momentum,
+            freeze_bn,
+            qconfig,
+            dim=1,
+        )
+
+
+class ConvBnReLU1d(ConvBn1d):
+    r"""
+    A ConvBnReLU1d module is a module fused from Conv1d, BatchNorm1d and ReLU,
+    attached with FakeQuantize modules for weight,
+    used in quantization aware training.
+
+    We combined the interface of :class:`torch.nn.Conv1d` and
+    :class:`torch.nn.BatchNorm1d` and :class:`torch.nn.ReLU`.
+
+    Similar to `torch.nn.Conv1d`, with FakeQuantize modules initialized to
+    default.
+
+    Attributes:
+        weight_fake_quant: fake quant module for weight
+
+    """
+
+    # base class defines _FLOAT_MODULE as "ConvBn1d"
+    _FLOAT_MODULE: ClassVar[type[nn.Module]] = nni.ConvBnReLU1d
+    _FLOAT_CONV_MODULE: ClassVar[type[nn.Conv1d]] = nn.Conv1d
+    _FLOAT_BN_MODULE: ClassVar[type[nn.BatchNorm1d]] = nn.BatchNorm1d
+    _FLOAT_RELU_MODULE: ClassVar[type[nn.Module] | None] = nn.ReLU
+    # module class after fusing bn into conv
+    _FUSED_FLOAT_MODULE: ClassVar[type[nn.Module] | None] = nni.ConvReLU1d
+
+    def forward(self, input):
+        r"""Performs forward pass through fused Conv1d, BatchNorm1d, and ReLU."""
+        return F.relu(self._forward(input))
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        r"""Creates a QAT module from a floating point module."""
+        return super().from_float(mod, use_precomputed_fake_quant)
+
+
+class ConvReLU1d(nnqat.Conv1d, nni._FusedModule):
+    r"""A ConvReLU1d module is a fused module of Conv1d and ReLU, attached with
+    FakeQuantize modules for weight for
+    quantization aware training.
+
+    We combined the interface of :class:`~torch.nn.Conv1d` and
+    :class:`~torch.nn.BatchNorm1d`.
+
+    Attributes:
+        weight_fake_quant: fake quant module for weight
+
+    """
+
+    _FLOAT_MODULE: ClassVar[type[nni.ConvReLU1d]] = nni.ConvReLU1d  # type: ignore[assignment]
+    _FLOAT_CONV_MODULE: ClassVar[type[nn.Conv1d]] = nn.Conv1d
+    _FLOAT_BN_MODULE: ClassVar[type[nn.Module] | None] = None
+    _FLOAT_RELU_MODULE: ClassVar[type[nn.Module] | None] = nn.ReLU
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=True,
+        padding_mode="zeros",
+        qconfig=None,
+    ):
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+            # pyrefly: ignore [bad-argument-type]
+            padding_mode=padding_mode,
+            qconfig=qconfig,
+        )
+        assert qconfig, "qconfig must be provided for QAT module"
+        self.qconfig = qconfig
+        self.weight_fake_quant = self.qconfig.weight()
+
+    def forward(self, input):
+        r"""Performs forward pass through fused Conv1d and ReLU."""
+        return F.relu(
+            self._conv_forward(input, self.weight_fake_quant(self.weight), self.bias)
+        )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):  # type: ignore[override]
+        r"""Creates a QAT module from a floating point module."""
+        return super().from_float(
+            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+
+class ConvBn2d(_ConvBnNd, nn.Conv2d):
+    r"""
+    A ConvBn2d module is a module fused from Conv2d and BatchNorm2d,
+    attached with FakeQuantize modules for weight,
+    used in quantization aware training.
+
+    We combined the interface of :class:`torch.nn.Conv2d` and
+    :class:`torch.nn.BatchNorm2d`.
+
+    Similar to :class:`torch.nn.Conv2d`, with FakeQuantize modules initialized
+    to default.
+
+    Attributes:
+        freeze_bn:
+        weight_fake_quant: fake quant module for weight
+
+    """
+
+    _FLOAT_MODULE: ClassVar[type[nni.ConvBn2d]] = nni.ConvBn2d  # type: ignore[assignment]
+    _FLOAT_CONV_MODULE: ClassVar[type[nn.Conv2d]] = nn.Conv2d
+    _FLOAT_BN_MODULE: ClassVar[type[nn.Module] | None] = nn.BatchNorm2d
+    _FLOAT_RELU_MODULE: ClassVar[type[nn.Module] | None] = None
+
+    def __init__(
+        self,
+        # ConvNd args
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=None,
+        padding_mode="zeros",
+        # BatchNorm2d args
+        # num_features: out_channels
+        eps=1e-05,
+        momentum=0.1,
+        # affine: True
+        # track_running_stats: True
+        # Args for this module
+        freeze_bn=False,
+        qconfig=None,
+    ):
+        kernel_size = _pair(kernel_size)
+        stride = _pair(stride)
+        padding = _pair(padding)
+        dilation = _pair(dilation)
+        _ConvBnNd.__init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            False,
+            _pair(0),
+            groups,
+            bias,
+            padding_mode,
+            eps,
+            momentum,
+            freeze_bn,
+            qconfig,
+            dim=2,
+        )
+
+
+class ConvBnReLU2d(ConvBn2d):
+    r"""
+    A ConvBnReLU2d module is a module fused from Conv2d, BatchNorm2d and ReLU,
+    attached with FakeQuantize modules for weight,
+    used in quantization aware training.
+
+    We combined the interface of :class:`torch.nn.Conv2d` and
+    :class:`torch.nn.BatchNorm2d` and :class:`torch.nn.ReLU`.
+
+    Similar to `torch.nn.Conv2d`, with FakeQuantize modules initialized to
+    default.
+
+    Attributes:
+        weight_fake_quant: fake quant module for weight
+
+    """
+
+    # base class defines _FLOAT_MODULE as "ConvBn2d"
+    _FLOAT_MODULE: ClassVar[type[nni.ConvBnReLU2d]] = nni.ConvBnReLU2d  # type: ignore[assignment]
+    _FLOAT_CONV_MODULE: ClassVar[type[nn.Conv2d]] = nn.Conv2d
+    _FLOAT_BN_MODULE: ClassVar[type[nn.BatchNorm2d]] = nn.BatchNorm2d
+    _FLOAT_RELU_MODULE: ClassVar[type[nn.Module] | None] = nn.ReLU
+    # module class after fusing bn into conv
+    _FUSED_FLOAT_MODULE: ClassVar[type[nni.ConvReLU2d] | None] = nni.ConvReLU2d
+
+    def forward(self, input):
+        r"""Performs forward pass through fused Conv2d, BatchNorm2d, and ReLU."""
+        return F.relu(self._forward(input))
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        r"""Creates a QAT module from a floating point module."""
+        return super().from_float(mod, use_precomputed_fake_quant)
+
+
+class ConvReLU2d(nnqat.Conv2d, nni._FusedModule):
+    r"""A ConvReLU2d module is a fused module of Conv2d and ReLU, attached with
+    FakeQuantize modules for weight for
+    quantization aware training.
+
+    We combined the interface of :class:`~torch.nn.Conv2d` and
+    :class:`~torch.nn.BatchNorm2d`.
+
+    Attributes:
+        weight_fake_quant: fake quant module for weight
+
+    """
+
+    _FLOAT_MODULE: ClassVar[type[nn.Module]] = nni.ConvReLU2d  # type: ignore[assignment]
+    _FLOAT_CONV_MODULE: ClassVar[type[nn.Conv2d]] = nn.Conv2d
+    _FLOAT_BN_MODULE: ClassVar[type[nn.Module] | None] = None
+    _FLOAT_RELU_MODULE: ClassVar[type[nn.Module] | None] = nn.ReLU
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=True,
+        padding_mode="zeros",
+        qconfig=None,
+    ):
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+            # pyrefly: ignore [bad-argument-type]
+            padding_mode=padding_mode,
+            qconfig=qconfig,
+        )
+        assert qconfig, "qconfig must be provided for QAT module"
+        self.qconfig = qconfig
+        self.weight_fake_quant = self.qconfig.weight()
+
+    def forward(self, input):
+        r"""Performs forward pass through fused Conv2d and ReLU."""
+        return F.relu(
+            self._conv_forward(input, self.weight_fake_quant(self.weight), self.bias)
+        )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):  # type: ignore[override]
+        r"""Creates a QAT module from a floating point module."""
+        return super().from_float(
+            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+
+class ConvBn3d(_ConvBnNd, nn.Conv3d):
+    r"""
+    A ConvBn3d module is a module fused from Conv3d and BatchNorm3d,
+    attached with FakeQuantize modules for weight,
+    used in quantization aware training.
+
+    We combined the interface of :class:`torch.nn.Conv3d` and
+    :class:`torch.nn.BatchNorm3d`.
+
+    Similar to :class:`torch.nn.Conv3d`, with FakeQuantize modules initialized
+    to default.
+
+    Attributes:
+        freeze_bn:
+        weight_fake_quant: fake quant module for weight
+
+    """
+
+    _FLOAT_MODULE: ClassVar[type[nni.ConvBn3d]] = nni.ConvBn3d  # type: ignore[assignment]
+    _FLOAT_CONV_MODULE: ClassVar[type[nn.Conv3d]] = nn.Conv3d
+    _FLOAT_BN_MODULE: ClassVar[type[nn.Module] | None] = nn.BatchNorm3d
+    _FLOAT_RELU_MODULE: ClassVar[type[nn.Module] | None] = None
+
+    def __init__(
+        self,
+        # ConvNd args
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=None,
+        padding_mode="zeros",
+        # BatchNorm3d args
+        # num_features: out_channels
+        eps=1e-05,
+        momentum=0.1,
+        # affine: True
+        # track_running_stats: True
+        # Args for this module
+        freeze_bn=False,
+        qconfig=None,
+    ):
+        kernel_size = _triple(kernel_size)
+        stride = _triple(stride)
+        padding = _triple(padding)
+        dilation = _triple(dilation)
+        _ConvBnNd.__init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            False,
+            _triple(0),
+            groups,
+            bias,
+            padding_mode,
+            eps,
+            momentum,
+            freeze_bn,
+            qconfig,
+            dim=3,
+        )
+
+
+class ConvBnReLU3d(ConvBn3d):
+    r"""
+    A ConvBnReLU3d module is a module fused from Conv3d, BatchNorm3d and ReLU,
+    attached with FakeQuantize modules for weight,
+    used in quantization aware training.
+
+    We combined the interface of :class:`torch.nn.Conv3d` and
+    :class:`torch.nn.BatchNorm3d` and :class:`torch.nn.ReLU`.
+
+    Similar to `torch.nn.Conv3d`, with FakeQuantize modules initialized to
+    default.
+
+    Attributes:
+        weight_fake_quant: fake quant module for weight
+
+    """
+
+    _FLOAT_MODULE: ClassVar[type[nni.ConvBnReLU3d]] = nni.ConvBnReLU3d  # type: ignore[assignment]
+    _FLOAT_CONV_MODULE: ClassVar[type[nn.Conv3d]] = nn.Conv3d
+    _FLOAT_BN_MODULE: ClassVar[type[nn.BatchNorm3d]] = nn.BatchNorm3d
+    _FLOAT_RELU_MODULE: ClassVar[type[nn.ReLU] | None] = nn.ReLU
+    # module class after fusing bn into conv
+    _FUSED_FLOAT_MODULE: ClassVar[type[nni.ConvReLU3d] | None] = nni.ConvReLU3d
+
+    def forward(self, input):
+        r"""Performs forward pass through fused Conv3d, BatchNorm3d, and ReLU."""
+        return F.relu(ConvBn3d._forward(self, input))
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        r"""Creates a QAT module from a floating point module."""
+        return super().from_float(
+            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+
+class ConvReLU3d(nnqat.Conv3d, nni._FusedModule):
+    r"""A ConvReLU3d module is a fused module of Conv3d and ReLU, attached with
+    FakeQuantize modules for weight for
+    quantization aware training.
+
+    We combined the interface of :class:`~torch.nn.Conv3d` and
+    :class:`~torch.nn.BatchNorm3d`.
+
+    Attributes:
+        weight_fake_quant: fake quant module for weight
+
+    """
+
+    _FLOAT_MODULE: ClassVar[type[nni.ConvReLU3d]] = nni.ConvReLU3d  # type: ignore[assignment]
+    _FLOAT_CONV_MODULE: ClassVar[type[nn.Conv3d]] = nn.Conv3d
+    _FLOAT_BN_MODULE: ClassVar[type[nn.Module] | None] = None
+    _FLOAT_RELU_MODULE: ClassVar[type[nn.Module] | None] = nn.ReLU
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=True,
+        padding_mode="zeros",
+        qconfig=None,
+    ):
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+            # pyrefly: ignore [bad-argument-type]
+            padding_mode=padding_mode,
+            qconfig=qconfig,
+        )
+        assert qconfig, "qconfig must be provided for QAT module"
+        self.qconfig = qconfig
+        self.weight_fake_quant = self.qconfig.weight()
+
+    def forward(self, input):
+        r"""Performs forward pass through fused Conv3d and ReLU."""
+        return F.relu(
+            self._conv_forward(input, self.weight_fake_quant(self.weight), self.bias)
+        )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):  # type: ignore[override]
+        r"""Creates a QAT module from a floating point module."""
+        return super().from_float(
+            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+
+def update_bn_stats(mod):
+    if type(mod) in {
+        ConvBnReLU1d,
+        ConvBnReLU2d,
+        ConvBnReLU3d,
+        ConvBn1d,
+        ConvBn2d,
+        ConvBn3d,
+    }:
+        mod.update_bn_stats()
+
+
+def freeze_bn_stats(mod):
+    if type(mod) in {
+        ConvBnReLU1d,
+        ConvBnReLU2d,
+        ConvBnReLU3d,
+        ConvBn1d,
+        ConvBn2d,
+        ConvBn3d,
+    }:
+        mod.freeze_bn_stats()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_fused.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_fused.py
new file mode 100644
index 0000000000000000000000000000000000000000..8458cef76ee3a37bce33d924d2d60d2ca971a614
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_fused.py
@@ -0,0 +1,191 @@
+# mypy: allow-untyped-defs
+import torch
+import torch.ao.nn.intrinsic as nni
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn import init
+from torch.nn.parameter import Parameter
+from torch.nn.utils.fusion import fuse_linear_bn_weights
+
+
+__all__ = [
+    "LinearBn1d",
+]
+
+
+class LinearBn1d(nn.modules.linear.Linear, nni._FusedModule):
+    r"""
+    A LinearBn1d module is a module fused from Linear and BatchNorm1d, attached
+    with FakeQuantize modules for weight, used in quantization aware training.
+
+    We combined the interface of :class:`torch.nn.Linear` and
+    :class:torch.nn.BatchNorm1d`.
+
+    Similar to :class:`torch.nn.Linear`, with FakeQuantize modules initialized
+    to default.
+
+    Attributes:
+        freeze_bn:
+        weight_fake_quant: fake quant module for weight
+
+    """
+
+    def __init__(
+        self,
+        # Linear args
+        in_features,
+        out_features,
+        bias=True,
+        # BatchNorm1d args
+        # num_features: out_features
+        eps=1e-05,
+        momentum=0.1,
+        # affine: True
+        # track_running_stats: True
+        # Args for this module
+        freeze_bn=False,
+        qconfig=None,
+    ):
+        nn.modules.linear.Linear.__init__(self, in_features, out_features, bias)
+        assert qconfig, "qconfig must be provided for QAT module"
+        self.qconfig = qconfig
+        self.freeze_bn = freeze_bn if self.training else True
+        self.bn = nn.BatchNorm1d(out_features, eps, momentum, True, True)
+        self.weight_fake_quant = self.qconfig.weight()
+        if bias:
+            self.bias = Parameter(torch.empty(out_features))
+        else:
+            self.register_parameter("bias", None)
+        self.reset_bn_parameters()
+
+        # this needs to be called after reset_bn_parameters,
+        # as they modify the same state
+        if self.training:
+            if freeze_bn:
+                self.freeze_bn_stats()
+            else:
+                self.update_bn_stats()
+        else:
+            self.freeze_bn_stats()
+
+    def reset_running_stats(self):
+        self.bn.reset_running_stats()
+
+    def reset_bn_parameters(self):
+        self.bn.reset_running_stats()
+        init.uniform_(self.bn.weight)
+        init.zeros_(self.bn.bias)
+
+    def update_bn_stats(self):
+        self.freeze_bn = False
+        self.bn.training = True
+        return self
+
+    def freeze_bn_stats(self):
+        self.freeze_bn = True
+        self.bn.training = False
+        return self
+
+    def forward(self, input):
+        assert self.bn.running_var is not None
+
+        # Scale the linear weights by BN's running statistics to reduce
+        # weight jitter, see https://arxiv.org/pdf/1806.08342.pdf, page 18
+        # for motivation.
+        #
+        # Instead of
+        #
+        #   x1 = F.linear(x0, fq(w), b)
+        #   x2 = self.bn(x1)
+        #
+        # We have
+        #
+        #   # scale the weight by previous batch's running statistics
+        #   scale_factor = bn.w / bn.running_std_from_prev_batch
+        #   # do the linear transformation without bias
+        #   x1_scaled = F.linear(x0, fq(w * scale_factor), 0)
+        #   # reverse the scaling and add original bias
+        #   x1_orig = x1_scaled / scale_factor + b
+        #   x2 = self.bn(x1_orig)
+
+        running_std = torch.sqrt(self.bn.running_var + self.bn.eps)
+        scale_factor = self.bn.weight / running_std
+        weight_shape = [1] * len(self.weight.shape)
+        weight_shape[0] = -1
+        bias_shape = [1] * len(self.weight.shape)
+        bias_shape[1] = -1
+        scaled_weight = self.weight_fake_quant(
+            self.weight * scale_factor.reshape(weight_shape)
+        )
+        if self.bias is not None:
+            zero_bias = torch.zeros_like(self.bias)
+        else:
+            zero_bias = torch.zeros(self.out_features, device=scaled_weight.device)
+        linear_out = F.linear(input, scaled_weight, zero_bias)
+        linear_out_orig = linear_out / scale_factor.reshape(bias_shape)
+        if self.bias is not None:
+            linear_out_orig = linear_out_orig + self.bias.reshape(bias_shape)
+        bn_out = self.bn(linear_out_orig)
+        return bn_out
+
+    def train(self, mode=True):
+        """
+        Batchnorm's training behavior is using the self.training flag. Prevent
+        changing it if BN is frozen. This makes sure that calling `model.train()`
+        on a model with a frozen BN will behave properly.
+        """
+        self.training = mode
+        if not self.freeze_bn:
+            for module in self.children():
+                module.train(mode)
+        return self
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        r"""Create a qat module from a float module or qparams_dict
+
+        Args:
+            mod: A float module, either produced by torch.ao.quantization
+                utilities or directly from the user.
+        """
+        assert type(mod) is nni.LinearBn1d, (
+            "qat."
+            + cls.__name__
+            + ".from_float only works for "
+            + nni.LinearBn1d.__name__
+        )
+        assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
+        assert mod.qconfig, "Input float module must have a valid config"
+        qconfig = mod.qconfig
+        linear, bn = mod[0], mod[1]
+        qat_linearbn = cls(
+            linear.in_features,
+            linear.out_features,
+            linear.bias is not None,
+            bn.eps,
+            bn.momentum,
+            False,
+            qconfig,
+        )
+        qat_linearbn.weight = linear.weight  # type: ignore[assignment]
+        qat_linearbn.bias = linear.bias  # type: ignore[assignment]
+        qat_linearbn.bn.weight = bn.weight  # type: ignore[assignment]
+        qat_linearbn.bn.bias = bn.bias  # type: ignore[assignment]
+        qat_linearbn.bn.running_mean = bn.running_mean  # type: ignore[assignment]
+        qat_linearbn.bn.running_var = bn.running_var  # type: ignore[assignment]
+        qat_linearbn.bn.num_batches_tracked = bn.num_batches_tracked  # type: ignore[assignment]
+        return qat_linearbn
+
+    def to_float(self):
+        linear = torch.nn.Linear(self.in_features, self.out_features)
+        assert self.bn.running_var is not None and self.bn.running_mean is not None
+        linear.weight, linear.bias = fuse_linear_bn_weights(
+            self.weight,
+            self.bias,
+            self.bn.running_mean,
+            self.bn.running_var,
+            self.bn.eps,
+            self.bn.weight,
+            self.bn.bias,
+        )
+        return linear
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py
new file mode 100644
index 0000000000000000000000000000000000000000..183286ebb8dad25e49cd2fcd7c2dba2436003823
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py
@@ -0,0 +1,74 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import torch
+import torch.ao.nn.intrinsic as nni
+import torch.ao.nn.qat as nnqat
+import torch.nn.functional as F
+from torch.ao.nn.intrinsic.modules.fused import _FusedModule
+
+
+if TYPE_CHECKING:
+    from torch.ao.quantization.qconfig import QConfigAny
+
+
+__all__ = ["LinearReLU"]
+
+
+class LinearReLU(nnqat.Linear, _FusedModule):
+    r"""
+    A LinearReLU module fused from Linear and ReLU modules, attached with
+    FakeQuantize modules for weight, used in
+    quantization aware training.
+
+    We adopt the same interface as :class:`torch.nn.Linear`.
+
+    Similar to `torch.ao.nn.intrinsic.LinearReLU`, with FakeQuantize modules initialized to
+    default.
+
+    Attributes:
+        weight: fake quant module for weight
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> m = nn.qat.LinearReLU(20, 30)
+        >>> input = torch.randn(128, 20)
+        >>> output = m(input)
+        >>> print(output.size())
+        torch.Size([128, 30])
+    """
+
+    # pyrefly: ignore [bad-override]
+    _FLOAT_MODULE = nni.LinearReLU
+
+    def __init__(
+        self,
+        in_features: int,
+        out_features: int,
+        bias: bool = True,
+        qconfig: QConfigAny = None,
+    ) -> None:
+        super().__init__(in_features, out_features, bias, qconfig)
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        return F.relu(F.linear(input, self.weight_fake_quant(self.weight), self.bias))
+
+    @classmethod
+    def from_float(
+        cls,
+        mod: torch.nn.Module,
+        use_precomputed_fake_quant: bool = False,
+    ) -> LinearReLU:
+        return super().from_float(mod, use_precomputed_fake_quant)  # type: ignore[no-untyped-call,no-any-return]
+
+    def to_float(self) -> nni.LinearReLU:
+        linear = torch.nn.Linear(
+            self.in_features, self.out_features, self.bias is not None
+        )
+        linear.weight = torch.nn.Parameter(self.weight.detach())
+        if self.bias is not None:
+            linear.bias = torch.nn.Parameter(self.bias.detach())
+        relu = torch.nn.ReLU()
+        return torch.ao.nn.intrinsic.LinearReLU(linear, relu)  # type: ignore[no-untyped-call]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6af3b4aeee893966323cc4e73a27ff41814fc251
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/__init__.py
@@ -0,0 +1,15 @@
+from .modules import *  # noqa: F403
+
+
+__all__ = [
+    "BNReLU2d",
+    "BNReLU3d",
+    "ConvReLU1d",
+    "ConvReLU2d",
+    "ConvReLU3d",
+    "LinearReLU",
+    "LinearLeakyReLU",
+    "LinearTanh",
+    "ConvAdd2d",
+    "ConvAddReLU2d",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d79bdbfe83209f18b17cc8c7b245f322871d6c0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/__init__.py
@@ -0,0 +1 @@
+from .modules import *  # noqa: F403
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7a6c3c57c7828861b574e76b134aee2c23f0aad
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/__init__.py
@@ -0,0 +1,6 @@
+from .linear_relu import LinearReLU
+
+
+__all__ = [
+    "LinearReLU",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py
new file mode 100644
index 0000000000000000000000000000000000000000..620d24ae43e466ecd7883acf7df627641ebfdb24
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py
@@ -0,0 +1,72 @@
+from typing import Any
+from typing_extensions import Self
+
+import torch
+import torch.ao.nn.intrinsic as nni
+import torch.ao.nn.quantized.dynamic as nnqd
+
+
+__all__ = ["LinearReLU"]
+
+
+class LinearReLU(nnqd.Linear):
+    r"""
+    A LinearReLU module fused from Linear and ReLU modules that can be used
+    for dynamic quantization.
+    Supports both, FP16 and INT8 quantization.
+
+    We adopt the same interface as :class:`torch.ao.nn.quantized.dynamic.Linear`.
+
+    Attributes:
+        Same as torch.ao.nn.quantized.dynamic.Linear
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> m = nn.intrinsic.quantized.dynamic.LinearReLU(20, 30)
+        >>> input = torch.randn(128, 20)
+        >>> output = m(input)
+        >>> print(output.size())
+        torch.Size([128, 30])
+    """
+
+    # pyrefly: ignore [bad-override]
+    _FLOAT_MODULE = nni.LinearReLU
+
+    def __init__(
+        self,
+        in_features: int,
+        out_features: int,
+        bias: bool = True,
+        dtype: torch.dtype = torch.qint8,
+    ) -> None:
+        super().__init__(in_features, out_features, bias, dtype)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if self._packed_params.dtype == torch.qint8:
+            # TODO check if we should set reduce_rage = True by default here
+            Y = torch.ops.quantized.linear_relu_dynamic(
+                x, self._packed_params._packed_params, reduce_range=True
+            )
+        elif self._packed_params.dtype == torch.float16:
+            Y = torch.ops.quantized.linear_relu_dynamic_fp16(
+                x, self._packed_params._packed_params
+            )
+        else:
+            raise RuntimeError("Unsupported dtype on dynamic quantized linear relu!")
+        return Y.to(x.dtype)
+
+    def _get_name(self) -> str:
+        return "DynamicQuantizedLinearReLU"
+
+    @classmethod
+    def from_float(
+        cls, mod: torch.nn.Module, use_precomputed_fake_quant: bool = False
+    ) -> Self:
+        return super().from_float(
+            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+    @classmethod
+    def from_reference(cls, ref_qlinear_relu: Any) -> Self:  # type: ignore[override]
+        return super().from_reference(ref_qlinear_relu[0])
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/modules/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7fa4dcec2597e18c002489405894ea7251d5156
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/modules/__init__.py
@@ -0,0 +1,18 @@
+from .bn_relu import BNReLU2d, BNReLU3d
+from .conv_add import ConvAdd2d, ConvAddReLU2d
+from .conv_relu import ConvReLU1d, ConvReLU2d, ConvReLU3d
+from .linear_relu import LinearLeakyReLU, LinearReLU, LinearTanh
+
+
+__all__ = [
+    "LinearReLU",
+    "ConvReLU1d",
+    "ConvReLU2d",
+    "ConvReLU3d",
+    "BNReLU2d",
+    "BNReLU3d",
+    "LinearLeakyReLU",
+    "LinearTanh",
+    "ConvAdd2d",
+    "ConvAddReLU2d",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/modules/bn_relu.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/modules/bn_relu.py
new file mode 100644
index 0000000000000000000000000000000000000000..f05618c0949e1164f05cbd1edbfb8eb6440063e9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/modules/bn_relu.py
@@ -0,0 +1,113 @@
+# mypy: allow-untyped-defs
+
+import torch
+import torch.ao.nn.intrinsic
+import torch.ao.nn.intrinsic.qat
+import torch.ao.nn.quantized as nnq
+
+
+__all__ = ["BNReLU2d", "BNReLU3d"]
+
+
+class BNReLU2d(nnq.BatchNorm2d):
+    r"""
+    A BNReLU2d module is a fused module of BatchNorm2d and ReLU
+
+    We adopt the same interface as :class:`torch.ao.nn.quantized.BatchNorm2d`.
+
+    Attributes:
+        Same as torch.ao.nn.quantized.BatchNorm2d
+
+    """
+
+    _FLOAT_MODULE = torch.ao.nn.intrinsic.BNReLU2d
+
+    def __init__(self, num_features, eps=1e-5, momentum=0.1, device=None, dtype=None):
+        super().__init__(
+            num_features, eps=eps, momentum=momentum, device=device, dtype=dtype
+        )
+
+    def forward(self, input):
+        r"""Applies fused BatchNorm2d and ReLU."""
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 4:
+            raise ValueError("Input shape must be `(N, C, H, W)`!")
+        return torch.ops.quantized.batch_norm2d_relu(
+            input,
+            self.weight,
+            self.bias,
+            self.running_mean,
+            self.running_var,
+            self.eps,
+            self.scale,
+            self.zero_point,
+        )
+
+    def _get_name(self):
+        return "QuantizedBNReLU2d"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):  # type: ignore[override]
+        r"""Creates a quantized module from a float module."""
+        # TODO: Add qat support for BNReLU2d
+        return super().from_float(
+            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+    @classmethod
+    def from_reference(cls, bn_relu, output_scale, output_zero_point):
+        r"""Creates a quantized module from a reference module."""
+        return super().from_reference(bn_relu[0], output_scale, output_zero_point)
+
+
+class BNReLU3d(nnq.BatchNorm3d):
+    r"""
+    A BNReLU3d module is a fused module of BatchNorm3d and ReLU
+
+    We adopt the same interface as :class:`torch.ao.nn.quantized.BatchNorm3d`.
+
+    Attributes:
+        Same as torch.ao.nn.quantized.BatchNorm3d
+
+    """
+
+    _FLOAT_MODULE = torch.ao.nn.intrinsic.BNReLU3d
+
+    def __init__(self, num_features, eps=1e-5, momentum=0.1, device=None, dtype=None):
+        super().__init__(
+            num_features, eps=eps, momentum=momentum, device=device, dtype=dtype
+        )
+
+    def forward(self, input):
+        r"""Applies fused BatchNorm3d and ReLU."""
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 5:
+            raise ValueError("Input shape must be `(N, C, D, H, W)`!")
+        return torch.ops.quantized.batch_norm3d_relu(
+            input,
+            self.weight,
+            self.bias,
+            self.running_mean,
+            self.running_var,
+            self.eps,
+            self.scale,
+            self.zero_point,
+        )
+
+    def _get_name(self):
+        return "QuantizedBNReLU3d"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):  # type: ignore[override]
+        r"""Creates a quantized module from a float module."""
+        # TODO: Add qat support for BNReLU3d
+        return super().from_float(
+            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+    @classmethod
+    def from_reference(cls, bn_relu, output_scale, output_zero_point):
+        r"""Creates a quantized module from a reference module."""
+        return super().from_reference(bn_relu[0], output_scale, output_zero_point)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/modules/conv_add.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/modules/conv_add.py
new file mode 100644
index 0000000000000000000000000000000000000000..82d5673e7173c56b5b56d2bd48a0b154bbfdfe9e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/modules/conv_add.py
@@ -0,0 +1,153 @@
+# mypy: allow-untyped-defs
+import torch
+import torch.ao.nn.intrinsic
+import torch.ao.nn.intrinsic.qat
+import torch.ao.nn.quantized as nnq
+import torch.nn.functional as F
+
+
+_reverse_repeat_padding = nnq.modules.conv._reverse_repeat_padding
+
+
+class ConvAdd2d(nnq.Conv2d):
+    r"""
+    A ConvAdd2d module is a fused module of Conv2d and Add
+
+    We adopt the same interface as :class:`torch.ao.nn.quantized.Conv2d`.
+
+    Attributes:
+        Same as torch.ao.nn.quantized.Conv2d
+
+    """
+
+    _FLOAT_MODULE = torch.ao.nn.intrinsic.ConvAdd2d  # type: ignore[assignment]
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=True,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ):
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+            padding_mode=padding_mode,
+            device=device,
+            dtype=dtype,
+        )
+
+    def forward(self, input, extra_input):  # type: ignore[override]
+        r"""Applies fused quantized Conv2d and addition."""
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 4:
+            raise ValueError("Input shape must be `(N, C, H, W)`!")
+        if self.padding_mode != "zeros":
+            _reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding)
+            input = F.pad(
+                input, _reversed_padding_repeated_twice, mode=self.padding_mode
+            )
+        return torch.ops.quantized.conv2d_add(
+            input, extra_input, self._packed_params, self.scale, self.zero_point
+        )
+
+    def _get_name(self):
+        return "QuantizedConvAdd2d"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):  # type: ignore[override]
+        r"""Creates a quantized module from a float module."""
+        return super().from_float(
+            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+    @classmethod
+    def from_reference(cls, ref_qconv, output_scale, output_zero_point):
+        r"""Creates a quantized module from a reference module."""
+        return super().from_reference(ref_qconv[0], output_scale, output_zero_point)
+
+
+class ConvAddReLU2d(nnq.Conv2d):
+    r"""
+    A ConvAddReLU2d module is a fused module of Conv2d, Add and Relu
+
+    We adopt the same interface as :class:`torch.ao.nn.quantized.Conv2d`.
+
+    Attributes:
+        Same as torch.ao.nn.quantized.Conv2d
+
+    """
+
+    _FLOAT_MODULE = torch.ao.nn.intrinsic.ConvAddReLU2d  # type: ignore[assignment]
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=True,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ):
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+            padding_mode=padding_mode,
+            device=device,
+            dtype=dtype,
+        )
+
+    def forward(self, input, extra_input):  # type: ignore[override]
+        r"""Applies fused quantized Conv2d, addition, and ReLU."""
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 4:
+            raise ValueError("Input shape must be `(N, C, H, W)`!")
+        if self.padding_mode != "zeros":
+            _reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding)
+            input = F.pad(
+                input, _reversed_padding_repeated_twice, mode=self.padding_mode
+            )
+        return torch.ops.quantized.conv2d_add_relu(
+            input, extra_input, self._packed_params, self.scale, self.zero_point
+        )
+
+    def _get_name(self):
+        return "QuantizedConvAddReLU2d"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):  # type: ignore[override]
+        r"""Creates a quantized module from a float module."""
+        return super().from_float(
+            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+    @classmethod
+    def from_reference(cls, ref_qconv, output_scale, output_zero_point):
+        r"""Creates a quantized module from a reference module."""
+        return super().from_reference(ref_qconv[0], output_scale, output_zero_point)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/modules/conv_relu.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/modules/conv_relu.py
new file mode 100644
index 0000000000000000000000000000000000000000..c31df28905cd7c9c17147c965f5bd2199af2920a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/modules/conv_relu.py
@@ -0,0 +1,276 @@
+# mypy: allow-untyped-defs
+
+import torch
+import torch.ao.nn.intrinsic
+import torch.ao.nn.intrinsic.qat
+import torch.ao.nn.quantized as nnq
+import torch.nn.functional as F
+from torch.nn.utils import fuse_conv_bn_weights
+
+
+__all__ = [
+    "ConvReLU1d",
+    "ConvReLU2d",
+    "ConvReLU3d",
+]
+
+_reverse_repeat_padding = nnq.modules.conv._reverse_repeat_padding
+
+
+# TODO: factor out the common parts to ConvNd
+class ConvReLU1d(nnq.Conv1d):
+    r"""
+    A ConvReLU1d module is a fused module of Conv1d and ReLU
+
+    We adopt the same interface as :class:`torch.ao.nn.quantized.Conv1d`.
+
+    Attributes:
+        Same as torch.ao.nn.quantized.Conv1d
+
+    """
+
+    _FLOAT_MODULE = torch.ao.nn.intrinsic.ConvReLU1d  # type: ignore[assignment]
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=True,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ):
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+            # pyrefly: ignore [bad-argument-type]
+            padding_mode=padding_mode,
+            device=device,
+            dtype=dtype,
+        )
+
+    def forward(self, input):
+        r"""Applies fused quantized Conv1d and ReLU."""
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 3:
+            raise ValueError("Input shape must be `(N, C, L)`!")
+        if self.padding_mode != "zeros":
+            # Padding in Conv1d is stored as (p, p), need to get (p,)
+            _reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding[:1])
+            input = F.pad(
+                input, _reversed_padding_repeated_twice, mode=self.padding_mode
+            )
+        return torch.ops.quantized.conv1d_relu(
+            input, self._packed_params, self.scale, self.zero_point
+        )
+
+    def _get_name(self):
+        return "QuantizedConvReLU1d"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):  # type: ignore[override]
+        r"""Creates a quantized module from a float module."""
+        if type(mod) is torch.ao.nn.intrinsic.qat.ConvBnReLU1d:
+            assert mod.bn.running_var is not None and mod.bn.running_mean is not None
+            mod.weight, mod.bias = fuse_conv_bn_weights(
+                mod.weight,
+                mod.bias,
+                mod.bn.running_mean,
+                mod.bn.running_var,
+                mod.bn.eps,
+                mod.bn.weight,
+                mod.bn.bias,
+            )
+        return super().from_float(mod, use_precomputed_fake_quant)
+
+    @classmethod
+    def from_reference(cls, ref_qconv, output_scale, output_zero_point):
+        r"""Creates a quantized module from a reference module."""
+        assert type(ref_qconv) is not torch.ao.nn.intrinsic.ConvBnReLU1d, (
+            "BatchNorm1d should be fused into Conv1d before converting to reference module"
+        )
+        return super().from_reference(ref_qconv[0], output_scale, output_zero_point)
+
+
+class ConvReLU2d(nnq.Conv2d):
+    r"""
+    A ConvReLU2d module is a fused module of Conv2d and ReLU
+
+    We adopt the same interface as :class:`torch.ao.nn.quantized.Conv2d`.
+
+    Attributes:
+        Same as torch.ao.nn.quantized.Conv2d
+
+    """
+
+    _FLOAT_MODULE = torch.ao.nn.intrinsic.ConvReLU2d  # type: ignore[assignment]
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=True,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ):
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+            padding_mode=padding_mode,
+            device=device,
+            dtype=dtype,
+        )
+
+    def forward(self, input):
+        r"""Applies fused quantized Conv2d and ReLU."""
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 4:
+            raise ValueError("Input shape must be `(N, C, H, W)`!")
+        if self.padding_mode != "zeros":
+            _reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding)
+            input = F.pad(
+                input, _reversed_padding_repeated_twice, mode=self.padding_mode
+            )
+        return torch.ops.quantized.conv2d_relu(
+            input, self._packed_params, self.scale, self.zero_point
+        )
+
+    def _get_name(self):
+        return "QuantizedConvReLU2d"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):  # type: ignore[override]
+        r"""Creates a quantized module from a float module."""
+        if type(mod) is torch.ao.nn.intrinsic.qat.ConvBnReLU2d:
+            assert mod.bn.running_var is not None and mod.bn.running_mean is not None
+            mod.weight, mod.bias = fuse_conv_bn_weights(
+                mod.weight,
+                mod.bias,
+                mod.bn.running_mean,
+                mod.bn.running_var,
+                mod.bn.eps,
+                mod.bn.weight,
+                mod.bn.bias,
+            )
+        return super().from_float(
+            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+    @classmethod
+    def from_reference(cls, ref_qconv, output_scale, output_zero_point):
+        r"""Creates a quantized module from a reference module."""
+        assert type(ref_qconv) is not torch.ao.nn.intrinsic.ConvBnReLU2d, (
+            "BatchNorm2d should be fused into Conv2d before converting to reference module"
+        )
+        return super().from_reference(ref_qconv[0], output_scale, output_zero_point)
+
+
+class ConvReLU3d(nnq.Conv3d):
+    r"""
+    A ConvReLU3d module is a fused module of Conv3d and ReLU
+
+    We adopt the same interface as :class:`torch.ao.nn.quantized.Conv3d`.
+
+    Attributes: Same as torch.ao.nn.quantized.Conv3d
+
+    """
+
+    _FLOAT_MODULE = torch.ao.nn.intrinsic.ConvReLU3d  # type: ignore[assignment]
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=True,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ):
+        assert padding_mode != "reflect", "Conv3d does not support reflection padding"
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            bias=bias,
+            padding_mode=padding_mode,
+            device=device,
+            dtype=dtype,
+        )
+
+    def forward(self, input):
+        r"""Applies fused quantized Conv3d and ReLU."""
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 5:
+            raise ValueError("Input shape must be `(N, C, D, H, W)`!")
+        if self.padding_mode != "zeros":
+            _reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding)
+            input = F.pad(
+                input, _reversed_padding_repeated_twice, mode=self.padding_mode
+            )
+        return torch.ops.quantized.conv3d_relu(
+            input, self._packed_params, self.scale, self.zero_point
+        )
+
+    def _get_name(self):
+        return "QuantizedConvReLU3d"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):  # type: ignore[override]
+        r"""Creates a quantized module from a float module."""
+        if type(mod) is torch.ao.nn.intrinsic.qat.ConvBnReLU3d:
+            assert mod.bn.running_var is not None and mod.bn.running_mean is not None
+            mod.weight, mod.bias = fuse_conv_bn_weights(
+                mod.weight,
+                mod.bias,
+                mod.bn.running_mean,
+                mod.bn.running_var,
+                mod.bn.eps,
+                mod.bn.weight,
+                mod.bn.bias,
+            )
+        return super().from_float(
+            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+    @classmethod
+    def from_reference(cls, ref_qconv, output_scale, output_zero_point):
+        r"""Creates a quantized module from a reference module."""
+        assert type(ref_qconv) is not torch.ao.nn.intrinsic.ConvBnReLU3d, (
+            "BatchNorm3d should be fused into Conv3d before converting to reference module"
+        )
+        return super().from_reference(ref_qconv[0], output_scale, output_zero_point)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ec84101ee0da62e3923362f444368b2a429d8b3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py
@@ -0,0 +1,190 @@
+# mypy: allow-untyped-defs
+import torch
+import torch.ao.nn.intrinsic as nni
+import torch.ao.nn.quantized as nnq
+from torch.ao.nn.quantized.modules.utils import _quantize_weight
+
+
+__all__ = [
+    "LinearReLU",
+    "LinearLeakyReLU",
+    "LinearTanh",
+]
+
+
+class LinearReLU(nnq.Linear):
+    r"""
+    A LinearReLU module fused from Linear and ReLU modules
+
+    We adopt the same interface as :class:`torch.ao.nn.quantized.Linear`.
+
+    Attributes:
+        Same as torch.ao.nn.quantized.Linear
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> m = nn.intrinsic.LinearReLU(20, 30)
+        >>> input = torch.randn(128, 20)
+        >>> output = m(input)
+        >>> print(output.size())
+        torch.Size([128, 30])
+    """
+
+    _FLOAT_MODULE = nni.LinearReLU  # type: ignore[assignment]
+
+    def __init__(self, in_features, out_features, bias=True, dtype=torch.qint8):
+        super().__init__(in_features, out_features, bias, dtype)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return torch.ops.quantized.linear_relu(
+            x, self._packed_params._packed_params, self.scale, self.zero_point
+        )
+
+    def _get_name(self):
+        return "QuantizedLinearReLU"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return super().from_float(mod, use_precomputed_fake_quant)
+
+    @classmethod
+    def from_reference(cls, ref_linear_relu, output_scale, output_zero_point):
+        return super().from_reference(
+            ref_linear_relu[0], output_scale, output_zero_point
+        )
+
+
+class LinearLeakyReLU(nnq.Linear):
+    r"""
+    For onednn backend only
+    A LinearLeakyReLU module fused from Linear and LeakyReLU modules
+    We adopt the same interface as :class:`torch.ao.nn.quantized.Linear`.
+    Attributes:
+        Same as torch.ao.nn.quantized.Linear
+        + negative_slope
+    Examples::
+        >>> # xdoctest: +SKIP
+        >>> m = nn.intrinsic.LinearLeakyReLU(20, 30, 0.01)
+        >>> input = torch.randn(128, 20)
+        >>> output = m(input)
+        >>> print(output.size())
+        torch.Size([128, 30])
+    """
+
+    _FLOAT_MODULE = nni.LinearLeakyReLU  # type: ignore[assignment]
+
+    def __init__(
+        self, in_features, out_features, negative_slope, bias=True, dtype=torch.qint8
+    ):
+        super().__init__(in_features, out_features, bias, dtype)
+        self.negative_slope = negative_slope
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return torch.ops.quantized.linear_leaky_relu(
+            x,
+            self._packed_params._packed_params,
+            self.scale,
+            self.zero_point,
+            self.negative_slope,
+        )
+
+    def _get_name(self):
+        return "QuantizedLinearLeakyReLU"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        assert type(mod) is nni.LinearLeakyReLU, (
+            "Input float module should be LinearLeakyReLU"
+        )
+        assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
+        activation_post_process = mod.activation_post_process
+        leaky_relu = mod[1]
+        mod = mod[0]
+        weight_post_process = mod.qconfig.weight()  # type: ignore[union-attr, operator]
+        weight_post_process(mod.weight)
+        dtype = weight_post_process.dtype
+        act_scale, act_zp = activation_post_process.calculate_qparams()  # type: ignore[union-attr,operator]
+        assert dtype == torch.qint8, "Weight observer must have dtype torch.qint8"
+        qweight = _quantize_weight(mod.weight.float(), weight_post_process)
+        qlinear_leaky_relu = cls(
+            mod.in_features, mod.out_features, leaky_relu.negative_slope, dtype=dtype
+        )
+        qlinear_leaky_relu.set_weight_bias(qweight, mod.bias)  # type: ignore[arg-type]
+        qlinear_leaky_relu.scale = float(act_scale)
+        qlinear_leaky_relu.zero_point = int(act_zp)
+        return qlinear_leaky_relu
+
+    @classmethod
+    def from_reference(cls, ref_mod, output_scale, output_zero_point):
+        linear = ref_mod[0]
+        leaky_relu = ref_mod[1]
+        qlinear_leaky_relu = cls(
+            linear.in_features, linear.out_features, leaky_relu.negative_slope
+        )
+        qweight = linear.get_quantized_weight()
+        qlinear_leaky_relu.set_weight_bias(qweight, linear.bias)
+        qlinear_leaky_relu.scale = float(output_scale)
+        qlinear_leaky_relu.zero_point = int(output_zero_point)
+        return qlinear_leaky_relu
+
+
+class LinearTanh(nnq.Linear):
+    r"""
+    A LinearTanh module fused from Linear and Tanh modules
+
+    We adopt the same interface as :class:`torch.ao.nn.quantized.Linear`.
+
+    Attributes:
+        Same as torch.ao.nn.quantized.Linear
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> m = nn.intrinsic.LinearTanh(20, 30)
+        >>> input = torch.randn(128, 20)
+        >>> output = m(input)
+        >>> print(output.size())
+        torch.Size([128, 30])
+    """
+
+    _FLOAT_MODULE = nni.LinearTanh  # type: ignore[assignment]
+
+    def __init__(self, in_features, out_features, bias=True, dtype=torch.qint8):
+        super().__init__(in_features, out_features, bias, dtype)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return torch.ops.quantized.linear_tanh(
+            x, self._packed_params._packed_params, self.scale, self.zero_point
+        )
+
+    def _get_name(self):
+        return "QuantizedLinearTanh"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        assert type(mod) is nni.LinearTanh, "Input float module should be LinearTanh"
+        assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
+        activation_post_process = mod.activation_post_process
+        mod = mod[0]
+        weight_post_process = mod.qconfig.weight()  # type: ignore[union-attr,operator]
+        weight_post_process(mod.weight)
+        dtype = weight_post_process.dtype
+        act_scale, act_zp = activation_post_process.calculate_qparams()  # type: ignore[union-attr,operator]
+        assert dtype == torch.qint8, "Weight observer must have dtype torch.qint8"
+        qweight = _quantize_weight(mod.weight.float(), weight_post_process)
+        qlinear_tanh = cls(mod.in_features, mod.out_features, dtype=dtype)
+        qlinear_tanh.set_weight_bias(qweight, mod.bias)  # type: ignore[arg-type]
+        qlinear_tanh.scale = float(act_scale)
+        qlinear_tanh.zero_point = int(act_zp)
+        return qlinear_tanh
+
+    @classmethod
+    def from_reference(cls, ref_mod, output_scale, output_zero_point):
+        linear = ref_mod[0]
+        qlinear_tanh = cls(linear.in_features, linear.out_features)
+        qweight = linear.get_quantized_weight()
+        qlinear_tanh.set_weight_bias(qweight, linear.bias)
+        qlinear_tanh.scale = float(output_scale)
+        qlinear_tanh.zero_point = int(output_zero_point)
+        return qlinear_tanh
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/qat/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/qat/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d79bdbfe83209f18b17cc8c7b245f322871d6c0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/qat/__init__.py
@@ -0,0 +1 @@
+from .modules import *  # noqa: F403
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/qat/dynamic/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/qat/dynamic/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d79bdbfe83209f18b17cc8c7b245f322871d6c0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/qat/dynamic/__init__.py
@@ -0,0 +1 @@
+from .modules import *  # noqa: F403
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/qat/dynamic/modules/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/qat/dynamic/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..dca71fcf09b019f3e197576eb415ba4fd54fa28a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/qat/dynamic/modules/__init__.py
@@ -0,0 +1,4 @@
+from .linear import Linear
+
+
+__all__ = ["Linear"]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/qat/dynamic/modules/linear.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/qat/dynamic/modules/linear.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc2238eedf6f902174421a94702a4188fa463098
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/qat/dynamic/modules/linear.py
@@ -0,0 +1,40 @@
+from typing import Optional, TYPE_CHECKING
+
+import torch
+
+
+if TYPE_CHECKING:
+    from torch.ao.quantization.qconfig import QConfig
+
+
+__all__ = ["Linear"]
+
+
+class Linear(torch.ao.nn.qat.Linear):
+    r"""
+    A linear module attached with FakeQuantize modules for weight,
+    used for dynamic quantization aware training.
+
+    We adopt the same interface as `torch.nn.Linear`, please see
+    https://pytorch.org/docs/stable/nn.html#torch.nn.Linear
+    for documentation.
+
+    Similar to `torch.nn.Linear`, with FakeQuantize modules initialized to
+    default.
+    """
+
+    def __init__(
+        self,
+        in_features: int,
+        out_features: int,
+        bias: bool = True,
+        qconfig: Optional["QConfig"] = None,
+        device: int | str | torch.device | None = None,
+        dtype: str | None = None,
+    ) -> None:
+        super().__init__(in_features, out_features, bias, qconfig, device, dtype)
+        if not torch.ao.quantization.qconfig._activation_is_memoryless(qconfig):  # type: ignore[arg-type]
+            raise ValueError(
+                "Dynamic QAT requires a memoryless observer."
+                + "This means a MovingAverage observer with averaging constant equal to 1"
+            )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/qat/modules/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/qat/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e28e0968a60d7612ebbd26d5f607b4407c2d380
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/qat/modules/__init__.py
@@ -0,0 +1,13 @@
+from .conv import Conv1d, Conv2d, Conv3d
+from .embedding_ops import Embedding, EmbeddingBag
+from .linear import Linear
+
+
+__all__ = [
+    "Linear",
+    "Conv1d",
+    "Conv2d",
+    "Conv3d",
+    "Embedding",
+    "EmbeddingBag",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/qat/modules/conv.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/qat/modules/conv.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d228d56fce129860f0ebad805b042771b941804
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/qat/modules/conv.py
@@ -0,0 +1,312 @@
+# mypy: allow-untyped-defs
+from typing import ClassVar, Literal
+
+import torch
+import torch.nn as nn
+from torch.ao.nn.intrinsic import _FusedModule
+from torch.nn.common_types import _size_1_t, _size_2_t, _size_3_t
+from torch.nn.modules.utils import _pair, _single, _triple
+
+
+__all__ = ["Conv1d", "Conv2d", "Conv3d"]
+
+
+class _ConvNd(nn.modules.conv._ConvNd):
+    _FLOAT_MODULE: ClassVar[type[nn.modules.conv._ConvNd]]
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: tuple[int, ...],
+        stride: tuple[int, ...],
+        padding: str | tuple[int, ...],
+        dilation: tuple[int, ...],
+        transposed: bool,
+        output_padding: tuple[int, ...],
+        groups: int,
+        bias: bool,
+        padding_mode: Literal["zeros", "reflect", "replicate", "circular"],
+        qconfig=None,
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        nn.modules.conv._ConvNd.__init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            transposed,
+            output_padding,
+            groups,
+            bias,
+            padding_mode,
+            **factory_kwargs,
+        )
+        assert qconfig, "qconfig must be provided for QAT module"
+        self.qconfig = qconfig
+        self.weight_fake_quant = qconfig.weight(factory_kwargs=factory_kwargs)
+
+    def forward(self, input):
+        return self._conv_forward(input, self.weight_fake_quant(self.weight), self.bias)
+
+    @staticmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        r"""Create a qat module from a float module
+
+        Args:
+           `mod`: a float module, either produced by torch.ao.quantization utilities
+           or directly from user
+        """
+        assert type(mod) is cls._FLOAT_MODULE, (
+            "qat."
+            + cls.__name__
+            + ".from_float only works for "
+            + cls._FLOAT_MODULE.__name__
+        )
+        assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
+        assert mod.qconfig, "Input float module must have a valid qconfig"
+        if issubclass(type(mod), _FusedModule):
+            mod = mod[0]
+        qconfig = mod.qconfig
+        qat_conv = cls(
+            mod.in_channels,
+            mod.out_channels,
+            mod.kernel_size,
+            stride=mod.stride,
+            padding=mod.padding,
+            dilation=mod.dilation,
+            groups=mod.groups,
+            bias=mod.bias is not None,
+            padding_mode=mod.padding_mode,
+            qconfig=qconfig,
+        )
+        qat_conv.weight = mod.weight
+        qat_conv.bias = mod.bias
+        return qat_conv
+
+    def to_float(self):
+        """This works for both single qat conv, and the qat conv - relu modules
+        to convert the qat module to a floating point module
+        """
+        cls = type(self)
+        conv = cls._FLOAT_CONV_MODULE(  # type: ignore[attr-defined]
+            self.in_channels,
+            self.out_channels,
+            self.kernel_size,
+            self.stride,
+            self.padding,
+            self.dilation,
+            self.groups,
+            self.bias is not None,
+            self.padding_mode,
+        )
+        conv.weight = torch.nn.Parameter(self.weight.detach())
+        if self.bias is not None:
+            conv.bias = torch.nn.Parameter(self.bias.detach())
+        # conv relu
+        if issubclass(cls, _FusedModule):
+            modules = [conv]
+            assert hasattr(cls, "_FLOAT_RELU_MODULE")
+            relu = cls._FLOAT_RELU_MODULE()
+            modules.append(relu)
+            # pyrefly: ignore [missing-attribute]
+            fused = cls._FLOAT_MODULE(*modules)
+            fused.train(self.training)
+            return fused
+        else:
+            return conv
+
+
+class Conv1d(_ConvNd, nn.Conv1d):
+    r"""
+    A Conv1d module attached with FakeQuantize modules for weight,
+    used for quantization aware training.
+
+    We adopt the same interface as :class:`~torch.nn.Conv1d`
+
+    Similar to :class:`~torch.nn.Conv2d`, with FakeQuantize modules initialized to
+    default.
+
+    Attributes:
+        weight_fake_quant: fake quant module for weight
+    """
+
+    _FLOAT_MODULE: ClassVar[type[nn.Conv1d]] = nn.Conv1d
+    _FLOAT_CONV_MODULE: ClassVar[type[nn.Conv1d]] = nn.Conv1d
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: _size_1_t,
+        stride: _size_1_t = 1,
+        padding: str | _size_1_t = 0,
+        dilation: _size_1_t = 1,
+        groups: int = 1,
+        bias: bool = True,
+        padding_mode: Literal["zeros", "reflect", "replicate", "circular"] = "zeros",
+        qconfig=None,
+        device=None,
+        dtype=None,
+    ) -> None:
+        kernel_size_ = _single(kernel_size)
+        stride_ = _single(stride)
+        padding_ = padding if isinstance(padding, str) else _single(padding)
+        dilation_ = _single(dilation)
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size_,
+            stride=stride_,
+            padding=padding_,
+            dilation=dilation_,
+            transposed=False,
+            output_padding=_single(0),
+            groups=groups,
+            bias=bias,
+            padding_mode=padding_mode,
+            qconfig=qconfig,
+            device=device,
+            dtype=dtype,
+        )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):  # type: ignore[override]
+        return super().from_float(
+            cls, mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+
+class Conv2d(_ConvNd, nn.Conv2d):
+    r"""
+    A Conv2d module attached with FakeQuantize modules for weight,
+    used for quantization aware training.
+
+    We adopt the same interface as `torch.nn.Conv2d`, please see
+    https://pytorch.org/docs/stable/nn.html?highlight=conv2d#torch.nn.Conv2d
+    for documentation.
+
+    Similar to `torch.nn.Conv2d`, with FakeQuantize modules initialized to
+    default.
+
+    Attributes:
+        weight_fake_quant: fake quant module for weight
+    """
+
+    _FLOAT_MODULE: ClassVar[type[nn.Conv2d]] = nn.Conv2d
+    _FLOAT_CONV_MODULE: ClassVar[type[nn.Conv2d]] = nn.Conv2d
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: _size_2_t,
+        stride: _size_2_t = 1,
+        padding: str | _size_2_t = 0,
+        dilation: _size_2_t = 1,
+        groups: int = 1,
+        bias: bool = True,
+        padding_mode: Literal["zeros", "reflect", "replicate", "circular"] = "zeros",
+        qconfig=None,
+        device=None,
+        dtype=None,
+    ) -> None:
+        kernel_size_ = _pair(kernel_size)
+        stride_ = _pair(stride)
+        padding_ = padding if isinstance(padding, str) else _pair(padding)
+        dilation_ = _pair(dilation)
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size_,
+            stride=stride_,
+            padding=padding_,
+            dilation=dilation_,
+            transposed=False,
+            output_padding=_pair(0),
+            groups=groups,
+            bias=bias,
+            padding_mode=padding_mode,
+            qconfig=qconfig,
+            device=device,
+            dtype=dtype,
+        )
+
+    def forward(self, input):
+        return self._conv_forward(input, self.weight_fake_quant(self.weight), self.bias)
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):  # type: ignore[override]
+        return super().from_float(
+            cls, mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+
+class Conv3d(_ConvNd, nn.Conv3d):
+    r"""
+    A Conv3d module attached with FakeQuantize modules for weight,
+    used for quantization aware training.
+
+    We adopt the same interface as `torch.nn.Conv3d`, please see
+    https://pytorch.org/docs/stable/nn.html?highlight=conv3d#torch.nn.Conv3d
+    for documentation.
+
+    Similar to `torch.nn.Conv3d`, with FakeQuantize modules initialized to
+    default.
+
+    Attributes:
+        weight_fake_quant: fake quant module for weight
+    """
+
+    _FLOAT_MODULE: ClassVar[type[nn.Conv3d]] = nn.Conv3d
+    _FLOAT_CONV_MODULE: ClassVar[type[nn.Conv3d]] = nn.Conv3d
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: _size_3_t,
+        stride: _size_3_t = 1,
+        padding: str | _size_3_t = 0,
+        dilation: _size_3_t = 1,
+        groups: int = 1,
+        bias: bool = True,
+        padding_mode: Literal["zeros", "reflect", "replicate", "circular"] = "zeros",
+        qconfig=None,
+        device=None,
+        dtype=None,
+    ) -> None:
+        kernel_size_ = _triple(kernel_size)
+        stride_ = _triple(stride)
+        padding_ = padding if isinstance(padding, str) else _triple(padding)
+        dilation_ = _triple(dilation)
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size_,
+            stride=stride_,
+            padding=padding_,
+            dilation=dilation_,
+            transposed=False,
+            output_padding=_triple(0),
+            groups=groups,
+            bias=bias,
+            padding_mode=padding_mode,
+            qconfig=qconfig,
+            device=device,
+            dtype=dtype,
+        )
+
+    def forward(self, input):
+        return self._conv_forward(input, self.weight_fake_quant(self.weight), self.bias)
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):  # type: ignore[override]
+        return super().from_float(
+            cls, mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/qat/modules/embedding_ops.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/qat/modules/embedding_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f69e70abcf1d43c4a96ca15dae355c31f66a627
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/qat/modules/embedding_ops.py
@@ -0,0 +1,251 @@
+# mypy: allow-untyped-defs
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import Tensor
+
+
+__all__ = ["Embedding", "EmbeddingBag"]
+
+
+class Embedding(nn.Embedding):
+    r"""
+    An embedding bag module attached with FakeQuantize modules for weight,
+    used for quantization aware training.
+
+    We adopt the same interface as `torch.nn.Embedding`, please see
+    https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html#torch.nn.Embedding
+    for documentation.
+
+    Similar to `torch.nn.Embedding`, with FakeQuantize modules initialized to
+    default.
+
+    Attributes:
+        weight: fake quant module for weight
+    """
+
+    _FLOAT_MODULE = nn.Embedding
+
+    def __init__(
+        self,
+        num_embeddings,
+        embedding_dim,
+        padding_idx=None,
+        max_norm=None,
+        norm_type=2.0,
+        scale_grad_by_freq=False,
+        sparse=False,
+        _weight=None,
+        device=None,
+        dtype=None,
+        qconfig=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__(
+            num_embeddings,
+            embedding_dim,
+            padding_idx,
+            max_norm,
+            norm_type,
+            scale_grad_by_freq,
+            sparse,
+            _weight,
+            # pyrefly: ignore [bad-argument-type]
+            **factory_kwargs,
+        )
+        assert qconfig, "qconfig must be provided for QAT module"
+        assert qconfig.weight().qscheme == torch.per_channel_affine_float_qparams, (
+            "Embedding weights requires a qscheme of torch.per_channel_affine_float_qparams Got "
+            + str(qconfig.weight().qscheme)
+        )
+        self.qconfig = qconfig
+        self.weight_fake_quant = qconfig.weight(factory_kwargs=factory_kwargs)
+
+    def forward(self, input) -> Tensor:
+        return F.embedding(
+            input,
+            self.weight_fake_quant(self.weight),
+            self.padding_idx,
+            self.max_norm,
+            self.norm_type,
+            self.scale_grad_by_freq,
+            self.sparse,
+        )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        r"""Create a qat module from a float module
+
+        Args: `mod` a float module, either produced by torch.ao.quantization utilities
+        or directly from user
+        """
+        assert type(mod) is cls._FLOAT_MODULE, (
+            " qat."
+            + cls.__name__
+            + ".from_float only works for "
+            + cls._FLOAT_MODULE.__name__
+        )
+        assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
+        assert mod.qconfig, "Input float module must have a valid qconfig"
+        weight_qscheme = mod.qconfig.weight().qscheme  # type: ignore[union-attr, operator]
+        assert weight_qscheme == torch.per_channel_affine_float_qparams, (
+            "Embedding weights requires a qscheme of torch.per_channel_affine_float_qparams Got "
+            + str(weight_qscheme)
+        )
+
+        qconfig = mod.qconfig
+        qat_embedding_bag = cls(
+            mod.num_embeddings,
+            mod.embedding_dim,
+            mod.padding_idx,
+            mod.max_norm,
+            mod.norm_type,
+            mod.scale_grad_by_freq,
+            mod.sparse,
+            mod.weight,
+            qconfig=qconfig,
+        )
+
+        return qat_embedding_bag
+
+    def to_float(self):
+        embedding_bag = torch.nn.Embedding(
+            self.num_embeddings,
+            self.embedding_dim,
+            self.padding_idx,
+            self.max_norm,
+            self.norm_type,
+            self.scale_grad_by_freq,
+            self.sparse,
+            None,
+        )
+        embedding_bag.weight = torch.nn.Parameter(self.weight.detach())
+        embedding_bag.train(self.training)
+        return embedding_bag
+
+
+class EmbeddingBag(nn.EmbeddingBag):
+    r"""
+    An embedding bag module attached with FakeQuantize modules for weight,
+    used for quantization aware training.
+
+    We adopt the same interface as `torch.nn.EmbeddingBag`, please see
+    https://pytorch.org/docs/stable/generated/torch.nn.EmbeddingBag.html#torch.nn.EmbeddingBag
+    for documentation.
+
+    Similar to `torch.nn.EmbeddingBag`, with FakeQuantize modules initialized to
+    default.
+
+    Attributes:
+        weight: fake quant module for weight
+    """
+
+    _FLOAT_MODULE = nn.EmbeddingBag
+
+    def __init__(
+        self,
+        num_embeddings,
+        embedding_dim,
+        max_norm=None,
+        norm_type=2.0,
+        scale_grad_by_freq=False,
+        mode="mean",
+        sparse=False,
+        _weight=None,
+        include_last_offset=False,
+        padding_idx=None,
+        qconfig=None,
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__(
+            num_embeddings,
+            embedding_dim,
+            max_norm,
+            norm_type,
+            scale_grad_by_freq,
+            mode,
+            sparse,
+            _weight,
+            include_last_offset,
+            padding_idx,
+            **factory_kwargs,
+        )
+        assert qconfig, "qconfig must be provided for QAT module"
+        assert qconfig.weight().qscheme == torch.per_channel_affine_float_qparams, (
+            "Embedding Bag weights requires a qscheme of torch.per_channel_affine_float_qparams Got "
+            + str(qconfig.weight().qscheme)
+        )
+        self.qconfig = qconfig
+        self.weight_fake_quant = qconfig.weight(factory_kwargs=factory_kwargs)
+
+    def forward(self, input, offsets=None, per_sample_weights=None) -> Tensor:
+        return F.embedding_bag(
+            input,
+            self.weight_fake_quant(self.weight),
+            offsets,
+            self.max_norm,
+            self.norm_type,
+            self.scale_grad_by_freq,
+            self.mode,
+            self.sparse,
+            per_sample_weights,
+            self.include_last_offset,
+            self.padding_idx,
+        )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        r"""Create a qat module from a float module
+
+        Args: `mod` a float module, either produced by torch.ao.quantization utilities
+        or directly from user
+        """
+        assert type(mod) is cls._FLOAT_MODULE, (
+            " qat."
+            + cls.__name__
+            + ".from_float only works for "
+            + cls._FLOAT_MODULE.__name__
+        )
+        assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
+        assert mod.qconfig, "Input float module must have a valid qconfig"
+        weight_qscheme = mod.qconfig.weight().qscheme  # type: ignore[union-attr, operator]
+        assert weight_qscheme == torch.per_channel_affine_float_qparams, (
+            "Embedding Bag weights requires a qscheme of torch.per_channel_affine_float_qparams Got "
+            + str(weight_qscheme)
+        )
+
+        qconfig = mod.qconfig
+        qat_embedding_bag = cls(
+            mod.num_embeddings,
+            mod.embedding_dim,
+            mod.max_norm,
+            mod.norm_type,
+            mod.scale_grad_by_freq,
+            mod.mode,
+            mod.sparse,
+            mod.weight,
+            mod.include_last_offset,
+            mod.padding_idx,
+            qconfig=qconfig,
+        )
+
+        return qat_embedding_bag
+
+    def to_float(self):
+        embedding_bag = torch.nn.EmbeddingBag(
+            self.num_embeddings,
+            self.embedding_dim,
+            self.max_norm,
+            self.norm_type,
+            self.scale_grad_by_freq,
+            self.mode,
+            self.sparse,
+            None,
+            self.include_last_offset,
+            self.padding_idx,
+        )
+        embedding_bag.weight = torch.nn.Parameter(self.weight.detach())
+        embedding_bag.train(self.training)
+        return embedding_bag
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/qat/modules/linear.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/qat/modules/linear.py
new file mode 100644
index 0000000000000000000000000000000000000000..5edf16ed3ea53d0323eda248b95703d5245b1786
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/qat/modules/linear.py
@@ -0,0 +1,97 @@
+# mypy: allow-untyped-defs
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.ao.nn.intrinsic import LinearReLU
+from torch.nn.utils.parametrize import (
+    is_parametrized,
+    transfer_parametrizations_and_params,
+    type_before_parametrizations,
+)
+
+
+__all__ = ["Linear"]
+
+
+class Linear(nn.Linear):
+    r"""
+    A linear module attached with FakeQuantize modules for weight,
+    used for quantization aware training.
+
+    We adopt the same interface as `torch.nn.Linear`, please see
+    https://pytorch.org/docs/stable/nn.html#torch.nn.Linear
+    for documentation.
+
+    Similar to `torch.nn.Linear`, with FakeQuantize modules initialized to
+    default.
+
+    Attributes:
+        weight: fake quant module for weight
+    """
+
+    _FLOAT_MODULE = nn.Linear
+
+    def __init__(
+        self,
+        in_features,
+        out_features,
+        bias=True,
+        qconfig=None,
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__(in_features, out_features, bias, **factory_kwargs)
+        assert qconfig, "qconfig must be provided for QAT module"
+        self.qconfig = qconfig
+        self.weight_fake_quant = qconfig.weight(factory_kwargs=factory_kwargs)
+
+    def forward(self, input):
+        return F.linear(input, self.weight_fake_quant(self.weight), self.bias)
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        r"""Create a qat module from a float module or qparams_dict
+        Args: `mod` a float module, either produced by torch.ao.quantization utilities
+        or directly from user
+        """
+        assert type_before_parametrizations(mod) == cls._FLOAT_MODULE, (
+            " qat."
+            + cls.__name__
+            + ".from_float only works for "
+            + cls._FLOAT_MODULE.__name__
+        )
+        assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
+        assert mod.qconfig, "Input float module must have a valid qconfig"
+        if type_before_parametrizations(mod) == LinearReLU:
+            mod = mod[0]
+
+        qconfig = mod.qconfig
+        qat_linear = cls(
+            mod.in_features,
+            mod.out_features,
+            bias=mod.bias is not None,
+            qconfig=qconfig,
+        )
+
+        if is_parametrized(mod, "weight"):
+            transfer_parametrizations_and_params(mod, qat_linear, "weight")
+        else:
+            qat_linear.weight = mod.weight
+
+        if is_parametrized(mod, "bias"):
+            transfer_parametrizations_and_params(mod, qat_linear, "bias")
+        else:
+            qat_linear.bias = mod.bias
+
+        return qat_linear
+
+    def to_float(self):
+        linear = torch.nn.Linear(
+            self.in_features, self.out_features, self.bias is not None
+        )
+        linear.weight = torch.nn.Parameter(self.weight.detach())
+        if self.bias is not None:
+            linear.bias = torch.nn.Parameter(self.bias.detach())
+        linear.train(self.training)
+        return linear
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantizable/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantizable/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d79bdbfe83209f18b17cc8c7b245f322871d6c0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantizable/__init__.py
@@ -0,0 +1 @@
+from .modules import *  # noqa: F403
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantizable/modules/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantizable/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..221107660158171ada5d1823cc193666c9e152e7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantizable/modules/__init__.py
@@ -0,0 +1,9 @@
+from .activation import MultiheadAttention
+from .rnn import LSTM, LSTMCell
+
+
+__all__ = [
+    "LSTM",
+    "LSTMCell",
+    "MultiheadAttention",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantizable/modules/activation.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantizable/modules/activation.py
new file mode 100644
index 0000000000000000000000000000000000000000..d808d50c366c68b8aa0d61a50b9f6db2d72c9ff2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantizable/modules/activation.py
@@ -0,0 +1,579 @@
+# mypy: allow-untyped-defs
+import warnings
+
+import torch
+import torch.jit  # this is needed to avoid a circular import
+import torch.nn.functional as F
+from torch import nn, Tensor
+
+
+__all__ = ["MultiheadAttention"]
+
+
+class MultiheadAttention(nn.MultiheadAttention):
+    _FLOAT_MODULE = nn.MultiheadAttention
+
+    r"""Quantizable implementation of the MultiheadAttention.
+
+    Note::
+        Please, refer to :class:`~torch.nn.MultiheadAttention` for more
+        information
+
+    Allows the model to jointly attend to information from different
+    representation subspaces.
+    See reference: Attention Is All You Need
+
+    The original MHA module is not quantizable.
+    This reimplements it by explicitly instantiating the linear layers.
+
+    .. math::
+        \text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O
+        \text{where} head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V)
+
+    Args:
+        embed_dim: total dimension of the model.
+        num_heads: parallel attention heads.
+        dropout: a Dropout layer on attn_output_weights. Default: 0.0.
+        bias: add bias as module parameter. Default: True.
+        add_bias_kv: add bias to the key and value sequences at dim=0.
+        add_zero_attn: add a new batch of zeros to the key and
+                       value sequences at dim=1.
+        kdim: total number of features in key. Default: None.
+        vdim: total number of features in value. Default: None.
+        batch_first: If ``True``, then the input and output tensors are provided
+            as (batch, seq, feature). Default: ``False`` (seq, batch, feature).
+
+    Note that if :attr:`kdim` and :attr:`vdim` are None, they will be set
+    to :attr:`embed_dim` such that query, key, and value have the same
+    number of features.
+
+    Examples::
+
+        >>> import torch.ao.nn.quantizable as nnqa
+        >>> multihead_attn = nnqa.MultiheadAttention(embed_dim, num_heads)
+        >>> attn_output, attn_output_weights = multihead_attn(query, key, value)
+
+    Note::
+        Please, follow the quantization flow to convert the quantizable MHA.
+    """
+    __constants__ = ["batch_first"]
+
+    def __init__(
+        self,
+        embed_dim: int,
+        num_heads: int,
+        dropout: float = 0.0,
+        bias: bool = True,
+        add_bias_kv: bool = False,
+        add_zero_attn: bool = False,
+        kdim: int | None = None,
+        vdim: int | None = None,
+        batch_first: bool = False,
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__(
+            embed_dim,
+            num_heads,
+            dropout,
+            bias,
+            add_bias_kv,
+            add_zero_attn,
+            kdim,
+            vdim,
+            batch_first,
+            **factory_kwargs,
+        )
+        self.linear_Q = nn.Linear(
+            self.embed_dim, self.embed_dim, bias=bias, **factory_kwargs
+        )
+        self.linear_K = nn.Linear(
+            self.kdim, self.embed_dim, bias=bias, **factory_kwargs
+        )
+        self.linear_V = nn.Linear(
+            self.vdim, self.embed_dim, bias=bias, **factory_kwargs
+        )
+        # for the type: ignore, see https://github.com/pytorch/pytorch/issues/58969
+        self.out_proj = nn.Linear(
+            self.embed_dim, self.embed_dim, bias=bias, **factory_kwargs
+        )  # type: ignore[assignment]
+
+        # Functionals
+        self.q_scaling_product = torch.ao.nn.quantized.FloatFunctional()
+        # note: importing torch.ao.nn.quantized at top creates a circular import
+
+        # Quant/Dequant
+        self.quant_attn_output = torch.ao.quantization.QuantStub()
+        self.quant_attn_output_weights = torch.ao.quantization.QuantStub()
+        self.dequant_q = torch.ao.quantization.DeQuantStub()
+        self.dequant_k = torch.ao.quantization.DeQuantStub()
+        self.dequant_v = torch.ao.quantization.DeQuantStub()
+
+    def _get_name(self):
+        return "QuantizableMultiheadAttention"
+
+    @classmethod
+    def from_float(cls, other):
+        assert type(other) is cls._FLOAT_MODULE
+        assert hasattr(other, "qconfig"), "The float module must have 'qconfig'"
+        # Setting the dropout to 0.0!
+        observed = cls(
+            other.embed_dim,
+            other.num_heads,
+            other.dropout,
+            (other.in_proj_bias is not None),
+            (other.bias_k is not None),
+            other.add_zero_attn,
+            other.kdim,
+            other.vdim,
+            other.batch_first,
+        )
+        observed.bias_k = other.bias_k
+        observed.bias_v = other.bias_v
+        observed.qconfig = other.qconfig
+
+        # Set the linear weights
+        # for the type: ignores, see https://github.com/pytorch/pytorch/issues/58969
+        observed.out_proj.weight = other.out_proj.weight
+        observed.out_proj.bias = other.out_proj.bias
+        if other._qkv_same_embed_dim:
+            # Use separate params
+            bias = other.in_proj_bias
+            _start = 0
+            _end = _start + other.embed_dim
+            weight = other.in_proj_weight[_start:_end, :]
+            if bias is not None:
+                bias = torch.nn.Parameter(bias[_start:_end], bias.requires_grad)
+            observed.linear_Q.weight = torch.nn.Parameter(weight, weight.requires_grad)
+            observed.linear_Q.bias = bias
+
+            bias = other.in_proj_bias
+            _start = _end
+            _end = _start + other.embed_dim
+            weight = other.in_proj_weight[_start:_end, :]
+            if bias is not None:
+                bias = torch.nn.Parameter(bias[_start:_end], bias.requires_grad)
+            observed.linear_K.weight = torch.nn.Parameter(weight, weight.requires_grad)
+            observed.linear_K.bias = bias
+
+            bias = other.in_proj_bias
+            _start = _end
+            weight = other.in_proj_weight[_start:, :]
+            if bias is not None:
+                bias = torch.nn.Parameter(bias[_start:], bias.requires_grad)
+            observed.linear_V.weight = torch.nn.Parameter(weight, weight.requires_grad)
+            observed.linear_V.bias = bias
+        else:
+            observed.linear_Q.weight = nn.Parameter(other.q_proj_weight)
+            observed.linear_K.weight = nn.Parameter(other.k_proj_weight)
+            observed.linear_V.weight = nn.Parameter(other.v_proj_weight)
+            if other.in_proj_bias is None:
+                # pyrefly: ignore [bad-assignment]
+                observed.linear_Q.bias = None
+                # pyrefly: ignore [bad-assignment]
+                observed.linear_K.bias = None
+                # pyrefly: ignore [bad-assignment]
+                observed.linear_V.bias = None
+            else:
+                observed.linear_Q.bias = nn.Parameter(
+                    other.in_proj_bias[0 : other.embed_dim]
+                )
+                observed.linear_K.bias = nn.Parameter(
+                    other.in_proj_bias[other.embed_dim : (other.embed_dim * 2)]
+                )
+                observed.linear_V.bias = nn.Parameter(
+                    other.in_proj_bias[(other.embed_dim * 2) :]
+                )
+        observed.eval()
+        # Explicit prepare
+        observed = torch.ao.quantization.prepare(observed, inplace=True)
+        return observed
+
+    @torch.jit.unused
+    def dequantize(self):
+        r"""Utility to convert the quantized MHA back to float.
+
+        The motivation for this is that it is not trivial to convert the weights
+        from the format that is used in the quantized version back to the
+        float.
+        """
+        fp = self._FLOAT_MODULE(
+            self.embed_dim,
+            self.num_heads,
+            self.dropout,
+            (self.linear_Q._weight_bias()[1] is not None),  # type: ignore[operator]
+            (self.bias_k is not None),
+            self.add_zero_attn,
+            self.kdim,
+            self.vdim,
+            self.batch_first,
+        )
+        assert fp._qkv_same_embed_dim == self._qkv_same_embed_dim
+        if self.bias_k is not None:
+            fp.bias_k = nn.Parameter(self.bias_k.dequantize())
+        if self.bias_v is not None:
+            fp.bias_v = nn.Parameter(self.bias_v.dequantize())
+
+        # Set the linear weights
+        # Note: Because the linear layers are quantized, mypy does not know how
+        # to deal with them -- might need to ignore the typing checks.
+        # for the type: ignore[has-type], see https://github.com/pytorch/pytorch/issues/58969
+        w, b = self.out_proj._weight_bias()  # type: ignore[operator, has-type]
+        fp.out_proj.weight = nn.Parameter(w.dequantize())
+        if b is not None:
+            fp.out_proj.bias = nn.Parameter(b)
+
+        wQ, bQ = self.linear_Q._weight_bias()  # type: ignore[operator]
+        wQ = wQ.dequantize()
+        wK, bK = self.linear_K._weight_bias()  # type: ignore[operator]
+        wK = wK.dequantize()
+        wV, bV = self.linear_V._weight_bias()  # type: ignore[operator]
+        wV = wV.dequantize()
+        if fp._qkv_same_embed_dim:
+            # Use separate params
+            _start = 0
+            _end = _start + fp.embed_dim
+            fp.in_proj_weight[_start:_end, :] = wQ
+            if fp.in_proj_bias is not None:
+                # pyrefly: ignore [bad-argument-type]
+                assert all(bQ == 0)
+                fp.in_proj_bias[_start:_end] = bQ
+
+            _start = _end
+            _end = _start + fp.embed_dim
+            fp.in_proj_weight[_start:_end, :] = wK
+            if fp.in_proj_bias is not None:
+                # pyrefly: ignore [bad-argument-type]
+                assert all(bK == 0)
+                fp.in_proj_bias[_start:_end] = bK
+
+            _start = _end
+            fp.in_proj_weight[_start:, :] = wV
+            if fp.in_proj_bias is not None:
+                # pyrefly: ignore [bad-argument-type]
+                assert all(bV == 0)
+                fp.in_proj_bias[_start:] = bV
+        else:
+            fp.q_proj_weight = nn.Parameter(wQ)
+            fp.k_proj_weight = nn.Parameter(wK)
+            fp.v_proj_weight = nn.Parameter(wV)
+            if fp.in_proj_bias is None:
+                # pyrefly: ignore [bad-assignment]
+                self.linear_Q.bias = None
+                # pyrefly: ignore [bad-assignment]
+                self.linear_K.bias = None
+                # pyrefly: ignore [bad-assignment]
+                self.linear_V.bias = None
+            else:
+                fp.in_proj_bias[0 : fp.embed_dim] = bQ
+                fp.in_proj_bias[fp.embed_dim : (fp.embed_dim * 2)] = bK
+                fp.in_proj_bias[(fp.embed_dim * 2) :] = bV
+
+        return fp
+
+    @classmethod
+    def from_observed(cls, other):
+        # The whole flow is float -> observed -> quantized
+        # This class does float -> observed only
+        # See nn.quantized.MultiheadAttention
+        raise NotImplementedError(
+            "It looks like you are trying to prepare an "
+            "MHA module. Please, see "
+            "the examples on quantizable MHAs."
+        )
+
+    def forward(
+        self,
+        query: Tensor,
+        key: Tensor,
+        value: Tensor,
+        key_padding_mask: Tensor | None = None,
+        need_weights: bool = True,
+        attn_mask: Tensor | None = None,
+        average_attn_weights: bool = True,
+        is_causal: bool = False,
+    ) -> tuple[Tensor, Tensor | None]:
+        r"""
+        Note::
+            Please, refer to :func:`~torch.nn.MultiheadAttention.forward` for more
+            information
+
+        Args:
+            query, key, value: map a query and a set of key-value pairs to an output.
+                See "Attention Is All You Need" for more details.
+            key_padding_mask: if provided, specified padding elements in the key will
+                be ignored by the attention. When given a binary mask and a value is True,
+                the corresponding value on the attention layer will be ignored.
+            need_weights: output attn_output_weights.
+            attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all
+                the batches while a 3D mask allows to specify a different mask for the entries of each batch.
+
+        Shape:
+            - Inputs:
+            - query: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is
+              the embedding dimension. :math:`(N, L, E)` if ``batch_first`` is ``True``.
+            - key: :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is
+              the embedding dimension. :math:`(N, S, E)` if ``batch_first`` is ``True``.
+            - value: :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is
+              the embedding dimension. :math:`(N, S, E)` if ``batch_first`` is ``True``.
+            - key_padding_mask: :math:`(N, S)` where N is the batch size, S is the source sequence length.
+              If a BoolTensor is provided, the positions with the
+              value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged.
+            - attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length.
+              3D mask :math:`(N*num_heads, L, S)` where N is the batch size, L is the target sequence length,
+              S is the source sequence length. attn_mask ensure that position i is allowed to attend the unmasked
+              positions. If a BoolTensor is provided, positions with ``True``
+              is not allowed to attend while ``False`` values will be unchanged. If a FloatTensor
+              is provided, it will be added to the attention weight.
+            - is_causal: If specified, applies a causal mask as attention mask. Mutually exclusive with providing attn_mask.
+              Default: ``False``.
+            - average_attn_weights: If true, indicates that the returned ``attn_weights`` should be averaged across
+              heads. Otherwise, ``attn_weights`` are provided separately per head. Note that this flag only has an
+              effect when ``need_weights=True.``. Default: True (i.e. average weights across heads)
+
+            - Outputs:
+            - attn_output: :math:`(L, N, E)` where L is the target sequence length, N is the batch size,
+              E is the embedding dimension. :math:`(N, L, E)` if ``batch_first`` is ``True``.
+            - attn_output_weights: If ``average_attn_weights=True``, returns attention weights averaged
+              across heads of shape :math:`(N, L, S)`, where N is the batch size, L is the target sequence length,
+              S is the source sequence length. If ``average_attn_weights=False``, returns attention weights per
+              head of shape :math:`(N, num_heads, L, S)`.
+        """
+        return self._forward_impl(
+            query,
+            key,
+            value,
+            key_padding_mask,
+            need_weights,
+            attn_mask,
+            average_attn_weights,
+            is_causal,
+        )
+
+    def _forward_impl(
+        self,
+        query: Tensor,
+        key: Tensor,
+        value: Tensor,
+        key_padding_mask: Tensor | None = None,
+        need_weights: bool = True,
+        attn_mask: Tensor | None = None,
+        average_attn_weights: bool = True,
+        is_causal: bool = False,
+    ) -> tuple[Tensor, Tensor | None]:
+        # This version will not deal with the static key/value pairs.
+        # Keeping it here for future changes.
+        #
+        # TODO: This method has some duplicate lines with the
+        # `torch.nn.functional.multi_head_attention`. Will need to refactor.
+        static_k = None
+        static_v = None
+
+        if attn_mask is not None and is_causal:
+            raise AssertionError("Only allow causal mask or attn_mask")
+
+        if is_causal:
+            raise AssertionError("causal mask not supported by AO MHA module")
+
+        if self.batch_first:
+            query, key, value = (x.transpose(0, 1) for x in (query, key, value))
+
+        tgt_len, bsz, embed_dim_to_check = query.size()
+        assert self.embed_dim == embed_dim_to_check
+        # allow MHA to have different sizes for the feature dimension
+        assert key.size(0) == value.size(0) and key.size(1) == value.size(1)
+
+        head_dim = self.embed_dim // self.num_heads
+        assert head_dim * self.num_heads == self.embed_dim, (
+            "embed_dim must be divisible by num_heads"
+        )
+        scaling = float(head_dim) ** -0.5
+
+        q = self.linear_Q(query)
+        k = self.linear_K(key)
+        v = self.linear_V(value)
+
+        q = self.q_scaling_product.mul_scalar(q, scaling)
+
+        if attn_mask is not None:
+            if attn_mask.dtype == torch.uint8:
+                warnings.warn(
+                    "Byte tensor for `attn_mask` in `nn.MultiheadAttention` is deprecated. "
+                    "Use bool tensor instead.",
+                    stacklevel=3,
+                )
+                attn_mask = attn_mask.to(torch.bool)
+            assert attn_mask.is_floating_point() or attn_mask.dtype == torch.bool, (
+                f"Only float and bool types are supported for attn_mask, not {attn_mask.dtype}"
+            )
+
+            if attn_mask.dim() == 2:
+                attn_mask = attn_mask.unsqueeze(0)
+                if list(attn_mask.size()) != [1, query.size(0), key.size(0)]:
+                    raise RuntimeError("The size of the 2D attn_mask is not correct.")
+            elif attn_mask.dim() == 3:
+                if list(attn_mask.size()) != [
+                    bsz * self.num_heads,
+                    query.size(0),
+                    key.size(0),
+                ]:
+                    raise RuntimeError("The size of the 3D attn_mask is not correct.")
+            else:
+                raise RuntimeError(
+                    f"attn_mask's dimension {attn_mask.dim()} is not supported"
+                )
+            # attn_mask's dim is 3 now.
+
+        # convert ByteTensor key_padding_mask to bool
+        if key_padding_mask is not None and key_padding_mask.dtype == torch.uint8:
+            warnings.warn(
+                "Byte tensor for `key_padding_mask` in `nn.MultiheadAttention` is deprecated. "
+                "Use bool tensor instead.",
+                stacklevel=3,
+            )
+            key_padding_mask = key_padding_mask.to(torch.bool)
+        if self.bias_k is not None and self.bias_v is not None:
+            if static_k is None and static_v is None:
+                # Explicitly assert that bias_k and bias_v are not None
+                # in a way that TorchScript can understand.
+                bias_k = self.bias_k
+                assert bias_k is not None
+                bias_v = self.bias_v
+                assert bias_v is not None
+
+                k = torch.cat([k, bias_k.repeat(1, bsz, 1)])
+                v = torch.cat([v, bias_v.repeat(1, bsz, 1)])
+                if attn_mask is not None:
+                    attn_mask = F.pad(attn_mask, (0, 1))
+                if key_padding_mask is not None:
+                    key_padding_mask = F.pad(key_padding_mask, (0, 1))
+            else:
+                assert static_k is None, "bias cannot be added to static key."
+                assert static_v is None, "bias cannot be added to static value."
+        else:
+            assert self.bias_k is None
+            assert self.bias_v is None
+
+        q = q.contiguous().view(tgt_len, bsz * self.num_heads, head_dim).transpose(0, 1)
+        if k is not None:
+            k = k.contiguous().view(-1, bsz * self.num_heads, head_dim).transpose(0, 1)
+        if v is not None:
+            v = v.contiguous().view(-1, bsz * self.num_heads, head_dim).transpose(0, 1)
+
+        if static_k is not None:
+            assert static_k.size(0) == bsz * self.num_heads
+            assert static_k.size(2) == head_dim
+            k = static_k
+
+        if static_v is not None:
+            assert static_v.size(0) == bsz * self.num_heads
+            assert static_v.size(2) == head_dim
+            v = static_v
+
+        # pyrefly: ignore [missing-attribute]
+        src_len = k.size(1)
+
+        if key_padding_mask is not None:
+            assert key_padding_mask.size(0) == bsz
+            assert key_padding_mask.size(1) == src_len
+
+        if self.add_zero_attn:
+            src_len += 1
+            # pyrefly: ignore [missing-attribute]
+            k_zeros = torch.zeros((k.size(0), 1) + k.size()[2:])
+            # pyrefly: ignore [missing-attribute]
+            if k.is_quantized:
+                k_zeros = torch.quantize_per_tensor(
+                    k_zeros,
+                    # pyrefly: ignore [missing-attribute]
+                    k.q_scale(),
+                    # pyrefly: ignore [missing-attribute]
+                    k.q_zero_point(),
+                    # pyrefly: ignore [missing-attribute]
+                    k.dtype,
+                )
+            # pyrefly: ignore [no-matching-overload]
+            k = torch.cat([k, k_zeros], dim=1)
+            # pyrefly: ignore [missing-attribute]
+            v_zeros = torch.zeros((v.size(0), 1) + k.size()[2:])
+            # pyrefly: ignore [missing-attribute]
+            if v.is_quantized:
+                v_zeros = torch.quantize_per_tensor(
+                    v_zeros,
+                    # pyrefly: ignore [missing-attribute]
+                    v.q_scale(),
+                    # pyrefly: ignore [missing-attribute]
+                    v.q_zero_point(),
+                    # pyrefly: ignore [missing-attribute]
+                    v.dtype,
+                )
+            # pyrefly: ignore [no-matching-overload]
+            v = torch.cat([v, v_zeros], dim=1)
+
+            if attn_mask is not None:
+                attn_mask = F.pad(attn_mask, (0, 1))
+            if key_padding_mask is not None:
+                key_padding_mask = F.pad(key_padding_mask, (0, 1))
+
+        # Leaving the quantized zone here
+        q = self.dequant_q(q)
+        k = self.dequant_k(k)
+        v = self.dequant_v(v)
+        attn_output_weights = torch.bmm(q, k.transpose(1, 2))
+        assert list(attn_output_weights.size()) == [
+            bsz * self.num_heads,
+            tgt_len,
+            src_len,
+        ]
+
+        if attn_mask is not None:
+            if attn_mask.dtype == torch.bool:
+                attn_output_weights.masked_fill_(attn_mask, float("-inf"))
+            else:
+                attn_output_weights += attn_mask
+
+        if key_padding_mask is not None:
+            attn_output_weights = attn_output_weights.view(
+                bsz, self.num_heads, tgt_len, src_len
+            )
+            attn_output_weights = attn_output_weights.masked_fill(
+                key_padding_mask.unsqueeze(1).unsqueeze(2),
+                float("-inf"),
+            )
+            attn_output_weights = attn_output_weights.view(
+                bsz * self.num_heads, tgt_len, src_len
+            )
+
+        attn_output_weights = F.softmax(attn_output_weights, dim=-1)
+        attn_output_weights = F.dropout(
+            attn_output_weights, p=self.dropout, training=self.training
+        )
+
+        attn_output = torch.bmm(attn_output_weights, v)
+        assert list(attn_output.size()) == [bsz * self.num_heads, tgt_len, head_dim]
+        if self.batch_first:
+            attn_output = attn_output.view(bsz, tgt_len, self.embed_dim)
+        else:
+            attn_output = (
+                attn_output.transpose(0, 1)
+                .contiguous()
+                .view(tgt_len, bsz, self.embed_dim)
+            )
+
+        # Reentering the quantized zone
+        attn_output = self.quant_attn_output(attn_output)
+        # for the type: ignore[has-type], see https://github.com/pytorch/pytorch/issues/58969
+        attn_output = self.out_proj(attn_output)  # type: ignore[has-type]
+        attn_output_weights = self.quant_attn_output_weights(attn_output_weights)
+
+        if need_weights:
+            # average attention weights over heads
+            attn_output_weights = attn_output_weights.view(
+                bsz, self.num_heads, tgt_len, src_len
+            )
+            if average_attn_weights:
+                attn_output_weights = attn_output_weights.mean(dim=1)
+            return attn_output, attn_output_weights
+        else:
+            return attn_output, None
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantizable/modules/rnn.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantizable/modules/rnn.py
new file mode 100644
index 0000000000000000000000000000000000000000..74e4bd902d1565360f72a5c4098b6e6d1590a146
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantizable/modules/rnn.py
@@ -0,0 +1,604 @@
+"""
+We will recreate all the RNN modules as we require the modules to be decomposed
+into its building blocks to be able to observe.
+"""
+
+# mypy: allow-untyped-defs
+
+import numbers
+import warnings
+
+import torch
+from torch import Tensor
+
+
+__all__ = ["LSTMCell", "LSTM"]
+
+
+class LSTMCell(torch.nn.Module):
+    r"""A quantizable long short-term memory (LSTM) cell.
+
+    For the description and the argument types, please, refer to :class:`~torch.nn.LSTMCell`
+
+    `split_gates`: specify True to compute the input/forget/cell/output gates separately
+    to avoid an intermediate tensor which is subsequently chunk'd. This optimization can
+    be beneficial for on-device inference latency. This flag is cascaded down from the
+    parent classes.
+
+    Examples::
+
+        >>> import torch.ao.nn.quantizable as nnqa
+        >>> rnn = nnqa.LSTMCell(10, 20)
+        >>> input = torch.randn(6, 10)
+        >>> hx = torch.randn(3, 20)
+        >>> cx = torch.randn(3, 20)
+        >>> output = []
+        >>> for i in range(6):
+        ...     hx, cx = rnn(input[i], (hx, cx))
+        ...     output.append(hx)
+    """
+
+    _FLOAT_MODULE = torch.nn.LSTMCell
+    __constants__ = ["split_gates"]  # for jit.script
+
+    def __init__(
+        self,
+        input_dim: int,
+        hidden_dim: int,
+        bias: bool = True,
+        device=None,
+        dtype=None,
+        *,
+        split_gates=False,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        self.input_size = input_dim
+        self.hidden_size = hidden_dim
+        self.bias = bias
+        self.split_gates = split_gates
+
+        if not split_gates:
+            self.igates: torch.nn.Module = torch.nn.Linear(
+                input_dim, 4 * hidden_dim, bias=bias, **factory_kwargs
+            )
+            self.hgates: torch.nn.Module = torch.nn.Linear(
+                hidden_dim, 4 * hidden_dim, bias=bias, **factory_kwargs
+            )
+            self.gates: torch.nn.Module = torch.ao.nn.quantized.FloatFunctional()
+        else:
+            # keep separate Linear layers for each gate
+            self.igates = torch.nn.ModuleDict()
+            self.hgates = torch.nn.ModuleDict()
+            self.gates = torch.nn.ModuleDict()
+            for g in ["input", "forget", "cell", "output"]:
+                # pyre-fixme[29]: `Union[torch._tensor.Tensor, torch.nn.modules.module.Module]`
+                self.igates[g] = torch.nn.Linear(
+                    input_dim, hidden_dim, bias=bias, **factory_kwargs
+                )
+                # pyre-fixme[29]: `Union[torch._tensor.Tensor, torch.nn.modules.module.Module]`
+                self.hgates[g] = torch.nn.Linear(
+                    hidden_dim, hidden_dim, bias=bias, **factory_kwargs
+                )
+                # pyre-fixme[29]: `Union[torch._tensor.Tensor, torch.nn.modules.module.Module]`
+                self.gates[g] = torch.ao.nn.quantized.FloatFunctional()
+
+        self.input_gate = torch.nn.Sigmoid()
+        self.forget_gate = torch.nn.Sigmoid()
+        self.cell_gate = torch.nn.Tanh()
+        self.output_gate = torch.nn.Sigmoid()
+
+        self.fgate_cx = torch.ao.nn.quantized.FloatFunctional()
+        self.igate_cgate = torch.ao.nn.quantized.FloatFunctional()
+        self.fgate_cx_igate_cgate = torch.ao.nn.quantized.FloatFunctional()
+
+        self.ogate_cy = torch.ao.nn.quantized.FloatFunctional()
+
+        self.initial_hidden_state_qparams: tuple[float, int] = (1.0, 0)
+        self.initial_cell_state_qparams: tuple[float, int] = (1.0, 0)
+        self.hidden_state_dtype: torch.dtype = torch.quint8
+        self.cell_state_dtype: torch.dtype = torch.quint8
+
+    def forward(
+        self, x: Tensor, hidden: tuple[Tensor, Tensor] | None = None
+    ) -> tuple[Tensor, Tensor]:
+        if hidden is None or hidden[0] is None or hidden[1] is None:
+            hidden = self.initialize_hidden(x.shape[0], x.is_quantized)
+        hx, cx = hidden
+
+        if not self.split_gates:
+            igates = self.igates(x)
+            hgates = self.hgates(hx)
+            gates = self.gates.add(igates, hgates)  # type: ignore[operator]
+
+            input_gate, forget_gate, cell_gate, out_gate = gates.chunk(4, 1)
+
+            input_gate = self.input_gate(input_gate)
+            forget_gate = self.forget_gate(forget_gate)
+            cell_gate = self.cell_gate(cell_gate)
+            out_gate = self.output_gate(out_gate)
+        else:
+            # apply each input + hidden projection and add together
+            gate = {}
+            for (key, gates), igates, hgates in zip(
+                self.gates.items(),  # type: ignore[operator]
+                self.igates.values(),  # type: ignore[operator]
+                self.hgates.values(),  # type: ignore[operator]
+            ):
+                gate[key] = gates.add(igates(x), hgates(hx))
+
+            input_gate = self.input_gate(gate["input"])
+            forget_gate = self.forget_gate(gate["forget"])
+            cell_gate = self.cell_gate(gate["cell"])
+            out_gate = self.output_gate(gate["output"])
+
+        fgate_cx = self.fgate_cx.mul(forget_gate, cx)
+        igate_cgate = self.igate_cgate.mul(input_gate, cell_gate)
+        fgate_cx_igate_cgate = self.fgate_cx_igate_cgate.add(fgate_cx, igate_cgate)
+        cy = fgate_cx_igate_cgate
+
+        # TODO: make this tanh a member of the module so its qparams can be configured
+        tanh_cy = torch.tanh(cy)
+        hy = self.ogate_cy.mul(out_gate, tanh_cy)
+        return hy, cy
+
+    def initialize_hidden(
+        self, batch_size: int, is_quantized: bool = False
+    ) -> tuple[Tensor, Tensor]:
+        h, c = (
+            torch.zeros((batch_size, self.hidden_size)),
+            torch.zeros((batch_size, self.hidden_size)),
+        )
+        if is_quantized:
+            (h_scale, h_zp) = self.initial_hidden_state_qparams
+            (c_scale, c_zp) = self.initial_cell_state_qparams
+            h = torch.quantize_per_tensor(
+                h, scale=h_scale, zero_point=h_zp, dtype=self.hidden_state_dtype
+            )
+            c = torch.quantize_per_tensor(
+                c, scale=c_scale, zero_point=c_zp, dtype=self.cell_state_dtype
+            )
+        return h, c
+
+    def _get_name(self):
+        return "QuantizableLSTMCell"
+
+    @classmethod
+    def from_params(cls, wi, wh, bi=None, bh=None, split_gates=False):
+        """Uses the weights and biases to create a new LSTM cell.
+
+        Args:
+            wi, wh: Weights for the input and hidden layers
+            bi, bh: Biases for the input and hidden layers
+        """
+        assert (bi is None) == (bh is None)  # Either both None or both have values
+        input_size = wi.shape[1]
+        hidden_size = wh.shape[1]
+        cell = cls(
+            input_dim=input_size,
+            hidden_dim=hidden_size,
+            bias=(bi is not None),
+            split_gates=split_gates,
+        )
+
+        if not split_gates:
+            cell.igates.weight = torch.nn.Parameter(wi)
+            if bi is not None:
+                cell.igates.bias = torch.nn.Parameter(bi)
+            cell.hgates.weight = torch.nn.Parameter(wh)
+            if bh is not None:
+                cell.hgates.bias = torch.nn.Parameter(bh)
+        else:
+            # split weight/bias
+            for w, b, gates in zip([wi, wh], [bi, bh], [cell.igates, cell.hgates]):
+                for w_chunk, gate in zip(w.chunk(4, dim=0), gates.values()):  # type: ignore[operator]
+                    gate.weight = torch.nn.Parameter(w_chunk)
+
+                if b is not None:
+                    for b_chunk, gate in zip(b.chunk(4, dim=0), gates.values()):  # type: ignore[operator]
+                        gate.bias = torch.nn.Parameter(b_chunk)
+
+        return cell
+
+    @classmethod
+    def from_float(cls, other, use_precomputed_fake_quant=False, split_gates=False):
+        assert type(other) is cls._FLOAT_MODULE
+        assert hasattr(other, "qconfig"), "The float module must have 'qconfig'"
+        observed = cls.from_params(
+            other.weight_ih,
+            other.weight_hh,
+            other.bias_ih,
+            other.bias_hh,
+            split_gates=split_gates,
+        )
+        observed.qconfig = other.qconfig
+        observed.igates.qconfig = other.qconfig
+        observed.hgates.qconfig = other.qconfig
+        if split_gates:
+            # also apply qconfig directly to Linear modules
+            for g in observed.igates.values():
+                g.qconfig = other.qconfig
+            for g in observed.hgates.values():
+                g.qconfig = other.qconfig
+        return observed
+
+
+class _LSTMSingleLayer(torch.nn.Module):
+    r"""A single one-directional LSTM layer.
+
+    The difference between a layer and a cell is that the layer can process a
+    sequence, while the cell only expects an instantaneous value.
+    """
+
+    def __init__(
+        self,
+        input_dim: int,
+        hidden_dim: int,
+        bias: bool = True,
+        device=None,
+        dtype=None,
+        *,
+        split_gates=False,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        self.cell = LSTMCell(
+            input_dim, hidden_dim, bias=bias, split_gates=split_gates, **factory_kwargs
+        )
+
+    def forward(self, x: Tensor, hidden: tuple[Tensor, Tensor] | None = None):
+        result = []
+        seq_len = x.shape[0]
+        for i in range(seq_len):
+            hidden = self.cell(x[i], hidden)
+            result.append(hidden[0])  # type: ignore[index]
+        result_tensor = torch.stack(result, 0)
+        return result_tensor, hidden
+
+    @classmethod
+    def from_params(cls, *args, **kwargs):
+        cell = LSTMCell.from_params(*args, **kwargs)
+        layer = cls(
+            cell.input_size, cell.hidden_size, cell.bias, split_gates=cell.split_gates
+        )
+        layer.cell = cell
+        return layer
+
+
+class _LSTMLayer(torch.nn.Module):
+    r"""A single bi-directional LSTM layer."""
+
+    def __init__(
+        self,
+        input_dim: int,
+        hidden_dim: int,
+        bias: bool = True,
+        batch_first: bool = False,
+        bidirectional: bool = False,
+        device=None,
+        dtype=None,
+        *,
+        split_gates=False,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        self.batch_first = batch_first
+        self.bidirectional = bidirectional
+        self.layer_fw = _LSTMSingleLayer(
+            input_dim, hidden_dim, bias=bias, split_gates=split_gates, **factory_kwargs
+        )
+        if self.bidirectional:
+            self.layer_bw = _LSTMSingleLayer(
+                input_dim,
+                hidden_dim,
+                bias=bias,
+                split_gates=split_gates,
+                **factory_kwargs,
+            )
+
+    def forward(self, x: Tensor, hidden: tuple[Tensor, Tensor] | None = None):
+        if self.batch_first:
+            x = x.transpose(0, 1)
+        if hidden is None:
+            hx_fw, cx_fw = (None, None)
+        else:
+            hx_fw, cx_fw = hidden
+        hidden_bw: tuple[Tensor, Tensor] | None = None
+        if self.bidirectional:
+            if hx_fw is None:
+                hx_bw = None
+            else:
+                hx_bw = hx_fw[1]
+                hx_fw = hx_fw[0]
+            if cx_fw is None:
+                cx_bw = None
+            else:
+                cx_bw = cx_fw[1]
+                cx_fw = cx_fw[0]
+            if hx_bw is not None and cx_bw is not None:
+                hidden_bw = hx_bw, cx_bw
+        if hx_fw is None and cx_fw is None:
+            hidden_fw = None
+        else:
+            hidden_fw = (
+                torch.jit._unwrap_optional(hx_fw),
+                torch.jit._unwrap_optional(cx_fw),
+            )
+        result_fw, hidden_fw = self.layer_fw(x, hidden_fw)
+
+        if hasattr(self, "layer_bw") and self.bidirectional:
+            x_reversed = x.flip(0)
+            result_bw, hidden_bw = self.layer_bw(x_reversed, hidden_bw)
+            result_bw = result_bw.flip(0)
+
+            result = torch.cat([result_fw, result_bw], result_fw.dim() - 1)
+            if hidden_fw is None and hidden_bw is None:
+                h = None
+                c = None
+            elif hidden_fw is None:
+                (h, c) = torch.jit._unwrap_optional(hidden_bw)
+            elif hidden_bw is None:
+                (h, c) = torch.jit._unwrap_optional(hidden_fw)
+            else:
+                h = torch.stack([hidden_fw[0], hidden_bw[0]], 0)  # type: ignore[list-item]
+                c = torch.stack([hidden_fw[1], hidden_bw[1]], 0)  # type: ignore[list-item]
+        else:
+            result = result_fw
+            h, c = torch.jit._unwrap_optional(hidden_fw)  # type: ignore[assignment]
+
+        if self.batch_first:
+            result.transpose_(0, 1)
+
+        return result, (h, c)
+
+    @classmethod
+    def from_float(cls, other, layer_idx=0, qconfig=None, **kwargs):
+        r"""
+        There is no FP equivalent of this class. This function is here just to
+        mimic the behavior of the `prepare` within the `torch.ao.quantization`
+        flow.
+        """
+        assert hasattr(other, "qconfig") or (qconfig is not None)
+
+        input_size = kwargs.get("input_size", other.input_size)
+        hidden_size = kwargs.get("hidden_size", other.hidden_size)
+        bias = kwargs.get("bias", other.bias)
+        batch_first = kwargs.get("batch_first", other.batch_first)
+        bidirectional = kwargs.get("bidirectional", other.bidirectional)
+        split_gates = kwargs.get("split_gates", False)
+
+        layer = cls(
+            input_size,
+            hidden_size,
+            bias,
+            batch_first,
+            bidirectional,
+            split_gates=split_gates,
+        )
+        # pyrefly: ignore [bad-argument-type]
+        layer.qconfig = getattr(other, "qconfig", qconfig)
+        wi = getattr(other, f"weight_ih_l{layer_idx}")
+        wh = getattr(other, f"weight_hh_l{layer_idx}")
+        bi = getattr(other, f"bias_ih_l{layer_idx}", None)
+        bh = getattr(other, f"bias_hh_l{layer_idx}", None)
+
+        layer.layer_fw = _LSTMSingleLayer.from_params(
+            wi, wh, bi, bh, split_gates=split_gates
+        )
+
+        if other.bidirectional:
+            wi = getattr(other, f"weight_ih_l{layer_idx}_reverse")
+            wh = getattr(other, f"weight_hh_l{layer_idx}_reverse")
+            bi = getattr(other, f"bias_ih_l{layer_idx}_reverse", None)
+            bh = getattr(other, f"bias_hh_l{layer_idx}_reverse", None)
+            layer.layer_bw = _LSTMSingleLayer.from_params(
+                wi, wh, bi, bh, split_gates=split_gates
+            )
+        return layer
+
+
+class LSTM(torch.nn.Module):
+    r"""A quantizable long short-term memory (LSTM).
+
+    For the description and the argument types, please, refer to :class:`~torch.nn.LSTM`
+
+    Attributes:
+        layers : instances of the `_LSTMLayer`
+
+    .. note::
+        To access the weights and biases, you need to access them per layer.
+        See examples below.
+
+    Examples::
+
+        >>> import torch.ao.nn.quantizable as nnqa
+        >>> rnn = nnqa.LSTM(10, 20, 2)
+        >>> input = torch.randn(5, 3, 10)
+        >>> h0 = torch.randn(2, 3, 20)
+        >>> c0 = torch.randn(2, 3, 20)
+        >>> output, (hn, cn) = rnn(input, (h0, c0))
+        >>> # To get the weights:
+        >>> # xdoctest: +SKIP
+        >>> print(rnn.layers[0].weight_ih)
+        tensor([[...]])
+        >>> print(rnn.layers[0].weight_hh)
+        AssertionError: There is no reverse path in the non-bidirectional layer
+    """
+
+    _FLOAT_MODULE = torch.nn.LSTM
+
+    def __init__(
+        self,
+        input_size: int,
+        hidden_size: int,
+        num_layers: int = 1,
+        bias: bool = True,
+        batch_first: bool = False,
+        dropout: float = 0.0,
+        bidirectional: bool = False,
+        device=None,
+        dtype=None,
+        *,
+        split_gates: bool = False,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        self.input_size = input_size
+        self.hidden_size = hidden_size
+        self.num_layers = num_layers
+        self.bias = bias
+        self.batch_first = batch_first
+        self.dropout = float(dropout)
+        self.bidirectional = bidirectional
+        self.training = False  # Default to eval mode. If we want to train, we will explicitly set to training.
+
+        if (
+            not isinstance(dropout, numbers.Number)
+            # pyrefly: ignore [unsupported-operation]
+            or not 0 <= dropout <= 1
+            or isinstance(dropout, bool)
+        ):
+            raise ValueError(
+                "dropout should be a number in range [0, 1] "
+                "representing the probability of an element being "
+                "zeroed"
+            )
+        # pyrefly: ignore [unsupported-operation]
+        if dropout > 0:
+            warnings.warn(
+                "dropout option for quantizable LSTM is ignored. "
+                "If you are training, please, use nn.LSTM version "
+                "followed by `prepare` step.",
+                stacklevel=2,
+            )
+            if num_layers == 1:
+                warnings.warn(
+                    "dropout option adds dropout after all but last "
+                    "recurrent layer, so non-zero dropout expects "
+                    f"num_layers greater than 1, but got dropout={dropout} "
+                    f"and num_layers={num_layers}",
+                    stacklevel=2,
+                )
+
+        layers = [
+            _LSTMLayer(
+                self.input_size,
+                self.hidden_size,
+                self.bias,
+                batch_first=False,
+                bidirectional=self.bidirectional,
+                split_gates=split_gates,
+                **factory_kwargs,
+            )
+        ]
+        layers.extend(
+            _LSTMLayer(
+                self.hidden_size,
+                self.hidden_size,
+                self.bias,
+                batch_first=False,
+                bidirectional=self.bidirectional,
+                split_gates=split_gates,
+                **factory_kwargs,
+            )
+            for _ in range(1, num_layers)
+        )
+        self.layers = torch.nn.ModuleList(layers)
+
+    def forward(self, x: Tensor, hidden: tuple[Tensor, Tensor] | None = None):
+        if self.batch_first:
+            x = x.transpose(0, 1)
+
+        max_batch_size = x.size(1)
+        num_directions = 2 if self.bidirectional else 1
+        if hidden is None:
+            zeros = torch.zeros(
+                num_directions,
+                max_batch_size,
+                self.hidden_size,
+                dtype=torch.float,
+                device=x.device,
+            )
+            zeros.squeeze_(0)
+            if x.is_quantized:
+                zeros = torch.quantize_per_tensor(
+                    zeros, scale=1.0, zero_point=0, dtype=x.dtype
+                )
+            hxcx = [(zeros, zeros) for _ in range(self.num_layers)]
+        else:
+            hidden_non_opt = torch.jit._unwrap_optional(hidden)
+            if isinstance(hidden_non_opt[0], Tensor):
+                hx = hidden_non_opt[0].reshape(
+                    self.num_layers, num_directions, max_batch_size, self.hidden_size
+                )
+                cx = hidden_non_opt[1].reshape(
+                    self.num_layers, num_directions, max_batch_size, self.hidden_size
+                )
+                hxcx = [
+                    (hx[idx].squeeze(0), cx[idx].squeeze(0))
+                    for idx in range(self.num_layers)
+                ]
+            else:
+                hxcx = hidden_non_opt
+
+        hx_list = []
+        cx_list = []
+        for idx, layer in enumerate(self.layers):
+            x, (h, c) = layer(x, hxcx[idx])
+            hx_list.append(torch.jit._unwrap_optional(h))
+            cx_list.append(torch.jit._unwrap_optional(c))
+        hx_tensor = torch.stack(hx_list)
+        cx_tensor = torch.stack(cx_list)
+
+        # We are creating another dimension for bidirectional case
+        # need to collapse it
+        hx_tensor = hx_tensor.reshape(-1, hx_tensor.shape[-2], hx_tensor.shape[-1])
+        cx_tensor = cx_tensor.reshape(-1, cx_tensor.shape[-2], cx_tensor.shape[-1])
+
+        if self.batch_first:
+            x = x.transpose(0, 1)
+
+        return x, (hx_tensor, cx_tensor)
+
+    def _get_name(self):
+        return "QuantizableLSTM"
+
+    @classmethod
+    def from_float(cls, other, qconfig=None, split_gates=False):
+        assert isinstance(other, cls._FLOAT_MODULE)
+        assert hasattr(other, "qconfig") or qconfig
+        observed = cls(
+            other.input_size,
+            other.hidden_size,
+            other.num_layers,
+            other.bias,
+            other.batch_first,
+            other.dropout,
+            other.bidirectional,
+            split_gates=split_gates,
+        )
+        # pyrefly: ignore [bad-argument-type]
+        observed.qconfig = getattr(other, "qconfig", qconfig)
+        for idx in range(other.num_layers):
+            observed.layers[idx] = _LSTMLayer.from_float(
+                other, idx, qconfig, batch_first=False, split_gates=split_gates
+            )
+
+        # Prepare the model
+        if other.training:
+            observed.train()
+            observed = torch.ao.quantization.prepare_qat(observed, inplace=True)
+        else:
+            observed.eval()
+            observed = torch.ao.quantization.prepare(observed, inplace=True)
+        return observed
+
+    @classmethod
+    def from_observed(cls, other):
+        # The whole flow is float -> observed -> quantized
+        # This class does float -> observed only
+        raise NotImplementedError(
+            "It looks like you are trying to convert a "
+            "non-quantizable LSTM module. Please, see "
+            "the examples on quantizable LSTMs."
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..77e97d8595282f3d69963ee129fa473249e3ae29
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/__init__.py
@@ -0,0 +1,39 @@
+from . import functional
+from .modules import *  # noqa: F403
+from .modules import MaxPool2d
+
+
+__all__ = [
+    "BatchNorm2d",
+    "BatchNorm3d",
+    "Conv1d",
+    "Conv2d",
+    "Conv3d",
+    "ConvTranspose1d",
+    "ConvTranspose2d",
+    "ConvTranspose3d",
+    "DeQuantize",
+    "ELU",
+    "Embedding",
+    "EmbeddingBag",
+    "GroupNorm",
+    "Hardswish",
+    "InstanceNorm1d",
+    "InstanceNorm2d",
+    "InstanceNorm3d",
+    "LayerNorm",
+    "LeakyReLU",
+    "Linear",
+    "LSTM",
+    "MultiheadAttention",
+    "Quantize",
+    "ReLU6",
+    "Sigmoid",
+    "Softmax",
+    "Dropout",
+    "PReLU",
+    # Wrapper modules
+    "FloatFunctional",
+    "FXFloatFunctional",
+    "QFunctional",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d79bdbfe83209f18b17cc8c7b245f322871d6c0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/__init__.py
@@ -0,0 +1 @@
+from .modules import *  # noqa: F403
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..969fd6f121f5ddb72ed2e8e158e3ee7e990cfd0c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/__init__.py
@@ -0,0 +1,26 @@
+from .conv import (
+    Conv1d,
+    Conv2d,
+    Conv3d,
+    ConvTranspose1d,
+    ConvTranspose2d,
+    ConvTranspose3d,
+)
+from .linear import Linear
+from .rnn import GRU, GRUCell, LSTM, LSTMCell, RNNCell
+
+
+__all__ = [
+    "Linear",
+    "LSTM",
+    "GRU",
+    "LSTMCell",
+    "RNNCell",
+    "GRUCell",
+    "Conv1d",
+    "Conv2d",
+    "Conv3d",
+    "ConvTranspose1d",
+    "ConvTranspose2d",
+    "ConvTranspose3d",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py
new file mode 100644
index 0000000000000000000000000000000000000000..68c3f6acd093477a44057ade1fb48107709eda89
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py
@@ -0,0 +1,530 @@
+# mypy: allow-untyped-defs
+r"""Dynamically quantized convolution modules."""
+
+import warnings
+from typing import ClassVar, Literal
+
+import torch
+import torch.ao.nn.quantized as nnq
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import Tensor
+from torch._ops import ops
+from torch.ao.nn.quantized.modules.conv import _reverse_repeat_padding
+from torch.nn.common_types import _size_1_t
+from torch.nn.modules.utils import _pair, _single, _triple
+
+
+__all__ = [
+    "Conv1d",
+    "Conv2d",
+    "Conv3d",
+    "ConvTranspose1d",
+    "ConvTranspose2d",
+    "ConvTranspose3d",
+]
+
+
+class Conv1d(nnq.Conv1d):
+    r"""A dynamically quantized conv module with floating point tensors as inputs and outputs.
+
+    For details on input arguments, parameters, and implementation see
+    :class:`~torch.nn.Conv1d` and :class:`~torch.ao.nn.quantized.dynamic.Conv1d` and
+
+    Attributes:
+        weight (Tensor):     packed tensor derived from the learnable weight
+                             parameter.
+        scale (Tensor):      scalar for the output scale
+        zero_point (Tensor): scalar for the output zero point
+
+    See :class:`~torch.nn.Conv1d` for other attributes.
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> m = nn.quantized.dynamic.Conv1d(16, 33, 3, stride=2)
+        >>> input = torch.randn(20, 16, 100)
+        >>> output = m(input)
+
+    """
+
+    _FLOAT_MODULE: ClassVar[type[nn.Conv1d]] = nn.Conv1d
+    _NNIQAT_CONV_BN_MODULE: ClassVar[type[nn.Module] | None] = None
+    _NNI_CONV_RELU_MODULE: ClassVar[type[nn.Module] | None] = None
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: _size_1_t,
+        stride: _size_1_t = 1,
+        padding: _size_1_t = 0,
+        dilation: _size_1_t = 1,
+        groups: int = 1,
+        bias: bool = True,
+        padding_mode: Literal["zeros", "reflect", "replicate", "circular"] = "zeros",
+        device=None,
+        dtype=None,
+        reduce_range=True,
+    ):
+        warnings.warn(
+            f"The current implementation of the {self._get_name()} module has poor numerical accuracy and its use is not recommended",  # noqa: B950
+            stacklevel=2,
+        )
+        factory_kwargs = {"device": device, "dtype": dtype}
+        kernel_size = _single(kernel_size)
+        stride = _single(stride)
+        # pyrefly: ignore [bad-assignment]
+        padding = padding if isinstance(padding, str) else _single(padding)
+        dilation = _single(dilation)
+
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            groups,
+            bias,
+            padding_mode,
+            **factory_kwargs,
+        )
+
+    def _get_name(self):
+        return "DynamicQuantizedConv1d"
+
+    def forward(self, input: Tensor, reduce_range: bool = True) -> Tensor:
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 3:
+            raise ValueError("Input shape must be `(N, C, L)`!")
+        if self.padding_mode != "zeros":
+            # Padding in Conv1d is stored as (p, p), need to get (p,)
+            _reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding[:1])
+            input = F.pad(
+                input, _reversed_padding_repeated_twice, mode=self.padding_mode
+            )
+        return ops.quantized.conv1d_dynamic(input, self._packed_params, reduce_range)
+
+
+class Conv2d(nnq.Conv2d):
+    r"""A dynamically quantized conv module with floating point tensors as inputs and outputs.
+
+    For details on input arguments, parameters, and implementation see
+    :class:`~torch.nn.Conv2d` and :class:`~torch.ao.nn.quantized.dynamic.Conv2d` and
+
+    Attributes:
+        weight (Tensor):     packed tensor derived from the learnable weight
+                             parameter.
+        scale (Tensor):      scalar for the output scale
+        zero_point (Tensor): scalar for the output zero point
+
+    See :class:`~torch.nn.Conv2d` for other attributes.
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> # With square kernels and equal stride
+        >>> m = nn.quantized.dynamic.Conv2d(16, 33, 3, stride=2)
+        >>> # non-square kernels and unequal stride and with padding
+        >>> m = nn.quantized.dynamic.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))
+        >>> # non-square kernels and unequal stride and with padding and dilation
+        >>> m = nn.quantized.dynamic.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2), dilation=(3, 1))
+        >>> input = torch.randn(20, 16, 50, 100)
+        >>> output = m(input)
+
+    """
+
+    _FLOAT_MODULE: ClassVar[type[nn.Conv2d]] = nn.Conv2d
+    _NNIQAT_CONV_BN_MODULE: ClassVar[type[nn.Module] | None] = None
+    _NNI_CONV_RELU_MODULE: ClassVar[type[nn.Module] | None] = None
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=True,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ):
+        warnings.warn(
+            f"The current implementation of the {self._get_name()} module "
+            "has poor numerical accuracy and its use is not recommended",
+            stacklevel=2,
+        )
+        factory_kwargs = {"device": device, "dtype": dtype}
+        kernel_size = _pair(kernel_size)
+        stride = _pair(stride)
+        padding = _pair(padding)
+        dilation = _pair(dilation)
+
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            groups,
+            bias,
+            padding_mode,
+            **factory_kwargs,
+        )
+
+    def _get_name(self):
+        return "DynamicQuantizedConv2d"
+
+    def forward(self, input: Tensor, reduce_range: bool = True) -> Tensor:
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 4:
+            raise ValueError("Input shape must be `(N, C, H, W)`!")
+        if self.padding_mode != "zeros":
+            _reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding)
+            input = F.pad(
+                input, _reversed_padding_repeated_twice, mode=self.padding_mode
+            )
+        return ops.quantized.conv2d_dynamic(input, self._packed_params, reduce_range)
+
+
+class Conv3d(nnq.Conv3d):
+    r"""A dynamically quantized conv module with floating point tensors as inputs and outputs.
+
+    For details on input arguments, parameters, and implementation see
+    :class:`~torch.nn.Conv3d` and :class:`~torch.ao.nn.quantized.dynamic.Conv3d` and
+
+    Attributes:
+        weight (Tensor):     packed tensor derived from the learnable weight
+                             parameter.
+        scale (Tensor):      scalar for the output scale
+        zero_point (Tensor): scalar for the output zero point
+
+    See :class:`~torch.nn.Conv3d` for other attributes.
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> # With square kernels and equal stride
+        >>> m = nn.quantized.dynamic.Conv3d(16, 33, 3, stride=2)
+        >>> # non-square kernels and unequal stride and with padding
+        >>> m = nn.quantized.dynamic.Conv3d(16, 33, (3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2))
+        >>> # non-square kernels and unequal stride and with padding and dilation
+        >>> m = nn.quantized.dynamic.Conv3d(16, 33, (3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2), dilation=(1, 2, 2))
+        >>> input = torch.randn(20, 16, 56, 56, 56)
+        >>> output = m(input)
+
+    """
+
+    _FLOAT_MODULE: ClassVar[type[nn.Conv3d]] = nn.Conv3d
+    _NNIQAT_CONV_BN_MODULE: ClassVar[type[nn.Module] | None] = None
+    _NNI_CONV_RELU_MODULE: ClassVar[type[nn.Module] | None] = None
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=True,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ):
+        warnings.warn(
+            f"The current implementation of the {self._get_name()} module has poor numerical accuracy and its use is not recommended",  # noqa: B950
+            stacklevel=2,
+        )
+        assert padding_mode != "reflect", "Conv3d does not support reflection padding"
+        factory_kwargs = {"device": device, "dtype": dtype}
+        kernel_size = _triple(kernel_size)
+        stride = _triple(stride)
+        padding = _triple(padding)
+        dilation = _triple(dilation)
+        super()._init(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            False,
+            _triple(0),
+            groups,
+            bias,
+            padding_mode,
+            **factory_kwargs,
+        )
+
+    def _get_name(self):
+        return "DynamicQuantizedConv3d"
+
+    def forward(self, input: Tensor, reduce_range: bool = True) -> Tensor:
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 5:
+            raise ValueError("Input shape must be `(N, C, D, H, W)`!")
+        if self.padding_mode != "zeros":
+            _reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding)
+            input = F.pad(
+                input, _reversed_padding_repeated_twice, mode=self.padding_mode
+            )
+        return ops.quantized.conv3d_dynamic(input, self._packed_params, reduce_range)
+
+
+class ConvTranspose1d(nnq.ConvTranspose1d):
+    r"""A dynamically quantized transposed convolution module with floating point tensors as inputs and outputs.
+
+    For details on input arguments, parameters, and implementation see
+    :class:`~torch.nn.ConvTranspose1d`.
+
+    For special notes, please, see :class:`~torch.ao.nn.quantized.dynamic.Conv1d`
+
+    Attributes:
+        weight (Tensor):     packed tensor derived from the learnable weight
+                             parameter.
+        scale (Tensor):      scalar for the output scale
+        zero_point (Tensor): scalar for the output zero point
+    See :class:`~torch.nn.ConvTranspose1d` for other attributes.
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> # With square kernels and equal stride
+        >>> m = nndq.ConvTranspose1d(16, 33, 3, stride=2)
+        >>> # non-square kernels and unequal stride and with padding
+        >>> m = nndq.ConvTranspose1d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))
+        >>> output = m(input)
+        >>> # exact output size can be also specified as an argument
+        >>> downsample = nndq.Conv1d(16, 16, 3, stride=2, padding=1)
+        >>> upsample = nndq.ConvTranspose1d(16, 16, 3, stride=2, padding=1)
+        >>> h = downsample(input)
+        >>> h.size()
+        torch.Size([1, 16, 6])
+        >>> output = upsample(h, output_size=input.size())
+        >>> output.size()
+        torch.Size([1, 16, 12])
+    """
+
+    _FLOAT_MODULE: ClassVar[type[nn.ConvTranspose1d]] = nn.ConvTranspose1d
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        output_padding=0,
+        groups=1,
+        bias=True,
+        dilation=1,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ):
+        warnings.warn(
+            f"The current implementation of the {self._get_name()} module has poor numerical accuracy and its use is not recommended",  # noqa: B950
+            stacklevel=2,
+        )
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            output_padding,
+            groups,
+            bias,
+            dilation,
+            padding_mode,
+            **factory_kwargs,
+        )
+
+    def _get_name(self):
+        return "DynamicQuantizedConvTranspose1d"
+
+    def forward(self, input: Tensor, reduce_range: bool = True) -> Tensor:
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 3:
+            raise ValueError("Input shape must be `(N, C, L)`!")
+        return torch.ops.quantized.conv_transpose1d_dynamic(
+            input, self._packed_params, reduce_range
+        )
+
+
+class ConvTranspose2d(nnq.ConvTranspose2d):
+    r"""A dynamically quantized transposed convolution module with floating point tensors as inputs and outputs.
+
+    For details on input arguments, parameters, and implementation see
+    :class:`~torch.nn.ConvTranspose2d`.
+
+    For special notes, please, see :class:`~torch.ao.nn.quantized.dynamic.Conv2d`
+
+    Attributes:
+        weight (Tensor):     packed tensor derived from the learnable weight
+                             parameter.
+        scale (Tensor):      scalar for the output scale
+        zero_point (Tensor): scalar for the output zero point
+    See :class:`~torch.nn.ConvTranspose2d` for other attributes.
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> # With square kernels and equal stride
+        >>> m = nnq.ConvTranspose2d(16, 33, 3, stride=2)
+        >>> # non-square kernels and unequal stride and with padding
+        >>> m = nnq.ConvTranspose2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))
+        >>> output = m(input)
+        >>> # exact output size can be also specified as an argument
+        >>> downsample = nnq.Conv2d(16, 16, 3, stride=2, padding=1)
+        >>> upsample = nnq.ConvTranspose2d(16, 16, 3, stride=2, padding=1)
+        >>> h = downsample(input)
+        >>> h.size()
+        torch.Size([1, 16, 6, 6])
+        >>> output = upsample(h, output_size=input.size())
+        >>> output.size()
+        torch.Size([1, 16, 12, 12])
+    """
+
+    _FLOAT_MODULE: ClassVar[type[nn.ConvTranspose2d]] = nn.ConvTranspose2d
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        output_padding=0,
+        groups=1,
+        bias=True,
+        dilation=1,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ):
+        warnings.warn(
+            f"The current implementation of the {self._get_name()} module has poor numerical accuracy and its use is not recommended",  # noqa: B950
+            stacklevel=2,
+        )
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            output_padding,
+            groups,
+            bias,
+            dilation,
+            padding_mode,
+            **factory_kwargs,
+        )
+
+    def _get_name(self):
+        return "DynamicQuantizedConvTranspose2d"
+
+    def forward(self, input: Tensor, reduce_range: bool = True) -> Tensor:
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 4:
+            raise ValueError("Input shape must be `(N, C, H, W)`!")
+        return ops.quantized.conv_transpose2d_dynamic(
+            input, self._packed_params, reduce_range
+        )
+
+
+class ConvTranspose3d(nnq.ConvTranspose3d):
+    r"""A dynamically quantized transposed convolution module with floating point tensors as inputs and outputs.
+
+    For details on input arguments, parameters, and implementation see
+    :class:`~torch.nn.ConvTranspose3d`.
+
+    For special notes, please, see :class:`~torch.ao.nn.quantized.dynamic.Conv3d`
+
+    Attributes:
+        weight (Tensor):     packed tensor derived from the learnable weight
+                             parameter.
+        scale (Tensor):      scalar for the output scale
+        zero_point (Tensor): scalar for the output zero point
+    See :class:`~torch.nn.ConvTranspose3d` for other attributes.
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> # With cubic kernels and equal stride
+        >>> m = nnq.ConvTranspose3d(16, 33, 3, stride=2)
+        >>> # non-cubic kernels and unequal stride and with padding
+        >>> m = nnq.ConvTranspose3d(16, 33, (3, 3, 5), stride=(2, 1, 1), padding=(4, 2, 2))
+        >>> output = m(input)
+        >>> # exact output size can be also specified as an argument
+        >>> downsample = nnq.Conv3d(16, 16, 3, stride=2, padding=1)
+        >>> upsample = nnq.ConvTranspose3d(16, 16, 3, stride=2, padding=1)
+        >>> h = downsample(input)
+        >>> h.size()
+        torch.Size([1, 16, 6, 6, 6])
+        >>> output = upsample(h, output_size=input.size())
+        >>> output.size()
+        torch.Size([1, 16, 12, 12, 12])
+    """
+
+    _FLOAT_MODULE: ClassVar[type[nn.ConvTranspose3d]] = nn.ConvTranspose3d
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        output_padding=0,
+        groups=1,
+        bias=True,
+        dilation=1,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ):
+        warnings.warn(
+            f"The current implementation of the {self._get_name()} module has poor numerical accuracy and its use is not recommended",  # noqa: B950
+            stacklevel=2,
+        )
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            output_padding,
+            groups,
+            bias,
+            dilation,
+            padding_mode,
+            **factory_kwargs,
+        )
+
+    def _get_name(self):
+        return "DynamicQuantizedConvTranspose3d"
+
+    def forward(self, input: Tensor, reduce_range: bool = True) -> Tensor:
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 5:
+            raise ValueError("Input shape must be `(N, C, T, H, W)`!")
+        return ops.quantized.conv_transpose3d_dynamic(
+            input, self._packed_params, reduce_range
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py
new file mode 100644
index 0000000000000000000000000000000000000000..523ff78c31cf141e680e0a3374bcb5f1252cf7d7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py
@@ -0,0 +1,168 @@
+# mypy: allow-untyped-defs
+import torch
+import torch.ao.nn.intrinsic as nni
+import torch.ao.nn.quantized as nnq
+from torch.ao.nn.quantized.modules.utils import _quantize_weight
+
+
+__all__ = [
+    "Linear",
+]
+
+
+class Linear(nnq.Linear):
+    r"""
+    A dynamic quantized linear module with floating point tensor as inputs and outputs.
+    We adopt the same interface as `torch.nn.Linear`, please see
+    https://pytorch.org/docs/stable/nn.html#torch.nn.Linear for documentation.
+
+    Similar to :class:`torch.nn.Linear`, attributes will be randomly
+    initialized at module creation time and will be overwritten later
+
+    Attributes:
+        weight (Tensor): the non-learnable quantized weights of the module which are of
+                         shape :math:`(\text{out\_features}, \text{in\_features})`.
+        bias (Tensor): the non-learnable floating point bias of the module of shape
+                       :math:`(\text{out\_features})`. If :attr:`bias` is ``True``,
+                       the values are initialized to zero.
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> m = nn.quantized.dynamic.Linear(20, 30)
+        >>> input = torch.randn(128, 20)
+        >>> output = m(input)
+        >>> print(output.size())
+        torch.Size([128, 30])
+    """
+
+    # version used in this class is different from the parent class nnq.Linear
+    _version = 4
+
+    def __init__(self, in_features, out_features, bias_=True, dtype=torch.qint8):
+        super().__init__(in_features, out_features, bias_, dtype=dtype)
+        # We don't muck around with buffers or attributes or anything here
+        # to keep the module simple. *everything* is simply a Python attribute.
+        # Serialization logic is explicitly handled in the below serialization and
+        # deserialization modules
+        self.version = 4
+
+    def forward(self, x):
+        # Note that we can handle self.bias == None case.
+        if self._packed_params.dtype == torch.qint8:
+            if self.version is None or self.version < 4:
+                Y = torch.ops.quantized.linear_dynamic(
+                    x, self._packed_params._packed_params
+                )
+            else:
+                Y = torch.ops.quantized.linear_dynamic(
+                    x, self._packed_params._packed_params, reduce_range=True
+                )
+        elif self._packed_params.dtype == torch.float16:
+            Y = torch.ops.quantized.linear_dynamic_fp16(
+                x, self._packed_params._packed_params
+            )
+        else:
+            raise RuntimeError("Unsupported dtype on dynamic quantized linear!")
+        return Y.to(x.dtype)
+
+    def _get_name(self):
+        return "DynamicQuantizedLinear"
+
+    def extra_repr(self):
+        extra_repr_str = f"in_features={self.in_features}, out_features={self.out_features}, dtype={self._packed_params.dtype}"
+        if self._packed_params.dtype == torch.qint8:
+            extra_repr_str += f", qscheme={self.weight().qscheme()}"
+        return extra_repr_str
+
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        version = local_metadata.get("version", None)
+        self.version = version
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            False,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        r"""Create a dynamic quantized module from a float module or qparams_dict
+
+        Args:
+            mod (Module): a float module, either produced by torch.ao.quantization
+                          utilities or provided by the user
+        """
+        float_modules = [
+            torch.nn.Linear,
+            torch.nn.modules.linear.NonDynamicallyQuantizableLinear,
+            torch.ao.nn.intrinsic.modules.fused.LinearReLU,
+            torch.ao.nn.qat.dynamic.Linear,
+        ]
+
+        assert type(mod) in float_modules, (
+            "nn.quantized.dynamic.Linear.from_float only works for one of"
+            + str([float_mod.__name__ for float_mod in float_modules])
+        )
+        assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
+        if type(mod) is nni.LinearReLU:
+            mod = mod[0]
+        # pyrefly: ignore [missing-attribute]
+        if mod.qconfig is not None and mod.qconfig.weight is not None:
+            # pyrefly: ignore [not-callable]
+            weight_observer = mod.qconfig.weight()
+        else:
+            # We have the circular import issues if we import the qconfig in the beginning of this file:
+            # https://github.com/pytorch/pytorch/pull/24231. The current workaround is to postpone the
+            # import until we need it.
+            from torch.ao.quantization.qconfig import default_dynamic_qconfig
+
+            weight_observer = default_dynamic_qconfig.weight()
+        dtype = weight_observer.dtype
+        assert dtype in [torch.qint8, torch.float16], (
+            "The only supported dtypes for "
+            f"dynamic quantized linear are qint8 and float16 got: {dtype}"
+        )
+        weight_observer(mod.weight)
+        if dtype == torch.qint8:
+            qweight = _quantize_weight(mod.weight.float(), weight_observer)
+        elif dtype == torch.float16:
+            qweight = mod.weight.float()
+        else:
+            raise RuntimeError(
+                "Unsupported dtype specified for dynamic quantized Linear!"
+            )
+        qlinear = cls(mod.in_features, mod.out_features, dtype=dtype)
+        # pyrefly: ignore [bad-argument-type]
+        qlinear.set_weight_bias(qweight, mod.bias)
+        return qlinear
+
+    @classmethod
+    def from_reference(cls, ref_qlinear):  # type: ignore[override]
+        """Create a (fbgemm/qnnpack) dynamic quantized module from a reference quantized
+        module
+        Args:
+            ref_qlinear (Module): a reference quantized  module, either produced by
+            torch.ao.quantization functions or provided by the user
+        """
+        qlinear = cls(
+            ref_qlinear.in_features,
+            ref_qlinear.out_features,
+            dtype=ref_qlinear.weight_dtype,
+        )
+        qweight = ref_qlinear.get_quantized_weight()
+        bias = ref_qlinear.bias
+        qlinear.set_weight_bias(qweight, bias)
+        return qlinear
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ebe4b6a15af499f38a0d70ca93870cf1d6c224f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py
@@ -0,0 +1,1366 @@
+# mypy: allow-untyped-defs
+import numbers
+import warnings
+from typing_extensions import deprecated
+
+import torch
+import torch.nn as nn
+from torch import Tensor  # noqa: F401
+from torch._jit_internal import Dict, List, Optional, Tuple, Union  # noqa: F401
+from torch.ao.nn.quantized.modules.utils import _quantize_weight
+from torch.nn.utils.rnn import PackedSequence
+
+
+__all__ = [
+    "pack_weight_bias",
+    "PackedParameter",
+    "RNNBase",
+    "LSTM",
+    "GRU",
+    "RNNCellBase",
+    "RNNCell",
+    "LSTMCell",
+    "GRUCell",
+    "apply_permutation",
+]
+
+
+def _apply_permutation(tensor: Tensor, permutation: Tensor, dim: int = 1) -> Tensor:
+    return tensor.index_select(dim, permutation)
+
+
+@deprecated(
+    "`apply_permutation` is deprecated, please use `tensor.index_select(dim, permutation)` instead",
+    category=FutureWarning,
+)
+def apply_permutation(tensor: Tensor, permutation: Tensor, dim: int = 1) -> Tensor:
+    return _apply_permutation(tensor, permutation, dim)
+
+
+def pack_weight_bias(qweight, bias, dtype):
+    if dtype == torch.qint8:
+        # for each layer, for each direction we need to quantize and pack
+        # weights and pack parameters in this order:
+        #
+        #   w_ih, w_hh
+        packed_weight = torch.ops.quantized.linear_prepack(qweight, bias)
+
+        return packed_weight
+    else:
+        # for each layer, for each direction we need to quantize and pack
+        # weights and pack parameters in this order:
+        #
+        #   packed_ih, packed_hh, b_ih, b_hh
+        packed_weight = torch.ops.quantized.linear_prepack_fp16(qweight, bias)
+
+        return packed_weight
+
+
+class PackedParameter(torch.nn.Module):
+    def __init__(self, param):
+        super().__init__()
+        self.param = param
+
+    def _save_to_state_dict(self, destination, prefix, keep_vars):
+        super()._save_to_state_dict(destination, prefix, keep_vars)
+        destination[prefix + "param"] = self.param
+
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        self.param = state_dict[prefix + "param"]
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            False,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+
+class RNNBase(torch.nn.Module):
+    _FLOAT_MODULE = nn.RNNBase
+
+    _version = 2
+
+    def __init__(
+        self,
+        mode,
+        input_size,
+        hidden_size,
+        num_layers=1,
+        bias=True,
+        batch_first=False,
+        dropout=0.0,
+        bidirectional=False,
+        dtype=torch.qint8,
+    ):
+        super().__init__()
+
+        self.mode = mode
+        self.input_size = input_size
+        self.hidden_size = hidden_size
+        self.num_layers = num_layers
+        self.bias = bias
+        self.batch_first = batch_first
+        self.dropout = float(dropout)
+        self.bidirectional = bidirectional
+        self.dtype = dtype
+        self.version = 2
+        self.training = False
+        num_directions = 2 if bidirectional else 1
+
+        # "type: ignore" is required since ints and Numbers are not fully comparable
+        # https://github.com/python/mypy/issues/8566
+        if (
+            not isinstance(dropout, numbers.Number)
+            or not 0 <= dropout <= 1  # type: ignore[operator]
+            or isinstance(dropout, bool)
+        ):
+            raise ValueError(
+                "dropout should be a number in range [0, 1] "
+                "representing the probability of an element being "
+                "zeroed"
+            )
+        if dropout > 0 and num_layers == 1:  # type: ignore[operator]
+            warnings.warn(
+                "dropout option adds dropout after all but last "
+                "recurrent layer, so non-zero dropout expects "
+                f"num_layers greater than 1, but got dropout={dropout} and "
+                f"num_layers={num_layers}",
+                stacklevel=2,
+            )
+
+        if mode == "LSTM":
+            gate_size = 4 * hidden_size
+        elif mode == "GRU":
+            gate_size = 3 * hidden_size
+        else:
+            raise ValueError("Unrecognized RNN mode: " + mode)
+
+        _all_weight_values = []
+        for layer in range(num_layers):
+            for _ in range(num_directions):
+                layer_input_size = (
+                    input_size if layer == 0 else hidden_size * num_directions
+                )
+
+                w_ih = torch.randn(gate_size, layer_input_size).to(torch.float)
+                w_hh = torch.randn(gate_size, hidden_size).to(torch.float)
+                b_ih = torch.randn(gate_size).to(torch.float)
+                b_hh = torch.randn(gate_size).to(torch.float)
+                if dtype == torch.qint8:
+                    w_ih = torch.quantize_per_tensor(
+                        w_ih, scale=0.1, zero_point=0, dtype=torch.qint8
+                    )
+                    w_hh = torch.quantize_per_tensor(
+                        w_hh, scale=0.1, zero_point=0, dtype=torch.qint8
+                    )
+                    packed_ih = torch.ops.quantized.linear_prepack(w_ih, b_ih)
+                    packed_hh = torch.ops.quantized.linear_prepack(w_hh, b_hh)
+                    if self.version is None or self.version < 2:
+                        cell_params = (
+                            torch.ops.quantized.make_quantized_cell_params_dynamic(
+                                packed_ih, packed_hh, b_ih, b_hh
+                            )
+                        )
+                    else:
+                        cell_params = (
+                            torch.ops.quantized.make_quantized_cell_params_dynamic(
+                                packed_ih, packed_hh, b_ih, b_hh, True
+                            )
+                        )
+                else:
+                    packed_ih = torch.ops.quantized.linear_prepack_fp16(w_ih, b_ih)
+                    packed_hh = torch.ops.quantized.linear_prepack_fp16(w_hh, b_hh)
+                    cell_params = torch.ops.quantized.make_quantized_cell_params_fp16(
+                        packed_ih, packed_hh
+                    )
+
+                _all_weight_values.append(PackedParameter(cell_params))
+        self._all_weight_values = torch.nn.ModuleList(_all_weight_values)
+
+    def _get_name(self):
+        return "DynamicQuantizedRNN"
+
+    def extra_repr(self):
+        s = "{input_size}, {hidden_size}"
+        if self.num_layers != 1:
+            s += ", num_layers={num_layers}"
+        if self.bias is not True:
+            s += ", bias={bias}"
+        if self.batch_first is not False:
+            s += ", batch_first={batch_first}"
+        if self.dropout != 0:
+            s += ", dropout={dropout}"
+        if self.bidirectional is not False:
+            s += ", bidirectional={bidirectional}"
+        return s.format(**self.__dict__)
+
+    def __repr__(self):
+        # We don't want to show `ModuleList` children, hence custom
+        # `__repr__`. This is the same as nn.Module.__repr__, except the check
+        # for the `PackedParameter` and `nn.ModuleList`.
+        # You should still override `extra_repr` to add more info.
+        extra_lines = []
+        extra_repr = self.extra_repr()
+        # empty string will be split into list ['']
+        if extra_repr:
+            extra_lines = extra_repr.split("\n")
+        child_lines = []
+        for key, module in self._modules.items():
+            if isinstance(module, (PackedParameter, nn.ModuleList)):
+                continue
+            mod_str = repr(module)
+            mod_str = nn.modules.module._addindent(mod_str, 2)
+            child_lines.append("(" + key + "): " + mod_str)
+        lines = extra_lines + child_lines
+
+        main_str = self._get_name() + "("
+        if lines:
+            # simple one-liner info, which most builtin Modules will use
+            if len(extra_lines) == 1 and not child_lines:
+                main_str += extra_lines[0]
+            else:
+                main_str += "\n  " + "\n  ".join(lines) + "\n"
+
+        main_str += ")"
+        return main_str
+
+    def check_input(self, input: Tensor, batch_sizes: Optional[Tensor]) -> None:
+        expected_input_dim = 2 if batch_sizes is not None else 3
+        if input.dim() != expected_input_dim:
+            raise RuntimeError(
+                f"input must have {expected_input_dim} dimensions, got {input.dim()}"
+            )
+        if self.input_size != input.size(-1):
+            raise RuntimeError(
+                f"input.size(-1) must be equal to input_size. Expected {self.input_size}, got {input.size(-1)}"
+            )
+
+    def get_expected_hidden_size(
+        self, input: Tensor, batch_sizes: Optional[Tensor]
+    ) -> tuple[int, int, int]:
+        if batch_sizes is not None:
+            mini_batch = int(batch_sizes[0])
+        else:
+            mini_batch = input.size(0) if self.batch_first else input.size(1)
+        num_directions = 2 if self.bidirectional else 1
+        expected_hidden_size = (
+            self.num_layers * num_directions,
+            mini_batch,
+            self.hidden_size,
+        )
+        return expected_hidden_size
+
+    def check_hidden_size(
+        self,
+        hx: Tensor,
+        expected_hidden_size: tuple[int, int, int],
+        msg: str = "Expected hidden size {}, got {}",
+    ) -> None:
+        if hx.size() != expected_hidden_size:
+            raise RuntimeError(msg.format(expected_hidden_size, list(hx.size())))
+
+    def check_forward_args(
+        self, input: Tensor, hidden: Tensor, batch_sizes: Optional[Tensor]
+    ) -> None:
+        self.check_input(input, batch_sizes)
+        expected_hidden_size = self.get_expected_hidden_size(input, batch_sizes)
+        self.check_hidden_size(
+            hidden, expected_hidden_size, msg="Expected hidden size {}, got {}"
+        )
+
+    def permute_hidden(self, hx: Tensor, permutation: Optional[Tensor]) -> Tensor:
+        if permutation is None:
+            return hx
+        return _apply_permutation(hx, permutation)
+
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        version = local_metadata.get("version", None)
+        self.version = version
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            False,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+    def set_weight_bias(self, weight_bias_dict):
+        def weight_bias_name(ihhh, layer, suffix):
+            weight_name = f"weight_{ihhh}_l{layer}{suffix}"
+            bias_name = f"bias_{ihhh}_l{layer}{suffix}"
+            return weight_name, bias_name
+
+        num_directions = 2 if self.bidirectional else 1
+        # TODO: dedup with __init__ of RNNBase
+        _all_weight_values = []
+        for layer in range(self.num_layers):
+            for direction in range(num_directions):
+                suffix = "_reverse" if direction == 1 else ""
+                w_ih_name, b_ih_name = weight_bias_name("ih", layer, suffix)
+                w_hh_name, b_hh_name = weight_bias_name("hh", layer, suffix)
+                w_ih = weight_bias_dict[w_ih_name]
+                b_ih = weight_bias_dict[b_ih_name]
+                w_hh = weight_bias_dict[w_hh_name]
+                b_hh = weight_bias_dict[b_hh_name]
+                if w_ih.dtype == torch.qint8:
+                    packed_ih = torch.ops.quantized.linear_prepack(w_ih, b_ih)
+                    packed_hh = torch.ops.quantized.linear_prepack(w_hh, b_hh)
+                    if self.version is None or self.version < 2:
+                        cell_params = (
+                            torch.ops.quantized.make_quantized_cell_params_dynamic(
+                                packed_ih, packed_hh, b_ih, b_hh
+                            )
+                        )
+                    else:
+                        cell_params = (
+                            torch.ops.quantized.make_quantized_cell_params_dynamic(
+                                packed_ih, packed_hh, b_ih, b_hh, True
+                            )
+                        )
+                else:
+                    packed_ih = torch.ops.quantized.linear_prepack_fp16(w_ih, b_ih)
+                    packed_hh = torch.ops.quantized.linear_prepack_fp16(w_hh, b_hh)
+                    cell_params = torch.ops.quantized.make_quantized_cell_params_fp16(
+                        packed_ih, packed_hh
+                    )
+
+                _all_weight_values.append(PackedParameter(cell_params))
+        self._all_weight_values = torch.nn.ModuleList(_all_weight_values)
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        assert type(mod) in {
+            torch.nn.LSTM,
+            torch.nn.GRU,
+        }, "nn.quantized.dynamic.RNNBase.from_float only works for nn.LSTM and nn.GRU"
+        assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
+
+        if mod.qconfig is not None and mod.qconfig.weight is not None:
+            weight_observer_method = mod.qconfig.weight
+        else:
+            # We have the circular import issues if we import the qconfig in the beginning of this file:
+            # https://github.com/pytorch/pytorch/pull/24231. The current workaround is to postpone the
+            # import until we need it.
+            from torch.ao.quantization.qconfig import default_dynamic_qconfig
+
+            weight_observer_method = default_dynamic_qconfig.weight
+
+        dtype = weight_observer_method().dtype
+        supported_scalar_types = [torch.qint8, torch.float16]
+        if dtype not in supported_scalar_types:
+            raise RuntimeError(
+                f"Unsupported dtype for dynamic RNN quantization: {dtype}"
+            )
+        # RNNBase can be either LSTM or GRU
+        qRNNBase: Union[LSTM, GRU]
+        if mod.mode == "LSTM":
+            qRNNBase = LSTM(
+                mod.input_size,
+                mod.hidden_size,
+                mod.num_layers,
+                mod.bias,
+                mod.batch_first,
+                mod.dropout,
+                mod.bidirectional,
+                dtype,
+            )
+        elif mod.mode == "GRU":
+            qRNNBase = GRU(
+                mod.input_size,
+                mod.hidden_size,
+                mod.num_layers,
+                mod.bias,
+                mod.batch_first,
+                mod.dropout,
+                mod.bidirectional,
+                dtype,
+            )
+        else:
+            raise NotImplementedError(
+                "Only LSTM/GRU is supported for QuantizedRNN for now"
+            )
+
+        num_directions = 2 if mod.bidirectional else 1
+
+        assert mod.bias
+
+        _all_weight_values = []
+        for layer in range(qRNNBase.num_layers):
+            for direction in range(num_directions):
+                suffix = "_reverse" if direction == 1 else ""
+
+                def retrieve_weight_bias(ihhh):
+                    weight_name = f"weight_{ihhh}_l{layer}{suffix}"
+                    bias_name = f"bias_{ihhh}_l{layer}{suffix}"
+                    weight = getattr(mod, weight_name)
+                    bias = getattr(mod, bias_name)
+                    return weight, bias
+
+                weight_ih, bias_ih = retrieve_weight_bias("ih")
+                weight_hh, bias_hh = retrieve_weight_bias("hh")
+
+                if dtype == torch.qint8:
+
+                    def quantize_and_pack(w, b):
+                        weight_observer = weight_observer_method()
+                        weight_observer(w)
+                        qweight = _quantize_weight(w.float(), weight_observer)
+                        packed_weight = torch.ops.quantized.linear_prepack(qweight, b)
+                        return packed_weight
+
+                    packed_ih = quantize_and_pack(weight_ih, bias_ih)
+                    packed_hh = quantize_and_pack(weight_hh, bias_hh)
+                    if qRNNBase.version is None or qRNNBase.version < 2:
+                        cell_params = (
+                            torch.ops.quantized.make_quantized_cell_params_dynamic(
+                                packed_ih, packed_hh, bias_ih, bias_hh
+                            )
+                        )
+                    else:
+                        cell_params = (
+                            torch.ops.quantized.make_quantized_cell_params_dynamic(
+                                packed_ih, packed_hh, bias_ih, bias_hh, True
+                            )
+                        )
+
+                elif dtype == torch.float16:
+                    packed_ih = torch.ops.quantized.linear_prepack_fp16(
+                        weight_ih.float(), bias_ih
+                    )
+                    packed_hh = torch.ops.quantized.linear_prepack_fp16(
+                        weight_hh.float(), bias_hh
+                    )
+
+                    cell_params = torch.ops.quantized.make_quantized_cell_params_fp16(
+                        packed_ih, packed_hh
+                    )
+                else:
+                    raise RuntimeError(
+                        "Unsupported dtype specified for dynamic quantized LSTM!"
+                    )
+
+                _all_weight_values.append(PackedParameter(cell_params))
+        qRNNBase._all_weight_values = torch.nn.ModuleList(_all_weight_values)
+
+        return qRNNBase
+
+    def _weight_bias(self):
+        # Returns a dict of weights and biases
+        weight_bias_dict: Dict[str, Dict] = {"weight": {}, "bias": {}}
+        count = 0
+        num_directions = 2 if self.bidirectional else 1
+        for layer in range(self.num_layers):
+            for direction in range(num_directions):
+                suffix = "_reverse" if direction == 1 else ""
+                key_name1 = f"weight_ih_l{layer}{suffix}"
+                key_name2 = f"weight_hh_l{layer}{suffix}"
+                # packed weights are part of torchbind class, CellParamsSerializationType
+                # Within the packed weight class, the weight and bias are accessible as Tensors
+                packed_weight_bias = self._all_weight_values[  # type: ignore[index]
+                    count
+                ].param.__getstate__()[0][4]
+                weight_bias_dict["weight"][key_name1] = packed_weight_bias[
+                    0
+                ].__getstate__()[0][0]
+                weight_bias_dict["weight"][key_name2] = packed_weight_bias[
+                    1
+                ].__getstate__()[0][0]
+                key_name1 = f"bias_ih_l{layer}{suffix}"
+                key_name2 = f"bias_hh_l{layer}{suffix}"
+                weight_bias_dict["bias"][key_name1] = packed_weight_bias[
+                    0
+                ].__getstate__()[0][1]
+                weight_bias_dict["bias"][key_name2] = packed_weight_bias[
+                    1
+                ].__getstate__()[0][1]
+                count = count + 1
+        return weight_bias_dict
+
+    def get_weight(self):
+        return self._weight_bias()["weight"]
+
+    def get_bias(self):
+        return self._weight_bias()["bias"]
+
+
+class LSTM(RNNBase):
+    r"""
+    A dynamic quantized LSTM module with floating point tensor as inputs and outputs.
+    We adopt the same interface as `torch.nn.LSTM`, please see
+    https://pytorch.org/docs/stable/nn.html#torch.nn.LSTM for documentation.
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> rnn = nn.LSTM(10, 20, 2)
+        >>> input = torch.randn(5, 3, 10)
+        >>> h0 = torch.randn(2, 3, 20)
+        >>> c0 = torch.randn(2, 3, 20)
+        >>> output, (hn, cn) = rnn(input, (h0, c0))
+    """
+
+    # pyrefly: ignore [bad-override]
+    _FLOAT_MODULE = nn.LSTM
+
+    __overloads__ = {"forward": ["forward_packed", "forward_tensor"]}
+
+    def __init__(self, *args, **kwargs):
+        super().__init__("LSTM", *args, **kwargs)
+
+    def _get_name(self):
+        return "DynamicQuantizedLSTM"
+
+    def forward_impl(
+        self,
+        input: Tensor,
+        hx: Optional[tuple[Tensor, Tensor]],
+        batch_sizes: Optional[Tensor],
+        max_batch_size: int,
+        sorted_indices: Optional[Tensor],
+    ) -> tuple[Tensor, tuple[Tensor, Tensor]]:
+        if hx is None:
+            num_directions = 2 if self.bidirectional else 1
+            zeros = torch.zeros(
+                self.num_layers * num_directions,
+                max_batch_size,
+                self.hidden_size,
+                dtype=input.dtype,
+                device=input.device,
+            )
+            hx = (zeros, zeros)
+        else:
+            # Each batch of the hidden state should match the input sequence that
+            # the user believes he/she is passing in.
+            hx = self.permute_hidden(hx, sorted_indices)
+
+        self.check_forward_args(input, hx, batch_sizes)
+
+        _all_params = [m.param for m in self._all_weight_values]
+        if batch_sizes is None:
+            result = torch.quantized_lstm(
+                input,
+                hx,
+                _all_params,
+                self.bias,
+                self.num_layers,
+                float(self.dropout),
+                self.training,
+                self.bidirectional,
+                self.batch_first,
+                dtype=self.dtype,
+                use_dynamic=True,
+            )
+        else:
+            result = torch.quantized_lstm(
+                input,
+                batch_sizes,
+                hx,
+                _all_params,
+                self.bias,
+                self.num_layers,
+                float(self.dropout),
+                self.training,
+                self.bidirectional,
+                dtype=self.dtype,
+                use_dynamic=True,
+            )
+        output = result[0]
+        hidden = result[1:]
+
+        return output, hidden
+
+    @torch.jit.export
+    def forward_tensor(
+        self, input: Tensor, hx: Optional[tuple[Tensor, Tensor]] = None
+    ) -> tuple[Tensor, tuple[Tensor, Tensor]]:
+        batch_sizes = None
+        max_batch_size = input.size(0) if self.batch_first else input.size(1)
+        sorted_indices = None
+        unsorted_indices = None
+
+        output, hidden = self.forward_impl(
+            input, hx, batch_sizes, max_batch_size, sorted_indices
+        )
+
+        return output, self.permute_hidden(hidden, unsorted_indices)
+
+    @torch.jit.export
+    def forward_packed(
+        self, input: PackedSequence, hx: Optional[tuple[Tensor, Tensor]] = None
+    ) -> tuple[PackedSequence, tuple[Tensor, Tensor]]:
+        input_, batch_sizes, sorted_indices, unsorted_indices = input
+        max_batch_size = int(batch_sizes[0])
+
+        output_, hidden = self.forward_impl(
+            input_, hx, batch_sizes, max_batch_size, sorted_indices
+        )
+
+        output = PackedSequence(output_, batch_sizes, sorted_indices, unsorted_indices)
+        return output, self.permute_hidden(hidden, unsorted_indices)
+
+    # "type: ignore" is required due to issue #43072
+    def permute_hidden(  # type: ignore[override]
+        self,
+        hx: tuple[Tensor, Tensor],
+        permutation: Optional[Tensor],
+    ) -> tuple[Tensor, Tensor]:
+        if permutation is None:
+            return hx
+        return _apply_permutation(hx[0], permutation), _apply_permutation(
+            hx[1], permutation
+        )
+
+    # "type: ignore" is required due to issue #43072
+    def check_forward_args(  # type: ignore[override]
+        self,
+        input: Tensor,
+        hidden: tuple[Tensor, Tensor],
+        batch_sizes: Optional[Tensor],
+    ) -> None:
+        self.check_input(input, batch_sizes)
+        expected_hidden_size = self.get_expected_hidden_size(input, batch_sizes)
+
+        self.check_hidden_size(
+            hidden[0], expected_hidden_size, "Expected hidden[0] size {}, got {}"
+        )
+        self.check_hidden_size(
+            hidden[1], expected_hidden_size, "Expected hidden[1] size {}, got {}"
+        )
+
+    @torch.jit.ignore
+    def forward(self, input, hx=None):
+        if isinstance(input, PackedSequence):
+            return self.forward_packed(input, hx)
+        else:
+            return self.forward_tensor(input, hx)
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return super().from_float(
+            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+    @classmethod
+    def from_reference(cls, ref_mod):
+        assert hasattr(ref_mod, "weight_ih_l0_dtype"), "We are assuming weight_ih_l0 "
+        "exists in LSTM, may need to relax the assumption to support the use case"
+        qmod = cls(
+            ref_mod.input_size,
+            ref_mod.hidden_size,
+            ref_mod.num_layers,
+            ref_mod.bias,
+            ref_mod.batch_first,
+            ref_mod.dropout,
+            ref_mod.bidirectional,
+            # assuming there is layer 0, which should be OK
+            ref_mod.weight_ih_l0_dtype,
+        )
+        qmod.set_weight_bias(ref_mod.get_quantized_weight_bias_dict())
+        return qmod
+
+
+class GRU(RNNBase):
+    r"""Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence.
+
+
+    For each element in the input sequence, each layer computes the following
+    function:
+
+    .. math::
+        \begin{array}{ll}
+            r_t = \sigma(W_{ir} x_t + b_{ir} + W_{hr} h_{(t-1)} + b_{hr}) \\
+            z_t = \sigma(W_{iz} x_t + b_{iz} + W_{hz} h_{(t-1)} + b_{hz}) \\
+            n_t = \tanh(W_{in} x_t + b_{in} + r_t \odot (W_{hn} h_{(t-1)}+ b_{hn})) \\
+            h_t = (1 - z_t) \odot n_t + z_t \odot h_{(t-1)}
+        \end{array}
+
+    where :math:`h_t` is the hidden state at time `t`, :math:`x_t` is the input
+    at time `t`, :math:`h_{(t-1)}` is the hidden state of the layer
+    at time `t-1` or the initial hidden state at time `0`, and :math:`r_t`,
+    :math:`z_t`, :math:`n_t` are the reset, update, and new gates, respectively.
+    :math:`\sigma` is the sigmoid function, and :math:`\odot` is the Hadamard product.
+
+    In a multilayer GRU, the input :math:`x^{(l)}_t` of the :math:`l` -th layer
+    (:math:`l >= 2`) is the hidden state :math:`h^{(l-1)}_t` of the previous layer multiplied by
+    dropout :math:`\delta^{(l-1)}_t` where each :math:`\delta^{(l-1)}_t` is a Bernoulli random
+    variable which is :math:`0` with probability :attr:`dropout`.
+
+    Args:
+        input_size: The number of expected features in the input `x`
+        hidden_size: The number of features in the hidden state `h`
+        num_layers: Number of recurrent layers. E.g., setting ``num_layers=2``
+            would mean stacking two GRUs together to form a `stacked GRU`,
+            with the second GRU taking in outputs of the first GRU and
+            computing the final results. Default: 1
+        bias: If ``False``, then the layer does not use bias weights `b_ih` and `b_hh`.
+            Default: ``True``
+        batch_first: If ``True``, then the input and output tensors are provided
+            as (batch, seq, feature). Default: ``False``
+        dropout: If non-zero, introduces a `Dropout` layer on the outputs of each
+            GRU layer except the last layer, with dropout probability equal to
+            :attr:`dropout`. Default: 0
+        bidirectional: If ``True``, becomes a bidirectional GRU. Default: ``False``
+
+    Inputs: input, h_0
+        - **input** of shape `(seq_len, batch, input_size)`: tensor containing the features
+          of the input sequence. The input can also be a packed variable length
+          sequence. See :func:`torch.nn.utils.rnn.pack_padded_sequence`
+          for details.
+        - **h_0** of shape `(num_layers * num_directions, batch, hidden_size)`: tensor
+          containing the initial hidden state for each element in the batch.
+          Defaults to zero if not provided. If the RNN is bidirectional,
+          num_directions should be 2, else it should be 1.
+
+    Outputs: output, h_n
+        - **output** of shape `(seq_len, batch, num_directions * hidden_size)`: tensor
+          containing the output features h_t from the last layer of the GRU,
+          for each `t`. If a :class:`torch.nn.utils.rnn.PackedSequence` has been
+          given as the input, the output will also be a packed sequence.
+          For the unpacked case, the directions can be separated
+          using ``output.view(seq_len, batch, num_directions, hidden_size)``,
+          with forward and backward being direction `0` and `1` respectively.
+
+          Similarly, the directions can be separated in the packed case.
+        - **h_n** of shape `(num_layers * num_directions, batch, hidden_size)`: tensor
+          containing the hidden state for `t = seq_len`
+
+          Like *output*, the layers can be separated using
+          ``h_n.view(num_layers, num_directions, batch, hidden_size)``.
+
+    Shape:
+        - Input1: :math:`(L, N, H_{in})` tensor containing input features where
+          :math:`H_{in}=\text{input\_size}` and `L` represents a sequence length.
+        - Input2: :math:`(S, N, H_{out})` tensor
+          containing the initial hidden state for each element in the batch.
+          :math:`H_{out}=\text{hidden\_size}`
+          Defaults to zero if not provided. where :math:`S=\text{num\_layers} * \text{num\_directions}`
+          If the RNN is bidirectional, num_directions should be 2, else it should be 1.
+        - Output1: :math:`(L, N, H_{all})` where :math:`H_{all}=\text{num\_directions} * \text{hidden\_size}`
+        - Output2: :math:`(S, N, H_{out})` tensor containing the next hidden state
+          for each element in the batch
+
+    Attributes:
+        weight_ih_l[k] : the learnable input-hidden weights of the :math:`\text{k}^{th}` layer
+            (W_ir|W_iz|W_in), of shape `(3*hidden_size, input_size)` for `k = 0`.
+            Otherwise, the shape is `(3*hidden_size, num_directions * hidden_size)`
+        weight_hh_l[k] : the learnable hidden-hidden weights of the :math:`\text{k}^{th}` layer
+            (W_hr|W_hz|W_hn), of shape `(3*hidden_size, hidden_size)`
+        bias_ih_l[k] : the learnable input-hidden bias of the :math:`\text{k}^{th}` layer
+            (b_ir|b_iz|b_in), of shape `(3*hidden_size)`
+        bias_hh_l[k] : the learnable hidden-hidden bias of the :math:`\text{k}^{th}` layer
+            (b_hr|b_hz|b_hn), of shape `(3*hidden_size)`
+
+    .. note::
+        All the weights and biases are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`
+        where :math:`k = \frac{1}{\text{hidden\_size}}`
+
+    .. note::
+        The calculation of new gate :math:`n_t` subtly differs from the original paper and other frameworks.
+        In the original implementation, the Hadamard product :math:`(\odot)` between :math:`r_t` and the
+        previous hidden state :math:`h_{(t-1)}` is done before the multiplication with the weight matrix
+        `W` and addition of bias:
+
+        .. math::
+            \begin{aligned}
+                n_t = \tanh(W_{in} x_t + b_{in} + W_{hn} ( r_t \odot h_{(t-1)} ) + b_{hn})
+            \end{aligned}
+
+        This is in contrast to PyTorch implementation, which is done after :math:`W_{hn} h_{(t-1)}`
+
+        .. math::
+            \begin{aligned}
+                n_t = \tanh(W_{in} x_t + b_{in} + r_t \odot (W_{hn} h_{(t-1)}+ b_{hn}))
+            \end{aligned}
+
+        This implementation differs on purpose for efficiency.
+
+    .. include:: ../cudnn_persistent_rnn.rst
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> rnn = nn.GRU(10, 20, 2)
+        >>> input = torch.randn(5, 3, 10)
+        >>> h0 = torch.randn(2, 3, 20)
+        >>> output, hn = rnn(input, h0)
+    """
+
+    # pyrefly: ignore [bad-override]
+    _FLOAT_MODULE = nn.GRU
+
+    __overloads__ = {"forward": ["forward_packed", "forward_tensor"]}
+
+    def __init__(self, *args, **kwargs):
+        super().__init__("GRU", *args, **kwargs)
+
+    def _get_name(self):
+        return "DynamicQuantizedGRU"
+
+    def check_forward_args(
+        self, input: Tensor, hidden: Tensor, batch_sizes: Optional[Tensor]
+    ) -> None:
+        self.check_input(input, batch_sizes)
+        expected_hidden_size = self.get_expected_hidden_size(input, batch_sizes)
+
+        self.check_hidden_size(
+            hidden, expected_hidden_size, "Expected hidden size {}, got {}"
+        )
+
+    def forward_impl(
+        self,
+        input: Tensor,
+        hx: Optional[Tensor],
+        batch_sizes: Optional[Tensor],
+        max_batch_size: int,
+        sorted_indices: Optional[Tensor],
+    ) -> tuple[Tensor, Tensor]:
+        if hx is None:
+            num_directions = 2 if self.bidirectional else 1
+            zeros = torch.zeros(
+                self.num_layers * num_directions,
+                max_batch_size,
+                self.hidden_size,
+                dtype=input.dtype,
+                device=input.device,
+            )
+            hx = zeros
+        else:
+            # Each batch of the hidden state should match the input sequence that
+            # the user believes he/she is passing in.
+            hx = self.permute_hidden(hx, sorted_indices)
+
+        self.check_forward_args(input, hx, batch_sizes)
+
+        _all_params = [m.param for m in self._all_weight_values]
+        if batch_sizes is None:
+            result = torch.quantized_gru(
+                input,
+                hx,
+                _all_params,
+                self.bias,
+                self.num_layers,
+                self.dropout,
+                self.training,
+                self.bidirectional,
+                self.batch_first,
+            )
+        else:
+            result = torch.quantized_gru(
+                input,
+                batch_sizes,
+                hx,
+                _all_params,
+                self.bias,
+                self.num_layers,
+                self.dropout,
+                self.training,
+                self.bidirectional,
+            )
+        output = result[0]
+        hidden = result[1]
+
+        return output, hidden
+
+    @torch.jit.export
+    def forward_tensor(
+        self, input: Tensor, hx: Optional[Tensor] = None
+    ) -> tuple[Tensor, Tensor]:
+        batch_sizes = None
+        max_batch_size = input.size(0) if self.batch_first else input.size(1)
+        sorted_indices = None
+        unsorted_indices = None
+
+        output, hidden = self.forward_impl(
+            input, hx, batch_sizes, max_batch_size, sorted_indices
+        )
+
+        return output, self.permute_hidden(hidden, unsorted_indices)
+
+    @torch.jit.export
+    def forward_packed(
+        self, input: PackedSequence, hx: Optional[Tensor] = None
+    ) -> tuple[PackedSequence, Tensor]:
+        input_, batch_sizes, sorted_indices, unsorted_indices = input
+        max_batch_size = int(batch_sizes[0])
+        output_, hidden = self.forward_impl(
+            input_, hx, batch_sizes, max_batch_size, sorted_indices
+        )
+
+        output = PackedSequence(output_, batch_sizes, sorted_indices, unsorted_indices)
+        return output, self.permute_hidden(hidden, unsorted_indices)
+
+    def permute_hidden(self, hx: Tensor, permutation: Optional[Tensor]) -> Tensor:
+        if permutation is None:
+            return hx
+        return _apply_permutation(hx, permutation)
+
+    @torch.jit.ignore
+    def forward(self, input, hx=None):
+        if isinstance(input, PackedSequence):
+            return self.forward_packed(input, hx)
+        else:
+            return self.forward_tensor(input, hx)
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return super().from_float(
+            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+    @classmethod
+    def from_reference(cls, ref_mod):
+        assert hasattr(ref_mod, "weight_ih_l0_dtype"), "We are assuming weight_ih_l0 "
+        "exists in LSTM, may need to relax the assumption to support the use case"
+        qmod = cls(
+            ref_mod.input_size,
+            ref_mod.hidden_size,
+            ref_mod.num_layers,
+            ref_mod.bias,
+            ref_mod.batch_first,
+            ref_mod.dropout,
+            ref_mod.bidirectional,
+            # assuming there is layer 0, which should be OK
+            ref_mod.weight_ih_l0_dtype,
+        )
+        qmod.set_weight_bias(ref_mod.get_quantized_weight_bias_dict())
+        return qmod
+
+
+class RNNCellBase(torch.nn.Module):
+    # _FLOAT_MODULE = nn.CellRNNBase
+    __constants__ = ["input_size", "hidden_size", "bias"]
+
+    def __init__(
+        self, input_size, hidden_size, bias=True, num_chunks=4, dtype=torch.qint8
+    ):
+        super().__init__()
+        self.input_size = input_size
+        self.hidden_size = hidden_size
+        self.bias = bias
+        self.weight_dtype = dtype
+        if bias:
+            self.bias_ih = torch.randn(num_chunks * hidden_size).to(dtype=torch.float)
+            self.bias_hh = torch.randn(num_chunks * hidden_size).to(dtype=torch.float)
+        else:
+            self.register_parameter("bias_ih", None)
+            self.register_parameter("bias_hh", None)
+
+        weight_ih = torch.randn(num_chunks * hidden_size, input_size).to(torch.float)
+        weight_hh = torch.randn(num_chunks * hidden_size, hidden_size).to(torch.float)
+        if dtype == torch.qint8:
+            weight_ih = torch.quantize_per_tensor(
+                weight_ih, scale=1, zero_point=0, dtype=torch.qint8
+            )
+            weight_hh = torch.quantize_per_tensor(
+                weight_hh, scale=1, zero_point=0, dtype=torch.qint8
+            )
+
+        if dtype == torch.qint8:
+            # for each layer, for each direction we need to quantize and pack
+            # weights and pack parameters in this order:
+            #
+            #   w_ih, w_hh
+            packed_weight_ih = torch.ops.quantized.linear_prepack(
+                weight_ih, self.bias_ih
+            )
+            packed_weight_hh = torch.ops.quantized.linear_prepack(
+                weight_hh, self.bias_hh
+            )
+        else:
+            # for each layer, for each direction we need to quantize and pack
+            # weights and pack parameters in this order:
+            #
+            #   packed_ih, packed_hh, b_ih, b_hh
+            packed_weight_ih = torch.ops.quantized.linear_prepack_fp16(
+                weight_ih, self.bias_ih
+            )
+            packed_weight_hh = torch.ops.quantized.linear_prepack_fp16(
+                weight_hh, self.bias_hh
+            )
+
+        self._packed_weight_ih = packed_weight_ih
+        self._packed_weight_hh = packed_weight_hh
+
+    def _get_name(self):
+        return "DynamicQuantizedRNNBase"
+
+    def extra_repr(self):
+        s = "{input_size}, {hidden_size}"
+        if "bias" in self.__dict__ and self.bias is not True:
+            s += ", bias={bias}"
+        if "nonlinearity" in self.__dict__ and self.nonlinearity != "tanh":
+            s += ", nonlinearity={nonlinearity}"
+        return s.format(**self.__dict__)
+
+    def check_forward_input(self, input):
+        if input.size(1) != self.input_size:
+            raise RuntimeError(
+                f"input has inconsistent input_size: got {input.size(1)}, expected {self.input_size}"
+            )
+
+    def check_forward_hidden(
+        self, input: Tensor, hx: Tensor, hidden_label: str = ""
+    ) -> None:
+        if input.size(0) != hx.size(0):
+            raise RuntimeError(
+                f"Input batch size {input.size(0)} doesn't match hidden{hidden_label} batch size {hx.size(0)}"
+            )
+
+        if hx.size(1) != self.hidden_size:
+            raise RuntimeError(
+                f"hidden{hidden_label} has inconsistent hidden_size: got {hx.size(1)}, expected {self.hidden_size}"
+            )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        assert type(mod) in {
+            torch.nn.LSTMCell,
+            torch.nn.GRUCell,
+            torch.nn.RNNCell,
+        }, (
+            "nn.quantized.dynamic.RNNCellBase.from_float \
+                                 only works for nn.LSTMCell, nn.GRUCell and nn.RNNCell"
+        )
+        assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
+
+        if mod.qconfig is not None and mod.qconfig.weight is not None:
+            weight_observer_method = mod.qconfig.weight
+        else:
+            # We have the circular import issues if we import the qconfig in the beginning of this file:
+            # https://github.com/pytorch/pytorch/pull/24231. The current workaround is to postpone the
+            # import until we need it.
+            from torch.ao.quantization.qconfig import default_dynamic_qconfig
+
+            weight_observer_method = default_dynamic_qconfig.weight
+
+        dtype = weight_observer_method().dtype
+        supported_scalar_types = [torch.qint8, torch.float16]
+        if dtype not in supported_scalar_types:
+            raise RuntimeError(
+                f"Unsupported dtype for dynamic RNN quantization: {dtype}"
+            )
+
+        qRNNCellBase: Union[LSTMCell, GRUCell, RNNCell]
+
+        if type(mod) is torch.nn.LSTMCell:
+            qRNNCellBase = LSTMCell(
+                mod.input_size, mod.hidden_size, bias=mod.bias, dtype=dtype
+            )
+        elif type(mod) is torch.nn.GRUCell:
+            qRNNCellBase = GRUCell(
+                mod.input_size, mod.hidden_size, bias=mod.bias, dtype=dtype
+            )
+        elif type(mod) is torch.nn.RNNCell:
+            qRNNCellBase = RNNCell(
+                mod.input_size,
+                mod.hidden_size,
+                bias=mod.bias,
+                nonlinearity=mod.nonlinearity,
+                dtype=dtype,
+            )
+        else:
+            raise NotImplementedError(
+                "Only LSTMCell, GRUCell and RNNCell \
+            are supported for QuantizedRNN for now"
+            )
+
+        assert mod.bias
+
+        def _observe_and_quantize_weight(weight):
+            if dtype == torch.qint8:
+                weight_observer = weight_observer_method()
+                weight_observer(weight)
+                qweight = _quantize_weight(weight.float(), weight_observer)
+                return qweight
+            else:
+                return weight.float()
+
+        qRNNCellBase._packed_weight_ih = pack_weight_bias(
+            _observe_and_quantize_weight(mod.weight_ih), mod.bias_ih, dtype
+        )
+        qRNNCellBase._packed_weight_hh = pack_weight_bias(
+            _observe_and_quantize_weight(mod.weight_hh), mod.bias_hh, dtype
+        )
+        return qRNNCellBase
+
+    @classmethod
+    def from_reference(cls, ref_mod):
+        assert hasattr(ref_mod, "weight_ih_dtype"), "We are assuming weight_ih "
+        "exists in reference module, may need to relax the assumption to support the use case"
+        if hasattr(ref_mod, "nonlinearity"):
+            qmod = cls(
+                ref_mod.input_size,
+                ref_mod.hidden_size,
+                ref_mod.bias,
+                ref_mod.nonlinearity,
+                dtype=ref_mod.weight_ih_dtype,
+            )
+        else:
+            qmod = cls(
+                ref_mod.input_size,
+                ref_mod.hidden_size,
+                ref_mod.bias,
+                dtype=ref_mod.weight_ih_dtype,
+            )
+        weight_bias_dict = {
+            "weight": {
+                "weight_ih": ref_mod.get_quantized_weight_ih(),
+                "weight_hh": ref_mod.get_quantized_weight_hh(),
+            },
+            "bias": {
+                "bias_ih": ref_mod.bias_ih,
+                "bias_hh": ref_mod.bias_hh,
+            },
+        }
+        qmod.set_weight_bias(weight_bias_dict)
+        return qmod
+
+    def _weight_bias(self):
+        # Returns a dict of weights and biases
+        weight_bias_dict: Dict[str, Dict] = {"weight": {}, "bias": {}}
+        w1, b1 = self._packed_weight_ih.__getstate__()[0]
+        w2, b2 = self._packed_weight_hh.__getstate__()[0]
+        # TODO: these can be simplified to one level? e.g. using weight_ih as key
+        # directly
+        weight_bias_dict["weight"]["weight_ih"] = w1
+        weight_bias_dict["weight"]["weight_hh"] = w2
+        weight_bias_dict["bias"]["bias_ih"] = b1
+        weight_bias_dict["bias"]["bias_hh"] = b2
+        return weight_bias_dict
+
+    def get_weight(self):
+        return self._weight_bias()["weight"]
+
+    def get_bias(self):
+        return self._weight_bias()["bias"]
+
+    def set_weight_bias(self, weight_bias_dict):
+        # TODO: these can be simplified to one level? e.g. using weight_ih as key
+        # directly
+        self._packed_weight_ih = pack_weight_bias(
+            weight_bias_dict["weight"]["weight_ih"],
+            weight_bias_dict["bias"]["bias_ih"],
+            self.weight_dtype,
+        )
+        self._packed_weight_hh = pack_weight_bias(
+            weight_bias_dict["weight"]["weight_hh"],
+            weight_bias_dict["bias"]["bias_hh"],
+            self.weight_dtype,
+        )
+
+    def _save_to_state_dict(self, destination, prefix, keep_vars):
+        super()._save_to_state_dict(destination, prefix, keep_vars)
+        destination[prefix + "_packed_weight_ih"] = self._packed_weight_ih
+        destination[prefix + "_packed_weight_hh"] = self._packed_weight_hh
+
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        self._packed_weight_ih = state_dict.pop(prefix + "_packed_weight_ih")
+        self._packed_weight_hh = state_dict.pop(prefix + "_packed_weight_hh")
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            False,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+
+class RNNCell(RNNCellBase):
+    r"""An Elman RNN cell with tanh or ReLU non-linearity.
+    A dynamic quantized RNNCell module with floating point tensor as inputs and outputs.
+    Weights are quantized to 8 bits. We adopt the same interface as `torch.nn.RNNCell`,
+    please see https://pytorch.org/docs/stable/nn.html#torch.nn.RNNCell for documentation.
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> rnn = nn.RNNCell(10, 20)
+        >>> input = torch.randn(6, 3, 10)
+        >>> hx = torch.randn(3, 20)
+        >>> output = []
+        >>> for i in range(6):
+        ...     hx = rnn(input[i], hx)
+        ...     output.append(hx)
+    """
+
+    __constants__ = ["input_size", "hidden_size", "bias", "nonlinearity"]
+
+    def __init__(
+        self, input_size, hidden_size, bias=True, nonlinearity="tanh", dtype=torch.qint8
+    ):
+        super().__init__(input_size, hidden_size, bias, num_chunks=1, dtype=dtype)
+        self.nonlinearity = nonlinearity
+
+    def _get_name(self):
+        return "DynamicQuantizedRNNCell"
+
+    def forward(self, input: Tensor, hx: Optional[Tensor] = None) -> Tensor:
+        self.check_forward_input(input)
+        if hx is None:
+            hx = torch.zeros(
+                input.size(0), self.hidden_size, dtype=input.dtype, device=input.device
+            )
+        self.check_forward_hidden(input, hx, "")
+        if self.nonlinearity == "tanh":
+            ret = torch.ops.quantized.quantized_rnn_tanh_cell_dynamic(
+                input,
+                hx,
+                self._packed_weight_ih,
+                self._packed_weight_hh,
+                self.bias_ih,
+                self.bias_hh,
+            )
+        elif self.nonlinearity == "relu":
+            ret = torch.ops.quantized.quantized_rnn_relu_cell_dynamic(
+                input,
+                hx,
+                self._packed_weight_ih,
+                self._packed_weight_hh,
+                self.bias_ih,
+                self.bias_hh,
+            )
+        else:
+            ret = input  # TODO: remove when jit supports exception flow
+            raise RuntimeError(f"Unknown nonlinearity: {self.nonlinearity}")
+        return ret
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return super().from_float(
+            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+
+class LSTMCell(RNNCellBase):
+    r"""A long short-term memory (LSTM) cell.
+
+    A dynamic quantized LSTMCell module with floating point tensor as inputs and outputs.
+    Weights are quantized to 8 bits. We adopt the same interface as `torch.nn.LSTMCell`,
+    please see https://pytorch.org/docs/stable/nn.html#torch.nn.LSTMCell for documentation.
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> rnn = nn.LSTMCell(10, 20)
+        >>> input = torch.randn(6, 3, 10)
+        >>> hx = torch.randn(3, 20)
+        >>> cx = torch.randn(3, 20)
+        >>> output = []
+        >>> for i in range(6):
+        ...     hx, cx = rnn(input[i], (hx, cx))
+        ...     output.append(hx)
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, num_chunks=4, **kwargs)  # type: ignore[misc]
+
+    def _get_name(self):
+        return "DynamicQuantizedLSTMCell"
+
+    def forward(
+        self, input: Tensor, hx: Optional[tuple[Tensor, Tensor]] = None
+    ) -> tuple[Tensor, Tensor]:
+        self.check_forward_input(input)
+        if hx is None:
+            zeros = torch.zeros(
+                input.size(0), self.hidden_size, dtype=input.dtype, device=input.device
+            )
+            hx = (zeros, zeros)
+        self.check_forward_hidden(input, hx[0], "[0]")
+        self.check_forward_hidden(input, hx[1], "[1]")
+        return torch.ops.quantized.quantized_lstm_cell_dynamic(
+            input,
+            hx,
+            self._packed_weight_ih,
+            self._packed_weight_hh,
+            self.bias_ih,
+            self.bias_hh,
+        )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return super().from_float(
+            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+
+class GRUCell(RNNCellBase):
+    r"""A gated recurrent unit (GRU) cell
+
+    A dynamic quantized GRUCell module with floating point tensor as inputs and outputs.
+    Weights are quantized to 8 bits. We adopt the same interface as `torch.nn.GRUCell`,
+    please see https://pytorch.org/docs/stable/nn.html#torch.nn.GRUCell for documentation.
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> rnn = nn.GRUCell(10, 20)
+        >>> input = torch.randn(6, 3, 10)
+        >>> hx = torch.randn(3, 20)
+        >>> output = []
+        >>> for i in range(6):
+        ...     hx = rnn(input[i], hx)
+        ...     output.append(hx)
+    """
+
+    def __init__(self, input_size, hidden_size, bias=True, dtype=torch.qint8):
+        super().__init__(input_size, hidden_size, bias, num_chunks=3, dtype=dtype)
+
+    def _get_name(self):
+        return "DynamicQuantizedGRUCell"
+
+    def forward(self, input: Tensor, hx: Optional[Tensor] = None) -> Tensor:
+        self.check_forward_input(input)
+        if hx is None:
+            hx = torch.zeros(
+                input.size(0), self.hidden_size, dtype=input.dtype, device=input.device
+            )
+        self.check_forward_hidden(input, hx, "")
+        return torch.ops.quantized.quantized_gru_cell_dynamic(
+            input,
+            hx,
+            self._packed_weight_ih,
+            self._packed_weight_hh,
+            self.bias_ih,
+            self.bias_hh,
+        )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return super().from_float(
+            mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/functional.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/functional.py
new file mode 100644
index 0000000000000000000000000000000000000000..f84d41b58503ad1d86244c7aa358f09ad16acad2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/functional.py
@@ -0,0 +1,781 @@
+# mypy: allow-untyped-defs
+r"""Functional interface (quantized)."""
+
+import warnings
+
+import torch
+from torch import Tensor
+from torch.jit.annotations import BroadcastingList2
+from torch.nn.modules.utils import _pair, _triple
+
+from .modules.utils import _pair_from_first
+
+
+# Although some of the functions and docstrings are mirrored from the torch.nn,
+# we want to have them here for future changes.
+
+__all__ = [
+    "avg_pool2d",
+    "avg_pool3d",
+    "adaptive_avg_pool2d",
+    "adaptive_avg_pool3d",
+    "conv1d",
+    "conv2d",
+    "conv3d",
+    "interpolate",
+    "linear",
+    "max_pool1d",
+    "max_pool2d",
+    "celu",
+    "leaky_relu",
+    "hardtanh",
+    "hardswish",
+    "threshold",
+    "elu",
+    "hardsigmoid",
+    "clamp",
+    "upsample",
+    "upsample_bilinear",
+    "upsample_nearest",
+]
+
+
+def avg_pool2d(
+    input,
+    kernel_size,
+    stride=None,
+    padding=0,
+    ceil_mode=False,
+    count_include_pad=True,
+    divisor_override=None,
+):
+    r"""
+    Applies 2D average-pooling operation in :math:`kH \times kW` regions by step size
+    :math:`sH \times sW` steps. The number of output features is equal to the number of
+    input planes.
+
+    .. note:: The input quantization parameters propagate to the output.
+
+    See :class:`~torch.ao.nn.quantized.AvgPool2d` for details and output shape.
+
+    Args:
+        input: quantized input tensor :math:`(\text{minibatch} , \text{in\_channels} , iH , iW)`
+        kernel_size: size of the pooling region. Can be a single number or a
+          tuple `(kH, kW)`
+        stride: stride of the pooling operation. Can be a single number or a
+          tuple `(sH, sW)`. Default: :attr:`kernel_size`
+        padding: implicit zero paddings on both sides of the input. Can be a
+          single number or a tuple `(padH, padW)`. Default: 0
+        ceil_mode: when True, will use `ceil` instead of `floor` in the formula
+            to compute the output shape. Default: ``False``
+        count_include_pad: when True, will include the zero-padding in the
+            averaging calculation. Default: ``True``
+        divisor_override: if specified, it will be used as divisor, otherwise
+             size of the pooling region will be used. Default: None
+    """
+    if not input.is_quantized:
+        raise ValueError("Input to 'quantized.avg_pool2d' must be quantized!")
+    return torch.nn.functional.avg_pool2d(
+        input,
+        kernel_size,
+        stride,
+        padding,
+        ceil_mode,
+        count_include_pad,
+        divisor_override,
+    )
+
+
+def avg_pool3d(
+    input,
+    kernel_size,
+    stride=None,
+    padding=0,
+    ceil_mode=False,
+    count_include_pad=True,
+    divisor_override=None,
+):
+    r"""
+    Applies 3D average-pooling operation in :math:`kD \ times kH \times kW` regions by step size
+    :math:`sD \times sH \times sW` steps. The number of output features is equal to the number of
+    input planes.
+
+    .. note:: The input quantization parameters propagate to the output.
+
+    Args:
+        input: quantized input tensor :math:`(\text{minibatch} , \text{in\_channels} , iH , iW)`
+        kernel_size: size of the pooling region. Can be a single number or a
+          tuple `(kD, kH, kW)`
+        stride: stride of the pooling operation. Can be a single number or a
+          tuple `(sD, sH, sW)`. Default: :attr:`kernel_size`
+        padding: implicit zero paddings on both sides of the input. Can be a
+          single number or a tuple `(padD, padH, padW)`. Default: 0
+        ceil_mode: when True, will use `ceil` instead of `floor` in the formula
+            to compute the output shape. Default: ``False``
+        count_include_pad: when True, will include the zero-padding in the
+            averaging calculation. Default: ``True``
+        divisor_override: if specified, it will be used as divisor, otherwise
+             size of the pooling region will be used. Default: None
+    """
+    if not input.is_quantized:
+        raise ValueError("Input to 'quantized.avg_pool3d' must be quantized!")
+    return torch.nn.functional.avg_pool3d(
+        input,
+        kernel_size,
+        stride,
+        padding,
+        ceil_mode,
+        count_include_pad,
+        divisor_override,
+    )
+
+
+def adaptive_avg_pool2d(input: Tensor, output_size: BroadcastingList2[int]) -> Tensor:
+    r"""
+    Applies a 2D adaptive average pooling over a quantized input signal composed
+    of several quantized input planes.
+
+    .. note:: The input quantization parameters propagate to the output.
+
+    See :class:`~torch.ao.nn.quantized.AdaptiveAvgPool2d` for details and output shape.
+
+    Args:
+        output_size: the target output size (single integer or
+                     double-integer tuple)
+    """
+    if not input.is_quantized:
+        raise ValueError(
+            "Input to 'quantized.functional.adaptive_avg_pool2d' must be quantized!"
+        )
+    return torch.nn.functional.adaptive_avg_pool2d(input, output_size)
+
+
+def adaptive_avg_pool3d(input: Tensor, output_size: BroadcastingList2[int]) -> Tensor:
+    r"""
+    Applies a 3D adaptive average pooling over a quantized input signal composed
+    of several quantized input planes.
+
+    .. note:: The input quantization parameters propagate to the output.
+
+    See :class:`~torch.ao.nn.quantized.AdaptiveAvgPool3d` for details and output shape.
+
+    Args:
+        output_size: the target output size (single integer or
+                     double-integer tuple)
+    """
+    if not input.is_quantized:
+        raise ValueError(
+            "Input to 'quantized.functional.adaptive_avg_pool3d' must be quantized!"
+        )
+    return torch.nn.functional.adaptive_avg_pool3d(input, output_size)
+
+
+def conv1d(
+    input,
+    weight,
+    bias,
+    stride=1,
+    padding=0,
+    dilation=1,
+    groups=1,
+    padding_mode="zeros",
+    scale=1.0,
+    zero_point=0,
+    dtype=torch.quint8,
+):
+    r"""
+    Applies a 1D convolution over a quantized 1D input composed of several input
+    planes.
+
+    See :class:`~torch.ao.nn.quantized.Conv1d` for details and output shape.
+
+    Args:
+        input: quantized input tensor of shape :math:`(\text{minibatch} , \text{in\_channels} , iW)`
+        weight: quantized filters of shape :math:`(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , iW)`
+        bias: **non-quantized** bias tensor of shape :math:`(\text{out\_channels})`. The tensor type must be `torch.float`.
+        stride: the stride of the convolving kernel. Can be a single number or a
+          tuple `(sW,)`. Default: 1
+        padding: implicit paddings on both sides of the input. Can be a
+          single number or a tuple `(padW,)`. Default: 0
+        dilation: the spacing between kernel elements. Can be a single number or
+          a tuple `(dW,)`. Default: 1
+        groups: split input into groups, :math:`\text{in\_channels}` should be divisible by the
+          number of groups. Default: 1
+        padding_mode: the padding mode to use. Only "zeros" is supported for quantized convolution at the moment. Default: "zeros"
+        scale: quantization scale for the output. Default: 1.0
+        zero_point: quantization zero_point for the output. Default: 0
+        dtype: quantization data type to use. Default: ``torch.quint8``
+
+    Examples::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
+        >>> from torch.ao.nn.quantized import functional as qF
+        >>> filters = torch.randn(33, 16, 3, dtype=torch.float)
+        >>> inputs = torch.randn(20, 16, 50, dtype=torch.float)
+        >>> bias = torch.randn(33, dtype=torch.float)
+        >>>
+        >>> scale, zero_point = 1.0, 0
+        >>> dtype_inputs = torch.quint8
+        >>> dtype_filters = torch.qint8
+        >>>
+        >>> q_filters = torch.quantize_per_tensor(filters, scale, zero_point, dtype_filters)
+        >>> q_inputs = torch.quantize_per_tensor(inputs, scale, zero_point, dtype_inputs)
+        >>> qF.conv1d(q_inputs, q_filters, bias, padding=1, scale=scale, zero_point=zero_point)
+    """  # noqa: E501
+    if padding_mode != "zeros":
+        raise NotImplementedError("Only zero-padding is supported!")
+    if input.dtype != torch.quint8:
+        raise NotImplementedError(
+            "Only torch.quint8 is supported for activation tensor!"
+        )
+    if weight.dtype != torch.qint8:
+        raise NotImplementedError("Only torch.qint8 is supported for weight tensor!")
+    if input.ndim != 3:
+        raise ValueError("Input shape must be `(N, C, L)`!")
+    stride = _pair_from_first(stride)
+    padding = _pair_from_first(padding)
+    dilation = _pair_from_first(dilation)
+
+    packed_params = torch.ops.quantized.conv1d_prepack(
+        weight, bias, stride, padding, dilation, groups
+    )
+    return torch.ops.quantized.conv1d(input, packed_params, scale, zero_point)
+
+
+def conv2d(
+    input,
+    weight,
+    bias,
+    stride=1,
+    padding=0,
+    dilation=1,
+    groups=1,
+    padding_mode="zeros",
+    scale=1.0,
+    zero_point=0,
+    dtype=torch.quint8,
+):
+    r"""
+    Applies a 2D convolution over a quantized 2D input composed of several input
+    planes.
+
+    See :class:`~torch.ao.nn.quantized.Conv2d` for details and output shape.
+
+    Args:
+        input: quantized input tensor of shape :math:`(\text{minibatch} , \text{in\_channels} , iH , iW)`
+        weight: quantized filters of shape :math:`(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , kH , kW)`
+        bias: **non-quantized** bias tensor of shape :math:`(\text{out\_channels})`. The tensor type must be `torch.float`.
+        stride: the stride of the convolving kernel. Can be a single number or a
+          tuple `(sH, sW)`. Default: 1
+        padding: implicit paddings on both sides of the input. Can be a
+          single number or a tuple `(padH, padW)`. Default: 0
+        dilation: the spacing between kernel elements. Can be a single number or
+          a tuple `(dH, dW)`. Default: 1
+        groups: split input into groups, :math:`\text{in\_channels}` should be divisible by the
+          number of groups. Default: 1
+        padding_mode: the padding mode to use. Only "zeros" is supported for quantized convolution at the moment. Default: "zeros"
+        scale: quantization scale for the output. Default: 1.0
+        zero_point: quantization zero_point for the output. Default: 0
+        dtype: quantization data type to use. Default: ``torch.quint8``
+
+    Examples::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
+        >>> from torch.ao.nn.quantized import functional as qF
+        >>> filters = torch.randn(8, 4, 3, 3, dtype=torch.float)
+        >>> inputs = torch.randn(1, 4, 5, 5, dtype=torch.float)
+        >>> bias = torch.randn(8, dtype=torch.float)
+        >>>
+        >>> scale, zero_point = 1.0, 0
+        >>> dtype_inputs = torch.quint8
+        >>> dtype_filters = torch.qint8
+        >>>
+        >>> q_filters = torch.quantize_per_tensor(filters, scale, zero_point, dtype_filters)
+        >>> q_inputs = torch.quantize_per_tensor(inputs, scale, zero_point, dtype_inputs)
+        >>> qF.conv2d(q_inputs, q_filters, bias, padding=1, scale=scale, zero_point=zero_point)
+    """  # noqa: E501
+    if padding_mode != "zeros":
+        raise NotImplementedError("Only zero-padding is supported!")
+    if input.dtype != torch.quint8:
+        raise NotImplementedError(
+            "Only torch.quint8 is supported for activation tensor!"
+        )
+    if weight.dtype != torch.qint8:
+        raise NotImplementedError("Only torch.qint8 is supported for weight tensor!")
+    if input.ndim != 4:
+        raise ValueError("Input shape must be `(N, C, H, W)`!")
+    stride = _pair(stride)
+    padding = _pair(padding)
+    dilation = _pair(dilation)
+
+    packed_params = torch.ops.quantized.conv2d_prepack(
+        weight, bias, stride, padding, dilation, groups
+    )
+    return torch.ops.quantized.conv2d(input, packed_params, scale, zero_point)
+
+
+def conv3d(
+    input,
+    weight,
+    bias,
+    stride=1,
+    padding=0,
+    dilation=1,
+    groups=1,
+    padding_mode="zeros",
+    scale=1.0,
+    zero_point=0,
+    dtype=torch.quint8,
+):
+    r"""
+    Applies a 3D convolution over a quantized 3D input composed of several input
+    planes.
+
+    See :class:`~torch.ao.nn.quantized.Conv3d` for details and output shape.
+
+    Args:
+        input: quantized input tensor of shape
+          :math:`(\text{minibatch} , \text{in\_channels} , iD , iH , iW)`
+        weight: quantized filters of shape
+          :math:`(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , kD , kH , kW)`
+        bias: **non-quantized** bias tensor of shape
+          :math:`(\text{out\_channels})`. The tensor type must be `torch.float`.
+        stride: the stride of the convolving kernel. Can be a single number or a
+          tuple `(sD, sH, sW)`. Default: 1
+        padding: implicit paddings on both sides of the input. Can be a
+          single number or a tuple `(padD, padH, padW)`. Default: 0
+        dilation: the spacing between kernel elements. Can be a single number or
+          a tuple `(dD, dH, dW)`. Default: 1
+        groups: split input into groups, :math:`\text{in\_channels}` should be
+          divisible by the number of groups. Default: 1
+        padding_mode: the padding mode to use. Only "zeros" is supported for
+          quantized convolution at the moment. Default: "zeros"
+        scale: quantization scale for the output. Default: 1.0
+        zero_point: quantization zero_point for the output. Default: 0
+        dtype: quantization data type to use. Default: ``torch.quint8``
+
+    Examples::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
+        >>> from torch.ao.nn.quantized import functional as qF
+        >>> filters = torch.randn(8, 4, 3, 3, 3, dtype=torch.float)
+        >>> inputs = torch.randn(1, 4, 5, 5, 5, dtype=torch.float)
+        >>> bias = torch.randn(8, dtype=torch.float)
+        >>>
+        >>> scale, zero_point = 1.0, 0
+        >>> dtype_inputs = torch.quint8
+        >>> dtype_filters = torch.qint8
+        >>>
+        >>> q_filters = torch.quantize_per_tensor(filters, scale, zero_point, dtype_filters)
+        >>> q_inputs = torch.quantize_per_tensor(inputs, scale, zero_point, dtype_inputs)
+        >>> qF.conv3d(q_inputs, q_filters, bias, padding=1, scale=scale, zero_point=zero_point)
+    """  # noqa: E501
+    if padding_mode != "zeros":
+        raise NotImplementedError("Only zero-padding is supported!")
+    if input.dtype != torch.quint8:
+        raise NotImplementedError(
+            "Only torch.quint8 is supported for activation tensor!"
+        )
+    if weight.dtype != torch.qint8:
+        raise NotImplementedError("Only torch.qint8 is supported for weight tensor!")
+    if input.ndim != 5:
+        raise ValueError("Input shape must be `(N, C, D, H, W)`!")
+    stride = _triple(stride)
+    padding = _triple(padding)
+    dilation = _triple(dilation)
+
+    packed_params = torch.ops.quantized.conv3d_prepack(
+        weight, bias, stride, padding, dilation, groups
+    )
+    return torch.ops.quantized.conv3d(input, packed_params, scale, zero_point)
+
+
+def interpolate(
+    input, size=None, scale_factor=None, mode="nearest", align_corners=None
+):
+    r"""Down/up samples the input to either the given :attr:`size` or the given
+    :attr:`scale_factor`
+
+    See :func:`torch.nn.functional.interpolate` for implementation details.
+
+    The input dimensions are interpreted in the form:
+    `mini-batch x channels x [optional depth] x [optional height] x width`.
+
+    .. note:: The input quantization parameters propagate to the output.
+
+    .. note:: Only 2D/3D input is supported for quantized inputs
+
+    .. note:: Only the following modes are supported for the quantized inputs:
+
+        - `bilinear`
+        - `nearest`
+
+    Args:
+        input (Tensor): the input tensor
+        size (int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int]):
+            output spatial size.
+        scale_factor (float or Tuple[float]): multiplier for spatial size. Has to match input size if it is a tuple.
+        mode (str): algorithm used for upsampling:
+            ``'nearest'`` | ``'bilinear'``
+        align_corners (bool, optional): Geometrically, we consider the pixels of the
+            input and output as squares rather than points.
+            If set to ``True``, the input and output tensors are aligned by the
+            center points of their corner pixels, preserving the values at the corner pixels.
+            If set to ``False``, the input and output tensors are aligned by the corner
+            points of their corner pixels, and the interpolation uses edge value padding
+            for out-of-boundary values, making this operation *independent* of input size
+            when :attr:`scale_factor` is kept the same. This only has an effect when :attr:`mode`
+            is ``'bilinear'``.
+            Default: ``False``
+    """
+    if not input.is_quantized:
+        raise ValueError("Input to 'quantized.interpolate' must be quantized!")
+    return torch.nn.functional.interpolate(
+        input, size, scale_factor, mode, align_corners
+    )
+
+
+def linear(
+    input: Tensor,
+    weight: Tensor,
+    bias: Tensor | None = None,
+    scale: float | None = None,
+    zero_point: int | None = None,
+) -> Tensor:
+    r"""
+    Applies a linear transformation to the incoming quantized data:
+    :math:`y = xA^T + b`.
+    See :class:`~torch.ao.nn.quantized.Linear`
+
+    .. note::
+
+      Current implementation packs weights on every call, which has penalty on performance.
+      If you want to avoid the overhead, use :class:`~torch.ao.nn.quantized.Linear`.
+
+    Args:
+      input (Tensor): Quantized input of type `torch.quint8`
+      weight (Tensor): Quantized weight of type `torch.qint8`
+      bias (Tensor): None or fp32 bias of type `torch.float`
+      scale (double): output scale. If None, derived from the input scale
+      zero_point (long): output zero point. If None, derived from the input zero_point
+
+    Shape:
+        - Input: :math:`(N, *, in\_features)` where `*` means any number of
+          additional dimensions
+        - Weight: :math:`(out\_features, in\_features)`
+        - Bias: :math:`(out\_features)`
+        - Output: :math:`(N, *, out\_features)`
+    """
+    if scale is None:
+        scale = input.q_scale()
+    if zero_point is None:
+        zero_point = input.q_zero_point()
+    _packed_params = torch.ops.quantized.linear_prepack(weight, bias)
+    return torch.ops.quantized.linear(input, _packed_params, scale, zero_point)
+
+
+def max_pool1d(
+    input,
+    kernel_size,
+    stride=None,
+    padding=0,
+    dilation=1,
+    ceil_mode=False,
+    return_indices=False,
+):
+    r"""Applies a 1D max pooling over a quantized input signal composed of
+    several quantized input planes.
+
+    .. note:: The input quantization parameters are propagated to the output.
+
+    See :class:`~torch.ao.nn.quantized.MaxPool1d` for details.
+    """
+    if return_indices:
+        raise NotImplementedError("return_indices is not yet implemented!")
+    if stride is None:
+        stride = torch.jit.annotate(list[int], [])
+    return torch.nn.functional.max_pool1d(
+        input,
+        kernel_size,
+        stride,
+        padding,
+        dilation,
+        ceil_mode=ceil_mode,
+        return_indices=return_indices,
+    )
+
+
+def max_pool2d(
+    input,
+    kernel_size,
+    stride=None,
+    padding=0,
+    dilation=1,
+    ceil_mode=False,
+    return_indices=False,
+):
+    r"""Applies a 2D max pooling over a quantized input signal composed of
+    several quantized input planes.
+
+    .. note:: The input quantization parameters are propagated to the output.
+
+    See :class:`~torch.ao.nn.quantized.MaxPool2d` for details.
+    """
+    if return_indices:
+        raise NotImplementedError("return_indices is not yet implemented!")
+    if stride is None:
+        stride = torch.jit.annotate(list[int], [])
+    return torch.nn.functional.max_pool2d(
+        input,
+        kernel_size,
+        stride,
+        padding,
+        dilation,
+        ceil_mode=ceil_mode,
+        return_indices=return_indices,
+    )
+
+
+def celu(input: Tensor, scale: float, zero_point: int, alpha: float = 1.0) -> Tensor:
+    r"""celu(input, scale, zero_point, alpha=1.) -> Tensor
+
+    Applies the quantized CELU function element-wise.
+
+    .. math::
+        \text{CELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x / \alpha) - 1))
+
+    Args:
+        input: quantized input
+        alpha: the :math:`\alpha` value for the CELU formulation. Default: 1.0
+    """
+    if not input.is_quantized:
+        raise ValueError("Input to 'quantized.celu' must be quantized!")
+    return torch.ops.quantized.celu(input, scale, zero_point, alpha)
+
+
+def leaky_relu(
+    input: Tensor,
+    negative_slope: float = 0.01,
+    inplace: bool = False,
+    scale: float | None = None,
+    zero_point: int | None = None,
+):
+    r"""
+    Quantized version of the.
+    leaky_relu(input, negative_slope=0.01, inplace=False, scale, zero_point) -> Tensor
+
+    Applies element-wise,
+    :math:`\text{LeakyReLU}(x) = \max(0, x) + \text{negative\_slope} * \min(0, x)`
+
+    Args:
+        input: Quantized input
+        negative_slope: The slope of the negative input
+        inplace: Inplace modification of the input tensor
+        scale, zero_point: Scale and zero point of the output tensor.
+
+    See :class:`~torch.nn.LeakyReLU` for more details.
+    """
+    if scale is not None and zero_point is not None:
+        assert not inplace, "Cannot rescale with `inplace`"
+        output = torch._empty_affine_quantized(
+            input.shape, scale=scale, zero_point=int(zero_point), dtype=input.dtype
+        )
+        torch._C._nn.leaky_relu(input, negative_slope, out=output)
+        return output
+    if inplace:
+        result = torch._C._nn.leaky_relu_(input, negative_slope)
+    else:
+        result = torch._C._nn.leaky_relu(input, negative_slope)
+    return result
+
+
+def hardtanh(
+    input: Tensor, min_val: float = -1.0, max_val: float = 1.0, inplace: bool = False
+) -> Tensor:
+    r"""This is the quantized version of :func:`~torch.nn.functional.hardtanh`."""
+    if not input.is_quantized:
+        raise ValueError("Input to 'quantized.hardtanh' must be quantized!")
+    if inplace:
+        return torch._C._nn.hardtanh_(input, min_val, max_val)
+    return torch._C._nn.hardtanh(input, min_val, max_val)
+
+
+def hardswish(input: Tensor, scale: float, zero_point: int) -> Tensor:
+    r"""This is the quantized version of :func:`~torch.nn.functional.hardswish`.
+
+    Args:
+        input: quantized input
+        scale: quantization scale of the output tensor
+        zero_point: quantization zero point of the output tensor
+    """
+    if not input.is_quantized:
+        raise ValueError("Input to 'quantized.hardswish' must be quantized!")
+    return torch._ops.ops.quantized.hardswish(input, scale, zero_point)
+
+
+def threshold(input: Tensor, threshold: float, value: float) -> Tensor:
+    r"""Applies the quantized version of the threshold function element-wise:
+
+    .. math::
+        x = \begin{cases}
+                x & \text{if~} x > \text{threshold} \\
+                \text{value} & \text{otherwise}
+            \end{cases}
+
+    See :class:`~torch.nn.Threshold` for more details.
+    """
+    if not input.is_quantized:
+        raise ValueError("Input to 'quantized.threshold' must be quantized!")
+    if threshold is None:
+        raise ValueError("Input to 'threshold' must be specified!")
+    if value is None:
+        raise ValueError("Input to 'value' must be specified!")
+    return torch._ops.ops.quantized.threshold(input, threshold, value)
+
+
+def elu(input: Tensor, scale: float, zero_point: int, alpha: float = 1.0) -> Tensor:
+    r"""This is the quantized version of :func:`~torch.nn.functional.elu`.
+
+    Args:
+        input: quantized input
+        scale: quantization scale of the output tensor
+        zero_point: quantization zero point of the output tensor
+        alpha: the alpha constant
+    """
+    if not input.is_quantized:
+        raise ValueError("Input to 'quantized.elu' must be quantized!")
+    return torch.ops.quantized.elu(input, scale, zero_point, alpha)
+
+
+def hardsigmoid(input: Tensor, inplace: bool = False) -> Tensor:
+    r"""This is the quantized version of :func:`~torch.nn.functional.hardsigmoid`."""
+    if not input.is_quantized:
+        raise ValueError("Input to 'quantized.hardsigmoid' must be quantized!")
+    if inplace:
+        return torch._C._nn.hardsigmoid_(input)  # type: ignore[attr-defined]
+    return torch._C._nn.hardsigmoid(input)
+
+
+def clamp(input: Tensor, min_: float, max_: float) -> Tensor:
+    r"""float(input, min\_, max\_) -> Tensor
+
+    Applies the clamp function element-wise.
+    See :class:`~torch.ao.nn.quantized.clamp` for more details.
+
+    Args:
+        input: quantized input
+        min_: minimum value for clamping
+        max_: maximum value for clamping
+    """
+    if not input.is_quantized:
+        raise ValueError("Input to 'quantized.clamp' must be quantized!")
+    return torch.clamp(input, min_, max_)
+
+
+def upsample(input, size=None, scale_factor=None, mode="nearest", align_corners=None):
+    r"""Upsamples the input to either the given :attr:`size` or the given
+    :attr:`scale_factor`
+
+    .. warning::
+        This function is deprecated in favor of
+        :func:`torch.ao.nn.quantized.functional.interpolate`.
+        This is equivalent with ``nn.quantized.functional.interpolate(...)``.
+
+    See :func:`torch.nn.functional.interpolate` for implementation details.
+
+    The input dimensions are interpreted in the form:
+    `mini-batch x channels x [optional depth] x [optional height] x width`.
+
+    .. note:: The input quantization parameters propagate to the output.
+
+    .. note:: Only 2D input is supported for quantized inputs
+
+    .. note:: Only the following modes are supported for the quantized inputs:
+
+        - `bilinear`
+        - `nearest`
+
+    Args:
+        input (Tensor): quantized input tensor
+        size (int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int]):
+            output spatial size.
+        scale_factor (float or Tuple[float]): multiplier for spatial size. Has to be an integer.
+        mode (str): algorithm used for upsampling:
+            ``'nearest'`` | ``'bilinear'``
+        align_corners (bool, optional): Geometrically, we consider the pixels of the
+            input and output as squares rather than points.
+            If set to ``True``, the input and output tensors are aligned by the
+            center points of their corner pixels, preserving the values at the corner pixels.
+            If set to ``False``, the input and output tensors are aligned by the corner
+            points of their corner pixels, and the interpolation uses edge value padding
+            for out-of-boundary values, making this operation *independent* of input size
+            when :attr:`scale_factor` is kept the same. This only has an effect when :attr:`mode`
+            is ``'bilinear'``.
+            Default: ``False``
+
+    .. warning::
+        With ``align_corners = True``, the linearly interpolating modes
+        (`bilinear`) don't proportionally align the
+        output and input pixels, and thus the output values can depend on the
+        input size. This was the default behavior for these modes up to version
+        0.3.1. Since then, the default behavior is ``align_corners = False``.
+        See :class:`~torch.nn.Upsample` for concrete examples on how this
+        affects the outputs.
+    """
+    warnings.warn(
+        "nn.quantized.functional.upsample is deprecated. Use nn.quantized.functional.interpolate instead.",
+        stacklevel=2,
+    )
+    return interpolate(input, size, scale_factor, mode, align_corners)
+
+
+def upsample_bilinear(input, size=None, scale_factor=None):
+    r"""Upsamples the input, using bilinear upsampling.
+
+    .. warning::
+        This function is deprecated in favor of
+        :func:`torch.ao.nn.quantized.functional.interpolate`.
+        This is equivalent with
+        ``nn.quantized.functional.interpolate(..., mode='bilinear', align_corners=True)``.
+
+    .. note:: The input quantization parameters propagate to the output.
+
+    .. note:: Only 2D inputs are supported
+
+    Args:
+        input (Tensor): quantized input
+        size (int or Tuple[int, int]): output spatial size.
+        scale_factor (int or Tuple[int, int]): multiplier for spatial size
+    """
+    # DeprecationWarning is ignored by default
+    warnings.warn(
+        "nn.quantized.functional.upsample_bilinear is deprecated. Use nn.quantized.functional.interpolate instead.",
+        stacklevel=2,
+    )
+    return interpolate(input, size, scale_factor, mode="bilinear", align_corners=True)
+
+
+def upsample_nearest(input, size=None, scale_factor=None):
+    r"""Upsamples the input, using nearest neighbours' pixel values.
+
+    .. warning::
+        This function is deprecated in favor of
+        :func:`torch.ao.nn.quantized.functional.interpolate`.
+        This is equivalent with ``nn.quantized.functional.interpolate(..., mode='nearest')``.
+
+    .. note:: The input quantization parameters propagate to the output.
+
+    .. note:: Only 2D inputs are supported
+
+    Args:
+        input (Tensor): quantized input
+        size (int or Tuple[int, int] or Tuple[int, int, int]): output spatial
+            size.
+        scale_factor (int): multiplier for spatial size. Has to be an integer.
+    """
+    # DeprecationWarning is ignored by default
+    warnings.warn(
+        "nn.quantized.functional.upsample_nearest is deprecated. Use nn.quantized.functional.interpolate instead.",
+        stacklevel=2,
+    )
+    return interpolate(input, size, scale_factor, mode="nearest")
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3bad8c49350f56e5e58235570799a8d0968296d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/__init__.py
@@ -0,0 +1,162 @@
+# mypy: allow-untyped-defs
+import torch
+
+# The quantized modules use `torch.nn` and `torch.ao.nn.quantizable`
+# packages. However, the `quantizable` package uses "lazy imports"
+# to avoid circular dependency.
+# Hence we need to include it here to make sure it is resolved before
+# they are used in the modules.
+import torch.ao.nn.quantizable
+from torch.nn.modules.pooling import MaxPool2d
+
+from .activation import (
+    ELU,
+    Hardswish,
+    LeakyReLU,
+    MultiheadAttention,
+    PReLU,
+    ReLU6,
+    Sigmoid,
+    Softmax,
+)
+from .batchnorm import BatchNorm2d, BatchNorm3d
+from .conv import (
+    Conv1d,
+    Conv2d,
+    Conv3d,
+    ConvTranspose1d,
+    ConvTranspose2d,
+    ConvTranspose3d,
+)
+from .dropout import Dropout
+from .embedding_ops import Embedding, EmbeddingBag
+from .functional_modules import FloatFunctional, FXFloatFunctional, QFunctional
+from .linear import Linear
+from .normalization import (
+    GroupNorm,
+    InstanceNorm1d,
+    InstanceNorm2d,
+    InstanceNorm3d,
+    LayerNorm,
+)
+from .rnn import LSTM
+
+
+__all__ = [
+    "BatchNorm2d",
+    "BatchNorm3d",
+    "Conv1d",
+    "Conv2d",
+    "Conv3d",
+    "ConvTranspose1d",
+    "ConvTranspose2d",
+    "ConvTranspose3d",
+    "DeQuantize",
+    "ELU",
+    "Embedding",
+    "EmbeddingBag",
+    "GroupNorm",
+    "Hardswish",
+    "InstanceNorm1d",
+    "InstanceNorm2d",
+    "InstanceNorm3d",
+    "LayerNorm",
+    "LeakyReLU",
+    "Linear",
+    "LSTM",
+    "MultiheadAttention",
+    "Quantize",
+    "ReLU6",
+    "Sigmoid",
+    "Softmax",
+    "Dropout",
+    "PReLU",
+    # Wrapper modules
+    "FloatFunctional",
+    "FXFloatFunctional",
+    "QFunctional",
+]
+
+
+class Quantize(torch.nn.Module):
+    r"""Quantizes an incoming tensor
+
+    Args:
+     `scale`: scale of the output Quantized Tensor
+     `zero_point`: zero_point of output Quantized Tensor
+     `dtype`: data type of output Quantized Tensor
+     `factory_kwargs`: Dictionary of kwargs used for configuring initialization
+         of internal buffers. Currently, `device` and `dtype` are supported.
+         Example: `factory_kwargs={'device': 'cuda', 'dtype': torch.float64}`
+         will initialize internal buffers as type `torch.float64` on the current CUDA device.
+         Note that `dtype` only applies to floating-point buffers.
+
+    Examples::
+        >>> t = torch.tensor([[1., -1.], [1., -1.]])
+        >>> scale, zero_point, dtype = 1.0, 2, torch.qint8
+        >>> qm = Quantize(scale, zero_point, dtype)
+        >>> # xdoctest: +SKIP
+        >>> qt = qm(t)
+        >>> print(qt)
+        tensor([[ 1., -1.],
+                [ 1., -1.]], size=(2, 2), dtype=torch.qint8, scale=1.0, zero_point=2)
+    """
+
+    scale: torch.Tensor
+    zero_point: torch.Tensor
+
+    def __init__(self, scale, zero_point, dtype, factory_kwargs=None):
+        factory_kwargs = torch.nn.factory_kwargs(factory_kwargs)
+        super().__init__()
+        self.register_buffer("scale", torch.tensor([scale], **factory_kwargs))
+        self.register_buffer(
+            "zero_point",
+            torch.tensor(
+                [zero_point],
+                dtype=torch.long,
+                **{k: v for k, v in factory_kwargs.items() if k != "dtype"},
+            ),
+        )
+        self.dtype = dtype
+
+    def forward(self, X):
+        return torch.quantize_per_tensor(
+            X, float(self.scale), int(self.zero_point), self.dtype
+        )
+
+    @staticmethod
+    def from_float(mod, use_precomputed_fake_quant=False):
+        assert hasattr(mod, "activation_post_process")
+        scale, zero_point = mod.activation_post_process.calculate_qparams()
+        return Quantize(
+            scale.float().item(),
+            zero_point.long().item(),
+            mod.activation_post_process.dtype,
+        )
+
+    def extra_repr(self):
+        return f"scale={self.scale}, zero_point={self.zero_point}, dtype={self.dtype}"
+
+
+class DeQuantize(torch.nn.Module):
+    r"""Dequantizes an incoming tensor
+
+    Examples::
+        >>> input = torch.tensor([[1., -1.], [1., -1.]])
+        >>> scale, zero_point, dtype = 1.0, 2, torch.qint8
+        >>> qm = Quantize(scale, zero_point, dtype)
+        >>> # xdoctest: +SKIP
+        >>> quantized_input = qm(input)
+        >>> dqm = DeQuantize()
+        >>> dequantized = dqm(quantized_input)
+        >>> print(dequantized)
+        tensor([[ 1., -1.],
+                [ 1., -1.]], dtype=torch.float32)
+    """
+
+    def forward(self, Xq):
+        return Xq.dequantize()
+
+    @staticmethod
+    def from_float(mod, use_precomputed_fake_quant=False):
+        return DeQuantize()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/activation.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/activation.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ecf1d5c9a1e2c198d89f284e109dd9410994b60
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/activation.py
@@ -0,0 +1,351 @@
+# mypy: allow-untyped-defs
+from warnings import warn
+
+import torch
+
+
+__all__ = [
+    "ReLU6",
+    "Hardswish",
+    "ELU",
+    "LeakyReLU",
+    "Sigmoid",
+    "Softmax",
+    "MultiheadAttention",
+    "PReLU",
+]
+
+
+class ReLU6(torch.nn.ReLU):
+    r"""Applies the element-wise function:
+
+    :math:`\text{ReLU6}(x) = \min(\max(x_0, x), q(6))`, where :math:`x_0` is the
+    zero_point, and :math:`q(6)` is the quantized representation of number 6.
+
+    Args:
+        inplace: can optionally do the operation in-place. Default: ``False``
+
+    Shape:
+        - Input: :math:`(N, *)` where `*` means, any number of additional
+          dimensions
+        - Output: :math:`(N, *)`, same shape as the input
+
+    .. image:: ../scripts/activation_images/ReLU6.png
+
+    Examples::
+
+        >>> m = nn.quantized.ReLU6()
+        >>> input = torch.randn(2)
+        >>> # xdoctest: +SKIP
+        >>> input = torch.quantize_per_tensor(input, 1.0, 0, dtype=torch.qint32)
+        >>> output = m(input)
+    """
+
+    def __init__(self, inplace=False):
+        super().__init__(inplace)
+        self.inplace = inplace
+
+    def forward(self, input):
+        return torch.ops.quantized.relu6(input, self.inplace)
+
+    def _get_name(self):
+        return "QuantizedReLU6"
+
+    @staticmethod
+    def from_float(mod, use_precomputed_fake_quant=False):
+        return ReLU6(mod.inplace)
+
+
+class Hardswish(torch.nn.Hardswish):
+    r"""This is the quantized version of :class:`~torch.nn.Hardswish`.
+
+    Args:
+        scale: quantization scale of the output tensor
+        zero_point: quantization zero point of the output tensor
+    """
+
+    def __init__(self, scale, zero_point, device=None, dtype=None):
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        # pyrefly: ignore [bad-argument-type]
+        self.register_buffer("scale", torch.tensor(scale, **factory_kwargs))
+        # pyrefly: ignore [bad-argument-type]
+        self.register_buffer("zero_point", torch.tensor(zero_point, **factory_kwargs))
+
+    def forward(self, input):
+        return torch.ops.quantized.hardswish(input, self.scale, self.zero_point)
+
+    def _get_name(self):
+        return "QuantizedHardswish"
+
+    @staticmethod
+    def from_float(mod, use_precomputed_fake_quant=False):
+        scale, zero_point = mod.activation_post_process.calculate_qparams()
+        return Hardswish(float(scale), int(zero_point))
+
+    @classmethod
+    def from_reference(cls, mod, scale, zero_point):
+        return cls(float(scale), int(zero_point))
+
+
+class ELU(torch.nn.ELU):
+    r"""This is the quantized equivalent of :class:`~torch.nn.ELU`.
+
+    Args:
+        scale: quantization scale of the output tensor
+        zero_point: quantization zero point of the output tensor
+        alpha: the alpha constant
+    """
+
+    def __init__(self, scale, zero_point, alpha=1.0):
+        super().__init__(alpha)
+        self.scale = scale
+        self.zero_point = zero_point
+
+    def forward(self, input):
+        return torch.ao.nn.quantized.functional.elu(
+            input, self.scale, self.zero_point, self.alpha
+        )
+
+    def _get_name(self):
+        return "QuantizedELU"
+
+    @staticmethod
+    def from_float(mod, use_precomputed_fake_quant=False):
+        scale, zero_point = mod.activation_post_process.calculate_qparams()
+        return ELU(float(scale), int(zero_point), mod.alpha)
+
+    @classmethod
+    def from_reference(cls, mod, scale, zero_point):
+        return cls(float(scale), int(zero_point), mod.alpha)
+
+
+class LeakyReLU(torch.nn.LeakyReLU):
+    r"""This is the quantized equivalent of :class:`~torch.nn.LeakyReLU`.
+
+    Args:
+        scale: quantization scale of the output tensor
+        zero_point: quantization zero point of the output tensor
+        negative_slope: Controls the angle of the negative slope. Default: 1e-2
+    """
+
+    def __init__(
+        self,
+        scale: float,
+        zero_point: int,
+        negative_slope: float = 1e-2,
+        inplace: bool = False,
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__(negative_slope, inplace)
+        # pyrefly: ignore [bad-argument-type]
+        self.register_buffer("scale", torch.tensor(scale, **factory_kwargs))
+        # pyrefly: ignore [bad-argument-type]
+        self.register_buffer("zero_point", torch.tensor(zero_point, **factory_kwargs))
+
+    def forward(self, input):
+        return torch.ops.quantized.leaky_relu(
+            input, self.negative_slope, self.inplace, self.scale, self.zero_point
+        )
+
+    def _get_name(self):
+        return "QuantizedLeakyReLU"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        scale, zero_point = mod.activation_post_process.calculate_qparams()
+        return cls(float(scale), int(zero_point), mod.negative_slope, mod.inplace)
+
+    @classmethod
+    def from_reference(cls, mod, scale, zero_point):
+        return cls(float(scale), int(zero_point), mod.negative_slope, mod.inplace)
+
+
+class Sigmoid(torch.nn.Sigmoid):
+    r"""This is the quantized equivalent of :class:`~torch.nn.Sigmoid`.
+
+    Args:
+        scale: quantization scale of the output tensor
+        zero_point: quantization zero point of the output tensor
+    """
+
+    def __init__(self, output_scale: float, output_zero_point: int):
+        super().__init__()
+        self.output_scale = output_scale
+        self.output_zero_point = output_zero_point
+
+    def forward(self, input):
+        return torch.ops.quantized.sigmoid(
+            input, self.output_scale, self.output_zero_point
+        )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        (
+            output_scale,
+            output_zero_point,
+        ) = mod.activation_post_process.calculate_qparams()
+        return cls(float(output_scale), int(output_zero_point))
+
+
+class Softmax(torch.nn.Softmax):
+    r"""This is the quantized version of :class:`~torch.nn.Softmax`.
+
+    Args:
+        dim: A dimension along which Softmax will be computed (so every slice along dim will sum to 1).
+        scale: quantization scale of the output tensor
+        zero_point: quantization zero point of the output tensor
+    """
+
+    def __init__(self, dim=None, scale=1.0, zero_point=0):
+        super().__init__()
+        self.dim = dim
+        self.scale = scale
+        self.zero_point = zero_point
+
+    def forward(self, input):
+        dim = self.dim
+        if dim is None:
+            stacklevel = 3
+            # Note: adding the mypy ignore on _get_softmax_dim seems less bad
+            # than making `_get_softmax_dim` an official API.
+            dim = torch.nn.functional._get_softmax_dim(  # type: ignore[attr-defined]
+                "softmax", input.dim(), stacklevel
+            )
+        return torch.ops.quantized.softmax(input, dim, self.scale, self.zero_point)
+
+    def _get_name(self):
+        return "QuantizedSoftmax"
+
+    @staticmethod
+    def from_float(mod, use_precomputed_fake_quant=False):
+        scale, zero_point = mod.activation_post_process.calculate_qparams()
+        return Softmax(mod.dim, float(scale), int(zero_point))
+
+    @classmethod
+    def from_reference(cls, mod, scale, zero_point):
+        return cls(mod.dim, float(scale), int(zero_point))
+
+
+class MultiheadAttention(torch.ao.nn.quantizable.MultiheadAttention):
+    # pyrefly: ignore [bad-override]
+    _FLOAT_MODULE = torch.ao.nn.quantizable.MultiheadAttention
+
+    def _get_name(self):
+        return "QuantizedMultiheadAttention"
+
+    @classmethod
+    def from_float(cls, other):
+        # The whole flow is float -> observed -> quantized
+        # This class does observed -> quantized only
+        raise NotImplementedError(
+            "It looks like you are trying to convert a "
+            "non-observed MHA module. Please, see "
+            "the examples on quantizable MHAs."
+        )
+
+    @classmethod
+    def from_observed(cls, other):
+        converted = torch.ao.quantization.convert(
+            other,
+            mapping=None,
+            inplace=False,
+            remove_qconfig=True,
+            convert_custom_config_dict=None,
+        )
+        converted.__class__ = cls
+        # Remove the parameters for the bias_k and bias_v to quantize them
+        # TODO: This is a potential source of accuracy drop.
+        #       quantized cat takes the scale and zp of the first
+        #       element, which might lose the precision in the bias_k
+        #       and the bias_v (which are cat'ed with k/v being first).
+        if converted.bias_k is not None:
+            bias_k = converted._parameters.pop("bias_k")
+            sc, zp = torch._choose_qparams_per_tensor(bias_k, reduce_range=False)
+            bias_k = torch.quantize_per_tensor(bias_k, sc, zp, torch.quint8)
+            setattr(converted, "bias_k", bias_k)  # noqa: B010
+
+        if converted.bias_v is not None:
+            bias_v = converted._parameters.pop("bias_v")
+            sc, zp = torch._choose_qparams_per_tensor(
+                bias_k,  # type: ignore[possibly-undefined]
+                reduce_range=False,
+            )
+            bias_v = torch.quantize_per_tensor(bias_v, sc, zp, torch.quint8)
+            setattr(converted, "bias_v", bias_v)  # noqa: B010
+
+        del converted.in_proj_weight
+        del converted.in_proj_bias
+
+        return converted
+
+
+class PReLU(torch.nn.Module):
+    r"""This is the quantized equivalent of :class:`~torch.nn.PReLU`.
+
+    Args:
+        scale: quantization scale of the output tensor
+        zero_point: quantization zero point of the output tensor
+        num_parameters: number of parameters: 1, or the number of channels at input. Default: 1
+    """
+
+    def __init__(
+        self, output_scale: float, output_zero_point: int, num_parameters: int = 1
+    ) -> None:
+        super().__init__()
+        self.num_parameters = num_parameters
+        self.scale = output_scale
+        self.zero_point = output_zero_point
+        w = torch.randn(num_parameters, dtype=torch.float)
+        qw = torch.quantize_per_tensor(w, scale=1.0, zero_point=0, dtype=torch.quint8)
+        self.set_weight(qw)
+
+    def set_weight(self, w: torch.Tensor) -> None:
+        self.weight = w
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        return torch.ops.quantized.prelu(
+            input, self.weight, self.scale, self.zero_point
+        )
+
+    def _get_name(self):
+        return "QuantizedPReLU"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        scale, zero_point = mod.activation_post_process.calculate_qparams()
+        qprelu = cls(float(scale), int(zero_point), mod.num_parameters)
+        float_wt = mod.weight.float()
+        observer = mod.qconfig.weight()
+        observer(float_wt)
+        if observer.dtype != torch.quint8:
+            warn(
+                f"PReLU's weight observer should have dtype quint8 but got {observer.dtype}",
+                stacklevel=2,
+            )
+        wt_scale, wt_zp = observer.calculate_qparams()
+        qweight = torch.quantize_per_tensor(
+            float_wt, float(wt_scale), int(wt_zp), torch.quint8
+        )
+        qprelu.set_weight(qweight)
+        return qprelu
+
+    @classmethod
+    def from_reference(cls, mod, scale, zero_point):
+        qprelu = cls(float(scale), int(zero_point), mod.num_parameters)
+        float_wt = mod.weight.float()
+        observer = mod.qconfig.weight()
+        observer(float_wt)
+        if observer.dtype != torch.quint8:
+            warn(
+                f"PReLU's weight observer should have dtype quint8 but got {observer.dtype}",
+                stacklevel=2,
+            )
+        wt_scale, wt_zp = observer.calculate_qparams()
+        qweight = torch.quantize_per_tensor(
+            float_wt, float(wt_scale), int(wt_zp), torch.quint8
+        )
+        qprelu.set_weight(qweight)
+        return qprelu
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/batchnorm.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/batchnorm.py
new file mode 100644
index 0000000000000000000000000000000000000000..f1e6779c08b1f6af61c2377335b984c7f75a29a6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/batchnorm.py
@@ -0,0 +1,130 @@
+# mypy: allow-untyped-defs
+import torch
+import torch.ao.nn.intrinsic as nni
+
+
+__all__ = ["BatchNorm2d", "BatchNorm3d"]
+
+
+class _BatchNorm(torch.nn.modules.batchnorm._BatchNorm):
+    def __init__(
+        self, num_features, eps=1e-5, momentum=0.1, device=None, dtype=None
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__(num_features, eps, momentum, True, True, **factory_kwargs)
+        # pyrefly: ignore [bad-argument-type]
+        self.register_buffer("scale", torch.tensor(1.0, **factory_kwargs))
+        # pyrefly: ignore [bad-argument-type]
+        self.register_buffer("zero_point", torch.tensor(0, **factory_kwargs))
+
+    @staticmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        activation_post_process = mod.activation_post_process
+        if type(mod) is cls._NNI_BN_RELU_MODULE:
+            mod = mod[0]
+        scale, zero_point = activation_post_process.calculate_qparams()
+        new_mod = cls(mod.num_features, mod.eps)
+        new_mod.weight = mod.weight
+        new_mod.bias = mod.bias
+        new_mod.running_mean = mod.running_mean
+        new_mod.running_var = mod.running_var
+        new_mod.scale = scale
+        new_mod.zero_point = zero_point
+        return new_mod
+
+    @classmethod
+    def from_reference(cls, bn, output_scale, output_zero_point):
+        qbn = cls(
+            bn.num_features,
+            bn.eps,
+            bn.momentum,
+            device=bn.weight.device,
+            dtype=bn.weight.dtype,
+        )
+        qbn.weight = bn.weight
+        qbn.bias = bn.bias
+        qbn.running_mean = bn.running_mean
+        qbn.running_var = bn.running_var
+        qbn.scale = output_scale
+        qbn.zero_point = output_zero_point
+        return qbn
+
+
+class BatchNorm2d(_BatchNorm):
+    r"""This is the quantized version of :class:`~torch.nn.BatchNorm2d`."""
+
+    _NNI_BN_RELU_MODULE = nni.BNReLU2d
+
+    def __init__(
+        self, num_features, eps=1e-5, momentum=0.1, device=None, dtype=None
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__(num_features, eps, momentum, **factory_kwargs)
+
+    def _get_name(self):
+        return "QuantizedBatchNorm2d"
+
+    def _check_input_dim(self, input):
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 4:
+            raise ValueError("Input shape must be `(N, C, H, W)`!")
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        # disabling this since this is not symbolically traceable
+        # self._check_input_dim(input)
+        return torch.ops.quantized.batch_norm2d(
+            input,
+            self.weight,
+            self.bias,
+            self.running_mean,
+            self.running_var,
+            self.eps,
+            self.scale,
+            self.zero_point,
+        )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):  # type: ignore[override]
+        return _BatchNorm.from_float(
+            cls, mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+
+class BatchNorm3d(_BatchNorm):
+    r"""This is the quantized version of :class:`~torch.nn.BatchNorm3d`."""
+
+    _NNI_BN_RELU_MODULE = nni.BNReLU3d
+
+    def __init__(self, num_features, eps=1e-5, momentum=0.1, device=None, dtype=None):
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__(num_features, eps, momentum, **factory_kwargs)
+
+    def _get_name(self):
+        return "QuantizedBatchNorm3d"
+
+    def _check_input_dim(self, input):
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 5:
+            raise ValueError("Input shape must be `(N, C, H, W)`!")
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        # disabling this since this is not symbolically traceable
+        # self._check_input_dim(input)
+        return torch.ops.quantized.batch_norm3d(
+            input,
+            self.weight,
+            self.bias,
+            self.running_mean,
+            self.running_var,
+            self.eps,
+            self.scale,
+            self.zero_point,
+        )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):  # type: ignore[override]
+        return _BatchNorm.from_float(
+            cls, mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/conv.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/conv.py
new file mode 100644
index 0000000000000000000000000000000000000000..a292d616a86c31d22550faa7d38d256350e4e91a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/conv.py
@@ -0,0 +1,1244 @@
+# mypy: allow-untyped-defs
+r"""Quantized convolution modules."""
+
+from typing import ClassVar, Literal, Optional
+
+import torch
+import torch.ao.nn.intrinsic as nni
+import torch.ao.nn.intrinsic.qat as nniqat
+import torch.nn as nn
+import torch.nn.functional as F
+from torch._ops import ops
+from torch.nn.common_types import _size_1_t
+from torch.nn.modules.utils import _pair, _single, _triple
+from torch.nn.utils import fuse_conv_bn_weights
+
+from .utils import _quantize_weight, WeightedQuantizedModule
+
+
+__all__ = [
+    "Conv1d",
+    "Conv2d",
+    "Conv3d",
+    "ConvTranspose1d",
+    "ConvTranspose2d",
+    "ConvTranspose3d",
+]
+
+_SUPPORTED_PADDING = {"zeros", "reflect"}
+
+
+def _reverse_repeat_padding(padding: list[int]) -> list[int]:
+    _reversed_padding_repeated_twice: list[int] = []
+    N = len(padding)
+    for idx in range(N):
+        _reversed_padding_repeated_twice.extend(padding[N - idx - 1] for _ in range(2))
+    return _reversed_padding_repeated_twice
+
+
+class _ConvNd(WeightedQuantizedModule):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=True,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ):
+        # All subclasses have this signature - See PR #49702s
+        raise NotImplementedError
+
+    def _init(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride,
+        padding,
+        dilation,
+        transposed,
+        output_padding,
+        groups,
+        bias,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+
+        if in_channels % groups != 0:
+            raise ValueError("in_channels must be divisible by groups")
+        if out_channels % groups != 0:
+            raise ValueError("out_channels must be divisible by groups")
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.kernel_size = kernel_size
+        self.stride = stride
+        self.padding = padding
+        self.dilation = dilation
+        self.transposed = transposed
+        self.output_padding = output_padding
+        self.groups = groups
+        if padding_mode not in _SUPPORTED_PADDING:
+            raise ValueError(
+                f"'padding_mode' {padding_mode} is not supported by quantized convolution"
+            )
+        self.padding_mode = padding_mode
+        # Initialize as NCHW. set_weight will internally transpose to NHWC.
+        if self.transposed:
+            weight_shape = [in_channels, out_channels // self.groups]
+        else:
+            weight_shape = [out_channels, in_channels // self.groups]
+        qweight = torch._empty_affine_quantized(
+            weight_shape + list(kernel_size),
+            scale=1,
+            zero_point=0,
+            dtype=torch.qint8,
+            **{k: v for k, v in factory_kwargs.items() if k != "dtype"},
+        )
+        bias_float = (
+            torch.zeros(
+                out_channels,
+                dtype=torch.float,
+                **{k: v for k, v in factory_kwargs.items() if k != "dtype"},
+            )
+            if bias
+            else None
+        )
+
+        self.set_weight_bias(qweight, bias_float)
+        self.scale = 1.0
+        self.zero_point = 0
+
+    def set_weight_bias(self, qweight, bias_float):
+        raise NotImplementedError
+
+    def bias(self):
+        raise NotImplementedError
+
+    def _weight_bias(self):
+        raise NotImplementedError
+
+    def extra_repr(self):
+        s = (
+            "{in_channels}, {out_channels}, kernel_size={kernel_size}"
+            ", stride={stride}, scale={scale}, zero_point={zero_point}"
+        )
+        if self.padding != (0,) * len(self.padding):
+            s += ", padding={padding}"
+        if self.dilation != (1,) * len(self.dilation):
+            s += ", dilation={dilation}"
+        if self.output_padding != (0,) * len(self.output_padding):
+            s += ", output_padding={output_padding}"
+        if self.groups != 1:
+            s += ", groups={groups}"
+        if self.bias() is None:
+            s += ", bias=False"
+        return s.format(**self.__dict__)
+
+    # ===== Serialization methods =====
+    # The special consideration here is that we have to unpack the weights into
+    # their regular QTensor form for serialization. Packed weights should not
+    # live outside the process in which they were created, rather they should be
+    # derived from the QTensor weight.
+    #   self
+    #   |--- weight : Tensor
+    #   |--- bias : Tensor
+    #
+    # TODO: maybe change to this when https://github.com/pytorch/pytorch/pull/32958 is landed
+    #   self
+    #   |--- _packed_params : Conv2dPackedParamsBase or Conv3dPackedParamsBase
+    def _save_to_state_dict(self, destination, prefix, keep_vars):
+        super()._save_to_state_dict(destination, prefix, keep_vars)
+        (w, b) = self._weight_bias()
+        destination[prefix + "weight"] = w
+        destination[prefix + "bias"] = b
+        destination[prefix + "scale"] = torch.tensor(self.scale)
+        destination[prefix + "zero_point"] = torch.tensor(self.zero_point)
+
+    @torch.jit.export
+    def __getstate__(self):
+        (w, b) = self._weight_bias()
+        return (
+            self.in_channels,
+            self.out_channels,
+            self.kernel_size,
+            self.stride,
+            self.padding,
+            self.dilation,
+            self.transposed,
+            self.output_padding,
+            self.groups,
+            self.padding_mode,
+            w,
+            b,
+            self.scale,
+            self.zero_point,
+            self.training,
+        )
+
+    # ===== Deserialization methods =====
+    # Counterpart to the serialization methods, we must pack the serialized
+    # QTensor weight into its packed format for use by the FBGEMM ops.
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        self.set_weight_bias(state_dict[prefix + "weight"], state_dict[prefix + "bias"])
+        state_dict.pop(prefix + "weight")
+        state_dict.pop(prefix + "bias")
+        self.scale = float(state_dict[prefix + "scale"])
+        state_dict.pop(prefix + "scale")
+        self.zero_point = int(state_dict[prefix + "zero_point"])
+        state_dict.pop(prefix + "zero_point")
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            False,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+    @torch.jit.export
+    def __setstate__(self, state):
+        self.in_channels = state[0]
+        self.out_channels = state[1]
+        self.kernel_size = state[2]
+        self.stride = state[3]
+        self.padding = state[4]
+        self.dilation = state[5]
+        self.transposed = state[6]
+        self.output_padding = state[7]
+        self.groups = state[8]
+        self.padding_mode = state[9]
+        self.set_weight_bias(state[10], state[11])
+        self.scale = state[12]
+        self.zero_point = state[13]
+        self.training = state[14]
+
+    def __deepcopy__(self, memo):
+        new_instance = type(self).__new__(type(self))
+        torch.nn.Module.__init__(new_instance)
+        state = self.__getstate__()
+        new_instance.__setstate__(state)
+        return new_instance
+
+    def __copy__(self):
+        return self.__deepcopy__({})
+
+    @classmethod
+    def get_qconv(cls, mod, activation_post_process, weight_post_process=None):
+        r"""Creates a qconv object and returns it."""
+        if weight_post_process is None:
+            weight_post_process = mod.qconfig.weight()
+        weight_post_process(mod.weight)
+        assert weight_post_process.dtype == torch.qint8, (
+            "Weight observer must have a dtype of qint8"
+        )
+        qweight = _quantize_weight(mod.weight.float(), weight_post_process)
+        # the __init__ call used is the one from derived classes and not the one from _ConvNd
+        qconv = cls(
+            mod.in_channels,
+            mod.out_channels,
+            mod.kernel_size,
+            mod.stride,
+            mod.padding,
+            mod.dilation,
+            mod.groups,
+            mod.bias is not None,
+            mod.padding_mode,
+        )
+        qconv.set_weight_bias(qweight, mod.bias)
+        if (
+            activation_post_process is None
+            or activation_post_process.dtype == torch.float
+        ):
+            return qconv  # dynamic quantization doesn't need scale/zero_point
+        else:
+            act_scale, act_zp = activation_post_process.calculate_qparams()
+            qconv.scale = float(act_scale)
+            qconv.zero_point = int(act_zp)
+            return qconv
+
+    @staticmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        if hasattr(mod, "weight_fake_quant"):
+            # assert type(mod) is cls.__QAT_MODULE, " nnq." + cls.__name__ + \
+            # ".from_float only works for " + cls.__QAT_MODULE.__name__
+            if type(mod) is cls._NNIQAT_CONV_BN_MODULE:
+                mod.weight, mod.bias = fuse_conv_bn_weights(
+                    mod.weight,
+                    mod.bias,
+                    mod.bn.running_mean,
+                    mod.bn.running_var,
+                    mod.bn.eps,
+                    mod.bn.weight,
+                    mod.bn.bias,
+                )
+            assert hasattr(mod, "activation_post_process"), (
+                "Input QAT module must have observer attached"
+            )
+            weight_post_process = mod.weight_fake_quant
+            activation_post_process = mod.activation_post_process
+        else:
+            assert type(mod) is cls._FLOAT_MODULE, (
+                " nnq."
+                + cls.__name__
+                + ".from_float only works for "
+                + cls._FLOAT_MODULE.__name__
+                + " but got:"
+                + str(type(mod))
+            )
+            assert hasattr(mod, "qconfig"), (
+                "Input float module must have qconfig defined."
+            )
+            activation_post_process = (
+                None
+                if not hasattr(mod, "activation_post_process")
+                else mod.activation_post_process
+            )
+            if type(mod) in [
+                cls._NNI_CONV_RELU_MODULE,
+                cls._NNI_CONV_ADD_MODULE,
+                cls._NNI_CONV_ADD_RELU_MODULE,
+            ]:
+                mod = mod[0]
+            weight_post_process = mod.qconfig.weight()
+        return cls.get_qconv(mod, activation_post_process, weight_post_process)
+
+    @classmethod
+    def from_reference(cls, ref_qconv, output_scale, output_zero_point):
+        r"""Create a (fbgemm/qnnpack) quantized module from a reference quantized module
+        Args:
+            ref_qconv (Module): a reference quantized  module, either produced by torch.ao.quantization
+                                utilities or provided by the user
+            output_scale (float): scale for output Tensor
+            output_zero_point (int): zero point for output Tensor
+        """
+        qconv = cls(
+            ref_qconv.in_channels,
+            ref_qconv.out_channels,
+            ref_qconv.kernel_size,  # type: ignore[arg-type]
+            ref_qconv.stride,  # type: ignore[arg-type]
+            ref_qconv.padding,  # type: ignore[arg-type]
+            ref_qconv.dilation,  # type: ignore[arg-type]
+            ref_qconv.groups,
+            ref_qconv.bias is not None,  # type: ignore[arg-type]
+            ref_qconv.padding_mode,
+            device=ref_qconv.weight.device,
+            dtype=ref_qconv.weight.dtype,
+        )
+        qweight = ref_qconv.get_quantized_weight()
+        qconv.set_weight_bias(qweight, ref_qconv.bias)
+        qconv.scale = float(output_scale)
+        qconv.zero_point = int(output_zero_point)
+        return qconv
+
+
+class Conv1d(_ConvNd):
+    r"""Applies a 1D convolution over a quantized input signal composed of
+    several quantized input planes.
+
+    For details on input arguments, parameters, and implementation see
+    :class:`~torch.nn.Conv1d`.
+
+    .. note::
+        Only `zeros` is supported for the :attr:`padding_mode` argument.
+
+    .. note::
+        Only `torch.quint8` is supported for the input data type.
+
+
+    Attributes:
+        weight (Tensor):     packed tensor derived from the learnable weight
+                             parameter.
+        scale (Tensor):      scalar for the output scale
+        zero_point (Tensor): scalar for the output zero point
+
+    See :class:`~torch.nn.Conv1d` for other attributes.
+
+    Examples::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
+        >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2)
+        >>> input = torch.randn(20, 16, 100)
+        >>> # quantize input to quint8
+        >>> # xdoctest: +SKIP
+        >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0,
+        ...                                     dtype=torch.quint8)
+        >>> output = m(q_input)
+
+    """
+
+    _FLOAT_MODULE: ClassVar[type[nn.Conv1d]] = nn.Conv1d
+    _NNIQAT_CONV_BN_MODULE: ClassVar[Optional[type[nn.Module]]] = nniqat.ConvBn1d
+    _NNI_CONV_RELU_MODULE: ClassVar[Optional[type[nn.Module]]] = nni.ConvReLU1d
+    _NNI_CONV_ADD_MODULE: ClassVar[Optional[type[nn.Module]]] = None
+    _NNI_CONV_ADD_RELU_MODULE: ClassVar[Optional[type[nn.Module]]] = None
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: _size_1_t,
+        stride: _size_1_t = 1,
+        padding: _size_1_t = 0,
+        dilation: _size_1_t = 1,
+        groups: int = 1,
+        bias: bool = True,
+        padding_mode: Literal["zeros", "reflect", "replicate", "circular"] = "zeros",
+        device=None,
+        dtype=None,
+    ):
+        factory_kwargs = {"device": device, "dtype": dtype}
+        kernel_size = _single(kernel_size)
+        stride = _single(stride)
+        # pyrefly: ignore [bad-assignment]
+        padding = padding if isinstance(padding, str) else _single(padding)
+        dilation = _single(dilation)
+
+        # Subclasses of _ConvNd needs to call _init rather than __init__. See
+        # discussion on PR #49702
+        super()._init(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            False,
+            _single(0),
+            groups,
+            bias,
+            padding_mode,
+            **factory_kwargs,
+        )
+
+    def _get_name(self):
+        return "QuantizedConv1d"
+
+    def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None:
+        if self.padding_mode == "zeros":
+            self._packed_params = torch.ops.quantized.conv1d_prepack(
+                w, b, self.stride, self.padding, self.dilation, self.groups
+            )
+        else:
+            self._packed_params = torch.ops.quantized.conv1d_prepack(
+                w, b, self.stride, _pair(0), self.dilation, self.groups
+            )
+
+    def _weight_bias(self):
+        w, b = torch.ops.quantized.conv1d_unpack(self._packed_params)
+        return w, b
+
+    def weight(self):
+        return self._weight_bias()[0]
+
+    def bias(self):
+        return self._weight_bias()[1]
+
+    def forward(self, input):
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 3:
+            raise ValueError("Input shape must be `(N, C, L)`!")
+        if self.padding_mode != "zeros":
+            # Padding in Conv1d is stored as (p, p), need to get (p,)
+            _reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding[:1])
+            input = F.pad(
+                input, _reversed_padding_repeated_twice, mode=self.padding_mode
+            )
+        return ops.quantized.conv1d(
+            input, self._packed_params, self.scale, self.zero_point
+        )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):  # type: ignore[override]
+        r"""Creates a quantized module from a float module or qparams_dict.
+
+        Args:
+            mod (Module): a float module, either produced by torch.ao.quantization
+              utilities or provided by the user
+        """
+        return _ConvNd.from_float(
+            cls, mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+
+class Conv2d(_ConvNd):
+    r"""Applies a 2D convolution over a quantized input signal composed of
+    several quantized input planes.
+
+    For details on input arguments, parameters, and implementation see
+    :class:`~torch.nn.Conv2d`.
+
+    .. note::
+        Only `zeros` is supported for the :attr:`padding_mode` argument.
+
+    .. note::
+        Only `torch.quint8` is supported for the input data type.
+
+
+    Attributes:
+        weight (Tensor):     packed tensor derived from the learnable weight
+                             parameter.
+        scale (Tensor):      scalar for the output scale
+        zero_point (Tensor): scalar for the output zero point
+
+    See :class:`~torch.nn.Conv2d` for other attributes.
+
+    Examples::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
+        >>> # With square kernels and equal stride
+        >>> m = nn.quantized.Conv2d(16, 33, 3, stride=2)
+        >>> # non-square kernels and unequal stride and with padding
+        >>> m = nn.quantized.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))
+        >>> # non-square kernels and unequal stride and with padding and dilation
+        >>> m = nn.quantized.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2), dilation=(3, 1))
+        >>> input = torch.randn(20, 16, 50, 100)
+        >>> # quantize input to quint8
+        >>> # xdoctest: +SKIP
+        >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
+        >>> output = m(q_input)
+
+    """
+
+    _FLOAT_MODULE: ClassVar[type[nn.Conv2d]] = nn.Conv2d
+    _NNIQAT_CONV_BN_MODULE: ClassVar[Optional[type[nn.Module]]] = nniqat.ConvBn2d
+    _NNI_CONV_RELU_MODULE: ClassVar[Optional[type[nn.Module]]] = nni.ConvReLU2d
+    _NNI_CONV_ADD_MODULE: ClassVar[type[nni.ConvAdd2d]] = nni.ConvAdd2d
+    _NNI_CONV_ADD_RELU_MODULE: ClassVar[type[nni.ConvAddReLU2d]] = nni.ConvAddReLU2d
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=True,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ):
+        factory_kwargs = {"device": device, "dtype": dtype}
+        kernel_size = _pair(kernel_size)
+        stride = _pair(stride)
+        padding = _pair(padding)
+        dilation = _pair(dilation)
+        # Subclasses of _ConvNd need to call _init rather than __init__. See
+        # discussion on PR #49702
+        super()._init(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            False,
+            _pair(0),
+            groups,
+            bias,
+            padding_mode,
+            **factory_kwargs,
+        )
+
+    def _get_name(self):
+        return "QuantizedConv2d"
+
+    def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None:
+        if self.padding_mode == "zeros":
+            self._packed_params = torch.ops.quantized.conv2d_prepack(
+                w, b, self.stride, self.padding, self.dilation, self.groups
+            )
+        else:
+            self._packed_params = torch.ops.quantized.conv2d_prepack(
+                w, b, self.stride, _pair(0), self.dilation, self.groups
+            )
+
+    def _weight_bias(self):
+        return self._packed_params.unpack()
+
+    def weight(self):
+        return self._weight_bias()[0]
+
+    def bias(self):
+        return self._weight_bias()[1]
+
+    def forward(self, input):
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 4:
+            raise ValueError("Input shape must be `(N, C, H, W)`!")
+        if self.padding_mode != "zeros":
+            _reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding)
+            input = F.pad(
+                input, _reversed_padding_repeated_twice, mode=self.padding_mode
+            )
+        return ops.quantized.conv2d(
+            input, self._packed_params, self.scale, self.zero_point
+        )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):  # type: ignore[override]
+        r"""Creates a quantized module from a float module or qparams_dict.
+
+        Args:
+            mod (Module): a float module, either produced by torch.ao.quantization
+              utilities or provided by the user
+        """
+        return _ConvNd.from_float(
+            cls, mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+
+class Conv3d(_ConvNd):
+    r"""Applies a 3D convolution over a quantized input signal composed of
+    several quantized input planes.
+
+    For details on input arguments, parameters, and implementation see
+    :class:`~torch.nn.Conv3d`.
+
+    .. note::
+        Only `zeros` is supported for the :attr:`padding_mode` argument.
+
+    .. note::
+        Only `torch.quint8` is supported for the input data type.
+
+
+    Attributes:
+        weight (Tensor):     packed tensor derived from the learnable weight
+                             parameter.
+        scale (Tensor):      scalar for the output scale
+        zero_point (Tensor): scalar for the output zero point
+
+    See :class:`~torch.nn.Conv3d` for other attributes.
+
+    Examples::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
+        >>> # With square kernels and equal stride
+        >>> m = nn.quantized.Conv3d(16, 33, 3, stride=2)
+        >>> # non-square kernels and unequal stride and with padding
+        >>> m = nn.quantized.Conv3d(16, 33, (3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2))
+        >>> # non-square kernels and unequal stride and with padding and dilation
+        >>> m = nn.quantized.Conv3d(16, 33, (3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2), dilation=(1, 2, 2))
+        >>> input = torch.randn(20, 16, 56, 56, 56)
+        >>> # quantize input to quint8
+        >>> # xdoctest: +SKIP
+        >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
+        >>> output = m(q_input)
+
+    """
+
+    _FLOAT_MODULE: ClassVar[type[nn.Conv3d]] = nn.Conv3d
+    _NNIQAT_CONV_BN_MODULE: ClassVar[Optional[type[nn.Module]]] = nniqat.ConvBn3d
+    _NNI_CONV_RELU_MODULE: ClassVar[Optional[type[nn.Module]]] = nni.ConvReLU3d
+    _NNI_CONV_ADD_MODULE: ClassVar[Optional[type[nn.Module]]] = None
+    _NNI_CONV_ADD_RELU_MODULE: ClassVar[Optional[type[nn.Module]]] = None
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=True,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ):
+        assert padding_mode != "reflect", "Conv3d does not support reflection padding"
+        factory_kwargs = {"device": device, "dtype": dtype}
+        kernel_size = _triple(kernel_size)
+        stride = _triple(stride)
+        padding = _triple(padding)
+        dilation = _triple(dilation)
+        # Subclasses of _ConvNd need to call _init rather than __init__. See
+        # discussion on PR #49702
+        super()._init(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            False,
+            _triple(0),
+            groups,
+            bias,
+            padding_mode,
+            **factory_kwargs,
+        )
+
+    def _get_name(self):
+        return "QuantizedConv3d"
+
+    def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None:
+        if self.padding_mode == "zeros":
+            self._packed_params = torch.ops.quantized.conv3d_prepack(
+                w, b, self.stride, self.padding, self.dilation, self.groups
+            )
+        else:
+            self._packed_params = torch.ops.quantized.conv3d_prepack(
+                w, b, self.stride, _triple(0), self.dilation, self.groups
+            )
+
+    def _weight_bias(self):
+        return self._packed_params.unpack()
+
+    def weight(self):
+        return self._weight_bias()[0]
+
+    def bias(self):
+        return self._weight_bias()[1]
+
+    def forward(self, input):
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 5:
+            raise ValueError("Input shape must be `(N, C, D, H, W)`!")
+        if self.padding_mode != "zeros":
+            _reversed_padding_repeated_twice = _reverse_repeat_padding(self.padding)
+            input = F.pad(
+                input, _reversed_padding_repeated_twice, mode=self.padding_mode
+            )
+        return ops.quantized.conv3d(
+            input, self._packed_params, self.scale, self.zero_point
+        )
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):  # type: ignore[override]
+        r"""Creates a quantized module from a float module or qparams_dict.
+
+        Args:
+            mod (Module): a float module, either produced by torch.ao.quantization
+              utilities or provided by the user
+        """
+        return _ConvNd.from_float(
+            cls, mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+        )
+
+
+# === Transposed Convolutions ===
+
+
+class _ConvTransposeNd(_ConvNd):
+    _FLOAT_MODULE: ClassVar[type[nn.modules.conv._ConvNd]]
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride,
+        padding,
+        dilation,
+        transposed,
+        output_padding,
+        groups,
+        bias,
+        padding_mode,
+        device=None,
+        dtype=None,
+    ):
+        if padding_mode != "zeros":
+            raise ValueError(
+                f'Only "zeros" padding mode is supported for {self.__class__.__name__}'
+            )
+        factory_kwargs = {"device": device, "dtype": dtype}
+        # Subclasses of _ConvNd need to call _init rather than __init__. See
+        # discussion on PR #49702
+        super()._init(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            transposed,
+            output_padding,
+            groups,
+            bias,
+            padding_mode,
+            **factory_kwargs,
+        )
+
+    def _input_padding(
+        self, kernel_size: list[int], dilation: list[int], padding: list[int]
+    ) -> list[int]:
+        res = torch.jit.annotate(list[int], [])
+        for kdx in range(len(kernel_size)):
+            pad = dilation[kdx] * (kernel_size[kdx] - 1) - padding[kdx]
+            res.append(pad)
+        return res
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):  # type: ignore[override]
+        r"""Creates a quantized module from a float module or qparams_dict.
+        Args:
+            mod (Module): a float module, either produced by torch.ao.quantization
+              utilities or provided by the user
+        """
+        # derived classes override cls._FLOAT_MODULE attribute
+        msg = (
+            " nnq."
+            + cls.__name__
+            + ".from_float only works for "
+            + cls._FLOAT_MODULE.__name__  # type: ignore[attr-defined]
+        )
+        assert type(mod) is cls._FLOAT_MODULE, msg
+        assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined."
+        weight_post_process = mod.qconfig.weight()  # type: ignore[operator, union-attr]
+        weight_post_process(mod.weight)
+        assert weight_post_process.dtype == torch.qint8, (
+            "Weight observer must have a dtype of qint8"
+        )
+        qweight = _quantize_weight(mod.weight.float(), weight_post_process)
+        # the __init__ call used is the one from derived classes and not the one from _ConvTransposeNd
+        qconv = cls(
+            mod.in_channels,
+            mod.out_channels,
+            mod.kernel_size,  # type: ignore[call-arg]
+            mod.stride,
+            mod.padding,
+            mod.output_padding,
+            mod.groups,
+            mod.bias is not None,
+            mod.dilation,
+            mod.padding_mode,
+        )
+        qconv.set_weight_bias(qweight, mod.bias)
+        if (
+            not hasattr(mod, "activation_post_process")
+            or mod.activation_post_process.dtype == torch.float
+        ):
+            return qconv  # dynamic quantization doesn't need scale/zero_point
+        else:
+            act_scale, act_zp = mod.activation_post_process.calculate_qparams()  # type: ignore[operator, union-attr]
+            qconv.scale = float(act_scale)
+            qconv.zero_point = int(act_zp)
+            return qconv
+
+    @staticmethod
+    def from_reference(cls, ref_qconvt, output_scale, output_zero_point):  # type: ignore[override]
+        r"""Create a (fbgemm/qnnpack) quantized module from a reference quantized module
+        Args:
+            ref_qconvt (Module): a reference quantized  module, either produced by torch.ao.quantization
+                                 utilities or provided by the user
+            output_scale (float): scale for output Tensor
+            output_zero_point (int): zero point for output Tensor
+        """
+        qconv = cls(
+            ref_qconvt.in_channels,
+            ref_qconvt.out_channels,
+            ref_qconvt.kernel_size,  # type: ignore[arg-type]
+            ref_qconvt.stride,  # type: ignore[arg-type]
+            ref_qconvt.padding,  # type: ignore[arg-type]
+            ref_qconvt.output_padding,  # type: ignore[arg-type]
+            ref_qconvt.groups,
+            ref_qconvt.bias is not None,  # type: ignore[arg-type]
+            ref_qconvt.dilation,  # type: ignore[arg-type]
+            ref_qconvt.padding_mode,
+            device=ref_qconvt.weight.device,
+            dtype=ref_qconvt.weight.dtype,
+        )
+        qweight = ref_qconvt.get_quantized_weight()
+        qconv.set_weight_bias(qweight, ref_qconvt.bias)
+        qconv.scale = float(output_scale)
+        qconv.zero_point = int(output_zero_point)
+        return qconv
+
+
+class ConvTranspose1d(_ConvTransposeNd):
+    r"""Applies a 1D transposed convolution operator over an input image
+    composed of several input planes.
+    For details on input arguments, parameters, and implementation see
+    :class:`~torch.nn.ConvTranspose1d`.
+
+    .. note:: Currently only the QNNPACK engine is implemented.
+        Please, set the `torch.backends.quantized.engine = 'qnnpack'`
+
+    For special notes, please, see :class:`~torch.ao.nn.quantized.Conv1d`
+
+    Attributes:
+        weight (Tensor):     packed tensor derived from the learnable weight
+                             parameter.
+        scale (Tensor):      scalar for the output scale
+        zero_point (Tensor): scalar for the output zero point
+    See :class:`~torch.nn.ConvTranspose2d` for other attributes.
+
+    Examples::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
+        >>> torch.backends.quantized.engine = 'qnnpack'
+        >>> from torch.ao.nn import quantized as nnq
+        >>> # With square kernels and equal stride
+        >>> m = nnq.ConvTranspose1d(16, 33, 3, stride=2)
+        >>> # non-square kernels and unequal stride and with padding
+        >>> m = nnq.ConvTranspose1d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))
+        >>> input = torch.randn(20, 16, 50)
+        >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
+        >>> output = m(q_input)
+        >>> # exact output size can be also specified as an argument
+        >>> input = torch.randn(1, 16, 12)
+        >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
+        >>> downsample = nnq.Conv1d(16, 16, 3, stride=2, padding=1)
+        >>> upsample = nnq.ConvTranspose1d(16, 16, 3, stride=2, padding=1)
+        >>> h = downsample(q_input)
+        >>> h.size()
+        torch.Size([1, 16, 6])
+        >>> # xdoctest: +SKIP("FIXME: output_size is not a parameter)
+        >>> output = upsample(h, output_size=input.size())
+        >>> output.size()
+        torch.Size([1, 16, 12])
+    """
+
+    _FLOAT_MODULE: ClassVar[type[nn.ConvTranspose1d]] = nn.ConvTranspose1d
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        output_padding=0,
+        groups=1,
+        bias=True,
+        dilation=1,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ):
+        factory_kwargs = {"device": device, "dtype": dtype}
+        kernel_size = _single(kernel_size)
+        stride = _single(stride)
+        padding = _single(padding)
+        dilation = _single(dilation)
+        output_padding = _single(output_padding)
+
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            True,
+            output_padding,
+            groups,
+            bias,
+            padding_mode,
+            **factory_kwargs,
+        )
+
+    def _get_name(self):
+        return "QuantizedConvTranspose1d"
+
+    def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None:
+        self._packed_params = torch.ops.quantized.conv_transpose1d_prepack(
+            w,
+            b,
+            self.stride,
+            self.padding,
+            self.output_padding,
+            self.dilation,
+            self.groups,
+        )
+
+    def _weight_bias(self):
+        w, b = torch.ops.quantized.conv_transpose1d_unpack(self._packed_params)
+        return w, b
+
+    def weight(self):
+        (w, _) = self._weight_bias()
+        return w
+
+    def bias(self):
+        (_, b) = self._weight_bias()
+        return b
+
+    def forward(self, input):
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 3:
+            raise ValueError("Input shape must be `(N, C, L)`!")
+        return torch.ops.quantized.conv_transpose1d(
+            input, self._packed_params, self.scale, self.zero_point
+        )
+
+    @classmethod
+    def from_reference(cls, ref_qconvt, output_scale, output_zero_point):  # type: ignore[override]
+        return _ConvTransposeNd.from_reference(
+            cls, ref_qconvt, output_scale, output_zero_point
+        )
+
+
+class ConvTranspose2d(_ConvTransposeNd):
+    r"""Applies a 2D transposed convolution operator over an input image
+    composed of several input planes.
+    For details on input arguments, parameters, and implementation see
+    :class:`~torch.nn.ConvTranspose2d`.
+
+    For special notes, please, see :class:`~torch.ao.nn.quantized.Conv2d`
+
+    Attributes:
+        weight (Tensor):     packed tensor derived from the learnable weight
+                             parameter.
+        scale (Tensor):      scalar for the output scale
+        zero_point (Tensor): scalar for the output zero point
+    See :class:`~torch.nn.ConvTranspose2d` for other attributes.
+
+    Examples::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
+        >>> # QNNPACK or FBGEMM as backend
+        >>> torch.backends.quantized.engine = 'qnnpack'
+        >>> # With square kernels and equal stride
+        >>> import torch.ao.nn.quantized as nnq
+        >>> m = nnq.ConvTranspose2d(16, 33, 3, stride=2)
+        >>> # non-square kernels and unequal stride and with padding
+        >>> m = nnq.ConvTranspose2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))
+        >>> input = torch.randn(20, 16, 50, 100)
+        >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
+        >>> output = m(q_input)
+        >>> # exact output size can be also specified as an argument
+        >>> input = torch.randn(1, 16, 12, 12)
+        >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
+        >>> downsample = nnq.Conv2d(16, 16, 3, stride=2, padding=1)
+        >>> upsample = nnq.ConvTranspose2d(16, 16, 3, stride=2, padding=1)
+        >>> h = downsample(q_input)
+        >>> h.size()
+        torch.Size([1, 16, 6, 6])
+        >>> # xdoctest: +SKIP("FIXME: output_size is not a parameter)
+        >>> output = upsample(h, output_size=input.size())
+        >>> output.size()
+        torch.Size([1, 16, 12, 12])
+    """
+
+    _FLOAT_MODULE: ClassVar[type[nn.ConvTranspose2d]] = nn.ConvTranspose2d
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        output_padding=0,
+        groups=1,
+        bias=True,
+        dilation=1,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ):
+        factory_kwargs = {"device": device, "dtype": dtype}
+        kernel_size = _pair(kernel_size)
+        stride = _pair(stride)
+        padding = _pair(padding)
+        dilation = _pair(dilation)
+        output_padding = _pair(output_padding)
+
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            True,
+            output_padding,
+            groups,
+            bias,
+            padding_mode,
+            **factory_kwargs,
+        )
+
+    def _get_name(self):
+        return "QuantizedConvTranspose2d"
+
+    def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None:
+        self._packed_params = torch.ops.quantized.conv_transpose2d_prepack(
+            w,
+            b,
+            self.stride,
+            self.padding,
+            self.output_padding,
+            self.dilation,
+            self.groups,
+        )
+
+    def _weight_bias(self):
+        w, b = torch.ops.quantized.conv2d_unpack(self._packed_params)
+        return w, b
+
+    def weight(self):
+        (w, _) = self._weight_bias()
+        return w
+
+    def bias(self):
+        (_, b) = self._weight_bias()
+        return b
+
+    def forward(self, input):
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 4:
+            raise ValueError("Input shape must be `(N, C, H, W)`!")
+        return ops.quantized.conv_transpose2d(
+            input, self._packed_params, self.scale, self.zero_point
+        )
+
+    @classmethod
+    def from_reference(cls, ref_qconvt, output_scale, output_zero_point):  # type: ignore[override]
+        return _ConvTransposeNd.from_reference(
+            cls, ref_qconvt, output_scale, output_zero_point
+        )
+
+
+class ConvTranspose3d(_ConvTransposeNd):
+    r"""Applies a 3D transposed convolution operator over an input image
+    composed of several input planes.
+    For details on input arguments, parameters, and implementation see
+    :class:`~torch.nn.ConvTranspose3d`.
+
+    .. note:: Currently only the FBGEMM engine is implemented.
+        Please, set the `torch.backends.quantized.engine = 'fbgemm'`
+
+    For special notes, please, see :class:`~torch.ao.nn.quantized.Conv3d`
+
+    Attributes:
+        weight (Tensor):     packed tensor derived from the learnable weight
+                             parameter.
+        scale (Tensor):      scalar for the output scale
+        zero_point (Tensor): scalar for the output zero point
+    See :class:`~torch.nn.ConvTranspose3d` for other attributes.
+
+    Examples::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
+        >>> torch.backends.quantized.engine = 'fbgemm'
+        >>> from torch.ao.nn import quantized as nnq
+        >>> # With cubic kernels and equal stride
+        >>> m = nnq.ConvTranspose3d(16, 33, 3, stride=2)
+        >>> # non-cubic kernels and unequal stride and with padding
+        >>> m = nnq.ConvTranspose3d(16, 33, (3, 3, 5), stride=(2, 1, 1), padding=(4, 2, 2))
+        >>> input = torch.randn(20, 16, 50, 100, 100)
+        >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
+        >>> output = m(q_input)
+        >>> # exact output size can be also specified as an argument
+        >>> input = torch.randn(1, 16, 12, 12, 12)
+        >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
+        >>> downsample = nnq.Conv3d(16, 16, 3, stride=2, padding=1)
+        >>> upsample = nnq.ConvTranspose3d(16, 16, 3, stride=2, padding=1)
+        >>> h = downsample(q_input)
+        >>> h.size()
+        torch.Size([1, 16, 6, 6, 6])
+        >>> # xdoctest: +SKIP("FIXME: output_size is not a parameter)
+        >>> output = upsample(h, output_size=input.size())
+        >>> output.size()
+        torch.Size([1, 16, 12, 12, 12])
+    """
+
+    _FLOAT_MODULE: ClassVar[type[nn.ConvTranspose3d]] = nn.ConvTranspose3d
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        output_padding=0,
+        groups=1,
+        bias=True,
+        dilation=1,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+    ):
+        factory_kwargs = {"device": device, "dtype": dtype}
+        kernel_size = _triple(kernel_size)
+        stride = _triple(stride)
+        padding = _triple(padding)
+        dilation = _triple(dilation)
+        output_padding = _triple(output_padding)
+
+        super().__init__(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            True,
+            output_padding,
+            groups,
+            bias,
+            padding_mode,
+            **factory_kwargs,
+        )
+
+    def _get_name(self):
+        return "QuantizedConvTranspose3d"
+
+    def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor]) -> None:
+        self._packed_params = torch.ops.quantized.conv_transpose3d_prepack(
+            w,
+            b,
+            self.stride,
+            self.padding,
+            self.output_padding,
+            self.dilation,
+            self.groups,
+        )
+
+    def _weight_bias(self):
+        w, b = torch.ops.quantized.conv3d_unpack(self._packed_params)
+        return w, b
+
+    def weight(self):
+        (w, _) = self._weight_bias()
+        return w
+
+    def bias(self):
+        (_, b) = self._weight_bias()
+        return b
+
+    def forward(self, input):
+        # Temporarily using len(shape) instead of ndim due to JIT issue
+        # https://github.com/pytorch/pytorch/issues/23890
+        if len(input.shape) != 5:
+            raise ValueError("Input shape must be `(N, C, T, H, W)`!")
+        return ops.quantized.conv_transpose3d(
+            input, self._packed_params, self.scale, self.zero_point
+        )
+
+    @classmethod
+    def from_reference(cls, ref_qconvt, output_scale, output_zero_point):  # type: ignore[override]
+        return _ConvTransposeNd.from_reference(
+            cls, ref_qconvt, output_scale, output_zero_point
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/dropout.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/dropout.py
new file mode 100644
index 0000000000000000000000000000000000000000..3744ca30d5a49ba92cbb86690f2683af02d594fe
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/dropout.py
@@ -0,0 +1,30 @@
+# mypy: allow-untyped-defs
+import torch
+
+
+__all__ = ["Dropout"]
+
+
+class Dropout(torch.nn.Dropout):
+    r"""This is the quantized equivalent of :class:`~torch.nn.Dropout`.
+        And this is a placeholder to enable models where fp32 tensors
+        had dropout to work with quantized tensors in train and eval mode.
+
+    Args:
+        p: probability of an element to be zeroed
+        inplace: can optionally do the operation in-place. Default: ``False``
+    """
+
+    def forward(self, input):
+        return input
+
+    def _get_name(self):
+        return "QuantizedDropout"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        return cls(mod.p, mod.inplace)
+
+    @classmethod
+    def from_reference(cls, mod, scale, zero_point):
+        return cls(mod.p, mod.inplace)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e843653ed27a49fa62d0f7e3408a7ac04f48fdf
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py
@@ -0,0 +1,413 @@
+# mypy: allow-untyped-defs
+import torch
+import torch.nn as nn
+from torch import Tensor  # noqa: F401
+from torch._jit_internal import List, Optional  # noqa: F401
+
+from .utils import _hide_packed_params_repr, _quantize_weight
+
+
+__all__ = ["EmbeddingPackedParams", "Embedding", "EmbeddingBag"]
+
+
+class EmbeddingPackedParams(torch.nn.Module):
+    _version = 1
+
+    def __init__(self, num_embeddings, embedding_dim, dtype=torch.quint8):
+        super().__init__()
+        self.dtype = dtype
+        if self.dtype in [torch.quint8, torch.quint4x2]:
+            scales = torch.ones(num_embeddings, dtype=torch.float)
+            zero_points = torch.zeros(num_embeddings, dtype=torch.float)
+            wq = torch._empty_per_channel_affine_quantized(
+                [num_embeddings, embedding_dim],
+                scales=scales,
+                zero_points=zero_points,
+                axis=0,
+                dtype=self.dtype,
+            )
+            self.set_weight(wq)
+        else:
+            raise NotImplementedError(
+                f"Unsupported dtype on quantized embedding! Supports quint8 and quint4x2. Got dtype: {dtype}"
+            )
+
+    @torch.jit.export
+    def set_weight(self, weight: torch.Tensor) -> None:
+        if self.dtype in [torch.quint8, torch.quint4x2]:
+            self._packed_weight = torch.ops.quantized.embedding_bag_prepack(weight)
+        else:
+            raise NotImplementedError(
+                "Unsupported dtype for quantized embedding prepack! Supports quint8 and quint4x2."
+            )
+
+    @torch.jit.export
+    def _weight(self):
+        if self.dtype in [torch.quint8, torch.quint4x2]:
+            return torch.ops.quantized.embedding_bag_unpack(self._packed_weight)
+        else:
+            raise NotImplementedError(
+                "Unsupported dtype for quantized embedding unpack! Supports quint8 and quint4x2."
+            )
+
+    def forward(self, x):
+        return x
+
+    # Version 1
+    #   self
+    #   |--- _packed_weight : Tensor representing weight of EmbeddingPackedParamsBase
+    #   |--- dtype : torch.dtype
+
+    def _save_to_state_dict(self, destination, prefix, keep_vars):
+        super()._save_to_state_dict(destination, prefix, keep_vars)
+        destination[prefix + "dtype"] = self.dtype
+        destination[prefix + "_packed_weight"] = self._weight()
+
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        self.dtype = state_dict[prefix + "dtype"]
+        state_dict.pop(prefix + "dtype")
+
+        weight = state_dict[prefix + "_packed_weight"]
+        state_dict.pop(prefix + "_packed_weight")
+        self.set_weight(weight)
+
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            False,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+    def __repr__(self):
+        return self._weight().__repr__()
+
+
+class Embedding(torch.nn.Module):
+    r"""
+    A quantized Embedding module with quantized packed weights as inputs.
+    We adopt the same interface as `torch.nn.Embedding`, please see
+    https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html for documentation.
+
+    Similar to :class:`~torch.nn.Embedding`, attributes will be randomly
+    initialized at module creation time and will be overwritten later
+
+    Attributes:
+        weight (Tensor): the non-learnable quantized weights of the module of
+                         shape :math:`(\text{num\_embeddings}, \text{embedding\_dim})`.
+
+    Examples::
+        >>> m = nn.quantized.Embedding(num_embeddings=10, embedding_dim=12)
+        >>> indices = torch.tensor([9, 6, 5, 7, 8, 8, 9, 2, 8])
+        >>> output = m(indices)
+        >>> print(output.size())
+        torch.Size([9, 12])
+
+    """
+
+    _version = 1
+
+    def __init__(
+        self,
+        num_embeddings: int,
+        embedding_dim: int,
+        padding_idx: Optional[int] = None,
+        max_norm: Optional[float] = None,
+        norm_type: float = 2.0,
+        scale_grad_by_freq: bool = False,
+        sparse: bool = False,
+        _weight: Optional[Tensor] = None,
+        dtype=torch.quint8,
+    ) -> None:
+        super().__init__()
+        self.num_embeddings = num_embeddings
+        self.embedding_dim = embedding_dim
+        self.dtype = dtype
+
+        if _weight is None:
+            scales = torch.ones(num_embeddings, dtype=torch.float)
+            zero_points = torch.zeros(num_embeddings, dtype=torch.float)
+            qweight = torch._empty_per_channel_affine_quantized(
+                [num_embeddings, embedding_dim],
+                scales=scales,
+                zero_points=zero_points,
+                axis=0,
+                dtype=torch.quint8,
+            )
+        else:
+            assert list(_weight.shape) == [
+                num_embeddings,
+                embedding_dim,
+            ], "Shape of weight does not match num_embeddings and embedding_dim"
+            qweight = _weight
+
+        self._packed_params = EmbeddingPackedParams(
+            num_embeddings, embedding_dim, dtype
+        )
+        self._packed_params.set_weight(qweight)
+
+    def forward(self, indices: Tensor) -> Tensor:
+        if self.dtype == torch.quint4x2:
+            return torch.ops.quantized.embedding_4bit(
+                self._packed_params._packed_weight, indices
+            )
+        else:
+            return torch.ops.quantized.embedding_byte(
+                self._packed_params._packed_weight, indices
+            )
+
+    def _get_name(self):
+        return "QuantizedEmbedding"
+
+    def __repr__(self):
+        return _hide_packed_params_repr(self, EmbeddingPackedParams)
+
+    def extra_repr(self):
+        extra_repr_str = (
+            f"num_embeddings={self.num_embeddings}, embedding_dim={self.embedding_dim}, "
+            f"dtype={self._packed_params.dtype}, qscheme={self.weight().qscheme()}"
+        )
+
+        return extra_repr_str
+
+    def set_weight(self, w: torch.Tensor) -> None:
+        self._packed_params.set_weight(w)
+
+    def weight(self):
+        return self._packed_params._weight()
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        r"""Create a quantized embedding module from a float module
+
+        Args:
+            mod (Module): a float module, either produced by torch.ao.quantization
+                          utilities or provided by user
+        """
+        if hasattr(mod, "weight_fake_quant"):
+            assert type(mod) is torch.ao.nn.qat.Embedding, (
+                "nnq."
+                + cls.__name__
+                + ".from_float "
+                + "with fake quant only works for "
+                + torch.ao.nn.qat.Embedding.__name__
+            )
+            weight_observer = mod.weight_fake_quant
+        else:
+            assert type(mod) is nn.Embedding, (
+                "nnq."
+                + cls.__name__
+                + ".from_float only works for "
+                + nn.Embedding.__name__
+            )
+            assert hasattr(mod, "qconfig"), (
+                "Embedding input float module must have qconfig defined"
+            )
+            from torch.ao.quantization import float_qparams_weight_only_qconfig
+
+            if mod.qconfig is not None and mod.qconfig.weight is not None:  # type: ignore[union-attr]
+                weight_observer = mod.qconfig.weight()  # type: ignore[union-attr, operator]
+            else:
+                weight_observer = float_qparams_weight_only_qconfig.weight()
+
+        dtype = weight_observer.dtype
+        is_float_qparams_qconfig = (
+            weight_observer.qscheme == torch.per_channel_affine_float_qparams
+        )
+        assert is_float_qparams_qconfig, (
+            "Embedding quantization is only supported with float_qparams_weight_only_qconfig."
+        )
+
+        assert dtype == torch.quint8 or dtype == torch.quint4x2, (
+            f"The only supported dtype for nnq.Embedding is torch.quint8 and torch.quint4x2, got {dtype}"
+        )
+
+        # Run the observer to calculate qparams.
+        weight_observer(mod.weight)
+        qweight = _quantize_weight(mod.weight.float(), weight_observer)
+
+        # Create quantized Embedding module and pass in the quantized weight
+        qembedding = Embedding(mod.num_embeddings, mod.embedding_dim)
+        qembedding.set_weight(qweight)
+        return qembedding
+
+    @classmethod
+    def from_reference(cls, ref_embedding):
+        qembedding = cls(
+            ref_embedding.num_embeddings,
+            ref_embedding.embedding_dim,
+            ref_embedding.padding_idx,
+            ref_embedding.max_norm,
+            ref_embedding.norm_type,
+            ref_embedding.scale_grad_by_freq,
+            ref_embedding.sparse,
+            ref_embedding.get_quantized_weight(),
+            ref_embedding.weight_dtype,
+        )
+        return qembedding
+
+
+class EmbeddingBag(Embedding):
+    r"""
+    A quantized EmbeddingBag module with quantized packed weights as inputs.
+    We adopt the same interface as `torch.nn.EmbeddingBag`, please see
+    https://pytorch.org/docs/stable/generated/torch.nn.EmbeddingBag.html for documentation.
+
+    Similar to :class:`~torch.nn.EmbeddingBag`, attributes will be randomly
+    initialized at module creation time and will be overwritten later
+
+    Attributes:
+        weight (Tensor): the non-learnable quantized weights of the module of
+                         shape :math:`(\text{num\_embeddings}, \text{embedding\_dim})`.
+
+    Examples::
+        >>> m = nn.quantized.EmbeddingBag(num_embeddings=10, embedding_dim=12, include_last_offset=True, mode='sum')
+        >>> indices = torch.tensor([9, 6, 5, 7, 8, 8, 9, 2, 8, 6, 6, 9, 1, 6, 8, 8, 3, 2, 3, 6, 3, 6, 5, 7, 0, 8, 4, 6, 5, 8, 2, 3])
+        >>> offsets = torch.tensor([0, 19, 20, 28, 28, 32])
+        >>> output = m(indices, offsets)
+        >>> print(output.size())
+        torch.Size([5, 12])
+
+    """
+
+    _version = 1
+
+    def __init__(
+        self,
+        num_embeddings: int,
+        embedding_dim: int,
+        max_norm: Optional[float] = None,
+        norm_type: float = 2.0,
+        scale_grad_by_freq: bool = False,
+        mode: str = "sum",
+        sparse: bool = False,
+        _weight: Optional[Tensor] = None,
+        include_last_offset: bool = False,
+        dtype=torch.quint8,
+    ) -> None:
+        super().__init__(num_embeddings, embedding_dim, _weight=_weight, dtype=dtype)
+
+        self.mode = mode
+        self.pruned_weights = False
+        self.include_last_offset = include_last_offset
+        self.dtype = dtype
+
+    def forward(
+        self,
+        indices: Tensor,
+        offsets: Optional[Tensor] = None,
+        per_sample_weights: Optional[Tensor] = None,
+        compressed_indices_mapping: Optional[Tensor] = None,
+    ) -> Tensor:
+        if self.dtype == torch.quint4x2:
+            return torch.ops.quantized.embedding_bag_4bit(
+                self._packed_params._packed_weight,
+                indices,
+                offsets,
+                False,
+                0,
+                self.pruned_weights,
+                per_sample_weights,
+                compressed_indices_mapping,
+                self.include_last_offset,
+            )
+        else:
+            return torch.ops.quantized.embedding_bag_byte(
+                self._packed_params._packed_weight,
+                indices,
+                offsets,
+                False,
+                0,
+                self.pruned_weights,
+                per_sample_weights,
+                compressed_indices_mapping,
+                self.include_last_offset,
+            )
+
+    def _get_name(self):
+        return "QuantizedEmbeddingBag"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        r"""Create a quantized embedding_bag module from a float module
+
+        Args:
+            mod (Module): a float module, either produced by torch.ao.quantization
+                          utilities or provided by user
+        """
+        if hasattr(mod, "weight_fake_quant"):
+            weight_observer = mod.weight_fake_quant
+        else:
+            assert type(mod) is nn.EmbeddingBag, (
+                "nnq."
+                + cls.__name__
+                + ".from_float only works for "
+                + nn.EmbeddingBag.__name__
+            )
+            assert hasattr(mod, "qconfig"), (
+                "EmbeddingBag input float module must have qconfig defined"
+            )
+            from torch.ao.quantization.qconfig import float_qparams_weight_only_qconfig
+
+            if mod.qconfig is not None and mod.qconfig.weight is not None:  # type: ignore[union-attr]
+                weight_observer = mod.qconfig.weight()  # type: ignore[union-attr, operator]
+            else:
+                weight_observer = float_qparams_weight_only_qconfig.weight()
+
+        dtype = weight_observer.dtype
+        is_float_qparams_qconfig = (
+            weight_observer.qscheme == torch.per_channel_affine_float_qparams
+        )
+        assert is_float_qparams_qconfig, (
+            "EmbeddingBag quantization is only supported with float_qparams_weight_only_qconfig."
+        )
+
+        assert dtype == torch.quint8 or dtype == torch.quint4x2, (
+            f"The only supported dtype for nnq.EmbeddingBag is torch.quint8 and torch.quint4x2, got {dtype}"
+        )
+
+        # Run the observer to calculate qparams.
+        weight_observer(mod.weight)
+        qweight = _quantize_weight(mod.weight.float(), weight_observer)
+
+        # Create quantized EmbeddingBag module and pass in the quantized weight
+        qembedding_bag = EmbeddingBag(
+            mod.num_embeddings,
+            mod.embedding_dim,
+            max_norm=mod.max_norm,
+            norm_type=mod.norm_type,
+            scale_grad_by_freq=mod.scale_grad_by_freq,
+            mode=mod.mode,
+            sparse=mod.sparse,
+            include_last_offset=mod.include_last_offset,
+            dtype=dtype,
+        )
+        qembedding_bag.set_weight(qweight)
+        return qembedding_bag
+
+    @classmethod
+    def from_reference(cls, ref_embedding_bag):
+        qembedding_bag = cls(
+            ref_embedding_bag.num_embeddings,
+            ref_embedding_bag.embedding_dim,
+            ref_embedding_bag.max_norm,
+            ref_embedding_bag.norm_type,
+            ref_embedding_bag.scale_grad_by_freq,
+            ref_embedding_bag.mode,
+            ref_embedding_bag.sparse,
+            ref_embedding_bag.get_quantized_weight(),
+            ref_embedding_bag.include_last_offset,
+            ref_embedding_bag.weight_dtype,
+        )
+        return qembedding_bag
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/functional_modules.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/functional_modules.py
new file mode 100644
index 0000000000000000000000000000000000000000..acb578d0cc7989ecedd92fcb30664d50b4c18f87
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/functional_modules.py
@@ -0,0 +1,298 @@
+# mypy: allow-untyped-defs
+
+import torch
+from torch import Tensor
+from torch._ops import ops
+
+
+__all__ = ["FloatFunctional", "FXFloatFunctional", "QFunctional"]
+
+
+class FloatFunctional(torch.nn.Module):
+    r"""State collector class for float operations.
+
+    The instance of this class can be used instead of the ``torch.`` prefix for
+    some operations. See example usage below.
+
+    .. note::
+
+        This class does not provide a ``forward`` hook. Instead, you must use
+        one of the underlying functions (e.g. ``add``).
+
+    Examples::
+
+        >>> f_add = FloatFunctional()
+        >>> a = torch.tensor(3.0)
+        >>> b = torch.tensor(4.0)
+        >>> f_add.add(a, b)  # Equivalent to ``torch.add(a, b)``
+
+    Valid operation names:
+        - add
+        - cat
+        - mul
+        - add_relu
+        - add_scalar
+        - mul_scalar
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.activation_post_process = torch.nn.Identity()
+
+    def forward(self, x):
+        raise RuntimeError(
+            "FloatFunctional is not intended to use the "
+            + "'forward'. Please use the underlying operation"
+        )
+
+    r"""Operation equivalent to ``torch.add(Tensor, Tensor)``"""
+
+    def add(self, x: Tensor, y: Tensor) -> Tensor:
+        r = torch.add(x, y)
+        r = self.activation_post_process(r)
+        return r
+
+    r"""Operation equivalent to ``torch.add(Tensor, float)``"""
+
+    def add_scalar(self, x: Tensor, y: float) -> Tensor:
+        r = torch.add(x, y)
+        # Note: this operation is not observed because the observation is not
+        # needed for the quantized op.
+        return r
+
+    r"""Operation equivalent to ``torch.mul(Tensor, Tensor)``"""
+
+    def mul(self, x: Tensor, y: Tensor) -> Tensor:
+        r = torch.mul(x, y)
+        r = self.activation_post_process(r)
+        return r
+
+    r"""Operation equivalent to ``torch.mul(Tensor, float)``"""
+
+    def mul_scalar(self, x: Tensor, y: float) -> Tensor:
+        r = torch.mul(x, y)
+        # Note: this operation is not observed because the observation is not
+        # needed for the quantized op.
+        return r
+
+    r"""Operation equivalent to ``torch.cat``"""
+
+    def cat(self, x: list[Tensor], dim: int = 0) -> Tensor:
+        r = torch.cat(x, dim=dim)
+        r = self.activation_post_process(r)
+        return r
+
+    r"""Operation equivalent to ``relu(torch.add(x,y))``"""
+
+    def add_relu(self, x: Tensor, y: Tensor) -> Tensor:
+        r = torch.add(x, y)
+        r = torch.nn.functional.relu(r)
+        r = self.activation_post_process(r)
+        return r
+
+    r"""Operation equivalent to ``torch.matmul(Tensor, Tensor)``"""
+
+    def matmul(self, x: Tensor, y: Tensor) -> Tensor:
+        r = torch.matmul(x, y)
+        r = self.activation_post_process(r)
+        return r
+
+
+class FXFloatFunctional(torch.nn.Module):
+    r"""module to replace FloatFunctional module before FX graph mode quantization,
+    since activation_post_process will be inserted in top level module directly
+
+    Valid operation names:
+        - add
+        - cat
+        - mul
+        - add_relu
+        - add_scalar
+        - mul_scalar
+    """
+
+    def forward(self, x):
+        raise RuntimeError(
+            "FloatFunctional is not intended to use the "
+            + "'forward'. Please use the underlying operation"
+        )
+
+    r"""Operation equivalent to ``torch.add(Tensor, Tensor)``"""
+
+    def add(self, x: Tensor, y: Tensor) -> Tensor:
+        r = torch.add(x, y)
+        return r
+
+    r"""Operation equivalent to ``torch.add(Tensor, float)``"""
+
+    def add_scalar(self, x: Tensor, y: float) -> Tensor:
+        r = torch.add(x, y)
+        return r
+
+    r"""Operation equivalent to ``torch.mul(Tensor, Tensor)``"""
+
+    def mul(self, x: Tensor, y: Tensor) -> Tensor:
+        r = torch.mul(x, y)
+        return r
+
+    r"""Operation equivalent to ``torch.mul(Tensor, float)``"""
+
+    def mul_scalar(self, x: Tensor, y: float) -> Tensor:
+        r = torch.mul(x, y)
+        return r
+
+    r"""Operation equivalent to ``torch.cat``"""
+
+    def cat(self, x: list[Tensor], dim: int = 0) -> Tensor:
+        r = torch.cat(x, dim=dim)
+        return r
+
+    r"""Operation equivalent to ``relu(torch.add(x,y))``"""
+
+    def add_relu(self, x: Tensor, y: Tensor) -> Tensor:
+        r = torch.add(x, y)
+        r = torch.nn.functional.relu(r)
+        return r
+
+    r"""Operation equivalent to ``torch.matmul(Tensor, Tensor)``"""
+
+    def matmul(self, x: Tensor, y: Tensor) -> Tensor:
+        r = torch.matmul(x, y)
+        return r
+
+
+class QFunctional(torch.nn.Module):
+    r"""Wrapper class for quantized operations.
+
+    The instance of this class can be used instead of the
+    ``torch.ops.quantized`` prefix. See example usage below.
+
+    .. note::
+
+        This class does not provide a ``forward`` hook. Instead, you must use
+        one of the underlying functions (e.g. ``add``).
+
+    Examples::
+
+        >>> q_add = QFunctional()
+        >>> # xdoctest: +SKIP
+        >>> a = torch.quantize_per_tensor(torch.tensor(3.0), 1.0, 0, torch.qint32)
+        >>> b = torch.quantize_per_tensor(torch.tensor(4.0), 1.0, 0, torch.qint32)
+        >>> q_add.add(a, b)  # Equivalent to ``torch.ops.quantized.add(a, b, 1.0, 0)``
+
+    Valid operation names:
+        - add
+        - cat
+        - mul
+        - add_relu
+        - add_scalar
+        - mul_scalar
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.scale = 1.0
+        self.zero_point = 0
+        self.activation_post_process = torch.nn.Identity()
+
+    def _save_to_state_dict(self, destination, prefix, keep_vars):
+        super()._save_to_state_dict(destination, prefix, keep_vars)
+        destination[prefix + "scale"] = torch.tensor(self.scale)
+        destination[prefix + "zero_point"] = torch.tensor(self.zero_point)
+
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        self.scale = float(state_dict.pop(prefix + "scale"))
+        self.zero_point = int(state_dict.pop(prefix + "zero_point"))
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            False,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+    def _get_name(self):
+        return "QFunctional"
+
+    def extra_repr(self):
+        return f"scale={self.scale}, zero_point={self.zero_point}"
+
+    def forward(self, x):
+        raise RuntimeError(
+            "Functional is not intended to use the "
+            + "'forward'. Please use the underlying operation"
+        )
+
+    r"""Operation equivalent to ``torch.ops.quantized.add``"""
+
+    def add(self, x: Tensor, y: Tensor) -> Tensor:
+        r = ops.quantized.add(x, y, scale=self.scale, zero_point=self.zero_point)
+        r = self.activation_post_process(r)
+        return r
+
+    r"""Operation equivalent to ``torch.ops.quantized.add(Tensor, float)``"""
+
+    def add_scalar(self, x: Tensor, y: float) -> Tensor:
+        r = ops.quantized.add_scalar(x, y)
+        # Note: this operation is not observed because the observation is not
+        # needed for the quantized op.
+        return r
+
+    r"""Operation equivalent to ``torch.ops.quantized.mul(Tensor, Tensor)``"""
+
+    def mul(self, x: Tensor, y: Tensor) -> Tensor:
+        r = ops.quantized.mul(x, y, scale=self.scale, zero_point=self.zero_point)
+        r = self.activation_post_process(r)
+        return r
+
+    r"""Operation equivalent to ``torch.ops.quantized.mul(Tensor, float)``"""
+
+    def mul_scalar(self, x: Tensor, y: float) -> Tensor:
+        r = ops.quantized.mul_scalar(x, y)
+        # Note: this operation is not observed because the observation is not
+        # needed for the quantized op.
+        return r
+
+    r"""Operation equivalent to ``torch.ops.quantized.cat``"""
+
+    def cat(self, x: list[Tensor], dim: int = 0) -> Tensor:
+        r = ops.quantized.cat(x, scale=self.scale, zero_point=self.zero_point, dim=dim)
+        r = self.activation_post_process(r)
+        return r
+
+    r"""Operation equivalent to ``torch.ops.quantized.add_relu``"""
+
+    def add_relu(self, x: Tensor, y: Tensor) -> Tensor:
+        r = ops.quantized.add_relu(x, y, scale=self.scale, zero_point=self.zero_point)
+        r = self.activation_post_process(r)
+        return r
+
+    r"""Operation equivalent to ``torch.ops.quantized.matmul(Tensor, Tensor)``"""
+
+    def matmul(self, x: Tensor, y: Tensor) -> Tensor:
+        r = ops.quantized.matmul(x, y, scale=self.scale, zero_point=self.zero_point)
+        # Note: this operation is not observed because the observation is not
+        # needed for the quantized op.
+        return r
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        assert type(mod) is FloatFunctional, (
+            "QFunctional.from_float expects an instance of FloatFunctional"
+        )
+        scale, zero_point = mod.activation_post_process.calculate_qparams()  # type: ignore[operator]
+        new_mod = QFunctional()
+        new_mod.scale = float(scale)
+        new_mod.zero_point = int(zero_point)
+        return new_mod
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/linear.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/linear.py
new file mode 100644
index 0000000000000000000000000000000000000000..84fa07b4a02207a34c16747d52d7283ad2ecfc8f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/linear.py
@@ -0,0 +1,361 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+from collections.abc import Iterable
+
+import torch
+import torch.ao.nn.intrinsic as nni
+import torch.ao.nn.intrinsic.qat as nniqat
+import torch.nn as nn
+from torch.nn.utils.fusion import fuse_linear_bn_weights
+from torch.nn.utils.parametrize import type_before_parametrizations
+
+from .utils import _hide_packed_params_repr, _quantize_weight, WeightedQuantizedModule
+
+
+__all__ = ["LinearPackedParams", "Linear"]
+
+
+class LinearPackedParams(torch.nn.Module):
+    _version = 3
+
+    def __init__(self, dtype=torch.qint8):
+        super().__init__()
+        self.dtype = dtype
+        if self.dtype == torch.qint8:
+            wq = torch._empty_affine_quantized(
+                [1, 1], scale=1.0, zero_point=0, dtype=torch.qint8
+            )
+        elif self.dtype == torch.float16:
+            wq = torch.zeros([1, 1], dtype=torch.float)
+        self.set_weight_bias(wq, None)  # type: ignore[possibly-undefined]
+
+    @torch.jit.export
+    def set_weight_bias(self, weight: torch.Tensor, bias: torch.Tensor | None) -> None:
+        if self.dtype == torch.qint8:
+            self._packed_params = torch.ops.quantized.linear_prepack(weight, bias)
+        elif self.dtype == torch.float16:
+            self._packed_params = torch.ops.quantized.linear_prepack_fp16(weight, bias)
+        else:
+            raise RuntimeError("Unsupported dtype on dynamic quantized linear!")
+
+    @torch.jit.export
+    def _weight_bias(self):
+        if self.dtype == torch.qint8:
+            return torch.ops.quantized.linear_unpack(self._packed_params)
+        elif self.dtype == torch.float16:
+            return torch.ops.quantized.linear_unpack_fp16(self._packed_params)
+        else:
+            raise RuntimeError("Unsupported dtype on dynamic quantized linear!")
+
+    def forward(self, x):
+        return x
+
+    # Version 1
+    #   self
+    #   |--- weight : Tensor
+    #   |--- bias : Tensor
+    #
+    # Version 2
+    #   self
+    #   |--- weight : Tensor
+    #   |--- bias : Tensor
+    #   |--- dtype : torch.dtype
+    #
+    # Version 3
+    #   self
+    #   |--- _packed_params : (Tensor, Tensor) representing (weight, bias)
+    #                         of LinearPackedParams
+    #   |--- dtype : torch.dtype
+    def _save_to_state_dict(self, destination, prefix, keep_vars):
+        super()._save_to_state_dict(destination, prefix, keep_vars)
+        destination[prefix + "dtype"] = self.dtype
+        destination[prefix + "_packed_params"] = self._weight_bias()
+
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        version = local_metadata.get("version", None)
+        if version is None or version < 2:
+            self.dtype = torch.qint8
+        else:
+            self.dtype = state_dict[prefix + "dtype"]
+            state_dict.pop(prefix + "dtype")
+
+        if version is None or version < 3:
+            self.set_weight_bias(
+                state_dict[prefix + "weight"], state_dict[prefix + "bias"]
+            )
+            state_dict.pop(prefix + "weight")
+            state_dict.pop(prefix + "bias")
+
+        if version == 3:
+            weight, bias = state_dict[prefix + "_packed_params"]
+            state_dict.pop(prefix + "_packed_params")
+            self.set_weight_bias(weight, bias)
+
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            False,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+    def __repr__(self):
+        return self._weight_bias().__repr__()
+
+
+class Linear(WeightedQuantizedModule):
+    r"""
+    A quantized linear module with quantized tensor as inputs and outputs.
+    We adopt the same interface as `torch.nn.Linear`, please see
+    https://pytorch.org/docs/stable/nn.html#torch.nn.Linear for documentation.
+
+    Similar to :class:`~torch.nn.Linear`, attributes will be randomly
+    initialized at module creation time and will be overwritten later
+
+    Attributes:
+        weight (Tensor): the non-learnable quantized weights of the module of
+                         shape :math:`(\text{out\_features}, \text{in\_features})`.
+        bias (Tensor): the non-learnable bias of the module of shape :math:`(\text{out\_features})`.
+                If :attr:`bias` is ``True``, the values are initialized to zero.
+        scale: `scale` parameter of output Quantized Tensor, type: double
+        zero_point: `zero_point` parameter for output Quantized Tensor, type: long
+
+    Examples::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE)
+        >>> m = nn.quantized.Linear(20, 30)
+        >>> input = torch.randn(128, 20)
+        >>> # xdoctest: +SKIP
+        >>> input = torch.quantize_per_tensor(input, 1.0, 0, torch.quint8)
+        >>> output = m(input)
+        >>> print(output.size())
+        torch.Size([128, 30])
+    """
+
+    _version = 3
+    _FLOAT_MODULE = (nn.Linear, nn.modules.linear.NonDynamicallyQuantizableLinear)
+
+    def __init__(self, in_features, out_features, bias_=True, dtype=torch.qint8):
+        super().__init__()
+        # We don't muck around with buffers or attributes or anything here
+        # to keep the module simple. *everything* is simply a Python attribute.
+        # Serialization logic is explicitly handled in the below serialization and
+        # deserialization modules
+        self.in_features = in_features
+        self.out_features = out_features
+        bias = None
+        if bias_:
+            bias = torch.zeros(out_features, dtype=torch.float)
+
+        if dtype == torch.qint8:
+            qweight = torch._empty_affine_quantized(
+                [out_features, in_features], scale=1, zero_point=0, dtype=torch.qint8
+            )
+        elif dtype == torch.float16:
+            qweight = torch.zeros([out_features, in_features], dtype=torch.float)
+        else:
+            raise RuntimeError("Unsupported dtype specified for quantized Linear!")
+
+        self._packed_params = LinearPackedParams(dtype)
+        self._packed_params.set_weight_bias(qweight, bias)
+        self.scale = 1.0
+        self.zero_point = 0
+
+    def _get_name(self):
+        return "QuantizedLinear"
+
+    def extra_repr(self):
+        return (
+            f"in_features={self.in_features}, out_features={self.out_features}, scale={self.scale}, "
+            f"zero_point={self.zero_point}, qscheme={self.weight().qscheme()}"
+        )
+
+    def __repr__(self):
+        return _hide_packed_params_repr(self, LinearPackedParams)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return torch.ops.quantized.linear(
+            x, self._packed_params._packed_params, self.scale, self.zero_point
+        )
+
+    # ===== Serialization methods =====
+    # The special consideration here is that we have to unpack the weights into their
+    # regular QTensor form for serialization. Packed weights should not live
+    # outside the process in which they were created, rather they should be derived
+    # from the QTensor weight.
+    #
+    # Version 1
+    #   self
+    #   |--- scale : float
+    #   |--- zero_point : int
+    #   |--- weight : Tensor
+    #   |--- bias : Tensor
+    #
+    # Version 2
+    #   self
+    #   |--- scale : float
+    #   |--- zero_point : int
+    #   |--- _packed_params : Module
+    #        |--- weight : Tensor
+    #        |--- bias : Tensor
+    #
+    # Version 3
+    #   self
+    #   |--- scale : float
+    #   |--- zero_point : int
+    #   |--- _packed_params : Module
+    #        |--- _packed_params : (Tensor, Tensor) representing weight, bias
+    #                              of LinearPackedParams C++ struct
+    #
+    def _save_to_state_dict(self, destination, prefix, keep_vars):
+        super()._save_to_state_dict(destination, prefix, keep_vars)
+        destination[prefix + "scale"] = torch.tensor(self.scale)
+        destination[prefix + "zero_point"] = torch.tensor(self.zero_point)
+
+    # ===== Deserialization methods =====
+    # Counterpart to the serialization methods, we must pack the serialized QTensor
+    # weight into its packed format for use by the FBGEMM ops.
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        self.scale = float(state_dict[prefix + "scale"])
+        state_dict.pop(prefix + "scale")
+
+        self.zero_point = int(state_dict[prefix + "zero_point"])
+        state_dict.pop(prefix + "zero_point")
+
+        version = local_metadata.get("version", None)
+
+        if version is None or version == 1:
+            # We moved the parameters into a LinearPackedParameters submodule
+            weight = state_dict.pop(prefix + "weight")
+            bias = state_dict.pop(prefix + "bias")
+            state_dict.update(
+                {
+                    prefix + "_packed_params.weight": weight,
+                    prefix + "_packed_params.bias": bias,
+                }
+            )
+
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            False,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+    # Function rather than property to make sure that JIT serialization doesn't
+    # register this as an attribute
+    def _weight_bias(self):
+        return self._packed_params._weight_bias()
+
+    def weight(self):
+        return self._weight_bias()[0]
+
+    def bias(self):
+        return self._weight_bias()[1]
+
+    def set_weight_bias(self, w: torch.Tensor, b: torch.Tensor | None) -> None:
+        self._packed_params.set_weight_bias(w, b)
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        r"""Create a quantized module from an observed float module
+
+        Args:
+            mod (Module): a float module, either produced by torch.ao.quantization
+                          utilities or provided by the user
+            use_precomputed_fake_quant (bool): if True, the module will reuse min/max
+                          values from the precomputed fake quant module.
+        """
+        if hasattr(mod, "weight_fake_quant"):
+            if type_before_parametrizations(mod) == nniqat.LinearBn1d:
+                mod.weight, mod.bias = fuse_linear_bn_weights(
+                    mod.weight,
+                    mod.bias,
+                    mod.bn.running_mean,
+                    mod.bn.running_var,
+                    mod.bn.eps,
+                    mod.bn.weight,
+                    mod.bn.bias,
+                )
+            weight_post_process = mod.weight_fake_quant
+            activation_post_process = mod.activation_post_process
+        else:
+            # This function does not participate in JIT, so it is OK to ignore
+            # the type mismatch in assignment. Also, mypy has an issue with
+            # iterables not being implemented, so we are ignoring those too.
+            if not isinstance(cls._FLOAT_MODULE, Iterable):
+                # pyrefly: ignore [bad-assignment]
+                cls._FLOAT_MODULE = [cls._FLOAT_MODULE]
+            supported_modules = ", ".join(
+                [float_mod.__name__ for float_mod in cls._FLOAT_MODULE]
+            )
+            error_msg = f"nnq.{cls.__name__}.from_float only works for {supported_modules}, but got: {type(mod)}"
+            assert type_before_parametrizations(mod) in cls._FLOAT_MODULE, (
+                error_msg.format()
+            )
+            assert hasattr(mod, "qconfig"), (
+                "Input float module must have qconfig defined"
+            )
+            activation_post_process = mod.activation_post_process
+            if type_before_parametrizations(mod) == nni.LinearReLU:
+                mod = mod[0]
+            weight_post_process = (
+                mod.qconfig.weight()
+                if not hasattr(mod, "weight_fake_quant")
+                else mod.weight_fake_quant
+            )
+
+        if not use_precomputed_fake_quant:
+            # Observer may not have been called yet
+            # Observer might have been called in the previous stage via PTQ algorithm e.g. AdaRound
+            weight_post_process(mod.weight)
+        dtype = weight_post_process.dtype
+        act_scale, act_zp = activation_post_process.calculate_qparams()
+        assert dtype == torch.qint8, "Weight observer must have dtype torch.qint8"
+        qweight = _quantize_weight(mod.weight.float(), weight_post_process)
+        qlinear = cls(mod.in_features, mod.out_features, dtype=dtype)
+        qlinear.set_weight_bias(qweight, mod.bias)
+        qlinear.scale = float(act_scale)
+        qlinear.zero_point = int(act_zp)
+        return qlinear
+
+    @classmethod
+    def from_reference(cls, ref_qlinear, output_scale, output_zero_point):
+        r"""Create a (fbgemm/qnnpack) quantized module from a reference quantized module
+
+        Args:
+            ref_qlinear (Module): a reference quantized linear module, either produced by torch.ao.quantization
+                          utilities or provided by the user
+            output_scale (float): scale for output Tensor
+            output_zero_point (int): zero point for output Tensor
+        """
+        qlinear = cls(ref_qlinear.in_features, ref_qlinear.out_features)
+        qweight = ref_qlinear.get_quantized_weight()
+        qlinear.set_weight_bias(qweight, ref_qlinear.bias)
+
+        qlinear.scale = float(output_scale)
+        qlinear.zero_point = int(output_zero_point)
+        return qlinear
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/normalization.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/normalization.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa335b4699db5519e2e53f27aa18958b5afced94
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/normalization.py
@@ -0,0 +1,358 @@
+# mypy: allow-untyped-defs
+import torch
+
+
+__all__ = [
+    "LayerNorm",
+    "GroupNorm",
+    "InstanceNorm1d",
+    "InstanceNorm2d",
+    "InstanceNorm3d",
+]
+
+
+class LayerNorm(torch.nn.LayerNorm):
+    r"""This is the quantized version of :class:`~torch.nn.LayerNorm`.
+
+    Additional args:
+        * **scale** - quantization scale of the output, type: double.
+        * **zero_point** - quantization zero point of the output, type: long.
+
+    """
+
+    def __init__(
+        self,
+        normalized_shape,
+        weight,
+        bias,
+        scale,
+        zero_point,
+        eps=1e-5,
+        elementwise_affine=True,
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__(
+            normalized_shape,
+            eps=eps,
+            elementwise_affine=elementwise_affine,
+            # pyrefly: ignore [bad-argument-type]
+            **factory_kwargs,
+        )
+        self.weight = weight
+        self.bias = bias
+        # pyrefly: ignore [bad-argument-type]
+        self.register_buffer("scale", torch.tensor(scale, **factory_kwargs))
+        # pyrefly: ignore [bad-argument-type]
+        self.register_buffer("zero_point", torch.tensor(zero_point, **factory_kwargs))
+
+    def forward(self, input):
+        return torch.ops.quantized.layer_norm(
+            input,
+            self.normalized_shape,
+            weight=self.weight,
+            bias=self.bias,
+            eps=self.eps,
+            output_scale=self.scale,
+            output_zero_point=self.zero_point,
+        )
+
+    def _get_name(self):
+        return "QuantizedLayerNorm"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        scale, zero_point = mod.activation_post_process.calculate_qparams()
+        new_mod = cls(
+            mod.normalized_shape,
+            mod.weight,
+            mod.bias,
+            float(scale),
+            int(zero_point),
+            mod.eps,
+            mod.elementwise_affine,
+        )
+        return new_mod
+
+    @classmethod
+    def from_reference(cls, mod, scale, zero_point):
+        return cls(
+            mod.normalized_shape,
+            mod.weight,
+            mod.bias,
+            float(scale),
+            int(zero_point),
+            mod.eps,
+            mod.elementwise_affine,
+        )
+
+
+class GroupNorm(torch.nn.GroupNorm):
+    r"""This is the quantized version of :class:`~torch.nn.GroupNorm`.
+
+    Additional args:
+        * **scale** - quantization scale of the output, type: double.
+        * **zero_point** - quantization zero point of the output, type: long.
+
+    """
+
+    __constants__ = ["num_groups", "num_channels", "eps", "affine"]
+
+    def __init__(
+        self,
+        num_groups,
+        num_channels,
+        weight,
+        bias,
+        scale,
+        zero_point,
+        eps=1e-5,
+        affine=True,
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__(num_groups, num_channels, eps, affine, **factory_kwargs)
+        self.weight = weight
+        self.bias = bias
+        # pyrefly: ignore [bad-argument-type]
+        self.register_buffer("scale", torch.tensor(scale, **factory_kwargs))
+        # pyrefly: ignore [bad-argument-type]
+        self.register_buffer("zero_point", torch.tensor(zero_point, **factory_kwargs))
+
+    def forward(self, input):
+        return torch.ops.quantized.group_norm(
+            input,
+            self.num_groups,
+            self.weight,
+            self.bias,
+            self.eps,
+            self.scale,
+            self.zero_point,
+        )
+
+    def _get_name(self):
+        return "QuantizedGroupNorm"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        scale, zero_point = mod.activation_post_process.calculate_qparams()
+        new_mod = cls(
+            mod.num_groups,
+            mod.num_channels,
+            mod.weight,
+            mod.bias,
+            float(scale),
+            int(zero_point),
+            mod.eps,
+            mod.affine,
+        )
+        return new_mod
+
+
+class InstanceNorm1d(torch.nn.InstanceNorm1d):
+    r"""This is the quantized version of :class:`~torch.nn.InstanceNorm1d`.
+
+    Additional args:
+        * **scale** - quantization scale of the output, type: double.
+        * **zero_point** - quantization zero point of the output, type: long.
+
+    """
+
+    def __init__(
+        self,
+        num_features,
+        weight,
+        bias,
+        scale,
+        zero_point,
+        eps=1e-5,
+        momentum=0.1,
+        affine=False,
+        track_running_stats=False,
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__(
+            num_features, eps, momentum, affine, track_running_stats, **factory_kwargs
+        )
+        self.weight = weight
+        self.bias = bias
+        # pyrefly: ignore [bad-argument-type]
+        self.register_buffer("scale", torch.tensor(scale, **factory_kwargs))
+        # pyrefly: ignore [bad-argument-type]
+        self.register_buffer("zero_point", torch.tensor(zero_point, **factory_kwargs))
+
+    def forward(self, input):
+        return torch.ops.quantized.instance_norm(
+            input, self.weight, self.bias, self.eps, self.scale, self.zero_point
+        )
+
+    def _get_name(self):
+        return "QuantizedInstanceNorm1d"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        scale, zero_point = mod.activation_post_process.calculate_qparams()
+        new_mod = cls(
+            mod.num_features,
+            mod.weight,
+            mod.bias,
+            float(scale),
+            int(zero_point),
+            mod.eps,
+            mod.affine,
+        )
+        return new_mod
+
+    @classmethod
+    def from_reference(cls, mod, scale, zero_point):
+        return cls(
+            mod.num_features,
+            mod.weight,
+            mod.bias,
+            float(scale),
+            int(zero_point),
+            mod.eps,
+            mod.affine,
+        )
+
+
+class InstanceNorm2d(torch.nn.InstanceNorm2d):
+    r"""This is the quantized version of :class:`~torch.nn.InstanceNorm2d`.
+
+    Additional args:
+        * **scale** - quantization scale of the output, type: double.
+        * **zero_point** - quantization zero point of the output, type: long.
+
+    """
+
+    def __init__(
+        self,
+        num_features,
+        weight,
+        bias,
+        scale,
+        zero_point,
+        eps=1e-5,
+        momentum=0.1,
+        affine=False,
+        track_running_stats=False,
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__(
+            num_features, eps, momentum, affine, track_running_stats, **factory_kwargs
+        )
+        self.weight = weight
+        self.bias = bias
+        # pyrefly: ignore [bad-argument-type]
+        self.register_buffer("scale", torch.tensor(scale, **factory_kwargs))
+        # pyrefly: ignore [bad-argument-type]
+        self.register_buffer("zero_point", torch.tensor(zero_point, **factory_kwargs))
+
+    def forward(self, input):
+        return torch.ops.quantized.instance_norm(
+            input, self.weight, self.bias, self.eps, self.scale, self.zero_point
+        )
+
+    def _get_name(self):
+        return "QuantizedInstanceNorm2d"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        scale, zero_point = mod.activation_post_process.calculate_qparams()
+        new_mod = cls(
+            mod.num_features,
+            mod.weight,
+            mod.bias,
+            float(scale),
+            int(zero_point),
+            mod.eps,
+            mod.affine,
+        )
+        return new_mod
+
+    @classmethod
+    def from_reference(cls, mod, scale, zero_point):
+        return cls(
+            mod.num_features,
+            mod.weight,
+            mod.bias,
+            float(scale),
+            int(zero_point),
+            mod.eps,
+            mod.affine,
+        )
+
+
+class InstanceNorm3d(torch.nn.InstanceNorm3d):
+    r"""This is the quantized version of :class:`~torch.nn.InstanceNorm3d`.
+
+    Additional args:
+        * **scale** - quantization scale of the output, type: double.
+        * **zero_point** - quantization zero point of the output, type: long.
+
+    """
+
+    def __init__(
+        self,
+        num_features,
+        weight,
+        bias,
+        scale,
+        zero_point,
+        eps=1e-5,
+        momentum=0.1,
+        affine=False,
+        track_running_stats=False,
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__(
+            num_features, eps, momentum, affine, track_running_stats, **factory_kwargs
+        )
+        self.weight = weight
+        self.bias = bias
+        # pyrefly: ignore [bad-argument-type]
+        self.register_buffer("scale", torch.tensor(scale, **factory_kwargs))
+        # pyrefly: ignore [bad-argument-type]
+        self.register_buffer("zero_point", torch.tensor(zero_point, **factory_kwargs))
+
+    def forward(self, input):
+        return torch.ops.quantized.instance_norm(
+            input, self.weight, self.bias, self.eps, self.scale, self.zero_point
+        )
+
+    def _get_name(self):
+        return "QuantizedInstanceNorm3d"
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        scale, zero_point = mod.activation_post_process.calculate_qparams()
+        new_mod = cls(
+            mod.num_features,
+            mod.weight,
+            mod.bias,
+            float(scale),
+            int(zero_point),
+            mod.eps,
+            mod.affine,
+        )
+        return new_mod
+
+    @classmethod
+    def from_reference(cls, mod, scale, zero_point):
+        return cls(
+            mod.num_features,
+            mod.weight,
+            mod.bias,
+            float(scale),
+            int(zero_point),
+            mod.eps,
+            mod.affine,
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/rnn.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/rnn.py
new file mode 100644
index 0000000000000000000000000000000000000000..5040b8c97d050102779c742989dd4f52cd3bffa8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/rnn.py
@@ -0,0 +1,59 @@
+from typing import Any
+
+import torch
+
+
+__all__ = [
+    "LSTM",
+]
+
+
+class LSTM(torch.ao.nn.quantizable.LSTM):
+    r"""A quantized long short-term memory (LSTM).
+
+    For the description and the argument types, please, refer to :class:`~torch.nn.LSTM`
+
+    Attributes:
+        layers : instances of the `_LSTMLayer`
+
+    .. note::
+        To access the weights and biases, you need to access them per layer.
+        See examples in :class:`~torch.ao.nn.quantizable.LSTM`
+
+    Examples::
+        >>> # xdoctest: +SKIP
+        >>> custom_module_config = {
+        ...     'float_to_observed_custom_module_class': {
+        ...         nn.LSTM: nn.quantizable.LSTM,
+        ...     },
+        ...     'observed_to_quantized_custom_module_class': {
+        ...         nn.quantizable.LSTM: nn.quantized.LSTM,
+        ...     }
+        ... }
+        >>> tq.prepare(model, prepare_custom_module_class=custom_module_config)
+        >>> tq.convert(model, convert_custom_module_class=custom_module_config)
+    """
+
+    _FLOAT_MODULE = torch.ao.nn.quantizable.LSTM  # type: ignore[assignment]
+
+    def _get_name(self) -> str:
+        return "QuantizedLSTM"
+
+    @classmethod
+    def from_float(cls, *args: Any, **kwargs: Any) -> None:
+        # The whole flow is float -> observed -> quantized
+        # This class does observed -> quantized only
+        raise NotImplementedError(
+            "It looks like you are trying to convert a "
+            "non-observed LSTM module. Please, see "
+            "the examples on quantizable LSTMs."
+        )
+
+    @classmethod
+    def from_observed(cls: type["LSTM"], other: torch.ao.nn.quantizable.LSTM) -> "LSTM":
+        assert isinstance(other, cls._FLOAT_MODULE)  # type: ignore[has-type]
+        converted = torch.ao.quantization.convert(
+            other, inplace=False, remove_qconfig=True
+        )
+        converted.__class__ = cls
+        return converted
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..330070913a7521871f123a3e076264498a6ef612
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/modules/utils.py
@@ -0,0 +1,144 @@
+# mypy: allow-untyped-defs
+import abc
+import collections
+import itertools
+
+import torch
+from torch.nn.modules.module import _addindent
+
+
+__all__ = [
+    "WeightedQuantizedModule",
+]
+
+
+class WeightedQuantizedModule(torch.nn.Module, metaclass=abc.ABCMeta):
+    """Wrapper for quantized modules than can be lowered from reference modules."""
+
+    @classmethod
+    @abc.abstractmethod
+    def from_reference(cls, ref_module, output_scale, output_zero_point):
+        raise NotImplementedError
+
+
+def _get_weight_observer(observer):
+    # FakeQuantize observer
+    if hasattr(observer, "activation_post_process"):
+        observer = observer.activation_post_process
+    # UniformQuantizationObserverBase observer
+    return observer
+
+
+def _needs_weight_clamping(observer, dtype):
+    observer = _get_weight_observer(observer)
+    if dtype in [torch.qint8, torch.quint8, torch.qint32]:
+        info = torch.iinfo(dtype)
+        return observer.quant_min > info.min or observer.quant_max < info.max
+    return False
+
+
+def _clamp_weights(qweight, observer, scale, zp):
+    if not _needs_weight_clamping(observer, qweight.dtype):
+        return qweight
+
+    observer = _get_weight_observer(observer)
+    min_, max_ = observer.quant_min, observer.quant_max
+
+    # Doing this because can't use torch.ops.quantized.clamp() with per_channel qscheme yet.
+    qw_int_max = torch.clone(qweight.int_repr()).fill_(max_)
+    qw_int_min = torch.clone(qweight.int_repr()).fill_(min_)
+    qw_int = torch.minimum(torch.maximum(qweight.int_repr(), qw_int_min), qw_int_max)
+
+    if observer.qscheme in [torch.per_tensor_symmetric, torch.per_tensor_affine]:
+        qweight = torch._make_per_tensor_quantized_tensor(
+            qw_int, scale.item(), zp.item()
+        )
+    elif observer.qscheme in [
+        torch.per_channel_symmetric,
+        torch.per_channel_affine,
+        torch.per_channel_affine_float_qparams,
+    ]:
+        qweight = torch._make_per_channel_quantized_tensor(
+            qw_int, scale, zp, axis=observer.ch_axis
+        )
+    else:
+        raise ValueError("Unexpected qscheme " + observer.qscheme)
+    return qweight
+
+
+def _quantize_weight(float_wt, observer):
+    wt_scale, wt_zp = observer.calculate_qparams()
+    if observer.qscheme in [torch.per_tensor_symmetric, torch.per_tensor_affine]:
+        qweight = torch.quantize_per_tensor(
+            float_wt, float(wt_scale), int(wt_zp), torch.qint8
+        )
+        qweight = _clamp_weights(qweight, observer, wt_scale, wt_zp)
+    elif observer.qscheme in [torch.per_channel_symmetric, torch.per_channel_affine]:
+        wt_axis = observer.ch_axis
+        qweight = torch.quantize_per_channel(
+            float_wt,
+            wt_scale.to(torch.double),
+            wt_zp.to(torch.int64),
+            wt_axis,
+            torch.qint8,
+        )
+        qweight = _clamp_weights(qweight, observer, wt_scale, wt_zp)
+    elif observer.qscheme == torch.per_channel_affine_float_qparams:
+        qweight = torch.quantize_per_channel(
+            float_wt,
+            wt_scale.to(torch.float),
+            wt_zp.to(torch.float),
+            observer.ch_axis,
+            observer.dtype,
+        )
+        qweight = _clamp_weights(qweight, observer, wt_scale, wt_zp)
+    else:
+        raise ValueError("Unexpected qscheme " + observer.qscheme)
+    return qweight
+
+
+def _ntuple_from_first(n):
+    """Converts the argument to a tuple of size n
+    with the first element repeated."""
+
+    def parse(x):
+        while isinstance(x, collections.abc.Sequence):
+            if len(x) == n:
+                break
+            x = x[0]
+        return tuple(itertools.repeat(x, n))
+
+    return parse
+
+
+def _hide_packed_params_repr(self, params):
+    # We don't want to show `PackedParams` children, hence custom
+    # `__repr__`. This is the same as nn.Module.__repr__, except the check
+    # for the `params module`.
+    extra_lines = []
+    extra_repr = self.extra_repr()
+    # empty string will be split into list ['']
+    if extra_repr:
+        extra_lines = extra_repr.split("\n")
+    child_lines = []
+    for key, module in self._modules.items():
+        if isinstance(module, params):
+            continue
+        mod_str = repr(module)
+        mod_str = _addindent(mod_str, 2)
+        child_lines.append("(" + key + "): " + mod_str)
+    lines = extra_lines + child_lines
+
+    main_str = self._get_name() + "("
+    if lines:
+        # simple one-liner info, which most builtin Modules will use
+        if len(extra_lines) == 1 and not child_lines:
+            main_str += extra_lines[0]
+        else:
+            main_str += "\n  " + "\n  ".join(lines) + "\n"
+
+    main_str += ")"
+    return main_str
+
+
+_pair_from_first = _ntuple_from_first(2)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/reference/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/reference/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e1e15e9c1516d30f7ca9ee47b21b267533de75b6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/reference/__init__.py
@@ -0,0 +1,19 @@
+from .modules import *  # noqa: F403
+
+
+__all__ = [
+    "Linear",
+    "Conv1d",
+    "Conv2d",
+    "Conv3d",
+    "ConvTranspose1d",
+    "ConvTranspose2d",
+    "ConvTranspose3d",
+    "RNNCell",
+    "LSTMCell",
+    "GRUCell",
+    "LSTM",
+    "GRU",
+    "Embedding",
+    "EmbeddingBag",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/reference/modules/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/reference/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..fbe97c22f5a46a5eafc1432075fc57dd44c3aa8d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/reference/modules/__init__.py
@@ -0,0 +1,29 @@
+from .conv import (
+    Conv1d,
+    Conv2d,
+    Conv3d,
+    ConvTranspose1d,
+    ConvTranspose2d,
+    ConvTranspose3d,
+)
+from .linear import Linear
+from .rnn import GRU, GRUCell, LSTM, LSTMCell, RNNCell
+from .sparse import Embedding, EmbeddingBag
+
+
+__all__ = [
+    "Linear",
+    "Conv1d",
+    "Conv2d",
+    "Conv3d",
+    "ConvTranspose1d",
+    "ConvTranspose2d",
+    "ConvTranspose3d",
+    "RNNCell",
+    "LSTMCell",
+    "GRUCell",
+    "LSTM",
+    "GRU",
+    "Embedding",
+    "EmbeddingBag",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/reference/modules/conv.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/reference/modules/conv.py
new file mode 100644
index 0000000000000000000000000000000000000000..3273b89cc70ab21a87a0369e71c3ceff19615111
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/reference/modules/conv.py
@@ -0,0 +1,518 @@
+# mypy: allow-untyped-defs
+from typing import Any, Literal, Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.common_types import _size_1_t
+
+from .utils import ReferenceQuantizedModule
+
+
+__all__ = [
+    "Conv1d",
+    "Conv2d",
+    "Conv3d",
+    "ConvTranspose1d",
+    "ConvTranspose2d",
+    "ConvTranspose3d",
+]
+
+
+class _ConvNd(torch.nn.modules.conv._ConvNd, ReferenceQuantizedModule):
+    """A reference version of nn.quantized.Conv2d
+    we will not pack the parameters in this module, since weight packing is an
+    optimization for quantized backends supported in PyTorch (fbgemm/qnnpack),
+    this is useful when user want to use this module in other backends like Glow.
+    """
+
+    __annotations__ = {"bias": Optional[torch.Tensor]}
+    _IS_REFERENCE = True
+
+    @staticmethod
+    def from_float(cls, float_conv, weight_qparams):
+        qref_conv = cls(
+            float_conv.in_channels,
+            float_conv.out_channels,
+            float_conv.kernel_size,  # type: ignore[arg-type]
+            float_conv.stride,  # type: ignore[arg-type]
+            float_conv.padding,  # type: ignore[arg-type]
+            float_conv.dilation,  # type: ignore[arg-type]
+            float_conv.groups,
+            float_conv.bias is not None,  # type: ignore[arg-type]
+            float_conv.padding_mode,
+            device=float_conv.weight.device,
+            dtype=float_conv.weight.dtype,
+            weight_qparams=weight_qparams,
+        )
+        qref_conv.weight = torch.nn.Parameter(float_conv.weight.detach())
+        if float_conv.bias is not None:
+            qref_conv.bias = torch.nn.Parameter(float_conv.bias.detach())
+        return qref_conv
+
+
+class Conv1d(_ConvNd, nn.Conv1d):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: _size_1_t,
+        stride: _size_1_t = 1,
+        padding: _size_1_t = 0,
+        dilation: _size_1_t = 1,
+        groups: int = 1,
+        bias: bool = True,
+        padding_mode: Literal["zeros", "reflect", "replicate", "circular"] = "zeros",
+        device=None,
+        dtype=None,
+        weight_qparams: dict[str, Any] | None = None,
+    ):
+        nn.Conv1d.__init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            groups,
+            bias,
+            padding_mode,
+            device,
+            dtype,
+        )
+        self._init_weight_qparams(weight_qparams, device)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        we have:
+        w(float) -- quant - dequant \
+        x(float) ------------- F.conv1d ---
+
+        In the full model, we will see
+        w(float) -- quant - *dequant \
+        x -- quant --- *dequant --  *F.conv1d --- *quant - dequant
+        and the backend should be able to fuse the ops with `*` into a quantized conv1d
+        """
+        weight_quant_dequant = self.get_weight()
+
+        result = F.conv1d(
+            x,
+            weight_quant_dequant,
+            self.bias,
+            self.stride,
+            self.padding,
+            self.dilation,
+            self.groups,
+        )
+        return result
+
+    def _get_name(self):
+        return "QuantizedConv1d(Reference)"
+
+    @classmethod
+    def from_float(cls, float_conv, weight_qparams):  # type: ignore[override]
+        return _ConvNd.from_float(cls, float_conv, weight_qparams)
+
+
+class Conv2d(_ConvNd, nn.Conv2d):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=True,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+        weight_qparams: dict[str, Any] | None = None,
+    ):
+        nn.Conv2d.__init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            groups,
+            bias,
+            # pyrefly: ignore [bad-argument-type]
+            padding_mode,
+            device,
+            dtype,
+        )
+        self._init_weight_qparams(weight_qparams, device)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        we have:
+        w(float) -- quant - dequant \
+        x(float) ------------- F.conv2d ---
+
+        In the full model, we will see
+        w(float) -- quant - *dequant \
+        x -- quant --- *dequant --  *F.conv2d --- *quant - dequant
+        and the backend should be able to fuse the ops with `*` into a quantized conv2d
+        """
+        weight_quant_dequant = self.get_weight()
+
+        result = F.conv2d(
+            x,
+            weight_quant_dequant,
+            self.bias,
+            self.stride,
+            self.padding,
+            self.dilation,
+            self.groups,
+        )
+        return result
+
+    def _get_name(self):
+        return "QuantizedConv2d(Reference)"
+
+    @classmethod
+    def from_float(cls, float_conv, weight_qparams):  # type: ignore[override]
+        return _ConvNd.from_float(cls, float_conv, weight_qparams)
+
+
+class Conv3d(_ConvNd, nn.Conv3d):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        dilation=1,
+        groups=1,
+        bias=True,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+        weight_qparams: dict[str, Any] | None = None,
+    ):
+        nn.Conv3d.__init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            dilation,
+            groups,
+            bias,
+            # pyrefly: ignore [bad-argument-type]
+            padding_mode,
+            device,
+            dtype,
+        )
+        self._init_weight_qparams(weight_qparams, device)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        we have:
+        w(float) -- quant - dequant \
+        x(float) ------------- F.conv3d ---
+
+        In the full model, we will see
+        w(float) -- quant - *dequant \
+        x -- quant --- *dequant --  *F.conv3d --- *quant - dequant
+        and the backend should be able to fuse the ops with `*` into a quantized conv3d
+        """
+        weight_quant_dequant = self.get_weight()
+
+        result = F.conv3d(
+            x,
+            weight_quant_dequant,
+            self.bias,
+            self.stride,
+            self.padding,
+            self.dilation,
+            self.groups,
+        )
+        return result
+
+    def _get_name(self):
+        return "QuantizedConv3d(Reference)"
+
+    @classmethod
+    def from_float(cls, float_conv, weight_qparams):  # type: ignore[override]
+        return _ConvNd.from_float(cls, float_conv, weight_qparams)
+
+
+class _ConvTransposeNd(_ConvNd, torch.nn.modules.conv._ConvTransposeNd):
+    """A reference version of nn.quantized.ConvTranspose2d
+    we will not pack the parameters in this module, since weight packing is an
+    optimization for quantized backends supported in PyTorch (fbgemm/qnnpack),
+    this is useful when user want to use this module in other backends like Glow.
+    """
+
+    @staticmethod
+    def from_float(cls, float_conv, weight_qparams):
+        qref_conv = cls(
+            float_conv.in_channels,
+            float_conv.out_channels,
+            float_conv.kernel_size,  # type: ignore[arg-type]
+            float_conv.stride,  # type: ignore[arg-type]
+            float_conv.padding,  # type: ignore[arg-type]
+            float_conv.output_padding,  # type: ignore[arg-type]
+            float_conv.groups,
+            float_conv.bias is not None,  # type: ignore[arg-type]
+            float_conv.dilation,  # type: ignore[arg-type]
+            float_conv.padding_mode,
+            device=float_conv.weight.device,
+            dtype=float_conv.weight.dtype,
+            weight_qparams=weight_qparams,
+        )
+        qref_conv.weight = torch.nn.Parameter(float_conv.weight.detach())
+        if float_conv.bias is not None:
+            qref_conv.bias = torch.nn.Parameter(float_conv.bias.detach())
+        return qref_conv
+
+
+class ConvTranspose1d(_ConvTransposeNd, nn.ConvTranspose1d):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: _size_1_t,
+        stride: _size_1_t = 1,
+        padding: _size_1_t = 0,
+        output_padding: _size_1_t = 0,
+        groups: int = 1,
+        bias: bool = True,
+        dilation: _size_1_t = 1,
+        padding_mode: Literal["zeros", "reflect", "replicate", "circular"] = "zeros",
+        device=None,
+        dtype=None,
+        weight_qparams: dict[str, Any] | None = None,
+    ):
+        nn.ConvTranspose1d.__init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            output_padding,
+            groups,
+            bias,
+            dilation,
+            padding_mode,
+            device,
+            dtype,
+        )
+        self._init_weight_qparams(weight_qparams, device)
+
+    def forward(
+        self, x: torch.Tensor, output_size: list[int] | None = None
+    ) -> torch.Tensor:
+        """
+        we have:
+        w(float) -- quant - dequant \
+        x(float) ------------- F.convTranspose1d ---
+        In the full model, we will see
+        w(float) -- quant - *dequant \
+        x -- quant --- *dequant --  *F.convTranspose1d --- *quant - dequant
+        and the backend should be able to fuse the ops with `*` into a quantized conv1d
+        """
+
+        assert isinstance(self.padding, tuple)
+        # One cannot replace List by Tuple or Sequence in "_output_padding" because
+        # TorchScript does not support `Sequence[T]` or `Tuple[T, ...]`.
+        output_padding = self._output_padding(
+            input,  # type: ignore[arg-type]
+            output_size,
+            self.stride,  # type: ignore[arg-type]
+            self.padding,  # type: ignore[arg-type]
+            self.kernel_size,  # type: ignore[arg-type]
+            self.dilation,  # type: ignore[arg-type]
+        )
+
+        weight_quant_dequant = self.get_weight()
+        result = F.conv_transpose1d(
+            x,
+            weight_quant_dequant,
+            self.bias,
+            self.stride,
+            self.padding,
+            output_padding,
+            self.groups,
+            self.dilation,
+        )
+        return result
+
+    def _get_name(self):
+        return "QuantizedConvTranspose1d(Reference)"
+
+    @classmethod
+    def from_float(cls, float_conv, weight_qparams):  # type: ignore[override]
+        return _ConvTransposeNd.from_float(cls, float_conv, weight_qparams)
+
+
+class ConvTranspose2d(_ConvTransposeNd, nn.ConvTranspose2d):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        output_padding=0,
+        groups=1,
+        bias=True,
+        dilation=1,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+        weight_qparams: dict[str, Any] | None = None,
+    ):
+        nn.ConvTranspose2d.__init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            output_padding,
+            groups,
+            bias,
+            dilation,
+            # pyrefly: ignore [bad-argument-type]
+            padding_mode,
+            device,
+            dtype,
+        )
+        self._init_weight_qparams(weight_qparams, device)
+
+    def forward(
+        self, x: torch.Tensor, output_size: list[int] | None = None
+    ) -> torch.Tensor:
+        """
+        we have:
+        w(float) -- quant - dequant \
+        x(float) ------------- F.convTranspose2d ---
+        In the full model, we will see
+        w(float) -- quant - *dequant \
+        x -- quant --- *dequant --  *F.convTranspose2d --- *quant - dequant
+        and the backend should be able to fuse the ops with `*` into a quantized conv2d
+        """
+        assert isinstance(self.padding, tuple)
+        # One cannot replace List by Tuple or Sequence in "_output_padding" because
+        # TorchScript does not support `Sequence[T]` or `Tuple[T, ...]`.
+
+        output_padding = self._output_padding(
+            input,  # type: ignore[arg-type]
+            output_size,
+            self.stride,  # type: ignore[arg-type]
+            self.padding,  # type: ignore[arg-type]
+            self.kernel_size,  # type: ignore[arg-type]
+            self.dilation,  # type: ignore[arg-type]
+        )
+
+        weight_quant_dequant = self.get_weight()
+        result = F.conv_transpose2d(
+            x,
+            weight_quant_dequant,
+            self.bias,
+            self.stride,
+            self.padding,
+            output_padding,
+            self.groups,
+            self.dilation,
+        )
+
+        return result
+
+    def _get_name(self):
+        return "QuantizedConvTranspose2d(Reference)"
+
+    @classmethod
+    def from_float(cls, float_conv, weight_qparams):  # type: ignore[override]
+        return _ConvTransposeNd.from_float(cls, float_conv, weight_qparams)
+
+
+class ConvTranspose3d(_ConvTransposeNd, nn.ConvTranspose3d):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride=1,
+        padding=0,
+        output_padding=0,
+        groups=1,
+        bias=True,
+        dilation=1,
+        padding_mode="zeros",
+        device=None,
+        dtype=None,
+        weight_qparams: dict[str, Any] | None = None,
+    ):
+        nn.ConvTranspose3d.__init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding,
+            output_padding,
+            groups,
+            bias,
+            dilation,
+            # pyrefly: ignore [bad-argument-type]
+            padding_mode,
+            device,
+            dtype,
+        )
+        self._init_weight_qparams(weight_qparams, device)
+
+    def forward(
+        self, x: torch.Tensor, output_size: list[int] | None = None
+    ) -> torch.Tensor:
+        """
+        we have:
+        w(float) -- quant - dequant \
+        x(float) ------------- F.convTranspose3d ---
+        In the full model, we will see
+        w(float) -- quant - *dequant \
+        x -- quant --- *dequant --  *F.convTranspose3d --- *quant - dequant
+        and the backend should be able to fuse the ops with `*` into a quantized conv3d
+        """
+
+        assert isinstance(self.padding, tuple)
+        # One cannot replace List by Tuple or Sequence in "_output_padding" because
+        # TorchScript does not support `Sequence[T]` or `Tuple[T, ...]`.
+        output_padding = self._output_padding(
+            input,  # type: ignore[arg-type]
+            output_size,
+            self.stride,  # type: ignore[arg-type]
+            self.padding,  # type: ignore[arg-type]
+            self.kernel_size,  # type: ignore[arg-type]
+            self.dilation,  # type: ignore[arg-type]
+        )
+
+        weight_quant_dequant = self.get_weight()
+        result = F.conv_transpose3d(
+            x,
+            weight_quant_dequant,
+            self.bias,
+            self.stride,
+            self.padding,
+            output_padding,
+            self.groups,
+            self.dilation,
+        )
+        return result
+
+    def _get_name(self):
+        return "QuantizedConvTranspose3d(Reference)"
+
+    @classmethod
+    def from_float(cls, float_conv, weight_qparams):  # type: ignore[override]
+        return _ConvTransposeNd.from_float(cls, float_conv, weight_qparams)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/reference/modules/linear.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/reference/modules/linear.py
new file mode 100644
index 0000000000000000000000000000000000000000..6014fab24036c30b183f5622d12aae4a345baedb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/reference/modules/linear.py
@@ -0,0 +1,69 @@
+from typing import Any
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .utils import ReferenceQuantizedModule
+
+
+__all__ = ["Linear"]
+
+
+class Linear(nn.Linear, ReferenceQuantizedModule):
+    """A reference quantized linear module that fits into the FX
+    Graph Mode Quantization workflow
+    activation will be floating point Tensor, we will store floating
+    point weight as well in the module, but in forward we'll quantize
+    and dequantize the weight before running the floating point functional
+    linear operator.
+    """
+
+    _IS_REFERENCE = True
+
+    def __init__(
+        self,
+        in_features: int,
+        out_features: int,
+        bias_: bool = True,
+        device: torch.device | None = None,
+        dtype: torch.dtype | None = None,
+        weight_qparams: dict[str, Any] | None = None,
+    ) -> None:
+        super().__init__(in_features, out_features, bias_, device, dtype)
+        self._init_weight_qparams(weight_qparams, device)
+
+    def _get_name(self) -> str:
+        return "QuantizedLinear(Reference)"
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        we have:
+        w(float) -- quant - dequant \
+        x(float) ------------- F.linear ---
+
+        In the full model, we will see
+        w(float) -- quant - *dequant \
+        x -- quant --- *dequant --  *F.linear --- *quant - dequant
+        and the backend should be able to fuse the ops with `*` into a quantized linear
+        """
+        weight_quant_dequant = self.get_weight()
+        result = F.linear(x, weight_quant_dequant, self.bias)
+        return result
+
+    @classmethod
+    def from_float(
+        cls, float_linear: nn.Linear, weight_qparams: dict[str, Any]
+    ) -> "Linear":
+        qref_linear = Linear(
+            float_linear.in_features,
+            float_linear.out_features,
+            float_linear.bias is not None,
+            device=float_linear.weight.device,
+            dtype=float_linear.weight.dtype,
+            weight_qparams=weight_qparams,
+        )
+        qref_linear.weight = torch.nn.Parameter(float_linear.weight.detach())
+        if float_linear.bias is not None:
+            qref_linear.bias = torch.nn.Parameter(float_linear.bias.detach())
+        return qref_linear
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/reference/modules/rnn.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/reference/modules/rnn.py
new file mode 100644
index 0000000000000000000000000000000000000000..1bdbfb81430b4db9e09ea752310732b89f47bfa1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/reference/modules/rnn.py
@@ -0,0 +1,861 @@
+# mypy: allow-untyped-defs
+from typing import Any
+
+import torch
+import torch.nn as nn
+from torch import _VF, Tensor
+from torch.nn.utils.rnn import PackedSequence
+
+from .utils import _quantize_and_dequantize_weight, _quantize_weight
+
+
+__all__ = [
+    "RNNCellBase",
+    "RNNCell",
+    "LSTMCell",
+    "GRUCell",
+    "RNNBase",
+    "LSTM",
+    "GRU",
+    "get_quantized_weight",
+]
+
+
+def _apply_permutation(tensor: Tensor, permutation: Tensor, dim: int = 1) -> Tensor:
+    return tensor.index_select(dim, permutation)
+
+
+def _get_weight_and_quantization_params(module, wn):
+    weight = getattr(module, wn)
+    params = [weight]
+    for param_name in [
+        wn + n for n in ["_qscheme", "_dtype", "_scale", "_zero_point", "_axis_int"]
+    ]:
+        if hasattr(module, param_name):
+            param = getattr(module, param_name)
+        else:
+            param = None
+        params.append(param)
+    return params
+
+
+def get_quantized_weight(module, wn):
+    if not hasattr(module, wn):
+        return None
+    params = _get_weight_and_quantization_params(module, wn)
+    weight = _quantize_weight(*params)
+    return weight
+
+
+def _get_quantize_and_dequantized_weight(module, wn):
+    if not hasattr(module, wn):
+        return None
+    params = _get_weight_and_quantization_params(module, wn)
+    weight = _quantize_and_dequantize_weight(*params)
+    return weight
+
+
+class RNNCellBase(nn.RNNCellBase):
+    def __init__(
+        self,
+        input_size: int,
+        hidden_size: int,
+        bias: bool,
+        num_chunks: int,
+        device=None,
+        dtype=None,
+        weight_qparams_dict=None,
+    ) -> None:
+        super().__init__(
+            input_size, hidden_size, bias, num_chunks, device=device, dtype=dtype
+        )
+        # TODO(jerryzh168): maybe make this arg a required arg
+        if weight_qparams_dict is None:
+            weight_qparams = {
+                "qscheme": torch.per_tensor_affine,
+                "dtype": torch.quint8,
+                "scale": 1.0,
+                "zero_point": 0,
+            }
+            weight_qparams_dict = {
+                "weight_ih": weight_qparams,
+                "weight_hh": weight_qparams,
+                "is_decomposed": False,
+            }
+        assert len(weight_qparams_dict) == 3, (
+            "Expected length for weight_qparams_dict to be 3 for QuantizedRNNCellBase(Reference)"
+        )
+        self._init_weight_qparams_dict(weight_qparams_dict, device)
+
+    def _init_weight_qparams_dict(self, weight_qparams_dict, device):
+        assert weight_qparams_dict is not None
+        self.is_decomposed = weight_qparams_dict["is_decomposed"]
+        for key, weight_qparams in weight_qparams_dict.items():
+            if key == "is_decomposed":
+                continue
+            # TODO: refactor the duplicated code to utils.py
+            weight_qscheme = weight_qparams["qscheme"]
+            weight_dtype = weight_qparams["dtype"]
+            setattr(self, key + "_qscheme", weight_qscheme)
+            setattr(self, key + "_dtype", weight_dtype)
+            assert weight_qscheme in [
+                None,
+                torch.per_tensor_affine,
+                torch.per_channel_affine,
+            ], Exception(
+                f"qscheme: {weight_qscheme} is not support in {self._get_name()}"
+            )
+            if weight_qscheme is not None:
+                scale = weight_qparams["scale"]
+                scale_tensor = (
+                    scale.detach().clone()
+                    if isinstance(scale, torch.Tensor)
+                    else torch.tensor(scale, dtype=torch.float, device=device)
+                )
+                self.register_buffer(key + "_scale", scale_tensor)
+                zp = weight_qparams["zero_point"]
+                zp_tensor = (
+                    zp.detach().clone()
+                    if isinstance(zp, torch.Tensor)
+                    else torch.tensor(zp, dtype=torch.int, device=device)
+                )
+                self.register_buffer(key + "_zero_point", zp_tensor)
+                if weight_qscheme == torch.per_channel_affine:
+                    axis = weight_qparams["axis"]
+                    axis_tensor = (
+                        axis.detach().clone()
+                        if isinstance(axis, torch.Tensor)
+                        else torch.tensor(axis, dtype=torch.int, device=device)
+                    )
+                    self.register_buffer(key + "_axis", axis_tensor)
+                else:
+                    # added for TorchScriptability, not used
+                    self.register_buffer(
+                        key + "_axis", torch.tensor(0, dtype=torch.int, device=device)
+                    )
+                setattr(self, key + "_axis_int", getattr(self, key + "_axis").item())
+
+    def _get_name(self):
+        return "QuantizedRNNCellBase(Reference)"
+
+    def get_quantized_weight_ih(self):
+        return get_quantized_weight(self, "weight_ih")
+
+    def get_quantized_weight_hh(self):
+        return get_quantized_weight(self, "weight_hh")
+
+    def get_weight_ih(self):
+        return _get_quantize_and_dequantized_weight(self, "weight_ih")
+
+    def get_weight_hh(self):
+        return _get_quantize_and_dequantized_weight(self, "weight_hh")
+
+
+class RNNCell(RNNCellBase):
+    """
+    We'll store weight_qparams for all the weights (weight_ih and weight_hh),
+    we need to pass in a `weight_qparams_dict` that maps from weight name,
+    e.g. weight_ih, to the weight_qparams for that weight
+    """
+
+    def __init__(
+        self,
+        input_size: int,
+        hidden_size: int,
+        bias: bool = True,
+        nonlinearity: str = "tanh",
+        device=None,
+        dtype=None,
+        weight_qparams_dict: dict[str, Any] | None = None,
+    ) -> None:
+        factory_kwargs = {
+            "device": device,
+            "dtype": dtype,
+            "weight_qparams_dict": weight_qparams_dict,
+        }
+        super().__init__(input_size, hidden_size, bias, num_chunks=1, **factory_kwargs)
+        self.nonlinearity = nonlinearity
+
+    def _get_name(self):
+        return "QuantizedRNNCell(Reference)"
+
+    # TODO: refactor nn.RNNCell to have a _forward that takes weight_ih and weight_hh as input
+    # and remove duplicated code, same for the other two Cell modules
+    def forward(self, input: Tensor, hx: Tensor | None = None) -> Tensor:
+        assert input.dim() in (
+            1,
+            2,
+        ), (
+            f"RNNCell: Expected input to be 1-D or 2-D but received {input.dim()}-D tensor"
+        )
+        is_batched = input.dim() == 2
+        if not is_batched:
+            input = input.unsqueeze(0)
+
+        if hx is None:
+            hx = torch.zeros(
+                input.size(0), self.hidden_size, dtype=input.dtype, device=input.device
+            )
+        else:
+            hx = hx.unsqueeze(0) if not is_batched else hx
+
+        if self.nonlinearity == "tanh":
+            ret = _VF.rnn_tanh_cell(
+                input,
+                hx,
+                self.get_weight_ih(),
+                self.get_weight_hh(),
+                self.bias_ih,
+                self.bias_hh,
+            )
+        elif self.nonlinearity == "relu":
+            ret = _VF.rnn_relu_cell(
+                input,
+                hx,
+                self.get_weight_ih(),
+                self.get_weight_hh(),
+                self.bias_ih,
+                self.bias_hh,
+            )
+        else:
+            ret = input  # TODO: remove when jit supports exception flow
+            raise RuntimeError(f"Unknown nonlinearity: {self.nonlinearity}")
+
+        if not is_batched:
+            ret = ret.squeeze(0)
+
+        return ret
+
+    @classmethod
+    def from_float(cls, mod, weight_qparams_dict):
+        ref_mod = cls(
+            mod.input_size,
+            mod.hidden_size,
+            mod.bias,
+            mod.nonlinearity,
+            mod.weight_ih.device,
+            mod.weight_ih.dtype,
+            weight_qparams_dict,
+        )
+        ref_mod.weight_ih = mod.weight_ih
+        ref_mod.weight_hh = mod.weight_hh
+        ref_mod.bias_ih = mod.bias_ih
+        ref_mod.bias_hh = mod.bias_hh
+        return ref_mod
+
+
+class LSTMCell(RNNCellBase):
+    """
+    We'll store weight_qparams for all the weights (weight_ih and weight_hh),
+    we need to pass in a `weight_qparams_dict` that maps from weight name,
+    e.g. weight_ih, to the weight_qparams for that weight
+    """
+
+    def __init__(
+        self,
+        input_size: int,
+        hidden_size: int,
+        bias: bool = True,
+        device=None,
+        dtype=None,
+        weight_qparams_dict: dict[str, Any] | None = None,
+    ) -> None:
+        factory_kwargs = {
+            "device": device,
+            "dtype": dtype,
+            "weight_qparams_dict": weight_qparams_dict,
+        }
+        super().__init__(input_size, hidden_size, bias, num_chunks=4, **factory_kwargs)
+
+    def _get_name(self):
+        return "QuantizedLSTMCell(Reference)"
+
+    def forward(
+        self, input: Tensor, hx: tuple[Tensor, Tensor] | None = None
+    ) -> tuple[Tensor, Tensor]:
+        assert input.dim() in (
+            1,
+            2,
+        ), (
+            f"LSTMCell: Expected input to be 1-D or 2-D but received {input.dim()}-D tensor"
+        )
+        is_batched = input.dim() == 2
+        if not is_batched:
+            input = input.unsqueeze(0)
+
+        if hx is None:
+            zeros = torch.zeros(
+                input.size(0), self.hidden_size, dtype=input.dtype, device=input.device
+            )
+            hx = (zeros, zeros)
+        else:
+            hx = (hx[0].unsqueeze(0), hx[1].unsqueeze(0)) if not is_batched else hx
+
+        ret = _VF.lstm_cell(
+            input,
+            hx,
+            self.get_weight_ih(),
+            self.get_weight_hh(),
+            self.bias_ih,
+            self.bias_hh,
+        )
+
+        if not is_batched:
+            ret = (ret[0].squeeze(0), ret[1].squeeze(0))
+        return ret
+
+    @classmethod
+    def from_float(cls, mod, weight_qparams_dict, use_precomputed_fake_quant=False):
+        ref_mod = cls(
+            mod.input_size,
+            mod.hidden_size,
+            mod.bias,
+            mod.weight_ih.device,
+            mod.weight_ih.dtype,
+            weight_qparams_dict,
+        )
+        ref_mod.weight_ih = mod.weight_ih
+        ref_mod.weight_hh = mod.weight_hh
+        ref_mod.bias_ih = mod.bias_ih
+        ref_mod.bias_hh = mod.bias_hh
+        return ref_mod
+
+
+class GRUCell(RNNCellBase):
+    """
+    We'll store weight_qparams for all the weights (weight_ih and weight_hh),
+    we need to pass in a `weight_qparams_dict` that maps from weight name,
+    e.g. weight_ih, to the weight_qparams for that weight
+    """
+
+    def __init__(
+        self,
+        input_size: int,
+        hidden_size: int,
+        bias: bool = True,
+        device=None,
+        dtype=None,
+        weight_qparams_dict: dict[str, Any] | None = None,
+    ) -> None:
+        factory_kwargs = {
+            "device": device,
+            "dtype": dtype,
+            "weight_qparams_dict": weight_qparams_dict,
+        }
+        super().__init__(input_size, hidden_size, bias, num_chunks=3, **factory_kwargs)
+
+    def _get_name(self):
+        return "QuantizedGRUCell(Reference)"
+
+    def forward(self, input: Tensor, hx: Tensor | None = None) -> Tensor:
+        assert input.dim() in (
+            1,
+            2,
+        ), (
+            f"GRUCell: Expected input to be 1-D or 2-D but received {input.dim()}-D tensor"
+        )
+        is_batched = input.dim() == 2
+        if not is_batched:
+            input = input.unsqueeze(0)
+
+        if hx is None:
+            hx = torch.zeros(
+                input.size(0), self.hidden_size, dtype=input.dtype, device=input.device
+            )
+        else:
+            hx = hx.unsqueeze(0) if not is_batched else hx
+
+        ret = _VF.gru_cell(
+            input,
+            hx,
+            self.get_weight_ih(),
+            self.get_weight_hh(),
+            self.bias_ih,
+            self.bias_hh,
+        )
+
+        if not is_batched:
+            ret = ret.squeeze(0)
+
+        return ret
+
+    @classmethod
+    def from_float(cls, mod, weight_qparams_dict):
+        ref_mod = cls(
+            mod.input_size,
+            mod.hidden_size,
+            mod.bias,
+            mod.weight_ih.device,
+            mod.weight_ih.dtype,
+            weight_qparams_dict,
+        )
+        ref_mod.weight_ih = mod.weight_ih
+        ref_mod.weight_hh = mod.weight_hh
+        ref_mod.bias_ih = mod.bias_ih
+        ref_mod.bias_hh = mod.bias_hh
+        return ref_mod
+
+
+class RNNBase(nn.RNNBase):
+    def __init__(
+        self,
+        mode: str,
+        input_size: int,
+        hidden_size: int,
+        num_layers: int = 1,
+        bias: bool = True,
+        batch_first: bool = False,
+        dropout: float = 0.0,
+        bidirectional: bool = False,
+        proj_size: int = 0,
+        device=None,
+        dtype=None,
+        weight_qparams_dict: dict[str, Any] | None = None,
+    ) -> None:
+        super().__init__(
+            mode,
+            input_size,
+            hidden_size,
+            num_layers,
+            bias,
+            batch_first,
+            dropout,
+            bidirectional,
+            proj_size,
+            device,
+            dtype,
+        )
+        # TODO(jerryzh168): maybe make this arg a required arg
+        if weight_qparams_dict is None:
+            weight_qparams = {
+                "qscheme": torch.per_tensor_affine,
+                "dtype": torch.quint8,
+                "scale": 1.0,
+                "zero_point": 0,
+            }
+            weight_qparams_dict = {"is_decomposed": False}  # type: ignore[dict-item]
+            for wn in self._flat_weights_names:
+                if wn.startswith("weight"):
+                    weight_qparams_dict[wn] = weight_qparams
+        self._init_weight_qparams_dict(weight_qparams_dict, device)
+
+    def _init_weight_qparams_dict(self, weight_qparams_dict, device):
+        self.is_decomposed = weight_qparams_dict["is_decomposed"]
+        for key, weight_qparams in weight_qparams_dict.items():
+            if key == "is_decomposed":
+                continue
+            weight_qscheme = weight_qparams["qscheme"]
+            weight_dtype = weight_qparams["dtype"]
+            setattr(self, key + "_qscheme", weight_qscheme)
+            setattr(self, key + "_dtype", weight_dtype)
+            assert weight_qscheme in [
+                None,
+                torch.per_tensor_affine,
+                torch.per_channel_affine,
+            ], Exception(
+                f"qscheme: {weight_qscheme} is not support in {self._get_name()}"
+            )
+            if weight_qscheme is not None:
+                self.register_buffer(
+                    key + "_scale",
+                    torch.tensor(
+                        weight_qparams["scale"], dtype=torch.float, device=device
+                    ),
+                )
+                self.register_buffer(
+                    key + "_zero_point",
+                    torch.tensor(
+                        weight_qparams["zero_point"], dtype=torch.int, device=device
+                    ),
+                )
+                if weight_qscheme == torch.per_channel_affine:
+                    self.register_buffer(
+                        key + "_axis",
+                        torch.tensor(
+                            weight_qparams["axis"], dtype=torch.int, device=device
+                        ),
+                    )
+                else:
+                    # added for TorchScriptability, not used
+                    self.register_buffer(
+                        key + "_axis", torch.tensor(0, dtype=torch.int, device=device)
+                    )
+                setattr(self, key + "_axis_int", getattr(self, key + "_axis").item())
+
+
+class LSTM(RNNBase):
+    """Reference Quantized LSTM Module
+    We'll store weight_qparams for all the weights in _flat_weights, we need to pass in
+    a `weight_qparams_dict` that maps from weight name, e.g. weight_ih_l0,
+    to the weight_qparams for that weight
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__("LSTM", *args, **kwargs)
+
+    # Same as above, see torch/nn/modules/module.py::_forward_unimplemented
+    def permute_hidden(  # type: ignore[override]
+        self,
+        hx: tuple[Tensor, Tensor],
+        permutation: Tensor | None,
+    ) -> tuple[Tensor, Tensor]:
+        if permutation is None:
+            return hx
+        return _apply_permutation(hx[0], permutation), _apply_permutation(
+            hx[1], permutation
+        )
+
+    def get_expected_cell_size(
+        self, input: Tensor, batch_sizes: Tensor | None
+    ) -> tuple[int, int, int]:
+        if batch_sizes is not None:
+            mini_batch = int(batch_sizes[0])
+        else:
+            mini_batch = input.size(0) if self.batch_first else input.size(1)
+        num_directions = 2 if self.bidirectional else 1
+        expected_hidden_size = (
+            self.num_layers * num_directions,
+            mini_batch,
+            self.hidden_size,
+        )
+        return expected_hidden_size
+
+    # In the future, we should prevent mypy from applying contravariance rules here.
+    # See torch/nn/modules/module.py::_forward_unimplemented
+    def check_forward_args(  # type: ignore[override]
+        self,
+        input: Tensor,
+        hidden: tuple[Tensor, Tensor],
+        batch_sizes: Tensor | None,
+    ):
+        self.check_input(input, batch_sizes)
+        self.check_hidden_size(
+            hidden[0],
+            self.get_expected_hidden_size(input, batch_sizes),
+            "Expected hidden[0] size {}, got {}",
+        )
+        self.check_hidden_size(
+            hidden[1],
+            self.get_expected_cell_size(input, batch_sizes),
+            "Expected hidden[1] size {}, got {}",
+        )
+
+    def get_quantized_weight_bias_dict(self):
+        """dictionary from flat_weight_name to quantized weight or (unquantized) bias
+        e.g.
+        {
+          "weight_ih_l0": quantized_weight,
+          "bias_ih_l0": unquantized_bias,
+          ...
+        }
+        """
+        quantized_weight_bias_dict = {}
+        for wn in self._flat_weights_names:
+            if hasattr(self, wn):
+                if wn.startswith("weight"):
+                    weight_or_bias = get_quantized_weight(self, wn)
+                else:
+                    weight_or_bias = getattr(self, wn)
+            else:
+                weight_or_bias = None
+            quantized_weight_bias_dict[wn] = weight_or_bias
+        return quantized_weight_bias_dict
+
+    def get_flat_weights(self):
+        flat_weights = []
+        for wn in self._flat_weights_names:
+            if hasattr(self, wn):
+                weight = getattr(self, wn)
+                if wn.startswith("weight"):
+                    params = _get_weight_and_quantization_params(self, wn)
+                    weight = _quantize_and_dequantize_weight(*params)
+            else:
+                weight = None
+            flat_weights.append(weight)
+        return flat_weights
+
+    def forward(self, input, hx=None):  # noqa: F811
+        orig_input = input
+        # xxx: isinstance check needs to be in conditional for TorchScript to compile
+        batch_sizes = None
+        if isinstance(orig_input, PackedSequence):
+            input, batch_sizes, sorted_indices, unsorted_indices = input
+            max_batch_size = int(batch_sizes[0])
+        else:
+            batch_sizes = None
+            is_batched = input.dim() == 3
+            batch_dim = 0 if self.batch_first else 1
+            if not is_batched:
+                input = input.unsqueeze(batch_dim)
+            max_batch_size = input.size(0) if self.batch_first else input.size(1)
+            sorted_indices = None
+            unsorted_indices = None
+
+        if hx is None:
+            num_directions = 2 if self.bidirectional else 1
+            real_hidden_size = (
+                self.proj_size if self.proj_size > 0 else self.hidden_size
+            )
+            h_zeros = torch.zeros(
+                self.num_layers * num_directions,
+                max_batch_size,
+                real_hidden_size,
+                dtype=input.dtype,
+                device=input.device,
+            )
+            c_zeros = torch.zeros(
+                self.num_layers * num_directions,
+                max_batch_size,
+                self.hidden_size,
+                dtype=input.dtype,
+                device=input.device,
+            )
+            hx = (h_zeros, c_zeros)
+        else:
+            if batch_sizes is None:  # If not PackedSequence input.
+                if is_batched:  # type: ignore[possibly-undefined]
+                    if hx[0].dim() != 3 or hx[1].dim() != 3:
+                        msg = (
+                            "For batched 3-D input, hx and cx should "
+                            f"also be 3-D but got ({hx[0].dim()}-D, {hx[1].dim()}-D) tensors"
+                        )
+                        raise RuntimeError(msg)
+                else:
+                    if hx[0].dim() != 2 or hx[1].dim() != 2:
+                        msg = (
+                            "For unbatched 2-D input, hx and cx should "
+                            f"also be 2-D but got ({hx[0].dim()}-D, {hx[1].dim()}-D) tensors"
+                        )
+                        raise RuntimeError(msg)
+                    hx = (hx[0].unsqueeze(1), hx[1].unsqueeze(1))
+
+            # Each batch of the hidden state should match the input sequence that
+            # the user believes he/she is passing in.
+            hx = self.permute_hidden(hx, sorted_indices)
+
+        self.check_forward_args(input, hx, batch_sizes)
+        if batch_sizes is None:
+            result = _VF.lstm(
+                input,
+                hx,
+                self.get_flat_weights(),
+                self.bias,
+                self.num_layers,
+                self.dropout,
+                self.training,
+                self.bidirectional,
+                self.batch_first,
+            )
+        else:
+            result = _VF.lstm(
+                input,
+                batch_sizes,
+                hx,
+                self.get_flat_weights(),
+                self.bias,
+                self.num_layers,
+                self.dropout,
+                self.training,
+                self.bidirectional,
+            )
+        output = result[0]
+        hidden = result[1:]
+        # xxx: isinstance check needs to be in conditional for TorchScript to compile
+        if isinstance(orig_input, PackedSequence):
+            output_packed = PackedSequence(
+                output,
+                # pyrefly: ignore [bad-argument-type]
+                batch_sizes,
+                sorted_indices,
+                unsorted_indices,
+            )
+            return output_packed, self.permute_hidden(hidden, unsorted_indices)
+        else:
+            if not is_batched:  # type: ignore[possibly-undefined]
+                output = output.squeeze(batch_dim)  # type: ignore[possibly-undefined]
+                hidden = (hidden[0].squeeze(1), hidden[1].squeeze(1))
+            return output, self.permute_hidden(hidden, unsorted_indices)
+
+    def _get_name(self):
+        return "QuantizedLSTM(Reference)"
+
+    @classmethod
+    def from_float(cls, mod, weight_qparams_dict):
+        ref_mod = cls(
+            mod.input_size,
+            mod.hidden_size,
+            mod.num_layers,
+            mod.bias,
+            mod.batch_first,
+            mod.dropout,
+            mod.bidirectional,
+            weight_qparams_dict=weight_qparams_dict,
+        )
+        for wn in mod._flat_weights_names:
+            setattr(ref_mod, wn, getattr(mod, wn))
+        return ref_mod
+
+
+class GRU(RNNBase):
+    """Reference Quantized GRU Module
+    We'll store weight_qparams for all the weights in _flat_weights, we need to pass in
+    a `weight_qparams_dict` that maps from weight name, e.g. weight_ih_l0,
+    to the weight_qparams for that weight
+    """
+
+    def __init__(self, *args, **kwargs):
+        if "proj_size" in kwargs:
+            raise ValueError(
+                "proj_size argument is only supported for LSTM, not RNN or GRU"
+            )
+        super().__init__("GRU", *args, **kwargs)
+
+    def get_quantized_weight_bias_dict(self):
+        """dictionary from flat_weight_name to quantized weight or (unquantized) bias
+        e.g.
+        {
+          "weight_ih_l0": quantized_weight,
+          "bias_ih_l0": unquantized_bias,
+          ...
+        }
+        """
+        quantized_weight_bias_dict = {}
+        for wn in self._flat_weights_names:
+            if hasattr(self, wn):
+                if wn.startswith("weight"):
+                    weight_or_bias = get_quantized_weight(self, wn)
+                else:
+                    weight_or_bias = getattr(self, wn)
+            else:
+                weight_or_bias = None
+            quantized_weight_bias_dict[wn] = weight_or_bias
+        return quantized_weight_bias_dict
+
+    def get_flat_weights(self):
+        flat_weights = []
+        for wn in self._flat_weights_names:
+            if hasattr(self, wn):
+                weight = getattr(self, wn)
+                if wn.startswith("weight"):
+                    params = _get_weight_and_quantization_params(self, wn)
+                    weight = _quantize_and_dequantize_weight(*params)
+            else:
+                weight = None
+            flat_weights.append(weight)
+        return flat_weights
+
+    def forward(self, input, hx=None):  # noqa: F811
+        # Note: this is copied from the forward of GRU in https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/rnn.py
+        # only changed self._flat_weights to self.get_flat_weights()
+        # TODO: maybe we can try inheriting from that class and define get_flat_weights
+        # as a @property? this might interfere with TorchScript, if we remove that
+        # requirement in the future we should be able to do this
+        orig_input = input
+        # xxx: isinstance check needs to be in conditional for TorchScript to compile
+        if isinstance(orig_input, PackedSequence):
+            input, batch_sizes, sorted_indices, unsorted_indices = input
+            max_batch_size = int(batch_sizes[0])
+        else:
+            batch_sizes = None
+            assert input.dim() in (
+                2,
+                3,
+            ), (
+                f"GRU: Expected input to be 2-D or 3-D but received {input.dim()}-D tensor"
+            )
+            is_batched = input.dim() == 3
+            batch_dim = 0 if self.batch_first else 1
+            if not is_batched:
+                input = input.unsqueeze(batch_dim)
+                if hx is not None:
+                    if hx.dim() != 2:
+                        raise RuntimeError(
+                            f"For unbatched 2-D input, hx should also be 2-D but got {hx.dim()}-D tensor"
+                        )
+                    hx = hx.unsqueeze(1)
+            else:
+                if hx is not None and hx.dim() != 3:
+                    raise RuntimeError(
+                        f"For batched 3-D input, hx should also be 3-D but got {hx.dim()}-D tensor"
+                    )
+            max_batch_size = input.size(0) if self.batch_first else input.size(1)
+            sorted_indices = None
+            unsorted_indices = None
+
+        if hx is None:
+            num_directions = 2 if self.bidirectional else 1
+            hx = torch.zeros(
+                self.num_layers * num_directions,
+                max_batch_size,
+                self.hidden_size,
+                dtype=input.dtype,
+                device=input.device,
+            )
+        else:
+            # Each batch of the hidden state should match the input sequence that
+            # the user believes he/she is passing in.
+            hx = self.permute_hidden(hx, sorted_indices)
+
+        self.check_forward_args(input, hx, batch_sizes)
+        if batch_sizes is None:
+            result = _VF.gru(
+                input,
+                hx,
+                self.get_flat_weights(),
+                self.bias,
+                self.num_layers,
+                self.dropout,
+                self.training,
+                self.bidirectional,
+                self.batch_first,
+            )
+        else:
+            result = _VF.gru(
+                input,
+                batch_sizes,
+                hx,
+                self.get_flat_weights(),
+                self.bias,
+                self.num_layers,
+                self.dropout,
+                self.training,
+                self.bidirectional,
+            )
+        output = result[0]
+        hidden = result[1]
+
+        # xxx: isinstance check needs to be in conditional for TorchScript to compile
+        if isinstance(orig_input, PackedSequence):
+            output_packed = PackedSequence(
+                output,
+                # pyrefly: ignore [bad-argument-type]
+                batch_sizes,
+                sorted_indices,
+                unsorted_indices,
+            )
+            return output_packed, self.permute_hidden(hidden, unsorted_indices)
+        else:
+            if not is_batched:  # type: ignore[possibly-undefined]
+                output = output.squeeze(batch_dim)  # type: ignore[possibly-undefined]
+                hidden = hidden.squeeze(1)
+
+            return output, self.permute_hidden(hidden, unsorted_indices)
+
+    def _get_name(self):
+        return "QuantizedGRU(Reference)"
+
+    @classmethod
+    def from_float(cls, mod, weight_qparams_dict):
+        ref_mod = cls(
+            mod.input_size,
+            mod.hidden_size,
+            mod.num_layers,
+            mod.bias,
+            mod.batch_first,
+            mod.dropout,
+            mod.bidirectional,
+            weight_qparams_dict=weight_qparams_dict,
+        )
+        for wn in mod._flat_weights_names:
+            setattr(ref_mod, wn, getattr(mod, wn))
+        return ref_mod
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/reference/modules/sparse.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/reference/modules/sparse.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ff80997c1439c50a456df328b4068ae0c419a01
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/reference/modules/sparse.py
@@ -0,0 +1,163 @@
+# mypy: allow-untyped-defs
+from typing import Any
+
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import Tensor
+
+from .utils import ReferenceQuantizedModule
+
+
+__all__ = ["Embedding", "EmbeddingBag"]
+
+
+class Embedding(nn.Embedding, ReferenceQuantizedModule):
+    """A reference quantized Embedding module that fits into the
+    FX Graph Mode Quantization workflow, activation will be floating point Tensor,
+    we will store floating point weight as well in the module, but in forward we'll
+    quantize and dequantize the weight before running the floating point functional
+    embedding operator.
+    """
+
+    def __init__(
+        self,
+        num_embeddings: int,
+        embedding_dim: int,
+        padding_idx: int | None = None,
+        max_norm: float | None = None,
+        norm_type: float = 2.0,
+        scale_grad_by_freq: bool = False,
+        sparse: bool = False,
+        _weight: Tensor | None = None,
+        device=None,
+        dtype=None,
+        weight_qparams: dict[str, Any] | None = None,
+    ) -> None:
+        super().__init__(
+            num_embeddings,
+            embedding_dim,
+            padding_idx,
+            max_norm,
+            norm_type,
+            scale_grad_by_freq,
+            sparse,
+            _weight,
+            # pyrefly: ignore [bad-argument-type]
+            device,
+            dtype,
+        )
+        self._init_weight_qparams(weight_qparams, device)
+
+    def _get_name(self):
+        return "QuantizedEmbedding(Reference)"
+
+    def forward(self, input: Tensor) -> Tensor:
+        weight_quant_dequant = self.get_weight()
+        return F.embedding(
+            input,
+            weight_quant_dequant,
+            self.padding_idx,
+            self.max_norm,
+            self.norm_type,
+            self.scale_grad_by_freq,
+            self.sparse,
+        )
+
+    @classmethod
+    def from_float(cls, mod, weight_qparams):
+        return cls(
+            mod.num_embeddings,
+            mod.embedding_dim,
+            mod.padding_idx,
+            mod.max_norm,
+            mod.norm_type,
+            mod.scale_grad_by_freq,
+            mod.sparse,
+            mod.weight,
+            mod.weight.device,
+            mod.weight.dtype,
+            weight_qparams,
+        )
+
+
+class EmbeddingBag(nn.EmbeddingBag, ReferenceQuantizedModule):
+    """A reference quantized EmbeddingBag module that fits into the
+    FX Graph Mode Quantization workflow, activation will be floating point Tensor,
+    we will store floating point weight as well in the module, but in forward we'll
+    quantize and dequantize the weight before running the floating point functional
+    embedding operator.
+    """
+
+    def __init__(
+        self,
+        num_embeddings: int,
+        embedding_dim: int,
+        max_norm: float | None = None,
+        norm_type: float = 2.0,
+        scale_grad_by_freq: bool = False,
+        mode: str = "mean",
+        sparse: bool = False,
+        _weight: Tensor | None = None,
+        include_last_offset: bool = False,
+        padding_idx: int | None = None,
+        device=None,
+        dtype=None,
+        weight_qparams: dict[str, Any] | None = None,
+    ) -> None:
+        super().__init__(
+            num_embeddings,
+            embedding_dim,
+            max_norm,
+            norm_type,
+            scale_grad_by_freq,
+            mode,
+            sparse,
+            _weight,
+            include_last_offset,
+            padding_idx,
+            device,
+            dtype,
+        )
+        self._init_weight_qparams(weight_qparams, device)
+
+    def _get_name(self):
+        return "QuantizedEmbedding(Reference)"
+
+    def forward(
+        self,
+        input: Tensor,
+        offsets: Tensor | None = None,
+        per_sample_weights: Tensor | None = None,
+    ) -> Tensor:
+        weight_quant_dequant = self.get_weight()
+        return F.embedding_bag(
+            input,
+            weight_quant_dequant,
+            offsets,
+            self.max_norm,
+            self.norm_type,
+            self.scale_grad_by_freq,
+            self.mode,
+            self.sparse,
+            per_sample_weights,
+            self.include_last_offset,
+            self.padding_idx,
+        )
+
+    @classmethod
+    def from_float(cls, mod, weight_qparams, use_precomputed_fake_quant=False):
+        return cls(
+            mod.num_embeddings,
+            mod.embedding_dim,
+            mod.max_norm,
+            mod.norm_type,
+            mod.scale_grad_by_freq,
+            mod.mode,
+            mod.sparse,
+            mod.weight,
+            mod.include_last_offset,
+            mod.padding_idx,
+            mod.weight.device,
+            mod.weight.dtype,
+            weight_qparams,
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/reference/modules/utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/reference/modules/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..7bdbcd4a6739e528e679c67b6a6614ea373801d3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/quantized/reference/modules/utils.py
@@ -0,0 +1,438 @@
+# mypy: allow-untyped-defs
+import typing
+
+import torch
+
+
+__all__ = [
+    "ReferenceQuantizedModule",
+]
+
+
+class ReferenceQuantizedModule(torch.nn.Module):
+    def _init_weight_qparams(self, weight_qparams, device):
+        if weight_qparams is None:
+            weight_qparams = {
+                "qscheme": torch.per_tensor_affine,
+                "dtype": torch.quint8,
+                "scale": 1.0,
+                "zero_point": 0,
+            }
+        # pyrefly: ignore [bad-assignment]
+        self.weight_qscheme: torch.qscheme = weight_qparams["qscheme"]
+        self.weight_dtype = weight_qparams["dtype"]
+        assert self.weight_qscheme in [
+            None,
+            torch.per_tensor_affine,
+            torch.per_channel_affine,
+            torch.per_channel_affine_float_qparams,
+        ], (
+            f"qscheme: {self.weight_qscheme} is not support in reference quantized {self._get_name()}"
+        )
+        if self.weight_dtype in [
+            torch.quint8,
+            torch.qint8,
+            torch.quint4x2,
+            torch.qint32,
+        ]:
+            zero_point_dtype = (
+                weight_qparams["zero_point"].dtype
+                if isinstance(weight_qparams["zero_point"], torch.Tensor)
+                else torch.int
+            )
+            w_scale = weight_qparams["scale"]
+            w_scale_tensor = (
+                w_scale.detach().clone()
+                if isinstance(w_scale, torch.Tensor)
+                else torch.tensor(w_scale, dtype=torch.float, device=device)
+            )
+            self.register_buffer("weight_scale", w_scale_tensor)
+            w_zp = weight_qparams["zero_point"]
+            w_zp_tensor = (
+                w_zp.detach().clone()
+                if isinstance(w_zp, torch.Tensor)
+                else torch.tensor(w_zp, dtype=zero_point_dtype, device=device)
+            )
+            self.register_buffer("weight_zero_point", w_zp_tensor)
+            if self.weight_qscheme in [
+                torch.per_channel_affine,
+                torch.per_channel_affine_float_qparams,
+            ]:
+                w_axis = weight_qparams["axis"]
+                w_axis_tensor = (
+                    w_axis.detach().clone()
+                    if isinstance(w_axis, torch.Tensor)
+                    else torch.tensor(w_axis, dtype=torch.int, device=device)
+                )
+                self.register_buffer("weight_axis", w_axis_tensor)
+            else:
+                # added for TorchScriptability, not used
+                self.register_buffer(
+                    "weight_axis", torch.tensor(0, dtype=torch.int, device=device)
+                )
+        else:
+            # added for TorchScriptability, and for torch.float
+            self.register_buffer(
+                "weight_scale", torch.tensor(1.0, dtype=torch.float, device=device)
+            )
+            self.register_buffer(
+                "weight_zero_point", torch.tensor(0, dtype=torch.int, device=device)
+            )
+            self.register_buffer(
+                "weight_axis", torch.tensor(0, dtype=torch.int, device=device)
+            )
+        # pyrefly: ignore [bad-assignment]
+        self.is_decomposed: bool = weight_qparams.get("is_decomposed", False)
+        # store weight_axis as weight_axis_int due to some constraints of torchdynamo.export
+        # for capturing `.item` operations
+        self.weight_axis_int: int = self.weight_axis.item()  # type: ignore[operator, assignment]
+        # pyrefly: ignore [bad-assignment]
+        self.weight_quant_min: int | None = weight_qparams.get("quant_min")
+        # pyrefly: ignore [bad-assignment]
+        self.weight_quant_max: int | None = weight_qparams.get("quant_max")
+
+    def get_weight(self):
+        """
+        Fake quantize (quantize and dequantize) the weight with
+        the quantization parameters for weight, this is used to
+        simulate the numerics for the quantized weight in a quantized
+        model
+        """
+        # suppress mypy warning
+        assert isinstance(self.weight_scale, torch.Tensor)
+        assert isinstance(self.weight_zero_point, torch.Tensor)
+        if self.is_decomposed:
+            return _quantize_and_dequantize_weight_decomposed(
+                self.weight,  # type: ignore[arg-type]
+                self.weight_qscheme,
+                # pyrefly: ignore [bad-argument-type]
+                self.weight_dtype,
+                self.weight_scale,
+                self.weight_zero_point,
+                self.weight_axis_int,
+                self.weight_quant_min,
+                self.weight_quant_max,
+            )
+        else:
+            return _quantize_and_dequantize_weight(
+                self.weight,  # type: ignore[arg-type]
+                self.weight_qscheme,
+                # pyrefly: ignore [bad-argument-type]
+                self.weight_dtype,
+                self.weight_scale,
+                self.weight_zero_point,
+                self.weight_axis_int,
+            )
+
+    def get_quantized_weight(self):
+        # suppress mypy warning
+        assert isinstance(self.weight_scale, torch.Tensor)
+        assert isinstance(self.weight_zero_point, torch.Tensor)
+        # assert isinstance(self.weight_axis, torch.Tensor)
+        if self.is_decomposed:
+            return _quantize_weight_decomposed(
+                self.weight,  # type: ignore[arg-type]
+                self.weight_qscheme,
+                # pyrefly: ignore [bad-argument-type]
+                self.weight_dtype,
+                self.weight_scale,
+                self.weight_zero_point,
+                self.weight_axis_int,
+                self.weight_quant_min,
+                self.weight_quant_max,
+            )
+        else:
+            return _quantize_weight(
+                self.weight,  # type: ignore[arg-type]
+                self.weight_qscheme,
+                # pyrefly: ignore [bad-argument-type]
+                self.weight_dtype,
+                self.weight_scale,
+                self.weight_zero_point,
+                self.weight_axis_int,
+            )
+
+    def _save_to_state_dict(self, destination, prefix, keep_vars):
+        super()._save_to_state_dict(destination, prefix, keep_vars)
+        _save_weight_qparams(
+            destination,
+            prefix,
+            self.weight_qscheme,
+            self.weight_dtype,
+            self.weight_scale,
+            self.weight_zero_point,
+            self.weight_axis,
+        )
+
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        for key in _get_weight_qparam_keys(state_dict, prefix):
+            setattr(self, key, state_dict[prefix + key])
+            state_dict.pop(prefix + key)
+
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            False,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+
+def _quantize_weight_decomposed(
+    weight: torch.Tensor,
+    weight_qscheme: torch.qscheme,
+    weight_dtype: torch.dtype,
+    weight_scale: torch.Tensor,
+    weight_zero_point: torch.Tensor,
+    weight_axis: int,
+    weight_quant_min: int | None,
+    weight_quant_max: int | None,
+) -> torch.Tensor:
+    _DTYPE_TO_QVALUE_BOUNDS: dict[torch.dtype, tuple[int, int]] = {
+        torch.uint8: (0, 255),
+        torch.int8: (-128, 127),
+        torch.int32: (-2147483648, 2147483647),  # torch.jit interprets 2**31 as a float
+    }
+
+    # TODO: add an util function for converting qdtype to dtype
+    _QDTYPE_TO_UNDERLYING_INT_REPR_DTYPE = {
+        torch.quint8: torch.uint8,
+        torch.qint8: torch.int8,
+        torch.qint32: torch.int32,
+    }
+    if weight_qscheme == torch.per_tensor_affine:
+        if weight_dtype in [torch.quint8, torch.qint8, torch.qint32]:
+            weight_dtype_ = _QDTYPE_TO_UNDERLYING_INT_REPR_DTYPE[weight_dtype]
+            if weight_quant_min is None or weight_quant_max is None:
+                weight_quant_min, weight_quant_max = _DTYPE_TO_QVALUE_BOUNDS[
+                    weight_dtype_
+                ]
+            weight = torch.ops.quantized_decomposed.quantize_per_tensor(
+                weight,
+                weight_scale,
+                weight_zero_point,
+                weight_quant_min,
+                weight_quant_max,
+                weight_dtype_,
+            )
+            return weight
+    elif weight_qscheme in [
+        torch.per_channel_affine,
+        torch.per_channel_affine_float_qparams,
+    ]:
+        # TODO: torch.quint4x2 is not supported
+        if weight_dtype in [torch.quint8, torch.qint8, torch.qint32]:
+            weight_dtype_ = _QDTYPE_TO_UNDERLYING_INT_REPR_DTYPE[weight_dtype]
+            if weight_quant_min is None or weight_quant_max is None:
+                weight_quant_min, weight_quant_max = _DTYPE_TO_QVALUE_BOUNDS[
+                    weight_dtype_
+                ]
+            weight = torch.ops.quantized_decomposed.quantize_per_channel(
+                weight,
+                weight_scale,
+                weight_zero_point,
+                weight_axis,
+                weight_quant_min,
+                weight_quant_max,
+                weight_dtype_,
+            )  # type: ignore[arg-type]
+            return weight
+    raise ValueError(f"Unsupported dtype and qscheme: {weight_dtype}, {weight_qscheme}")
+
+
+def _dequantize_weight_decomposed(
+    weight: torch.Tensor,
+    weight_qscheme: torch.qscheme,
+    weight_dtype: torch.dtype,
+    weight_scale: torch.Tensor,
+    weight_zero_point: torch.Tensor,
+    weight_axis: int,
+    weight_quant_min: int | None,
+    weight_quant_max: int | None,
+) -> torch.Tensor:
+    # TODO: get the quant_min and quant_max from activation_post_process
+    _DTYPE_TO_QVALUE_BOUNDS: dict[torch.dtype, tuple[int, int]] = {
+        torch.uint8: (0, 255),
+        torch.int8: (-128, 127),
+        torch.int32: (-2147483648, 2147483647),  # torch.jit interprets 2**31 as a float
+    }
+    # TODO: add an util function for converting qdtype to dtype
+    _QDTYPE_TO_UNDERLYING_INT_REPR_DTYPE = {
+        torch.quint8: torch.uint8,
+        torch.qint8: torch.int8,
+        torch.qint32: torch.int32,
+    }
+    weight_dtype_ = _QDTYPE_TO_UNDERLYING_INT_REPR_DTYPE[weight_dtype]
+    if weight_quant_min is None or weight_quant_max is None:
+        weight_quant_min, weight_quant_max = _DTYPE_TO_QVALUE_BOUNDS[weight_dtype_]
+    if weight_qscheme == torch.per_tensor_affine:
+        if weight_dtype in [torch.quint8, torch.qint8, torch.qint32]:
+            weight = torch.ops.quantized_decomposed.dequantize_per_tensor(
+                weight,
+                weight_scale,
+                weight_zero_point,
+                weight_quant_min,
+                weight_quant_max,
+                weight_dtype_,
+            )
+            return weight
+    elif weight_qscheme in [
+        torch.per_channel_affine,
+        torch.per_channel_affine_float_qparams,
+    ]:
+        # TODO: torch.quint4x2 is not supported
+        if weight_dtype in [torch.quint8, torch.qint8, torch.qint32]:
+            weight = torch.ops.quantized_decomposed.dequantize_per_channel(
+                weight,
+                weight_scale,
+                weight_zero_point,
+                weight_axis,
+                weight_quant_min,
+                weight_quant_max,
+                weight_dtype_,
+            )  # type: ignore[arg-type]
+            return weight
+    raise ValueError(f"Unsupported dtype and qscheme: {weight_dtype}, {weight_qscheme}")
+
+
+def _quantize_weight(
+    weight: torch.Tensor,
+    weight_qscheme: torch.qscheme,
+    weight_dtype: torch.dtype,
+    weight_scale: torch.Tensor,
+    weight_zero_point: torch.Tensor,
+    weight_axis_int: int,
+) -> torch.Tensor:
+    if weight_dtype == torch.float16:
+        weight = weight.to(weight_dtype)
+        return weight
+
+    if weight_qscheme == torch.per_tensor_affine:
+        if weight_dtype in [torch.quint8, torch.qint8, torch.qint32]:
+            weight = torch.quantize_per_tensor(
+                weight, weight_scale, weight_zero_point, weight_dtype
+            )
+            return weight
+    elif weight_qscheme in [
+        torch.per_channel_affine,
+        torch.per_channel_affine_float_qparams,
+    ]:
+        if weight_dtype in [torch.quint8, torch.qint8, torch.quint4x2, torch.qint32]:
+            weight = torch.quantize_per_channel(
+                weight, weight_scale, weight_zero_point, weight_axis_int, weight_dtype
+            )  # type: ignore[arg-type]
+            return weight
+    raise ValueError(f"Unsupported dtype and qscheme: {weight_dtype}, {weight_qscheme}")
+
+
+def _quantize_and_dequantize_weight_decomposed(
+    weight: torch.Tensor,
+    weight_qscheme: torch.qscheme,
+    weight_dtype: torch.dtype,
+    weight_scale: torch.Tensor,
+    weight_zero_point: torch.Tensor,
+    weight_axis_int: int,
+    weight_quant_min: int | None,
+    weight_quant_max: int | None,
+) -> torch.Tensor:
+    """Quantize and then dequantize the weight based on
+    the quantization parameters
+    """
+    if weight_qscheme in [
+        torch.per_tensor_affine,
+        torch.per_channel_affine,
+        torch.per_channel_affine_float_qparams,
+    ]:
+        weight_quant = _quantize_weight_decomposed(
+            weight,
+            weight_qscheme,
+            weight_dtype,
+            weight_scale,
+            weight_zero_point,
+            weight_axis_int,
+            weight_quant_min,
+            weight_quant_max,
+        )
+        weight_dequant = _dequantize_weight_decomposed(
+            weight_quant,
+            weight_qscheme,
+            weight_dtype,
+            weight_scale,
+            weight_zero_point,
+            weight_axis_int,
+            weight_quant_min,
+            weight_quant_max,
+        )
+    else:
+        weight_dequant = weight
+    return weight_dequant
+
+
+def _quantize_and_dequantize_weight(
+    weight: torch.Tensor,
+    weight_qscheme: torch.qscheme,
+    weight_dtype: torch.dtype,
+    weight_scale: torch.Tensor,
+    weight_zero_point: torch.Tensor,
+    weight_axis_int: int,
+) -> torch.Tensor:
+    """Quantize and then dequantize the weight based on
+    the quantization parameters
+    """
+    if weight_qscheme in [
+        torch.per_tensor_affine,
+        torch.per_channel_affine,
+        torch.per_channel_affine_float_qparams,
+    ]:
+        weight_quant = _quantize_weight(
+            weight,
+            weight_qscheme,
+            weight_dtype,
+            weight_scale,
+            weight_zero_point,
+            weight_axis_int,
+        )
+        weight_dequant = weight_quant.dequantize()
+    else:
+        weight_dequant = weight
+    return weight_dequant
+
+
+def _save_weight_qparams(
+    destination,
+    prefix,
+    weight_qscheme,
+    weight_dtype,
+    weight_scale,
+    weight_zero_point,
+    weight_axis,
+):
+    destination[prefix + "weight_qscheme"] = weight_qscheme
+    destination[prefix + "weight_dtype"] = weight_dtype
+    if weight_qscheme is not None:
+        destination[prefix + "weight_scale"] = weight_scale
+        destination[prefix + "weight_zero_point"] = weight_zero_point
+        if weight_qscheme == torch.per_channel_affine:
+            destination[prefix + "weight_axis"] = weight_axis
+
+
+def _get_weight_qparam_keys(state_dict: dict[str, typing.Any], prefix: str):
+    keys = ["weight_qscheme", "weight_dtype"]
+    weight_qscheme = state_dict[prefix + "weight_qscheme"]
+    if weight_qscheme is not None:
+        keys.append("weight_scale")
+        keys.append("weight_zero_point")
+        if weight_qscheme == torch.quantize_per_channel:
+            keys.append("weight_axis")
+    return keys
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/sparse/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/sparse/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0fda5a58f2984ee05b0d167297b458f62c37fc59
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/sparse/__init__.py
@@ -0,0 +1 @@
+from . import quantized
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/sparse/quantized/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/sparse/quantized/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef66c90b0e8ecdbc7cd2cfb4c1cecf0bc38e8466
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/sparse/quantized/__init__.py
@@ -0,0 +1,10 @@
+from torch.ao.nn.sparse.quantized import dynamic
+
+from .linear import Linear, LinearPackedParams
+
+
+__all__ = [
+    "dynamic",
+    "Linear",
+    "LinearPackedParams",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/sparse/quantized/dynamic/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/sparse/quantized/dynamic/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..91ecfd8793dc08b96ed64f47f531724aa8a866d0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/sparse/quantized/dynamic/__init__.py
@@ -0,0 +1,6 @@
+from .linear import Linear
+
+
+__all__ = [
+    "Linear",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/sparse/quantized/dynamic/linear.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/sparse/quantized/dynamic/linear.py
new file mode 100644
index 0000000000000000000000000000000000000000..d327cabd0d3681cce4ec4b7d62f0f9e734ad0730
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/sparse/quantized/dynamic/linear.py
@@ -0,0 +1,191 @@
+# mypy: allow-untyped-defs
+
+import torch
+import torch.ao.nn.intrinsic as nni
+from torch.ao.nn.quantized.modules.utils import (
+    _hide_packed_params_repr,
+    _quantize_weight,
+)
+from torch.ao.nn.sparse.quantized import linear
+from torch.ao.nn.sparse.quantized.utils import LinearBlockSparsePattern
+
+
+__all__ = ["Linear"]
+
+
+class Linear(torch.nn.Module):
+    r"""
+    A dynamically quantized sparse linear module with float tensor as inputs and outputs.
+    """
+
+    _version = 1
+    _op_type = "sparse_dynamic"
+    _FLOAT_MODULE = torch.nn.Linear
+
+    def __init__(
+        self,
+        in_features,
+        out_features,
+        row_block_size,
+        col_block_size,
+        bias=True,
+        dtype=torch.qint8,
+    ):
+        super().__init__()
+
+        if dtype != torch.qint8:
+            raise NotImplementedError(
+                "Only QINT8 is supported for Sparse Quantized Linear Dynamic"
+            )
+
+        self.in_features = in_features
+        self.out_features = out_features
+
+        if bias:
+            bias = torch.zeros(self.out_features, dtype=torch.float)
+        else:
+            bias = None
+
+        qweight = torch._empty_affine_quantized(
+            [out_features, in_features], scale=1, zero_point=0, dtype=torch.qint8
+        )
+        self._packed_params = linear.LinearPackedParams(
+            row_block_size=row_block_size, col_block_size=col_block_size, dtype=dtype
+        )
+        self._packed_params.set_weight_bias(
+            qweight, bias, row_block_size, col_block_size
+        )
+
+    def _get_name(self):
+        return "SparseQuantizedDynamicLinear"
+
+    def extra_repr(self):
+        return f"in_features={self.in_features}, out_features={self.out_features}, qscheme={self.weight().qscheme()}"
+
+    def __repr__(self):
+        return _hide_packed_params_repr(self, linear.LinearPackedParams)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return torch.ops.sparse.qlinear_dynamic(x, self._packed_params._packed_params)
+
+    def _save_to_state_dict(self, destination, prefix, keep_vars):
+        super()._save_to_state_dict(destination, prefix, keep_vars)
+        destination[prefix + "op_type"] = self._op_type
+
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        op_type = int(state_dict[prefix + "op_type"])
+        assert op_type == "sparse", (
+            f"Cannot load from op_type [{op_type}], expecting [{self._op_type}]"
+        )
+        state_dict.pop(prefix + "op_type")
+
+        version = local_metadata.get("version", None)
+        assert version <= self._version
+
+        # Is this code valid? In old quantization it seemed to be used to load
+        # older model
+        weight = state_dict.pop(prefix + "weight")
+        bias = state_dict.pop(prefix + "bias")
+        state_dict.update(
+            {
+                prefix + "_packed_params.weight": weight,
+                prefix + "_packed_params.bias": bias,
+            }
+        )
+
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            False,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+    def _weight_bias(self):
+        return self._packed_params._weight_bias()
+
+    def weight(self):
+        return self._weight_bias()[0]
+
+    def bias(self):
+        return self._weight_bias()[1]
+
+    def set_weight_bias(
+        self,
+        w: torch.Tensor,
+        b: torch.Tensor | None,
+        row_block_size: int | None,
+        col_block_size: int | None,
+    ) -> None:
+        assert row_block_size is not None and col_block_size is not None
+        self.out_features = w.shape[0]
+        self.in_features = w.shape[1]
+        self._packed_params.set_weight_bias(w, b, row_block_size, col_block_size)
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        r"""Create a quantized sparse dynamic module from a float module.
+
+        We only care about the convert at this stage, no need for observers just yet.
+        """
+        assert type(mod) is cls._FLOAT_MODULE, (
+            " nnq."
+            + cls.__name__
+            + ".from_float only works for "
+            + cls._FLOAT_MODULE.__name__
+        )
+        # TODO: Need to add options to qconfig to avoid the calibration.
+        # TODO: Add calibration for the sparsity
+        assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
+        if type(mod) is nni.LinearReLU:
+            mod = mod[0]
+        # pyrefly: ignore [missing-attribute]
+        if mod.qconfig is not None and mod.qconfig.weight is not None:
+            # pyrefly: ignore [not-callable]
+            weight_observer = mod.qconfig.weight()
+        else:
+            # We have the circular import issues if we import the qconfig in the beginning of this file:
+            # https://github.com/pytorch/pytorch/pull/24231. The current workaround is to postpone the
+            # import until we need it.
+            from torch.ao.quantization.qconfig import default_dynamic_qconfig
+
+            weight_observer = default_dynamic_qconfig.weight()
+
+        # It is important to multiply by the mask BEFORE calling the `weight_observer`
+        # TODO (zaf): Mask might not be part of the qconfig (T83295194)
+        weight = mod.weight
+        if getattr(mod.qconfig, "mask", False):
+            weight = mod.qconfig.mask * mod.weight
+
+        weight_observer(weight)
+        dtype = weight_observer.dtype
+        assert dtype == torch.qint8, "Weight observer must have dtype torch.qint8"
+        _w_sc, w_zp = weight_observer.calculate_qparams()
+        if isinstance(w_zp, torch.Tensor):
+            assert not torch.any(w_zp.bool()), "All weight zero points must map to 0"
+        else:
+            assert w_zp == 0, "Weight zero point must map to 0"
+        qweight = _quantize_weight(weight.float(), weight_observer)
+
+        row_block_size, col_block_size = LinearBlockSparsePattern.block_size()
+        qlinear = cls(
+            mod.in_features,
+            mod.out_features,
+            row_block_size,
+            col_block_size,
+            dtype=dtype,
+        )
+        # pyrefly: ignore [bad-argument-type]
+        qlinear.set_weight_bias(qweight, mod.bias, row_block_size, col_block_size)
+        return qlinear
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/sparse/quantized/linear.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/sparse/quantized/linear.py
new file mode 100644
index 0000000000000000000000000000000000000000..f106a32abfbf960b989c8eba860db2dec4a7fe4c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/sparse/quantized/linear.py
@@ -0,0 +1,274 @@
+# mypy: allow-untyped-defs
+
+import torch
+from torch.ao.nn.quantized.modules.utils import (
+    _hide_packed_params_repr,
+    _quantize_weight,
+)
+
+
+__all__ = ["LinearPackedParams", "Linear"]
+
+
+# TODO (zaf): Inherit from `quantized.LinearPackedParams` (T83294430)
+class LinearPackedParams(torch.nn.Module):
+    _version = 1
+
+    def __init__(self, row_block_size=1, col_block_size=4, dtype=torch.qint8):
+        super().__init__()
+
+        if dtype != torch.qint8:
+            raise NotImplementedError("Linear prepacking only supports QINT8")
+        self.dtype = dtype
+        wq = torch._empty_affine_quantized(
+            [1, 1], scale=1.0, zero_point=0, dtype=torch.qint8
+        )
+        self.set_weight_bias(wq, None, row_block_size, col_block_size)
+
+    def _get_name(self):
+        return "SparseQuantizedLinearPackedParams"
+
+    @torch.jit.export
+    def set_weight_bias(
+        self,
+        weight: torch.Tensor,
+        bias: torch.Tensor | None,
+        row_block_size: int | None,
+        col_block_size: int | None,
+    ) -> None:
+        assert row_block_size is not None and col_block_size is not None
+        self._packed_params = torch.ops.sparse.qlinear_prepack(
+            weight, bias, row_block_size, col_block_size
+        )
+
+    @torch.jit.export
+    def _weight_bias(self):
+        (weight, bias, block_sizes) = torch.ops.sparse.qlinear_unpack(
+            self._packed_params
+        )
+        return (weight, bias, block_sizes[0], block_sizes[1])
+
+    def forward(self, x):
+        return x
+
+    def _save_to_state_dict(self, destination, prefix, keep_vars):
+        super()._save_to_state_dict(destination, prefix, keep_vars)
+        destination[prefix + "dtype"] = self.dtype
+        destination[prefix + "_packed_params"] = self._weight_bias()
+
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        version = local_metadata.get("version", None)
+        assert version <= self._version
+
+        self.dtype = state_dict.pop(prefix + "dtype")
+        weight, bias, row_block_size, col_block_size = state_dict.pop(
+            prefix + "_packed_params"
+        )
+        self.set_weight_bias(weight, bias, row_block_size, col_block_size)
+
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            False,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+    @torch.jit.export
+    def __getstate__(self):
+        return self._packed_params, self.training, self.dtype
+
+    @torch.jit.export
+    def __setstate__(self, state):
+        (self._packed_params, self.training, self.dtype) = state
+
+    def __repr__(self):
+        return self._weight_bias().__repr__()
+
+
+# TODO (zaf): Inherit from `quantized.Linear` (T83294430)
+class Linear(torch.nn.Module):
+    r"""
+    A quantized sparse linear module with quantized tensor as inputs and outputs.
+    """
+
+    _version = 1
+    _FLOAT_MODULE = torch.nn.Linear
+
+    def __init__(
+        self,
+        in_features,
+        out_features,
+        row_block_size,
+        col_block_size,
+        bias=True,
+        dtype=torch.qint8,
+    ):
+        super().__init__()
+
+        if dtype != torch.qint8:
+            raise NotImplementedError(
+                "Only QINT8 is supported for Sparse Quantized Linear"
+            )
+
+        self.in_features = in_features
+        self.out_features = out_features
+
+        if bias:
+            bias = torch.zeros(self.out_features, dtype=torch.float)
+        else:
+            bias = None
+
+        qweight = torch._empty_affine_quantized(
+            [out_features, in_features], scale=1, zero_point=0, dtype=torch.qint8
+        )
+        self._packed_params = LinearPackedParams(
+            row_block_size=row_block_size, col_block_size=col_block_size, dtype=dtype
+        )
+        self._packed_params.set_weight_bias(
+            qweight, bias, row_block_size, col_block_size
+        )
+        self.scale = 1.0
+        self.zero_point = 0
+
+    @classmethod
+    def _get_name(cls):
+        return "SparseQuantizedLinear"
+
+    def extra_repr(self):
+        return (
+            f"in_features={self.in_features}, out_features={self.out_features}, scale={self.scale}, "
+            f"zero_point={self.zero_point}, qscheme={self.weight().qscheme()}"
+        )
+
+    def __repr__(self):
+        return _hide_packed_params_repr(self, LinearPackedParams)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return torch.ops.sparse.qlinear(
+            x, self._packed_params._packed_params, self.scale, self.zero_point
+        )
+
+    def _save_to_state_dict(self, destination, prefix, keep_vars):
+        super()._save_to_state_dict(destination, prefix, keep_vars)
+        destination[prefix + "scale"] = torch.tensor(self.scale)
+        destination[prefix + "zero_point"] = torch.tensor(self.zero_point)
+
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        self.scale = float(state_dict[prefix + "scale"])
+        state_dict.pop(prefix + "scale")
+
+        self.zero_point = int(state_dict[prefix + "zero_point"])
+        state_dict.pop(prefix + "zero_point")
+
+        state_dict.pop(prefix + "op_type")
+
+        version = local_metadata.get("version", None)
+        assert version <= self._version
+
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            False,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+    def _weight_bias(self):
+        return self._packed_params._weight_bias()
+
+    def weight(self):
+        return self._weight_bias()[0]
+
+    def bias(self):
+        return self._weight_bias()[1]
+
+    def set_weight_bias(
+        self,
+        w: torch.Tensor,
+        b: torch.Tensor | None,
+        row_block_size: int | None,
+        col_block_size: int | None,
+    ) -> None:
+        assert row_block_size is not None and col_block_size is not None
+        self._packed_params.set_weight_bias(w, b, row_block_size, col_block_size)
+
+    @classmethod
+    def from_float(cls, mod, use_precomputed_fake_quant=False):
+        r"""Create a quantized sparse module from a float module.
+
+        We only care about the convert at this stage, no need for observers just yet.
+
+        TODO(zaf): Need to add the sparse params to the qconfig
+        """
+        assert type(mod) is cls._FLOAT_MODULE, (
+            cls._get_name() + ".from_float only works for " + cls._FLOAT_MODULE.__name__
+        )
+        assert hasattr(mod, "sparse_params"), (
+            "Expecting the Linear to have `sparse_params`. Make sure you have provided arguments "
+            'in the `sparsifier.squash_mask(params_to_save=("sparse_block_shape",))` method.'
+        )
+        sparse_block_shape = mod.sparse_params.get("sparse_block_shape", None)  # type: ignore[operator, union-attr]
+        assert isinstance(sparse_block_shape, (tuple, list))
+        assert len(sparse_block_shape) == 2
+        # TODO: Need to add options to qconfig to avoid the calibration.
+        # TODO: Add calibration for the sparsity
+        assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
+        activation_post_process = mod.activation_post_process
+        weight_post_process = mod.qconfig.weight()  # type: ignore[operator, union-attr]
+
+        # Assumption is that the weight is already sparsified by the
+        # `sparsifier.convert`
+        weight = mod.weight
+
+        weight_post_process(weight)
+        dtype = weight_post_process.dtype
+        act_scale, act_zp = activation_post_process.calculate_qparams()  # type: ignore[operator, union-attr]
+        assert dtype == torch.qint8, "Weight observer must have dtype torch.qint8"
+        w_sc, w_zp = weight_post_process.calculate_qparams()
+        if isinstance(w_zp, torch.Tensor):
+            assert not torch.any(w_zp.bool()), "All weight zero points must map to 0"
+        else:
+            assert w_zp == 0, "Weight zero point must map to 0"
+        qweight = _quantize_weight(weight.float(), weight_post_process)
+
+        row_block_size = mod.sparse_params["sparse_block_shape"][0]  # type: ignore[index]
+        col_block_size = mod.sparse_params["sparse_block_shape"][1]  # type: ignore[index]
+        qlinear = cls(
+            mod.in_features,
+            mod.out_features,
+            row_block_size,
+            col_block_size,
+            dtype=dtype,
+        )
+        qlinear.set_weight_bias(
+            qweight,
+            mod.bias,
+            row_block_size,  # type: ignore[arg-type]
+            col_block_size,  # type: ignore[arg-type]
+        )
+        qlinear.scale = float(act_scale)
+        qlinear.zero_point = int(act_zp)
+        return qlinear
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/sparse/quantized/utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/sparse/quantized/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..2cfd4a5973dfa8a5219f5ca97246424ae17a6308
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/nn/sparse/quantized/utils.py
@@ -0,0 +1,62 @@
+import threading
+
+
+__all__ = ["LinearBlockSparsePattern"]
+
+
+def _is_valid_linear_block_sparse_pattern(
+    row_block_size: int, col_block_size: int
+) -> bool:
+    return (row_block_size == 1 and col_block_size == 4) or (
+        row_block_size == 8 and col_block_size == 1
+    )
+
+
+# This is a stop-gap measure as current flow does not allow module
+# specific block sparse pattern.
+# In fact there is no way to convey sparse pattern via module config
+# of quantization flow. Thus using the global context to convey
+# sparsity pattern.
+# Once the flow supports it, this should be removed.
+class LinearBlockSparsePattern:
+    rlock = threading.RLock()
+    row_block_size: int = 1
+    col_block_size: int = 4
+    prev_row_block_size: int = 1
+    prev_col_block_size: int = 4
+
+    def __init__(self, row_block_size: int = 1, col_block_size: int = 4):
+        assert _is_valid_linear_block_sparse_pattern(row_block_size, col_block_size)
+        LinearBlockSparsePattern.rlock.acquire()
+        LinearBlockSparsePattern.prev_row_block_size = (
+            LinearBlockSparsePattern.row_block_size
+        )
+        LinearBlockSparsePattern.prev_col_block_size = (
+            LinearBlockSparsePattern.col_block_size
+        )
+        LinearBlockSparsePattern.row_block_size = row_block_size
+        LinearBlockSparsePattern.col_block_size = col_block_size
+
+    def __enter__(self) -> None:
+        pass
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        backtrace: object | None,
+    ) -> None:
+        LinearBlockSparsePattern.row_block_size = (
+            LinearBlockSparsePattern.prev_row_block_size
+        )
+        LinearBlockSparsePattern.col_block_size = (
+            LinearBlockSparsePattern.prev_col_block_size
+        )
+        LinearBlockSparsePattern.rlock.release()
+
+    @staticmethod
+    def block_size() -> tuple[int, int]:
+        return (
+            LinearBlockSparsePattern.row_block_size,
+            LinearBlockSparsePattern.col_block_size,
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/_numeric_suite.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/_numeric_suite.py
new file mode 100644
index 0000000000000000000000000000000000000000..026ac73606e307bedd500a801a76ba1a97c4c655
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/_numeric_suite.py
@@ -0,0 +1,568 @@
+# mypy: allow-untyped-defs
+from collections.abc import Callable
+from typing import Any
+
+import torch
+import torch.ao.nn.quantized as nnq
+import torch.ao.nn.quantized.dynamic as nnqd
+import torch.nn as nn
+from torch.ao.quantization import prepare
+from torch.ao.quantization.quantization_mappings import (
+    get_default_compare_output_module_list,
+)
+
+
+NON_LEAF_MODULE_TO_ADD_OBSERVER_ALLOW_LIST = {
+    nnqd.Linear,
+    nnq.Linear,
+    nnqd.LSTM,
+    nn.LSTM,
+}
+
+
+def _find_match(
+    str_list: dict[str, Any] | list[str],
+    key_str: str,
+    postfix: str,
+) -> str | None:
+    split_str = key_str.split(".")
+    if split_str[-1] == postfix:
+        match_string = "".join(key_str.split(".")[0:-1])
+        for s2 in str_list:
+            pattern1 = "".join(s2.split(".")[0:-1])
+            pattern2 = "".join(s2.split(".")[0:-2])
+            if match_string == pattern1:
+                return s2
+            if match_string == pattern2:
+                return s2
+
+        # For matching "fc.weight" and "fc._packed_params._packed_params"
+        if postfix == "_packed_params":
+            match_string = "".join(key_str.split(".")[0:-2])
+            if len(match_string) == 0:
+                return None
+            for s2 in str_list:
+                pattern1 = "".join(s2.split(".")[0:-1])
+                pattern2 = "".join(s2.split(".")[0:-2])
+                if match_string == pattern1:
+                    return s2
+                if match_string == pattern2:
+                    return s2
+        return None
+    else:
+        return None
+
+
+def compare_weights(
+    float_dict: dict[str, Any], quantized_dict: dict[str, Any]
+) -> dict[str, dict[str, torch.Tensor]]:
+    r"""Compare the weights of the float module with its corresponding quantized
+    module. Return a dict with key corresponding to module names and each entry being
+    a dictionary with two keys 'float' and 'quantized', containing the float and
+    quantized weights. This dict can be used to compare and compute the quantization
+    error of the weights of float and quantized models.
+
+    Example usage::
+
+        wt_compare_dict = compare_weights(float_model.state_dict(), qmodel.state_dict())
+        for key in wt_compare_dict:
+            print(
+                key,
+                compute_error(
+                    wt_compare_dict[key]["float"],
+                    wt_compare_dict[key]["quantized"].dequantize(),
+                ),
+            )
+
+    Args:
+        float_dict: state dict of the float model
+        quantized_dict: state dict of the quantized model
+
+    Return:
+        weight_dict: dict with key corresponding to module names and each entry being
+        a dictionary with two keys 'float' and 'quantized', containing the float and
+        quantized weights
+    """
+    torch._C._log_api_usage_once("quantization_api._numeric_suite.compare_weights")
+    weight_dict: dict[str, dict] = {}
+    for key in quantized_dict:
+        match_key = _find_match(float_dict, key, "weight")
+        if match_key is not None:
+            weight_dict[key] = {}
+            weight_dict[key]["float"] = float_dict[match_key]
+            weight_dict[key]["quantized"] = quantized_dict[key]
+            continue
+
+        # For matching "fc.weight" and "fc._packed_params._packed_params"
+        match_key = _find_match(float_dict, key, "_packed_params")
+        if match_key is not None:
+            weight_dict[key] = {}
+            weight_dict[key]["float"] = float_dict[match_key]
+            weight_dict[key]["quantized"] = quantized_dict[key][0]
+
+        # For LSTM
+        split_str = key.split(".")
+        if split_str[-1] == "param" and split_str[-3] == "_all_weight_values":
+            layer = split_str[-2]
+            module_name = ".".join(split_str[:-3])
+            float_weight_ih_key = module_name + ".weight_ih_l" + layer
+            float_weight_hh_key = module_name + ".weight_hh_l" + layer
+            if float_weight_ih_key in float_dict and float_weight_hh_key in float_dict:
+                weight_dict[key] = {}
+                weight_dict[key]["float"] = float_dict[float_weight_ih_key]
+                weight_dict[key]["quantized"] = (
+                    quantized_dict[key].__getstate__()[0][4][0].__getstate__()[0][0]
+                )
+                weight_dict[key]["float"] = float_dict[float_weight_hh_key]
+                weight_dict[key]["quantized"] = (
+                    quantized_dict[key].__getstate__()[0][4][1].__getstate__()[0][0]
+                )
+
+    return weight_dict
+
+
+def _get_logger_dict_helper(
+    mod: nn.Module,
+    target_dict: dict[str, Any],
+    prefix: str = "",
+) -> None:
+    r"""This is the helper function for get_logger_dict
+
+    Args:
+        mod: module we want to save all logger stats
+        prefix: prefix for the current module
+        target_dict: the dictionary used to save all logger stats
+    """
+
+    def get_prefix(prefix):
+        return prefix if prefix == "" else prefix + "."
+
+    for child in mod.children():
+        if isinstance(child, Logger):
+            target_dict[get_prefix(prefix) + "stats"] = child.stats
+            break
+
+    for name, child in mod.named_children():
+        module_prefix = get_prefix(prefix) + name if prefix else name
+        _get_logger_dict_helper(child, target_dict, module_prefix)
+
+
+def get_logger_dict(mod: nn.Module, prefix: str = "") -> dict[str, dict]:
+    r"""Traverse the modules and save all logger stats into target dict.
+    This is mainly used for quantization accuracy debug.
+
+    Type of loggers supported:
+        ShadowLogger: used to log the outputs of the quantized module and its matching float shadow module,
+        OutputLogger: used to log the outputs of the modules
+
+    Args:
+        mod: module we want to save all logger stats
+        prefix: prefix for the current module
+
+    Return:
+        target_dict: the dictionary used to save all logger stats
+
+    """
+    torch._C._log_api_usage_once("quantization_api._numeric_suite.get_logger_dict")
+
+    target_dict: dict[str, dict] = {}
+    _get_logger_dict_helper(mod, target_dict, prefix)
+    return target_dict
+
+
+class Logger(nn.Module):
+    r"""Base class for stats logging"""
+
+    def __init__(self):
+        super().__init__()
+        self.stats = {}
+        # We only insert observer if the op is quantized with static quantization,
+        # which is identified by activation_observer.dtype == quint8.  This is needed
+        # when attaching Logger as observer for FX mode
+        self.dtype = torch.quint8
+
+    def forward(self, x):
+        # fmt: off
+        """
+        """  # blank docblock to make autodoc happy
+        # fmt: on
+
+
+class ShadowLogger(Logger):
+    r"""Class used in Shadow module to record the outputs of the original and
+    shadow modules.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.stats["float"] = []
+        self.stats["quantized"] = []
+
+    def forward(self, x, y):  # type: ignore[override]
+        # fmt: off
+        """
+        """  # blank docblock to make autodoc happy
+        # fmt: on
+        if len(x) > 1:
+            x = x[0]
+        if len(y) > 1:
+            y = y[0]
+        self.stats["quantized"].append(x.detach())
+        self.stats["float"].append(y.detach())
+
+
+class OutputLogger(Logger):
+    r"""Class used to log the outputs of the module"""
+
+    def __init__(self):
+        super().__init__()
+        self.stats["tensor_val"] = []
+
+    def forward(self, x):
+        # fmt: off
+        """
+        """  # blank docblock to make autodoc happy
+        # fmt: on
+        self.stats["tensor_val"].append(x)
+        return x
+
+
+def _convert_tuple_to_list(t: Any) -> Any:
+    return [_convert_tuple_to_list(x) for x in t] if type(t) is tuple else t
+
+
+def _dequantize_tensor_list(t: Any) -> Any:
+    return (
+        [_dequantize_tensor_list(x) for x in t]
+        if type(t) is list
+        else t.dequantize()
+        if t.is_quantized
+        else t
+    )
+
+
+class Shadow(nn.Module):
+    r"""Shadow module attaches the float module to its matching quantized module
+    as the shadow. Then it uses Logger module to process the outputs of both
+    modules.
+
+    Args:
+        q_module: module quantized from float_module that we want to shadow
+        float_module: float module used to shadow q_module
+        logger_cls: type of logger used to process the outputs of q_module and
+            float_module. ShadowLogger or custom loggers can be used.
+    """
+
+    def __init__(self, q_module, float_module, logger_cls):
+        super().__init__()
+        self.orig_module = q_module
+        self.shadow_module = float_module
+        self.dequant = nnq.DeQuantize()
+        self.logger = logger_cls()
+
+    def forward(self, *x) -> torch.Tensor:
+        # fmt: off
+        """
+        """  # blank docblock to make autodoc happy
+        # fmt: on
+        xl = _convert_tuple_to_list(x)
+        output = self.orig_module(*xl)
+        xl_float = _dequantize_tensor_list(xl)
+        shadow_output = self.shadow_module(*xl_float)
+        self.logger(output, shadow_output)
+        return output
+
+    def add(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+        # fmt: off
+        """
+        """  # blank docblock to make autodoc happy
+        # fmt: on
+        output = self.orig_module.add(x, y)
+        x = x.dequantize()
+        y = y.dequantize()
+        shadow_output = self.shadow_module.add(x, y)
+        self.logger(output, shadow_output)
+        return output
+
+    def add_scalar(self, x: torch.Tensor, y: float) -> torch.Tensor:
+        # fmt: off
+        """
+        """  # blank docblock to make autodoc happy
+        # fmt: on
+        output = self.orig_module.add_scalar(x, y)
+        x = x.dequantize()
+        shadow_output = self.shadow_module.add_scalar(x, y)
+        self.logger(output, shadow_output)
+        return output
+
+    def mul(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+        # fmt: off
+        """
+        """  # blank docblock to make autodoc happy
+        # fmt: on
+        output = self.orig_module.mul(x, y)
+        x = x.dequantize()
+        y = y.dequantize()
+        shadow_output = self.shadow_module.mul(x, y)
+        self.logger(output, shadow_output)
+        return output
+
+    def mul_scalar(self, x: torch.Tensor, y: float) -> torch.Tensor:
+        # fmt: off
+        """
+        """  # blank docblock to make autodoc happy
+        # fmt: on
+        output = self.orig_module.mul_scalar(x, y)
+        x = x.dequantize()
+        shadow_output = self.shadow_module.mul_scalar(x, y)
+        self.logger(output, shadow_output)
+        return output
+
+    def cat(self, x: list[torch.Tensor], dim: int = 0) -> torch.Tensor:
+        # fmt: off
+        """
+        """  # blank docblock to make autodoc happy
+        # fmt: on
+        output = self.orig_module.cat(x, dim)
+        x = [y.dequantize() for y in x]
+        shadow_output = self.shadow_module.cat(x, dim)
+        self.logger(output, shadow_output)
+        return output
+
+    def add_relu(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+        # fmt: off
+        """
+        """  # blank docblock to make autodoc happy
+        # fmt: on
+        output = self.orig_module.add_relu(x, y)
+        x = x.dequantize()
+        y = y.dequantize()
+        shadow_output = self.shadow_module.add_relu(x, y)
+        self.logger(output, shadow_output)
+        return output
+
+
+def prepare_model_with_stubs(
+    float_module: nn.Module,
+    q_module: nn.Module,
+    module_swap_list: set[type],
+    logger_cls: Callable,
+) -> None:
+    r"""Prepare the model by attaching the float module to its matching quantized
+    module as the shadow if the float module type is in module_swap_list.
+
+    Example usage::
+
+        prepare_model_with_stubs(float_model, q_model, module_swap_list, Logger)
+        q_model(data)
+        ob_dict = get_logger_dict(q_model)
+
+    Args:
+        float_module: float module used to generate the q_module
+        q_module: module quantized from float_module
+        module_swap_list: list of float module types to attach the shadow
+        logger_cls: type of logger to be used in shadow module to process the outputs of
+            quantized module and its float shadow module
+    """
+    torch._C._log_api_usage_once(
+        "quantization_api._numeric_suite.prepare_model_with_stubs"
+    )
+
+    float_module_children = dict(float_module.named_children())
+
+    reassign = {}
+    for name, mod in q_module.named_children():
+        if name not in float_module_children:
+            continue
+
+        float_mod = float_module_children[name]
+
+        if type(float_mod) not in module_swap_list:
+            prepare_model_with_stubs(float_mod, mod, module_swap_list, logger_cls)
+
+        # Insert shadow module only if the module is not of the same type as
+        # the floating point module
+        if type(float_mod) in module_swap_list and not _is_identical_module_type(
+            mod, float_mod
+        ):
+            reassign[name] = Shadow(mod, float_mod, logger_cls)
+
+    for key, value in reassign.items():
+        q_module._modules[key] = value
+
+
+def _is_identical_module_type(mod1, mod2):
+    # Compare if two modules have the same dtype
+    mod1_module_types = [type(mod) for mod in mod1.modules()]
+    mod2_module_types = [type(mod) for mod in mod2.modules()]
+    return mod1_module_types == mod2_module_types
+
+
+def compare_model_stub(
+    float_model: nn.Module,
+    q_model: nn.Module,
+    module_swap_list: set[type],
+    *data,
+    logger_cls=ShadowLogger,
+) -> dict[str, dict]:
+    r"""Compare quantized module in a model with its floating point counterpart,
+    feeding both of them the same input. Return a dict with key corresponding to
+    module names and each entry being a dictionary with two keys 'float' and
+    'quantized', containing the output tensors of quantized and its matching
+    float shadow module. This dict can be used to compare and compute the module
+    level quantization error.
+
+    This function first call prepare_model_with_stubs() to swap the quantized
+    module that we want to compare with the Shadow module, which takes quantized
+    module, corresponding float module and logger as input, and creates a forward
+    path inside to make the float module to shadow quantized module sharing the
+    same input. The logger can be customizable, default logger is ShadowLogger
+    and it will save the outputs of the quantized module and float module that
+    can be used to compute the module level quantization error.
+
+    Example usage::
+
+        module_swap_list = [
+            torchvision.models.quantization.resnet.QuantizableBasicBlock
+        ]
+        ob_dict = compare_model_stub(float_model, qmodel, module_swap_list, data)
+        for key in ob_dict:
+            print(
+                key,
+                compute_error(
+                    ob_dict[key]["float"], ob_dict[key]["quantized"].dequantize()
+                ),
+            )
+
+    Args:
+        float_model: float model used to generate the q_model
+        q_model: model quantized from float_model
+        module_swap_list: list of float module types at which shadow modules will
+            be attached.
+        data: input data used to run the prepared q_model
+        logger_cls: type of logger to be used in shadow module to process the outputs of
+            quantized module and its float shadow module
+    """
+    torch._C._log_api_usage_once("quantization_api._numeric_suite.compare_model_stub")
+    prepare_model_with_stubs(float_model, q_model, module_swap_list, logger_cls)
+    q_model(*data)
+    ob_dict = get_logger_dict(q_model)
+    return ob_dict
+
+
+def get_matching_activations(
+    float_module: nn.Module,
+    q_module: nn.Module,
+) -> dict[str, dict[str, torch.Tensor]]:
+    r"""Find the matching activation between float and quantized modules.
+
+    Args:
+        float_module: float module used to generate the q_module
+        q_module: module quantized from float_module
+
+    Return:
+        act_dict: dict with key corresponding to quantized module names and each
+        entry being a dictionary with two keys 'float' and 'quantized', containing
+        the matching float and quantized activations
+    """
+    torch._C._log_api_usage_once(
+        "quantization_api._numeric_suite.get_matching_activations"
+    )
+    float_dict = get_logger_dict(float_module)
+    quantized_dict = get_logger_dict(q_module)
+    act_dict: dict[str, dict] = {}
+    for key in quantized_dict:
+        if len(quantized_dict[key]["tensor_val"]) == 0:
+            continue
+        match_key = _find_match(sorted(float_dict, reverse=True), key, "stats")
+        if match_key is not None:
+            act_dict[key] = {}
+            act_dict[key]["float"] = float_dict[match_key]["tensor_val"]
+            act_dict[key]["quantized"] = quantized_dict[key]["tensor_val"]
+    return act_dict
+
+
+def prepare_model_outputs(
+    float_module: nn.Module,
+    q_module: nn.Module,
+    logger_cls=OutputLogger,
+    allow_list=None,
+) -> None:
+    r"""Prepare the model by attaching the logger to both float module
+    and quantized module if they are in the allow_list.
+
+    Args:
+        float_module: float module used to generate the q_module
+        q_module: module quantized from float_module
+        logger_cls: type of logger to be attached to float_module and q_module
+        allow_list: list of module types to attach logger
+    """
+    torch._C._log_api_usage_once(
+        "quantization_api._numeric_suite.prepare_model_outputs"
+    )
+    if allow_list is None:
+        allow_list = get_default_compare_output_module_list()
+
+    qconfig_debug = torch.ao.quantization.QConfig(activation=logger_cls, weight=None)
+    float_module.qconfig = qconfig_debug  # type: ignore[assignment]
+    prepare(
+        float_module, inplace=True, allow_list=allow_list, prepare_custom_config_dict={}
+    )
+    q_module.qconfig = qconfig_debug  # type: ignore[assignment]
+    prepare(
+        q_module,
+        inplace=True,
+        allow_list=allow_list,
+        observer_non_leaf_module_list=NON_LEAF_MODULE_TO_ADD_OBSERVER_ALLOW_LIST,
+        prepare_custom_config_dict={},
+    )
+
+
+def compare_model_outputs(
+    float_model: nn.Module,
+    q_model: nn.Module,
+    *data,
+    logger_cls=OutputLogger,
+    allow_list=None,
+) -> dict[str, dict[str, torch.Tensor]]:
+    r"""Compare output activations between float and quantized models at
+    corresponding locations for the same input. Return a dict with key corresponding
+    to quantized module names and each entry being a dictionary with two keys
+    'float' and 'quantized', containing the activations of quantized model and
+    float model at matching locations. This dict can be used to compare and
+    compute the propagation quantization error.
+
+    Example usage::
+
+        act_compare_dict = compare_model_outputs(float_model, qmodel, data)
+        for key in act_compare_dict:
+            print(
+                key,
+                compute_error(
+                    act_compare_dict[key]["float"],
+                    act_compare_dict[key]["quantized"].dequantize(),
+                ),
+            )
+
+    Args:
+        float_model: float model used to generate the q_model
+        q_model: model quantized from float_model
+        data: input data used to run the prepared float_model and q_model
+        logger_cls: type of logger to be attached to float_module and q_module
+        allow_list: list of module types to attach logger
+
+    Return:
+        act_compare_dict: dict with key corresponding to quantized module names
+        and each entry being a dictionary with two keys 'float' and 'quantized',
+        containing the matching float and quantized activations
+    """
+    torch._C._log_api_usage_once(
+        "quantization_api._numeric_suite.compare_model_outputs"
+    )
+    if allow_list is None:
+        allow_list = get_default_compare_output_module_list()
+    prepare_model_outputs(float_model, q_model, logger_cls, allow_list)
+    float_model(*data)
+    q_model(*data)
+    act_compare_dict = get_matching_activations(float_model, q_model)
+    return act_compare_dict
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/_numeric_suite_fx.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/_numeric_suite_fx.py
new file mode 100644
index 0000000000000000000000000000000000000000..1861d0160db152e73debda3bda7f714ca4bbf601
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/_numeric_suite_fx.py
@@ -0,0 +1,1121 @@
+# mypy: allow-untyped-defs
+"""
+This module contains tooling to compare weights and activations
+across models. Example usage::
+
+    import copy
+    import torch
+    import torch.ao.quantization.quantize_fx as quantize_fx
+    import torch.ao.ns._numeric_suite_fx as ns
+
+    m = torch.nn.Sequential(torch.nn.Conv2d(1, 1, 1)).eval()
+    mp = quantize_fx.prepare_fx(m, {"": torch.ao.quantization.default_qconfig})
+    # We convert a copy because we need the original prepared model
+    # to be available for comparisons, and `quantize_fx.convert_fx` is inplace.
+    mq = quantize_fx.convert_fx(copy.deepcopy(mp))
+
+    #
+    # Comparing weights
+    #
+
+    # extract weight pairs
+    weight_comparison = ns.extract_weights("a", mp, "b", mq)
+
+    # add SQNR for each comparison, inplace
+    ns.extend_logger_results_with_comparison(
+        weight_comparison, "a", "b", torch.ao.ns.fx.utils.compute_sqnr, "sqnr"
+    )
+
+    # weight_comparison contains the weights from `mp` and `mq` stored
+    # in pairs, and can be used for further analysis.
+
+
+    #
+    # Comparing activations, with error propagation
+    #
+
+    # add loggers
+    mp_ns, mq_ns = ns.add_loggers(
+        "a", copy.deepcopy(mp), "b", copy.deepcopy(mq), ns.OutputLogger
+    )
+
+    # send an example datum to capture intermediate activations
+    datum = torch.randn(1, 1, 1, 1)
+    mp_ns(datum)
+    mq_ns(datum)
+
+    # extract intermediate activations
+    act_comparison = ns.extract_logger_info(mp_ns, mq_ns, ns.OutputLogger, "b")
+
+    # add SQNR for each comparison, inplace
+    ns.extend_logger_results_with_comparison(
+        act_comparison, "a", "b", torch.ao.ns.fx.utils.compute_sqnr, "sqnr"
+    )
+
+    # act_comparison contains the activations from `mp_ns` and `mq_ns` stored
+    # in pairs, and can be used for further analysis.
+
+    #
+    # Comparing activations, without error propagation
+    #
+
+    # create shadow model
+    mp_shadows_mq = ns.add_shadow_loggers(
+        "a", copy.deepcopy(mp), "b", copy.deepcopy(mq), ns.OutputLogger
+    )
+
+    # send an example datum to capture intermediate activations
+    datum = torch.randn(1, 1, 1, 1)
+    mp_shadows_mq(datum)
+
+    # extract intermediate activations
+    shadow_act_comparison = ns.extract_shadow_logger_info(
+        mp_shadows_mq, ns.OutputLogger, "b"
+    )
+
+    # add SQNR for each comparison, inplace
+    ns.extend_logger_results_with_comparison(
+        shadow_act_comparison, "a", "b", torch.ao.ns.fx.utils.compute_sqnr, "sqnr"
+    )
+
+    # shadow_act_comparison contains the activations from `mp_ns` and `mq_ns` stored
+    # in pairs, and can be used for further analysis.
+
+"""
+
+import collections
+from collections.abc import Callable
+from typing import Any, TYPE_CHECKING
+
+import torch
+import torch.ao.quantization.quantize_fx as quantize_fx
+import torch.nn as nn
+from torch.ao.ns.fx.graph_matcher import get_matching_subgraph_pairs
+from torch.ao.ns.fx.mappings import get_base_name_to_sets_of_related_ops
+from torch.ao.ns.fx.n_shadows_utils import (
+    _get_dedup_subgraphs,
+    create_add_loggers_graph,
+    create_n_transformed_and_logged_copies_of_subgraph,
+    create_results_comparison,
+    extract_weight_comparison,
+    group_results_by_subgraph,
+    OutputProp,
+    print_n_shadows_summary,
+    SHADOW_WRAPPER_NODE_NAME_PREFIX,
+)
+from torch.ao.ns.fx.qconfig_multi_mapping import QConfigMultiMapping
+from torch.ao.quantization import QConfigMapping
+from torch.ao.quantization.backend_config import BackendConfig
+from torch.ao.quantization.backend_config.utils import (
+    get_fusion_pattern_to_root_node_getter,
+)
+from torch.ao.quantization.fx.graph_module import _get_observed_graph_module_attr
+from torch.ao.quantization.fx.match_utils import _find_matches
+from torch.ao.quantization.fx.qconfig_mapping_utils import (
+    _generate_node_name_to_qconfig,
+)
+from torch.ao.quantization.fx.quantize_handler import _get_pattern_to_quantize_handlers
+from torch.fx import GraphModule
+from torch.fx.graph import Node
+
+from .fx.graph_passes import add_loggers_to_model, create_a_shadows_b
+from .fx.ns_types import NSNodeTargetType, NSResultsType, NSSingleResultValuesType
+from .fx.utils import (
+    get_target_type_str,
+    maybe_add_missing_fqns,
+    rekey_logger_info_on_node_name_of_model,
+)
+from .fx.weight_utils import extract_weight_from_node
+
+
+if TYPE_CHECKING:
+    from torch.ao.quantization.qconfig import QConfigAny
+
+RNNReturnType = tuple[torch.Tensor, tuple[torch.Tensor, torch.Tensor]]
+
+
+class OutputLogger(nn.Module):
+    """
+    Base class for capturing intermediate values.
+    """
+
+    stats: list[torch.Tensor]
+    stats_rnn: list[RNNReturnType]
+
+    # Mark as impure so that calls to it will not be removed during DCE.
+    _is_impure = True
+
+    def __init__(
+        self,
+        ref_node_name: str,
+        prev_node_name: str,
+        model_name: str,
+        ref_name: str,
+        prev_node_target_type: str,
+        ref_node_target_type: str,
+        results_type: str,
+        index_within_arg: int,
+        index_of_arg: int,
+        fqn: str | None,
+        qconfig_str: str | None = "",
+    ):
+        super().__init__()
+        self.stats: list[torch.Tensor] = []
+        self.stats_rnn: list[RNNReturnType] = []
+
+        # name of the node which was responsible for adding this logger
+        # Note:
+        # - if we are logging node outputs, this is the same as prev_node_name
+        # - if we are logging node inputs, this is the name of the node
+        #   whose input this logger is logging.
+        #
+        # example, where logger1 is logging input of op1 and logger2 is logging
+        #    the output of op1:
+        #
+        #  x1 -> logger1 -> op1 -> logger2 -> x2
+        #
+        # in this example,
+        #   - logger1's prev_node_name is x1 and ref_node_name is op1
+        #   - logger2's prev_node_name is op1 and ref_node_name is op1
+        self.ref_node_name = ref_node_name
+        # name of the node whose output this Logger is capturing
+        self.prev_node_name = prev_node_name
+
+        # name of the model from which the node originated from
+        self.model_name = model_name
+        # reference name, used to match loggers from separate models
+        # to each other
+        self.ref_name = ref_name
+        # type of the target of the node whose output this logger is logging
+        self.prev_node_target_type = prev_node_target_type
+        # type of the target of the node which was responsible for adding this
+        # logger
+        self.ref_node_target_type = ref_node_target_type
+        # what kind of values are inside of stats
+        self.results_type = results_type
+        # index of this node within the arg of the input/output node
+        # for example, in cat([x1, x2, x3], dim=0), x2 would have index_within_arg == 1
+        self.index_within_arg = index_within_arg
+        # index of this node within the args of the input/output node
+        # for example, in add(x1, x2), x2 would have index_of_arg == 1
+        self.index_of_arg = index_of_arg
+        # fully qualified name
+        self.fqn = fqn
+        # if loggers are added before prepare_fx, but we do not want
+        # collect results of calibration, only results after convert_fx
+        # so, we add a flag to control whether this logger collects data
+        self.enabled = True
+        # string representation of qconfig
+        self.qconfig_str = qconfig_str
+        # this can be turned off to reduce memory usage during calibration
+        self.save_activations = True
+
+    # Note: cannot annotate the type of x because TorchScript does not support
+    #   the Union type.
+    def forward(self, x):
+        # fmt: off
+        """
+        """  # blank docblock to make autodoc happy
+        # fmt: on
+        # TODO(future PR): consider designing this better, as the difference
+        # between these two flags is subtle and not obvious.
+        if not self.enabled:
+            return x
+        if not self.save_activations:
+            return x
+        # TODO(future PR): consider refactoring this to better reuse the parent
+        # class
+        if isinstance(x, torch.Tensor):
+            self.stats.append(x.detach())
+        elif isinstance(x, tuple) and len(x) == 2 and len(x[1]) == 2:
+            new_res = (x[0].detach(), (x[1][0].detach(), x[1][1].detach()))
+            self.stats_rnn.append(new_res)
+        return x
+
+    def __repr__(self):
+        clean_dict = {
+            k: v
+            for k, v in self.__dict__.items()
+            # skip nn.Module keys
+            if (k != "training") and not k.startswith("_")
+        }
+        return f"OutputLogger({clean_dict})"
+
+
+class OutputComparisonLogger(OutputLogger):
+    """
+    Same as OutputLogger, but also requires the original activation
+    in order to calculate the comparison at calibration time
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        # TODO(future PR): make the comparison function configurable
+        self.comparison_fn = torch.ao.ns.fx.utils.compute_sqnr
+        self.comparison_fn_name = "sqnr"
+        # precalculated comparisons of logger output versus reference
+        self.comparisons = []
+        # precalculated comparisons function
+
+    def forward(self, x, x_ref):  # type: ignore[override]
+        # fmt: off
+        """
+        """  # blank docblock to make autodoc happy
+        # fmt: on
+        if not self.enabled:
+            return x
+        if not isinstance(x, torch.Tensor):
+            raise AssertionError("non-tensor inputs not yet supported")
+        if self.save_activations:
+            # save the activation, for debugging
+            self.stats.append(x.detach())
+        # save the comparison
+        self.comparisons.append(self.comparison_fn(x, x_ref))
+        return x
+
+    def __repr__(self):
+        clean_dict = {
+            k: v
+            for k, v in self.__dict__.items()
+            # skip nn.Module keys
+            if (k != "training") and not k.startswith("_")
+        }
+        return f"OutputComparisonLogger({clean_dict})"
+
+
+class NSTracer(quantize_fx.QuantizationTracer):
+    """
+    Just like a regular FX quantization tracer, but treats observers and fake_quantize
+    modules as leaf modules.
+    """
+
+    def is_leaf_module(self, m: torch.nn.Module, module_qualified_name: str) -> bool:
+        # fmt: off
+        """
+        """  # blank docblock to make autodoc happy
+        # fmt: on
+        if isinstance(m, torch.ao.quantization.ObserverBase):
+            return True
+        elif isinstance(m, torch.ao.quantization.FakeQuantizeBase):
+            return True
+        return super().is_leaf_module(m, module_qualified_name)
+
+
+def _extract_weights_one_model(
+    model_name: str,
+    model: GraphModule,
+    nodes_and_names_to_instrument: list[tuple[Node, str]],
+    results: NSResultsType,
+    op_to_type_to_weight_extraction_fn: dict[str, dict[Callable, Callable]]
+    | None = None,
+) -> None:
+    torch._C._log_api_usage_once(
+        "quantization_api._numeric_suite_fx._extract_weights_one_model"
+    )
+    for node, ref_name in nodes_and_names_to_instrument:
+        res_type = NSSingleResultValuesType.WEIGHT.value
+        extracted_weight = extract_weight_from_node(
+            node, model, op_to_type_to_weight_extraction_fn
+        )
+        if extracted_weight:
+            if ref_name not in results:
+                results[ref_name] = {res_type: {}}
+            results[ref_name][res_type][model_name] = [extracted_weight]
+
+
+def _extract_weights_impl(
+    model_name_a: str,
+    gm_a: GraphModule,
+    model_name_b: str,
+    gm_b: GraphModule,
+    base_name_to_sets_of_related_ops: dict[str, set[NSNodeTargetType]] | None = None,
+    unmatchable_types_map: dict[str, set[NSNodeTargetType]] | None = None,
+    op_to_type_to_weight_extraction_fn: dict[str, dict[Callable, Callable]]
+    | None = None,
+) -> NSResultsType:
+    torch._C._log_api_usage_once(
+        "quantization_api._numeric_suite_fx._extract_weights_impl"
+    )
+    matched_subgraph_pairs = get_matching_subgraph_pairs(
+        gm_a, gm_b, base_name_to_sets_of_related_ops, unmatchable_types_map
+    )
+
+    # split the subgraph pairs into one data structure for each model
+    nodes_and_names_to_instrument_a: list[tuple[Node, str]] = []
+    nodes_and_names_to_instrument_b: list[tuple[Node, str]] = []
+    for match_name, match in matched_subgraph_pairs.items():
+        subgraph_a, subgraph_b = match
+        nodes_and_names_to_instrument_a.append((subgraph_a.base_op_node, match_name))
+        nodes_and_names_to_instrument_b.append((subgraph_b.base_op_node, match_name))
+
+    # populate the results, one model at a time
+    results: NSResultsType = {}
+    _extract_weights_one_model(
+        model_name_a,
+        gm_a,
+        nodes_and_names_to_instrument_a,
+        results,
+        op_to_type_to_weight_extraction_fn,
+    )
+    _extract_weights_one_model(
+        model_name_b,
+        gm_b,
+        nodes_and_names_to_instrument_b,
+        results,
+        op_to_type_to_weight_extraction_fn,
+    )
+
+    # fill in missing fqn entries
+    maybe_add_missing_fqns(results)
+
+    # rekey on names of nodes in gm_b
+    results = rekey_logger_info_on_node_name_of_model(results, model_name_b)
+
+    return results
+
+
+def extract_weights(
+    model_name_a: str,
+    model_a: nn.Module,
+    model_name_b: str,
+    model_b: nn.Module,
+    base_name_to_sets_of_related_ops: dict[str, set[NSNodeTargetType]] | None = None,
+    unmatchable_types_map: dict[str, set[NSNodeTargetType]] | None = None,
+    op_to_type_to_weight_extraction_fn: dict[str, dict[Callable, Callable]]
+    | None = None,
+) -> NSResultsType:
+    """
+    Extract weights from model A and model B, and return a comparison.
+
+    Args:
+        model_name_a: string name of model A to use in results
+        model_a: model A
+        model_name_b: string name of model B to use in results
+        model_b: model B
+        base_name_to_sets_of_related_ops: optional override of subgraph base nodes, subject to change
+        unmatchable_types_map: optional override of unmatchable types, subject to change
+        op_to_type_to_weight_extraction_fn: optional override of function which extracts weight
+            from a type, subject to change
+
+    Return:
+        NSResultsType, containing the weight comparisons
+    """
+
+    torch._C._log_api_usage_once("quantization_api._numeric_suite_fx.extract_weights")
+    if base_name_to_sets_of_related_ops is None:
+        base_name_to_sets_of_related_ops = get_base_name_to_sets_of_related_ops()
+
+    # TODO(future PR): expose these
+    skipped_module_names: list[str] = []
+    skipped_module_classes: list[Callable] = []
+    tracer_a = NSTracer(skipped_module_names, skipped_module_classes)
+    tracer_b = NSTracer(skipped_module_names, skipped_module_classes)
+    gm_a = GraphModule(model_a, tracer_a.trace(model_a))
+    maybe_model_a_node_name_to_scope = _get_observed_graph_module_attr(
+        model_a, "node_name_to_scope"
+    )
+    if maybe_model_a_node_name_to_scope is not None:
+        gm_a._node_name_to_scope = maybe_model_a_node_name_to_scope
+    gm_b = GraphModule(model_b, tracer_b.trace(model_b))
+    maybe_model_b_node_name_to_scope = _get_observed_graph_module_attr(
+        model_b, "node_name_to_scope"
+    )
+    if maybe_model_b_node_name_to_scope is not None:
+        gm_b._node_name_to_scope = maybe_model_b_node_name_to_scope
+    return _extract_weights_impl(
+        model_name_a,
+        gm_a,
+        model_name_b,
+        gm_b,
+        base_name_to_sets_of_related_ops,
+        unmatchable_types_map,
+        op_to_type_to_weight_extraction_fn,
+    )
+
+
+def _add_loggers_one_model(
+    model_name: str,
+    model: GraphModule,
+    nodes_and_names_to_instrument_inputs: list[tuple[Node, str, str]],
+    nodes_and_names_to_instrument_outputs: list[tuple[Node, str, str]],
+    logger_cls: Callable,
+) -> nn.Module:
+    torch._C._log_api_usage_once(
+        "quantization_api._numeric_suite_fx._add_loggers_one_model"
+    )
+
+    # TODO(future PR): do not observe nodes we do not care
+    #   about (both fp32, denylist, etc)
+    node_to_instrument_inputs_to_ref_name: dict[Node, tuple[str, str]] = {}
+    node_to_instrument_outputs_to_ref_name: dict[Node, tuple[str, str]] = {}
+    for node, ref_name, ref_node_type in nodes_and_names_to_instrument_inputs:
+        node_to_instrument_inputs_to_ref_name[node] = (ref_name, ref_node_type)
+    for node, ref_name, ref_node_type in nodes_and_names_to_instrument_outputs:
+        node_to_instrument_outputs_to_ref_name[node] = (ref_name, ref_node_type)
+
+    model = add_loggers_to_model(
+        model,
+        node_to_instrument_inputs_to_ref_name,
+        node_to_instrument_outputs_to_ref_name,
+        logger_cls,
+        model_name,
+    )
+    return model
+
+
+def _add_loggers_impl(
+    name_a: str,
+    gm_a: GraphModule,
+    name_b: str,
+    gm_b: GraphModule,
+    logger_cls: Callable,
+    should_log_inputs: bool,
+    base_name_to_sets_of_related_ops: dict[str, set[NSNodeTargetType]] | None = None,
+    unmatchable_types_map: dict[str, set[NSNodeTargetType]] | None = None,
+) -> tuple[nn.Module, nn.Module]:
+    torch._C._log_api_usage_once("quantization_api._numeric_suite_fx._add_loggers_impl")
+    matched_subgraph_pairs = get_matching_subgraph_pairs(
+        gm_a, gm_b, base_name_to_sets_of_related_ops, unmatchable_types_map
+    )
+    nodes_and_names_to_instrument_inputs_a = []
+    nodes_and_names_to_instrument_inputs_b = []
+    nodes_and_names_to_instrument_outputs_a = []
+    nodes_and_names_to_instrument_outputs_b = []
+    for match_name, (subgraph_a, subgraph_b) in matched_subgraph_pairs.items():
+        ref_node_type_a = get_target_type_str(subgraph_a.base_op_node, gm_a)
+        ref_node_type_b = get_target_type_str(subgraph_b.base_op_node, gm_b)
+        # Note: for matching inputs we use start_node, such as observing
+        # the input of linear in linear-relu
+        if should_log_inputs:
+            nodes_and_names_to_instrument_inputs_a.append(
+                (subgraph_a.start_node, match_name, ref_node_type_a)
+            )
+            nodes_and_names_to_instrument_inputs_b.append(
+                (subgraph_b.start_node, match_name, ref_node_type_b)
+            )
+        # Note: for matching activations we always use end_node,
+        # such as observing the output of relu in linear-relu
+        nodes_and_names_to_instrument_outputs_a.append(
+            (subgraph_a.end_node, match_name, ref_node_type_a)
+        )
+        nodes_and_names_to_instrument_outputs_b.append(
+            (subgraph_b.end_node, match_name, ref_node_type_b)
+        )
+
+    new_model_a = _add_loggers_one_model(
+        name_a,
+        gm_a,
+        nodes_and_names_to_instrument_inputs_a,
+        nodes_and_names_to_instrument_outputs_a,
+        logger_cls,
+    )
+    new_model_b = _add_loggers_one_model(
+        name_b,
+        gm_b,
+        nodes_and_names_to_instrument_inputs_b,
+        nodes_and_names_to_instrument_outputs_b,
+        logger_cls,
+    )
+    return (new_model_a, new_model_b)
+
+
+def add_loggers(
+    name_a: str,
+    model_a: nn.Module,
+    name_b: str,
+    model_b: nn.Module,
+    logger_cls: Callable,
+    should_log_inputs: bool = False,
+    base_name_to_sets_of_related_ops: dict[str, set[NSNodeTargetType]] | None = None,
+    unmatchable_types_map: dict[str, set[NSNodeTargetType]] | None = None,
+) -> tuple[nn.Module, nn.Module]:
+    """
+    Instrument model A and model B with loggers.
+
+    Args:
+        name_a: string name of model A to use in results
+        model_a: model A
+        name_b: string name of model B to use in results
+        model_b: model B
+        logger_cls: class of Logger to use
+        base_name_to_sets_of_related_ops: optional override of subgraph base nodes, subject to change
+        unmatchable_types_map: optional override of unmatchable types, subject to change
+
+    Return:
+        Returns a tuple of (model_a_with_loggers, model_b_with_loggers).  Modifies both models inplace.
+    """
+
+    torch._C._log_api_usage_once("quantization_api._numeric_suite_fx.add_loggers")
+    # TODO(future PR): expose these
+    skipped_module_names: list[str] = []
+    skipped_module_classes: list[Callable] = []
+    tracer_a = NSTracer(skipped_module_names, skipped_module_classes)
+    tracer_b = NSTracer(skipped_module_names, skipped_module_classes)
+    gm_a = GraphModule(model_a, tracer_a.trace(model_a))
+    maybe_model_a_node_name_to_scope = _get_observed_graph_module_attr(
+        model_a, "node_name_to_scope"
+    )
+    if maybe_model_a_node_name_to_scope is not None:
+        gm_a._node_name_to_scope = maybe_model_a_node_name_to_scope
+    gm_b = GraphModule(model_b, tracer_b.trace(model_b))
+    maybe_model_b_node_name_to_scope = _get_observed_graph_module_attr(
+        model_b, "node_name_to_scope"
+    )
+    if maybe_model_b_node_name_to_scope is not None:
+        gm_b._node_name_to_scope = maybe_model_b_node_name_to_scope
+    return _add_loggers_impl(
+        name_a,
+        gm_a,
+        name_b,
+        gm_b,
+        logger_cls,
+        should_log_inputs=should_log_inputs,
+        base_name_to_sets_of_related_ops=base_name_to_sets_of_related_ops,
+        unmatchable_types_map=unmatchable_types_map,
+    )
+
+
+def _extract_logger_info_one_model(
+    model: nn.Module,
+    results: NSResultsType,
+    logger_cls: Callable,
+) -> None:
+    torch._C._log_api_usage_once(
+        "quantization_api._numeric_suite_fx._extract_logger_info_one_model"
+    )
+    for _gm_name, mod in model.named_modules():
+        # TODO(future PR): better check when scripted
+        is_logger = isinstance(mod, logger_cls) or (  # type: ignore[arg-type]
+            isinstance(mod, torch.jit.RecursiveScriptModule)
+            and mod.original_name == "OutputLogger"
+        )
+        if is_logger:
+            key = mod.ref_name
+            if key not in results:
+                results[key] = {}
+            if mod.model_name in results[key]:
+                raise AssertionError(f"{mod.model_name} is already present in results")
+            if mod.results_type not in results[key]:
+                results[key][mod.results_type] = {}
+            if mod.model_name not in results[key][mod.results_type]:
+                results[key][mod.results_type][mod.model_name] = []
+            stats_to_use = mod.stats
+            if len(mod.stats_rnn) > 0:
+                stats_to_use = mod.stats_rnn
+            data = {
+                "type": mod.results_type,
+                "values": stats_to_use,
+                "ref_node_name": mod.ref_node_name,
+                "ref_node_target_type": mod.ref_node_target_type,
+                "prev_node_name": mod.prev_node_name,
+                "prev_node_target_type": mod.prev_node_target_type,
+                "index_within_arg": mod.index_within_arg,
+                "index_of_arg": mod.index_of_arg,
+                "fqn": mod.fqn,
+                "qconfig_str": mod.qconfig_str,
+            }
+            if hasattr(mod, "comparisons"):
+                data["comparisons"] = mod.comparisons
+                data["comparison_fn_name"] = mod.comparison_fn_name
+            else:
+                data["comparisons"] = []
+                data["comparison_fn_name"] = ""
+            results[key][mod.results_type][mod.model_name].append(data)
+            # ensure the list stays sorted
+            results[key][mod.results_type][mod.model_name].sort(
+                key=lambda res: f"{res['index_of_arg']}:{res['index_within_arg']}"
+            )
+
+
+# TODO(future PR): align on naming
+# this is equivalent of just the comparison extraction part of `ns.compare_model_outputs`
+def extract_logger_info(
+    model_a: nn.Module,
+    model_b: nn.Module,
+    logger_cls: Callable,
+    model_name_to_use_for_layer_names: str,
+) -> NSResultsType:
+    """
+    Traverse all loggers in `model_a` and `model_b`, and extract the logged
+    information.
+
+    Args:
+        model_a: model A
+        model_b: model B
+        logger_cls: class of Logger to use
+        model_name_to_use_for_layer_names: string name of model to use for
+          layer names in the output
+
+    Return:
+        NSResultsType, containing the logged comparisons
+    """
+    torch._C._log_api_usage_once(
+        "quantization_api._numeric_suite_fx.extract_logger_info"
+    )
+    results: NSResultsType = {}
+    for model in (model_a, model_b):
+        _extract_logger_info_one_model(model, results, logger_cls)
+    # fill in missing fqn entries
+    maybe_add_missing_fqns(results)
+    # rekey on the name of model b
+    results = rekey_logger_info_on_node_name_of_model(
+        results, model_name_to_use_for_layer_names
+    )
+    return results
+
+
+def _add_shadow_loggers_impl(
+    name_a: str,
+    gm_a: GraphModule,
+    name_b: str,
+    gm_b: GraphModule,
+    logger_cls: Callable,
+    should_log_inputs: bool,
+    base_name_to_sets_of_related_ops: dict[str, set[NSNodeTargetType]] | None = None,
+    node_type_to_io_type_map: dict[str, set[NSNodeTargetType]] | None = None,
+    unmatchable_types_map: dict[str, set[NSNodeTargetType]] | None = None,
+) -> nn.Module:
+    torch._C._log_api_usage_once(
+        "quantization_api._numeric_suite_fx._add_shadow_loggers_impl"
+    )
+    matched_subgraph_pairs = get_matching_subgraph_pairs(
+        gm_a, gm_b, base_name_to_sets_of_related_ops, unmatchable_types_map
+    )
+    gm_a_shadows_b = create_a_shadows_b(
+        name_a,
+        gm_a,
+        name_b,
+        gm_b,
+        matched_subgraph_pairs,
+        logger_cls,
+        should_log_inputs=should_log_inputs,
+        node_type_to_io_type_map=node_type_to_io_type_map,
+    )
+    return gm_a_shadows_b
+
+
+def add_shadow_loggers(
+    name_a: str,
+    model_a: nn.Module,
+    name_b: str,
+    model_b: nn.Module,
+    logger_cls: Callable,
+    should_log_inputs: bool = False,
+    base_name_to_sets_of_related_ops: dict[str, set[NSNodeTargetType]] | None = None,
+    node_type_to_io_type_map: dict[str, set[NSNodeTargetType]] | None = None,
+    unmatchable_types_map: dict[str, set[NSNodeTargetType]] | None = None,
+) -> nn.Module:
+    """
+    Instrument model A and model B with shadow loggers.
+
+    Args:
+        name_a: string name of model A to use in results
+        model_a: model A
+        name_b: string name of model B to use in results
+        model_b: model B
+        logger_cls: class of Logger to use
+        should_log_inputs: whether to log inputs
+        base_name_to_sets_of_related_ops: optional override of subgraph base nodes, subject to change
+        unmatchable_types_map: optional override of unmatchable types, subject to change
+    """
+    torch._C._log_api_usage_once(
+        "quantization_api._numeric_suite_fx.add_shadow_loggers"
+    )
+    # TODO(future PR): expose these
+    skipped_module_names: list[str] = []
+    skipped_module_classes: list[Callable] = []
+    tracer_a = NSTracer(skipped_module_names, skipped_module_classes)
+    tracer_b = NSTracer(skipped_module_names, skipped_module_classes)
+    gm_a = GraphModule(model_a, tracer_a.trace(model_a))
+    maybe_model_a_node_name_to_scope = _get_observed_graph_module_attr(
+        model_a, "node_name_to_scope"
+    )
+    if maybe_model_a_node_name_to_scope is not None:
+        gm_a._node_name_to_scope = maybe_model_a_node_name_to_scope
+    gm_b = GraphModule(model_b, tracer_b.trace(model_b))
+    maybe_model_b_node_name_to_scope = _get_observed_graph_module_attr(
+        model_b, "node_name_to_scope"
+    )
+    if maybe_model_b_node_name_to_scope is not None:
+        gm_b._node_name_to_scope = maybe_model_b_node_name_to_scope
+    return _add_shadow_loggers_impl(
+        name_a,
+        gm_a,
+        name_b,
+        gm_b,
+        logger_cls,
+        should_log_inputs=should_log_inputs,
+        base_name_to_sets_of_related_ops=base_name_to_sets_of_related_ops,
+        node_type_to_io_type_map=node_type_to_io_type_map,
+        unmatchable_types_map=unmatchable_types_map,
+    )
+
+
+def extract_shadow_logger_info(
+    model_a_shadows_b: nn.Module,
+    logger_cls: Callable,
+    model_name_to_use_for_layer_names: str,
+) -> NSResultsType:
+    """
+    Traverse all loggers in a shadow model, and extract the logged
+    information.
+
+    Args:
+        model_a_shadows_b: shadow model
+        logger_cls: class of Logger to use
+        model_name_to_use_for_layer_names: string name of model to use for
+          layer names in the output
+
+    Return:
+        NSResultsType, containing the logged comparisons
+    """
+    torch._C._log_api_usage_once(
+        "quantization_api._numeric_suite_fx.extract_shadow_logger_info"
+    )
+    results: NSResultsType = collections.defaultdict(dict)
+    _extract_logger_info_one_model(model_a_shadows_b, results, logger_cls)
+    # fill in missing fqn entries
+    maybe_add_missing_fqns(results)
+    # rekey on the name of model b
+    results = rekey_logger_info_on_node_name_of_model(
+        results, model_name_to_use_for_layer_names
+    )
+    return dict(results)
+
+
+def extend_logger_results_with_comparison(
+    results: NSResultsType,
+    model_name_1: str,
+    model_name_2: str,
+    comparison_fn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor],
+    comparison_name: str,
+) -> None:
+    """
+    Compares the logged values from `model_name_2` against the corresponding
+    values in `model_name_1`, using `comparison_fn`. Records the result
+    in `model_name_2`'s results under `comparison_name`. Modifies `results` inplace.
+
+    Args:
+        results: the result data structure from `extract_logger_info` or
+          `extract_shadow_logger_info`.
+        model_name_1: string name of model 1
+        model_name_2: string name of model 2
+        comparison_fn: function to compare two Tensors
+        comparison_name: string name of model to use for
+          layer names in the output
+    """
+    for results_type_to_results in results.values():
+        for model_name_to_results in results_type_to_results.values():
+            if model_name_1 not in model_name_to_results:
+                raise AssertionError(f"{model_name_1} not found in results")
+            if model_name_2 not in model_name_to_results:
+                raise AssertionError(f"{model_name_2} not found in results")
+
+            results_1 = model_name_to_results[model_name_1]
+            results_2 = model_name_to_results[model_name_2]
+
+            for result_2 in results_2:
+                index_within_arg_2 = result_2["index_within_arg"]
+                index_of_arg_2 = result_2["index_of_arg"]
+                # find corresponding result_1
+                result_1 = None
+                for cur_result_1 in results_1:
+                    index_within_arg_1 = cur_result_1["index_within_arg"]
+                    index_of_arg_1 = cur_result_1["index_of_arg"]
+                    if (index_within_arg_1 == index_within_arg_2) and (
+                        index_of_arg_1 == index_of_arg_2
+                    ):
+                        result_1 = cur_result_1
+                        break
+                if result_1 is None:
+                    raise AssertionError("Expected result_1 to be not None")
+
+                values_1 = result_1["values"]
+                values_2 = result_2["values"]
+                result_2[comparison_name] = []
+                for value_1, value_2 in zip(values_1, values_2):
+                    comparison_result = comparison_fn(value_1, value_2)
+                    result_2[comparison_name].append(comparison_result)
+
+
+def prepare_n_shadows_model(
+    model: torch.nn.Module,
+    example_inputs: Any,
+    qconfig_multi_mapping: QConfigMultiMapping,
+    backend_config: BackendConfig,
+    custom_prepare_fn: Callable | None = None,
+    custom_prepare_kwargs: dict[str, Any] | None = None,
+    custom_tracer: Any = None,
+) -> GraphModule:
+    """
+    Given a model with a graph with M ops such as
+
+
+      args_kwargs_m -> op_m -> output_m
+
+
+    And a set of N qconfigs for each op, creates a new model, with
+    each of the subgraph of `op_m` transformed into
+
+    .. code::
+
+           |---------> op_m_n -> log_m_n
+           |                     /
+      args_kwargs_m ---------> op_m -> log_m_0
+
+    Where op_m_n is op_m wrapped in a submodule and transformed with
+    qconfig_n, and its inner graph looks like
+
+    .. code::
+
+      args_m -------- op_m_prepared_with_qconfig_n -> out_m_n
+                  /
+      kwargs_m ---
+
+    This is useful for testing different quantization of multiple layers in
+    a single pass through the model.
+
+    High level TODOs for future PRs:
+    * figure out a better way to name the output structure
+    * return a results data structure instead of printing it out
+    * add examples to docblocks
+    """
+
+    if custom_tracer is None:
+        tracer = quantize_fx.QuantizationTracer([], [])
+    else:
+        tracer = custom_tracer
+    mt = torch.fx.GraphModule(model, tracer.trace(model))
+    # this is necessary to ensure logger FQNs get populated
+    mt._node_name_to_scope = tracer.node_name_to_scope  # type: ignore[assignment]
+
+    # run example input propagation, we need this to call prepare_fx on
+    # individual subgraphs
+    output_prop = OutputProp(mt)
+    output_prop.propagate(*example_inputs)
+
+    # Find the set of subgraphs in the original graph which we need to
+    # consider.
+    modules = dict(mt.named_modules(remove_duplicate=False))
+    patterns = _get_pattern_to_quantize_handlers(backend_config)
+    root_node_getter_mapping = get_fusion_pattern_to_root_node_getter(backend_config)
+    standalone_module_names: list[str] = []
+    standalone_module_classes: list[type] = []
+    custom_module_classes: list[type] = []
+    matches = _find_matches(
+        mt.graph,
+        modules,
+        patterns,
+        root_node_getter_mapping,
+        standalone_module_names,
+        standalone_module_classes,
+        custom_module_classes,
+    )
+    subgraphs_dedup: dict[str, list[Node]] = _get_dedup_subgraphs(matches)
+
+    # generate node to qconfig for each subgraph
+    # TODO(future PR): deduplicate repeating entries
+    list_of_node_name_to_qconfig: list[dict[str, QConfigAny]] = []
+    for qconfig_mapping in qconfig_multi_mapping.qconfig_mappings_list:
+        node_name_to_qconfig = _generate_node_name_to_qconfig(
+            mt, modules, mt.graph, qconfig_mapping, tracer.node_name_to_scope
+        )
+        list_of_node_name_to_qconfig.append(node_name_to_qconfig)
+
+    # For each region in the model, do the following:
+    #   For each qconfig for that region, do the following:
+    #     1. create a copy of the region wrapped in a module
+    #     2. pass original args, original kwargs, and expected output to module
+    #     3. add an output comparison logger and hook it up to compare
+    #        actual output to expected output
+    #     4. run `prepare_fx` on the module
+    for subgraph_idx, (match_name, nodes_in_this_subgraph) in enumerate(
+        subgraphs_dedup.items()
+    ):
+        create_n_transformed_and_logged_copies_of_subgraph(
+            mt,
+            subgraph_idx,
+            match_name,
+            nodes_in_this_subgraph,
+            qconfig_multi_mapping.qconfig_mappings_list,
+            list_of_node_name_to_qconfig,
+            custom_prepare_fn,
+            custom_prepare_kwargs,  # type: ignore[arg-type]
+        )
+
+    return mt
+
+
+# TODO(future PR): we should rethink the names of all the PNP APIs
+def _prepare_n_shadows_add_loggers_model(
+    model: torch.nn.Module,
+    example_inputs: Any,
+    qconfig_mapping: QConfigMapping,
+    backend_config: BackendConfig,
+) -> torch.nn.Module:
+    r"""
+    Note: this API is not recommended for wide usage, it is only
+    provided for customers who need to migrate from the `add_loggers`
+    API.
+
+    This creates a model which provides logging for the following
+    problem: if we quantize `model` with `qconfig_mapping` and feed
+    the same input through both models, log the comparisons of
+    corresponding intermediate layers.
+
+    The problem is solved with a single model.  Specifically, we
+    partition `model` into N subgraphs, create a copy of each relevant
+    subgraph, wrap it in a module, apply the quantization API to that
+    module, and hook up loggers to measure the comparisons.
+
+    Example starting graph:
+
+      x0 -> op0 -> x1 -> op1 -> x2
+
+    Example config: quantize op0 to int8, do nothing to op1.
+    The following graph will be created:
+
+    .. code::
+
+      x0_0 -> op0_0 -> x1_0 -> log -----> op1_0 -> x2_0 -> log
+       \                        \                           \       # noqa: W605
+         ---> op0_1 -> x1_1 ----> clog -> op1_0 -> x2_1 ----> clog
+
+    Where op0_0 is op0, op0_1 is op0 wrapped in a submodule and quantized
+    to int8, op1_0 is op1 (appearing in the graph twice), log is a logger,
+    and clog is a comparison logger.
+    """
+
+    tracer = quantize_fx.QuantizationTracer([], [])
+    mt = torch.fx.GraphModule(model, tracer.trace(model))
+    # this is necessary to ensure logger FQNs get populated
+    mt._node_name_to_scope = tracer.node_name_to_scope  # type: ignore[assignment]
+
+    # run example input propagation, we need this to call prepare_fx on
+    # individual subgraphs
+    output_prop = OutputProp(mt)
+    output_prop.propagate(*example_inputs)
+
+    # Find the set of subgraphs in the original graph which we need to
+    # consider.
+    modules = dict(mt.named_modules(remove_duplicate=False))
+    patterns = _get_pattern_to_quantize_handlers(backend_config)
+    root_node_getter_mapping = get_fusion_pattern_to_root_node_getter(backend_config)
+    standalone_module_names: list[str] = []
+    standalone_module_classes: list[type] = []
+    custom_module_classes: list[type] = []
+    matches = _find_matches(
+        mt.graph,
+        modules,
+        patterns,
+        root_node_getter_mapping,
+        standalone_module_names,
+        standalone_module_classes,
+        custom_module_classes,
+    )
+    subgraphs_dedup: dict[str, list[Node]] = _get_dedup_subgraphs(matches)
+
+    # generate node to qconfig for each subgraph
+    node_name_to_qconfig = _generate_node_name_to_qconfig(
+        mt, modules, mt.graph, qconfig_mapping, tracer.node_name_to_scope
+    )
+
+    # Now, mutate the graph to be the add_loggers graph with propagation
+    # error.
+    create_add_loggers_graph(mt, subgraphs_dedup, qconfig_mapping, node_name_to_qconfig)
+
+    return mt
+
+
+# TODO(future PR): we should rethink the names of all the PNP APIs
+def _n_shadows_compare_weights(
+    model: torch.nn.Module,
+    example_inputs: Any,
+    qconfig_mapping: QConfigMapping,
+    backend_config: BackendConfig,
+) -> NSResultsType:
+    """
+    Note: this API is not recommended for wide usage, it is only
+    provided for customers who need to migrate from the `add_loggers`
+    API.
+    """
+    qconfig_multi_mapping = QConfigMultiMapping.from_list_qconfig_mapping(
+        [qconfig_mapping]
+    )
+    mp = prepare_n_shadows_model(
+        model, example_inputs, qconfig_multi_mapping, backend_config
+    )
+    # passing inputs through the model is necessary to populate
+    # observers which observe weights with real values
+    mp(*example_inputs)
+    mq = convert_n_shadows_model(mp)
+    weight_comparison = extract_weight_comparison(mq)
+    return weight_comparison
+
+
+# TODO(future PR): consider aligning API signature with other similar quantization
+# functions (enable_fake_quant, etc)
+def loggers_set_enabled(model: torch.nn.Module, enabled: bool) -> None:
+    """
+    Sets the `enabled` setting on a `model`'s loggers
+    """
+    for _, child in model.named_modules():
+        if isinstance(child, OutputLogger):
+            child.enabled = enabled
+
+
+# TODO(future PR): consider aligning API signature with other similar quantization
+# functions (enable_fake_quant, etc)
+def loggers_set_save_activations(
+    model: torch.nn.Module,
+    save_activations: bool,
+) -> None:
+    """
+    Sets the `save_activations` setting on a `model`'s loggers
+    """
+    for _name, child in model.named_modules():
+        if isinstance(child, OutputLogger):
+            child.save_activations = save_activations
+
+
+def convert_n_shadows_model(
+    model: GraphModule,
+    custom_convert_fn: Callable | None = None,
+    custom_convert_kwargs: dict[str, Any] | None = None,
+) -> GraphModule:
+    """
+    Given a model from `prepare_n_shadows_model`, runs `convert_fx`
+    on each shadow submodule.
+    """
+    for node in model.graph.nodes:
+        # TODO(future PR): consider matching in a safer way than
+        # node name string match
+        if node.name.startswith(SHADOW_WRAPPER_NODE_NAME_PREFIX):
+            orig_mod = getattr(model, node.name)
+            if custom_convert_fn is None:
+                converted_mod = torch.ao.quantization.quantize_fx.convert_fx(orig_mod)
+            else:
+                if custom_convert_kwargs is None:
+                    custom_convert_kwargs = {}
+                converted_mod = custom_convert_fn(orig_mod, **custom_convert_kwargs)
+            setattr(model, node.name, converted_mod)
+
+    return model
+
+
+def extract_results_n_shadows_model(model: torch.nn.Module) -> NSResultsType:
+    """
+    Extracts logger results from `model`.
+    """
+    results: NSResultsType = {}
+    _extract_logger_info_one_model(model, results, OutputLogger)
+    return results
+
+
+def print_comparisons_n_shadows_model(results: NSResultsType) -> None:
+    """
+    Prints a summary of extracted `results`.
+    """
+    results_grouped = group_results_by_subgraph(results)
+    results_comparison = create_results_comparison(results_grouped)
+    print_n_shadows_summary(results_comparison)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/fx/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/fx/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/fx/graph_matcher.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/fx/graph_matcher.py
new file mode 100644
index 0000000000000000000000000000000000000000..4fdad3f2d9bc49094c0da3264012cc206c28ab86
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/fx/graph_matcher.py
@@ -0,0 +1,485 @@
+# mypy: allow-untyped-defs
+import collections
+import enum
+from typing import Any
+
+import torch
+from torch.ao.quantization import FakeQuantizeBase, ObserverBase
+from torch.ao.quantization.utils import getattr_from_fqn
+from torch.fx import GraphModule
+from torch.fx.graph import Graph, Node
+
+from .mappings import get_base_name_to_sets_of_related_ops, get_unmatchable_types_map
+from .ns_types import NSNodeTargetType, NSSubgraph
+from .pattern_utils import (
+    end_node_matches_reversed_fusion,
+    get_reversed_fusions,
+    get_type_a_related_to_b,
+)
+
+
+toq = torch.ops.quantized
+
+
+def _get_output_nodes(g: Graph) -> list[Node]:
+    return [n for n in g.nodes if n.op == "output"]
+
+
+class _NSGraphMatchableSubgraphsIterator:
+    """
+    Iterates through the graph of gm, starting with the output nodes
+    and continuing backwards.
+    1. Returns matchable subgraphs, in order. A subgraph is defined by
+       (start_node, end_node).
+    2. Skips over non-matchable subgraphs
+    """
+
+    def __init__(
+        self,
+        gm: GraphModule,
+        non_matchable_functions: set[NSNodeTargetType],
+        non_matchable_modules: set[NSNodeTargetType],
+        non_matchable_methods: set[NSNodeTargetType],
+    ):
+        self.gm: GraphModule = gm
+        self.non_matchable_functions: set[NSNodeTargetType] = non_matchable_functions
+        self.non_matchable_modules: set[NSNodeTargetType] = non_matchable_modules
+        self.non_matchable_methods: set[NSNodeTargetType] = non_matchable_methods
+        self.seen_nodes: set[Node] = set()
+        self.stack: list[Node] = []
+        for start_node in _get_output_nodes(self.gm.graph):
+            self.stack.append(start_node)
+
+    def __iter__(self):
+        return self
+
+    def __next__(self) -> NSSubgraph:
+        """
+        Returns the next matchable subgraph.
+        """
+        while len(self.stack) > 0:
+            cur_end_node = self.stack.pop()
+            if cur_end_node in self.seen_nodes:
+                continue
+
+            # for subgraphs which are single nodes, start_node == end_node
+            # for subgraphs with more than one node, start node != end_node
+            cur_start_node = cur_end_node
+            # Subgraphs like linear-relu have the base node as the start node.
+            # Subgraphs like dequantize-linear-relu-to(torch.float16) have the
+            #   base node as the second node.
+            # The cur_base_op_node var will move to the actual node during
+            #   the fusion matching later in this code block.
+            cur_base_op_node = cur_end_node
+
+            # Check for potential fusions. For now, we are greedy
+            # and always skip all non-base nodes of a fusion.  For example,
+            # if we match linear-relu backwards, we will always skip the
+            # relu node and attempt to match the linear node.  This can
+            # be made configurable later if needed.
+            for _reverse_fusion_ops, base_op_idx in get_reversed_fusions():
+                is_match = end_node_matches_reversed_fusion(
+                    cur_end_node, _reverse_fusion_ops, self.gm, self.seen_nodes
+                )
+                if is_match:
+                    # navigate to the base node
+                    for rev_fusion_idx in range(len(_reverse_fusion_ops) - 1):
+                        # pyrefly: ignore [bad-argument-type]
+                        self.seen_nodes.add(cur_start_node)
+                        # for now, assume that there are no other nodes
+                        # which need to be added to the stack
+                        cur_start_node = cur_start_node.args[0]  # type: ignore[assignment]
+                        # if the base op index matches the current node, set it
+                        rev_base_op_idx = len(_reverse_fusion_ops) - 2 - base_op_idx
+                        if rev_fusion_idx == rev_base_op_idx:
+                            cur_base_op_node = cur_start_node
+                    break
+
+            # pyrefly: ignore [bad-argument-type]
+            self.seen_nodes.add(cur_start_node)
+            # add args of previous nodes to stack
+            # pyrefly: ignore [missing-attribute]
+            for arg in cur_start_node.all_input_nodes:
+                self._recursively_add_node_arg_to_stack(arg)
+
+            # skip unmatchable nodes
+            # note: this check is done on the start_node, i.e.
+            # if we are matching linear-relu in reverse, this would do the matchable
+            # check on the linear
+            # pyrefly: ignore [bad-argument-type]
+            if not self._is_matchable(cur_base_op_node):
+                continue
+
+            # If an observer or a fake_quant was not matched as a part of
+            # a pattern of multiple nodes, ignore it. One case where this is
+            # relevant is an observer on a graph input, which was added because
+            # it is necessary for the next node.
+            if cur_end_node.op == "call_module" and cur_start_node is cur_end_node:
+                maybe_obs = getattr_from_fqn(self.gm, cur_end_node.target)  # type: ignore[arg-type]
+                if isinstance(maybe_obs, (ObserverBase, FakeQuantizeBase)):
+                    continue
+
+            return NSSubgraph(
+                # pyrefly: ignore [bad-argument-type]
+                start_node=cur_start_node,
+                end_node=cur_end_node,
+                # pyrefly: ignore [bad-argument-type]
+                base_op_node=cur_base_op_node,
+            )
+
+        raise StopIteration
+
+    def _recursively_add_node_arg_to_stack(self, arg: Any) -> None:
+        """
+        Adds all of the nodes in this arg to the stack, properly navigating
+        through list, dicts and tuples.
+        """
+        if isinstance(arg, Node):
+            self.stack.append(arg)
+        elif (
+            isinstance(arg, torch.fx.immutable_collections.immutable_list)
+            or type(arg) is tuple
+        ):
+            for inner_arg in arg:
+                self._recursively_add_node_arg_to_stack(inner_arg)
+        elif isinstance(arg, torch.fx.immutable_collections.immutable_dict):
+            for value in arg.values():
+                self._recursively_add_node_arg_to_stack(value)
+
+    def _is_matchable(self, node: Node) -> bool:
+        if node.op == "call_function":
+            return node.target not in self.non_matchable_functions
+        elif node.op == "call_module":
+            if not isinstance(node.target, str):
+                raise AssertionError(f"Expected str, got {type(node.target)}")
+            target_mod = getattr_from_fqn(self.gm, node.target)
+            return not any(
+                isinstance(target_mod, t)  # type: ignore[arg-type]
+                for t in self.non_matchable_modules
+            )
+        elif node.op == "call_method":
+            return node.target not in self.non_matchable_methods
+        else:
+            return False
+
+
+class GraphMatchingException(Exception):
+    """
+    Exception raised when two graphs cannot be matched.
+    """
+
+
+class SubgraphTypeRelationship(enum.Enum):
+    # same type, known
+    # example: F.linear and F.linear, or nn.Conv2d and nn.Conv2d
+    EQUAL = enum.auto()
+    # same type, but the type is not known to Numerical Suite
+    # (user defined type, etc).
+    EQUAL_BUT_UKNOWN = enum.auto()
+    # known, same subgraph_relationship set, but not the same type
+    # example: F.linear and toq.linear
+    RELATED_BUT_NOT_EQUAL = enum.auto()
+    # not related
+    NOT_RELATED = enum.auto()
+
+
+def _get_subgraph_relationship_type(
+    subgraph_a: NSSubgraph,
+    subgraph_b: NSSubgraph,
+    gm_a: GraphModule,
+    gm_b: GraphModule,
+    type_a_related_to_b: set[tuple[NSNodeTargetType, NSNodeTargetType]],
+) -> SubgraphTypeRelationship:
+    node_a = subgraph_a.base_op_node
+    node_b = subgraph_b.base_op_node
+
+    # TODO(next): make this code handle matching by what is before the base op
+    if node_a.op != node_b.op:
+        if not (
+            node_a.op in ("call_function", "call_method")
+            and node_b.op in ("call_function", "call_method")
+        ):
+            return SubgraphTypeRelationship.NOT_RELATED
+
+    if node_a.op in ("call_function", "call_method"):
+        key = (node_a.target, node_b.target)
+
+        if key not in type_a_related_to_b:
+            if node_a.target == node_b.target:
+                return SubgraphTypeRelationship.EQUAL_BUT_UKNOWN
+            else:
+                return SubgraphTypeRelationship.NOT_RELATED
+        # after this point, we are dealing with known types
+
+        if node_a.target == node_b.target:
+            node_a_has_prev = subgraph_a.base_op_node == subgraph_a.start_node
+            node_b_has_prev = subgraph_b.base_op_node == subgraph_b.start_node
+            if node_a_has_prev and (not node_b_has_prev):
+                return SubgraphTypeRelationship.RELATED_BUT_NOT_EQUAL
+            elif (not node_a_has_prev) and node_b_has_prev:
+                return SubgraphTypeRelationship.RELATED_BUT_NOT_EQUAL
+            elif (not node_a_has_prev) and (not node_b_has_prev):
+                return SubgraphTypeRelationship.EQUAL
+            else:
+                # TODO(future PR): check for matches start_op_node and base_op_node
+                return SubgraphTypeRelationship.EQUAL
+
+        if key in type_a_related_to_b:
+            return SubgraphTypeRelationship.RELATED_BUT_NOT_EQUAL
+        else:
+            return SubgraphTypeRelationship.NOT_RELATED
+    elif node_a.op == "call_module":
+        if (
+            subgraph_a.base_op_node != subgraph_a.start_node
+            or subgraph_b.base_op_node != subgraph_b.start_node
+        ):
+            raise AssertionError(
+                "Matching call_module patterns where base_op_node != start_node is not supported yet"
+            )
+        # for call_module, we need to look up the modules to do the type check
+        if not isinstance(node_a.target, str):
+            raise AssertionError(f"Expected str, got {type(node_a.target)}")
+        mod_a = getattr_from_fqn(gm_a, node_a.target)
+        if not isinstance(node_b.target, str):
+            raise AssertionError(f"Expected str, got {type(node_b.target)}")
+        mod_b = getattr_from_fqn(gm_b, node_b.target)
+
+        key = (type(mod_a), type(mod_b))
+
+        if key not in type_a_related_to_b:
+            if type(mod_a) is type(mod_b):
+                return SubgraphTypeRelationship.EQUAL_BUT_UKNOWN
+            else:
+                return SubgraphTypeRelationship.NOT_RELATED
+        elif type(mod_a) is type(mod_b):
+            return SubgraphTypeRelationship.EQUAL
+        else:
+            return SubgraphTypeRelationship.RELATED_BUT_NOT_EQUAL
+
+    return SubgraphTypeRelationship.NOT_RELATED
+
+
+def _get_name_for_subgraph(
+    subgraph_a: NSSubgraph,
+    gm_a: GraphModule,
+    base_name_to_sets_of_related_ops: dict[str, set[NSNodeTargetType]],
+    existing_names: set[str],
+) -> str:
+    """
+    Returns a unique name for a subgraph. This name is based on two things:
+    1. the name of the set containing the underlying type of the base op in the
+       subgraph (i.e. 'torch.nn.functional.linear' if this is related to a linear op)
+    2. the number of previous subgraphs with related underlying type of the base op
+
+    For example, in the graph
+
+    linear0 -> relu0 -> linear1 -> relu1
+
+    The subgraphs are (linear0, relu0) and (linear1, relu1).  If we iterate
+    from the output node backwards, the name given to (linear1, relu1) will be
+    `base_op_torch.nn.functional.linear_0`, and the name given to (linear0, relu0)
+    will be `base_op_torch.nn.functional.linear_1`.
+
+    Why are we not just using the node name? Answer: because of two requirements:
+    A. fusions must be supported
+    B. some Numeric Suite APIs can be called without having all of the models in memory
+
+    For example, let's say we need to match nodes of
+
+    (1) ... -> linear0 -> relu0 -> ...
+
+    And
+
+    (2) ... -> linear_relu0 -> ...
+
+    Without being able to inspect them together. With the current naming scheme, if
+    we iterate through both of these graphs in the same order, and assuming the rest
+    of the graphs match, both of these subgraphs will get the same name without
+    (1) and (2) knowing anything about each other.
+    """
+    target_type = _get_node_target_type(subgraph_a.base_op_node, gm_a)
+    target_base_type = None
+    for base_name, sets_of_related_ops in base_name_to_sets_of_related_ops.items():
+        if target_type in sets_of_related_ops:
+            target_base_type = base_name
+    target_base_name = "base_op_" + str(target_base_type)
+    counter = 0
+    proposed_name = target_base_name + "_" + str(counter)
+    while proposed_name in existing_names:
+        counter += 1
+        proposed_name = target_base_name + "_" + str(counter)
+    existing_names.add(proposed_name)
+    return proposed_name
+
+
+def _get_node_target_type(node: Node, gm: GraphModule) -> NSNodeTargetType | None:
+    if node.op in ("call_function", "call_method"):
+        return node.target
+    elif node.op == "call_module":
+        if not isinstance(node.target, str):
+            raise AssertionError(f"Expected str, got {type(node.target)}")
+        mod = getattr_from_fqn(gm, node.target)
+        return type(mod)
+    return None
+
+
+def get_matching_subgraph_pairs(
+    gm_a: GraphModule,
+    gm_b: GraphModule,
+    base_name_to_sets_of_related_ops: dict[str, set[NSNodeTargetType]] | None = None,
+    unmatchable_types_map: dict[str, set[NSNodeTargetType]] | None = None,
+) -> dict[str, tuple[NSSubgraph, NSSubgraph]]:
+    """
+    Matches matchable subgraphs of graph_a to graph_b.
+
+    For a node, "matchable" is defined as a node which is not an observer,
+    fake_quants, quant or dequant.
+
+    A subgraph can contain one or more nodes.  A subgraph is matchable if
+    at least one node inside of it is matchable.  Currently, all nodes in
+    a subgraph must be matchable (because we assume no observers will be
+    inserted in the middle of a fusion).
+
+    A subgraph is defined by (start_node, end_node).  We assume that only
+    start_node and end_node are linked with the surrounding graph, all other
+    nodes in a subgraph are self-contained.
+
+    A pair of nodes is "related" if both nodes represent the same mathematical
+    operation across different quantization flavors. For example,
+    `F.linear` and `torch.ops.quantized.linear` are related, and
+    `F.linear` and `torch.nn.Conv` are not related.
+
+    For each matchable pair of nodes node_a and node_b, they will match
+    if node_a and node_b are related.
+
+    For graphs A and B, they will match iff:
+    1. the number of matchable subgraphs in A and B is equivalent
+    2. when iterating through the matchable subgraphs of A and B in the same order, each
+       corresponding pair of base nodes is related.
+
+    This enables us to find the corresponding subgraphs between
+    graphs of related models.  For example, if we had two graphs such as:
+
+    graph_a: x0 -> conv_0 (type: nn.Conv2d) -> obs_0 -> x1
+             w  -/
+             b  -/
+
+    graph_b: x0 -> quant_0 -> qconv_0 (type: nnq.Conv2d) -> dequant_0 -> x1
+           packed_params_0 -/
+
+    This function will return the following result:
+    {
+        'conv_0': (  # the name of the node in graph_b
+          (conv_0, conv_0),  # (start_node_a, end_node_a)
+          (qconv_0, qconv_0),  # (start_node_b, end_node_b)
+        ),
+    }
+
+    Or, if we have a fusion pattern,
+
+    graph_a: x0 -> linear_0 -> relu_0 -> obs_0 -> x1
+             w  -/
+             b  -/
+
+    graph_b: x0 -> quant_0 -> linear_relu_0 -> dequant_0 -> x1
+           packed_params_0 -/
+
+    This function will return the following result:
+    {
+        'linear_relu_0': (  # the name of the node in graph_b
+          (linear_0, relu_0),  # (start_node_a, end_node_a)
+          (linear_relu_0, linear_relu_0),  # (start_node_b, end_node_b)
+        ),
+    }
+    """
+    if unmatchable_types_map is None:
+        unmatchable_types_map = get_unmatchable_types_map()
+    non_matchable_functions = unmatchable_types_map["funs_unmatchable"]
+    non_matchable_modules = unmatchable_types_map["mods_unmatchable"]
+    non_matchable_methods = unmatchable_types_map["meths_unmatchable"]
+
+    graph_a_iterator = _NSGraphMatchableSubgraphsIterator(
+        gm_a, non_matchable_functions, non_matchable_modules, non_matchable_methods
+    )
+    graph_b_iterator = _NSGraphMatchableSubgraphsIterator(
+        gm_b, non_matchable_functions, non_matchable_modules, non_matchable_methods
+    )
+    results = collections.OrderedDict()
+    if base_name_to_sets_of_related_ops is None:
+        base_name_to_sets_of_related_ops = get_base_name_to_sets_of_related_ops()
+    type_a_related_to_b = get_type_a_related_to_b(base_name_to_sets_of_related_ops)
+
+    existing_names_a: set[str] = set()
+    existing_names_b: set[str] = set()
+
+    while True:
+        # fetch the next subgraphs from a and b
+        cur_subgraph_a, cur_subgraph_b = None, None
+        try:
+            cur_subgraph_a = next(graph_a_iterator)
+        except StopIteration:
+            pass
+        try:
+            cur_subgraph_b = next(graph_b_iterator)
+        except StopIteration:
+            pass
+
+        # look up types of a and b for useful error messages
+        type_start_a, type_start_b = None, None
+        if cur_subgraph_a is not None:
+            type_start_a = _get_node_target_type(cur_subgraph_a.start_node, gm_a)
+        if cur_subgraph_b is not None:
+            type_start_b = _get_node_target_type(cur_subgraph_b.start_node, gm_b)
+
+        # check for results and determine what to do next
+        if cur_subgraph_a is not None and cur_subgraph_b is not None:
+            # both nodes were fetched, check for subgraph_relationship
+            # note: subgraph_relationship is checked on the start node, i.e.
+            # if a linear-relu pattern is checked, we would check for subgraph_relationship
+            # of the linear
+            subgraph_relationship = _get_subgraph_relationship_type(
+                cur_subgraph_a, cur_subgraph_b, gm_a, gm_b, type_a_related_to_b
+            )
+            if subgraph_relationship == SubgraphTypeRelationship.NOT_RELATED:
+                msg = f"""
+The subgraphs
+({cur_subgraph_a}, {type_start_a}) and
+({cur_subgraph_b}, {type_start_b})
+are not related. Please ensure that the two models you pass in have the same number
+of subgraphs, and each pair of subgraphs is related to each other."""
+                raise GraphMatchingException(msg)
+            elif subgraph_relationship == SubgraphTypeRelationship.EQUAL_BUT_UKNOWN:
+                # skip matching but unknown types
+                continue
+            key_name_a = _get_name_for_subgraph(
+                cur_subgraph_a, gm_a, base_name_to_sets_of_related_ops, existing_names_a
+            )
+            key_name_b = _get_name_for_subgraph(
+                cur_subgraph_b, gm_b, base_name_to_sets_of_related_ops, existing_names_b
+            )
+            if key_name_a != key_name_b:
+                raise AssertionError(
+                    f"Subgraph names {key_name_a} and {key_name_b} do not match"
+                )
+            results[key_name_a] = (cur_subgraph_a, cur_subgraph_b)
+            continue
+        elif cur_subgraph_a is None and cur_subgraph_b is None:
+            # we reached the end of both graphs
+            break
+        else:
+            # only one node was fetched, no match possible, throw error
+            msg = f"""
+Attempting to match
+({cur_subgraph_a}, {type_start_a}) and
+({cur_subgraph_b}, {type_start_b}),
+one of which is empty. Please ensure that the two models you pass in have the same number
+of subgraphs."""
+            raise GraphMatchingException(msg)
+
+    # The subgraph pairs are originally created by traversing the two graphs
+    # from the outputs to the inputs. Reverse the results to return the
+    # subgraphs in their order of execution.
+    results = collections.OrderedDict(reversed(results.items()))
+
+    # pyrefly: ignore [bad-return]
+    return results
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/fx/graph_passes.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/fx/graph_passes.py
new file mode 100644
index 0000000000000000000000000000000000000000..338db28ce41d96ec5d3de38591f5937543d65394
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/fx/graph_passes.py
@@ -0,0 +1,1155 @@
+# mypy: allow-untyped-defs
+from collections.abc import Callable
+from typing import Any
+
+import torch
+from torch.ao.ns.fx.mappings import get_node_type_to_io_type_map
+from torch.ao.quantization.fx.utils import get_new_attr_name_with_prefix
+from torch.ao.quantization.observer import _is_activation_post_process
+from torch.fx import GraphModule, map_arg
+from torch.fx.graph import Graph, Node
+
+from .ns_types import NSNodeTargetType, NSSingleResultValuesType, NSSubgraph
+from .utils import (
+    get_arg_indices_of_inputs_to_log,
+    get_node_first_input_and_output_type,
+    get_node_input_qparams,
+    get_normalized_nth_input,
+    get_number_of_non_param_args,
+    get_target_type_str,
+    getattr_from_fqn,
+    NodeInputOrOutputType,
+    op_type_supports_shadowing,
+    return_first_non_observer_node,
+)
+
+
+def _maybe_get_fqn(node: Node, gm: GraphModule) -> str | None:
+    fqn = None
+    if hasattr(gm, "_node_name_to_scope"):
+        # fqn on observers is not present, because they do not
+        # exist when the fqns are created during tracing. If this is
+        # an observer, get the fqn of the node being observed.
+        node_to_use_for_fqn = node
+        if node.op == "call_module":
+            if not isinstance(node.target, str):
+                raise AssertionError(f"Expected str, got {type(node.target)}")
+            module = getattr_from_fqn(gm, node.target)
+            if _is_activation_post_process(module):
+                node_to_use_for_fqn = get_normalized_nth_input(node, gm, 0)
+        fqn = gm._node_name_to_scope[node_to_use_for_fqn.name][0]  # type: ignore[index]
+    return fqn  # type: ignore[return-value]
+
+
+def _insert_logger_after_node(
+    node: Node,
+    gm: GraphModule,
+    logger_cls: Callable,
+    logger_node_name_suffix: str,
+    ref_node_name: str,
+    model_name: str,
+    ref_name: str,
+    ref_node_target_type: str,
+    results_type: str,
+    index_within_arg: int,
+    index_of_arg: int,
+    fqn: str | None,
+) -> Node:
+    """
+    Given a starting graph of
+
+    prev_node -> node -> next_node
+
+    This function creates a new logger_cls obj and adds it
+    after node, resulting in
+
+    prev_node -> node -> logger_obj -> next_node
+    """
+    # create new name
+    logger_node_name = get_new_attr_name_with_prefix(
+        node.name + logger_node_name_suffix
+    )(gm)
+    target_type = get_target_type_str(node, gm)
+    # create the logger object
+    logger_obj = logger_cls(
+        ref_node_name,
+        node.name,
+        model_name,
+        ref_name,
+        target_type,
+        ref_node_target_type,
+        results_type,
+        index_within_arg,
+        index_of_arg,
+        fqn,
+    )
+    # attach the logger object to the parent module
+    setattr(gm, logger_node_name, logger_obj)
+    logger_node = node.graph.create_node("call_module", logger_node_name, (node,), {})
+    return logger_node
+
+
+def add_loggers_to_model(
+    gm: GraphModule,
+    node_to_instrument_inputs_to_ref_node_name: dict[Node, tuple[str, str]],
+    node_to_instrument_outputs_to_ref_node_name: dict[Node, tuple[str, str]],
+    logger_cls: Callable,
+    model_name: str,
+) -> GraphModule:
+    """
+    Takes the graph of gm, adds loggers to the output
+    of each node in nodes_to_instrument. Returns a GraphModule with the new
+    graph.
+    """
+
+    new_graph = Graph()
+    env: dict[str, Any] = {}
+
+    def load_arg(a):
+        return map_arg(a, lambda node: env[node.name])
+
+    for node in gm.graph.nodes:
+        if node.op == "output":
+            new_graph.output(map_arg(get_normalized_nth_input(node, gm, 0), load_arg))
+            continue
+
+        if (node in node_to_instrument_inputs_to_ref_node_name) or (
+            node in node_to_instrument_outputs_to_ref_node_name
+        ):
+            fqn = _maybe_get_fqn(node, gm)
+
+            if node in node_to_instrument_inputs_to_ref_node_name:
+                ref_name, ref_node_type = node_to_instrument_inputs_to_ref_node_name[
+                    node
+                ]
+                # Ops such add and mul are special because either
+                # one or two of the first two arguments can be tensors,
+                # and if one argument is a tensor it can be first or
+                # second (x + 1 versus 1 + x).
+                arg_indices_to_log = get_arg_indices_of_inputs_to_log(node)
+                for node_arg_idx in arg_indices_to_log:
+                    node_arg = get_normalized_nth_input(node, gm, node_arg_idx)
+                    if type(node_arg) is Node:
+                        # create a single input logger
+                        prev_node = env[node_arg.name]
+                        env[node_arg.name] = _insert_logger_after_node(
+                            prev_node,
+                            gm,
+                            logger_cls,
+                            "_ns_logger_",
+                            node.name,
+                            model_name,
+                            ref_name,
+                            ref_node_type,
+                            NSSingleResultValuesType.NODE_INPUT.value,
+                            index_within_arg=0,
+                            index_of_arg=node_arg_idx,
+                            fqn=fqn,
+                        )
+                    elif (
+                        type(node_arg) is torch.fx.immutable_collections.immutable_list
+                    ):
+                        # create N input loggers, one for each node
+                        for arg_idx, arg in enumerate(node_arg):  # type: ignore[var-annotated, arg-type]
+                            prev_node = env[arg.name]
+                            env[prev_node.name] = _insert_logger_after_node(
+                                prev_node,
+                                gm,
+                                logger_cls,
+                                "_ns_logger_",
+                                node.name,
+                                model_name,
+                                ref_name,
+                                ref_node_type,
+                                NSSingleResultValuesType.NODE_INPUT.value,
+                                index_within_arg=arg_idx,
+                                index_of_arg=node_arg_idx,
+                                fqn=fqn,
+                            )
+
+            # ensure env is populated with base node
+            # Note: runs for both inputs and outputs
+            env[node.name] = new_graph.node_copy(node, load_arg)
+
+            if node in node_to_instrument_outputs_to_ref_node_name:
+                ref_name, ref_node_type = node_to_instrument_outputs_to_ref_node_name[
+                    node
+                ]
+                # add the logger after the base node
+                env[node.name] = _insert_logger_after_node(
+                    env[node.name],
+                    gm,
+                    logger_cls,
+                    "_ns_logger_",
+                    node.name,
+                    model_name,
+                    ref_name,
+                    ref_node_type,
+                    NSSingleResultValuesType.NODE_OUTPUT.value,
+                    index_within_arg=0,
+                    index_of_arg=0,
+                    fqn=fqn,
+                )
+
+        else:
+            env[node.name] = new_graph.node_copy(node, load_arg)
+
+    new_gm = GraphModule(gm, new_graph)
+    return new_gm
+
+
+def _insert_quantize_per_tensor_node(
+    prev_node_c: Node,
+    node_a: Node,
+    gm_b: GraphModule,
+    graph_c: Graph,
+    scale: torch.Tensor | float,
+    zero_point: torch.Tensor | int,
+    dtype_cast_name: str,
+) -> Node:
+    # copy scale
+    scale_node_name = get_new_attr_name_with_prefix(node_a.name + "_input_scale_")(gm_b)
+    setattr(gm_b, scale_node_name, scale)
+    scale_node = graph_c.create_node(
+        "get_attr", scale_node_name, (), {}, scale_node_name
+    )
+    # copy zero_point
+    zero_point_node_name = get_new_attr_name_with_prefix(
+        node_a.name + "_input_zero_point_"
+    )(gm_b)
+    setattr(gm_b, zero_point_node_name, zero_point)
+    zero_point_node = graph_c.create_node(
+        "get_attr", zero_point_node_name, (), {}, zero_point_node_name
+    )
+    # create the quantize_per_tensor call
+    return graph_c.create_node(
+        "call_function",
+        torch.quantize_per_tensor,
+        (prev_node_c, scale_node, zero_point_node, torch.quint8),
+        {},
+        dtype_cast_name,
+    )
+
+
+def _insert_dtype_cast_after_node(
+    node_a: Node,
+    node_c: Node,
+    prev_node_c: Node | list[Node],
+    gm_a: GraphModule,
+    gm_b: GraphModule,
+    graph_c: Graph,
+    node_name_prefix: str,
+    logger_cls: Callable,
+    node_type_to_io_type_map: dict[str, set[NSNodeTargetType]],
+) -> Node | list[Node]:
+    """
+    Given a starting graph C (derived from graph B) of
+
+    ... -> prev_node_c -> node_c -> ...
+
+    And a corresponding related node_a, inserts the correct dtype
+    cast node after prev_node_c to cast into the dtype expected
+    by node_a, resulting in:
+
+                          dtype_cast
+                        /
+    ... -> prev_node_c -> node_c -> ...
+
+    For example, if node_c is an int8 op and node_a is an fp32 op, this function
+    will insert a dequant.
+    """
+    dtype_cast_op = None
+    dtype_cast_mod_cls = None
+    dtype_cast_method = None
+    dtype_cast_method_dtype = None
+    dtype_cast_scale = None
+    dtype_cast_zero_point = None
+    node_input_type_a, _node_output_type_a = get_node_first_input_and_output_type(
+        node_a, gm_a, logger_cls, node_type_to_io_type_map
+    )
+    node_input_type_c, _node_output_type_c = get_node_first_input_and_output_type(
+        node_c, gm_b, logger_cls, node_type_to_io_type_map
+    )
+
+    if (
+        (
+            node_input_type_a == NodeInputOrOutputType.FP32
+            and node_input_type_c == NodeInputOrOutputType.INT8
+        )
+        or (
+            node_input_type_a == NodeInputOrOutputType.FP32
+            and node_input_type_c == NodeInputOrOutputType.FP16
+        )
+        or
+        # TODO(future PR): determine the actual dtype of node_c,
+        # the current code only works because dequantize works with
+        # multiple input dtypes.
+        (
+            node_input_type_a == NodeInputOrOutputType.FP32
+            and node_input_type_c == NodeInputOrOutputType.FP32_OR_INT8
+        )
+    ):
+        dtype_cast_op = torch.dequantize
+    elif (
+        node_input_type_a == node_input_type_c
+        and node_input_type_a != NodeInputOrOutputType.UNKNOWN
+    ):
+        dtype_cast_mod_cls = torch.nn.Identity
+    elif (
+        node_input_type_a == NodeInputOrOutputType.INT8
+        and node_input_type_c == NodeInputOrOutputType.FP32
+    ):
+        # int8 shadows fp32, the dtype cast needs to quantize to int8
+        # with the right qparams.
+        node_a_input_qparams = get_node_input_qparams(
+            node_a, gm_a, node_type_to_io_type_map
+        )
+        if node_a_input_qparams is not None:
+            dtype_cast_op = torch.quantize_per_tensor  # type: ignore[assignment]
+            dtype_cast_scale, dtype_cast_zero_point = node_a_input_qparams
+    elif (
+        node_input_type_a == NodeInputOrOutputType.FP16
+        and node_input_type_c == NodeInputOrOutputType.FP32
+    ):
+        dtype_cast_method = "to"
+        dtype_cast_method_dtype = torch.float16
+    else:
+        raise AssertionError(
+            f"dtype cast from {node_input_type_c} {node_c.format_node()} to "
+            + f"{node_input_type_a} {node_a.format_node()} needs to be implemented"
+        )
+
+    if isinstance(prev_node_c, Node):
+        new_dtype_cast_name = get_new_attr_name_with_prefix(node_name_prefix)(gm_b)
+        if dtype_cast_op:
+            if dtype_cast_scale is not None and dtype_cast_zero_point is not None:
+                return _insert_quantize_per_tensor_node(
+                    prev_node_c,
+                    node_a,
+                    gm_b,
+                    graph_c,
+                    dtype_cast_scale,
+                    dtype_cast_zero_point,
+                    new_dtype_cast_name,
+                )
+            else:
+                return graph_c.create_node(
+                    "call_function",
+                    dtype_cast_op,
+                    (prev_node_c,),
+                    {},
+                    new_dtype_cast_name,
+                )
+        elif dtype_cast_method:
+            return graph_c.create_node(
+                "call_method",
+                dtype_cast_method,
+                (prev_node_c, dtype_cast_method_dtype),
+                {},
+                new_dtype_cast_name,
+            )
+        else:
+            if not dtype_cast_mod_cls:
+                raise AssertionError("Expected dtype_cast_mod_cls to be not None")
+            dtype_cast_mod = dtype_cast_mod_cls()
+            setattr(gm_b, new_dtype_cast_name, dtype_cast_mod)
+            return graph_c.create_node(
+                "call_module",
+                new_dtype_cast_name,
+                (prev_node_c,),
+                {},
+                new_dtype_cast_name,
+            )
+    elif isinstance(prev_node_c, list):
+        results = []
+        for prev_node_c_inner in prev_node_c:
+            new_dtype_cast_name = get_new_attr_name_with_prefix(node_name_prefix)(gm_b)
+            if dtype_cast_op:
+                # TODO(future PR): add handling for quantize_per_tensor
+                new_dtype_cast_node = graph_c.create_node(
+                    "call_function",
+                    dtype_cast_op,
+                    (prev_node_c_inner,),
+                    {},
+                    new_dtype_cast_name,
+                )
+                results.append(new_dtype_cast_node)
+            else:
+                if not dtype_cast_mod_cls:
+                    raise AssertionError("Expected dtype_cast_mod_cls to be not None")
+                dtype_cast_mod = dtype_cast_mod_cls()
+                setattr(gm_b, new_dtype_cast_name, dtype_cast_mod)
+                new_dtype_cast_node = graph_c.create_node(
+                    "call_module",
+                    new_dtype_cast_name,
+                    (prev_node_c_inner,),
+                    {},
+                    new_dtype_cast_name,
+                )
+                results.append(new_dtype_cast_node)
+        return results
+    else:
+        raise AssertionError(f"type f{type(prev_node_c)} is not handled")
+
+
+# TODO(future PR): look into using copy_node API instead
+def _copy_node_from_a_to_c(
+    node_a: Node,
+    gm_a: GraphModule,
+    gm_b: GraphModule,
+    graph_c: Graph,
+) -> Node:
+    """
+    Simple copy of node_a to graph_c.
+    """
+    if node_a.op == "get_attr":
+        node_a_copy_name = get_new_attr_name_with_prefix(node_a.name + "_shadow_copy_")(
+            gm_b
+        )
+        node_a_obj = getattr_from_fqn(gm_a, node_a.target)  # type: ignore[arg-type]
+        if torch.is_tensor(node_a_obj):
+            node_a_obj = node_a_obj.detach()
+        setattr(gm_b, node_a_copy_name, node_a_obj)
+        node_a_copy = graph_c.create_node(
+            node_a.op, node_a_copy_name, (), {}, node_a_copy_name
+        )
+        return node_a_copy
+    elif node_a.op == "call_method":
+        if node_a.target not in ("dequantize", "to"):
+            raise AssertionError(f"target {node_a.target} is not implemented")
+        if node_a.target == "dequantize":
+            arg_copy = _copy_node_from_a_to_c(
+                get_normalized_nth_input(node_a, gm_a, 0), gm_a, gm_b, graph_c
+            )  # type: ignore[arg-type]
+            node_a_copy_name = get_new_attr_name_with_prefix(
+                node_a.name + "_shadow_copy_"
+            )(gm_b)
+            node_a_copy = graph_c.create_node(
+                node_a.op, node_a.target, (arg_copy,), {}, node_a_copy_name
+            )
+            return node_a_copy
+        else:  # to
+            arg_copy = _copy_node_from_a_to_c(
+                get_normalized_nth_input(node_a, gm_a, 0), gm_a, gm_b, graph_c
+            )  # type: ignore[arg-type]
+            node_a_copy_name = get_new_attr_name_with_prefix(
+                node_a.name + "_shadow_copy_"
+            )(gm_b)
+            node_a_copy = graph_c.create_node(
+                node_a.op,
+                node_a.target,
+                (arg_copy, get_normalized_nth_input(node_a, gm_a, 1)),
+                {},
+                node_a_copy_name,
+            )
+            return node_a_copy
+
+    else:
+        raise AssertionError(
+            f"handling of node {node_a.format_node()} with op {node_a.op} is not implemented"
+        )
+
+
+def _can_insert_copy_of_subgraph_a(
+    subgraph_a: NSSubgraph,
+    gm_a: GraphModule,
+    num_non_param_args_node_a: int,
+) -> bool:
+    """
+    This function returns `False` if the input subgraph cannot be copied by
+    `_insert_copy_of_subgraph_a_after_input_node_c`. This usually means
+    that there is a corner case logic for which copy is not yet implemented.
+    """
+    # populate the list of nodes we need to check
+    nodes = []
+    cur_node = subgraph_a.end_node
+    while cur_node != subgraph_a.start_node:
+        nodes.append(cur_node)
+        cur_node = get_normalized_nth_input(cur_node, gm_a, 0)  # type: ignore[assignment]
+    nodes.append(cur_node)
+    nodes.reverse()
+
+    def _can_insert(node_a_arg, gm_a):
+        if isinstance(node_a_arg, Node):
+            arg_a = return_first_non_observer_node(node_a_arg, gm_a)
+            if arg_a.op == "call_method":
+                return arg_a.target in ("dequantize", "to")
+            elif arg_a.op == "get_attr":
+                return True
+            else:
+                return False
+        elif isinstance(node_a_arg, (list, tuple)):
+            for el in node_a_arg:
+                if not isinstance(el, Node):
+                    return False
+        return True
+
+    # For each node, check if we handle the copy behavior. This follows the
+    # logic in `_insert_copy_of_subgraph_a_after_input_node_c`.
+    for node_a in nodes:
+        local_num_non_param_args_node_a = (
+            num_non_param_args_node_a if node_a is nodes[0] else 1
+        )
+
+        norm_args_kwargs = node_a.normalized_arguments(
+            gm_a, normalize_to_only_use_kwargs=True
+        )
+        if norm_args_kwargs is not None:
+            norm_args, norm_kwargs = norm_args_kwargs
+        else:
+            norm_args, norm_kwargs = node_a.args, node_a.kwargs
+
+        cur_idx = 0
+
+        while cur_idx < len(norm_args):
+            if cur_idx == 0:
+                pass
+            elif cur_idx == 1 and local_num_non_param_args_node_a == 2:
+                pass
+            else:
+                if not _can_insert(norm_args[cur_idx], gm_a):
+                    return False
+            cur_idx += 1
+
+        for kwarg_val in norm_kwargs.values():
+            # stitch the inputs from base graph
+            if cur_idx == 0:
+                pass
+            elif cur_idx == 1 and local_num_non_param_args_node_a == 2:
+                pass
+            else:
+                if not _can_insert(kwarg_val, gm_a):
+                    return False
+            cur_idx += 1
+
+    return True
+
+
+def _insert_copy_of_subgraph_a_after_input_node_c(
+    input_node_c: Node | list[Node],
+    input_node_c_2: Node | list[Node] | None,
+    subgraph_a: NSSubgraph,
+    gm_a: GraphModule,
+    gm_b: GraphModule,
+    node_name_prefix: str,
+) -> Node:
+    """
+    TODO(before land): real docblock
+    """
+    if not isinstance(input_node_c, (Node, list)):
+        raise AssertionError(f"Expected Node or list, got {type(input_node_c)}")
+
+    # create a sequential list of the subgraphs' nodes from start to end,
+    # because we need to add the nodes to graph C in non-reverse order
+    nodes_of_a = [subgraph_a.end_node]
+    cur_node = subgraph_a.end_node
+    while cur_node != subgraph_a.start_node:
+        cur_node = get_normalized_nth_input(cur_node, gm_a, 0)  # type: ignore[assignment]
+        nodes_of_a.insert(0, cur_node)
+
+    # go through nodes of a in order, and insert them into the graph of c
+    # sequentially
+    cur_node_a = nodes_of_a[0]
+    cur_node_c = _insert_copy_of_node_a_after_input_node_c(
+        input_node_c, input_node_c_2, cur_node_a, gm_a, gm_b, node_name_prefix
+    )
+    for cur_idx_a in range(1, len(nodes_of_a)):
+        cur_node_a = nodes_of_a[cur_idx_a]
+        prev_node_c = cur_node_c  # previous added node is the input to next node
+        cur_node_c = _insert_copy_of_node_a_after_input_node_c(
+            prev_node_c,
+            # TODO(future PR): enable multiple inputs for nodes which are not at start of subgraph
+            None,
+            cur_node_a,
+            gm_a,
+            gm_b,
+            node_name_prefix,
+        )
+    # return the last inserted node
+    return cur_node_c
+
+
+def _insert_copy_of_node_a_after_input_node_c(
+    input_node_c: Node | list[Node],
+    input_node_c_2: Node | list[Node] | None,
+    node_a: Node,
+    gm_a: GraphModule,
+    gm_b: GraphModule,
+    node_name_prefix: str,
+) -> Node:
+    """
+    Assume that node_a from graph_a has
+      args (input, (input2)?, arg1, ...), and
+      kwargs {kw0: kwarg0, ...}
+
+    Note: input2 is optional. If it equals to None, we assume that the op
+    has a single non-param input.  If it is specified, we assume that the op
+    has two non-param inputs.
+
+    Copies the underlying values of arg1..argn and kwarg0..kwargn into gm_b,
+    and creates the corresponding nodes in graph_c. Note: observers are ignored,
+    so if an arg is an observer we navigate up until we find a non-observer parent.
+
+    If node_a is a call_module, points the module pointed to by node_a to gm_b.
+
+    Creates the copy of node_a in graph_c, with input as the first arg,
+    and all other args and kwargs pointing to the copies of the objects
+    in gm_b created above.
+
+    An example in pictures:
+
+    graph A:
+    ========
+
+    input -------------> node_a
+                         / / /
+    (input_2)?----------/ / /
+                         / /
+    weight -> weight_obs  /
+                         /
+    bias ----------------
+
+    graph C (derived from B):
+    =========================
+
+    input_node_c --> node_a_copy
+                     / / /
+    (input_node_c_2)? / /
+                     / /
+    weight_copy ----/ /
+                     /
+    bias_copy ------/
+    """
+    if isinstance(input_node_c, Node):
+        graph_c = input_node_c.graph
+    else:
+        if not isinstance(input_node_c, list):
+            raise AssertionError(f"Expected list, got {type(input_node_c)}")
+        graph_c = input_node_c[0].graph
+
+    norm_args_kwargs = node_a.normalized_arguments(
+        gm_a, normalize_to_only_use_kwargs=True
+    )
+    if norm_args_kwargs is not None:
+        norm_args, norm_kwargs = norm_args_kwargs
+    else:
+        norm_args, norm_kwargs = node_a.args, node_a.kwargs
+
+    new_args = []
+    new_kwargs = {}
+
+    def _copy_arg(arg):
+        # copy the other inputs from the other graph
+        if isinstance(arg, Node):
+            arg = return_first_non_observer_node(arg, gm_a)
+            arg = _copy_node_from_a_to_c(arg, gm_a, gm_b, graph_c)
+            return arg
+        elif isinstance(arg, (int, float, torch.dtype)):
+            return arg
+        elif isinstance(kwarg_val, (list, tuple)):
+            for el in kwarg_val:
+                if isinstance(el, Node):
+                    raise AssertionError(
+                        "handling of Node inside list is not implemented"
+                    )
+            return arg
+        else:
+            raise AssertionError(
+                f"handling for kwarg of type {type(kwarg_val)} is not implemented"
+            )
+
+    cur_idx = 0
+
+    while cur_idx < len(norm_args):
+        if cur_idx == 0:
+            new_arg = input_node_c
+        elif cur_idx == 1 and input_node_c_2 is not None:
+            new_arg = input_node_c_2
+        else:
+            new_arg = _copy_arg(norm_args[cur_idx])
+        new_args.append(new_arg)
+        cur_idx += 1
+
+    for kwarg_name, kwarg_val in norm_kwargs.items():
+        # stitch the inputs from base graph
+        if cur_idx == 0:
+            new_kwargs[kwarg_name] = input_node_c
+        elif cur_idx == 1 and input_node_c_2 is not None:
+            new_kwargs[kwarg_name] = input_node_c_2
+        else:
+            new_kwargs[kwarg_name] = _copy_arg(kwarg_val)
+        cur_idx += 1
+
+    new_args = tuple(new_args)  # type: ignore[assignment]
+
+    node_a_shadows_c_name = get_new_attr_name_with_prefix(node_name_prefix)(gm_b)
+
+    if node_a.op == "call_module":
+        # if target is a module, we point to the module from gm_b
+        new_mod_copy_name = get_new_attr_name_with_prefix(node_name_prefix)(gm_b)
+        # fetch the corresponding module from gm_a
+        if not isinstance(node_a.target, str):
+            raise AssertionError(f"Expected str, got {type(node_a.target)}")
+        mod_a = getattr_from_fqn(gm_a, node_a.target)
+        setattr(gm_b, new_mod_copy_name, mod_a)
+        node_a_shadows_c = graph_c.create_node(
+            node_a.op,
+            new_mod_copy_name,
+            new_args,  # type: ignore[arg-type]
+            new_kwargs,  # type: ignore[arg-type]
+            node_a_shadows_c_name,
+        )
+        return node_a_shadows_c
+    else:
+        if node_a.op not in ("call_function", "call_method"):
+            raise AssertionError(f"Unexpected op: {node_a.op}")
+        node_a_shadows_c = graph_c.create_node(
+            node_a.op,
+            node_a.target,
+            new_args,  # type: ignore[arg-type]
+            new_kwargs,  # type: ignore[arg-type]
+            node_a_shadows_c_name,
+        )
+        return node_a_shadows_c
+
+
+def create_a_shadows_b(
+    name_a: str,
+    gm_a: GraphModule,
+    name_b: str,
+    gm_b: GraphModule,
+    matched_subgraph_pairs: dict[str, tuple[NSSubgraph, NSSubgraph]],
+    logger_cls: Callable,
+    should_log_inputs: bool,
+    node_type_to_io_type_map: dict[str, set[NSNodeTargetType]] | None = None,
+) -> GraphModule:
+    """
+    Creates a new GraphModule consisting of the graph of C, with the meaningful
+    nodes of A shadowing the corresponding nodes of B.  For example,
+
+    Graph A:
+    a0 -> op0_fp32 -> a1 -> op1_fp32 -> a2
+
+    Graph B:
+    b0 -> op0_int8 -> b1 -> op1_int8 -> b2
+
+    matched_node_pairs: {'op0': (op0_fp32, op0_int8), 'op1': (op1_fp32, op1_int8)}
+
+    Graph C (A shadows B):
+
+        / dequant0 -> op0_fp32 -> logger_a_0  / dequant_1 -> op1_fp32 -> logger_a_1
+       /                                     /
+    b0 -------------> op0_int8 -> logger_b_0 --------------> op1_int8 -> logger_b_1
+
+    In a nutshell, this function does the following for each node pair:
+    * copies the necessary attributes and modules from gm_a to gm_b,
+      keeping names unique
+    * adds a dtype cast op (dequant, quant, etc)
+    * adds a copy of node_a in gm_b's graph
+    * adds loggers to the outputs of node_a and node_b
+    """
+
+    if node_type_to_io_type_map is None:
+        node_type_to_io_type_map = get_node_type_to_io_type_map()
+
+    # graph_c is the graph created from copying the nodes of graph_b and inserting
+    # the shadows with the nodes copied from graph_a
+    graph_c = Graph()
+    env_c: dict[str, Any] = {}
+
+    def load_arg(a):
+        return map_arg(a, lambda node: env_c[node.name])
+
+    start_node_b_to_matched_subgraph_a_and_name = {}
+    end_node_b_to_matched_subgraph_a_and_name = {}
+    for match_name, match in matched_subgraph_pairs.items():
+        subgraph_a, subgraph_b = match
+        ref_node_type_a = get_target_type_str(subgraph_a.base_op_node, gm_a)
+        ref_node_type_b = get_target_type_str(subgraph_b.base_op_node, gm_b)
+        start_node_b_to_matched_subgraph_a_and_name[subgraph_b.start_node] = (
+            subgraph_a,
+            match_name,
+            ref_node_type_a,
+            ref_node_type_b,
+        )
+        end_node_b_to_matched_subgraph_a_and_name[subgraph_b.end_node] = (
+            subgraph_a,
+            match_name,
+            ref_node_type_a,
+            ref_node_type_b,
+        )
+
+    for node_b in gm_b.graph.nodes:
+        if node_b.op == "output":
+            graph_c.output(map_arg(node_b.args[0], load_arg))
+            continue
+
+        # calculate the flags to determine what to do with this node
+        node_b_is_start_node = node_b in start_node_b_to_matched_subgraph_a_and_name
+        node_b_is_end_node = node_b in end_node_b_to_matched_subgraph_a_and_name
+
+        if node_b_is_start_node or node_b_is_end_node:
+            if node_b_is_start_node:
+                (
+                    subgraph_a,
+                    ref_name,
+                    ref_node_type_a,
+                    ref_node_type_b,
+                ) = start_node_b_to_matched_subgraph_a_and_name[node_b]
+            else:
+                if not node_b_is_end_node:
+                    raise AssertionError("Expected node_b_is_end_node to be not false")
+                (
+                    subgraph_a,
+                    ref_name,
+                    ref_node_type_a,
+                    ref_node_type_b,
+                ) = end_node_b_to_matched_subgraph_a_and_name[node_b]
+
+            all_op_types_support_shadowing = op_type_supports_shadowing(
+                subgraph_a.start_node
+            ) and op_type_supports_shadowing(node_b)
+            if not all_op_types_support_shadowing:
+                print(
+                    f"skipping shadow loggers for node_b: {get_target_type_str(node_b, gm_b)}"
+                    + f", start_node_a: {get_target_type_str(subgraph_a.start_node, gm_a)}"
+                    + ", unsupported"
+                )
+                env_c[node_b.name] = graph_c.node_copy(node_b, load_arg)
+                continue
+
+            # For both start_node and end_node verify that we know how to do
+            # the dtype cast. If we do not, skip.
+            (
+                node_input_type_a,
+                node_output_type_a,
+            ) = get_node_first_input_and_output_type(
+                subgraph_a.start_node, gm_a, logger_cls, node_type_to_io_type_map
+            )
+            (
+                node_input_type_b,
+                node_output_type_b,
+            ) = get_node_first_input_and_output_type(
+                node_b, gm_b, logger_cls, node_type_to_io_type_map
+            )
+            node_io_types_known_a_and_b = (
+                node_input_type_a != NodeInputOrOutputType.UNKNOWN
+                and node_output_type_a != NodeInputOrOutputType.UNKNOWN
+                and node_input_type_b != NodeInputOrOutputType.UNKNOWN
+                and node_output_type_b != NodeInputOrOutputType.UNKNOWN
+            )
+            if not node_io_types_known_a_and_b:
+                print(
+                    f"skipping shadow loggers for node_b: {get_target_type_str(node_b, gm_b)}"
+                    + f", start_node_a: {get_target_type_str(subgraph_a.start_node, gm_a)}"
+                    + ", unknown dtype cast"
+                )
+                env_c[node_b.name] = graph_c.node_copy(node_b, load_arg)
+                continue
+
+            # If we are shadowing from fp32 to int8, we need to insert
+            # quantize_per_tensor call with qparams from the previous node.
+            # Only do this if we are able to infer these qparams from the graph.
+            if (
+                node_input_type_a == NodeInputOrOutputType.INT8
+                and node_input_type_b == NodeInputOrOutputType.FP32
+            ):
+                node_a_input_qparams = get_node_input_qparams(
+                    subgraph_a.start_node, gm_a, node_type_to_io_type_map
+                )
+                if not node_a_input_qparams:
+                    print(
+                        f"skipping shadow loggers for node_b: {get_target_type_str(node_b, gm_b)}"
+                        + f", start_node_a: {get_target_type_str(subgraph_a.start_node, gm_a)}"
+                        + ", unknown input qparams"
+                    )
+                    env_c[node_b.name] = graph_c.node_copy(node_b, load_arg)
+                    continue
+
+            num_non_param_args_node_a = get_number_of_non_param_args(
+                subgraph_a.start_node, gm_a
+            )
+            if not _can_insert_copy_of_subgraph_a(
+                subgraph_a, gm_a, num_non_param_args_node_a
+            ):
+                print(
+                    f"skipping shadow loggers for node_b: {get_target_type_str(node_b, gm_b)}"
+                    + f", start_node_a: {get_target_type_str(subgraph_a.start_node, gm_a)}"
+                    + ", unhandled logic in subgraph copy"
+                )
+                env_c[node_b.name] = graph_c.node_copy(node_b, load_arg)
+                continue
+
+            fqn_base_a = _maybe_get_fqn(subgraph_a.base_op_node, gm_a)
+            fqn_base_b = _maybe_get_fqn(subgraph_b.base_op_node, gm_b)  # type: ignore[possibly-undefined]
+
+            if node_b_is_start_node:
+                # if necessary, log the input of node_c
+                if should_log_inputs:
+                    prev_node_b = get_normalized_nth_input(node_b, gm_b, 0)
+                    if isinstance(prev_node_b, Node):
+                        prev_node_c = env_c[prev_node_b.name]
+                        env_c[prev_node_c.name] = _insert_logger_after_node(
+                            prev_node_c,
+                            gm_b,
+                            logger_cls,
+                            "_ns_logger_b_inp_",
+                            node_b.name,
+                            name_b,
+                            ref_name,
+                            ref_node_type_b,
+                            NSSingleResultValuesType.NODE_INPUT.value,
+                            index_within_arg=0,
+                            index_of_arg=0,
+                            fqn=fqn_base_b,
+                        )
+                    elif isinstance(prev_node_b, list):
+                        # first, save the prev_node instances, because they
+                        # will be overwritten in the env after the first logger
+                        # is added
+                        prev_node_c_list = [env_c[arg.name] for arg in prev_node_b]
+
+                        for arg_idx, prev_node_c in enumerate(prev_node_c_list):
+                            env_c[prev_node_c.name] = _insert_logger_after_node(
+                                prev_node_c,
+                                gm_b,
+                                logger_cls,
+                                "_ns_logger_b_inp_",
+                                node_b.name,
+                                name_b,
+                                ref_name,
+                                ref_node_type_b,
+                                NSSingleResultValuesType.NODE_INPUT.value,
+                                index_within_arg=arg_idx,
+                                index_of_arg=0,
+                                fqn=fqn_base_b,
+                            )
+                    else:
+                        # logging of inputs which are not lists is not supported yet
+                        raise AssertionError(
+                            f"type {type(prev_node_b)} is not handled yet"
+                        )
+                # subgraph so far:
+                #
+                # (prev_node_c)+ -> (logger_c_input)?
+
+            # Note: this if statement is always True, spelling it out to clarify code
+            # intent.
+            if node_b_is_start_node or node_b_is_end_node:
+                # ensure env_c is populated with base node
+                env_c[node_b.name] = graph_c.node_copy(node_b, load_arg)
+                node_c = env_c[node_b.name]
+
+                # after this point,
+                #
+                # node_a is the original node from graph_a, with parent module gm_a
+                # node_b is the original node from graph_b, with parent module gm_b
+                # node_c is the copy of node_b in graph_c
+                #
+                # subgraph so far:
+                #
+                # (prev_node_c)+ -> (logger_c_input)? -> node_start_c
+
+            if node_b_is_start_node:
+                # cast dtype from the dtype of node_c's input to the dtype of
+                # node_a's input (dequant, etc)
+                # prev_node_c = node_c.args[0]
+                prev_node_c = get_normalized_nth_input(node_c, gm_b, 0)  # type: ignore[possibly-undefined]
+                if should_log_inputs:
+                    # skip the input logger when inserting a dtype cast
+                    if isinstance(prev_node_c, Node):
+                        # pyrefly: ignore [unbound-name]
+                        prev_node_c = get_normalized_nth_input(node_c, gm_b, 0)
+                    elif isinstance(prev_node_c, list):
+                        prev_node_c = [
+                            get_normalized_nth_input(arg, gm_b, 0)
+                            for arg in prev_node_c
+                        ]
+                dtype_cast_node = _insert_dtype_cast_after_node(
+                    subgraph_a.start_node,
+                    # pyrefly: ignore [unbound-name]
+                    node_c,
+                    prev_node_c,
+                    gm_a,
+                    gm_b,
+                    graph_c,
+                    node_b.name + "_dtype_cast_",
+                    logger_cls,
+                    node_type_to_io_type_map,
+                )
+                # note: not inserting to env_c because all nodes which use the dtype
+                #   casts are copied from graph_a
+                #
+                # subgraph so far:
+                #
+                #           (dtype_cast_node)+
+                #                  /
+                # (prev_node_c)+ -> (logger_c_input)? -> node_start_c
+
+                # if input logging is enabled, log the input to the subgraph
+                if should_log_inputs:
+                    # TODO: explain this
+                    ref_node_name = ""
+                    if isinstance(dtype_cast_node, Node):
+                        dtype_cast_node = _insert_logger_after_node(
+                            dtype_cast_node,
+                            gm_b,
+                            logger_cls,
+                            "_ns_logger_a_inp_",
+                            ref_node_name,
+                            name_a,
+                            ref_name,
+                            ref_node_type_a,
+                            NSSingleResultValuesType.NODE_INPUT.value,
+                            index_within_arg=0,
+                            index_of_arg=0,
+                            fqn=fqn_base_a,
+                        )
+                        input_logger: Node | list[Node] = dtype_cast_node
+                    else:
+                        if not isinstance(dtype_cast_node, list):
+                            raise AssertionError(
+                                f"Expected list, got {type(dtype_cast_node)}"
+                            )
+                        new_loggers = []
+                        for dtype_cast_idx, dtype_cast_node_inner in enumerate(
+                            dtype_cast_node
+                        ):
+                            dtype_cast_logger = _insert_logger_after_node(
+                                dtype_cast_node_inner,
+                                gm_b,
+                                logger_cls,
+                                "_ns_logger_a_inp_",
+                                ref_node_name,
+                                name_a,
+                                ref_name,
+                                ref_node_type_a,
+                                NSSingleResultValuesType.NODE_INPUT.value,
+                                index_within_arg=dtype_cast_idx,
+                                index_of_arg=0,
+                                fqn=fqn_base_a,
+                            )
+                            new_loggers.append(dtype_cast_logger)
+                        dtype_cast_node = new_loggers
+                        input_logger = dtype_cast_node
+                    # subgraph so far:
+                    #
+                    #       (dtype_cast_node)+ -> (logger_a_input)?
+                    #                  /
+                    # prev_node_c -> (logger_c_input)? -> node_start_c
+
+                # hook up the new mod_a copy to be in the graph, receiving the
+                # same inputs as mod_b does, with dtype cast to match a
+                # Some ops, such as LSTMs, have two non-param inputs. If we have
+                # such an op, pass the second param as well. Note: dtype casting
+                # for the second param is not implemented yet, it can be added
+                # later if there is a use case.
+                node_c_second_non_param_arg = None
+                num_non_param_args_node_a = get_number_of_non_param_args(
+                    subgraph_a.start_node, gm_a
+                )
+                if num_non_param_args_node_a == 2:
+                    # node_c_second_non_param_arg = node_c.args[1]
+                    node_c_second_non_param_arg = get_normalized_nth_input(
+                        # pyrefly: ignore [unbound-name]
+                        node_c,
+                        gm_b,
+                        1,
+                    )
+                node_a_shadows_c = _insert_copy_of_subgraph_a_after_input_node_c(
+                    dtype_cast_node,
+                    node_c_second_non_param_arg,
+                    subgraph_a,
+                    gm_a,
+                    gm_b,
+                    # pyrefly: ignore [unbound-name]
+                    node_c.name + "_shadow_copy_",
+                )
+                env_c[node_a_shadows_c.name] = node_a_shadows_c
+                # subgraph so far:
+                #
+                #       dtype_cast_node -> (logger_a_input)? -> subgraph_a_copy(args/kwargs not shown)
+                #                  /
+                # (prev_node_c)+ -> (logger_c_input)? -> node_start_c
+
+                if should_log_inputs:
+                    # When we created the input logger, we left the ref_node_name
+                    # as an empty string, because the subgraph copy did not exist
+                    # yet. Now that the subgraph copy exists, we modify this name
+                    # to its true value.
+                    # Note: the alternative to this is to create the input logger
+                    # after creating the subgraph, which is slightly more
+                    # complicated. This is the lesser of two evils.
+                    # input_logger = env_c[dtype_cast_node.name]
+                    # Find the first node in the subgraph
+                    cur_node = node_a_shadows_c
+                    while get_normalized_nth_input(cur_node, gm_b, 0) != input_logger:  # type: ignore[possibly-undefined]
+                        cur_node = get_normalized_nth_input(cur_node, gm_b, 0)  # type: ignore[assignment]
+                    # pyrefly: ignore [unbound-name]
+                    if isinstance(input_logger, Node):
+                        # pyrefly: ignore [unbound-name]
+                        input_logger_mod = getattr(gm_b, input_logger.name)
+                        input_logger_mod.ref_node_name = cur_node.name
+                    else:
+                        # pyrefly: ignore [unbound-name]
+                        if not isinstance(input_logger, list):
+                            raise AssertionError(
+                                # pyrefly: ignore [unbound-name]
+                                f"Expected list, got {type(input_logger)}"
+                            )
+                        # pyrefly: ignore [unbound-name]
+                        for input_logger_inner in input_logger:
+                            input_logger_mod = getattr(gm_b, input_logger_inner.name)
+                            input_logger_mod.ref_node_name = cur_node.name
+
+                # hook up a logger to the mod_a copy
+                env_c[node_a_shadows_c.name] = _insert_logger_after_node(
+                    env_c[node_a_shadows_c.name],
+                    gm_b,
+                    logger_cls,
+                    "_ns_logger_a_",
+                    node_a_shadows_c.name,
+                    name_a,
+                    ref_name,
+                    ref_node_type_a,
+                    NSSingleResultValuesType.NODE_OUTPUT.value,
+                    index_within_arg=0,
+                    index_of_arg=0,
+                    fqn=fqn_base_a,
+                )
+                # subgraph so far:
+                #
+                #       dtype_cast_node -> (logger_a_input)? -> subgraph_a_copy -> logger_a
+                #                  /
+                # (prev_node_c)+ -> (logger_c_input)? -> node_start_c
+
+            if node_b_is_end_node:
+                # hook up a logger to the mod_b copy
+                env_c[node_b.name] = _insert_logger_after_node(
+                    env_c[node_b.name],
+                    gm_b,
+                    logger_cls,
+                    "_ns_logger_b_",
+                    node_b.name,
+                    name_b,
+                    ref_name,
+                    ref_node_type_b,
+                    NSSingleResultValuesType.NODE_OUTPUT.value,
+                    index_within_arg=0,
+                    index_of_arg=0,
+                    fqn=fqn_base_b,
+                )
+                # subgraph so far:
+                #
+                #       dtype_cast_node -> (logger_a_input)? -> subgraph_a_copy -> logger_a
+                #                  /
+                # (prev_node_c+) -> (logger_c_input)? -> node_start_c -> ... -> node_end_c -> logger_c
+                #
+                # Note: node_start_c may be the same node as node_end_c, or they
+                # may have nodes in between.
+
+        else:
+            env_c[node_b.name] = graph_c.node_copy(node_b, load_arg)
+
+    gm_c = GraphModule(gm_b, graph_c)
+    return gm_c
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/fx/mappings.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/fx/mappings.py
new file mode 100644
index 0000000000000000000000000000000000000000..275291789f1c5461af366038d7702801bf5fc303
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/fx/mappings.py
@@ -0,0 +1,763 @@
+import operator
+from typing import TYPE_CHECKING
+
+import torch
+import torch.ao.nn.intrinsic as nni
+import torch.ao.nn.intrinsic.qat as nniqat
+import torch.ao.nn.intrinsic.quantized as nniq
+import torch.ao.nn.intrinsic.quantized.dynamic as nniqd
+import torch.ao.nn.qat as nnqat
+import torch.ao.nn.qat.dynamic as nnqatd
+import torch.ao.nn.quantized as nnq
+import torch.ao.nn.quantized.dynamic as nnqd
+import torch.ao.quantization.fx._lower_to_native_backend as _lower_to_native_backend
+import torch.ao.quantization.quantization_mappings as quantization_mappings
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.ao.quantization.backend_config import get_native_backend_config
+
+from .ns_types import NSNodeTargetType
+
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+
+toq = torch.ops.quantized
+
+
+def get_base_name_to_sets_of_related_ops() -> dict[str, set[NSNodeTargetType]]:
+    # note: this set is modified below by items from backend_config
+    sets_of_related_ops: list[set[NSNodeTargetType]] = [
+        # conv modules
+        {
+            nn.Conv1d,
+        },
+        {
+            nn.Conv2d,
+        },
+        {
+            nn.Conv3d,
+        },
+        # conv functionals
+        {
+            F.conv1d,
+        },
+        {
+            F.conv2d,
+        },
+        {
+            F.conv3d,
+        },
+        # linear modules
+        {
+            nn.Linear,
+        },
+        # linear functionals
+        {
+            F.linear,
+        },
+        # average pool
+        {
+            nn.AvgPool1d,
+            torch.avg_pool1d,
+        },
+        {
+            nn.AvgPool2d,
+            torch._C._nn.avg_pool2d,
+        },
+        {
+            nn.AvgPool3d,
+            torch._C._nn.avg_pool3d,
+        },
+        # adaptive average pool
+        {
+            nn.AdaptiveAvgPool1d,
+            F.adaptive_avg_pool1d,
+        },
+        {
+            nn.AdaptiveAvgPool2d,
+            F.adaptive_avg_pool2d,
+        },
+        {
+            nn.AdaptiveAvgPool3d,
+            F.adaptive_avg_pool3d,
+        },
+        # LSTM
+        {
+            nn.LSTM,
+        },
+        # add
+        {
+            torch.add,
+            operator.add,  # x + y
+        },
+        # cat
+        {
+            torch.cat,
+        },
+        # mul
+        {
+            torch.mul,
+            operator.mul,
+        },
+        # relu
+        {
+            F.relu,
+            nn.ReLU,
+            "relu",
+            "relu_",
+            torch.relu,
+        },
+        # maxpool
+        {
+            nn.MaxPool1d,
+            F.max_pool1d,
+        },
+        {
+            nn.MaxPool2d,
+            F.max_pool2d,
+        },
+        {
+            nn.MaxPool3d,
+            F.max_pool3d,
+        },
+        # sigmoid
+        {
+            torch.sigmoid,
+            "sigmoid",
+            "sigmoid_",
+            nn.Sigmoid,
+            F.sigmoid,
+        },
+        # BatchNorm
+        {
+            nn.BatchNorm2d,
+        },
+        {
+            nn.BatchNorm3d,
+        },
+        # ConvTranspose
+        {
+            nn.ConvTranspose1d,
+        },
+        {
+            nn.ConvTranspose2d,
+        },
+        {
+            nn.ConvTranspose3d,
+        },
+        # functional transposed conv
+        {
+            F.conv_transpose1d,
+        },
+        {
+            F.conv_transpose2d,
+        },
+        {
+            F.conv_transpose3d,
+        },
+        # ELU
+        {
+            nn.ELU,
+        },
+        # Embedding
+        {
+            nn.Embedding,
+        },
+        # EmbeddingBag
+        {
+            nn.EmbeddingBag,
+        },
+        # GroupNorm
+        {
+            nn.GroupNorm,
+        },
+        # Hardswish
+        {
+            nn.Hardswish,
+        },
+        # InstanceNorm
+        {
+            nn.InstanceNorm1d,
+        },
+        {
+            nn.InstanceNorm2d,
+        },
+        {
+            nn.InstanceNorm3d,
+        },
+        # LayerNorm
+        {
+            nn.LayerNorm,
+        },
+        # LeakyReLU
+        {
+            nn.LeakyReLU,
+        },
+        # ReLU6
+        {
+            nn.ReLU6,
+            F.relu6,
+        },
+        # F.elu
+        {
+            F.elu,
+        },
+        # F.hardswish
+        {
+            F.hardswish,
+        },
+        # F.group_norm
+        {
+            F.group_norm,
+        },
+        # F.instance_norm
+        {
+            F.instance_norm,
+        },
+        # F.layer_norm
+        {
+            F.layer_norm,
+        },
+        # F.leaky_relu
+        {
+            F.leaky_relu,
+        },
+        # F.silu
+        {
+            nn.SiLU,
+            F.silu,
+        },
+        # F.mish
+        {
+            nn.Mish,
+            F.mish,
+        },
+        # F.tanh
+        {
+            nn.Tanh,
+            F.tanh,
+            torch.tanh,
+            "tanh_",
+            "tanh",
+        },
+        # F.hardsigmoid
+        {
+            "hardsigmoid_",
+            "hardsigmoid",
+            F.hardsigmoid,
+            nn.Hardsigmoid,
+        },
+        # F.hardtanh
+        {
+            nn.Hardtanh,
+            F.hardtanh,
+            F.hardtanh_,
+        },
+        # floordiv
+        {
+            operator.floordiv,
+        },
+        # unsqueeze
+        {
+            torch.unsqueeze,
+        },
+        # stack
+        {
+            torch.stack,
+        },
+        # squeeze
+        {
+            torch.squeeze,
+        },
+        # sort
+        {
+            torch.sort,
+        },
+        # repeat_interleave
+        {
+            torch.repeat_interleave,
+        },
+        # min
+        {
+            torch.min,
+        },
+        # mean
+        {
+            torch.mean,
+        },
+        # max
+        {
+            torch.max,
+        },
+        # transpose
+        {
+            torch.transpose,
+        },
+        # flatten
+        {
+            torch.flatten,
+        },
+        # clamp
+        {
+            torch.clamp,
+        },
+        # chunk
+        {
+            torch.chunk,
+        },
+        # interpolate
+        {
+            torch.nn.functional.interpolate,
+        },
+        # dropout
+        {
+            nn.Dropout,
+        },
+        # F.dropout
+        {
+            F.dropout,
+        },
+        # matmul
+        {
+            torch.matmul,
+        },
+        # Softmax
+        {
+            nn.Softmax,
+        },
+        # PReLU
+        {
+            nn.PReLU,
+            nnq.PReLU,
+        },
+        # F.prelu
+        {
+            F.prelu,
+            toq.prelu,
+        },
+        # pixel shuffle
+        {
+            nn.PixelShuffle,
+        },
+        {
+            F.pixel_shuffle,
+        },
+        # pixel unshuffle
+        {
+            nn.PixelUnshuffle,
+        },
+        {
+            F.pixel_unshuffle,
+        },
+        # narrow
+        {
+            torch.narrow,
+        },
+    ]
+
+    # for each floating point op, add versions of the op added by
+    # backend_config
+    backend_config = get_native_backend_config()
+
+    new_connections: list[tuple[Callable, Callable]] = [
+        # technical debt edge case
+        (nn.Linear, nn.modules.linear.NonDynamicallyQuantizableLinear),
+    ]
+
+    for pattern, config in backend_config._pattern_complex_format_to_config.items():
+        # pattern format: (c, (b, a))
+        first_element = pattern
+        # look from the end, because pattern is in reverse order
+        while isinstance(first_element, (list, tuple)):
+            first_element = first_element[-1]
+
+        if config.fused_module is not None:
+            # case 1: pattern fuses a pattern of ops into an op
+            # example: nn.Conv1d, nn.ReLU fused into nni.ConvReLU1d
+            new_connections.append((first_element, config.fused_module))
+
+        if config.qat_module is not None:
+            # case 2: pattern swaps a module into a QAT module
+            # example: nni.ConvReLU1d swapped into nniqat.ConvReLU1d
+            new_connections.append((first_element, config.qat_module))
+
+        if config.reference_quantized_module is not None:
+            # case 3: reference version of floating point module, such as
+            # nn.Conv2d and nnqr.Conv2d
+            new_connections.append((first_element, config.reference_quantized_module))
+
+    #
+    # Add reference module swaps from default lowering path
+    #
+
+    for source_to_target in (
+        _lower_to_native_backend.STATIC_LOWER_MODULE_MAP,
+        _lower_to_native_backend.DYNAMIC_LOWER_MODULE_MAP,
+        _lower_to_native_backend.WEIGHT_ONLY_LOWER_MODULE_MAP,
+        _lower_to_native_backend.SPECIAL_PATTERN_LOWER_MODULE_MAP,
+    ):
+        for source, target in source_to_target.items():  # type: ignore[attr-defined]
+            new_connections.append((source, target))
+
+    for source_to_double_target in (
+        _lower_to_native_backend.STATIC_LOWER_FUSED_MODULE_MAP,
+        _lower_to_native_backend.STATIC_LOWER_FUSED_MODULE_TWO_INPUTS_MAP,
+        _lower_to_native_backend.DYNAMIC_LOWER_FUSED_MODULE_MAP,
+    ):
+        for source, (target1, target2) in source_to_double_target.items():  # type: ignore[attr-defined]
+            new_connections.append((source, target1))
+            new_connections.append((source, target2))
+
+    #
+    # Add function swaps from default lowering path
+    #
+
+    for source, (  # type:ignore[assignment]
+        target1,
+        target2,
+    ) in _lower_to_native_backend.STATIC_LOWER_FUNCTIONAL_MAP.items():
+        new_connections.append((source, target1))
+        # pyrefly: ignore [bad-argument-type]
+        new_connections.append((source, target2))
+
+    for source_to_target in (
+        _lower_to_native_backend.QBIN_OP_MAPPING,
+        _lower_to_native_backend.QBIN_RELU_OP_MAPPING,
+        quantization_mappings.DEFAULT_FLOAT_TO_QUANTIZED_OPERATOR_MAPPINGS,
+    ):
+        for source, target in source_to_target.items():  # type:ignore[assignment]
+            # pyrefly: ignore [bad-argument-type]
+            new_connections.append((source, target))
+
+    #
+    # Add other swaps, ideally in the future this could be removed
+    # after the lowering code stops using these.
+    #
+    for source_to_target in (
+        quantization_mappings.DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS,
+    ):
+        for source, target in source_to_target.items():  # type:ignore[assignment]
+            new_connections.append((source, target))
+
+    # add the new connections from backend_config
+    for item1, item2 in new_connections:
+        for set_of_related_ops in sets_of_related_ops:
+            if item1 in set_of_related_ops or item2 in set_of_related_ops:
+                set_of_related_ops.add(item1)
+                set_of_related_ops.add(item2)
+                break
+
+    base_name_to_sets_of_related_ops: dict[str, set[NSNodeTargetType]] = {}
+
+    for counter, set_of_related_ops in enumerate(sets_of_related_ops):
+        base_name = str(counter)
+        base_name_to_sets_of_related_ops[base_name] = set_of_related_ops
+
+    return base_name_to_sets_of_related_ops
+
+
+def get_base_name_for_op(
+    base_name_to_sets_of_related_ops: dict[str, set[NSNodeTargetType]],
+    op: NSNodeTargetType,
+) -> str | None:
+    for base_name, set_of_related_ops in base_name_to_sets_of_related_ops.items():
+        if op in set_of_related_ops:
+            return base_name
+    return None
+
+
+def add_op_to_sets_of_related_ops(
+    base_name_to_sets_of_related_ops: dict[str, set[NSNodeTargetType]],
+    op: NSNodeTargetType,
+    related_op: NSNodeTargetType | None,
+) -> None:
+    if related_op is not None:
+        for set_of_related_ops in base_name_to_sets_of_related_ops.values():
+            if related_op in set_of_related_ops:
+                set_of_related_ops.add(op)
+                return
+        # if we got here, related_op was not found
+        raise AssertionError(f"{related_op} was not found")
+    else:
+        counter = 0
+        while str(counter) in base_name_to_sets_of_related_ops:
+            counter += 1
+        base_name_to_sets_of_related_ops[str(counter)] = {op}
+
+
+# TODO(future PR): clean this up
+def get_node_type_to_io_type_map() -> dict[str, set[NSNodeTargetType]]:
+    FUNS_IO_TYPE_FP32: set[NSNodeTargetType] = {
+        F.linear,
+        F.conv1d,
+        F.conv2d,
+        F.conv3d,
+        torch.cat,
+        F.elu,
+        F.hardswish,
+        F.instance_norm,
+        F.layer_norm,
+        F.leaky_relu,
+        F.dropout,
+        F.silu,
+        F.mish,
+        operator.add,
+        torch.add,
+        operator.mul,
+        torch.mul,
+        torch.sum,
+        F.prelu,
+    }
+
+    FUNS_IO_TYPE_FP16: set[NSNodeTargetType] = set()
+
+    FUNS_IO_TYPE_INT8: set[NSNodeTargetType] = {
+        toq.linear,
+        toq.linear_relu,
+        toq.conv1d,
+        toq.conv1d_relu,
+        toq.conv2d,
+        toq.conv2d_relu,
+        toq.conv3d,
+        toq.conv3d_relu,
+        toq.cat,
+        toq.elu,
+        toq.hardswish,
+        toq.instance_norm,
+        toq.layer_norm,
+        toq.leaky_relu,
+        toq.dropout,
+        toq.prelu,
+        # TODO(future PR): implement shadowing for binary ops and
+        # uncomment below
+        # toq.add,
+        # toq.mul,
+    }
+
+    FUNS_IO_TYPE_FP32_OR_INT8: set[NSNodeTargetType] = {
+        F.relu,
+        F.tanh,
+        torch.tanh,
+        F.sigmoid,
+        torch.sigmoid,
+        F.hardsigmoid,
+        operator.floordiv,
+        torch.adaptive_avg_pool1d,
+        F.adaptive_avg_pool2d,
+        F.adaptive_avg_pool3d,
+        F.dropout,
+        F.hardtanh,
+        F.hardtanh_,
+        F.interpolate,
+        F.max_pool1d,
+        F.max_pool2d,
+        F.max_pool3d,
+        F.relu6,
+        F.pixel_shuffle,
+        F.pixel_unshuffle,
+        torch.avg_pool1d,
+        torch._C._nn.avg_pool2d,
+        torch._C._nn.avg_pool3d,
+        torch.cat,
+        torch.chunk,
+        torch.clamp,
+        torch.flatten,
+        torch.transpose,
+        torch.max,
+        torch.mean,
+        torch.min,
+        torch.narrow,
+        torch.repeat_interleave,
+        torch.sort,
+        torch.squeeze,
+        torch.stack,
+        torch.unsqueeze,
+        operator.add,
+    }
+
+    MODS_IO_TYPE_FP32: set[NSNodeTargetType] = {
+        nn.Linear,
+        nnqat.Linear,
+        nnqatd.Linear,
+        nnqd.Linear,
+        torch.nn.modules.linear.NonDynamicallyQuantizableLinear,
+        nn.Conv1d,
+        nn.Conv2d,
+        nn.Conv3d,
+        nnqat.Conv1d,
+        nnqat.Conv2d,
+        nnqat.Conv3d,
+        nnqat.Embedding,
+        nnqat.EmbeddingBag,
+        nn.LSTM,
+        # note: nnqd.Linear is an instance of nnq.Linear, so this
+        # check has to happen before the int8 module check
+        nnqd.LSTM,
+        nn.BatchNorm2d,
+        nn.BatchNorm3d,
+        nn.Dropout,
+        nn.ConvTranspose1d,
+        nn.ConvTranspose2d,
+        nn.ConvTranspose3d,
+        nn.ELU,
+        nn.GroupNorm,
+        nn.InstanceNorm1d,
+        nn.InstanceNorm2d,
+        nn.InstanceNorm3d,
+        nn.LayerNorm,
+        nn.Hardswish,
+        nn.LeakyReLU,
+        nn.ReLU6,
+        nn.SiLU,
+        nn.Mish,
+        nn.Softmax,
+        nn.PReLU,
+        nni.BNReLU2d,
+        nni.BNReLU3d,
+        nni.ConvReLU1d,
+        nni.ConvReLU2d,
+        nni.ConvReLU3d,
+        nni.LinearReLU,
+        nni.LinearBn1d,
+        nni.ConvBn1d,
+        nni.ConvBn2d,
+        nni.ConvBn3d,
+        nniqat.ConvBn1d,
+        nniqat.ConvBn2d,
+        nniqat.ConvBn3d,
+        nniqat.ConvBnReLU1d,
+        nniqat.ConvBnReLU2d,
+        nniqat.ConvBnReLU3d,
+        nniqat.ConvReLU1d,
+        nniqat.ConvReLU2d,
+        nniqat.ConvReLU3d,
+        nniqat.LinearReLU,
+        nniqat.LinearBn1d,
+        nniqd.LinearReLU,
+        nni.LinearLeakyReLU,
+        nni.LinearTanh,
+        nni.ConvAdd2d,
+        nni.ConvAddReLU2d,
+    }
+
+    MODS_IO_TYPE_INT8: set[NSNodeTargetType] = {
+        nnq.Linear,
+        nnq.Conv1d,
+        nnq.Conv2d,
+        nnq.Conv3d,
+        nnq.BatchNorm2d,
+        nnq.BatchNorm3d,
+        nnq.Dropout,
+        nnq.ConvTranspose1d,
+        nnq.ConvTranspose2d,
+        nnq.ELU,
+        nnq.InstanceNorm1d,
+        nnq.InstanceNorm2d,
+        nnq.InstanceNorm3d,
+        nnq.LayerNorm,
+        nnq.Hardswish,
+        nnq.LeakyReLU,
+        nnq.Embedding,
+        nnq.EmbeddingBag,
+        nnq.Dropout,
+        nnq.Softmax,
+        nnq.PReLU,
+        nniq.BNReLU2d,
+        nniq.BNReLU3d,
+        nniq.ConvReLU1d,
+        nniq.ConvReLU2d,
+        nniq.ConvReLU3d,
+        nniq.LinearReLU,
+        nniq.LinearLeakyReLU,
+        nniq.LinearTanh,
+        nniq.ConvAdd2d,
+        nniq.ConvAddReLU2d,
+    }
+
+    MODS_IO_TYPE_FP32_OR_INT8: set[NSNodeTargetType] = {
+        nn.ReLU,
+        nn.Tanh,
+        nn.Sigmoid,
+        nn.Hardsigmoid,
+        nn.AdaptiveAvgPool1d,
+        nn.AdaptiveAvgPool2d,
+        nn.AdaptiveAvgPool3d,
+        nn.AvgPool1d,
+        nn.AvgPool2d,
+        nn.AvgPool3d,
+        nn.Dropout,
+        nn.Hardtanh,
+        nn.Identity,
+        nn.MaxPool1d,
+        nn.MaxPool2d,
+        nn.MaxPool3d,
+        nn.PixelShuffle,
+        nn.PixelUnshuffle,
+        nn.ReLU6,
+    }
+
+    METHS_IO_TYPE_FP32_OR_INT8: set[NSNodeTargetType] = {
+        "sigmoid_",
+        "sigmoid",
+        "tanh_",
+        "tanh",
+        "hardsigmoid_",
+        "hardsigmoid",
+        "relu_",
+        "relu",
+    }
+
+    return {
+        "funs_io_type_fp32": FUNS_IO_TYPE_FP32,
+        "funs_io_type_fp16": FUNS_IO_TYPE_FP16,
+        "funs_io_type_int8": FUNS_IO_TYPE_INT8,
+        "funs_io_type_fp32_or_int8": FUNS_IO_TYPE_FP32_OR_INT8,
+        "mods_io_type_fp32": MODS_IO_TYPE_FP32,
+        "mods_io_type_int8": MODS_IO_TYPE_INT8,
+        "mods_io_type_fp32_or_int8": MODS_IO_TYPE_FP32_OR_INT8,
+        "meths_io_type_fp32_or_int8": METHS_IO_TYPE_FP32_OR_INT8,
+    }
+
+
+def get_unmatchable_types_map() -> dict[str, set[NSNodeTargetType]]:
+    FUNS_UNMATCHABLE: set[NSNodeTargetType] = {
+        torch.quantize_per_tensor,
+        operator.getitem,
+    }
+
+    MODS_UNMATCHABLE: set[NSNodeTargetType] = {
+        nn.Identity,
+    }
+
+    METHS_UNMATCHABLE: set[NSNodeTargetType] = {
+        "to",
+        "dequantize",
+        "reshape",
+        "view",
+        "unsqueeze_",
+        "unsqueeze",
+        "transpose",
+        "squeeze_",
+        "squeeze",
+        "size",
+        "shape",
+        "resize_",
+        "repeat_interleave",
+        "repeat",
+        "permute",
+        "numel",
+        "mean",
+        "detach_",
+        "detach",
+        "contiguous",
+        "clamp",
+        "chunk",
+    }
+
+    return {
+        "funs_unmatchable": FUNS_UNMATCHABLE,
+        "mods_unmatchable": MODS_UNMATCHABLE,
+        "meths_unmatchable": METHS_UNMATCHABLE,
+    }
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/fx/n_shadows_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/fx/n_shadows_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..95d467d9337ea24d676d282740df042d5bdd16f3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/fx/n_shadows_utils.py
@@ -0,0 +1,1416 @@
+# mypy: allow-untyped-defs
+import collections
+import copy
+import operator
+from collections.abc import Callable
+from typing import Any
+
+import torch
+import torch.fx
+from torch.ao.ns.fx.graph_passes import _maybe_get_fqn
+from torch.ao.ns.fx.ns_types import NSResultsType, NSSingleResultValuesType
+from torch.ao.ns.fx.utils import (  # TODO(future PR): make this work correctly for methods
+    get_normalized_nth_input,
+    get_target_type_str,
+)
+from torch.ao.quantization import QConfigMapping
+from torch.ao.quantization.fx.match_utils import _MatchResult
+from torch.ao.quantization.qconfig import QConfigAny
+from torch.ao.quantization.utils import getattr_from_fqn
+from torch.fx import Graph, GraphModule, Node
+from torch.utils._pytree import tree_map
+
+
+SHADOW_NODE_NAME_PREFIX = "shadow"
+SHADOW_WRAPPER_NODE_NAME_PREFIX = "shadow_wrapper"
+
+# TODO(future PR): reuse existing mapping instead of creating a new one
+BINARY_FUNCTIONS = {
+    torch.add,
+    torch.Tensor.add,
+    operator.add,
+    torch.mul,
+    torch.Tensor.mul,
+    operator.mul,
+}
+
+
+def _get_attr_name(subgraph_idx, subgraph_candidate_idx):
+    return f"{SHADOW_NODE_NAME_PREFIX}_{subgraph_idx}_{subgraph_candidate_idx}"
+
+
+def _get_attr_wrapper_name(subgraph_idx, subgraph_candidate_idx):
+    return f"{SHADOW_WRAPPER_NODE_NAME_PREFIX}_{subgraph_idx}_{subgraph_candidate_idx}"
+
+
+class OutputProp:
+    """
+    Output propagation (modeled from shape propagation).
+
+    Given a GraphModule and an example input, saves the output flowing
+    through each node on `node.traced_result`.
+
+    Code based on the example from
+    https://pytorch.org/docs/stable/fx.html#the-interpreter-pattern
+    """
+
+    def __init__(self, mod):
+        self.mod = mod
+        self.graph = mod.graph
+        self.modules = dict(self.mod.named_modules())
+
+    def propagate(self, *args):
+        args_iter = iter(args)
+        env: dict[str, Node] = {}
+
+        def load_arg(a):
+            return torch.fx.graph.map_arg(a, lambda n: env[n.name])
+
+        def fetch_attr(target: str):
+            target_atoms = target.split(".")
+            attr_itr = self.mod
+            for i, atom in enumerate(target_atoms):
+                if not hasattr(attr_itr, atom):
+                    raise RuntimeError(
+                        f"Node referenced nonexistent target {'.'.join(target_atoms[:i])}"
+                    )
+                attr_itr = getattr(attr_itr, atom)
+            return attr_itr
+
+        for node in self.graph.nodes:
+            if node.op == "placeholder":
+                result = next(args_iter)
+            elif node.op == "get_attr":
+                result = fetch_attr(node.target)
+            elif node.op == "call_function":
+                result = node.target(*load_arg(node.args), **load_arg(node.kwargs))
+            elif node.op == "call_method":
+                self_obj, *args = load_arg(node.args)
+                kwargs = load_arg(node.kwargs)
+                result = getattr(self_obj, node.target)(*args, **kwargs)
+            elif node.op == "call_module":
+                result = self.modules[node.target](
+                    *load_arg(node.args), **load_arg(node.kwargs)
+                )
+
+            if isinstance(result, torch.Tensor):  # type: ignore[possibly-undefined]
+                # pyrefly: ignore [unbound-name]
+                node.traced_result = result
+
+            # pyrefly: ignore [unsupported-operation]
+            # pyrefly: ignore [unbound-name]
+            env[node.name] = result
+
+        return None
+
+
+def _get_dedup_subgraphs(matches: dict[str, _MatchResult]) -> dict[str, list[Node]]:
+    # the original matches variable is unique by node, make it unique by subgraph
+    # instead
+    seen_nodes = set()
+    subgraphs_dedup = {}
+
+    # Dict items are not reversible until Python 3.8, so we hack it
+    # to be compatible with previous Python versions
+    # TODO(future PR): try reversed(list(matches.items()))
+    matches_items_reversed: list[tuple[str, _MatchResult]] = list(
+        reversed(matches.items())
+    )
+
+    # Note: the order is important.  `matches` currently provides the matches
+    # in reverse order.  We would like to process the matches in non-reverse
+    # order, so that we can create an intuitive naming scheme, such as
+    # naming the first op's submodules `shadow_0_0` through `shadow_0_(n-1)`
+    for name, cur_match in matches_items_reversed:  # type: ignore[call-overload]
+        was_seen = False
+        for node_or_tuple in cur_match[1]:
+            # Cur_match[1] has an unusual type. It says that it's a `List[Node]`,
+            # but it is really not. Furthermore, the contents of this field
+            # can change from match results of multiple nodes of the same pattern
+            #
+            # For example, for conv -> bn -> relu, we see
+            # match_results = {
+            #   'conv': (relu, [(bn, conv), relu], ...),
+            #   'bn': (relu, [(bn, conv), relu], ...),
+            #   'relu': (relu, [(bn, conv), relu], ...),
+            # }
+            #
+            # Ideally we should clean up the `find_matches` function to make
+            # this more intuitive. For the purposes of this prototype, we hack
+            # around it.
+
+            if isinstance(node_or_tuple, Node):
+                if node_or_tuple in seen_nodes:
+                    was_seen = True
+                seen_nodes.add(node_or_tuple)
+
+            else:
+                if not isinstance(node_or_tuple, tuple):
+                    raise AssertionError(f"Expected tuple, got {type(node_or_tuple)}")
+                for node in node_or_tuple:
+                    if not isinstance(node, Node):
+                        raise AssertionError(f"Expected Node, got {type(node)}")
+                    if node in seen_nodes:
+                        was_seen = True
+                    seen_nodes.add(node)
+
+        if was_seen:
+            continue
+
+        # Start with the unusual type, convert it to [op_0, ..., op_n]
+        list_of_nodes = []
+
+        if len(cur_match[1]) == 1:
+            list_of_nodes = cur_match[1]
+        else:
+            if len(cur_match[1]) != 2:
+                raise ValueError(
+                    f"Expected cur_match[1] to have length 2, got {len(cur_match[1])}"
+                )
+            # either (a, b), or ((a, b), c) or (c, (a, b))
+            # cannot make any assumptions on order, not clear what the
+            # _find_matches function is doing to populate this
+            # TODO(future PR): make this code less confusing,  see discussion
+            # in https://github.com/pytorch/pytorch/pull/80521/files#r975918836
+
+            def _order_nodes(node_a, node_b, node_c) -> list[Node]:
+                nodes = [node_a, node_b, node_c]
+                first_node = None
+                mid_node = None
+                last_node = None
+                for n in nodes:
+                    prev_n = n.args[0]
+                    next_n = next(iter(n.users))
+                    if prev_n not in nodes:
+                        first_node = n
+                    elif next_n not in nodes:
+                        last_node = n
+                    else:
+                        mid_node = n
+                if first_node is None or mid_node is None or last_node is None:
+                    raise AssertionError("Expected all nodes to be non-None")
+                if mid_node.args[0] is not first_node:
+                    raise AssertionError("Expected mid_node.args[0] to be first_node")
+                if last_node.args[0] is not mid_node:
+                    raise AssertionError("Expected last_node.args[0] to be mid_node")
+                return [last_node, mid_node, first_node]
+
+            if isinstance(cur_match[1][0], Node) and isinstance(cur_match[1][1], Node):
+                # (a, b)
+                list_of_nodes = cur_match[1]
+            elif isinstance(cur_match[1][0], tuple):
+                # ((a, b), c)
+                node_a, node_b = cur_match[1][0]
+                node_c = cur_match[1][1]
+                list_of_nodes = _order_nodes(node_a, node_b, node_c)
+            elif isinstance(cur_match[1][1], tuple):
+                # (a, (b, c))
+                node_a, node_b = cur_match[1][1]
+                node_c = cur_match[1][0]
+                list_of_nodes = _order_nodes(node_a, node_b, node_c)
+
+        # [node_n, ..., node_0], note that the order is reversed
+        # to make it chronological for simple subgraphs
+        list_of_nodes.reverse()
+        subgraphs_dedup[name] = list_of_nodes
+
+    return subgraphs_dedup
+
+
+def _get_logger_for_subgraph(
+    model: GraphModule,
+    first_node: Node,
+    last_node: Node,
+    subgraph_idx: int,
+    subgraph_candidate_idx: int,
+    qconfig_str: str,
+    logger_cls: Callable,
+    fqn: str | None,
+) -> torch.nn.Module:
+    """
+    Given a model and a linear subgraph starting from `first_node` and
+    ending with `last_node`, creates a logger for the end of this
+    subgraph.
+    """
+    if fqn is None:
+        fqn = ""
+    logger_mod_orig = logger_cls(
+        first_node.name,  # ref_node_name
+        last_node.name,  # prev_node_name
+        f"subgraph_{subgraph_idx}_{subgraph_candidate_idx}",  # model_name
+        "model",  # ref_name
+        get_target_type_str(last_node, model),  # prev_node_target_type
+        get_target_type_str(first_node, model),  # ref_node_target_type
+        NSSingleResultValuesType.NODE_OUTPUT.value,  # results_type
+        0,  # index_within_arg
+        0,  # index_of_arg
+        fqn,  # fqn
+        qconfig_str,
+    )
+    # Usually we expect the user to add loggers, then calibrate, then convert,
+    # and then populate loggers.  This is why the loggers start disabled.
+    # TODO(future PR): reconsider the design to make this more intuitive.
+    logger_mod_orig.enabled = False
+    return logger_mod_orig
+
+
+def create_submodule_from_subgraph(
+    model: torch.nn.Module,
+    first_node: Node,
+    last_node: Node,
+) -> GraphModule:
+    """
+    Input: a model, and a linear subgraph within the model from first_node to
+      last_node.
+
+    Output: a new submodule containing a copy of the subgraph, with the inputs
+      to the first node becoming the inputs to the submodule, and all other
+      nodes in the subgraph being copied.
+
+    Example inputs:
+
+    `model`: a module with graph
+
+      x0 -> op1 -> x1 -> op2 -> x2
+             |
+            arg1
+
+    `first_node`: op1
+    `last_node`: op2
+
+    Example output: a new module with graph
+
+      input1 -> op1_copy -> x1 -> op2_copy -> output1
+                   |
+                  arg1
+    """
+
+    #
+    # create a blank GraphModule with an empty graph
+    #
+
+    class M(torch.nn.Module):
+        def forward(self, x):
+            pass
+
+    m = M()
+    gm = torch.fx.symbolic_trace(m)
+    g = gm.graph
+    for node in reversed(gm.graph.nodes):
+        g.erase_node(node)
+
+    #
+    # modify the graph to have a copy of our subgraph
+    #
+
+    cur_node_orig = first_node
+
+    cur_name_idx = 0
+
+    iteration_limit = 100
+    cur_iteration = 0
+
+    while True:
+        if cur_node_orig is first_node:
+            # we are at the first node, we need to set up graph inputs
+            # TODO(future): some graphs could have placeholders which are unrelated
+            # to the first node, need to handle this
+            cur_args_copy = []
+            cur_kwargs_copy = {}
+            seen_names: set[str] = set()
+            old_name_to_new_node: dict[str, Node] = {}
+
+            def _add_placeholder(
+                g: Graph, node: Node, seen_names, old_name_to_new_node
+            ):
+                # note: for graphs starting with patterns such as `y = x + x`, we
+                # need to ensure we do not add multiple placeholders with the
+                # same name
+                counter = 0
+                while node.name + "_" + str(counter) in seen_names:
+                    counter += 1
+                cur_name = node.name + "_" + str(counter)
+                seen_names.add(cur_name)
+                placeholder = g.placeholder(cur_name)
+                old_name_to_new_node[node.name] = placeholder
+                return placeholder
+
+            for arg in cur_node_orig.args:
+                if isinstance(arg, Node):
+                    p = _add_placeholder(g, arg, seen_names, old_name_to_new_node)
+                    cur_args_copy.append(p)
+                elif isinstance(arg, (list, tuple)):
+                    new_arg = []
+                    for inner_arg in arg:
+                        if isinstance(inner_arg, Node):
+                            new_arg.append(
+                                _add_placeholder(
+                                    g, inner_arg, seen_names, old_name_to_new_node
+                                )
+                            )
+                        else:
+                            new_arg.append(inner_arg)
+                    cur_args_copy.append(new_arg)
+                else:
+                    cur_args_copy.append(arg)
+
+            # TODO(future PR): handle non-normalized kwargs
+            for kwarg_name, kwarg in cur_node_orig.kwargs.items():
+                if isinstance(kwarg, Node):
+                    cur_kwargs_copy[kwarg_name] = _add_placeholder(
+                        g, kwarg, seen_names, old_name_to_new_node
+                    )
+                elif isinstance(kwarg, (list, tuple)):
+                    new_kwarg = []
+                    for inner_kwarg in kwarg:
+                        p = _add_placeholder(
+                            g,
+                            inner_kwarg,  # type: ignore[arg-type]
+                            seen_names,
+                            old_name_to_new_node,
+                        )
+                        new_kwarg.append(p)
+                    cur_kwargs_copy[kwarg_name] = new_kwarg
+                else:
+                    cur_kwargs_copy[kwarg_name] = kwarg
+
+            cur_args_copy = tuple(cur_args_copy)  # type: ignore[assignment]
+        else:
+            # we are not at first node, first arg is from the previous node,
+            # and all other args are copied
+
+            # the current implementation is simplistic and cannot handle
+            # ops with two or more arguments which need to be passed from
+            # the previous op, so we assert them out
+            if cur_node_orig.target in BINARY_FUNCTIONS:
+                raise AssertionError(
+                    f"Unexpected binary function target: {cur_node_orig.target}"
+                )
+
+            # at this point in the code, cur_node_copy is pointing to the copy
+            # of the previous node
+            # TODO(future PR): this is not handling complicated graphs correctly, need to
+            # look at actual relationships instead of assuming sequential graph
+            # TODO(future PR): this is ignoring kwargs, will need to support kwargs
+            # for any fusion pattern which has them for a node that is not the
+            # first node.
+            cur_args_copy = [cur_node_copy]  # type: ignore[has-type, possibly-undefined]  # noqa: F821
+
+            if len(cur_node_orig.args) > 1:
+                for arg in cur_node_orig.args[1:]:
+                    if isinstance(arg, torch.nn.Parameter):
+                        new_arg = arg.detach().clone()  # type: ignore[assignment]
+                        mod_name = f"mod_{cur_name_idx}"
+                        cur_name_idx += 1
+                        setattr(gm, mod_name, new_arg)
+                        new_arg_placeholder = gm.placeholder(mod_name)  # type: ignore[operator]
+                        # pyrefly: ignore [missing-attribute]
+                        cur_args_copy.append(new_arg_placeholder)
+                    elif isinstance(arg, (float, int, torch.dtype)):
+                        # pyrefly: ignore [missing-attribute]
+                        cur_args_copy.append(arg)
+                    else:
+                        raise AssertionError(f"arg of type {type(arg)} not handled yet")
+            cur_args_copy = tuple(cur_args_copy)  # type: ignore[assignment]
+
+        # copy the node
+        if cur_node_orig.op == "call_module":
+            orig_mod = getattr_from_fqn(model, cur_node_orig.target)  # type: ignore[arg-type]
+            orig_mod_copy = copy.deepcopy(orig_mod)
+            mod_name = f"mod_{cur_name_idx}"
+            setattr(gm, mod_name, orig_mod_copy)
+            cur_name_idx += 1
+            cur_node_copy = g.call_module(mod_name, cur_args_copy, cur_kwargs_copy)  # type: ignore[possibly-undefined,arg-type]
+
+        elif cur_node_orig.op == "call_function":
+            cur_node_copy = g.call_function(
+                cur_node_orig.target,  # type: ignore[arg-type]
+                cur_args_copy,  # type: ignore[arg-type]
+                cur_kwargs_copy,  # type: ignore[possibly-undefined]
+            )
+
+        elif cur_node_orig.op == "call_method":
+            cur_node_copy = g.call_method(
+                cur_node_orig.target,  # type: ignore[arg-type]
+                cur_args_copy,  # type: ignore[arg-type]
+                cur_kwargs_copy,  # type: ignore[possibly-undefined]
+            )
+
+        else:
+            raise AssertionError(f"{cur_node_orig.op} not supported yet")
+
+        if cur_node_orig is last_node:
+            break
+
+        # go to next node
+        if len(cur_node_orig.users.keys()) != 1:
+            raise AssertionError(
+                f"{cur_node_orig} has more than 1 users, not supported yet"
+            )
+        cur_node_orig = next(iter(cur_node_orig.users.keys()))
+        cur_iteration += 1
+        if cur_iteration > iteration_limit:
+            raise AssertionError("iteration limit exceeded")
+
+    # set up outputs
+    g.output(cur_node_copy)
+
+    gm.recompile()
+    return gm
+
+
+def create_one_transformed_and_logged_copy_of_subgraph(
+    mt: GraphModule,
+    subgraph_idx: int,
+    subgraph_candidate_idx: int,
+    first_node: Node,
+    last_node: Node,
+    fqn: str | None,
+    list_of_node_name_to_qconfig: list[dict[str, QConfigAny]],
+    example_inputs: Any,
+    last_added_shadow_node_list: list[Node | None],
+    custom_prepare_fn: Callable | None = None,
+    custom_prepare_kwargs: dict[str, Any] | None = None,
+) -> None:
+    """
+    Given a subgraph in `mt` and a subgraph candidate idx, inserts the
+    subgraph candidate copy and instruments it with loggers.
+
+    If subgraph_candidate_idx is 0, this is the baseline fp32 subgraph and we just
+    add a logger to the end.
+
+    If subgraph_candidate_idx is not 0, we create a copy of the subgraph and
+    prepare it with `prepare_fx`.
+    """
+
+    # TODO(future PR): move logger classes to utils to remove circular dependency
+    from torch.ao.ns._numeric_suite_fx import OutputComparisonLogger, OutputLogger
+
+    if subgraph_candidate_idx == 0:
+        # idx = 0 is the floating point (original) version of the subgraph
+        # We keep the subgraph as is, and add a logger at the end
+
+        qconfig_str = ""
+        logger_mod_orig = _get_logger_for_subgraph(
+            mt,
+            first_node,
+            last_node,
+            subgraph_idx,
+            subgraph_candidate_idx,
+            qconfig_str,
+            OutputLogger,
+            fqn,
+        )
+
+        attr_name = _get_attr_name(subgraph_idx, subgraph_candidate_idx)
+        if hasattr(mt, attr_name):
+            raise AssertionError(f"Unexpected attribute '{attr_name}' found in {mt}")
+        setattr(mt, attr_name, logger_mod_orig)
+        with mt.graph.inserting_after(last_node):
+            new_node = mt.graph.call_module(attr_name, args=(last_node,), kwargs={})
+            last_added_shadow_node_list[0] = new_node
+
+    else:
+        # idx > 0 means we have a candidate qconfig to try, so we need
+        # to make a copy of the subgraph, feed it with the right inputs,
+        # and add a logger at the end
+
+        # get the qconfig
+        # subtract one because the first candidate is the floating point
+        # version of the subgraph
+        node_name_to_qconfig = list_of_node_name_to_qconfig[subgraph_candidate_idx - 1]
+        qconfig = node_name_to_qconfig[first_node.name]
+
+        # if no quantization is requested, skip
+        # TODO(future PR): deduplicate equivalent qconfigs that come from
+        #   different qconfig mapping objects
+        if qconfig is None:
+            return
+
+        qconfig_mapping = QConfigMapping().set_global(qconfig)
+
+        # create a copy of the submodule, wrapped in a separate module
+        orig_mod_copy_wrapped = create_submodule_from_subgraph(
+            mt, first_node, last_node
+        )
+
+        # add a call to prepare_fx on the wrapper module
+        if custom_prepare_fn is None:
+            orig_mod_copy_wrapped = torch.ao.quantization.quantize_fx.prepare_fx(
+                orig_mod_copy_wrapped, qconfig_mapping, example_inputs=example_inputs
+            )
+        else:
+            if custom_prepare_kwargs is None:
+                custom_prepare_kwargs = {}
+            for kwarg_name in [
+                "example_inputs",
+                "prepare_custom_config",
+                "qconfig_mapping",
+            ]:
+                if kwarg_name in custom_prepare_kwargs:
+                    raise AssertionError(
+                        f"cannot specify {kwarg_name} in custom_prepare_kwargs"
+                    )
+            prepare_kwargs: dict[str, Any] = {
+                "example_inputs": example_inputs,
+                "qconfig_mapping": qconfig_mapping,
+            }
+            prepare_kwargs.update(custom_prepare_kwargs)
+            orig_mod_copy_wrapped = custom_prepare_fn(
+                orig_mod_copy_wrapped, **prepare_kwargs
+            )
+
+        # attach the wrapper to the model
+        attr_name = _get_attr_wrapper_name(subgraph_idx, subgraph_candidate_idx)
+        if hasattr(mt, attr_name):
+            raise AssertionError(f"Unexpected attribute '{attr_name}' found in {mt}")
+        setattr(mt, attr_name, orig_mod_copy_wrapped)
+
+        # add a call to the wrapper module from the parent graph
+        insert_after_node = last_added_shadow_node_list[0]
+        with mt.graph.inserting_after(insert_after_node):
+            # TODO(future PR): handle fusion patterns where non-first nodes
+            # need inputs
+
+            # pass in all node args and kwargs
+
+            new_args = []
+            for arg in first_node.args:
+                if isinstance(arg, Node):
+                    new_args.append(arg)
+                elif (
+                    isinstance(arg, (list, tuple))
+                    and len(arg)
+                    and isinstance(arg[0], Node)
+                ):
+                    new_args.extend(
+                        inner_arg for inner_arg in arg if isinstance(inner_arg, Node)
+                    )
+
+            new_kwargs = {}
+            for name, old_kwarg in first_node.kwargs.items():
+                if isinstance(old_kwarg, Node):
+                    new_kwargs[name] = old_kwarg
+                elif isinstance(old_kwarg, (list, tuple)) and len(old_kwarg):
+                    # TODO(future PR): clarify why we are adding kwargs to args
+                    new_args.extend(old_kwarg)  # type: ignore[arg-type]
+
+            new_args = tuple(new_args)  # type: ignore[assignment]
+
+            new_node = mt.graph.call_module(attr_name, args=new_args, kwargs=new_kwargs)  # type: ignore[arg-type]
+
+        # add a logger to parent graph to observe the shadow wrapper
+        logger_mod_orig = _get_logger_for_subgraph(
+            mt,
+            first_node,
+            last_node,
+            subgraph_idx,
+            subgraph_candidate_idx,
+            str(qconfig),
+            OutputComparisonLogger,
+            fqn,
+        )
+
+        attr_name = _get_attr_name(subgraph_idx, subgraph_candidate_idx)
+        if hasattr(mt, attr_name):
+            raise AssertionError(f"Unexpected attribute '{attr_name}' found in {mt}")
+        setattr(mt, attr_name, logger_mod_orig)
+        with mt.graph.inserting_after(new_node):
+            logger = mt.graph.call_module(
+                attr_name, args=(new_node, last_node), kwargs={}
+            )
+            last_added_shadow_node_list[0] = logger
+
+    mt.recompile()
+
+
+def create_n_transformed_and_logged_copies_of_subgraph(
+    mt: GraphModule,
+    subgraph_idx: int,
+    match_name: str,
+    nodes_in_this_subgraph: list[Any],
+    qconfig_mappings: list[QConfigMapping],
+    list_of_node_name_to_qconfig: list[dict[str, QConfigAny]],
+    custom_prepare_fn: Callable | None = None,
+    custom_prepare_kwargs: dict[str, Any] | None = None,
+) -> None:
+    """
+    Given a model `mt` and a subgraph_idx, creates the needed copies
+    of the subgraph for all qconfigs, and instruments them with loggers.
+    """
+    # for now, assume that
+    # 1. the first node has one input
+    # 2. the last node has one output
+
+    # for now, ignore all subgraphs that contain non-nodes (tuples, etc)
+    # TODO(future PR): implement this
+    if any(not isinstance(node, Node) for node in nodes_in_this_subgraph):
+        return
+
+    first_node = nodes_in_this_subgraph[0]
+    last_node = nodes_in_this_subgraph[-1]
+    # We used output propagation to populate example values on each
+    # node. Use the example values from the previous node as the input
+    # to the current node.
+    prev_node = get_normalized_nth_input(first_node, mt, 0)
+    if isinstance(prev_node, list):
+        example_inputs = [x.traced_result for x in prev_node]
+    elif isinstance(prev_node, tuple):
+        example_inputs = (x.traced_result for x in prev_node)  # type: ignore[assignment]
+    else:
+        # currently some customer models do not have a traced_result in
+        # every node, so we have to guard for this case since we cannot
+        # quantize without an example input
+        # TODO(future PR): add a test case for this once we have an easy
+        # repro, see https://github.com/pytorch/pytorch/pull/80521/files#r975940489
+        # for additional context
+        if hasattr(prev_node, "traced_result"):
+            example_inputs = (prev_node.traced_result,)  # type: ignore[attr-defined, assignment]
+        else:
+            print(
+                "unable to get example input for node "
+                + f"{first_node.format_node()}, skipping"
+            )
+            return
+
+    # If there are no quantization configs for this subgraph, skip adding
+    # loggers. This reduces memory usage for models where not all layers are
+    # quantized.
+    # TODO(future): consider making this configurable
+    found_at_least_one_qconfig = False
+    for subgraph_candidate_idx in range(len(qconfig_mappings) + 1):
+        if subgraph_candidate_idx == 0:
+            # fp32 baseline does not need a qconfig
+            continue
+
+        # a. we have N shadows, so len(qconfig_mappings) is N
+        # b. we will have the fp32 layer + N shadows, so overall number of
+        #    (original_op) + (*shadows) will be N+1
+        # c. since `subgraph_candidate_idx` represents (b), we need
+        #    to subtract 1 to query from (a)
+        node_name_to_qconfig = list_of_node_name_to_qconfig[subgraph_candidate_idx - 1]
+        qconfig = node_name_to_qconfig[first_node.name]
+        if qconfig is not None:
+            found_at_least_one_qconfig = True
+            break
+    if not found_at_least_one_qconfig:
+        print(
+            "unable to find at least one qconfig for node "
+            + f"{first_node.format_node()}, skipping"
+        )
+        return
+
+    fqn = _maybe_get_fqn(first_node, mt)
+
+    # We want the results to contain the subgraphs in natural order,
+    # and the graph to also contain shadow wrappers and shadow loggers
+    # in natural order.
+    # If we just iterate in reverse, the graph will be in natural
+    # order but the eventual results will be in reverse order.
+    # So, we keep track of the last shadow logger we added and
+    # always insert after it.
+    last_added_shadow_node_list: list[Node | None] = [None]
+    for subgraph_candidate_idx in range(len(qconfig_mappings) + 1):
+        create_one_transformed_and_logged_copy_of_subgraph(
+            mt,
+            subgraph_idx,
+            subgraph_candidate_idx,
+            first_node,
+            last_node,
+            fqn,
+            list_of_node_name_to_qconfig,
+            example_inputs,
+            last_added_shadow_node_list,
+            custom_prepare_fn,
+            custom_prepare_kwargs,
+        )
+
+
+def create_add_loggers_graph(
+    model: GraphModule,
+    subgraphs_dedup: dict[str, list[Node]],
+    qconfig_mapping: QConfigMapping,
+    node_name_to_qconfig: dict[str, QConfigAny],
+) -> None:
+    r"""
+    Given a model, a model graph partition (currently a set of matched
+    subgraphs) and instructions how to transform each subgraph
+    (currently quantizing it according to qconfig_mapping), modifies
+    the model graph to create an alternate path through the original graph,
+    with each of the subgraphs quantized.  This is useful to compare
+    propagation error of a transformation such as quantization.
+
+    For example, given layer op0 and op1, there are four cases when handling op1:
+    1. op0 and op1 quantized
+    2. op0 and op1 unquantized
+    3. op0 quantized, op1 unquantized
+    4. op0 unquantized, op1 quantized
+
+    Example input, case 1:
+
+    .. code::
+
+      x0_0 -> op0_0 -> x1_0 -> log -----> op1_0 -> x2_0 -> log
+       \                        \          \                 \       # noqa: W605
+         ---> op0_1 -> x1_1 ----> clog    op1_1 -> x2_1 ----> clog
+
+    Example output, case 1:
+
+    .. code::
+
+      x0_0 -> op0_0 -> x1_0 -> log -----> op1_0 -> x2_0 -> log
+       \                        \                           \        # noqa: W605
+         ---> op0_1 -> x1_1 ----> clog -> op1_1 -> x2_1 ----> clog
+
+    """
+    # TODO(future PR): move logger classes to utils to remove circular dependency
+    from torch.ao.ns._numeric_suite_fx import OutputComparisonLogger, OutputLogger
+
+    def _get_subgraph_containing_node(node, subgraphs_dedup):
+        for subgraph in subgraphs_dedup.values():
+            if node in subgraph:
+                return subgraph
+        return None
+
+    # First, we need to create shadow branches, going from
+    #
+    #   x0 -> op0 -> x1 -> ...
+    #
+    #
+    # to
+    #
+    #   x0 -> op0_0 -> x1_0 -> log -> ...
+    #    \                     \
+    #      -> op0_1 -> x1_1 -> clog
+    #
+    # Later, the outputs of each shadow will be rerouted to calculate
+    # propagation error.
+
+    # Note: we cannot iterate over matched subgraphs because some nodes
+    # may not be matched. So, we iterate over nodes in the graph, and
+    # associate them to matched subgraphs if possible.
+
+    nodes_to_skip = set()
+    # for each subgraph, save a mapping from first node of subgraph
+    # to first and last node of the shadow of this subgraph
+    orig_first_node_to_shadow_in_node = {}
+    orig_first_node_to_shadow_out_node = {}
+    # need to record original list because we will mutate the graph as we go
+    orig_nodes = list(model.graph.nodes)  # type: ignore[union-attr, arg-type]
+    cur_subgraph_idx = 0
+    for n in orig_nodes:
+        if n.op in ("placeholder", "get_attr", "output") or n in nodes_to_skip:
+            continue
+
+        maybe_subgraph = _get_subgraph_containing_node(n, subgraphs_dedup)
+        insert_submodule_copy = False
+        if maybe_subgraph is not None:
+            first_node, last_node = maybe_subgraph[0], maybe_subgraph[-1]
+            nodes_to_skip.update(maybe_subgraph)
+            qconfig = node_name_to_qconfig[first_node.name]
+            if qconfig is not None:
+                insert_submodule_copy = True
+        else:
+            first_node, last_node = n, n
+
+        if insert_submodule_copy:
+            match_name = first_node.name
+            create_n_transformed_and_logged_copies_of_subgraph(
+                model,
+                cur_subgraph_idx,
+                match_name,
+                # pyrefly: ignore [bad-argument-type]
+                maybe_subgraph,
+                [qconfig_mapping],
+                [node_name_to_qconfig],
+                None,
+                None,  # type: ignore[arg-type]
+            )
+            # find the created shadow module and record it so we
+            # can find it easily in step 2
+            expected_shadow_target = f"shadow_wrapper_{cur_subgraph_idx}_1"
+            new_shadow_mod = None
+            for maybe_shadow_mod in model.graph.nodes:
+                if (
+                    maybe_shadow_mod.op == "call_module"
+                    and maybe_shadow_mod.target == expected_shadow_target
+                ):
+                    new_shadow_mod = maybe_shadow_mod
+                    break
+            if new_shadow_mod is None:
+                raise AssertionError("Expected new_shadow_mod to be non-None")
+            orig_first_node_to_shadow_in_node[first_node] = new_shadow_mod
+            orig_first_node_to_shadow_out_node[first_node] = new_shadow_mod
+
+        else:
+            # create a copy of the subgraph by only copying FX nodes
+            # but not copying any parameters, to minimize memory usage
+            subgraph_to_use = (
+                maybe_subgraph if maybe_subgraph is not None else [first_node]
+            )
+
+            # add a regular logger after last_node
+            qconfig_str = ""
+            subgraph_candidate_idx = 0
+            fqn = _maybe_get_fqn(first_node, model)
+            logger_mod_orig = _get_logger_for_subgraph(
+                model,
+                first_node,
+                last_node,
+                cur_subgraph_idx,
+                subgraph_candidate_idx,
+                qconfig_str,
+                OutputLogger,
+                fqn,
+            )
+            attr_name = _get_attr_name(cur_subgraph_idx, subgraph_candidate_idx)
+            if hasattr(model, attr_name):
+                raise AssertionError(
+                    f"Unexpected attribute '{attr_name}' found in {model}"
+                )
+            setattr(model, attr_name, logger_mod_orig)
+            insertion_point = last_node
+            with model.graph.inserting_after(insertion_point):
+                logger = model.graph.call_module(
+                    attr_name, args=(last_node,), kwargs={}
+                )
+                insertion_point = logger
+
+            # create a copy of the subgraph
+            cur_node_orig = first_node
+            cur_node_copy = None
+            first_node_copy = None
+            # pyrefly: ignore [bad-assignment]
+            while cur_node_orig in subgraph_to_use:
+                # TODO(future PR): make this support all possible args/kwargs
+                if cur_node_orig is first_node:
+                    new_args = cur_node_orig.args
+                    new_kwargs = cur_node_orig.kwargs
+                else:
+                    first_arg_for_copy: Node | None = cur_node_copy
+                    new_args = (first_arg_for_copy, *cur_node_orig.args[1:])
+                    new_kwargs = cur_node_orig.kwargs
+                # make a copy of cur_node_orig
+                with model.graph.inserting_after(insertion_point):
+                    cur_node_copy = model.graph.create_node(
+                        cur_node_orig.op,
+                        cur_node_orig.target,
+                        new_args,
+                        new_kwargs,
+                        # cur_node_orig.name,  # TODO(future PR): set name explicitly
+                    )
+                    if first_node_copy is None:
+                        first_node_copy = cur_node_copy
+                # since now only linear subgraphs are supported, all nodes
+                # except the last one must have only one user
+                if cur_node_orig != last_node:
+                    if len(cur_node_orig.users.keys()) != 1:
+                        raise AssertionError(
+                            f"Expected exactly 1, but got {len(cur_node_orig.users)}"
+                        )
+                cur_node_orig = next(iter(cur_node_orig.users.keys()))
+                if cur_node_orig.name.startswith(SHADOW_NODE_NAME_PREFIX):
+                    raise AssertionError(
+                        "cur_node_orig should not start with SHADOW_NODE_NAME_PREFIX"
+                    )
+                insertion_point = cur_node_copy
+
+            # add a comparison logger after last_node's copy
+            subgraph_candidate_idx = 1
+            logger_mod_orig = _get_logger_for_subgraph(
+                model,
+                first_node,
+                last_node,
+                cur_subgraph_idx,
+                subgraph_candidate_idx,
+                qconfig_str,
+                OutputComparisonLogger,
+                fqn,
+            )
+            attr_name = _get_attr_name(cur_subgraph_idx, subgraph_candidate_idx)
+            if hasattr(model, attr_name):
+                raise AssertionError(
+                    f"Unexpected attribute '{attr_name}' found in {model}"
+                )
+            setattr(model, attr_name, logger_mod_orig)
+            with model.graph.inserting_after(insertion_point):
+                logger = model.graph.call_module(
+                    attr_name, args=(cur_node_copy, last_node), kwargs={}
+                )
+
+            # save the final node so we can use it in step 2
+            orig_first_node_to_shadow_in_node[first_node] = first_node_copy
+            orig_first_node_to_shadow_out_node[first_node] = cur_node_copy
+
+        cur_subgraph_idx += 1
+
+    model.recompile()
+
+    # Now, we go from
+    #
+    #   x0 -> op0_0 -> x1_0 -> log -> x1 -> op1_0 -> ...
+    #    \                     \       \
+    #      -> op0_1 -> x1_1 -> clog      -> op1_1 -> ...
+    #
+    # to
+    #
+    #   x0 -> op0_0 -> x1_0 -> log --> x1_0 -> op1_0 -> ...
+    #    \                     \
+    #      -> op0_1 -> x1_1 -> clog -> x1_1 -> op1_1 -> ...
+    #
+    # sample values of key internal variables for the example above:
+    #
+    #   orig_first_node_to_shadow_in_node = {op0_0: op0_1, op1_0: op1_1}
+    #   orig_first_node_to_shadow_out_node = {op0_0: op0_1, op1_0: op1_1}
+    #
+    # note: for subgraphs with more than one node, in_node will be different
+    # compared to out_node
+
+    nodes_to_skip = set()
+    for n in orig_nodes:
+        if n.op in ("placeholder", "get_attr", "output") or n in nodes_to_skip:
+            continue
+
+        maybe_subgraph = _get_subgraph_containing_node(n, subgraphs_dedup)
+        if maybe_subgraph is not None:
+            first_node, last_node = maybe_subgraph[0], maybe_subgraph[-1]
+            nodes_to_skip.update(maybe_subgraph)
+        else:
+            first_node, last_node = n, n
+
+        def maybe_remap_node_to_shadow(node):
+            """
+            If unshadowed `node` has a shadow version, return that. If not,
+            return `node`.
+            """
+            if not isinstance(node, Node):
+                # handle scalars
+                return node
+
+            if node.op in ("placeholder", "get_attr"):
+                return node
+
+            # Find the shadowed version of this arg from the previous
+            # subgraph. For this, we need to:
+            # 1. navigate to the first node of the previous subgraph
+            # 2. get the output of the shadow wrapper which has (1) as an input
+
+            # For now, assume the arg is in matched subgraphs. In the
+            # future we may have to handle the case where this is not true.
+            prev_subgraph = _get_subgraph_containing_node(node, subgraphs_dedup)
+            if prev_subgraph is None:
+                prev_subgraph = [node]
+            prev_first_node = prev_subgraph[0]
+            prev_shadow_output = orig_first_node_to_shadow_out_node[prev_first_node]
+            return prev_shadow_output
+
+        cur_shadow_input = orig_first_node_to_shadow_in_node[first_node]
+        if cur_shadow_input is None:
+            raise AssertionError("Expected cur_shadow_input to be non-None")
+        cur_shadow_input.args = tree_map(
+            maybe_remap_node_to_shadow, cur_shadow_input.args
+        )
+        cur_shadow_input.kwargs = tree_map(
+            maybe_remap_node_to_shadow, cur_shadow_input.kwargs
+        )
+
+        model.recompile()
+
+
+def _get_weight_info_from_shadow_wrapper(shadow_wrapper: torch.nn.Module):
+    # input: shadow wrapper module
+    # output if shadow wrapper module has a weighted op:
+    #   (quantize_fn, (quantize_fn_args))
+    # output if shadow wrapper module doesn't have a weighted op:
+    #   None
+
+    # For now, assume that the weight is the second input
+    # to the shadow module. If that changes, we can fix it later.
+    placeholders_seen = 0
+    for shadow_n in shadow_wrapper.graph.nodes:  # type: ignore[union-attr]
+        if shadow_n.op != "placeholder":
+            continue
+
+        placeholders_seen += 1
+        if placeholders_seen != 2:
+            continue
+
+        # the subgraph looks like
+        #
+        #   _input_scale_1 = self._input_scale_1
+        #   _input_zero_point_1 = self._input_zero_point_1
+        #   quantize_per_channel = torch.quantize_per_channel(
+        #       w2_0, _input_scale_1, _input_zero_point_1,
+        #       0, torch.qint8)
+        #
+        #  we have `w2_0`, and are navigating this subgraph
+        #  to get `_input_scale_1` and `_input_zero_point_1`
+
+        if len(shadow_n.users) != 1:
+            raise AssertionError(f"Expected exactly 1, got {len(shadow_n.users)}")
+        quant_node = next(iter(shadow_n.users.keys()))
+        new_args: Any = None
+        if quant_node.target is torch.quantize_per_channel:
+            _weight, scale_node, zp_node, axis, dtype = quant_node.args
+            scale_val = getattr_from_fqn(shadow_wrapper, scale_node.target)
+            zp_val = getattr_from_fqn(shadow_wrapper, zp_node.target)
+            new_args = (scale_val, zp_val, axis, dtype)
+        else:
+            if quant_node.target != torch.quantize_per_tensor:
+                raise AssertionError(
+                    f"Expected torch.quantize_per_tensor, but got {quant_node.target}"
+                )
+            _weight, scale_node, zp_node, dtype = quant_node.args
+            scale_val = getattr_from_fqn(shadow_wrapper, scale_node.target)
+            zp_val = getattr_from_fqn(shadow_wrapper, zp_node.target)
+            new_args = (scale_val, zp_val, dtype)
+        return (quant_node.target, new_args)
+
+    return None
+
+
+def extract_weight_comparison(m: GraphModule) -> NSResultsType:
+    # example graph:
+    #
+    #   w1 = self.w1
+    #   b1 = self.b1
+    #   linear = torch._C._nn.linear(x, w1, b1)
+    #   shadow_0_0 = self.shadow_0_0(linear)
+    #   shadow_wrapper_0_1 = self.shadow_wrapper_0_1(x, w1, b1)
+    #   shadow_0_1 = self.shadow_0_1(shadow_wrapper_0_1, linear)
+    #
+    # algorithm:
+    # 1. for each call_function node matching our allowlist:
+    # 2.   if corresponding shadow wrapper exists, extract the weight pair
+    #
+    # Note: this is not super robust, but that's ok because this is
+    # just for legacy customers who depend on the previous two-model version
+    # of this API. TBD if we need to make this robust.
+    # Note: modules are not supported, since existing customers only
+    # use functions.
+
+    # TODO(future PR): move this to config
+    weighted_ops = {
+        torch.nn.functional.linear,
+    }
+
+    results: NSResultsType = {"model": {NSSingleResultValuesType.WEIGHT.value: {}}}
+
+    for n in m.graph.nodes:  # type: ignore[union-attr]
+        if not (n.op == "call_function" and n.target in weighted_ops):
+            continue
+
+        # Check if we have a corresponding shadow wrapper
+        # TODO(future PR, if needed): support kwargs
+        # TODO(future PR, if needed): support multiple shadow users
+        first_arg = n.args[0]
+        shadow_wrapper_node = None
+        for user in first_arg.users:
+            # TODO(before land): fix string match
+            if user.op == "call_module" and user.target.startswith("shadow_wrapper"):
+                shadow_wrapper_node = user
+                break
+
+        if shadow_wrapper_node is None:
+            continue
+
+        shadow_wrapper = getattr_from_fqn(m, shadow_wrapper_node.target)  # type: ignore[arg-type]
+        weight_info = _get_weight_info_from_shadow_wrapper(shadow_wrapper)
+        if weight_info is None:
+            continue
+
+        # get weight
+        w_node = n.args[1]
+        w_obj = getattr_from_fqn(m, w_node.target).detach()
+
+        # get a quantized version of weight
+        quant_fn, quant_fn_args_except_first = weight_info
+        new_args = (w_obj, *quant_fn_args_except_first)
+        w_obj_q = quant_fn(*new_args)
+
+        # add a comparison
+        ref_node_name = n.name
+        prev_node_name = n.name
+        ref_node_type = get_target_type_str(n, m)
+        prev_node_type = ref_node_type
+        fqn = None
+        if hasattr(m, "_node_name_to_scope"):
+            fqn = m._node_name_to_scope[n.name][0]  # type: ignore[index]
+        comparison = torch.ao.ns.fx.utils.compute_sqnr(w_obj, w_obj_q)
+        result_fp32 = {
+            "res_type": NSSingleResultValuesType.WEIGHT.value,
+            "values": [w_obj],
+            "prev_node_name": prev_node_name,
+            "prev_node_target_type": prev_node_type,
+            "ref_node_name": ref_node_name,
+            "ref_node_target_type": ref_node_type,
+            "index_within_arg": 0,
+            "index_of_arg": 0,
+            "fqn": fqn,
+            "qconfig_str": "",
+            "comparisons": [comparison],
+            "comparison_fn_name": "sqnr",
+        }
+        result_q = {
+            "res_type": NSSingleResultValuesType.WEIGHT.value,
+            "values": [w_obj_q],
+            "prev_node_name": prev_node_name,
+            "prev_node_target_type": prev_node_type,
+            "ref_node_name": ref_node_name,
+            "ref_node_target_type": ref_node_type,
+            "index_within_arg": 0,
+            "index_of_arg": 0,
+            "fqn": fqn,
+            "qconfig_str": "",
+            "comparisons": [comparison],
+            "comparison_fn_name": "sqnr",
+        }
+
+        # go from subgraph_n_1 to subgraph_n_0
+        _1, _2, node_idx, _3 = shadow_wrapper_node.target.split("_")
+        name_fp32 = f"subgraph_{node_idx}_0"
+        name_q = f"subgraph_{node_idx}_1"
+
+        results["model"][NSSingleResultValuesType.WEIGHT.value][name_fp32] = [
+            result_fp32
+        ]
+        results["model"][NSSingleResultValuesType.WEIGHT.value][name_q] = [result_q]
+
+    return results
+
+
+# TODO(future PR): redesign this to make it easier to consume outputs
+def group_results_by_subgraph(results: NSResultsType) -> Any:
+    """
+    Creates a comparison of results
+
+    Input:
+
+    {
+      'model': {
+        'node_output': {
+          'subgraph_0_0': [
+            'values': [torch.tensor(...), ...], ...
+            'ref_node_name': ...,
+            'ref_node_target_type': ...,
+            'qconfig_str': ...,
+            'comparisons': [], ...
+            'comparison_fn_name': '',
+            'fqn': '...',
+          ],
+          'subgraph_0_1': [
+            'values': [torch.tensor(...), ...], ...
+            'ref_node_name': ...,
+            'ref_node_target_type': ...,
+            'qconfig_str': ...,
+            'comparisons': [torch.tensor(...), ...], ...
+            'comparison_fn_name': '...',
+            'fqn': '...',
+          ],
+          ...
+        },
+      },
+    }
+
+    Output:
+    {
+      'subgraph_0': {
+        '0': {
+          'ref_node_name': '...',
+          'ref_node_target_type': ...,
+          'values': [torch.tensor(...), ...],
+          'qconfig_str': None,
+          'comparisons': [torch.tensor(...), ...], ...
+          'comparison_fn_name': '...',
+          'fqn': '...',
+        },
+        '1': {
+          'ref_node_name': '...',
+          'ref_node_target_type': ...,
+          'values': [torch.tensor(...), ...],
+          'qconfig_str': '...',
+          'comparisons': [torch.tensor(...), ...], ...
+          'comparison_fn_name': '...',
+          'fqn': '...',
+        },
+      },
+    }
+
+    """
+    subgraph_name_to_subgraph_results: Any = collections.defaultdict(dict)
+
+    # node_output or weight
+    key_to_use = next(iter(results["model"].keys()))
+
+    for subgraph_name_with_idx, subgraph_candidate_results in results["model"][
+        key_to_use
+    ].items():
+        # convert from `subgraph_m_n` to `subgraph_m` and `n`
+        (
+            subgraph_str,
+            subgraph_idx,
+            subgraph_candidate_idx,
+        ) = subgraph_name_with_idx.split("_")
+        subgraph_name = f"{subgraph_str}_{subgraph_idx}"
+
+        subgraph_results = {
+            "ref_node_name": subgraph_candidate_results[0]["ref_node_name"],
+            "ref_node_target_type": subgraph_candidate_results[0][
+                "ref_node_target_type"
+            ],
+            "fqn": subgraph_candidate_results[0]["fqn"],
+            "values": subgraph_candidate_results[0]["values"],
+            "qconfig_str": subgraph_candidate_results[0]["qconfig_str"],
+            "comparisons": subgraph_candidate_results[0]["comparisons"],
+            "comparison_fn_name": subgraph_candidate_results[0]["comparison_fn_name"],
+        }
+
+        subgraph_name_to_subgraph_results[subgraph_name][subgraph_candidate_idx] = (
+            subgraph_results
+        )
+
+    return dict(subgraph_name_to_subgraph_results)
+
+
+# TODO(future PR): redesign this to make it easier to consume outputs
+def create_results_comparison(
+    results_grouped,
+) -> Any:
+    """
+    Input:
+
+    {
+      'subgraph_0': {
+        '0': {
+          'ref_node_name': '...',
+          'ref_node_target_type': ...,
+          'values': [torch.tensor(...), ...],
+          'qconfig_str': '',
+          'comparisons': [],
+          'comparison_fn_name': '',
+          'fqn': '...',
+        },
+        '1': {
+          'ref_node_name': '...',
+          'ref_node_target_type': ...,
+          'values': [torch.tensor(...), ...],
+          'qconfig_str': '...',
+          'comparisons': [torch.tensor(...), ...],
+          'comparison_fn_name': 'sqnr',
+          'fqn': '...',
+        },
+      },
+    }
+
+    Output:
+    {
+      'subgraph_0': {
+        'ref_node_name': '...',
+        'ref_node_target_type': '...',
+        'fqn': '...',
+        'candidates': {
+          '1': {
+            'qconfig_str': ...,
+            'comparison_fn_name': 'sqnr',
+            'cmp_raw': [..., ...],
+            'cmp_mean': ...,
+          },
+          ...,
+        },
+      },
+    }
+    """
+
+    results_comparison = {}
+
+    for subgraph_name, subgraph_results in results_grouped.items():
+        candidates = {}
+        for subgraph_inner_name, subgraph_inner_result in subgraph_results.items():
+            # skip comparing baseline to baseline
+            if subgraph_inner_name == "0":
+                continue
+
+            # we expect the comparisons to be precalculated from
+            # calibration, so we just fetch them here
+            cmp_raw = subgraph_inner_result["comparisons"]
+            cmp_raw_tensor = torch.stack(cmp_raw)
+
+            candidates[subgraph_inner_name] = {
+                "qconfig_str": subgraph_inner_result["qconfig_str"],
+                "comparison_fn_name": subgraph_inner_result["comparison_fn_name"],
+                "cmp_raw": cmp_raw_tensor,
+                "cmp_mean": torch.mean(cmp_raw_tensor),
+            }
+
+        results_comparison[subgraph_name] = {
+            "ref_node_name": subgraph_results["0"]["ref_node_name"],
+            "ref_node_target_type": subgraph_results["0"]["ref_node_target_type"],
+            "fqn": subgraph_results["0"]["fqn"],
+            "candidates": candidates,
+        }
+
+    return results_comparison
+
+
+# TODO(future PR): redesign this to make it easier to consume outputs
+def print_n_shadows_summary(
+    results_comparison,
+) -> None:
+    """
+    Input:
+
+    {
+      'subgraph_0': {
+        'ref_node_name': 'linear1',
+        'ref_node_target_type': '...',
+        'fqn': '...',
+        'candidates': {
+          '1': {
+            'qconfig_str': ...,
+            'comparison_fn_name': ...,
+            'cmp_raw': [45.0, 55.0],
+            'cmp_mean': 50.0,
+          },
+          ...,
+        },
+      },
+    }
+
+    Prints:
+
+    node_name | node_type | fqn | 0    | 1    | ...
+    linear1   | ...       | ... | 45.0 | 50.0 | ...
+    """
+
+    try:
+        from tabulate import tabulate
+    except ImportError:
+        print(
+            "`print_tabular` relies on the library `tabulate`, "
+            "which could not be found on this machine. Run `pip "
+            "install tabulate` to install the library."
+        )
+        return
+
+    results = []
+    for subgraph_data in results_comparison.values():
+        mean_all_candidates = [
+            candidate["cmp_mean"]
+            for candidate_name, candidate in subgraph_data["candidates"].items()
+        ]
+
+        data_row = [
+            subgraph_data["ref_node_name"],
+            subgraph_data["ref_node_target_type"],
+            subgraph_data["fqn"],
+            *mean_all_candidates,
+        ]
+        results.append(data_row)
+
+    max_candidate_idx_len = -1
+    for data_row in results:
+        max_candidate_idx_len = max(max_candidate_idx_len, len(data_row[1]))
+    candidate_idx_headers = [str(x) for x in range(max_candidate_idx_len)]
+
+    headers = ["node_name", "node_type", "fqn", *candidate_idx_headers]
+    print(tabulate(results, headers=headers))
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/fx/ns_types.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/fx/ns_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..134fd485130e0069ab992197ea6e176e1e1e216b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/fx/ns_types.py
@@ -0,0 +1,66 @@
+import enum
+from collections.abc import Callable
+from typing import Any, NamedTuple, Union
+
+from torch.fx.graph import Node
+
+
+class NSSingleResultValuesType(str, enum.Enum):
+    WEIGHT = "weight"
+    NODE_OUTPUT = "node_output"
+    NODE_INPUT = "node_input"
+
+
+class NSSubgraph(NamedTuple):
+    start_node: Node
+    end_node: Node
+    base_op_node: Node
+
+
+# TODO(future PR): see if we can use typing_extensions's TypedDict instead
+# to properly type the various keys
+# {
+#   # one of NSSingleResultValuesType
+#   'type': 'weight',
+#   # the values of type specified above
+#   'values': [torch.tensor(...), ...],
+#   # name of the node directly before the logger
+#   'prev_node_name': 'linear1',
+#   # type of the underlying function or module
+#   'prev_node_target_type': torch.nn.functional.linear  # or torch.nn.Linear, etc
+#   # name of the node responsible for adding this logger
+#   # Note: this may differ from prev_node_name if we are logging inputs
+#   'ref_node_name': 'linear1',
+#   # index of this node within the arg of the input/output node
+#   # for example, in cat([x1, x2, x3], dim=0), x2 would have index_within_arg == 1
+#   'index_within_arg': 0,
+#   # index of this node within the args of the input/output node
+#   # for example, in add(x1, x2), x2 would have index_of_arg == 1
+#   'index_of_arg': 0,
+#   # precomputed comparisons of logger values to reference values
+#   'comparisons': [torch.tensor(...), ...]
+#   # name of function used for precomputed comparisons
+#   'comparison_fn_name': 'sqnr',
+#   # string representation of qconfig responsible for creating this logger
+#   'qconfig_str': 'QConfig(...)',
+# }
+NSSingleResultType = dict[str, Any]
+
+# {
+#   'layer_name_1': {  # subgraph name
+#     'node_output': {  # results type (node_output, node_input, weight)
+#       'model_name_a':  # model name
+#          [NSSingleResultType, ...],  # results, ordered by index_within_arg
+#       'model_name_b':
+#          [NSSingleResultType, ...],
+#     },
+#   },
+# }
+#
+NSResultsType = dict[str, dict[str, dict[str, list[NSSingleResultType]]]]
+
+# Defines the underlying target type of a node, for example:
+# `F.conv1d` for a `call_function` conv node
+# `nn.Conv1d` for a `call_module` node calling the forward of a `nn.Conv1d` module
+# `'sigmoid'` for a `call_method` node calling `x.sigmoid()`
+NSNodeTargetType = Union[Callable, str]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/fx/pattern_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/fx/pattern_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..d10fdd39da9080144d3f6ef577d3ca5aca313538
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/fx/pattern_utils.py
@@ -0,0 +1,214 @@
+from collections.abc import Callable
+from typing import Any, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.ao.quantization import FakeQuantizeBase, ObserverBase
+from torch.ao.quantization.backend_config import get_native_backend_config
+from torch.ao.quantization.fx.quantize_handler import _get_pattern_to_quantize_handlers
+from torch.ao.quantization.utils import getattr_from_fqn
+from torch.fx import GraphModule
+from torch.fx.graph import Node
+
+from .ns_types import NSNodeTargetType
+
+
+toq = torch.ops.quantized
+
+
+def get_type_a_related_to_b(
+    base_name_to_sets_of_related_ops: dict[str, set[NSNodeTargetType]],
+) -> set[tuple[NSNodeTargetType, NSNodeTargetType]]:
+    # TODO(future PR): allow customizations
+    # TODO(future PR): reuse existing quantization mappings
+    # TODO(future PR): add the rest of modules and ops here
+    type_a_related_to_b: set[tuple[NSNodeTargetType, NSNodeTargetType]] = set()
+
+    for s in base_name_to_sets_of_related_ops.values():
+        s_list = list(s)
+        # add every bidirectional pair
+        for idx_0 in range(len(s_list)):
+            for idx_1 in range(idx_0, len(s_list)):
+                type_a_related_to_b.add((s_list[idx_0], s_list[idx_1]))
+                type_a_related_to_b.add((s_list[idx_1], s_list[idx_0]))
+
+    return type_a_related_to_b
+
+
+NSFusionElType = Union[
+    Callable,  # call_function or call_module type, example: F.linear or nn.Conv2d
+    str,  # call_method name, example: "dequantize"
+    tuple[
+        str, Any
+    ],  # call_method name and first argument, example: ("to", torch.float16)
+]
+NSFusionType = Union[
+    tuple[NSFusionElType, NSFusionElType],
+    tuple[NSFusionElType, NSFusionElType, NSFusionElType, NSFusionElType],
+]
+
+
+def get_reversed_fusions() -> list[tuple[NSFusionType, int]]:
+    """
+    Set of potential fusions, in reverse order.  The order is reversed
+    to match how fusion patterns are defined in quantization code.
+
+    Fusion format:
+    ((fusion_op_0, fusion_op_1), base_op_idx)
+
+    Where base_op_idx is the idx of the op we should use to match other related
+    ops. Note: base_op_idx is specified in non-reverse order, i.e. a base_op_idx
+    of 0 represents the first op in regular (non-reverse) order, 1 represents the
+    second op, etc.
+    """
+    results: list[tuple[NSFusionType, int]] = []
+
+    # Possible syntaxes:
+    # * single op: torch.nn.Conv2d
+    # * multiple ops: (torch.nn.ReLU, torch.nn.Conv2d)
+    # For fusions, we only care about patterns composed of multiple ops.
+    # TODO(future PR): allow customizations from default patterns.
+    all_quant_patterns = _get_pattern_to_quantize_handlers(get_native_backend_config())
+
+    default_base_op_idx = 0
+    for quant_pattern in all_quant_patterns:
+        # TODO: this is a temporary hack to flatten the patterns from quantization so
+        # that it works with the ns matcher function, maybe we should use `_is_match`
+        # in torch.ao.quantization.fx.match_utils to match the patterns
+        if (
+            isinstance(quant_pattern, tuple)
+            and len(quant_pattern) == 2
+            and isinstance(quant_pattern[1], tuple)
+            and len(quant_pattern[1]) == 2
+        ):
+            # flatten the pattern with form (nn.ReLU, (nn.BatchNorm2d, nn.Conv2d))
+            quant_pattern = (quant_pattern[0], quant_pattern[1][0], quant_pattern[1][1])
+
+        # Only patterns of multiple ops are fusions, ignore
+        # patterns which contain a single ops (they get matched
+        # without caring about fusions).
+        if isinstance(quant_pattern, tuple):
+            results.append((quant_pattern, default_base_op_idx))  # type: ignore[arg-type]
+
+        # For each pattern, add additional patterns with observers and
+        # fake quants at the end.
+        # TODO(future PR): if needed, implement matching for a node
+        #   having multiple output observers.
+        for cls in (ObserverBase, FakeQuantizeBase):
+            if isinstance(quant_pattern, tuple):
+                new_pattern = (cls, *quant_pattern)
+            else:
+                new_pattern = (cls, quant_pattern)
+            results.append((new_pattern, default_base_op_idx))  # type: ignore[arg-type]
+
+    # After this point, results contains values such as
+    # [..., ((torch.nn.Relu, torch.nn.Conv2d), 0), ...]
+
+    # Patterns for matching fp16 emulation are not specified in the quantization
+    # fusion mappings.  For now, define them here.
+    fp16_em_base_op_idx = 1
+    patterns_to_add = [
+        # linear-relu fp16 emulation:
+        # fp16_to_fp32 -> linear -> relu -> fp32_to_fp16
+        (
+            (("to", torch.float16), F.relu, F.linear, "dequantize"),
+            fp16_em_base_op_idx,
+        ),
+        # Conv-BN fusion (this happens outside of quantization patterns,
+        # which is why it is defined separately here).
+        ((nn.BatchNorm1d, nn.Conv1d), default_base_op_idx),
+        ((nn.BatchNorm2d, nn.Conv2d), default_base_op_idx),
+        ((nn.BatchNorm3d, nn.Conv3d), default_base_op_idx),
+        ((nn.ReLU, nn.BatchNorm1d, nn.Conv1d), default_base_op_idx),
+        ((nn.ReLU, nn.BatchNorm2d, nn.Conv2d), default_base_op_idx),
+        ((nn.ReLU, nn.BatchNorm3d, nn.Conv3d), default_base_op_idx),
+    ]
+    for p in patterns_to_add:
+        results.append(p)  # type: ignore[arg-type]
+        results.append(((ObserverBase, *p[0]), p[1]))  # type: ignore[arg-type]
+        results.append(((FakeQuantizeBase, *p[0]), p[1]))  # type: ignore[arg-type]
+
+    return results
+
+
+def end_node_matches_reversed_fusion(
+    end_node: Node,
+    reversed_fusion: NSFusionType,
+    gm: GraphModule,
+    seen_nodes: set[Node],
+) -> bool:
+    """
+    Returns true if a pattern ending with `end_node` matches
+    the fusion pattern.
+    """
+    cur_node = end_node
+    for fusion_idx in range(len(reversed_fusion)):
+        # each node can only belong to one matched pattern
+        if cur_node in seen_nodes:
+            return False
+
+        cur_fusion_el = reversed_fusion[fusion_idx]
+
+        if cur_node.op == "call_function":
+            fusion_el_is_fun = (not isinstance(cur_fusion_el, str)) and (
+                not isinstance(cur_fusion_el, type)
+            )
+            if fusion_el_is_fun:
+                if cur_node.target != cur_fusion_el:
+                    return False
+                if len(cur_node.args) > 0 and isinstance(cur_node.args[0], Node):
+                    cur_node = cur_node.args[0]
+                else:
+                    return False
+            else:
+                return False
+
+        elif cur_node.op == "call_module":
+            fusion_el_is_mod = isinstance(cur_fusion_el, type)
+            if fusion_el_is_mod:
+                if not isinstance(cur_node.target, str):
+                    raise AssertionError(f"Expected str, got {type(cur_node.target)}")
+                target_mod = getattr_from_fqn(gm, cur_node.target)
+                if not isinstance(cur_fusion_el, type):
+                    return False
+                if not isinstance(target_mod, cur_fusion_el):
+                    return False
+                if len(cur_node.args) > 0 and isinstance(cur_node.args[0], Node):
+                    cur_node = cur_node.args[0]
+                else:
+                    return False
+            else:
+                return False
+
+        elif cur_node.op == "call_method":
+            fusion_el_is_meth_with_second_arg = (
+                isinstance(cur_fusion_el, tuple) and len(cur_fusion_el) == 2
+            )
+            fusion_el_is_meth_without_args = isinstance(cur_fusion_el, str)
+            if fusion_el_is_meth_without_args or fusion_el_is_meth_with_second_arg:
+                if fusion_el_is_meth_without_args:
+                    if cur_node.target != cur_fusion_el:
+                        return False
+                else:
+                    if not isinstance(cur_fusion_el, tuple):
+                        raise AssertionError(
+                            f"Expected tuple, got {type(cur_fusion_el)}"
+                        )
+                    if cur_node.target != cur_fusion_el[0]:
+                        return False
+                    elif len(cur_node.args) < 2:
+                        return False
+                    elif cur_node.args[1] != cur_fusion_el[1]:
+                        return False
+
+                if len(cur_node.args) > 0 and isinstance(cur_node.args[0], Node):
+                    cur_node = cur_node.args[0]
+                else:
+                    return False
+            else:
+                return False
+        else:
+            return False
+
+    return True
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/fx/qconfig_multi_mapping.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/fx/qconfig_multi_mapping.py
new file mode 100644
index 0000000000000000000000000000000000000000..d36914b46929d7eb8311097cd6b5d0fdc0c82f12
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/fx/qconfig_multi_mapping.py
@@ -0,0 +1,251 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import copy
+from typing import Any, TYPE_CHECKING
+
+import torch
+from torch.ao.quantization import QConfigMapping
+from torch.ao.quantization.qconfig_mapping import _QCONFIG_STYLE_ORDER
+
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+    from torch.ao.quantization.qconfig import QConfigAny
+
+__all__ = ["QConfigMultiMapping"]
+
+_QCONFIG_STYLE_TO_METHOD: dict[str, str] = {
+    "global_qconfig": "set_global",
+    "object_type_qconfigs": "set_object_type",
+    "module_name_regex_qconfigs": "set_module_name_regex",
+    "module_name_qconfigs": "set_module_name",
+    "module_name_object_type_order_qconfigs": "set_module_name_object_type_order",
+}
+
+
+def _remove_duplicates_and_none(qconfig_list: list[QConfigAny]) -> None:
+    to_remove = []
+    for index, cur_qconfig in enumerate(qconfig_list):
+        if cur_qconfig is None:
+            to_remove.append(index)
+            break
+        for checked_qconfig in qconfig_list[:index]:
+            if torch.ao.quantization.qconfig_equals(cur_qconfig, checked_qconfig):
+                to_remove.append(index)
+                break
+    for index in to_remove[::-1]:
+        qconfig_list.pop(index)
+
+
+class QConfigMultiMapping:
+    """
+    This class, used with the prepare_n_shadows_model API, stores a list of :class:`torch.ao.quantization.QConfigMapping`s
+    so that multiple QConfigs can be specified for each QConfig matching style.
+
+    The user can specify QConfigs using the following methods (in increasing match priority):
+
+        ``set_global`` : sets the global (default) QConfigs
+
+        ``set_object_type`` : sets the QConfigs for a given module type, function, or method name
+
+        ``set_module_name_regex`` : sets the QConfigs for modules matching the given regex string
+
+        ``set_module_name`` : sets the QConfigs for modules matching the given module name
+
+        ``set_module_name_object_type_order`` : sets the QConfigs for modules matching a combination
+        of the given module name, object type, and the index at which the module appears
+
+    Note: Usage of set methods is the same as in QConfigMapping except with a passed in list of QConfigs rather than a
+    single QConfig.
+
+    Example usage::
+
+        qconfig_mapping = QConfigMultiMapping()
+            .set_global([qconfig1, qconfig2])
+            .set_object_type(torch.nn.Linear, [qconfig2, qconfig3])
+            .set_object_type(torch.nn.ReLU, [qconfig1])
+            .set_module_name_regex("foo.*bar.*conv[0-9]+", [qconfig2])
+            .set_module_name_regex("foo.*", [qconfig1, qconfig2, qconfig3])
+            .set_module_name("module1", [None])
+            .set_module_name("module2", [qconfig2])
+            .set_module_name_object_type_order("foo.bar", torch.nn.functional.linear, 0, [qconfig3])
+
+    """
+
+    def __init__(self) -> None:
+        # initialize this with 1 QConfigMapping to avoid corner cases
+        self.qconfig_mappings_list: list[QConfigMapping] = [QConfigMapping()]
+
+    def _handle_list_size_mismatch(
+        self, qconfig_list: list[QConfigAny], style: str
+    ) -> None:
+        # this method handles cases where the size of qconfig_list does not match
+        # the size of qconfig_mappings_list.
+        # Issue: Consider a user inserting global_qconfig A and B first, then inserting
+        # qconfig C as an object_type_qconfig for conv ops. If we internally store
+        # 1 QConfigMapping with A and C and another with just B, then the
+        # second QConfigMapping will match B to conv ops (which is not wanted), since B is global.
+
+        # we avoid this by maintaining the invariant that if any QConfigMapping
+        # has a qconfig style+key with a qconfig in it, all QConfigMappings must
+        # have either a qconfig or None for that same style+key. In the above
+        # example, a None qconfig would prevent the unwanted match in the
+        # second QConfigMapping
+
+        if len(qconfig_list) > len(self.qconfig_mappings_list):
+            # Case: we have more qconfigs (in qconfig_list) than QConfigMappings
+
+            # Add new QConfigMappings (initialized so we maintain the `invariant`)
+
+            new_qconfig_mapping = QConfigMapping()
+            # searches other QConfigMappings for qconfig style+keys
+            # that need to be inserted as `None` into the new QConfigMapping
+            for qconfig_mapping in self.qconfig_mappings_list:
+                # global_qconfig has None by default
+                for check_style in _QCONFIG_STYLE_ORDER[1:]:
+                    qconfigs_dict = getattr(qconfig_mapping, check_style)
+                    target_qconfigs_dict = getattr(new_qconfig_mapping, check_style)
+                    for key in qconfigs_dict:
+                        target_qconfigs_dict[key] = None
+                break
+
+            # insert copies of this new QConfigMapping until all entries
+            # in qconfig_list can fit among the QConfigMappings
+            while len(qconfig_list) > len(self.qconfig_mappings_list):
+                self.qconfig_mappings_list.append(copy.deepcopy(new_qconfig_mapping))
+        else:
+            # Case: we have fewer qconfigs in qconfig_list than QConfigMappings
+
+            # pad qconfig_list with `None` until length is same
+            while len(qconfig_list) < len(self.qconfig_mappings_list):
+                qconfig_list.append(None)
+
+    # this function applies the insertion method across each QConfigMapping
+    def _insert_qconfig_list(
+        self,
+        style: str,
+        args: list[str | int | Callable],
+        qconfig_list: list[QConfigAny],
+    ) -> None:
+        # we remove duplicates and None to make the ordering of qconfigs
+        # deterministic upon insertion.
+        _remove_duplicates_and_none(qconfig_list)
+
+        self._handle_list_size_mismatch(qconfig_list, style)
+        method_name = _QCONFIG_STYLE_TO_METHOD[style]
+        for qconfig_mapping, qconfig in zip(self.qconfig_mappings_list, qconfig_list):
+            # uses QConfigMapping set method to insert qconfig
+            set_method = getattr(qconfig_mapping, method_name)
+            set_method(*args, qconfig)
+
+    def set_global(self, global_qconfig_list: list[QConfigAny]) -> QConfigMultiMapping:
+        """
+        Set global QConfigs
+        see :func:`~torch.ao.quantization.QConfigMapping.set_global()` for more info
+        """
+        self._insert_qconfig_list("global_qconfig", [], global_qconfig_list)
+        return self
+
+    def set_object_type(
+        self, object_type: Callable | str, qconfig_list: list[QConfigAny]
+    ) -> QConfigMultiMapping:
+        """
+        Set object type QConfigs
+        see :func:`~torch.ao.quantization.QConfigMapping.set_object_type()` for more info
+        """
+        self._insert_qconfig_list("object_type_qconfigs", [object_type], qconfig_list)
+        return self
+
+    def set_module_name_regex(
+        self, module_name_regex: str, qconfig_list: list[QConfigAny]
+    ) -> QConfigMultiMapping:
+        """
+        Set module_name_regex QConfigs
+        see :func:`~torch.ao.quantization.QConfigMapping.set_module_name_regex()` for more info
+        """
+        self._insert_qconfig_list(
+            "module_name_regex_qconfigs", [module_name_regex], qconfig_list
+        )
+        return self
+
+    def set_module_name(
+        self, module_name: str, qconfig_list: list[QConfigAny]
+    ) -> QConfigMultiMapping:
+        """
+        Set module_name QConfigs
+        see :func:`~torch.ao.quantization.QConfigMapping.set_module_name()` for more info
+        """
+        self._insert_qconfig_list("module_name_qconfigs", [module_name], qconfig_list)
+        return self
+
+    def set_module_name_object_type_order(
+        self,
+        module_name: str,
+        object_type: Callable,
+        index: int,
+        qconfig_list: list[QConfigAny],
+    ) -> QConfigMultiMapping:
+        """
+        Set module_name QConfigs
+        see :func:`~torch.ao.quantization.QConfigMapping.set_module_name_object_type_order()` for more info
+        """
+        self._insert_qconfig_list(
+            "module_name_object_type_order_qconfigs",
+            [module_name, object_type, index],
+            qconfig_list,
+        )
+        return self
+
+    def __repr__(self):
+        return (
+            self.__class__.__name__
+            + " ["
+            + "".join(
+                f"\n{qconfig_mapping.__repr__()},"
+                for qconfig_mapping in self.qconfig_mappings_list
+            )
+            + "\n]"
+        )
+
+    @classmethod
+    def from_list_qconfig_mapping(
+        cls, qconfig_mapping_list: list[QConfigMapping]
+    ) -> QConfigMultiMapping:
+        """
+        Creates a QConfigMultiMapping from a list of QConfigMappings
+        """
+        new_qconfig_multi_mapping = cls()
+
+        new_qconfig_multi_mapping.qconfig_mappings_list = copy.deepcopy(
+            qconfig_mapping_list
+        )
+
+        # we need to avoid the issue described in _handle_list_size_mismatch,
+        # so we reinsert all the qconfigs using the QConfigMultiMapping
+        # set methods
+
+        # go through all qconfig styles
+        # note: global can be ignored since it is None by default
+        for style in _QCONFIG_STYLE_ORDER[1:]:
+            # gather all key+qconfigs for current style
+            # into qconfig_dict_list
+            qconfig_dict_list: dict[Any, list[QConfigAny]] = {}
+            for qconfig_mapping in qconfig_mapping_list:
+                qconfig_dict = getattr(qconfig_mapping, style)
+                for key, qconfig in qconfig_dict.items():
+                    if key not in qconfig_dict_list:
+                        qconfig_dict_list[key] = []
+                    qconfig_dict_list[key].append(qconfig)
+
+            # reinsert all gathered key+qconfigs
+            set_method_name = _QCONFIG_STYLE_TO_METHOD[style]
+            set_method = getattr(new_qconfig_multi_mapping, set_method_name)
+            for key, qconfig_list in qconfig_dict_list.items():
+                if isinstance(key, tuple):
+                    set_method(*key, qconfig_list)
+                else:
+                    set_method(key, qconfig_list)
+
+        return new_qconfig_multi_mapping
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/fx/utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/fx/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..93e72ae2fd4b64ae1b529e06bb8af988a747f690
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/fx/utils.py
@@ -0,0 +1,579 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+import enum
+import operator
+from collections.abc import Callable
+
+import torch
+import torch.ao.nn.intrinsic.quantized as nniq
+import torch.ao.nn.quantized as nnq
+import torch.nn as nn
+from torch.ao.quantization import FakeQuantizeBase, ObserverBase
+from torch.ao.quantization.observer import _is_activation_post_process
+from torch.ao.quantization.utils import getattr_from_fqn
+from torch.fx import GraphModule
+from torch.fx.graph import Node
+
+from .ns_types import NSNodeTargetType, NSResultsType
+
+
+toq = torch.ops.quantized
+
+
+# TODO(future PR): consider deleting this enum and using the torch types
+# directly.  This might be tricky because it is not a one to one mapping.
+class NodeInputOrOutputType(enum.Enum):
+    FP32 = enum.auto()  # torch.float
+    INT8 = enum.auto()  # torch.qint8 or torch.quint8
+    FP16 = enum.auto()  # torch.float16
+    UNKNOWN = enum.auto()  # we cannot determine input/output dtype
+    # TODO(future PR): while these functions can support multiple dtypes,
+    #   for the purposes of numerical debugging we want to get the actual
+    #   dtype used in the model. We will likely need some kind of dtype
+    #   propagation to estimate this.
+    FP32_OR_INT8 = enum.auto()  # either torch.float or torch.quint8 or torch.qint8
+    # TODO(future PRs): dynamic quant, fake quant, etc
+
+
+def get_node_first_input_and_output_type(
+    node: Node,
+    gm: GraphModule,
+    logger_cls: Callable,
+    node_type_to_io_type_map: dict[str, set[NSNodeTargetType]],
+) -> tuple[NodeInputOrOutputType, NodeInputOrOutputType]:
+    # TODO(future PR): clean this up
+    FUNS_IO_TYPE_FP32 = node_type_to_io_type_map["funs_io_type_fp32"]
+    FUNS_IO_TYPE_FP16 = node_type_to_io_type_map["funs_io_type_fp16"]
+    FUNS_IO_TYPE_INT8 = node_type_to_io_type_map["funs_io_type_int8"]
+    FUNS_IO_TYPE_FP32_OR_INT8 = node_type_to_io_type_map["funs_io_type_fp32_or_int8"]
+    MODS_IO_TYPE_FP32 = node_type_to_io_type_map["mods_io_type_fp32"]
+    MODS_IO_TYPE_INT8 = node_type_to_io_type_map["mods_io_type_int8"]
+    MODS_IO_TYPE_FP32_OR_INT8 = node_type_to_io_type_map["mods_io_type_fp32_or_int8"]
+    METHS_IO_TYPE_FP32_OR_INT8 = node_type_to_io_type_map["meths_io_type_fp32_or_int8"]
+
+    if node.op == "call_function":
+        if node.target in FUNS_IO_TYPE_FP32:
+            return (NodeInputOrOutputType.FP32, NodeInputOrOutputType.FP32)
+        if node.target in FUNS_IO_TYPE_FP16:
+            return (NodeInputOrOutputType.FP16, NodeInputOrOutputType.FP16)
+        elif node.target in FUNS_IO_TYPE_INT8:
+            return (NodeInputOrOutputType.INT8, NodeInputOrOutputType.INT8)
+        elif node.target in FUNS_IO_TYPE_FP32_OR_INT8:
+            first_arg = get_normalized_nth_input(node, gm, 0)
+            if not isinstance(first_arg, Node):
+                raise AssertionError(f"Expected Node, got {type(first_arg)}")
+            (
+                _prev_node_input_type,
+                prev_node_output_type,
+            ) = get_node_first_input_and_output_type(
+                first_arg, gm, logger_cls, node_type_to_io_type_map
+            )
+            return (prev_node_output_type, prev_node_output_type)
+        else:
+            return (NodeInputOrOutputType.UNKNOWN, NodeInputOrOutputType.UNKNOWN)
+
+    elif node.op == "call_module":
+        if node.op != "call_module":
+            raise AssertionError(f"Expected call_module, got '{node.op}'")
+        if not isinstance(node.target, str):
+            raise AssertionError(f"Expected str, but got {type(node.target)}")
+
+        mod = getattr_from_fqn(gm, node.target)
+        is_known_fp32_or_int8_input_module = any(
+            isinstance(mod, target_type)  # type: ignore[arg-type]
+            for target_type in MODS_IO_TYPE_FP32_OR_INT8
+        )
+        if (
+            isinstance(mod, (logger_cls, ObserverBase, FakeQuantizeBase))  # type: ignore[arg-type]
+            or is_known_fp32_or_int8_input_module
+        ):
+            # A logger or observer's input and output type is the output
+            # type of the preceding node.
+            first_arg = get_normalized_nth_input(node, gm, 0)
+            if not isinstance(first_arg, Node):
+                raise AssertionError(f"Expected Node, got {type(first_arg)}")
+            (
+                _prev_node_input_type,
+                prev_node_output_type,
+            ) = get_node_first_input_and_output_type(
+                first_arg, gm, logger_cls, node_type_to_io_type_map
+            )
+            return (prev_node_output_type, prev_node_output_type)
+        is_known_fp32_input_module = any(
+            isinstance(mod, target_type)  # type: ignore[arg-type]
+            for target_type in MODS_IO_TYPE_FP32
+        )
+        is_known_int8_input_module = any(
+            isinstance(mod, target_type)  # type: ignore[arg-type]
+            for target_type in MODS_IO_TYPE_INT8
+        )
+        if is_known_fp32_input_module:
+            return (NodeInputOrOutputType.FP32, NodeInputOrOutputType.FP32)
+        elif is_known_int8_input_module:
+            return (NodeInputOrOutputType.INT8, NodeInputOrOutputType.INT8)
+        else:
+            return (NodeInputOrOutputType.UNKNOWN, NodeInputOrOutputType.UNKNOWN)
+
+    elif node.op == "call_method":
+        if node.target == "dequantize":
+            # Dequantize is a special node because it allows multiple input types.
+            # So, we look up the output type of the previous node and return that
+            # as the input type of this node instance.
+            prev_node = get_normalized_nth_input(node, gm, 0)
+            if not isinstance(prev_node, Node):
+                raise AssertionError(f"Expected Node, got {type(prev_node)}")
+            (
+                _prev_node_input_type,
+                prev_node_output_type,
+            ) = get_node_first_input_and_output_type(
+                prev_node, gm, logger_cls, node_type_to_io_type_map
+            )
+            return (prev_node_output_type, NodeInputOrOutputType.FP32)
+
+        elif node.target == "to":
+            # to is a special node because it allows multiple input types.
+            # So, we look up the output type of the previous node and return that
+            # as the input type of this node instance. We also look up the target
+            # of to and return the correct output type.
+            prev_node = get_normalized_nth_input(node, gm, 0)
+            if not isinstance(prev_node, Node):
+                raise AssertionError(f"Expected Node, got {type(prev_node)}")
+            (
+                _prev_node_input_type,
+                prev_node_output_type,
+            ) = get_node_first_input_and_output_type(
+                prev_node, gm, logger_cls, node_type_to_io_type_map
+            )
+
+            cur_node_dtype_target = get_normalized_nth_input(node, gm, 1)
+            if cur_node_dtype_target is not torch.float16:
+                raise AssertionError(
+                    f"{cur_node_dtype_target} handling needs to be added"
+                )
+
+            return (prev_node_output_type, NodeInputOrOutputType.FP16)
+
+        elif node.target in METHS_IO_TYPE_FP32_OR_INT8:
+            first_arg = get_normalized_nth_input(node, gm, 0)
+            if not isinstance(first_arg, Node):
+                raise AssertionError(f"Expected Node, got {type(first_arg)}")
+            (
+                _prev_node_input_type,
+                prev_node_output_type,
+            ) = get_node_first_input_and_output_type(
+                first_arg, gm, logger_cls, node_type_to_io_type_map
+            )
+            return (prev_node_output_type, prev_node_output_type)
+
+        return (NodeInputOrOutputType.UNKNOWN, NodeInputOrOutputType.UNKNOWN)
+    else:
+        return (NodeInputOrOutputType.UNKNOWN, NodeInputOrOutputType.UNKNOWN)
+
+
+def get_node_input_qparams(
+    node: Node,
+    gm: GraphModule,
+    node_type_to_io_type_map: dict[str, set[NSNodeTargetType]],
+) -> tuple[torch.Tensor | float, torch.Tensor | int] | None:
+    """
+    Returns the qparams (scale, zero_point) of the first input to `node`,
+    if they can be inferred from the graph.
+    """
+    prev_node = get_normalized_nth_input(node, gm, 0)
+
+    if not isinstance(prev_node, Node):
+        return None
+
+    MODS_IO_TYPE_FP32_OR_INT8 = node_type_to_io_type_map["mods_io_type_fp32_or_int8"]
+
+    def _get_scale_zp_from_function_args(node, gm, scale_arg_idx, zp_arg_idx):
+        scale_node = get_normalized_nth_input(node, gm, scale_arg_idx)
+        zp_node = get_normalized_nth_input(node, gm, zp_arg_idx)
+        if not isinstance(scale_node, Node):
+            raise AssertionError(f"Expected Node, got {type(scale_node)}")
+        if not isinstance(scale_node.target, str):
+            raise AssertionError(f"Expected str, got {type(scale_node.target)}")
+        if not isinstance(zp_node, Node):
+            raise AssertionError(f"Expected Node, got {type(zp_node)}")
+        if not isinstance(zp_node.target, str):
+            raise AssertionError(f"Expected str, got {type(zp_node.target)}")
+        scale_obj = getattr_from_fqn(gm, scale_node.target)
+        zp_obj = getattr_from_fqn(gm, zp_node.target)
+        return (scale_obj, zp_obj)
+
+    if prev_node.op == "call_function":
+        # quantize - read the args directly
+        if prev_node.target is torch.quantize_per_tensor:
+            return _get_scale_zp_from_function_args(prev_node, gm, 1, 2)
+        elif prev_node.target in (toq.add, toq.add_relu, toq.mul, toq.mul_relu):
+            return _get_scale_zp_from_function_args(prev_node, gm, 2, 3)
+
+        return None
+        # TODO(future PR): handle more functionals
+        # TODO(future PR): handle functional ops which inherit qparams from input
+
+    elif prev_node.op == "call_module":
+        # get type of the module
+        if not isinstance(prev_node.target, str):
+            raise AssertionError(f"Expected str, got {type(prev_node.target)}")
+        module_obj = getattr_from_fqn(gm, prev_node.target)
+        if isinstance(
+            module_obj,
+            (
+                nnq.Linear,
+                nnq.Conv1d,
+                nnq.Conv2d,
+                nniq.ConvReLU2d,
+                nnq.Conv3d,
+                nnq.BatchNorm2d,
+                nnq.BatchNorm3d,
+                nnq.ConvTranspose1d,
+                nnq.ConvTranspose2d,
+                nnq.ELU,
+                nnq.GroupNorm,
+                nnq.InstanceNorm1d,
+                nnq.InstanceNorm2d,
+                nnq.InstanceNorm3d,
+                nnq.LayerNorm,
+                nnq.Hardswish,
+                nnq.LeakyReLU,
+                nnq.ReLU6,
+                nniq.BNReLU2d,
+                nniq.BNReLU3d,
+                nniq.ConvReLU1d,
+                nniq.ConvReLU2d,
+                nniq.ConvReLU3d,
+                nniq.LinearReLU,
+            ),
+        ):
+            return (module_obj.scale, module_obj.zero_point)  # type: ignore[return-value]
+
+        is_known_fp32_or_int8_input_module = any(
+            isinstance(module_obj, target_type)  # type: ignore[arg-type]
+            for target_type in MODS_IO_TYPE_FP32_OR_INT8
+        )
+        if is_known_fp32_or_int8_input_module:
+            return get_node_input_qparams(prev_node, gm, node_type_to_io_type_map)
+
+    return None
+
+
+def return_first_non_observer_node(
+    node: Node,
+    gm: GraphModule,
+) -> Node:
+    """
+    If node is not an observer, returns it.  If node is an observer,
+    navigates up the graph and returns the first parent which is not an
+    observer.  For example,
+
+    graph: (node_non_obs), node = node_non_obs : returns node_non_obs
+    graph: (node_non_obs -> obs0), node = obs0 : returns node_non_obs
+    graph: (node_non_obs -> obs0 -> fq0), node = fq0 : returns node_non_obs
+    """
+    if node.op == "call_module":
+        node_obj = getattr_from_fqn(gm, node.target)  # type: ignore[arg-type]
+        if _is_activation_post_process(node_obj):
+            if len(node.args) != 1:
+                raise AssertionError(
+                    f"Expected node.args to have length 1, got {len(node.args)}"
+                )
+            if not isinstance(node.args[0], Node):
+                raise AssertionError(f"Expected Node, got {type(node.args[0])}")
+            node = node.args[0]
+            # code duplication intended, not worth refactoring
+            if not isinstance(node.target, str):
+                raise AssertionError(f"Expected str, got {type(node.target)}")
+            node_obj = getattr_from_fqn(gm, node.target)
+            if _is_activation_post_process(node_obj):
+                if len(node.args) != 1:
+                    raise AssertionError(
+                        f"Expected node.args to have length 1, got {len(node.args)}"
+                    )
+                if not isinstance(node.args[0], Node):
+                    raise AssertionError(f"Expected Node, got {type(node.args[0])}")
+                node = node.args[0]
+    return node
+
+
+def get_number_of_non_param_args(
+    node: Node,
+    gm: GraphModule,
+) -> int:
+    """
+    Assumes that all non-param args occur first. Returns the number of
+    non-param args expected for a node.  For example, for
+
+      F.linear(x, weight, bias)
+
+    Returns 1, because x is a non-param arg and weight and bias are params.
+    For
+
+      lstm_mod(x, hid)
+
+    Returns 2, because both x and hid are non-param args.
+    """
+    if node.op == "call_module":
+        node_obj = getattr_from_fqn(gm, node.target)  # type: ignore[arg-type]
+        if isinstance(node_obj, nn.LSTM):
+            return 2
+
+    # default is 1
+    return 1
+
+
+def get_arg_indices_of_inputs_to_log(node: Node) -> list[int]:
+    """
+    Returns the indices of args of the node which we should attach
+    loggers to, if input logging is enabled.
+
+    For example,
+    * for (x + y), returns [0, 1]
+    * for (1 + y), returns [1]
+    * for (x + 1), returns [0]
+    * for (linear(x, w, b)) returns [0]
+    * by default, returns [0]
+    """
+    if len(node.args) == 0:
+        return []
+    if node.op == "call_function" and (
+        # TODO(future PR): use relationship map instead of hardcoding
+        node.target in (torch.add, torch.ops.quantized.add, operator.add)
+        or node.target in (torch.mul, torch.ops.quantized.mul, operator.mul)
+    ):
+        result = [i for i in range(2) if type(node.args[i]) is Node]
+        return result
+    return [0]
+
+
+def get_target_type_str(node: Node, gm: GraphModule) -> str:
+    """
+    Returns a string representation of the type of the function or module
+    pointed to by this node, or '' for other node types.
+    """
+    target_type = ""
+    if node.op in ("call_function", "call_method"):
+        target_type = torch.typename(node.target)
+    elif node.op == "call_module":
+        if not isinstance(node.target, str):
+            raise AssertionError(f"Expected str, got {type(node.target)}")
+        target_mod = getattr_from_fqn(gm, node.target)
+        target_type = torch.typename(target_mod)
+    return target_type
+
+
+def rekey_logger_info_on_node_name_of_model(
+    results: NSResultsType,
+    model_name: str,
+) -> NSResultsType:
+    """
+    Rekeys the layer name of a results dictionary to use node names
+    from `model_name`.
+
+    For example, transforms
+
+        {'base_op_1_0': {'node_output': {'model_a':
+          [{'ref_node_name': 'linear1', ...}]}}}
+
+    into
+
+        {'linear1': {'node_output': {'model_a':
+          [{'ref_node_name': 'linear1', ...}]}}}
+
+    Note: we cannot use these node names directly because they are not
+    guaranteed to be consistent across models. This is why we extract
+    the results first and rekey afterwards.
+    """
+    new_results = {}
+    for old_layer_name, result_type_to_results in results.items():
+        new_layer_name = None
+        for model_name_to_results in result_type_to_results.values():
+            for cur_model_name, list_of_results in model_name_to_results.items():
+                if cur_model_name == model_name:
+                    if len(list_of_results) == 0:
+                        raise AssertionError("Expected list_of_results to be not empty")
+                    new_layer_name = list_of_results[0]["ref_node_name"]
+                else:
+                    continue
+        if new_layer_name is not None:
+            new_results[new_layer_name] = result_type_to_results
+        else:
+            new_results[old_layer_name] = result_type_to_results
+    return new_results
+
+
+def maybe_add_missing_fqns(results: NSResultsType) -> None:
+    """
+    If `fqn` entries are filled in for one of the models in `results`, copies
+    them over to any models which do not have them filled out.
+
+    A common use case benefitting from this is comparing a model prepared by
+    quantization to a quantized model. In this case, the model prepared by
+    quantization would have `fqn` entries, and the quantized model would not.
+    """
+
+    # Check in the first result to find any model with fqn entries defined.
+    model_name_with_fqns = None
+    for result_type_to_results in results.values():
+        for model_name_to_results in result_type_to_results.values():
+            for model_name, model_results in model_name_to_results.items():
+                if len(model_results) > 0:
+                    if model_results[0]["fqn"] is not None:
+                        model_name_with_fqns = model_name
+                        break
+            break
+        break
+
+    if model_name_with_fqns:
+        for result_type_to_results in results.values():
+            for model_name_to_results in result_type_to_results.values():
+                ref_model_results = model_name_to_results[model_name_with_fqns]
+                for model_name, model_results in model_name_to_results.items():
+                    if model_name == model_name_with_fqns:
+                        continue
+
+                    for i in range(len(model_results)):
+                        fqn = ref_model_results[i]["fqn"]
+                        model_results[i]["fqn"] = fqn
+
+
+def maybe_dequantize_first_two_tensor_args_and_handle_tuples(f):
+    def inner(*args, **kwargs):
+        a0, a1, *a_other = args
+
+        if (isinstance(a0, tuple) and isinstance(a1, tuple)) or (
+            isinstance(a0, list) and isinstance(a1, list)
+        ):
+            results = []
+            for el0, el1 in zip(a0, a1):
+                new_args = (el0, el1, *a_other)
+                results.append(inner(*new_args, **kwargs))
+            return results
+
+        elif isinstance(a0, torch.Tensor) and isinstance(a1, torch.Tensor):
+            if a0.is_quantized:
+                a0 = a0.dequantize()
+            if a1.is_quantized:
+                a1 = a1.dequantize()
+
+        # for the purposes of this util, only handle floats
+        if a0.dtype != torch.float or a1.dtype != torch.float:
+            return None
+
+        new_args = (a0, a1, *a_other)
+        return f(*new_args, **kwargs)
+
+    return inner
+
+
+@maybe_dequantize_first_two_tensor_args_and_handle_tuples
+def compute_sqnr(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+    """
+    Computes the SQNR between `x` and `y`.
+
+    Args:
+        x: Tensor or tuple of tensors
+        y: Tensor or tuple of tensors
+
+    Return:
+        float or tuple of floats
+    """
+    Ps = torch.norm(x)
+    Pn = torch.norm(x - y)
+    return 20 * torch.log10(Ps / Pn)
+
+
+@maybe_dequantize_first_two_tensor_args_and_handle_tuples
+def compute_normalized_l2_error(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+    """
+    Computes the normalized L2 error between `x` and `y`.
+
+    Args:
+        x: Tensor or tuple of tensors
+        y: Tensor or tuple of tensors
+
+    Return:
+        float or tuple of floats
+    """
+    # pyrefly: ignore [unsupported-operation]
+    return torch.sqrt(((x - y) ** 2).sum() / (x**2).sum())
+
+
+@maybe_dequantize_first_two_tensor_args_and_handle_tuples
+def compute_cosine_similarity(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+    """
+    Computes the cosine similarity between `x` and `y`.
+
+    Args:
+        x: Tensor or tuple of tensors
+        y: Tensor or tuple of tensors
+
+    Return:
+        float or tuple of floats
+    """
+    # For convolutions, the shape of the quantized weight has one additional
+    # dimension compared to the shape of the fp32 weight. Match the shapes
+    # to enable cosine similarity comparison.
+    x = x.reshape(1, -1)
+    y = y.reshape(1, -1)
+    return torch.nn.functional.cosine_similarity(x, y)
+
+
+def op_type_supports_shadowing(node: Node) -> bool:
+    if node.op == "call_function":
+        if node.target in (
+            torch.add,
+            torch.mul,
+            operator.add,
+            operator.mul,
+            torch.cat,
+            torch.stack,
+        ):
+            # shadowing for ops with multiple tensor inputs is not implemented yet
+            return False
+    return True
+
+
+def get_normalized_nth_input(node: Node, gm: GraphModule, idx: int) -> Node:
+    """
+    Given a node, gets the n'th input to that node, normalizing
+    args and kwargs to the best of its ability.
+    """
+    try:
+        norm_args_and_kwargs = node.normalized_arguments(
+            gm, normalize_to_only_use_kwargs=True
+        )
+        if norm_args_and_kwargs is not None:
+            norm_args, norm_kwargs = norm_args_and_kwargs
+            if len(norm_args) + len(norm_kwargs) <= idx:
+                raise AssertionError(
+                    f"Index {idx} out of range: total = {len(norm_args) + len(norm_kwargs)}"
+                )
+            if idx < len(norm_args):
+                return norm_args[idx]
+            else:
+                # note: in Python 3.7+ dicts are ordered
+                return list(norm_kwargs.values())[idx]
+        else:
+            if len(node.args) + len(node.kwargs) <= idx:
+                raise AssertionError(
+                    f"Index {idx} out of range: total = {len(node.args) + len(node.kwargs)}"
+                )
+            if idx < len(node.args):
+                return node.args[idx]  # type: ignore[return-value]
+            else:
+                kwargs_idx = idx + len(node.args)
+                return list(node.kwargs.values())[kwargs_idx]  # type: ignore[return-value]
+    except RuntimeError:
+        # this RuntimeError happens when node argument normalization
+        # requires typehints to proceed, such as for torch.add where
+        # either the first, second or both arguments could be tensors
+        if len(node.args) + len(node.kwargs) <= idx:
+            raise AssertionError(
+                f"Index {idx} out of range: total = {len(node.args) + len(node.kwargs)}"
+            ) from None
+        if idx < len(node.args):
+            return node.args[idx]  # type: ignore[return-value]
+        else:
+            kwargs_idx = idx + len(node.args)
+            return list(node.kwargs.values())[kwargs_idx]  # type: ignore[return-value]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/fx/weight_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/fx/weight_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..6bff44215e46174856918883f35aac92b4491c25
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/ns/fx/weight_utils.py
@@ -0,0 +1,302 @@
+from collections.abc import Callable
+
+import torch
+import torch.ao.nn.intrinsic as nni
+import torch.ao.nn.intrinsic.qat as nniqat
+import torch.ao.nn.intrinsic.quantized as nniq
+import torch.ao.nn.qat as nnqat
+import torch.ao.nn.quantized as nnq
+import torch.ao.nn.quantized.dynamic as nnqd
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.fx import GraphModule
+from torch.fx.graph import Node
+
+from .ns_types import NSSingleResultType, NSSingleResultValuesType
+from .utils import get_target_type_str, getattr_from_fqn, return_first_non_observer_node
+
+
+toq = torch.ops.quantized
+
+
+def mod_weight_detach(mod: nn.Module) -> torch.Tensor:
+    return mod.weight.detach()  # type: ignore[operator]
+
+
+def mod_0_weight_detach(mod: nn.Module) -> torch.Tensor:
+    return mod[0].weight.detach()  # type: ignore[index]
+
+
+def mod_weight_bias_0(mod: nn.Module) -> torch.Tensor:
+    return mod._weight_bias()[0]  # type: ignore[operator]
+
+
+def get_lstm_weight(mod: nn.Module) -> list[torch.Tensor]:
+    res = []
+    for idx, param_name in enumerate(mod._flat_weights_names):  # type: ignore[arg-type]
+        if "weight_ih_l" in param_name or "weight_hh_l" in param_name:
+            param_value = mod._flat_weights[idx].detach()  # type: ignore[index,union-attr]
+            res.append(param_value)
+    return res
+
+
+def get_qlstm_weight(mod: nn.Module) -> list[torch.Tensor]:
+    res = []
+    for weight_value in mod._all_weight_values:  # type: ignore[union-attr]
+        res.append(weight_value.param.__getstate__()[0][4][0].__getstate__()[0][0])
+        res.append(weight_value.param.__getstate__()[0][4][1].__getstate__()[0][0])
+    return res
+
+
+def get_conv_mod_weight(mod: nn.Module) -> torch.Tensor:
+    if isinstance(mod, (nn.Conv1d, nn.Conv2d, nn.Conv3d)):
+        return mod.weight.detach()
+    elif isinstance(mod, (nni.ConvReLU1d, nni.ConvReLU2d, nni.ConvReLU3d)):
+        return mod[0].weight.detach()  # type: ignore[operator]
+    else:
+        return mod._weight_bias()[0]  # type: ignore[operator]
+
+
+def get_linear_mod_weight(mod: nn.Module) -> torch.Tensor:
+    if isinstance(mod, nn.Linear):
+        return mod.weight.detach()
+    elif isinstance(mod, nni.LinearReLU):
+        return mod[0].weight.detach()  # type: ignore[operator]
+    else:
+        return mod._weight_bias()[0]  # type: ignore[operator]
+
+
+def get_lstm_mod_weights(mod: nn.Module) -> list[torch.Tensor]:
+    # TODO(future PR): make more generic, handle everything
+    if isinstance(mod, nn.LSTM):
+        res = []
+        for idx, param_name in enumerate(mod._flat_weights_names):
+            if "weight_ih_l" in param_name or "weight_hh_l" in param_name:
+                param_value = mod._flat_weights[idx].detach()  # type: ignore[index,union-attr]
+                res.append(param_value)
+        return res
+    else:
+        if not isinstance(mod, nnqd.LSTM):
+            raise AssertionError(f"type {type(mod)} not handled yet")
+        res = []
+        for weight_value in mod._all_weight_values:
+            res.append(
+                weight_value.param.__getstate__()[0][4][0].__getstate__()[0][0]  # type: ignore[index]
+            )
+            res.append(
+                weight_value.param.__getstate__()[0][4][1].__getstate__()[0][0]  # type: ignore[index]
+            )
+        return res
+
+
+def get_conv_fun_weight(node: Node, gm: GraphModule) -> torch.Tensor:
+    # traverse backwards from the weight arg, accounting for any observers
+    weight_arg_node = node.args[1]
+    if not isinstance(weight_arg_node, Node):
+        raise AssertionError(f"Expected Node, got {type(weight_arg_node)}")
+    weight_node = return_first_non_observer_node(weight_arg_node, gm)
+    if not isinstance(weight_node, Node):
+        raise AssertionError(f"Expected Node, got {type(weight_node)}")
+    if weight_node.op != "get_attr":
+        raise AssertionError(f"Expected get_attr, got {weight_node.op}")
+    weight = getattr_from_fqn(gm, weight_node.target)  # type: ignore[arg-type]
+    return weight.detach()
+
+
+def get_qconv_fun_weight(node: Node, gm: GraphModule) -> torch.Tensor:
+    # qconv state is arg 1
+    qconv_state_node = node.args[1]
+    if not isinstance(qconv_state_node, Node):
+        raise AssertionError(f"Expected Node, got {type(qconv_state_node)}")
+    if qconv_state_node.op != "get_attr":
+        raise AssertionError(f"Expected get_attr, got {qconv_state_node.op}")
+    qconv_state_obj = getattr_from_fqn(gm, qconv_state_node.target)  # type: ignore[arg-type]
+    return qconv_state_obj.weight()
+
+
+def get_linear_fun_weight(node: Node, gm: GraphModule) -> torch.Tensor:
+    # traverse backwards from the weight arg, accounting for any observers
+    # supported patterns:
+    # weight -> obs -> linear
+    # weight -> to(torch.float16) -> dequantize -> linear
+    linear_second_arg = node.args[1]
+    if not isinstance(linear_second_arg, Node):
+        raise AssertionError(f"Expected Node, got {type(linear_second_arg)}")
+
+    if linear_second_arg.op == "call_module":
+        # weight -> obs -> linear
+        weight_arg_node = node.args[1]
+        if not isinstance(weight_arg_node, Node):
+            raise AssertionError(f"Expected Node, got {type(weight_arg_node)}")
+        weight_node = weight_arg_node.args[0]
+        if not isinstance(weight_node, Node):
+            raise AssertionError(f"Expected Node, got {type(weight_node)}")
+        if weight_node.op != "get_attr":
+            raise AssertionError(f"Expected get_attr, got {weight_node.op}")
+        weight = getattr_from_fqn(gm, weight_node.target)  # type: ignore[arg-type]
+        return weight.detach()
+    elif linear_second_arg.op == "call_method":
+        # weight -> to(torch.float16) -> dequantize -> linear
+        if linear_second_arg.op != "call_method":
+            raise AssertionError(f"Expected call_method, got {linear_second_arg.op}")
+        dequant_node = node.args[1]
+        if not isinstance(dequant_node, Node):
+            raise AssertionError(f"Expected Node, got {type(dequant_node)}")
+        to_fp16_node = dequant_node.args[0]
+        if not isinstance(to_fp16_node, Node):
+            raise AssertionError(f"Expected Node, got {type(to_fp16_node)}")
+        # extract the dtype, so we can cast to it before returning
+        target_dtype = to_fp16_node.args[1]
+        weight_node = to_fp16_node.args[0]
+        if not isinstance(weight_node, Node):
+            raise AssertionError(f"Expected Node, got {type(weight_node)}")
+        if weight_node.op != "get_attr":
+            raise AssertionError(f"Expected get_attr, got {weight_node.op}")
+        weight = getattr_from_fqn(gm, weight_node.target)  # type: ignore[arg-type]
+        # return the weight with fp16 cast
+        return weight.detach().to(target_dtype)
+    else:
+        if linear_second_arg.op != "get_attr":
+            raise AssertionError(f"Expected get_attr, got {linear_second_arg.op}")
+        weight = getattr_from_fqn(gm, linear_second_arg.target)  # type: ignore[arg-type]
+        return weight.detach()
+
+
+def get_qlinear_fun_weight(node: Node, gm: GraphModule) -> torch.Tensor:
+    # packed weight is arg 1
+    packed_weight_node = node.args[1]
+    if not isinstance(packed_weight_node, Node):
+        raise AssertionError(f"Expected Node, got {type(packed_weight_node)}")
+    if packed_weight_node.op != "get_attr":
+        raise AssertionError(f"Expected get_attr, got {packed_weight_node.op}")
+    packed_weight = getattr_from_fqn(gm, packed_weight_node.target)  # type: ignore[arg-type]
+    # TODO(future PR): why does packed_weight.unpack() not work?
+    (weight, _bias), _name = packed_weight.__getstate__()
+    return weight
+
+
+def get_op_to_type_to_weight_extraction_fn() -> dict[str, dict[Callable, Callable]]:
+    op_to_type_to_weight_extraction_fn: dict[str, dict[Callable, Callable]] = {
+        "call_module": {
+            # Conv1d
+            nn.Conv1d: mod_weight_detach,
+            nni.ConvReLU1d: mod_0_weight_detach,
+            nnq.Conv1d: mod_weight_bias_0,
+            nnqat.Conv1d: mod_weight_detach,
+            nniqat.ConvBn1d: mod_weight_detach,
+            nniqat.ConvBnReLU1d: mod_weight_detach,
+            nniqat.ConvReLU1d: mod_weight_detach,
+            nniq.ConvReLU1d: mod_weight_bias_0,
+            # Conv2d
+            nn.Conv2d: mod_weight_detach,
+            nni.ConvReLU2d: mod_0_weight_detach,
+            nnq.Conv2d: mod_weight_bias_0,
+            nnqat.Conv2d: mod_weight_detach,
+            nniqat.ConvBn2d: mod_weight_detach,
+            nniqat.ConvBnReLU2d: mod_weight_detach,
+            nniqat.ConvReLU2d: mod_weight_detach,
+            nniq.ConvReLU2d: mod_weight_bias_0,
+            # Conv3d
+            nn.Conv3d: mod_weight_detach,
+            nni.ConvReLU3d: mod_0_weight_detach,
+            nnq.Conv3d: mod_weight_bias_0,
+            nnqat.Conv3d: mod_weight_detach,
+            nniqat.ConvBn3d: mod_weight_detach,
+            nniqat.ConvBnReLU3d: mod_weight_detach,
+            nniqat.ConvReLU3d: mod_weight_detach,
+            nniq.ConvReLU3d: mod_weight_bias_0,
+            # Linear
+            nn.Linear: mod_weight_detach,
+            nnq.Linear: mod_weight_bias_0,
+            nni.LinearReLU: mod_0_weight_detach,
+            nniq.LinearReLU: mod_weight_bias_0,
+            nnqat.Linear: mod_weight_detach,
+            nnqd.Linear: mod_weight_bias_0,
+            nniqat.LinearReLU: mod_weight_detach,
+            nniqat.LinearBn1d: mod_weight_detach,
+            nn.modules.linear.NonDynamicallyQuantizableLinear: mod_weight_detach,
+            # LSTM
+            nn.LSTM: get_lstm_weight,
+            nnqd.LSTM: get_qlstm_weight,
+        },
+        "call_function": {
+            # Conv
+            F.conv1d: get_conv_fun_weight,
+            F.conv2d: get_conv_fun_weight,
+            F.conv3d: get_conv_fun_weight,
+            toq.conv1d: get_qconv_fun_weight,
+            toq.conv2d: get_qconv_fun_weight,
+            toq.conv3d: get_qconv_fun_weight,
+            toq.conv1d_relu: get_qconv_fun_weight,
+            toq.conv2d_relu: get_qconv_fun_weight,
+            toq.conv3d_relu: get_qconv_fun_weight,
+            # Linear
+            F.linear: get_linear_fun_weight,
+            toq.linear: get_qlinear_fun_weight,
+            toq.linear_relu: get_qlinear_fun_weight,
+        },
+    }
+
+    return op_to_type_to_weight_extraction_fn
+
+
+def extract_weight_from_node(
+    node: Node,
+    gm: GraphModule,
+    op_to_type_to_weight_extraction_fn: dict[str, dict[Callable, Callable]]
+    | None = None,
+) -> NSSingleResultType | None:
+    res_type = NSSingleResultValuesType.WEIGHT.value
+
+    # Not all graphmodules have _node_name_to_scope, so only fill it
+    # out if it exists.
+    fqn = None
+    if hasattr(gm, "_node_name_to_scope"):
+        fqn = gm._node_name_to_scope[node.name][0]  # type: ignore[index]
+
+    if op_to_type_to_weight_extraction_fn is None:
+        op_to_type_to_weight_extraction_fn = get_op_to_type_to_weight_extraction_fn()
+
+    ref_node_type = get_target_type_str(node, gm)
+    # for extracting weights, these are always the same
+    prev_node_type = ref_node_type
+
+    if node.op == "call_function":
+        function_mapping = op_to_type_to_weight_extraction_fn["call_function"]
+        for target_fn_type, weight_extraction_fn in function_mapping.items():
+            if node.target == target_fn_type:
+                weight = weight_extraction_fn(node, gm)
+                return {
+                    "type": res_type,
+                    "values": [weight],
+                    "prev_node_name": node.name,
+                    "prev_node_target_type": prev_node_type,
+                    "ref_node_name": node.name,
+                    "ref_node_target_type": ref_node_type,
+                    "index_within_arg": 0,
+                    "index_of_arg": 0,
+                    "fqn": fqn,
+                }
+
+    elif node.op == "call_module":
+        # for call_module, we need to look up the modules to do the type check
+        if not isinstance(node.target, str):
+            raise AssertionError(f"Expected str, got {type(node.target)}")
+        mod = getattr_from_fqn(gm, node.target)
+        module_mapping = op_to_type_to_weight_extraction_fn["call_module"]
+        for target_mod_type, weight_extraction_fn in module_mapping.items():
+            if type(mod) is target_mod_type:
+                weight = weight_extraction_fn(mod)
+                return {
+                    "type": res_type,
+                    "values": [weight],
+                    "prev_node_name": node.name,
+                    "prev_node_target_type": prev_node_type,
+                    "ref_node_name": node.name,
+                    "ref_node_target_type": ref_node_type,
+                    "index_within_arg": 0,
+                    "index_of_arg": 0,
+                    "fqn": fqn,
+                }
+
+    return None
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..52fc301befd34642d51f1c27e07600a1f3ef26ff
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/__init__.py
@@ -0,0 +1,23 @@
+# Variables
+from ._mappings import (
+    get_dynamic_sparse_quantized_mapping,
+    get_static_sparse_quantized_mapping,
+)
+
+# Scheduler
+from .scheduler.base_scheduler import BaseScheduler
+from .scheduler.cubic_scheduler import CubicSL
+from .scheduler.lambda_scheduler import LambdaSL
+
+# Sparsifier
+from .sparsifier.base_sparsifier import BaseSparsifier
+from .sparsifier.nearly_diagonal_sparsifier import NearlyDiagonalSparsifier
+
+# Parametrizations
+from .sparsifier.utils import (
+    FakeSparsity,
+    fqn_to_module,
+    get_arg_info_from_tensor_fqn,
+    module_to_fqn,
+)
+from .sparsifier.weight_norm_sparsifier import WeightNormSparsifier
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py
new file mode 100644
index 0000000000000000000000000000000000000000..241b4e70e8196e66b471e3855033e55e3e426249
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py
@@ -0,0 +1,482 @@
+# mypy: allow-untyped-defs
+import copy
+import warnings
+from collections import defaultdict
+from typing import Any
+
+import torch
+from torch import nn
+from torch.ao.pruning.sparsifier.utils import fqn_to_module, module_to_fqn
+
+
+__all__ = ["ActivationSparsifier"]
+
+
+class ActivationSparsifier:
+    r"""
+    The Activation sparsifier class aims to sparsify/prune activations in a neural
+    network. The idea is to attach the sparsifier to a layer (or layers) and it
+    zeroes out the activations based on the mask_fn (or sparsification function)
+    input by the user.
+    The mask_fn is applied once all the inputs are aggregated and reduced i.e.
+    mask = mask_fn(reduce_fn(aggregate_fn(activations)))
+
+    Note::
+        The sparsification mask is computed on the input **before it goes through the attached layer**.
+
+    Args:
+        model (nn.Module):
+            The model whose layers will be sparsified. The layers that needs to be
+            sparsified should be added separately using the register_layer() function
+        aggregate_fn (Optional, Callable):
+            default aggregate_fn that is used if not specified while registering the layer.
+            specifies how inputs should be aggregated over time.
+            The aggregate_fn should usually take 2 torch tensors and return the aggregated tensor.
+            Example
+                def add_agg_fn(tensor1, tensor2):  return tensor1 + tensor2
+                reduce_fn (Optional, Callable):
+                    default reduce_fn that is used if not specified while registering the layer.
+                    reduce_fn will be called on the aggregated tensor i.e. the tensor obtained after
+                    calling agg_fn() on all inputs.
+                    Example
+                def mean_reduce_fn(agg_tensor):    return agg_tensor.mean(dim=0)
+                mask_fn (Optional, Callable):
+                    default mask_fn that is used to create the sparsification mask using the tensor obtained after
+                    calling the reduce_fn(). This is used by default if a custom one is passed in the
+                    register_layer().
+                    Note that the mask_fn() definition should contain the sparse arguments that is passed in sparse_config
+                    arguments.
+                features (Optional, list):
+                    default selected features to sparsify.
+                    If this is non-empty, then the mask_fn will be applied for each feature of the input.
+                    For example,
+                mask = [mask_fn(reduce_fn(aggregated_fn(input[feature])) for feature in features]
+                feature_dim (Optional, int):
+                    default dimension of input features. Again, features along this dim will be chosen
+                    for sparsification.
+                sparse_config (Dict):
+                    Default configuration for the mask_fn. This config will be passed
+                    with the mask_fn()
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> model = SomeModel()
+        >>> act_sparsifier = ActivationSparsifier(...)  # init activation sparsifier
+        >>> # Initialize aggregate_fn
+        >>> def agg_fn(x, y):
+        >>>     return x + y
+        >>>
+        >>> # Initialize reduce_fn
+        >>> def reduce_fn(x):
+        >>>     return torch.mean(x, dim=0)
+        >>>
+        >>> # Initialize mask_fn
+        >>> def mask_fn(data):
+        >>>     return torch.eye(data.shape).to(data.device)
+        >>>
+        >>>
+        >>> act_sparsifier.register_layer(
+        ...     model.some_layer,
+        ...     aggregate_fn=agg_fn,
+        ...     reduce_fn=reduce_fn,
+        ...     mask_fn=mask_fn,
+        ... )
+        >>>
+        >>> # start training process
+        >>> for _ in [...]:
+        >>> # epoch starts
+        >>> # model.forward(), compute_loss() and model.backwards()
+        >>> # epoch ends
+        >>>     act_sparsifier.step()
+        >>> # end training process
+        >>> sparsifier.squash_mask()
+    """
+
+    def __init__(
+        self,
+        model: nn.Module,
+        aggregate_fn=None,
+        reduce_fn=None,
+        mask_fn=None,
+        features=None,
+        feature_dim=None,
+        **sparse_config,
+    ):
+        self.model = model
+        self.defaults: dict[str, Any] = defaultdict()
+        self.defaults["sparse_config"] = sparse_config
+
+        # functions
+        self.defaults["aggregate_fn"] = aggregate_fn
+        self.defaults["reduce_fn"] = reduce_fn
+        self.defaults["mask_fn"] = mask_fn
+
+        # default feature and feature_dim
+        self.defaults["features"] = features
+        self.defaults["feature_dim"] = feature_dim
+
+        self.data_groups: dict[str, dict] = defaultdict(
+            dict
+        )  # contains all relevant info w.r.t each registered layer
+
+        self.state: dict[str, Any] = defaultdict(dict)  # layer name -> mask
+
+    @staticmethod
+    def _safe_rail_checks(args):
+        """Makes sure that some of the functions and attributes are not passed incorrectly"""
+
+        # if features are not None, then feature_dim must not be None
+        features, feature_dim = args["features"], args["feature_dim"]
+        if features is not None:
+            if feature_dim is None:
+                raise AssertionError("need feature dim to select features")
+
+        # all the *_fns should be callable
+        fn_keys = ["aggregate_fn", "reduce_fn", "mask_fn"]
+        for key in fn_keys:
+            fn = args[key]
+            if not callable(fn):
+                raise AssertionError(f"{fn} must be callable")
+
+    def _aggregate_hook(self, name):
+        """Returns hook that computes aggregate of activations passing through."""
+
+        # gather some data
+        feature_dim = self.data_groups[name]["feature_dim"]
+        features = self.data_groups[name]["features"]
+        agg_fn = self.data_groups[name]["aggregate_fn"]
+
+        def hook(module, input) -> None:
+            input_data = input[0]
+
+            data = self.data_groups[name].get("data")  # aggregated data
+            if features is None:
+                # no features associated, data should not be a list
+                if data is None:
+                    data = torch.zeros_like(input_data)
+                    self.state[name]["mask"] = torch.ones_like(input_data)
+                out_data = agg_fn(data, input_data)
+            else:
+                # data should be a list [aggregated over each feature only]
+                if data is None:
+                    out_data = [
+                        0 for _ in range(len(features))
+                    ]  # create one in case of 1st forward
+                    self.state[name]["mask"] = [0 for _ in range(len(features))]
+                else:
+                    out_data = data  # a list
+
+                # compute aggregate over each feature
+                for feature_idx in range(len(features)):
+                    # each feature is either a list or scalar, convert it to torch tensor
+                    feature_tensor = (
+                        torch.Tensor([features[feature_idx]])
+                        .long()
+                        .to(input_data.device)
+                    )
+                    data_feature = torch.index_select(
+                        input_data, feature_dim, feature_tensor
+                    )
+                    if data is None:
+                        curr_data = torch.zeros_like(data_feature)
+                        self.state[name]["mask"][feature_idx] = torch.ones_like(
+                            data_feature
+                        )
+                    else:
+                        curr_data = data[feature_idx]
+                    out_data[feature_idx] = agg_fn(curr_data, data_feature)
+            self.data_groups[name]["data"] = out_data
+
+        return hook
+
+    def register_layer(
+        self,
+        layer: nn.Module,
+        aggregate_fn=None,
+        reduce_fn=None,
+        mask_fn=None,
+        features=None,
+        feature_dim=None,
+        **sparse_config,
+    ):
+        r"""
+        Registers a layer for sparsification. The layer should be part of self.model.
+        Specifically, registers a pre-forward hook to the layer. The hook will apply the aggregate_fn
+        and store the aggregated activations that is input over each step.
+
+        Note::
+            - There is no need to pass in the name of the layer as it is automatically computed as per
+              the fqn convention.
+
+            - All the functions (fn) passed as argument will be called at a dim, feature level.
+        """
+        name = module_to_fqn(self.model, layer)
+        if name is None:
+            raise AssertionError("layer not found in the model")
+
+        if name in self.data_groups:  # unregister layer if already present
+            warnings.warn(
+                "layer already attached to the sparsifier, deregistering the layer and registering with new config",
+                stacklevel=2,
+            )
+            self.unregister_layer(name=name)
+
+        local_args = copy.deepcopy(self.defaults)
+        update_dict = {
+            "aggregate_fn": aggregate_fn,
+            "reduce_fn": reduce_fn,
+            "mask_fn": mask_fn,
+            "features": features,
+            "feature_dim": feature_dim,
+            "layer": layer,
+        }
+        local_args.update(
+            (arg, val) for arg, val in update_dict.items() if val is not None
+        )
+        local_args["sparse_config"].update(sparse_config)
+
+        self._safe_rail_checks(local_args)
+
+        self.data_groups[name] = local_args
+        agg_hook = layer.register_forward_pre_hook(self._aggregate_hook(name=name))
+
+        self.state[name]["mask"] = (
+            None  # mask will be created when model forward is called.
+        )
+
+        # attach agg hook
+        self.data_groups[name]["hook"] = agg_hook
+
+        # for serialization purposes, we know whether aggregate_hook is attached
+        # or sparsify_hook()
+        self.data_groups[name]["hook_state"] = "aggregate"  # aggregate hook is attached
+
+    def get_mask(self, name: str | None = None, layer: nn.Module | None = None):
+        """
+        Returns mask associated to the layer.
+
+        The mask is
+            - a torch tensor is features for that layer is None.
+            - a list of torch tensors for each feature, otherwise
+
+        Note::
+            The shape of the mask is unknown until model.forward() is applied.
+            Hence, if get_mask() is called before model.forward(), an
+            error will be raised.
+        """
+        if name is None and layer is None:
+            raise AssertionError("Need at least name or layer obj to retrieve mask")
+
+        if name is None:
+            if layer is None:
+                raise AssertionError("layer must be provided when name is None")
+            name = module_to_fqn(self.model, layer)
+            if name is None:
+                raise AssertionError("layer not found in the specified model")
+
+        if name not in self.state:
+            raise ValueError("Error: layer with the given name not found")
+
+        mask = self.state[name].get("mask", None)
+
+        if mask is None:
+            raise ValueError(
+                "Error: shape unknown, call layer() routine at least once to infer mask"
+            )
+        return mask
+
+    def unregister_layer(self, name):
+        """Detaches the sparsifier from the layer"""
+
+        # detach any hooks attached
+        self.data_groups[name]["hook"].remove()
+
+        # pop from the state dict
+        self.state.pop(name)
+
+        # pop from the data groups
+        self.data_groups.pop(name)
+
+    def step(self):
+        """Internally calls the update_mask() function for each layer"""
+        with torch.no_grad():
+            for name, configs in self.data_groups.items():
+                data = configs["data"]
+                self.update_mask(name, data, configs)
+
+                self.data_groups[name].pop("data")  # reset the accumulated data
+
+    def update_mask(self, name, data, configs):
+        """
+        Called for each registered layer and does the following-
+            1. apply reduce_fn on the aggregated activations
+            2. use mask_fn to compute the sparsification mask
+
+        Note:
+            the reduce_fn and mask_fn is called for each feature, dim over the data
+        """
+        mask = self.get_mask(name)
+        sparse_config = configs["sparse_config"]
+        features = configs["features"]
+        reduce_fn = configs["reduce_fn"]
+        mask_fn = configs["mask_fn"]
+        if features is None:
+            data = reduce_fn(data)
+            mask.data = mask_fn(data, **sparse_config)
+        else:
+            for feature_idx in range(len(features)):
+                data_feature = reduce_fn(data[feature_idx])
+                mask[feature_idx].data = mask_fn(data_feature, **sparse_config)
+
+    def _sparsify_hook(self, name):
+        """Returns hook that applies sparsification mask to input entering the attached layer"""
+        mask = self.get_mask(name)
+        features = self.data_groups[name]["features"]
+        feature_dim = self.data_groups[name]["feature_dim"]
+
+        def hook(module, input):
+            input_data = input[0]
+            if features is None:
+                # apply to all the features
+                return input_data * mask
+            else:
+                # apply per feature, feature_dim
+                for feature_idx in range(len(features)):
+                    feature = (
+                        torch.Tensor([features[feature_idx]])
+                        .long()
+                        .to(input_data.device)
+                    )
+                    sparsified = (
+                        torch.index_select(input_data, feature_dim, feature)
+                        * mask[feature_idx]
+                    )
+                    input_data.index_copy_(feature_dim, feature, sparsified)
+                return input_data
+
+        return hook
+
+    def squash_mask(self, attach_sparsify_hook=True, **kwargs):
+        """
+        Unregisters aggregate hook that was applied earlier and registers sparsification hooks if
+        attach_sparsify_hook = True.
+        """
+        for name, configs in self.data_groups.items():
+            # unhook agg hook
+            configs["hook"].remove()
+            configs.pop("hook")
+            self.data_groups[name]["hook_state"] = "None"
+            if attach_sparsify_hook:
+                configs["hook"] = configs["layer"].register_forward_pre_hook(
+                    self._sparsify_hook(name)
+                )
+            configs["hook_state"] = (
+                "sparsify"  # signals that sparsify hook is now attached
+            )
+
+    def _get_serializable_data_groups(self):
+        """Exclude hook and layer from the config keys before serializing
+
+        TODO: Might have to treat functions (reduce_fn, mask_fn etc) in a different manner while serializing.
+              For time-being, functions are treated the same way as other attributes
+        """
+        data_groups: dict[str, Any] = defaultdict()
+        for name, config in self.data_groups.items():
+            new_config = {
+                key: value
+                for key, value in config.items()
+                if key not in ["hook", "layer"]
+            }
+            data_groups[name] = new_config
+        return data_groups
+
+    def _convert_mask(self, states_dict, sparse_coo=True):
+        r"""Converts the mask to sparse coo or dense depending on the `sparse_coo` argument.
+        If `sparse_coo=True`, then the mask is stored as sparse coo else dense tensor
+        """
+        states = copy.deepcopy(states_dict)
+        for state in states.values():
+            if state["mask"] is not None:
+                if isinstance(state["mask"], list):
+                    for idx in range(len(state["mask"])):
+                        if sparse_coo:
+                            state["mask"][idx] = state["mask"][idx].to_sparse_coo()
+                        else:
+                            state["mask"][idx] = state["mask"][idx].to_dense()
+                else:
+                    if sparse_coo:
+                        state["mask"] = state["mask"].to_sparse_coo()
+                    else:
+                        state["mask"] = state["mask"].to_dense()
+        return states
+
+    def state_dict(self) -> dict[str, Any]:
+        r"""Returns the state of the sparsifier as a :class:`dict`.
+
+        It contains:
+        * state - contains name -> mask mapping.
+        * data_groups - a dictionary containing all config information for each
+            layer
+        * defaults - the default config while creating the constructor
+        """
+        data_groups = self._get_serializable_data_groups()
+        state = self._convert_mask(self.state)
+        return {"state": state, "data_groups": data_groups, "defaults": self.defaults}
+
+    def load_state_dict(self, state_dict: dict[str, Any]) -> None:
+        r"""The load_state_dict() restores the state of the sparsifier based on the state_dict
+
+        Args:
+        * state_dict - the dictionary that to which the current sparsifier needs to be restored to
+        """
+        state = state_dict["state"]
+        data_groups, defaults = state_dict["data_groups"], state_dict["defaults"]
+
+        self.__set_state__(
+            {"state": state, "data_groups": data_groups, "defaults": defaults}
+        )
+
+    def __get_state__(self) -> dict[str, Any]:
+        data_groups = self._get_serializable_data_groups()
+        state = self._convert_mask(self.state)
+        return {
+            "defaults": self.defaults,
+            "state": state,
+            "data_groups": data_groups,
+        }
+
+    def __set_state__(self, state: dict[str, Any]) -> None:
+        state["state"] = self._convert_mask(
+            state["state"], sparse_coo=False
+        )  # convert mask to dense tensor
+        self.__dict__.update(state)
+
+        # need to attach layer and hook info into the data_groups
+        for name, config in self.data_groups.items():
+            # fetch layer
+            layer = fqn_to_module(self.model, name)
+            if layer is None:
+                raise AssertionError(f"layer {name} not found in the model")
+
+            # if agg_mode is True, then layer in aggregate mode
+            if "hook_state" in config and config["hook_state"] == "aggregate":
+                hook = layer.register_forward_pre_hook(self._aggregate_hook(name))
+
+            elif "hook_state" in config and config["hook_state"] == "sparsify":
+                hook = layer.register_forward_pre_hook(self._sparsify_hook(name))
+
+            config["layer"] = layer
+            config["hook"] = hook  # type: ignore[possibly-undefined]
+
+    def __repr__(self):
+        format_string = self.__class__.__name__ + " ("
+        for name, config in self.data_groups.items():
+            format_string += "\n"
+            format_string += "\tData Group\n"
+            format_string += f"\t    name: {name}\n"
+            for key in sorted(config.keys()):
+                if key in ["data", "hook", "reduce_fn", "mask_fn", "aggregate_fn"]:
+                    continue
+                format_string += f"\t    {key}: {config[key]}\n"
+        format_string += ")"
+        return format_string
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_scheduler/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_scheduler/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a7564fe408b36e5fb62eb4cb2272ef432095981
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_scheduler/__init__.py
@@ -0,0 +1,6 @@
+from .base_data_scheduler import BaseDataScheduler
+
+
+__all__ = [
+    "BaseDataScheduler",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py
new file mode 100644
index 0000000000000000000000000000000000000000..c2f48abfc9deec2393816ffd227cc414f9f14a29
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py
@@ -0,0 +1,199 @@
+# mypy: allow-untyped-defs
+import abc
+import warnings
+import weakref
+from functools import wraps
+
+from torch.ao.pruning._experimental.data_sparsifier import BaseDataSparsifier
+
+
+__all__ = ["BaseDataScheduler"]
+
+
+class BaseDataScheduler:
+    r"""
+    The BaseDataScheduler is the abstract scheduler class specifically for the
+    BaseDataSparsifier class. This class controls a specific hyperparameter of
+    the sparsifier class and varies it across the training process (or across time).
+
+    Args:
+        data_sparsifier (instance of BaseDataSparsifier)
+            Implemented class data sparsifier class wherein the update_mask is implemented
+        schedule_param (str)
+            A specific hyperparameter of the passed sparsifier that needs to be scheduled/varied
+        last_epoch (int, default=-1)
+            This is specifically is passed when training needs to be resumed from a particular
+            point.
+        verbose (bool, default=False)
+            Verbosity of the BaseDataScheduler
+
+    The *get_hyperparam()* function needs to be implemented by the user.
+    """
+
+    def __init__(
+        self, data_sparsifier, schedule_param: str, last_epoch=-1, verbose=False
+    ):
+        # Attach sparsifier
+        if not isinstance(data_sparsifier, BaseDataSparsifier):
+            raise TypeError(
+                f"{type(data_sparsifier).__name__} is not an instance of torch.ao.pruning.BaseDataSparsifier"
+            )
+        self.data_sparsifier = data_sparsifier
+        self.schedule_param = schedule_param
+
+        # Initialize epoch and base hyper-params
+        self.base_param = {
+            name: config.get(schedule_param, None)
+            for name, config in self.data_sparsifier.data_groups.items()
+        }
+
+        self.last_epoch = last_epoch
+
+        # Following https://github.com/pytorch/pytorch/issues/20124
+        # We would like to ensure that `scheduler.step()` is called after
+        # `sparsifier.step()`
+        def with_counter(method):
+            if getattr(method, "_with_counter", False):
+                # `sparsifier.step()` has already been replaced, return.
+                return method
+
+            # Keep a weak reference to the sparsifier instance to prevent
+            # cyclic references.
+            instance_ref = weakref.ref(method.__self__)
+            # Get the unbound method for the same purpose.
+            func = method.__func__
+            cls = instance_ref().__class__
+            del method
+
+            @wraps(func)
+            def wrapper(*args, **kwargs):
+                instance = instance_ref()
+                instance._step_count += 1  # type: ignore[union-attr]
+                wrapped = func.__get__(instance, cls)
+                return wrapped(*args, **kwargs)
+
+            # Note that the returned function here is no longer a bound method,
+            # so attributes like `__func__` and `__self__` no longer exist.
+            wrapper._with_counter = True  # type: ignore[attr-defined]
+            return wrapper
+
+        self.data_sparsifier.step = with_counter(self.data_sparsifier.step)  # type: ignore[assignment]
+        self.data_sparsifier._step_count = 0  # type: ignore[attr-defined]
+        self._step_count: int = 0
+        self.verbose = verbose
+
+        # Housekeeping
+        self._get_sp_called_within_step: bool = False  # sp -> schedule parameter
+        self.step()
+
+    @abc.abstractmethod
+    def get_schedule_param(self):
+        r"""
+        Abstract method that needs to be implemented by the child class.
+        The expected return type should is a dictionary of name to schedule_param value
+        The returned values will be updated in sparsifier when the scheduler step() function
+        is called.
+
+        Example:
+            >>> def get_schedule_param(self):
+            ...     new_param = {}
+            ...     for name in self.sparsifier.data_groups.keys():
+            ...         new_param[name] = (
+            ...             self.sparsifier.data_groups[name][self.schedule_param] * 0.5
+            ...         )
+            ...     return new_param
+
+        When the step() function is called, the value in self.sparsifier.data_groups[name][self.schedule_param]
+        would be halved
+        """
+        raise NotImplementedError
+
+    def __repr__(self):
+        format_string = self.__class__.__name__ + " ("
+        format_string += "\n"
+        format_string += f"Data Sparsifier {self.data_sparsifier}\n"
+        format_string += f"    {self.schedule_param}: {self.base_param}\n"
+        format_string += ")"
+        return format_string
+
+    def state_dict(self):
+        """Returns the state of the scheduler as a :class:`dict`.
+
+        It contains an entry for every variable in self.__dict__ which
+        is not the sparsifier.
+
+        Note:
+            The scheduler class does not track the state of the data_sparsifier.
+            Make sure to store the state of the sparsifier before storing the
+            state of the scheduler
+        """
+        return {
+            key: value
+            for key, value in self.__dict__.items()
+            if key != "data_sparsifier"
+        }
+
+    def load_state_dict(self, state_dict):
+        """Loads the schedulers state.
+
+        Note:
+            Remember to restore the state of the data_sparsifier before the scheduler.
+
+        Args:
+            state_dict (dict): scheduler state. Should be an object returned
+                from a call to :meth:`state_dict`.
+        """
+        self.__dict__.update(state_dict)
+
+    def get_last_param(self):
+        return self._last_param
+
+    def step(self):
+        # Raise warning if trying to call scheduler step before the sparsifier.
+        # https://github.com/pytorch/pytorch/issues/20124
+        if self._step_count == 1:
+            if not hasattr(self.data_sparsifier.step, "_with_counter"):
+                warnings.warn(
+                    "Seems like `data_sparsifier.step()` has been overridden after sparsity scheduler "
+                    "initialization. Please, make sure to call `data_sparsifier.step()` before "
+                    "`scheduler.step()`.",
+                    UserWarning,
+                    stacklevel=2,
+                )
+
+            # Just check if there were two first scheduler.step() calls before sparsifier.step()
+            elif self.data_sparsifier._step_count < 1:  # type: ignore[attr-defined]
+                warnings.warn(
+                    "Detected call of `scheduler.step()` before `data_sparsifier.step()`. "
+                    "You have to make sure you run the data_sparsifier.step() BEFORE any "
+                    "calls to the scheduler.step().",
+                    UserWarning,
+                    stacklevel=2,
+                )
+        self._step_count += 1
+
+        class _enable_get_sp_call:
+            def __init__(self, o):
+                self.o = o
+
+            def __enter__(self):
+                self.o._get_sp_called_within_step = True
+                return self
+
+            def __exit__(self, type, value, traceback):
+                self.o._get_sp_called_within_step = False
+
+        with _enable_get_sp_call(self):
+            self.last_epoch += 1
+            updated_scheduler_params = self.get_schedule_param()
+
+        for name, param in updated_scheduler_params.items():
+            self.data_sparsifier.data_groups[name][self.schedule_param] = param
+            if self.verbose:
+                print(f"Adjusting {self.schedule_param} for group {name} to {param}")
+
+        self._last_param = {
+            name: config.get(self.schedule_param, None)
+            for name, config in self.data_sparsifier.data_groups.items()
+        }
+        self.data_sparsifier.enable_mask_update = True
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_sparsifier/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_sparsifier/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b1b5b9b96ec96fffdb0b66e21686a927a0c41b4a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_sparsifier/__init__.py
@@ -0,0 +1,8 @@
+from .base_data_sparsifier import BaseDataSparsifier
+from .data_norm_sparsifier import DataNormSparsifier
+
+
+__all__ = [
+    "BaseDataSparsifier",
+    "DataNormSparsifier",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py
new file mode 100644
index 0000000000000000000000000000000000000000..e76b5ccd7b5c571f636cce6a2f8beb907f50004e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py
@@ -0,0 +1,334 @@
+# mypy: allow-untyped-defs
+import abc
+import copy
+import sys
+import warnings
+from collections import defaultdict
+from typing import Any
+
+import torch
+from torch import nn
+from torch.ao.pruning.sparsifier import base_sparsifier, utils
+from torch.nn.utils import parametrize
+
+
+if not sys.warnoptions:
+    # to suppress repeated warnings when being used in a training loop.
+    warnings.simplefilter("once")
+
+__all__ = ["BaseDataSparsifier"]
+
+EMBEDDING_TYPES = {
+    nn.Embedding,
+    nn.EmbeddingBag,
+}
+
+SUPPORTED_TYPES = {
+    torch.Tensor,
+    nn.Parameter,
+    *EMBEDDING_TYPES,
+}
+
+
+class _Container(nn.Module):
+    pass
+
+
+class BaseDataSparsifier(base_sparsifier.BaseSparsifier):
+    r"""
+    Base Data Sparsifier class for all Data sparsifiers.
+    The abstract class accepts raw torch tensors / embedding / embedding bags (refer to SUPPORTED_TYPES above)
+    to prepare for sparsification.
+    In this case, mask (and parametrizations) is owned by the class and not by the user.
+    Specifically, the container object inside the class maintains the mask and parametrizations of the input data
+
+    Args:
+        data_list (list of tuples)
+            list of (name, data) tuples to sparsify. Lookup SUPPORTED_TYPES
+            for type of data. Internally, a container module handles the data sparsification.
+
+        defaults (dict)
+            default configurations will be attached to the
+            configuration. Only the keys that don't exist in the `config` will
+            be updated.
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> data_list = [('tensor_1', torch.randn(3,3)), ('tensor_2', torch.randn(4,4))]
+        >>> defaults = {'sparsity_level': 0.7}
+        >>> sparsifier = DerivedDataSparsifier(data_list = data_list, **defaults) # Some sparsifier that inherits BaseDataSparsifier
+        >>> new_tensor_to_add = {'name': 'tensor_3', 'data': torch.randn(5,5), 'sparsity_level': 0.3}
+        >>> sparsifier.add_data(**new_tensor_to_add)
+        >>> # tensor_1 and tensor_2 will have sparsity_level of 0.7 but tensor_3 will have sparsity_level=0.3
+    """
+
+    def __init__(self, data_list: list[tuple[str, Any]] | None = None, **defaults):
+        super().__init__(defaults=defaults)
+
+        self._container = _Container()
+
+        self.data_groups: dict[str, dict] = defaultdict(dict)  # name -> {**config}
+        if data_list is not None:
+            # add data with default config here
+            [self.add_data(name, data, **self.defaults) for name, data in data_list]
+
+    def prepare(self, model, config):
+        raise NotImplementedError("this function is undefined for this class")
+
+    def _extract_weight(self, data):
+        # extract the weight parameter instead of underlying data
+        if type(data) in [torch.Tensor, nn.Parameter]:
+            return data
+        elif type(data) in EMBEDDING_TYPES:
+            return data.weight
+
+    def add_data(self, name: str, data, reuse_mask=True, **config):
+        r"""Configures and parametrizes the internal container model with name and data.
+
+        **Note**:
+            1. If the data with name already exists, it replaces the data.
+            2. While replacing, the old mask is reused when `reuse_mask=True`
+            3. If `reuse_mask=True`, then the replacing data needs to have the same shape as that of old data.
+            4. By default, the config of the replaced data is used as config for the replacing data, unless something
+               is specified in the config dictionary.
+        """
+        if type(data) not in SUPPORTED_TYPES:
+            raise AssertionError(
+                f"specified data type:{type(data)} not  supported at the moment"
+            )
+        local_args = copy.deepcopy(self.defaults)
+        local_args.update(config)
+        weight = self._extract_weight(data)
+
+        # Bookkeeping in the container class
+        mask = local_args.get("mask", torch.ones_like(weight))
+        param_class = local_args.get("parametrization", utils.FakeSparsity)
+
+        if name in self.state:
+            # If the named data already exists - replace
+            warnings.warn(
+                "Replacing existing data of the same name. - Did you mean a different name?",
+                stacklevel=2,
+            )
+
+            # reuse old config
+            old_args = self.data_groups[name]
+            local_args = copy.deepcopy(old_args)
+            local_args.update(config)
+
+            if reuse_mask:
+                current_data = self.get_data(name=name)
+                if weight.shape != current_data.shape:
+                    raise AssertionError(
+                        "to retain the old mask, the shape of the new data must be the same as the previous one"
+                    )
+                mask = self.get_mask(
+                    name=name
+                )  # reuse mask instead of creating a new one
+
+            self._delete_data(name=name)
+
+        # parameter creates a deepcopy of the weight inside, so create a buffer
+        self._container.register_buffer(name=name, tensor=weight)
+        parametrize.register_parametrization(self._container, name, param_class(mask))
+        self.state[name]["mask"] = mask
+        self.data_groups[name] = local_args
+        return getattr(self._container, name)
+
+    def get_data(self, name: str, return_original: bool = True):
+        r"""Returns weight tensor (or data)
+        Args:
+            - name: name of the data to be returned
+            - return_original returns weight tensor without applying parametrization if True
+                else - returns the sparsified version (parametrized)
+        """
+        if name not in self.data_groups:
+            raise ValueError("data with specified name does not exist")
+
+        if return_original:
+            if not parametrize.is_parametrized(self._container, name):
+                raise ValueError("mask squashed - original mask value does not exist")
+            data = getattr(self._container.parametrizations, name).original
+            return data
+        else:
+            return getattr(self._container, name)
+
+    def _convert_mask(self, states, sparse_coo=True):
+        r"""Converts the mask to sparse coo or dense tensors depending on the `sparse_coo` argument."""
+        states = copy.deepcopy(states)
+        for state in states.values():
+            if sparse_coo:
+                state["mask"] = state["mask"].to_sparse_coo()
+            else:
+                state["mask"] = state["mask"].to_dense()
+
+        return states
+
+    def state_dict(self):
+        r"""Returns the state of the optimizer as a :class:`dict`.
+
+        It contains:
+        * state - contains name -> mask mapping.
+        * data_groups - a list containing all sparsity configuration groups
+            with the key name specifying the name of the data
+        * container_state_dict - the state dictionary of the internal
+            container model used for sparsification
+        """
+        state = self._convert_mask(self.state)
+        return {
+            "state": state,
+            "data_groups": self.data_groups,
+            "_container": self._container.state_dict(),
+        }
+
+    def _load_container_from_state(self, states, data_groups, container_state_dict):
+        r"""This restores the state of the container specifically based on the data present in state and data_groups
+        If the data was parametrized, then the data would be added to the container and then parametrized,
+        else it would just add the attribute the container.
+        """
+        for name, state in states.items():
+            config_name = data_groups.get(name, None)
+            if config_name is None:
+                raise RuntimeError(f"Error loading {name}")
+
+            # check if the data with such a name was parametrized, if so parametrize
+            # otherwise just set the attribute and continue
+            parametrized_name = f"parametrizations.{name}.original"
+            parametrized = False
+            data = container_state_dict.get(name, None)
+            if name in container_state_dict:
+                # the parametrization was probably removed for this
+                data = container_state_dict.get(name)
+
+            elif parametrized_name in container_state_dict:
+                # so the weight was parametrized
+                data = container_state_dict.get(parametrized_name)
+                parametrized = True
+
+            else:
+                raise RuntimeError(f"Error loading {name}")
+
+            self._container.register_buffer(name=name, tensor=data)
+
+            if parametrized:
+                # register parameter if parametrized
+                mask = state.get("mask", torch.ones_like(data))
+                param_class = data_groups.get(
+                    "parametrization", utils.FakeSparsity
+                )  # change once public_api for utils is fixed!
+                parametrize.register_parametrization(
+                    self._container, name, param_class(mask)
+                )
+
+    def load_state_dict(self, state_dict, strict=True):
+        r"""The load_state_dict() restores the state of the sparsifier based on the state_dict
+
+        Args:
+        * state_dict - the dictionary that to which the current sparsifier needs to be restored to
+        * strict - If True - the sparsifier is reset and is restored exactly to the state in state_dict.
+            If False - the current sparsifier is not reset before loading the state_dict i.e. data added
+            before loading the state_dict is not erased.
+        """
+        states = copy.deepcopy(state_dict["state"])
+        data_groups = copy.deepcopy(state_dict["data_groups"])
+        container_state_dict = copy.deepcopy(state_dict["_container"])
+
+        states = self._convert_mask(
+            states, sparse_coo=False
+        )  # convert sparse coo mask to dense
+        if strict:
+            # if strict load -> then reset container
+            self._container = _Container()
+
+        self._load_container_from_state(states, data_groups, container_state_dict)
+
+        if not strict:
+            states.update(self.state)
+            data_groups.update(self.data_groups)
+
+        self.__setstate__({"state": states, "data_groups": data_groups})
+
+    def __setstate__(self, state):
+        if "_container" in state:  # If container object is in state then load model
+            container_dict = state.pop("_container")
+            self._container = _Container()
+            state["state"] = self._convert_mask(
+                state["state"], sparse_coo=False
+            )  # convert sparse coo mask to dense
+            self._load_container_from_state(
+                state["state"], state["data_groups"], container_dict
+            )
+
+        self.__dict__.update(state)
+
+    def __getstate__(self):
+        state = self._convert_mask(self.state)
+        return {
+            "defaults": self.defaults,
+            "state": state,
+            "data_groups": self.data_groups,
+            "_container": self._container.state_dict(),
+        }
+
+    def __repr__(self):  # type:ignore[override]
+        format_string = self.__class__.__name__ + " ("
+        for name, sparse_args in self.data_groups.items():
+            format_string += "\n"
+            format_string += "\tData Group\n"
+            format_string += f"\t    name: {name}\n"
+            for key in sorted(sparse_args.keys()):
+                if key == "data":
+                    continue
+                format_string += f"\t    {key}: {sparse_args[key]}\n"
+        format_string += ")"
+        return format_string
+
+    def get_mask(self, name: str):
+        if name not in self.state:
+            raise ValueError("data with specified name does not exist")
+        return self.state[name]["mask"]
+
+    def squash_mask(self, *args, leave_parametrized=True, names=None, **kwargs):
+        r"""Squashes the sparse masks into the appropriate tensors. Also, accepts list of strings
+        to squash mask for. If none, squashes mask for all the keys
+        kwargs:
+            * names: list of strings to squash mask for
+            * sparsified: if true - applies the mask before squashing
+                          if false - does not apply the mask before squashing
+        """
+        if names is None:
+            names = list(self.data_groups.keys())
+        for name in names:
+            parametrize.remove_parametrizations(
+                self._container, name, leave_parametrized=leave_parametrized
+            )
+
+    def step(self):  # type:ignore[override]
+        if not self.enable_mask_update:
+            return
+        with torch.no_grad():
+            for name, config in self.data_groups.items():
+                # get non-sparsified data
+                data = self.get_data(name)
+                # need name for the mask otherwise can directly pass mask?
+                self.update_mask(name, data, **config)
+
+    @abc.abstractmethod
+    def update_mask(self, name, data, **kwargs):  # type: ignore[override]
+        pass
+
+    def _delete_data(self, name):
+        """Detaches some data from the sparsifier.
+
+        Args:
+            name (str)
+                Name of the data to be removed from the sparsifier
+
+        Note:
+            Currently private. Kind of used as a helper function when replacing data of the same name
+        """
+        self.squash_mask(
+            names=[name], leave_parametrized=False
+        )  # do not apply the mask while deleting
+        delattr(self._container, name)
+        self.state.pop(name)
+        self.data_groups.pop(name)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_sparsifier/data_norm_sparsifier.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_sparsifier/data_norm_sparsifier.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa2971cd0b3d0cae7afb8763bee319ac819ad2f8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_sparsifier/data_norm_sparsifier.py
@@ -0,0 +1,204 @@
+# mypy: allow-untyped-defs
+import operator
+from functools import reduce
+from typing import Any
+
+import torch
+from torch.nn import functional as F
+
+from .base_data_sparsifier import BaseDataSparsifier
+
+
+__all__ = ["DataNormSparsifier"]
+
+
+class DataNormSparsifier(BaseDataSparsifier):
+    r"""L1-Norm Sparsifier
+    This sparsifier computes the *L1-norm* of every sparse block and "zeroes-out" the
+    ones with the lowest norm. The level of sparsity defines how many of the
+    blocks is removed.
+    This sparsifier is controlled by three variables:
+    1. `sparsity_level` defines the number of *sparse blocks* that are zeroed-out
+    2. `sparse_block_shape` defines the shape of the sparse blocks. Note that
+        the sparse blocks originate at the zero-index of the tensor.
+    3. `zeros_per_block` is the number of zeros that we are expecting in each
+        sparse block. By default we assume that all elements within a block are
+        zeroed-out. However, setting this variable sets the target number of
+        zeros per block. The zeros within each block are chosen as the *smallest
+        absolute values*.
+    Args:
+        sparsity_level: The target level of sparsity
+        sparse_block_shape: The shape of a sparse block
+        zeros_per_block: Number of zeros in a sparse block
+    Note::
+        All arguments to the DataNormSparsifier constructor are "default"
+        arguments and could be overridden by the configuration provided in the
+        `add_data` step.
+    """
+
+    def __init__(
+        self,
+        data_list: list[tuple[str, Any]] | None = None,
+        sparsity_level: float = 0.5,
+        sparse_block_shape: tuple[int, int] = (1, 4),
+        zeros_per_block: int | None = None,
+        norm: str = "L1",
+    ):
+        if zeros_per_block is None:
+            zeros_per_block = reduce(operator.mul, sparse_block_shape)
+
+        if norm not in ["L1", "L2"]:
+            raise AssertionError("only L1 and L2 norm supported at the moment")
+
+        defaults = {
+            "sparsity_level": sparsity_level,
+            "sparse_block_shape": sparse_block_shape,
+            "zeros_per_block": zeros_per_block,
+        }
+        self.norm = norm
+        super().__init__(data_list=data_list, **defaults)
+
+    def __get_scatter_folded_mask(
+        self, data, dim, indices, output_size, sparse_block_shape
+    ):
+        mask = torch.ones_like(data)
+        mask.scatter_(dim=dim, index=indices, value=0)  # zeroing out
+        mask = F.fold(
+            mask,
+            output_size=output_size,
+            kernel_size=sparse_block_shape,
+            stride=sparse_block_shape,
+        )
+        mask = mask.to(torch.int8)
+        return mask
+
+    def __get_block_level_mask(self, data, sparse_block_shape, zeros_per_block):
+        # Assume data is a squeezed tensor
+        height, width = data.shape[-2], data.shape[-1]
+        block_height, block_width = sparse_block_shape
+        values_per_block = block_height * block_width
+
+        # just return zeros if zeroing all elements in block
+        if values_per_block == zeros_per_block:
+            return torch.zeros_like(data, dtype=torch.int8)
+
+        # creating additional height and width to support padding
+        dh = (block_height - height % block_height) % block_height
+        dw = (block_width - width % block_width) % block_width
+
+        # create a new padded tensor like data (to match the block_shape)
+        padded_data = torch.ones(
+            height + dh, width + dw, dtype=data.dtype, device=data.device
+        )
+        padded_data = (
+            padded_data * torch.nan
+        )  # can also be replaced with 0 to stop the removal of edge data
+        padded_data[0:height, 0:width] = data
+        unfolded_data = F.unfold(
+            padded_data[None, None, :],
+            kernel_size=sparse_block_shape,
+            stride=sparse_block_shape,
+        )
+
+        _, sorted_idx = torch.sort(unfolded_data, dim=1)
+        sorted_idx = sorted_idx[
+            :, :zeros_per_block, :
+        ]  # zero out zeros_per_block number of elements
+
+        mask = self.__get_scatter_folded_mask(
+            data=unfolded_data,
+            dim=1,
+            indices=sorted_idx,
+            output_size=padded_data.shape,
+            sparse_block_shape=sparse_block_shape,
+        )
+
+        mask = (
+            mask.squeeze(0).squeeze(0)[:height, :width].contiguous()
+        )  # remove padding and make contiguous
+        return mask
+
+    def __get_data_level_mask(self, data, sparsity_level, sparse_block_shape):
+        height, width = data.shape[-2], data.shape[-1]
+        block_height, block_width = sparse_block_shape
+        dh = (block_height - height % block_height) % block_height
+        dw = (block_width - width % block_width) % block_width
+
+        data_norm = F.avg_pool2d(
+            data[None, None, :],
+            kernel_size=sparse_block_shape,
+            stride=sparse_block_shape,
+            ceil_mode=True,
+        )
+
+        values_per_block = reduce(operator.mul, sparse_block_shape)
+
+        data_norm = data_norm.flatten()
+        num_blocks = len(data_norm)
+
+        data_norm = data_norm.repeat(
+            1, values_per_block, 1
+        )  # get similar shape after unfold
+        _, sorted_idx = torch.sort(data_norm, dim=2)
+
+        threshold_idx = round(sparsity_level * num_blocks)  # number of blocks to remove
+        sorted_idx = sorted_idx[:, :, :threshold_idx]
+
+        mask = self.__get_scatter_folded_mask(
+            data=data_norm,
+            dim=2,
+            indices=sorted_idx,
+            output_size=(height + dh, width + dw),
+            sparse_block_shape=sparse_block_shape,
+        )
+
+        mask = mask.squeeze(0).squeeze(0)[
+            :height, :width
+        ]  # squeeze only the first 2 dimension
+        return mask
+
+    def update_mask(  # type: ignore[override]
+        self, name, data, sparsity_level, sparse_block_shape, zeros_per_block, **kwargs
+    ):
+        values_per_block = reduce(operator.mul, sparse_block_shape)
+        if zeros_per_block > values_per_block:
+            raise ValueError(
+                "Number of zeros per block cannot be more than "
+                "the total number of elements in that block."
+            )
+        if zeros_per_block < 0:
+            raise ValueError("Number of zeros per block should be positive.")
+
+        if self.norm == "L1":
+            data_norm = torch.abs(data).squeeze()  # absolute value based (L1)
+        else:
+            data_norm = (data * data).squeeze()  # square every element for L2
+
+        if len(data_norm.shape) > 2:  # only supports 2 dimensional data at the moment
+            raise ValueError("only supports 2-D at the moment")
+
+        elif len(data_norm.shape) == 1:  # in case the data is bias (or 1D)
+            data_norm = data_norm[None, :]
+
+        mask = self.get_mask(name)
+        if sparsity_level <= 0 or zeros_per_block == 0:
+            mask.data = torch.ones_like(mask)
+        elif sparsity_level >= 1.0 and (zeros_per_block == values_per_block):
+            mask.data = torch.zeros_like(mask)
+
+        # Fetch the high level mask that zeros out entire blocks
+        data_lvl_mask = self.__get_data_level_mask(
+            data=data_norm,
+            sparsity_level=sparsity_level,
+            sparse_block_shape=sparse_block_shape,
+        )
+
+        # Fetch block level mask that zeros out 'zeros_per_block' number of elements in every block
+        block_lvl_mask = self.__get_block_level_mask(
+            data=data_norm,
+            sparse_block_shape=sparse_block_shape,
+            zeros_per_block=zeros_per_block,
+        )
+
+        # zero out the entries inside those blocks whose block is sparsified
+        mask.data = torch.where(data_lvl_mask == 1, data_lvl_mask, block_lvl_mask)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_sparsifier/lightning/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_sparsifier/lightning/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_sparsifier/lightning/callbacks/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_sparsifier/lightning/callbacks/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_sparsifier/lightning/callbacks/_data_sparstity_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_sparsifier/lightning/callbacks/_data_sparstity_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..50d5684961bc807d5ae1b02615ade168416c9b3d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_sparsifier/lightning/callbacks/_data_sparstity_utils.py
@@ -0,0 +1,44 @@
+# mypy: allow-untyped-defs
+import logging
+
+from torch.ao.pruning._experimental.data_sparsifier.base_data_sparsifier import (
+    SUPPORTED_TYPES,
+)
+
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+
+def _attach_model_to_data_sparsifier(module, data_sparsifier, config=None):
+    """Attaches a data sparsifier to all the layers of the module.
+    Essentially, loop over all the weight parameters in the module and
+    attach it to the data sparsifier.
+    Note::
+        The '.' in the layer names are replaced with '_' (refer to _get_valid_name() below)
+        before attaching to the sparsifier. This is because, the data
+        sparsifier uses a dummy model inside to store the weight parameters.
+    """
+    if config is None:
+        config = {}
+    for name, parameter in module.named_parameters():
+        if type(parameter) in SUPPORTED_TYPES:
+            valid_name = _get_valid_name(name)
+            # will be defaulted to default configs
+            data_sparsifier.add_data(
+                name=valid_name, data=parameter, **config.get(valid_name, {})
+            )
+
+
+def _get_valid_name(name):
+    return name.replace(".", "_")  # . is not allowed as a name
+
+
+def _log_sparsified_level(model, data_sparsifier) -> None:
+    # Show the level of sparsity AFTER step:
+    for name, parameter in model.named_parameters():
+        if type(parameter) not in SUPPORTED_TYPES:
+            continue
+        valid_name = _get_valid_name(name)
+        mask = data_sparsifier.get_mask(name=valid_name)
+        sparsity_level = 1.0 - mask.float().mean()
+        logger.info("Sparsity in layer %s = % .2%", name, sparsity_level)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_sparsifier/lightning/callbacks/data_sparsity.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_sparsifier/lightning/callbacks/data_sparsity.py
new file mode 100644
index 0000000000000000000000000000000000000000..c1c8a91c5c9dcea9ad5cceaa9ecc80e3f32bd8a7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_sparsifier/lightning/callbacks/data_sparsity.py
@@ -0,0 +1,181 @@
+# mypy: allow-untyped-defs
+from collections import defaultdict
+from copy import deepcopy
+from typing import Any, TYPE_CHECKING
+
+import pytorch_lightning as pl  # type: ignore[import]
+
+from ._data_sparstity_utils import (
+    _attach_model_to_data_sparsifier,
+    _get_valid_name,
+    _log_sparsified_level,
+)
+
+
+if TYPE_CHECKING:
+    import torch
+
+
+class PostTrainingDataSparsity(pl.callbacks.Callback):
+    """Lightning callback that enables post-training sparsity.
+
+    This callback aims to sparsify the model inside lightning module after training.
+    **Note that the model is copied and then sparsified, so the existing model is not modified**
+
+    The sparsified model can be used for comparison and can be accessed using
+        .sparsified
+
+    Args:
+        data_sparsifier_class (some implemented class of BaseDataSparsifier)
+            The data sparsifier object of this class is created when the
+            training starts.
+            Note: Objects should not be passed in here as they are created
+            once the training completes.
+
+        data_sparsifier_args (Dict)
+            Dictionary of args to be passed to the data sparsifier.
+            Note: data_list arg should be ignored
+
+    Hooks implemented:
+        on_fit_end()
+            1. copies the model and attaches it to the sparsifier
+            2. sparsier step() is called
+            3. squashes the mask()
+    """
+
+    def __init__(self, data_sparsifier_class, data_sparsifier_args):
+        super().__init__()
+        self.data_sparsifier_class = data_sparsifier_class
+        self.data_sparsifier_args = data_sparsifier_args
+        self.data_sparsifier: Any = None
+        self.sparsified: torch.nn.Module | None = None
+
+    def on_fit_end(self, trainer, pl_module) -> None:
+        self.sparsified = deepcopy(pl_module.model).eval()
+        self.data_sparsifier = self.data_sparsifier_class(**self.data_sparsifier_args)
+
+        _attach_model_to_data_sparsifier(self.sparsified, self.data_sparsifier)
+
+        self.data_sparsifier.step()
+
+        self.data_sparsifier.squash_mask()  # currently squashes params for all mask
+
+        _log_sparsified_level(self.sparsified, self.data_sparsifier)
+
+
+class TrainingAwareDataSparsity(pl.callbacks.Callback):
+    """Lightning callback that enables in-training sparsity.
+
+    This callback aims to sparsify the model inside lightning module during training.
+    **Note that the model is copied and then sparsified, so the existing model is not modified**
+
+    The sparsified model can be used for comparison and can be accessed using
+        .sparsified
+
+    Args:
+        data_sparsifier_class (some implemented class of BaseDataSparsifier)
+            The data sparsifier object of this class is created when the
+            training starts.
+            Note: Objects should not be passed in here as they are created
+            when the training starts.
+
+        data_sparsifier_args (Dict)
+            Dictionary of args to be passed to the data sparsifier.
+            Note: data_list arg should be ignored
+
+        data_scheduler_class (some implemented class of BaseDataScheduler)
+            The data scheduler of this class is created when the training starts
+            Note: Objects should not be passed in here as they are created
+            when the training starts.
+
+        data_scheduler_args(Dict)
+            Dictionary of args to be passed to the data scheduler.
+            **Note: data_sparsifier arg should be ignored as the recipe
+            creates and pass sparsifier object into the class**
+
+    Hooks implemented:
+        on_train_start()
+            Data sparsifier and scheduler objects are created.
+            Pytorch model attached to the sparsifier
+
+        on_train_epoch_start()
+            Loads the state_dict of the data sparsifier
+
+        on_train_epoch_end()
+            1. Copies the model and attaches it to the sparsifier
+            2. sparsifier step() and scheduler step()
+            3. Dump state_dict of the current sparsifier
+
+        on_train_end()
+            squash mask
+    """
+
+    def __init__(
+        self,
+        data_sparsifier_class,
+        data_sparsifier_args,
+        data_scheduler_class,
+        data_scheduler_args,
+    ):
+        super().__init__()
+        # data sparsifier objects
+        self.data_sparsifier_class = data_sparsifier_class
+        self.data_sparsifier_args = data_sparsifier_args
+
+        # scheduler objects
+        self.data_scheduler_class = data_scheduler_class
+        self.data_scheduler_args = data_scheduler_args
+
+        # fields
+        self.data_sparsifier: Any = None
+        self.data_scheduler: Any = None
+        self.sparsified: torch.nn.Module | None = None
+
+        self.data_sparsifier_state_dict: Any = None
+
+    def on_train_start(self, trainer, pl_module) -> None:
+        # create sparsifier
+        self.data_sparsifier = self.data_sparsifier_class(**self.data_sparsifier_args)
+        self.sparsified = deepcopy(pl_module.model)
+
+        _attach_model_to_data_sparsifier(
+            self.sparsified, self.data_sparsifier
+        )  # just to populate the base_sl in the scheduler
+
+        # create scheduler
+        args = deepcopy(self.data_scheduler_args)
+        args["data_sparsifier"] = self.data_sparsifier
+        self.data_scheduler = self.data_scheduler_class(**args)
+
+    def on_train_epoch_start(self, trainer, pl_module):
+        if self.data_sparsifier_state_dict is None:
+            return  # probably first epoch
+
+        # load the existing config for each data
+        self.data_sparsifier.load_state_dict(self.data_sparsifier_state_dict)
+
+    def __create_config_based_on_state(self, pl_module):
+        config: dict = defaultdict()
+        if self.data_sparsifier_state_dict is None:
+            return config
+        for name, _ in pl_module.model.named_parameters():
+            valid_name = _get_valid_name(name)
+            config[valid_name] = self.data_sparsifier.data_groups[valid_name]
+
+        return config
+
+    def on_train_epoch_end(self, trainer, pl_module):
+        self.sparsified = deepcopy(pl_module.model)
+        config = self.__create_config_based_on_state(pl_module)
+
+        # attach model to the data sparsifier
+        _attach_model_to_data_sparsifier(
+            self.sparsified, self.data_sparsifier, config=config
+        )
+        self.data_sparsifier.step()
+        self.data_scheduler.step()
+
+        self.data_sparsifier_state_dict = self.data_sparsifier.state_dict()
+
+    def on_train_end(self, trainer, pl_module):
+        self.data_sparsifier.squash_mask()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_sparsifier/quantization_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_sparsifier/quantization_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..b727635d08151abd39c94ee40b0417afea97a05b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/data_sparsifier/quantization_utils.py
@@ -0,0 +1,154 @@
+# mypy: allow-untyped-defs
+
+import torch
+import torch.nn as nn
+from torch.ao.pruning.sparsifier.utils import fqn_to_module, module_to_fqn
+
+
+SUPPORTED_MODULES = {nn.Embedding, nn.EmbeddingBag}
+
+
+def _fetch_all_embeddings(model):
+    """Fetches Embedding and EmbeddingBag modules from the model"""
+    embedding_modules = []
+    stack = [model]
+    while stack:
+        module = stack.pop()
+        for _, child in module.named_children():
+            fqn_name = module_to_fqn(model, child)
+            if type(child) in SUPPORTED_MODULES:
+                embedding_modules.append((fqn_name, child))
+            else:
+                stack.append(child)
+    return embedding_modules
+
+
+def post_training_sparse_quantize(
+    model,
+    data_sparsifier_class,
+    sparsify_first=True,
+    select_embeddings: list[nn.Module] | None = None,
+    **sparse_config,
+):
+    """Takes in a model and applies sparsification and quantization to only embeddings & embeddingbags.
+    The quantization step can happen before or after sparsification depending on the `sparsify_first` argument.
+
+    Args:
+        - model (nn.Module)
+            model whose embeddings needs to be sparsified
+        - data_sparsifier_class (type of data sparsifier)
+            Type of sparsification that needs to be applied to model
+        - sparsify_first (bool)
+            if true, sparsifies first and then quantizes
+            otherwise, quantizes first and then sparsifies.
+        - select_embeddings (List of Embedding modules)
+            List of embedding modules to in the model to be sparsified & quantized.
+            If None, all embedding modules with be sparsified
+        - sparse_config (Dict)
+            config that will be passed to the constructor of data sparsifier object.
+
+    Note:
+        1. When `sparsify_first=False`, quantization occurs first followed by sparsification.
+            - before sparsifying, the embedding layers are dequantized.
+            - scales and zero-points are saved
+            - embedding layers are sparsified and `squash_mask` is applied
+            - embedding weights are requantized using the saved scales and zero-points
+        2. When `sparsify_first=True`, sparsification occurs first followed by quantization.
+            - embeddings are sparsified first
+            - quantization is applied on the sparsified embeddings
+    """
+    data_sparsifier = data_sparsifier_class(**sparse_config)
+
+    # if select_embeddings is None, perform it on all embeddings
+    if select_embeddings is None:
+        embedding_modules = _fetch_all_embeddings(model)
+
+    else:
+        embedding_modules = []
+        if not isinstance(select_embeddings, list):
+            raise AssertionError(
+                "the embedding_modules must be a list of embedding modules"
+            )
+        for emb in select_embeddings:
+            if type(emb) not in SUPPORTED_MODULES:
+                raise AssertionError(
+                    "the embedding_modules list must be an embedding or embedding bags"
+                )
+            fqn_name = module_to_fqn(model, emb)
+            if fqn_name is None:
+                raise AssertionError(
+                    "the embedding modules must be part of input model"
+                )
+            embedding_modules.append((fqn_name, emb))
+
+    if sparsify_first:
+        # sparsify
+        for name, emb_module in embedding_modules:
+            valid_name = name.replace(".", "_")
+            data_sparsifier.add_data(name=valid_name, data=emb_module)
+
+        data_sparsifier.step()
+        data_sparsifier.squash_mask()
+
+        # quantize
+        for _, emb_module in embedding_modules:
+            emb_module.qconfig = torch.ao.quantization.float_qparams_weight_only_qconfig
+
+        torch.ao.quantization.prepare(model, inplace=True)
+        torch.ao.quantization.convert(model, inplace=True)
+
+    else:
+        # quantize
+        for _, emb_module in embedding_modules:
+            emb_module.qconfig = torch.ao.quantization.float_qparams_weight_only_qconfig
+
+        torch.ao.quantization.prepare(model, inplace=True)
+        torch.ao.quantization.convert(model, inplace=True)
+
+        # retrieve scale & zero_points
+        quantize_params: dict[str, dict] = {
+            "scales": {},
+            "zero_points": {},
+            "dequant_weights": {},
+            "axis": {},
+            "dtype": {},
+        }
+
+        for name, _ in embedding_modules:
+            quantized_emb = fqn_to_module(model, name)
+            if quantized_emb is None:
+                raise AssertionError(f"quantized embedding {name} not found in model")
+
+            quantized_weight = quantized_emb.weight()  # type: ignore[operator]
+            quantize_params["scales"][name] = quantized_weight.q_per_channel_scales()
+            quantize_params["zero_points"][name] = (
+                quantized_weight.q_per_channel_zero_points()
+            )
+            quantize_params["dequant_weights"][name] = torch.dequantize(
+                quantized_weight
+            )
+            quantize_params["axis"][name] = quantized_weight.q_per_channel_axis()
+            quantize_params["dtype"][name] = quantized_weight.dtype
+
+            # attach data to sparsifier
+            data_sparsifier.add_data(
+                name=name.replace(".", "_"),
+                data=quantize_params["dequant_weights"][name],
+            )
+
+        data_sparsifier.step()
+        data_sparsifier.squash_mask()
+
+        for name, _ in embedding_modules:
+            quantized_emb = fqn_to_module(model, name)
+            if quantized_emb is None:
+                raise AssertionError(f"quantized embedding {name} not found in model")
+            requantized_vector = torch.quantize_per_channel(
+                quantize_params["dequant_weights"][name],
+                scales=quantize_params["scales"][name],
+                zero_points=quantize_params["zero_points"][name],
+                dtype=quantize_params["dtype"][name],
+                axis=quantize_params["axis"][name],
+            )
+
+            quantized_emb.set_weight(requantized_vector)  # type: ignore[operator]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/pruner/FPGM_pruner.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/pruner/FPGM_pruner.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a89de12bd9345a05acee98309f90d38d70daac1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/pruner/FPGM_pruner.py
@@ -0,0 +1,96 @@
+# mypy: allow-untyped-defs
+from collections.abc import Callable
+
+import torch
+
+from .base_structured_sparsifier import BaseStructuredSparsifier
+
+
+__all__ = ["FPGMPruner"]
+
+
+class FPGMPruner(BaseStructuredSparsifier):
+    r"""Filter Pruning via Geometric Median (FPGM) Structured Pruner
+    This sparsifier prune filter (row) in a tensor according to distances among filters according to
+    `Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration `_.
+
+    This sparsifier is controlled by three variables:
+    1. `sparsity_level` defines the number of filters (rows) that are zeroed-out.
+    2. `dist` defines the distance measurement type. Default: 3 (L2 distance).
+    Available options are: [1, 2, (custom callable distance function)].
+
+    Note::
+        Inputs should be a 4D convolutional tensor of shape (N, C, H, W).
+            - N: output channels size
+            - C: input channels size
+            - H: height of kernel
+            - W: width of kernel
+    """
+
+    def __init__(self, sparsity_level: float = 0.5, dist: Callable | int | None = None):
+        defaults = {
+            "sparsity_level": sparsity_level,
+        }
+
+        if dist is None:
+            dist = 2
+
+        if callable(dist):
+            self.dist_fn = dist
+        elif dist == 1:
+            self.dist_fn = lambda x: torch.cdist(x, x, p=1)
+        elif dist == 2:
+            self.dist_fn = lambda x: torch.cdist(x, x, p=2)
+        else:
+            raise NotImplementedError("Distance function is not yet implemented.")
+        super().__init__(defaults=defaults)
+
+    def _compute_distance(self, t):
+        r"""Compute distance across all entries in tensor `t` along all dimension
+        except for the one identified by dim.
+        Args:
+            t (torch.Tensor): tensor representing the parameter to prune
+        Returns:
+            distance (torch.Tensor): distance computed across filtters
+        """
+        dim = 0  # prune filter (row)
+
+        size = t.size(dim)
+        slc = [slice(None)] * t.dim()
+
+        # flatten the tensor along the dimension
+        t_flatten = [
+            t[tuple(slc[:dim] + [slice(i, i + 1)] + slc[dim + 1 :])].reshape(-1)
+            for i in range(size)
+        ]
+        t_flatten = torch.stack(t_flatten)
+
+        # distance measurement
+        dist_matrix = self.dist_fn(t_flatten)
+
+        # more similar with other filter indicates large in the sum of row
+        # pyrefly: ignore [bad-argument-type]
+        distance = torch.sum(torch.abs(dist_matrix), 1)
+
+        return distance
+
+    def update_mask(  # type: ignore[override]
+        self, module, tensor_name, sparsity_level, **kwargs
+    ):
+        tensor_weight = getattr(module, tensor_name)
+        mask = getattr(module.parametrizations, tensor_name)[0].mask
+
+        if sparsity_level <= 0:
+            mask.data = torch.ones_like(mask).bool()
+        elif sparsity_level >= 1.0:
+            mask.data = torch.zeros_like(mask).bool()
+        else:
+            distance = self._compute_distance(tensor_weight)
+
+            tensor_size = tensor_weight.shape[0]  # prune filter (row)
+            nparams_toprune = round(sparsity_level * tensor_size)
+            nparams_toprune = min(
+                max(nparams_toprune, 0), tensor_size
+            )  # clamp to [0, tensor_size]
+            topk = torch.topk(distance, k=nparams_toprune, largest=False)
+            mask[topk.indices] = False
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/pruner/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/pruner/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a57db6a8d8cde9a89c7cbda4dff6f6075559b59b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/pruner/__init__.py
@@ -0,0 +1,5 @@
+from .base_structured_sparsifier import BaseStructuredSparsifier
+from .FPGM_pruner import FPGMPruner
+from .lstm_saliency_pruner import LSTMSaliencyPruner
+from .parametrization import BiasHook, FakeStructuredSparsity
+from .saliency_pruner import SaliencyPruner
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/pruner/base_structured_sparsifier.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/pruner/base_structured_sparsifier.py
new file mode 100644
index 0000000000000000000000000000000000000000..d1676292f7d74c4a620de0a53334d6dcd33aa764
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/pruner/base_structured_sparsifier.py
@@ -0,0 +1,313 @@
+# mypy: allow-untyped-defs
+from collections.abc import Callable
+from itertools import chain
+from operator import getitem
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torch.ao.pruning.sparsifier.base_sparsifier import BaseSparsifier
+from torch.fx import symbolic_trace
+from torch.nn.utils import parametrize
+
+from .match_utils import apply_match, MatchAllNode
+from .parametrization import BiasHook, FakeStructuredSparsity, module_contains_param
+from .prune_functions import (
+    prune_conv2d,
+    prune_conv2d_activation_conv2d,
+    prune_conv2d_activation_pool_conv2d,
+    prune_conv2d_conv2d,
+    prune_conv2d_pool_activation_conv2d,
+    prune_conv2d_pool_flatten_linear,
+    prune_linear,
+    prune_linear_activation_linear,
+    prune_linear_linear,
+    prune_lstm_output_layernorm_linear,
+    prune_lstm_output_linear,
+)
+
+
+def _get_supported_structured_pruning_modules():
+    SUPPORTED_STRUCTURED_PRUNING_MODULES = {  # added to config if None given
+        nn.Linear,
+        nn.Conv2d,
+        nn.LSTM,
+    }
+    return SUPPORTED_STRUCTURED_PRUNING_MODULES
+
+
+def _get_supported_activation_functions():
+    SUPPORTED_ACTIVATION_FUNCTIONS = {
+        F.relu,
+        F.rrelu,
+        F.hardtanh,
+        F.relu6,
+        F.sigmoid,
+        F.hardsigmoid,
+        F.tanh,
+        F.silu,
+        F.mish,
+        F.hardswish,
+        F.elu,
+        F.celu,
+        F.selu,
+        F.hardshrink,
+        F.leaky_relu,
+        F.logsigmoid,
+        F.softplus,
+        F.prelu,
+        F.softsign,
+        F.tanhshrink,
+        F.gelu,
+    }
+    return SUPPORTED_ACTIVATION_FUNCTIONS
+
+
+def _get_supported_activation_modules():
+    SUPPORTED_ACTIVATION_MODULES = {
+        nn.ReLU,
+        nn.RReLU,
+        nn.Hardtanh,
+        nn.ReLU6,
+        nn.Sigmoid,
+        nn.Hardsigmoid,
+        nn.Tanh,
+        nn.SiLU,
+        nn.Mish,
+        nn.Hardswish,
+        nn.ELU,
+        nn.CELU,
+        nn.SELU,
+        nn.Hardshrink,
+        nn.LeakyReLU,
+        nn.LogSigmoid,
+        nn.Softplus,
+        nn.PReLU,
+        nn.Softsign,
+        nn.Tanhshrink,
+        nn.GELU,
+    }
+    return SUPPORTED_ACTIVATION_MODULES
+
+
+def _get_default_structured_pruning_patterns() -> dict[
+    tuple[type[nn.Module] | Callable | MatchAllNode | str, ...],
+    Callable[..., None],
+]:
+    """
+    Returns the patterns for conv2d / linear conversion for each element in the activation functions/modules defined above.
+    """
+    patterns: dict[
+        tuple[type[nn.Module] | Callable | MatchAllNode | str, ...],
+        Callable[..., None],
+    ] = {
+        # linear -> linear
+        (nn.Linear, "output"): prune_linear,
+        (nn.Linear, nn.Linear): prune_linear_linear,
+        # conv2d -> conv2d
+        (nn.Conv2d, "output"): prune_conv2d,
+        (nn.Conv2d, nn.Conv2d): prune_conv2d_conv2d,
+        # TODO LSTM Structured pruning does not support returned state currently.
+        # Should find a way to explicitly match getitem(0) instead of getitem.
+        # This will also require changing the pruning function.
+        # lstm -> getitem(0) -> linear
+        (nn.LSTM, getitem, nn.Linear): prune_lstm_output_linear,
+        # lstm -> getitem(0) -> layernorm -> linear
+        (nn.LSTM, getitem, nn.LayerNorm, nn.Linear): prune_lstm_output_layernorm_linear,
+    }
+
+    for activation in chain(
+        _get_supported_activation_functions(), _get_supported_activation_modules()
+    ):
+        patterns.update(
+            {
+                # linear -> activation -> linear
+                (nn.Linear, activation, nn.Linear): prune_linear_activation_linear,
+                # conv2d -> activation -> conv2d
+                (nn.Conv2d, activation, nn.Conv2d): prune_conv2d_activation_conv2d,
+                # conv2d -> activation -> pool -> conv2d
+                (
+                    nn.Conv2d,
+                    activation,
+                    nn.AvgPool2d,
+                    nn.Conv2d,
+                ): prune_conv2d_activation_pool_conv2d,
+                (
+                    nn.Conv2d,
+                    activation,
+                    F.avg_pool2d,
+                    nn.Conv2d,
+                ): prune_conv2d_activation_pool_conv2d,
+                (
+                    nn.Conv2d,
+                    activation,
+                    nn.MaxPool2d,
+                    nn.Conv2d,
+                ): prune_conv2d_activation_pool_conv2d,
+                (
+                    nn.Conv2d,
+                    activation,
+                    F.max_pool2d,
+                    nn.Conv2d,
+                ): prune_conv2d_activation_pool_conv2d,
+                # conv2d -> pool -> activation -> conv2d
+                (
+                    nn.Conv2d,
+                    nn.AvgPool2d,
+                    activation,
+                    nn.Conv2d,
+                ): prune_conv2d_pool_activation_conv2d,
+                (
+                    nn.Conv2d,
+                    F.avg_pool2d,
+                    activation,
+                    nn.Conv2d,
+                ): prune_conv2d_pool_activation_conv2d,
+                (
+                    nn.Conv2d,
+                    nn.MaxPool2d,
+                    activation,
+                    nn.Conv2d,
+                ): prune_conv2d_pool_activation_conv2d,
+                (
+                    nn.Conv2d,
+                    F.max_pool2d,
+                    activation,
+                    nn.Conv2d,
+                ): prune_conv2d_pool_activation_conv2d,
+                # conv2d -> adaptive pool -> flatten -> linear
+                (
+                    nn.Conv2d,
+                    nn.AdaptiveAvgPool2d,
+                    nn.Flatten,
+                    nn.Linear,
+                ): prune_conv2d_pool_flatten_linear,
+                (
+                    nn.Conv2d,
+                    nn.AdaptiveAvgPool2d,
+                    torch.flatten,
+                    nn.Linear,
+                ): prune_conv2d_pool_flatten_linear,
+                (
+                    nn.Conv2d,
+                    nn.AdaptiveMaxPool2d,
+                    nn.Flatten,
+                    nn.Linear,
+                ): prune_conv2d_pool_flatten_linear,
+                (
+                    nn.Conv2d,
+                    nn.AdaptiveMaxPool2d,
+                    torch.flatten,
+                    nn.Linear,
+                ): prune_conv2d_pool_flatten_linear,
+            }
+        )
+    return patterns
+
+
+class BaseStructuredSparsifier(BaseSparsifier):
+    r"""Base class for structured pruning.
+
+    Abstract methods that need to be implemented:
+        - update_mask: Function to compute a new mask for all keys in the
+            `groups` attribute.
+
+    Args:
+        - defaults [dict]: default configurations will be attached to the
+            configuration. Only the keys that don't exist in the `config` will
+            be updated.
+    """
+
+    def __init__(self, defaults, patterns=None):
+        super().__init__(defaults)
+        if patterns is None:
+            patterns = _get_default_structured_pruning_patterns()
+        self.patterns = patterns
+
+    def make_config_from_model(
+        self,
+        model: nn.Module,
+        SUPPORTED_MODULES: set[type] | None = None,
+    ) -> None:
+        if SUPPORTED_MODULES is None:
+            SUPPORTED_MODULES = _get_supported_structured_pruning_modules()
+        super().make_config_from_model(model, SUPPORTED_MODULES=SUPPORTED_MODULES)
+
+    def _prepare(self, *args, **kwargs) -> None:
+        r"""This function will attach the FakeStructuredSparsity parameterizations
+        and BiasHooks at the appropriate points in the model.
+        """
+        for config in self.groups:
+            module = config["module"]
+            tensor_name = config["tensor_name"]
+            parametrization = config.get("parametrization", FakeStructuredSparsity)
+            tensor = getattr(module, tensor_name)
+
+            mask = config.get(
+                "mask",
+                torch.ones(tensor.shape[0], dtype=torch.bool, device=tensor.device),
+            )
+            self.state[config["tensor_fqn"]]["mask"] = mask
+            parametrize.register_parametrization(
+                module, tensor_name, parametrization(mask)
+            )
+
+            # if linear / conv, we add in bias hooks
+            if isinstance(module, (nn.Linear, nn.Conv2d)):
+                prune_bias = config.get("prune_bias", True)
+                if module.bias is not None:
+                    module.register_parameter(
+                        "_bias", nn.Parameter(module.bias.detach())
+                    )
+                    # pyrefly: ignore [bad-assignment]
+                    module.bias = None
+                    module.prune_bias = prune_bias
+
+                module.register_forward_hook(
+                    BiasHook(module.parametrizations.weight[0], prune_bias)  # type: ignore[union-attr, index]
+                )
+
+    def prune(self) -> None:
+        r"""
+        This function will FX symbolically trace the model and then find instances of the patterns
+        defined in self.patterns (by default SUPPORTED_STRUCTURED_PRUNING_PATTERNS ).
+
+        For each pattern, it will apply to corresponding conversion function, which will modify the output
+        and input size expected by the modules within the pattern
+        """
+
+        self.traced = symbolic_trace(self.model)
+        modules = dict(self.traced.named_modules())
+
+        # Right now we check for matches simply by iterating across all the patterns
+        # if this is slow we can store patterns in a trie-structure and modify this code for faster lookup
+        for node in self.traced.graph.nodes:
+            for pattern, convert_fn in self.patterns.items():
+                matched = apply_match(modules, pattern, node, [])
+                if matched is None:
+                    continue
+
+                first_module = modules.get(node.target)
+                # check if first module exists and has appropriate parameterization, otherwise skip
+                if (
+                    first_module is not None
+                    and parametrize.is_parametrized(first_module)
+                    and module_contains_param(first_module, FakeStructuredSparsity)
+                ):
+                    convert_block = []
+                    for node in matched:
+                        if node.op == "call_module":
+                            convert_block.append(modules.get(node.target))
+                        elif node.op == "call_function":
+                            convert_block.append(node.target)
+                    convert_fn(*convert_block)
+
+        for module in self.traced.modules():
+            if module_contains_param(module, FakeStructuredSparsity):
+                raise Exception(  # noqa: TRY002
+                    f"Error: {module} still contains FakeStructuredSparsity parametrizations!"
+                )
+
+        self.traced.graph.lint()
+        self.traced.recompile()
+        return self.traced  # type: ignore[return-value]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/pruner/lstm_saliency_pruner.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/pruner/lstm_saliency_pruner.py
new file mode 100644
index 0000000000000000000000000000000000000000..f904cc3ab8c4c34a193dd30926fff164010287a8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/pruner/lstm_saliency_pruner.py
@@ -0,0 +1,54 @@
+from typing import Any, cast
+
+import torch
+from torch import nn
+
+from .base_structured_sparsifier import BaseStructuredSparsifier
+from .parametrization import FakeStructuredSparsity
+
+
+class LSTMSaliencyPruner(BaseStructuredSparsifier):
+    """
+    Prune packed LSTM weights based on saliency.
+    For each layer {k} inside a LSTM, we have two packed weight matrices
+    - weight_ih_l{k}
+    - weight_hh_l{k}
+
+    These tensors pack the weights for the 4 linear layers together for efficiency.
+
+    [W_ii | W_if | W_ig | W_io]
+
+    Pruning this tensor directly will lead to weights being misassigned when unpacked.
+    To ensure that each packed linear layer is pruned the same amount:
+        1. We split the packed weight into the 4 constituent linear parts
+        2. Update the mask for each individual piece using saliency individually
+
+    This applies to both weight_ih_l{k} and weight_hh_l{k}.
+    """
+
+    def update_mask(self, module: nn.Module, tensor_name: str, **kwargs: Any) -> None:
+        weights = getattr(module, tensor_name)
+
+        for p in getattr(module.parametrizations, tensor_name):
+            if isinstance(p, FakeStructuredSparsity):
+                mask = cast(torch.Tensor, p.mask)
+
+                # select weights based on magnitude
+                if weights.dim() <= 1:
+                    raise Exception(  # noqa: TRY002
+                        "Structured pruning can only be applied to a 2+dim weight tensor!"
+                    )
+                # take norm over all but first dim
+                dims = tuple(range(1, weights.dim()))
+                saliency = weights.norm(dim=dims, p=1)
+
+                # handle weights in 4 groups
+                split_size = len(mask) // 4
+                masks = torch.split(mask, split_size)
+                saliencies = torch.split(saliency, split_size)
+
+                for keep_mask, sal in zip(masks, saliencies):
+                    # mask smallest k values to be removed
+                    k = int(len(keep_mask) * kwargs["sparsity_level"])
+                    prune = sal.topk(k, largest=False, sorted=False).indices
+                    keep_mask.data[prune] = False  # modifies underlying p.mask directly
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/pruner/match_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/pruner/match_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..e22b979ab900c63a9a975b7a07c9b2a64ed8c0b5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/pruner/match_utils.py
@@ -0,0 +1,65 @@
+"""
+Contains utility functions to check if a pattern is in the graph and return the matching nodes
+"""
+
+from typing import Any
+
+import torch
+from torch import nn
+from torch.ao.quantization.utils import MatchAllNode
+from torch.fx import Node
+from torch.nn.utils import parametrize
+
+
+def _match(
+    modules: dict[str, nn.ModuleDict],
+    node: Node,
+    current: nn.Module | Any,
+) -> bool:
+    r"""
+    checks to see if a single node of a pattern matches
+    """
+    if isinstance(current, type) and issubclass(current, MatchAllNode):
+        return True
+    if not isinstance(node, Node):
+        return False
+    if isinstance(current, type) and issubclass(current, torch.nn.Module):
+        return (
+            node.op == "call_module"
+            and parametrize.type_before_parametrizations(modules[node.target])  # type: ignore[index]
+            == current
+        )
+    elif callable(current):
+        return node.op == "call_function" and node.target is current
+    elif isinstance(current, str):
+        return node.target == current
+    return False
+
+
+def apply_match(
+    modules: dict[str, nn.ModuleDict],
+    pattern: tuple[Any] | Any,
+    node: Node,
+    matched_node_pattern: list[Node],
+) -> list[Node] | None:
+    r"""
+    This function will return the matched nodes if the pattern matches the node given
+    If there is no match, it will return None
+    """
+    if isinstance(pattern, tuple):
+        if len(pattern) == 1:
+            if _match(modules, node, pattern[0]):
+                return matched_node_pattern + [node]
+
+        first, *rest = pattern
+        if _match(modules, node, first):
+            if rest is None:
+                return matched_node_pattern + [node]
+
+            for user in node.users:
+                return apply_match(
+                    modules, tuple(rest), user, matched_node_pattern + [node]
+                )
+    elif _match(modules, node, pattern):
+        return [node]
+    return None
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/pruner/parametrization.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/pruner/parametrization.py
new file mode 100644
index 0000000000000000000000000000000000000000..4256d6fd01750d4408b92342bfb8d12239bf129a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/pruner/parametrization.py
@@ -0,0 +1,63 @@
+# mypy: allow-untyped-defs
+import torch
+from torch import nn
+from torch.nn.utils.parametrize import is_parametrized
+
+
+def module_contains_param(module, parametrization):
+    if is_parametrized(module):
+        # see if any of the module tensors have a parametriztion attached that matches the one passed in
+        return any(
+            any(isinstance(param, parametrization) for param in param_list)
+            for key, param_list in module.parametrizations.items()
+        )
+    return False
+
+
+# Structured Pruning Parameterizations
+class FakeStructuredSparsity(nn.Module):
+    r"""
+    Parametrization for Structured Pruning. Like FakeSparsity, this should be attached to
+    the  'weight' or any other parameter that requires a mask.
+
+    Instead of an element-wise bool mask, this parameterization uses a row-wise bool mask.
+    """
+
+    def __init__(self, mask):
+        super().__init__()
+        self.register_buffer("mask", mask)
+
+    def forward(self, x):
+        if not isinstance(self.mask, torch.Tensor):
+            raise AssertionError("mask must be a torch.Tensor")
+        if self.mask.shape[0] != x.shape[0]:
+            raise AssertionError(
+                f"mask shape[0] ({self.mask.shape[0]}) must match x shape[0] ({x.shape[0]})"
+            )
+        shape = [1] * len(x.shape)
+        shape[0] = -1
+        return self.mask.reshape(shape) * x
+
+    def state_dict(self, *args, **kwargs):
+        # avoid double saving masks
+        return {}
+
+
+class BiasHook:
+    def __init__(self, parametrization, prune_bias):
+        self.param = parametrization
+        self.prune_bias = prune_bias
+
+    def __call__(self, module, input, output):
+        if getattr(module, "_bias", None) is not None:
+            bias = module._bias.data
+            if self.prune_bias:
+                bias[~self.param.mask] = 0
+
+            # reshape bias to broadcast over output dimensions
+            idx = [1] * len(output.shape)
+            idx[1] = -1
+            bias = bias.reshape(idx)
+
+            output += bias
+        return output
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/pruner/prune_functions.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/pruner/prune_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..14a1c9a97b07ccb87a5ffab2923a105b7abbd6d4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/pruner/prune_functions.py
@@ -0,0 +1,485 @@
+# mypy: allow-untyped-defs
+"""
+Collection of conversion functions for linear / conv2d structured pruning
+Also contains utilities for bias propagation
+"""
+
+from collections.abc import Callable
+from typing import cast
+
+import torch
+from torch import nn, Tensor
+from torch.nn.utils import parametrize
+from torch.nn.utils.parametrize import ParametrizationList
+
+from .parametrization import BiasHook, FakeStructuredSparsity
+
+
+# BIAS PROPAGATION
+def _remove_bias_handles(module: nn.Module) -> None:
+    if hasattr(module, "_forward_hooks"):
+        bias_hooks: list[int] = []
+        for key, hook in module._forward_hooks.items():
+            if isinstance(hook, BiasHook):
+                bias_hooks.append(key)
+
+        for key in bias_hooks:
+            del module._forward_hooks[key]
+
+
+def _get_adjusted_next_layer_bias(
+    next_layer: nn.Module, pruned_biases: Tensor, mask: Tensor
+) -> nn.Parameter:
+    r"""Returns new adjusted bias for the second supported module"""
+    if parametrize.is_parametrized(next_layer):
+        # need to access original weight
+        parametrization_dict = cast(nn.ModuleDict, next_layer.parametrizations)
+        weight_parameterizations = cast(
+            ParametrizationList, parametrization_dict.weight
+        )
+        next_weight = weight_parameterizations.original
+    else:
+        next_weight = cast(Tensor, next_layer.weight)
+
+    scaling_weight = next_weight[:, ~mask]
+    if isinstance(next_layer, nn.Conv2d):  # checking for Conv2d
+        # Propagating first layer pruned biases and calculating the new second layer bias
+        # involves more steps since the Conv2d scaling weight has extra dimensions,
+        # so adding bias involves broadcasting, logically:
+        # for each channel k in range(oC):
+        #     scaled_biases = sum(first_bias[pruned_idx] @ next_weight[k, pruned_idx, :, :].T)
+        #     new_next_bias[k] = old_next_bias[k] + scaled_biases
+        scaling_product = torch.matmul(
+            pruned_biases.reshape(1, -1), torch.transpose(scaling_weight, 1, 2)
+        )
+        sum_range = list(range(len(scaling_product.shape)))[
+            1:
+        ]  # all but the first dimension
+        scaled_biases = torch.sum(scaling_product, sum_range)
+    elif isinstance(next_layer, nn.Linear):  # Linear
+        scaled_biases = torch.matmul(
+            pruned_biases, torch.transpose(scaling_weight, 0, 1)
+        )  # recall b2_new = b1 @ w2.T + b2
+    else:
+        raise NotImplementedError(f"Type {type(next_layer)} not supported yet.")
+
+    if (
+        parametrize.is_parametrized(next_layer)
+        and getattr(next_layer, "_bias", None) is not None
+    ):  # next_layer is parametrized & has original bias ._bias
+        adjusted_bias = nn.Parameter(scaled_biases + next_layer._bias)  # type: ignore[operator]
+    elif (
+        not parametrize.is_parametrized(next_layer) and next_layer.bias is not None
+    ):  # next_layer not parametrized & has .bias
+        adjusted_bias = nn.Parameter(scaled_biases + next_layer.bias)  # type: ignore[operator]
+    else:  # next_layer has no bias
+        adjusted_bias = nn.Parameter(scaled_biases)
+    return adjusted_bias
+
+
+def _prune_module_bias(module: nn.Module, mask: Tensor) -> None:
+    r"""Applies mask to given modules bias"""
+    # prune bias along with weights, discard pruned indices of bias
+    original_bias = cast(Tensor, getattr(module, "_bias", module.bias))
+    if original_bias is not None:
+        module.bias = nn.Parameter(original_bias[mask])
+
+    #  remove _bias parameter
+    if hasattr(module, "_bias"):
+        delattr(module, "_bias")
+
+
+def _propagate_module_bias(module: nn.Module, mask: Tensor) -> Tensor | None:
+    r"""
+    In the case that we need to propagate biases, this function will return the biases we need
+    """
+    # set current module bias
+    if module.bias is not None:
+        module.bias = nn.Parameter(cast(Tensor, module.bias)[mask])
+    elif getattr(module, "_bias", None) is not None:
+        # pyrefly: ignore [bad-assignment]
+        module.bias = nn.Parameter(cast(Tensor, module._bias)[mask])
+
+    # get pruned biases to propagate to subsequent layer
+    if getattr(module, "_bias", None) is not None:
+        pruned_biases = cast(Tensor, module._bias)[~mask]
+    else:
+        pruned_biases = None
+
+    if hasattr(module, "_bias"):
+        delattr(module, "_bias")
+
+    return pruned_biases
+
+
+# LINEAR
+def _prune_linear_helper(linear: nn.Linear) -> Tensor:
+    # expects linear to be a parameterized linear module
+    parametrization_dict = cast(nn.ModuleDict, linear.parametrizations)
+    weight_parameterizations = cast(ParametrizationList, parametrization_dict.weight)
+    for p in weight_parameterizations:
+        if isinstance(p, FakeStructuredSparsity):
+            mask = cast(Tensor, p.mask)
+
+    with torch.no_grad():
+        parametrize.remove_parametrizations(linear, "weight", leave_parametrized=True)
+        linear.weight = nn.Parameter(linear.weight[mask])  # type: ignore[possibly-undefined]
+    linear.out_features = linear.weight.shape[0]
+    _remove_bias_handles(linear)
+
+    # pyrefly: ignore [unbound-name]
+    return mask
+
+
+def prune_linear(linear: nn.Linear) -> None:
+    mask = _prune_linear_helper(linear)
+    if getattr(linear, "prune_bias", False):
+        _prune_module_bias(linear, mask)
+
+
+def prune_linear_linear(linear1: nn.Linear, linear2: nn.Linear) -> None:
+    prune_linear_activation_linear(linear1, None, linear2)
+
+
+def prune_linear_activation_linear(
+    linear1: nn.Linear,
+    activation: Callable[[Tensor], Tensor] | None,
+    linear2: nn.Linear,
+):
+    mask = _prune_linear_helper(linear1)
+    if getattr(linear1, "prune_bias", False):
+        _prune_module_bias(linear1, mask)
+    else:
+        pruned_biases = _propagate_module_bias(linear1, mask)
+        if pruned_biases is not None:
+            if activation:
+                pruned_biases = activation(pruned_biases)
+            linear2.bias = _get_adjusted_next_layer_bias(linear2, pruned_biases, mask)
+
+    with torch.no_grad():
+        if parametrize.is_parametrized(linear2):
+            parametrization_dict = cast(nn.ModuleDict, linear2.parametrizations)
+            weight_parameterizations = cast(
+                ParametrizationList, parametrization_dict.weight
+            )
+
+            weight_parameterizations.original = nn.Parameter(
+                weight_parameterizations.original[:, mask]
+            )
+            linear2.in_features = weight_parameterizations.original.shape[1]
+        else:
+            linear2.weight = nn.Parameter(linear2.weight[:, mask])
+            linear2.in_features = linear2.weight.shape[1]
+
+
+# CONV2D
+def _prune_conv2d_helper(conv2d: nn.Conv2d) -> Tensor:
+    parametrization_dict = cast(nn.ModuleDict, conv2d.parametrizations)
+    weight_parameterizations = cast(ParametrizationList, parametrization_dict.weight)
+    for p in weight_parameterizations:
+        if isinstance(p, FakeStructuredSparsity):
+            mask = cast(Tensor, p.mask)
+
+    with torch.no_grad():
+        parametrize.remove_parametrizations(conv2d, "weight", leave_parametrized=True)
+        conv2d.weight = nn.Parameter(conv2d.weight[mask])  # type: ignore[possibly-undefined]
+    conv2d.out_channels = conv2d.weight.shape[0]
+
+    _remove_bias_handles(conv2d)
+    # pyrefly: ignore [unbound-name]
+    return mask
+
+
+def prune_conv2d_padded(conv2d_1: nn.Conv2d) -> None:
+    parametrization_dict = cast(nn.ModuleDict, conv2d_1.parametrizations)
+    weight_parameterizations = cast(ParametrizationList, parametrization_dict.weight)
+    for p in weight_parameterizations:
+        if isinstance(p, FakeStructuredSparsity):
+            mask = cast(Tensor, p.mask)
+
+    with torch.no_grad():
+        parametrize.remove_parametrizations(conv2d_1, "weight", leave_parametrized=True)
+
+    if getattr(conv2d_1, "_bias", None) is not None:
+        if (
+            conv2d_1.bias is not None
+        ):  # conv2d_1 has original bias and bias propagated from previous layer
+            new_bias = torch.zeros(conv2d_1.bias.shape)
+            new_bias[mask] = conv2d_1.bias[mask]  # type: ignore[possibly-undefined]
+            # adjusted bias that to keep in conv2d_1
+            # pyrefly: ignore [unbound-name]
+            new_bias[~mask] = cast(Tensor, conv2d_1._bias)[~mask]
+            # pruned biases that are kept instead of propagated
+            conv2d_1.bias = nn.Parameter(new_bias)
+        else:  # conv2d_1 has only original bias
+            conv2d_1.bias = nn.Parameter(cast(Tensor, conv2d_1._bias))
+    else:
+        # no original bias, only propagated bias
+        if (
+            conv2d_1.bias is not None
+        ):  # conv2d_1 has bias propagated from previous layer
+            conv2d_1.bias.data[~mask] = 0  # type: ignore[possibly-undefined]
+
+    if hasattr(conv2d_1, "_bias"):
+        delattr(conv2d_1, "_bias")
+
+
+def prune_conv2d(conv2d: nn.Conv2d) -> None:
+    mask = _prune_conv2d_helper(conv2d)
+    if getattr(conv2d, "prune_bias", False):
+        _prune_module_bias(conv2d, mask)
+
+
+def prune_conv2d_conv2d(conv2d_1: nn.Conv2d, conv2d_2: nn.Conv2d) -> None:
+    prune_conv2d_activation_conv2d(conv2d_1, None, conv2d_2)
+
+
+def prune_conv2d_activation_conv2d(
+    conv2d_1: nn.Conv2d,
+    activation: Callable[[Tensor], Tensor] | None,
+    conv2d_2: nn.Conv2d,
+):
+    r"""
+    Fusion Pattern for conv2d -> some activation module / function -> conv2d layers
+    """
+    parametrization_dict = cast(nn.ModuleDict, conv2d_1.parametrizations)
+    weight_parameterizations = cast(ParametrizationList, parametrization_dict.weight)
+    for p in weight_parameterizations:
+        if isinstance(p, FakeStructuredSparsity):
+            mask = cast(Tensor, p.mask)
+
+    prune_bias = getattr(conv2d_1, "prune_bias", False)
+    if (
+        hasattr(conv2d_2, "padding")
+        and cast(tuple[int], conv2d_2.padding) > (0, 0)
+        and (conv2d_1.bias is not None or getattr(conv2d_1, "_bias", None) is not None)
+    ):
+        prune_conv2d_padded(conv2d_1)
+    else:
+        mask = _prune_conv2d_helper(conv2d_1)
+        if prune_bias:
+            _prune_module_bias(conv2d_1, mask)
+        else:
+            pruned_biases = _propagate_module_bias(conv2d_1, mask)
+            if pruned_biases is not None:
+                if activation:
+                    pruned_biases = activation(pruned_biases)
+                conv2d_2.bias = _get_adjusted_next_layer_bias(
+                    conv2d_2, pruned_biases, mask
+                )
+
+        if (
+            not (
+                hasattr(conv2d_2, "padding")
+                and cast(tuple[int], conv2d_2.padding) > (0, 0)
+            )
+            or conv2d_1.bias is None
+        ):
+            with torch.no_grad():
+                if parametrize.is_parametrized(conv2d_2):
+                    parametrization_dict = cast(
+                        nn.ModuleDict, conv2d_2.parametrizations
+                    )
+                    weight_parameterizations = cast(
+                        ParametrizationList, parametrization_dict.weight
+                    )
+                    weight_parameterizations.original = nn.Parameter(
+                        weight_parameterizations.original[:, mask]
+                    )
+                    conv2d_2.in_channels = weight_parameterizations.original.shape[1]
+                else:
+                    conv2d_2.weight = nn.Parameter(conv2d_2.weight[:, mask])
+                    conv2d_2.in_channels = conv2d_2.weight.shape[1]
+
+
+def prune_conv2d_pool_activation_conv2d(
+    c1: nn.Conv2d,
+    pool: nn.Module,
+    activation: Callable[[Tensor], Tensor] | None,
+    c2: nn.Conv2d,
+) -> None:
+    prune_conv2d_activation_conv2d(c1, activation, c2)
+
+
+def prune_conv2d_activation_pool_conv2d(
+    c1: nn.Conv2d,
+    activation: Callable[[Tensor], Tensor] | None,
+    pool: nn.Module,
+    c2: nn.Conv2d,
+) -> None:
+    prune_conv2d_activation_conv2d(c1, activation, c2)
+
+
+def prune_conv2d_pool_flatten_linear(
+    conv2d: nn.Conv2d,
+    pool: nn.Module,
+    flatten: Callable[[Tensor], Tensor] | None,
+    linear: nn.Linear,
+) -> None:
+    mask = _prune_conv2d_helper(conv2d)
+
+    # We map the pruned indices of the Conv2d output to the flattened indices of the Linear following the Flatten layer.
+    # we determine the flattening scale (h * w), and readjust `first_pruned_indices`
+    # (each idx maps to range idx * h * w to (idx+1) * h * w), `first_valid_indices`,
+    # and `pruned_biases` (repeat each bias by h * w).
+    if parametrize.is_parametrized(linear):
+        parametrization_dict = cast(nn.ModuleDict, linear.parametrizations)
+        weight_parameterizations = cast(
+            ParametrizationList, parametrization_dict.weight
+        )
+        linear_ic = weight_parameterizations.original.shape[1]
+    else:
+        linear_ic = linear.weight.shape[1]
+
+    conv2d_oc = len(mask)
+    if linear_ic % conv2d_oc != 0:
+        raise AssertionError(
+            f"Flattening from dimensions {conv2d_oc} to {linear_ic} not supported"
+        )
+
+    flatten_scale = linear_ic // conv2d_oc
+    flattened_mask = torch.tensor(
+        [[val] * flatten_scale for val in mask], dtype=torch.bool, device=mask.device
+    ).flatten()
+
+    if getattr(conv2d, "prune_bias", False):
+        _prune_module_bias(conv2d, mask)
+    else:
+        pruned_biases = cast(Tensor, _propagate_module_bias(conv2d, mask))
+        flattened_pruned_biases = torch.tensor(
+            [[bias] * flatten_scale for bias in pruned_biases], device=mask.device
+        ).flatten()
+        linear.bias = _get_adjusted_next_layer_bias(
+            linear, flattened_pruned_biases, flattened_mask
+        )
+
+    with torch.no_grad():
+        if parametrize.is_parametrized(linear):
+            parametrization_dict = cast(nn.ModuleDict, linear.parametrizations)
+            weight_parameterizations = cast(
+                ParametrizationList, parametrization_dict.weight
+            )
+            weight_parameterizations.original = nn.Parameter(
+                weight_parameterizations.original[:, flattened_mask]
+            )
+            linear.in_features = weight_parameterizations.original.shape[1]
+        else:
+            linear.weight = nn.Parameter(linear.weight[:, flattened_mask])
+            linear.in_features = linear.weight.shape[1]
+
+
+def prune_lstm_output_linear(
+    lstm: nn.LSTM, getitem: Callable, linear: nn.Linear
+) -> None:
+    prune_lstm_output_layernorm_linear(lstm, getitem, None, linear)
+
+
+def prune_lstm_output_layernorm_linear(
+    lstm: nn.LSTM,
+    getitem: Callable,
+    layernorm: nn.LayerNorm | None,
+    linear: nn.Linear,
+) -> None:
+    for i in range(lstm.num_layers):
+        if parametrize.is_parametrized(lstm, f"weight_ih_l{i}"):
+            parametrization_dict = cast(nn.ModuleDict, lstm.parametrizations)
+            weight_parameterizations = cast(
+                ParametrizationList, parametrization_dict[f"weight_ih_l{i}"]
+            )
+            mask = weight_parameterizations[0].mask
+
+            with torch.no_grad():
+                parametrize.remove_parametrizations(
+                    lstm, f"weight_ih_l{i}", leave_parametrized=True
+                )
+                setattr(
+                    lstm,
+                    f"weight_ih_l{i}",
+                    nn.Parameter(getattr(lstm, f"weight_ih_l{i}")[mask]),
+                )
+                setattr(
+                    lstm,
+                    f"bias_ih_l{i}",
+                    nn.Parameter(getattr(lstm, f"bias_ih_l{i}")[mask]),
+                )
+
+        if parametrize.is_parametrized(lstm, f"weight_hh_l{i}"):
+            parametrization_dict = cast(nn.ModuleDict, lstm.parametrizations)
+            weight_parameterizations = cast(
+                ParametrizationList, parametrization_dict[f"weight_hh_l{i}"]
+            )
+            mask = weight_parameterizations[0].mask
+
+            with torch.no_grad():
+                parametrize.remove_parametrizations(
+                    lstm, f"weight_hh_l{i}", leave_parametrized=True
+                )
+                # splitting out hidden-hidden masks
+                W_hi, W_hf, W_hg, W_ho = torch.split(
+                    getattr(lstm, f"weight_hh_l{i}"), lstm.hidden_size
+                )
+                M_hi, M_hf, M_hg, M_ho = torch.split(mask, lstm.hidden_size)  # type: ignore[arg-type]
+
+                # resize each individual weight separately
+                W_hi = W_hi[M_hi][:, M_hi]
+                W_hf = W_hf[M_hf][:, M_hf]
+                W_hg = W_hg[M_hg][:, M_hg]
+                W_ho = W_ho[M_ho][:, M_ho]
+
+                # concat, use this as new weight
+                new_weight = torch.cat((W_hi, W_hf, W_hg, W_ho))
+                setattr(lstm, f"weight_hh_l{i}", nn.Parameter(new_weight))
+                setattr(
+                    lstm,
+                    f"bias_hh_l{i}",
+                    nn.Parameter(getattr(lstm, f"bias_hh_l{i}")[mask]),
+                )
+
+            # If this is the final layer, then we need to prune linear layer columns
+            if i + 1 == lstm.num_layers:
+                lstm.hidden_size = int(M_hi.sum())
+                with torch.no_grad():
+                    if parametrize.is_parametrized(linear):
+                        parametrization_dict = cast(
+                            nn.ModuleDict, linear.parametrizations
+                        )
+                        weight_parameterizations = cast(
+                            ParametrizationList, parametrization_dict.weight
+                        )
+
+                        weight_parameterizations.original = nn.Parameter(
+                            weight_parameterizations.original[:, M_ho]
+                        )
+                        linear.in_features = weight_parameterizations.original.shape[1]
+                    else:
+                        linear.weight = nn.Parameter(linear.weight[:, M_ho])
+                        linear.in_features = linear.weight.shape[1]
+
+                    # if layernorm module, prune weight and bias
+                    if layernorm is not None:
+                        layernorm.normalized_shape = (linear.in_features,)
+                        layernorm.weight = nn.Parameter(layernorm.weight[M_ho])
+                        layernorm.bias = nn.Parameter(layernorm.bias[M_ho])
+
+            # otherwise need to prune the columns of the input of the next LSTM layer
+            else:
+                with torch.no_grad():
+                    if parametrize.is_parametrized(lstm, f"weight_ih_l{i + 1}"):
+                        parametrization_dict = cast(
+                            nn.ModuleDict, lstm.parametrizations
+                        )
+                        weight_parameterizations = cast(
+                            ParametrizationList,
+                            getattr(parametrization_dict, f"weight_ih_l{i + 1}"),
+                        )
+
+                        weight_parameterizations.original = nn.Parameter(
+                            weight_parameterizations.original[:, M_ho]
+                        )
+                    else:
+                        next_layer_weight = getattr(lstm, f"weight_ih_l{i + 1}")
+                        setattr(
+                            lstm,
+                            f"weight_ih_l{i + 1}",
+                            nn.Parameter(next_layer_weight[:, M_ho]),
+                        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/pruner/saliency_pruner.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/pruner/saliency_pruner.py
new file mode 100644
index 0000000000000000000000000000000000000000..11c4652a7f0dafe2d3dd94f85c68fece035fd827
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_experimental/pruner/saliency_pruner.py
@@ -0,0 +1,35 @@
+# mypy: allow-untyped-defs
+from .base_structured_sparsifier import BaseStructuredSparsifier
+
+
+class SaliencyPruner(BaseStructuredSparsifier):
+    """
+    Prune rows based on the saliency (L1 norm) of each row.
+
+    This pruner works on N-Dimensional weight tensors.
+    For each row, we will calculate the saliency, which is the sum the L1 norm of all weights in that row.
+    We expect that the resulting saliency vector has the same shape as our mask.
+    We then pick elements to remove until we reach the target sparsity_level.
+    """
+
+    def update_mask(self, module, tensor_name, **kwargs):
+        # tensor_name will give you the FQN, all other entries in sparse config is present in kwargs
+        weights = getattr(module, tensor_name)
+        mask = getattr(module.parametrizations, tensor_name)[0].mask
+
+        # use negative weights so we can use topk (we prune out the smallest)
+        if weights.dim() <= 1:
+            raise Exception(  # noqa: TRY002
+                "Structured pruning can only be applied to a 2+dim weight tensor!"
+            )
+        saliency = -weights.norm(dim=tuple(range(1, weights.dim())), p=1)
+        if saliency.shape != mask.shape:
+            raise AssertionError(
+                f"saliency shape ({saliency.shape}) must match mask shape ({mask.shape})"
+            )
+
+        num_to_pick = int(len(mask) * kwargs["sparsity_level"])
+        prune = saliency.topk(num_to_pick).indices
+
+        # Set the mask to be false for the rows we want to prune
+        mask.data[prune] = False
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_mappings.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_mappings.py
new file mode 100644
index 0000000000000000000000000000000000000000..6fc2c4f10aef5585072f36116282a2048965197a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/_mappings.py
@@ -0,0 +1,23 @@
+# mypy: allow-untyped-defs
+__all__ = [
+    "get_static_sparse_quantized_mapping",
+    "get_dynamic_sparse_quantized_mapping",
+]
+
+
+def get_static_sparse_quantized_mapping():
+    import torch.ao.nn.sparse
+
+    _static_sparse_quantized_mapping = {
+        torch.nn.Linear: torch.ao.nn.sparse.quantized.Linear,
+    }
+    return _static_sparse_quantized_mapping
+
+
+def get_dynamic_sparse_quantized_mapping():
+    import torch.ao.nn.sparse
+
+    _dynamic_sparse_quantized_mapping = {
+        torch.nn.Linear: torch.ao.nn.sparse.quantized.dynamic.Linear,
+    }
+    return _dynamic_sparse_quantized_mapping
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/scheduler/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/scheduler/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/scheduler/base_scheduler.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/scheduler/base_scheduler.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac8916713dae6fe008b75e6dca9d63851560ab6e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/scheduler/base_scheduler.py
@@ -0,0 +1,173 @@
+# mypy: allow-untyped-defs
+
+import warnings
+import weakref
+from functools import wraps
+
+from torch.ao.pruning.sparsifier.base_sparsifier import BaseSparsifier
+
+
+__all__ = ["BaseScheduler"]
+
+
+class BaseScheduler:
+    def __init__(self, sparsifier, last_epoch=-1, verbose=False):
+        # Attach sparsifier
+        if not isinstance(sparsifier, BaseSparsifier):
+            raise TypeError(
+                f"{type(sparsifier).__name__} is not an instance of torch.ao.pruning.BaseSparsifier"
+            )
+        self.sparsifier = sparsifier
+
+        # Initialize epoch and base sparsity levels
+
+        self.base_sl = [group["sparsity_level"] for group in sparsifier.groups]
+        self.last_epoch = last_epoch
+
+        # Following https://github.com/pytorch/pytorch/issues/20124
+        # We would like to ensure that `scheduler.step()` is called after
+        # `sparsifier.step()`
+        def with_counter(method):
+            if getattr(method, "_with_counter", False):
+                # `sparsifier.step()` has already been replaced, return.
+                return method
+
+            # Keep a weak reference to the sparsifier instance to prevent
+            # cyclic references.
+            instance_ref = weakref.ref(method.__self__)
+            # Get the unbound method for the same purpose.
+            func = method.__func__
+            cls = instance_ref().__class__
+            del method
+
+            @wraps(func)
+            def wrapper(*args, **kwargs):
+                instance = instance_ref()
+                instance._step_count += 1  # type: ignore[union-attr]
+                wrapped = func.__get__(instance, cls)
+                return wrapped(*args, **kwargs)
+
+            # Note that the returned function here is no longer a bound method,
+            # so attributes like `__func__` and `__self__` no longer exist.
+            wrapper._with_counter = True  # type: ignore[attr-defined]
+            return wrapper
+
+        self.sparsifier.step = with_counter(self.sparsifier.step)  # type: ignore[assignment]
+        self.sparsifier._step_count = 0  # type: ignore[attr-defined]
+        self._step_count: int = 0
+        self.verbose = verbose
+
+        # Housekeeping
+        self._get_sl_called_within_step: bool = False
+
+        self.step()
+
+    def state_dict(self):
+        """Returns the state of the scheduler as a :class:`dict`.
+
+        It contains an entry for every variable in self.__dict__ which
+        is not the sparsifier.
+        """
+        return {
+            key: value for key, value in self.__dict__.items() if key != "sparsifier"
+        }
+
+    def load_state_dict(self, state_dict):
+        """Loads the schedulers state.
+
+        Args:
+            state_dict (dict): scheduler state. Should be an object returned
+                from a call to :meth:`state_dict`.
+        """
+        self.__dict__.update(state_dict)
+
+    def get_last_sl(self):
+        """Return last computed sparsity level by current scheduler."""
+        return self._last_sl
+
+    def get_sl(self):
+        # Compute sparsity level using chainable form of the scheduler
+        # Note: This method is not intended to be called directly, and is only
+        #       used by the ".step" method. Use .get_last_sl() instead.
+        if not self._get_sl_called_within_step:
+            warnings.warn(
+                "To get the last sparsity level computed by the scheduler, "
+                "please use `get_last_sl()`.",
+                stacklevel=2,
+            )
+        raise NotImplementedError
+
+    def print_sl(self, is_verbose, group, sl, epoch=None):
+        """Display the current sparsity level."""
+        if is_verbose:
+            if epoch is None:
+                print(f"Adjusting sparsity level of group {group} to {sl:.4e}.")
+            else:
+                print(
+                    f"Epoch {epoch:5d}: adjusting sparsity level of group {group} to {sl:.4e}."
+                )
+
+    def __repr__(self):
+        format_string = self.__class__.__name__ + " ("
+        format_string += "\n"
+        format_string += f"Sparsifier {self.sparsifier}\n"
+        format_string += f"    base_sl: {self.base_sl}\n"
+        format_string += ")"
+        return format_string
+
+    def step(self, epoch=None):
+        # Raise warning if trying to call scheduler step before the sparsifier.
+        # https://github.com/pytorch/pytorch/issues/20124
+        if self._step_count == 1:
+            if not hasattr(self.sparsifier.step, "_with_counter"):
+                warnings.warn(
+                    "Seems like `sparsifier.step()` has been overridden after sparsity scheduler "
+                    "initialization. Please, make sure to call `sparsifier.step()` before "
+                    "`scheduler.step()`.",
+                    UserWarning,
+                    stacklevel=2,
+                )
+
+            # Just check if there were two first scheduler.step() calls before sparsifier.step()
+            elif self.sparsifier._step_count < 1:  # type: ignore[attr-defined]
+                warnings.warn(
+                    "Detected call of `scheduler.step()` before `sparsifier.step()`. "
+                    "You have to make sure you run the sparsifier.step() BEFORE any "
+                    "calls to the scheduler.step().",
+                    UserWarning,
+                    stacklevel=2,
+                )
+        self._step_count += 1
+
+        class _enable_get_sl_call:
+            def __init__(self, o):
+                self.o = o
+
+            def __enter__(self):
+                self.o._get_sl_called_within_step = True
+                return self
+
+            def __exit__(self, type, value, traceback):
+                self.o._get_sl_called_within_step = False
+
+        with _enable_get_sl_call(self):
+            self.last_epoch += 1
+            values = self.get_sl()
+
+        for i, data in enumerate(zip(self.sparsifier.groups, values)):
+            param_group, sl = data
+            param_group["sparsity_level"] = sl
+            self.print_sl(self.verbose, i, sl, epoch)
+
+        self._last_sl = [group["sparsity_level"] for group in self.sparsifier.groups]
+        self.sparsifier.enable_mask_update = True
+
+    def _make_sure_a_list(self, var):
+        r"""Utility that extends it to the same length as the .groups, ensuring it is a list"""
+        n = len(self.sparsifier.groups)
+        if not isinstance(var, (list, tuple)):
+            return [var] * n
+        else:
+            if len(var) != n:
+                raise ValueError(f"Expected variable of length {n}, but got {len(var)}")
+            return list(var)  # We want the result to be in a list, not tuple
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/scheduler/cubic_scheduler.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/scheduler/cubic_scheduler.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4706900762adf411eb68dfd7fee3ff9fed36b51
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/scheduler/cubic_scheduler.py
@@ -0,0 +1,114 @@
+# mypy: allow-untyped-defs
+import warnings
+
+from .base_scheduler import BaseScheduler
+
+
+__all__ = ["CubicSL"]
+
+
+def _clamp(x, lo, hi):
+    return max(lo, min(hi, x))
+
+
+class CubicSL(BaseScheduler):
+    r"""Sets the sparsity level of each parameter group to the final sl
+    plus a given exponential function.
+
+    .. math::
+
+        s_i = s_f + (s_0 - s_f) \cdot \left( 1 - \frac{t - t_0}{n\Delta t} \right)^3
+
+    where :math:`s_i` is the sparsity at epoch :math:`t`, :math;`s_f` is the final
+    sparsity level, :math:`f(i)` is the function to be applied to the current epoch
+    :math:`t`, initial epoch :math:`t_0`, and final epoch :math:`t_f`.
+    :math:`\Delta t` is used to control how often the update of the sparsity level
+    happens. By default,
+
+    Args:
+        sparsifier (BaseSparsifier): Wrapped sparsifier.
+        init_sl (int, list): Initial level of sparsity
+        init_t (int, list): Initial step, when pruning starts
+        delta_t (int, list): Pruning frequency
+        total_t (int, list): Total number of pruning steps
+        initially_zero (bool, list): If True, sets the level of sparsity to 0
+            before init_t (:math:`t_0`). Otherwise, the sparsity level before
+            init_t (:math:`t_0`) is set to init_sl(:math:`s_0`)
+        last_epoch (int): The index of last epoch. Default: -1.
+        verbose (bool): If ``True``, prints a message to stdout for
+            each update. Default: ``False``.
+    """
+
+    def __init__(
+        self,
+        sparsifier,
+        init_sl=0.0,
+        init_t=0,
+        delta_t=10,
+        total_t=100,
+        initially_zero=False,
+        last_epoch=-1,
+        verbose=False,
+    ):
+        self.sparsifier = sparsifier
+
+        self.init_sl = self._make_sure_a_list(init_sl)
+        self.init_t = self._make_sure_a_list(init_t)
+        self.delta_t = self._make_sure_a_list(delta_t)
+        self.total_t = self._make_sure_a_list(total_t)
+
+        self.initially_zero = self._make_sure_a_list(initially_zero)
+
+        super().__init__(sparsifier, last_epoch, verbose)
+
+    @staticmethod
+    def sparsity_compute_fn(s_0, s_f, t, t_0, dt, n, initially_zero=False):
+        r""" "Computes the current level of sparsity.
+
+        Based on https://arxiv.org/pdf/1710.01878.pdf
+
+        Args:
+            s_0: Initial level of sparsity, :math:`s_i`
+            s_f: Target level of sparsity, :math:`s_f`
+            t: Current step, :math:`t`
+            t_0: Initial step, :math:`t_0`
+            dt: Pruning frequency, :math:`\Delta T`
+            n: Pruning steps, :math:`n`
+            initially_zero: Sets the level of sparsity to 0 before t_0.
+                If False, sets to s_0
+
+        Returns:
+            The sparsity level :math:`s_t` at the current step :math:`t`
+        """
+        if initially_zero and t < t_0:
+            return 0
+        s_t = s_f + (s_0 - s_f) * (1.0 - (t - t_0) / (dt * n)) ** 3
+        s_t = _clamp(s_t, s_0, s_f)
+        return s_t
+
+    def get_sl(self):
+        if not self._get_sl_called_within_step:
+            warnings.warn(
+                "To get the last sparsity level computed by the scheduler, "
+                "please use `get_last_sl()`.",
+                stacklevel=2,
+            )
+        return [
+            self.sparsity_compute_fn(
+                s_0=initial_sparsity,
+                s_f=final_sparsity,
+                t=self.last_epoch,
+                t_0=initial_epoch,
+                dt=delta_epoch,
+                n=interval_epochs,
+                initially_zero=initially_zero,
+            )
+            for initial_sparsity, final_sparsity, initial_epoch, delta_epoch, interval_epochs, initially_zero in zip(
+                self.init_sl,
+                self.base_sl,
+                self.init_t,
+                self.delta_t,
+                self.total_t,
+                self.initially_zero,
+            )
+        ]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe5737095bf6662ba13a22a8ee8287d07263c05f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py
@@ -0,0 +1,64 @@
+import warnings
+from collections.abc import Callable
+
+from torch.ao.pruning.sparsifier.base_sparsifier import BaseSparsifier
+
+from .base_scheduler import BaseScheduler
+
+
+__all__ = ["LambdaSL"]
+
+
+class LambdaSL(BaseScheduler):
+    """Sets the sparsity level of each parameter group to the final sl
+    times a given function. When last_epoch=-1, sets initial sl as zero.
+    Args:
+        sparsifier (BaseSparsifier): Wrapped sparsifier.
+        sl_lambda (function or list): A function which computes a multiplicative
+            factor given an integer parameter epoch, or a list of such
+            functions, one for each group in sparsifier.param_groups.
+        last_epoch (int): The index of last epoch. Default: -1.
+        verbose (bool): If ``True``, prints a message to stdout for
+            each update. Default: ``False``.
+    Example:
+        >>> # Assuming sparsifier has two groups.
+        >>> lambda1 = lambda epoch: epoch // 30
+        >>> lambda2 = lambda epoch: 0.95**epoch
+        >>> # xdoctest: +SKIP
+        >>> scheduler = LambdaSL(sparsifier, sl_lambda=[lambda1, lambda2])
+        >>> for epoch in range(100):
+        >>>     train(...)
+        >>>     validate(...)
+        >>>     scheduler.step()
+    """
+
+    def __init__(
+        self,
+        sparsifier: BaseSparsifier,
+        sl_lambda: Callable[[int], float] | list[Callable[[int], float]],
+        last_epoch: int = -1,
+        verbose: bool = False,
+    ) -> None:
+        self.sparsifier = sparsifier
+
+        if not isinstance(sl_lambda, list) and not isinstance(sl_lambda, tuple):
+            self.sl_lambdas = [sl_lambda] * len(sparsifier.groups)
+        else:
+            if len(sl_lambda) != len(sparsifier.groups):
+                raise ValueError(
+                    f"Expected {len(sparsifier.groups)} lr_lambdas, but got {len(sl_lambda)}"
+                )
+            self.sl_lambdas = list(sl_lambda)
+        super().__init__(sparsifier, last_epoch, verbose)  # type: ignore[no-untyped-call]
+
+    def get_sl(self) -> list[float]:
+        if not self._get_sl_called_within_step:
+            warnings.warn(
+                "To get the last sparsity level computed by the scheduler, "
+                "please use `get_last_sl()`.",
+                stacklevel=2,
+            )
+        return [
+            base_sl * lmbda(self.last_epoch)
+            for lmbda, base_sl in zip(self.sl_lambdas, self.base_sl)
+        ]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/sparsifier/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/sparsifier/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f55d63a26781a3875a5d3ee36fb0ee906a5a0d9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py
@@ -0,0 +1,359 @@
+# mypy: allow-untyped-defs
+import abc
+import copy
+from collections import defaultdict
+from typing import Any
+
+import torch
+from torch import nn
+from torch.nn.utils import parametrize
+from torch.nn.utils.parametrize import type_before_parametrizations
+
+from .utils import (
+    FakeSparsity,
+    get_arg_info_from_tensor_fqn,
+    module_contains_param,
+    module_to_fqn,
+    swap_module,
+)
+
+
+__all__ = ["BaseSparsifier"]
+
+SUPPORTED_MODULES = {nn.Linear}
+
+KEYS_NOT_IN_STATE_DICT = ["module", "module_fqn", "tensor_name"]
+
+
+# TODO update desc with new config args
+class BaseSparsifier(abc.ABC):
+    r"""Base class for all sparsifiers.
+
+    Abstract methods that need to be implemented:
+
+    - update_mask: Function to compute a new mask for all keys in the
+        `groups`.
+
+    Args:
+        - model [nn.Module]: model to configure. The model itself is not saved
+            but used for the state_dict saving / loading.
+        - config [list]: configuration elements should be a dict map that includes
+            `tensor_fqn` of tensors to sparsify
+        - defaults [dict]: default configurations will be attached to the
+            configuration. Only the keys that don't exist in the `config` will
+            be updated.
+
+    Example::
+
+        >>> # xdoctest: +SKIP("Can't instantiate abstract class BaseSparsifier with abstract method update_mask")
+        >>> config = [{'tensor_fqn': 'layer1.weight', 'tensor_fqn': 'linear2.weight2', 'sparsity_level': 0.5}]
+        >>> defaults = {'sparsity_level': 0.7}
+        >>> # model.layer1.weight will have `sparsity_level` = 0.7 (getting default)
+        >>> sparsifier = BaseSparsifier(config, defaults)
+    """
+
+    def __init__(self, defaults: dict[str, Any] | None = None):
+        super().__init__()
+        self.defaults: dict[str, Any] = defaults or {}
+
+        self.state: dict[str, dict] = defaultdict(dict)
+        self.groups: list[dict[str, Any]] = []
+        self.enable_mask_update = True
+
+    def __getstate__(self) -> dict[str, Any]:
+        return {
+            "defaults": self.defaults,
+            "state": self.state,
+            "groups": self.groups,
+        }
+
+    def __setstate__(self, state: dict[str, dict[str, Any]]) -> None:
+        self.__dict__.update(state)
+
+    def __repr__(self):
+        format_string = self.__class__.__name__ + " ("
+        for i, sparse_args in enumerate(self.groups):
+            module = sparse_args["module"]
+            format_string += "\n"
+            format_string += f"\tGroup {i}\n"
+            format_string += f"\t    module: {module}\n"
+            for key in sorted(sparse_args.keys()):
+                if key == "module":
+                    continue
+                format_string += f"\t    {key}: {sparse_args[key]}\n"
+        format_string += ")"
+        return format_string
+
+    def state_dict(self) -> dict[str, Any]:
+        r"""Returns the state of the optimizer as a :class:`dict`.
+
+        It contains:
+        * state - current state of the sparsification.
+        * groups - a list containing all sparsity configuration groups
+            with the key 'tensor_fqn' specifying the path to the sparsified tensor within a model
+
+        TODO: Need a clean way of loading the state of the "prepared" module
+        """
+
+        groups: list[dict[str, Any]] = [
+            dict(
+                filter(
+                    lambda key_value: key_value[0] not in KEYS_NOT_IN_STATE_DICT,
+                    mg.items(),
+                )
+            )
+            for mg in self.groups
+        ]
+
+        return {
+            "state": self.state,
+            "groups": groups,
+        }
+
+    def load_state_dict(self, state_dict: dict[str, Any], strict: bool = True):
+        groups = copy.deepcopy(state_dict["groups"])
+        states = state_dict["state"]
+        for tensor_fqn, s in states.items():
+            arg_info = get_arg_info_from_tensor_fqn(self.model, tensor_fqn)
+            module = arg_info["module"]
+            tensor_name = arg_info["tensor_name"]
+            if strict and module is None:
+                raise RuntimeError(f"Error loading {tensor_fqn} into the model")
+
+            found = False
+            for p in module.parametrizations[tensor_name]:
+                if isinstance(p, FakeSparsity):
+                    found = True
+                    break
+            if not found:
+                p = FakeSparsity(torch.ones(getattr(module, tensor_name).shape))
+                parametrize.register_parametrization(module, tensor_name, p)
+            if s.get("mask", None) is not None:
+                mask = s.pop("mask")
+                p.mask = mask
+
+            for mg in groups:
+                if mg["tensor_fqn"] == tensor_fqn:
+                    mg.update(arg_info)
+        self.__setstate__({"state": states, "groups": groups})
+
+    def make_config_from_model(
+        self,
+        model: nn.Module,
+        SUPPORTED_MODULES: set[type[nn.Linear]] = SUPPORTED_MODULES,
+    ) -> None:
+        self.config = []
+        stack = [model]
+        while stack:
+            module = stack.pop()
+            for _name, child in module.named_children():
+                if type(child) in SUPPORTED_MODULES:
+                    module_fqn = module_to_fqn(model, child)
+                    if not isinstance(module_fqn, str):
+                        raise AssertionError("module_fqn must be a string")
+                    self.config.append({"tensor_fqn": module_fqn + ".weight"})
+                else:
+                    stack.append(child)
+
+    def prepare(self, model, config):
+        r"""Prepares a model, by adding the parametrizations.
+
+        Note::
+
+            The model is modified inplace. If you need to preserve the original
+            model, use copy.deepcopy.
+        """
+        self.model = model  # TODO: Need to figure out how to load without this.
+        self.config = config
+
+        # If no config -- try getting all the supported layers
+        if self.config is None:
+            self.make_config_from_model(model)
+
+        # TODO: Remove the configuration by reference ('module')
+        # pyrefly: ignore [not-iterable]
+        for module_config in self.config:
+            if not isinstance(module_config, dict):
+                raise AssertionError(
+                    "config elements should be dicts not modules i.e.:"
+                    "[{`tensor_fqn`: `foo.bar.weight`}, {`tensor_fqn`: ... }, ...]"
+                )
+
+            if not isinstance(self.defaults, dict):
+                raise AssertionError("defaults must be a dict")
+            local_args = copy.deepcopy(self.defaults)
+            local_args.update(module_config)
+
+            tensor_fqn = local_args.get("tensor_fqn", None)
+            if tensor_fqn is None:
+                raise AssertionError(
+                    "tensor_fqn is a required argument in the sparsity config which"
+                    "replaces previous `module` and [module]`fqn` arguments"
+                )
+
+            # populate all information from tensor_fqn
+            info_from_tensor_fqn = get_arg_info_from_tensor_fqn(model, tensor_fqn)
+
+            # check that whatever was put into local_args agrees with what was obtained
+            # from tensor_fqn
+            for key in info_from_tensor_fqn:
+                if key in local_args:
+                    if not (
+                        info_from_tensor_fqn[key] == local_args[key]
+                        or (
+                            key == "tensor_fqn"
+                            and "." + info_from_tensor_fqn[key] == local_args[key]
+                        )
+                        # info_from_tensor_fqn will chop leading '.' from tensor_fqn so ignore that
+                    ):
+                        raise AssertionError(
+                            f"Given both `{key}` and `tensor_fqn` in the config, it is expected them to agree!"
+                        )
+            local_args.update(info_from_tensor_fqn)
+            self.groups.append(local_args)
+        self._prepare()
+
+    def _prepare(self, *args, **kwargs):
+        r"""Adds mask parametrization to the layer weight"""
+        for config in self.groups:
+            module = config["module"]
+            tensor_name = config["tensor_name"]
+            parametrization = config.get("parametrization", FakeSparsity)
+            mask = config.get("mask", torch.ones_like(getattr(module, tensor_name)))
+            self.state[config["tensor_fqn"]]["mask"] = mask
+            parametrize.register_parametrization(
+                module, tensor_name, parametrization(mask)
+            )
+
+    def squash_mask(
+        self,
+        params_to_keep: tuple[str, ...] | None = None,
+        params_to_keep_per_layer: dict[str, tuple[str, ...]] | None = None,
+        *args,
+        **kwargs,
+    ):
+        r"""Squashes the sparse masks into the appropriate tensors.
+
+        If either the `params_to_keep` or `params_to_keep_per_layer` is set,
+        the module will have a `sparse_params` dict attached to it.
+
+        Args:
+            params_to_keep: List of keys to save in the module or a dict
+                            representing the modules and keys that will have
+                            sparsity parameters saved
+            params_to_keep_per_layer: Dict to specify the params that should be
+                            saved for specific layers. The keys in the dict
+                            should be the module fqn, while the values should
+                            be a list of strings with the names of the variables
+                            to save in the `sparse_params`
+
+        Examples:
+            >>> # xdoctest: +SKIP("locals are undefined")
+            >>> # Don't save any sparse params
+            >>> sparsifier.squash_mask()
+            >>> hasattr(model.submodule1, "sparse_params")
+            False
+
+            >>> # Keep sparse params per layer
+            >>> sparsifier.squash_mask(
+            ...     params_to_keep_per_layer={
+            ...         "submodule1.linear1": ("foo", "bar"),
+            ...         "submodule2.linear42": ("baz",),
+            ...     }
+            ... )
+            >>> print(model.submodule1.linear1.sparse_params)
+            {'foo': 42, 'bar': 24}
+            >>> print(model.submodule2.linear42.sparse_params)
+            {'baz': 0.1}
+
+            >>> # Keep sparse params for all layers
+            >>> sparsifier.squash_mask(params_to_keep=("foo", "bar"))
+            >>> print(model.submodule1.linear1.sparse_params)
+            {'foo': 42, 'bar': 24}
+            >>> print(model.submodule2.linear42.sparse_params)
+            {'foo': 42, 'bar': 24}
+
+            >>> # Keep some sparse params for all layers, and specific ones for
+            >>> # some other layers
+            >>> sparsifier.squash_mask(
+            ...     params_to_keep=("foo", "bar"),
+            ...     params_to_keep_per_layer={"submodule2.linear42": ("baz",)},
+            ... )
+            >>> print(model.submodule1.linear1.sparse_params)
+            {'foo': 42, 'bar': 24}
+            >>> print(model.submodule2.linear42.sparse_params)
+            {'foo': 42, 'bar': 24, 'baz': 0.1}
+        """
+        for config in self.groups:
+            module = config["module"]
+            tensor_name = config["tensor_name"]
+            parametrize.remove_parametrizations(
+                module, tensor_name, leave_parametrized=True
+            )
+            sparse_params = {}
+            if params_to_keep is not None:
+                global_params = {k: config[k] for k in params_to_keep}
+                sparse_params.update(global_params)
+            if params_to_keep_per_layer is not None:
+                params = params_to_keep_per_layer.get(config["module_fqn"], None)
+                if params is not None:
+                    per_layer_params = {k: config[k] for k in params}
+                    sparse_params.update(per_layer_params)
+            if sparse_params:
+                # TODO handle multiple tensor being quantized on a single module, where to store sparse_params?
+                module.sparse_params = sparse_params
+
+    def convert(
+        self,
+        module: nn.Module,
+        mapping: dict[type[nn.Module], type[nn.Module]] | None = None,
+        inplace: bool = False,
+        parameterization: type[nn.Module] = FakeSparsity,
+    ):
+        r"""Converts submodules in input module to a different module according to `mapping`
+        by calling `from_dense` method on the target module class
+        Args:
+            module: input module
+            mapping: a dictionary that maps from source module type to target
+                module type, can be overwritten to allow swapping user defined
+                Modules
+            inplace: carry out model transformations in-place, the original module
+                is mutated
+        """
+        if mapping is None:
+            raise NotImplementedError("Need to auto generate mapping ")
+        if not inplace:
+            module = copy.deepcopy(module)
+
+        reassign = {}
+        for name, mod in module.named_children():
+            # leaf node
+            if (
+                module_contains_param(mod, parameterization)
+                and type_before_parametrizations(mod) in mapping
+            ):
+                reassign[name] = swap_module(mod, mapping)
+            else:
+                # recurse
+                reassign[name] = self.convert(
+                    mod,
+                    mapping=mapping,
+                    inplace=True,
+                    parameterization=parameterization,
+                )
+
+        for key, value in reassign.items():
+            module._modules[key] = value
+
+        return module
+
+    def step(self, use_path: bool = True) -> None:
+        if not self.enable_mask_update:
+            return
+        with torch.no_grad():
+            for config in self.groups:
+                self.update_mask(**config)
+
+    @abc.abstractmethod
+    def update_mask(self, module: nn.Module, tensor_name: str, **kwargs):
+        pass
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/sparsifier/nearly_diagonal_sparsifier.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/sparsifier/nearly_diagonal_sparsifier.py
new file mode 100644
index 0000000000000000000000000000000000000000..26fb3a98b8fb7d37e6bd5965d1d41b091d3e4818
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/sparsifier/nearly_diagonal_sparsifier.py
@@ -0,0 +1,60 @@
+# mypy: allow-untyped-defs
+import torch
+
+from . import base_sparsifier
+
+
+class NearlyDiagonalSparsifier(base_sparsifier.BaseSparsifier):
+    r"""Nearly Diagonal Sparsifier
+
+    This sparsifier creates a nearly diagonal mask to be applied to the weight matrix.
+    Nearly Diagonal Matrix is a matrix that contains non-zero elements near the diagonal and the rest are zero.
+    An example of a nearly diagonal matrix with degree (or nearliness) 3 and 5 are follows respectively.
+    1 1 0 0       1 1 1 0
+    1 1 1 0       1 1 1 1
+    0 1 1 1       1 1 1 1
+    0 0 1 1       0 1 1 1
+    Note that a nearly diagonal matrix with degree 1 is just a matrix with main diagonal populated
+
+    This sparsifier is controlled by one variable:
+    1. `nearliness` defines the number of non-zero diagonal lines that are closest to the main diagonal.
+        Currently - supports only odd number
+
+    Note:
+        This can be accelerated (vectorized) once the Spdiagonal feature (PR: #78439) is landed or the banded matrix
+        feature is landed: https://stackoverflow.com/questions/52463972/generating-banded-matrices-using-numpy
+
+    Args:
+        nearliness: The degree of nearliness (default = 1)
+
+    """
+
+    def __init__(self, nearliness: int = 1):
+        defaults = {"nearliness": nearliness}
+        super().__init__(defaults=defaults)
+
+    def update_mask(  # type:ignore[override]
+        self, module, tensor_name, nearliness, **kwargs
+    ):
+        mask = getattr(module.parametrizations, tensor_name)[0].mask
+        mask.data = torch.zeros_like(mask)
+        if nearliness <= 0:
+            return
+
+        tensor = getattr(module, tensor_name)
+        height, width = tensor.shape
+
+        if nearliness % 2 == 0:
+            raise ValueError("nearliness can only be an odd number")
+        dist_to_diagonal = nearliness // 2
+        # check
+        if dist_to_diagonal >= min(height, width):
+            raise ValueError(
+                "nearliness cannot be larger than the dimensions of tensor."
+            )
+
+        for row in range(height):
+            # Bounds of entries that needs to be set to 1
+            low = max(0, row - dist_to_diagonal)
+            high = min(width, row + dist_to_diagonal + 1)
+            mask[row, low:high].fill_(1)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/sparsifier/utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/sparsifier/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..97461630bc3ae9ce60cd02ce13a2371d9ba05536
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/sparsifier/utils.py
@@ -0,0 +1,141 @@
+# mypy: allow-untyped-defs
+from itertools import chain
+from typing import Any
+
+from torch import nn
+from torch.nn.utils.parametrize import is_parametrized, type_before_parametrizations
+
+
+__all__ = [
+    "module_contains_param",
+    "swap_module",
+    "module_to_fqn",
+    "fqn_to_module",
+    "get_arg_info_from_tensor_fqn",
+    "FakeSparsity",
+]
+
+
+def module_contains_param(module: nn.Module, parametrization: type[nn.Module]) -> bool:
+    if is_parametrized(module):
+        # see if any of the module tensors have a parametriztion attached that matches the one passed in
+        return any(
+            any(isinstance(param, parametrization) for param in param_list)
+            for key, param_list in module.parametrizations.items()  # type: ignore[union-attr,operator]
+        )
+    return False
+
+
+def swap_module(
+    mod: nn.Module, mapping: dict[type[nn.Module], type[nn.Module]]
+) -> nn.Module:
+    r"""Swaps the module using from_dense according to the mapping passed in.
+    Args:
+        mod: input module
+        mapping: a dictionary that maps from nn module to sparse nn module
+    Return:
+        The corresponding sparse module of `mod` according to mapping, created using from_dense
+    """
+    if type_before_parametrizations(mod) in mapping:
+        sparse_mod = mapping[type_before_parametrizations(mod)]
+
+        # TODO Fix this typing, as Type[Module] has no attribute "from_dense"
+        new_mod = sparse_mod.from_dense(mod)  # type: ignore[attr-defined]
+
+        # Preserve module's pre forward hooks. They'll be called on quantized input
+        for pre_hook_fn in mod._forward_pre_hooks.values():
+            new_mod.register_forward_pre_hook(pre_hook_fn)
+        # Preserve module's post forward hooks except _observer_forward_hook
+        # After convert they'll work with quantized output
+        for hook_fn in mod._forward_hooks.values():
+            new_mod.register_forward_hook(hook_fn)
+
+        # respect device affinity when swapping modules
+        # pyrefly: ignore [bad-argument-type]
+        devices = {p.device for p in chain(mod.parameters(), mod.buffers())}
+        if len(devices) > 1:
+            raise AssertionError(
+                f"swap_module only works with cpu or single-device CUDA modules, but got devices {devices}"
+            )
+        device = next(iter(devices)) if len(devices) > 0 else None
+        if device:
+            new_mod.to(device)
+
+        return new_mod
+
+    else:
+        return mod
+
+
+def module_to_fqn(model: nn.Module, module: nn.Module, prefix: str = "") -> str | None:
+    """
+    Returns the fqn for a module or None if module not a descendent of model.
+    """
+    if module is model:
+        return ""
+    for name, child in model.named_children():
+        fqn = module_to_fqn(child, module, ".")
+        if isinstance(fqn, str):
+            return prefix + name + fqn
+    return None
+
+
+def fqn_to_module(model: nn.Module | None, path: str) -> nn.Module | None:
+    """
+    Given an fqn, returns the corresponding module or tensor or None if the fqn given by `path`
+    doesn't correspond to anything. Similar to model.get_submodule(path) but works for tensors.
+    """
+    if path != "":
+        for name in path.split("."):
+            model = getattr(model, name, None)
+    return model
+
+
+def get_arg_info_from_tensor_fqn(model: nn.Module, tensor_fqn: str) -> dict[str, Any]:
+    """
+    Uses tensor_fqn to obtain a dict containing module_fqn, module and tensor_name
+    """
+    # string manip to split tensor_fqn into module_fqn and tensor_name
+    # if tensor_fqn is 'weight' then module_fqn and tensor_name are '' and 'weight'
+    # if tensor_fqn is 'linear.weight' then module_fqn and tensor_name are 'linear' and 'weight'
+    tensor_name = tensor_fqn.rsplit(".", maxsplit=1)[-1]
+    module_fqn = tensor_fqn[: -len(tensor_name) - ("." in tensor_fqn)]
+
+    module = fqn_to_module(model, module_fqn)
+
+    return {
+        "module_fqn": module_fqn,
+        "module": module,
+        "tensor_name": tensor_name,
+        "tensor_fqn": tensor_fqn,
+    }
+
+
+# Parametrizations
+class FakeSparsity(nn.Module):
+    r"""Parametrization for the weights. Should be attached to the 'weight' or
+    any other parameter that requires a mask applied to it.
+
+    Note::
+
+        Once the mask is passed, the variable should not change the id. The
+        contents of the mask can change, but the mask reference itself should
+        not.
+    """
+
+    def __init__(self, mask):
+        super().__init__()
+        self.register_buffer("mask", mask)
+
+    def forward(self, x):
+        if self.mask.shape != x.shape:
+            raise AssertionError(
+                f"mask shape ({self.mask.shape}) must match x shape ({x.shape})"
+            )
+        return self.mask * x
+
+    def state_dict(self, *args, **kwargs):
+        # We don't want to let the parametrizations to save the mask.
+        # That way we make sure that the linear module doesn't store the masks
+        # alongside their parametrizations.
+        return {}
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/sparsifier/weight_norm_sparsifier.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/sparsifier/weight_norm_sparsifier.py
new file mode 100644
index 0000000000000000000000000000000000000000..0fd0368f156744f1af362670fa73baf505f50251
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/pruning/sparsifier/weight_norm_sparsifier.py
@@ -0,0 +1,250 @@
+# mypy: allow-untyped-defs
+import operator
+from collections.abc import Callable
+from functools import reduce
+
+import torch
+import torch.nn.functional as F
+
+from .base_sparsifier import BaseSparsifier
+
+
+__all__ = ["WeightNormSparsifier"]
+
+
+def _flat_idx_to_2d(idx, shape):
+    rows = idx // shape[1]
+    cols = idx % shape[1]
+    return rows, cols
+
+
+class WeightNormSparsifier(BaseSparsifier):
+    r"""Weight-Norm Sparsifier
+
+    This sparsifier computes the norm of every sparse block and "zeroes-out" the
+    ones with the lowest norm. The level of sparsity defines how many of the
+    blocks is removed.
+
+    This sparsifier is controlled by three variables:
+    1. `sparsity_level` defines the number of *sparse blocks* that are zeroed-out
+    2. `sparse_block_shape` defines the shape of the sparse blocks. Note that
+        the sparse blocks originate at the zero-index of the tensor.
+    3. `zeros_per_block` is the number of zeros that we are expecting in each
+        sparse block. By default we assume that all elements within a block are
+        zeroed-out. However, setting this variable sets the target number of
+        zeros per block. The zeros within each block are chosen as the *smallest
+        absolute values*.
+
+    Args:
+
+        sparsity_level: The target level of sparsity
+        sparse_block_shape: The shape of a sparse block (see note below)
+        zeros_per_block: Number of zeros in a sparse block
+        norm: Norm to use. Could be either `int` or a callable.
+            If `int`, only L1 and L2 are implemented.
+
+    Note::
+        The `sparse_block_shape` is tuple representing (block_ROWS, block_COLS),
+        irrespective of what the rows / cols mean in the data tensor. That means,
+        if you were to sparsify a weight tensor in the nn.Linear, which has a
+        weight shape `(Cout, Cin)`, the `block_ROWS` would refer to the output
+        channels, while the `block_COLS` would refer to the input channels.
+
+    Note::
+        All arguments to the WeightNormSparsifier constructor are "default"
+        arguments and could be overridden by the configuration provided in the
+        `prepare` step.
+    """
+
+    def __init__(
+        self,
+        sparsity_level: float = 0.5,
+        sparse_block_shape: tuple[int, int] = (1, 4),
+        zeros_per_block: int | None = None,
+        norm: Callable | int | None = None,
+    ):
+        if zeros_per_block is None:
+            zeros_per_block = reduce(operator.mul, sparse_block_shape)
+        defaults = {
+            "sparsity_level": sparsity_level,
+            "sparse_block_shape": sparse_block_shape,
+            "zeros_per_block": zeros_per_block,
+        }
+        if norm is None:
+            norm = 2
+        if callable(norm):
+            self.norm_fn = norm
+        elif norm == 1:
+            self.norm_fn = lambda T: T.abs()
+        elif norm == 2:
+            self.norm_fn = lambda T: T * T
+        else:
+            raise NotImplementedError(f"L-{norm} is not yet implemented.")
+        super().__init__(defaults=defaults)
+
+    def _scatter_fold_block_mask(
+        self,
+        output_shape,
+        dim,
+        indices,
+        block_shape,
+        mask=None,
+        input_shape=None,
+        device=None,
+    ):
+        r"""Creates patches of size `block_shape` after scattering the indices."""
+        if mask is None:
+            if input_shape is None:
+                raise AssertionError("input_shape must be provided when mask is None")
+            mask = torch.ones(input_shape, device=device)
+        mask.scatter_(dim=dim, index=indices, value=0)
+        mask.data = F.fold(
+            mask, output_size=output_shape, kernel_size=block_shape, stride=block_shape
+        )
+        return mask
+
+    def _make_tensor_mask(
+        self, data, input_shape, sparsity_level, sparse_block_shape, mask=None
+    ):
+        r"""Creates a tensor-level mask.
+
+        Tensor-level mask is described as a mask, where the granularity of sparsification of the
+        smallest patch is the sparse_block_shape. That means, that for a given mask and a
+        sparse_block_shape, the smallest "patch" of zeros/ones could be the sparse_block_shape.
+
+        In this context, `sparsity_level` describes the fraction of sparse patches.
+        """
+        h, w = data.shape[-2:]
+        block_h, block_w = sparse_block_shape
+        dh = (block_h - h % block_h) % block_h
+        dw = (block_w - w % block_w) % block_w
+
+        if mask is None:
+            mask = torch.ones(h + dh, w + dw, device=data.device)
+
+        if sparsity_level >= 1.0:
+            mask.data = torch.zeros_like(mask)
+            return mask
+        elif sparsity_level <= 0.0:
+            mask.data = torch.ones_like(mask)
+            return mask
+
+        values_per_block = reduce(operator.mul, sparse_block_shape)
+        if values_per_block > 1:
+            # Reduce the data
+            data = F.avg_pool2d(
+                data[None, None, :],
+                kernel_size=sparse_block_shape,
+                stride=sparse_block_shape,
+                ceil_mode=True,
+            )
+        data = data.flatten()
+        num_blocks = len(data)
+
+        data = data.repeat(1, values_per_block, 1)
+
+        threshold_idx = round(sparsity_level * num_blocks)
+        threshold_idx = max(0, min(num_blocks - 1, threshold_idx))  # Sanity check
+        _, sorted_idx = torch.topk(data, k=threshold_idx, dim=2, largest=False)
+
+        # Temp reshape for mask
+        mask_reshape = mask.reshape(data.shape)  # data might be reshaped
+        self._scatter_fold_block_mask(
+            dim=2,
+            output_shape=(h + dh, w + dw),
+            indices=sorted_idx,
+            block_shape=sparse_block_shape,
+            mask=mask_reshape,
+        )
+        mask.data = mask_reshape.squeeze().reshape(mask.shape)[:h, :w].contiguous()
+        return mask
+
+    def _make_block_mask(self, data, sparse_block_shape, zeros_per_block, mask=None):
+        r"""Creates a block-level mask.
+
+        Block-level mask is described as a mask, where the granularity of sparsification of the
+        largest patch is the sparse_block_shape. That means that for a given mask and a
+        sparse_block_shape, the sparsity is computed only within a patch of a size sparse_block_shape.
+
+        In this context the `zeros_per_block` describes the number of zeroed-out elements within a patch.
+        """
+        h, w = data.shape[-2:]
+        block_h, block_w = sparse_block_shape
+        dh = (block_h - h % block_h) % block_h
+        dw = (block_w - w % block_w) % block_w
+        values_per_block = reduce(operator.mul, sparse_block_shape)
+
+        if mask is None:
+            mask = torch.ones((h + dh, w + dw), device=data.device)
+
+        if values_per_block == zeros_per_block:
+            # Everything should be sparsified
+            mask.data = torch.zeros_like(mask)
+            return mask
+
+        # create a new padded tensor like data (to match the block_shape)
+        padded_data = torch.ones(h + dh, w + dw, dtype=data.dtype, device=data.device)
+        padded_data.fill_(torch.nan)
+        padded_data[:h, :w] = data
+        unfolded_data = F.unfold(
+            padded_data[None, None, :],
+            kernel_size=sparse_block_shape,
+            stride=sparse_block_shape,
+        )
+
+        # Temp reshape for mask
+        mask_reshape = mask.reshape(unfolded_data.shape)
+        _, sorted_idx = torch.topk(
+            unfolded_data, k=zeros_per_block, dim=1, largest=False
+        )
+
+        self._scatter_fold_block_mask(
+            dim=1,
+            indices=sorted_idx,
+            output_shape=padded_data.shape,
+            block_shape=sparse_block_shape,
+            mask=mask_reshape,
+        )
+
+        mask.data = mask_reshape.squeeze().reshape(mask.shape).contiguous()
+        return mask
+
+    def update_mask(  # type: ignore[call-override, override]
+        self,
+        module,
+        tensor_name,
+        sparsity_level,
+        sparse_block_shape,
+        zeros_per_block,
+        **kwargs,
+    ):
+        values_per_block = reduce(operator.mul, sparse_block_shape)
+        if zeros_per_block > values_per_block:
+            raise ValueError(
+                "Number of zeros per block cannot be more than the total number of elements in that block."
+            )
+        if zeros_per_block < 0:
+            raise ValueError("Number of zeros per block should be positive.")
+
+        mask = getattr(module.parametrizations, tensor_name)[0].mask
+        if sparsity_level <= 0 or zeros_per_block == 0:
+            mask.data = torch.ones_like(mask)
+        elif sparsity_level >= 1.0 and (zeros_per_block == values_per_block):
+            mask.data = torch.zeros_like(mask)
+        else:
+            ww = self.norm_fn(getattr(module, tensor_name))
+            tensor_mask = self._make_tensor_mask(
+                data=ww,
+                # pyrefly: ignore [missing-attribute]
+                input_shape=ww.shape,
+                sparsity_level=sparsity_level,
+                sparse_block_shape=sparse_block_shape,
+            )
+            if values_per_block != zeros_per_block:
+                block_mask = self._make_block_mask(
+                    data=ww,
+                    sparse_block_shape=sparse_block_shape,
+                    zeros_per_block=zeros_per_block,
+                )
+                tensor_mask = torch.logical_or(tensor_mask, block_mask)
+            mask.data = tensor_mask
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2efc24081b0c13d94b7ab256f635eafce8614543
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/__init__.py
@@ -0,0 +1,247 @@
+# mypy: allow-untyped-defs
+
+import sys
+from collections.abc import Callable
+from typing import Optional, Union
+
+import torch
+from torch import Tensor
+
+from .fake_quantize import *  # noqa: F403
+from .fuse_modules import fuse_modules, fuse_modules_qat  # noqa: F403
+from .fuser_method_mappings import *  # noqa: F403
+from .observer import *  # noqa: F403
+from .pt2e._numeric_debugger import (  # noqa: F401
+    compare_results,
+    CUSTOM_KEY,
+    extract_results_from_loggers,
+    generate_numeric_debug_handle,
+    NUMERIC_DEBUG_HANDLE_KEY,
+    prepare_for_propagation_comparison,
+)
+from .pt2e.export_utils import (
+    _allow_exported_model_train_eval as allow_exported_model_train_eval,
+    _move_exported_model_to_eval as move_exported_model_to_eval,
+    _move_exported_model_to_train as move_exported_model_to_train,
+)
+
+# pyrefly: ignore [deprecated]
+from .qconfig import *  # noqa: F403
+from .qconfig_mapping import *  # noqa: F403
+from .quant_type import *  # noqa: F403
+from .quantization_mappings import *  # noqa: F403 # type: ignore[no-redef]
+from .quantize import *  # noqa: F403
+from .quantize_jit import *  # noqa: F403
+from .stubs import *  # noqa: F403
+
+
+# ensure __module__ is set correctly for public APIs
+if sys.version_info < (3, 12):
+    ObserverOrFakeQuantize = Union[ObserverBase, FakeQuantizeBase]
+    ObserverOrFakeQuantize.__module__ = "torch.ao.quantization"
+else:
+    from typing import TypeAliasType
+
+    ObserverOrFakeQuantize = TypeAliasType(
+        "ObserverOrFakeQuantize", ObserverBase | FakeQuantizeBase
+    )
+
+for _f in [
+    compare_results,
+    extract_results_from_loggers,
+    generate_numeric_debug_handle,
+    prepare_for_propagation_comparison,
+]:
+    _f.__module__ = "torch.ao.quantization"
+
+__all__ = [
+    "DeQuantStub",
+    "FakeQuantize",
+    "FakeQuantizeBase",
+    "FixedQParamsFakeQuantize",
+    "FixedQParamsObserver",
+    "FusedMovingAvgObsFakeQuantize",
+    "HistogramObserver",
+    "MatchAllNode",
+    "MinMaxObserver",
+    "MovingAverageMinMaxObserver",
+    "MovingAveragePerChannelMinMaxObserver",
+    "NoopObserver",
+    "ObserverBase",
+    "ObserverOrFakeQuantize",
+    "Pattern",
+    "PerChannelMinMaxObserver",
+    "PlaceholderObserver",
+    "QConfig",
+    "QConfigAny",
+    "QConfigDynamic",
+    "QConfigMapping",
+    "QuantStub",
+    "QuantType",
+    "QuantWrapper",
+    "RecordingObserver",
+    "ReuseInputObserver",
+    "UniformQuantizationObserverBase",
+    "add_quant_dequant",
+    "convert",
+    "convert_dynamic_jit",
+    "convert_jit",
+    "default_affine_fixed_qparams_fake_quant",
+    "default_affine_fixed_qparams_observer",
+    "default_debug_observer",
+    "default_dynamic_fake_quant",
+    "default_dynamic_quant_observer",
+    "default_embedding_fake_quant",
+    "default_embedding_fake_quant_4bit",
+    "default_eval_fn",
+    "default_fake_quant",
+    "default_fixed_qparams_range_0to1_fake_quant",
+    "default_fixed_qparams_range_0to1_observer",
+    "default_fixed_qparams_range_neg1to1_fake_quant",
+    "default_fixed_qparams_range_neg1to1_observer",
+    "default_float_qparams_observer",
+    "default_float_qparams_observer_4bit",
+    "default_fused_act_fake_quant",
+    "default_fused_per_channel_wt_fake_quant",
+    "default_fused_wt_fake_quant",
+    "default_histogram_fake_quant",
+    "default_histogram_observer",
+    "default_observer",
+    "default_per_channel_weight_fake_quant",
+    "default_per_channel_weight_observer",
+    "default_placeholder_observer",
+    "default_reuse_input_observer",
+    "default_symmetric_fixed_qparams_fake_quant",
+    "default_symmetric_fixed_qparams_observer",
+    "default_weight_fake_quant",
+    "default_weight_observer",
+    "disable_fake_quant",
+    "disable_observer",
+    "enable_fake_quant",
+    "enable_observer",
+    "fuse_conv_bn",
+    "fuse_conv_bn_jit",
+    "fuse_conv_bn_relu",
+    "fuse_convtranspose_bn",
+    "fuse_linear_bn",
+    "fuse_modules",
+    "fuse_modules_qat",
+    "fused_per_channel_wt_fake_quant_range_neg_127_to_127",
+    "fused_wt_fake_quant_range_neg_127_to_127",
+    "get_combined_dict",
+    "get_default_compare_output_module_list",
+    "get_default_custom_config_dict",
+    "get_default_dynamic_quant_module_mappings",
+    "get_default_dynamic_sparse_quant_module_mappings",
+    "get_default_float_to_quantized_operator_mappings",
+    "get_default_qat_module_mappings",
+    "get_default_qat_qconfig",
+    "get_default_qat_qconfig_dict",
+    "get_default_qat_qconfig_mapping",
+    "get_default_qconfig",
+    "get_default_qconfig_dict",
+    "get_default_qconfig_mapping",
+    "get_default_qconfig_propagation_list",
+    "get_default_static_quant_module_mappings",
+    "get_default_static_quant_reference_module_mappings",
+    "get_default_static_sparse_quant_module_mappings",
+    "get_dynamic_quant_module_class",
+    "get_embedding_qat_module_mappings",
+    "get_embedding_static_quant_module_mappings",
+    "get_fuser_method",
+    "get_fuser_method_new",
+    "get_observer_state_dict",
+    "get_quantized_operator",
+    "get_static_quant_module_class",
+    "load_observer_state_dict",
+    "move_exported_model_to_eval",
+    "move_exported_model_to_train",
+    "allow_exported_model_train_eval",
+    "no_observer_set",
+    "per_channel_weight_observer_range_neg_127_to_127",
+    "prepare",
+    "prepare_dynamic_jit",
+    "prepare_jit",
+    "prepare_qat",
+    "propagate_qconfig_",
+    "qconfig_equals",
+    "quantize",
+    "quantize_dynamic",
+    "quantize_dynamic_jit",
+    "quantize_jit",
+    "quantize_qat",
+    "script_qconfig",
+    "script_qconfig_dict",
+    "swap_module",
+    "weight_observer_range_neg_127_to_127",
+    "generate_numeric_debug_handle",
+    "CUSTOM_KEY",
+    "NUMERIC_DEBUG_HANDLE_KEY",
+    "prepare_for_propagation_comparison",
+    "extract_results_from_loggers",
+    "compare_results",
+    # from torchao, should be merged with torchao
+    # in the future
+    "AffineQuantizedObserverBase",
+    "Granularity",
+    "MappingType",
+    "PerAxis",
+    "PerBlock",
+    "PerGroup",
+    "PerRow",
+    "PerTensor",
+    "PerToken",
+    "TorchAODType",
+    "ZeroPointDomain",
+    "get_block_size",
+]
+
+
+def default_eval_fn(model, calib_data):
+    r"""Define the default evaluation function.
+
+    Default evaluation function takes a torch.utils.data.Dataset or a list of
+    input Tensors and run the model on the dataset
+    """
+    for data, _target in calib_data:
+        model(data)
+
+
+class _DerivedObserverOrFakeQuantize(ObserverBase):
+    r"""This observer is used to describe an observer whose quantization parameters
+    are derived from other observers
+    """
+
+    def __init__(
+        self,
+        dtype: torch.dtype,
+        obs_or_fqs: list[ObserverOrFakeQuantize],
+        derive_qparams_fn: Callable[
+            [list[ObserverOrFakeQuantize]], tuple[Tensor, Tensor]
+        ],
+        quant_min: int | None = None,
+        quant_max: int | None = None,
+        qscheme: torch.qscheme | None = None,
+        ch_axis: int | None = None,
+    ):
+        super().__init__(dtype)
+        self.obs_or_fqs = obs_or_fqs
+        self.derive_qparams_fn = derive_qparams_fn
+        self.quant_min = quant_min
+        self.quant_max = quant_max
+        self.qscheme = qscheme
+        self.ch_axis = ch_axis
+
+        from .utils import is_per_channel
+
+        if is_per_channel(self.qscheme):
+            if self.ch_axis is None:
+                raise AssertionError(
+                    "Must provide a valid ch_axis if qscheme is per channel"
+                )
+
+    def forward(self, x: Tensor) -> Tensor:
+        return x
+
+    def calculate_qparams(self):  # type:ignore[override]
+        return self.derive_qparams_fn(self.obs_or_fqs)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/_correct_bias.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/_correct_bias.py
new file mode 100644
index 0000000000000000000000000000000000000000..4309e4530cb72bd6620a69527cbe87e2a533c323
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/_correct_bias.py
@@ -0,0 +1,156 @@
+# mypy: allow-untyped-defs
+import torch
+import torch.ao.nn.quantized as nnq
+import torch.ao.ns._numeric_suite as ns
+import torch.ao.quantization
+import torch.nn as nn
+
+
+__all__ = [
+    "get_module",
+    "parent_child_names",
+    "get_param",
+    "MeanShadowLogger",
+    "bias_correction",
+]
+
+_supported_modules = {nn.Linear, nn.Conv2d}
+_supported_modules_quantized = {nnq.Linear, nnq.Conv2d}
+
+
+def get_module(model, name):
+    """Given name of submodule, this function grabs the submodule from given model."""
+    return dict(model.named_modules())[name]
+
+
+def parent_child_names(name):
+    """Split full name of submodule into parent submodule's full name and submodule's name."""
+    split_name = name.rsplit(".", 1)
+    if len(split_name) == 1:
+        return "", split_name[0]
+    else:
+        return split_name[0], split_name[1]
+
+
+def get_param(module, attr):
+    """Get the parameter given a module and attribute.
+
+    Sometimes the weights/bias attribute gives you the raw tensor, but sometimes
+    gives a function that will give you the raw tensor, this function takes care of that logic
+    """
+    param = getattr(module, attr, None)
+    if callable(param):
+        return param()
+    else:
+        return param
+
+
+class MeanShadowLogger(ns.Logger):
+    """Mean Logger for a Shadow module.
+
+    A logger for a Shadow module whose purpose is to record the rolling mean
+    of the data passed to the floating point and quantized models
+    """
+
+    def __init__(self):
+        """Set up initial values for float and quantized stats, count, float sum, and quant sum."""
+        super().__init__()
+        self.stats["float"] = None
+        self.stats["quantized"] = None
+        self.count = 0
+        self.float_sum = None
+        self.quant_sum = None
+
+    def forward(self, x, y):  # type: ignore[override]
+        """Compute the average of quantized and floating-point data from modules.
+
+        The inputs x,y are output data from the quantized and floating-point modules.
+        x is for the quantized module, y is for the floating point module
+        """
+        if x.is_quantized:
+            x = x.dequantize()
+
+        self.count += 1
+        if self.stats["quantized"] is None:
+            self.stats["quantized"] = x
+            self.quant_sum = x
+        else:
+            self.quant_sum += x
+            self.stats["quantized"] = self.quant_sum / self.count
+
+        if self.stats["float"] is None:
+            self.stats["float"] = y
+            self.float_sum = y
+        else:
+            self.float_sum += y
+            self.stats["float"] = self.float_sum / self.count
+
+    def clear(self):
+        self.stats["float"] = None
+        self.stats["quantized"] = None
+        self.count = 0
+        self.float_sum = None
+        self.quant_sum = None
+
+
+def bias_correction(
+    float_model,
+    quantized_model,
+    img_data,
+    target_modules=_supported_modules_quantized,
+    neval_batches=None,
+):
+    """Perform bias correction on a module.
+
+    Using numeric suite shadow module, the expected output of the floating point and quantized modules
+    is recorded. Using that data the bias of supported modules is shifted to compensate for the drift caused
+    by quantization
+    Paper reference: https://arxiv.org/pdf/1906.04721.pdf (Section 4.2)
+
+    Args:
+        float_model: a trained model that serves as a reference to what bias correction should aim for
+        quantized_model: quantized form of float_model that bias correction is to applied to
+        img_data: calibration data to estimate the expected output (used to find quantization error)
+        target_modules: specifies what submodules in quantized_model need bias correction (can be extended to
+                unquantized submodules)
+        neval_batches: a cap to the number of batches you want to be used for estimating the expected output
+    """
+    ns.prepare_model_with_stubs(
+        float_model, quantized_model, _supported_modules, MeanShadowLogger
+    )
+
+    uncorrected_modules = {
+        name: submodule
+        for name, submodule in quantized_model.named_modules()
+        if type(submodule) in target_modules
+    }
+
+    for uncorrected_module in uncorrected_modules:
+        quantized_submodule = get_module(quantized_model, uncorrected_module)
+        bias = get_param(quantized_submodule, "bias")
+        if bias is not None:
+            for count, data in enumerate(img_data, start=1):
+                quantized_model(data[0])
+                if count == neval_batches:
+                    break
+            ob_dict = ns.get_logger_dict(quantized_model)
+            parent_name, _ = parent_child_names(uncorrected_module)
+
+            float_data = ob_dict[parent_name + ".stats"]["float"]
+            quant_data = ob_dict[parent_name + ".stats"]["quantized"]
+
+            # math for expected_error
+            quantization_error = quant_data - float_data
+            dims = list(range(quantization_error.dim()))
+            # Note: we don't want to take the mean over the output channel dimension
+            dims.remove(1)
+            expected_error = torch.mean(quantization_error, dims)
+
+            updated_bias = bias.data - expected_error
+
+            bias.data = updated_bias
+
+            # Resets the data contained in the loggers
+            for submodule in quantized_model.modules():
+                if isinstance(submodule, MeanShadowLogger):
+                    submodule.clear()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/_equalize.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/_equalize.py
new file mode 100644
index 0000000000000000000000000000000000000000..e4ff327f285aa4c17f05a9cbf61b7323a0536a12
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/_equalize.py
@@ -0,0 +1,279 @@
+# mypy: allow-untyped-defs
+import copy
+from itertools import chain
+from typing import Any
+
+import torch
+
+
+__all__ = [
+    "set_module_weight",
+    "set_module_bias",
+    "has_bias",
+    "get_module_weight",
+    "get_module_bias",
+    "max_over_ndim",
+    "min_over_ndim",
+    "channel_range",
+    "get_name_by_module",
+    "cross_layer_equalization",
+    "process_paired_modules_list_to_name",
+    "expand_groups_in_paired_modules_list",
+    "equalize",
+    "converged",
+]
+
+_supported_types = {torch.nn.Conv2d, torch.nn.Linear, torch.nn.Conv1d}
+_supported_intrinsic_types = {
+    torch.ao.nn.intrinsic.ConvReLU2d,
+    torch.ao.nn.intrinsic.LinearReLU,
+    torch.ao.nn.intrinsic.ConvReLU1d,
+}
+_all_supported_types = _supported_types.union(_supported_intrinsic_types)
+
+
+def set_module_weight(module, weight) -> None:
+    if type(module) in _supported_types:
+        module.weight = torch.nn.Parameter(weight)
+    else:
+        module[0].weight = torch.nn.Parameter(weight)
+
+
+def set_module_bias(module, bias) -> None:
+    if type(module) in _supported_types:
+        module.bias = torch.nn.Parameter(bias)
+    else:
+        module[0].bias = torch.nn.Parameter(bias)
+
+
+def has_bias(module) -> bool:
+    if type(module) in _supported_types:
+        return module.bias is not None
+    else:
+        return module[0].bias is not None
+
+
+def get_module_weight(module):
+    if type(module) in _supported_types:
+        return module.weight
+    else:
+        return module[0].weight
+
+
+def get_module_bias(module):
+    if type(module) in _supported_types:
+        return module.bias
+    else:
+        return module[0].bias
+
+
+def max_over_ndim(input, axis_list, keepdim=False):
+    """Apply 'torch.max' over the given axes."""
+    axis_list.sort(reverse=True)
+    for axis in axis_list:
+        input, _ = input.max(axis, keepdim)
+    return input
+
+
+def min_over_ndim(input, axis_list, keepdim=False):
+    """Apply 'torch.min' over the given axes."""
+    axis_list.sort(reverse=True)
+    for axis in axis_list:
+        input, _ = input.min(axis, keepdim)
+    return input
+
+
+def channel_range(input, axis=0):
+    """Find the range of weights associated with a specific channel."""
+    size_of_tensor_dim = input.ndim
+    axis_list = list(range(size_of_tensor_dim))
+    axis_list.remove(axis)
+
+    mins = min_over_ndim(input, axis_list)
+    maxs = max_over_ndim(input, axis_list)
+
+    if mins.size(0) != input.size(axis):
+        raise AssertionError(
+            "Dimensions of resultant channel range does not match size of requested axis"
+        )
+    return maxs - mins
+
+
+def get_name_by_module(model, module):
+    """Get the name of a module within a model.
+
+    Args:
+        model: a model (nn.module) that equalization is to be applied on
+        module: a module within the model
+
+    Returns:
+        name: the name of the module within the model
+    """
+    for name, m in model.named_modules():
+        if m is module:
+            return name
+    raise ValueError("module is not in the model")
+
+
+def cross_layer_equalization(module1, module2, output_axis=0, input_axis=1):
+    """Scale the range of Tensor1.output to equal Tensor2.input.
+
+    Given two adjacent tensors', the weights are scaled such that
+    the ranges of the first tensors' output channel are equal to the
+    ranges of the second tensors' input channel
+    """
+    if (
+        type(module1) not in _all_supported_types
+        or type(module2) not in _all_supported_types
+    ):
+        raise ValueError(
+            "module type not supported:", type(module1), " ", type(module2)
+        )
+
+    bias = get_module_bias(module1) if has_bias(module1) else None
+
+    weight1 = get_module_weight(module1)
+    weight2 = get_module_weight(module2)
+
+    if weight1.size(output_axis) != weight2.size(input_axis):
+        raise TypeError(
+            "Number of output channels of first arg do not match \
+        number input channels of second arg"
+        )
+
+    weight1_range = channel_range(weight1, output_axis)
+    weight2_range = channel_range(weight2, input_axis)
+
+    # producing scaling factors to applied
+    weight2_range += 1e-9
+    scaling_factors = torch.sqrt(weight1_range / weight2_range)
+    inverse_scaling_factors = torch.reciprocal(scaling_factors)
+
+    if bias is not None:
+        bias = bias * inverse_scaling_factors
+
+    # formatting the scaling (1D) tensors to be applied on the given argument tensors
+    # pads axis to (1D) tensors to then be broadcasted
+    size1 = [1] * weight1.ndim
+    size1[output_axis] = weight1.size(output_axis)
+    size2 = [1] * weight2.ndim
+    size2[input_axis] = weight2.size(input_axis)
+
+    scaling_factors = torch.reshape(scaling_factors, size2)
+    inverse_scaling_factors = torch.reshape(inverse_scaling_factors, size1)
+
+    weight1 = weight1 * inverse_scaling_factors
+    weight2 = weight2 * scaling_factors
+
+    set_module_weight(module1, weight1)
+    if bias is not None:
+        set_module_bias(module1, bias)
+    set_module_weight(module2, weight2)
+
+
+def process_paired_modules_list_to_name(model, paired_modules_list):
+    """Processes a list of paired modules to a list of names of paired modules."""
+
+    for group in paired_modules_list:
+        for i, item in enumerate(group):
+            if isinstance(item, torch.nn.Module):
+                group[i] = get_name_by_module(model, item)
+            elif not isinstance(item, str):
+                raise TypeError("item must be a nn.Module or a string")
+    return paired_modules_list
+
+
+def expand_groups_in_paired_modules_list(paired_modules_list):
+    """Expands module pair groups larger than two into groups of two modules."""
+    new_list = []
+
+    for group in paired_modules_list:
+        if len(group) == 1:
+            raise ValueError("Group must have at least two modules")
+        elif len(group) == 2:
+            new_list.append(group)
+        elif len(group) > 2:
+            new_list.extend([group[i], group[i + 1]] for i in range(len(group) - 1))
+
+    return new_list
+
+
+def equalize(model, paired_modules_list, threshold=1e-4, inplace=True):
+    """Equalize modules until convergence is achieved.
+
+    Given a list of adjacent modules within a model, equalization will
+    be applied between each pair, this will repeated until convergence is achieved
+
+    Keeps a copy of the changing modules from the previous iteration, if the copies
+    are not that different than the current modules (determined by converged_test),
+    then the modules have converged enough that further equalizing is not necessary
+
+    Reference is section 4.1 of this paper https://arxiv.org/pdf/1906.04721.pdf
+
+    Args:
+        model: a model (nn.Module) that equalization is to be applied on
+            paired_modules_list (List(List[nn.module || str])): a list of lists
+            where each sublist is a pair of two submodules found in the model,
+            for each pair the two modules have to be adjacent in the model,
+            with only piece-wise-linear functions like a (P)ReLU or LeakyReLU in between
+            to get expected results.
+            The list can contain either modules, or names of modules in the model.
+            If you pass multiple modules in the same list, they will all be equalized together.
+            threshold (float): a number used by the converged function to determine what degree
+            of similarity between models is necessary for them to be called equivalent
+        inplace (bool): determines if function is inplace or not
+    """
+
+    paired_modules_list = process_paired_modules_list_to_name(
+        model, paired_modules_list
+    )
+
+    if not inplace:
+        model = copy.deepcopy(model)
+
+    paired_modules_list = expand_groups_in_paired_modules_list(paired_modules_list)
+
+    name_to_module: dict[str, torch.nn.Module] = {}
+    previous_name_to_module: dict[str, Any] = {}
+    name_set = set(chain.from_iterable(paired_modules_list))
+
+    for name, module in model.named_modules():
+        if name in name_set:
+            name_to_module[name] = module
+            previous_name_to_module[name] = None
+    while not converged(name_to_module, previous_name_to_module, threshold):
+        for pair in paired_modules_list:
+            previous_name_to_module[pair[0]] = copy.deepcopy(name_to_module[pair[0]])
+            previous_name_to_module[pair[1]] = copy.deepcopy(name_to_module[pair[1]])
+
+            cross_layer_equalization(name_to_module[pair[0]], name_to_module[pair[1]])
+
+    return model
+
+
+def converged(curr_modules, prev_modules, threshold=1e-4):
+    """Test whether modules are converged to a specified threshold.
+
+    Tests for the summed norm of the differences between each set of modules
+    being less than the given threshold
+
+    Takes two dictionaries mapping names to modules, the set of names for each dictionary
+    should be the same, looping over the set of names, for each name take the difference
+    between the associated modules in each dictionary
+
+    """
+    if curr_modules.keys() != prev_modules.keys():
+        raise ValueError(
+            "The keys to the given mappings must have the same set of names of modules"
+        )
+
+    summed_norms = torch.tensor(0.0)
+    if None in prev_modules.values():
+        return False
+    for name in curr_modules:
+        curr_weight = get_module_weight(curr_modules[name])
+        prev_weight = get_module_weight(prev_modules[name])
+
+        difference = curr_weight.sub(prev_weight)
+        summed_norms += torch.norm(difference)
+    return bool(summed_norms < threshold)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/_learnable_fake_quantize.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/_learnable_fake_quantize.py
new file mode 100644
index 0000000000000000000000000000000000000000..00b824f8d1ecfe2086576eb3a4c16c4321e9e892
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/_learnable_fake_quantize.py
@@ -0,0 +1,199 @@
+# mypy: allow-untyped-defs
+
+import torch
+from torch.nn.parameter import Parameter
+
+
+__all__: list[str] = []
+
+
+class _LearnableFakeQuantize(torch.ao.quantization.FakeQuantizeBase):
+    r"""Generalized extension of the FakeQuantize module in fake_quantize.py.
+
+    This is an extension of the FakeQuantize module in fake_quantize.py, which
+    supports more generalized lower-bit quantization and supports learning of the scale
+    and zero point parameters through backpropagation.
+
+    In addition to the attributes in the original FakeQuantize module, the _LearnableFakeQuantize
+    module also includes the following attributes to support quantization parameter learning.
+
+    * :attr:`channel_len` defines the length of the channel when initializing scale and zero point
+      for the per channel case.
+
+    * :attr:`use_grad_scaling` defines the flag for whether the gradients for scale and zero point are
+      normalized by the constant, which is proportional to the square root of the number of
+      elements in the tensor. The related literature justifying the use of this particular constant
+      can be found here: https://openreview.net/pdf?id=rkgO66VKDS.
+
+    * :attr:`fake_quant_enabled` defines the flag for enabling fake quantization on the output.
+
+    * :attr:`static_enabled` defines the flag for using observer's static estimation for
+      scale and zero point.
+
+    * :attr:`learning_enabled` defines the flag for enabling backpropagation for scale and zero point.
+    """
+
+    def __init__(
+        self,
+        observer,
+        quant_min=0,
+        quant_max=255,
+        scale=1.0,
+        zero_point=0.0,
+        channel_len=-1,
+        use_grad_scaling=False,
+        **observer_kwargs,
+    ):
+        super().__init__()
+        if quant_min >= quant_max:
+            raise AssertionError("quant_min must be strictly less than quant_max.")
+        self.quant_min = quant_min
+        self.quant_max = quant_max
+        # also pass quant_min and quant_max to observer
+        observer_kwargs["quant_min"] = quant_min
+        observer_kwargs["quant_max"] = quant_max
+        self.use_grad_scaling = use_grad_scaling
+        if channel_len == -1:
+            self.scale = Parameter(torch.tensor([scale]))
+            self.zero_point = Parameter(torch.tensor([zero_point]))
+        else:
+            if not (isinstance(channel_len, int) and channel_len > 0):
+                raise AssertionError("Channel size must be a positive integer.")
+            self.scale = Parameter(torch.tensor([scale] * channel_len))
+            self.zero_point = Parameter(torch.tensor([zero_point] * channel_len))
+
+        self.activation_post_process = observer(**observer_kwargs)
+        if torch.iinfo(self.activation_post_process.dtype).min > quant_min:
+            raise AssertionError("quant_min out of bound")
+        if quant_max > torch.iinfo(self.activation_post_process.dtype).max:
+            raise AssertionError("quant_max out of bound")
+        self.dtype = self.activation_post_process.dtype
+        self.qscheme = self.activation_post_process.qscheme
+        self.ch_axis = (
+            self.activation_post_process.ch_axis
+            if hasattr(self.activation_post_process, "ch_axis")
+            else -1
+        )
+        self.register_buffer("fake_quant_enabled", torch.tensor([1], dtype=torch.uint8))
+        self.register_buffer("static_enabled", torch.tensor([1], dtype=torch.uint8))
+        self.register_buffer("learning_enabled", torch.tensor([0], dtype=torch.uint8))
+
+        bitrange = torch.tensor(quant_max - quant_min + 1).double()
+        self.bitwidth = int(torch.log2(bitrange).item())
+        self.register_buffer("eps", torch.tensor([torch.finfo(torch.float32).eps]))
+
+    @torch.jit.export
+    def enable_param_learning(self):
+        r"""Enable parameter learning over static observer estimates.
+
+        Enables learning of quantization parameters and
+        disables static observer estimates. Forward path returns fake quantized X.
+        """
+        self.toggle_qparam_learning(enabled=True).toggle_fake_quant(
+            enabled=True
+        ).toggle_observer_update(enabled=False)
+        return self
+
+    @torch.jit.export
+    def enable_static_estimate(self):
+        """Enable static estimates of quantization parameters.
+
+        Enables static observer estimates and disables learning of
+        quantization parameters. Forward path returns fake quantized X.
+        """
+        self.toggle_qparam_learning(enabled=False).toggle_fake_quant(
+            enabled=True
+        ).toggle_observer_update(enabled=True)
+
+    @torch.jit.export
+    def enable_static_observation(self):
+        """Enable accumulation of data without updating quantization parameters.
+
+        Enables static observer accumulating data from input but doesn't
+        update the quantization parameters. Forward path returns the original X.
+        """
+        self.toggle_qparam_learning(enabled=False).toggle_fake_quant(
+            enabled=False
+        ).toggle_observer_update(enabled=True)
+
+    @torch.jit.export
+    def toggle_observer_update(self, enabled=True):
+        self.static_enabled[0] = int(enabled)  # type: ignore[operator]
+        return self
+
+    @torch.jit.export
+    def enable_observer(self, enabled=True):
+        self.toggle_observer_update(enabled)
+
+    @torch.jit.export
+    def toggle_qparam_learning(self, enabled=True):
+        self.learning_enabled[0] = int(enabled)  # type: ignore[operator]
+        self.scale.requires_grad = enabled
+        self.zero_point.requires_grad = enabled
+        return self
+
+    @torch.jit.export
+    def toggle_fake_quant(self, enabled=True):
+        self.fake_quant_enabled[0] = int(enabled)
+        return self
+
+    @torch.jit.export
+    def observe_quant_params(self):
+        print(f"_LearnableFakeQuantize Scale: {self.scale.detach()}")
+        print(f"_LearnableFakeQuantize Zero Point: {self.zero_point.detach()}")
+
+    @torch.jit.export
+    def calculate_qparams(self):  # type: ignore[override]
+        self.scale.data.clamp_(min=self.eps.item())  # type: ignore[operator]
+        scale = self.scale.detach()
+        zero_point = (
+            self.zero_point.detach()
+            .round()
+            .clamp(self.quant_min, self.quant_max)
+            .long()
+        )
+        return scale, zero_point
+
+    def forward(self, X):
+        if self.static_enabled[0] == 1:  # type: ignore[index]
+            self.activation_post_process(X.detach())
+            _scale, _zero_point = self.activation_post_process.calculate_qparams()
+            _scale = _scale.to(self.scale.device)
+            _zero_point = _zero_point.to(self.zero_point.device)
+            self.scale.data.copy_(_scale)
+            self.zero_point.data.copy_(_zero_point)
+        else:
+            self.scale.data.clamp_(min=self.eps.item())  # type: ignore[operator]
+
+        if self.fake_quant_enabled[0] == 1:
+            if self.qscheme in (
+                torch.per_channel_symmetric,
+                torch.per_tensor_symmetric,
+            ):
+                self.zero_point.data.zero_()
+
+            if self.use_grad_scaling:
+                grad_factor = 1.0 / (X.numel() * self.quant_max) ** 0.5
+            else:
+                grad_factor = 1.0
+            if self.qscheme in (torch.per_channel_symmetric, torch.per_channel_affine):
+                X = torch._fake_quantize_learnable_per_channel_affine(
+                    X,
+                    self.scale,
+                    self.zero_point,
+                    self.ch_axis,
+                    self.quant_min,
+                    self.quant_max,
+                    grad_factor,
+                )
+            else:
+                X = torch._fake_quantize_learnable_per_tensor_affine(
+                    X,
+                    self.scale,
+                    self.zero_point,
+                    self.quant_min,
+                    self.quant_max,
+                    grad_factor,
+                )
+
+        return X
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d452359d41c36dc719e6df932b6fb018ca6a36b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/__init__.py
@@ -0,0 +1,30 @@
+from .backend_config import (
+    BackendConfig,
+    BackendPatternConfig,
+    DTypeConfig,
+    DTypeWithConstraints,
+    ObservationType,
+)
+from .executorch import get_executorch_backend_config
+from .fbgemm import get_fbgemm_backend_config
+from .native import get_native_backend_config, get_native_backend_config_dict
+from .onednn import get_onednn_backend_config
+from .qnnpack import get_qnnpack_backend_config
+from .tensorrt import get_tensorrt_backend_config, get_tensorrt_backend_config_dict
+
+
+__all__ = [
+    "get_fbgemm_backend_config",
+    "get_native_backend_config",
+    "get_native_backend_config_dict",
+    "get_qnnpack_backend_config",
+    "get_tensorrt_backend_config",
+    "get_tensorrt_backend_config_dict",
+    "get_executorch_backend_config",
+    "BackendConfig",
+    "BackendPatternConfig",
+    "DTypeConfig",
+    "DTypeWithConstraints",
+    "ObservationType",
+    "get_onednn_backend_config",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/_common_operator_config_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/_common_operator_config_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..9cb322fd85d2c2b07a68f3b436e4f0536ac87e28
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/_common_operator_config_utils.py
@@ -0,0 +1,782 @@
+# mypy: allow-untyped-defs
+import copy
+import operator
+from collections import namedtuple
+from collections.abc import Callable
+
+import torch
+import torch.ao.nn.intrinsic as nni
+import torch.ao.nn.intrinsic.qat as nniqat
+import torch.ao.nn.qat as nnqat
+import torch.ao.nn.quantized.reference as nnqr
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.ao.quantization.fuser_method_mappings import (
+    _sequential_wrapper2,
+    fuse_conv_bn,
+    fuse_conv_bn_relu,
+    fuse_convtranspose_bn,
+    fuse_linear_bn,
+)
+
+from .backend_config import (
+    BackendPatternConfig,
+    DTypeConfig,
+    DTypeWithConstraints,
+    ObservationType,
+)
+
+
+__all__: list[str] = []
+
+# TODO: rename to be more explicit, e.g. qat_conv_relu
+_ConvMetadata = namedtuple(
+    "_ConvMetadata",
+    [
+        "root",
+        "transpose",
+        "bn",
+        "reference",
+        "transpose_reference",
+        "fused_conv_relu",
+        "fused_conv_bn",
+        "fused_conv_bn_relu",
+        "qat",
+        "relu_qat",
+        "bn_qat",
+        "bn_relu_qat",
+        "func",
+        "func_transpose",
+    ],
+)
+_Conv1dMetadata = _ConvMetadata(
+    nn.Conv1d,
+    nn.ConvTranspose1d,
+    nn.BatchNorm1d,
+    nnqr.Conv1d,
+    nnqr.ConvTranspose1d,
+    nni.ConvReLU1d,
+    nni.ConvBn1d,
+    nni.ConvBnReLU1d,
+    nnqat.Conv1d,
+    nniqat.ConvReLU1d,
+    nniqat.ConvBn1d,
+    nniqat.ConvBnReLU1d,
+    F.conv1d,
+    F.conv_transpose1d,
+)
+_Conv2dMetadata = _ConvMetadata(
+    nn.Conv2d,
+    nn.ConvTranspose2d,
+    nn.BatchNorm2d,
+    nnqr.Conv2d,
+    nnqr.ConvTranspose2d,
+    nni.ConvReLU2d,
+    nni.ConvBn2d,
+    nni.ConvBnReLU2d,
+    nnqat.Conv2d,
+    nniqat.ConvReLU2d,
+    nniqat.ConvBn2d,
+    nniqat.ConvBnReLU2d,
+    F.conv2d,
+    F.conv_transpose2d,
+)
+_Conv3dMetadata = _ConvMetadata(
+    nn.Conv3d,
+    nn.ConvTranspose3d,
+    nn.BatchNorm3d,
+    nnqr.Conv3d,
+    nnqr.ConvTranspose3d,
+    nni.ConvReLU3d,
+    nni.ConvBn3d,
+    nni.ConvBnReLU3d,
+    nnqat.Conv3d,
+    nniqat.ConvReLU3d,
+    nniqat.ConvBn3d,
+    nniqat.ConvBnReLU3d,
+    F.conv3d,
+    F.conv_transpose3d,
+)
+
+# Add constraints for fixed qparams ops like sigmoid and tanh to ensure values
+# fall within the proper ranges, e.g. [0, 1] for sigmoid, [-1, 1] for tanh
+_FIXED_QPARAM_OP_0TO1_CONSTRAINTS = DTypeWithConstraints(
+    dtype=torch.quint8,
+    quant_min_lower_bound=0,
+    quant_max_upper_bound=255,
+    scale_exact_match=1.0 / 256.0,
+    zero_point_exact_match=0,
+)
+_FIXED_QPARAM_OP_NEG1TO1_CONSTRAINTS = DTypeWithConstraints(
+    dtype=torch.quint8,
+    quant_min_lower_bound=0,
+    quant_max_upper_bound=255,
+    scale_exact_match=2.0 / 256.0,
+    zero_point_exact_match=128,
+)
+_FIXED_QPARAMS_OP_TO_CONSTRAINTS: dict[Callable | str, DTypeWithConstraints] = {
+    torch.nn.Hardsigmoid: _FIXED_QPARAM_OP_0TO1_CONSTRAINTS,
+    torch.nn.functional.hardsigmoid: _FIXED_QPARAM_OP_0TO1_CONSTRAINTS,
+    "hardsigmoid": _FIXED_QPARAM_OP_0TO1_CONSTRAINTS,
+    "hardsigmoid_": _FIXED_QPARAM_OP_0TO1_CONSTRAINTS,
+    torch.nn.Sigmoid: _FIXED_QPARAM_OP_0TO1_CONSTRAINTS,
+    torch.sigmoid: _FIXED_QPARAM_OP_0TO1_CONSTRAINTS,
+    "sigmoid": _FIXED_QPARAM_OP_0TO1_CONSTRAINTS,
+    "sigmoid_": _FIXED_QPARAM_OP_0TO1_CONSTRAINTS,
+    torch.nn.Softmax: _FIXED_QPARAM_OP_0TO1_CONSTRAINTS,
+    torch.nn.Tanh: _FIXED_QPARAM_OP_NEG1TO1_CONSTRAINTS,
+    torch.tanh: _FIXED_QPARAM_OP_NEG1TO1_CONSTRAINTS,
+    "tanh": _FIXED_QPARAM_OP_NEG1TO1_CONSTRAINTS,
+    "tanh_": _FIXED_QPARAM_OP_NEG1TO1_CONSTRAINTS,
+}
+
+
+def _get_binary_op_configs(
+    dtype_configs: list[DTypeConfig],
+) -> list[BackendPatternConfig]:
+    binary_op_configs: list[BackendPatternConfig] = []
+    num_tensor_args_to_observation_type_mapping = {
+        # TODO: this is not used right now since we have extra check in prepare
+        # will need to change this to NO_OBSERVER later after we implemented
+        # Tensor dtype inference properly
+        0: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
+        1: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
+        2: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
+    }
+    for op_with_quantized_bop_scalar_variant in [
+        operator.add,
+        torch.add,
+        operator.mul,
+        torch.mul,
+    ]:
+        bop_patterns = [
+            (op_with_quantized_bop_scalar_variant, nn.ReLU),
+            (op_with_quantized_bop_scalar_variant, F.relu),
+            (op_with_quantized_bop_scalar_variant, torch.relu),
+            op_with_quantized_bop_scalar_variant,
+        ]
+        binary_op_configs.extend(
+            BackendPatternConfig(bop_pattern)
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            ._set_num_tensor_args_to_observation_type(
+                num_tensor_args_to_observation_type_mapping
+            )
+            for bop_pattern in bop_patterns
+        )
+    # matmul
+    binary_op_configs.append(
+        BackendPatternConfig(torch.matmul).set_dtype_configs(dtype_configs)  # noqa: E131
+    )
+    return binary_op_configs
+
+
+def _get_linear_configs(dtype_configs: list[DTypeConfig]) -> list[BackendPatternConfig]:
+    """
+    Return all configs related to linear modules and ops.
+    """
+    observation_type = ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+    linear_configs: list[BackendPatternConfig] = []
+
+    # (1) Single linear modules/functions
+    # -------------------------------------
+    # linear module
+    linear_configs.append(
+        BackendPatternConfig(torch.nn.Linear)
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        .set_root_module(torch.nn.Linear)
+        .set_reference_quantized_module(nnqr.Linear)
+        .set_qat_module(nnqat.Linear)
+    )
+    # linear qat module
+    linear_configs.append(
+        BackendPatternConfig(nnqat.Linear)
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        .set_root_module(torch.nn.Linear)
+        .set_reference_quantized_module(nnqr.Linear)
+    )
+    # functional linear
+    linear_configs.append(
+        BackendPatternConfig(torch.nn.functional.linear)
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        ._set_input_type_to_index({"weight": 1, "bias": 2})
+    )
+
+    # (2) Linear + relu
+    # -------------------
+    # 2.1 linear module + relu fusion config
+    # linear relu, linear module + relu module
+    linear_configs.append(
+        BackendPatternConfig((torch.nn.Linear, torch.nn.ReLU))
+        .set_dtype_configs(dtype_configs)  # noqa: E131
+        .set_fuser_method(_sequential_wrapper2(nni.LinearReLU))
+        .set_fused_module(nni.LinearReLU)
+    )
+    # linear relu, linear module + functional relu
+    linear_configs.append(
+        BackendPatternConfig((torch.nn.Linear, torch.nn.functional.relu))
+        .set_dtype_configs(dtype_configs)  # noqa: E131
+        .set_fuser_method(_sequential_wrapper2(nni.LinearReLU))
+        .set_fused_module(nni.LinearReLU)
+    )
+
+    # 2.2 linear module + relu, fused module configs
+    # linear relu, fused module
+    linear_configs.append(
+        BackendPatternConfig(nni.LinearReLU)
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        .set_root_module(torch.nn.Linear)
+        .set_reference_quantized_module(nnqr.Linear)
+        .set_qat_module(nniqat.LinearReLU)
+    )
+    # linear relu, qat fused module
+    linear_configs.append(
+        BackendPatternConfig(nniqat.LinearReLU)
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        .set_root_module(torch.nn.Linear)
+        .set_reference_quantized_module(nnqr.Linear)
+    )
+    # 2.3 functional linear + relu configs
+    # linear relu, functional linear + relu module
+    linear_configs.append(
+        BackendPatternConfig((F.linear, torch.nn.ReLU))
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+    )
+    # linear relu, functional linear + functional relu
+    linear_configs.append(
+        BackendPatternConfig((F.linear, F.relu))
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+    )
+
+    # (3) Linear + batchnorm
+    # ------------------------
+    # 3.1 linear bn fusion
+    linear_configs.append(
+        BackendPatternConfig((nn.Linear, nn.BatchNorm1d))
+        .set_dtype_configs(dtype_configs)  # noqa: E131
+        .set_fuser_method(fuse_linear_bn)
+        .set_fused_module(nni.LinearBn1d)
+    )
+
+    # 3.2 linear bn fused
+    # linear bn, fused module
+    linear_configs.append(
+        BackendPatternConfig(nni.LinearBn1d)
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        .set_root_module(torch.nn.Linear)
+        .set_reference_quantized_module(nnqr.Linear)
+        .set_qat_module(nniqat.LinearBn1d)
+    )
+    # linear bn, qat fused module
+    linear_configs.append(
+        BackendPatternConfig(nniqat.LinearBn1d)
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        .set_root_module(torch.nn.Linear)
+        .set_reference_quantized_module(nnqr.Linear)
+    )
+    return linear_configs
+
+
+def _get_conv_configs(dtype_configs):
+    """
+    Return all configs related to conv modules and ops.
+    """
+    conv_configs = []
+    observation_type = ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+    for convs in [_Conv1dMetadata, _Conv2dMetadata, _Conv3dMetadata]:
+        # (1) Single conv modules/functions
+        # -----------------------------------
+        # conv module
+        conv_configs.append(
+            BackendPatternConfig(convs.root)
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_root_module(convs.root)
+            .set_reference_quantized_module(convs.reference)
+            .set_qat_module(convs.qat)
+        )
+        # conv qat module
+        conv_configs.append(
+            BackendPatternConfig(convs.qat)
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_root_module(convs.root)
+            .set_reference_quantized_module(convs.reference)
+        )
+        # functional conv
+        conv_configs.append(
+            BackendPatternConfig(convs.func)
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            ._set_input_type_to_index({"weight": 1, "bias": 2})
+        )
+
+        # (2) Conv + relu
+        # -----------------
+        # 2.1 conv module + relu fusion configs
+        # conv relu fusion, conv module + relu module
+        conv_configs.append(
+            BackendPatternConfig((convs.root, torch.nn.ReLU))
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_fuser_method(_sequential_wrapper2(convs.fused_conv_relu))
+            .set_fused_module(convs.fused_conv_relu)
+        )
+        # conv relu fusion, conv module + functional relu
+        conv_configs.append(
+            BackendPatternConfig((convs.root, F.relu))
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_fuser_method(_sequential_wrapper2(convs.fused_conv_relu))
+            .set_fused_module(convs.fused_conv_relu)
+        )
+        # 2.2 conv module + relu fused module configs
+        # conv relu, fused module
+        conv_configs.append(
+            BackendPatternConfig(convs.fused_conv_relu)
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_root_module(convs.root)
+            .set_reference_quantized_module(convs.reference)
+            .set_qat_module(convs.relu_qat)
+        )
+        # conv relu, qat fused module
+        conv_configs.append(
+            BackendPatternConfig(convs.relu_qat)
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_root_module(convs.root)
+            .set_reference_quantized_module(convs.reference)
+        )
+        # 2.3 functional conv + relu configs
+        # conv relu, functional conv + relu module
+        conv_configs.append(
+            BackendPatternConfig((convs.func, torch.nn.ReLU))
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+        )
+        # conv relu, functional conv + functional relu
+        conv_configs.append(
+            BackendPatternConfig((convs.func, F.relu))
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+        )
+
+        # fused conv relu
+        conv_configs.append(
+            BackendPatternConfig(convs.fused_conv_relu)
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_qat_module(convs.relu_qat)
+        )
+
+        conv_configs.append(
+            BackendPatternConfig(convs.relu_qat)
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_root_module(convs.root)
+            .set_reference_quantized_module(convs.reference)
+        )
+
+        # (3) Conv + batchnorm (+ relu)
+        # -------------------------------
+        # 3.1 conv bn fusion configs
+        # conv + bn fusion
+        conv_configs.append(
+            BackendPatternConfig((convs.root, convs.bn))
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_fuser_method(fuse_conv_bn)
+            .set_fused_module(convs.fused_conv_bn)
+        )
+        # conv + bn + relu module fusion
+        conv_configs.append(
+            BackendPatternConfig((convs.root, convs.bn, nn.ReLU))
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_fuser_method(fuse_conv_bn_relu)
+            .set_fused_module(convs.fused_conv_bn_relu)
+        )
+        # conv + bn + relu functional fusion
+        conv_configs.append(
+            BackendPatternConfig((convs.root, convs.bn, F.relu))
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_root_module(convs.root)
+            .set_fuser_method(fuse_conv_bn_relu)
+            .set_fused_module(convs.fused_conv_bn_relu)
+        )
+        # TODO: we can add fusion for torch.relu as well
+
+        # 3.2 conv + bn (+ relu) fused module configs
+        # fused conv bn
+        conv_configs.append(
+            BackendPatternConfig(convs.fused_conv_bn)
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_qat_module(convs.bn_qat)
+        )
+
+        # fused conv bn relu
+        conv_configs.append(
+            BackendPatternConfig(convs.fused_conv_bn_relu)
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_qat_module(convs.bn_relu_qat)
+        )
+
+        # conv bn, qat fused module
+        conv_configs.append(
+            BackendPatternConfig(convs.bn_qat)
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_root_module(convs.root)
+            .set_reference_quantized_module(convs.reference)
+        )
+        # conv bn relu, qat fused module
+        conv_configs.append(
+            BackendPatternConfig(convs.bn_relu_qat)
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_root_module(convs.root)
+            .set_reference_quantized_module(convs.reference)
+        )
+
+        # (4) conv transpose and its fusion
+        # 4.1 conv transpose config
+        conv_configs.append(
+            BackendPatternConfig(convs.transpose)
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_root_module(convs.transpose)
+            .set_reference_quantized_module(convs.transpose_reference)
+        )
+
+        # 4.2 conv transpose + bn fusion
+        conv_configs.append(
+            BackendPatternConfig((convs.transpose, convs.bn))
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_fuser_method(fuse_convtranspose_bn)
+            .set_root_module(convs.transpose)
+            .set_reference_quantized_module(convs.transpose_reference)
+        )
+
+        # 4.3 functional conv transpose
+        conv_configs.append(
+            BackendPatternConfig(convs.func_transpose)
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            ._set_input_type_to_index({"weight": 1, "bias": 2})
+        )
+
+    return conv_configs
+
+
+def _get_cat_config(dtype_configs: list[DTypeConfig]) -> BackendPatternConfig:
+    return (
+        BackendPatternConfig(torch.cat)
+        .set_observation_type(ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT)
+        .set_dtype_configs(dtype_configs)
+    )
+
+
+def _get_ln_configs(dtype_configs: list[DTypeConfig]) -> list[BackendPatternConfig]:
+    ln_configs = []
+    ln_configs.append(
+        BackendPatternConfig(torch.nn.LayerNorm)
+        .set_observation_type(ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+    )
+    ln_configs.append(
+        BackendPatternConfig(torch.nn.functional.layer_norm)
+        .set_observation_type(ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        ._set_input_type_to_index({"weight": 2, "bias": 3})
+    )
+    return ln_configs
+
+
+def _get_default_op_configs(
+    dtype_configs: list[DTypeConfig],
+) -> list[BackendPatternConfig]:
+    default_ops = [
+        torch.nn.ELU,
+        torch.nn.LeakyReLU,
+        torch.nn.Hardswish,
+        torch.nn.InstanceNorm1d,
+        torch.nn.InstanceNorm2d,
+        torch.nn.InstanceNorm3d,
+        torch.nn.Dropout,
+        torch.nn.PReLU,
+        torch.nn.functional.elu,
+        torch.nn.functional.hardswish,
+        torch.nn.functional.leaky_relu,
+        torch.nn.functional.dropout,
+    ]
+    configs = [
+        BackendPatternConfig(op)
+        .set_observation_type(ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        for op in default_ops
+    ]
+
+    configs.append(
+        BackendPatternConfig(torch.nn.functional.group_norm)
+        .set_observation_type(ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        ._set_input_type_to_index({"weight": 2, "bias": 3})
+    )
+
+    configs.append(
+        BackendPatternConfig(torch.nn.functional.instance_norm)
+        .set_observation_type(ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        ._set_input_type_to_index({"weight": 3, "bias": 4})
+    )
+    return configs
+
+
+def _add_fixed_qparams_to_dtype_configs(
+    dtype_configs: list[DTypeConfig],
+    constraints: DTypeWithConstraints,
+) -> list[DTypeConfig]:
+    """
+    Return a copy of the list of DTypeConfigs where activations are subject to the specified
+    constraints required for fixed qparams ops.
+
+    If the data type doesn't match the one in the constraints, simply leave the corresponding
+    DTypeConfig unchanged.
+
+    If `scale_min_lower_bound` or `scale_max_upper_bound` is specified in the activations,
+    throw an exception since these settings are incompatible with fixed qparams ops.
+    """
+    new_dtype_configs = []
+    for dtype_config in dtype_configs:
+        dc = copy.deepcopy(dtype_config)
+        for orig_constraints in [
+            dc.input_dtype_with_constraints,
+            dc.output_dtype_with_constraints,
+        ]:
+            if orig_constraints.dtype != constraints.dtype:
+                continue
+            if orig_constraints.scale_min_lower_bound is not None:
+                raise ValueError(
+                    f"scale_min_lower_bound is invalid for fixed qparams ops: {dtype_config}"
+                )
+            if orig_constraints.scale_max_upper_bound is not None:
+                raise ValueError(
+                    f"scale_max_upper_bound is invalid for fixed qparams ops: {dtype_config}"
+                )
+            orig_constraints.quant_min_lower_bound = constraints.quant_min_lower_bound
+            orig_constraints.quant_max_upper_bound = constraints.quant_max_upper_bound
+            orig_constraints.scale_exact_match = constraints.scale_exact_match
+            orig_constraints.zero_point_exact_match = constraints.zero_point_exact_match
+        new_dtype_configs.append(dc)
+    return new_dtype_configs
+
+
+def _get_fixed_qparams_op_configs(
+    dtype_configs: list[DTypeConfig],
+) -> list[BackendPatternConfig]:
+    fixed_qparams_op_configs = []
+    for fixed_qparam_op, constraints in _FIXED_QPARAMS_OP_TO_CONSTRAINTS.items():
+        new_dtype_configs = _add_fixed_qparams_to_dtype_configs(
+            dtype_configs, constraints
+        )
+        fixed_qparams_op_configs.append(
+            BackendPatternConfig(fixed_qparam_op)
+            .set_observation_type(
+                ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+            )  # noqa: E131
+            .set_dtype_configs(new_dtype_configs)
+        )
+    return fixed_qparams_op_configs
+
+
+def _get_share_qparams_op_configs(dtype_configs):
+    """Get the operator config for the operators that works for both float and quantized input
+    if input is quantized, the output Tensor shares the same quantization parameter
+    with input.
+    Example operator: avgpool2d, reshape, transpose, maxpool2d
+    Example observed operator:
+    observer_0 - avgpool2d - observer_0 (same observer instance as input)
+    """
+
+    def _get_share_qprams_op_backend_config(op):
+        return (
+            BackendPatternConfig(op)
+            .set_observation_type(ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT)
+            .set_dtype_configs(dtype_configs)
+        )
+
+    share_qparams_ops = [
+        torch.nn.AdaptiveAvgPool1d,
+        torch.nn.AdaptiveAvgPool2d,
+        torch.nn.AdaptiveAvgPool3d,
+        torch.nn.AvgPool1d,
+        torch.nn.AvgPool2d,
+        torch.nn.AvgPool3d,
+        torch.nn.Hardtanh,
+        torch.nn.Identity,
+        torch.nn.MaxPool1d,
+        torch.nn.MaxPool2d,
+        torch.nn.MaxPool3d,
+        torch.nn.PixelShuffle,
+        torch.nn.PixelUnshuffle,
+        torch.nn.ReLU,
+        torch.nn.ReLU6,
+        torch.adaptive_avg_pool1d,
+        torch.nn.functional.adaptive_avg_pool2d,
+        torch.nn.functional.adaptive_avg_pool3d,
+        torch.nn.functional.hardtanh,
+        torch.nn.functional.hardtanh_,
+        torch.nn.functional.interpolate,
+        torch.nn.functional.max_pool1d,
+        torch.nn.functional.max_pool2d,
+        torch.nn.functional.max_pool3d,
+        torch.nn.functional.pixel_shuffle,
+        torch.nn.functional.pixel_unshuffle,
+        torch.nn.functional.relu,
+        torch.nn.functional.relu6,
+        torch.avg_pool1d,
+        torch._C._nn.avg_pool2d,
+        torch._C._nn.avg_pool3d,
+        torch.clamp,
+        torch.flatten,
+        torch.mean,
+        torch.narrow,
+        torch.repeat_interleave,
+        torch.transpose,
+        torch.squeeze,
+        torch.stack,
+        torch.unsqueeze,
+        operator.floordiv,
+        "contiguous",
+        "clamp",
+        "detach",
+        "detach_",
+        "mean",
+        "permute",
+        "repeat",
+        "repeat_interleave",
+        "reshape",
+        "resize_",
+        "relu",
+        "relu_",
+        "squeeze",
+        "squeeze_",
+        "transpose",
+        "unsqueeze",
+        "unsqueeze_",
+        "view",
+    ]
+    return [_get_share_qprams_op_backend_config(op) for op in share_qparams_ops]
+
+
+def _get_bn_configs(dtype_configs: list[DTypeConfig]) -> list[BackendPatternConfig]:
+    """Get configs related to batchnorm."""
+    bn_configs = []
+    bn_to_fused_bn = {
+        torch.nn.BatchNorm2d: nni.BNReLU2d,
+        torch.nn.BatchNorm3d: nni.BNReLU3d,
+    }
+    for bn in bn_to_fused_bn:
+        fused_bn = bn_to_fused_bn[bn]
+        # bn module + relu module fusion config
+        bn_configs.append(
+            BackendPatternConfig((bn, nn.ReLU))
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_fuser_method(_sequential_wrapper2(fused_bn))
+            .set_fused_module(fused_bn)
+        )
+        # bn module + F.relu fusion config
+        bn_configs.append(
+            BackendPatternConfig((bn, F.relu))
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_fuser_method(_sequential_wrapper2(fused_bn))
+            .set_fused_module(fused_bn)
+        )
+        bn_configs.append(
+            BackendPatternConfig(bn)
+            .set_observation_type(
+                ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+            )  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+        )
+
+    # fused bn configs
+    for fused_bn in bn_to_fused_bn.values():
+        bn_configs.append(
+            BackendPatternConfig(fused_bn)
+            .set_observation_type(
+                ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+            )  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+        )
+    return bn_configs
+
+
+def _get_rnn_op_configs(dtype_configs: list[DTypeConfig]) -> list[BackendPatternConfig]:
+    rnn_op_configs = []
+    for rnn_op, ref_rnn_op in [
+        (nn.GRUCell, nnqr.GRUCell),
+        (nn.LSTMCell, nnqr.LSTMCell),
+        (nn.RNNCell, nnqr.RNNCell),
+        (nn.LSTM, nnqr.LSTM),
+        (nn.GRU, nnqr.GRU),
+    ]:
+        rnn_op_configs.append(
+            BackendPatternConfig(rnn_op)
+            .set_observation_type(
+                ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+            )  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_root_module(rnn_op)
+            .set_reference_quantized_module(ref_rnn_op)
+        )
+    return rnn_op_configs
+
+
+def _get_embedding_op_configs(
+    dtype_configs: list[DTypeConfig],
+) -> list[BackendPatternConfig]:
+    embedding_op_configs = []
+    for embedding_op, qat_embedding_op, ref_embedding_op in [
+        (nn.Embedding, nnqat.Embedding, nnqr.Embedding),
+        (nn.EmbeddingBag, nnqat.EmbeddingBag, nnqr.EmbeddingBag),
+    ]:
+        embedding_op_configs.append(
+            BackendPatternConfig(embedding_op)
+            .set_observation_type(
+                ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+            )  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_qat_module(qat_embedding_op)
+            .set_root_module(embedding_op)
+            .set_reference_quantized_module(ref_embedding_op)
+        )
+
+        # config for qat op
+        embedding_op_configs.append(
+            BackendPatternConfig(qat_embedding_op)
+            .set_observation_type(
+                ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+            )  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_root_module(embedding_op)
+            .set_reference_quantized_module(ref_embedding_op)
+        )
+    return embedding_op_configs
+
+
+def _get_tensor_info_op_configs(dtype_configs):
+    """
+    These ops work on tensors of different dtypes but return non-tensors
+    containing information about the input tensor.
+    """
+
+    def _get_config(op):
+        return (
+            BackendPatternConfig(op)
+            .set_observation_type(ObservationType.INPUT_OUTPUT_NOT_OBSERVED)
+            .set_dtype_configs(dtype_configs)
+        )
+
+    return [_get_config(op) for op in ("shape", "size")]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/_qnnpack_pt2e.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/_qnnpack_pt2e.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4e67b79c370207c228fd66d33fadad03a58ed2a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/_qnnpack_pt2e.py
@@ -0,0 +1,181 @@
+# mypy: allow-untyped-defs
+import operator
+
+import torch
+from torch.ao.quantization.backend_config import (
+    BackendConfig,
+    BackendPatternConfig,
+    DTypeConfig,
+    ObservationType,
+)
+
+
+weighted_op_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.quint8,
+    weight_dtype=torch.qint8,
+    bias_dtype=torch.float,
+)
+
+
+def get_linear_configs():
+    linear_configs = []
+    observation_type = ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+    dtype_configs = [weighted_op_quint8_dtype_config]
+
+    # TODO: need to fix the way we insert observers for this pattern
+    # should be solved in the new fusion API
+    # reason that this doesn't work: the pattern is a bit complicated and we don't
+    # have a way to specify which input of the pattern we would like to observe
+    # pattern:
+    # bias input weight
+    # \     |    /
+    #  \    |   t
+    #   \   |  /
+    #    addmm
+    # we want to observe "weight" as weight, but there is not way to convey this
+    # information with current pattern language
+    #
+    # right now:
+    # original:
+    #         weight - t \
+    #         input  - addmm
+    # observed (no hack):
+    #      weight - t - observer \
+    #       input - observer - addmm
+    # target:
+    #      weight - observer - t \
+    #        input - observer - addmm
+
+    # def root_node_getter(node_pattern):
+    #     addmm, bias, act, weight = node_pattern
+    #     return addmm
+
+    # linear_configs.append(
+    #     BackendPatternConfig((torch.ops.aten.addmm.default, MatchAllNode, MatchAllNode, torch.ops.aten.t.default))
+    #     .set_observation_type(observation_type)  # noqa: E131
+    #     .set_dtype_configs(dtype_configs)
+    #     ._set_root_node_getter(root_node_getter))
+
+    linear_configs.append(
+        BackendPatternConfig(torch.ops.aten.addmm.default)
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        ._set_input_type_to_index({"weight": 2, "bias": 0})
+    )
+    # linear is decomposed to `t - mm` if bias is not present
+    linear_configs.append(
+        BackendPatternConfig(torch.ops.aten.mm.default)
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        ._set_input_type_to_index({"weight": 1})
+    )
+    return linear_configs
+
+
+def get_conv_configs():
+    conv_configs = []
+    observation_type = ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+    dtype_configs = [weighted_op_quint8_dtype_config]
+    conv_configs.append(
+        BackendPatternConfig(torch.ops.aten.convolution.default)
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        ._set_input_type_to_index({"weight": 1, "bias": 2})
+    )
+    conv_configs.append(
+        BackendPatternConfig(
+            (torch.ops.aten.convolution.default, torch.ops.aten.relu.default)
+        )
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        ._set_input_type_to_index({"weight": 1, "bias": 2})
+    )
+    # TODO: remove when functionalization is supported in PT2 mode
+    conv_configs.append(
+        BackendPatternConfig(
+            (torch.ops.aten.convolution.default, torch.ops.aten.relu_.default)
+        )
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        ._set_input_type_to_index({"weight": 1, "bias": 2})
+    )
+    return conv_configs
+
+
+def get_pooling_configs():
+    backend_pattern_configs = []
+    observation_type = ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT
+    dtype_configs = [weighted_op_quint8_dtype_config]
+
+    def root_node_getter(node_pattern):
+        _getitem, maxpool, _index = node_pattern
+        return maxpool
+
+    backend_pattern_configs.append(
+        BackendPatternConfig()
+        ._set_pattern_complex_format(
+            (operator.getitem, torch.ops.aten.max_pool2d_with_indices.default, 0)
+        )
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        ._set_root_node_getter(root_node_getter)
+    )
+
+    return backend_pattern_configs
+
+
+def get_relu_configs():
+    backend_pattern_configs = []
+    observation_type = ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT
+    dtype_configs = [weighted_op_quint8_dtype_config]
+    backend_pattern_configs.append(
+        BackendPatternConfig(torch.ops.aten.relu.default)
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+    )
+    return backend_pattern_configs
+
+
+def get_binary_op_configs():
+    binary_op_configs: list[BackendPatternConfig] = []
+    dtype_configs = [weighted_op_quint8_dtype_config]
+    num_tensor_args_to_observation_type_mapping = {
+        # TODO: this is not used right now since we have extra check in prepare
+        # will need to change this to NO_OBSERVER later after we implemented
+        # Tensor dtype inference properly
+        0: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
+        1: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
+        2: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
+    }
+    for op_with_quantized_bop_scalar_variant in [
+        torch.ops.aten.add.Tensor,
+        torch.ops.aten.add_.Tensor,
+    ]:
+        bop_patterns = [
+            (op_with_quantized_bop_scalar_variant, torch.ops.aten.relu.default),
+            op_with_quantized_bop_scalar_variant,
+            # TODO: remove when functionalization is supported in pt2_mode
+            (op_with_quantized_bop_scalar_variant, torch.ops.aten.relu_.default),
+        ]
+        binary_op_configs.extend(
+            BackendPatternConfig(bop_pattern)
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            ._set_num_tensor_args_to_observation_type(
+                num_tensor_args_to_observation_type_mapping
+            )
+            for bop_pattern in bop_patterns
+        )
+
+    return binary_op_configs
+
+
+def get_qnnpack_pt2e_backend_config():
+    return (
+        BackendConfig("qnnpack_pytorch_2.0_export")
+        .set_backend_pattern_configs(get_linear_configs())
+        .set_backend_pattern_configs(get_binary_op_configs())
+        .set_backend_pattern_configs(get_conv_configs())
+        .set_backend_pattern_configs(get_pooling_configs())
+        .set_backend_pattern_configs(get_relu_configs())
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/backend_config.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/backend_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..96a0b44a3afdf5a663319e67e1b422e17136d4d5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/backend_config.py
@@ -0,0 +1,751 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+from dataclasses import dataclass
+from enum import Enum
+from typing import Any, TYPE_CHECKING
+
+import torch
+
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+    from torch.ao.quantization.utils import Pattern
+
+
+__all__ = [
+    "BackendConfig",
+    "BackendPatternConfig",
+    "DTypeConfig",
+    "DTypeWithConstraints",
+    "ObservationType",
+]
+
+
+# DTypeConfig dict keys
+INPUT_DTYPE_DICT_KEY = "input_dtype"
+OUTPUT_DTYPE_DICT_KEY = "output_dtype"
+WEIGHT_DTYPE_DICT_KEY = "weight_dtype"
+BIAS_DTYPE_DICT_KEY = "bias_dtype"
+IS_DYNAMIC_DICT_KEY = "is_dynamic"
+
+# BackendConfig dict keys
+NAME_DICT_KEY = "name"
+CONFIGS_DICT_KEY = "configs"
+
+# BackendPatternConfig dict keys
+PATTERN_DICT_KEY = "pattern"
+PATTERN_COMPLEX_FORMAT_DICT_KEY = "pattern_complex_format"
+OBSERVATION_TYPE_DICT_KEY = "observation_type"
+DTYPE_CONFIGS_DICT_KEY = "dtype_configs"
+ROOT_MODULE_DICT_KEY = "root_module"
+QAT_MODULE_DICT_KEY = "qat_module"
+REFERENCE_QUANTIZED_MODULE_DICT_KEY = "reference_quantized_module_for_root"
+FUSED_MODULE_DICT_KEY = "fused_module"
+FUSER_METHOD_DICT_KEY = "fuser_method"
+ROOT_NODE_GETTER_DICT_KEY = "root_node_getter"
+EXTRA_INPUTS_GETTER_DICT_KEY = "extra_inputs_getter"
+NUM_TENSOR_ARGS_TO_OBSERVATION_TYPE_DICT_KEY = "num_tensor_args_to_observation_type"
+INPUT_TYPE_TO_INDEX_DICT_KEY = "input_type_to_index"
+
+
+# TODO: maybe rename this to something that's not related to observer
+# e.g. QParamsType
+class ObservationType(Enum):
+    """An enum that represents different ways of how an operator/operator pattern
+    should be observed
+    """
+
+    OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT = 0
+    """this means input and output are observed with different observers, based
+    on qconfig.activation
+    example: conv, linear, softmax
+    """
+
+    OUTPUT_SHARE_OBSERVER_WITH_INPUT = 1
+    """this means the output will use the same observer instance as input, based
+    on qconfig.activation
+    example: torch.cat, maxpool
+    """
+
+    INPUT_OUTPUT_NOT_OBSERVED = 2
+    """this means the input and output are never observed
+    example: x.shape, x.size
+    """
+
+
+@dataclass
+class DTypeWithConstraints:
+    """
+    Config for specifying additional constraints for a given dtype, such as quantization
+    value ranges, scale value ranges, and fixed quantization params, to be used in
+    :class:`~torch.ao.quantization.backend_config.DTypeConfig`.
+
+    The constraints currently supported are:
+
+    * `quant_min_lower_bound` and `quant_max_upper_bound`: Lower and upper
+      bounds for the minimum and maximum quantized values respectively. If
+      the QConfig's `quant_min` and `quant_max` fall outside this range,
+      then the QConfig will be ignored.
+
+    * `scale_min_lower_bound` and `scale_max_upper_bound`: Lower and upper
+      bounds for the minimum and maximum scale values respectively. If the
+      QConfig's minimum scale value (currently exposed as `eps`) falls below
+      the lower bound, then the QConfig will be ignored. Note that the upper
+      bound is currently not enforced.
+
+    * `scale_exact_match` and `zero_point_exact_match`: Exact match requirements
+      for scale and zero point, to be used for operators with fixed quantization
+      parameters such as sigmoid and tanh. If the observer specified in the QConfig
+      is neither `FixedQParamsObserver` nor `FixedQParamsFakeQuantize`, or if
+      the quantization parameters don't match, then the QConfig will be ignored.
+    """
+
+    dtype: torch.dtype | None = None
+    quant_min_lower_bound: int | float | None = None
+    quant_max_upper_bound: int | float | None = None
+    scale_min_lower_bound: int | float | None = None
+    scale_max_upper_bound: int | float | None = None
+    scale_exact_match: float | None = None
+    zero_point_exact_match: int | None = None
+
+
+@dataclass
+class DTypeConfig:
+    """
+    Config object that specifies the supported data types passed as arguments to
+    quantize ops in the reference model spec, for input and output activations,
+    weights, and biases.
+
+    For example, consider the following reference model:
+
+      quant1 - [dequant1 - fp32_linear - quant2] - dequant2
+
+    The pattern in the square brackets refers to the reference pattern of
+    statically quantized linear. Setting the input dtype as `torch.quint8`
+    in the DTypeConfig means we pass in `torch.quint8` as the dtype argument
+    to the first quantize op (quant1). Similarly, setting the output dtype as
+    `torch.quint8` means we pass in `torch.quint8` as the dtype argument to
+    the second quantize op (quant2).
+
+    Note that the dtype here does not refer to the interface dtypes of the
+    op. For example, the "input dtype" here is not the dtype of the input
+    tensor passed to the quantized linear op. Though it can still be the
+    same as the interface dtype, this is not always the case, e.g. the
+    interface dtype is fp32 in dynamic quantization but the "input dtype"
+    specified in the DTypeConfig would still be quint8. The semantics of
+    dtypes here are the same as the semantics of the dtypes specified in
+    the observers.
+
+    These dtypes are matched against the ones specified in the user's
+    QConfig. If there is a match, and the QConfig satisfies the constraints
+    specified in the DTypeConfig (if any), then we will quantize the given
+    pattern using this DTypeConfig. Otherwise, the QConfig is ignored and
+    the pattern will not be quantized.
+
+    Example usage::
+
+        >>> # xdoctest: +SKIP(failing)
+        >>> dtype_config1 = DTypeConfig(
+        ...     input_dtype=torch.quint8,
+        ...     output_dtype=torch.quint8,
+        ...     weight_dtype=torch.qint8,
+        ...     bias_dtype=torch.float)
+
+        >>> dtype_config2 = DTypeConfig(
+        ...     input_dtype=DTypeWithConstraints(
+        ...         dtype=torch.quint8,
+        ...         quant_min_lower_bound=0,
+        ...         quant_max_upper_bound=255,
+        ...     ),
+        ...     output_dtype=DTypeWithConstraints(
+        ...         dtype=torch.quint8,
+        ...         quant_min_lower_bound=0,
+        ...         quant_max_upper_bound=255,
+        ...     ),
+        ...     weight_dtype=DTypeWithConstraints(
+        ...         dtype=torch.qint8,
+        ...         quant_min_lower_bound=-128,
+        ...         quant_max_upper_bound=127,
+        ...     ),
+        ...     bias_dtype=torch.float)
+
+        >>> dtype_config1.input_dtype
+        torch.quint8
+
+        >>> dtype_config2.input_dtype
+        torch.quint8
+
+        >>> dtype_config2.input_dtype_with_constraints
+        DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, \
+scale_min_lower_bound=None, scale_max_upper_bound=None)
+    """
+
+    input_dtype_with_constraints: DTypeWithConstraints
+    output_dtype_with_constraints: DTypeWithConstraints
+    weight_dtype_with_constraints: DTypeWithConstraints
+    bias_dtype: torch.dtype | None
+    is_dynamic: bool | None
+
+    def __init__(
+        self,
+        input_dtype: torch.dtype | DTypeWithConstraints | None = None,
+        output_dtype: torch.dtype | DTypeWithConstraints | None = None,
+        weight_dtype: torch.dtype | DTypeWithConstraints | None = None,
+        bias_dtype: torch.dtype | None = None,
+        is_dynamic: bool | None = None,
+    ):
+        if isinstance(input_dtype, DTypeWithConstraints):
+            self.input_dtype_with_constraints = input_dtype
+        else:
+            self.input_dtype_with_constraints = DTypeWithConstraints(dtype=input_dtype)
+
+        if isinstance(output_dtype, DTypeWithConstraints):
+            self.output_dtype_with_constraints = output_dtype
+        else:
+            self.output_dtype_with_constraints = DTypeWithConstraints(
+                dtype=output_dtype
+            )
+
+        if isinstance(weight_dtype, DTypeWithConstraints):
+            self.weight_dtype_with_constraints = weight_dtype
+        else:
+            self.weight_dtype_with_constraints = DTypeWithConstraints(
+                dtype=weight_dtype
+            )
+
+        self.bias_dtype = bias_dtype
+        self.is_dynamic = is_dynamic
+
+    @property
+    def input_dtype(self) -> torch.dtype | None:
+        return self.input_dtype_with_constraints.dtype
+
+    @property
+    def output_dtype(self) -> torch.dtype | None:
+        return self.output_dtype_with_constraints.dtype
+
+    @property
+    def weight_dtype(self) -> torch.dtype | None:
+        return self.weight_dtype_with_constraints.dtype
+
+    @classmethod
+    def from_dict(cls, dtype_config_dict: dict[str, Any]) -> DTypeConfig:
+        """
+        Create a ``DTypeConfig`` from a dictionary with the following items (all optional):
+            "input_dtype": torch.dtype or ``DTypeWithConstraints``
+            "output_dtype": torch.dtype or ``DTypeWithConstraints``
+            "weight_dtype": torch.dtype or ``DTypeWithConstraints``
+            "bias_type": torch.dtype
+            "is_dynamic": bool
+        """
+        input_dtype = dtype_config_dict.get(INPUT_DTYPE_DICT_KEY)
+        if input_dtype is not None and not isinstance(
+            input_dtype, (torch.dtype, DTypeWithConstraints)
+        ):
+            raise ValueError(
+                "Expected input_dtype to be a torch.dtype or DTypeWithConstraints"
+            )
+        output_dtype = dtype_config_dict.get(OUTPUT_DTYPE_DICT_KEY)
+        if output_dtype is not None and not isinstance(
+            output_dtype, (torch.dtype, DTypeWithConstraints)
+        ):
+            raise ValueError(
+                "Expected output_dtype to be a torch.dtype or DTypeWithConstraints"
+            )
+        weight_dtype = dtype_config_dict.get(WEIGHT_DTYPE_DICT_KEY)
+        if weight_dtype is not None and not isinstance(
+            weight_dtype, (torch.dtype, DTypeWithConstraints)
+        ):
+            raise ValueError(
+                "Expected weight_dtype to be a torch.dtype or DTypeWithConstraints"
+            )
+        bias_dtype = dtype_config_dict.get(BIAS_DTYPE_DICT_KEY)
+        is_dynamic = dtype_config_dict.get(IS_DYNAMIC_DICT_KEY)
+        return cls(input_dtype, output_dtype, weight_dtype, bias_dtype, is_dynamic)
+
+    def to_dict(self) -> dict[str, Any]:
+        """
+        Convert this ``DTypeConfig`` to a dictionary with the items described in
+        :func:`~torch.ao.quantization.backend_config.DTypeConfig.from_dict`.
+        """
+        dtype_config_dict: dict[str, Any] = {}
+        if self.input_dtype is not None:
+            dtype_config_dict[INPUT_DTYPE_DICT_KEY] = self.input_dtype_with_constraints
+        if self.output_dtype is not None:
+            dtype_config_dict[OUTPUT_DTYPE_DICT_KEY] = (
+                self.output_dtype_with_constraints
+            )
+        if self.weight_dtype is not None:
+            dtype_config_dict[WEIGHT_DTYPE_DICT_KEY] = (
+                self.weight_dtype_with_constraints
+            )
+        if self.bias_dtype is not None:
+            dtype_config_dict[BIAS_DTYPE_DICT_KEY] = self.bias_dtype
+        if self.is_dynamic is not None:
+            dtype_config_dict[IS_DYNAMIC_DICT_KEY] = self.is_dynamic
+        return dtype_config_dict
+
+
+class BackendConfig:
+    # TODO: refer to NativeBackendConfig once that is implemented
+    """Config that defines the set of patterns that can be quantized on a given backend, and how reference
+    quantized models can be produced from these patterns.
+
+    A pattern in this context refers to a module, a functional, an operator, or a directed acyclic graph
+    of the above. Each pattern supported on the target backend can be individually configured through
+    :class:`~torch.ao.quantization.backend_config.BackendPatternConfig` in terms of:
+
+    (1) The supported input/output activation, weight, and bias data types
+
+    (2) How observers and quant/dequant ops are inserted in order to construct the reference pattern, and
+
+    (3) (Optionally) Fusion, QAT, and reference module mappings.
+
+    The format of the patterns is described in:
+    https://github.com/pytorch/pytorch/blob/master/torch/ao/quantization/backend_config/README.md
+
+    Example usage::
+
+        import torch
+        from torch.ao.quantization.backend_config import (
+            BackendConfig,
+            BackendPatternConfig,
+            DTypeConfig,
+            ObservationType,
+        )
+
+        weighted_int8_dtype_config = DTypeConfig(
+            input_dtype=torch.quint8,
+            output_dtype=torch.quint8,
+            weight_dtype=torch.qint8,
+            bias_dtype=torch.float)
+
+        def fuse_conv2d_relu(is_qat, conv, relu):
+            return torch.ao.nn.intrinsic.ConvReLU2d(conv, relu)
+
+        # For quantizing Linear
+        linear_config = BackendPatternConfig(torch.nn.Linear) \
+            .set_observation_type(ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT) \
+            .add_dtype_config(weighted_int8_dtype_config) \
+            .set_root_module(torch.nn.Linear) \
+            .set_qat_module(torch.ao.nn.qat.Linear) \
+            .set_reference_quantized_module(torch.ao.nn.quantized.reference.Linear)
+
+        # For fusing Conv2d + ReLU into ConvReLU2d
+        conv_relu_config = BackendPatternConfig((torch.nn.Conv2d, torch.nn.ReLU)) \
+            .set_observation_type(ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT) \
+            .add_dtype_config(weighted_int8_dtype_config) \
+            .set_fused_module(torch.ao.nn.intrinsic.ConvReLU2d) \
+            .set_fuser_method(fuse_conv2d_relu)
+
+        # For quantizing ConvReLU2d
+        fused_conv_relu_config = BackendPatternConfig(torch.ao.nn.intrinsic.ConvReLU2d) \
+            .set_observation_type(ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT) \
+            .add_dtype_config(weighted_int8_dtype_config) \
+            .set_root_module(torch.nn.Conv2d) \
+            .set_qat_module(torch.ao.nn.intrinsic.qat.ConvReLU2d) \
+            .set_reference_quantized_module(torch.ao.nn.quantized.reference.Conv2d)
+
+        backend_config = BackendConfig("my_backend") \
+            .set_backend_pattern_config(linear_config) \
+            .set_backend_pattern_config(conv_relu_config) \
+            .set_backend_pattern_config(fused_conv_relu_config)
+
+    """
+
+    def __init__(self, name: str = ""):
+        self.name = name
+        # Store all BackendPatternConfigs in a map to handle duplicates
+        # Note: the key in this map uses the complex reversed tuple format.
+        # This is intended only for internal use; users who wish to access
+        # the original patterns should go through `self.configs` instead.
+        self._pattern_complex_format_to_config: dict[Pattern, BackendPatternConfig] = {}
+
+    def __repr__(self):
+        return f"BackendConfig({self.__dict__})"
+
+    def set_name(self, name: str) -> BackendConfig:
+        """
+        Set the name of the target backend.
+        """
+        self.name = name
+        return self
+
+    def set_backend_pattern_config(self, config: BackendPatternConfig) -> BackendConfig:
+        """
+        Set the config for an pattern that can be run on the target backend.
+        This overrides any existing config for the given pattern.
+        """
+        # Avoid circular dependencies
+        pattern_complex_format = torch.ao.quantization.backend_config.utils._get_pattern_in_reversed_nested_tuple_format(
+            config
+        )  # type: ignore[attr-defined]
+        self._pattern_complex_format_to_config[pattern_complex_format] = config
+        return self
+
+    def set_backend_pattern_configs(
+        self, configs: list[BackendPatternConfig]
+    ) -> BackendConfig:
+        """
+        Set the configs for patterns that can be run on the target backend.
+        This overrides any existing config for a given pattern if it was previously registered already.
+        """
+        for conf in configs:
+            self.set_backend_pattern_config(conf)
+        return self
+
+    @property
+    def configs(self) -> list[BackendPatternConfig]:
+        """
+        Return a copy of the list of configs set in this `BackendConfig`.
+        """
+        return list(self._pattern_complex_format_to_config.values())
+
+    @classmethod
+    def from_dict(cls, backend_config_dict: dict[str, Any]) -> BackendConfig:
+        """
+        Create a ``BackendConfig`` from a dictionary with the following items:
+
+            "name": the name of the target backend
+
+            "configs": a list of dictionaries that each represents a `BackendPatternConfig`
+
+        """
+        conf = cls(backend_config_dict.get(NAME_DICT_KEY, ""))
+        for d in backend_config_dict.get(CONFIGS_DICT_KEY, []):
+            if isinstance(d, BackendPatternConfig):
+                conf.set_backend_pattern_config(d)
+            elif isinstance(d, dict):
+                conf.set_backend_pattern_config(BackendPatternConfig.from_dict(d))
+            else:
+                raise ValueError(
+                    f"Expected backend_config_dict['{CONFIGS_DICT_KEY}'] to be a dictionary"
+                )
+        return conf
+
+    def to_dict(self) -> dict[str, Any]:
+        """
+        Convert this ``BackendConfig`` to a dictionary with the items described in
+        :func:`~torch.ao.quantization.backend_config.BackendConfig.from_dict`.
+        """
+        return {
+            NAME_DICT_KEY: self.name,
+            CONFIGS_DICT_KEY: [c.to_dict() for c in self.configs],
+        }
+
+
+class BackendPatternConfig:
+    """
+    Config object that specifies quantization behavior for a given operator pattern.
+    For a detailed example usage, see :class:`~torch.ao.quantization.backend_config.BackendConfig`.
+    """
+
+    def __init__(self, pattern: Pattern | None = None):
+        self.pattern: Pattern | None = pattern
+        self.observation_type = ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+        self.dtype_configs: list[DTypeConfig] = []
+        self.root_module: type[torch.nn.Module] | None = None
+        self.qat_module: type[torch.nn.Module] | None = None
+        self.reference_quantized_module: type[torch.nn.Module] | None = None
+        self.fused_module: type[torch.nn.Module] | None = None
+        self.fuser_method: Callable | None = None
+
+        # Temporary/internal configs
+        self._root_node_getter: Callable | None = None
+        self._extra_inputs_getter: Callable | None = None
+        self._num_tensor_args_to_observation_type: dict[int, ObservationType] = {}
+        self._input_type_to_index: dict[str, int] = {}
+        self._pattern_complex_format: Pattern | None = None
+
+    def __repr__(self):
+        dict_nonempty = {
+            k: v
+            for k, v in self.__dict__.items()
+            if (
+                (not isinstance(v, (list, dict)) and v is not None)
+                or (isinstance(v, (list, dict)) and len(v) > 0)
+            )
+        }
+        return f"BackendPatternConfig({dict_nonempty})"
+
+    def set_pattern(self, pattern: Pattern) -> BackendPatternConfig:
+        """
+        Set the pattern to configure.
+
+        The pattern can be a float module, functional operator, pytorch operator, or a tuple
+        combination of the above. Tuple patterns are treated as sequential patterns, and
+        currently only tuples of 2 or 3 elements are supported.
+        """
+        if self._pattern_complex_format is not None:
+            raise ValueError(
+                "Only one of 'pattern' or 'pattern_complex_format' can be set"
+            )
+        self.pattern = pattern
+        return self
+
+    def set_observation_type(
+        self, observation_type: ObservationType
+    ) -> BackendPatternConfig:
+        """
+        Set how observers should be inserted in the graph for this pattern.
+
+        Observation type here refers to how observers (or quant-dequant ops) will be placed
+        in the graph. This is used to produce the desired reference patterns understood by
+        the backend. Weighted ops such as linear and conv require different observers
+        (or quantization parameters passed to quantize ops in the reference model) for the
+        input and the output.
+
+        There are two observation types:
+
+            `OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT` (default): the output observer instance
+            will be different from the input. This is the most common observation type.
+
+            `OUTPUT_SHARE_OBSERVER_WITH_INPUT`: the output observer instance will be the
+            same as the input. This is useful for operators like `cat`.
+
+        Note: This will be renamed in the near future, since we will soon insert QuantDeQuantStubs
+        with observers (and fake quantizes) attached instead of observers themselves.
+        """
+        self.observation_type = observation_type
+        return self
+
+    def add_dtype_config(self, dtype_config: DTypeConfig) -> BackendPatternConfig:
+        """
+        Add a set of supported data types passed as arguments to quantize ops in the
+        reference model spec.
+        """
+        self.dtype_configs.append(dtype_config)
+        return self
+
+    def set_dtype_configs(
+        self, dtype_configs: list[DTypeConfig]
+    ) -> BackendPatternConfig:
+        """
+        Set the supported data types passed as arguments to quantize ops in the
+        reference model spec, overriding all previously registered data types.
+        """
+        self.dtype_configs = dtype_configs
+        return self
+
+    def set_root_module(
+        self, root_module: type[torch.nn.Module]
+    ) -> BackendPatternConfig:
+        """
+        Set the module that represents the root for this pattern.
+
+        When we construct the reference quantized model during the convert phase,
+        the root modules (e.g. torch.nn.Linear for torch.ao.nn.intrinsic.LinearReLU)
+        will be swapped to the corresponding reference quantized modules (e.g.
+        torch.ao.nn.reference.quantized.Linear). This allows custom backends to
+        specify custom reference quantized module implementations to match the
+        numerics of their lowered operators. Since this is a one-to-one mapping,
+        both the root module and the reference quantized module must be specified
+        in the same BackendPatternConfig in order for the conversion to take place.
+        """
+        self.root_module = root_module
+        return self
+
+    def set_qat_module(self, qat_module: type[torch.nn.Module]) -> BackendPatternConfig:
+        """
+        Set the module that represents the QAT implementation for this pattern.
+        """
+        self.qat_module = qat_module
+        return self
+
+    def set_reference_quantized_module(
+        self, reference_quantized_module: type[torch.nn.Module]
+    ) -> BackendPatternConfig:
+        """
+        Set the module that represents the reference quantized implementation for
+        this pattern's root module.
+
+        For more detail, see :func:`~torch.ao.quantization.backend_config.BackendPatternConfig.set_root_module`.
+        """
+        self.reference_quantized_module = reference_quantized_module
+        return self
+
+    def set_fused_module(
+        self, fused_module: type[torch.nn.Module]
+    ) -> BackendPatternConfig:
+        """
+        Set the module that represents the fused implementation for this pattern.
+        """
+        self.fused_module = fused_module
+        return self
+
+    def set_fuser_method(self, fuser_method: Callable) -> BackendPatternConfig:
+        """
+        Set the function that specifies how to fuse this BackendPatternConfig's pattern.
+
+        The first argument of this function should be `is_qat`, and the rest of the arguments
+        should be the items in the tuple pattern. The return value of this function should be
+        the resulting fused module.
+
+        For example, the fuser method for the pattern `(torch.nn.Linear, torch.nn.ReLU)` can be:
+
+            def fuse_linear_relu(is_qat, linear, relu):
+                return torch.ao.nn.intrinsic.LinearReLU(linear, relu)
+
+        For a more complicated example, see https://gist.github.com/jerryzh168/8bea7180a8ba3c279f2c9b050f2a69a6.
+        """
+        self.fuser_method = fuser_method
+        return self
+
+    def _set_root_node_getter(self, root_node_getter: Callable) -> BackendPatternConfig:
+        self._root_node_getter = root_node_getter
+        return self
+
+    def _set_extra_inputs_getter(
+        self, extra_inputs_getter: Callable
+    ) -> BackendPatternConfig:
+        self._extra_inputs_getter = extra_inputs_getter
+        return self
+
+    def _set_num_tensor_args_to_observation_type(
+        self, num_tensor_args_to_observation_type: dict[int, ObservationType]
+    ) -> BackendPatternConfig:
+        self._num_tensor_args_to_observation_type = num_tensor_args_to_observation_type
+        return self
+
+    def _set_input_type_to_index(
+        self, input_type_to_index: dict[str, int]
+    ) -> BackendPatternConfig:
+        self._input_type_to_index = input_type_to_index
+        return self
+
+    def _set_pattern_complex_format(self, pattern: Pattern) -> BackendPatternConfig:
+        """
+        Set the pattern to configure, using the reversed nested tuple format.
+
+        See the BackendConfig README for more detail:
+        https://github.com/pytorch/pytorch/blob/master/torch/ao/quantization/backend_config/README.md#advanced-pattern-specification
+        """
+        if self.pattern is not None:
+            raise ValueError(
+                "Only one of 'pattern' or 'pattern_complex_format' can be set"
+            )
+        self._pattern_complex_format = pattern
+        return self
+
+    @classmethod
+    def from_dict(
+        cls, backend_pattern_config_dict: dict[str, Any]
+    ) -> BackendPatternConfig:
+        """
+        Create a ``BackendPatternConfig`` from a dictionary with the following items:
+
+            "pattern": the pattern being configured
+            "observation_type": the :class:`~torch.ao.quantization.backend_config.ObservationType` that specifies how
+            observers should be inserted for this pattern
+            "dtype_configs": a list of dictionaries that represents :class:`~torch.ao.quantization.backend_config.DTypeConfig` s
+            "root_module": a :class:`torch.nn.Module` that represents the root for this pattern
+            "qat_module": a :class:`torch.nn.Module` that represents the QAT implementation for this pattern
+            "reference_quantized_module": a :class:`torch.nn.Module` that represents the reference quantized
+            implementation for this pattern's root module.
+            "fused_module": a :class:`torch.nn.Module` that represents the fused implementation for this pattern
+            "fuser_method": a function that specifies how to fuse the pattern for this pattern
+            "pattern_complex_format": the pattern specified in the reversed nested tuple format (deprecated)
+
+        """
+
+        def _get_dtype_config(obj: Any) -> DTypeConfig:
+            """
+            Convert the given object into a ``DTypeConfig`` if possible, else throw an exception.
+            """
+            if isinstance(obj, DTypeConfig):
+                return obj
+            if isinstance(obj, dict):
+                return DTypeConfig.from_dict(obj)
+            raise ValueError(
+                f"Expected a list of DTypeConfigs in "
+                f"backend_pattern_config_dict[\"{DTYPE_CONFIGS_DICT_KEY}\"], got '{type(obj)}'"
+            )
+
+        conf = cls()
+        if PATTERN_DICT_KEY in backend_pattern_config_dict:
+            conf.set_pattern(backend_pattern_config_dict[PATTERN_DICT_KEY])
+        if OBSERVATION_TYPE_DICT_KEY in backend_pattern_config_dict:
+            conf.set_observation_type(
+                backend_pattern_config_dict[OBSERVATION_TYPE_DICT_KEY]
+            )
+        for d in backend_pattern_config_dict.get(DTYPE_CONFIGS_DICT_KEY, []):
+            conf.add_dtype_config(_get_dtype_config(d))
+        conf.set_root_module(
+            backend_pattern_config_dict.get(ROOT_MODULE_DICT_KEY)  # type: ignore[arg-type]
+        )
+        conf.set_qat_module(backend_pattern_config_dict.get(QAT_MODULE_DICT_KEY))  # type: ignore[arg-type]
+        conf.set_reference_quantized_module(
+            backend_pattern_config_dict.get(REFERENCE_QUANTIZED_MODULE_DICT_KEY)  # type: ignore[arg-type]
+        )
+        conf.set_fused_module(
+            backend_pattern_config_dict.get(FUSED_MODULE_DICT_KEY)  # type: ignore[arg-type]
+        )
+        conf.set_fuser_method(
+            backend_pattern_config_dict.get(FUSER_METHOD_DICT_KEY)  # type: ignore[arg-type]
+        )
+        conf._set_root_node_getter(
+            backend_pattern_config_dict.get(ROOT_NODE_GETTER_DICT_KEY)  # type: ignore[arg-type]
+        )
+        conf._set_extra_inputs_getter(
+            backend_pattern_config_dict.get(EXTRA_INPUTS_GETTER_DICT_KEY)  # type: ignore[arg-type]
+        )
+        conf._set_num_tensor_args_to_observation_type(
+            backend_pattern_config_dict.get(
+                NUM_TENSOR_ARGS_TO_OBSERVATION_TYPE_DICT_KEY, {}
+            )
+        )
+        conf._set_input_type_to_index(
+            backend_pattern_config_dict.get(INPUT_TYPE_TO_INDEX_DICT_KEY, {})
+        )
+        if PATTERN_COMPLEX_FORMAT_DICT_KEY in backend_pattern_config_dict:
+            conf._set_pattern_complex_format(
+                backend_pattern_config_dict[PATTERN_COMPLEX_FORMAT_DICT_KEY]
+            )
+        return conf
+
+    def to_dict(self) -> dict[str, Any]:
+        """
+        Convert this ``BackendPatternConfig`` to a dictionary with the items described in
+        :func:`~torch.ao.quantization.backend_config.BackendPatternConfig.from_dict`.
+        """
+        backend_pattern_config_dict: dict[str, Any] = {
+            OBSERVATION_TYPE_DICT_KEY: self.observation_type,
+            DTYPE_CONFIGS_DICT_KEY: [c.to_dict() for c in self.dtype_configs],
+        }
+        if self.pattern is not None:
+            backend_pattern_config_dict[PATTERN_DICT_KEY] = self.pattern
+        if self.root_module is not None:
+            backend_pattern_config_dict[ROOT_MODULE_DICT_KEY] = self.root_module
+        if self.qat_module is not None:
+            backend_pattern_config_dict[QAT_MODULE_DICT_KEY] = self.qat_module
+        if self.reference_quantized_module is not None:
+            backend_pattern_config_dict[REFERENCE_QUANTIZED_MODULE_DICT_KEY] = (
+                self.reference_quantized_module
+            )
+        if self.fused_module is not None:
+            backend_pattern_config_dict[FUSED_MODULE_DICT_KEY] = self.fused_module
+        if self.fuser_method is not None:
+            backend_pattern_config_dict[FUSER_METHOD_DICT_KEY] = self.fuser_method
+        if self._root_node_getter is not None:
+            backend_pattern_config_dict[ROOT_NODE_GETTER_DICT_KEY] = (
+                self._root_node_getter
+            )
+        if self._extra_inputs_getter is not None:
+            backend_pattern_config_dict[EXTRA_INPUTS_GETTER_DICT_KEY] = (
+                self._extra_inputs_getter
+            )
+        if len(self._num_tensor_args_to_observation_type) > 0:
+            backend_pattern_config_dict[
+                NUM_TENSOR_ARGS_TO_OBSERVATION_TYPE_DICT_KEY
+            ] = self._num_tensor_args_to_observation_type
+        if len(self._input_type_to_index) > 0:
+            backend_pattern_config_dict[INPUT_TYPE_TO_INDEX_DICT_KEY] = (
+                self._input_type_to_index
+            )
+        if self._pattern_complex_format is not None:
+            backend_pattern_config_dict[PATTERN_COMPLEX_FORMAT_DICT_KEY] = (
+                self._pattern_complex_format
+            )
+        return backend_pattern_config_dict
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/executorch.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/executorch.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b9b16492821b73dba1ff3ce6e2617d844d94229
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/executorch.py
@@ -0,0 +1,498 @@
+# TODO: rename executorch to qnnpack_executorch since executorch is a general runtime
+# not a specific backend
+
+import operator
+
+import torch
+import torch.ao.nn.qat as nnqat
+import torch.ao.nn.quantized.reference as nnqr
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.ao.quantization.fuser_method_mappings import (
+    _sequential_wrapper2,
+    fuse_conv_bn,
+    fuse_conv_bn_relu,
+)
+
+from ._common_operator_config_utils import _Conv2dMetadata
+from .backend_config import (
+    BackendConfig,
+    BackendPatternConfig,
+    DTypeConfig,
+    DTypeWithConstraints,
+    ObservationType,
+)
+from .qnnpack import (
+    qnnpack_default_op_qint8_symmetric_dtype_config,
+    qnnpack_weighted_op_qint8_symmetric_dtype_config,
+)
+
+
+__all__ = [
+    "get_executorch_backend_config",
+]
+
+
+# ===================
+# |  DTYPE CONFIGS  |
+# ===================
+
+executorch_weighted_op_int8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.quint8,
+    weight_dtype=torch.qint8,
+    bias_dtype=torch.float,
+)
+
+executorch_default_op_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.quint8,
+)
+
+executorch_default_dynamic_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.float,
+    weight_dtype=torch.qint8,
+    bias_dtype=torch.float,
+    is_dynamic=True,
+)
+
+executorch_act_qint8_scale_min_2_neg_12 = DTypeWithConstraints(
+    dtype=torch.qint8,
+    scale_min_lower_bound=2**-12,
+)
+
+executorch_weight_qint8_neg_127_to_127_scale_min_2_neg_12 = DTypeWithConstraints(
+    dtype=torch.qint8,
+    quant_min_lower_bound=-127,
+    quant_max_upper_bound=127,
+    scale_min_lower_bound=2**-12,
+)
+
+executorch_default_dynamic_qint8_dtype_config = DTypeConfig(
+    input_dtype=executorch_act_qint8_scale_min_2_neg_12,
+    output_dtype=torch.float,
+    weight_dtype=executorch_weight_qint8_neg_127_to_127_scale_min_2_neg_12,
+    bias_dtype=torch.float,
+    is_dynamic=True,
+)
+
+executorch_default_dynamic_float16_dtype_config = DTypeConfig(
+    input_dtype=torch.float16,
+    output_dtype=torch.float,
+    weight_dtype=torch.float16,
+    bias_dtype=torch.float,
+    is_dynamic=True,
+)
+
+executorch_weight_only_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.float,
+    output_dtype=torch.float,
+    weight_dtype=torch.quint8,
+)
+
+
+# =============================
+# |  BACKEND PATTERN CONFIGS  |
+# =============================
+
+
+def _get_linear_configs() -> list[BackendPatternConfig]:
+    """
+    Return all configs related to linear modules and ops.
+    """
+    observation_type = ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+    dtype_configs = [
+        qnnpack_weighted_op_qint8_symmetric_dtype_config,
+        executorch_weighted_op_int8_dtype_config,
+        executorch_default_dynamic_quint8_dtype_config,
+        executorch_default_dynamic_qint8_dtype_config,
+        executorch_default_dynamic_float16_dtype_config,
+    ]
+    linear_configs: list[BackendPatternConfig] = []
+    # linear module
+    linear_configs.append(
+        BackendPatternConfig(torch.nn.Linear)
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        .set_root_module(torch.nn.Linear)
+        .set_reference_quantized_module(nnqr.Linear)
+        .set_qat_module(nnqat.Linear)
+    )
+    # linear qat module
+    linear_configs.append(
+        BackendPatternConfig(nnqat.Linear)
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        .set_root_module(torch.nn.Linear)
+        .set_reference_quantized_module(nnqr.Linear)
+    )
+    # functional linear
+    linear_configs.append(
+        BackendPatternConfig(torch.nn.functional.linear)
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        ._set_input_type_to_index({"weight": 1, "bias": 2})
+    )
+    return linear_configs
+
+
+def _get_conv_configs() -> list[BackendPatternConfig]:
+    """
+    Return all configs related to conv modules and ops.
+    """
+    observation_type = ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+    dtype_configs = [
+        qnnpack_weighted_op_qint8_symmetric_dtype_config,
+        executorch_weighted_op_int8_dtype_config,
+    ]
+    conv_configs = []
+    for convs in [_Conv2dMetadata]:
+        # (1) Single conv modules/functions
+        # -----------------------------------
+        # conv module
+        conv_configs.append(
+            BackendPatternConfig(convs.root)
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_root_module(convs.root)
+            .set_reference_quantized_module(convs.reference)
+            .set_qat_module(convs.qat)
+        )
+        # conv qat module
+        conv_configs.append(
+            BackendPatternConfig(convs.qat)
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_root_module(convs.root)
+            .set_reference_quantized_module(convs.reference)
+        )
+        # functional conv
+        conv_configs.append(
+            BackendPatternConfig(convs.func)
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            ._set_input_type_to_index({"weight": 1, "bias": 2})
+        )
+
+        # (2) Conv + relu
+        # -----------------------------------
+        # conv module + relu module
+        conv_configs.append(
+            BackendPatternConfig((convs.root, nn.ReLU))
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_fuser_method(_sequential_wrapper2(convs.fused_conv_relu))
+            .set_fused_module(convs.fused_conv_relu)
+        )
+        # conv module + functional relu
+        conv_configs.append(
+            BackendPatternConfig((convs.root, F.relu))
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_fuser_method(_sequential_wrapper2(convs.fused_conv_relu))
+            .set_fused_module(convs.fused_conv_relu)
+        )
+        # fused conv relu module
+        conv_configs.append(
+            BackendPatternConfig(convs.fused_conv_relu)
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_root_module(convs.root)
+            .set_reference_quantized_module(convs.reference)
+            .set_qat_module(convs.relu_qat)
+        )
+        # conv relu, qat fused module
+        conv_configs.append(
+            BackendPatternConfig(convs.relu_qat)
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_root_module(convs.root)
+            .set_reference_quantized_module(convs.reference)
+        )
+        # functional conv + relu module
+        conv_configs.append(
+            BackendPatternConfig((convs.func, nn.ReLU))
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+        )
+        # functional conv + functional relu
+        conv_configs.append(
+            BackendPatternConfig((convs.func, F.relu))
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+        )
+        # fused conv relu
+        conv_configs.append(
+            BackendPatternConfig(convs.fused_conv_relu)
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_qat_module(convs.relu_qat)
+        )
+
+        conv_configs.append(
+            BackendPatternConfig(convs.relu_qat)
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_root_module(convs.root)
+            .set_reference_quantized_module(convs.reference)
+        )
+
+        # (3) Conv + batchnorm (+ relu)
+        # -------------------------------
+        # conv + batchnorm (+ relu)
+        conv_configs.append(
+            BackendPatternConfig((convs.root, convs.bn))
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_fuser_method(fuse_conv_bn)
+            .set_fused_module(convs.fused_conv_bn)
+        )
+        # conv + bn + relu module fusion
+        conv_configs.append(
+            BackendPatternConfig((convs.root, convs.bn, nn.ReLU))
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_fuser_method(fuse_conv_bn_relu)
+            .set_fused_module(convs.fused_conv_bn_relu)
+        )
+        # conv + bn + relu functional fusion
+        conv_configs.append(
+            BackendPatternConfig((convs.root, convs.bn, F.relu))
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_root_module(convs.root)
+            .set_fuser_method(fuse_conv_bn_relu)
+            .set_fused_module(convs.fused_conv_bn_relu)
+        )
+        # TODO: we can add fusion for torch.relu as well
+        # 3.2 conv + bn (+ relu) fused module configs
+        # fused conv bn
+        conv_configs.append(
+            BackendPatternConfig(convs.fused_conv_bn)
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_qat_module(convs.bn_qat)
+        )
+
+        # fused conv bn relu
+        conv_configs.append(
+            BackendPatternConfig(convs.fused_conv_bn_relu)
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            .set_qat_module(convs.bn_relu_qat)
+        )
+
+        # conv bn, qat fused module
+        conv_configs.append(
+            BackendPatternConfig(convs.bn_qat)
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_root_module(convs.root)
+            .set_reference_quantized_module(convs.reference)
+        )
+        # conv bn relu, qat fused module
+        conv_configs.append(
+            BackendPatternConfig(convs.bn_relu_qat)
+            .set_observation_type(observation_type)  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_root_module(convs.root)
+            .set_reference_quantized_module(convs.reference)
+        )
+    return conv_configs
+
+
+def _get_binary_ops_configs() -> list[BackendPatternConfig]:
+    """
+    Return all configs related to binary ops.
+    """
+    dtype_configs = [
+        qnnpack_default_op_qint8_symmetric_dtype_config,
+        executorch_weighted_op_int8_dtype_config,
+    ]
+    num_tensor_args_to_observation_type_mapping = {
+        # TODO: this is not used right now since we have extra check in prepare
+        # will need to change this to NO_OBSERVER later after we implemented
+        # Tensor dtype inference properly
+        0: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
+        1: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
+        2: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
+    }
+    binary_op_configs: list[BackendPatternConfig] = []
+    for op in [
+        operator.add,
+        torch.add,
+        operator.sub,
+        torch.sub,
+        operator.mul,
+        torch.mul,
+    ]:
+        bop_patterns = [
+            (op, torch.nn.ReLU),
+            (op, torch.nn.functional.relu),
+            (op, torch.relu),
+            op,
+        ]
+        binary_op_configs.extend(
+            BackendPatternConfig(bop_pattern)
+            .set_dtype_configs(dtype_configs)  # noqa: E131
+            ._set_num_tensor_args_to_observation_type(
+                num_tensor_args_to_observation_type_mapping
+            )
+            for bop_pattern in bop_patterns
+        )
+    return binary_op_configs
+
+
+def _get_share_qparams_ops_configs() -> list[BackendPatternConfig]:
+    """
+    Return the operator configs for the operators that works for both float and quantized
+    input if input is quantized, the output Tensor shares the same quantization parameter
+    with input.
+
+    Example operator: avgpool2d, reshape, transpose, maxpool2d
+    Example observed operator:
+    observer_0 - avgpool2d - observer_0 (same observer instance as input)
+    """
+    observation_type = ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT
+    dtype_configs = [
+        qnnpack_default_op_qint8_symmetric_dtype_config,
+        executorch_default_op_quint8_dtype_config,
+    ]
+    share_qparams_ops = [
+        torch.nn.Flatten,
+        F.adaptive_avg_pool2d,
+        F.elu,
+        F.hardtanh,
+        F.max_pool2d,
+        F.pad,
+        F.relu,
+        F.relu6,
+        F.leaky_relu,
+        F.leaky_relu_,
+        torch.nn.AdaptiveAvgPool2d,
+        torch.nn.ConstantPad2d,
+        torch.nn.ELU,
+        torch.nn.MaxPool2d,
+        torch.nn.ReLU6,
+        torch.nn.Hardtanh,
+        torch.nn.LeakyReLU,
+        torch.clamp,
+        torch.flatten,
+        torch.mean,
+        torch.permute,
+        torch.permute_copy,
+        torch.squeeze,
+        "clamp",
+        "mean",
+        "permute",
+        "reshape",
+        "relu",
+        "relu_",
+        "squeeze",
+        "squeeze_",
+        "leaky_relu",
+    ]
+    share_qparams_op_configs: list[BackendPatternConfig] = [
+        BackendPatternConfig(op)
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        for op in share_qparams_ops
+    ]
+    return share_qparams_op_configs
+
+
+def _get_bn_configs() -> list[BackendPatternConfig]:
+    """
+    Return all configs related to batchnorm.
+    """
+    observation_type = ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+    dtype_configs = [
+        qnnpack_default_op_qint8_symmetric_dtype_config,
+        executorch_default_op_quint8_dtype_config,
+    ]
+    bn_configs = []
+    bn_configs.append(
+        BackendPatternConfig(nn.BatchNorm2d)
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+    )
+    return bn_configs
+
+
+def _get_cat_configs() -> list[BackendPatternConfig]:
+    dtype_configs = [
+        qnnpack_default_op_qint8_symmetric_dtype_config,
+        executorch_default_op_quint8_dtype_config,
+    ]
+    cat_configs = []
+    cat_configs.append(
+        BackendPatternConfig(torch.cat)
+        .set_observation_type(ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT)
+        .set_dtype_configs(dtype_configs)
+    )
+    cat_configs.append(
+        BackendPatternConfig(torch.concat)
+        .set_observation_type(ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT)
+        .set_dtype_configs(dtype_configs)
+    )
+    cat_configs.append(
+        BackendPatternConfig(torch.concatenate)
+        .set_observation_type(ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT)
+        .set_dtype_configs(dtype_configs)
+    )
+    return cat_configs
+
+
+def _get_embedding_op_configs() -> list[BackendPatternConfig]:
+    dtype_configs = [
+        executorch_weight_only_quint8_dtype_config,
+    ]
+    embedding_op_configs = []
+    for embedding_op, qat_embedding_op, ref_embedding_op in [
+        (nn.Embedding, nnqat.Embedding, nnqr.Embedding),
+        (nn.EmbeddingBag, nnqat.EmbeddingBag, nnqr.EmbeddingBag),
+    ]:
+        embedding_op_configs.append(
+            BackendPatternConfig(embedding_op)
+            .set_observation_type(
+                ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+            )  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_qat_module(qat_embedding_op)
+            .set_root_module(embedding_op)
+            .set_reference_quantized_module(ref_embedding_op)
+        )
+        # config for qat op
+        embedding_op_configs.append(
+            BackendPatternConfig(qat_embedding_op)
+            .set_observation_type(
+                ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+            )  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            .set_root_module(embedding_op)
+            .set_reference_quantized_module(ref_embedding_op)
+        )
+
+        # config for functional embedding
+        embedding_op_configs.append(
+            BackendPatternConfig(torch.nn.functional.embedding)
+            .set_observation_type(
+                ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+            )  # noqa: E131
+            .set_dtype_configs(dtype_configs)
+            ._set_input_type_to_index({"weight": 1})
+        )
+    return embedding_op_configs
+
+
+# =====================
+# |  BACKEND CONFIGS  |
+# =====================
+
+
+def get_executorch_backend_config() -> BackendConfig:
+    """
+    Return the `BackendConfig` for backends PyTorch lowers to through the Executorch stack.
+    """
+    return (
+        BackendConfig("executorch")
+        .set_backend_pattern_configs(_get_linear_configs())
+        .set_backend_pattern_configs(_get_conv_configs())
+        .set_backend_pattern_configs(_get_binary_ops_configs())
+        .set_backend_pattern_configs(_get_share_qparams_ops_configs())
+        .set_backend_pattern_configs(_get_bn_configs())
+        .set_backend_pattern_configs(_get_cat_configs())
+        .set_backend_pattern_configs(_get_embedding_op_configs())
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/fbgemm.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/fbgemm.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d665f4fd030aba47c98ee692f0d9e7eca41cbc6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/fbgemm.py
@@ -0,0 +1,129 @@
+import torch
+
+from ._common_operator_config_utils import (
+    _get_binary_op_configs,
+    _get_bn_configs,
+    _get_cat_config,
+    _get_conv_configs,
+    _get_default_op_configs,
+    _get_embedding_op_configs,
+    _get_fixed_qparams_op_configs,
+    _get_linear_configs,
+    _get_rnn_op_configs,
+    _get_share_qparams_op_configs,
+    _get_tensor_info_op_configs,
+)
+from .backend_config import BackendConfig, DTypeConfig
+
+
+__all__ = [
+    "get_fbgemm_backend_config",
+]
+
+# ===================
+# |  DTYPE CONFIGS  |
+# ===================
+
+# TODO: For now, these DTypeConfigs are identical to the ones defined in native.py
+# In the future, once we support specifying quant_min/quant_max and scale_min/scale_max,
+# these will diverge. In particular, for FBGEMM, we will restrict the activation quantized
+# values to within [0, 127].
+
+fbgemm_weighted_op_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.quint8,
+    weight_dtype=torch.qint8,
+    bias_dtype=torch.float,
+)
+
+fbgemm_default_op_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.quint8,
+)
+
+fbgemm_default_op_fp16_dtype_config = DTypeConfig(
+    input_dtype=torch.float16,
+    output_dtype=torch.float16,
+    weight_dtype=torch.float16,
+    bias_dtype=torch.float16,
+)
+
+fbgemm_default_dynamic_int8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.float,
+    weight_dtype=torch.qint8,
+    bias_dtype=torch.float,
+    is_dynamic=True,
+)
+
+fbgemm_default_dynamic_float16_dtype_config = DTypeConfig(
+    input_dtype=torch.float16,
+    output_dtype=torch.float,
+    weight_dtype=torch.float16,
+    bias_dtype=torch.float,
+    is_dynamic=True,
+)
+
+fbgemm_weight_only_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.float,
+    output_dtype=torch.float,
+    weight_dtype=torch.quint8,
+)
+
+fbgemm_weight_only_quint4x2_dtype_config = DTypeConfig(
+    input_dtype=torch.float,
+    output_dtype=torch.float,
+    weight_dtype=torch.quint4x2,
+)
+
+
+# =====================
+# |  BACKEND CONFIGS  |
+# =====================
+
+
+def get_fbgemm_backend_config() -> BackendConfig:
+    """
+    Return the `BackendConfig` for PyTorch's native FBGEMM backend.
+    """
+    conv_dtype_configs = [fbgemm_weighted_op_quint8_dtype_config]
+    linear_dtype_configs = [
+        fbgemm_weighted_op_quint8_dtype_config,
+        fbgemm_default_dynamic_int8_dtype_config,
+        fbgemm_default_dynamic_float16_dtype_config,
+    ]
+    binary_op_dtype_configs = [fbgemm_default_op_quint8_dtype_config]
+    default_op_dtype_configs = [fbgemm_default_op_quint8_dtype_config]
+    fixed_qparams_op_dtype_configs = [fbgemm_default_op_quint8_dtype_config]
+    share_qparams_op_dtype_configs = [fbgemm_default_op_quint8_dtype_config]
+    tensor_info_op_dtype_configs = [fbgemm_default_op_quint8_dtype_config]
+    rnn_op_dtype_configs = [
+        fbgemm_default_dynamic_int8_dtype_config,
+        fbgemm_default_dynamic_float16_dtype_config,
+    ]
+    embedding_op_dtype_configs = [
+        fbgemm_weight_only_quint8_dtype_config,
+        fbgemm_weight_only_quint4x2_dtype_config,
+    ]
+    return (
+        BackendConfig("fbgemm")
+        .set_backend_pattern_configs(_get_conv_configs(conv_dtype_configs))
+        .set_backend_pattern_configs(_get_linear_configs(linear_dtype_configs))
+        .set_backend_pattern_configs(_get_binary_op_configs(binary_op_dtype_configs))
+        .set_backend_pattern_config(_get_cat_config(default_op_dtype_configs))
+        .set_backend_pattern_configs(_get_default_op_configs(default_op_dtype_configs))
+        .set_backend_pattern_configs(
+            _get_fixed_qparams_op_configs(fixed_qparams_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(
+            _get_share_qparams_op_configs(share_qparams_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(
+            _get_tensor_info_op_configs(tensor_info_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(_get_bn_configs(default_op_dtype_configs))
+        .set_backend_pattern_configs(_get_rnn_op_configs(rnn_op_dtype_configs))
+        .set_backend_pattern_configs(
+            _get_embedding_op_configs(embedding_op_dtype_configs)
+        )
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/native.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/native.py
new file mode 100644
index 0000000000000000000000000000000000000000..a98d1a9a3d41b43b1c0ce55a2471d3342af71a55
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/native.py
@@ -0,0 +1,231 @@
+# mypy: allow-untyped-defs
+import torch
+
+from ._common_operator_config_utils import (
+    _get_binary_op_configs,
+    _get_bn_configs,
+    _get_cat_config,
+    _get_conv_configs,
+    _get_default_op_configs,
+    _get_embedding_op_configs,
+    _get_fixed_qparams_op_configs,
+    _get_linear_configs,
+    _get_ln_configs,
+    _get_rnn_op_configs,
+    _get_share_qparams_op_configs,
+    _get_tensor_info_op_configs,
+)
+from .backend_config import BackendConfig, DTypeConfig
+
+
+__all__ = [
+    "get_test_only_legacy_native_backend_config",
+    "default_op_quint8_dtype_config",
+    "default_op_fp16_dtype_config",
+    "default_dynamic_int8_dtype_config",
+    "default_dynamic_float16_dtype_config",
+    "input_output_only_quint8_dtype_config",
+    "weight_only_quint8_dtype_config",
+    "weight_only_quint4x2_dtype_config",
+    "get_native_backend_config",
+    "get_native_backend_config_dict",
+    "get_test_only_legacy_native_backend_config_dict",
+]
+
+# ===================
+# |  DTYPE CONFIGS  |
+# ===================
+
+# weighted op int8 dtype config
+# this is config for ops that has quantized weights, like linear, conv
+weighted_op_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.quint8,
+    weight_dtype=torch.qint8,
+    bias_dtype=torch.float,
+)
+
+default_op_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.quint8,
+)
+
+default_op_fp16_dtype_config = DTypeConfig(
+    input_dtype=torch.float16,
+    output_dtype=torch.float16,
+    weight_dtype=torch.float16,
+    bias_dtype=torch.float16,
+)
+
+default_dynamic_int8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.float,
+    weight_dtype=torch.qint8,
+    bias_dtype=torch.float,
+    # currently the dtype check is not yet enabled, so we provided the dtype_configs but
+    # it is not really used yet,
+    # we will enable it a bit later after we moved everything to backend_config_dict
+    is_dynamic=True,
+)
+
+default_dynamic_float16_dtype_config = DTypeConfig(
+    input_dtype=torch.float16,
+    output_dtype=torch.float,
+    weight_dtype=torch.float16,
+    bias_dtype=torch.float,
+    # currently the dtype check is not yet enabled, so we provided the dtype_configs but
+    # it is not really used yet,
+    # we will enable it a bit later after we moved everything to backend_config_dict
+    is_dynamic=True,
+)
+
+# Needed for LayerNorm and f.layer_norm, since currently the kernel only supports float weights
+input_output_only_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.quint8,
+    weight_dtype=torch.float,
+    bias_dtype=torch.float,
+)
+
+weight_only_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.float,
+    output_dtype=torch.float,
+    weight_dtype=torch.quint8,
+)
+
+weight_only_quint4x2_dtype_config = DTypeConfig(
+    input_dtype=torch.float,
+    output_dtype=torch.float,
+    weight_dtype=torch.quint4x2,
+)
+
+
+# =====================
+# |  BACKEND CONFIGS  |
+# =====================
+
+
+def get_test_only_legacy_native_backend_config() -> BackendConfig:
+    """
+    Return the `BackendConfig` for PyTorch Native backend (fbgemm/qnnpack) with various additional fp16 ops.
+    """
+    conv_dtype_configs = [weighted_op_quint8_dtype_config]
+    linear_dtype_configs = [
+        weighted_op_quint8_dtype_config,
+        default_dynamic_int8_dtype_config,
+        default_dynamic_float16_dtype_config,
+        default_op_fp16_dtype_config,
+    ]
+    binary_op_dtype_configs = [
+        default_op_quint8_dtype_config,
+        default_op_fp16_dtype_config,
+    ]
+    default_op_dtype_configs = [default_op_quint8_dtype_config]
+    fixed_qparams_op_dtype_configs = [
+        default_op_quint8_dtype_config,
+        default_op_fp16_dtype_config,
+    ]
+    share_qparams_op_dtype_configs = [
+        default_op_quint8_dtype_config,
+        default_op_fp16_dtype_config,
+    ]
+    tensor_info_op_dtype_configs = [
+        default_op_quint8_dtype_config,
+    ]
+    rnn_op_dtype_configs = [
+        default_dynamic_int8_dtype_config,
+        default_dynamic_float16_dtype_config,
+    ]
+    embedding_op_dtype_configs = [
+        weight_only_quint8_dtype_config,
+        weight_only_quint4x2_dtype_config,
+    ]
+    layer_norm_op_dtype_configs = [input_output_only_quint8_dtype_config]
+    return (
+        BackendConfig("_native_and_fp16")
+        .set_backend_pattern_configs(_get_conv_configs(conv_dtype_configs))
+        .set_backend_pattern_configs(_get_linear_configs(linear_dtype_configs))
+        .set_backend_pattern_configs(_get_binary_op_configs(binary_op_dtype_configs))
+        .set_backend_pattern_config(_get_cat_config(default_op_dtype_configs))
+        .set_backend_pattern_configs(_get_default_op_configs(default_op_dtype_configs))
+        .set_backend_pattern_configs(
+            _get_fixed_qparams_op_configs(fixed_qparams_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(
+            _get_share_qparams_op_configs(share_qparams_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(
+            _get_tensor_info_op_configs(tensor_info_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(_get_bn_configs(default_op_dtype_configs))
+        .set_backend_pattern_configs(_get_ln_configs(layer_norm_op_dtype_configs))
+        .set_backend_pattern_configs(_get_rnn_op_configs(rnn_op_dtype_configs))
+        .set_backend_pattern_configs(
+            _get_embedding_op_configs(embedding_op_dtype_configs)
+        )
+    )
+
+
+def get_native_backend_config() -> BackendConfig:
+    """
+    Return the `BackendConfig` for PyTorch Native backend (fbgemm/qnnpack).
+    """
+    # TODO: express this BackendConfig as a union of the FBGEMM and QNNPACK BackendConfigs
+    conv_dtype_configs = [weighted_op_quint8_dtype_config]
+    linear_dtype_configs = [
+        weighted_op_quint8_dtype_config,
+        default_dynamic_int8_dtype_config,
+        default_dynamic_float16_dtype_config,
+    ]
+    binary_op_dtype_configs = [default_op_quint8_dtype_config]
+    default_op_dtype_configs = [default_op_quint8_dtype_config]
+    fixed_qparams_op_dtype_configs = [default_op_quint8_dtype_config]
+    share_qparams_op_dtype_configs = [default_op_quint8_dtype_config]
+    tensor_info_op_dtype_configs = [default_op_quint8_dtype_config]
+    rnn_op_dtype_configs = [
+        default_dynamic_int8_dtype_config,
+        default_dynamic_float16_dtype_config,
+    ]
+    embedding_op_dtype_configs = [
+        weight_only_quint8_dtype_config,
+        weight_only_quint4x2_dtype_config,
+    ]
+    layer_norm_op_dtype_configs = [input_output_only_quint8_dtype_config]
+    return (
+        BackendConfig("native")
+        .set_backend_pattern_configs(_get_conv_configs(conv_dtype_configs))
+        .set_backend_pattern_configs(_get_linear_configs(linear_dtype_configs))
+        .set_backend_pattern_configs(_get_binary_op_configs(binary_op_dtype_configs))
+        .set_backend_pattern_config(_get_cat_config(default_op_dtype_configs))
+        .set_backend_pattern_configs(_get_default_op_configs(default_op_dtype_configs))
+        .set_backend_pattern_configs(
+            _get_fixed_qparams_op_configs(fixed_qparams_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(
+            _get_share_qparams_op_configs(share_qparams_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(
+            _get_tensor_info_op_configs(tensor_info_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(_get_bn_configs(default_op_dtype_configs))
+        .set_backend_pattern_configs(_get_ln_configs(layer_norm_op_dtype_configs))
+        .set_backend_pattern_configs(_get_rnn_op_configs(rnn_op_dtype_configs))
+        .set_backend_pattern_configs(
+            _get_embedding_op_configs(embedding_op_dtype_configs)
+        )
+    )
+
+
+def get_native_backend_config_dict():
+    """
+    Return the `BackendConfig` for PyTorch Native backend (fbgemm/qnnpack) in dictionary form.
+    """
+    return get_native_backend_config().to_dict()
+
+
+def get_test_only_legacy_native_backend_config_dict():
+    """
+    Return the `BackendConfig` for PyTorch Native backend (fbgemm/qnnpack) with various additional
+    fp16 ops in dictionary form.
+    """
+    return get_test_only_legacy_native_backend_config().to_dict()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/onednn.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/onednn.py
new file mode 100644
index 0000000000000000000000000000000000000000..3cc7a2cf4c669742583fb1fe23d9948e04dbecc1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/onednn.py
@@ -0,0 +1,641 @@
+# mypy: allow-untyped-defs
+import itertools
+import operator
+
+import torch
+import torch.ao.nn.intrinsic as nni
+import torch.ao.nn.quantized.reference as nnqr
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.ao.quantization.fuser_method_mappings import _sequential_wrapper2
+from torch.ao.quantization.utils import MatchAllNode
+
+from ._common_operator_config_utils import (
+    _get_binary_op_configs,
+    _get_bn_configs,
+    _get_cat_config,
+    _get_conv_configs,
+    _get_default_op_configs,
+    _get_embedding_op_configs,
+    _get_fixed_qparams_op_configs,
+    _get_linear_configs,
+    _get_ln_configs,
+    _get_rnn_op_configs,
+    _get_share_qparams_op_configs,
+)
+from .backend_config import (
+    BackendConfig,
+    BackendPatternConfig,
+    DTypeConfig,
+    ObservationType,
+)
+
+
+# ===================
+# |  DTYPE CONFIGS  |
+# ===================
+
+onednn_weighted_op_int8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.quint8,
+    weight_dtype=torch.qint8,
+    bias_dtype=torch.float,
+)
+
+onednn_op_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.quint8,
+)
+
+onednn_dynamic_int8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.float,
+    weight_dtype=torch.qint8,
+    bias_dtype=torch.float,
+    is_dynamic=True,
+)
+
+onednn_weight_only_qint8_dtype_config = DTypeConfig(
+    input_dtype=torch.float,
+    output_dtype=torch.float,
+    weight_dtype=torch.qint8,
+)
+
+onednn_input_output_only_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.quint8,
+    weight_dtype=torch.float,
+    bias_dtype=torch.float,
+)
+
+# ===================
+# |  FUSER METHODS  |
+# ===================
+
+
+def _fuse_linear_bn_leaky_relu(is_qat, linear, bn, leaky_relu):
+    r"""Given the linear, bn and leaky_relu modules, fuses them and returns the fused module
+    Args:
+        is_qat: a flag for whether we are using quantization aware training fusion
+                or post training quantization fusion
+        linear: Module instance of type Linear
+        bn: BatchNorm1d instance that needs to be fused with the linear layer
+        leaky_relu: LeakyReLU instance that needs to be fused with the linear layer
+    Examples::
+        >>> # xdoctest: +SKIP(failing)
+        >>> m1 = nn.Linear(20, 10)
+        >>> b1 = nn.BatchNorm1d(10)
+        >>> lr = nn.LeakyReLU(0.01)
+        >>> m2 = _fuse_linear_bn_leaky_relu(m1, b1, lr)
+    """
+    if linear.training != bn.training or bn.training != leaky_relu.training:
+        raise AssertionError(
+            "Linear, BN and LeakyReLU all must be in the same mode (train or eval)."
+        )
+
+    if is_qat:
+        raise NotImplementedError(
+            f"Cannot fuse train modules: {(linear, bn, leaky_relu)}"
+        )
+    else:
+        map_to_fused_module_eval = {
+            nn.Linear: nni.LinearLeakyReLU,
+        }
+        fused_module = map_to_fused_module_eval.get(type(linear), None)
+        if fused_module is not None:
+            fused_linear = nn.utils.fusion.fuse_linear_bn_eval(linear, bn)
+            fm = fused_module(fused_linear, leaky_relu)
+            return fm
+        else:
+            raise NotImplementedError(
+                f"Cannot fuse eval modules: {(linear, bn, leaky_relu)}"
+            )
+
+
+# ======================
+# |  CONFIGS FOR CONV  |
+# ======================
+observation_type = ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT
+
+conv_dtype_configs = [onednn_weighted_op_int8_dtype_config]
+conv_configs = _get_conv_configs(conv_dtype_configs)
+
+# (1) Conv2d + Add
+
+# conv2d   Y
+#   \   /
+#    add
+
+# include:
+# conv2d conv2d
+#   \   /
+#    add
+
+
+def _fuse_conv_add_left(is_qat, add, conv, _):
+    return nni.ConvAdd2d(conv, add)
+
+
+def _conv_add_root_node_getter_left(pattern):
+    _, conv, _ = pattern
+    return conv
+
+
+def _conv_add_extra_inputs_getter_left(pattern):
+    """get inputs pattern for extra inputs, inputs for root node
+    are assumed to be copied over from root node to the fused node
+    """
+    _, _conv, extra_input = pattern
+    return [extra_input]
+
+
+# conv2d
+#  \
+#  bn   Y
+#   \   /
+#    add
+
+
+def _fuse_conv_bn_add_left(is_qat, add, bn_conv, _):
+    bn, conv = bn_conv
+    if is_qat:
+        raise NotImplementedError(f"Cannot fuse train modules: {(conv, bn, add)}")
+    else:
+        fused_conv = nn.utils.fusion.fuse_conv_bn_eval(conv, bn)
+        return nni.ConvAdd2d(fused_conv, add)
+
+
+def _conv_bn_add_root_node_getter_left(add_pattern):
+    _, bn_conv, _ = add_pattern
+    _bn, conv = bn_conv
+    return conv
+
+
+def _conv_bn_add_extra_inputs_getter_left(add_pattern):
+    """get inputs pattern for extra inputs, inputs for root node
+    are assumed to be copied over from root node to the fused node
+    """
+    _, _bn_conv, extra_input = add_pattern
+    return [extra_input]
+
+
+conv_add_left_optioins = itertools.product(
+    [True, False],  # with_bn
+    [torch.add, operator.add],  # add_op
+)
+
+for with_bn, add_op in conv_add_left_optioins:
+    if with_bn:
+        conv_configs.append(
+            BackendPatternConfig()
+            ._set_pattern_complex_format(
+                (add_op, (nn.BatchNorm2d, nn.Conv2d), MatchAllNode)
+            )  # noqa: E131
+            .set_observation_type(observation_type)
+            .set_dtype_configs(conv_dtype_configs)
+            .set_fuser_method(_fuse_conv_bn_add_left)
+            ._set_root_node_getter(_conv_bn_add_root_node_getter_left)
+            ._set_extra_inputs_getter(_conv_bn_add_extra_inputs_getter_left)
+            .set_fused_module(nni.ConvAdd2d)
+        )
+    else:
+        conv_configs.append(
+            BackendPatternConfig()
+            ._set_pattern_complex_format((add_op, nn.Conv2d, MatchAllNode))  # noqa: E131
+            .set_observation_type(observation_type)
+            .set_dtype_configs(conv_dtype_configs)
+            .set_fuser_method(_fuse_conv_add_left)
+            ._set_root_node_getter(_conv_add_root_node_getter_left)
+            ._set_extra_inputs_getter(_conv_add_extra_inputs_getter_left)
+            .set_fused_module(nni.ConvAdd2d)
+        )
+
+#  Y   conv2d
+#   \   /
+#    add
+
+
+def _fuse_conv_add_right(is_qat, add, _, conv):
+    return nni.ConvAdd2d(conv, add)
+
+
+def _conv_add_root_node_getter_right(pattern):
+    _add, _, conv = pattern
+    return conv
+
+
+def _conv_add_extra_inputs_getter_right(pattern):
+    """get inputs pattern for extra inputs, inputs for root node
+    are assumed to be copied over from root node to the fused node
+    """
+    _, extra_input, _conv = pattern
+    return [extra_input]
+
+
+#      conv2d
+#        /
+#  Y    bn
+#   \   /
+#    add
+
+
+def _fuse_conv_bn_add_right(is_qat, add, _, bn_conv):
+    bn, conv = bn_conv
+    if is_qat:
+        raise NotImplementedError(f"Cannot fuse train modules: {(conv, bn, add)}")
+    else:
+        fused_conv = nn.utils.fusion.fuse_conv_bn_eval(conv, bn)
+        return nni.ConvAdd2d(fused_conv, add)
+
+
+def _conv_bn_add_root_node_getter_right(pattern):
+    _add, _, bn_conv = pattern
+    _bn, conv = bn_conv
+    return conv
+
+
+def _conv_bn_add_extra_inputs_getter_right(pattern):
+    """get inputs pattern for extra inputs, inputs for root node
+    are assumed to be copied over from root node to the fused node
+    """
+    _, extra_input, _bn_conv = pattern
+    return [extra_input]
+
+
+conv_add_optioins = itertools.product(
+    [True, False],  # with_bn
+    [torch.add, operator.add],  # add_op
+)
+
+for with_bn, add_op in conv_add_optioins:
+    if with_bn:
+        conv_configs.append(
+            BackendPatternConfig()
+            ._set_pattern_complex_format(
+                (add_op, MatchAllNode, (nn.BatchNorm2d, nn.Conv2d))
+            )  # noqa: E131
+            .set_observation_type(observation_type)
+            .set_dtype_configs(conv_dtype_configs)
+            .set_fuser_method(_fuse_conv_bn_add_right)
+            ._set_root_node_getter(_conv_bn_add_root_node_getter_right)
+            ._set_extra_inputs_getter(_conv_bn_add_extra_inputs_getter_right)
+            .set_fused_module(nni.ConvAdd2d)
+        )
+    else:
+        conv_configs.append(
+            BackendPatternConfig()
+            ._set_pattern_complex_format((add_op, MatchAllNode, nn.Conv2d))  # noqa: E131
+            .set_observation_type(observation_type)
+            .set_dtype_configs(conv_dtype_configs)
+            .set_fuser_method(_fuse_conv_add_right)
+            ._set_root_node_getter(_conv_add_root_node_getter_right)
+            ._set_extra_inputs_getter(_conv_add_extra_inputs_getter_right)
+            .set_fused_module(nni.ConvAdd2d)
+        )
+
+conv_configs.append(
+    BackendPatternConfig(nni.ConvAdd2d)
+    .set_observation_type(observation_type)  # noqa: E131
+    .set_dtype_configs(conv_dtype_configs)
+    .set_root_module(nn.Conv2d)
+    .set_reference_quantized_module(nnqr.Conv2d)
+)
+
+# (2) Conv2d + Add + Relu
+
+# conv2d Y
+#   \   /
+#    add
+#     \
+#     relu
+
+
+def _fuse_conv_add_relu_left(is_qat, relu, add_pattern):
+    add, conv, _ = add_pattern
+    return nni.ConvAddReLU2d(conv, add, relu)
+
+
+def _conv_add_relu_root_node_getter_left(pattern):
+    _relu, add_pattern = pattern
+    _, conv, _ = add_pattern
+    return conv
+
+
+def _conv_add_relu_extra_inputs_getter_left(pattern):
+    """get inputs pattern for extra inputs, inputs for root node
+    are assumed to be copied over from root node to the fused node
+    """
+    _relu, add_pattern = pattern
+    _, _conv, extra_input = add_pattern
+    return [extra_input]
+
+
+# conv2d
+#  \
+#  bn   Y
+#   \   /
+#    add
+#     \
+#     relu
+
+
+def _fuse_conv_bn_add_relu_left(is_qat, relu, add_pattern):
+    add, bn_conv, _ = add_pattern
+    bn, conv = bn_conv
+    if is_qat:
+        raise NotImplementedError(f"Cannot fuse train modules: {(conv, bn, add, relu)}")
+    else:
+        fused_conv = nn.utils.fusion.fuse_conv_bn_eval(conv, bn)
+        return nni.ConvAddReLU2d(fused_conv, add, relu)
+
+
+def _conv_bn_add_relu_root_node_getter_left(pattern):
+    _relu, add_pattern = pattern
+    _, bn_conv, _ = add_pattern
+    _bn, conv = bn_conv
+    return conv
+
+
+def _conv_bn_add_relu_extra_inputs_getter_left(pattern):
+    """get inputs pattern for extra inputs, inputs for root node
+    are assumed to be copied over from root node to the fused node
+    """
+    _relu, add_pattern = pattern
+    _, _bn_conv, extra_input = add_pattern
+    return [extra_input]
+
+
+conv_add_relu_left_optioins = itertools.product(
+    [True, False],  # with_bn
+    [torch.add, operator.add],  # add_op
+)
+
+for with_bn, add_op in conv_add_relu_left_optioins:
+    if with_bn:
+        conv_configs.append(
+            BackendPatternConfig()
+            ._set_pattern_complex_format(
+                (nn.ReLU, (add_op, (nn.BatchNorm2d, nn.Conv2d), MatchAllNode))
+            )  # noqa: E131
+            .set_observation_type(observation_type)
+            .set_dtype_configs(conv_dtype_configs)
+            .set_fuser_method(_fuse_conv_bn_add_relu_left)
+            ._set_root_node_getter(_conv_bn_add_relu_root_node_getter_left)
+            ._set_extra_inputs_getter(_conv_bn_add_relu_extra_inputs_getter_left)
+            .set_fused_module(nni.ConvAddReLU2d)
+        )
+    else:
+        conv_configs.append(
+            BackendPatternConfig()
+            ._set_pattern_complex_format((nn.ReLU, (add_op, nn.Conv2d, MatchAllNode)))  # noqa: E131
+            .set_observation_type(observation_type)
+            .set_dtype_configs(conv_dtype_configs)
+            .set_fuser_method(_fuse_conv_add_relu_left)
+            ._set_root_node_getter(_conv_add_relu_root_node_getter_left)
+            ._set_extra_inputs_getter(_conv_add_relu_extra_inputs_getter_left)
+            .set_fused_module(nni.ConvAddReLU2d)
+        )
+
+#  Y   conv2d
+#   \   /
+#    add
+#     \
+#     relu
+
+
+def _fuse_conv_add_relu_right(is_qat, relu, add_pattern):
+    add, _, conv = add_pattern
+    return nni.ConvAddReLU2d(conv, add, relu)
+
+
+def _conv_add_relu_root_node_getter_right(pattern):
+    _relu, add_pattern = pattern
+    _, _extra_input, conv = add_pattern
+    return conv
+
+
+def _conv_add_relu_extra_inputs_getter_right(pattern):
+    """get inputs pattern for extra inputs, inputs for root node
+    are assumed to be copied over from root node to the fused node
+    """
+    _relu, add_pattern = pattern
+    _, extra_input, _conv = add_pattern
+    return [extra_input]
+
+
+#      conv2d
+#        /
+#  Y    bn
+#   \   /
+#    add
+#     \
+#     relu
+
+
+def _fuse_conv_bn_add_relu_right(is_qat, relu, add_pattern):
+    add, _, bn_conv = add_pattern
+    bn, conv = bn_conv
+    if is_qat:
+        raise NotImplementedError(f"Cannot fuse train modules: {(conv, bn, add, relu)}")
+    else:
+        fused_conv = nn.utils.fusion.fuse_conv_bn_eval(conv, bn)
+        return nni.ConvAddReLU2d(fused_conv, add, relu)
+
+
+def _conv_bn_add_relu_root_node_getter_right(pattern):
+    _relu, add_pattern = pattern
+    _, _, bn_conv = add_pattern
+    _bn, conv = bn_conv
+    return conv
+
+
+def _conv_bn_add_relu_extra_inputs_getter_right(pattern):
+    """get inputs pattern for extra inputs, inputs for root node
+    are assumed to be copied over from root node to the fused node
+    """
+    _relu, add_pattern = pattern
+    _, extra_input, _bn_conv = add_pattern
+    return [extra_input]
+
+
+conv_add_relu_left_optioins = itertools.product(
+    [True, False],  # with_bn
+    [torch.add, operator.add],  # add_op
+)
+
+for with_bn, add_op in conv_add_relu_left_optioins:
+    if with_bn:
+        conv_configs.append(
+            BackendPatternConfig()
+            ._set_pattern_complex_format(
+                (nn.ReLU, (add_op, MatchAllNode, (nn.BatchNorm2d, nn.Conv2d)))
+            )  # noqa: E131
+            .set_observation_type(observation_type)
+            .set_dtype_configs(conv_dtype_configs)
+            .set_fuser_method(_fuse_conv_bn_add_relu_right)
+            ._set_root_node_getter(_conv_bn_add_relu_root_node_getter_right)
+            ._set_extra_inputs_getter(_conv_bn_add_relu_extra_inputs_getter_right)
+            .set_fused_module(nni.ConvAddReLU2d)
+        )
+    else:
+        conv_configs.append(
+            BackendPatternConfig()
+            ._set_pattern_complex_format((nn.ReLU, (add_op, MatchAllNode, nn.Conv2d)))  # noqa: E131
+            .set_observation_type(observation_type)
+            .set_dtype_configs(conv_dtype_configs)
+            .set_fuser_method(_fuse_conv_add_relu_right)
+            ._set_root_node_getter(_conv_add_relu_root_node_getter_right)
+            ._set_extra_inputs_getter(_conv_add_relu_extra_inputs_getter_right)
+            .set_fused_module(nni.ConvAddReLU2d)
+        )
+
+conv_configs.append(
+    BackendPatternConfig(nni.ConvAddReLU2d)
+    .set_observation_type(observation_type)  # noqa: E131
+    .set_dtype_configs(conv_dtype_configs)
+    .set_root_module(nn.Conv2d)
+    .set_reference_quantized_module(nnqr.Conv2d)
+)
+
+# ========================
+# |  CONFIGS FOR LINEAR  |
+# ========================
+
+linear_dtype_configs = [
+    onednn_weighted_op_int8_dtype_config,
+    onednn_dynamic_int8_dtype_config,
+]
+linear_configs = _get_linear_configs(linear_dtype_configs)
+
+
+def _add_eltwise_fusion_configs(
+    configs,
+    root_module,
+    root_op,
+    post_module,
+    post_op,
+    dtype_configs,
+    fuser_method,
+    fused_module,
+    observation_type,
+    ref_quant_module,
+):
+    # 1 base module + op module fusion config
+    configs.append(
+        BackendPatternConfig((root_module, post_module))
+        .set_dtype_configs(dtype_configs)  # noqa: E131
+        .set_fuser_method(fuser_method)
+        .set_fused_module(fused_module)
+    )
+    # base module + functional post op
+    configs.append(
+        BackendPatternConfig((root_module, post_op))
+        .set_dtype_configs(dtype_configs)  # noqa: E131
+        .set_fuser_method(fuser_method)
+        .set_fused_module(fused_module)
+    )
+
+    # 2 fused module configs
+    configs.append(
+        BackendPatternConfig(fused_module)
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+        .set_root_module(root_module)
+        .set_reference_quantized_module(ref_quant_module)
+    )
+
+    # 3 functional base op + post op configs
+    configs.append(
+        BackendPatternConfig((root_op, post_module))
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+    )
+    configs.append(
+        BackendPatternConfig((root_op, post_op))
+        .set_observation_type(observation_type)  # noqa: E131
+        .set_dtype_configs(dtype_configs)
+    )
+
+
+# Configs for linear + leaky_relu fusion
+_add_eltwise_fusion_configs(
+    linear_configs,
+    nn.Linear,
+    F.linear,
+    nn.LeakyReLU,
+    F.leaky_relu,
+    linear_dtype_configs,
+    _sequential_wrapper2(nni.LinearLeakyReLU),
+    nni.LinearLeakyReLU,
+    observation_type,
+    nnqr.Linear,
+)
+
+# Configs for linear module + batchnorm + leaky_relu
+linear_configs.append(
+    BackendPatternConfig((nn.Linear, nn.BatchNorm1d, nn.LeakyReLU))
+    .set_dtype_configs(linear_dtype_configs)  # noqa: E131
+    .set_fuser_method(_fuse_linear_bn_leaky_relu)
+    .set_fused_module(nni.LinearLeakyReLU)
+)
+
+# Configs for linear + tanh fusion
+_add_eltwise_fusion_configs(
+    linear_configs,
+    nn.Linear,
+    F.linear,
+    nn.Tanh,
+    torch.tanh,
+    linear_dtype_configs,
+    _sequential_wrapper2(nni.LinearTanh),
+    nni.LinearTanh,
+    observation_type,
+    nnqr.Linear,
+)
+
+# ===========================
+# |  CONFIGS FOR OTHER OPS  |
+# ===========================
+
+binary_op_dtype_configs = [onednn_op_quint8_dtype_config]
+default_op_dtype_configs = [onednn_op_quint8_dtype_config]
+fixed_qparams_op_dtype_configs = [onednn_op_quint8_dtype_config]
+share_qparams_op_dtype_configs = [onednn_op_quint8_dtype_config]
+rnn_op_dtype_configs = [onednn_dynamic_int8_dtype_config]
+embedding_op_dtype_configs = [onednn_weight_only_qint8_dtype_config]
+layer_norm_op_dtype_configs = [onednn_input_output_only_quint8_dtype_config]
+
+# =====================
+# |  BACKEND CONFIGS  |
+# =====================
+
+
+def get_onednn_backend_config() -> BackendConfig:
+    """
+    Return the `BackendConfig` for PyTorch's native ONEDNN backend.
+    """
+    return (
+        BackendConfig("onednn")
+        .set_backend_pattern_configs(conv_configs)
+        .set_backend_pattern_configs(linear_configs)
+        .set_backend_pattern_configs(_get_binary_op_configs(binary_op_dtype_configs))
+        .set_backend_pattern_config(_get_cat_config(default_op_dtype_configs))
+        .set_backend_pattern_configs(_get_default_op_configs(default_op_dtype_configs))
+        .set_backend_pattern_configs(
+            _get_fixed_qparams_op_configs(fixed_qparams_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(
+            _get_share_qparams_op_configs(share_qparams_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(_get_bn_configs(default_op_dtype_configs))
+        .set_backend_pattern_configs(_get_ln_configs(layer_norm_op_dtype_configs))
+        .set_backend_pattern_configs(_get_rnn_op_configs(rnn_op_dtype_configs))
+        .set_backend_pattern_configs(
+            _get_embedding_op_configs(embedding_op_dtype_configs)
+        )
+    )
+
+
+__all__ = [
+    "get_onednn_backend_config",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/qnnpack.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/qnnpack.py
new file mode 100644
index 0000000000000000000000000000000000000000..841bac512a6549f39f757b9531591f1e47e72a83
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/qnnpack.py
@@ -0,0 +1,171 @@
+import torch
+
+from ._common_operator_config_utils import (
+    _get_binary_op_configs,
+    _get_bn_configs,
+    _get_cat_config,
+    _get_conv_configs,
+    _get_default_op_configs,
+    _get_embedding_op_configs,
+    _get_fixed_qparams_op_configs,
+    _get_linear_configs,
+    _get_rnn_op_configs,
+    _get_share_qparams_op_configs,
+)
+from .backend_config import BackendConfig, DTypeConfig, DTypeWithConstraints
+
+
+__all__ = [
+    "get_qnnpack_backend_config",
+]
+
+# ===================
+# |  DTYPE CONFIGS  |
+# ===================
+
+qnnpack_weighted_op_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.quint8,
+    weight_dtype=torch.qint8,
+    bias_dtype=torch.float,
+)
+
+qnnpack_default_op_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.quint8,
+)
+
+qnnpack_default_op_fp16_dtype_config = DTypeConfig(
+    input_dtype=torch.float16,
+    output_dtype=torch.float16,
+    weight_dtype=torch.float16,
+    bias_dtype=torch.float16,
+)
+
+qnnpack_default_dynamic_int8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.float,
+    weight_dtype=torch.qint8,
+    bias_dtype=torch.float,
+    is_dynamic=True,
+)
+
+qnnpack_default_dynamic_float16_dtype_config = DTypeConfig(
+    input_dtype=torch.float16,
+    output_dtype=torch.float,
+    weight_dtype=torch.float16,
+    bias_dtype=torch.float,
+    is_dynamic=True,
+)
+
+qnnpack_weight_only_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.float,
+    output_dtype=torch.float,
+    weight_dtype=torch.quint8,
+)
+
+qnnpack_weight_only_quint4x2_dtype_config = DTypeConfig(
+    input_dtype=torch.float,
+    output_dtype=torch.float,
+    weight_dtype=torch.quint4x2,
+)
+
+# xnnpack compatible dtype configs
+
+# We restrict scale values to be 2 ** -12 to ensure the
+# requantization scale never falls below the xnnpack lower
+# threshold. Additionally, for qint8 weight, we restrict
+# the quantization values to [-127, +127], excluding -128.
+# For more detail, refer to the description of
+# `default_symmetric_qnnpack_qconfig`.
+
+# TODO: add additional restriction on qscheme to ensure it
+# is either per_tensor_symmetric or per_channel_symmetric
+
+qnnpack_act_qint8_scale_min_2_neg_12 = DTypeWithConstraints(
+    dtype=torch.qint8,
+    scale_min_lower_bound=2**-12,
+)
+
+qnnpack_weight_qint8_neg_127_to_127_scale_min_2_neg_12 = DTypeWithConstraints(
+    dtype=torch.qint8,
+    quant_min_lower_bound=-127,
+    quant_max_upper_bound=127,
+    scale_min_lower_bound=2**-12,
+)
+
+qnnpack_weighted_op_qint8_symmetric_dtype_config = DTypeConfig(
+    input_dtype=qnnpack_act_qint8_scale_min_2_neg_12,
+    output_dtype=qnnpack_act_qint8_scale_min_2_neg_12,
+    weight_dtype=qnnpack_weight_qint8_neg_127_to_127_scale_min_2_neg_12,
+    bias_dtype=torch.float,
+)
+
+qnnpack_default_op_qint8_symmetric_dtype_config = DTypeConfig(
+    input_dtype=qnnpack_act_qint8_scale_min_2_neg_12,
+    output_dtype=qnnpack_act_qint8_scale_min_2_neg_12,
+)
+
+
+# =====================
+# |  BACKEND CONFIGS  |
+# =====================
+
+
+def get_qnnpack_backend_config() -> BackendConfig:
+    """
+    Return the `BackendConfig` for PyTorch's native QNNPACK backend.
+    """
+    conv_dtype_configs = [
+        qnnpack_weighted_op_qint8_symmetric_dtype_config,
+        qnnpack_weighted_op_quint8_dtype_config,
+    ]
+    linear_dtype_configs = [
+        qnnpack_weighted_op_qint8_symmetric_dtype_config,
+        qnnpack_weighted_op_quint8_dtype_config,
+        qnnpack_default_dynamic_int8_dtype_config,
+        qnnpack_default_dynamic_float16_dtype_config,
+    ]
+    binary_op_dtype_configs = [
+        qnnpack_default_op_qint8_symmetric_dtype_config,
+        qnnpack_default_op_quint8_dtype_config,
+    ]
+    default_op_dtype_configs = [
+        qnnpack_default_op_qint8_symmetric_dtype_config,
+        qnnpack_default_op_quint8_dtype_config,
+    ]
+    fixed_qparams_op_dtype_configs = [
+        qnnpack_default_op_qint8_symmetric_dtype_config,
+        qnnpack_default_op_quint8_dtype_config,
+    ]
+    share_qparams_op_dtype_configs = [
+        qnnpack_default_op_qint8_symmetric_dtype_config,
+        qnnpack_default_op_quint8_dtype_config,
+    ]
+    rnn_op_dtype_configs = [
+        qnnpack_default_dynamic_int8_dtype_config,
+        qnnpack_default_dynamic_float16_dtype_config,
+    ]
+    embedding_op_dtype_configs = [
+        qnnpack_weight_only_quint8_dtype_config,
+        qnnpack_weight_only_quint4x2_dtype_config,
+    ]
+    return (
+        BackendConfig("qnnpack")
+        .set_backend_pattern_configs(_get_conv_configs(conv_dtype_configs))
+        .set_backend_pattern_configs(_get_linear_configs(linear_dtype_configs))
+        .set_backend_pattern_configs(_get_binary_op_configs(binary_op_dtype_configs))
+        .set_backend_pattern_config(_get_cat_config(default_op_dtype_configs))
+        .set_backend_pattern_configs(_get_default_op_configs(default_op_dtype_configs))
+        .set_backend_pattern_configs(
+            _get_fixed_qparams_op_configs(fixed_qparams_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(
+            _get_share_qparams_op_configs(share_qparams_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(_get_bn_configs(default_op_dtype_configs))
+        .set_backend_pattern_configs(_get_rnn_op_configs(rnn_op_dtype_configs))
+        .set_backend_pattern_configs(
+            _get_embedding_op_configs(embedding_op_dtype_configs)
+        )
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/tensorrt.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/tensorrt.py
new file mode 100644
index 0000000000000000000000000000000000000000..d0490e2071f4f2df59b4bb6eb2a1d7885b4aa036
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/tensorrt.py
@@ -0,0 +1,98 @@
+# mypy: allow-untyped-defs
+import torch
+
+from ._common_operator_config_utils import (
+    _get_binary_op_configs,
+    _get_conv_configs,
+    _get_linear_configs,
+    _get_share_qparams_op_configs,
+    _get_tensor_info_op_configs,
+)
+from .backend_config import (
+    BackendConfig,
+    BackendPatternConfig,
+    DTypeConfig,
+    ObservationType,
+)
+
+
+__all__ = [
+    "get_tensorrt_backend_config",
+    "get_tensorrt_backend_config_dict",
+]
+
+
+def get_tensorrt_backend_config() -> BackendConfig:
+    """
+    Return the `BackendConfig` for the TensorRT backend.
+    NOTE: Current api will change in the future, it's just to unblock experimentation for
+    new backends, please don't use it right now.
+    TODO: add a README when it's more stable
+    """
+    # dtype configs
+    weighted_op_qint8_dtype_config = DTypeConfig(
+        input_dtype=torch.qint8,
+        output_dtype=torch.qint8,
+        weight_dtype=torch.qint8,
+        bias_dtype=torch.float,
+    )
+    non_weighted_op_qint8_dtype_config = DTypeConfig(
+        input_dtype=torch.qint8,
+        output_dtype=torch.qint8,
+    )
+
+    addmm_config = (
+        BackendPatternConfig(torch.addmm)
+        .set_observation_type(ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT)
+        .add_dtype_config(weighted_op_qint8_dtype_config)
+        ._set_input_type_to_index(
+            {
+                "bias": 0,
+                "input": 1,
+                "weight": 2,
+            }
+        )
+    )
+    cat_config = (
+        BackendPatternConfig(torch.cat)
+        .set_observation_type(ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT)
+        .add_dtype_config(non_weighted_op_qint8_dtype_config)
+    )
+    conv_dtype_configs = [
+        weighted_op_qint8_dtype_config,
+    ]
+    linear_dtype_configs = [
+        weighted_op_qint8_dtype_config,
+    ]
+    binary_op_dtype_configs = [
+        weighted_op_qint8_dtype_config,
+    ]
+    share_qparams_op_dtype_configs = [
+        non_weighted_op_qint8_dtype_config,
+    ]
+    tensor_info_op_dtype_configs = [
+        non_weighted_op_qint8_dtype_config,
+    ]
+    # there might be things not supported in fx2trt, but it will error out
+    # during fx2trt conversion and can support them after that
+    return (
+        BackendConfig("tensorrt")
+        .set_backend_pattern_configs(_get_conv_configs(conv_dtype_configs))
+        .set_backend_pattern_config(addmm_config)
+        .set_backend_pattern_config(cat_config)
+        .set_backend_pattern_configs(_get_linear_configs(linear_dtype_configs))
+        .set_backend_pattern_configs(_get_binary_op_configs(binary_op_dtype_configs))
+        .set_backend_pattern_configs(
+            _get_share_qparams_op_configs(share_qparams_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(
+            _get_tensor_info_op_configs(tensor_info_op_dtype_configs)
+        )
+    )
+
+
+def get_tensorrt_backend_config_dict():
+    """
+    Return the `BackendConfig` for the TensorRT backend in dictionary form.
+    """
+    return get_tensorrt_backend_config().to_dict()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..4d486a061129324a311cdf74ebb58a51bf2dd9d8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/utils.py
@@ -0,0 +1,317 @@
+# mypy: allow-untyped-defs
+from collections.abc import Callable
+from typing import Any
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.ao.quantization.fuser_method_mappings import _reverse2, _reverse3
+from torch.ao.quantization.utils import Pattern
+
+from .backend_config import BackendConfig, BackendPatternConfig, DTypeConfig
+
+
+__all__ = [
+    "get_pattern_to_dtype_configs",
+    "get_qat_module_classes",
+    "get_fused_module_classes",
+    "get_pattern_to_input_type_to_index",
+    "get_root_module_to_quantized_reference_module",
+    "get_fuser_method_mapping",
+    "get_module_to_qat_module",
+    "get_fusion_pattern_to_root_node_getter",
+    "get_fusion_pattern_to_extra_inputs_getter",
+    "remove_boolean_dispatch_from_name",
+    "pattern_to_human_readable",
+    "entry_to_pretty_str",
+]
+
+
+def get_pattern_to_dtype_configs(
+    backend_config: BackendConfig,
+) -> dict[Pattern, list[DTypeConfig]]:
+    pattern_to_dtype_configs: dict[Pattern, list[DTypeConfig]] = {}
+    for pattern, config in backend_config._pattern_complex_format_to_config.items():
+        pattern_to_dtype_configs[pattern] = config.dtype_configs
+    return pattern_to_dtype_configs
+
+
+def get_qat_module_classes(backend_config: BackendConfig) -> tuple[type, ...]:
+    qat_module_classes = [
+        config.qat_module
+        for config in backend_config.configs
+        if config.qat_module is not None
+    ]
+    return tuple(set(qat_module_classes))
+
+
+def get_fused_module_classes(backend_config: BackendConfig) -> tuple[type, ...]:
+    fused_module_classes = [
+        config.fused_module
+        for config in backend_config.configs
+        if config.fused_module is not None
+    ]
+    return tuple(set(fused_module_classes))
+
+
+def get_pattern_to_input_type_to_index(
+    backend_config: BackendConfig,
+) -> dict[Pattern, dict[str, int]]:
+    pattern_to_input_type_to_index: dict[Pattern, dict[str, int]] = {}
+    for pattern, config in backend_config._pattern_complex_format_to_config.items():
+        pattern_to_input_type_to_index[pattern] = config._input_type_to_index
+    return pattern_to_input_type_to_index
+
+
+def get_root_module_to_quantized_reference_module(
+    backend_config: BackendConfig,
+) -> dict[type[torch.nn.Module], type[torch.nn.Module]]:
+    mapping: dict[type[torch.nn.Module], type[torch.nn.Module]] = {}
+    for config in backend_config.configs:
+        if (
+            config.root_module is not None
+            and config.reference_quantized_module is not None
+        ):
+            mapping[config.root_module] = config.reference_quantized_module
+    return mapping
+
+
+def get_fuser_method_mapping(
+    backend_config: BackendConfig,
+) -> dict[Pattern, nn.Sequential | Callable]:
+    fuser_method_mapping: dict[Pattern, nn.Sequential | Callable] = {}
+    for pattern, config in backend_config._pattern_complex_format_to_config.items():
+        if config.fuser_method is not None:
+            # Note: both the fuser method and the pattern are specified in forward order in the
+            # BackendConfig, but the internal pattern matching code uses the reversed nested tuple
+            # format, so we need to convert both to the internal format
+            fuser_method = _get_fuser_method_in_reversed_nested_tuple_format(config)
+            fuser_method_mapping[pattern] = fuser_method
+    return fuser_method_mapping
+
+
+def get_module_to_qat_module(
+    backend_config: BackendConfig,
+) -> dict[Pattern, type[torch.nn.Module]]:
+    module_to_qat_module: dict[Pattern, type[torch.nn.Module]] = {}
+    for pattern, config in backend_config._pattern_complex_format_to_config.items():
+        if config.qat_module is not None:
+            module_to_qat_module[pattern] = config.qat_module
+    return module_to_qat_module
+
+
+def get_fusion_pattern_to_root_node_getter(
+    backend_config: BackendConfig,
+) -> dict[Pattern, Callable]:
+    """Get a map from fusion pattern to a function that returns the root node
+    from the fusion pattern, e.g. the most common one is:
+    def get_root_node(node_pattern):
+        while not isinstance(node_pattern[-1], Node):
+            node_pattern = node_pattern[-1]
+        return node_pattern[-1]
+    This can work for all patterns whose root node is the "last node" in the pattern,
+    e.g. (torch.add, MatchAllNode, (torch.ReLU, torch.Conv2d))
+    """
+    root_node_getter_mapping: dict[Pattern, Callable] = {}
+    for pattern, config in backend_config._pattern_complex_format_to_config.items():
+        if config._root_node_getter is not None:
+            root_node_getter_mapping[pattern] = config._root_node_getter
+    return root_node_getter_mapping
+
+
+def get_fusion_pattern_to_extra_inputs_getter(
+    backend_config: BackendConfig,
+) -> dict[Pattern, Callable]:
+    """Get a map from fusion pattern to a function that returns extra input nodes
+    from the fusion pattern, in the order required by the root node. This is optional,
+    if not specified, we will not copy over any extra inputs for the root node.
+    Example:
+    # Let's say we have the pattern (torch.add, MatchAllNode, (torch.nn.BatchNorm2d, torch.nn.Conv2d))
+    # and root node is torch.nn.Conv2d, and the node in MatchAllNode would be an extra
+    # argument to the fused module, we can unpack the pattern and return the node at
+    # MatchAllNode here
+    # we can implement extra_inputs_getter as follows:
+    def extra_inputs_getter(pattern) -> List[Any]:
+        add, extra_input, conv_pattern = pattern
+        return [extra_input]
+    """
+    extra_inputs_getter_mapping: dict[Pattern, Callable] = {}
+    for pattern, config in backend_config._pattern_complex_format_to_config.items():
+        if config._extra_inputs_getter is not None:
+            extra_inputs_getter_mapping[pattern] = config._extra_inputs_getter
+    return extra_inputs_getter_mapping
+
+
+def remove_boolean_dispatch_from_name(p) -> Any:
+    """
+    Some ops have a default string representation such as
+    '.fn at 0x7ff1106bf280>',
+    this function replaces them with the hardcoded function names.
+    """
+    if p is F.fractional_max_pool2d:
+        return "torch.nn.functional.fractional_max_pool2d"
+    elif p is F.fractional_max_pool3d:
+        return "torch.nn.functional.fractional_max_pool3d"
+    elif p is F.max_pool1d:
+        return "torch.nn.functional.max_pool1d"
+    elif p is F.max_pool2d:
+        return "torch.nn.functional.max_pool2d"
+    elif p is F.max_pool3d:
+        return "torch.nn.functional.max_pool3d"
+    elif p is F.adaptive_max_pool1d:
+        return "torch.nn.functional.adaptive_max_pool1d"
+    elif p is F.adaptive_max_pool2d:
+        return "torch.nn.functional.adaptive_max_pool2d"
+    elif p is F.adaptive_max_pool3d:
+        return "torch.nn.functional.adaptive_max_pool3d"
+    if "boolean_dispatch" in str(p):
+        raise AssertionError(
+            f"{p} does not have a human readable representation in "
+            + "quantization documentation"
+        )
+    return p
+
+
+def pattern_to_human_readable(p) -> Any:
+    if isinstance(p, tuple):
+        # nested patterns, recurse
+        return tuple(pattern_to_human_readable(inner_p) for inner_p in p)
+    elif isinstance(p, str):
+        # method names are already human readable
+        return p
+    else:
+        p = remove_boolean_dispatch_from_name(p)
+        return p
+
+
+# TODO(future PR): move backend_config_dict to use dataclass and move this logic to
+# the corresponding __str__ function
+def entry_to_pretty_str(entry) -> str:
+    """
+    Given a backend_config_dict entry, returns a string with the human readable
+    representation of it.
+    """
+    s = "{\n"
+
+    # always output the pattern first
+    if "pattern" in entry:
+        pattern_str = pattern_to_human_readable(entry["pattern"])
+
+        s += f"  'pattern': {pattern_str},\n"
+
+    # custom output for dtype_configs to make it look nice
+    if "dtype_configs" in entry:
+        s += "  'dtype_configs': [\n"
+        for dtype_config in entry["dtype_configs"]:
+            s += "    {\n"
+            for k, v in dtype_config.items():
+                s += f"      '{k}': {v},\n"
+            s += "    },\n"
+        s += "  ],\n"
+
+    # custom output for num_tensor_args_to_observation_type to make it look nice
+    if "num_tensor_args_to_observation_type" in entry:
+        s += "  'num_tensor_args_to_observation_type': {\n"
+        for k, v in entry["num_tensor_args_to_observation_type"].items():
+            s += f"    {k}: {v},\n"
+        s += "  },\n"
+
+    # output all the other fields
+    custom_handled_fields = [
+        "pattern",
+        "dtype_configs",
+        "num_tensor_args_to_observation_type",
+    ]
+    for field_name in entry:
+        if field_name in custom_handled_fields:
+            continue
+        s += f"  '{field_name}': {entry[field_name]},\n"
+
+    s += "}"
+    return s
+
+
+def _get_pattern_in_reversed_nested_tuple_format(
+    config: BackendPatternConfig,
+) -> Pattern:
+    """
+    Return the pattern specified in the given config in the reversed nested tuple format
+    used internally in the quantization pattern matching code.
+
+    If the pattern is not a tuple, or the pattern is already specified in the reversed
+    nested tuple format, return the pattern as is. Otherwise:
+
+    For 2-tuples (a, b), return (b, a).
+    For 3-tuples (a, b, c), return (c, (b, a)).
+
+    For example:
+        * Given nn.Linear, return nn.Linear
+        * Given (nn.Linear, nn.ReLU), return (nn.ReLU, nn.Linear)
+        * Given (nn.Conv2d, nn.BatchNorm2d, nn.ReLU), return
+          (nn.ReLU, (nn.BatchNorm2d, nn.Conv2d))
+
+    For context, the reason why this is needed is the user-facing BackendConfig
+    API accepts the flat 2-or-3-tuple format in forward order. While this simple
+    format handles the vast majority of use cases, it does not handle the more
+    complex ones, and so the internal pattern matching code for quantization uses
+    the following, more general reversed nested tuple format instead:
+
+        operator = module_type | functional | torch op | native op | MatchAllNode
+        Pattern = (operator, Pattern, Pattern, ...) | operator
+
+    In the future, we expect to replace the above complex format with the one used
+    by the subgraph rewriter in torch.fx, so we don't have to maintain our own
+    complex pattern matching code. Then we won't need this helper function anymore.
+    """
+    if config._pattern_complex_format is not None:
+        return config._pattern_complex_format
+    if config.pattern is None:
+        raise ValueError(
+            "Either 'pattern' or 'pattern_complex_format' must be specified"
+        )
+    if not isinstance(config.pattern, tuple):
+        return config.pattern
+
+    # Pattern is specified in the simple tuple format, need to convert
+    if len(config.pattern) == 2:
+        (a, b) = config.pattern
+        return (b, a)
+    elif len(config.pattern) == 3:
+        (a, b, c) = config.pattern
+        return (c, (b, a))
+    else:
+        raise ValueError("Expected a tuple with 2 or 3 elements, got: ", config.pattern)
+
+
+def _get_fuser_method_in_reversed_nested_tuple_format(
+    config: BackendPatternConfig,
+) -> Callable:
+    """
+    Return the fuser method specified in the given config in the reversed nested
+    tuple format used internally in the quantization pattern matching code.
+
+    If pattern is specified in the reversed nested tuple format, we assume the
+    fuser method is also specified in this format and simply return it as is.
+    Otherwise, we convert the fuser method as follows:
+
+        * Given f(is_qat, conv, relu), return f'(is_qat, relu, conv)
+        * Given f(is_qat, conv, bn, relu), return f'(is_qat, relu, bn_conv),
+          where bn_conv is a 2-tuple (bn, conv)
+
+    The first argument of a fuser method is always `is_qat` and is not affected
+    in the conversion. We currently only support functions with 3 or 4 arguments.
+    """
+    if config.fuser_method is None:
+        raise AssertionError("config.fuser_method must be provided")
+    if config._pattern_complex_format is not None:
+        return config.fuser_method
+    if not isinstance(config.pattern, tuple):
+        raise ValueError("Expected pattern to be a tuple, got: ", config.pattern)
+
+    # Pattern is specified in the simple tuple format, need to convert
+    if len(config.pattern) == 2:
+        return _reverse2(config.fuser_method)
+    elif len(config.pattern) == 3:
+        return _reverse3(config.fuser_method)
+    else:
+        raise ValueError("Expected a tuple with 2 or 3 elements, got: ", config.pattern)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/x86.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/x86.py
new file mode 100644
index 0000000000000000000000000000000000000000..c64b56c981b391140f63038ac507b0708ee876f4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/backend_config/x86.py
@@ -0,0 +1,126 @@
+import torch
+
+from ._common_operator_config_utils import (
+    _get_binary_op_configs,
+    _get_bn_configs,
+    _get_cat_config,
+    _get_conv_configs,
+    _get_default_op_configs,
+    _get_embedding_op_configs,
+    _get_fixed_qparams_op_configs,
+    _get_linear_configs,
+    _get_rnn_op_configs,
+    _get_share_qparams_op_configs,
+    _get_tensor_info_op_configs,
+)
+from .backend_config import BackendConfig, DTypeConfig
+
+
+__all__ = [
+    "get_x86_backend_config",
+]
+
+# ===================
+# |  DTYPE CONFIGS  |
+# ===================
+
+# X86 aligns with FBGEMM for now
+
+x86_weighted_op_int8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.quint8,
+    weight_dtype=torch.qint8,
+    bias_dtype=torch.float,
+)
+
+x86_default_op_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.quint8,
+)
+
+x86_default_op_fp16_dtype_config = DTypeConfig(
+    input_dtype=torch.float16,
+    output_dtype=torch.float16,
+    weight_dtype=torch.float16,
+    bias_dtype=torch.float16,
+)
+
+x86_default_dynamic_int8_dtype_config = DTypeConfig(
+    input_dtype=torch.quint8,
+    output_dtype=torch.float,
+    weight_dtype=torch.qint8,
+    bias_dtype=torch.float,
+    is_dynamic=True,
+)
+
+x86_default_dynamic_float16_dtype_config = DTypeConfig(
+    input_dtype=torch.float16,
+    output_dtype=torch.float,
+    weight_dtype=torch.float16,
+    bias_dtype=torch.float,
+    is_dynamic=True,
+)
+
+x86_weight_only_quint8_dtype_config = DTypeConfig(
+    input_dtype=torch.float,
+    output_dtype=torch.float,
+    weight_dtype=torch.quint8,
+)
+
+x86_weight_only_quint4x2_dtype_config = DTypeConfig(
+    input_dtype=torch.float,
+    output_dtype=torch.float,
+    weight_dtype=torch.quint4x2,
+)
+
+
+# =====================
+# |  BACKEND CONFIGS  |
+# =====================
+
+
+def get_x86_backend_config() -> BackendConfig:
+    """
+    Return the `BackendConfig` for PyTorch's native x86 backend.
+    """
+    conv_dtype_configs = [x86_weighted_op_int8_dtype_config]
+    linear_dtype_configs = [
+        x86_weighted_op_int8_dtype_config,
+        x86_default_dynamic_int8_dtype_config,
+        x86_default_dynamic_float16_dtype_config,
+    ]
+    binary_op_dtype_configs = [x86_weighted_op_int8_dtype_config]
+    default_op_dtype_configs = [x86_default_op_quint8_dtype_config]
+    fixed_qparams_op_dtype_configs = [x86_weighted_op_int8_dtype_config]
+    share_qparams_op_dtype_configs = [x86_default_op_quint8_dtype_config]
+    tensor_info_op_dtype_configs = [x86_default_op_quint8_dtype_config]
+    rnn_op_dtype_configs = [
+        x86_default_dynamic_int8_dtype_config,
+        x86_default_dynamic_float16_dtype_config,
+    ]
+    embedding_op_dtype_configs = [
+        x86_weight_only_quint8_dtype_config,
+        x86_weight_only_quint4x2_dtype_config,
+    ]
+    return (
+        BackendConfig("x86")
+        .set_backend_pattern_configs(_get_conv_configs(conv_dtype_configs))
+        .set_backend_pattern_configs(_get_linear_configs(linear_dtype_configs))
+        .set_backend_pattern_configs(_get_binary_op_configs(binary_op_dtype_configs))
+        .set_backend_pattern_config(_get_cat_config(default_op_dtype_configs))
+        .set_backend_pattern_configs(_get_default_op_configs(default_op_dtype_configs))
+        .set_backend_pattern_configs(
+            _get_fixed_qparams_op_configs(fixed_qparams_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(
+            _get_share_qparams_op_configs(share_qparams_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(
+            _get_tensor_info_op_configs(tensor_info_op_dtype_configs)
+        )
+        .set_backend_pattern_configs(_get_bn_configs(default_op_dtype_configs))
+        .set_backend_pattern_configs(_get_rnn_op_configs(rnn_op_dtype_configs))
+        .set_backend_pattern_configs(
+            _get_embedding_op_configs(embedding_op_dtype_configs)
+        )
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fake_quantize.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fake_quantize.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4a380946c8a06dd884680fc52cf1350f49772f8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fake_quantize.py
@@ -0,0 +1,663 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+"""Implements modules  used to perform fake quantization."""
+
+import re
+from abc import ABC, abstractmethod
+from typing import Any
+
+import torch
+from torch.ao.quantization.observer import (
+    _with_args,
+    default_fixed_qparams_range_0to1_observer,
+    default_fixed_qparams_range_neg1to1_observer,
+    FixedQParamsObserver,
+    HistogramObserver,
+    MovingAverageMinMaxObserver,
+    MovingAveragePerChannelMinMaxObserver,
+)
+from torch.nn import Module
+
+
+__all__ = [
+    "FakeQuantizeBase",
+    "FakeQuantize",
+    "FixedQParamsFakeQuantize",
+    "FusedMovingAvgObsFakeQuantize",
+    "disable_fake_quant",
+    "disable_observer",
+    "enable_fake_quant",
+    "enable_observer",
+    "default_fake_quant",
+    "default_weight_fake_quant",
+    "default_dynamic_fake_quant",
+    "default_fixed_qparams_range_neg1to1_fake_quant",
+    "default_fixed_qparams_range_0to1_fake_quant",
+    "default_symmetric_fixed_qparams_fake_quant",
+    "default_affine_fixed_qparams_fake_quant",
+    "default_per_channel_weight_fake_quant",
+    "default_embedding_fake_quant",
+    "default_embedding_fake_quant_4bit",
+    "default_histogram_fake_quant",
+    "default_fused_act_fake_quant",
+    "default_fused_wt_fake_quant",
+    "default_fused_per_channel_wt_fake_quant",
+    "fused_wt_fake_quant_range_neg_127_to_127",
+    "fused_per_channel_wt_fake_quant_range_neg_127_to_127",
+]
+
+
+def _is_per_channel(qscheme: "torch.qscheme") -> bool:
+    return qscheme in [
+        torch.per_channel_symmetric,
+        torch.per_channel_affine,
+        torch.per_channel_affine_float_qparams,
+    ]
+
+
+def _is_per_tensor(qscheme: "torch.qscheme") -> bool:
+    return qscheme in [torch.per_tensor_symmetric, torch.per_tensor_affine]
+
+
+def _is_symmetric_quant(qscheme: "torch.qscheme") -> bool:
+    return qscheme in [torch.per_tensor_symmetric, torch.per_channel_symmetric]
+
+
+def _is_float_qparams(qscheme: "torch.qscheme") -> bool:
+    return qscheme == torch.per_channel_affine_float_qparams
+
+
+class FakeQuantizeBase(ABC, Module):
+    r"""Base fake quantize module.
+
+    Base fake quantize module
+    Any fake quantize implementation should derive from this class.
+
+    Concrete fake quantize module should follow the same API. In forward, they will update
+    the statistics of the observed Tensor and fake quantize the input. They should also provide a
+    `calculate_qparams` function that computes the quantization parameters given
+    the collected statistics.
+
+    """
+
+    fake_quant_enabled: torch.Tensor
+    observer_enabled: torch.Tensor
+
+    def __init__(self) -> None:
+        """Set fake_quant_enabled and observer_enabled."""
+        super().__init__()
+        # fake_quant_enabled and observer_enabled are buffers to support their
+        # replication in DDP. Data type is uint8 because NCCL does not support
+        # bool tensors.
+        self.register_buffer("fake_quant_enabled", torch.tensor([1], dtype=torch.uint8))
+        self.register_buffer("observer_enabled", torch.tensor([1], dtype=torch.uint8))
+
+    @abstractmethod
+    def forward(self, x):
+        pass
+
+    @abstractmethod
+    def calculate_qparams(self, **kwargs):
+        pass
+
+    @torch.jit.export
+    def enable_fake_quant(self, enabled: bool = True) -> None:
+        self.fake_quant_enabled[0] = 1 if enabled else 0
+
+    @torch.jit.export
+    def disable_fake_quant(self):
+        self.enable_fake_quant(False)
+
+    @torch.jit.export
+    def enable_observer(self, enabled: bool = True) -> None:
+        self.observer_enabled[0] = 1 if enabled else 0
+
+    @torch.jit.export
+    def disable_observer(self):
+        self.enable_observer(False)
+
+    @classmethod
+    def with_args(cls, **kwargs):
+        fake_quant_constructor = _with_args(cls, **kwargs)
+        # need to assign the correct module to fake_quantize
+        # constructors to satisfy public v private requirements
+        fake_quant_constructor.__module__ = "torch.ao.quantization.fake_quantize"
+        return fake_quant_constructor
+
+
+class FakeQuantize(FakeQuantizeBase):
+    r"""Simulate the quantize and dequantize operations in training time.
+
+    The output of this module is given by::
+
+        x_out = (
+            clamp(round(x / scale + zero_point), quant_min, quant_max) - zero_point
+        ) * scale
+
+    * :attr:`is_dynamic` indicates whether the fake quantie is a placeholder for dynamic quantization
+      operators (choose_qparams -> q -> dq) or static quantization operators (q -> dq)
+
+    * :attr:`scale` defines the scale factor used for quantization.
+
+    * :attr:`zero_point` specifies the quantized value to which 0 in floating point maps to
+
+    * :attr:`fake_quant_enabled` controls the application of fake quantization on tensors, note that
+      statistics can still be updated.
+
+    * :attr:`observer_enabled` controls statistics collection on tensors
+
+    * :attr:`dtype` specifies the quantized dtype that is being emulated with fake-quantization,
+        allowable values are torch.qint8 and torch.quint8.
+
+    Args:
+
+        observer (module): Module for observing statistics on input tensors and calculating scale
+          and zero-point.
+        observer_kwargs (optional): Arguments for the observer module
+
+    Attributes:
+        activation_post_process (Module): User provided module that collects statistics on the input tensor and
+          provides a method to calculate scale and zero-point.
+
+    """
+
+    scale: torch.Tensor
+    zero_point: torch.Tensor
+
+    def __init__(
+        self,
+        observer=MovingAverageMinMaxObserver,
+        quant_min=None,
+        quant_max=None,
+        is_dynamic=False,
+        **observer_kwargs,
+    ):
+        super().__init__()
+        # Populate quant_min/quant_max to observer_kwargs if valid
+        if quant_min is not None and quant_max is not None:
+            if quant_min > quant_max:
+                raise AssertionError(
+                    "quant_min must be less than or equal to quant_max"
+                )
+            dtype = observer_kwargs.get("dtype", torch.quint8)
+            if hasattr(observer, "p"):
+                # In case observer is _PartialWrapper, dtype can be stored in
+                # observer.p.keywords["dtype"]
+                dtype = getattr(getattr(observer, "p", {}), "keywords", {}).get(
+                    "dtype", dtype
+                )
+            # pyrefly: ignore [bad-argument-type]
+            if torch.iinfo(dtype).min > quant_min:
+                raise AssertionError("quant_min out of bound")
+            # pyrefly: ignore [bad-argument-type]
+            if quant_max > torch.iinfo(dtype).max:
+                raise AssertionError("quant_max out of bound")
+            observer_kwargs.update({"quant_min": quant_min, "quant_max": quant_max})
+        observer_kwargs["is_dynamic"] = is_dynamic
+        self.activation_post_process = observer(**observer_kwargs)
+        # TODO: keeping self.quant_min/max for BC; remove after a couple releases
+        # Users should use self.activation_post_process.quant_min
+        self.quant_min = self.activation_post_process.quant_min
+        self.quant_max = self.activation_post_process.quant_max
+        self.is_dynamic = self.activation_post_process.is_dynamic
+        if _is_float_qparams(self.activation_post_process.qscheme):
+            zero_point_dtype = torch.float
+        else:
+            zero_point_dtype = torch.int
+        self.register_buffer("scale", torch.tensor([1.0], dtype=torch.float))
+        self.register_buffer("zero_point", torch.tensor([0], dtype=zero_point_dtype))
+        self.dtype = self.activation_post_process.dtype
+        self.qscheme = self.activation_post_process.qscheme
+        self.ch_axis = (
+            self.activation_post_process.ch_axis
+            if hasattr(self.activation_post_process, "ch_axis")
+            else -1
+        )
+        if not (_is_per_channel(self.qscheme) or _is_per_tensor(self.qscheme)):
+            raise AssertionError(
+                "Only per channel and per tensor quantization are supported in fake quantize"
+                + " got qscheme: "
+                + str(self.qscheme)
+            )
+        self.is_per_channel = _is_per_channel(self.qscheme)
+
+    @torch.jit.export
+    def calculate_qparams(self):  # type: ignore[override]
+        return self.activation_post_process.calculate_qparams()
+
+    def forward(self, X):
+        if self.observer_enabled[0] == 1:
+            self.activation_post_process(X.detach())
+            _scale, _zero_point = self.calculate_qparams()
+            _scale, _zero_point = (
+                _scale.to(self.scale.device),
+                _zero_point.to(self.zero_point.device),
+            )
+            if self.scale.shape != _scale.shape:
+                self.scale.resize_(_scale.shape)
+                self.zero_point.resize_(_zero_point.shape)
+            self.scale.copy_(_scale)
+            self.zero_point.copy_(_zero_point)
+
+        if self.fake_quant_enabled[0] == 1:
+            if self.is_per_channel:
+                X = torch.fake_quantize_per_channel_affine(
+                    X,
+                    self.scale,
+                    self.zero_point,
+                    self.ch_axis,
+                    self.activation_post_process.quant_min,
+                    self.activation_post_process.quant_max,
+                )
+            else:
+                X = torch.fake_quantize_per_tensor_affine(
+                    X,
+                    self.scale,
+                    self.zero_point,
+                    self.activation_post_process.quant_min,
+                    self.activation_post_process.quant_max,
+                )
+        return X
+
+    @torch.jit.export
+    def extra_repr(self):
+        return (
+            f"fake_quant_enabled={self.fake_quant_enabled}, observer_enabled={self.observer_enabled}, "
+            f"quant_min={self.activation_post_process.quant_min}, quant_max={self.activation_post_process.quant_max}, "
+            f"dtype={self.dtype}, qscheme={self.qscheme}, ch_axis={self.ch_axis}, "
+            f"scale={self.scale}, zero_point={self.zero_point}"
+        )
+
+    def _save_to_state_dict(self, destination, prefix, keep_vars):
+        # We cannot currently register scalar values as buffers, so need to manually
+        # specify serialization here.
+        super()._save_to_state_dict(destination, prefix, keep_vars)
+        destination[prefix + "scale"] = self.scale
+        destination[prefix + "zero_point"] = self.zero_point
+
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        # Removing this function throws an error that the size of the loaded tensor does not match the original size
+        # i.e., These buffers start out with numel 0 and become numel 1 once they have their first forward pass.
+        local_state = ["scale", "zero_point"]
+        for name in local_state:
+            key = prefix + name
+            if key in state_dict:
+                val = state_dict[key]
+                # Custom handling to allow loading scale and zero_point
+                # of size N into uninitialized buffers of size 0. The
+                # buffers are resized here, and the values are copied in
+                # the default state_dict loading code of the parent.
+                if name == "scale":
+                    self.scale.resize_(val.shape)
+                else:
+                    if name != "zero_point":
+                        raise AssertionError(
+                            "Expected 'zero_point' but got different state key"
+                        )
+                    self.zero_point.resize_(val.shape)
+                # For torchscript module we need to update the attributes here since we do not
+                # call the `_load_from_state_dict` function defined module.py
+                if torch.jit.is_scripting():
+                    if name == "scale":
+                        self.scale.copy_(val)
+                    else:
+                        if name != "zero_point":
+                            raise AssertionError(
+                                "Expected 'zero_point' but got different state key"
+                            )
+                        self.zero_point.copy_(val)
+            elif strict:
+                missing_keys.append(key)
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            strict,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+
+class FixedQParamsFakeQuantize(FakeQuantize):
+    """Simulate quantize and dequantize in training time.
+
+    Simulate quantize and dequantize with fixed quantization
+    parameters in training time. Only per tensor quantization
+    is supported.
+    """
+
+    # TODO: rename observer to observer_ctr
+    def __init__(self, observer):
+        super().__init__(observer=observer)
+        if type(self.activation_post_process) is not FixedQParamsObserver:
+            raise AssertionError(
+                f"{self.__class__.__name__}'s observer must be a {FixedQParamsObserver.__name__}"
+            )
+        self._observer_ctr = observer
+        self.scale = self.activation_post_process.scale
+        self.zero_point = self.activation_post_process.zero_point
+        if not _is_per_tensor(self.qscheme):
+            raise AssertionError(
+                "Only per tensor quantization is supported"
+                + " FixedQParamsFakeQuantize module, got qscheme:"
+                + str(self.qscheme)
+            )
+
+    @torch.jit.export
+    def calculate_qparams(self):  # type: ignore[override]
+        return self.scale, self.zero_point
+
+    @torch.jit.export
+    def extra_repr(self):
+        """Define a string representation of the object's attributes."""
+        return (
+            f"fake_quant_enabled={self.fake_quant_enabled}, observer_enabled={self.observer_enabled}, "
+            f"scale={self.scale}, zero_point={self.zero_point}, "
+            f"dtype={self.dtype}, quant_min={self.activation_post_process.quant_min}, "
+            f"quant_max={self.activation_post_process.quant_max}, qscheme={self.qscheme}"
+        )
+
+
+class FusedMovingAvgObsFakeQuantize(FakeQuantize):
+    r"""Define a fused module to observe the tensor.
+
+    Fused module that is used to observe the input tensor (compute min/max), compute
+    scale/zero_point and fake_quantize the tensor.
+    This module uses calculation similar MovingAverageMinMaxObserver for the inputs,
+    to compute the min/max values in order to compute the scale/zero_point.
+    The qscheme input in the observer is used to differentiate between symmetric/affine
+    quantization scheme.
+
+    The output of this module is given by
+    x_out = (clamp(round(x/scale + zero_point), quant_min, quant_max)-zero_point)*scale
+
+    Similar to :class:`~torch.ao.quantization.FakeQuantize`, and accepts the same attributes as the
+    base class.
+
+    """
+
+    def __init__(
+        self,
+        observer: Any = MovingAverageMinMaxObserver,
+        quant_min: int = 0,
+        quant_max: int = 255,
+        **observer_kwargs: Any,
+    ) -> None:
+        super().__init__(observer, quant_min, quant_max, **observer_kwargs)
+        if not isinstance(
+            self.activation_post_process,
+            (MovingAverageMinMaxObserver, MovingAveragePerChannelMinMaxObserver),
+        ):
+            raise AssertionError(
+                "Fused observer+fake_quant module only works with MovingAverageMinMaxObserver"
+            )
+        self.register_buffer("fake_quant_enabled", torch.tensor([1], dtype=torch.long))
+        self.register_buffer("observer_enabled", torch.tensor([1], dtype=torch.long))
+        self.is_symmetric_quant = _is_symmetric_quant(
+            self.activation_post_process.qscheme
+        )
+
+    @torch.jit.export
+    def calculate_qparams(self) -> tuple[torch.Tensor, torch.Tensor]:  # type: ignore[override]
+        return self.activation_post_process.calculate_qparams()
+
+    @torch.jit.export
+    def extra_repr(self) -> str:
+        return (
+            f"fake_quant_enabled={self.fake_quant_enabled}, observer_enabled={self.observer_enabled}, "
+            f"scale={self.scale}, zero_point={self.zero_point}, dtype={self.dtype}, "
+            f"quant_min={self.activation_post_process.quant_min}, quant_max={self.activation_post_process.quant_max}, "
+            f"qscheme={self.qscheme}, reduce_range={self.activation_post_process.reduce_range}"
+        )
+
+    def forward(self, X: torch.Tensor) -> torch.Tensor:
+        return torch.fused_moving_avg_obs_fake_quant(
+            X,
+            self.observer_enabled,
+            self.fake_quant_enabled,
+            self.activation_post_process.min_val,
+            self.activation_post_process.max_val,
+            self.scale,
+            self.zero_point,
+            self.activation_post_process.averaging_constant,
+            self.activation_post_process.quant_min,
+            self.activation_post_process.quant_max,
+            self.ch_axis,
+            self.is_per_channel,
+            self.is_symmetric_quant,
+        )
+
+
+default_fake_quant = FakeQuantize.with_args(
+    observer=MovingAverageMinMaxObserver,
+    quant_min=0,
+    quant_max=255,
+    dtype=torch.quint8,
+    qscheme=torch.per_tensor_affine,
+    reduce_range=True,
+)
+"""
+Default fake_quant for activations.
+"""
+
+default_weight_fake_quant = FakeQuantize.with_args(
+    observer=MovingAverageMinMaxObserver,
+    quant_min=-128,
+    quant_max=127,
+    dtype=torch.qint8,
+    qscheme=torch.per_tensor_symmetric,
+    reduce_range=False,
+)
+"""
+Default fake_quant for weights.
+Observer is memoryless since averaging_constant is 1.
+"""
+
+default_dynamic_fake_quant = FakeQuantize.with_args(
+    observer=MovingAverageMinMaxObserver,
+    quant_min=0,
+    quant_max=255,
+    is_dynamic=True,
+    dtype=torch.quint8,
+    averaging_constant=1,
+)
+"""
+Default dynamic fake_quant for activations.
+"""
+
+default_fixed_qparams_range_neg1to1_fake_quant = FixedQParamsFakeQuantize.with_args(
+    observer=default_fixed_qparams_range_neg1to1_observer
+)
+default_fixed_qparams_range_0to1_fake_quant = FixedQParamsFakeQuantize.with_args(
+    observer=default_fixed_qparams_range_0to1_observer
+)
+# TODO: the following 2 variables are kept for backwards compatibility; remove after a few releases
+default_symmetric_fixed_qparams_fake_quant = (
+    default_fixed_qparams_range_neg1to1_fake_quant
+)
+default_affine_fixed_qparams_fake_quant = default_fixed_qparams_range_0to1_fake_quant
+
+default_per_channel_weight_fake_quant = FakeQuantize.with_args(
+    observer=MovingAveragePerChannelMinMaxObserver,
+    quant_min=-128,
+    quant_max=127,
+    dtype=torch.qint8,
+    qscheme=torch.per_channel_symmetric,
+    reduce_range=False,
+    ch_axis=0,
+)
+"""
+Default fake_quant for per-channel weights.
+Observer is memoryless since averaging_constant is 1.
+"""
+default_embedding_fake_quant = FakeQuantize.with_args(
+    observer=MovingAveragePerChannelMinMaxObserver,
+    qscheme=torch.per_channel_affine_float_qparams,
+    dtype=torch.quint8,
+    quant_min=0,
+    quant_max=255,
+    ch_axis=0,
+    averaging_constant=1,
+)
+"""
+Default fake_quant for embeddings.
+Observer is memoryless since averaging_constant is 1.
+"""
+
+default_embedding_fake_quant_4bit = FakeQuantize.with_args(
+    observer=MovingAveragePerChannelMinMaxObserver,
+    qscheme=torch.per_channel_affine_float_qparams,
+    ch_axis=0,
+    dtype=torch.quint4x2,
+    averaging_constant=1,
+)
+
+default_histogram_fake_quant = FakeQuantize.with_args(
+    observer=HistogramObserver,
+    quant_min=0,
+    quant_max=255,
+    dtype=torch.quint8,
+    qscheme=torch.per_tensor_affine,
+    reduce_range=True,
+)
+"""
+Fake_quant for activations using a histogram..
+"""
+
+
+default_fused_act_fake_quant = FusedMovingAvgObsFakeQuantize.with_args(
+    observer=MovingAverageMinMaxObserver,
+    quant_min=0,
+    quant_max=255,
+    dtype=torch.quint8,
+)
+
+"""
+Fused version of `default_fake_quant`, with improved performance.
+"""
+
+
+default_fused_wt_fake_quant = FusedMovingAvgObsFakeQuantize.with_args(
+    observer=MovingAverageMinMaxObserver,
+    quant_min=-128,
+    quant_max=127,
+    dtype=torch.qint8,
+    qscheme=torch.per_tensor_symmetric,
+)
+"""
+Fused version of `default_weight_fake_quant`, with improved performance.
+"""
+
+default_fused_per_channel_wt_fake_quant = FusedMovingAvgObsFakeQuantize.with_args(
+    observer=MovingAveragePerChannelMinMaxObserver,
+    quant_min=-128,
+    quant_max=127,
+    dtype=torch.qint8,
+    qscheme=torch.per_channel_symmetric,
+)
+"""
+Fused version of `default_per_channel_weight_fake_quant`, with improved performance.
+"""
+
+fused_wt_fake_quant_range_neg_127_to_127 = FusedMovingAvgObsFakeQuantize.with_args(
+    observer=MovingAverageMinMaxObserver,
+    quant_min=-127,
+    quant_max=127,
+    dtype=torch.qint8,
+    qscheme=torch.per_tensor_symmetric,
+    eps=2**-12,
+)
+"""
+Fused version of `default_weight_fake_quant`, with the 8-bit values restricted to [-127, +127], excluding -128.
+"""
+
+fused_per_channel_wt_fake_quant_range_neg_127_to_127 = (
+    FusedMovingAvgObsFakeQuantize.with_args(
+        observer=MovingAveragePerChannelMinMaxObserver,
+        quant_min=-127,
+        quant_max=127,
+        dtype=torch.qint8,
+        qscheme=torch.per_channel_symmetric,
+        eps=2**-12,
+    )
+)
+
+"""
+Fused version of `default_per_channel_weight_fake_quant`, with the 8-bit values restricted to [-127, +127], excluding -128.
+"""
+
+
+def _is_fake_quant_script_module(mod):
+    """Return true if given mod is an instance of FakeQuantize script module."""
+    if isinstance(mod, torch.jit.RecursiveScriptModule):
+        # qualified name looks like '__torch__.torch.ao.quantization.fake_quantize.___torch_mangle_2.FakeQuantize'
+        suffix = mod._c.qualified_name.split(".", 1)[1]
+        name = re.sub(r"\.___torch_mangle_\d+", "", suffix)
+        return (
+            name == "torch.ao.quantization.fake_quantize.FakeQuantize"
+            or name
+            == "torch.ao.quantization.fake_quantize.FusedMovingAvgObsFakeQuantize"
+        )
+    return False
+
+
+def disable_fake_quant(mod):
+    """Disable fake quantization for the module.
+
+    Disable fake quantization for this module, if applicable. Example usage::
+
+      # model is any PyTorch model
+      model.apply(torch.ao.quantization.disable_fake_quant)
+
+    """
+    if isinstance(mod, FakeQuantizeBase) or _is_fake_quant_script_module(mod):
+        mod.disable_fake_quant()
+
+
+def enable_fake_quant(mod):
+    """Enable fake quantization for the module.
+
+    Enable fake quantization for this module, if applicable. Example usage::
+
+      # model is any PyTorch model
+      model.apply(torch.ao.quantization.enable_fake_quant)
+
+    """
+    if isinstance(mod, FakeQuantizeBase) or _is_fake_quant_script_module(mod):
+        mod.enable_fake_quant()
+
+
+def disable_observer(mod):
+    """Disable observation for this module.
+
+    Disable observation for this module, if applicable. Example usage::
+
+      # model is any PyTorch model
+      model.apply(torch.ao.quantization.disable_observer)
+
+    """
+    if isinstance(mod, FakeQuantizeBase) or _is_fake_quant_script_module(mod):
+        mod.disable_observer()
+
+
+def enable_observer(mod):
+    """Enable observation for this module.
+
+    Enable observation for this module, if applicable. Example usage::
+
+      # model is any PyTorch model
+      model.apply(torch.ao.quantization.enable_observer)
+
+    """
+    if isinstance(mod, FakeQuantizeBase) or _is_fake_quant_script_module(mod):
+        mod.enable_observer()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fuse_modules.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fuse_modules.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f664c699144917d3314eee7bdf5dd92f9697108
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fuse_modules.py
@@ -0,0 +1,215 @@
+# mypy: allow-untyped-defs
+import copy
+
+import torch.nn as nn
+
+# for backward compatibility
+from torch.ao.quantization.fuser_method_mappings import (  # noqa: F401  # noqa: F401
+    fuse_conv_bn,
+    fuse_conv_bn_relu,
+    get_fuser_method,
+)
+from torch.nn.utils.parametrize import type_before_parametrizations
+
+
+__all__ = [
+    "fuse_known_modules",
+    "fuse_modules",
+    "fuse_modules_qat",
+]
+
+
+# Generalization of getattr
+def _get_module(model, submodule_key):
+    tokens = submodule_key.split(".")
+    cur_mod = model
+    for s in tokens:
+        cur_mod = getattr(cur_mod, s)
+    return cur_mod
+
+
+# Generalization of setattr
+def _set_module(model, submodule_key, module):
+    tokens = submodule_key.split(".")
+    sub_tokens = tokens[:-1]
+    cur_mod = model
+    for s in sub_tokens:
+        cur_mod = getattr(cur_mod, s)
+
+    setattr(cur_mod, tokens[-1], module)
+
+
+def fuse_known_modules(mod_list, is_qat, additional_fuser_method_mapping=None):
+    r"""Return a list of known fuse modules.
+
+    Returns a list of modules that fuses the operations specified
+     in the input module list.
+
+    Fuses only the following sequence of modules:
+    conv, bn
+    conv, bn, relu
+    conv, relu
+    linear, bn
+    linear, relu
+    For these sequences, the first element in the output module list performs
+    the fused operation. The rest of the elements are set to nn.Identity()
+    """
+    types = tuple(type_before_parametrizations(m) for m in mod_list)
+    fuser_method = get_fuser_method(types, additional_fuser_method_mapping)
+    if fuser_method is None:
+        raise NotImplementedError(f"Cannot fuse modules: {types}")
+    new_mod: list[nn.Module | None] = [None] * len(mod_list)
+    fused = fuser_method(is_qat, *mod_list)
+    # NOTE: forward hooks not processed in the two following for loops will be lost after the fusion
+    # Move pre forward hooks of the base module to resulting fused module
+    for pre_hook_fn in mod_list[0]._forward_pre_hooks.values():
+        fused.register_forward_pre_hook(pre_hook_fn)
+    mod_list[0]._forward_pre_hooks.clear()
+    # Move post forward hooks of the last module to resulting fused module
+    for hook_fn in mod_list[-1]._forward_hooks.values():
+        fused.register_forward_hook(hook_fn)
+    mod_list[-1]._forward_hooks.clear()
+    new_mod[0] = fused
+
+    for i in range(1, len(mod_list)):
+        identity = nn.Identity()
+        identity.training = mod_list[0].training
+        new_mod[i] = identity
+
+    return new_mod
+
+
+def _fuse_modules_helper(
+    model,
+    modules_to_fuse,
+    is_qat,
+    fuser_func=fuse_known_modules,
+    fuse_custom_config_dict=None,
+):
+    if fuse_custom_config_dict is None:
+        fuse_custom_config_dict = {}
+    additional_fuser_method_mapping = fuse_custom_config_dict.get(
+        "additional_fuser_method_mapping", {}
+    )
+    mod_list = [_get_module(model, item) for item in modules_to_fuse]
+
+    # Fuse list of modules
+    new_mod_list = fuser_func(mod_list, is_qat, additional_fuser_method_mapping)
+
+    # Replace original module list with fused module list
+    for i, item in enumerate(modules_to_fuse):
+        _set_module(model, item, new_mod_list[i])
+
+
+def _fuse_modules(
+    model,
+    modules_to_fuse,
+    is_qat,
+    inplace=False,
+    fuser_func=fuse_known_modules,
+    fuse_custom_config_dict=None,
+):
+    if not inplace:
+        model = copy.deepcopy(model)
+
+    if all(isinstance(module_element, str) for module_element in modules_to_fuse):
+        # Handle case of modules_to_fuse being a list
+        _fuse_modules_helper(
+            model, modules_to_fuse, is_qat, fuser_func, fuse_custom_config_dict
+        )
+    else:
+        # Handle case of modules_to_fuse being a list of lists
+        for module_list in modules_to_fuse:
+            _fuse_modules_helper(
+                model, module_list, is_qat, fuser_func, fuse_custom_config_dict
+            )
+    return model
+
+
+def fuse_modules(
+    model,
+    modules_to_fuse,
+    inplace=False,
+    fuser_func=fuse_known_modules,
+    fuse_custom_config_dict=None,
+):
+    r"""Fuse a list of modules into a single module.
+
+    Fuses only the following sequence of modules:
+    conv, bn
+    conv, bn, relu
+    conv, relu
+    linear, relu
+    bn, relu
+    All other sequences are left unchanged.
+    For these sequences, replaces the first item in the list
+    with the fused module, replacing the rest of the modules
+    with identity.
+
+    Args:
+        model: Model containing the modules to be fused
+        modules_to_fuse: list of list of module names to fuse. Can also be a list
+                         of strings if there is only a single list of modules to fuse.
+        inplace: bool specifying if fusion happens in place on the model, by default
+                 a new model is returned
+        fuser_func: Function that takes in a list of modules and outputs a list of fused modules
+                    of the same length. For example,
+                    fuser_func([convModule, BNModule]) returns the list [ConvBNModule, nn.Identity()]
+                    Defaults to torch.ao.quantization.fuse_known_modules
+        `fuse_custom_config_dict`: custom configuration for fusion
+
+    .. code-block:: python
+
+       # Example of fuse_custom_config_dict
+       fuse_custom_config_dict = {
+           # Additional fuser_method mapping
+           "additional_fuser_method_mapping": {
+               (torch.nn.Conv2d, torch.nn.BatchNorm2d): fuse_conv_bn
+           },
+       }
+
+    Returns:
+        model with fused modules. A new copy is created if inplace=True.
+
+    Examples::
+
+            >>> # xdoctest: +SKIP
+            >>> m = M().eval()
+            >>> # m is a module containing the sub-modules below
+            >>> modules_to_fuse = [ ['conv1', 'bn1', 'relu1'], ['submodule.conv', 'submodule.relu']]
+            >>> fused_m = torch.ao.quantization.fuse_modules(m, modules_to_fuse)
+            >>> output = fused_m(input)
+
+            >>> m = M().eval()
+            >>> # Alternately provide a single list of modules to fuse
+            >>> modules_to_fuse = ['conv1', 'bn1', 'relu1']
+            >>> fused_m = torch.ao.quantization.fuse_modules(m, modules_to_fuse)
+            >>> output = fused_m(input)
+
+    """
+    return _fuse_modules(
+        model,
+        modules_to_fuse,
+        is_qat=False,
+        inplace=inplace,
+        fuser_func=fuser_func,
+        fuse_custom_config_dict=fuse_custom_config_dict,
+    )
+
+
+def fuse_modules_qat(
+    model,
+    modules_to_fuse,
+    inplace=False,
+    fuser_func=fuse_known_modules,
+    fuse_custom_config_dict=None,
+):
+    """QAT version for `fuse_modules`."""
+    return _fuse_modules(
+        model,
+        modules_to_fuse,
+        is_qat=True,
+        inplace=inplace,
+        fuser_func=fuser_func,
+        fuse_custom_config_dict=fuse_custom_config_dict,
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fuser_method_mappings.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fuser_method_mappings.py
new file mode 100644
index 0000000000000000000000000000000000000000..d72a3579438bc3e5e2687982ab4b550c680d2110
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fuser_method_mappings.py
@@ -0,0 +1,314 @@
+# mypy: allow-untyped-defs
+import itertools
+from collections.abc import Callable
+from typing import Any
+
+import torch.ao.nn.intrinsic as nni
+import torch.nn as nn
+from torch.ao.quantization.utils import get_combined_dict, MatchAllNode, Pattern
+
+
+__all__ = [
+    "fuse_conv_bn",
+    "fuse_conv_bn_relu",
+    "fuse_linear_bn",
+    "fuse_convtranspose_bn",
+    "get_fuser_method",
+    "get_fuser_method_new",
+]
+
+
+def fuse_conv_bn(is_qat, conv, bn):
+    r"""Return the fused the conv and bn modules.
+    Given the conv and bn modules, fuses them and returns the fused module
+
+    Args:
+        is_qat: a flag for whether we are using quantization aware training fusion
+        or post training quantization fusion
+        conv: Module instance of type conv2d/conv3d
+        bn: Spatial BN instance that needs to be fused with the conv
+
+    Examples::
+
+        >>> m1 = nn.Conv2d(10, 20, 3)
+        >>> b1 = nn.BatchNorm2d(20)
+        >>> # xdoctest: +SKIP
+        >>> m2 = fuse_conv_bn(m1, b1)
+    """
+    if conv.training != bn.training:
+        raise AssertionError(
+            "Conv and BN both must be in the same mode (train or eval)."
+        )
+
+    fused_module_class_map = {
+        nn.Conv1d: nni.ConvBn1d,
+        nn.Conv2d: nni.ConvBn2d,
+        nn.Conv3d: nni.ConvBn3d,
+    }
+
+    if is_qat:
+        if bn.num_features != conv.out_channels:
+            raise AssertionError(
+                "Output channel of Conv2d must match num_features of BatchNorm2d."
+            )
+        if not bn.affine:
+            raise AssertionError(
+                "Only support fusing BatchNorm2d with affine set to True"
+            )
+        if not bn.track_running_stats:
+            raise AssertionError(
+                "Only support fusing BatchNorm2d with tracking_running_stats set to True"
+            )
+        fused_module_class = fused_module_class_map.get((type(conv)), None)
+        if fused_module_class is not None:
+            return fused_module_class(conv, bn)
+        else:
+            raise NotImplementedError(f"Cannot fuse train modules: {(conv, bn)}")
+    else:
+        return nn.utils.fuse_conv_bn_eval(conv, bn)
+
+
+def fuse_conv_bn_relu(is_qat, conv, bn, relu):
+    r"""Return the fused conv and bv modules.
+
+    Given the conv and bn modules, fuses them and returns the fused module
+
+    Args:
+        is_qat: a flag for whether we are using quantization aware training fusion
+        or post training quantization fusion
+        conv: Module instance of type conv2d/conv3d
+        bn: Spatial BN instance that needs to be fused with the conv
+
+    Examples::
+
+        >>> m1 = nn.Conv2d(10, 20, 3)
+        >>> b1 = nn.BatchNorm2d(20)
+        >>> r1 = nn.ReLU(inplace=False)
+        >>> # xdoctest: +SKIP
+        >>> m2 = fuse_conv_bn_relu(m1, b1, r1)
+    """
+    if not (conv.training == bn.training == relu.training):
+        raise AssertionError(
+            "Conv and BN both must be in the same mode (train or eval)."
+        )
+    fused_module: type[nn.Sequential] | None = None
+    if is_qat:
+        map_to_fused_module_train = {
+            nn.Conv1d: nni.ConvBnReLU1d,
+            nn.Conv2d: nni.ConvBnReLU2d,
+            nn.Conv3d: nni.ConvBnReLU3d,
+        }
+        if bn.num_features != conv.out_channels:
+            raise AssertionError(
+                "Output channel of Conv2d must match num_features of BatchNorm2d"
+            )
+        if not bn.affine:
+            raise AssertionError(
+                "Only support fusing BatchNorm2d with affine set to True"
+            )
+        if not bn.track_running_stats:
+            raise AssertionError(
+                "Only support fusing BatchNorm2d with tracking_running_stats set to True"
+            )
+        fused_module = map_to_fused_module_train.get(type(conv), None)
+        if fused_module is not None:
+            return fused_module(conv, bn, relu)
+        else:
+            raise NotImplementedError(f"Cannot fuse train modules: {(conv, bn, relu)}")
+    else:
+        map_to_fused_module_eval = {
+            nn.Conv1d: nni.ConvReLU1d,
+            nn.Conv2d: nni.ConvReLU2d,
+            nn.Conv3d: nni.ConvReLU3d,
+        }
+        fused_module = map_to_fused_module_eval.get(type(conv), None)
+        if fused_module is not None:
+            fused_conv = nn.utils.fusion.fuse_conv_bn_eval(conv, bn)
+            return fused_module(fused_conv, relu)
+        else:
+            raise NotImplementedError(f"Cannot fuse eval modules: {(conv, bn, relu)}")
+
+
+def fuse_linear_bn(is_qat, linear, bn):
+    r"""Return the fused linear and bn modules.
+    Given the linear and bn modules, fuses them and returns the fused module
+
+    Args:
+        is_qat: a flag for whether we are using quantization aware training fusion
+        or post training quantization fusion
+        linear: Module instance of type Linear
+        bn: BatchNorm1d instance that needs to be fused with the linear layer
+
+    Examples::
+
+        >>> m1 = nn.Linear(20, 10)
+        >>> b1 = nn.BatchNorm1d(10)
+        >>> # xdoctest: +SKIP
+        >>> m2 = fuse_linear_bn(m1, b1)
+    """
+    if linear.training != bn.training:
+        raise AssertionError(
+            "Linear and BN both must be in the same mode (train or eval)."
+        )
+
+    if is_qat:
+        if bn.num_features != linear.out_features:
+            raise AssertionError(
+                "Output features of Linear must match num_features of BatchNorm1d"
+            )
+        if not bn.affine:
+            raise AssertionError(
+                "Only support fusing BatchNorm1d with affine set to True"
+            )
+        if not bn.track_running_stats:
+            raise AssertionError(
+                "Only support fusing BatchNorm1d with tracking_running_stats set to True"
+            )
+        return nni.LinearBn1d(linear, bn)
+    else:
+        return nn.utils.fusion.fuse_linear_bn_eval(linear, bn)
+
+
+def fuse_convtranspose_bn(is_qat, convt, bn):
+    r"""Return the fused ConvTranspose and bn modules.
+    Given ConvTranspose and bn modules, fuses them and returns the fused module
+
+    Args:
+        convt: Module instance of type ConvTransposeNd
+        bn: BatchNormNd instance that needs to be fused with the linear layer.
+            batch norm N should match the ConvTranspose N
+
+    Examples::
+
+        >>> m1 = nn.ConvTranspose2d(10, 20, 3)
+        >>> b1 = nn.BatchNorm2d(20)
+        >>> # xdoctest: +SKIP
+        >>> m2 = fuse_convtranspose_bn(m1, b1)
+    """
+    if convt.training != bn.training:
+        raise AssertionError(
+            "ConvTranspose and BN both must be in the same mode (train or eval)."
+        )
+
+    if is_qat:
+        raise Exception(  # noqa: TRY002
+            "Fusing ConvTranspose+BatchNorm not yet supported in QAT."
+        )
+    else:
+        return nn.utils.fusion.fuse_conv_bn_eval(convt, bn, transpose=True)
+
+
+def _sequential_wrapper2(sequential):
+    """Return a sequential wrapped that for is_qat and two modules.
+    Given a sequential class for two modules, return a function that takes
+    is_qat, and then two modules as argument, that ignores the is_qat flag
+    and always returns the sequential that combines the two input modules
+    """
+
+    def fuser_method(is_qat, m1, m2):
+        return sequential(m1, m2)
+
+    return fuser_method
+
+
+_DEFAULT_OP_LIST_TO_FUSER_METHOD: dict[tuple, nn.Sequential | Callable] = {
+    (nn.Conv1d, nn.BatchNorm1d): fuse_conv_bn,
+    (nn.Conv1d, nn.BatchNorm1d, nn.ReLU): fuse_conv_bn_relu,
+    (nn.Conv2d, nn.BatchNorm2d): fuse_conv_bn,
+    (nn.Conv2d, nn.BatchNorm2d, nn.ReLU): fuse_conv_bn_relu,
+    (nn.Conv3d, nn.BatchNorm3d): fuse_conv_bn,
+    (nn.Conv3d, nn.BatchNorm3d, nn.ReLU): fuse_conv_bn_relu,
+    (nn.Conv1d, nn.ReLU): _sequential_wrapper2(nni.ConvReLU1d),
+    (nn.Conv2d, nn.ReLU): _sequential_wrapper2(nni.ConvReLU2d),
+    (nn.Conv3d, nn.ReLU): _sequential_wrapper2(nni.ConvReLU3d),
+    (nn.Linear, nn.BatchNorm1d): fuse_linear_bn,
+    (nn.Linear, nn.ReLU): _sequential_wrapper2(nni.LinearReLU),
+    (nn.BatchNorm2d, nn.ReLU): _sequential_wrapper2(nni.BNReLU2d),
+    (nn.BatchNorm3d, nn.ReLU): _sequential_wrapper2(nni.BNReLU3d),
+    (nn.ConvTranspose1d, nn.BatchNorm1d): fuse_convtranspose_bn,
+    (nn.ConvTranspose2d, nn.BatchNorm2d): fuse_convtranspose_bn,
+    (nn.ConvTranspose3d, nn.BatchNorm3d): fuse_convtranspose_bn,
+}
+
+
+def get_fuser_method(op_list, additional_fuser_method_mapping=None):
+    """Get fuser method for the given list of module types.
+
+    Get fuser method for the given list of module types,
+    return None if fuser method does not exist
+    """
+    if additional_fuser_method_mapping is None:
+        additional_fuser_method_mapping = {}
+    all_mappings = get_combined_dict(
+        _DEFAULT_OP_LIST_TO_FUSER_METHOD, additional_fuser_method_mapping
+    )
+    fuser_method = all_mappings.get(op_list, None)
+    if fuser_method is None:
+        raise AssertionError(f"did not find fuser method for: {op_list} ")
+    return fuser_method
+
+
+def _reverse2(f):
+    def reversed(is_qat, x, y):
+        return f(is_qat, y, x)
+
+    return reversed
+
+
+def _reverse3(f):
+    def reversed(is_qat, x, w):
+        y, z = w
+        return f(is_qat, z, y, x)
+
+    return reversed
+
+
+def _get_valid_patterns(op_pattern):
+    """Return a list of valid patterns generated from the op_pattern.
+
+    Returns a list of valid patterns generated from the op_pattern,
+    since MatchAllNode can match all types of nodes,
+    e.g. pattern (torch.nn.Conv2d, torch.add) should also be able to match keys like
+    (MatchAllNode, torch.add) and (torch.nn.Conv2d, MatchAllNode)
+
+    Example Input:
+    (torch.add, (torch.nn.ReLU, torch.nn.Conv2d))
+
+    Example Output:
+    [(torch.add, (torch.nn.ReLU, torch.nn.Conv2d)),
+     (torch.add, (torch.nn.ReLU, MatchAllNode)),
+     (torch.add, (MatchAllNode, torch.nn.Conv2d)),
+     (torch.add, (MatchAllNode, MatchAllNode)),
+     (MatchAllNode, (torch.nn.ReLU, torch.nn.Conv2d)),
+     (MatchAllNode, (torch.nn.ReLU, MatchAllNode)),
+     (MatchAllNode, (MatchAllNode, torch.nn.Conv2d)),
+     (MatchAllNode, (MatchAllNode, MatchAllNode)),
+    ]
+    """
+    result: list[Any]
+    if isinstance(op_pattern, (tuple, list)):
+        sub_combs = [_get_valid_patterns(sub_pattern) for sub_pattern in op_pattern]
+        result = list(itertools.product(*sub_combs))
+    else:
+        result = [op_pattern, MatchAllNode]
+    return result
+
+
+def get_fuser_method_new(
+    op_pattern: Pattern,
+    fuser_method_mapping: dict[Pattern, nn.Sequential | Callable],
+):
+    """Get fuser method.
+
+    This will be made default after we deprecate the get_fuser_method
+    Would like to implement this first and have a separate PR for deprecation
+    """
+    op_patterns = _get_valid_patterns(op_pattern)
+    fuser_method = None
+    for op_pattern in op_patterns:
+        fuser_method = fuser_method_mapping.get(op_pattern)
+        if fuser_method is not None:
+            break
+    if fuser_method is None:
+        raise AssertionError(f"did not find fuser method for: {op_pattern} ")
+    return fuser_method
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..72d624ad7d6a3926c5d34afab3b7066928f9933d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/__init__.py
@@ -0,0 +1,3 @@
+from .convert import convert
+from .fuse import fuse
+from .prepare import prepare
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/_decomposed.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/_decomposed.py
new file mode 100644
index 0000000000000000000000000000000000000000..0754627a19dd1241dda4c53121f994b1b63ff025
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/_decomposed.py
@@ -0,0 +1,1268 @@
+# mypy: allow-untyped-defs
+import math
+
+import torch
+from torch._refs import _unsqueeze_multiple
+from torch.ao.quantization.utils import determine_qparams, validate_qmin_qmax
+from torch.library import impl, Library
+
+
+# Note: decomposed means decomposed quantized tensor, using decomposed so that the
+# name is not too long
+quantized_decomposed_lib = Library("quantized_decomposed", "DEF")
+
+_INTEGER_DTYPES = [torch.uint8, torch.int8, torch.uint16, torch.int16, torch.int32]
+_FLOAT_DTYPES = [torch.float8_e5m2, torch.float8_e4m3fn]
+
+_DTYPE_TO_QVALUE_BOUNDS = {
+    k: (torch.iinfo(k).min, torch.iinfo(k).max) for k in _INTEGER_DTYPES
+}
+_DTYPE_TO_QVALUE_BOUNDS.update(
+    {k: (int(torch.finfo(k).min), int(torch.finfo(k).max)) for k in _FLOAT_DTYPES}
+)
+
+
+# Helper to check the passed in quant min and max are valid for the dtype
+def _quant_min_max_bounds_check(quant_min, quant_max, dtype):
+    if dtype not in _DTYPE_TO_QVALUE_BOUNDS:
+        raise ValueError(f"Unsupported dtype: {dtype}")
+    quant_min_lower_bound, quant_max_upper_bound = _DTYPE_TO_QVALUE_BOUNDS[dtype]
+
+    if quant_min < quant_min_lower_bound:
+        raise AssertionError(
+            "quant_min out of bound for dtype, "
+            f"quant_min_lower_bound: {quant_min_lower_bound} quant_min: {quant_min}"
+        )
+
+    if quant_max > quant_max_upper_bound:
+        raise AssertionError(
+            "quant_max out of bound for dtype, "
+            f"quant_max_upper_bound: {quant_max_upper_bound} quant_max: {quant_max}"
+        )
+
+
+quantized_decomposed_lib.define(
+    "quantize_per_tensor(Tensor input, float scale, int zero_point, "
+    "int quant_min, int quant_max, ScalarType dtype) -> Tensor"
+)
+
+
+@impl(quantized_decomposed_lib, "quantize_per_tensor", "CompositeExplicitAutograd")
+def quantize_per_tensor(
+    input: torch.Tensor,
+    scale: float,
+    zero_point: int,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+) -> torch.Tensor:
+    """Affine quantization for the Tensor using the same quantization parameters to map
+    from floating point to quantized values
+
+    Args:
+       input (torch.Tensor): original float32 or bfloat16 Tensor
+       scale (float): quantization parameter for affine quantization
+       zero_point (int): quantization parameter for affine quantization
+       quant_min (int): minimum quantized value for output Tensor
+       quant_max (int): maximum quantized value for output Tensor
+       dtype (torch.dtype): requested dtype (e.g. torch.uint8) for output Tensor
+
+    Returns:
+       Tensor with requested dtype (e.g. torch.uint8), note the quantization parameters
+       are not stored in the Tensor, we are storing them in function arguments instead
+    """
+    if input.dtype in [torch.float16, torch.bfloat16]:
+        input = input.to(torch.float32)
+    if input.dtype != torch.float32:
+        raise AssertionError(
+            f"Expecting input to have dtype torch.float32, but got dtype: {input.dtype}"
+        )
+    _quant_min_max_bounds_check(quant_min, quant_max, dtype)
+
+    inv_scale = 1.0 / scale
+    return torch.clamp(
+        torch.round(input * inv_scale) + zero_point, quant_min, quant_max
+    ).to(dtype)
+
+
+@impl(quantized_decomposed_lib, "quantize_per_tensor", "Meta")
+def quantize_per_tensor_meta(
+    input: torch.Tensor,
+    scale: float,
+    zero_point: int,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+) -> torch.Tensor:
+    if input.dtype in [torch.float16, torch.bfloat16]:
+        input = input.to(torch.float32)
+    if input.dtype != torch.float32:
+        raise AssertionError(
+            f"Expecting input to have dtype torch.float32, but got dtype: {input.dtype}"
+        )
+    return torch.empty_like(input, dtype=dtype)
+
+
+quantized_decomposed_lib.define(
+    "quantize_per_tensor.tensor(Tensor input, Tensor scale, Tensor zero_point, "
+    "int quant_min, int quant_max, ScalarType dtype) -> Tensor"
+)
+
+
+@impl(
+    quantized_decomposed_lib, "quantize_per_tensor.tensor", "CompositeExplicitAutograd"
+)
+def quantize_per_tensor_tensor(
+    input: torch.Tensor,
+    scale: torch.Tensor,
+    zero_point: torch.Tensor,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+) -> torch.Tensor:
+    """Affine quantization for the Tensor using the same quantization parameters to map
+    from floating point to quantized values
+    Same as `quantize_per_tensor` but scale and zero_point are Scalar Tensor instead of
+    scalar values
+    """
+    if zero_point.numel() != 1:
+        raise AssertionError(
+            f"Expecting zero_point tensor to be one element, but received : {zero_point.numel()}"
+        )
+    if scale.numel() != 1:
+        raise AssertionError(
+            f"Expecting scale tensor to be one element, but received : {scale.numel()}"
+        )
+    return quantize_per_tensor(
+        input,
+        scale.item(),
+        zero_point.item(),  # type: ignore[arg-type]
+        quant_min,  # type: ignore[arg-type]
+        quant_max,  # type: ignore[arg-type]
+        dtype,
+    )
+
+
+@impl(quantized_decomposed_lib, "quantize_per_tensor.tensor", "Meta")
+def quantize_per_tensor_tensor_meta(
+    input: torch.Tensor,
+    scale: torch.Tensor,
+    zero_point: torch.Tensor,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+) -> torch.Tensor:
+    if input.dtype in [torch.float16, torch.bfloat16]:
+        input = input.to(torch.float32)
+    if zero_point.numel() != 1:
+        raise AssertionError(
+            f"Expecting zero_point tensor to be one element, but received : {zero_point.numel()}"
+        )
+    if scale.numel() != 1:
+        raise AssertionError(
+            f"Expecting scale tensor to be one element, but received : {scale.numel()}"
+        )
+    if input.dtype != torch.float32:
+        raise AssertionError(
+            f"Expecting input to have dtype torch.float32, but got dtype: {input.dtype}"
+        )
+    return torch.empty_like(input, dtype=dtype)
+
+
+# TODO: remove other variants and keep this one
+quantized_decomposed_lib.define(
+    "quantize_per_tensor.tensor2(Tensor input, Tensor scale, Tensor zero_point, "
+    "Tensor quant_min, Tensor quant_max, ScalarType dtype) -> Tensor"
+)
+
+
+@impl(
+    quantized_decomposed_lib, "quantize_per_tensor.tensor2", "CompositeExplicitAutograd"
+)
+def quantize_per_tensor_tensor2(
+    input: torch.Tensor,
+    scale: torch.Tensor,
+    zero_point: torch.Tensor,
+    quant_min: torch.Tensor,
+    quant_max: torch.Tensor,
+    dtype: torch.dtype,
+) -> torch.Tensor:
+    """Affine quantization for the Tensor using the same quantization parameters to map
+    from floating point to quantized values
+    Same as `quantize_per_tensor` but scale and zero_point are Scalar Tensor instead of
+    scalar values
+    """
+    if zero_point.numel() != 1:
+        raise AssertionError(
+            f"Expecting zero_point tensor to be one element, but received : {zero_point.numel()}"
+        )
+    if scale.numel() != 1:
+        raise AssertionError(
+            f"Expecting scale tensor to be one element, but received : {scale.numel()}"
+        )
+    return quantize_per_tensor(
+        input,
+        scale.item(),
+        zero_point.item(),  # type: ignore[arg-type]
+        quant_min.item(),  # type: ignore[arg-type]
+        quant_max.item(),  # type: ignore[arg-type]
+        dtype,
+    )
+
+
+@impl(quantized_decomposed_lib, "quantize_per_tensor.tensor2", "Meta")
+def quantize_per_tensor_tensor2_meta(
+    input: torch.Tensor,
+    scale: torch.Tensor,
+    zero_point: torch.Tensor,
+    quant_min: torch.Tensor,
+    quant_max: torch.Tensor,
+    dtype: torch.dtype,
+) -> torch.Tensor:
+    return quantize_per_tensor_tensor_meta(
+        input,
+        scale,
+        zero_point,  # type: ignore[arg-type]
+        quant_min,  # type: ignore[arg-type]
+        quant_max,  # type: ignore[arg-type]
+        dtype,
+    )
+
+
+# Note: quant_min/quant_max/dtype are not used in the operator, but for now it's kept in
+# the signature as metadata for the input Tensor, this might be useful for pattern
+# matching in the future
+# We will revisit this later if we found there are no use cases for it
+quantized_decomposed_lib.define(
+    "dequantize_per_tensor(Tensor input, float scale, int zero_point, "
+    "int quant_min, int quant_max, ScalarType dtype, *, ScalarType? out_dtype=None) -> Tensor"
+)
+
+
+@impl(quantized_decomposed_lib, "dequantize_per_tensor", "CompositeExplicitAutograd")
+def dequantize_per_tensor(
+    input: torch.Tensor,
+    scale: float,
+    zero_point: int,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+    *,
+    out_dtype: torch.dtype | None = None,
+) -> torch.Tensor:
+    """Affine dequantization for the Tensor using the same quantization parameters to map
+    from quantized values to floating point values
+
+    Args:
+       input (torch.Tensor): Tensor with dtype matching `dtype` argument,
+       e.g. (`torch.uint8`), it is a per tensor quantized Tensor if combined with
+       quantization parameters in the argument of this function (scale/zero_point)
+
+       scale (float): quantization parameter for affine quantization
+
+       zero_point (int): quantization parameter for affine quantization
+
+       quant_min (int): minimum quantized value for input Tensor (not used in computation,
+       reserved for pattern matching)
+
+       quant_max (int): maximum quantized value for input Tensor (not used in computation,
+       reserved for pattern matching)
+
+       dtype (torch.dtype): dtype for input Tensor (not used in computation,
+       reserved for pattern matching)
+
+       out_dtype (torch.dtype?): optional dtype for output Tensor
+
+    Returns:
+       dequantized float32 Tensor
+    """
+    if input.dtype != dtype:
+        raise AssertionError(
+            f"Expecting input to have dtype: {dtype}, but got {input.dtype}"
+        )
+    if out_dtype is None:
+        out_dtype = torch.float32
+    if dtype in _DTYPE_TO_QVALUE_BOUNDS:
+        # TODO: investigate why
+        # (input - zero_point).to(torch.float32) * scale
+        # failed the test
+        return (input.to(out_dtype) - zero_point) * scale
+    else:
+        raise ValueError(f"Unsupported dtype in dequantize_per_tensor: {dtype}")
+
+
+@impl(quantized_decomposed_lib, "dequantize_per_tensor", "Meta")
+def dequantize_per_tensor_meta(
+    input: torch.Tensor,
+    scale: torch.Tensor,
+    zero_point: torch.Tensor,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+    *,
+    out_dtype: torch.dtype | None = None,
+) -> torch.Tensor:
+    if out_dtype is None:
+        out_dtype = torch.float32
+    return torch.empty_like(input, dtype=out_dtype)
+
+
+quantized_decomposed_lib.define(
+    "dequantize_per_tensor.tensor(Tensor input, Tensor scale, Tensor zero_point, "
+    "int quant_min, int quant_max, ScalarType dtype, *, ScalarType? out_dtype=None) -> Tensor"
+)
+
+
+@impl(
+    quantized_decomposed_lib,
+    "dequantize_per_tensor.tensor",
+    "CompositeExplicitAutograd",
+)
+def dequantize_per_tensor_tensor(
+    input: torch.Tensor,
+    scale: torch.Tensor,
+    zero_point: torch.Tensor,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+    *,
+    out_dtype: torch.dtype | None = None,
+) -> torch.Tensor:
+    """Affine dequantization for the Tensor using the same quantization parameters to map
+    from quantized values to floating point values
+    Same as `dequantize_per_tensor` but scale and zero_point are Scalar Tensor instead of
+    scalar values
+    """
+    if zero_point.numel() != 1:
+        raise AssertionError(
+            f"Expecting zero_point tensor to be one element, but received : {zero_point.numel()}"
+        )
+    if scale.numel() != 1:
+        raise AssertionError(
+            f"Expecting scale tensor to be one element, but received : {scale.numel()}"
+        )
+    return dequantize_per_tensor(
+        input,
+        scale.item(),
+        zero_point.item(),  # type: ignore[arg-type]
+        quant_min,
+        quant_max,
+        dtype,
+        out_dtype=out_dtype,
+    )
+
+
+@impl(quantized_decomposed_lib, "dequantize_per_tensor.tensor", "Meta")
+def dequantize_per_tensor_tensor_meta(
+    input: torch.Tensor,
+    scale: torch.Tensor,
+    zero_point: torch.Tensor,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+    *,
+    out_dtype: torch.dtype | None = None,
+) -> torch.Tensor:
+    if out_dtype is None:
+        out_dtype = torch.float32
+    if zero_point.numel() != 1:
+        raise AssertionError(
+            f"Expecting zero_point tensor to be one element, but received : {zero_point.numel()}"
+        )
+    if scale.numel() != 1:
+        raise AssertionError(
+            f"Expecting scale tensor to be one element, but received : {scale.numel()}"
+        )
+    if input.dtype != dtype:
+        raise AssertionError(
+            f"Expecting input to have dtype: {dtype}, but got {input.dtype}"
+        )
+    if dtype in _DTYPE_TO_QVALUE_BOUNDS:
+        return torch.empty_like(input, dtype=out_dtype)
+    else:
+        raise ValueError(f"Unsupported dtype in dequantize_per_tensor: {dtype}")
+
+
+# TODO: remove other variants and keep this one
+quantized_decomposed_lib.define(
+    "dequantize_per_tensor.tensor2(Tensor input, Tensor scale, Tensor zero_point, "
+    "Tensor quant_min, Tensor quant_max, ScalarType dtype, *, ScalarType? out_dtype=None) -> Tensor"
+)
+
+
+@impl(
+    quantized_decomposed_lib,
+    "dequantize_per_tensor.tensor2",
+    "CompositeExplicitAutograd",
+)
+def dequantize_per_tensor_tensor2(
+    input: torch.Tensor,
+    scale: torch.Tensor,
+    zero_point: torch.Tensor,
+    quant_min: torch.Tensor,
+    quant_max: torch.Tensor,
+    dtype: torch.dtype,
+    *,
+    out_dtype: torch.dtype | None = None,
+) -> torch.Tensor:
+    """Affine dequantization for the Tensor using the same quantization parameters to map
+    from quantized values to floating point values
+    Same as `dequantize_per_tensor` but scale and zero_point are Scalar Tensor instead of
+    scalar values
+    """
+    if zero_point.numel() != 1:
+        raise AssertionError(
+            f"Expecting zero_point tensor to be one element, but received : {zero_point.numel()}"
+        )
+    if scale.numel() != 1:
+        raise AssertionError(
+            f"Expecting scale tensor to be one element, but received : {scale.numel()}"
+        )
+    return dequantize_per_tensor(
+        input,
+        scale.item(),
+        zero_point.item(),  # type: ignore[arg-type]
+        quant_min.item(),  # type: ignore[arg-type]
+        quant_max.item(),  # type: ignore[arg-type]
+        dtype,
+        out_dtype=out_dtype,
+    )
+
+
+@impl(quantized_decomposed_lib, "dequantize_per_tensor.tensor2", "Meta")
+def dequantize_per_tensor_tensor2_meta(
+    input,
+    scale,
+    zero_point,
+    quant_min,
+    quant_max,
+    dtype,
+    *,
+    out_dtype: torch.dtype | None = None,
+) -> torch.Tensor:
+    return dequantize_per_tensor_tensor_meta(
+        input, scale, zero_point, quant_min, quant_max, dtype, out_dtype=out_dtype
+    )
+
+
+quantized_decomposed_lib.define(
+    "choose_qparams.tensor(Tensor input, int quant_min, int quant_max, "
+    "float eps, ScalarType dtype) -> (Tensor, Tensor)"
+)
+
+
+@impl(quantized_decomposed_lib, "choose_qparams.tensor", "CompositeExplicitAutograd")
+def choose_qparams_tensor(
+    input: torch.Tensor, qmin: int, qmax: int, eps: float, dtype: torch.dtype
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Given an input Tensor, derive the per tensor affine quantization parameter
+    (scale and zero_point) for target quantized Tensor from the Tensor
+
+    Args:
+       input (torch.Tensor): floating point input Tensor
+       quant_min (int): minimum quantized value for target quantized Tensor
+       quant_max (int): maximum quantized value for target quantized Tensor
+       dtype (torch.dtype): dtype for target quantized Tensor
+
+    Returns:
+       scale (float): quantization parameter for the target quantized Tensor
+       zero_point (int): quantization parameter for the target quantized Tensor
+    """
+    if input.dtype not in [
+        torch.float32,
+        torch.float16,
+        torch.bfloat16,
+    ]:
+        raise AssertionError(
+            f"Expecting input to have dtype torch.float32/16/b16, but got dtype: {input.dtype}"
+        )
+    if dtype not in _DTYPE_TO_QVALUE_BOUNDS:
+        raise AssertionError(
+            f"Expecting target dtype to be one of {_DTYPE_TO_QVALUE_BOUNDS.keys()}, but got: {dtype}"
+        )
+    validate_qmin_qmax(qmin, qmax)
+
+    min_val, max_val = torch.aminmax(input)
+
+    return determine_qparams(
+        min_val,
+        max_val,
+        qmin,
+        qmax,
+        dtype,
+        torch.Tensor([eps]),
+        has_customized_qrange=False,
+    )
+
+
+quantized_decomposed_lib.define(
+    "choose_qparams_symmetric.tensor(Tensor input, int quant_min, int quant_max, "
+    "float eps, ScalarType dtype) -> (Tensor, Tensor)"
+)
+
+
+@impl(
+    quantized_decomposed_lib,
+    "choose_qparams_symmetric.tensor",
+    "CompositeExplicitAutograd",
+)
+def choose_qparams_symmetric_tensor(
+    input: torch.Tensor, qmin: int, qmax: int, eps: float, dtype: torch.dtype
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Given an input Tensor, derive the per tensor affine quantization parameter
+    (scale and zero_point) for target quantized Tensor from the Tensor
+
+    Args:
+       input (torch.Tensor): floating point input Tensor
+       quant_min (int): minimum quantized value for target quantized Tensor
+       quant_max (int): maximum quantized value for target quantized Tensor
+       dtype (torch.dtype): dtype for target quantized Tensor
+
+    Returns:
+       scale (float): quantization parameter for the target quantized Tensor
+       zero_point (int): quantization parameter for the target quantized Tensor
+    """
+    if input.dtype not in [
+        torch.float32,
+        torch.float16,
+        torch.bfloat16,
+    ]:
+        raise AssertionError(
+            f"Expecting input to have dtype torch.float32/16/b16, but got dtype: {input.dtype}"
+        )
+    if dtype not in _DTYPE_TO_QVALUE_BOUNDS:
+        raise AssertionError(
+            f"Expecting target dtype to be one of {_DTYPE_TO_QVALUE_BOUNDS.keys()}, but got: {dtype}"
+        )
+    validate_qmin_qmax(qmin, qmax)
+
+    min_val, max_val = torch.aminmax(input)
+    return determine_qparams(
+        min_val,
+        max_val,
+        qmin,
+        qmax,
+        dtype,
+        torch.Tensor([eps]),
+        has_customized_qrange=False,
+        qscheme=torch.per_tensor_symmetric,
+    )
+
+
+@impl(quantized_decomposed_lib, "choose_qparams.tensor", "Meta")
+def choose_qparams_tensor_meta(
+    input: torch.Tensor, quant_min: int, quant_max: int, eps: float, dtype: torch.dtype
+) -> tuple[torch.Tensor, torch.Tensor]:
+    if input.dtype not in [
+        torch.float32,
+        torch.float16,
+        torch.bfloat16,
+    ]:
+        raise AssertionError(
+            f"Expecting input to have dtype torch.float32/16/b16, but got dtype: {input.dtype}"
+        )
+    if quant_min >= quant_max:
+        raise AssertionError(
+            f"Expecting quant_min to be smaller than quant_max but received min: {quant_min} max: {quant_max}"
+        )
+    return torch.empty(1, dtype=torch.double, device=input.device), torch.empty(
+        1, dtype=torch.int64, device=input.device
+    )
+
+
+@impl(quantized_decomposed_lib, "choose_qparams_symmetric.tensor", "Meta")
+def choose_qparams_symmetric_tensor_meta(
+    input: torch.Tensor, quant_min: int, quant_max: int, eps: float, dtype: torch.dtype
+) -> tuple[torch.Tensor, torch.Tensor]:
+    return torch.empty(1, dtype=torch.double, device=input.device), torch.empty(
+        1, dtype=torch.int64, device=input.device
+    )
+
+
+# Helper function used to implement per-channel quantization against any axis
+def _permute_to_axis_zero(x, axis):
+    new_axis_list = list(range(x.dim()))
+    new_axis_list[axis] = 0
+    new_axis_list[0] = axis
+    y = x.permute(tuple(new_axis_list))
+    return y, new_axis_list
+
+
+quantized_decomposed_lib.define(
+    "quantize_per_channel(Tensor input, Tensor scales, Tensor zero_points, int axis, "
+    "int quant_min, int quant_max, ScalarType dtype) -> Tensor"
+)
+
+
+@impl(quantized_decomposed_lib, "quantize_per_channel", "CompositeExplicitAutograd")
+def quantize_per_channel(
+    input: torch.Tensor,
+    scales: torch.Tensor,
+    zero_points: torch.Tensor,
+    axis: int,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+) -> torch.Tensor:
+    """Affine per channel quantization for the Tensor using the same quantization
+    parameters for each channel/axis to map from floating point to quantized values
+
+    Args:
+       input (torch.Tensor): original float32 or bfloat16 Tensor
+       scales (torch.Tensor): a list of scale quantization parameter for
+       affine quantization, one per channel
+       zero_point (torch.Tensor): a list of zero_point quantization parameter for
+       affine quantization, one per channel
+       quant_min (int): minimum quantized value for output Tensor
+       quant_max (int): maximum quantized value for output Tensor
+       dtype (torch.dtype): requested dtype (e.g. torch.uint8) for output Tensor
+
+    Returns:
+       Tensor with requested dtype (e.g. torch.uint8), note the quantization parameters
+       are not stored in the Tensor, we are storing them in function arguments instead
+    """
+    if input.dtype in [torch.float16, torch.bfloat16]:
+        input = input.to(torch.float32)
+    if input.dtype != torch.float32:
+        raise AssertionError(
+            f"Expecting input to have dtype torch.float32, but got dtype: {input.dtype}"
+        )
+    if axis >= input.dim():
+        raise AssertionError(f"Expecting axis to be < {input.dim()}")
+    _quant_min_max_bounds_check(quant_min, quant_max, dtype)
+    input, permute_axis_list = _permute_to_axis_zero(input, axis)
+
+    new_shape = [1] * input.dim()
+    new_shape[0] = scales.shape[0]
+    scales = scales.view(new_shape)
+    zero_points = zero_points.view(new_shape)
+
+    res = torch.clamp(
+        torch.round(input * (1.0 / scales)) + zero_points, quant_min, quant_max
+    )
+    out = res.permute(tuple(permute_axis_list))
+    return out.to(dtype)
+
+
+@impl(quantized_decomposed_lib, "quantize_per_channel", "Meta")
+def quantize_per_channel_meta(
+    input: torch.Tensor,
+    scales: torch.Tensor,
+    zero_points: torch.Tensor,
+    axis: int,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+) -> torch.Tensor:
+    if input.dtype in [torch.float16, torch.bfloat16]:
+        input = input.to(torch.float32)
+    if input.dtype != torch.float32:
+        raise AssertionError(
+            f"Expecting input to have dtype torch.float32, but got dtype: {input.dtype}"
+        )
+    if axis >= input.dim():
+        raise AssertionError(f"Expecting axis to be < {input.dim()}")
+    _quant_min_max_bounds_check(quant_min, quant_max, dtype)
+    return torch.empty_like(input, dtype=dtype)
+
+
+# Note: quant_min/quant_max/dtype are not used in the operator, but for now it's kept in
+# the signature as metadata for the input Tensor, this might be useful for pattern
+# matching in the future
+# We will revisit this later if we found there are no use cases for it
+quantized_decomposed_lib.define(
+    "dequantize_per_channel(Tensor input, Tensor scales, Tensor? zero_points, int axis, "
+    "int quant_min, int quant_max, ScalarType dtype, *, ScalarType? out_dtype=None) -> Tensor"
+)
+
+
+@impl(quantized_decomposed_lib, "dequantize_per_channel", "CompositeExplicitAutograd")
+def dequantize_per_channel(
+    input: torch.Tensor,
+    scales: torch.Tensor,
+    zero_points: torch.Tensor | None,
+    axis: int,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+    *,
+    out_dtype: torch.dtype | None = None,
+) -> torch.Tensor:
+    """Affine per channel dequantization for the Tensor using the same quantization
+    parameters for each channel/axis to map from quantized values to floating point values
+
+    Args:
+       input (torch.Tensor): Tensor with dtype matching `dtype` argument,
+       e.g. (`torch.uint8`), it is a per channel quantized Tensor if combined with
+       quantization parameter in the argument of this function (scales/zero_points/axis)
+
+       scales (torch.Tensor): a list of scale quantization parameter for
+       affine quantization, one per channel
+
+       zero_points (torch.Tensor): a list of zero_point quantization parameter for
+       affine quantization, one per channel
+
+       quant_min (int): minimum quantized value for output Tensor (not used in computation,
+       reserved for pattern matching)
+
+       quant_max (int): maximum quantized value for output Tensor (not used in computation,
+       reserved for pattern matching)
+
+       dtype (torch.dtype): requested dtype for output Tensor (not used in computation,
+       reserved for pattern matching)
+
+       out_dtype (torch.dtype?): optional dtype for output Tensor
+
+    Returns:
+       dequantized float32 Tensor
+    """
+    if input.dtype != dtype:
+        raise AssertionError(
+            f"Expecting input to have dtype: {dtype}, but got dtype: {input.dtype}"
+        )
+    if out_dtype is None:
+        out_dtype = torch.float32
+    if axis >= input.dim():
+        raise AssertionError(f"Expecting axis to be < {input.dim()}")
+    _quant_min_max_bounds_check(quant_min, quant_max, dtype)
+    input, permute_axis_list = _permute_to_axis_zero(input, axis)
+
+    new_shape = [1] * input.dim()
+    new_shape[0] = scales.shape[0]
+    scales = scales.view(new_shape)
+    if zero_points is not None:
+        res = (input - zero_points.view(new_shape)) * scales
+    else:
+        res = input * scales
+
+    res = res.to(out_dtype)
+
+    out = res.permute(tuple(permute_axis_list))
+    return out
+
+
+@impl(quantized_decomposed_lib, "dequantize_per_channel", "Meta")
+def dequantize_per_channel_meta(
+    input: torch.Tensor,
+    scales: torch.Tensor,
+    zero_points: torch.Tensor | None,
+    axis: int,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+    *,
+    out_dtype: torch.dtype | None = None,
+) -> torch.Tensor:
+    if input.dtype != dtype:
+        raise AssertionError(
+            f"Expecting input to have dtype {dtype}, but got dtype: {input.dtype}"
+        )
+    if out_dtype is None:
+        out_dtype = torch.float32
+    if axis >= input.dim():
+        raise AssertionError(f"Expecting axis to be < {input.dim()}")
+    _quant_min_max_bounds_check(quant_min, quant_max, dtype)
+    return torch.empty_like(input, dtype=out_dtype)
+
+
+quantized_decomposed_lib.define(
+    "choose_qparams_per_token(Tensor input, ScalarType dtype) -> (Tensor, Tensor)"
+)
+
+
+@impl(
+    quantized_decomposed_lib,
+    "choose_qparams_per_token",
+    "CompositeExplicitAutograd",
+)
+def choose_qparams_per_token(
+    input: torch.Tensor,
+    dtype: torch.dtype,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Choose quantization parameters for per token quantization. This means for a N dimension Tensor
+    (M1, M2, ...Mn, N), we calculate scales/zero_points for each N elements and quantize
+    every N elements with the same quantization parameter. The dimension for scales/zero_points
+    will be (M1 * M2 ... * Mn)
+
+    Args:
+       input (torch.Tensor): original float32/float16 Tensor
+       dtype (torch.dtype): dtype (e.g. torch.uint8) for input Tensor
+
+    Returns:
+        scales and zero_points, both float32 Tensors
+    """
+
+    scales = input.abs().amax(dim=-1, keepdim=True)
+    if scales.dtype == torch.float16:
+        scales = (
+            scales.float()
+        )  # want float scales to avoid overflows for fp16, (bf16 has wide enough range)
+    if dtype == torch.int8:
+        n_bits = 8
+        quant_max = 2 ** (n_bits - 1) - 1
+    else:
+        raise Exception(  # noqa: TRY002
+            f"unsupported dtype in choose_qparams_per_token: {dtype}"
+        )
+
+    scales = scales.clamp(min=1e-5).div(quant_max)
+    zero_points = torch.zeros_like(scales)
+    return scales, zero_points
+
+
+@impl(
+    quantized_decomposed_lib,
+    "choose_qparams_per_token",
+    "Meta",
+)
+def choose_qparams_per_token_meta(
+    input: torch.Tensor,
+    dtype: torch.dtype,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    size = list(input.shape[:-1]) + [1]
+    return torch.empty(size, dtype=torch.double, device=input.device), torch.empty(
+        size, dtype=torch.int64, device=input.device
+    )
+
+
+quantized_decomposed_lib.define(
+    "_choose_qparams_per_token_asymmetric_impl(Tensor input, ScalarType dtype) -> (Tensor, Tensor)"
+)
+
+
+@impl(
+    quantized_decomposed_lib,
+    "_choose_qparams_per_token_asymmetric_impl",
+    "CompositeImplicitAutograd",
+)
+def _choose_qparams_per_token_asymmetric_impl(
+    input: torch.Tensor,
+    dtype: torch.dtype,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Choose quantization parameters for per token quantization. This means for a N dimension Tensor
+    (M1, M2, ...Mn, N), we calculate scales/zero_points for each N elements and quantize
+    every N elements with the same quantization parameter. The dimension for scales/zero_points
+    will be (M1 * M2 ... * Mn)
+
+    Args:
+       input (torch.Tensor): original float32/float16 Tensor
+       dtype (torch.dtype): dtype (e.g. torch.uint8) for input Tensor
+
+    Returns:
+        scales and zero_points, both float32 Tensors
+    """
+    # Based on https://github.com/google/XNNPACK/blob/df156f0cf3db5a4576cc711123eeb54915f82ffc/src/xnnpack/quantization.h#L18
+    qmin, qmax = -128, 127
+    min_val = torch.amin(input, dim=-1, keepdim=True)
+    max_val = torch.amax(input, dim=-1, keepdim=True)
+    min_val_neg = torch.min(min_val, torch.zeros_like(min_val))
+    max_val_pos = torch.max(max_val, torch.zeros_like(max_val))
+    eps = torch.finfo(torch.float32).eps  # use xnnpack eps?
+
+    # scale
+    scale = (max_val_pos - min_val_neg) / float(qmax - qmin)
+    scale = scale.clamp(min=eps)
+
+    # zero point
+    descaled_min = min_val_neg / scale
+    descaled_max = max_val_pos / scale
+    zero_point_from_min_error = qmin + descaled_min
+    zero_point_from_max_error = qmax + descaled_max
+    zero_point = torch.where(
+        zero_point_from_min_error + zero_point_from_max_error > 0,
+        qmin - descaled_min,
+        qmax - descaled_max,
+    )
+    zero_point = torch.clamp(zero_point, qmin, qmax).round()
+
+    return scale.to(torch.float64), zero_point.to(torch.int64)
+
+
+quantized_decomposed_lib.define(
+    "choose_qparams_per_token_asymmetric(Tensor input, ScalarType dtype) -> (Tensor, Tensor)"
+)
+
+
+@impl(
+    quantized_decomposed_lib,
+    "choose_qparams_per_token_asymmetric",
+    "CompositeExplicitAutograd",
+)
+def choose_qparams_per_token_asymmetric(
+    input: torch.Tensor,
+    dtype: torch.dtype,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    return _choose_qparams_per_token_asymmetric_impl(input, dtype)
+
+
+@impl(
+    quantized_decomposed_lib,
+    "choose_qparams_per_token_asymmetric",
+    "Meta",
+)
+def choose_qparams_per_token_asymmetric_meta(
+    input: torch.Tensor,
+    dtype: torch.dtype,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    size = list(input.shape[:-1]) + [1]
+    return torch.empty(size, dtype=torch.double, device=input.device), torch.empty(
+        size, dtype=torch.int64, device=input.device
+    )
+
+
+def _per_token_quant_qparam_dim_check(input, scales, zero_points):
+    num_tokens = math.prod(list(input.size())[:-1])
+    if num_tokens != scales.numel():
+        raise AssertionError(f"num_tokens: {num_tokens} scales: {scales.size()}")
+    if num_tokens != zero_points.numel():
+        raise AssertionError(
+            f"num_tokens: {num_tokens} zero_points: {zero_points.size()}"
+        )
+
+
+quantized_decomposed_lib.define(
+    "quantize_per_token(Tensor input, Tensor scales, Tensor zero_points, "
+    "int quant_min, int quant_max, ScalarType dtype) -> Tensor"
+)
+
+
+@impl(quantized_decomposed_lib, "quantize_per_token", "CompositeExplicitAutograd")
+def quantize_per_token(
+    input: torch.Tensor,
+    scales: torch.Tensor,
+    zero_points: torch.Tensor,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+):
+    """Per token quantization for the Tensor using the quantization parameters to map
+    from floating point to quantized values. This means for a N dimension Tensor
+    (M1, M2, ...Mn, N), we calculate scales/zero_points for each N elements and quantize
+    every N elements with the same quantization parameter. The dimension for scales/zero_points
+    will be (M1 * M2 ... * Mn)
+
+    Args:
+       input (torch.Tensor): original float32 or bfloat16 Tensor
+       scales (float32 torch.Tensor): quantization parameter for per token affine quantization
+       zero_points (int32 torch.Tensor): quantization parameter for per token affine quantization
+       quant_min (int): minimum quantized value for output Tensor
+       quant_max (int): maximum quantized value for output Tensor
+       dtype (torch.dtype): requested dtype (e.g. torch.uint8) for output Tensor
+
+    Returns:
+       Tensor with requested dtype (e.g. torch.uint8), note the quantization parameters
+       are not stored in the Tensor, we are storing them in function arguments instead
+    """
+    _quant_min_max_bounds_check(quant_min, quant_max, dtype)
+    _per_token_quant_qparam_dim_check(input, scales, zero_points)
+    input = (
+        input.mul(1.0 / scales)
+        .add(zero_points)
+        .round()
+        .clamp(quant_min, quant_max)
+        .to(dtype)
+    )
+    return input
+
+
+@impl(quantized_decomposed_lib, "quantize_per_token", "Meta")
+def quantize_per_token_meta(
+    input: torch.Tensor,
+    scales: torch.Tensor,
+    zero_points: torch.Tensor,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+):
+    _quant_min_max_bounds_check(quant_min, quant_max, dtype)
+    return torch.empty_like(input, dtype=dtype)
+
+
+quantized_decomposed_lib.define(
+    "dequantize_per_token(Tensor input, Tensor scales, Tensor zero_points, "
+    "int quant_min, int quant_max, ScalarType dtype, ScalarType output_dtype) -> Tensor"
+)
+
+
+@impl(quantized_decomposed_lib, "dequantize_per_token", "CompositeExplicitAutograd")
+def dequantize_per_token(
+    input: torch.Tensor,
+    scales: torch.Tensor,
+    zero_points: torch.Tensor,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+    output_dtype: torch.dtype = torch.float32,
+):
+    """Per token dequantization for the Tensor using the quantization parameters to map
+    from floating point to quantized values. This means for a N dimension Tensor
+    (M1, M2, ...Mn, N), we calculate scales/zero_points for each N elements and quantize
+    every N elements with the same quantization parameter. The dimension for scales/zero_points
+    will be (M1 * M2 ... * Mn)
+
+    Args:
+       input (torch.Tensor): quantized Tensor (uint8, int8 etc.)
+       scales (float64 torch.Tensor): quantization parameter for per token affine quantization
+       zero_points (int64 torch.Tensor): quantization parameter for per token affine quantization
+       quant_min (int): minimum quantized value for input Tensor
+       quant_max (int): maximum quantized value for input Tensor
+       dtype (torch.dtype): dtype (e.g. torch.uint8) for input Tensor
+       output_dtype (torch.dtype): dtype (e.g. torch.float32) for output Tensor
+
+    Returns:
+       dequantized Tensor with dtype `output_dtype`
+    """
+    input = input - zero_points
+    input = input * scales
+    # Since scales are of float64 type, we need to cast it to output dtype requested
+    return input.to(output_dtype)
+
+
+@impl(quantized_decomposed_lib, "dequantize_per_token", "Meta")
+def dequantize_per_token_meta(
+    input: torch.Tensor,
+    scales: torch.Tensor,
+    zero_points: torch.Tensor,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+    output_dtype: torch.dtype = torch.float32,
+):
+    _quant_min_max_bounds_check(quant_min, quant_max, dtype)
+    # TODO: support fp16
+    return torch.empty_like(input, dtype=output_dtype)
+
+
+quantized_decomposed_lib.define(
+    "quantize_per_channel_group(Tensor input, Tensor scales, Tensor zero_points, int quant_min, "
+    "int quant_max, ScalarType dtype, int group_size) -> Tensor"
+)
+
+
+# TODO: dtype is ignored for now
+@impl(
+    quantized_decomposed_lib, "quantize_per_channel_group", "CompositeExplicitAutograd"
+)
+def quantize_per_channel_group(
+    input: torch.Tensor,
+    scales: torch.Tensor,
+    zero_points: torch.Tensor,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+    group_size=128,
+):
+    if group_size <= 1:
+        raise AssertionError("group_size must be > 1")
+    # needed for GPTQ single column quantize
+    if group_size > input.shape[-1] and scales.shape[-1] == 1:
+        group_size = input.shape[-1]
+
+    if input.shape[-1] % group_size != 0:
+        raise AssertionError("input.shape[-1] must be divisible by group_size")
+    if input.dim() != 2:
+        raise AssertionError("input must be 2-dimensional")
+
+    # TODO: check for dtype, currently we can't express torch.int4 so it's omitted
+    to_quant = input.reshape(-1, group_size)
+    if torch.isnan(to_quant).sum() != 0:
+        raise AssertionError("to_quant must not contain NaNs")
+
+    scales = scales.reshape(-1, 1)
+    zero_points = zero_points.reshape(-1, 1)
+
+    input_int8 = (
+        to_quant.mul(1.0 / scales)
+        .add(zero_points)
+        .round()
+        .clamp_(quant_min, quant_max)
+        .to(dtype)
+        .reshape_as(input)
+    )
+
+    return input_int8
+
+
+@impl(quantized_decomposed_lib, "quantize_per_channel_group", "Meta")
+def quantize_per_channel_group_meta(
+    input: torch.Tensor,
+    scales: torch.Tensor,
+    zero_points: torch.Tensor,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+    group_size=128,
+):
+    """Groupwise quantization within each channel for an 2-d Tensor using the quantization parameters
+    to map from floating point to quantized values. This means for each row of a 2-d Tensor
+    (M, N), we calculate scales/zero_points for each `group_size` elements
+    and quantize every `group_size` elements with the same quantization parameter.
+    The dimension for scales/zero_points will be (M * ceil(N, group_size),)
+
+    Args:
+       input (torch.Tensor): original float32 or bfloat16 Tensor
+       scales (float32 torch.Tensor): quantization parameter for per channel group affine quantization
+       zero_points (int32 torch.Tensor): quantization parameter for per channel group affine quantization
+       quant_min (int): minimum quantized value for output Tensor
+       quant_max (int): maximum quantized value for output Tensor
+       dtype (torch.dtype): requested dtype (e.g. torch.uint8) for output Tensor
+
+    Returns:
+       Tensor with requested dtype (e.g. torch.uint8), note the quantization parameters
+       are not stored in the Tensor, we are storing them in function arguments instead
+    """
+    if group_size <= 1:
+        raise AssertionError("group_size must be > 1")
+    # needed for GPTQ single column quantize
+    if group_size > input.shape[-1] and scales.shape[-1] == 1:
+        group_size = input.shape[-1]
+
+    if input.shape[-1] % group_size != 0:
+        raise AssertionError("input.shape[-1] must be divisible by group_size")
+    if input.dim() != 2:
+        raise AssertionError("input must be 2-dimensional")
+    return torch.empty_like(input, dtype=dtype)
+
+
+quantized_decomposed_lib.define(
+    "dequantize_per_channel_group(Tensor input, Tensor scales, Tensor? zero_points, int quant_min, "
+    "int quant_max, ScalarType dtype, int group_size, ScalarType output_dtype) -> Tensor"
+)
+
+
+@impl(
+    quantized_decomposed_lib,
+    "dequantize_per_channel_group",
+    "CompositeExplicitAutograd",
+)
+def dequantize_per_channel_group(
+    w_int8: torch.Tensor,
+    scales: torch.Tensor,
+    zero_points: torch.Tensor | None,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+    group_size: int = 128,
+    output_dtype: torch.dtype = torch.float32,
+):
+    """Groupwise dequantization within each channel for an 2-d Tensor using the quantization parameters
+    to map from floating point to quantized values. This means for each row of a 2-d Tensor
+    (M, N), we calculate scales/zero_points for each `group_size` elements
+    and quantize every `group_size` elements with the same quantization parameter.
+    The dimension for scales/zero_points will be (M * ceil(N, group_size),)
+
+    Args:
+       input (torch.Tensor): quantized Tensor (uint8/int8 etc.)
+       scales (float32 torch.Tensor): quantization parameter for per channel group affine quantization
+       zero_points (int32 torch.Tensor): quantization parameter for per channel group affine quantization
+       quant_min (int): minimum quantized value for input Tensor
+       quant_max (int): maximum quantized value for input Tensor
+       dtype (torch.dtype): dtype (e.g. torch.uint8) for input Tensor
+       output_dtype (torch.dtype): dtype (e.g. torch.float32) for output Tensor
+
+    Returns:
+       dequantized Tensor with dtype `output_dtype`
+    """
+
+    if group_size <= 1:
+        raise AssertionError("group_size must be > 1")
+    # needed for GPTQ single column dequantize
+    if group_size > w_int8.shape[-1] and scales.shape[-1] == 1:
+        group_size = w_int8.shape[-1]
+    if w_int8.shape[-1] % group_size != 0:
+        raise AssertionError("w_int8.shape[-1] must be divisible by group_size")
+    if w_int8.dim() != 2:
+        raise AssertionError("w_int8 must be 2-dimensional")
+
+    w_int8_grouped = w_int8.reshape(-1, group_size)
+    scales = scales.reshape(-1, 1)
+    if zero_points is not None:
+        zp = zero_points.reshape(-1, 1)
+    else:
+        zp = torch.zeros([], dtype=torch.int32, device=scales.device)
+    w_dq = w_int8_grouped.sub(zp).mul(scales).reshape_as(w_int8).to(output_dtype)
+    return w_dq
+
+
+quantized_decomposed_lib.define(
+    "fake_quant_per_channel(Tensor input, Tensor scales, Tensor zero_points, int axis, "
+    "int quant_min, int quant_max) -> Tensor"
+)
+
+
+class FakeQuantPerChannel(torch.autograd.Function):
+    @staticmethod
+    # pyrefly: ignore [bad-override]
+    def forward(ctx, input, scales, zero_points, axis, quant_min, quant_max):
+        if scales.dtype != torch.float32:
+            scales = scales.to(torch.float32)
+        if zero_points.dtype != torch.int32:
+            zero_points = zero_points.to(torch.int32)
+        if input.dtype != torch.float32:
+            raise AssertionError(
+                f"Expecting input to have dtype torch.float32, but got dtype: {input.dtype}"
+            )
+        if axis >= input.dim():
+            raise AssertionError(f"Expecting axis to be < {input.dim()}")
+        broadcast_dims = list(range(axis)) + list(range(axis + 1, input.ndim))
+        unsqueeze_scales = _unsqueeze_multiple(scales, broadcast_dims)
+        unsqueeze_zero_points = _unsqueeze_multiple(zero_points, broadcast_dims)
+        temp = torch.round(input * (1.0 / unsqueeze_scales)) + unsqueeze_zero_points
+        out = (
+            torch.clamp(temp, quant_min, quant_max) - unsqueeze_zero_points
+        ) * unsqueeze_scales
+        mask = torch.logical_and((temp >= quant_min), (temp <= quant_max))
+
+        ctx.save_for_backward(mask)
+        return out
+
+    @staticmethod
+    # pyrefly: ignore [bad-override]
+    def backward(ctx, gy):
+        (mask,) = ctx.saved_tensors
+        return gy * mask, None, None, None, None, None
+
+
+@impl(quantized_decomposed_lib, "fake_quant_per_channel", "Autograd")
+def fake_quant_per_channel(
+    input: torch.Tensor,
+    scales: torch.Tensor,
+    zero_points: torch.Tensor,
+    axis: int,
+    quant_min: int,
+    quant_max: int,
+) -> torch.Tensor:
+    return FakeQuantPerChannel.apply(
+        input, scales, zero_points, axis, quant_min, quant_max
+    )
+
+
+@impl(quantized_decomposed_lib, "fake_quant_per_channel", "Meta")
+def fake_quant_per_channel_meta(
+    input: torch.Tensor,
+    scales: torch.Tensor,
+    zero_points: torch.Tensor,
+    axis: int,
+    quant_min: int,
+    quant_max: int,
+) -> torch.Tensor:
+    return torch.empty_like(input)
+
+
+quantized_decomposed_lib.define(
+    "convert_element_type.no_fuse(Tensor input, ScalarType dtype) -> Tensor"
+)
+
+
+@impl(
+    quantized_decomposed_lib,
+    "convert_element_type.no_fuse",
+    "CompositeExplicitAutograd",
+)
+def convert_element_type(input: torch.Tensor, dtype: torch.dtype) -> torch.Tensor:
+    return torch.ops.prims.convert_element_type.default(input, dtype)
+
+
+@impl(quantized_decomposed_lib, "convert_element_type.no_fuse", "Meta")
+def convert_element_type_meta(input: torch.Tensor, dtype: torch.dtype) -> torch.Tensor:
+    return torch.empty_like(input, dtype=dtype)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/_equalize.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/_equalize.py
new file mode 100644
index 0000000000000000000000000000000000000000..dda37214210e34bb7676b9877d2e44876366a07f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/_equalize.py
@@ -0,0 +1,1020 @@
+# mypy: allow-untyped-defs
+import operator
+import warnings
+from collections import namedtuple
+from typing import Any
+
+import torch
+import torch.ao.nn.intrinsic as nni
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.ao.quantization.fx.graph_module import _get_observed_graph_module_attr
+from torch.ao.quantization.observer import (
+    _with_args,
+    ObserverBase,
+    PerChannelMinMaxObserver,
+)
+from torch.ao.quantization.utils import _parent_name, check_min_max_valid
+from torch.fx import GraphModule
+from torch.fx.graph import Node
+
+from .utils import (
+    get_new_attr_name_with_prefix,
+    maybe_get_next_module,
+    node_arg_is_weight,
+)
+
+
+CUSTOM_MODULE_SUPP_LIST: list[Any] = []
+
+
+def reshape_scale(scale: torch.Tensor, axis: int, input: torch.Tensor) -> torch.Tensor:
+    """Reshapes the scale so that we can multiply it to the input by the given axis."""
+    new_shape = [1] * input.ndim
+    new_shape[axis] = input.size(axis)
+    return scale.view(new_shape)
+
+
+qsheme_mapping_per_tensor_to_per_channel = {
+    torch.per_tensor_affine: torch.per_channel_affine,
+    torch.per_tensor_symmetric: torch.per_channel_symmetric,
+}
+
+
+class _InputEqualizationObserver(nn.Module):
+    r"""Observer for tracking the running min/max values of input columns, and
+    computing the quantization parameters for the overall min/max input values.
+
+    Args:
+        dtype: Quantized data type
+        qscheme: Quantization scheme
+        quant_min: Minimum quantization value. If unspecified, it will
+            follow the 8-bit setup.
+        quant_max: Maximum quantization value. If unspecified, it will
+            follow the 8-bit setup.
+
+    The running minimum/maximum :math:`x_\text{min/max}` are computed in the
+    same way as :class:`~torch.ao.quantization.observer.PerChannelMinMaxObserver`,
+    with the difference that the running min/max values are stored per column.
+    This observer is intended to be used along with a WeightEqualizationObserver
+    to calculate the equalization scale.
+    """
+
+    def __init__(
+        self,
+        dtype=torch.quint8,
+        qscheme=torch.per_tensor_affine,
+        quant_min=None,
+        quant_max=None,
+        factory_kwargs=None,
+    ) -> None:
+        super().__init__()
+
+        if qscheme not in {torch.per_tensor_affine, torch.per_tensor_symmetric}:
+            raise TypeError("Input qscheme must be per-tensor")
+
+        self.dtype = dtype
+        self.qscheme = qscheme
+
+        per_channel_qscheme = qsheme_mapping_per_tensor_to_per_channel[qscheme]
+        self.input_obs = PerChannelMinMaxObserver(
+            ch_axis=1,
+            dtype=dtype,
+            qscheme=per_channel_qscheme,
+            quant_min=quant_min,
+            quant_max=quant_max,
+            factory_kwargs=factory_kwargs,
+        )
+
+        self.equalization_scale = torch.tensor(1)
+        self.equalization_shape: list[int] = []
+
+    def forward(self, x_orig):
+        if x_orig.ndim < 2 or x_orig.ndim > 5:
+            raise ValueError(
+                "InputEqualizationObserver only supports Linear and Conv layers"
+            )
+
+        # Calculate the shape needed to reshape the equalization scale later (needed for Conv layers)
+        self.equalization_shape = [1] * x_orig.ndim
+        self.equalization_shape[1] = x_orig.size(1)
+
+        return self.input_obs(x_orig)
+
+    def get_input_minmax(self):
+        return (self.input_obs.min_val, self.input_obs.max_val)
+
+    def set_equalization_scale(self, equalization_scale):
+        # Reshape the equalization scale along axis=1 so that it can be
+        # multiplied with the input along axis=1
+        if equalization_scale.nelement() == 1 and equalization_scale == torch.tensor(1):
+            return
+        self.equalization_scale = torch.reshape(
+            equalization_scale, self.equalization_shape
+        )
+
+    def calculate_scaled_minmax(self):
+        r"""Returns the scaled min/max inputs"""
+        if (
+            self.equalization_scale.nelement() == 1
+            and self.equalization_scale == torch.tensor(1)
+        ):
+            warnings.warn(
+                "Must call calculate_equalization_scale before calling calculate_scaled_minmax. "
+                + "Will not scale the next quantization observer.",
+                stacklevel=2,
+            )
+            return None, None
+
+        # Calculate qparams for the scaled min/max inputs
+        # Scale the input by the equalization scale located at the same column
+        # index
+        (min_inputs, max_inputs) = self.get_input_minmax()
+        equalization_scale_reshaped = reshape_scale(
+            self.equalization_scale, 0, min_inputs
+        )
+        min_input_scaled = torch.min(torch.mul(min_inputs, equalization_scale_reshaped))
+        max_input_scaled = torch.max(torch.mul(max_inputs, equalization_scale_reshaped))
+
+        return min_input_scaled, max_input_scaled
+
+    with_args = classmethod(_with_args)
+
+
+class _WeightEqualizationObserver(nn.Module):
+    r"""Observer for tracking the running min/max values of weight columns and
+    rows, and computing the quantization parameters for the weight rows.
+
+    Args:
+        dtype: Quantized data type
+        qscheme: Quantization scheme
+        quant_min: Minimum quantization value. If unspecified, it will
+            follow the 8-bit setup.
+        quant_max: Maximum quantization value. If unspecified, it will
+            follow the 8-bit setup.
+
+    This observer is made up of 1 PerChannelMinMaxObserver `weight_col_obs` used
+    to record the running minimum and maximum of columns of incoming weight
+    tensors. This observer is intended to be used along with an
+    InputEqualizationObserver to calculate the equalization scale.
+
+    The running minimum/maximum :math:`w_\text{min/max}` are computed in the
+    same way as :class:`~torch.ao.quantization.observer.PerChannelMinMaxObserver`.
+    """
+
+    def __init__(
+        self,
+        dtype=torch.qint8,
+        qscheme=torch.per_tensor_affine,
+        quant_min=None,
+        quant_max=None,
+        factory_kwargs=None,
+    ) -> None:
+        super().__init__()
+
+        self.dtype = dtype
+        self.qscheme = qscheme
+        self.ch_axis = 1
+
+        per_channel_qscheme = qscheme
+        if qscheme in {torch.per_tensor_affine, torch.per_tensor_symmetric}:
+            per_channel_qscheme = qsheme_mapping_per_tensor_to_per_channel[qscheme]
+        self.weight_col_obs = PerChannelMinMaxObserver(
+            ch_axis=1,
+            dtype=dtype,
+            qscheme=per_channel_qscheme,
+            quant_min=quant_min,
+            quant_max=quant_max,
+            factory_kwargs=factory_kwargs,
+        )
+
+        self.equalization_scale = torch.tensor(1)
+
+    def forward(self, w_orig):
+        if w_orig.ndim < 2 or w_orig.ndim > 5:
+            raise ValueError(
+                "InputEqualizationObserver only supports Linear and Conv layers"
+            )
+
+        return self.weight_col_obs(w_orig)
+
+    def get_weight_col_minmax(self):
+        return (self.weight_col_obs.min_val, self.weight_col_obs.max_val)
+
+    def set_equalization_scale(self, equalization_scale):
+        self.equalization_scale = equalization_scale
+
+    with_args = classmethod(_with_args)
+
+
+def calculate_equalization_scale(
+    input_obs: _InputEqualizationObserver, weight_obs: _WeightEqualizationObserver
+) -> torch.Tensor:
+    r"""Calculates the equalization scale and sets the equalization_scale value
+    in the observers.
+
+    Args:
+        input_obs: Observer that tracks the ranges for the input columns
+        weight_obs: Observer that tracks the ranges for the weight columns
+    """
+
+    (min_inputs, max_inputs) = input_obs.get_input_minmax()
+    (min_weights, max_weights) = weight_obs.get_weight_col_minmax()
+
+    if not (
+        check_min_max_valid(min_inputs, max_inputs)
+        and check_min_max_valid(min_weights, max_weights)
+    ):
+        warnings.warn(
+            "Must run observer before calling calculate_equalization_scale. "
+            + "Returning default equalization scale torch.tensor(1).",
+            stacklevel=2,
+        )
+        return torch.tensor(1)
+
+    if min_inputs.shape != min_weights.shape:
+        raise ValueError(
+            "Input and Weight must have the same column dimension. "
+            + f"Found {min_inputs.shape} and {min_weights.shape} shapes instead."
+        )
+
+    equalization_scale = torch.sqrt(
+        (max_weights - min_weights) / (max_inputs - min_inputs)
+    )
+    # Replace all 'inf', 'nan', 0's with 1s to prevent errors
+    equalization_scale[equalization_scale == 0.0] = 1
+    equalization_scale = torch.nan_to_num(equalization_scale, nan=1, posinf=1, neginf=1)
+    return equalization_scale
+
+
+class EqualizationQConfig(
+    # pyrefly: ignore [invalid-inheritance]
+    namedtuple("EqualizationQConfig", ["input_activation", "weight"])
+):
+    """
+    Describes how to quantize a layer or a part of the network specifically for
+    input-weight equalization by providing settings (observer classes) for
+    inputs, outputs, and weights.
+
+    Note that EqualizationQConfig needs to contain observer **classes** (like
+    MinMaxObserver) or a callable that returns instances on invocation, not the
+    concrete observer instances themselves.
+    Quantization function will instantiate observers multiple times for each of
+    the layers.
+
+    Observer classes have usually reasonable default arguments, but they can be
+    overwritten with `with_args` method (that behaves like functools.partial):
+
+    my_qconfig = EqualizationQConfig(input_activation=_InputEqualizationObserver.with_args(dtype=torch.qint8),
+                                    weight=_WeightEqualizationObserver.with_args(dtype=torch.qint8))
+    """
+
+    __slots__ = ()
+
+    def __new__(cls, input_activation=torch.nn.Identity, weight=torch.nn.Identity):
+        if isinstance(input_activation, nn.Module) or isinstance(weight, nn.Module):
+            raise ValueError(
+                "EqualizationQConfig received observer instance, please pass observer class instead. "
+                + "Use MyObserver.with_args(x=1) to override arguments to constructor if needed"
+            )
+        self = super().__new__(cls, input_activation, weight)
+        return self
+
+
+input_equalization_observer = _InputEqualizationObserver.with_args(
+    dtype=torch.quint8, qscheme=torch.per_tensor_symmetric
+)
+weight_equalization_observer = _WeightEqualizationObserver.with_args(
+    dtype=torch.qint8, qscheme=torch.per_channel_symmetric
+)
+default_equalization_qconfig = EqualizationQConfig(
+    input_activation=input_equalization_observer, weight=weight_equalization_observer
+)
+
+
+def fused_module_supports_equalization(module) -> bool:
+    """Checks if the fused node supports equalization."""
+    return type(module) in [
+        nni.LinearReLU,
+        nni.ConvReLU1d,
+        nni.ConvReLU2d,
+        nni.ConvReLU3d,
+    ]
+
+
+def nn_module_supports_equalization(module) -> bool:
+    """Checks if the torch.nn node supports equalization."""
+    return type(module) in [nn.Linear, nn.Conv1d, nn.Conv2d, nn.Conv3d]
+
+
+def custom_module_supports_equalization(module) -> bool:
+    """Checks if the custom node supports equalization."""
+    return type(module) in CUSTOM_MODULE_SUPP_LIST
+
+
+def node_supports_equalization(node: Node, modules) -> bool:
+    """Checks if the current node supports equalization
+    Currently we only support nn.Linear/F.Linear and nn.Conv/F.conv layers
+    """
+    if node.op == "call_module":
+        return (
+            nn_module_supports_equalization(modules[str(node.target)])
+            or fused_module_supports_equalization(modules[str(node.target)])
+            or custom_module_supports_equalization(modules[str(node.target)])
+        )
+    elif node.op == "call_function":
+        return node.target in [F.linear, F.conv1d, F.conv2d, F.conv3d]
+    return False
+
+
+def is_equalization_observer(observer: nn.Module) -> bool:
+    return isinstance(
+        observer, (_InputEqualizationObserver, _WeightEqualizationObserver)
+    )
+
+
+###############################################################################
+# Functions for equalization during convert                                   #
+###############################################################################
+
+
+def get_op_node_and_weight_eq_obs(
+    input_eq_obs_node: Node, model: GraphModule, modules: dict[str, nn.Module]
+) -> tuple[Node | None, _WeightEqualizationObserver | None]:
+    """Gets the following weight equalization observer. There should always
+    exist a weight equalization observer after an input equalization observer.
+
+    Returns the operation node that follows the input equalization observer node
+    and the weight equalization observer
+    """
+
+    # Find the op node that comes directly after the input equalization observer
+    op_node = None
+    for user in input_eq_obs_node.users:
+        if node_supports_equalization(user, modules):
+            op_node = user
+            break
+
+    if op_node is None:
+        raise AssertionError(
+            "Expected an operation node after the input equalization observer"
+        )
+    if op_node.op == "call_module":
+        # If the op_node is a nn.Linear layer, then it must have a
+        # WeightEqualizationObserver configuration
+        maybe_equalization_node_name_to_config = _get_observed_graph_module_attr(
+            model, "equalization_node_name_to_qconfig"
+        )
+        if maybe_equalization_node_name_to_config is None:
+            raise AssertionError(
+                "Expected 'equalization_node_name_to_qconfig' attribute in observed graph module"
+            )
+        equalization_node_name_to_qconfig: dict[str, Any] = (
+            maybe_equalization_node_name_to_config  # type: ignore[assignment]
+        )
+        if equalization_node_name_to_qconfig.get(op_node.name, None) is None:
+            raise AssertionError(
+                f"No equalization qconfig found for op node {op_node.name}"
+            )
+        weight_eq_obs = equalization_node_name_to_qconfig.get(  # type: ignore[union-attr]
+            op_node.name, None
+        ).weight()
+
+        if not isinstance(weight_eq_obs, _WeightEqualizationObserver):
+            raise AssertionError(
+                "Expected weight equalization observer to be a _WeightEqualizationObserver"
+            )
+        return op_node, weight_eq_obs
+
+    elif op_node.op == "call_function":
+        weight_node = maybe_get_weight_eq_obs_node(op_node, modules)
+        if weight_node is not None:
+            weight_eq_obs = modules[str(weight_node.target)]
+            if not isinstance(weight_eq_obs, _WeightEqualizationObserver):
+                raise AssertionError(
+                    "Expected weight equalization observer to be a _WeightEqualizationObserver"
+                )
+            return op_node, weight_eq_obs
+
+    return None, None
+
+
+def maybe_get_weight_eq_obs_node(
+    op_node: Node, modules: dict[str, nn.Module]
+) -> Node | None:
+    """Gets the weight equalization observer node if it exists."""
+    if op_node.op != "call_function":
+        raise AssertionError(
+            "maybe_get_weight_eq_obs_node expects a call_function op_node"
+        )
+    for node_arg in op_node.args:
+        if node_arg_is_weight(op_node, node_arg):
+            if (
+                isinstance(node_arg, Node)
+                and node_arg.op == "call_module"
+                and isinstance(
+                    modules[str(node_arg.target)], _WeightEqualizationObserver
+                )
+            ):
+                return node_arg
+    return None
+
+
+def maybe_get_next_input_eq_obs(
+    node: Node, modules: dict[str, nn.Module]
+) -> _InputEqualizationObserver | None:
+    """Gets the following input equalization observer if it exists.
+
+    For example, in the case of connecting linear layers:
+        x -> inp_obs1 -> eq_obs1 -> linear1 -> out_obs1 -> eq_obs2 -> linear2 -> out_obs2
+    If the node being passed in is the linear1 node, then we want to return eq_obs2,
+    the following equalization observer for linear2.
+
+    However, if there are no connecting layers:
+        x -> inp_obs1 -> eq_obs1 -> linear1 -> out_obs1 -> add
+    Then we want to return None.
+
+    In the case of an unfused linear-relu layer with a connecting linear layer:
+        linear1 -> relu -> out_obs1 -> eq_obs2 -> linear2 -> out_obs2
+    Since it is unfused, we want to skip over the relu layer and return eq_obs2,
+    the following equalization observer for linear2.
+    """
+
+    if not node_supports_equalization(node, modules):
+        raise AssertionError("Node does not support equalization")
+
+    # Locate the following nn.ReLU or F.relu node if it exists
+    maybe_relu_node = maybe_get_next_module(node, modules, nn.ReLU)
+    if maybe_relu_node is None:
+        maybe_relu_node = maybe_get_next_module(
+            node, modules, target_functional_type=F.relu
+        )
+
+    # Locate the following output observer if it exists.
+    # We will skip the relu node if it exists.
+    maybe_obs_node = (
+        maybe_get_next_module(node, modules, ObserverBase)
+        if maybe_relu_node is None
+        else maybe_get_next_module(maybe_relu_node, modules, ObserverBase)
+    )
+    if maybe_obs_node is None:
+        return None
+
+    maybe_eq_obs_node = maybe_get_next_module(
+        maybe_obs_node, modules, _InputEqualizationObserver
+    )
+    if maybe_eq_obs_node is None:
+        return None
+
+    maybe_eq_obs = modules[str(maybe_eq_obs_node)]
+    if not isinstance(maybe_eq_obs, _InputEqualizationObserver):
+        raise AssertionError(
+            "Expected the following equalization observer to be an _InputEqualizationObserver"
+        )
+    return maybe_eq_obs
+
+
+def maybe_get_next_equalization_scale(
+    node: Node, modules: dict[str, nn.Module]
+) -> torch.Tensor | None:
+    """If the next next node is an InputEqualizationObserver then we want to
+    return its equalization scale, else we return 1
+
+    This is used in the case where there are two connecting linear layers:
+        linear1 -> LinearOutObs -> InputEqObs -> linear2
+    In this case, the node given is linear1 and we want to locate the InputEqObs.
+    """
+    next_inp_eq_obs = maybe_get_next_input_eq_obs(node, modules)
+    # pyrefly: ignore [invalid-argument]
+    if next_inp_eq_obs:
+        if (
+            next_inp_eq_obs.equalization_scale.nelement() == 1
+            and next_inp_eq_obs.equalization_scale == torch.tensor(1)
+        ):
+            return None
+        return next_inp_eq_obs.equalization_scale
+    return None
+
+
+def scale_input_observer(node: Node, modules: dict[str, nn.Module]) -> None:
+    """Scales the following input quantization observer's min/max values by
+    updating the values with the scaled min/max values calculated by the input
+    equalization observer
+    """
+    input_eq_obs = modules[str(node.target)]
+    if not isinstance(input_eq_obs, _InputEqualizationObserver):
+        raise AssertionError(
+            "Expected the module at node.target to be an _InputEqualizationObserver"
+        )
+
+    input_quant_obs_node = node.args[0]
+    if not isinstance(input_quant_obs_node, Node):
+        raise AssertionError(
+            "Expected the input quantization observer node to be a Node"
+        )
+
+    input_quant_obs = modules[str(input_quant_obs_node.target)]
+    if not isinstance(input_quant_obs, ObserverBase):
+        return
+
+    min_input_scaled, max_input_scaled = input_eq_obs.calculate_scaled_minmax()
+    if min_input_scaled is None and max_input_scaled is None:
+        return
+    input_quant_obs.min_val = min_input_scaled
+    input_quant_obs.max_val = max_input_scaled
+
+
+def scale_weight_node(
+    node: Node,
+    modules: dict[str, nn.Module],
+    equalization_scale: torch.Tensor,
+    next_equalization_scale: torch.Tensor | None,
+) -> None:
+    """Scale the weights for input-weight equalization by multiplying the
+    weight by 1/equalization_scale and next_equalization_scale
+
+    Args:
+        node: Current node whose weights we want to scale
+        equalization_scale: Current node's calculated equalization scale
+        next_equalization_scale: Next node's calculated equalization scale if
+           the following node needs to be equalized, 1 otherwise
+    """
+    if equalization_scale is None:
+        return
+
+    if fused_module_supports_equalization(modules[str(node.target)]):
+        op_module = modules[str(node.target)][0]  # type: ignore[index]
+    else:
+        op_module = modules[str(node.target)]
+    if not (
+        nn_module_supports_equalization(op_module)
+        or custom_module_supports_equalization(op_module)
+    ):
+        raise AssertionError(
+            "Expected operation module to support equalization (nn or custom)"
+        )
+
+    # Scale the weights for input-weight equalization
+    # If the following layer needs to be equalized then we will multiply its scale
+    weight = op_module.weight
+    if not isinstance(weight, torch.Tensor):
+        raise AssertionError("Expected op_module.weight to be a torch.Tensor")
+
+    # Scale the weights by the reciprocal of the equalization scale
+    # Reshape the equalization scale so that we can multiply it to the weight along axis=1
+    equalization_scale_reshaped = reshape_scale(equalization_scale, 1, weight)
+    scaled_weight = torch.mul(weight, torch.reciprocal(equalization_scale_reshaped))
+
+    if next_equalization_scale is None:
+        op_module.weight = nn.Parameter(scaled_weight)
+        return
+
+    # Multiply the weights row wise by the next equalization scale
+    # Reshape the equalization scale so that we can multiply it to the weight along axis=0
+    next_equalization_scale_reshaped = reshape_scale(next_equalization_scale, 0, weight)
+    scaled_weight = torch.mul(scaled_weight, next_equalization_scale_reshaped)
+
+    op_module.weight = nn.Parameter(scaled_weight)
+
+    # Multiply the bias element wise by the next equalization scale
+    bias = op_module.bias
+    if bias is None:
+        return
+    if not isinstance(bias, torch.Tensor):
+        raise AssertionError("Expected op_module.bias to be a torch.Tensor")
+
+    # Reshape the equalization scale so that we can multiply it element-wise to the bias
+    next_equalization_scale_reshaped = reshape_scale(next_equalization_scale, 0, bias)
+    scaled_bias = torch.mul(bias, next_equalization_scale_reshaped)
+    op_module.bias = nn.Parameter(scaled_bias)
+
+
+def scale_weight_functional(
+    op_node: Node,
+    model: GraphModule,
+    modules: dict[str, nn.Module],
+    equalization_scale: torch.Tensor,
+    next_equalization_scale: torch.Tensor | None,
+) -> None:
+    """Scales the weight value for functional layers"""
+    if equalization_scale is None:
+        return
+
+    # From the given op_node, the path looks like:
+    #   get_attr(weight) -> weight_quant_obs -> weight_eq_obs -> op_node
+    # So we want to trace back from the op_node to get the equalization observer
+    # node, then the quantization observer node, and then finally the weight
+    # node which contains the weight values.
+
+    # Get the equalization observer node
+    weight_eq_obs_node = maybe_get_weight_eq_obs_node(op_node, modules)
+    if weight_eq_obs_node is None:
+        return
+
+    # Get the quantization observer node
+    weight_quant_obs_node = weight_eq_obs_node.args[0]
+    if weight_quant_obs_node is None:
+        return
+    if not (
+        isinstance(weight_quant_obs_node, Node)
+        and isinstance(modules[str(weight_quant_obs_node.target)], ObserverBase)
+    ):
+        raise AssertionError(
+            "Expected weight_quant_obs_node to be a Node whose module is an ObserverBase"
+        )
+
+    # Get the get_attr(weight) node
+    weight_node = weight_quant_obs_node.args[0]
+    if weight_node is None:
+        return
+    if not (isinstance(weight_node, Node) and weight_node.op == "get_attr"):
+        raise AssertionError("Expected weight node to be a 'get_attr' Node")
+
+    weight_parent_name, weight_name = _parent_name(weight_node.target)
+    weight = getattr(modules[weight_parent_name], weight_name)
+
+    # Scale the weights for input-weight equalization
+    # If the following layer needs to be equalized then we will multiply its scale
+    # Reshape the equalization scale so that we can multiply it to the weight along axis=1
+    equalization_scale_reshaped = reshape_scale(equalization_scale, 1, weight)
+    scaled_weight = torch.mul(weight, torch.reciprocal(equalization_scale_reshaped))
+
+    if next_equalization_scale is None:
+        setattr(modules[weight_parent_name], weight_name, scaled_weight)
+        return
+
+    # Multiply the weights row wise by the next equalization scale
+    # Reshape the equalization scale so that we can multiply it to the weight along axis=1
+    next_equalization_scale_reshaped = reshape_scale(
+        next_equalization_scale, 0, scaled_weight
+    )
+    scaled_weight = torch.mul(scaled_weight, next_equalization_scale_reshaped)
+
+    setattr(modules[weight_parent_name], weight_name, scaled_weight)
+    if not torch.allclose(model.get_buffer(str(weight_node.target)), scaled_weight):
+        raise AssertionError("Model buffer for weight does not match the scaled weight")
+
+    # Multiply the bias element wise by the next equalization scale
+    bias_node = None
+    for node in op_node.args:
+        # Find the node containing the weight values
+        if isinstance(node, Node) and node.op == "get_attr" and "bias" in node.name:
+            bias_node = node
+            break
+    if bias_node is None:
+        return
+
+    bias_parent_name, bias_name = _parent_name(bias_node.target)
+    bias = getattr(modules[bias_parent_name], bias_name)
+
+    # Reshape the equalization scale so that we can multiply it element-wise to the bias
+    next_equalization_scale_reshaped = reshape_scale(next_equalization_scale, 0, bias)
+    scaled_bias = torch.mul(bias, next_equalization_scale_reshaped)
+    setattr(modules[bias_parent_name], bias_name, scaled_bias)
+
+
+def clear_weight_quant_obs_node(op_node: Node, modules: dict[str, nn.Module]) -> None:
+    """Given the operation node, we want find the corresponding quantization
+    observer and reset its min/max values
+    """
+    weight_eq_obs_node = maybe_get_weight_eq_obs_node(op_node, modules)
+    if weight_eq_obs_node is None:
+        return
+
+    weight_quant_obs_node = weight_eq_obs_node.args[0]
+    if weight_quant_obs_node is None:
+        return
+    if not isinstance(weight_quant_obs_node, Node):
+        raise AssertionError("Expected weight_quant_obs_node to be a Node")
+
+    weight_quant_obs = modules[str(weight_quant_obs_node.target)]
+    if not isinstance(modules[str(weight_quant_obs_node.target)], ObserverBase):
+        raise AssertionError(
+            "Expected the module at weight_quant_obs_node to be an ObserverBase"
+        )
+    weight_quant_obs.reset_min_max_vals()  # type: ignore[operator]
+
+
+def remove_node(model: GraphModule, node: Node, prev_node: Node):
+    """Removes the given node from the model by replacing all of its users with
+    the given previous node
+    """
+    # For all of the current node's users, replace the current node with
+    # the input quantization observer node
+    orig_users = list(node.users.keys())
+    for user_node in orig_users:
+        user_node.replace_input_with(node, prev_node)
+
+    # Erase the InputEqualizationObserver node
+    model.graph.erase_node(node)
+
+
+def update_obs_for_equalization(
+    model: GraphModule, modules: dict[str, nn.Module]
+) -> dict[str, _WeightEqualizationObserver]:
+    """Update all of the observer's equalization scale. For each
+    InputEqualizationObserver, we will find the location of the next
+    WeightEqualizationObserver, create it, and calculate the equalization scale
+    based on the two observers.
+
+    We will then return a dictionary mapping operation node names to
+    the corresponding WeightEqualizationObservers for that operation.
+    """
+    weight_eq_obs_dict = {}
+    for node in model.graph.nodes:
+        if node.op == "call_module" and isinstance(
+            modules[node.target], _InputEqualizationObserver
+        ):
+            input_eq_obs = modules[node.target]
+            if not isinstance(input_eq_obs, _InputEqualizationObserver):
+                raise AssertionError(
+                    "Expected module at node.target to be an _InputEqualizationObserver"
+                )
+            op_node, weight_eq_obs = get_op_node_and_weight_eq_obs(node, model, modules)
+
+            if op_node is None or weight_eq_obs is None:
+                continue
+
+            if op_node.op == "call_module":
+                # Calibrate the weight equalization observer since it has just
+                # been created
+                if fused_module_supports_equalization(modules[str(op_node.target)]):
+                    module = modules[str(op_node.target)][0]  # type: ignore[index]
+                    if not nn_module_supports_equalization(module):
+                        raise AssertionError(
+                            "Expected fused module to support equalization"
+                        )
+                    weight_eq_obs(module.weight)
+                else:
+                    weight_eq_obs(modules[str(op_node.target)].weight)
+
+            # Calculate and set the equalization scale values
+            equalization_scale = calculate_equalization_scale(
+                input_eq_obs, weight_eq_obs
+            )
+            input_eq_obs.set_equalization_scale(equalization_scale)
+            weight_eq_obs.set_equalization_scale(equalization_scale)
+
+            weight_eq_obs_dict[op_node.name] = weight_eq_obs
+
+    return weight_eq_obs_dict
+
+
+def convert_eq_obs(
+    model: GraphModule,
+    modules: dict[str, nn.Module],
+    weight_eq_obs_dict: dict[str, _WeightEqualizationObserver],
+) -> None:
+    """Converts the equalization operations and updates the other nodes in the
+    following way:
+        - Removes the input equalization observers and inserts a mul operator
+          along with an equalization scale node wherever applicable (we do not
+          want to insert a mul operator between connecting linear layers).
+        - Updates the input quantization observers with the scaled input min/max
+          values.
+        - Scales the weights by the current and next equalization scales.
+        - Removes the weight equalization observer node if it exists.
+
+    Before (after prepare):
+                                    weight values
+                                          |
+                                    WeightQuantObs
+                                          |
+                                      WeightEqObs
+                                          |
+        x -> InpQuantObs -> InpEqObs -> linear -> OutQuantObs
+
+    After this function:
+                                              scaled weight values
+                                                      |
+       equalization scale                       WeightQuantObs
+              |                                       |
+        x -> mul -> InpQuantObs (scaled min/max) -> linear -> OutQuantObs
+
+    After convert:
+       equalization scale                 scaled weight values
+              |                                    |
+        x -> mul -> quantize_per_tensor -> quantized::linear
+
+    Note that although the equalization observer appeared after the quantization
+    observer after prepare_fx, the mul node appears before the quantization node
+    after convert_fx. This is because placing the equalization observer after
+    the quantization observer in prepare_fx would allow us to keep the invariant
+    that the graph before the current node inserts its observers is not
+    modified.
+
+    Having the equalization observer before the quantization observer would also
+    cause some inconsistences between the ordering of the quantization and
+    equalization observers.
+    For example, a single linear layer would look like:
+        x -> InpEqObs1 -> InpQuantObs1 -> linear1 -> OutQuantObs1
+    But between two connected linear layers, it would look like:
+        linear1 -> OutQuantObs1 -> InpEqObs2 -> linear2 -> OutQuantObs2
+    """
+    for node in model.graph.nodes:
+        if node.op == "call_module" and isinstance(
+            modules[node.target], _InputEqualizationObserver
+        ):
+            inp_quant_obs_node = node.args[0]
+            prev_node = inp_quant_obs_node.args[0]
+
+            # If the previous node is a layer that needs to be equalized, then
+            # we will remove the current node because we do not need to add any
+            # equalization nodes between two layers that need to be equalized
+
+            # Before: linear1/relu (prev_node) -> output_quant_obs1 (inp_quant_obs_node) -> input_eq_obs2 (node) -> linear2
+            # After: linear1/relu (prev_node) -> output_quant_obs1 (inp_quant_obs_node) -> linear2
+            if (
+                node_supports_equalization(prev_node, modules)
+                or "relu" in prev_node.name
+            ):
+                remove_node(model, node, inp_quant_obs_node)
+                continue
+
+            # Update the following input quantization observer's min/max values
+            scale_input_observer(node, modules)
+
+            # Remove the InputEqualization node and add a mul operator before
+            # the quantization observer node that appears before the equalization node
+            # Before: x -> input_quant_obs -> input_eq_obs -> linear
+            # After: x -> mul -> input_quant_obs -> linear
+
+            # Create a node containing the equalization scale
+            with model.graph.inserting_before(inp_quant_obs_node):
+                get_new_eq_scale_name = get_new_attr_name_with_prefix(
+                    prev_node.name + "_equalization_scale"
+                )
+                name = get_new_eq_scale_name(modules)
+                setattr(model, name, modules[node.target].equalization_scale)
+                eq_scale_node = model.graph.create_node("get_attr", name)
+
+            # Create a node multiplying the input with the equalization scale
+            with model.graph.inserting_after(eq_scale_node):
+                inputs = (prev_node, eq_scale_node)
+                mul_node = model.graph.create_node("call_function", torch.mul, inputs)
+
+            # Set the mul nod to be the input_quant_obs_node's input instead of
+            # the previous node
+            inp_quant_obs_node.replace_input_with(prev_node, mul_node)
+            remove_node(model, node, inp_quant_obs_node)
+
+        elif weight_eq_obs_dict.get(node.name, None) is not None:
+            weight_eq_obs = weight_eq_obs_dict.get(node.name)
+            if not isinstance(weight_eq_obs, _WeightEqualizationObserver):
+                raise AssertionError(
+                    "Expected weight equalization observer to be a _WeightEqualizationObserver"
+                )
+            equalization_scale = weight_eq_obs.equalization_scale
+
+            if (
+                equalization_scale.nelement() == 1
+                and equalization_scale == torch.tensor(1)
+            ):
+                equalization_scale = None  # type: ignore[assignment]
+            maybe_next_equalization_scale = maybe_get_next_equalization_scale(
+                node, modules
+            )
+
+            # Scale the weight nodes
+            if node.op == "call_module":
+                scale_weight_node(
+                    node,
+                    modules,
+                    # pyrefly: ignore [bad-argument-type]
+                    equalization_scale,
+                    maybe_next_equalization_scale,
+                )
+            elif node.op == "call_function":
+                scale_weight_functional(
+                    node,
+                    model,
+                    modules,
+                    # pyrefly: ignore [bad-argument-type]
+                    equalization_scale,
+                    maybe_next_equalization_scale,
+                )
+
+                weight_eq_obs_node = maybe_get_weight_eq_obs_node(node, modules)
+                if weight_eq_obs_node is None:
+                    return
+                if not isinstance(
+                    modules[str(weight_eq_obs_node.target)], _WeightEqualizationObserver
+                ):
+                    raise AssertionError(
+                        "Expected weight equalization observer to be a _WeightEqualizationObserver"
+                    )
+
+                # Clear the quantization observer's min/max values so that they
+                # can get updated later based on the new scale values
+                clear_weight_quant_obs_node(node, modules)
+
+                # Erase the weight equalization observer node
+                prev_node = weight_eq_obs_node.args[0]
+                remove_node(model, weight_eq_obs_node, prev_node)  # type: ignore[arg-type]
+            else:
+                raise ValueError(
+                    "Expected operation node to be 'call_module' or 'call_function"
+                    + f"Instead got node {node.name} as '{node.op}'."
+                )
+
+
+def _convert_equalization_ref(model: GraphModule):
+    """Reference function which applies changes needed for equalization, but
+    does not quantize the nodes
+    """
+    modules = dict(model.named_modules(remove_duplicate=False))
+
+    # Calculate the equalization scale, update the observers with the scaled
+    # inputs, and scale the weight
+    weight_eq_obs_dict = update_obs_for_equalization(model, modules)
+    convert_eq_obs(model, modules, weight_eq_obs_dict)
+
+    return GraphModule(model, model.graph)
+
+
+###############################################################################
+# Functions for running the equalized model on the Numeric Suite              #
+###############################################################################
+
+
+def get_layer_sqnr_dict(
+    model_a: nn.Module, model_b: nn.Module, x: torch.Tensor
+) -> dict[str, float]:
+    """Runs the Numeric Suite on model_a and model_b and returns a dictionary
+    containing the SQNR between layers in model_a and model_b.
+
+    Note: In order to support equalized models, this function has a hacky fix in
+    which we do not match any torch.mul operators. This is because equalized
+    models contain extra mul operators to scale the input by the equalization
+    scale, but this edge case has not been resolved yet within the numeric suite code.
+
+    Args:
+        model_a: A float model
+        model_b: A quantized model
+        x: Inputs to use during calibration
+    """
+    import torch.ao.ns._numeric_suite_fx as ns
+    from torch.ao.ns.fx.mappings import get_unmatchable_types_map
+
+    unmatchable_types_map = get_unmatchable_types_map()
+    unmatchable_types_map["funs_unmatchable"].add(torch.mul)
+
+    model_a_ns, model_b_ns = ns.add_loggers(
+        "fp32",
+        model_a,
+        "int8",
+        model_b,
+        ns.OutputLogger,
+        unmatchable_types_map=unmatchable_types_map,
+    )
+
+    model_a_ns(x)
+    model_b_ns(x)
+
+    activation_comparison_dict = ns.extract_logger_info(
+        model_a_ns, model_b_ns, ns.OutputLogger, "int8"
+    )
+    ns.extend_logger_results_with_comparison(
+        activation_comparison_dict,
+        "fp32",
+        "int8",
+        torch.ao.ns.fx.utils.compute_sqnr,
+        "sqnr",
+    )
+
+    # Construct a dictionary mapping layer names to the SQNR values
+    layer_sqnr_dict = {}
+    for key in activation_comparison_dict:
+        layer = activation_comparison_dict[key]["node_output"]["int8"][0]["fqn"]
+        sqnr = activation_comparison_dict[key]["node_output"]["int8"][0]["sqnr"][0]
+        layer_sqnr_dict[layer] = sqnr
+
+    return layer_sqnr_dict
+
+
+def get_equalization_qconfig_dict(
+    layer_sqnr_dict: dict[str, float], num_layers_to_equalize: int
+) -> Any:
+    """Given the layer to SQNR dictionary, find the layers with the highest
+    quantization errors, and return an equalization_qconfig_dict
+    specifying to only equalize those top layers.
+
+    Args:
+        layer_sqnr_dict: Dictionary mapping layer names to SQNR values (found
+            when comparing an equalized model against a float model)
+        num_layers_to_equalize: Number of layers with the highest quantization
+           errors to equalize
+    """
+
+    # Sort the layer_sqnr_dictionary values and get the layers with the lowest
+    # SQNR values (aka highest quantization errors)
+    layer_sqnr_sorted = sorted(layer_sqnr_dict.items(), key=operator.itemgetter(1))
+    layers_to_equalize = layer_sqnr_sorted[:num_layers_to_equalize]
+
+    # Constructs an equalization_qconfig_dict that specifies to only equalize
+    # the layers with the highest quantization errors
+    module_to_qconfig_list = [
+        (item[0], default_equalization_qconfig) for item in layers_to_equalize
+    ]
+    equalization_qconfig_dict = {"module_name": module_to_qconfig_list}
+    return equalization_qconfig_dict
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/_lower_to_native_backend.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/_lower_to_native_backend.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad20bcc96251d8fb439e5201a2038e28e5ec675b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/_lower_to_native_backend.py
@@ -0,0 +1,1413 @@
+# mypy: allow-untyped-defs
+import operator
+from collections.abc import Callable
+from typing import Any
+
+import torch
+import torch.ao.nn.intrinsic as nni
+import torch.ao.nn.intrinsic.quantized as nniq
+import torch.ao.nn.intrinsic.quantized.dynamic as nniqd
+import torch.ao.nn.quantized as nnq
+import torch.ao.nn.quantized.dynamic as nnqd
+import torch.ao.nn.quantized.reference as nnqr
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.ao.nn.quantized.modules.utils import WeightedQuantizedModule
+from torch.ao.quantization.qconfig import QConfigAny
+from torch.ao.quantization.quantization_mappings import get_quantized_operator
+from torch.ao.quantization.utils import _parent_name
+from torch.fx import GraphModule, map_arg, Node
+from torch.fx.graph import Graph
+
+from .utils import (
+    collect_producer_nodes,
+    create_node_from_old_node_preserve_meta,
+    get_linear_prepack_op_for_dtype,
+    get_new_attr_name_with_prefix,
+    get_qconv_prepack_op,
+    graph_module_from_producer_nodes,
+)
+
+
+QOP_TO_ARG_NAMES_TO_SKIP: dict[Callable[..., Any], list[str]] = {
+    torch._ops.ops.quantized.hardswish: ["inplace"],
+    torch._ops.ops.quantized.elu: ["inplace"],
+    torch._ops.ops.quantized.dropout: ["inplace"],
+    torch._ops.ops.quantized.instance_norm: [
+        "running_mean",
+        "running_var",
+        "use_input_stats",
+        "momentum",
+    ],
+}
+
+
+def _is_node_in_list(node, modules, func_list, method_list, module_type_list):
+    is_call_function = node.op == "call_function" and node.target in func_list
+    is_call_method = node.op == "call_method" and node.target in method_list
+    is_call_module = (
+        node.op == "call_module" and type(modules[str(node.target)]) in module_type_list
+    )
+    return is_call_function, is_call_method, is_call_module
+
+
+def is_fixed_qparams_node(node, modules):
+    func_list = [
+        torch.nn.functional.hardsigmoid,
+        torch.nn.functional.sigmoid,
+        torch.sigmoid,
+        torch.tanh,
+    ]
+    method_list = [
+        "hardsigmoid",
+        "hardsigmoid_",
+        "sigmoid",
+        "sigmoid_",
+        "tanh",
+        "tanh_",
+    ]
+    module_type_list = [
+        torch.nn.Hardsigmoid,
+        torch.nn.Sigmoid,
+        torch.nn.Tanh,
+        torch.nn.Softmax,
+    ]
+    return _is_node_in_list(node, modules, func_list, method_list, module_type_list)
+
+
+def is_default_node(node, modules):
+    func_list = [
+        torch.nn.functional.elu,
+        torch.nn.functional.hardswish,
+        torch.nn.functional.instance_norm,
+        torch.nn.functional.layer_norm,
+        torch.nn.functional.leaky_relu,
+        torch.nn.functional.dropout,
+    ]
+    method_list: list[Any] = []
+    module_type_list = [
+        nnqr.ConvTranspose1d,
+        nnqr.ConvTranspose2d,
+        nnqr.ConvTranspose3d,
+        torch.nn.ELU,
+        torch.nn.LeakyReLU,
+        torch.nn.Hardswish,
+        torch.nn.InstanceNorm1d,
+        torch.nn.InstanceNorm2d,
+        torch.nn.InstanceNorm3d,
+        torch.nn.LayerNorm,
+        torch.nn.Dropout,
+        torch.nn.PReLU,
+        torch.nn.BatchNorm2d,
+        torch.nn.BatchNorm3d,
+        torch.ao.nn.intrinsic.BNReLU2d,
+        torch.ao.nn.intrinsic.BNReLU3d,
+    ]
+    return _is_node_in_list(node, modules, func_list, method_list, module_type_list)
+
+
+def is_copy_node(node, modules):
+    func_list = [
+        torch.adaptive_avg_pool1d,
+        torch.nn.functional.adaptive_avg_pool2d,
+        torch.nn.functional.adaptive_avg_pool3d,
+        torch.nn.functional.hardtanh,
+        torch.nn.functional.hardtanh_,
+        torch.nn.functional.interpolate,
+        torch.nn.functional.max_pool1d,
+        torch.nn.functional.max_pool2d,
+        torch.nn.functional.max_pool3d,
+        torch.nn.functional.relu,
+        torch.nn.functional.relu6,
+        torch.avg_pool1d,
+        torch._C._nn.avg_pool2d,
+        torch._C._nn.avg_pool3d,
+        torch.clamp,
+        torch.flatten,
+        torch.mean,
+        operator.floordiv,
+        # F.channel_shuffle and torch.channel_shuffle are essentially the same thing
+        # so we only need to put one of them here
+        torch.channel_shuffle,
+    ]
+    method_list = [
+        "clamp",
+        "mean",
+        "relu",
+        "relu_",
+    ]
+    module_type_list = [
+        torch.nn.AdaptiveAvgPool1d,
+        torch.nn.AdaptiveAvgPool2d,
+        torch.nn.AdaptiveAvgPool3d,
+        torch.nn.AvgPool1d,
+        torch.nn.AvgPool2d,
+        torch.nn.AvgPool3d,
+        torch.nn.Hardtanh,
+        torch.nn.MaxPool1d,
+        torch.nn.MaxPool2d,
+        torch.nn.MaxPool3d,
+        torch.nn.ReLU,
+        torch.nn.ReLU6,
+        torch.nn.ChannelShuffle,
+    ]
+    return _is_node_in_list(node, modules, func_list, method_list, module_type_list)
+
+
+def is_general_tensor_shape_node(node, modules):
+    func_list = [
+        torch.narrow,
+        torch.transpose,
+        torch.repeat_interleave,
+        torch.squeeze,
+        torch.stack,
+        torch.unsqueeze,
+        torch.nn.functional.pixel_shuffle,
+        torch.nn.functional.pixel_unshuffle,
+    ]
+    method_list = [
+        "contiguous",
+        "detach",
+        "detach_",
+        "permute",
+        "repeat",
+        "repeat_interleave",
+        "reshape",
+        "resize_",
+        "shape",
+        "size",
+        "squeeze",
+        "squeeze_",
+        "transpose",
+        "unsqueeze",
+        "unsqueeze_",
+        "view",
+    ]
+    module_type_list = [
+        torch.nn.Identity,
+        torch.nn.PixelShuffle,
+        torch.nn.PixelUnshuffle,
+    ]
+    return _is_node_in_list(node, modules, func_list, method_list, module_type_list)
+
+
+def is_other_node(node, modules):
+    func_list = [
+        torch.cat,
+    ]
+    method_list: list[Any] = []
+    module_type_list: list[Any] = []
+    return _is_node_in_list(node, modules, func_list, method_list, module_type_list)
+
+
+def is_special_pattern_node(node, modules):
+    res_function, res_method, res_module = False, False, False
+    for checker in [
+        is_fixed_qparams_node,
+        is_default_node,
+        is_copy_node,
+        is_general_tensor_shape_node,
+        is_other_node,
+    ]:
+        is_call_function, is_call_method, is_call_module = checker(node, modules)
+        res_function = res_function or is_call_function
+        res_method = res_method or is_call_method
+        res_module = res_module or is_call_module
+    return res_function, res_method, res_module
+
+
+def is_dequantize_node(node):
+    return (
+        isinstance(node, Node)
+        and node.op == "call_method"
+        and node.target == "dequantize"
+    )
+
+
+def is_getattr_tensor_metadata_node(node):
+    return (
+        node.op == "call_function"
+        and node.target is getattr
+        and node.args[1] == "shape"
+    )
+
+
+def is_get_tensor_info_node(node):
+    return node.op == "call_method" and node.target in ["shape", "size"]
+
+
+def should_skip_lowering(op: torch.fx.node.Node, qconfig_map: dict[str, QConfigAny]):
+    """
+    Return True if the op is configured with a None qconfig, False otherwise.
+    Note: maybe need to generalize this to also check for the dtype, and we
+    only lower when dtype matches, but right now fbgemm/qnnpack only support
+    a single dtype, so it is OK for now.
+    """
+    return op.name in qconfig_map and qconfig_map[op.name] is None
+
+
+# Mapping from reference module class to the replacement static quantized module class for lowering
+STATIC_LOWER_MODULE_MAP: dict[type[nn.Module], type[WeightedQuantizedModule]] = {
+    nnqr.Linear: nnq.Linear,
+    nnqr.Conv1d: nnq.Conv1d,
+    nnqr.Conv2d: nnq.Conv2d,
+    nnqr.Conv3d: nnq.Conv3d,
+}
+
+# Mapping from reference module class to the replacement dynamic quantized module class for lowering
+DYNAMIC_LOWER_MODULE_MAP: dict[type[nn.Module], type[nn.Module]] = {
+    nnqr.Linear: nnqd.Linear,
+    nnqr.GRUCell: nnqd.GRUCell,
+    nnqr.LSTMCell: nnqd.LSTMCell,
+    nnqr.RNNCell: nnqd.RNNCell,
+    nnqr.LSTM: nnqd.LSTM,
+    nnqr.GRU: nnqd.GRU,
+}
+
+# Mapping from reference module class to the replacement weight only quantized module class for lowering
+# TODO: correct the namespace for these modules
+WEIGHT_ONLY_LOWER_MODULE_MAP: dict[type[nn.Module], type[nn.Module]] = {
+    nnqr.Embedding: nnq.Embedding,
+    nnqr.EmbeddingBag: nnq.EmbeddingBag,
+}
+
+# TODO: merge with STATIC_LOWER_MODULE_MAP after we merge
+# _lower_static_weighted_ref_module and special_pattern_replacement
+SPECIAL_PATTERN_LOWER_MODULE_MAP = {
+    nn.BatchNorm2d: nnq.BatchNorm2d,
+    nn.BatchNorm3d: nnq.BatchNorm3d,
+    nnqr.ConvTranspose1d: nnq.ConvTranspose1d,
+    nnqr.ConvTranspose2d: nnq.ConvTranspose2d,
+    nnqr.ConvTranspose3d: nnq.ConvTranspose3d,
+    nn.ELU: nnq.ELU,
+    nn.LeakyReLU: nnq.LeakyReLU,
+    nn.Hardswish: nnq.Hardswish,
+    nn.InstanceNorm1d: nnq.InstanceNorm1d,
+    nn.InstanceNorm2d: nnq.InstanceNorm2d,
+    nn.InstanceNorm3d: nnq.InstanceNorm3d,
+    nn.LayerNorm: nnq.LayerNorm,
+    nn.Dropout: nnq.Dropout,
+    nn.Softmax: nnq.Softmax,
+    nn.PReLU: nnq.PReLU,
+    nni.BNReLU2d: nniq.BNReLU2d,
+    nni.BNReLU3d: nniq.BNReLU3d,
+}
+
+# Mapping from fused module class to a 2-tuple of:
+#   1) The inner reference module class
+#   2) The replacement static quantized module class for lowering
+STATIC_LOWER_FUSED_MODULE_MAP: dict[
+    type[nn.Module], tuple[type[nn.Module], type[WeightedQuantizedModule]]
+] = {
+    nni.LinearReLU: (nnqr.Linear, nniq.LinearReLU),
+    # TODO: LinearLeakyReLU is registered as global but it is only fused and
+    # lowered when ondnn's backend config is used. Maybe need to separate
+    # registration and lowering functions for different backends in the future.
+    nni.LinearLeakyReLU: (nnqr.Linear, nniq.LinearLeakyReLU),
+    nni.LinearTanh: (nnqr.Linear, nniq.LinearTanh),
+    nni.ConvReLU1d: (nnqr.Conv1d, nniq.ConvReLU1d),
+    nni.ConvReLU2d: (nnqr.Conv2d, nniq.ConvReLU2d),
+    nni.ConvReLU3d: (nnqr.Conv3d, nniq.ConvReLU3d),
+}
+
+# The difference between STATIC_LOWER_FUSED_MODULE_TWO_INPUTS_MAP and STATIC_LOWER_FUSED_MODULE_MAP:
+# The refer node inside STATIC_LOWER_FUSED_MODULE_TWO_INPUTS_MAP has 2 inputs.
+# Mapping from fused module class to a 2-tuple of:
+#   1) The inner reference module class
+#   2) The replacement static quantized module class for lowering
+STATIC_LOWER_FUSED_MODULE_TWO_INPUTS_MAP: dict[
+    type[nn.Module], tuple[type[nn.Module], type[WeightedQuantizedModule]]
+] = {
+    nni.ConvAdd2d: (nnqr.Conv2d, nniq.ConvAdd2d),
+    nni.ConvAddReLU2d: (nnqr.Conv2d, nniq.ConvAddReLU2d),
+}
+
+# Mapping from fused module class to a 2-tuple of:
+#   1) The inner reference module class
+#   2) The replacement dynamic quantized module class for lowering
+DYNAMIC_LOWER_FUSED_MODULE_MAP: dict[
+    type[nn.Module], tuple[type[nn.Module], type[nn.Module]]
+] = {
+    nni.LinearReLU: (nnqr.Linear, nniqd.LinearReLU),
+}
+
+# Mapping from a functional to lower to a 2-tuple of
+#   1) The quantized version of the op
+#   2) The quantized version of the op fused with relu, if it exists, else None
+STATIC_LOWER_FUNCTIONAL_MAP: dict[Callable, tuple[Callable, Callable | None]] = {
+    F.linear: (torch.ops.quantized.linear, torch.ops.quantized.linear_relu),
+    F.conv1d: (torch.ops.quantized.conv1d, torch.ops.quantized.conv1d_relu),
+    F.conv2d: (torch.ops.quantized.conv2d, torch.ops.quantized.conv2d_relu),
+    F.conv3d: (torch.ops.quantized.conv3d, torch.ops.quantized.conv3d_relu),
+    F.conv_transpose1d: (torch.ops.quantized.conv_transpose1d, None),
+    F.conv_transpose2d: (torch.ops.quantized.conv_transpose2d, None),
+    F.conv_transpose3d: (torch.ops.quantized.conv_transpose3d, None),
+}
+
+WEIGHT_PREPACK_OPS: set[Callable] = {
+    torch._ops.ops.quantized.linear_prepack,
+    torch._ops.ops.quantized.linear_prepack_fp16,
+    torch._ops.ops.quantized.conv1d_prepack,
+    torch._ops.ops.quantized.conv2d_prepack,
+    torch._ops.ops.quantized.conv3d_prepack,
+    torch.ops.quantized.conv_transpose1d_prepack,
+    torch.ops.quantized.conv_transpose2d_prepack,
+    torch.ops.quantized.conv_transpose3d_prepack,
+}
+
+# Mapping from a functional to a dictionary, where the key is a 2-tuple of
+# (input_activation_dtype, weight_dtype) and the value is a 2-tuple of
+#   1) The dynamically quantized version of the op
+#   2) The dynamically quantized version of the op fused with relu, if it exists, else None
+DYNAMIC_LOWER_FUNCTIONAL_MAP: dict[
+    Callable, dict[tuple[torch.dtype, torch.dtype], tuple[Callable, Callable | None]]
+] = {
+    F.linear: {
+        (torch.quint8, torch.qint8): (
+            torch.ops.quantized.linear_dynamic,
+            torch.ops.quantized.linear_relu_dynamic,
+        ),
+        (torch.float16, torch.float16): (
+            torch.ops.quantized.linear_dynamic_fp16,
+            torch.ops.quantized.linear_relu_dynamic_fp16,
+        ),
+    },
+    # dynamic conv + relu is not available yet
+    F.conv1d: {
+        (torch.quint8, torch.qint8): (torch.ops.quantized.conv1d_dynamic, None),
+    },
+    F.conv2d: {
+        (torch.quint8, torch.qint8): (torch.ops.quantized.conv2d_dynamic, None),
+    },
+    F.conv3d: {
+        (torch.quint8, torch.qint8): (torch.ops.quantized.conv3d_dynamic, None),
+    },
+}
+
+CONV_FUNCTIONAL_OPS: set[Callable] = {
+    F.conv1d,
+    F.conv2d,
+    F.conv3d,
+}
+
+CONV_TRANSPOSE_FUNCTIONAL_OPS: set[Callable] = {
+    F.conv_transpose1d,
+    F.conv_transpose2d,
+    F.conv_transpose3d,
+}
+
+# TODO: add tests for lowering these ops
+QBIN_OP_MAPPING: dict[Callable | str, Callable] = {
+    operator.add: torch.ops.quantized.add,
+    torch.add: torch.ops.quantized.add,
+    operator.mul: torch.ops.quantized.mul,
+    operator.matmul: torch.ops.quantized.matmul,
+    torch.mul: torch.ops.quantized.mul,
+    torch.matmul: torch.ops.quantized.matmul,
+}
+QBIN_RELU_OP_MAPPING: dict[Callable | str, Callable] = {
+    operator.add: torch.ops.quantized.add_relu,
+    torch.add: torch.ops.quantized.add_relu,
+    operator.mul: torch.ops.quantized.mul_relu,
+    torch.mul: torch.ops.quantized.mul_relu,
+}
+
+ORIGINAL_WEIGHTS_LOOKUP = "original_weights_lookup"
+
+
+def _save_packed_weight(self, destination, prefix, keep_vars):
+    for attr_name in dir(self):
+        if "_packed_weight" in attr_name and isinstance(
+            getattr(self, attr_name), torch._C.ScriptObject
+        ):  # type: ignore[attr-defined]
+            packed_weight = getattr(self, attr_name)
+            destination[prefix + attr_name] = packed_weight
+
+
+def _load_packed_weight(
+    self,
+    state_dict,
+    prefix,
+    local_metadata,
+    strict,
+    missing_keys,
+    unexpected_keys,
+    error_msgs,
+):
+    attrs_to_pop = []
+    for attr_name in state_dict:
+        if attr_name.startswith("_packed_weight") and isinstance(
+            state_dict[attr_name], torch._C.ScriptObject
+        ):  # type: ignore[attr-defined] # noqa: B950
+            setattr(self, attr_name, state_dict[attr_name])
+            attrs_to_pop.append(attr_name)
+
+    # pop the packed param attributesn
+    for attr_name in attrs_to_pop:
+        state_dict.pop(attr_name)
+
+
+def fold_weight(
+    quantized_model: GraphModule,
+    node_name_to_scope: dict[str, tuple[str, type]],
+    keep_original_weights: bool = False,
+) -> GraphModule:
+    """
+    Trace back from the weight node util we hit getattr, reconstruct the
+    graph module with the traced nodes and run the graph module to pack the
+    weight. then replace the original chain of ops with the packed weight.
+    """
+    packed_weights = {}
+    # map from folded node name to the prepacked weight name
+    folded_nodes = {}
+    original_weights_lookup: dict[str, list] = {}
+    lookup_counter = 0
+    # get packed weights
+    for node in quantized_model.graph.nodes:
+        if node.op == "call_function" and node.target in WEIGHT_PREPACK_OPS:
+            nodes_to_fold = collect_producer_nodes(node)
+            if nodes_to_fold is not None:
+                for node_to_fold in nodes_to_fold:
+                    folded_nodes[node_to_fold.name] = node
+
+                prepacking_module = graph_module_from_producer_nodes(
+                    quantized_model, nodes_to_fold
+                )
+                packed_weight = prepacking_module()
+                packed_weights[node.name] = packed_weight
+                if keep_original_weights:
+                    original_weights = list(prepacking_module.state_dict().values())
+                    original_weights_lookup[str(lookup_counter)] = sorted(
+                        original_weights, key=lambda x: x.numel(), reverse=True
+                    )
+                    if len(original_weights_lookup[str(lookup_counter)]) == 1:
+                        # bias is None
+                        original_weights_lookup[str(lookup_counter)].append(None)
+                    lookup_counter += 1
+    lookup_counter = 0
+
+    # remove folded nodes and replace the prepacking node with getattr
+    folded_graph = Graph()
+    env: dict[Any, Any] = {}
+
+    def load_arg(a):
+        return map_arg(a, lambda node: env[node.name])
+
+    for node in quantized_model.graph.nodes:
+        prepack_node = folded_nodes.get(node.name, None)
+        if prepack_node is node:
+            packed_weight = packed_weights[node.name]
+            # add a prepacked attribute to root
+            op_node = next(iter(prepack_node.users))
+            module_path, _ = node_name_to_scope[op_node.name]
+            get_new_packed_weight_name = get_new_attr_name_with_prefix(
+                module_path + "_packed_weight_"
+            )
+            packed_weight_name = get_new_packed_weight_name(quantized_model)
+            setattr(quantized_model, packed_weight_name, packed_weight)
+            # replace prepack node with a getattr node
+            env[node.name] = folded_graph.create_node(
+                "get_attr", packed_weight_name, (), {}
+            )
+            if keep_original_weights:
+                key_name = (
+                    packed_weight_name.replace(":", "_")
+                    .replace("/", "_")
+                    .replace("|", "_")
+                    .replace(" ", "")
+                    .lower()
+                )
+                original_weights_lookup[key_name] = original_weights_lookup[
+                    str(lookup_counter)
+                ]
+                del original_weights_lookup[str(lookup_counter)]
+                lookup_counter += 1
+        elif prepack_node is not None:
+            # remove the fold node
+            continue
+        else:
+            # copy other nodes
+            env[node.name] = folded_graph.node_copy(node, load_arg)
+
+    quantized_model = GraphModule(quantized_model, folded_graph)
+    quantized_model._register_state_dict_hook(_save_packed_weight)
+    quantized_model.register_load_state_dict_pre_hook(_load_packed_weight)
+
+    if keep_original_weights:
+        setattr(  # noqa: B010
+            quantized_model, ORIGINAL_WEIGHTS_LOOKUP, original_weights_lookup
+        )
+
+    return quantized_model
+
+
+def _get_module(node: Node, modules: dict[str, nn.Module]) -> nn.Module | None:
+    """
+    Return the `torch.nn.Module` that corresponds to the specified node's target.
+    If no such node exists, return None.
+    """
+    if node.op == "call_module" and str(node.target) in modules:
+        return modules[str(node.target)]
+    else:
+        return None
+
+
+def _match_static_pattern(
+    node: Node,
+    modules: dict[str, nn.Module],
+    qconfig_map: dict[str, QConfigAny],
+    matching_modules_or_ops: list[Callable],
+    dequantize_node_arg_indices: list[int],
+) -> tuple[Node, Node, Node] | tuple[None, None, None]:
+    """
+    Match the pattern (dequantize - ref node - quantize) against the node provided.
+
+    If there is a match, return a 3-tuple of:
+      1) q_node: the quantize node,
+      2) relu_node: a relu node wrapping the ref_node, and
+      3) ref_node: a reference module or functional node to replace with its quantized counterpart
+    Otherwise, if there is no match, return a 3-tuple of (None, None, None).
+
+    Parameters:
+      node: The `torch.fx.Node` to match against.
+      modules: A mapping from node names to modules in the model graph, used for module lookup.
+      qconfig_map: A mapping from node names to the qconfigs associated with the nodes.
+          If the corresponding qconfig for the reference node is None, then return no match.
+      matching_modules_or_ops: Either a list of functions or a list of `torch.nn.Module`s.
+          If the reference node is not in this list, then return no match.
+      dequantize_node_arg_indices: A list of indices in the reference node args where dequantize
+          nodes may be present. An empty list means skipping the check for dequantize nodes.
+    """
+    SKIP_LOWERING_VALUE = (None, None, None)
+
+    # Match quantize node
+    if node.op != "call_function" or node.target != torch.quantize_per_tensor:
+        return SKIP_LOWERING_VALUE
+    q_node = node
+    ref_node = q_node.args[0]
+    if not isinstance(ref_node, Node):
+        raise AssertionError("Expected the reference node to be a torch.fx Node")
+
+    # Handle cases where the node is wrapped in a ReLU
+    if (ref_node.op == "call_function" and ref_node.target in (F.relu, torch.relu)) or (
+        ref_node.op == "call_module" and type(_get_module(ref_node, modules)) is nn.ReLU
+    ):
+        relu_node = ref_node
+        ref_node = relu_node.args[0]
+        if not isinstance(ref_node, Node):
+            raise AssertionError(
+                "Expected the reference node after ReLU to be a torch.fx Node"
+            )
+    else:
+        relu_node = None
+    if should_skip_lowering(ref_node, qconfig_map):
+        return SKIP_LOWERING_VALUE
+
+    # Match reference module or functional
+    if isinstance(matching_modules_or_ops[0], type) and issubclass(
+        matching_modules_or_ops[0], nn.Module
+    ):
+        expected_op = "call_module"
+        match_key = type(_get_module(ref_node, modules))
+    else:
+        expected_op = "call_function"
+        match_key = ref_node.target  # type: ignore[assignment]
+    if ref_node.op != expected_op or match_key not in matching_modules_or_ops:
+        return SKIP_LOWERING_VALUE
+
+    # Match dequantize node(s). Both of the following conditions must pass:
+    # (1) All `torch.fx.Node`s at the matching indices must be a dequantize node
+    # (2) There must be at least one dequantize node
+    matched_dequantize = False
+    for i in dequantize_node_arg_indices:
+        if i >= len(ref_node.args):
+            raise AssertionError(
+                f"Dequantize index {i} exceeded reference node's arg length {len(ref_node.args)}"
+            )
+        arg = ref_node.args[i]
+        if is_dequantize_node(arg):
+            matched_dequantize = True
+        elif isinstance(arg, Node):
+            return SKIP_LOWERING_VALUE
+    if not matched_dequantize:
+        return SKIP_LOWERING_VALUE
+
+    return (q_node, relu_node, ref_node)  # type: ignore[return-value]
+
+
+def _match_static_pattern_with_two_inputs(
+    node: Node,
+    modules: dict[str, nn.Module],
+    qconfig_map: dict[str, QConfigAny],
+    matching_modules_or_ops: list[Callable],
+) -> tuple[Node, Node] | tuple[None, None]:
+    """
+                      (dequantize \
+    Match the pattern (dequantize - ref node - quantize) against the node provided.
+
+    If there is a match, return a 2-tuple of:
+      1) q_node: the quantize node,
+      2) ref_node: a reference module or functional node to replace with its quantized counterpart
+    Otherwise, if there is no match, return a 2-tuple of (None, None).
+
+    Parameters:
+      node: The `torch.fx.Node` to match against.
+      modules: A mapping from node names to modules in the model graph, used for module lookup.
+      qconfig_map: A mapping from node names to the qconfigs associated with the nodes.
+          If the corresponding qconfig for the reference node is None, then return no match.
+      matching_modules_or_ops: Either a list of functions or a list of `torch.nn.Module`s.
+          If the reference node is not in this list, then return no match.
+    """
+    SKIP_LOWERING_VALUE = (None, None)
+
+    # Match quantize node
+    if node.op != "call_function" or node.target != torch.quantize_per_tensor:
+        return SKIP_LOWERING_VALUE
+    q_node = node
+    ref_node = q_node.args[0]
+    if not isinstance(ref_node, Node):
+        raise AssertionError("Expected the reference node to be a torch.fx Node")
+
+    if should_skip_lowering(ref_node, qconfig_map):
+        return SKIP_LOWERING_VALUE
+
+    # Match reference module or functional
+    if isinstance(matching_modules_or_ops[0], type) and issubclass(
+        matching_modules_or_ops[0], nn.Module
+    ):
+        expected_op = "call_module"
+        match_key = type(_get_module(ref_node, modules))
+    else:
+        # This pass only support op of "call_module"
+        return SKIP_LOWERING_VALUE
+
+    if ref_node.op != expected_op or match_key not in matching_modules_or_ops:
+        return SKIP_LOWERING_VALUE
+
+    # Check ref_node has 2 input nodes, both are dq node.
+    if len(ref_node.args) != 2:
+        return SKIP_LOWERING_VALUE
+    for i in range(len(ref_node.args)):
+        arg = ref_node.args[i]
+        if not is_dequantize_node(arg):
+            return SKIP_LOWERING_VALUE
+
+    return (q_node, ref_node)
+
+
+def _lower_static_weighted_ref_module(
+    model: GraphModule, qconfig_map: dict[str, QConfigAny]
+):
+    """
+    Traverse the graph and find dequantize - ref module - quantize patterns
+    and replace them with the quantized version of the ref module.
+    """
+    modules = dict(model.named_modules(remove_duplicate=False))
+    for n in model.graph.nodes:
+        # Step 0: Find nodes that match this pattern (dequantize - ref module - quantize)
+        matching_modules = list(STATIC_LOWER_MODULE_MAP.keys()) + list(
+            STATIC_LOWER_FUSED_MODULE_MAP.keys()
+        )
+        q_node, _relu_node, ref_node = _match_static_pattern(
+            n,
+            modules,
+            qconfig_map,
+            matching_modules,  # type: ignore[arg-type]
+            dequantize_node_arg_indices=[0],
+        )
+        if q_node is None:
+            continue
+        if ref_node is None:
+            raise AssertionError(
+                "Expected a reference node when matching static pattern"
+            )
+        (_, scale_node, zero_point_node, _) = q_node.args
+        ref_module = _get_module(ref_node, modules)
+        ref_class = type(ref_module)
+        if not isinstance(scale_node, Node):
+            raise AssertionError("Expected scale_node to be a Node")
+        if not isinstance(zero_point_node, Node):
+            raise AssertionError("Expected zero_point_node to be a Node")
+        if not issubclass(ref_class, nn.Module):
+            raise AssertionError(
+                "Expected reference module class to be a subclass of nn.Module"
+            )
+
+        # Step 1: Change this pattern to use the corresponding quantized module
+        # For fused modules, we also check whether the inner module is a reference module
+        # If so, we replace the entire fused module with the corresponding quantized module
+        if ref_class in STATIC_LOWER_FUSED_MODULE_MAP:
+            inner_ref_class, q_class = STATIC_LOWER_FUSED_MODULE_MAP[ref_class]
+            if type(ref_module[0]) is not inner_ref_class:  # type: ignore[index]
+                continue
+        else:
+            q_class = STATIC_LOWER_MODULE_MAP[ref_class]
+        output_scale = getattr(model, scale_node.target)  # type: ignore[arg-type]
+        output_zero_point = getattr(model, zero_point_node.target)  # type: ignore[arg-type]
+        q_module = q_class.from_reference(ref_module, output_scale, output_zero_point)
+        # replace reference module with quantized module
+        parent_name, module_name = _parent_name(ref_node.target)
+        setattr(modules[parent_name], module_name, q_module)
+
+        # Step 2: Reroute around dq_node, and remove q_node and its args
+        if len(ref_node.args) != 1:
+            raise AssertionError("Expected reference node to have exactly 1 arg")
+        dq_node = ref_node.args[0]
+        if not isinstance(dq_node, Node):
+            raise AssertionError("Expected dq_node to be a Node")
+        ref_node.replace_input_with(dq_node, dq_node.args[0])  # type: ignore[arg-type]
+        q_node.replace_all_uses_with(ref_node)
+        model.graph.erase_node(q_node)
+        model.graph.erase_node(scale_node)
+        model.graph.erase_node(zero_point_node)
+
+
+def _lower_static_weighted_ref_module_with_two_inputs(
+    model: GraphModule, qconfig_map: dict[str, QConfigAny]
+):
+    """
+    Traverse the graph and find patterns
+    dequantize   dequantize
+       \\         //
+        ref module
+            \\
+          quantize
+    and replace them with the quantized version of the ref module.
+    """
+    modules = dict(model.named_modules(remove_duplicate=False))
+    for n in model.graph.nodes:
+        #                                            (dequantize \
+        # Step 0: Find nodes that match this pattern (dequantize - ref module - quantize)
+        matching_modules = list(STATIC_LOWER_FUSED_MODULE_TWO_INPUTS_MAP.keys())
+        (q_node, ref_node) = _match_static_pattern_with_two_inputs(
+            n,
+            modules,
+            qconfig_map,
+            matching_modules,  # type: ignore[arg-type]
+        )
+        if q_node is None:
+            continue
+        if ref_node is None:
+            raise AssertionError(
+                "Expected a reference node when matching static pattern with two inputs"
+            )
+        (_, scale_node, zero_point_node, _) = q_node.args
+        ref_module = _get_module(ref_node, modules)
+        ref_class = type(ref_module)
+        if not isinstance(scale_node, Node):
+            raise AssertionError("Expected scale_node to be a Node")
+        if not isinstance(zero_point_node, Node):
+            raise AssertionError("Expected zero_point_node to be a Node")
+        if not issubclass(ref_class, nn.Module):
+            raise AssertionError(
+                "Expected reference module class to be a subclass of nn.Module"
+            )
+
+        # Step 1: Change this pattern to use the corresponding quantized module
+        # For fused modules, we also check whether the inner module is a reference module
+        # If so, we replace the entire fused module with the corresponding quantized module
+        if ref_class in STATIC_LOWER_FUSED_MODULE_TWO_INPUTS_MAP:
+            inner_ref_class, q_class = STATIC_LOWER_FUSED_MODULE_TWO_INPUTS_MAP[
+                ref_class
+            ]
+            if type(ref_module[0]) is not inner_ref_class:  # type: ignore[index]
+                continue
+        else:
+            continue
+        output_scale = getattr(model, scale_node.target)  # type: ignore[arg-type]
+        output_zero_point = getattr(model, zero_point_node.target)  # type: ignore[arg-type]
+        q_module = q_class.from_reference(ref_module, output_scale, output_zero_point)
+        # replace reference module with quantized module
+        parent_name, module_name = _parent_name(ref_node.target)
+        setattr(modules[parent_name], module_name, q_module)
+
+        # Step 2: Reroute around dq_node, and remove q_node and its args
+        if len(ref_node.args) != 2:
+            raise AssertionError("Expected reference node to have exactly 2 args")
+        for arg in ref_node.args:
+            if not is_dequantize_node(arg):
+                continue
+            dq_node = arg
+            if not isinstance(dq_node, Node):
+                raise AssertionError("Expected dq_node to be a Node")
+            ref_node.replace_input_with(dq_node, dq_node.args[0])  # type: ignore[arg-type]
+
+        q_node.replace_all_uses_with(ref_node)
+        model.graph.erase_node(q_node)
+        model.graph.erase_node(scale_node)
+        model.graph.erase_node(zero_point_node)
+
+
+def _lower_dynamic_weighted_ref_module(model: GraphModule):
+    """
+    Traverse the graph and find quantize_per_tensor_dynamic - dequantize - ref_module patterns
+    and replace them with the dynamically quantized version of the ref module.
+    """
+    named_modules = dict(model.named_modules(remove_duplicate=False))
+    for n in model.graph.nodes:
+        if n.op != "call_module" or type(named_modules[str(n.target)]) not in set(
+            DYNAMIC_LOWER_MODULE_MAP.keys()
+        ).union(set(DYNAMIC_LOWER_FUSED_MODULE_MAP.keys())):
+            continue
+        ref_node = n
+        dq_node = ref_node.args[0]
+        if dq_node.op != "call_method" or dq_node.target != "dequantize":
+            continue
+
+        input_dynamic_q_node = dq_node.args[0]
+
+        if (
+            input_dynamic_q_node.op != "call_function"
+            or input_dynamic_q_node.target != torch.quantize_per_tensor_dynamic
+        ):
+            continue
+
+        activation_dtype = input_dynamic_q_node.args[1]
+        is_fp16 = activation_dtype == torch.float16
+        is_int8 = activation_dtype in [torch.quint8, torch.qint8]
+        if not is_int8 and not is_fp16:
+            continue
+
+        ref_module = named_modules[str(ref_node.target)]
+        ref_class = type(ref_module)
+        if ref_class in DYNAMIC_LOWER_FUSED_MODULE_MAP:
+            inner_ref_class, q_class = DYNAMIC_LOWER_FUSED_MODULE_MAP[ref_class]
+            if type(ref_module[0]) is not inner_ref_class:
+                continue
+        else:
+            q_class = DYNAMIC_LOWER_MODULE_MAP.get(ref_class)  # type: ignore[assignment]
+        # TODO: maybe define a WeightedDynamicallyQuantizedModule
+        q_module = q_class.from_reference(ref_module)  # type: ignore[attr-defined]
+
+        # replace reference module with dynamically quantized module
+        parent_name, module_name = _parent_name(ref_node.target)
+        setattr(named_modules[parent_name], module_name, q_module)
+        ref_node.replace_input_with(dq_node, input_dynamic_q_node.args[0])
+
+
+def _lower_weight_only_weighted_ref_module(model: GraphModule):
+    """
+    Traverse the graph and find ref_module patterns
+    and replace them with the weight only quantized version of the ref module.
+    """
+    named_modules = dict(model.named_modules(remove_duplicate=False))
+    for n in model.graph.nodes:
+        if n.op != "call_module" or type(named_modules[str(n.target)]) not in set(
+            WEIGHT_ONLY_LOWER_MODULE_MAP.keys()
+        ):
+            continue
+        ref_node = n
+        ref_module = named_modules[str(ref_node.target)]
+        ref_class = type(ref_module)
+        q_class = WEIGHT_ONLY_LOWER_MODULE_MAP.get(ref_class)
+        # TODO: WeightedQuantizedModule is currently assuming static quant apis
+        # with output_scale, output_zero_point in from_reference, we may want to
+        # relax that, or rename this
+        # TODO: maybe define a WeightedWeightOnlyQuantizedModule
+        q_module = q_class.from_reference(ref_module)  # type: ignore[union-attr]
+
+        # replace reference module with dynamically quantized module
+        parent_name, module_name = _parent_name(ref_node.target)
+        setattr(named_modules[parent_name], module_name, q_module)
+
+
+def _lower_static_weighted_ref_functional(
+    model: GraphModule, qconfig_map: dict[str, QConfigAny]
+):
+    """
+    Traverse the graph and replace functional reference patterns with their quantized versions.
+    """
+    modules = dict(model.named_modules(remove_duplicate=False))
+    for n in model.graph.nodes:
+        # Step 0: Find nodes that match this pattern (dequantize - functional op - quantize)
+        matching_ops = list(STATIC_LOWER_FUNCTIONAL_MAP.keys())
+        (q_node, relu_node, func_node) = _match_static_pattern(
+            n, modules, qconfig_map, matching_ops, dequantize_node_arg_indices=[0, 1]
+        )
+        if q_node is None:
+            continue
+        if func_node is None:
+            raise AssertionError(
+                "Expected a function node when matching static functional pattern"
+            )
+        (_, output_scale_node, output_zp_node, _) = q_node.args
+        (input_dq_node, weight_dq_node, *remaining_func_args) = func_node.args
+        if not isinstance(output_zp_node, Node):
+            raise AssertionError("Expected output_zp_node to be a Node")
+        if not isinstance(input_dq_node, Node):
+            raise AssertionError("Expected input_dq_node to be a Node")
+        if not isinstance(weight_dq_node, Node):
+            raise AssertionError("Expected weight_dq_node to be a Node")
+        quantized_weight = weight_dq_node.args[0]
+        if not isinstance(quantized_weight, Node):
+            raise AssertionError("Expected quantized_weight to be a Node")
+        if quantized_weight.op != "call_function" or quantized_weight.target not in (
+            torch.quantize_per_tensor,
+            torch.quantize_per_channel,
+        ):
+            continue
+
+        # Step 1: Replace quantized weights with packed weights, which will be folded later
+        # Use the right prepack op and prepare the corresponding args
+        # Linear prepack args: (quantized weights[, bias])
+        # Conv prepack args: (quantized weights[, bias, stride, padding, dilation, groups])
+        prepack_args = [quantized_weight] + remaining_func_args
+        if func_node.target is F.linear:
+            weight_dtype = quantized_weight.args[-1]
+            prepack_op = get_linear_prepack_op_for_dtype(weight_dtype)
+        elif func_node.target in CONV_FUNCTIONAL_OPS:
+            prepack_op = get_qconv_prepack_op(func_node.target)  # type: ignore[arg-type]
+            # For conv1d, the stride, padding, and dilation args may be ints,
+            # in which case we need to convert them to tuples
+            if func_node.target is F.conv1d:
+                for i in [2, 3, 4]:
+                    if len(prepack_args) > i and isinstance(prepack_args[i], int):
+                        prepack_args[i] = (prepack_args[i],)
+        elif func_node.target in CONV_TRANSPOSE_FUNCTIONAL_OPS:
+            prepack_op = get_qconv_prepack_op(func_node.target)  # type: ignore[arg-type]
+            # For conv_transpose1d, the stride, padding, and dilation args may be ints,
+            # in which case we need to convert them to tuples
+            if func_node.target is F.conv_transpose1d:
+                # Note prepack_args[5] is groups.
+                for i in [2, 3, 4, 6]:
+                    if len(prepack_args) > i and isinstance(prepack_args[i], int):
+                        prepack_args[i] = (prepack_args[i],)
+            # swap dilation and groups
+            # prepack op has arguments: {w, b, stride, padding, output_padding, dilation, groups}
+            # transposed conv op has arguments: {x, w, b, stride, padding, output_padding, groups, dilation}
+            if len(prepack_args) > 6:
+                prepack_args[5], prepack_args[6] = prepack_args[6], prepack_args[5]
+        else:
+            raise ValueError(f"Lowering is not supported for op '{func_node.target}'")
+        with model.graph.inserting_before(output_scale_node):  # type: ignore[arg-type]
+            # kwargs of the func node are needed for prepack op (i.e., quantized::linear_prepack)
+            # They are not needed for compute op (i.e., quantized::linear)
+            kwargs = func_node.kwargs
+            # F.linear uses 'bias' key for bias while qlinear_prepack uses 'B' for bias
+            if func_node.target is F.linear and "bias" in kwargs:
+                kwargs = kwargs.copy()
+                kwargs["B"] = kwargs["bias"]
+                del kwargs["bias"]
+            packed_weight = model.graph.create_node(
+                "call_function", prepack_op, tuple(prepack_args), kwargs
+            )
+
+        # Step 2: Replace reference pattern with the corresponding quantized op
+        (q_func, q_relu_func) = STATIC_LOWER_FUNCTIONAL_MAP[func_node.target]  # type: ignore[index]
+        # conv_transpose does not support fusion with relu yet. q_relu_func is None in such cases
+        if q_relu_func is not None:
+            func_node.target = q_relu_func if relu_node is not None else q_func
+        else:
+            func_node.target = q_func
+        func_node.args = (
+            input_dq_node.args[0],
+            packed_weight,
+            output_scale_node,
+            output_zp_node,
+        )
+        # kwargs for func_node has been moved to kwargs for prepack op
+        func_node.kwargs = {}
+        q_node.replace_all_uses_with(func_node)
+        # Move func_node after output_zp_node in the graph
+        output_zp_node.append(func_node)
+
+        # Clean up: Remove quantize node, and the relu node if it exists
+        model.graph.erase_node(q_node)
+        if relu_node is not None and q_relu_func is not None:
+            model.graph.erase_node(relu_node)
+
+
+def _lower_dynamic_weighted_ref_functional(
+    model: GraphModule, qconfig_map: dict[str, QConfigAny]
+):
+    """
+    Traverse the graph and replace functional reference patterns with their dynamically
+    quantized versions.
+    Examples:
+    quantize_per_tensor_dynamic - dequantize - functional linear --> linear_dynamic
+    to(torch.float16) - dequantize - functional linear --> linear_dynamic_fp16
+    """
+    modules = dict(model.named_modules(remove_duplicate=False))
+    # we want to search in reserved order so that we can match the larger patterns first
+    # e.g. we want to match linear - relu before linear.
+    for n in reversed(model.graph.nodes):
+        # Step 0: Find nodes that match this pattern
+        # (quantize_per_tensor_dynamic - dequantize - dynamically quantized op)
+        # We search for the pattern backwards, starting with the quantize node
+        # Quantize node args: (func, scale, zp, dtype)
+        func_node = n
+        # Handle cases where the functional op is wrapped in a ReLU
+        if (
+            func_node.op == "call_function"
+            and func_node.target is F.relu
+            or func_node.op == "call_module"
+            and type(modules[str(func_node.target)]) is torch.nn.ReLU
+        ):
+            relu_node = func_node
+            func_node = relu_node.args[0]
+        else:
+            relu_node = None
+        if should_skip_lowering(func_node, qconfig_map):
+            continue
+        # Linear args: (dequantized inputs, dequantized weights[, bias])
+        # Conv args: (dequantized inputs, dequantized weights[, bias, stride, padding, dilation, groups])
+        if (
+            func_node.op != "call_function"
+            or func_node.target not in DYNAMIC_LOWER_FUNCTIONAL_MAP
+        ):
+            continue
+        (input_dq_node, weight_dq_node, *remaining_func_args) = func_node.args
+        if (
+            input_dq_node.op != "call_method"
+            or input_dq_node.target != "dequantize"
+            or weight_dq_node.op != "call_method"
+            or weight_dq_node.target != "dequantize"
+        ):
+            continue
+
+        input_dynamic_q_node = input_dq_node.args[0]
+
+        if (
+            input_dynamic_q_node.op != "call_function"
+            or input_dynamic_q_node.target != torch.quantize_per_tensor_dynamic
+        ):
+            continue
+
+        reduce_range_node = None
+        (pattern_input, activation_dtype, reduce_range_node) = input_dynamic_q_node.args
+        is_fp16 = activation_dtype == torch.float16
+        is_int8 = activation_dtype in [torch.quint8, torch.qint8]
+        if not is_int8 and not is_fp16:
+            continue
+
+        quantized_weight = weight_dq_node.args[0]
+        weight_dtype = quantized_weight.args[-1]
+
+        # Step 1: Try to select reference pattern with the corresponding quantized op
+        dynamic_quant_dtype_key = (activation_dtype, weight_dtype)
+        if (
+            dynamic_quant_dtype_key
+            not in DYNAMIC_LOWER_FUNCTIONAL_MAP[func_node.target]
+        ):
+            print(
+                f"Didn't find dtype combination {dynamic_quant_dtype_key} during "
+                f"dynamic quantized op lowering for {func_node.target}"
+            )
+            continue
+        (q_func, q_relu_func) = DYNAMIC_LOWER_FUNCTIONAL_MAP[func_node.target][
+            dynamic_quant_dtype_key
+        ]
+
+        if q_func is None or q_relu_func is None:
+            print(
+                "Didn't find corresponding quantized function or quantized relu function "
+                f"for {func_node.target}, {dynamic_quant_dtype_key}"
+            )
+            continue
+
+        # Step 2: Replace quantized weights with packed weights, which will be folded later
+        # Use the right prepack op and prepare the corresponding args
+        # Linear prepack args: (quantized weights[, bias])
+        # Conv prepack args: (quantized weights[, bias, stride, padding, dilation, groups])
+        prepack_args = [quantized_weight] + remaining_func_args
+        prepack_kwargs = {}
+        if func_node.target is F.linear:
+            prepack_op = get_linear_prepack_op_for_dtype(weight_dtype)
+            kwargs = func_node.kwargs.copy()
+            if "bias" in kwargs:
+                prepack_kwargs["B"] = kwargs["bias"]
+                del kwargs["bias"]
+                func_node.kwargs = kwargs
+        elif func_node.target in CONV_FUNCTIONAL_OPS:
+            prepack_op = get_qconv_prepack_op(func_node.target)
+            # For conv1d, the stride, padding, and dilation args may be ints,
+            # in which case we need to convert them to tuples
+            if func_node.target is F.conv1d:
+                for i in [2, 3, 4]:
+                    if len(prepack_args) > i and isinstance(prepack_args[i], int):
+                        prepack_args[i] = (prepack_args[i],)
+        else:
+            raise ValueError(f"Lowering is not supported for op '{func_node.target}'")
+        with model.graph.inserting_before(func_node):
+            packed_weight = model.graph.create_node(
+                "call_function", prepack_op, tuple(prepack_args), prepack_kwargs
+            )
+
+        # Step 3: Replace reference pattern with the corresponding quantized op
+        func_node.target = q_relu_func if relu_node is not None else q_func
+        if is_int8:
+            func_node.args = (pattern_input, packed_weight, reduce_range_node)
+        else:
+            func_node.args = (pattern_input, packed_weight)
+
+        if relu_node is not None:
+            relu_node.replace_all_uses_with(func_node)
+
+        # Step 4: Remove the relu node if it exists
+        if relu_node is not None:
+            model.graph.erase_node(relu_node)
+
+
+def _lower_quantized_binary_op(model: GraphModule, qconfig_map: dict[str, QConfigAny]):
+    binary_ops_to_lower: list[Callable] = [
+        operator.add,
+        torch.add,
+        operator.mul,
+        torch.mul,
+        torch.matmul,
+    ]
+    modules = dict(model.named_modules(remove_duplicate=False))
+    for n in model.graph.nodes:
+        # Step 0: Find nodes that match this pattern (dequantize - ref module - quantize)
+        (q_node, relu_node, bop_node) = _match_static_pattern(
+            n,
+            modules,
+            qconfig_map,
+            binary_ops_to_lower,
+            dequantize_node_arg_indices=[0, 1],
+        )
+        if q_node is None:
+            continue
+        if bop_node is None:
+            raise AssertionError(
+                "Expected a binary op node when matching quantized binary op pattern"
+            )
+        (_, scale_node, zero_point_node, _) = q_node.args
+
+        # Step 1: Remove dequant nodes
+        num_dq_nodes = 0
+        for arg in bop_node.args:
+            if not is_dequantize_node(arg):
+                continue
+            dq_node = arg
+            if not isinstance(dq_node, Node):
+                raise AssertionError("Expected dq_node to be a Node")
+            dn_input = dq_node.args[0]
+            bop_node.replace_input_with(dq_node, dn_input)  # type: ignore[arg-type]
+            num_dq_nodes += 1
+        if num_dq_nodes <= 0:
+            raise AssertionError(
+                "Expected at least one dequantize node in binary op args"
+            )
+
+        # Step 2: Swap binary op to quantized binary op
+        if bop_node.target not in QBIN_OP_MAPPING:
+            raise AssertionError(
+                f"Unsupported binary op {bop_node.target} for lowering"
+            )
+        binop_to_qbinop = QBIN_OP_MAPPING if relu_node is None else QBIN_RELU_OP_MAPPING
+        qbin_op = binop_to_qbinop[bop_node.target]
+        # prepare the args for quantized binary op
+        # (x, y)
+        qop_node_args = list(bop_node.args)
+        # (x, y, scale, zero_point)
+        # add scale and zero_point arguments for Tensor - Tensor operation
+        if num_dq_nodes == 2:
+            qop_node_args.extend([scale_node, zero_point_node])
+        # insert a call to quantized binary op and remove the original binary op
+        with model.graph.inserting_after(q_node):
+            qop_node = create_node_from_old_node_preserve_meta(
+                model.graph,
+                ("call_function", qbin_op, tuple(qop_node_args), {}),
+                bop_node,
+            )
+            q_node.replace_all_uses_with(qop_node)
+
+        # Step 3: Remove quantize node, binary op node, and relu node if any
+        model.graph.erase_node(q_node)
+        if relu_node is not None:
+            model.graph.erase_node(relu_node)
+        model.graph.erase_node(bop_node)
+
+
+def special_pattern_replacement(model: GraphModule):
+    modules = dict(model.named_modules(remove_duplicate=False))
+    for n in model.graph.nodes:
+        q_node = n
+        is_quantize = q_node.target is torch.quantize_per_tensor
+        is_to_fp16 = (
+            q_node.op == "call_method"
+            and q_node.target == "to"
+            and len(q_node.args) == 2
+            and q_node.args[1] == torch.float16
+        )
+        # Only continue when neither quantize nor to_fp16
+        if not is_quantize and not is_to_fp16:
+            continue
+        ref_node = q_node.args[0]
+        # get output scale/zero_point/dtype from the quantize node
+        # ref_node, scale_node, zero_point_node, dtype = q_node.args
+        # TODO: add safety checks that users for the ref_node and dq_node needs to be one
+        is_call_function, is_call_method, is_call_module = is_fixed_qparams_node(
+            ref_node, modules
+        )
+        if is_to_fp16 and (is_call_function or is_call_method or is_call_module):
+            # TODO: add a warning or error out here? (bc-breaking if error out)
+            # warnings.warn(
+            #     "Only reference patterns are currently supported for {dtype} dtype with {op} op"
+            #     "".format(dtype=dtypes, op=ref_node))
+            continue
+
+        is_call_function, is_call_method, is_call_module = is_default_node(
+            ref_node, modules
+        )
+        if is_to_fp16 and (is_call_function or is_call_method or is_call_module):
+            # TODO: add a warning or error out here? (bc-breaking if error out)
+            continue
+
+        # This check includes all supported ops
+        is_call_function, is_call_method, is_call_module = is_special_pattern_node(
+            ref_node, modules
+        )
+        if not (is_call_module or is_call_function or is_call_method):
+            continue
+        if len(ref_node.args) <= 0 and len(ref_node.kwargs) <= 0:
+            raise AssertionError("Expected ref_node to have args or kwargs")
+        dq_node_or_nodes = (
+            ref_node.args[0]
+            if len(ref_node.args) > 0
+            else next(iter(ref_node.kwargs.values()))
+        )
+        if not isinstance(dq_node_or_nodes, (Node, tuple, list)):
+            raise AssertionError(
+                "Expected dq_node_or_nodes to be a Node, tuple, or list"
+            )
+        is_dequantize = False
+        if isinstance(dq_node_or_nodes, Node):
+            is_dequantize = (
+                dq_node_or_nodes.op == "call_method"
+                and dq_node_or_nodes.target == "dequantize"
+            )
+        elif isinstance(dq_node_or_nodes, (tuple, list)):
+            is_dequantize = all(
+                x.op == "call_method" and x.target == "dequantize"
+                for x in dq_node_or_nodes
+            )
+
+        if not is_dequantize:
+            continue
+
+        # TODO: enable we have patterns that needs to swap the modules
+        if is_call_module:
+            ref_module = modules[ref_node.target]
+            if type(ref_module) in SPECIAL_PATTERN_LOWER_MODULE_MAP and is_quantize:
+                qmodule_cls = SPECIAL_PATTERN_LOWER_MODULE_MAP.get(type(ref_module))
+                scale_node = q_node.args[1]
+                zero_point_node = q_node.args[2]
+                output_scale = getattr(model, scale_node.target)
+                output_zero_point = getattr(model, zero_point_node.target)
+
+                qmodule = qmodule_cls.from_reference(  # type:ignore[union-attr]
+                    ref_module, output_scale, output_zero_point
+                )
+                # replace reference module with quantized module
+                parent_name, module_name = _parent_name(ref_node.target)
+                setattr(modules[parent_name], module_name, qmodule)
+
+        # reroute around dq node:
+        dq_nodes: list[Node] = []
+        if isinstance(dq_node_or_nodes, Node):
+            dq_nodes = [dq_node_or_nodes]
+        elif isinstance(dq_node_or_nodes, (tuple, list)):
+            dq_nodes = list(dq_node_or_nodes)
+
+        for dq_node in dq_nodes:
+            dn_input = dq_node.args[0]
+            ref_node.replace_input_with(dq_node, dn_input)
+
+        # store q node args
+        qnode_qparams = list(q_node.args)[1:]
+        # replace uses of q node with input and remove q node
+        q_node_input = q_node.args[0]
+        q_node.replace_all_uses_with(q_node_input)
+        model.graph.erase_node(q_node)
+
+        is_call_function, is_call_method, is_call_module = is_default_node(
+            ref_node, modules
+        )
+        if is_call_function:
+            # pass scale/zer_point arguments from quantize_per_tensor to the default node operator
+            # insert an op after the zero_point node so that the scale/zero_point
+            # nodes are is available
+            qop = get_quantized_operator(ref_node.target)
+            args = list(ref_node.args)
+            kwargs = dict(ref_node.kwargs)
+            if qop in QOP_TO_ARG_NAMES_TO_SKIP:
+                args_to_skip = QOP_TO_ARG_NAMES_TO_SKIP[qop]
+                for arg in args_to_skip:
+                    if arg in kwargs:
+                        kwargs.pop(arg)
+            kwargs["output_scale"] = qnode_qparams[0]
+            kwargs["output_zero_point"] = qnode_qparams[1]
+            with model.graph.inserting_after(qnode_qparams[1]):
+                qop_node = create_node_from_old_node_preserve_meta(
+                    model.graph, ("call_function", qop, tuple(args), kwargs), ref_node
+                )
+                ref_node.replace_all_uses_with(qop_node)
+                model.graph.erase_node(ref_node)
+        else:
+            # remove scale/zero_point node for quantize node
+            for n in qnode_qparams:
+                if isinstance(n, Node):
+                    model.graph.erase_node(n)
+
+    return model
+
+
+def _lower_getattr_tensor_metadta_op(model: GraphModule):
+    """Modified the graph of the model inplace, to skip extra dequantize op before
+    the general tensor shape ops when possible
+    """
+    for n in model.graph.nodes:
+        if is_getattr_tensor_metadata_node(n):
+            maybe_dq = n.args[0]
+            if maybe_dq.op != "call_method" or maybe_dq.target != "dequantize":
+                continue
+            # skip the dequantize node
+            args = list(n.args)
+            args[0] = n.args[0].args[0]
+            n.args = tuple(args)
+
+
+def _lower_get_tensor_info_op(model: GraphModule):
+    """Modified the graph of the model inplace, to skip extra dequantize op before
+    the general tensor shape ops when possible
+    """
+    for n in model.graph.nodes:
+        if not is_get_tensor_info_node(n):
+            continue
+        maybe_dq = n.args[0]
+        if maybe_dq.op != "call_method" or maybe_dq.target != "dequantize":
+            continue
+        # skip the dequantize node
+        args = list(n.args)
+        args[0] = n.args[0].args[0]
+        n.args = tuple(args)
+
+
+def _lower_to_native_backend(
+    model: GraphModule,
+    qconfig_map: dict[str, QConfigAny],
+    node_name_to_scope: dict[str, tuple[str, type]],
+    keep_original_weights: bool = False,
+) -> GraphModule:
+    """Lower a quantized reference model (with reference quantized operator patterns)
+    to the native backend in PyTorch (fbgemm/qnnpack), both backends shares the same
+    operator signature so they can be lowered with the same function
+    """
+    _lower_static_weighted_ref_module(model, qconfig_map)
+    _lower_static_weighted_ref_module_with_two_inputs(model, qconfig_map)
+    _lower_dynamic_weighted_ref_module(model)
+    _lower_weight_only_weighted_ref_module(model)
+    _lower_static_weighted_ref_functional(model, qconfig_map)
+    _lower_dynamic_weighted_ref_functional(model, qconfig_map)
+    _lower_quantized_binary_op(model, qconfig_map)
+    _lower_getattr_tensor_metadta_op(model)
+    _lower_get_tensor_info_op(model)
+    special_pattern_replacement(model)
+    model.graph.eliminate_dead_code()
+    model = fold_weight(model, node_name_to_scope, keep_original_weights)
+    model.graph.eliminate_dead_code()
+    model.recompile()
+    model.graph.lint()
+    return model
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/_model_report/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/_model_report/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/_model_report/detector.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/_model_report/detector.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a48bbbaaee901871d41396e0583642c4d486dce
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/_model_report/detector.py
@@ -0,0 +1,1743 @@
+# mypy: allow-untyped-defs
+from abc import ABC, abstractmethod
+from collections.abc import Callable
+from typing import Any
+
+import torch
+import torch.ao.nn.qat as nnqat
+import torch.nn as nn
+from torch.ao.quantization.fake_quantize import FakeQuantize
+from torch.ao.quantization.fx._equalize import (
+    default_equalization_qconfig,
+    EqualizationQConfig,
+)
+from torch.ao.quantization.fx._model_report.model_report_observer import (
+    ModelReportObserver,
+)
+from torch.ao.quantization.fx.graph_module import GraphModule
+from torch.ao.quantization.observer import (
+    _is_activation_post_process,
+    default_dynamic_quant_observer,
+    default_observer,
+    default_per_channel_weight_observer,
+    default_weight_observer,
+    ObserverBase,
+)
+from torch.ao.quantization.qconfig import (
+    _assert_valid_qconfig,
+    default_qconfig,
+    QConfig,
+)
+
+
+# Names for observer insert keys
+DETECTOR_TARGET_NODE_KEY = "target_node"
+DETECTOR_OBS_TO_INSERT_KEY = "observer_to_insert"
+DETECTOR_IS_POST_OBS_KEY = "is_post_observer"
+DETECTOR_OBS_ARGS_KEY = "observer_args"
+
+
+# Mapping related code
+class DetectorQConfigInfo:
+    r"""
+    This class contains the QConfig information for a single module.
+    The list of variables / values this contains can grow depending on the
+    extensibility of the qconfig mapping feature set but this currently includes:
+    - if activation observer is dynamic
+    - if weight observer is per channel
+
+
+    Args:
+        module_fqn (str): The fully qualified name (fqn) of the module that this
+            information contains info relevant to qconfig for
+    """
+
+    def __init__(self, module_fqn: str):
+        super().__init__()
+        self.module_fqn = module_fqn
+
+        # populate this section with all the variables we might find important
+        # change from none if your detector is actually using this
+        self.is_activation_dynamic = False
+        self.is_weight_per_channel = False
+
+        # equalization related options
+        self.is_equalization_recommended = False
+
+    def generate_quantization_qconfig(self, module: torch.nn.Module) -> QConfig:
+        r"""
+        Args:
+            module (torch.nn.Module) The module we are generating
+            the qconfig for
+
+        Returns the generated quantization QConfig according to what a valid configuration is
+        """
+        # Apply suggestions to new qconfig
+        module_qconfig = default_qconfig
+
+        # keep track of dynamic and per_channel recommendations
+        recommendations_list = []
+        # append as if a list of combinations
+        recommendations_list.append(
+            (self.is_activation_dynamic, self.is_weight_per_channel)
+        )
+        recommendations_list.append(
+            (self.is_activation_dynamic, False)
+        )  # only trying dynamic rec
+        recommendations_list.append(
+            (False, self.is_weight_per_channel)
+        )  # only trying dynamic
+
+        # now we try each of the combinations
+        for rec in recommendations_list:
+            # rec[0] -> dynamic recommended
+            # rec[1] -> per channel recommended
+            activation = default_dynamic_quant_observer if rec[0] else default_observer
+            weight = (
+                default_per_channel_weight_observer
+                if rec[1]
+                else default_weight_observer
+            )
+            test_config = QConfig(activation, weight)
+            try:
+                _assert_valid_qconfig(test_config, module)
+                module_qconfig = test_config
+                break
+            except AssertionError:
+                # if not a valid configuration, we move on to the next one in priority
+                continue
+
+        # return the QConfig chosen
+        return module_qconfig
+
+    def generate_equalization_qconfig(self) -> EqualizationQConfig:
+        r"""
+        This returns the equalization configuration for a module.
+
+        For now, it just returns the default, but as more equalization options become
+        possible, this method can get more fleshed out with more nuanced granularity.
+
+
+        Returns the generated equalization QConfig according to what a valid configuration is
+        """
+        # in this case, we just return default equalization config
+        # we know this is valid because only valid modules would even
+        # have this option
+        return default_equalization_qconfig
+
+
+# Adding base class for detectors
+class DetectorBase(ABC):
+    r"""Base Detector Module
+    Any detector class should derive from this class.
+
+    Concrete detectors should follow the same general API, which includes:
+    - A method to calculate and return observer insertion points
+        - Should return both the fqns and the Observer class to insert
+    - A method to return a report based on the detector
+        - Should return a str-based report and dict info in Tuple[str,Dict] format
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.detector_config_info = None
+
+    @abstractmethod
+    def determine_observer_insert_points(self, model) -> dict:
+        r"""
+        Args
+            model (nn.Module or subclass): model to find observer insertion points
+
+        Returns a Dict mapping from unique observer fqns (where we want to insert them) to a Dict.
+            This dict maps string keys to detector specific information
+        """
+
+    @abstractmethod
+    def get_detector_name(self) -> str:
+        r"""Returns the name of the current detector"""
+
+    @abstractmethod
+    def get_qconfig_info(self, model) -> dict[str, DetectorQConfigInfo]:
+        r"""Returns the DetectorQConfigInfo for each module_fqn relevant
+        Args
+            model (nn.Module or subclass): model to find observer insertion points
+
+        Returns a Dict mapping from unique observer fqns (where we want to insert them) to:
+            A DetectorQConfigInfo with the information to generate a QConfig for a specific module
+        """
+
+    def _get_targeting_node(
+        self, prepared_fx_model: GraphModule, target_fqn: str
+    ) -> torch.fx.node.Node:
+        r"""
+        Takes in a GraphModule and the target_fqn and finds the node whose target is this fqn.
+
+        If it's not found, it means it is most likely inside a fused layer
+            We just go one layer up in terms of the fqn we are searching for until we find parent node
+            If we get to empty string, then we know that it doesn't exist
+
+        The reason for the recursion is that if the model that we are looking for got fused,
+        we will have module fqn as e.g. x.linear.0 but the graph will only have a node for the fused module,
+        which would have fqn as x.linear so they will not match.
+        To handle this, if we don't match, we then take off the last bit of the fqn e.g. x.linear.0 -> x.linear,
+        or more generally foo.bar.baz -> foo.bar and search again, this will allow us to locate the correct module
+        even in cases with fusion
+
+        Args:
+            prepared_fx_model (GraphModule):  The prepared Fx GraphModule
+            target_fqn (str): The fqn of the layer we are trying to target
+
+        Returns the node object we are trying to add observers around
+        """
+        for node in prepared_fx_model.graph.nodes:
+            # if the node's target is our target, return it
+            if node.target == target_fqn:
+                return node
+
+        # getting here means node not found
+        # if no "." we are already at base and failed
+        parent_fqn_sep_index = target_fqn.rfind(".")
+        if parent_fqn_sep_index == -1:
+            raise ValueError("passed in target_fqn not found in graph's targets.")
+        else:
+            # recursively call it with parent fqn
+            return self._get_targeting_node(
+                prepared_fx_model, target_fqn[:parent_fqn_sep_index]
+            )
+
+    @abstractmethod
+    def generate_detector_report(self, model) -> tuple[str, dict[str, Any]]:
+        r"""
+        Args
+            model (nn.Module or subclass): model to find observer insertion points
+
+        Returns a Tuple of two elements:
+            Str: string report of the suggested improvements
+            Dict: contains useful data collected by the observer pertinent to this report
+        """
+
+
+class PerChannelDetector(DetectorBase):
+    r"""This class is used to detect if any Linear or Conv layers in a model utilize per_channel quantization.
+    Only Linear and Conv layers can use per_channel as of now so only these two are currently checked.
+
+    per_channel quantization can lead to major benefits in the form of accuracy.
+    Therefore, if the backend used by the user supports it, it is recommended to use
+
+    Args:
+        backend (str, optional): the backend the user wishes to use in production
+            Default value is current torch.backends.quantized.engine
+    """
+
+    # Keys for return dictionary
+    BACKEND_KEY = "backend"
+    PER_CHAN_SUPPORTED_KEY = "per_channel_quantization_supported"
+    PER_CHAN_USED_KEY = "per_channel_quantization_used"
+
+    # Default map for representing supported per channel quantization modules for different backends
+    DEFAULT_BACKEND_PER_CHANNEL_SUPPORTED_MODULES: dict[str, set[Any]] = {
+        "fbgemm": {
+            nn.Linear,
+            nn.Conv1d,
+            nn.Conv2d,
+            nn.Conv3d,
+            nnqat.Linear,
+            nnqat.Conv1d,
+            nnqat.Conv2d,
+            nnqat.Conv3d,
+        },
+        "qnnpack": {
+            nn.Linear,
+            nn.Conv1d,
+            nn.Conv2d,
+            nn.Conv3d,
+            nnqat.Linear,
+            nnqat.Conv1d,
+            nnqat.Conv2d,
+            nnqat.Conv3d,
+        },
+        "onednn": {
+            nn.Linear,
+            nn.Conv1d,
+            nn.Conv2d,
+            nn.Conv3d,
+            nnqat.Linear,
+            nnqat.Conv1d,
+            nnqat.Conv2d,
+            nnqat.Conv3d,
+        },
+        "x86": {
+            nn.Linear,
+            nn.Conv1d,
+            nn.Conv2d,
+            nn.Conv3d,
+            nnqat.Linear,
+            nnqat.Conv1d,
+            nnqat.Conv2d,
+            nnqat.Conv3d,
+        },
+    }
+
+    def __init__(self, backend: str = torch.backends.quantized.engine):
+        super().__init__()
+
+        # store the backend information
+        self.backend_chosen = backend
+        self.supported_modules = set()
+        if self.backend_chosen in self.DEFAULT_BACKEND_PER_CHANNEL_SUPPORTED_MODULES:
+            self.supported_modules = self.DEFAULT_BACKEND_PER_CHANNEL_SUPPORTED_MODULES[
+                self.backend_chosen
+            ]
+        else:
+            raise ValueError(
+                f"Not configured to work with {self.backend_chosen}. Try a different default backend"
+            )
+
+    def get_detector_name(self) -> str:
+        r"""returns the string name of this detector"""
+        return "per_channel_detector"
+
+    def get_qconfig_info(self, model) -> dict[str, DetectorQConfigInfo]:
+        r"""Returns the DetectorQConfigInfo for each module_fqn relevant
+        Args
+            model (nn.Module or subclass): model to find observer insertion points
+
+        Returns a Dict mapping from unique observer fqns (where we want to insert them) to:
+            A DetectorQConfigInfo with the information to generate a QConfig for a specific module
+        """
+        # run the helper function to populate the dictionary
+        per_channel_info = self._detect_per_channel_helper(model)
+
+        # we actually have a qconfig info object we are populating
+        module_fqn_to_detector_qconfig_info = {}
+
+        for module_fqn in per_channel_info:
+            # create a detector info instance
+            detector_qconfig_info = DetectorQConfigInfo(module_fqn)
+
+            # see if per channel quantization is supported
+            per_chan_supported: bool = per_channel_info[module_fqn][
+                self.PER_CHAN_SUPPORTED_KEY
+            ]
+            detector_qconfig_info.is_weight_per_channel = per_chan_supported
+            module_fqn_to_detector_qconfig_info[module_fqn] = detector_qconfig_info
+
+        return module_fqn_to_detector_qconfig_info
+
+    def determine_observer_insert_points(self, model: nn.Module) -> dict:
+        r"""
+        There is no observers inserted for the PerChannelDetector.
+
+        Returns an empty dictionary since no observers are added or needed
+        """
+        return {}
+
+    def _detect_per_channel_helper(self, model: nn.Module):
+        r"""
+        determines if per_channel quantization is supported in modules and submodules.
+
+        Returns a dictionary in the higher level _detect_per_channel function.
+        Each entry maps the fully-qualified-name to information on whether per_channel quantization.
+
+        Args:
+            model: The current module that is being checked to see if it is per_channel quantizable
+
+        Returns dictionary mapping fqns to if per_channel quantization is possible
+        """
+        # create dict we will return
+        per_channel_info: dict = {}
+
+        # get the fully qualified name and check if in list of modules to include and list of modules to ignore
+        for fqn, module in model.named_modules():
+            is_in_include_list = any(
+                isinstance(module, x) for x in self.supported_modules
+            )
+
+            # check if the module per_channel is supported
+            # based on backend
+            per_channel_supported = False
+
+            if is_in_include_list:
+                per_channel_supported = True
+
+                # assert statement for MyPy
+                q_config_file = module.qconfig
+                if not isinstance(q_config_file, QConfig):
+                    raise AssertionError("module.qconfig must be a QConfig")
+
+                # this object should either be fake quant or observer
+                q_or_s_obj = module.qconfig.weight.p.func()
+                if not isinstance(q_or_s_obj, (FakeQuantize, ObserverBase)):
+                    raise AssertionError(
+                        "module.qconfig.weight must be a FakeQuantize or ObserverBase"
+                    )
+
+                per_channel_used = False  # will be true if found in qconfig
+
+                if hasattr(
+                    q_or_s_obj, "ch_axis"
+                ):  # then we know that per_channel quantization used
+                    # all fake quants have channel axis so need to check is_per_channel
+                    if isinstance(q_or_s_obj, FakeQuantize):
+                        if (
+                            hasattr(q_or_s_obj, "is_per_channel")
+                            and q_or_s_obj.is_per_channel
+                        ):
+                            per_channel_used = True
+                    elif isinstance(q_or_s_obj, ObserverBase):
+                        # should be an observer otherwise
+                        per_channel_used = True
+                    else:
+                        raise ValueError("Should be either observer or fake quant")
+
+                per_channel_info[fqn] = {
+                    self.PER_CHAN_SUPPORTED_KEY: per_channel_supported,
+                    self.PER_CHAN_USED_KEY: per_channel_used,
+                    self.BACKEND_KEY: self.backend_chosen,
+                }
+
+        return per_channel_info
+
+    def generate_detector_report(self, model: nn.Module) -> tuple[str, dict[str, Any]]:
+        r"""Checks if any Linear or Conv layers in the model utilize per_channel quantization.
+        Only Linear and Conv layers can use per_channel as of now so only these two are currently checked.
+
+        Looks at q_config format and backend to determine if per_channel can be utilized.
+        Uses the DEFAULT_BACKEND_PER_CHANNEL_SUPPORTED_MODULES structure to determine support
+
+        Args:
+            model: The prepared and calibrated model we want to check if using per_channel
+
+        Returns a tuple with two elements:
+            String report of potential actions to improve model (if per_channel quantization is available in backend)
+            Dictionary mapping per_channel quantizable elements to:
+                whether per_channel quantization is supported by the backend
+                if it is being utilized in the current model
+        """
+
+        # run the helper function to populate the dictionary
+        per_channel_info = self._detect_per_channel_helper(model)
+
+        # String to let the user know of further optimizations
+        further_optims_str = (
+            f"Further Optimizations for backend {self.backend_chosen}: \n"
+        )
+
+        optimizations_possible = False
+        for fqn in per_channel_info:
+            fqn_dict = per_channel_info[fqn]
+            if (
+                fqn_dict[self.PER_CHAN_SUPPORTED_KEY]
+                and not fqn_dict[self.PER_CHAN_USED_KEY]
+            ):
+                optimizations_possible = True
+                further_optims_str += (
+                    f"Module {fqn} can be configured to use per_channel quantization.\n"
+                )
+
+        if optimizations_possible:
+            further_optims_str += "To use per_channel quantization, make sure the qconfig has a per_channel weight observer."
+        else:
+            further_optims_str += "No further per_channel optimizations possible."
+
+        # return the string and the dictionary form of same information
+        return (further_optims_str, per_channel_info)
+
+
+class DynamicStaticDetector(DetectorBase):
+    r"""
+    Determines whether dynamic or static quantization is more appropriate for a given module.
+
+    Takes advantage of the ModelReportObserver that records range information.
+    Stationary distribution of data are strictly above tolerance level for the comparison statistic:
+
+        S = average_batch_activation_range/epoch_activation_range
+
+    Nonstationary distributions are below or at the tolerance level for this metric.
+
+    If the distribution of data right after the module is non-stationary, recommend dynamic quantization
+        Otherwise recommend static quantization
+
+    Args:
+        tolerance (float, optional): The threshold where S metric is stationary above and non-stationary otherwise. Default: 0.5
+    """
+
+    # names for the pre and post observers that are inserted
+    DEFAULT_PRE_OBSERVER_NAME = "model_report_pre_observer"
+    DEFAULT_POST_OBSERVER_NAME = "model_report_post_observer"
+
+    # naming conventions for stationary vs non-stationary data
+    STATIONARY_STR = "stationary"
+    NON_STATIONARY_STR = "non-stationary"
+
+    # naming for activation
+    INPUT_ACTIVATION_PREFIX = "input_activation_"
+    OUTPUT_ACTIVATION_PREFIX = "output_activation_"
+
+    # naming conventions for the keys of the return module info
+    TOLERANCE_KEY = "dynamic_static_tolerance"
+    DEFAULT_DYNAMIC_REC_KEY = "dynamic_recommended"
+    PRE_OBS_COMP_STAT_KEY = INPUT_ACTIVATION_PREFIX + "dynamic_static_comp_stat"
+    POST_OBS_COMP_STAT_KEY = OUTPUT_ACTIVATION_PREFIX + "dynamic_static_comp_stat"
+    PRE_OBS_DATA_DIST_KEY = (
+        INPUT_ACTIVATION_PREFIX + "dynamic_static_data_classification"
+    )
+    POST_OBS_DATA_DIST_KEY = (
+        OUTPUT_ACTIVATION_PREFIX + "dynamic_static_data_classification"
+    )
+    IS_CURRENTLY_SUPPORTED_KEY = "is_dynamic_supported"
+
+    # modules that are supported both dynamic and static for this report function
+    DEFAULT_DYNAMIC_STATIC_CHECK_SUPPORTED = {nn.Linear}
+
+    # modules that will be supported soon for both
+    DEFAULT_DYNAMIC_STATIC_FUTURE_SUPPORTED = {nn.Conv1d, nn.Conv2d, nn.Conv3d}
+
+    def __init__(self, tolerance=0.5):
+        super().__init__()
+
+        # set tolerance level and initialize a set to keep track of useful fqn locations
+        self.tolerance = tolerance
+        self.useful_observer_fqns: set[str] = set()
+
+    def determine_observer_insert_points(
+        self, prepared_fx_model: GraphModule
+    ) -> dict[str, dict[str, Any]]:
+        r"""
+        Determines where observers need to be inserted for the Dynamic vs Static detector.
+        For this detector, we want to place observers on either side of linear layers in the model.
+
+        Currently inserts observers for:
+            linear layers
+
+        Args:
+            prepared_fx_model (GraphModule):  The prepared Fx GraphModule
+
+        Returns a Dict mapping from unique observer fqns (where we want to insert them) to a Dict with:
+            key "target_node" -> the node we are trying to observe with this observer (torch.fx.node.Node)
+            key "observer_to_insert" -> the observer we wish to insert (ObserverBase)
+            key "is_post_observer" -> True if this is meant to be a post-observer for target_node, False if pre-observer
+            key "observer_args" -> The arguments that are meant to be passed into the observer
+        """
+
+        # observer for this detector is ModelReportObserver
+        obs_ctr = ModelReportObserver
+
+        # return dict
+        obs_fqn_to_info: dict[str, dict[str, Any]] = {}
+
+        for fqn, module in prepared_fx_model.named_modules():
+            # make sure module is supported
+            if self._is_supported(module, insert=True):
+                # if it's a supported type, we want to get node and add observer insert locations
+                targeted_node = self._get_targeting_node(prepared_fx_model, fqn)
+
+                # add entry for pre-observer
+                pre_obs_fqn = fqn + "." + self.DEFAULT_PRE_OBSERVER_NAME
+
+                obs_fqn_to_info[pre_obs_fqn] = {
+                    DETECTOR_TARGET_NODE_KEY: targeted_node,
+                    DETECTOR_OBS_TO_INSERT_KEY: obs_ctr(),
+                    DETECTOR_IS_POST_OBS_KEY: False,
+                    DETECTOR_OBS_ARGS_KEY: targeted_node.args,
+                }
+
+                # add entry for post-observer
+                post_obs_fqn = fqn + "." + self.DEFAULT_POST_OBSERVER_NAME
+
+                obs_fqn_to_info[post_obs_fqn] = {
+                    DETECTOR_TARGET_NODE_KEY: targeted_node,
+                    DETECTOR_OBS_TO_INSERT_KEY: obs_ctr(),
+                    DETECTOR_IS_POST_OBS_KEY: True,
+                    DETECTOR_OBS_ARGS_KEY: (targeted_node,),
+                }
+
+        return obs_fqn_to_info
+
+    def get_detector_name(self) -> str:
+        r"""returns the string name of this detector"""
+        return "dynamic_vs_static_detector"
+
+    def get_qconfig_info(self, model) -> dict[str, DetectorQConfigInfo]:
+        r"""Returns the DetectorQConfigInfo for each module_fqn relevant
+        Args
+            model (nn.Module or subclass): model to find observer insertion points
+
+        Returns a Dict mapping from unique observer fqns (where we want to insert them) to:
+            A DetectorQConfigInfo with the information to generate a QConfig for a specific module
+        """
+        # run the helper function to populate the dictionary
+        dynamic_static_info = self._generate_dict_info(model)
+
+        # we actually have a qconfig info object we are populating
+        module_fqn_to_detector_qconfig_info = {}
+
+        for module_fqn in dynamic_static_info:
+            # create a detector info instance
+            detector_qconfig_info = DetectorQConfigInfo(module_fqn)
+
+            # see if per channel quantization is supported
+            dynamic_static_recommended: bool = dynamic_static_info[module_fqn][
+                self.DEFAULT_DYNAMIC_REC_KEY
+            ]
+            detector_qconfig_info.is_activation_dynamic = dynamic_static_recommended
+            module_fqn_to_detector_qconfig_info[module_fqn] = detector_qconfig_info
+
+        return module_fqn_to_detector_qconfig_info
+
+    def _is_supported(self, module: nn.Module, insert: bool = False) -> bool:
+        r"""Returns whether the given module is supported for observers
+
+        Args
+            module: The module to check and ensure is supported
+            insert: True if this is check for observer insertion, false if for report gen
+
+        Returns True if the module is supported by observer, False otherwise
+        """
+        # check to see if module is of a supported type
+        is_supported_type = any(
+            isinstance(module, x) for x in self.DEFAULT_DYNAMIC_STATIC_CHECK_SUPPORTED
+        )
+
+        # check if it will be supported
+        future_supported_type = any(
+            isinstance(module, x) for x in self.DEFAULT_DYNAMIC_STATIC_FUTURE_SUPPORTED
+        )
+
+        # supported
+        supported = is_supported_type or future_supported_type
+
+        # this is check for observer insertion
+        if insert:
+            return supported
+        else:
+            # this is for report gen and we also need to check if it contains observers
+            has_obs = hasattr(module, self.DEFAULT_PRE_OBSERVER_NAME) and hasattr(
+                module, self.DEFAULT_POST_OBSERVER_NAME
+            )
+            return supported and has_obs
+
+    def _generate_dict_info(self, model: GraphModule) -> dict[str, Any]:
+        r"""
+        Helper function for generate_detector_report that does the generation of the dictionary.
+        This process is done as specified in generate_detector_report documentation
+
+        Args:
+            model (GraphModule): The prepared and calibrated GraphModule with inserted ModelReportObservers
+
+        Returns a Dictionary mapping modules with ModelReportObservers around them to:
+                whether dynamic quantization is recommended
+                their S metric of input to module
+                whether input to module is stationary or non-stationary
+                their S metric of output of module
+                whether output of module is stationary or non-stationary
+                the tolerance level to decided whether input/output is stationary or non-stationary
+                whether it is currently supported or planned for the future
+        """
+        # store modules dynamic vs static information
+        module_dynamic_static_info = {}
+
+        # This for loop goes through the modules, and extracts all relevant information into module_dynamic_static_info
+        #   This information primary includes whether the data distributions around a supported module is stationary or not
+        #   Based on this, it is recorded whether dynamic or static quantization is recommended
+
+        # loop through all submodules included nested ones
+        for fqn, module in model.named_modules():
+            # if module is Linear has the ModelReportObserver attached to it
+            if self._is_supported(module):
+                # get pre and post observers for the module
+                pre_obs = getattr(module, self.DEFAULT_PRE_OBSERVER_NAME)
+                post_obs = getattr(module, self.DEFAULT_POST_OBSERVER_NAME)
+
+                # get the statistics for each module
+                pre_stat = pre_obs.get_batch_to_epoch_ratio()
+                post_stat = post_obs.get_batch_to_epoch_ratio()
+
+                # record module, pre and post stat, and whether to do dynamic or static based off it
+                # true if post observer data distribution is non-stationary, false if it's stationary
+                dynamic_recommended = post_stat <= self.tolerance
+
+                # specify the classifications for whether data distributions considered stationary or non-stationary
+                pre_obs_dist_classif = (
+                    self.STATIONARY_STR
+                    if pre_stat > self.tolerance
+                    else self.NON_STATIONARY_STR
+                )
+                post_obs_dist_classif = (
+                    self.STATIONARY_STR
+                    if post_stat > self.tolerance
+                    else self.NON_STATIONARY_STR
+                )
+
+                # check if current support or future support
+                is_supported_type = any(
+                    isinstance(module, x)
+                    for x in self.DEFAULT_DYNAMIC_STATIC_CHECK_SUPPORTED
+                )
+
+                # store the set of important information for this module
+                module_info = {
+                    self.TOLERANCE_KEY: self.tolerance,
+                    self.DEFAULT_DYNAMIC_REC_KEY: dynamic_recommended,
+                    self.PRE_OBS_COMP_STAT_KEY: pre_stat,
+                    self.PRE_OBS_DATA_DIST_KEY: pre_obs_dist_classif,
+                    self.POST_OBS_COMP_STAT_KEY: post_stat,
+                    self.POST_OBS_DATA_DIST_KEY: post_obs_dist_classif,
+                    self.IS_CURRENTLY_SUPPORTED_KEY: is_supported_type,
+                }
+
+                module_dynamic_static_info[fqn] = module_info
+
+        return module_dynamic_static_info
+
+    def generate_detector_report(
+        self, model: GraphModule
+    ) -> tuple[str, dict[str, Any]]:
+        r"""
+        Determines whether dynamic or static quantization is more appropriate for a given module.
+
+        Takes advantage of the ModelReportObserver that records range information.
+        Stationary distribution of data are strictly above tolerance level for the comparison statistic:
+
+            S = average_batch_activation_range/epoch_activation_range
+
+        Nonstationary distributions are below or at the tolerance level for this metric.
+
+        If the distribution of data right after the module is non-stationary, recommend dynamic quantization
+            Otherwise recommend static quantization
+
+        This will then generate suggestions for dynamic vs static quantization focused around Linear.
+
+        Args:
+            model (GraphModule): The prepared and calibrated GraphModule with inserted ModelReportObservers
+
+        Returns a tuple with two elements:
+            String report of of whether dynamic or static quantization is recommended for certain modules
+            Dictionary mapping modules with ModelReportObservers around them to:
+                whether dynamic quantization is recommended
+                their S metric of input to module
+                whether input to module is stationary or non-stationary
+                their S metric of output of module
+                whether output of module is stationary or non-stationary
+                the tolerance level to decided whether input/output is stationary or non-stationary
+                whether it is currently supported or planned for the future
+        """
+
+        # get the dictionary of the information to format the string report
+        module_dynamic_static_info = self._generate_dict_info(model)
+
+        dynamic_vs_static_string = "Dynamic vs. Static Quantization suggestions: \n"
+
+        modules_added: bool = False  # check to make sure at least 1 module added.
+
+        dynamic_benefit = (
+            " You will get more accurate results if you use dynamic quantization"
+        )
+        static_benefit = (
+            " You can increase model efficiency if you use static quantization"
+        )
+        future_support_str = (
+            ". This layer is not yet supported for dynamic quantization"
+        )
+        # This for loop goes through the information collected in module_dynamic_static_info and:
+        #   Populates the string based report with the information from module_dynamic_static_info
+        #   Compiles the complete report by appending relevant formatted strings
+
+        for module_fqn in module_dynamic_static_info:
+            # there is at least 1 module for suggestion
+            modules_added = True
+            module_info = module_dynamic_static_info[module_fqn]
+            suggestion_string_template = (
+                "For module {} it is suggested to use {} quantization because {}.\n"
+            )
+
+            # decide what string formatting values will be
+            quantization_type = ""
+            quantization_reasoning = "the distribution of data before {} is {} and the distribution after is {}."
+
+            benefit_str = ""
+
+            # strings for if dynamic quantized per tensor is needed
+            recommend_per_tensor = (
+                ". We recommend to add a {} before this module if it is static."
+            )
+            rec_lay_to_add = "dynamic quantize per tensor layer"
+            dynamic_per_tensor_string = recommend_per_tensor.format(rec_lay_to_add)
+            dynamic_per_tensor_reasoning_string = " This is because the input to this module has a non-stationary distribution"
+
+            # start composing explanation
+            if module_info[self.DEFAULT_DYNAMIC_REC_KEY]:
+                quantization_type = "dynamic"
+                # check if currently supported or future supported
+                benefit_str = dynamic_benefit
+                if not module_info[self.IS_CURRENTLY_SUPPORTED_KEY]:
+                    benefit_str += future_support_str
+            else:
+                quantization_type = "static"
+                benefit_str = static_benefit
+
+            # now set the quantization explanation string
+            quantization_reasoning = (
+                quantization_reasoning.format(
+                    module_fqn,
+                    module_info[self.PRE_OBS_DATA_DIST_KEY],
+                    module_info[self.POST_OBS_DATA_DIST_KEY],
+                )
+                + benefit_str
+            )
+
+            # if we have a non-stationary input -> linear -> stationary we suggested static
+            # however, we want to also recommend they add a dynamic quantize per tensor right if this change is made
+            if (
+                module_info[self.PRE_OBS_DATA_DIST_KEY] == self.NON_STATIONARY_STR
+                and module_info[self.POST_OBS_DATA_DIST_KEY] == self.STATIONARY_STR
+            ):
+                quantization_reasoning = (
+                    quantization_reasoning
+                    + dynamic_per_tensor_string
+                    + dynamic_per_tensor_reasoning_string
+                )
+
+            # format the overall suggestion string with the specific inputs
+            module_suggestion_string = suggestion_string_template.format(
+                module_fqn, quantization_type, quantization_reasoning
+            )
+
+            # append to overall suggestion
+            dynamic_vs_static_string += module_suggestion_string
+
+        if not modules_added:
+            dynamic_vs_static_string += "No applicable layers for suggestions. Only linear and conv are valid.\n"
+
+        # return the string as well as the dictionary of information
+        return (dynamic_vs_static_string, module_dynamic_static_info)
+
+
+class InputWeightEqualizationDetector(DetectorBase):
+    r"""
+    Determines whether input-weight equalization can help improve quantization for certain modules.
+
+    Specifically, this list of modules includes:
+        linear
+        conv
+
+    Determines whether input-weight equalization is recommended based on the comp stat:
+        s_c = sqrt(w_c/W)/sqrt(i_c/I)
+        where:
+            w_c is range of weight for channel c, W is range of weight over all channels
+            i_c is range of input for channel c, I is range of input over all channels
+
+        if s_c >= threshold or <= 1 / threshold, recommends input-weight equalization
+
+    Args:
+        ratio_threshold (float): The threshold for s_c to determine if input-weight equalization is suggested
+            Should be between 0 and 1 (both non-inclusive)
+        ch_axis (int, optional): The channel axis being observed to determine input weight equalization
+            Default: 1
+
+    * :attr:`ratio_threshold`: The threshold for s_c to determine if input-weight equalization is suggested
+        Should be between 0 and 1
+
+    * :attr:`ch_axis`: The channel axis being observed to determine input weight equalization
+
+    * :attr:`SUPPORTED_MODULES`: This specifies the modules that are supported for input-weight equalization
+
+    * :attr:`DEFAULT_PRE_OBSERVER_NAME`: The name of the pre-observer to be inserted for this detector
+    """
+
+    SUPPORTED_MODULES: set[Callable] = {
+        nn.Linear,
+        nn.Conv1d,
+        nn.Conv2d,
+        nn.Conv3d,
+        nnqat.Linear,
+        nnqat.Conv1d,
+        nnqat.Conv2d,
+        nnqat.Conv3d,
+    }
+
+    # names for the pre and post observers that are inserted
+    DEFAULT_PRE_OBSERVER_NAME: str = "model_report_pre_observer"
+
+    # weight / activation prefix for each of the below info
+    WEIGHT_PREFIX = "weight_"
+    ACTIVATION_PREFIX = "input_activation_"
+
+    # string names for keys of info dictionaries
+    PER_CHANNEL_MAX_KEY = "per_channel_max"
+    PER_CHANNEL_MIN_KEY = "per_channel_min"
+    GLOBAL_MAX_KEY = "global_max"
+    GLOBAL_MIN_KEY = "global_min"
+
+    # keys for return dict of recommendations
+    RECOMMENDED_KEY = "input_weight_equalization_recommended"
+    COMP_METRIC_KEY = "input_weight_channel_comparison_metrics"
+    THRESHOLD_KEY = "input_weight_threshold"
+    CHANNEL_KEY = "input_weight_channel_axis"
+
+    # default weight and info strings
+    WEIGHT_STR = "weight"
+    INPUT_STR = "input"
+
+    # default for what ratio we recommend input weight
+    DEFAULT_RECOMMEND_INPUT_WEIGHT_CHANNEL_RATIO = 0.4
+
+    def __init__(self, ratio_threshold: float, ch_axis: int = 1):
+        # ensure passed in inputs are valid
+        if ratio_threshold <= 0 or ratio_threshold >= 1:
+            raise ValueError("Make sure threshold is > 0 and < 1")
+
+        # initialize attributes based on args
+        self.ratio_threshold: float = ratio_threshold
+        self.ch_axis: int = ch_axis
+
+    def _is_supported(self, module: nn.Module, insert: bool = False) -> bool:
+        r"""Returns whether the given module is supported for observers
+
+        Args
+            module: The module to check and ensure is supported
+            insert: True if this is check for observer insertion, false if for report gen
+
+        Returns True if the module is supported by observer, False otherwise
+        """
+        # check to see if module is of a supported type
+        is_supported_type = any(type(module) is x for x in self.SUPPORTED_MODULES)
+
+        # this is check for observer insertion
+        if insert:
+            return is_supported_type
+        else:
+            # this is for report gen and we also need to check if it contains observers
+            has_obs = hasattr(module, self.DEFAULT_PRE_OBSERVER_NAME)
+            return is_supported_type and has_obs
+
+    def get_qconfig_info(self, model) -> dict[str, DetectorQConfigInfo]:
+        r"""Returns the DetectorQConfigInfo for each module_fqn relevant
+        Args
+            model (nn.Module or subclass): model to find observer insertion points
+
+        Returns a Dict mapping from unique observer fqns (where we want to insert them) to:
+            A DetectorQConfigInfo with the information to generate a QConfig for a specific module
+        """
+        # run the helper function to populate the dictionary
+        # find the range of inputs
+        input_values: dict[str, dict] = self._extract_input_info(model)
+
+        # find the range of weights
+        weight_values: dict[str, dict] = self._extract_weight_info(model)
+
+        # calculate per_channel comparison statistic s_c
+        comp_stats: dict[str, torch.Tensor] = self._generate_comparison_values(
+            input_values, weight_values
+        )
+
+        # generate the return dictionary
+        input_weight_equalization_info: dict[str, dict] = self._generate_dict_info(
+            input_values, weight_values, comp_stats
+        )
+
+        # we actually have a qconfig info object we are populating
+        module_fqn_to_detector_qconfig_info = {}
+
+        for module_fqn in input_weight_equalization_info:
+            # create a detector info instance
+            detector_qconfig_info = DetectorQConfigInfo(module_fqn)
+
+            # see if per channel quantization is supported
+            input_weight_recommended: bool = input_weight_equalization_info[module_fqn][
+                self.RECOMMENDED_KEY
+            ]
+            detector_qconfig_info.is_equalization_recommended = input_weight_recommended
+            module_fqn_to_detector_qconfig_info[module_fqn] = detector_qconfig_info
+
+        return module_fqn_to_detector_qconfig_info
+
+    def determine_observer_insert_points(
+        self, prepared_fx_model: GraphModule
+    ) -> dict[str, dict[str, Any]]:
+        r"""Determines where observers need to be inserted for the Input Weight Equalization Detector.
+        For this detector, we want to place observers in front of supported layers.
+
+        Currently inserts observers for:
+            linear layers
+            conv layers
+
+        Args:
+            prepared_fx_model (GraphModule):  The prepared Fx GraphModule
+
+        Returns a Dict mapping from unique observer fqns (where we want to insert them) to a Dict with:
+            key "target_node" -> the node we are trying to observe with this observer (torch.fx.node.Node)
+            key "observer_to_insert" -> the observer we wish to insert (ObserverBase)
+            key "is_post_observer" -> True if this is meant to be a post-observer for target_node, False if pre-observer
+            key "observer_args" -> The arguments that are meant to be passed into the observer
+        """
+
+        # observer for this detector is ModelReportObserver
+        obs_ctr = ModelReportObserver
+
+        # return dict
+        obs_fqn_to_info: dict[str, dict[str, Any]] = {}
+
+        for fqn, module in prepared_fx_model.named_modules():
+            # check to see if module is of a supported type
+            if self._is_supported(module, insert=True):
+                # if it's a supported type, we want to get node and add observer insert locations
+                targeted_node = self._get_targeting_node(prepared_fx_model, fqn)
+
+                # add entry for pre-observer
+                pre_obs_fqn = fqn + "." + self.DEFAULT_PRE_OBSERVER_NAME
+
+                obs_fqn_to_info[pre_obs_fqn] = {
+                    DETECTOR_TARGET_NODE_KEY: targeted_node,
+                    DETECTOR_OBS_TO_INSERT_KEY: obs_ctr(ch_axis=self.ch_axis),
+                    DETECTOR_IS_POST_OBS_KEY: False,
+                    DETECTOR_OBS_ARGS_KEY: targeted_node.args,
+                }
+
+        return obs_fqn_to_info
+
+    def get_detector_name(self) -> str:
+        r"""Returns the name of this detector"""
+        return "input_weight_equalization_detector"
+
+    def _extract_input_info(self, model: GraphModule) -> dict[str, dict]:
+        r"""
+        Takes in a calibrated GraphModule and then finds the relevant observers.
+        It then extracts the input information for each observer returns it
+
+        Args
+            model (GraphModule): The prepared and calibrated GraphModule with inserted ModelReportObservers
+
+        Returns a dict mapping relevant module fqns (str) to a dict with keys:
+            "input_activation_per_channel_max" : maps to the per_channel max values
+            "input_activation_per_channel_min" : maps to the per_channel min values
+            "input_activation_global_max" : maps to the global max recorded
+            "input_activation_global_min" : maps to the global min recorded
+        """
+
+        # return dictionary mapping observer fqns to desired info
+        input_info: dict[str, dict] = {}
+
+        for fqn, module in model.named_modules():
+            # if module is supported and it has a pre-observer
+            if self._is_supported(module):
+                # get pre observer for the module
+                pre_obs = getattr(module, self.DEFAULT_PRE_OBSERVER_NAME)
+
+                input_info[fqn] = {
+                    self.ACTIVATION_PREFIX + self.PER_CHANNEL_MAX_KEY: pre_obs.max_val,
+                    self.ACTIVATION_PREFIX + self.PER_CHANNEL_MIN_KEY: pre_obs.min_val,
+                    self.ACTIVATION_PREFIX + self.GLOBAL_MAX_KEY: max(pre_obs.max_val),
+                    self.ACTIVATION_PREFIX + self.GLOBAL_MIN_KEY: min(pre_obs.min_val),
+                }
+
+        return input_info
+
+    def _extract_weight_info(self, model: GraphModule) -> dict[str, dict]:
+        r"""
+        Takes in a calibrated GraphModule and then finds the relevant observers.
+        It then extracts the weight information for each layer an observer is attached to.
+
+        Args
+            model (GraphModule): The prepared and calibrated GraphModule with inserted ModelReportObservers
+
+        Returns a dict mapping module fqns (str) to a dict with keys:
+            "per_channel_max" : maps to the per_channel max values
+            "per_channel_min" : maps to the per_channel min values
+            "global_max" : maps to the global max recorded
+            "global_min" : maps to the global min recorded
+        """
+        # return dictionary mapping observer fqns to desired info
+        weight_info: dict[str, dict] = {}
+
+        for fqn, module in model.named_modules():
+            # if module is supported and it has a pre-observer
+            if self._is_supported(module):
+                # we don't need actual observer, just the module weights
+                # calculate min and max vals
+                device = module.weight.device
+                min_val: torch.Tensor = torch.tensor([float("inf")], device=device)
+                max_val: torch.Tensor = torch.tensor([float("-inf")], device=device)
+                x_copy = module.weight
+                x_dim = x_copy.size()
+
+                new_axis_list = [i for i in range(len(x_dim))]  # noqa: C416
+                new_axis_list[self.ch_axis] = 0
+                new_axis_list[0] = self.ch_axis
+                y = x_copy.permute(new_axis_list)
+
+                # Need to match dtype of min/max because the updates to buffers
+                # are done in place and types need to match for comparisons
+                y = y.to(min_val.dtype)
+                y = torch.flatten(y, start_dim=1)
+                if min_val.numel() == 0 or max_val.numel() == 0:
+                    min_val, max_val = torch.aminmax(y, dim=1)
+                else:
+                    min_val_cur, max_val_cur = torch.aminmax(y, dim=1)
+                    min_val = torch.min(min_val_cur, min_val)
+                    max_val = torch.max(max_val_cur, max_val)
+
+                weight_info[fqn] = {
+                    self.WEIGHT_PREFIX + self.PER_CHANNEL_MAX_KEY: max_val,
+                    self.WEIGHT_PREFIX + self.PER_CHANNEL_MIN_KEY: min_val,
+                    self.WEIGHT_PREFIX + self.GLOBAL_MAX_KEY: max(max_val),
+                    self.WEIGHT_PREFIX + self.GLOBAL_MIN_KEY: min(min_val),
+                }
+
+        return weight_info
+
+    def _calculate_range_ratio(
+        self, info_dict: dict, info_str: str, module_fqn: str
+    ) -> torch.Tensor:
+        r"""
+        Takes in an info dict and calculates the s_c matrix.
+
+        Args:
+            info_dict (dict): A dictionary of either input or weight range info
+            info_str (str): A str describing whether currently looking at weight or input info
+                Either "weight" or "input"
+            module_fqn (str): The fqn of the module we are looking at
+
+        Returns a tensor of values, where each value is the s_c stat for a different channel
+        """
+        # calculate the ratios of the info
+        # get the prefix str
+        prefix_str = (
+            self.ACTIVATION_PREFIX if info_str == self.INPUT_STR else self.WEIGHT_PREFIX
+        )
+
+        per_channel_range = (
+            info_dict[prefix_str + self.PER_CHANNEL_MAX_KEY]
+            - info_dict[prefix_str + self.PER_CHANNEL_MIN_KEY]
+        )
+        global_range = (
+            info_dict[prefix_str + self.GLOBAL_MAX_KEY]
+            - info_dict[prefix_str + self.GLOBAL_MIN_KEY]
+        )
+
+        if global_range == 0:
+            range_zero_explanation = "We recommend removing this channel as it doesn't provide any useful information."
+            raise ValueError(
+                f"The range of the {info_str} data for module {module_fqn} is 0, "
+                f"which means you have a constant value channel. {range_zero_explanation}"
+            )
+
+        ratio = per_channel_range / global_range
+
+        return ratio
+
+    def _generate_comparison_values(
+        self, input_info: dict, weight_info: dict
+    ) -> dict[str, torch.Tensor]:
+        r"""
+        Takes in the information on the min and max values of the inputs and weights and:
+            Calculates the comp stat for each channel: s_c = sqrt(w_c/W)/sqrt(i_c/I)
+
+        Args:
+            input_info (dict): A dict mapping each observer to input range information
+            weight_info (dict): A dict mapping each observer to weight range information
+
+        Returns a dict mapping relevant observer fqns (str) to a 1-D tensor.
+            Each value is a different s_c value for a different channel
+        """
+        # create return dictionary for each observer
+        module_fqn_to_channel: dict[str, torch.Tensor] = {}
+
+        # for each module (both passed in dicts should have same keys)
+        for module_fqn in input_info:
+            # raise error if not in weight info
+            if module_fqn not in weight_info:
+                raise KeyError(
+                    f"Unable to find weight range stats for module {module_fqn}"
+                )
+
+            # calculate the ratios of the weight info and input info
+            weight_ratio = self._calculate_range_ratio(
+                weight_info[module_fqn], self.WEIGHT_STR, module_fqn
+            )
+            input_ratio = self._calculate_range_ratio(
+                input_info[module_fqn], self.INPUT_STR, module_fqn
+            )
+
+            # if mismatched size, because of grouping, we want to replicate weight enough times
+            weight_channels = len(weight_ratio)
+            input_channels = len(input_ratio)
+            if weight_channels != input_channels:
+                # we try to replicate
+                if input_channels % weight_channels != 0:
+                    raise AssertionError(
+                        "input channels should be divisible by weight channels."
+                    )
+                # get replication factor
+                rep_factor: int = input_channels // weight_channels
+
+                # weight ratio is (n,), input ratio is (k,), we just repeat weight ratio k // n
+                weight_ratio = weight_ratio.repeat(rep_factor)
+
+            # calculate the s metric per channel
+            s = torch.sqrt(weight_ratio) / torch.sqrt(input_ratio)
+            module_fqn_to_channel[module_fqn] = s
+
+        # return compiled observer ratios
+        return module_fqn_to_channel
+
+    def _generate_dict_info(
+        self, input_info: dict, weight_info: dict, comp_stats: dict
+    ) -> dict[str, dict]:
+        r"""
+        Helper function for generate_detector_report that does the generation of the dictionary.
+        This process is done as specified in generate_detector_report documentation
+
+        Args:
+            input_info (dict): A dict mapping each module to input range information
+            weight_info (dict): A dict mapping each module to weight range information
+            comp_stats (dict): A dict mapping each module to its corresponding comp stat
+
+        Returns a dictionary mapping each module with relevant ModelReportObservers around them to:
+            whether input weight equalization is recommended
+            their s_c metric compared to the threshold
+            the threshold used to make the recommendation
+            the channel used for recording data
+            the input channel range info
+            the weight channel range info
+        """
+        # store modules input weight equalization info
+        input_weight_equalization_info: dict[str, dict] = {}
+
+        # for each module we add separate set of suggestions
+        for module_fqn in input_info:
+            # get relevant info for this module
+            mod_input_info: dict = input_info[module_fqn]
+            mod_weight_info: dict = weight_info[module_fqn]
+            mod_comp_stat: dict = comp_stats[module_fqn]
+
+            # decide if each channel should have input weight equalization or not
+            channel_rec_vals: list = []
+
+            for val in mod_comp_stat:
+                float_rep: float = val.item()
+
+                # decide if recommending input weight equalization
+                recommended: bool = (
+                    float_rep >= self.ratio_threshold
+                    and float_rep <= 1 / self.ratio_threshold
+                )
+                channel_rec_vals.append(recommended)
+
+            # build the return dict input
+            # also unpack input and weight dicts into it
+            input_weight_equalization_info[module_fqn] = {
+                self.RECOMMENDED_KEY: channel_rec_vals,
+                self.COMP_METRIC_KEY: mod_comp_stat,
+                self.THRESHOLD_KEY: self.ratio_threshold,
+                self.CHANNEL_KEY: self.ch_axis,
+                **mod_input_info,
+                **mod_weight_info,
+            }
+
+        # return our compiled info for each module
+        return input_weight_equalization_info
+
+    def generate_detector_report(
+        self, model: GraphModule
+    ) -> tuple[str, dict[str, Any]]:
+        r"""
+        Determines whether input weight equalization is appropriate for a given module.
+
+        Takes advantage of the ModelReport Observer which records per channel information of input range
+        It then uses the passed in weight info inconjunction to compute the desired ratio
+        Finally, it gives suggestions based on this information for each module of interest
+
+        Args:
+            model (GraphModule): The prepared and calibrated GraphModule with inserted ModelReportObservers
+
+        Returns a tuple with two elements:
+            String report of of whether input weight equalization is recommended for certain modules
+            Dictionary mapping modules of interest to:
+                whether input weight equalization is recommended
+                their s_c metric compared to the threshold
+                the threshold used to make the recommendation
+                the channel used for recording data
+                the input channel range info
+                the weight channel range info
+        """
+
+        # find the range of inputs
+        input_values: dict[str, dict] = self._extract_input_info(model)
+
+        # find the range of weights
+        weight_values: dict[str, dict] = self._extract_weight_info(model)
+
+        # calculate per_channel comparison statistic s_c
+        comp_stats: dict[str, torch.Tensor] = self._generate_comparison_values(
+            input_values, weight_values
+        )
+
+        # generate the return dictionary
+        input_weight_equalization_info: dict[str, dict] = self._generate_dict_info(
+            input_values, weight_values, comp_stats
+        )
+
+        # now we can generate report based on this information
+        input_weight_string = "Input-Weight Equalization suggestions: \n"
+
+        # some strings to be formatted depending on module we are adding
+        module_suggestion_str = "For Module {} looked at with axis {}: \n"
+        channel_suggestion_str = (
+            "\tWe suggest {} input weight equalization because {}\n"
+        )
+        use_str = "to use"
+        no_use_str = "to not use"
+        input_weight_benefit_str = "{}/{} channels would benefit and we expect significant reduction in quantization error."
+        input_weight_non_benefit_reasoning = (
+            "{}/{} channels benefitting from input-weight equalization being applied."
+        )
+        input_weight_non_benefit_str = "we don't expect much improvement from input-weight equalization based on {}"
+
+        # added module check
+        added_module: bool = False
+
+        # compile the suggestion string
+        for module_fqn in input_weight_equalization_info:
+            # we added at least 1 module
+            added_module = True
+            # add the module level description
+            input_weight_string += module_suggestion_str.format(
+                module_fqn, self.ch_axis
+            )
+
+            mod_info: dict[str, Any] = input_weight_equalization_info[module_fqn]
+
+            # gather info on how many channels would benefit from input weight and
+            recommendation_per_channel: torch.Tensor = mod_info[self.RECOMMENDED_KEY]
+            num_recs = sum(recommendation_per_channel)
+
+            if (
+                num_recs / len(recommendation_per_channel)
+                >= self.DEFAULT_RECOMMEND_INPUT_WEIGHT_CHANNEL_RATIO
+            ):
+                input_benefit_formatted = input_weight_benefit_str.format(
+                    num_recs, len(recommendation_per_channel)
+                )
+                channel_str = channel_suggestion_str.format(
+                    use_str, input_benefit_formatted
+                )
+                input_weight_string += channel_str
+            else:
+                non_benefit_reason_formatted = (
+                    input_weight_non_benefit_reasoning.format(
+                        num_recs, len(recommendation_per_channel)
+                    )
+                )
+                non_benefit_str = input_weight_non_benefit_str.format(
+                    non_benefit_reason_formatted
+                )
+                channel_str = channel_suggestion_str.format(no_use_str, non_benefit_str)
+                input_weight_string += channel_str
+
+        # if no modules looked at, amend return string
+        if not added_module:
+            input_weight_string += (
+                "No applicable layers for suggestions. Only linear and conv valid.\n"
+            )
+
+        # return a tuple with the string explanation and the compiled dict info
+        return (input_weight_string, input_weight_equalization_info)
+
+
+class OutlierDetector(DetectorBase):
+    r"""
+    Determines whether there are significant outliers in activation data around a certain layer.
+
+    This is ideally used in conjunction with information on stationary vs. non-stationary distribution:
+        If the data is stationary, and there are significant outliers, then we want to flag them
+        We want to do this on a per channel basis for detecting outliers
+
+    Determines whether activation data is flagged as outlier based on if data is stationary and:
+        p_r = avg(100th percentile / "reference_percentile"th percentile)
+        where:
+            p_r is average percentile ratio across all batches in the epoch
+            reference_percentile is a percentile values between 0 and 100 exclusive
+
+        if p_r is above some threshold, then we consider the activations to have significant outliers
+
+    Args:
+        ratio_threshold (float, optional): The threshold for p_r to determine if there are outliers in activations
+            Should be >= 1
+            Default: 3.5
+        reference_percentile (float, optional): The denominator to find the relative scale of the 100th percentile
+            Should be between 0 and 1
+            Default: 0.975
+        fraction_batches_used_threshold (float, optional): Threshold of fraction of batches per channel to determine outlier
+            If fraction is below this, we deem number of samples used to calculate outliers as insignificant and alert user
+            regardless of whether we detected outliers or not in channel to take a closer look at channel results
+            Should be between 0 and 1
+            Default: 0.95
+        ch_axis (int, optional): The channel axis being observed to determine input weight equalization
+            Default: 1
+
+    * :attr:`ratio_threshold`: The threshold for p_r to determine if there are outliers in activations
+        The p_r value (average ratio of 100th percentile/reference_percentile) is compared to ratio_threshold
+        If it is significantly greater, then we consider it an outlier
+        This threshold was calculated based on the ratio of the percentiles in a normal distribution
+        The calculations behind value choice: https://drive.google.com/file/d/1N2wdtXWI-kOH8S7HH4-PYB_NmqzZil4p/view?usp=sharing
+
+    * :attr:`reference_percentile`: The denominator of the top fraction to find the relative scale of the 100th percentile
+        Should be between 0 and 1
+        The calculations behind value choice: https://drive.google.com/file/d/1N2wdtXWI-kOH8S7HH4-PYB_NmqzZil4p/view?usp=sharing
+
+    * :attr:`fraction_batches_used_threshold`: The fraction of batches to determine outliers for each channel should be above this
+        Some batches may not be used because of 0-based errors, so this is to ensure a good amount of the total batches are used
+        Should be between 0 and 1
+
+    * :attr:`ch_axis`: The channel axis being observed to determine outliers
+
+    * :attr:`DEFAULT_PRE_OBSERVER_NAME`: The name of the pre-observer to be inserted for this detector
+    """
+
+    # names for the pre observers that are inserted
+    DEFAULT_PRE_OBSERVER_NAME: str = "model_report_pre_observer"
+
+    # pre activation prefix
+    INPUT_ACTIVATION_PREFIX = "input_activation_"
+
+    # names for dict keys
+    OUTLIER_KEY = "outliers_detected"
+    NUM_BATCHES_KEY = "outlier_detection_batches_used"
+    IS_SUFFICIENT_BATCHES_KEY = "outlier_detection_is_sufficient_batches"
+    COMP_METRIC_KEY = "outlier_detection_percentile_ratios"
+    RATIO_THRES_KEY = "outlier_detection_ratio_threshold"
+    REF_PERCENTILE_KEY = "outlier_detection_reference_percentile"
+    CHANNEL_AXIS_KEY = "outlier_detection_channel_axis"
+    MAX_VALS_KEY = INPUT_ACTIVATION_PREFIX + "per_channel_max"
+    CONSTANT_COUNTS_KEY = "constant_batch_counts"
+
+    def __init__(
+        self,
+        ratio_threshold: float = 3.5,
+        reference_percentile: float = 0.975,
+        fraction_batches_used_threshold: float = 0.95,
+        ch_axis: int = 1,
+    ):
+        # initialize the variables of interest
+        self.ratio_threshold = ratio_threshold
+
+        # make sure passed in percentile is valid
+        if reference_percentile < 0 or reference_percentile > 1:
+            raise AssertionError("reference_percentile must be between 0 and 1")
+        if not (
+            fraction_batches_used_threshold >= 0
+            and fraction_batches_used_threshold <= 1
+        ):
+            raise AssertionError(
+                "fraction_batches_used_threshold must be between 0 and 1"
+            )
+        self.reference_percentile = reference_percentile
+        self.fraction_batches_used_threshold = fraction_batches_used_threshold
+        self.ch_axis = ch_axis
+
+    def get_detector_name(self) -> str:
+        r"""Returns the name of this detector"""
+        return "outlier_detector"
+
+    def _supports_insertion(self, module: nn.Module) -> bool:
+        r"""Returns whether the given module is supported for observers insertion
+
+        Any module that doesn't have children and isn't an observer itself is supported
+
+        Args
+            module: The module to check and ensure is supported
+
+        Returns True if the module is supported by observer, False otherwise
+        """
+        # case for insertion of module
+        # check if the module has any children and isn't observer
+        num_children = len(list(module.children()))
+        return num_children == 0 and not _is_activation_post_process(module)
+
+    def get_qconfig_info(self, model) -> dict[str, DetectorQConfigInfo]:
+        r"""Returns the DetectorQConfigInfo for each module_fqn relevant
+        Args
+            model (nn.Module or subclass): model to find observer insertion points
+
+        Returns a Dict mapping from unique observer fqns (where we want to insert them) to:
+            A DetectorQConfigInfo with the information to generate a QConfig for a specific module
+        """
+        # currently doesn't do anything for outlier detector
+        return {}
+
+    def _supports_report_gen(self, module: nn.Module) -> bool:
+        r"""Returns whether the given module is supported for report generation
+
+        Any module that has a model report pre-observer is supported
+
+        Args
+            module: The module to check and ensure is supported
+
+        Returns True if the module is supported by observer, False otherwise
+        """
+        return hasattr(module, self.DEFAULT_PRE_OBSERVER_NAME)
+
+    def determine_observer_insert_points(
+        self, prepared_fx_model: GraphModule
+    ) -> dict[str, dict[str, Any]]:
+        r"""Determines where observers need to be inserted for the Outlier Detector.
+
+        For this detector, we want to place observers in front of supported layers.
+
+        Currently inserts observers for:
+            all layers that do not have children (leaf level layers)
+
+        Args:
+            prepared_fx_model (GraphModule):  The prepared Fx GraphModule
+
+        Returns a Dict mapping from unique observer fqns (where we want to insert them) to a Dict with:
+            key "target_node" -> the node we are trying to observe with this observer (torch.fx.node.Node)
+            key "observer_to_insert" -> the observer we wish to insert (ObserverBase)
+            key "is_post_observer" -> True if this is meant to be a post-observer for target_node, False if pre-observer
+            key "observer_args" -> The arguments that are meant to be passed into the observer
+        """
+        # observer for this detector is ModelReportObserver
+        obs_ctr = ModelReportObserver
+
+        # return dict
+        obs_fqn_to_info: dict[str, dict[str, Any]] = {}
+
+        for fqn, module in prepared_fx_model.named_modules():
+            # check to see if module is of a supported type
+            if self._supports_insertion(module):
+                # if it's a supported type, we want to get node and add observer insert locations
+                targeted_node = self._get_targeting_node(prepared_fx_model, fqn)
+
+                # add entry for pre-observer
+                pre_obs_fqn = fqn + "." + self.DEFAULT_PRE_OBSERVER_NAME
+
+                obs_fqn_to_info[pre_obs_fqn] = {
+                    DETECTOR_TARGET_NODE_KEY: targeted_node,
+                    DETECTOR_OBS_TO_INSERT_KEY: obs_ctr(
+                        ch_axis=self.ch_axis, comp_percentile=self.reference_percentile
+                    ),
+                    DETECTOR_IS_POST_OBS_KEY: False,
+                    DETECTOR_OBS_ARGS_KEY: targeted_node.args,
+                }
+
+        return obs_fqn_to_info
+
+    def _calculate_outlier_info(
+        self,
+        percentile_ratios: torch.Tensor,
+        counted_batches: torch.Tensor,
+        total_batches: int,
+    ) -> dict[str, list[bool]]:
+        r"""
+        Gives info on whether the percentile ratios calculated would be considered outliers
+        Also gives information on whether the collected data is statistically significant to make this claim
+
+        Args:
+            percentile_ratios (torch.Tensor): The average percentile_ratios per channel calculated by the observer
+            counted_batches (torch.Tensor): The number of batches used for average calculation per tensor
+            total_batches (int): The total number of batches that passed through observer in this epoch
+
+        Returns a dictionary mapping:
+            "outliers_detected" : list of bools per channel that are true if it is considered an outlier
+            "is_sufficient_batches": if o_r was >= fraction_batches_used_threshold:
+                where o_r = counted_batches / total_batches
+        """
+        outlier_dict: dict[str, list[bool]] = {
+            self.OUTLIER_KEY: [],
+            self.IS_SUFFICIENT_BATCHES_KEY: [],
+        }
+
+        # get both as flattened lists for easy mapping
+        ratios_list: list = percentile_ratios.tolist()
+        num_batches_list: list = counted_batches.tolist()
+
+        # calculate whether channels were statistically significant
+        significant_size = [
+            batch_size / total_batches >= self.fraction_batches_used_threshold
+            for batch_size in num_batches_list
+        ]
+        outlier_dict[self.IS_SUFFICIENT_BATCHES_KEY] = significant_size
+
+        # calculate for each channel whether it's an outlier or not based on ratio
+        outlier_detected = [ratio > self.ratio_threshold for ratio in ratios_list]
+        outlier_dict[self.OUTLIER_KEY] = outlier_detected
+
+        # return the dictionary with the two lists
+        return outlier_dict
+
+    def _generate_info_dict(self, model: GraphModule) -> dict[str, dict]:
+        r"""
+        Helper function for generate_detector_report that does the generation of the dictionary.
+        This process is done as specified in generate_detector_report documentation
+
+        Args:
+            model (GraphModule): The prepared and calibrated GraphModule with inserted ModelReportObservers
+
+        Returns a dict mapping relevant module fqns to:
+            whether there were outliers found in activation before
+            the number of batches used for each channel
+            whether fraction of applicable batches used is above fraction_batches_used_threshold
+            their p_r metric compared to the threshold
+            the threshold used to make the recommendation
+            the reference_percentile used to make the recommendation
+            the channel axis used to determine individual channels
+            the constant batch counts per channel
+            the per channel max values
+        """
+        # return dictionary mapping observer fqns to desired info
+        info_dict: dict[str, dict] = {}
+
+        for fqn, module in model.named_modules():
+            # if module is supported and it has a pre-observer
+            if self._supports_report_gen(module):
+                # get pre observer for the module
+                pre_obs: ModelReportObserver = getattr(
+                    module, self.DEFAULT_PRE_OBSERVER_NAME
+                )
+
+                # get the number of batches and calculated ratio thresholds
+                num_batches: torch.Tensor = pre_obs.percentile_batches_tracked
+                average_ratios: torch.Tensor = pre_obs.average_percentile_ratio
+                channel_batch_cnts: torch.Tensor = pre_obs.constant_channels
+                total_batches: int = pre_obs.num_batches_tracked
+
+                # also get the max values
+                max_vals: torch.Tensor = pre_obs.max_val
+
+                # we have to specifically modify how we are recording negative ratio for pre-relu layers
+                for index, ratio_val in enumerate(average_ratios):
+                    # check if we have a negative ratio
+                    # a ratio might be negative if we have a situation where the 100th percentile is
+                    # > 0 while the nth percentile is < 0, in which case this would not be detected
+                    # as an outlier. Since we care more about magnitude, we make it positive.
+                    if ratio_val.item() < 0:
+                        # first make it positive
+                        average_ratios[index] = -ratio_val
+
+                    if ratio_val.item() < 1:
+                        # if it's less than 1 we have the flip it as well
+                        average_ratios[index] = 1 / ratio_val
+
+                outlier_calcs = self._calculate_outlier_info(
+                    average_ratios, num_batches, total_batches
+                )
+
+                # calculate whether ratios were outliers
+                info_dict[fqn] = {
+                    self.CHANNEL_AXIS_KEY: self.ch_axis,
+                    self.REF_PERCENTILE_KEY: self.reference_percentile,
+                    self.RATIO_THRES_KEY: self.ratio_threshold,
+                    self.COMP_METRIC_KEY: average_ratios,
+                    self.NUM_BATCHES_KEY: num_batches,
+                    self.OUTLIER_KEY: outlier_calcs[self.OUTLIER_KEY],
+                    self.IS_SUFFICIENT_BATCHES_KEY: outlier_calcs[
+                        self.IS_SUFFICIENT_BATCHES_KEY
+                    ],
+                    self.CONSTANT_COUNTS_KEY: channel_batch_cnts,
+                    self.MAX_VALS_KEY: max_vals,
+                }
+
+        return info_dict
+
+    def generate_detector_report(
+        self, model: GraphModule
+    ) -> tuple[str, dict[str, Any]]:
+        r"""
+        Determines whether input weight equalization is appropriate for a given module.
+
+        Takes advantage of the ModelReport Observer which records the relevant percentile information
+
+        Args:
+            model (GraphModule): The prepared and calibrated GraphModule with inserted ModelReportObservers
+
+        Returns a tuple with two elements:
+            String report of of whether there are outliers in the activations around certain modules
+            Dictionary mapping modules of interest to:
+                whether there were outliers found in activation before
+                the number of batches used for each channel
+                whether fraction of applicable batches used is above fraction_batches_used_threshold
+                their p_r metric compared to the threshold
+                the threshold used to make the recommendation
+                the reference_percentile used to make the recommendation
+                the channel axis used to determine individual channels
+                the constant batch counts per channel
+                the per channel max values
+        """
+        # generate the information dictionary of outlier information
+        info_dict = self._generate_info_dict(model)
+
+        # now we can generate report based on this information
+        outlier_string = "Outlier detection report: \n"
+
+        # added module check
+        added_module: bool = False
+
+        # some strings to be formatted depending on module we are adding
+        module_suggestion_str = "For Module {} looked at with axis {}: \n"
+        channel_suggestion_str = "\tFor channel {}, we found outliers in the preceding activation data with {}.\n"
+        channel_max_value_str = "a max value across all batches of {}"
+        note_string = "Note: outlier detection is only reliable for {}. We recommend {} to ensure the most accurate results."
+        note_distribution = "stationary distributions"
+        note_rec = "running the static vs. dynamic detector to ensure activation data before modules above is stationary"
+
+        # suggestion for constant batch check since that can make it no outliers
+        constant_str = "\tFor channel {}, we found {} constant value batches. {}\n"
+        constant_suggestion = "We recommend taking a look at the dict and data to see how frequent this occurred and why."
+
+        # compile the suggestion string
+        for module_fqn in info_dict:
+            # get module specific info
+            mod_info: dict[str, Any] = info_dict[module_fqn]
+            # check to see if we already added high level model desc
+            added_model_desc = False
+            # look at each individual channel and add a suggestion
+            for index, outlier_detected in enumerate(mod_info[self.OUTLIER_KEY]):
+                if outlier_detected:
+                    # we found at least 1 outlier
+                    if not added_model_desc:
+                        # add the module level description
+                        outlier_string += module_suggestion_str.format(
+                            module_fqn, self.ch_axis
+                        )
+                        added_model_desc = True
+
+                    # we mark that we found at least one outlier
+                    added_module = True
+                    max_value_found_str = channel_max_value_str.format(
+                        mod_info[self.MAX_VALS_KEY][index]
+                    )
+                    channel_str = channel_suggestion_str.format(
+                        index, max_value_found_str
+                    )
+                    outlier_string += channel_str
+
+                # also check if we found constant batch
+                if mod_info[self.CONSTANT_COUNTS_KEY][index] != 0:
+                    # make sure we add a module level highlight.
+                    if not added_model_desc:
+                        # add the module level description
+                        outlier_string += module_suggestion_str.format(
+                            module_fqn, self.ch_axis
+                        )
+                        added_model_desc = True
+
+                    constant_values_for_channel = mod_info[self.CONSTANT_COUNTS_KEY][
+                        index
+                    ]
+                    formatted_str = constant_str.format(
+                        index, constant_values_for_channel, constant_suggestion
+                    )
+                    outlier_string += formatted_str
+                    # we also added at least one thing to description
+                    added_module = True
+
+        # if found outlier, give suggestion, else give default response
+        if added_module:
+            # compose the note string
+            note_composed = note_string.format(note_distribution, note_rec)
+            outlier_string += note_composed
+        else:
+            outlier_string += "There were no outliers found in the activations.\n"
+
+        return (outlier_string, info_dict)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/_model_report/model_report.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/_model_report/model_report.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ffbff88dd2d80dc237ae779eddd6fad5d26daee
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/_model_report/model_report.py
@@ -0,0 +1,666 @@
+# mypy: allow-untyped-defs
+from collections import OrderedDict
+from collections.abc import Callable
+from typing import Any
+
+import torch
+from torch.ao.quantization.fx._equalize import EqualizationQConfig
+from torch.ao.quantization.fx._model_report.detector import (
+    DETECTOR_IS_POST_OBS_KEY,
+    DETECTOR_OBS_ARGS_KEY,
+    DETECTOR_OBS_TO_INSERT_KEY,
+    DETECTOR_TARGET_NODE_KEY,
+    DetectorBase,
+    DetectorQConfigInfo,
+)
+from torch.ao.quantization.fx._model_report.model_report_visualizer import (
+    ModelReportVisualizer,
+)
+from torch.ao.quantization.fx.graph_module import GraphModule
+from torch.ao.quantization.observer import ObserverBase
+from torch.ao.quantization.qconfig_mapping import QConfig, QConfigMapping
+
+
+class ModelReport:
+    r"""
+    The ModelReport class aims to provide users an easy way to diagnose issues that they run into
+    with their models. The class works with all traceable GraphModules to help diagnose issues,
+    though the requirements on the type of model more-so depends on the specific report the user
+    is trying to generate. With respect to the reports, the ModelReport class is initialized with
+    a set of Detector classes, each of which generate reports on quantization configuration
+    issues a use might have.
+
+    Currently supports generating reports on:
+    - Suggestions for per-channel vs. per-tensor quantization (nn.Module)
+    - Suggestions for dynamic vs static quantization for linear layers (Graph Modules)
+    - Suggestions for input-weight equalization for linear and conv layers (Graph Modules)
+    - Suggestions for outlier detection for all layers (Graph Modules)
+
+    The ModelReport class has the primary functionality of inserting observers (primarily the ModelReportObserver)
+    where needed for each detector to gather the information it needs, and then after calibration, the ModelReport
+    class compiles the report generated by each Detector class into a single report to return to the user. It also
+    has the capability to remove all the observers it inserted as well.
+
+    * :attr:`_model` The model we wish to generate the report for. Must be a traceable GraphModule
+
+    * :attr:`_desired_report_detectors` The set of Detectors representing desired reports from the ModelReport class
+        Make sure that these are all unique types of detectors [do not have more than 1 of the same class]
+
+    * :attr:`_desired_detector_names` The set of detector names of the _desired_report_detectors.
+        This set is generated by calling the get_detector_name() of each detector
+
+    * :attr:`_detector_name_to_observer_fqns` The mapping from each detector to fqns of observers of interest
+        The purpose of this is to keep track of what observers were inserted for each detector, so that they
+        can be removed at the end if desired
+
+    * :attr:`_prepared_flag` A boolean flag that keeps track of whether we have prepared the model or not
+        This is to ensure we only insert observers once with the ModelReport instance
+
+    * :attr:`_removed_observers` A boolean to track if we have removed observers already
+        The purpose is to ensure we don't attempt to remove observers twice with the same ModelReport
+        instance. This also allows the functionality where we can generate the report multiple times
+        as long as we haven't removed the observers yet.
+
+    Note:
+        This class was initially designed to work with the Fx Graph Mode workflow in mind. However,
+        full functionality is available as long as there is a traceable GraphModule that is being used.
+        One method to get a traceable GraphModule without going through the Fx workflow is to use
+        the QuantizationTracer class.
+
+    General Flow for Fx workflow:
+    1.) Initialize ModelReport object with reports of interest by passing in initialized detector objects and model
+    2.) Prepare your model with prepare_fx
+    3.) Call model_report.prepare_detailed_calibration to add relevant observers
+    4.) Calibrate your model with data
+    5.) Call model_report.generate_report on your model to generate report and optionally remove added observers
+    Optional
+        6.) Call model_report.generate_visualizer to get a ModelReportVisualizer instance
+        7.) To help in parsing report information and debugging, view report info as a:
+            - Table
+            - Histogram
+            - Line plot
+    8.) Call model_report.generate_qconfigs to generate the qconfigs based on the report suggestions
+
+    Example (with QuantizationTracer):
+        >>> # xdoctest: +SKIP
+        >>> # get the necessary qconfig
+        >>> config = PrepareCustomConfig()
+        >>> skipped_module_names, skipped_module_classes = (
+        ...     get_skipped_module_name_and_classes(config, False)
+        ... )
+
+        >>> # initialize our model and get GraphModule
+        >>> model = SomeModel()
+        >>> tracer = QuantizationTracer(skipped_module_names, skipped_module_classes)
+        >>> graph_module = GraphModule(model, tracer.trace(model))
+
+        >>> # get our set of detectors and ModelReport instance
+        >>> detector_set = set(
+        ...     [
+        ...         DynamicStaticDetector(tolerance=0.5),
+        ...         InputWeightEqualizationDetector(ratio_threshold=0.7),
+        ...     ]
+        ... )
+        >>> tracer_reporter = ModelReport(graph_module, tracer_detector_set)
+
+        >>> # now we insert the observers and calibrate the model
+        >>> tracer_model_with_observers = tracer_reporter.prepare_detailed_calibration()
+        >>> for i in range(num_callibration_batches):
+        >>>     example_input = get_callibration_input()
+        >>>     tracer_model_with_observers(example_input)
+
+        >>> # finally we generate the reports and optionally remove the observers we inserted
+        >>> reports = tracer_reporter.generate_model_report(
+        ...     remove_inserted_observers=True
+        ... )
+
+        >>> # Optional: we can generate the qconfig mapping based on the suggestions
+        >>> qconfigs = model_report.generate_qconfig_mapping()
+
+        >>> # Optional: we can generate the equalization mapping based on the suggestions
+        >>> qconfigs = model_report.generate_equalization_mapping()
+
+        >>> # Optional: we get a ModelReportVisualizer instance to do any visualizations desired
+        >>> model_report_visualizer = tracer_reporter.generate_visualizer()
+
+    """
+
+    def __init__(self, model: GraphModule, desired_report_detectors: set[DetectorBase]):
+        if len(desired_report_detectors) == 0:
+            raise ValueError("Should include at least 1 desired report")
+
+        # keep track of the model we wish to generate report for
+        self._model: GraphModule = model
+
+        # keep the reports private so they can't be modified
+        self._desired_report_detectors = desired_report_detectors
+        self._desired_detector_names = {
+            detector.get_detector_name() for detector in desired_report_detectors
+        }
+
+        # keep a mapping of desired reports to observers of interest
+        # this is to get the readings, and to remove them, can create a large set
+        # this set can then be used to traverse the graph and remove added observers
+        self._detector_name_to_observer_fqns: dict[str, set[str]] = {}
+
+        # initialize each report to have empty set of observers of interest
+        for desired_report in self._desired_detector_names:
+            self._detector_name_to_observer_fqns[desired_report] = set()
+
+        # flags to ensure that we can only prepare and remove observers once
+        self._prepared_flag = False
+        self._removed_observers = False
+
+        # store the reports that we generated for visualization purposes
+        # initially empty since no reports generated
+        self._generated_reports: dict[str, dict] = {}
+
+    def get_desired_reports_names(self) -> set[str]:
+        """Returns a copy of the desired reports for viewing"""
+        return self._desired_detector_names.copy()
+
+    def get_observers_of_interest(self) -> dict[str, set[str]]:
+        """Returns a copy of the observers of interest for viewing"""
+        return self._detector_name_to_observer_fqns.copy()
+
+    def prepare_detailed_calibration(self) -> GraphModule:
+        r"""
+        Takes in a graph model and inserts the following observers:
+        - ModelReportObserver
+
+        Each observer is inserted based on the desired_reports into the relevant locations
+
+        Right now, each report in self._desired_detector_names has independent insertions
+            However, if a module already has a Observer of the same type, the insertion will not occur
+            This is because all of the same type of Observer collect same information, so redundant
+
+        Returns the same GraphModule with the observers inserted
+        """
+
+        # if already prepared once, cannot prepare again
+        if self._prepared_flag:
+            raise ValueError(
+                "Already ran preparing detailed calibration. Run the report generation next after calibration."
+            )
+
+        # loop through each detector, find where placements should be, and keep track
+        insert_observers_fqns: dict[str, Any] = {}
+
+        for detector in self._desired_report_detectors:
+            # determine observer points for each detector
+            obs_fqn_to_info = detector.determine_observer_insert_points(self._model)
+            # map each insert point to the observer to use
+            insert_observers_fqns.update(obs_fqn_to_info)
+            # update the set of observers this report cares about
+            self._detector_name_to_observer_fqns[detector.get_detector_name()] = set(
+                obs_fqn_to_info.keys()
+            )
+
+        # now insert all the observers at their desired locations
+        for observer_fqn in insert_observers_fqns:
+            target_node = insert_observers_fqns[observer_fqn][DETECTOR_TARGET_NODE_KEY]
+            insert_obs = insert_observers_fqns[observer_fqn][DETECTOR_OBS_TO_INSERT_KEY]
+            insert_post = insert_observers_fqns[observer_fqn][DETECTOR_IS_POST_OBS_KEY]
+            observer_args = insert_observers_fqns[observer_fqn][DETECTOR_OBS_ARGS_KEY]
+            self._insert_observer_around_module(
+                observer_fqn, target_node, insert_obs, observer_args, insert_post
+            )
+
+        self._prepared_flag = True
+
+        return self._model
+
+    def _insert_observer_around_module(
+        self,
+        obs_fqn: str,
+        target_node: torch.fx.node.Node,
+        obs_to_insert: ObserverBase,
+        observer_args: tuple,
+        insert_post: bool,
+    ):
+        r"""
+        Helper function that inserts the observer into both the graph structure and the module of the model
+
+        Args
+            node_fqn (str): The fully qualified name of the observer we want to insert
+            target_node (torch.fx.node.Node): The node in model we are inserting observers around
+            obs_to_insert (ObserverBase): The observer we are inserting around target_node
+            observer_args (Tuple): The arguments we want to pass into the observer
+            insert_post (bool): whether this is meant to be a post observer for this node
+        """
+        # if we are inserting post, then our target node is the next node
+        if insert_post:
+            target_node = target_node.next
+
+        with self._model.graph.inserting_before(target_node):
+            self._model.add_submodule(obs_fqn, obs_to_insert)
+            self._model.graph.create_node(
+                op="call_module", target=obs_fqn, args=observer_args
+            )
+
+        # recompile model after inserts are made
+        self._model.recompile()
+
+    def _get_node_from_fqn(self, node_fqn: str) -> torch.fx.node.Node:
+        r"""
+        Takes in a node fqn and returns the node based on the fqn
+
+        Args
+            node_fqn (str): The fully qualified name of the node we want to find in model
+
+        Returns the Node object of the given node_fqn otherwise returns None
+        """
+        node_to_return = None
+        for node in self._model.graph.nodes:
+            # if the target matches the fqn, it's the node we are looking for
+            if node.target == node_fqn:
+                node_to_return = node
+                break
+
+        if node_to_return is None:
+            raise ValueError("The node_fqn is was not found within the module.")
+
+        # assert for MyPy
+        if not isinstance(node_to_return, torch.fx.node.Node):
+            raise AssertionError("node_to_return must be a torch.fx.node.Node")
+
+        return node_to_return
+
+    def generate_model_report(
+        self, remove_inserted_observers: bool
+    ) -> dict[str, tuple[str, dict]]:
+        r"""
+        Generates all the requested reports.
+
+        Note:
+            You should have calibrated the model with relevant data before calling this
+
+        The reports generated are specified by the desired_reports specified in desired_reports
+
+        Can optionally remove all the observers inserted by the ModelReport instance
+
+        Args:
+            remove_inserted_observers (bool): True to remove the observers inserted by this ModelReport instance
+
+        Returns a mapping of each desired report name to a tuple with:
+            The textual summary of that report information
+            A dictionary containing relevant statistics or information for that report
+
+        Note:
+            Throws exception if we try to generate report on model we already removed observers from
+            Throws exception if we try to generate report without preparing for calibration
+        """
+        # if we haven't prepped model for calibration, then we shouldn't generate report yet
+        if not self._prepared_flag:
+            raise Exception(  # noqa: TRY002
+                "Cannot generate report without preparing model for calibration"
+            )
+
+        # if we already removed the observers, we cannot generate report
+        if self._removed_observers:
+            raise Exception(  # noqa: TRY002
+                "Cannot generate report on model you already removed observers from"
+            )
+
+        # keep track of all the reports of interest and their outputs
+        reports_of_interest = {}
+
+        for detector in self._desired_report_detectors:
+            # generate the individual report for the detector
+            report_output = detector.generate_detector_report(self._model)
+            reports_of_interest[detector.get_detector_name()] = report_output
+
+        # if user wishes to remove inserted observers, go ahead and remove
+        if remove_inserted_observers:
+            self._removed_observers = True
+            # get the set of all Observers inserted by this instance of ModelReport
+            all_observers_of_interest: set[str] = set()
+            for desired_report in self._detector_name_to_observer_fqns:
+                observers_of_interest = self._detector_name_to_observer_fqns[
+                    desired_report
+                ]
+                all_observers_of_interest.update(observers_of_interest)
+
+            # go through all_observers_of_interest and remove them from the graph and model
+            for observer_fqn in all_observers_of_interest:
+                # remove the observer from the model
+                self._model.delete_submodule(observer_fqn)
+
+                # remove the observer from the graph structure
+                node_obj = self._get_node_from_fqn(observer_fqn)
+
+                if node_obj:
+                    self._model.graph.erase_node(node_obj)
+                else:
+                    raise ValueError("Node no longer exists in GraphModule structure")
+
+            # remember to recompile the model
+            self._model.recompile()
+
+        # save the generated reports for visualization purposes
+        saved_reports: dict[str, dict] = {
+            report_name: report_tuple[1]
+            for report_name, report_tuple in reports_of_interest.items()
+        }
+
+        self._generated_reports = saved_reports
+
+        # return the reports of interest
+        return reports_of_interest
+
+    def _is_same_info_for_same_key(self, info_dict_a: dict, info_dict_b: dict) -> bool:
+        r"""
+        Takes in two dictionaries and ensures that any common keys between the two have the same
+        values.
+
+        Args:
+            info_dict_a (Dict): First dictionary we wish to compare
+            info_dict_b (Dict): Second dictionary we wish to compare
+
+        Returns True if all shared keys have same values, false otherwise
+        """
+        # get the set of keys for both
+        dict_a_keys: set = set(info_dict_a.keys())
+        dict_b_keys: set = set(info_dict_b.keys())
+
+        # get the insersection keys and check if same value for both dicts
+        intersecting_keys: set = dict_a_keys.intersection(dict_b_keys)
+
+        for key in intersecting_keys:
+            dict_a_val = info_dict_a[key]
+            dict_b_val = info_dict_b[key]
+
+            # if it's a tensor we have to handle separately
+            if type(dict_a_val) is torch.Tensor:
+                # if dict_b_val not tensor, automatically false
+                if (
+                    type(dict_b_val) is not torch.Tensor
+                    or sum(dict_a_val != dict_b_val) != 0
+                ):
+                    return False
+            else:
+                # for non-tensor vals
+                if dict_a_val != dict_b_val:
+                    return False
+
+        # if no non matching shared keys found, return true
+        return True
+
+    def _reformat_reports_for_visualizer(self) -> OrderedDict:
+        r"""
+        Takes the generated reports and reformats them into the format that is desired by the
+        ModelReportVisualizer
+
+        Returns an OrderedDict mapping module_fqns to their features
+        """
+        # we want to reorder and reformat the information so it is ordered in terms of order
+        # found in the model
+
+        # first create new dict with all modules as keys and features under respective module
+        module_fqns_to_features: dict[str, dict] = {}
+
+        for report_name in self._generated_reports:
+            # get mod -> feature dict and go through
+            module_info = self._generated_reports[report_name]
+
+            for module_fqn in module_info:
+                # check if already in our accumulation dict
+                if module_fqn in module_fqns_to_features:
+                    # we merge all the features together
+                    new_info: dict = module_info[module_fqn]
+                    present_info: dict = module_fqns_to_features[module_fqn]
+
+                    # merge them together into the new unioned dict
+                    # same features keys -> same info, so okay if override
+
+                    # do safety check to make sure shared keys have same info
+                    if self._is_same_info_for_same_key(new_info, present_info):
+                        module_fqns_to_features[module_fqn] = {
+                            **new_info,
+                            **present_info,
+                        }
+                    else:
+                        error_str = "You have the same key with different values across detectors. "
+                        error_str += "Someone incorrectly implemented a detector with conflicting keys to existing detectors."
+                        raise ValueError(error_str)
+                else:
+                    # we just set it
+                    module_fqns_to_features[module_fqn] = module_info[module_fqn]
+
+        # our ordered dict so that modules can be ordered in order of how they appear in model
+        features_by_module: OrderedDict[str, dict] = OrderedDict()
+
+        # we loop through modules in graph in order
+        for fqn, _module in self._model.named_modules():
+            # find that fqn in fqns_to_features
+            if fqn in module_fqns_to_features:
+                # add it to our ordered dict
+                features_by_module[fqn] = module_fqns_to_features[fqn]
+
+        # return the ordered dict of info we created
+        return features_by_module
+
+    def generate_visualizer(self) -> ModelReportVisualizer:
+        r"""
+        Generates a ModelReportVisualizer instance using the reports generated
+        by the generate_model_report() method.
+
+        Returns the generated ModelReportVisualizer instance initialized
+
+        Note:
+            Throws exception if attempt to get visualizers without generating report
+        """
+        # check if user has generated reports at least once
+        if len(self._generated_reports) == 0:
+            raise Exception(  # noqa: TRY002
+                "Unable to generate visualizers without first generating reports"
+            )
+
+        # get the ordered dict mapping modules to their full set of collected features / stats
+        module_fqns_to_features: OrderedDict = self._reformat_reports_for_visualizer()
+
+        # create and return ModelReportVisualizer instance
+        visualizer: ModelReportVisualizer = ModelReportVisualizer(
+            module_fqns_to_features
+        )
+
+        return visualizer
+
+    def _generate_qconfig_mapping_helper(
+        self,
+        detector_qconfig_info_combined: dict[str, DetectorQConfigInfo],
+        generation_function: Callable,
+    ) -> QConfigMapping:
+        r"""
+        This helper takes in the compiled detector qconfig info that
+        has been compiled together and merges it into a QConfigMapping
+        """
+        # keep track of the qconfigmapping
+        qconfig_mapping = QConfigMapping()
+
+        # loop through each module / fqn and attempt to create QConfigMapping
+        for fqn, module in self._model.named_modules():
+            # if we have a qconfig info for this module
+            if fqn in detector_qconfig_info_combined:
+                qconfig_info_compiled = detector_qconfig_info_combined[fqn]
+
+                # now generate the qconfig and add it to the mapping
+                generated_qconfig = generation_function(qconfig_info_compiled, module)
+
+                # add to our config
+                qconfig_mapping.set_module_name(fqn, generated_qconfig)
+
+        # return compiled mapping
+        return qconfig_mapping
+
+    def _update_detector_quantizaiton_qconfig_info(
+        self, combined_info: DetectorQConfigInfo, new_info: DetectorQConfigInfo
+    ):
+        r"""
+        Takes in the old and new information and updates the combined information.
+
+        Args:
+            combined_info (DetectorQConfigInfo): The DetectorQConfigInfo we are compiling all of the information in
+            new_info (DetectorQConfigInfo): The DetectorQConfigInfo with the information we are trying to merge the new info
+                into it
+        """
+        combined_info.is_activation_dynamic = (
+            combined_info.is_activation_dynamic or new_info.is_activation_dynamic
+        )
+        combined_info.is_weight_per_channel = (
+            combined_info.is_weight_per_channel or new_info.is_weight_per_channel
+        )
+
+    def _update_detector_equalization_qconfig_info(
+        self, combined_info: DetectorQConfigInfo, new_info: DetectorQConfigInfo
+    ):
+        r"""
+        Takes in the old and new information and updates the combined information.
+
+        Args:
+            combined_info (DetectorQConfigInfo): The DetectorQConfigInfo we are compiling all of the information in
+            new_info (DetectorQConfigInfo): The DetectorQConfigInfo with the information we are trying to merge the new info
+                into it
+        """
+        is_equalization_recommended = (
+            combined_info.is_equalization_recommended
+            or new_info.is_equalization_recommended
+        )
+        combined_info.is_equalization_recommended = is_equalization_recommended
+
+    def _generate_module_fqn_to_detector_info_mapping(
+        self, update_qconfig_info_function: Callable
+    ) -> dict[str, DetectorQConfigInfo]:
+        r"""
+        Generates a QConfigMapping based on the suggestions of the
+        ModelReport API. The generated mapping encompasses all the
+        different types of feedback from the different detectors
+        all into one place.
+
+        These configs are based on the suggestions provided by the ModelReport API
+        and can only be generated once the reports have been generated.
+
+        Args:
+            update_qconfig_info_function (Callable) takes in a function that takes in two DetectorQConfigInfo
+            and updates the one that is being compiled
+
+        Returns a Dict mapping module_fqns to DetectorQConfigInfo objects
+
+        Note:
+            Throws exception if we try to generate mapping on model we already removed observers from
+            Throws exception if we try to generate mapping without preparing for calibration
+        """
+        # if we haven't prepped model for calibration, then we shouldn't generate mapping yet
+        if not self._prepared_flag:
+            raise Exception(  # noqa: TRY002
+                "Cannot generate report without preparing model for calibration"
+            )
+
+        # if we already removed the observers, we cannot mapping
+        if self._removed_observers:
+            raise Exception(  # noqa: TRY002
+                "Cannot generate report on model you already removed observers from"
+            )
+
+        # keep track of qconfig info for each module across detectors
+        detector_qconfig_info_combined: dict[str, DetectorQConfigInfo] = {}
+
+        for detector in self._desired_report_detectors:
+            # get the info from the detector
+            detector_info: dict[str, DetectorQConfigInfo] = detector.get_qconfig_info(
+                self._model
+            )
+
+            # we go through the modules
+            for module_fqn in detector_info:
+                # see if we already have info on it
+                if module_fqn in detector_qconfig_info_combined:
+                    # we combine the current options with what is there
+                    current_options = detector_qconfig_info_combined[module_fqn]
+                    detector_options = detector_info[module_fqn]
+
+                    update_qconfig_info_function(current_options, detector_options)
+                else:
+                    # we just use this for now
+                    detector_qconfig_info_combined[module_fqn] = detector_info[
+                        module_fqn
+                    ]
+
+        return detector_qconfig_info_combined
+
+    def generate_qconfig_mapping(self) -> QConfigMapping:
+        r"""
+        Generates a QConfigMapping based on the suggestions of the
+        ModelReport API. The generated mapping encompasses all the
+        different types of feedback from the different detectors
+        all into one place.
+
+        These configs are based on the suggestions provided by the ModelReport API
+        and can only be generated once the reports have been generated.
+
+        Returns a QConfigMapping for the quantization configuration
+
+        Note:
+            Throws exception if we try to generate mapping on model we already removed observers from
+            Throws exception if we try to generate mapping without preparing for calibration
+        """
+        # get the mapping info
+        detector_qconfig_info_combined = (
+            self._generate_module_fqn_to_detector_info_mapping(
+                self._update_detector_quantizaiton_qconfig_info
+            )
+        )
+
+        # we will do a bit of processing and remove fqns that don't have input weight recommended
+
+        # now we generate the QConfig for each of the options
+        mapping: QConfigMapping = self._generate_qconfig_mapping_helper(
+            detector_qconfig_info_combined, self._quantization_config_generator
+        )
+
+        # return the generated mapping
+        return mapping
+
+    def _quantization_config_generator(
+        self, detector_qconfig_info: DetectorQConfigInfo, module: torch.nn.Module
+    ) -> QConfig:
+        r"""
+        Returns the quantization configuration generated by the DetectorQConfigInfo object
+        """
+        return detector_qconfig_info.generate_quantization_qconfig(module)
+
+    def _equalization_config_generator(
+        self, detector_qconfig_info: DetectorQConfigInfo, module: torch.nn.Module
+    ) -> EqualizationQConfig:
+        r"""
+        We ignore the module argument here, and only focus on thedetector_qconfig_info
+
+        Returns the equalization configuration generated by the DetectorQConfigInfo object
+        """
+        return detector_qconfig_info.generate_equalization_qconfig()
+
+    def generate_equalization_mapping(self) -> QConfigMapping:
+        r"""
+        Generates a QConfigMapping based on the suggestions of the
+        ModelReport API for equalization. The generated mapping encompasses all the
+        different types of feedback from the input-weight equalization detector.
+
+        These configs are based on the suggestions provided by the ModelReport API
+        and can only be generated once the reports have been generated.
+
+        Returns a QConfigMapping for the equalization configuration
+        """
+        # get the mapping info
+        detector_qconfig_info_combined = (
+            self._generate_module_fqn_to_detector_info_mapping(
+                self._update_detector_equalization_qconfig_info
+            )
+        )
+
+        # now we generate the QConfig for each of the options
+        mapping: QConfigMapping = self._generate_qconfig_mapping_helper(
+            detector_qconfig_info_combined, self._equalization_config_generator
+        )
+
+        # return the generated mapping
+        return mapping
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/_model_report/model_report_observer.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/_model_report/model_report_observer.py
new file mode 100644
index 0000000000000000000000000000000000000000..a809dc60838e574e0bd484ee9698e9d1a0a5ee47
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/_model_report/model_report_observer.py
@@ -0,0 +1,285 @@
+# mypy: allow-untyped-defs
+import torch
+from torch.ao.quantization.observer import ObserverBase
+
+
+class ModelReportObserver(ObserverBase):
+    r"""This observer is used to record additional information regarding keeping track
+    of S = average_batch_activation_range/epoch_activation_range.
+
+    The purpose of this information is to prepare a report to present to users on whether
+    Dynamic or Static Quantization is more appropriate for their model given the general
+    distributions of their data.
+
+    Args:
+        ch_axis (int, optional): The channel axis for which the range and outlier stats are computed
+            Default: 1
+        comp_percentile (float, optional): The percentile to compare against 100 percentile to find outliers
+            Should be between 0 and 1 exclusive
+            Default: 0.9
+
+    * :attr:`num_batches_tracked` specifies number of batches passed through the observer
+
+    * :attr:`average_batch_activation_range` defines average across the ranges of each batch passed through
+
+    * :attr:`epoch_activation_min` defines the minimum value passed through the observer
+
+    * :attr:`epoch_activation_max` defines the maximum value passed through the observer
+
+    * :attr:`ch_axis` defines the channel being used to compute per channel min max stats
+
+    * :attr:`min_val` defines the per channel minimum values passed through
+
+    * :attr:`max_val` defines the per channel maximum values passed through
+
+    * :attr:`comp_percentile` defines comparison percentile to find outliers
+
+    * :attr:`average_percentile_ratio` defines the per channel average percentile ratios
+
+    * :attr:`percentile_batches_tracked` defines the number of percentile batches tracked for each channel
+
+    * :attr:`constant_channels` defines the number of batches that aren't constant channels per channel
+
+    Note: this tool is meant for FX Graph Mode Quantization
+    """
+
+    epoch_activation_min: torch.Tensor
+    epoch_activation_max: torch.Tensor
+    min_val: torch.Tensor
+    max_val: torch.Tensor
+    comp_percentile: torch.Tensor
+    average_percentile_ratio: torch.Tensor
+    percentile_batches_tracked: torch.Tensor
+    constant_channels: torch.Tensor
+
+    def __init__(self, ch_axis: int = 1, comp_percentile: float = 0.9):
+        super().__init__(torch.qint8)
+        self.num_batches_tracked = 0
+
+        # keep track of the min and mix of the range for average batch and epoch as a whole
+        self.average_batch_activation_range: torch.Tensor = torch.tensor(float(0))
+        self.register_buffer("epoch_activation_min", torch.tensor(float("inf")))
+        self.register_buffer("epoch_activation_max", torch.tensor(float("-inf")))
+
+        # keep track of per channel min max information using the given channel
+        self.ch_axis: int = ch_axis
+        self.register_buffer("min_val", torch.tensor([]))
+        self.register_buffer("max_val", torch.tensor([]))
+
+        # keep track of percentile ratio information per channel
+        self.register_buffer("comp_percentile", torch.tensor([comp_percentile]))
+        self.register_buffer("average_percentile_ratio", torch.tensor([]))
+        self.register_buffer("percentile_batches_tracked", torch.tensor([]))
+        self.register_buffer("constant_channels", torch.tensor([]))
+
+    def forward(self, x):
+        x_copy = x.detach()  # avoid keeping autograd tape
+        x_copy = x_copy.to(self.epoch_activation_min.dtype)
+
+        x_copy = self._calculate_range_stats(x_copy)
+        x_copy = self._calculate_min_max_stats(x_copy)
+        x_copy = self._calculate_percentile_stats(x_copy)
+
+        # return the passed in the value
+        return x
+
+    def _calculate_range_stats(self, x_copy):
+        r"""Calculates and stores range stats with forward values.
+
+        Args
+            x_copy: A copy of the forward data
+
+        Returns the passed in x_copy
+        """
+        # get the min, max values of the data
+        min_val_cur, max_val_cur = torch.aminmax(x_copy)
+
+        # calculate new epoch range values
+        epoch_min_val = torch.min(self.epoch_activation_min, min_val_cur)
+        epoch_max_val = torch.max(self.epoch_activation_max, max_val_cur)
+
+        self.epoch_activation_min.copy_(epoch_min_val)
+        self.epoch_activation_max.copy_(epoch_max_val)
+
+        # calculate the average batch activation range
+        current_batch_range = max_val_cur - min_val_cur
+        new_range = (
+            self.average_batch_activation_range * self.num_batches_tracked
+            + current_batch_range
+        ) / (self.num_batches_tracked + 1)
+
+        self.average_batch_activation_range = new_range
+        self.num_batches_tracked += 1  # new batch was processed
+
+        return x_copy
+
+    def _calculate_min_max_stats(self, x_copy):
+        r"""Calculates and stores the per_channel min, max stats with forward values.
+        Does calculation based on channel axis: self.ch_axis
+
+        Args
+            x_copy: A copy of the forward data
+
+        Returns the passed in x_copy
+        """
+        # get the current min and max vals
+        min_val = self.min_val
+        max_val = self.max_val
+        x_dim = x_copy.size()
+
+        new_axis_list = [i for i in range(len(x_dim))]  # noqa: C416
+        new_axis_list[self.ch_axis] = 0
+        new_axis_list[0] = self.ch_axis
+        y = x_copy.permute(new_axis_list)
+        # Need to match dtype of min/max because the updates to buffers
+        # are done in place and types need to match for comparisons
+        y = y.to(self.min_val.dtype)
+        y = torch.flatten(y, start_dim=1)
+        if min_val.numel() == 0 or max_val.numel() == 0:
+            min_val, max_val = torch.aminmax(y, dim=1)
+        else:
+            min_val_cur, max_val_cur = torch.aminmax(y, dim=1)
+            min_val = torch.min(min_val_cur, min_val)
+            max_val = torch.max(max_val_cur, max_val)
+
+        self.min_val.resize_(min_val.shape)
+        self.max_val.resize_(max_val.shape)
+        self.min_val.copy_(min_val)
+        self.max_val.copy_(max_val)
+
+        return x_copy
+
+    def _calculate_percentile_stats(self, x_copy):
+        r"""Calculates and stores the per_channel percentile stats with forward values.
+        Does calculation based on channel axis: self.ch_axis
+
+        Args
+            x_copy: A copy of the forward data
+
+        Returns the passed in x_copy
+        """
+        # get the dimension of the copy
+        x_dim = x_copy.size()
+
+        new_axis_list = [i for i in range(len(x_dim))]  # noqa: C416
+        new_axis_list[self.ch_axis] = 0
+        new_axis_list[0] = self.ch_axis
+        y = x_copy.permute(new_axis_list)
+        # Need to match dtype of min/max because the updates to buffers
+        # are done in place and types need to match for comparisons
+        y = y.to(self.min_val.dtype)
+        y = torch.flatten(y, start_dim=1)
+        y = y.to(dtype=self.min_val.dtype, device="cpu")
+
+        # find the percentile values along the axis
+        # we want both 100th percentile and comp_percentile
+        # we also want to find 0th quartile to see if we have constant channel
+        quantiles_list = [0, self.comp_percentile, 1.00]
+        quantiles_to_find = torch.tensor(quantiles_list, dtype=self.min_val.dtype)
+
+        # find the quantiles
+        desired_quantiles = torch.quantile(
+            y, quantiles_to_find, dim=self.ch_axis, interpolation="lower"
+        )
+        zero_quantile = desired_quantiles[0]
+        comp_quantile = desired_quantiles[1]
+        hundreth_quartile = desired_quantiles[2]
+
+        # if any of the channels have 0s, we ignore that channel for this calculation
+        any_non_zero_quantile_value: torch.Tensor = (
+            comp_quantile != torch.tensor([0])
+        ) | (hundreth_quartile != torch.tensor([0]))
+        any_non_zero_quantile_value = (
+            any_non_zero_quantile_value.int()
+        )  # transform boolean values to int values
+
+        # we also check if we have a constant channel
+        any_constant_channels: torch.Tensor = (
+            hundreth_quartile - zero_quantile
+        ) == torch.tensor([0])
+        any_constant_channels = (
+            any_constant_channels.int()
+        )  # transform boolean values to int values
+
+        # possibilities to get nan as an answer
+        #   will ignore any of these three cases with 0s and just not deal with them for now
+        # case (1) 0 in numerator: issue if 0 is largest, all negative, and rest are really negative
+        # case (2) 0 in denominator: is possible unless case 3, we just ignore
+        # case (3) 0 in both: not outlier, channel just kinda useless, ignore
+
+        # get the ratio and get rid of nan values
+        quantile_ratios = hundreth_quartile / comp_quantile
+        quantile_ratios = torch.nan_to_num(quantile_ratios)
+        # update averages, remembering to only update if didn't have zeros
+        ratio_if_not_zero = any_non_zero_quantile_value * quantile_ratios
+
+        # if num_batches and average_ratio are not initialized, we want to initialize them
+        if (
+            self.percentile_batches_tracked.shape[0] == 0
+            or self.average_percentile_ratio.shape[0] == 0
+        ):
+            self.percentile_batches_tracked = torch.zeros_like(
+                any_non_zero_quantile_value
+            )
+            self.average_percentile_ratio = torch.zeros_like(ratio_if_not_zero)
+
+        # also initialize the constant channel var if that is not initialized separately
+        if self.constant_channels.shape[0] == 0:
+            self.constant_channels = torch.zeros_like(any_constant_channels)
+
+        # get current num batches and average ratio
+        num_batches = self.percentile_batches_tracked
+        average_ratio = self.average_percentile_ratio
+
+        # calculate new_number of batches, new_ratios, and get rid of nans because of 0 size batches
+        new_number_of_batches: torch.Tensor = num_batches + any_non_zero_quantile_value
+        new_ratios: torch.Tensor = (
+            (average_ratio * num_batches) + ratio_if_not_zero
+        ) / new_number_of_batches
+        new_ratios = torch.nan_to_num(new_ratios)
+
+        # update the number of non-constant channels
+        new_constant_count: torch.Tensor = (
+            self.constant_channels + any_constant_channels
+        )
+
+        # update the values locally
+        self.percentile_batches_tracked.copy_(new_number_of_batches)
+        self.average_percentile_ratio.copy_(new_ratios)
+        self.constant_channels.copy_(new_constant_count)
+
+        return x_copy
+
+    @torch.jit.export
+    def get_batch_to_epoch_ratio(self):
+        epoch_activation_range = self.epoch_activation_max - self.epoch_activation_min
+
+        if epoch_activation_range == torch.tensor(float(0)):
+            raise ValueError("Range for Epoch is 0")
+        elif epoch_activation_range == torch.tensor(float("inf")):
+            raise ValueError(
+                "No data has been run through observer or infinity value present"
+            )
+        else:
+            return self.average_batch_activation_range / epoch_activation_range
+
+    @torch.jit.export
+    def reset_batch_and_epoch_values(self):
+        # set all the values back to their original defaults for a new epoch
+        # keep device
+        device = self.max_val.device
+        self.num_batches_tracked = 0
+        self.average_batch_activation_range = torch.tensor(float(0), device=device)
+        self.epoch_activation_min = torch.tensor(float("inf"), device=device)
+        self.epoch_activation_max = torch.tensor(float("-inf"), device=device)
+        self.min_val = torch.tensor([], device=device)
+        self.max_val = torch.tensor([], device=device)
+        self.average_percentile_ratio = torch.tensor([], device=device)
+        self.percentile_batches_tracked = torch.tensor([], device=device)
+        self.constant_channels = torch.tensor([], device=device)
+
+    @torch.jit.export
+    def calculate_qparams(self):  # type: ignore[override]
+        raise Exception(  # noqa: TRY002
+            "calculate_qparams should not be called for ModelReportObserver"
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e58772660c5a9067f727bf066b5519f65f37637
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py
@@ -0,0 +1,712 @@
+# mypy: allow-untyped-defs
+from collections import OrderedDict, OrderedDict as OrdDict
+from typing import Any
+
+import torch
+
+
+# try to import tablate
+got_tabulate = True
+try:
+    from tabulate import tabulate
+except ImportError:
+    got_tabulate = False
+
+
+# var to see if we could import matplotlib
+got_matplotlib = True
+try:
+    import matplotlib.pyplot as plt
+except ImportError:
+    got_matplotlib = False
+
+
+class ModelReportVisualizer:
+    r"""
+    The ModelReportVisualizer class aims to provide users a way to visualize some of the statistics
+    that were generated by the ModelReport API. However, at a higher level, the class aims to provide
+    some level of visualization of statistics to PyTorch in order to make it easier to parse data and
+    diagnose any potential issues with data or a specific model. With respect to the visualizations,
+    the ModelReportVisualizer class currently supports several methods of visualizing data.
+
+    Supported Visualization Methods Include:
+    - Table format
+    - Plot format (line graph)
+    - Histogram format
+
+    For all of the existing visualization methods, there is the option to filter data based on:
+    - A module fqn prefix
+    - Feature [required for the plot and histogram]
+
+    * :attr:`generated_reports` The reports generated by the ModelReport class in the structure below
+        Ensure sure that features that are the same across different report contain the same name
+        Ensure that objects representing the same features are the same type / dimension (where applicable)
+
+    Note:
+        Currently, the ModelReportVisualizer class supports visualization of data generated by the
+        ModelReport class. However, this structure is extensible and should allow the visualization of
+        other information as long as the information is structured in the following general format:
+
+        Report Structure
+        -- module_fqn [module with attached detectors]
+            |
+            -- feature keys [not every detector extracts same information]
+                                    [same collected info has same keys, unless can be specific to detector]
+
+
+    The goal behind the class is that the generated visualizations can be used in conjunction with the generated
+    report for people to get a better understanding of issues and what the fix might be. It is also just to provide
+    a good visualization platform, since it might be hard to parse through the ModelReport returned dictionary as
+    that grows in size.
+
+    General Use Flow Expected
+    1.) Initialize ModelReport object with reports of interest by passing in initialized detector objects
+    2.) Prepare your model with prepare_fx
+    3.) Call model_report.prepare_detailed_calibration on your model to add relevant observers
+    4.) Calibrate your model with data
+    5.) Call model_report.generate_report on your model to generate report and optionally remove added observers
+    6.) Use output of model_report.generate_report to initialize ModelReportVisualizer instance
+    7.) Use instance to view different views of data as desired, applying filters as needed
+        8.) Either see the super detailed information or just the actual printed or shown table / plot / histogram
+
+    """
+
+    # keys for table dict
+    TABLE_TENSOR_KEY = "tensor_level_info"
+    TABLE_CHANNEL_KEY = "channel_level_info"
+
+    # Constants for header vals
+    NUM_NON_FEATURE_TENSOR_HEADERS = 2
+    NUM_NON_FEATURE_CHANNEL_HEADERS = 3
+
+    # Constants for row index in header
+    CHANNEL_NUM_INDEX = 2
+
+    def __init__(self, generated_reports: OrderedDict[str, Any]):
+        r"""
+        Initializes the ModelReportVisualizer instance with the necessary reports.
+
+        Args:
+            generated_reports (Dict[str, Any]): The reports generated by the ModelReport class
+                can also be a dictionary generated in another manner, as long as format is same
+        """
+        self.generated_reports = generated_reports
+
+    def get_all_unique_module_fqns(self) -> set[str]:
+        r"""
+        The purpose of this method is to provide a user the set of all module_fqns so that if
+        they wish to use some of the filtering capabilities of the ModelReportVisualizer class,
+        they don't need to manually parse the generated_reports dictionary to get this information.
+
+        Returns all the unique module fqns present in the reports the ModelReportVisualizer
+        instance was initialized with.
+        """
+        # returns the keys of the ordered dict
+        return set(self.generated_reports.keys())
+
+    def get_all_unique_feature_names(
+        self, plottable_features_only: bool = True
+    ) -> set[str]:
+        r"""
+        The purpose of this method is to provide a user the set of all feature names so that if
+        they wish to use the filtering capabilities of the generate_table_view(), or use either of
+        the generate_plot_view() or generate_histogram_view(), they don't need to manually parse
+        the generated_reports dictionary to get this information.
+
+        Args:
+            plottable_features_only (bool): True if the user is only looking for plottable features,
+                False otherwise
+                plottable features are those that are tensor values
+                Default: True (only return those feature names that are plottable)
+
+        Returns all the unique module fqns present in the reports the ModelReportVisualizer
+        instance was initialized with.
+        """
+        unique_feature_names = set()
+        for module_fqn in self.generated_reports:
+            # get dict of the features
+            feature_dict: dict[str, Any] = self.generated_reports[module_fqn]
+
+            # loop through features
+            for feature_name in feature_dict:
+                # if we need plottable, ensure type of val is tensor
+                if (
+                    not plottable_features_only
+                    or type(feature_dict[feature_name]) is torch.Tensor
+                ):
+                    unique_feature_names.add(feature_name)
+
+        # return our compiled set of unique feature names
+        return unique_feature_names
+
+    def _get_filtered_data(
+        self, feature_filter: str, module_fqn_filter: str
+    ) -> OrderedDict[str, Any]:
+        r"""
+        Filters the data and returns it in the same ordered dictionary format so the relevant views can be displayed.
+
+        Args:
+            feature_filter (str): The feature filter, if we want to filter the set of data to only include
+                a certain set of features that include feature_filter
+                If feature = "", then we do not filter based on any features
+            module_fqn_filter (str): The filter on prefix for the module fqn. All modules that have fqn with
+                this prefix will be included
+                If module_fqn_filter = "" we do not filter based on module fqn, and include all modules
+
+        First, the data is filtered based on module_fqn, and then filtered based on feature
+        Returns an OrderedDict (sorted in order of model) mapping:
+            module_fqns -> feature_names -> values
+        """
+        # create return dict
+        filtered_dict: OrderedDict[str, Any] = OrdDict()
+
+        for module_fqn in self.generated_reports:
+            # first filter based on module
+            if module_fqn_filter == "" or module_fqn_filter in module_fqn:
+                # create entry for module and loop through features
+                filtered_dict[module_fqn] = {}
+                module_reports = self.generated_reports[module_fqn]
+                for feature_name in module_reports:
+                    # check if filtering on features and do so if desired
+                    if feature_filter == "" or feature_filter in feature_name:
+                        filtered_dict[module_fqn][feature_name] = module_reports[
+                            feature_name
+                        ]
+
+        # we have populated the filtered dict, and must return it
+
+        return filtered_dict
+
+    def _generate_tensor_table(
+        self,
+        filtered_data: OrderedDict[str, dict[str, Any]],
+        tensor_features: list[str],
+    ) -> tuple[list, list]:
+        r"""
+        Takes in the filtered data and features list and generates the tensor headers and table
+
+        Currently meant to generate the headers and table for both the tensor information.
+
+        Args:
+            filtered_data (OrderedDict[str, Dict[str, Any]]): An OrderedDict (sorted in order of model) mapping:
+                module_fqns -> feature_names -> values
+            tensor_features (List[str]): A list of the tensor level features
+
+        Returns a tuple with:
+            A list of the headers of the tensor table
+            A list of lists containing the table information row by row
+            The 0th index row will contain the headers of the columns
+            The rest of the rows will contain data
+        """
+        # now we compose the tensor information table
+        tensor_table: list[list[Any]] = []
+        tensor_headers: list[str] = []
+
+        # append the table row to the table only if we have features
+        if len(tensor_features) > 0:
+            # now we add all the data
+            for index, module_fqn in enumerate(filtered_data):
+                # we make a new row for the tensor table
+                tensor_table_row = [index, module_fqn]
+                for feature in tensor_features:
+                    # we iterate in same order of added features
+
+                    if feature in filtered_data[module_fqn]:
+                        # add value if applicable to module
+                        feature_val = filtered_data[module_fqn][feature]
+                    else:
+                        # add that it is not applicable
+                        feature_val = "Not Applicable"
+
+                    # if it's a tensor we want to extract val
+                    if isinstance(feature_val, torch.Tensor):
+                        feature_val = feature_val.item()
+
+                    # we add to our list of values
+                    # pyrefly: ignore [bad-argument-type]
+                    tensor_table_row.append(feature_val)
+
+                tensor_table.append(tensor_table_row)
+
+        # add row of headers of we actually have something, otherwise just empty
+        if len(tensor_table) != 0:
+            tensor_headers = ["idx", "layer_fqn"] + tensor_features
+
+        return (tensor_headers, tensor_table)
+
+    def _generate_channels_table(
+        self,
+        filtered_data: OrderedDict[str, Any],
+        channel_features: list[str],
+        num_channels: int,
+    ) -> tuple[list, list]:
+        r"""
+        Takes in the filtered data and features list and generates the channels headers and table
+
+        Currently meant to generate the headers and table for both the channels information.
+
+        Args:
+            filtered_data (OrderedDict[str, Any]): An OrderedDict (sorted in order of model) mapping:
+                module_fqns -> feature_names -> values
+            channel_features (List[str]): A list of the channel level features
+            num_channels (int): Number of channels in the channel data
+
+        Returns a tuple with:
+            A list of the headers of the channel table
+            A list of lists containing the table information row by row
+            The 0th index row will contain the headers of the columns
+            The rest of the rows will contain data
+        """
+        # now we compose the table for the channel information table
+        channel_table: list[list[Any]] = []
+        channel_headers: list[str] = []
+
+        # counter to keep track of number of entries in
+        channel_table_entry_counter: int = 0
+
+        if len(channel_features) > 0:
+            # now we add all channel data
+            for module_fqn in filtered_data:
+                # we iterate over all channels
+                for channel in range(num_channels):
+                    # we make a new row for the channel
+                    new_channel_row = [channel_table_entry_counter, module_fqn, channel]
+                    for feature in channel_features:
+                        if feature in filtered_data[module_fqn]:
+                            # add value if applicable to module
+                            feature_val = filtered_data[module_fqn][feature][channel]
+                        else:
+                            # add that it is not applicable
+                            feature_val = "Not Applicable"
+
+                        # if it's a tensor we want to extract val
+                        if type(feature_val) is torch.Tensor:
+                            feature_val = feature_val.item()
+
+                        # add value to channel specific row
+                        # pyrefly: ignore [bad-argument-type]
+                        new_channel_row.append(feature_val)
+
+                    # add to table and increment row index counter
+                    channel_table.append(new_channel_row)
+                    channel_table_entry_counter += 1
+
+        # add row of headers of we actually have something, otherwise just empty
+        if len(channel_table) != 0:
+            channel_headers = ["idx", "layer_fqn", "channel"] + channel_features
+
+        return (channel_headers, channel_table)
+
+    def generate_filtered_tables(
+        self, feature_filter: str = "", module_fqn_filter: str = ""
+    ) -> dict[str, tuple[list, list]]:
+        r"""
+        Takes in optional filter values and generates two tables with desired information.
+
+        The generated tables are presented in both a list-of-lists format
+
+        The reason for the two tables are that they handle different things:
+        1.) the first table handles all tensor level information
+        2.) the second table handles and displays all channel based information
+
+        The reasoning for this is that having all the info in one table can make it ambiguous which collected
+            statistics are global, and which are actually per-channel, so it's better to split it up into two
+            tables. This also makes the information much easier to digest given the plethora of statistics collected
+
+        Tensor table columns:
+            idx  layer_fqn  feature_1   feature_2   feature_3   .... feature_n
+            ----  ---------  ---------   ---------   ---------        ---------
+
+        Per-Channel table columns:
+            idx  layer_fqn  channel  feature_1   feature_2   feature_3   .... feature_n
+            ----  ---------  -------  ---------   ---------   ---------        ---------
+
+        Args:
+            feature_filter (str, optional): Filters the features presented to only those that
+                contain this filter substring
+                Default = "", results in all the features being printed
+            module_fqn_filter (str, optional): Only includes modules that contains this string
+                Default = "", results in all the modules in the reports to be visible in the table
+
+        Returns a dictionary with two keys:
+            (Dict[str, Tuple[List, List]]) A dict containing two keys:
+            "tensor_level_info", "channel_level_info"
+                Each key maps to a tuple with:
+                    A list of the headers of each table
+                    A list of lists containing the table information row by row
+                    The 0th index row will contain the headers of the columns
+                    The rest of the rows will contain data
+
+        Example Use:
+            >>> # xdoctest: +SKIP("undefined variables")
+            >>> mod_report_visualizer.generate_filtered_tables(
+            ...     feature_filter="per_channel_min", module_fqn_filter="block1"
+            ... )  # generates table with per_channel_min info for all modules in block 1 of the model
+        """
+        # first get the filtered data
+        filtered_data: OrderedDict[str, Any] = self._get_filtered_data(
+            feature_filter, module_fqn_filter
+        )
+
+        # now we split into tensor and per-channel data
+        tensor_features: set[str] = set()
+        channel_features: set[str] = set()
+
+        # keep track of the number of channels we have
+        num_channels: int = 0
+
+        for module_fqn in filtered_data:
+            for feature_name in filtered_data[module_fqn]:
+                # get the data for that specific feature
+                feature_data = filtered_data[module_fqn][feature_name]
+
+                # check if not zero dim tensor
+                is_tensor: bool = isinstance(feature_data, torch.Tensor)
+                is_not_zero_dim: bool = is_tensor and len(feature_data.shape) != 0
+
+                if is_not_zero_dim or isinstance(feature_data, list):
+                    # works means per channel
+                    channel_features.add(feature_name)
+                    num_channels = len(feature_data)
+                else:
+                    # means is per-tensor
+                    tensor_features.add(feature_name)
+
+        # we make them lists for iteration purposes
+        tensor_features_list: list[str] = sorted(tensor_features)
+        channel_features_list: list[str] = sorted(channel_features)
+
+        # get the tensor info
+        tensor_headers, tensor_table = self._generate_tensor_table(
+            filtered_data, tensor_features_list
+        )
+
+        # get the channel info
+        channel_headers, channel_table = self._generate_channels_table(
+            filtered_data, channel_features_list, num_channels
+        )
+
+        # let's now create the dictionary to return
+        table_dict = {
+            self.TABLE_TENSOR_KEY: (tensor_headers, tensor_table),
+            self.TABLE_CHANNEL_KEY: (channel_headers, channel_table),
+        }
+
+        # return the two tables
+        return table_dict
+
+    def generate_table_visualization(
+        self, feature_filter: str = "", module_fqn_filter: str = ""
+    ):
+        r"""
+        Takes in optional filter values and prints out formatted tables of the information.
+
+        The reason for the two tables printed out instead of one large one are that they handle different things:
+        1.) the first table handles all tensor level information
+        2.) the second table handles and displays all channel based information
+
+        The reasoning for this is that having all the info in one table can make it ambiguous which collected
+            statistics are global, and which are actually per-channel, so it's better to split it up into two
+            tables. This also makes the information much easier to digest given the plethora of statistics collected
+
+        Tensor table columns:
+         idx  layer_fqn  feature_1   feature_2   feature_3   .... feature_n
+        ----  ---------  ---------   ---------   ---------        ---------
+
+        Per-Channel table columns:
+
+         idx  layer_fqn  channel  feature_1   feature_2   feature_3   .... feature_n
+        ----  ---------  -------  ---------   ---------   ---------        ---------
+
+        Args:
+            feature_filter (str, optional): Filters the features presented to only those that
+                contain this filter substring
+                Default = "", results in all the features being printed
+            module_fqn_filter (str, optional): Only includes modules that contains this string
+                Default = "", results in all the modules in the reports to be visible in the table
+
+        Example Use:
+            >>> # xdoctest: +SKIP("undefined variables")
+            >>> mod_report_visualizer.generate_table_visualization(
+            ...     feature_filter="per_channel_min", module_fqn_filter="block1"
+            ... )
+            >>> # prints out neatly formatted table with per_channel_min info
+            >>> # for all modules in block 1 of the model
+        """
+        # see if we got tabulate
+        if not got_tabulate:
+            print("Make sure to install tabulate and try again.")
+            return None
+
+        # get the table dict and the specific tables of interest
+        table_dict = self.generate_filtered_tables(feature_filter, module_fqn_filter)
+        tensor_headers, tensor_table = table_dict[self.TABLE_TENSOR_KEY]
+        channel_headers, channel_table = table_dict[self.TABLE_CHANNEL_KEY]
+
+        # get the table string and print it out
+        # now we have populated the tables for each one
+        # let's create the strings to be returned
+        table_str = ""
+        # the tables will have some headers columns that are non-feature
+        # ex. table index, module name, channel index, etc.
+        # we want to look at header columns for features, that come after those headers
+        if len(tensor_headers) > self.NUM_NON_FEATURE_TENSOR_HEADERS:
+            # if we have at least one tensor level feature to be added we add tensor table
+            table_str += "Tensor Level Information \n"
+            table_str += tabulate(tensor_table, headers=tensor_headers)
+        if len(channel_headers) > self.NUM_NON_FEATURE_CHANNEL_HEADERS:
+            # if we have at least one channel level feature to be added we add tensor table
+            table_str += "\n\n Channel Level Information \n"
+            table_str += tabulate(channel_table, headers=channel_headers)
+
+        # if no features at all, let user know
+        if table_str == "":
+            table_str = "No data points to generate table with."
+
+        print(table_str)
+
+    def _get_plottable_data(
+        self, feature_filter: str, module_fqn_filter: str
+    ) -> tuple[list, list[list], bool]:
+        r"""
+        Takes in the feature filters and module filters and outputs the x and y data for plotting
+
+        Args:
+            feature_filter (str): Filters the features presented to only those that
+                contain this filter substring
+            module_fqn_filter (str): Only includes modules that contains this string
+
+        Returns a tuple of three elements
+            The first is a list containing relevant x-axis data
+            The second is a list containing the corresponding y-axis data
+            If the data is per channel
+        """
+        # get the table dict and the specific tables of interest
+        table_dict = self.generate_filtered_tables(feature_filter, module_fqn_filter)
+        tensor_headers, tensor_table = table_dict[self.TABLE_TENSOR_KEY]
+        channel_headers, channel_table = table_dict[self.TABLE_CHANNEL_KEY]
+
+        # make sure it is only 1 feature that is being plotted
+        # get the number of features in each of these
+        tensor_info_features_count = (
+            len(tensor_headers) - ModelReportVisualizer.NUM_NON_FEATURE_TENSOR_HEADERS
+        )
+        channel_info_features_count = (
+            len(channel_headers) - ModelReportVisualizer.NUM_NON_FEATURE_CHANNEL_HEADERS
+        )
+
+        # see if valid tensor or channel plot
+        is_valid_per_tensor_plot: bool = tensor_info_features_count == 1
+        is_valid_per_channel_plot: bool = channel_info_features_count == 1
+
+        # offset should either be one of tensor or channel table or neither
+        feature_column_offset = ModelReportVisualizer.NUM_NON_FEATURE_TENSOR_HEADERS
+        table = tensor_table
+
+        # if a per_channel plot, we have different offset and table
+        if is_valid_per_channel_plot:
+            feature_column_offset = (
+                ModelReportVisualizer.NUM_NON_FEATURE_CHANNEL_HEADERS
+            )
+            table = channel_table
+
+        x_data: list = []
+        y_data: list[list] = []
+        # the feature will either be a tensor feature or channel feature
+        if is_valid_per_tensor_plot:
+            for table_row_num, row in enumerate(table):
+                # get x_value to append
+                x_val_to_append = table_row_num
+                # the index of the feature will the 0 + num non feature columns
+                tensor_feature_index = feature_column_offset
+                row_value = row[tensor_feature_index]
+                if type(row_value) is not str:
+                    x_data.append(x_val_to_append)
+                    y_data.append(row_value)
+        elif is_valid_per_channel_plot:
+            # gather the x_data and multiple y_data
+            # calculate the number of channels
+            num_channels: int = max(row[self.CHANNEL_NUM_INDEX] for row in table) + 1
+
+            # separate data list per channel
+            y_data.extend([] for _ in range(num_channels))
+
+            for table_row_num, row in enumerate(table):
+                # get x_value to append
+                x_val_to_append = table_row_num
+                current_channel = row[
+                    self.CHANNEL_NUM_INDEX
+                ]  # initially chose current channel
+                new_module_index: int = table_row_num // num_channels
+                x_val_to_append = new_module_index
+
+                # the index of the feature will the 0 + num non feature columns
+                tensor_feature_index = feature_column_offset
+                row_value = row[tensor_feature_index]
+                if type(row_value) is not str:
+                    # only append if new index we are appending
+                    if len(x_data) == 0 or x_data[-1] != x_val_to_append:
+                        x_data.append(x_val_to_append)
+
+                    # append value for that channel
+                    y_data[current_channel].append(row_value)
+        else:
+            # more than one feature was chosen
+            error_str = "Make sure to pick only a single feature with your filter to plot a graph."
+            error_str += " We recommend calling get_all_unique_feature_names() to find unique feature names."
+            error_str += " Pick one of those features to plot."
+            raise ValueError(error_str)
+
+        # return x, y values, and if data is per-channel
+        return (x_data, y_data, is_valid_per_channel_plot)
+
+    def generate_plot_visualization(
+        self, feature_filter: str, module_fqn_filter: str = ""
+    ):
+        r"""
+        Takes in a feature and optional module_filter and plots of the desired data.
+
+        For per channel features, it averages the value across the channels and plots a point
+        per module. The reason for this is that for models with hundreds of channels, it can
+        be hard to differentiate one channel line from another, and so the point of generating
+        a single average point per module is to give a sense of general trends that encourage
+        further deep dives.
+
+        Note:
+            Only features in the report that have tensor value data are plottable by this class
+            When the tensor information is plotted, it will plot:
+                idx as the x val, feature value as the y_val
+            When the channel information is plotted, it will plot:
+                the first idx of each module as the x val, feature value as the y_val [for each channel]
+                The reason for this is that we want to be able to compare values across the
+                channels for same layer, and it will be hard if values are staggered by idx
+                This means each module is represented by only 1 x value
+        Args:
+            feature_filter (str): Filters the features presented to only those that
+                contain this filter substring
+            module_fqn_filter (str, optional): Only includes modules that contains this string
+                Default = "", results in all the modules in the reports to be visible in the table
+
+        Example Use:
+            >>> # xdoctest: +SKIP("undefined variables")
+            >>> mod_report_visualizer.generate_plot_visualization(
+            ...     feature_filter="per_channel_min", module_fqn_filter="block1"
+            ... )
+            >>> # outputs line plot of per_channel_min information for all
+            >>> # modules in block1 of model each channel gets it's own line,
+            >>> # and it's plotted across the in-order modules on the x-axis
+        """
+        # checks if we have matplotlib and let's user know to install it if don't
+        if not got_matplotlib:
+            print("make sure to install matplotlib and try again.")
+            return None
+
+        # get the x and y data and if per channel
+        x_data, y_data, data_per_channel = self._get_plottable_data(
+            feature_filter, module_fqn_filter
+        )
+
+        # plot based on whether data is per channel or not
+        ax = plt.subplot()
+        ax.set_ylabel(feature_filter)
+        ax.set_title(feature_filter + " Plot")
+        plt.xticks(x_data)  # only show ticks for actual points
+
+        if data_per_channel:
+            ax.set_xlabel("First idx of module")
+            # set the legend as well
+            # plot a single line that is average of the channel values
+            num_modules = len(
+                y_data[0]
+            )  # all y_data have same length, so get num modules
+            num_channels = len(
+                y_data
+            )  # we want num channels to be able to calculate average later
+
+            avg_vals = [
+                sum(y_data[:][index]) / num_channels for index in range(num_modules)
+            ]
+
+            # plot the three things we measured
+            ax.plot(
+                x_data, avg_vals, label=f"Average Value Across {num_channels} Channels"
+            )
+            ax.legend(loc="upper right")
+        else:
+            ax.set_xlabel("idx")
+            ax.plot(x_data, y_data)
+
+        # actually show the plot
+        plt.show()
+
+    def generate_histogram_visualization(
+        self, feature_filter: str, module_fqn_filter: str = "", num_bins: int = 10
+    ):
+        r"""
+        Takes in a feature and optional module_filter and plots the histogram of desired data.
+
+        Note:
+            Only features in the report that have tensor value data can be viewed as a histogram
+            If you want to plot a histogram from all the channel values of a specific feature for
+                a specific model, make sure to specify both the model and the feature properly
+                in the filters and you should be able to see a distribution of the channel data
+
+        Args:
+            feature_filter (str, optional): Filters the features presented to only those that
+                contain this filter substring
+                Default = "", results in all the features being printed
+            module_fqn_filter (str, optional): Only includes modules that contains this string
+                Default = "", results in all the modules in the reports to be visible in the table
+            num_bins (int, optional): The number of bins to create the histogram with
+                Default = 10, the values will be split into 10 equal sized bins
+
+        Example Use:
+            >>> # xdoctest: +SKIP
+            >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization(
+            ...     feature_filter="per_channel_min", module_fqn_filter="block1"
+            ... )
+            # outputs histogram of per_channel_min information for all modules in block1 of model
+                information is gathered across all channels for all modules in block 1 for the
+                per_channel_min and is displayed in a histogram of equally sized bins
+        """
+        # checks if we have matplotlib and let's user know to install it if don't
+        if not got_matplotlib:
+            print("make sure to install matplotlib and try again.")
+            return None
+
+        # get the x and y data and if per channel
+        _x_data, y_data, data_per_channel = self._get_plottable_data(
+            feature_filter, module_fqn_filter
+        )
+
+        # for histogram, we just care about plotting the y data
+        # plot based on whether data is per channel or not
+        ax = plt.subplot()
+        ax.set_xlabel(feature_filter)
+        ax.set_ylabel("Frequency")
+        ax.set_title(feature_filter + " Histogram")
+
+        if data_per_channel:
+            # set the legend as well
+            # combine all the data
+            all_data = []
+            for channel_info in y_data:
+                all_data.extend(channel_info)
+
+            _val, bins, _ = plt.hist(
+                all_data,
+                bins=num_bins,
+                stacked=True,
+                rwidth=0.8,
+            )
+            plt.xticks(bins)
+        else:
+            _val, bins, _ = plt.hist(
+                y_data,
+                bins=num_bins,
+                stacked=False,
+                rwidth=0.8,
+            )
+            plt.xticks(bins)
+
+        plt.show()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/convert.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/convert.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a19a40cab908baa78fffeb89f46eedc71976736
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/convert.py
@@ -0,0 +1,1323 @@
+# mypy: ignore-errors
+
+import copy
+import operator
+import warnings
+from typing import Any, TYPE_CHECKING
+
+import torch
+from torch.ao.quantization import CUSTOM_KEY, NUMERIC_DEBUG_HANDLE_KEY
+from torch.ao.quantization.backend_config import (
+    BackendConfig,
+    get_native_backend_config,
+)
+from torch.ao.quantization.backend_config.utils import (
+    get_fused_module_classes,
+    get_pattern_to_dtype_configs,
+    get_qat_module_classes,
+    get_root_module_to_quantized_reference_module,
+)
+from torch.ao.quantization.observer import _is_activation_post_process
+from torch.ao.quantization.qconfig import qconfig_equals, QConfigAny
+from torch.ao.quantization.qconfig_mapping import QConfigMapping
+from torch.ao.quantization.quant_type import QuantType
+from torch.ao.quantization.quantize import _remove_qconfig
+from torch.ao.quantization.stubs import DeQuantStub
+from torch.ao.quantization.utils import (
+    _parent_name,
+    activation_is_statically_quantized,
+    get_qparam_dict,
+    get_swapped_custom_module_class,
+    is_per_channel,
+    to_underlying_dtype,
+    weight_is_quantized,
+)
+from torch.fx import GraphModule
+from torch.fx.graph import Argument, Graph, Node
+from torch.nn.utils.parametrize import type_before_parametrizations
+
+# importing the lib so that the quantized_decomposed ops are registered
+from ._decomposed import quantized_decomposed_lib  # noqa: F401
+from ._equalize import convert_eq_obs, update_obs_for_equalization
+from .custom_config import ConvertCustomConfig, PrepareCustomConfig
+from .graph_module import _is_observed_module, _is_observed_standalone_module
+from .lower_to_fbgemm import lower_to_fbgemm
+from .qconfig_mapping_utils import (
+    _compare_prepare_convert_qconfig_mappings,
+    _generate_node_name_to_qconfig,
+    _is_qconfig_supported_by_dtype_configs,
+    _update_qconfig_for_fusion,
+    _update_qconfig_for_qat,
+)
+from .utils import (
+    _get_module,
+    _is_custom_module_lstm,
+    _is_custom_module_mha,
+    assert_and_get_unique_device,
+    collect_producer_nodes,
+    create_getattr_from_value,
+    get_custom_module_class_keys,
+    graph_module_from_producer_nodes,
+    node_arg_is_weight,
+)
+
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+
+__all__ = [
+    "convert",
+    "convert_custom_module",
+    "convert_standalone_module",
+    "convert_weighted_module",
+]
+
+SUPPORTED_QDTYPES = [
+    torch.quint8,
+    torch.qint8,
+    torch.qint32,
+    torch.uint8,
+    torch.int8,
+    torch.uint16,
+    torch.int16,
+    torch.int32,
+    torch.float8_e5m2,
+    torch.float8_e4m3fn,
+]
+
+_QSCHEME_TO_CHOOSE_QPARAMS_OP = {
+    torch.per_tensor_affine: torch.ops.quantized_decomposed.choose_qparams.tensor,
+    torch.per_tensor_symmetric: torch.ops.quantized_decomposed.choose_qparams_symmetric.tensor,
+}
+
+
+def _replace_observer_with_quantize_dequantize_node_decomposed(
+    model: torch.fx.GraphModule,
+    node: Node,
+    modules: dict[str, torch.nn.Module],
+    node_name_to_scope: dict[str, tuple[str, type]],
+    node_name_to_qconfig: dict[str, QConfigAny],
+    model_device: torch.device | None = None,
+) -> None:
+    """Replace activation_post_process module call node with quantize and
+    dequantize node working with decomposed Tensor
+
+    Before:
+    ... -> observer_0(x) -> ...
+    After:
+    ... -> torch.ops.quantized_decomposed.quantize_per_tensor(x, ...) ->
+    torch.ops.quantized_decomposed.dequantize_per_tensor() -> ...
+
+    or quantize_per_channel and dequantize_per_channel
+    """
+    graph = model.graph
+    if modules is None:
+        raise AssertionError("modules must not be None")
+    if not isinstance(node.target, str):
+        raise AssertionError(
+            f"Expected node.target to be a str, but got {type(node.target)}"
+        )
+    module_path, prefix = _get_module_path_and_prefix(
+        node, node_name_to_scope, node_name_to_qconfig
+    )
+    activation_post_process = modules[node.target]
+    if hasattr(activation_post_process, "convert"):
+        activation_post_process.convert(model, node)
+        return
+    # skip replacing observers to quant/dequant nodes if the qconfigs of all
+    # consumers and producers of this observer are None
+    skip_replacement = all(
+        _has_none_qconfig(n, node_name_to_qconfig)
+        for n in list(node.args) + list(node.users.keys())
+    )
+    if skip_replacement or not _is_conversion_supported(activation_post_process):
+        # didn't find corresponding quantize op and info for the activation_post_process
+        # so we just remove the observer
+        with graph.inserting_before(node):
+            node.replace_all_uses_with(node.args[0])
+            graph.erase_node(node)
+        return
+
+    # otherwise, we can convert the activation_post_process module call to quantize/dequantize node
+
+    # 1. extract the information from activation_post_process module for generating
+    # the quantize and dequantize operator
+    dtype = activation_post_process.dtype  # type: ignore[attr-defined]
+
+    is_dynamic = False
+    if hasattr(activation_post_process, "is_dynamic"):
+        is_dynamic = activation_post_process.is_dynamic  # type: ignore[assignment]
+
+    def add_dequantize_op_kwargs(dequantize_op, input_node):
+        dequantize_op_kwargs = {}
+        if "val" in input_node.meta:
+            dq_out_dtype = input_node.meta["val"].dtype
+            if dq_out_dtype != torch.float32:
+                dequantize_op_kwargs = {"out_dtype": dq_out_dtype}
+        return dequantize_op_kwargs
+
+    if dtype in SUPPORTED_QDTYPES and (not is_dynamic):
+        # TODO: probably should cleanup this condition check, it's hard
+        # to reason about this if and the following elif
+
+        # uint8/int8/int32 static quantization branch
+
+        # 1. extract information for inserting q/dq node from activation_post_process
+        node_type = "call_function"
+        quantize_op: Callable | None = None
+        scale, zero_point = activation_post_process.calculate_qparams()  # type: ignore[attr-defined, operator]
+        if is_per_channel(activation_post_process.qscheme):  # type: ignore[attr-defined]
+            ch_axis = int(activation_post_process.ch_axis)  # type: ignore[attr-defined, arg-type]
+            quantize_op = torch.ops.quantized_decomposed.quantize_per_channel.default
+            dequantize_op = (
+                torch.ops.quantized_decomposed.dequantize_per_channel.default
+            )
+            quant_min = activation_post_process.quant_min
+            quant_max = activation_post_process.quant_max
+            dtype_ = to_underlying_dtype(dtype)
+            qparams = {
+                "_scale_": scale,
+                "_zero_point_": zero_point,
+                "_axis_": ch_axis,
+                "_quant_min_": quant_min,
+                "_quant_max_": quant_max,
+                "_dtype_": dtype_,
+            }
+        else:
+            quantize_op = torch.ops.quantized_decomposed.quantize_per_tensor.default
+            dequantize_op = torch.ops.quantized_decomposed.dequantize_per_tensor.default
+            scale = float(scale)
+            zero_point = int(zero_point)
+            quant_min = activation_post_process.quant_min  # type: ignore[attr-defined]
+            quant_max = activation_post_process.quant_max  # type: ignore[attr-defined]
+            dtype_ = to_underlying_dtype(dtype)
+            qparams = {
+                "_scale_": scale,
+                "_zero_point_": zero_point,
+                "_quant_min_": quant_min,
+                "_quant_max_": quant_max,
+                "_dtype_": dtype_,
+            }
+
+        # 2. replace activation_post_process node with quantize and dequantize
+        with graph.inserting_before(node):
+            input_node = node.args[0]
+            quantize_op_inputs = [input_node]
+            for key, value_or_node in qparams.items():
+                # TODO: we can add the information of whether a value needs to
+                # be registered as an attribute in qparams dict itself
+                if key in ["_scale_", "_zero_point_"] and (
+                    not isinstance(value_or_node, (float, int))
+                ):
+                    # For scale and zero_point values we register them as buffers in the root module.
+                    # However, note that when the values are not tensors, as in the case of
+                    # per_tensor quantization, they will be treated as literals.
+                    # However, registering them as a node seems to cause issue with dynamo
+                    # tracing where it may consider tensor overload as opposed to default.
+                    # With extra check of scale and zero_point being scalar, it makes
+                    # sure that the default overload can be used.
+                    # TODO: maybe need more complex attr name here
+                    qparam_node = create_getattr_from_value(
+                        model,
+                        graph,
+                        module_path + prefix + key,
+                        value_or_node,
+                        model_device,
+                    )
+                    quantize_op_inputs.append(qparam_node)
+                else:
+                    # for qparams that are not scale/zero_point (like axis, dtype) we store them as literals in the graph.
+                    quantize_op_inputs.append(value_or_node)
+
+            quantized_node = graph.create_node(
+                node_type, quantize_op, tuple(quantize_op_inputs), {}
+            )
+            # use the same qparams from quantize op
+            dq_inputs = [quantized_node] + quantize_op_inputs[1:]
+            dequantized_node = graph.call_function(
+                dequantize_op,
+                tuple(dq_inputs),
+                add_dequantize_op_kwargs(dequantize_op, input_node),
+            )
+
+            node.replace_all_uses_with(dequantized_node)
+            # propagate numeric debug handle from observer/fake_quant node to dequantize node
+            if (
+                CUSTOM_KEY in node.meta
+                and NUMERIC_DEBUG_HANDLE_KEY in node.meta[CUSTOM_KEY]
+            ):
+                if CUSTOM_KEY not in dequantized_node.meta:
+                    dequantized_node.meta[CUSTOM_KEY] = {}
+                dequantized_node.meta[CUSTOM_KEY][NUMERIC_DEBUG_HANDLE_KEY] = node.meta[
+                    CUSTOM_KEY
+                ][NUMERIC_DEBUG_HANDLE_KEY]
+            graph.erase_node(node)
+    elif is_dynamic:
+        # uint8/int8/fp16 dynamic quantization
+
+        # 1. extract information for inserting q/dq node from activation_post_process
+        node_type = "call_function"
+        quantize_op = torch.ops.quantized_decomposed.quantize_per_tensor.tensor
+        # we only use choose_qparams for is_decomposed now,
+        # but we should probably align the non-decomposed path with this as well,
+        # and that can be done after we remove reduce_range flag
+        # 1. extract qparams from activation_post_process module
+        dtype_ = to_underlying_dtype(dtype)
+        if dtype_ not in [torch.uint8, torch.int8]:
+            raise AssertionError(
+                "only uint8 and int8 are supported in reference flow for dynamic quantization right now"
+            )
+        quant_min = activation_post_process.quant_min  # type: ignore[attr-defined]
+        quant_max = activation_post_process.quant_max  # type: ignore[attr-defined]
+        qscheme = getattr(activation_post_process, "qscheme", torch.per_tensor_affine)  # type: ignore[attr-defined]
+        eps = getattr(activation_post_process, "eps", torch.finfo(torch.float32).eps)  # type: ignore[attr-defined]
+        # note: scale and zero_point are missing for quantize_per_tensor op
+        # we'll need to get this from choose_qparams op, which we'll add after
+        # this step
+        qparams = {
+            "_quant_min_": quant_min,
+            "_quant_max_": quant_max,
+            "_eps_": eps,
+            "_dtype_": dtype_,
+        }
+
+        choose_qparams_op = _QSCHEME_TO_CHOOSE_QPARAMS_OP[qscheme]
+        # 2. insert choose_qparams op and update the qparams list
+        with graph.inserting_before(node):
+            input_node = node.args[0]
+            choose_qparams_op_inputs = [node.args[0]] + list(qparams.values())
+            choose_qparams_node = graph.create_node(
+                "call_function", choose_qparams_op, tuple(choose_qparams_op_inputs), {}
+            )
+            # choose_qparms returns (scale, zero_point)
+            scale_node = graph.create_node(
+                "call_function", operator.getitem, (choose_qparams_node, 0), {}
+            )
+            zero_point_node = graph.create_node(
+                "call_function", operator.getitem, (choose_qparams_node, 1), {}
+            )
+            # we have quant_min, quant_max and dtype, all should be stored
+            # as literals
+            quant_min = qparams["_quant_min_"]
+            quant_max = qparams["_quant_max_"]
+            dtype = qparams["_dtype_"]
+            qparams = {
+                "_scale_": scale_node,
+                "_zero_point_": zero_point_node,
+                "_quant_min_": quant_min,
+                "_quant_max_": quant_max,
+                "_dtype_": dtype,
+            }
+
+        # 3. replace activation_post_process node to quantize and dequantize node
+        with graph.inserting_before(node):
+            input_node = node.args[0]
+            quantize_op_inputs = [input_node]
+            for key, value_or_node in qparams.items():
+                # TODO: we can add the information of whether a value needs to
+                # be registered as an attribute in qparams dict itself
+                if key in ["_scale_", "_zero_point_"]:
+                    # in this case we have a node in the graph since it's dynamically
+                    # computed from the input, with choose_qparams op
+                    qparam_node = value_or_node
+                    quantize_op_inputs.append(qparam_node)
+                else:
+                    # for qparams that are not scale/zero_point (like axis, dtype) we
+                    # store them as literals in the graph.
+                    quantize_op_inputs.append(value_or_node)
+
+            quantized_node = graph.create_node(
+                node_type, quantize_op, tuple(quantize_op_inputs), {}
+            )
+            # use the same qparams from quantize op
+            dq_inputs = [quantized_node] + quantize_op_inputs[1:]
+            # need to use the tensor variant of this op, since scale and zero_point
+            # from choose_qparam are Tensors, instead of float/int, this is to
+            # prevent these nodes being traced away by downstream systems
+            dequantize_op = torch.ops.quantized_decomposed.dequantize_per_tensor.tensor
+            dequantized_node = graph.call_function(
+                dequantize_op,
+                tuple(dq_inputs),
+                add_dequantize_op_kwargs(dequantize_op, input_node),
+            )
+
+            node.replace_all_uses_with(dequantized_node)
+            # propagate numeric debug handle from observer/fake_quant node to dequantize node
+            if NUMERIC_DEBUG_HANDLE_KEY in node.meta:
+                dequantized_node.meta[NUMERIC_DEBUG_HANDLE_KEY] = node.meta[
+                    NUMERIC_DEBUG_HANDLE_KEY
+                ]
+            graph.erase_node(node)
+    elif dtype == torch.float16:
+        # Insert to_fp16 -> to_fp32 node
+        dtype_convert_op = torch.ops.quantized_decomposed.convert_element_type.no_fuse
+        with graph.inserting_before(node):
+            input_node = node.args[0]
+            convert_fp16_node = graph.create_node(
+                "call_function", dtype_convert_op, (input_node, torch.float16), {}
+            )
+            convert_fp32_node = graph.create_node(
+                "call_function", dtype_convert_op, (convert_fp16_node, torch.float), {}
+            )
+            node.replace_all_uses_with(convert_fp32_node)
+            graph.erase_node(node)
+
+    # should not reach since we have checks in the beginning to make sure the
+    # activation_post_process is supported
+
+
+def _replace_observer_with_quantize_dequantize_node(
+    model: torch.fx.GraphModule,
+    node: Node,
+    modules: dict[str, torch.nn.Module],
+    node_name_to_scope: dict[str, tuple[str, type]],
+    node_name_to_qconfig: dict[str, QConfigAny],
+    model_device: torch.device | None = None,
+) -> None:
+    """Replace activation_post_process module call node with quantize and
+    dequantize node
+
+    Before:
+    ... -> observer_0(x) -> ...
+    After:
+    ... -> torch.quantize_per_tensor(x, ...) -> x.dequantize() -> ...
+    """
+    if modules is None:
+        raise AssertionError("modules must not be None")
+    if not isinstance(node.target, str):
+        raise AssertionError(
+            f"Expected node.target to be a str, but got {type(node.target)}"
+        )
+    graph = model.graph
+    module_path, prefix = _get_module_path_and_prefix(
+        node, node_name_to_scope, node_name_to_qconfig
+    )
+    activation_post_process = modules[node.target]
+    # skip replacing observers to quant/dequant nodes if the qconfigs of all
+    # consumers and producers of this observer are None
+    skip_replacement = all(
+        _has_none_qconfig(n, node_name_to_qconfig)
+        for n in list(node.args) + list(node.users.keys())
+    )
+    if skip_replacement or not _is_conversion_supported(activation_post_process):
+        # didn't find corresponding quantize op and info for the activation_post_process
+        # so we just remove the observer
+        with graph.inserting_before(node):
+            node.replace_all_uses_with(node.args[0])
+            graph.erase_node(node)
+        return
+
+    # otherwise, we can convert the activation_post_process module call to quantize/dequantize node
+    dtype = activation_post_process.dtype  # type: ignore[attr-defined]
+
+    is_dynamic = False
+    if hasattr(activation_post_process, "is_dynamic"):
+        is_dynamic = activation_post_process.is_dynamic  # type: ignore[attr-defined, assignment]
+
+    if dtype in [
+        torch.quint8,
+        torch.qint8,
+        torch.qint32,
+        torch.float8_e5m2,
+        torch.float8_e4m3fn,
+    ] and (not is_dynamic):
+        # TODO: probably should cleanup this condition check, it's hard
+        # to reason about this if and the following elif
+
+        # uint8/int8/int32 static quantization branch
+
+        # 1. extract the information from activation_post_process module for generating
+        # the quantize and dequantize operator
+        node_type = "call_function"
+        quantize_op: Callable | None = None
+        scale, zero_point = activation_post_process.calculate_qparams()  # type: ignore[attr-defined, operator]
+        if is_per_channel(activation_post_process.qscheme):  # type: ignore[attr-defined]
+            ch_axis = int(activation_post_process.ch_axis)  # type: ignore[attr-defined, arg-type]
+            qparams = {
+                "_scale_": scale,
+                "_zero_point_": zero_point,
+                "_axis_": ch_axis,
+                "_dtype_": dtype,
+            }
+            quantize_op = torch.quantize_per_channel
+        else:
+            scale = float(scale)
+            zero_point = int(zero_point)
+            qparams = {"_scale_": scale, "_zero_point_": zero_point, "_dtype_": dtype}
+            quantize_op = torch.quantize_per_tensor
+
+        # 2. replace activation_post_process node with quantize and dequantize
+        with graph.inserting_before(node):
+            input_node = node.args[0]
+            quantize_op_inputs = [input_node]
+            for key, value_or_node in qparams.items():
+                # TODO: we can add the information of whether a value needs to
+                # be registered as an attribute in qparams dict itself
+                if key in ["_scale_", "_zero_point_"]:
+                    # For scale and zero_point values we register them as buffers in the root module.
+                    # TODO: maybe need more complex attr name here
+                    qparam_node = create_getattr_from_value(
+                        model,
+                        graph,
+                        module_path + prefix + key,
+                        value_or_node,
+                        model_device,
+                    )
+                    quantize_op_inputs.append(qparam_node)
+                else:
+                    # for qparams that are not scale/zero_point (like axis, dtype) we store them as literals in the graph.
+                    quantize_op_inputs.append(value_or_node)
+
+            quantized_node = graph.create_node(
+                node_type, quantize_op, tuple(quantize_op_inputs), {}
+            )
+            dequantized_node = graph.call_method("dequantize", args=(quantized_node,))
+            node.replace_all_uses_with(dequantized_node)
+            graph.erase_node(node)
+    elif is_dynamic:
+        # uint8/int8/fp16 dynamic quantization branch
+
+        node_type = "call_function"
+        quantize_op = torch.quantize_per_tensor_dynamic
+        # TODO: get reduce range from observer
+        # reduce_range = activation_post_process.reduce_range
+        reduce_range = torch.backends.quantized.engine in ("fbgemm", "x86")
+        qparams = {"_dtype_": dtype, "_reduce_range_": reduce_range}
+
+        with graph.inserting_before(node):
+            input_node = node.args[0]
+            quantize_op_inputs = [input_node]
+            for value in qparams.values():
+                quantize_op_inputs.append(value)
+
+            quantized_node = graph.create_node(
+                node_type, quantize_op, tuple(quantize_op_inputs), {}
+            )
+            dequantized_node = graph.call_method("dequantize", args=(quantized_node,))
+            node.replace_all_uses_with(dequantized_node)
+            graph.erase_node(node)
+    elif dtype == torch.float16:
+        node_type = "call_method"
+        quantize_op = "to"  # type: ignore[assignment]
+        qparams = {"_dtype_": dtype}
+        with graph.inserting_before(node):
+            input_node = node.args[0]
+            quantize_op_inputs = [input_node]
+            for value in qparams.values():
+                # TODO: we can add the information of whether a value needs to
+                # be registered as an attribute in qparams dict itself
+                quantize_op_inputs.append(value)
+
+            quantized_node = graph.create_node(
+                node_type, quantize_op, tuple(quantize_op_inputs), {}
+            )
+            dequantized_node = graph.call_method("dequantize", args=(quantized_node,))
+            node.replace_all_uses_with(dequantized_node)
+            graph.erase_node(node)
+
+    # should not reach since we have checks in the beginning to make sure the
+    # activation_post_process is supported
+
+
+# this is a temporary hack for custom module, we may want to implement
+# this properly after the custom module class design is finalized
+# TODO: DeQuantStubs are currently inserted only after custom module LSTM, while observers are inserted
+# after all other custom modules. In the future, we should simply insert QuantStubs before and DeQuantStubs
+# after custom modules in general, and replace these with "quantize" and "dequantize" nodes respectively.
+def _replace_observer_or_dequant_stub_with_dequantize_node(
+    node: Node, graph: Graph
+) -> None:
+    call_custom_module_node = node.args[0]
+    if not isinstance(call_custom_module_node, Node):
+        raise AssertionError(
+            f"Expecting the for call custom module node to be a Node, but got {call_custom_module_node}"
+        )
+    node.replace_all_uses_with(call_custom_module_node)
+    graph.erase_node(node)
+    _insert_dequantize_node(call_custom_module_node, graph)
+
+
+def _is_conversion_supported(activation_post_process: torch.nn.Module) -> bool:
+    dtype = activation_post_process.dtype  # type: ignore[attr-defined]
+
+    is_dynamic = False
+    if hasattr(activation_post_process, "is_dynamic"):
+        is_dynamic = activation_post_process.is_dynamic  # type: ignore[attr-defined, assignment]
+
+    return (
+        (dtype in SUPPORTED_QDTYPES and (not is_dynamic))
+        or is_dynamic  # type: ignore[return-value]
+        or dtype == torch.float16
+    )
+
+
+def _has_none_qconfig(
+    node: Argument, node_name_to_qconfig: dict[str, QConfigAny]
+) -> bool:
+    """Check if a node has a qconfig of None, i.e. user requested to not quantize
+    the node
+    """
+    return (
+        isinstance(node, Node)
+        and node.name in node_name_to_qconfig
+        and node_name_to_qconfig[node.name] is None
+    )
+
+
+def _run_weight_observers(observed: GraphModule, backend_config: BackendConfig) -> None:
+    """Extract the subgraph that produces the weight for dynamic quant
+    or weight only quant node and run the subgraph to observe the weight.
+    Note that the observers of dynamic quant or weight only quant ops are
+    run during the convert step.
+    """
+    for node in observed.graph.nodes:
+        if node.op != "call_function":
+            continue
+        for node_arg in node.args:
+            # node_arg is weight
+            if node_arg and node_arg_is_weight(node, node_arg):
+                weight_observer_nodes = collect_producer_nodes(node_arg)
+                if weight_observer_nodes is None:
+                    continue
+                weight_observer_module = graph_module_from_producer_nodes(
+                    observed, weight_observer_nodes
+                )
+                # run the weight observer
+                weight_observer_module()
+
+
+def _maybe_recursive_remove_dequantize(arg: Any, node: Node, graph: Graph) -> None:
+    """If the arg is a dequantize Node, or a list/tuple/dict of dequantize Node,
+    we'll recursively remove the dequantize Node
+    """
+    if isinstance(arg, Node) and arg.op == "call_method" and arg.target == "dequantize":
+        quantize_node = arg.args[0]
+        # we only replace the specific use since dequantize could be used by other nodes
+        # as well
+        node.replace_input_with(arg, quantize_node)
+    elif isinstance(arg, (list, tuple)):
+        for arg_element in arg:
+            _maybe_recursive_remove_dequantize(arg_element, node, graph)
+    elif isinstance(arg, dict):
+        for arg_element in arg.values():
+            _maybe_recursive_remove_dequantize(arg_element, node, graph)
+    else:
+        warnings.warn(
+            f"Unsupported node type in recursive remove dequantize: {type(arg)}",
+            stacklevel=2,
+        )
+
+
+def _get_module_path_and_prefix(
+    obs_node: Node,
+    node_name_to_scope: dict[str, tuple[str, type]],
+    node_name_to_qconfig: dict[str, QConfigAny],
+) -> tuple[str, str]:
+    """Given and observer node, get the `Scope` or the fully qualified name for
+    the submodule containing the observed node, also return a prefix of "_input"
+    when the observed node is an input of a F.linear op, and not the output of another
+    quantized op.
+    TODO: this logic is hacky, we should think about how to remove it or make it more
+    general
+    """
+    observed_node = obs_node.args[0]
+    # an observer can be inserted for both input of the next operator or output of the previous
+    # operator (they can be the same)
+    # this flag identifies if the observer is inserted only because the observed node is
+    # the input of the next operator
+    if not isinstance(observed_node, Node):
+        raise AssertionError(
+            f"Expecting observed node to be a Node, but got {observed_node}"
+        )
+    is_input_observer_only = (
+        node_name_to_qconfig[observed_node.name] is None
+        if observed_node.name in node_name_to_qconfig
+        else None
+    )
+    if is_input_observer_only:
+        # if the quantize function is at the input of op, then we find the first user of the observer_node
+        # to get the path. If a linear call_function is in the user list, we return the first instance
+        # of linear node to get the FQN.
+        users = list(obs_node.users)
+        first_linear_use_or_first_use = users[0] if users else None
+        linear_node = None
+        for n in users:
+            if n.op == "call_function" and n.target is torch.nn.functional.linear:
+                linear_node = n
+                break
+        if linear_node:
+            first_linear_use_or_first_use = linear_node
+        prefix = "_input"
+    else:
+        # if the quantize function is at the output of the op, we use the observer input node to get the path
+        first_linear_use_or_first_use = observed_node
+        prefix = ""
+
+    if (
+        first_linear_use_or_first_use
+        and first_linear_use_or_first_use.name in node_name_to_scope
+    ):
+        module_path, _ = node_name_to_scope[first_linear_use_or_first_use.name]
+    else:
+        # TODO: it's not used, so actually we can skip quantization
+        # but this requires changing return type of quantize_node
+        # we can fix it later if needed
+        module_path = ""
+    return module_path, prefix
+
+
+def _insert_dequantize_node(node: Node, graph: Graph) -> None:
+    """Inserts dequantize node for `node` in `graph`"""
+    with graph.inserting_after(node):
+        dequantize_node = graph.call_method("dequantize", (node,))
+        for user_node in dict(node.users):
+            if user_node is not dequantize_node:
+                user_node.replace_input_with(node, dequantize_node)
+
+
+def _maybe_get_observer_for_node(
+    node: Node, modules: dict[str, torch.nn.Module]
+) -> torch.nn.Module | None:
+    """
+    If the node is observed, return the observer
+    instance. Otherwise, return None.
+    """
+    for maybe_obs_node in node.users:
+        if maybe_obs_node.op == "call_module":
+            maybe_obs = modules[str(maybe_obs_node.target)]
+            if _is_activation_post_process(maybe_obs):
+                return maybe_obs
+    return None
+
+
+def convert_standalone_module(
+    node: Node,
+    modules: dict[str, torch.nn.Module],
+    model: torch.fx.GraphModule,
+    is_reference: bool,
+    backend_config: BackendConfig | None,
+) -> None:
+    """Converts a observed standalone module to a quantized standalone module by calling
+    the fx convert api, currently using the same `is_reference` flag as parent, but we may
+    changing this behavior in the future (e.g. separating quantization and lowering for
+    standalone module as well)
+
+    Args:
+      - node: The call_module node of the observed standalone module
+      - modules: named_module of original model
+      - model: original model
+      - is_reference: a flag from parent provided by user to decide if we want to
+        produce a reference model or a fbgemm/qnnpack model
+      - backend_config: backend configuration of the target backend of quantization
+    """
+    # TODO: remove is_reference flag
+    if is_reference:
+        convert_fn = torch.ao.quantization.quantize_fx.convert_to_reference_fx
+    else:
+        convert_fn = torch.ao.quantization.quantize_fx.convert_fx  # type: ignore[attr-defined]
+    # We know that observed standalone module is a GraphModule since
+    # it's produced by us
+    observed_standalone_module: GraphModule = modules[str(node.target)]  # type: ignore[assignment]
+    sm_input_quantized_idxs = observed_standalone_module.meta[
+        "_observed_graph_module_attrs"
+    ].standalone_module_input_quantized_idxs
+    # remove the dequantize nodes for inputs
+    args = list(node.args)
+    for idx in range(len(args)):
+        if idx in sm_input_quantized_idxs:
+            arg = args[idx]
+            if arg.op == "call_method" and arg.target == "dequantize":  # type: ignore[union-attr]
+                quantize_node = arg.args[0]  # type: ignore[union-attr]
+                node.replace_input_with(arg, quantize_node)
+                if len(arg.users) == 0:  # type: ignore[union-attr]
+                    model.graph.erase_node(arg)
+    # add dequantize node for output
+    sm_output_quantized_idxs = observed_standalone_module.meta[
+        "_observed_graph_module_attrs"
+    ].standalone_module_output_quantized_idxs
+    if len(sm_output_quantized_idxs) > 0:
+        if sm_output_quantized_idxs[0] != 0:
+            raise AssertionError(
+                "Currently only quantized output idxs = [0] is supported"
+            )
+
+        # if it's non-empty, then it means the output is kept in quantized form
+        # we'll just add a dequantize node after this node
+        _insert_dequantize_node(node, model.graph)
+
+    # TODO: allow convert_custom_config to override backend_config
+    # for standalone module
+    quantized_standalone_module = convert_fn(
+        observed_standalone_module, backend_config=backend_config
+    )
+    parent_name, name = _parent_name(node.target)
+    # update the modules dict
+    setattr(modules[parent_name], name, quantized_standalone_module)
+    modules[str(node.target)] = quantized_standalone_module
+
+
+def convert_weighted_module(
+    node: Node,
+    modules: dict[str, torch.nn.Module],
+    observed_node_names: set[str],
+    node_name_to_qconfig: dict[str, QConfigAny],
+    backend_config: BackendConfig,
+    is_decomposed: bool = False,
+    is_reference: bool = False,
+    model_device: torch.device | None = None,
+) -> None:
+    """Convert a weighted module to reference quantized module in the model
+    If the QConfig of a QAT module is not set, the module will still be converted to
+    a float module.
+
+    Args:
+      - node: The call_module node of the observed standalone module
+      - modules: named_module of original model
+      - observed_node_names: names for the set of observed fx node, we can skip
+        this conversion if the node is not observed
+    """
+    original_module = modules[str(node.target)]
+    qconfig: QConfigAny = original_module.qconfig  # type: ignore[assignment]
+    weight_post_process = None
+    qat_module_classes = get_qat_module_classes(backend_config)
+
+    if isinstance(original_module, qat_module_classes):
+        # Converting qat module to a float module, we need to attach
+        # weight fake_quant to the module, weight fake_quant is assumed to be run during
+        # QAT so we don't need to run it again here
+        weight_post_process = original_module.weight_fake_quant
+        original_module = original_module.to_float()  # type: ignore[operator]
+        # change qat module to float module
+        parent_name, name = _parent_name(node.target)
+        setattr(modules[parent_name], name, original_module)
+
+    is_observed = node.name in observed_node_names
+    # If a qconfig is not defined for this node, then skip converting to a reference module
+    if (
+        qconfig is None
+        or _has_none_qconfig(node, node_name_to_qconfig)
+        or not is_observed
+    ):
+        return
+
+    # skip converting to reference quantized module if the qconfig is not supported
+    pattern_to_dtype_configs = get_pattern_to_dtype_configs(backend_config)
+    dtype_configs = pattern_to_dtype_configs.get(type(original_module), [])
+    if not _is_qconfig_supported_by_dtype_configs(qconfig, dtype_configs):
+        return
+
+    # TODO: rename weight_is_statically_quantized to weight_is_int8_quantized
+    is_weight_quantized = weight_is_quantized(qconfig)
+
+    # the condition for swapping the module to reference quantized module is:
+    # weights need to be quantized
+    if not is_weight_quantized:
+        return
+
+    fused_module = None
+    float_module = original_module
+    # extract the individual float_module and fused module
+    if isinstance(original_module, torch.ao.nn.intrinsic._FusedModule):
+        fused_module = float_module
+        float_module = fused_module[0]  # type: ignore[index]
+
+    # TODO: move this to the reference quantized module
+    # weight_qparams or weight_qparams dict
+    wq_or_wq_dict = {"is_decomposed": is_decomposed}
+    if isinstance(float_module, torch.nn.RNNCellBase):
+        weight_post_process_ih = qconfig.weight()  # type: ignore[union-attr, operator]
+        weight_post_process_hh = qconfig.weight()  # type: ignore[union-attr, operator]
+        weight_post_process_ih(float_module.weight_ih)
+        weight_post_process_hh(float_module.weight_hh)
+        weight_qparams_ih = get_qparam_dict(weight_post_process_ih)
+        weight_qparams_hh = get_qparam_dict(weight_post_process_hh)
+        wq_or_wq_dict.update(
+            {
+                "weight_ih": weight_qparams_ih,
+                "weight_hh": weight_qparams_hh,
+            }
+        )
+    elif isinstance(float_module, (torch.nn.LSTM, torch.nn.GRU)):
+        # format for wq_or_wq_dict (flattened attributes):
+        # {"weight_ih_l0_scale": ..., "weight_ih_l0_qscheme": ..., ...}
+        for wn in float_module._flat_weights_names:
+            if hasattr(float_module, wn) and wn.startswith("weight"):
+                weight = getattr(float_module, wn)
+                weight_post_process = qconfig.weight()  # type: ignore[union-attr, operator]
+                if weight_post_process.dtype == torch.qint8:  # type: ignore[union-attr]
+                    weight_post_process(weight)  # type: ignore[operator, misc]
+                wq_or_wq_dict[wn] = get_qparam_dict(weight_post_process)
+    else:
+        # weight_post_process is None means the original module is not a QAT module
+        # we need to get weight_post_process from qconfig in this case
+        is_ptq = weight_post_process is None
+        if is_ptq:
+            weight_post_process = qconfig.weight()  # type: ignore[union-attr, operator]
+            if model_device is not None:
+                device = model_device
+            else:
+                device = assert_and_get_unique_device(float_module)
+            if device:
+                weight_post_process.to(device)
+
+        # Call weight observer/fake_quant at least once to ensure the scales and zero points
+        # have the right shapes. Note: there are two cases where we don't have to do this:
+        #
+        # (1) QAT: The model's forward method already calls the weight observer/fake_quant,
+        #     and this typically happens during training, so we don't need to do it here.
+        #
+        # (2) Non-reference (lowered) case: The quantized module's from_float method already
+        #     calls the weight observer/fake_quant, so we don't have to do it here.
+        #
+        # Currently we ignore both cases and call the weight observer/fake_quant here
+        # regardless, which is technically incorrect. For (1), this is mainly to preserve BC
+        # in test code, which may not always train before convert. In the future, we should
+        # break BC for these two cases. See https://github.com/pytorch/pytorch/issues/73941.
+        #
+        # For PT2, however, we don't need to preserve BC here, so we can skip this hack
+        # for QAT. We identify this case as (is_decomposed + is_reference + is_qat).
+        # Note that we still need it for PTQ in the PT2 flow since the model's forward
+        # method doesn't call the weight observer.
+        is_qat = not is_ptq
+        if not (is_decomposed and is_reference and is_qat):
+            weight_post_process(float_module.weight)  # type: ignore[operator]
+
+        wq_or_wq_dict.update(get_qparam_dict(weight_post_process))
+
+    # We use the same reference module for all modes of quantization: static, dynamic, weight_only
+    # root_module_to_quantized_reference_module: module mapping from root (floating point) module class
+    # to quantized reference module class, e.g. nn.Conv2d to nn.quantized._reference.Conv2d
+    root_module_to_quantized_reference_module = (
+        get_root_module_to_quantized_reference_module(backend_config)
+    )
+    ref_qmodule_cls = root_module_to_quantized_reference_module.get(
+        type_before_parametrizations(float_module), None
+    )
+    if ref_qmodule_cls is None:
+        raise AssertionError(
+            f"No reference quantized module class configured for {type_before_parametrizations(float_module)}"
+        )
+    ref_qmodule = ref_qmodule_cls.from_float(float_module, wq_or_wq_dict)  # type: ignore[attr-defined]
+    if fused_module is not None:
+        fused_module[0] = ref_qmodule  # type: ignore[operator]
+    else:
+        parent_name, name = _parent_name(node.target)
+        setattr(modules[parent_name], name, ref_qmodule)
+
+
+def _remove_previous_dequantize_in_custom_module(
+    node: Node, prev_node: Node, graph: Graph
+) -> None:
+    """
+    Given a custom module `node`, if the previous node is a dequantize, reroute the custom as follows:
+
+    Before: quantize - dequantize - custom_module
+    After: quantize - custom_module
+                 \\ - dequantize
+    """
+    # expecting the input node for a custom module node to be a Node
+    if not isinstance(prev_node, Node):
+        raise AssertionError(
+            f"Expecting the argument for custom module node to be a Node, but got {prev_node}"
+        )
+    if prev_node.op == "call_method" and prev_node.target == "dequantize":
+        node.replace_input_with(prev_node, prev_node.args[0])
+        # Remove the dequantize node if it doesn't have other users
+        if len(prev_node.users) == 0:
+            graph.erase_node(prev_node)
+
+
+def convert_custom_module(
+    node: Node,
+    graph: Graph,
+    modules: dict[str, torch.nn.Module],
+    custom_module_class_mapping: dict[QuantType, dict[type, type]],
+    statically_quantized_custom_module_nodes: set[Node],
+) -> None:
+    """Converts an observed custom module to a quantized custom module based on
+    `custom_module_class_mapping`
+    For static quantization, we'll also remove the previous `dequantize` node and
+    attach the observer node for output to the module, the observer for the node
+    will be converted to a dequantize node instead of quantize-dequantize pairs
+    later in the graph. In the end we would have a quantized custom module that
+    has the same interface as a default quantized module in nn.quantized namespace,
+    i.e. quantized input and quantized output.
+
+    Args:
+      - node: The call_module node of the observed standalone module
+      - graph: The graph containing the node
+      - modules: named_module of original model
+      - custom_module_class_mapping: mapping from observed custom module class to
+        quantized custom module class, used to swap custom modules
+      - statically_quantized_custom_module_nodes: we'll add the custom module node
+        if we find it is statically quantized, this will be used later when converting
+        observers to quant/dequant node pairs, if the observed node is a statically
+        quantized custom module nodes, we'll convert the observer to a dequantize node,
+        this is to keep the interface the same as the default quantized module.
+        TODO: maybe we want to redesign this part to align with reference model design
+        as well, but there has been some discussions around the interface, so we can do
+        it later.
+    """
+    observed_custom_module = modules[str(node.target)]
+    qconfig = observed_custom_module.qconfig
+    if activation_is_statically_quantized(qconfig):
+        statically_quantized_custom_module_nodes.add(node)
+        if _is_custom_module_lstm(node, modules):
+            # The inputs are tuples in the form (input, (hidden0, hidden1))
+            # Ensure all three input nodes are quantized
+            if not (
+                len(node.args) == 2
+                and isinstance(node.args[1], tuple)
+                and len(node.args[1]) == 2
+            ):
+                raise AssertionError(
+                    "Expected LSTM custom module inputs to be (input, (hidden0, hidden1))"
+                )
+            (inputs, (hidden0, hidden1)) = node.args  # type: ignore[misc]
+            if not isinstance(inputs, Node):
+                raise AssertionError("Expected inputs to be a Node")
+            if not isinstance(hidden0, Node):
+                raise AssertionError("Expected hidden0 to be a Node")
+            if not isinstance(hidden1, Node):
+                raise AssertionError("Expected hidden1 to be a Node")
+            _remove_previous_dequantize_in_custom_module(node, inputs, graph)
+            _remove_previous_dequantize_in_custom_module(node, hidden0, graph)
+            _remove_previous_dequantize_in_custom_module(node, hidden1, graph)
+        elif _is_custom_module_mha(node, modules):
+            # Inputs are in the form (query, key, value)
+            # TODO: This is the first step in enabling the full fx custom module
+            # quantization path for MultiheadAttention, and only covers the inputs
+            # to the module.
+            # Additional handling is yet to be implemented for the outputs, similar
+            # to LSTM custom module
+            if len(node.args) != 3:
+                raise AssertionError(
+                    "Expected MHA custom module inputs to be (query, key, value)"
+                )
+            query, key, value = node.args
+            if not isinstance(query, Node):
+                raise AssertionError("Expected query to be a Node")
+            if not isinstance(key, Node):
+                raise AssertionError("Expected key to be a Node")
+            if not isinstance(value, Node):
+                raise AssertionError("Expected value to be a Node")
+            _remove_previous_dequantize_in_custom_module(node, query, graph)
+            _remove_previous_dequantize_in_custom_module(node, key, graph)
+            _remove_previous_dequantize_in_custom_module(node, value, graph)
+        else:
+            # remove the previous dequant node to ensure the inputs are quantized
+            arg = node.args[0]
+            if not isinstance(arg, Node):
+                raise AssertionError("Expected arg to be a Node")
+            _remove_previous_dequantize_in_custom_module(node, arg, graph)
+            # absorb the following observer into the module conversion
+            activation_post_process = _maybe_get_observer_for_node(node, modules)
+            if activation_post_process is None:
+                raise AssertionError(
+                    "Expected activation_post_process to be present for observed custom module"
+                )
+            observed_custom_module.activation_post_process = activation_post_process
+
+    # swap the observed custom module to quantized custom module
+    quantized_custom_module_class = get_swapped_custom_module_class(
+        observed_custom_module, custom_module_class_mapping, qconfig
+    )
+    quantized_custom_module = quantized_custom_module_class.from_observed(
+        observed_custom_module
+    )
+    parent_name, name = _parent_name(node.target)
+    setattr(modules[parent_name], name, quantized_custom_module)
+
+
+def convert(
+    model: GraphModule,
+    is_reference: bool = False,
+    convert_custom_config: ConvertCustomConfig | dict[str, Any] | None = None,
+    is_standalone_module: bool = False,
+    _remove_qconfig_flag: bool = True,
+    qconfig_mapping: QConfigMapping | dict[str, Any] | None = None,
+    backend_config: BackendConfig | dict[str, Any] | None = None,
+    is_decomposed: bool = False,
+    keep_original_weights: bool = False,
+) -> GraphModule:
+    """
+    We will convert an observed model (a module with observer calls) to a reference
+    quantized model, the rule is simple:
+    1. for each observer module call in the graph, we'll convert it to calls to
+       quantize and dequantize functions based on the observer instance
+    2. for weighted operations like linear/conv, we need to convert them to reference
+       quantized module, this requires us to know whether the dtype configured for the
+       weight is supported in the backend, this is done in prepare step and the result
+       is stored in observed_node_names, we can decide whether we need to swap the
+       module based on this set
+
+    Args:
+       * `is_standalone_module`: when this flag is True, it means we are quantizing
+       a submodule that is not inlined in parent module, and will be quantized
+       separately as one unit.
+
+       * `is_decomposed`: a boolean flag to indicate whether we want to use the
+        quantize operator for decomposed quantized tensor
+        (torch.ops.quantized_decomposed.quantize_per_tensor) or default/standalone
+        quantized tensor (torch.quantize_per_tensor)
+
+    Returns:
+         a quantized standalone module, whether input/output is quantized is
+         specified by prepare_custom_config, with
+         input_quantized_idxs, output_quantized_idxs, please
+         see docs for :func:`~torch.ao.quantization.prepare_fx` for details
+    """
+    if convert_custom_config is None:
+        convert_custom_config = ConvertCustomConfig()
+
+    if isinstance(convert_custom_config, dict):
+        warnings.warn(
+            "Passing a convert_custom_config_dict to convert is deprecated and will not be supported "
+            "in a future version. Please pass in a ConvertCustomConfig instead.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        convert_custom_config = ConvertCustomConfig.from_dict(convert_custom_config)
+
+    if isinstance(qconfig_mapping, dict):
+        warnings.warn(
+            "Passing a QConfig dictionary to convert is deprecated and will not be supported "
+            "in a future version. Please pass in a QConfigMapping instead.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        qconfig_mapping = (
+            QConfigMapping.from_dict(qconfig_mapping) if qconfig_mapping else None
+        )
+    qconfig_mapping = copy.deepcopy(qconfig_mapping)
+    if not (qconfig_mapping is None or isinstance(qconfig_mapping, QConfigMapping)):
+        raise AssertionError("qconfig_mapping must be None or a QConfigMapping")
+
+    if isinstance(backend_config, dict):
+        warnings.warn(
+            "Passing a backend_config_dict to prepare is deprecated and will not be supported "
+            "in a future version. Please pass in a BackendConfig instead.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        backend_config = BackendConfig.from_dict(backend_config)
+
+    if backend_config is None:
+        backend_config = get_native_backend_config()
+
+    if not _is_observed_module(model):
+        raise AssertionError("incoming model must be produced by prepare_fx")
+    observed_graph_module_attrs = model.meta["_observed_graph_module_attrs"]
+    node_name_to_scope: dict[str, tuple[str, type]] = (
+        observed_graph_module_attrs.node_name_to_scope
+    )
+    prepare_custom_config: PrepareCustomConfig = (
+        observed_graph_module_attrs.prepare_custom_config
+    )
+    observed_node_names: set[str] = observed_graph_module_attrs.observed_node_names
+    node_name_to_qconfig: dict[str, QConfigAny] = (
+        observed_graph_module_attrs.node_name_to_qconfig
+    )  # type: ignore[assignment]
+
+    # mapping from fully qualified module name to module instance
+    # for example,
+    # {
+    #   '': Model(...),
+    #   'linear': Linear(...),
+    #   'linear.weight_fake_quant': PerChannelMinMaxObserver(...),
+    # }
+    # We use remove_duplicate=False here because torch.cat uses
+    # the same activation_post_process module instance but different names
+    modules = dict(model.named_modules(remove_duplicate=False))
+
+    # TODO refactor this code once we update the prepare logic to have additional information on
+    # which graph nodes have been observed and share that with convert to decide which observers to ignore.
+    if qconfig_mapping:
+        prepare_qconfig_mapping: QConfigMapping = (
+            observed_graph_module_attrs.qconfig_mapping
+        )  # type: ignore[assignment]
+        modules_copy = copy.deepcopy(modules)
+
+        if observed_graph_module_attrs.is_qat:
+            _update_qconfig_for_qat(qconfig_mapping, backend_config)
+        _update_qconfig_for_fusion(model, qconfig_mapping)
+
+        _compare_prepare_convert_qconfig_mappings(
+            prepare_qconfig_mapping, qconfig_mapping
+        )  # type: ignore[arg-type]
+        convert_node_name_to_qconfig = _generate_node_name_to_qconfig(
+            model, modules_copy, model.graph, qconfig_mapping, node_name_to_scope
+        )
+        # check the convert_node_name_to_qconfig generated and ensure that
+        # all the values either match what was set in prepare node_name_to_qconfig
+        # or are set to None in the convert_node_name_to_qconfig.
+        for k, v in node_name_to_qconfig.items():
+            if k not in convert_node_name_to_qconfig:
+                raise AssertionError(
+                    f"Expected key {k} in convert node_name_to_qconfig"
+                )
+            if convert_node_name_to_qconfig[k] is not None:
+                if not qconfig_equals(v, convert_node_name_to_qconfig[k]):
+                    raise AssertionError(
+                        f"Expected k {k} to have the same value in prepare and convert QConfigMappings, "
+                        f"but {v} was updated to {convert_node_name_to_qconfig[k]}"
+                    )
+        node_name_to_qconfig = convert_node_name_to_qconfig
+
+    custom_module_classes = get_custom_module_class_keys(
+        convert_custom_config.observed_to_quantized_mapping
+    )
+    custom_module_class_mapping = convert_custom_config.observed_to_quantized_mapping
+
+    if observed_graph_module_attrs.equalization_node_name_to_qconfig is not None:
+        # If we want to do equalization then do the following:
+        # Calculate the equalization scale, update the observers with the scaled
+        # inputs, and scale the weight
+        weight_eq_obs_dict = update_obs_for_equalization(model, modules)
+        convert_eq_obs(model, modules, weight_eq_obs_dict)
+
+    # always run weight observers in the top level forward method
+    # for dynamic quant ops or weight only quant ops
+    _run_weight_observers(model, backend_config)
+
+    # additional state to override inputs to be quantized, if specified
+    # by the user
+    placeholder_node_seen_cnt = 0
+    input_quantized_idxs: list[int] = prepare_custom_config.input_quantized_indexes
+    output_quantized_idxs: list[int] = prepare_custom_config.output_quantized_indexes
+
+    root_module_to_quantized_reference_module = (
+        get_root_module_to_quantized_reference_module(backend_config)
+    )
+    # convert tuples so that it can work with isinstance(module, tuple_of_classes)
+    root_module_classes = tuple(root_module_to_quantized_reference_module.keys())
+    qat_module_classes = get_qat_module_classes(backend_config)
+    fused_module_classes = get_fused_module_classes(backend_config)
+    statically_quantized_custom_module_nodes: set[Node] = set()
+    model_device = assert_and_get_unique_device(model)
+
+    for node in list(model.graph.nodes):
+        if node.op == "placeholder":
+            cur_placeholder_node_idx = placeholder_node_seen_cnt
+            placeholder_node_seen_cnt += 1
+            if cur_placeholder_node_idx in input_quantized_idxs:
+                # Inputs are assumed to be quantized if the user specified the
+                # input_quantized_idxs override.
+                # we need to dequantize the inputs since all operators took
+                # floating point inputs in reference quantized models
+                _insert_dequantize_node(node, model.graph)
+        elif node.op == "output":
+            # If the argument is empty we don't need to do anything
+            if len(output_quantized_idxs) == 0:
+                continue
+            # Result are kept quantized if the user specified the
+            # output_quantized_idxs override.
+            # Remove the dequantize operator for the node in the end if any
+            return_node = node
+            output = node.args[0]
+            # outputs can be Node, list, tuple, dict, other cases are not supported yet
+            if isinstance(output, (list, tuple)):
+                for idx in output_quantized_idxs:
+                    _maybe_recursive_remove_dequantize(
+                        output[idx], return_node, model.graph
+                    )
+            elif isinstance(output, (Node, dict)):
+                # we treat dict as a single argument currently, but it can be extended
+                # to support {"key": dtype} after we change output_quantized_idxs to
+                # dict
+                if 0 in output_quantized_idxs:
+                    _maybe_recursive_remove_dequantize(output, return_node, model.graph)
+            else:
+                warnings.warn(
+                    f"Unsupported node type for output_quantized_idxs: {type(output)}",
+                    stacklevel=2,
+                )
+        elif node.op == "call_module":
+            mod = _get_module(node, modules)
+            if mod is None:
+                raise AssertionError(
+                    "Expected module for call_module node to be present in modules mapping"
+                )
+            if _is_activation_post_process(mod):
+                observed_node = node.args[0]
+                if observed_node in statically_quantized_custom_module_nodes:
+                    _replace_observer_or_dequant_stub_with_dequantize_node(
+                        node, model.graph
+                    )
+                else:
+                    if is_decomposed:
+                        _replace_observer_with_quantize_dequantize_node_decomposed(
+                            model,
+                            node,
+                            modules,
+                            node_name_to_scope,
+                            node_name_to_qconfig,
+                            model_device,
+                        )
+                    else:
+                        _replace_observer_with_quantize_dequantize_node(
+                            model,
+                            node,
+                            modules,
+                            node_name_to_scope,
+                            node_name_to_qconfig,
+                            model_device,
+                        )
+            elif isinstance(mod, DeQuantStub):
+                _replace_observer_or_dequant_stub_with_dequantize_node(
+                    node, model.graph
+                )
+            elif _is_observed_standalone_module(mod):
+                convert_standalone_module(
+                    node, modules, model, is_reference, backend_config
+                )
+            # below this point `type_before_parametrizations` is used
+            # instead of `type` to handle situations with fx quant + sparsity
+            elif type_before_parametrizations(mod) in set(root_module_classes).union(
+                qat_module_classes
+            ).union(fused_module_classes):
+                # extra check for fused module classes to make sure they are fused module classes
+                # of target modules
+                if (
+                    type_before_parametrizations(mod) in fused_module_classes
+                    and type_before_parametrizations(mod[0]) not in root_module_classes
+                ):  # type: ignore[index]
+                    continue
+                convert_weighted_module(
+                    node,
+                    modules,
+                    observed_node_names,
+                    node_name_to_qconfig,
+                    backend_config,
+                    is_decomposed,
+                    is_reference,
+                    model_device,
+                )
+            elif type_before_parametrizations(mod) in custom_module_classes:
+                convert_custom_module(
+                    node,
+                    model.graph,
+                    modules,
+                    custom_module_class_mapping,
+                    statically_quantized_custom_module_nodes,
+                )
+
+    # remove deadcode after converting observers to quant/dequant ops
+    model.graph.eliminate_dead_code()
+    model = GraphModule(model, model.graph)
+
+    # TODO: maybe move this to quantize_fx.py
+    if not is_reference:
+        model = lower_to_fbgemm(
+            model, node_name_to_qconfig, node_name_to_scope, keep_original_weights
+        )
+
+    # TODO: this looks hacky, we want to check why we need this and see if we can
+    # remove this
+    # removes qconfig and activation_post_process modules
+    if _remove_qconfig_flag:
+        _remove_qconfig(model)
+    model.delete_all_unused_submodules()
+    model.meta.pop("_observed_graph_module_attrs", None)
+    return model
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/custom_config.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/custom_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..e749de94bd5c3d1eb0c34a14cfcf38d441aedbff
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/custom_config.py
@@ -0,0 +1,521 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any
+
+from torch.ao.quantization import QConfigMapping
+from torch.ao.quantization.backend_config import BackendConfig
+from torch.ao.quantization.quant_type import (
+    _get_quant_type_to_str,
+    _quant_type_from_str,
+    QuantType,
+)
+
+
+__all__ = [
+    "ConvertCustomConfig",
+    "FuseCustomConfig",
+    "PrepareCustomConfig",
+    "StandaloneModuleConfigEntry",
+]
+
+
+# TODO: replace all usages with these constants
+STANDALONE_MODULE_NAME_DICT_KEY = "standalone_module_name"
+STANDALONE_MODULE_CLASS_DICT_KEY = "standalone_module_class"
+FLOAT_TO_OBSERVED_DICT_KEY = "float_to_observed_custom_module_class"
+OBSERVED_TO_QUANTIZED_DICT_KEY = "observed_to_quantized_custom_module_class"
+NON_TRACEABLE_MODULE_NAME_DICT_KEY = "non_traceable_module_name"
+NON_TRACEABLE_MODULE_CLASS_DICT_KEY = "non_traceable_module_class"
+INPUT_QUANTIZED_INDEXES_DICT_KEY = "input_quantized_idxs"
+OUTPUT_QUANTIZED_INDEXES_DICT_KEY = "output_quantized_idxs"
+PRESERVED_ATTRIBUTES_DICT_KEY = "preserved_attributes"
+
+
+@dataclass
+class StandaloneModuleConfigEntry:
+    # qconfig_mapping for the prepare function called in the submodule,
+    # None means use qconfig from parent qconfig_mapping
+    qconfig_mapping: QConfigMapping | None
+    example_inputs: tuple[Any, ...]
+    prepare_custom_config: PrepareCustomConfig | None
+    backend_config: BackendConfig | None
+
+
+class PrepareCustomConfig:
+    """
+    Custom configuration for :func:`~torch.ao.quantization.quantize_fx.prepare_fx` and
+    :func:`~torch.ao.quantization.quantize_fx.prepare_qat_fx`.
+
+    Example usage::
+
+        prepare_custom_config = PrepareCustomConfig() \
+            .set_standalone_module_name("module1", qconfig_mapping, example_inputs, \
+                child_prepare_custom_config, backend_config) \
+            .set_standalone_module_class(MyStandaloneModule, qconfig_mapping, example_inputs, \
+                child_prepare_custom_config, backend_config) \
+            .set_float_to_observed_mapping(FloatCustomModule, ObservedCustomModule) \
+            .set_non_traceable_module_names(["module2", "module3"]) \
+            .set_non_traceable_module_classes([NonTraceableModule1, NonTraceableModule2]) \
+            .set_input_quantized_indexes([0]) \
+            .set_output_quantized_indexes([0]) \
+            .set_preserved_attributes(["attr1", "attr2"])
+    """
+
+    def __init__(self) -> None:
+        self.standalone_module_names: dict[str, StandaloneModuleConfigEntry] = {}
+        self.standalone_module_classes: dict[type, StandaloneModuleConfigEntry] = {}
+        self.float_to_observed_mapping: dict[QuantType, dict[type, type]] = {}
+        self.non_traceable_module_names: list[str] = []
+        self.non_traceable_module_classes: list[type] = []
+        self.input_quantized_indexes: list[int] = []
+        self.output_quantized_indexes: list[int] = []
+        self.preserved_attributes: list[str] = []
+
+    def __repr__(self):
+        dict_nonempty = {k: v for k, v in self.__dict__.items() if len(v) > 0}
+        return f"PrepareCustomConfig({dict_nonempty})"
+
+    def set_standalone_module_name(
+        self,
+        module_name: str,
+        qconfig_mapping: QConfigMapping | None,
+        example_inputs: tuple[Any, ...],
+        prepare_custom_config: PrepareCustomConfig | None,
+        backend_config: BackendConfig | None,
+    ) -> PrepareCustomConfig:
+        """
+        Set the configuration for running a standalone module identified by ``module_name``.
+
+        If ``qconfig_mapping`` is None, the parent ``qconfig_mapping`` will be used instead.
+        If ``prepare_custom_config`` is None, an empty ``PrepareCustomConfig`` will be used.
+        If ``backend_config`` is None, the parent ``backend_config`` will be used instead.
+        """
+        self.standalone_module_names[module_name] = StandaloneModuleConfigEntry(
+            qconfig_mapping, example_inputs, prepare_custom_config, backend_config
+        )
+        return self
+
+    def set_standalone_module_class(
+        self,
+        module_class: type,
+        qconfig_mapping: QConfigMapping | None,
+        example_inputs: tuple[Any, ...],
+        prepare_custom_config: PrepareCustomConfig | None,
+        backend_config: BackendConfig | None,
+    ) -> PrepareCustomConfig:
+        """
+        Set the configuration for running a standalone module identified by ``module_class``.
+
+        If ``qconfig_mapping`` is None, the parent ``qconfig_mapping`` will be used instead.
+        If ``prepare_custom_config`` is None, an empty ``PrepareCustomConfig`` will be used.
+        If ``backend_config`` is None, the parent ``backend_config`` will be used instead.
+        """
+        self.standalone_module_classes[module_class] = StandaloneModuleConfigEntry(
+            qconfig_mapping, example_inputs, prepare_custom_config, backend_config
+        )
+        return self
+
+    def set_float_to_observed_mapping(
+        self,
+        float_class: type,
+        observed_class: type,
+        quant_type: QuantType = QuantType.STATIC,
+    ) -> PrepareCustomConfig:
+        """
+        Set the mapping from a custom float module class to a custom observed module class.
+
+        The observed module class must have a ``from_float`` class method that converts the float module class
+        to the observed module class. This is currently only supported for static quantization.
+        """
+        if quant_type != QuantType.STATIC:
+            raise ValueError(
+                "set_float_to_observed_mapping is currently only supported for static quantization"
+            )
+        if quant_type not in self.float_to_observed_mapping:
+            self.float_to_observed_mapping[quant_type] = {}
+        self.float_to_observed_mapping[quant_type][float_class] = observed_class
+        return self
+
+    def set_non_traceable_module_names(
+        self, module_names: list[str]
+    ) -> PrepareCustomConfig:
+        """
+        Set the modules that are not symbolically traceable, identified by name.
+        """
+        self.non_traceable_module_names = module_names
+        return self
+
+    def set_non_traceable_module_classes(
+        self, module_classes: list[type]
+    ) -> PrepareCustomConfig:
+        """
+        Set the modules that are not symbolically traceable, identified by class.
+        """
+        self.non_traceable_module_classes = module_classes
+        return self
+
+    def set_input_quantized_indexes(self, indexes: list[int]) -> PrepareCustomConfig:
+        """
+        Set the indexes of the inputs of the graph that should be quantized.
+        Inputs are otherwise assumed to be in fp32 by default instead.
+        """
+        self.input_quantized_indexes = indexes
+        return self
+
+    def set_output_quantized_indexes(self, indexes: list[int]) -> PrepareCustomConfig:
+        """
+        Set the indexes of the outputs of the graph that should be quantized.
+        Outputs are otherwise assumed to be in fp32 by default instead.
+        """
+        self.output_quantized_indexes = indexes
+        return self
+
+    def set_preserved_attributes(self, attributes: list[str]) -> PrepareCustomConfig:
+        """
+        Set the names of the attributes that will persist in the graph module even if they are not used in
+        the model's ``forward`` method.
+        """
+        self.preserved_attributes = attributes
+        return self
+
+    # TODO: remove this
+    @classmethod
+    def from_dict(
+        cls, prepare_custom_config_dict: dict[str, Any]
+    ) -> PrepareCustomConfig:
+        """
+        Create a ``PrepareCustomConfig`` from a dictionary with the following items:
+
+            "standalone_module_name": a list of (module_name, qconfig_mapping, example_inputs,
+            child_prepare_custom_config, backend_config) tuples
+
+            "standalone_module_class" a list of (module_class, qconfig_mapping, example_inputs,
+            child_prepare_custom_config, backend_config) tuples
+
+            "float_to_observed_custom_module_class": a nested dictionary mapping from quantization
+            mode to an inner mapping from float module classes to observed module classes, e.g.
+            {"static": {FloatCustomModule: ObservedCustomModule}}
+
+            "non_traceable_module_name": a list of modules names that are not symbolically traceable
+            "non_traceable_module_class": a list of module classes that are not symbolically traceable
+            "input_quantized_idxs": a list of indexes of graph inputs that should be quantized
+            "output_quantized_idxs": a list of indexes of graph outputs that should be quantized
+            "preserved_attributes": a list of attributes that persist even if they are not used in ``forward``
+
+        This function is primarily for backward compatibility and may be removed in the future.
+        """
+
+        def _get_qconfig_mapping(obj: Any, dict_key: str) -> QConfigMapping | None:
+            """
+            Convert the given object into a QConfigMapping if possible, else throw an exception.
+            """
+            if isinstance(obj, QConfigMapping) or obj is None:
+                return obj
+            if isinstance(obj, dict):
+                return QConfigMapping.from_dict(obj)
+            raise ValueError(
+                f"Expected QConfigMapping in prepare_custom_config_dict[\"{dict_key}\"], got '{type(obj)}'"
+            )
+
+        def _get_prepare_custom_config(
+            obj: Any, dict_key: str
+        ) -> PrepareCustomConfig | None:
+            """
+            Convert the given object into a PrepareCustomConfig if possible, else throw an exception.
+            """
+            if isinstance(obj, PrepareCustomConfig) or obj is None:
+                return obj
+            if isinstance(obj, dict):
+                return PrepareCustomConfig.from_dict(obj)
+            raise ValueError(
+                f"Expected PrepareCustomConfig in prepare_custom_config_dict[\"{dict_key}\"], got '{type(obj)}'"
+            )
+
+        def _get_backend_config(obj: Any, dict_key: str) -> BackendConfig | None:
+            """
+            Convert the given object into a BackendConfig if possible, else throw an exception.
+            """
+            if isinstance(obj, BackendConfig) or obj is None:
+                return obj
+            if isinstance(obj, dict):
+                return BackendConfig.from_dict(obj)
+            raise ValueError(
+                f"Expected BackendConfig in prepare_custom_config_dict[\"{dict_key}\"], got '{type(obj)}'"
+            )
+
+        conf = cls()
+        for (
+            module_name,
+            qconfig_dict,
+            example_inputs,
+            _prepare_custom_config_dict,
+            backend_config_dict,
+        ) in prepare_custom_config_dict.get(STANDALONE_MODULE_NAME_DICT_KEY, []):
+            qconfig_mapping = _get_qconfig_mapping(
+                qconfig_dict, STANDALONE_MODULE_NAME_DICT_KEY
+            )
+            prepare_custom_config = _get_prepare_custom_config(
+                _prepare_custom_config_dict, STANDALONE_MODULE_NAME_DICT_KEY
+            )
+            backend_config = _get_backend_config(
+                backend_config_dict, STANDALONE_MODULE_NAME_DICT_KEY
+            )
+            conf.set_standalone_module_name(
+                module_name,
+                qconfig_mapping,
+                example_inputs,
+                prepare_custom_config,
+                backend_config,
+            )
+        for (
+            module_class,
+            qconfig_dict,
+            example_inputs,
+            _prepare_custom_config_dict,
+            backend_config_dict,
+        ) in prepare_custom_config_dict.get(STANDALONE_MODULE_CLASS_DICT_KEY, []):
+            qconfig_mapping = _get_qconfig_mapping(
+                qconfig_dict, STANDALONE_MODULE_CLASS_DICT_KEY
+            )
+            prepare_custom_config = _get_prepare_custom_config(
+                _prepare_custom_config_dict, STANDALONE_MODULE_CLASS_DICT_KEY
+            )
+            backend_config = _get_backend_config(
+                backend_config_dict, STANDALONE_MODULE_CLASS_DICT_KEY
+            )
+            conf.set_standalone_module_class(
+                module_class,
+                qconfig_mapping,
+                example_inputs,
+                prepare_custom_config,
+                backend_config,
+            )
+        for quant_type_name, custom_module_mapping in prepare_custom_config_dict.get(
+            FLOAT_TO_OBSERVED_DICT_KEY, {}
+        ).items():
+            quant_type = _quant_type_from_str(quant_type_name)
+            for float_class, observed_class in custom_module_mapping.items():
+                conf.set_float_to_observed_mapping(
+                    float_class, observed_class, quant_type
+                )
+        conf.set_non_traceable_module_names(
+            prepare_custom_config_dict.get(NON_TRACEABLE_MODULE_NAME_DICT_KEY, [])
+        )
+        conf.set_non_traceable_module_classes(
+            prepare_custom_config_dict.get(NON_TRACEABLE_MODULE_CLASS_DICT_KEY, [])
+        )
+        conf.set_input_quantized_indexes(
+            prepare_custom_config_dict.get(INPUT_QUANTIZED_INDEXES_DICT_KEY, [])
+        )
+        conf.set_output_quantized_indexes(
+            prepare_custom_config_dict.get(OUTPUT_QUANTIZED_INDEXES_DICT_KEY, [])
+        )
+        conf.set_preserved_attributes(
+            prepare_custom_config_dict.get(PRESERVED_ATTRIBUTES_DICT_KEY, [])
+        )
+        return conf
+
+    def to_dict(self) -> dict[str, Any]:
+        """
+        Convert this ``PrepareCustomConfig`` to a dictionary with the items described in
+        :func:`~torch.ao.quantization.fx.custom_config.PrepareCustomConfig.from_dict`.
+        """
+
+        def _make_tuple(key: Any, e: StandaloneModuleConfigEntry):
+            qconfig_dict = e.qconfig_mapping.to_dict() if e.qconfig_mapping else None
+            prepare_custom_config_dict = (
+                e.prepare_custom_config.to_dict() if e.prepare_custom_config else None
+            )
+            return (
+                key,
+                qconfig_dict,
+                e.example_inputs,
+                prepare_custom_config_dict,
+                e.backend_config,
+            )
+
+        d: dict[str, Any] = {}
+        for module_name, sm_config_entry in self.standalone_module_names.items():
+            if STANDALONE_MODULE_NAME_DICT_KEY not in d:
+                d[STANDALONE_MODULE_NAME_DICT_KEY] = []
+            d[STANDALONE_MODULE_NAME_DICT_KEY].append(
+                _make_tuple(module_name, sm_config_entry)
+            )
+        for module_class, sm_config_entry in self.standalone_module_classes.items():
+            if STANDALONE_MODULE_CLASS_DICT_KEY not in d:
+                d[STANDALONE_MODULE_CLASS_DICT_KEY] = []
+            d[STANDALONE_MODULE_CLASS_DICT_KEY].append(
+                _make_tuple(module_class, sm_config_entry)
+            )
+        for (
+            quant_type,
+            float_to_observed_mapping,
+        ) in self.float_to_observed_mapping.items():
+            if FLOAT_TO_OBSERVED_DICT_KEY not in d:
+                d[FLOAT_TO_OBSERVED_DICT_KEY] = {}
+            d[FLOAT_TO_OBSERVED_DICT_KEY][_get_quant_type_to_str(quant_type)] = (
+                float_to_observed_mapping
+            )
+        if len(self.non_traceable_module_names) > 0:
+            d[NON_TRACEABLE_MODULE_NAME_DICT_KEY] = self.non_traceable_module_names
+        if len(self.non_traceable_module_classes) > 0:
+            d[NON_TRACEABLE_MODULE_CLASS_DICT_KEY] = self.non_traceable_module_classes
+        if len(self.input_quantized_indexes) > 0:
+            d[INPUT_QUANTIZED_INDEXES_DICT_KEY] = self.input_quantized_indexes
+        if len(self.output_quantized_indexes) > 0:
+            d[OUTPUT_QUANTIZED_INDEXES_DICT_KEY] = self.output_quantized_indexes
+        if len(self.preserved_attributes) > 0:
+            d[PRESERVED_ATTRIBUTES_DICT_KEY] = self.preserved_attributes
+        return d
+
+
+class ConvertCustomConfig:
+    """
+    Custom configuration for :func:`~torch.ao.quantization.quantize_fx.convert_fx`.
+
+    Example usage::
+
+        convert_custom_config = ConvertCustomConfig() \
+            .set_observed_to_quantized_mapping(ObservedCustomModule, QuantizedCustomModule) \
+            .set_preserved_attributes(["attr1", "attr2"])
+    """
+
+    def __init__(self) -> None:
+        self.observed_to_quantized_mapping: dict[QuantType, dict[type, type]] = {}
+        self.preserved_attributes: list[str] = []
+
+    def __repr__(self):
+        dict_nonempty = {k: v for k, v in self.__dict__.items() if len(v) > 0}
+        return f"ConvertCustomConfig({dict_nonempty})"
+
+    def set_observed_to_quantized_mapping(
+        self,
+        observed_class: type,
+        quantized_class: type,
+        quant_type: QuantType = QuantType.STATIC,
+    ) -> ConvertCustomConfig:
+        """
+        Set the mapping from a custom observed module class to a custom quantized module class.
+
+        The quantized module class must have a ``from_observed`` class method that converts the observed module class
+        to the quantized module class.
+        """
+        if quant_type not in self.observed_to_quantized_mapping:
+            self.observed_to_quantized_mapping[quant_type] = {}
+        self.observed_to_quantized_mapping[quant_type][observed_class] = quantized_class
+        return self
+
+    def set_preserved_attributes(self, attributes: list[str]) -> ConvertCustomConfig:
+        """
+        Set the names of the attributes that will persist in the graph module even if they are not used in
+        the model's ``forward`` method.
+        """
+        self.preserved_attributes = attributes
+        return self
+
+    # TODO: remove this
+    @classmethod
+    def from_dict(
+        cls, convert_custom_config_dict: dict[str, Any]
+    ) -> ConvertCustomConfig:
+        """
+        Create a ``ConvertCustomConfig`` from a dictionary with the following items:
+
+            "observed_to_quantized_custom_module_class": a nested dictionary mapping from quantization
+            mode to an inner mapping from observed module classes to quantized module classes, e.g.::
+            {
+            "static": {FloatCustomModule: ObservedCustomModule},
+            "dynamic": {FloatCustomModule: ObservedCustomModule},
+            "weight_only": {FloatCustomModule: ObservedCustomModule}
+            }
+            "preserved_attributes": a list of attributes that persist even if they are not used in ``forward``
+
+        This function is primarily for backward compatibility and may be removed in the future.
+        """
+        conf = cls()
+        for quant_type_name, custom_module_mapping in convert_custom_config_dict.get(
+            OBSERVED_TO_QUANTIZED_DICT_KEY, {}
+        ).items():
+            quant_type = _quant_type_from_str(quant_type_name)
+            for observed_class, quantized_class in custom_module_mapping.items():
+                conf.set_observed_to_quantized_mapping(
+                    observed_class, quantized_class, quant_type
+                )
+        conf.set_preserved_attributes(
+            convert_custom_config_dict.get(PRESERVED_ATTRIBUTES_DICT_KEY, [])
+        )
+        return conf
+
+    def to_dict(self) -> dict[str, Any]:
+        """
+        Convert this ``ConvertCustomConfig`` to a dictionary with the items described in
+        :func:`~torch.ao.quantization.fx.custom_config.ConvertCustomConfig.from_dict`.
+        """
+        d: dict[str, Any] = {}
+        for (
+            quant_type,
+            observed_to_quantized_mapping,
+        ) in self.observed_to_quantized_mapping.items():
+            if OBSERVED_TO_QUANTIZED_DICT_KEY not in d:
+                d[OBSERVED_TO_QUANTIZED_DICT_KEY] = {}
+            d[OBSERVED_TO_QUANTIZED_DICT_KEY][_get_quant_type_to_str(quant_type)] = (
+                observed_to_quantized_mapping
+            )
+        if len(self.preserved_attributes) > 0:
+            d[PRESERVED_ATTRIBUTES_DICT_KEY] = self.preserved_attributes
+        return d
+
+
+class FuseCustomConfig:
+    """
+    Custom configuration for :func:`~torch.ao.quantization.quantize_fx.fuse_fx`.
+
+    Example usage::
+
+        fuse_custom_config = FuseCustomConfig().set_preserved_attributes(
+            ["attr1", "attr2"]
+        )
+    """
+
+    def __init__(self) -> None:
+        self.preserved_attributes: list[str] = []
+
+    def __repr__(self):
+        dict_nonempty = {k: v for k, v in self.__dict__.items() if len(v) > 0}
+        return f"FuseCustomConfig({dict_nonempty})"
+
+    def set_preserved_attributes(self, attributes: list[str]) -> FuseCustomConfig:
+        """
+        Set the names of the attributes that will persist in the graph module even if they are not used in
+        the model's ``forward`` method.
+        """
+        self.preserved_attributes = attributes
+        return self
+
+    # TODO: remove this
+    @classmethod
+    def from_dict(cls, fuse_custom_config_dict: dict[str, Any]) -> FuseCustomConfig:
+        """
+        Create a ``ConvertCustomConfig`` from a dictionary with the following items:
+
+            "preserved_attributes": a list of attributes that persist even if they are not used in ``forward``
+
+        This function is primarily for backward compatibility and may be removed in the future.
+        """
+        conf = cls()
+        conf.set_preserved_attributes(
+            fuse_custom_config_dict.get(PRESERVED_ATTRIBUTES_DICT_KEY, [])
+        )
+        return conf
+
+    def to_dict(self) -> dict[str, Any]:
+        """
+        Convert this ``FuseCustomConfig`` to a dictionary with the items described in
+        :func:`~torch.ao.quantization.fx.custom_config.ConvertCustomConfig.from_dict`.
+        """
+        d: dict[str, Any] = {}
+        if len(self.preserved_attributes) > 0:
+            d[PRESERVED_ATTRIBUTES_DICT_KEY] = self.preserved_attributes
+        return d
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/fuse.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/fuse.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f4ee15779a180ea88c7dda47c7e6a45da092714
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/fuse.py
@@ -0,0 +1,195 @@
+# mypy: allow-untyped-defs
+import warnings
+from collections.abc import Callable
+from typing import Any
+
+from torch.ao.quantization.backend_config import (
+    BackendConfig,
+    get_native_backend_config,
+)
+from torch.ao.quantization.backend_config.utils import (
+    get_fuser_method_mapping,
+    get_fusion_pattern_to_extra_inputs_getter,
+    get_fusion_pattern_to_root_node_getter,
+)
+from torch.ao.quantization.utils import NodePattern, Pattern
+from torch.fx import GraphModule, map_arg, Node
+from torch.fx.graph import Graph
+
+from .custom_config import FuseCustomConfig
+from .fuse_handler import _get_fusion_pattern_to_fuse_handler_cls, FuseHandler
+from .match_utils import _is_match, MatchAllNode
+from .pattern_utils import _sorted_patterns_dict
+
+
+__all__ = [
+    "fuse",
+    # TODO: We should make this private in the future
+    # This is currently needed for test_public_bindings for some reason
+    "FuseHandler",
+]
+
+
+def fuse(
+    model: GraphModule,
+    is_qat: bool,
+    fuse_custom_config: FuseCustomConfig | dict[str, Any] | None = None,
+    backend_config: BackendConfig | dict[str, Any] | None = None,
+) -> GraphModule:
+    if fuse_custom_config is None:
+        fuse_custom_config = FuseCustomConfig()
+
+    if isinstance(fuse_custom_config, dict):
+        warnings.warn(
+            "Passing a fuse_custom_config_dict to fuse is deprecated and will not be supported "
+            "in a future version. Please pass in a FuseCustomConfig instead.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        fuse_custom_config = FuseCustomConfig.from_dict(fuse_custom_config)
+
+    if isinstance(backend_config, dict):
+        warnings.warn(
+            "Passing a backend_config_dict to prepare is deprecated and will not be supported "
+            "in a future version. Please pass in a BackendConfig instead.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        backend_config = BackendConfig.from_dict(backend_config)
+
+    named_modules = dict(model.named_modules())
+
+    if backend_config is None:
+        backend_config = get_native_backend_config()
+
+    fusion_pattern_to_fuse_handler_cls = _sorted_patterns_dict(
+        _get_fusion_pattern_to_fuse_handler_cls(backend_config)
+    )
+    fuser_method_mapping = get_fuser_method_mapping(backend_config)
+    fusion_pattern_to_root_node_getter = get_fusion_pattern_to_root_node_getter(
+        backend_config
+    )
+    fusion_pattern_to_extra_inputs_getter = get_fusion_pattern_to_extra_inputs_getter(
+        backend_config
+    )
+
+    # find fusion
+    fusion_pairs = _find_matches(model, model.graph, fusion_pattern_to_fuse_handler_cls)
+    # TODO: change this to inplace changes to graph, since we no longer construct
+    # new GraphModule anymore
+    fused_graph = Graph()
+    env: dict[Any, Any] = {}
+
+    def load_arg(a):
+        return map_arg(a, lambda node: env[node.name])
+
+    def default_root_node_getter(node_pattern):
+        while not isinstance(node_pattern[-1], Node):
+            node_pattern = node_pattern[-1]
+        return node_pattern[-1]
+
+    for node in model.graph.nodes:
+        (
+            maybe_last_node,
+            pattern,
+            matched_node_pattern,
+            obj,
+            node_to_subpattern,
+        ) = fusion_pairs.get(node.name, (None, None, None, None, None))
+        # get the corresponding subpattern for the current node
+        if node_to_subpattern is not None:
+            node_subpattern = node_to_subpattern.get(node, None)
+        else:
+            node_subpattern = None
+        if maybe_last_node is node:
+            if obj is None:
+                raise AssertionError(
+                    "fuse handler object must not be None for matched root node"
+                )
+            root_node_getter = fusion_pattern_to_root_node_getter.get(
+                pattern, default_root_node_getter
+            )
+            root_node = root_node_getter(matched_node_pattern)  # type: ignore[index]
+            extra_inputs_getter = fusion_pattern_to_extra_inputs_getter.get(
+                pattern, None
+            )
+            extra_inputs = []
+            if extra_inputs_getter is not None:
+                extra_inputs = extra_inputs_getter(matched_node_pattern)
+            # TODO: add validation that root_node is a module and has the same type
+            # as the root_module in the configuration
+            env[node.name] = obj.fuse(
+                load_arg,
+                named_modules,
+                fused_graph,
+                root_node,
+                extra_inputs,
+                matched_node_pattern,  # type: ignore[arg-type]
+                fuse_custom_config,
+                fuser_method_mapping,
+                is_qat,
+            )
+        elif maybe_last_node is None or node_subpattern is MatchAllNode:
+            env[node.name] = fused_graph.node_copy(node, load_arg)
+        # node matched in patterns and is not root is removed here
+
+    model = GraphModule(model, fused_graph)
+    return model
+
+
+def _find_matches(
+    root: GraphModule,
+    graph: Graph,
+    pattern_to_fuse_handler_cls: dict[Pattern, Callable],
+) -> dict[str, tuple[Node, Pattern, NodePattern, FuseHandler, dict[Node, Any]]]:
+    modules = dict(root.named_modules())
+    # node name -> (root_node, match_value)
+    match_map: dict[
+        str, tuple[Node, Pattern, NodePattern, FuseHandler, dict[Node, Any]]
+    ] = {}
+    # a map from node to the matched subpattern
+    node_to_subpattern: dict[Node, Any] = {}
+
+    # TODO: dedup with quantization matching function in match_utils.py
+    def apply_match(pattern, node, match, matched_node_pattern, node_to_subpattern):
+        if isinstance(pattern, tuple):
+            s, *args = pattern
+            current_node_pattern: list[Node] = []
+            apply_match(s, node, match, current_node_pattern, node_to_subpattern)
+            for subpattern, arg in zip(args, node.args):
+                apply_match(
+                    subpattern, arg, match, current_node_pattern, node_to_subpattern
+                )
+            matched_node_pattern.append(tuple(current_node_pattern))
+        else:
+            # the first pattern matches will take precedence
+            if node.name not in match_map:
+                matched_node_pattern.append(node)
+                # MatchAllNode here is actually MatchAllInputNode which should not
+                # be added to match_map
+                if pattern is not MatchAllNode:
+                    node_to_subpattern[node] = pattern
+                    root_node, pattern, handler = match
+                    match_map[node.name] = (
+                        root_node,
+                        pattern,
+                        matched_node_pattern,
+                        handler,
+                        node_to_subpattern,
+                    )
+
+    for node in reversed(graph.nodes):
+        if node.name not in match_map:
+            for pattern, fuse_handler_cls in pattern_to_fuse_handler_cls.items():
+                matched_node_pattern: list[Node] = []
+                if _is_match(modules, node, pattern):
+                    apply_match(
+                        pattern,
+                        node,
+                        (node, pattern, fuse_handler_cls(node)),
+                        matched_node_pattern,
+                        node_to_subpattern,
+                    )
+                    break
+
+    return match_map
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/fuse_handler.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/fuse_handler.py
new file mode 100644
index 0000000000000000000000000000000000000000..76fe84c2c3ad5fd88303d5f04e83c3dccfd24e5a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/fuse_handler.py
@@ -0,0 +1,129 @@
+# mypy: allow-untyped-defs
+from abc import ABC, abstractmethod
+from collections.abc import Callable
+from typing import Any
+
+import torch
+from torch.ao.quantization.backend_config import BackendConfig
+from torch.ao.quantization.fuser_method_mappings import get_fuser_method_new
+from torch.ao.quantization.utils import _parent_name, NodePattern, Pattern
+from torch.fx.graph import Graph, Node
+from torch.nn.utils.parametrize import type_before_parametrizations
+
+from .custom_config import FuseCustomConfig
+from .match_utils import MatchAllNode
+
+
+__all__ = [
+    "DefaultFuseHandler",
+    "FuseHandler",
+]
+
+
+# ----------------------------
+# Fusion Pattern Registrations
+# ----------------------------
+
+
+# Base Pattern Handler
+class FuseHandler(ABC):
+    """Base handler class for the fusion patterns"""
+
+    @abstractmethod
+    def __init__(self, node: Node):
+        pass
+
+    @abstractmethod
+    def fuse(
+        self,
+        load_arg: Callable,
+        named_modules: dict[str, torch.nn.Module],
+        fused_graph: Graph,
+        root_node: Node,
+        extra_inputs: list[Any],
+        matched_node_pattern: NodePattern,
+        fuse_custom_config: FuseCustomConfig,
+        fuser_method_mapping: dict[Pattern, torch.nn.Sequential | Callable],
+        is_qat: bool,
+    ) -> Node:
+        pass
+
+
+class DefaultFuseHandler(FuseHandler):
+    def __init__(self, node: Node):  # pylint: disable=useless-parent-delegation
+        super().__init__(node)  # type:ignore[safe-super]
+
+    def fuse(
+        self,
+        load_arg: Callable,
+        named_modules: dict[str, torch.nn.Module],
+        fused_graph: Graph,
+        root_node: Node,
+        extra_inputs: list[Any],
+        matched_node_pattern: NodePattern,
+        fuse_custom_config: FuseCustomConfig,
+        fuser_method_mapping: dict[Pattern, torch.nn.Sequential | Callable],
+        is_qat: bool,
+    ) -> Node:
+        if root_node.op != "call_module":
+            raise AssertionError("Expecting module node to be a call_module Node")
+        root_module = named_modules[str(root_node.target)]
+
+        def get_modules(pattern):
+            """Given a node pattern, extract the corresponding modules
+            e.g. input: (relu_node, (bn_node, conv_node))
+                 output: (relu_module, (bn_module, conv_module))
+            """
+            if isinstance(pattern, (tuple, list)):
+                n, *args = pattern
+                modules: list[torch.nn.Module] = []
+                modules.append(get_modules(n))
+                modules.extend(get_modules(a) for a in args)
+                return tuple(modules)
+            else:
+                n = pattern
+                if n.op == "call_module":
+                    return named_modules[n.target]
+                elif n.op == "call_function" and n.target is torch.nn.functional.relu:
+                    relu = torch.nn.ReLU()
+                    relu.training = root_module.training
+                    return relu
+                elif n.op == "call_function" or n.op == "call_method":
+                    return n.target
+                else:
+                    return MatchAllNode
+
+        # since relu can be used multiple times, we'll need to create a relu module for each match
+        matched_modules = get_modules(matched_node_pattern)
+
+        def get_matched_types(m):
+            if isinstance(m, tuple):
+                return tuple(map(get_matched_types, m))
+            if isinstance(m, torch.nn.Module):
+                return type_before_parametrizations(m)
+            return m
+
+        matched_module_types = get_matched_types(matched_modules)
+        module_parent_name, module_name = _parent_name(root_node.target)
+        fuser_method = get_fuser_method_new(matched_module_types, fuser_method_mapping)
+        # TODO: change the signature for fuser_method to take matched module patterns
+        # as input
+        fused_module = fuser_method(is_qat, *matched_modules)
+        setattr(named_modules[module_parent_name], module_name, fused_module)
+        extra_args = [load_arg(input) for input in extra_inputs]
+        node = fused_graph.node_copy(root_node, load_arg)
+        args = list(node.args)
+        args.extend(extra_args)
+        node.args = tuple(args)
+        return node
+
+
+def _get_fusion_pattern_to_fuse_handler_cls(
+    backend_config: BackendConfig,
+) -> dict[Pattern, Callable]:
+    fusion_pattern_to_fuse_handlers: dict[Pattern, Callable] = {}
+    for pattern, config in backend_config._pattern_complex_format_to_config.items():
+        if config.fuser_method is not None:
+            # TODO: is this logic right?
+            fusion_pattern_to_fuse_handlers[pattern] = DefaultFuseHandler
+    return fusion_pattern_to_fuse_handlers
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/graph_module.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/graph_module.py
new file mode 100644
index 0000000000000000000000000000000000000000..87ec3179a68ee26a5b2199c3f7543fdfd73e2864
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/graph_module.py
@@ -0,0 +1,205 @@
+# mypy: allow-untyped-defs
+import copy
+from typing import Any
+
+import torch
+from torch.fx import GraphModule
+from torch.fx.graph import Graph
+
+
+__all__ = [
+    "FusedGraphModule",
+    "ObservedGraphModule",
+    "ObservedStandaloneGraphModule",
+    "QuantizedGraphModule",
+]
+
+
+class FusedGraphModule(GraphModule):
+    def __init__(
+        self,
+        root: torch.nn.Module | dict[str, Any],
+        graph: Graph,
+        preserved_attr_names: set[str],
+    ):
+        self.preserved_attr_names = preserved_attr_names
+        preserved_attrs = {
+            attr: getattr(root, attr)
+            for attr in self.preserved_attr_names
+            if hasattr(root, attr)
+        }
+        super().__init__(root, graph)
+        for attr in preserved_attrs:
+            setattr(self, attr, preserved_attrs[attr])
+
+    # GraphModule does not copy attributes which are not in the __dict__
+    # of vanilla nn.Module.  So, we override __deepcopy__ in order
+    # to copy the quantization specific attributes correctly.
+    def __deepcopy__(self, memo):
+        fake_mod = torch.nn.Module()
+        fake_mod.__dict__ = copy.deepcopy(self.__dict__)
+        return FusedGraphModule(
+            fake_mod,
+            copy.deepcopy(self.graph),
+            copy.deepcopy(self.preserved_attr_names),
+        )
+
+
+class ObservedGraphModule(GraphModule):
+    def __init__(
+        self,
+        root: torch.nn.Module | dict[str, Any],
+        graph: Graph,
+        preserved_attr_names: set[str],
+    ):
+        self.preserved_attr_names = {
+            "_activation_post_process_map",
+            "_activation_post_process_indexes",
+            "_patterns",
+            "_node_name_to_qconfig",
+            "_prepare_custom_config",
+            "_equalization_node_name_to_qconfig",
+            "_node_name_to_scope",
+            "_qconfig_mapping",
+            "_is_qat",
+            "_observed_node_names",
+        }.union(preserved_attr_names)
+        preserved_attrs = {
+            attr: getattr(root, attr)
+            for attr in self.preserved_attr_names
+            if hasattr(root, attr)
+        }
+        super().__init__(root, graph)
+        for attr in preserved_attrs:
+            setattr(self, attr, preserved_attrs[attr])
+
+    # GraphModule does not copy attributes which are not in the __dict__
+    # of vanilla nn.Module.  So, we override __deepcopy__ in order
+    # to copy the quantization specific attributes correctly.
+    def __deepcopy__(self, memo):
+        fake_mod = torch.nn.Module()
+        fake_mod.__dict__ = copy.deepcopy(self.__dict__)
+        return ObservedGraphModule(
+            fake_mod,
+            copy.deepcopy(self.graph),
+            copy.deepcopy(self.preserved_attr_names),
+        )
+
+
+def _is_observed_module(module: Any) -> bool:
+    return hasattr(module, "meta") and "_observed_graph_module_attrs" in module.meta
+
+
+def _get_observed_graph_module_attr(
+    model: torch.nn.Module | GraphModule, attr_name: str
+) -> Any:
+    if hasattr(model, "meta") and "_observed_graph_module_attrs" in model.meta:  # type: ignore[operator, index]
+        return getattr(model.meta["_observed_graph_module_attrs"], attr_name)  # type: ignore[index]
+    return None
+
+
+class ObservedStandaloneGraphModule(ObservedGraphModule):
+    def __init__(
+        self,
+        root: torch.nn.Module | dict[str, Any],
+        graph: Graph,
+        preserved_attr_names: set[str],
+    ):
+        preserved_attr_names = preserved_attr_names.union(
+            {
+                "_standalone_module_input_quantized_idxs",
+                "_standalone_module_output_quantized_idxs",
+            }
+        )
+        super().__init__(root, graph, preserved_attr_names)
+
+    def __deepcopy__(self, memo):
+        fake_mod = torch.nn.Module()
+        fake_mod.__dict__ = copy.deepcopy(self.__dict__)
+        return ObservedStandaloneGraphModule(
+            fake_mod,
+            copy.deepcopy(self.graph),
+            copy.deepcopy(self.preserved_attr_names),
+        )
+
+
+def _is_observed_standalone_module(module: Any) -> bool:
+    return (
+        _is_observed_module(module)
+        and module.meta["_observed_graph_module_attrs"].is_observed_standalone_module
+    )
+
+
+def _save_packed_weight(self, destination, prefix, keep_vars):
+    for attr_name in dir(self):
+        if "_packed_weight" in attr_name and isinstance(
+            getattr(self, attr_name), torch._C.ScriptObject
+        ):  # type: ignore[attr-defined]
+            packed_weight = getattr(self, attr_name)
+            destination[prefix + attr_name] = packed_weight
+
+
+class QuantizedGraphModule(GraphModule):
+    """This class is created to make sure PackedParams
+    (e.g. LinearPackedParams, Conv2dPackedParams) to appear in state_dict
+    so that we can serialize and deserialize quantized graph module with
+    torch.save(m.state_dict()) and m.load_state_dict(state_dict)
+    """
+
+    def __init__(
+        self,
+        root: torch.nn.Module | dict[str, Any],
+        graph: Graph,
+        preserved_attr_names: set[str],
+    ):
+        self.preserved_attr_names = preserved_attr_names
+        preserved_attrs = {
+            attr: getattr(root, attr)
+            for attr in self.preserved_attr_names
+            if hasattr(root, attr)
+        }
+        super().__init__(root, graph)
+        for attr in preserved_attrs:
+            setattr(self, attr, preserved_attrs[attr])
+        self._register_state_dict_hook(_save_packed_weight)
+
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        attrs_to_pop = []
+        for attr_name in state_dict:
+            if attr_name.startswith("_packed_weight") and isinstance(
+                state_dict[attr_name], torch._C.ScriptObject
+            ):  # type: ignore[attr-defined] # noqa: B950
+                setattr(self, attr_name, state_dict[attr_name])
+                attrs_to_pop.append(attr_name)
+
+        # pop the packed param attributesn
+        for attr_name in attrs_to_pop:
+            state_dict.pop(attr_name)
+
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            strict,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+    def __deepcopy__(self, memo):
+        fake_mod = torch.nn.Module()
+        fake_mod.__dict__ = copy.deepcopy(self.__dict__)
+        return QuantizedGraphModule(
+            fake_mod,
+            copy.deepcopy(self.graph),
+            copy.deepcopy(self.preserved_attr_names),
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/lower_to_fbgemm.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/lower_to_fbgemm.py
new file mode 100644
index 0000000000000000000000000000000000000000..73fd3e8741b6d6c26d5a352d25d4cf6986de4d9d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/lower_to_fbgemm.py
@@ -0,0 +1,21 @@
+from torch.ao.quantization.qconfig import QConfigAny
+from torch.fx import GraphModule
+
+from ._lower_to_native_backend import _lower_to_native_backend
+
+
+__all__ = ["lower_to_fbgemm"]
+
+
+def lower_to_fbgemm(
+    model: GraphModule,
+    qconfig_map: dict[str, QConfigAny],
+    node_name_to_scope: dict[str, tuple[str, type]],
+    keep_original_weights: bool = False,
+) -> GraphModule:
+    """Lower a quantized reference model (with reference quantized operator patterns)
+    to fbgemm
+    """
+    return _lower_to_native_backend(
+        model, qconfig_map, node_name_to_scope, keep_original_weights
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/lower_to_qnnpack.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/lower_to_qnnpack.py
new file mode 100644
index 0000000000000000000000000000000000000000..f1fa3ecf3f5a3b2b5dc67d769853f8424bae7efb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/lower_to_qnnpack.py
@@ -0,0 +1,18 @@
+from torch.ao.quantization.qconfig import QConfigAny
+from torch.fx import GraphModule
+
+from ._lower_to_native_backend import _lower_to_native_backend
+
+
+__all__ = ["lower_to_qnnpack"]
+
+
+def lower_to_qnnpack(
+    model: GraphModule,
+    qconfig_map: dict[str, QConfigAny],
+    node_name_to_scope: dict[str, tuple[str, type]],
+) -> GraphModule:
+    """Lower a quantized reference model (with reference quantized operator patterns)
+    to qnnpack
+    """
+    return _lower_to_native_backend(model, qconfig_map, node_name_to_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/lstm_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/lstm_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..78849692a45efab6b7ce3af208ee16f6d77286c6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/lstm_utils.py
@@ -0,0 +1,228 @@
+import copy
+import operator
+from typing import Any, TYPE_CHECKING
+
+import torch
+from torch.ao.quantization import (
+    default_weight_fake_quant,
+    default_weight_observer,
+    FakeQuantizeBase,
+    QConfig,
+    QConfigMapping,
+)
+from torch.ao.quantization.backend_config import BackendConfig
+from torch.ao.quantization.observer import _PartialWrapper
+from torch.ao.quantization.quantize_fx import convert_to_reference_fx, prepare_fx
+
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+
+# TODO: move all LSTM util functions from fx/utils.py to this file
+def _get_lstm_with_individually_observed_parts(
+    float_lstm: torch.nn.LSTM,
+    example_inputs: tuple[Any, ...],
+    backend_config: BackendConfig | None = None,
+    linear_output_obs_ctr: _PartialWrapper | None = None,
+    sigmoid_obs_ctr: _PartialWrapper | None = None,
+    tanh_obs_ctr: _PartialWrapper | None = None,
+    cell_state_obs_ctr: _PartialWrapper | None = None,
+    hidden_state_obs_ctr: _PartialWrapper | None = None,
+    split_gates: bool = False,
+) -> torch.ao.nn.quantizable.LSTM:
+    """
+    Return an observed `torch.ao.nn.quantizable.LSTM` created from a `torch.nn.LSTM`
+    with specific observers or fake quantizes assigned to the inner ops or submodules.
+
+    In both eager and FX graph mode quantization, `torch.ao.nn.quantizable.LSTM` is
+    used as an observed custom module, which is responsible for inserting its own
+    observers. By default, all inner ops inherit the parent custom module's QConfig.
+    Users who wish to override this behavior may extend `torch.ao.nn.quantizable.LSTM`
+    and use this helper function to customize the observer insertion logic.
+
+    This is meant to be used to convert a float module to an observed module in the
+    custom module flow.
+
+    Args:
+        `float_lstm`: The float LSTM module
+        `example_inputs`: example inputs for the forward function of the LSTM module
+        `backend_config`: BackendConfig to use to observe the LSTM module
+        `linear_output_obs_ctr`: observer or fake quantize for linear outputs Wx + b,
+            where W is the weight matrix, b is the bias, and x is either the inputs
+            or the hidden state from the previous layer (if any)
+        `sigmoid_obs_ctr`: observer or fake quantize for sigmoid activations
+        `tanh_obs_ctr`: observer or fake quantize for tanh activations
+        `cell_state_obs_ctr`: observer or fake quantize for the cell state
+        `hidden_state_obs_ctr`: observer or fake quantize for the hidden state and
+            the output
+
+    Return:
+        A `torch.ao.nn.quantizable.LSTM` with the specified observers or fake quantizes
+        assigned to the inner ops.
+    """
+
+    def make_qconfig(obs_ctr: _PartialWrapper) -> QConfig:
+        """
+        Make a QConfig with fixed qparams observers or fake quantizes.
+        """
+        if isinstance(obs_ctr(), FakeQuantizeBase):
+            weight = default_weight_fake_quant
+        else:
+            weight = default_weight_observer
+        return QConfig(activation=obs_ctr, weight=weight)
+
+    quantizable_lstm = torch.ao.nn.quantizable.LSTM(
+        float_lstm.input_size,
+        float_lstm.hidden_size,
+        float_lstm.num_layers,
+        float_lstm.bias,
+        float_lstm.batch_first,
+        float_lstm.dropout,
+        float_lstm.bidirectional,
+        split_gates=split_gates,
+    )
+    quantizable_lstm.qconfig = float_lstm.qconfig
+
+    for idx in range(float_lstm.num_layers):
+        quantizable_lstm.layers[idx] = (
+            torch.ao.nn.quantizable.modules.rnn._LSTMLayer.from_float(
+                float_lstm,
+                idx,
+                float_lstm.qconfig,
+                batch_first=False,
+                split_gates=split_gates,
+            )
+        )
+
+    # Build QConfigMapping for the LSTM cell
+    # Note: FloatFunctional qconfigs will be configured separately below
+    cell_qm = QConfigMapping().set_global(float_lstm.qconfig)  # type: ignore[arg-type]
+    if sigmoid_obs_ctr is not None:
+        cell_qm.set_module_name("input_gate", make_qconfig(sigmoid_obs_ctr))
+        cell_qm.set_module_name("forget_gate", make_qconfig(sigmoid_obs_ctr))
+        cell_qm.set_module_name("output_gate", make_qconfig(sigmoid_obs_ctr))
+    if tanh_obs_ctr is not None:
+        cell_qm.set_module_name("cell_gate", make_qconfig(tanh_obs_ctr))
+
+    # Insert observers into each LSTM cell
+    # TODO: maybe make this work for layer_bw as well
+    for layer in quantizable_lstm.layers:
+        cell = layer.layer_fw.cell  # type: ignore[union-attr]
+        if not isinstance(cell, torch.nn.Module):
+            raise AssertionError("cell should be a nn.Module")
+        cell = prepare_fx(cell, cell_qm, example_inputs, backend_config=backend_config)
+        # HACK: Manually replace the activation_post_process following these ops.
+        # This is needed for FloatFunctional ops because there is currently no way
+        # to configure these ops in FX graph mode quantization today. This is because
+        # the FloatFunctional modules simply disappear from the graph after tracing.
+        # In the future, we should rewrite quantizable LSTM without FloatFunctionals.
+        if not split_gates:
+            op_index_to_activation_post_process_ctr = {
+                (torch.add, 0): linear_output_obs_ctr,  # gates.add
+                (torch.mul, 0): cell_state_obs_ctr,  # fgate_cx.mul
+                (torch.mul, 1): cell_state_obs_ctr,  # igate_cgate.mul
+                (torch.add, 1): cell_state_obs_ctr,  # fgate_cx_igate_cgate.add
+                (torch.mul, 2): hidden_state_obs_ctr,  # ogate_cy.mul
+            }
+        else:
+            op_index_to_activation_post_process_ctr = {
+                (torch.add, 0): linear_output_obs_ctr,  # gates.add (input)
+                (torch.add, 1): linear_output_obs_ctr,  # gates.add (forget)
+                (torch.add, 2): linear_output_obs_ctr,  # gates.add (cell)
+                (torch.add, 3): linear_output_obs_ctr,  # gates.add (output)
+                (torch.mul, 0): cell_state_obs_ctr,  # fgate_cx.mul
+                (torch.mul, 1): cell_state_obs_ctr,  # igate_cgate.mul
+                (torch.add, 4): cell_state_obs_ctr,  # fgate_cx_igate_cgate.add
+                (torch.mul, 2): hidden_state_obs_ctr,  # ogate_cy.mul
+            }
+        add_count = 0
+        mul_count = 0
+        for node in cell.graph.nodes:
+            op_index: tuple[Callable, int] | None = None  # e.g. (torch.add, 1)
+            if node.target is torch.add:
+                op_index = (torch.add, add_count)
+                add_count += 1
+            elif node.target is torch.mul:
+                op_index = (torch.mul, mul_count)
+                mul_count += 1
+            else:
+                # Neither torch.add nor torch.mul
+                continue
+            if op_index not in op_index_to_activation_post_process_ctr:
+                continue
+            if len(node.users) != 1:
+                raise AssertionError("expected exactly one user for the node")
+            activation_post_process_name = next(iter(node.users.keys())).name
+            activation_post_process_ctr = op_index_to_activation_post_process_ctr[
+                op_index
+            ]
+            if activation_post_process_ctr is not None:
+                setattr(
+                    cell, activation_post_process_name, activation_post_process_ctr()
+                )
+        layer.layer_fw.cell = cell  # type: ignore[union-attr]
+    return quantizable_lstm
+
+
+def _get_reference_quantized_lstm_module(
+    observed_lstm: torch.ao.nn.quantizable.LSTM,
+    backend_config: BackendConfig | None = None,
+) -> torch.ao.nn.quantized.LSTM:
+    """
+    Return a `torch.ao.nn.quantized.LSTM` created from a `torch.ao.nn.quantizable.LSTM`
+    with observers or fake quantizes inserted through `prepare_fx`, e.g. from
+    `_get_lstm_with_individually_observed_parts`.
+
+    This is meant to be used to convert an observed module to a quantized module in the
+    custom module flow.
+
+    Args:
+        `observed_lstm`: a `torch.ao.nn.quantizable.LSTM` observed through `prepare_fx`
+        `backend_config`: BackendConfig to use to produce the reference quantized model
+
+    Return:
+        A reference `torch.ao.nn.quantized.LSTM` module.
+    """
+    quantized_lstm = torch.ao.nn.quantized.LSTM(
+        observed_lstm.input_size,
+        observed_lstm.hidden_size,
+        observed_lstm.num_layers,
+        observed_lstm.bias,
+        observed_lstm.batch_first,
+        observed_lstm.dropout,
+        observed_lstm.bidirectional,
+    )
+
+    for i, layer in enumerate(quantized_lstm.layers):
+        cell = copy.deepcopy(observed_lstm.layers.get_submodule(str(i)).layer_fw.cell)  # type: ignore[union-attr]
+        cell = convert_to_reference_fx(cell, backend_config=backend_config)  # type: ignore[arg-type]
+        if not isinstance(cell, torch.fx.GraphModule):
+            raise AssertionError("cell must be converted to a torch.fx.GraphModule")
+        # HACK: Manually remove input quantize nodes and output dequantize nodes,
+        # since custom modules expect quint8 inputs and outputs for now. Note that
+        # this functionality is supposedly handled through PrepareCustomConfig's
+        # `set_input_quantized_indexes` and `set_output_quantized_indexes`, but that
+        # API doesn't currently handle tuple inputs and outputs, so we have to do
+        # this manually for now. In the future we should (1) relax the restriction
+        # on custom module input/output dtypes, and (2) expand support for complex
+        # input/output structures.
+        for node in cell.graph.nodes:
+            if node.target is torch.quantize_per_tensor:
+                arg = node.args[0]
+                # Remove quantize(x), quantize(hidden[0]), and quantize(hidden[1])
+                if arg.target == "x" or (
+                    arg.target is operator.getitem and arg.args[0].target == "hidden"
+                ):
+                    with cell.graph.inserting_before(node):
+                        node.replace_all_uses_with(arg)
+                        cell.graph.erase_node(node)
+            if node.target == "output":
+                # Remove all dequantize nodes in the output tuple
+                for arg in node.args[0]:
+                    with cell.graph.inserting_before(node):
+                        node.replace_input_with(arg, arg.args[0])
+        cell.graph.eliminate_dead_code()
+        cell.recompile()
+        layer.layer_fw.cell = cell  # type: ignore[union-attr]
+    return quantized_lstm
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/match_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/match_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..79194caa4a17b9f2db99981b184081d09df80e84
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/match_utils.py
@@ -0,0 +1,231 @@
+# mypy: allow-untyped-defs
+import sys
+from collections.abc import Callable, Iterable
+from typing import Any
+
+import torch
+from torch.ao.quantization.qconfig import QConfigAny
+from torch.ao.quantization.utils import MatchAllNode, Pattern
+from torch.fx.graph import Graph, Node
+from torch.nn.utils.parametrize import type_before_parametrizations
+
+from .graph_module import _is_observed_standalone_module
+from .quantize_handler import QuantizeHandler
+
+
+__all__: list[str] = []
+
+# TODO(future PR): the 1st argument is typed as `List[Node]`, but a better type
+# would be a recursive `List[Union[Node, Tuple[Union[Node, ...]]]]`
+_MatchResult = tuple[Node, list[Node], Pattern | None, QuantizeHandler]
+
+_MatchResultWithQConfig = tuple[
+    Node, list[Node], Pattern | None, QuantizeHandler, QConfigAny
+]
+
+
+# Note: The order of patterns is important! match function will take whatever is matched first, so we'll
+# need to put the fusion patterns before single patterns. For example, add_relu should be registered come before relu.
+# decorators are applied in the reverse order we see. Also when we match the nodes in the graph with these patterns,
+# we'll start from the last node of the graph and traverse back.
+def _is_match(modules, node, pattern, max_uses=sys.maxsize):
+    """Matches a node in fx against a pattern"""
+    if isinstance(pattern, tuple):
+        self_match, *arg_matches = pattern
+        if self_match is getattr:
+            if len(pattern) != 2:
+                raise AssertionError("Expecting getattr pattern to have two elements")
+            arg_matches = []
+    else:
+        self_match = pattern
+        arg_matches = []
+
+    if isinstance(self_match, type) and issubclass(self_match, MatchAllNode):
+        return True
+
+    if node == pattern:
+        return True
+
+    if not isinstance(node, Node) or len(node.users) > max_uses:
+        return False
+
+    if isinstance(self_match, type) and issubclass(self_match, torch.nn.Module):
+        if node.op != "call_module":
+            return False
+        if type_before_parametrizations(modules[node.target]) != self_match:
+            return False
+    elif callable(self_match):
+        if node.op != "call_function" or node.target is not self_match:
+            return False
+        elif node.target is getattr:
+            if node.args[1] != pattern[1]:
+                return False
+    elif isinstance(self_match, str):
+        if node.op != "call_method" or node.target != self_match:
+            return False
+    elif node.target != self_match:
+        return False
+
+    if not arg_matches:
+        return True
+
+    if len(arg_matches) != len(node.args):
+        return False
+
+    return all(
+        _is_match(modules, node, arg_match, max_uses=1)
+        for node, arg_match in zip(node.args, arg_matches)
+    )
+
+
+def _find_matches(
+    graph: Graph,
+    modules: dict[str, torch.nn.Module],
+    patterns: dict[Pattern, QuantizeHandler],
+    root_node_getter_mapping: dict[Pattern, Callable],
+    standalone_module_names: list[str] | None = None,
+    standalone_module_classes: list[type] | None = None,
+    custom_module_classes: list[Any] | None = None,
+) -> dict[str, _MatchResult]:
+    """
+    Matches the nodes in the input graph to quantization patterns, and
+    outputs the information needed to quantize them in future steps.
+
+    Inputs:
+      - graph: an fx.Graph object
+      - modules: a mapping of fully qualified module name to instance,
+          for example, {'foo': ModuleFoo, ...}
+      - patterns: a mapping from a tuple of nodes in reverse order to
+          uninitialized QuantizeHandler subclass.
+
+    Outputs a map of
+      node_name ->
+        (node, matched_values, matched_pattern, QuantizeHandler instance,
+         qconfig)
+
+    For example, {
+      'relu_1': (relu_1, [relu_1], torch.nn.functional.relu,
+                 , QConfig(...)),
+      ...
+    }
+    """
+    if custom_module_classes is None:
+        custom_module_classes = []
+
+    if standalone_module_classes is None:
+        standalone_module_classes = []
+
+    if standalone_module_names is None:
+        standalone_module_names = []
+
+    match_map: dict[str, _MatchResult] = {}
+    all_matched: set[str] = set()
+
+    def _recursive_record_node_in_match_map(
+        last_node, match_map, node_pattern, matched_node_pattern, pattern, match_value
+    ):
+        if isinstance(node_pattern, Node):
+            match_map[node_pattern.name] = (
+                last_node,
+                matched_node_pattern,
+                pattern,
+                match_value,
+            )
+        elif not isinstance(node_pattern, Iterable):
+            return
+        else:
+            for n in node_pattern:
+                _recursive_record_node_in_match_map(
+                    last_node, match_map, n, matched_node_pattern, pattern, match_value
+                )
+
+    # TODO: 1. merge with fuse matcher 2. document the code
+    def record_match(pattern, node, last_node, matched_node_pattern, match_map):
+        if isinstance(pattern, tuple):
+            s, *args = pattern
+            is_single_arg = len(args) == 1
+            current_node_pattern: list[Node] = []
+            record_match(s, node, last_node, matched_node_pattern, match_map)
+            if pattern[0] is not getattr:
+                for subpattern, arg in zip(args, node.args):
+                    record_match(subpattern, arg, node, current_node_pattern, match_map)
+            if len(current_node_pattern) > 1:
+                # current_node_pattern is  the node pattern we get from matching
+                # the subpattern with arguments of the node
+                # we use is_single_arg to recover the original structure of the pattern
+                # if the original pattern has a single argument, we will have
+                # (original_op, (original_arg, ...))
+                # otherwise, we'll have a list of arguments
+                # (original_op, arg0, arg1, arg2, ...)
+                if is_single_arg:
+                    matched_node_pattern.append(tuple(current_node_pattern))
+                else:
+                    matched_node_pattern.extend(list(current_node_pattern))
+            else:
+                matched_node_pattern.append(current_node_pattern[0])
+        else:
+            matched_node_pattern.append(node)
+
+    for node in reversed(graph.nodes):
+        if node.name not in match_map and node.name not in all_matched:
+            for pattern, quantize_handler_cls in patterns.items():
+                root_node_getter = root_node_getter_mapping.get(pattern)
+                if _is_match(modules, node, pattern) and node.name not in match_map:
+                    matched_node_pattern: list[Node] = []
+                    record_match(pattern, node, node, matched_node_pattern, match_map)
+                    quantize_handler = quantize_handler_cls(  # type: ignore[operator]
+                        matched_node_pattern, modules, root_node_getter
+                    )
+                    last_node = node
+                    # record the match for all nodes in the pattern
+                    _recursive_record_node_in_match_map(
+                        last_node,
+                        match_map,
+                        # we need to record all nodes in the matched pattern in the match_map
+                        matched_node_pattern,
+                        # this is a part of the value corresponding to the node
+                        matched_node_pattern,
+                        pattern,
+                        quantize_handler,
+                    )
+                    break
+
+    # add custom module instances to the match result
+    if modules is None:
+        raise AssertionError("modules must not be None")
+    for node in graph.nodes:
+        if (
+            node.op == "call_module"
+            and type(modules[node.target]) in custom_module_classes
+        ):
+            match_map[node.name] = (
+                node,
+                node,
+                None,
+                QuantizeHandler(node, modules, is_custom_module=True),
+            )
+
+    def is_standalone_module(node_target: str, modules: dict[str, torch.nn.Module]):
+        if modules is None:
+            raise AssertionError("modules must not be None")
+        return (
+            node_target in standalone_module_names
+            or type(modules[node_target])  # type: ignore[operator]
+            in standalone_module_classes  # type: ignore[operator]
+        )
+
+    # add standalone modules to the match
+    for node in graph.nodes:
+        if node.op == "call_module" and (
+            is_standalone_module(node.target, modules)
+            or _is_observed_standalone_module(modules[node.target])
+        ):
+            # add node to matched nodes
+            match_map[node.name] = (
+                node,
+                node,
+                None,
+                QuantizeHandler(node, modules, is_standalone_module=True),
+            )
+
+    return match_map
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/pattern_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/pattern_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..e86f95d67aba092daff6a3a14a14767f29d249a2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/pattern_utils.py
@@ -0,0 +1,112 @@
+# mypy: allow-untyped-defs
+import copy
+from collections import OrderedDict
+from typing import Any
+
+from torch.ao.quantization.fake_quantize import FixedQParamsFakeQuantize
+from torch.ao.quantization.observer import ObserverBase
+from torch.ao.quantization.utils import Pattern
+
+
+__all__ = [
+    "get_default_fusion_patterns",
+    "get_default_quant_patterns",
+    "get_default_output_activation_post_process_map",
+]
+
+# TODO(future PR): fix the typing on QuantizeHandler (currently a circular dependency)
+QuantizeHandler = Any
+
+# pattern for conv bn fusion
+_DEFAULT_FUSION_PATTERNS: dict[Pattern, QuantizeHandler] = OrderedDict()
+
+
+def _register_fusion_pattern(pattern):
+    def insert(fn):
+        _DEFAULT_FUSION_PATTERNS[pattern] = fn
+        return fn
+
+    return insert
+
+
+def get_default_fusion_patterns() -> dict[Pattern, QuantizeHandler]:
+    return copy.copy(_DEFAULT_FUSION_PATTERNS)
+
+
+_DEFAULT_QUANTIZATION_PATTERNS: dict[Pattern, QuantizeHandler] = OrderedDict()
+
+# Mapping from pattern to activation_post_process(observer/fake_quant) constructor for output activation
+# e.g. pattern: torch.sigmoid,
+#      output_activation_post_process: default_fixed_qparams_range_0to1_fake_quant
+_DEFAULT_OUTPUT_FAKE_QUANTIZE_MAP: dict[Pattern, QuantizeHandler] = {}
+_DEFAULT_OUTPUT_OBSERVER_MAP: dict[Pattern, QuantizeHandler] = {}
+
+
+# Register pattern for both static quantization and qat
+def _register_quant_pattern(pattern, fixed_qparams_observer=None):
+    def insert(fn):
+        _DEFAULT_QUANTIZATION_PATTERNS[pattern] = fn
+        if fixed_qparams_observer is not None:
+            _DEFAULT_OUTPUT_FAKE_QUANTIZE_MAP[pattern] = (
+                FixedQParamsFakeQuantize.with_args(observer=fixed_qparams_observer)
+            )
+            _DEFAULT_OUTPUT_OBSERVER_MAP[pattern] = fixed_qparams_observer
+        return fn
+
+    return insert
+
+
+# Get patterns for both static quantization and qat
+def get_default_quant_patterns() -> dict[Pattern, QuantizeHandler]:
+    return copy.copy(_DEFAULT_QUANTIZATION_PATTERNS)
+
+
+# a map from pattern to output activation post process constructor
+# e.g. torch.sigmoid -> default_affine_fixed_qparam_fake_quant
+def get_default_output_activation_post_process_map(
+    is_training,
+) -> dict[Pattern, ObserverBase]:
+    if is_training:
+        return copy.copy(_DEFAULT_OUTPUT_FAKE_QUANTIZE_MAP)
+    else:
+        return copy.copy(_DEFAULT_OUTPUT_OBSERVER_MAP)
+
+
+# Example use of register pattern function:
+# @_register_fusion_pattern(torch.nn.ReLU, (torch.nn.BatchNorm2d, torch.nn.Conv2d)))
+# class ConvOrLinearBNReLUFusion():
+#     def __init__(...):
+#         ...
+#
+
+
+def _sorted_patterns_dict(
+    patterns_dict: dict[Pattern, QuantizeHandler],
+) -> dict[Pattern, QuantizeHandler]:
+    """
+    Return a sorted version of the patterns dictionary such that longer patterns are matched first,
+    e.g. match (F.relu, F.linear) before F.relu.
+    This works for current use cases, but we may need to have a more clever way to sort
+    things to address more complex patterns
+    """
+
+    def get_len(pattern):
+        """this will calculate the length of the pattern by counting all the entries
+        in the pattern.
+        this will make sure (nn.ReLU, (nn.BatchNorm, nn.Conv2d)) comes before
+        (nn.BatchNorm, nn.Conv2d) so that we can match the former first
+        """
+        len = 0
+        if isinstance(pattern, tuple):
+            for item in pattern:
+                len += get_len(item)
+        else:
+            len += 1
+        return len
+
+    return OrderedDict(
+        sorted(
+            patterns_dict.items(),
+            key=lambda kv: -get_len(kv[0]) if isinstance(kv[0], tuple) else 1,
+        )
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/prepare.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/prepare.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c2fab3f27eb917b22368dae04cd908f2f81a7c2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/prepare.py
@@ -0,0 +1,2251 @@
+# mypy: allow-untyped-defs
+import copy
+import warnings
+from dataclasses import asdict
+from typing import Any
+
+import torch
+from torch._subclasses import FakeTensor
+from torch.ao.quantization import (
+    _DerivedObserverOrFakeQuantize,
+    FixedQParamsFakeQuantize,
+    FixedQParamsObserver,
+    ObserverBase,
+    ObserverOrFakeQuantize,
+    PlaceholderObserver,
+)
+from torch.ao.quantization.backend_config import (
+    BackendConfig,
+    DTypeConfig,
+    get_native_backend_config,
+)
+from torch.ao.quantization.backend_config.utils import (
+    get_fusion_pattern_to_root_node_getter,
+    get_module_to_qat_module,
+    get_pattern_to_dtype_configs,
+)
+from torch.ao.quantization.observer import _is_activation_post_process, _PartialWrapper
+from torch.ao.quantization.qconfig import _is_reuse_input_qconfig, QConfigAny
+from torch.ao.quantization.qconfig_mapping import QConfigMapping
+from torch.ao.quantization.quantize import convert, propagate_qconfig_
+from torch.ao.quantization.quantizer import (
+    DerivedQuantizationSpec,
+    EdgeOrNode,
+    FixedQParamsQuantizationSpec,
+    QuantizationSpec,
+    QuantizationSpecBase,
+    SharedQuantizationSpec,
+)
+from torch.ao.quantization.utils import (
+    _parent_name,
+    get_qconfig_dtypes,
+    get_swapped_custom_module_class,
+    NodePattern,
+    Pattern,
+)
+from torch.fx import GraphModule
+from torch.fx.graph import Graph, Node
+from torch.fx.node import Argument
+
+from ._equalize import is_equalization_observer, node_supports_equalization
+from .custom_config import PrepareCustomConfig, StandaloneModuleConfigEntry
+from .match_utils import _find_matches, _MatchResultWithQConfig
+from .pattern_utils import _sorted_patterns_dict
+from .qconfig_mapping_utils import (
+    _generate_node_name_to_qconfig,
+    _get_flattened_qconfig_dict,
+    _update_qconfig_for_fusion,
+    _update_qconfig_for_qat,
+)
+from .quantize_handler import (
+    _default_root_node_getter,
+    _get_pattern_to_quantize_handlers,
+    QuantizeHandler,
+)
+from .utils import (
+    _insert_dequant_stubs_for_custom_module_lstm_output,
+    _is_custom_module_lstm,
+    _maybe_get_custom_module_lstm_from_node_arg,
+    _qconfig_satisfies_dtype_config_constraints,
+    all_node_args_have_no_tensors,
+    assert_and_get_unique_device,
+    get_custom_module_class_keys,
+    get_new_attr_name_with_prefix,
+    get_non_observable_arg_indexes_and_types,
+    node_arg_is_bias,
+    node_arg_is_weight,
+    NON_QUANTIZABLE_WEIGHT_OPS,
+    ObservedGraphModuleAttrs,
+)
+
+
+__all__ = [
+    "insert_observers_for_model",
+    "prepare",
+    "propagate_dtypes_for_known_nodes",
+]
+
+
+# list of dtypes to not add observers to
+_DO_NOT_OBS_DTYPE_LIST = [int, float, torch.bool, None]
+_OBS_DTYPE_LIST = [
+    torch.quint8,
+    torch.qint8,
+    torch.qint32,
+    torch.float16,
+    torch.uint8,
+    torch.int8,
+    torch.int16,
+    torch.int32,
+    torch.float8_e5m2,
+    torch.float8_e4m3fn,
+]
+
+_DEFAULT_FP32_OBS_OR_FQ_CTR = PlaceholderObserver.with_args(dtype=torch.float)
+
+# note: the following default target dtype info dicts are temporary,
+# should be moved to the new programmable API class soon
+_DEFAULT_FP32_QCONFIG_FOR_TARGET_DTYPE_INFO = {
+    "input_act_obs_or_fq_ctr": torch.ao.quantization.qconfig._default_fp32_placeholder_qconfig.activation,
+    "output_act_obs_or_fq_ctr": torch.ao.quantization.qconfig._default_fp32_placeholder_qconfig.activation,
+}
+
+_DEFAULT_QUINT8_QCONFIG_FOR_TARGET_DTYPE_INFO = {
+    "input_act_obs_or_fq_ctr": torch.ao.quantization.qconfig._default_quint8_placeholder_qconfig.activation,
+    "output_act_obs_or_fq_ctr": torch.ao.quantization.qconfig._default_quint8_placeholder_qconfig.activation,
+}
+
+
+def _get_observer_kwargs(
+    quant_spec: QuantizationSpec | FixedQParamsQuantizationSpec,
+):
+    kwargs_dict = asdict(quant_spec)
+    return copy.deepcopy(kwargs_dict)
+
+
+def _get_qspec_for_arg(
+    arg: Node,
+    input_qspec_map: dict[Node, QuantizationSpecBase],
+    named_modules: dict[str, torch.nn.Module],
+) -> QuantizationSpecBase | None:
+    while _is_activation_post_process_node(arg, named_modules):
+        arg = arg.args[0]  # type: ignore[assignment]
+    return input_qspec_map.get(arg)
+
+
+def _create_obs_or_fq_from_qspec(
+    quantization_spec: QuantizationSpecBase | None,
+    obs_or_fq_map: dict[EdgeOrNode, ObserverOrFakeQuantize],
+    is_qat: bool,
+):
+    """Create observer or fake quantize objects based on quantization spec
+
+    Args:
+       quantization_spec: used to store parameters to create the observer or fake quantizer
+       obs_or_fq_map: this is a map from edge/output to the corresponding observer/fake_quant
+       instance, it may be reused for different edge/output depending on configuration
+    """
+    if quantization_spec is None:
+        return None
+    if isinstance(quantization_spec, SharedQuantizationSpec):
+        edge_or_node = quantization_spec.edge_or_node
+        if edge_or_node not in obs_or_fq_map:
+            raise AssertionError(
+                "please make sure only refer to edge or node that has "
+                f"observer/fake_quant inserted: '{edge_or_node}' not in\n{obs_or_fq_map.keys()}"
+            )
+        return obs_or_fq_map[edge_or_node]
+    elif isinstance(quantization_spec, DerivedQuantizationSpec):
+        # can't use asdict, so not calling get_observer_kwargs here
+        kwargs = {
+            "dtype": quantization_spec.dtype,
+            "derive_qparams_fn": quantization_spec.derive_qparams_fn,
+            "quant_min": quantization_spec.quant_min,
+            "quant_max": quantization_spec.quant_max,
+            "qscheme": quantization_spec.qscheme,
+            "ch_axis": quantization_spec.ch_axis,
+        }
+        edge_or_nodes = quantization_spec.derived_from
+        obs_or_fqs = [obs_or_fq_map[k] for k in edge_or_nodes]
+        # pyrefly: ignore [unsupported-operation]
+        kwargs["obs_or_fqs"] = obs_or_fqs
+        return _DerivedObserverOrFakeQuantize.with_args(**kwargs)()
+    elif isinstance(quantization_spec, FixedQParamsQuantizationSpec):
+        kwargs = _get_observer_kwargs(quantization_spec)
+        observer_ctr = FixedQParamsObserver.with_args(**kwargs)
+        if is_qat:
+            return FixedQParamsFakeQuantize.with_args(observer=observer_ctr)()
+        else:
+            return observer_ctr()
+
+    if not isinstance(quantization_spec, QuantizationSpec):
+        raise AssertionError("quantization_spec must be a QuantizationSpec")
+    observer_or_fake_quant_ctr = quantization_spec.observer_or_fake_quant_ctr
+    kwargs = _get_observer_kwargs(quantization_spec)
+    kwargs.pop("observer_or_fake_quant_ctr")
+    # we will remove is_dynamic from QuantizationSpec because
+    # it seems that dynamic range quantization
+    obs_or_fq_class = observer_or_fake_quant_ctr
+    if isinstance(observer_or_fake_quant_ctr, _PartialWrapper):
+        obs_or_fq_class = observer_or_fake_quant_ctr.p.func  # type: ignore[union-attr, assignment]
+    if "PerChannel" not in obs_or_fq_class.__name__:  # type: ignore[operator, union-attr]
+        kwargs.pop("ch_axis")
+    return observer_or_fake_quant_ctr.with_args(**kwargs)()
+
+
+def _needs_obs_or_fq(
+    prev_output_dtype: Any,
+    prev_output_is_dynamic: bool,
+    cur_target_dtype: Any,
+    cur_target_is_dynamic: bool,
+    reuse_input_obs_or_fq: bool,
+    is_zeroth_arg: bool = False,
+) -> bool:
+    """
+    note: we will treat "not specified" as torch.float for now
+    utility function that checks if we should insert an observer or fake quant node
+    base on the requested dtype for the nodes from user
+
+    is_zeroth_arg: we only dynamically quantize the first arg of the node right now
+      this should be removed when we enable configuring dynamic quantization
+      for a specific argument, this can be removed if we deprecate fx graph mode
+      quantization
+
+    """
+
+    # need to insert placeholder observer for dynamic quantization so that it can
+    # be converted to choose_qparams -> q -> dq in convert step
+    if cur_target_is_dynamic:
+        if cur_target_dtype not in _OBS_DTYPE_LIST:
+            raise AssertionError(
+                f"Expected cur_target_dtype to be torch.float, but got: {cur_target_dtype}"
+            )
+        if prev_output_dtype in _DO_NOT_OBS_DTYPE_LIST:
+            raise AssertionError(
+                "prev_output_dtype must not be in _DO_NOT_OBS_DTYPE_LIST"
+            )
+        return is_zeroth_arg
+    if reuse_input_obs_or_fq:
+        return False
+    # non dynamic quantization
+    if cur_target_dtype in _OBS_DTYPE_LIST:
+        return (
+            prev_output_dtype in _OBS_DTYPE_LIST + [torch.float]
+            and cur_target_dtype != prev_output_dtype
+        )
+
+    # lots of error checking are skipped here for now
+    return False
+
+
+def _is_activation_post_process_node(
+    node: Node, named_modules: dict[str, torch.nn.Module]
+) -> bool:
+    return (
+        isinstance(node, torch.fx.Node)
+        and node.op == "call_module"
+        and _is_activation_post_process(named_modules[str(node.target)])
+    )
+
+
+def _get_dtype_and_is_dynamic(
+    obs_or_fq: ObserverOrFakeQuantize | None,
+) -> tuple[torch.dtype | None, bool]:
+    """Given a constructor for observer or fake quant module, returns
+    a Tuple of dtype and is_dynamic
+    """
+    # TODO: instead of instantiating the instance, we can use inspect to get the default args
+    if obs_or_fq is None:
+        return None, False
+    else:
+        return obs_or_fq.dtype, getattr(obs_or_fq, "is_dynamic", False)  # type: ignore[return-value]
+
+
+def _is_input_arg_dtype_supported_by_backend(
+    arg: Argument,
+    node: Node,
+    qconfig: QConfigAny,
+    dtype_config: DTypeConfig,
+    backend_config: BackendConfig,
+) -> bool:
+    """Check if the configured qconfig for the argument
+    is supported by the backend or not
+    """
+    if isinstance(arg, (list, tuple)):
+        return all(
+            _is_input_arg_dtype_supported_by_backend(
+                a, node, qconfig, dtype_config, backend_config
+            )
+            for a in arg
+        )
+    if not isinstance(arg, Node):
+        return True
+    # TODO: support check for standalone module
+    is_weight = node_arg_is_weight(node, arg)
+    is_bias = node_arg_is_bias(node, arg)
+    is_activation = not is_weight and not is_bias
+    if is_activation:
+        input_act_obs_or_fq_ctr = node.meta["target_dtype_info"].get(
+            "input_act_obs_or_fq_ctr"
+        )
+        input_act_obs_or_fq = (
+            input_act_obs_or_fq_ctr() if input_act_obs_or_fq_ctr else None
+        )
+        qconfig_dtype, qconfig_is_dynamic = _get_dtype_and_is_dynamic(
+            input_act_obs_or_fq
+        )
+        # TODO(future PR): remove the cast to bool below after figuring
+        # out why backend_config has is_dynamic set to None in some cases.
+        return (dtype_config.input_dtype is None) or (
+            dtype_config.input_dtype == qconfig_dtype
+            and bool(dtype_config.is_dynamic) == bool(qconfig_is_dynamic)
+            and _qconfig_satisfies_dtype_config_constraints(
+                qconfig, dtype_config.input_dtype_with_constraints
+            )
+        )
+    elif is_weight:
+        # TODO: move dtype check into `_qconfig_satisfies_dtype_config_constraints` as well
+        weight_obs_or_fq_ctr = node.meta["target_dtype_info"].get(
+            "weight_obs_or_fq_ctr", None
+        )
+        weight_obs_or_fq = weight_obs_or_fq_ctr() if weight_obs_or_fq_ctr else None
+        qconfig_weight_dtype, _ = _get_dtype_and_is_dynamic(weight_obs_or_fq)
+        backend_config_weight_dtype = dtype_config.weight_dtype
+        dtype_matches = qconfig_weight_dtype == backend_config_weight_dtype
+        qconfig_satisfies_constraints = _qconfig_satisfies_dtype_config_constraints(
+            qconfig, dtype_config.weight_dtype_with_constraints, is_activation=False
+        )
+        return backend_config_weight_dtype is None or (
+            dtype_matches and qconfig_satisfies_constraints
+        )
+    else:  # bias
+        # TODO: move dtype check into `_qconfig_satisfies_dtype_config_constraints` as well
+        bias_obs_or_fq_ctr = node.meta["target_dtype_info"].get(
+            "bias_obs_or_fq_ctr", None
+        )
+        bias_obs_or_fq = bias_obs_or_fq_ctr() if bias_obs_or_fq_ctr else None
+        qconfig_bias_dtype, _ = _get_dtype_and_is_dynamic(bias_obs_or_fq)
+        backend_config_bias_dtype = dtype_config.bias_dtype
+        return (
+            backend_config_bias_dtype is None
+            or qconfig_bias_dtype == backend_config_bias_dtype
+        )
+
+
+def _is_output_dtype_supported_by_backend(
+    node: Node,
+    qconfig: QConfigAny,
+    dtype_config: DTypeConfig,
+) -> bool:
+    """Check if the configured qconfig for the output
+    is supported by the backend or not
+    """
+    # TODO: move dtype check into `_qconfig_satisfies_dtype_config_constraints` as well
+    backend_config_output_dtype = dtype_config.output_dtype
+    # TODO: we should check is_dynamic here as well, the code from _is_input_arg_dtype_supported_by_backend
+    # from input activation check can be reused here
+    qconfig_output_dtype = None
+    output_act_obs_or_fq_ctr = node.meta["target_dtype_info"].get(
+        "output_act_obs_or_fq_ctr", _DEFAULT_FP32_OBS_OR_FQ_CTR
+    )
+    output_act_obs_or_fq = (
+        output_act_obs_or_fq_ctr() if output_act_obs_or_fq_ctr else None
+    )
+    qconfig_output_dtype, qconfig_output_is_dynamic = _get_dtype_and_is_dynamic(
+        output_act_obs_or_fq
+    )
+    # TODO: this is a hack because we can only specify one activation_obs_or_fq for
+    # qconfig (qconfig.activation), and we are only supporting dynamically quantized
+    # linear op which has fp32 output dtype, this should be removed if we generalize
+    # the structure of qconfig in the future
+    if qconfig_output_is_dynamic:
+        qconfig_output_dtype = torch.float32
+    dtype_matches = qconfig_output_dtype == backend_config_output_dtype
+    qconfig_satisfies_constraints = _qconfig_satisfies_dtype_config_constraints(
+        qconfig, dtype_config.output_dtype_with_constraints
+    )
+    return backend_config_output_dtype is None or (
+        dtype_matches and qconfig_satisfies_constraints
+    )
+
+
+def _is_observer_in_same_graph(
+    node: Node,
+    named_modules: dict[str, torch.nn.Module],
+    obs_or_fq_map: dict[EdgeOrNode, ObserverOrFakeQuantize],
+    is_qat,
+):
+    """Check if observer in same graph
+    when the node output is not fp32 and input is 'placeholder'
+    the input is assumed to be quantized, so it is observed
+    in a different place rather than not observed.
+    """
+    node_output_dtype = _get_arg_target_dtype_as_output(
+        node, named_modules, obs_or_fq_map, is_qat
+    )
+    if len(node.args) > 0 and isinstance(node.args[0], Node):
+        if (
+            node_output_dtype in [torch.quint8, torch.uint8]
+            and node.args[0].op == "placeholder"
+        ):
+            return False
+    return True
+
+
+def _is_pattern_dtype_config_and_qconfig_supported_by_backend(
+    pattern: Pattern | None,
+    matched_node_pattern: list[Node] | None,
+    qconfig: QConfigAny,
+    backend_config: BackendConfig,
+) -> bool:
+    """Check if the dtype configuration of a pattern is supported by
+    the backend or not, and whether the qconfig satisfies constraints
+    specified in the corresponding dtype config.
+    """
+    if backend_config is None or pattern is None:
+        return True
+    if matched_node_pattern is None or len(matched_node_pattern) < 1:
+        raise AssertionError("matched_node_pattern must be non-empty")
+    pattern_to_dtype_configs = get_pattern_to_dtype_configs(backend_config)
+    dtype_configs: list[DTypeConfig] = pattern_to_dtype_configs.get(pattern, [])
+    pattern_to_root_node_getter = get_fusion_pattern_to_root_node_getter(backend_config)
+
+    root_node_getter = pattern_to_root_node_getter.get(
+        pattern, _default_root_node_getter
+    )
+    root_node = root_node_getter(matched_node_pattern)
+    input_node = root_node
+    output_node = matched_node_pattern[0]
+    for dtype_config in dtype_configs:
+        # check if arg dtype are supported
+        supported = True
+        for arg in list(input_node.args) + list(input_node.kwargs.values()):
+            supported = supported and _is_input_arg_dtype_supported_by_backend(
+                arg, input_node, qconfig, dtype_config, backend_config
+            )
+        # check if output dtype is supported
+        supported = supported and _is_output_dtype_supported_by_backend(
+            output_node, qconfig, dtype_config
+        )
+        if supported:
+            return True
+    return False
+
+
+def _get_standalone_module_configs(
+    node: Node,
+    named_modules: dict[str, torch.nn.Module],
+    prepare_custom_config: PrepareCustomConfig,
+    parent_qconfig: QConfigAny,
+    parent_backend_config: BackendConfig | None,
+) -> tuple[QConfigMapping, tuple[Any, ...], PrepareCustomConfig, BackendConfig | None]:
+    """
+    Returns the standalone module QConfigMapping and PrepareCustomConfig
+    for `node`, assuming that the module pointed to by `node` is
+    a standalone modules.
+    """
+    module_name = str(node.target)
+    module_type = type(named_modules[module_name])  # type: ignore[index]
+    # name config has precedence over type config
+    config_entry = StandaloneModuleConfigEntry(None, (), None, None)
+    config_entry = prepare_custom_config.standalone_module_classes.get(
+        module_type, config_entry
+    )
+    config_entry = prepare_custom_config.standalone_module_names.get(
+        module_name, config_entry
+    )
+    # fallback to use parent module's qconfig if user didn't specify qconfig dict
+    qconfig_mapping = config_entry.qconfig_mapping or QConfigMapping().set_global(
+        parent_qconfig
+    )
+    example_inputs = config_entry.example_inputs
+    prepare_custom_config = config_entry.prepare_custom_config or PrepareCustomConfig()
+    backend_config = config_entry.backend_config or parent_backend_config
+    return (qconfig_mapping, example_inputs, prepare_custom_config, backend_config)
+
+
+def _qat_swap_modules(
+    root: torch.nn.Module, module_to_qat_module: dict[Pattern, type[torch.nn.Module]]
+) -> None:
+    convert(root, mapping=module_to_qat_module, inplace=True, remove_qconfig=False)
+
+
+def _add_matched_node_name_to_set(matched_node_pattern: NodePattern, s: set[str]):
+    if isinstance(matched_node_pattern, Node):
+        s.add(matched_node_pattern.name)
+    elif isinstance(matched_node_pattern, (list, tuple)):
+        for maybe_node in matched_node_pattern:
+            _add_matched_node_name_to_set(maybe_node, s)
+
+
+def _insert_obs_or_fq(
+    node: Node,
+    obs_or_fq: ObserverOrFakeQuantize,
+    model: torch.nn.Module,
+    named_modules: dict[str, torch.nn.Module],
+    graph: Graph,
+    model_device: torch.device | None = None,
+) -> Node:
+    """
+    Attaches `obs_or_fq` to `model`, and creates a node which calls
+    `obs_or_fq` on the output of `node`.
+
+    obs_or_fq: an instance of Observer or FakeQuantize module
+    """
+    if model_device is None:
+        model_device = assert_and_get_unique_device(model)
+    if model_device:
+        obs_or_fq.to(model_device)
+    # add obs_or_fq module as attribute
+    if is_equalization_observer(obs_or_fq):
+        prefix = node.name + "_equalization_process_"
+    else:
+        prefix = "activation_post_process_"
+    get_new_obs_or_fq_name = get_new_attr_name_with_prefix(prefix)
+    obs_or_fq_name = get_new_obs_or_fq_name(model)
+    setattr(model, obs_or_fq_name, obs_or_fq)
+    named_modules[obs_or_fq_name] = obs_or_fq
+    with graph.inserting_after(node):
+        new_obs = graph.create_node("call_module", obs_or_fq_name, (node,), {})
+    return new_obs
+
+
+def _set_target_dtype_info_for_matched_node_pattern(
+    matched_node_pattern: NodePattern,
+    last_node: Node,
+    qconfig: QConfigAny,
+    qhandler: QuantizeHandler | None,
+    backend_config: BackendConfig,
+    named_modules: dict[str, torch.nn.Module],
+    cache_for_no_tensor_check: dict[Node, bool],
+    processed_nodes: set[Node],
+) -> None:
+    """Sets the target_dtype_info for each node in matched_node_pattern
+    Note: processed_nodes is used to ensure we only process each node once
+    """
+    if isinstance(matched_node_pattern, (list, tuple)):
+        for node_pattern in matched_node_pattern:
+            _set_target_dtype_info_for_matched_node_pattern(
+                node_pattern,
+                last_node,
+                qconfig,
+                qhandler,
+                backend_config,
+                named_modules,
+                cache_for_no_tensor_check,
+                processed_nodes,
+            )
+
+    # set target_dtype_info if matched_node_pattern is a Node
+    # other types of matched object, e.g. int, float literals, are ignored
+    elif isinstance(matched_node_pattern, Node):
+        # for pyre
+        if not isinstance(matched_node_pattern, Node):
+            raise AssertionError("matched_node_pattern must be a Node")
+        node = matched_node_pattern
+        if node in processed_nodes:
+            return
+        processed_nodes.add(node)
+
+        if qconfig is None:
+            return
+        # TODO: refactor the following code in terms of apply a qconfig to a pattern
+        # e.g. for a pattern with op1 -> op2 -> op3, and qconfig = QConfig(input_act=obs0, output_act=obs1)
+        # we set the input_obs_or_fq_ctr for the arguments of op1 to based on qconfig.input_act,
+        # and set output_obs_or_fq_ctr based on qconfig.output_act
+        # this also requires we extend the structure of QConfig to support more fine
+        # grained configurations
+        target_dtype_info: dict[str, Any] = _get_target_activation_dtype_for_node(
+            node,
+            qconfig,
+            qhandler,
+            named_modules,
+            backend_config,
+            cache_for_no_tensor_check,
+        )
+        node.meta["target_dtype_info"] = target_dtype_info
+
+
+def _get_target_activation_dtype_for_node(
+    node: Node,
+    qconfig: QConfigAny,
+    qhandler: QuantizeHandler | None,
+    named_modules: dict[str, torch.nn.Module],
+    backend_config: BackendConfig,
+    cache_for_no_tensor_check: dict[Node, bool],
+) -> dict[str, Any]:
+    """
+    For each op attribute in the op's input activation, output activation,
+    weight, bias - returns the settings of dtype and is_dynamic we expect
+    for the `quantize` call in the reference model representation, or None
+    if there is no `quantize` call needed.
+
+    For example, if we have a node corresponding to `op0` in
+
+      x0 -> op0 -> x1
+
+    And we want a reference quantized representation to be
+
+      x0 -> quant_static -> dequant -> op0 -> quant_dynamic -> dequant -> x1
+
+    Then this function will return
+
+      {
+        "input_act_obs_or_fq_ctr": MinMaxObserver.with_args(dtype=torch.quint8, is_dynamic=False),
+        "output_act_obs_or_fq_ctr": MinMaxObserver.with_args(dtype=torch.quint8, is_dynamic=False),
+      }
+
+    TODO(future PR, if needed): explicitly spell out the non-Tensor
+    dtypes.
+    """
+    args_have_no_tensors = all_node_args_have_no_tensors(
+        node, named_modules, cache_for_no_tensor_check
+    )
+    if args_have_no_tensors:
+        return {
+            "input_act_obs_or_fq_ctr": None,
+            "output_act_obs_or_fq_ctr": None,
+        }
+    # get qconfig to determine the eventual dtype of this node
+    if qconfig is not None:
+        act_dtype, weight_dtype, input_act_is_dynamic = get_qconfig_dtypes(qconfig)
+
+        # Currently `QConfig` only has one `activation` field.
+        # For static quantization, it is reused for both input
+        # and output activation. For dynamic quantization, this
+        # field is currently only used for the input activation,
+        # with the output activation being in fp32.
+        # In the future this may change as we add more fields
+        # to the `QConfig` object.
+        bias_dtype = (
+            torch.float16
+            if (
+                act_dtype == torch.float16
+                and weight_dtype == torch.float16
+                and (not input_act_is_dynamic)
+            )
+            else torch.float
+        )
+
+        is_general_tensor_value_op = (
+            qhandler is not None and qhandler.is_general_tensor_value_op()
+        )
+
+        _is_standalone_module = qhandler is not None and qhandler.is_standalone_module()
+
+        weight_index = None
+        if (
+            isinstance(node, Node)
+            and node.op == "call_function"
+            and node.target in backend_config._pattern_complex_format_to_config
+        ):
+            weight_index = backend_config._pattern_complex_format_to_config[
+                node.target
+            ]._input_type_to_index.get("weight")
+
+        bias_index = None
+        if (
+            isinstance(node, Node)
+            and node.op == "call_function"
+            and node.target in backend_config._pattern_complex_format_to_config
+        ):
+            bias_index = backend_config._pattern_complex_format_to_config[
+                node.target
+            ]._input_type_to_index.get("bias")
+
+        return {
+            "input_act_obs_or_fq_ctr": qconfig.activation,
+            "weight_obs_or_fq_ctr": qconfig.weight,
+            "bias_obs_or_fq_ctr": PlaceholderObserver.with_args(dtype=bias_dtype),
+            "weight_index": weight_index,
+            "bias_index": bias_index,
+            "output_act_obs_or_fq_ctr": qconfig.activation,
+            "reuse_input_obs_or_fq": _is_reuse_input_qconfig(qconfig),
+            "input_output_share_observers": is_general_tensor_value_op,
+            "_is_standalone_module": _is_standalone_module,
+        }
+    return copy.copy(_DEFAULT_FP32_QCONFIG_FOR_TARGET_DTYPE_INFO)
+
+
+def _get_output_act_obs_or_fq(
+    arg: Node,
+    named_modules: dict[str, torch.nn.Module],
+    obs_or_fq_map: dict[EdgeOrNode, ObserverOrFakeQuantize],
+    is_qat: bool,
+) -> ObserverOrFakeQuantize | None:
+    """Get the constructor for observer or fake quant object for
+    the argument in the original graph as the output of previous node,
+    skipping inserted observers
+
+    We are assuming that the observers are inserted correctly, and the dtype for
+    argument in quantized graph will match what is specified by the qconfig
+    """
+    if not isinstance(arg, Node):
+        raise AssertionError("arg must be a Node")
+    if "quantization_annotation" in arg.meta:
+        return _create_obs_or_fq_from_qspec(
+            arg.meta["quantization_annotation"].output_qspec, obs_or_fq_map, is_qat
+        )
+
+    # Custom module LSTM output is a tuple that we broke down into the internal nodes in order
+    # to insert DeQuantStubs (see `_insert_dequant_stubs_for_custom_module_lstm_output`).
+    # Since we modified the graph in this case, we must trace back from the args through
+    # the specific nodes we added in order to reach the original LSTM node. Otherwise, we would
+    # not be able to accurately detect whether this node is a consumer of custom module LSTM.
+    custom_module_lstm_node = _maybe_get_custom_module_lstm_from_node_arg(
+        arg, named_modules
+    )
+    output_act_obs_or_fq_ctr = None
+    if custom_module_lstm_node is not None:
+        output_act_obs_or_fq_ctr = custom_module_lstm_node.meta["target_dtype_info"][
+            "output_act_obs_or_fq_ctr"
+        ]
+        output_act_obs_or_fq = (
+            output_act_obs_or_fq_ctr() if output_act_obs_or_fq_ctr else None
+        )
+    elif _is_activation_post_process_node(arg, named_modules):
+        observed_arg = arg.args[0]
+        if not isinstance(observed_arg, Node):
+            raise AssertionError("Currently we only support observing Node")
+        if "quantization_annotation" in observed_arg.meta:
+            output_act_obs_or_fq = _create_obs_or_fq_from_qspec(
+                observed_arg.meta["quantization_annotation"].output_qspec,
+                obs_or_fq_map,
+                is_qat,
+            )
+        else:
+            if "target_dtype_info" not in observed_arg.meta:
+                raise AssertionError(
+                    "expected 'target_dtype_info' in observed_arg.meta"
+                )
+            output_act_obs_or_fq_ctr = observed_arg.meta["target_dtype_info"][
+                "output_act_obs_or_fq_ctr"
+            ]
+            output_act_obs_or_fq = (
+                output_act_obs_or_fq_ctr() if output_act_obs_or_fq_ctr else None
+            )
+    else:
+        if "target_dtype_info" in arg.meta:
+            output_act_obs_or_fq_ctr = arg.meta["target_dtype_info"].get(
+                "output_act_obs_or_fq_ctr", _DEFAULT_FP32_OBS_OR_FQ_CTR
+            )
+        else:
+            output_act_obs_or_fq_ctr = _DEFAULT_FP32_OBS_OR_FQ_CTR
+        output_act_obs_or_fq = (
+            output_act_obs_or_fq_ctr() if output_act_obs_or_fq_ctr else None
+        )
+
+    return output_act_obs_or_fq
+
+
+def _get_arg_target_dtype_as_output(
+    arg: Node,
+    named_modules: dict[str, torch.nn.Module],
+    obs_or_fq_map: dict[EdgeOrNode, ObserverOrFakeQuantize],
+    is_qat: bool,
+) -> torch.dtype | None:
+    arg_as_output_act_obs_or_fq = _get_output_act_obs_or_fq(
+        arg, named_modules, obs_or_fq_map, is_qat
+    )
+    arg_as_output_target_dtype, _ = _get_dtype_and_is_dynamic(
+        arg_as_output_act_obs_or_fq
+    )
+    return arg_as_output_target_dtype
+
+
+def _get_arg_as_input_act_obs_or_fq(
+    arg: Node,
+    node: Node,
+    named_modules: dict[str, torch.nn.Module],
+    obs_or_fq_map: dict[EdgeOrNode, ObserverOrFakeQuantize],
+    is_qat: bool,
+) -> ObserverOrFakeQuantize | None:
+    """Get the observer or fake quant constructor for the Argument `arg`, as input
+    to Node `node`
+    """
+    if not isinstance(arg, Node):
+        raise AssertionError("arg must be a Node")
+    # "input_qspec_map" is the more general design we'll use for pt2e path
+    # it is a map from input argument node to observer or fake quant constructor, for example
+    # for the following graph:
+    # x -> conv -> output
+    #
+    # we may annotate conv node like the following:
+    # conv.meta[...] = QuantizationAnnotation("input_qspec_map": {x: MinMaxObserver.with_args(dtype=torch.qint8)}, ...)
+    #
+    if "quantization_annotation" in node.meta:
+        input_qspec_map = node.meta["quantization_annotation"].input_qspec_map
+        input_arg_qspec = _get_qspec_for_arg(arg, input_qspec_map, named_modules)
+        if input_arg_qspec is None:
+            input_arg_obs_or_fq = _DEFAULT_FP32_OBS_OR_FQ_CTR()
+        else:
+            input_arg_obs_or_fq = _create_obs_or_fq_from_qspec(
+                input_arg_qspec, obs_or_fq_map, is_qat
+            )
+        return input_arg_obs_or_fq
+
+    # we can remove the following path in the future if fx graph mode quantization is
+    # no longer used
+    is_weight = node_arg_is_weight(node, arg)
+    is_bias = node_arg_is_bias(node, arg)
+    is_activation = not is_weight and not is_bias
+    obs_or_fq_ctr = None
+    if is_activation:
+        obs_or_fq_ctr = node.meta["target_dtype_info"].get(
+            "input_act_obs_or_fq_ctr", _DEFAULT_FP32_OBS_OR_FQ_CTR
+        )
+    elif is_weight:
+        if node.target not in NON_QUANTIZABLE_WEIGHT_OPS:
+            obs_or_fq_ctr = node.meta["target_dtype_info"].get(
+                "weight_obs_or_fq_ctr", _DEFAULT_FP32_OBS_OR_FQ_CTR
+            )
+    else:
+        obs_or_fq_ctr = node.meta["target_dtype_info"].get(
+            "bias_obs_or_fq_ctr", _DEFAULT_FP32_OBS_OR_FQ_CTR
+        )
+    return obs_or_fq_ctr() if obs_or_fq_ctr else None
+
+
+def _maybe_insert_input_observer_for_arg_or_kwarg(
+    node: Node | Any,
+    arg: Argument,
+    qconfig: QConfigAny,
+    model: torch.nn.Module,
+    named_modules: dict[str, torch.nn.Module],
+    graph: Graph,
+    qhandler: QuantizeHandler | None,
+    prepare_custom_config: PrepareCustomConfig,
+    obs_or_fq_map: dict[EdgeOrNode, ObserverOrFakeQuantize],
+    is_qat: bool,
+    backend_config: BackendConfig | None = None,
+    model_device: torch.device | None = None,
+) -> Argument:
+    """
+    Given a `node` and an `arg`, inserts an input observer between
+    `node` and `arg` if necessary.
+    """
+    # for ops such as torch.cat([x0, x1]),
+    # traverse through the list
+    if isinstance(arg, (list, tuple)):
+        new_arg_to_return = []
+        for inner_arg in arg:
+            new_inner_arg = _maybe_insert_input_observer_for_arg_or_kwarg(
+                node,
+                inner_arg,
+                qconfig,
+                model,
+                named_modules,
+                graph,
+                qhandler,
+                prepare_custom_config,
+                obs_or_fq_map,
+                is_qat,
+                backend_config,
+                model_device,
+            )
+            new_arg_to_return.append(new_inner_arg)
+        return type(arg)(new_arg_to_return)
+
+    if not isinstance(arg, Node):
+        return arg
+    if not isinstance(arg, Node):
+        raise AssertionError("arg must be a Node")
+    # default (no observer)
+    new_arg = arg
+
+    is_standalone_module = qhandler is not None and qhandler.is_standalone_module()
+    # TODO: move this to a separate function
+    if not is_standalone_module:
+        # Note: qconfig can be None in this branch this we are getting act/fq from
+        # node.meta now
+        # regular flow for most nodes, except standalone modules
+
+        if "quantization_annotation" in node.meta:
+            reuse_input_obs_or_fq = node.meta[
+                "quantization_annotation"
+            ]._reuse_input_obs_or_fq
+        else:
+            if "target_dtype_info" not in node.meta:
+                raise AssertionError("expected 'target_dtype_info' in node.meta")
+            # TODO: we are assuming "target_dtype_info" exists here, maybe
+            # a default value also need to be provided here
+            target_dtype_info = node.meta["target_dtype_info"]
+            # for nodes that doesn't have `reuse_input_obs_or_fq` configured,
+            # we'll default to False, this makes configuring this field optional for users
+            reuse_input_obs_or_fq = target_dtype_info.get(
+                "reuse_input_obs_or_fq", False
+            )
+        arg_as_input_act_obs_or_fq = _get_arg_as_input_act_obs_or_fq(
+            arg, node, named_modules, obs_or_fq_map, is_qat
+        )
+        (
+            arg_as_input_target_dtype,
+            arg_as_input_target_is_dynamic,
+        ) = _get_dtype_and_is_dynamic(arg_as_input_act_obs_or_fq)
+
+        arg_as_output_act_obs_or_fq = _get_output_act_obs_or_fq(
+            arg, named_modules, obs_or_fq_map, is_qat
+        )
+        (
+            arg_as_output_target_dtype,
+            arg_as_output_target_is_dynamic,
+        ) = _get_dtype_and_is_dynamic(arg_as_output_act_obs_or_fq)
+
+        needs_obs_or_fq = _needs_obs_or_fq(
+            arg_as_output_target_dtype,
+            arg_as_output_target_is_dynamic,
+            arg_as_input_target_dtype,
+            arg_as_input_target_is_dynamic,
+            reuse_input_obs_or_fq,
+            is_zeroth_arg=len(node.args) > 0 and arg is node.args[0],
+        )
+
+    else:
+        if qconfig is None:
+            raise AssertionError("qconfig must not be None")
+        # custom flow for standalone modules
+        _, _, sm_prepare_custom_config, _ = _get_standalone_module_configs(
+            node, named_modules, prepare_custom_config, qconfig, backend_config
+        )
+        sm_input_quantized_idxs = sm_prepare_custom_config.input_quantized_indexes
+
+        # for args, this is set to the index of the current arg
+        # for kwargs, this is left at None
+        cur_input_idx = None
+        for arg_idx, arg_to_check in enumerate(node.args):
+            if arg_to_check is arg:
+                cur_input_idx = arg_idx
+                break
+
+        if cur_input_idx is None:
+            needs_obs_or_fq = False
+        else:
+            arg_as_output_target_dtype = _get_arg_target_dtype_as_output(
+                arg, named_modules, obs_or_fq_map, is_qat
+            )
+            arg_as_input_target_dtype = (
+                torch.quint8
+                if cur_input_idx in sm_input_quantized_idxs
+                else torch.float
+            )
+            needs_obs_or_fq = (
+                arg_as_output_target_dtype != arg_as_input_target_dtype
+            ) and (arg_as_input_target_dtype != torch.float)
+
+        act_post_process_ctr = qconfig.activation
+        arg_as_input_act_obs_or_fq = (
+            act_post_process_ctr() if act_post_process_ctr else None
+        )
+
+    if needs_obs_or_fq:
+        existing_obs_node = None
+
+        # Before using the new observer, check if an observer
+        # of the correct type already exists. If it does, use it.
+        # This prevents duplicate observer insertions if a node is
+        # used by multiple nodes.
+        # TODO: this is looking into how the value is used in the future
+        # we should remove this
+        # removing this means we insert one observer for each use, even if they
+        # have the same dtype, we can have an extra pass that removes the extra observers
+        for maybe_obs_node in arg.users:
+            if maybe_obs_node.op == "call_module":
+                maybe_obs_mod = named_modules[maybe_obs_node.target]  # type: ignore[index]
+                if (
+                    type(maybe_obs_mod) is type(arg_as_input_act_obs_or_fq)
+                    and maybe_obs_mod.dtype == arg_as_input_target_dtype  # type: ignore[possibly-undefined]
+                ):
+                    arg_as_input_act_obs_or_fq = maybe_obs_mod  # type: ignore[assignment]
+                    existing_obs_node = maybe_obs_node
+                    break
+
+        if arg_as_input_act_obs_or_fq is None:
+            raise AssertionError("arg_as_input_act_obs_or_fq must not be None")
+        obs_or_fq_map[(arg, node)] = arg_as_input_act_obs_or_fq
+        if existing_obs_node is None:
+            new_obs_node = _insert_obs_or_fq(
+                arg,
+                arg_as_input_act_obs_or_fq,
+                model,
+                named_modules,
+                graph,
+                model_device,
+            )
+            # override this arg to be the observed arg
+            new_arg = new_obs_node
+        else:
+            new_arg = existing_obs_node
+
+    return new_arg
+
+
+def _maybe_insert_input_observers_for_node(
+    node: Node,
+    qconfig: QConfigAny,
+    model: torch.nn.Module,
+    named_modules: dict[str, torch.nn.Module],
+    graph: Graph,
+    qhandler: QuantizeHandler | None,
+    prepare_custom_config: PrepareCustomConfig,
+    obs_or_fq_map: dict[EdgeOrNode, ObserverOrFakeQuantize],
+    is_qat: bool,
+    backend_config: BackendConfig | None = None,
+    model_device: torch.device | None = None,
+) -> None:
+    """
+    If needed, inserts observers to the input args and kwargs of `node`.
+    Note: modifies `node` inplace.
+
+    For example, if cur_node needs an observer after prev_node, we change from
+
+      prev_node -> cur_node
+
+    To
+
+      prev_node -> obs -> cur_node
+
+    Note: backend_config only needed for standalone_module node
+    """
+    # Look through every input arg.  If that arg's target dtype does not
+    # match the current node's target dtype, insert an observer.
+    new_args = []
+    for arg in node.args:
+        new_arg = _maybe_insert_input_observer_for_arg_or_kwarg(
+            node,
+            arg,
+            qconfig,
+            model,
+            named_modules,
+            graph,
+            qhandler,
+            prepare_custom_config,
+            obs_or_fq_map,
+            is_qat,
+            backend_config,
+            model_device,
+        )
+        new_args.append(new_arg)
+
+    new_kwargs = {}
+    for k, kwarg in node.kwargs.items():
+        new_kwarg = _maybe_insert_input_observer_for_arg_or_kwarg(
+            node,
+            kwarg,
+            qconfig,
+            model,
+            named_modules,
+            graph,
+            qhandler,
+            prepare_custom_config,
+            obs_or_fq_map,
+            is_qat,
+            backend_config,
+            model_device,
+        )
+        new_kwargs[k] = new_kwarg
+
+    # assign the new args and kwargs to the node, inplace
+    node.args = tuple(new_args)
+    node.kwargs = new_kwargs
+
+
+def _maybe_insert_input_equalization_observers_for_node(
+    node: Node,
+    equalization_qconfig: Any,
+    model: torch.nn.Module,
+    named_modules: dict[str, torch.nn.Module],
+    graph: Graph,
+    is_branch: bool,
+) -> None:
+    """
+    If `node` needs to be equalized, find the input/weight observers it needs in
+    `equalization_qconfig`, creates them, and inserts it into `graph`.
+
+    If `node` does not need an equalization observer, returns None.
+    """
+    if equalization_qconfig is None or not node_supports_equalization(
+        node, named_modules
+    ):
+        return
+
+    if is_branch:
+        warnings.warn(
+            f"Cannot equalize {node} because it is part of a branch.", stacklevel=2
+        )
+        return
+
+    new_args = []
+    for arg in node.args:
+        if not isinstance(arg, Node) or node_arg_is_bias(node, arg):
+            new_args.append(arg)
+            continue
+
+        is_weight = node_arg_is_weight(node, arg)
+
+        act_eq_process_ctr = (
+            equalization_qconfig.weight
+            if is_weight
+            else equalization_qconfig.input_activation
+        )
+
+        new_eq_obs_mod = act_eq_process_ctr()
+        new_eq_obs_node = _insert_obs_or_fq(
+            arg, new_eq_obs_mod, model, named_modules, graph
+        )
+
+        new_args.append(new_eq_obs_node)
+
+    # assign the new args and kwargs to the node, inplace
+    node.args = tuple(new_args)
+
+
+def _maybe_insert_output_observer_for_node(
+    node: Node,
+    model: torch.nn.Module,
+    named_modules: dict[str, torch.nn.Module],
+    graph: Graph,
+    obs_or_fq_map: dict[EdgeOrNode, ObserverOrFakeQuantize],
+    is_qat: bool,
+) -> Node | None:
+    """
+    If `node` needs an output observer, creates it, inserts it into `graph`
+    and returns it.
+
+    If `node` does not need an output observer, returns None.
+
+    Note: inserting dynamic quantization ops for output is not supported in fx graph mode
+    quantization code path right now
+    """
+    if node.op == "output":
+        raise AssertionError("observer insertion for outputs is handled elsewhere")
+
+    is_standalone_module = False
+    if "quantization_annotation" in node.meta:
+        output_act_obs_or_fq = _create_obs_or_fq_from_qspec(
+            node.meta["quantization_annotation"].output_qspec, obs_or_fq_map, is_qat
+        )
+    else:
+        if "target_dtype_info" not in node.meta:
+            raise AssertionError("expected 'target_dtype_info' in node.meta")
+        is_standalone_module = node.meta["target_dtype_info"].get(
+            "_is_standalone_module", False
+        )
+        output_act_obs_or_fq_ctr = node.meta["target_dtype_info"].get(
+            "output_act_obs_or_fq_ctr"
+        )
+        output_act_obs_or_fq = (
+            output_act_obs_or_fq_ctr() if output_act_obs_or_fq_ctr else None
+        )
+    target_dtype, target_is_dynamic = _get_dtype_and_is_dynamic(output_act_obs_or_fq)
+    # uncomment after we support reuse_input_obs_or_fq properly by having separate
+    # implementations for this key instead of reusing the input_output_share_observers
+    # code
+    # reuse_input_obs_or_fq = node.meta["target_dtype_info"].get("reuse_input_obs_or_fq", False)
+    # for now we set this to False since reuse_input_obs_or_fq for
+    # the output of a node is implementation in the same code path as observer sharing,
+    # we should refactor this part to make it clearer in the future
+    # and we would be able to read this from config directly
+    reuse_input_obs_or_fq = False
+
+    # Note: prev_output_dtype = torch.float and prev_output_is_dynamic=False
+    # because the prev_output is the output of an fp32 op, although technically
+    # we should get the dtype of the output from node.meta["val"] in the future
+    # if we deprecate fx graph mode quantization
+    needs_obs_or_fq = _needs_obs_or_fq(
+        torch.float, False, target_dtype, target_is_dynamic, reuse_input_obs_or_fq
+    )
+    # currently the activation in QConfig(activation=...,) is for both input
+    # and output, and when the activation is configured to be dynamic quantization
+    # e.g. PlaceholderObserver(dtype=torch.quint8, is_dynamic=True, ...), it means
+    # the input should by dynamically quantized, but output should not be quantized
+    #
+    # there is no way we can specify different observer/fq for input and output
+    # activation through QConfig today, this limitation is lifted in the
+    # quantizer/annotation API in pytorch 2.0 export quantization code path,
+    # but since this code is reused, annotating output to be dynamically quantized
+    # would not work either for that.
+    # we can change QConfig to support input/output activation if we want
+    # to remove the following check, or if we can deprecate fx graph mode quantization
+    if target_is_dynamic:
+        needs_obs_or_fq = False
+
+    # we never insert observers to output of standalone module, we assume
+    # if needed, they are inserted inside the standalone module
+    needs_obs_or_fq = needs_obs_or_fq and (not is_standalone_module)
+
+    if needs_obs_or_fq:
+        obs_or_fq_map[node] = output_act_obs_or_fq
+        return _insert_obs_or_fq(
+            node, output_act_obs_or_fq, model, named_modules, graph
+        )
+    else:
+        return None
+
+
+def _maybe_insert_observers_before_graph_output(
+    graph_output_node: Node,
+    model: torch.nn.Module,
+    named_modules: dict[str, torch.nn.Module],
+    graph: Graph,
+    obs_or_fq_map: dict[EdgeOrNode, ObserverOrFakeQuantize],
+    is_qat: bool,
+) -> None:
+    """
+    If the output needs to be quantized and there are any nodes
+    in the output which are not already observed, inserts observers
+    for those nodes.
+    """
+
+    def _recursive_maybe_replace_node_with_obs(
+        maybe_node: Argument,
+        model: torch.nn.Module,
+        named_modules: dict[str, torch.nn.Module],
+        graph: Graph,
+    ) -> Argument:
+        """
+        Navigate an arbitrary data structure of lists, tuples, dicts.
+        For each container type, recurse on all inputs. Once any Node
+        is found, insert an observer if needed and do not recurse further.
+
+        For example, given a structure of
+
+          {'foo1': [[bar1]], 'foo2': {'foo3': [[[bar3]]]}}
+
+        we recurse down to bar1 and bar3, observe them if necessary,
+        and if we inserted an observer then replace the original node
+        with its observer.
+
+        Returns the data structure with all nodes needing observation being
+        replaced by their observers.
+        """
+        if isinstance(maybe_node, Node):
+            # check dtype of this node
+            arg_as_output_target_dtype = _get_arg_target_dtype_as_output(
+                maybe_node, named_modules, obs_or_fq_map, is_qat
+            )
+            observer_mod = None
+            arg_as_input_target_dtype = torch.float
+            if "target_dtype_info" in maybe_node.meta:
+                observer_cls = maybe_node.meta["target_dtype_info"].get(
+                    "input_act_obs_or_fq_ctr", None
+                )
+                if observer_cls is not None:
+                    observer_mod = observer_cls()
+                    arg_as_input_target_dtype = observer_mod.dtype
+            # TODO: this does not handle dynamic quantization yet
+            need_obs = (
+                arg_as_output_target_dtype != arg_as_input_target_dtype
+                and arg_as_input_target_dtype != torch.float
+            )
+            if need_obs:
+                if observer_mod is None:
+                    raise AssertionError(
+                        "observer_mod must not be None when need_obs is True"
+                    )
+                # insert observer
+                observer_node = _insert_obs_or_fq(
+                    maybe_node, observer_mod, model, named_modules, graph
+                )
+                return observer_node
+            else:
+                return maybe_node
+        elif isinstance(maybe_node, (list, tuple)):
+            results = [
+                _recursive_maybe_replace_node_with_obs(
+                    inner_node, model, named_modules, graph
+                )
+                for inner_node in maybe_node
+            ]
+            if isinstance(maybe_node, list):
+                return results
+            else:
+                return tuple(results)
+        elif isinstance(maybe_node, dict):
+            results_dict = {}
+            for k, inner_v in maybe_node.items():
+                results_dict[k] = _recursive_maybe_replace_node_with_obs(
+                    inner_v, model, named_modules, graph
+                )
+            return results_dict
+        elif maybe_node is None:
+            return None
+        else:
+            raise Exception(  # noqa: TRY002
+                "Unhandled type for returned node:", maybe_node
+            )
+
+    new_args = [
+        _recursive_maybe_replace_node_with_obs(old_arg, model, named_modules, graph)
+        for old_arg in graph_output_node.args
+    ]
+
+    graph_output_node.args = tuple(new_args)  # type: ignore[assignment]
+
+
+def _maybe_propagate_dtype_for_node(
+    node: Node,
+    target_dtype: torch.dtype | type,
+    node_name_to_match_result_with_qconfig: dict[str, _MatchResultWithQConfig],
+) -> None:
+    """
+    Assigns `target_dtype` to `node`, setting `is_dynamic` to False. If `node`
+    is a general tensor shape op, also call this function recursively on
+    the first argument, to propagate the dtype to the caller.
+    """
+    node.meta["target_dtype_info"]["input_act_obs_or_fq_ctr"] = None
+    node.meta["target_dtype_info"]["output_act_obs_or_fq_ctr"] = None
+    # if this is a copy node, propagate to first arg
+    (
+        _root_node,
+        _,
+        _pattern,
+        qhandler,
+        _qconfig,
+    ) = node_name_to_match_result_with_qconfig.get(
+        node.name, (None, None, None, None, None)
+    )
+    # TODO: probably need to remove `is_general_tensor_value_op`
+    if qhandler is not None and qhandler.is_general_tensor_value_op():
+        prev_node = node.args[0]
+        if isinstance(prev_node, Node):
+            _maybe_propagate_dtype_for_node(
+                prev_node, target_dtype, node_name_to_match_result_with_qconfig
+            )
+
+
+def propagate_dtypes_for_known_nodes(
+    graph: Graph,
+    node_name_to_match_result_with_qconfig: dict[str, _MatchResultWithQConfig],
+) -> None:
+    """
+    Currently we assume that inputs to the graph are either `torch.float` or
+    `torch.quint8`, which is not always correct. For ops such as
+    `x.masked_fill(mask, value)`, we know that the dtype of  `mask` is a
+    `BoolTensor`. Propagate this information throughout the graph.
+
+    Note: not all dtypes in the graph will be correct after this pass, but a
+    higher percentage of them will be correct. Hopefully in the future we can
+    replace this with a better way to reason about dtypes of tensors.
+    """
+    for node in graph.nodes:
+        non_observable_arg_dict = get_non_observable_arg_indexes_and_types(node)
+
+        for arg_type in non_observable_arg_dict:
+            non_observable_indices = non_observable_arg_dict[arg_type](node)
+
+            for index in non_observable_indices:
+                arg = node.args[index]
+
+                # when an argument is a tuple, it does not show up as another node so we need to go through
+                # all elements of the tuple manually
+                if isinstance(arg, (tuple, list)):
+                    arg_list = list(arg)
+                else:
+                    arg_list = [arg]
+
+                for cur_arg in arg_list:
+                    # hard coded arguments show up but aren't `Node` typed and do not need dtype propagated
+                    if isinstance(cur_arg, torch.fx.node.Node):
+                        _maybe_propagate_dtype_for_node(
+                            cur_arg, arg_type, node_name_to_match_result_with_qconfig
+                        )
+
+
+def _maybe_make_input_output_share_observers(
+    node: Node,
+    model: torch.nn.Module,
+    named_modules: dict[str, torch.nn.Module],
+) -> bool:
+    """
+    Ensures that we share an observer
+    for all input arguments as well as the output argument. In detail, given
+    a graph of
+
+      x0 -> obs0 -> op -> x2
+                  /
+      x1 -> obs1 /
+
+    where node obs0 points to observer instance observer0,
+    obs1 points to observer1 and obs2 points to observer2, we make nodes obs1
+    and ob2 point to observer0.
+    Returns: whether the operation succeeded or not
+    """
+    first_arg = None
+    # find the first non-Tensor arg
+    for i in range(len(node.args)):
+        if isinstance(node.args[i], (Node, list, tuple)):
+            first_arg = node.args[i]
+            break
+
+    # if there is no non-Tensor arg, return directly
+    if first_arg is None:
+        return False
+
+    if isinstance(first_arg, (list, tuple)):
+        first_arg_arg = first_arg[0]
+    elif isinstance(first_arg, Node):
+        first_arg_arg = first_arg
+    else:
+        return False
+
+    # if we have a graph such as
+    #   observed_node -> non_observed_node -> cat
+    # we need to navigate up to the first observer
+    iteration_guard = 0
+    while not _is_activation_post_process_node(first_arg_arg, named_modules):
+        if not isinstance(first_arg_arg, Node):
+            return False
+        # did not find an activation_post_process for the op
+        if first_arg_arg.op == "placeholder":
+            return False
+        # trace back the args until we found the first Tensor/Node
+        trace_back_node = None
+        for i in range(len(first_arg_arg.args)):
+            trace_back_node = first_arg_arg.args[i]
+            if isinstance(trace_back_node, Node):
+                break
+        if trace_back_node is None:
+            return False
+        first_arg_arg = trace_back_node
+
+        iteration_guard += 1
+        if iteration_guard > 10000:
+            raise AssertionError("Unable to find observer of previous node")
+
+    if not isinstance(first_arg_arg, Node):
+        raise AssertionError("first_arg_arg must be a Node")
+    target_to_use = first_arg_arg.target
+    if not isinstance(target_to_use, str):
+        raise AssertionError("target_to_use must be a string")
+    obs_mod_to_use = named_modules[target_to_use]
+
+    if isinstance(first_arg, (list, tuple)):
+        # set all other input observer nodes to use that module
+        for input_idx, input_arg in enumerate(first_arg):
+            if input_idx == 0:
+                continue
+            iteration_guard = 0
+            while not _is_activation_post_process_node(input_arg, named_modules):
+                # failed to trace back since no input arg for the current node
+                if len(input_arg.args) < 1:
+                    return False
+                input_arg = input_arg.args[0]
+                iteration_guard += 1
+                if iteration_guard > 10000:
+                    raise AssertionError("Unable to find observer of previous node")
+
+            parent_name, name = _parent_name(input_arg.target)
+            setattr(named_modules[parent_name], name, obs_mod_to_use)
+
+    # set the output observer node to use that module
+    for output_obs_node in node.users:
+        if not _is_activation_post_process_node(output_obs_node, named_modules):
+            raise AssertionError(
+                "output_obs_node must be an activation post process node"
+            )
+        parent_name, name = _parent_name(output_obs_node.target)
+        setattr(named_modules[parent_name], name, obs_mod_to_use)
+
+    # TODO(future PR): delete the orphaned observer modules
+    return True
+
+
+def _remove_output_observer(
+    node: Node, model: torch.nn.Module, named_modules: dict[str, torch.nn.Module]
+):
+    items = list(node.users.items())
+    for output_obs_node, _ in items:
+        if not _is_activation_post_process_node(output_obs_node, named_modules):
+            raise AssertionError(
+                "output_obs_node must be an activation post process node"
+            )
+        output_obs_node.replace_all_uses_with(node)
+        model.graph.erase_node(output_obs_node)  # type: ignore[union-attr, operator]
+
+
+def _swap_custom_module_to_observed(
+    node: Node,
+    qconfig: QConfigAny,
+    named_modules: dict[str, torch.nn.Module],
+    prepare_custom_config: PrepareCustomConfig,
+):
+    custom_module = named_modules[node.target]  # type: ignore[index]
+    custom_module_class_mapping = prepare_custom_config.float_to_observed_mapping
+    observed_custom_module_class = get_swapped_custom_module_class(
+        custom_module, custom_module_class_mapping, qconfig
+    )
+    observed_custom_module = observed_custom_module_class.from_float(custom_module)
+    parent_name, name = _parent_name(node.target)
+    setattr(named_modules[parent_name], name, observed_custom_module)
+
+
+def insert_observers_for_model(
+    model: GraphModule,
+    node_name_to_match_result_with_qconfig: dict[str, _MatchResultWithQConfig],
+    node_name_to_qconfig: dict[str, QConfigAny],
+    prepare_custom_config: PrepareCustomConfig,
+    equalization_config_map: dict[str, Any],
+    backend_config: BackendConfig,
+    observed_node_names: set[str],
+    is_qat: bool,
+) -> Node | None:
+    """
+    Inserts observers, using the following high level algorithm:
+
+    For each node in the graph:
+      1. determine the target dtype of this node in the quantized graph, and save
+           it for future steps
+      2. determine the target dtype or all args and kwargs of this node
+      3. if any arg or kwarg's target dtype does not match the current node's
+           dtype, insert an observer
+      4. if the current node needs an output observer, insert it
+
+    For example:
+
+    - starting graph:
+        x0 -> linear -> x1
+
+    - observed graph after processing x0:
+        x0(fp32)
+
+    - observed graph after processing linear:
+        x0(fp32) -> x0_obs0(int8) -> linear(int8) -> linear_obs0(int8)
+
+    - observed graph after processing x1:
+        x0(fp32) -> x0_obs0(int8) -> linear(int8) -> linear_obs0(int8) -> x1
+
+    After a node is processed, the naive observer placement is guaranteed to be
+    complete for that node and all of its predecessors. There can be future
+    passes which optimize the graph by deduplicating observers, etc.
+    """
+
+    # node.meta["target_dtype_info"] stores the target dtype information
+    # that's derived from qconfig for the Node, for example, if we have
+    # a conv2d node that has a qconfig
+    # qconfig = QConfig(activation=..., weight=...)
+    # # information for input and bias node omitted
+    # # for getattr node
+    # # weight = getattr(self, 'weight')
+    # weight.meta["target_dtype_info"] = {
+    #    'output_act_obs_or_fq_ctr': qconfig.weight,
+    # }
+    # # for conv2d node
+    # # conv2d = call_function[target=torch.nn.functional.conv2d](
+    # #            args=(input, weight, bias))
+    # conv2d.meta["target_dtype_info"] = {
+    #   'input_act_obs_or_fq_ctr': qconfig.activation
+    #   'weight_obs_or_fq_ctr': qconfig.weight,
+    #   'bias_obs_or_fq_ctr': PlaceholderObserver.with_args(dtype=torch.float32),
+    #   'output_act_obs_or_fq_ctr': qconfig.activation,
+    # }
+    #
+    cache_for_no_tensor_check: dict[Node, bool] = {}
+
+    # first, populate the dtype map based only on qconfig and qhandler
+    # this assumes:
+    # graph inputs are fp32 by default, and int8 where overridden
+    # other nodes output dtype is specified by the qconfig
+    named_modules = dict(model.named_modules(remove_duplicate=False))
+
+    input_quantized_idxs: list[int] = prepare_custom_config.input_quantized_indexes
+    output_quantized_idxs: list[int] = prepare_custom_config.output_quantized_indexes
+    processed_nodes: set[Node] = set()
+    # initialize target_dtype_info
+    for node in model.graph.nodes:
+        node.meta["target_dtype_info"] = copy.copy(
+            _DEFAULT_FP32_QCONFIG_FOR_TARGET_DTYPE_INFO
+        )
+
+    inputs_seen_counter = 0
+    outputs_seen_counter = 0
+    placeholder_node_to_input_index: dict[Node, int] = {}
+    # TODO: we probably don't need this counter since each graph will only have
+    # one output node?
+    output_node_to_output_index: dict[Node, int] = {}
+    for node in model.graph.nodes:
+        if node.op == "placeholder":
+            placeholder_node_to_input_index[node] = inputs_seen_counter
+            inputs_seen_counter += 1
+        if node.op == "output":
+            output_node_to_output_index[node] = outputs_seen_counter
+            outputs_seen_counter += 1
+
+    # Step 1, set the observer or fake quantize module constructor for each node in the
+    # matched_node_pattern
+
+    for match_res_with_qconfig in node_name_to_match_result_with_qconfig.values():
+        (
+            last_node,
+            matched_node_pattern,
+            pattern,
+            qhandler,
+            qconfig,
+        ) = match_res_with_qconfig
+        if qhandler is None:
+            raise AssertionError("qhandler must not be None")
+        _set_target_dtype_info_for_matched_node_pattern(
+            matched_node_pattern,
+            last_node,
+            qconfig,
+            qhandler,
+            backend_config,
+            named_modules,
+            cache_for_no_tensor_check,
+            processed_nodes,
+        )
+
+    # Step 2. Special cases for some operators, we might be able to remove them
+    # in the future if we know dtype information of each node better
+
+    # Step 2.1. some settings are not based on patterns, we need to process each node
+    # instead
+    for node in model.graph.nodes:
+        if (
+            node.op == "placeholder"
+            and placeholder_node_to_input_index[node] in input_quantized_idxs
+        ):
+            # users are not supposed to call calculate_qparams on PlaceholderObserver, and
+            # this is OK because we are using this as a way to encode the dtypes of input
+            # tensor, we won't actually insert these observers in the graph and won't
+            # actually call calculate_qparams
+            node.meta["target_dtype_info"] = copy.copy(
+                _DEFAULT_QUINT8_QCONFIG_FOR_TARGET_DTYPE_INFO
+            )
+        elif node.op in ("call_module", "call_method", "call_function"):
+            args_have_no_tensors = all_node_args_have_no_tensors(
+                node, named_modules, cache_for_no_tensor_check
+            )
+            if args_have_no_tensors:
+                node.meta["target_dtype_info"] = {
+                    "input_act_obs_or_fq_ctr": None,
+                    "output_act_obs_or_fq_ctr": None,
+                }
+        elif (
+            node.op == "output"
+            and output_node_to_output_index[node] in output_quantized_idxs
+        ):
+            # TODO(future PR): update the output_quantized_idxs API to match
+            # arbitrary data structures. There is always a single output, and
+            # that output can have arbitrary nesting of values. List[int] is
+            # not the right data type for this.
+
+            # TODO(future PR): support more dtypes in model outputs, if necessary
+            node.meta["target_dtype_info"] = copy.copy(
+                _DEFAULT_QUINT8_QCONFIG_FOR_TARGET_DTYPE_INFO
+            )
+
+    # Step 2.2, for nodes with known input dtypes, propagate them throughout the
+    # graph. For example, if there is a call such as
+    #   x1 = x0.masked_fill(mask, 1)
+    # we propagate the type of mask to be torch.bool
+    propagate_dtypes_for_known_nodes(
+        model.graph, node_name_to_match_result_with_qconfig
+    )
+
+    # Step 3, check if the requested target_dtype_info is supported by backend or not
+    # if not, we'll reset the target_dtye_info to use the default (float Tensor)
+
+    # reset the counters and set of processed_nodes
+    processed_nodes: set[Node] = set()
+    for match_res_with_qconfig in node_name_to_match_result_with_qconfig.values():
+        (
+            last_node,
+            matched_node_pattern,
+            pattern,
+            qhandler,
+            qconfig,
+        ) = match_res_with_qconfig
+        is_supported_by_backend = (
+            _is_pattern_dtype_config_and_qconfig_supported_by_backend(
+                pattern, matched_node_pattern, qconfig, backend_config
+            )
+        )
+        if qhandler is None:
+            raise AssertionError("qhandler must not be None")
+
+        # get output_act_dtype so that we don't also reset the special typed nodes
+        # TODO: we might want to handle these more uniformly with the default path
+        # this can be improved if we can use node.meta["val"]
+        output_act_or_fq_ctr = node.meta["target_dtype_info"][
+            "output_act_obs_or_fq_ctr"
+        ]
+        output_act_or_fq = output_act_or_fq_ctr() if output_act_or_fq_ctr else None
+        output_act_dtype, _ = _get_dtype_and_is_dynamic(output_act_or_fq)
+        if not is_supported_by_backend and output_act_dtype not in [
+            None,
+            int,
+            float,
+            torch.bool,
+        ]:
+            # restore target_dtype_info to default if it is not supported by backend
+            _set_target_dtype_info_for_matched_node_pattern(
+                matched_node_pattern,
+                last_node,
+                torch.ao.quantization.qconfig._default_fp32_placeholder_qconfig,
+                None,
+                backend_config,
+                named_modules,
+                cache_for_no_tensor_check,
+                processed_nodes,
+            )
+
+    # After this point, the current node and all of its arguments
+    # have a target_dtype_info assigned. Now, we insert observers for inputs
+    # of this node (if needed for this node), and the output of this node
+    # (if needed for this node).
+
+    # Since we are mutating the graph as we go, we iterate over the original
+    # nodes before observer insertion, instead of model.graph.nodes.
+    nodes_before_observation = list(model.graph.nodes)
+
+    # Avoid duplicates custom module swaps for multiple nodes with same target.
+    custom_module_names_already_swapped: set[str] = set()
+
+    # TODO: reuse placeholder_node_to_input_index and output_node_to_output_index
+    # reset inputs/outputs counters
+    inputs_seen_counter = 0
+    outputs_seen_counter = 0
+    results_node = None
+    obs_or_fq_map: dict[EdgeOrNode, ObserverOrFakeQuantize] = {}
+    model_device = assert_and_get_unique_device(model)
+
+    # TODO: change this to insert obs/fq by pattern instead of by node
+    for node in nodes_before_observation:
+        if node.op == "placeholder":
+            # if a graph input is in fp32, it does not need observation
+            # if a graph input is in int8, we assume the observation happens
+            #   outside of the graph, and no additional observation is needed
+            pass
+
+        elif node.op in ("call_module", "call_method", "call_function", "output"):
+            # check for matches
+            (
+                last_node,
+                matched_node_pattern,
+                pattern,
+                qhandler,
+                qconfig,
+            ) = node_name_to_match_result_with_qconfig.get(  # type: ignore[assignment]
+                node.name, (None, None, None, None, None)
+            )
+            equalization_qconfig = equalization_config_map.get(node.name, None)
+
+            this_node_dtype_info = node.meta["target_dtype_info"]
+            if "val" in node.meta:
+                output_is_a_tensor = this_node_dtype_info is not None and isinstance(
+                    node.meta["val"], FakeTensor
+                )
+            else:
+                output_is_a_tensor = this_node_dtype_info is not None
+
+            skip_inserting_observers = (
+                (qconfig is None) or not output_is_a_tensor
+            ) and (node.op != "output")
+
+            # TODO: take a closer look to see if we can remove this check
+            # right now it is here because of `observed_node_names`, we are using
+            # it as an indicator for swapping the modules to reference modules in
+            # convert
+            is_supported_by_backend = (
+                _is_pattern_dtype_config_and_qconfig_supported_by_backend(
+                    pattern, matched_node_pattern, qconfig, backend_config
+                )
+            )
+
+            if not skip_inserting_observers and is_supported_by_backend:
+                named_modules = dict(model.named_modules(remove_duplicate=False))
+                if node.op != "output":
+                    if matched_node_pattern is None:
+                        raise AssertionError("matched_node_pattern must not be None")
+                    # add matched nodes to the observed node name set
+                    _add_matched_node_name_to_set(
+                        matched_node_pattern, observed_node_names
+                    )
+
+                    # This is currently only used for equalization.
+                    # Checks if the current node is in a branch in which the two
+                    # first layers are both being quantized.
+                    #
+                    # ex.       conv2
+                    #         /
+                    #      x -> conv1
+                    #
+                    # If this is the case, we will not apply equalization to the
+                    # initial two layers.
+                    is_quantized_branch = False
+                    if (
+                        len(node.args) > 0
+                        and isinstance(node.args[0], Node)
+                        and len(node.args[0].users) > 1
+                    ):
+                        for user in node.args[0].users:
+                            # Checks if there exists another user being quantized
+                            is_user_quantized = node_name_to_qconfig.get(
+                                user.name, None
+                            ) is not None or (
+                                user.op == "call_module"
+                                and isinstance(
+                                    named_modules[str(user.target)], ObserverBase
+                                )
+                            )
+                            if user != node and is_user_quantized:
+                                is_quantized_branch = True
+
+                    pattern_to_root_node_getter = (
+                        get_fusion_pattern_to_root_node_getter(backend_config)
+                    )
+                    root_node_getter = pattern_to_root_node_getter.get(
+                        pattern, _default_root_node_getter
+                    )
+                    root_node = root_node_getter(matched_node_pattern)
+                    is_input_node_of_the_pattern = node is root_node
+                    if is_input_node_of_the_pattern:
+                        # this modifies node inplace
+                        _maybe_insert_input_observers_for_node(
+                            node,
+                            qconfig,
+                            model,
+                            named_modules,
+                            model.graph,
+                            qhandler,
+                            prepare_custom_config,
+                            obs_or_fq_map,
+                            is_qat,
+                            backend_config,
+                            model_device,
+                        )
+
+                        # insert equalization input observers if needed
+                        _maybe_insert_input_equalization_observers_for_node(
+                            node,
+                            equalization_qconfig,
+                            model,
+                            named_modules,
+                            model.graph,
+                            is_quantized_branch,
+                        )
+
+                    is_last_node_of_pattern = node is last_node
+                    input_output_share_observers = node.meta["target_dtype_info"].get(
+                        "input_output_share_observers", False
+                    )
+                    reuse_input_obs_or_fq = node.meta["target_dtype_info"].get(
+                        "reuse_input_obs_or_fq", False
+                    )
+
+                    if is_last_node_of_pattern:
+                        if _is_custom_module_lstm(
+                            node, named_modules, qconfig, qhandler
+                        ):
+                            # Currently custom module outputs are assumed to be already quantized,
+                            # so we need to insert a DeQuantStub after the output. For custom module
+                            # LSTM specifically, the outputs are also a nested tuple, so we must first
+                            # break down the tuple to insert DeQuantStubs after the internal nodes.
+
+                            # TODO: This currently diverges from how custom modules are handled today,
+                            # where we insert observers after the output instead of DeQuantStubs, and
+                            # replace these observers with "dequantize" nodes during convert. Conceptually,
+                            # these output observers are the same as DeQuantStubs. In the future, we
+                            # should resolve this inconsistency by inserting DeQuantStubs for all custom
+                            # modules, not just for LSTM.
+                            _insert_dequant_stubs_for_custom_module_lstm_output(
+                                node, model, named_modules, model.graph
+                            )
+                            if node.target not in custom_module_names_already_swapped:
+                                custom_module_names_already_swapped.add(node.target)
+                                _swap_custom_module_to_observed(
+                                    node, qconfig, named_modules, prepare_custom_config
+                                )
+                        else:
+                            # this returns the new observer node if it was needed
+                            maybe_output_obs_node = (
+                                _maybe_insert_output_observer_for_node(
+                                    node,
+                                    model,
+                                    named_modules,
+                                    model.graph,
+                                    obs_or_fq_map,
+                                    is_qat,
+                                )
+                            )
+
+                            if maybe_output_obs_node is not None:
+                                # Update users of original node to use the output observer
+                                # instead. For example, change
+                                #
+                                #           next_node
+                                #          /
+                                #   cur_node -> obs
+                                #
+                                # to
+                                #
+                                #                 next_node
+                                #                 /
+                                #   cur_node -> obs
+                                #
+                                # We need to save orig users before updating uses because
+                                # the list of users will change as we update uses
+                                orig_users = list(node.users.keys())
+                                for user_node in orig_users:
+                                    if user_node is maybe_output_obs_node:
+                                        continue
+                                    user_node.replace_input_with(
+                                        node, maybe_output_obs_node
+                                    )
+
+                                _is_observer_in_same_graph_ = (
+                                    _is_observer_in_same_graph(
+                                        node, named_modules, obs_or_fq_map, is_qat
+                                    )
+                                )
+
+                                # for ops whose inputs and outputs share observer/fqs, we modify the graph
+                                # to make all inputs and outputs use the first input's
+                                # observer/fq
+                                if (
+                                    input_output_share_observers
+                                    and _is_observer_in_same_graph_
+                                ) or reuse_input_obs_or_fq:
+                                    if not _maybe_make_input_output_share_observers(
+                                        node, model, named_modules
+                                    ):
+                                        _remove_output_observer(
+                                            node, model, named_modules
+                                        )
+
+                                if qhandler is not None and qhandler.is_custom_module():
+                                    if (
+                                        node.target
+                                        not in custom_module_names_already_swapped
+                                    ):
+                                        custom_module_names_already_swapped.add(
+                                            node.target
+                                        )
+                                        _swap_custom_module_to_observed(
+                                            node,
+                                            qconfig,
+                                            named_modules,
+                                            prepare_custom_config,
+                                        )
+
+                else:  # output
+                    _maybe_insert_observers_before_graph_output(
+                        node, model, named_modules, model.graph, obs_or_fq_map, is_qat
+                    )
+
+        #
+        # After this point, the current node has input and output observers
+        # that it needs for itself inserted.
+        #
+
+        # increment the counters, so future inputs and outputs are assigned
+        # correct dtypes
+        if node.op == "placeholder":
+            inputs_seen_counter += 1
+        elif node.op == "output":
+            outputs_seen_counter += 1
+            results_node = node
+
+    return results_node
+
+
+def _run_prepare_fx_on_standalone_modules(
+    model: torch.nn.Module,
+    is_qat: bool,
+    named_modules: dict[str, torch.nn.Module],
+    node_name_to_match_result_with_qconfig: Any,
+    prepare_custom_config: PrepareCustomConfig,
+    backend_config: BackendConfig,
+) -> None:
+    """
+    Runs prepare_fx on each standalone module. Note: this does
+    not modify the graph, it just replaces the unobserved modules with
+    their observed versions.
+    """
+    for (
+        root_node,
+        _,
+        _pattern,
+        qhandler,
+        qconfig,
+    ) in node_name_to_match_result_with_qconfig.values():
+        if qhandler is None:
+            continue
+        elif not qhandler.is_standalone_module():
+            continue
+
+        (
+            sm_qconfig_mapping,
+            sm_example_inputs,
+            sm_prepare_custom_config,
+            sm_backend_config,
+        ) = _get_standalone_module_configs(
+            root_node, named_modules, prepare_custom_config, qconfig, backend_config
+        )
+
+        standalone_module = named_modules[root_node.target]
+        prepare = torch.ao.quantization.quantize_fx._prepare_standalone_module_fx  # type: ignore[attr-defined]
+        observed_standalone_module = prepare(
+            standalone_module,
+            sm_qconfig_mapping,
+            is_qat,
+            example_inputs=sm_example_inputs,
+            prepare_custom_config=sm_prepare_custom_config,
+            backend_config=sm_backend_config,
+        )
+        parent_name, name = _parent_name(root_node.target)
+        setattr(named_modules[parent_name], name, observed_standalone_module)
+        named_modules[root_node.target] = observed_standalone_module
+
+
+def _save_state(
+    observed: GraphModule,
+    node_name_to_qconfig: dict[str, QConfigAny],
+    node_name_to_scope: dict[str, tuple[str, type]],
+    prepare_custom_config: PrepareCustomConfig,
+    equalization_node_name_to_qconfig: dict[str, Any],
+    qconfig_mapping: QConfigMapping,
+    is_qat: bool,
+    observed_node_names: set[str],
+) -> None:
+    observed.meta["_observed_graph_module_attrs"] = ObservedGraphModuleAttrs(
+        node_name_to_qconfig=node_name_to_qconfig,
+        node_name_to_scope=node_name_to_scope,
+        prepare_custom_config=prepare_custom_config,
+        equalization_node_name_to_qconfig=equalization_node_name_to_qconfig,
+        qconfig_mapping=qconfig_mapping,
+        is_qat=is_qat,
+        observed_node_names=observed_node_names,
+    )
+
+
+def prepare(
+    model: GraphModule,
+    qconfig_mapping: QConfigMapping | dict[str, Any],
+    is_qat: bool,
+    node_name_to_scope: dict[str, tuple[str, type]],
+    example_inputs: tuple[Any, ...],
+    prepare_custom_config: PrepareCustomConfig | dict[str, Any] | None = None,
+    _equalization_config: QConfigMapping | dict[str, Any] | None = None,
+    backend_config: BackendConfig | dict[str, Any] | None = None,
+    is_standalone_module: bool = False,
+) -> GraphModule:
+    """standalone_module means it a submodule that is not inlined in
+    parent module, and will be quantized separately as one unit.
+
+    How the standalone module is observed is specified by `input_quantized_idxs` and
+    `output_quantized_idxs` in the prepare_custom_config for the standalone module
+    Args:
+        node_name_to_scope: mapping from node name to the scope of the module which contains the node.
+        The scope is a tuple of fully qualified path of the module and the type of the module
+    Returns:
+        model(GraphModule): prepared standalone module
+        attributes related to standalone module
+        in model.meta["_observed_graph_module_attrs"]:
+            is_observed_standalone_module (bool): boolean value that shows whether the
+            current model is a observed standalone module or not
+            standalone_module_input_quantized_idxs(List[Int]): a list of
+                indexes for the graph input that is expected to be quantized,
+                same as input_quantized_idxs configuration provided
+                for the standalone module
+            standalone_module_output_quantized_idxs(List[Int]): a list of
+                indices for the graph output that is quantized
+                same as input_quantized_idxs configuration provided
+                for the standalone module
+    """
+    if prepare_custom_config is None:
+        prepare_custom_config = PrepareCustomConfig()
+    if _equalization_config is None:
+        _equalization_config = QConfigMapping()
+
+    if isinstance(qconfig_mapping, dict):
+        warnings.warn(
+            "Passing a QConfig dictionary to prepare is deprecated and will not be supported "
+            "in a future version. Please pass in a QConfigMapping instead.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        qconfig_mapping = QConfigMapping.from_dict(qconfig_mapping)
+
+    if isinstance(_equalization_config, dict):
+        warnings.warn(
+            "Passing a QConfig dictionary to prepare for equalization is deprecated and will not "
+            "be supported in a future version. Please pass in a QConfigMapping instead.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        _equalization_config = QConfigMapping.from_dict(_equalization_config)
+
+    if isinstance(prepare_custom_config, dict):
+        warnings.warn(
+            "Passing a prepare_custom_config_dict to prepare is deprecated and will not be supported "
+            "in a future version. Please pass in a PrepareCustomConfig instead.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        prepare_custom_config = PrepareCustomConfig.from_dict(prepare_custom_config)
+
+    if isinstance(backend_config, dict):
+        warnings.warn(
+            "Passing a backend_config_dict to prepare is deprecated and will not be supported "
+            "in a future version. Please pass in a BackendConfig instead.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        backend_config = BackendConfig.from_dict(backend_config)
+
+    if not isinstance(qconfig_mapping, QConfigMapping):
+        raise AssertionError("qconfig_mapping must be a QConfigMapping")
+    if not isinstance(_equalization_config, QConfigMapping):
+        raise AssertionError("_equalization_config must be a QConfigMapping")
+    qconfig_mapping = copy.deepcopy(qconfig_mapping)
+    _equalization_config = copy.deepcopy(_equalization_config)
+
+    # mapping from a tuple of nodes in reverse order to uninitialized
+    #   QuantizeHandler subclass. For example,
+    # {
+    #   # match a single node
+    #   (:
+    #     ),
+    #   # match multiple nodes in reverse order
+    #   ((, ):
+    #     ),
+    # }
+
+    pattern_to_quantize_handler: dict[Pattern, QuantizeHandler] = {}
+    if backend_config is None:
+        backend_config = get_native_backend_config()
+    pattern_to_quantize_handler = _get_pattern_to_quantize_handlers(backend_config)
+    pattern_to_quantize_handler = _sorted_patterns_dict(pattern_to_quantize_handler)
+
+    root_node_getter_mapping = get_fusion_pattern_to_root_node_getter(backend_config)
+
+    # pyrefly: ignore [bad-argument-type]
+    _update_qconfig_for_fusion(model, qconfig_mapping)
+    # pyrefly: ignore [bad-argument-type]
+    _update_qconfig_for_fusion(model, _equalization_config)
+    # pyrefly: ignore [bad-argument-type]
+    flattened_qconfig_dict = _get_flattened_qconfig_dict(qconfig_mapping)
+    # TODO: support regex as well
+    propagate_qconfig_(model, flattened_qconfig_dict, prepare_custom_config.to_dict())
+
+    if is_qat:
+        module_to_qat_module = get_module_to_qat_module(backend_config)
+        _qat_swap_modules(model, module_to_qat_module)
+        # pyrefly: ignore [bad-argument-type]
+        _update_qconfig_for_qat(qconfig_mapping, backend_config)
+
+    # mapping from fully qualified module name to module instance
+    # for example,
+    # {
+    #   '': Model(...),
+    #   'linear': Linear(...),
+    #   'linear.weight_fake_quant': PerChannelMinMaxObserver(...),
+    # }
+    named_modules = dict(model.named_modules(remove_duplicate=False))
+
+    # fill node_name_to_qconfig, a map from node name to qconfig, used in _find_matches
+    equalization_node_name_to_qconfig = _generate_node_name_to_qconfig(
+        model,
+        named_modules,
+        model.graph,
+        # pyrefly: ignore [bad-argument-type]
+        _equalization_config,
+        node_name_to_scope,
+    )
+    node_name_to_qconfig = _generate_node_name_to_qconfig(
+        model,
+        named_modules,
+        model.graph,
+        # pyrefly: ignore [bad-argument-type]
+        qconfig_mapping,
+        node_name_to_scope,
+    )
+
+    # match the patterns that will get quantized
+    standalone_module_names = list(prepare_custom_config.standalone_module_names.keys())
+    standalone_module_classes = list(
+        prepare_custom_config.standalone_module_classes.keys()
+    )
+
+    custom_module_classes = get_custom_module_class_keys(
+        prepare_custom_config.float_to_observed_mapping
+    )
+    matches_without_qconfig = _find_matches(
+        model.graph,
+        named_modules,
+        pattern_to_quantize_handler,
+        root_node_getter_mapping,
+        standalone_module_names,
+        standalone_module_classes,
+        custom_module_classes,
+    )
+
+    # map qconfig instances to matches
+    node_name_to_match_result_with_qconfig = {}
+    for node_name, match_without_qconfig in matches_without_qconfig.items():
+        match_with_qconfig = (*match_without_qconfig, node_name_to_qconfig[node_name])
+        node_name_to_match_result_with_qconfig[node_name] = match_with_qconfig
+
+    _run_prepare_fx_on_standalone_modules(
+        model,
+        is_qat,
+        named_modules,
+        node_name_to_match_result_with_qconfig,
+        prepare_custom_config,
+        backend_config,
+    )
+
+    # record names for the set of observed node, so that in convert step
+    # we know whether we need to convert a floating point module to reference
+    # quantized module or not
+    observed_node_names: set[str] = set()
+
+    result_node = insert_observers_for_model(
+        model,
+        node_name_to_match_result_with_qconfig,
+        node_name_to_qconfig,
+        prepare_custom_config,
+        equalization_node_name_to_qconfig,
+        backend_config,
+        observed_node_names,
+        is_qat,
+    )
+    model = GraphModule(model, model.graph)
+
+    _save_state(
+        model,
+        node_name_to_qconfig,
+        node_name_to_scope,
+        prepare_custom_config,
+        equalization_node_name_to_qconfig,
+        # pyrefly: ignore [bad-argument-type]
+        qconfig_mapping,
+        is_qat,
+        observed_node_names,
+    )
+
+    if is_standalone_module:
+        if result_node is None:
+            raise AssertionError("result_node must not be None for standalone modules")
+        if not isinstance(result_node.args[0], Node):
+            raise AssertionError(
+                "standalone module only supports returning simple value currently (not tuple, dict etc.)"
+            )
+        # these inputs are observed in parent
+        # converting List[int] to Tensor since module attribute is
+        # Union[Tensor, Module]
+        input_quantized_idxs: list[int] = prepare_custom_config.input_quantized_indexes
+        output_quantized_idxs: list[int] = (
+            prepare_custom_config.output_quantized_indexes
+        )
+        observed_graph_module_attrs = model.meta["_observed_graph_module_attrs"]
+        # inplace modification
+        observed_graph_module_attrs.is_observed_standalone_module = True
+        observed_graph_module_attrs.standalone_module_input_quantized_idxs = (
+            input_quantized_idxs
+        )
+        observed_graph_module_attrs.standalone_module_output_quantized_idxs = (
+            output_quantized_idxs
+        )
+    return model
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/qconfig_mapping_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/qconfig_mapping_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..783cba8149e6e09164d01c7f9ebafdc2e6240428
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/qconfig_mapping_utils.py
@@ -0,0 +1,401 @@
+# mypy: allow-untyped-defs
+import re
+from collections import defaultdict, OrderedDict
+from collections.abc import Callable
+from typing import Any
+
+import torch
+from torch.ao.nn.intrinsic import _FusedModule
+from torch.ao.quantization import QConfig
+from torch.ao.quantization.backend_config import BackendConfig, DTypeConfig
+from torch.ao.quantization.backend_config.utils import get_module_to_qat_module
+from torch.ao.quantization.observer import _is_activation_post_process
+from torch.ao.quantization.qconfig import (
+    _add_module_to_qconfig_obs_ctr,
+    qconfig_equals,
+    QConfigAny,
+)
+from torch.ao.quantization.qconfig_mapping import (
+    _MODULE_NAME_DICT_KEY,
+    _MODULE_NAME_REGEX_DICT_KEY,
+    _OBJECT_TYPE_DICT_KEY,
+    QConfigMapping,
+)
+from torch.ao.quantization.utils import _parent_name, get_qconfig_dtypes
+from torch.fx import GraphModule
+from torch.fx.graph import Graph
+
+
+__all__: list[str] = []
+
+
+def _maybe_adjust_qconfig_for_module_name_object_type_order(
+    qconfig_mapping: QConfigMapping,
+    cur_module_path: str,
+    cur_object_type: Callable,
+    cur_object_type_idx: int,
+    fallback_qconfig: QConfigAny,
+) -> QConfigAny:
+    for (
+        module_name,
+        object_type,
+        index,
+    ), qconfig in qconfig_mapping.module_name_object_type_order_qconfigs.items():
+        if (
+            (module_name == cur_module_path)
+            and (object_type == cur_object_type)
+            and (index == cur_object_type_idx)
+        ):
+            return qconfig
+    return fallback_qconfig
+
+
+def _update_qconfig_for_fusion(model: GraphModule, qconfig_mapping: QConfigMapping):
+    """
+    Update the QConfigMapping to account for fused modules such as LinearReLU.
+    This assumes the QConfigMapping's attributes have already been converted to OrderedDicts.
+    """
+    object_type_dict = qconfig_mapping.object_type_qconfigs
+    if len(object_type_dict) == 0:
+        return qconfig_mapping
+
+    modules = dict(model.named_modules())
+
+    for node in model.graph.nodes:
+        if node.op == "call_module" and node.target in modules:
+            maybe_fused_module = modules[str(node.target)]
+            if not isinstance(maybe_fused_module, _FusedModule):
+                continue
+
+            ops = list(maybe_fused_module._modules.values())
+            fused_qconfig = object_type_dict.get(type(ops[0]), None)
+
+            # Raise an error if the modules in the fused module have
+            # different qconfigs specified in the qconfig_dict
+            # TODO: currently it only works for modules,
+            # need to make this work for torch.nn.functional.relu
+            # TODO: currently it only works for object_type configurations,
+            # ideally it should work for different types of configurations,
+            # maybe we want to redesign this part
+            for op in ops[1:]:
+                if not qconfig_equals(
+                    object_type_dict.get(type(op), None), fused_qconfig
+                ):
+                    raise LookupError(
+                        "During fusion, we need to specify the same "
+                        + f"qconfigs for all module types in {type(maybe_fused_module)} "
+                        + f"offending type: {type(op)}"
+                    )
+
+            if fused_qconfig is not None:
+                object_type_dict[type(maybe_fused_module)] = fused_qconfig
+
+
+def _generate_node_name_to_qconfig(
+    root: torch.nn.Module,
+    modules: dict[str, torch.nn.Module],
+    input_graph: Graph,
+    qconfig_mapping: QConfigMapping,
+    node_name_to_scope: dict[str, tuple[str, type]],
+) -> dict[str, QConfigAny]:
+    global_qconfig = qconfig_mapping.global_qconfig
+    node_name_to_qconfig = {}
+
+    # example:
+    #
+    #   {'foo.bar': {F.linear: 0, F.conv2d: 1, ...}, ...}
+    #
+    # meaning in submodule 'foo.bar', we have seen 0 F.linear and
+    # 1 F.conv2d invocations so far.
+    submodule_to_object_type_to_cur_idx: dict[str, dict[Callable, int]] = defaultdict(
+        lambda: defaultdict(int)
+    )
+    for node in input_graph.nodes:
+        qconfig = None
+        if node.op == "get_attr":
+            module_name, _ = _parent_name(node.target)
+            qconfig = _maybe_adjust_qconfig_for_module_type_or_name(
+                qconfig_mapping, type(modules[module_name]), module_name, global_qconfig
+            )
+            qconfig_with_device_check = _add_module_to_qconfig_obs_ctr(
+                qconfig, modules.get(node.target, None)
+            )
+        elif node.op == "call_function":
+            # precedence: module_name_qconfig
+            # > function_qconfig > global_qconfig
+            # module_name takes precedence over function qconfig
+            function_qconfig = _get_object_type_qconfig(
+                qconfig_mapping, node.target, global_qconfig
+            )
+            module_path, module_type = node_name_to_scope[node.name]
+            qconfig = _maybe_adjust_qconfig_for_module_type_or_name(
+                qconfig_mapping, module_type, module_path, function_qconfig
+            )
+
+            cur_object_type_idx = submodule_to_object_type_to_cur_idx[module_path][
+                node.target
+            ]
+            submodule_to_object_type_to_cur_idx[module_path][node.target] += 1
+            qconfig = _maybe_adjust_qconfig_for_module_name_object_type_order(
+                qconfig_mapping, module_path, node.target, cur_object_type_idx, qconfig
+            )
+            qconfig_with_device_check = _add_module_to_qconfig_obs_ctr(
+                qconfig, modules.get(node.target, None)
+            )
+
+        elif node.op == "call_method":
+            module_path, module_type = node_name_to_scope[node.name]
+            # first use node.target (string) to get the qconfig
+            # this is to support configs like
+            # "object_type": [("reshape", qconfig)]
+            qconfig = _maybe_adjust_qconfig_for_module_type_or_name(
+                qconfig_mapping, node.target, module_path, global_qconfig
+            )
+            # if there is no special config for the method, we'll fall back to the
+            # config for the module that contains the call_method node
+            qconfig = _maybe_adjust_qconfig_for_module_type_or_name(
+                qconfig_mapping, module_type, module_path, qconfig
+            )
+            # currently call_method does not support modifying qconfig
+            # by order, we can add this later if it is needed.
+            qconfig_with_device_check = _add_module_to_qconfig_obs_ctr(
+                qconfig, modules.get(node.target, None)
+            )
+
+        elif node.op == "call_module":
+            # if the node is an observer, just continue - don't add it to the qconfig_map
+            if _is_activation_post_process(modules[node.target]):
+                continue
+            qconfig = _maybe_adjust_qconfig_for_module_type_or_name(
+                qconfig_mapping, type(modules[node.target]), node.target, global_qconfig
+            )
+
+            module_path, module_type = node_name_to_scope[node.name]
+            # Note: for call_module, the module_path is the current module's name.
+            # to meaningfully count invocations, we need to count them in the parent
+            # module.
+            parent_name, _ = _parent_name(module_path)
+            cur_object_type_idx = submodule_to_object_type_to_cur_idx[parent_name][
+                module_type
+            ]
+            submodule_to_object_type_to_cur_idx[parent_name][module_type] += 1
+            qconfig = _maybe_adjust_qconfig_for_module_name_object_type_order(
+                qconfig_mapping, parent_name, module_type, cur_object_type_idx, qconfig
+            )
+            qconfig_with_device_check = _add_module_to_qconfig_obs_ctr(
+                qconfig, modules.get(node.target, None)
+            )
+
+            # regex is not supported eager mode propagate_qconfig_, we'll
+            # need to set the qconfig explicitly here in case regex
+            # is used
+            modules[node.target].qconfig = qconfig_with_device_check
+        else:
+            qconfig_with_device_check = None
+
+        node_name_to_qconfig[node.name] = qconfig_with_device_check
+    return node_name_to_qconfig
+
+
+def _check_is_valid_config_dict(
+    config_dict: Any, allowed_keys: set[str], dict_name: str
+) -> None:
+    r"""Checks if the given config_dict has the correct keys
+
+    Args:
+      `config_dict`: dictionary whose keys we want to check
+    """
+
+    for k in config_dict:
+        if k not in allowed_keys:
+            raise ValueError(
+                "Expected "
+                + dict_name
+                + " to have the following keys: "
+                + str(allowed_keys)
+                + ". But found '"
+                + k
+                + "' instead."
+            )
+
+
+def _compare_prepare_convert_qconfig_mappings(
+    prepare_qconfig_mapping: QConfigMapping, convert_qconfig_mapping: QConfigMapping
+):
+    r"""Compare the qconfig_mapping passed in convert to the one from prepare and check the values
+
+    Args:
+      `prepare_qconfig_mapping`: configuration for prepare quantization step
+      `convert_qconfig_mapping`: configuration for convert quantization step
+    """
+    if not qconfig_equals(
+        prepare_qconfig_mapping.global_qconfig, convert_qconfig_mapping.global_qconfig
+    ):
+        raise AssertionError(
+            "Expected global qconfigs to be the same in the prepare and convert quantization configs"
+        )
+    prepare_dicts: list[OrderedDict] = [
+        prepare_qconfig_mapping.object_type_qconfigs,
+        prepare_qconfig_mapping.module_name_qconfigs,
+        prepare_qconfig_mapping.module_name_regex_qconfigs,
+    ]
+    convert_dicts: list[OrderedDict] = [
+        convert_qconfig_mapping.object_type_qconfigs,
+        convert_qconfig_mapping.module_name_qconfigs,
+        convert_qconfig_mapping.module_name_regex_qconfigs,
+    ]
+    dict_names = [
+        _OBJECT_TYPE_DICT_KEY,
+        _MODULE_NAME_DICT_KEY,
+        _MODULE_NAME_REGEX_DICT_KEY,
+    ]
+    for i in range(len(prepare_dicts)):
+        for name in prepare_dicts[i]:
+            if name not in convert_dicts[i]:
+                raise AssertionError(
+                    f"Missing key {dict_names[i]} {name} in convert QConfigMapping when it was present in prepare"
+                )
+            if convert_dicts[i][name] is not None and not qconfig_equals(
+                prepare_dicts[i][name], convert_dicts[i][name]
+            ):
+                raise AssertionError(
+                    "Expected convert QConfigMapping to have the same qconfig as prepare for key "
+                    f"{dict_names[i]} {name}; prepare: {prepare_dicts[i][name]}; convert: {convert_dicts[i][name]}"
+                )
+
+
+def _is_qconfig_supported_by_dtype_configs(
+    qconfig: QConfig, dtype_configs: list[DTypeConfig]
+):
+    for dtype_config in dtype_configs:
+        is_dynamic = dtype_config.is_dynamic
+        if is_dynamic is None:
+            is_dynamic = False
+        input_dtype = dtype_config.input_dtype or torch.float
+        weight_dtype = dtype_config.weight_dtype or torch.float
+        bias_dtype = dtype_config.bias_dtype or torch.float
+        output_dtype = dtype_config.output_dtype or torch.float
+        (
+            qconfig_activation_dtype,
+            qconfig_weight_dtype,
+            qconfig_input_act_is_dynamic,
+        ) = get_qconfig_dtypes(qconfig)
+        qconfig_bias_dtype = (
+            torch.float16
+            if (
+                qconfig_activation_dtype == torch.float16
+                and qconfig_weight_dtype == torch.float16
+                and not is_dynamic
+            )
+            else torch.float
+        )
+
+        if is_dynamic:
+            is_match = (
+                qconfig_input_act_is_dynamic
+                and input_dtype == qconfig_activation_dtype
+                and output_dtype == torch.float
+                and weight_dtype == qconfig_weight_dtype
+            )
+        else:
+            is_match = (
+                input_dtype == qconfig_activation_dtype
+                and output_dtype == qconfig_activation_dtype
+                and weight_dtype == qconfig_weight_dtype
+                and bias_dtype == qconfig_bias_dtype
+            )
+        if is_match:
+            return True
+    return False
+
+
+def _get_object_type_qconfig(
+    qconfig_mapping: QConfigMapping,
+    object_type: Callable | str,
+    fallback_qconfig: QConfigAny,
+) -> QConfigAny:
+    return qconfig_mapping.object_type_qconfigs.get(object_type, fallback_qconfig)
+
+
+def _get_module_name_regex_qconfig(qconfig_mapping, module_name, fallback_qconfig):
+    for regex_pattern, qconfig in qconfig_mapping.module_name_regex_qconfigs.items():
+        if re.match(regex_pattern, module_name):
+            # first match wins
+            return qconfig
+    return fallback_qconfig
+
+
+def _get_module_name_qconfig(qconfig_mapping, module_name, fallback_qconfig):
+    if module_name == "":
+        # module name qconfig not found
+        return fallback_qconfig
+    if module_name in qconfig_mapping.module_name_qconfigs:
+        return qconfig_mapping.module_name_qconfigs[module_name]
+    else:
+        parent, _ = _parent_name(module_name)
+        return _get_module_name_qconfig(qconfig_mapping, parent, fallback_qconfig)
+
+
+def _maybe_adjust_qconfig_for_module_type_or_name(
+    qconfig_mapping, module_type, module_name, global_qconfig
+):
+    # get qconfig for module_name,
+    # fallback to module_name_regex_qconfig, module_type_qconfig,
+    # global_qconfig if necessary
+    module_type_qconfig = _get_object_type_qconfig(
+        qconfig_mapping, module_type, global_qconfig
+    )
+    module_name_regex_qconfig = _get_module_name_regex_qconfig(
+        qconfig_mapping, module_name, module_type_qconfig
+    )
+    module_name_qconfig = _get_module_name_qconfig(
+        qconfig_mapping, module_name, module_name_regex_qconfig
+    )
+    return module_name_qconfig
+
+
+def _get_flattened_qconfig_dict(
+    qconfig_mapping: QConfigMapping,
+) -> dict[Callable | str, QConfigAny]:
+    """flatten the global, object_type and module_name qconfig
+    to the same qconfig_dict so that it can be used by
+    propagate_qconfig_ function.
+    "module_name_regex" is ignored for now since it's not supported
+    in propagate_qconfig_, but it can be fixed later.
+
+    For example:
+    Input: {
+      "": qconfig,
+      "object_type": [
+        (torch.add, qconfig)
+      ],
+      "module_name": [
+        ("conv", qconfig)
+      ]
+    }
+
+    Output: {
+      "": qconfig,
+      torch.add: qconfig,
+      "conv": qconfig
+    }
+    """
+    flattened: dict[Callable | str, QConfigAny] = {"": qconfig_mapping.global_qconfig}
+    flattened.update(qconfig_mapping.object_type_qconfigs)
+    flattened.update(qconfig_mapping.module_name_qconfigs)  # type: ignore[arg-type]
+    return flattened
+
+
+def _update_qconfig_for_qat(
+    qconfig_mapping: QConfigMapping, backend_config: BackendConfig
+):
+    """
+    Update the qconfig_mapping to account for module swaps during QAT.
+    During QAT we perform a module swap on the nn.Module types to the corresponding nn.qat.modules types.
+    """
+    module_to_qat_module_class = get_module_to_qat_module(backend_config)
+    object_type_dict = qconfig_mapping.object_type_qconfigs
+    new_object_type_dict = object_type_dict.copy()
+    for k, v in new_object_type_dict.items():
+        if k in module_to_qat_module_class:
+            object_type_dict[module_to_qat_module_class[k]] = v
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/quantize_handler.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/quantize_handler.py
new file mode 100644
index 0000000000000000000000000000000000000000..0bd8d7fe3a17439b46ad673a5aaf7eae28b7082f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/quantize_handler.py
@@ -0,0 +1,226 @@
+# mypy: allow-untyped-defs
+from abc import ABC
+from collections.abc import Callable
+
+import torch
+from torch.ao.quantization.backend_config import (
+    BackendConfig,
+    DTypeConfig,
+    ObservationType,
+)
+from torch.ao.quantization.utils import NodePattern, Pattern, QuantizerCls
+from torch.fx.graph import Node
+
+from .utils import all_node_args_have_no_tensors
+
+
+__all__ = [
+    "QuantizeHandler",
+    "BinaryOpQuantizeHandler",
+    "CatQuantizeHandler",
+    "ConvReluQuantizeHandler",
+    "LinearReLUQuantizeHandler",
+    "BatchNormQuantizeHandler",
+    "EmbeddingQuantizeHandler",
+    "RNNDynamicQuantizeHandler",
+    "DefaultNodeQuantizeHandler",
+    "FixedQParamsOpQuantizeHandler",
+    "CopyNodeQuantizeHandler",
+    "GeneralTensorShapeOpQuantizeHandler",
+    "CustomModuleQuantizeHandler",
+    "StandaloneModuleQuantizeHandler",
+]
+
+
+def _default_root_node_getter(node_pattern):
+    if node_pattern is None:
+        return node_pattern
+    while not isinstance(node_pattern, Node):
+        node_pattern = node_pattern[-1]
+    return node_pattern
+
+
+# Base Pattern Handler
+class QuantizeHandler(ABC):  # noqa: B024
+    """Base handler class for the quantizer patterns"""
+
+    def __init__(
+        self,
+        node_pattern: NodePattern,
+        modules: dict[str, torch.nn.Module],
+        root_node_getter: Callable | None = None,
+        is_custom_module=False,
+        is_standalone_module=False,
+    ):
+        """Records pattern information in __init__, which will be used
+        in convert
+        """
+        self.node_pattern = node_pattern
+        self.modules = modules
+        if root_node_getter is None:
+            root_node_getter = _default_root_node_getter
+        self.root_node = root_node_getter(node_pattern)
+        self.is_custom_module_ = is_custom_module
+        self.is_standalone_module_ = is_standalone_module
+        self.num_tensor_args = 0
+        # determine how many of the first two args are Tensors (versus scalars)
+        # this distinguishes things like "x + y" from "x + 2" or "2 + x"
+        if isinstance(self.root_node, Node):
+            cache_for_no_tensor_check: dict[Node, bool] = {}
+            for arg_idx in range(len(self.root_node.args)):
+                arg = self.root_node.args[arg_idx]
+                if isinstance(arg, Node) and (
+                    not all_node_args_have_no_tensors(
+                        arg, self.modules, cache_for_no_tensor_check
+                    )
+                ):
+                    self.num_tensor_args += 1
+
+    def is_general_tensor_value_op(self) -> bool:
+        """
+        Returns True if the operator works for both floating point and
+        quantized input, and does some computation based on the input Tensor,
+        or the ops that only re-arranges the Tensor values or query some metadata
+        about the Tensor
+        so we need to insert observer/fake_quant for the output of the
+        operator (same observer instance as input)
+        since the distribution of values is different for input and output
+        Tensors (for HistogramObserver) while they share the same quantization
+        parameters
+        Example operator: avgpool2d, reshape, transpose, maxpool2d
+        Example observed operator:
+        observer_0 - avgpool2d - observer_0 (same observer instance as input)
+        """
+        return False
+
+    def is_custom_module(self):
+        return self.is_custom_module_
+
+    def is_standalone_module(self):
+        return self.is_standalone_module_
+
+
+def _get_quantize_handler_cls(
+    observation_type: ObservationType,
+    dtype_configs: list[DTypeConfig],
+    num_tensor_args_to_observation_type: dict[int, ObservationType],
+) -> type[QuantizeHandler]:
+    """
+    Return a configurable QuantizeHandler that matches the given specifications from the backend.
+    """
+
+    class ConfigurableQuantizeHandler(QuantizeHandler):
+        def __init__(
+            self,
+            node_pattern: NodePattern,
+            modules: dict[str, torch.nn.Module],
+            root_node_getter: Callable | None = None,
+        ):
+            super().__init__(node_pattern, modules, root_node_getter)
+            if num_tensor_args_to_observation_type:
+                if self.num_tensor_args not in num_tensor_args_to_observation_type:
+                    raise AssertionError(
+                        f"Must provide observation_type config for tensor number {self.num_tensor_args}"
+                        f" in num_tensor_args_to_observation_type for {node_pattern}"
+                    )
+                self.observation_type = num_tensor_args_to_observation_type[
+                    self.num_tensor_args
+                ]
+            else:
+                self.observation_type = observation_type
+            self.dtype_configs = dtype_configs
+
+        def is_general_tensor_value_op(self) -> bool:
+            return (
+                self.observation_type
+                == ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT
+            )
+
+    return ConfigurableQuantizeHandler
+
+
+def _get_pattern_to_quantize_handlers(
+    backend_config: BackendConfig,
+) -> dict[Pattern, QuantizerCls]:
+    """
+    Note: Quantize handler is just a holder for some check methods like
+    (should_insert_observer_for_output), maybe this can be a enum as well,
+    we can refactor this after we convert the path for fbgemm/qnnpack fully to the
+    new path, this is not exposed to backend developers
+    """
+    pattern_to_quantize_handlers = {}
+    for pattern, config in backend_config._pattern_complex_format_to_config.items():
+        observation_type = config.observation_type
+        dtype_configs = config.dtype_configs
+        num_tensor_args_to_observation_type = (
+            config._num_tensor_args_to_observation_type
+        )
+        pattern_to_quantize_handlers[pattern] = _get_quantize_handler_cls(
+            observation_type, dtype_configs, num_tensor_args_to_observation_type
+        )
+    return pattern_to_quantize_handlers
+
+
+# TODO: remove this class, this is still exposed in torch.ao.quantization
+# but we should be able to break bc
+class BinaryOpQuantizeHandler(QuantizeHandler):
+    pass
+
+
+class CatQuantizeHandler(QuantizeHandler):
+    pass
+
+
+# TODO: remove this class
+class ConvReluQuantizeHandler(QuantizeHandler):
+    pass
+
+
+# TODO: remove this class
+class LinearReLUQuantizeHandler(QuantizeHandler):
+    pass
+
+
+# TODO: remove this class
+class BatchNormQuantizeHandler(QuantizeHandler):
+    pass
+
+
+# TODO: remove this class
+class EmbeddingQuantizeHandler(QuantizeHandler):
+    pass
+
+
+# TODO: remove this class
+class RNNDynamicQuantizeHandler(QuantizeHandler):
+    pass
+
+
+# TODO: remove this class
+class DefaultNodeQuantizeHandler(QuantizeHandler):
+    """Common quantized op, first input and first output will be quantized"""
+
+
+# TODO: remove this class
+class FixedQParamsOpQuantizeHandler(QuantizeHandler):
+    pass
+
+
+# TODO: remove
+class CopyNodeQuantizeHandler(QuantizeHandler):
+    pass
+
+
+# TODO: remove
+class GeneralTensorShapeOpQuantizeHandler(QuantizeHandler):
+    pass
+
+
+# TODO: not used, can be removed after torch.ao.quantization namespace is deprecated
+class CustomModuleQuantizeHandler(QuantizeHandler):
+    pass
+
+
+# TODO: not used, can be removed after torch.ao.quantization namespace is deprecated
+class StandaloneModuleQuantizeHandler(QuantizeHandler):
+    pass
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/tracer.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/tracer.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c1635936845a44ab895a3c6b0c5e07e9ec9951e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/tracer.py
@@ -0,0 +1,48 @@
+from collections.abc import Callable
+
+import torch
+from torch.ao.nn.intrinsic import _FusedModule
+from torch.fx._symbolic_trace import Tracer
+from torch.fx.proxy import Scope
+
+
+__all__ = [
+    "QuantizationTracer",
+]
+
+
+class ScopeContextManager(torch.fx.proxy.ScopeContextManager):
+    def __init__(
+        self, scope: Scope, current_module: torch.nn.Module, current_module_path: str
+    ):
+        super().__init__(scope, Scope(current_module_path, type(current_module)))
+
+
+class QuantizationTracer(Tracer):
+    def __init__(
+        self, skipped_module_names: list[str], skipped_module_classes: list[Callable]
+    ):
+        super().__init__()
+        self.skipped_module_names = skipped_module_names
+        self.skipped_module_classes = skipped_module_classes
+        # NB: initialized the module_type of top level module to None
+        # we are assuming people won't configure the model with the type of top level
+        # module here, since people can use "" for global config
+        # We can change this if there is a use case that configures
+        # qconfig using top level module type
+        self.scope = Scope("", None)
+        self.record_stack_traces = True
+
+    def is_leaf_module(self, m: torch.nn.Module, module_qualified_name: str) -> bool:
+        return (
+            (
+                (
+                    m.__module__.startswith("torch.nn")
+                    or m.__module__.startswith("torch.ao.nn")
+                )
+                and not isinstance(m, torch.nn.Sequential)
+            )
+            or module_qualified_name in self.skipped_module_names
+            or type(m) in self.skipped_module_classes
+            or isinstance(m, _FusedModule)
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a46d2057c5480ae036bbb847cf3d9bb185b29ce
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/fx/utils.py
@@ -0,0 +1,997 @@
+# mypy: allow-untyped-defs
+import copy
+import functools
+import operator
+import warnings
+from collections import namedtuple
+from collections.abc import Callable
+from dataclasses import dataclass
+from typing import Any
+
+import torch
+import torch.nn as nn
+from torch.ao.quantization import QConfigAny, QuantType
+from torch.ao.quantization.backend_config import DTypeWithConstraints
+from torch.ao.quantization.fake_quantize import (
+    FakeQuantizeBase,
+    FixedQParamsFakeQuantize,
+)
+from torch.ao.quantization.observer import (
+    _is_activation_post_process,
+    FixedQParamsObserver,
+    ObserverBase,
+)
+from torch.ao.quantization.qconfig import (
+    float16_dynamic_qconfig,
+    float16_static_qconfig,
+    qconfig_equals,
+)
+from torch.ao.quantization.qconfig_mapping import QConfigMapping
+from torch.ao.quantization.stubs import DeQuantStub
+from torch.ao.quantization.utils import (
+    _assert_and_get_unique_device,
+    activation_is_statically_quantized,
+)
+from torch.fx import GraphModule, map_arg
+from torch.fx.graph import Graph, Node
+
+# importing the lib so that the quantized_decomposed ops are registered
+from ._decomposed import quantized_decomposed_lib  # noqa: F401
+from .custom_config import PrepareCustomConfig
+
+
+# TODO: revisit this list. Many helper methods shouldn't be public
+__all__ = [
+    "all_node_args_except_first",
+    "all_node_args_have_no_tensors",
+    "assert_and_get_unique_device",
+    "collect_producer_nodes",
+    "create_getattr_from_value",
+    "create_node_from_old_node_preserve_meta",
+    "EMPTY_ARG_DICT",
+    "get_custom_module_class_keys",
+    "get_linear_prepack_op_for_dtype",
+    "get_new_attr_name_with_prefix",
+    "get_non_observable_arg_indexes_and_types",
+    "get_qconv_prepack_op",
+    "get_skipped_module_name_and_classes",
+    "graph_module_from_producer_nodes",
+    "maybe_get_next_module",
+    "NodeInfo",
+    "node_arg_is_bias",
+    "node_arg_is_weight",
+    "NON_OBSERVABLE_ARG_DICT",
+    "NON_QUANTIZABLE_WEIGHT_OPS",
+    "return_arg_list",
+    "ObservedGraphModuleAttrs",
+]
+
+NON_QUANTIZABLE_WEIGHT_OPS = {
+    torch.nn.functional.layer_norm,
+    torch.nn.functional.group_norm,
+    torch.nn.functional.instance_norm,
+}
+
+
+@dataclass
+class ObservedGraphModuleAttrs:
+    node_name_to_qconfig: dict[str, QConfigAny]
+    node_name_to_scope: dict[str, tuple[str, type]]
+    prepare_custom_config: PrepareCustomConfig
+    equalization_node_name_to_qconfig: dict[str, Any]
+    qconfig_mapping: QConfigMapping
+    is_qat: bool
+    observed_node_names: set[str]
+    is_observed_standalone_module: bool = False
+    standalone_module_input_quantized_idxs: list[int] | None = None
+    standalone_module_output_quantized_idxs: list[int] | None = None
+
+
+def node_arg_is_weight(node: Node, arg: Any) -> bool:
+    """Returns if node arg is weight"""
+    weight_index = None
+    if "target_dtype_info" in node.meta:
+        weight_index = node.meta["target_dtype_info"].get("weight_index", None)
+    if (
+        weight_index is not None
+        and weight_index < len(node.args)
+        and node.args[weight_index] is arg
+    ):
+        return True
+    return node.kwargs.get("weight") is arg
+
+
+def node_arg_is_bias(node: Node, arg: Any) -> bool:
+    """Returns if node arg is bias"""
+    bias_index = None
+    if "target_dtype_info" in node.meta:
+        bias_index = node.meta["target_dtype_info"].get("bias_index", None)
+    if (
+        bias_index is not None
+        and bias_index < len(node.args)
+        and node.args[bias_index] is arg
+    ):
+        return True
+    return node.kwargs.get("bias") is arg
+
+
+def get_custom_module_class_keys(
+    custom_module_mapping: dict[QuantType, dict[type, type]],
+) -> list[Any]:
+    r"""Get all the unique custom module keys in the custom config dict
+    e.g.
+    Input:
+    {
+        QuantType.STATIC: {
+            CustomModule1: ObservedCustomModule
+        },
+        QuantType.DYNAMIC: {
+            CustomModule2: DynamicObservedCustomModule
+        },
+        QuantType.WEIGHT_ONLY: {
+            CustomModule3: WeightOnlyObservedCustomModule
+        },
+    }
+
+    Output:
+    # extract the keys across all inner STATIC, DYNAMIC, and WEIGHT_ONLY dicts
+    [CustomModule1, CustomModule2, CustomModule3]
+    """
+    # using set to dedup
+    float_custom_module_classes: set[Any] = set()
+    for quant_mode in [QuantType.STATIC, QuantType.DYNAMIC, QuantType.WEIGHT_ONLY]:
+        quant_mode_custom_module_config = custom_module_mapping.get(quant_mode, {})
+        quant_mode_custom_module_classes = set(quant_mode_custom_module_config.keys())
+        float_custom_module_classes |= quant_mode_custom_module_classes
+    return list(float_custom_module_classes)
+
+
+def get_linear_prepack_op_for_dtype(dtype):
+    if dtype == torch.float16:
+        return torch.ops.quantized.linear_prepack_fp16
+    elif dtype == torch.qint8:
+        return torch.ops.quantized.linear_prepack
+    else:
+        raise Exception("can't get linear prepack op for dtype:", dtype)  # noqa: TRY002
+
+
+def get_qconv_prepack_op(conv_op: Callable) -> Callable:
+    prepack_ops = {
+        torch.nn.functional.conv1d: torch.ops.quantized.conv1d_prepack,
+        torch.nn.functional.conv2d: torch.ops.quantized.conv2d_prepack,
+        torch.nn.functional.conv3d: torch.ops.quantized.conv3d_prepack,
+        torch.nn.functional.conv_transpose1d: torch.ops.quantized.conv_transpose1d_prepack,
+        torch.nn.functional.conv_transpose2d: torch.ops.quantized.conv_transpose2d_prepack,
+        torch.nn.functional.conv_transpose3d: torch.ops.quantized.conv_transpose3d_prepack,
+    }
+    prepack_op = prepack_ops.get(conv_op)
+    if prepack_op is None:
+        raise AssertionError(f"Didn't find prepack op for {conv_op}")
+    return prepack_op
+
+
+# Returns a function that can get a new attribute name for module with given
+# prefix, for example,
+# >> get_new_observer_name = get_new_attr_name_with_prefix('_observer')
+# >> new_name = get_new_observer_name(module)
+# new_name will be an unused attribute name on module, e.g. `_observer_1`
+def get_new_attr_name_with_prefix(prefix: str) -> Callable:
+    prefix = prefix.replace(".", "_")
+
+    def get_new_attr_name(module: torch.nn.Module):
+        def get_attr_name(i: int):
+            return prefix + str(i)
+
+        i = 0
+        attr_name = get_attr_name(i)
+        while hasattr(module, attr_name):
+            i += 1
+            attr_name = get_attr_name(i)
+        return attr_name
+
+    return get_new_attr_name
+
+
+def collect_producer_nodes(node: Node) -> list[Node] | None:
+    r"""Starting from a target node, trace back until we hit input or
+    getattr node. This is used to extract the chain of operators
+    starting from getattr to the target node, for example::
+
+        def forward(self, x):
+            observed = self.observer(self.weight)
+            return F.linear(x, observed)
+
+    collect_producer_nodes(observed) will either return a list of nodes that
+    produces the observed node or None if we can't extract a self contained
+    graph without free variables(inputs of the forward function).
+    """
+    nodes = [node]
+    frontier = [node]
+    while frontier:
+        node = frontier.pop()
+        all_args = list(node.args) + list(node.kwargs.values())
+        for arg in all_args:
+            if not isinstance(arg, Node):
+                continue
+            if arg.op == "placeholder":
+                # hit input, can't fold in this case
+                return None
+            nodes.append(arg)
+            if not (arg.op == "call_function" and arg.target is getattr):
+                frontier.append(arg)
+    return nodes
+
+
+def graph_module_from_producer_nodes(
+    root: GraphModule, producer_nodes: list[Node]
+) -> GraphModule:
+    r"""Construct a graph module from extracted producer nodes
+    from `collect_producer_nodes` function
+    Args:
+      root: the root module for the original graph
+      producer_nodes: a list of nodes we use to construct the graph
+    Return:
+      A graph module constructed from the producer nodes
+    """
+    if len(producer_nodes) == 0:
+        raise AssertionError("list of producer nodes can not be empty")
+    # since we traced back from node to getattr
+    producer_nodes.reverse()
+    graph = Graph()
+    env: dict[Any, Any] = {}
+
+    def load_arg(a):
+        return map_arg(a, lambda node: env[node])
+
+    for producer_node in producer_nodes:
+        env[producer_node] = graph.node_copy(producer_node, load_arg)
+    graph.output(load_arg(producer_nodes[-1]))
+    graph_module = GraphModule(root, graph)
+    return graph_module
+
+
+# TODO: delete
+@functools.cache
+def assert_and_get_unique_device(module: torch.nn.Module) -> Any:
+    """
+    Returns the unique device for a module, or None if no device is found.
+    Throws an error if multiple devices are detected.
+    """
+    return _assert_and_get_unique_device(module)
+
+
+def create_getattr_from_value(
+    module: torch.nn.Module,
+    graph: Graph,
+    prefix: str,
+    value: Any,
+    device: torch.device | None = None,
+) -> Node:
+    """
+    Given a value of any type, creates a getattr node corresponding to the value and
+    registers the value as a buffer to the module.
+    """
+    get_new_attr_name = get_new_attr_name_with_prefix(prefix)
+    attr_name = get_new_attr_name(module)
+    if device is None:
+        device = assert_and_get_unique_device(module)
+    new_value = (
+        value.detach().clone()
+        if isinstance(value, torch.Tensor)
+        else torch.tensor(value, device=device)
+    )
+    module.register_buffer(attr_name, new_value)
+    # Create get_attr with value
+    attr_node = graph.create_node("get_attr", attr_name)
+    return attr_node
+
+
+def all_node_args_have_no_tensors(
+    node: Node, modules: dict[str, torch.nn.Module], cache: dict[Node, bool]
+) -> bool:
+    """
+    If we know for sure that all of this node's args have no
+    tensors (are primitives), return True.  If we either
+    find a tensor or are not sure, return False. Note: this
+    function is not exact.
+    """
+    if cache and node in cache:
+        return cache[node]
+
+    result = False  # will be overwritten
+    if not isinstance(node, Node):
+        result = True
+    elif node.op == "placeholder":
+        result = False
+    elif node.op == "call_module":
+        if not isinstance(node.target, str):
+            raise AssertionError("node.target must be a string for call_module nodes")
+        if _is_activation_post_process(modules[node.target]):
+            result = all_node_args_have_no_tensors(node.args[0], modules, cache)  # type: ignore[arg-type]
+    elif node.op == "call_module":
+        result = False
+    elif node.op == "call_function" and node.target is operator.getitem:
+        result = all_node_args_have_no_tensors(node.args[0], modules, cache)  # type: ignore[arg-type]
+    elif node.op == "get_attr":
+        result = False
+    elif node.target is getattr and node.args[1] in ["ndim", "shape"]:
+        # x1 = x0.ndim
+        result = True
+    elif node.op == "call_method" and node.target == "size":
+        # x1 = x0.size(0)
+        result = True
+    else:
+        found_one_tensor = False
+        for arg in node.args:
+            if isinstance(arg, list):
+                for list_el in arg:
+                    if isinstance(list_el, Node):
+                        this_list_el_args_have_no_tensors = (
+                            all_node_args_have_no_tensors(list_el, modules, cache)
+                        )
+                        found_one_tensor = found_one_tensor or (
+                            not this_list_el_args_have_no_tensors
+                        )
+                        # If found_one_tensor is True, there is no point in
+                        # recursing further as the end result will always
+                        # be True.
+                        # TODO(future PR): remove this entire function  and
+                        # change to dtype inference without recursion.
+                        if found_one_tensor:
+                            result = not found_one_tensor
+                            if cache:
+                                cache[node] = result
+                            return result
+            elif isinstance(arg, int):
+                pass
+            else:
+                if isinstance(arg, Node):
+                    this_arg_args_have_no_tensors = all_node_args_have_no_tensors(
+                        arg, modules, cache
+                    )
+                    found_one_tensor = found_one_tensor or (
+                        not this_arg_args_have_no_tensors
+                    )
+                    # If found_one_tensor is True, there is no point in
+                    # recursing further as the end result will always
+                    # be True.
+                    # TODO(future PR): remove this entire function  and
+                    # change to dtype inference without recursion.
+                    if found_one_tensor:
+                        result = not found_one_tensor
+                        if cache:
+                            cache[node] = result
+                        return result
+                else:
+                    found_one_tensor = True
+            result = not found_one_tensor
+    if cache:
+        cache[node] = result
+    return result
+
+
+def all_node_args_except_first(node: Node) -> list[int]:
+    """
+    Returns all node arg indices after first
+    """
+    return list(range(1, len(node.args)))
+
+
+def return_arg_list(arg_indices: list[int]) -> Callable[[Node], list[int]]:
+    """
+    Constructs a function that takes a node as arg and returns the arg_indices
+    that are valid for node.args
+    """
+
+    def arg_indices_func(node: Node) -> list[int]:
+        return [i for i in arg_indices if i < len(node.args)]
+
+    return arg_indices_func
+
+
+NodeInfo = namedtuple("NodeInfo", "op target")
+
+# this dict identifies which indices of a node are non tensors
+# so that they can be propagated correctly since inserting observers
+# for them would cause errors
+
+NON_OBSERVABLE_ARG_DICT: dict[
+    NodeInfo, dict[type | torch.dtype, Callable[[Node], list[int]]]
+] = {
+    NodeInfo("call_method", "masked_fill"): {
+        torch.bool: return_arg_list([1]),
+        float: return_arg_list([2]),
+    },
+    NodeInfo("call_method", "permute"): {int: all_node_args_except_first},
+    NodeInfo("call_method", "repeat"): {int: all_node_args_except_first},
+    NodeInfo("call_method", "reshape"): {int: all_node_args_except_first},
+    NodeInfo("call_method", "size"): {int: return_arg_list([1])},
+    NodeInfo("call_method", "transpose"): {int: all_node_args_except_first},
+    NodeInfo("call_method", torch.transpose): {int: all_node_args_except_first},
+    NodeInfo("call_method", "unsqueeze"): {int: return_arg_list([1])},
+    NodeInfo("call_method", "unsqueeze_"): {int: return_arg_list([1])},
+    NodeInfo("call_method", torch.unsqueeze): {int: return_arg_list([1])},
+    NodeInfo("call_method", "view"): {int: all_node_args_except_first},
+}
+
+EMPTY_ARG_DICT: dict[type | torch.dtype, Callable[[Node], list[int]]] = {}
+
+
+def get_non_observable_arg_indexes_and_types(
+    node: Node,
+) -> dict[type | torch.dtype, Callable[[Node], list[int]]]:
+    """
+    Returns a dict with of non float tensor types as keys and values which correspond to a
+    function to retrieve the list (which takes the node as an argument)
+    """
+    info = NodeInfo(node.op, node.target)
+
+    return NON_OBSERVABLE_ARG_DICT.get(info, EMPTY_ARG_DICT)
+
+
+def maybe_get_next_module(
+    node: Node,
+    modules: dict[str, nn.Module],
+    target_module_type: type[nn.Module] | None = None,
+    target_functional_type: Any = None,
+) -> Node | None:
+    """Gets the next module that matches what is needed in
+    is_target_module_type if it exists
+
+    Args:
+        node: The node whose users we want to look at
+        target_module_type: Module type that we want to check
+        target_functional_type: Functional type that we want to check
+    """
+
+    for user in node.users:
+        if (
+            user.op == "call_module"
+            and target_module_type is not None
+            and isinstance(modules[str(user.target)], target_module_type)
+        ):
+            return user
+        elif (
+            user.op == "call_function"
+            and target_functional_type is not None
+            and user.target == target_functional_type
+        ):
+            return user
+
+    return None
+
+
+def create_node_from_old_node_preserve_meta(
+    quantized_graph: Graph,
+    create_node_args: tuple[Any, ...],
+    old_node: Node,
+) -> Node:
+    """
+    Creates `new_node` and copies the necessary metadata to it from `old_node`.
+    """
+    new_node = quantized_graph.create_node(*create_node_args)
+    new_node.stack_trace = old_node.stack_trace
+    return new_node
+
+
+def get_skipped_module_name_and_classes(
+    prepare_custom_config: PrepareCustomConfig, is_standalone_module: bool
+) -> tuple[list[str], list[type[Any]]]:
+    skipped_module_names = copy.copy(prepare_custom_config.non_traceable_module_names)
+    skipped_module_classes = copy.copy(
+        prepare_custom_config.non_traceable_module_classes
+    )
+    if not is_standalone_module:
+        # standalone module and custom module config are applied in top level module
+        skipped_module_names += list(
+            prepare_custom_config.standalone_module_names.keys()
+        )
+        skipped_module_classes += list(
+            prepare_custom_config.standalone_module_classes.keys()
+        )
+        skipped_module_classes += get_custom_module_class_keys(
+            prepare_custom_config.float_to_observed_mapping
+        )
+
+    return skipped_module_names, skipped_module_classes
+
+
+def _is_custom_module_lstm(
+    node: Node,
+    named_modules: dict[str, torch.nn.Module],
+    qconfig: QConfigAny = None,
+    # QuantizeHandler, but we cannot include the type here due to circular imports
+    qhandler: Any | None = None,
+) -> bool:
+    """
+    Return whether this refers to the custom module LSTM flow.
+    """
+    mod = _get_module(node, named_modules)
+    if qconfig is not None and qhandler is not None:
+        if not isinstance(
+            qhandler, torch.ao.quantization.fx.quantize_handler.QuantizeHandler
+        ):  # type: ignore[attr-defined]
+            raise AssertionError("qhandler must be a QuantizeHandler when provided")
+        return (
+            isinstance(mod, torch.nn.LSTM)
+            and activation_is_statically_quantized(qconfig)
+            and qhandler.is_custom_module()
+        )
+    else:
+        return isinstance(mod, torch.ao.nn.quantizable.LSTM)
+
+
+def _is_custom_module_mha(
+    node: Node,
+    named_modules: dict[str, torch.nn.Module],
+    qconfig: QConfigAny = None,
+    # QuantizeHandler, but we cannot include the type here due to circular imports
+    qhandler: Any | None = None,
+) -> bool:
+    """
+    Return whether this refers to the custom module MultiheadAttention flow.
+    """
+    mod = _get_module(node, named_modules)
+    if qconfig is not None and qhandler is not None:
+        if not isinstance(
+            qhandler, torch.ao.quantization.fx.quantize_handler.QuantizeHandler
+        ):  # type: ignore[attr-defined]
+            raise AssertionError("qhandler must be a QuantizeHandler when provided")
+        return (
+            isinstance(mod, torch.nn.MultiheadAttention)
+            and activation_is_statically_quantized(qconfig)
+            and qhandler.is_custom_module()
+        )
+    else:
+        return isinstance(mod, torch.ao.nn.quantizable.MultiheadAttention)
+
+
+def _get_module(
+    node: Node, named_modules: dict[str, torch.nn.Module]
+) -> torch.nn.Module | None:
+    """
+    If `node` refers to a call_module node, return the module, else None.
+    """
+    if node.op == "call_module" and str(node.target) in named_modules:
+        return named_modules[str(node.target)]
+    else:
+        return None
+
+
+def _insert_dequant_stub(
+    node: Node,
+    model: torch.nn.Module,
+    named_modules: dict[str, torch.nn.Module],
+    graph: Graph,
+) -> Node:
+    """
+    Attach a `DeQuantStub` to the model and create a node that calls this
+    `DeQuantStub` on the output of `node`, similar to how observers are inserted.
+    """
+    prefix = "dequant_stub_"
+    get_new_dequant_stub_name = get_new_attr_name_with_prefix(prefix)
+    dequant_stub_name = get_new_dequant_stub_name(model)
+    dequant_stub = DeQuantStub()
+    setattr(model, dequant_stub_name, dequant_stub)
+    named_modules[dequant_stub_name] = dequant_stub
+    with graph.inserting_after(node):
+        return graph.call_module(dequant_stub_name, (node,))
+
+
+def _insert_dequant_stubs_for_custom_module_lstm_output(
+    node: Node,
+    model: torch.nn.Module,
+    named_modules: dict[str, torch.nn.Module],
+    graph: Graph,
+) -> Node:
+    """
+    Insert DeQuantStubs after each internal output node of custom module LSTM.
+
+    Custom module LSTM outputs are nested tuples of the structure (output, (hidden0, hidden1)),
+    Since we cannot dequantize a tuple as a whole, we must first break down the tuple into its
+    components through `getitem`. This function transforms the graph as follows:
+
+      (1) Split the LSTM node into (output, (hidden0, hidden1))
+      (2) Insert a DeQuantStub after each internal node
+      (3) Recombine the DeQuantStubs into the same structure as before
+      (4) Reroute all consumers of the original LSTM node and its sub-nodes
+          (e.g. lstm[0])
+
+    Before:
+                   lstm_output
+                        |
+                        v
+                  original_user(s)
+    After:
+                   lstm_output
+                  /           \\
+                 /  (getitem)  \\
+                /               \\
+               v                 v
+             output            hidden
+               |               /   \\
+         (DeQuantStub)        (getitem)
+               |             /       \\
+               v            v         v
+           output_dq     hidden0    hidden1
+               |            |         |
+               |    (DeQuantStub) (DeQuantStub)
+               |            |         |
+               |            v         v
+               |      hidden0_dq  hidden1_dq
+               |            \\       /
+               |              (tuple)
+               |              \\   /
+               |               v  v
+               |             hidden_dq
+               \\               /
+                \\   (tuple)   /
+                 v            v
+                 lstm_output_dq
+                       |
+                       v
+                original_user(s)
+
+    For step (4), reroute all users of the original LSTM node(s) as follows:
+      lstm_output -> lstm_output_dq
+      lstm_output[0] -> output_dq
+      lstm_output[1] -> hidden_dq
+      lstm_output[1][0] -> hidden0_dq
+      lstm_output[1][1] -> hidden1_dq
+
+    Return the node `lstm_output_dq`.
+    """
+    # (1) Split the LSTM node into (output, (hidden0, hidden1))
+    # (2) Insert a DeQuantStub after each internal node
+    with graph.inserting_after(node):
+        output = graph.call_function(operator.getitem, (node, 0))
+        output_dq = _insert_dequant_stub(output, model, named_modules, graph)
+    with graph.inserting_after(output_dq):
+        hidden = graph.call_function(operator.getitem, (node, 1))
+    with graph.inserting_after(hidden):
+        hidden0 = graph.call_function(operator.getitem, (hidden, 0))
+        hidden0_dq = _insert_dequant_stub(hidden0, model, named_modules, graph)
+    with graph.inserting_after(hidden0_dq):
+        hidden1 = graph.call_function(operator.getitem, (hidden, 1))
+        hidden1_dq = _insert_dequant_stub(hidden1, model, named_modules, graph)
+
+    # (3) Recombine the DeQuantStubs into the same structure as before
+    with graph.inserting_after(hidden1_dq):
+        hidden_dq = graph.call_function(tuple, ([hidden0_dq, hidden1_dq],))
+    with graph.inserting_after(hidden_dq):
+        lstm_output_dq = graph.call_function(tuple, ([output_dq, hidden_dq],))
+
+    # (4) Reroute all consumers of the original LSTM node and its sub-nodes
+    for user in list(node.users.keys()):
+        if user != output and user != hidden:
+            user.replace_input_with(node, lstm_output_dq)
+    # The getitem and tuple nodes we added here may interfere with reference quantized
+    # pattern matching, so we need to redirect the consumers of internal nodes to the
+    # corresponding nodes with DeQuantStubs (e.g. lstm_output_dq[0] -> output_dq) attached,
+    # in order to preserve reference patterns like "dequantize - consumer - quantize".
+    _reroute_tuple_getitem_pattern(graph)
+    return lstm_output_dq
+
+
+def _maybe_get_custom_module_lstm_from_node_arg(
+    arg: Node,
+    named_modules: dict[str, torch.nn.Module],
+) -> Node | None:
+    """
+    Given an argument of a node, if the argument refers to the path through which the node
+    is a consumer of custom module LSTM, return the custom module LSTM node, or None otherwise.
+
+    This is used to determine whether a node is a consumer of custom module LSTM, and, if so,
+    skip inserting input observers for this node. This is because custom module LSTM produces
+    quantized outputs, so inserting an input observer for the consumer of custom module LSTM
+    would unnecessarily quantize the outputs again.
+
+      lstm -> consumer
+
+    In practice, however, custom module LSTM outputs a tuple (output, (hidden0, hidden1)) with
+    DeQuantStubs attached to each internal node (see `_insert_dequant_stubs_for_custom_module_lstm_output`).
+    This tuple can be consumed in one of four ways:
+
+      lstm -> getitem -> DeQuantStub -> consumer                       # consume lstm[0]
+      lstm -> getitem -> getitem -> DeQuantStub -> tuple -> consumer   # consume lstm[1]
+      lstm -> getitem -> getitem -> DeQuantStub -> consumer            # consume lstm[1][0] or lstm[1][1]
+      lstm -> getitem -> DeQuantStub -> tuple -> consumer              # consume lstm
+
+    Thus, we must match against the above patterns instead of simply checking the parent node
+    to determine whether this node is a consumer of a custom module LSTM.
+    """
+
+    def match_dq(a):
+        return isinstance(_get_module(a, named_modules), DeQuantStub)
+
+    def match_lstm(a):
+        return _is_custom_module_lstm(a, named_modules)
+
+    def match_getitem(a):
+        return a.op == "call_function" and a.target is operator.getitem
+
+    def match_tuple(a):
+        return a.op == "call_function" and a.target is tuple
+
+    def _match_pattern(match_pattern: list[Callable]) -> Node | None:
+        """
+        Traverse up the graph and match the args one by one.
+        If there is a match, return the last matched node, or None otherwise.
+        """
+        a = arg
+        for i, match in enumerate(match_pattern):
+            if not match(a):
+                return None
+            # Match next arg, for tuple the arg is a tuple of a list, e.g. ([dq_1, other_node],)
+            if i < len(match_pattern) - 1:
+                if match is match_tuple:
+                    a = a.args[0][0]  # type: ignore[assignment,index]
+                else:
+                    a = a.args[0]  # type: ignore[assignment]
+        # pyrefly: ignore [bad-return]
+        return a
+
+    all_match_patterns = [
+        [match_dq, match_getitem, match_lstm],
+        [match_tuple, match_dq, match_getitem, match_getitem, match_lstm],
+        [match_dq, match_getitem, match_getitem, match_lstm],
+        [match_tuple, match_dq, match_getitem, match_lstm],
+    ]
+
+    for p in all_match_patterns:
+        matched_node = _match_pattern(p)
+        if matched_node is not None:
+            return matched_node
+    return None
+
+
+def _reroute_tuple_getitem_pattern(graph: Graph):
+    """
+    Search for patterns where N consecutive `tuple` call_function nodes are followed by
+    N consecutive `getitem` call_function nodes that are "reverses" of the `tuple` nodes.
+    If we find this pattern, reroute the consumers of the last `getitem` to skip these
+    N `tuple` and `getitem` nodes.
+
+    Before:
+
+        a   b     c
+        |   \\   /
+        \\   tuple
+         \\   /
+          tuple
+            |
+        getitem(1)
+            |
+        getitem(0)
+            |
+            d
+
+    After:
+
+        b
+        |
+        d
+    """
+
+    def find_patterns(
+        node: Node,
+        index_stack: list[int],
+        current_pattern: list[Node],
+        matched_patterns: list[list[Node]],
+        seen: set[tuple[Node, tuple[int, ...]]],
+    ):
+        """
+        Traverse the graph recursively to match for the N-tuple - N-getitem patterns,
+        starting at the given node.
+
+        We use a stack to keep track of the expected `getitem` indices, since these are
+        reversed from the `tuple` indices. In the above example, the stack after
+        (b -> tuple -> tuple) will be [0, 1], which will be popped by getitem(1) first
+        and then by getitem(0).
+
+        TODO: traverse upwards from the output and handle the case when tuple is not a
+        separate node, e.g. graph.call_function(operator.getitem, args=(a, (b, c)))
+        """
+        if len(index_stack) == 0 and len(current_pattern) > 0:
+            matched_patterns.append(copy.copy(current_pattern))
+            current_pattern.clear()
+
+        # Avoid duplicating work
+        state = (node, tuple(index_stack))
+        if state in seen:
+            return
+        seen.add(state)
+
+        # Iterate through users of this node to find tuple/getitem nodes to match
+        for user in node.users:
+            if user.op == "call_function" and user.target is tuple:
+                for i, user_arg in enumerate(user.args[0]):  # type: ignore[arg-type]
+                    if user_arg == node:
+                        index_stack.append(i)
+                        current_pattern.append(user)
+                        find_patterns(
+                            user, index_stack, current_pattern, matched_patterns, seen
+                        )
+            elif user.op == "call_function" and user.target is operator.getitem:
+                if len(index_stack) > 0:
+                    if user.args[1] == index_stack[-1]:
+                        index_stack.pop()
+                        current_pattern.append(user)
+                        find_patterns(
+                            user, index_stack, current_pattern, matched_patterns, seen
+                        )
+        return matched_patterns
+
+    # Collect all matched patterns
+    matched_patterns: list[list[Node]] = []
+    seen: set[tuple[Node, tuple[int, ...]]] = set()  # (node, index_stack)
+    for node in graph.nodes:
+        find_patterns(node, [], [], matched_patterns, seen)
+
+    # For each pattern, redirect all consumers of the last getitem node to the correct input
+    # of the first tuple node
+    for pattern in matched_patterns:
+        first_tuple = pattern[0]
+        last_getitem = pattern[-1]
+        if not (first_tuple.op == "call_function" and first_tuple.target is tuple):
+            raise AssertionError(
+                "first tuple node must be a call_function with target tuple"
+            )
+        if not (
+            last_getitem.op == "call_function"
+            and last_getitem.target is operator.getitem
+        ):
+            raise AssertionError(
+                "last getitem node must be a call_function with target operator.getitem"
+            )
+        last_getitem_index = last_getitem.args[1]
+        new_input = first_tuple.args[0][last_getitem_index]  # type: ignore[index]
+        for user in list(last_getitem.users.keys()):
+            user.replace_input_with(last_getitem, new_input)  # type: ignore[arg-type]
+
+
+def _get_observer_from_activation_post_process(
+    activation_post_process: ObserverBase | FakeQuantizeBase,
+) -> ObserverBase:
+    """
+    If `activation_post_process` is an observer, return the observer.
+    If `activation_post_process` is a fake quantize, return the internal observer.
+    """
+    if isinstance(activation_post_process, ObserverBase):
+        return activation_post_process
+    else:
+        if not isinstance(activation_post_process, FakeQuantizeBase):
+            raise AssertionError(
+                "activation_post_process must be an ObserverBase or FakeQuantizeBase"
+            )
+        return activation_post_process.activation_post_process  # type: ignore[return-value]
+
+
+def _qconfig_satisfies_dtype_config_constraints(
+    qconfig: QConfigAny,
+    dtype_with_constraints: DTypeWithConstraints,
+    is_activation: bool = True,
+) -> bool:
+    """
+    Return whether `qconfig` satisfies the following constraints from the backend,
+    specified through the activation and weight DTypeWithConstraints.
+
+        1. QConfig specified a quantization range that falls within the backend's, if any
+        2. QConfig specified a min scale value that is >= the backend's, if any
+        3. QConfig specified a FixedQParamsObserver or FixedQParamsFakeQuantize that has
+           scale and zero point that match the backend's, if any
+
+    If `is_activation` is True, we check `qconfig.activation`, else we check `qconfig.weight`.
+    If `qconfig` or `dtype_with_constraints.dtype` is None, or the dtypes do not match, return True.
+    """
+
+    # TODO: log warnings only when the user enabled a debug flag
+    def _activation_post_process_satisfies_dtype_config_constraints(
+        activation_post_process: ObserverBase | FakeQuantizeBase,
+        dtype_with_constraints: DTypeWithConstraints,
+        debug_string: str,
+    ) -> bool:
+        observer = _get_observer_from_activation_post_process(activation_post_process)
+        app_quant_min = getattr(observer, "quant_min", None)
+        app_quant_max = getattr(observer, "quant_max", None)
+        # TODO: for now, just use the existing eps value as scale_min. In the future, we should
+        # resolve the differences between the two, either by renaming eps or some other way
+        app_scale_min = getattr(observer, "eps", None)
+        backend_quant_min = dtype_with_constraints.quant_min_lower_bound
+        backend_quant_max = dtype_with_constraints.quant_max_upper_bound
+        backend_scale_min = dtype_with_constraints.scale_min_lower_bound
+        backend_scale_exact_match = dtype_with_constraints.scale_exact_match
+        backend_zero_point_exact_match = dtype_with_constraints.zero_point_exact_match
+        # check quantization ranges
+        if backend_quant_min is not None and backend_quant_max is not None:
+            if app_quant_min is None or app_quant_max is None:
+                warnings.warn(
+                    f"QConfig {debug_string} must specify 'quant_min' and 'quant_max', ignoring {qconfig}",
+                    stacklevel=2,
+                )
+                return False
+            elif app_quant_min < backend_quant_min or app_quant_max > backend_quant_max:
+                warnings.warn(
+                    f"QConfig {debug_string} quantization range must fall within the backend's:\n"
+                    f"QConfig range = ({app_quant_min}, {app_quant_max}), "
+                    f"BackendConfig range = ({backend_quant_min}, {backend_quant_max}), "
+                    f"ignoring {qconfig}",
+                    stacklevel=2,
+                )
+                return False
+        # check scale min
+        if backend_scale_min is not None:
+            if app_scale_min is None:
+                warnings.warn(
+                    f"QConfig {debug_string} must specify 'eps', ignoring {qconfig}",
+                    stacklevel=2,
+                )
+                return False
+            if app_scale_min < backend_scale_min:
+                warnings.warn(
+                    f"QConfig {debug_string} eps ({app_scale_min}) must be greater than or equal to "
+                    f"the backend's min scale value ({backend_scale_min}), ignoring {qconfig}",
+                    stacklevel=2,
+                )
+                return False
+        # check fixed scale and zero point
+        if (
+            backend_scale_exact_match is not None
+            and backend_zero_point_exact_match is not None
+        ):
+            # For tests only, accept the following qconfigs for now
+            # TODO: handle fp16 qconfigs properly
+            for accepted_qconfig in [float16_static_qconfig, float16_dynamic_qconfig]:
+                if qconfig_equals(qconfig, accepted_qconfig):
+                    return True
+            suggestion_str = (
+                "Please use torch.ao.quantization.get_default_qconfig_mapping or "
+                "torch.ao.quantization.get_default_qat_qconfig_mapping. Example:\n"
+                '    qconfig_mapping = get_default_qconfig_mapping("fbgemm")\n'
+                "    model = prepare_fx(model, qconfig_mapping, example_inputs)"
+            )
+            if not isinstance(
+                activation_post_process, FixedQParamsObserver
+            ) and not isinstance(activation_post_process, FixedQParamsFakeQuantize):
+                warnings.warn(
+                    f"QConfig must specify a FixedQParamsObserver or a FixedQParamsFakeQuantize "
+                    f"for fixed qparams ops, ignoring {qconfig}.\n{suggestion_str}",
+                    stacklevel=2,
+                )
+                return False
+            if (
+                observer.scale != backend_scale_exact_match
+                or observer.zero_point != backend_zero_point_exact_match
+            ):
+                warnings.warn(
+                    f"QConfig fixed scale ({observer.scale}) and zero point ({observer.zero_point}) "
+                    f"do not match the backend's ({backend_scale_exact_match} and {backend_zero_point_exact_match}), "
+                    f"ignoring {qconfig}.\n{suggestion_str}",
+                    stacklevel=2,
+                )
+                return False
+        return True
+
+    if qconfig is None or dtype_with_constraints.dtype is None:
+        return True
+
+    activation_post_process_ctr = (
+        qconfig.activation if is_activation else qconfig.weight
+    )
+    debug_string = "activation" if is_activation else "weight"
+    satisfies_constraints = True
+    if activation_post_process_ctr is not None:
+        activation_post_process = activation_post_process_ctr()
+        if not _is_activation_post_process(activation_post_process):
+            raise AssertionError(
+                "activation_post_process must be an activation post process"
+            )
+        # If dtypes don't match, don't check the activation_post_process and return True early
+        if activation_post_process.dtype != dtype_with_constraints.dtype:
+            return True
+        satisfies_constraints = (
+            _activation_post_process_satisfies_dtype_config_constraints(
+                activation_post_process, dtype_with_constraints, debug_string
+            )
+        )
+    return satisfies_constraints
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/observer.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/observer.py
new file mode 100644
index 0000000000000000000000000000000000000000..abb81c2a54d0091e16ff7cbbf6ef6bb2112485de
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/observer.py
@@ -0,0 +1,2155 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+# temporarily skip RUF for this file for now, we can re-enable
+# after move the affine quantization related things to torchao
+# noqa: RUF
+"""
+This module implements observers which are used to collect statistics about
+the values observed during calibration (PTQ) or training (QAT).
+"""
+
+import operator
+import re
+import warnings
+from abc import ABCMeta, abstractmethod
+from collections import OrderedDict
+from functools import partial
+from typing import Any
+
+import torch
+import torch.nn as nn
+from torch.ao.quantization.utils import (
+    calculate_qmin_qmax,
+    check_min_max_valid,
+    is_per_channel,
+    is_per_tensor,
+    validate_qmin_qmax,
+)
+from torch.fx import Node
+
+
+__all__ = [
+    "default_affine_fixed_qparams_observer",
+    "default_debug_observer",
+    "default_dynamic_quant_observer",
+    "default_fixed_qparams_range_0to1_observer",
+    "default_fixed_qparams_range_neg1to1_observer",
+    "default_float_qparams_observer",
+    "default_float_qparams_observer_4bit",
+    "default_histogram_observer",
+    "default_observer",
+    "default_per_channel_weight_observer",
+    "default_placeholder_observer",
+    "default_reuse_input_observer",
+    "default_symmetric_fixed_qparams_observer",
+    "default_weight_observer",
+    "get_observer_state_dict",
+    "load_observer_state_dict",
+    "per_channel_weight_observer_range_neg_127_to_127",
+    "weight_observer_range_neg_127_to_127",
+    "FixedQParamsObserver",
+    "HistogramObserver",
+    "MinMaxObserver",
+    "MovingAverageMinMaxObserver",
+    "MovingAveragePerChannelMinMaxObserver",
+    "NoopObserver",
+    "ObserverBase",
+    "PerChannelMinMaxObserver",
+    "PlaceholderObserver",
+    "RecordingObserver",
+    "ReuseInputObserver",
+    "UniformQuantizationObserverBase",
+    "AffineQuantizedObserverBase",
+    "Granularity",
+    "MappingType",
+    "PerAxis",
+    "PerBlock",
+    "PerGroup",
+    "PerRow",
+    "PerTensor",
+    "PerToken",
+    "TorchAODType",
+    "ZeroPointDomain",
+    "get_block_size",
+]
+
+
+class _PartialWrapper:
+    def __init__(self, p):
+        self.p = p
+        self.callable_args = {}
+
+    def __call__(self, *args, **keywords):
+        # call each arg in callable_args and add them partial, then run with keywords
+        # skip if arg_name in keywords so its possible to overwrite
+        for arg_name in self.callable_args:
+            if arg_name not in keywords:
+                keywords = {**keywords, arg_name: self.callable_args[arg_name]()}
+        return self.p(*args, **keywords)
+
+    def __repr__(self):
+        return self.p.__repr__() + self.callable_args.__repr__()
+
+    def with_args(self, **kwargs):
+        return _with_args(self, **kwargs)
+
+    def with_callable_args(self, **kwargs):
+        result = _PartialWrapper(p=self.p)
+        result.callable_args = {**self.callable_args, **kwargs}
+        return result
+
+
+def _with_args(cls_or_self, **kwargs):
+    r"""Wrapper that allows creation of class factories.
+
+    This can be useful when there is a need to create classes with the same
+    constructor arguments, but different instances. Can be used in conjunction with
+    _callable_args
+
+    Example::
+
+        >>> # xdoctest: +SKIP("Undefined vars")
+        >>> Foo.with_args = classmethod(_with_args)
+        >>> foo_builder = Foo.with_args(a=3, b=4).with_args(answer=42)
+        >>> foo_instance1 = foo_builder()
+        >>> foo_instance2 = foo_builder()
+        >>> id(foo_instance1) == id(foo_instance2)
+        False
+    """
+    r = _PartialWrapper(partial(cls_or_self, **kwargs))
+    return r
+
+
+def _with_callable_args(cls_or_self, **kwargs):
+    r"""Wrapper that allows creation of class factories args that need to be
+    called at construction time.
+
+    This can be useful when there is a need to create classes with the same
+    constructor arguments, but different instances and those arguments should only
+    be calculated at construction time. Can be used in conjunction with _with_args
+
+    Example::
+
+        >>> # xdoctest: +SKIP("Undefined vars")
+        >>> Foo.with_callable_args = classmethod(_with_callable_args)
+        >>> Foo.with_args = classmethod(_with_args)
+        >>> foo_builder = Foo.with_callable_args(cur_time=get_time_func).with_args(name="dan")
+        >>> foo_instance1 = foo_builder()
+        >>> # wait 50
+        >>> foo_instance2 = foo_builder()
+        >>> id(foo_instance1.creation_time) == id(foo_instance2.creation_time)
+        False
+    """
+    r = _PartialWrapper(partial(cls_or_self))
+    return r.with_callable_args(**kwargs)
+
+
+ABC: Any = ABCMeta("ABC", (object,), {})  # compatible with Python 2 *and* 3:
+
+
+class ObserverBase(ABC, nn.Module):
+    r"""Base observer Module.
+    Any observer implementation should derive from this class.
+
+    Concrete observers should follow the same API. In forward, they will update
+    the statistics of the observed Tensor. And they should provide a
+    `calculate_qparams` function that computes the quantization parameters given
+    the collected statistics.
+
+    Args:
+        dtype: dtype argument to the `quantize` node needed to implement the
+               reference model spec.
+        is_dynamic: indicator for whether the observer is a placeholder for dynamic quantization
+        or static quantization
+    """
+
+    def __init__(self, dtype, is_dynamic: bool = False):
+        super().__init__()
+        self.dtype = dtype
+        self.is_dynamic = is_dynamic
+
+    @abstractmethod
+    def forward(self, x):
+        pass
+
+    @abstractmethod
+    def calculate_qparams(self, **kwargs):
+        pass
+
+    with_args = classmethod(_with_args)
+    with_callable_args = classmethod(_with_callable_args)
+
+
+class UniformQuantizationObserverBase(ObserverBase):
+    r"""Common base for all observers using uniform quantization to calculate
+    scale and zero_point.
+
+    Args:
+        dtype: dtype argument to the `quantize` node needed to implement the
+               reference model spec.
+        qscheme: Quantization scheme to be used.
+        reduce_range: Reduces the range of the quantized data type by 1 bit.
+                      This is sometimes required to avoid instruction overflow.
+        quant_min: Minimum quantization value. If unspecified, it will follow the 8-bit setup.
+        quant_max: Maximum quantization value. If unspecified, it will follow the 8-bit setup.
+        eps: Epsilon value for float32, Defaults to `torch.finfo(torch.float32).eps`.
+
+    .. warning::
+
+        :attr:`dtype` can only take ``torch.qint8`` or ``torch.quint8``.
+               or `torch.int8` or `torch.uint8`
+
+    .. warning::
+
+        :attr:`qscheme` can only take one of the following options:
+
+        - ``torch.per_tensor_affine``
+        - ``torch.per_tensor_symmetric``
+        - ``torch.per_channel_affine``
+        - ``torch.per_channel_symmetric``
+    """
+
+    # Note: the version is shared by all observer types
+    #
+    # Version 1/None
+    #   self
+    #
+    # Version 2 (base class only, does not include child class buffers)
+    #   self
+    #   |--- eps : Tensor
+    #
+    # Version 3
+    #   for HistogramObserver only, changed the shape of uninitialized
+    #   min_val and max_val buffers from torch.Size([0]) to torch.Size([])
+    #   for PerChannelObservers, changed the name of the buffers from min_vals
+    #   to min_val and from max_vals to max_val.
+    _version = 3
+
+    eps: torch.Tensor
+
+    def __init__(
+        self,
+        dtype=torch.quint8,
+        qscheme=torch.per_tensor_affine,
+        reduce_range=False,
+        quant_min=None,
+        quant_max=None,
+        factory_kwargs=None,
+        eps=torch.finfo(torch.float32).eps,
+        is_dynamic=False,
+        **kwargs,
+    ) -> None:
+        factory_kwargs = torch.nn.factory_kwargs(factory_kwargs)
+        super().__init__(dtype=dtype, is_dynamic=is_dynamic, **kwargs)
+        self.qscheme = qscheme
+        if reduce_range:
+            warnings.warn(
+                "Please use quant_min and quant_max to specify the range for observers. \
+                    reduce_range will be deprecated in a future release of PyTorch.",
+                stacklevel=2,
+            )
+        self.reduce_range = reduce_range
+        self.register_buffer("eps", torch.tensor([eps], **factory_kwargs))
+        if self.qscheme not in (
+            torch.per_tensor_affine,
+            torch.per_tensor_symmetric,
+            torch.per_channel_affine,
+            torch.per_channel_symmetric,
+            torch.per_channel_affine_float_qparams,
+        ):
+            raise AssertionError(
+                "Default Observer only works for per_tensor_affine, per_tensor_symmetric, "
+                "per_channel_affine, per_channel_symmetric and per_channel_float_qparams quantization scheme"
+            )
+
+        _ALLOWED_DTYPES = (
+            torch.qint8,
+            torch.quint8,
+            torch.quint4x2,
+            torch.qint32,
+            torch.int8,
+            torch.uint8,
+            torch.int16,
+            torch.int32,
+            torch.float8_e5m2,
+            torch.float8_e4m3fn,
+            torch.uint16,
+        )
+
+        if self.dtype not in _ALLOWED_DTYPES:
+            raise AssertionError(
+                f"Default Observer only works for {_ALLOWED_DTYPES} data type"
+            )
+        self.has_customized_qrange = (quant_min is not None) and (quant_max is not None)
+        if self.has_customized_qrange:
+            # pyrefly: ignore [bad-argument-type]
+            validate_qmin_qmax(quant_min, quant_max)
+        self.quant_min, self.quant_max = calculate_qmin_qmax(
+            # pyrefly: ignore [bad-argument-type]
+            quant_min,
+            # pyrefly: ignore [bad-argument-type]
+            quant_max,
+            self.has_customized_qrange,
+            self.dtype,
+            self.reduce_range,
+        )
+
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        version = local_metadata.get("version", None)
+
+        if version is None or version == 1:
+            # eps was moved to a buffer in version 2
+            eps = torch.tensor([torch.finfo(torch.float32).eps])
+            state_dict[prefix + "eps"] = eps
+
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            strict,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+    @torch.jit.export
+    def _validate_qmin_qmax(self, quant_min: int, quant_max: int) -> None:
+        r"""Validates that the user-specified quantization range is properly initialized
+        and within the given bound supported by the observer dtype.
+
+        To accommodate lower-bit quantization with respect to the existing torch.qint8 and
+        torch.quint8 datatypes, the user can choose to use dynamic quantization range by passing
+        in a tuple of initial qmin and qmax values. One use case is these customized qmin and qmax
+        values are used to calculate static estimates of the scale and zero point for aggressive lower-bit
+        fake quantization. These estimates are compared against parameters learned through backpropagation.
+        The related literatures for scale and zero point via backpropagation are as follows:
+
+        Learned Step Size Quantization: https://openreview.net/pdf?id=rkgO66VKDS
+        Trained Quantization Thresholds: https://arxiv.org/pdf/1903.08066.pdf
+        """
+        # The variable names are prefixed with "initial" because their values (qmin and qmax) might be adjusted
+        # based on whether quantization range is reduced and the datatype (signed/unsigned) used by the observer.
+        if not quant_min <= 0 <= quant_max:
+            raise AssertionError("Used-specified quantization range must include 0.")
+        if quant_min >= quant_max:
+            raise AssertionError(
+                "qmin must be strictly less than qmax for user-specified quantization range."
+            )
+
+    @torch.jit.export
+    def _calculate_qparams(
+        self, min_val: torch.Tensor, max_val: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        r"""Calculates the quantization parameters, given min and max
+        value tensors. Works for both per tensor and per channel cases
+
+        Args:
+            min_val: Minimum values per channel
+            max_val: Maximum values per channel
+
+        Returns:
+            scales: Scales tensor of shape (#channels,)
+            zero_points: Zero points tensor of shape (#channels,)
+        """
+        # Functionally equivalent to 'determine_qparams' in utils.py. Observers must be torchscriptable however and qscheme
+        # as far as I can tell is not allowed to passed as a parameter in torchscript functions. This makes refactoring observer
+        # to use this utility a massive pain and very gross. For now Im opting just to duplicate as this code
+        # seems unlikely to change (last update over 1 year ago) and when torchscript is fully deprecated we can refactor.
+        # TODO(jakeszwe, jerryzh168)
+        if not check_min_max_valid(min_val, max_val):
+            return torch.tensor([1.0], device=min_val.device.type), torch.tensor(
+                [0], device=min_val.device.type
+            )
+
+        quant_min, quant_max = self.quant_min, self.quant_max
+        min_val_neg = torch.min(min_val, torch.zeros_like(min_val))
+        max_val_pos = torch.max(max_val, torch.zeros_like(max_val))
+
+        device = min_val_neg.device
+        scale = torch.ones(min_val_neg.size(), dtype=torch.float32, device=device)
+        zero_point = torch.zeros(min_val_neg.size(), dtype=torch.int64, device=device)
+
+        if (
+            self.qscheme == torch.per_tensor_symmetric
+            or self.qscheme == torch.per_channel_symmetric
+        ):
+            max_val_pos = torch.max(-min_val_neg, max_val_pos)
+            scale = max_val_pos / (float(quant_max - quant_min) / 2)
+            scale = torch.max(scale, self.eps)
+            if self.dtype in [torch.quint8, torch.uint8]:
+                if self.has_customized_qrange:
+                    # When customized quantization range is used, down-rounded midpoint of the range is chosen.
+                    zero_point = zero_point.new_full(
+                        zero_point.size(), (quant_min + quant_max) // 2
+                    )
+                else:
+                    zero_point = zero_point.new_full(zero_point.size(), 128)
+            elif self.dtype == torch.uint16:
+                zero_point = zero_point.new_full(zero_point.size(), 2**15)
+        elif self.qscheme == torch.per_channel_affine_float_qparams:
+            scale = (max_val - min_val) / float(quant_max - quant_min)
+            scale = torch.where(scale > self.eps, scale, torch.ones_like(scale))
+            # We use the quantize function
+            # xq = Round(Xf * inv_scale + zero_point),
+            # setting zero_point to (-1 * min *inv_scale) we get
+            # Xq = Round((Xf - min) * inv_scale)
+            zero_point = -1 * min_val / scale
+        else:
+            scale = (max_val_pos - min_val_neg) / float(quant_max - quant_min)
+            scale = torch.max(scale, self.eps)
+            zero_point = quant_min - torch.round(min_val_neg / scale).to(torch.int)
+            zero_point = torch.clamp(zero_point, quant_min, quant_max)
+
+        # For scalar values, cast them to Tensors of size 1 to keep the shape
+        # consistent with default values in FakeQuantize.
+        if len(scale.shape) == 0:
+            # TODO: switch to scale.item() after adding JIT support
+            scale = torch.tensor([float(scale)], dtype=scale.dtype, device=device)
+        if len(zero_point.shape) == 0:
+            # TODO: switch to zero_point.item() after adding JIT support
+            zero_point = torch.tensor(
+                [int(zero_point)], dtype=zero_point.dtype, device=device
+            )
+            if self.qscheme == torch.per_channel_affine_float_qparams:
+                zero_point = torch.tensor(
+                    [float(zero_point)], dtype=zero_point.dtype, device=device
+                )
+
+        return scale, zero_point
+
+    @torch.jit.export
+    def reset_min_max_vals(self):
+        raise NotImplementedError("Cannot reset min/max values in the given observer.")
+
+
+# Originally, this class was called `_ObserverBase`.  Keeping the old name around
+# for backwards compatibility.
+# TODO(after v1.13): delete this
+_ObserverBase = UniformQuantizationObserverBase
+
+
+class MinMaxObserver(UniformQuantizationObserverBase):
+    r"""Observer module for computing the quantization parameters based on the
+    running min and max values.
+
+    This observer uses the tensor min/max statistics to compute the quantization
+    parameters. The module records the running minimum and maximum of incoming
+    tensors, and uses this statistic to compute the quantization parameters.
+
+    Args:
+        dtype: dtype argument to the `quantize` node needed to implement the
+               reference model spec.
+        qscheme: Quantization scheme to be used
+        reduce_range: Reduces the range of the quantized data type by 1 bit
+        quant_min: Minimum quantization value. If unspecified, it will follow the 8-bit setup.
+        quant_max: Maximum quantization value. If unspecified, it will follow the 8-bit setup.
+        eps: Epsilon value for float32, Defaults to `torch.finfo(torch.float32).eps`.
+
+    Given running min/max as :math:`x_\text{min}` and :math:`x_\text{max}`,
+    scale :math:`s` and zero point :math:`z` are computed as:
+
+    The running minimum/maximum :math:`x_\text{min/max}` is computed as:
+
+    .. math::
+
+        \begin{array}{ll}
+        x_\text{min} &= \begin{cases}
+            \min(X) & \text{if~}x_\text{min} = \text{None} \\
+            \min\left(x_\text{min}, \min(X)\right) & \text{otherwise}
+        \end{cases}\\
+        x_\text{max} &= \begin{cases}
+            \max(X) & \text{if~}x_\text{max} = \text{None} \\
+            \max\left(x_\text{max}, \max(X)\right) & \text{otherwise}
+        \end{cases}\\
+        \end{array}
+
+    where :math:`X` is the observed tensor.
+
+    The scale :math:`s` and zero point :math:`z` are then computed as:
+
+    .. math::
+
+        \begin{aligned}
+            \text{if Symmetric:}&\\
+            &s = 2 \max(|x_\text{min}|, x_\text{max}) /
+                \left( Q_\text{max} - Q_\text{min} \right) \\
+            &z = \begin{cases}
+                0 & \text{if dtype is qint8} \\
+                128 & \text{otherwise}
+            \end{cases}\\
+            \text{Otherwise:}&\\
+                &s = \left( x_\text{max} - x_\text{min}  \right ) /
+                    \left( Q_\text{max} - Q_\text{min} \right ) \\
+                &z = Q_\text{min} - \text{round}(x_\text{min} / s)
+        \end{aligned}
+
+    where :math:`Q_\text{min}` and :math:`Q_\text{max}` are the minimum and
+    maximum of the quantized data type.
+
+    .. warning:: :attr:`dtype` can only take ``torch.qint8`` or ``torch.quint8``.
+
+    .. note:: If the running minimum equals to the running maximum, the scale
+              and zero_point are set to 1.0 and 0.
+    """
+
+    min_val: torch.Tensor
+    max_val: torch.Tensor
+
+    def __init__(
+        self,
+        dtype=torch.quint8,
+        qscheme=torch.per_tensor_affine,
+        reduce_range=False,
+        quant_min=None,
+        quant_max=None,
+        factory_kwargs=None,
+        eps=torch.finfo(torch.float32).eps,
+        is_dynamic=False,
+        **kwargs,
+    ) -> None:
+        if not is_per_tensor(qscheme):
+            raise NotImplementedError(
+                "MinMaxObserver's qscheme only support torch.per_tensor_symmetric \
+                    and torch.per_tensor_affine."
+            )
+        # TODO: MinMaxObserver by itself doesn't support dynamic quantization, but
+        # if it's inherited by MovingAverageObserver, and averaging_constant is 1, it
+        # supports dynamic quantization, we may need to better error checking here
+
+        # For x86 quantized kernels, we need to ensure that the vpmaddubsw
+        # instruction does not overflow. We allow for a reduce_range argument to
+        # observers that reduces the quantized range to (0,127) or (-64, 63).
+        # For more details see aten/src/ATen/native/quantized/cpu/qconv.cpp
+        # This is not an optimal choice for non x86 backends as it loses a bit
+        # of precision for activations.
+        super().__init__(
+            dtype=dtype,
+            qscheme=qscheme,
+            reduce_range=reduce_range,
+            quant_min=quant_min,
+            quant_max=quant_max,
+            factory_kwargs=factory_kwargs,
+            eps=eps,
+            is_dynamic=is_dynamic,
+            **kwargs,
+        )
+        factory_kwargs = torch.nn.factory_kwargs(factory_kwargs)
+        self.register_buffer("min_val", torch.tensor(float("inf"), **factory_kwargs))
+        self.register_buffer("max_val", torch.tensor(float("-inf"), **factory_kwargs))
+        if (
+            self.qscheme == torch.per_tensor_symmetric
+            and self.reduce_range
+            and self.dtype == torch.quint8
+        ):
+            raise NotImplementedError(
+                "Cannot reduce range for symmetric \
+                                       quantization for quint8"
+            )
+
+    def forward(self, x_orig):
+        r"""Records the running minimum and maximum of ``x``."""
+        if x_orig.numel() == 0:
+            return x_orig
+        x = x_orig.detach()  # avoid keeping autograd tape
+        x = x.to(self.min_val.dtype)
+        min_val_cur, max_val_cur = torch.aminmax(x)
+        min_val = torch.min(min_val_cur, self.min_val)
+        max_val = torch.max(max_val_cur, self.max_val)
+        self.min_val.copy_(min_val)
+        self.max_val.copy_(max_val)
+        return x_orig
+
+    @torch.jit.export
+    def calculate_qparams(self):  # type: ignore[override]
+        r"""Calculates the quantization parameters."""
+        return self._calculate_qparams(self.min_val, self.max_val)
+
+    @torch.jit.export
+    def extra_repr(self):
+        return f"min_val={self.min_val}, max_val={self.max_val}"
+
+    @torch.jit.export
+    def reset_min_max_vals(self):
+        """Resets the min/max values."""
+        self.min_val.copy_(torch.tensor(float("inf")))
+        self.max_val.copy_(torch.tensor(float("-inf")))
+
+
+class MovingAverageMinMaxObserver(MinMaxObserver):
+    r"""Observer module for computing the quantization parameters based on the
+    moving average of the min and max values.
+
+    This observer computes the quantization parameters based on the moving
+    averages of minimums and maximums of the incoming tensors. The module
+    records the average minimum and maximum of incoming tensors, and uses this
+    statistic to compute the quantization parameters.
+
+    Args:
+        averaging_constant: Averaging constant for min/max.
+        dtype: dtype argument to the `quantize` node needed to implement the
+               reference model spec.
+        qscheme: Quantization scheme to be used
+        reduce_range: Reduces the range of the quantized data type by 1 bit
+        quant_min: Minimum quantization value. If unspecified, it will follow the 8-bit setup.
+        quant_max: Maximum quantization value. If unspecified, it will follow the 8-bit setup.
+        eps: Epsilon value for float32, Defaults to `torch.finfo(torch.float32).eps`.
+
+    The moving average min/max is computed as follows
+
+    .. math::
+
+        \begin{array}{ll}
+                x_\text{min} = \begin{cases}
+                    \min(X) & \text{if~}x_\text{min} = \text{None} \\
+                    (1 - c) x_\text{min} + c \min(X) & \text{otherwise}
+                \end{cases}\\
+                x_\text{max} = \begin{cases}
+                    \max(X) & \text{if~}x_\text{max} = \text{None} \\
+                    (1 - c) x_\text{max} + c \max(X) & \text{otherwise}
+                \end{cases}\\
+        \end{array}
+
+    where :math:`x_\text{min/max}` is the running average min/max, :math:`X` is
+    is the incoming tensor, and :math:`c` is the ``averaging_constant``.
+
+    The scale and zero point are then computed as in
+    :class:`~torch.ao.quantization.observer.MinMaxObserver`.
+
+    .. note:: Only works with ``torch.per_tensor_affine`` quantization scheme.
+
+    .. note:: If the running minimum equals to the running maximum, the scale
+              and zero_point are set to 1.0 and 0.
+    """
+
+    def __init__(
+        self,
+        averaging_constant=0.01,
+        dtype=torch.quint8,
+        qscheme=torch.per_tensor_affine,
+        reduce_range=False,
+        quant_min=None,
+        quant_max=None,
+        eps=torch.finfo(torch.float32).eps,
+        is_dynamic=False,
+        **kwargs,
+    ) -> None:
+        if not is_per_tensor(qscheme):
+            raise NotImplementedError(
+                f"MovingAverageMinMaxObserver's qscheme only support \
+                torch.per_tensor_symmetric and torch.per_tensor_affine. \
+                but got: {qscheme}"
+            )
+        self.averaging_constant = averaging_constant
+        if is_dynamic and self.averaging_constant != 1:
+            raise NotImplementedError(
+                "MovingAverageMinMaxObserver doesn't support dynamic quantization for "
+                f"averaging constant of {self.averaging_constant}"
+            )
+        super().__init__(
+            dtype=dtype,
+            qscheme=qscheme,
+            reduce_range=reduce_range,
+            quant_min=quant_min,
+            quant_max=quant_max,
+            eps=eps,
+            is_dynamic=is_dynamic,
+            **kwargs,
+        )
+
+    def forward(self, x_orig):
+        if x_orig.numel() == 0:
+            return x_orig
+        x = x_orig.detach()  # avoid keeping autograd tape
+        x = x.to(self.min_val.dtype)
+        min_val = self.min_val
+        max_val = self.max_val
+        if min_val == float("inf") and max_val == float("-inf"):
+            min_val, max_val = torch.aminmax(x)
+        else:
+            min_val_cur, max_val_cur = torch.aminmax(x)
+            min_val = min_val + self.averaging_constant * (min_val_cur - min_val)
+            max_val = max_val + self.averaging_constant * (max_val_cur - max_val)
+        self.min_val.copy_(min_val)
+        self.max_val.copy_(max_val)
+        return x_orig
+
+
+class PerChannelMinMaxObserver(UniformQuantizationObserverBase):
+    r"""Observer module for computing the quantization parameters based on the
+    running per channel min and max values.
+
+    This observer uses the tensor min/max statistics to compute the per channel
+    quantization parameters. The module records the running minimum and maximum
+    of incoming tensors, and uses this statistic to compute the quantization
+    parameters.
+
+    Args:
+        ch_axis: Channel axis
+        dtype: dtype argument to the `quantize` node needed to implement the
+               reference model spec.
+        qscheme: Quantization scheme to be used
+        reduce_range: Reduces the range of the quantized data type by 1 bit
+        quant_min: Minimum quantization value. If unspecified, it will follow the 8-bit setup.
+        quant_max: Maximum quantization value. If unspecified, it will follow the 8-bit setup.
+        eps: Epsilon value for float32, Defaults to `torch.finfo(torch.float32).eps`.
+
+    The quantization parameters are computed the same way as in
+    :class:`~torch.ao.quantization.observer.MinMaxObserver`, with the difference
+    that the running min/max values are stored per channel.
+    Scales and zero points are thus computed per channel as well.
+
+    .. note:: If the running minimum equals to the running maximum, the scales
+              and zero_points are set to 1.0 and 0.
+    """
+
+    min_val: torch.Tensor
+    max_val: torch.Tensor
+
+    def __init__(
+        self,
+        ch_axis=0,
+        dtype=torch.quint8,
+        qscheme=torch.per_channel_affine,
+        reduce_range=False,
+        quant_min=None,
+        quant_max=None,
+        factory_kwargs=None,
+        eps=torch.finfo(torch.float32).eps,
+        is_dynamic=False,
+        **kwargs,
+    ) -> None:
+        if not is_per_channel(qscheme):
+            raise NotImplementedError(
+                "PerChannelMinMaxObserver's qscheme only support \
+                    torch.per_channel_symmetric, torch.per_channel_affine and torch.per_channel_affine_float_qparams."
+            )
+        if is_dynamic:
+            raise NotImplementedError(
+                "PerChannelMinMaxObserver doesn't support dynamic quantization"
+            )
+        super().__init__(
+            dtype=dtype,
+            qscheme=qscheme,
+            reduce_range=reduce_range,
+            quant_min=quant_min,
+            quant_max=quant_max,
+            factory_kwargs=factory_kwargs,
+            eps=eps,
+            is_dynamic=is_dynamic,
+            **kwargs,
+        )
+        factory_kwargs = torch.nn.factory_kwargs(factory_kwargs)
+        self.ch_axis = ch_axis
+        self.register_buffer("min_val", torch.tensor([], **factory_kwargs))
+        self.register_buffer("max_val", torch.tensor([], **factory_kwargs))
+        if (
+            self.qscheme == torch.per_channel_symmetric
+            and self.reduce_range
+            and self.dtype == torch.quint8
+        ):
+            raise NotImplementedError(
+                "Cannot reduce range for symmetric quantization for quint8"
+            )
+
+    def forward(self, x_orig):
+        return self._forward(x_orig)
+
+    def _forward(self, x_orig):
+        if x_orig.numel() == 0:
+            return x_orig
+        x = x_orig.detach()  # avoid keeping autograd tape
+        min_val = self.min_val
+        max_val = self.max_val
+        x_dim = x.size()
+
+        new_axis_list = [i for i in range(len(x_dim))]  # noqa: C416
+        new_axis_list[self.ch_axis] = 0
+        new_axis_list[0] = self.ch_axis
+        y = x.permute(new_axis_list)
+        # Need to match dtype of min/max because the updates to buffers
+        # are done in place and types need to match for comparisons
+        y = y.to(self.min_val.dtype)
+        y = torch.flatten(y, start_dim=1)
+        if min_val.numel() == 0 or max_val.numel() == 0:
+            min_val, max_val = torch.aminmax(y, dim=1)
+        else:
+            min_val_cur, max_val_cur = torch.aminmax(y, dim=1)
+            min_val = torch.min(min_val_cur, min_val)
+            max_val = torch.max(max_val_cur, max_val)
+        self.min_val.resize_(min_val.shape)
+        self.max_val.resize_(max_val.shape)
+        self.min_val.copy_(min_val)
+        self.max_val.copy_(max_val)
+        return x_orig
+
+    @torch.jit.export
+    def calculate_qparams(self):  # type: ignore[override]
+        return self._calculate_qparams(self.min_val, self.max_val)
+
+    def extra_repr(self):
+        return f"min_val={self.min_val}, max_val={self.max_val}"
+
+    def _load_from_state_dict(
+        self,
+        state_dict: dict[str, Any],
+        prefix: str,
+        local_metadata: dict[str, torch.Tensor],
+        strict: bool,
+        missing_keys: list[str],
+        unexpected_keys: list[str],
+        error_msgs: list[str],
+    ):
+        version = local_metadata.get("version")
+        if version is not None and version < 3:
+            local_state = ["min_vals", "max_vals"]
+            expected_min_name = "min_vals"
+            expected_max_name = "max_vals"
+        else:
+            local_state = ["min_val", "max_val"]
+            expected_min_name = "min_val"
+            expected_max_name = "max_val"
+        for name in local_state:
+            key = prefix + name
+            if key in state_dict:
+                val = state_dict[key]
+                # Custom handling to allow loading min_val or max_val
+                # of size N into uninitialized buffers of size 0. The
+                # buffers are resized here, and the values are copied in
+                # the default state_dict loading code of the parent.
+                if name == expected_min_name:
+                    self.min_val.resize_(val.shape)
+                elif name == expected_max_name:
+                    self.max_val.resize_(val.shape)
+                else:
+                    warnings.warn(
+                        f"Observer load_from_state_dict got unexpected name {name}",
+                        stacklevel=2,
+                    )
+                # For torchscript module we need to update the attributes here since we do not
+                # call the `_load_from_state_dict` function defined module.py
+                if torch.jit.is_scripting():
+                    if name == expected_min_name:
+                        self.min_val.copy_(val)
+                    elif name == expected_max_name:
+                        self.max_val.copy_(val)
+                    else:
+                        warnings.warn(
+                            f"Observer load_from_state_dict got unexpected name {name}",
+                            stacklevel=2,
+                        )
+            elif strict:
+                missing_keys.append(key)
+
+        if not torch.jit.is_scripting():
+            super()._load_from_state_dict(
+                state_dict,
+                prefix,
+                local_metadata,
+                False,
+                missing_keys,
+                unexpected_keys,
+                error_msgs,
+            )
+
+    def _load_from_state_dict_script(
+        self,
+        state_dict: dict[str, Any],
+        prefix: str,
+        local_metadata: dict[str, torch.Tensor],
+        strict: bool,
+        missing_keys: list[str],
+        unexpected_keys: list[str],
+        error_msgs: list[str],
+    ):
+        self._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            strict,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+    @torch.jit.export
+    def reset_min_max_vals(self):
+        """Resets the min/max values."""
+        # This used to be torch.ones but that does not work because
+        # JIT compiler can optimize it via common subexpression elimination
+        # in which case both min_val and max_val point to the same tensor.
+        self.min_val = torch.rand(
+            0,
+        )
+        self.max_val = torch.rand(
+            0,
+        )
+
+
+class MovingAveragePerChannelMinMaxObserver(PerChannelMinMaxObserver):
+    r"""Observer module for computing the quantization parameters based on the
+    running per channel min and max values.
+
+    This observer uses the tensor min/max statistics to compute the per channel
+    quantization parameters. The module records the running minimum and maximum
+    of incoming tensors, and uses this statistic to compute the quantization
+    parameters.
+
+    Args:
+        averaging_constant: Averaging constant for min/max.
+        ch_axis: Channel axis
+        dtype: Quantized data type
+        qscheme: Quantization scheme to be used
+        reduce_range: Reduces the range of the quantized data type by 1 bit
+        quant_min: Minimum quantization value. If unspecified, it will follow the 8-bit setup.
+        quant_max: Maximum quantization value. If unspecified, it will follow the 8-bit setup.
+        eps: Epsilon value for float32, Defaults to `torch.finfo(torch.float32).eps`.
+
+    The quantization parameters are computed the same way as in
+    :class:`~torch.ao.quantization.observer.MovingAverageMinMaxObserver`, with the
+    difference that the running min/max values are stored per channel.
+    Scales and zero points are thus computed per channel as well.
+
+    .. note:: If the running minimum equals to the running maximum, the scales
+              and zero_points are set to 1.0 and 0.
+    """
+
+    def __init__(
+        self,
+        averaging_constant=0.01,
+        ch_axis=0,
+        dtype=torch.quint8,
+        qscheme=torch.per_channel_affine,
+        reduce_range=False,
+        quant_min=None,
+        quant_max=None,
+        eps=torch.finfo(torch.float32).eps,
+        is_dynamic=False,
+        **kwargs,
+    ) -> None:
+        if not is_per_channel(qscheme):
+            raise NotImplementedError(
+                "MovingAveragePerChannelMinMaxObserver's qscheme only support \
+                    torch.per_channel_symmetric, torch.per_channel_affine and torch.per_channel_affine_float_qparams."
+            )
+        if is_dynamic:
+            raise NotImplementedError(
+                "MovingAveragePerChannelMinMaxObserver doesn't support dynamic quantization"
+            )
+        super().__init__(
+            ch_axis=ch_axis,
+            dtype=dtype,
+            qscheme=qscheme,
+            reduce_range=reduce_range,
+            quant_min=quant_min,
+            quant_max=quant_max,
+            eps=eps,
+            is_dynamic=is_dynamic,
+            **kwargs,
+        )
+        self.averaging_constant = averaging_constant
+
+    def forward(self, x_orig):
+        if x_orig.numel() == 0:
+            return x_orig
+        x = x_orig.detach()  # avoid keeping autograd tape
+        x = x.to(self.min_val.dtype)
+        min_val = self.min_val
+        max_val = self.max_val
+        x_dim = x.size()
+
+        new_axis_list = [i for i in range(len(x_dim))]  # noqa: C416
+        new_axis_list[self.ch_axis] = 0
+        new_axis_list[0] = self.ch_axis
+        y = x.permute(new_axis_list)
+        y = torch.flatten(y, start_dim=1)
+        if min_val.numel() == 0 or max_val.numel() == 0:
+            min_val, max_val = torch.aminmax(y, dim=1)
+        else:
+            min_val_cur, max_val_cur = torch.aminmax(y, dim=1)
+            min_val = min_val + self.averaging_constant * (min_val_cur - min_val)
+            max_val = max_val + self.averaging_constant * (max_val_cur - max_val)
+        self.min_val.resize_(min_val.shape)
+        self.max_val.resize_(max_val.shape)
+        self.min_val.copy_(min_val)
+        self.max_val.copy_(max_val)
+        return x_orig
+
+
+class HistogramObserver(UniformQuantizationObserverBase):
+    r"""
+    The module records the running histogram of tensor values along with
+    min/max values. ``calculate_qparams`` will calculate scale and zero_point.
+
+    Args:
+        bins: Number of bins to use for the histogram
+        dtype: dtype argument to the `quantize` node needed to implement the
+               reference model spec
+        qscheme: Quantization scheme to be used
+        reduce_range: Reduces the range of the quantized data type by 1 bit
+        eps: Epsilon value for float32, Defaults to `torch.finfo(torch.float32).eps`.
+
+    The scale and zero point are computed as follows:
+
+    1. Create the histogram of the incoming inputs.
+        The histogram is computed continuously, and the ranges per bin change
+        with every new tensor observed.
+    2. Search the distribution in the histogram for optimal min/max values.
+        The search for the min/max values ensures the minimization of the
+        quantization error with respect to the floating point model.
+    3. Compute the scale and zero point the same way as in the
+        :class:`~torch.ao.quantization.MinMaxObserver`
+    """
+
+    histogram: torch.Tensor
+    min_val: torch.Tensor
+    max_val: torch.Tensor
+
+    def __init__(
+        self,
+        bins: int = 2048,
+        dtype: torch.dtype = torch.quint8,
+        qscheme=torch.per_tensor_affine,
+        reduce_range=False,
+        quant_min=None,
+        quant_max=None,
+        factory_kwargs=None,
+        eps=torch.finfo(torch.float32).eps,
+        is_dynamic=False,
+        **kwargs,
+    ) -> None:
+        if not is_per_tensor(qscheme):
+            raise NotImplementedError(
+                "HistogramObserver's qscheme only support torch.per_tensor_symmetric \
+                    and torch.per_tensor_affine."
+            )
+        if is_dynamic:
+            raise NotImplementedError(
+                "HistogramObserver doesn't support dynamic quantization"
+            )
+        # bins: The number of bins used for histogram calculation.
+        super().__init__(
+            dtype=dtype,
+            qscheme=qscheme,
+            reduce_range=reduce_range,
+            quant_min=quant_min,
+            quant_max=quant_max,
+            factory_kwargs=factory_kwargs,
+            eps=eps,
+            is_dynamic=is_dynamic,
+            **kwargs,
+        )
+        factory_kwargs = torch.nn.factory_kwargs(factory_kwargs)
+        self.bins = bins
+        self.register_buffer("histogram", torch.zeros(self.bins, **factory_kwargs))
+        self.register_buffer("min_val", torch.tensor(float("inf"), **factory_kwargs))
+        self.register_buffer("max_val", torch.tensor(float("-inf"), **factory_kwargs))
+        self.dst_nbins = 2 ** torch.iinfo(self.dtype).bits
+        self.upsample_rate = (
+            16  # used to reduce quantization errors when upscaling histogram
+        )
+
+    def _get_norm(
+        self, delta_begin: torch.Tensor, delta_end: torch.Tensor, density: torch.Tensor
+    ) -> torch.Tensor:
+        r"""
+        Compute the norm of the values uniformaly distributed between
+        delta_begin and delta_end.
+        Currently only L2 norm is supported.
+
+        norm = density * (integral_{begin, end} x^2)
+             = density * (end^3 - begin^3) / 3
+        """
+        norm = (
+            delta_end * delta_end * delta_end - delta_begin * delta_begin * delta_begin
+        ) / 3
+        return density * norm
+
+    def _compute_quantization_error(self, next_start_bin: int, next_end_bin: int):
+        r"""
+        Compute the quantization error if we use start_bin to end_bin as the
+        min and max to do the quantization.
+        """
+        bin_width = (self.max_val.item() - self.min_val.item()) / self.bins
+
+        dst_bin_width = bin_width * (next_end_bin - next_start_bin + 1) / self.dst_nbins
+        if dst_bin_width == 0.0:
+            return 0.0
+
+        src_bin = torch.arange(self.bins, device=self.histogram.device)
+        # distances from the beginning of first dst_bin to the beginning and
+        # end of src_bin
+        src_bin_begin = (src_bin - next_start_bin) * bin_width
+        src_bin_end = src_bin_begin + bin_width
+
+        # which dst_bins the beginning and end of src_bin belong to?
+        dst_bin_of_begin = torch.clamp(
+            torch.div(src_bin_begin, dst_bin_width, rounding_mode="floor"),
+            0,
+            self.dst_nbins - 1,
+        )
+        dst_bin_of_begin_center = (dst_bin_of_begin + 0.5) * dst_bin_width
+
+        dst_bin_of_end = torch.clamp(
+            torch.div(src_bin_end, dst_bin_width, rounding_mode="floor"),
+            0,
+            self.dst_nbins - 1,
+        )
+        density = self.histogram / bin_width
+
+        norm = torch.zeros(self.bins, device=self.histogram.device)
+
+        delta_begin = src_bin_begin - dst_bin_of_begin_center
+        delta_end = dst_bin_width / 2
+        norm += self._get_norm(
+            delta_begin,
+            torch.ones(self.bins, device=self.histogram.device) * delta_end,
+            density,
+        )
+
+        norm += (dst_bin_of_end - dst_bin_of_begin - 1) * self._get_norm(
+            torch.tensor(-dst_bin_width / 2), torch.tensor(dst_bin_width / 2), density
+        )
+
+        dst_bin_of_end_center = dst_bin_of_end * dst_bin_width + dst_bin_width / 2
+
+        delta_begin = -dst_bin_width / 2
+        delta_end = src_bin_end - dst_bin_of_end_center
+        norm += self._get_norm(torch.tensor(delta_begin), delta_end, density)
+
+        return norm.sum().item()
+
+    def _non_linear_param_search(self) -> tuple[torch.Tensor, torch.Tensor]:
+        r"""Non-linear parameter search.
+
+        An approximation for L2 error minimization for selecting min/max.
+        By selecting new min/max, we filter out outliers in input distribution.
+        This follows the implementation of NormMinimization::NonlinearQuantizationParamsSearch in
+        caffe2/quantization/server/norm_minimization.cc
+        """
+        if self.histogram.size()[0] != self.bins:
+            raise AssertionError("bins mismatch")
+        bin_width = (self.max_val - self.min_val) / self.bins
+
+        # cumulative sum
+        total = torch.sum(self.histogram).item()
+        cSum = torch.cumsum(self.histogram, dim=0)
+
+        stepsize = 1e-5  # granularity
+        alpha = 0.0  # lower bound
+        beta = 1.0  # upper bound
+        start_bin = 0
+        end_bin = self.bins - 1
+        norm_min = float("inf")
+
+        while alpha < beta:
+            # Find the next step
+            next_alpha = alpha + stepsize
+            next_beta = beta - stepsize
+
+            # find the left and right bins between the quantile bounds
+            l = start_bin
+            r = end_bin
+            while l < end_bin and cSum[l] < next_alpha * total:
+                l = l + 1
+            while r > start_bin and cSum[r] > next_beta * total:
+                r = r - 1
+
+            # decide the next move
+            next_start_bin = start_bin
+            next_end_bin = end_bin
+            if (l - start_bin) > (end_bin - r):
+                # move the start bin
+                next_start_bin = l
+                alpha = next_alpha
+            else:
+                # move the end bin
+                next_end_bin = r
+                beta = next_beta
+
+            if next_start_bin == start_bin and next_end_bin == end_bin:
+                continue
+
+            # calculate the quantization error using next_start_bin and next_end_bin
+            norm = self._compute_quantization_error(next_start_bin, next_end_bin)
+
+            if norm > norm_min:
+                break
+            norm_min = norm
+            start_bin = next_start_bin
+            end_bin = next_end_bin
+
+        new_min = self.min_val + bin_width * start_bin
+        new_max = self.min_val + bin_width * (end_bin + 1)
+        return new_min, new_max
+
+    def _upscale_histogram(
+        self,
+        histogram: torch.Tensor,
+        orig_min: torch.Tensor,
+        orig_max: torch.Tensor,
+        update_min: torch.Tensor,
+        update_max: torch.Tensor,
+    ):
+        # this turns the histogram into a more fine-coarsed histogram to reduce
+        # bin quantization errors
+        histogram = histogram.repeat_interleave(self.upsample_rate) / self.upsample_rate
+        bin_size = (orig_max - orig_min) / (self.bins * self.upsample_rate)
+        mid_points_histogram = (
+            torch.linspace(
+                orig_min,
+                orig_max,
+                self.bins * self.upsample_rate + 1,
+                device=orig_min.device,
+            )[:-1].to(histogram.device)
+            + 0.5 * bin_size
+        )
+        boundaries_new_histogram = torch.linspace(
+            update_min, update_max, self.bins + 1, device=update_min.device
+        ).to(histogram.device)
+        # this maps the mid-points of the histogram to the new histogram's space
+        bucket_assignments = (
+            torch.bucketize(mid_points_histogram, boundaries_new_histogram, right=True)
+            - 1
+        )
+        # this then maps the histogram mid-points in the new space, weighted by the original histogram's values
+        # this is just the old histogram in the new histogram's space
+
+        # In case due to numerical issues the values land higher/lower than the maximum/minimum
+        bucket_assignments[bucket_assignments >= self.bins] = self.bins - 1
+        bucket_assignments[bucket_assignments < 0] = 0
+
+        update_histogram = torch.bincount(
+            bucket_assignments, weights=histogram, minlength=self.bins
+        )
+        return update_histogram
+
+    def _combine_histograms(
+        self,
+        orig_hist: torch.Tensor,
+        orig_min: torch.Tensor,
+        orig_max: torch.Tensor,
+        update_hist: torch.Tensor,
+        update_min: torch.Tensor,
+        update_max: torch.Tensor,
+    ) -> torch.Tensor:
+        # If the new min and max are the same as the current min and max,
+        # we can just add the new histogram to the original histogram
+        if update_min == orig_min and update_max == orig_max:
+            return orig_hist + update_hist
+
+        # If the orig hist only has one value (i.e., the min and max are the same)
+        # we can just add it into new histogram
+        if orig_min == orig_max:
+            bin_value = torch.sum(orig_hist)
+            transformed_orig_hist = (
+                torch.histc(orig_min, bins=self.bins, min=update_min, max=update_max)  # type: ignore[arg-type]
+                * bin_value
+            )
+            return transformed_orig_hist + update_hist
+
+        # We assume the update_hist is already in the target range, we will map the orig_max to it
+        if update_min > orig_min:
+            raise AssertionError("update_min must be <= orig_min")
+        if update_max < orig_max:
+            raise AssertionError("update_max must be >= orig_max")
+
+        # Now we need to turn the old_histogram, into the range of the new histogram
+        transformed_orig_hist = self._upscale_histogram(
+            orig_hist,
+            orig_min,
+            orig_max,
+            update_min,
+            update_max,
+        )
+
+        return update_hist + transformed_orig_hist
+
+    def reset_histogram(
+        self, x: torch.Tensor, min_val: torch.Tensor, max_val: torch.Tensor
+    ) -> None:
+        self.min_val.resize_(min_val.shape)
+        self.min_val.copy_(min_val)
+        self.max_val.resize_(max_val.shape)
+        self.max_val.copy_(max_val)
+        if min_val.numel() != 1 or max_val.numel() != 1:
+            raise AssertionError("histogram min/max values must be scalar.")
+        new_histogram = torch.histc(x, self.bins, min=min_val, max=max_val)  # type: ignore[arg-type]
+        self.histogram.detach_().resize_(new_histogram.shape)
+        self.histogram.copy_(new_histogram)
+
+    def forward(self, x_orig: torch.Tensor) -> torch.Tensor:  # pyre-ignore[14]
+        if x_orig.numel() == 0:
+            return x_orig
+        x = x_orig.detach()
+        x_min, x_max = torch.aminmax(x)
+        # want to ignore torch.inf since we don't actually
+        # want to make our quantization range infinite
+        # and in practice those values will be clamped
+        if x_min == -torch.inf or x_max == torch.inf:
+            warnings.warn(
+                "torch.inf detected in input tensor, ignoring input", stacklevel=2
+            )
+            x = x[x.abs() != torch.inf]
+            if x.numel() == 0:
+                return x_orig
+            x_min, x_max = torch.aminmax(x)
+
+        current_min = self.min_val
+        current_max = self.max_val
+
+        is_uninitialized = self.min_val == float("inf") or self.max_val == float("-inf")
+        if is_uninitialized:
+            self.reset_histogram(x, x_min, x_max)
+        else:
+            update_min, update_max = x_min, x_max
+            new_min = torch.min(current_min, update_min)
+            new_max = torch.max(current_max, update_max)
+
+            # TODO: For some reason, this is required for it to pass torchscript test
+            # new_min and new_max should already have requires_grad set to False
+            new_min, new_max = new_min.detach(), new_max.detach()
+            update_histogram = torch.histc(
+                x,
+                self.bins,
+                min=new_min,  # type: ignore[arg-type]
+                max=new_max,  # type: ignore[arg-type]
+            ).to(self.histogram.device)
+            if new_min == current_min and new_max == current_max:
+                combined_histogram = self.histogram + update_histogram
+                self.histogram.detach_().resize_(combined_histogram.shape)
+                self.histogram.copy_(combined_histogram)
+            else:
+                combined_histogram = self._combine_histograms(
+                    self.histogram,
+                    current_min,
+                    current_max,
+                    update_histogram,
+                    new_min,
+                    new_max,
+                )
+                self.histogram.detach_().resize_(combined_histogram.shape)
+                self.histogram.copy_(combined_histogram)
+                self.min_val.detach_().resize_(new_min.shape)
+                self.min_val.copy_(new_min)
+                self.max_val.detach_().resize_(new_max.shape)
+                self.max_val.copy_(new_max)
+
+        return x_orig
+
+    @torch.jit.export
+    def calculate_qparams(self):  # type: ignore[override]
+        is_uninitialized = self.min_val == float("inf") and self.max_val == float(
+            "-inf"
+        )
+        if is_uninitialized:
+            warnings.warn(
+                "must run observer before calling calculate_qparams.\
+                                    Returning default scale and zero point ",
+                stacklevel=2,
+            )
+            return torch.tensor([1.0], device=self.min_val.device.type), torch.tensor(
+                [0], device=self.min_val.device.type
+            )
+        if self.bins != len(self.histogram):
+            raise AssertionError(
+                "The number of bins in histogram should be equal to the number of bins "
+                "supplied while making this observer"
+            )
+
+        new_min, new_max = self._non_linear_param_search()
+
+        return self._calculate_qparams(new_min, new_max)
+
+    def _save_to_state_dict(self, destination, prefix, keep_vars):
+        super()._save_to_state_dict(destination, prefix, keep_vars)
+        destination[prefix + "min_val"] = self.min_val
+        destination[prefix + "max_val"] = self.max_val
+
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        version = local_metadata.get("version", None)
+
+        if version is None or version < 3:
+            # if min_val and max_val are not initialized, update their shape
+            # to account for the differences between v2 and v3
+            min_val_name, max_val_name = prefix + "min_val", prefix + "max_val"
+            if min_val_name in state_dict:
+                if state_dict[min_val_name].shape == torch.Size([0]):
+                    state_dict[min_val_name] = torch.tensor(float("inf"))
+            if max_val_name in state_dict:
+                if state_dict[max_val_name].shape == torch.Size([0]):
+                    state_dict[max_val_name] = torch.tensor(float("-inf"))
+
+        local_state = ["min_val", "max_val"]
+        for name in local_state:
+            key = prefix + name
+            if key in state_dict:
+                val = state_dict[key]
+                setattr(self, name, val)
+            elif strict:
+                missing_keys.append(key)
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            strict,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+    def extra_repr(self):
+        return f"min_val={self.min_val}, max_val={self.max_val}"
+
+
+class FixedQParamsObserver(ObserverBase):
+    r"""
+    Observer that simulates quantize and dequantize with fixed
+    quantization parameters in training time. Only per tensor
+    quantization is supported.
+
+    Args:
+        `scale` (float): fixed scale for the observer
+        `zero_point` (int): fixed zero point for the observer
+        `dtype`, `qscheme`, `quant_min`, `quant_max`
+    """
+
+    scale: torch.Tensor
+    zero_point: torch.Tensor
+
+    def __init__(
+        self,
+        scale,
+        zero_point,
+        dtype=torch.quint8,
+        qscheme=torch.per_tensor_affine,
+        quant_min=0,
+        quant_max=255,
+        is_dynamic=False,
+        **kwargs,
+    ):
+        if is_dynamic:
+            raise NotImplementedError(
+                "FixedQParamsObserver doesn't support dynamic quantization"
+            )
+        super().__init__(dtype=dtype, is_dynamic=is_dynamic, **kwargs)
+        self.quant_min = quant_min
+        self.quant_max = quant_max
+        self.register_buffer("scale", torch.tensor([scale], dtype=torch.float))
+        self.register_buffer("zero_point", torch.tensor([zero_point], dtype=torch.int))
+        self.dtype = dtype
+        self.qscheme = qscheme
+
+    def forward(self, X):
+        return X
+
+    @torch.jit.export
+    def calculate_qparams(self):  # type: ignore[override]
+        return self.scale, self.zero_point
+
+
+class PlaceholderObserver(ObserverBase):
+    r"""
+    Observer that doesn't do anything and just passes its configuration to the
+    quantized module's ``.from_float()``.
+
+    Can be used for quantization to float16 which doesn't require determining
+    ranges.
+
+    Args:
+        dtype: dtype argument to the `quantize` node needed to implement the
+               reference model spec.
+        quant_min: minimum value in quantized domain (TODO: align behavior with other observers)
+        quant_max: maximum value in quantized domain
+        custom_op_name: (temporary) specify this observer for an operator that doesn't require any observation
+                        (Can be used in Graph Mode Passes for special case ops).
+        compute_dtype (deprecated): if set, marks the future quantize function to use
+                       dynamic quantization instead of static quantization.
+                       This field is deprecated, use `is_dynamic=True` instead.
+        is_dynamic: if True, the `quantize` function in the reference model
+                    representation taking stats from this observer instance will
+                    use dynamic quantization.
+    """
+
+    def __init__(
+        self,
+        dtype=torch.float32,
+        custom_op_name="",
+        compute_dtype=None,
+        quant_min=None,
+        quant_max=None,
+        qscheme=None,
+        eps=None,
+        is_dynamic=False,
+    ) -> None:
+        super().__init__(dtype=dtype, is_dynamic=is_dynamic)
+        if qscheme is None:
+            qscheme = torch.per_tensor_affine
+        if eps is None:
+            eps = torch.finfo(torch.float32).eps
+
+        # dtype of input of the target operator, e.g. for dynamic quantization
+        # ops, the dtype will be float32
+        self.dtype = dtype
+        self.qscheme = qscheme
+        self.quant_min = quant_min
+        self.quant_max = quant_max
+        self.eps = eps
+        self.custom_op = custom_op_name
+        # used for configuration of computation type for dynamic quantization
+        if compute_dtype:
+            is_dynamic = True
+            warnings.warn(
+                "Please use `is_dynamic` instead of `compute_dtype`. \
+                    `compute_dtype` will be deprecated in a future release \
+                    of PyTorch.",
+                stacklevel=2,
+            )
+
+    def forward(self, x):
+        return x
+
+    @torch.jit.export
+    def extra_repr(self):
+        return f"dtype={self.dtype}, is_dynamic={self.is_dynamic}"
+
+    @torch.jit.export
+    def calculate_qparams(self):  # type: ignore[override]
+        raise Exception(  # noqa: TRY002
+            "calculate_qparams should not be called for PlaceholderObserver"
+        )
+
+
+class RecordingObserver(ObserverBase):
+    r"""
+    The module is mainly for debug and records the tensor values during runtime.
+
+    Args:
+        dtype: Quantized data type
+        qscheme: Quantization scheme to be used
+        reduce_range: Reduces the range of the quantized data type by 1 bit
+    """
+
+    __annotations__ = {"tensor_val": list[torch.Tensor | None]}
+
+    def __init__(self, dtype=torch.quint8):
+        super().__init__(dtype=dtype, is_dynamic=False)
+        self.tensor_val = []
+
+    def forward(self, x):
+        self.tensor_val.append(x.clone())
+        return x
+
+    @torch.jit.export
+    def calculate_qparams(self):  # type: ignore[override]
+        raise Exception(  # noqa: TRY002
+            "calculate_qparams should not be called for RecordingObserver"
+        )
+
+    @torch.jit.export
+    def get_tensor_value(self):
+        return self.tensor_val
+
+
+class NoopObserver(ObserverBase):
+    r"""
+    Observer that doesn't do anything and just passes its configuration to the
+    quantized module's ``.from_float()``.
+
+    Primarily used for quantization to float16 which doesn't require determining
+    ranges.
+
+    Args:
+        dtype: Quantized data type
+        custom_op_name: (temporary) specify this observer for an operator that doesn't require any observation
+                        (Can be used in Graph Mode Passes for special case ops).
+    """
+
+    def __init__(self, dtype=torch.float16, custom_op_name="") -> None:
+        super().__init__(dtype=dtype, is_dynamic=False)
+        self.dtype = dtype
+        self.custom_op = custom_op_name
+
+    def forward(self, x):
+        return x
+
+    @torch.jit.export
+    def calculate_qparams(self):  # type: ignore[override]
+        raise Exception(  # noqa: TRY002
+            "calculate_qparams should not be called for NoopObserver"
+        )
+
+
+class ReuseInputObserver(ObserverBase):
+    r"""This observer is used when we want to reuse the observer from the operator
+    that produces the input Tensor, typically used for operators like reshape, e.g.
+    ```
+    x0 = ...
+    x1 = x0.reshape()
+    ```
+    if we configure x0 to be observed by some observer, let's say MinMaxObserver,
+    and reshape is configured with ReuseInputObserver, we'll reuse the observer instance
+    for x0 for x1 (output of reshape). If x0 is not observed, we also won't observe x1.
+
+    Note: this is only enabled in FX Graph Mode Quantization
+    """
+
+    def __init__(self) -> None:
+        super().__init__(torch.quint8, is_dynamic=False)
+
+    def forward(self, x):
+        return x
+
+    @torch.jit.export
+    def calculate_qparams(self):  # type: ignore[override]
+        raise Exception(  # noqa: TRY002
+            "calculate_qparams should not be called for ReuseInputObserver"
+        )
+
+
+"""
+# Experimental Affine Quantization Feature START
+We plan to merge the following with torchao repo after we move pt2e flow to torchao
+copied from https://github.com/pytorch/ao/blob/main/torchao/quantization/observer.py
+"""
+from dataclasses import dataclass
+from enum import auto, Enum
+
+
+class MappingType(Enum):
+    """How floating point number is mapped to integer number
+
+    symmetric mapping means floating point range is symmetrically mapped to integer range
+    let's say we have floating point range (-3.5, 10.2) and integer range (-8, 7) (int4)
+    we'll use (-10.2, 10.2) as the range for floating point and map that to (-8, 7)
+    e.g. scale = (10.2 - (-10.2)) / (7 - (-8))
+
+    SYMMETRIC_NO_CLIPPING_ERR is a variant of symmetric mapping, where the scale is the max of smin
+    and smax, where smin = min_val_neg / quant_min, and smax = max_val_pos / quant_max. By calculating
+    smin and smax individually, there can be less round error on negative values, and no out-of-range
+    of all floating point values.
+
+    asymmetric mapping means we just directly map the floating point range to integer range,
+    for the above example, we will map (-3.5, 10.2) to (-8, 7) and calculate quantization parameter
+    based on this mapping
+    e.g. scale = (10.2 - (-3.5)) / (7 - (-8))
+    """
+
+    SYMMETRIC = auto()
+    SYMMETRIC_NO_CLIPPING_ERR = auto()
+    ASYMMETRIC = auto()
+
+
+class ZeroPointDomain(Enum):
+    """Enum that indicate whether zero_point is in integer domain or floating point domain
+
+    integer domain: quantized_val = (float_val / scale) (integer) + zero_point (integer)
+    float domain: quantized_val = (float_val - (zero_point (float) - scale * mid_point)) / scale
+    none domain: quantized_val = (float_val / scale)
+    """
+
+    INT = auto()
+    FLOAT = auto()
+    NONE = auto()
+
+
+class TorchAODType(Enum):
+    """
+    Placeholder for dtypes that do not exist in PyTorch core yet.
+    """
+
+    # torch.int1 to torch.int7 will be added to PyTorch 2.6
+    # These will remain here for BC with older PyTorch versions
+    INT1 = auto()
+    INT2 = auto()
+    INT3 = auto()
+    INT4 = auto()
+    INT5 = auto()
+    INT6 = auto()
+    INT7 = auto()
+
+
+@dataclass(frozen=True)
+class Granularity:
+    """
+    Base class for representing the granularity of quantization.
+
+    This class serves as a parent for specific granularity types used in
+    quantization operations, such as per-tensor or per-axis quantization.
+    """
+
+
+@dataclass(frozen=True)
+class PerBlock(Granularity):
+    """
+    Represents per-block granularity in quantization. See
+    :func:`~torchao.quantization.quant_primitives.quantize_affine` for docs for
+    `block_size`
+
+    Attributes:
+        block_size (Tuple[int, ...]): The size of each quantization group
+    """
+
+    block_size: tuple[int, ...]
+
+
+@dataclass(frozen=True)
+class PerTensor(Granularity):
+    """
+    Represents per-tensor granularity in quantization.
+
+    This granularity type calculates the quantization parameters
+    based off the entire tensor.
+
+    """
+
+
+@dataclass(frozen=True)
+class PerAxis(Granularity):
+    """
+    Represents per-axis granularity in quantization.
+
+    This granularity type calculates different quantization parameters
+    along a specified axis of the tensor.
+
+    For example if the input tensor is shape [8, 16] and axis=0, then
+    the quantization parameters are calculated for each row of the tensor.
+    Giving a total of 8 quantization parameters.
+
+    Attributes:
+        axis (int): The axis along which reduction is performed.
+    """
+
+    axis: int
+
+
+@dataclass(frozen=True)
+class PerGroup(Granularity):
+    """
+    Represents per-channel group granularity in quantization.
+
+    This granularity type calculates different quantization parameters
+    for each group of  elements.
+
+    For example if the input tensor is shape [8, 16], and the group size is 4, then
+    the input tensor is reshaped to [64, 4]
+    quantization parameters are calculated for each group of 4 elements,
+    giving a total of 64 quantization parameters.
+
+    Attributes:
+        group_size (int): The size of each quantization group
+
+    """
+
+    group_size: int
+
+
+class PerRow(Granularity):
+    """
+    Represents row-wise granularity in quantization.
+
+    This is a special case of per-axis quantization and is unique to Float8 matmuls
+    where the input is quantized with a block_size of (1, ..., input.shape[-1]). And the weight
+    is quantized with a block_size of (1, weight.shape[1]).
+    """
+
+
+class PerToken(Granularity):
+    """
+    Represents per-token granularity in quantization.
+
+    This granularity type calculates a different set of quantization parameters
+    for each token, which is represented as the last dimension of the tensor.
+
+    For example, if the input tensor has shape [2, 3, 4], then there are 6 tokens
+    with 4 elements each, and we will calculate 6 sets of quantization parameters,
+    one for each token.
+
+    If the input tensor has only two dimensions, e.g. [8, 16], then this is
+    equivalent to `PerAxis(axis=0)`, which yields 8 sets of quantization parameters.
+    """
+
+
+def get_block_size(
+    input_shape: tuple[int, ...], granularity: Granularity
+) -> tuple[int, ...]:
+    """Get the block size based on the input shape and granularity type.
+
+    Args:
+        input_shape: The input tensor shape possibly more than 2 dimensions
+        granularity: The granularity type of the quantization
+    """
+    if not isinstance(granularity, Granularity):
+        raise AssertionError(
+            "Please provide an instance of Granularity, not subclass of it"
+        )
+    if isinstance(granularity, PerTensor):
+        return input_shape
+    elif isinstance(granularity, PerAxis):
+        block_size = list(input_shape)
+        block_size[granularity.axis] = 1
+        return tuple(block_size)
+    elif isinstance(granularity, PerRow):
+        return (1,) * (len(input_shape) - 1) + (input_shape[-1],)
+    elif isinstance(granularity, PerGroup):
+        if len(input_shape) != 2:
+            raise AssertionError(
+                f"Expecting input shape dim to be 2 for per group quantization, gotinput shape: {input_shape}"
+            )
+        return (1, granularity.group_size)
+    elif isinstance(granularity, PerToken):
+        block_size = [1] * len(input_shape)
+        block_size[-1] = input_shape[-1]
+        return tuple(block_size)
+    raise ValueError(f"Unsupported Granularity: {granularity}")
+
+
+class AffineQuantizedObserverBase(ABC, torch.nn.Module):
+    """Observer module for affine quantization (https://github.com/pytorch/ao/tree/main/torchao/quantization#affine-quantization)
+
+    Args:
+      `granularity` and `block_size`: The granularity of the quantization,
+        must specify at least one, if both are specified `block_size` takes precedence
+        Current supported granularity type are `PerTensor` and `PerAxis`
+      other args: please see `:class:torchao.dtypes.AffineQuantizedTensor`
+    """
+
+    with_args = classmethod(_with_args)
+
+    def __init__(
+        self,
+        mapping_type: MappingType,
+        target_dtype: torch.dtype,
+        granularity: Granularity,
+        quant_min: int | None = None,
+        quant_max: int | None = None,
+        eps: float | None = None,
+        scale_dtype: torch.dtype | None = None,
+        zero_point_dtype: torch.dtype | None = None,
+        preserve_zero: bool = True,
+        zero_point_domain: ZeroPointDomain | None = ZeroPointDomain.INT,
+        # there could be some extra args that's ignored
+        **kwargs,
+    ):
+        super().__init__()
+        if granularity is None:
+            raise AssertionError("granularity is None")
+        self.mapping_type = mapping_type
+        self.target_dtype = target_dtype
+        self.granularity = granularity
+        self.quant_min = quant_min
+        self.quant_max = quant_max
+        self.eps = eps
+        self.scale_dtype = scale_dtype
+        self.zero_point_dtype = zero_point_dtype
+        self.preserve_zero = preserve_zero
+        self.zero_point_domain = zero_point_domain
+        # populatd during forward
+        self.block_size = None
+        self.original_dtype = None
+
+    @abstractmethod
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        """forward function should take the input tensor
+        and updates internal stats and return the original input Tensor
+        """
+
+    @abstractmethod
+    def calculate_qparams(self) -> tuple[torch.Tensor, torch.Tensor]:
+        """Calculate quantization parameter based on the stats attached to the observer module
+        and returns a tuple of scale and zero_point Tensor
+        """
+
+    def convert(self, model: torch.fx.GraphModule, observer_node: Node):
+        """
+        Converts the observer node in the graph into its quantized representation
+
+        Args:
+            model: graph module to convert the observer node in
+            observer_node: the observer node to convert
+        """
+        from torch.ao.quantization.fx.utils import create_getattr_from_value
+
+        with model.graph.inserting_before(observer_node):
+            if self.block_size is None:
+                raise AssertionError("Expecting block_size to be populated")
+            if self.original_dtype is None:
+                raise AssertionError("Expecting original_dtype to be populated")
+            if hasattr(self, "is_dynamic") and self.is_dynamic:
+                choose_qparams_affine = model.graph.call_function(
+                    torch.ops.pt2e_quant.choose_qparams_affine,
+                    (
+                        observer_node.args[0],
+                        self.mapping_type.name,
+                        self.block_size,
+                        self.target_dtype,
+                        self.quant_min,
+                        self.quant_max,
+                        self.eps,
+                        self.scale_dtype,
+                        self.zero_point_dtype,
+                        self.preserve_zero,
+                        self.zero_point_domain.name,
+                    ),
+                )
+                scale_node = model.graph.call_function(
+                    operator.getitem, (choose_qparams_affine, 0)
+                )
+                zero_point_node = model.graph.call_function(
+                    operator.getitem, (choose_qparams_affine, 1)
+                )
+            else:
+                scale, zero_point = self.calculate_qparams()
+                scale_node = create_getattr_from_value(
+                    model,
+                    model.graph,
+                    "_scale",
+                    scale,
+                    scale.device if isinstance(scale, torch.Tensor) else None,
+                )
+                zero_point_node = create_getattr_from_value(
+                    model,
+                    model.graph,
+                    "_zero_point",
+                    zero_point,
+                    zero_point.device if isinstance(zero_point, torch.Tensor) else None,
+                )
+
+            q_node = model.graph.call_function(
+                torch.ops.pt2e_quant.quantize_affine,
+                (
+                    observer_node.args[0],
+                    self.block_size,
+                    scale_node,
+                    zero_point_node,
+                    self.target_dtype,
+                    self.quant_min,
+                    self.quant_max,
+                    self.zero_point_domain.name,
+                ),
+                {},
+            )
+            dq_node = model.graph.call_function(
+                torch.ops.pt2e_quant.dequantize_affine,
+                (
+                    q_node,
+                    self.block_size,
+                    scale_node,
+                    zero_point_node,
+                    self.target_dtype,
+                    self.quant_min,
+                    self.quant_max,
+                    self.zero_point_domain.name,
+                ),
+                {"output_dtype": self.original_dtype},
+            )
+            observer_node.replace_all_uses_with(dq_node)
+            model.graph.erase_node(observer_node)
+
+
+def _is_observer_script_module(mod, obs_type_name):
+    """Returns true if given mod is an instance of Observer script module."""
+    if isinstance(mod, torch.jit.RecursiveScriptModule):
+        # qualified name looks like '__torch__.torch.ao.quantization.observer.___torch_mangle_2.MinMaxObserver'
+        suffix = mod._c.qualified_name.split(".", 1)[1]
+        name = re.sub(r"\.___torch_mangle_\d+", "", suffix)
+        return obs_type_name in name
+    return False
+
+
+# Experimental Affine Quantization Feature END
+
+
+def _is_activation_post_process(module):
+    return isinstance(
+        module,
+        (
+            torch.ao.quantization.ObserverBase,
+            torch.ao.quantization.FakeQuantizeBase,
+            AffineQuantizedObserverBase,
+        ),
+    ) or _is_observer_script_module(module, "quantization.observer")
+
+
+def _is_per_channel_script_obs_instance(module):
+    if isinstance(module, torch.jit.RecursiveScriptModule):
+        return _is_observer_script_module(
+            module, "quantization.observer.PerChannelMinMaxObserver"
+        ) or _is_observer_script_module(
+            module, "quantization.observer.MovingAveragePerChannelMinMaxObserver"
+        )
+    return False
+
+
+def get_observer_state_dict(mod):
+    r"""
+    Returns the state dict corresponding to the observer stats.
+    Traverse the model state_dict and extract out the stats.
+    """
+    od = OrderedDict()
+    if isinstance(mod, torch.jit.RecursiveScriptModule):
+        for k, v in mod.state_dict().items():
+            if "observer" in k:
+                od[k] = v
+    else:
+        # path for GraphModule and nn.Module (eager mode)
+        for k, v in mod.state_dict().items():
+            if "activation_post_process" in k:
+                od[k] = v
+    od._metadata = mod.state_dict()._metadata  # type: ignore[attr-defined]
+    return od
+
+
+def load_observer_state_dict(mod, obs_dict):
+    r"""
+    Given input model and a state_dict containing model observer stats,
+    load the stats back into the model. The observer state_dict can be saved
+    using torch.ao.quantization.get_observer_state_dict
+    """
+    missing_keys: list[str] = []
+    unexpected_keys: list[str] = []
+    for name, module in mod.named_modules():
+        prefix = name + "."
+        if _is_activation_post_process(module):
+            if _is_per_channel_script_obs_instance(module):
+                # For per-channel observers we need to call a custom load_from_state_dict to resize the tensor.
+                # However this is not called when the module is scripted and we end up calling the default one in module.py
+                module._load_from_state_dict_script(
+                    obs_dict, prefix, {}, True, missing_keys, unexpected_keys, []
+                )
+            else:
+                module._load_from_state_dict(
+                    obs_dict, prefix, {}, False, missing_keys, unexpected_keys, []
+                )
+    for k in missing_keys:
+        if "observer" in k or "activation_post_process" in k:
+            raise Exception(  # noqa: TRY002
+                f"Missing keys for observer {k} in state_dict"
+            )
+    for k in unexpected_keys:
+        if "observer" in k or "activation_post_process" in k:
+            raise Exception(  # noqa: TRY002
+                f"Unexpected keys for observer {k} in state_dict"
+            )
+
+
+# Restrict activations to be in the range (0,127)
+default_observer = MinMaxObserver.with_args(quant_min=0, quant_max=127)
+"""
+Default observer for static quantization, usually used for debugging.
+"""
+
+default_placeholder_observer = PlaceholderObserver
+"""
+Default placeholder observer, usually used for quantization to torch.float16.
+"""
+
+default_debug_observer = RecordingObserver
+"""
+Default debug-only observer.
+"""
+
+default_weight_observer = MinMaxObserver.with_args(
+    dtype=torch.qint8, qscheme=torch.per_tensor_symmetric
+)
+"""
+Default weight observer.
+"""
+
+weight_observer_range_neg_127_to_127 = MinMaxObserver.with_args(
+    dtype=torch.qint8,
+    qscheme=torch.per_tensor_symmetric,
+    quant_min=-127,
+    quant_max=127,
+    eps=2**-12,
+)
+"""
+Symmetric weight observer with the 8-bit values restricted to [-127, +127], excluding -128.
+"""
+
+default_histogram_observer = HistogramObserver.with_args(quant_min=0, quant_max=127)
+"""
+Default histogram observer, usually used for PTQ.
+"""
+
+default_per_channel_weight_observer = PerChannelMinMaxObserver.with_args(
+    dtype=torch.qint8, qscheme=torch.per_channel_symmetric
+)
+"""
+Default per-channel weight observer, usually used on backends where per-channel
+weight quantization is supported, such as `fbgemm`.
+"""
+
+per_channel_weight_observer_range_neg_127_to_127 = PerChannelMinMaxObserver.with_args(
+    dtype=torch.qint8,
+    qscheme=torch.per_channel_symmetric,
+    quant_min=-127,
+    quant_max=127,
+    eps=2**-12,
+)
+"""
+Per-channel, symmetric weight observer with the 8-bit values restricted to [-127, +127], excluding -128.
+"""
+
+default_dynamic_quant_observer = PlaceholderObserver.with_args(
+    dtype=torch.quint8,
+    quant_min=0,
+    quant_max=255,
+    is_dynamic=True,
+)
+"""
+Default observer for dynamic quantization.
+"""
+
+default_float_qparams_observer = PerChannelMinMaxObserver.with_args(
+    dtype=torch.quint8, qscheme=torch.per_channel_affine_float_qparams, ch_axis=0
+)
+"""
+Default observer for a floating point zero-point.
+"""
+
+default_float_qparams_observer_4bit = PerChannelMinMaxObserver.with_args(
+    dtype=torch.quint4x2, qscheme=torch.per_channel_affine_float_qparams, ch_axis=0
+)
+"""
+Default observer for a floating point zero-point and 4 bit activations.
+"""
+
+# TODO(future PR): remove these defaults and enforce activation functions
+# to explicitly specify their output range
+default_fixed_qparams_range_neg1to1_observer = FixedQParamsObserver.with_args(
+    scale=2.0 / 256.0, zero_point=128, dtype=torch.quint8, quant_min=0, quant_max=255
+)
+default_fixed_qparams_range_0to1_observer = FixedQParamsObserver.with_args(
+    scale=1.0 / 256.0, zero_point=0, dtype=torch.quint8, quant_min=0, quant_max=255
+)
+# TODO: the following 2 variables are kept for backwards compatibility; remove after a few releases
+default_symmetric_fixed_qparams_observer = default_fixed_qparams_range_neg1to1_observer
+default_affine_fixed_qparams_observer = default_fixed_qparams_range_0to1_observer
+
+"""
+Default observers for fixed qparams operations.
+"""
+
+default_reuse_input_observer = ReuseInputObserver
+"""
+Default observer for operators like reshape that reuses the observer of input to
+the operator
+"""
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/__init__.py
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diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py
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+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py
@@ -0,0 +1,891 @@
+# copied from https://github.com/pytorch/ao/blob/main/torchao/quantization/observer.py
+# and https://github.com/pytorch/ao/blob/main/torchao/quantization/quant_primitives.py
+# PLEASE DON'T MODIFY THIS FILE SO THAT WE DON'T GET OUT OF SYNC
+import logging
+from abc import ABCMeta
+from typing import Any
+
+import torch
+from torch.ao.quantization.observer import (
+    AffineQuantizedObserverBase,
+    get_block_size,
+    Granularity,
+    MappingType,
+    TorchAODType,
+    ZeroPointDomain,
+)
+
+
+ABC: Any = ABCMeta("ABC", (object,), {})  # compatible with Python 2 *and* 3:
+
+logger = logging.getLogger(__name__)
+
+FP8_TYPES = {
+    torch.float8_e4m3fn,
+    torch.float8_e5m2,
+    torch.float8_e4m3fnuz,
+    torch.float8_e5m2fnuz,
+}
+_SUB_BYTE_UINT_BOUNDS = {
+    torch.uint1: (0, 2**1 - 1),
+    torch.uint2: (0, 2**2 - 1),
+    torch.uint3: (0, 2**3 - 1),
+    torch.uint4: (0, 2**4 - 1),
+    torch.uint5: (0, 2**5 - 1),
+    torch.uint6: (0, 2**6 - 1),
+    torch.uint7: (0, 2**7 - 1),
+}
+
+"""
+Map from dtype to the bound value of integers
+TODO: maybe can replace this with call to torch.iinfo
+"""
+_DTYPE_TO_QVALUE_BOUNDS: dict[torch.dtype | TorchAODType, tuple[int, int]] = {
+    torch.uint8: (0, 255),
+    torch.int8: (-128, 127),
+    torch.int16: (-(2**15), 2**15 - 1),
+    torch.int32: (-(2**31), 2**31 - 1),
+}
+_DTYPE_TO_QVALUE_BOUNDS.update(_SUB_BYTE_UINT_BOUNDS)
+
+
+def _is_float8_type(dtype: torch.dtype) -> bool:
+    fp8_types = {
+        torch.float8_e4m3fn,
+        torch.float8_e4m3fnuz,
+        torch.float8_e5m2,
+        torch.float8_e5m2fnuz,
+    }
+    return dtype in fp8_types
+
+
+# TODO: decide on if we want to allow custom quant_min/quant_max here
+def _get_and_check_qmin_qmax(dtype, quant_min, quant_max):
+    """Get quant_min and quant_max args based on dtype and also
+    verify that they are within the range of possible quant_min/quant_max
+    for dtype
+    """
+    if dtype in FP8_TYPES:
+        quant_min_lower_bound, quant_max_upper_bound = (
+            torch.finfo(dtype).min,
+            torch.finfo(dtype).max,
+        )
+    elif dtype not in _DTYPE_TO_QVALUE_BOUNDS:
+        raise ValueError(f"Unsupported dtype: {dtype}")
+    else:
+        quant_min_lower_bound, quant_max_upper_bound = _DTYPE_TO_QVALUE_BOUNDS[dtype]
+    if quant_min is None:
+        quant_min = quant_min_lower_bound
+    if quant_max is None:
+        quant_max = quant_max_upper_bound
+
+    if quant_min < quant_min_lower_bound:
+        raise AssertionError(
+            "quant_min out of bound for dtype, "
+            f"quant_min_lower_bound: {quant_min_lower_bound} quant_min: {quant_min}"
+        )
+
+    if quant_max > quant_max_upper_bound:
+        raise AssertionError(
+            "quant_max out of bound for dtype, "
+            f"quant_max_upper_bound: {quant_max_upper_bound} quant_max: {quant_max}"
+        )
+    return quant_min, quant_max
+
+
+def _get_reduction_params(block_size, input_size):
+    """Given block_size and input size find the parameters for reduction:
+
+    Output:
+        shape_for_reduction: the shape we use to `view` input to prepare it for reduction
+        reduction_dims: the dims we'll do reduction over
+
+    Example::
+        Input:
+          block_size: (3, 3, 2, 10)
+          input_size: (3, 3, 10, 10)
+
+        Output:
+          shape_for_reduction: (3, 3, 5, 2, 10)
+          reduction_dim: [0, 1, 3, 4]
+    """
+    if len(block_size) != len(input_size):
+        raise AssertionError(
+            "block_size length must equal input_size length, got "
+            f"block_size={block_size}, input_size={input_size}"
+        )
+    shape_for_reduction = []
+    reduction_dims = []
+    cur_dim = 0
+    for i in range(len(block_size)):
+        if block_size[i] != input_size[i] and block_size[i] > 1:
+            if input_size[i] % block_size[i] != 0:
+                raise AssertionError(
+                    f"Expecting input size at {i} dimension: {input_size[i]} to be divisible "
+                    f"by block_size at {i} dimension: {block_size[i]}"
+                )
+            shape_for_reduction.append(input_size[i] // block_size[i])
+            shape_for_reduction.append(block_size[i])
+            # reduce over the block_size[i] dim
+            reduction_dims.append(cur_dim + 1)
+            cur_dim += 2
+        else:
+            # block_size[i] == input_size[i] or block_size[i] == 1
+            shape_for_reduction.append(input_size[i])
+            # we only need to reduce over the dimension if block_size is greater than 1
+            # otherwise it's already the same as reduced dimension
+            if block_size[i] != 1:
+                reduction_dims.append(cur_dim)
+            cur_dim += 1
+    return shape_for_reduction, reduction_dims
+
+
+def _register_custom_op(lib):
+    """This decorator is used to preserve some high level operators for torch.export.export
+    while still allow them to be decomposed for inductor path
+
+    requirement: make sure `fn.__name__[1:]` is the operator name you want to register
+
+    NOTE: This should be applied at the top, after all other decorators have been applied
+    NOTE: We haven't tested the case when `fn` accepts tensor subclass instance as input,
+    e.g. uint4 tensor subclass instance, and we'll probably need to figure out what would make
+    sense for downstream system (like executorch) to accept as well
+
+    Example:
+        lib = torch.library.Library("my_namespace', "FRAGMENT")
+
+        register_custom_op = _register_custom_op(lib)
+
+        @register_custom_op
+        def _the_op_that_needs_to_be_preserved(...)
+            ...
+
+        # after this, `_the_op_that_needs_to_be_preserved` will be preserved as
+        # torch.ops.my_namespace.the_op_that_needs_to_be_preserved operator after
+        # torch.export.export / torch._export.export_for_training
+
+    """
+    from torch._inductor.decomposition import register_decomposition
+
+    def decorator(fn):
+        from torch._library.infer_schema import infer_schema
+
+        # expecting fn.__name__ starts with `_` and we want to take the rest
+        # to be the name of the custom op
+        if fn.__name__[0] != "_":
+            raise AssertionError(
+                f"Expecting function name starts with `_`, got {fn.__name__}"
+            )
+        if any(c in fn.__name__ for c in ".<>"):
+            raise AssertionError(
+                f"Expecting op to be defined in normal functions, not lambda or local: {fn.__name__}"
+            )
+        op_name = fn.__name__[1:]
+        schema = op_name + infer_schema(fn, mutates_args={})
+        lib.define(schema)
+        lib.impl(op_name, fn, "CompositeImplicitAutograd")
+
+        lib_namespace = lib.ns
+        op = getattr(getattr(torch.ops, lib_namespace), op_name)
+        register_decomposition([op])(fn)
+        return op
+
+    return decorator
+
+
+quant_lib = torch.library.Library("pt2e_quant", "FRAGMENT")  # noqa: TOR901
+
+register_custom_op = _register_custom_op(quant_lib)
+
+
+def choose_qparams_affine_with_min_max(
+    min_val: torch.Tensor,
+    max_val: torch.Tensor,
+    mapping_type: MappingType,
+    block_size: tuple[int, ...],
+    target_dtype: torch.dtype,
+    quant_min: int | None = None,
+    quant_max: int | None = None,
+    eps: float | None = None,
+    scale_dtype: torch.dtype | None = None,
+    zero_point_dtype: torch.dtype | None = None,
+    preserve_zero: bool = True,
+    zero_point_domain: ZeroPointDomain | None = ZeroPointDomain.INT,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """A variant of :func:`~torchao.quantization.quant_primitives.choose_qparams_affine`
+    operator that pass in min_val and max_val directly instead of deriving these from a single input.
+    This is used for observers in static quantization where min_val and max_val may be obtained through
+    tracking all the data in calibration data set.
+
+    Args:
+      Mostly same as :func:`~torchao.quantization.quant_primitives.choose_qparams_affine`. with one
+      difference: instead of passing in `input` Tensor and use that to calculate min_val/max_val
+      and then scale/zero_point, we pass in min_val/max_val directly
+    """
+    return _choose_qparams_affine(
+        None,
+        mapping_type.name,
+        block_size,
+        target_dtype,
+        quant_min,
+        quant_max,
+        eps,
+        scale_dtype,
+        zero_point_dtype,
+        preserve_zero,
+        zero_point_domain.name if zero_point_domain is not None else None,
+        min_val,
+        max_val,
+    )
+
+
+@register_custom_op
+def _choose_qparams_affine(
+    input: torch.Tensor | None,
+    mapping_type: str,
+    block_size: list[int],
+    target_dtype: torch.dtype,
+    quant_min: int | float | bool | None = None,
+    quant_max: int | float | bool | None = None,
+    eps: float | None = None,
+    scale_dtype: torch.dtype | None = None,
+    zero_point_dtype: torch.dtype | None = None,
+    preserve_zero: bool = True,
+    zero_point_domain: str | None = "INT",
+    min_val: torch.Tensor | None = None,
+    max_val: torch.Tensor | None = None,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """op definition that has compatible signatures with custom op library
+
+    The op does the following:
+    1. figure out the dimension for reduction based on block_size
+    2. find min_val/max_val based on the dimension for reduction
+    3. calculate quantization parameters based on min_val/max_val based on args like `preserve_zero`
+       and `zero_point_domain`
+    """
+    quant_min, quant_max = _get_and_check_qmin_qmax(target_dtype, quant_min, quant_max)
+    if mapping_type not in [
+        MappingType.SYMMETRIC.name,
+        MappingType.SYMMETRIC_NO_CLIPPING_ERR.name,
+        MappingType.ASYMMETRIC.name,
+    ]:
+        raise AssertionError(f"Unsupported mapping type: {mapping_type}")
+    if target_dtype in FP8_TYPES:
+        if mapping_type != MappingType.SYMMETRIC.name:
+            raise AssertionError(
+                f"Only symmetric quantization is supported for FP8 types, got {mapping_type}"
+            )
+
+    if input is not None:
+        if scale_dtype is None:
+            scale_dtype = input.dtype
+        if zero_point_dtype is None:
+            zero_point_dtype = input.dtype
+        if eps is None:
+            eps = torch.finfo(input.dtype).eps
+
+        if len(block_size) != input.dim():
+            raise AssertionError(
+                f"Got input dim:{input.dim()}, block_size: {block_size}"
+            )
+        shape_for_reduction, reduction_dims = _get_reduction_params(
+            block_size, input.size()
+        )
+        input = input.view(shape_for_reduction)
+
+        min_val = torch.amin(input, dim=reduction_dims, keepdim=False)
+        max_val = torch.amax(input, dim=reduction_dims, keepdim=False)
+    else:
+        if min_val is None or max_val is None:
+            raise AssertionError(
+                f"Need to provide `min_val` and `max_val` when `input` is None, got: {min_val, max_val}"
+            )
+        if min_val.dtype != max_val.dtype:
+            raise AssertionError(
+                f"Expecting `min_val` and `max_val` to have the same dtype, got: {min_val.dtype, max_val.dtype}"
+            )
+
+        if scale_dtype is None:
+            scale_dtype = min_val.dtype
+        if zero_point_dtype is None:
+            zero_point_dtype = min_val.dtype
+        if eps is None:
+            eps = torch.finfo(min_val.dtype).eps
+
+    if preserve_zero:
+        min_val_neg = torch.min(min_val, torch.zeros_like(min_val))
+        max_val_pos = torch.max(max_val, torch.zeros_like(max_val))
+    else:
+        min_val_neg = min_val
+        max_val_pos = max_val
+
+    if (
+        mapping_type == MappingType.SYMMETRIC.name
+        or mapping_type == MappingType.SYMMETRIC_NO_CLIPPING_ERR.name
+    ):
+        # scales
+        if mapping_type == MappingType.SYMMETRIC.name:
+            max_val_pos = torch.max(-min_val_neg, max_val_pos)
+            scale = max_val_pos / (float(quant_max - quant_min) / 2)
+        else:
+            if mapping_type != MappingType.SYMMETRIC_NO_CLIPPING_ERR.name:
+                raise AssertionError(
+                    f"Expected mapping_type to be SYMMETRIC_NO_CLIPPING_ERR, got {mapping_type}"
+                )
+            # calculate smin and smax individually and choose the larger one. For example, if quant_min = -8 and
+            # quant_max = 7.
+            # - If smin is bigger: There would be coverage on negative values down to -8, and less rounding
+            # error than the existing SYMMETRIC case.
+            # - If smax is bigger: it covers the positive values up to 7. The round
+            # error may be bigger than the existing SYMMETRIC case. Either way, there's no out-of-range fp values after
+            # quantization.
+            smin = min_val_neg / float(quant_min)
+            smax = max_val_pos / float(quant_max)
+            mask = smin > smax
+            scale = torch.where(mask, smin, smax)
+        # zeros
+        if not preserve_zero:
+            raise ValueError(
+                "preserve_zero == False is not supported for symmetric quantization"
+            )
+        if (
+            zero_point_domain is not None
+            and zero_point_domain != ZeroPointDomain.INT.name
+        ):
+            raise ValueError(
+                "zero_point_domain != ZeroPointDomain.INT is not supported for symmetric quantization"
+            )
+        scale = torch.clamp(scale, min=eps)
+        zero_point = torch.full_like(scale, int((quant_max + quant_min + 1) / 2))
+    else:
+        if mapping_type != MappingType.ASYMMETRIC.name:
+            raise AssertionError(
+                f"Expected mapping_type to be ASYMMETRIC, got {mapping_type}"
+            )
+        scale = (max_val_pos - min_val_neg) / float(quant_max - quant_min)
+        scale = torch.clamp(scale, min=eps)
+        if zero_point_domain == ZeroPointDomain.NONE.name:
+            zero_point = None
+        else:
+            if preserve_zero:
+                zero_point = quant_min - torch.round(min_val_neg / scale)
+                zero_point = torch.clamp(zero_point, quant_min, quant_max)
+            else:
+                if zero_point_domain != ZeroPointDomain.FLOAT.name:
+                    raise AssertionError(
+                        "if not preserve_zero, zero_point must be in FLOAT domain"
+                    )
+                mid_point = (quant_max + quant_min + 1) / 2
+                zero_point = min_val_neg + scale * mid_point
+
+    if zero_point is not None:
+        zero_point = zero_point.to(dtype=zero_point_dtype)
+    return scale.to(dtype=scale_dtype), zero_point
+
+
+@torch.no_grad()
+def quantize_affine(
+    input: torch.Tensor,
+    block_size: tuple[int, ...],
+    scale: torch.Tensor,
+    zero_point: torch.Tensor | None,
+    output_dtype: torch.dtype,
+    quant_min: int | float | None = None,
+    quant_max: int | float | None = None,
+    zero_point_domain: ZeroPointDomain | None = ZeroPointDomain.INT,
+) -> torch.Tensor:
+    """
+    Args:
+      input (torch.Tensor): original float32, float16 or bfloat16 Tensor
+      block_size: (Tuple[int, ...]): granularity of quantization,
+           this means the size of the tensor elements that's sharing the same qparam
+           e.g. when size is the same as the input tensor dimension, we are using per tensor quantization
+      scale (float): quantization parameter for affine quantization
+      zero_point (int): quantization parameter for affine quantization
+      output_dtype (torch.dtype): requested dtype (e.g. torch.uint8) for output Tensor
+      quant_min (Optional[int]): minimum quantized value for output Tensor, if not specified, it will be derived from dtype
+      quant_max (Optional[int]): maximum quantized value for output Tensor, if not specified, it will be derived from dtype
+      zero_point_domain (ZeroPointDomain): the domain that zero_point is in, should be either integer or float
+        if zero_point is in integer domain, zero point is added to the quantized integer value during
+        quantization
+        if zero_point is in floating point domain, zero point is subtracted from the floating point (unquantized)
+        value during quantization
+        default is ZeroPointDomain.INT
+
+    Note:
+      How can block_size represent different granularities?
+      let's say we have a Tensor of size: (3, 3, 10, 10), here is the table showing how block_size represents different
+      granularities:
+
+       granularity type       |     block_size
+         per_tensor           |    (3, 3, 10, 10)
+         per_axis (axis=0)    |    (1, 3, 10, 10)
+         per_axis (axis=1)    |    (3, 1, 10, 10)
+     per_group (groupsize=2)  |    (3, 3, 10, 2)
+     per_group (groupsize=2) for axis = 3 | (3, 3, 2, 10)
+
+
+    Output:
+      quantized tensor with requested dtype
+    """
+    return _quantize_affine(
+        input,
+        block_size,
+        scale,
+        zero_point,
+        output_dtype,
+        quant_min,
+        quant_max,
+        zero_point_domain.name if zero_point_domain is not None else None,
+    )
+
+
+@register_custom_op
+def _quantize_affine(
+    input: torch.Tensor,
+    block_size: list[int],
+    scale: torch.Tensor,
+    zero_point: torch.Tensor | None,
+    output_dtype: torch.dtype,
+    quant_min: int | float | bool | None = None,
+    quant_max: int | float | bool | None = None,
+    zero_point_domain: str | None = ZeroPointDomain.INT.name,
+) -> torch.Tensor:
+    """op definition that has compatible signatures with custom op library
+
+    Note:
+        zero_point_domain is optional specifies how we quantize the floating point to quantized data:
+        INT: quantized_val = (float_val / scale) (integer) + zero_point (integer)
+        FLOAT: quantized_val = (float_val - (zero_point (float) - scale * mid_point)) / scale
+        None: quantized_val = (float_val / scale) | this is primarily used for floatx quantization
+            Where we do not want to round values to nearest integer and instead scale and cast.
+    """
+    quant_min, quant_max = _get_and_check_qmin_qmax(output_dtype, quant_min, quant_max)
+    # workaround for uintx dtypes, since we don't have native Uintx dtype connected with
+    # torch.uintx dtypes yet
+    if output_dtype in _SUB_BYTE_UINT_BOUNDS:
+        output_dtype = torch.uint8
+    return _quantize_affine_no_dtype_cast(
+        input,
+        block_size,
+        scale,
+        zero_point,
+        quant_min,
+        quant_max,
+        zero_point_domain,
+    ).to(output_dtype)
+
+
+def _quantize_affine_no_dtype_cast(
+    input: torch.Tensor,
+    block_size: list[int],
+    scale: torch.Tensor,
+    zero_point: torch.Tensor | None,
+    quant_min: int | float,
+    quant_max: int | float,
+    zero_point_domain: str | None = ZeroPointDomain.INT.name,
+) -> torch.Tensor:
+    """
+    The op does the following:
+    1. figure out the dimension for reduction based on block_size, also reshape the input to align with
+       the shape after reduction
+    2. quantize the input based on the quantization parameters scale and zero_point and args like zero_point_domain
+    3. reshape the quantized result to original shape
+    """
+    # TODO: validations
+    # TODO: validate scale/zero_point dimensions are compatible with block_size
+    if input.dtype not in [torch.float32, torch.float16, torch.bfloat16]:
+        raise AssertionError(f"Unsupported input dtype: {input.dtype}")
+    if len(block_size) != input.dim():
+        raise AssertionError(f"Got input dim:{input.dim()}, block_size: {block_size}")
+    shape_for_reduction, reduction_dims = _get_reduction_params(
+        block_size, input.size()
+    )
+    original_shape = input.shape
+    input = input.view(shape_for_reduction)
+    shape_after_reduction = shape_for_reduction
+    for i in reduction_dims:
+        shape_after_reduction[i] = 1
+    scale = scale.view(shape_after_reduction)
+    if zero_point is not None:
+        zero_point = zero_point.view(shape_after_reduction)
+
+    if zero_point_domain == ZeroPointDomain.INT.name:
+        quant = torch.clamp(
+            torch.round(input * (1.0 / scale)) + zero_point, quant_min, quant_max
+        )
+    elif zero_point_domain == ZeroPointDomain.NONE.name:
+        if zero_point is not None:
+            raise AssertionError(
+                "zero_point should be None when zero_point_domain is NONE"
+            )
+        quant = torch.clamp(torch.round(input * (1.0 / scale)), quant_min, quant_max)
+    elif zero_point_domain is None:
+        # This case handles quantization for float8 we expect no zero point and no zero point domain
+        if zero_point is not None:
+            raise AssertionError(
+                "zero_point should be None when zero_point_domain is None"
+            )
+        quant = torch.clamp(input * scale.reciprocal(), quant_min, quant_max)
+    else:
+        if zero_point_domain != ZeroPointDomain.FLOAT.name:
+            raise AssertionError(f"Unexpected zero_point_domain: {zero_point_domain}")
+        mid_point = (quant_max + quant_min + 1) / 2
+        min_val = zero_point - scale * mid_point
+        quant = torch.clamp(
+            torch.round((input - min_val) / scale), quant_min, quant_max
+        )
+    quant = quant.view(original_shape)
+
+    return quant
+
+
+def dequantize_affine(
+    input: torch.Tensor,
+    block_size: tuple[int, ...],
+    scale: torch.Tensor,
+    zero_point: torch.Tensor | None,
+    input_dtype: torch.dtype,
+    quant_min: int | float | None = None,
+    quant_max: int | float | None = None,
+    zero_point_domain: ZeroPointDomain = ZeroPointDomain.INT,
+    *,
+    output_dtype: torch.dtype = torch.float32,
+) -> torch.Tensor:
+    """
+    Args:
+      input (torch.Tensor): quantized tensor, should match the dtype `dtype` argument
+      block_size: (List[int]): granularity of quantization,
+        this means the size of the tensor elements that's sharing the same qparam
+        e.g. when size is the same as the input tensor dimension, we are using per tensor quantization
+      scale (Tensor): quantization parameter for affine quantization
+      zero_point (Tensor): quantization parameter for affine quantization
+      input_dtype (torch.dtype): requested dtype (e.g. torch.uint8) for output Tensor
+      quant_min (Optional[int]): minimum quantized value for input Tensor
+      quant_max (Optional[int]): maximum quantized value for input Tensor
+      output_dtype (torch.dtype): dtype for output Tensor, default is fp32
+      zero_point_domain (ZeroPointDomain): the domain that zero_point is in, should be either integer or float
+        if zero_point is in integer domain, zero point is added to the quantized integer value during
+        quantization
+        if zero_point is in floating point domain, zero point is subtracted from the floating point (unquantized)
+        value during quantization
+        default is ZeroPointDomain.INT
+
+    Output:
+      dequantized Tensor, with requested dtype or fp32
+    """
+    return _dequantize_affine(
+        input,
+        block_size,
+        scale,
+        zero_point,
+        input_dtype,
+        quant_min,
+        quant_max,
+        zero_point_domain.name if zero_point_domain is not None else None,
+        output_dtype=output_dtype,
+    )
+
+
+@register_custom_op
+def _dequantize_affine(
+    input: torch.Tensor,
+    block_size: list[int],
+    scale: torch.Tensor,
+    zero_point: torch.Tensor | None,
+    input_dtype: torch.dtype,
+    quant_min: int | float | bool | None = None,
+    quant_max: int | float | bool | None = None,
+    zero_point_domain: str | None = ZeroPointDomain.INT.name,
+    output_dtype: torch.dtype = torch.float32,
+) -> torch.Tensor:
+    """op definition that has compatible signatures with custom op library"""
+    # TODO: validate scale/zero_point dimensions are compatible with block_size
+    if input_dtype not in _SUB_BYTE_UINT_BOUNDS:
+        if input.dtype != input_dtype:
+            raise AssertionError(f"Expected: {input_dtype}, got: {input.dtype}")
+    if output_dtype not in [torch.float32, torch.float16, torch.bfloat16]:
+        raise AssertionError(f"Unsupported output dtype: {output_dtype}")
+    quant_min, quant_max = _get_and_check_qmin_qmax(input_dtype, quant_min, quant_max)
+    return _dequantize_affine_no_dtype_check(
+        input,
+        block_size,
+        scale,
+        zero_point,
+        quant_min,
+        quant_max,
+        zero_point_domain,
+        output_dtype,
+    )
+
+
+def _dequantize_affine_no_dtype_check(
+    input: torch.Tensor,
+    block_size: list[int],
+    scale: torch.Tensor,
+    zero_point: torch.Tensor | None,
+    quant_min: int | float,
+    quant_max: int | float,
+    zero_point_domain: str | None = ZeroPointDomain.INT.name,
+    output_dtype: torch.dtype = torch.float32,
+) -> torch.Tensor:
+    """This function converts AQT tensors to their high precision floating point representation
+
+    The op does the following:
+    1. figure out the dimension for reduction based on block_size, also reshape the input to align with
+       the shape after reduction
+    2. dequantize the input based on the quantization parameters scale and zero_point and args like zero_point_domain
+    3. reshape the quantized result to original shape and change dtype to the output_dtype
+    """
+    if len(block_size) != input.dim():
+        raise AssertionError(f"Got input dim:{input.dim()}, block_size: {block_size}")
+    shape_for_reduction, reduction_dims = _get_reduction_params(
+        block_size, input.size()
+    )
+    original_shape = input.shape
+    input = input.view(shape_for_reduction)
+    shape_after_reduction = shape_for_reduction
+    for i in reduction_dims:
+        shape_after_reduction[i] = 1
+    scale = scale.view(shape_after_reduction)
+
+    if zero_point is not None:
+        zero_point = zero_point.view(shape_after_reduction)
+
+    if zero_point_domain == ZeroPointDomain.INT.name:
+        # Force a copy to avoid input modification due
+        # to upcoming in-place operations.
+        dequant = input.to(torch.int32, copy=True)
+        if zero_point is not None:
+            dequant = dequant - zero_point.to(torch.int32)
+        dequant = dequant.to(output_dtype)
+        dequant = dequant * scale
+    elif zero_point_domain == ZeroPointDomain.NONE.name:
+        if zero_point is not None:
+            raise AssertionError(
+                "zero_point should be None when zero_point_domain is NONE"
+            )
+        dequant = input.to(output_dtype)
+        dequant = dequant * scale
+    elif zero_point_domain is None:
+        # This case handles dequantization for float8 we expect no zero point and no zero point domain
+        if zero_point is not None:
+            raise AssertionError(
+                "zero_point should be None when zero_point_domain is None"
+            )
+        if not _is_float8_type(input.dtype):
+            raise AssertionError(
+                f"dequantiztion with no zero point domain is only supported with FP8 types, got {input.dtype}"
+            )
+        dequant = input.to(output_dtype)
+        dequant = dequant * scale
+    else:
+        if zero_point_domain != ZeroPointDomain.FLOAT.name:
+            raise AssertionError(f"Unexpected zero point domain: {zero_point_domain}")
+        # TODO: this seems to be a detail for tinygemm (converting from uint to int, probably need to refactor this)
+        mid_point = (quant_max + quant_min + 1) / 2
+        # This should allocate new memory and avoid input modification
+        dequant = input - mid_point
+        dequant = dequant.to(output_dtype)
+        dequant *= scale
+        if zero_point is not None:
+            dequant += zero_point
+
+    return dequant.view(original_shape).to(output_dtype)
+
+
+class AffineQuantizedMinMaxObserver(AffineQuantizedObserverBase):
+    def forward(self, input: torch.Tensor):
+        if input.numel() == 0:
+            return input
+
+        input_detached = input.detach()
+        self.original_dtype = input_detached.dtype
+        if self.granularity is None:
+            raise AssertionError("granularity is None")
+        self.block_size = get_block_size(input_detached.shape, self.granularity)
+
+        shape_for_reduction, reduction_dims = _get_reduction_params(
+            self.block_size, input_detached.size()
+        )
+        input_detached = input_detached.view(shape_for_reduction)
+        min_val = torch.amin(input_detached, dim=reduction_dims, keepdim=False)
+        max_val = torch.amax(input_detached, dim=reduction_dims, keepdim=False)
+        if not hasattr(self, "min_val") or not hasattr(self, "max_val"):
+            self.min_val = min_val
+            self.max_val = max_val
+        else:
+            if self.min_val.shape != min_val.shape:
+                raise AssertionError(
+                    f"Can't update existing min_val - shape mismatch, self.min_val:{self.min_val.shape} != min_val:{min_val.shape}"
+                )
+            if self.max_val.shape != max_val.shape:
+                raise AssertionError(
+                    f"Can't update existing max_val - shape mismatch, self.max_val {self.max_val.shape} != max_val:{max_val.shape}"
+                )
+            min_val = torch.min(self.min_val, min_val)
+            max_val = torch.max(self.max_val, max_val)
+            self.min_val.copy_(min_val)
+            self.max_val.copy_(max_val)
+        # returning original input
+        return input
+
+    def calculate_qparams(self) -> tuple[torch.Tensor, torch.Tensor]:
+        if not (hasattr(self, "min_val") and hasattr(self, "max_val")):
+            raise AssertionError(
+                "Expecting the observer has min_val and max_val, please run the observer before calling calculate_qparams"
+            )
+        return choose_qparams_affine_with_min_max(
+            self.min_val,
+            self.max_val,
+            self.mapping_type,
+            [],  # BlockSize is not needed because the min/max are already reduced
+            self.target_dtype,
+            self.quant_min,
+            self.quant_max,
+            self.eps,
+            self.scale_dtype,
+            self.zero_point_dtype,
+            self.preserve_zero,
+            self.zero_point_domain,
+        )
+
+
+class AffineQuantizedMovingAverageMinMaxObserver(AffineQuantizedObserverBase):
+    def __init__(
+        self,
+        mapping_type: MappingType,
+        target_dtype: torch.dtype,
+        granularity: Granularity,
+        averaging_constant=0.01,
+        quant_min: int | None = None,
+        quant_max: int | None = None,
+        eps: float | None = None,
+        is_dynamic=False,
+        scale_dtype: torch.dtype | None = None,
+        zero_point_dtype: torch.dtype | None = None,
+        preserve_zero: bool = True,
+        zero_point_domain: ZeroPointDomain | None = ZeroPointDomain.INT,
+        # there could be some extra args that's ignored
+        **kwargs,
+    ):
+        self.is_dynamic = is_dynamic
+        self.averaging_constant = averaging_constant
+        if is_dynamic and self.averaging_constant != 1:
+            raise NotImplementedError(
+                "MovingAverageMinMaxObserver doesn't support dynamic quantization for "
+                f"averaging constant of {self.averaging_constant}"
+            )
+
+        super().__init__(
+            mapping_type=mapping_type,
+            target_dtype=target_dtype,
+            granularity=granularity,
+            quant_min=quant_min,
+            quant_max=quant_max,
+            eps=eps,
+            scale_dtype=scale_dtype,
+            zero_point_dtype=zero_point_dtype,
+            preserve_zero=preserve_zero,
+            zero_point_domain=zero_point_domain,
+        )
+
+    def forward(self, input: torch.Tensor):
+        if input.numel() == 0:
+            return input
+
+        input_detached = input.detach()
+        self.original_dtype = input_detached.dtype
+        if self.granularity is None:
+            raise AssertionError("granularity is None")
+        self.block_size = get_block_size(input_detached.shape, self.granularity)
+
+        shape_for_reduction, reduction_dims = _get_reduction_params(
+            self.block_size, input_detached.size()
+        )
+        input_detached = input_detached.view(shape_for_reduction)
+        min_val = torch.amin(input_detached, dim=reduction_dims, keepdim=False)
+        max_val = torch.amax(input_detached, dim=reduction_dims, keepdim=False)
+        if not hasattr(self, "min_val") or not hasattr(self, "max_val"):
+            self.min_val = min_val
+            self.max_val = max_val
+        else:
+            if self.min_val.shape != min_val.shape:
+                raise AssertionError(
+                    f"Can't update existing min_val - shape mismatch, self.min_val:{self.min_val.shape} != min_val:{min_val.shape}"
+                )
+            if self.max_val.shape != max_val.shape:
+                raise AssertionError(
+                    f"Can't update existing max_val - shape mismatch, self.max_val {self.max_val.shape} != max_val:{max_val.shape}"
+                )
+            min_val = self.min_val + self.averaging_constant * (min_val - self.min_val)
+            max_val = self.max_val + self.averaging_constant * (max_val - self.max_val)
+            self.min_val.copy_(min_val)
+            self.max_val.copy_(max_val)
+
+        # returning original input
+        return input
+
+    def calculate_qparams(self) -> tuple[torch.Tensor, torch.Tensor]:
+        if not (hasattr(self, "min_val") and hasattr(self, "max_val")):
+            raise AssertionError(
+                "Expecting the observer has min_val and max_val, please run the observer before calling calculate_qparams"
+            )
+
+        return choose_qparams_affine_with_min_max(
+            self.min_val,
+            self.max_val,
+            self.mapping_type,
+            [],  # BlockSize is not needed because the min/max are already reduced
+            self.target_dtype,
+            self.quant_min,
+            self.quant_max,
+            self.eps,
+            self.scale_dtype,
+            self.zero_point_dtype,
+            self.preserve_zero,
+            self.zero_point_domain,
+        )
+
+
+class AffineQuantizedPlaceholderObserver(AffineQuantizedObserverBase):
+    def __init__(
+        self,
+        mapping_type: MappingType,
+        target_dtype: torch.dtype,
+        granularity: Granularity,
+        quant_min: int | None = None,
+        quant_max: int | None = None,
+        eps: float | None = None,
+        is_dynamic=False,
+        scale_dtype: torch.dtype | None = None,
+        zero_point_dtype: torch.dtype | None = None,
+        preserve_zero: bool = True,
+        zero_point_domain: ZeroPointDomain | None = ZeroPointDomain.INT,
+        # there could be some extra args that's ignored
+        **kwargs,
+    ):
+        self.is_dynamic = is_dynamic
+
+        super().__init__(
+            mapping_type=mapping_type,
+            target_dtype=target_dtype,
+            granularity=granularity,
+            quant_min=quant_min,
+            quant_max=quant_max,
+            eps=eps,
+            scale_dtype=scale_dtype,
+            zero_point_dtype=zero_point_dtype,
+            preserve_zero=preserve_zero,
+            zero_point_domain=zero_point_domain,
+        )
+
+    def forward(self, input):
+        self.block_size = get_block_size(input.shape, self.granularity)
+        self.original_dtype = input.dtype
+        return input
+
+    def calculate_qparams(self):
+        raise Exception(  # noqa: TRY002
+            "calculate_qparams should not be called for PlaceholderObserver"
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/_numeric_debugger.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/_numeric_debugger.py
new file mode 100644
index 0000000000000000000000000000000000000000..6eaeaa46a924893fa0aace363f3040d5e2d692de
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/_numeric_debugger.py
@@ -0,0 +1,341 @@
+import copy
+import logging
+from collections.abc import Callable, Sequence
+from dataclasses import dataclass
+
+import torch
+from torch.ao.ns.fx.utils import compute_sqnr
+from torch.ao.quantization.pt2e.graph_utils import bfs_trace_with_node_process
+from torch.export import ExportedProgram
+from torch.fx import GraphModule, Node
+from torch.nn import functional as F
+
+
+NUMERIC_DEBUG_HANDLE_KEY = "numeric_debug_handle"
+CUSTOM_KEY = "custom"
+
+log = logging.getLogger(__name__)
+
+
+def generate_numeric_debug_handle(ep: ExportedProgram) -> None:
+    """
+    Attach numeric_debug_handle_id for all nodes in the graph module of the given
+    ExportedProgram, like conv2d, squeeze, conv1d, etc, except for placeholder.
+    Notice that nodes like getattr are out of scope since they are not in the graph.
+
+    The graph nodes of input exported program are modified inplace.
+
+    Here's an example of using debug handle quantize flow::
+
+        ep = export_for_training(eager_model, example_inputs)
+        generate_numeric_debug_handle(ep)
+
+        m = ep.module()
+        quantizer = XNNPACKQuantizer()
+        m = prepare_pt2e(m, quantizer)
+        m = convert_pt2e(m)
+    """
+
+    # Sanity check the input data type
+    if not isinstance(ep, ExportedProgram):
+        raise ValueError(
+            f"Expected ep to be ExportedProgram, got {type(ExportedProgram)}"
+        )
+
+    unique_id = 0
+
+    def _find_max_id(node: torch.fx.Node) -> None:
+        nonlocal unique_id
+        unique_id = max(
+            unique_id, node.meta.get(CUSTOM_KEY, {}).get(NUMERIC_DEBUG_HANDLE_KEY, 0)
+        )
+
+    def _assign_debug_handle(node: torch.fx.Node) -> None:
+        nonlocal unique_id
+        if CUSTOM_KEY not in node.meta:
+            node.meta[CUSTOM_KEY] = {}
+
+        if NUMERIC_DEBUG_HANDLE_KEY not in node.meta[CUSTOM_KEY]:
+            node.meta[CUSTOM_KEY][NUMERIC_DEBUG_HANDLE_KEY] = unique_id
+            unique_id += 1
+
+    # Find the max ID that exists in the graph first, in case part of the graph
+    # has already been annotated. This way we guarantee there are no duplicate
+    # handle IDs.
+    bfs_trace_with_node_process(ep, _find_max_id)
+
+    unique_id += 1
+
+    # Assign debug handles to all nodes in the graph that don't have one based on the
+    # max ID found in the previous step.
+    bfs_trace_with_node_process(ep, _assign_debug_handle)
+
+
+def _detach(x: object) -> object:
+    detached: object = None
+    if isinstance(x, torch.Tensor):
+        detached = x.detach()
+    elif isinstance(x, (list, tuple)):
+        detached = type(x)([_detach(e) for e in x])
+    elif isinstance(x, dict):
+        detached = {k: _detach(e) for k, e in x.items()}
+    else:
+        detached = x
+    return detached
+
+
+def _tensor_shape_equals(x: object, y: object) -> bool:
+    if isinstance(x, torch.Tensor) and isinstance(y, torch.Tensor):
+        return x.shape == y.shape
+    elif isinstance(x, (list, tuple)) and isinstance(y, (list, tuple)):
+        return all(_tensor_shape_equals(e1, e2) for e1, e2 in zip(x, y))
+    elif isinstance(x, dict) and isinstance(y, dict):
+        all_equal = True
+        for k in x:
+            all_equal = all_equal and k in y and (_tensor_shape_equals(x[k], y[k]))
+        return all_equal
+    else:
+        log.debug("Comparing non Tensors: %s and %s, they must be equal", x, y)
+        return type(x) is type(y) and x == y
+
+
+def _loss_fn(
+    loss: Callable[[torch.Tensor, torch.Tensor], torch.Tensor], x: object, y: object
+) -> object:
+    """The returned loss will have the same structure as `x` and `y`, e.g.
+    if both are Tensor, we'll return a Tensor
+    if both are list, we'll return a list of Tensors
+    if both are dict, we'll return a dict with the same key, and value being the loss between the
+    two Tensors
+    """
+    if isinstance(x, torch.Tensor) and isinstance(y, torch.Tensor):
+        return loss(x.to(torch.float32), y.to(torch.float32))
+    elif isinstance(x, (list, tuple)) and isinstance(y, (list, tuple)):
+        return type(x)([_loss_fn(loss, e1, e2) for e1, e2 in zip(x, y)])
+    elif isinstance(x, dict) and isinstance(y, dict):
+        return {k: _loss_fn(loss, e, y[k]) for k, e in x.items()}
+    else:
+        return None
+
+
+class OutputLogger(torch.nn.Module):
+    """
+    Base class for capturing output values for nodes in a GraphModule, it only captures
+    Tensor output currently, but we can extend it to work for other types of inputs later if needed
+    """
+
+    # Mark as impure so that calls to it will not be removed during DCE.
+    _is_impure = True
+
+    def __init__(
+        self,
+        debug_handle: int,
+        node_name: str | None = None,
+        nn_module_stack: object | None = None,
+    ) -> None:
+        super().__init__()
+        self.node_name = node_name
+        self.nn_module_stack = nn_module_stack
+        self.debug_handle = debug_handle
+        self.stats: list[object] = []
+
+    def forward(self, x: object) -> object:
+        self.stats.append(_detach(x))
+        return x
+
+    def __extra_repr__(self) -> str:
+        return (
+            f"debug_handle={self.debug_handle}, node_name={self.node_name}, "
+            "nn_module_stack={self.nn_module_stack}, num_stats={len(self.stats)})"
+        )
+
+
+def _insert_logger(model: GraphModule, node: Node, debug_handle: int) -> Node:
+    """For a given node, adds an OutputLogger that observes the output of that node,
+    and all its users use the OutputLogger output instead.
+    The OutputLogger will contain the debug_handle which can be used to compare
+    graphs after transforms"""
+
+    # to avoid circular dep
+    from torch.ao.quantization.fx.utils import get_new_attr_name_with_prefix
+
+    # add a logger after the node
+    with model.graph.inserting_after(node):
+        get_new_attr_name = get_new_attr_name_with_prefix(f"{node.name}_logger")
+        logger_name = get_new_attr_name(model)
+        setattr(
+            model,
+            logger_name,
+            OutputLogger(debug_handle, node.name, node.meta.get("nn_module_stack")),
+        )
+        logger_node = model.graph.call_module(logger_name, (node,), {})
+
+    orig_users = list(node.users.keys())
+    for user_node in orig_users:
+        if user_node is logger_node:
+            continue
+        user_node.replace_input_with(node, logger_node)
+
+    return logger_node
+
+
+def prepare_for_propagation_comparison(model: GraphModule) -> GraphModule:
+    """Add output loggers to node that has numeric_debug_handle
+
+    Args:
+        model (GraphModule): original model
+    Returns:
+        a model with output loggers for all nodes that has numeric_debug_handle_id
+    """
+    # don't change the original model
+    model = copy.deepcopy(model)
+    for n in model.graph.nodes:
+        if (
+            CUSTOM_KEY not in n.meta
+            or NUMERIC_DEBUG_HANDLE_KEY not in n.meta[CUSTOM_KEY]
+        ):
+            continue
+        numeric_debug_handle = n.meta[CUSTOM_KEY][NUMERIC_DEBUG_HANDLE_KEY]
+        _insert_logger(model, n, numeric_debug_handle)
+
+    model.recompile()
+    return model
+
+
+@dataclass(frozen=True)
+class QuantizationComparisonResult:
+    actual: torch.Tensor
+    ref: torch.Tensor
+
+    @property
+    def mse_loss(self) -> object:
+        return self.loss(F.mse_loss)
+
+    @property
+    def sqnr(self) -> object:
+        return self.loss(compute_sqnr)
+
+    def loss(
+        self, loss_function: Callable[[torch.Tensor, torch.Tensor], torch.Tensor]
+    ) -> object:
+        return _loss_fn(loss_function, self.actual, self.ref)
+
+    def __repr__(self) -> str:
+        # Don't include the tensors themselves as they are quite large to print
+        # out.
+        return (
+            f"QuantizationComparisonResult(mse_loss={self.mse_loss}, sqnr={self.sqnr})"
+        )
+
+    def __post_init__(self) -> None:
+        if not isinstance(self.actual, (torch.Tensor, list, tuple, dict)):
+            raise ValueError(
+                f"`self.actual` value must be a Tensor, list, tuple or dict, got: {self.actual}"
+            )
+
+        if not isinstance(self.ref, (torch.Tensor, list, tuple, dict)):
+            raise ValueError(
+                f"`self.ref` value must be a Tensor, list, tuple or dict, got: {self.ref}"
+            )
+
+        if not _tensor_shape_equals(self.ref, self.actual):
+            raise ValueError(
+                f"Cannot compare tensors with different shapes: ref={self.ref} vs actual={self.actual}"
+            )
+
+
+@dataclass(frozen=True)
+class NodeAccuracySummary:
+    handle: int
+    actual_node_name: str
+    actual_module_stack: str
+    ref_node_name: str
+    ref_module_stack: str
+    results: Sequence[QuantizationComparisonResult]
+
+
+def _module_stack_to_str(module_stack: object) -> str:
+    """Simplifies the stack from ("mod", "mod.foo", "mod.foo.0", "mod.foo.0.linear")
+    to "mod.foo.0.linear"
+    """
+    if not isinstance(module_stack, dict):
+        return str(module_stack)
+    module_values_list = list(module_stack.values())
+    if len(module_values_list) > 0:
+        owning_module = module_values_list[-1][0]
+        return str(owning_module)
+    else:
+        return str(module_stack)
+
+
+def extract_results_from_loggers(
+    model: GraphModule,
+) -> dict[int, tuple[str | None, object, list[object]]]:
+    """For a given model, extract the tensors stats and related information for each debug handle.
+    The reason we have a list of object, instead of Tensor is because the output of node may not be
+    a Tensor, it could be (nested) list, tuple or dict as well.
+
+    Returns:
+        A dict is keyed by the debug_handle id and the values are a list of object recorded
+        in loggers
+
+    """
+    # Results maps debug handle to a tensor list for each model being compared.
+    handles: dict[int, tuple[str | None, object, list[object]]] = {}
+    for _name, module in model.named_children():
+        if isinstance(module, OutputLogger) and len(module.stats) > 0:
+            handles[module.debug_handle] = (
+                module.node_name,
+                module.nn_module_stack,
+                module.stats,
+            )
+
+    return handles
+
+
+def compare_results(
+    ref_results: dict[int, tuple[str | None, object, list[torch.Tensor]]],
+    actual_results: dict[int, tuple[str | None, object, list[torch.Tensor]]],
+) -> dict[int, NodeAccuracySummary]:
+    """Given two dict mapping from `debug_handle_id` (int) to list of tensors
+    return a map from `debug_handle_id` to `NodeAccuracySummary` that contains
+    comparison information like SQNR, MSE etc.
+
+    Args:
+        ref_results (Dict[int, Tuple[str, object, List[torch.Tensor]]]): reference results for each debug_handle_id
+        actual_results (Dict[int, Tuple[str, object, List[torch.Tensor]]]): actual results for each debug_handle_id
+
+    Returns:
+        Dict[int, NodeAccuracySummary]
+    """
+    comparisons = {}
+    for debug_handle, (ref_name, ref_stack, ref_stats) in ref_results.items():
+        if debug_handle not in actual_results:
+            log.debug(
+                "Cannot compare for handle %s because it wasn't found in the transformed model",
+                debug_handle,
+            )
+            continue
+        actual_name, actual_stack, actual_stats = actual_results[debug_handle]
+        try:
+            results = [
+                QuantizationComparisonResult(actual=a, ref=b)
+                for a, b in zip(actual_stats, ref_stats)
+            ]
+        except Exception as e:
+            # Add extra information for an exception from QuantizationComparisonResult
+            # if the shapes didn't match, to include the handle and the node names.
+            raise ValueError(
+                f"For numeric_debug_handle={debug_handle} from ref node {ref_name} and actual node {actual_name}"
+            ) from e
+
+        comparisons[debug_handle] = NodeAccuracySummary(
+            handle=debug_handle,
+            actual_node_name=actual_name or "",
+            actual_module_stack=_module_stack_to_str(actual_stack),
+            ref_node_name=ref_name or "",
+            ref_module_stack=_module_stack_to_str(ref_stack),
+            results=results,
+        )
+
+    return comparisons
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/duplicate_dq_pass.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/duplicate_dq_pass.py
new file mode 100644
index 0000000000000000000000000000000000000000..81c03e51414320e3faee0f6b8906ea38910c41ee
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/duplicate_dq_pass.py
@@ -0,0 +1,81 @@
+import logging
+import operator
+
+import torch
+from torch.ao.quantization.pt2e.utils import (
+    _filter_sym_size_users,
+    _is_valid_annotation,
+)
+from torch.fx.node import map_arg
+from torch.fx.passes.infra.pass_base import PassBase, PassResult
+
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.WARNING)
+
+__all__ = ["DuplicateDQPass"]
+
+_QUANTIZE_OPS = [
+    torch.ops.quantized_decomposed.quantize_per_tensor.default,
+    torch.ops.quantized_decomposed.quantize_per_tensor.tensor,
+    torch.ops.quantized_decomposed.quantize_per_channel.default,
+]
+
+_DEQUANTIZE_OPS = [
+    torch.ops.quantized_decomposed.dequantize_per_tensor.default,
+    torch.ops.quantized_decomposed.dequantize_per_tensor.tensor,
+    torch.ops.quantized_decomposed.dequantize_per_channel.default,
+]
+
+
+def _maybe_duplicate_dq(
+    gm: torch.fx.GraphModule, dq_node: torch.fx.Node, user: torch.fx.Node
+) -> None:
+    annotation = user.meta.get("quantization_annotation", None)
+    if not _is_valid_annotation(annotation):  # type: ignore[arg-type]
+        return
+    with gm.graph.inserting_after(dq_node):
+        new_node = gm.graph.node_copy(dq_node)
+
+        def maybe_replace_node(n: torch.fx.Node) -> torch.fx.Node:
+            if n == dq_node:
+                return new_node
+            else:
+                return n
+
+        new_args = map_arg(user.args, maybe_replace_node)
+        new_kwargs = map_arg(user.kwargs, maybe_replace_node)
+        user.args = new_args  # type: ignore[assignment]
+        user.kwargs = new_kwargs  # type: ignore[assignment]
+
+
+class DuplicateDQPass(PassBase):
+    def call(self, graph_module: torch.fx.GraphModule) -> PassResult:
+        for node in graph_module.graph.nodes:
+            if node.op == "call_function" and node.target in _DEQUANTIZE_OPS:
+                dq_users = _filter_sym_size_users(node)
+                if len(dq_users) <= 1:
+                    continue
+                # Do not duplicate dq for dynamic quantization
+                # Pattern: choose_qparam - getitem - q - dq
+                q_node = node.args[0]
+                if q_node.op == "call_function" and q_node.target in _QUANTIZE_OPS:
+                    getitem_node = q_node.args[1]
+                    if (
+                        isinstance(getitem_node, torch.fx.node.Node)
+                        and getitem_node.op == "call_function"
+                        and getitem_node.target is operator.getitem
+                    ):
+                        choose_qparam_node = getitem_node.args[0]
+                        if (
+                            isinstance(choose_qparam_node, torch.fx.node.Node)
+                            and choose_qparam_node.op == "call_function"
+                            and choose_qparam_node.target
+                            == torch.ops.quantized_decomposed.choose_qparams.tensor
+                        ):
+                            continue
+                for user in dq_users:
+                    _maybe_duplicate_dq(graph_module, node, user)
+        graph_module.graph.eliminate_dead_code()
+        graph_module.recompile()
+        return PassResult(graph_module, True)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/export_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/export_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..70cca73dd00dcb4bd865dda4f2718a610496323e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/export_utils.py
@@ -0,0 +1,240 @@
+# mypy: allow-untyped-defs
+import types
+
+import torch
+import torch.nn.functional as F
+from torch.ao.quantization.utils import _assert_and_get_unique_device
+
+
+__all__ = [
+    "model_is_exported",
+]
+
+_EXPORTED_TRAINING_ATTR = "_exported_training"
+
+
+class _WrapperModule(torch.nn.Module):
+    """Class to wrap a callable in an :class:`torch.nn.Module`. Use this if you
+    are trying to export a callable.
+    """
+
+    def __init__(self, fn):
+        super().__init__()
+        self.fn = fn
+
+    def forward(self, *args, **kwargs):
+        """Simple forward that just calls the ``fn`` provided to :meth:`WrapperModule.__init__`."""
+        return self.fn(*args, **kwargs)
+
+
+def model_is_exported(m: torch.nn.Module) -> bool:
+    """
+    Return True if the `torch.nn.Module` was exported, False otherwise
+    (e.g. if the model was FX symbolically traced or not traced at all).
+    """
+    return isinstance(m, torch.fx.GraphModule) and any(
+        "val" in n.meta for n in m.graph.nodes
+    )
+
+
+def _replace_dropout(m: torch.fx.GraphModule, train_to_eval: bool):
+    """
+    Switch dropout patterns in the model between train and eval modes.
+
+    Dropout has different behavior in train vs eval mode. For exported models,
+    however, calling `model.train()` or `model.eval()` does not automatically switch
+    the dropout behavior between the two modes, so here we need to rewrite the aten
+    dropout patterns manually to achieve the same effect.
+
+    See https://github.com/pytorch/pytorch/issues/103681.
+    """
+    # Avoid circular dependencies
+    from .utils import _get_aten_graph_module_for_pattern
+
+    # Needed to ensure subgraph matches are self-contained
+    m.graph.eliminate_dead_code()
+    m.recompile()
+
+    for inplace in [False, True]:
+
+        def dropout_train(x):
+            return F.dropout(x, p=0.5, training=True, inplace=inplace)
+
+        def dropout_eval(x):
+            return F.dropout(x, p=0.5, training=False, inplace=inplace)
+
+        example_inputs = (torch.randn(1),)
+        if train_to_eval:
+            match_pattern = _get_aten_graph_module_for_pattern(
+                _WrapperModule(dropout_train),
+                example_inputs,
+            )
+            replacement_pattern = _get_aten_graph_module_for_pattern(
+                _WrapperModule(dropout_eval),
+                example_inputs,
+            )
+        else:
+            match_pattern = _get_aten_graph_module_for_pattern(
+                _WrapperModule(dropout_eval),
+                example_inputs,
+            )
+            replacement_pattern = _get_aten_graph_module_for_pattern(
+                _WrapperModule(dropout_train),
+                example_inputs,
+            )
+
+        from torch.fx.subgraph_rewriter import replace_pattern_with_filters
+
+        replace_pattern_with_filters(
+            m,
+            match_pattern,
+            replacement_pattern,
+            match_filters=[],
+            ignore_literals=True,
+        )
+        m.recompile()
+
+
+def _replace_batchnorm(m: torch.fx.GraphModule, train_to_eval: bool):
+    """
+    Switch batchnorm patterns in the model between train and eval modes.
+
+    Batchnorm has different behavior in train vs eval mode. For exported models,
+    however, calling `model.train()` or `model.eval()` does not automatically switch
+    the batchnorm behavior between the two modes, so here we need to rewrite the aten
+    batchnorm patterns manually to achieve the same effect.
+    """
+    # TODO(Leslie): This function still fails to support custom momentum and eps value.
+    # Enable this support in future updates.
+
+    # Avoid circular dependencies
+    from .utils import _get_aten_graph_module_for_pattern
+
+    # Needed to ensure subgraph matches are self-contained
+    m.graph.eliminate_dead_code()
+    m.recompile()
+
+    def bn_train(
+        x: torch.Tensor,
+        bn_weight: torch.Tensor,
+        bn_bias: torch.Tensor,
+        bn_running_mean: torch.Tensor,
+        bn_running_var: torch.Tensor,
+    ):
+        return F.batch_norm(
+            x, bn_running_mean, bn_running_var, bn_weight, bn_bias, training=True
+        )
+
+    def bn_eval(
+        x: torch.Tensor,
+        bn_weight: torch.Tensor,
+        bn_bias: torch.Tensor,
+        bn_running_mean: torch.Tensor,
+        bn_running_var: torch.Tensor,
+    ):
+        return F.batch_norm(
+            x, bn_running_mean, bn_running_var, bn_weight, bn_bias, training=False
+        )
+
+    example_inputs = (
+        torch.randn(1, 1, 3, 3),  # x
+        torch.randn(1),  # bn_weight
+        torch.randn(1),  # bn_bias
+        torch.randn(1),  # bn_running_mean
+        torch.randn(1),  # bn_running_var
+    )
+
+    device = _assert_and_get_unique_device(m)
+    is_cuda = device is not None and device.type == "cuda"
+    bn_train_aten = _get_aten_graph_module_for_pattern(
+        _WrapperModule(bn_train),
+        example_inputs,
+        is_cuda,
+    )
+    bn_eval_aten = _get_aten_graph_module_for_pattern(
+        _WrapperModule(bn_eval),
+        example_inputs,
+        is_cuda,
+    )
+
+    if train_to_eval:
+        match_pattern = bn_train_aten
+        replacement_pattern = bn_eval_aten
+    else:
+        match_pattern = bn_eval_aten
+        replacement_pattern = bn_train_aten
+
+    from torch.fx.subgraph_rewriter import replace_pattern_with_filters
+
+    replace_pattern_with_filters(
+        m,
+        match_pattern,
+        replacement_pattern,
+        match_filters=[],
+        ignore_literals=True,
+    )
+    m.recompile()
+
+
+# TODO: expose these under this namespace?
+def _move_exported_model_to_eval(model: torch.fx.GraphModule):
+    """
+    Move an exported GraphModule to eval mode.
+
+    This is equivalent to model.eval() but only for certain special ops like dropout, batchnorm.
+    QAT users should call this before performing inference on the model.
+
+    This call is idempotent; if the model is already in eval mode, nothing will happen.
+    """
+    is_training = getattr(model, _EXPORTED_TRAINING_ATTR, True)
+    if not is_training:
+        return model
+    setattr(model, _EXPORTED_TRAINING_ATTR, False)
+    _replace_dropout(model, train_to_eval=True)
+    _replace_batchnorm(model, train_to_eval=True)
+    return model
+
+
+def _move_exported_model_to_train(model: torch.fx.GraphModule):
+    """
+    Move an exported GraphModule to train mode.
+
+    This is equivalent to model.train() but only for certain special ops like dropout, batchnorm.
+    QAT users should call this before performing training on the model.
+
+    This call is idempotent; if the model is already in train mode, nothing will happen.
+    """
+    is_training = getattr(model, _EXPORTED_TRAINING_ATTR, False)
+    if is_training:
+        return model
+    setattr(model, _EXPORTED_TRAINING_ATTR, True)
+    _replace_dropout(model, train_to_eval=False)
+    _replace_batchnorm(model, train_to_eval=False)
+    return model
+
+
+def _allow_exported_model_train_eval(model: torch.fx.GraphModule):
+    """
+    Allow users to call `model.train()` and `model.eval()` on an exported model,
+    but with the effect of changing behavior between the two modes limited to special
+    ops only, which are currently dropout and batchnorm.
+
+    Note: This does not achieve the same effect as what `model.train()` and `model.eval()`
+    does in eager models, but only provides an approximation. In particular, user code
+    branching on `training` flag will not function correctly in general because the branch
+    is already specialized at export time. Additionally, other ops beyond dropout and batchnorm
+    that have different train/eval behavior will also not be converted properly.
+    """
+
+    def _train(self, mode: bool = True):
+        if mode:
+            _move_exported_model_to_train(self)
+        else:
+            _move_exported_model_to_eval(self)
+
+    def _eval(self):
+        _move_exported_model_to_eval(self)
+
+    model.train = types.MethodType(_train, model)  # type: ignore[method-assign]
+    model.eval = types.MethodType(_eval, model)  # type: ignore[method-assign]
+    return model
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/graph_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/graph_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..a6b46011d8c41d3dc0d980e33caecb22b615fd22
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/graph_utils.py
@@ -0,0 +1,191 @@
+# mypy: allow-untyped-defs
+import itertools
+import operator
+from collections import OrderedDict
+from collections.abc import Callable, Sequence
+from typing import Any
+
+import torch
+from torch.export import ExportedProgram
+from torch.fx import Node
+from torch.fx.passes.utils.source_matcher_utils import (
+    check_subgraphs_connected,
+    get_source_partitions,
+    SourcePartition,
+)
+
+
+__all__ = [
+    "find_sequential_partitions",
+    "get_equivalent_types",
+    "update_equivalent_types_dict",
+    "bfs_trace_with_node_process",
+]
+
+_EQUIVALENT_TYPES: list[set] = [
+    {torch.nn.Conv1d, torch.nn.functional.conv1d},
+    {torch.nn.Conv2d, torch.nn.functional.conv2d},
+    {torch.nn.AdaptiveAvgPool2d, torch.nn.functional.adaptive_avg_pool2d},
+    {torch.nn.ReLU, torch.nn.functional.relu, torch.nn.functional.relu_},
+    {torch.nn.BatchNorm2d, torch.nn.functional.batch_norm},
+    {torch.nn.Hardtanh, torch.nn.functional.hardtanh, torch.nn.functional.hardtanh_},
+    {torch.add, operator.add, operator.iadd, "add", "add_"},
+    {torch.mul, operator.mul, operator.imul, "mul", "mul_"},
+]
+
+
+def _create_equivalent_types_dict():
+    _DICT = {}
+    for values in _EQUIVALENT_TYPES:
+        for v in values:
+            _DICT[v] = list(values)
+    return _DICT
+
+
+_EQUIVALENT_TYPES_DICT = _create_equivalent_types_dict()
+
+
+def get_equivalent_types() -> list[set]:
+    return _EQUIVALENT_TYPES
+
+
+def update_equivalent_types_dict(customized_equivalent_types=None):
+    """Help function for user who wants to customize the _EQUIVALENT_TYPES and _EQUIVALENT_TYPES_DICT.
+    When customized_equivalent_types passes in,
+    re-generate _EQUIVALENT_TYPES and _EQUIVALENT_TYPES_DICT.
+    """
+    if customized_equivalent_types is None:
+        raise ValueError("customized_equivalent_types should not be None")
+    global _EQUIVALENT_TYPES
+    global _EQUIVALENT_TYPES_DICT
+    _EQUIVALENT_TYPES = customized_equivalent_types
+    _EQUIVALENT_TYPES_DICT = _create_equivalent_types_dict()
+
+
+def _partitions_sequential(partitions: Sequence[SourcePartition]):
+    prev_partition = None
+    for partition in partitions:
+        if prev_partition is not None and not check_subgraphs_connected(
+            prev_partition, partition
+        ):
+            return False
+        prev_partition = partition
+    return True
+
+
+def _get_matching_types(partition_type):
+    matching_types = [partition_type]
+    if partition_type in _EQUIVALENT_TYPES_DICT:
+        matching_types.extend(_EQUIVALENT_TYPES_DICT[partition_type])
+    return matching_types
+
+
+def _valid_type_sequence(partition_types: list[Any]):
+    partition_types_set = set()  # type: ignore[var-annotated]
+    for partition_type in partition_types:
+        matching_types = _get_matching_types(partition_type)
+        matching_types_set = set(matching_types)
+        if len(partition_types_set & matching_types_set) > 0:
+            return False
+        partition_types_set |= matching_types_set
+    return True
+
+
+def find_sequential_partitions(
+    gm: torch.fx.GraphModule,
+    partition_types: list[Any],
+    include_functional_equivalent=True,
+    filter_fn: Callable[[Node], bool] | None = None,
+):
+    if not _valid_type_sequence(partition_types):
+        raise ValueError(
+            f"Invalid partition types: {partition_types}. Each type in the sequence must be unique"
+        )
+
+    typed_partitions: OrderedDict[Any, list[SourcePartition]] = OrderedDict()
+    for partition_type in partition_types:
+        types_to_match = _get_matching_types(partition_type)
+        partitions = get_source_partitions(gm.graph, types_to_match, filter_fn)
+        typed_partitions[partition_type] = list(
+            itertools.chain.from_iterable(partitions.values())
+        )
+
+    typed_partitions_list = list(typed_partitions.values())
+    fusion_candidates = itertools.product(*typed_partitions_list)
+    fused_partitions = [
+        candidate
+        for candidate in fusion_candidates
+        if _partitions_sequential(candidate)
+    ]
+    return fused_partitions
+
+
+def _get_submodule(
+    graph_module: torch.fx.GraphModule, node: torch.fx.Node, arg_index: int
+) -> tuple[str, torch.nn.Module, torch.fx.Node]:
+    submod_node = node.args[arg_index]
+    if not isinstance(submod_node, torch.fx.Node):
+        raise AssertionError(
+            f"Expected submod_node to be a torch.fx.Node, got {type(submod_node)}"
+        )
+    if submod_node.op != "get_attr":
+        raise AssertionError(
+            f"Expected submod_node.op to be 'get_attr', got {submod_node.op}"
+        )
+    if not isinstance(submod_node.target, str):
+        raise AssertionError(
+            f"Expected submod_node.target to be a string attribute name, got {type(submod_node.target)}"
+        )
+    submodule = graph_module.get_submodule(submod_node.target)
+    # pyre-ignore
+    return submod_node.target, submodule, node
+
+
+def _get_control_flow_submodules(
+    graph_module: torch.fx.GraphModule,
+) -> list[tuple[str, torch.nn.Module, torch.fx.Node]]:
+    """
+    Returns a list of submodules used for control flow operations
+    (torch.ops.higher_order.cond/map) that are in the given toplevel graph (does not look
+    into submodules). Specifically, the returned value is a list containing a
+    tuple of (name of the submodule that's stored in the graph module, the
+    submodule itself, and the fx node that uses this submodule).
+    """
+    control_flow_submodules = []
+    for node in graph_module.graph.nodes:
+        if node.op != "call_function":
+            continue
+
+        if node.target is torch.ops.higher_order.cond:
+            control_flow_submodules.append(_get_submodule(graph_module, node, 1))
+            control_flow_submodules.append(_get_submodule(graph_module, node, 2))
+        if node.target is torch.ops.higher_order.map_impl:
+            control_flow_submodules.append(_get_submodule(graph_module, node, 0))
+
+    return control_flow_submodules
+
+
+def bfs_trace_with_node_process(
+    model: ExportedProgram | torch.fx.GraphModule, node_op: Callable
+) -> None:
+    """Traverse the graph module and apply node_op to each node."""
+
+    if not isinstance(model, (ExportedProgram, torch.fx.GraphModule)):
+        raise AssertionError(
+            f"Expected GraphModule or ExportedProgram, got {type(model)}"
+        )
+    gm = model.graph_module if isinstance(model, ExportedProgram) else model
+    queue = [gm]
+    while queue:
+        current_graph_module = queue.pop(0)
+        for node in current_graph_module.graph.nodes:
+            if node.op in ["output", "placeholder"]:
+                continue
+
+            node_op(node)
+
+        control_flow_submodules = [
+            submodule
+            for _, submodule, _ in _get_control_flow_submodules(current_graph_module)
+        ]
+        queue.extend(control_flow_submodules)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/lowering.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/lowering.py
new file mode 100644
index 0000000000000000000000000000000000000000..c306b1745badaf575060a6a2fb4ed21f6977ab75
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/lowering.py
@@ -0,0 +1,60 @@
+import torch
+from torch._inductor.constant_folding import constant_fold
+from torch._inductor.fx_passes.freezing_patterns import freezing_passes
+
+
+__all__ = [
+    "lower_pt2e_quantized_to_x86",
+]
+
+
+def lower_pt2e_quantized_to_x86(
+    model: torch.fx.GraphModule,
+    example_inputs: tuple[torch.Tensor, ...],
+) -> torch.fx.GraphModule:
+    """Lower a PT2E-quantized model to x86 backend.
+
+    Args:
+    * `model` (torch.fx.GraphModule): a model quantized by PT2E quantization flow.
+    * `example_inputs` (tuple[torch.Tensor, ...]): example inputs for the model.
+
+    Return:
+    A GraphModule lowered to x86 backend.
+    """
+
+    def _post_autograd_decomp_table():  # type: ignore[no-untyped-def]
+        decomp_table = torch.export.default_decompositions()
+
+        # if we are post-autograd, we shouldn't
+        # decomp prim ops.
+        for k in list(decomp_table.keys()):
+            if not torch._export.utils._is_cia_op(k):
+                del decomp_table[k]
+
+        return decomp_table
+
+    def _node_replace(m):  # type: ignore[no-untyped-def]
+        # Replace aten.t(x) with aten.permute(x, [1, 0])
+        aten = torch.ops.aten
+        g = m.graph
+        for node in g.nodes:
+            if node.target is aten.t.default:
+                with g.inserting_before(node):
+                    x = node.args[0]
+                    dims = [1, 0]
+                    perm_node = g.call_function(aten.permute.default, args=(x, dims))
+                    node.replace_all_uses_with(perm_node)
+                    g.erase_node(node)
+
+        g.lint()
+        m.recompile()
+
+    lowered_model = (
+        torch.export.export(model, example_inputs, strict=True)
+        .run_decompositions(_post_autograd_decomp_table())
+        .module()
+    )
+    _node_replace(lowered_model)
+    freezing_passes(lowered_model, example_inputs)
+    constant_fold(lowered_model)
+    return lowered_model
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/port_metadata_pass.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/port_metadata_pass.py
new file mode 100644
index 0000000000000000000000000000000000000000..be5878042b046447e446c3f4ee1cb1d761f29f27
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/port_metadata_pass.py
@@ -0,0 +1,217 @@
+# mypy: allow-untyped-defs
+import logging
+
+import torch
+from torch._export.error import InternalError
+from torch.ao.quantization.pt2e.utils import (
+    _filter_sym_size_users,
+    _find_q_dq_node_for_user,
+    _is_valid_annotation,
+)
+from torch.ao.quantization.quantizer import QuantizationSpecBase
+from torch.fx.passes.infra.pass_base import PassBase, PassResult
+
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.ERROR)
+
+__all__ = ["PortNodeMetaForQDQ"]
+
+_METADATA_TO_PORT = [
+    "stack_trace",
+    "quantization_tag",
+]
+
+_QUANTIZE_OPS = [
+    torch.ops.quantized_decomposed.quantize_per_tensor.default,
+    torch.ops.quantized_decomposed.quantize_per_tensor.tensor,
+    torch.ops.quantized_decomposed.quantize_per_channel.default,
+    torch.ops.pt2e_quant.quantize_affine,
+]
+
+_DEQUANTIZE_OPS = [
+    torch.ops.quantized_decomposed.dequantize_per_tensor.default,
+    torch.ops.quantized_decomposed.dequantize_per_tensor.tensor,
+    torch.ops.quantized_decomposed.dequantize_per_channel.default,
+    torch.ops.pt2e_quant.dequantize_affine,
+]
+
+_CHOOSE_QPARAMS_OPS = [
+    torch.ops.quantized_decomposed.choose_qparams.tensor,
+    torch.ops.quantized_decomposed.choose_qparams_symmetric.tensor,
+    torch.ops.pt2e_quant.choose_qparams_affine,
+]
+
+
+def _add_metadata(to_node: torch.fx.Node, from_node: torch.fx.Node) -> None:
+    from_meta = from_node.meta
+    for meta_name in _METADATA_TO_PORT:
+        if meta_name in from_meta:
+            to_node.meta[meta_name] = from_meta[meta_name]
+
+
+def _has_quant_annotation(node: torch.fx.Node) -> bool:
+    return "quantization_annotation" in node.meta
+
+
+def _find_choose_qparams_node(node: torch.fx.Node) -> torch.fx.Node | None:
+    # BFS to look for choose qparams
+    from collections import deque
+
+    queue = deque(list(node.users.keys()))
+    while len(queue):
+        n = queue.popleft()
+        if n.op == "output":
+            continue
+        if n.op == "call_function" and n.target in _CHOOSE_QPARAMS_OPS:
+            return n
+        for k in n.users:
+            queue.append(k)
+    return None
+
+
+def _port_metadata_for_input_quant_nodes(
+    input_node: torch.fx.Node,
+    node: torch.fx.Node,
+    qspec: QuantizationSpecBase | None,
+):
+    if qspec is None:
+        return
+
+    is_dynamic_quant = getattr(qspec, "is_dynamic", None)
+    if is_dynamic_quant is not None and is_dynamic_quant is True:
+        choose_qparams_node = _find_choose_qparams_node(input_node)
+        if choose_qparams_node is None:
+            raise ValueError(f"No chose qparams node found for {node}")
+        choose_qparam_users = _filter_sym_size_users(choose_qparams_node)
+        if len(choose_qparam_users) != 2:
+            raise InternalError(f"Expecting exactly two user for {choose_qparams_node}")
+        scale_node = choose_qparam_users.pop()
+        dynamic_q_node = next(iter(scale_node.users.keys()))
+        dynamic_q_node_users = _filter_sym_size_users(dynamic_q_node)
+        if len(dynamic_q_node_users) > 1:
+            raise InternalError(f"Expecting single user for {dynamic_q_node}")
+        dynamic_dq_node = dynamic_q_node_users.pop()
+        _add_metadata(choose_qparams_node, node)
+        _add_metadata(dynamic_q_node, node)
+        _add_metadata(dynamic_dq_node, node)
+    else:
+        q_node, dq_node = _find_q_dq_node_for_user(input_node, node)
+        if q_node is None or dq_node is None:
+            return
+        # add metadata for all the node between q_node and get_attr node
+        # if the q_node can be traced back to get_attr node
+        q_to_get_attr_nodes = [q_node]
+        q_node_input = q_node.args[0]
+        while (
+            isinstance(q_node_input, torch.fx.Node)
+            and q_node_input.op == "call_function"
+            and q_node_input.target
+            in [
+                torch.ops.aten.flatten.using_ints,
+                torch.ops.aten.permute.default,
+                torch.ops.aten.permute_copy.default,
+                torch.ops.aten.slice_copy.Tensor,
+                torch.ops.aten.squeeze.dim,
+                torch.ops.aten.squeeze_copy.dim,
+                torch.ops.aten.transpose.Dimname,
+                torch.ops.aten.transpose.int,
+                torch.ops.aten.transpose_,
+                torch.ops.aten.view_copy.default,
+                torch.ops.aten.view.default,
+                torch.ops.aten._mkldnn_transpose,
+            ]
+        ):
+            q_to_get_attr_nodes.append(q_node_input)
+            q_node_input = q_node_input.args[0]
+        if isinstance(q_node_input, torch.fx.Node) and q_node_input.op == "get_attr":
+            for n in q_to_get_attr_nodes:
+                _add_metadata(n, q_node_input)
+        _add_metadata(dq_node, node)
+
+
+def _port_metadata_for_output_quant_nodes(
+    node: torch.fx.Node, qspec: QuantizationSpecBase | None
+):
+    if qspec is None:
+        return
+
+    node_users = _filter_sym_size_users(node)
+    if len(node.users) == 0:
+        return
+    if len(node_users) != 1:
+        logger.warning(f"Expecting {node} to have single user")  # noqa: G004
+    q_node = node_users.pop()
+    if q_node.op != "call_function" or q_node.target not in _QUANTIZE_OPS:
+        logger.warning(
+            f"Expecting {node} user to be a quantized op but got {q_node}"  # noqa: G004
+        )  # noqa: G004
+        return
+
+    _add_metadata(q_node, node)
+
+
+class PortNodeMetaForQDQ(PassBase):
+    """
+    Port metadata for nodes added by quantization flow.
+    For static quant these are:
+    - quantizer_per_tensor.default, dequantize_per_tensor.default
+    - quantizer_per_channel.default, dequantize_per_channel.default
+    For dynamic quant these are:
+    - choose_qparams.tensor
+    - quantizer_per_tensor.tensor, dequantize_per_tensor.tensor
+    - quantizer_per_channel.default, dequantize_per_channel.default
+
+    Rules of porting metadata:
+    - Metadata to be ported:
+      - nn_module_stack
+      - stack_trace
+      - quantization_tag
+    - Metadata to NOT be ported:
+      - Everything else
+    - Rules:
+      - Statically quantized patterns:
+        - Dequantize nodes on the inputs to be quantized inherit metadata of the consumer node.
+        - Quantize nodes on the outputs inherit metadata of the producer node.
+        - Example 1:
+          - Original: [Conv -> AvgPool -> Linear]
+          - Quantized [Q-> DQ -> Conv -> Q -> DQ -> AvgPool -> Q -> DQ -> Linear -> Q -> DQ]
+          - Inner brackets specify which nodes Q/DQ inherit metadata from
+          - [Q-> [DQ -> Conv -> Q] -> [DQ -> AvgPool -> Q] -> [DQ -> Linear -> Q] -> DQ]
+          - Note first Q and last DQ do not inherit metadata from any nodes
+        - Example 2:
+          - Original: [Conv -> AvgPool -> Linear]
+          - AvgPool is not quantized
+          - Quantized [Q-> DQ -> Conv -> Q -> DQ -> AvgPool -> Q -> DQ -> Linear -> Q -> DQ]
+          - Inner brackets specify which nodes Q/DQ inherit metadata from
+          - [Q-> [DQ -> Conv -> Q] -> DQ -> [AvgPool] -> Q -> [DQ -> Linear -> Q] -> DQ]
+          - Note DQ and Q nodes around AvgPool do not inherit metadata from AvgPool because
+            AvgPool was not supposed to be quantized. Metadata porting relies on quantization_annotation
+            on the nodes (in this case AvgPool node) to conclude if the node or pattern was
+            supposed to be quantized. And subsequently decide if the preceding Q, if any, should
+            inherit metadata from AvgPool.
+      - Dynamically quantized patterns:
+        - Input that are dynamically quantized have choose_qparams, quantize and dequantize nodes
+        - For example, below linear is dynamically quantized while rest statically:
+          - Original: [Conv -> AvgPool -> Linear]
+          - Quantized [Q-> DQ -> Conv -> Q -> DQ -> AvgPool -> Q -> DQ -> choose_params -> Q -> DQ -> Linear]
+          - Quantized [Q-> [DQ -> Conv -> Q] -> [DQ -> AvgPool -> Q] -> DQ -> [choose_params -> Q -> DQ -> Linear]]
+          - Note first Q does not inherit metadata from any nodes
+    NB:
+    - The best place for porting metadata is during observer conversion to q/dq. This is because it precisely
+      knows which quantization spec is converted to q/dq and thus from where the metadata should be ported.
+      However, since FX and PT2E quant workflow are on a common code-base, this hurts readability quite a bit.
+      Doing it via a separate pass, helps readability of the code. Once we are able to refactor PT2E quant
+      code, this pass should like to be integrated in the refactored variant of "convert" step.
+    """
+
+    def call(self, graph_module: torch.fx.GraphModule) -> PassResult:
+        for node in graph_module.graph.nodes:
+            annotation = node.meta.get("quantization_annotation", None)
+            if _is_valid_annotation(annotation):
+                input_qspec_map = node.meta["quantization_annotation"].input_qspec_map
+                output_qspec = node.meta["quantization_annotation"].output_qspec
+                for input_node, qspec in input_qspec_map.items():
+                    _port_metadata_for_input_quant_nodes(input_node, node, qspec)
+                _port_metadata_for_output_quant_nodes(node, output_qspec)
+        return PassResult(graph_module, True)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/prepare.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/prepare.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e3c8b4b33d881feda0864cc65698972a3226c7c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/prepare.py
@@ -0,0 +1,610 @@
+# mypy: allow-untyped-defs
+from typing import Any
+
+import torch
+from torch._subclasses import FakeTensor
+from torch.ao.quantization import (
+    CUSTOM_KEY,
+    NUMERIC_DEBUG_HANDLE_KEY,
+    ObserverOrFakeQuantize,
+    QConfigMapping,
+)
+from torch.ao.quantization.fx.custom_config import PrepareCustomConfig
+from torch.ao.quantization.fx.prepare import (
+    _create_obs_or_fq_from_qspec,
+    _insert_obs_or_fq,
+    _is_activation_post_process_node,
+    _save_state,
+)
+from torch.ao.quantization.qconfig import QConfigAny
+from torch.ao.quantization.quantizer import (
+    EdgeOrNode,
+    QuantizationSpecBase,
+    SharedQuantizationSpec,
+)
+from torch.ao.quantization.utils import _assert_and_get_unique_device
+from torch.fx import Graph, GraphModule, Node
+from torch.fx.node import Argument
+
+
+# TODO: make pt2e folder private?
+__all__ = [
+    "prepare",
+]
+
+
+def _find_root_edge_or_node(
+    edge_or_node: EdgeOrNode, shared_with_map: dict[EdgeOrNode, EdgeOrNode]
+) -> EdgeOrNode:
+    """Find the root node for the sharing tree
+    Args:
+        edge_or_node: edge/node that we want to find the root
+        shared_with_map: each edge/node points to the parent, the root node will points to itself
+
+    Returns:
+        root edge/node
+    """
+    parent = shared_with_map[edge_or_node]
+    if parent == edge_or_node:
+        return edge_or_node
+    root = _find_root_edge_or_node(parent, shared_with_map)
+    # path compression
+    shared_with_map[edge_or_node] = root
+    return root
+
+
+def _union(
+    parent: EdgeOrNode,
+    child: EdgeOrNode,
+    shared_with_map: dict[EdgeOrNode, EdgeOrNode],
+) -> None:
+    """Merge the subtree for `child` with `parent`, the order is important here"""
+    root_parent = _find_root_edge_or_node(parent, shared_with_map)
+    root_child = _find_root_edge_or_node(child, shared_with_map)
+    # union the two trees by pointing the root of child to root of parent
+    shared_with_map[root_child] = root_parent
+
+
+def _update_shared_with(
+    child: EdgeOrNode,
+    qspec: QuantizationSpecBase,
+    shared_with_map: dict[EdgeOrNode, EdgeOrNode],
+):
+    """Update the `shared_with_map` based on the qspec, this applies the `SharedQuantizationSpec`
+    configuration and established the relationship between `edge_or_node` with the edge/node that it
+    is pointing to, we'll use this information in the end to get the group id
+    """
+    if isinstance(qspec, SharedQuantizationSpec):
+        parent = qspec.edge_or_node
+        # we point from edge_or_node to the node that it is sharing_with, e.g.
+        # qspec for a = SharedQuantizationSpec(b) means `a` points to `b`
+        _union(parent, child, shared_with_map)
+
+
+def _unwrap_shared_qspec(
+    qspec: QuantizationSpecBase,
+    edge_or_node_to_qspec: dict[EdgeOrNode, QuantizationSpecBase],
+    shared_with_map: dict[EdgeOrNode, EdgeOrNode],
+) -> QuantizationSpecBase:
+    """Unwraps qspec to get the final root qspec (non SharedQuantizationSpec)
+    if qspec is SharedQuantizationSpec
+       (1). tries to find the root edge or node for the node that the qspec points to
+       (2). recursively find the root qspec based on the qspec for the root node
+    """
+    if isinstance(qspec, SharedQuantizationSpec):
+        sharing_with = qspec.edge_or_node
+        root = _find_root_edge_or_node(sharing_with, shared_with_map)
+        qspec = edge_or_node_to_qspec[root]
+        return _unwrap_shared_qspec(qspec, edge_or_node_to_qspec, shared_with_map)
+    return qspec
+
+
+def _has_same_attr(
+    qspec_a: QuantizationSpecBase, qspec_b: QuantizationSpecBase, attr_name: str
+):
+    return (
+        hasattr(qspec_a, attr_name)
+        and hasattr(qspec_b, attr_name)
+        and getattr(qspec_a, attr_name) == getattr(qspec_b, attr_name)
+    ) or (not hasattr(qspec_a, attr_name) and not hasattr(qspec_b, attr_name))
+
+
+def _get_edge_or_node_to_qspec(
+    model: torch.fx.GraphModule,
+) -> dict[EdgeOrNode, QuantizationSpecBase]:
+    """Get a map from EdgeOrNode to quantization spec based on annotations on the nodes"""
+    edge_or_node_to_qspec: dict[EdgeOrNode, QuantizationSpecBase] = {}
+    for n in model.graph.nodes:
+        if hasattr(n, "meta") and "quantization_annotation" in n.meta:
+            qa = n.meta["quantization_annotation"]
+            for input_to_n, qspec in qa.input_qspec_map.items():
+                input_edge = (input_to_n, n)
+                edge_or_node_to_qspec[input_edge] = qspec
+            if qa.output_qspec is not None:
+                output_node = n
+                qspec = qa.output_qspec
+                edge_or_node_to_qspec[output_node] = qspec
+    return edge_or_node_to_qspec
+
+
+def _union_input_edge_with(
+    input_edge,
+    input_edge_root_qspec,
+    edge_or_node,
+    edge_or_node_to_qspec,
+    shared_with_map,
+):
+    """Union input edge with another edge or node, used in implicit sharing to point the current input
+    edge to other user edges of the producer node, or the output of producer node since these are
+    referring to the same Tensor
+    """
+    root_qspec = None
+    if edge_or_node in edge_or_node_to_qspec:
+        qspec = edge_or_node_to_qspec[edge_or_node]
+        root_qspec = _unwrap_shared_qspec(qspec, edge_or_node_to_qspec, shared_with_map)
+    # TODO: add assertions for types of root qspecs
+    if root_qspec is not None and all(
+        _has_same_attr(root_qspec, input_edge_root_qspec, attr)
+        for attr in [
+            "dtype",
+            "is_dynamic",
+            "quant_min",
+            "quant_max",
+            "qscheme",
+            "ch_axis",
+            "scale",
+            "zero_point",
+        ]
+    ):
+        # the input arg to the node should reuse the existing output observer for arg
+        # since dtype is the same (we may want to extend this to be a more strict check
+        # in the future)
+        # so we point from `input_edge` to `arg` (output of the argument)
+        _union(edge_or_node, input_edge, shared_with_map)
+
+
+def _get_edge_or_node_to_group_id(
+    edge_or_node_to_qspec: dict[EdgeOrNode, QuantizationSpecBase],
+) -> dict[EdgeOrNode, int]:
+    """Map from edge/node to the group ID, generated from quantization annotations,
+    edge/node with the same group ID should use the same observer/fake_quant instance
+
+    This is applying SharedQuantizationSpec configuration and map each edge/node to a group
+    There is another implicit sharing that's built in the quantization, when we have the following:
+       * op1 -> op2
+       * output of op1: int8_qspec
+       * (op1 -> op2) input edge: int8_qspec
+    we'll assume sharing between the output of op1 and input of (op1 -> op2) since these are the same Tensor.
+
+    Figuring out the correct group ID for all edge/node is a standard union find problem:
+    https://www.geeksforgeeks.org/introduction-to-disjoint-set-data-structure-or-union-find-algorithm/
+
+    Args:
+        edge_or_node_to_qspec: Dictionary from edge_or_node to the qspec, derived from annotations
+    Returns:
+        edge_or_node_to_group_id: Dictionary from edge_or_node to group_id (int), all edge or node that
+        belongs to the same group should have the same id
+
+    Example:
+        op2 -> cat1 -> cat2
+           op1 /        /
+                     op3
+        edge_or_node_to_qspec: {
+            op1: int8_qspec,
+            op2: int8_qspec,
+            (op1, cat1): int8_qspc,
+            (op2, cat1): SharedQuantizationSpec((op1, cat1)),
+            cat1: SharedQuantizationSpec((op1, cat1)),
+            (op3, cat2): int8_qspec,
+            (cat1, cat2): SharedQuantizationSpec((op3, cat2)),
+            cat2: SharedQuantizationSpec((op3, cat2)),
+        }
+
+        edge_or_node_to_group_id = _get_edge_or_node_to_group_id(edge_or_node_to_qspec)
+        edge_or_node_to_group_id: {
+            op1: 1,
+            op2: 1,
+            (op1, cat1): 1,
+            (op2, cat1): 1,
+            cat1: 1,
+            (op3, cat2): 1,
+            (cat1, cat2): 1,
+            cat2: 1,
+        }
+        # everything are in the same group because (cat1) and (cat1, cat2) are implicitly shared, which
+        # connects the two sharing group around cat1 and cat2 op due to transitive sharing
+    """
+    # means the observer of key should be shared with observer with value, by default it will
+    # be shared with itself
+    shared_with_map: dict[EdgeOrNode, EdgeOrNode] = {
+        k: k for k in edge_or_node_to_qspec
+    }
+    for edge_or_node, qspec in edge_or_node_to_qspec.items():
+        if isinstance(edge_or_node, torch.fx.Node):
+            output_node = edge_or_node
+            _update_shared_with(output_node, qspec, shared_with_map)
+        else:
+            input_edge = edge_or_node
+            input_edge_root_qspec = _unwrap_shared_qspec(
+                qspec, edge_or_node_to_qspec, shared_with_map
+            )
+
+            if not isinstance(input_edge, tuple):
+                raise AssertionError(
+                    f"input_edge must be a tuple (arg, user), got {type(input_edge)}"
+                )
+            arg, n = input_edge
+            if n.meta["quantization_annotation"].allow_implicit_sharing:
+                # NOTE: the order is important here, we first share with other users and then share with previous
+                # output because the reverse order could cause circular dependency
+                # e.g node1 -> node2
+                #          \ -> node3
+                # when processing (node1, node2), if we first point (node1, node2) to node1
+                # Step 1. shared_map = {(node1, node2): node1}
+                # Step 2. after that, we point the (node1, node2) to its other user (node1, node3) ,
+                # which means shared_map = {(node1, node2): node1, node1: (node1, node3)}
+                # because we will point the root of (node1, node2) (in this case node1) to the root of (node1, node3)
+                # Step 3. and when we process (node1, node3), it can try to point to node1 as well, then we'll
+                # have a circular dependency
+                # the following order works around this issue, but this does not allow arbitrary configuration
+                # of sharing so it might break in a different case in the future, when it breaks
+                # quantizer writer can check the notes here to debug the issue
+
+                # sharing with other users of the producer node
+                # (arg, user)
+                if not isinstance(arg, Node) or not isinstance(n, Node):
+                    raise Exception(  # noqa: TRY002
+                        f"Expected input_edge to have type Tuple[Node, Node], but got: {arg, n}"
+                    )
+                for user in arg.users:
+                    if user is n:
+                        continue
+                    arg_to_user_edge = (arg, user)
+                    _union_input_edge_with(
+                        input_edge,
+                        input_edge_root_qspec,
+                        arg_to_user_edge,
+                        edge_or_node_to_qspec,
+                        shared_with_map,
+                    )
+
+                # sharing with output of producer node
+                _union_input_edge_with(
+                    input_edge,
+                    input_edge_root_qspec,
+                    arg,
+                    edge_or_node_to_qspec,
+                    shared_with_map,
+                )
+
+            _update_shared_with(input_edge, qspec, shared_with_map)
+
+    # now that we get the sharing relations between all edges and nodes, we can assign group ids
+    cur_group_id = 0
+    edge_or_node_to_group_id: dict[EdgeOrNode, int] = {}
+    for edge_or_node in shared_with_map:
+        root = _find_root_edge_or_node(edge_or_node, shared_with_map)
+        if root not in edge_or_node_to_group_id:
+            edge_or_node_to_group_id[root] = cur_group_id
+            cur_group_id += 1
+        edge_or_node_to_group_id[edge_or_node] = edge_or_node_to_group_id[root]
+
+    return edge_or_node_to_group_id
+
+
+def _get_obs_or_fq_map(
+    edge_or_node_to_group_id: dict[EdgeOrNode, int],
+    edge_or_node_to_qspec: dict[EdgeOrNode, QuantizationSpecBase],
+    is_qat: bool,
+) -> dict[EdgeOrNode, ObserverOrFakeQuantize]:
+    """Generates the EdgeOrNode to observer/fake_quant instances
+    Makes sure that for EdgeOrNode that has the same group_id should have the same observer or fake quant
+    instances
+    """
+    obs_or_fq_map: dict[EdgeOrNode, ObserverOrFakeQuantize] = {}
+    group_id_to_obs_or_fq: dict[int, ObserverOrFakeQuantize] = {}
+    for edge_or_node, qspec in edge_or_node_to_qspec.items():
+        group_id = edge_or_node_to_group_id[edge_or_node]
+        if group_id not in group_id_to_obs_or_fq:
+            # TODO: maybe edge_or_node_to_qspec should be edge_or_node_to_root_qspec, this will simplify
+            # the implementation for _create_obs_or_fq_from_qspec
+            group_id_to_obs_or_fq[group_id] = _create_obs_or_fq_from_qspec(
+                qspec, obs_or_fq_map, is_qat
+            )
+        obs_or_fq_map[edge_or_node] = group_id_to_obs_or_fq[group_id]
+    return obs_or_fq_map
+
+
+def _maybe_insert_input_observer_for_arg_or_kwarg(
+    node: Node | Any,
+    arg: Argument,
+    qconfig: QConfigAny,
+    model: torch.nn.Module,
+    named_modules: dict[str, torch.nn.Module],
+    obs_or_fq_map: dict[EdgeOrNode, ObserverOrFakeQuantize],
+    is_qat: bool,
+    model_device: torch.device | None = None,
+) -> Argument:
+    """
+    Given a `node` and an `arg`, inserts an input observer between
+    `node` and `arg` if necessary.
+    """
+    # for ops such as torch.cat([x0, x1]),
+    # traverse through the list
+    if isinstance(arg, (list, tuple)):
+        new_arg_to_return = []
+        for inner_arg in arg:
+            new_inner_arg = _maybe_insert_input_observer_for_arg_or_kwarg(
+                node,
+                inner_arg,
+                qconfig,
+                model,
+                named_modules,
+                obs_or_fq_map,
+                is_qat,
+                model_device,
+            )
+            new_arg_to_return.append(new_inner_arg)
+        return type(arg)(new_arg_to_return)
+
+    if not isinstance(arg, Node):
+        return arg
+    if not isinstance(arg, Node):
+        raise AssertionError(
+            f"expect original argument to be a Node, but got: {type(arg)}"
+        )
+    # default (no observer)
+    new_arg = arg
+
+    # find the original `arg` node to the current node, skipping inserted observer/fake_quant nodes
+    original_arg = arg
+    while _is_activation_post_process_node(original_arg, named_modules):
+        original_arg = original_arg.args[0]  # type: ignore[assignment]
+    if not isinstance(original_arg, Node):
+        raise AssertionError(
+            f"expect original argument to be a Node, but got: {type(original_arg)}"
+        )
+
+    input_edge = (original_arg, node)
+    if input_edge not in obs_or_fq_map:
+        return new_arg
+    # input_edge needs to be observed
+    input_edge_obs_or_fq = obs_or_fq_map[input_edge]
+    if input_edge_obs_or_fq is None:
+        return new_arg
+
+    arg_as_output_obs_or_fq = obs_or_fq_map.get(original_arg)
+    # the arg is observed as the output and is using the same instance as the input_edge
+    # we'll reuse the inserted observer/fake_quant
+    if arg_as_output_obs_or_fq is not None and id(arg_as_output_obs_or_fq) == id(
+        input_edge_obs_or_fq
+    ):
+        return new_arg
+
+    # otherwise, we'll insert a new observer/fake_quant node
+
+    # skip inserting new observers if the same observer instance is inserted before for another user
+    # Example:
+    # conv1 -> obs1 -> existing_obs -> conv2
+    #             \ -> conv3
+    #
+    # instead of inserting new observers we will have:
+    # conv1 -> obs1 -> existing_obs -> conv2
+    #                            \ -> conv3
+    for maybe_obs_node in arg.users:
+        if not _is_activation_post_process_node(maybe_obs_node, named_modules):
+            continue
+        maybe_obs_mod = named_modules[maybe_obs_node.target]  # type: ignore[index]
+        if id(maybe_obs_mod) == id(input_edge_obs_or_fq):
+            return maybe_obs_node
+
+    if not isinstance(model.graph, Graph):
+        raise AssertionError(
+            f"Expected model.graph to be a torch.fx.Graph, got {type(model.graph)}"
+        )
+    new_arg = _insert_obs_or_fq(
+        arg,
+        input_edge_obs_or_fq,
+        model,
+        named_modules,
+        model.graph,
+        model_device,
+    )
+    return new_arg
+
+
+def _maybe_insert_input_observers_for_node(
+    node: Node,
+    qconfig: QConfigAny,
+    model: torch.nn.Module,
+    named_modules: dict[str, torch.nn.Module],
+    obs_or_fq_map: dict[EdgeOrNode, ObserverOrFakeQuantize],
+    is_qat: bool,
+    model_device: torch.device | None = None,
+) -> None:
+    """
+    If needed, inserts observers to the input args and kwargs of `node`.
+    Note: modifies `node` inplace.
+
+    For example, if cur_node needs an observer after prev_node, we change from
+
+      prev_node -> cur_node
+
+    To
+
+      prev_node -> obs -> cur_node
+
+    """
+    # Look through every input arg.  If that arg's target dtype does not
+    # match the current node's target dtype, insert an observer.
+    new_args = []
+    for arg in node.args:
+        new_arg = _maybe_insert_input_observer_for_arg_or_kwarg(
+            node,
+            arg,
+            qconfig,
+            model,
+            named_modules,
+            obs_or_fq_map,
+            is_qat,
+            model_device,
+        )
+        new_args.append(new_arg)
+
+    # Clone has a memory_format kwarg, zeros_like has a pin_memory kwarg, and
+    # gelu has a has an approximate kwarg that persist in exported graph.
+    # This is just a work around for these.
+    if not (
+        node.target is torch.ops.aten.clone.default
+        or node.target is torch.ops.aten.zeros_like.default
+        or node.target is torch.ops.aten.gelu.default
+        or len(node.kwargs) == 0
+    ):
+        raise AssertionError(" expecting kwargs for aten op IR to be empty")
+
+    # assign the new args to the node, inplace
+    node.args = tuple(new_args)
+
+
+def _maybe_insert_output_observer_for_node(
+    node: Node,
+    model: torch.nn.Module,
+    named_modules: dict[str, torch.nn.Module],
+    graph: Graph,
+    obs_or_fq_map: dict[EdgeOrNode, ObserverOrFakeQuantize],
+    is_qat: bool,
+    model_device: torch.device | None = None,
+) -> Node | None:
+    if node in obs_or_fq_map:
+        output_act_obs_or_fq = obs_or_fq_map[node]
+        new_output = _insert_obs_or_fq(
+            node,
+            output_act_obs_or_fq,
+            model,
+            named_modules,
+            graph,
+            model_device,
+        )
+        # propagate numeric debug handle from original node to observer/fake_quant node
+        if (
+            isinstance(node, Node)
+            and isinstance(new_output, Node)
+            and CUSTOM_KEY in node.meta
+            and NUMERIC_DEBUG_HANDLE_KEY in node.meta[CUSTOM_KEY]
+        ):
+            if CUSTOM_KEY not in new_output.meta:
+                new_output.meta[CUSTOM_KEY] = {}
+            new_output.meta[CUSTOM_KEY][NUMERIC_DEBUG_HANDLE_KEY] = node.meta[
+                CUSTOM_KEY
+            ][NUMERIC_DEBUG_HANDLE_KEY]
+        return new_output
+    return None
+
+
+def _maybe_insert_input_and_output_observers_for_node(
+    node: Node,
+    model: torch.fx.GraphModule,
+    obs_or_fq_map: dict[EdgeOrNode, ObserverOrFakeQuantize],
+    is_qat: bool,
+    model_device: torch.device | None = None,
+):
+    this_node_quantization_annotation = node.meta.get("quantization_annotation", None)
+    if this_node_quantization_annotation is None:
+        return
+
+    named_modules = dict(model.named_modules(remove_duplicate=False))
+    _maybe_insert_input_observers_for_node(
+        node,
+        None,  # qconfig
+        model,
+        named_modules,
+        obs_or_fq_map,
+        is_qat,
+        model_device,
+    )
+
+    output_is_a_tensor = "val" in node.meta and isinstance(node.meta["val"], FakeTensor)
+    if not output_is_a_tensor:
+        return
+
+    # this returns the new observer node if it was needed
+    maybe_output_obs_node = _maybe_insert_output_observer_for_node(
+        node,
+        model,
+        named_modules,
+        model.graph,
+        obs_or_fq_map,
+        is_qat,
+        model_device,
+    )
+
+    if maybe_output_obs_node is None:
+        return
+    # Update users of original node to use the output observer
+    # instead. For example, change
+    #
+    #           next_node
+    #          /
+    #   cur_node -> obs
+    #
+    # to
+    #
+    #                 next_node
+    #                 /
+    #   cur_node -> obs
+    #
+    # We need to save orig users before updating uses because
+    # the list of users will change as we update uses
+    orig_users = list(node.users.keys())
+    for user_node in orig_users:
+        if user_node is maybe_output_obs_node:
+            continue
+        user_node.replace_input_with(node, maybe_output_obs_node)
+
+
+def prepare(
+    model: GraphModule,
+    node_name_to_scope: dict[str, tuple[str, type]],
+    is_qat: bool,
+    obs_or_fq_callback=None,
+) -> GraphModule:
+    # Since we are mutating the graph as we go, we iterate over the original
+    # nodes before observer insertion, instead of model.graph.nodes.
+    nodes_before_observation = list(model.graph.nodes)
+
+    # At the high level we construct a map from EdgeOrNode to a observer_or_fake_quant instance
+    # all edge/nodes that belongs to the same group will use the same instance
+    # and when we insert observers we'll just query this map to get the correct observer_or_fake_quant
+    # instance
+    edge_or_node_to_qspec = _get_edge_or_node_to_qspec(model)
+    edge_or_node_to_group_id = _get_edge_or_node_to_group_id(edge_or_node_to_qspec)
+    obs_or_fq_map = _get_obs_or_fq_map(
+        edge_or_node_to_group_id, edge_or_node_to_qspec, is_qat
+    )
+    if obs_or_fq_callback:
+        obs_or_fq_callback(model, obs_or_fq_map)
+    model_device = _assert_and_get_unique_device(model)
+
+    for node in nodes_before_observation:
+        # TODO: simplify logic for inserting observers
+        _maybe_insert_input_and_output_observers_for_node(
+            node,
+            model,
+            obs_or_fq_map,
+            is_qat,
+            model_device,
+        )
+
+    model = GraphModule(model, model.graph)
+
+    _save_state(
+        model,
+        {},  # node_name_to_qconfig
+        node_name_to_scope,
+        PrepareCustomConfig(),
+        {},  # equalization_node_name_to_qconfig
+        QConfigMapping(),
+        is_qat,
+        set(),  # observed_node_names
+    )
+    return model
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/qat_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/qat_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..9498a4f16f78f256baba85246dabeb458c9764c2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/qat_utils.py
@@ -0,0 +1,1058 @@
+# mypy: allow-untyped-defs
+import copy
+import dataclasses
+import itertools
+import operator
+from collections.abc import Callable
+from typing import Any, TYPE_CHECKING
+
+import torch
+import torch.nn.functional as F
+from torch.ao.quantization.fx._decomposed import quantized_decomposed_lib  # noqa: F401
+from torch.ao.quantization.pt2e.export_utils import _WrapperModule
+from torch.ao.quantization.quantizer import (
+    DerivedQuantizationSpec,
+    EdgeOrNode,
+    QuantizationSpecBase,
+    SharedQuantizationSpec,
+)
+from torch.fx import Graph, GraphModule, Node
+from torch.fx.subgraph_rewriter import replace_pattern_with_filters, ReplacedPatterns
+
+from .utils import (
+    _get_aten_graph_module_for_pattern,
+    _is_bn_node,
+    _is_conv_or_conv_transpose_node,
+    _is_conv_transpose_fn,
+    fold_bn_weights_into_conv_node,
+)
+
+
+if TYPE_CHECKING:
+    from torch.fx.passes.utils.matcher_with_name_node_map_utils import InternalMatch
+
+__all__ = []  # type: ignore[var-annotated]
+
+
+def _get_quantized_conv_bn_example_inputs_kwargs(
+    is_per_channel: bool,
+    has_bias: bool,
+    bias_is_quantized: bool,
+    is_cuda: bool,
+) -> dict[str, Any]:
+    """
+    Optional example inputs for quantized and folded conv-bn patterns
+    used in convert, expressed as kwargs.
+    """
+    kwargs = {}
+    # Per tensor quantization uses literals to represent scale and zero
+    # point, so there is no need to include them here as kwargs
+    if is_per_channel:
+        kwargs["weight_scale"] = torch.tensor([1], dtype=torch.float)
+        kwargs["weight_zero_point"] = torch.tensor([0], dtype=torch.int)
+        if has_bias and bias_is_quantized:
+            kwargs["bias_scale"] = torch.tensor([1], dtype=torch.float)
+            kwargs["bias_zero_point"] = torch.tensor([0], dtype=torch.int)
+    if has_bias:
+        kwargs["conv_bias"] = torch.randn(1)
+    if is_cuda:
+        for k, v in kwargs.items():
+            if isinstance(v, torch.Tensor):
+                kwargs[k] = v.cuda()
+    return kwargs
+
+
+def _get_conv_bn_pattern(conv_fn: Callable) -> Callable:
+    def _conv_bn_pattern(
+        x: torch.Tensor,
+        conv_weight: torch.Tensor,
+        conv_bias: torch.Tensor,
+        bn_weight: torch.Tensor,
+        bn_bias: torch.Tensor,
+        bn_running_mean: torch.Tensor,
+        bn_running_var: torch.Tensor,
+    ) -> torch.Tensor:
+        x = conv_fn(x, conv_weight, conv_bias)
+        x = F.batch_norm(
+            x, bn_running_mean, bn_running_var, bn_weight, bn_bias, training=True
+        )
+        return x
+
+    return _WrapperModule(_conv_bn_pattern)
+
+
+# TODO: merge this with the `no_conv_bias` case
+def _get_qat_conv_bn_pattern(conv_fn: Callable) -> Callable:
+    def _qat_conv_bn_pattern(
+        x: torch.Tensor,
+        conv_weight: torch.Tensor,
+        conv_bias: torch.Tensor,
+        bn_weight: torch.Tensor,
+        bn_bias: torch.Tensor,
+        bn_running_mean: torch.Tensor,
+        bn_running_var: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        Approximated method to fuse conv and bn. It requires only one forward pass.
+        conv_orig = conv / scale_factor where scale_factor = bn.weight / running_std.
+        This is based on `nniqat.ConvBn2d._forward_approximate`.
+        """
+        # TODO: allow setting eps
+        bn_eps = 1e-5
+        running_std = torch.sqrt(bn_running_var + bn_eps)
+        scale_factor = bn_weight / running_std
+        weight_shape = [1] * len(conv_weight.shape)
+        weight_in_channel_axis = 1 if _is_conv_transpose_fn(conv_fn) else 0
+        weight_shape[weight_in_channel_axis] = -1
+        bias_shape = [1] * len(conv_weight.shape)
+        bias_shape[1] = -1
+        scaled_weight = conv_weight * scale_factor.reshape(weight_shape)
+        zero_bias = torch.zeros_like(conv_bias, dtype=x.dtype)
+        x = conv_fn(x, scaled_weight, zero_bias)
+        x = x / scale_factor.reshape(bias_shape)
+        x = x + conv_bias.reshape(bias_shape)
+        x = F.batch_norm(
+            x,
+            bn_running_mean,
+            bn_running_var,
+            bn_weight,
+            bn_bias,
+            training=True,
+            eps=bn_eps,
+        )
+        return x
+
+    return _WrapperModule(_qat_conv_bn_pattern)
+
+
+def _get_qat_conv_bn_pattern_no_conv_bias(conv_fn: Callable) -> Callable:
+    def _qat_conv_bn_pattern_no_conv_bias(
+        x: torch.Tensor,
+        conv_weight: torch.Tensor,
+        # Not used, only for matching convenience
+        conv_bias: torch.Tensor,
+        bn_weight: torch.Tensor,
+        bn_bias: torch.Tensor,
+        bn_running_mean: torch.Tensor,
+        bn_running_var: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        Same as `_get_qat_conv_bn_pattern`, but handles the case with no conv bias.
+        """
+        # TODO: allow setting eps
+        bn_eps = 1e-5
+        running_std = torch.sqrt(bn_running_var + bn_eps)
+        scale_factor = bn_weight / running_std
+        weight_shape = [1] * len(conv_weight.shape)
+        weight_in_channel_axis = 1 if _is_conv_transpose_fn(conv_fn) else 0
+        weight_shape[weight_in_channel_axis] = -1
+        bias_shape = [1] * len(conv_weight.shape)
+        bias_shape[1] = -1
+        scaled_weight = conv_weight * scale_factor.reshape(weight_shape)
+        x = conv_fn(x, scaled_weight, None)
+        x = x / scale_factor.reshape(bias_shape)
+        x = F.batch_norm(
+            x,
+            bn_running_mean,
+            bn_running_var,
+            bn_weight,
+            bn_bias,
+            training=True,
+            eps=bn_eps,
+        )
+        return x
+
+    return _WrapperModule(_qat_conv_bn_pattern_no_conv_bias)
+
+
+def _append_qdq(x, is_per_channel, is_bias, kwargs):
+    """
+    Helper function to append q-dq ops after `x`, using dummy values for the qparams
+    and qmin/qmax. We use dummy values here because we match with `ignore_literals=True`
+    and will manually replace these values after subgraph rewriting.
+
+    Return the dq node.
+    """
+    # Dummy args to be passed into q-dq ops
+    per_channel_axis = 0
+    scale_key = "bias_scale" if is_bias else "weight_scale"
+    zp_key = "bias_zero_point" if is_bias else "weight_zero_point"
+    scale = kwargs[scale_key] if is_per_channel else 1.0
+    zp = kwargs[zp_key] if is_per_channel else 0
+    qmin = -127
+    qmax = 127
+    dtype = torch.int8
+
+    qd = torch.ops.quantized_decomposed
+    if is_per_channel:
+        x = qd.quantize_per_channel(x, scale, zp, per_channel_axis, qmin, qmax, dtype)
+        x = qd.dequantize_per_channel(x, scale, zp, per_channel_axis, qmin, qmax, dtype)
+    else:
+        x = qd.quantize_per_tensor(x, scale, zp, qmin, qmax, dtype)
+        x = qd.dequantize_per_tensor(x, scale, zp, qmin, qmax, dtype)
+    return x
+
+
+def _get_quantized_qat_conv_bn_pattern(
+    is_per_channel: bool,
+    has_bias: bool,
+    bias_is_quantized: bool,
+    conv_fn: Callable,
+    bn_is_training: bool,
+) -> Callable:
+    """
+    Return the quantized version of QAT conv + BN pattern.
+    This is based on `nniqat.ConvBn2d._forward_approximate`,
+    used in QAT convert. We first match this pattern and replace
+    it with the normal [conv - bn] pattern, then fold the BN
+    weights into conv.
+    """
+    # TODO: allow setting eps
+    bn_eps = 1e-5
+
+    def _quantized_qat_conv_bn_pattern(
+        x: torch.Tensor,
+        conv_weight: torch.Tensor,
+        bn_weight: torch.Tensor,
+        bn_bias: torch.Tensor,
+        bn_running_mean: torch.Tensor,
+        bn_running_var: torch.Tensor,
+        **kwargs,
+    ) -> torch.Tensor:
+        running_std = torch.sqrt(bn_running_var + bn_eps)
+        scale_factor = bn_weight / running_std
+        weight_shape = [1] * len(conv_weight.shape)
+        weight_shape[0] = -1
+        bias_shape = [1] * len(conv_weight.shape)
+        bias_shape[1] = -1
+        scaled_weight = conv_weight * scale_factor.reshape(weight_shape)
+        scaled_weight = _append_qdq(
+            scaled_weight,
+            is_per_channel,
+            is_bias=False,
+            kwargs=kwargs,
+        )
+        if has_bias:
+            zero_bias = torch.zeros_like(kwargs["conv_bias"], dtype=x.dtype)
+            if bias_is_quantized:
+                zero_bias = _append_qdq(
+                    zero_bias,
+                    is_per_channel,
+                    is_bias=True,
+                    kwargs=kwargs,
+                )
+            x = conv_fn(x, scaled_weight, zero_bias)
+        else:
+            x = conv_fn(x, scaled_weight, None)
+        x = x / scale_factor.reshape(bias_shape)
+        if has_bias:
+            x = x + kwargs["conv_bias"].reshape(bias_shape)
+        x = F.batch_norm(
+            x,
+            bn_running_mean,
+            bn_running_var,
+            bn_weight,
+            bn_bias,
+            training=bn_is_training,
+            eps=bn_eps,
+        )
+        return x
+
+    return _WrapperModule(_quantized_qat_conv_bn_pattern)
+
+
+def _get_folded_quantized_qat_conv_bn_pattern(
+    is_per_channel: bool,
+    has_bias: bool,
+    bias_is_quantized: bool,
+    conv_fn: Callable,
+    bn_is_training: bool,
+) -> Callable:
+    """
+    Quantized QAT conv - bn pattern with bn weights being folded into conv.
+    """
+    # TODO: allow setting eps
+    bn_eps = 1e-5
+
+    def _folded_quantized_qat_conv_bn_pattern(
+        x: torch.Tensor,
+        conv_weight: torch.Tensor,
+        bn_weight: torch.Tensor,
+        bn_bias: torch.Tensor,
+        bn_running_mean: torch.Tensor,
+        bn_running_var: torch.Tensor,
+        **kwargs,
+    ) -> torch.Tensor:
+        conv_weight = _append_qdq(
+            conv_weight,
+            is_per_channel,
+            is_bias=False,
+            kwargs=kwargs,
+        )
+        if has_bias:
+            bias = kwargs["conv_bias"]
+            if bias_is_quantized:
+                bias = _append_qdq(
+                    bias,
+                    is_per_channel,
+                    is_bias=True,
+                    kwargs=kwargs,
+                )
+        else:
+            bias = None
+        x = conv_fn(x, conv_weight, bias)
+        x = F.batch_norm(
+            x,
+            bn_running_mean,
+            bn_running_var,
+            bn_weight,
+            bn_bias,
+            training=bn_is_training,
+            eps=bn_eps,
+        )
+        return x
+
+    return _WrapperModule(_folded_quantized_qat_conv_bn_pattern)
+
+
+def _has_conv_bias_filter(
+    match: "InternalMatch",
+    original_graph: Graph,
+    pattern_graph: Graph,
+) -> bool:
+    """
+    Match filter for the subgraph rewriter that returns True if the conv node in
+    the original graph has bias.
+    """
+    for n in match.nodes_map.values():
+        if _is_conv_or_conv_transpose_node(n):
+            return len(n.args) > 2 and n.args[2] is not None
+    raise ValueError("Could not find conv node in matched conv + bn pattern")
+
+
+def _no_conv_bias_filter(
+    match: "InternalMatch",
+    original_graph: Graph,
+    pattern_graph: Graph,
+) -> bool:
+    """
+    Match filter for the subgraph rewriter that returns True if the conv node in
+    the original graph does NOT have bias.
+    """
+    return not _has_conv_bias_filter(match, original_graph, pattern_graph)
+
+
+def _is_quantize(n: Node) -> bool:
+    return n.target in [
+        torch.ops.quantized_decomposed.quantize_per_tensor.default,
+        torch.ops.quantized_decomposed.quantize_per_tensor.tensor,
+        torch.ops.quantized_decomposed.quantize_per_channel.default,
+    ]
+
+
+def _is_dequantize(n: Node) -> bool:
+    return n.target in [
+        torch.ops.quantized_decomposed.dequantize_per_tensor.default,
+        torch.ops.quantized_decomposed.dequantize_per_tensor.tensor,
+        torch.ops.quantized_decomposed.dequantize_per_channel.default,
+    ]
+
+
+def _get_conv_bn_pattern_nodes(r: ReplacedPatterns) -> dict[str, tuple[Node, Node]]:
+    """
+    Helper function to extract the nodes in the conv-bn fusion pattern after
+    subgraph rewriting, in the form of a map:
+
+        {name: (original_node, replacement_node)}
+
+    The following names must exist in the map:
+
+        "conv", "conv_weight", "conv_input", "bn", "getitem"
+
+    The following names may exist in the map:
+
+        "conv_weight_q", "conv_weight_dq", "conv_bias",
+        "conv_bias_q", "conv_bias_dq"
+    """
+
+    def _get_nodes(nodes: list[Node]) -> tuple[Node, Node, Node | None]:
+        """
+        Return a 3-tuple of (conv_node, bn_node, getitem_node).
+        This asserts that the match contains exactly one of each node.
+        """
+        conv_node, bn_node, getitem_node = None, None, None
+        for n in nodes:
+            if n.op != "call_function":
+                continue
+            if _is_conv_or_conv_transpose_node(n):
+                if conv_node is not None:
+                    raise AssertionError(
+                        f"Found multiple conv nodes in match, previous: {conv_node}, new: {n}"
+                    )
+                conv_node = n
+            if _is_bn_node(n):
+                if bn_node is not None:
+                    raise AssertionError(
+                        f"Found multiple bn nodes in match, previous: {bn_node}, new: {n}"
+                    )
+                bn_node = n
+            if n.target is operator.getitem:
+                if getitem_node is not None:
+                    raise AssertionError(
+                        f"Found multiple getitem nodes in match, previous: {getitem_node}, new: {n}"
+                    )
+                getitem_node = n
+        if conv_node is None:
+            raise AssertionError(
+                "Expected exactly one conv node in the match, found none"
+            )
+        if bn_node is None:
+            raise AssertionError(
+                "Expected exactly one bn node in the match, found none"
+            )
+        return (conv_node, bn_node, getitem_node)
+
+    def _get_q_dq_nodes(n: Node) -> tuple[Node, Node, Node]:
+        """
+        Return a 3-tuple of (orig_node, q_node, dq_node).
+        """
+        if not _is_dequantize(n):
+            raise AssertionError(f"Expected a dequantize node, got: {n}")
+        q_node = n.args[0]
+        if not isinstance(q_node, Node):
+            raise AssertionError(
+                f"Expected quantize node to be a torch.fx.Node, got {type(q_node)}"
+            )
+        if not _is_quantize(q_node):
+            raise AssertionError(
+                f"Expected q_node to be a quantize node, got target={q_node.target}"
+            )
+        orig_node = q_node.args[0]
+        if not isinstance(orig_node, Node):
+            raise AssertionError(
+                f"Expected original node to be a torch.fx.Node, got {type(orig_node)}"
+            )
+        return (orig_node, q_node, n)
+
+    original_nodes = list(_filter_nodes_map(r.nodes_map).values())
+    o_conv, o_bn, o_getitem = _get_nodes(original_nodes)
+    r_conv, r_bn, r_getitem = _get_nodes(r.replacements)
+
+    # Create the mapping from original node to replacement node
+    if o_getitem is not None:
+        raise AssertionError(f"Expected o_getitem to be None, got {o_getitem}")
+    if r_getitem is not None:
+        raise AssertionError(f"Expected r_getitem to be None, got {r_getitem}")
+    mapping = {
+        "conv": (o_conv, r_conv),
+        "bn": (o_bn, r_bn),
+    }
+
+    # Extract conv input and weight
+    # Note: here we extract the original nodes indirectly through the pattern nodes
+    # because the args of the original nodes are no longer available after replacement
+    (p_conv, _, _) = _get_nodes(list(r.nodes_map.keys()))
+    (p_conv_input, p_conv_weight, *_) = p_conv.args
+    (r_conv_input, r_conv_weight, *_) = r_conv.args
+    if not isinstance(p_conv_input, Node):
+        raise AssertionError(
+            f"Expected p_conv_input to be a Node, got {type(p_conv_input)}"
+        )
+    if not isinstance(p_conv_weight, Node):
+        raise AssertionError(
+            f"Expected p_conv_weight to be a Node, got {type(p_conv_weight)}"
+        )
+    if not isinstance(r_conv_input, Node):
+        raise AssertionError(
+            f"Expected r_conv_input to be a Node, got {type(r_conv_input)}"
+        )
+    if not isinstance(r_conv_weight, Node):
+        raise AssertionError(
+            f"Expected r_conv_weight to be a Node, got {type(r_conv_weight)}"
+        )
+    o_conv_input = r.nodes_map[p_conv_input]
+    o_conv_weight = r.nodes_map[p_conv_weight]
+
+    # If conv weight is quantized, extract the q - dq nodes
+    if _is_dequantize(p_conv_weight):
+        p_conv_weight, p_conv_weight_q, p_conv_weight_dq = _get_q_dq_nodes(
+            p_conv_weight
+        )
+        r_conv_weight, r_conv_weight_q, r_conv_weight_dq = _get_q_dq_nodes(
+            r_conv_weight
+        )
+        o_conv_weight = r.nodes_map[p_conv_weight]
+        o_conv_weight_q = r.nodes_map[p_conv_weight_q]
+        o_conv_weight_dq = r.nodes_map[p_conv_weight_dq]
+        mapping["conv_weight_q"] = (o_conv_weight_q, r_conv_weight_q)
+        mapping["conv_weight_dq"] = (o_conv_weight_dq, r_conv_weight_dq)
+    mapping["conv_input"] = (o_conv_input, r_conv_input)
+    mapping["conv_weight"] = (o_conv_weight, r_conv_weight)
+
+    # Extract conv bias
+    if len(p_conv.args) > 2 and len(r_conv.args) > 2:
+        p_conv_bias = p_conv.args[2]
+        r_conv_bias = r_conv.args[2]
+        if not isinstance(p_conv_bias, Node):
+            raise AssertionError(
+                f"Expected p_conv_bias to be a Node, got {type(p_conv_bias)}"
+            )
+        if not isinstance(r_conv_bias, Node):
+            raise AssertionError(
+                f"Expected r_conv_bias to be a Node, got {type(r_conv_bias)}"
+            )
+        o_conv_bias = r.nodes_map[p_conv_bias]
+
+        # If conv bias is quantized, extract the q - dq nodes
+        if _is_dequantize(p_conv_bias):
+            p_conv_bias, p_conv_bias_q, p_conv_bias_dq = _get_q_dq_nodes(p_conv_bias)
+            r_conv_bias, r_conv_bias_q, r_conv_bias_dq = _get_q_dq_nodes(r_conv_bias)
+            o_conv_bias = r.nodes_map[p_conv_bias]
+            o_conv_bias_q = r.nodes_map[p_conv_bias_q]
+            o_conv_bias_dq = r.nodes_map[p_conv_bias_dq]
+            mapping["conv_bias_q"] = (o_conv_bias_q, r_conv_bias_q)
+            mapping["conv_bias_dq"] = (o_conv_bias_dq, r_conv_bias_dq)
+        mapping["conv_bias"] = (o_conv_bias, r_conv_bias)
+    return mapping
+
+
+def _filter_nodes_map(nodes_map: dict[Node, Node]) -> dict[Node, Node]:
+    """
+    Return a filtered `nodes_map` returned from the subgraph rewriter.
+    The filtered `nodes_map` will contain only nodes that are actually
+    matched in the pattern, excluding None or placeholder nodes.
+    """
+    new_nodes_map: dict[Node, Node] = {}
+    for pattern_node, graph_node in nodes_map.items():
+        # bias can be None
+        if graph_node is None:
+            continue
+        # skip pattern placeholder nodes
+        if pattern_node.op == "placeholder":
+            continue
+        new_nodes_map[pattern_node] = graph_node
+    return new_nodes_map
+
+
+# TODO: this is error prone, use the replace_literals_with_placeholders hack instead
+def _copy_over_literal_conv_args(original_node: Node, new_node: Node):
+    """
+    Copy over literal args in conv, such as stride and padding, from the matched node
+    in the original graph to its replacement in the new graph.
+
+    This is needed due to the following limitation in the subgraph rewriter when used
+    with dynamo export: literal (non-tensor) args are not supported in the match and
+    replacement patterns. This is because dynamo export automatically inlines these
+    literal args, making them dead placeholder nodes. In the future, we should check
+    if dynamo export can optionally disable this inlining, or if subgraph rewriter
+    can do the copying for us. See https://github.com/pytorch/pytorch/issues/100419.
+
+    Note: Unlike other tensor args like conv weights and biases, literal args are
+    preserved in the original nodes after replacement, so we can access them here.
+    """
+    if not _is_conv_or_conv_transpose_node(original_node):
+        raise AssertionError(
+            f"Expected original_node to be a conv node, got {original_node}"
+        )
+    if not _is_conv_or_conv_transpose_node(new_node):
+        raise AssertionError(f"Expected new_node to be a conv node, got {new_node}")
+    # x, weight, bias, [stride, padding, dilation, transposed, output_padding, groups]
+    new_args = list(new_node.args)
+    if len(new_args) < 3:
+        # bias is optional, when it is not present, it means it is None
+        new_args.append(None)
+    new_node.args = tuple(new_args[:3]) + original_node.args[3:]
+
+
+def _update_conv_input_qspec_map_after_replacement(
+    original_node: Node, replacement_node: Node
+):
+    """
+    Update the `input_qspec_map` in the annotation after subgraph rewriting.
+
+    The original annotation referred to the nodes in the original graph,
+    so the keys in the `input_qspec_map` will need to be updated to reflect
+    the corresponding nodes in the replacement graph.
+    """
+    if not _is_conv_or_conv_transpose_node(original_node):
+        raise AssertionError(
+            f"Expected original_node to be a conv node, got {original_node}"
+        )
+    if not _is_conv_or_conv_transpose_node(replacement_node):
+        raise AssertionError(
+            f"Expected replacement_node to be a conv node, got {replacement_node}"
+        )
+    if "quantization_annotation" not in original_node.meta:
+        return
+    original_input_qspec_map = original_node.meta[
+        "quantization_annotation"
+    ].input_qspec_map
+    input_qspec_map = {}
+    # get the list of configs, it should be ordered as input, weight, bias
+    # note: this is really hacky, we need a better solution, hopefully
+    # in subgraph_rewriter, issue tracking the problem: https://github.com/pytorch/pytorch/issues/101820
+    all_configs = list(original_input_qspec_map.items())
+    # input activation
+    input_qspec_map[replacement_node.args[0]] = all_configs[0][1]
+    # weight
+    input_qspec_map[replacement_node.args[1]] = all_configs[1][1]
+    # bias
+    if len(replacement_node.args) > 2 and len(all_configs) > 2:
+        input_qspec_map[replacement_node.args[2]] = all_configs[2][1]
+    replacement_node.meta["quantization_annotation"].input_qspec_map = input_qspec_map
+
+
+def _update_special_qspecs_after_replacement(
+    node: Node,
+    original_to_replacement_node: dict[Node, Node],
+):
+    """
+    Update the `SharedQuantizationSpec`s and `DerivedQuantizationSpec`s
+    used in `node`'s quantization annotation after subgraph rewriting.
+
+    The original annotation referred to the nodes in the original graph,
+    so the nodes used in these special quantization specs will need to
+    be updated to the corresponding nodes in the replacement graph.
+    """
+
+    def _get_new_edge_or_node(edge_or_node: EdgeOrNode):
+        if isinstance(edge_or_node, Node):
+            _node = edge_or_node
+            return original_to_replacement_node.get(_node, _node)
+        elif (
+            isinstance(edge_or_node, tuple)
+            and len(edge_or_node) == 2
+            and all(isinstance(x, Node) for x in edge_or_node)
+        ):
+            src, dest = edge_or_node
+            return (
+                original_to_replacement_node.get(src, src),
+                original_to_replacement_node.get(dest, dest),
+            )
+        else:
+            raise ValueError("unexpected type for edge_or_node: ", type(edge_or_node))
+
+    def _get_new_qspec(qspec: QuantizationSpecBase):
+        if isinstance(qspec, SharedQuantizationSpec):
+            new_edge_or_node = _get_new_edge_or_node(qspec.edge_or_node)
+            return SharedQuantizationSpec(new_edge_or_node)
+        elif isinstance(qspec, DerivedQuantizationSpec):
+            new_derived_from = [_get_new_edge_or_node(x) for x in qspec.derived_from]
+            return dataclasses.replace(qspec, derived_from=new_derived_from)
+        else:
+            return qspec
+
+    if "quantization_annotation" not in node.meta:
+        return
+    annotation = node.meta["quantization_annotation"]
+    for input_node, qspec in annotation.input_qspec_map.items():
+        annotation.input_qspec_map[input_node] = _get_new_qspec(qspec)
+    annotation.output_qspec = _get_new_qspec(annotation.output_qspec)
+
+
+def _fuse_conv_bn_qat(m: GraphModule) -> GraphModule:
+    # Example inputs for conv-bn1d patterns
+    _conv1d_bn_example_inputs = (
+        torch.randn(1, 1, 3),  # x
+        torch.randn(1, 1, 1),  # conv_weight
+        torch.randn(1),  # conv_bias
+        torch.randn(1),  # bn_weight
+        torch.randn(1),  # bn_bias
+        torch.randn(1),  # bn_running_mean
+        torch.randn(1),  # bn_running_var
+    )
+
+    # Example inputs for conv-bn2d patterns
+    _conv2d_bn_example_inputs = (
+        torch.randn(1, 1, 3, 3),  # x
+        torch.randn(1, 1, 1, 1),  # conv_weight
+        torch.randn(1),  # conv_bias
+        torch.randn(1),  # bn_weight
+        torch.randn(1),  # bn_bias
+        torch.randn(1),  # bn_running_mean
+        torch.randn(1),  # bn_running_var
+    )
+
+    has_bn = any(_is_bn_node(n) for n in m.graph.nodes)
+    if not has_bn:
+        return m
+    is_cuda_options = [True, False] if torch.cuda.is_available() else [False]
+    for is_cuda in is_cuda_options:
+        m = _fuse_conv_bn_qat_helper(
+            m, F.conv1d, _conv1d_bn_example_inputs, is_cuda=is_cuda
+        )
+        m = _fuse_conv_bn_qat_helper(
+            m, F.conv2d, _conv2d_bn_example_inputs, is_cuda=is_cuda
+        )
+        m = _fuse_conv_bn_qat_helper(
+            m, F.conv_transpose1d, _conv1d_bn_example_inputs, is_cuda=is_cuda
+        )
+        m = _fuse_conv_bn_qat_helper(
+            m, F.conv_transpose2d, _conv2d_bn_example_inputs, is_cuda=is_cuda
+        )
+    return m
+
+
+def _fuse_conv_bn_qat_helper(
+    m: GraphModule,
+    conv_fn: Callable,
+    example_inputs: tuple[Any, ...],
+    is_cuda: bool,
+) -> GraphModule:
+    """
+    Given a graph of decomposed aten ops, replace the (conv + bn) pattern with
+    the fused QAT subgraph equivalent. The input graph should already be annotated.
+    The annotations in the original nodes will be preserved in the corresponding
+    nodes in the new subgraph.
+
+    Note: This also handles the (conv + bn + relu) pattern.
+    """
+    m.graph.eliminate_dead_code()
+    m.recompile()
+
+    conv_bn_pattern = _get_conv_bn_pattern(conv_fn)
+    match_pattern = _get_aten_graph_module_for_pattern(
+        conv_bn_pattern,
+        example_inputs,
+        is_cuda,
+    )
+
+    # Step (1): Replace patterns with conv bias
+    #
+    # Here we do replacement separately for cases with and without conv bias, since
+    # the replacement patterns for these two cases are substantially different.
+    # TODO: use the public replace_pattern API once it also returns replacement nodes
+
+    qat_conv_bn_pattern = _get_qat_conv_bn_pattern(conv_fn)
+    replacement_pattern_with_conv_bias = _get_aten_graph_module_for_pattern(
+        qat_conv_bn_pattern,
+        example_inputs,
+        is_cuda,
+    )
+    replacements_with_conv_bias = replace_pattern_with_filters(
+        m,
+        match_pattern,
+        replacement_pattern_with_conv_bias,
+        match_filters=[_has_conv_bias_filter],
+        ignore_literals=True,
+    )
+    m.recompile()
+
+    # Step (2): Replace patterns without conv bias
+
+    qat_conv_bn_pattern_no_conv_bias = _get_qat_conv_bn_pattern_no_conv_bias(conv_fn)
+    replacement_pattern_no_conv_bias = _get_aten_graph_module_for_pattern(
+        qat_conv_bn_pattern_no_conv_bias,
+        example_inputs,
+        is_cuda,
+    )
+    replacements_no_conv_bias = replace_pattern_with_filters(
+        m,
+        match_pattern,
+        replacement_pattern_no_conv_bias,
+        match_filters=[_no_conv_bias_filter],
+        ignore_literals=True,
+    )
+    m.recompile()
+
+    # Step (3): Post processing
+    #
+    # Due to limited functionality in the subgraph rewriter, here we manually
+    # update the replacement graph as follows:
+    #
+    #   (a) Copy over metadata from original subgraph. This ensures the stack traces
+    #       and annotations are preserved in the new subgraph
+    #
+    #   (b) Copy over literal args for conv from the original subgraph
+    #       TODO: do this for literal args for batchnorm as well
+    #
+    #   (c) Update all references of the old nodes in the original subgraph to refer
+    #       to the corresponding nodes in the new subgraph in the annotations
+    #
+    # In the future, we should try to push as much of this functionality into the
+    # subgraph rewriter as possible, so we don't have to manually copy anything over.
+    # For more detail, see https://github.com/pytorch/pytorch/issues/100419.
+
+    all_original_to_replacement_nodes = {}
+    for r in replacements_with_conv_bias + replacements_no_conv_bias:
+        replacement_dict = _get_conv_bn_pattern_nodes(r)
+        # The original conv node's "nn_module_stack"
+        conv_nn_module = replacement_dict["conv"][0].meta.get("nn_module_stack", None)
+        for k, node_tuple in replacement_dict.items():
+            original_node, replacement_node = node_tuple
+            # Step (3a): Copy over metadata for all nodes in [conv - bn - getitem]
+            replacement_node.meta = original_node.meta
+            # If original_node is a get_attr node, it doesn't have nn_module_stack.
+            # In this case, we copy nn_module_stack from the original conv node.
+            if (
+                k in ["conv_input", "conv_weight"]
+                and conv_nn_module
+                and "nn_module_stack" not in replacement_node.meta
+            ):
+                replacement_node.meta["nn_module_stack"] = copy.deepcopy(conv_nn_module)
+            if _is_conv_or_conv_transpose_node(original_node):
+                # Step (3b): Copy over conv literal args
+                _copy_over_literal_conv_args(original_node, replacement_node)
+                # Step (3c): Update old references in the conv node's input_qspec_map
+                _update_conv_input_qspec_map_after_replacement(
+                    original_node, replacement_node
+                )
+            all_original_to_replacement_nodes[original_node] = replacement_node
+
+    # Step (3c): Update old references in the special qspecs for all nodes in the graph
+    for n in m.graph.nodes:
+        _update_special_qspecs_after_replacement(n, all_original_to_replacement_nodes)
+
+    return m
+
+
+def _duplicate_dequantize_node(m: GraphModule):
+    """
+    Helper function to duplicate all dequantize nodes in the graph if the
+    node has more than one user. For example:
+
+    Before:
+      quantize -> dequantize -> a
+                          \\--> b
+                          \\--> c
+
+    After:
+      quantize -> dequantize_1 -> a
+            \\--> dequantize_2 -> b
+            \\--> dequantize_3 -> c
+
+    This is useful for subgraph rewriting. E.g. if we wish to match the
+    pattern [dequantize - a] above, subgraph matching would fail because
+    the dequantize node has users outside the matched portion of the graph.
+    Instead, we match [dequantize_1 - a], which is safe.
+    """
+    dq_op = torch.ops.quantized_decomposed.dequantize_per_tensor
+    for n in m.graph.nodes:
+        if n.op != "call_function" or n.target != dq_op or len(n.users) == 1:
+            continue
+        for user in list(n.users):
+            with m.graph.inserting_before(n):
+                new_node = m.graph.create_node("call_function", dq_op, n.args, n.kwargs)
+            user.replace_input_with(n, new_node)
+        m.graph.erase_node(n)
+    m.recompile()
+
+
+def _remove_extra_dequantize(m: GraphModule):
+    """
+    Removes duplicate dequant nodes in the graph, for an operator that has
+    multiple dequant nodes as a user. Replace them with a single dequant node
+    that can be shared across all uses. This should be seen as the "reverse"
+    of `_duplicate_dequantize_node`.
+    """
+    dq_op = torch.ops.quantized_decomposed.dequantize_per_tensor
+    for n in m.graph.nodes:
+        dq_users = [
+            user
+            for user in n.users
+            if user.op == "call_function" and user.target == dq_op
+        ]
+        if len(dq_users) > 1:
+            with m.graph.inserting_after(dq_users[0]):
+                new_node = m.graph.create_node(
+                    "call_function", dq_op, dq_users[0].args, {}
+                )
+            for dq_user in dq_users:
+                dq_user.replace_all_uses_with(new_node)
+                m.graph.erase_node(dq_user)
+    m.recompile()
+
+
+def _copy_over_q_dq_args(original_node: Node, replacement_node: Node):
+    """
+    Given a pair of quantize or dequantize nodes, copy over all literal args
+    from the original node to the replacement node.
+    """
+    # For quantize_per_tensor, scale and zp are literals and need to be copied
+    # For quantize_per_channel, scale and zp are get_attr nodes and should be skipped
+    if original_node.target != replacement_node.target:
+        raise AssertionError(
+            "Expected original and replacement nodes to have the same target, got "
+            f"{original_node.target} != {replacement_node.target}"
+        )
+    if original_node.target in (
+        torch.ops.quantized_decomposed.quantize_per_tensor.default,
+        torch.ops.quantized_decomposed.dequantize_per_tensor.default,
+    ):
+        # Args: input, [scale, zp, qmin, qmax, dtype]
+        start_copy_arg_index = 1
+    elif original_node.target in (
+        torch.ops.quantized_decomposed.quantize_per_channel.default,
+        torch.ops.quantized_decomposed.dequantize_per_channel.default,
+    ):
+        # Args: input, scale, zp, [axis, qmin, qmax, dtype]
+        start_copy_arg_index = 3
+    else:
+        raise ValueError(
+            f"Expected quantize/dequantize nodes, got '{original_node.target}'"
+        )
+    replacement_node.args = (
+        replacement_node.args[:start_copy_arg_index]
+        + original_node.args[start_copy_arg_index:]
+    )
+
+
+def _fold_conv_bn_qat(m: GraphModule) -> GraphModule:
+    # Example inputs for quantized and folded conv-bn1d patterns used in convert
+    _quantized_conv1d_bn_example_inputs = (
+        torch.randn(1, 1, 3),  # x
+        torch.randn(1, 1, 1),  # conv_weight
+        torch.randn(1),  # bn_weight
+        torch.randn(1),  # bn_bias
+        torch.randn(1),  # bn_running_mean
+        torch.randn(1),  # bn_running_var
+    )
+
+    # Example inputs for quantized and folded conv-bn2d patterns used in convert
+    _quantized_conv2d_bn_example_inputs = (
+        torch.randn(1, 1, 3, 3),  # x
+        torch.randn(1, 1, 1, 1),  # conv_weight
+        torch.randn(1),  # bn_weight
+        torch.randn(1),  # bn_bias
+        torch.randn(1),  # bn_running_mean
+        torch.randn(1),  # bn_running_var
+    )
+
+    has_bn = any(_is_bn_node(n) for n in m.graph.nodes)
+    if not has_bn:
+        return m
+    is_cuda_options = [True, False] if torch.cuda.is_available() else [False]
+    for is_cuda in is_cuda_options:
+        m = _fold_conv_bn_qat_helper(
+            m, F.conv1d, _quantized_conv1d_bn_example_inputs, is_cuda=is_cuda
+        )
+        m = _fold_conv_bn_qat_helper(
+            m, F.conv2d, _quantized_conv2d_bn_example_inputs, is_cuda=is_cuda
+        )
+        m = _fold_conv_bn_qat_helper(
+            m, F.conv_transpose1d, _quantized_conv1d_bn_example_inputs, is_cuda=is_cuda
+        )
+        m = _fold_conv_bn_qat_helper(
+            m, F.conv_transpose2d, _quantized_conv2d_bn_example_inputs, is_cuda=is_cuda
+        )
+
+    # remove in place add from batchnorm tracking training stats
+    for node in m.graph.nodes:
+        if (
+            node.target is torch.ops.aten.add_.Tensor
+            and node.args[0].op == "get_attr"
+            and node.args[1] == 1
+            and (
+                torch.nn.modules.batchnorm.BatchNorm2d
+                in [val[1] for val in node.meta["source_fn_stack"]]
+                or torch.nn.modules.batchnorm.BatchNorm1d
+                in [val[1] for val in node.meta["source_fn_stack"]]
+            )
+        ):
+            m.graph.erase_node(node)
+
+    m.graph.eliminate_dead_code()
+    m.recompile()
+
+    return m
+
+
+def _fold_conv_bn_qat_helper(
+    m: GraphModule,
+    conv_fn: Callable,
+    example_inputs: tuple[Any, ...],
+    is_cuda: bool,
+) -> GraphModule:
+    """
+    Replace the quantized (conv + bn) pattern with conv with bn weights folded into the weights of conv.
+    """
+
+    m.graph.eliminate_dead_code()
+    m.recompile()
+    _duplicate_dequantize_node(m)
+
+    # Step (1): Replace QAT pattern with simple [conv - bn] pattern
+    replacements = []
+    replacement_options = itertools.product(
+        [True, False],  # is_per_channel
+        [True, False],  # has_bias
+        [True, False],  # bias_is_quantized
+        [True, False],  # bn_is_training
+    )
+    for (
+        is_per_channel,
+        has_bias,
+        bias_is_quantized,
+        bn_is_training,
+    ) in replacement_options:
+        # For the cases without bias, `bias_is_quantized` is irrelevant, so here we arbitrarily
+        # filter out one of the values for this flag to avoid having duplicate patterns
+        if not has_bias and bias_is_quantized:
+            continue
+        kwargs = _get_quantized_conv_bn_example_inputs_kwargs(
+            is_per_channel, has_bias, bias_is_quantized, is_cuda
+        )
+        match_pattern = _get_quantized_qat_conv_bn_pattern(
+            is_per_channel, has_bias, bias_is_quantized, conv_fn, bn_is_training
+        )
+        match_pattern = _get_aten_graph_module_for_pattern(
+            match_pattern,
+            example_inputs,
+            is_cuda,
+            **kwargs,
+        )
+        replacement_pattern = _get_folded_quantized_qat_conv_bn_pattern(
+            is_per_channel, has_bias, bias_is_quantized, conv_fn, bn_is_training
+        )
+        replacement_pattern = _get_aten_graph_module_for_pattern(
+            replacement_pattern,
+            example_inputs,
+            is_cuda,
+            **kwargs,
+        )
+        replacements.extend(
+            replace_pattern_with_filters(
+                m,
+                match_pattern,
+                replacement_pattern,
+                ignore_literals=True,
+            )
+        )
+    m.recompile()
+    _remove_extra_dequantize(m)
+
+    for r in replacements:
+        node_map = _get_conv_bn_pattern_nodes(r)
+
+        # Step (2): Copy over metadata from original subgraph
+        for original_node, replacement_node in node_map.values():
+            replacement_node.meta = original_node.meta
+
+        # Step (3): Copy over args for weight (and optionally bias) q - dq nodes
+        _copy_over_q_dq_args(*node_map["conv_weight_q"])
+        _copy_over_q_dq_args(*node_map["conv_weight_dq"])
+        if "conv_bias_q" in node_map:
+            if "conv_bias_dq" not in node_map:
+                raise AssertionError(
+                    "Expected 'conv_bias_dq' to be present in node_map when 'conv_bias_q' is present"
+                )
+            _copy_over_q_dq_args(*node_map["conv_bias_q"])
+            _copy_over_q_dq_args(*node_map["conv_bias_dq"])
+
+        # Step (4): Fold BN weights into conv
+        conv_bias = None
+        (_, conv_node) = node_map["conv"]
+        (_, bn_node) = node_map["bn"]
+        (_, conv_weight) = node_map["conv_weight"]
+        if "conv_bias" in node_map:
+            (_, conv_bias) = node_map["conv_bias"]
+        fold_bn_weights_into_conv_node(conv_node, conv_weight, conv_bias, bn_node, m)
+
+        # Copy over literal args for conv
+        for original_node in _filter_nodes_map(r.nodes_map).values():
+            if _is_conv_or_conv_transpose_node(original_node):
+                _copy_over_literal_conv_args(original_node, conv_node)
+
+    m.graph.eliminate_dead_code()
+    m.recompile()
+    return m
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/representation/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/representation/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8876d439feb41929ca9b64f3f023db499eac007b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/representation/__init__.py
@@ -0,0 +1,6 @@
+from .rewrite import reference_representation_rewrite
+
+
+__all__ = [
+    "reference_representation_rewrite",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/representation/rewrite.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/representation/rewrite.py
new file mode 100644
index 0000000000000000000000000000000000000000..52084784f5036a92a909ad7f044d733677e48618
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/representation/rewrite.py
@@ -0,0 +1,825 @@
+# mypy: allow-untyped-defs
+from collections.abc import Callable
+from dataclasses import dataclass
+from functools import partial
+from typing import Any
+
+import torch
+from torch._export.utils import _disable_aten_to_metadata_assertions
+from torch._higher_order_ops.out_dtype import out_dtype
+from torch.ao.quantization.fx._decomposed import quantized_decomposed_lib  # noqa: F401
+from torch.ao.quantization.pt2e.export_utils import _WrapperModule
+from torch.ao.quantization.pt2e.utils import (
+    _get_aten_graph_module_for_pattern,
+    _replace_literals_with_existing_placeholders,
+    _replace_literals_with_new_placeholders,
+    remove_tensor_overload_for_qdq_ops,
+)
+from torch.fx import GraphModule
+from torch.fx.subgraph_rewriter import replace_pattern
+
+
+__all__ = [
+    "reference_representation_rewrite",
+]
+
+
+def _qdq_quantized_linear(
+    x_i8,
+    x_scale,
+    x_zero_point,
+    x_quant_min,
+    x_quant_max,
+    weight_i8,
+    weight_scale,
+    weight_zero_point,
+    weight_quant_min,
+    weight_quant_max,
+    bias_fp32,
+    out_scale,
+    out_zero_point,
+    out_quant_min,
+    out_quant_max,
+):
+    x_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(
+        x_i8, x_scale, x_zero_point, x_quant_min, x_quant_max, torch.int8
+    )
+    weight_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(
+        weight_i8,
+        weight_scale,
+        weight_zero_point,
+        weight_quant_min,
+        weight_quant_max,
+        torch.int8,
+    )
+    out_fp32 = torch.ops.aten.linear.default(x_fp32, weight_fp32, bias_fp32)
+    out_i8 = torch.ops.quantized_decomposed.quantize_per_tensor(
+        out_fp32, out_scale, out_zero_point, out_quant_min, out_quant_max, torch.int8
+    )
+    return out_i8
+
+
+def _reference_quantized_linear(
+    x_i8,
+    x_scale,
+    x_zero_point,
+    x_quant_min,
+    x_quant_max,
+    weight_i8,
+    weight_scale,
+    weight_zero_point,
+    weight_quant_min,
+    weight_quant_max,
+    bias_fp32,
+    out_scale,
+    out_zero_point,
+    out_quant_min,
+    out_quant_max,
+):
+    # without using quant_min/max in clamp, the traced graph will not have quant_mi/max args.
+    # This results in failure to match the pattern.
+    # Therefore, we call a torch.ops.aten.clamp here
+    x_i8 = torch.ops.aten.clamp(x_i8, x_quant_min, x_quant_max)
+    weight_i8 = torch.ops.aten.clamp(weight_i8, weight_quant_min, weight_quant_max)
+
+    x_i16 = x_i8.to(torch.int16)
+    weight_i16 = weight_i8.to(torch.int16)
+    # always set bias to None so that the same representation can work for the case
+    # no matter if bias_scale == x_scale * weight_scale or not
+    acc_i32 = out_dtype(
+        torch.ops.aten.linear.default,
+        torch.int32,
+        x_i16 - x_zero_point,
+        weight_i16 - weight_zero_point,
+        None,
+    )
+    # TODO: change to mul.Scalar
+    # Note: we are quantizing bias with these scales without signal from user, but it might be OK
+    bias_scale = x_scale * weight_scale
+    bias_i32 = out_dtype(torch.ops.aten.div.Tensor, torch.int32, bias_fp32, bias_scale)
+    acc_i32 = acc_i32 + bias_i32
+    # TODO: change to mul.Scalar when we make x_scale/weight_scale etc. Scalar values
+    acc_i32 = (
+        out_dtype(
+            torch.ops.aten.mul.Tensor,
+            torch.int32,
+            acc_i32,
+            x_scale * weight_scale / out_scale,
+        )
+        + out_zero_point
+    )
+    out_i8 = torch.ops.aten.clamp(acc_i32, out_quant_min, out_quant_max).to(torch.int8)
+    return out_i8
+
+
+def _qdq_dynamic_quantized_linear(
+    x_fp32,
+    x_quant_min,
+    x_quant_max,
+    x_eps,
+    weight_i8,
+    weight_scale,
+    weight_zero_point,
+    weight_quant_min,
+    weight_quant_max,
+    bias_fp32,
+):
+    x_scale, x_zero_point = torch.ops.quantized_decomposed.choose_qparams(
+        x_fp32, x_quant_min, x_quant_max, x_eps, torch.int8
+    )
+    x_i8 = torch.ops.quantized_decomposed.quantize_per_tensor(
+        x_fp32, x_scale, x_zero_point, x_quant_min, x_quant_max, torch.int8
+    )
+    x_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(
+        x_i8, x_scale, x_zero_point, x_quant_min, x_quant_max, torch.int8
+    )
+    weight_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(
+        weight_i8,
+        weight_scale,
+        weight_zero_point,
+        weight_quant_min,
+        weight_quant_max,
+        torch.int8,
+    )
+    out_fp32 = torch.ops.aten.linear.default(x_fp32, weight_fp32, bias_fp32)
+    return out_fp32
+
+
+def _reference_dynamic_quantized_linear(
+    x_fp32,
+    x_quant_min,
+    x_quant_max,
+    x_eps,
+    weight_i8,
+    weight_scale,
+    weight_zero_point,
+    weight_quant_min,
+    weight_quant_max,
+    bias_fp32,
+):
+    x_scale, x_zero_point = torch.ops.quantized_decomposed.choose_qparams(
+        x_fp32, x_quant_min, x_quant_max, x_eps, torch.int8
+    )
+    # decomposed representation for quantize_per_tensor
+    # TODO: use out_dtype(mul, ...) here when the op is ready
+    x_fp32 = x_fp32 / x_scale  # fp32
+    # round modes might be different here
+    # pytorch is rounding to even, which is also common for most of the backends
+    x_fp32 = torch.round(x_fp32)  # fp32
+    x_i32 = x_fp32.to(dtype=torch.int32)  # int32
+    x_i32 = x_i32 + x_zero_point  # int32
+    # clamp works for fp32, int32 and int8 dtypes
+    x_i32 = torch.clamp(x_i32, x_quant_min, x_quant_max)  # int32
+    x_i8 = x_i32.to(dtype=torch.int8)
+
+    weight_i8 = torch.ops.aten.clamp(weight_i8, weight_quant_min, weight_quant_max)
+
+    x_i16 = x_i8.to(torch.int16)
+    weight_i16 = weight_i8.to(torch.int16)
+    # always set bias to None so that the same representation can work for the case
+    # no matter if bias_scale == x_scale * weight_scale or not
+    acc_i32 = out_dtype(
+        torch.ops.aten.linear.default,
+        torch.int32,
+        x_i16 - x_zero_point,
+        weight_i16 - weight_zero_point,
+        None,
+    )
+    bias_scale = x_scale * weight_scale
+    bias_i32 = out_dtype(torch.ops.aten.div.Tensor, torch.int32, bias_fp32, bias_scale)
+    acc_i32 = acc_i32 + bias_i32
+    out_fp32 = acc_i32 * (x_scale * weight_scale)
+    return out_fp32
+
+
+def _qdq_quantized_conv2d(
+    x_i8,
+    x_scale,
+    x_zero_point,
+    x_quant_min,
+    x_quant_max,
+    weight_i8,
+    weight_scale,
+    weight_zero_point,
+    weight_quant_min,
+    weight_quant_max,
+    bias_fp32,
+    out_scale,
+    out_zero_point,
+    out_quant_min,
+    out_quant_max,
+):
+    stride = [1, 1]
+    padding = [0, 0]
+    dilation = [1, 1]
+    transposed = False
+    output_padding = [0, 0]
+    groups = 1
+    x_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(
+        x_i8, x_scale, x_zero_point, x_quant_min, x_quant_max, torch.int8
+    )
+    weight_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(
+        weight_i8,
+        weight_scale,
+        weight_zero_point,
+        weight_quant_min,
+        weight_quant_max,
+        torch.int8,
+    )
+    out_fp32 = torch.ops.aten.convolution.default(
+        x_fp32,
+        weight_fp32,
+        bias_fp32,
+        stride,
+        padding,
+        dilation,
+        transposed,
+        output_padding,
+        groups,
+    )
+    out_i8 = torch.ops.quantized_decomposed.quantize_per_tensor(
+        out_fp32, out_scale, out_zero_point, out_quant_min, out_quant_max, torch.int8
+    )
+    return out_i8
+
+
+def _reference_quantized_conv2d(
+    x_i8,
+    x_scale,
+    x_zero_point,
+    x_quant_min,
+    x_quant_max,
+    weight_i8,
+    weight_scale,
+    weight_zero_point,
+    weight_quant_min,
+    weight_quant_max,
+    bias_fp32,
+    out_scale,
+    out_zero_point,
+    out_quant_min,
+    out_quant_max,
+):
+    stride = [1, 1]
+    padding = [0, 0]
+    dilation = [1, 1]
+    transposed = False
+    output_padding = [0, 0]
+    groups = 1
+    # without using quant_min/max in clamp, the traced graph will not have quant_mi/max args.
+    # This results in failure to match the pattern.
+    # Therefore, we call a torch.ops.aten.clamp here
+    x_i8 = torch.ops.aten.clamp(x_i8, x_quant_min, x_quant_max)
+    weight_i8 = torch.ops.aten.clamp(weight_i8, weight_quant_min, weight_quant_max)
+
+    x_i16 = x_i8.to(torch.int16)
+    weight_i16 = weight_i8.to(torch.int16)
+    # always set bias to None so that the same representation can work for the case
+    # no matter if bias_scale == x_scale * weight_scale or not
+    acc_i32 = out_dtype(
+        torch.ops.aten.convolution.default,
+        torch.int32,
+        x_i16 - x_zero_point,
+        weight_i16 - weight_zero_point,
+        None,
+        stride,
+        padding,
+        dilation,
+        transposed,
+        output_padding,
+        groups,
+    )
+    # Note: we are quantizing bias with these scales without signal from user, but it might be OK
+    bias_scale = x_scale * weight_scale
+    # bias quantization to int32 uses bias_scale = x_scale * weight_scale due to:
+    # Take linear calculation for example
+    # Out_(i, j)_fp32 = Sum_(over k)[X_(i, k)_fp32 * W_(i, k)_fp32] + bias_(i)_fp32
+    # Represent X, W fp32 as their dequant transforms
+    # A_fp32 = (A_q - A_zero_point)/A_scale
+    # Out_(i, j)_fp32 = Sum_(over k)[(X_(i, k)_fp32 - X_zp) * X_scale * (W_(i, k)_fp32 - W_zp) * W_scale] + bias_(i)_fp32
+    # Factor out X_scale and W_scale
+    # Out_(i, j)_fp32 = ((X_scale * W_scale) * Sum_(over k)[(X_(i, k)_fp32 - X_zp) * (W_(i, k)_fp32 - W_zp)]) + bias_(i)_fp32
+    # In order to addition of bias_(i)_fp32 inside, we must do
+    # Out_(i, j)_fp32 = (X_scale * W_scale) * (Sum_(over k)[(X_(i, k)_fp32 - X_zp) * (W_(i, k)_fp32 - W_zp)] + (1 / (X_scale * W_scale)) * bias_(i)_fp32)W_scale  # noqa: B950
+    # Note we had to multiply bias_fp32 with X_scale * W_scale = bias_scale
+    # Thus bias quantization to int32 must be with X_scale * W_scale
+
+    bias_i32 = out_dtype(torch.ops.aten.div.Tensor, torch.int32, bias_fp32, bias_scale)
+    # Unsqueeze to match broadcast dims
+    # Unfortnuately I cannot do bias_i32.unsqueeze(0) due to literal matching nightmare
+    # in graph pattern replacement
+    bias_i32 = bias_i32.unsqueeze(-1)
+    bias_i32 = bias_i32.unsqueeze(-1)
+    acc_i32 = acc_i32 + bias_i32
+    # TODO: change to mul.Scalar when we make x_scale/weight_scale etc. Scalar values
+    acc_i32 = (
+        out_dtype(
+            torch.ops.aten.mul.Tensor,
+            torch.int32,
+            acc_i32,
+            x_scale * weight_scale / out_scale,
+        )
+        + out_zero_point
+    )
+    out_i8 = torch.ops.aten.clamp(acc_i32, out_quant_min, out_quant_max).to(torch.int8)
+    return out_i8
+
+
+def _qdq_quantized_add_relu(
+    x_i8,
+    x_scale,
+    x_zero_point,
+    y_i8,
+    y_scale,
+    y_zero_point,
+    out_scale,
+    out_zero_point,
+    quant_min,
+    quant_max,
+):
+    x_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(
+        x_i8, x_scale, x_zero_point, quant_min, quant_max, torch.int8
+    )
+    y_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(
+        y_i8, y_scale, y_zero_point, quant_min, quant_max, torch.int8
+    )
+    out_fp32 = x_fp32 + y_fp32
+    out_fp32 = torch.ops.aten.relu(out_fp32)
+    out_i8 = torch.ops.quantized_decomposed.quantize_per_tensor(
+        out_fp32, out_scale, out_zero_point, quant_min, quant_max, torch.int8
+    )
+    return out_i8
+
+
+def _reference_quantized_add_relu(
+    x_i8,
+    x_scale,
+    x_zero_point,
+    y_i8,
+    y_scale,
+    y_zero_point,
+    out_scale,
+    out_zero_point,
+    quant_min,
+    quant_max,
+):
+    """
+    See comments for `_reference_quantized_add` for more information on
+    how to derive the formula for out_i8 based on x_i8 and y_i8
+    """
+    x_i32 = x_i8.to(torch.int32)
+    y_i32 = y_i8.to(torch.int32)
+    # TODO: change this to mul.Scalar?
+    x_i32 = out_dtype(
+        torch.ops.aten.mul.Tensor,
+        torch.int32,
+        (x_i32 - x_zero_point),
+        (x_scale / out_scale),
+    )
+    y_i32 = out_dtype(
+        torch.ops.aten.mul.Tensor,
+        torch.int32,
+        (y_i32 - y_zero_point),
+        (y_scale / out_scale),
+    )
+    out_i32 = x_i32 + y_i32 + out_zero_point
+    # out_i32 = torch.ops.aten.clamp(out_i32, out_zero_point)
+    out_i8 = torch.ops.aten.clamp(out_i32, out_zero_point, quant_max).to(torch.int8)
+    return out_i8
+
+
+def _qdq_quantized_add(
+    x_i8,
+    x_scale,
+    x_zero_point,
+    y_i8,
+    y_scale,
+    y_zero_point,
+    out_scale,
+    out_zero_point,
+    quant_min,
+    quant_max,
+):
+    x_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(
+        x_i8, x_scale, x_zero_point, quant_min, quant_max, torch.int8
+    )
+    y_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(
+        y_i8, y_scale, y_zero_point, quant_min, quant_max, torch.int8
+    )
+    out_fp32 = x_fp32 + y_fp32
+    out_i8 = torch.ops.quantized_decomposed.quantize_per_tensor(
+        out_fp32, out_scale, out_zero_point, quant_min, quant_max, torch.int8
+    )
+    return out_i8
+
+
+def _reference_quantized_add(
+    x_i8,
+    x_scale,
+    x_zero_point,
+    y_i8,
+    y_scale,
+    y_zero_point,
+    out_scale,
+    out_zero_point,
+    quant_min,
+    quant_max,
+):
+    """
+        # How to Derive the formula for out_i8 based on x_i8 and y_i8
+        # (since quantized add takes x_i8, y_i8 and their quantization parameters, and produce an out_i8)
+
+        # out_i8 is quantized output, we can write down the formula for it first:
+    out_i8 = out_f32 / out_scale + out_zero_point           (1)
+
+        # then out_fp32 is computed from x_f32 + y_f32, and the x_fp32 and y_fp32 are the dequantized x_i8 and y_i8
+        out_f32 = x_f32 + y_f32           (2)
+        x_fp32 = (x_i8 - x_zero_point) * x_scale         (3)
+        y_fp32 = (y_i8 - y_zero_point) * y_scale         (4)
+
+        # applying the above formula to the out_i8 equation we can get the following:
+        out_i8 = out_fp32 / out_scale + out_zero_point             # (1)
+           = (x_f32 + y_f32) / out_scale + out_zero_point      # applying (2) to substitute out_fp32 with x_fp32 + y_fp32
+           = ((x_i8 - x_zero_point) * x_scale + (y_i8 - y_zero_point) * y_scale) / out_scale + out_zero_point  # apply (3) and (4)
+    """
+    x_i32 = x_i8.to(torch.int32)
+    y_i32 = y_i8.to(torch.int32)
+    # TODO: use out_dtype op
+    x_i32 = torch.round((x_scale / out_scale) * (x_i32 - x_zero_point)).to(torch.int32)
+    y_i32 = torch.round((y_scale / out_scale) * (y_i32 - y_zero_point)).to(torch.int32)
+    out_i32 = x_i32 + y_i32 + out_zero_point
+    quant_min = -128
+    quant_max = 127
+    out_i8 = torch.ops.aten.clamp(out_i32, quant_min, quant_max).to(torch.int8)
+    return out_i8
+
+
+def _qdq_quantized_max_pool2d(
+    x_i8,
+    x_scale,
+    x_zero_point,
+    x_quant_min,
+    x_quant_max,
+    out_scale,
+    out_zero_point,
+    out_quant_min,
+    out_quant_max,
+):
+    kernel_size = 1
+    stride = 1
+    padding = 0
+    dilation = 1
+    ceil_mode = False
+    x_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(
+        x_i8, x_scale, x_zero_point, x_quant_min, x_quant_max, torch.int8
+    )
+    out_fp32, _ = torch.ops.aten.max_pool2d_with_indices.default(
+        x_fp32, kernel_size, stride, padding, dilation, ceil_mode
+    )
+    out_i8 = torch.ops.quantized_decomposed.quantize_per_tensor(
+        out_fp32, out_scale, out_zero_point, out_quant_min, out_quant_max, torch.int8
+    )
+    return out_i8
+
+
+def _reference_quantized_max_pool2d(
+    x_i8,
+    x_scale,
+    x_zero_point,
+    x_quant_min,
+    x_quant_max,
+    out_scale,
+    out_zero_point,
+    out_quant_min,
+    out_quant_max,
+):
+    kernel_size = 1
+    stride = 1
+    padding = 0
+    dilation = 1
+    ceil_mode = False
+    # to preserve x_quant_min, x_quant_max in the graph for pattern matching
+    x_i8 = torch.clamp(x_i8, x_quant_min, x_quant_max)
+    x_i32 = x_i8.to(torch.int32)
+    out_i32, _ = torch.ops.aten.max_pool2d_with_indices.default(
+        x_i32 - x_zero_point, kernel_size, stride, padding, dilation, ceil_mode
+    )
+    out_fp32 = out_i32 * (x_scale / out_scale) + out_zero_point
+    out_fp32 = torch.clamp(out_fp32, out_quant_min, out_quant_max)
+    out_i8 = out_fp32.to(torch.int8)
+    return out_i8
+
+
+def _quantize_per_tensor_int8(x_fp32, scale, zero_point, quant_min, quant_max):
+    x = torch.ops.quantized_decomposed.quantize_per_tensor(
+        x_fp32, scale, zero_point, quant_min, quant_max, torch.int8
+    )
+    return x
+
+
+def _reference_quantize_per_tensor_int8(
+    x_fp32, scale, zero_point, quant_min, quant_max
+):
+    # TODO: use out_dtype(mul, ...) here when the op is ready
+    x = x_fp32 / scale  # fp32
+    # round modes might be different here
+    # pytorch is rounding to even, which is also common for most of the backends
+    x = torch.round(x)  # fp32
+    x = x.to(dtype=torch.int32)  # int32
+    x = x + zero_point  # int32
+    # clamp works for fp32, int32 and int8 dtypes
+    x = torch.clamp(x, quant_min, quant_max)  # int32
+    x = x.to(dtype=torch.int8)
+    return x
+
+
+def _dequantize_per_tensor_int8(x_i8, scale, zero_point, quant_min, quant_max):
+    x_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(
+        x_i8, scale, zero_point, quant_min, quant_max, torch.int8
+    )
+    return x_fp32
+
+
+def _reference_dequantize_per_tensor_int8(
+    x_i8, scale, zero_point, quant_min, quant_max
+):
+    # without using quant_min/max in clamp, the traced graph will not have quant_mi/max args.
+    # This results in failure to match the pattern.
+    # Therefore, we call a torch.ops.aten.clamp here
+    x_i8 = torch.ops.aten.clamp(x_i8, quant_min, quant_max)
+    # TODO: use out_dtype op
+    # note: x_i8.to(torch.int32) does not work here
+    # TODO: debug the implementation later when torchdynamo time out issue is resolved
+    return ((x_i8.to(torch.float32) - zero_point) * scale).to(dtype=torch.float32)
+
+
+def _quantize_per_channel_int8(
+    x_fp32, scales, zero_points, ch_axis, quant_min, quant_max
+):
+    out_i8 = torch.ops.quantized_decomposed.quantize_per_channel(
+        x_fp32, scales, zero_points, ch_axis, quant_min, quant_max, torch.int8
+    )
+    return out_i8
+
+
+def _reference_quantize_per_channel_int8(
+    x_fp32, scales, zero_points, ch_axis, quant_min, quant_max
+):
+    x_fp32 = torch.transpose(x_fp32, ch_axis, -1)
+    out_i32 = torch.ops.aten.clamp(
+        torch.round(x_fp32 / scales).to(torch.int32) + zero_points, quant_min, quant_max
+    )
+    out_i32 = torch.transpose(out_i32, ch_axis, -1)
+    return out_i32.to(torch.int8)
+
+
+def _dequantize_per_channel_int8(
+    x_i8, scales, zero_points, ch_axis, quant_min, quant_max
+):
+    # the following will be replaced as placeholders
+    out_fp32 = torch.ops.quantized_decomposed.dequantize_per_channel(
+        x_i8, scales, zero_points, ch_axis, quant_min, quant_max, torch.int8
+    )
+    return out_fp32
+
+
+def _reference_dequantize_per_channel_int8(
+    x_i8, scales, zero_points, ch_axis, quant_min, quant_max
+):
+    # the following will be replaced as placeholders
+    # in order to preserve the quant_min/quant_max args for pattern matching (e.g. matching for int4 quantized ops)
+    # we call a torch.ops.aten.clamp here
+    x_i8 = torch.ops.aten.clamp(x_i8, quant_min, quant_max)
+    x_i8 = torch.transpose(x_i8, ch_axis, -1)
+    x_i32 = x_i8.to(torch.int32)
+    out_fp32 = (x_i32 - zero_points).to(torch.float) * scales
+    out_fp32 = torch.transpose(out_fp32, ch_axis, -1)
+    return out_fp32
+
+
+def _replace_ph_qdq_per_channel_replacement(gm: torch.fx.GraphModule):
+    return _replace_literals_with_existing_placeholders(
+        gm, exclude_literals=[-1], literal_to_ph_idx={1: 3, -128: 4, 127: 5}
+    )
+
+
+@dataclass
+class _RewriteInfo:
+    """Data needed for rewrite, this includes example inputs, pattern and replacement functions
+    and post transformation functions for the exported pattern and replacement GraphModule
+    """
+
+    # example inputs used for exporting the pattern into GraphModule
+    example_inputs: tuple[Any, ...]
+    pattern: Callable
+    replacement: Callable
+    # post transformation on the exported pattern and replacement GraphModule
+    pattern_post_trans: Callable[[GraphModule], GraphModule] | None = None
+    replacement_post_trans: Callable[[GraphModule], GraphModule] | None = None
+
+
+def reference_representation_rewrite(model: GraphModule) -> GraphModule:
+    _QUANTIZED_LINEAR_EXAMPLE_INPUTS = (
+        torch.randint(-128, 127, (2, 5), dtype=torch.int8),
+        torch.randn(1, dtype=torch.float),
+        torch.zeros(1, dtype=torch.int),
+        torch.tensor([-128], dtype=torch.int),
+        torch.tensor([127], dtype=torch.int),
+        torch.randint(-128, 127, (5, 5), dtype=torch.int8),
+        torch.randn(1, dtype=torch.float),
+        torch.zeros(1, dtype=torch.int),
+        torch.tensor([-127], dtype=torch.int),
+        torch.tensor([127], dtype=torch.int),
+        torch.randn(1, dtype=torch.float),
+        torch.randn(1, dtype=torch.float),
+        torch.zeros(1, dtype=torch.int),
+        torch.tensor([-128], dtype=torch.int),
+        torch.tensor([127], dtype=torch.int),
+    )
+
+    _DYNAMIC_QUANTIZED_LINEAR_EXAMPLE_INPUTS = (
+        torch.randn((2, 5), dtype=torch.float),
+        -128,
+        127,
+        torch.finfo(torch.float32).eps,
+        torch.randint(-128, 127, (5, 5), dtype=torch.int8),
+        torch.randn(1, dtype=torch.float),
+        torch.zeros(1, dtype=torch.int),
+        torch.tensor([-127], dtype=torch.int),
+        torch.tensor([127], dtype=torch.int),
+        torch.randn(1, dtype=torch.float),
+    )
+
+    _QUANTIZED_CONV2d_EXAMPLE_INPUTS = (
+        torch.randint(-128, 127, (1, 3, 3, 3), dtype=torch.int8),
+        torch.randn(1, dtype=torch.float),
+        torch.zeros(1, dtype=torch.int),
+        torch.tensor([-128], dtype=torch.int),
+        torch.tensor([127], dtype=torch.int),
+        torch.randint(-128, 127, (1, 3, 3, 3), dtype=torch.int8),
+        torch.randn(1, dtype=torch.float),
+        torch.zeros(1, dtype=torch.int),
+        torch.tensor([-127], dtype=torch.int),
+        torch.tensor([127], dtype=torch.int),
+        torch.randn(1, dtype=torch.float),
+        torch.randn(1, dtype=torch.float),
+        torch.zeros(1, dtype=torch.int),
+        torch.tensor([-128], dtype=torch.int),
+        torch.tensor([127], dtype=torch.int),
+    )
+
+    _QUANTIZED_ADD_OR_ADD_RELU_EXAMPLE_INPUTS = (
+        torch.randint(-128, 127, (1, 3, 3, 3), dtype=torch.int8),
+        torch.randn(1, dtype=torch.float),
+        torch.zeros(1, dtype=torch.int),
+        torch.randint(-128, 127, (1, 3, 3, 3), dtype=torch.int8),
+        torch.randn(1, dtype=torch.float),
+        torch.zeros(1, dtype=torch.int),
+        torch.randn(1, dtype=torch.float),
+        torch.zeros(1, dtype=torch.int),
+        torch.tensor([-128], dtype=torch.int),
+        torch.tensor([127], dtype=torch.int),
+    )
+
+    _QUANTIZED_MAX_POOL2D_EXAMPLE_INPUTS = (
+        torch.randint(-128, 127, (1, 3, 3, 3), dtype=torch.int8),
+        torch.randn(1, dtype=torch.float),
+        torch.zeros(1, dtype=torch.int),
+        torch.tensor([-128], dtype=torch.int),
+        torch.tensor([127], dtype=torch.int),
+        torch.randn(1, dtype=torch.float),
+        torch.zeros(1, dtype=torch.int),
+        torch.tensor([-128], dtype=torch.int),
+        torch.tensor([127], dtype=torch.int),
+    )
+
+    _QUANTIZE_PER_TENSOR_INT8_EXAMPLE_INPUTS = (
+        torch.randn(1, 3, 3, 3, dtype=torch.float),
+        torch.randn(1, dtype=torch.float),
+        torch.zeros(1, dtype=torch.int),
+        torch.tensor([-128], dtype=torch.int),
+        torch.tensor([127], dtype=torch.int),
+    )
+
+    _DEQUANTIZE_PER_TENSOR_INT8_EXAMPLE_INPUTS = (
+        torch.randint(-128, 127, (1, 3, 3, 3), dtype=torch.int8),
+        torch.randn(1, dtype=torch.float),
+        torch.zeros(1, dtype=torch.int),
+        torch.tensor([-128], dtype=torch.int),
+        torch.tensor([127], dtype=torch.int),
+    )
+
+    _QUANTIZE_PER_CHANNEL_INT8_EXAMPLE_INPUTS = (
+        torch.randn(1, 3, 3, 3, dtype=torch.float),
+        torch.randn(3, dtype=torch.float),
+        torch.zeros(3, dtype=torch.int),
+        1,
+        -128,
+        127,
+    )
+
+    _DEQUANTIZE_PER_CHANNEL_INT8_EXAMPLE_INPUTS = (
+        torch.randint(-128, 127, (1, 3, 3, 3), dtype=torch.int8),
+        torch.randn(3, dtype=torch.float),
+        torch.zeros(3, dtype=torch.int),
+        1,
+        -128,
+        127,
+    )
+
+    _REWRITE_INFO_LIST = [
+        _RewriteInfo(
+            _DYNAMIC_QUANTIZED_LINEAR_EXAMPLE_INPUTS,
+            _WrapperModule(_qdq_dynamic_quantized_linear),
+            _WrapperModule(_reference_dynamic_quantized_linear),
+            partial(
+                _replace_literals_with_existing_placeholders,
+                literal_to_ph_idx={-128: 1, 127: 2, torch.finfo(torch.float32).eps: 3},
+            ),
+            partial(
+                _replace_literals_with_existing_placeholders,
+                literal_to_ph_idx={-128: 1, 127: 2, torch.finfo(torch.float32).eps: 3},
+            ),
+        ),
+        _RewriteInfo(
+            _QUANTIZED_LINEAR_EXAMPLE_INPUTS,
+            _WrapperModule(_qdq_quantized_linear),
+            _WrapperModule(_reference_quantized_linear),
+            _replace_literals_with_new_placeholders,
+            _replace_literals_with_new_placeholders,
+        ),
+        _RewriteInfo(
+            _QUANTIZED_CONV2d_EXAMPLE_INPUTS,
+            _WrapperModule(_qdq_quantized_conv2d),
+            _WrapperModule(_reference_quantized_conv2d),
+            partial(_replace_literals_with_new_placeholders, exclude_literals=[-1]),
+            partial(_replace_literals_with_new_placeholders, exclude_literals=[-1]),
+        ),
+        _RewriteInfo(
+            _QUANTIZED_ADD_OR_ADD_RELU_EXAMPLE_INPUTS,
+            _WrapperModule(_qdq_quantized_add_relu),
+            _WrapperModule(_reference_quantized_add_relu),
+        ),
+        _RewriteInfo(
+            _QUANTIZED_ADD_OR_ADD_RELU_EXAMPLE_INPUTS,
+            _WrapperModule(_qdq_quantized_add),
+            _WrapperModule(_reference_quantized_add),
+        ),
+        _RewriteInfo(
+            _QUANTIZED_MAX_POOL2D_EXAMPLE_INPUTS,
+            _WrapperModule(_qdq_quantized_max_pool2d),
+            _WrapperModule(_reference_quantized_max_pool2d),
+            _replace_literals_with_new_placeholders,
+            _replace_literals_with_new_placeholders,
+        ),
+        _RewriteInfo(
+            _QUANTIZE_PER_TENSOR_INT8_EXAMPLE_INPUTS,
+            _WrapperModule(_quantize_per_tensor_int8),
+            _WrapperModule(_reference_quantize_per_tensor_int8),
+        ),
+        _RewriteInfo(
+            _DEQUANTIZE_PER_TENSOR_INT8_EXAMPLE_INPUTS,
+            _WrapperModule(_dequantize_per_tensor_int8),
+            _WrapperModule(_reference_dequantize_per_tensor_int8),
+        ),
+        _RewriteInfo(
+            _QUANTIZE_PER_CHANNEL_INT8_EXAMPLE_INPUTS,
+            _WrapperModule(_quantize_per_channel_int8),
+            _WrapperModule(_reference_quantize_per_channel_int8),
+            _replace_ph_qdq_per_channel_replacement,
+            _replace_ph_qdq_per_channel_replacement,
+        ),
+        _RewriteInfo(
+            _DEQUANTIZE_PER_CHANNEL_INT8_EXAMPLE_INPUTS,
+            _WrapperModule(_dequantize_per_channel_int8),
+            _WrapperModule(_reference_dequantize_per_channel_int8),
+            _replace_ph_qdq_per_channel_replacement,
+            _replace_ph_qdq_per_channel_replacement,
+        ),
+    ]
+
+    remove_tensor_overload_for_qdq_ops(model)
+
+    with _disable_aten_to_metadata_assertions():
+        for rewrite_info in _REWRITE_INFO_LIST:
+            example_inputs = rewrite_info.example_inputs
+            pattern = rewrite_info.pattern
+            replacement = rewrite_info.replacement
+            pattern_post_trans = rewrite_info.pattern_post_trans
+            replacement_post_trans = rewrite_info.replacement_post_trans
+            pattern = _get_aten_graph_module_for_pattern(pattern, example_inputs)  # type: ignore[arg-type, assignment]
+            remove_tensor_overload_for_qdq_ops(pattern)  # type: ignore[arg-type]
+            replacement = _get_aten_graph_module_for_pattern(  # type: ignore[assignment]
+                replacement,
+                example_inputs,  # type: ignore[arg-type]
+            )
+            remove_tensor_overload_for_qdq_ops(replacement)  # type: ignore[arg-type]
+            if pattern_post_trans:
+                pattern = pattern_post_trans(pattern)
+            if replacement_post_trans:
+                replacement = replacement_post_trans(replacement)
+            pattern.recompile()  # type: ignore[attr-defined]
+            replacement.recompile()  # type: ignore[attr-defined]
+            replace_pattern(model, pattern, replacement)
+
+    return model
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..69a74ea6a0dfaf541a7617a16419013cce597bdd
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/pt2e/utils.py
@@ -0,0 +1,625 @@
+# mypy: allow-untyped-defs
+import operator
+import types
+from collections.abc import Callable
+from typing import Any
+
+import torch
+import torch.ao.quantization.pt2e._affine_quantization  # noqa: F401
+import torch.nn.functional as F
+import torch.utils._pytree as pytree
+
+# Makes sure that quantized_decomposed ops are registered
+from torch.ao.quantization.fx._decomposed import quantized_decomposed_lib  # noqa: F401
+from torch.ao.quantization.quantizer import QuantizationAnnotation
+from torch.export.unflatten import _assign_attr, _AttrKind
+from torch.fx import GraphModule, Node
+from torch.nn.utils.fusion import fuse_conv_bn_weights
+
+
+__all__ = [
+    "fold_bn_weights_into_conv_node",
+    "remove_tensor_overload_for_qdq_ops",
+]
+
+_QUANTIZE_OPS = [
+    torch.ops.quantized_decomposed.quantize_per_tensor.default,
+    torch.ops.quantized_decomposed.quantize_per_tensor.tensor,
+    torch.ops.quantized_decomposed.quantize_per_channel.default,
+]
+
+
+_DEQUANTIZE_OPS = [
+    torch.ops.quantized_decomposed.dequantize_per_tensor.default,
+    torch.ops.quantized_decomposed.dequantize_per_tensor.tensor,
+    torch.ops.quantized_decomposed.dequantize_per_channel.default,
+]
+
+
+def _is_connected(source: torch.fx.Node, dest: torch.fx.Node) -> bool:
+    """
+    Assuming dest is one of the ops inserted by quant workflow, this function
+    finds if source and dest are connected. Assumption is that only quant workflow
+    inserted ops exist between source and dest
+    """
+    quant_workflow_ops = _QUANTIZE_OPS + _DEQUANTIZE_OPS
+    quant_workflow_ops.append(torch.ops.quantized_decomposed.choose_qparams.tensor)
+    while dest.target in quant_workflow_ops:
+        if not isinstance(dest.args[0], torch.fx.Node):
+            raise ValueError(
+                f"expected arg[0] of quant workflow ops to be a node but found {dest.args[0]}"
+            )
+        dest = dest.args[0]
+    return dest == source
+
+
+def _find_q_dq_node_for_user(
+    produer: torch.fx.Node, user: torch.fx.Node
+) -> tuple[Any, Any]:
+    """
+    Find q, dq pair corresponding to [producer -> q -> dq -> user]
+    Utils works by finding dq arg of user and ensuring it is connected to
+    producer
+    """
+    dq_node = None
+    for n in user.args:
+        if (
+            isinstance(n, torch.fx.Node)
+            and n.op == "call_function"
+            and n.target in _DEQUANTIZE_OPS
+        ):
+            if _is_connected(produer, n):
+                dq_node = n
+                break
+    if dq_node is None:
+        for n in user.kwargs:
+            if (
+                isinstance(n, torch.fx.Node)
+                and n.op == "call_function"
+                and n.target in _DEQUANTIZE_OPS
+            ):
+                if _is_connected(produer, n):
+                    dq_node = n
+                    break
+    if dq_node is None:
+        return (None, None)
+
+    q_node = None
+    if (
+        isinstance(arg := dq_node.args[0], torch.fx.Node)
+        and arg.op == "call_function"
+        and arg.target in _QUANTIZE_OPS
+    ):
+        q_node = arg
+    return (q_node, dq_node)
+
+
+def _is_sym_size_node(node: Node):
+    return (
+        node.op == "call_function"
+        and node.target is torch.ops.aten.sym_size.default
+        or node.target is torch.ops.aten.sym_numel.default
+        or node.target is torch.ops.aten.sym_numel
+        or node.target is torch.ops.aten.sym_size
+    )
+
+
+def _filter_sym_size_users(node: torch.fx.Node) -> list[torch.fx.Node]:
+    node_users = list(filter((lambda x: (_is_sym_size_node(x) is False)), node.users))
+    return node_users
+
+
+def _is_valid_annotation(annotation: QuantizationAnnotation) -> bool:
+    if annotation is None:
+        return False
+    input_qspec_map = annotation.input_qspec_map
+    output_qspec = annotation.output_qspec
+    if len(input_qspec_map) == 0 and output_qspec is None:
+        return False
+    return True
+
+
+def _get_tensor_constant_from_node(node, m):
+    if node is None:
+        return None
+    if node.op != "get_attr":
+        raise AssertionError(f"Expected node.op to be 'get_attr', got {node.op}")
+    target_atoms = node.target.split(".")
+    attr_itr = m
+    for i, atom in enumerate(target_atoms):
+        if not hasattr(attr_itr, atom):
+            raise RuntimeError(
+                f"Node referenced nonexistent target {'.'.join(target_atoms[:i])}"
+            )
+        attr_itr = getattr(attr_itr, atom)
+    return attr_itr
+
+
+def _get_all_arguments(orig_args, orig_kwargs, args_schema):
+    all_args = []
+    for i, schema in enumerate(args_schema):
+        if schema.name in orig_kwargs:
+            all_args.append(orig_kwargs[schema.name])
+        elif not schema.kwarg_only and i < len(orig_args):
+            all_args.append(orig_args[i])
+        else:
+            all_args.append(schema.default_value)
+    return all_args
+
+
+def _is_supported_batch_norm_for_training(node: Node):
+    """
+    Return True if the given node refers to an aten batch norm op QAT supports.
+    """
+    supported_ops = [
+        torch.ops.aten.batch_norm.default,
+        torch.ops.aten._native_batch_norm_legit.default,
+        # Note: we won't need this op anymore after batch norm consolidation
+        # For now, we need to continue to support it because it gives better
+        # training numerics than `_native_batch_norm_legit`
+        torch.ops.aten.cudnn_batch_norm.default,
+        torch.ops.aten.miopen_batch_norm.default,
+    ]
+    return node.target in supported_ops
+
+
+# TODO: move this to torch/ao/quantization/utils.py
+def _is_conv_node(n: Node):
+    """
+    Return whether the node refers to an aten conv op.
+    """
+    return n.op == "call_function" and n.target in [
+        torch.ops.aten.conv1d.default,
+        torch.ops.aten.conv1d.padding,
+        torch.ops.aten.conv2d.default,
+        torch.ops.aten.conv2d.padding,
+        torch.ops.aten.conv3d.default,
+        torch.ops.aten.conv3d.padding,
+    ]
+
+
+def _is_conv_transpose_node(n: Node):
+    """
+    Return whether the node refers to an aten conv_transpose op.
+    """
+    return n.op == "call_function" and n.target in [
+        torch.ops.aten.conv_transpose1d,
+        torch.ops.aten.conv_transpose1d.default,
+        torch.ops.aten.conv_transpose2d,
+        torch.ops.aten.conv_transpose2d.input,
+    ]
+
+
+def _is_conv_or_conv_transpose_node(n: Node):
+    """
+    Return whether the node refers to an aten conv or conv transpose op.
+    """
+    return _is_conv_node(n) or _is_conv_transpose_node(n)
+
+
+def _is_conv_transpose_fn(conv_fn: Callable):
+    return conv_fn in [F.conv_transpose1d, F.conv_transpose2d]
+
+
+def _is_bn_node(n: Node):
+    return (
+        _is_supported_batch_norm_for_training(n)
+        or n.target is torch.ops.aten._native_batch_norm_legit_no_training.default
+    )
+
+
+def fold_bn_weights_into_conv_node(
+    conv_node: Node,
+    conv_weight_node: Node,
+    conv_bias_node: Node | None,
+    bn_node: Node,
+    m: GraphModule,
+) -> None:
+    # conv args: input, weight, bias, stride, padding, dilation, ...
+    conv_w = _get_tensor_constant_from_node(conv_weight_node, m)
+    conv_b = _get_tensor_constant_from_node(conv_bias_node, m)
+    transpose = _is_conv_transpose_node(conv_node)
+
+    # eval bn args: input, weight, bias, running mean, running var, momentum, eps
+    # train bn args: input, weight, bias, running mean, running var, training, momentum, eps
+    bn_args_schema = bn_node.target._schema.arguments  # type: ignore[union-attr]
+    bn_args = _get_all_arguments(bn_node.args, bn_node.kwargs, bn_args_schema)
+    bn_w = _get_tensor_constant_from_node(bn_args[1], m)
+    bn_b = _get_tensor_constant_from_node(bn_args[2], m)
+    bn_rm = _get_tensor_constant_from_node(bn_args[3], m)
+    bn_rv = _get_tensor_constant_from_node(bn_args[4], m)
+    if bn_node.target is torch.ops.aten._native_batch_norm_legit_no_training.default:
+        eps_arg_index = 6
+    elif _is_supported_batch_norm_for_training(bn_node):
+        eps_arg_index = 7
+    else:
+        raise ValueError("BN node target is unexpected ", bn_node.target)
+    bn_eps = bn_args[eps_arg_index]
+
+    fused_weight, fused_bias = fuse_conv_bn_weights(
+        conv_w, conv_b, bn_rm, bn_rv, bn_eps, bn_w, bn_b, transpose=transpose
+    )
+
+    # update the weight and bias for conv
+    conv_args = list(conv_node.args)
+    # filling in the default bias argument
+    if len(conv_args) == 2:
+        conv_args.append(None)
+
+    # calling data since the fused_weight and fused_bias are nn.Parameter
+    weight_attr_name = conv_weight_node.target
+    if not isinstance(weight_attr_name, str):
+        raise AssertionError(
+            f"Expected conv_weight_node.target to be a string attribute name, got {type(weight_attr_name)}"
+        )
+    _assign_attr(fused_weight, m, weight_attr_name, _AttrKind.PARAMETER)
+    if conv_bias_node is not None:
+        bias_attr_name = conv_bias_node.target
+        _assign_attr(fused_bias, m, str(bias_attr_name), _AttrKind.PARAMETER)
+    else:
+        bias_attr_name = weight_attr_name + "_bias"
+        _assign_attr(fused_bias, m, bias_attr_name, _AttrKind.PARAMETER)
+        with m.graph.inserting_before(conv_node):
+            get_bias_node = m.graph.get_attr(bias_attr_name)
+        # NOTE: here we assume the bias of conv is not quantized!
+        conv_args[2] = get_bias_node
+    conv_node.args = tuple(conv_args)
+
+    # native_batch_norm has 3 outputs, we expect getitem calls on the output
+    # and we want to replace the uses of getitem 0 with the output of conv
+    #
+    if bn_node.target is torch.ops.aten.batch_norm.default:
+        # With the new training ir, instead of batch_norm + getitem,
+        # we only have the batch_norm node.
+        #
+        # Before:
+        # conv -> bn -> users
+        # After:
+        # conv -> users
+        #       bn has no users now
+        bn_node.replace_all_uses_with(conv_node)
+    else:
+        # Before:
+        # conv -> bn - (first output) -> users1
+        #          \ - (second output) -> users2
+        #          \ - (third output) -> users3
+        # After:
+        # conv -> (first output) -> users1
+        #       bn -
+        #          \ - (second output) -> users2
+        #          \ - (third output) -> users3
+        # if users2 and users3 are empty then bn will be removed through dead code elimination
+        for user in bn_node.users:
+            if (
+                user.op != "call_function"
+                or user.target != operator.getitem
+                or user.args[1] != 0
+            ):
+                continue
+            user.replace_all_uses_with(conv_node)
+
+    # If the BN node does not have users, erase it from the graph
+    # Note: we need to do this manually because the model can still be in train
+    # mode at this point, in which case DCE won't erase the BN node automatically
+    # since the node refers to a mutating op. Here we still need to call DCE first
+    # to get rid of the unused getitem nodes that consume the BN node.
+    m.graph.eliminate_dead_code()
+    if len(bn_node.users) == 0:
+        m.graph.erase_node(bn_node)
+
+
+# fuse conv bn weights, inplace modification of the graph_module and graph
+def _fuse_conv_bn_(m: GraphModule) -> None:
+    has_bn = any(_is_bn_node(n) for n in m.graph.nodes)
+    if not has_bn:
+        return
+    for n in m.graph.nodes:
+        if n.op != "call_function" or n.target not in (
+            torch.ops.aten._native_batch_norm_legit_no_training.default,
+            torch.ops.aten.batch_norm.default,
+        ):
+            continue
+        bn_node = n
+        n = bn_node.args[0]
+        if not _is_conv_or_conv_transpose_node(n):
+            continue
+        conv_node = n
+        conv_weight_node = conv_node.args[1]
+        conv_bias_node = conv_node.args[2] if len(conv_node.args) > 2 else None
+        fold_bn_weights_into_conv_node(
+            conv_node, conv_weight_node, conv_bias_node, bn_node, m
+        )
+
+    m.graph.eliminate_dead_code()
+    m.recompile()
+
+
+def _get_node_name_to_scope(model: GraphModule) -> dict[str, tuple[str, type]]:
+    # TODO: move this information to fx node itself
+    node_name_to_scope: dict[str, tuple[str, type]] = {}
+    for n in model.graph.nodes:
+        nn_module_stack = n.meta.get("nn_module_stack", None)
+        current_scope = ("", type(None))
+        if nn_module_stack:
+            bt = list(nn_module_stack.values())[-1]
+            current_scope = (bt[0].split(".")[-1], bt[1])
+        node_name_to_scope[n.name] = current_scope
+    return node_name_to_scope
+
+
+def _get_aten_graph_module_for_pattern(
+    pattern: Callable,
+    example_inputs: tuple[Any, ...],
+    is_cuda: bool = False,
+    **kwargs,
+) -> GraphModule:
+    """
+    Convert the pattern to an FX graph with decomposed aten ops.
+    """
+    if is_cuda:
+        example_inputs = tuple(
+            x.cuda() if isinstance(x, torch.Tensor) else x for x in example_inputs
+        )
+
+    with torch._export.config.patch(use_new_tracer_experimental=True):
+        aten_pattern = torch.export.export(
+            pattern,  # type: ignore[arg-type]
+            example_inputs,
+            kwargs,
+            strict=True,
+        ).module(check_guards=False)
+
+    aten_pattern.graph.eliminate_dead_code()  # type: ignore[operator, union-attr]
+    aten_pattern.recompile()  # type: ignore[operator]
+
+    # ep.module() adds copy_ nodes for the mutated inputs.
+    # For patterns, it doesn't matter
+    for node in aten_pattern.graph.nodes:  # type: ignore[union-attr]
+        if (
+            node.op == "call_function"
+            and node.target is torch.ops.aten.copy_.default
+            and len(node.users) == 0
+        ):
+            aten_pattern.graph.erase_node(node)  # type: ignore[operator, union-attr]
+
+    aten_pattern.graph.eliminate_dead_code()  # type: ignore[operator, union-attr]
+    aten_pattern.recompile()  # type: ignore[operator]
+
+    return aten_pattern  # type: ignore[return-value]
+
+
+def remove_tensor_overload_for_qdq_ops(match_pattern: GraphModule) -> None:
+    """Remove .tensor overload for quantize/dequantize ops so that we can
+    use the match_pattern that we get from torchdynamo export to match the output of convert_pt2e
+    """
+    _MAP = {
+        torch.ops.quantized_decomposed.quantize_per_tensor.default: torch.ops.quantized_decomposed.quantize_per_tensor,
+        torch.ops.quantized_decomposed.dequantize_per_tensor.default: torch.ops.quantized_decomposed.dequantize_per_tensor,
+        torch.ops.quantized_decomposed.quantize_per_tensor.tensor: torch.ops.quantized_decomposed.quantize_per_tensor,
+        torch.ops.quantized_decomposed.dequantize_per_tensor.tensor: torch.ops.quantized_decomposed.dequantize_per_tensor,
+        torch.ops.quantized_decomposed.quantize_per_tensor.tensor2: torch.ops.quantized_decomposed.quantize_per_tensor,
+        torch.ops.quantized_decomposed.dequantize_per_tensor.tensor2: torch.ops.quantized_decomposed.dequantize_per_tensor,
+        torch.ops.quantized_decomposed.quantize_per_channel.default: torch.ops.quantized_decomposed.quantize_per_channel,
+        torch.ops.quantized_decomposed.dequantize_per_channel.default: torch.ops.quantized_decomposed.dequantize_per_channel,
+        torch.ops.aten.clamp.Tensor: torch.ops.aten.clamp,
+    }
+    for n in match_pattern.graph.nodes:
+        if n.op != "call_function":
+            continue
+        if n.target in _MAP:
+            n.target = _MAP[n.target]
+
+
+def _is_literal(arg):
+    if isinstance(arg, (int, float)):
+        return True
+    if isinstance(arg, (tuple, list)):
+        return all(map(_is_literal, arg))
+    return False
+
+
+def _replace_literals_with_new_placeholders(
+    gm: torch.fx.GraphModule,
+    merge_dup: bool = False,
+    exclude_literals: list[Any] | None = None,
+):
+    """Replace the literals in the graph with placeholder nodes that's created on the fly while we
+    traverse the graph, so that the literal arguments in the graph can be matched and replaced
+
+    To use this, the pattern and replacement graph should have the exact same number of literal args
+    and they should be used in the exact same order in the pattern and replacement graph.
+
+    If the literal arguments are not used in the same order in pattern and replacement graph, please
+    use `_replace_literals_with_existing_placeholders` instead
+
+    Args:
+        `gm`: input GraphModule that we'll transform
+        `merge_dup`: boolean flag to indicate that if the same literal appears multiple times in
+         the graph, whether they should correspond to the same placeholder or not
+        `exclude_literals`: a list of literals that will not be replaced with placeholders
+
+    Example:
+
+    # 1. Original Graph
+    def pattern(self, x):
+        return x + 3
+
+    def replacement(self, x):
+        return x - 3
+
+    example_inputs = (torch.randn(1, 3, 3, 3),)
+    pattern_gm = _get_aten_graph_module_for_pattern(pattern, example_inputs)
+    replacement_gm = _get_aten_graph_module_for_pattern(pattern, example_inptus)
+
+    # 2. Before calling replace literals we'll see the following graph:
+    def pattern(self, x):
+        return x + 3
+
+    def replacement(self, x):
+        return x - 3
+
+    pattern_gm = _replace_literals_with_new_placeholders(pattern_gm)
+    replacement_gm = _replace_literals_with_new_placeholders(replacement_gm)
+
+    # 3. After replacing literals with new placeholder nodes
+
+    def pattern(self, x, new_ph):
+        return x + new_ph
+
+    def pattern(self, x, new_ph):
+        return x - new_ph
+
+    """
+    last_ph = None
+    cnt = 0
+    literal_to_ph: dict[float | bool | int | torch.dtype, Node] = {}
+    if exclude_literals is None:
+        exclude_literals = []
+
+    in_spec = gm._in_spec
+    assert in_spec.type is tuple
+    args_spec = in_spec.child(0)
+    assert args_spec.type is tuple
+    args_spec_children = args_spec.children()
+    for node in gm.graph.nodes:
+        if node.op == "placeholder":
+            last_ph = node
+            cnt += 1
+            continue
+        with gm.graph.inserting_after(last_ph):
+            new_args = []
+            for arg in node.args:
+                if _is_literal(arg) and arg not in exclude_literals:
+                    if merge_dup and arg in literal_to_ph:
+                        new_args.append(literal_to_ph[arg])
+                    else:
+                        ph_node = gm.graph.placeholder("arg" + str(cnt))
+                        new_args.append(ph_node)
+                        args_spec_children.append(pytree.treespec_leaf())
+                        cnt += 1
+                        if merge_dup:
+                            literal_to_ph[arg] = ph_node
+                else:
+                    new_args.append(arg)
+            new_args = tuple(new_args)
+
+        node.args = new_args
+
+    # Update `num_nodes`, `num_leaves`, `num_children`.
+    args_spec = pytree.treespec_tuple(args_spec_children)
+    gm._in_spec = in_spec = pytree.treespec_tuple([args_spec, *in_spec.children()[1:]])
+    return gm
+
+
+def _replace_literals_with_existing_placeholders(
+    gm: torch.fx.GraphModule,
+    exclude_literals: list[Any] | None = None,
+    literal_to_ph_idx: dict[float | int | bool | torch.dtype, int] | None = None,
+):
+    """Replace the literals in the graph with **existing** placeholder nodes, so that the literal arguments
+    in the graph can be matched and replaced
+
+    To use this, all literal args in the graph should be unique and each of them should correspond
+    to exactly one placeholder node
+
+    # 1. Original Graph
+    def pattern(self, x_i8, scale, zero_point, quant_min, quant_max):
+        return torch.dequantize_per_tensor(x_i8, scale, zero_point, quant_min, quant_max)
+
+    def replacement(x_i8, scale, zero_point, quant_min, quant_max):
+        x_i8 = torch.clamp(x_i8, quant_min, quant_max)
+        return ((x_i8.to(torch.float32) - zero_point) * scale).to(dtype=torch.float32)
+
+    example_inputs = (
+        torch.randn(1, 3, 3, 3),
+        1.0,
+        0,
+        -128,
+        127,
+    )
+    pattern_gm = _get_aten_graph_module_for_pattern(pattern, example_inputs)
+    replacement_gm = _get_aten_graph_module_for_pattern(pattern, example_inptus)
+
+    # 2. Before calling replace literals we'll see the following graph:
+    def pattern(self, x_i8, scale, zero_point, quant_min, quant_max):
+        # scale/zero_point/quant_min/quant_max are burnt in since they are scalar values
+        return torch.dequantize_per_tensor(x_i8, 1.0, 0, -128, 127)
+
+    def replacement(x_i8, scale, zero_point, quant_min, quant_max):
+        # scale/zero_point/quant_min/quant_max are burnt in since they are scalar values
+        x_i8 = torch.clamp(x_i8, -128, 127)
+        return ((x_i8.to(torch.float32) - 0) * 1.0).to(dtype=torch.float32)
+
+    # Note that literal args appear in different order in pattern and replacement graph, so
+    # we can't use _replace_literals_with_new_placeholders
+
+    literal_to_ph_idx = {1.0: 1, 0: 2, -128: 3, 127: 4}
+    pattern_gm = _replace_literals_with_existing_placeholders(pattern_gm, literal_to_ph_idx)
+    replacement_gm = _replace_literals_with_existing_placeholders(replacement_gm, literal_to_ph_idx)
+
+    # 3. After replacing literals with existing placeholder nodes
+
+    def pattern(self, x_i8, scale, zero_point, quant_min, quant_max):
+        # scale/zero_point/quant_min/quant_max are burnt in since they are scalar values
+        return torch.dequantize_per_tensor(x_i8, scale, zero_point, quant_min, quant_max)
+
+    def replacement(x_i8, scale, zero_point, quant_min, quant_max):
+        # scale/zero_point/quant_min/quant_max are burnt in since they are scalar values
+        x_i8 = torch.clamp(x_i8, quant_min, quant_max)
+        return ((x_i8.to(torch.float32) - zero_point) * scale).to(dtype=torch.float32)
+    """
+    if exclude_literals is None:
+        exclude_literals = []
+
+    if literal_to_ph_idx is None:
+        literal_to_ph_idx = {}
+
+    phs = [node for node in gm.graph.nodes if node.op == "placeholder"]
+
+    for node in gm.graph.nodes:
+        if node.op != "call_function":
+            continue
+        new_args = []
+        for arg in node.args:
+            if (
+                _is_literal(arg)
+                and arg not in exclude_literals
+                and arg in literal_to_ph_idx
+            ):
+                ph_idx = literal_to_ph_idx[arg]
+                ph_node = phs[ph_idx]
+                new_args.append(ph_node)
+            else:
+                new_args.append(arg)
+        new_args = tuple(new_args)
+        node.args = new_args
+    return gm
+
+
+# TODO: Handle this in export itself and don't wrap the model in another GraphModule
+# in prepare and convert
+def _disallow_eval_train(model: GraphModule):
+    """
+    Disallow calling `model.train()` or `model.eval()` on the given GraphModule.
+    This is useful for exported models, where these methods don't actually behave as expected.
+    """
+    error_message = """
+        Calling train() or eval() is not supported for exported models.
+        Please call `torch.ao.quantization.move_exported_model_to_train(model)` (or eval) instead.
+
+        If you cannot replace the calls to `model.train()` and `model.eval()`, you may override
+        the behavior for these methods by calling `torch.ao.quantization.allow_exported_model_train_eval(model)`,
+        which does the above automatically for you. Note that this has limited effect on switching
+        behavior between train and eval modes, and should be used only for special ops such as dropout
+        and batchnorm.
+        """
+
+    def _train(self, mode: bool = True):
+        raise NotImplementedError(error_message)
+
+    def _eval(self, mode: bool = True):
+        raise NotImplementedError(error_message)
+
+    model.train = types.MethodType(_train, model)  # type: ignore[method-assign]
+    model.eval = types.MethodType(_eval, model)  # type: ignore[method-assign]
+    return model
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/qconfig.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/qconfig.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff5d1f341751a3b0ea4f720978d3c380e26ccc41
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/qconfig.py
@@ -0,0 +1,715 @@
+# mypy: allow-untyped-defs
+import copy
+import sys
+import warnings
+from collections import namedtuple
+from typing import Any, Optional, Union
+from typing_extensions import deprecated
+
+import torch
+import torch.nn as nn
+from torch.ao.quantization.fake_quantize import (
+    default_dynamic_fake_quant,
+    default_embedding_fake_quant,
+    default_embedding_fake_quant_4bit,
+    default_fake_quant,
+    default_fused_act_fake_quant,
+    default_fused_per_channel_wt_fake_quant,
+    default_fused_wt_fake_quant,
+    default_per_channel_weight_fake_quant,
+    default_weight_fake_quant,
+    FakeQuantize,
+    FakeQuantizeBase,
+    fused_per_channel_wt_fake_quant_range_neg_127_to_127,
+    fused_wt_fake_quant_range_neg_127_to_127,
+    FusedMovingAvgObsFakeQuantize,
+)
+
+from .observer import (
+    _PartialWrapper,
+    default_debug_observer,
+    default_dynamic_quant_observer,
+    default_float_qparams_observer,
+    default_float_qparams_observer_4bit,
+    default_observer,
+    default_per_channel_weight_observer,
+    default_placeholder_observer,
+    default_reuse_input_observer,
+    default_weight_observer,
+    HistogramObserver,
+    MinMaxObserver,
+    MovingAverageMinMaxObserver,
+    NoopObserver,
+    ObserverBase,
+    per_channel_weight_observer_range_neg_127_to_127,
+    PlaceholderObserver,
+    ReuseInputObserver,
+    weight_observer_range_neg_127_to_127,
+)
+
+
+__all__ = [
+    "QConfig",
+    # TODO: deprecated, remove
+    "QConfigDynamic",
+    "default_qconfig",
+    "default_debug_qconfig",
+    "default_per_channel_qconfig",
+    "default_dynamic_qconfig",
+    "float16_dynamic_qconfig",
+    "float16_static_qconfig",
+    "per_channel_dynamic_qconfig",
+    "float_qparams_weight_only_qconfig",
+    "float_qparams_weight_only_qconfig_4bit",
+    "default_quint8_weight_qconfig",
+    "default_qat_qconfig",
+    "default_dynamic_qat_qconfig",
+    "default_weight_only_qconfig",
+    "default_activation_only_qconfig",
+    "default_qat_qconfig_v2",
+    "default_reuse_input_qconfig",
+    "default_symmetric_qnnpack_qconfig",
+    "default_per_channel_symmetric_qnnpack_qconfig",
+    "default_symmetric_qnnpack_qat_qconfig",
+    "default_per_channel_symmetric_qnnpack_qat_qconfig",
+    "default_embedding_qat_qconfig",
+    "default_embedding_qat_qconfig_4bit",
+    "get_default_qconfig",
+    "get_default_qat_qconfig",
+    "get_default_qconfig_dict",
+    "get_default_qat_qconfig_dict",
+    "QConfigAny",
+    "qconfig_equals",
+]
+
+
+# pyrefly: ignore [invalid-inheritance]
+class QConfig(namedtuple("QConfig", ["activation", "weight"])):
+    """
+    Describes how to quantize a layer or a part of the network by providing
+    settings (observer classes) for activations and weights respectively.
+
+
+    Note that QConfig needs to contain observer **classes** (like MinMaxObserver) or a callable that returns
+    instances on invocation, not the concrete observer instances themselves.
+    Quantization preparation function will instantiate observers multiple times for each of the layers.
+
+
+    Observer classes have usually reasonable default arguments, but they can be overwritten with `with_args`
+    method (that behaves like functools.partial)::
+
+      my_qconfig = QConfig(
+          activation=MinMaxObserver.with_args(dtype=torch.qint8),
+          weight=default_observer.with_args(dtype=torch.qint8),
+      )
+
+    """
+
+    __slots__ = ()
+
+    def __new__(cls, activation, weight):
+        # catch common mistakes
+        if isinstance(activation, nn.Module) or isinstance(weight, nn.Module):
+            raise ValueError(
+                "QConfig received observer instance, please pass observer class instead. "
+                + "Use MyObserver.with_args(x=1) to override arguments to constructor if needed"
+            )
+        return super().__new__(cls, activation, weight)
+
+
+@deprecated(
+    "`QConfigDynamic` is going to be deprecated in PyTorch 1.12, please use `QConfig` instead",
+    category=FutureWarning,
+)
+# pyrefly: ignore [invalid-inheritance]
+class QConfigDynamic(namedtuple("QConfigDynamic", ["activation", "weight"])):
+    """
+    Describes how to dynamically quantize a layer or a part of the network by providing
+    settings (observer classes) for weights.
+
+    It's like QConfig, but for dynamic quantization.
+
+    Note that QConfigDynamic needs to contain observer **classes** (like MinMaxObserver) or a callable that returns
+    instances on invocation, not the concrete observer instances themselves.
+    Quantization function will instantiate observers multiple times for each of the layers.
+
+    Observer classes have usually reasonable default arguments, but they can be overwritten with `with_args`
+    method (that behaves like functools.partial)::
+
+      my_qconfig = QConfigDynamic(weight=default_observer.with_args(dtype=torch.qint8))
+    """
+
+    __slots__ = ()
+
+    def __new__(cls, activation=torch.nn.Identity, weight=torch.nn.Identity):
+        # catch common mistakes
+        if isinstance(weight, nn.Module):
+            raise ValueError(
+                "QConfigDynamic received observer instance, please pass observer class instead. "
+                + "Use MyObserver.with_args(x=1) to override arguments to constructor if needed"
+            )
+        return super().__new__(cls, activation, weight)
+
+
+default_qconfig = QConfig(activation=default_observer, weight=default_weight_observer)
+"""
+Default qconfig configuration.
+"""
+
+default_debug_qconfig = QConfig(
+    weight=default_weight_observer, activation=default_debug_observer
+)
+"""
+Default qconfig configuration for debugging.
+"""
+
+default_per_channel_qconfig = QConfig(
+    activation=default_observer, weight=default_per_channel_weight_observer
+)
+"""
+Default qconfig configuration for per channel weight quantization.
+"""
+
+default_dynamic_qconfig = QConfig(
+    activation=default_dynamic_quant_observer, weight=default_weight_observer
+)
+"""
+Default dynamic qconfig.
+"""
+
+float16_dynamic_qconfig = QConfig(
+    activation=PlaceholderObserver.with_args(dtype=torch.float16, is_dynamic=True),
+    weight=PlaceholderObserver.with_args(dtype=torch.float16),
+)
+"""
+Dynamic qconfig with weights quantized to `torch.float16`.
+"""
+
+float16_static_qconfig = QConfig(
+    activation=PlaceholderObserver.with_args(dtype=torch.float16),
+    weight=PlaceholderObserver.with_args(dtype=torch.float16),
+)
+"""
+Dynamic qconfig with both activations and weights quantized to `torch.float16`.
+"""
+
+per_channel_dynamic_qconfig = QConfig(
+    activation=default_dynamic_quant_observer,
+    weight=default_per_channel_weight_observer,
+)
+"""
+Dynamic qconfig with weights quantized per channel.
+"""
+
+float_qparams_weight_only_qconfig = QConfig(
+    activation=default_placeholder_observer, weight=default_float_qparams_observer
+)
+"""
+Dynamic qconfig with weights quantized with a floating point zero_point.
+"""
+
+float_qparams_weight_only_qconfig_4bit = QConfig(
+    activation=default_placeholder_observer, weight=default_float_qparams_observer_4bit
+)
+
+default_qat_qconfig = QConfig(
+    activation=default_fake_quant, weight=default_weight_fake_quant
+)
+"""
+Default qconfig for QAT.
+"""
+
+default_dynamic_qat_qconfig = QConfig(
+    activation=default_dynamic_fake_quant, weight=default_weight_fake_quant
+)
+"""
+Default qconfig for dynamic QAT.
+"""
+
+default_weight_only_qconfig = QConfig(
+    activation=torch.nn.Identity, weight=default_weight_fake_quant
+)
+"""
+Default qconfig for quantizing weights only.
+"""
+
+default_activation_only_qconfig = QConfig(
+    activation=default_fake_quant, weight=torch.nn.Identity
+)
+"""
+Default qconfig for quantizing activations only.
+"""
+
+# QAT config that uses a fused observer + fake quant modules for optimized training performance.
+# to modify the activation/weight observers, the default entries in fake_quantize.py can be modified.
+default_qat_qconfig_v2 = QConfig(
+    activation=default_fused_act_fake_quant, weight=default_fused_wt_fake_quant
+)
+"""
+Fused version of `default_qat_config`, has performance benefits.
+"""
+
+default_reuse_input_qconfig = QConfig(
+    activation=default_reuse_input_observer, weight=NoopObserver
+)
+"""
+Default qconfig for operators that reuse the observers from input Tensor, e.g. reshape
+"""
+
+
+def get_default_qconfig(backend="x86", version=0):
+    """
+    Returns the default PTQ qconfig for the specified backend.
+
+    Args:
+      * `backend` (str): a string representing the target backend. Currently supports
+        `x86` (default), `fbgemm`, `qnnpack` and `onednn`.
+
+    Return:
+        qconfig
+    """
+    supported_backends = ["fbgemm", "x86", "qnnpack", "onednn"]
+    if backend not in supported_backends:
+        raise AssertionError(
+            "backend: "
+            + str(backend)
+            + f" not supported. backend must be one of {supported_backends}"
+        )
+
+    if version == 0:
+        if backend == "fbgemm":
+            qconfig = QConfig(
+                activation=HistogramObserver.with_args(reduce_range=True),
+                weight=default_per_channel_weight_observer,
+            )
+        elif backend == "qnnpack":
+            # TODO: make this compatible with xnnpack constraints
+            qconfig = QConfig(
+                activation=HistogramObserver.with_args(reduce_range=False),
+                weight=default_weight_observer,
+            )
+        elif backend == "onednn":
+            if not torch.cpu._is_vnni_supported():
+                warnings.warn(
+                    "Default qconfig of oneDNN backend with reduce_range of false may have accuracy issues "
+                    "on CPU without Vector Neural Network Instruction support.",
+                    stacklevel=2,
+                )
+            qconfig = QConfig(
+                activation=HistogramObserver.with_args(reduce_range=False),
+                weight=default_per_channel_weight_observer,
+            )
+        elif backend == "x86":
+            qconfig = QConfig(
+                activation=HistogramObserver.with_args(reduce_range=True),
+                weight=default_per_channel_weight_observer,
+            )
+        else:
+            # won't reach
+            qconfig = default_qconfig
+    else:
+        raise AssertionError(
+            "Version number: "
+            + str(version)
+            + " in get_default_qconfig is not supported. Version number must be 0"
+        )
+
+    return qconfig
+
+
+"""
+Default, symmetric PTQ qconfig for the specified backend. And a per_channel
+variant of the same.
+
+Symmetric here applies to signed weights with zero point = 0, and additional
+value restrictions. The activations are also signed 8-bit integers with this
+qconfig.
+
+    * Once this change is merged [as of 3/17/22], with backend or qengine =
+    'qnnpack', some quantized operators with this symmetric qconfig may use
+    operators from xnnpack library.
+
+        ** Support to use xnnpack ops with `qnnpack` backed for asymmetric
+        qconfig (returned by get_default_qconfig()) is not available yet.
+
+    * This qconfig uses signed activations and weights. Weights have added
+    restrictions such as zero point is forced to be 0, making the weights
+    symmetric, hence the name. And the 8-bit quantized values are
+    restricting to to [-127, +127], excluding -128.
+
+    * xnnpack has a requantization scale value restriction, 0x1p-32 <=
+    requantization_scale < 256.0 where, `requantization_scale = (input_scale
+    * kernel_scale) / (output_scale)`. Using this eps (w/ assumed max value
+    of 256) is to prevent requantization_scale to go below xnnpack lower
+    threshold.
+"""
+default_symmetric_qnnpack_qconfig = QConfig(
+    activation=HistogramObserver.with_args(
+        dtype=torch.qint8, reduce_range=False, eps=2**-12
+    ),
+    weight=weight_observer_range_neg_127_to_127,
+)
+
+default_per_channel_symmetric_qnnpack_qconfig = QConfig(
+    activation=HistogramObserver.with_args(
+        dtype=torch.qint8, reduce_range=False, eps=2**-12
+    ),
+    weight=per_channel_weight_observer_range_neg_127_to_127,
+)
+
+default_embedding_qat_qconfig = QConfig(
+    activation=NoopObserver.with_args(dtype=torch.float32),
+    weight=default_embedding_fake_quant,
+)
+
+default_embedding_qat_qconfig_4bit = QConfig(
+    activation=NoopObserver.with_args(dtype=torch.float32),
+    weight=default_embedding_fake_quant_4bit,
+)
+
+default_quint8_weight_qconfig = QConfig(
+    activation=HistogramObserver, weight=MinMaxObserver
+)
+
+
+def get_default_qat_qconfig(backend="x86", version=1):
+    """
+    Returns the default QAT qconfig for the specified backend.
+
+    Args:
+      * `backend` (str): a string representing the target backend. Currently supports
+        `x86` (default), `fbgemm`, `qnnpack` and `onednn`.
+      * `version`: version, for backwards compatibility. Can be `None` or `1`.
+
+    Return:
+        qconfig
+    """
+    supported_backends = ["fbgemm", "x86", "qnnpack", "onednn"]
+    if backend not in supported_backends:
+        raise AssertionError(
+            "backend: "
+            + str(backend)
+            + f" not supported. backend must be one of {supported_backends}"
+        )
+
+    # Histogram observer is too slow for quantization aware training
+    if version == 0:
+        if backend == "fbgemm":
+            qconfig = QConfig(
+                activation=FakeQuantize.with_args(
+                    observer=MovingAverageMinMaxObserver,
+                    quant_min=0,
+                    quant_max=255,
+                    reduce_range=True,
+                ),
+                weight=default_per_channel_weight_fake_quant,
+            )
+        elif backend == "qnnpack":
+            qconfig = QConfig(
+                activation=FakeQuantize.with_args(
+                    observer=MovingAverageMinMaxObserver,
+                    quant_min=0,
+                    quant_max=255,
+                    reduce_range=False,
+                ),
+                weight=default_weight_fake_quant,
+            )
+        elif backend == "onednn":
+            qconfig = QConfig(
+                activation=FakeQuantize.with_args(
+                    observer=MovingAverageMinMaxObserver, quant_min=0, quant_max=255
+                ),
+                weight=default_per_channel_weight_fake_quant,
+            )
+        elif backend == "x86":
+            qconfig = QConfig(
+                activation=FakeQuantize.with_args(
+                    observer=MovingAverageMinMaxObserver,
+                    quant_min=0,
+                    quant_max=255,
+                    reduce_range=True,
+                ),
+                weight=default_per_channel_weight_fake_quant,
+            )
+        else:
+            qconfig = default_qat_qconfig
+    # Use the fused observe + fake_quant modules for doing QAT.
+    elif version == 1:
+        if backend == "fbgemm":
+            qconfig = QConfig(
+                activation=FusedMovingAvgObsFakeQuantize.with_args(
+                    observer=MovingAverageMinMaxObserver,
+                    quant_min=0,
+                    quant_max=255,
+                    reduce_range=True,
+                ),
+                weight=default_fused_per_channel_wt_fake_quant,
+            )
+        elif backend == "qnnpack":
+            # TODO: make this compatible with xnnpack constraints
+            qconfig = QConfig(
+                activation=FusedMovingAvgObsFakeQuantize.with_args(
+                    observer=MovingAverageMinMaxObserver,
+                    quant_min=0,
+                    quant_max=255,
+                    reduce_range=False,
+                ),
+                weight=default_fused_wt_fake_quant,
+            )
+        elif backend == "onednn":
+            qconfig = QConfig(
+                activation=FusedMovingAvgObsFakeQuantize.with_args(
+                    observer=MovingAverageMinMaxObserver, quant_min=0, quant_max=255
+                ),
+                weight=default_fused_per_channel_wt_fake_quant,
+            )
+        elif backend == "x86":
+            qconfig = QConfig(
+                activation=FusedMovingAvgObsFakeQuantize.with_args(
+                    observer=MovingAverageMinMaxObserver,
+                    quant_min=0,
+                    quant_max=255,
+                    reduce_range=True,
+                ),
+                weight=default_fused_per_channel_wt_fake_quant,
+            )
+        else:
+            qconfig = default_qat_qconfig_v2
+    else:
+        raise AssertionError(
+            "Version number: "
+            + str(version)
+            + "in get_default_qat_qconfig is not supported. Version number must be 0 or 1"
+        )
+
+    return qconfig
+
+
+"""
+Default symmetric QAT qconfig for qnnpack. And its per channel weight variant.
+"""
+default_symmetric_qnnpack_qat_qconfig = QConfig(
+    activation=FusedMovingAvgObsFakeQuantize.with_args(
+        observer=MovingAverageMinMaxObserver,
+        quant_min=-128,
+        quant_max=127,
+        dtype=torch.qint8,
+        reduce_range=False,
+        eps=2**-12,
+    ),
+    weight=fused_wt_fake_quant_range_neg_127_to_127,
+)
+
+default_per_channel_symmetric_qnnpack_qat_qconfig = QConfig(
+    activation=FusedMovingAvgObsFakeQuantize.with_args(
+        observer=MovingAverageMinMaxObserver,
+        quant_min=-128,
+        quant_max=127,
+        dtype=torch.qint8,
+        reduce_range=False,
+        eps=2**-12,
+    ),
+    weight=fused_per_channel_wt_fake_quant_range_neg_127_to_127,
+)
+
+_default_fp32_placeholder_qconfig = QConfig(
+    activation=PlaceholderObserver.with_args(dtype=torch.float32),
+    weight=PlaceholderObserver.with_args(dtype=torch.float32),
+)
+
+_default_quint8_placeholder_qconfig = QConfig(
+    activation=PlaceholderObserver.with_args(dtype=torch.quint8),
+    # operators using this qconfig doesn't have weights
+    weight=None,
+)
+
+
+@deprecated(
+    "`torch.ao.quantization.get_default_qconfig_dict` is deprecated and will be removed in "
+    "a future version. Please use `torch.ao.quantization.get_default_qconfig_mapping` instead.",
+    category=FutureWarning,
+)
+def get_default_qconfig_dict(backend="x86", version=0):
+    return torch.ao.quantization.get_default_qconfig_mapping(backend, version).to_dict()
+
+
+@deprecated(
+    "`torch.ao.quantization.get_default_qat_qconfig_dict` is deprecated and will be removed in "
+    "a future version. Please use `torch.ao.quantization.get_default_qat_qconfig_mapping` instead.",
+    category=FutureWarning,
+)
+def get_default_qat_qconfig_dict(backend="x86", version=1):
+    return torch.ao.quantization.get_default_qat_qconfig_mapping(
+        backend, version
+    ).to_dict()
+
+
+def _assert_valid_qconfig(qconfig: QConfig | None, mod: torch.nn.Module) -> None:
+    """
+    Verifies that this `qconfig` is valid.
+    """
+    if qconfig is None:
+        return
+    is_conv_transpose_mod = isinstance(
+        mod,
+        (torch.nn.ConvTranspose1d, torch.nn.ConvTranspose2d, torch.nn.ConvTranspose3d),
+    )
+    if is_conv_transpose_mod:
+        if qconfig.weight is None:
+            # for now, we assume that any qconfig for ConvTranspose without a weight is valid
+            return
+        example_observer = qconfig.weight()
+        is_per_channel = isinstance(
+            example_observer,
+            (
+                torch.ao.quantization.PerChannelMinMaxObserver,
+                torch.ao.quantization.MovingAveragePerChannelMinMaxObserver,
+            ),
+        )
+        if is_per_channel:
+            raise AssertionError(
+                "Per channel weight observer is not supported yet for ConvTranspose{n}d."
+            )
+
+
+if sys.version_info < (3, 12):
+    QConfigAny = Optional[QConfig]
+    QConfigAny.__module__ = "torch.ao.quantization.qconfig"
+else:
+    from typing import TypeAliasType
+
+    QConfigAny = TypeAliasType("QConfigAny", QConfig | None)
+
+
+def _add_module_to_qconfig_obs_ctr(
+    qconfig: QConfigAny, module: nn.Module | None
+) -> Any:
+    r"""This is a helper function for use in quantization prepare that updates a qconfig so that
+    the constructors stored in the qconfig will create observers on the same device that
+    'module' is on. This is intended to be used when the qconfigs are propagated to each
+    module in order to avoid potential device alignment issues.
+
+    Args:
+        qconfig: QConfig with obs constructors stored in activation and weight
+        module: module which the qconfig is related to
+
+    Return:
+        qconfig: configured so that obs constructors set to construct on the same device as module
+    """
+
+    if module is None or qconfig is None or qconfig._fields != ("activation", "weight"):
+        return qconfig
+
+    def get_factory_kwargs_based_on_module_device():
+        if not isinstance(module, torch.nn.Module):
+            raise AssertionError("module must be an instance of torch.nn.Module")
+        devices = {p.device for p in module.parameters()} | {
+            p.device for p in module.buffers()
+        }
+        device = next(iter(devices)) if len(devices) > 0 else None
+        return None if device is None else {"device": device}
+
+    def configure_constructor_to_put_obs_on_module_device(original_constructor):
+        try:
+            # check if constructor can accept factory_kwargs
+            check = original_constructor.with_args(factory_kwargs=None)
+            check()
+            return original_constructor.with_callable_args(
+                factory_kwargs=get_factory_kwargs_based_on_module_device
+            )
+        except AttributeError:  # qconfig doesn't have activation or weight
+            return original_constructor
+        except TypeError:  # the class doesn't accept factory_kwargs argument
+            return original_constructor
+
+    activation = configure_constructor_to_put_obs_on_module_device(qconfig.activation)
+    weight = configure_constructor_to_put_obs_on_module_device(qconfig.weight)
+
+    return QConfig(activation, weight)
+
+
+_ObserverOrFakeQuantizeConstructor = Union[
+    _PartialWrapper, type[ObserverBase], type[FakeQuantizeBase]
+]
+
+
+def _obs_or_fq_ctr_equals(
+    obs_or_fq1: _ObserverOrFakeQuantizeConstructor,
+    obs_or_fq2: _ObserverOrFakeQuantizeConstructor,
+):
+    if isinstance(obs_or_fq1, _PartialWrapper) and isinstance(
+        obs_or_fq2, _PartialWrapper
+    ):
+        return _partial_wrapper_equals(obs_or_fq1, obs_or_fq2)
+    return obs_or_fq1 == obs_or_fq2
+
+
+def _partial_wrapper_equals(obs_or_fq1: _PartialWrapper, obs_or_fq2: _PartialWrapper):
+    """
+    Return whether the two partial wrappers are equal,
+    """
+    # functools.partial has no __eq__ operator defined so '==' defaults to 'is'
+    obs_or_fq1_keywords = copy.copy(obs_or_fq1.p.keywords)
+    obs_or_fq2_keywords = copy.copy(obs_or_fq2.p.keywords)
+    keywords_equal = True
+    # compare observer constructor with _obs_or_fq_ctr_equals since direct compare would fail
+    if "observer" in obs_or_fq1_keywords and "observer" in obs_or_fq2_keywords:
+        keywords_equal = keywords_equal and _obs_or_fq_ctr_equals(
+            obs_or_fq1_keywords["observer"], obs_or_fq2_keywords["observer"]
+        )
+        obs_or_fq1_keywords.pop("observer")
+        obs_or_fq2_keywords.pop("observer")
+    keywords_equal = keywords_equal and obs_or_fq1_keywords == obs_or_fq2_keywords
+    return (
+        obs_or_fq1.p.func == obs_or_fq2.p.func
+        and obs_or_fq1.p.args == obs_or_fq2.p.args
+        and keywords_equal
+    )
+
+
+def qconfig_equals(q1: QConfigAny, q2: QConfigAny):
+    """
+    Returns `True` if `q1` equals `q2`, and `False` otherwise.
+    """
+    if q1 is None or q2 is None:
+        return q1 == q2
+    else:
+        if q1 is None or q2 is None:
+            raise AssertionError(
+                "Both q1 and q2 must be non-None for qconfig comparison"
+            )
+        try:
+            # Qconfig weight and activation can be either a partial wrapper,
+            # or an observer class. Special handling is required (above) for
+            # comparing partial wrappers.
+            activation_same = _obs_or_fq_ctr_equals(q1.activation, q2.activation)
+            weight_same = _obs_or_fq_ctr_equals(q1.weight, q2.weight)
+            return activation_same and weight_same
+        except AttributeError:
+            return q1 == q2
+
+
+def _activation_is_memoryless(qconfig: QConfig):
+    """
+    Return whether the observer for activations defined in the given QConfig is memoryless.
+    This means a MovingAverage observer with averaging constant equal to 1.
+    """
+
+    def _is_memoryless(observer):
+        return (
+            hasattr(observer, "averaging_constant") and observer.averaging_constant == 1
+        )
+
+    act = qconfig.activation()
+    if isinstance(act, FakeQuantizeBase) and hasattr(act, "activation_post_process"):
+        return _is_memoryless(act.activation_post_process)
+    else:
+        return _is_memoryless(act)
+
+
+def _is_reuse_input_qconfig(qconfig: QConfig | None):
+    return (
+        qconfig is not None
+        and isinstance(qconfig.activation(), ReuseInputObserver)
+        and isinstance(qconfig.weight(), NoopObserver)
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/qconfig_mapping.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/qconfig_mapping.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf896a96da055ea99d1e165c12dc450f50ad77dc
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/qconfig_mapping.py
@@ -0,0 +1,385 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+from collections import OrderedDict
+from typing import Any, TYPE_CHECKING
+
+import torch
+
+from .fake_quantize import default_weight_fake_quant, FixedQParamsFakeQuantize
+from .observer import (
+    _PartialWrapper,
+    default_fixed_qparams_range_0to1_observer,
+    default_fixed_qparams_range_neg1to1_observer,
+    default_placeholder_observer,
+    default_weight_observer,
+)
+from .qconfig import (
+    default_quint8_weight_qconfig,
+    default_reuse_input_qconfig,
+    default_symmetric_qnnpack_qat_qconfig,
+    default_symmetric_qnnpack_qconfig,
+    get_default_qat_qconfig,
+    get_default_qconfig,
+    QConfig,
+    QConfigAny,
+)
+
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+
+__all__ = [
+    "get_default_qconfig_mapping",
+    "get_default_qat_qconfig_mapping",
+    "QConfigMapping",
+]
+
+
+# TODO: replace all usages with these constants
+_GLOBAL_DICT_KEY = ""
+_OBJECT_TYPE_DICT_KEY = "object_type"
+_MODULE_NAME_REGEX_DICT_KEY = "module_name_regex"
+_MODULE_NAME_DICT_KEY = "module_name"
+_MODULE_NAME_OBJECT_TYPE_ORDER_DICT_KEY = "module_name_object_type_order"
+
+# TODO: derive this map from the BackendConfig
+_FIXED_QPARAMS_OP_TO_OBSERVER: dict[Callable | str, _PartialWrapper] = {
+    torch.nn.Hardsigmoid: default_fixed_qparams_range_0to1_observer,
+    torch.nn.functional.hardsigmoid: default_fixed_qparams_range_0to1_observer,
+    "hardsigmoid": default_fixed_qparams_range_0to1_observer,
+    "hardsigmoid_": default_fixed_qparams_range_0to1_observer,
+    torch.nn.Sigmoid: default_fixed_qparams_range_0to1_observer,
+    torch.sigmoid: default_fixed_qparams_range_0to1_observer,
+    "sigmoid": default_fixed_qparams_range_0to1_observer,
+    "sigmoid_": default_fixed_qparams_range_0to1_observer,
+    torch.nn.Softmax: default_fixed_qparams_range_0to1_observer,
+    torch.nn.Tanh: default_fixed_qparams_range_neg1to1_observer,
+    torch.tanh: default_fixed_qparams_range_neg1to1_observer,
+    "tanh": default_fixed_qparams_range_neg1to1_observer,
+    "tanh_": default_fixed_qparams_range_neg1to1_observer,
+}
+
+
+def _get_default_qconfig_mapping(
+    is_qat: bool, backend: str, version: int
+) -> QConfigMapping:
+    """
+    Return the default QConfigMapping for the given quantization type and backend.
+    """
+    if is_qat:
+        qconfig = get_default_qat_qconfig(backend, version)
+    else:
+        qconfig = get_default_qconfig(backend, version)
+    default_weight = default_weight_fake_quant if is_qat else default_weight_observer
+
+    # default_per_channel_weight_observer is not currently compatible with fbgemm backend
+    # so we have to modify the weight observer to default_weight_observer or another
+    # per tensor supported observer.
+    # see https://github.com/pytorch/pytorch/issues/47535
+    if backend in ("fbgemm", "x86"):
+        qconfig_transpose = QConfig(
+            activation=qconfig.activation, weight=default_weight
+        )
+    else:
+        qconfig_transpose = qconfig
+
+    # currently layernorm only supports float weights
+    # we have to add this because otherwise there will be a extra quantize-dequantize pair
+    qconfig_layernorm = QConfig(
+        activation=qconfig.activation, weight=default_placeholder_observer
+    )
+
+    qconfig_mapping = (
+        QConfigMapping()
+        .set_global(qconfig)
+        .set_object_type("reshape", default_reuse_input_qconfig)
+        .set_object_type(torch.nn.ConvTranspose1d, qconfig_transpose)
+        .set_object_type(torch.nn.ConvTranspose2d, qconfig_transpose)
+        .set_object_type(torch.nn.ConvTranspose3d, qconfig_transpose)
+        .set_object_type(torch.nn.functional.conv_transpose1d, qconfig_transpose)
+        .set_object_type(torch.nn.functional.conv_transpose2d, qconfig_transpose)
+        .set_object_type(torch.nn.functional.conv_transpose3d, qconfig_transpose)
+        .set_object_type(torch.nn.functional.layer_norm, qconfig_layernorm)
+        .set_object_type(torch.nn.LayerNorm, qconfig_layernorm)
+        .set_object_type(torch.nn.PReLU, default_quint8_weight_qconfig)
+    )
+    # Use special observers for ops with fixed qparams
+    fixed_qparams_observer_to_qconfig: dict[Any, QConfigAny] = {}
+    for fixed_qparams_op, observer in _FIXED_QPARAMS_OP_TO_OBSERVER.items():
+        if observer in fixed_qparams_observer_to_qconfig:
+            fixed_qparams_qconfig = fixed_qparams_observer_to_qconfig[observer]
+        else:
+            if is_qat:
+                activation = FixedQParamsFakeQuantize.with_args(observer=observer)
+            else:
+                activation = observer
+            fixed_qparams_qconfig = QConfig(
+                activation=activation, weight=default_weight
+            )
+            fixed_qparams_observer_to_qconfig[observer] = fixed_qparams_qconfig
+        qconfig_mapping.set_object_type(fixed_qparams_op, fixed_qparams_qconfig)
+
+    # TODO Currently it's required that separate ops in a fused op/module have the same qconfig.
+    #      Need to be able to support fusion of ops with different qconfigs
+
+    return qconfig_mapping
+
+
+def get_default_qconfig_mapping(backend="x86", version=0) -> QConfigMapping:
+    """
+    Return the default QConfigMapping for post training quantization.
+
+    Args:
+      * ``backend`` (str) : the quantization backend for the default qconfig mapping, should be
+         one of ["x86" (default), "fbgemm", "qnnpack", "onednn"]
+      * ``version`` (int) : the version for the default qconfig mapping
+    """
+    # TODO: add assert for backend choices
+    return _get_default_qconfig_mapping(False, backend, version)
+
+
+def get_default_qat_qconfig_mapping(backend="x86", version=1) -> QConfigMapping:
+    """
+    Return the default QConfigMapping for quantization aware training.
+
+    Args:
+      * ``backend`` (str) : the quantization backend for the default qconfig mapping, should be
+         one of ["x86" (default), "fbgemm", "qnnpack", "onednn"]
+      * ``version`` (int) : the version for the default qconfig mapping
+    """
+    return _get_default_qconfig_mapping(True, backend, version)
+
+
+def _get_symmetric_qnnpack_qconfig_mapping() -> QConfigMapping:
+    """
+    Return a QConfigMapping that uses `torch.ao.quantization.default_symmetric_qnnpack_qconfig`
+    as the default QConfig.
+    """
+    default_qconfig = default_symmetric_qnnpack_qconfig
+    return _get_default_qconfig_mapping_with_default_qconfig(
+        False, "qnnpack", default_qconfig
+    )
+
+
+def _get_symmetric_qnnpack_qat_qconfig_mapping() -> QConfigMapping:
+    """
+    Return a QConfigMapping that uses `torch.ao.quantization.default_symmetric_qnnpack_qat_qconfig`
+    as the default QConfig.
+    """
+    default_qconfig = default_symmetric_qnnpack_qat_qconfig
+    return _get_default_qconfig_mapping_with_default_qconfig(
+        True, "qnnpack", default_qconfig
+    )
+
+
+def _get_default_qconfig_mapping_with_default_qconfig(
+    is_qat: bool,
+    backend: str,
+    default_qconfig: QConfig,
+) -> QConfigMapping:
+    """
+    Return a QConfigMapping that uses the provided qconfig as the default QConfig.
+    """
+    if is_qat:
+        qconfig_mapping = get_default_qat_qconfig_mapping(backend)
+    else:
+        qconfig_mapping = get_default_qconfig_mapping(backend)
+    qconfig_mapping.set_global(default_qconfig)
+    for pattern in qconfig_mapping.object_type_qconfigs:
+        if pattern not in _FIXED_QPARAMS_OP_TO_OBSERVER:
+            qconfig_mapping.set_object_type(pattern, default_qconfig)
+    return qconfig_mapping
+
+
+_QCONFIG_STYLE_ORDER: list[str] = [
+    "global_qconfig",
+    "object_type_qconfigs",
+    "module_name_regex_qconfigs",
+    "module_name_qconfigs",
+    "module_name_object_type_order_qconfigs",
+]
+
+
+class QConfigMapping:
+    """
+    Mapping from model ops to :class:`torch.ao.quantization.QConfig` s.
+
+    The user can specify QConfigs using the following methods (in increasing match priority):
+
+        ``set_global`` : sets the global (default) QConfig
+
+        ``set_object_type`` : sets the QConfig for a given module type, function, or method name
+
+        ``set_module_name_regex`` : sets the QConfig for modules matching the given regex string
+
+        ``set_module_name`` : sets the QConfig for modules matching the given module name
+
+        ``set_module_name_object_type_order`` : sets the QConfig for modules matching a combination
+        of the given module name, object type, and the index at which the module appears
+
+    Example usage::
+
+        qconfig_mapping = QConfigMapping()
+            .set_global(global_qconfig)
+            .set_object_type(torch.nn.Linear, qconfig1)
+            .set_object_type(torch.nn.ReLU, qconfig1)
+            .set_module_name_regex("foo.*bar.*conv[0-9]+", qconfig1)
+            .set_module_name_regex("foo.*", qconfig2)
+            .set_module_name("module1", qconfig1)
+            .set_module_name("module2", qconfig2)
+            .set_module_name_object_type_order("foo.bar", torch.nn.functional.linear, 0, qconfig3)
+
+    """
+
+    def __init__(self) -> None:
+        # In increasing match priority:
+        self.global_qconfig: QConfigAny = None
+        self.object_type_qconfigs: OrderedDict[Callable | str, QConfigAny] = (
+            OrderedDict()
+        )
+        self.module_name_regex_qconfigs: OrderedDict[str, QConfigAny] = OrderedDict()
+        self.module_name_qconfigs: OrderedDict[str, QConfigAny] = OrderedDict()
+        self.module_name_object_type_order_qconfigs: OrderedDict[
+            tuple[str, Callable, int], QConfigAny
+        ] = OrderedDict()
+
+    def set_global(self, global_qconfig: QConfigAny) -> QConfigMapping:
+        """
+        Set the global (default) QConfig.
+        """
+        self.global_qconfig = global_qconfig
+        return self
+
+    def set_object_type(
+        self, object_type: Callable | str, qconfig: QConfigAny
+    ) -> QConfigMapping:
+        """
+        Set the QConfig for a given module type, function, or method name.
+        If the QConfig for an existing object type was already set, the new QConfig will override the old one.
+        """
+        self.object_type_qconfigs[object_type] = qconfig
+        return self
+
+    def set_module_name_regex(
+        self, module_name_regex: str, qconfig: QConfigAny
+    ) -> QConfigMapping:
+        """
+        Set the QConfig for modules matching the given regex string.
+
+        Regexes will be matched in the order in which they are registered through this method.
+        Thus, the caller should register more specific patterns first, e.g.::
+
+            qconfig_mapping = QConfigMapping()
+                .set_module_name_regex("foo.*bar.*conv[0-9]+", qconfig1)
+                .set_module_name_regex("foo.*bar.*", qconfig2)
+                .set_module_name_regex("foo.*", qconfig3)
+
+        In this example, "foo.bar.conv0" would match qconfig1, "foo.bar.linear" would match qconfig2,
+        and "foo.baz.relu" would match qconfig3.
+
+        If the QConfig for an existing module name regex was already set, the new QConfig will override the
+        old one while preserving the order in which the regexes were originally registered.
+        """
+        self.module_name_regex_qconfigs[module_name_regex] = qconfig
+        return self
+
+    def set_module_name(self, module_name: str, qconfig: QConfigAny) -> QConfigMapping:
+        """
+        Set the QConfig for modules matching the given module name.
+        If the QConfig for an existing module name was already set, the new QConfig will override the old one.
+        """
+        self.module_name_qconfigs[module_name] = qconfig
+        return self
+
+    def set_module_name_object_type_order(
+        self, module_name: str, object_type: Callable, index: int, qconfig: QConfigAny
+    ) -> QConfigMapping:
+        """
+        Set the QConfig for modules matching a combination of the given module name, object type,
+        and the index at which the module appears.
+
+        If the QConfig for an existing (module name, object type, index)  was already set, the new QConfig
+        will override the old one.
+        """
+        self.module_name_object_type_order_qconfigs[
+            (module_name, object_type, index)
+        ] = qconfig
+        return self
+
+    def __repr__(self) -> str:
+        output = self.__class__.__name__ + " ("
+        for style_name in _QCONFIG_STYLE_ORDER:
+            output += f"\n {style_name}"
+            qconfigs = getattr(self, style_name)
+            if isinstance(qconfigs, OrderedDict) and len(qconfigs) > 0:
+                for key, qconfig in qconfigs.items():
+                    output += f"\n  {key}: {qconfig}"
+            else:
+                output += f"\n  {qconfigs}"
+        return output + "\n)"
+
+    # TODO: remove this
+    def to_dict(self) -> dict[str, Any]:
+        """
+        Convert this ``QConfigMapping`` to a dictionary with the following keys:
+
+            "" (for global QConfig)
+
+            "object_type"
+
+            "module_name_regex"
+
+            "module_name"
+
+            "module_name_object_type_order"
+
+        The values of this dictionary are lists of tuples.
+        """
+        return {
+            _GLOBAL_DICT_KEY: self.global_qconfig,
+            _OBJECT_TYPE_DICT_KEY: list(self.object_type_qconfigs.items()),
+            _MODULE_NAME_REGEX_DICT_KEY: list(self.module_name_regex_qconfigs.items()),
+            _MODULE_NAME_DICT_KEY: list(self.module_name_qconfigs.items()),
+            _MODULE_NAME_OBJECT_TYPE_ORDER_DICT_KEY: [
+                (*k, v) for k, v in self.module_name_object_type_order_qconfigs.items()
+            ],
+        }
+
+    # TODO: remove this
+    @classmethod
+    def from_dict(cls, qconfig_dict: dict[str, Any]) -> QConfigMapping:
+        """
+        Create a ``QConfigMapping`` from a dictionary with the following keys (all optional):
+
+            "" (for global QConfig)
+
+            "object_type"
+
+            "module_name_regex"
+
+            "module_name"
+
+            "module_name_object_type_order"
+
+        The values of this dictionary are expected to be lists of tuples.
+        """
+        conf = cls()
+        if _GLOBAL_DICT_KEY in qconfig_dict:
+            conf.set_global(qconfig_dict[_GLOBAL_DICT_KEY])
+        for object_type, qconfig in qconfig_dict.get(_OBJECT_TYPE_DICT_KEY, []):
+            conf.set_object_type(object_type, qconfig)
+        for module_name_regex, qconfig in qconfig_dict.get(
+            _MODULE_NAME_REGEX_DICT_KEY, []
+        ):
+            conf.set_module_name_regex(module_name_regex, qconfig)
+        for module_name, qconfig in qconfig_dict.get(_MODULE_NAME_DICT_KEY, []):
+            conf.set_module_name(module_name, qconfig)
+        for module_name, object_type, index, qconfig in qconfig_dict.get(
+            _MODULE_NAME_OBJECT_TYPE_ORDER_DICT_KEY, []
+        ):
+            conf.set_module_name_object_type_order(
+                module_name, object_type, index, qconfig
+            )
+        return conf
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quant_type.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quant_type.py
new file mode 100644
index 0000000000000000000000000000000000000000..18488d7f9ccba604ca8f1df7ea0ef4a88546d63e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quant_type.py
@@ -0,0 +1,35 @@
+import enum
+
+
+__all__ = [
+    "QuantType",
+]
+
+
+# Quantization type (dynamic quantization, static quantization).
+# Should match the c++ enum in quantization_type.h
+class QuantType(enum.IntEnum):
+    DYNAMIC = 0
+    STATIC = 1
+    QAT = 2
+    WEIGHT_ONLY = 3
+
+
+_quant_type_to_str = {
+    QuantType.STATIC: "static",
+    QuantType.DYNAMIC: "dynamic",
+    QuantType.QAT: "qat",
+    QuantType.WEIGHT_ONLY: "weight_only",
+}
+
+
+# TODO: make this private
+def _get_quant_type_to_str(quant_type: QuantType) -> str:
+    return _quant_type_to_str[quant_type]
+
+
+def _quant_type_from_str(name: str) -> QuantType:
+    for quant_type, s in _quant_type_to_str.items():
+        if name == s:
+            return quant_type
+    raise ValueError(f"Unknown QuantType name '{name}'")
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantization_mappings.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantization_mappings.py
new file mode 100644
index 0000000000000000000000000000000000000000..647ed5a4d4f3946626ef360a7a45541719136006
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantization_mappings.py
@@ -0,0 +1,369 @@
+import copy
+from collections.abc import Callable
+from typing import Any
+
+import torch
+import torch.ao.nn as ao_nn
+import torch.ao.nn.intrinsic as nni
+import torch.ao.nn.intrinsic.qat as nniqat
+import torch.ao.nn.intrinsic.quantized as nniq
+import torch.ao.nn.intrinsic.quantized.dynamic as nniqd
+import torch.ao.nn.qat as nnqat
+import torch.ao.nn.qat.dynamic as nnqatd
+import torch.ao.nn.quantized as nnq
+import torch.ao.nn.quantized.dynamic as nnqd
+import torch.ao.nn.quantized.reference as nnqr
+
+# Because `torch.ao.nn` uses lazy imports, we need to make
+# sure we import the contents explicitly here.
+import torch.ao.nn.sparse
+import torch.nn.functional as F
+from torch import nn
+from torch.ao.quantization.fake_quantize import (
+    default_fixed_qparams_range_0to1_fake_quant,
+    default_fixed_qparams_range_neg1to1_fake_quant,
+)
+from torch.ao.quantization.stubs import DeQuantStub, QuantStub
+from torch.ao.quantization.utils import get_combined_dict
+from torch.nn.utils.parametrize import type_before_parametrizations
+
+
+__all__ = [
+    "DEFAULT_REFERENCE_STATIC_QUANT_MODULE_MAPPINGS",
+    "DEFAULT_STATIC_QUANT_MODULE_MAPPINGS",
+    "DEFAULT_QAT_MODULE_MAPPINGS",
+    "DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS",
+    "DEFAULT_FLOAT_TO_QUANTIZED_OPERATOR_MAPPINGS",
+    "DEFAULT_MODULE_TO_ACT_POST_PROCESS",
+    "DEFAULT_STATIC_SPARSE_QUANT_MODULE_MAPPINGS",
+    "DEFAULT_DYNAMIC_SPARSE_QUANT_MODULE_MAPPINGS",
+    "no_observer_set",
+    "get_default_static_quant_module_mappings",
+    "get_default_static_quant_reference_module_mappings",
+    "get_embedding_static_quant_module_mappings",
+    "get_default_static_sparse_quant_module_mappings",
+    "get_static_quant_module_class",
+    "get_dynamic_quant_module_class",
+    "get_default_qat_module_mappings",
+    "get_embedding_qat_module_mappings",
+    "get_default_dynamic_quant_module_mappings",
+    "get_default_dynamic_sparse_quant_module_mappings",
+    "get_default_qconfig_propagation_list",
+    "get_default_compare_output_module_list",
+    "get_default_float_to_quantized_operator_mappings",
+    "get_quantized_operator",
+]
+
+# Default map for swapping float module to reference quantized modules
+DEFAULT_REFERENCE_STATIC_QUANT_MODULE_MAPPINGS: dict[Callable, Any] = {
+    QuantStub: nnq.Quantize,
+    DeQuantStub: nnq.DeQuantize,
+    nn.Linear: nnqr.Linear,
+    nn.Conv1d: nnqr.Conv1d,
+    nn.Conv2d: nnqr.Conv2d,
+    nn.Conv3d: nnqr.Conv3d,
+    nn.ConvTranspose1d: nnqr.ConvTranspose1d,
+    nn.ConvTranspose2d: nnqr.ConvTranspose2d,
+    nn.ConvTranspose3d: nnqr.ConvTranspose3d,
+    nn.Embedding: nnqr.Embedding,
+    nn.EmbeddingBag: nnqr.EmbeddingBag,
+    nn.GRUCell: nnqr.GRUCell,
+    nn.LSTMCell: nnqr.LSTMCell,
+    nn.RNNCell: nnqr.RNNCell,
+    nn.LSTM: nnqr.LSTM,
+}
+
+# Default map for swapping float module to quantized ones
+DEFAULT_STATIC_QUANT_MODULE_MAPPINGS: dict[Callable, Any] = {
+    QuantStub: nnq.Quantize,
+    DeQuantStub: nnq.DeQuantize,
+    nn.BatchNorm2d: nnq.BatchNorm2d,
+    nn.BatchNorm3d: nnq.BatchNorm3d,
+    nn.Dropout: nnq.Dropout,
+    nn.Conv1d: nnq.Conv1d,
+    nn.Conv2d: nnq.Conv2d,
+    nn.Conv3d: nnq.Conv3d,
+    nn.ConvTranspose1d: nnq.ConvTranspose1d,
+    nn.ConvTranspose2d: nnq.ConvTranspose2d,
+    nn.ConvTranspose3d: nnq.ConvTranspose3d,
+    nn.ELU: nnq.ELU,
+    nn.Embedding: nnq.Embedding,
+    nn.EmbeddingBag: nnq.EmbeddingBag,
+    nn.GroupNorm: nnq.GroupNorm,
+    nn.Hardswish: nnq.Hardswish,
+    nn.InstanceNorm1d: nnq.InstanceNorm1d,
+    nn.InstanceNorm2d: nnq.InstanceNorm2d,
+    nn.InstanceNorm3d: nnq.InstanceNorm3d,
+    nn.LayerNorm: nnq.LayerNorm,
+    nn.LeakyReLU: nnq.LeakyReLU,
+    nn.modules.linear.NonDynamicallyQuantizableLinear: nnq.Linear,
+    nn.Linear: nnq.Linear,
+    nn.ReLU6: nnq.ReLU6,
+    nn.PReLU: nnq.PReLU,
+    # Wrapper Modules:
+    nnq.FloatFunctional: nnq.QFunctional,
+    # Intrinsic modules:
+    nni.BNReLU2d: nniq.BNReLU2d,
+    nni.BNReLU3d: nniq.BNReLU3d,
+    nni.ConvReLU1d: nniq.ConvReLU1d,
+    nni.ConvReLU2d: nniq.ConvReLU2d,
+    nni.ConvReLU3d: nniq.ConvReLU3d,
+    nni.ConvAdd2d: nniq.ConvAdd2d,
+    nni.ConvAddReLU2d: nniq.ConvAddReLU2d,
+    nni.LinearReLU: nniq.LinearReLU,
+    nni.LinearLeakyReLU: nniq.LinearLeakyReLU,
+    nni.LinearTanh: nniq.LinearTanh,
+    nniqat.ConvBn1d: nnq.Conv1d,
+    nniqat.ConvBn2d: nnq.Conv2d,
+    nniqat.ConvBn3d: nnq.Conv3d,
+    nniqat.ConvBnReLU1d: nniq.ConvReLU1d,
+    nniqat.ConvBnReLU2d: nniq.ConvReLU2d,
+    nniqat.ConvBnReLU3d: nniq.ConvReLU3d,
+    nniqat.ConvReLU2d: nniq.ConvReLU2d,
+    nniqat.ConvReLU3d: nniq.ConvReLU3d,
+    nniqat.LinearReLU: nniq.LinearReLU,
+    nniqat.LinearBn1d: nnq.Linear,
+    # QAT modules:
+    nnqat.Linear: nnq.Linear,
+    nnqat.Conv2d: nnq.Conv2d,
+    nnqat.Conv3d: nnq.Conv3d,
+}
+
+# Default map for swapping float module to qat modules
+DEFAULT_QAT_MODULE_MAPPINGS: dict[Callable, Any] = {
+    nn.Conv2d: nnqat.Conv2d,
+    nn.Conv3d: nnqat.Conv3d,
+    nn.Linear: nnqat.Linear,
+    nn.modules.linear.NonDynamicallyQuantizableLinear: nnqat.Linear,
+    # Intrinsic modules:
+    nni.ConvBn1d: nniqat.ConvBn1d,
+    nni.ConvBn2d: nniqat.ConvBn2d,
+    nni.ConvBn3d: nniqat.ConvBn3d,
+    nni.ConvBnReLU1d: nniqat.ConvBnReLU1d,
+    nni.ConvBnReLU2d: nniqat.ConvBnReLU2d,
+    nni.ConvBnReLU3d: nniqat.ConvBnReLU3d,
+    nni.ConvReLU2d: nniqat.ConvReLU2d,
+    nni.ConvReLU3d: nniqat.ConvReLU3d,
+    nni.LinearReLU: nniqat.LinearReLU,
+    nni.LinearBn1d: nniqat.LinearBn1d,
+}
+
+# Default map for swapping dynamic modules
+DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS: dict[Callable, Any] = {
+    nn.GRUCell: nnqd.GRUCell,
+    nn.Linear: nnqd.Linear,
+    nnqatd.Linear: nnqd.Linear,
+    nn.modules.linear.NonDynamicallyQuantizableLinear: nnqd.Linear,
+    nn.LSTM: nnqd.LSTM,
+    nn.GRU: nnqd.GRU,
+    nn.LSTMCell: nnqd.LSTMCell,
+    nn.RNNCell: nnqd.RNNCell,
+    nni.LinearReLU: nniqd.LinearReLU,
+    nn.EmbeddingBag: nnq.EmbeddingBag,
+    nn.Embedding: nnq.Embedding,
+    # Don't want to enable these by default because the numerical
+    # accuracy is poor compared to other dynamic ops
+    # nn.Conv1d: nnqd.Conv1d,
+    # nn.Conv2d: nnqd.Conv2d,
+    # nn.Conv3d: nnqd.Conv3d,
+    # nn.ConvTranspose1d: nnqd.ConvTranspose1d,
+    # nn.ConvTranspose2d: nnqd.ConvTranspose2d,
+    # nn.ConvTranspose3d: nnqd.ConvTranspose3d,
+}
+
+# Allowlist for propagating the qconfig
+_INCLUDE_QCONFIG_PROPAGATE_LIST: set[Callable] = {
+    nn.Sequential,
+}
+
+# Default mapping from floating point function or torch ops to quantized ops
+# TODO: merge with default static mapping
+DEFAULT_FLOAT_TO_QUANTIZED_OPERATOR_MAPPINGS: dict[Callable | str, Callable] = {
+    F.elu: torch.ops.quantized.elu,
+    F.hardswish: torch.ops.quantized.hardswish,
+    F.instance_norm: torch.ops.quantized.instance_norm,
+    F.layer_norm: torch.ops.quantized.layer_norm,
+    F.leaky_relu: torch.ops.quantized.leaky_relu,
+    F.dropout: torch.ops.quantized.dropout,
+}
+
+# mapping from module to output activation post process class
+DEFAULT_MODULE_TO_ACT_POST_PROCESS: dict[Callable, Callable] = {
+    nn.Hardsigmoid: default_fixed_qparams_range_0to1_fake_quant,
+    nn.Sigmoid: default_fixed_qparams_range_0to1_fake_quant,
+    nn.Softmax: default_fixed_qparams_range_0to1_fake_quant,
+    nn.Tanh: default_fixed_qparams_range_neg1to1_fake_quant,
+}
+
+# Default map for swapping float module to static sparse quantized ones
+DEFAULT_STATIC_SPARSE_QUANT_MODULE_MAPPINGS: dict[Callable, Any] = {
+    nn.Linear: ao_nn.sparse.quantized.Linear
+}
+
+# Default map for swapping float module to dynamic sparse quantized ones
+DEFAULT_DYNAMIC_SPARSE_QUANT_MODULE_MAPPINGS: dict[Callable, Any] = {
+    nn.Linear: ao_nn.sparse.quantized.dynamic.Linear
+}
+
+
+def no_observer_set() -> set[Any]:
+    r"""These modules cannot have observers inserted by default."""
+    no_observers = {nn.quantizable.LSTM, nn.quantizable.MultiheadAttention}
+    return no_observers
+
+
+def get_default_static_quant_module_mappings() -> dict[Callable, Any]:
+    """Get module mapping for post training static quantization"""
+    return copy.deepcopy(DEFAULT_STATIC_QUANT_MODULE_MAPPINGS)
+
+
+def get_default_static_quant_reference_module_mappings() -> dict[Callable, Any]:
+    """Get reference module mapping for post training static quantization"""
+    return copy.deepcopy(DEFAULT_REFERENCE_STATIC_QUANT_MODULE_MAPPINGS)
+
+
+def get_embedding_static_quant_module_mappings() -> dict[Callable, Any]:
+    """Get module mapping, including mapping for embedding QAT"""
+    mapping = copy.deepcopy(DEFAULT_STATIC_QUANT_MODULE_MAPPINGS)
+    mapping[nnqat.EmbeddingBag] = nnq.EmbeddingBag
+    mapping[nnqat.Embedding] = nnq.Embedding
+    return mapping
+
+
+def get_default_static_sparse_quant_module_mappings() -> dict[Callable, Any]:
+    """Get module mapping for post training static sparse quantization"""
+    return copy.deepcopy(DEFAULT_STATIC_SPARSE_QUANT_MODULE_MAPPINGS)
+
+
+def get_static_quant_module_class(
+    float_module_class: Callable,
+    additional_static_quant_mapping: dict[Callable, Any] | None = None,
+    is_reference: bool = False,
+) -> Any:
+    r"""n Get the statically quantized module class corresponding to
+    the floating point module class
+    """
+    if additional_static_quant_mapping is None:
+        additional_static_quant_mapping = {}
+    all_mappings = get_combined_dict(
+        DEFAULT_REFERENCE_STATIC_QUANT_MODULE_MAPPINGS
+        if is_reference
+        else DEFAULT_STATIC_QUANT_MODULE_MAPPINGS,
+        additional_static_quant_mapping,
+    )
+    static_quant_module_class = all_mappings.get(float_module_class, None)
+    if static_quant_module_class is None:
+        raise AssertionError(
+            f"Floating point module class {str(float_module_class)}"
+            + " does not have a corresponding quantized module class"
+        )
+    return copy.deepcopy(static_quant_module_class)
+
+
+def get_dynamic_quant_module_class(
+    float_module_class: Callable,
+    additional_dynamic_quant_mapping: dict[Callable, Any] | None = None,
+) -> Any:
+    r"""n Get the dynamically quantized module class corresponding to
+    the floating point module class
+    """
+    if additional_dynamic_quant_mapping is None:
+        additional_dynamic_quant_mapping = {}
+    all_mappings = get_combined_dict(
+        DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS, additional_dynamic_quant_mapping
+    )
+    dynamic_quant_module_class = all_mappings.get(float_module_class, None)
+    if dynamic_quant_module_class is None:
+        raise AssertionError(
+            f"Floating point module class {str(float_module_class)}"
+            + " does not have a corresponding quantized module class"
+        )
+    return copy.deepcopy(dynamic_quant_module_class)
+
+
+def get_default_qat_module_mappings() -> dict[Callable, Any]:
+    """Get default module mapping for quantization aware training"""
+    return copy.deepcopy(DEFAULT_QAT_MODULE_MAPPINGS)
+
+
+def get_embedding_qat_module_mappings() -> dict[Callable, Any]:
+    """Get module mapping for quantization aware training
+    This is includes default values in addition to
+    enabling qat for embeddings.
+    """
+    mapping = copy.deepcopy(DEFAULT_QAT_MODULE_MAPPINGS)
+    mapping[nn.EmbeddingBag] = nnqat.EmbeddingBag
+    mapping[nn.Embedding] = nnqat.Embedding
+    return mapping
+
+
+def get_default_dynamic_quant_module_mappings() -> dict[Callable, Any]:
+    """Get module mapping for post training dynamic quantization"""
+    return DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS
+
+
+def get_default_dynamic_sparse_quant_module_mappings() -> dict[Callable, Any]:
+    """Get module mapping for post training dynamic sparse quantization"""
+    return DEFAULT_DYNAMIC_SPARSE_QUANT_MODULE_MAPPINGS
+
+
+def get_default_qconfig_propagation_list() -> set[Callable]:
+    """Get the default list of module types that we'll attach qconfig
+    attribute to in prepare
+    """
+    QCONFIG_PROPAGATE_MODULE_CLASS_LIST = (
+        set(DEFAULT_STATIC_QUANT_MODULE_MAPPINGS.keys())
+        | set(DEFAULT_QAT_MODULE_MAPPINGS.keys())
+        | set(DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS.keys())
+        | _INCLUDE_QCONFIG_PROPAGATE_LIST
+    )
+    return copy.deepcopy(QCONFIG_PROPAGATE_MODULE_CLASS_LIST)
+
+
+def get_default_compare_output_module_list() -> set[Callable]:
+    """Get list of module class types that we will record output
+    in numeric suite
+    """
+    NUMERIC_SUITE_COMPARE_MODEL_OUTPUT_MODULE_LIST = (
+        set(DEFAULT_STATIC_QUANT_MODULE_MAPPINGS.values())
+        | set(DEFAULT_QAT_MODULE_MAPPINGS.values())
+        | set(DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS.values())
+        | set(DEFAULT_STATIC_QUANT_MODULE_MAPPINGS.keys())
+        | set(DEFAULT_QAT_MODULE_MAPPINGS.keys())
+        | set(DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS.keys())
+        | _INCLUDE_QCONFIG_PROPAGATE_LIST
+    )
+    return copy.deepcopy(NUMERIC_SUITE_COMPARE_MODEL_OUTPUT_MODULE_LIST)
+
+
+def get_default_float_to_quantized_operator_mappings() -> dict[
+    Callable | str, Callable
+]:
+    return copy.deepcopy(DEFAULT_FLOAT_TO_QUANTIZED_OPERATOR_MAPPINGS)
+
+
+# TODO: merge with get_static_quant_module_class
+def get_quantized_operator(float_op: Callable | str) -> Callable:
+    """Get the quantized operator corresponding to the float operator"""
+    quantized_op = DEFAULT_FLOAT_TO_QUANTIZED_OPERATOR_MAPPINGS.get(float_op)
+    if quantized_op is None:
+        raise AssertionError(
+            f"Operator {str(float_op)} does not have corresponding quantized op"
+        )
+    return quantized_op
+
+
+def _get_special_act_post_process(module: torch.nn.Module) -> Callable | None:
+    r"""Get the special activation post process for `module`, this has
+    higher priority than the activation post process in `qconfig`
+    e.g.
+    input: torch.nn.Sigmoid
+    output: default_affine_fixed_qparam_fake_quant
+    """
+    return DEFAULT_MODULE_TO_ACT_POST_PROCESS.get(
+        type_before_parametrizations(module), None
+    )
+
+
+def _has_special_act_post_process(module: torch.nn.Module) -> bool:
+    return module.training and type(module) in DEFAULT_MODULE_TO_ACT_POST_PROCESS
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantize.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantize.py
new file mode 100644
index 0000000000000000000000000000000000000000..e71dd24fda745d7f23f671eedaa1ff43df147a9a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantize.py
@@ -0,0 +1,829 @@
+# mypy: allow-untyped-defs
+import copy
+import inspect
+import itertools
+import typing_extensions
+import warnings
+
+import torch
+import torch.ao.nn.quantized as nnq
+import torch.nn as nn
+from torch.ao.nn.intrinsic import _FusedModule
+from torch.ao.quantization.observer import _is_activation_post_process
+from torch.ao.quantization.qconfig import (
+    _activation_is_memoryless,
+    _add_module_to_qconfig_obs_ctr,
+    default_dynamic_qconfig,
+    float16_dynamic_qconfig,
+    float_qparams_weight_only_qconfig,
+    float_qparams_weight_only_qconfig_4bit,
+)
+from torch.ao.quantization.quantization_mappings import (
+    _get_special_act_post_process,
+    _has_special_act_post_process,
+    get_default_dynamic_quant_module_mappings,
+    get_default_qat_module_mappings,
+    get_default_qconfig_propagation_list,
+    get_default_static_quant_module_mappings,
+    get_default_static_quant_reference_module_mappings,
+    no_observer_set,
+)
+from torch.ao.quantization.stubs import DeQuantStub, QuantWrapper
+from torch.nn.utils.parametrize import type_before_parametrizations
+
+from .utils import (
+    DEPRECATION_WARNING,
+    get_qparam_dict,
+    has_no_children_ignoring_parametrizations,
+)
+
+
+__all__ = [
+    "get_default_custom_config_dict",
+    "propagate_qconfig_",
+    "add_quant_dequant",
+    "prepare",
+    "quantize",
+    "quantize_dynamic",
+    "prepare_qat",
+    "quantize_qat",
+    "convert",
+    "swap_module",
+]
+
+
+# TODO remove this once BC is no longer required to avoid a SEV
+is_activation_post_process = _is_activation_post_process
+
+
+_DEFAULT_CUSTOM_CONFIG_DICT = {
+    "float_to_observed_custom_module_class": {
+        nn.LSTM: nn.quantizable.LSTM,
+        nn.MultiheadAttention: nn.quantizable.MultiheadAttention,
+    },
+    "observed_to_quantized_custom_module_class": {
+        nn.quantizable.LSTM: nn.quantized.LSTM,
+        nn.quantizable.MultiheadAttention: nn.quantized.MultiheadAttention,
+    },
+}
+
+
+def get_default_custom_config_dict():
+    r"""Defines the default custom config dict."""
+    return _DEFAULT_CUSTOM_CONFIG_DICT
+
+
+def _propagate_qconfig_helper(
+    module,
+    qconfig_dict,
+    qconfig_parent=None,
+    prefix="",
+    prepare_custom_config_dict=None,
+):
+    r"""This is a helper function for `propagate_qconfig_`
+
+    Args:
+        module: input module
+        qconfig_dict: dictionary that maps from name of submodule to quantization
+                     configuration
+        qconfig_parent: quantization config of parent module, we will fallback to
+                       this config when there is no specified config for current
+                       module
+        prefix: corresponding prefix of the current module, used as key in
+                qconfig_dict
+        prepare_custom_config_dict: dictionary for custom handling of modules
+                                    see docs for :func:`~torch.ao.quantization.prepare_fx`
+
+    Return:
+        None, module is modified inplace with qconfig attached
+    """
+
+    module_qconfig = qconfig_dict.get(
+        type_before_parametrizations(module), qconfig_parent
+    )
+    module_qconfig = qconfig_dict.get(prefix, module_qconfig)
+    module_qconfig = getattr(module, "qconfig", module_qconfig)
+
+    torch.ao.quantization.qconfig._assert_valid_qconfig(module_qconfig, module)
+
+    qconfig_with_device_check = _add_module_to_qconfig_obs_ctr(module_qconfig, module)
+    module.qconfig = qconfig_with_device_check
+
+    for name, child in module.named_children():
+        module_prefix = prefix + "." + name if prefix else name
+        #  do no not propagate qconfig to child if child is non traceable
+        if prepare_custom_config_dict is None or not (
+            name in prepare_custom_config_dict.get("non_traceable_module_name", [])
+            or type(child)
+            in prepare_custom_config_dict.get("non_traceable_module_class", [])
+        ):
+            _propagate_qconfig_helper(
+                child, qconfig_dict, qconfig_with_device_check, module_prefix
+            )
+
+
+def propagate_qconfig_(module, qconfig_dict=None, prepare_custom_config_dict=None):
+    r"""Propagate qconfig through the module hierarchy and assign `qconfig`
+    attribute on each leaf module
+
+    Args:
+        module: input module
+        qconfig_dict: dictionary that maps from name or type of submodule to
+            quantization configuration, qconfig applies to all submodules of a
+            given module unless qconfig for the submodules are specified (when
+            the submodule already has qconfig attribute)
+        prepare_custom_config_dict: dictionary for custom handling of modules
+            see docs for :func:`~torch.ao.quantization.prepare_fx`
+
+    Return:
+        None, module is modified inplace with qconfig attached
+    """
+    if qconfig_dict is None:
+        qconfig_dict = {}
+    if prepare_custom_config_dict is None:
+        prepare_custom_config_dict = {}
+    _propagate_qconfig_helper(
+        module, qconfig_dict, prepare_custom_config_dict=prepare_custom_config_dict
+    )
+
+
+def _observer_forward_hook(self, input, output):
+    r"""Forward hook that calls observer on the output"""
+    return self.activation_post_process(output)
+
+
+def _observer_forward_pre_hook(self, input):
+    r"""Forward pre hook that calls observer on the output"""
+    return self.activation_post_process(input[0])
+
+
+def _register_activation_post_process_hook(module, pre_hook=False):
+    if not hasattr(module, "activation_post_process"):
+        raise AssertionError(
+            "Expect activation_post_process attribute already attached to the module"
+        )
+    if pre_hook:
+        module.register_forward_pre_hook(_observer_forward_pre_hook, prepend=True)
+    else:
+        module.register_forward_hook(_observer_forward_hook, prepend=True)
+
+
+def _add_observer_(
+    module,
+    qconfig_propagation_list=None,
+    non_leaf_module_list=None,
+    device=None,
+    custom_module_class_mapping=None,
+):
+    r"""Add observer for the leaf child of the module.
+
+    This function insert observer module to all leaf child module that
+    has a valid qconfig attribute.
+
+    Args:
+        module: input module with qconfig attributes for all the leaf modules that we want to quantize
+        qconfig_propagation_list: a list of quantizable modules that will have observers added to them
+            if they are leaf nodes
+        device: parent device, if any
+        non_leaf_module_list: list of non-leaf modules we want to add observer
+
+    Return:
+        None, module is modified inplace with added observer modules and forward_hooks
+    """
+    if qconfig_propagation_list is None:
+        qconfig_propagation_list = get_default_qconfig_propagation_list()
+
+    if custom_module_class_mapping is None:
+        custom_module_class_mapping = {}
+
+    # respect device affinity when adding observers
+    if device is None:
+        devices = _get_unique_devices_(module)
+        if len(devices) > 1:
+            raise AssertionError(
+                f"_add_observer_ only works with cpu or single-device CUDA modules, but got devices {devices}"
+            )
+        device = next(iter(devices)) if len(devices) > 0 else None
+
+    def get_activation_post_process(qconfig, device, special_act_post_process=None):
+        activation = (
+            qconfig.activation()
+            if special_act_post_process is None
+            else special_act_post_process()
+        )
+        if device is not None:
+            activation.to(device)
+        return activation
+
+    def needs_observation(m):
+        return hasattr(m, "qconfig") and m.qconfig is not None
+
+    def insert_activation_post_process(m, special_act_post_process=None):
+        """Adds an activation post process module and register
+        a pre or post hook that calls the module
+        """
+        # We don't insert observer/fake_quantize for DeQuantStub
+        if needs_observation(m) and not isinstance(m, DeQuantStub):
+            # observer and hook will be gone after we swap the module
+            m.add_module(
+                "activation_post_process",
+                get_activation_post_process(
+                    m.qconfig, device, special_act_post_process
+                ),
+            )
+            # Register observer as the first entry in the hook list
+            # All post forward hooks are preserved and will be executed after the observer before convert
+            _register_activation_post_process_hook(
+                m, pre_hook=_activation_is_memoryless(m.qconfig)
+            )
+
+    for name, child in module.named_children():
+        # TODO remove Dropout special after codebase stable
+        if type_before_parametrizations(child) is nn.Dropout:
+            continue
+        elif issubclass(
+            type_before_parametrizations(child), (nnq.FloatFunctional, nnq.QFunctional)
+        ):
+            if needs_observation(child):
+                if not hasattr(child, "activation_post_process"):
+                    raise AssertionError(
+                        f"functional class {type_before_parametrizations(child)} has no pre-defined `activation_post_process`"
+                    )
+                child.activation_post_process = get_activation_post_process(
+                    child.qconfig, device
+                )
+        elif isinstance(child, _FusedModule):
+            # activation_post_process are now added directly to nn.Sequential/_FusedModule
+            if needs_observation(child):
+                insert_activation_post_process(child)
+        elif (
+            non_leaf_module_list is not None
+            and type_before_parametrizations(child) in non_leaf_module_list
+        ):
+            if needs_observation(child):
+                insert_activation_post_process(child)
+        elif _has_special_act_post_process(child):
+            special_act_post_process = _get_special_act_post_process(child)
+            insert_activation_post_process(child, special_act_post_process)
+        elif (
+            needs_observation(child)
+            and type_before_parametrizations(child) in custom_module_class_mapping
+        ):
+            observed_class = custom_module_class_mapping[
+                type_before_parametrizations(child)
+            ]
+            observed_child = observed_class.from_float(child)
+            setattr(module, name, observed_child)
+            # TODO: These are the modules that cannot be observed
+            #       Once there are more, we should move them to a separate list
+            if not issubclass(observed_class, tuple(no_observer_set())):
+                insert_activation_post_process(observed_child)
+        else:
+            _add_observer_(
+                child,
+                qconfig_propagation_list,
+                non_leaf_module_list,
+                device,
+                custom_module_class_mapping,
+            )
+
+    # Insert observers only for leaf nodes, note that this observer is for
+    # the output of the module, for input QuantStub will observe them
+    if (
+        has_no_children_ignoring_parametrizations(module)
+        and not isinstance(module, torch.nn.Sequential)
+        and type_before_parametrizations(module) in qconfig_propagation_list
+    ):
+        insert_activation_post_process(module)
+    # This is a special case for AdaRound eager mode
+    # AdaRound contains weight_fake_quant to be propagated from API to convert
+    # leaf node check with a number of children looks naive assumption that blocks
+    # Adding an exception case for AdaRound
+    if (
+        hasattr(module, "weight_fake_quant")
+        and not isinstance(module, torch.nn.Sequential)
+        and type_before_parametrizations(module) in qconfig_propagation_list
+    ):
+        insert_activation_post_process(module)
+
+
+def _get_unique_devices_(module):
+    return {p.device for p in module.parameters() if p.device.type != "meta"} | {
+        p.device for p in module.buffers() if p.device.type != "meta"
+    }
+
+
+def add_quant_dequant(module):
+    r"""Wrap the leaf child module in QuantWrapper if it has a valid qconfig
+    Note that this function will modify the children of module inplace and it
+    can return a new module which wraps the input module as well.
+
+    Args:
+        module: input module with qconfig attributes for all the leaf modules
+        that we want to quantize
+
+    Return:
+        Either the inplace modified module with submodules wrapped in
+        `QuantWrapper` based on qconfig or a new `QuantWrapper` module which
+        wraps the input module, the latter case only happens when the input
+        module is a leaf module and we want to quantize it.
+    """
+    if (
+        has_no_children_ignoring_parametrizations(module)
+        and hasattr(module, "qconfig")
+        and module.qconfig
+    ):
+        return QuantWrapper(module)
+
+    for name, child in module.named_children():
+        module._modules[name] = add_quant_dequant(child)
+    return module
+
+
+@typing_extensions.deprecated(DEPRECATION_WARNING)
+def prepare(
+    model,
+    inplace=False,
+    allow_list=None,
+    observer_non_leaf_module_list=None,
+    prepare_custom_config_dict=None,
+):
+    r"""Prepares a copy of the model for quantization calibration or quantization-aware training.
+
+    Quantization configuration should be assigned preemptively
+    to individual submodules in `.qconfig` attribute.
+
+    The model will be attached with observer or fake quant modules, and qconfig
+    will be propagated.
+
+    Args:
+        `model`: input model to be modified in-place
+        `inplace`: carry out model transformations in-place, the original module is mutated
+        `allow_list`: list of quantizable modules
+        `observer_non_leaf_module_list`: list of non-leaf modules we want to add observer
+        `prepare_custom_config_dict`: customization configuration dictionary for prepare function
+
+    .. code-block:: python
+
+       # Example of prepare_custom_config_dict:
+       prepare_custom_config_dict = {
+           # user will manually define the corresponding observed
+           # module class which has a from_float class method that converts
+           # float custom module to observed custom module
+           "float_to_observed_custom_module_class": {CustomModule: ObservedCustomModule}
+       }
+
+    """
+    torch._C._log_api_usage_once("quantization_api.quantize.prepare")
+    if prepare_custom_config_dict is None:
+        prepare_custom_config_dict = get_default_custom_config_dict()
+    custom_module_class_mapping = prepare_custom_config_dict.get(
+        "float_to_observed_custom_module_class", {}
+    )
+
+    if not inplace:
+        model = copy.deepcopy(model)
+
+    # TODO: remove allow_list
+    qconfig_propagation_list = allow_list
+    if allow_list is None:
+        qconfig_propagation_list = get_default_qconfig_propagation_list()
+    propagate_qconfig_(model, qconfig_dict=None)
+
+    # sanity check common API misusage
+    if not any(hasattr(m, "qconfig") and m.qconfig for m in model.modules()):
+        warnings.warn(
+            "None of the submodule got qconfig applied. Make sure you "
+            "passed correct configuration through `qconfig_dict` or "
+            "by assigning the `.qconfig` attribute directly on submodules",
+            stacklevel=2,
+        )
+
+    _add_observer_(
+        model,
+        qconfig_propagation_list,
+        observer_non_leaf_module_list,
+        custom_module_class_mapping=custom_module_class_mapping,
+    )
+    return model
+
+
+def _remove_activation_post_process(module):
+    # TODO: maybe we should change activation_post_process to _activation_post_process
+    # to prevent it from being used by user
+    if hasattr(module, "activation_post_process") and _is_activation_post_process(
+        module.activation_post_process
+    ):
+        delattr(module, "activation_post_process")
+
+    # remove activation_post_process pre and post hooks
+    def remove_hooks(pre_hook=False):
+        hook_map = module._forward_pre_hooks if pre_hook else module._forward_hooks
+        observer_hook = (
+            _observer_forward_pre_hook if pre_hook else _observer_forward_hook
+        )
+        handle_ids_to_remove = set()
+        for handle_id, hook_fn in hook_map.items():
+            if hook_fn is observer_hook:
+                handle_ids_to_remove.add(handle_id)
+        for handle_id in handle_ids_to_remove:
+            hook_map.pop(handle_id)
+
+    remove_hooks(pre_hook=True)
+    remove_hooks(pre_hook=False)
+
+
+# TODO: rename to something more general
+def _remove_qconfig(module):
+    r"""Clean up the qconfig left in the module so that new qconfig can be
+    propagated.
+
+    Args:
+        module: module to be cleaned up
+    """
+    for child in module.children():
+        _remove_qconfig(child)
+
+    if hasattr(module, "qconfig"):
+        del module.qconfig
+
+    _remove_activation_post_process(module)
+
+
+@typing_extensions.deprecated(DEPRECATION_WARNING)
+def quantize(model, run_fn, run_args, mapping=None, inplace=False):
+    r"""Quantize the input float model with post training static quantization.
+
+    First it will prepare the model for calibration, then it calls
+    `run_fn` which will run the calibration step, after that we will
+    convert the model to a quantized model.
+
+    Args:
+        model: input float model
+        run_fn: a calibration function for calibrating the prepared model
+        run_args: positional arguments for `run_fn`
+        inplace: carry out model transformations in-place, the original module is mutated
+        mapping: correspondence between original module types and quantized counterparts
+
+    Return:
+        Quantized model.
+    """
+    torch._C._log_api_usage_once("quantization_api.quantize.quantize")
+    if mapping is None:
+        mapping = get_default_static_quant_module_mappings()
+    if not inplace:
+        model = copy.deepcopy(model)
+    model.eval()
+    prepare(model, inplace=True)
+    run_fn(model, *run_args)
+    convert(model, mapping, inplace=True)
+    return model
+
+
+@typing_extensions.deprecated(DEPRECATION_WARNING)
+def quantize_dynamic(
+    model, qconfig_spec=None, dtype=torch.qint8, mapping=None, inplace=False
+):
+    r"""Converts a float model to dynamic (i.e. weights-only) quantized model.
+
+    Replaces specified modules with dynamic weight-only quantized versions and output the quantized model.
+
+    For simplest usage provide `dtype` argument that can be float16 or qint8. Weight-only quantization
+    by default is performed for layers with large weights size - i.e. Linear and RNN variants.
+
+    Fine grained control is possible with `qconfig` and `mapping` that act similarly to `quantize()`.
+    If `qconfig` is provided, the `dtype` argument is ignored.
+
+    Args:
+        model: input model
+        qconfig_spec: Either:
+
+            - A dictionary that maps from name or type of submodule to quantization
+              configuration, qconfig applies to all submodules of a given
+              module unless qconfig for the submodules are specified (when the
+              submodule already has qconfig attribute). Entries in the dictionary
+              need to be QConfig instances.
+
+            - A set of types and/or submodule names to apply dynamic quantization to,
+              in which case the `dtype` argument is used to specify the bit-width
+
+        inplace: carry out model transformations in-place, the original module is mutated
+        mapping: maps type of a submodule to a type of corresponding dynamically quantized version
+            with which the submodule needs to be replaced
+
+    """
+    torch._C._log_api_usage_once("quantization_api.quantize.quantize_dynamic")
+    if qconfig_spec is None:
+        if dtype == torch.qint8:
+            qconfig_spec = {
+                nn.Linear: default_dynamic_qconfig,
+                nn.LSTM: default_dynamic_qconfig,
+                nn.GRU: default_dynamic_qconfig,
+                nn.LSTMCell: default_dynamic_qconfig,
+                nn.RNNCell: default_dynamic_qconfig,
+                nn.GRUCell: default_dynamic_qconfig,
+            }
+        elif dtype == torch.float16:
+            qconfig_spec = {
+                nn.Linear: float16_dynamic_qconfig,
+                nn.LSTM: float16_dynamic_qconfig,
+                nn.GRU: float16_dynamic_qconfig,
+                nn.LSTMCell: float16_dynamic_qconfig,
+                nn.RNNCell: float16_dynamic_qconfig,
+                nn.GRUCell: float16_dynamic_qconfig,
+            }
+        elif dtype == torch.quint8:
+            qconfig_spec = {
+                nn.EmbeddingBag: float_qparams_weight_only_qconfig,
+                nn.Embedding: float_qparams_weight_only_qconfig,
+            }
+        elif dtype == torch.quint4x2:
+            qconfig_spec = {
+                nn.EmbeddingBag: float_qparams_weight_only_qconfig_4bit,
+            }
+        else:
+            raise ValueError(
+                f"Don't know how to quantize with default settings for {dtype}. Provide full qconfig please"
+            )
+    elif isinstance(qconfig_spec, set):
+        if dtype is torch.qint8:
+            default_qconfig = default_dynamic_qconfig
+        elif dtype is torch.float16:
+            default_qconfig = float16_dynamic_qconfig
+        elif dtype is torch.quint8:
+            default_qconfig = float_qparams_weight_only_qconfig
+        elif dtype is torch.quint4x2:
+            default_qconfig = float_qparams_weight_only_qconfig_4bit
+        else:
+            raise RuntimeError(
+                "Unknown dtype specified for quantize_dynamic: ", str(dtype)
+            )
+        qconfig_spec = dict(zip(qconfig_spec, itertools.repeat(default_qconfig)))
+
+    if mapping is None:
+        mapping = get_default_dynamic_quant_module_mappings()
+
+    if not inplace:
+        model = copy.deepcopy(model)
+    model.eval()
+    propagate_qconfig_(model, qconfig_spec)
+    convert(model, mapping, inplace=True)
+    return model
+
+
+@typing_extensions.deprecated(DEPRECATION_WARNING)
+def prepare_qat(model, mapping=None, inplace=False):
+    r"""
+    Prepares a copy of the model for quantization calibration or
+    quantization-aware training and converts it to quantized version.
+
+    Quantization configuration should be assigned preemptively
+    to individual submodules in `.qconfig` attribute.
+
+    Args:
+        model: input model to be modified in-place
+        mapping: dictionary that maps float modules to quantized modules to be
+                 replaced.
+        inplace: carry out model transformations in-place, the original module
+                 is mutated
+    """
+    torch._C._log_api_usage_once("quantization_api.quantize.prepare_qat")
+    if not model.training:
+        raise AssertionError("prepare_qat only works on models in training mode")
+    if mapping is None:
+        mapping = get_default_qat_module_mappings()
+
+    if not inplace:
+        model = copy.deepcopy(model)
+
+    propagate_qconfig_(model, qconfig_dict=None)
+    convert(model, mapping=mapping, inplace=True, remove_qconfig=False)
+    prepare(model, observer_non_leaf_module_list=set(mapping.values()), inplace=True)
+    return model
+
+
+@typing_extensions.deprecated(DEPRECATION_WARNING)
+def quantize_qat(model, run_fn, run_args, inplace=False):
+    r"""Do quantization aware training and output a quantized model
+
+    Args:
+        model: input model
+        run_fn: a function for evaluating the prepared model, can be a
+                function that simply runs the prepared model or a training
+                loop
+        run_args: positional arguments for `run_fn`
+
+    Return:
+        Quantized model.
+    """
+    torch._C._log_api_usage_once("quantization_api.quantize.quantize_qat")
+    if not inplace:
+        model = copy.deepcopy(model)
+    model.train()
+    prepare_qat(model, inplace=True)
+    run_fn(model, *run_args)
+    convert(model, inplace=True)
+    return model
+
+
+@typing_extensions.deprecated(DEPRECATION_WARNING)
+def convert(
+    module,
+    mapping=None,
+    inplace=False,
+    remove_qconfig=True,
+    is_reference=False,
+    convert_custom_config_dict=None,
+    use_precomputed_fake_quant=False,
+):
+    r"""Converts submodules in input module to a different module according to `mapping`
+    by calling `from_float` method on the target module class. And remove qconfig at the
+    end if remove_qconfig is set to True.
+
+    Args:
+        `module`: prepared and calibrated module
+        `mapping`: a dictionary that maps from source module type to target
+                   module type, can be overwritten to allow swapping user defined
+                   Modules
+        `inplace`: carry out model transformations in-place, the original module
+                   is mutated
+        `convert_custom_config_dict`: custom configuration dictionary for convert function
+        `use_precomputed_fake_quant`: a flag to enable use of precomputed fake quant
+
+    .. code-block:: python
+
+       # Example of convert_custom_config_dict:
+       convert_custom_config_dict = {
+           # user will manually define the corresponding quantized
+           # module class which has a from_observed class method that converts
+           # observed custom module to quantized custom module
+           "observed_to_quantized_custom_module_class": {
+               ObservedCustomModule: QuantizedCustomModule
+           }
+       }
+
+    """
+    torch._C._log_api_usage_once("quantization_api.quantize.convert")
+    if not inplace:
+        module = copy.deepcopy(module)
+    _convert(
+        module,
+        mapping,
+        inplace=True,
+        is_reference=is_reference,
+        convert_custom_config_dict=convert_custom_config_dict,
+        use_precomputed_fake_quant=use_precomputed_fake_quant,
+    )
+    if remove_qconfig:
+        _remove_qconfig(module)
+    return module
+
+
+def _convert(
+    module,
+    mapping=None,
+    inplace=False,
+    is_reference=False,
+    convert_custom_config_dict=None,
+    use_precomputed_fake_quant=False,
+):
+    r"""Converts submodules in input module to a different module according to `mapping`
+    by calling `from_float` method on the target module class
+
+    Args:
+        module: input module
+        mapping: a dictionary that maps from source module type to target
+                 module type, can be overwritten to allow swapping user defined
+                 Modules
+        inplace: carry out model transformations in-place, the original module
+                 is mutated
+        is_reference: a flag to enable quantized reference module
+        use_precomputed_fake_quant: a flag to enable use of precomputed fake quant
+
+    """
+    if mapping is None:
+        mapping = (
+            get_default_static_quant_reference_module_mappings()
+            if is_reference
+            else get_default_static_quant_module_mappings()
+        )
+    if convert_custom_config_dict is None:
+        convert_custom_config_dict = get_default_custom_config_dict()
+    custom_module_class_mapping = convert_custom_config_dict.get(
+        "observed_to_quantized_custom_module_class", {}
+    )
+
+    if not inplace:
+        module = copy.deepcopy(module)
+    reassign = {}
+    for name, mod in module.named_children():
+        # both fused modules and observed custom modules are
+        # swapped as one unit
+        if (
+            not isinstance(mod, _FusedModule)
+            and type_before_parametrizations(mod) not in custom_module_class_mapping
+        ):
+            _convert(
+                mod,
+                mapping,
+                True,  # inplace
+                is_reference,
+                convert_custom_config_dict,
+                use_precomputed_fake_quant=use_precomputed_fake_quant,
+            )
+        reassign[name] = swap_module(
+            mod, mapping, custom_module_class_mapping, use_precomputed_fake_quant
+        )
+
+    for key, value in reassign.items():
+        module._modules[key] = value
+
+    return module
+
+
+def swap_module(
+    mod, mapping, custom_module_class_mapping, use_precomputed_fake_quant=False
+):
+    r"""Swaps the module if it has a quantized counterpart and it has an
+    `observer` attached.
+
+    Args:
+        mod: input module
+        mapping: a dictionary that maps from nn module to nnq module
+
+    Return:
+        The corresponding quantized module of `mod`
+    """
+    new_mod = mod
+    if hasattr(mod, "qconfig") and mod.qconfig is not None:
+        swapped = False
+        if type_before_parametrizations(mod) in custom_module_class_mapping:
+            new_mod = custom_module_class_mapping[
+                type_before_parametrizations(mod)
+            ].from_observed(mod)
+            swapped = True
+        elif type_before_parametrizations(mod) in mapping:
+            qmod = mapping[type_before_parametrizations(mod)]
+            if hasattr(qmod, "_IS_REFERENCE") and qmod._IS_REFERENCE:
+                if mod.qconfig is None:
+                    raise AssertionError(
+                        "module qconfig must not be None when swapping to reference module"
+                    )
+                weight_post_process = mod.qconfig.weight()
+                weight_post_process(mod.weight)
+                weight_qparams = get_qparam_dict(weight_post_process)
+                new_mod = qmod.from_float(mod, weight_qparams)
+            else:
+                sig = inspect.signature(qmod.from_float)
+                if "use_precomputed_fake_quant" in sig.parameters:
+                    new_mod = qmod.from_float(
+                        mod, use_precomputed_fake_quant=use_precomputed_fake_quant
+                    )
+                else:
+                    new_mod = qmod.from_float(mod)
+            swapped = True
+
+        if swapped:
+            # Preserve module's pre forward hooks. They'll be called on quantized input
+            for pre_hook_fn in mod._forward_pre_hooks.values():
+                new_mod.register_forward_pre_hook(pre_hook_fn)
+            # Preserve module's post forward hooks except _observer_forward_hook
+            # After convert they'll work with quantized output
+            for hook_fn in mod._forward_hooks.values():
+                if hook_fn is not _observer_forward_hook:
+                    new_mod.register_forward_hook(hook_fn)
+
+            # respect device affinity when swapping modules
+            devices = _get_unique_devices_(mod)
+            if not (
+                len(devices) <= 1
+                or (len(devices) == 2 and torch.device("meta") in devices)
+            ):
+                raise AssertionError(
+                    f"swap_module only works with cpu or single-device CUDA modules, but got devices {devices}"
+                )
+            device = next(iter(devices)) if len(devices) > 0 else None
+            if device:
+                new_mod.to(device)
+    return new_mod
+
+
+def _get_observer_dict(mod, target_dict, prefix=""):
+    r"""Traverse the modules and save all observers into dict.
+    This is mainly used for quantization accuracy debug
+    Args:
+        mod: the top module we want to save all observers
+        prefix: the prefix for the current module
+        target_dict: the dictionary used to save all the observers
+    """
+
+    def get_prefix(prefix):
+        return prefix if prefix == "" else prefix + "."
+
+    if hasattr(mod, "activation_post_process"):
+        target_dict[get_prefix(prefix) + "activation_post_process"] = (
+            mod.activation_post_process
+        )
+    for name, child in mod.named_children():
+        module_prefix = get_prefix(prefix) + name if prefix else name
+        _get_observer_dict(child, target_dict, module_prefix)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantize_fx.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantize_fx.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba6ab86aaa048fbd128f9a89cc32d4e438d3fe12
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantize_fx.py
@@ -0,0 +1,759 @@
+import copy
+import typing_extensions
+import warnings
+from typing import Any
+
+import torch
+from torch.fx import GraphModule
+from torch.fx.graph_module import _USER_PRESERVED_ATTRIBUTES_KEY
+
+from .backend_config import BackendConfig, get_tensorrt_backend_config  # noqa: F401
+from .fx.convert import convert
+from .fx.custom_config import ConvertCustomConfig, FuseCustomConfig, PrepareCustomConfig
+from .fx.fuse import fuse  # noqa: F401
+from .fx.graph_module import ObservedGraphModule  # noqa: F401
+from .fx.prepare import prepare  # noqa: F401
+from .fx.tracer import QuantizationTracer, Scope, ScopeContextManager  # noqa: F401
+from .fx.utils import (  # noqa: F401
+    get_custom_module_class_keys,
+    get_skipped_module_name_and_classes,
+)
+from .qconfig_mapping import QConfigMapping
+from .utils import DEPRECATION_WARNING
+
+
+def attach_preserved_attrs_to_model(
+    model: GraphModule | torch.nn.Module,
+    preserved_attrs: dict[str, Any],
+) -> None:
+    """Store preserved attributes to the model.meta so that it can be preserved during deepcopy"""
+    model.meta[_USER_PRESERVED_ATTRIBUTES_KEY] = copy.copy(preserved_attrs)  # type: ignore[operator, index, assignment]
+    # set the preserved attributes in the model so that user can call
+    # model.attr as they do before calling fx graph mode quantization
+    for attr_name, attr in model.meta[_USER_PRESERVED_ATTRIBUTES_KEY].items():  # type: ignore[index, union-attr]
+        setattr(model, attr_name, attr)
+
+
+def _check_is_graph_module(model: torch.nn.Module) -> None:
+    if not isinstance(model, GraphModule):
+        raise ValueError(
+            "input model must be a GraphModule, "
+            + "Got type:"
+            + str(type(model))
+            + " Please make "
+            + "sure to follow the tutorials."
+        )
+
+
+def _attach_meta_to_node_if_not_exist(model: GraphModule) -> None:
+    """Attach meta field to all nodes of the graph if it does not exist,
+    meta field is a field stores some meta information about the node, such
+    as dtype and shape information for output of the node, this only exists
+    if the program is captured by make_fx (used in quantize_pt2e flow), if
+    the program is captured by torch.fx symbolic tracing, this field may not exist,
+    so we add it here to avoid checking this all over the places
+    """
+    for node in model.graph.nodes:
+        if not hasattr(node, "meta"):
+            node.meta = {}
+
+
+def _swap_ff_with_fxff(model: torch.nn.Module) -> None:
+    r"""Swap FloatFunctional with FXFloatFunctional"""
+    modules_to_swap = []
+    for name, module in model.named_children():
+        if isinstance(module, torch.ao.nn.quantized.FloatFunctional):
+            modules_to_swap.append(name)
+        else:
+            _swap_ff_with_fxff(module)
+
+    for name in modules_to_swap:
+        del model._modules[name]
+        model._modules[name] = torch.ao.nn.quantized.FXFloatFunctional()
+
+
+def _fuse_fx(
+    model: GraphModule,
+    is_qat: bool,
+    fuse_custom_config: FuseCustomConfig | dict[str, Any] | None = None,
+    backend_config: BackendConfig | dict[str, Any] | None = None,
+) -> GraphModule:
+    r"""Internal helper function to fuse modules in preparation for quantization
+
+    Args:
+        model: GraphModule object from symbolic tracing (torch.fx.symbolic_trace)
+    """
+    _check_is_graph_module(model)
+    return fuse(model, is_qat, fuse_custom_config, backend_config)  # type: ignore[operator]
+
+
+def _prepare_fx(
+    model: torch.nn.Module,
+    qconfig_mapping: QConfigMapping | dict[str, Any],
+    is_qat: bool,
+    example_inputs: tuple[Any, ...],
+    prepare_custom_config: PrepareCustomConfig | dict[str, Any] | None = None,
+    _equalization_config: QConfigMapping | dict[str, Any] | None = None,
+    backend_config: BackendConfig | dict[str, Any] | None = None,
+    is_standalone_module: bool = False,
+) -> GraphModule:
+    r"""Internal helper function for prepare_fx
+        Args:
+          `model`, `qconfig_mapping`, `prepare_custom_config`, `_equalization_config`:
+          see docs for :func:`~torch.ao.quantization.prepare_fx`
+          `is_standalone_module`: a boolean flag indicates whether we are
+          quantizing a standalone module or not, a standalone module
+          is a submodule of the parent module that is not inlined in the
+    forward graph of the parent module,
+          the way we quantize standalone module is described in:
+          :func:`~torch.ao.quantization._prepare_standalone_module_fx`
+    """
+    if prepare_custom_config is None:
+        prepare_custom_config = PrepareCustomConfig()
+    if _equalization_config is None:
+        _equalization_config = QConfigMapping()
+
+    if isinstance(prepare_custom_config, dict):
+        warnings.warn(
+            "Passing a prepare_custom_config_dict to prepare is deprecated and will not be supported "
+            "in a future version. Please pass in a PrepareCustomConfig instead.",
+            FutureWarning,
+            stacklevel=3,
+        )
+        prepare_custom_config = PrepareCustomConfig.from_dict(prepare_custom_config)
+
+    # swap FloatFunctional with FXFloatFunctional
+    _swap_ff_with_fxff(model)
+
+    skipped_module_names, skipped_module_classes = get_skipped_module_name_and_classes(
+        prepare_custom_config, is_standalone_module
+    )
+    preserved_attr_names = prepare_custom_config.preserved_attributes
+    preserved_attrs = {
+        attr: getattr(model, attr)
+        for attr in preserved_attr_names
+        if hasattr(model, attr)
+    }
+    # symbolically trace the model
+    tracer = QuantizationTracer(skipped_module_names, skipped_module_classes)  # type: ignore[arg-type]
+    graph_module = GraphModule(model, tracer.trace(model))
+    _attach_meta_to_node_if_not_exist(graph_module)
+
+    fuse_custom_config = FuseCustomConfig().set_preserved_attributes(
+        prepare_custom_config.preserved_attributes
+    )
+    graph_module = _fuse_fx(graph_module, is_qat, fuse_custom_config, backend_config)
+    prepared = prepare(
+        graph_module,
+        qconfig_mapping,
+        is_qat,
+        tracer.node_name_to_scope,
+        example_inputs=example_inputs,
+        prepare_custom_config=prepare_custom_config,
+        _equalization_config=_equalization_config,
+        backend_config=backend_config,
+        is_standalone_module=is_standalone_module,
+    )  # type: ignore[operator]
+
+    attach_preserved_attrs_to_model(prepared, preserved_attrs)
+    return prepared
+
+
+def _prepare_standalone_module_fx(
+    model: torch.nn.Module,
+    qconfig_mapping: QConfigMapping | dict[str, Any],
+    is_qat: bool,
+    example_inputs: tuple[Any, ...],
+    prepare_custom_config: PrepareCustomConfig | dict[str, Any] | None = None,
+    backend_config: BackendConfig | dict[str, Any] | None = None,
+) -> GraphModule:
+    r"""[Internal use only] Prepare a standalone module, so that it can be used when quantizing the
+    parent module.
+    standalone_module means it a submodule that is not inlined in parent module,
+    and will be quantized separately as one unit.
+
+    How the standalone module is observed is specified by `input_quantized_idxs` and
+    `output_quantized_idxs` in the prepare_custom_config for the standalone module
+
+    Returns:
+
+        * model(GraphModule): prepared standalone module. It has these attributes in
+          model.meta:
+
+            * `standalone_module_input_quantized_idxs(List[Int])`: a list of
+              indexes for the graph input that is expected to be quantized,
+              same as input_quantized_idxs configuration provided
+              for the standalone module
+            * `standalone_module_output_quantized_idxs(List[Int])`: a list of
+              indices for the graph output that is quantized
+              same as input_quantized_idxs configuration provided
+              for the standalone module
+
+    """
+    return _prepare_fx(
+        model,
+        qconfig_mapping,
+        is_qat,
+        example_inputs,
+        prepare_custom_config,
+        backend_config=backend_config,
+        is_standalone_module=True,
+    )
+
+
+def fuse_fx(
+    model: torch.nn.Module,
+    fuse_custom_config: FuseCustomConfig | dict[str, Any] | None = None,
+    backend_config: BackendConfig | dict[str, Any] | None = None,
+) -> GraphModule:
+    r"""Fuse modules like conv+bn, conv+bn+relu etc, model must be in eval mode.
+    Fusion rules are defined in torch.ao.quantization.fx.fusion_pattern.py
+
+    Args:
+
+        * `model` (torch.nn.Module): a torch.nn.Module model
+        * `fuse_custom_config` (FuseCustomConfig): custom configurations for fuse_fx.
+            See :class:`~torch.ao.quantization.fx.custom_config.FuseCustomConfig` for more details
+    Example::
+
+        from torch.ao.quantization import fuse_fx
+
+        m = Model().eval()
+        m = fuse_fx(m)
+
+    """
+    if fuse_custom_config is None:
+        fuse_custom_config = FuseCustomConfig()
+
+    if isinstance(fuse_custom_config, dict):
+        warnings.warn(
+            "Passing a fuse_custom_config_dict to fuse is deprecated and will not be supported "
+            "in a future version. Please pass in a FuseCustomConfig instead.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        fuse_custom_config = FuseCustomConfig.from_dict(fuse_custom_config)
+
+    torch._C._log_api_usage_once("quantization_api.quantize_fx.fuse_fx")
+    preserved_attr_names = fuse_custom_config.preserved_attributes
+    preserved_attrs = {
+        attr: getattr(model, attr)
+        for attr in preserved_attr_names
+        if hasattr(model, attr)
+    }
+
+    graph_module = torch.fx.symbolic_trace(model)
+    _attach_meta_to_node_if_not_exist(graph_module)
+    graph_module = _fuse_fx(graph_module, False, fuse_custom_config, backend_config)
+
+    attach_preserved_attrs_to_model(graph_module, preserved_attrs)
+    return graph_module
+
+
+@typing_extensions.deprecated(DEPRECATION_WARNING)
+def prepare_fx(
+    model: torch.nn.Module,
+    qconfig_mapping: QConfigMapping | dict[str, Any],
+    example_inputs: tuple[Any, ...],
+    prepare_custom_config: PrepareCustomConfig | dict[str, Any] | None = None,
+    _equalization_config: QConfigMapping | dict[str, Any] | None = None,
+    backend_config: BackendConfig | dict[str, Any] | None = None,
+) -> GraphModule:
+    r""" Prepare a model for post training quantization
+
+    Args:
+      * `model` (torch.nn.Module): torch.nn.Module model
+
+      * `qconfig_mapping` (QConfigMapping): QConfigMapping object to configure how a model is
+         quantized, see :class:`~torch.ao.quantization.qconfig_mapping.QConfigMapping`
+         for more details
+
+      * `example_inputs` (Tuple[Any, ...]): Example inputs for forward function of the model,
+         Tuple of positional args (keyword args can be passed as positional args as well)
+
+      * `prepare_custom_config` (PrepareCustomConfig): customization configuration for quantization tool.
+          See :class:`~torch.ao.quantization.fx.custom_config.PrepareCustomConfig` for more details
+
+      * `_equalization_config`: config for specifying how to perform equalization on the model
+
+      * `backend_config` (BackendConfig): config that specifies how operators are quantized
+         in a backend, this includes how the operators are observed,
+         supported fusion patterns, how quantize/dequantize ops are
+         inserted, supported dtypes etc. See :class:`~torch.ao.quantization.backend_config.BackendConfig` for more details
+
+    Return:
+      A GraphModule with observer (configured by qconfig_mapping), ready for calibration
+
+    Example::
+
+        import torch
+        from torch.ao.quantization import get_default_qconfig_mapping
+        from torch.ao.quantization.quantize_fx import prepare_fx
+
+        class Submodule(torch.nn.Module):
+            def __init__(self) -> None:
+                super().__init__()
+                self.linear = torch.nn.Linear(5, 5)
+            def forward(self, x):
+                x = self.linear(x)
+                return x
+
+        class M(torch.nn.Module):
+            def __init__(self) -> None:
+                super().__init__()
+                self.linear = torch.nn.Linear(5, 5)
+                self.sub = Submodule()
+
+            def forward(self, x):
+                x = self.linear(x)
+                x = self.sub(x) + x
+                return x
+
+        # initialize a floating point model
+        float_model = M().eval()
+
+        # define calibration function
+        def calibrate(model, data_loader):
+            model.eval()
+            with torch.no_grad():
+                for image, target in data_loader:
+                    model(image)
+
+        # qconfig is the configuration for how we insert observers for a particular
+        # operator
+        # qconfig = get_default_qconfig("fbgemm")
+        # Example of customizing qconfig:
+        # qconfig = torch.ao.quantization.QConfig(
+        #    activation=MinMaxObserver.with_args(dtype=torch.qint8),
+        #    weight=MinMaxObserver.with_args(dtype=torch.qint8))
+        # `activation` and `weight` are constructors of observer module
+
+        # qconfig_mapping is a collection of quantization configurations, user can
+        # set the qconfig for each operator (torch op calls, functional calls, module calls)
+        # in the model through qconfig_mapping
+        # the following call will get the qconfig_mapping that works best for models
+        # that target "fbgemm" backend
+        qconfig_mapping = get_default_qconfig_mapping("fbgemm")
+
+        # We can customize qconfig_mapping in different ways.
+        # e.g. set the global qconfig, which means we will use the same qconfig for
+        # all operators in the model, this can be overwritten by other settings
+        # qconfig_mapping = QConfigMapping().set_global(qconfig)
+        # e.g. quantize the linear submodule with a specific qconfig
+        # qconfig_mapping = QConfigMapping().set_module_name("linear", qconfig)
+        # e.g. quantize all nn.Linear modules with a specific qconfig
+        # qconfig_mapping = QConfigMapping().set_object_type(torch.nn.Linear, qconfig)
+        # for a more complete list, please see the docstring for :class:`torch.ao.quantization.QConfigMapping`
+        # argument
+
+        # example_inputs is a tuple of inputs, that is used to infer the type of the
+        # outputs in the model
+        # currently it's not used, but please make sure model(*example_inputs) runs
+        example_inputs = (torch.randn(1, 3, 224, 224),)
+
+        # TODO: add backend_config after we split the backend_config for fbgemm and qnnpack
+        # e.g. backend_config = get_default_backend_config("fbgemm")
+        # `prepare_fx` inserts observers in the model based on qconfig_mapping and
+        # backend_config. If the configuration for an operator in qconfig_mapping
+        # is supported in the backend_config (meaning it's supported by the target
+        # hardware), we'll insert observer modules according to the qconfig_mapping
+        # otherwise the configuration in qconfig_mapping will be ignored
+        #
+        # Example:
+        # in qconfig_mapping, user sets linear module to be quantized with quint8 for
+        # activation and qint8 for weight:
+        # qconfig = torch.ao.quantization.QConfig(
+        #     observer=MinMaxObserver.with_args(dtype=torch.quint8),
+        #     weight=MinMaxObserver.with-args(dtype=torch.qint8))
+        # Note: current qconfig api does not support setting output observer, but
+        # we may extend this to support these more fine grained control in the
+        # future
+        #
+        # qconfig_mapping = QConfigMapping().set_object_type(torch.nn.Linear, qconfig)
+        # in backend config, linear module also supports in this configuration:
+        # weighted_int8_dtype_config = DTypeConfig(
+        #   input_dtype=torch.quint8,
+        #   output_dtype=torch.quint8,
+        #   weight_dtype=torch.qint8,
+        #   bias_type=torch.float)
+
+        # linear_pattern_config = BackendPatternConfig(torch.nn.Linear) \
+        #    .set_observation_type(ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT) \
+        #    .add_dtype_config(weighted_int8_dtype_config) \
+        #    ...
+
+        # backend_config = BackendConfig().set_backend_pattern_config(linear_pattern_config)
+        # `prepare_fx` will check that the setting requested by suer in qconfig_mapping
+        # is supported by the backend_config and insert observers and fake quant modules
+        # in the model
+        prepared_model = prepare_fx(float_model, qconfig_mapping, example_inputs)
+        # Run calibration
+        calibrate(prepared_model, sample_inference_data)
+    """
+    torch._C._log_api_usage_once("quantization_api.quantize_fx.prepare_fx")
+    return _prepare_fx(
+        model,
+        qconfig_mapping,
+        False,  # is_qat
+        example_inputs,
+        prepare_custom_config,
+        _equalization_config,
+        backend_config,
+    )
+
+
+@typing_extensions.deprecated(DEPRECATION_WARNING)
+def prepare_qat_fx(
+    model: torch.nn.Module,
+    qconfig_mapping: QConfigMapping | dict[str, Any],
+    example_inputs: tuple[Any, ...],
+    prepare_custom_config: PrepareCustomConfig | dict[str, Any] | None = None,
+    backend_config: BackendConfig | dict[str, Any] | None = None,
+) -> GraphModule:
+    r"""Prepare a model for quantization aware training
+
+    Args:
+      * `model` (torch.nn.Module): torch.nn.Module model
+      * `qconfig_mapping` (QConfigMapping): see :func:`~torch.ao.quantization.prepare_fx`
+      * `example_inputs` (Tuple[Any, ...]): see :func:`~torch.ao.quantization.prepare_fx`
+      * `prepare_custom_config` (PrepareCustomConfig): see :func:`~torch.ao.quantization.prepare_fx`
+      * `backend_config` (BackendConfig): see :func:`~torch.ao.quantization.prepare_fx`
+
+    Return:
+      A GraphModule with fake quant modules (configured by qconfig_mapping and backend_config), ready for
+      quantization aware training
+
+    Example::
+
+        import torch
+        from torch.ao.quantization import get_default_qat_qconfig_mapping
+        from torch.ao.quantization.quantize_fx import prepare_qat_fx
+
+
+        class Submodule(torch.nn.Module):
+            def __init__(self) -> None:
+                super().__init__()
+                self.linear = torch.nn.Linear(5, 5)
+
+            def forward(self, x):
+                x = self.linear(x)
+                return x
+
+
+        class M(torch.nn.Module):
+            def __init__(self) -> None:
+                super().__init__()
+                self.linear = torch.nn.Linear(5, 5)
+                self.sub = Submodule()
+
+            def forward(self, x):
+                x = self.linear(x)
+                x = self.sub(x) + x
+                return x
+
+
+        # initialize a floating point model
+        float_model = M().train()
+        # (optional, but preferred) load the weights from pretrained model
+        # float_model.load_weights(...)
+
+
+        # define the training loop for quantization aware training
+        def train_loop(model, train_data):
+            model.train()
+            for image, target in data_loader:
+                ...
+
+
+        # qconfig is the configuration for how we insert observers for a particular
+        # operator
+        # qconfig = get_default_qconfig("fbgemm")
+        # Example of customizing qconfig:
+        # qconfig = torch.ao.quantization.QConfig(
+        #    activation=FakeQuantize.with_args(observer=MinMaxObserver.with_args(dtype=torch.qint8)),
+        #    weight=FakeQuantize.with_args(observer=MinMaxObserver.with_args(dtype=torch.qint8)))
+        # `activation` and `weight` are constructors of observer module
+
+        # qconfig_mapping is a collection of quantization configurations, user can
+        # set the qconfig for each operator (torch op calls, functional calls, module calls)
+        # in the model through qconfig_mapping
+        # the following call will get the qconfig_mapping that works best for models
+        # that target "fbgemm" backend
+        qconfig_mapping = get_default_qat_qconfig_mapping("fbgemm")
+
+        # We can customize qconfig_mapping in different ways, please take a look at
+        # the docstring for :func:`~torch.ao.quantization.prepare_fx` for different ways
+        # to configure this
+
+        # example_inputs is a tuple of inputs, that is used to infer the type of the
+        # outputs in the model
+        # currently it's not used, but please make sure model(*example_inputs) runs
+        example_inputs = (torch.randn(1, 3, 224, 224),)
+
+        # TODO: add backend_config after we split the backend_config for fbgemm and qnnpack
+        # e.g. backend_config = get_default_backend_config("fbgemm")
+        # `prepare_qat_fx` inserts observers in the model based on qconfig_mapping and
+        # backend_config, if the configuration for an operator in qconfig_mapping
+        # is supported in the backend_config (meaning it's supported by the target
+        # hardware), we'll insert fake_quantize modules according to the qconfig_mapping
+        # otherwise the configuration in qconfig_mapping will be ignored
+        # see :func:`~torch.ao.quantization.prepare_fx` for a detailed explanation of
+        # how qconfig_mapping interacts with backend_config
+        prepared_model = prepare_qat_fx(float_model, qconfig_mapping, example_inputs)
+        # Run training
+        train_loop(prepared_model, train_loop)
+
+    """
+    torch._C._log_api_usage_once("quantization_api.quantize_fx.prepare_qat_fx")
+    return _prepare_fx(
+        model,
+        qconfig_mapping,
+        True,  # is_qat
+        example_inputs,
+        prepare_custom_config,
+        backend_config=backend_config,
+    )
+
+
+def _convert_fx(
+    graph_module: GraphModule,
+    is_reference: bool,
+    convert_custom_config: ConvertCustomConfig | dict[str, Any] | None = None,
+    is_standalone_module: bool = False,
+    _remove_qconfig: bool = True,
+    qconfig_mapping: QConfigMapping | dict[str, Any] | None = None,
+    backend_config: BackendConfig | dict[str, Any] | None = None,
+    is_decomposed: bool = False,
+    keep_original_weights: bool = False,
+) -> GraphModule:
+    """`is_standalone_module`: see docs in :func:`~torch.ao.quantization.prepare_standalone_module_fx`"""
+    if convert_custom_config is None:
+        convert_custom_config = ConvertCustomConfig()
+
+    if isinstance(convert_custom_config, dict):
+        warnings.warn(
+            "Passing a convert_custom_config_dict to convert is deprecated and will not be supported "
+            "in a future version. Please pass in a ConvertCustomConfig instead.",
+            FutureWarning,
+            stacklevel=3,
+        )
+        convert_custom_config = ConvertCustomConfig.from_dict(convert_custom_config)
+
+    _check_is_graph_module(graph_module)
+    preserved_attr_names = convert_custom_config.preserved_attributes
+    preserved_attrs = {
+        attr: getattr(graph_module, attr)
+        for attr in preserved_attr_names
+        if hasattr(graph_module, attr)
+    }
+
+    quantized = convert(
+        graph_module,
+        is_reference,
+        convert_custom_config,
+        is_standalone_module,
+        _remove_qconfig_flag=_remove_qconfig,
+        qconfig_mapping=qconfig_mapping,
+        backend_config=backend_config,
+        is_decomposed=is_decomposed,
+        keep_original_weights=keep_original_weights,
+    )
+
+    attach_preserved_attrs_to_model(quantized, preserved_attrs)
+    return quantized
+
+
+@typing_extensions.deprecated(DEPRECATION_WARNING)
+def convert_fx(
+    graph_module: GraphModule,
+    convert_custom_config: ConvertCustomConfig | dict[str, Any] | None = None,
+    _remove_qconfig: bool = True,
+    qconfig_mapping: QConfigMapping | dict[str, Any] | None = None,
+    backend_config: BackendConfig | dict[str, Any] | None = None,
+    keep_original_weights: bool = False,
+) -> GraphModule:
+    r"""Convert a calibrated or trained model to a quantized model
+
+    Args:
+        * `graph_module` (torch.fx.GraphModule): A prepared and calibrated/trained model (GraphModule)
+
+        * `convert_custom_config` (ConvertCustomConfig): custom configurations for convert function.
+            See :class:`~torch.ao.quantization.fx.custom_config.ConvertCustomConfig` for more details
+
+        * `_remove_qconfig` (bool): Option to remove the qconfig attributes in the model after convert.
+
+        * `qconfig_mapping` (QConfigMapping): config for specifying how to convert a model for quantization.
+
+           The keys must include the ones in the qconfig_mapping passed to `prepare_fx` or `prepare_qat_fx`,
+           with the same values or `None`. Additional keys can be specified with values set to `None`.
+
+          For each entry whose value is set to None, we skip quantizing that entry in the model::
+
+            qconfig_mapping = QConfigMapping
+                .set_global(qconfig_from_prepare)
+                .set_object_type(torch.nn.functional.add, None)  # skip quantizing torch.nn.functional.add
+                .set_object_type(torch.nn.functional.linear, qconfig_from_prepare)
+                .set_module_name("foo.bar", None)  # skip quantizing module "foo.bar"
+
+         * `backend_config` (BackendConfig): A configuration for the backend which describes how
+            operators should be quantized in the backend, this includes quantization
+            mode support (static/dynamic/weight_only), dtype support (quint8/qint8 etc.),
+            observer placement for each operators and fused operators.
+            See :class:`~torch.ao.quantization.backend_config.BackendConfig` for more details
+
+    Return:
+        A quantized model (torch.nn.Module)
+
+    Example::
+
+        # prepared_model: the model after prepare_fx/prepare_qat_fx and calibration/training
+        # convert_fx converts a calibrated/trained model to a quantized model for the
+        # target hardware, this includes converting the model first to a reference
+        # quantized model, and then lower the reference quantized model to a backend
+        # Currently, the supported backends are fbgemm (onednn), qnnpack (xnnpack) and
+        # they share the same set of quantized operators, so we are using the same
+        # lowering procedure
+        #
+        # backend_config defines the corresponding reference quantized module for
+        # the weighted modules in the model, e.g. nn.Linear
+        # TODO: add backend_config after we split the backend_config for fbgemm and qnnpack
+        # e.g. backend_config = get_default_backend_config("fbgemm")
+        quantized_model = convert_fx(prepared_model)
+
+    """
+    torch._C._log_api_usage_once("quantization_api.quantize_fx.convert_fx")
+    return _convert_fx(
+        graph_module,
+        is_reference=False,
+        convert_custom_config=convert_custom_config,
+        _remove_qconfig=_remove_qconfig,
+        qconfig_mapping=qconfig_mapping,
+        backend_config=backend_config,
+        keep_original_weights=keep_original_weights,
+    )
+
+
+def convert_to_reference_fx(
+    graph_module: GraphModule,
+    convert_custom_config: ConvertCustomConfig | dict[str, Any] | None = None,
+    _remove_qconfig: bool = True,
+    qconfig_mapping: QConfigMapping | dict[str, Any] | None = None,
+    backend_config: BackendConfig | dict[str, Any] | None = None,
+) -> GraphModule:
+    r"""Convert a calibrated or trained model to a reference quantized model,
+    see https://github.com/pytorch/rfcs/blob/master/RFC-0019-Extending-PyTorch-Quantization-to-Custom-Backends.md for more details,
+    reference quantized model is a standard representation of a quantized model provided
+    by FX Graph Mode Quantization, it can be further lowered to run on the target
+    hardware, like accelerators
+
+    Args:
+        * `graph_module` (GraphModule): A prepared and calibrated/trained model (GraphModule)
+
+        * `convert_custom_config` (ConvertCustomConfig): custom configurations for convert function.
+            See :func:`~torch.ao.quantization.quantize_fx.convert_fx` for more details.
+
+        * `_remove_qconfig` (bool): Option to remove the qconfig attributes in the model after convert.
+
+        * `qconfig_mapping` (QConfigMapping): config for specifying how to convert a model for quantization.
+            See :func:`~torch.ao.quantization.quantize_fx.convert_fx` for more details.
+
+         * `backend_config` (BackendConfig): A configuration for the backend which describes how
+            operators should be quantized in the backend. See
+            :func:`~torch.ao.quantization.quantize_fx.convert_fx` for more details.
+
+    Return:
+        A reference quantized model (GraphModule)
+
+    Example::
+
+        # prepared_model: the model after prepare_fx/prepare_qat_fx and calibration/training
+        # TODO: add backend_config after we split the backend_config for fbgemm and qnnpack
+        # e.g. backend_config = get_default_backend_config("fbgemm")
+        reference_quantized_model = convert_to_reference_fx(prepared_model)
+
+    """
+    torch._C._log_api_usage_once("quantization_api.quantize_fx.convert_to_reference_fx")
+    return _convert_fx(
+        graph_module,
+        is_reference=True,
+        convert_custom_config=convert_custom_config,
+        _remove_qconfig=_remove_qconfig,
+        qconfig_mapping=qconfig_mapping,
+        backend_config=backend_config,
+    )
+
+
+def _convert_to_reference_decomposed_fx(
+    graph_module: GraphModule,
+    convert_custom_config: ConvertCustomConfig | dict[str, Any] | None = None,
+    qconfig_mapping: QConfigMapping | dict[str, Any] | None = None,
+    backend_config: BackendConfig | dict[str, Any] | None = None,
+) -> GraphModule:
+    r"""Convert a calibrated or trained model to a reference quantized model, with
+    decomposed representation for quantized Tensor
+    see https://github.com/pytorch/rfcs/blob/master/RFC-0019-Extending-PyTorch-Quantization-to-Custom-Backends.md for more details,
+    reference quantized model is a standard representation of a quantized model provided
+    by FX Graph Mode Quantization, it can be further lowered to run on the target
+    hardware, like accelerators
+
+    Note: this is not public API
+
+    Args:
+        * `graph_module` (GraphModule): A prepared and calibrated/trained model (GraphModule)
+
+        * `convert_custom_config` (ConvertCustomConfig): custom configurations for convert function.
+            See :func:`~torch.ao.quantization.quantize_fx.convert_fx` for more details.
+
+        * `_remove_qconfig` (bool): Option to remove the qconfig attributes in the model after convert.
+
+        * `qconfig_mapping` (QConfigMapping): config for specifying how to convert a model for quantization.
+            See :func:`~torch.ao.quantization.quantize_fx.convert_fx` for more details.
+
+         * `backend_config` (BackendConfig): A configuration for the backend which describes how
+            operators should be quantized in the backend. See
+            :func:`~torch.ao.quantization.quantize_fx.convert_fx` for more details.
+
+    Return:
+        A reference quantized model (GraphModule) with operators working with decomposed quantized Tensor
+
+    Example::
+
+        # prepared_model: the model after prepare_fx/prepare_qat_fx and calibration/training
+        # TODO: add backend_config after we split the backend_config for fbgemm and qnnpack
+        # e.g. backend_config = get_default_backend_config("fbgemm")
+        reference_quantized_model = _convert_to_reference_decomposed_fx(prepared_model)
+
+    """
+    torch._C._log_api_usage_once(
+        "quantization_api.quantize_fx._convert_to_reference_decomposed_fx"
+    )
+    return _convert_fx(
+        graph_module,
+        is_reference=True,
+        convert_custom_config=convert_custom_config,
+        _remove_qconfig=False,
+        qconfig_mapping=qconfig_mapping,
+        backend_config=backend_config,
+        is_decomposed=True,
+    )
+
+
+def _convert_standalone_module_fx(
+    graph_module: GraphModule,
+    is_reference: bool = False,
+    convert_custom_config: ConvertCustomConfig | dict[str, Any] | None = None,
+) -> GraphModule:
+    r"""[Internal use only] Convert a model produced by :func:`~torch.ao.quantization.prepare_standalone_module_fx`
+    and convert it to a quantized model
+
+    Returns a quantized standalone module, whether input/output is quantized is
+    specified by prepare_custom_config, with
+    input_quantized_idxs, output_quantized_idxs, please
+    see docs for prepare_fx for details
+    """
+    return _convert_fx(
+        graph_module,
+        is_reference,
+        convert_custom_config,
+        is_standalone_module=True,
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantize_jit.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantize_jit.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec4caab1edcd010a66032cab51cae77ad8e4ed62
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantize_jit.py
@@ -0,0 +1,423 @@
+# mypy: allow-untyped-defs
+
+import torch
+from torch.ao.quantization.qconfig import QConfig
+from torch.ao.quantization.quant_type import QuantType
+from torch.jit._recursive import wrap_cpp_module
+
+
+__all__ = [
+    "script_qconfig",
+    "script_qconfig_dict",
+    "fuse_conv_bn_jit",
+    "prepare_jit",
+    "prepare_dynamic_jit",
+    "convert_jit",
+    "convert_dynamic_jit",
+    "quantize_jit",
+    "quantize_dynamic_jit",
+]
+
+
+def _check_is_script_module(model):
+    if not isinstance(model, torch.jit.ScriptModule):
+        raise ValueError("input must be a script module, got: " + str(type(model)))
+
+
+def _check_forward_method(model):
+    if not model._c._has_method("forward"):
+        raise ValueError("input script module does not have forward method")
+
+
+def script_qconfig(qconfig):
+    r"""Instantiate the activation and weight observer modules and script
+    them, these observer module instances will be deepcopied during
+    prepare_jit step.
+    """
+    return QConfig(
+        activation=torch.jit.script(qconfig.activation())._c,
+        weight=torch.jit.script(qconfig.weight())._c,
+    )
+
+
+def script_qconfig_dict(qconfig_dict):
+    r"""Helper function used by `prepare_jit`.
+    Apply `script_qconfig` for all entries in `qconfig_dict` that is
+    not None.
+    """
+    return {k: script_qconfig(v) if v else None for k, v in qconfig_dict.items()}
+
+
+def fuse_conv_bn_jit(model, inplace=False):
+    r"""Fuse conv - bn module
+    Works for eval model only.
+
+    Args:
+        model: TorchScript model from scripting or tracing
+    """
+    torch._C._log_api_usage_once("quantization_api.quantize_jit.fuse_conv_bn_jit")
+    model_c = model._c
+    model_c = torch._C._jit_pass_fold_convbn(model_c)
+    if inplace:
+        model._reconstruct(model_c)
+    else:
+        model = wrap_cpp_module(model_c)
+    return model
+
+
+def _prepare_jit(model, qconfig_dict, inplace=False, quant_type=QuantType.STATIC):
+    _check_is_script_module(model)
+    _check_forward_method(model)
+    if not all(isinstance(x, str) for x in qconfig_dict):
+        raise ValueError("qconfig_dict should only contain names(str) as keys.")
+    scripted_qconfig_dict = script_qconfig_dict(qconfig_dict)
+    model = fuse_conv_bn_jit(model, inplace)
+    model_c = torch._C._jit_pass_insert_observers(
+        model._c, "forward", scripted_qconfig_dict, inplace, quant_type
+    )
+    if inplace:
+        model._reconstruct(model_c)
+    else:
+        model = wrap_cpp_module(model_c)
+    return model
+
+
+def _prepare_ondevice_jit(
+    model,
+    qconfig_dict,
+    method_name="forward",
+    inplace=False,
+    quant_type=QuantType.STATIC,
+):
+    _check_is_script_module(model)
+    if not all(isinstance(x, str) for x in qconfig_dict):
+        raise ValueError("qconfig_dict should only contain names(str) as keys.")
+    scripted_qconfig_dict = script_qconfig_dict(qconfig_dict)
+    method_graph = model._c._get_method(method_name).graph
+    torch._C._jit_pass_inline(method_graph)
+    model = fuse_conv_bn_jit(model, inplace)
+    model_c = torch._C._jit_pass_insert_observer_method_for_ondevice_ptq(
+        model._c, method_name, scripted_qconfig_dict, inplace, quant_type
+    )
+    if inplace:
+        model._reconstruct(model_c)
+    else:
+        model = wrap_cpp_module(model_c)
+    return model
+
+
+def prepare_jit(model, qconfig_dict, inplace=False):
+    torch._C._log_api_usage_once("quantization_api.quantize_jit.prepare_jit")
+    return _prepare_jit(model, qconfig_dict, inplace, quant_type=QuantType.STATIC)
+
+
+def prepare_dynamic_jit(model, qconfig_dict, inplace=False):
+    torch._C._log_api_usage_once("quantization_api.quantize_jit.prepare_dynamic_jit")
+    return _prepare_jit(model, qconfig_dict, inplace, quant_type=QuantType.DYNAMIC)
+
+
+def _prepare_ondevice_dynamic_jit(
+    model, qconfig_dict, method_name="forward", inplace=False
+):
+    return _prepare_ondevice_jit(
+        model, qconfig_dict, method_name, inplace, quant_type=QuantType.DYNAMIC
+    )
+
+
+def _convert_jit(
+    model, inplace=False, debug=False, quant_type=QuantType.STATIC, preserved_attrs=None
+):
+    _check_is_script_module(model)
+    model.eval()
+    model_c = model._c
+    model_c = torch._C._jit_pass_insert_quant_dequant(
+        model_c, "forward", inplace, debug, quant_type
+    )
+    if not debug:
+        is_xpu = all(p.device.type == "xpu" for p in model.parameters())
+        if not is_xpu:
+            # Moving model parameters to CPU since quantized operators
+            # are only supported on CPU and XPU right now
+            model.cpu()
+        if preserved_attrs is None:
+            preserved_attrs = []
+        model_c = torch._C._jit_pass_quant_finalize(
+            model_c, quant_type, preserved_attrs
+        )
+    if inplace:
+        model._reconstruct(model_c)
+    else:
+        model = wrap_cpp_module(model_c)
+    torch._C._jit_pass_constant_propagation(model.graph)
+    torch._C._jit_pass_dce(model.graph)
+    return model
+
+
+def _convert_ondevice_jit(
+    model, method_name, inplace=False, debug=False, quant_type=QuantType.STATIC
+):
+    _check_is_script_module(model)
+    if quant_type != QuantType.DYNAMIC:
+        raise AssertionError(
+            "This API, while should work for static quant, is only tested for dynamic quant."
+        )
+    if method_name.startswith("observe_"):
+        raise AssertionError("Pass in valid method to be quantized, e.g. forward")
+    observe_method_name = "observe_" + method_name
+    quantize_method_name = "quantize_" + method_name
+    model_c = model._c
+    model_c = torch._C._jit_pass_insert_quant_dequant_for_ondevice_ptq(
+        model._c, observe_method_name, inplace, debug, QuantType.DYNAMIC
+    )
+    model_c = torch._C._jit_pass_quant_finalize_for_ondevice_ptq(
+        model_c, QuantType.DYNAMIC, quantize_method_name
+    )
+    if inplace:
+        model._reconstruct(model_c)
+    else:
+        model = wrap_cpp_module(model_c)
+    return model
+
+
+def convert_jit(model, inplace=False, debug=False, preserved_attrs=None):
+    torch._C._log_api_usage_once("quantization_api.quantize_jit.convert_jit")
+    return _convert_jit(
+        model,
+        inplace,
+        debug,
+        quant_type=QuantType.STATIC,
+        preserved_attrs=preserved_attrs,
+    )
+
+
+def convert_dynamic_jit(model, inplace=False, debug=False, preserved_attrs=None):
+    torch._C._log_api_usage_once("quantization_api.quantize_jit.convert_dynamic_jit")
+    return _convert_jit(
+        model,
+        inplace,
+        debug,
+        quant_type=QuantType.DYNAMIC,
+        preserved_attrs=preserved_attrs,
+    )
+
+
+def _convert_ondevice_dynamic_jit(model, method_name, inplace=False, debug=False):
+    return _convert_ondevice_jit(
+        model, method_name, inplace, debug, quant_type=QuantType.DYNAMIC
+    )
+
+
+def _quantize_ondevice_dynamic_jit_impl(
+    model, qconfig_dict, method_name, inplace=False
+):
+    model = _prepare_ondevice_dynamic_jit(model, qconfig_dict, method_name, inplace)
+    model = _convert_ondevice_dynamic_jit(model, method_name, inplace)
+    return model
+
+
+def _quantize_jit(
+    model,
+    qconfig_dict,
+    run_fn=None,
+    run_args=None,
+    inplace=False,
+    debug=False,
+    quant_type=QuantType.STATIC,
+):
+    # Always do inplace convert because the Tensor is already
+    # copied in prepare_jit when inplace is False
+    if quant_type == QuantType.DYNAMIC:
+        model = prepare_dynamic_jit(model, qconfig_dict, inplace)
+        model = convert_dynamic_jit(model, True, debug)
+    else:
+        if not run_fn:
+            raise AssertionError(
+                "Must provide calibration function for post training static quantization"
+            )
+        if not run_args:
+            raise AssertionError(
+                "Must provide calibration dataset for post training static quantization"
+            )
+        model = prepare_jit(model, qconfig_dict, inplace)
+        run_fn(model, *run_args)
+        model = convert_jit(model, True, debug)
+
+    torch._C._jit_pass_constant_propagation(model.graph)
+    torch._C._jit_pass_dce(model.graph)
+    return model
+
+
+def quantize_jit(model, qconfig_dict, run_fn, run_args, inplace=False, debug=False):
+    r"""Quantize the input float TorchScript model with
+    post training static quantization.
+
+    First it will prepare the model for calibration, then it calls
+    `run_fn` which will run the calibration step, after that we will
+    convert the model to a quantized model.
+
+    Args:
+        `model`: input float TorchScript model
+        `qconfig_dict`: qconfig_dict is a dictionary with names of sub modules as key and
+        qconfig for that module as value, empty key means the qconfig will be applied
+        to whole model unless it's overwritten by more specific configurations, the
+        qconfig for each module is either found in the dictionary or fallback to
+         the qconfig of parent module.
+
+        Right now qconfig_dict is the only way to configure how the model is quantized,
+        and it is done in the granularity of module, that is, we only support one type
+        of qconfig for each torch.nn.Module, and the qconfig for sub module will
+        override the qconfig for parent module, empty string means global configuration.
+        `run_fn`: a calibration function for calibrating the prepared model
+        `run_args`: positional arguments for `run_fn`
+        `inplace`: carry out model transformations in-place, the original module is
+        mutated
+        `debug`: flag for producing a debug friendly model (preserve weight attribute)
+
+    Return:
+        Quantized TorchSciprt model.
+
+    Example:
+    ```python
+    import torch
+    from torch.ao.quantization import get_default_qconfig
+    from torch.ao.quantization import quantize_jit
+
+    ts_model = torch.jit.script(
+        float_model.eval()
+    )  # or torch.jit.trace(float_model, input)
+    qconfig = get_default_qconfig("fbgemm")
+
+
+    def calibrate(model, data_loader):
+        model.eval()
+        with torch.no_grad():
+            for image, target in data_loader:
+                model(image)
+
+
+    quantized_model = quantize_jit(
+        ts_model, {"": qconfig}, calibrate, [data_loader_test]
+    )
+    ```
+    """
+    torch._C._log_api_usage_once("quantization_api.quantize_jit.quantize_jit")
+    return _quantize_jit(
+        model,
+        qconfig_dict,
+        run_fn,
+        run_args,
+        inplace,
+        debug,
+        quant_type=QuantType.STATIC,
+    )
+
+
+def quantize_dynamic_jit(model, qconfig_dict, inplace=False, debug=False):
+    r"""Quantize the input float TorchScript model with
+    post training dynamic quantization.
+    Currently only qint8 quantization of torch.nn.Linear is supported.
+
+    Args:
+        `model`: input float TorchScript model
+        `qconfig_dict`: qconfig_dict is a dictionary with names of sub modules as key and
+        qconfig for that module as value, please see detailed
+        descriptions in :func:`~torch.ao.quantization.quantize_jit`
+        `inplace`: carry out model transformations in-place, the original module is
+        mutated
+        `debug`: flag for producing a debug friendly model (preserve weight attribute)
+
+    Return:
+        Quantized TorchSciprt model.
+
+    Example:
+    ```python
+    import torch
+    from torch.ao.quantization import per_channel_dynamic_qconfig
+    from torch.ao.quantization import quantize_dynamic_jit
+
+    ts_model = torch.jit.script(
+        float_model.eval()
+    )  # or torch.jit.trace(float_model, input)
+    qconfig = get_default_qconfig("fbgemm")
+
+
+    def calibrate(model, data_loader):
+        model.eval()
+        with torch.no_grad():
+            for image, target in data_loader:
+                model(image)
+
+
+    quantized_model = quantize_dynamic_jit(
+        ts_model, {"": qconfig}, calibrate, [data_loader_test]
+    )
+    ```
+    """
+    torch._C._log_api_usage_once("quantization_api.quantize_jit.quantize_dynamic_jit")
+    return _quantize_jit(
+        model, qconfig_dict, inplace=inplace, debug=debug, quant_type=QuantType.DYNAMIC
+    )
+
+
+def _quantize_ondevice_dynamic_jit(
+    model, qconfig_dict, method_name="forward", inplace=False
+):
+    r"""Prepares the input float TorchScript model with
+    *on-device* post training dynamic quantization.
+    Currently only qint8 quantization of torch.nn.Linear is supported.
+
+    Args:
+        `model`: input float TorchScript model
+        `qconfig_dict`: qconfig_dict is a dictionary with names of sub modules as key and
+        qconfig for that module as value, please see detailed
+        `method_name`: Name of the method within the model, to be prepared for quantization
+        descriptions in :func:`~torch.ao.quantization.quantize_jit`
+        `inplace`: carry out model transformations in-place, the original module is
+        mutated
+
+    Return:
+        TorchScript model that is ready for on device quantization.
+        This means that the returned
+        model has:
+        - Method is inlined.
+        - Model has observer modules inserted in the model.
+        - Model has packed params inserted in the model. However they are empty as in they dont
+          contain valid quantized weights.
+        - observe_ is added that observe the values to be quantized.
+        - reset_observers_ to reset observers.
+        - quantize_ is added to the model.
+          - This method extract scale, zero points.
+          - Quantizes observed weights.
+          - Creates packed params from it and update the attribute of the model with the new values
+            for the packed params.
+          - Reset the original fp32 weights with empty tensor using SetAttr.
+        - quantized_ is added to the model.
+          - This method uses quantized weights and quantized linear ops instead of fp32 op.
+          - This method should be used for inference post PTQ.
+        - Note that all method's signatures should be the same as method_name.
+
+        Later on device:
+        - Run reset_observers_
+        - Run observe_
+        - Run quantize_
+        - Now model can be saved and loaded later.
+        - Run model with quantized_
+
+    Example:
+    ```python
+    import torch
+    from torch.ao.quantization import per_channel_dynamic_qconfig
+    from torch.ao.quantization.quantize_jit import _quantize_ondevice_dynamic_jit
+
+    ts_model = torch.jit.script(
+        float_model.eval()
+    )  # or torch.jit.trace(float_model, input)
+    qconfig = get_default_qconfig("fbgemm")
+    quant_ready_model = _quantize_ondevice_dynamic_jit(
+        ts_model, {"": qconfig}, "forward", True
+    )
+    ```
+    """
+    return _quantize_ondevice_dynamic_jit_impl(
+        model, qconfig_dict, method_name, inplace=inplace
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantize_pt2e.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantize_pt2e.py
new file mode 100644
index 0000000000000000000000000000000000000000..169e2905ddbdcc2ec86d92d1b858abe7e91af298
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantize_pt2e.py
@@ -0,0 +1,262 @@
+import typing_extensions
+
+import torch
+from torch._export.passes.constant_folding import constant_fold
+from torch.ao.quantization.pt2e.duplicate_dq_pass import DuplicateDQPass
+from torch.ao.quantization.pt2e.port_metadata_pass import PortNodeMetaForQDQ
+from torch.ao.quantization.quantizer import (  # noqa: F401
+    DerivedQuantizationSpec,
+    FixedQParamsQuantizationSpec,
+    QuantizationAnnotation,
+    QuantizationSpec,
+    QuantizationSpecBase,
+    Quantizer,
+    SharedQuantizationSpec,
+)
+from torch.fx import GraphModule, Node
+from torch.fx.passes.infra.pass_manager import PassManager
+
+from .pt2e.prepare import prepare
+from .pt2e.qat_utils import _fold_conv_bn_qat, _fuse_conv_bn_qat
+from .pt2e.representation import reference_representation_rewrite
+from .pt2e.utils import _disallow_eval_train, _fuse_conv_bn_, _get_node_name_to_scope
+from .quantize_fx import _convert_to_reference_decomposed_fx
+from .utils import DEPRECATION_WARNING
+
+
+__all__ = [
+    "prepare_pt2e",
+    "prepare_qat_pt2e",
+    "convert_pt2e",
+]
+
+
+@typing_extensions.deprecated(DEPRECATION_WARNING)
+def prepare_pt2e(
+    model: GraphModule,
+    quantizer: Quantizer,
+) -> GraphModule:
+    """Prepare a model for post training quantization
+
+    Args:
+      * `model` (torch.fx.GraphModule): a model captured by `torch.export.export_for_training` API.
+      * `quantizer`: A backend specific quantizer that conveys how user want the
+        model to be quantized. Tutorial for how to write a quantizer can be found here:
+        https://pytorch.org/tutorials/prototype/pt2e_quantizer.html
+
+    Return:
+      A GraphModule with observer (based on quantizer annotation), ready for calibration
+
+    Example::
+
+        import torch
+        from torch.ao.quantization.quantize_pt2e import prepare_pt2e
+        from torch.ao.quantization.quantizer import (
+            XNNPACKQuantizer,
+            get_symmetric_quantization_config,
+        )
+
+        class M(torch.nn.Module):
+            def __init__(self) -> None:
+                super().__init__()
+                self.linear = torch.nn.Linear(5, 10)
+
+           def forward(self, x):
+               return self.linear(x)
+
+        # initialize a floating point model
+        float_model = M().eval()
+
+        # define calibration function
+        def calibrate(model, data_loader):
+            model.eval()
+            with torch.no_grad():
+                for image, target in data_loader:
+                    model(image)
+
+        # Step 1. program capture
+        # NOTE: this API will be updated to torch.export API in the future, but the captured
+        # result should mostly stay the same
+        m = torch.export.export_for_training(m, *example_inputs).module()
+        # we get a model with aten ops
+
+        # Step 2. quantization
+        # backend developer will write their own Quantizer and expose methods to allow
+        # users to express how they
+        # want the model to be quantized
+        quantizer = XNNPACKQuantizer().set_global(get_symmetric_quantization_config())
+        m = prepare_pt2e(m, quantizer)
+
+        # run calibration
+        # calibrate(m, sample_inference_data)
+    """
+    torch._C._log_api_usage_once("quantization_api.quantize_pt2e.prepare_pt2e")
+    original_graph_meta = model.meta
+    node_name_to_scope = _get_node_name_to_scope(model)
+    # TODO: check qconfig_mapping to make sure conv and bn are both configured
+    # to be quantized before fusion
+    # TODO: (maybe) rewrite this with subgraph_rewriter
+    _fuse_conv_bn_(model)
+    model = quantizer.transform_for_annotation(model)
+    quantizer.annotate(model)
+    quantizer.validate(model)
+    model = prepare(
+        model,
+        node_name_to_scope,
+        is_qat=False,
+        obs_or_fq_callback=quantizer.prepare_obs_or_fq_callback,
+    )
+    model.meta.update(original_graph_meta)
+    model = _disallow_eval_train(model)
+    return model
+
+
+@typing_extensions.deprecated(DEPRECATION_WARNING)
+def prepare_qat_pt2e(
+    model: GraphModule,
+    quantizer: Quantizer,
+) -> GraphModule:
+    """Prepare a model for quantization aware training
+
+    Args:
+      * `model` (torch.fx.GraphModule): see :func:`~torch.ao.quantization.quantize_pt2e.prepare_pt2e`
+      * `quantizer`: see :func:`~torch.ao.quantization.quantize_pt2e.prepare_pt2e`
+
+    Return:
+      A GraphModule with fake quant modules (based on quantizer annotation), ready for
+      quantization aware training
+
+    Example::
+        import torch
+        from torch.ao.quantization.quantize_pt2e import prepare_qat_pt2e
+        from torch.ao.quantization.quantizer import (
+            XNNPACKQuantizer,
+            get_symmetric_quantization_config,
+        )
+
+        class M(torch.nn.Module):
+            def __init__(self) -> None:
+                super().__init__()
+                self.linear = torch.nn.Linear(5, 10)
+
+           def forward(self, x):
+               return self.linear(x)
+
+        # initialize a floating point model
+        float_model = M().eval()
+
+        # define the training loop for quantization aware training
+        def train_loop(model, train_data):
+            model.train()
+            for image, target in data_loader:
+                ...
+
+        # Step 1. program capture
+        # NOTE: this API will be updated to torch.export API in the future, but the captured
+        # result should mostly stay the same
+        m = torch.export.export_for_training(m, *example_inputs).module()
+        # we get a model with aten ops
+
+        # Step 2. quantization
+        # backend developer will write their own Quantizer and expose methods to allow
+        # users to express how they
+        # want the model to be quantized
+        quantizer = XNNPACKQuantizer().set_global(get_symmetric_quantization_config())
+        m = prepare_qat_pt2e(m, quantizer)
+
+        # run quantization aware training
+        train_loop(prepared_model, train_loop)
+
+    """
+    torch._C._log_api_usage_once("quantization_api.quantize_pt2e.prepare_qat_pt2e")
+    original_graph_meta = model.meta
+    node_name_to_scope = _get_node_name_to_scope(model)
+    model = quantizer.transform_for_annotation(model)
+    quantizer.annotate(model)
+    quantizer.validate(model)
+    # Perform fusion after annotate to avoid quantizing ops in the new
+    # subgraph that don't need to be quantized
+    # TODO: only fuse if conv and bn are both configured to be quantized
+    _fuse_conv_bn_qat(model)
+    model = prepare(
+        model,
+        node_name_to_scope,
+        is_qat=True,
+        obs_or_fq_callback=quantizer.prepare_obs_or_fq_callback,
+    )
+    model.meta.update(original_graph_meta)
+    model = _disallow_eval_train(model)
+    return model
+
+
+_QUANT_OPS = [
+    torch.ops.quantized_decomposed.quantize_per_tensor.default,
+    torch.ops.quantized_decomposed.quantize_per_tensor.tensor,
+    torch.ops.quantized_decomposed.quantize_per_channel.default,
+    torch.ops.pt2e_quant.quantize_affine,
+]
+
+
+def _quant_node_constraint(n: Node) -> bool:
+    """If there is any pure ops between get_attr and quantize op they will be const propagated
+    e.g. get_attr(weight) -> transpose -> quantize -> dequantize*
+    (Note: dequantize op is not going to be constant propagated)
+
+    This filter is added because we don't want to constant fold the things that are not
+    related to quantization
+    """
+    return n.op == "call_function" and n.target in _QUANT_OPS
+
+
+@typing_extensions.deprecated(DEPRECATION_WARNING)
+def convert_pt2e(
+    model: GraphModule,
+    use_reference_representation: bool = False,
+    fold_quantize: bool = True,
+) -> GraphModule:
+    """Convert a calibrated/trained model to a quantized model
+
+    Args:
+      * `model` (torch.fx.GraphModule): calibrated/trained model
+      * `use_reference_representation` (bool): boolean flag to indicate whether to produce reference representation or not
+      * `fold_quantize` (bool): boolean flag for whether fold the quantize op or not
+
+    Returns:
+        quantized model, either in q/dq representation or reference representation
+
+    Example::
+
+        # prepared_model: the model produced by `prepare_pt2e`/`prepare_qat_pt2e` and calibration/training
+        # `convert_pt2e` produces a quantized model that represents quantized computation with
+        # quantize dequantize ops and fp32 ops by default.
+        # Please refer to
+        # https://pytorch.org/tutorials/prototype/pt2e_quant_ptq_static.html#convert-the-calibrated-model-to-a-quantized-model
+        # for detailed explanation of output quantized model
+        quantized_model = convert_pt2e(prepared_model)
+
+    """
+    torch._C._log_api_usage_once("quantization_api.quantize_pt2e.convert_pt2e")
+    if not isinstance(use_reference_representation, bool):
+        raise ValueError(
+            "Unexpected argument type for `use_reference_representation`, "
+            f"please make sure you intend to pass argument {use_reference_representation} to convert_pt2e"
+        )
+    original_graph_meta = model.meta
+    model = _convert_to_reference_decomposed_fx(model)
+    model = _fold_conv_bn_qat(model)
+
+    pm = PassManager([DuplicateDQPass()])
+    model = pm(model).graph_module
+
+    pm = PassManager([PortNodeMetaForQDQ()])
+    model = pm(model).graph_module
+
+    if fold_quantize:
+        constant_fold(model, _quant_node_constraint)
+
+    if use_reference_representation:
+        model = reference_representation_rewrite(model)
+
+    model.meta.update(original_graph_meta)
+    model = _disallow_eval_train(model)
+    return model
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantizer/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantizer/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f5cd5e8696d39781004960f47e6f44d3b1987ff4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantizer/__init__.py
@@ -0,0 +1,22 @@
+from .quantizer import (
+    DerivedQuantizationSpec,
+    EdgeOrNode,
+    FixedQParamsQuantizationSpec,
+    QuantizationAnnotation,
+    QuantizationSpec,
+    QuantizationSpecBase,
+    Quantizer,
+    SharedQuantizationSpec,
+)
+
+
+__all__ = [
+    "EdgeOrNode",
+    "Quantizer",
+    "QuantizationSpecBase",
+    "QuantizationSpec",
+    "FixedQParamsQuantizationSpec",
+    "SharedQuantizationSpec",
+    "DerivedQuantizationSpec",
+    "QuantizationAnnotation",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantizer/composable_quantizer.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantizer/composable_quantizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..15404cc560117713bf8c952f594c051b1c13e3a4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantizer/composable_quantizer.py
@@ -0,0 +1,83 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from .quantizer import QuantizationAnnotation, Quantizer
+
+
+if TYPE_CHECKING:
+    import torch
+    from torch.fx import Node
+
+__all__ = [
+    "ComposableQuantizer",
+]
+
+
+class ComposableQuantizer(Quantizer):
+    """
+    ComposableQuantizer allows users to combine more than one quantizer into a single quantizer.
+    This allows users to quantize a model with multiple quantizers. E.g., embedding quantization
+    maybe supported by one quantizer while linear layers and other ops might be supported by another
+    quantizer.
+
+    ComposableQuantizer is initialized with a list of `Quantizer` instances.
+    The order of the composition matters since that is the order in which the quantizers will be
+    applies.
+    Example:
+    ```
+    embedding_quantizer = EmbeddingQuantizer()
+    linear_quantizer = MyLinearQuantizer()
+    xnnpack_quantizer = (
+        XNNPackQuantizer()
+    )  # to handle ops not quantized by previous two quantizers
+    composed_quantizer = ComposableQuantizer(
+        [embedding_quantizer, linear_quantizer, xnnpack_quantizer]
+    )
+    prepared_m = prepare_pt2e(model, composed_quantizer)
+    ```
+    """
+
+    def __init__(self, quantizers: list[Quantizer]):
+        super().__init__()
+        self.quantizers = quantizers
+        self._graph_annotations: dict[Node, QuantizationAnnotation] = {}
+
+    def _record_and_validate_annotations(
+        self, gm: torch.fx.GraphModule, quantizer: Quantizer
+    ) -> None:
+        for n in gm.graph.nodes:
+            if "quantization_annotation" in n.meta:
+                # check if the annotation has been changed by
+                # comparing QuantizationAnnotation object id
+                if n in self._graph_annotations and (
+                    id(self._graph_annotations[n])
+                    != id(n.meta["quantization_annotation"])
+                ):
+                    raise RuntimeError(
+                        f"Quantizer {quantizer.__class__.__name__} has changed annotations on node {n}"
+                    )
+                else:
+                    self._graph_annotations[n] = n.meta["quantization_annotation"]
+            else:
+                if n in self._graph_annotations:
+                    raise RuntimeError(
+                        f"Quantizer {quantizer.__class__.__name__} has removed annotations on node {n}"
+                    )
+
+    def annotate(self, model: torch.fx.GraphModule) -> torch.fx.GraphModule:
+        """just handling global spec for now"""
+        for quantizer in self.quantizers:
+            quantizer.annotate(model)
+            self._record_and_validate_annotations(model, quantizer)
+        return model
+
+    def transform_for_annotation(
+        self, model: torch.fx.GraphModule
+    ) -> torch.fx.GraphModule:
+        for quantizer in self.quantizers:
+            model = quantizer.transform_for_annotation(model)
+        return model
+
+    def validate(self, model: torch.fx.GraphModule) -> None:
+        pass
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantizer/embedding_quantizer.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantizer/embedding_quantizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b8ef1030bfdcdeb88b58179f4f2ea83c895aad2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantizer/embedding_quantizer.py
@@ -0,0 +1,94 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import copy
+
+import torch
+import torch.nn.functional as F
+from torch.ao.quantization.observer import PerChannelMinMaxObserver
+from torch.ao.quantization.quantizer.quantizer import (
+    QuantizationAnnotation,
+    QuantizationSpec,
+    Quantizer,
+)
+from torch.ao.quantization.quantizer.xnnpack_quantizer_utils import (
+    OperatorConfig,
+    OperatorPatternType,
+    QuantizationConfig,
+)
+
+
+__all__ = [
+    "get_embedding_operators_config",
+    "EmbeddingQuantizer",
+]
+
+
+def get_embedding_operators_config() -> OperatorConfig:
+    weight_quantization_spec = QuantizationSpec(
+        dtype=torch.uint8,
+        qscheme=torch.per_channel_affine_float_qparams,
+        ch_axis=0,
+        observer_or_fake_quant_ctr=PerChannelMinMaxObserver.with_args(eps=2**-12),
+    )
+    quantization_config = QuantizationConfig(None, None, weight_quantization_spec, None)
+    ops: list[OperatorPatternType] = [[torch.nn.Embedding]]
+    ops.append([F.embedding])
+    supported_config_and_operators = OperatorConfig(
+        config=quantization_config, operators=ops
+    )
+    return copy.deepcopy(supported_config_and_operators)
+
+
+class EmbeddingQuantizer(Quantizer):
+    @classmethod
+    def get_supported_quantization_configs(cls) -> list[QuantizationConfig]:
+        op_configs: set[QuantizationConfig] = {
+            spec for spec, _ in cls.get_supported_operators()
+        }
+        return list(op_configs)
+
+    @classmethod
+    def get_supported_operator_for_quantization_config(
+        cls, quantization_config: QuantizationConfig
+    ) -> list[OperatorPatternType]:
+        for config, ops in cls.get_supported_operators():
+            # note: this assumes each entry in cls.supported_spec_and_operators
+            # corresponds to one spec, e.g. we don't have
+            # [(spec1, op_list1), (spec1, op_list2), (spec2, op_list3)]
+            # where the first and second entry have the same spec but did not
+            # merge the op list
+            if config == quantization_config:
+                return ops
+        return []
+
+    def annotate(self, model: torch.fx.GraphModule) -> torch.fx.GraphModule:
+        """just handling global spec for now"""
+        self._annotate_embedding_ops(model.graph)
+        return model
+
+    def _annotate_embedding_ops(self, graph: torch.fx.Graph) -> None:
+        embedding_config: OperatorConfig = get_embedding_operators_config()
+        for node in graph.nodes:
+            # Keep node parsing based annotations instead of module partitioners
+            # just as an example of alternate ways of annotating
+            if (
+                node.op == "call_function"
+                and node.target is torch.ops.aten.embedding.default
+            ):
+                if embedding_config.config.weight is None:
+                    raise ValueError(
+                        "Embedding config must have a valid weight quantization spec."
+                    )
+                node.meta["quantization_annotation"] = QuantizationAnnotation(
+                    input_qspec_map={
+                        node.args[0]: embedding_config.config.weight,
+                    }
+                )
+
+    def validate(self, model: torch.fx.GraphModule) -> None:
+        pass
+
+    @classmethod
+    def get_supported_operators(cls) -> list[OperatorConfig]:
+        return [get_embedding_operators_config()]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantizer/quantizer.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantizer/quantizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..d6e10526b4cc4ca58d099523d32ebd57a393a1dd
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantizer/quantizer.py
@@ -0,0 +1,182 @@
+# mypy: allow-untyped-defs
+from abc import ABC, abstractmethod
+from collections.abc import Callable
+from dataclasses import dataclass, field
+from typing import Annotated
+
+import torch
+from torch import Tensor
+from torch.ao.quantization import ObserverOrFakeQuantize
+from torch.ao.quantization.qconfig import _ObserverOrFakeQuantizeConstructor
+from torch.fx import Node
+
+
+__all__ = [
+    "Quantizer",
+    "QuantizationSpecBase",
+    "QuantizationSpec",
+    "FixedQParamsQuantizationSpec",
+    "EdgeOrNode",
+    "SharedQuantizationSpec",
+    "DerivedQuantizationSpec",
+    "QuantizationAnnotation",
+]
+
+
+class QuantizationSpecBase(ABC):  # noqa: B024
+    """Base class for different types of quantization specs that allows users to
+    specify how to quantize a Tensor (input/output of a Node) in the model
+    """
+
+
+@dataclass(eq=True, frozen=True)
+class QuantizationSpec(QuantizationSpecBase):
+    """Quantization spec for common operators that allows user to specify how to
+    quantize a Tensor, this includes dtype, quant_min, quant_max etc.
+    """
+
+    dtype: torch.dtype
+    # observer or fake_quantize constructor such as
+    # MinMaxObserver, PerChannelHistogramObserver etc.
+    # or we can attach some custom args to them
+    # e.g. MinMaxObserver.with_args(eps=eps)
+    observer_or_fake_quant_ctr: _ObserverOrFakeQuantizeConstructor
+    quant_min: int | None = None
+    quant_max: int | None = None
+    qscheme: torch.qscheme | None = None
+    ch_axis: int | None = None
+    is_dynamic: bool = False
+
+    def __post_init__(self):
+        # TODO: add init for quant_min/quant_max
+        # quant_min must be less than quant_max
+        if (
+            self.quant_min is not None
+            and self.quant_max is not None
+            and self.quant_min > self.quant_max
+        ):
+            raise ValueError(
+                f"quant_min {self.quant_min} must be <= quant_max {self.quant_max}."
+            )
+
+        # ch_axis must be less than the number of channels
+        # but no way to check here. Just check that it is not < 0.
+        if self.ch_axis is not None and self.ch_axis < 0:
+            raise ValueError("Ch_axis is < 0.")
+
+
+@dataclass(eq=True, frozen=True)
+class FixedQParamsQuantizationSpec(QuantizationSpecBase):
+    dtype: torch.dtype
+    scale: float
+    zero_point: int
+    quant_min: int | None = None
+    quant_max: int | None = None
+    qscheme: torch.qscheme | None = None
+    is_dynamic: bool = False
+
+
+"""
+The way we refer to other points of quantization in the graph will be either
+an input edge or an output value
+input edge is the connection between input node and the node consuming the input, so it's a Tuple[Node, Node]
+output value is an fx Node
+"""
+EdgeOrNode = Annotated[tuple[Node, Node] | Node, None]
+EdgeOrNode.__module__ = "torch.ao.quantization.quantizer.quantizer"
+
+
+@dataclass(eq=True, frozen=True)
+class SharedQuantizationSpec(QuantizationSpecBase):
+    """
+    Quantization spec for the Tensors whose quantization parameters are shared with other Tensors
+    """
+
+    # the edge or node to share observer or fake quant instances with
+    edge_or_node: EdgeOrNode
+
+
+@dataclass(eq=True, frozen=True)
+class DerivedQuantizationSpec(QuantizationSpecBase):
+    """Quantization spec for the Tensors whose quantization parameters are derived from other Tensors"""
+
+    derived_from: list[EdgeOrNode]
+    derive_qparams_fn: Callable[[list[ObserverOrFakeQuantize]], tuple[Tensor, Tensor]]
+    dtype: torch.dtype
+    quant_min: int | None = None
+    quant_max: int | None = None
+    qscheme: torch.qscheme | None = None
+    ch_axis: int | None = None
+    is_dynamic: bool = False
+
+
+@dataclass
+class QuantizationAnnotation:
+    """How are input argument or output should be quantized,
+    expressed as QuantizationSpec, this corresponds to how a Tensor in the
+    operator Graph is observed (PTQ) or fake quantized (QAT)
+    """
+
+    # a map from torch.fx.Node to a type of QuantizationSpecBase
+    input_qspec_map: dict[Node, QuantizationSpecBase | None] = field(
+        default_factory=dict
+    )
+
+    # How the output of this node is quantized, expressed as QuantizationSpec
+    # TODO: change the value to QuantizationSpec in a separate PR
+    output_qspec: QuantizationSpecBase | None = None
+
+    # For a Node: node1 and edge: (node1, node2), since they are observing the same
+    # Tensor, we may want to implicitly share observers, this flag allows people to
+    # turn off this behavior for the output of the node
+    allow_implicit_sharing: bool = True
+
+    # whether the node is annotated or not
+    _annotated: bool = False
+
+
+class Quantizer(ABC):
+    def transform_for_annotation(
+        self, model: torch.fx.GraphModule
+    ) -> torch.fx.GraphModule:
+        """Allows for user defined transforms to run before annotating the graph.
+        This allows quantizer to allow quantizing part of the model that are otherwise not quantizable.
+        For example quantizer can
+        a) decompose a compound operator like scaled dot product attention,
+        into bmm and softmax if quantizer knows how to quantize bmm/softmax but not sdpa
+        or b) transform scalars to tensor to allow quantizing scalares.
+
+        Note: this is an optional method
+        """
+        return model
+
+    # annotate nodes in the graph with observer or fake quant constructors
+    # to convey the desired way of quantization
+    @abstractmethod
+    def annotate(self, model: torch.fx.GraphModule) -> torch.fx.GraphModule:
+        pass
+
+    # validate the annotated graph is supported by the backend
+    @abstractmethod
+    def validate(self, model: torch.fx.GraphModule) -> None:
+        pass
+
+    def prepare_obs_or_fq_callback(
+        self,
+        model: torch.fx.GraphModule,
+        edge_or_node_to_obs_or_fq: dict[EdgeOrNode, ObserverOrFakeQuantize],
+    ) -> None:
+        """A callback that will be called after the observers or fake quants are created
+        for each sharing group, but before they are inserted into the graph. The
+        callback can be used to make final quantization adjustments, such as enforcing
+        specific scale and zero point on model input or output.
+
+        Args:
+          * `model`: the graph module being prepared.
+          * `edge_or_node_to_obs_or_fq`: a dictionary mapping each annotated edge and
+            node to the corresponding observer or fake quant object. Note that multiple
+            edges and/or nodes can map to the same observer / fake quant instance if
+            they were annotated with SharedQuantizationSpec. This dictionary can be
+            modified by the callback.
+        """
+        return
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantizer/utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantizer/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..06463ae0f2f3adb815d34b0f539fb6cde423e1ab
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantizer/utils.py
@@ -0,0 +1,90 @@
+from collections.abc import Callable
+
+from torch.ao.quantization.pt2e.utils import _is_sym_size_node
+from torch.ao.quantization.quantizer.quantizer import (
+    QuantizationAnnotation,
+    QuantizationSpecBase,
+)
+from torch.fx import Node
+
+
+__all__: list[str] = []
+
+
+def _annotate_input_qspec_map(
+    node: Node, input_node: Node, qspec: QuantizationSpecBase | None
+) -> None:
+    quantization_annotation = node.meta.get(
+        "quantization_annotation", QuantizationAnnotation()
+    )
+    if quantization_annotation.input_qspec_map is None:
+        quantization_annotation.input_qspec_map = {}
+    quantization_annotation.input_qspec_map[input_node] = qspec
+    node.meta["quantization_annotation"] = quantization_annotation
+
+
+def _annotate_output_qspec(node: Node, qspec: QuantizationSpecBase | None) -> None:
+    quantization_annotation = node.meta.get(
+        "quantization_annotation", QuantizationAnnotation()
+    )
+    quantization_annotation.output_qspec = qspec
+    node.meta["quantization_annotation"] = quantization_annotation
+
+
+def _node_only_used_for_sym_size(node: Node, partition_nodes: list[Node]) -> bool:
+    """
+    This utility is used to handle cases when dynami_shape=True tracing leads
+    to symint nodes in the pattern of linear module. In those cases, we need to
+    distinguish between the nodes that are in input for just extracting value of
+    some dimensions (and symint nodes) vs. the one that is activation.
+    For example:
+    graph(x, y, weight):
+       size_0 = torch.ops.aten.sym_size([x], [0])
+       size_1 = torch.ops.aten.sym_size([y], [1])
+       view_size = size_0 * size_1
+       size_3 = torch.ops.aten.sym_size([x], [2])
+       vie_out = torch.ops.aten.view(x, [view_size, size_3])
+       return mm(view_out, weight)
+    In the example above y node is not actual input. It exist only to extract size_1
+    """
+    if _is_sym_size_node(node):
+        return True
+
+    return all(
+        ((user not in partition_nodes) or _is_sym_size_node(user))
+        for user in node.users
+    )
+
+
+def _get_module_name_filter(module_name: str) -> Callable[[Node], bool]:
+    """Get the module_name_filter function for a given module name, the filter accepts
+    a node and checks if the node comes from a module that has certain module name
+
+    For example:
+        node: linear_op = call_function[...](...)  # comes from a module with name blocks.sub.linear1
+
+
+    >> module_name_filter = _get_module_name_filter("blocks.sub")
+    >> print(module_name_filter(node))
+    True  # the node is from "blocks.sub" based on the fully qualified name "blocks.sub.linear1"
+    """
+
+    def module_name_filter(n: Node) -> bool:
+        # example: {
+        #    'L__self___sub': ("L['self'].sub", ),
+        #    'L__self___sub_linear': ("L['self'].sub.linear", )
+        # }
+        # get_attr nodes doesn't have nn_module_stack?
+        nn_module_stack = n.meta.get("nn_module_stack", {})
+
+        def _normalize_path(n: str) -> str:
+            prefix = 0
+            # TODO This is non standard behavior and should be removed when we migrate off capture_pre_autograd_graph.
+            if n.startswith("L['self']."):
+                prefix = len("L['self'].")
+            return n[prefix:]
+
+        names = [_normalize_path(n) for n, _ in nn_module_stack.values()]
+        return module_name in names
+
+    return module_name_filter
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantizer/x86_inductor_quantizer.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantizer/x86_inductor_quantizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9cde0e2d12a6d00abfef6c2564b679286d99262
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantizer/x86_inductor_quantizer.py
@@ -0,0 +1,1605 @@
+# mypy: allow-untyped-defs
+import functools
+import itertools
+import operator
+import warnings
+from collections.abc import Callable, Sequence
+from dataclasses import dataclass
+from typing import Any, Optional, TYPE_CHECKING, TypeAlias
+
+import torch
+import torch.nn.functional as F
+from torch.ao.quantization.fake_quantize import (
+    FakeQuantize,
+    FusedMovingAvgObsFakeQuantize,
+)
+from torch.ao.quantization.observer import (
+    HistogramObserver,
+    MovingAverageMinMaxObserver,
+    MovingAveragePerChannelMinMaxObserver,
+    PerChannelMinMaxObserver,
+    PlaceholderObserver,
+)
+from torch.ao.quantization.pt2e.graph_utils import find_sequential_partitions
+from torch.ao.quantization.quantizer.quantizer import (
+    QuantizationAnnotation,
+    QuantizationSpec,
+    Quantizer,
+    SharedQuantizationSpec,
+)
+from torch.ao.quantization.quantizer.utils import _get_module_name_filter
+from torch.ao.quantization.quantizer.xnnpack_quantizer_utils import (
+    get_bias_qspec,
+    get_input_act_qspec,
+    get_output_act_qspec,
+    get_weight_qspec,
+    QuantizationConfig,
+)
+from torch.fx import Node
+from torch.fx.passes.utils.source_matcher_utils import (
+    get_source_partitions,
+    SourcePartition,
+)
+
+
+FilterFn: TypeAlias = Callable[[list[Node]], bool]
+
+
+if TYPE_CHECKING:
+    from torch.ao.quantization.qconfig import _ObserverOrFakeQuantizeConstructor
+
+__all__ = [
+    "X86InductorQuantizer",
+    "get_default_x86_inductor_quantization_config",
+    "get_x86_inductor_linear_dynamic_fp16_config",
+]
+
+
+@dataclass
+class _X86InductorQuantizationAnnotation(QuantizationAnnotation):
+    # _is_output_of_quantized_pattern:
+    #  * Node as output node of a fusion pattern.
+    #  * The fusion pattern supports int8 data type.
+    #  * The fusion pattern has inputs annotated to insert observer.
+    #  * The quantization_config is not `None`.
+    _is_output_of_quantized_pattern: bool = False
+
+
+# Operators that:
+# 1. Operators are optimized to run with int8 when int8 input provided.
+# 2. Operators do not support int8 input and produce fp32 output.
+int8_in_int8_out_ops: set = {
+    torch.ops.aten.max_pool2d.default,
+    torch.ops.aten.cat.default,
+    torch.ops.aten.avg_pool2d.default,
+    torch.ops.aten.adaptive_avg_pool2d.default,
+    torch.ops.aten.flatten.using_ints,
+}
+
+# Operators that support the int8 data type for quantization config propagation.
+# A superset of int8_in_int8_out_ops incorporating additional operators.
+propagation_quantizable_ops = int8_in_int8_out_ops
+
+# Operators support the int8 data type
+# and recipe is configured by default in X86InductorQuantizer.
+default_quantizable_ops = propagation_quantizable_ops | {
+    torch.ops.aten.conv1d.default,
+    torch.ops.aten.conv2d.default,
+    torch.ops.aten.linear.default,
+}
+
+# A superset of default_quantizable_ops includes operators support the int8 data type
+# but not enabled by default recipe of X86InductorQuantizer.
+quantizable_ops = default_quantizable_ops | {
+    torch.ops.aten.matmul.default,
+}
+
+QUANT_ANNOTATION_KEY = "quantization_annotation"
+
+
+def _skip_annotate(nodes: list[Node], filter_fn: FilterFn | None = None) -> bool:
+    """Determine whether to skip annotation for a list of nodes."""
+
+    # 1) Skip annotate if any node is already annotated
+    if _is_any_annotated(nodes):
+        return True
+
+    # 2) Proceed annotate if a) a filter function is provided
+    # and b) the given nodes list passes the filter function check.
+    if filter_fn and filter_fn(nodes):
+        return False
+
+    return True
+
+
+def _create_module_name_filter(module_name: str) -> FilterFn:
+    """Create a filter function for a given module name.
+
+    The filter function takes a list of nodes (as determined by the annotate function)
+    and return True if *all* nodes come from the specified module name, False otherwise.
+
+    For example:
+        linear_1: "f32[3, 10]" = torch.ops.aten.linear.default(...) # comes from a module with name `sub.linear1`
+        relu: "f32[3, 10]" = torch.ops.aten.relu.default(linear_1); # comes from a module with name `sub.relu1`
+
+    >> module_name_filter = _create_module_name_filter_inner("sub")
+    >> print(module_name_filter([relu, linear_1]))
+    # True  # These two nodes are determined by `_annotate_linear_unary` function and from "sub".
+    """
+
+    filter_fn = _get_module_name_filter(module_name)
+
+    def check_all_nodes_from_module(nodes: list[Node]) -> bool:
+        all_nodes_from_module_name: bool = all(filter_fn(n) for n in nodes)
+        return all_nodes_from_module_name
+
+    return check_all_nodes_from_module
+
+
+def _create_operator_type_filter(
+    operator_type: Callable,
+) -> FilterFn:
+    """Create a filter function for a given operator type.
+
+    The filter function takes a list of nodes and returns True if it contains
+    exactly one node with the specified operator type, False otherwise.
+
+    For example:
+        linear_1: "f32[3, 10]" = torch.ops.aten.linear.default(...) # comes from a module with name `sub.linear1`
+        relu: "f32[3, 10]" = torch.ops.aten.relu.default(linear_1); # comes from a module with name `sub.relu1`
+
+    >> operator_type_filter = _create_operator_type_filter(torch.ops.aten.linear.default)
+    >> print(operator_type_filter([relu, linear_1]))
+    # True  # These two nodes are determined by `_annotate_linear_unary` function and the second node is `linear`.
+    """
+
+    def operator_type_filter(nodes: list[Node]):
+        num_nodes_with_operator_type = sum(
+            node.target == operator_type for node in nodes
+        )
+        if num_nodes_with_operator_type > 1:
+            raise NotImplementedError(
+                f"Several nodes within a single pattern are {operator_type}."
+            )
+        return num_nodes_with_operator_type == 1
+
+    return operator_type_filter
+
+
+def _global_config_filter(nodes: list[Node]) -> bool:
+    """Filter function for global configuration.
+
+    This filter function takes a list of nodes and returns True if there is exactly one node
+    in the list that is a default quantizable operation, False otherwise.
+    """
+    num_nodes_in_default_quantizable_ops = sum(
+        node.target in default_quantizable_ops for node in nodes
+    )
+    if num_nodes_in_default_quantizable_ops > 1:
+        raise NotImplementedError(
+            "Several nodes within a single pattern are default quantizable operations."
+        )
+    return num_nodes_in_default_quantizable_ops == 1
+
+
+def _map_module_function_to_aten_operator_type():
+    module_function_to_aten_operator: dict[Callable, torch._ops.OpOverloadPacket] = {}
+    map_list = (
+        ([torch.nn.Conv2d, F.conv1d], torch.ops.aten.conv1d.default),
+        ([torch.nn.Conv2d, F.conv2d], torch.ops.aten.conv2d.default),
+        ([torch.nn.Linear, F.linear], torch.ops.aten.linear.default),
+        ([torch.nn.MaxPool2d, F.max_pool2d], torch.ops.aten.max_pool2d.default),
+        (
+            [
+                torch.cat,
+            ],
+            torch.ops.aten.cat.default,
+        ),
+        ([torch.nn.AvgPool2d, F.avg_pool2d], torch.ops.aten.avg_pool2d.default),
+        (
+            [torch.nn.AdaptiveAvgPool2d, F.adaptive_avg_pool2d],
+            torch.ops.aten.adaptive_avg_pool2d.default,
+        ),
+        (
+            [
+                torch.flatten,
+            ],
+            torch.ops.aten.flatten.using_ints,
+        ),
+        (
+            [
+                torch.matmul,
+            ],
+            torch.ops.aten.matmul.default,
+        ),
+    )
+    for map_item in map_list:
+        module_function_to_aten_operator.update(dict.fromkeys(map_item[0], map_item[1]))  # type: ignore[arg-type, call-overload]
+    return module_function_to_aten_operator
+
+
+def _mark_nodes_as_annotated(nodes: list[Node]):
+    for node in nodes:
+        if node is not None:
+            if QUANT_ANNOTATION_KEY not in node.meta:
+                node.meta[QUANT_ANNOTATION_KEY] = _X86InductorQuantizationAnnotation()
+            node.meta[QUANT_ANNOTATION_KEY]._annotated = True
+
+
+def _is_node_annotated(_node):
+    """
+    return True if the node is annotated, otherwise return False
+    """
+    return (
+        QUANT_ANNOTATION_KEY in _node.meta
+        and _node.meta[QUANT_ANNOTATION_KEY]._annotated
+    )
+
+
+def _is_any_annotated(nodes: list[Node]):
+    """
+    Given a list of nodes (that represents an operator pattern),
+    check if any of the node is annotated, return True if any of the node
+    is annotated, otherwise return False.
+    """
+    return any(_is_node_annotated(node) for node in nodes)
+
+
+def _is_all_annotated(nodes: list[Node]):
+    """
+    Given a list of nodes (that represents an operator pattern),
+    return True if all of the node is annotated, otherwise return False.
+    """
+    return all(_is_node_annotated(node) for node in nodes)
+
+
+def _is_quantized_op_pt2e(node: torch.fx.Node):
+    """
+    Used for pt2e flow to check if the node is a quantized node:
+    Case1: the node has been annotated as output node of a fusion pattern.
+    Case2: the node has been annotated as single quantized node.
+    """
+    if not _is_any_annotated([node]):
+        # The node has not been annotated, directly return False
+        return False
+    quantization_annotation = node.meta.get(QUANT_ANNOTATION_KEY, None)
+    if not isinstance(quantization_annotation, _X86InductorQuantizationAnnotation):
+        raise AssertionError(
+            "quantization_annotation must be an _X86InductorQuantizationAnnotation"
+        )
+    return quantization_annotation._is_output_of_quantized_pattern
+
+
+@functools.lru_cache
+def get_default_x86_inductor_quantization_config(
+    is_qat: bool = False,
+    is_dynamic: bool = False,
+    reduce_range: bool = False,
+):
+    """
+    reduce_range is False by default. Set it to True on earlier CPUs without VNNI to avoid accuracy issue.
+    """
+    extra_args: dict[str, Any] = {"eps": 2**-12}
+    if is_qat:
+        if is_dynamic:
+            act_observer_or_fake_quant_ctr = FakeQuantize
+            dynamic_quant_observer = MovingAverageMinMaxObserver.with_args(
+                averaging_constant=1
+            )
+            extra_args["observer"] = dynamic_quant_observer
+        else:
+            act_observer_or_fake_quant_ctr = FusedMovingAvgObsFakeQuantize  # type: ignore[assignment]
+    else:
+        if is_dynamic:
+            act_observer_or_fake_quant_ctr = PlaceholderObserver  # type: ignore[assignment]
+        else:
+            act_observer_or_fake_quant_ctr = HistogramObserver  # type: ignore[assignment]
+
+    # Copy from x86 default qconfig from torch/ao/quantization/qconfig.py
+    act_quantization_spec = QuantizationSpec(
+        dtype=torch.uint8,
+        quant_min=0,
+        quant_max=127 if reduce_range else 255,
+        qscheme=torch.per_tensor_affine,
+        is_dynamic=is_dynamic,
+        observer_or_fake_quant_ctr=act_observer_or_fake_quant_ctr.with_args(
+            **extra_args
+        ),
+    )
+
+    weight_observer_or_fake_quant_ctr: _ObserverOrFakeQuantizeConstructor = (
+        FusedMovingAvgObsFakeQuantize if is_qat else PerChannelMinMaxObserver
+    )
+
+    if is_qat:
+        # Only support per channel quant for now
+        extra_args["observer"] = MovingAveragePerChannelMinMaxObserver  # type: ignore[dict-item]
+    weight_quantization_spec = QuantizationSpec(
+        dtype=torch.int8,
+        quant_min=-128,
+        quant_max=127,
+        qscheme=torch.per_channel_symmetric,
+        ch_axis=0,  # 0 corresponding to weight shape = (oc, ic, kh, kw) of conv
+        is_dynamic=False,
+        observer_or_fake_quant_ctr=weight_observer_or_fake_quant_ctr.with_args(
+            **extra_args
+        ),
+    )
+    bias_quantization_spec = None  # will use placeholder observer by default
+    quantization_config = QuantizationConfig(
+        act_quantization_spec,
+        act_quantization_spec,
+        weight_quantization_spec,
+        bias_quantization_spec,
+        is_qat,
+    )
+    return quantization_config
+
+
+@functools.lru_cache
+def get_x86_inductor_linear_dynamic_fp16_config():
+    """
+    For linear_dynamic_fp16. The name may be confusing.
+    The op's behavior is fp32_input * (fp16_weight -> to_fp32) -> fp32_output.
+    """
+    weight_quantization_spec = QuantizationSpec(
+        dtype=torch.float16,
+        observer_or_fake_quant_ctr=PlaceholderObserver,
+    )
+    quantization_config = QuantizationConfig(
+        None,  # input_quantization_spec
+        None,  # output_quantization_spec
+        weight_quantization_spec,
+        None,  # bias_quantization_spec
+    )
+    return quantization_config
+
+
+def _annotate_nodes_not_quantize(nodes: Node | list[Node]) -> None:
+    """Annotate nodes to exclude them from quantization (their `quantization_config` is `None`)."""
+    if not isinstance(nodes, list):
+        nodes = [nodes]
+    for node in nodes:
+        node.meta[QUANT_ANNOTATION_KEY] = _X86InductorQuantizationAnnotation(
+            _annotated=True
+        )
+
+
+def _config_checker(method: Callable) -> Callable:
+    @functools.wraps(method)
+    def wrapper(
+        quantizer: "X86InductorQuantizer",
+        name: Any,
+        quantization_config: Optional["QuantizationConfig"],
+    ) -> "X86InductorQuantizer":
+        if quantizer._need_skip_config(quantization_config):
+            warnings.warn(
+                f"Skip the quantization config for {name}.",
+                stacklevel=2,
+            )
+            return quantizer
+        return method(quantizer, name, quantization_config)
+
+    return wrapper
+
+
+@dataclass
+class _CurrentQuantizationMode:
+    r"""Configuration defining the current quantization mode for the quantizer.
+
+    All possible current quantization modes are listed below:
+    ----------------------------------------------------------------------------------------------------------
+                |                                       dynamic_state
+     qat_state  |---------------------------------------------------------------------------------------------
+                |                           None                              |    True       |  False
+    ----------------------------------------------------------------------------------------------------------
+        None    | quantizer does not receive a non-None `quantization_config` | \             | \
+        False   | quantizer will not do QAT                                   | dynamic       | static
+        True    | quantizer will do QAT                                       | QAT + dynamic | QAT + static
+    """
+
+    qat_state: bool | None
+    dynamic_state: bool | None
+
+
+class X86InductorQuantizer(Quantizer):
+    module_function_to_aten_operator_type = _map_module_function_to_aten_operator_type()
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.global_config: QuantizationConfig | None = None
+        self.operator_type_qconfig: dict[
+            torch._ops.OpOverloadPacket, QuantizationConfig | None
+        ] = {}
+        self.module_name_qconfig: dict[str, QuantizationConfig | None] = {}
+
+    def _get_current_quantization_mode(self) -> _CurrentQuantizationMode:
+        """Retrieves the current quantization mode based on all configurations."""
+        qat_state = None
+        dynamic_state = None
+
+        # As we use `_need_skip_config` to skip all invalid configurations,
+        # we can safely assume that the all existing non-None configurations
+        # have the same quantization mode.
+        # pyrefly: ignore [bad-assignment]
+        for qconfig in (
+            list(self.module_name_qconfig.values())
+            + list(self.operator_type_qconfig.values())
+            + [self.global_config]
+        ):
+            if qconfig is not None:
+                # Query the `is_qat` state
+                if qat_state is None:
+                    qat_state = qconfig.is_qat
+                else:
+                    if qat_state != qconfig.is_qat:
+                        raise AssertionError(
+                            f"All non-None quantization configs should have the same `is_qat`,"
+                            f"but got {qat_state} and {qconfig.is_qat}."
+                        )
+                # Query the `is_dynamic` state
+                input_activation_spec = qconfig.input_activation
+                if input_activation_spec is not None:
+                    if dynamic_state is None:
+                        dynamic_state = input_activation_spec.is_dynamic
+                    else:
+                        if dynamic_state != input_activation_spec.is_dynamic:
+                            raise AssertionError(
+                                f"All non-None `input_activation_spec` should have the same `is_dynamic`,"
+                                f"but got {dynamic_state} and {input_activation_spec.is_dynamic}."
+                            )
+        return _CurrentQuantizationMode(
+            qat_state=qat_state, dynamic_state=dynamic_state
+        )
+
+    def _need_skip_config(self, quantization_config: QuantizationConfig | None) -> bool:
+        """Check if the provided quantization config is valid for X86InductorQuantizer.
+
+        Mixed static/dynamic configurations or mixed QAT/non-QAT configurations are not supported.
+        To avoid such a mix, we compare the incoming configuration with current configuration status.
+        Refer the `_CurrentQuantizationMode` definition for all possible modes.
+        """
+        if quantization_config is None:
+            return False
+
+        need_skip = False
+        current_mode = self._get_current_quantization_mode()
+        if (
+            current_mode.qat_state is not None
+            and current_mode.qat_state != quantization_config.is_qat
+        ):
+            warnings.warn(
+                "Mixed QAT and Non-QAT quantization config is not supported.",
+                stacklevel=2,
+            )
+            need_skip = True
+        if current_mode.dynamic_state is not None:
+            input_activation_spec = quantization_config.input_activation
+            if (
+                input_activation_spec is not None
+                and current_mode.dynamic_state != input_activation_spec.is_dynamic
+            ):
+                warnings.warn(
+                    "Mixed dynamic and static quantization config is not supported.",
+                    stacklevel=2,
+                )
+                need_skip = True
+        return need_skip
+
+    def set_global(self, quantization_config: QuantizationConfig):
+        if self._need_skip_config(quantization_config):
+            warnings.warn("Skip the global quantization config.", stacklevel=2)
+            return self
+        self.global_config = quantization_config
+        return self
+
+    def get_global_quantization_config(self):
+        if not isinstance(self.global_config, QuantizationConfig):
+            warnings.warn(
+                "The global_config for X86InductorQuantizer is currently invalid. \
+                Please ensure that you use set_global to establish the global quantization configuration.",
+                stacklevel=2,
+            )
+        return self.global_config
+
+    @_config_checker
+    def set_function_type_qconfig(
+        self,
+        function_type: Callable,
+        quantization_config: QuantizationConfig | None,
+    ) -> "X86InductorQuantizer":
+        if function_type in X86InductorQuantizer.module_function_to_aten_operator_type:
+            self._set_aten_operator_qconfig(
+                X86InductorQuantizer.module_function_to_aten_operator_type[
+                    function_type
+                ],
+                quantization_config,
+            )
+        else:
+            warnings.warn(
+                f"function: Unable to customize quantization config for {function_type} by X86InductorQuantizer.",
+                stacklevel=2,
+            )
+        return self
+
+    @_config_checker
+    def set_module_type_qconfig(
+        self,
+        module_type: torch.nn.Module,
+        quantization_config: QuantizationConfig | None,
+    ) -> "X86InductorQuantizer":
+        if module_type in X86InductorQuantizer.module_function_to_aten_operator_type:
+            self._set_aten_operator_qconfig(
+                X86InductorQuantizer.module_function_to_aten_operator_type[module_type],
+                quantization_config,
+            )
+        else:
+            warnings.warn(
+                f"Module: Unable to customize quantization config for {module_type} by X86InductorQuantizer.",
+                stacklevel=2,
+            )
+        return self
+
+    @_config_checker
+    def set_module_name_qconfig(
+        self, module_name: str, quantization_config: QuantizationConfig | None
+    ):
+        """Set quantization_config for a submodule with name: `module_name`, for example:
+        quantizer.set_module_name_qconfig("blocks.sub"), it will quantize all supported operator/operator
+        patterns in the submodule with this module name with the given `quantization_config`
+
+        The supported operators include `quantizable_ops` and `propagation_quantizable_ops`.
+        """
+        self.module_name_qconfig[module_name] = quantization_config
+        return self
+
+    def _set_aten_operator_qconfig(
+        self,
+        operator_type: torch._ops.OpOverloadPacket,
+        quantization_config: QuantizationConfig | None,
+    ) -> "X86InductorQuantizer":
+        if operator_type in quantizable_ops:
+            self.operator_type_qconfig[operator_type] = quantization_config
+        else:
+            warnings.warn(
+                f"operator: Unable to quantize {operator} by X86InductorQuantizer.",
+                stacklevel=2,
+            )
+        return self
+
+    def _annotate_conv_node_helper(
+        self,
+        conv_node: torch.fx.Node,
+        annotate_output: bool,
+        quantization_config: QuantizationConfig | None,
+    ) -> None:
+        """Helper function to annotate the conv node"""
+        if quantization_config is None:
+            _annotate_nodes_not_quantize(conv_node)
+            return
+        input_qspec_map = {}
+        input_node = conv_node.args[0]
+        if not isinstance(input_node, Node):
+            raise AssertionError("input_node must be a FX Node")
+        input_qspec_map[input_node] = get_input_act_qspec(quantization_config)
+        weight_node = conv_node.args[1]
+        if not isinstance(weight_node, Node):
+            raise AssertionError("weight_node must be a FX Node")
+        input_qspec_map[weight_node] = get_weight_qspec(quantization_config)
+        bias_node = None if len(conv_node.args) == 2 else conv_node.args[2]
+        if isinstance(bias_node, Node):
+            input_qspec_map[bias_node] = get_bias_qspec(quantization_config)
+        if annotate_output:
+            conv_node.meta[QUANT_ANNOTATION_KEY] = _X86InductorQuantizationAnnotation(
+                input_qspec_map=input_qspec_map,
+                _annotated=True,
+                _is_output_of_quantized_pattern=True,
+            )
+        else:
+            conv_node.meta[QUANT_ANNOTATION_KEY] = _X86InductorQuantizationAnnotation(
+                input_qspec_map=input_qspec_map,
+                _annotated=True,
+            )
+
+    def _annotate_linear_node_helper(
+        self,
+        linear_node: torch.fx.Node,
+        annotate_output: bool,
+        quantization_config: QuantizationConfig | None,
+    ) -> None:
+        """Helper function to annotate the linear node"""
+        if quantization_config is None:
+            _annotate_nodes_not_quantize(linear_node)
+            return
+        input_qspec_map = {}
+        if linear_node.target is not torch.ops.aten.linear.default:
+            raise AssertionError(
+                "linear_node.target must be torch.ops.aten.linear.default"
+            )
+        has_bias = len(linear_node.args) == 3
+        input_index = 0
+        weight_index = 1
+        bias_index = 2
+
+        input_node = linear_node.args[input_index]
+        if not isinstance(input_node, Node):
+            raise AssertionError("input_node must be a FX Node")
+        input_qspec_map[input_node] = get_input_act_qspec(quantization_config)
+
+        weight_node = linear_node.args[weight_index]
+        if not isinstance(weight_node, Node):
+            raise AssertionError("weight_node must be a FX Node")
+        input_qspec_map[weight_node] = get_weight_qspec(quantization_config)
+
+        bias_node = linear_node.args[bias_index] if has_bias else None
+        if isinstance(bias_node, Node):
+            input_qspec_map[bias_node] = get_bias_qspec(quantization_config)
+
+        if annotate_output:
+            linear_node.meta[QUANT_ANNOTATION_KEY] = _X86InductorQuantizationAnnotation(
+                input_qspec_map=input_qspec_map,
+                _annotated=True,
+                _is_output_of_quantized_pattern=True,
+            )
+        else:
+            linear_node.meta[QUANT_ANNOTATION_KEY] = _X86InductorQuantizationAnnotation(
+                input_qspec_map=input_qspec_map, _annotated=True
+            )
+
+    def _get_output_nodes_of_partitions(
+        self,
+        partition_list: list[SourcePartition],
+    ) -> list[torch.fx.Node]:
+        """Helper function to get the output node list from partition list"""
+        output_node_list = []
+        for partition in partition_list:
+            if len(partition.output_nodes) > 1:
+                raise ValueError("Input partition has more than one output node")
+            output_node = partition.output_nodes[0]
+            if not isinstance(output_node, Node):
+                raise AssertionError("output_node must be a FX Node")
+            output_node_list.append(output_node)
+        if len(output_node_list) != len(partition_list):
+            raise ValueError(
+                "length of output_node_list should equal to length of partition_list"
+            )
+        return output_node_list
+
+    def _get_input_idx_for_binary_node(
+        self,
+        conv_gemm_node: torch.fx.Node,
+        binary_node: torch.fx.Node,
+    ):
+        """Helper function to check conv_gemm and extra input node index
+        for binary node fused with conv_gemm.
+        """
+        conv_gemm_node_idx = None
+        extra_input_node_idx = None
+        if (binary_node.args[0].op == "call_function") and (  # type: ignore[union-attr]
+            binary_node.args[0] == conv_gemm_node
+        ):
+            conv_gemm_node_idx = 0
+            extra_input_node_idx = 1
+        elif (binary_node.args[1].op == "call_function") and (  # type: ignore[union-attr]
+            binary_node.args[1] == conv_gemm_node
+        ):
+            conv_gemm_node_idx = 1
+            extra_input_node_idx = 0
+        extra_input_node = binary_node.args[extra_input_node_idx]  # type: ignore[index]
+        if not isinstance(extra_input_node, Node):
+            raise AssertionError("extra_input_node must be a FX Node")
+        return conv_gemm_node_idx, extra_input_node_idx
+
+    def annotate(self, model: torch.fx.GraphModule) -> torch.fx.GraphModule:
+        """Annotate the given model with quantization configurations.
+
+        Annotation contracts:
+        1. Annotate each node according to the user's qconfig in the following order:
+        `module_name_qconfig`, `operator_type_qconfig`, and `global_config`.
+        2. Avoid re-annotating nodes already annotated in prior stages. For example,
+        if `linear1` has been annotated by `module_name_qconfig`, it won't be annotated again
+        during the processing of the 'operator_type_qconfig' or 'global_config'.
+        3. For config is `None`, the node will be annotated with `_X86InductorQuantizationAnnotation(_annotated=True)`.
+
+        For each pair of (module_name_or_operator_type_or_global, qconfig), a filter function is created.
+        This filter function checks if the node is marked by current stage and not annotated by the previous stage.
+        """
+        for module_name, quantization_config in self.module_name_qconfig.items():
+            self._annotate_with_config(
+                model, quantization_config, _create_module_name_filter(module_name)
+            )
+
+        for operator_type, quantization_config in self.operator_type_qconfig.items():
+            self._annotate_with_config(
+                model, quantization_config, _create_operator_type_filter(operator_type)
+            )
+
+        if self.global_config:
+            self._annotate_with_config(
+                model,
+                self.global_config,
+                _global_config_filter,
+            )
+
+        # Once we've annotated the model with quantization configurations, we also need to annotate
+        # the output of quantizable operations. For example, if we annotated `maxpool2d` to quantize its inputs,
+        # we will quantize its output accordingly. This enables us to fuse the dq-operator-q into a quantized op.
+        # Refer to
+        # https://github.com/intel/intel-extension-for-pytorch/blob/90d19323d96afc53fcc22ba5a7bb3fb07fdd6c1c/intel_extension_for_pytorch/quantization/_recipe.py#L487  # noqa: B950
+
+        self._annotate_output_for_int8_in_int8_out_pattern_entry(model)
+
+        return model
+
+    def _annotate_with_config(
+        self,
+        model: torch.fx.GraphModule,
+        quantization_config: QuantizationConfig | None,
+        filter_fn: FilterFn,
+    ) -> None:
+        """Annotate the model with the given quantization configuration.
+
+        High-level description of quantization recipe for X86 Inductor Backend:
+        Step 1: Apply quantization recipe for fusion patterns of conv/linear to enable int8 data type actively.
+        Step 2: Propagate quantization annotation for patterns besides conv/linear. Go through the pattern in model
+        from start to the end. If a pattern supports computation with int8 data type and inputs connected to
+        quantized patterns, annotate its inputs as quantized pattern.
+        """
+
+        # Step1: Recipe of fusion patterns like conv/linear.
+        self._annotate_conv2d_fusion_pattern(model, quantization_config, filter_fn)
+        self._annotate_linear_fusion_pattern(model, quantization_config, filter_fn)
+        self._annotate_matmul(model, quantization_config, filter_fn)
+
+        # Step2: Recipe to propagate annotation for patterns beside conv/linear.
+        # Go through all the nodes from start to end.
+        # Recipe refer to
+        # https://github.com/intel/intel-extension-for-pytorch/blob/90d19323d96afc53fcc22ba5a7bb3fb07fdd6c1c/intel_extension_for_pytorch/quantization/_recipe.py#L538  # noqa: B950
+
+        self._annotate_propagation_quantizable_pattern_entry(
+            model, quantization_config, filter_fn
+        )
+
+    def _annotate_qat_conv2d_fusion_pattern(
+        self,
+        model: torch.fx.GraphModule,
+        quantization_config: QuantizationConfig | None,
+        filter_fn: FilterFn | None = None,
+    ):
+        # Annotate QAT Specific patterns
+        self._annotate_qat_conv2d_bn_binary_unary(model, quantization_config, filter_fn)
+        self._annotate_qat_conv2d_bn_binary(model, quantization_config, filter_fn)
+        self._annotate_qat_conv2d_bn_unary(model, quantization_config, filter_fn)
+        self._annotate_qat_conv2d_bn(model, quantization_config, filter_fn)
+
+    def _annotate_qat_conv2d_bn_binary_unary(
+        self,
+        gm: torch.fx.GraphModule,
+        quantization_config: QuantizationConfig | None,
+        filter_fn: FilterFn | None = None,
+    ) -> None:
+        fused_partitions = find_sequential_partitions(
+            gm, [torch.nn.Conv2d, torch.nn.BatchNorm2d, operator.add, torch.nn.ReLU]
+        )
+        for fused_partition in fused_partitions:
+            (
+                conv_partition,
+                bn_partition,
+                binary_partition,
+                unary_partition,
+            ) = fused_partition
+
+            (
+                conv_node,
+                bn_output_node,
+                binary_node,
+                unary_node,
+            ) = self._get_output_nodes_of_partitions(
+                [conv_partition, bn_partition, binary_partition, unary_partition]
+            )
+            if len(bn_output_node.users) != 1:
+                # Conv BN pattern should only has 1 user.
+                continue
+            (
+                bn_output_node_idx,
+                extra_input_node_idx,
+            ) = self._get_input_idx_for_binary_node(bn_output_node, binary_node)
+            if (bn_output_node_idx is None) or (extra_input_node_idx is None):
+                continue
+            if bn_output_node != binary_node.args[bn_output_node_idx]:
+                raise ValueError(f"{bn_output_node} doesn't match input of binary node")
+            extra_input_node = binary_node.args[extra_input_node_idx]
+
+            if (
+                conv_node.op != "call_function"
+                or conv_node.target != torch.ops.aten.conv2d.default
+            ):
+                continue
+
+            if _skip_annotate(
+                [unary_node, binary_node, bn_output_node, conv_node], filter_fn
+            ):
+                continue
+
+            self._annotate_conv_node_helper(conv_node, False, quantization_config)
+
+            if quantization_config is not None:
+                binary_node_input_qspec_map = {}
+                binary_node_input_qspec_map[extra_input_node] = get_input_act_qspec(
+                    quantization_config
+                )
+                binary_node.meta[QUANT_ANNOTATION_KEY] = (
+                    _X86InductorQuantizationAnnotation(
+                        # pyrefly: ignore [bad-argument-type]
+                        input_qspec_map=binary_node_input_qspec_map,
+                        _annotated=True,
+                    )
+                )
+                unary_node.meta[QUANT_ANNOTATION_KEY] = (
+                    _X86InductorQuantizationAnnotation(
+                        # TODO Remove the annotate of output in QAT when qat util support pattern matcher.
+                        output_qspec=get_output_act_qspec(quantization_config),  # type: ignore[arg-type]
+                        _annotated=True,
+                        _is_output_of_quantized_pattern=True,
+                    )
+                )
+            else:
+                _annotate_nodes_not_quantize([binary_node, unary_node])
+            nodes_to_mark_annotated = list(conv_partition.nodes)
+            nodes_to_mark_annotated.extend(list(bn_partition.nodes))
+            nodes_to_mark_annotated.extend(list(binary_partition.nodes))
+            nodes_to_mark_annotated.extend(list(unary_partition.nodes))
+            _mark_nodes_as_annotated(nodes_to_mark_annotated)
+
+    def _annotate_qat_conv2d_bn_binary(
+        self,
+        gm: torch.fx.GraphModule,
+        quantization_config: QuantizationConfig | None,
+        filter_fn: FilterFn | None = None,
+    ) -> None:
+        fused_partitions = find_sequential_partitions(
+            gm, [torch.nn.Conv2d, torch.nn.BatchNorm2d, operator.add]
+        )
+        for fused_partition in fused_partitions:
+            conv_partition, bn_partition, binary_partition = fused_partition
+            (
+                conv_node,
+                bn_output_node,
+                binary_node,
+            ) = self._get_output_nodes_of_partitions(
+                [conv_partition, bn_partition, binary_partition]
+            )
+            if len(bn_output_node.users) != 1:
+                # Conv BN pattern should only has 1 user.
+                continue
+            (
+                bn_output_node_idx,
+                extra_input_node_idx,
+            ) = self._get_input_idx_for_binary_node(bn_output_node, binary_node)
+            if (bn_output_node_idx is None) or (extra_input_node_idx is None):
+                continue
+            if bn_output_node != binary_node.args[bn_output_node_idx]:
+                raise ValueError(f"{bn_output_node} doesn't match input of binary node")
+
+            extra_input_node = binary_node.args[extra_input_node_idx]
+
+            if (
+                conv_node.op != "call_function"
+                or conv_node.target != torch.ops.aten.conv2d.default
+            ):
+                continue
+
+            if _skip_annotate([binary_node, bn_output_node, conv_node], filter_fn):
+                continue
+
+            self._annotate_conv_node_helper(conv_node, False, quantization_config)
+
+            if quantization_config is not None:
+                binary_node_input_qspec_map = {}
+                binary_node_input_qspec_map[extra_input_node] = get_input_act_qspec(
+                    quantization_config
+                )
+                binary_node.meta[QUANT_ANNOTATION_KEY] = (
+                    _X86InductorQuantizationAnnotation(
+                        # pyrefly: ignore [bad-argument-type]
+                        input_qspec_map=binary_node_input_qspec_map,
+                        # TODO Remove the annotate of output in QAT when qat util support pattern matcher.
+                        output_qspec=get_output_act_qspec(quantization_config),  # type: ignore[arg-type]
+                        _annotated=True,
+                        _is_output_of_quantized_pattern=True,
+                    )
+                )
+            else:
+                _annotate_nodes_not_quantize(binary_node)
+            nodes_to_mark_annotated = list(conv_partition.nodes)
+            nodes_to_mark_annotated.extend(list(bn_partition.nodes))
+            nodes_to_mark_annotated.extend(list(binary_partition.nodes))
+            _mark_nodes_as_annotated(nodes_to_mark_annotated)
+
+    def _annotate_qat_conv2d_bn_unary(
+        self,
+        gm: torch.fx.GraphModule,
+        quantization_config: QuantizationConfig | None,
+        filter_fn: FilterFn | None = None,
+    ) -> None:
+        fused_partitions = []
+        unary_patterns = [
+            [torch.nn.Conv2d, torch.nn.BatchNorm2d, torch.nn.ReLU],
+            [torch.nn.Conv2d, torch.nn.BatchNorm2d, torch.nn.Hardtanh],
+            [torch.nn.Conv2d, torch.nn.BatchNorm2d, torch.nn.Hardswish],
+            [torch.nn.Conv2d, torch.nn.BatchNorm2d, torch.nn.ReLU6],
+            [torch.nn.Conv2d, torch.nn.BatchNorm2d, torch.nn.SiLU],
+        ]
+        for unary_pattern in unary_patterns:
+            partitions = find_sequential_partitions(gm, unary_pattern)
+            if partitions:
+                # Extend the fused_partitions if partitions is not empty
+                fused_partitions.extend(partitions)
+
+        for fused_partition in fused_partitions:
+            conv_partition, bn_partition, unary_partition = fused_partition
+            (
+                conv_node,
+                bn_output_node,
+                unary_node,
+            ) = self._get_output_nodes_of_partitions(
+                [conv_partition, bn_partition, unary_partition]
+            )
+
+            if (
+                conv_node.op != "call_function"
+                or conv_node.target != torch.ops.aten.conv2d.default
+            ):
+                continue
+
+            if _skip_annotate([unary_node, bn_output_node, conv_node], filter_fn):
+                continue
+
+            self._annotate_conv_node_helper(conv_node, False, quantization_config)
+            if quantization_config is not None:
+                unary_node.meta[QUANT_ANNOTATION_KEY] = (
+                    _X86InductorQuantizationAnnotation(
+                        # TODO Remove the annotate of output in QAT when qat util support pattern matcher.
+                        output_qspec=get_output_act_qspec(quantization_config),  # type: ignore[arg-type]
+                        _annotated=True,
+                        _is_output_of_quantized_pattern=True,
+                    )
+                )
+            else:
+                _annotate_nodes_not_quantize(unary_node)
+            nodes_to_mark_annotated = list(conv_partition.nodes)
+            nodes_to_mark_annotated.extend(list(bn_partition.nodes))
+            nodes_to_mark_annotated.extend(list(unary_partition.nodes))
+            _mark_nodes_as_annotated(nodes_to_mark_annotated)
+
+    def _annotate_qat_conv2d_bn(
+        self,
+        gm: torch.fx.GraphModule,
+        quantization_config: QuantizationConfig | None,
+        filter_fn: FilterFn | None = None,
+    ) -> None:
+        fused_partitions = find_sequential_partitions(
+            gm, [torch.nn.Conv2d, torch.nn.BatchNorm2d]
+        )
+        for fused_partition in fused_partitions:
+            conv_partition, bn_partition = fused_partition
+            conv_node, bn_output_node = self._get_output_nodes_of_partitions(
+                [conv_partition, bn_partition]
+            )
+
+            if (
+                conv_node.op != "call_function"
+                or conv_node.target != torch.ops.aten.conv2d.default
+            ):
+                continue
+
+            if _skip_annotate([bn_output_node, conv_node], filter_fn):
+                continue
+
+            self._annotate_conv_node_helper(conv_node, False, quantization_config)
+            if quantization_config is not None:
+                bn_output_node.meta[QUANT_ANNOTATION_KEY] = (
+                    _X86InductorQuantizationAnnotation(
+                        # TODO Remove the annotate of output in QAT when qat util support pattern matcher.
+                        output_qspec=get_output_act_qspec(quantization_config),  # type: ignore[arg-type]
+                        _annotated=True,
+                        _is_output_of_quantized_pattern=True,
+                    )
+                )
+            else:
+                _annotate_nodes_not_quantize(bn_output_node)
+            nodes_to_mark_annotated = list(conv_partition.nodes)
+            nodes_to_mark_annotated.extend(list(bn_partition.nodes))
+            _mark_nodes_as_annotated(nodes_to_mark_annotated)
+
+    def _annotate_conv2d_fusion_pattern(
+        self,
+        model: torch.fx.GraphModule,
+        quantization_config: QuantizationConfig | None,
+        filter_fn: FilterFn | None = None,
+    ):
+        if (quantization_config is None) or (quantization_config.is_qat):
+            # Annotate QAT specific pattern: mainly due to BN not folded in prepare_qat
+            self._annotate_qat_conv2d_fusion_pattern(
+                model, quantization_config, filter_fn
+            )
+        self._annotate_conv2d_binary_unary(model, quantization_config, filter_fn)
+        self._annotate_conv2d_binary(model, quantization_config, filter_fn)
+        self._annotate_conv2d_unary(model, quantization_config, filter_fn)
+        self._annotate_conv2d(model, quantization_config, filter_fn)
+
+    def _annotate_linear_fusion_pattern(
+        self,
+        model: torch.fx.GraphModule,
+        quantization_config: QuantizationConfig | None,
+        filter_fn: FilterFn | None = None,
+    ):
+        self._annotate_linear_binary_unary(model, quantization_config, filter_fn)
+        self._annotate_linear_unary(model, quantization_config, filter_fn)
+        self._annotate_linear(model, quantization_config, filter_fn)
+
+    def _annotate_matmul(
+        self,
+        model: torch.fx.GraphModule,
+        quantization_config: QuantizationConfig | None,
+        filter_fn: FilterFn | None = None,
+    ):
+        for node in model.graph.nodes:
+            if node.target != torch.ops.aten.matmul.default:
+                continue
+            if _skip_annotate([node], filter_fn):
+                continue
+
+            if quantization_config is None:
+                _annotate_nodes_not_quantize(node)
+                continue
+
+            input_qspec_map = {}
+            matmul_node = node
+            for input_node in matmul_node.args:
+                input_qspec_map[input_node] = get_input_act_qspec(quantization_config)
+            matmul_node.meta[QUANT_ANNOTATION_KEY] = _X86InductorQuantizationAnnotation(
+                input_qspec_map=input_qspec_map,
+                _annotated=True,
+                _is_output_of_quantized_pattern=True,
+            )
+
+    def _annotate_conv2d_binary_unary(
+        self,
+        gm: torch.fx.GraphModule,
+        quantization_config: QuantizationConfig | None,
+        filter_fn: FilterFn | None = None,
+    ) -> None:
+        # Conv2d + add + unary op
+        fused_partitions = find_sequential_partitions(
+            gm, [torch.nn.Conv2d, operator.add, torch.nn.ReLU]
+        )
+        for fused_partition in fused_partitions:
+            conv_partition, binary_partition, unary_partition = fused_partition
+            conv_node, binary_node, unary_node = self._get_output_nodes_of_partitions(
+                [conv_partition, binary_partition, unary_partition]
+            )
+            if len(conv_node.users) != 1:
+                # Conv Node should only has 1 user node
+                continue
+            conv_node_idx, extra_input_node_idx = self._get_input_idx_for_binary_node(
+                conv_node, binary_node
+            )
+            if (conv_node_idx is None) or (extra_input_node_idx is None):
+                continue
+            if conv_node != binary_node.args[conv_node_idx]:
+                raise ValueError(f"{conv_node} doesn't match input of binary node")
+            extra_input_node = binary_node.args[extra_input_node_idx]
+            if (
+                conv_node.op != "call_function"
+                or conv_node.target != torch.ops.aten.conv2d.default
+            ):
+                # No conv node found to be fused with add
+                continue
+            if _skip_annotate([unary_node, binary_node, conv_node], filter_fn):
+                continue
+
+            if quantization_config is None:
+                _annotate_nodes_not_quantize([conv_node, binary_node, unary_node])
+                continue
+
+            self._annotate_conv_node_helper(conv_node, False, quantization_config)
+            binary_node_input_qspec_map = {}
+            binary_node_input_qspec_map[extra_input_node] = get_input_act_qspec(
+                quantization_config
+            )
+            binary_node.meta[QUANT_ANNOTATION_KEY] = _X86InductorQuantizationAnnotation(
+                # pyrefly: ignore [bad-argument-type]
+                input_qspec_map=binary_node_input_qspec_map,
+                _annotated=True,
+            )
+            unary_node.meta[QUANT_ANNOTATION_KEY] = _X86InductorQuantizationAnnotation(
+                _annotated=True,
+                _is_output_of_quantized_pattern=True,
+            )
+
+    def _annotate_conv2d_binary(
+        self,
+        gm: torch.fx.GraphModule,
+        quantization_config: QuantizationConfig | None,
+        filter_fn: FilterFn | None = None,
+    ) -> None:
+        # Conv2d + add
+        fused_partitions = find_sequential_partitions(
+            gm, [torch.nn.Conv2d, operator.add]
+        )
+        for fused_partition in fused_partitions:
+            conv_partition, binary_partition = fused_partition
+            conv_node, binary_node = self._get_output_nodes_of_partitions(
+                [conv_partition, binary_partition]
+            )
+            if len(conv_node.users) != 1:
+                # Conv Node should only has 1 user node
+                continue
+            conv_node_idx, extra_input_node_idx = self._get_input_idx_for_binary_node(
+                conv_node, binary_node
+            )
+            if (conv_node_idx is None) or (extra_input_node_idx is None):
+                continue
+            if conv_node != binary_node.args[conv_node_idx]:
+                raise ValueError(f"{conv_node} doesn't match input of binary node")
+            extra_input_node = binary_node.args[extra_input_node_idx]
+            if not isinstance(conv_node, Node):
+                raise AssertionError("conv_node must be a FX Node")
+            if (
+                conv_node.op != "call_function"
+                or conv_node.target != torch.ops.aten.conv2d.default
+            ):
+                # No conv node found to be fused with add
+                continue
+            if _skip_annotate([binary_node, conv_node], filter_fn):
+                continue
+
+            if quantization_config is None:
+                _annotate_nodes_not_quantize([conv_node, binary_node])
+                continue
+
+            self._annotate_conv_node_helper(conv_node, False, quantization_config)
+            binary_node_input_qspec_map = {}
+            binary_node_input_qspec_map[extra_input_node] = get_input_act_qspec(
+                quantization_config
+            )
+            binary_node.meta[QUANT_ANNOTATION_KEY] = _X86InductorQuantizationAnnotation(
+                # pyrefly: ignore [bad-argument-type]
+                input_qspec_map=binary_node_input_qspec_map,
+                _annotated=True,
+                _is_output_of_quantized_pattern=True,
+            )
+
+    def _annotate_conv2d_unary(
+        self,
+        gm: torch.fx.GraphModule,
+        quantization_config: QuantizationConfig | None,
+        filter_fn: FilterFn | None = None,
+    ) -> None:
+        fused_partitions = []
+        unary_patterns = [
+            [torch.nn.Conv2d, torch.nn.ReLU],
+            [torch.nn.Conv2d, torch.nn.Hardtanh],
+            [torch.nn.Conv2d, torch.nn.Hardswish],
+            [torch.nn.Conv2d, torch.nn.ReLU6],
+            [torch.nn.Conv2d, torch.nn.SiLU],
+            [torch.nn.Conv1d, torch.nn.ReLU],
+        ]
+        for unary_pattern in unary_patterns:
+            partitions = find_sequential_partitions(gm, unary_pattern)
+            if partitions:
+                # Extend the fused_partitions if partitions is not empty
+                fused_partitions.extend(partitions)
+
+        for fused_partition in fused_partitions:
+            conv_partition, unary_partition = fused_partition
+            conv_node, unary_node = self._get_output_nodes_of_partitions(
+                [conv_partition, unary_partition]
+            )
+            if conv_node.op != "call_function" or conv_node.target not in (
+                torch.ops.aten.conv2d.default,
+                torch.ops.aten.conv1d.default,
+            ):
+                continue
+            if _skip_annotate([unary_node, conv_node], filter_fn):
+                continue
+
+            if quantization_config is None:
+                _annotate_nodes_not_quantize([conv_node, unary_node])
+                continue
+
+            self._annotate_conv_node_helper(conv_node, False, quantization_config)
+            unary_node.meta[QUANT_ANNOTATION_KEY] = _X86InductorQuantizationAnnotation(
+                _annotated=True,
+                _is_output_of_quantized_pattern=True,
+            )
+
+    def _annotate_conv2d(
+        self,
+        gm: torch.fx.GraphModule,
+        quantization_config: QuantizationConfig | None,
+        filter_fn: FilterFn | None = None,
+    ) -> None:
+        conv_partitions = get_source_partitions(
+            gm.graph, [torch.nn.Conv2d, torch.nn.functional.conv2d]
+        )
+        conv_partitions = list(itertools.chain.from_iterable(conv_partitions.values()))
+        for conv_partition in conv_partitions:
+            if len(conv_partition.output_nodes) > 1:
+                raise ValueError("conv partition has more than one output node")
+            conv_node = conv_partition.output_nodes[0]
+            if (
+                conv_node.op != "call_function"
+                or conv_node.target != torch.ops.aten.conv2d.default
+            ):
+                raise ValueError(f"{conv_node} is not an aten conv2d operator")
+            # skip annotation if it is already annotated
+            if _skip_annotate([conv_node], filter_fn):
+                continue
+            self._annotate_conv_node_helper(conv_node, True, quantization_config)
+
+    def _annotate_maxpool2d(
+        self,
+        node: Node,
+        quantization_config: QuantizationConfig | None,
+    ) -> None:
+        if node.target is not torch.ops.aten.max_pool2d.default:
+            return
+        if quantization_config is None:
+            _annotate_nodes_not_quantize(node)
+            return
+
+        maxpool_node = node
+        if _is_any_annotated(
+            [
+                maxpool_node,
+            ]
+        ):
+            return
+
+        input_node = maxpool_node.args[0]
+        if not isinstance(input_node, Node):
+            raise AssertionError("input_node must be a FX Node")
+        input_qspec_map = {}
+        input_qspec_map[input_node] = get_input_act_qspec(quantization_config)
+        maxpool_node.meta[QUANT_ANNOTATION_KEY] = _X86InductorQuantizationAnnotation(
+            input_qspec_map=input_qspec_map,
+            _annotated=True,
+            _is_output_of_quantized_pattern=True,
+        )
+
+    def _annotate_cat(
+        self, node: Node, quantization_config: QuantizationConfig
+    ) -> None:
+        if quantization_config is None:
+            _annotate_nodes_not_quantize(node)
+            return
+        cat_node = node
+        input_nodes = cat_node.args[0]
+        if not isinstance(input_nodes, Sequence):
+            raise AssertionError("input_nodes must be a Sequence of FX Nodes")
+        first_input_node = input_nodes[0]
+        input_qspec_map = {}
+        if not isinstance(first_input_node, Node):
+            raise AssertionError("first_input_node must be a FX Node")
+        if not isinstance(cat_node, Node):
+            raise AssertionError("cat_node must be a FX Node")
+        input_qspec_map[first_input_node] = get_input_act_qspec(quantization_config)
+        share_qparams_with_input_act0_qspec = SharedQuantizationSpec(
+            (first_input_node, cat_node)
+        )
+
+        for input_node in input_nodes[1:]:
+            if input_node not in input_qspec_map:
+                # There has the case of cat same nodes: torch.cat([input0, input0], 1)
+                if not isinstance(input_node, Node):
+                    raise AssertionError("input_node must be a FX Node")
+                input_qspec_map[input_node] = share_qparams_with_input_act0_qspec
+
+        cat_node.meta[QUANT_ANNOTATION_KEY] = _X86InductorQuantizationAnnotation(
+            input_qspec_map=input_qspec_map,
+            _annotated=True,
+            _is_output_of_quantized_pattern=True,
+        )
+
+    def _annotate_propagation_quantizable_pattern_entry(
+        self,
+        gm: torch.fx.GraphModule,
+        quantization_config: QuantizationConfig | None,
+        filter_fn: FilterFn | None = None,
+    ):
+        for node in gm.graph.nodes:
+            self._annotate_propagation_quantizable_pattern(
+                node, quantization_config, filter_fn
+            )
+
+    def _annotate_propagation_quantizable_pattern(
+        self, node: Node, quantization_config, filter_fn
+    ) -> None:
+        # Propagate annotation to quantizable patterns.
+        if (
+            (node.target in propagation_quantizable_ops)
+            and (not _is_any_annotated([node]))
+            and (node.op == "call_function")
+        ):
+
+            def is_all_inputs_connected_to_quantized_op(input_nodes):
+                # Ensure all the inputs connect to fusion pattern or quantized node
+                for input_node in input_nodes:
+                    if not _is_quantized_op_pt2e(input_node):
+                        return False
+                return True
+
+            if _skip_annotate([node], filter_fn):
+                return
+
+            if quantization_config is None:
+                _annotate_nodes_not_quantize(node)
+                return
+
+            if node.target is torch.ops.aten.max_pool2d.default:
+                # Recipe of maxpool2d: check input arg[0] of maxpool2d is quantized or not
+                input_nodes_to_check = [node.all_input_nodes[0]]
+                if not is_all_inputs_connected_to_quantized_op(input_nodes_to_check):
+                    if quantization_config is not None:
+                        warnings.warn(
+                            f"The input of maxpool2d is not quantized, skip annotate maxpool2d with config {quantization_config}.",
+                            stacklevel=2,
+                        )
+                    return
+
+                self._annotate_maxpool2d(node, quantization_config)
+                return
+            elif node.target is torch.ops.aten.cat.default:
+                input_nodes_to_check = node.all_input_nodes
+                if not is_all_inputs_connected_to_quantized_op(input_nodes_to_check):
+                    return
+                self._annotate_cat(node, quantization_config)
+            elif (
+                node.target is torch.ops.aten.flatten.using_ints
+                and len(node.users) > 0
+                and not any(user.target in quantizable_ops for user in node.users)
+            ):
+                # Recipe of flatten: check if any users of flatten node are quantizable ops or not
+                return
+            else:
+                input_node = node.all_input_nodes[0]
+                if not is_all_inputs_connected_to_quantized_op(
+                    [
+                        input_node,
+                    ]
+                ):
+                    return
+                input_qspec_map = {}
+                input_qspec_map[input_node] = get_input_act_qspec(quantization_config)
+                node.meta[QUANT_ANNOTATION_KEY] = _X86InductorQuantizationAnnotation(
+                    input_qspec_map=input_qspec_map,
+                    _annotated=True,
+                    _is_output_of_quantized_pattern=True,
+                )
+        return
+
+    def _annotate_output_share_observer_as_input(
+        self, input_node: Node, source_node: Node
+    ):
+        source_node_quantization_annotation = source_node.meta.get(QUANT_ANNOTATION_KEY)
+        if (
+            source_node_quantization_annotation
+            and source_node_quantization_annotation._is_output_of_quantized_pattern
+        ):
+            edge_or_node = (input_node, source_node)
+            source_node_quantization_annotation.output_qspec = SharedQuantizationSpec(
+                edge_or_node
+            )
+        return
+
+    def _annotate_output_for_int8_in_int8_out_pattern_entry(
+        self,
+        model: torch.fx.GraphModule,
+    ):
+        for node in model.graph.nodes:
+            self._annotate_output_for_int8_in_int8_out_pattern(node)
+
+    def _annotate_output_for_int8_in_int8_out_pattern(
+        self,
+        node: Node,
+    ) -> None:
+        r"""
+        Check and insert observer at output of node in int8_in_int8_out_ops if needed.
+        Recipe refers to
+        https://github.com/intel/intel-extension-for-pytorch/blob/90d19323d96afc53fcc22ba5a7bb3fb07fdd6c1c/intel_extension_for_pytorch/quantization/_utils.py#L495
+        """  # noqa: B950
+        edge_or_node: tuple[Node, Node]
+        if (node.target in int8_in_int8_out_ops) and (_is_any_annotated([node])):
+            if node.target is torch.ops.aten.max_pool2d.default:
+                maxpool_node = node
+                if not _is_all_annotated(
+                    [
+                        maxpool_node,
+                    ]
+                ):
+                    return
+
+                # Get the quantization_annotation from getitem_node
+                maxpool_node_quantization_annotation = maxpool_node.meta.get(
+                    QUANT_ANNOTATION_KEY
+                )
+                if (
+                    maxpool_node_quantization_annotation
+                    and maxpool_node_quantization_annotation._is_output_of_quantized_pattern
+                ):
+                    # Annotate the output_qspec of getitem_node
+                    input_act = maxpool_node.args[0]
+                    if not isinstance(input_act, Node):
+                        raise AssertionError("input_act must be a FX Node")
+                    if not isinstance(maxpool_node, Node):
+                        raise AssertionError("maxpool_node must be a FX Node")
+                    edge_or_node = (input_act, maxpool_node)
+                    maxpool_node_quantization_annotation.output_qspec = (
+                        SharedQuantizationSpec(edge_or_node)
+                    )
+            else:
+                input_node = node.all_input_nodes[0]
+                self._annotate_output_share_observer_as_input(input_node, node)
+        return
+
+    def _annotate_linear(
+        self,
+        gm: torch.fx.GraphModule,
+        quantization_config: QuantizationConfig | None,
+        filter_fn: FilterFn | None = None,
+    ) -> None:
+        linear_partitions = get_source_partitions(
+            gm.graph, [torch.nn.Linear, torch.nn.functional.linear]
+        )
+        linear_partitions = list(
+            itertools.chain.from_iterable(linear_partitions.values())
+        )
+        for partition in linear_partitions:
+            if len(partition.output_nodes) > 1:
+                raise ValueError(
+                    "Linear partition cannot have more than one output node"
+                )
+            linear_node = partition.output_nodes[0]
+            if (
+                linear_node.op != "call_function"
+                or linear_node.target != torch.ops.aten.linear.default
+            ):
+                raise ValueError(f"{linear_node} is not an aten linear operator")
+            # skip annotation if it is already annotated
+            if _skip_annotate([linear_node], filter_fn):
+                continue
+            self._annotate_linear_node_helper(linear_node, True, quantization_config)
+
+    def _annotate_linear_unary(
+        self,
+        gm: torch.fx.GraphModule,
+        quantization_config: QuantizationConfig | None,
+        filter_fn: FilterFn | None = None,
+    ) -> None:
+        postop_list = [
+            torch.nn.ReLU,
+            torch.nn.LeakyReLU,
+            torch.nn.Tanh,
+            torch.nn.GELU,
+        ]
+        fused_partitions: list[tuple] = []
+        for postop in postop_list:
+            fused_partitions = fused_partitions + find_sequential_partitions(
+                gm, [torch.nn.Linear, postop]
+            )
+        for fused_partition in fused_partitions:
+            linear_partition, unary_partition = fused_partition
+            linear_node, unary_node = self._get_output_nodes_of_partitions(
+                [linear_partition, unary_partition]
+            )
+            if (
+                linear_node.op != "call_function"
+                or linear_node.target != torch.ops.aten.linear.default
+            ):
+                continue
+            if _skip_annotate([unary_node, linear_node], filter_fn):
+                continue
+
+            if quantization_config is None:
+                _annotate_nodes_not_quantize([linear_node, unary_node])
+                continue
+
+            self._annotate_linear_node_helper(linear_node, False, quantization_config)
+            unary_node.meta[QUANT_ANNOTATION_KEY] = _X86InductorQuantizationAnnotation(
+                _annotated=True,
+                _is_output_of_quantized_pattern=True,
+            )
+
+    def _annotate_linear_binary_unary(
+        self,
+        gm: torch.fx.GraphModule,
+        quantization_config: QuantizationConfig | None,
+        filter_fn: FilterFn | None = None,
+    ) -> None:
+        # linear + binary_op + (optional) unary op
+        binary_op_list = [operator.add]
+        unary_op_list = [torch.nn.ReLU, None]
+        combinations = itertools.product(binary_op_list, unary_op_list)
+        for binary_op, unary_op in combinations:
+            has_unary = unary_op is not None
+            seq_partition = [torch.nn.Linear, binary_op]
+            if has_unary:
+                # pyrefly: ignore [bad-argument-type]
+                seq_partition.append(unary_op)
+            fused_partitions = find_sequential_partitions(gm, seq_partition)
+            for fused_partition in fused_partitions:
+                unary_partition, unary_node = None, None
+                if has_unary:
+                    (
+                        linear_partition,
+                        binary_partition,
+                        unary_partition,
+                    ) = fused_partition
+                    (
+                        linear_node,
+                        binary_node,
+                        unary_node,
+                    ) = self._get_output_nodes_of_partitions(
+                        [linear_partition, binary_partition, unary_partition]
+                    )
+                else:
+                    linear_partition, binary_partition = fused_partition
+                    linear_node, binary_node = self._get_output_nodes_of_partitions(
+                        [linear_partition, binary_partition]
+                    )
+                if len(linear_node.users) != 1:
+                    # Linear Node should only has 1 user node
+                    continue
+                (
+                    linear_node_idx,
+                    extra_input_node_idx,
+                ) = self._get_input_idx_for_binary_node(linear_node, binary_node)
+                if (linear_node_idx is None) or (extra_input_node_idx is None):
+                    continue
+                if linear_node != binary_node.args[linear_node_idx]:
+                    raise ValueError(
+                        f"{linear_node} doesn't match input of binary node"
+                    )
+                if not isinstance(linear_node, Node):
+                    raise AssertionError("linear_node must be a FX Node")
+                if (
+                    linear_node.op != "call_function"
+                    or linear_node.target != torch.ops.aten.linear.default
+                ):
+                    # No linear node found to be fused with add
+                    continue
+                node_list = (
+                    [binary_node, linear_node]
+                    if unary_node is None
+                    else [unary_node, binary_node, linear_node]
+                )
+                if _skip_annotate(node_list, filter_fn):
+                    continue
+
+                if quantization_config is None:
+                    _annotate_nodes_not_quantize(node_list)
+                    continue
+
+                self._annotate_linear_node_helper(
+                    linear_node, False, quantization_config
+                )
+                # We don't insert q-dq before the binary input node due to accuracy issues
+                binary_node.meta[QUANT_ANNOTATION_KEY] = (
+                    _X86InductorQuantizationAnnotation(
+                        input_qspec_map={},
+                        _annotated=True,
+                        _is_output_of_quantized_pattern=(not has_unary),
+                    )
+                )
+                if unary_node is not None:
+                    unary_node.meta[QUANT_ANNOTATION_KEY] = (
+                        _X86InductorQuantizationAnnotation(
+                            _annotated=True,
+                            _is_output_of_quantized_pattern=True,
+                        )
+                    )
+
+    def validate(self, model: torch.fx.GraphModule) -> None:
+        pass
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantizer/xnnpack_quantizer.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantizer/xnnpack_quantizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..d3a2234fdff3f137170d2810ef82fe8b7c706c0c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantizer/xnnpack_quantizer.py
@@ -0,0 +1,451 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import copy
+import functools
+import typing_extensions
+from typing import Any, TYPE_CHECKING
+
+import torch
+import torch._dynamo as torchdynamo
+import torch.nn.functional as F
+from torch.ao.quantization.fake_quantize import (
+    FakeQuantize,
+    FusedMovingAvgObsFakeQuantize,
+)
+from torch.ao.quantization.observer import (
+    HistogramObserver,
+    MinMaxObserver,
+    MovingAverageMinMaxObserver,
+    MovingAveragePerChannelMinMaxObserver,
+    PerChannelMinMaxObserver,
+    PlaceholderObserver,
+)
+from torch.ao.quantization.quantizer import QuantizationSpec, Quantizer
+from torch.ao.quantization.quantizer.utils import _get_module_name_filter
+from torch.ao.quantization.quantizer.xnnpack_quantizer_utils import (
+    _convert_scalars_to_attrs,
+    OP_TO_ANNOTATOR,
+    OperatorConfig,
+    OperatorPatternType,
+    propagate_annotation,
+    QuantizationConfig,
+)
+from torch.fx._compatibility import compatibility
+
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+    from torch.ao.quantization.qconfig import _ObserverOrFakeQuantizeConstructor
+    from torch.fx import Node
+
+
+__all__ = [
+    "XNNPACKQuantizer",
+    "get_symmetric_quantization_config",
+]
+
+
+def _get_dynamo_graph(function: Callable, inputs) -> torch.fx.Graph:
+    gm, _ = torchdynamo.export(function, aten_graph=True)(*inputs)
+    gm.graph.eliminate_dead_code()
+    return gm.graph
+
+
+def _get_linear_patterns(input_size: list[int]):
+    in_channels = input_size[-1]
+    out_channels = 8  # hard coding but this should not matter
+    weight = torch.ones((out_channels, in_channels))
+    bias = torch.ones((out_channels,))
+    act = torch.ones(input_size)
+
+    def linear_op(act, weight, bias=None):
+        return F.linear(act, weight, bias)
+
+    pattern_w_bias = _get_dynamo_graph(linear_op, (act, weight, bias))
+    pattern_wo_bias = _get_dynamo_graph(linear_op, (act, weight))
+    return [pattern_w_bias, pattern_wo_bias]
+
+
+def _supported_symmetric_quantized_operators() -> dict[str, list[OperatorPatternType]]:
+    supported_operators: dict[str, list[OperatorPatternType]] = {
+        # Both conv and linear should be able to handle relu + hardtanh fusion since
+        # those are clamp ops
+        "conv2d": [
+            [torch.nn.Conv2d, torch.nn.ReLU],
+            [torch.nn.Conv2d, F.relu],
+            [F.conv2d, torch.nn.ReLU],
+            [F.conv2d, F.relu],
+        ],
+        "linear": [[torch.nn.Linear], [F.linear]],
+        "add": [[torch.add]],
+        "adaptive_avg_pool2d": [
+            [torch.nn.AdaptiveAvgPool2d],
+            [F.adaptive_avg_pool2d],
+        ],
+    }
+    return copy.deepcopy(supported_operators)
+
+
+def _get_supported_symmetric_config_and_operators() -> list[OperatorConfig]:
+    supported_config_and_operators: list[OperatorConfig] = []
+    for quantization_config in [
+        get_symmetric_quantization_config(),
+        get_symmetric_quantization_config(is_qat=True),
+        get_symmetric_quantization_config(is_per_channel=True),
+        get_symmetric_quantization_config(is_per_channel=True, is_qat=True),
+    ]:
+        ops = _supported_symmetric_quantized_operators()
+        supported_config_and_operators.extend(
+            OperatorConfig(quantization_config, pattern_list)
+            for pattern_list in ops.values()
+        )
+    return copy.deepcopy(supported_config_and_operators)
+
+
+@functools.lru_cache
+def get_symmetric_quantization_config(
+    is_per_channel: bool = False,
+    is_qat: bool = False,
+    is_dynamic: bool = False,
+    act_qmin: int = -128,
+    act_qmax: int = 127,
+    weight_qmin: int = -127,
+    weight_qmax: int = 127,
+):
+    extra_args: dict[str, Any] = {"eps": 2**-12}
+    if is_qat:
+        if is_dynamic:
+            act_observer_or_fake_quant_ctr = FakeQuantize
+            dynamic_quant_observer = MovingAverageMinMaxObserver.with_args(
+                averaging_constant=1
+            )
+            extra_args["observer"] = dynamic_quant_observer
+        else:
+            act_observer_or_fake_quant_ctr = FusedMovingAvgObsFakeQuantize  # type: ignore[assignment]
+    else:
+        if is_dynamic:
+            act_observer_or_fake_quant_ctr = PlaceholderObserver  # type: ignore[assignment]
+        else:
+            act_observer_or_fake_quant_ctr = HistogramObserver  # type: ignore[assignment]
+
+    act_quantization_spec = QuantizationSpec(
+        dtype=torch.int8,
+        quant_min=act_qmin,
+        quant_max=act_qmax,
+        qscheme=torch.per_tensor_affine,
+        is_dynamic=is_dynamic,
+        observer_or_fake_quant_ctr=act_observer_or_fake_quant_ctr.with_args(
+            **extra_args,
+        ),
+    )
+    weight_qscheme = (
+        torch.per_channel_symmetric if is_per_channel else torch.per_tensor_symmetric
+    )
+    weight_observer_or_fake_quant_ctr: _ObserverOrFakeQuantizeConstructor = (
+        MinMaxObserver
+    )
+    if is_qat:
+        # TODO: qat + per channel?
+        weight_observer_or_fake_quant_ctr = FusedMovingAvgObsFakeQuantize
+    elif is_per_channel:
+        weight_observer_or_fake_quant_ctr = PerChannelMinMaxObserver
+
+    extra_args: dict[str, Any] = {"eps": 2**-12}
+    if is_qat:
+        if weight_qscheme == torch.per_tensor_symmetric:
+            extra_args["observer"] = MovingAverageMinMaxObserver
+        else:
+            extra_args["observer"] = MovingAveragePerChannelMinMaxObserver  # type: ignore[dict-item]
+    weight_quantization_spec = QuantizationSpec(
+        dtype=torch.int8,
+        quant_min=weight_qmin,
+        quant_max=weight_qmax,
+        qscheme=weight_qscheme,
+        ch_axis=0,
+        is_dynamic=False,
+        observer_or_fake_quant_ctr=weight_observer_or_fake_quant_ctr.with_args(
+            **extra_args
+        ),
+    )
+
+    bias_quantization_spec = None
+    if is_dynamic:
+        quantization_config = QuantizationConfig(
+            act_quantization_spec,
+            None,
+            weight_quantization_spec,
+            bias_quantization_spec,
+            is_qat,
+        )
+    else:
+        quantization_config = QuantizationConfig(
+            act_quantization_spec,
+            act_quantization_spec,
+            weight_quantization_spec,
+            bias_quantization_spec,
+            is_qat,
+        )
+    return quantization_config
+
+
+def _get_supported_config_and_operators() -> list[OperatorConfig]:
+    return _get_supported_symmetric_config_and_operators()
+
+
+def _get_module_type_filter(tp: Callable):
+    """Get the module_type_filter function for a given module type, the filter accepts
+    a node and checks if the node comes from a module that has certain module type
+
+    For example:
+        node: linear_op = call_function[...](...)  # comes from a module with type Block -> Sub -> Linear
+
+
+    >> module_type_filter = _get_module_type_filter(Sub)  # submodule with type `Sub`, under the `Block` submodule
+    >> print(module_type_filter(node))
+    True  # the node is from the submodule `Sub` (same for `Block` and `Linear` as well)
+    """
+
+    tp_str = tp.__module__ + "." + tp.__qualname__
+
+    def module_type_filter(n: Node) -> bool:
+        # example: {
+        #     'L__self___sub': ("L['self'].sub", ),
+        #     'L__self___sub_linear': ("L['self'].sub.linear", )
+        # }
+        nn_module_stack = n.meta.get("nn_module_stack", {})
+        types = []
+        for _, t in nn_module_stack.values():
+            # export() returns str, but older APIs (e.g. capture_pre_autograd_graph)
+            # return type. Handle both cases.
+            if isinstance(t, type):
+                t = t.__module__ + "." + t.__qualname__
+            types.append(t)
+        return tp_str in types
+
+    return module_type_filter
+
+
+def _get_not_module_type_or_name_filter(
+    tp_list: list[Callable], module_name_list: list[str]
+) -> Callable[[Node], bool]:
+    module_type_filters = [_get_module_type_filter(tp) for tp in tp_list]
+    module_name_list_filters = [_get_module_name_filter(m) for m in module_name_list]
+
+    def not_module_type_or_name_filter(n: Node) -> bool:
+        return not any(f(n) for f in module_type_filters + module_name_list_filters)
+
+    return not_module_type_or_name_filter
+
+
+@compatibility(is_backward_compatible=False)
+@typing_extensions.deprecated(
+    "XNNPACKQuantizer is deprecated! Please use xnnpack quantizer in "
+    "ExecuTorch (https://github.com/pytorch/executorch/tree/main/backends/xnnpack/quantizer) instead."
+)
+class XNNPACKQuantizer(Quantizer):
+    """
+    !!! DEPRECATED !!!
+    XNNPACKQuantizer is a marked as deprecated. It will be removed in the future.
+    It has been moved to executorch.backends.xnnpack.quantizer.xnnpack_quantizer.XNNPACKQuantizer.
+    Please use the new quantizer instead.
+    """
+
+    supported_config_and_operators = _get_supported_config_and_operators()
+    STATIC_QAT_ONLY_OPS = [
+        "conv_bn_relu",
+        "conv_bn",
+        "conv_transpose_bn_relu",
+        "conv_transpose_bn",
+    ]
+
+    # static quantization ops (both PTQ and QAT)
+    # Preserve the order that fusions come before singular ops
+    STATIC_OPS = [
+        "linear_relu",
+        "linear",
+        "conv_relu",
+        "conv",
+        "conv_transpose_relu",
+        "adaptive_avg_pool2d",
+        # TODO: move this to BoltNNQuantizer?
+        "gru_io_only",
+        "add_relu",
+        "add",
+        "mul_relu",
+        "mul",
+        "cat",
+    ]
+
+    DYNAMIC_OPS = [
+        "linear",
+    ]
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.global_config: QuantizationConfig | None = None
+        self.operator_type_config: dict[
+            torch._ops.OpOverloadPacket, QuantizationConfig | None
+        ] = {}
+        self.module_type_config: dict[Callable, QuantizationConfig | None] = {}
+        self.module_name_config: dict[str, QuantizationConfig | None] = {}
+
+    @classmethod
+    def get_supported_quantization_configs(cls) -> list[QuantizationConfig]:
+        op_configs: set[QuantizationConfig] = {
+            spec for spec, _ in cls.supported_config_and_operators
+        }
+        return list(op_configs)
+
+    @classmethod
+    def get_supported_operator_for_quantization_config(
+        cls, quantization_config: QuantizationConfig | None
+    ) -> list[OperatorPatternType]:
+        if quantization_config is None:
+            all_ops = []
+            for _, ops in cls.supported_config_and_operators:
+                all_ops.extend(ops)
+            return all_ops
+
+        for config, ops in cls.supported_config_and_operators:
+            # note: this assumes each entry in cls.supported_spec_and_operators
+            # corresponds to one spec, e.g. we don't have
+            # [(spec1, op_list1), (spec1, op_list2), (spec2, op_list3)]
+            # where the first and second entry have the same spec but did not
+            # merge the op list
+            if config == quantization_config:
+                return ops
+        return []
+
+    def set_global(self, quantization_config: QuantizationConfig) -> XNNPACKQuantizer:
+        self.global_config = quantization_config
+        return self
+
+    def set_operator_type(
+        self,
+        operator_type: torch._ops.OpOverloadPacket,
+        quantization_config: QuantizationConfig,
+    ) -> XNNPACKQuantizer:
+        self.operator_type_config[operator_type] = quantization_config
+        return self
+
+    def set_module_type(
+        self, module_type: Callable, quantization_config: QuantizationConfig
+    ):
+        """Set quantization_config for a submodule with type: `module_type`, for example:
+        quantizer.set_module_name(Sub) or quantizer.set_module_name(nn.Linear), it will quantize all supported operator/operator
+        patterns in the submodule with this module type with the given `quantization_config`
+        """
+        self.module_type_config[module_type] = quantization_config
+        return self
+
+    def set_module_name(
+        self, module_name: str, quantization_config: QuantizationConfig | None
+    ):
+        """Set quantization_config for a submodule with name: `module_name`, for example:
+        quantizer.set_module_name("blocks.sub"), it will quantize all supported operator/operator
+        patterns in the submodule with this module name with the given `quantization_config`
+        """
+        if quantization_config is None:
+            raise AssertionError("quantization_config == None is not supported yet")
+        self.module_name_config[module_name] = quantization_config
+        return self
+
+    def transform_for_annotation(
+        self, model: torch.fx.GraphModule
+    ) -> torch.fx.GraphModule:
+        """Transforms scalar values to tensor attributes"""
+        return _convert_scalars_to_attrs(model)
+
+    def annotate(self, model: torch.fx.GraphModule) -> torch.fx.GraphModule:
+        """just handling global spec for now"""
+        # hacked for handling dynamic linear quant. will fix later.
+        if self.global_config and self.global_config.input_activation.is_dynamic:  # type: ignore[union-attr]
+            model = self._annotate_for_dynamic_quantization_config(model)
+        else:
+            model = self._annotate_for_static_quantization_config(model)
+        propagate_annotation(model)
+        return model
+
+    def _annotate_all_static_patterns(
+        self,
+        model: torch.fx.GraphModule,
+        quantization_config: QuantizationConfig | None,
+        filter_fn: Callable[[Node], bool] | None = None,
+    ) -> torch.fx.GraphModule:
+        # TODO: implement the support for None to be canceling out previous annotations
+        if quantization_config is None:
+            return model
+
+        if quantization_config.is_qat:
+            for op in self.STATIC_QAT_ONLY_OPS:
+                OP_TO_ANNOTATOR[op](model, quantization_config, filter_fn)
+        for op in self.STATIC_OPS:
+            OP_TO_ANNOTATOR[op](model, quantization_config, filter_fn)
+        return model
+
+    def _annotate_all_dynamic_patterns(
+        self,
+        model: torch.fx.GraphModule,
+        quantization_config: QuantizationConfig | None,
+        filter_fn: Callable[[Node], bool] | None = None,
+    ) -> torch.fx.GraphModule:
+        # TODO: implement the support for None to be canceling out previous annotations
+        if quantization_config is None:
+            return model
+
+        for op in self.DYNAMIC_OPS:
+            OP_TO_ANNOTATOR[op](model, quantization_config, filter_fn)
+        return model
+
+    def _annotate_for_static_quantization_config(
+        self, model: torch.fx.GraphModule
+    ) -> torch.fx.GraphModule:
+        module_name_list = list(self.module_name_config.keys())
+        for module_name, config in self.module_name_config.items():
+            self._annotate_all_static_patterns(
+                model, config, _get_module_name_filter(module_name)
+            )
+
+        tp_list = list(self.module_type_config.keys())
+        for module_type, config in self.module_type_config.items():
+            self._annotate_all_static_patterns(
+                model, config, _get_module_type_filter(module_type)
+            )
+
+        self._annotate_all_static_patterns(
+            model,
+            self.global_config,
+            _get_not_module_type_or_name_filter(tp_list, module_name_list),
+        )
+        return model
+
+    def _annotate_for_dynamic_quantization_config(
+        self, model: torch.fx.GraphModule
+    ) -> torch.fx.GraphModule:
+        module_name_list = list(self.module_name_config.keys())
+        for module_name, config in self.module_name_config.items():
+            self._annotate_all_dynamic_patterns(
+                model, config, _get_module_name_filter(module_name)
+            )
+
+        tp_list = list(self.module_type_config.keys())
+        for module_type, config in self.module_type_config.items():
+            self._annotate_all_dynamic_patterns(
+                model, config, _get_module_type_filter(module_type)
+            )
+
+        self._annotate_all_dynamic_patterns(
+            model,
+            self.global_config,
+            _get_not_module_type_or_name_filter(tp_list, module_name_list),
+        )
+        return model
+
+    def validate(self, model: torch.fx.GraphModule) -> None:
+        pass
+
+    @classmethod
+    def get_supported_operators(cls) -> list[OperatorConfig]:
+        return cls.supported_config_and_operators
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantizer/xnnpack_quantizer_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantizer/xnnpack_quantizer_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..22282d3d071a899e31cd4607027aa3abec249c7f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantizer/xnnpack_quantizer_utils.py
@@ -0,0 +1,1152 @@
+# mypy: allow-untyped-defs
+import itertools
+import typing
+from collections.abc import Callable
+from dataclasses import dataclass
+from typing import NamedTuple
+
+import torch
+import torch.nn.functional as F
+from torch._subclasses import FakeTensor
+from torch.ao.quantization.fx.utils import get_new_attr_name_with_prefix
+from torch.ao.quantization.pt2e.export_utils import _WrapperModule
+from torch.ao.quantization.pt2e.utils import (
+    _get_aten_graph_module_for_pattern,
+    _is_conv_node,
+    _is_conv_transpose_node,
+)
+from torch.ao.quantization.quantizer import (
+    QuantizationAnnotation,
+    QuantizationSpec,
+    SharedQuantizationSpec,
+)
+from torch.ao.quantization.quantizer.utils import (
+    _annotate_input_qspec_map,
+    _annotate_output_qspec,
+)
+from torch.fx import Node
+from torch.fx.passes.utils.matcher_with_name_node_map_utils import (
+    SubgraphMatcherWithNameNodeMap,
+)
+from torch.fx.passes.utils.source_matcher_utils import get_source_partitions
+
+
+__all__ = [
+    "OperatorConfig",
+    "OperatorPatternType",
+    "QuantizationConfig",
+    "get_input_act_qspec",
+    "get_output_act_qspec",
+    "get_weight_qspec",
+    "get_bias_qspec",
+    "OP_TO_ANNOTATOR",
+    "propagate_annotation",
+]
+
+
+# In the absence of better name, just winging it with QuantizationConfig
+@dataclass(eq=True, frozen=True)
+class QuantizationConfig:
+    input_activation: QuantizationSpec | None
+    output_activation: QuantizationSpec | None
+    weight: QuantizationSpec | None
+    bias: QuantizationSpec | None
+    # TODO: remove, since we can use observer_or_fake_quant_ctr to express this
+    is_qat: bool = False
+
+
+# Use Annotated because list[Callable].__module__ is read-only.
+OperatorPatternType = typing.Annotated[list[Callable], None]
+OperatorPatternType.__module__ = (
+    "torch.ao.quantization.quantizer.xnnpack_quantizer_utils"
+)
+
+AnnotatorType = Callable[
+    [
+        torch.fx.GraphModule,
+        QuantizationConfig | None,
+        Callable[[Node], bool] | None,
+    ],
+    list[list[Node]] | None,
+]
+OP_TO_ANNOTATOR: dict[str, AnnotatorType] = {}
+
+
+def register_annotator(op: str) -> Callable[[AnnotatorType], None]:
+    def decorator(annotator: AnnotatorType) -> None:
+        OP_TO_ANNOTATOR[op] = annotator
+
+    return decorator
+
+
+class OperatorConfig(NamedTuple):
+    # fix List[str] with List[List[Union[nn.Module, FunctionType, BuiltinFunctionType]]]
+    # Basically we are mapping a quantization config to some list of patterns.
+    # a pattern is defined as a list of nn module, function or builtin function names
+    # e.g. [nn.Conv2d, torch.relu, torch.add]
+    # We have not resolved whether fusion can be considered internal details of the
+    # quantizer hence it does not need communication to user.
+    # Note this pattern is not really informative since it does not really
+    # tell us the graph structure resulting from the list of ops.
+    config: QuantizationConfig
+    operators: list[OperatorPatternType]
+
+
+def _is_annotated(nodes: list[Node]):
+    """
+    Given a list of nodes (that represents an operator pattern),
+    check if any of the node is annotated, return True if any of the node
+    is annotated, otherwise return False
+    """
+    annotated = False
+    for node in nodes:
+        annotated = annotated or (
+            "quantization_annotation" in node.meta
+            and node.meta["quantization_annotation"]._annotated
+        )
+    return annotated
+
+
+def _mark_nodes_as_annotated(nodes: list[Node]):
+    for node in nodes:
+        if node is not None:
+            if "quantization_annotation" not in node.meta:
+                node.meta["quantization_annotation"] = QuantizationAnnotation()
+            node.meta["quantization_annotation"]._annotated = True
+
+
+def get_input_act_qspec(quantization_config: QuantizationConfig | None):
+    if quantization_config is None:
+        return None
+    if quantization_config.input_activation is None:
+        return None
+    quantization_spec: QuantizationSpec = quantization_config.input_activation
+    if quantization_spec.qscheme not in [
+        torch.per_tensor_affine,
+        torch.per_tensor_symmetric,
+    ]:
+        raise AssertionError(
+            f"Unsupported activation qscheme: {quantization_spec.qscheme}"
+        )
+    return quantization_spec
+
+
+def get_output_act_qspec(quantization_config: QuantizationConfig | None):
+    if quantization_config is None:
+        return None
+    if quantization_config.output_activation is None:
+        return None
+    quantization_spec: QuantizationSpec = quantization_config.output_activation
+    if quantization_spec.qscheme not in [
+        torch.per_tensor_affine,
+        torch.per_tensor_symmetric,
+    ]:
+        raise AssertionError(
+            f"Unsupported activation qscheme: {quantization_spec.qscheme}"
+        )
+    return quantization_spec
+
+
+def get_weight_qspec(quantization_config: QuantizationConfig | None):
+    if quantization_config is None:
+        return None
+    if quantization_config is None:
+        raise AssertionError("quantization_config must not be None")
+    if quantization_config.weight is None:
+        return None
+    quantization_spec: QuantizationSpec = quantization_config.weight
+    if quantization_spec.qscheme not in [
+        torch.per_tensor_symmetric,
+        torch.per_channel_symmetric,
+        None,
+    ]:
+        raise ValueError(
+            f"Unsupported quantization_spec {quantization_spec} for weight"
+        )
+    return quantization_spec
+
+
+def get_bias_qspec(quantization_config: QuantizationConfig | None):
+    if quantization_config is None:
+        return None
+    if quantization_config is None:
+        raise AssertionError("quantization_config must not be None")
+    if quantization_config.bias is None:
+        return None
+    quantization_spec: QuantizationSpec = quantization_config.bias
+    if quantization_spec.dtype != torch.float:
+        raise AssertionError(
+            "Only float dtype for bias is supported for bias right now"
+        )
+    return quantization_spec
+
+
+@register_annotator("linear")
+def _annotate_linear(
+    gm: torch.fx.GraphModule,
+    quantization_config: QuantizationConfig | None,
+    filter_fn: Callable[[Node], bool] | None = None,
+) -> list[list[Node]] | None:
+    annotated_partitions = []
+    input_act_qspec = get_input_act_qspec(quantization_config)
+    output_act_qspec = get_output_act_qspec(quantization_config)
+    weight_qspec = get_weight_qspec(quantization_config)
+    bias_qspec = get_bias_qspec(quantization_config)
+    for node in gm.graph.nodes:
+        if node.op != "call_function" or node.target != torch.ops.aten.linear.default:
+            continue
+        if filter_fn and not filter_fn(node):
+            continue
+        act_node = node.args[0]
+        weight_node = node.args[1]
+        bias_node = None
+        if len(node.args) > 2:
+            bias_node = node.args[2]
+
+        if _is_annotated([node]) is False:  # type: ignore[list-item]
+            _annotate_input_qspec_map(
+                node,
+                act_node,
+                input_act_qspec,
+            )
+            _annotate_input_qspec_map(
+                node,
+                weight_node,
+                weight_qspec,
+            )
+            nodes_to_mark_annotated = [node, weight_node]
+            if bias_node:
+                _annotate_input_qspec_map(
+                    node,
+                    bias_node,
+                    bias_qspec,
+                )
+                nodes_to_mark_annotated.append(bias_node)
+            _annotate_output_qspec(node, output_act_qspec)
+            _mark_nodes_as_annotated(nodes_to_mark_annotated)
+            annotated_partitions.append(nodes_to_mark_annotated)
+
+    return annotated_partitions
+
+
+@register_annotator("linear_relu")
+def _annotate_linear_relu(
+    gm: torch.fx.GraphModule,
+    quantization_config: QuantizationConfig | None,
+    filter_fn: Callable[[Node], bool] | None = None,
+) -> list[list[Node]] | None:
+    annotated_partitions = []
+    input_act_qspec = get_input_act_qspec(quantization_config)
+    output_act_qspec = get_output_act_qspec(quantization_config)
+    weight_qspec = get_weight_qspec(quantization_config)
+    bias_qspec = get_bias_qspec(quantization_config)
+    for node in gm.graph.nodes:
+        if node.op != "call_function" or node.target not in [
+            torch.ops.aten.relu.default,
+            torch.ops.aten.relu_.default,
+        ]:
+            continue
+        relu_node = node
+        maybe_linear_node = node.args[0]
+        if (
+            not isinstance(maybe_linear_node, Node)
+            or maybe_linear_node.op != "call_function"
+            or maybe_linear_node.target != torch.ops.aten.linear.default
+        ):
+            continue
+
+        linear_node = maybe_linear_node
+        if len(linear_node.users) > 1:
+            # if linear node has multiple users, then it can't be fused with relu
+            continue
+
+        input_qspec_map = {}
+        input_act = linear_node.args[0]
+        if not isinstance(input_act, Node):
+            raise AssertionError("input activation must be a FX Node")
+        input_qspec_map[input_act] = input_act_qspec
+
+        weight = linear_node.args[1]
+        if not isinstance(weight, Node):
+            raise AssertionError("weight must be a FX Node")
+        input_qspec_map[weight] = weight_qspec
+
+        # adding weight node to the partition as well
+        partition = [relu_node, linear_node, weight]
+        bias = linear_node.args[2] if len(linear_node.args) > 2 else None
+        if isinstance(bias, Node):
+            input_qspec_map[bias] = bias_qspec
+            partition.append(bias)
+
+        if _is_annotated(partition):
+            continue
+
+        if filter_fn and any(not filter_fn(n) for n in partition):
+            continue
+
+        linear_node.meta["quantization_annotation"] = QuantizationAnnotation(
+            input_qspec_map=input_qspec_map,
+            _annotated=True,
+        )
+        relu_node.meta["quantization_annotation"] = QuantizationAnnotation(
+            output_qspec=output_act_qspec,
+            _annotated=True,
+        )
+        _mark_nodes_as_annotated(partition)
+        annotated_partitions.append(partition)
+    return annotated_partitions
+
+
+@register_annotator("conv")
+def _annotate_conv(
+    gm: torch.fx.GraphModule,
+    quantization_config: QuantizationConfig | None,
+    filter_fn: Callable[[Node], bool] | None = None,
+) -> list[list[Node]] | None:
+    annotated_partitions = []
+    for n in gm.graph.nodes:
+        if n.op != "call_function" or n.target not in [
+            torch.ops.aten.conv1d.default,
+            torch.ops.aten.conv2d.default,
+        ]:
+            continue
+        conv_node = n
+
+        input_qspec_map = {}
+        input_act = conv_node.args[0]
+        if not isinstance(input_act, Node):
+            raise AssertionError("input activation must be a FX Node")
+        input_qspec_map[input_act] = get_input_act_qspec(quantization_config)
+
+        weight = conv_node.args[1]
+        if not isinstance(weight, Node):
+            raise AssertionError("weight must be a FX Node")
+        input_qspec_map[weight] = get_weight_qspec(quantization_config)
+
+        # adding weight node to the partition as well
+        partition = [conv_node, conv_node.args[1]]
+
+        bias = conv_node.args[2] if len(conv_node.args) > 2 else None
+        if isinstance(bias, Node):
+            input_qspec_map[bias] = get_bias_qspec(quantization_config)
+            partition.append(bias)
+
+        if _is_annotated(partition):
+            continue
+
+        if filter_fn and any(not filter_fn(n) for n in partition):
+            continue
+
+        conv_node.meta["quantization_annotation"] = QuantizationAnnotation(
+            input_qspec_map=input_qspec_map,
+            output_qspec=get_output_act_qspec(quantization_config),
+            _annotated=True,
+        )
+        _mark_nodes_as_annotated(partition)
+        annotated_partitions.append(partition)
+    return annotated_partitions
+
+
+def _do_annotate_conv_relu(
+    gm: torch.fx.GraphModule,
+    quantization_config: QuantizationConfig | None,
+    filter_fn: Callable[[Node], bool] | None = None,
+    is_conv_transpose: bool = False,
+):
+    annotated_partitions = []
+    for n in gm.graph.nodes:
+        if n.op != "call_function" or n.target not in [
+            torch.ops.aten.relu.default,
+            torch.ops.aten.relu_.default,
+        ]:
+            continue
+        relu_node = n
+        maybe_conv_node = n.args[0]
+
+        is_conv_node = _is_conv_transpose_node if is_conv_transpose else _is_conv_node
+        if not isinstance(maybe_conv_node, Node) or not is_conv_node(maybe_conv_node):
+            continue
+        conv_node = maybe_conv_node
+
+        if len(conv_node.users) > 1:
+            # relu shouldn't be fuseable to conv if there are other users
+            # of convolution
+            continue
+
+        input_qspec_map = {}
+        input_act = conv_node.args[0]
+        if not isinstance(input_act, Node):
+            raise AssertionError("input activation must be a FX Node")
+        input_qspec_map[input_act] = get_input_act_qspec(quantization_config)
+
+        weight = conv_node.args[1]
+        if not isinstance(weight, Node):
+            raise AssertionError("weight must be a FX Node")
+        input_qspec_map[weight] = get_weight_qspec(quantization_config)
+
+        # adding weight node to the partition as well
+        partition = [relu_node, conv_node, conv_node.args[1]]
+        bias = conv_node.args[2] if len(conv_node.args) > 2 else None
+        if isinstance(bias, Node):
+            input_qspec_map[bias] = get_bias_qspec(quantization_config)
+            partition.append(bias)
+
+        # pyrefly: ignore [bad-argument-type]
+        if _is_annotated(partition):
+            continue
+
+        # pyrefly: ignore [bad-argument-type]
+        if filter_fn and any(not filter_fn(n) for n in partition):
+            continue
+
+        conv_node.meta["quantization_annotation"] = QuantizationAnnotation(
+            input_qspec_map=input_qspec_map, _annotated=True
+        )
+        relu_node.meta["quantization_annotation"] = QuantizationAnnotation(
+            output_qspec=get_output_act_qspec(quantization_config),  # type: ignore[arg-type]
+            _annotated=True,
+        )
+        # pyrefly: ignore [bad-argument-type]
+        _mark_nodes_as_annotated(partition)
+        annotated_partitions.append(partition)
+    return annotated_partitions
+
+
+@register_annotator("conv_relu")
+def _annotate_conv_relu(
+    gm: torch.fx.GraphModule,
+    quantization_config: QuantizationConfig | None,
+    filter_fn: Callable[[Node], bool] | None = None,
+) -> list[list[Node]] | None:
+    return _do_annotate_conv_relu(
+        gm, quantization_config, filter_fn, is_conv_transpose=False
+    )
+
+
+@register_annotator("conv_transpose_relu")
+def _annotate_conv_transpose_relu(
+    gm: torch.fx.GraphModule,
+    quantization_config: QuantizationConfig | None,
+    filter_fn: Callable[[Node], bool] | None = None,
+) -> list[list[Node]] | None:
+    return _do_annotate_conv_relu(
+        gm, quantization_config, filter_fn, is_conv_transpose=True
+    )
+
+
+@register_annotator("conv_bn")
+def _annotate_conv_bn(
+    gm: torch.fx.GraphModule,
+    quantization_config: QuantizationConfig | None,
+    filter_fn: Callable[[Node], bool] | None = None,
+) -> list[list[Node]] | None:
+    """
+    Find conv + batchnorm partitions
+    Note: This is only used for QAT. In PTQ, batchnorm should already be fused into the conv.
+    """
+    return _do_annotate_conv_bn(gm, quantization_config, filter_fn, has_relu=False)
+
+
+@register_annotator("conv_bn_relu")
+def _annotate_conv_bn_relu(
+    gm: torch.fx.GraphModule,
+    quantization_config: QuantizationConfig | None,
+    filter_fn: Callable[[Node], bool] | None = None,
+) -> list[list[Node]] | None:
+    """
+    Find conv + batchnorm + relu partitions
+    Note: This is only used for QAT. In PTQ, batchnorm should already be fused into the conv.
+    """
+    return _do_annotate_conv_bn(gm, quantization_config, filter_fn, has_relu=True)
+
+
+@register_annotator("conv_transpose_bn")
+def _annotate_conv_transpose_bn(
+    gm: torch.fx.GraphModule,
+    quantization_config: QuantizationConfig | None,
+    filter_fn: Callable[[Node], bool] | None = None,
+) -> list[list[Node]] | None:
+    """
+    Find conv_transpose + batchnorm partitions
+    Note: This is only used for QAT. In PTQ, batchnorm should already be fused into the conv.
+    """
+    return _do_annotate_conv_bn(
+        gm, quantization_config, filter_fn, has_relu=False, is_conv_transpose=True
+    )
+
+
+@register_annotator("conv_transpose_bn_relu")
+def _annotate_conv_transpose_bn_relu(
+    gm: torch.fx.GraphModule,
+    quantization_config: QuantizationConfig | None,
+    filter_fn: Callable[[Node], bool] | None = None,
+) -> list[list[Node]] | None:
+    """
+    Find conv_transpose + batchnorm + relu partitions
+    Note: This is only used for QAT. In PTQ, batchnorm should already be fused into the conv.
+    """
+    return _do_annotate_conv_bn(
+        gm, quantization_config, filter_fn, has_relu=True, is_conv_transpose=True
+    )
+
+
+def _do_annotate_conv_bn(
+    gm: torch.fx.GraphModule,
+    quantization_config: QuantizationConfig | None,
+    filter_fn: Callable[[Node], bool] | None,
+    has_relu: bool,
+    is_conv_transpose: bool = False,
+) -> list[list[Node]]:
+    """
+    Given a function that takes in a `conv_fn` and returns a conv-bn[-relu] pattern,
+    return a list of annotated partitions.
+
+    The output of the pattern must include a dictionary from string name to node
+    for the following names: "input", "conv", "weight", "bias", and "output".
+    """
+
+    # Example inputs for conv-bn1d patterns
+    _conv1d_bn_example_inputs = (
+        torch.randn(1, 1, 3),  # x
+        torch.randn(1, 1, 1),  # conv_weight
+        torch.randn(1),  # conv_bias
+        torch.randn(1),  # bn_weight
+        torch.randn(1),  # bn_bias
+        torch.randn(1),  # bn_running_mean
+        torch.randn(1),  # bn_running_var
+    )
+
+    # Example inputs for conv-bn2d patterns
+    _conv2d_bn_example_inputs = (
+        torch.randn(1, 1, 3, 3),  # x
+        torch.randn(1, 1, 1, 1),  # conv_weight
+        torch.randn(1),  # conv_bias
+        torch.randn(1),  # bn_weight
+        torch.randn(1),  # bn_bias
+        torch.randn(1),  # bn_running_mean
+        torch.randn(1),  # bn_running_var
+    )
+
+    def get_pattern(conv_fn: Callable, relu_is_inplace: bool):
+        def _conv_bn(x, conv_weight, conv_bias, bn_weight, bn_bias, bn_rm, bn_rv):
+            conv = conv_fn(x, conv_weight, conv_bias)
+            bn = F.batch_norm(conv, bn_rm, bn_rv, bn_weight, bn_bias, training=True)
+            if has_relu:
+                output = F.relu_(bn) if relu_is_inplace else F.relu(bn)
+            else:
+                output = bn
+            return output, {
+                "input": x,
+                "conv": conv,
+                "weight": conv_weight,
+                "bias": conv_bias,
+                "output": output,
+            }
+
+        return _WrapperModule(_conv_bn)
+
+    # Needed for matching, otherwise the matches gets filtered out due to unused
+    # nodes returned by batch norm
+    gm.graph.eliminate_dead_code()
+    gm.recompile()
+
+    matches = []
+    if is_conv_transpose:
+        combinations = [
+            (F.conv_transpose1d, _conv1d_bn_example_inputs),
+            (F.conv_transpose2d, _conv2d_bn_example_inputs),
+        ]
+    else:
+        combinations = [
+            (F.conv1d, _conv1d_bn_example_inputs),  # type: ignore[list-item]
+            (F.conv2d, _conv2d_bn_example_inputs),  # type: ignore[list-item]
+        ]
+
+    # Add `is_cuda` and `relu_is_inplace` dimensions
+    combinations = itertools.product(  # type: ignore[assignment]
+        combinations,
+        [True, False] if torch.cuda.is_available() else [False],  # is_cuda
+        [True, False] if has_relu else [False],  # relu_is_inplace
+    )
+
+    # Match against all conv dimensions and cuda variants
+    for (conv_fn, example_inputs), is_cuda, relu_is_inplace in combinations:  # type: ignore[misc]
+        pattern = get_pattern(conv_fn, relu_is_inplace)  # type: ignore[has-type]
+        pattern = _get_aten_graph_module_for_pattern(pattern, example_inputs, is_cuda)  # type: ignore[has-type]
+        pattern.graph.eliminate_dead_code()
+        pattern.recompile()
+        matcher = SubgraphMatcherWithNameNodeMap(pattern, ignore_literals=True)
+        matches.extend(matcher.match(gm.graph))
+
+    # Annotate nodes returned in the matches
+    annotated_partitions = []
+    for match in matches:
+        name_node_map = match.name_node_map
+        input_node = name_node_map["input"]
+        conv_node = name_node_map["conv"]
+        weight_node = name_node_map["weight"]
+        bias_node = name_node_map["bias"]
+        output_node = name_node_map["output"]
+
+        # TODO: annotate the uses of input, weight, and bias separately instead
+        # of assuming they come from a single conv node. This is not possible today
+        # because input may have multiple users, and we can't rely on the conv node
+        # always being the first user. This was the case in models with skip
+        # connections like resnet18
+
+        # Validate conv args
+        if conv_node.args[0] is not input_node:
+            raise ValueError("Conv arg did not contain input node ", input_node)
+        if conv_node.args[1] is not weight_node:
+            raise ValueError("Conv arg did not contain weight node ", weight_node)
+        if len(conv_node.args) > 2 and conv_node.args[2] is not bias_node:
+            raise ValueError("Conv arg did not contain bias node ", bias_node)
+
+        # Skip if the partition is already annotated or is filtered out by the user
+        partition = [conv_node, weight_node]
+        if bias_node is not None:
+            partition.append(bias_node)
+        if _is_annotated(partition):
+            continue
+        if filter_fn and any(not filter_fn(n) for n in partition):
+            continue
+
+        # Annotate conv inputs and pattern output
+        input_qspec_map = {}
+        input_qspec_map[input_node] = get_input_act_qspec(quantization_config)
+        input_qspec_map[weight_node] = get_weight_qspec(quantization_config)
+        if bias_node is not None:
+            input_qspec_map[bias_node] = get_bias_qspec(quantization_config)
+        conv_node.meta["quantization_annotation"] = QuantizationAnnotation(
+            input_qspec_map=input_qspec_map,
+            _annotated=True,
+        )
+        output_node.meta["quantization_annotation"] = QuantizationAnnotation(
+            output_qspec=get_output_act_qspec(quantization_config),  # type: ignore[arg-type]
+            _annotated=True,
+        )
+        _mark_nodes_as_annotated(partition)
+        annotated_partitions.append(partition)
+    return annotated_partitions
+
+
+@register_annotator("gru_io_only")
+def _annotate_gru_io_only(
+    gm: torch.fx.GraphModule,
+    quantization_config: QuantizationConfig | None,
+    filter_fn: Callable[[Node], bool] | None = None,
+) -> list[list[Node]] | None:
+    gru_partitions = get_source_partitions(gm.graph, [torch.nn.GRU], filter_fn)
+    gru_partitions = list(itertools.chain.from_iterable(gru_partitions.values()))
+    annotated_partitions = []
+    for gru_partition in gru_partitions:
+        annotated_partitions.append(gru_partition.nodes)
+        output_nodes = gru_partition.output_nodes
+        input_nodes = gru_partition.input_nodes
+        # skip annotation if it is already annotated
+        if _is_annotated(input_nodes + output_nodes):
+            continue
+        # inside each GRU partition, we should be able to annotate each linear
+        # subgraph
+        input_act = input_nodes[0]
+        input_act_user = next(iter(input_act.users.keys()))
+        if not isinstance(input_act, Node):
+            raise AssertionError("input activation must be a FX Node")
+        if not isinstance(input_act_user, Node):
+            raise AssertionError("input activation user must be a FX Node")
+        input_act_user.meta["quantization_annotation"] = QuantizationAnnotation(
+            input_qspec_map={
+                input_act: get_input_act_qspec(quantization_config),
+            },
+            _annotated=True,
+        )
+
+        hidden_state = input_nodes[1]
+        hidden_state_user = next(iter(hidden_state.users.keys()))
+        if not isinstance(hidden_state, Node):
+            raise AssertionError("hidden state must be a FX Node")
+        if not isinstance(hidden_state_user, Node):
+            raise AssertionError("hidden state user must be a FX Node")
+        hidden_state_user.meta["quantization_annotation"] = QuantizationAnnotation(
+            input_qspec_map={
+                hidden_state: get_input_act_qspec(quantization_config),
+            },
+            _annotated=True,
+        )
+
+        if len(output_nodes) != 2:
+            raise AssertionError("expecting GRU to have two outputs")
+        for output in output_nodes:
+            output.meta["quantization_annotation"] = QuantizationAnnotation(
+                output_qspec=get_output_act_qspec(quantization_config),
+                _annotated=True,
+            )
+        nodes_to_mark_annotated = list(gru_partition.nodes)
+        _mark_nodes_as_annotated(nodes_to_mark_annotated)
+    return annotated_partitions
+
+
+@register_annotator("adaptive_avg_pool2d")
+def _annotate_adaptive_avg_pool2d(
+    gm: torch.fx.GraphModule,
+    quantization_config: QuantizationConfig | None,
+    filter_fn: Callable[[Node], bool] | None = None,
+) -> list[list[Node]] | None:
+    """Always annotate adaptive_avg_pool2d op"""
+    module_partitions = get_source_partitions(
+        gm.graph, [torch.nn.AdaptiveAvgPool2d, F.adaptive_avg_pool2d], filter_fn
+    )
+    partitions = list(itertools.chain.from_iterable(module_partitions.values()))
+    annotated_partitions = []
+    for partition in partitions:
+        pool_node = partition.output_nodes[0]
+        if (
+            pool_node.op != "call_function"
+            or pool_node.target != torch.ops.aten.adaptive_avg_pool2d.default
+        ):
+            raise ValueError(f"{pool_node} is not an aten adaptive_avg_pool2d operator")
+
+        if _is_annotated([pool_node]):
+            continue
+
+        annotated_partitions.append(partition.nodes)
+        input_act = pool_node.args[0]
+        if not isinstance(input_act, Node):
+            raise AssertionError("input activation must be a FX Node")
+
+        # only annotate input output sharing operator
+        # when the output of the input node is annotated
+        if (
+            "quantization_annotation" not in input_act.meta
+            or not input_act.meta["quantization_annotation"]._annotated
+            or input_act.meta["quantization_annotation"].output_qspec is None
+        ):
+            input_act_qspec = get_input_act_qspec(quantization_config)
+        else:
+            input_act_qspec = SharedQuantizationSpec(input_act)
+
+        # output sharing with input
+        output_act_qspec = SharedQuantizationSpec((input_act, pool_node))
+        pool_node.meta["quantization_annotation"] = QuantizationAnnotation(
+            input_qspec_map={
+                input_act: input_act_qspec,
+            },
+            output_qspec=output_act_qspec,
+            _annotated=True,
+        )
+    return annotated_partitions
+
+
+def _is_input_large_scalar(node: Node, gm: torch.fx.GraphModule):
+    """Check if input is a large scalar value. So that we can skip quantization for the node
+    since histc op (in HistogramObserver) only works for values up to certain upper bound
+    """
+    if node.op == "get_attr":
+        qualified_name = str(node.target)
+        module_path, _, name = qualified_name.rpartition(".")
+        submod = gm.get_submodule(module_path)
+        tensor = getattr(submod, name)
+        # torch.histc works until this upper bound
+        HISTC_UPPER_BOUND = 3.4028235e15
+        return tensor.numel() == 1 and abs(tensor.item()) > HISTC_UPPER_BOUND
+    return False
+
+
+def _is_input_non_float_tensor(node: Node):
+    """Check if the input is not a float tensor, so that we can skip quantization for the node
+    since observers only works with float Tensors
+    """
+    if "val" not in node.meta or not isinstance(node.meta["val"], FakeTensor):
+        return True
+    return node.meta["val"].dtype != torch.float32
+
+
+@register_annotator("add_relu")
+def _annotate_add_relu(
+    gm: torch.fx.GraphModule,
+    quantization_config: QuantizationConfig | None,
+    filter_fn: Callable[[Node], bool] | None = None,
+) -> list[list[Node]] | None:
+    annotated_partitions = []
+    for node in gm.graph.nodes:
+        if node.op != "call_function" or node.target not in [
+            torch.ops.aten.relu.default,
+            torch.ops.aten.relu_.default,
+        ]:
+            continue
+        relu_node = node
+        maybe_add = node.args[0]
+        if (
+            not isinstance(maybe_add, Node)
+            or maybe_add.op != "call_function"
+            or maybe_add.target
+            not in [
+                torch.ops.aten.add.Tensor,
+                torch.ops.aten.add_.Tensor,
+            ]
+        ):
+            continue
+
+        add_node = maybe_add
+
+        if len(add_node.users) > 1:
+            # add can't be fused with ReLU if the result of add is being used
+            # else where in the graph
+            continue
+
+        partition = [relu_node, add_node]
+
+        if _is_annotated(partition):
+            continue
+
+        if filter_fn and any(not filter_fn(n) for n in partition):
+            continue
+
+        input_act_qspec = get_input_act_qspec(quantization_config)
+        output_act_qspec = get_output_act_qspec(quantization_config)
+
+        input_qspec_map = {}
+        input_act0 = add_node.args[0]
+        if isinstance(input_act0, Node):
+            if _is_input_large_scalar(input_act0, gm):
+                continue
+            if _is_input_non_float_tensor(input_act0):
+                continue
+            partition.append(input_act0)
+            input_qspec_map[input_act0] = input_act_qspec
+
+        input_act1 = add_node.args[1]
+        if isinstance(input_act1, Node):
+            if _is_input_large_scalar(input_act1, gm):
+                continue
+            if _is_input_non_float_tensor(input_act1):
+                continue
+            partition.append(input_act1)
+            input_qspec_map[input_act1] = input_act_qspec
+
+        add_node.meta["quantization_annotation"] = QuantizationAnnotation(
+            input_qspec_map=input_qspec_map,
+            _annotated=True,
+        )
+        relu_node.meta["quantization_annotation"] = QuantizationAnnotation(
+            output_qspec=output_act_qspec,
+            _annotated=True,
+        )
+        annotated_partitions.append(partition)
+    return annotated_partitions
+
+
+@register_annotator("add")
+def _annotate_add(
+    gm: torch.fx.GraphModule,
+    quantization_config: QuantizationConfig | None,
+    filter_fn: Callable[[Node], bool] | None = None,
+) -> list[list[Node]] | None:
+    annotated_partitions = []
+    for node in gm.graph.nodes:
+        if node.op != "call_function" or node.target not in [
+            torch.ops.aten.add.Tensor,
+            torch.ops.aten.add_.Tensor,
+        ]:
+            continue
+        add_node = node
+        partition = [add_node]
+
+        if _is_annotated(partition):
+            continue
+
+        if filter_fn and any(not filter_fn(n) for n in partition):
+            continue
+
+        input_act_qspec = get_input_act_qspec(quantization_config)
+        output_act_qspec = get_output_act_qspec(quantization_config)
+
+        input_qspec_map = {}
+        input_act0 = add_node.args[0]
+        if isinstance(input_act0, Node):
+            if _is_input_large_scalar(input_act0, gm):
+                continue
+            if _is_input_non_float_tensor(input_act0):
+                continue
+            input_qspec_map[input_act0] = input_act_qspec
+            partition.append(input_act0)
+
+        input_act1 = add_node.args[1]
+        if isinstance(input_act1, Node):
+            if _is_input_large_scalar(input_act1, gm):
+                continue
+            if _is_input_non_float_tensor(input_act1):
+                continue
+            input_qspec_map[input_act1] = input_act_qspec
+            partition.append(input_act1)
+
+        add_node.meta["quantization_annotation"] = QuantizationAnnotation(
+            input_qspec_map=input_qspec_map,
+            output_qspec=output_act_qspec,
+            _annotated=True,
+        )
+        annotated_partitions.append(partition)
+    return annotated_partitions
+
+
+@register_annotator("mul_relu")
+def _annotate_mul_relu(
+    gm: torch.fx.GraphModule,
+    quantization_config: QuantizationConfig | None,
+    filter_fn: Callable[[Node], bool] | None = None,
+) -> list[list[Node]] | None:
+    annotated_partitions = []
+    for node in gm.graph.nodes:
+        if node.op != "call_function" or node.target not in [
+            torch.ops.aten.relu.default,
+            torch.ops.aten.relu_.default,
+        ]:
+            continue
+        relu_node = node
+        maybe_mul = node.args[0]
+        if (
+            not isinstance(maybe_mul, Node)
+            or maybe_mul.op != "call_function"
+            or maybe_mul.target
+            not in [
+                torch.ops.aten.mul.Tensor,
+                torch.ops.aten.mul_.Tensor,
+            ]
+        ):
+            continue
+
+        mul_node = maybe_mul
+        if len(mul_node.users) > 1:
+            # mul can't be fused with ReLU if the result of mul is being used
+            # else where in the graph
+            continue
+
+        partition = [relu_node, mul_node]
+
+        if _is_annotated(partition):
+            continue
+
+        if filter_fn and any(not filter_fn(n) for n in partition):
+            continue
+
+        input_act_qspec = get_input_act_qspec(quantization_config)
+        output_act_qspec = get_output_act_qspec(quantization_config)
+
+        input_qspec_map = {}
+        input_act0 = mul_node.args[0]
+        if isinstance(input_act0, Node):
+            if _is_input_large_scalar(input_act0, gm):
+                continue
+            if _is_input_non_float_tensor(input_act0):
+                continue
+            partition.append(input_act0)
+            input_qspec_map[input_act0] = input_act_qspec
+
+        input_act1 = mul_node.args[1]
+        if isinstance(input_act1, Node):
+            if _is_input_large_scalar(input_act1, gm):
+                continue
+            if _is_input_non_float_tensor(input_act1):
+                continue
+            partition.append(input_act1)
+            input_qspec_map[input_act1] = input_act_qspec
+
+        mul_node.meta["quantization_annotation"] = QuantizationAnnotation(
+            input_qspec_map=input_qspec_map,
+            _annotated=True,
+        )
+        relu_node.meta["quantization_annotation"] = QuantizationAnnotation(
+            output_qspec=output_act_qspec,
+            _annotated=True,
+        )
+        annotated_partitions.append(partition)
+    return annotated_partitions
+
+
+@register_annotator("mul")
+def _annotate_mul(
+    gm: torch.fx.GraphModule,
+    quantization_config: QuantizationConfig | None,
+    filter_fn: Callable[[Node], bool] | None = None,
+) -> list[list[Node]] | None:
+    annotated_partitions = []
+    for node in gm.graph.nodes:
+        if node.op != "call_function" or node.target not in [
+            torch.ops.aten.mul.Tensor,
+            torch.ops.aten.mul_.Tensor,
+        ]:
+            continue
+
+        mul_node = node
+        partition = [mul_node]
+        if _is_annotated(partition):
+            continue
+
+        if filter_fn and any(not filter_fn(n) for n in partition):
+            continue
+
+        input_act_qspec = get_input_act_qspec(quantization_config)
+        output_act_qspec = get_output_act_qspec(quantization_config)
+
+        input_qspec_map = {}
+        input_act0 = mul_node.args[0]
+        if isinstance(input_act0, Node):
+            if _is_input_large_scalar(input_act0, gm):
+                continue
+            if _is_input_non_float_tensor(input_act0):
+                continue
+            input_qspec_map[input_act0] = input_act_qspec
+            partition.append(input_act0)
+
+        input_act1 = mul_node.args[1]
+        if isinstance(input_act1, Node):
+            if _is_input_large_scalar(input_act1, gm):
+                continue
+            if _is_input_non_float_tensor(input_act1):
+                continue
+            input_qspec_map[input_act1] = input_act_qspec
+            partition.append(input_act0)
+
+        mul_node.meta["quantization_annotation"] = QuantizationAnnotation(
+            input_qspec_map=input_qspec_map,
+            output_qspec=output_act_qspec,
+            _annotated=True,
+        )
+        annotated_partitions.append(partition)
+    return annotated_partitions
+
+
+# TODO: remove Optional in return type, fix annotated_partitions logic
+@register_annotator("cat")
+def _annotate_cat(
+    gm: torch.fx.GraphModule,
+    quantization_config: QuantizationConfig | None,
+    filter_fn: Callable[[Node], bool] | None = None,
+) -> list[list[Node]] | None:
+    cat_partitions = get_source_partitions(gm.graph, [torch.cat], filter_fn)
+    cat_partitions = list(itertools.chain.from_iterable(cat_partitions.values()))
+    annotated_partitions = []
+    for cat_partition in cat_partitions:
+        cat_node = cat_partition.output_nodes[0]
+        if _is_annotated([cat_node]):
+            continue
+
+        if cat_node.target != torch.ops.aten.cat.default:
+            # TODO: change this to AnnotationException
+            raise Exception(  # noqa: TRY002
+                f"Expected cat node: torch.ops.aten.cat.default, but found {cat_node.target}"
+                " please check if you are calling the correct capture API"
+            )
+
+        annotated_partitions.append(cat_partition.nodes)
+
+        input_act_qspec = get_input_act_qspec(quantization_config)
+        inputs = cat_node.args[0]
+
+        input_qspec_map = {}
+        input_act0 = inputs[0]  # type: ignore[index]
+        if isinstance(input_act0, Node):
+            input_qspec_map[input_act0] = input_act_qspec
+
+        shared_with_input0_qspec = SharedQuantizationSpec((input_act0, cat_node))  # type: ignore[arg-type]
+        for input_act in inputs[1:]:  # type: ignore[index, union-attr]
+            if input_act not in input_qspec_map:
+                input_qspec_map[input_act] = shared_with_input0_qspec  # type: ignore[index]
+
+        output_act_qspec = shared_with_input0_qspec
+
+        cat_node.meta["quantization_annotation"] = QuantizationAnnotation(
+            input_qspec_map=input_qspec_map,
+            output_qspec=output_act_qspec,
+            _annotated=True,
+        )
+    return annotated_partitions
+
+
+def _is_share_obs_or_fq_op(op: Callable) -> bool:
+    return op in [
+        torch.ops.aten.relu.default,
+        torch.ops.aten.hardtanh.default,
+        torch.ops.aten.hardtanh_.default,
+        torch.ops.aten.max_pool2d.default,
+        torch.ops.aten.mean.default,
+        torch.ops.aten.mean.dim,
+        torch.ops.aten.permute.default,
+        torch.ops.aten.permute_copy.default,
+        torch.ops.aten.squeeze.dim,
+        torch.ops.aten.squeeze_copy.dim,
+        # TODO: remove?
+        torch.ops.aten.adaptive_avg_pool2d.default,
+        torch.ops.aten.view_copy.default,
+        torch.ops.aten.view.default,
+        torch.ops.aten.slice_copy.Tensor,
+        torch.ops.aten.flatten.using_ints,
+    ]
+
+
+def propagate_annotation(model: torch.fx.GraphModule) -> None:
+    for n in model.graph.nodes:
+        if n.op != "call_function" or not _is_share_obs_or_fq_op(n.target):
+            continue
+
+        prev_node = n.args[0]
+        if not isinstance(prev_node, Node):
+            continue
+
+        quantization_annotation = prev_node.meta.get("quantization_annotation", None)
+        if not quantization_annotation:
+            continue
+
+        output_qspec = quantization_annotation.output_qspec
+        if not output_qspec:
+            continue
+
+        # make sure current node is not annotated
+        if (
+            "quantization_annotation" in n.meta
+            and n.meta["quantization_annotation"]._annotated
+        ):
+            continue
+
+        shared_qspec = SharedQuantizationSpec(prev_node)
+        # propagate the previous output_qspec to the current node
+        n.meta["quantization_annotation"] = QuantizationAnnotation(
+            input_qspec_map={
+                prev_node: shared_qspec,
+            },
+            output_qspec=shared_qspec,
+            _annotated=True,
+        )
+
+
+# TODO: make the list of ops customizable
+def _convert_scalars_to_attrs(model: torch.fx.GraphModule) -> torch.fx.GraphModule:
+    for n in model.graph.nodes:
+        if n.op != "call_function" or n.target not in [
+            torch.ops.aten.add.Tensor,
+            torch.ops.aten.mul.Tensor,
+        ]:
+            continue
+        args = list(n.args)
+        new_args = []
+        for i in range(len(args)):
+            if isinstance(args[i], torch.fx.Node):
+                new_args.append(args[i])
+                continue
+            prefix = "_tensor_constant_"
+            get_new_attr_name = get_new_attr_name_with_prefix(prefix)
+            tensor_constant_name = get_new_attr_name(model)
+            float_tensor = torch.tensor(float(args[i]))
+            model.register_buffer(tensor_constant_name, float_tensor)
+            fake_mode = n.meta["val"].fake_mode
+            with model.graph.inserting_before(n):
+                get_attr_node = model.graph.create_node(
+                    "get_attr", tensor_constant_name, (), {}
+                )
+                get_attr_node.meta["val"] = fake_mode.from_tensor(
+                    float_tensor, static_shapes=True
+                )
+                new_args.append(get_attr_node)
+        n.args = tuple(new_args)
+    model.recompile()
+    return model
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantizer/xpu_inductor_quantizer.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantizer/xpu_inductor_quantizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c0fc48fd54fa17b6ed0db900677ab339d62a988
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/quantizer/xpu_inductor_quantizer.py
@@ -0,0 +1,117 @@
+# mypy: allow-untyped-defs
+import functools
+from typing import Any, TYPE_CHECKING
+
+import torch
+from torch.ao.quantization.observer import HistogramObserver, PerChannelMinMaxObserver
+from torch.ao.quantization.quantizer.quantizer import QuantizationSpec
+from torch.ao.quantization.quantizer.x86_inductor_quantizer import (
+    _is_any_annotated,
+    FilterFn,
+    int8_in_int8_out_ops,
+    X86InductorQuantizer,
+)
+from torch.ao.quantization.quantizer.xnnpack_quantizer_utils import QuantizationConfig
+from torch.fx import Node
+
+
+if TYPE_CHECKING:
+    from torch.ao.quantization.qconfig import _ObserverOrFakeQuantizeConstructor
+
+__all__ = [
+    "XPUInductorQuantizer",
+    "get_default_xpu_inductor_quantization_config",
+]
+
+
+@functools.lru_cache
+def get_default_xpu_inductor_quantization_config():
+    extra_args: dict[str, Any] = {"eps": 2**-12}
+    act_observer_or_fake_quant_ctr = HistogramObserver
+    act_quantization_spec = QuantizationSpec(
+        dtype=torch.int8,
+        quant_min=-128,
+        quant_max=127,
+        qscheme=torch.per_tensor_affine,
+        is_dynamic=False,
+        observer_or_fake_quant_ctr=act_observer_or_fake_quant_ctr.with_args(
+            **extra_args
+        ),
+    )
+
+    weight_observer_or_fake_quant_ctr: _ObserverOrFakeQuantizeConstructor = (
+        PerChannelMinMaxObserver
+    )
+
+    weight_quantization_spec = QuantizationSpec(
+        dtype=torch.int8,
+        quant_min=-128,
+        quant_max=127,
+        qscheme=torch.per_channel_symmetric,
+        ch_axis=0,  # 0 corresponding to weight shape = (oc, ic, kh, kw) of conv
+        is_dynamic=False,
+        observer_or_fake_quant_ctr=weight_observer_or_fake_quant_ctr.with_args(
+            **extra_args
+        ),
+    )
+
+    bias_quantization_spec = None  # will use placeholder observer by default
+    quantization_config = QuantizationConfig(
+        act_quantization_spec,
+        act_quantization_spec,
+        weight_quantization_spec,
+        bias_quantization_spec,
+        False,
+    )
+    return quantization_config
+
+
+class XPUInductorQuantizer(X86InductorQuantizer):
+    """
+    XPUInductorQuantizer is a class designed to facilitate
+    quantization capability at Intel GPU backend. The class
+    highly reuses the existing implementation of
+    X86InductorQuantizer as both are intended to take advantage
+    of the optimized kernels in oneDNN library.
+    """
+
+    """
+        Following annotate_xx overrides the impls in base class, as
+        no XPU implementation for these operators currently. We would
+        gradually enable the XPU implementation and remove following
+        overrides. We keep the annotate methods but make the function
+        body empty, aiming to let `_generate_qdq_quantized_model`
+        generate qdq around op and graph execute on fp32 dtype for
+        unsupported operators.
+    """
+
+    def _annotate_qat_conv2d_fusion_pattern(
+        self,
+        model: torch.fx.GraphModule,
+        quantization_config: QuantizationConfig | None,
+        filter_fn: FilterFn | None = None,
+    ):
+        pass
+
+    def _annotate_maxpool2d(
+        self,
+        node: Node,
+        quantization_config: QuantizationConfig | None,
+    ) -> None:
+        """
+        Here we skip the annotate logic for maxpool at XPU backend
+        as the quantized::max_pool2d is only implemented for CPU.
+        """
+        return
+
+    def _annotate_output_for_int8_in_int8_out_pattern(
+        self,
+        node: Node,
+    ) -> None:
+        if (node.target in int8_in_int8_out_ops) and (_is_any_annotated([node])):
+            if node.target is torch.ops.aten.max_pool2d.default:
+                return
+            else:
+                input_node = node.all_input_nodes[0]
+                self._annotate_output_share_observer_as_input(input_node, node)
+        return
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/stubs.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/stubs.py
new file mode 100644
index 0000000000000000000000000000000000000000..8dd05374eff844be2cec2d913b88a338aded4e6a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/stubs.py
@@ -0,0 +1,74 @@
+from typing import Any
+
+import torch
+from torch import nn
+from torch.ao.quantization import QConfig
+
+
+__all__ = ["QuantStub", "DeQuantStub", "QuantWrapper"]
+
+
+class QuantStub(nn.Module):
+    r"""Quantize stub module, before calibration, this is same as an observer,
+    it will be swapped as `nnq.Quantize` in `convert`.
+
+    Args:
+        qconfig: quantization configuration for the tensor,
+            if qconfig is not provided, we will get qconfig from parent modules
+    """
+
+    def __init__(self, qconfig: QConfig | None = None):
+        super().__init__()
+        if qconfig:
+            self.qconfig = qconfig
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return x
+
+
+class DeQuantStub(nn.Module):
+    r"""Dequantize stub module, before calibration, this is same as identity,
+    this will be swapped as `nnq.DeQuantize` in `convert`.
+
+    Args:
+        qconfig: quantization configuration for the tensor,
+            if qconfig is not provided, we will get qconfig from parent modules
+    """
+
+    def __init__(self, qconfig: Any | None = None):
+        super().__init__()
+        if qconfig:
+            self.qconfig = qconfig
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return x
+
+
+class QuantWrapper(nn.Module):
+    r"""A wrapper class that wraps the input module, adds QuantStub and
+    DeQuantStub and surround the call to module with call to quant and dequant
+    modules.
+
+    This is used by the `quantization` utility functions to add the quant and
+    dequant modules, before `convert` function `QuantStub` will just be observer,
+    it observes the input tensor, after `convert`, `QuantStub`
+    will be swapped to `nnq.Quantize` which does actual quantization. Similarly
+    for `DeQuantStub`.
+    """
+
+    quant: QuantStub
+    dequant: DeQuantStub
+    module: nn.Module
+
+    def __init__(self, module: nn.Module):
+        super().__init__()
+        qconfig = getattr(module, "qconfig", None)
+        self.add_module("quant", QuantStub(qconfig))
+        self.add_module("dequant", DeQuantStub(qconfig))
+        self.add_module("module", module)
+        self.train(module.training)
+
+    def forward(self, X: torch.Tensor) -> torch.Tensor:
+        X = self.quant(X)
+        X = self.module(X)
+        return self.dequant(X)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..84a027e17e6b07cfbddc8b7b436ba0299b32ef91
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/ao/quantization/utils.py
@@ -0,0 +1,875 @@
+# mypy: allow-untyped-defs
+"""
+Utils shared by different modes of quantization (eager/graph)
+"""
+
+import functools
+import sys
+import warnings
+from collections import OrderedDict
+from collections.abc import Callable
+from inspect import getfullargspec, signature
+from typing import Any, Union
+
+import torch
+from torch.ao.quantization.quant_type import QuantType
+from torch.fx import Node
+from torch.nn.utils.parametrize import is_parametrized
+
+
+if sys.version_info < (3, 12):
+    NodePattern = Union[tuple[Node, Node], tuple[Node, tuple[Node, Node]], Any]
+    NodePattern.__module__ = "torch.ao.quantization.utils"
+else:
+    from typing import TypeAliasType
+
+    NodePattern = TypeAliasType(
+        "NodePattern", tuple[Node, Node] | tuple[Node, tuple[Node, Node]] | Any
+    )
+
+
+# This is the Quantizer class instance from torch/quantization/fx/quantize.py.
+# Define separately to prevent circular imports.
+# TODO(future PR): improve this.
+# make this public once fixed (can't be public as is because setting the module directly
+# doesn't work)
+QuantizerCls = Any
+
+# Type for fusion patterns, it can be more complicated than the following actually,
+# see pattern.md for docs
+# TODO: not sure if typing supports recursive data types
+
+if sys.version_info < (3, 12):
+    Pattern = Union[
+        Callable,
+        tuple[Callable, Callable],
+        tuple[Callable, tuple[Callable, Callable]],
+        Any,
+    ]
+    Pattern.__module__ = "torch.ao.quantization.utils"
+else:
+    from typing import TypeAliasType
+
+    Pattern = TypeAliasType(
+        "Pattern",
+        Callable
+        | tuple[Callable, Callable]
+        | tuple[Callable, tuple[Callable, Callable]]
+        | Any,
+    )
+
+
+# TODO: maybe rename this to MatchInputNode
+class MatchAllNode:
+    """A node pattern that matches all nodes, used in defining
+    fusion patterns in FX Graph Mode Quantization
+    """
+
+
+module_type_list = {
+    torch.nn.ReLU,
+    torch.nn.ReLU6,
+    torch.nn.AdaptiveAvgPool1d,
+    torch.nn.AdaptiveAvgPool2d,
+    torch.nn.AdaptiveAvgPool3d,
+    torch.nn.AvgPool1d,
+    torch.nn.AvgPool2d,
+    torch.nn.AvgPool3d,
+    torch.nn.MaxPool1d,
+    torch.nn.MaxPool2d,
+    torch.nn.MaxPool3d,
+    torch.nn.Identity,
+    torch.nn.Hardsigmoid,
+    torch.nn.Sigmoid,
+    torch.nn.Tanh,
+}
+func_list = {
+    torch.nn.functional.adaptive_avg_pool1d,
+    torch.nn.functional.adaptive_avg_pool2d,
+    torch.nn.functional.adaptive_avg_pool3d,
+    torch.nn.functional.elu,
+    torch.nn.functional.hardswish,
+    torch.nn.functional.instance_norm,
+    torch.nn.functional.layer_norm,
+    torch.nn.functional.leaky_relu,
+    torch.nn.functional.silu,
+    torch.nn.functional.mish,
+    torch.nn.functional.dropout,
+    torch.nn.functional.max_pool1d,
+    torch.nn.functional.max_pool2d,
+    torch.nn.functional.max_pool3d,
+    torch.nn.functional.relu,
+    torch.nn.functional.hardtanh,
+    torch.nn.functional.hardtanh_,
+    torch.nn.functional.hardsigmoid,
+    torch.nn.functional.sigmoid,
+    torch.transpose,
+    torch.repeat_interleave,
+    torch.sigmoid,
+    torch.squeeze,
+    torch.stack,
+    torch.sum,
+    torch.tanh,
+    torch.unsqueeze,
+    torch.cat,
+}
+method_list = {
+    torch.mean,
+    "relu",
+    "relu_",
+    "contiguous",
+    "detach",
+    "detach_",
+    "hardsigmoid",
+    "hardsigmoid_",
+    "permute",
+    "repeat",
+    "repeat_interleave",
+    "reshape",
+    "resize_",
+    "shape",
+    "sigmoid",
+    "sigmoid_",
+    "size",
+    "squeeze",
+    "squeeze_",
+    "tanh",
+    "tanh_",
+    "transpose",
+    "unsqueeze",
+    "unsqueeze_",
+    "view",
+}
+
+
+# TODO: not used now, remove
+def check_node(node, modules):
+    # TODO: reuse is_fixed_qparam_node after we move this function to _lower_to_native_backend.py
+    is_call_function = node.op == "call_function" and node.target in func_list
+    is_call_method = node.op == "call_method" and node.target in method_list
+    is_call_module = (
+        node.op == "call_module" and type(modules[str(node.target)]) in module_type_list
+    )
+    return is_call_function, is_call_method, is_call_module
+
+
+def get_combined_dict(default_dict, additional_dict):
+    """
+    Combines two dictionaries.
+
+    This function takes two dictionaries as input and returns a new dictionary
+    that contains all the key-value pairs from both input dictionaries.
+    If there are any duplicate keys in the `additional_dict`, the values
+    from the `additional_dict` will overwrite those in the `default_dict`.
+    Args:
+        default_dict (dict): The main dictionary that will be used as the base
+        additional_dict (dict): The dictionary used to update `default_dict`
+
+    Returns:
+        dict: The resulting dictionary
+    Example:
+        >>> x = dict(a=1, b=1)
+        >>> y = dict(b=2, c=3)
+        >>> get_combined_dict(x, y)
+        {'a': 1, 'b': 2, 'c': 3}
+    """
+    d = default_dict.copy()
+    d.update(additional_dict)
+    return d
+
+
+def is_per_tensor(qscheme):
+    return qscheme == torch.per_tensor_affine or qscheme == torch.per_tensor_symmetric
+
+
+def is_per_channel(qscheme):
+    return qscheme in [
+        torch.per_channel_affine,
+        torch.per_channel_affine_float_qparams,
+        torch.per_channel_symmetric,
+    ]
+
+
+def getattr_from_fqn(obj: Any, fqn: str) -> Any:
+    """
+    Given an obj and a fqn such as "foo.bar.baz", returns gm.foo.bar.baz.
+    """
+    return functools.reduce(getattr, fqn.split("."), obj)
+
+
+def to_underlying_dtype(qdtype):
+    DTYPE_MAPPING = {
+        torch.quint8: torch.uint8,
+        torch.qint8: torch.int8,
+        torch.qint32: torch.int32,
+        torch.quint4x2: torch.uint8,
+        torch.quint2x4: torch.uint8,
+        torch.uint8: torch.uint8,
+        torch.int8: torch.int8,
+        torch.uint16: torch.uint16,
+        torch.int16: torch.int16,
+        torch.int32: torch.int32,
+        torch.float8_e5m2: torch.float8_e5m2,
+        torch.float8_e4m3fn: torch.float8_e4m3fn,
+    }
+    if qdtype not in DTYPE_MAPPING:
+        raise AssertionError("Unsupported dtype: " + str(qdtype))
+    return DTYPE_MAPPING[qdtype]
+
+
+def get_qparam_dict(observer_or_fake_quant):
+    from torch.ao.quantization.observer import PlaceholderObserver
+
+    qscheme = getattr(observer_or_fake_quant, "qscheme", None)
+    dtype = observer_or_fake_quant.dtype
+    qparams = {"qscheme": qscheme, "dtype": dtype}
+
+    if not qscheme or isinstance(observer_or_fake_quant, PlaceholderObserver):
+        return {"qscheme": None, "dtype": dtype}
+
+    if is_per_tensor(qscheme):
+        qscheme = torch.per_tensor_affine
+    elif is_per_channel(qscheme):
+        # change symmetric to affine since we do not have symmetric
+        # quantized Tensor
+        if qscheme == torch.per_channel_symmetric:
+            qscheme = torch.per_channel_affine
+        qparams["axis"] = observer_or_fake_quant.ch_axis
+    else:
+        raise RuntimeError(f"Unrecognized qscheme: {qscheme}")
+    # update qscheme, since we don't have symmetric quant qscheme
+    # in quantized Tensor
+    qparams["qscheme"] = qscheme
+
+    scale, zero_point = observer_or_fake_quant.calculate_qparams()
+    qparams["scale"] = scale
+    qparams["zero_point"] = zero_point
+
+    if hasattr(observer_or_fake_quant, "quant_min"):
+        qparams["quant_min"] = observer_or_fake_quant.quant_min
+    if hasattr(observer_or_fake_quant, "quant_max"):
+        qparams["quant_max"] = observer_or_fake_quant.quant_max
+
+    return qparams
+
+
+def get_swapped_custom_module_class(
+    custom_module, custom_module_class_mapping, qconfig
+):
+    """Get the observed/quantized custom module class that we need
+    to swap `custom_module` to
+    Input:
+        custom_module: input, can be an instance of either a float or observed custom module
+        custom_module_class_mapping: the float to observed or observed to quantized custom module class mapping
+        qconfig: qconfig configured for the custom module
+
+    Output:
+        corresponding observed/quantized custom module class for input custom module instance
+    """
+    quant_type = get_quant_type(qconfig)
+    class_mapping = custom_module_class_mapping.get(quant_type, {})
+    if type(custom_module) not in class_mapping:
+        raise AssertionError(
+            "did not find corresponding observed "
+            f"module class for {type(custom_module)} in mapping: {class_mapping}"
+        )
+    return class_mapping[type(custom_module)]
+
+
+def activation_dtype(qconfig):
+    if qconfig is None:
+        raise AssertionError("qconfig must be provided to determine activation dtype")
+    activation = qconfig.activation()
+    return activation.dtype
+
+
+def weight_dtype(qconfig):
+    if qconfig is None:
+        raise AssertionError("qconfig must be provided to determine weight dtype")
+    weight = qconfig.weight()
+    return weight.dtype
+
+
+def activation_is_statically_quantized(qconfig):
+    """Given a qconfig, decide if the activation needs to be
+    quantized or not, this includes quantizing to quint8, qint8 and qint32 and float16
+    """
+    return activation_dtype(qconfig) in [
+        torch.quint8,
+        torch.qint8,
+        torch.qint32,
+        torch.float16,
+        torch.uint8,
+        torch.int8,
+        torch.int16,
+        torch.int32,
+        torch.float8_e5m2,
+        torch.float8_e4m3fn,
+    ] and (not activation_is_dynamically_quantized(qconfig))
+
+
+def activation_is_dynamically_quantized(qconfig):
+    """Given a qconfig, decide if the activation needs to be
+    dynamically quantized or not, this includes dynamically quantizing to
+    quint8, qint8 and float16
+    """
+    _activation_dtype, _, activation_is_dynamic = get_qconfig_dtypes(qconfig)
+    return activation_is_dynamic
+
+
+def activation_is_int8_quantized(qconfig):
+    """Given a qconfig, decide if the activation needs to be
+    quantized to int8 or not, this includes quantizing to quint8, qint8
+    """
+    return activation_dtype(qconfig) in [
+        torch.quint8,
+        torch.qint8,
+        torch.uint8,
+        torch.int8,
+    ]
+
+
+def activation_is_int32_quantized(qconfig):
+    """Given a qconfig, decide if the activation needs to be
+    quantized to int32 or not
+    """
+    return activation_dtype(qconfig) in [torch.qint32, torch.int32]
+
+
+def weight_is_quantized(qconfig):
+    """Given a qconfig, decide if the weight needs to be
+    quantized or not
+    """
+    return weight_dtype(qconfig) in [
+        torch.quint8,
+        torch.qint8,
+        torch.float16,
+        torch.quint4x2,
+        torch.uint8,
+        torch.int8,
+        torch.int16,
+        torch.int32,
+        torch.float8_e5m2,
+        torch.float8_e4m3fn,
+    ]
+
+
+def weight_is_statically_quantized(qconfig):
+    """Given a qconfig, decide if the weight needs to be statically
+    quantized or not
+    """
+    return weight_dtype(qconfig) in [torch.quint8, torch.qint8, torch.uint8, torch.int8]
+
+
+def op_is_int8_dynamically_quantized(qconfig) -> bool:
+    """Given a qconfig, returns True if this op is using int8 dynamic
+    quantization
+    """
+    activation_dtype, weight_dtype, activation_is_dynamic = get_qconfig_dtypes(qconfig)
+    return (
+        activation_dtype in [torch.quint8, torch.uint8]
+        and
+        # for now, the lines below assume fbgemm or qnnpack
+        weight_dtype in [torch.qint8, torch.int8]
+        and activation_is_dynamic
+    )
+
+
+def get_qconfig_dtypes(qconfig):
+    r"""returns the qconfig tuple for qconfig:
+    (activation_dtype, weight_dtype, activation_is_dynamic)
+    """
+    if qconfig is None:
+        raise AssertionError("qconfig must be provided to extract dtypes")
+    activation = qconfig.activation()
+    weight = qconfig.weight()
+    act_is_dynamic = getattr(activation, "is_dynamic", False)
+    return (activation.dtype, weight.dtype, act_is_dynamic)
+
+
+def get_quant_type(qconfig):
+    if qconfig is None:
+        raise AssertionError("qconfig must be provided to determine quant type")
+    activation = qconfig.activation()
+    weight = qconfig.weight()
+    static_dtypes = [
+        torch.quint8,
+        torch.qint8,
+        torch.quint4x2,
+        torch.qint32,
+        torch.uint8,
+        torch.int8,
+        torch.int16,
+        torch.int32,
+        torch.float8_e5m2,
+        torch.float8_e4m3fn,
+    ]
+    if weight.dtype in static_dtypes:
+        if hasattr(activation, "is_dynamic") and activation.is_dynamic:
+            return QuantType.DYNAMIC
+        elif activation.dtype in static_dtypes:
+            return QuantType.STATIC
+        else:
+            return QuantType.WEIGHT_ONLY
+
+    if weight.dtype == torch.float16:
+        if hasattr(activation, "is_dynamic") and activation.is_dynamic:
+            return QuantType.DYNAMIC
+        elif activation.dtype == torch.float16:
+            return QuantType.STATIC
+
+    raise Exception(  # noqa: TRY002
+        f"Unrecognized dtype combination in get_quant_type: activation({activation.dtype}),"
+        f"weight({weight.dtype})"
+    )
+
+
+def check_min_max_valid(min_val: torch.Tensor, max_val: torch.Tensor) -> bool:
+    """Checks if the given minimum and maximum values are valid, meaning that
+    they exist and the min value is less than the max value.
+    """
+    if min_val.numel() == 0 or max_val.numel() == 0:
+        warnings.warn(
+            "must run observer before calling calculate_qparams. "
+            + "Returning default values.",
+            stacklevel=2,
+        )
+        return False
+
+    if min_val.dim() == 0 or max_val.dim() == 0:
+        if min_val == float("inf") and max_val == float("-inf"):
+            warnings.warn(
+                "must run observer before calling calculate_qparams. "
+                + "Returning default values.",
+                stacklevel=2,
+            )
+
+            return False
+
+        if min_val > max_val:
+            raise AssertionError(f"min {min_val} should be less than max {max_val}")
+    else:
+        if torch.any(min_val > max_val):
+            raise AssertionError(f"min {min_val} should be less than max {max_val}")
+
+    return True
+
+
+def calculate_qmin_qmax(
+    quant_min: int,
+    quant_max: int,
+    has_customized_qrange: bool,
+    dtype: torch.dtype,
+    reduce_range: bool,
+) -> tuple[int, int]:
+    r"""Calculates actual qmin and qmax based on the quantization range,
+    observer datatype and if range is reduced.
+    """
+    # TODO(jerryzh): Figure out why custom quant_min/quant_max are still adjusted.
+    if has_customized_qrange:
+        # This initialization here is to be resolve TorchScript compilation issues and allow
+        # using of refinement to decouple initial_qmin and initial_qmax from quantization range.
+        # The actual values of initial_qmin and initial_qmax will be reset below.
+        if dtype in [torch.qint32, torch.int32]:
+            initial_quant_min, initial_quant_max = 0, 2**32 - 1
+        else:
+            initial_quant_min, initial_quant_max = 0, 255
+        # The following assignment of self.qmin and self.qmax to the local variables and the if check refine the
+        # attribute from Optional valid integers for use, based on TorchScript's requirements.
+        custom_quant_min, custom_quant_max = quant_min, quant_max
+        if custom_quant_min is not None and custom_quant_max is not None:
+            initial_quant_min, initial_quant_max = (
+                custom_quant_min,
+                custom_quant_max,
+            )
+
+        qrange_len = initial_quant_max - initial_quant_min + 1
+        if dtype in [torch.qint8, torch.int8]:
+            if not (0 < qrange_len <= 256):
+                raise AssertionError(
+                    "quantization range should be positive and not exceed the maximum bit range (=256)."
+                )
+        elif dtype in [torch.qint32, torch.int32]:
+            if not (0 < qrange_len <= 2**32):
+                raise AssertionError(
+                    "quantization range should be positive and not exceed the maximum bit range (=4294967296)."
+                )
+        if reduce_range:
+            quant_min, quant_max = quant_min // 2, quant_max // 2
+    else:
+        # Fallback onto default 8-bit qmin and qmax calculation if dynamic range is not used.
+        if dtype in [torch.qint8, torch.int8]:
+            if reduce_range:
+                quant_min, quant_max = -64, 63
+            else:
+                quant_min, quant_max = -128, 127
+        elif dtype in [torch.quint8, torch.uint8]:
+            if reduce_range:
+                quant_min, quant_max = 0, 127
+            else:
+                quant_min, quant_max = 0, 255
+        elif dtype in [torch.qint32, torch.int32]:
+            quant_min, quant_max = -1 * (2**31), (2**31) - 1
+        elif dtype == torch.uint16:
+            quant_min, quant_max = 0, 2**16 - 1
+        elif dtype == torch.int16:
+            quant_min, quant_max = -(2**15), 2**15 - 1
+        else:
+            quant_min, quant_max = 0, 15
+    return quant_min, quant_max
+
+
+def _parent_name(target):
+    """
+    Turn 'foo.bar' into ['foo', 'bar']
+    """
+    r = target.rsplit(".", 1)
+    if len(r) == 1:
+        return "", r[0]
+    else:
+        return r[0], r[1]
+
+
+def has_no_children_ignoring_parametrizations(module):
+    """
+    Checks if module._modules is empty or
+    if module is a parametrization, checks that module._modules only has
+    the 'parametrizations' module
+    """
+    if len(module._modules) == 0:
+        return True
+    elif is_parametrized(module):
+        return len(module._modules) == 1 and "parametrizations" in module._modules
+    else:
+        return False
+
+
+def _get_path_of_module(
+    root: torch.nn.Module, submodule: torch.nn.Module
+) -> str | None:
+    """Get the path (fully qualified name) of a submodule
+
+    Example::
+
+    >> class M(torch.nn.Module):
+           def __init__(self) -> None:
+               self.linear = torch.nn.Linear(5, 5)
+           def forward(self, x):
+               return self.linear(x)
+
+    >> m = M()
+    >> l = m.linear
+    >> _get_path_of_module(m, l)
+    "linear"
+    """
+    for n, p in root.named_modules():
+        if submodule is p:
+            return n
+    return None
+
+
+def _get_signature_locals(f: Callable, loc: dict[str, Any]) -> dict[str, Any]:
+    """Get local keyword arguments
+
+    Example::
+
+    >> def f(self, a, b=9):
+           pass
+    >> loc = {"a": 6, "c": 7}
+    >> _get_signature_locals(f, loc)
+    {"a": 6}
+    """
+    return {k: v for k, v in loc.items() if k in signature(f).parameters}
+
+
+def _get_default_kwargs(f: Callable) -> "OrderedDict[str, Any]":
+    """Get all default keyword arguments from function signature
+
+    Example::
+
+    >> def f(self, a, b=9):
+           pass
+    >> _get_default_kwargs(f)
+    {"b": 9}
+    """
+    kwargs = {}
+    for name, param in signature(f).parameters.items():
+        if param.default is not param.empty:
+            kwargs[name] = param.default
+        elif param.kind is param.VAR_POSITIONAL:
+            kwargs[name] = ()
+        elif param.kind is param.VAR_KEYWORD:
+            kwargs[name] = {}
+    return OrderedDict(kwargs)
+
+
+def _normalize_kwargs(func: Callable, loc: dict[str, Any]) -> "OrderedDict[str, Any]":
+    """Given a function and local function arguments, normalize the keyword
+    arguments by filling in default arguments from function signature
+
+    Example::
+
+    >> def f(self, key1=3, key2=3):
+           pass
+    >> loc = {"key2": 6}
+    >> _normalize_kwargs(f, loc)
+    {"key1": 3, "key2": 6}
+    """
+    default_kwargs = _get_default_kwargs(func)
+    local_kwargs = _get_signature_locals(func, loc)
+    normalized_kwargs = default_kwargs.copy()
+    for attr, val in local_kwargs.items():
+        if attr in normalized_kwargs:
+            # override the default keyword arguments
+            normalized_kwargs[attr] = val
+    return normalized_kwargs
+
+
+def validate_qmin_qmax(quant_min: int, quant_max: int) -> None:
+    r"""Validates that the user-specified quantization range is properly initialized
+    and within the given bound supported by the observer dtype.
+
+    To accommodate lower-bit quantization with respect to the existing torch.qint8 and
+    torch.quint8 datatypes, the user can choose to use dynamic quantization range by passing
+    in a tuple of initial qmin and qmax values. One use case is these customized qmin and qmax
+    values are used to calculate static estimates of the scale and zero point for aggressive lower-bit
+    fake quantization. These estimates are compared against parameters learned through backpropagation.
+    The related literatures for scale and zero point via backpropagation are as follows:
+
+    Learned Step Size Quantization: https://openreview.net/pdf?id=rkgO66VKDS
+    Trained Quantization Thresholds: https://arxiv.org/pdf/1903.08066.pdf
+    """
+    # The variable names are prefixed with "initial" because their values (qmin and qmax) might be adjusted
+    # based on whether quantization range is reduced and the datatype (signed/unsigned) used by the observer.
+    if not (quant_min <= 0 <= quant_max):
+        raise AssertionError("Used-specified quantization range must include 0.")
+    if quant_min >= quant_max:
+        raise AssertionError(
+            "qmin must be strictly less than qmax for user-specified quantization range."
+        )
+
+
+# Functionally equivalent to '_calculate_qparams' in observer.py. Observers must be torchscriptable however and qscheme
+# as far as I can tell is not allowed to passed as a parameter in torchscript functions. This makes refactoring observer
+# to use this utility a massive pain and very gross. For now Im opting just to duplicate as this code seems unlikely to change
+# (last update over 1 year ago) and when torchscript is fully deprecated we can refactor. TODO(jakeszwe, jerryzh168)
+def determine_qparams(
+    min_val: torch.Tensor,
+    max_val: torch.Tensor,
+    quant_min: int,
+    quant_max: int,
+    dtype: torch.dtype,
+    eps: torch.Tensor,
+    has_customized_qrange: bool,
+    qscheme: torch.qscheme = torch.per_tensor_affine,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    r"""Calculates the quantization parameters, given min and max
+    value tensors. Works for both per tensor and per channel cases
+
+    Args:
+        min_val: Minimum values per channel
+        max_val: Maximum values per channel
+
+    Returns:
+        scales: Scales tensor of shape (#channels,)
+        zero_points: Zero points tensor of shape (#channels,)
+    """
+    if not check_min_max_valid(min_val, max_val):
+        return torch.tensor([1.0], device=min_val.device.type), torch.tensor(
+            [0], device=min_val.device.type
+        )
+
+    min_val_neg = torch.min(min_val, torch.zeros_like(min_val))
+    max_val_pos = torch.max(max_val, torch.zeros_like(max_val))
+
+    device = min_val_neg.device
+    scale = torch.ones(min_val_neg.size(), dtype=torch.double, device=device)
+    zero_point = torch.zeros(min_val_neg.size(), dtype=torch.int64, device=device)
+    eps = eps.to(device)
+
+    if qscheme == torch.per_tensor_symmetric or qscheme == torch.per_channel_symmetric:
+        max_val_pos = torch.max(-min_val_neg, max_val_pos)
+        scale = max_val_pos / (float(quant_max - quant_min) / 2)
+        scale = torch.max(scale, eps)
+        if dtype in [torch.uint8, torch.quint8]:
+            if has_customized_qrange:
+                # When customized quantization range is used, down-rounded midpoint of the range is chosen.
+                zero_point = zero_point.new_full(
+                    zero_point.size(), (quant_min + quant_max) // 2
+                )
+            else:
+                zero_point = zero_point.new_full(zero_point.size(), 128)
+    elif qscheme == torch.per_channel_affine_float_qparams:
+        scale = (max_val - min_val) / float(quant_max - quant_min)
+        scale = torch.where(scale > eps, scale, torch.ones_like(scale))
+        # We use the quantize function
+        # xq = Round(Xf * inv_scale + zero_point),
+        # setting zero_point to (-1 * min *inv_scale) we get
+        # Xq = Round((Xf - min) * inv_scale)
+        zero_point = -1 * min_val / scale
+    else:
+        scale = (max_val_pos - min_val_neg) / float(quant_max - quant_min)
+        scale = torch.max(scale, eps)
+        zero_point = quant_min - torch.round(min_val_neg / scale).to(torch.int)
+        zero_point = torch.clamp(zero_point, quant_min, quant_max)
+
+    # For scalar values, cast them to Tensors of size 1 to keep the shape
+    # consistent with default values in FakeQuantize.
+    if len(scale.shape) == 0:
+        # TODO: switch to scale.item() after adding JIT support
+        scale = torch.tensor([float(scale)], dtype=scale.dtype, device=device)
+    if len(zero_point.shape) == 0:
+        # TODO: switch to zero_point.item() after adding JIT support
+        zero_point = torch.tensor(
+            [int(zero_point)], dtype=zero_point.dtype, device=device
+        )
+        if qscheme == torch.per_channel_affine_float_qparams:
+            zero_point = torch.tensor(
+                [float(zero_point)], dtype=zero_point.dtype, device=device
+            )
+
+    return scale.to(torch.double), zero_point.to(torch.int64)
+
+
+def _get_num_pos_args(f: Callable) -> int:
+    """Get number of positional args for a function
+
+    Example::
+
+    >> def f(self, key1=3, key2=3):
+           pass
+    >> _get_num_pos_args(f)
+    3
+    """
+    return len(getfullargspec(f).args)
+
+
+def get_fqn_to_example_inputs(
+    model: torch.nn.Module, example_inputs: tuple[Any, ...]
+) -> dict[str, tuple[Any, ...]]:
+    """Given a model and its example inputs, return a dictionary from
+    fully qualified name of submodules to example_inputs for that submodule,
+    e.g. {"linear1": (tensor1,), "linear2": (tensor2,), "sub": (tensor3,),
+          "sub.linear1": (tensor4,), ...}
+
+    Used to make quantizing submodules easier now that FX Graph Mode Quantization requires
+    example inputs.
+
+    Also works for keyword arguments with default values, we would flatten keyword
+    arguments as positional arguments and fill in the missing keyword args with default
+    values, e.g. if we have a forward function:
+    def forward(self, x, key1=3, key2=3):
+        ...
+
+    and we call it with self.submodule(x, key2=6)
+    we'll get example_inputs: (x, 3, 6)
+
+    user can also override `key1` with positional arguments as well:
+    for self.submodule(x, 5, key2=6)
+    we'll get: (x, 5, 6)
+
+    variable positional arguments and variable positional keyword arguments in forward
+    function are not supported currently, so please make sure no submodules is using
+    them.
+    """
+    root = model
+    fqn_to_example_inputs = {}
+
+    def _patched_module_call(self, *args, **kwargs):
+        submodule_example_inputs = list(args).copy()
+        normalized_kwargs = _normalize_kwargs(self.forward, kwargs)
+        # minus 1 to skipping counting `self`
+        num_args = _get_num_pos_args(self.forward) - 1
+        num_to_pop = num_args - len(submodule_example_inputs)
+        while num_to_pop and normalized_kwargs:
+            normalized_kwargs.popitem(last=False)
+            num_to_pop -= 1
+        submodule_example_inputs.extend(normalized_kwargs.values())
+        submodule_example_inputs_tuple = tuple(submodule_example_inputs)
+        fqn = _get_path_of_module(root, self)
+        if fqn is not None:
+            fqn_to_example_inputs[fqn] = submodule_example_inputs_tuple
+        return orig_module_call(self, *args, **kwargs)
+
+    orig_module_call = torch.nn.Module.__call__
+    torch.nn.Module.__call__ = _patched_module_call  # type: ignore[method-assign]
+    try:
+        model(*example_inputs)
+    finally:
+        # restore the module call even if there is an exception
+        torch.nn.Module.__call__ = orig_module_call  # type: ignore[method-assign]
+    return fqn_to_example_inputs
+
+
+def _assert_and_get_unique_device(module: torch.nn.Module) -> Any:
+    """
+    Returns the unique device for a module, or None if no device is found.
+    Throws an error if multiple devices are detected.
+    """
+    devices = {p.device for p in module.parameters()} | {
+        p.device for p in module.buffers()
+    }
+    """
+    As a temp workaround for AIMP HHC publish we added CPU check.remove it later. T163614564
+    """
+    if {torch.device("cpu"), torch.device("meta")} == devices:
+        warnings.warn(
+            "Both 'meta' and 'cpu' are present in the list of devices. Module can have one device. We Select 'cpu'.",
+            stacklevel=2,
+        )
+        devices = {torch.device("cpu")}
+    ""
+    if len(devices) > 1:
+        raise AssertionError(
+            "prepare only works with cpu or single-device CUDA modules, "
+            f"but got devices {devices}"
+        )
+    device = next(iter(devices)) if len(devices) > 0 else None
+    return device
+
+
+DEPRECATION_WARNING = (
+    "torch.ao.quantization is deprecated and will be removed in 2.10. \n"
+    "For migrations of users: \n"
+    "1. Eager mode quantization (torch.ao.quantization.quantize, "
+    "torch.ao.quantization.quantize_dynamic), please migrate to use torchao eager mode "
+    "quantize_ API instead \n"
+    "2. FX graph mode quantization (torch.ao.quantization.quantize_fx.prepare_fx,"
+    "torch.ao.quantization.quantize_fx.convert_fx, please migrate to use torchao pt2e quantization "
+    "API instead (prepare_pt2e, convert_pt2e) \n"
+    "3. pt2e quantization has been migrated to torchao (https://github.com/pytorch/ao/tree/main/torchao/quantization/pt2e) \n"
+    "see https://github.com/pytorch/ao/issues/2259 for more details"
+)
+
+
+__all__ = [
+    "NodePattern",
+    "Pattern",
+    "MatchAllNode",
+    "check_node",
+    "get_combined_dict",
+    "is_per_tensor",
+    "is_per_channel",
+    "getattr_from_fqn",
+    "get_qparam_dict",
+    "get_swapped_custom_module_class",
+    "activation_dtype",
+    "weight_dtype",
+    "activation_is_statically_quantized",
+    "activation_is_dynamically_quantized",
+    "activation_is_int8_quantized",
+    "activation_is_int32_quantized",
+    "weight_is_quantized",
+    "weight_is_statically_quantized",
+    "op_is_int8_dynamically_quantized",
+    "get_qconfig_dtypes",
+    "get_quant_type",
+    "check_min_max_valid",
+    "calculate_qmin_qmax",
+    "has_no_children_ignoring_parametrizations",
+    "get_fqn_to_example_inputs",
+    "to_underlying_dtype",
+    "determine_qparams",
+    "validate_qmin_qmax",
+    "DEPRECATION_WARNING",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..cfab4fa5e2d5fc7363475cb217bea6dfa0b49042
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/__init__.py
@@ -0,0 +1,618 @@
+# mypy: allow-untyped-defs
+"""
+``torch.autograd`` provides classes and functions implementing automatic differentiation of arbitrary scalar valued functions.
+
+It requires minimal changes to the existing code - you only need to declare :class:`Tensor` s
+for which gradients should be computed with the ``requires_grad=True`` keyword.
+As of now, we only support autograd for floating point :class:`Tensor` types (
+half, float, double and bfloat16) and complex :class:`Tensor` types (cfloat, cdouble).
+"""
+
+import warnings
+from collections.abc import Sequence
+from typing import cast, Optional, Union
+
+import torch
+from torch import _vmap_internals
+from torch.overrides import handle_torch_function, has_torch_function, is_tensor_like
+from torch.types import _size, _TensorOrTensors, _TensorOrTensorsOrGradEdge
+
+from . import forward_ad, functional, graph
+from .anomaly_mode import detect_anomaly, set_detect_anomaly
+from .function import Function, NestedIOFunction
+from .grad_mode import (
+    _force_original_view_tracking,
+    _unsafe_preserve_version_counter,
+    enable_grad,
+    inference_mode,
+    no_grad,
+    set_grad_enabled,
+    set_multithreading_enabled,
+)
+from .gradcheck import gradcheck, gradgradcheck
+from .graph import _engine_run_backward
+from .variable import Variable
+
+
+__all__ = [
+    "Variable",
+    "Function",
+    "backward",
+    "grad_mode",
+    "NestedIOFunction",
+    "detect_anomaly",
+    "enable_grad",
+    "grad",
+    "gradcheck",
+    "gradgradcheck",
+    "inference_mode",
+    "no_grad",
+    "set_detect_anomaly",
+    "set_grad_enabled",
+    "set_multithreading_enabled",
+    "variable",
+]
+
+_OptionalTensor = Optional[torch.Tensor]
+_ShapeorNestedShape = Union[_size, Sequence[_size], torch.Tensor]
+
+
+def _calculate_shape(
+    output: Union[torch.Tensor, graph.GradientEdge],
+    grad: torch.Tensor,
+    is_grads_batched: bool,
+) -> tuple[_ShapeorNestedShape, _ShapeorNestedShape]:
+    # is_same_size ensures that both tensors are either nested or non nested
+    # circular import
+    from torch.nested._internal.nested_tensor import NestedTensor
+
+    if isinstance(output, graph.GradientEdge):
+        # We have already checked that we are not a C++ NestedTensor
+        if is_grads_batched:
+            raise RuntimeError("Batched grads are not supported with GradientEdge")
+        out_metadata = output.node._input_metadata[output.output_nr]
+        return torch.Size(out_metadata.shape), grad.shape
+
+    if output.is_nested and not isinstance(output, NestedTensor):
+        if is_grads_batched:
+            raise RuntimeError("Batched grads are not supported with Nested Tensor.")
+        out_shape = output._nested_tensor_size()
+        grad_shape = grad._nested_tensor_size()
+
+        return out_shape, grad_shape
+
+    reg_out_shape = output.shape
+    reg_grad_shape = grad.shape if not is_grads_batched else grad.shape[1:]
+    return reg_out_shape, reg_grad_shape
+
+
+def _make_grads(
+    outputs: Union[Sequence[torch.Tensor], Sequence[graph.GradientEdge]],
+    grads: Sequence[_OptionalTensor],
+    is_grads_batched: bool,
+) -> tuple[_OptionalTensor, ...]:
+    new_grads: list[_OptionalTensor] = []
+
+    for out, grad in zip(outputs, grads):
+        out = cast(Union[torch.Tensor, graph.GradientEdge], out)
+        out_size = None
+        out_device = None
+
+        if isinstance(out, graph.GradientEdge):
+            out_metadata = out.node._input_metadata[out.output_nr]
+            out_size = torch.Size(out_metadata.shape)
+            out_dtype = out_metadata.dtype
+            out_device = out_metadata.device
+            out_is_nested = out_metadata.is_nested_tensor
+            if out_metadata.is_cpp_nested_tensor:
+                raise RuntimeError(
+                    "C++ NestedTensor are not supported with GradientEdge"
+                )
+            out_is_cpp_nested = False
+        else:
+            # circular import
+            from torch.nested._internal.nested_tensor import NestedTensor
+
+            if not isinstance(out, torch.Tensor):
+                raise AssertionError("Expected output to be a torch.Tensor")
+            out_dtype = out.dtype
+            out_is_nested = out.is_nested
+            out_is_cpp_nested = out_is_nested and not isinstance(out, NestedTensor)
+            if not out_is_cpp_nested:
+                out_size = out.shape
+
+        if isinstance(grad, torch.Tensor):
+            from torch.fx.experimental.symbolic_shapes import expect_true, sym_eq
+
+            first_grad = grad if not is_grads_batched else grad[0]
+
+            # TODO: We can remove this conditional once we uniformly use
+            # singleton int to represent jagged dimension, so that size() call
+            # on nested tensor works.
+            if out_is_cpp_nested:
+                if not isinstance(out, torch.Tensor):
+                    raise AssertionError("Expected output to be a torch.Tensor.")
+                shape_matches = torch.is_same_size(out, first_grad)
+            else:
+                # We need to do a regular size check, without going through
+                # the operator, to be able to handle unbacked symints
+                # (expect_true ensures we can deal with unbacked)
+                if out_size is None:
+                    raise AssertionError("Expected out_size to be set.")
+                shape_matches = expect_true(sym_eq(out_size, first_grad.size()))
+
+            if not shape_matches:
+                out = cast(Union[torch.Tensor, graph.GradientEdge], out)  # type: ignore[redundant-cast]
+                out_shape, grad_shape = _calculate_shape(
+                    out, first_grad, is_grads_batched
+                )
+                if is_grads_batched:
+                    raise RuntimeError(
+                        "If `is_grads_batched=True`, we interpret the first "
+                        "dimension of each grad_output as the batch dimension. "
+                        "The sizes of the remaining dimensions are expected to match "
+                        "the shape of corresponding output, but a mismatch "
+                        "was detected: grad_output["
+                        + str(grads.index(grad))
+                        + "] has a shape of "
+                        + str(grad_shape)
+                        + " and output["
+                        + str(outputs.index(out))
+                        + "] has a shape of "
+                        + str(out_shape)
+                        + ". "
+                        "If you only want some tensors in `grad_output` to be considered "
+                        "batched, consider using vmap."
+                    )
+                else:
+                    raise RuntimeError(
+                        "Mismatch in shape: grad_output["
+                        + str(grads.index(grad))
+                        + "] has a shape of "
+                        + str(grad_shape)
+                        + " and output["
+                        + str(outputs.index(out))
+                        + "] has a shape of "
+                        + str(out_shape)
+                        + "."
+                    )
+            if out_dtype.is_complex != grad.dtype.is_complex:
+                raise RuntimeError(
+                    "For complex Tensors, both grad_output and output"
+                    " are required to have the same dtype."
+                    " Mismatch in dtype: grad_output["
+                    + str(grads.index(grad))
+                    + "] has a dtype of "
+                    + str(grad.dtype)
+                    + " and output["
+                    + str(outputs.index(out))
+                    + "] has a dtype of "
+                    + str(out_dtype)
+                    + "."
+                )
+            new_grads.append(grad)
+        elif grad is None:
+            if isinstance(out, graph.GradientEdge) or out.requires_grad:  # type: ignore[attr-defined]
+                if isinstance(out, graph.GradientEdge):
+                    if out_size is None:
+                        raise AssertionError("Expected out_size to be set.")
+                    out_numel_is_1 = all(o == 1 for o in out_size)
+                else:
+                    if not isinstance(out, torch.Tensor):
+                        raise AssertionError("Expected output to be a torch.Tensor")
+                    out_numel_is_1 = out.numel() == 1
+                if not out_numel_is_1:
+                    raise RuntimeError(
+                        "grad can be implicitly created only for scalar outputs"
+                    )
+                if not out_dtype.is_floating_point:
+                    msg = (
+                        "grad can be implicitly created only for real scalar outputs"
+                        f" but got {out_dtype}"
+                    )
+                    raise RuntimeError(msg)
+                if isinstance(out, graph.GradientEdge):
+                    if out_size is None:
+                        raise AssertionError("Expected out_size to be set.")
+                    if out_device is None:
+                        raise AssertionError("Expected out_device to be set.")
+                    new_grads.append(
+                        torch.ones(
+                            out_size,
+                            dtype=out_dtype,
+                            device=out_device,
+                        )
+                    )
+                else:
+                    if not isinstance(out, torch.Tensor):
+                        raise AssertionError("Expected output to be a torch.Tensor")
+                    new_grads.append(
+                        torch.ones_like(out, memory_format=torch.preserve_format)
+                    )
+            else:
+                new_grads.append(None)
+        else:
+            raise TypeError(
+                "gradients can be either Tensors or None, but got "
+                + type(grad).__name__
+            )
+    return tuple(new_grads)
+
+
+def _tensor_or_tensors_to_tuple(
+    tensors: Optional[_TensorOrTensors], length: int
+) -> tuple[_OptionalTensor, ...]:
+    if tensors is None:
+        return (None,) * length
+    if isinstance(tensors, torch.Tensor):
+        return (tensors,)
+    return tuple(tensors)
+
+
+def backward(
+    tensors: _TensorOrTensorsOrGradEdge,
+    grad_tensors: Optional[_TensorOrTensors] = None,
+    retain_graph: Optional[bool] = None,
+    create_graph: bool = False,
+    grad_variables: Optional[_TensorOrTensors] = None,
+    inputs: Optional[_TensorOrTensorsOrGradEdge] = None,
+) -> None:
+    r"""Compute the sum of gradients of given tensors with respect to graph leaves.
+
+    The graph is differentiated using the chain rule. If any of ``tensors``
+    are non-scalar (i.e. their data has more than one element) and require
+    gradient, then the Jacobian-vector product would be computed, in this
+    case the function additionally requires specifying ``grad_tensors``.
+    It should be a sequence of matching length, that contains the "vector"
+    in the Jacobian-vector product, usually the gradient of the differentiated
+    function w.r.t. corresponding tensors (``None`` is an acceptable value for
+    all tensors that don't need gradient tensors).
+
+    This function accumulates gradients in the leaves - you might need to zero
+    ``.grad`` attributes or set them to ``None`` before calling it.
+    See :ref:`Default gradient layouts`
+    for details on the memory layout of accumulated gradients.
+
+    .. note::
+        Using this method with ``create_graph=True`` will create a reference cycle
+        between the parameter and its gradient which can cause a memory leak.
+        We recommend using ``autograd.grad`` when creating the graph to avoid this.
+        If you have to use this function, make sure to reset the ``.grad`` fields of your
+        parameters to ``None`` after use to break the cycle and avoid the leak.
+
+    .. note::
+
+        If you run any forward ops, create ``grad_tensors``, and/or call ``backward``
+        in a user-specified CUDA stream context, see
+        :ref:`Stream semantics of backward passes`.
+
+    .. note::
+
+        When ``inputs`` are provided and a given input is not a leaf,
+        the current implementation will call its grad_fn (even though it is not strictly needed to get this gradients).
+        It is an implementation detail on which the user should not rely.
+        See https://github.com/pytorch/pytorch/pull/60521#issuecomment-867061780 for more details.
+
+    Args:
+        tensors (Sequence[Tensor] or Tensor or Sequence[GradientEdge] or GradientEdge): Tensors of which
+            the derivative will be computed.
+        grad_tensors (Sequence[Tensor or None] or Tensor, optional): The "vector" in
+            the Jacobian-vector product, usually gradients w.r.t. each element of
+            corresponding tensors. None values can be specified for scalar Tensors or
+            ones that don't require grad. If a None value would be acceptable for all
+            grad_tensors, then this argument is optional.
+        retain_graph (bool, optional): If ``False``, the graph used to compute the grad
+            will be freed. Note that in nearly all cases setting this option to ``True``
+            is not needed and often can be worked around in a much more efficient
+            way. Defaults to the value of ``create_graph``.
+        create_graph (bool, optional): If ``True``, graph of the derivative will
+            be constructed, allowing to compute higher order derivative products.
+            Defaults to ``False``.
+        inputs (Sequence[Tensor] or Tensor or Sequence[GradientEdge], optional): Inputs w.r.t. which the gradient
+            be will accumulated into ``.grad``. All other Tensors will be ignored. If
+            not provided, the gradient is accumulated into all the leaf Tensors that
+            were used to compute the :attr:`tensors`.
+    """
+    if torch._C._are_functorch_transforms_active():
+        raise RuntimeError(
+            "backward() called inside a functorch transform. This is not "
+            "supported, please use functorch.grad or functorch.vjp instead "
+            "or call backward() outside of functorch transforms."
+        )
+
+    if grad_variables is not None:
+        warnings.warn(
+            "`grad_variables` is deprecated. Use `grad_tensors` instead.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        if grad_tensors is None:
+            grad_tensors = grad_variables
+        else:
+            raise RuntimeError(
+                "`grad_tensors` and `grad_variables` (deprecated) "
+                "arguments both passed to `backward()`. Please only "
+                "use `grad_tensors`."
+            )
+
+    inputs_tuple: tuple[Union[torch.Tensor, graph.GradientEdge], ...]
+    if inputs is None:
+        inputs_tuple = ()
+    elif isinstance(inputs, (torch.Tensor, graph.GradientEdge)):
+        inputs_tuple = (inputs,)
+    else:
+        inputs_tuple = tuple(inputs)
+        if len(inputs_tuple) == 0:
+            raise RuntimeError("`inputs` argument to `backward()` cannot be empty.")
+
+    if is_tensor_like(tensors) or isinstance(tensors, graph.GradientEdge):
+        tensors = cast(
+            Union[tuple[torch.Tensor], tuple[graph.GradientEdge]], (tensors,)
+        )
+    else:
+        # pyrefly: ignore [bad-argument-type]
+        tensors = tuple(tensors)
+
+    grad_tensors_ = _tensor_or_tensors_to_tuple(grad_tensors, len(tensors))
+    grad_tensors_ = _make_grads(tensors, grad_tensors_, is_grads_batched=False)
+    if retain_graph is None:
+        retain_graph = create_graph
+
+    # The reason we repeat the same comment below is that
+    # some Python versions print out the first line of a multi-line function
+    # calls in the traceback and some print out the last line
+    _engine_run_backward(
+        tensors,
+        grad_tensors_,
+        retain_graph,
+        create_graph,
+        inputs_tuple,
+        allow_unreachable=True,
+        accumulate_grad=True,
+    )
+
+
+def grad(
+    outputs: _TensorOrTensorsOrGradEdge,
+    inputs: _TensorOrTensorsOrGradEdge,
+    grad_outputs: Optional[_TensorOrTensors] = None,
+    retain_graph: Optional[bool] = None,
+    create_graph: bool = False,
+    only_inputs: bool = True,
+    allow_unused: Optional[bool] = None,
+    is_grads_batched: bool = False,
+    materialize_grads: bool = False,
+) -> tuple[torch.Tensor, ...]:
+    r"""Compute and return the sum of gradients of outputs with respect to the inputs.
+
+    ``grad_outputs`` should be a sequence of length matching ``output``
+    containing the "vector" in vector-Jacobian product, usually the pre-computed
+    gradients w.r.t. each of the outputs. If an output doesn't require_grad,
+    then the gradient can be ``None``).
+
+    .. note::
+
+        If you run any forward ops, create ``grad_outputs``, and/or call ``grad``
+        in a user-specified CUDA stream context, see
+        :ref:`Stream semantics of backward passes`.
+
+    .. note::
+
+        ``only_inputs`` argument is deprecated and is ignored now (defaults to ``True``).
+        To accumulate gradient for other parts of the graph, please use
+        ``torch.autograd.backward``.
+
+    Args:
+        outputs (sequence of Tensor or GradientEdge): outputs of the differentiated function.
+        inputs (sequence of Tensor or GradientEdge): Inputs w.r.t. which the gradient will be
+            returned (and not accumulated into ``.grad``).
+        grad_outputs (sequence of Tensor): The "vector" in the vector-Jacobian product.
+            Usually gradients w.r.t. each output. None values can be specified for scalar
+            Tensors or ones that don't require grad. If a None value would be acceptable
+            for all grad_tensors, then this argument is optional. Default: None.
+        retain_graph (bool, optional): If ``False``, the graph used to compute the grad
+            will be freed. Note that in nearly all cases setting this option to ``True``
+            is not needed and often can be worked around in a much more efficient
+            way. Defaults to the value of ``create_graph``.
+        create_graph (bool, optional): If ``True``, graph of the derivative will
+            be constructed, allowing to compute higher order derivative products.
+            Default: ``False``.
+        allow_unused (Optional[bool], optional): If ``False``, specifying inputs
+            that were not used when computing outputs (and therefore their grad is
+            always zero) is an error. Defaults to the value of ``materialize_grads``.
+        is_grads_batched (bool, optional): If ``True``, the first dimension of each
+            tensor in ``grad_outputs`` will be interpreted as the batch dimension.
+            Instead of computing a single vector-Jacobian product, we compute a
+            batch of vector-Jacobian products for each "vector" in the batch.
+            We use the vmap prototype feature as the backend to vectorize calls
+            to the autograd engine so that this computation can be performed in a
+            single call. This should lead to performance improvements when compared
+            to manually looping and performing backward multiple times. Note that
+            due to this feature being experimental, there may be performance
+            cliffs. Please use ``torch._C._debug_only_display_vmap_fallback_warnings(True)``
+            to show any performance warnings and file an issue on github if warnings exist
+            for your use case. Defaults to ``False``.
+        materialize_grads (bool, optional): If ``True``, set the gradient for unused inputs
+            to zero instead of None. This is useful when computing higher-order derivatives.
+            If ``materialize_grads`` is ``True`` and ``allow_unused`` is ``False``, an error
+            will be raised. Defaults to ``False``.
+
+    """
+    if materialize_grads and allow_unused is False:
+        raise ValueError(
+            "Expected allow_unused to be True or not passed when materialize_grads=True, "
+            "but got: allow_unused=False."
+        )
+    if allow_unused is None:
+        allow_unused = materialize_grads
+    if is_tensor_like(outputs) or isinstance(outputs, graph.GradientEdge):
+        outputs = cast(
+            Union[Sequence[torch.Tensor], Sequence[graph.GradientEdge]], (outputs,)
+        )
+    else:
+        # pyrefly: ignore [bad-argument-type]
+        outputs = tuple(outputs)
+    if is_tensor_like(inputs) or isinstance(inputs, graph.GradientEdge):
+        inputs = cast(_TensorOrTensorsOrGradEdge, (inputs,))
+    else:
+        # pyrefly: ignore [bad-argument-type]
+        inputs = tuple(inputs)
+    t_outputs = tuple(i for i in outputs if is_tensor_like(i))
+    t_inputs = tuple(i for i in inputs if is_tensor_like(i))
+    overridable_args = t_outputs + t_inputs
+    if has_torch_function(overridable_args):
+        return handle_torch_function(
+            grad,
+            overridable_args,
+            outputs,
+            inputs,
+            grad_outputs=grad_outputs,
+            retain_graph=retain_graph,
+            create_graph=create_graph,
+            only_inputs=only_inputs,
+            allow_unused=allow_unused,
+            is_grads_batched=is_grads_batched,
+            materialize_grads=materialize_grads,
+        )
+
+    if not only_inputs:
+        warnings.warn(
+            "only_inputs argument is deprecated and is ignored now "
+            "(defaults to True). To accumulate gradient for other "
+            "parts of the graph, please use torch.autograd.backward.",
+            FutureWarning,
+            stacklevel=2,
+        )
+
+    grad_outputs_ = _tensor_or_tensors_to_tuple(grad_outputs, len(outputs))
+    grad_outputs_ = _make_grads(
+        outputs, grad_outputs_, is_grads_batched=is_grads_batched
+    )
+
+    if retain_graph is None:
+        retain_graph = create_graph
+
+    # The reason we repeat the same comment several times below is because
+    # some Python versions print out the first line of multi-line function
+    # calls in the traceback and some print out the last line
+    if is_grads_batched:
+
+        def vjp(gO):
+            return _engine_run_backward(
+                outputs,
+                gO,
+                retain_graph,
+                create_graph,
+                inputs,
+                allow_unused,
+                accumulate_grad=False,
+            )
+
+        result = _vmap_internals._vmap(vjp, 0, 0, allow_none_pass_through=True)(
+            grad_outputs_
+        )
+    else:
+        result = _engine_run_backward(
+            outputs,
+            grad_outputs_,
+            retain_graph,
+            create_graph,
+            inputs,
+            allow_unused,
+            accumulate_grad=False,
+        )
+    if materialize_grads:
+        if any(
+            result[i] is None and not is_tensor_like(inputs[i])
+            for i in range(len(inputs))
+        ):
+            raise RuntimeError(
+                "materialize_grads cannot be used when the given input is a GradientEdge"
+            )
+        result = tuple(
+            output
+            if output is not None
+            else torch.zeros_like(input, requires_grad=create_graph)
+            for (output, input) in zip(result, inputs)
+        )
+    return result
+
+
+# This function applies in case of gradient checkpointing for memory
+# optimization. Currently, gradient checkpointing is supported only if the
+# execution engine is invoked through torch.autograd.backward() and its
+# inputs argument is not passed. It is not supported for torch.autograd.grad().
+# This is because if inputs are specified, the gradient won't be calculated for
+# anything else e.g. model parameters like weights, bias etc.
+#
+# This function returns whether the checkpointing is valid i.e. torch.autograd.backward
+# or not i.e. torch.autograd.grad. The implementation works by maintaining a thread
+# local variable in torch/csrc/autograd/engine.cpp which looks at the NodeTask
+# in the stack and before a NodeTask is executed in evaluate_function, it
+# checks for whether reentrant backwards is imperative or not.
+# See https://github.com/pytorch/pytorch/pull/4594 for more discussion/context
+def _is_checkpoint_valid():
+    return Variable._execution_engine.is_checkpoint_valid()
+
+
+def variable(*args, **kwargs):  # noqa: D103
+    raise RuntimeError(
+        "torch.autograd.variable(...) is deprecated, use torch.tensor(...) instead"
+    )
+
+
+# Monkey patching variable.Variable to fix FX codegen. FX generates a call by roughly doing
+# f"{fn.__module__}.{fn.__name__}(...). This yields torch.autograd.variable.Variable(...) in the
+# output of an FX graph.  Unfortunately the module name torch.autograd.variable is shadowed by the
+# deprecated function - variable(...).
+variable.Variable = Variable  # type: ignore[attr-defined]
+
+if not torch._C._autograd_init():
+    raise RuntimeError("autograd initialization failed")
+
+# Import all native method/classes
+from torch._C._autograd import (
+    _add_metadata_json,
+    _disable_profiler,
+    _disable_profiler_legacy,
+    _enable_profiler,
+    _enable_profiler_legacy,
+    _enable_record_function,
+    _get_sequence_nr,
+    _kineto_step,
+    _KinetoEvent,
+    _pop_saved_tensors_default_hooks,
+    _prepare_profiler,
+    _profiler_enabled,
+    _ProfilerResult,
+    _push_saved_tensors_default_hooks,
+    _record_function_with_args_enter,
+    _record_function_with_args_exit,
+    _set_empty_test_observer,
+    _supported_activities,
+    _toggle_collection_dynamic,
+    DeviceType,
+    kineto_available,
+    ProfilerEvent,
+    SavedTensor,
+)
+from torch._C._profiler import ProfilerActivity, ProfilerConfig, ProfilerState
+
+from . import profiler
+
+
+def _register_py_tensor_class_for_device(device, cls):
+    if not isinstance(cls, type):
+        raise RuntimeError("cls isn't a typeinfo object")
+    torch._C._register_py_class_for_device(device, cls)
+
+
+is_multithreading_enabled = torch._C._is_multithreading_enabled
+torch._C._add_docstr(
+    is_multithreading_enabled, "Returns True if multithreading is currently enabled."
+)
+
+is_view_replay_enabled = torch._C._is_view_replay_enabled
+torch._C._add_docstr(
+    is_view_replay_enabled, "Returns True if view-replay is currently enabled."
+)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/_functions/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/_functions/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4170fad3eeac788dcb36b6ae1ddbee1b44dc25a1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/_functions/__init__.py
@@ -0,0 +1 @@
+from .tensor import *  # noqa: F403
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/_functions/tensor.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/_functions/tensor.py
new file mode 100644
index 0000000000000000000000000000000000000000..716ae1db726ad5b397426e0669cfd241ee7ee556
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/_functions/tensor.py
@@ -0,0 +1,72 @@
+# mypy: allow-untyped-defs
+import operator
+from functools import reduce
+from typing_extensions import deprecated
+
+import torch
+import torch._utils
+from torch.autograd.function import Function
+
+
+class Type(Function):
+    @staticmethod
+    @deprecated(
+        "`torch.autograd._functions.Type` is deprecated as of PyTorch 2.1, "
+        "please use `torch.tensor.to(dtype=dtype)` instead.",
+        category=FutureWarning,
+    )
+    # pyrefly: ignore [bad-override]
+    def forward(ctx, i, dest_type):
+        ctx.input_type = type(i)
+        ctx.input_device = -1 if not i.is_cuda else i.get_device()
+        return i.type(dest_type)
+
+    @staticmethod
+    # pyrefly: ignore [bad-override]
+    def backward(ctx, grad_output):
+        if ctx.input_device == -1:
+            return grad_output.type(ctx.input_type), None
+        else:
+            with torch.accelerator.device_index(ctx.input_device):
+                return grad_output.type(ctx.input_type), None
+
+
+# TODO: deprecate this
+class Resize(Function):
+    @staticmethod
+    # pyrefly: ignore [bad-override]
+    def forward(ctx, tensor, sizes):
+        ctx.sizes = sizes
+        ctx.numel = reduce(operator.mul, sizes, 1)
+        if tensor.numel() != ctx.numel:
+            raise RuntimeError(
+                (
+                    "requested resize to {} ({} elements in total), "
+                    "but the given tensor has a size of {} ({} elements). "
+                    "autograd's resize can only change the shape of a given "
+                    "tensor, while preserving the number of elements. "
+                ).format(
+                    "x".join(map(str, sizes)),
+                    ctx.numel,
+                    "x".join(map(str, tensor.size())),
+                    tensor.numel(),
+                )
+            )
+        ctx.input_sizes = tensor.size()
+        if tensor.is_quantized:
+            tensor.copy_(tensor)
+            return tensor.contiguous().view(*sizes)
+        if tensor.is_contiguous():
+            result = tensor.new(tensor).contiguous().view(*sizes)
+            return result
+        else:
+            return tensor.contiguous().view(*sizes)
+
+    @staticmethod
+    # pyrefly: ignore [bad-override]
+    def backward(ctx, grad_output):
+        if grad_output.numel() != ctx.numel:
+            raise AssertionError(
+                f"Expected grad_output to have {ctx.numel} elements, but got {grad_output.numel()}"
+            )
+        return grad_output.contiguous().view(ctx.input_sizes), None
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/_functions/utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/_functions/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..1e74e21d3cef22c0fd459eff5934d4e531d5456d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/_functions/utils.py
@@ -0,0 +1,26 @@
+# mypy: allow-untyped-defs
+
+
+def maybe_view(tensor, size, check_same_size=True):
+    if check_same_size and tensor.size() == size:
+        return tensor
+    return tensor.contiguous().view(size)
+
+
+def maybe_unexpand(tensor, old_size, check_same_size=True):
+    if check_same_size and tensor.size() == old_size:
+        return tensor
+    num_unsqueezed = tensor.dim() - len(old_size)
+    expanded_dims = [
+        dim
+        for dim, (expanded, original) in enumerate(
+            zip(tensor.size()[num_unsqueezed:], old_size)
+        )
+        if expanded != original
+    ]
+
+    for _ in range(num_unsqueezed):
+        tensor = tensor.sum(0, keepdim=False)
+    for dim in expanded_dims:
+        tensor = tensor.sum(dim, keepdim=True)
+    return tensor
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/anomaly_mode.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/anomaly_mode.py
new file mode 100644
index 0000000000000000000000000000000000000000..0277f1b75541f669272c9e3334a7a7783e9b773b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/anomaly_mode.py
@@ -0,0 +1,123 @@
+# mypy: allow-untyped-defs
+r"""Autograd anomaly mode."""
+
+import warnings
+
+import torch
+
+
+__all__ = ["detect_anomaly", "set_detect_anomaly"]
+
+
+class detect_anomaly:
+    r"""Context-manager that enable anomaly detection for the autograd engine.
+
+    This does two things:
+
+    - Running the forward pass with detection enabled will allow the backward
+      pass to print the traceback of the forward operation that created the failing
+      backward function.
+    - If ``check_nan`` is ``True``, any backward computation that generate "nan"
+      value will raise an error. Default ``True``.
+
+    .. warning::
+        This mode should be enabled only for debugging as the different tests
+        will slow down your program execution.
+
+    Example:
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_ANOMALY)
+        >>> import torch
+        >>> from torch import autograd
+        >>> class MyFunc(autograd.Function):
+        ...     @staticmethod
+        ...     def forward(ctx, inp):
+        ...         return inp.clone()
+        ...
+        ...     @staticmethod
+        ...     def backward(ctx, gO):
+        ...         # Error during the backward pass
+        ...         raise RuntimeError("Some error in backward")
+        ...         return gO.clone()
+        >>> def run_fn(a):
+        ...     out = MyFunc.apply(a)
+        ...     return out.sum()
+        >>> inp = torch.rand(10, 10, requires_grad=True)
+        >>> out = run_fn(inp)
+        >>> out.backward()
+            Traceback (most recent call last):
+              File "", line 1, in 
+              File "/your/pytorch/install/torch/_tensor.py", line 93, in backward
+                torch.autograd.backward(self, gradient, retain_graph, create_graph)
+              File "/your/pytorch/install/torch/autograd/__init__.py", line 90, in backward
+                allow_unreachable=True)  # allow_unreachable flag
+              File "/your/pytorch/install/torch/autograd/function.py", line 76, in apply
+                return self._forward_cls.backward(self, *args)
+              File "", line 8, in backward
+            RuntimeError: Some error in backward
+        >>> with autograd.detect_anomaly():
+        ...     inp = torch.rand(10, 10, requires_grad=True)
+        ...     out = run_fn(inp)
+        ...     out.backward()
+            Traceback of forward call that caused the error:
+              File "tmp.py", line 53, in 
+                out = run_fn(inp)
+              File "tmp.py", line 44, in run_fn
+                out = MyFunc.apply(a)
+            Traceback (most recent call last):
+              File "", line 4, in 
+              File "/your/pytorch/install/torch/_tensor.py", line 93, in backward
+                torch.autograd.backward(self, gradient, retain_graph, create_graph)
+              File "/your/pytorch/install/torch/autograd/__init__.py", line 90, in backward
+                allow_unreachable=True)  # allow_unreachable flag
+              File "/your/pytorch/install/torch/autograd/function.py", line 76, in apply
+                return self._forward_cls.backward(self, *args)
+              File "", line 8, in backward
+            RuntimeError: Some error in backward
+
+    """
+
+    def __init__(self, check_nan=True) -> None:  # noqa: D107
+        self.prev = torch.is_anomaly_enabled()
+        self.check_nan = check_nan
+        self.prev_check_nan = torch.is_anomaly_check_nan_enabled()
+        warnings.warn(
+            "Anomaly Detection has been enabled. "
+            "This mode will increase the runtime "
+            "and should only be enabled for debugging.",
+            stacklevel=2,
+        )
+
+    def __enter__(self) -> None:  # noqa: D105
+        torch.set_anomaly_enabled(True, self.check_nan)
+
+    def __exit__(self, *args: object) -> None:  # noqa: D105
+        torch.set_anomaly_enabled(self.prev, self.prev_check_nan)
+
+
+class set_detect_anomaly:
+    r"""Context-manager that sets the anomaly detection for the autograd engine on or off.
+
+    ``set_detect_anomaly`` will enable or disable the autograd anomaly detection
+    based on its argument :attr:`mode`.
+    It can be used as a context-manager or as a function.
+
+    See ``detect_anomaly`` above for details of the anomaly detection behaviour.
+
+    Args:
+        mode (bool): Flag whether to enable anomaly detection (``True``),
+                     or disable (``False``).
+        check_nan (bool): Flag whether to raise an error when the backward
+                          generate "nan"
+
+    """
+
+    def __init__(self, mode: bool, check_nan: bool = True) -> None:  # noqa: D107
+        self.prev = torch.is_anomaly_enabled()
+        self.prev_check_nan = torch.is_anomaly_check_nan_enabled()
+        torch.set_anomaly_enabled(mode, check_nan)
+
+    def __enter__(self) -> None:  # noqa: D105
+        pass
+
+    def __exit__(self, *args: object) -> None:  # noqa: D105
+        torch.set_anomaly_enabled(self.prev, self.prev_check_nan)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/forward_ad.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/forward_ad.py
new file mode 100644
index 0000000000000000000000000000000000000000..8fcb64beba3b4741b1562a682f369a1cc7009761
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/forward_ad.py
@@ -0,0 +1,229 @@
+# mypy: allow-untyped-defs
+import os
+from typing import Any, NamedTuple, Optional
+
+import torch
+
+from .grad_mode import _DecoratorContextManager
+
+
+__all__ = [
+    "UnpackedDualTensor",
+    "enter_dual_level",
+    "exit_dual_level",
+    "make_dual",
+    "unpack_dual",
+    "dual_level",
+]
+
+# Global variable used to make the python API simpler to use
+_current_level = -1
+
+
+def enter_dual_level():
+    r"""Enter a new forward grad level.
+
+    This level can be used to make and unpack dual Tensors to compute
+    forward gradients.
+
+    This function also updates the current level that is used by default
+    by the other functions in this API.
+    """
+    global _current_level
+    new_level = torch._C._enter_dual_level()
+    if new_level != _current_level + 1:
+        raise RuntimeError(
+            "Entering a new forward AD level but the current level "
+            "is not valid. Make sure you did not modified it directly."
+        )
+    _current_level = new_level
+    return new_level
+
+
+def exit_dual_level(*, level=None):
+    r"""Exit a forward grad level.
+
+    This function deletes all the gradients associated with this
+    level. Only deleting the latest entered level is allowed.
+
+    This function also updates the current level that is used by default
+    by the other functions in this API.
+    """
+    global _current_level
+    if level is None:
+        level = _current_level
+    if level != _current_level:
+        raise RuntimeError(
+            "Trying to exit a forward AD level that was not the last one "
+            "that was created. This is not supported."
+        )
+    torch._C._exit_dual_level(level=level)
+    _current_level = level - 1
+
+
+def _maybe_load_decompositions():
+    if os.environ.get("PYTORCH_JIT", "1") == "1" and __debug__:
+        from torch._decomp import decompositions_for_jvp  # noqa: F401
+
+
+def make_dual(tensor, tangent, *, level=None):
+    r"""Associate a tensor value with its tangent to create a "dual tensor" for forward AD gradient computation.
+
+    The result is a new tensor aliased to :attr:`tensor` with :attr:`tangent` embedded
+    as an attribute as-is if it has the same storage layout or copied otherwise.
+    The tangent attribute can be recovered with :func:`unpack_dual`.
+
+    This function is backward differentiable.
+
+    Given a function `f` whose jacobian is `J`, it allows one to compute the Jacobian-vector product (`jvp`)
+    between `J` and a given vector `v` as follows.
+
+    Example::
+
+        >>> # xdoctest: +SKIP("Undefined variables")
+        >>> with dual_level():
+        ...     inp = make_dual(x, v)
+        ...     out = f(inp)
+        ...     y, jvp = unpack_dual(out)
+
+    Please see the `forward-mode AD tutorial `__
+    for detailed steps on how to use this API.
+
+    """
+    # See NOTE: [forward-mode AD decompositions mechanism]
+    #
+    # Import from torch._decomp import decompositions_for_jvp to register
+    # decompositions for jvp to the jit registry
+    #
+    # FIXME: We specify that __debug__ must be True because
+    # if python is run with -OO or -O flags (i.e., __debug__ is False), we encounter the
+    # following error:
+    #
+    # Return value was annotated as having type Tuple[NoneType, NoneType] but is actually of
+    # type Tuple[Tensor, Tensor]:
+    #   File ".../torch/_decomp/__init__.py", line 1585
+    #     else:
+    #         buffer = z
+    #     return min - torch.log1p(z), buffer
+    #     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
+    _maybe_load_decompositions()
+
+    if level is None:
+        level = _current_level
+
+    if level < 0:
+        raise RuntimeError(
+            "Trying to create a dual Tensor for forward AD but no level "
+            "exists, make sure to enter_dual_level() first."
+        )
+    if not (tensor.is_floating_point() or tensor.is_complex()):
+        raise ValueError(
+            f"Expected primal to be floating point or complex, but got: {tensor.dtype}"
+        )
+    if not (tangent.is_floating_point() or tangent.is_complex()):
+        raise ValueError(
+            f"Expected tangent to be floating point or complex, but got: {tangent.dtype}"
+        )
+
+    return torch._VF._make_dual(tensor, tangent, level=level)
+
+
+class UnpackedDualTensor(NamedTuple):
+    r"""Namedtuple returned by :func:`unpack_dual` containing the primal and tangent components of the dual tensor.
+
+    See :func:`unpack_dual` for more details.
+    """
+
+    primal: torch.Tensor
+    tangent: Optional[torch.Tensor]
+
+
+def unpack_dual(tensor, *, level=None):
+    r"""Unpack a "dual tensor" to get both its Tensor value and its forward AD gradient.
+
+    The result is a namedtuple ``(primal, tangent)`` where ``primal`` is a view of
+    :attr:`tensor`'s primal and ``tangent`` is :attr:`tensor`'s tangent as-is.
+    Neither of these tensors can be dual tensor of level :attr:`level`.
+
+    This function is backward differentiable.
+
+    Example::
+
+        >>> # xdoctest: +SKIP("Undefined variables")
+        >>> with dual_level():
+        ...     inp = make_dual(x, x_t)
+        ...     out = f(inp)
+        ...     y, jvp = unpack_dual(out)
+        ...     jvp = unpack_dual(out).tangent
+
+    Please see the `forward-mode AD tutorial `__
+    for detailed steps on how to use this API.
+    """
+    if level is None:
+        level = _current_level
+
+    if level < 0:
+        return UnpackedDualTensor(tensor, None)
+
+    primal, dual = torch._VF._unpack_dual(tensor, level=level)
+
+    return UnpackedDualTensor(primal, dual)
+
+
+class dual_level(_DecoratorContextManager):
+    r"""Context-manager for forward AD, where all forward AD computation must occur within the ``dual_level`` context.
+
+    .. Note::
+
+        The ``dual_level`` context appropriately enters and exit the dual level to
+        controls the current forward AD level, which is used by default by the other
+        functions in this API.
+
+        We currently don't plan to support nested ``dual_level`` contexts, however, so
+        only a single forward AD level is supported. To compute higher-order
+        forward grads, one can use :func:`torch.func.jvp`.
+
+    Example::
+
+        >>> # xdoctest: +SKIP("Undefined variables")
+        >>> x = torch.tensor([1])
+        >>> x_t = torch.tensor([1])
+        >>> with dual_level():
+        ...     inp = make_dual(x, x_t)
+        ...     # Do computations with inp
+        ...     out = your_fn(inp)
+        ...     _, grad = unpack_dual(out)
+        >>> grad is None
+        False
+        >>> # After exiting the level, the grad is deleted
+        >>> _, grad_after = unpack_dual(out)
+        >>> grad is None
+        True
+
+    Please see the `forward-mode AD tutorial `__
+    for detailed steps on how to use this API.
+    """
+
+    def __enter__(self):
+        return enter_dual_level()
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        exit_dual_level()
+
+
+# Private helper functions
+_is_fwd_grad_enabled = torch._C._is_fwd_grad_enabled
+
+
+# Private helper function to enable or disable fwd grad.
+# If you're a user and want to use this, please file an issue to discuss the use case.
+class _set_fwd_grad_enabled(_DecoratorContextManager):
+    def __init__(self, mode: bool) -> None:
+        self.prev = _is_fwd_grad_enabled()
+        torch._C._set_fwd_grad_enabled(mode)
+
+    def __enter__(self) -> None:
+        pass
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        torch._C._set_fwd_grad_enabled(self.prev)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/function.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/function.py
new file mode 100644
index 0000000000000000000000000000000000000000..70c6cad4e99678ac86ddd6d2cafca182ca8047f4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/function.py
@@ -0,0 +1,856 @@
+# mypy: allow-untyped-defs
+import functools
+import inspect
+import itertools
+import warnings
+from collections import OrderedDict
+from collections.abc import Callable
+from typing import Any, Concatenate, Optional, TypeVar
+from typing_extensions import deprecated, ParamSpec
+
+import torch
+import torch._C as _C
+import torch._functorch as _functorch
+import torch.utils.hooks as hooks
+from torch._C import _functions
+from torch._functorch.autograd_function import custom_function_call
+
+
+__all__ = [
+    "FunctionCtx",
+    "BackwardCFunction",
+    "FunctionMeta",
+    "Function",
+    "once_differentiable",
+    "InplaceFunction",
+    "NestedIOFunction",
+]
+
+# Unique id provider for each class inheriting from Function
+# This is incremented in FunctionMeta during class definition
+AUTOGRAD_FUNCTION_COUNTER = itertools.count()
+
+_T = TypeVar("_T")
+_R = TypeVar("_R")
+_P = ParamSpec("_P")
+
+
+# Formerly known as: _ContextMethodMixin
+class FunctionCtx:
+    def save_for_backward(self, *tensors: torch.Tensor):
+        r"""Save given tensors for a future call to :func:`~Function.backward`.
+
+        ``save_for_backward`` should be called at most once, in either the
+        :func:`setup_context` or :func:`forward` methods, and only with tensors.
+
+        All tensors intended to be used in the backward pass should be saved
+        with ``save_for_backward`` (as opposed to directly on ``ctx``) to prevent
+        incorrect gradients and memory leaks, and enable the application of saved
+        tensor hooks. See :class:`torch.autograd.graph.saved_tensors_hooks`.
+        See :ref:`extending-autograd` for more details.
+
+        Note that if intermediary tensors, tensors that are neither inputs
+        nor outputs of :func:`forward`, are saved for backward, your custom Function
+        may not support double backward.
+        Custom Functions that do not support double backward should decorate their
+        :func:`backward` method with ``@once_differentiable`` so that performing
+        double backward raises an error. If you'd like to support double backward,
+        you can either recompute intermediaries based on the inputs during backward
+        or return the intermediaries as the outputs of the custom Function. See the
+        `double backward tutorial `_
+        for more details.
+
+        In :func:`backward`, saved tensors can be accessed through the :attr:`saved_tensors`
+        attribute. Before returning them to the user, a check is made to ensure
+        they weren't used in any in-place operation that modified their content.
+
+        Arguments can also be ``None``. This is a no-op.
+
+        See :ref:`extending-autograd` for more details on how to use this method.
+
+        Example::
+
+            >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+            >>> class Func(Function):
+            >>>     @staticmethod
+            >>>     def forward(ctx, x: torch.Tensor, y: torch.Tensor, z: int):
+            >>>         w = x * z
+            >>>         out = x * y + y * z + w * y
+            >>>         ctx.save_for_backward(x, y, w, out)
+            >>>         ctx.z = z  # z is not a tensor
+            >>>         return out
+            >>>
+            >>>     @staticmethod
+            >>>     @once_differentiable
+            >>>     def backward(ctx, grad_out):
+            >>>         x, y, w, out = ctx.saved_tensors
+            >>>         z = ctx.z
+            >>>         gx = grad_out * (y + y * z)
+            >>>         gy = grad_out * (x + z + w)
+            >>>         gz = None
+            >>>         return gx, gy, gz
+            >>>
+            >>> a = torch.tensor(1., requires_grad=True, dtype=torch.double)
+            >>> b = torch.tensor(2., requires_grad=True, dtype=torch.double)
+            >>> c = 4
+            >>> d = Func.apply(a, b, c)
+
+        """
+        self.to_save = tensors
+
+    def save_for_forward(self, *tensors: torch.Tensor):
+        r"""Save given tensors for a future call to :func:`~Function.jvp`.
+
+        ``save_for_forward`` should be called at most once, in either the
+        :func:`setup_context` or :func:`forward` methods, and all arguments
+        should be tensors.
+
+        In :func:`jvp`, saved objects can be accessed through the :attr:`saved_tensors`
+        attribute.
+
+        Arguments can also be ``None``. This is a no-op.
+
+        See :ref:`extending-autograd` for more details on how to use this method.
+
+        Example::
+
+            >>> # xdoctest: +SKIP
+            >>> class Func(torch.autograd.Function):
+            >>>     @staticmethod
+            >>>     def forward(ctx, x: torch.Tensor, y: torch.Tensor, z: int):
+            >>>         ctx.save_for_backward(x, y)
+            >>>         ctx.save_for_forward(x, y)
+            >>>         ctx.z = z
+            >>>         return x * y * z
+            >>>
+            >>>     @staticmethod
+            >>>     def jvp(ctx, x_t, y_t, _):
+            >>>         x, y = ctx.saved_tensors
+            >>>         z = ctx.z
+            >>>         return z * (y * x_t + x * y_t)
+            >>>
+            >>>     @staticmethod
+            >>>     def vjp(ctx, grad_out):
+            >>>         x, y = ctx.saved_tensors
+            >>>         z = ctx.z
+            >>>         return z * grad_out * y, z * grad_out * x, None
+            >>>
+            >>>     a = torch.tensor(1., requires_grad=True, dtype=torch.double)
+            >>>     t = torch.tensor(1., dtype=torch.double)
+            >>>     b = torch.tensor(2., requires_grad=True, dtype=torch.double)
+            >>>     c = 4
+            >>>
+            >>>     with fwAD.dual_level():
+            >>>         a_dual = fwAD.make_dual(a, t)
+            >>>         d = Func.apply(a_dual, b, c)
+
+        """
+        for tensor in tensors:
+            if not (isinstance(tensor, torch.Tensor) or tensor is None):
+                raise AssertionError(
+                    "save_for_forward expects all arguments to be tensors; you should "
+                    "save non-tensors as attributes on ctx."
+                )
+
+        self.saved_for_forward = tensors
+
+    def mark_dirty(self, *args: torch.Tensor):
+        r"""Mark given tensors as modified in an in-place operation.
+
+        This should be called at most once, in either the :func:`setup_context`
+        or :func:`forward` methods, and all arguments should be inputs.
+
+        Every tensor that's been modified in-place in a call to :func:`forward`
+        should be given to this function, to ensure correctness of our checks.
+        It doesn't matter whether the function is called before or after
+        modification.
+
+        Examples::
+            >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+            >>> class Inplace(Function):
+            >>>     @staticmethod
+            >>>     def forward(ctx, x):
+            >>>         x_npy = x.numpy() # x_npy shares storage with x
+            >>>         x_npy += 1
+            >>>         ctx.mark_dirty(x)
+            >>>         return x
+            >>>
+            >>>     @staticmethod
+            >>>     @once_differentiable
+            >>>     def backward(ctx, grad_output):
+            >>>         return grad_output
+            >>>
+            >>> a = torch.tensor(1., requires_grad=True, dtype=torch.double).clone()
+            >>> b = a * a
+            >>> Inplace.apply(a)  # This would lead to wrong gradients!
+            >>>                   # but the engine would not know unless we mark_dirty
+            >>> # xdoctest: +SKIP
+            >>> b.backward() # RuntimeError: one of the variables needed for gradient
+            >>>              # computation has been modified by an inplace operation
+
+        """
+        self.dirty_tensors = args
+
+    @deprecated(
+        "`mark_shared_storage` is deprecated. "
+        "Tensors with shared storages are automatically tracked. "
+        "Note that calls to `set_()` are not tracked",
+        category=FutureWarning,
+    )
+    def mark_shared_storage(self, *pairs):
+        pass
+
+    def mark_non_differentiable(self, *args: torch.Tensor):
+        r"""Mark outputs as non-differentiable.
+
+        This should be called at most once, in either the :func:`setup_context`
+        or :func:`forward` methods, and all arguments should be tensor outputs.
+
+        This will mark outputs as not requiring gradients, increasing the
+        efficiency of backward computation. You still need to accept a gradient
+        for each output in :meth:`~Function.backward`, but it's always going to
+        be a zero tensor with the same shape as the shape of a corresponding
+        output.
+
+        This is used e.g. for indices returned from a sort. See example::
+            >>> class Func(Function):
+            >>>     @staticmethod
+            >>>     def forward(ctx, x):
+            >>>         sorted, idx = x.sort()
+            >>>         ctx.mark_non_differentiable(idx)
+            >>>         ctx.save_for_backward(x, idx)
+            >>>         return sorted, idx
+            >>>
+            >>>     @staticmethod
+            >>>     @once_differentiable
+            >>>     def backward(ctx, g1, g2):  # still need to accept g2
+            >>>         x, idx = ctx.saved_tensors
+            >>>         grad_input = torch.zeros_like(x)
+            >>>         grad_input.index_add_(0, idx, g1)
+            >>>         return grad_input
+
+        """
+        self.non_differentiable = args
+
+    def set_materialize_grads(self, value: bool):
+        r"""Set whether to materialize grad tensors. Default is ``True``.
+
+        This should be called only from either the :func:`setup_context` or
+        :func:`forward` methods.
+
+        If ``True``, undefined grad tensors will be expanded to tensors full of zeros
+        prior to calling the :func:`backward` and :func:`jvp` methods.
+
+        Example::
+
+            >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+            >>> class SimpleFunc(Function):
+            >>>     @staticmethod
+            >>>     def forward(ctx, x):
+            >>>         return x.clone(), x.clone()
+            >>>
+            >>>     @staticmethod
+            >>>     @once_differentiable
+            >>>     def backward(ctx, g1, g2):
+            >>>         return g1 + g2  # No check for None necessary
+            >>>
+            >>> # We modify SimpleFunc to handle non-materialized grad outputs
+            >>> class Func(Function):
+            >>>     @staticmethod
+            >>>     def forward(ctx, x):
+            >>>         ctx.set_materialize_grads(False)
+            >>>         ctx.save_for_backward(x)
+            >>>         return x.clone(), x.clone()
+            >>>
+            >>>     @staticmethod
+            >>>     @once_differentiable
+            >>>     def backward(ctx, g1, g2):
+            >>>         x, = ctx.saved_tensors
+            >>>         grad_input = torch.zeros_like(x)
+            >>>         if g1 is not None:  # We must check for None now
+            >>>             grad_input += g1
+            >>>         if g2 is not None:
+            >>>             grad_input += g2
+            >>>         return grad_input
+            >>>
+            >>> a = torch.tensor(1., requires_grad=True)
+            >>> b, _ = Func.apply(a)  # induces g2 to be undefined
+
+        """
+        self.materialize_grads = value
+
+
+# DO NOT USE: This is only defined to be able to load old serialized models
+_ContextMethodMixin = FunctionCtx
+
+
+class _HookMixin:
+    @staticmethod
+    def _register_hook(backward_hooks, hook):
+        if backward_hooks is None:
+            backward_hooks = OrderedDict()
+        handle = hooks.RemovableHandle(backward_hooks)
+        backward_hooks[handle.id] = hook
+        return backward_hooks, handle
+
+
+class BackwardCFunction(_C._FunctionBase, FunctionCtx, _HookMixin):
+    r"""
+    This class is used for internal autograd work. Do not use.
+    """
+
+    def apply(self, *args):
+        r"""
+        Apply method used when executing this Node during the backward
+        """
+        # _forward_cls is defined by derived class
+        # The user should define either backward or vjp but never both.
+        backward_fn = self._forward_cls.backward  # type: ignore[attr-defined]
+        vjp_fn = self._forward_cls.vjp  # type: ignore[attr-defined]
+        if backward_fn is not Function.backward and vjp_fn is not Function.vjp:
+            raise RuntimeError(
+                "Implementing both 'backward' and 'vjp' for a custom "
+                "Function is not allowed. You should only implement one "
+                "of them."
+            )
+        user_fn = vjp_fn if vjp_fn is not Function.vjp else backward_fn
+        return user_fn(self, *args)
+
+    def apply_jvp(self, *args):
+        r"""
+        Apply method used when executing forward mode AD during the forward
+        """
+        # _forward_cls is defined by derived class
+        return self._forward_cls.jvp(self, *args)  # type: ignore[attr-defined]
+
+    def _compiled_autograd_key(self):
+        return self._forward_cls._compiled_autograd_key(self)  # type: ignore[attr-defined]
+
+
+class FunctionMeta(type):
+    """Function metaclass.
+
+    This metaclass sets up the following properties:
+        _backward_cls: The Function class corresponding to the differentiated
+            version of this function (which is generated on the fly by this
+            metaclass).
+    """
+
+    def __init__(cls, name, bases, attrs):
+        backward_fn = type(
+            name + "Backward", (BackwardCFunction,), {"_forward_cls": cls}
+        )
+        backward_fn._autograd_function_id = next(AUTOGRAD_FUNCTION_COUNTER)  # type: ignore[attr-defined]
+        cls._backward_cls = backward_fn
+
+        super().__init__(name, bases, attrs)
+
+
+class _SingleLevelFunction(
+    _C._FunctionBase, FunctionCtx, _HookMixin, metaclass=FunctionMeta
+):
+    @staticmethod
+    def forward(*args: Any, **kwargs: Any) -> Any:
+        r"""Define the forward of the custom autograd Function.
+
+        This function is to be overridden by all subclasses.
+        There are two ways to define forward:
+
+        Usage 1 (Combined forward and ctx)::
+
+            @staticmethod
+            def forward(ctx: Any, *args: Any, **kwargs: Any) -> Any:
+                pass
+
+        - It must accept a context ctx as the first argument, followed by any
+          number of arguments (tensors or other types).
+        - See :ref:`combining-forward-context` for more details
+
+        Usage 2 (Separate forward and ctx)::
+
+            @staticmethod
+            def forward(*args: Any, **kwargs: Any) -> Any:
+                pass
+
+
+            @staticmethod
+            def setup_context(ctx: Any, inputs: Tuple[Any, ...], output: Any) -> None:
+                pass
+
+        - The forward no longer accepts a ctx argument.
+        - Instead, you must also override the :meth:`torch.autograd.Function.setup_context`
+          staticmethod to handle setting up the ``ctx`` object.
+          ``output`` is the output of the forward, ``inputs`` are a Tuple of inputs
+          to the forward.
+        - See :ref:`extending-autograd` for more details
+
+        The context can be used to store arbitrary data that can be then
+        retrieved during the backward pass. Tensors should not be stored
+        directly on `ctx` (though this is not currently enforced for
+        backward compatibility). Instead, tensors should be saved either with
+        :func:`ctx.save_for_backward` if they are intended to be used in
+        ``backward`` (equivalently, ``vjp``) or :func:`ctx.save_for_forward`
+        if they are intended to be used for in ``jvp``.
+        """
+        raise NotImplementedError(
+            "You must implement the forward function for custom autograd.Function."
+        )
+
+    @staticmethod
+    def setup_context(ctx: Any, inputs: tuple[Any, ...], output: Any) -> Any:
+        r"""There are two ways to define the forward pass of an autograd.Function.
+
+        Either:
+
+        1. Override forward with the signature ``forward(ctx, *args, **kwargs)``.
+           ``setup_context`` is not overridden. Setting up the ctx for backward
+           happens inside the ``forward``.
+        2. Override forward with the signature ``forward(*args, **kwargs)`` and
+           override ``setup_context``. Setting up the ctx for backward happens
+           inside ``setup_context`` (as opposed to inside the ``forward``)
+
+        See :meth:`torch.autograd.Function.forward` and :ref:`extending-autograd` for more details.
+        """
+        raise NotImplementedError("setup_context is not implemented.")
+
+    @staticmethod
+    def backward(ctx: Any, *grad_outputs: Any) -> Any:
+        r"""Define a formula for differentiating the operation with backward mode automatic differentiation.
+
+        This function is to be overridden by all subclasses.
+        (Defining this function is equivalent to defining the ``vjp`` function.)
+
+        It must accept a context :attr:`ctx` as the first argument, followed by
+        as many outputs as the :func:`forward` returned (None will be passed in
+        for non tensor outputs of the forward function),
+        and it should return as many tensors, as there were inputs to
+        :func:`forward`. Each argument is the gradient w.r.t the given output,
+        and each returned value should be the gradient w.r.t. the
+        corresponding input. If an input is not a Tensor or is a Tensor not
+        requiring grads, you can just pass None as a gradient for that input.
+
+        The context can be used to retrieve tensors saved during the forward
+        pass. It also has an attribute :attr:`ctx.needs_input_grad` as a tuple
+        of booleans representing whether each input needs gradient. E.g.,
+        :func:`backward` will have ``ctx.needs_input_grad[0] = True`` if the
+        first input to :func:`forward` needs gradient computed w.r.t. the
+        output.
+        """
+        raise NotImplementedError(
+            "You must implement either the backward or vjp method for "
+            "your custom autograd.Function to use it with backward "
+            "mode AD."
+        )
+
+    # vjp and backward are alias of each other
+    vjp = backward
+
+    @staticmethod
+    def jvp(ctx: Any, *grad_inputs: Any) -> Any:
+        r"""Define a formula for differentiating the operation with forward mode automatic differentiation.
+
+        This function is to be overridden by all subclasses.
+        It must accept a context :attr:`ctx` as the first argument, followed by
+        as many inputs as the :func:`forward` got (None will be passed in
+        for non tensor inputs of the forward function),
+        and it should return as many tensors as there were outputs to
+        :func:`forward`. Each argument is the gradient w.r.t the given input,
+        and each returned value should be the gradient w.r.t. the
+        corresponding output. If an output is not a Tensor or the function is not
+        differentiable with respect to that output, you can just pass None as a
+        gradient for that input.
+
+        You can use the :attr:`ctx` object to pass any value from the forward to this
+        functions.
+        """
+        raise NotImplementedError(
+            "You must implement the jvp function for custom "
+            "autograd.Function to use it with forward mode AD."
+        )
+
+
+class Function(_SingleLevelFunction):
+    r"""Base class to create custom `autograd.Function`.
+
+    To create a custom `autograd.Function`, subclass this class and implement
+    the :meth:`forward` and :meth:`backward` static methods. Then, to use your custom
+    op in the forward pass, call the class method ``apply``. Do not call
+    :meth:`forward` directly.
+
+    To ensure correctness and best performance, make sure you are calling the
+    correct methods on ``ctx`` and validating your backward function using
+    :func:`torch.autograd.gradcheck`.
+
+    See :ref:`extending-autograd` for more details on how to use this class.
+
+    Examples::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+        >>> class Exp(Function):
+        >>>     @staticmethod
+        >>>     def forward(ctx, i):
+        >>>         result = i.exp()
+        >>>         ctx.save_for_backward(result)
+        >>>         return result
+        >>>
+        >>>     @staticmethod
+        >>>     def backward(ctx, grad_output):
+        >>>         result, = ctx.saved_tensors
+        >>>         return grad_output * result
+        >>>
+        >>> # Use it by calling the apply method:
+        >>> # xdoctest: +SKIP
+        >>> output = Exp.apply(input)
+    """
+
+    def __init__(self, *args, **kwargs):
+        warnings.warn(
+            f"{self.__class__} should not be instantiated. Methods on autograd functions "
+            "are all static, so you should invoke them on the class itself. "
+            "Instantiating an autograd function will raise an "
+            "error in a future version of PyTorch.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
+
+    def __call__(self, *args, **kwargs):
+        raise RuntimeError(
+            "Legacy autograd function with non-static forward method is deprecated. "
+            "Please use new-style autograd function with static forward method. "
+            "(Example: https://pytorch.org/docs/stable/autograd.html#torch.autograd.Function)"
+        )
+
+    """
+    Bool that specifies if PyTorch should attempt to autogenerate
+    :func:`torch.vmap` support for this autograd.Function. You may set this to
+    True only if this autograd.Function's forward, backward, and jvp (if they
+    exist) are written using PyTorch operations; otherwise, please override
+    :meth:`torch.autograd.Function.vmap` to add support for :func:`torch.vmap`.
+
+    Please see :ref:`func-autograd-function` for more details.
+    """
+    generate_vmap_rule = False
+
+    @staticmethod
+    def vmap(info, in_dims, *args):
+        r"""Define the behavior for this autograd.Function underneath :func:`torch.vmap`.
+
+        For a :func:`torch.autograd.Function` to support
+        :func:`torch.vmap`, you must either override this static method, or set
+        ``generate_vmap_rule`` to ``True`` (you may not do both).
+
+        If you choose to override this staticmethod: it must accept
+
+        - an ``info`` object as the first argument. ``info.batch_size``
+          specifies the size of the dimension being vmapped over,
+          while ``info.randomness`` is the randomness option passed to
+          :func:`torch.vmap`.
+        - an ``in_dims`` tuple as the second argument.
+          For each arg in ``args``, ``in_dims`` has a corresponding
+          ``Optional[int]``. It is ``None`` if the arg is not a Tensor or if
+          the arg is not being vmapped over, otherwise, it is an integer
+          specifying what dimension of the Tensor is being vmapped over.
+        - ``*args``, which is the same as the args to :meth:`~Function.forward`.
+
+        The return of the vmap staticmethod is a tuple of ``(output, out_dims)``.
+        Similar to ``in_dims``, ``out_dims`` should be of the same structure as
+        ``output`` and contain one ``out_dim`` per output that specifies if the
+        output has the vmapped dimension and what index it is in.
+
+        Please see :ref:`func-autograd-function` for more details.
+        """
+        raise NotImplementedError(
+            "To use autograd.Function with vmap, you must either override the "
+            "vmap staticmethod or set generate_vmap_rule=True."
+        )
+
+    @classmethod
+    def apply(cls, *args, **kwargs):
+        def bind_default_args(func, *args, **kwargs):
+            signature = inspect.signature(func)
+            bound_args = signature.bind(*args, **kwargs)
+            bound_args.apply_defaults()
+
+            return bound_args.args
+
+        is_setup_ctx_defined = _is_setup_context_defined(cls.setup_context)
+        if is_setup_ctx_defined:
+            args = bind_default_args(cls.forward, *args, **kwargs)
+
+        if not torch._C._are_functorch_transforms_active():
+            # See NOTE: [functorch vjp and autograd interaction]
+            args = _functorch.utils.unwrap_dead_wrappers(args)
+            return super().apply(*args, **kwargs)  # type: ignore[misc]
+
+        if not is_setup_ctx_defined:
+            raise RuntimeError(
+                "In order to use an autograd.Function with functorch transforms "
+                "(vmap, grad, jvp, jacrev, ...), it must override the setup_context "
+                "staticmethod. For more details, please see "
+                "https://pytorch.org/docs/main/notes/extending.func.html"
+            )
+
+        return custom_function_call(cls, *args, **kwargs)
+
+    @staticmethod
+    def _compiled_autograd_key(ctx):
+        return (ctx._autograd_function_id,)
+
+
+def _is_setup_context_defined(fn):
+    return fn != _SingleLevelFunction.setup_context
+
+
+def once_differentiable(
+    fn: Callable[Concatenate[_T, _P], _R],
+) -> Callable[Concatenate[_T, _P], _R]:
+    @functools.wraps(fn)
+    def wrapper(ctx: _T, *args: _P.args, **kwargs: _P.kwargs) -> _R:
+        with torch.no_grad():
+            outputs = fn(ctx, *args, **kwargs)
+
+        if not torch.is_grad_enabled():
+            return outputs
+
+        # If any of the inputs have requires_grad=True, we force the outputs
+        # to have requires_grad=True but point to a grad_fn which throws an
+        # error message during (double) back-propagation.
+        # XXX: this is only an approximation of requires_grad - there's no way
+        # to figure out if fn didn't use ctx.saved_tensors and as a result
+        # some Tensors might require grad, even if no args do.
+        # Unfortunately, this leads to unexpected error messages ("no nodes
+        # require computing gradients"), but I don't have a better idea.
+        # These functions would raise an error in backward anyway.
+        requires_grad = any(
+            isinstance(arg, torch.Tensor) and arg.requires_grad for arg in args
+        )
+        if not requires_grad:
+            return outputs
+
+        if not isinstance(outputs, tuple):
+            outputs_ = (outputs,)
+        else:
+            outputs_ = outputs
+
+        err_fn = _functions.DelayedError(
+            b"trying to differentiate twice a function that was marked "
+            b"with @once_differentiable",
+            len(outputs_),
+        )
+
+        # Create aliases of each output that has requires_grad=True. We need
+        # at least one of the inputs to err_fn to require grad so that the
+        # output will have a grad_fn.
+        def fake_requires_grad(var):
+            if var is not None:
+                var = var.detach()
+                var.requires_grad = True
+            return var
+
+        return err_fn(*[fake_requires_grad(v) for v in outputs_])  # type: ignore[return-value]
+
+    return wrapper
+
+
+class InplaceFunction(Function):
+    r"""
+    This class is here only for backward compatibility reasons.
+    Use :class:`Function` instead of this for any new use case.
+    """
+
+    def __init__(self, inplace=False):
+        super().__init__()
+        self.inplace = inplace
+
+
+def _nested_map(condition, fn, condition_msg=None):
+    def _map(obj):
+        if condition(obj):
+            return fn(obj)
+        elif obj is None:
+            return None
+        elif isinstance(obj, (list, tuple)):
+            mapped = (_map(x) for x in obj)
+            if hasattr(obj, "_fields"):
+                # obj is namedtuple
+                return type(obj)(*mapped)
+            return type(obj)(mapped)
+        elif isinstance(obj, dict):
+            return {x: _map(obj[x]) for x in obj}
+        else:
+            raise ValueError(
+                "Auto nesting doesn't know how to process "
+                "an input object of type "
+                + torch.typename(obj)
+                + (
+                    ". Accepted types: " + condition_msg + ", or lists/tuples of them"
+                    if condition_msg
+                    else ""
+                )
+            )
+
+    return _map
+
+
+def _jit_unwrap_structured(obj):
+    if hasattr(obj, "_jit_unwrap"):
+        return obj._jit_unwrap()
+    return obj
+
+
+def _iter_filter(condition, allow_unknown=False, condition_msg=None, conversion=None):
+    def _iter(obj):
+        if conversion is not None:
+            obj = conversion(obj)
+        if condition(obj):
+            yield obj
+        elif obj is None:
+            return
+        elif isinstance(obj, (list, tuple)):
+            for o in obj:
+                yield from _iter(o)
+        elif isinstance(obj, dict):
+            # We only accept primitive key types, so we needn't inspect them
+            for o in obj.values():
+                yield from _iter(o)
+        elif allow_unknown:
+            yield obj
+        else:
+            raise ValueError(
+                "Auto nesting doesn't know how to process "
+                "an input object of type "
+                + torch.typename(obj)
+                + (
+                    ". Accepted types: " + condition_msg + ", or lists/tuples of them"
+                    if condition_msg
+                    else ""
+                )
+            )
+
+    return _iter
+
+
+def _unflatten(input, proto):
+    # unflatten a list or tuple input into a nested list/tuple structure
+    # specified by proto
+    def unflatten_helper(input, proto):
+        res: list[Optional[torch.Tensor]] = []
+        if hasattr(proto, "_jit_wrap"):
+            return proto._jit_wrap(input)
+        if not isinstance(proto, (list, tuple)):
+            return input[0], input[1:]
+        for e in proto:
+            if e is None:
+                res.append(e)
+            else:
+                res_e, input = unflatten_helper(input, e)
+                res.append(res_e)
+        return type(proto)(res), input
+
+    return unflatten_helper(input, proto)[0]
+
+
+_iter_jit_values = _iter_filter(
+    lambda o: o is None or isinstance(o, torch._C.Value),
+    condition_msg="jit's Values or None",
+)
+_iter_tensors = _iter_filter(
+    lambda x: isinstance(x, torch.Tensor),
+    condition_msg="Tensors",
+    conversion=_jit_unwrap_structured,
+)
+_iter_tensors_permissive = _iter_filter(
+    lambda x: isinstance(x, torch.Tensor),
+    allow_unknown=True,
+    condition_msg="Tensors (permissive)",
+)
+_iter_None_tensors = _iter_filter(
+    lambda o: o is None or isinstance(o, torch.Tensor), condition_msg="Tensors or None"
+)
+_map_tensor_data = _nested_map(
+    lambda x: isinstance(x, torch.Tensor), lambda o: o.data, condition_msg="Tensors"
+)
+
+
+class NestedIOFunction(Function):
+    r"""
+    This class is here only for backward compatibility reasons.
+    Use :class:`Function` instead of this for any new use case.
+    """
+
+    # The 'type: ignore' statements are needed here because these functions are declared as '@staticmethod' in the
+    # superclass (Function) but are instance methods here, which mypy reports as incompatible.
+
+    def _do_forward(self, *input):
+        self._nested_input = input
+        flat_input = tuple(_iter_tensors(input))
+        flat_output = super()._do_forward(*flat_input)  # type: ignore[misc]
+        nested_tensors = _unflatten(flat_output, self._nested_output)
+        return nested_tensors
+
+    def _do_backward(self, gradients, retain_variables):
+        self.retain_variables = retain_variables
+        result = super()._do_backward(gradients, retain_variables)  # type: ignore[misc]
+        if not retain_variables:
+            del self._nested_output
+            del self._to_save_nested
+        return result
+
+    def backward(self, *gradients: Any) -> Any:  # type: ignore[override]
+        r"""
+        Shared backward utility.
+        """
+        nested_gradients = _unflatten(gradients, self._nested_output)
+        result = self.backward_extended(*nested_gradients)  # type: ignore[func-returns-value]
+        return tuple(_iter_None_tensors(result))
+
+    __call__ = _do_forward
+
+    def forward(self, *args: Any) -> Any:  # type: ignore[override]
+        r"""
+        Shared forward utility.
+        """
+        nested_tensors = _map_tensor_data(self._nested_input)
+        result = self.forward_extended(*nested_tensors)  # type: ignore[func-returns-value]
+        del self._nested_input
+        self._nested_output = result
+        return tuple(_iter_tensors(result))
+
+    def save_for_backward(self, *args: Any) -> None:
+        r"""
+        See :meth:`Function.save_for_backward`.
+        """
+        self.to_save = tuple(_iter_tensors(args))
+        self._to_save_nested = args
+
+    @property
+    def saved_tensors(self):  # type: ignore[override]
+        r"""
+        See :meth:`Function.saved_tensors`.
+        """
+        flat_tensors = super().saved_tensors  # type: ignore[misc]
+        return _unflatten(flat_tensors, self._to_save_nested)
+
+    def mark_dirty(self, *args: Any, **kwargs: Any) -> None:
+        r"""
+        See :meth:`Function.mark_dirty`.
+        """
+        self.dirty_tensors = tuple(_iter_tensors((args, kwargs)))
+
+    def mark_non_differentiable(self, *args: Any, **kwargs: Any) -> None:
+        r"""
+        See :meth:`Function.mark_non_differentiable`.
+        """
+        self.non_differentiable = tuple(_iter_tensors((args, kwargs)))
+
+    def forward_extended(self, *input: Any) -> None:
+        r"""
+        User defined forward.
+        """
+        raise NotImplementedError
+
+    def backward_extended(self, *grad_output: Any) -> None:
+        r"""
+        User defined backward.
+        """
+        raise NotImplementedError
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/functional.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/functional.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8bce9ed7c54f0a333ea8043c3fab60d46b7b94c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/functional.py
@@ -0,0 +1,1210 @@
+# mypy: allow-untyped-defs
+
+import torch
+from torch._vmap_internals import _vmap
+
+from . import forward_ad as fwAD
+
+
+__all__ = ["vjp", "jvp", "jacobian", "hessian", "hvp", "vhp"]
+
+# Utility functions
+
+
+def _as_tuple_nocheck(x):
+    if isinstance(x, tuple):
+        return x
+    elif isinstance(x, list):
+        return tuple(x)
+    else:
+        return (x,)
+
+
+def _as_tuple(inp, arg_name=None, fn_name=None):
+    # Ensures that inp is a tuple of Tensors
+    # Returns whether or not the original inp was a tuple and the tupled version of the input
+    if arg_name is None and fn_name is None:
+        return _as_tuple_nocheck(inp)
+
+    is_inp_tuple = True
+    if not isinstance(inp, tuple):
+        inp = (inp,)
+        is_inp_tuple = False
+
+    for i, el in enumerate(inp):
+        if not isinstance(el, torch.Tensor):
+            if is_inp_tuple:
+                raise TypeError(
+                    f"The {arg_name} given to {fn_name} must be either a Tensor or a tuple of Tensors but the"
+                    f" value at index {i} has type {type(el)}."
+                )
+            else:
+                raise TypeError(
+                    f"The {arg_name} given to {fn_name} must be either a Tensor or a tuple of Tensors but the"
+                    f" given {arg_name} has type {type(el)}."
+                )
+
+    return is_inp_tuple, inp
+
+
+def _tuple_postprocess(res, to_unpack):
+    # Unpacks a potentially nested tuple of Tensors
+    # to_unpack should be a single boolean or a tuple of two booleans.
+    # It is used to:
+    # - invert _as_tuple when res should match the inp given to _as_tuple
+    # - optionally remove nesting of two tuples created by multiple calls to _as_tuple
+    if isinstance(to_unpack, tuple):
+        if len(to_unpack) != 2:
+            raise AssertionError("Expected to_unpack tuple to have exactly 2 elements")
+        if not to_unpack[1]:
+            res = tuple(el[0] for el in res)
+        if not to_unpack[0]:
+            res = res[0]
+    else:
+        if not to_unpack:
+            res = res[0]
+    return res
+
+
+def _grad_preprocess(inputs, create_graph, need_graph):
+    # Preprocess the inputs to make sure they require gradient
+    # inputs is a tuple of Tensors to preprocess
+    # create_graph specifies if the user wants gradients to flow back to the Tensors in inputs
+    # need_graph specifies if we internally want gradients to flow back to the Tensors in res
+    # Note that we *always* create a new Tensor object to be able to see the difference between
+    # inputs given as arguments and the same Tensors automatically captured by the user function.
+    # Check this issue for more details on how that can happen: https://github.com/pytorch/pytorch/issues/32576
+    res = []
+    for inp in inputs:
+        if create_graph and inp.requires_grad:
+            # Create at least a new Tensor object in a differentiable way
+            if not inp.is_sparse:
+                # Use .view_as() to get a shallow copy
+                res.append(inp.view_as(inp))
+            else:
+                # We cannot use view for sparse Tensors so we clone
+                res.append(inp.clone())
+        else:
+            res.append(inp.detach().requires_grad_(need_graph))
+    return tuple(res)
+
+
+def _grad_postprocess(inputs, create_graph):
+    # Postprocess the generated Tensors to avoid returning Tensors with history when the user did not
+    # request it.
+    if isinstance(inputs[0], torch.Tensor):
+        if not create_graph:
+            return tuple(inp.detach() for inp in inputs)
+        else:
+            return inputs
+    else:
+        return tuple(_grad_postprocess(inp, create_graph) for inp in inputs)
+
+
+def _validate_v(v, other, is_other_tuple):
+    # This assumes that other is the correct shape, and v should match
+    # Both are assumed to be tuples of Tensors
+    if len(other) != len(v):
+        if is_other_tuple:
+            raise RuntimeError(
+                f"v is a tuple of invalid length: should be {len(other)} but got {len(v)}."
+            )
+        else:
+            raise RuntimeError("The given v should contain a single Tensor.")
+
+    for idx, (el_v, el_other) in enumerate(zip(v, other)):
+        if el_v.size() != el_other.size():
+            prepend = ""
+            if is_other_tuple:
+                prepend = f"Entry {idx} in "
+            raise RuntimeError(
+                f"{prepend}v has invalid size: should be {el_other.size()} but got {el_v.size()}."
+            )
+
+
+def _check_requires_grad(inputs, input_type, strict):
+    # Used to make all the necessary checks to raise nice errors in strict mode.
+    if not strict:
+        return
+
+    if input_type not in ["outputs", "grad_inputs", "jacobian", "hessian"]:
+        raise RuntimeError("Invalid input_type to _check_requires_grad")
+    for i, inp in enumerate(inputs):
+        if inp is None:
+            # This can only be reached for grad_inputs.
+            raise RuntimeError(
+                f"The output of the user-provided function is independent of input {i}."
+                " This is not allowed in strict mode."
+            )
+        if not inp.requires_grad:
+            if input_type == "hessian":
+                raise RuntimeError(
+                    f"The hessian of the user-provided function with respect to input {i}"
+                    " is independent of the input. This is not allowed in strict mode."
+                    " You should ensure that your function is thrice differentiable and that"
+                    " the hessian depends on the inputs."
+                )
+            elif input_type == "jacobian":
+                raise RuntimeError(
+                    "While computing the hessian, found that the jacobian of the user-provided"
+                    f" function with respect to input {i} is independent of the input. This is not"
+                    " allowed in strict mode. You should ensure that your function is twice"
+                    " differentiable and that the jacobian depends on the inputs (this would be"
+                    " violated by a linear function for example)."
+                )
+            elif input_type == "grad_inputs":
+                raise RuntimeError(
+                    f"The gradient with respect to input {i} is independent of the inputs of the"
+                    " user-provided function. This is not allowed in strict mode."
+                )
+            else:
+                raise RuntimeError(
+                    f"Output {i} of the user-provided function does not require gradients."
+                    " The outputs must be computed in a differentiable manner from the input"
+                    " when running in strict mode."
+                )
+
+
+def _autograd_grad(
+    outputs,
+    inputs,
+    grad_outputs=None,
+    create_graph=False,
+    retain_graph=None,
+    is_grads_batched=False,
+):
+    # Version of autograd.grad that accepts `None` in outputs and do not compute gradients for them.
+    # This has the extra constraint that inputs has to be a tuple
+    if not isinstance(outputs, tuple):
+        raise AssertionError("Expected outputs to be a tuple")
+    if grad_outputs is None:
+        grad_outputs = (None,) * len(outputs)
+    if not isinstance(grad_outputs, tuple):
+        raise AssertionError("Expected grad_outputs to be a tuple")
+    if len(outputs) != len(grad_outputs):
+        raise AssertionError(
+            f"Expected outputs and grad_outputs to have the same length, "
+            f"but got {len(outputs)} and {len(grad_outputs)}"
+        )
+
+    new_outputs: tuple[torch.Tensor, ...] = ()
+    new_grad_outputs: tuple[torch.Tensor, ...] = ()
+    for out, grad_out in zip(outputs, grad_outputs):
+        if out is not None and out.requires_grad:
+            new_outputs += (out,)
+            new_grad_outputs += (grad_out,)
+
+    if len(new_outputs) == 0:
+        # No differentiable output, we don't need to call the autograd engine
+        return (None,) * len(inputs)
+    else:
+        return torch.autograd.grad(
+            new_outputs,
+            inputs,
+            new_grad_outputs,
+            allow_unused=True,
+            create_graph=create_graph,
+            retain_graph=retain_graph,
+            is_grads_batched=is_grads_batched,
+        )
+
+
+def _fill_in_zeros(grads, refs, strict, create_graph, stage):
+    # Used to detect None in the grads and depending on the flags, either replace them
+    # with Tensors full of 0s of the appropriate size based on the refs or raise an error.
+    # strict and create graph allow us to detect when it is appropriate to raise an error
+    # stage gives us information of which backward call we consider to give good error message
+    if stage not in ["back", "back_trick", "double_back", "double_back_trick"]:
+        raise RuntimeError(f"Invalid stage argument '{stage}' to _fill_in_zeros")
+
+    res: tuple[torch.Tensor, ...] = ()
+    for i, grads_i in enumerate(grads):
+        if grads_i is None:
+            if strict:
+                if stage == "back":
+                    raise RuntimeError(
+                        "The output of the user-provided function is independent of "
+                        f"input {i}. This is not allowed in strict mode."
+                    )
+                elif stage == "back_trick":
+                    raise RuntimeError(
+                        f"The gradient with respect to the input is independent of entry {i}"
+                        " in the grad_outputs when using the double backward trick to compute"
+                        " forward mode gradients. This is not allowed in strict mode."
+                    )
+                elif stage == "double_back":
+                    raise RuntimeError(
+                        "The jacobian of the user-provided function is independent of "
+                        f"input {i}. This is not allowed in strict mode."
+                    )
+                else:
+                    raise RuntimeError(
+                        "The hessian of the user-provided function is independent of "
+                        f"entry {i} in the grad_jacobian. This is not allowed in strict "
+                        "mode as it prevents from using the double backward trick to "
+                        "replace forward mode AD."
+                    )
+
+            grads_i = torch.zeros_like(refs[i])
+        else:
+            if strict and create_graph and not grads_i.requires_grad:
+                if "double" not in stage:
+                    raise RuntimeError(
+                        "The jacobian of the user-provided function is independent of "
+                        f"input {i}. This is not allowed in strict mode when create_graph=True."
+                    )
+                else:
+                    raise RuntimeError(
+                        "The hessian of the user-provided function is independent of "
+                        f"input {i}. This is not allowed in strict mode when create_graph=True."
+                    )
+
+        res += (grads_i,)
+
+    return res
+
+
+# Public API
+
+
+def vjp(func, inputs, v=None, create_graph=False, strict=False):
+    r"""Compute the dot product between a vector ``v`` and the Jacobian of the given function at the point given by the inputs.
+
+    Args:
+        func (function): a Python function that takes Tensor inputs and returns
+            a tuple of Tensors or a Tensor.
+        inputs (tuple of Tensors or Tensor): inputs to the function ``func``.
+        v (tuple of Tensors or Tensor): The vector for which the vector
+            Jacobian product is computed.  Must be the same size as the output
+            of ``func``. This argument is optional when the output of ``func``
+            contains a single element and (if it is not provided) will be set
+            as a Tensor containing a single ``1``.
+        create_graph (bool, optional): If ``True``, both the output and result
+            will be computed in a differentiable way. Note that when ``strict``
+            is ``False``, the result can not require gradients or be
+            disconnected from the inputs.  Defaults to ``False``.
+        strict (bool, optional): If ``True``, an error will be raised when we
+            detect that there exists an input such that all the outputs are
+            independent of it. If ``False``, we return a Tensor of zeros as the
+            vjp for said inputs, which is the expected mathematical value.
+            Defaults to ``False``.
+
+    Returns:
+        output (tuple): tuple with:
+            func_output (tuple of Tensors or Tensor): output of ``func(inputs)``
+
+            vjp (tuple of Tensors or Tensor): result of the dot product with
+            the same shape as the inputs.
+
+    Example:
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+        >>> def exp_reducer(x):
+        ...     return x.exp().sum(dim=1)
+        >>> inputs = torch.rand(4, 4)
+        >>> v = torch.ones(4)
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> vjp(exp_reducer, inputs, v)
+        (tensor([5.7817, 7.2458, 5.7830, 6.7782]),
+         tensor([[1.4458, 1.3962, 1.3042, 1.6354],
+                [2.1288, 1.0652, 1.5483, 2.5035],
+                [2.2046, 1.1292, 1.1432, 1.3059],
+                [1.3225, 1.6652, 1.7753, 2.0152]]))
+
+        >>> vjp(exp_reducer, inputs, v, create_graph=True)
+        (tensor([5.7817, 7.2458, 5.7830, 6.7782], grad_fn=),
+         tensor([[1.4458, 1.3962, 1.3042, 1.6354],
+                [2.1288, 1.0652, 1.5483, 2.5035],
+                [2.2046, 1.1292, 1.1432, 1.3059],
+                [1.3225, 1.6652, 1.7753, 2.0152]], grad_fn=))
+
+        >>> def adder(x, y):
+        ...     return 2 * x + 3 * y
+        >>> inputs = (torch.rand(2), torch.rand(2))
+        >>> v = torch.ones(2)
+        >>> vjp(adder, inputs, v)
+        (tensor([2.4225, 2.3340]),
+         (tensor([2., 2.]), tensor([3., 3.])))
+    """
+    with torch.enable_grad():
+        is_inputs_tuple, inputs = _as_tuple(inputs, "inputs", "vjp")
+        inputs = _grad_preprocess(inputs, create_graph=create_graph, need_graph=True)
+
+        outputs = func(*inputs)
+        is_outputs_tuple, outputs = _as_tuple(
+            outputs, "outputs of the user-provided function", "vjp"
+        )
+        _check_requires_grad(outputs, "outputs", strict=strict)
+
+        if v is not None:
+            _, v = _as_tuple(v, "v", "vjp")
+            v = _grad_preprocess(v, create_graph=create_graph, need_graph=False)
+            _validate_v(v, outputs, is_outputs_tuple)
+        else:
+            if len(outputs) != 1 or outputs[0].nelement() != 1:
+                raise RuntimeError(
+                    "The vector v can only be None if the "
+                    "user-provided function returns "
+                    "a single Tensor with a single element."
+                )
+
+    enable_grad = True if create_graph else torch.is_grad_enabled()
+    with torch.set_grad_enabled(enable_grad):
+        grad_res = _autograd_grad(outputs, inputs, v, create_graph=create_graph)
+        vjp = _fill_in_zeros(grad_res, inputs, strict, create_graph, "back")
+
+    # Cleanup objects and return them to the user
+    outputs = _grad_postprocess(outputs, create_graph)
+    vjp = _grad_postprocess(vjp, create_graph)
+
+    return _tuple_postprocess(outputs, is_outputs_tuple), _tuple_postprocess(
+        vjp, is_inputs_tuple
+    )
+
+
+def jvp(func, inputs, v=None, create_graph=False, strict=False):
+    r"""Compute the dot product between the Jacobian of the given function at the point given by the inputs and a vector ``v``.
+
+    Args:
+        func (function): a Python function that takes Tensor inputs and returns
+            a tuple of Tensors or a Tensor.
+        inputs (tuple of Tensors or Tensor): inputs to the function ``func``.
+        v (tuple of Tensors or Tensor): The vector for which the Jacobian
+            vector product is computed. Must be the same size as the input of
+            ``func``. This argument is optional when the input to ``func``
+            contains a single element and (if it is not provided) will be set
+            as a Tensor containing a single ``1``.
+        create_graph (bool, optional): If ``True``, both the output and result
+            will be computed in a differentiable way. Note that when ``strict``
+            is ``False``, the result can not require gradients or be
+            disconnected from the inputs.  Defaults to ``False``.
+        strict (bool, optional): If ``True``, an error will be raised when we
+            detect that there exists an input such that all the outputs are
+            independent of it. If ``False``, we return a Tensor of zeros as the
+            jvp for said inputs, which is the expected mathematical value.
+            Defaults to ``False``.
+
+    Returns:
+        output (tuple): tuple with:
+            func_output (tuple of Tensors or Tensor): output of ``func(inputs)``
+
+            jvp (tuple of Tensors or Tensor): result of the dot product with
+            the same shape as the output.
+
+    Note:
+        ``autograd.functional.jvp`` computes the jvp by using the backward of
+        the backward (sometimes called the double backwards trick). This is not
+        the most performant way of computing the jvp. Please consider using
+        :func:`torch.func.jvp` or the
+        :ref:`low-level forward-mode AD API ` instead.
+
+    Example:
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+        >>> def exp_reducer(x):
+        ...     return x.exp().sum(dim=1)
+        >>> inputs = torch.rand(4, 4)
+        >>> v = torch.ones(4, 4)
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> jvp(exp_reducer, inputs, v)
+        (tensor([6.3090, 4.6742, 7.9114, 8.2106]),
+         tensor([6.3090, 4.6742, 7.9114, 8.2106]))
+
+        >>> jvp(exp_reducer, inputs, v, create_graph=True)
+        (tensor([6.3090, 4.6742, 7.9114, 8.2106], grad_fn=),
+         tensor([6.3090, 4.6742, 7.9114, 8.2106], grad_fn=))
+
+        >>> def adder(x, y):
+        ...     return 2 * x + 3 * y
+        >>> inputs = (torch.rand(2), torch.rand(2))
+        >>> v = (torch.ones(2), torch.ones(2))
+        >>> jvp(adder, inputs, v)
+        (tensor([2.2399, 2.5005]),
+         tensor([5., 5.]))
+
+    """
+    with torch.enable_grad():
+        is_inputs_tuple, inputs = _as_tuple(inputs, "inputs", "jvp")
+        inputs = _grad_preprocess(inputs, create_graph=create_graph, need_graph=True)
+
+        if v is not None:
+            _, v = _as_tuple(v, "v", "jvp")
+            v = _grad_preprocess(v, create_graph=create_graph, need_graph=False)
+            _validate_v(v, inputs, is_inputs_tuple)
+        else:
+            if len(inputs) != 1 or inputs[0].nelement() != 1:
+                raise RuntimeError(
+                    "The vector v can only be None if the input to "
+                    "the user-provided function is a single Tensor "
+                    "with a single element."
+                )
+
+        outputs = func(*inputs)
+        is_outputs_tuple, outputs = _as_tuple(
+            outputs, "outputs of the user-provided function", "jvp"
+        )
+        _check_requires_grad(outputs, "outputs", strict=strict)
+        # The backward is linear so the value of grad_outputs is not important as
+        # it won't appear in the double backward graph. We only need to ensure that
+        # it does not contain inf or nan.
+        grad_outputs = tuple(
+            torch.zeros_like(out, requires_grad=True) for out in outputs
+        )
+
+        grad_inputs = _autograd_grad(outputs, inputs, grad_outputs, create_graph=True)
+        _check_requires_grad(grad_inputs, "grad_inputs", strict=strict)
+
+    if create_graph:
+        with torch.enable_grad():
+            grad_res = _autograd_grad(
+                grad_inputs, grad_outputs, v, create_graph=create_graph
+            )
+            jvp = _fill_in_zeros(grad_res, outputs, strict, create_graph, "back_trick")
+    else:
+        grad_res = _autograd_grad(
+            grad_inputs, grad_outputs, v, create_graph=create_graph
+        )
+        jvp = _fill_in_zeros(grad_res, outputs, strict, create_graph, "back_trick")
+
+    # Cleanup objects and return them to the user
+    outputs = _grad_postprocess(outputs, create_graph)
+    jvp = _grad_postprocess(jvp, create_graph)
+
+    return _tuple_postprocess(outputs, is_outputs_tuple), _tuple_postprocess(
+        jvp, is_outputs_tuple
+    )
+
+
+def _construct_standard_basis_for(
+    tensors: tuple[torch.Tensor, ...], tensor_numels: tuple[int, ...]
+) -> tuple[torch.Tensor, ...]:
+    # This function:
+    # - constructs a N=sum(tensor_numels) standard basis. i.e. an NxN identity matrix.
+    # - Splits the identity matrix into chunks with each chunk size determined by `tensor_numels`.
+    # - Each chunk corresponds to one tensor. The chunk has the same dtype and
+    #   device as the tensor
+    #
+    # For example, with tensor_numels = [1, 2, 1], this function returns:
+    # ( tensor([[1],     tensor([[0, 0],      tensor([[0],
+    #           [0],             [1, 0],              [0],
+    #           [0],             [0, 1],              [0],
+    #           [0]])  ,         [0, 0]])  ,          [1]])  )
+    #
+    # Precondition: tensor_numels == tuple(tensor.numel() for tensor in tensors)
+    # Precondition: tensors always has at least one element.
+    #
+    # See NOTE: [Computing jacobian with vmap and grad for multiple tensors]
+    # for context behind this function. All the pre-conditions are guarded for
+    # in torch.autograd.functional.jacobian.
+    if len(tensors) != len(tensor_numels):
+        raise AssertionError(
+            f"Expected tensors and tensor_numels to have the same length, "
+            f"but got {len(tensors)} and {len(tensor_numels)}"
+        )
+    if len(tensors) == 0:
+        raise AssertionError("Expected at least one tensor")
+    total_numel = sum(tensor_numels)
+    chunks = tuple(
+        tensor.new_zeros(total_numel, tensor_numel)
+        for tensor, tensor_numel in zip(tensors, tensor_numels)
+    )
+    diag_start_idx = 0
+    for chunk, numel in zip(chunks, tensor_numels):
+        chunk.diagonal(diag_start_idx).fill_(1)
+        diag_start_idx -= numel
+    return chunks
+
+
+def _jacfwd(func, inputs, strict=False, vectorize=False):
+    if strict:
+        raise RuntimeError(
+            "torch.autograd.functional.jacobian: `strict=True` "
+            'and `strategy="forward-mode"` are not supported together (yet). '
+            "Please either set `strict=False` or "
+            '`strategy="reverse-mode"`.'
+        )
+    is_inputs_tuple, inputs = _as_tuple(inputs, "inputs", "jacobian")
+    output_info = []
+
+    if vectorize:
+        # See NOTE: [Computing jacobian with vmap and grad for multiple outputs]
+        input_numels = tuple(input.numel() for input in inputs)
+
+        # Step 1: Prepare tangents
+        tangents = _construct_standard_basis_for(inputs, input_numels)
+
+        # Step 2: Compute vmap over computation with dual tensors
+        def jvp(tangents):
+            with fwAD.dual_level():
+                dual_inputs = tuple(
+                    fwAD.make_dual(input, tangent.view_as(input))
+                    for input, tangent in zip(inputs, tangents)
+                )
+                _is_outputs_tuple, dual_outputs = _as_tuple(
+                    func(*dual_inputs), "outputs"
+                )
+                output_info.append(_is_outputs_tuple)
+                jv = []
+                primal_outs = []
+                for dual_out in dual_outputs:
+                    primal, tangent = fwAD.unpack_dual(dual_out)
+                    primal_outs.append(primal)
+                    if tangent is not None:
+                        jv.append(tangent)
+                    else:
+                        jv.append(torch.zeros_like(primal))
+                output_info.append(primal_outs)
+                return tuple(jv)
+
+        outputs_before_split = _vmap(jvp)(tangents)
+        is_outputs_tuple, outputs = output_info
+        # Step 3: for each of the output tangents, split along dim 0
+        jacobian_input_output = []
+        for jac_output_i, output_i in zip(outputs_before_split, outputs):
+            jacobian_output_i_output = []
+            for jac, input_j in zip(jac_output_i.split(input_numels, dim=0), inputs):
+                # We need to transpose the Jacobian because in forward AD, the
+                # batch dimension represents that of the inputs
+                jacobian_input_i_output_j = jac.permute(*range(1, jac.ndim), 0).reshape(
+                    (*output_i.shape, *input_j.shape)
+                )  # noqa: C409
+
+                jacobian_output_i_output.append(jacobian_input_i_output_j)
+            jacobian_input_output.append(jacobian_output_i_output)
+
+        # Omit [Step 4] because everything is already transposed w/ forward AD
+        return _tuple_postprocess(
+            jacobian_input_output, (is_outputs_tuple, is_inputs_tuple)
+        )
+    else:
+        raise NotImplementedError(
+            "Computing Jacobian using forward-AD or forward-over-reverse Hessian is"
+            "only implemented for `vectorize=True`."
+        )
+
+
+def jacobian(
+    func,
+    inputs,
+    create_graph=False,
+    strict=False,
+    vectorize=False,
+    strategy="reverse-mode",
+):
+    r"""Compute the Jacobian of a given function.
+
+    Args:
+        func (function): a Python function that takes Tensor inputs and returns
+            a tuple of Tensors or a Tensor.
+        inputs (tuple of Tensors or Tensor): inputs to the function ``func``.
+        create_graph (bool, optional): If ``True``, the Jacobian will be
+            computed in a differentiable manner. Note that when ``strict`` is
+            ``False``, the result can not require gradients or be disconnected
+            from the inputs.  Defaults to ``False``.
+        strict (bool, optional): If ``True``, an error will be raised when we
+            detect that there exists an input such that all the outputs are
+            independent of it. If ``False``, we return a Tensor of zeros as the
+            jacobian for said inputs, which is the expected mathematical value.
+            Defaults to ``False``.
+        vectorize (bool, optional): This feature is experimental.
+            Please consider using :func:`torch.func.jacrev` or
+            :func:`torch.func.jacfwd` instead if you are looking for something
+            less experimental and more performant.
+            When computing the jacobian, usually we invoke
+            ``autograd.grad`` once per row of the jacobian. If this flag is
+            ``True``, we perform only a single ``autograd.grad`` call with
+            ``batched_grad=True`` which uses the vmap prototype feature.
+            Though this should lead to performance improvements in many cases,
+            because this feature is still experimental, there may be performance
+            cliffs. See :func:`torch.autograd.grad`'s ``batched_grad`` parameter for
+            more information.
+        strategy (str, optional): Set to ``"forward-mode"`` or ``"reverse-mode"`` to
+            determine whether the Jacobian will be computed with forward or reverse
+            mode AD. Currently, ``"forward-mode"`` requires ``vectorized=True``.
+            Defaults to ``"reverse-mode"``. If ``func`` has more outputs than
+            inputs, ``"forward-mode"`` tends to be more performant. Otherwise,
+            prefer to use ``"reverse-mode"``.
+
+    Returns:
+        Jacobian (Tensor or nested tuple of Tensors): if there is a single
+        input and output, this will be a single Tensor containing the
+        Jacobian for the linearized inputs and output. If one of the two is
+        a tuple, then the Jacobian will be a tuple of Tensors. If both of
+        them are tuples, then the Jacobian will be a tuple of tuple of
+        Tensors where ``Jacobian[i][j]`` will contain the Jacobian of the
+        ``i``\th output and ``j``\th input and will have as size the
+        concatenation of the sizes of the corresponding output and the
+        corresponding input and will have same dtype and device as the
+        corresponding input. If strategy is ``forward-mode``, the dtype will be
+        that of the output; otherwise, the input.
+
+    Example:
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+        >>> def exp_reducer(x):
+        ...     return x.exp().sum(dim=1)
+        >>> inputs = torch.rand(2, 2)
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> jacobian(exp_reducer, inputs)
+        tensor([[[1.4917, 2.4352],
+                 [0.0000, 0.0000]],
+                [[0.0000, 0.0000],
+                 [2.4369, 2.3799]]])
+
+        >>> jacobian(exp_reducer, inputs, create_graph=True)
+        tensor([[[1.4917, 2.4352],
+                 [0.0000, 0.0000]],
+                [[0.0000, 0.0000],
+                 [2.4369, 2.3799]]], grad_fn=)
+
+        >>> def exp_adder(x, y):
+        ...     return 2 * x.exp() + 3 * y
+        >>> inputs = (torch.rand(2), torch.rand(2))
+        >>> jacobian(exp_adder, inputs)
+        (tensor([[2.8052, 0.0000],
+                [0.0000, 3.3963]]),
+         tensor([[3., 0.],
+                 [0., 3.]]))
+
+        >>> def linear_model(x):
+        ...     W = torch.tensor([[2.0, -1.0], [0.0, 1.0]])
+        ...     b = torch.tensor([1.0, 0.5])
+        ...     return x @ W.T + b
+
+        >>> x = torch.randn(4, 2, requires_grad=True)
+        >>> jac = jacobian(linear_model, x, vectorize=True)
+        >>> jac.shape
+        torch.Size([4, 2, 4, 2])
+    """
+    if strategy not in ("forward-mode", "reverse-mode"):
+        raise AssertionError(
+            'Expected strategy to be either "forward-mode" or "reverse-mode". Hint: If your '
+            'function has more outputs than inputs, "forward-mode" tends to be more performant. '
+            'Otherwise, prefer to use "reverse-mode".'
+        )
+    if strategy == "forward-mode":
+        if create_graph:
+            raise NotImplementedError(
+                "torch.autograd.functional.jacobian: `create_graph=True` "
+                'and `strategy="forward-mode"` are not supported together (yet). '
+                "Please either set `create_graph=False` or "
+                '`strategy="reverse-mode"`.'
+            )
+        return _jacfwd(func, inputs, strict, vectorize)
+
+    with torch.enable_grad():
+        is_inputs_tuple, inputs = _as_tuple(inputs, "inputs", "jacobian")
+        inputs = _grad_preprocess(inputs, create_graph=create_graph, need_graph=True)
+
+        outputs = func(*inputs)
+        is_outputs_tuple, outputs = _as_tuple(
+            outputs, "outputs of the user-provided function", "jacobian"
+        )
+        _check_requires_grad(outputs, "outputs", strict=strict)
+
+        if vectorize:
+            if strict:
+                raise RuntimeError(
+                    "torch.autograd.functional.jacobian: `strict=True` "
+                    "and `vectorized=True` are not supported together. "
+                    "Please either set `strict=False` or "
+                    "`vectorize=False`."
+                )
+            # NOTE: [Computing jacobian with vmap and grad for multiple outputs]
+            #
+            # Let's consider f(x) = (x**2, x.sum()) and let x = torch.randn(3).
+            # It turns out we can compute the jacobian of this function with a single
+            # call to autograd.grad by using vmap over the correct grad_outputs.
+            #
+            # Firstly, one way to compute the jacobian is to stack x**2 and x.sum()
+            # into a 4D vector. E.g., use g(x) = torch.stack([x**2, x.sum()])
+            #
+            # To get the first row of the jacobian, we call
+            # >>> autograd.grad(g(x), x, grad_outputs=torch.tensor([1, 0, 0, 0]))
+            # To get the 2nd row of the jacobian, we call
+            # >>> autograd.grad(g(x), x, grad_outputs=torch.tensor([0, 1, 0, 0]))
+            # and so on.
+            #
+            # Using vmap, we can vectorize all 4 of these computations into one by
+            # passing the standard basis for R^4 as the grad_output.
+            # vmap(partial(autograd.grad, g(x), x))(torch.eye(4)).
+            #
+            # Now, how do we compute the jacobian *without stacking the output*?
+            # We can just split the standard basis across the outputs. So to
+            # compute the jacobian of f(x), we'd use
+            # >>> autograd.grad(f(x), x, grad_outputs=_construct_standard_basis_for(...))
+            # The grad_outputs looks like the following:
+            # ( torch.tensor([[1, 0, 0],
+            #                 [0, 1, 0],
+            #                 [0, 0, 1],
+            #                 [0, 0, 0]]),
+            #   torch.tensor([[0],
+            #                 [0],
+            #                 [0],
+            #                 [1]]) )
+            #
+            # But we're not done yet!
+            # >>> vmap(partial(autograd.grad(f(x), x, grad_outputs=...)))
+            # returns a Tensor of shape [4, 3]. We have to remember to split the
+            # jacobian of shape [4, 3] into two:
+            # - one of shape [3, 3] for the first output
+            # - one of shape [   3] for the second output
+
+            # Step 1: Construct grad_outputs by splitting the standard basis
+            output_numels = tuple(output.numel() for output in outputs)
+            grad_outputs = _construct_standard_basis_for(outputs, output_numels)
+            flat_outputs = tuple(output.reshape(-1) for output in outputs)
+
+            # Step 2: Call vmap + autograd.grad
+            def vjp(grad_output):
+                vj = list(
+                    _autograd_grad(
+                        flat_outputs,
+                        inputs,
+                        grad_output,
+                        create_graph=create_graph,
+                        is_grads_batched=True,
+                    )
+                )
+                for el_idx, vj_el in enumerate(vj):
+                    if vj_el is not None:
+                        continue
+                    vj[el_idx] = torch.zeros_like(inputs[el_idx]).expand(
+                        (sum(output_numels),) + inputs[el_idx].shape
+                    )
+                return tuple(vj)
+
+            jacobians_of_flat_output = vjp(grad_outputs)
+
+            # Step 3: The returned jacobian is one big tensor per input. In this step,
+            # we split each Tensor by output.
+            jacobian_input_output = []
+            for jac_input_i, input_i in zip(jacobians_of_flat_output, inputs):
+                jacobian_input_i_output = []
+                for jac, output_j in zip(
+                    jac_input_i.split(output_numels, dim=0), outputs
+                ):
+                    jacobian_input_i_output_j = jac.view(output_j.shape + input_i.shape)
+                    jacobian_input_i_output.append(jacobian_input_i_output_j)
+                jacobian_input_output.append(jacobian_input_i_output)
+
+            # Step 4: Right now, `jacobian` is a List[List[Tensor]].
+            # The outer List corresponds to the number of inputs,
+            # the inner List corresponds to the number of outputs.
+            # We need to exchange the order of these and convert to tuples
+            # before returning.
+            jacobian_output_input = tuple(zip(*jacobian_input_output))
+
+            jacobian_output_input = _grad_postprocess(
+                jacobian_output_input, create_graph
+            )
+            return _tuple_postprocess(
+                jacobian_output_input, (is_outputs_tuple, is_inputs_tuple)
+            )
+
+        jacobian: tuple[torch.Tensor, ...] = ()
+
+        for i, out in enumerate(outputs):
+            # mypy complains that expression and variable have different types due to the empty list
+            jac_i: tuple[list[torch.Tensor]] = tuple([] for _ in range(len(inputs)))  # type: ignore[assignment]
+            for j in range(out.nelement()):
+                vj = _autograd_grad(
+                    (out.reshape(-1)[j],),
+                    inputs,
+                    retain_graph=True,
+                    create_graph=create_graph,
+                )
+
+                for el_idx, (jac_i_el, vj_el, inp_el) in enumerate(
+                    zip(jac_i, vj, inputs)
+                ):
+                    if vj_el is not None:
+                        if strict and create_graph and not vj_el.requires_grad:
+                            msg = (
+                                "The jacobian of the user-provided function is "
+                                f"independent of input {i}. This is not allowed in "
+                                "strict mode when create_graph=True."
+                            )
+                            raise RuntimeError(msg)
+                        jac_i_el.append(vj_el)
+                    else:
+                        if strict:
+                            msg = (
+                                f"Output {i} of the user-provided function is "
+                                f"independent of input {el_idx}. This is not allowed in "
+                                "strict mode."
+                            )
+                            raise RuntimeError(msg)
+                        jac_i_el.append(torch.zeros_like(inp_el))
+
+            jacobian += (
+                tuple(
+                    torch.stack(jac_i_el, dim=0).view(
+                        out.size() + inputs[el_idx].size()  # type: ignore[operator]
+                    )
+                    for (el_idx, jac_i_el) in enumerate(jac_i)
+                ),
+            )
+
+        jacobian = _grad_postprocess(jacobian, create_graph)
+
+        return _tuple_postprocess(jacobian, (is_outputs_tuple, is_inputs_tuple))
+
+
+def hessian(
+    func,
+    inputs,
+    create_graph=False,
+    strict=False,
+    vectorize=False,
+    outer_jacobian_strategy="reverse-mode",
+):
+    r"""Compute the Hessian of a given scalar function.
+
+    Args:
+        func (function): a Python function that takes Tensor inputs and returns
+            a Tensor with a single element.
+        inputs (tuple of Tensors or Tensor): inputs to the function ``func``.
+        create_graph (bool, optional): If ``True``, the Hessian will be computed in
+            a differentiable manner. Note that when ``strict`` is ``False``, the result can not
+            require gradients or be disconnected from the inputs.
+            Defaults to ``False``.
+        strict (bool, optional): If ``True``, an error will be raised when we detect that there exists an input
+            such that all the outputs are independent of it. If ``False``, we return a Tensor of zeros as the
+            hessian for said inputs, which is the expected mathematical value.
+            Defaults to ``False``.
+        vectorize (bool, optional): This feature is experimental.
+            Please consider using :func:`torch.func.hessian`
+            instead if you are looking for something less experimental and more performant.
+            When computing the hessian, usually we invoke
+            ``autograd.grad`` once per row of the hessian. If this flag is
+            ``True``, we use the vmap prototype feature as the backend to
+            vectorize calls to ``autograd.grad`` so we only invoke it once
+            instead of once per row. This should lead to performance
+            improvements in many use cases, however, due to this feature
+            being incomplete, there may be performance cliffs. Please
+            use `torch._C._debug_only_display_vmap_fallback_warnings(True)`
+            to show any performance warnings and file us issues if
+            warnings exist for your use case. Defaults to ``False``.
+        outer_jacobian_strategy (str, optional): The Hessian is computed by
+            computing the Jacobian of a Jacobian. The inner Jacobian is always
+            computed in reverse-mode AD. Setting strategy to ``"forward-mode"``
+            or ``"reverse-mode"`` determines whether the outer Jacobian will be
+            computed with forward or reverse mode AD. Currently, computing the outer
+            Jacobian in ``"forward-mode"`` requires ``vectorized=True``. Defaults
+            to ``"reverse-mode"``.
+
+    Returns:
+        Hessian (Tensor or a tuple of tuple of Tensors): if there is a single input,
+        this will be a single Tensor containing the Hessian for the input.
+        If it is a tuple, then the Hessian will be a tuple of tuples where
+        ``Hessian[i][j]`` will contain the Hessian of the ``i``\th input
+        and ``j``\th input with size the sum of the size of the ``i``\th input plus
+        the size of the ``j``\th input. ``Hessian[i][j]`` will have the same
+        dtype and device as the corresponding ``i``\th input.
+
+    Example:
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+        >>> def pow_reducer(x):
+        ...     return x.pow(3).sum()
+        >>> inputs = torch.rand(2, 2)
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> hessian(pow_reducer, inputs)
+        tensor([[[[5.2265, 0.0000],
+                  [0.0000, 0.0000]],
+                 [[0.0000, 4.8221],
+                  [0.0000, 0.0000]]],
+                [[[0.0000, 0.0000],
+                  [1.9456, 0.0000]],
+                 [[0.0000, 0.0000],
+                  [0.0000, 3.2550]]]])
+
+        >>> hessian(pow_reducer, inputs, create_graph=True)
+        tensor([[[[5.2265, 0.0000],
+                  [0.0000, 0.0000]],
+                 [[0.0000, 4.8221],
+                  [0.0000, 0.0000]]],
+                [[[0.0000, 0.0000],
+                  [1.9456, 0.0000]],
+                 [[0.0000, 0.0000],
+                  [0.0000, 3.2550]]]], grad_fn=)
+
+
+        >>> def pow_adder_reducer(x, y):
+        ...     return (2 * x.pow(2) + 3 * y.pow(2)).sum()
+        >>> inputs = (torch.rand(2), torch.rand(2))
+        >>> hessian(pow_adder_reducer, inputs)
+        ((tensor([[4., 0.],
+                  [0., 4.]]),
+          tensor([[0., 0.],
+                  [0., 0.]])),
+         (tensor([[0., 0.],
+                  [0., 0.]]),
+          tensor([[6., 0.],
+                  [0., 6.]])))
+    """
+    is_inputs_tuple, inputs = _as_tuple(inputs, "inputs", "hessian")
+    if outer_jacobian_strategy not in (
+        "forward-mode",
+        "reverse-mode",
+    ):
+        raise AssertionError(
+            'Expected strategy to be either "forward-mode" or "reverse-mode".'
+        )
+
+    def ensure_single_output_function(*inp):
+        out = func(*inp)
+        is_out_tuple, t_out = _as_tuple(
+            out, "outputs of the user-provided function", "hessian"
+        )
+        _check_requires_grad(t_out, "outputs", strict=strict)
+
+        if is_out_tuple or not isinstance(out, torch.Tensor):
+            raise RuntimeError(
+                "The function given to hessian should return a single Tensor"
+            )
+
+        if out.nelement() != 1:
+            raise RuntimeError(
+                "The Tensor returned by the function given to hessian should contain a single element"
+            )
+
+        return out.squeeze()
+
+    def jac_func(*inp):
+        if outer_jacobian_strategy == "forward-mode":
+            # _grad_preprocess requires create_graph=True and input to require_grad
+            # or else the input will be detached
+            inp = tuple(t.requires_grad_(True) for t in inp)
+        jac = jacobian(ensure_single_output_function, inp, create_graph=True)
+        _check_requires_grad(jac, "jacobian", strict=strict)
+        return jac
+
+    res = jacobian(
+        jac_func,
+        inputs,
+        create_graph=create_graph,
+        strict=strict,
+        vectorize=vectorize,
+        strategy=outer_jacobian_strategy,
+    )
+    return _tuple_postprocess(res, (is_inputs_tuple, is_inputs_tuple))
+
+
+def vhp(func, inputs, v=None, create_graph=False, strict=False):
+    r"""Compute the dot product between vector ``v`` and Hessian of a  given scalar function at a specified point.
+
+    Args:
+        func (function): a Python function that takes Tensor inputs and returns
+            a Tensor with a single element.
+        inputs (tuple of Tensors or Tensor): inputs to the function ``func``.
+        v (tuple of Tensors or Tensor): The vector for which the vector Hessian
+            product is computed. Must be the same size as the input of
+            ``func``. This argument is optional when ``func``'s input contains
+            a single element and (if it is not provided) will be set as a
+            Tensor containing a single ``1``.
+        create_graph (bool, optional): If ``True``, both the output and result
+            will be computed in a differentiable way. Note that when ``strict``
+            is ``False``, the result can not require gradients or be
+            disconnected from the inputs.
+            Defaults to ``False``.
+        strict (bool, optional): If ``True``, an error will be raised when we
+            detect that there exists an input such that all the outputs are
+            independent of it. If ``False``, we return a Tensor of zeros as the
+            vhp for said inputs, which is the expected mathematical value.
+            Defaults to ``False``.
+
+    Returns:
+        output (tuple): tuple with:
+            func_output (tuple of Tensors or Tensor): output of ``func(inputs)``
+
+            vhp (tuple of Tensors or Tensor): result of the dot product with the
+            same shape as the inputs.
+
+    Example:
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+        >>> def pow_reducer(x):
+        ...     return x.pow(3).sum()
+        >>> inputs = torch.rand(2, 2)
+        >>> v = torch.ones(2, 2)
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> vhp(pow_reducer, inputs, v)
+        (tensor(0.5591),
+         tensor([[1.0689, 1.2431],
+                 [3.0989, 4.4456]]))
+        >>> vhp(pow_reducer, inputs, v, create_graph=True)
+        (tensor(0.5591, grad_fn=),
+         tensor([[1.0689, 1.2431],
+                 [3.0989, 4.4456]], grad_fn=))
+        >>> def pow_adder_reducer(x, y):
+        ...     return (2 * x.pow(2) + 3 * y.pow(2)).sum()
+        >>> inputs = (torch.rand(2), torch.rand(2))
+        >>> v = (torch.zeros(2), torch.ones(2))
+        >>> vhp(pow_adder_reducer, inputs, v)
+        (tensor(4.8053),
+         (tensor([0., 0.]),
+          tensor([6., 6.])))
+    """
+    with torch.enable_grad():
+        is_inputs_tuple, inputs = _as_tuple(inputs, "inputs", "vhp")
+        inputs = _grad_preprocess(inputs, create_graph=create_graph, need_graph=True)
+
+        if v is not None:
+            _, v = _as_tuple(v, "v", "vhp")
+            v = _grad_preprocess(v, create_graph=create_graph, need_graph=False)
+            _validate_v(v, inputs, is_inputs_tuple)
+        else:
+            if len(inputs) != 1 or inputs[0].nelement() != 1:
+                raise RuntimeError(
+                    "The vector v can only be None if the input to the user-provided function "
+                    "is a single Tensor with a single element."
+                )
+        outputs = func(*inputs)
+        is_outputs_tuple, outputs = _as_tuple(
+            outputs, "outputs of the user-provided function", "vhp"
+        )
+        _check_requires_grad(outputs, "outputs", strict=strict)
+
+        if is_outputs_tuple or not isinstance(outputs[0], torch.Tensor):
+            raise RuntimeError(
+                "The function given to vhp should return a single Tensor"
+            )
+
+        if outputs[0].nelement() != 1:
+            raise RuntimeError(
+                "The Tensor returned by the function given to vhp should contain a single element"
+            )
+
+        jac = _autograd_grad(outputs, inputs, create_graph=True)
+        _check_requires_grad(jac, "jacobian", strict=strict)
+
+    enable_grad = True if create_graph else torch.is_grad_enabled()
+    with torch.set_grad_enabled(enable_grad):
+        grad_res = _autograd_grad(jac, inputs, v, create_graph=create_graph)
+        vhp = _fill_in_zeros(grad_res, inputs, strict, create_graph, "double_back")
+
+    outputs = _grad_postprocess(outputs, create_graph)
+    vhp = _grad_postprocess(vhp, create_graph)
+
+    return _tuple_postprocess(outputs, is_outputs_tuple), _tuple_postprocess(
+        vhp, is_inputs_tuple
+    )
+
+
+def hvp(func, inputs, v=None, create_graph=False, strict=False):
+    r"""Compute the dot product between the scalar function's Hessian and a vector ``v`` at a specified point.
+
+    Args:
+        func (function): a Python function that takes Tensor inputs and returns
+            a Tensor with a single element.
+        inputs (tuple of Tensors or Tensor): inputs to the function ``func``.
+        v (tuple of Tensors or Tensor): The vector for which the Hessian vector
+            product is computed. Must be the same size as the input of
+            ``func``. This argument is optional when ``func``'s input contains
+            a single element and (if it is not provided) will be set as a
+            Tensor containing a single ``1``.
+        create_graph (bool, optional): If ``True``, both the output and result will be
+            computed in a differentiable way. Note that when ``strict`` is
+            ``False``, the result can not require gradients or be disconnected
+            from the inputs.  Defaults to ``False``.
+        strict (bool, optional): If ``True``, an error will be raised when we
+            detect that there exists an input such that all the outputs are
+            independent of it. If ``False``, we return a Tensor of zeros as the
+            hvp for said inputs, which is the expected mathematical value.
+            Defaults to ``False``.
+    Returns:
+        output (tuple): tuple with:
+            func_output (tuple of Tensors or Tensor): output of ``func(inputs)``
+
+            hvp (tuple of Tensors or Tensor): result of the dot product with
+            the same shape as the inputs.
+
+    Example:
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+        >>> def pow_reducer(x):
+        ...     return x.pow(3).sum()
+        >>> inputs = torch.rand(2, 2)
+        >>> v = torch.ones(2, 2)
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> hvp(pow_reducer, inputs, v)
+        (tensor(0.1448),
+         tensor([[2.0239, 1.6456],
+                 [2.4988, 1.4310]]))
+
+        >>> hvp(pow_reducer, inputs, v, create_graph=True)
+        (tensor(0.1448, grad_fn=),
+         tensor([[2.0239, 1.6456],
+                 [2.4988, 1.4310]], grad_fn=))
+
+
+        >>> def pow_adder_reducer(x, y):
+        ...     return (2 * x.pow(2) + 3 * y.pow(2)).sum()
+        >>> inputs = (torch.rand(2), torch.rand(2))
+        >>> v = (torch.zeros(2), torch.ones(2))
+        >>> hvp(pow_adder_reducer, inputs, v)
+        (tensor(2.3030),
+         (tensor([0., 0.]),
+          tensor([6., 6.])))
+
+    Note:
+
+        This function is significantly slower than `vhp` due to backward mode AD constraints.
+        If your functions is twice continuously differentiable, then hvp = vhp.t(). So if you
+        know that your function satisfies this condition, you should use vhp instead that is
+        much faster with the current implementation.
+
+    """
+    with torch.enable_grad():
+        is_inputs_tuple, inputs = _as_tuple(inputs, "inputs", "hvp")
+        inputs = _grad_preprocess(inputs, create_graph=create_graph, need_graph=True)
+
+        if v is not None:
+            _, v = _as_tuple(v, "v", "hvp")
+            v = _grad_preprocess(v, create_graph=create_graph, need_graph=False)
+            _validate_v(v, inputs, is_inputs_tuple)
+        else:
+            if len(inputs) != 1 or inputs[0].nelement() != 1:
+                raise RuntimeError(
+                    "The vector v can only be None if the input to the user-provided function "
+                    "is a single Tensor with a single element."
+                )
+        outputs = func(*inputs)
+        is_outputs_tuple, outputs = _as_tuple(
+            outputs, "outputs of the user-provided function", "hvp"
+        )
+        _check_requires_grad(outputs, "outputs", strict=strict)
+
+        if is_outputs_tuple or not isinstance(outputs[0], torch.Tensor):
+            raise RuntimeError(
+                "The function given to hvp should return a single Tensor"
+            )
+
+        if outputs[0].nelement() != 1:
+            raise RuntimeError(
+                "The Tensor returned by the function given to hvp should contain a single element"
+            )
+
+        jac = _autograd_grad(outputs, inputs, create_graph=True)
+        _check_requires_grad(jac, "jacobian", strict=strict)
+
+        grad_jac = tuple(torch.zeros_like(inp, requires_grad=True) for inp in inputs)
+
+        double_back = _autograd_grad(jac, inputs, grad_jac, create_graph=True)
+        _check_requires_grad(jac, "hessian", strict=strict)
+
+    enable_grad = True if create_graph else torch.is_grad_enabled()
+    with torch.set_grad_enabled(enable_grad):
+        grad_res = _autograd_grad(double_back, grad_jac, v, create_graph=create_graph)
+        hvp = _fill_in_zeros(
+            grad_res, inputs, strict, create_graph, "double_back_trick"
+        )
+
+    outputs = _grad_postprocess(outputs, create_graph)
+    hvp = _grad_postprocess(hvp, create_graph)
+
+    return _tuple_postprocess(outputs, is_outputs_tuple), _tuple_postprocess(
+        hvp, is_inputs_tuple
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/grad_mode.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/grad_mode.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc882ec08cb76d2864e673e496f01af155ccc2a4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/grad_mode.py
@@ -0,0 +1,420 @@
+# mypy: allow-untyped-defs
+from typing import Any, Union
+
+import torch
+from torch.utils._contextlib import (
+    _DecoratorContextManager,
+    _NoParamDecoratorContextManager,
+    F,
+)
+
+
+__all__ = [
+    "no_grad",
+    "enable_grad",
+    "set_grad_enabled",
+    "inference_mode",
+    "set_multithreading_enabled",
+]
+
+
+class no_grad(_NoParamDecoratorContextManager):
+    r"""Context-manager that disables gradient calculation.
+
+    Disabling gradient calculation is useful for inference, when you are sure
+    that you will not call :meth:`Tensor.backward()`. It will reduce memory
+    consumption for computations that would otherwise have `requires_grad=True`.
+
+    In this mode, the result of every computation will have
+    `requires_grad=False`, even when the inputs have `requires_grad=True`.
+    There is an exception! All factory functions, or functions that create
+    a new Tensor and take a requires_grad kwarg, will NOT be affected by
+    this mode.
+
+    This context manager is thread local; it will not affect computation
+    in other threads.
+
+    Also functions as a decorator.
+
+    .. note::
+        No-grad is one of several mechanisms that can enable or
+        disable gradients locally see :ref:`locally-disable-grad-doc` for
+        more information on how they compare.
+
+    .. note::
+        This API does not apply to :ref:`forward-mode AD `.
+        If you want to disable forward AD for a computation, you can unpack
+        your dual tensors.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> x = torch.tensor([1.], requires_grad=True)
+        >>> with torch.no_grad():
+        ...     y = x * 2
+        >>> y.requires_grad
+        False
+        >>> @torch.no_grad()
+        ... def doubler(x):
+        ...     return x * 2
+        >>> z = doubler(x)
+        >>> z.requires_grad
+        False
+        >>> @torch.no_grad()
+        ... def tripler(x):
+        ...     return x * 3
+        >>> z = tripler(x)
+        >>> z.requires_grad
+        False
+        >>> # factory function exception
+        >>> with torch.no_grad():
+        ...     a = torch.nn.Parameter(torch.rand(10))
+        >>> a.requires_grad
+        True
+    """
+
+    def __init__(self) -> None:
+        if not torch._jit_internal.is_scripting():
+            super().__init__()
+        self.prev = False
+
+    def __enter__(self) -> None:
+        self.prev = torch.is_grad_enabled()
+        torch.set_grad_enabled(False)
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        torch.set_grad_enabled(self.prev)
+
+
+class enable_grad(_NoParamDecoratorContextManager):
+    r"""Context-manager that enables gradient calculation.
+
+    Enables gradient calculation, if it has been disabled via :class:`~no_grad`
+    or :class:`~set_grad_enabled`.
+
+    This context manager is thread local; it will not affect computation
+    in other threads.
+
+    Also functions as a decorator.
+
+    .. note::
+        enable_grad is one of several mechanisms that can enable or
+        disable gradients locally see :ref:`locally-disable-grad-doc` for
+        more information on how they compare.
+
+    .. note::
+        This API does not apply to :ref:`forward-mode AD `.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> x = torch.tensor([1.], requires_grad=True)
+        >>> with torch.no_grad():
+        ...     with torch.enable_grad():
+        ...         y = x * 2
+        >>> y.requires_grad
+        True
+        >>> y.backward()
+        >>> x.grad
+        tensor([2.])
+        >>> @torch.enable_grad()
+        ... def doubler(x):
+        ...     return x * 2
+        >>> with torch.no_grad():
+        ...     z = doubler(x)
+        >>> z.requires_grad
+        True
+        >>> @torch.enable_grad()
+        ... def tripler(x):
+        ...     return x * 3
+        >>> with torch.no_grad():
+        ...     z = tripler(x)
+        >>> z.requires_grad
+        True
+
+    """
+
+    def __enter__(self) -> None:
+        self.prev = torch.is_grad_enabled()
+        torch._C._set_grad_enabled(True)
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        torch._C._set_grad_enabled(self.prev)
+
+
+class set_grad_enabled(_DecoratorContextManager):
+    r"""Context-manager that sets gradient calculation on or off.
+
+    ``set_grad_enabled`` will enable or disable grads based on its argument :attr:`mode`.
+    It can be used as a context-manager or as a function.
+
+    This context manager is thread local; it will not affect computation
+    in other threads.
+
+    Args:
+        mode (bool): Flag whether to enable grad (``True``), or disable
+                     (``False``). This can be used to conditionally enable
+                     gradients.
+
+    .. note::
+        set_grad_enabled is one of several mechanisms that can enable or
+        disable gradients locally see :ref:`locally-disable-grad-doc` for
+        more information on how they compare.
+
+    .. note::
+        This API does not apply to :ref:`forward-mode AD `.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> x = torch.tensor([1.], requires_grad=True)
+        >>> is_train = False
+        >>> with torch.set_grad_enabled(is_train):
+        ...     y = x * 2
+        >>> y.requires_grad
+        False
+        >>> _ = torch.set_grad_enabled(True)
+        >>> y = x * 2
+        >>> y.requires_grad
+        True
+        >>> _ = torch.set_grad_enabled(False)
+        >>> y = x * 2
+        >>> y.requires_grad
+        False
+
+    """
+
+    def __init__(self, mode: bool) -> None:
+        self.prev = torch.is_grad_enabled()
+        self.mode = mode
+        torch._C._set_grad_enabled(mode)
+
+    def __call__(self, orig_func: F) -> F:
+        torch._C._set_grad_enabled(self.prev)
+        return super().__call__(orig_func)
+
+    def __enter__(self) -> None:
+        torch._C._set_grad_enabled(self.mode)
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        torch._C._set_grad_enabled(self.prev)
+
+    def __str__(self) -> str:
+        return f"{torch.typename(self)}(mode={self.mode})"
+
+    def __repr__(self) -> str:
+        return str(self)
+
+    def clone(self) -> "set_grad_enabled":
+        r"""
+        Create a copy of this class
+        """
+        return self.__class__(self.mode)
+
+
+class inference_mode(_DecoratorContextManager):
+    r"""Context manager that enables or disables inference mode.
+
+    InferenceMode is analogous to :class:`~no_grad` and should be used
+    when you are certain your operations will not interact with autograd
+    (e.g., during data loading or model evaluation). Compared to
+    :class:`~no_grad`, it removes additional overhead by disabling view
+    tracking and version counter bumps. It is also more restrictive, in
+    that tensors created in this mode cannot be used in computations
+    recorded by autograd.
+
+    This context manager is thread-local; it does not affect computation
+    in other threads.
+
+    Also functions as a decorator.
+
+    .. note::
+        Inference mode is one of several mechanisms that can locally enable
+        or disable gradients. See :ref:`locally-disable-grad-doc` for a
+        comparison. If avoiding the use of tensors created in inference mode
+        in autograd-tracked regions is difficult, consider benchmarking your
+        code with and without inference mode to weigh the performance benefits
+        against the trade-offs. You can always use :class:`~no_grad` instead.
+
+    .. note::
+       Unlike some other mechanisms that locally enable or disable grad,
+       entering inference_mode also disables :ref:`forward-mode AD `.
+
+    .. warning::
+        `inference_mode` does NOT automatically set the model to evaluation mode.
+        For proper inference behavior (e.g., disabling dropout, using running statistics
+        in batch normalization), you must explicitly set your model to evaluation mode using
+        `model.eval()` in addition to using this context manager.
+
+    Args:
+        mode (bool or function): Either a boolean flag to enable or disable
+            inference mode, or a Python function to decorate with inference
+            mode enabled.
+
+    Example::
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+        >>> import torch
+        >>> x = torch.ones(1, 2, 3, requires_grad=True)
+        >>> with torch.inference_mode():
+        ...     y = x * x
+        >>> y.requires_grad
+        False
+        >>> # xdoctest: +SKIP("want string isn't quite right")
+        >>> y._version
+        Traceback (most recent call last):
+        File "", line 1, in 
+        RuntimeError: Inference tensors do not track version counter.
+        >>> @torch.inference_mode()
+        ... def func(x):
+        ...     return x * x
+        >>> out = func(x)
+        >>> out.requires_grad
+        False
+        >>> @torch.inference_mode()
+        ... def doubler(x):
+        ...     return x * 2
+        >>> out = doubler(x)
+        >>> out.requires_grad
+        False
+
+    """
+
+    def __init__(self, mode: bool = True) -> None:
+        if not torch._jit_internal.is_scripting():
+            super().__init__()
+        self.mode = mode
+
+    def __new__(cls, mode=True):
+        if isinstance(mode, bool):
+            return super().__new__(cls)
+        return cls()(mode)
+
+    def __enter__(self) -> None:
+        self._inference_mode_context = torch._C._InferenceMode(self.mode)
+        self._inference_mode_context.__enter__()
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        self._inference_mode_context.__exit__(exc_type, exc_value, traceback)
+
+    def clone(self) -> "inference_mode":
+        r"""
+        Create a copy of this class
+        """
+        return self.__class__(self.mode)
+
+
+def _enter_inference_mode(mode):
+    mode_context = torch._C._InferenceMode(mode)
+    mode_context.__enter__()
+    return mode_context
+
+
+def _exit_inference_mode(mode):
+    mode.__exit__(None, None, None)
+
+
+class set_multithreading_enabled(_DecoratorContextManager):
+    r"""Context-manager that sets multithreaded backwards on or off.
+
+    ``set_multithreading_enabled`` will enable or disable multithreaded backwards based on its argument :attr:`mode`.
+    It can be used as a context-manager or as a function.
+
+    This context manager is thread local; it will not affect computation
+    in other threads.
+
+    Args:
+        mode (bool): Flag whether to enable multithreaded backwards (``True``), or disable
+                     (``False``).
+
+    .. note::
+        This API does not apply to :ref:`forward-mode AD `.
+
+    """
+
+    def __init__(self, mode: bool) -> None:
+        self.prev = torch._C._is_multithreading_enabled()
+        torch._C._set_multithreading_enabled(mode)
+        self.mode = mode
+
+    def __enter__(self) -> None:
+        pass
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        torch._C._set_multithreading_enabled(self.prev)
+
+    def clone(self) -> "set_multithreading_enabled":
+        r"""
+        Create a copy of this class
+        """
+        return self.__class__(self.mode)
+
+
+class _force_original_view_tracking(_DecoratorContextManager):
+    r"""Context-manager that sets whether or not to always enable view-replay in autograd.
+
+    ``set_view_replay_enabled`` will enable or disable view-replay based on its argument :attr:`mode`.
+    It can be used as a context-manager or as a function.
+
+    This context manager is thread local; it will not affect computation
+    in other threads.
+
+    When a tensor view is mutated, the autograd engine needs to decide whether or not
+    to regenerate the "updated view" by either replaying the chain of views from the updated base,
+    or with a single call to as_strided.
+
+    If set_view_replay_enabled is set to True, then autograd will always use view replay.
+    Otherwise, it will fall back to its existing logic.
+
+    Args:
+        mode (bool): Flag whether to enable view-replay (``True``), or disable
+                     (``False``).
+
+    """
+
+    def __init__(self, mode: bool) -> None:
+        self.prev = torch._C._is_view_replay_enabled()
+        torch._C._set_view_replay_enabled(mode)
+        self.mode = mode
+
+    def __enter__(self) -> None:
+        pass
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        torch._C._set_view_replay_enabled(self.prev)
+
+    def clone(self):
+        return self.__class__(self.mode)
+
+
+class _unsafe_preserve_version_counter(_DecoratorContextManager):
+    r"""DO NOT USE THIS UNLESS YOU KNOW EXACTLY WHAT YOU'RE DOING.
+
+    This context manager can lead to arbitrary silent-correctness issues in any other part of your code
+    (even the ones not touched directly by the context manager)!
+
+    Ordinarily, autograd will track mutations to tensors by incrementing it's `._version` attribute.
+    This is generally important for correctness, as for example, mutating a tensor that autograd has saved
+    for the backwards pass can result in incorrect gradients, and autograd uses the version counter to detect
+    and error out in this situation.
+
+    However, there are rare instances where it might be useful to hide mutations from autograd. For example:
+    if a tensor is very large, and you'd like to free its memory by storing it elsewhere, and re-populate
+    the tensor right before it is needed by autograd.
+
+    Args:
+        tensor (torch.Tensor): the tensor in question, that you would like to preserve the version counter of.
+
+    .. note::
+        This API does not apply to :ref:`forward-mode AD `.
+
+    """
+
+    def __init__(self, tensors: Union[torch.Tensor, tuple[torch.Tensor, ...]]) -> None:
+        self.tensors = (tensors,) if isinstance(tensors, torch.Tensor) else tensors
+        if not isinstance(self.tensors, tuple):
+            raise AssertionError("Expected tensors to be a tuple")
+        self.prev_versions = tuple(t._version for t in self.tensors)
+
+    def __enter__(self) -> None:
+        pass
+
+    # pyrefly: ignore [bad-override]
+    def __exit__(self, *args) -> None:
+        torch._C._autograd._unsafe_set_version_counter(self.tensors, self.prev_versions)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/gradcheck.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/gradcheck.py
new file mode 100644
index 0000000000000000000000000000000000000000..053be3450d6d226431f3b70b582f627658e319f2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/gradcheck.py
@@ -0,0 +1,2306 @@
+# mypy: allow-untyped-defs
+import collections
+import functools
+import warnings
+from collections.abc import Callable, Iterable
+from itertools import product
+from typing import Optional, Union
+from typing_extensions import deprecated
+
+import torch
+import torch.testing
+
+# pyrefly: ignore [deprecated]
+from torch._vmap_internals import _vmap, vmap
+from torch.overrides import is_tensor_like
+from torch.types import _TensorOrTensors
+
+
+# Note: `get_*_jacobian` functions are added here even though we didn't intend to make them public
+# since they have been exposed from before we added `__all__`  and we already maintain BC for them
+# We should eventually deprecate them and remove them from `__all__`
+__all__ = [
+    "gradcheck",
+    "gradgradcheck",
+    "GradcheckError",
+    "get_numerical_jacobian",
+    "get_analytical_jacobian",
+    "get_numerical_jacobian_wrt_specific_input",
+]
+
+
+class GradcheckError(RuntimeError):
+    r"""Error raised by :func:`gradcheck` and :func:`gradgradcheck`."""
+
+
+def _is_sparse_compressed_tensor(obj: torch.Tensor):
+    return obj.layout in {
+        torch.sparse_csr,
+        torch.sparse_csc,
+        torch.sparse_bsr,
+        torch.sparse_bsc,
+    }
+
+
+def _is_sparse_any_tensor(obj: torch.Tensor):
+    return _is_sparse_compressed_tensor(obj) or obj.layout is torch.sparse_coo
+
+
+def _is_float_or_complex_tensor(obj):
+    return is_tensor_like(obj) and (obj.is_floating_point() or obj.is_complex())
+
+
+def _allocate_jacobians_with_inputs(
+    input_tensors: tuple, numel_output
+) -> tuple[torch.Tensor, ...]:
+    # Makes zero-filled tensors from inputs. If `numel_output` is not None, for
+    # each tensor in `input_tensors`, returns a new zero-filled tensor with height
+    # of `t.numel` and width of `numel_output`. Otherwise, for each tensor, returns
+    # a 1-d tensor with size `(t.numel,)`. Each new tensor will be strided and have
+    # the same dtype and device as those of the corresponding input.
+    out: list[torch.Tensor] = [
+        t.new_zeros((t.numel(), numel_output), layout=torch.strided)
+        for t in input_tensors
+        if _is_float_or_complex_tensor(t) and t.requires_grad
+    ]
+    return tuple(out)
+
+
+def _allocate_jacobians_with_outputs(
+    output_tensors: tuple, numel_input, dtype=None, device=None
+) -> tuple[torch.Tensor, ...]:
+    # Makes zero-filled tensors from outputs. If `dim` is not None, for each tensor
+    # in `output_tensors`, returns a new zero-filled tensor with height of `dim` and
+    # width of `t.numel`. Otherwise, for each tensor, returns a 1-d tensor with size
+    # (t.numel,).
+    options = {"dtype": dtype, "device": device, "layout": torch.strided}
+    out: list[torch.Tensor] = [
+        t.new_zeros((numel_input, t.numel()), **options)
+        for t in output_tensors
+        if _is_float_or_complex_tensor(t)
+    ]
+    return tuple(out)
+
+
+def _iter_tensors(
+    x: Union[torch.Tensor, Iterable[torch.Tensor]], only_requiring_grad: bool = False
+) -> Iterable[torch.Tensor]:
+    if is_tensor_like(x):
+        # mypy doesn't narrow type of `x` to torch.Tensor
+        if x.requires_grad or not only_requiring_grad:  # type: ignore[union-attr]
+            yield x  # type: ignore[misc]
+    elif isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
+        for elem in x:
+            yield from _iter_tensors(elem, only_requiring_grad)
+
+
+def _densify(x):
+    # return a copy of sparse x with all unspecified elements
+    # "replaced" with zero-valued elements
+    if isinstance(x, (list, tuple)):
+        return type(x)(map(_densify, x))
+    elif not is_tensor_like(x) or x.layout in {torch.strided, torch._mkldnn}:  # type: ignore[attr-defined] # no attr _mkldnn
+        return x
+    elif x.layout is torch.sparse_coo:
+        device = x.device
+        indices_dtype = x._indices().dtype
+        tmp = torch.ones(x.shape[: x.sparse_dim()], dtype=torch.int8, device=device)
+        indices = tmp.nonzero().t().to(dtype=indices_dtype)
+        values = torch.zeros(
+            (tmp.numel(), *x.shape[x.sparse_dim() :]), dtype=x.dtype, device=device
+        )
+        x_coalesced = x.detach().coalesce()
+        if x_coalesced.numel() > 0:
+            stride = tmp.stride()
+            flat_indices = (
+                x_coalesced.indices()
+                .mul(
+                    torch.tensor(stride, dtype=indices_dtype, device=device).unsqueeze(
+                        1
+                    )
+                )
+                .sum(0)
+            )
+            values[flat_indices] = x_coalesced.values()
+        return (
+            torch.sparse_coo_tensor(indices, values, x.shape)
+            ._coalesced_(True)
+            .requires_grad_(x.requires_grad)
+        )
+    elif _is_sparse_compressed_tensor(x):
+        blocksize = (
+            x.values().shape[1:3]
+            if x.layout in {torch.sparse_bsr, torch.sparse_bsc}
+            else None
+        )
+        compressed_indices = (
+            x.crow_indices()
+            if x.layout in {torch.sparse_csr, torch.sparse_bsr}
+            else x.ccol_indices()
+        )
+        # We'll use intermediate sparse COO for simplicity
+        r = _densify(x.detach().to_sparse(layout=torch.sparse_coo)).to_sparse(
+            layout=x.layout, blocksize=blocksize
+        )
+        # Check that all elements are specified also after `to_sparse` op:
+        dense_numel = r.values().numel() // max(1, r.values().shape[0])
+        batch_numel = compressed_indices.numel() // compressed_indices.shape[-1]
+        sparse_numel = r.numel() // max(1, dense_numel * batch_numel)
+        if sparse_numel != r._nnz():
+            raise AssertionError(
+                f"{x.layout} densify failed: expected nnz={sparse_numel} but got {r._nnz()}"
+            )
+        return r.requires_grad_(x.requires_grad)
+    elif _is_sparse_any_tensor(x):
+        raise NotImplementedError(x.layout)
+    return x
+
+
+def _iter_tensor(x_tensor):
+    # (Only used for slow gradcheck) Returns a generator that yields the following
+    # elements at each iteration:
+    #  1) a tensor: the same tensor is returned across all iterations. The tensor
+    #     is not the same as the original x_tensor as given as input - it is
+    #     prepared so that it can be modified in-place. Depending on whether the
+    #     input tensor is strided, sparse, or dense, the returned tensor may or may
+    #     not share storage with x_tensor.
+    #  2) a tuple of indices that can be used with advanced indexing (yielded in
+    #     dictionary order)
+    #  3) flattened index that will be used to index into the Jacobian tensor
+    #
+    # For a tensor t with size (2, 2), _iter_tensor yields:
+    #     `x, (0, 0), 0`, `x, (0, 1), 1`, `x, (1, 0), 2`, `x, (1, 1), 3`
+    #
+    # where x is the t.data of the original tensor. Perturbing the entry of x
+    # at index (1, 1) yields the 3rd column of the overall Jacobian matrix.
+    if _is_sparse_any_tensor(x_tensor):
+
+        def get_stride(size):
+            dim = len(size)
+            tmp = 1
+            stride = [0] * dim
+            for i in reversed(range(dim)):
+                stride[i] = tmp
+                tmp *= size[i]
+            return stride
+
+        x_nnz = x_tensor._nnz()
+        x_size = list(x_tensor.size())
+        if x_tensor.layout is torch.sparse_coo:
+            x_indices = x_tensor._indices().t()
+            x_values = x_tensor._values()
+        elif x_tensor.layout is torch.sparse_csr:
+            x_indices = torch._convert_indices_from_csr_to_coo(
+                x_tensor.crow_indices(), x_tensor.col_indices()
+            ).t()
+            x_values = x_tensor.values()
+        elif x_tensor.layout is torch.sparse_csc:
+            x_indices = torch._convert_indices_from_csr_to_coo(
+                x_tensor.ccol_indices(), x_tensor.row_indices(), transpose=True
+            ).t()
+            x_values = x_tensor.values()
+        elif x_tensor.layout is torch.sparse_bsr:
+            x_block_values = x_tensor.values()
+            x_blocksize = x_block_values.size()[1:3]
+            x_indices = (
+                torch._convert_indices_from_csr_to_coo(
+                    x_tensor.crow_indices(), x_tensor.col_indices()
+                )
+                .repeat_interleave(x_blocksize[0] * x_blocksize[1], 1)
+                .mul_(torch.tensor(x_blocksize, device=x_tensor.device).reshape(2, 1))
+                .add_(
+                    torch.stack(
+                        torch.where(torch.ones(x_blocksize, device=x_tensor.device))
+                    ).repeat(1, x_nnz)
+                )
+                .t()
+            )
+            x_values = x_block_values.flatten(0, 2)
+            x_nnz = x_values.size(0)
+        elif x_tensor.layout is torch.sparse_bsc:
+            x_block_values = x_tensor.values()
+            x_blocksize = x_block_values.size()[1:3]
+            x_indices = (
+                torch._convert_indices_from_csr_to_coo(
+                    x_tensor.ccol_indices(), x_tensor.row_indices(), transpose=True
+                )
+                .repeat_interleave(x_blocksize[0] * x_blocksize[1], 1)
+                .mul_(torch.tensor(x_blocksize, device=x_tensor.device).reshape(2, 1))
+                .add_(
+                    torch.stack(
+                        torch.where(torch.ones(x_blocksize, device=x_tensor.device))
+                    ).repeat(1, x_nnz)
+                )
+                .t()
+            )
+            x_values = x_block_values.flatten(0, 2)
+            x_nnz = x_values.size(0)
+        else:
+            raise NotImplementedError(f"_iter_tensor for {x_tensor.layout} input")
+        x_stride = get_stride(x_size)
+        # Use .data here to get around the version check
+        x_values = x_values.data
+        for i in range(x_nnz):
+            x_value = x_values[i]
+            for x_idx in product(*[range(m) for m in x_values.size()[1:]]):
+                indices = x_indices[i].tolist() + list(x_idx)
+                d_idx = sum(indices[k] * x_stride[k] for k in range(len(x_size)))
+                yield x_value, x_idx, d_idx
+    elif x_tensor.layout == torch._mkldnn:  # type: ignore[attr-defined]
+        for d_idx, x_idx in enumerate(product(*[range(m) for m in x_tensor.size()])):
+            # this is really inefficient, but without indexing implemented, there's
+            # not really a better way than converting back and forth
+            x_tensor_dense = x_tensor.to_dense()
+            yield x_tensor_dense, x_idx, d_idx
+    else:
+        # Use .data here to get around the version check
+        x_tensor = x_tensor.data
+        for d_idx, x_idx in enumerate(product(*[range(m) for m in x_tensor.size()])):
+            yield x_tensor, x_idx, d_idx
+
+
+def _get_numerical_jacobian(
+    fn, inputs, outputs=None, target=None, eps=1e-3, is_forward_ad=False
+) -> list[tuple[torch.Tensor, ...]]:
+    """Compute the numerical Jacobian of `fn(inputs)` with respect to `target`.
+
+    If not specified, targets are the input. Returns M * N Jacobians where N is the
+    number of tensors in target that require grad and M is the number of non-integral
+    outputs.
+
+    Args:
+        fn: the function to compute the jacobian for
+        inputs: inputs to `fn`
+        outputs: provide precomputed outputs to avoid one extra invocation of fn
+        target: the Tensors wrt whom Jacobians are calculated (default=`inputs`)
+        eps: the magnitude of the perturbation during finite differencing
+             (default=`1e-3`)
+        is_forward_ad: if this numerical jacobian is computed to be checked wrt
+                       forward AD gradients (this is used for error checking only)
+
+    Returns:
+        A list of M N-tuples of tensors
+
+    Note that `target` may not even be part of `input` to `fn`, so please be
+    **very careful** in this to not clone `target`.
+    """
+    jacobians: list[tuple[torch.Tensor, ...]] = []
+    if outputs is None:
+        outputs = _as_tuple(fn(*_as_tuple(inputs)))
+    if not is_forward_ad and any(o.is_complex() for o in outputs):
+        raise ValueError(
+            "Expected output to be non-complex. get_numerical_jacobian no "
+            "longer supports functions that return complex outputs."
+        )
+    if target is None:
+        target = inputs
+    inp_indices = [
+        i for i, a in enumerate(target) if is_tensor_like(a) and a.requires_grad
+    ]
+    for inp, inp_idx in zip(_iter_tensors(target, True), inp_indices):
+        jacobians += [
+            get_numerical_jacobian_wrt_specific_input(
+                fn,
+                inp_idx,
+                inputs,
+                outputs,
+                eps,
+                input=inp,
+                is_forward_ad=is_forward_ad,
+            )
+        ]
+    return jacobians
+
+
+@deprecated(
+    "`get_numerical_jacobian` was part of PyTorch's private API and not "
+    "meant to be exposed. We are deprecating it and it will be removed "
+    "in a future version of PyTorch. If you have a specific use for "
+    "this or feature request for this to be a stable API, please file "
+    "us an issue at https://github.com/pytorch/pytorch/issues/new",
+    category=FutureWarning,
+)
+def get_numerical_jacobian(fn, inputs, target=None, eps=1e-3, grad_out=1.0):
+    """Compute the numerical Jacobian for a given fn and its inputs.
+
+    This is a Deprecated API.
+
+    Args:
+        fn: the function to compute the Jacobian for (must take inputs as a tuple)
+        inputs: input to `fn`
+        target: the Tensors wrt whom Jacobians are calculated (default=`input`)
+        eps: the magnitude of the perturbation during finite differencing
+             (default=`1e-3`)
+        grad_out: defaults to 1.0.
+
+    Returns:
+        A list of Jacobians of `fn` (restricted to its first output) with respect to
+        each input or target, if provided.
+
+    Note that `target` may not even be part of `input` to `fn`, so please be
+    **very careful** in this to not clone `target`.
+    """
+    if (
+        grad_out != 1.0
+    ):  # grad_out param is only kept for backward compatibility reasons
+        raise ValueError(
+            "Expected grad_out to be 1.0. get_numerical_jacobian no longer "
+            "supports values of grad_out != 1.0."
+        )
+
+    def fn_pack_inps(*inps):
+        return fn(inps)
+
+    jacobians = _get_numerical_jacobian(fn_pack_inps, inputs, None, target, eps)
+
+    return tuple(jacobian_for_each_output[0] for jacobian_for_each_output in jacobians)
+
+
+def _compute_numerical_gradient(fn, entry, v, norm_v, nbhd_checks_fn):
+    # Computes numerical directional derivative as finite difference
+    # of function `fn` at input `entry`, perturbed by vector `v`.
+    if _is_sparse_compressed_tensor(entry):
+        # sparse compressed tensors don't implement sub/add/copy_
+        # yet. However, in non-masked semantics context entry and v
+        # have the same sparse indices ...
+        if entry.layout != v.layout:
+            raise AssertionError(
+                f"Expected entry and v to have the same layout, but got {entry.layout} and {v.layout}"
+            )
+        if entry._nnz() != v._nnz():
+            raise AssertionError(
+                f"Expected entry and v to have the same nnz, but got {entry._nnz()} and {v._nnz()} "
+                f"with entry shape {entry.shape}"
+            )
+        # ... the finite differencing can be performed on values only:
+        entry = entry.values()
+        v = v.values()
+        # we'll detach to avoid backward computations that sparse
+        # tensors have limited support for.
+        entry = entry.detach()
+
+    orig = entry.clone()
+    entry.copy_(orig - v)
+    outa = fn()
+    entry.copy_(orig + v)
+    outb = fn()
+    entry.copy_(orig)
+
+    def compute(a, b):
+        nbhd_checks_fn(a, b)
+        ret = (b - a) / (2 * norm_v)  # use central difference approx
+        return ret.detach().reshape(-1)
+
+    return tuple(compute(a, b) for (a, b) in zip(outa, outb))
+
+
+def _compute_numerical_jvps_wrt_specific_input(
+    jvp_fn, delta, input_is_complex, is_forward_ad=False
+) -> list[torch.Tensor]:
+    # Computing the jacobian only works for real delta
+    # For details on the algorithm used here, refer:
+    # Section 3.5.3 https://arxiv.org/pdf/1701.00392.pdf
+    # s = fn(z) where z = x for real valued input
+    # and z = x + yj for complex valued input
+    jvps: list[torch.Tensor] = []
+    ds_dx_tup = jvp_fn(delta[0] if isinstance(delta, tuple) else delta)
+
+    if input_is_complex:  # C -> R
+        ds_dy_tup = (
+            jvp_fn(delta[1] * 1j) if isinstance(delta, tuple) else jvp_fn(delta * 1j)
+        )
+        for ds_dx, ds_dy in zip(ds_dx_tup, ds_dy_tup):
+            if ds_dx.is_complex():
+                raise AssertionError("Expected ds_dx to be real-valued, not complex")
+            # conjugate wirtinger derivative
+            conj_w_d = ds_dx + ds_dy * 1j
+            jvps.append(conj_w_d)
+    else:
+        for ds_dx in ds_dx_tup:  # R -> R or (R -> C for the forward AD case)
+            if not is_forward_ad and ds_dx.is_complex():
+                raise AssertionError("Expected ds_dx to be real-valued, not complex.")
+            jvps.append(ds_dx)
+    return jvps
+
+
+def _combine_jacobian_cols(
+    jacobians_cols: dict[int, list[torch.Tensor]], outputs, input, numel
+) -> tuple[torch.Tensor, ...]:
+    # jacobian_cols maps column_idx -> output_idx -> single column of jacobian Tensor
+    # we return a list that maps output_idx -> full jacobian Tensor
+    jacobians = _allocate_jacobians_with_outputs(
+        outputs, numel, dtype=input.dtype if input.dtype.is_complex else None
+    )
+    for i, jacobian in enumerate(jacobians):
+        for k, v in jacobians_cols.items():
+            jacobian[k] = v[i]
+    return jacobians
+
+
+def _prepare_input(
+    input: torch.Tensor, maybe_perturbed_input: Optional[torch.Tensor], fast_mode=False
+) -> torch.Tensor:
+    # Prepares the inputs to be passed into the function while including the new
+    # modified input.
+    if input.layout == torch._mkldnn:  # type: ignore[attr-defined] # no attr _mkldnn
+        # Convert back to mkldnn
+        if maybe_perturbed_input is not None:
+            return maybe_perturbed_input.to_mkldnn()
+        else:
+            return input
+    elif _is_sparse_any_tensor(input):
+        if fast_mode and maybe_perturbed_input is not None:
+            # entry is already a "cloned" version of the original tensor
+            # thus changes to entry are not reflected in the input
+            return maybe_perturbed_input
+        else:
+            return input
+    else:
+        # We cannot use entry (input.data) if we want gradgrad to work because
+        # fn (in the gradgrad case) needs to compute grad wrt input
+        return input
+
+
+def _check_outputs_same_dtype_and_shape(output1, output2, eps, idx=None) -> None:
+    # Check that the returned outputs don't have different dtype or shape when you
+    # perturb the input
+    on_index = f"on index {idx} " if idx is not None else ""
+    if output1.shape != output2.shape:
+        raise AssertionError(
+            f"Expected `func` to return outputs with the same shape"
+            f" when inputs are perturbed {on_index}by {eps}, but got:"
+            f" shapes {output1.shape} and {output2.shape}."
+        )
+    if output1.dtype != output2.dtype:
+        raise AssertionError(
+            f"Expected `func` to return outputs with the same dtype"
+            f" when inputs are perturbed {on_index}by {eps}, but got:"
+            f" dtypes {output1.dtype} and {output2.dtype}."
+        )
+
+
+def get_numerical_jacobian_wrt_specific_input(
+    fn, input_idx, inputs, outputs, eps, input=None, is_forward_ad=False
+) -> tuple[torch.Tensor, ...]:
+    # Computes the numerical jacobians wrt to a single input. Returns N jacobian
+    # tensors, where N is the number of outputs. We use a dictionary for
+    # jacobian_cols because indices aren't necessarily consecutive for sparse inputs
+    # When we perturb only a single element of the input tensor at a time, the jvp
+    # is equivalent to a single col of the Jacobian matrix of fn.
+    jacobian_cols: dict[int, list[torch.Tensor]] = {}
+    input = inputs[input_idx] if input is None else input
+    if not input.requires_grad:
+        raise AssertionError("Expected input to have requires_grad=True")
+    for x, idx, d_idx in _iter_tensor(input):
+        wrapped_fn = _with_prepare_inputs(fn, inputs, input_idx, x)
+        input_to_perturb = x[idx]
+        nbhd_checks_fn = functools.partial(
+            _check_outputs_same_dtype_and_shape, idx=idx, eps=eps
+        )
+        jvp_fn = _get_numerical_jvp_fn(
+            wrapped_fn, input_to_perturb, eps, nbhd_checks_fn
+        )
+        jacobian_cols[d_idx] = _compute_numerical_jvps_wrt_specific_input(
+            jvp_fn, eps, x.is_complex(), is_forward_ad
+        )
+    return _combine_jacobian_cols(jacobian_cols, outputs, input, input.numel())
+
+
+def _get_analytical_jacobian_forward_ad(
+    fn, inputs, outputs, *, check_grad_dtypes=False, all_u=None
+) -> tuple[tuple[torch.Tensor, ...], ...]:
+    """Compute the analytical Jacobian using forward mode AD of `fn(inputs)` using forward mode AD with respect to `target`.
+
+    Return N * M Jacobians where N is the number of tensors in target that require grad and
+    M is the number of non-integral outputs.
+    Contrary to other functions here, this function requires "inputs" to actually be used by the function.
+    The computed value is expected to be wrong if the function captures the inputs by side effect instead of
+    using the passed ones (many torch.nn tests do this).
+
+    Args:
+        fn: the function to compute the jacobian for
+        inputs: inputs to `fn`
+        outputs: provide precomputed outputs to avoid one extra invocation of fn
+        check_grad_dtypes: if True, will check that the gradient dtype are valid
+        all_u (optional): if provided, the Jacobian will be right multiplied with this vector
+
+    Returns:
+        A tuple of M N-tuples of tensors
+    """
+    # To avoid early import issues
+    fwAD = torch.autograd.forward_ad
+
+    tensor_inputs = tuple(i for i in inputs if is_tensor_like(i) and i.requires_grad)
+
+    if any(i.is_complex() for i in tensor_inputs):
+        raise ValueError(
+            "Expected inputs to be non-complex for _get_analytical_jacobian_forward_ad."
+        )
+
+    if all_u:
+        jacobians = tuple(
+            _allocate_jacobians_with_outputs(outputs, 1) for i in tensor_inputs
+        )
+    else:
+        jacobians = tuple(
+            _allocate_jacobians_with_outputs(outputs, i.numel()) for i in tensor_inputs
+        )
+
+    with fwAD.dual_level():
+        fw_grads = []
+        dual_inputs = []
+        for inp in inputs:
+            if is_tensor_like(inp) and inp.requires_grad:
+                if inp.layout == torch._mkldnn:  # type: ignore[attr-defined]
+                    raise ValueError(
+                        "MKLDNN inputs are not support for forward AD gradcheck."
+                    )
+
+                inp = fwAD.make_dual(inp.detach(), torch.zeros_like(inp))
+                # If inp is a differentiable view, the dual might not be the tangent given to
+                # make_dual, so read it explicitly from the dual tensor
+                fw_grads.append(fwAD.unpack_dual(inp)[1])
+            dual_inputs.append(inp)
+
+        if all_u:
+            # Do the full reduction in one pass
+            # To be consistent with numerical evaluation, we actually compute one reduction per input
+            for i, (fw_grad, u) in enumerate(zip(fw_grads, all_u)):
+                fw_grad.copy_(u.view_as(fw_grad))
+                raw_outputs = _as_tuple(fn(*dual_inputs))
+                dual_outputs = filter(_is_float_or_complex_tensor, raw_outputs)
+                for index_o, d_o in enumerate(dual_outputs):
+                    val, res = fwAD.unpack_dual(d_o)
+                    if (
+                        check_grad_dtypes
+                        and res is not None
+                        and val.is_complex() != res.is_complex()
+                    ):
+                        raise GradcheckError("Forward AD gradient has dtype mismatch.")
+
+                    # Remove extra dimension of size 1 corresponding to the reduced input
+                    jacobians[i][index_o].squeeze_(0)
+                    if res is None:
+                        jacobians[i][index_o].zero_()
+                    else:
+                        jacobians[i][index_o].copy_(res.reshape(-1))
+                fw_grad.zero_()
+        else:
+            # Reconstruct the full Jacobian column by column
+            for i, fw_grad in enumerate(fw_grads):
+                for lin_idx, grad_idx in enumerate(
+                    product(*[range(m) for m in fw_grad.size()])
+                ):
+                    fw_grad[grad_idx] = 1.0
+                    raw_outputs = _as_tuple(fn(*dual_inputs))
+                    dual_outputs = filter(_is_float_or_complex_tensor, raw_outputs)
+                    for index_o, d_o in enumerate(dual_outputs):
+                        val, res = fwAD.unpack_dual(d_o)
+                        if (
+                            check_grad_dtypes
+                            and res is not None
+                            and val.is_complex() != res.is_complex()
+                        ):
+                            raise GradcheckError(
+                                "Forward AD gradient has dtype mismatch."
+                            )
+
+                        if res is None:
+                            jacobians[i][index_o][lin_idx].zero_()
+                        else:
+                            jacobians[i][index_o][lin_idx].copy_(res.reshape(-1))
+                    fw_grad[grad_idx] = 0.0
+
+    return jacobians
+
+
+def _get_input_to_perturb(input):
+    # Prepare the input so that it can be modified in-place and do certain
+    # operations that require the tensor to have strides. If fast_mode=False,
+    # _iter_tensor would handle the below cases:
+    if input.layout == torch._mkldnn:  # type: ignore[attr-defined] # no attr _mkldnn
+        # Convert to dense so we can perform operations that require strided tensors
+        input_to_perturb = input.to_dense()
+    elif _is_sparse_any_tensor(input):
+        # Clone because input may require grad, and copy_ calls resize_,
+        # which is not allowed for .data
+        input_to_perturb = input.clone()
+    else:
+        input_to_perturb = input.data
+    return input_to_perturb
+
+
+def _with_prepare_inputs(fn, inputs, input_idx, input_to_perturb, fast_mode=False):
+    # Wraps `fn` so that its inputs are already supplied
+    def wrapped_fn():
+        inp = tuple(
+            _prepare_input(a, input_to_perturb if i == input_idx else None, fast_mode)
+            if is_tensor_like(a)
+            else a
+            for i, a in enumerate(_as_tuple(inputs))
+        )
+        return tuple(a.clone() for a in _as_tuple(fn(*inp)))
+
+    return wrapped_fn
+
+
+def _get_numerical_jvp_fn(wrapped_fn, input_to_perturb, eps, nbhd_checks_fn):
+    # Wraps jvp_fn so that certain arguments are already supplied
+    def jvp_fn(delta):
+        return _compute_numerical_gradient(
+            wrapped_fn, input_to_perturb, delta, eps, nbhd_checks_fn
+        )
+
+    return jvp_fn
+
+
+def _reshape_tensor_or_tuple(u, shape):
+    # We don't need to reshape when input corresponding to u is sparse
+    if isinstance(u, tuple):
+        if not _is_sparse_any_tensor(u[0]):
+            return (u[0].reshape(shape), u[1].reshape(shape))
+    else:
+        if not _is_sparse_any_tensor(u):
+            return u.reshape(shape)
+    return u
+
+
+def _mul_tensor_or_tuple(u, k):
+    if isinstance(u, tuple):
+        return (k * u[0], k * u[1])
+    else:
+        return k * u
+
+
+def _get_numerical_jvp_wrt_specific_input(
+    fn, input_idx, inputs, u, eps, is_forward_ad=False
+) -> list[torch.Tensor]:
+    input = inputs[input_idx]
+    input_to_perturb = _get_input_to_perturb(input)
+    wrapped_fn = _with_prepare_inputs(fn, inputs, input_idx, input_to_perturb, True)
+    nbhd_checks_fn = functools.partial(_check_outputs_same_dtype_and_shape, eps=eps)
+    jvp_fn = _get_numerical_jvp_fn(wrapped_fn, input_to_perturb, eps, nbhd_checks_fn)
+    u = _reshape_tensor_or_tuple(u, input_to_perturb.shape)
+    u = _mul_tensor_or_tuple(u, eps)
+    return _compute_numerical_jvps_wrt_specific_input(
+        jvp_fn, u, input.is_complex(), is_forward_ad
+    )
+
+
+def _get_numerical_vJu(
+    fn, inputs, inp_indices, func_out, all_u, all_v, eps, is_forward_ad
+):
+    # Note that all_v can also be None, in that case, this function only computes Ju.
+    reduced_jacobians: list[list[torch.Tensor]] = []
+    for inp_idx, u in zip(inp_indices, all_u):
+        all_Ju = _get_numerical_jvp_wrt_specific_input(
+            fn, inp_idx, inputs, u, eps, is_forward_ad
+        )
+        # Filter out the Ju for non floating point outputs
+        filtered_Ju = []
+        func_out = _as_tuple(func_out)
+        if len(all_Ju) != len(func_out):
+            raise AssertionError(
+                f"Expected all_Ju and func_out to have the same length, "
+                f"but got {len(all_Ju)} and {len(func_out)}"
+            )
+        for Ju, output in zip(all_Ju, func_out):
+            if _is_float_or_complex_tensor(output):
+                filtered_Ju.append(Ju)
+            else:
+                # TODO: handle the other Ju
+                pass
+        if all_v is not None:
+            jacobian_scalars: list[torch.Tensor] = []
+            for v, Ju in zip(all_v, filtered_Ju):
+                jacobian_scalars.append(_dot_with_type_promotion(v, Ju))
+            reduced_jacobians.append(jacobian_scalars)
+        else:
+            reduced_jacobians.append(filtered_Ju)
+    return reduced_jacobians
+
+
+def _check_jacobians_equal(j1, j2, atol):
+    # Check whether the max difference between two Jacobian tensors are within some
+    # tolerance `atol`.
+    for j1_x, j2_x in zip(j1, j2):
+        if j1_x.numel() != 0 and (j1_x - j2_x).abs().max() > atol:
+            return False
+    return True
+
+
+def _stack_and_check_tensors(
+    list_of_list_of_tensors, inputs, numel_outputs
+) -> tuple[tuple[torch.Tensor, ...], bool, bool]:
+    # For the ith tensor in the inner list checks whether it has the same size and
+    # dtype as the ith differentiable input.
+    out_jacobians = _allocate_jacobians_with_inputs(inputs, numel_outputs)
+    diff_input_list = list(_iter_tensors(inputs, True))
+    correct_grad_sizes = True
+    correct_grad_types = True
+    for i, tensor_list in enumerate(list_of_list_of_tensors):
+        inp = diff_input_list[i]
+        out_jacobian = out_jacobians[i]
+        for j, tensor in enumerate(tensor_list):
+            if tensor is not None and tensor.size() != inp.size():
+                correct_grad_sizes = False
+            elif tensor is not None and tensor.dtype != inp.dtype:
+                correct_grad_types = False
+            if tensor is None:
+                out_jacobian[:, j].zero_()
+            else:
+                dense = tensor.to_dense() if tensor.layout != torch.strided else tensor
+                if out_jacobian[:, j].numel() != dense.numel():
+                    raise AssertionError(
+                        f"Expected out_jacobian column to have {dense.numel()} elements, "
+                        f"but got {out_jacobian[:, j].numel()}"
+                    )
+                out_jacobian[:, j] = dense.reshape(-1)
+    return out_jacobians, correct_grad_sizes, correct_grad_types
+
+
+FAILED_NONDET_MSG = """\n
+NOTE: If your op relies on non-deterministic operations i.e., it is listed here:
+https://pytorch.org/docs/stable/generated/torch.use_deterministic_algorithms.html
+this failure might be expected.
+
+If you are adding a new operator, please file an issue and then use one of the
+workarounds. The workaround depends on how your test invokes gradcheck/gradgradcheck.
+If the test
+- manually invokes gradcheck/gradgradcheck, then call gradcheck/gradgradcheck
+  with `nondet_tol=` as a keyword argument.
+- is OpInfo-based (e.g., in test_ops_gradients.py), then modify the OpInfo for the test
+  to have `gradcheck_nondet_tol=`.
+- is a Module test (e.g., in common_nn.py), then modify the corresponding
+  module_test entry to have `gradcheck_nondet_tol=`
+"""
+
+
+def _check_analytical_jacobian_attributes(
+    inputs, output, nondet_tol, check_grad_dtypes, fast_mode=False, v=None
+) -> tuple[torch.Tensor, ...]:
+    # This is used by both fast and slow mode:
+    #  - For slow mode, vjps[i][j] is the jth row of the Jacobian wrt the ith
+    #    input.
+    #  - For fast mode, vjps[i][0] is a linear combination of the rows
+    #    of the Jacobian wrt the ith input
+    diff_input_list = list(_iter_tensors(inputs, True))
+
+    def vjp_fn(grad_output):
+        return torch.autograd.grad(
+            output, diff_input_list, grad_output, retain_graph=True, allow_unused=True
+        )
+
+    # Compute everything twice to check for nondeterminism (which we call reentrancy)
+    if fast_mode:
+        vjps1 = _get_analytical_vjps_wrt_specific_output(vjp_fn, output.clone(), v)
+        vjps2 = _get_analytical_vjps_wrt_specific_output(vjp_fn, output.clone(), v)
+    else:
+        vjps1 = _compute_analytical_jacobian_rows(vjp_fn, output.clone())
+        vjps2 = _compute_analytical_jacobian_rows(vjp_fn, output.clone())
+
+    output_numel = output.numel() if not fast_mode else 1
+    jacobians1, types_ok, sizes_ok = _stack_and_check_tensors(
+        vjps1, inputs, output_numel
+    )
+    jacobians2, _, _ = _stack_and_check_tensors(vjps2, inputs, output_numel)
+    reentrant = _check_jacobians_equal(jacobians1, jacobians2, nondet_tol)
+
+    if not types_ok and check_grad_dtypes:
+        raise GradcheckError("Gradient has dtype mismatch")
+    if not sizes_ok:
+        raise GradcheckError("Analytical gradient has incorrect size")
+    if not reentrant:
+        raise GradcheckError(
+            "Backward is not reentrant, i.e., running backward with "
+            "same input and grad_output multiple times gives different values, "
+            "although analytical gradient matches numerical gradient."
+            f"The tolerance for nondeterminism was {nondet_tol}." + FAILED_NONDET_MSG
+        )
+    return jacobians1
+
+
+def _get_analytical_vJu_backward_mode(
+    inputs, outputs, nondet_tol, check_grad_dtypes, all_v, all_u
+):
+    reduced_jacobians: list[list[torch.Tensor]] = []
+    for output, v in zip(outputs, all_v):
+        all_vJ = _check_analytical_jacobian_attributes(
+            inputs, output, nondet_tol, check_grad_dtypes, fast_mode=True, v=v
+        )
+        jacobian_scalars: list[torch.Tensor] = []
+        for vJ, u in zip(all_vJ, all_u):
+            # Why do we need squeeze here? vJ is a 2-d tensor so that we can reuse
+            # the error checking logic from slow mode
+            vJ = vJ.T.squeeze(0)
+            if vJ.is_complex():  # C -> R
+                tv = torch.view_as_real(vJ.resolve_conj())
+                tr = tv.select(-1, 0)
+                ti = tv.select(-1, 1)
+                jacobian_scalars.append(tr.dot(u[0]) + 1j * ti.dot(u[1]))
+            else:  # R -> R
+                jacobian_scalars.append(vJ.dot(u))
+        reduced_jacobians.append(jacobian_scalars)
+    return reduced_jacobians
+
+
+@deprecated(
+    "`get_analytical_jacobian` was part of PyTorch's private API and not "
+    "meant to be exposed. We are deprecating it and it will be removed "
+    "in a future version of PyTorch. If you have a specific use for "
+    "this or feature request for this to be a stable API, please file "
+    "us an issue at https://github.com/pytorch/pytorch/issues/new",
+    category=FutureWarning,
+)
+def get_analytical_jacobian(inputs, output, nondet_tol=0.0, grad_out=1.0):
+    # Replicates the behavior of the old get_analytical_jacobian before the refactor
+    # This shares much of its code with _check_analytical_jacobian_attributes
+    if (
+        grad_out != 1.0
+    ):  # grad_out param is only kept for backward compatibility reasons
+        raise ValueError(
+            "Expected grad_out to be 1.0. get_analytical_jacobian no longer "
+            "supports values of grad_out != 1.0."
+        )
+    if output.is_complex():
+        raise ValueError(
+            "Expected output to be non-complex. get_analytical_jacobian no "
+            "longer supports functions that return complex outputs."
+        )
+    diff_input_list = list(_iter_tensors(inputs, True))
+
+    def vjp_fn(grad_output):
+        return torch.autograd.grad(
+            output, diff_input_list, grad_output, retain_graph=True, allow_unused=True
+        )
+
+    # Compute everything twice to check for nondeterminism (which we call reentrancy)
+    vjps1 = _compute_analytical_jacobian_rows(vjp_fn, output.clone())
+    vjps2 = _compute_analytical_jacobian_rows(vjp_fn, output.clone())
+
+    output_numel = output.numel()
+    jacobians1, types_ok, sizes_ok = _stack_and_check_tensors(
+        vjps1, inputs, output_numel
+    )
+    jacobians2, _, _ = _stack_and_check_tensors(vjps2, inputs, output_numel)
+    reentrant = _check_jacobians_equal(jacobians1, jacobians2, nondet_tol)
+
+    return jacobians1, reentrant, sizes_ok, types_ok
+
+
+def _get_analytical_jacobian(inputs, outputs, input_idx, output_idx):
+    # Computes the analytical Jacobian in slow mode for a single input-output pair.
+    # Forgoes performing checks on dtype, shape, and reentrancy.
+    jacobians = _check_analytical_jacobian_attributes(
+        inputs, outputs[output_idx], nondet_tol=float("inf"), check_grad_dtypes=False
+    )
+    return jacobians[input_idx]
+
+
+def _compute_analytical_jacobian_rows(
+    vjp_fn, sample_output
+) -> list[list[Optional[torch.Tensor]]]:
+    # Computes Jacobian row-by-row by projecting `vjp_fn` = v^T J on standard basis
+    # vectors: vjp_fn(e) = e^T J is a corresponding row of the Jacobian.
+    # NB: this function does not assume vjp_fn(v) to return tensors with the same
+    # number of elements for different v. This is checked when we later combine the
+    # rows into a single tensor.
+    grad_out_base = torch.zeros_like(
+        sample_output, memory_format=torch.legacy_contiguous_format
+    )
+    flat_grad_out = grad_out_base.view(-1)
+    # jacobians_rows[i][j] is the Jacobian jth row for the ith input
+    jacobians_rows: list[list[Optional[torch.Tensor]]] = []
+    for j in range(flat_grad_out.numel()):
+        flat_grad_out.zero_()
+        flat_grad_out[j] = 1.0  # projection for jth row of Jacobian
+        grad_inputs = vjp_fn(grad_out_base)
+        for i, d_x in enumerate(grad_inputs):
+            if j == 0:
+                jacobians_rows.append([])
+            jacobians_rows[i] += [
+                d_x.clone() if isinstance(d_x, torch.Tensor) else None
+            ]
+    return jacobians_rows
+
+
+def _get_analytical_vjps_wrt_specific_output(
+    vjp_fn, sample_output, v
+) -> list[list[Optional[torch.Tensor]]]:
+    grad_inputs = vjp_fn(v.reshape(sample_output.shape))
+    vjps: list[list[Optional[torch.Tensor]]] = [
+        [vjp.clone() if isinstance(vjp, torch.Tensor) else None] for vjp in grad_inputs
+    ]
+    return vjps
+
+
+def _check_inputs(tupled_inputs) -> bool:
+    # Make sure that gradients are saved for at least one input
+    any_input_requiring_grad = False
+    for idx, inp in enumerate(tupled_inputs):
+        if is_tensor_like(inp) and inp.requires_grad:
+            if not (inp.dtype == torch.float64 or inp.dtype == torch.complex128):
+                warnings.warn(
+                    f"Input #{idx} requires gradient and "
+                    "is not a double precision floating point or complex. "
+                    "This check will likely fail if all the inputs are "
+                    "not of double precision floating point or complex. ",
+                    stacklevel=2,
+                )
+            if inp.is_sparse:
+                content = inp._values()
+            elif _is_sparse_compressed_tensor(inp):
+                content = inp.values()
+            else:
+                content = inp
+            # TODO: To cover more problematic cases, replace stride = 0 check with
+            # "any overlap in memory" once we have a proper function to check it.
+            if content.layout is not torch._mkldnn:  # type: ignore[attr-defined]
+                if not all(
+                    st > 0 or sz <= 1
+                    for st, sz in zip(content.stride(), content.size())
+                ):
+                    raise RuntimeError(
+                        f"The {idx}th input has a dimension with stride 0. gradcheck only "
+                        "supports inputs that are non-overlapping to be able to "
+                        "compute the numerical gradients correctly. You should call "
+                        ".contiguous on the input before passing it to gradcheck."
+                    )
+            any_input_requiring_grad = True
+
+    if not any_input_requiring_grad:
+        raise ValueError(
+            "gradcheck expects at least one input tensor to require gradient, "
+            "but none of the them have requires_grad=True."
+        )
+    return True
+
+
+def _check_outputs(outputs) -> None:
+    if any(_is_sparse_any_tensor(t) for t in outputs if isinstance(t, torch.Tensor)):
+        # it is easier to call to_dense() on the sparse output than
+        # to modify analytical jacobian
+        raise ValueError(
+            "Sparse output is not supported at gradcheck yet. "
+            "Please call to_dense(masked_grad=...) on the output of fn for gradcheck."
+        )
+    if any(t.layout == torch._mkldnn for t in outputs if isinstance(t, torch.Tensor)):  # type: ignore[attr-defined]
+        raise ValueError(
+            "MKLDNN output is not supported at gradcheck yet. "
+            "Please call to_dense(masked_grad=...) on the output of fn for gradcheck."
+        )
+
+
+def _check_no_differentiable_outputs(
+    func, inputs, func_out, eps, *, is_forward_ad
+) -> bool:
+    # When there are no differentiable outputs, numerical gradient for a function is
+    # expected to be zero.
+    jacobians_all_inputs_outputs = _get_numerical_jacobian(
+        func, inputs, func_out, eps=eps, is_forward_ad=is_forward_ad
+    )
+    for jacobians_all_outputs_and_fixed_input in jacobians_all_inputs_outputs:
+        for jacobian in jacobians_all_outputs_and_fixed_input:
+            if torch.ne(jacobian, 0).sum() > 0:
+                raise GradcheckError(
+                    "Numerical gradient for function expected to be zero"
+                )
+    return True
+
+
+def _check_no_differentiable_outputs_fast(
+    func, func_out, all_inputs, inputs_indices, all_u, eps, nondet_tol
+):
+    for inp_idx, u in zip(inputs_indices, all_u):
+        jvps = _get_numerical_jvp_wrt_specific_input(func, inp_idx, all_inputs, u, eps)
+        for jvp in jvps:
+            if jvp.numel() == 0:
+                continue
+            if (jvp - torch.zeros_like(jvp)).abs().max() > nondet_tol:
+                raise GradcheckError(
+                    "Numerical gradient for function expected to be zero"
+                )
+    return True
+
+
+FAILED_BATCHED_GRAD_MSG = """
+gradcheck or gradgradcheck failed while testing batched gradient computation.
+This could have been invoked in a number of ways (via a test that calls
+gradcheck/gradgradcheck directly or via an autogenerated test).
+
+If you are adding a new operator, please file an issue and then use one of the
+workarounds. The workaround depends on how your test invokes gradcheck/gradgradcheck.
+If the test
+- manually invokes gradcheck/gradgradcheck, then call gradcheck/gradgradcheck
+  with `check_batched_grad=False` as a keyword argument.
+- is OpInfo-based (e.g., in test_ops_gradients.py), then modify the OpInfo for the test
+  to have `check_batched_grad=False` and/or `check_batched_gradgrad=False`.
+
+If you're modifying an existing operator that supports batched grad computation,
+or wish to make a new operator work with batched grad computation, please read
+the following.
+
+To compute batched grads (e.g., jacobians, hessians), we vmap over the backward
+computation. The most common failure case is if there is a 'vmap-incompatible
+operation' in the backward pass. Please see
+NOTE: [How to write vmap-compatible backward formulas]
+in the codebase for an explanation of how to fix this.
+""".strip()
+
+FAILED_BATCHED_GRAD_MSG_FWD_AD = """
+gradcheck failed while testing batched gradient computation with forward-mode AD.
+This test is enabled automatically when both `check_batched_grad=True`
+and `check_forward_ad=True`, but can be disabled in the following ways
+dependong on how the test was invoked (via a test that calls gradcheck
+directly or via an autogenerated test).
+
+If you are adding a new operator, please file an issue and then use one of the
+workarounds. The workaround depends on how your test invokes gradcheck/gradgradcheck.
+If the test
+- manually invokes gradcheck/gradgradcheck, then call gradcheck/gradgradcheck
+  with `check_batched_forward_grad=False` as a keyword argument.
+- is OpInfo-based (e.g., in test_ops_gradients.py), then modify the OpInfo for the test
+  to have `check_batched_forward_grad=False`
+"""
+
+
+def _get_failed_batched_grad_test_msg(
+    output_idx, input_idx, res, exp, is_forward_ad=False
+):
+    return f"""
+For output {output_idx} and input {input_idx}:
+
+{FAILED_BATCHED_GRAD_MSG_FWD_AD if is_forward_ad else FAILED_BATCHED_GRAD_MSG}
+
+Got:
+{res}
+
+Expected:
+{exp}
+""".strip()
+
+
+def _test_batched_grad_forward_ad(func, inputs) -> bool:
+    fwAD = torch.autograd.forward_ad  # To avoid early import issues (do we need this?)
+    if not isinstance(inputs, tuple):
+        raise AssertionError("Expected inputs to be a tuple")
+
+    for input_idx, current_input in enumerate(inputs):
+        if not (is_tensor_like(current_input) and current_input.requires_grad):
+            continue
+
+        def jvp(tangent: torch.Tensor):
+            with fwAD.dual_level():
+                dual = fwAD.make_dual(current_input.detach(), tangent)
+                inputs_with_dual = tuple(
+                    dual
+                    if idx == input_idx
+                    else (inp.detach() if is_tensor_like(inp) else inp)
+                    for idx, inp in enumerate(inputs)
+                )
+                dual_outputs = _as_tuple(func(*inputs_with_dual))
+                ret = []
+                for dual_output in dual_outputs:
+                    if dual_output is None:
+                        continue
+                    primal_out, tangent_out = fwAD.unpack_dual(dual_output)
+                    if tangent_out is not None:
+                        ret.append(tangent_out)
+                    else:
+                        ret.append(
+                            torch.zeros(
+                                [], dtype=primal_out.dtype, device=primal_out.device
+                            ).expand(primal_out.shape)
+                        )
+                return tuple(ret)
+
+        if not _is_float_or_complex_tensor(current_input):
+            continue
+
+        tangents = [torch.randn_like(current_input) for _ in range(2)]
+        expected = [jvp(t) for t in tangents]
+        expected = [torch.stack(shards) for shards in zip(*expected)]
+
+        try:
+            result = _vmap(jvp)(torch.stack(tangents))
+        except RuntimeError as ex:
+            # Rethrow to provide a better error message
+            raise GradcheckError(
+                f"While computing batched gradients, got: {ex}\n\n{FAILED_BATCHED_GRAD_MSG_FWD_AD}"
+            ) from ex
+
+        for input_idx, (res, exp) in enumerate(zip(result, expected)):
+            if torch.allclose(res, exp):
+                continue
+            raise GradcheckError(
+                _get_failed_batched_grad_test_msg(
+                    input_idx, input_idx, res, exp, is_forward_ad=True
+                )
+            )
+    return True
+
+
+def _test_batched_grad(input, output, output_idx) -> bool:
+    # NB: _test_batched_grad compares two autograd.grad invocations with a single
+    # vmap(autograd.grad) invocation. It's not exactly a "gradcheck" in the
+    # sense that we're not comparing an analytical jacobian with a numeric one,
+    # but it is morally similar (we could have computed a full analytic jac
+    # via vmap, but that is potentially slow)
+    diff_input_list = list(_iter_tensors(input, True))
+    grad = functools.partial(
+        torch.autograd.grad,
+        output,
+        diff_input_list,
+        retain_graph=True,
+        allow_unused=True,
+    )
+
+    def vjp(v):
+        results = grad(v)
+        results = tuple(
+            grad
+            if grad is not None
+            else torch.zeros([], dtype=inp.dtype, device=inp.device).expand(inp.shape)
+            for grad, inp in zip(results, diff_input_list)
+        )
+        return results
+
+    grad_outputs = [torch.randn_like(output) for _ in range(2)]
+
+    expected = [vjp(gO) for gO in grad_outputs]
+    expected = [torch.stack(shards) for shards in zip(*expected)]
+
+    # Squash warnings since these are expected to happen in most cases
+    # NB: this doesn't work for CUDA tests: https://github.com/pytorch/pytorch/issues/50209
+    with warnings.catch_warnings():
+        warnings.filterwarnings("ignore", message="There is a performance drop")
+        warnings.filterwarnings("ignore", message="Please use `torch.vmap`")
+        try:
+            result = vmap(vjp)(torch.stack(grad_outputs))
+        except RuntimeError as ex:
+            # It's OK that we're not raising the error at the correct callsite.
+            # That's because the callsite is always going to inside the Python
+            # autograd.grad instead of the C++ traceback of what line in the
+            # backward formula
+            raise GradcheckError(
+                f"While computing batched gradients, got: {ex}\n\n{FAILED_BATCHED_GRAD_MSG}"
+            ) from ex
+
+    for input_idx, (res, exp) in enumerate(zip(result, expected)):
+        if torch.allclose(res, exp):
+            continue
+        raise GradcheckError(
+            _get_failed_batched_grad_test_msg(output_idx, input_idx, res, exp)
+        )
+    return True
+
+
+def _test_backward_mul_by_grad_output(outputs, inputs, masked) -> bool:
+    # Tests that backward is multiplied by grad_output
+    diff_input_list: list[torch.Tensor] = list(_iter_tensors(inputs, True))
+    if not diff_input_list:
+        raise GradcheckError("no Tensors requiring grad found in input")
+    grads_input = torch.autograd.grad(
+        outputs,
+        diff_input_list,
+        [
+            torch.zeros_like(o, memory_format=torch.legacy_contiguous_format)
+            for o in outputs
+        ],
+        allow_unused=True,
+    )
+    for gi, di in zip(grads_input, diff_input_list):
+        if gi is None:
+            continue
+        if isinstance(gi, torch.Tensor) and gi.layout != torch.strided:
+            if gi.layout != di.layout:
+                raise GradcheckError(
+                    "grad is incorrect layout ("
+                    + str(gi.layout)
+                    + " is not "
+                    + str(di.layout)
+                    + ")"
+                )
+            if _is_sparse_any_tensor(gi):
+                sparse_kind = str(gi.layout).replace("torch.", "").replace("_coo", "")
+                if gi.sparse_dim() != di.sparse_dim():
+                    raise GradcheckError(
+                        f"grad is {sparse_kind} tensor, but has incorrect sparse_dim"
+                        f" {gi.sparse_dim()}, expected {di.sparse_dim()}"
+                    )
+                if gi.dense_dim() != di.dense_dim():
+                    raise GradcheckError(
+                        f"grad is {sparse_kind} tensor, but has incorrect dense_dim"
+                        f" {gi.dense_dim()}, expected {di.dense_dim()}"
+                    )
+            gi = gi.to_dense()
+            di = di.to_dense()
+        if masked:
+            if not torch.allclose(gi, torch.zeros_like(gi)):
+                raise GradcheckError("backward not multiplied by grad_output")
+        elif not gi.eq(0).all():
+            raise GradcheckError("backward not multiplied by grad_output")
+        if gi.dtype != di.dtype:
+            raise GradcheckError("grad is incorrect type")
+        if gi.device != di.device:
+            raise GradcheckError("grad is incorrect device")
+        if gi.size() != di.size():
+            raise GradcheckError("grad is incorrect size")
+    return True
+
+
+def _test_undefined_forward_mode(func, outputs, inputs):
+    fwAD = torch.autograd.forward_ad
+
+    _inp_tensors_idx, inp_tensors = _get_inp_tensors(inputs)
+    _all_v, all_u, _all_u_dense = _make_vectors(
+        inp_tensors, outputs, use_forward_ad=True
+    )
+
+    with fwAD.dual_level():
+        fw_grads = []
+        dual_inputs = []
+        tensor_indices = set()
+        for i, inp in enumerate(inputs):
+            if is_tensor_like(inp) and inp.requires_grad:
+                if inp.layout == torch._mkldnn:  # type: ignore[attr-defined]
+                    raise ValueError(
+                        "MKLDNN inputs are not support for forward AD gradcheck."
+                    )
+
+                inp = fwAD.make_dual(inp.detach(), torch.zeros_like(inp))
+                # If inp is a differentiable view, the dual might not be the tangent given to
+                # make_dual, so read it explicitly from the dual tensor
+                fw_grads.append(fwAD.unpack_dual(inp)[1])
+                tensor_indices.add(i)
+            dual_inputs.append(inp)
+
+        for fw_grad, u in zip(fw_grads, all_u):
+            fw_grad.copy_(u.view_as(fw_grad))
+
+        for idx, inp in enumerate(inputs):
+            if idx not in tensor_indices:
+                continue
+            dual_inp_obj = dual_inputs[idx]
+
+            # case 1 (Materialized Zero Tensor Tangent)
+            dual_inputs[idx] = fwAD.make_dual(inp.detach(), torch.zeros_like(inp))
+            raw_outputs = _as_tuple(func(*dual_inputs))
+            dual_outputs1 = filter(_is_float_or_complex_tensor, raw_outputs)
+
+            # case 2 (Efficient Zero Tensor Tangent since we don't make a dual object and pass a regular tensor)
+            dual_inputs[idx] = inp.detach()
+            raw_outputs = _as_tuple(func(*dual_inputs))
+            dual_outputs2 = filter(_is_float_or_complex_tensor, raw_outputs)
+
+            # reset
+            dual_inputs[idx] = dual_inp_obj
+
+            for index_o, (d_o1, d_o2) in enumerate(zip(dual_outputs1, dual_outputs2)):
+                _val1, res1 = fwAD.unpack_dual(d_o1)
+                _val2, res2 = fwAD.unpack_dual(d_o2)
+
+                if not (res1 is None or res2 is None):
+                    if not torch.allclose(res1, res2):
+                        raise GradcheckError(
+                            "Mismatch in tangent values for output with index: ",
+                            index_o,
+                            " when input: ",
+                            inp,
+                            " has an undefined tangent value. ",
+                            " Got: ",
+                            res1,
+                            " but expected: ",
+                            res2,
+                        )
+    return True
+
+
+def _test_undefined_backward_mode(func, outputs, inputs) -> bool:
+    diff_input_list: list[torch.Tensor] = list(_iter_tensors(inputs, True))
+    if not diff_input_list:
+        raise GradcheckError("no Tensors requiring grad found in input")
+
+    def warn_bc_breaking():
+        warnings.warn(
+            "Backwards compatibility: New undefined gradient support checking "
+            "feature is enabled by default, but it may break existing callers "
+            "of this function. If this is true for you, you can call this "
+            'function with "check_undefined_grad=False" to disable the feature',
+            stacklevel=2,
+        )
+
+    def check_undefined_grad_support(output_to_check):
+        grads_output = [
+            torch.zeros_like(o, memory_format=torch.legacy_contiguous_format)
+            for o in output_to_check
+        ]
+        try:
+            grads_input = torch.autograd.grad(
+                output_to_check, diff_input_list, grads_output, allow_unused=True
+            )
+        except RuntimeError as e:
+            warn_bc_breaking()
+            raise GradcheckError(
+                "Expected backward function to handle undefined output grads. "
+                'Please look at "Notes about undefined output gradients" in '
+                '"tools/autograd/derivatives.yaml"'
+            ) from e
+
+        for gi in grads_input:
+            if (gi is not None) and (not gi.eq(0).all()):
+                warn_bc_breaking()
+                raise GradcheckError(
+                    "Expected all input grads to be undefined or zero when all output grads are undefined "
+                    'or zero. Please look at "Notes about undefined output gradients" in '
+                    '"tools/autograd/derivatives.yaml"'
+                )
+        return True
+
+    # All backward functions must work properly if all output grads are undefined
+    outputs_to_check = [
+        [
+            torch._C._functions.UndefinedGrad()(o)
+            for o in _differentiable_outputs(func(*inputs))
+            # This check filters out Tensor-likes that aren't instances of Tensor.
+            if isinstance(o, torch.Tensor)
+        ]
+    ]
+
+    # If there are multiple output grads, we should be able to undef one at a time without error
+    if len(outputs_to_check[0]) > 1:
+        for undef_grad_idx in range(len(outputs)):
+            output_to_check = _differentiable_outputs(func(*inputs))
+            outputs_to_check.append(
+                [
+                    torch._C._functions.UndefinedGrad()(o)
+                    if idx == undef_grad_idx
+                    else o
+                    for idx, o in enumerate(output_to_check)
+                ]
+            )
+
+    return all(check_undefined_grad_support(output) for output in outputs_to_check)
+
+
+def _as_tuple(x):
+    if isinstance(x, tuple):
+        return x
+    elif isinstance(x, list):
+        return tuple(x)
+    else:
+        return (x,)
+
+
+def _differentiable_outputs(x):
+    return tuple(o for o in _as_tuple(x) if o.requires_grad)
+
+
+def _get_notallclose_msg(
+    analytical,
+    numerical,
+    output_idx,
+    input_idx,
+    complex_indices,
+    test_imag=False,
+    is_forward_ad=False,
+) -> str:
+    out_is_complex = (
+        (not is_forward_ad) and complex_indices and output_idx in complex_indices
+    )
+    inp_is_complex = is_forward_ad and complex_indices and input_idx in complex_indices
+    part = "imaginary" if test_imag else "real"
+    element = "inputs" if is_forward_ad else "outputs"
+    prefix = (
+        ""
+        if not (out_is_complex or inp_is_complex)
+        else f"While considering the {part} part of complex {element} only, "
+    )
+    mode = "computed with forward mode " if is_forward_ad else ""
+    return (
+        prefix
+        + f"Jacobian {mode}mismatch for output {output_idx:d} with respect to input {input_idx:d},\n"
+        f"numerical:{numerical}\nanalytical:{analytical}\n"
+    )
+
+
+def _transpose(matrix_of_tensors):
+    # returns list of tuples
+    return list(zip(*matrix_of_tensors))
+
+
+def _real_and_imag_output(fn):
+    # returns new functions real(fn), and imag(fn) where real(fn) and imag(fn) behave the same as
+    # the original fn, except torch.real or torch.imag are applied to the complex outputs
+    def apply_to_c_outs(fn, fn_to_apply):
+        def wrapped_fn(*inputs):
+            outs = _as_tuple(fn(*inputs))
+            return tuple(fn_to_apply(o) if o.is_complex() else o for o in outs)
+
+        return wrapped_fn
+
+    return apply_to_c_outs(fn, torch.real), apply_to_c_outs(fn, torch.imag)
+
+
+def _real_and_imag_input(fn, complex_inp_indices, tupled_inputs):
+    # returns new functions that take real inputs instead of complex inputs as
+    # (x, y) -> fn(x + y * 1j). And it computes: inp -> fn(inp + y * 1j) and inp -> fn(x + inp * 1j).
+    # In each case, the other part is considered constant.
+    # We do not use 0 for the constant here to make sure we always call the user function with a valid input.
+    def apply_to_c_inps(fn, fn_to_apply):
+        def wrapped_fn(*inputs):
+            new_inputs = list(inputs)
+            for should_be_complex in complex_inp_indices:
+                new_inputs[should_be_complex] = fn_to_apply(
+                    new_inputs[should_be_complex], tupled_inputs[should_be_complex]
+                )
+            return _as_tuple(fn(*new_inputs))
+
+        return wrapped_fn
+
+    real_fn = apply_to_c_inps(fn, lambda inp, orig: inp + orig.imag * 1j)
+    imag_fn = apply_to_c_inps(fn, lambda inp, orig: orig.real + inp * 1j)
+    return real_fn, imag_fn
+
+
+def _gradcheck_real_imag(
+    gradcheck_fn,
+    func,
+    func_out,
+    tupled_inputs,
+    outputs,
+    eps,
+    rtol,
+    atol,
+    check_grad_dtypes,
+    check_forward_ad,
+    check_backward_ad,
+    nondet_tol,
+    check_undefined_grad,
+):
+    complex_out_indices = [i for i, o in enumerate(outputs) if o.is_complex()]
+    has_any_complex_output = any(o.is_complex() for o in _as_tuple(func_out))
+    if check_backward_ad:
+        if has_any_complex_output:
+            real_fn, imag_fn = _real_and_imag_output(func)
+
+            imag_func_out = imag_fn(*tupled_inputs)
+            imag_outputs = _differentiable_outputs(imag_func_out)
+            gradcheck_fn(
+                imag_fn,
+                imag_func_out,
+                tupled_inputs,
+                imag_outputs,
+                eps,
+                rtol,
+                atol,
+                check_grad_dtypes,
+                nondet_tol,
+                complex_indices=complex_out_indices,
+                test_imag=True,
+            )
+
+            real_func_out = real_fn(*tupled_inputs)
+            real_outputs = _differentiable_outputs(real_func_out)
+            gradcheck_fn(
+                real_fn,
+                real_func_out,
+                tupled_inputs,
+                real_outputs,
+                eps,
+                rtol,
+                atol,
+                check_grad_dtypes,
+                nondet_tol,
+                complex_indices=complex_out_indices,
+            )
+        else:
+            gradcheck_fn(
+                func,
+                func_out,
+                tupled_inputs,
+                outputs,
+                eps,
+                rtol,
+                atol,
+                check_grad_dtypes,
+                nondet_tol,
+            )
+
+    if check_forward_ad:
+        complex_inp_indices = [
+            i
+            for i, inp in enumerate(tupled_inputs)
+            if is_tensor_like(inp) and inp.is_complex()
+        ]
+        if complex_inp_indices:
+            real_fn, imag_fn = _real_and_imag_input(
+                func, complex_inp_indices, tupled_inputs
+            )
+
+            imag_inputs = [
+                inp.imag if is_tensor_like(inp) and inp.is_complex() else inp
+                for inp in tupled_inputs
+            ]
+            imag_func_out = imag_fn(*imag_inputs)
+            diff_imag_func_out = _differentiable_outputs(imag_func_out)
+            gradcheck_fn(
+                imag_fn,
+                imag_func_out,
+                imag_inputs,
+                diff_imag_func_out,
+                eps,
+                rtol,
+                atol,
+                check_grad_dtypes,
+                nondet_tol,
+                complex_indices=complex_inp_indices,
+                test_imag=True,
+                use_forward_ad=True,
+            )
+
+            real_inputs = [
+                inp.real if is_tensor_like(inp) and inp.is_complex() else inp
+                for inp in tupled_inputs
+            ]
+            real_func_out = real_fn(*real_inputs)
+            diff_real_func_out = _differentiable_outputs(real_func_out)
+            gradcheck_fn(
+                real_fn,
+                real_func_out,
+                real_inputs,
+                diff_real_func_out,
+                eps,
+                rtol,
+                atol,
+                check_grad_dtypes,
+                nondet_tol,
+                complex_indices=complex_inp_indices,
+                use_forward_ad=True,
+            )
+            if check_undefined_grad:
+                _test_undefined_forward_mode(imag_fn, imag_func_out, imag_inputs)
+                _test_undefined_forward_mode(real_fn, real_func_out, real_inputs)
+        else:
+            gradcheck_fn(
+                func,
+                func_out,
+                tupled_inputs,
+                outputs,
+                eps,
+                rtol,
+                atol,
+                check_grad_dtypes,
+                nondet_tol,
+                use_forward_ad=True,
+            )
+            if check_undefined_grad:
+                _test_undefined_forward_mode(func, outputs, tupled_inputs)
+
+
+def _slow_gradcheck(
+    func,
+    func_out,
+    tupled_inputs,
+    outputs,
+    eps,
+    rtol,
+    atol,
+    check_grad_dtypes,
+    nondet_tol,
+    *,
+    use_forward_ad=False,
+    complex_indices=None,
+    test_imag=False,
+    masked=False,
+):
+    func_out = _as_tuple(func_out)
+    if not outputs:
+        return _check_no_differentiable_outputs(
+            func, tupled_inputs, func_out, eps=eps, is_forward_ad=use_forward_ad
+        )
+    tupled_inputs_numerical = tupled_inputs if masked else _densify(tupled_inputs)
+
+    numerical = _transpose(
+        _get_numerical_jacobian(
+            func,
+            tupled_inputs_numerical,
+            func_out,
+            eps=eps,
+            is_forward_ad=use_forward_ad,
+        )
+    )
+    # Note: [numerical vs analytical output length]
+    # The numerical path returns jacobian quantity for all outputs, even if requires_grad of that
+    # output is False. This behavior is necessary for _check_no_differentiable_outputs to work.
+    numerical = [nj for o, nj in zip(func_out, numerical) if o.requires_grad]
+    if use_forward_ad:
+        analytical_forward = _get_analytical_jacobian_forward_ad(
+            func, tupled_inputs, func_out, check_grad_dtypes=check_grad_dtypes
+        )
+
+        for i, n_per_out in enumerate(numerical):
+            for j, n in enumerate(n_per_out):
+                a = analytical_forward[j][i]
+                if not _allclose_with_type_promotion(a, n.to(a.device), rtol, atol):
+                    raise GradcheckError(
+                        _get_notallclose_msg(
+                            a, n, i, j, complex_indices, test_imag, is_forward_ad=True
+                        )
+                    )
+    else:
+        for i, o in enumerate(outputs):
+            analytical = _check_analytical_jacobian_attributes(
+                tupled_inputs, o, nondet_tol, check_grad_dtypes
+            )
+
+            for j, (a, n) in enumerate(zip(analytical, numerical[i])):
+                if not _allclose_with_type_promotion(a, n.to(a.device), rtol, atol):
+                    raise GradcheckError(
+                        _get_notallclose_msg(a, n, i, j, complex_indices, test_imag)
+                    )
+
+    return True
+
+
+def _dot_with_type_promotion(u, v):
+    if u.dim() != 1 or v.dim() != 1:
+        raise AssertionError(
+            f"Expected u and v to be 1D tensors, but got dims {u.dim()} and {v.dim()}"
+        )
+    return (u * v).sum()
+
+
+def _allclose_with_type_promotion(a, b, rtol, atol):
+    promoted_type = torch.promote_types(a.dtype, b.dtype)
+    a = a.to(dtype=promoted_type)
+    b = b.to(dtype=promoted_type)
+    return torch.allclose(a, b, rtol, atol)
+
+
+def _to_real_dtype(dtype):
+    if dtype == torch.complex128:
+        return torch.float64
+    elif dtype == torch.complex64:
+        return torch.float32
+    else:
+        return dtype
+
+
+def _vec_from_tensor(x, generator, downcast_complex=False):
+    # Create a random vector with the same number of elements as x and the same
+    # dtype/device. If x is complex and downcast_complex is False, we create a
+    # complex tensor with only real component.
+    if x.layout == torch.sparse_coo:
+        # For sparse, create a random sparse vec with random values in the same
+        # indices. Make sure size is set so that it isn't inferred to be smaller.
+        x_values = x._values()
+        dtype = _to_real_dtype(x.dtype) if downcast_complex else x.dtype
+        values = (
+            torch.rand(x_values.numel(), generator=generator)
+            .to(dtype=dtype, device=x.device)
+            .view(x_values.shape)
+        )
+        values /= values.norm()
+        vec = torch.sparse_coo_tensor(x._indices(), values, x.size(), device=x.device)
+    elif _is_sparse_compressed_tensor(x):
+        if x.layout in {torch.sparse_csr, torch.sparse_bsr}:
+            compressed_indices, plain_indices = x.crow_indices(), x.col_indices()
+        else:
+            compressed_indices, plain_indices = x.ccol_indices(), x.row_indices()
+        x_values = x.values()
+        dtype = _to_real_dtype(x.dtype) if downcast_complex else x.dtype
+        values = (
+            torch.rand(x_values.numel(), generator=generator)
+            .to(dtype=dtype, device=x.device)
+            .view(x_values.shape)
+        )
+        values /= values.norm()
+        vec = torch.sparse_compressed_tensor(
+            compressed_indices,
+            plain_indices,
+            values,
+            x.size(),
+            layout=x.layout,
+            device=x.device,
+        )
+    else:
+        dtype = _to_real_dtype(x.dtype) if downcast_complex else x.dtype
+        vec = torch.rand(x.numel(), generator=generator).to(
+            dtype=dtype, device=x.device
+        )
+        vec /= vec.norm()
+    return vec
+
+
+def _get_inp_tensors(tupled_inputs):
+    inp_idx_tup = [
+        (i, t)
+        for i, t in enumerate(tupled_inputs)
+        if is_tensor_like(t) and t.requires_grad
+    ]
+    return [tup[0] for tup in inp_idx_tup], [tup[1] for tup in inp_idx_tup]
+
+
+def _adjusted_atol(atol, u, v):
+    # In slow gradcheck, we compare A and B element-wise, i.e., for some a, b we
+    # allow: |a - b| < atol + rtol * b. But since we now compare q1 = v^T A u and
+    # q2 = v^T B u, we must allow |q1 - q2| < v^T E u + rtol * v^T B u, where E is
+    # the correctly sized matrix in which each entry is atol.
+    #
+    # We see that atol needs to be scaled by v^T M u (where M is an all-ones M x N
+    # matrix): v^T M u = \sum_{i} \sum_{j} u_i * v_j = (\sum_{i} u_i)(\sum_{i} v_i)
+    # TODO: properly handle case when u is tuple instead of only taking first element
+    u = u[0] if isinstance(u, tuple) else u
+    sum_u = u.sum()
+    sum_v = 1.0 if v is None else v.sum()
+    return atol * float(sum_u) * float(sum_v)
+
+
+FAST_FAIL_SLOW_OK_MSG = """
+Fast gradcheck failed but element-wise differences are small. This means that the
+test might've passed in slow_mode!
+
+If you are adding a new operator, please file an issue and then use one of the
+workarounds. The workaround depends on how your test invokes gradcheck/gradgradcheck:
+
+If the test
+- manually invokes gradcheck/gradgradcheck, then call gradcheck/gradgradcheck
+  with `fast_mode=False` as a keyword argument.
+- is OpInfo-based (e.g., in test_ops_gradients.py), then modify the OpInfo for the test
+  to have `gradcheck_fast_mode=False`
+- is a Module test (e.g., in common_nn.py), then modify the corresponding
+  module_test entry to have `gradcheck_fast_mode=False`
+""".strip()
+
+
+def _run_slow_mode_and_get_error(
+    func, tupled_inputs, outputs, input_idx, output_idx, rtol, atol, eps, is_forward_ad
+):
+    # Compute jacobians in slow mode for better error message
+    slow_numerical = _get_numerical_jacobian(
+        func, tupled_inputs, outputs, eps=eps, is_forward_ad=is_forward_ad
+    )[input_idx][output_idx]
+    if is_forward_ad:
+
+        def new_fn(inp):
+            new_inputs = list(tupled_inputs)
+            new_inputs[input_idx] = inp
+            return _as_tuple(func(*new_inputs))[output_idx]
+
+        slow_analytical = _get_analytical_jacobian_forward_ad(
+            new_fn, (tupled_inputs[input_idx],), (outputs[output_idx],)
+        )[0][0]
+    else:
+        slow_analytical = _get_analytical_jacobian(
+            tupled_inputs, outputs, input_idx, output_idx
+        )
+
+    # Assume jacobians are non-empty and have the same shape
+    slow_max_diff = (slow_numerical - slow_analytical).abs().max()
+
+    slow_allclose = torch.allclose(slow_analytical, slow_numerical, rtol, atol)
+    msg = (
+        "\nThe above quantities relating the numerical and analytical jacobians are computed \n"
+        "in fast mode. See: https://github.com/pytorch/pytorch/issues/53876 for more background \n"
+        "about fast mode. Below, we recompute numerical and analytical jacobians in slow mode:\n\n"
+        f"Numerical:\n {slow_numerical}\n"
+        f"Analytical:\n{slow_analytical}\n\n"
+        f"The max per-element difference (slow mode) is: {slow_max_diff}.\n"
+    )
+    if slow_allclose:
+        # Slow gradcheck would've passed!
+        msg += FAST_FAIL_SLOW_OK_MSG
+    return msg
+
+
+def _to_flat_dense_if_sparse(tensor):
+    if _is_sparse_any_tensor(tensor):
+        return tensor.to_dense().reshape(-1)
+    else:
+        return tensor
+
+
+def _make_vectors(inp_tensors, outputs, *, use_forward_ad):
+    # Use our own generator to avoid messing with the user's RNG state
+    g_cpu = torch.Generator()
+
+    def _vec_from_tensor_cpu(*args):
+        # Default allocate all tensors on CPU, so they are on the same device as the generator
+        # even if the user specified a default device
+        with torch.device("cpu"):
+            return _vec_from_tensor(*args)
+
+    all_u = []
+    all_u_dense = []
+    for inp in inp_tensors:
+        ur = _vec_from_tensor_cpu(inp, g_cpu, True)
+        ur_dense = _to_flat_dense_if_sparse(ur)
+        if inp.is_complex():
+            ui = _vec_from_tensor_cpu(inp, g_cpu, True)
+            all_u.append((ur, ui))
+            ui_dense = _to_flat_dense_if_sparse(ui)
+            all_u_dense.append((ur_dense, ui_dense))
+        else:
+            all_u.append(ur)
+            all_u_dense.append(ur_dense)
+    all_v = (
+        None
+        if use_forward_ad
+        else [_vec_from_tensor_cpu(out, g_cpu) for out in outputs]
+    )
+    return all_v, all_u, all_u_dense
+
+
+def _check_analytical_numerical_equal(
+    all_analytical,
+    all_numerical,
+    complex_indices,
+    tupled_inputs,
+    outputs,
+    func,
+    all_v,
+    all_u,
+    rtol,
+    atol,
+    eps,
+    test_imag,
+    *,
+    is_forward_ad=False,
+):
+    for i, all_numerical_for_input_i in enumerate(all_numerical):
+        for j, n in enumerate(all_numerical_for_input_i):
+            # Forward AD generates the transpose of what this function expects
+            if is_forward_ad:
+                a = all_analytical[i][j]
+            else:
+                a = all_analytical[j][i]
+            n = n.to(device=a.device)
+            updated_atol = _adjusted_atol(atol, all_u[i], all_v[j] if all_v else None)
+            if not _allclose_with_type_promotion(a, n.to(a.device), rtol, updated_atol):
+                jacobians_str = _run_slow_mode_and_get_error(
+                    func, tupled_inputs, outputs, i, j, rtol, atol, eps, is_forward_ad
+                )
+                raise GradcheckError(
+                    _get_notallclose_msg(
+                        a, n, j, i, complex_indices, test_imag, is_forward_ad
+                    )
+                    + jacobians_str
+                )
+
+
+def _fast_gradcheck(
+    func,
+    func_out,
+    inputs,
+    outputs,
+    eps,
+    rtol,
+    atol,
+    check_grad_dtypes,
+    nondet_tol,
+    *,
+    use_forward_ad=False,
+    complex_indices=None,
+    test_imag=False,
+    masked=False,
+):
+    # See https://github.com/pytorch/pytorch/issues/53876 for details
+    inp_tensors_idx, inp_tensors = _get_inp_tensors(inputs)
+    # Backward mode computes v^T * J (VJP)
+    # Since we computed J * u (JVP) through finite difference method, we perform an equality check
+    # between VJP * u, v * JVP
+    # ----
+    # Forward mode computes J * u (JVP)
+    # Since we already compute JVP through finite difference method,
+    # we don't need v for correctness check here as asserted below
+    all_v, all_u, all_u_dense = _make_vectors(
+        inp_tensors, outputs, use_forward_ad=use_forward_ad
+    )
+
+    inputs_numerical, all_u_numerical, all_v_numerical = (
+        (inputs, all_u, all_v) if masked else _densify((inputs, all_u, all_v))
+    )
+
+    numerical_vJu = _get_numerical_vJu(
+        func,
+        inputs_numerical,
+        inp_tensors_idx,
+        func_out,
+        all_u_numerical,
+        all_v_numerical,
+        eps,
+        is_forward_ad=use_forward_ad,
+    )
+    # TODO: replicate https://github.com/pytorch/pytorch/pull/77743 for fast gradcheck as well
+    if use_forward_ad:
+        if all_v is not None:
+            raise AssertionError("Expected all_v to be None.")
+        analytical_vJu = _get_analytical_jacobian_forward_ad(
+            func,
+            inputs,
+            _as_tuple(func_out),
+            all_u=all_u,
+            check_grad_dtypes=check_grad_dtypes,
+        )
+    else:
+        if not outputs:
+            _check_no_differentiable_outputs_fast(
+                func, func_out, inputs, inp_tensors_idx, all_u, eps, nondet_tol
+            )
+
+        analytical_vJu = _get_analytical_vJu_backward_mode(
+            inputs, outputs, nondet_tol, check_grad_dtypes, all_v, all_u_dense
+        )
+
+    _check_analytical_numerical_equal(
+        analytical_vJu,
+        numerical_vJu,
+        complex_indices,
+        inputs,
+        outputs,
+        func,
+        all_v,
+        all_u,
+        rtol,
+        atol,
+        eps,
+        test_imag,
+        is_forward_ad=use_forward_ad,
+    )
+
+    return True
+
+
+# Note [VarArg of Tensors]
+# ~~~~~~~~~~~~~~~~~~~~~~~~
+# 'func' accepts a vararg of tensors, which isn't expressible in the type system at the moment.
+# If https://mypy.readthedocs.io/en/latest/additional_features.html?highlight=callable#extended-callable-types is accepted,
+# the '...' first argument of Callable can be replaced with VarArg(Tensor).
+# For now, we permit any input.
+def gradcheck(
+    func: Callable[..., Union[_TensorOrTensors]],  # See Note [VarArg of Tensors]
+    inputs: _TensorOrTensors,
+    *,
+    eps: float = 1e-6,
+    atol: float = 1e-5,
+    rtol: float = 1e-3,
+    raise_exception: bool = True,
+    nondet_tol: float = 0.0,
+    check_undefined_grad: bool = True,
+    check_grad_dtypes: bool = False,
+    check_batched_grad: bool = False,
+    check_batched_forward_grad: bool = False,
+    check_forward_ad: bool = False,
+    check_backward_ad: bool = True,
+    fast_mode: bool = False,
+    masked: Optional[bool] = None,
+) -> bool:  # noqa: D400,D205
+    r"""Check gradients computed via small finite differences against analytical
+    gradients wrt tensors in :attr:`inputs` that are of floating point or complex type
+    and with ``requires_grad=True``.
+
+    The check between numerical and analytical gradients uses :func:`~torch.allclose`.
+
+    For most of the complex functions we consider for optimization purposes, no notion of
+    Jacobian exists. Instead, gradcheck verifies if the numerical and analytical values of
+    the Wirtinger and Conjugate Wirtinger derivatives are consistent. Because the gradient
+    computation is done under the assumption that the overall function has a real-valued
+    output, we treat functions with complex output in a special way. For these functions,
+    gradcheck is applied to two real-valued functions corresponding to taking the real
+    components of the complex outputs for the first, and taking the imaginary components
+    of the complex outputs for the second. For more details, check out
+    :ref:`complex_autograd-doc`.
+
+    .. note::
+        The default values are designed for :attr:`input` of double precision.
+        This check will likely fail if :attr:`input` is of less precision, e.g.,
+        ``FloatTensor``.
+
+    .. note::
+        Gradcheck may fail when evaluated on non-differentiable points
+        because the numerically computed gradients via finite differencing may differ
+        those computed analytically (not necessarily because either is incorrect).
+        For more context, see :ref:`non-differentiable-func-grad`.
+
+    .. warning::
+       If any checked tensor in :attr:`input` has overlapping memory, i.e.,
+       different indices pointing to the same memory address (e.g., from
+       :func:`torch.Tensor.expand`), this check will likely fail because the numerical
+       gradients computed by point perturbation at such indices will change
+       values at all other indices that share the same memory address.
+
+    Args:
+        func (function): a Python function that takes Tensor inputs and returns
+            a Tensor or a tuple of Tensors
+        inputs (tuple of Tensor or Tensor): inputs to the function
+        eps (float, optional): perturbation for finite differences
+        atol (float, optional): absolute tolerance
+        rtol (float, optional): relative tolerance
+        raise_exception (bool, optional): indicating whether to raise an exception if
+            the check fails. The exception gives more information about the
+            exact nature of the failure. This is helpful when debugging gradchecks.
+        nondet_tol (float, optional): tolerance for non-determinism. When running
+            identical inputs through the differentiation, the results must either match
+            exactly (default, 0.0) or be within this tolerance.
+        check_undefined_grad (bool, optional): if ``True``, check if undefined output grads
+            are supported and treated as zeros, for ``Tensor`` outputs.
+        check_batched_grad (bool, optional): if ``True``, check if we can compute
+            batched gradients using prototype vmap support. Defaults to False.
+        check_batched_forward_grad (bool, optional): if ``True``, checks if we can compute
+            batched forward gradients using forward ad and prototype vmap support. Defaults to ``False``.
+        check_forward_ad (bool, optional): if ``True``, check that the gradients computed with forward
+            mode AD match the numerical ones. Defaults to ``False``.
+        check_backward_ad (bool, optional): if ``False``, do not perform any checks that rely on
+            backward mode AD to be implemented. Defaults to ``True``.
+        fast_mode (bool, optional): Fast mode for gradcheck and gradgradcheck is currently only
+            implemented for R to R functions. If none of the inputs and outputs are complex
+            a faster implementation of gradcheck that no longer computes the entire jacobian
+            is run; otherwise, we fall back to the slow implementation.
+        masked (bool, optional): if ``True``, the gradients of unspecified elements of
+            sparse tensors are ignored. Defaults to ``False``.
+    Returns:
+        ``True`` if all differences satisfy allclose condition
+
+    """
+    if not (check_forward_ad or check_backward_ad):
+        raise AssertionError(
+            "Expected at least one of check_forward_ad or check_backward_ad to be True"
+        )
+    if check_batched_grad and not check_backward_ad:
+        raise AssertionError(
+            "Setting check_batched_grad=True requires check_backward_ad to be True"
+        )
+    if check_batched_forward_grad and not check_forward_ad:
+        raise AssertionError(
+            "Setting check_batched_forward_grad=True requires check_forward_ad to be True"
+        )
+    args = locals().copy()
+    args.pop("raise_exception")
+    if not raise_exception:
+        try:
+            return _gradcheck_helper(**args)
+        except GradcheckError:
+            return False
+    else:
+        return _gradcheck_helper(**args)
+
+
+def _gradcheck_helper(
+    func,
+    inputs,
+    eps,
+    atol,
+    rtol,
+    nondet_tol,
+    check_undefined_grad,
+    check_grad_dtypes,
+    check_batched_grad,
+    check_batched_forward_grad,
+    check_forward_ad,
+    check_backward_ad,
+    fast_mode,
+    masked,
+):
+    tupled_inputs = _as_tuple(inputs)
+    _check_inputs(tupled_inputs)
+
+    func_out = func(*tupled_inputs)
+    outputs = _differentiable_outputs(func_out)
+    _check_outputs(outputs)
+
+    gradcheck_fn = functools.partial(
+        _fast_gradcheck if fast_mode else _slow_gradcheck, masked=masked
+    )
+    _gradcheck_real_imag(
+        gradcheck_fn,
+        func,
+        func_out,
+        tupled_inputs,
+        outputs,
+        eps,
+        rtol,
+        atol,
+        check_grad_dtypes,
+        check_forward_ad=check_forward_ad,
+        check_backward_ad=check_backward_ad,
+        nondet_tol=nondet_tol,
+        check_undefined_grad=check_undefined_grad,
+    )
+
+    if check_batched_forward_grad:
+        _test_batched_grad_forward_ad(func, tupled_inputs)
+
+    # Short circuit because remaining tests rely on backward AD to be implemented
+    if not check_backward_ad:
+        return True
+
+    for i, o in enumerate(outputs):
+        if check_batched_grad:
+            _test_batched_grad(tupled_inputs, o, i)
+
+    _test_backward_mul_by_grad_output(outputs, tupled_inputs, masked)
+
+    if check_undefined_grad and check_backward_ad:
+        _test_undefined_backward_mode(func, outputs, tupled_inputs)
+    return True
+
+
+def gradgradcheck(
+    func: Callable[..., _TensorOrTensors],  # See Note [VarArg of Tensors]
+    inputs: _TensorOrTensors,
+    grad_outputs: Optional[_TensorOrTensors] = None,
+    *,
+    eps: float = 1e-6,
+    atol: float = 1e-5,
+    rtol: float = 1e-3,
+    gen_non_contig_grad_outputs: bool = False,
+    raise_exception: bool = True,
+    nondet_tol: float = 0.0,
+    check_undefined_grad: bool = True,
+    check_grad_dtypes: bool = False,
+    check_batched_grad: bool = False,
+    check_fwd_over_rev: bool = False,
+    check_rev_over_rev: bool = True,
+    fast_mode: bool = False,
+    masked: bool = False,
+) -> bool:  # noqa: D400,D205
+    r"""Check gradients of gradients computed via small finite differences
+    against analytical gradients wrt tensors in :attr:`inputs` and
+    :attr:`grad_outputs` that are of floating point or complex type and with
+    ``requires_grad=True``.
+
+    This function checks that backpropagating through the gradients computed
+    to the given :attr:`grad_outputs` are correct.
+
+    The check between numerical and analytical gradients uses :func:`~torch.allclose`.
+
+    .. note::
+        The default values are designed for :attr:`input` and
+        :attr:`grad_outputs` of double precision. This check will likely fail if
+        they are of less precision, e.g., ``FloatTensor``.
+
+    .. warning::
+       If any checked tensor in :attr:`input` and :attr:`grad_outputs` has
+       overlapping memory, i.e., different indices pointing to the same memory
+       address (e.g., from :func:`torch.Tensor.expand`), this check will likely fail
+       because the numerical gradients computed by point perturbation at such
+       indices will change values at all other indices that share the same
+       memory address.
+
+    Args:
+        func (function): a Python function that takes Tensor inputs and returns
+            a Tensor or a tuple of Tensors
+        inputs (tuple of Tensor or Tensor): inputs to the function
+        grad_outputs (tuple of Tensor or Tensor, optional): The gradients with
+            respect to the function's outputs.
+        eps (float, optional): perturbation for finite differences
+        atol (float, optional): absolute tolerance
+        rtol (float, optional): relative tolerance
+        gen_non_contig_grad_outputs (bool, optional): if :attr:`grad_outputs` is
+            ``None`` and :attr:`gen_non_contig_grad_outputs` is ``True``, the
+            randomly generated gradient outputs are made to be noncontiguous
+        raise_exception (bool, optional): indicating whether to raise an exception if
+            the check fails. The exception gives more information about the
+            exact nature of the failure. This is helpful when debugging gradchecks.
+        nondet_tol (float, optional): tolerance for non-determinism. When running
+            identical inputs through the differentiation, the results must either match
+            exactly (default, 0.0) or be within this tolerance. Note that a small amount
+            of nondeterminism in the gradient will lead to larger inaccuracies in
+            the second derivative.
+        check_undefined_grad (bool, optional): if True, check if undefined output grads
+            are supported and treated as zeros
+        check_batched_grad (bool, optional): if True, check if we can compute
+            batched gradients using prototype vmap support. Defaults to False.
+        fast_mode (bool, optional): if True, run a faster implementation of gradgradcheck that
+            no longer computes the entire jacobian.
+        masked (bool, optional): if True, the gradients of unspecified elements of
+            sparse tensors are ignored (default, False).
+    Returns:
+        True if all differences satisfy allclose condition
+    """
+    if not (check_fwd_over_rev or check_rev_over_rev):
+        raise AssertionError(
+            "Expected at least one of check_fwd_over_rev or check_rev_over_rev to be True"
+        )
+    if check_undefined_grad and not check_rev_over_rev:
+        raise AssertionError(
+            "Setting check_undefined_grad=True requires check_rev_over_rev to be True"
+        )
+    if check_batched_grad and not check_rev_over_rev:
+        raise AssertionError(
+            "Setting check_batched_grad=True requires check_rev_over_rev to be True"
+        )
+    # TODO: do we want to test this too?
+    # assert not (check_batched_forward_grad and not check_fwd_over_rev), (
+    #     "Setting check_batched_forward_grad=True requires check_fwd_over_rev to be True")
+    tupled_inputs = _as_tuple(inputs)
+
+    if grad_outputs is None:
+        # If grad_outputs is not specified, create random Tensors of the same shape, type, and device as the outputs
+
+        outputs = _differentiable_outputs(func(*tupled_inputs))
+        tupled_grad_outputs = tuple(
+            torch.testing.make_tensor(
+                x.shape,
+                dtype=x.dtype
+                if x.is_floating_point() or x.is_complex()
+                else torch.double,
+                device=x.device,
+                low=-1,
+                high=1,
+                requires_grad=True,
+                noncontiguous=gen_non_contig_grad_outputs,
+            )
+            for x in outputs
+        )
+    else:
+        tupled_grad_outputs = _as_tuple(grad_outputs)
+
+    num_outputs = len(tupled_grad_outputs)
+
+    # NB: We need to save the requires_grad information about the inputs here because gradcheck detaches inputs
+    #     before running forward mode AD
+    diff_input_args_indices = {
+        i for i, x in enumerate(tupled_inputs) if is_tensor_like(x) and x.requires_grad
+    }
+    diff_grad_output_indices = {
+        i for i, x in enumerate(tupled_grad_outputs) if x.requires_grad
+    }
+
+    def new_func(*args):
+        # Restore the requires_grad information
+        input_args = tuple(
+            x.requires_grad_() if i in diff_input_args_indices else x
+            for i, x in enumerate(args[:-num_outputs])
+        )
+        outputs = _differentiable_outputs(func(*input_args))
+        grad_outputs = tuple(
+            x.requires_grad_() if i in diff_grad_output_indices else x
+            for i, x in enumerate(args[-num_outputs:])
+        )
+        diff_input_args = tuple(
+            x for i, x in enumerate(input_args) if i in diff_input_args_indices
+        )
+        grad_inputs = torch.autograd.grad(
+            outputs, diff_input_args, grad_outputs, create_graph=True, allow_unused=True
+        )
+        grad_inputs = tuple(g for g in grad_inputs if g is not None)
+        return grad_inputs
+
+    return gradcheck(
+        new_func,
+        tupled_inputs + tupled_grad_outputs,
+        eps=eps,
+        atol=atol,
+        rtol=rtol,
+        raise_exception=raise_exception,
+        nondet_tol=nondet_tol,
+        check_undefined_grad=check_undefined_grad,
+        check_grad_dtypes=check_grad_dtypes,
+        check_batched_grad=check_batched_grad,
+        fast_mode=fast_mode,
+        check_forward_ad=check_fwd_over_rev,
+        check_backward_ad=check_rev_over_rev,
+        masked=masked,
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/graph.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/graph.py
new file mode 100644
index 0000000000000000000000000000000000000000..f7c7150aa7e9d5bd88ea7dfc7cc86a565c9f9cb5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/graph.py
@@ -0,0 +1,870 @@
+import abc
+import contextlib
+import functools
+import logging
+import threading
+from collections import defaultdict, deque
+from collections.abc import (
+    Callable,
+    Generator,
+    Iterable,
+    Iterator,
+    MutableMapping,
+    Sequence,
+)
+from typing import (
+    Any,
+    cast,
+    Literal,
+    NamedTuple,
+    Optional,
+    TYPE_CHECKING,
+    TypeAlias,
+    Union,
+)
+from weakref import WeakKeyDictionary, WeakValueDictionary
+
+import torch
+from torch.autograd.variable import Variable
+from torch.utils._python_dispatch import TorchDispatchMode
+from torch.utils.hooks import RemovableHandle
+
+
+if TYPE_CHECKING:
+    from torch._ops import OpOverload
+
+
+__all__ = [
+    "saved_tensors_hooks",
+    "save_on_cpu",
+    "disable_saved_tensors_hooks",
+    "register_multi_grad_hook",
+    "allow_mutation_on_saved_tensors",
+    "Node",
+    "GradientEdge",
+    "get_gradient_edge",
+    "increment_version",
+    "set_warn_on_accumulate_grad_stream_mismatch",
+]
+
+
+log = logging.getLogger(__name__)
+
+
+class Node(abc.ABC):
+    @abc.abstractmethod
+    def name(self) -> str:
+        r"""Return the name.
+
+        Example::
+
+            >>> import torch
+            >>> a = torch.tensor([0., 0., 0.], requires_grad=True)
+            >>> b = a.clone()
+            >>> assert isinstance(b.grad_fn, torch.autograd.graph.Node)
+            >>> print(b.grad_fn.name())
+            CloneBackward0
+        """
+        raise NotImplementedError
+
+    @property
+    @abc.abstractmethod
+    def next_functions(self) -> tuple[tuple[Optional["Node"], int], ...]:
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def metadata(self) -> dict:
+        r"""Return the metadata."""
+        raise NotImplementedError
+
+    @property
+    @abc.abstractmethod
+    def _input_metadata(self) -> list[Any]:
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def _register_hook_dict(self, tensor: torch.Tensor) -> None:
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def register_hook(self, fn: Callable[..., Any]) -> RemovableHandle:
+        r"""Register a backward hook.
+
+        The hook will be called every time a gradient with respect to the
+        Node is computed. The hook should have the following signature::
+
+            hook(grad_inputs: Tuple[Tensor], grad_outputs: Tuple[Tensor]) -> Tuple[Tensor] or None
+
+
+        The hook should not modify its argument, but it can optionally return
+        a new gradient which will be used in place of :attr:`grad_inputs`.
+
+        This function returns a handle with a method ``handle.remove()``
+        that removes the hook from the module.
+
+        .. note::
+            See :ref:`backward-hooks-execution` for more information on how when this hook
+            is executed, and how its execution is ordered relative to other hooks.
+
+        .. note::
+            In the rare case where the hook is registered while the Node has already
+            begun execution, there is no longer any guarantee on :attr:`grad_outputs`
+            content (it might be as usual or empty depending on other factors). The
+            hook can still optionally return a new gradient to be used in place of
+            :attr:`grad_inputs` independent of :attr:`grad_outputs`.
+
+        Example::
+
+            >>> import torch
+            >>> a = torch.tensor([0., 0., 0.], requires_grad=True)
+            >>> b = a.clone()
+            >>> assert isinstance(b.grad_fn, torch.autograd.graph.Node)
+            >>> handle = b.grad_fn.register_hook(lambda gI, gO: (gO[0] * 2,))
+            >>> b.sum().backward(retain_graph=True)
+            >>> print(a.grad)
+            tensor([2., 2., 2.])
+            >>> handle.remove() # Removes the hook
+            >>> a.grad = None
+            >>> b.sum().backward(retain_graph=True)
+            >>> print(a.grad)
+            tensor([1., 1., 1.])
+        """
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def register_prehook(self, fn: Callable[..., Any]) -> RemovableHandle:
+        r"""Register a backward pre-hook.
+
+        The hook will be called every time a gradient with respect to the
+        Node is computed. The hook should have the following signature::
+
+            hook(grad_outputs: Tuple[Tensor]) -> Tuple[Tensor] or None
+
+        The hook should not modify its argument, but it can optionally return
+        a new gradient which will be used in place of :attr:`grad_outputs`.
+
+        This function returns a handle with a method ``handle.remove()``
+        that removes the hook from the module.
+
+        .. note::
+            See :ref:`backward-hooks-execution` for more information on how when this hook
+            is executed, and how its execution is ordered relative to other hooks.
+
+        Example::
+
+            >>> a = torch.tensor([0., 0., 0.], requires_grad=True)
+            >>> b = a.clone()
+            >>> assert isinstance(b.grad_fn, torch.autograd.graph.Node)
+            >>> handle = b.grad_fn.register_prehook(lambda gI: (gI[0] * 2,))
+            >>> b.sum().backward(retain_graph=True)
+            >>> print(a.grad)
+            tensor([2., 2., 2.])
+            >>> handle.remove()
+            >>> a.grad = None
+            >>> b.sum().backward(retain_graph=True)
+            >>> print(a.grad)
+            tensor([1., 1., 1.])
+        """
+        raise NotImplementedError
+
+    @classmethod
+    def __subclasshook__(cls, subclass: type) -> bool:
+        if cls is Node and (
+            (
+                subclass is not None
+                and subclass is getattr(torch._C._functions, subclass.__name__, None)
+            )
+            or issubclass(subclass, torch.autograd.function.BackwardCFunction)
+        ):
+            return True
+        return NotImplemented
+
+
+def _get_grad_fn_or_grad_acc(t: Union[torch.Tensor, "GradientEdge"]) -> Node:
+    if isinstance(t, GradientEdge):
+        return t.node
+    if t.requires_grad and t.grad_fn is None:
+        with torch.enable_grad():
+            node = t.view_as(t).grad_fn.next_functions[0][0]  # type: ignore[union-attr]
+    else:
+        node = t.grad_fn
+    if node is None:
+        raise AssertionError("Expected gradient function to be set")
+    return node
+
+
+class GradientEdge(NamedTuple):
+    """Object representing a given gradient edge within the autograd graph.
+
+    To get the gradient edge where a given Tensor gradient will be computed,
+    you can do ``edge = autograd.graph.get_gradient_edge(tensor)``.
+    """
+
+    node: Node
+    output_nr: int
+    # This token can be used to ensure the graph stays alive when it cannot be
+    # done via the node field
+    ownership_token: Optional[Node] = None
+
+
+def get_gradient_edge(tensor: torch.Tensor) -> GradientEdge:
+    """Get the gradient edge for computing the gradient of the given Tensor.
+
+    In particular, it is equivalent to call
+    ``g = autograd.grad(loss, input)`` and ``g = autograd.grad(loss, get_gradient_edge(input))``.
+    """
+    if not tensor.requires_grad:
+        raise RuntimeError(
+            "It is not possible to get the gradient edge for a Tensor "
+            "that does not require gradients",
+        )
+    grad_fn = _get_grad_fn_or_grad_acc(tensor)
+
+    # Python-based Node are owned by the C++ side meaning the python grad_fn
+    # object we hold here does NOT keep the C++ graph alive.
+    # Create an ownership token by creating a new C++ node that own the graph
+    # we care about here.
+    token = None
+    if isinstance(grad_fn, torch._C._FunctionBase):
+        with torch.enable_grad():
+            token = tensor.view_as(tensor).grad_fn
+
+    # Note that output_nr default to 0 which is the right value
+    # for the AccumulateGrad node.
+    # pyrefly: ignore [bad-argument-type]
+    return GradientEdge(grad_fn, tensor.output_nr, ownership_token=token)
+
+
+def increment_version(tensor: Union[torch.Tensor, Iterable[torch.Tensor]]) -> None:
+    """Update autograd metadata tracking whether the given Tensor was modified in place.
+
+    This is to enable more accurate error checking within the autograd engine.
+    It is already done automatically by PyTorch functions and within custom Function
+    when mark_dirty() is called appropriately so you only need to call this explicitly
+    if you are doing inplace operation on the Tensor data in a way that Pytorch doesn't
+    know about. For example a custom kernel that reads the Tensor data_ptr and modifies
+    the memory inplace based on this pointer. Can accept either a tensor, or a list of tensors.
+
+    Note that incrementing the version counter multiple times for a single inplace operation
+    is not problematic.
+
+    Note that if you pass in tensor constructed under torch.inference_mode(),
+    we will not bump its version counter (because your tensor does not have one).
+    """
+    if isinstance(tensor, torch.Tensor):
+        tensor = (tensor,)
+    torch._C._increment_version(tensor)
+
+
+class saved_tensors_hooks:
+    """Context-manager that sets a pair of pack / unpack hooks for saved tensors.
+
+    Use this context-manager to define how intermediary results of an operation
+    should be packed before saving, and unpacked on retrieval.
+
+    In that context, the ``pack_hook`` function will be called every time an
+    operation saves a tensor for backward (this includes intermediary results
+    saved using
+    :func:`~torch.autograd.function._ContextMethodMixin.save_for_backward` but
+    also those recorded by a PyTorch-defined operation). The output of
+    ``pack_hook`` is then stored in the computation graph instead of the
+    original tensor.
+
+    The ``unpack_hook`` is called when the saved tensor needs to be accessed,
+    namely when executing :func:`torch.Tensor.backward()` or
+    :func:`torch.autograd.grad()`. It takes as argument the *packed* object
+    returned by ``pack_hook`` and should return a tensor which has the same
+    content as the original tensor (passed as input to the corresponding
+    ``pack_hook``).
+
+    The hooks should have the following signatures:
+
+        pack_hook(tensor: Tensor) -> Any
+
+        unpack_hook(Any) -> Tensor
+
+    where the return value of ``pack_hook`` is a valid input to ``unpack_hook``.
+
+    In general, you want ``unpack_hook(pack_hook(t))`` to be equal to ``t`` in terms
+    of value, size, dtype and device.
+
+    Example::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+        >>> def pack_hook(x):
+        ...     print("Packing", x)
+        ...     return x.detach()
+        >>>
+        >>> def unpack_hook(x):
+        ...     print("Unpacking", x)
+        ...     return x
+        >>>
+        >>> a = torch.ones(5, requires_grad=True)
+        >>> b = torch.ones(5, requires_grad=True) * 2
+        >>> with torch.autograd.graph.saved_tensors_hooks(pack_hook, unpack_hook):
+        ...     y = a * b
+        Packing tensor([1., 1., 1., 1., 1.], requires_grad=True)
+        Packing tensor([2., 2., 2., 2., 2.], grad_fn=)
+        >>> y.sum().backward()
+        Unpacking tensor([1., 1., 1., 1., 1.], requires_grad=True)
+        Unpacking tensor([2., 2., 2., 2., 2.], grad_fn=)
+
+    .. warning ::
+        Performing an inplace operation on the input to either hooks may lead
+        to undefined behavior.
+
+    .. warning ::
+        Only one pair of hooks is allowed at a time. When recursively nesting this
+        context-manager, only the inner-most pair of hooks will be applied.
+
+    .. warning ::
+        To avoid reference cycle, the return value of ``pack_hook`` cannot hold a
+        reference to the input tensor. For example, use `lambda x: x.detach()`
+        instead of `lambda x: x` as the pack hook.
+    """
+
+    def __init__(
+        self,
+        pack_hook: Callable[[torch.Tensor], Any],
+        unpack_hook: Callable[[Any], torch.Tensor],
+    ) -> None:
+        self.pack_hook = pack_hook
+        self.unpack_hook = unpack_hook
+
+    def __enter__(self) -> None:
+        torch._C._autograd._push_saved_tensors_default_hooks(
+            self.pack_hook, self.unpack_hook
+        )
+
+    def __exit__(self, *args: object) -> None:
+        torch._C._autograd._pop_saved_tensors_default_hooks()
+
+
+class save_on_cpu(saved_tensors_hooks):
+    """Context manager under which tensors saved by the forward pass will be stored on cpu, then retrieved for backward.
+
+    When performing operations within this context manager, intermediary
+    results saved in the graph during the forward pass will be moved to CPU,
+    then copied back to the original device when needed for the backward pass.
+    If the graph was already on CPU, no tensor copy is performed.
+
+    Use this context-manager to trade compute for GPU memory usage (e.g.
+    when your model doesn't fit in GPU memory during training).
+
+    Args:
+        pin_memory (bool): If ``True`` tensors will be saved to CPU pinned memory
+                           during packing and copied to GPU asynchronously during unpacking.
+                           Defaults to ``False``.
+                           Also see :ref:`cuda-memory-pinning`.
+
+
+    Example::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD)
+        >>> a = torch.randn(5, requires_grad=True, device="cuda")
+        >>> b = torch.randn(5, requires_grad=True, device="cuda")
+        >>> c = torch.randn(5, requires_grad=True, device="cuda")
+        >>>
+        >>> def f(a, b, c):
+        ...     prod_1 = a * b           # a and b are saved on GPU
+        ...     with torch.autograd.graph.save_on_cpu():
+        ...         prod_2 = prod_1 * c  # prod_1 and c are saved on CPU
+        ...     y = prod_2 * a           # prod_2 and a are saved on GPU
+        ...     return y
+        >>>
+        >>> y = f(a, b, c)
+        >>> del a, b, c  # for illustration only
+        >>> # the content of a, b, and prod_2 are still alive on GPU
+        >>> # the content of prod_1 and c only live on CPU
+        >>> y.sum().backward()  # all CPU tensors are moved back to GPU, for backward
+        >>> # all intermediary tensors are released (deleted) after the call to backward
+    """
+
+    def __init__(self, pin_memory: bool = False, device_type: str = "cuda") -> None:
+        device_module = getattr(torch, device_type, torch.cuda)
+
+        def pack_to_cpu(tensor: torch.Tensor) -> tuple[torch.device, torch.Tensor]:
+            if not pin_memory:
+                return (tensor.device, tensor.cpu())
+            packed = torch.empty(
+                tensor.size(),
+                dtype=tensor.dtype,
+                layout=tensor.layout,
+                pin_memory=(device_module.is_available() and not tensor.is_sparse),
+            )
+            packed.copy_(tensor)
+            return (tensor.device, packed)
+
+        def unpack_from_cpu(packed: tuple[torch.device, torch.Tensor]) -> torch.Tensor:
+            device, tensor = packed
+            return tensor.to(device, non_blocking=pin_memory)
+
+        super().__init__(pack_to_cpu, unpack_from_cpu)
+
+
+@contextlib.contextmanager
+def disable_saved_tensors_hooks(error_message: str) -> Generator[None, None, None]:
+    """Context-manager that disables the saved tensors default hooks feature.
+
+    Useful for if you are creating a feature that does not work with saved
+    tensors default hooks.
+
+    Args:
+        error_message (str): When saved tensors default hooks are used when they
+                             have been are disabled, a RuntimeError with this
+                             error message gets raised.
+
+    Example::
+
+        >>> # xdoctest: +SKIP(failing)
+        >>> message = "saved tensors default hooks are disabled"
+        >>> with torch.autograd.graph.disable_saved_tensors_hooks(message):
+        ...     # Raises RuntimeError: saved tensors default hooks are disabled
+        ...     with torch.autograd.graph.save_on_cpu():
+        ...         pass
+    """
+    maybe_prev_message = None
+    try:
+        maybe_prev_message = (
+            torch._C._autograd._saved_tensors_hooks_get_disabled_error_message()
+        )
+        torch._C._autograd._saved_tensors_hooks_disable(error_message)
+        yield
+    finally:
+        # See NOTE: [disabled_error_message invariant]
+        if maybe_prev_message is None:
+            torch._C._autograd._saved_tensors_hooks_enable()
+        else:
+            torch._C._autograd._saved_tensors_hooks_disable(maybe_prev_message)
+
+
+def set_warn_on_accumulate_grad_stream_mismatch(enabled: bool) -> None:
+    """Whether to warn when the AccumulateGrad node's stream does not match the stream
+    of the node that produced the incoming gradient.
+    """
+    return torch._C._set_warn_on_accumulate_grad_stream_mismatch(enabled)
+
+
+class _MultiHandle(RemovableHandle):
+    handles: tuple[RemovableHandle, ...]
+
+    def __init__(self, handles: tuple[RemovableHandle, ...]) -> None:
+        self.handles = handles
+
+    def remove(self) -> None:
+        for handle in self.handles:
+            handle.remove()
+
+    def __getstate__(self) -> tuple[RemovableHandle, ...]:
+        return self.handles
+
+    def __setstate__(self, state: tuple[RemovableHandle, ...]) -> None:
+        self.handles = state
+
+
+def register_multi_grad_hook(
+    tensors: Sequence[torch.Tensor],
+    fn: Union[
+        Callable[[Sequence[Optional[torch.Tensor]]], None],
+        Callable[[torch.Tensor], None],
+    ],
+    *,
+    mode: Literal["all", "any"] = "all",
+) -> RemovableHandle:
+    r"""Register a multi-grad backward hook.
+
+    There are two supported modes: ``"all"`` and ``"any"``.
+
+    Under the ``"all"`` mode, the hook will be called after gradients with respect to every tensor in
+    :attr:`tensors` have been computed. If a tensor is in :attr:`tensors` but
+    is not part of the graph, or if a tensor is not needed to compute the gradients
+    for any ``inputs`` specified for the current ``.backward()`` or ``.grad()`` call,
+    this tensor will be ignored and the hook will not wait for its gradient to be
+    computed.
+
+    After every non-ignored tensor's gradient has been computed, :attr:`fn` will be
+    called with those gradients. ``None`` will be passed for tensors that did not
+    have their gradients computed.
+
+    Under the ``"any"`` mode, the hook will be called after the first gradient
+    with respect to a tensor in :attr:`tensors` has been computed. The hook
+    will be called with that gradient as its argument.
+
+    The hook should not modify its arguments.
+
+    This function returns a handle with a method ``handle.remove()`` that removes the hook.
+
+    .. note::
+        See :ref:`backward-hooks-execution` for more information on how when this hook
+        is executed, and how its execution is ordered relative to other hooks.
+
+    Example::
+
+        >>> import torch
+        >>>
+        >>> a = torch.rand(2, 3, requires_grad=True)
+        >>> b = torch.rand(2, 3, requires_grad=True)
+        >>> c = a * b
+        >>> d = a * b
+        >>>
+        >>> def fn(grads):
+        ...     print([g is not None for g in grads])
+        ...
+        >>> torch.autograd.graph.register_multi_grad_hook((a, b, c, d), fn)
+        >>>
+        >>> c.sum().backward(retain_graph=True)
+        [True, True, True, False]
+        >>> c.sum().backward(inputs=(a,), retain_graph=True)
+        [True, False, True, False]
+        >>>
+    """
+    supported_modes = ("all", "any")
+    lock = threading.Lock()
+
+    if mode not in supported_modes:
+        raise ValueError(f"Expects mode to be one of {supported_modes} but got {mode}")
+
+    if mode == "all":
+        count: dict[int, int] = {}
+        nb_calls = None
+        buffer: dict[int, list[Optional[torch.Tensor]]] = {}
+
+        grad_fns = list(map(_get_grad_fn_or_grad_acc, tensors))
+        len_tensors = len(tensors)
+
+        def get_inner_hook(idx: int) -> Callable[[torch.Tensor], None]:
+            def inner_hook(grad: torch.Tensor) -> None:
+                nonlocal count, nb_calls, buffer, fn
+                id = torch._C._current_graph_task_id()
+                if id == -1:
+                    raise AssertionError(
+                        "expected this hook to be called inside a backward call"
+                    )
+                count[id] = count.get(id, 0)
+                # pyrefly: ignore [unsupported-operation]
+                buffer[id] = buffer.get(id, [None] * len_tensors)
+
+                with lock:
+                    curr_count, count[id] = count[id], count[id] + 1
+
+                    if curr_count == 0:
+                        # On the first call, compute the actual nb_calls and buffer
+                        nb_calls = sum(
+                            map(torch._C._will_engine_execute_node, grad_fns)
+                        )
+
+                buffer[id][idx] = grad
+
+                if nb_calls is None:
+                    raise AssertionError("Expected nb_calls to be set")
+                if curr_count == nb_calls - 1:
+                    fn = cast(Callable[[Sequence[Optional[torch.Tensor]]], None], fn)
+                    fn(buffer[id])
+                    del count[id]
+                    del buffer[id]
+
+            return inner_hook
+
+        handles = tuple(
+            t.register_hook(get_inner_hook(i)) for i, t in enumerate(tensors)
+        )
+    elif mode == "any":
+        fn = cast(Callable[[torch.Tensor], None], fn)
+        ran_hook: dict[int, bool] = defaultdict(bool)
+
+        @functools.wraps(fn)
+        def wrapped_fn(grad: torch.Tensor) -> None:
+            nonlocal ran_hook
+            id = torch._C._current_graph_task_id()
+            if id == -1:
+                raise AssertionError(
+                    "expected this hook to be called inside a backward call"
+                )
+            with lock:
+                prev, ran_hook[id] = ran_hook[id], True
+            if prev:
+                return
+            fn(grad)
+
+        handles = tuple(
+            tensor.register_hook(wrapped_fn)
+            for tensor in tensors
+            if tensor.requires_grad
+        )
+
+    return _MultiHandle(handles)  # type: ignore[possibly-undefined]
+
+
+# NOTE [Allow mutation on tensors saved for backward]
+#
+# 1. Tensor gets saved for backward
+#    - remember the python object id and the version of the tensor
+#    - remember aliasing information (data_ptr of base + version)
+#    - save the original so we control its lifetime
+# 2. Any time a tensor gets in-placed
+#    - for each tensor aliased to it:
+#      - check using its object id and version to see if it has been saved
+#      - if it has been saved, clone it
+#      - delete the reference to the original
+# 3. during backward
+#    - if the clone exists, the tensor must've been modified in-place
+_allow_mutation_on_saved_tensors_enabled: bool = False
+
+
+_TID: TypeAlias = tuple[int, int, int]
+_SID: TypeAlias = tuple[int, int]
+
+
+def _get_tid(tensor: torch.Tensor) -> _TID:
+    # FIXME: This is almost definitely a bug.
+    if isinstance(
+        tensor,
+        (
+            torch._subclasses.fake_tensor.FakeTensor,
+            torch._subclasses.functional_tensor.FunctionalTensor,
+        ),
+    ):
+        data_ptr = 0
+    else:
+        data_ptr = tensor.data_ptr()
+    return (id(tensor), data_ptr, tensor._version)
+
+
+def _get_sid(tensor: torch.Tensor) -> _SID:
+    # FIXME: This is almost definitely a bug.
+    if isinstance(
+        tensor,
+        (
+            torch._subclasses.fake_tensor.FakeTensor,
+            torch._subclasses.functional_tensor.FunctionalTensor,
+        ),
+    ):
+        data_ptr = 0
+    else:
+        data_ptr = tensor.data_ptr()
+    return (data_ptr, tensor._version)
+
+
+class _Handle:
+    pass
+
+
+class _swap_with_cloned(saved_tensors_hooks):
+    def __init__(self, ctx: "_AllowMutationOnSavedContext") -> None:
+        def pack_hook(tensor: torch.Tensor) -> _Handle:
+            tid = _get_tid(tensor)
+            sid = _get_sid(tensor)
+            # Tensors saved for backward have an entry in _tid_to_weakhandle
+            handle: Optional[_Handle] = None
+
+            # Save aliasing information
+            ctx.sid_to_tid[sid].add(tid)
+
+            # NB: The same tensor (of the same version) can be saved multiple times
+            if tid not in ctx.tid_to_weakhandle:
+                handle = _Handle()
+                ctx.tid_to_weakhandle[tid] = handle
+                ctx.original[handle] = tensor
+            else:
+                # Store an additional strong reference to the handle
+                handle = ctx.tid_to_weakhandle[tid]
+            return handle
+
+        def unpack_hook(handle: _Handle) -> torch.Tensor:
+            error_msg = (
+                "Trying to backward outside of the 'allow_mutation_on_saved_tensors' context"
+                "in which the graph was originally recorded."
+            )
+            if not _allow_mutation_on_saved_tensors_enabled:
+                raise AssertionError(error_msg)
+            if handle in ctx.cloned:
+                res = ctx.cloned[handle]
+            else:
+                if handle not in ctx.original:
+                    raise AssertionError(error_msg)
+                res = ctx.original[handle]
+            return res
+
+        super().__init__(pack_hook, unpack_hook)
+
+
+class _CloneArgBeforeMutateMode(TorchDispatchMode):
+    def __init__(self, ctx: "_AllowMutationOnSavedContext") -> None:
+        self.ctx = ctx
+
+    def __torch_dispatch__(
+        self,
+        func: "OpOverload",
+        types: Iterable[type],
+        args: tuple[Any, ...] = (),
+        kwargs: Optional[dict[Any, Any]] = None,
+    ) -> Any:
+        kwargs = kwargs or {}
+
+        def maybe_clone(t: torch.Tensor) -> None:
+            tid = _get_tid(t)
+            sid = _get_sid(t)
+            ctx = self.ctx
+            if sid in ctx.sid_to_tid:
+                for tid in ctx.sid_to_tid[sid]:
+                    if tid not in ctx.tid_to_weakhandle:
+                        # We know that if tid is in sid_to_tid, then it must also be in
+                        # tid_to_weakhandle. However, it is possible for the tensor to be
+                        # saved at one point, but cleared by backward before it is modified
+                        # in-place. Consider the following example:
+                        #
+                        # >>> a = torch.randn(2, 3, requires_grad=True).clone()
+                        # >>> out = (a**2).sum()
+                        # >>> out.backward()
+                        # >>> a.sin_()
+                        continue
+                    handle = ctx.tid_to_weakhandle[tid]
+                    if handle in ctx.cloned:
+                        # The same exact tensor has been cloned already
+                        continue
+                    ctx.cloned[handle] = ctx.original[handle].clone()
+                    del ctx.original[handle]
+
+        for idx, arg in enumerate(func._schema.arguments):
+            if arg.alias_info is not None and arg.alias_info.is_write:
+                if arg.is_out:
+                    maybe_clone(kwargs["out"])
+                elif isinstance(args[idx], list):
+                    # Foreach case. (Possible optimization: if most of the
+                    # tensors need to be cloned, use a for each clone?)
+                    for t in args[idx]:
+                        maybe_clone(t)
+                else:
+                    maybe_clone(args[idx])
+
+        return func(*args, **kwargs)
+
+
+class _AllowMutationOnSavedContext:
+    def __init__(self) -> None:
+        self.cloned: MutableMapping[_Handle, torch.Tensor] = WeakKeyDictionary()
+        self.original: MutableMapping[_Handle, torch.Tensor] = WeakKeyDictionary()
+        self.tid_to_weakhandle: MutableMapping[_TID, _Handle] = WeakValueDictionary()
+        self.sid_to_tid: dict[_SID, set[_TID]] = defaultdict(set)
+
+    def clear(self) -> None:
+        self.cloned.clear()
+        self.original.clear()
+        self.tid_to_weakhandle.clear()
+        self.sid_to_tid.clear()
+
+
+@contextlib.contextmanager
+def allow_mutation_on_saved_tensors() -> Generator[
+    _AllowMutationOnSavedContext, None, None
+]:
+    """Context manager under which mutating tensors saved for backward is allowed.
+
+    Under this context manager, tensors saved for backward are cloned on mutation,
+    so the original version can still be used during backward. Normally, mutating a tensor
+    saved for backward will result in an error raised when it's used during backward.
+
+    To ensure the correct behavior, both the forward and backward should be run under
+    the same context manager.
+
+    Returns:
+        An _AllowMutationOnSavedContext object storing the state managed by this
+        context manager. This object can be useful for debugging purposes. The state
+        managed by the context manager is automatically cleared upon exiting.
+
+    Example::
+
+        >>> import torch
+        >>> with torch.autograd.graph.allow_mutation_on_saved_tensors():
+        ...     # forward
+        ...     a = torch.ones(2, 3, requires_grad=True)
+        ...     b = a.clone()
+        ...     out = (b**2).sum()
+        ...     b.sin_()
+        ...     # backward
+        ...     out.sum().backward()
+        ...
+        tensor([[0.8415, 0.8415, 0.8415],
+                [0.8415, 0.8415, 0.8415]], grad_fn=)
+    """
+    global _allow_mutation_on_saved_tensors_enabled
+
+    ctx = _AllowMutationOnSavedContext()
+
+    with _swap_with_cloned(ctx), _CloneArgBeforeMutateMode(ctx):
+        try:
+            if _allow_mutation_on_saved_tensors_enabled:
+                raise RuntimeError(
+                    "allow_mutation_on_saved_tensors contexts cannot be nested"
+                )
+            _allow_mutation_on_saved_tensors_enabled = True
+            yield ctx
+        finally:
+            ctx.clear()
+            _allow_mutation_on_saved_tensors_enabled = False
+
+
+def _register_logging_hooks_on_whole_graph(
+    t_outputs: Sequence[Union[torch.Tensor, GradientEdge]],
+) -> Callable[[], None]:
+    grad_fns = list(map(_get_grad_fn_or_grad_acc, t_outputs))
+
+    def iter_graph(roots: list[Node]) -> Iterator[Node]:
+        if not roots:
+            return
+        seen: set[Node] = set()
+        q: deque[Node] = deque()
+        for node in roots:
+            if node is not None:
+                seen.add(node)
+                q.append(node)
+
+        while q:
+            node = q.popleft()
+            for fn, _ in node.next_functions:
+                if fn in seen or fn is None:
+                    continue
+                seen.add(fn)
+                q.append(fn)
+
+            yield node
+
+    def fmt(t: Optional[torch.Tensor]) -> str:
+        # Avoid circular import
+        from torch.utils._dtype_abbrs import dtype_abbrs
+
+        if t is None:
+            return "None"
+        return f"{dtype_abbrs[t.dtype]}[{', '.join(map(str, t.shape))}]"
+
+    def prehook(grad_outputs: Sequence[Optional[torch.Tensor]]) -> None:
+        node = torch._C._current_autograd_node()
+        grad_outputs_str = f"[{','.join(fmt(t) for t in grad_outputs)}]"
+        log_str = f"Executing: {node} with grad_outputs: {grad_outputs_str}"
+        log.debug(log_str)
+
+    handles = [node.register_prehook(prehook) for node in iter_graph(grad_fns)]
+
+    def unregister_hooks() -> None:
+        for handle in handles:
+            handle.remove()
+
+    return unregister_hooks
+
+
+def _engine_run_backward(
+    t_outputs: Sequence[Union[torch.Tensor, GradientEdge]],
+    *args: Any,
+    **kwargs: Any,
+) -> tuple[torch.Tensor, ...]:
+    attach_logging_hooks = log.getEffectiveLevel() <= logging.DEBUG
+    if attach_logging_hooks:
+        unregister_hooks = _register_logging_hooks_on_whole_graph(t_outputs)
+    try:
+        return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
+            t_outputs, *args, **kwargs
+        )  # Calls into the C++ engine to run the backward pass
+    finally:
+        if attach_logging_hooks:
+            unregister_hooks()  # type: ignore[possibly-undefined]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/profiler.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/profiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa43af270117109158e7e6c79f2eb19322c7ab7e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/profiler.py
@@ -0,0 +1,1242 @@
+# mypy: allow-untyped-defs
+import uuid
+from collections import defaultdict
+from collections.abc import Iterable
+from dataclasses import dataclass
+from time import perf_counter_ns
+from typing import Any, Optional
+from warnings import warn
+
+import torch
+import torch.cuda
+from torch._C import _get_privateuse1_backend_name
+from torch._C._profiler import _ExperimentalConfig
+from torch.autograd import (
+    _disable_profiler,
+    _enable_profiler,
+    _kineto_step,
+    _prepare_profiler,
+    _ProfilerResult,
+    _supported_activities,
+    _toggle_collection_dynamic,
+    DeviceType,
+    kineto_available,
+    ProfilerActivity,
+    ProfilerConfig,
+    ProfilerState,
+)
+from torch.autograd.profiler_util import (
+    _filter_name,
+    _filter_stack_entry,
+    _rewrite_name,
+    EventList,
+    FunctionEvent,
+    MEMORY_EVENT_NAME,
+    MemRecordsAcc,
+    OUT_OF_MEMORY_EVENT_NAME,
+)
+from torch.futures import Future
+
+
+__all__ = [
+    "profile",
+    "record_function",
+    "emit_itt",
+    "emit_nvtx",
+    "load_nvprof",
+    "EnforceUnique",
+    "parse_nvprof_trace",
+    "KinetoStepTracker",
+    "EventList",
+    "FunctionEvent",
+    "MemRecordsAcc",
+]
+
+try:
+    # Available in Python >= 3.2
+    from contextlib import ContextDecorator as _ContextDecorator
+except ImportError:
+    import functools
+
+    class _ContextDecorator:  # type: ignore[no-redef]
+        def __enter__(self):
+            raise NotImplementedError
+
+        def __exit__(self, exc_type, exc_val, exc_tb):
+            raise NotImplementedError
+
+        def __call__(self, func):
+            @functools.wraps(func)
+            def wrapped(*args, **kwargs):
+                with self:
+                    return func(*args, **kwargs)
+
+            return wrapped
+
+
+# global python state - whether profiler is currently enabled
+# useful for fast python checks to reduce latency
+_is_profiler_enabled: bool = False
+
+
+def _set_is_profiler_enabled(enable: bool):
+    global _is_profiler_enabled
+    _is_profiler_enabled = enable
+
+
+def _run_on_profiler_start():
+    _set_is_profiler_enabled(True)
+
+
+def _run_on_profiler_stop():
+    _set_is_profiler_enabled(False)
+
+
+@dataclass
+class _ProfilerStats:
+    "Profiler timing and stats used by developers to catch issues/regressions"
+
+    profiling_window_duration_sec: float = 0
+    number_of_events: int = 0
+    profiler_prepare_call_duration_us: int = 0
+    profiler_enable_call_duration_us: int = 0
+    profiler_disable_call_duration_us: int = 0
+    parse_kineto_call_duration_us: int = 0
+    function_events_build_tree_call_duration_us: int = 0
+
+
+class profile:
+    """Context manager that manages autograd profiler state and holds a summary of results.
+
+    .. note::
+        This is the backend, most people should use :mod:`torch.profiler` instead.
+
+    Under the hood it just records events of functions being executed in C++ and
+    exposes those events to Python. You can wrap any code into it and it will
+    only report runtime of PyTorch functions.
+    Note: profiler is thread local and is automatically propagated into the async tasks
+
+    Args:
+        enabled (bool, optional): Setting this to False makes this context manager a no-op.
+
+        use_cuda (bool, optional): Enables timing of CUDA events as well
+            using the cudaEvent API. (will be deprecated)
+
+        use_device (str, optional): Enables timing of device events.
+            Adds approximately 4us of overhead to each tensor operation when use cuda.
+            The valid devices options are 'cuda', 'xpu', 'mtia' and 'privateuseone'.
+
+        record_shapes (bool, optional): If shapes recording is set, information
+            about input dimensions will be collected. This allows one to see which
+            dimensions have been used under the hood and further group by them
+            using prof.key_averages(group_by_input_shape=True). Please note that
+            shape recording might skew your profiling data. It is recommended to
+            use separate runs with and without shape recording to validate the timing.
+            Most likely the skew will be negligible for bottom most events (in a case
+            of nested function calls). But for higher level functions the total
+            self cpu time might be artificially increased because of the shape
+            collection.
+
+        with_flops (bool, optional): If with_flops is set, the profiler will estimate
+            the FLOPs (floating point operations) value using the operator's input shape.
+            This allows one to estimate the hardware performance. Currently,
+            this option only works for the matrix multiplication and 2D convolution operators.
+
+        profile_memory (bool, optional): track tensor memory allocation/deallocation.
+
+        with_stack (bool, optional): record source information (file and line number) for the ops.
+
+        with_modules (bool): record module hierarchy (including function names)
+            corresponding to the callstack of the op. e.g. If module A's forward call's
+            module B's forward which contains an aten::add op,
+            then aten::add's module hierarchy is A.B
+            Note that this support exist, at the moment, only for TorchScript models
+            and not eager mode models.
+
+        use_kineto (bool, optional): experimental, enable profiling with Kineto profiler.
+
+        use_cpu (bool, optional): profile CPU events; setting to ``False`` requires
+            ``use_kineto=True`` and can be used to lower the overhead for GPU-only profiling.
+
+        experimental_config (_ExperimentalConfig) : A set of experimental options
+            used by profiler libraries like Kineto. Note, backward compatibility is not guaranteed.
+
+        acc_events (bool): Enable the accumulation of FunctionEvents across multiple profiling cycles
+
+
+    .. warning::
+        Enabling memory profiling or source attribution incurs additional profiler
+        overhead
+
+    .. warning::
+        This context managers should not be called recursively, i.e. no nested
+        instances are allowed
+
+    .. warning::
+        Due to some CUDA multiprocessing limitations (see :ref:`multiprocessing-cuda-note`),
+        one cannot use the profiler with ``use_device = 'cuda'`` to benchmark
+        DataLoaders with ``num_workers > 0``. If you wish to benchmark data loading,
+        please use ``use_device = None`` or ``num_workers = 0``.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD_PROFILER)
+        >>> x = torch.randn((1, 1), requires_grad=True)
+        >>> with torch.autograd.profiler.profile() as prof:
+        >>>     for _ in range(100):  # any normal python code, really!
+        >>>         y = x ** 2
+        >>>         y.backward()
+        >>> # NOTE: some columns were removed for brevity
+        >>> print(prof.key_averages().table(sort_by="self_cpu_time_total"))
+        -----------------------------------  ---------------  ---------------  ---------------
+        Name                                 Self CPU total   CPU time avg     Number of Calls
+        -----------------------------------  ---------------  ---------------  ---------------
+        mul                                  32.048ms         32.048ms         200
+        pow                                  27.041ms         27.041ms         200
+        PowBackward0                         9.727ms          55.483ms         100
+        torch::autograd::AccumulateGrad      9.148ms          9.148ms          100
+        torch::autograd::GraphRoot           691.816us        691.816us        100
+        -----------------------------------  ---------------  ---------------  ---------------
+
+    """
+
+    def __init__(
+        self,
+        enabled=True,
+        *,
+        use_cuda=False,  # Deprecated
+        use_device=None,
+        record_shapes=False,
+        with_flops=False,
+        profile_memory=False,
+        with_stack=False,
+        with_modules=False,
+        use_kineto=False,
+        use_cpu=True,
+        experimental_config=None,
+        acc_events=False,
+        custom_trace_id_callback=None,
+    ):
+        self.enabled: bool = enabled
+        if not self.enabled:
+            return
+        self.use_cuda = use_cuda
+        if self.use_cuda:
+            warn(
+                "The attribute `use_cuda` will be deprecated soon, "
+                "please use ``use_device = 'cuda'`` instead.",
+                FutureWarning,
+                stacklevel=2,
+            )
+            self.use_device: Optional[str] = "cuda"
+        else:
+            self.use_device = use_device
+        # TODO Consider changing _function_events into data structure with size cap
+        self._function_events: Optional[EventList] = None
+        self._old_function_events: Optional[EventList] = None
+        # Function event processing is done lazily
+        self._needs_processing = False
+        self.entered = False
+        self.record_shapes = record_shapes
+        self.with_flops = with_flops
+        self.record_shapes |= self.with_flops
+        self.profile_memory = profile_memory
+        self.with_stack = with_stack
+        self.with_modules = with_modules
+        self.use_cpu = use_cpu
+        self.acc_events = acc_events
+        if experimental_config is None:
+            experimental_config = _ExperimentalConfig()
+        self.experimental_config = experimental_config
+        self.kineto_results: Optional[_ProfilerResult] = None
+        self.profiling_start_time_ns = 0
+        self.profiling_end_time_ns = 0
+        self._stats = _ProfilerStats()
+        self.custom_trace_id_callback = custom_trace_id_callback
+        self.trace_id = ""
+        if not self.use_cpu:
+            if not use_kineto:
+                raise AssertionError(
+                    "Device-only events supported only with Kineto (use_kineto=True)"
+                )
+
+        if self.use_device is not None:
+            VALID_DEVICE_OPTIONS = ["cuda", "xpu", "mtia", "hpu"]
+            if _get_privateuse1_backend_name() != "privateuseone":
+                VALID_DEVICE_OPTIONS.append(_get_privateuse1_backend_name())
+            if self.use_device not in VALID_DEVICE_OPTIONS:
+                warn(
+                    f"The {self.use_device} is not a valid device option.", stacklevel=2
+                )
+                self.use_device = None
+
+            if self.use_device == "cuda" and not torch.cuda.is_available():
+                warn("CUDA is not available, disabling CUDA profiling", stacklevel=2)
+                self.use_cuda = False
+                self.use_device = None
+
+            if self.use_device == "xpu" and not torch.xpu.is_available():
+                warn("XPU is not available, disabling XPU profiling", stacklevel=2)
+                self.use_device = None
+
+            if self.use_device == "hpu" and not (
+                hasattr(torch, "hpu") and torch.hpu.is_available()
+            ):
+                warn("HPU is not available, disabling HPU profiling", stacklevel=2)
+                self.use_device = None
+
+        self.kineto_activities = set()
+        if self.use_cpu:
+            self.kineto_activities.add(ProfilerActivity.CPU)
+
+        self.profiler_kind = ProfilerState.KINETO
+        if self.use_device == "cuda":
+            if not use_kineto or ProfilerActivity.CUDA not in _supported_activities():
+                if not self.use_cpu:
+                    raise AssertionError("Legacy CUDA profiling requires use_cpu=True")
+                self.profiler_kind = ProfilerState.KINETO_GPU_FALLBACK
+            else:
+                self.kineto_activities.add(ProfilerActivity.CUDA)
+        elif self.use_device == "xpu":
+            if not (use_kineto and ProfilerActivity.XPU in _supported_activities()):
+                raise AssertionError(
+                    "Legacy XPU profiling is not supported. Requires use_kineto=True on XPU devices."
+                )
+            self.kineto_activities.add(ProfilerActivity.XPU)
+        elif self.use_device == "mtia":
+            if not (use_kineto and ProfilerActivity.MTIA in _supported_activities()):
+                raise AssertionError(
+                    "Legacy MTIA profiling is not supported. Requires use_kineto=True on MTIA devices."
+                )
+            self.kineto_activities.add(ProfilerActivity.MTIA)
+        elif self.use_device == "hpu":
+            if not (use_kineto and ProfilerActivity.HPU in _supported_activities()):
+                raise AssertionError(
+                    "Legacy HPU profiling is not supported. Requires use_kineto=True on HPU devices."
+                )
+            self.kineto_activities.add(ProfilerActivity.HPU)
+        elif self.use_device is not None and self.use_device != "privateuseone":
+            if (
+                not use_kineto
+                or ProfilerActivity.PrivateUse1 not in _supported_activities()
+            ):
+                if not self.use_cpu:
+                    raise AssertionError(
+                        "Legacy custombackend profiling requires use_cpu=True"
+                    )
+                self.profiler_kind = ProfilerState.KINETO_PRIVATEUSE1_FALLBACK
+            else:
+                self.kineto_activities.add(ProfilerActivity.PrivateUse1)
+
+        if len(self.kineto_activities) == 0:
+            raise AssertionError("No activities specified for the profiler")
+
+    def default_trace_id(self):
+        # Generate a UUID
+        uuid_raw = uuid.uuid4()
+
+        return f"{uuid_raw.int:032X}"
+
+    def create_trace_id(self):
+        if self.custom_trace_id_callback:
+            return self.custom_trace_id_callback()
+        return self.default_trace_id()
+
+    def config(self, create_trace_id=False):
+        # only need to generate new trace id upon prepare trace not start trace
+        if create_trace_id:
+            trace_id = self.create_trace_id()
+            self.trace_id = trace_id
+        return ProfilerConfig(
+            self.profiler_kind,
+            self.record_shapes,
+            self.profile_memory,
+            self.with_stack,
+            self.with_flops,
+            self.with_modules,
+            self.experimental_config,
+            self.trace_id,
+        )
+
+    def __enter__(self):
+        if not self.enabled:
+            return
+        if self.entered:
+            raise RuntimeError("Profiler context manager is not reentrant")
+        self._prepare_trace()
+        self._start_trace()
+        return self
+
+    def _prepare_trace(self):
+        self.entered = True
+        t0 = perf_counter_ns()
+        _prepare_profiler(self.config(create_trace_id=True), self.kineto_activities)
+        t1 = perf_counter_ns()
+        self._stats.profiler_prepare_call_duration_us = int((t1 - t0) / 1000)
+
+    def _start_trace(self):
+        self.entered = True
+        _run_on_profiler_start()
+        t0 = perf_counter_ns()
+        _enable_profiler(self.config(create_trace_id=False), self.kineto_activities)
+        t1 = perf_counter_ns()
+        self._stats.profiler_enable_call_duration_us = int((t1 - t0) / 1000)
+        self.profiling_start_time_ns = t1
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        if not self.enabled:
+            return
+        if self.use_device and hasattr(torch, self.use_device):
+            device_module = getattr(torch, self.use_device)
+            if hasattr(device_module, "synchronize"):
+                device_module.synchronize()
+
+        if self._function_events and self.acc_events:
+            self._old_function_events = self._function_events
+        self._function_events = None
+        self._needs_processing = True
+
+        t0 = perf_counter_ns()
+
+        self.kineto_results = _disable_profiler()
+        t1 = perf_counter_ns()
+        self._stats.profiler_disable_call_duration_us = int((t1 - t0) / 1000)
+        self.profiling_end_time_ns = t0
+
+        _run_on_profiler_stop()
+
+        self._stats.profiling_window_duration_sec = (
+            (self.profiling_end_time_ns - self.profiling_start_time_ns) * 1.0 / 1e9
+        )
+
+        # If we plan to accumulate events we should post process the function events
+        # right away to retain the state across multiple start/stop calls
+        if self.acc_events:
+            self._ensure_function_events()
+        return False
+
+    def __repr__(self):
+        if self._needs_processing:
+            self._ensure_function_events()
+        if self._function_events is None:
+            return ""
+        return repr(self._function_events)
+
+    def __str__(self):
+        if self._needs_processing:
+            self._ensure_function_events()
+        if self._function_events is None:
+            return ""
+        return str(self._function_events)
+
+    def _ensure_function_events(self):
+        """Process function events lazily if required"""
+        if self._function_events is not None:
+            return
+        self._needs_processing = False
+
+        t0 = perf_counter_ns()
+        parsed_results = []
+        if self.kineto_results:
+            parsed_results = self._parse_kineto_results(self.kineto_results)
+        t1 = perf_counter_ns()
+        self._stats.parse_kineto_call_duration_us = int((t1 - t0) / 1000)
+
+        self._function_events = EventList(
+            parsed_results,
+            use_device=self.use_device,
+            profile_memory=self.profile_memory,
+            with_flops=self.with_flops,
+        )
+        t0 = perf_counter_ns()
+        self._function_events._build_tree()
+        t1 = perf_counter_ns()
+        self._stats.function_events_build_tree_call_duration_us = int((t1 - t0) / 1000)
+        self._stats.number_of_events = len(self._function_events)
+
+        if self._old_function_events and self.acc_events:
+            for evt in self._old_function_events:
+                self._function_events.append(evt)
+            self._old_function_events = None
+
+        if self._function_events is None:
+            raise RuntimeError("Profiler didn't finish running")
+
+    @property
+    def function_events(self):
+        if self._function_events is None or self._needs_processing:
+            self._ensure_function_events()
+        return self._function_events
+
+    def table(
+        self,
+        sort_by=None,
+        row_limit=100,
+        max_src_column_width=75,
+        max_name_column_width=55,
+        max_shapes_column_width=80,
+        header=None,
+        top_level_events_only=False,
+    ):
+        self._ensure_function_events()
+        if self._function_events is None:
+            raise AssertionError("Expected profiling results")
+        return self._function_events.table(
+            sort_by=sort_by,
+            row_limit=row_limit,
+            max_src_column_width=max_src_column_width,
+            max_name_column_width=max_name_column_width,
+            max_shapes_column_width=max_shapes_column_width,
+            header=header,
+            top_level_events_only=top_level_events_only,
+        )
+
+    table.__doc__ = EventList.table.__doc__
+
+    def export_chrome_trace(self, path):
+        """
+        Exports the collected trace in Chrome JSON format. If kineto is enabled, only
+        last cycle in schedule is exported.
+        """
+        if kineto_available():
+            self.kineto_results.save(path)  # type: ignore[union-attr]
+        else:
+            self._ensure_function_events()
+            return self._function_events.export_chrome_trace(path)  # type: ignore[union-attr]
+
+    export_chrome_trace.__doc__ = EventList.export_chrome_trace.__doc__
+
+    def export_stacks(self, path: str, metric: str = "self_cpu_time_total"):
+        self._ensure_function_events()
+        if self._function_events is None:
+            raise AssertionError("Expected profiling results")
+        if not self.with_stack:
+            raise AssertionError("export_stacks() requires with_stack=True")
+        return self._function_events.export_stacks(path, metric)
+
+    def toggle_collection_dynamic(
+        self, enabled: bool, activities: Iterable[ProfilerActivity]
+    ):
+        """
+        Toggles the collection of activities for the current profiler instance.
+        """
+        return _toggle_collection_dynamic(enabled, set(activities))
+
+    def key_averages(
+        self,
+        group_by_input_shape=False,
+        group_by_stack_n=0,
+        group_by_overload_name=False,
+    ):
+        self._ensure_function_events()
+        if self._function_events is None:
+            raise AssertionError("Expected profiling results")
+        return self._function_events.key_averages(
+            group_by_input_shape, group_by_stack_n, group_by_overload_name
+        )
+
+    key_averages.__doc__ = EventList.key_averages.__doc__
+
+    def total_average(self):
+        self._ensure_function_events()
+        if self._function_events is None:
+            raise AssertionError("Expected profiling results")
+        return self._function_events.total_average()
+
+    total_average.__doc__ = EventList.total_average.__doc__
+
+    @property
+    def self_cpu_time_total(self):
+        """Returns total time spent on CPU.
+
+        The total time is a sum of all self times across all the events.
+        """
+        self._ensure_function_events()
+        if self._function_events is None:
+            raise AssertionError("Expected profiling results")
+        return self._function_events.self_cpu_time_total
+
+    def _parse_kineto_results(self, result: _ProfilerResult):
+        # result.events() has most of the events - PyTorch op-level and device-level events
+
+        trace_start_ns = result.trace_start_ns()
+        mem_records = [
+            [evt, False] for evt in result.events() if evt.name() == MEMORY_EVENT_NAME
+        ]
+        oom_records = [
+            evt for evt in result.events() if evt.name() == OUT_OF_MEMORY_EVENT_NAME
+        ]
+        mem_records_acc = MemRecordsAcc(mem_records)
+
+        def _cpu_memory_usage(mem_record):
+            return (
+                mem_record.nbytes()
+                if mem_record.device_type()
+                in [DeviceType.CPU, DeviceType.MKLDNN, DeviceType.IDEEP]
+                else 0
+            )
+
+        def _device_memory_usage(mem_record):
+            return (
+                mem_record.nbytes()
+                if mem_record.device_type()
+                in [
+                    DeviceType.CUDA,
+                    DeviceType.PrivateUse1,
+                    DeviceType.HIP,
+                    DeviceType.XPU,
+                ]
+                else 0
+            )
+
+        # Create and return FunctionEvent list, which contains all function events
+        # Here 2 function events are created:
+        # all_function_events contains all events associated with each kineto event from result
+        all_function_events = []
+        # frontend_function_events contains the events in aten or torch frontend level,
+        # whose correlation id is 0
+        frontend_function_events = []
+        device_corr_map: dict[int, list[FunctionEvent]] = {}
+        max_evt_id = 0
+        for kineto_event in result.events():
+            if (
+                _filter_name(kineto_event.name())
+                or getattr(kineto_event, "is_hidden_event", lambda: False)()
+            ):
+                continue
+            rel_start_ns = kineto_event.start_ns() - trace_start_ns
+            rel_end_ns = kineto_event.end_ns() - trace_start_ns
+            abs_end_ns = kineto_event.end_ns()
+
+            cpu_memory_usage = 0
+            device_memory_usage = 0
+            if kineto_event.device_type() == DeviceType.CPU:
+                # find the corresponding memory allocation events
+                for mem_record in mem_records_acc.in_interval(
+                    kineto_event.start_ns() / 1000, abs_end_ns / 1000
+                ):
+                    cpu_memory_usage += _cpu_memory_usage(mem_record[0])
+                    device_memory_usage += _device_memory_usage(mem_record[0])
+                    mem_record[1] = True
+
+            is_async = kineto_event.is_async() or (
+                kineto_event.start_thread_id() != kineto_event.end_thread_id()
+            )
+
+            fe = FunctionEvent(
+                id=kineto_event.correlation_id(),
+                name=_rewrite_name(name=kineto_event.name(), with_wildcard=True),
+                overload_name=kineto_event.overload_name(),
+                trace_name=_rewrite_name(name=kineto_event.name(), with_wildcard=False),
+                thread=kineto_event.start_thread_id(),
+                start_us=rel_start_ns / 1000,
+                end_us=rel_end_ns / 1000,
+                fwd_thread=kineto_event.fwd_thread_id(),
+                input_shapes=kineto_event.shapes(),
+                concrete_inputs=kineto_event.concrete_inputs(),
+                kwinputs=kineto_event.kwinputs(),
+                stack=[
+                    entry
+                    for entry in kineto_event.stack()
+                    if _filter_stack_entry(entry)
+                ],
+                scope=kineto_event.scope(),
+                use_device=self.use_device,
+                cpu_memory_usage=cpu_memory_usage,
+                device_memory_usage=device_memory_usage,
+                is_async=is_async,
+                sequence_nr=kineto_event.sequence_nr(),
+                device_type=kineto_event.device_type(),
+                device_index=kineto_event.device_index(),
+                device_resource_id=kineto_event.device_resource_id(),
+                flops=kineto_event.flops(),
+                is_user_annotation=kineto_event.is_user_annotation(),
+                metadata_json=kineto_event.metadata_json(),
+            )
+            max_evt_id = max(max_evt_id, fe.id)
+            if fe.device_type == DeviceType.CPU and not fe.is_async:
+                if self.use_device == _get_privateuse1_backend_name():
+                    privateuse1_time = kineto_event.privateuse1_elapsed_us()
+                    if privateuse1_time > 0:
+                        fe.append_kernel(fe.name, fe.device_index, privateuse1_time)
+                        fe.is_legacy = True
+                elif self.use_device == "cuda":
+                    # Check if we have CUDA time as a fallback
+                    cuda_time = kineto_event.cuda_elapsed_us()
+                    if cuda_time > 0:
+                        fe.append_kernel(fe.name, fe.device_index, cuda_time)
+                        fe.is_legacy = True
+            all_function_events.append(fe)
+            corr_id = kineto_event.linked_correlation_id()
+            if corr_id > 0:
+                if corr_id not in device_corr_map:
+                    device_corr_map[corr_id] = []
+                device_corr_map[corr_id].append(fe)
+            elif corr_id == 0:
+                frontend_function_events.append(fe)
+            else:
+                raise RuntimeError(
+                    f"Got negative correlation id {corr_id} in profiler post processing"
+                )
+
+        # associate device kernels and device runtime (CPU) with CPU events
+        for fe in frontend_function_events:
+            if (
+                fe.device_type == DeviceType.CPU
+                and not fe.is_async
+                and fe.id in device_corr_map
+            ):
+                for f_evt in device_corr_map[fe.id]:
+                    if (
+                        f_evt.device_type == DeviceType.CUDA
+                        or f_evt.device_type == DeviceType.PrivateUse1
+                    ):
+                        fe.append_kernel(
+                            f_evt.name,
+                            f_evt.device_index,
+                            f_evt.time_range.end - f_evt.time_range.start,
+                        )
+                    elif f_evt.device_type == DeviceType.CPU:
+                        # make sure that 'thread' of a CPU Kineto (e.g. Device Runtime) event is associated
+                        # with the 'thread' of the corresponding linked PyTorch event to properly track
+                        # parents and children
+                        f_evt.thread = fe.thread
+
+        def createFunctionEventForMemoryEvents(evt):
+            rel_start_ns = evt.start_ns() - trace_start_ns
+            fe = FunctionEvent(
+                id=max_evt_id,
+                name=evt.name(),
+                overload_name="",
+                trace_name=None,  # not outputting in the trace
+                thread=evt.start_thread_id(),
+                start_us=rel_start_ns / 1000,
+                end_us=rel_start_ns / 1000,  # no duration
+                fwd_thread=evt.start_thread_id(),
+                input_shapes=[],
+                stack=[],
+                scope=0,  # RecordScope::FUNCTION
+                use_device=self.use_device,
+                cpu_memory_usage=_cpu_memory_usage(evt),
+                device_memory_usage=_device_memory_usage(evt),
+                is_async=False,
+                sequence_nr=-1,
+                device_type=DeviceType.CPU,
+                device_index=0,
+            )
+            return fe
+
+        # output top-level memory events
+        for mem_record in mem_records:
+            if not mem_record[1]:
+                max_evt_id += 1
+                fe = createFunctionEventForMemoryEvents(mem_record[0])
+                all_function_events.append(fe)
+
+        for oom_record in oom_records:
+            max_evt_id += 1
+            fe = createFunctionEventForMemoryEvents(oom_record)
+            all_function_events.append(fe)
+
+        all_function_events.sort(
+            key=lambda evt: [evt.time_range.start, -evt.time_range.end]
+        )
+        return all_function_events
+
+
+# pyrefly: ignore [invalid-inheritance]
+class record_function(_ContextDecorator):
+    """Context manager/function decorator that adds a label to a code block/function when running autograd profiler.
+    Label will only appear if CPU activity tracing is enabled.
+
+    It is useful when tracing the code profile.
+
+    Args:
+        name (str): Label assigned to the block of code.
+        node_id (int): ID of node, for distributed profiling. Unset in
+        non-distributed cases.
+
+    Example:
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD_PROFILER)
+        >>> x = torch.randn((1, 1), requires_grad=True)
+        >>> with torch.autograd.profiler.profile() as prof:
+        ...     y = x**2
+        ...     with torch.autograd.profiler.record_function(
+        ...         "label-z"
+        ...     ):  # label the block
+        ...         z = y**3
+        ...     y.backward()
+        >>> # xdoctest: +IGNORE_WANT
+        >>> # NOTE: some columns were removed for brevity
+        >>> print(prof.key_averages().table(sort_by="self_cpu_time_total"))
+        -----------------------------------  ---------------  ---------------  ---------------
+        Name                                 Self CPU total %  CPU time avg     Number of Calls
+        -----------------------------------  ---------------  ---------------  ---------------
+        pow                                  60.77%           47.470us         3
+        mul                                  21.73%           25.465us         2
+        PowBackward0                         12.03%           121.891us        1
+        torch::autograd::AccumulateGrad      2.70%            6.324us          1
+        label-z                              2.13%            12.421us         1
+        torch::autograd::GraphRoot           0.64%            1.503us          1
+        -----------------------------------  ---------------  ---------------  ---------------
+        Self CPU time total: 234.344us
+        CUDA time total: 0.000us
+
+    """
+
+    def __init__(self, name: str, args: Optional[str] = None):
+        self.name: str = name
+        self.args: Optional[str] = args
+        # Whether or not we should run record function's end callbacks when exiting.
+        self.run_callbacks_on_exit: bool = True
+        # TODO: TorchScript ignores standard type annotation here
+        # self.record: Optional["torch.classes.profiler._RecordFunction"] = None
+        self.record = torch.jit.annotate(
+            # pyrefly: ignore [not-a-type]
+            Optional["torch.classes.profiler._RecordFunction"],
+            None,
+        )
+
+    def __enter__(self):
+        self.record = torch.ops.profiler._record_function_enter_new(
+            self.name, self.args
+        )
+        return self
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any):
+        if not self.run_callbacks_on_exit:
+            return
+
+        # Local variable is needed by TorchScript to refine Optional[T] to T
+        record = self.record
+        if record is None:
+            raise AssertionError("Expected record to be set")
+
+        # TODO: Too slow with __torch_function__ handling enabled
+        # See https://github.com/pytorch/pytorch/issues/76410
+        if not torch.jit.is_scripting():
+            with torch._C.DisableTorchFunctionSubclass():
+                torch.ops.profiler._record_function_exit._RecordFunction(record)
+        else:
+            torch.ops.profiler._record_function_exit(record)
+
+    def _call_end_callbacks_on_future(self, fut: Future[Any]) -> Future[Any]:
+        """Use for profiling async calls that return a future.
+
+        Calling this function will extend recording beyond this scope, until the future is
+        satisfied. It is useful for profiling the end to end time of asynchronous calls.
+        This function should only be called once to attach the callback onto the future, and
+        will throw if called multiple times.
+
+        Args:
+            fut: (torch._C.Future): future for which to schedule
+            callback for.
+
+        Returns:
+            A future that completes with the value of the passed in future when
+            the profiling callbacks have ran.
+
+        """
+        # Throw if we have already attached a callback onto the future.
+        if not self.run_callbacks_on_exit:
+            raise RuntimeError("_call_end_callbacks_on_future can only be called once.")
+
+        # We are scheduling to run this RecordFunction's end callbacks when the
+        # passed in future completes, so don't run end callbacks on exit.
+        self.run_callbacks_on_exit = False
+
+        # Local variable is needed by TorchScript to refine Optional[T] to T
+        record = self.record
+        if record is None:
+            raise AssertionError("Expected record to be set")
+
+        # TODO: Too slow with __torch_function__ handling enabled
+        # See https://github.com/pytorch/pytorch/issues/76410
+        if not torch.jit.is_scripting():
+            with torch._C.DisableTorchFunctionSubclass():
+                profiled_future = (
+                    torch.ops.profiler._call_end_callbacks_on_jit_fut._RecordFunction(
+                        record, fut
+                    )
+                )
+        else:
+            profiled_future = torch.ops.profiler._call_end_callbacks_on_jit_fut(
+                record, fut
+            )
+        return profiled_future
+
+
+class emit_itt:
+    """Context manager that makes every autograd operation emit an ITT range.
+
+    It is useful when running the program under Intel(R) VTune Profiler::
+
+        vtune <--vtune-flags> 
+
+    The Instrumentation and Tracing Technology (ITT) API enables your application to generate and
+    control the collection of trace data during its execution across different Intel tools.
+    This context manager is to annotate Intel(R) VTune Profiling trace. With help of this context manager,
+    you will be able to see labeled ranges in Intel(R) VTune Profiler GUI.
+
+    .. warning:
+        This context manager should not be called recursively, i.e. at most one
+        instance should be enabled at any given time.
+
+    Args:
+        enabled (bool, optional): Setting ``enabled=False`` makes this context manager a no-op.
+            Default: ``True``.
+        record_shapes (bool, optional): If ``record_shapes=True``, the itt range wrapping
+            each autograd op will append information about the sizes of Tensor arguments received
+            by that op, in the following format:
+            ``[[arg0.size(0), arg0.size(1), ...], [arg1.size(0), arg1.size(1), ...], ...]``
+            Non-tensor arguments will be represented by ``[]``.
+            Arguments will be listed in the order they are received by the backend op.
+            Please note that this order may not match the order in which those arguments were passed
+            on the Python side.  Also note that shape recording may increase the overhead of itt range creation.
+            Default: ``False``
+
+    Example:
+        >>> # xdoctest: +SKIP("Undefined variables")
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD_PROFILER)
+        >>> with torch.autograd.profiler.emit_itt():
+        ...     model(x)
+
+    """
+
+    def __init__(self, enabled=True, record_shapes=False):
+        self.enabled = enabled
+        self.entered = False
+        self.record_shapes = record_shapes
+
+    def __enter__(self):
+        if not self.enabled:
+            return
+        if self.entered:
+            raise RuntimeError("ITT annotation context manager is not reentrant")
+        self.entered = True
+        _run_on_profiler_start()
+        _enable_profiler(
+            ProfilerConfig(
+                ProfilerState.ITT,
+                self.record_shapes,
+                False,
+                False,
+                False,
+                False,
+                _ExperimentalConfig(),
+            ),
+            set(),
+        )
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        if not self.enabled:
+            return
+        _disable_profiler()
+        _run_on_profiler_stop()
+        return False
+
+
+class emit_nvtx:
+    """Context manager that makes every autograd operation emit an NVTX range.
+
+    It is useful when running the program under nvprof::
+
+        nvprof --profile-from-start off -o trace_name.prof -- 
+
+    Unfortunately, there's no way to force nvprof to flush the data it collected
+    to disk, so for CUDA profiling one has to use this context manager to annotate
+    nvprof traces and wait for the process to exit before inspecting them.
+    Then, either NVIDIA Visual Profiler (nvvp) can be used to visualize the timeline, or
+    :func:`torch.autograd.profiler.load_nvprof` can load the results for inspection
+    e.g. in Python REPL.
+
+    .. warning:
+        This context manager should not be called recursively, i.e. at most one
+        instance should be enabled at any given time.
+
+    Args:
+        enabled (bool, optional): Setting ``enabled=False`` makes this context manager a no-op.
+            Default: ``True``.
+        record_shapes (bool, optional): If ``record_shapes=True``, the nvtx range wrapping
+            each autograd op will append information about the sizes of Tensor arguments received
+            by that op, in the following format:
+            ``[[arg0.size(0), arg0.size(1), ...], [arg1.size(0), arg1.size(1), ...], ...]``
+            Non-tensor arguments will be represented by ``[]``.
+            Arguments will be listed in the order they are received by the backend op.
+            Please note that this order may not match the order in which those arguments were passed
+            on the Python side.  Also note that shape recording may increase the overhead of nvtx range creation.
+            Default: ``False``
+
+    Example:
+        >>> # xdoctest: +SKIP("undefined variables")
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD_PROFILER)
+        >>> with torch.cuda.profiler.profile():
+        ...     model(x)  # Warmup CUDA memory allocator and profiler
+        ...     with torch.autograd.profiler.emit_nvtx():
+        ...         model(x)
+
+    **Forward-backward correlation**
+
+    When viewing a profile created using :class:`emit_nvtx` in the Nvidia Visual Profiler,
+    correlating each backward-pass op with the corresponding forward-pass op can be difficult.
+    To ease this task, :class:`emit_nvtx` appends sequence number information to the ranges it
+    generates.
+
+    During the forward pass, each function range is decorated with ``seq=``.  ``seq`` is a running
+    counter, incremented each time a new backward Function object is created and stashed for backward.
+    Thus, the ``seq=`` annotation associated with each forward function range tells you that
+    if a backward Function object is created by this forward function,
+    the backward object will receive sequence number N.
+    During the backward pass, the top-level range wrapping each C++ backward Function's
+    ``apply()`` call is decorated with ``stashed seq=``.  ``M`` is the sequence number that
+    the backward object was created with.  By comparing ``stashed seq`` numbers in backward with ``seq``
+    numbers in forward, you can track down which forward op created each backward Function.
+
+    Any functions executed during the backward pass are also decorated with ``seq=``.  During
+    default backward (with ``create_graph=False``) this information is irrelevant, and in fact,
+    ``N`` may simply be 0 for all such functions.  Only the top-level ranges associated with
+    backward Function objects' ``apply()`` methods are useful, as a way to correlate these Function
+    objects with the earlier forward pass.
+
+    **Double-backward**
+
+    If, on the other hand, a backward pass with ``create_graph=True`` is underway (in other words,
+    if you are setting up for a double-backward), each function's execution during backward
+    is given a nonzero, useful ``seq=``.  Those functions may themselves create Function objects
+    to be executed later during double-backward, just as the original functions in the forward pass did.
+    The relationship between backward and double-backward is conceptually the same as the relationship
+    between forward and backward: The functions still emit current-sequence-number-tagged ranges,
+    the Function objects they create still stash those sequence numbers, and during the eventual
+    double-backward, the Function objects' ``apply()`` ranges are still tagged with ``stashed seq``
+    numbers, which can be compared to `seq` numbers from the backward pass.
+
+    .. warning:
+        The sequence number is thread-local, and some forward functions don't create an associated
+        backward Function object (instead delegating that to sub-functions further down the call chain).
+        For these reasons, the correspondence of stashed sequence numbers in
+        backward Function ``apply()`` ranges with `seq` numbers in forward-pass ranges is
+        not guaranteed to be 1 to 1.  The sequence numbers alone may not be enough to fully
+        disambiguate which forward function created which
+        backward Function object.  You may need to make a judgment based on analytic knowledge of what
+        the expected correspondence should be.
+    """
+
+    def __init__(self, enabled=True, record_shapes=False):
+        self.enabled = enabled
+        self.entered = False
+        self.record_shapes = record_shapes
+
+    def __enter__(self):
+        if not self.enabled:
+            return
+        if self.entered:
+            raise RuntimeError("NVTX annotation context manager is not reentrant")
+        self.entered = True
+        torch.cuda.synchronize()
+        _run_on_profiler_start()
+        _enable_profiler(
+            ProfilerConfig(
+                ProfilerState.NVTX,
+                self.record_shapes,
+                False,
+                False,
+                False,
+                False,
+                _ExperimentalConfig(),
+            ),
+            set(),
+        )
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        if not self.enabled:
+            return
+        torch.cuda.synchronize()
+        _disable_profiler()
+        _run_on_profiler_stop()
+        return False
+
+
+def load_nvprof(path):
+    """Open an nvprof trace file and parses autograd annotations.
+
+    Args:
+        path (str): path to nvprof trace
+    """
+    return EventList(parse_nvprof_trace(path))
+
+
+class EnforceUnique:
+    """Raises an error if a key is seen more than once."""
+
+    def __init__(self):
+        self.seen = set()
+
+    def see(self, *key):
+        r"""
+        Observe a key and raise an error if it is seen multiple times.
+        """
+        if key in self.seen:
+            raise RuntimeError("duplicate key: " + str(key))
+        self.seen.add(key)
+
+
+def parse_nvprof_trace(path):
+    import sqlite3
+
+    conn = sqlite3.connect(path)
+    conn.row_factory = sqlite3.Row
+
+    # Parse strings table
+    strings = {}
+    for r in conn.execute("SELECT _id_ as id, value FROM StringTable"):
+        strings[r["id"]] = torch._C._demangle(r["value"])
+
+    # First, find all functions and create FunctionEvents for them
+    marker_query = """
+    SELECT
+        start.id AS marker_id, start.name, start.timestamp AS start_time, end.timestamp AS end_time
+    FROM
+        CUPTI_ACTIVITY_KIND_MARKER AS start INNER JOIN CUPTI_ACTIVITY_KIND_MARKER AS end
+        ON start.id = end.id
+    WHERE
+        start.name != 0 AND end.name = 0
+    """
+    functions = []
+    functions_map = {}
+    unique = EnforceUnique()
+    for row in conn.execute(marker_query):
+        unique.see(row["marker_id"])
+        evt = FunctionEvent(
+            id=row["marker_id"],
+            node_id=0,  # missing a node_id when calling FunctionEvent. This is just to ensure
+            # that pytorch doesn't crash when creating a FunctionEvent() object
+            name=strings[row["name"]],
+            start_us=row["start_time"],
+            end_us=row["end_time"],
+            thread=0,
+        )  # TODO: find in sqlite database
+        functions.append(evt)
+        functions_map[evt.id] = evt
+
+    # Now, correlate all kernels with FunctionEvents
+    kernel_query = """
+    SELECT
+        start.id AS marker_id, start.name, start.timestamp, end.timestamp,
+        runtime._id_ AS runtime_id, runtime.cbid, runtime.start AS runtime_start, runtime.end AS runtime_end,
+        kernel.start AS kernel_start, kernel.end AS kernel_end, kernel.name AS kernel_name
+    FROM
+        CUPTI_ACTIVITY_KIND_MARKER AS start
+        INNER JOIN CUPTI_ACTIVITY_KIND_MARKER AS end
+            ON start.id = end.id
+        INNER JOIN CUPTI_ACTIVITY_KIND_RUNTIME as runtime
+            ON (start.timestamp < runtime.start AND runtime.end < end.timestamp)
+        INNER JOIN CUPTI_ACTIVITY_KIND_CONCURRENT_KERNEL AS kernel
+            ON kernel.correlationId = runtime.correlationId
+    """
+    unique = EnforceUnique()
+    for row in conn.execute(kernel_query):
+        unique.see(row["marker_id"], row["runtime_id"])
+        # 211 is cudaKernelLaunch for cuda >= 9.2
+        if row["cbid"] != 211:
+            raise AssertionError(f"Expected cbid to be 211, but got {row['cbid']}")
+        evt = functions_map[row["marker_id"]]
+        evt.append_kernel(
+            row["kernel_name"], 0, row["kernel_end"] - row["kernel_start"]
+        )
+
+    functions.sort(key=lambda evt: evt.time_range.start)
+    return functions
+
+
+class KinetoStepTracker:
+    """Provides an abstraction for incrementing the step count globally.
+
+    Previously, we only had one place to mark that a step() has occurred
+    in the program via pytorch profiler step(). We will now add step hooks
+    in the Optimizer class https://github.com/pytorch/pytorch/issues/88446
+
+    - This could mean programs that already call profiler.step() every
+      iteration can end up double incrementing step count.
+    - If a model uses multiple optimizers we can also have double or more
+      counting of the step.
+
+    We fix this by adding a layer of abstraction before calling step()
+    to the kineto library. The idea is to maintain steps per requester in a dict:
+
+    .. code-block::
+
+        {
+           "ProfilerStep": 100,  # triggered by profiler step() call
+           "Optimizer1Step": 100,   # Optimizer 1 or 2 are just examples, could be SGD, Adam etc
+           "Optimizer2Step": 100,
+        }
+
+    To figure out the global step count just take the max of dict values (100).
+
+    If one of the count increments the max will go up.
+
+    .. code-block::
+
+        {
+           "ProfilerStep": 100,
+           "Optimizer1Step": 101,   # Optimizer1 got incremented first say
+           "Optimizer2Step": 100,
+        }
+
+    Then global step count is 101
+    We only call the kineto step() function when global count increments.
+
+    NOTE: Please do not use the KinetoStepTracker in modules beside the Optimizer
+    for now. The result could be incorrect increments of the step count.
+    """
+
+    _current_step = 0
+    _step_dict: dict[str, int] = defaultdict(int)
+
+    @classmethod
+    def init_step_count(cls, requester: str):
+        r"""
+        Initialize for a given requester.
+        """
+        cls._step_dict[requester] = cls._current_step
+
+    @classmethod
+    def erase_step_count(cls, requester: str) -> bool:
+        r"""
+        Remove a given requester.
+        """
+        return cls._step_dict.pop(requester, None) is not None
+
+    @classmethod
+    def increment_step(cls, requester: str) -> int:
+        """Increments the step count for the requester.
+
+        Additionally if the max over all step counts has incremented then
+        trigger the _kineto_step() returns global step count
+        """
+        if requester not in cls._step_dict:
+            cls.init_step_count(requester)
+        cls._step_dict[requester] += 1
+
+        new_step = max(cls._step_dict.values())
+        if new_step > cls._current_step:
+            delta = new_step - cls._current_step
+            if delta > 1:
+                warn(
+                    "Profiler step count has increased more than 1 - "
+                    f"current_step = {cls._current_step} step dict =  {cls._step_dict}",
+                    stacklevel=2,
+                )
+            for _ in range(delta):
+                _kineto_step()
+            cls._current_step = new_step
+        return cls._current_step
+
+    @classmethod
+    def current_step(cls) -> int:
+        r"""
+        Get the latest step for any requester
+        """
+        return cls._current_step
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/profiler_legacy.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/profiler_legacy.py
new file mode 100644
index 0000000000000000000000000000000000000000..5dd26c0881370486ffbaf7896fcffb8e334ddd6b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/profiler_legacy.py
@@ -0,0 +1,327 @@
+# mypy: allow-untyped-defs
+import itertools
+import warnings
+from typing_extensions import deprecated
+
+import torch
+import torch.cuda
+from torch.autograd import (
+    _disable_profiler_legacy,
+    _enable_profiler_legacy,
+    DeviceType,
+    ProfilerConfig,
+    ProfilerState,
+)
+from torch.autograd.profiler_util import (
+    _filter_name,
+    _filter_stack_entry,
+    _rewrite_name,
+    EventList,
+    FunctionEvent,
+    MEMORY_EVENT_NAME,
+)
+
+
+__all__ = ["profile"]
+
+
+@deprecated(
+    "`torch.autograd.profiler_legacy.profile` is deprecated and will be removed in a future release. "
+    "Please use `torch.profiler` instead.",
+    category=None,  # TODO: change to `FutureWarning`
+)
+class profile:
+    """DEPRECATED: use torch.profiler instead."""
+
+    def __init__(
+        self,
+        enabled=True,
+        *,
+        use_cuda=False,
+        record_shapes=False,
+        with_flops=False,
+        profile_memory=False,
+        with_stack=False,
+        with_modules=False,
+    ):
+        self.enabled: bool = enabled
+        if not self.enabled:
+            return
+        self.use_cuda = use_cuda
+        self.function_events = None
+        self.entered = False
+        self.record_shapes = record_shapes
+        self.with_flops = with_flops
+        self.record_shapes |= self.with_flops
+        self.profile_memory = profile_memory
+        self.with_stack = with_stack
+        self.with_modules = with_modules
+
+        if self.use_cuda and not torch.cuda.is_available():
+            warnings.warn(
+                "CUDA is not available, disabling CUDA profiling",
+                stacklevel=2,
+            )
+            self.use_cuda = False
+
+        if self.use_cuda:
+            self.profiler_kind = ProfilerState.CUDA
+        else:
+            self.profiler_kind = ProfilerState.CPU
+
+    def config(self):
+        return ProfilerConfig(
+            self.profiler_kind,
+            self.record_shapes,
+            self.profile_memory,
+            self.with_stack,
+            self.with_flops,
+            self.with_modules,
+            # avoid exposing _ExperimentalConfig this in legacy public API
+            torch._C._profiler._ExperimentalConfig(),
+        )
+
+    def __enter__(self):
+        if not self.enabled:
+            return
+        if self.entered:
+            raise RuntimeError("Profiler context manager is not reentrant")
+        self.entered = True
+        self._start_trace()
+        return self
+
+    def _start_trace(self):
+        _enable_profiler_legacy(self.config())
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        if not self.enabled:
+            return
+        if self.use_cuda:
+            torch.cuda.synchronize()
+
+        records = _disable_profiler_legacy()
+        parsed_results = _parse_legacy_records(records)
+        # pyrefly: ignore [bad-assignment]
+        self.function_events = EventList(
+            parsed_results,
+            use_device="cuda" if self.use_cuda else None,
+            profile_memory=self.profile_memory,
+            with_flops=self.with_flops,
+        )
+        # pyrefly: ignore [missing-attribute]
+        self.function_events._build_tree()
+        return False
+
+    def __repr__(self):
+        if self.function_events is None:
+            return ""
+        return repr(self.function_events)
+
+    def __str__(self):
+        if self.function_events is None:
+            return ""
+        return str(self.function_events)
+
+    def _check_finish(self):
+        if self.function_events is None:
+            raise RuntimeError("Profiler didn't finish running")
+
+    def table(
+        self,
+        sort_by=None,
+        row_limit=100,
+        max_src_column_width=75,
+        max_name_column_width=55,
+        max_shapes_column_width=80,
+        header=None,
+        top_level_events_only=False,
+    ):
+        self._check_finish()
+        if self.function_events is None:
+            raise AssertionError("Expected profiling results")
+        return self.function_events.table(
+            sort_by=sort_by,
+            row_limit=row_limit,
+            max_src_column_width=max_src_column_width,
+            max_name_column_width=max_name_column_width,
+            max_shapes_column_width=max_shapes_column_width,
+            header=header,
+            top_level_events_only=top_level_events_only,
+        )
+
+    table.__doc__ = EventList.table.__doc__
+
+    def export_chrome_trace(self, path):
+        self._check_finish()
+        if self.function_events is None:
+            raise AssertionError("Expected profiling results")
+        return self.function_events.export_chrome_trace(path)
+
+    export_chrome_trace.__doc__ = EventList.export_chrome_trace.__doc__
+
+    def export_stacks(self, path: str, metric: str = "self_cpu_time_total"):
+        self._check_finish()
+        if self.function_events is None:
+            raise AssertionError("Expected profiling results")
+        if not self.with_stack:
+            raise AssertionError("export_stacks() requires with_stack=True")
+        return self.function_events.export_stacks(path, metric)
+
+    def key_averages(self, group_by_input_shape=False, group_by_stack_n=0):
+        self._check_finish()
+        if self.function_events is None:
+            raise AssertionError("Expected profiling results")
+        return self.function_events.key_averages(group_by_input_shape, group_by_stack_n)
+
+    key_averages.__doc__ = EventList.key_averages.__doc__
+
+    def total_average(self):
+        self._check_finish()
+        if self.function_events is None:
+            raise AssertionError("Expected profiling results")
+        return self.function_events.total_average()
+
+    total_average.__doc__ = EventList.total_average.__doc__
+
+    @property
+    def self_cpu_time_total(self):
+        """Return CPU time as the sum of self times across all events."""
+        self._check_finish()
+        if self.function_events is None:
+            raise AssertionError("Expected profiling results")
+        return self.function_events.self_cpu_time_total
+
+
+def _parse_legacy_records(thread_records):
+    def _get_record_key(record):
+        """Return a tuple for correlating start and end records in `_parse_legacy_records`."""
+        return (record.handle(), record.node_id())
+
+    start_record = None
+    functions = []
+
+    # '__start_profile' is not guaranteed to be first, so we must find it here
+    for record in itertools.chain.from_iterable(thread_records):
+        name = record.name()
+        if start_record is None and name == "__start_profile":
+            start_record = record
+
+    if start_record is None or start_record.is_remote():
+        raise AssertionError("Expected a valid local start_record")
+
+    for thread_record_list in thread_records:
+        # accumulated memory allocations per handle
+        cpu_memory_allocs = {}
+        cuda_memory_allocs = {}
+        # ranges per handle
+        range_starts = {}
+
+        filtered_handles = set()
+        prev_record = None
+        for record in thread_record_list:
+            record_key = _get_record_key(record)
+            if _filter_name(record.name()) or record_key in filtered_handles:
+                filtered_handles.add(record_key)
+                continue
+
+            if record.kind() == "push":
+                # workaround to reduce double logging from operator
+                # wrappers and redispatch
+                if prev_record is not None:
+                    duplicate = (
+                        prev_record.name() == record.name()
+                        and prev_record.kind() == record.kind()
+                        and prev_record.node_id() == record.node_id()
+                    )
+                    if duplicate:
+                        filtered_handles.add(record_key)
+                        continue
+
+                range_starts[record_key] = record
+                cpu_memory_allocs[record_key] = 0
+                cuda_memory_allocs[record_key] = 0
+            elif record.kind() == "pop":
+                if record_key not in range_starts:
+                    raise AssertionError(
+                        f"Expected record with key {record_key} to exist in range_starts. "
+                        "This means that the pop event did not have a corresponding push."
+                    )
+
+                start = range_starts[record_key]
+
+                cpu_memory_usage = cpu_memory_allocs[record_key]
+                cuda_memory_usage = cuda_memory_allocs[record_key]
+                is_async = start.is_async() or (start.thread_id() != record.thread_id())
+                is_remote_event = record.is_remote()
+                start_flops = start.flops()
+
+                fe = FunctionEvent(
+                    id=record.handle(),
+                    node_id=record.node_id(),
+                    name=_rewrite_name(name=start.name(), with_wildcard=True),
+                    trace_name=_rewrite_name(name=start.name(), with_wildcard=False),
+                    thread=start.thread_id(),
+                    start_us=start_record.cpu_elapsed_us(start),
+                    end_us=start_record.cpu_elapsed_us(record),
+                    fwd_thread=start.fwd_thread_id(),
+                    input_shapes=start.shapes(),
+                    stack=[
+                        entry for entry in start.stack() if _filter_stack_entry(entry)
+                    ],
+                    scope=start.scope(),
+                    use_device="cuda" if start.has_cuda() else None,
+                    cpu_memory_usage=cpu_memory_usage,
+                    device_memory_usage=cuda_memory_usage,
+                    is_async=is_async,
+                    is_remote=is_remote_event,
+                    sequence_nr=start.sequence_nr(),
+                    device_type=DeviceType.CPU,
+                    is_legacy=True,
+                    flops=start_flops,
+                )
+                # note: async events have only cpu total time
+                if not is_async and start.has_cuda():
+                    duration = start.cuda_elapsed_us(record)
+                    if duration > 0:
+                        fe.append_kernel(start.name(), start.device(), duration)
+                functions.append(fe)
+                del range_starts[record_key]
+                del cpu_memory_allocs[record_key]
+                del cuda_memory_allocs[record_key]
+            elif record.kind() == "memory_alloc":
+                num_open_handles_cpu = len(cpu_memory_allocs)
+                num_open_handles_cuda = len(cuda_memory_allocs)
+                if num_open_handles_cpu != num_open_handles_cuda:
+                    raise AssertionError(
+                        f"Expected CPU and CUDA memory allocation handles to match, "
+                        f"but got {num_open_handles_cpu} CPU and {num_open_handles_cuda} CUDA"
+                    )
+                for handle in cpu_memory_allocs:
+                    cpu_memory_allocs[handle] += record.cpu_memory_usage()
+                for handle in cuda_memory_allocs:
+                    cuda_memory_allocs[handle] += record.cuda_memory_usage()
+                if num_open_handles_cpu == 0:
+                    # output event as a top-level memory event
+                    fe = FunctionEvent(
+                        id=0,
+                        name=MEMORY_EVENT_NAME,
+                        trace_name=None,
+                        thread=0,
+                        start_us=0,
+                        end_us=0,
+                        stack=[],
+                        cpu_memory_usage=record.cpu_memory_usage(),
+                        device_memory_usage=record.cuda_memory_usage(),
+                        is_legacy=True,
+                    )
+                    functions.append(fe)
+            prev_record = record
+
+    # Sort functions by start time then by end time ascending.
+    # This ensures that--in the case of nested events which
+    # have the same start time (which may happen due to the
+    # granularity of the given clock tick)--we always show
+    # the outermost nested call first. This adds stability
+    # in how FunctionEvents appear
+    functions.sort(key=lambda evt: [evt.time_range.start, -evt.time_range.end])
+    return functions
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/profiler_util.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/profiler_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..1db1e15d9ef243e4f261932b74ba6bbf48d2ea0f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/profiler_util.py
@@ -0,0 +1,1445 @@
+# mypy: allow-untyped-defs
+import bisect
+import itertools
+import math
+from collections import defaultdict, namedtuple
+from operator import attrgetter
+from typing import Any, Optional
+from typing_extensions import deprecated
+
+import torch
+from torch.autograd import DeviceType
+
+
+__all__ = [
+    "EventList",
+    "FormattedTimesMixin",
+    "Interval",
+    "Kernel",
+    "FunctionEvent",
+    "FunctionEventAvg",
+    "StringTable",
+    "MemRecordsAcc",
+]
+
+
+class EventList(list):
+    """A list of profiling events with helper methods for analysis and visualization.
+
+    EventList extends the standard Python list to provide specialized methods for
+    working with profiling events (FunctionEvent or FunctionEventAvg objects).
+    It includes utilities for aggregating statistics, formatting output tables,
+    and exporting profiling data.
+
+    This class is typically returned by profiler methods and should not be
+    instantiated directly by users.
+
+    Args:
+        *args: Standard list arguments.
+        use_device (str, optional): Device type for profiling ("cuda", "xpu", etc.).
+        profile_memory (bool, optional): Whether memory profiling was enabled. Default: False.
+        with_flops (bool, optional): Whether to include FLOP counts. Default: False.
+
+    Attributes:
+        _use_device (str): Device type being profiled.
+        _profile_memory (bool): Whether memory profiling is enabled.
+        _with_flops (bool): Whether FLOP counting is enabled.
+        _tree_built (bool): Whether the event tree structure has been built.
+
+    Key Methods:
+        table(...): Format events as a table string for display.
+        export_chrome_trace(path): Export to Chrome tracing format.
+        export_stacks(path, metric): Export stack traces with metrics.
+        key_averages(...): Compute averaged statistics grouped by operation name.
+        total_average(): Compute aggregate totals across all events (sums, not averages).
+
+    Properties:
+        self_cpu_time_total: Sum of self CPU time across all events.
+
+    Example::
+
+        import torch
+        from torch.profiler import profile, ProfilerActivity
+
+        with profile(activities=[ProfilerActivity.CPU]) as prof:
+            x = torch.randn(100, 100)
+            y = torch.matmul(x, x)
+
+        # EventList is returned by prof.events()
+        events = prof.events()
+
+        # Display as formatted table
+        print(
+            events.table(
+                sort_by="cpu_time_total", row_limit=20, top_level_events_only=False
+            )
+        )
+
+        # Export to Chrome tracing format
+        events.export_chrome_trace("trace.json")
+
+        # Get averaged statistics
+        avg_events = events.key_averages()
+        print(avg_events.table())
+
+        # Export stack traces
+        events.export_stacks("stacks.txt", "self_cpu_time_total")
+
+    See Also:
+        - :class:`FunctionEvent`: Individual profiling event
+        - :class:`FunctionEventAvg`: Averaged profiling statistics
+        - :meth:`table`: Format events as a readable table
+        - :meth:`key_averages`: Aggregate events by operation name
+    """
+
+    def __init__(self, *args, **kwargs):
+        use_device = kwargs.pop("use_device", None)
+        profile_memory = kwargs.pop("profile_memory", False)
+        with_flops = kwargs.pop("with_flops", False)
+        # pyrefly: ignore [not-iterable]
+        super().__init__(*args, **kwargs)
+        self._use_device = use_device
+        self._profile_memory = profile_memory
+        self._tree_built = False
+        self._with_flops = with_flops
+
+    def _build_tree(self):
+        self._populate_cpu_children()
+        self._remove_dup_nodes()
+        self._set_backward_stacktraces()
+        self._tree_built = True
+
+    def __str__(self):
+        return self.table()
+
+    def _remove_dup_nodes(self):
+        while True:
+            to_delete = set()
+
+            for idx in range(len(self)):
+                if (
+                    self[idx].cpu_parent is not None
+                    and self[idx].cpu_parent.name == self[idx].name
+                    and len(self[idx].cpu_parent.cpu_children) == 1
+                ):
+                    self[idx].cpu_parent.cpu_children = self[idx].cpu_children
+                    self[idx].cpu_parent.kernels = self[idx].kernels  # lift kernels up
+                    for ch in self[idx].cpu_children:
+                        ch.cpu_parent = self[idx].cpu_parent
+                    to_delete.add(idx)
+            if len(to_delete) == 0:
+                break
+
+            new_evts = [ev for ind, ev in enumerate(self) if ind not in to_delete]
+
+            self.clear()
+
+            self.extend(new_evts)
+
+    def _populate_cpu_children(self):
+        """Populate child events into each underlying FunctionEvent object.
+
+        One event is a child of another if [s1, e1) is inside [s2, e2). Where
+        s1 and e1 would be start and end of the child event's interval. And
+        s2 and e2 start and end of the parent event's interval
+
+        Example: In event list [[0, 10], [1, 3], [3, 4]] would have make [0, 10]
+        be a parent of two other intervals.
+
+        If for any reason two intervals intersect only partially, this function
+        will not record a parent child relationship between then.
+        """
+        # Some events can be async (i.e. start and end on different threads),
+        # since it's generally undefined how to attribute children ranges to
+        # async ranges, we do not use them when calculating nested ranges and stats
+        sync_events = [
+            evt
+            for evt in self
+            if not evt.is_async and evt.device_type == DeviceType.CPU
+        ]
+        events = sorted(
+            sync_events,
+            key=attrgetter("thread"),
+        )
+        # Group by both thread and node_id, so that events that happen to have
+        # the same thread_id but are from different nodes aren't incorrectly
+        # grouped together.
+        threads = itertools.groupby(
+            events, key=lambda event: (event.thread, event.node_id)
+        )
+
+        # For each thread we keep a stack of current nested parents.
+        # We maintain the invariant that each interval is a subset of all other
+        # intervals lower in the stack.
+        #
+        # First we sort the intervals by their start time. Then we iterate over them.
+        # Every time we see a new interval we remove several parents from
+        # the top until we restore the invariant. Then parent child relationship
+        # if recorded if the stack is not empty.
+        # Finally we add new interval to the list
+        #
+        # Algorithm has O(N * log(N)) complexity where N is number of
+        # intervals
+        for _thread_id, thread_events in threads:
+            thread_events_ = sorted(
+                thread_events,
+                key=lambda event: [event.time_range.start, -event.time_range.end],
+            )
+            current_events: list[FunctionEvent] = []
+            for event in thread_events_:
+                while len(current_events) > 0:
+                    parent = current_events[-1]
+                    if (
+                        event.time_range.start >= parent.time_range.end
+                        or event.time_range.end > parent.time_range.end
+                    ):
+                        # this can't be a parent
+                        current_events.pop()
+                    else:
+                        parent.append_cpu_child(event)
+                        if event.cpu_parent is not None:
+                            raise AssertionError(
+                                f"There is already a CPU parent event for {event.key}"
+                            )
+                        event.set_cpu_parent(parent)
+                        break
+
+                current_events.append(event)
+
+    def _set_backward_stacktraces(self):
+        def bw_parent(evt):
+            if evt is None:
+                return None
+            elif evt.scope == 1:  # BACKWARD_FUNCTION
+                return evt
+            else:
+                return bw_parent(evt.cpu_parent)
+
+        fwd_stacks = {}
+        for evt in self:
+            if bw_parent(evt) is None and evt.stack is not None:
+                t = (evt.sequence_nr, evt.thread)
+                if t not in fwd_stacks:
+                    fwd_stacks[t] = evt.stack
+
+        for evt in self:
+            p = bw_parent(evt)
+            if p is not None:
+                if p.fwd_thread is None:
+                    raise AssertionError(
+                        "Expected fwd_thread to be set for backward parent"
+                    )
+                t = (p.sequence_nr, p.fwd_thread)
+                evt.stack = fwd_stacks.get(t, [])
+
+    @property
+    def self_cpu_time_total(self):
+        return sum(event.self_cpu_time_total for event in self)
+
+    def table(
+        self,
+        sort_by=None,
+        row_limit=100,
+        max_src_column_width=75,
+        max_name_column_width=55,
+        max_shapes_column_width=80,
+        header=None,
+        top_level_events_only=False,
+        time_unit=None,
+    ):
+        """Print an EventList as a nicely formatted table.
+
+        Args:
+            sort_by (str, optional): Attribute used to sort entries. By default
+                they are printed in the same order as they were registered.
+                Valid keys include: ``cpu_time``, ``cuda_time``, ``xpu_time``,
+                ``cpu_time_total``, ``cuda_time_total``, ``xpu_time_total``,
+                ``cpu_memory_usage``, ``cuda_memory_usage``, ``xpu_memory_usage``,
+                ``self_cpu_memory_usage``, ``self_cuda_memory_usage``,
+                ``self_xpu_memory_usage``, ``count``.
+            top_level_events_only(bool, optional): Boolean flag to determine the
+                selection of events to display. If true, the profiler will only
+                display events at top level like top-level invocation of python
+                `lstm`, python `add` or other functions, nested events like low-level
+                cpu/cuda/xpu ops events are omitted for profiler result readability.
+            time_unit(str, optional): A time unit to be used for all values in the
+                table. Valid options are: ``s``, ``ms`` and ``us``.
+
+        Returns:
+            A string containing the table.
+        """
+        return _build_table(
+            self,
+            sort_by=sort_by,
+            row_limit=row_limit,
+            max_src_column_width=max_src_column_width,
+            max_name_column_width=max_name_column_width,
+            max_shapes_column_width=max_shapes_column_width,
+            header=header,
+            profile_memory=self._profile_memory,
+            with_flops=self._with_flops,
+            top_level_events_only=top_level_events_only,
+            time_unit=time_unit,
+        )
+
+    def export_chrome_trace(self, path):
+        """Export an EventList as a Chrome tracing tools file.
+
+        The checkpoint can be later loaded and inspected under ``chrome://tracing`` URL.
+
+        Args:
+            path (str): Path where the trace will be written.
+        """
+        import os
+
+        device_name = "cuda" if not self._use_device else self._use_device
+        with open(path, "w") as f:
+            next_id = 0
+            # Use file IO over using json.dump since JSON dumping is very slow and
+            # this technique is proven to give a 4x speedup.
+            f.write("[")
+            for evt in self:
+                if evt.trace_name is None:
+                    continue
+                f.write(
+                    '{{"name": "{}", '
+                    '"ph": "X", '
+                    '"ts": {}, '
+                    '"dur": {}, '
+                    '"tid": {}, '
+                    '"pid": "CPU functions", '
+                    '"args": {{}}}}, '.format(
+                        evt.trace_name,
+                        evt.time_range.start,
+                        evt.time_range.elapsed_us(),
+                        evt.thread
+                        if not evt.is_remote
+                        else f'" node_id:{evt.node_id}, thread_id:{evt.thread} "',
+                    )
+                )
+                for _ in evt.kernels:
+                    # 's' and 'f' draw Flow arrows from
+                    # the CPU launch to the GPU kernel
+                    f.write(
+                        f'{{"name": "{evt.trace_name}", '
+                        '"ph": "s", '
+                        f'"ts": {evt.time_range.start}, '
+                        f'"tid": {evt.thread}, '
+                        '"pid": "CPU functions", '
+                        f'"id": {next_id}, '
+                        f'"cat": "cpu_to_{device_name}", '
+                        '"args": {}}, '
+                    )
+                    # Note: use torch.profiler to get device kernel trace
+                    next_id += 1
+            if len(self) > 0:
+                # remove trailing whitespace and comma
+                f.seek(f.tell() - 2, os.SEEK_SET)
+                f.truncate()
+            f.write("]")
+
+    def supported_export_stacks_metrics(self):
+        return [
+            "self_cpu_time_total",
+            "self_cuda_time_total",
+            "self_xpu_time_total",
+            "self_privateuse1_time_total",
+        ]
+
+    def export_stacks(self, path: str, metric: str):
+        if metric not in self.supported_export_stacks_metrics():
+            raise ValueError(
+                "metric should be one of: "
+                + str(self.supported_export_stacks_metrics())
+            )
+        translate_table = str.maketrans(" ;\t\n", "____")
+        with open(path, "w") as f:
+            for evt in self:
+                if evt.stack and len(evt.stack) > 0:
+                    metric_value = getattr(
+                        evt,
+                        metric.replace("cuda", "device")
+                        .replace("xpu", "device")
+                        .replace("privateuse1", "device"),
+                    )
+                    if int(metric_value) > 0:
+                        stack_str = ""
+                        for entry in reversed(evt.stack):
+                            stack_str += entry.translate(translate_table)
+                            stack_str += ";"
+                        stack_str = stack_str[:-1] + " " + str(int(metric_value))
+                        f.write(stack_str + "\n")
+
+    def key_averages(
+        self,
+        group_by_input_shapes=False,
+        group_by_stack_n=0,
+        group_by_overload_name=False,
+    ):
+        """Averages all function events over their keys.
+
+        Args:
+            group_by_input_shapes: group entries by
+                (event name, input shapes) rather than just event name.
+                This is useful to see which input shapes contribute to the runtime
+                the most and may help with size-specific optimizations or
+                choosing the best candidates for quantization (aka fitting a roof line)
+
+            group_by_stack_n: group by top n stack trace entries
+
+            group_by_overload_name: Differentiate operators by their overload name e.g. aten::add.Tensor
+            and aten::add.out will be aggregated separately
+
+        Returns:
+            An EventList containing FunctionEventAvg objects.
+        """
+        if not self._tree_built:
+            raise AssertionError(
+                "Expected tree to be built before calling key_averages"
+            )
+        stats: dict[tuple[str, ...], FunctionEventAvg] = defaultdict(FunctionEventAvg)
+
+        def get_key(
+            event, group_by_input_shapes, group_by_stack_n, group_by_overload_name
+        ) -> tuple[str, ...]:
+            key = [
+                str(event.key),
+                str(event.node_id),
+                str(event.device_type),
+                str(event.is_legacy),
+                str(event.is_user_annotation),
+            ]
+            if group_by_overload_name:
+                key.append(evt.overload_name)
+            if group_by_input_shapes:
+                key.append(str(event.input_shapes))
+            if group_by_stack_n > 0:
+                key += event.stack[:group_by_stack_n]
+            return tuple(key)
+
+        for evt in self:
+            stats[
+                get_key(
+                    evt, group_by_input_shapes, group_by_stack_n, group_by_overload_name
+                )
+            ].add(evt)
+
+        avg_list = EventList(
+            stats.values(),
+            use_device=self._use_device,
+            profile_memory=self._profile_memory,
+            with_flops=self._with_flops,
+        )
+        for evt in avg_list:
+            evt.stack = evt.stack[:group_by_stack_n]
+            if not group_by_input_shapes:
+                evt.input_shapes = ""
+            if not group_by_overload_name:
+                evt.overload_name = ""
+        return avg_list
+
+    def total_average(self):
+        """Compute aggregate statistics across all events.
+
+        Accumulates statistics from all events into a single FunctionEventAvg object.
+        This is primarily useful for computing total metrics (total CPU time, total
+        memory usage, etc.) across the entire profiling session, regardless of
+        operation type.
+
+        Note:
+            This sums up times and counts across ALL different operations, so the
+            "average" metrics (like cpu_time) represent the average time per operation
+            call across the entire session, mixing all operation types together.
+            For per-operation averages, use :meth:`key_averages` instead.
+
+        Returns:
+            FunctionEventAvg: A single aggregate object with key="Total" containing
+                accumulated statistics.
+
+        """
+        total_stat = FunctionEventAvg()
+        for evt in self:
+            total_stat += evt
+            total_stat.key = None
+        total_stat.key = "Total"
+        return total_stat
+
+
+def _format_time(time_us):
+    """Define how to format time in FunctionEvent."""
+    US_IN_SECOND = 1000.0 * 1000.0
+    US_IN_MS = 1000.0
+    if time_us >= US_IN_SECOND:
+        return f"{time_us / US_IN_SECOND:.3f}s"
+    if time_us >= US_IN_MS:
+        return f"{time_us / US_IN_MS:.3f}ms"
+    return f"{time_us:.3f}us"
+
+
+def _format_time_share(time_us, total_time_us):
+    """Define how to format time in FunctionEvent."""
+    if total_time_us == 0:
+        if time_us != 0:
+            raise AssertionError(f"Expected time_us == 0 but got {time_us}")
+        return "NaN"
+    return f"{time_us * 100.0 / total_time_us:.2f}%"
+
+
+def _format_memory(nbytes):
+    """Return a formatted memory size string."""
+    KB = 1024
+    MB = 1024 * KB
+    GB = 1024 * MB
+    if abs(nbytes) >= GB:
+        return f"{nbytes * 1.0 / GB:.2f} GB"
+    elif abs(nbytes) >= MB:
+        return f"{nbytes * 1.0 / MB:.2f} MB"
+    elif abs(nbytes) >= KB:
+        return f"{nbytes * 1.0 / KB:.2f} KB"
+    else:
+        return str(nbytes) + " B"
+
+
+def _attr_formatter(name):
+    return property(lambda self: _format_time(getattr(self, name)))
+
+
+class FormattedTimesMixin:
+    """Helpers for FunctionEvent and FunctionEventAvg.
+
+    The subclass should define `*_time_total` and `count` attributes.
+    """
+
+    cpu_time_str = _attr_formatter("cpu_time")
+    device_time_str = _attr_formatter("device_time")
+    cpu_time_total_str = _attr_formatter("cpu_time_total")
+    device_time_total_str = _attr_formatter("device_time_total")
+    self_cpu_time_total_str = _attr_formatter("self_cpu_time_total")
+    self_device_time_total_str = _attr_formatter("self_device_time_total")
+
+    @property
+    def cpu_time(self):
+        return 0.0 if self.count == 0 else 1.0 * self.cpu_time_total / self.count  # type: ignore[attr-defined]
+
+    @property
+    def device_time(self):
+        return 0.0 if self.count == 0 else 1.0 * self.device_time_total / self.count  # type: ignore[attr-defined]
+
+    @property
+    @deprecated(
+        "`cuda_time` is deprecated, please use `device_time` instead.",
+        category=FutureWarning,
+    )
+    def cuda_time(self):  # To be deprecated
+        return self.device_time
+
+
+class Interval:
+    def __init__(self, start, end):
+        self.start = start
+        self.end = end
+
+    def elapsed_us(self):
+        r"""
+        Returns the length of the interval
+        """
+        return self.end - self.start
+
+
+Kernel = namedtuple("Kernel", ["name", "device", "duration"])
+
+
+class FunctionEvent(FormattedTimesMixin):
+    """Profiling information about a single function.
+
+    FunctionEvent records the execution of a single operation during profiling.
+    These events are obtained from the profiler/kineto and contain detailed
+    timing and memory usage information.
+
+    .. note::
+        FunctionEvent objects are typically created by the profiler/kineto and should not
+        be instantiated directly by users. Access them through the profiler's output.
+
+    Attributes:
+        id (int): Unique identifier for this event.
+        node_id (int): Node identifier for distributed profiling (-1 if not applicable).
+        name (str): Name of the profiled function/operator.
+        overload_name (str): Overload name for the operator (requires _ExperimentalConfig(capture_overload_names=True) set).
+        trace_name (str): Same as name, just changes ProfilerStep* to ProfilerStep#
+        time_range (Interval): Time interval containing start and end timestamps in microseconds.
+        thread (int): Thread ID where the operation started.
+        fwd_thread (int): Thread ID of the corresponding forward operation.
+        kernels (List[Kernel]): List of device kernels launched by this operation.
+        count (int): Number of times this event was called (usually 1).
+        cpu_children (List[FunctionEvent]): Direct CPU child operations.
+        cpu_parent (FunctionEvent): Direct CPU parent operation.
+        input_shapes (Tuple[int, ...]): Shapes of input tensors (requires record_shapes=true).
+        concrete_inputs (List[Any]): Concrete input values (requires record_shapes=true).
+        kwinputs (Dict[str, Any]): Keyword arguments (requires record_shapes=true).
+        stack (List[str]): Python stack trace where the operation was called (requires with_stack=true).
+        scope (int): at::RecordScope identifier (0=forward, 1=backward, etc.).
+        use_device (str): Device type being profiled ("cuda", "xpu", etc.).
+        cpu_memory_usage (int): CPU memory allocated in bytes.
+        device_memory_usage (int): Device memory allocated in bytes.
+        is_async (bool): Whether this is an asynchronous operation.
+        is_remote (bool): Whether this operation occurred on a remote node.
+        sequence_nr (int): Sequence number for autograd operations.
+        device_type (DeviceType): Type of device (CPU, CUDA, XPU, PrivateUse1, etc.).
+        device_index (int): Index of the device (e.g., GPU 0, 1, 2).
+        device_resource_id (int): Resource ID on the device (ie. stream ID).
+        is_legacy (bool): Whether this is from the legacy profiler.
+        flops (int): Estimated floating point operations.
+        is_user_annotation (bool): Whether this is a user-annotated region.
+        metadata_json (str): Additional metadata in JSON format.
+
+    Properties:
+        cpu_time_total (float): Total CPU time in microseconds.
+        device_time_total (float): Total device (CUDA/XPU/etc) time in microseconds.
+        self_cpu_time_total (float): CPU time excluding child operations.
+        self_device_time_total (float): Device time excluding child operations.
+        self_cpu_memory_usage (int): CPU memory usage excluding child operations.
+        self_device_memory_usage (int): Device memory usage excluding child operations.
+        cpu_time (float): Average CPU time per call.
+        device_time (float): Average device time per call.
+        key (str): Key used for grouping events (usually same as name).
+
+    See Also:
+        - :class:`torch.profiler.profile`: Context manager for profiling
+        - :class:`EventList`: List container for FunctionEvent objects with helper methods
+        - :class:`FunctionEventAvg`: Averaged statistics over multiple FunctionEvent objects
+    """
+
+    def __init__(
+        self,
+        id,
+        name,
+        thread,
+        start_us,
+        end_us,
+        overload_name=None,
+        fwd_thread=None,
+        input_shapes=None,
+        stack=None,
+        scope=0,
+        use_device=None,
+        cpu_memory_usage=0,
+        device_memory_usage=0,
+        is_async=False,
+        is_remote=False,
+        sequence_nr=-1,
+        node_id=-1,
+        device_type=DeviceType.CPU,
+        device_index=0,
+        device_resource_id=None,
+        is_legacy=False,
+        flops=None,
+        trace_name=None,
+        concrete_inputs=None,
+        kwinputs=None,
+        is_user_annotation=False,
+        metadata_json=None,
+    ):
+        self.id: int = id
+        self.node_id: int = node_id
+        self.name: str = name
+        # pyrefly: ignore [bad-assignment]
+        self.overload_name: str = overload_name
+        # pyrefly: ignore [bad-assignment]
+        self.trace_name: str = trace_name
+        self.time_range: Interval = Interval(start_us, end_us)
+        self.thread: int = thread
+        self.fwd_thread: Optional[int] = fwd_thread
+        self.kernels: list[Kernel] = []
+        self.count: int = 1
+        self.cpu_children: list[FunctionEvent] = []
+        self.cpu_parent: Optional[FunctionEvent] = None
+        # pyrefly: ignore [bad-assignment]
+        self.input_shapes: tuple[int, ...] = input_shapes
+        # pyrefly: ignore [bad-assignment]
+        self.concrete_inputs: list[Any] = concrete_inputs
+        # pyrefly: ignore [bad-assignment]
+        self.kwinputs: dict[str, Any] = kwinputs
+        # pyrefly: ignore [bad-assignment]
+        self.stack: list = stack
+        self.scope: int = scope
+        self.use_device: Optional[str] = use_device
+        self.cpu_memory_usage: int = cpu_memory_usage
+        self.device_memory_usage: int = device_memory_usage
+        self.is_async: bool = is_async
+        self.is_remote: bool = is_remote
+        self.sequence_nr: int = sequence_nr
+        self.device_type: DeviceType = device_type
+        self.device_index: int = device_index
+        self.device_resource_id: int = (
+            thread if device_resource_id is None else device_resource_id
+        )
+        self.is_legacy: bool = is_legacy
+        self.flops: Optional[int] = flops
+        self.is_user_annotation: Optional[bool] = is_user_annotation
+        self.self_cpu_percent = -1
+        self.total_cpu_percent = -1
+        self.total_device_percent = -1
+        self.metadata_json = metadata_json
+
+    def append_kernel(self, name, device, duration):
+        if self.device_type != DeviceType.CPU:
+            raise AssertionError("Expected device_type to be CPU")
+        self.kernels.append(Kernel(name, device, duration))
+
+    def append_cpu_child(self, child):
+        """Append a CPU child of type FunctionEvent.
+
+        One is supposed to append only direct children to the event to have
+        correct self cpu time being reported.
+        """
+        if self.device_type != DeviceType.CPU:
+            raise AssertionError("Expected device_type to be CPU")
+        if not isinstance(child, FunctionEvent):
+            raise AssertionError("Expected child to be a FunctionEvent")
+        if child.device_type != DeviceType.CPU:
+            raise AssertionError("Expected child device_type to be CPU")
+        self.cpu_children.append(child)
+
+    def set_cpu_parent(self, parent):
+        """Set the immediate CPU parent of type FunctionEvent.
+
+        One profiling FunctionEvent should have only one CPU parent such that
+        the child's range interval is completely inside the parent's. We use
+        this connection to determine the event is from top-level op or not.
+        """
+        if self.device_type != DeviceType.CPU:
+            raise AssertionError("Expected device_type to be CPU")
+        if not isinstance(parent, FunctionEvent):
+            raise AssertionError("Expected parent to be a FunctionEvent")
+        if parent.device_type != DeviceType.CPU:
+            raise AssertionError("Expected parent device_type to be CPU")
+        self.cpu_parent = parent
+
+    # Note: async events don't have children, are not used when computing 'self'
+    # metrics of other events, have only total cpu time
+    @property
+    def self_cpu_memory_usage(self):
+        if self.is_async or self.device_type != DeviceType.CPU:
+            return 0
+        return self.cpu_memory_usage - sum(
+            child.cpu_memory_usage for child in self.cpu_children
+        )
+
+    @property
+    def self_device_memory_usage(self):
+        if self.is_async or self.device_type != DeviceType.CPU:
+            return 0
+        return self.device_memory_usage - sum(
+            child.device_memory_usage for child in self.cpu_children
+        )
+
+    @property
+    @deprecated(
+        "`self_cuda_memory_usage` is deprecated. Use `self_device_memory_usage` instead.",
+        category=FutureWarning,
+    )
+    def self_cuda_memory_usage(self):  # To be deprecated
+        return self.self_device_memory_usage
+
+    @property
+    def cpu_time_total(self):
+        if self.device_type == DeviceType.CPU:
+            return self.time_range.elapsed_us()
+        else:
+            return 0
+
+    @property
+    def self_cpu_time_total(self):
+        if self.is_async or self.device_type != DeviceType.CPU:
+            return 0
+        return self.cpu_time_total - sum(
+            child.cpu_time_total for child in self.cpu_children
+        )
+
+    @property
+    def device_time_total(self):
+        if self.is_async or not self.use_device:
+            return 0
+        if self.device_type == DeviceType.CPU:
+            if not self.is_legacy:
+                # account for the kernels in the children ops
+                return sum(kinfo.duration for kinfo in self.kernels) + sum(
+                    ch.device_time_total for ch in self.cpu_children
+                )
+            else:
+                # each legacy cpu events has a single (fake) kernel
+                return sum(kinfo.duration for kinfo in self.kernels)
+        else:
+            if self.device_type not in [
+                DeviceType.CUDA,
+                DeviceType.PrivateUse1,
+                DeviceType.MTIA,
+                DeviceType.HPU,
+            ]:
+                raise AssertionError(
+                    f"Expected device_type to be CUDA, PrivateUse1, MTIA, or HPU, but got {self.device_type}"
+                )
+            return self.time_range.elapsed_us()
+
+    @property
+    @deprecated(
+        "`cuda_time_total` is deprecated. Use `device_time_total` instead.",
+        category=FutureWarning,
+    )
+    def cuda_time_total(self):  # To be deprecated
+        return self.device_time_total
+
+    @property
+    def self_device_time_total(self):
+        if self.is_async or not self.use_device:
+            return 0
+        if self.device_type == DeviceType.CPU:
+            return self.device_time_total - sum(
+                child.device_time_total for child in self.cpu_children
+            )
+        else:
+            if self.device_type not in [
+                DeviceType.CUDA,
+                DeviceType.PrivateUse1,
+                DeviceType.MTIA,
+                DeviceType.HPU,
+            ]:
+                raise AssertionError(
+                    f"Expected device_type to be CUDA, PrivateUse1, MTIA, or HPU, but got {self.device_type}"
+                )
+            return self.device_time_total
+
+    @property
+    @deprecated(
+        "`self_cuda_time_total` is deprecated. Use `self_device_time_total` instead.",
+        category=FutureWarning,
+    )
+    def self_cuda_time_total(self):  # To be deprecated
+        return self.self_device_time_total
+
+    @property
+    def key(self):
+        return self.name
+
+    def __repr__(self):
+        device_name = self.use_device
+        device_time = self.device_time_str
+        device_memory_usage = self.device_memory_usage
+        return (
+            f""
+        )
+
+
+class FunctionEventAvg(FormattedTimesMixin):
+    """Averaged profiling statistics over multiple FunctionEvent objects.
+
+    FunctionEventAvg aggregates statistics from multiple FunctionEvent objects
+    with the same key (typically same operation name). This is useful for getting
+    average performance metrics across multiple invocations of the same operation.
+
+    This class is typically created by calling :meth:`EventList.key_averages()` on
+    a profiler's event list.
+
+    Attributes:
+        key (str): Grouping key for the events (typically operation name).
+        count (int): Total number of events aggregated.
+        node_id (int): Node identifier for distributed profiling (-1 if not applicable).
+        is_async (bool): Whether the operations are asynchronous.
+        is_remote (bool): Whether the operations occurred on a remote node.
+        use_device (str): Device type being profiled ("cuda", "xpu", etc.).
+        cpu_time_total (int): Accumulated total CPU time in microseconds.
+        device_time_total (int): Accumulated total device time in microseconds.
+        self_cpu_time_total (int): Accumulated self CPU time (excluding children) in microseconds.
+        self_device_time_total (int): Accumulated self device time (excluding children) in microseconds.
+        input_shapes (List[List[int]]): Input tensor shapes (requires record_shapes=true).
+        overload_name (str): Operator overload name (requires _ExperimentalConfig(capture_overload_names=True) set).
+        stack (List[str]): Python stack trace where the operation was called (requires with_stack=true).
+        scope (int): at::RecordScope identifier (0=forward, 1=backward, etc.).
+        cpu_memory_usage (int): Accumulated CPU memory usage in bytes.
+        device_memory_usage (int): Accumulated device memory usage in bytes.
+        self_cpu_memory_usage (int): Accumulated self CPU memory usage in bytes.
+        self_device_memory_usage (int): Accumulated self device memory usage in bytes.
+        cpu_children (List[FunctionEvent]): CPU child events.
+        cpu_parent (FunctionEvent): CPU parent event.
+        device_type (DeviceType): Type of device (CPU, CUDA, XPU, PrivateUse1, etc.).
+        is_legacy (bool): Whether from legacy profiler.
+        flops (int): Total floating point operations.
+        is_user_annotation (bool): Whether this is a user-annotated region.
+
+    Properties:
+        cpu_time (float): Average CPU time per invocation.
+        device_time (float): Average device time per invocation.
+
+    See Also:
+        - :class:`EventList.key_averages`: Method that creates FunctionEventAvg objects
+        - :class:`FunctionEvent`: Individual profiling event
+        - :class:`EventList`: Container for profiling events
+    """
+
+    def __init__(self) -> None:
+        self.key: Optional[str] = None
+        self.count: int = 0
+        self.node_id: int = 0
+        self.is_async: bool = False
+        self.is_remote: bool = False
+        self.use_device: Optional[str] = None
+        self.cpu_time_total: int = 0
+        self.device_time_total: int = 0
+        self.self_cpu_time_total: int = 0
+        self.self_device_time_total: int = 0
+        self.input_shapes: Optional[list[list[int]]] = None
+        self.overload_name: Optional[str] = None
+        self.stack: Optional[list] = None
+        self.scope: Optional[int] = None
+        self.cpu_memory_usage: int = 0
+        self.device_memory_usage: int = 0
+        self.self_cpu_memory_usage: int = 0
+        self.self_device_memory_usage: int = 0
+        self.cpu_children: Optional[list[FunctionEvent]] = None
+        self.cpu_parent: Optional[FunctionEvent] = None
+        self.device_type: DeviceType = DeviceType.CPU
+        self.is_legacy: bool = False
+        self.flops: int = 0
+
+    def add(self, other):
+        if self.key is None:
+            # First function being recorded as part of FunctionEventAvg, propagate
+            # fields.
+            self.key = other.key
+            self.node_id = other.node_id
+            self.is_async = other.is_async
+            self.is_remote = other.is_remote
+            self.cpu_parent = other.cpu_parent
+            self.cpu_children = other.cpu_children
+
+            self.overload_name = other.overload_name
+            self.input_shapes = other.input_shapes
+            self.stack = other.stack
+            self.scope = other.scope
+            self.device_type = other.device_type
+            self.is_legacy = other.is_legacy
+            self.use_device = other.use_device
+            self.is_user_annotation = other.is_user_annotation
+
+        if not isinstance(other, (FunctionEvent, FunctionEventAvg)):
+            raise AssertionError(
+                "Expected other to be a FunctionEvent or FunctionEventAvg"
+            )
+        if other.key != self.key:
+            raise AssertionError(
+                f"Expected keys to match, but got {other.key} vs {self.key}"
+            )
+
+        self.cpu_time_total += other.cpu_time_total
+        self.device_time_total += other.device_time_total
+        self.self_cpu_time_total += other.self_cpu_time_total
+        self.self_device_time_total += other.self_device_time_total
+        self.cpu_memory_usage += other.cpu_memory_usage
+        self.device_memory_usage += other.device_memory_usage
+        self.self_cpu_memory_usage += other.self_cpu_memory_usage
+        self.self_device_memory_usage += other.self_device_memory_usage
+        self.count += other.count
+        if self.flops is None:
+            # pyrefly: ignore [bad-assignment]
+            self.flops = other.flops
+        elif other.flops is not None:
+            self.flops += other.flops
+        return self
+
+    def __iadd__(self, other):
+        return self.add(other)
+
+    def __repr__(self):
+        device_name = "cuda" if not self.use_device else self.use_device
+        self_device_time = self.self_device_time_total_str
+        device_time = self.device_time_str
+        device_memory = self.device_memory_usage
+        return (
+            f""
+        )
+
+
+class StringTable(defaultdict):
+    def __missing__(self, key):
+        # manage cases like 't' (demangled to 'unsigned short') separately,
+        # for now simply check the length to avoid unexpected results for
+        # the short sequences
+        self[key] = torch._C._demangle(key) if len(key) > 1 else key
+        return self[key]
+
+
+class MemRecordsAcc:
+    """Acceleration structure for accessing mem_records in interval."""
+
+    def __init__(self, mem_records):
+        self._mem_records = mem_records
+        self._start_nses: list[int] = []
+        self._indices: list[int] = []
+        if len(mem_records) > 0:
+            tmp = sorted([(r[0].start_ns(), i) for i, r in enumerate(mem_records)])
+            self._start_nses, self._indices = zip(*tmp)  # type: ignore[assignment]
+
+    def in_interval(self, start_us, end_us):
+        r"""
+        Return all records in the given interval
+        To maintain backward compatibility, convert us to ns in function
+        """
+        start_idx = bisect.bisect_left(self._start_nses, start_us * 1000)
+        end_idx = bisect.bisect_right(self._start_nses, end_us * 1000)
+        for i in range(start_idx, end_idx):
+            yield self._mem_records[self._indices[i]]
+
+
+def _filter_stack_entry(entry):
+    filtered_entries = [
+        ("autograd/__init__", "_make_grads"),
+        ("autograd/__init__", "backward"),
+        ("torch/tensor", "backward"),
+        ("_internal/common_utils", "prof_callable"),
+        ("_internal/common_utils", "prof_func_call"),
+        ("_internal/common_utils", "prof_meth_call"),
+    ]
+    return all(not (f[0] in entry and f[1] in entry) for f in filtered_entries)
+
+
+MEMORY_EVENT_NAME = "[memory]"
+OUT_OF_MEMORY_EVENT_NAME = "[OutOfMemory]"
+
+
+def _filter_name(name):
+    # ignoring the following utility ops
+    filtered_out_names = [
+        MEMORY_EVENT_NAME,  # used only for the top-level memory events
+        OUT_OF_MEMORY_EVENT_NAME,
+        "profiler::_record_function_enter",
+        "profiler::_record_function_enter_new",
+        "profiler::_record_function_exit",
+        "aten::is_leaf",
+        "aten::output_nr",
+        "aten::_version",
+    ]
+    return name in filtered_out_names
+
+
+# Demangles and optionally rewrites the provided event name,
+# with_wildcard - whether to replace certain numbered event names
+# with a wildcard name to aggregate them together in the profiler table
+# output
+def _rewrite_name(name, with_wildcard=False):
+    string_table = StringTable()
+    name = string_table[name]
+    if with_wildcard:
+        if name.startswith("ProfilerStep#"):
+            name = "ProfilerStep*"
+    return name
+
+
+def _build_table(
+    events,
+    sort_by=None,
+    header=None,
+    row_limit=100,
+    max_src_column_width=75,
+    max_name_column_width=55,
+    max_shapes_column_width=80,
+    with_flops=False,
+    profile_memory=False,
+    top_level_events_only=False,
+    time_unit=None,
+):
+    """Print a summary of events (which can be a list of FunctionEvent or FunctionEventAvg)."""
+    if len(events) == 0:
+        return ""
+
+    has_device_time = any(event.self_device_time_total > 0 for event in events)
+    has_device_mem = any(event.self_device_memory_usage > 0 for event in events)
+    use_device = events[0].use_device
+    # Running on PrivateUse1 device with profiler but not enable
+    # ProfilerActivity.PrivateUse1 can also catch privateuse1 memory usage.
+    # Here only need to check has_privateuse1_time if not use_device.
+    if not use_device and has_device_time:
+        raise RuntimeError("use_device is None, but there is device performance data.")
+
+    has_input_shapes = any(
+        (event.input_shapes is not None and len(event.input_shapes) > 0)
+        for event in events
+    )
+
+    has_overload_names = any(
+        (event.overload_name is not None and len(event.overload_name) > 0)
+        for event in events
+    )
+
+    if sort_by is not None:
+        events = EventList(
+            sorted(
+                events,
+                key=lambda evt: getattr(
+                    evt,
+                    sort_by.replace("cuda", "device")
+                    .replace("xpu", "device")
+                    .replace("privateuse1", "device"),
+                ),
+                reverse=True,
+            ),
+            use_device=use_device,
+            profile_memory=profile_memory,
+            with_flops=with_flops,
+        )
+
+    name_column_width = max(len(evt.key) for evt in events) + 4
+    if max_name_column_width is not None:
+        name_column_width = min(name_column_width, max_name_column_width)
+
+    shapes_column_width = max(len(str(evt.input_shapes)) for evt in events) + 4
+    if max_shapes_column_width is not None:
+        shapes_column_width = min(shapes_column_width, max_shapes_column_width)
+
+    DEFAULT_COLUMN_WIDTH = 12
+    flops_column_width = DEFAULT_COLUMN_WIDTH
+
+    src_column_width = None
+    stacks = [
+        evt.stack for evt in events if evt.stack is not None and len(evt.stack) > 0
+    ]
+    has_stack = len(stacks) > 0
+    if has_stack:
+        src_column_width = (
+            max(max(len(entry) for entry in stack) for stack in stacks) + 4
+        )
+        if max_src_column_width is not None:
+            src_column_width = min(src_column_width, max_src_column_width)
+
+    headers = ["Name"]
+    if has_overload_names:
+        headers.append("Overload Name")
+    headers += [
+        "Self CPU %",
+        "Self CPU",
+        "CPU total %",
+        "CPU total",
+        "CPU time avg",
+    ]
+
+    device_name = use_device.upper() if use_device is not None else "None"
+    if has_device_time:
+        headers.extend(
+            [
+                f"Self {device_name}",
+                f"Self {device_name} %",
+                f"{device_name} total",
+                f"{device_name} time avg",
+            ]
+        )
+    if profile_memory:
+        headers.extend(
+            [
+                "CPU Mem",
+                "Self CPU Mem",
+            ]
+        )
+        if use_device and has_device_mem:
+            headers.extend(
+                [
+                    f"{device_name} Mem",
+                    f"Self {device_name} Mem",
+                ]
+            )
+    headers.append("# of Calls")
+    # Only append Node ID if any event has a valid (>= 0) Node ID
+    append_node_id = any(evt.node_id != -1 for evt in events)
+    if append_node_id:
+        headers.append("Node ID")
+
+    # Have to use a list because nonlocal is Py3 only...
+    SPACING_SIZE = 2
+    row_format_lst = [""]
+    header_sep_lst = [""]
+    line_length_lst = [-SPACING_SIZE]
+
+    def add_column(padding, text_dir=">"):
+        row_format_lst[0] += (
+            "{: " + text_dir + str(padding) + "}" + (" " * SPACING_SIZE)
+        )
+        header_sep_lst[0] += "-" * padding + (" " * SPACING_SIZE)
+        line_length_lst[0] += padding + SPACING_SIZE
+
+    def auto_scale_flops(flops):
+        flop_headers = [
+            "FLOPs",
+            "KFLOPs",
+            "MFLOPs",
+            "GFLOPs",
+            "TFLOPs",
+            "PFLOPs",
+        ]
+        if flops <= 0:
+            raise AssertionError(f"Expected flops to be positive, but got {flops}")
+        # pyrefly: ignore [no-matching-overload]
+        log_flops = max(0, min(math.log10(flops) / 3, float(len(flop_headers) - 1)))
+        if not (log_flops >= 0 and log_flops < len(flop_headers)):
+            raise AssertionError(
+                f"Expected log_flops to be in range [0, {len(flop_headers)}), but got {log_flops}"
+            )
+        return (pow(10, (math.floor(log_flops) * -3.0)), flop_headers[int(log_flops)])
+
+    add_column(name_column_width)
+    if has_overload_names:
+        add_column(name_column_width)
+    for _ in headers[1 + has_overload_names :]:
+        add_column(DEFAULT_COLUMN_WIDTH)
+
+    if has_input_shapes:
+        headers.append("Input Shapes")
+        add_column(shapes_column_width)
+
+    if has_stack:
+        headers.append("Source Location")
+        add_column(src_column_width, text_dir="<")
+
+    if with_flops:
+        # Auto-scaling of flops header
+        raw_flops = [evt.flops for evt in events if evt.flops > 0]
+        if len(raw_flops) != 0:
+            (flops_scale, flops_header) = auto_scale_flops(min(raw_flops))
+            headers.append(f"Total {flops_header}")
+            add_column(flops_column_width)
+        else:
+            with_flops = False  # can't find any valid flops
+
+    row_format = row_format_lst[0]
+    header_sep = header_sep_lst[0]
+    line_length = line_length_lst[0]
+    add_column = None  # type: ignore[assignment]
+
+    # Have to use a list because nonlocal is Py3 only...
+    result = []
+
+    def append(s):
+        result.append(s)
+        result.append("\n")  # Yes, newline after the end as well
+
+    sum_self_cpu_time_total = 0
+    sum_self_device_time_total = 0
+    for evt in events:
+        sum_self_cpu_time_total += evt.self_cpu_time_total
+        if evt.device_type == DeviceType.CPU and evt.is_legacy:
+            # in legacy profiler, kernel info is stored in cpu events
+            sum_self_device_time_total += evt.self_device_time_total
+        elif (
+            evt.device_type
+            in [
+                DeviceType.CUDA,
+                DeviceType.PrivateUse1,
+                DeviceType.MTIA,
+            ]
+            and not evt.is_user_annotation
+        ):
+            # in kineto profiler, there're events with the correct device type (e.g. CUDA)
+            sum_self_device_time_total += evt.self_device_time_total
+
+    # Actual printing
+    if header is not None:
+        append("=" * line_length)
+        append(header)
+    if top_level_events_only:
+        append("=" * line_length)
+        append("This report only display top-level ops statistics")
+    append(header_sep)
+    append(row_format.format(*headers))
+
+    append(header_sep)
+
+    def trim_path(path, src_column_width):
+        if len(path) > src_column_width:
+            offset = len(path) - src_column_width
+            path = path[offset:]
+            if len(path) > 3:
+                path = "..." + path[3:]
+        return path
+
+    def override_time_unit(time_us, default_str, time_unit):
+        US_IN_SECOND = 1000.0 * 1000.0
+        US_IN_MS = 1000.0
+        if time_unit == "s":
+            return f"{time_us / US_IN_SECOND:.3f}s"
+        elif time_unit == "ms":
+            return f"{time_us / US_IN_MS:.3f}ms"
+        elif time_unit == "us":
+            return f"{time_us:.3f}us"
+        else:
+            return default_str
+
+    event_limit = 0
+    for evt in events:
+        if event_limit == row_limit:
+            break
+        if top_level_events_only and evt.cpu_parent is not None:
+            continue
+        else:
+            event_limit += 1
+        name = evt.key
+        if max_name_column_width is not None and len(name) >= max_name_column_width - 3:
+            name = name[: (max_name_column_width - 3)] + "..."
+
+        evt.self_cpu_percent = _format_time_share(
+            evt.self_cpu_time_total, sum_self_cpu_time_total
+        )
+        evt.total_cpu_percent = (
+            _format_time_share(evt.cpu_time_total, sum_self_cpu_time_total)
+            if not evt.is_async
+            else 0
+        )
+
+        row_values = [name]
+        if has_overload_names:
+            overload_name = evt.overload_name
+            if (
+                max_name_column_width is not None
+                and len(overload_name) >= max_name_column_width - 3
+            ):
+                overload_name = overload_name[: (max_name_column_width - 3)] + "..."
+            row_values += [overload_name]
+        row_values += [
+            # Self CPU total %, 0 for async events.
+            evt.self_cpu_percent,
+            override_time_unit(
+                evt.self_cpu_time_total, evt.self_cpu_time_total_str, time_unit
+            ),  # Self CPU total
+            # CPU total %, 0 for async events.
+            evt.total_cpu_percent,
+            override_time_unit(
+                evt.cpu_time_total, evt.cpu_time_total_str, time_unit
+            ),  # CPU total
+            override_time_unit(
+                evt.cpu_time, evt.cpu_time_str, time_unit
+            ),  # CPU time avg
+        ]
+        if has_device_time:
+            evt.total_device_percent = _format_time_share(
+                evt.self_device_time_total, sum_self_device_time_total
+            )
+            row_values.extend(
+                [
+                    override_time_unit(
+                        evt.self_device_time_total,
+                        evt.self_device_time_total_str,
+                        time_unit,
+                    ),
+                    # device time total %
+                    evt.total_device_percent,
+                    override_time_unit(
+                        evt.device_time_total, evt.device_time_total_str, time_unit
+                    ),
+                    override_time_unit(
+                        evt.device_time, evt.device_time_str, time_unit
+                    ),  # device time avg
+                ]
+            )
+        if profile_memory:
+            row_values.extend(
+                [
+                    # CPU Mem Total
+                    _format_memory(evt.cpu_memory_usage),
+                    # Self CPU Mem Total
+                    _format_memory(evt.self_cpu_memory_usage),
+                ]
+            )
+            if use_device and has_device_mem:
+                row_values.extend(
+                    [
+                        # Device Mem Total
+                        _format_memory(evt.device_memory_usage),
+                        # Self Device Mem Total
+                        _format_memory(evt.self_device_memory_usage),
+                    ]
+                )
+        row_values.append(
+            evt.count,  # Number of calls
+        )
+
+        if append_node_id:
+            row_values.append(evt.node_id)
+        if has_input_shapes:
+            row_values.append(str(evt.input_shapes)[:shapes_column_width])
+        if with_flops:
+            if evt.flops <= 0:
+                row_values.append("--")
+            else:
+                row_values.append(f"{evt.flops * flops_scale:8.3f}")  # type: ignore[possibly-undefined]
+        if has_stack:
+            src_field = ""
+            if len(evt.stack) > 0:
+                src_field = trim_path(evt.stack[0], src_column_width)
+            row_values.append(src_field)
+        append(row_format.format(*row_values))
+
+        if has_stack:
+            empty_headers = [""] * (len(headers) - 1)
+            for entry in evt.stack[1:]:
+                append(
+                    row_format.format(
+                        *(empty_headers + [trim_path(entry, src_column_width)])
+                    )
+                )
+            empty_headers.append("")
+            append(row_format.format(*empty_headers))
+
+    append(header_sep)
+    append(
+        f"Self CPU time total: {override_time_unit(sum_self_cpu_time_total, _format_time(sum_self_cpu_time_total), time_unit)}"
+    )
+    if has_device_time:
+        append(
+            f"Self {use_device.upper() if use_device is not None else 'None'} "
+            f"time total: {override_time_unit(sum_self_device_time_total, _format_time(sum_self_device_time_total), time_unit)}"
+        )
+    return "".join(result)
+
+
+# Collect all events with stack traces and format them canonically
+def _canonicalize_profiler_events(events):
+    """
+    Extract and format all events with stack traces in a canonical way
+    for deterministic testing.
+    """
+    events_with_traces = []
+
+    for event in events:
+        # Extract relevant fields
+        event_name = event.get("name", "")
+        node_name = event["args"].get("node_name", "")
+        stack_trace = event["args"].get("stack_trace", "")
+
+        # Get the last non-empty line of the stack trace
+        lines = [s.strip() for s in stack_trace.split("\n") if s.strip()]
+        stack_trace = lines[-1] if lines else ""
+
+        events_with_traces.append(
+            {
+                "event_name": event_name[:20],
+                "node_name": node_name,
+                "stack_trace": stack_trace,
+                "start_time": event.get("ts", 0),
+            }
+        )
+
+    # Sort by node_name for deterministic ordering
+    events_with_traces.sort(key=lambda x: x["start_time"])
+
+    # Format as a string
+    lines: list[str] = []
+    for evt in events_with_traces:
+        lines.append(
+            f"event={evt['event_name']} node={evt['node_name']} stack_trace={evt['stack_trace']}"
+        )
+
+    return "\n".join(lines)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/variable.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/variable.py
new file mode 100644
index 0000000000000000000000000000000000000000..84b504a9c82c7ab855df9ba58d934fa92d936253
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/autograd/variable.py
@@ -0,0 +1,15 @@
+# mypy: allow-untyped-defs
+import torch
+from torch._C import _ImperativeEngine as ImperativeEngine
+
+
+__all__ = ["VariableMeta", "Variable"]
+
+
+class VariableMeta(type):
+    def __instancecheck__(cls, other):
+        return isinstance(other, torch.Tensor)
+
+
+class Variable(torch._C._LegacyVariableBase, metaclass=VariableMeta):  # type: ignore[misc]
+    _execution_engine = ImperativeEngine()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f54a3fd6820c75f885aa50e6beacd3b2a5a12871
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/__init__.py
@@ -0,0 +1,139 @@
+# mypy: allow-untyped-defs
+import sys
+import types
+from contextlib import contextmanager
+
+import torch
+
+
+# The idea for this parameter is that we forbid bare assignment
+# to torch.backends..enabled and friends when running our
+# test suite, where it's very easy to forget to undo the change
+# later.
+__allow_nonbracketed_mutation_flag = True
+
+
+def disable_global_flags():
+    global __allow_nonbracketed_mutation_flag
+    __allow_nonbracketed_mutation_flag = False
+
+
+def flags_frozen():
+    return not __allow_nonbracketed_mutation_flag
+
+
+@contextmanager
+def __allow_nonbracketed_mutation():
+    global __allow_nonbracketed_mutation_flag
+    old = __allow_nonbracketed_mutation_flag
+    __allow_nonbracketed_mutation_flag = True
+    try:
+        yield
+    finally:
+        __allow_nonbracketed_mutation_flag = old
+
+
+class ContextProp:
+    def __init__(self, getter, setter):
+        self.getter = getter
+        self.setter = setter
+
+    def __get__(self, obj, objtype):
+        return self.getter()
+
+    def __set__(self, obj, val):
+        if not flags_frozen():
+            self.setter(val)
+        else:
+            raise RuntimeError(
+                f"not allowed to set {obj.__name__} flags "
+                "after disable_global_flags; please use flags() context manager instead"
+            )
+
+
+class PropModule(types.ModuleType):
+    def __init__(self, m, name):
+        super().__init__(name)
+        self.m = m
+
+    def __getattr__(self, attr):
+        return self.m.__getattribute__(attr)
+
+
+class _FP32Precision:
+    def __init__(self, backend, op):
+        self.backend = backend
+        self.op = op
+
+    def __setattr__(self, name, value):
+        if name == "fp32_precision":
+            torch._C._set_fp32_precision_setter(self.backend, self.op, value)
+        elif name in ("backend", "op"):
+            super().__setattr__(name, value)
+        else:
+            raise AttributeError("Unknown attribute " + name)
+
+    def __getattr__(self, name):
+        if name == "fp32_precision":
+            return torch._C._get_fp32_precision_getter(self.backend, self.op)
+        else:
+            raise AttributeError("Unknown attribute " + name)
+
+
+def set_flags(_fp32_precision="none"):
+    orig_flags = (torch._C._get_fp32_precision_getter("generic", "all"),)
+    if _fp32_precision is not None:
+        torch._C._set_fp32_precision_setter("generic", "all", _fp32_precision)
+    return orig_flags
+
+
+@contextmanager
+def flags(fp32_precision="none"):
+    with __allow_nonbracketed_mutation():
+        orig_flags = set_flags(fp32_precision)
+    try:
+        yield
+    finally:
+        with __allow_nonbracketed_mutation():
+            set_flags(*orig_flags)
+
+
+def _get_fp32_precision_getter(backend, op):
+    def inner():
+        return torch._C._get_fp32_precision_getter(backend, op)
+
+    return inner
+
+
+def _set_fp32_precision_setter(backend, op):
+    def inner(precision):
+        return torch._C._set_fp32_precision_setter(backend, op, precision)
+
+    return inner
+
+
+class GenericModule(PropModule):
+    fp32_precision = ContextProp(
+        _get_fp32_precision_getter("generic", "all"),
+        _set_fp32_precision_setter("generic", "all"),
+    )
+
+
+sys.modules[__name__] = GenericModule(sys.modules[__name__], __name__)
+
+from torch.backends import (
+    cpu as cpu,
+    cuda as cuda,
+    cudnn as cudnn,
+    cusparselt as cusparselt,
+    kleidiai as kleidiai,
+    mha as mha,
+    miopen as miopen,
+    mkl as mkl,
+    mkldnn as mkldnn,
+    mps as mps,
+    nnpack as nnpack,
+    openmp as openmp,
+    opt_einsum as opt_einsum,
+    quantized as quantized,
+)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/_coreml/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/_coreml/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/_coreml/preprocess.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/_coreml/preprocess.py
new file mode 100644
index 0000000000000000000000000000000000000000..3180e56a6baf96b56c88a712a4426108d8c8e2fc
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/_coreml/preprocess.py
@@ -0,0 +1,150 @@
+# mypy: allow-untyped-defs
+import hashlib
+import json
+
+import coremltools as ct  # type: ignore[import]
+from coremltools.converters.mil.input_types import TensorType  # type: ignore[import]
+from coremltools.converters.mil.mil import types  # type: ignore[import]
+from coremltools.models.neural_network import quantization_utils  # type: ignore[import]
+
+import torch
+
+
+CT_METADATA_VERSION = "com.github.apple.coremltools.version"
+CT_METADATA_SOURCE = "com.github.apple.coremltools.source"
+
+
+class ScalarType:
+    Float = 0
+    Double = 1
+    Int = 2
+    Long = 3
+    Undefined = 4
+
+
+# Supported Tensor types in coremltools:
+# https://github.com/apple/coremltools/blob/main/coremltools/converters/mil/frontend/torch/converter.py#L28
+torch_to_mil_types = {
+    ScalarType.Float: types.fp32,
+    ScalarType.Double: types.fp64,
+    ScalarType.Int: types.int32,
+    ScalarType.Long: types.int64,
+}
+
+
+class CoreMLComputeUnit:
+    CPU = "cpuOnly"
+    CPUAndGPU = "cpuAndGPU"
+    ALL = "all"
+
+
+class CoreMLQuantizationMode:
+    LINEAR = "linear"
+    LINEAR_SYMMETRIC = "linear_symmetric"
+    NONE = "none"
+
+
+def TensorSpec(shape, dtype=ScalarType.Float):
+    return (shape, dtype)
+
+
+def CompileSpec(
+    inputs,
+    outputs,
+    backend=CoreMLComputeUnit.CPU,
+    allow_low_precision=True,
+    quantization_mode=CoreMLQuantizationMode.NONE,
+    mlmodel_export_path=None,
+    convert_to=None,
+):
+    return (
+        inputs,
+        outputs,
+        backend,
+        allow_low_precision,
+        quantization_mode,
+        mlmodel_export_path,
+        convert_to,
+    )
+
+
+def _check_enumerated_shape(shape):
+    for s in shape:
+        if not isinstance(s, (list, tuple)):
+            return False
+    return True
+
+
+def _convert_to_mil_type(shape, dtype, name: str):
+    mil_shape = shape
+    if _check_enumerated_shape(shape):
+        mil_shape = ct.EnumeratedShapes(shape)
+    ml_type = TensorType(shape=mil_shape, dtype=torch_to_mil_types[dtype])
+    ml_type.name = name
+    return ml_type
+
+
+def preprocess(script_module: torch._C.ScriptObject, compile_spec: dict[str, tuple]):
+    spec = compile_spec["forward"]
+    (
+        input_specs,
+        output_specs,
+        backend,
+        allow_low_precision,
+        quantization_mode,
+        mlmodel_export_path,
+        convert_to,
+    ) = spec
+    mil_inputs = []
+    inputs = []
+    for index, input in enumerate(input_specs):
+        shape, dtype = input
+        name = "input_" + str(index)
+        inputs.append([name, str(dtype), str(shape)])
+        ml_type = _convert_to_mil_type(shape, dtype, name)
+        mil_inputs.append(ml_type)
+    model = torch.jit.RecursiveScriptModule._construct(script_module, lambda x: None)
+    mlmodel = ct.convert(model, inputs=mil_inputs, convert_to=convert_to)
+
+    if quantization_mode != CoreMLQuantizationMode.NONE:
+        quant_model_spec = quantization_utils.quantize_weights(
+            mlmodel, nbits=8, quantization_mode=quantization_mode
+        )
+        mlmodel = ct.models.MLModel(quant_model_spec)
+
+    spec = mlmodel.get_spec()
+    assert len(spec.description.output) == len(output_specs)  # type: ignore[attr-defined]
+    outputs = []
+    for index, output in enumerate(output_specs):
+        shape, dtype = output
+        name = spec.description.output[index].name  # type: ignore[attr-defined]
+        outputs.append([name, str(dtype), str(shape)])
+    mlmodel = ct.models.model.MLModel(spec)
+    print(mlmodel)
+
+    if mlmodel_export_path is not None:
+        print(f"Saving CoreML .mlmodel file to {mlmodel_export_path}")
+        mlmodel.save(mlmodel_export_path)
+
+    config = {
+        "spec_ver": str(spec.specificationVersion),  # type: ignore[attr-defined]
+        "backend": backend,
+        "allow_low_precision": str(allow_low_precision),
+    }
+    metadata = {
+        "coremltool_ver": mlmodel.user_defined_metadata[CT_METADATA_VERSION],
+        "torch_ver": mlmodel.user_defined_metadata[CT_METADATA_SOURCE],
+    }
+    coreml_compile_spec = {
+        "inputs": inputs,
+        "outputs": outputs,
+        "config": config,
+        "metadata": metadata,
+    }
+    mlmodel = spec.SerializeToString()  # type: ignore[attr-defined]
+
+    return {
+        "model": mlmodel,
+        "hash": str(hashlib.sha256(mlmodel).hexdigest()),
+        "extra": json.dumps(coreml_compile_spec),
+    }
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/_nnapi/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/_nnapi/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/_nnapi/prepare.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/_nnapi/prepare.py
new file mode 100644
index 0000000000000000000000000000000000000000..0fc48d711111ffd417fa1c544bd4b2362e75cf16
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/_nnapi/prepare.py
@@ -0,0 +1,199 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+from typing import Optional
+
+import torch
+from torch.backends._nnapi.serializer import _NnapiSerializer
+
+
+ANEURALNETWORKS_PREFER_LOW_POWER = 0
+ANEURALNETWORKS_PREFER_FAST_SINGLE_ANSWER = 1
+ANEURALNETWORKS_PREFER_SUSTAINED_SPEED = 2
+
+
+class NnapiModule(torch.nn.Module):
+    """Torch Module that wraps an NNAPI Compilation.
+
+    This module handles preparing the weights, initializing the
+    NNAPI TorchBind object, and adjusting the memory formats
+    of all inputs and outputs.
+    """
+
+    # _nnapi.Compilation is defined
+    comp: Optional[torch.classes._nnapi.Compilation]  # type: ignore[name-defined]
+    weights: list[torch.Tensor]
+    out_templates: list[torch.Tensor]
+
+    def __init__(
+        self,
+        shape_compute_module: torch.nn.Module,
+        ser_model: torch.Tensor,
+        weights: list[torch.Tensor],
+        inp_mem_fmts: list[int],
+        out_mem_fmts: list[int],
+        compilation_preference: int,
+        relax_f32_to_f16: bool,
+    ):
+        super().__init__()
+        self.shape_compute_module = shape_compute_module
+        self.ser_model = ser_model
+        self.weights = weights
+        self.inp_mem_fmts = inp_mem_fmts
+        self.out_mem_fmts = out_mem_fmts
+        self.out_templates = []
+        self.comp = None
+        self.compilation_preference = compilation_preference
+        self.relax_f32_to_f16 = relax_f32_to_f16
+
+    @torch.jit.export
+    def init(self, args: list[torch.Tensor]):
+        assert self.comp is None
+        self.out_templates = self.shape_compute_module.prepare(self.ser_model, args)  # type: ignore[operator]
+        self.weights = [w.contiguous() for w in self.weights]
+        comp = torch.classes._nnapi.Compilation()
+        comp.init2(
+            self.ser_model,
+            self.weights,
+            self.compilation_preference,
+            self.relax_f32_to_f16,
+        )
+
+        self.comp = comp
+
+    def forward(self, args: list[torch.Tensor]) -> list[torch.Tensor]:
+        if self.comp is None:
+            self.init(args)
+        comp = self.comp
+        assert comp is not None
+        outs = [torch.empty_like(out) for out in self.out_templates]
+
+        assert len(args) == len(self.inp_mem_fmts)
+        fixed_args = []
+        for idx in range(len(args)):
+            fmt = self.inp_mem_fmts[idx]
+            # These constants match the values in DimOrder in serializer.py
+            # TODO: See if it's possible to use those directly.
+            if fmt == 0:
+                fixed_args.append(args[idx].contiguous())
+            elif fmt == 1:
+                fixed_args.append(args[idx].permute(0, 2, 3, 1).contiguous())
+            else:
+                raise ValueError("Invalid mem_fmt")
+        comp.run(fixed_args, outs)
+        assert len(outs) == len(self.out_mem_fmts)
+        for idx in range(len(self.out_templates)):
+            fmt = self.out_mem_fmts[idx]
+            # These constants match the values in DimOrder in serializer.py
+            # TODO: See if it's possible to use those directly.
+            if fmt in (0, 2):
+                pass
+            elif fmt == 1:
+                outs[idx] = outs[idx].permute(0, 3, 1, 2)
+            else:
+                raise ValueError("Invalid mem_fmt")
+        return outs
+
+
+def convert_model_to_nnapi(
+    model,
+    inputs,
+    serializer=None,
+    return_shapes=None,
+    use_int16_for_qint16=False,
+    compilation_preference=ANEURALNETWORKS_PREFER_SUSTAINED_SPEED,
+    relax_f32_to_f16=False,
+):
+    (
+        shape_compute_module,
+        ser_model_tensor,
+        used_weights,
+        inp_mem_fmts,
+        out_mem_fmts,
+        retval_count,
+    ) = process_for_nnapi(
+        model, inputs, serializer, return_shapes, use_int16_for_qint16
+    )
+
+    nnapi_model = NnapiModule(
+        shape_compute_module,
+        ser_model_tensor,
+        used_weights,
+        inp_mem_fmts,
+        out_mem_fmts,
+        compilation_preference,
+        relax_f32_to_f16,
+    )
+
+    class NnapiInterfaceWrapper(torch.nn.Module):
+        """NNAPI list-ifying and de-list-ifying wrapper.
+
+        NNAPI always expects a list of inputs and provides a list of outputs.
+        This module allows us to accept inputs as separate arguments.
+        It returns results as either a single tensor or tuple,
+        matching the original module.
+        """
+
+        def __init__(self, mod):
+            super().__init__()
+            self.mod = mod
+
+    wrapper_model_py = NnapiInterfaceWrapper(nnapi_model)
+    wrapper_model = torch.jit.script(wrapper_model_py)
+    # TODO: Maybe make these names match the original.
+    arg_list = ", ".join(f"arg_{idx}" for idx in range(len(inputs)))
+    if retval_count < 0:
+        ret_expr = "retvals[0]"
+    else:
+        ret_expr = "".join(f"retvals[{idx}], " for idx in range(retval_count))
+    wrapper_model.define(
+        f"def forward(self, {arg_list}):\n"
+        f"    retvals = self.mod([{arg_list}])\n"
+        f"    return {ret_expr}\n"
+    )
+    return wrapper_model
+
+
+def process_for_nnapi(
+    model, inputs, serializer=None, return_shapes=None, use_int16_for_qint16=False
+):
+    model = torch.jit.freeze(model)
+
+    if isinstance(inputs, torch.Tensor):
+        inputs = [inputs]
+
+    serializer = serializer or _NnapiSerializer(
+        config=None, use_int16_for_qint16=use_int16_for_qint16
+    )
+    (
+        ser_model,
+        used_weights,
+        inp_mem_fmts,
+        out_mem_fmts,
+        shape_compute_lines,
+        retval_count,
+    ) = serializer.serialize_model(model, inputs, return_shapes)
+    ser_model_tensor = torch.tensor(ser_model, dtype=torch.int32)
+
+    # We have to create a new class here every time this function is called
+    # because module.define adds a method to the *class*, not the instance.
+    class ShapeComputeModule(torch.nn.Module):
+        """Code-gen-ed module for tensor shape computation.
+
+        module.prepare will mutate ser_model according to the computed operand
+        shapes, based on the shapes of args.  Returns a list of output templates.
+        """
+
+    shape_compute_module = torch.jit.script(ShapeComputeModule())
+    real_shape_compute_lines = [
+        "def prepare(self, ser_model: torch.Tensor, args: List[torch.Tensor]) -> List[torch.Tensor]:\n",
+    ] + [f"    {line}\n" for line in shape_compute_lines]
+    shape_compute_module.define("".join(real_shape_compute_lines))
+
+    return (
+        shape_compute_module,
+        ser_model_tensor,
+        used_weights,
+        inp_mem_fmts,
+        out_mem_fmts,
+        retval_count,
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/_nnapi/serializer.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/_nnapi/serializer.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ff09959f840c4b8c61147cc2180abc8d5d25b13
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/_nnapi/serializer.py
@@ -0,0 +1,2231 @@
+# mypy: allow-untyped-defs
+import array
+import enum
+import functools
+import logging
+import operator
+import struct
+import sys
+from typing import NamedTuple, Optional
+
+import torch
+
+
+# TODO: Add type annotations
+# TODO: Check tensor types for ops
+
+
+LOG = logging.getLogger("nnapi_serialize")
+
+
+class NNAPI_OperandCode:
+    FLOAT32 = 0
+    INT32 = 1
+    UINT32 = 2
+    TENSOR_FLOAT32 = 3
+    TENSOR_INT32 = 4
+    TENSOR_QUANT8_ASYMM = 5
+    BOOL = 6
+    TENSOR_QUANT16_SYMM = 7
+    TENSOR_FLOAT16 = 8
+    TENSOR_BOOL8 = 9
+    FLOAT16 = 10
+    TENSOR_QUANT8_SYMM_PER_CHANNEL = 11
+    TENSOR_QUANT16_ASYMM = 12
+
+
+class NNAPI_OperationCode:
+    ADD = 0
+    AVERAGE_POOL_2D = 1
+    CONCATENATION = 2
+    CONV_2D = 3
+    DEPTHWISE_CONV_2D = 4
+    DEPTH_TO_SPACE = 5
+    DEQUANTIZE = 6
+    EMBEDDING_LOOKUP = 7
+    FLOOR = 8
+    FULLY_CONNECTED = 9
+    HASHTABLE_LOOKUP = 10
+    L2_NORMALIZATION = 11
+    L2_POOL_2D = 12
+    LOCAL_RESPONSE_NORMALIZATION = 13
+    LOGISTIC = 14
+    LSH_PROJECTION = 15
+    LSTM = 16
+    MAX_POOL_2D = 17
+    MUL = 18
+    RELU = 19
+    RELU1 = 20
+    RELU6 = 21
+    RESHAPE = 22
+    RESIZE_BILINEAR = 23
+    RNN = 24
+    SOFTMAX = 25
+    SPACE_TO_DEPTH = 26
+    SVDF = 27
+    TANH = 28
+    BATCH_TO_SPACE_ND = 29
+    DIV = 30
+    MEAN = 31
+    PAD = 32
+    SPACE_TO_BATCH_ND = 33
+    SQUEEZE = 34
+    STRIDED_SLICE = 35
+    SUB = 36
+    TRANSPOSE = 37
+    ABS = 38
+    ARGMAX = 39
+    ARGMIN = 40
+    AXIS_ALIGNED_BBOX_TRANSFORM = 41
+    BIDIRECTIONAL_SEQUENCE_LSTM = 42
+    BIDIRECTIONAL_SEQUENCE_RNN = 43
+    BOX_WITH_NMS_LIMIT = 44
+    CAST = 45
+    CHANNEL_SHUFFLE = 46
+    DETECTION_POSTPROCESSING = 47
+    EQUAL = 48
+    EXP = 49
+    EXPAND_DIMS = 50
+    GATHER = 51
+    GENERATE_PROPOSALS = 52
+    GREATER = 53
+    GREATER_EQUAL = 54
+    GROUPED_CONV_2D = 55
+    HEATMAP_MAX_KEYPOINT = 56
+    INSTANCE_NORMALIZATION = 57
+    LESS = 58
+    LESS_EQUAL = 59
+    LOG = 60
+    LOGICAL_AND = 61
+    LOGICAL_NOT = 62
+    LOGICAL_OR = 63
+    LOG_SOFTMAX = 64
+    MAXIMUM = 65
+    MINIMUM = 66
+    NEG = 67
+    NOT_EQUAL = 68
+    PAD_V2 = 69
+    POW = 70
+    PRELU = 71
+    QUANTIZE = 72
+    QUANTIZED_16BIT_LSTM = 73
+    RANDOM_MULTINOMIAL = 74
+    REDUCE_ALL = 75
+    REDUCE_ANY = 76
+    REDUCE_MAX = 77
+    REDUCE_MIN = 78
+    REDUCE_PROD = 79
+    REDUCE_SUM = 80
+    ROI_ALIGN = 81
+    ROI_POOLING = 82
+    RSQRT = 83
+    SELECT = 84
+    SIN = 85
+    SLICE = 86
+    SPLIT = 87
+    SQRT = 88
+    TILE = 89
+    TOPK_V2 = 90
+    TRANSPOSE_CONV_2D = 91
+    UNIDIRECTIONAL_SEQUENCE_LSTM = 92
+    UNIDIRECTIONAL_SEQUENCE_RNN = 93
+    RESIZE_NEAREST_NEIGHBOR = 94
+
+
+class NNAPI_FuseCode:
+    FUSED_NONE = 0
+    FUSED_RELU = 1
+    FUSED_RELU1 = 2
+    FUSED_RELU6 = 3
+
+
+class OperandValueSourceType:
+    IMMEDIATE = 0
+    NUMBERED_BUFFER = 2
+    NUMBERED_MEMORY = 3
+
+
+# Scalar types that appear explicitly in models.
+# These must be kept in sync with
+# AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS.
+# TODO: Expose these directly to Python to avoid maintaining this list.
+class TorchScalarTypes(enum.Enum):
+    QUINT8 = 13
+
+
+def approx_equal(lhs, rhs, tolerance=1e-6):
+    return abs(lhs - rhs) <= tolerance * min(lhs, rhs)
+
+
+def tensor_size(op_type, dims):
+    ITEM_SIZES = {
+        NNAPI_OperandCode.TENSOR_FLOAT32: 4,
+        NNAPI_OperandCode.TENSOR_INT32: 4,
+        NNAPI_OperandCode.TENSOR_QUANT8_ASYMM: 1,
+        NNAPI_OperandCode.TENSOR_QUANT16_SYMM: 2,
+        NNAPI_OperandCode.TENSOR_QUANT16_ASYMM: 2,
+    }
+    size = ITEM_SIZES[op_type]
+    for d in dims:
+        size *= d
+    return size
+
+
+def change_element(tup, index, value):
+    ls = list(tup)
+    ls[index] = value
+    return tuple(ls)
+
+
+class ConvPoolArgs2d(NamedTuple):
+    """Configuration arguments for a convolution."""
+
+    kernel_h: int
+    kernel_w: int
+    stride_h: int
+    stride_w: int
+    pad_t: int
+    pad_b: int
+    pad_l: int
+    pad_r: int
+    dilation_h: int
+    dilation_w: int
+    group: int
+
+
+class DimOrder(enum.Enum):
+    PRESUMED_CONTIGUOUS = 0
+    CHANNELS_LAST = 1
+    SCALAR_OR_VECTOR = 2
+    UNKNOWN_CONSTANT = 999
+
+
+class Operand(NamedTuple):
+    """Representation of an NNAPI operand."""
+
+    # NNAPI operand type.  One of NNAPI_OperandCode.
+    # TODO: Make this an enum.
+    op_type: int
+
+    # This is always the PyTorch shape, which is NCHW for feature maps.
+    # The actual NNAPI operand might have a transposed shape.
+    # we use 0 for load time dynamic shapes & -1 for runtime dynamic shapes
+    shape: tuple[int, ...]
+
+    # Specifies how the shape of the operand that we define in NNAPI
+    # relates to the shape we track above.
+    # - PRESUMED_CONTIGUOUS: physical NNAPI operand will exactly match
+    #   the shape of the PyTorch tensor.
+    # - CHANNELS_LAST: The PyTorch tensor is expected to be NCHW, and
+    #   the NNAPI operand will be represented explicitly as NHWC.
+    dim_order: DimOrder
+
+    # Quantization params
+    scale: float
+    zero_point: int
+
+    def use_nchw(self):
+        if self.dim_order is DimOrder.PRESUMED_CONTIGUOUS:
+            return True
+        if self.dim_order is DimOrder.CHANNELS_LAST:
+            return False
+        raise Exception("Unknown dim order")  # noqa: TRY002
+
+
+def broadcast_shapes(shape1, shape2):
+    assert len(shape1) > 0
+    assert len(shape2) > 0
+    s1 = list(shape1)
+    s2 = list(shape2)
+    # TODO: Support non-equal-rank broadcast where semantics match.
+    # This can be tricky for NHWC tensors because dimension orders
+    # don't match between PT and NNAPI, even though semantics match.
+    if len(s1) > len(s2):
+        # s2 = [1] * (len(s1) - len(s2)) + s2
+        raise Exception(  # noqa: TRY002
+            "Non-equal-rank broadcast is not supported yet."
+        )  # noqa: TRY002
+    if len(s2) > len(s1):
+        # s3 = [1] * (len(s2) - len(s1)) + s1
+        raise Exception(  # noqa: TRY002
+            "Non-equal-rank broadcast is not supported yet."
+        )  # noqa: TRY002
+    ret = []
+    for d1, d2 in zip(s1, s2):
+        if d1 == 1:
+            ret.append(d2)
+        elif d2 == 1:
+            ret.append(d1)
+        elif d1 == d2:
+            ret.append(d1)
+        else:
+            raise Exception(  # noqa: TRY002
+                f"Cannot broadcast shapes: {shape1} and {shape2}"
+            )  # noqa: TRY002
+    return tuple(ret)
+
+
+def get_conv_pool_shape(image_shape, args, out_ch, transpose):
+    batch, _in_c, in_h, in_w = image_shape
+
+    # TODO: Handle dilation
+    if args.dilation_h != 1 or args.dilation_w != 1:
+        raise Exception("Dilation not supported yet.")  # noqa: TRY002
+
+    if transpose:
+        out_h = (in_h - 1) * args.stride_h + args.kernel_h - args.pad_t - args.pad_b
+        out_w = (in_w - 1) * args.stride_w + args.kernel_w - args.pad_l - args.pad_l
+    else:
+        out_h = (in_h - args.kernel_h + args.pad_t + args.pad_b) // args.stride_h + 1
+        out_w = (in_w - args.kernel_w + args.pad_l + args.pad_r) // args.stride_w + 1
+
+    # Handle variable-sized tensors.
+    if in_h == 0:
+        out_h = 0
+    if in_w == 0:
+        out_w = 0
+
+    out_shape = (batch, out_ch, out_h, out_w)
+    return out_shape
+
+
+def fix_shape(shape, dim_order):
+    # Return the actual shape that an operand should have in NNAPI,
+    # given a PyTorch shape and dimension order.  This is where we
+    # convert from PyTorch's "always NCHW" shape to explicit NHWC.
+    if dim_order is DimOrder.PRESUMED_CONTIGUOUS:
+        return shape
+    if dim_order is DimOrder.CHANNELS_LAST:
+        return tuple([shape[0]] + list(shape[2:]) + [shape[1]])
+    if dim_order is DimOrder.SCALAR_OR_VECTOR:
+        assert len(shape) == 0 or len(shape) == 1
+        return shape
+    if dim_order is DimOrder.UNKNOWN_CONSTANT:
+        # XXX think this through
+        return shape
+    raise Exception(f"Bad dim_order: {dim_order!r}.")  # noqa: TRY002
+
+
+def reverse_map_dim(dim_order, d):
+    # Return the original PyTorch dimension position for a given dimension.
+    # d should be the dimension that NNAPI will see.
+    # reverse_map_dim(PRESUMED_CONTIGUOUS, x) == x
+    # reverse_map_dim(CHANNELS_LAST, 3) == 1
+    if dim_order in (DimOrder.PRESUMED_CONTIGUOUS, DimOrder.SCALAR_OR_VECTOR):
+        return d
+    assert dim_order is DimOrder.CHANNELS_LAST
+    return [0, 2, 3, 1][d]
+
+
+def flex_name(op_id, dim):
+    # Return the local variable name for the computed flexible size
+    # for a given op and dimension.
+    return f"s_{op_id}_{dim}"
+
+
+class _NnapiSerializer:
+    def __init__(self, config, use_int16_for_qint16=False):
+        self.operands = []
+        self.values = []
+        self.operations = []
+        self.value_data = []
+        self.operation_args = []
+        self.inputs = []
+        self.outputs = []
+        self.flexible_shape_computation_lines = []
+
+        self.modules = {}
+        self.constants = {}
+        self.tensor_sequences = {}
+        self.jitval_operand_map = {}
+        self.cached_immediates = {}
+        self.used_weights = []
+        self.weight_offset = 0
+        self.use_int16_for_qint16 = use_int16_for_qint16
+
+        if config is None:
+            config = {}
+
+    def get_next_operand_id(self):
+        return len(self.operands)
+
+    # Add a tensor operand corresponding to a JIT Value.
+    # Returns the NNAPI operand ID.  Can be looked up later with
+    # get_tensor_operand_by_jitval.
+    def add_tensor_operand(self, jitval, oper):
+        assert isinstance(oper, Operand)
+        if jitval in self.jitval_operand_map:
+            raise Exception(f"Duplicate tensor: {jitval!r}")  # noqa: TRY002
+
+        operand_id = self.get_next_operand_id()
+        self.operands.append(oper)
+        self.jitval_operand_map[jitval] = operand_id
+        return operand_id
+
+    # Add a tensor operand that does not correspond to a JIT Value.
+    # Useful for cases where multiple NNAPI operands are required
+    # to implement one JIT IR node.  Returns the NNAPI operand ID.
+    def add_anonymous_tensor_operand(self, oper):
+        assert isinstance(oper, Operand)
+        operand_id = self.get_next_operand_id()
+        self.operands.append(oper)
+        return operand_id
+
+    def torch_tensor_to_operand(self, tensor, dim_order):
+        dtype = str(tensor.dtype).replace("torch.", "")
+        scale = 0.0
+        zero_point = 0
+        if dtype == "float32":
+            op_type = NNAPI_OperandCode.TENSOR_FLOAT32
+        elif dtype == "int32":
+            op_type = NNAPI_OperandCode.TENSOR_INT32
+        elif dtype == "quint8":
+            op_type = NNAPI_OperandCode.TENSOR_QUANT8_ASYMM
+            scale = tensor.q_scale()
+            zero_point = tensor.q_zero_point()
+        elif dtype == "qint32":
+            op_type = NNAPI_OperandCode.TENSOR_INT32
+            scale = tensor.q_scale()
+            zero_point = tensor.q_zero_point()
+            assert zero_point == 0
+        elif dtype == "int16":
+            if self.use_int16_for_qint16:
+                nnapi_dtype = getattr(tensor, "nnapi_dtype", None)
+                op_codes = (
+                    NNAPI_OperandCode.TENSOR_QUANT16_SYMM,
+                    NNAPI_OperandCode.TENSOR_QUANT16_ASYMM,
+                )
+                if nnapi_dtype in op_codes:
+                    op_type = nnapi_dtype
+                    scale = tensor.nnapi_scale
+                    zero_point = tensor.nnapi_zero_point
+                else:
+                    raise Exception(  # noqa: TRY002
+                        f"`nnapi_type` needs to be one of {op_codes} for `int16`"
+                    )
+            else:
+                raise Exception(  # noqa: TRY002
+                    "`int16` isn't supported. If you're trying to represent NNAPI"
+                    " qint16 with Pytorch int16, set `use_int16_for_qint16 = True`"
+                )
+        else:
+            raise Exception(  # noqa: TRY002
+                f"Can't handle input with dtype '{tensor.dtype}'"
+            )  # noqa: TRY002
+        return Operand(
+            shape=tuple(tensor.shape),
+            # pyrefly: ignore [bad-argument-type]
+            op_type=op_type,
+            dim_order=dim_order,
+            scale=scale,
+            zero_point=zero_point,
+        )
+
+    def add_tensor_operand_for_input(self, arg_idx, jitval, tensor):
+        dim_order = (
+            DimOrder.CHANNELS_LAST
+            if getattr(tensor, "nnapi_nhwc", False)
+            else DimOrder.PRESUMED_CONTIGUOUS
+        )
+        toper = self.torch_tensor_to_operand(tensor, dim_order)
+        operand_id = self.add_tensor_operand(jitval, toper)
+        self.inputs.append(operand_id)
+        for dim, size in enumerate(tensor.shape):
+            if size == 0:
+                self.compute_operand_shape(
+                    operand_id, dim, f"args[{arg_idx}].shape[{dim}]"
+                )
+        return operand_id
+
+    def add_tensor_operand_for_weight(
+        self, tensor, dim_order=DimOrder.UNKNOWN_CONSTANT
+    ):
+        toper = self.torch_tensor_to_operand(tensor, dim_order)
+        operand_id = len(self.operands)
+        self.operands.append(toper)
+        tsize = tensor_size(toper.op_type, toper.shape)
+        self.values.append((operand_id, OperandValueSourceType.NUMBERED_BUFFER))
+        buf_num = len(self.used_weights)
+        offset = 0
+        self.value_data.append(struct.pack("iii", buf_num, offset, tsize))
+        # For NHWC NNAPI op, lay out data in the same dim order by permuting torch tensor
+        if dim_order == DimOrder.CHANNELS_LAST:
+            tensor = tensor.permute(0, 2, 3, 1)
+        self.used_weights.append(tensor)
+        return operand_id
+
+    def add_immediate_operand(self, code, value, dims):
+        assert isinstance(dims, tuple)
+        cache_key = (code, value)
+        if cache_key not in self.cached_immediates:
+            operand_id = len(self.operands)
+            self.operands.append(Operand(code, dims, DimOrder.SCALAR_OR_VECTOR, 0.0, 0))
+            self.values.append((operand_id, OperandValueSourceType.IMMEDIATE))
+            self.value_data.append(value)
+            self.cached_immediates[cache_key] = operand_id
+        return self.cached_immediates[cache_key]
+
+    def add_immediate_int_scalar(self, value):
+        return self.add_immediate_operand(
+            NNAPI_OperandCode.INT32, struct.pack("i", value), ()
+        )
+
+    def add_immediate_float_scalar(self, value):
+        return self.add_immediate_operand(
+            NNAPI_OperandCode.FLOAT32, struct.pack("f", value), ()
+        )
+
+    def add_immediate_bool_scalar(self, value):
+        return self.add_immediate_operand(
+            NNAPI_OperandCode.BOOL, b"\x01" if value else b"\x00", ()
+        )
+
+    def add_immediate_int_vector(self, value):
+        return self.add_immediate_operand(
+            NNAPI_OperandCode.TENSOR_INT32,
+            array.array("i", value).tobytes(),
+            (len(value),),
+        )
+
+    def has_operand_for_jitval(self, jitval):
+        return jitval in self.jitval_operand_map
+
+    def get_tensor_operand_by_jitval(self, jitval):
+        operand_id = self.jitval_operand_map[jitval]
+        return (operand_id, self.operands[operand_id])
+
+    def get_tensor_operand_by_jitval_fixed_size(self, jitval):
+        op_id, oper = self.get_tensor_operand_by_jitval(jitval)
+        for s in oper.shape:
+            if s == 0:
+                # TODO: Improve this error message, possibly after converting
+                # many callsites to support flexible size.
+                raise Exception(  # noqa: TRY002
+                    "Flexible size is not supported for this operand."
+                )  # noqa: TRY002
+            if s < 0:
+                # runtime flex
+                LOG.warning("Operand %s has runtime flex shape", oper)
+        return op_id, oper
+
+    def get_tensor_operand_or_constant(
+        self, jitval, dim_order=DimOrder.PRESUMED_CONTIGUOUS
+    ):
+        operand_id = self.jitval_operand_map.get(jitval)
+        if operand_id is None:
+            _, value = self.get_constant_value(jitval, "TensorType")
+            operand_id = self.add_tensor_operand_for_weight(value, dim_order)
+        return (operand_id, self.operands[operand_id])
+
+    def get_tensor_operand_for_weight(self, jitval):
+        _, value = self.get_constant_value(jitval, "TensorType")
+        operand_id = self.add_tensor_operand_for_weight(value)
+        return (operand_id, self.operands[operand_id])
+
+    def add_operation(self, opcode, inputs, outputs):
+        self.operations.append((opcode, len(inputs), len(outputs)))
+        self.operation_args.extend(inputs + outputs)
+
+    def add_tensor_sequence(self, jitval, values):
+        assert jitval not in self.tensor_sequences
+        self.tensor_sequences[jitval] = values
+
+    def add_constant_value(self, jitval, ctype, value):
+        assert jitval not in self.constants
+        self.constants[jitval] = (ctype, value)
+
+    def get_constant_value(self, jitval, typekind=None):
+        record = self.constants.get(jitval)
+        if record is None:
+            raise Exception(  # noqa: TRY002
+                f"Could not find constant value for '{jitval!r}'."
+            )  # noqa: TRY002
+        ctype, _ = record
+        if typekind is not None and ctype.kind() != typekind:
+            raise Exception(  # noqa: TRY002
+                f"Expected constant value of type {typekind}, but got {ctype.kind()} for value '{jitval!r}'"
+            )
+        return record
+
+    def operand_to_template_torchscript(self, op_id, oper, shape=None):
+        """Return a TorchScript expression to build a template for a given operand."""
+        if shape is None:
+            shape = oper.shape
+        else:
+            assert len(shape) == len(oper.shape)
+
+        shape_parts = ["("]
+        for d, s in enumerate(shape):
+            if s > 0:
+                # Fixed shape dimension: just add the value.
+                shape_parts.append(str(s))
+            elif s == 0:
+                # Load time flexible shape dimension: it should have been computed in a variable.
+                shape_parts.append(flex_name(op_id, d))
+            elif s == -1:
+                # Runtime flexible shape
+                shape_parts.append("0")
+            else:
+                raise Exception(  # noqa: TRY002
+                    "Unknown dim value, dimensions should be >= -1"
+                )  # noqa: TRY002
+            shape_parts.append(",")
+        shape_parts.append(")")
+        shape_code = "".join(shape_parts)
+        if oper.op_type == NNAPI_OperandCode.TENSOR_FLOAT32:
+            return f"torch.zeros({shape_code}, dtype=torch.float32)"
+        elif oper.op_type == NNAPI_OperandCode.TENSOR_INT32:
+            return f"torch.zeros({shape_code}, dtype=torch.int32)"
+        elif oper.op_type == NNAPI_OperandCode.TENSOR_QUANT8_ASYMM:
+            return (
+                f"torch.quantize_per_tensor("
+                f"torch.zeros(1), scale={oper.scale}, zero_point={oper.zero_point}, dtype=torch.quint8)"
+                f".expand({shape_code}).contiguous()"
+            )
+        elif oper.op_type in (
+            NNAPI_OperandCode.TENSOR_QUANT16_ASYMM,
+            NNAPI_OperandCode.TENSOR_QUANT16_SYMM,
+        ):
+            if self.use_int16_for_qint16:
+                return f"torch.zeros({shape_code}, dtype=torch.int16)"
+            else:
+                raise Exception(  # noqa: TRY002
+                    "`int16` isn't supported. If you're trying to represent NNAPI"
+                    " qint16 with Pytorch int16, set `use_int16_for_qint16 = True`"
+                )
+
+        raise Exception(  # noqa: TRY002
+            f"Unsupported output operand type: {oper.op_type}"
+        )  # noqa: TRY002
+
+    def forward_operand_shape(self, out_op_id, out_dim, in_op_id, in_dim):
+        self.compute_operand_shape(out_op_id, out_dim, flex_name(in_op_id, in_dim))
+
+    def compute_operand_shape(self, op_id, dim, expr):
+        self.flexible_shape_computation_lines.append(
+            f"{flex_name(op_id, dim)} = {expr}"
+        )
+
+    def transpose_to_nhwc(self, in_id, oper):
+        if oper.shape[2:] != (1, 1):
+            raise Exception(  # noqa: TRY002
+                "Automatic transpose only supported for H,W == 1,1"
+            )  # noqa: TRY002
+
+        out_oper = oper._replace(dim_order=DimOrder.CHANNELS_LAST)
+
+        inputs = [None] * 2
+        inputs[0] = in_id
+        inputs[1] = self.add_immediate_int_vector([0, 2, 3, 1])
+
+        outputs = [None] * 1
+        outputs[0] = self.add_anonymous_tensor_operand(out_oper)
+
+        self.add_operation(NNAPI_OperationCode.TRANSPOSE, inputs, outputs)
+
+        return outputs[0], out_oper
+
+    # Transpose inputs as necessary to allow broadcasting.
+    def transpose_for_broadcast(self, in0_id, in0_oper, in1_id, in1_oper):
+        if in0_oper.dim_order == in1_oper.dim_order:
+            return in0_id, in0_oper, in1_id, in1_oper
+
+        # Assume NHWC is preferred if there is a mismatch.
+        orders = (in0_oper.dim_order, in1_oper.dim_order)
+        if orders == (DimOrder.PRESUMED_CONTIGUOUS, DimOrder.CHANNELS_LAST):
+            return self.transpose_to_nhwc(in0_id, in0_oper) + (in1_id, in1_oper)
+        if orders == (DimOrder.CHANNELS_LAST, DimOrder.PRESUMED_CONTIGUOUS):
+            return (in0_id, in0_oper) + self.transpose_to_nhwc(in1_id, in1_oper)
+
+        raise Exception(  # noqa: TRY002
+            f"Automatic transpose not supported for dim_orders: {in0_oper.dim_order!r}, {in1_oper.dim_order!r}"
+        )
+
+    def get_size_arg(self, jitval):
+        ctype, value = self.get_constant_value(jitval)
+        if ctype.kind() == "ListType":
+            assert ctype.getElementType().kind() == "IntType"
+            return value
+        raise Exception(  # noqa: TRY002
+            f"Can't handle size arg of type '{ctype!r}' for '{jitval!r}'"
+        )  # noqa: TRY002
+
+    def get_conv_pool_args_2d_from_pack(self, kernel_size, packed_config):
+        pc = [i.item() for i in packed_config]
+        assert pc[0] == 2
+        strides = [pc[1], pc[2]]
+        paddings = [pc[3], pc[4]]
+        dilations = [pc[5], pc[6]]
+        output_padding = [pc[7], pc[8]]
+        group_num = pc[9]
+
+        assert len(pc) == 11
+        assert output_padding == [0, 0]
+
+        return self.get_conv_pool_args_2d_common(
+            kernel_size, strides, paddings, dilations, group_num
+        )
+
+    def get_conv_pool_args_2d_from_jit(
+        self, kernel_size, stride, padding, dilation=None, group=None
+    ):
+        strides = self.get_size_arg(stride)
+        paddings = self.get_size_arg(padding)
+        if dilation is None:
+            dilations = [1, 1]
+        else:
+            dilations = self.get_size_arg(dilation)
+        if group is not None:
+            _, group_num = self.get_constant_value(group, "IntType")
+        else:
+            group_num = None
+        return self.get_conv_pool_args_2d_common(
+            kernel_size, strides, paddings, dilations, group_num
+        )
+
+    def get_conv_pool_args_2d_common(
+        self, kernel_size, strides, paddings, dilations, group_num
+    ):
+        kernels = list(kernel_size)
+
+        assert len(kernels) == 2
+        assert len(strides) == 2
+        assert len(paddings) == 2
+        assert len(dilations) == 2
+
+        # NNAPI uses 4 values for padding.
+        ph, pw = paddings
+        real_paddings = [ph, ph, pw, pw]
+
+        return ConvPoolArgs2d(
+            *(kernels + strides + real_paddings + dilations + [group_num])
+        )
+
+    def serialize_model(self, model, inputs, return_shapes=None):
+        self.add_immediate_bool_scalar(False)
+        self.add_immediate_bool_scalar(True)
+
+        inp_dim_orders = []
+        out_dim_orders = []
+
+        self_jitval = next(model.graph.inputs())
+        self.add_constant_value(self_jitval, self_jitval.type(), model)
+
+        for arg_idx, (input_value, input_tensor) in enumerate(
+            zip(list(model.graph.inputs())[1:], inputs)
+        ):
+            op_id = self.add_tensor_operand_for_input(
+                arg_idx, input_value, input_tensor
+            )
+            inp_dim_orders.append(self.operands[op_id].dim_order.value)
+
+        for idx, node in enumerate(model.graph.nodes()):
+            LOG.debug("Processing node #%d: %r", idx, node)
+            self.add_node(node)
+
+        retn = model.graph.return_node()
+        assert retn.inputsSize() == 1
+        assert retn.outputsSize() == 0
+        retn_input = retn.inputsAt(0)
+        template_return_lines = ["return ["]
+        if retn_input.type().kind() == "TensorType":
+            return_values = [retn_input]
+            retval_count = -1
+        elif retn_input.type().kind() == "TupleType":
+            return_values = self.tensor_sequences[retn_input]
+            retval_count = len(return_values)
+        else:
+            raise Exception(  # noqa: TRY002
+                f"Unsupported return type: {retn_input.type()}"
+            )  # noqa: TRY002
+
+        if return_shapes is not None:
+            assert len(return_shapes) == len(return_values)
+        for i, v in enumerate(return_values):
+            op_id = self.jitval_operand_map[v]
+            self.outputs.append(op_id)
+            out_dim_orders.append(self.operands[op_id].dim_order.value)
+            shape = return_shapes[i] if return_shapes else None
+            template_return_lines.append(
+                self.operand_to_template_torchscript(op_id, self.operands[op_id], shape)
+                + ","
+            )
+        template_return_lines.append("]")
+
+        model = []
+
+        version = 1
+        header = struct.pack(
+            "iiiiii",
+            version,
+            len(self.operands),
+            len(self.values),
+            len(self.operations),
+            len(self.inputs),
+            len(self.outputs),
+        )
+        model.append(header)
+
+        serialized_values, serialized_value_data = self.serialize_values()
+
+        model.extend(
+            struct.pack("iifi", t, len(d), s, z) for (t, d, _m, s, z) in self.operands
+        )
+        model.extend(serialized_values)
+        model.extend(struct.pack("iii", *x) for x in self.operations)
+
+        # Compact the model so we can get its length so far.
+        model = [b"".join(model)]
+        model_offset = len(model[0])
+        # Model offset is the index into the model (in 32-bit words, not bytes)
+        # of the next dimension we're about to serialize.  If it's 0,
+        # generate code to mutate it before passing to NNAPI.
+        assert model_offset % 4 == 0
+        model_offset = int(model_offset / 4)
+
+        for op_id, (_, dims, dim_order, _, _) in enumerate(self.operands):
+            shape = fix_shape(dims, dim_order)
+            for d, s in enumerate(shape):
+                if s == 0:
+                    pt_d = reverse_map_dim(dim_order, d)
+                    self.flexible_shape_computation_lines.append(
+                        f"ser_model[{model_offset}] = {flex_name(op_id, pt_d)}"
+                    )
+                model_offset += 1
+
+            # convert runtime flex shape from -1 to 0
+            shape = tuple(d if d != -1 else 0 for d in shape)
+            model.append(self.serialize_ints(shape))
+
+        model.extend(serialized_value_data)
+        model.append(self.serialize_ints(self.operation_args))
+        model.append(self.serialize_ints(self.inputs))
+        model.append(self.serialize_ints(self.outputs))
+
+        self.flexible_shape_computation_lines.extend(template_return_lines)
+
+        return (
+            array.array("i", b"".join(model)),
+            self.used_weights,
+            inp_dim_orders,
+            out_dim_orders,
+            self.flexible_shape_computation_lines,
+            retval_count,
+        )
+
+    def serialize_values(self):
+        serialized_values = []
+        serialized_value_data = []
+        assert len(self.values) == len(self.value_data)
+        for (op_index, source_type), data in zip(self.values, self.value_data):
+            source_length = len(data)
+
+            # Pad with 0 bytes out to a multiple of 4 for alignment.
+            physical_length = ((source_length - 1) | 0x3) + 1
+            padded_data = data + (b"\0" * (physical_length - source_length))
+
+            serialized_values.append(
+                struct.pack("iii", op_index, source_type, source_length)
+            )
+            serialized_value_data.append(padded_data)
+
+        return serialized_values, serialized_value_data
+
+    @staticmethod
+    def serialize_ints(ints):
+        return array.array("i", ints).tobytes()
+
+    ADDER_MAP = {
+        "prim::GetAttr": lambda self, node: self.add_getattr(node),
+        "prim::Constant": lambda self, node: self.add_constant_node(node),
+        "prim::ListConstruct": lambda self, node: self.add_list_construct(node),
+        "prim::TupleConstruct": lambda self, node: self.add_tuple_construct(node),
+        "aten::unsqueeze": lambda self, node: self.add_unsqueeze(node),
+        "aten::to": lambda self, node: self.add_to(node),
+        "aten::detach": lambda self, node: self._identity(node),
+        "aten::reshape": lambda self, node: self.add_reshape(node),
+        "aten::flatten": lambda self, node: self.add_flatten(node),
+        "aten::slice": lambda self, node: self.add_slice(node),
+        "aten::size": lambda self, node: self.add_size(node),
+        "aten::cat": lambda self, node: self.add_cat(node),
+        "aten::mean": lambda self, node: self.add_mean(node),
+        "aten::quantize_per_tensor": lambda self, node: self.add_quantize(node),
+        "aten::dequantize": lambda self, node: self.add_dequantize(node),
+        "aten::add": lambda self, node: self.add_add_sub_op(
+            node, NNAPI_OperationCode.ADD, NNAPI_FuseCode.FUSED_NONE
+        ),
+        "aten::sub": lambda self, node: self.add_add_sub_op(
+            node, NNAPI_OperationCode.SUB, NNAPI_FuseCode.FUSED_NONE
+        ),
+        "aten::mul": lambda self, node: self.add_pointwise_simple_binary_broadcast_op(
+            node, NNAPI_OperationCode.MUL, NNAPI_FuseCode.FUSED_NONE
+        ),
+        "aten::div": lambda self, node: self.add_pointwise_simple_binary_broadcast_op(
+            node, NNAPI_OperationCode.DIV, NNAPI_FuseCode.FUSED_NONE
+        ),
+        "aten::relu": lambda self, node: self.add_pointwise_simple_unary_op(
+            node, NNAPI_OperationCode.RELU
+        ),
+        "aten::sigmoid": lambda self, node: self.add_pointwise_simple_unary_op(
+            node, NNAPI_OperationCode.LOGISTIC
+        ),
+        "aten::softmax": lambda self, node: self.add_softmax(node),
+        "aten::hardtanh": lambda self, node: self.add_hardtanh(node),
+        "aten::avg_pool2d": lambda self, node: self.add_avg_pool2d(node),
+        "aten::max_pool2d": lambda self, node: self.add_pool2d_node(
+            node, NNAPI_OperationCode.MAX_POOL_2D
+        ),
+        "aten::adaptive_avg_pool2d": lambda self, node: self.add_adaptive_avg_pool2d(
+            node
+        ),
+        "aten::upsample_nearest2d": lambda self, node: self.add_upsample_nearest2d(
+            node
+        ),
+        "aten::prelu": lambda self, node: self.add_prelu_op(node),
+        "aten::addmm": lambda self, node: self.add_addmm(node),
+        "aten::linear": lambda self, node: self.add_linear(node),
+        "aten::_convolution": lambda self, node: self.add_conv_underscore(node),
+        "aten::conv2d": lambda self, node: self.add_conv2d(node),
+        "aten::log_softmax": lambda self, node: self.add_log_softmax(node),
+        "quantized::linear": lambda self, node: self.add_qlinear(node),
+        "quantized::conv2d": lambda self, node: self.add_qconv2d(
+            node, NNAPI_FuseCode.FUSED_NONE
+        ),
+        "quantized::conv2d_relu": lambda self, node: self.add_qconv2d(
+            node, NNAPI_FuseCode.FUSED_RELU
+        ),
+        "quantized::conv_transpose2d": lambda self, node: self.add_qconv2d(
+            node, NNAPI_FuseCode.FUSED_NONE, transpose=True
+        ),
+        "quantized::add": lambda self, node: self.add_qadd(
+            node, NNAPI_OperationCode.ADD, NNAPI_FuseCode.FUSED_NONE
+        ),
+        "quantized::add_relu": lambda self, node: self.add_qadd(
+            node, NNAPI_OperationCode.ADD, NNAPI_FuseCode.FUSED_RELU
+        ),
+        "quantized::mul": lambda self, node: self.add_qadd(
+            node, NNAPI_OperationCode.MUL, NNAPI_FuseCode.FUSED_NONE
+        ),
+    }
+
+    def add_node(self, node):
+        adder = self.ADDER_MAP.get(node.kind())
+        if not adder:
+            raise Exception(  # noqa: TRY002
+                f"Unsupported node kind ({node.kind()!r}) in node {node!r}"
+            )  # noqa: TRY002
+        adder(self, node)
+
+    def _identity(self, node):
+        in_id, _in_oper = self.get_tensor_operand_by_jitval(node.inputsAt(0))
+        jitval = node.outputsAt(0)
+        self.jitval_operand_map[jitval] = in_id
+
+    def add_getattr(self, node):
+        assert node.inputsSize() == 1
+        assert node.outputsSize() == 1
+        obj_ctype, obj = self.get_constant_value(node.inputsAt(0))
+        assert str(obj_ctype).startswith("__torch__.")
+        name = node.s("name")
+        value = getattr(obj, name)
+        output = node.outputsAt(0)
+        ctype = output.type()
+        self.add_constant_value(output, ctype, value)
+
+    def add_constant_node(self, node):
+        assert node.inputsSize() == 0
+        assert node.outputsSize() == 1
+        output = node.outputsAt(0)
+        ctype = output.type()
+        value = output.toIValue()
+        self.add_constant_value(output, ctype, value)
+
+    def add_list_construct(self, node):
+        assert node.outputsSize() == 1
+        output = node.outputsAt(0)
+        ctype = output.type()
+        const_vals: Optional[list] = []
+        tensors: Optional[list] = []
+        for inp in node.inputs():
+            if const_vals is not None and inp in self.constants:
+                _, val = self.get_constant_value(inp)
+                const_vals.append(val)
+            else:
+                const_vals = None
+            if tensors is not None and inp.type().kind() == "TensorType":
+                tensors.append(inp)
+            else:
+                tensors = None
+
+        if const_vals is not None:
+            # NOTE: Now that TorchScript supports list constants,
+            # this code path might not be used anymore.
+            self.add_constant_value(output, ctype, const_vals)
+        if tensors is not None:
+            self.add_tensor_sequence(output, tensors)
+        if const_vals is None and tensors is None:
+            raise Exception(  # noqa: TRY002
+                f"Unable to handle ListConstruct node.  Neither all constants nor all tensors. {node!r}"
+            )
+
+    def add_tuple_construct(self, node):
+        assert node.outputsSize() == 1
+        output = node.outputsAt(0)
+        values = list(node.inputs())
+        self.add_tensor_sequence(output, values)
+
+    def add_unsqueeze(self, node):
+        assert node.inputsSize() == 2
+        assert node.outputsSize() == 1
+
+        in_id, in_oper = self.get_tensor_operand_by_jitval_fixed_size(node.inputsAt(0))
+
+        _, dim = self.get_constant_value(node.inputsAt(1), "IntType")
+        assert in_oper.dim_order == DimOrder.PRESUMED_CONTIGUOUS
+
+        real_dim = dim if dim >= 0 else dim + len(in_oper.shape) + 1
+        out_shape_list = list(in_oper.shape)
+        out_shape_list.insert(real_dim, 1)
+        out_shape = tuple(out_shape_list)
+        out_oper = in_oper._replace(shape=out_shape)
+
+        inputs = [None] * 2
+        inputs[0] = in_id
+        inputs[1] = self.add_immediate_int_scalar(dim)
+
+        outputs = [None] * 1
+        outputs[0] = self.add_tensor_operand(node.outputsAt(0), out_oper)
+
+        self.add_operation(NNAPI_OperationCode.EXPAND_DIMS, inputs, outputs)
+
+    def add_to(self, node):
+        # Handle to("cpu") / to("gpu") case
+        self._identity(node)
+
+    def add_reshape(self, node):
+        assert node.inputsSize() == 2
+        assert node.outputsSize() == 1
+
+        in_id, in_oper = self.get_tensor_operand_by_jitval_fixed_size(node.inputsAt(0))
+
+        shape_ctype, shape = self.get_constant_value(node.inputsAt(1))
+        assert shape_ctype.kind() == "ListType"
+        assert shape_ctype.getElementType().kind() == "IntType"
+        is_trivial_reshape = len(shape) == 2 and shape[1] == -1
+
+        if in_oper.dim_order != DimOrder.PRESUMED_CONTIGUOUS and not is_trivial_reshape:
+            raise Exception(  # noqa: TRY002
+                "Currently, reshape is only supported on NHWC tensors if the target size is [X, -1]."
+            )
+
+        # Bit of a hack here.  Use a real tensor to infer the output shape.
+        out_shape = torch.zeros(1).expand(in_oper.shape).reshape(shape).shape
+        out_oper = in_oper._replace(
+            shape=out_shape, dim_order=DimOrder.PRESUMED_CONTIGUOUS
+        )
+
+        inputs = [None] * 2
+        inputs[0] = in_id
+        inputs[1] = self.add_immediate_int_vector(shape)
+
+        outputs = [None] * 1
+        outputs[0] = self.add_tensor_operand(node.outputsAt(0), out_oper)
+
+        self.add_operation(NNAPI_OperationCode.RESHAPE, inputs, outputs)
+
+    def add_flatten(self, node):
+        assert node.inputsSize() == 3
+        assert node.outputsSize() == 1
+
+        in_id, in_oper = self.get_tensor_operand_by_jitval(node.inputsAt(0))
+
+        _start_ctype, start_dim = self.get_constant_value(node.inputsAt(1), "IntType")
+        _end_ctype, end_dim = self.get_constant_value(node.inputsAt(2), "IntType")
+
+        # channels last with channels == 1 or (height & width both 1)
+        is_trivial_flatten = len(in_oper.shape) == 4 and (
+            in_oper.shape[1] == 1 or (in_oper.shape[2] == 1 and in_oper.shape[3] == 1)
+        )
+        if in_oper.dim_order != DimOrder.PRESUMED_CONTIGUOUS and not is_trivial_flatten:
+            raise Exception(  # noqa: TRY002
+                "Currently, flatten is not supported on NHWC tensors unless C=1 or H=W=1"
+            )
+
+        if start_dim < 0:
+            start_dim += len(in_oper.shape)
+        if end_dim < 0:
+            end_dim += len(in_oper.shape)
+
+        out_shape = (
+            in_oper.shape[:start_dim]
+            + (functools.reduce(operator.mul, in_oper.shape[start_dim : end_dim + 1]),)
+            + in_oper.shape[end_dim + 1 :]
+        )
+
+        if any(dim == 0 for dim in in_oper.shape[start_dim : end_dim + 1]):
+            raise Exception(  # noqa: TRY002
+                "Flattening flexible dims is not supported yet"
+            )  # noqa: TRY002
+        non_flattened_dims = in_oper.shape[:start_dim] + in_oper.shape[end_dim + 1 :]
+        if non_flattened_dims.count(0) > 1:
+            raise Exception("Only 1 dim can be flexible")  # noqa: TRY002
+
+        out_oper = in_oper._replace(
+            shape=out_shape, dim_order=DimOrder.PRESUMED_CONTIGUOUS
+        )
+        out_id = self.add_tensor_operand(node.outputsAt(0), out_oper)
+
+        for idx, dim in enumerate(out_shape):
+            if dim == 0:
+                self.forward_operand_shape(out_id, idx, in_id, in_oper.shape.index(0))
+
+        inputs_1 = tuple(dim if dim != 0 else -1 for dim in out_shape)
+        inputs = [None] * 2
+        inputs[0] = in_id
+        inputs[1] = self.add_immediate_int_vector(inputs_1)
+
+        outputs = [None] * 1
+        outputs[0] = out_id
+
+        self.add_operation(NNAPI_OperationCode.RESHAPE, inputs, outputs)
+
+    def add_slice(self, node):
+        assert node.inputsSize() == 5
+        assert node.outputsSize() == 1
+
+        in_id, in_oper = self.get_tensor_operand_by_jitval(node.inputsAt(0))
+        _, dim_value = self.get_constant_value(node.inputsAt(1))
+        _, start_value = self.get_constant_value(node.inputsAt(2))
+        _, stop_value = self.get_constant_value(node.inputsAt(3))
+        _, step_value = self.get_constant_value(node.inputsAt(4))
+
+        if start_value is None:
+            start_value = 0
+        if stop_value is None:
+            stop_value = sys.maxsize
+
+        if start_value < 0:
+            start_value += in_oper.shape[dim_value]
+        elif start_value == sys.maxsize:
+            start_value = 0
+
+        if start_value == 0 and stop_value == sys.maxsize:
+            self._identity(node)
+            return
+
+        if in_oper.shape[dim_value] == 0:
+            raise Exception("Unable to slice with flexible shape")  # noqa: TRY002
+
+        if stop_value < 0:
+            stop_value += in_oper.shape[dim_value]
+        elif stop_value == sys.maxsize:
+            stop_value = in_oper.shape[dim_value]
+
+        if start_value >= stop_value:
+            raise Exception(  # noqa: TRY002
+                "Slice start value should be less than stop value"
+            )  # noqa: TRY002
+
+        out_len = (stop_value - start_value) // step_value
+        out_shape = tuple(
+            out_len if i == dim_value else dim for i, dim in enumerate(in_oper.shape)
+        )
+        out_id = self.add_tensor_operand(
+            node.outputsAt(0), in_oper._replace(shape=out_shape)
+        )
+
+        # flex inputs
+        end_mask = 0
+        for idx, dim in enumerate(out_shape):
+            if dim == 0:
+                self.forward_operand_shape(out_id, idx, in_id, idx)
+                end_mask |= 1 << idx
+
+        inputs = [None] * 7
+        inputs[0] = in_id
+        inputs[1] = self.add_immediate_int_vector(
+            [start_value if i == dim_value else 0 for i in range(len(in_oper.shape))]
+        )
+        inputs[2] = self.add_immediate_int_vector(
+            [
+                stop_value if i == dim_value else dim
+                for i, dim in enumerate(in_oper.shape)
+            ]
+        )
+        inputs[3] = self.add_immediate_int_vector(
+            [step_value if i == dim_value else 1 for i in range(len(in_oper.shape))]
+        )
+        inputs[4] = self.add_immediate_int_scalar(0)  # begin mask
+        inputs[5] = self.add_immediate_int_scalar(end_mask)
+        inputs[6] = self.add_immediate_int_scalar(0)  # shrink axis mas
+
+        outputs = [None] * 1
+        outputs[0] = out_id
+
+        self.add_operation(NNAPI_OperationCode.STRIDED_SLICE, inputs, outputs)
+
+    def add_size(self, node):
+        assert node.inputsSize() == 2
+        assert node.outputsSize() == 1
+
+        _, in_oper = self.get_tensor_operand_by_jitval_fixed_size(node.inputsAt(0))
+        _, value = self.constants[node.inputsAt(1)]
+        res = in_oper.shape[value]
+        output = node.outputsAt(0)
+        self.add_constant_value(output, output.type(), res)
+
+    def add_cat(self, node):
+        assert node.inputsSize() == 2
+        assert node.outputsSize() == 1
+
+        tensors = self.tensor_sequences[node.inputsAt(0)]
+        _, dim = self.get_constant_value(node.inputsAt(1), "IntType")
+
+        assert len(tensors) > 0
+        in_ids = []
+        out_oper = None
+        out_dim_size = 0
+        for inp in tensors:
+            in_id, in_oper = self.get_tensor_operand_by_jitval(inp)
+            if out_oper is None:
+                out_shape = change_element(in_oper.shape, dim, -1)
+                out_oper = in_oper._replace(shape=out_shape)
+            assert in_oper.op_type == out_oper.op_type
+            assert in_oper.dim_order == out_oper.dim_order
+            assert change_element(in_oper.shape, dim, -1) == change_element(
+                out_oper.shape, dim, -1
+            )
+            # TODO: Possibly check scale and zero point.
+            in_ids.append(in_id)
+            # TODO: Possibly support variable-sized inputs.
+            out_dim_size += in_oper.shape[dim]
+
+        assert out_oper is not None
+        out_oper = out_oper._replace(
+            shape=change_element(out_oper.shape, dim, out_dim_size)
+        )
+
+        if in_oper.dim_order == DimOrder.CHANNELS_LAST:  # type: ignore[possibly-undefined]
+            assert len(out_oper.shape) == 4
+            nnapi_dim = [0, 3, 1, 2][dim]
+        else:
+            nnapi_dim = dim
+
+        out_id = self.add_tensor_operand(node.outputsAt(0), out_oper)
+        for idx, d in enumerate(out_oper.shape):
+            if d == 0:
+                if idx == dim:
+                    shape = " + ".join(flex_name(ip_id, dim) for ip_id in in_ids)
+                    self.compute_operand_shape(out_id, idx, shape)
+                else:
+                    self.forward_operand_shape(out_id, idx, in_ids[0], idx)
+
+        inputs = in_ids + [self.add_immediate_int_scalar(nnapi_dim)]
+
+        outputs = [None] * 1
+        outputs[0] = out_id
+
+        self.add_operation(NNAPI_OperationCode.CONCATENATION, inputs, outputs)
+
+    def add_mean(self, node):
+        assert node.inputsSize() == 4
+        assert node.outputsSize() == 1
+
+        in_id, in_oper = self.get_tensor_operand_by_jitval_fixed_size(node.inputsAt(0))
+        dim_ctype, dim = self.get_constant_value(node.inputsAt(1))
+        assert dim_ctype.kind() == "ListType"
+        assert dim_ctype.getElementType().kind() == "IntType"
+        _, keep_dim = self.get_constant_value(node.inputsAt(2), "BoolType")
+        # Expect None for dtype
+        self.get_constant_value(node.inputsAt(3), "NoneType")
+
+        if in_oper.dim_order == DimOrder.CHANNELS_LAST:
+            assert len(in_oper.shape) == 4
+            nnapi_dim = [[0, 3, 1, 2][d] for d in dim]
+        else:
+            nnapi_dim = dim
+
+        collapsed_dims = set()
+        for d in dim:
+            if d < 0:
+                d += len(in_oper.shape)
+            collapsed_dims.add(d)
+
+        if in_oper.dim_order == DimOrder.CHANNELS_LAST and not keep_dim:
+            assert collapsed_dims.issuperset({2, 3})
+            out_dim_order = DimOrder.PRESUMED_CONTIGUOUS
+        else:
+            out_dim_order = in_oper.dim_order
+
+        out_shape = []
+        for i, s in enumerate(in_oper.shape):
+            if i not in collapsed_dims:
+                out_shape.append(s)
+            elif keep_dim:
+                out_shape.append(1)
+
+        out_oper = in_oper._replace(shape=out_shape, dim_order=out_dim_order)
+
+        inputs = [None] * 3
+        inputs[0] = in_id
+        inputs[1] = self.add_immediate_int_vector(nnapi_dim)
+        inputs[2] = self.add_immediate_int_scalar(keep_dim)
+
+        outputs = [None] * 1
+        outputs[0] = self.add_tensor_operand(node.outputsAt(0), out_oper)
+
+        self.add_operation(NNAPI_OperationCode.MEAN, inputs, outputs)
+
+    def add_quantize(self, node):
+        assert node.inputsSize() == 4
+        assert node.outputsSize() == 1
+
+        in_id, in_oper = self.get_tensor_operand_by_jitval_fixed_size(node.inputsAt(0))
+        if in_oper.dim_order != DimOrder.CHANNELS_LAST:
+            raise Exception(  # noqa: TRY002
+                "Most hardware backends prefer NHWC quantized tensors.  "
+                "Try setting `t.nnapi_nhwc = True` on your tensor inputs.  "
+            )
+        _, scale = self.get_constant_value(node.inputsAt(1), "FloatType")
+        _, zero_point = self.get_constant_value(node.inputsAt(2), "IntType")
+        _, scalar_type = self.get_constant_value(node.inputsAt(3), "IntType")
+        if scalar_type != TorchScalarTypes.QUINT8.value:
+            raise Exception(  # noqa: TRY002
+                "PyTorch NNAPI export only supports quantized tensors "
+                "with the quint8 dtype."
+            )
+        op_type = NNAPI_OperandCode.TENSOR_QUANT8_ASYMM
+
+        out_oper = in_oper._replace(
+            op_type=op_type,
+            scale=scale,
+            zero_point=zero_point,
+        )
+
+        inputs = [None] * 1
+        inputs[0] = in_id
+
+        outputs = [None] * 1
+        outputs[0] = self.add_tensor_operand(node.outputsAt(0), out_oper)
+
+        self.add_operation(NNAPI_OperationCode.QUANTIZE, inputs, outputs)
+
+    def add_dequantize(self, node):
+        assert node.inputsSize() == 1
+        assert node.outputsSize() == 1
+
+        in_id, in_oper = self.get_tensor_operand_by_jitval_fixed_size(node.inputsAt(0))
+        out_oper = in_oper._replace(
+            op_type=NNAPI_OperandCode.TENSOR_FLOAT32,
+            scale=0.0,
+            zero_point=0,
+        )
+
+        inputs = [None] * 1
+        inputs[0] = in_id
+
+        outputs = [None] * 1
+        outputs[0] = self.add_tensor_operand(node.outputsAt(0), out_oper)
+
+        self.add_operation(NNAPI_OperationCode.DEQUANTIZE, inputs, outputs)
+
+    def add_pointwise_simple_unary_op(self, node, opcode):
+        assert node.inputsSize() == 1
+        assert node.outputsSize() == 1
+
+        in_id, in_oper = self.get_tensor_operand_by_jitval(node.inputsAt(0))
+
+        out_oper = in_oper
+        if opcode == NNAPI_OperationCode.LOGISTIC:
+            # NNAPI docs: For ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, the scale
+            # must be 1.f / 256 and the zeroPoint must be 0.
+            # https://fburl.com/h52stoog
+            if in_oper.op_type == NNAPI_OperandCode.TENSOR_QUANT8_ASYMM:
+                out_oper = in_oper._replace(zero_point=0, scale=1.0 / 256)
+
+        out_id = self.add_tensor_operand(node.outputsAt(0), out_oper)
+
+        for idx, dim in enumerate(in_oper.shape):
+            if dim == 0:
+                self.forward_operand_shape(out_id, idx, in_id, idx)
+
+        inputs = [None] * 1
+        inputs[0] = in_id
+
+        outputs = [None] * 1
+        outputs[0] = out_id
+
+        self.add_operation(opcode, inputs, outputs)
+
+    def _do_add_binary(self, node, opcode, fuse_code, *, qparams=None):  # noqa: D401
+        """Helper for pointwise binary broadcast ops with superfluous extra args."""
+        assert node.outputsSize() == 1
+
+        assert node.inputsAt(0).type().kind() == "TensorType"
+        assert node.inputsAt(1).type().kind() == "TensorType"
+
+        if self.has_operand_for_jitval(node.inputsAt(0)):
+            in0_id, in0_oper = self.get_tensor_operand_by_jitval(node.inputsAt(0))
+            in1_id, in1_oper = self.get_tensor_operand_or_constant(
+                node.inputsAt(1), in0_oper.dim_order
+            )
+        elif self.has_operand_for_jitval(node.inputsAt(1)):
+            in1_id, in1_oper = self.get_tensor_operand_by_jitval(node.inputsAt(1))
+            in0_id, in0_oper = self.get_tensor_operand_or_constant(
+                node.inputsAt(0), in1_oper.dim_order
+            )
+        else:
+            raise Exception(  # noqa: TRY002
+                f"Can't do a NNAPI binary op: {opcode} on two constants"
+            )  # noqa: TRY002
+
+        assert in0_oper.op_type == in1_oper.op_type
+        in0_id, in0_oper, in1_id, in1_oper = self.transpose_for_broadcast(
+            in0_id, in0_oper, in1_id, in1_oper
+        )
+        # NOTE: PyTorch and NNAPI have the same broadcast semantics.
+        out_shape = broadcast_shapes(in0_oper.shape, in1_oper.shape)
+        out_oper = in0_oper._replace(shape=out_shape)
+        if qparams is not None:
+            scale, zp = qparams
+            out_oper = out_oper._replace(scale=scale, zero_point=zp)
+
+        out_id = self.add_tensor_operand(node.outputsAt(0), out_oper)
+        for idx, (d0, d1) in enumerate(zip(in0_oper.shape, in1_oper.shape)):
+            if d0 == 1 and d1 == 0:
+                self.forward_operand_shape(out_id, idx, in1_id, idx)
+            elif d0 == 0 and d1 == 1:
+                self.forward_operand_shape(out_id, idx, in0_id, idx)
+            elif d0 == 0 and d1 == 0:
+                self.flexible_shape_computation_lines.append(
+                    f"assert {flex_name(in0_id, idx)} == {flex_name(in1_id, idx)}"
+                )
+                self.forward_operand_shape(out_id, idx, in0_id, idx)
+
+        inputs = [None] * 3
+        inputs[0] = in0_id
+        inputs[1] = in1_id
+        inputs[2] = self.add_immediate_int_scalar(fuse_code)
+
+        outputs = [None] * 1
+        outputs[0] = out_id
+
+        self.add_operation(opcode, inputs, outputs)
+
+    def add_pointwise_simple_binary_broadcast_op(self, node, opcode, fuse_code):
+        assert node.inputsSize() == 2
+        self._do_add_binary(node, opcode, fuse_code)
+
+    def add_add_sub_op(self, node, opcode, fuse_code):
+        assert node.inputsSize() == 3
+
+        _, alpha = self.get_constant_value(node.inputsAt(2), "IntType")
+        if alpha != 1:
+            raise Exception(  # noqa: TRY002
+                "NNAPI does not support add/sub with alpha."
+            )  # noqa: TRY002
+
+        self._do_add_binary(node, opcode, fuse_code)
+
+    def add_qadd(self, node, opcode, fuse_code):
+        assert node.inputsSize() == 4
+
+        _, scale = self.get_constant_value(node.inputsAt(2), "FloatType")
+        _, zero_point = self.get_constant_value(node.inputsAt(3), "IntType")
+
+        self._do_add_binary(node, opcode, fuse_code, qparams=(scale, zero_point))
+
+    def add_softmax(self, node):
+        assert node.inputsSize() == 3
+        in_id, in_oper = self.get_tensor_operand_by_jitval(node.inputsAt(0))
+
+        _, softmax_dim = self.get_constant_value(node.inputsAt(1), "IntType")
+
+        out_id = self.add_tensor_operand(node.outputsAt(0), in_oper)
+        for dim, size in enumerate(in_oper.shape):
+            if size == 0:
+                self.forward_operand_shape(out_id, dim, in_id, dim)
+
+        inputs = [None] * 3
+        inputs[0] = in_id
+        inputs[1] = self.add_immediate_float_scalar(
+            1.0
+        )  # positive scaling factor of exponent, beta
+        inputs[2] = self.add_immediate_int_scalar(softmax_dim)
+
+        outputs = [None] * 1
+        outputs[0] = out_id
+
+        self.add_operation(NNAPI_OperationCode.SOFTMAX, inputs, outputs)
+
+    def add_hardtanh(self, node):
+        assert node.inputsSize() == 3
+        assert node.outputsSize() == 1
+
+        in_id, in_oper = self.get_tensor_operand_by_jitval_fixed_size(node.inputsAt(0))
+        _, min_val = self.get_constant_value(node.inputsAt(1), "FloatType")
+        _, max_val = self.get_constant_value(node.inputsAt(2), "FloatType")
+
+        op_map = {
+            (-1, 1): NNAPI_OperationCode.RELU1,
+            (0, 6): NNAPI_OperationCode.RELU6,  # noqa: E201
+        }
+
+        opcode = op_map.get((min_val, max_val))
+        if opcode is None:
+            raise Exception(  # noqa: TRY002
+                "NNAPI only supports hardtanh with args (-1, 1) or (0, 6)."
+            )  # noqa: TRY002
+
+        inputs = [None] * 1
+        inputs[0] = in_id
+
+        outputs = [None] * 1
+        outputs[0] = self.add_tensor_operand(node.outputsAt(0), in_oper)
+
+        self.add_operation(opcode, inputs, outputs)
+
+    def add_prelu_op(self, node):
+        assert node.inputsSize() == 2
+        assert node.outputsSize() == 1
+
+        assert node.inputsAt(0).type().kind() == "TensorType"
+        assert node.inputsAt(1).type().kind() == "TensorType"
+
+        in_id, in_oper = self.get_tensor_operand_by_jitval(node.inputsAt(0))
+        w_id, w_oper = self.get_tensor_operand_for_weight(node.inputsAt(1))
+        assert len(w_oper.shape) == 1
+        assert w_oper.shape[0] > 0
+        if w_oper.shape[0] > 1:
+            if in_oper.use_nchw():
+                # TODO: Support this by adding trailing 1 dims.
+                raise Exception(  # noqa: TRY002
+                    "Per-channel PReLU only supports channels_last right now."
+                )
+
+        out_id = self.add_tensor_operand(node.outputsAt(0), in_oper)
+        for dim, size in enumerate(in_oper.shape):
+            if size > 0:
+                pass
+            elif dim <= 1:
+                raise Exception(  # noqa: TRY002
+                    "PReLU requires fixed size for dim 0 and dim 1."
+                )  # noqa: TRY002
+            else:
+                self.forward_operand_shape(out_id, dim, in_id, dim)
+
+        inputs = [None] * 2
+        inputs[0] = in_id
+        inputs[1] = w_id
+
+        outputs = [None] * 1
+        outputs[0] = out_id
+
+        self.add_operation(NNAPI_OperationCode.PRELU, inputs, outputs)
+
+    def add_pool2d_node(self, node, opcode):
+        assert node.inputsSize() == 6
+        assert node.outputsSize() == 1
+        image, kernel, stride, padding, dilation, _ceil_mode = node.inputs()
+
+        stride = stride or kernel
+
+        # TODO: Validate ceil_mode semantics.
+
+        args = self.get_conv_pool_args_2d_from_jit(
+            self.get_size_arg(kernel), stride, padding, dilation
+        )
+        if args.dilation_h != 1 or args.dilation_w != 1:
+            raise Exception("NNAPI does not support dilated pooling.")  # noqa: TRY002
+
+        image_id, image_oper = self.get_tensor_operand_by_jitval_fixed_size(image)
+        assert len(image_oper.shape) == 4
+
+        out_shape = get_conv_pool_shape(
+            image_oper.shape, args, image_oper.shape[1], False
+        )
+        use_nchw = image_oper.use_nchw()
+
+        inputs = [None] * 11
+        inputs[0] = image_id
+        inputs[1] = self.add_immediate_int_scalar(args.pad_l)
+        inputs[2] = self.add_immediate_int_scalar(args.pad_r)
+        inputs[3] = self.add_immediate_int_scalar(args.pad_t)
+        inputs[4] = self.add_immediate_int_scalar(args.pad_b)
+        inputs[5] = self.add_immediate_int_scalar(args.stride_w)
+        inputs[6] = self.add_immediate_int_scalar(args.stride_h)
+        inputs[7] = self.add_immediate_int_scalar(args.kernel_w)
+        inputs[8] = self.add_immediate_int_scalar(args.kernel_h)
+        inputs[9] = self.add_immediate_int_scalar(NNAPI_FuseCode.FUSED_NONE)
+        inputs[10] = self.add_immediate_bool_scalar(use_nchw)
+
+        outputs = [None] * 1
+        outputs[0] = self.add_tensor_operand(
+            node.outputsAt(0), image_oper._replace(shape=out_shape)
+        )
+
+        self.add_operation(opcode, inputs, outputs)
+
+    def add_avg_pool2d(self, node):
+        assert node.inputsSize() == 7
+        assert node.outputsSize() == 1
+        (
+            image,
+            kernel,
+            stride,
+            padding,
+            _ceil_mode,
+            count_include_pad,
+            divisor_override,
+        ) = node.inputs()
+
+        _, count_include_pad_value = self.get_constant_value(count_include_pad)
+        _, divisor_override_value = self.get_constant_value(divisor_override)
+        if not count_include_pad_value or divisor_override_value:
+            raise Exception(  # noqa: TRY002
+                "NNAPI doesn't support count_include_pad=False or divisor_override"
+            )
+
+        args = self.get_conv_pool_args_2d_from_jit(
+            self.get_size_arg(kernel), stride, padding
+        )
+
+        image_id, image_oper = self.get_tensor_operand_by_jitval(image)
+        assert len(image_oper.shape) == 4
+
+        out_shape = get_conv_pool_shape(
+            image_oper.shape, args, image_oper.shape[1], False
+        )
+        use_nchw = image_oper.use_nchw()
+
+        inputs = [None] * 11
+        inputs[0] = image_id
+        inputs[1] = self.add_immediate_int_scalar(args.pad_l)
+        inputs[2] = self.add_immediate_int_scalar(args.pad_r)
+        inputs[3] = self.add_immediate_int_scalar(args.pad_t)
+        inputs[4] = self.add_immediate_int_scalar(args.pad_b)
+        inputs[5] = self.add_immediate_int_scalar(args.stride_w)
+        inputs[6] = self.add_immediate_int_scalar(args.stride_h)
+        inputs[7] = self.add_immediate_int_scalar(args.kernel_w)
+        inputs[8] = self.add_immediate_int_scalar(args.kernel_h)
+        inputs[9] = self.add_immediate_int_scalar(NNAPI_FuseCode.FUSED_NONE)
+        inputs[10] = self.add_immediate_bool_scalar(use_nchw)
+
+        outputs = [None] * 1
+        out_id = self.add_tensor_operand(
+            node.outputsAt(0), image_oper._replace(shape=out_shape)
+        )
+        self._handle_conv_pool_flexible_input(out_id, image, args, False)
+        outputs[0] = out_id
+
+        self.add_operation(NNAPI_OperationCode.AVERAGE_POOL_2D, inputs, outputs)
+
+    def add_adaptive_avg_pool2d(self, node):
+        assert node.inputsSize() == 2
+        assert node.outputsSize() == 1
+
+        image_id, image_oper = self.get_tensor_operand_by_jitval_fixed_size(
+            node.inputsAt(0)
+        )
+        assert len(image_oper.shape) == 4
+
+        size_ctype, size_arg = self.get_constant_value(node.inputsAt(1))
+        assert size_ctype.kind() == "ListType"
+        assert size_ctype.getElementType().kind() == "IntType"
+        if size_arg != [1, 1]:
+            raise Exception(  # noqa: TRY002
+                "NNAPI only supports adaptive_avg_pool2d with output size (1, 1)."
+            )
+
+        out_shape = image_oper.shape[0:2] + tuple(size_arg)
+        use_nchw = image_oper.use_nchw()
+
+        inputs = [None] * 11
+        inputs[0] = image_id
+        inputs[1] = self.add_immediate_int_scalar(0)
+        inputs[2] = self.add_immediate_int_scalar(0)
+        inputs[3] = self.add_immediate_int_scalar(0)
+        inputs[4] = self.add_immediate_int_scalar(0)
+        inputs[5] = self.add_immediate_int_scalar(1)
+        inputs[6] = self.add_immediate_int_scalar(1)
+        inputs[7] = self.add_immediate_int_scalar(image_oper.shape[3])
+        inputs[8] = self.add_immediate_int_scalar(image_oper.shape[2])
+        inputs[9] = self.add_immediate_int_scalar(NNAPI_FuseCode.FUSED_NONE)
+        inputs[10] = self.add_immediate_bool_scalar(use_nchw)
+
+        outputs = [None] * 1
+        outputs[0] = self.add_tensor_operand(
+            node.outputsAt(0), image_oper._replace(shape=out_shape)
+        )
+
+        self.add_operation(NNAPI_OperationCode.AVERAGE_POOL_2D, inputs, outputs)
+
+    def add_upsample_nearest2d(self, node):
+        assert node.inputsSize() == 3 or node.inputsSize() == 4
+        assert node.outputsSize() == 1
+        if node.inputsSize() == 3:
+            image, size_jit, scale_jit = node.inputs()
+        else:
+            image, size_jit, scale_h_jit, scale_w_jit = node.inputs()
+        size_ctype, size_arg = self.get_constant_value(size_jit)
+
+        if node.inputsSize() == 3:
+            scale_ctype, scale_arg = self.get_constant_value(scale_jit)  # type: ignore[possibly-undefined]
+        else:
+            scale_h_ctype, scale_h_arg = self.get_constant_value(scale_h_jit)  # type: ignore[possibly-undefined]
+            scale_w_ctype, _scale_w_arg = self.get_constant_value(scale_w_jit)  # type: ignore[possibly-undefined]
+
+            # The only way for the 4-argument overload of upsample_nearest2d to
+            # have been added to the graph without error is if the scale_h and
+            # scale_w arguments are None
+            assert scale_h_ctype.kind() == "NoneType"
+            assert scale_w_ctype.kind() == "NoneType"
+
+            scale_ctype = scale_h_ctype
+            scale_arg = scale_h_arg
+
+        image_id, image_oper = self.get_tensor_operand_by_jitval(image)
+        assert len(image_oper.shape) == 4
+
+        if size_ctype.kind() != "NoneType" and scale_ctype.kind() != "NoneType":
+            raise Exception("Size and scale cannot both be non-None.")  # noqa: TRY002
+        elif size_ctype.kind() != "NoneType":
+            assert size_ctype.kind() == "ListType"
+            assert size_ctype.getElementType().kind() == "IntType"
+            assert scale_ctype.kind() == "NoneType"
+            assert scale_arg is None
+            assert isinstance(size_arg, list)
+            assert size_arg
+            assert all(isinstance(val, int) for val in size_arg)
+            if len(size_arg) == 1:
+                size_arg = size_arg * 2
+            assert len(size_arg) == 2
+            out_h = size_arg[0]
+            out_w = size_arg[1]
+            arg_h = self.add_immediate_int_scalar(out_h)
+            arg_w = self.add_immediate_int_scalar(out_w)
+        elif scale_ctype.kind() != "NoneType":
+            assert scale_ctype.kind() == "ListType"
+            assert scale_ctype.getElementType().kind() == "FloatType"
+            assert size_ctype.kind() == "NoneType"
+            assert size_arg is None
+            assert isinstance(scale_arg, list)
+            assert scale_arg
+            assert all(isinstance(val, float) for val in scale_arg)
+            if len(scale_arg) == 1:
+                scale_arg = scale_arg * 2
+            assert len(scale_arg) == 2
+            out_h = int(scale_arg[0] * image_oper.shape[2])
+            out_w = int(scale_arg[1] * image_oper.shape[3])
+            arg_h = self.add_immediate_float_scalar(scale_arg[0])
+            arg_w = self.add_immediate_float_scalar(scale_arg[1])
+        else:
+            raise Exception("Size and scale cannot both be None.")  # noqa: TRY002
+
+        out_shape = (image_oper.shape[0], image_oper.shape[1], out_h, out_w)
+        use_nchw = image_oper.use_nchw()
+        out_id = self.add_tensor_operand(
+            node.outputsAt(0), image_oper._replace(shape=out_shape)
+        )
+
+        if image_oper.shape[0] == 0 or image_oper.shape[1] == 0:
+            raise Exception("Flexible batch or channels not supported")  # noqa: TRY002
+
+        # Handle variable input size
+        for dim in (2, 3):  # h, w indices
+            if image_oper.shape[dim] == 0:
+                if size_ctype.kind() != "NoneType":
+                    # pyrefly: ignore [unsupported-operation]
+                    self.compute_operand_shape(out_id, dim, size_arg[dim - 2])
+                elif scale_ctype.kind() != "NoneType":
+                    self.compute_operand_shape(
+                        out_id,
+                        dim,
+                        # pyrefly: ignore [unsupported-operation]
+                        f"int({scale_arg[dim - 2]} * {flex_name(image_id, dim)})",
+                    )
+                else:
+                    raise Exception(  # noqa: TRY002
+                        "Size and scale cannot both be None."
+                    )  # noqa: TRY002
+
+        inputs = [None] * 4
+        inputs[0] = image_id
+        inputs[1] = arg_w
+        inputs[2] = arg_h
+        inputs[3] = self.add_immediate_bool_scalar(use_nchw)
+
+        outputs = [None] * 1
+        outputs[0] = out_id
+
+        self.add_operation(NNAPI_OperationCode.RESIZE_NEAREST_NEIGHBOR, inputs, outputs)
+
+    def add_addmm(self, node):
+        assert node.inputsSize() == 5
+        assert node.outputsSize() == 1
+        jit_bias, jit_input, jit_weight, jit_beta, jit_alpha = node.inputs()
+
+        for jitval in (jit_beta, jit_alpha):
+            scale_ctype, scale_value = self.get_constant_value(jitval)
+            assert scale_ctype.kind() in ("IntType", "FloatType")
+            if scale_value != 1:
+                raise Exception(  # noqa: TRY002
+                    "NNAPI Fully-Connected does not support alpha and beta."
+                )
+
+        self.add_addmm_or_linear(node, True, jit_input, jit_weight, jit_bias)
+
+    def add_linear(self, node):
+        assert node.inputsSize() == 3
+        assert node.outputsSize() == 1
+        jit_input, jit_weight, jit_bias = node.inputs()
+
+        self.add_addmm_or_linear(node, False, jit_input, jit_weight, jit_bias)
+
+    def add_addmm_or_linear(
+        self, node, transpose_weight, jit_input, jit_weight, jit_bias
+    ):
+        input_id, input_oper = self.get_tensor_operand_by_jitval(jit_input)
+        bias_id, bias_oper = self.get_tensor_operand_for_weight(jit_bias)
+
+        assert len(input_oper.shape) == 2
+        assert len(bias_oper.shape) == 1
+
+        # TODO: Transform at load time to share weights with CPU model.
+        _, weight_tensor = self.get_constant_value(jit_weight, "TensorType")
+        assert len(weight_tensor.shape) == 2
+        if transpose_weight:
+            nnapi_weight_tensor = weight_tensor.t().contiguous()
+        else:
+            nnapi_weight_tensor = weight_tensor.contiguous()
+        weight_id = self.add_tensor_operand_for_weight(nnapi_weight_tensor)
+        weight_oper = self.operands[weight_id]
+
+        out_shape = (input_oper.shape[0], weight_oper.shape[0])
+        out_id = self.add_tensor_operand(
+            node.outputsAt(0), input_oper._replace(shape=out_shape)
+        )
+
+        if input_oper.shape[0] == 0:
+            self.forward_operand_shape(out_id, 0, input_id, 0)
+
+        inputs = [None] * 4
+        inputs[0] = input_id
+        inputs[1] = weight_id
+        inputs[2] = bias_id
+        inputs[3] = self.add_immediate_int_scalar(NNAPI_FuseCode.FUSED_NONE)
+
+        outputs = [None] * 1
+        outputs[0] = out_id
+
+        self.add_operation(NNAPI_OperationCode.FULLY_CONNECTED, inputs, outputs)
+
+    def add_qlinear(self, node):
+        assert node.inputsSize() == 4
+        assert node.outputsSize() == 1
+        (
+            jit_input,
+            jit_packed_weight,
+            jit_scale,
+            jit_zero_point,
+        ) = node.inputs()
+
+        input_id, input_oper = self.get_tensor_operand_by_jitval_fixed_size(jit_input)
+        # TODO: Support automatic reshape
+        assert len(input_oper.shape) == 2
+
+        _, out_scale = self.get_constant_value(jit_scale, "FloatType")
+        _, out_zero_point = self.get_constant_value(jit_zero_point, "IntType")
+        weight_ctype, packed_weight = self.get_constant_value(jit_packed_weight)
+        assert weight_ctype.name() == "LinearPackedParamsBase"
+        raw_weight, raw_bias = packed_weight.__getstate__()[0]
+        assert raw_bias is not None
+
+        assert len(raw_weight.shape) == 2
+        assert len(raw_bias.shape) == 1
+        assert raw_bias.shape[0] == raw_weight.shape[0]
+        assert raw_weight.shape[1] == input_oper.shape[1]
+
+        assert raw_weight.qscheme() == torch.per_tensor_affine
+        if raw_weight.dtype == torch.quint8:
+            unsigned_weight = raw_weight
+        else:
+            assert raw_weight.dtype == torch.qint8
+            unsigned_weight = torch._make_per_tensor_quantized_tensor(
+                (raw_weight.int_repr().int() + 128).to(torch.uint8),
+                scale=raw_weight.q_scale(),
+                zero_point=raw_weight.q_zero_point() + 128,
+            )
+        weight_scale = unsigned_weight.q_scale()
+        bias_scale = input_oper.scale * weight_scale
+        int_bias = torch.quantize_per_tensor(raw_bias, bias_scale, 0, torch.qint32)
+        bias_id = self.add_tensor_operand_for_weight(int_bias)
+
+        multiplier = input_oper.scale * weight_scale / out_scale
+        assert multiplier > 0
+        if multiplier >= 1:
+            raise Exception(  # noqa: TRY002
+                "Quantized convolution multiplier is greater than 1.  "
+                "This is supported by NNAPI, but not by most hardware backends.  "
+                "Try training a model without quantization-aware training.  "
+            )
+
+        # TODO: Transform at load time to share weights with CPU model.
+        nnapi_weight_tensor = unsigned_weight.contiguous()
+        weight_id = self.add_tensor_operand_for_weight(nnapi_weight_tensor)
+        weight_oper = self.operands[weight_id]
+
+        out_shape = (input_oper.shape[0], weight_oper.shape[0])
+        out_oper = input_oper._replace(
+            shape=out_shape,
+            scale=out_scale,
+            zero_point=out_zero_point,
+        )
+
+        inputs = [None] * 4
+        inputs[0] = input_id
+        inputs[1] = weight_id
+        inputs[2] = bias_id
+        inputs[3] = self.add_immediate_int_scalar(NNAPI_FuseCode.FUSED_NONE)
+
+        outputs = [None] * 1
+        outputs[0] = self.add_tensor_operand(node.outputsAt(0), out_oper)
+
+        self.add_operation(NNAPI_OperationCode.FULLY_CONNECTED, inputs, outputs)
+
+    def get_optional_bias(self, jit_bias, weight_tensor, transpose=False):
+        ctype, _value = self.get_constant_value(jit_bias)
+        if ctype.kind() == "NoneType":
+            bias_idx = 1 if transpose else 0
+            nnapi_bias_tensor = torch.zeros(
+                weight_tensor.size()[bias_idx], dtype=weight_tensor.dtype
+            )
+            bias_id = self.add_tensor_operand_for_weight(nnapi_bias_tensor)
+            bias_oper = self.operands[bias_id]
+            return bias_id, bias_oper
+        else:
+            return self.get_tensor_operand_for_weight(jit_bias)
+
+    def add_conv2d(self, node):
+        assert node.inputsSize() == 7
+        assert node.outputsSize() == 1
+
+        (
+            jit_image,
+            jit_weight,
+            jit_bias,
+            jit_stride,
+            jit_pad,
+            jit_dilation,
+            jit_groups,
+        ) = node.inputs()
+
+        _, weight_tensor = self.get_constant_value(jit_weight, "TensorType")
+        bias_id, _bias_oper = self.get_optional_bias(jit_bias, weight_tensor)
+        args = self.get_conv_pool_args_2d_from_jit(
+            weight_tensor.shape[2:4], jit_stride, jit_pad, jit_dilation, jit_groups
+        )
+
+        return self.add_conv2d_common(
+            node.outputsAt(0),
+            0.0,
+            0,
+            jit_image,
+            weight_tensor,
+            bias_id,
+            args,
+            False,  # transpose
+            NNAPI_FuseCode.FUSED_NONE,
+        )
+
+    def add_conv_underscore(self, node):
+        assert node.inputsSize() == 13
+        assert node.outputsSize() == 1
+
+        (
+            jit_image,
+            jit_weight,
+            jit_bias,
+            jit_stride,
+            jit_pad,
+            jit_dilation,
+            jit_transpose,
+            _,
+            jit_groups,
+            _,
+            _,
+            _,
+            _,
+        ) = node.inputs()
+
+        _, weight_tensor = self.get_constant_value(jit_weight, "TensorType")
+        _, transpose = self.get_constant_value(jit_transpose)
+        bias_id, _bias_oper = self.get_optional_bias(jit_bias, weight_tensor, transpose)
+        args = self.get_conv_pool_args_2d_from_jit(
+            weight_tensor.shape[2:4], jit_stride, jit_pad, jit_dilation, jit_groups
+        )
+
+        return self.add_conv2d_common(
+            node.outputsAt(0),
+            0.0,
+            0,
+            jit_image,
+            weight_tensor,
+            bias_id,
+            args,
+            transpose,
+            NNAPI_FuseCode.FUSED_NONE,
+        )
+
+    def add_log_softmax(self, node):
+        assert node.inputsSize() == 3
+        assert node.outputsSize() == 1
+
+        jit_input, jit_dim, _jit_half_to_float = node.inputs()
+        input_id, input_oper = self.get_tensor_operand_by_jitval_fixed_size(jit_input)
+        _, dim = self.get_constant_value(jit_dim, "IntType")
+
+        out_shape = input_oper.shape
+
+        inputs = [None] * 3
+        inputs[0] = input_id
+        # specifying 1 as the scaling factor for the exponent, beta
+        inputs[1] = self.add_immediate_float_scalar(1)
+        inputs[2] = self.add_immediate_int_scalar(dim)
+
+        outputs = [None] * 1
+        outputs[0] = self.add_tensor_operand(
+            node.outputsAt(0), input_oper._replace(shape=out_shape)
+        )
+        self.add_operation(NNAPI_OperationCode.LOG_SOFTMAX, inputs, outputs)
+
+    def add_qconv2d(self, node, fuse_code, transpose=False):
+        assert node.inputsSize() == 4
+        assert node.outputsSize() == 1
+
+        (
+            jit_image,
+            jit_packed_weight,
+            jit_scale,
+            jit_zero_point,
+        ) = node.inputs()
+
+        _, out_scale = self.get_constant_value(jit_scale, "FloatType")
+        _, out_zero_point = self.get_constant_value(jit_zero_point, "IntType")
+        weight_ctype, packed_weight = self.get_constant_value(jit_packed_weight)
+        assert weight_ctype.name() == "Conv2dPackedParamsBase"
+        (
+            pack_version,
+            tensors,
+            opt_tensors,
+        ) = packed_weight.__getstate__()[0]
+        assert pack_version == "2"
+        packed_config, raw_weight = tensors
+        (raw_bias,) = opt_tensors
+        assert raw_bias is not None
+        args = self.get_conv_pool_args_2d_from_pack(
+            raw_weight.shape[2:4], packed_config
+        )
+
+        assert raw_weight.qscheme() == torch.per_tensor_affine
+        if raw_weight.dtype == torch.quint8:
+            unsigned_weight = raw_weight
+        else:
+            assert raw_weight.dtype == torch.qint8
+            unsigned_weight = torch._make_per_tensor_quantized_tensor(
+                (raw_weight.int_repr().int() + 128).to(torch.uint8),
+                scale=raw_weight.q_scale(),
+                zero_point=raw_weight.q_zero_point() + 128,
+            )
+        weight_scale = unsigned_weight.q_scale()
+        _, image_oper = self.get_tensor_operand_by_jitval(jit_image)
+        bias_scale = image_oper.scale * weight_scale
+        int_bias = torch.quantize_per_tensor(raw_bias, bias_scale, 0, torch.qint32)
+        bias_id = self.add_tensor_operand_for_weight(int_bias)
+
+        multiplier = image_oper.scale * weight_scale / out_scale
+        assert multiplier > 0
+        if multiplier >= 1:
+            raise Exception(  # noqa: TRY002
+                "Quantized convolution multiplier is greater than 1.  "
+                "This is supported by NNAPI, but not by most hardware backends.  "
+                "Try training a model without quantization-aware training.  "
+            )
+
+        return self.add_conv2d_common(
+            node.outputsAt(0),
+            out_scale,
+            out_zero_point,
+            jit_image,
+            unsigned_weight,
+            bias_id,
+            args,
+            transpose,
+            fuse_code,
+        )
+
+    def add_conv2d_common(
+        self,
+        jit_out,
+        out_scale,
+        out_zero_point,
+        jit_image,
+        weight_tensor,
+        bias_id,
+        args,
+        transpose,
+        fuse_code,
+    ):
+        image_id, image_oper = self.get_tensor_operand_by_jitval(jit_image)
+        in_c = image_oper.shape[1]
+
+        if args.group == 1:
+            # Full convolution
+            depthwise = False
+            if transpose:
+                weight_permutation = (1, 2, 3, 0)
+            else:
+                weight_permutation = (0, 2, 3, 1)
+        elif args.group == in_c:
+            # Depthwise convolution
+            depthwise = True
+            weight_permutation = (1, 2, 3, 0)
+        else:
+            raise Exception("Group convolution not supported yet.")  # noqa: TRY002
+
+        # TODO: Transform at load time to share weights with CPU model.
+        nnapi_weight_tensor = weight_tensor.permute(*weight_permutation).contiguous()
+        weight_id = self.add_tensor_operand_for_weight(nnapi_weight_tensor)
+        weight_oper = self.operands[weight_id]
+
+        bias_oper = self.operands[bias_id]
+
+        if image_oper.op_type == NNAPI_OperandCode.TENSOR_FLOAT32:
+            assert weight_oper.op_type == NNAPI_OperandCode.TENSOR_FLOAT32
+            assert bias_oper.op_type == NNAPI_OperandCode.TENSOR_FLOAT32
+        elif image_oper.op_type == NNAPI_OperandCode.TENSOR_QUANT8_ASYMM:
+            assert weight_oper.op_type == NNAPI_OperandCode.TENSOR_QUANT8_ASYMM
+            assert bias_oper.op_type == NNAPI_OperandCode.TENSOR_INT32
+            assert approx_equal(image_oper.scale * weight_oper.scale, bias_oper.scale)
+            assert bias_oper.zero_point == 0
+        else:
+            raise Exception(  # noqa: TRY002
+                f"Unsupported input type for conv2d: {image_oper.op_type}"
+            )  # noqa: TRY002
+
+        assert len(image_oper.shape) == 4
+        assert len(weight_oper.shape) == 4
+        assert len(bias_oper.shape) == 1
+
+        if depthwise:
+            # Depthwise convolution
+            one, _kern_h, _kern_w, out_c = weight_oper.shape
+            assert one == 1
+            assert out_c % in_c == 0
+            channel_multiplier = out_c // in_c
+            assert channel_multiplier == 1  # Don't support multiplier
+            assert out_c == in_c
+        else:
+            # Full convolution
+            out_c, _kern_h, _kern_w, kern_d = weight_oper.shape
+            assert kern_d == in_c
+
+        assert out_c == bias_oper.shape[0]
+
+        use_nchw = image_oper.use_nchw()
+
+        if depthwise:
+            num_args = 12
+            opcode = NNAPI_OperationCode.DEPTHWISE_CONV_2D
+        else:
+            num_args = 11
+            if transpose:
+                opcode = NNAPI_OperationCode.TRANSPOSE_CONV_2D
+            else:
+                opcode = NNAPI_OperationCode.CONV_2D
+
+        inputs = [None] * num_args
+        inputs[0] = image_id
+        inputs[1] = weight_id
+        inputs[2] = bias_id
+        inputs[3] = self.add_immediate_int_scalar(args.pad_l)
+        inputs[4] = self.add_immediate_int_scalar(args.pad_r)
+        inputs[5] = self.add_immediate_int_scalar(args.pad_t)
+        inputs[6] = self.add_immediate_int_scalar(args.pad_b)
+        inputs[7] = self.add_immediate_int_scalar(args.stride_w)
+        inputs[8] = self.add_immediate_int_scalar(args.stride_h)
+        if depthwise:
+            inputs[9] = self.add_immediate_int_scalar(1)
+            inputs[10] = self.add_immediate_int_scalar(fuse_code)
+            inputs[11] = self.add_immediate_bool_scalar(use_nchw)
+        else:
+            inputs[9] = self.add_immediate_int_scalar(fuse_code)
+            inputs[10] = self.add_immediate_bool_scalar(use_nchw)
+
+        outputs = [None] * 1
+        out_shape = get_conv_pool_shape(image_oper.shape, args, out_c, transpose)
+        out_oper = image_oper._replace(
+            shape=out_shape,
+            scale=out_scale,
+            zero_point=out_zero_point,
+        )
+        out_id = self.add_tensor_operand(jit_out, out_oper)
+        self._handle_conv_pool_flexible_input(out_id, jit_image, args, transpose)
+
+        outputs[0] = out_id
+        self.add_operation(opcode, inputs, outputs)
+
+    def _handle_conv_pool_flexible_input(self, out_id, jit_image, args, transpose):
+        image_id, image_oper = self.get_tensor_operand_by_jitval(jit_image)
+        batch, in_ch, in_h, in_w = image_oper.shape
+
+        if batch == 0:
+            self.forward_operand_shape(out_id, 0, image_id, 0)
+        if in_ch == 0:
+            raise Exception("Input channels can't be flexible")  # noqa: TRY002
+        # H & W
+        if transpose:
+            if in_h == 0:
+                self.compute_operand_shape(
+                    out_id,
+                    2,
+                    f"({flex_name(image_id, 2)} - 1) * {args.stride_h} + {args.kernel_h} - {args.pad_t} - {args.pad_b}",
+                )
+            if in_w == 0:
+                self.compute_operand_shape(
+                    out_id,
+                    3,
+                    f"({flex_name(image_id, 3)} - 1) * {args.stride_w} + {args.kernel_w} - {args.pad_l} - {args.pad_r}",
+                )
+        else:
+            if in_h == 0:
+                self.compute_operand_shape(
+                    out_id,
+                    2,
+                    f"({flex_name(image_id, 2)} - {args.kernel_h} + {args.pad_t} + {args.pad_b}) // {args.stride_h} + 1",
+                )
+            if in_w == 0:
+                self.compute_operand_shape(
+                    out_id,
+                    3,
+                    f"({flex_name(image_id, 3)} - {args.kernel_w} + {args.pad_l} + {args.pad_r}) // {args.stride_w} + 1",
+                )
+
+
+def serialize_model(
+    module, inputs, *, config=None, return_shapes=None, use_int16_for_qint16=False
+):
+    """Convert to NNAPI and serialize torchscript module.
+
+    Parameters:
+        module: Torchscript module to convert
+        inputs: Tensors used to specify input details for NNAPI
+        config (optional): Optional config to attach to module
+        return_shapes (optional): Specify shape of outputs if
+            your module uses runtime flexible shapes to set output
+            buffer size for NNAPI
+        use_int16_for_qint16 (optional): Use Pytorch int16 to represent NNAPI qint16 values
+    """
+    return _NnapiSerializer(config, use_int16_for_qint16).serialize_model(
+        module, inputs, return_shapes
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/cpu/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/cpu/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..82dc52cd4904c1cda023c876c586550a5a33ff7a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/cpu/__init__.py
@@ -0,0 +1,21 @@
+import torch
+
+
+__all__ = [
+    "get_cpu_capability",
+]
+
+
+def get_cpu_capability() -> str:
+    r"""Return cpu capability as a string value.
+
+    Possible values:
+    - "DEFAULT"
+    - "VSX"
+    - "Z VECTOR"
+    - "NO AVX"
+    - "AVX2"
+    - "AVX512"
+    - "SVE256"
+    """
+    return torch._C._get_cpu_capability()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/cuda/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/cuda/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d62c2b05a1ea1f3ecc5ceb0fbc17f5a714d87941
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/cuda/__init__.py
@@ -0,0 +1,593 @@
+# mypy: allow-untyped-defs
+import contextlib
+from typing import Any, Union
+from typing_extensions import deprecated
+
+import torch
+
+
+__all__ = [
+    "is_built",
+    "cuFFTPlanCacheAttrContextProp",
+    "cuFFTPlanCache",
+    "cuFFTPlanCacheManager",
+    "cuBLASModule",
+    "preferred_linalg_library",
+    "preferred_blas_library",
+    "preferred_rocm_fa_library",
+    "cufft_plan_cache",
+    "matmul",
+    "SDPAParams",
+    "enable_cudnn_sdp",
+    "cudnn_sdp_enabled",
+    "enable_flash_sdp",
+    "flash_sdp_enabled",
+    "enable_mem_efficient_sdp",
+    "mem_efficient_sdp_enabled",
+    "math_sdp_enabled",
+    "enable_math_sdp",
+    "allow_fp16_bf16_reduction_math_sdp",
+    "fp16_bf16_reduction_math_sdp_allowed",
+    "is_flash_attention_available",
+    "can_use_flash_attention",
+    "can_use_efficient_attention",
+    "can_use_cudnn_attention",
+    "sdp_kernel",
+]
+
+
+def is_built():
+    r"""
+    Return whether PyTorch is built with CUDA support.
+
+    Note that this doesn't necessarily mean CUDA is available; just that if this PyTorch
+    binary were run on a machine with working CUDA drivers and devices, we would be able to use it.
+    """
+    return torch._C._has_cuda
+
+
+class cuFFTPlanCacheAttrContextProp:
+    # Like regular ContextProp, but uses the `.device_index` attribute from the
+    # calling object as the first argument to the getter and setter.
+    def __init__(self, getter, setter):
+        self.getter = getter
+        self.setter = setter
+
+    def __get__(self, obj, objtype):
+        return self.getter(obj.device_index)
+
+    def __set__(self, obj, val):
+        if isinstance(self.setter, str):
+            raise RuntimeError(self.setter)
+        self.setter(obj.device_index, val)
+
+
+class cuFFTPlanCache:
+    r"""
+    Represent a specific plan cache for a specific `device_index`.
+
+    The attributes `size` and `max_size`, and method `clear`, can fetch and/ or
+    change properties of the C++ cuFFT plan cache.
+    """
+
+    def __init__(self, device_index):
+        self.device_index = device_index
+
+    size = cuFFTPlanCacheAttrContextProp(
+        torch._cufft_get_plan_cache_size,
+        ".size is a read-only property showing the number of plans currently in the "
+        "cache. To change the cache capacity, set cufft_plan_cache.max_size.",
+    )
+
+    max_size = cuFFTPlanCacheAttrContextProp(
+        torch._cufft_get_plan_cache_max_size, torch._cufft_set_plan_cache_max_size
+    )
+
+    def clear(self):
+        return torch._cufft_clear_plan_cache(self.device_index)
+
+
+class cuFFTPlanCacheManager:
+    r"""
+    Represent all cuFFT plan caches, return the cuFFTPlanCache for a given device when indexed.
+
+    Finally, this object, when used directly as a `cuFFTPlanCache` object (e.g.,
+    setting the `.max_size`) attribute, the current device's cuFFT plan cache is
+    used.
+    """
+
+    __initialized = False
+
+    def __init__(self):
+        self.caches = []
+        self.__initialized = True
+
+    def __getitem__(self, device):
+        index = torch.cuda._utils._get_device_index(device)
+        if index < 0 or index >= torch.cuda.device_count():
+            raise RuntimeError(
+                f"cufft_plan_cache: expected 0 <= device index < {torch.cuda.device_count()}, but got "
+                f"device with index {index}"
+            )
+        if len(self.caches) == 0:
+            self.caches.extend(
+                cuFFTPlanCache(index) for index in range(torch.cuda.device_count())
+            )
+        return self.caches[index]
+
+    def __getattr__(self, name):
+        return getattr(self[torch.cuda.current_device()], name)
+
+    def __setattr__(self, name, value):
+        if self.__initialized:
+            return setattr(self[torch.cuda.current_device()], name, value)
+        else:
+            return super().__setattr__(name, value)
+
+
+class cuBLASModule:
+    @staticmethod
+    def _parse_reduction_setting(value: Any, attr_name: str) -> tuple[bool, bool]:
+        def _ensure_bool(obj: Any, which: str) -> bool:
+            if isinstance(obj, bool):
+                return obj
+            raise TypeError(
+                f"{attr_name} expects a bool for {which}, but got {type(obj)!r}"
+            )
+
+        if isinstance(value, bool):
+            return value, True
+        if isinstance(value, (list, tuple)):
+            if not value:
+                raise TypeError(f"{attr_name} expects at least one boolean argument")
+            if len(value) > 2:
+                raise TypeError(f"{attr_name} expects at most two boolean arguments")
+            allow_reduced_precision = _ensure_bool(value[0], "allow_reduced_precision")
+            if len(value) == 1:
+                return allow_reduced_precision, True
+            allow_splitk = _ensure_bool(value[1], "allow_splitk")
+            return allow_reduced_precision, allow_splitk
+        raise TypeError(
+            f"{attr_name} expects a bool or a tuple/list of bools, but got {type(value)!r}"
+        )
+
+    def __getattr__(self, name):
+        if name == "allow_tf32":
+            return torch._C._get_cublas_allow_tf32()
+        elif name == "allow_fp16_reduced_precision_reduction":
+            allow_reduced_precision, _ = (
+                torch._C._get_cublas_allow_fp16_reduced_precision_reduction()
+            )
+            return allow_reduced_precision
+        elif name == "allow_fp16_reduced_precision_reduction_split_k":
+            _, allow_splitk = (
+                torch._C._get_cublas_allow_fp16_reduced_precision_reduction()
+            )
+            return allow_splitk
+        elif name == "allow_bf16_reduced_precision_reduction":
+            allow_reduced_precision, _ = (
+                torch._C._get_cublas_allow_bf16_reduced_precision_reduction()
+            )
+            return allow_reduced_precision
+        elif name == "allow_bf16_reduced_precision_reduction_split_k":
+            _, allow_splitk = (
+                torch._C._get_cublas_allow_bf16_reduced_precision_reduction()
+            )
+            return allow_splitk
+        elif name == "allow_fp16_accumulation":
+            return torch._C._get_cublas_allow_fp16_accumulation()
+        elif name == "fp32_precision":
+            return torch._C._get_fp32_precision_getter("cuda", "matmul")
+        raise AttributeError("Unknown attribute " + name)
+
+    def __setattr__(self, name, value):
+        if name == "allow_tf32":
+            return torch._C._set_cublas_allow_tf32(value)
+        elif name == "allow_fp16_reduced_precision_reduction":
+            allow_reduced_precision, allow_splitk = self._parse_reduction_setting(
+                value, "allow_fp16_reduced_precision_reduction"
+            )
+            return torch._C._set_cublas_allow_fp16_reduced_precision_reduction(
+                allow_reduced_precision,
+                allow_splitk,
+            )
+        elif name == "allow_bf16_reduced_precision_reduction":
+            allow_reduced_precision, allow_splitk = self._parse_reduction_setting(
+                value, "allow_bf16_reduced_precision_reduction"
+            )
+            return torch._C._set_cublas_allow_bf16_reduced_precision_reduction(
+                allow_reduced_precision,
+                allow_splitk,
+            )
+        elif name == "allow_fp16_accumulation":
+            return torch._C._set_cublas_allow_fp16_accumulation(value)
+        elif name == "fp32_precision":
+            return torch._C._set_fp32_precision_setter("cuda", "matmul", value)
+        raise AttributeError("Unknown attribute " + name)
+
+
+_LinalgBackends = {
+    "default": torch._C._LinalgBackend.Default,
+    "cusolver": torch._C._LinalgBackend.Cusolver,
+    "magma": torch._C._LinalgBackend.Magma,
+}
+_LinalgBackends_str = ", ".join(_LinalgBackends.keys())
+
+
+def preferred_linalg_library(
+    backend: Union[None, str, torch._C._LinalgBackend] = None,
+) -> torch._C._LinalgBackend:
+    r"""
+    Override the heuristic PyTorch uses to choose between cuSOLVER and MAGMA for CUDA linear algebra operations.
+
+    .. warning:: This flag is experimental and subject to change.
+
+    When PyTorch runs a CUDA linear algebra operation it often uses the cuSOLVER or MAGMA libraries,
+    and if both are available it decides which to use with a heuristic.
+    This flag (a :class:`str`) allows overriding those heuristics.
+
+    * If `"cusolver"` is set then cuSOLVER will be used wherever possible.
+    * If `"magma"` is set then MAGMA will be used wherever possible.
+    * If `"default"` (the default) is set then heuristics will be used to pick between
+      cuSOLVER and MAGMA if both are available.
+    * When no input is given, this function returns the currently preferred library.
+    * User may use the environment variable TORCH_LINALG_PREFER_CUSOLVER=1 to set the preferred library to cuSOLVER
+      globally.
+      This flag only sets the initial value of the preferred library and the preferred library
+      may still be overridden by this function call later in your script.
+
+    Note: When a library is preferred other libraries may still be used if the preferred library
+    doesn't implement the operation(s) called.
+    This flag may achieve better performance if PyTorch's heuristic library selection is incorrect
+    for your application's inputs.
+
+    Currently supported linalg operators:
+
+    * :func:`torch.linalg.inv`
+    * :func:`torch.linalg.inv_ex`
+    * :func:`torch.linalg.cholesky`
+    * :func:`torch.linalg.cholesky_ex`
+    * :func:`torch.cholesky_solve`
+    * :func:`torch.cholesky_inverse`
+    * :func:`torch.linalg.lu_factor`
+    * :func:`torch.linalg.lu`
+    * :func:`torch.linalg.lu_solve`
+    * :func:`torch.linalg.qr`
+    * :func:`torch.linalg.eigh`
+    * :func:`torch.linalg.eighvals`
+    * :func:`torch.linalg.svd`
+    * :func:`torch.linalg.svdvals`
+    """
+    if backend is None:
+        pass
+    elif isinstance(backend, str):
+        if backend not in _LinalgBackends:
+            raise RuntimeError(
+                f"Unknown input value. Choose from: {_LinalgBackends_str}."
+            )
+        torch._C._set_linalg_preferred_backend(_LinalgBackends[backend])
+    elif isinstance(backend, torch._C._LinalgBackend):
+        torch._C._set_linalg_preferred_backend(backend)
+    else:
+        raise RuntimeError("Unknown input value type.")
+
+    return torch._C._get_linalg_preferred_backend()
+
+
+_BlasBackends = {
+    "default": torch._C._BlasBackend.Default,
+    "cublas": torch._C._BlasBackend.Cublas,
+    "hipblas": torch._C._BlasBackend.Cublas,  # alias
+    "cublaslt": torch._C._BlasBackend.Cublaslt,
+    "hipblaslt": torch._C._BlasBackend.Cublaslt,  # alias
+    "ck": torch._C._BlasBackend.Ck,
+}
+_BlasBackends_str = ", ".join(_BlasBackends.keys())
+
+
+def preferred_blas_library(
+    backend: Union[None, str, torch._C._BlasBackend] = None,
+) -> torch._C._BlasBackend:
+    r"""
+    Override the library PyTorch uses for BLAS operations. Choose between cuBLAS, cuBLASLt, and CK [ROCm-only].
+
+    .. warning:: This flag is experimental and subject to change.
+
+    When PyTorch runs a CUDA BLAS operation it defaults to cuBLAS even if both cuBLAS and cuBLASLt are available.
+    For PyTorch built for ROCm, hipBLAS, hipBLASLt, and CK may offer different performance.
+    This flag (a :class:`str`) allows overriding which BLAS library to use.
+
+    * If `"cublas"` is set then cuBLAS will be used wherever possible.
+    * If `"cublaslt"` is set then cuBLASLt will be used wherever possible.
+    * If `"ck"` is set then CK will be used wherever possible.
+    * If `"default"` (the default) is set then heuristics will be used to pick between the other options.
+    * When no input is given, this function returns the currently preferred library.
+    * User may use the environment variable TORCH_BLAS_PREFER_CUBLASLT=1 to set the preferred library to cuBLASLt
+      globally.
+      This flag only sets the initial value of the preferred library and the preferred library
+      may still be overridden by this function call later in your script.
+
+    Note: When a library is preferred other libraries may still be used if the preferred library
+    doesn't implement the operation(s) called.
+    This flag may achieve better performance if PyTorch's library selection is incorrect
+    for your application's inputs.
+
+    """
+    if backend is None:
+        pass
+    elif isinstance(backend, str):
+        if backend not in _BlasBackends:
+            raise RuntimeError(
+                f"Unknown input value. Choose from: {_BlasBackends_str}."
+            )
+        torch._C._set_blas_preferred_backend(_BlasBackends[backend])
+    elif isinstance(backend, torch._C._BlasBackend):
+        torch._C._set_blas_preferred_backend(backend)
+    else:
+        raise RuntimeError("Unknown input value type.")
+
+    return torch._C._get_blas_preferred_backend()
+
+
+_ROCmFABackends = {
+    "default": torch._C._ROCmFABackend.Default,
+    "aotriton": torch._C._ROCmFABackend.AOTriton,
+    "ck": torch._C._ROCmFABackend.Ck,
+}
+_ROCmFABackends_str = ", ".join(_ROCmFABackends.keys())
+
+
+from torch._C import _SDPAParams as SDPAParams, _SDPBackend as SDPBackend
+
+
+def preferred_rocm_fa_library(
+    backend: Union[None, str, torch._C._ROCmFABackend] = None,
+) -> torch._C._ROCmFABackend:
+    r"""
+    [ROCm-only]
+    Override the backend PyTorch uses in ROCm environments for Flash Attention. Choose between AOTriton and CK
+
+    .. warning:: This flag is experimental and subject to change.
+
+    When Flash Attention is enabled and desired, PyTorch defaults to using AOTriton as the backend.
+    This flag (a :class:`str`) allows users to override this backend to use composable_kernel
+
+    * If `"default"` is set then the default backend will be used wherever possible. Currently AOTriton.
+    * If `"aotriton"` is set then AOTriton will be used wherever possible.
+    * If `"ck"` is set then CK will be used wherever possible.
+    * When no input is given, this function returns the currently preferred library.
+    * User may use the environment variable TORCH_ROCM_FA_PREFER_CK=1 to set the preferred library to CK
+      globally.
+
+    Note: When a library is preferred other libraries may still be used if the preferred library
+    doesn't implement the operation(s) called.
+    This flag may achieve better performance if PyTorch's library selection is incorrect
+    for your application's inputs.
+    """
+    if backend is None:
+        pass
+    elif isinstance(backend, str):
+        if backend not in _ROCmFABackends:
+            raise RuntimeError(
+                f"Unknown input value. Choose from: {_ROCmFABackends_str}."
+            )
+        torch._C._set_rocm_fa_preferred_backend(_ROCmFABackends[backend])
+    elif isinstance(backend, torch._C._ROCmFABackend):
+        torch._C._set_rocm_fa_preferred_backend(backend)
+    else:
+        raise ValueError(f"Unknown input value. Choose from: {_ROCmFABackends_str}.")
+
+    return torch._C._get_rocm_fa_preferred_backend()
+
+
+# Set the __module__ attribute
+SDPAParams.__module__ = "torch.backends.cuda"
+SDPAParams.__name__ = "SDPAParams"
+
+
+def flash_sdp_enabled():
+    r"""
+    .. warning:: This flag is beta and subject to change.
+
+    Returns whether flash scaled dot product attention is enabled or not.
+    """
+    return torch._C._get_flash_sdp_enabled()
+
+
+def enable_flash_sdp(enabled: bool):
+    r"""
+    .. warning:: This flag is beta and subject to change.
+
+    Enables or disables flash scaled dot product attention.
+    """
+    torch._C._set_sdp_use_flash(enabled)
+
+
+def mem_efficient_sdp_enabled():
+    r"""
+    .. warning:: This flag is beta and subject to change.
+
+    Returns whether memory efficient scaled dot product attention is enabled or not.
+    """
+    return torch._C._get_mem_efficient_sdp_enabled()
+
+
+def enable_mem_efficient_sdp(enabled: bool):
+    r"""
+    .. warning:: This flag is beta and subject to change.
+
+    Enables or disables memory efficient scaled dot product attention.
+    """
+    torch._C._set_sdp_use_mem_efficient(enabled)
+
+
+def math_sdp_enabled():
+    r"""
+    .. warning:: This flag is beta and subject to change.
+
+    Returns whether math scaled dot product attention is enabled or not.
+    """
+    return torch._C._get_math_sdp_enabled()
+
+
+def enable_math_sdp(enabled: bool):
+    r"""
+    .. warning:: This flag is beta and subject to change.
+
+    Enables or disables math scaled dot product attention.
+    """
+    torch._C._set_sdp_use_math(enabled)
+
+
+def allow_fp16_bf16_reduction_math_sdp(enabled: bool):
+    r"""
+    .. warning:: This flag is beta and subject to change.
+
+    Enables or disables fp16/bf16 reduction in math scaled dot product attention.
+    """
+    torch._C._set_math_sdp_allow_fp16_bf16_reduction(enabled)
+
+
+def fp16_bf16_reduction_math_sdp_allowed():
+    r"""
+    .. warning:: This flag is beta and subject to change.
+
+    Returns whether fp16/bf16 reduction in math scaled dot product attention is enabled or not.
+    """
+    return torch._C._get_math_sdp_allow_fp16_bf16_reduction()
+
+
+def is_flash_attention_available() -> bool:
+    r"""Check if PyTorch was built with FlashAttention for scaled_dot_product_attention.
+
+    Returns:
+        True if FlashAttention is built and available; otherwise, False.
+
+    Note:
+        This function is dependent on a CUDA-enabled build of PyTorch. It will return False
+        in non-CUDA environments.
+    """
+    return torch._C._is_flash_attention_available()
+
+
+def can_use_flash_attention(params: SDPAParams, debug: bool = False) -> bool:
+    r"""Check if FlashAttention can be utilized in scaled_dot_product_attention.
+
+    Args:
+        params: An instance of SDPAParams containing the tensors for query,
+                key, value, an optional attention mask, dropout rate, and
+                a flag indicating if the attention is causal.
+        debug: Whether to logging.warn debug information as to why FlashAttention could not be run.
+            Defaults to False.
+
+    Returns:
+        True if FlashAttention can be used with the given parameters; otherwise, False.
+
+    Note:
+        This function is dependent on a CUDA-enabled build of PyTorch. It will return False
+        in non-CUDA environments.
+    """
+    return torch._C._can_use_flash_attention(params, debug)
+
+
+def can_use_efficient_attention(params: SDPAParams, debug: bool = False) -> bool:
+    r"""Check if efficient_attention can be utilized in scaled_dot_product_attention.
+
+    Args:
+        params: An instance of SDPAParams containing the tensors for query,
+                key, value, an optional attention mask, dropout rate, and
+                a flag indicating if the attention is causal.
+        debug: Whether to logging.warn with information as to why efficient_attention could not be run.
+            Defaults to False.
+
+    Returns:
+        True if efficient_attention can be used with the given parameters; otherwise, False.
+
+    Note:
+        This function is dependent on a CUDA-enabled build of PyTorch. It will return False
+        in non-CUDA environments.
+    """
+    return torch._C._can_use_mem_efficient_attention(params, debug)
+
+
+def can_use_cudnn_attention(params: SDPAParams, debug: bool = False) -> bool:
+    r"""Check if cudnn_attention can be utilized in scaled_dot_product_attention.
+
+    Args:
+        params: An instance of SDPAParams containing the tensors for query,
+                key, value, an optional attention mask, dropout rate, and
+                a flag indicating if the attention is causal.
+        debug: Whether to logging.warn with information as to why cuDNN attention could not be run.
+            Defaults to False.
+
+    Returns:
+        True if cuDNN can be used with the given parameters; otherwise, False.
+
+    Note:
+        This function is dependent on a CUDA-enabled build of PyTorch. It will return False
+        in non-CUDA environments.
+    """
+    return torch._C._can_use_cudnn_attention(params, debug)
+
+
+def cudnn_sdp_enabled():
+    r"""
+    .. warning:: This flag is beta and subject to change.
+
+    Returns whether cuDNN scaled dot product attention is enabled or not.
+    """
+    return torch._C._get_cudnn_sdp_enabled()
+
+
+def enable_cudnn_sdp(enabled: bool):
+    r"""
+    .. warning:: This flag is beta and subject to change.
+
+    Enables or disables cuDNN scaled dot product attention.
+    """
+    torch._C._set_sdp_use_cudnn(enabled)
+
+
+@contextlib.contextmanager
+@deprecated(
+    (
+        "`torch.backends.cuda.sdp_kernel()` is deprecated. "
+        "In the future, this context manager will be removed. "
+        "Please see `torch.nn.attention.sdpa_kernel()` for the new context manager, "
+        "with updated signature."
+    ),
+    category=FutureWarning,
+)
+def sdp_kernel(
+    enable_flash: bool = True,
+    enable_math: bool = True,
+    enable_mem_efficient: bool = True,
+    enable_cudnn: bool = True,
+):
+    r"""
+    .. warning:: This flag is beta and subject to change.
+
+    This context manager can be used to temporarily enable or disable any of the three backends for scaled dot product attention.
+    Upon exiting the context manager, the previous state of the flags will be restored.
+    """
+    from torch.nn.attention import sdpa_kernel
+
+    backend_list = []
+    if enable_flash:
+        backend_list.append(SDPBackend.FLASH_ATTENTION)
+    if enable_mem_efficient:
+        backend_list.append(SDPBackend.EFFICIENT_ATTENTION)
+    if enable_math:
+        backend_list.append(SDPBackend.MATH)
+    if enable_cudnn:
+        backend_list.append(SDPBackend.CUDNN_ATTENTION)
+
+    with sdpa_kernel(backend_list) as context:
+        try:
+            yield context
+        finally:
+            pass
+
+
+cufft_plan_cache = cuFFTPlanCacheManager()
+matmul = cuBLASModule()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/cudnn/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/cudnn/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5cd6ec297c7a8a21c407e12112ba961b76624a6f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/cudnn/__init__.py
@@ -0,0 +1,248 @@
+# mypy: allow-untyped-defs
+import os
+import sys
+import warnings
+from contextlib import contextmanager
+from typing import Optional
+
+import torch
+from torch.backends import (
+    __allow_nonbracketed_mutation,
+    _FP32Precision,
+    _get_fp32_precision_getter,
+    _set_fp32_precision_setter,
+    ContextProp,
+    PropModule,
+)
+
+
+try:
+    from torch._C import _cudnn
+except ImportError:
+    _cudnn = None  # type: ignore[assignment]
+
+# Write:
+#
+#   torch.backends.cudnn.enabled = False
+#
+# to globally disable CuDNN/MIOpen
+
+__cudnn_version: Optional[int] = None
+
+if _cudnn is not None:
+
+    def _init():
+        global __cudnn_version
+        if __cudnn_version is None:
+            # pyrefly: ignore [missing-attribute]
+            __cudnn_version = _cudnn.getVersionInt()
+            # pyrefly: ignore [missing-attribute]
+            runtime_version = _cudnn.getRuntimeVersion()
+            # pyrefly: ignore [missing-attribute]
+            compile_version = _cudnn.getCompileVersion()
+            runtime_major, runtime_minor, _ = runtime_version
+            compile_major, compile_minor, _ = compile_version
+            # Different major versions are always incompatible
+            # Starting with cuDNN 7, minor versions are backwards-compatible
+            # Not sure about MIOpen (ROCm), so always do a strict check
+            if runtime_major != compile_major:
+                cudnn_compatible = False
+            # pyrefly: ignore [missing-attribute]
+            elif runtime_major < 7 or not _cudnn.is_cuda:
+                cudnn_compatible = runtime_minor == compile_minor
+            else:
+                cudnn_compatible = runtime_minor >= compile_minor
+            if not cudnn_compatible:
+                if os.environ.get("PYTORCH_SKIP_CUDNN_COMPATIBILITY_CHECK", "0") == "1":
+                    return True
+                base_error_msg = (
+                    f"cuDNN version incompatibility: "
+                    f"PyTorch was compiled  against {compile_version} "
+                    f"but found runtime version {runtime_version}. "
+                    f"PyTorch already comes bundled with cuDNN. "
+                    f"One option to resolving this error is to ensure PyTorch "
+                    f"can find the bundled cuDNN. "
+                )
+
+                if "LD_LIBRARY_PATH" in os.environ:
+                    ld_library_path = os.environ.get("LD_LIBRARY_PATH", "")
+                    if any(
+                        substring in ld_library_path for substring in ["cuda", "cudnn"]
+                    ):
+                        raise RuntimeError(
+                            f"{base_error_msg}"
+                            f"Looks like your LD_LIBRARY_PATH contains incompatible version of cudnn. "
+                            f"Please either remove it from the path or install cudnn {compile_version}"
+                        )
+                    else:
+                        raise RuntimeError(
+                            f"{base_error_msg}"
+                            f"one possibility is that there is a "
+                            f"conflicting cuDNN in LD_LIBRARY_PATH."
+                        )
+                else:
+                    raise RuntimeError(base_error_msg)
+            # Check if cuDNN version is compatible with available CUDA devices
+            if torch.cuda.is_available() and not torch.version.hip:
+                min_cc = min(
+                    [
+                        torch.cuda.get_device_capability(i)
+                        for i in range(torch.cuda.device_count())
+                    ]
+                )
+                if __cudnn_version >= 91100 and min_cc < (7, 5):
+                    raise RuntimeError(
+                        f"cuDNN version {__cudnn_version} is not compatible with devices with SM < 7.5. "
+                        f"Please install a version of PyTorch with a compatible cuDNN version. "
+                        f"https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-compatibility-matrix"
+                    )
+
+        return True
+
+else:
+
+    def _init():
+        return False
+
+
+def version():
+    """Return the version of cuDNN."""
+    if not _init():
+        return None
+    return __cudnn_version
+
+
+CUDNN_TENSOR_DTYPES = {
+    torch.half,
+    torch.float,
+    torch.double,
+}
+
+
+def is_available():
+    r"""Return a bool indicating if CUDNN is currently available."""
+    return torch._C._has_cudnn
+
+
+def is_acceptable(tensor):
+    if not torch._C._get_cudnn_enabled():
+        return False
+    if tensor.device.type != "cuda" or tensor.dtype not in CUDNN_TENSOR_DTYPES:
+        return False
+    if not is_available():
+        warnings.warn(
+            "PyTorch was compiled without cuDNN/MIOpen support. To use cuDNN/MIOpen, rebuild "
+            "PyTorch making sure the library is visible to the build system.",
+            stacklevel=2,
+        )
+        return False
+    if not _init():
+        warnings.warn(
+            "cuDNN/MIOpen library not found. Check your {libpath}".format(
+                libpath={"darwin": "DYLD_LIBRARY_PATH", "win32": "PATH"}.get(
+                    sys.platform, "LD_LIBRARY_PATH"
+                )
+            ),
+            stacklevel=2,
+        )
+        return False
+    return True
+
+
+def set_flags(
+    _enabled=None,
+    _benchmark=None,
+    _benchmark_limit=None,
+    _deterministic=None,
+    _allow_tf32=None,
+    _fp32_precision="none",
+):
+    orig_flags = (
+        torch._C._get_cudnn_enabled(),
+        torch._C._get_cudnn_benchmark(),
+        None if not is_available() else torch._C._cuda_get_cudnn_benchmark_limit(),
+        torch._C._get_cudnn_deterministic(),
+        torch._C._get_cudnn_allow_tf32(),
+        torch._C._get_fp32_precision_getter("cuda", "all"),
+    )
+    if _enabled is not None:
+        torch._C._set_cudnn_enabled(_enabled)
+    if _benchmark is not None:
+        torch._C._set_cudnn_benchmark(_benchmark)
+    if _benchmark_limit is not None and is_available():
+        torch._C._cuda_set_cudnn_benchmark_limit(_benchmark_limit)
+    if _deterministic is not None:
+        torch._C._set_cudnn_deterministic(_deterministic)
+    if _allow_tf32 is not None:
+        torch._C._set_cudnn_allow_tf32(_allow_tf32)
+    if _fp32_precision is not None:
+        torch._C._set_fp32_precision_setter("cuda", "all", _fp32_precision)
+    return orig_flags
+
+
+@contextmanager
+def flags(
+    enabled=False,
+    benchmark=False,
+    benchmark_limit=10,
+    deterministic=False,
+    allow_tf32=True,
+    fp32_precision="none",
+):
+    with __allow_nonbracketed_mutation():
+        orig_flags = set_flags(
+            enabled,
+            benchmark,
+            benchmark_limit,
+            deterministic,
+            allow_tf32,
+            fp32_precision,
+        )
+    try:
+        yield
+    finally:
+        # recover the previous values
+        with __allow_nonbracketed_mutation():
+            set_flags(*orig_flags)
+
+
+# The magic here is to allow us to intercept code like this:
+#
+#   torch.backends..enabled = True
+
+
+class CudnnModule(PropModule):
+    enabled = ContextProp(torch._C._get_cudnn_enabled, torch._C._set_cudnn_enabled)
+    deterministic = ContextProp(
+        torch._C._get_cudnn_deterministic, torch._C._set_cudnn_deterministic
+    )
+    benchmark = ContextProp(
+        torch._C._get_cudnn_benchmark, torch._C._set_cudnn_benchmark
+    )
+    benchmark_limit = None
+    if is_available():
+        benchmark_limit = ContextProp(
+            torch._C._cuda_get_cudnn_benchmark_limit,
+            torch._C._cuda_set_cudnn_benchmark_limit,
+        )
+    allow_tf32 = ContextProp(
+        torch._C._get_cudnn_allow_tf32, torch._C._set_cudnn_allow_tf32
+    )
+    conv = _FP32Precision("cuda", "conv")
+    rnn = _FP32Precision("cuda", "rnn")
+    fp32_precision = ContextProp(
+        _get_fp32_precision_getter("cuda", "all"),
+        _set_fp32_precision_setter("cuda", "all"),
+    )
+
+
+# This is the sys.modules replacement trick, see
+# https://stackoverflow.com/questions/2447353/getattr-on-a-module/7668273#7668273
+sys.modules[__name__] = CudnnModule(sys.modules[__name__], __name__)
+
+# Add type annotation for the replaced module
+enabled: bool
+deterministic: bool
+benchmark: bool
+allow_tf32: bool
+benchmark_limit: int
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/cudnn/rnn.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/cudnn/rnn.py
new file mode 100644
index 0000000000000000000000000000000000000000..0dc9ca80aa6fd10efc41910d38ba33d00852729c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/cudnn/rnn.py
@@ -0,0 +1,69 @@
+# mypy: allow-untyped-defs
+import torch.cuda
+
+
+try:
+    from torch._C import _cudnn
+except ImportError:
+    # Uses of all the functions below should be guarded by torch.backends.cudnn.is_available(),
+    # so it's safe to not emit any checks here.
+    _cudnn = None  # type: ignore[assignment]
+
+
+def get_cudnn_mode(mode):
+    if mode == "RNN_RELU":
+        # pyrefly: ignore [missing-attribute]
+        return int(_cudnn.RNNMode.rnn_relu)
+    elif mode == "RNN_TANH":
+        # pyrefly: ignore [missing-attribute]
+        return int(_cudnn.RNNMode.rnn_tanh)
+    elif mode == "LSTM":
+        # pyrefly: ignore [missing-attribute]
+        return int(_cudnn.RNNMode.lstm)
+    elif mode == "GRU":
+        # pyrefly: ignore [missing-attribute]
+        return int(_cudnn.RNNMode.gru)
+    else:
+        raise Exception(f"Unknown mode: {mode}")  # noqa: TRY002
+
+
+# NB: We don't actually need this class anymore (in fact, we could serialize the
+# dropout state for even better reproducibility), but it is kept for backwards
+# compatibility for old models.
+class Unserializable:
+    def __init__(self, inner):
+        self.inner = inner
+
+    def get(self):
+        return self.inner
+
+    def __getstate__(self):
+        # Note: can't return {}, because python2 won't call __setstate__
+        # if the value evaluates to False
+        return ""
+
+    def __setstate__(self, state):
+        self.inner = None
+
+
+def init_dropout_state(dropout, train, dropout_seed, dropout_state):
+    dropout_desc_name = "desc_" + str(torch.cuda.current_device())
+    dropout_p = dropout if train else 0
+    if (dropout_desc_name not in dropout_state) or (
+        dropout_state[dropout_desc_name].get() is None
+    ):
+        if dropout_p == 0:
+            dropout_state[dropout_desc_name] = Unserializable(None)
+        else:
+            dropout_state[dropout_desc_name] = Unserializable(
+                torch._cudnn_init_dropout_state(  # type: ignore[call-arg]
+                    dropout_p,
+                    train,
+                    dropout_seed,
+                    # pyrefly: ignore [unexpected-keyword]
+                    self_ty=torch.uint8,
+                    device=torch.device("cuda"),
+                )
+            )
+    dropout_ts = dropout_state[dropout_desc_name].get()
+    return dropout_ts
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/cusparselt/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/cusparselt/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e9b9df2acf144e00a193ee312f728ec30327f8a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/cusparselt/__init__.py
@@ -0,0 +1,57 @@
+from typing import Optional
+
+import torch
+
+
+__all__ = [
+    "version",
+    "is_available",
+    "get_max_alg_id",
+]
+
+try:
+    from torch._C import _cusparselt
+except ImportError:
+    _cusparselt = None  # type: ignore[assignment]
+
+__cusparselt_version: Optional[int] = None
+__MAX_ALG_ID: Optional[int] = None
+
+if _cusparselt is not None:
+
+    def _init() -> bool:
+        global __cusparselt_version
+        global __MAX_ALG_ID
+        if __cusparselt_version is None:
+            # pyrefly: ignore [missing-attribute]
+            __cusparselt_version = _cusparselt.getVersionInt()
+            if __cusparselt_version == 400:
+                __MAX_ALG_ID = 4
+            elif __cusparselt_version == 502:
+                __MAX_ALG_ID = 5
+            elif __cusparselt_version == 602:
+                __MAX_ALG_ID = 37
+        return True
+
+else:
+
+    def _init() -> bool:
+        return False
+
+
+def version() -> Optional[int]:
+    """Return the version of cuSPARSELt"""
+    if not _init():
+        return None
+    return __cusparselt_version
+
+
+def is_available() -> bool:
+    r"""Return a bool indicating if cuSPARSELt is currently available."""
+    return torch._C._has_cusparselt
+
+
+def get_max_alg_id() -> Optional[int]:
+    if not _init():
+        return None
+    return __MAX_ALG_ID
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/kleidiai/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/kleidiai/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a681b77ef58ce1f390232b82c4a9843d5559ca3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/kleidiai/__init__.py
@@ -0,0 +1,7 @@
+# mypy: allow-untyped-defs
+import torch
+
+
+def is_available():
+    r"""Return whether PyTorch is built with KleidiAI support."""
+    return torch._C._has_kleidiai
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/mha/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/mha/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e1dd2ebd688805bdf3359cb56b64d0854cf258c4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/mha/__init__.py
@@ -0,0 +1,25 @@
+# Config options to enable/disable C++ kernel for nn.functional.MHA
+# and nn.TransformerEncoder
+import torch
+
+
+_is_fastpath_enabled: bool = True
+
+
+def get_fastpath_enabled() -> bool:
+    """Returns whether fast path for TransformerEncoder and MultiHeadAttention
+    is enabled, or ``True`` if jit is scripting.
+
+    .. note::
+        The fastpath might not be run even if ``get_fastpath_enabled`` returns
+        ``True`` unless all conditions on inputs are met.
+    """
+    if not torch.jit.is_scripting():
+        return _is_fastpath_enabled
+    return True
+
+
+def set_fastpath_enabled(value: bool) -> None:
+    """Sets whether fast path is enabled"""
+    global _is_fastpath_enabled
+    _is_fastpath_enabled = value
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/miopen/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/miopen/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1b270b658e31a91dfb37380abec383009dfc5bfa
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/miopen/__init__.py
@@ -0,0 +1,50 @@
+# mypy: allow-untyped-defs
+import sys
+from contextlib import contextmanager
+
+import torch
+from torch.backends import __allow_nonbracketed_mutation, ContextProp, PropModule
+
+
+def set_flags(
+    _immediate=None,
+):
+    orig_flags = (torch._C._get_miopen_immediate(),)
+    if _immediate is not None:
+        torch._C._set_miopen_immediate(_immediate)
+    return orig_flags
+
+
+@contextmanager
+def flags(
+    immediate=False,
+):
+    with __allow_nonbracketed_mutation():
+        orig_flags = set_flags(
+            immediate,
+        )
+    try:
+        yield
+    finally:
+        # recover the previous values
+        with __allow_nonbracketed_mutation():
+            set_flags(*orig_flags)
+
+
+# The magic here is to allow us to intercept code like this:
+#
+#   torch.backends..immediate = True
+
+
+class MiopenModule(PropModule):
+    immediate = ContextProp(
+        torch._C._get_miopen_immediate, torch._C._set_miopen_immediate
+    )
+
+
+# This is the sys.modules replacement trick, see
+# https://stackoverflow.com/questions/2447353/getattr-on-a-module/7668273#7668273
+sys.modules[__name__] = MiopenModule(sys.modules[__name__], __name__)
+
+# Add type annotation for the replaced module
+immediate: bool
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/mkl/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/mkl/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae16922761afeafa53766757641bcc532b4d5ef4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/mkl/__init__.py
@@ -0,0 +1,58 @@
+# mypy: allow-untyped-defs
+import torch
+
+
+def is_available():
+    r"""Return whether PyTorch is built with MKL support."""
+    return torch._C.has_mkl
+
+
+VERBOSE_OFF = 0
+VERBOSE_ON = 1
+
+
+class verbose:
+    """
+    On-demand oneMKL verbosing functionality.
+
+    To make it easier to debug performance issues, oneMKL can dump verbose
+    messages containing execution information like duration while executing
+    the kernel. The verbosing functionality can be invoked via an environment
+    variable named `MKL_VERBOSE`. However, this methodology dumps messages in
+    all steps. Those are a large amount of verbose messages. Moreover, for
+    investigating the performance issues, generally taking verbose messages
+    for one single iteration is enough. This on-demand verbosing functionality
+    makes it possible to control scope for verbose message dumping. In the
+    following example, verbose messages will be dumped out for the second
+    inference only.
+
+    .. highlight:: python
+    .. code-block:: python
+
+        import torch
+
+        model(data)
+        with torch.backends.mkl.verbose(torch.backends.mkl.VERBOSE_ON):
+            model(data)
+
+    Args:
+        level: Verbose level
+            - ``VERBOSE_OFF``: Disable verbosing
+            - ``VERBOSE_ON``:  Enable verbosing
+    """
+
+    def __init__(self, enable):
+        self.enable = enable
+
+    def __enter__(self):
+        if self.enable == VERBOSE_OFF:
+            return
+        st = torch._C._verbose.mkl_set_verbose(self.enable)
+        assert st, (
+            "Failed to set MKL into verbose mode. Please consider to disable this verbose scope."
+        )
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        torch._C._verbose.mkl_set_verbose(VERBOSE_OFF)
+        return False
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/mkldnn/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/mkldnn/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..58e6b2c595e9853942b0a3a58a6e5ab2627d3608
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/mkldnn/__init__.py
@@ -0,0 +1,137 @@
+# mypy: allow-untyped-defs
+import sys
+from contextlib import contextmanager
+from typing import TYPE_CHECKING
+
+import torch
+from torch.backends import (
+    __allow_nonbracketed_mutation,
+    _FP32Precision,
+    _get_fp32_precision_getter,
+    _set_fp32_precision_setter,
+    ContextProp,
+    PropModule,
+)
+
+
+def is_available():
+    r"""Return whether PyTorch is built with MKL-DNN support."""
+    return torch._C._has_mkldnn
+
+
+def is_acl_available():
+    r"""Return whether PyTorch is built with MKL-DNN + ACL support."""
+    # pyrefly: ignore [missing-attribute]
+    return torch._C._has_mkldnn_acl
+
+
+VERBOSE_OFF = 0
+VERBOSE_ON = 1
+VERBOSE_ON_CREATION = 2
+
+
+class verbose:
+    """
+    On-demand oneDNN (former MKL-DNN) verbosing functionality.
+
+    To make it easier to debug performance issues, oneDNN can dump verbose
+    messages containing information like kernel size, input data size and
+    execution duration while executing the kernel. The verbosing functionality
+    can be invoked via an environment variable named `DNNL_VERBOSE`. However,
+    this methodology dumps messages in all steps. Those are a large amount of
+    verbose messages. Moreover, for investigating the performance issues,
+    generally taking verbose messages for one single iteration is enough.
+    This on-demand verbosing functionality makes it possible to control scope
+    for verbose message dumping. In the following example, verbose messages
+    will be dumped out for the second inference only.
+
+    .. highlight:: python
+    .. code-block:: python
+
+        import torch
+
+        model(data)
+        with torch.backends.mkldnn.verbose(torch.backends.mkldnn.VERBOSE_ON):
+            model(data)
+
+    Args:
+        level: Verbose level
+            - ``VERBOSE_OFF``: Disable verbosing
+            - ``VERBOSE_ON``:  Enable verbosing
+            - ``VERBOSE_ON_CREATION``: Enable verbosing, including oneDNN kernel creation
+    """
+
+    def __init__(self, level):
+        self.level = level
+
+    def __enter__(self):
+        if self.level == VERBOSE_OFF:
+            return
+        st = torch._C._verbose.mkldnn_set_verbose(self.level)
+        assert st, (
+            "Failed to set MKLDNN into verbose mode. Please consider to disable this verbose scope."
+        )
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        torch._C._verbose.mkldnn_set_verbose(VERBOSE_OFF)
+        return False
+
+
+def set_flags(
+    _enabled=None, _deterministic=None, _allow_tf32=None, _fp32_precision="none"
+):
+    orig_flags = (
+        torch._C._get_mkldnn_enabled(),
+        torch._C._get_mkldnn_deterministic(),
+        torch._C._get_onednn_allow_tf32(),
+        torch._C._get_fp32_precision_getter("mkldnn", "all"),
+    )
+    if _enabled is not None:
+        torch._C._set_mkldnn_enabled(_enabled)
+    if _deterministic is not None:
+        torch._C._set_mkldnn_deterministic(_deterministic)
+    if _allow_tf32 is not None:
+        torch._C._set_onednn_allow_tf32(_allow_tf32)
+    if _fp32_precision is not None:
+        torch._C._set_fp32_precision_setter("mkldnn", "all", _fp32_precision)
+    return orig_flags
+
+
+@contextmanager
+def flags(enabled=False, deterministic=False, allow_tf32=True, fp32_precision="none"):
+    with __allow_nonbracketed_mutation():
+        orig_flags = set_flags(enabled, deterministic, allow_tf32, fp32_precision)
+    try:
+        yield
+    finally:
+        with __allow_nonbracketed_mutation():
+            set_flags(*orig_flags)
+
+
+class MkldnnModule(PropModule):
+    def is_available(self):
+        return is_available()
+
+    enabled = ContextProp(torch._C._get_mkldnn_enabled, torch._C._set_mkldnn_enabled)
+    deterministic = ContextProp(
+        torch._C._get_mkldnn_deterministic, torch._C._set_mkldnn_deterministic
+    )
+    allow_tf32 = ContextProp(
+        torch._C._get_onednn_allow_tf32, torch._C._set_onednn_allow_tf32
+    )
+    matmul = _FP32Precision("mkldnn", "matmul")
+    conv = _FP32Precision("mkldnn", "conv")
+    rnn = _FP32Precision("mkldnn", "rnn")
+    fp32_precision = ContextProp(
+        _get_fp32_precision_getter("mkldnn", "all"),
+        _set_fp32_precision_setter("generic", "all"),
+    )
+
+
+if TYPE_CHECKING:
+    enabled: ContextProp
+    deterministic: ContextProp
+    allow_tf32: ContextProp
+
+sys.modules[__name__] = MkldnnModule(sys.modules[__name__], __name__)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/mps/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/mps/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c3c507428cfff85a02e1d9939b4951d7e8b84bf
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/mps/__init__.py
@@ -0,0 +1,78 @@
+from functools import lru_cache as _lru_cache
+from typing import Optional, TYPE_CHECKING
+
+import torch
+from torch.library import Library as _Library
+
+
+__all__ = [
+    "get_core_count",
+    "get_name",
+    "is_built",
+    "is_available",
+    "is_macos13_or_newer",
+    "is_macos_or_newer",
+]
+
+
+def is_built() -> bool:
+    r"""Return whether PyTorch is built with MPS support.
+
+    Note that this doesn't necessarily mean MPS is available; just that
+    if this PyTorch binary were run a machine with working MPS drivers
+    and devices, we would be able to use it.
+    """
+    return torch._C._has_mps
+
+
+@_lru_cache
+def is_available() -> bool:
+    r"""Return a bool indicating if MPS is currently available."""
+    return torch._C._mps_is_available()
+
+
+@_lru_cache
+def is_macos_or_newer(major: int, minor: int) -> bool:
+    r"""Return a bool indicating whether MPS is running on given MacOS or newer."""
+    return torch._C._mps_is_on_macos_or_newer(major, minor)
+
+
+@_lru_cache
+def is_macos13_or_newer(minor: int = 0) -> bool:
+    r"""Return a bool indicating whether MPS is running on MacOS 13 or newer."""
+    return torch._C._mps_is_on_macos_or_newer(13, minor)
+
+
+@_lru_cache
+def get_name() -> str:
+    r"""Return Metal device name"""
+    return torch._C._mps_get_name()
+
+
+@_lru_cache
+def get_core_count() -> int:
+    r"""Return GPU core count.
+
+    According to the documentation, one core is comprised of 16 Execution Units.
+    One execution Unit has 8 ALUs.
+    And one ALU can run 24 threads, i.e. one core is capable of executing 3072 threads concurrently.
+    """
+    return torch._C._mps_get_core_count()
+
+
+_lib: Optional[_Library] = None
+
+
+def _init() -> None:
+    r"""Register prims as implementation of var_mean and group_norm."""
+    global _lib
+
+    if _lib is not None or not is_built():
+        return
+
+    from torch._decomp.decompositions import native_group_norm_backward
+    from torch._refs import native_group_norm
+
+    _lib = _Library("aten", "IMPL")  # noqa: TOR901
+    _lib.impl("native_group_norm", native_group_norm, "MPS")
+    _lib.impl("native_group_norm_backward", native_group_norm_backward, "MPS")
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/nnpack/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/nnpack/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d8a72f3cda9b0da16702c0d7c6fe92ae8f3f153
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/nnpack/__init__.py
@@ -0,0 +1,32 @@
+# mypy: allow-untyped-defs
+from contextlib import contextmanager
+
+import torch
+from torch.backends import __allow_nonbracketed_mutation, ContextProp, PropModule
+
+
+__all__ = ["is_available", "flags", "set_flags"]
+
+
+def is_available():
+    r"""Return whether PyTorch is built with NNPACK support."""
+    return torch._nnpack_available()
+
+
+def set_flags(_enabled):
+    r"""Set if nnpack is enabled globally"""
+    orig_flags = (torch._C._get_nnpack_enabled(),)
+    torch._C._set_nnpack_enabled(_enabled)
+    return orig_flags
+
+
+@contextmanager
+def flags(enabled=False):
+    r"""Context manager for setting if nnpack is enabled globally"""
+    with __allow_nonbracketed_mutation():
+        orig_flags = set_flags(enabled)
+    try:
+        yield
+    finally:
+        with __allow_nonbracketed_mutation():
+            set_flags(orig_flags[0])
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/openmp/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/openmp/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..aff8d46cd4ac2d9ff49942542d99ac2afbb85896
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/openmp/__init__.py
@@ -0,0 +1,7 @@
+# mypy: allow-untyped-defs
+import torch
+
+
+def is_available():
+    r"""Return whether PyTorch is built with OpenMP support."""
+    return torch._C.has_openmp
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/opt_einsum/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/opt_einsum/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..264be78aa9a1c24a4624a87782e2b2c5afd29c05
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/opt_einsum/__init__.py
@@ -0,0 +1,117 @@
+# mypy: allow-untyped-defs
+import sys
+import warnings
+from contextlib import contextmanager
+from functools import lru_cache as _lru_cache
+from typing import Any
+
+from torch.backends import __allow_nonbracketed_mutation, ContextProp, PropModule
+
+
+try:
+    import opt_einsum as _opt_einsum  # type: ignore[import]
+except ImportError:
+    _opt_einsum = None
+
+
+@_lru_cache
+def is_available() -> bool:
+    r"""Return a bool indicating if opt_einsum is currently available.
+
+    You must install opt-einsum in order for torch to automatically optimize einsum. To
+    make opt-einsum available, you can install it along with torch: ``pip install torch[opt-einsum]``
+    or by itself: ``pip install opt-einsum``. If the package is installed, torch will import
+    it automatically and use it accordingly. Use this function to check whether opt-einsum
+    was installed and properly imported by torch.
+    """
+    return _opt_einsum is not None
+
+
+def get_opt_einsum() -> Any:
+    r"""Return the opt_einsum package if opt_einsum is currently available, else None."""
+    return _opt_einsum
+
+
+def _set_enabled(_enabled: bool) -> None:
+    if not is_available() and _enabled:
+        raise ValueError(
+            f"opt_einsum is not available, so setting `enabled` to {_enabled} will not reap "
+            "the benefits of calculating an optimal path for einsum. torch.einsum will "
+            "fall back to contracting from left to right. To enable this optimal path "
+            "calculation, please install opt-einsum."
+        )
+    global enabled
+    enabled = _enabled
+
+
+def _get_enabled() -> bool:
+    return enabled
+
+
+def _set_strategy(_strategy: str) -> None:
+    if not is_available():
+        raise ValueError(
+            f"opt_einsum is not available, so setting `strategy` to {_strategy} will not be meaningful. "
+            "torch.einsum will bypass path calculation and simply contract from left to right. "
+            "Please install opt_einsum or unset `strategy`."
+        )
+    if not enabled:
+        raise ValueError(
+            f"opt_einsum is not enabled, so setting a `strategy` to {_strategy} will not be meaningful. "
+            "torch.einsum will bypass path calculation and simply contract from left to right. "
+            "Please set `enabled` to `True` as well or unset `strategy`."
+        )
+    if _strategy not in ["auto", "greedy", "optimal"]:
+        raise ValueError(
+            f"`strategy` must be one of the following: [auto, greedy, optimal] but is {_strategy}"
+        )
+    global strategy
+    strategy = _strategy
+
+
+def _get_strategy() -> str:
+    # pyrefly: ignore [bad-return]
+    return strategy
+
+
+def set_flags(_enabled=None, _strategy=None):
+    orig_flags = (enabled, None if not is_available() else strategy)
+    if _enabled is not None:
+        _set_enabled(_enabled)
+    if _strategy is not None:
+        _set_strategy(_strategy)
+    return orig_flags
+
+
+@contextmanager
+def flags(enabled=None, strategy=None):
+    with __allow_nonbracketed_mutation():
+        orig_flags = set_flags(enabled, strategy)
+    try:
+        yield
+    finally:
+        # recover the previous values
+        with __allow_nonbracketed_mutation():
+            set_flags(*orig_flags)
+
+
+# The magic here is to allow us to intercept code like this:
+#
+#   torch.backends.opt_einsum.enabled = True
+
+
+class OptEinsumModule(PropModule):
+    global enabled
+    enabled = ContextProp(_get_enabled, _set_enabled)
+    global strategy
+    strategy = None
+    if is_available():
+        strategy = ContextProp(_get_strategy, _set_strategy)
+
+
+# This is the sys.modules replacement trick, see
+# https://stackoverflow.com/questions/2447353/getattr-on-a-module/7668273#7668273
+sys.modules[__name__] = OptEinsumModule(sys.modules[__name__], __name__)
+
+enabled = bool(is_available())
+strategy = "auto" if is_available() else None
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/quantized/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/quantized/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..caabfdf243783f2161a201c6a6ec9bd6eca83b18
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/quantized/__init__.py
@@ -0,0 +1,65 @@
+# mypy: allow-untyped-defs
+import sys
+import types
+
+import torch
+
+
+# This function should correspond to the enums present in c10/core/QEngine.h
+def _get_qengine_id(qengine: str) -> int:
+    if qengine == "none" or qengine == "" or qengine is None:
+        ret = 0
+    elif qengine == "fbgemm":
+        ret = 1
+    elif qengine == "qnnpack":
+        ret = 2
+    elif qengine == "onednn":
+        ret = 3
+    elif qengine == "x86":
+        ret = 4
+    else:
+        ret = -1
+        raise RuntimeError(f"{qengine} is not a valid value for quantized engine")
+    return ret
+
+
+# This function should correspond to the enums present in c10/core/QEngine.h
+def _get_qengine_str(qengine: int) -> str:
+    all_engines = {0: "none", 1: "fbgemm", 2: "qnnpack", 3: "onednn", 4: "x86"}
+    return all_engines.get(qengine, "*undefined")
+
+
+class _QEngineProp:
+    def __get__(self, obj, objtype) -> str:
+        return _get_qengine_str(torch._C._get_qengine())
+
+    def __set__(self, obj, val: str) -> None:
+        torch._C._set_qengine(_get_qengine_id(val))
+
+
+class _SupportedQEnginesProp:
+    def __get__(self, obj, objtype) -> list[str]:
+        qengines = torch._C._supported_qengines()
+        return [_get_qengine_str(qe) for qe in qengines]
+
+    def __set__(self, obj, val) -> None:
+        raise RuntimeError("Assignment not supported")
+
+
+class QuantizedEngine(types.ModuleType):
+    def __init__(self, m, name):
+        super().__init__(name)
+        self.m = m
+
+    def __getattr__(self, attr):
+        return self.m.__getattribute__(attr)
+
+    engine = _QEngineProp()
+    supported_engines = _SupportedQEnginesProp()
+
+
+# This is the sys.modules replacement trick, see
+# https://stackoverflow.com/questions/2447353/getattr-on-a-module/7668273#7668273
+sys.modules[__name__] = QuantizedEngine(sys.modules[__name__], __name__)
+engine: str
+supported_engines: list[str]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/xeon/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/xeon/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/xeon/run_cpu.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/xeon/run_cpu.py
new file mode 100644
index 0000000000000000000000000000000000000000..b6b6bdf78991dcc140d9fedf2be2ea3ba6dedf74
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/xeon/run_cpu.py
@@ -0,0 +1,947 @@
+# mypy: allow-untyped-defs
+"""
+This is a script for launching PyTorch inference on Intel(R) Xeon(R) Scalable Processors with optimal configurations.
+
+Single instance inference, multi-instance inference are enabled.
+
+Note: term "instance" here doesn't refer to a cloud instance. This script is executed as a single process. It invokes
+multiple "instances" which are formed from multiple threads for each. "instance" is kind of group of threads in this
+context.
+
+Illustrated as below:
+
+::
+
+    +-----------------------------+----------------------+-------+
+    |            process          |        thread        | core  |
+    +=============================+======================+=======+
+    | torch.backends.xeon.run_cpu | instance 0: thread 0 |   0   |
+    |                             |             thread 1 |   1   |
+    |                             +----------------------+-------+
+    |                             | instance 1: thread 0 |   2   |
+    |                             |             thread 1 |   3   |
+    |                             +----------------------+-------+
+    |                             | ...                  |  ...  |
+    |                             +----------------------+-------+
+    |                             | instance N: thread 0 |   M   |
+    |                             |             thread 1 |  M+1  |
+    +-----------------------------+----------------------+-------+
+
+To get the peak performance on Intel(R) Xeon(R) Scalable Processors, the script optimizes the configuration of thread and memory
+management. For thread management, the script configures thread affinity and the preload of Intel OMP library.
+For memory management, it configures NUMA binding and preload optimized memory allocation library (e.g. tcmalloc, jemalloc).
+
+Environment variables that will be set by this script:
+
++------------------+-------------------------------------------------------------------------------------------------+
+| Environ Variable |                                             Value                                               |
++==================+=================================================================================================+
+|    LD_PRELOAD    | Depending on knobs you set, /libiomp5.so, /libjemalloc.so, /libtcmalloc.so might |
+|                  | be appended to LD_PRELOAD.                                                                      |
++------------------+-------------------------------------------------------------------------------------------------+
+|   KMP_AFFINITY   | If libiomp5.so is preloaded, KMP_AFFINITY could be set to "granularity=fine,compact,1,0".       |
++------------------+-------------------------------------------------------------------------------------------------+
+|   KMP_BLOCKTIME  | If libiomp5.so is preloaded, KMP_BLOCKTIME is set to "1".                                       |
++------------------+-------------------------------------------------------------------------------------------------+
+|  OMP_NUM_THREADS | value of ncores_per_instance                                                                    |
++------------------+-------------------------------------------------------------------------------------------------+
+|    MALLOC_CONF   | If libjemalloc.so is preloaded, MALLOC_CONF will be set to                                      |
+|                  | "oversize_threshold:1,background_thread:true,metadata_thp:auto".                                |
++------------------+-------------------------------------------------------------------------------------------------+
+
+*Note*: This script respects environment variables set preliminarily. I.e. If you set the environment variables
+mentioned above before running the script, the script will not overwrite the values in the script.
+
+How to use this module:
+~~~~~~~~~~~~~~~~~~~~~~~
+
+Single instance inference
+-------------------------
+
+1. Run single-instance inference on a single node with all CPU nodes.
+
+::
+
+   python -m torch.backends.xeon.run_cpu --throughput-mode script.py args
+
+2. Run single-instance inference on a single CPU node.
+
+::
+
+   python -m torch.backends.xeon.run_cpu --node-id 1 script.py args
+
+Multi-instance inference
+------------------------
+
+1. Multi-instance
+   By default this tool runs one process per node. If you want to set the instance numbers and core per instance,
+   --ninstances and  --ncores-per-instance should be set.
+
+::
+
+   python -m torch.backends.xeon.run_cpu -- python_script args
+
+   eg: on an Intel(R) Xeon(R) Scalable Processor with 14 instance, 4 cores per instance
+
+::
+
+   python -m torch.backends.xeon.run_cpu --ninstances 14 --ncores-per-instance 4 python_script args
+
+2. Run single-instance inference among multiple instances.
+   By default, runs all ninstances. If you want to independently run a single instance among ninstances, specify rank.
+
+   eg: run 0th instance on an Intel(R) Xeon(R) Scalable Processor with 2 instance (i.e., numactl -C 0-27)
+
+::
+
+   python -m torch.backends.xeon.run_cpu --ninstances 2 --rank 0 python_script args
+
+   eg: run 1st instance on an Intel(R) Xeon(R) Scalable Processor with 2 instance (i.e., numactl -C 28-55)
+
+::
+
+   python -m torch.backends.xeon.run_cpu --ninstances 2 --rank 1 python_script args
+
+   eg: run 0th instance on an Intel(R) Xeon(R) Scalable Processor with 2 instance, 2 cores per instance,
+   first four cores (i.e., numactl -C 0-1)
+
+::
+
+   python -m torch.backends.xeon.run_cpu --core-list "0, 1, 2, 3" --ninstances 2 --ncores-per-instance 2
+   --rank 0 python_script args
+
+3. To look up what optional arguments this module offers:
+
+::
+
+    python -m torch.backends.xeon.run_cpu --help
+
+Memory allocator
+----------------
+
+"--enable-tcmalloc" and "--enable-jemalloc" can be used to enable different memory allocator.
+
+"""
+
+import glob
+import logging
+import os
+import platform
+import re
+import subprocess
+import sys
+from argparse import ArgumentParser, RawTextHelpFormatter, REMAINDER
+from os.path import expanduser
+
+from torch.distributed.elastic.multiprocessing import (
+    DefaultLogsSpecs as _DefaultLogsSpecs,
+    start_processes,
+    Std,
+)
+
+
+format_str = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+logging.basicConfig(level=logging.INFO, format=format_str)
+logger = logging.getLogger(__name__)
+
+
+class _CPUinfo:
+    """Get CPU information, such as cores list and NUMA information."""
+
+    def __init__(self, test_input=""):
+        self.cpuinfo = []
+        if platform.system() in ["Windows", "Darwin"]:
+            raise RuntimeError(f"{platform.system()} is not supported!!!")
+        elif platform.system() == "Linux":
+            # Sample output of: `lscpu --parse=CPU,Core,Socket,Node`
+            #
+            # # The following is the parsable format, which can be fed to other
+            # # programs. Each different item in every column has an unique ID
+            # # starting from zero.
+            # # CPU,Core,Socket,Node
+            # 0,0,0,0
+            # 1,1,0,0
+            # ...
+            if test_input == "":
+                lscpu_cmd = ["lscpu", "--parse=CPU,Core,Socket,Node"]
+                lscpu_info = subprocess.check_output(
+                    lscpu_cmd, universal_newlines=True
+                ).split("\n")
+            else:
+                lscpu_info = test_input.split("\n")
+
+            # Get information about  cpu, core, socket and node
+            for line in lscpu_info:
+                pattern = r"^([\d]+,[\d]+,[\d]+,[\d]?)"
+                regex_out = re.search(pattern, line)
+                if regex_out:
+                    self.cpuinfo.append(regex_out.group(1).strip().split(","))
+
+            # physical cores := core column in lscpu output
+            #  logical cores :=  cPU column in lscpu output
+            self.node_nums = int(max(line[3] for line in self.cpuinfo)) + 1
+            self.node_physical_cores: list[list[int]] = []  # node_id is index
+            self.node_logical_cores: list[list[int]] = []  # node_id is index
+            self.physical_core_node_map = {}  # physical core to numa node id
+            self.logical_core_node_map = {}  # logical core to numa node id
+
+            for node_id in range(self.node_nums):
+                cur_node_physical_core = []
+                cur_node_logical_core = []
+                for cpuinfo in self.cpuinfo:
+                    nid = cpuinfo[3] if cpuinfo[3] != "" else "0"
+                    if node_id == int(nid):
+                        if int(cpuinfo[1]) not in cur_node_physical_core:
+                            cur_node_physical_core.append(int(cpuinfo[1]))
+                            self.physical_core_node_map[int(cpuinfo[1])] = int(node_id)
+                        cur_node_logical_core.append(int(cpuinfo[0]))
+                        self.logical_core_node_map[int(cpuinfo[0])] = int(node_id)
+                self.node_physical_cores.append(cur_node_physical_core)
+                self.node_logical_cores.append(cur_node_logical_core)
+
+    def _physical_core_nums(self):
+        return len(self.node_physical_cores) * len(self.node_physical_cores[0])
+
+    def _logical_core_nums(self):
+        return len(self.node_logical_cores) * len(self.node_logical_cores[0])
+
+    def get_node_physical_cores(self, node_id):
+        if node_id < 0 or node_id > self.node_nums - 1:
+            raise ValueError(
+                f"Invalid node id: {node_id}. Valid node ids: {list(range(len(self.node_physical_cores)))}"
+            )
+        return self.node_physical_cores[node_id]
+
+    def get_node_logical_cores(self, node_id):
+        if node_id < 0 or node_id > self.node_nums - 1:
+            raise ValueError(
+                f"Invalid node id: {node_id}. Valid node ids: {list(range(len(self.node_physical_cores)))}"
+            )
+        return self.node_logical_cores[node_id]
+
+    def get_all_physical_cores(self):
+        all_cores = []
+        for cores in self.node_physical_cores:
+            all_cores.extend(cores)
+        return all_cores
+
+    def get_all_logical_cores(self):
+        all_cores = []
+        for cores in self.node_logical_cores:
+            all_cores.extend(cores)
+        return all_cores
+
+    def numa_aware_check(self, core_list):
+        """
+        Check whether all cores in core_list are in the same NUMA node.
+
+        Cross NUMA will reduce performance.
+        We strongly advice to not use cores on different nodes.
+        """
+        cores_numa_map = self.logical_core_node_map
+        numa_ids = []
+        for core in core_list:
+            numa_id = cores_numa_map[core]
+            if numa_id not in numa_ids:
+                numa_ids.append(numa_id)
+        if len(numa_ids) > 1:
+            logger.warning(
+                "Numa Aware: cores:%s on different NUMA nodes:%s. To avoid \
+this behavior, please use --ncores-per-instance knob to make sure number of cores is divisible by --ncores-per-\
+instance. Alternatively, please use --skip-cross-node-cores knob.",
+                str(core_list),
+                str(numa_ids),
+            )
+        if len(numa_ids) == 0:
+            raise RuntimeError(
+                "invalid number of NUMA nodes; please make sure numa_ids >= 1"
+            )
+        return numa_ids
+
+
+class _Launcher:
+    r"""Class for launcher."""
+
+    msg_lib_notfound = (
+        f"Unable to find the {{0}} library file lib{{1}}.so in $CONDA_PREFIX/lib or $VIRTUAL_ENV/lib \
+or /.local/lib/ or /usr/local/lib/ or /usr/local/lib64/ or /usr/lib or /usr/lib64 or \
+{expanduser('~')}/.local/lib/ so the LD_PRELOAD environment variable will not be set."
+    )
+
+    def __init__(self) -> None:
+        self.cpuinfo = _CPUinfo()
+
+    def add_lib_preload(self, lib_type):
+        """Enable TCMalloc/JeMalloc/intel OpenMP."""
+        library_paths = []
+        if "CONDA_PREFIX" in os.environ:
+            library_paths.append(f"{os.environ['CONDA_PREFIX']}/lib")
+        if "VIRTUAL_ENV" in os.environ:
+            library_paths.append(f"{os.environ['VIRTUAL_ENV']}/lib")
+
+        library_paths += [
+            f"{expanduser('~')}/.local/lib",
+            "/usr/local/lib",
+            "/usr/local/lib64",
+            "/usr/lib",
+            "/usr/lib64",
+        ]
+
+        lib_find = False
+        lib_set = False
+        for item in os.getenv("LD_PRELOAD", "").split(":"):
+            if item.endswith(f"lib{lib_type}.so"):
+                lib_set = True
+                break
+        if not lib_set:
+            for lib_path in library_paths:
+                library_file = os.path.join(lib_path, f"lib{lib_type}.so")
+                matches = glob.glob(library_file)
+                if len(matches) > 0:
+                    ld_preloads = [f"{matches[0]}", os.getenv("LD_PRELOAD", "")]
+                    os.environ["LD_PRELOAD"] = os.pathsep.join(
+                        [p.strip(os.pathsep) for p in ld_preloads if p]
+                    )
+                    lib_find = True
+                    break
+        return lib_set or lib_find
+
+    def is_numactl_available(self):
+        numactl_available = False
+        try:
+            cmd = ["numactl", "-C", "0", "-m", "0", "hostname"]
+            r = subprocess.run(
+                cmd,
+                env=os.environ,
+                stdout=subprocess.DEVNULL,
+                stderr=subprocess.DEVNULL,
+                check=False,
+            )
+            if r.returncode == 0:
+                numactl_available = True
+        except Exception:
+            pass
+        return numactl_available
+
+    def set_memory_allocator(
+        self, enable_tcmalloc=True, enable_jemalloc=False, use_default_allocator=False
+    ):
+        """
+        Enable TCMalloc/JeMalloc with LD_PRELOAD and set configuration for JeMalloc.
+
+        By default, PTMalloc will be used for PyTorch, but TCMalloc and JeMalloc can get better
+        memory reuse and reduce page fault to improve performance.
+        """
+        if enable_tcmalloc and enable_jemalloc:
+            raise RuntimeError(
+                "Unable to enable TCMalloc and JEMalloc at the same time."
+            )
+
+        if enable_tcmalloc:
+            find_tc = self.add_lib_preload(lib_type="tcmalloc")
+            if not find_tc:
+                msg = f'{self.msg_lib_notfound} you can use "conda install -c conda-forge gperftools" to install {{0}}'
+                logger.warning(msg.format("TCmalloc", "tcmalloc"))  # noqa: G001
+            else:
+                logger.info("Use TCMalloc memory allocator")
+
+        elif enable_jemalloc:
+            find_je = self.add_lib_preload(lib_type="jemalloc")
+            if not find_je:
+                msg = f'{self.msg_lib_notfound} you can use "conda install -c conda-forge jemalloc" to install {{0}}'
+                logger.warning(msg.format("Jemalloc", "jemalloc"))  # noqa: G001
+            else:
+                logger.info("Use JeMalloc memory allocator")
+                self.set_env(
+                    "MALLOC_CONF",
+                    "oversize_threshold:1,background_thread:true,metadata_thp:auto",
+                )
+
+        elif use_default_allocator:
+            pass
+
+        else:
+            find_tc = self.add_lib_preload(lib_type="tcmalloc")
+            if find_tc:
+                logger.info("Use TCMalloc memory allocator")
+                return
+            find_je = self.add_lib_preload(lib_type="jemalloc")
+            if find_je:
+                logger.info("Use JeMalloc memory allocator")
+                return
+            logger.warning(
+                """Neither TCMalloc nor JeMalloc is found in $CONDA_PREFIX/lib or $VIRTUAL_ENV/lib
+                            or /.local/lib/ or /usr/local/lib/ or /usr/local/lib64/ or /usr/lib or /usr/lib64 or
+                           %s/.local/lib/ so the LD_PRELOAD environment variable will not be set.
+                           This may drop the performance""",
+                expanduser("~"),
+            )
+
+    def log_env_var(self, env_var_name=""):
+        if env_var_name in os.environ:
+            logger.info("%s=%s", env_var_name, os.environ[env_var_name])
+
+    def set_env(self, env_name, env_value):
+        if not env_value:
+            logger.warning("%s is None", env_name)
+        if env_name not in os.environ:
+            os.environ[env_name] = env_value
+        elif os.environ[env_name] != env_value:
+            logger.warning(
+                "Overriding value with the one set in environment variable: %s. \
+Value applied: %s. Value ignored: %s",
+                env_name,
+                os.environ[env_name],
+                env_value,
+            )
+        self.log_env_var(env_name)
+
+    # set_kmp_affinity is used to control whether to set KMP_AFFINITY or not.
+    # In scenario that use all cores on all nodes, including logical cores, setting KMP_AFFINITY disables logical cores.
+    # In this case, KMP_AFFINITY should not be set.
+    def set_multi_thread_and_allocator(
+        self,
+        ncores_per_instance,
+        disable_iomp=False,
+        set_kmp_affinity=True,
+        enable_tcmalloc=True,
+        enable_jemalloc=False,
+        use_default_allocator=False,
+    ):
+        """
+        Set multi-thread configuration and enable Intel openMP and TCMalloc/JeMalloc.
+
+        By default, GNU openMP and PTMalloc are used in PyTorch. but Intel openMP and TCMalloc/JeMalloc are better alternatives
+        to get performance benefit.
+        """
+        self.set_memory_allocator(
+            enable_tcmalloc, enable_jemalloc, use_default_allocator
+        )
+        self.set_env("OMP_NUM_THREADS", str(ncores_per_instance))
+        if not disable_iomp:
+            find_iomp = self.add_lib_preload(lib_type="iomp5")
+            if not find_iomp:
+                msg = f'{self.msg_lib_notfound} you can use "conda install mkl" to install {{0}}'
+                logger.warning(msg.format("iomp", "iomp5"))  # noqa: G001
+            else:
+                logger.info("Using Intel OpenMP")
+                if set_kmp_affinity:
+                    self.set_env("KMP_AFFINITY", "granularity=fine,compact,1,0")
+                self.set_env("KMP_BLOCKTIME", "1")
+        self.log_env_var("LD_PRELOAD")
+
+    r"""
+     Launcher for single instance and multi-instance
+     """
+
+    def launch(self, args):
+        cores = []
+        set_kmp_affinity = True
+        enable_taskset = False
+        if args.core_list:  # user specify what cores will be used by params
+            cores = [int(x) for x in args.core_list.split(",")]
+            if args.ncores_per_instance == -1:
+                raise RuntimeError(
+                    'please specify the "--ncores-per-instance" if you have pass the --core-list params'
+                )
+            elif (
+                args.ninstances > 1
+                and args.ncores_per_instance * args.ninstances < len(cores)
+            ):
+                logger.warning(
+                    "only first %s cores will be used, \
+but you specify %s cores in core_list",
+                    args.ncores_per_instance * args.ninstances,
+                    len(cores),
+                )
+            else:
+                args.ninstances = len(cores) // args.ncores_per_instance
+
+        else:
+            if args.use_logical_core:
+                if args.node_id != -1:
+                    cores = self.cpuinfo.get_node_logical_cores(args.node_id)
+                else:
+                    cores = self.cpuinfo.get_all_logical_cores()
+                    # When using all cores on all nodes, including logical cores,
+                    # setting KMP_AFFINITY disables logical cores. Thus, KMP_AFFINITY should not be set.
+                    set_kmp_affinity = False
+            else:
+                if args.node_id != -1:
+                    cores = self.cpuinfo.get_node_physical_cores(args.node_id)
+                else:
+                    cores = self.cpuinfo.get_all_physical_cores()
+            if (
+                not args.multi_instance
+                and args.ninstances == -1
+                and args.ncores_per_instance == -1
+            ):
+                args.ninstances = 1
+                args.ncores_per_instance = len(cores)
+            elif (
+                args.multi_instance
+                and args.ninstances == -1
+                and args.ncores_per_instance == -1
+            ):
+                args.throughput_mode = True
+            elif args.ncores_per_instance == -1 and args.ninstances != -1:
+                if args.ninstances > len(cores):
+                    raise RuntimeError(
+                        f"there are {len(cores)} total cores but you specify {args.ninstances} ninstances; \
+please make sure ninstances <= total_cores)"
+                    )
+                else:
+                    args.ncores_per_instance = len(cores) // args.ninstances
+            elif args.ncores_per_instance != -1 and args.ninstances == -1:
+                if not args.skip_cross_node_cores:
+                    args.ninstances = len(cores) // args.ncores_per_instance
+                else:
+                    ncore_per_node = len(self.cpuinfo.node_physical_cores[0])
+                    num_leftover_cores = ncore_per_node % args.ncores_per_instance
+                    if args.ncores_per_instance > ncore_per_node:
+                        # too many ncores_per_instance to skip cross-node cores
+                        logger.warning(
+                            "there are %s core(s) per socket, but you specify %s ncores_per_instance and \
+skip_cross_node_cores. Please make sure --ncores-per-instance < core(s) per \
+socket",
+                            ncore_per_node,
+                            args.ncores_per_instance,
+                        )
+                        sys.exit(-1)
+                    elif num_leftover_cores == 0:
+                        # aren't any cross-node cores
+                        logger.info(
+                            "--skip-cross-node-cores is set, but there are no cross-node cores."
+                        )
+                        args.ninstances = len(cores) // args.ncores_per_instance
+                    else:
+                        # skip cross-node cores
+                        if args.ninstances != -1:
+                            logger.warning(
+                                "--skip-cross-node-cores is exclusive to --ninstances. --ninstances \
+won't take effect even if it is set explicitly."
+                            )
+
+                        i = 1
+                        leftover_cores = set()
+                        while ncore_per_node * i <= len(cores):
+                            leftover_cores.update(
+                                cores[
+                                    ncore_per_node * i
+                                    - num_leftover_cores : ncore_per_node * i
+                                ]
+                            )
+                            i += 1
+                        cores = list(set(cores) - leftover_cores)
+                        assert len(cores) % args.ncores_per_instance == 0
+                        args.ninstances = len(cores) // args.ncores_per_instance
+            else:
+                if args.ninstances * args.ncores_per_instance > len(cores):
+                    raise RuntimeError(
+                        "Please make sure ninstances * ncores_per_instance <= total_cores"
+                    )
+            if args.latency_mode:
+                logger.warning(
+                    "--latency-mode is exclusive to --ninstances, --ncores-per-instance, --node-id and \
+--use-logical-core. They won't take effect even they are set explicitly."
+                )
+                args.ncores_per_instance = 4
+                cores = self.cpuinfo.get_all_physical_cores()
+                args.ninstances = len(cores) // args.ncores_per_instance
+
+            if args.throughput_mode:
+                logger.warning(
+                    "--throughput-mode is exclusive to --ninstances, --ncores-per-instance, --node-id and \
+--use-logical-core. They won't take effect even they are set explicitly."
+                )
+                args.ninstances = self.cpuinfo.node_nums
+                cores = self.cpuinfo.get_all_physical_cores()
+                args.ncores_per_instance = len(cores) // args.ninstances
+
+        if args.ninstances > 1 and args.rank != -1:
+            logger.info(
+                "assigning %s cores for instance %s",
+                args.ncores_per_instance,
+                args.rank,
+            )
+
+        if not args.disable_numactl:
+            numactl_available = self.is_numactl_available()
+            if not numactl_available:
+                if not args.disable_taskset:
+                    logger.warning(
+                        "Core binding with numactl is not available. Disabling numactl and using taskset instead. \
+                    This may affect performance in multi-socket system; please use numactl if memory binding is needed."
+                    )
+                    args.disable_numactl = True
+                    enable_taskset = True
+                else:
+                    logger.warning(
+                        "Core binding with numactl is not available, and --disable_taskset is set. \
+                    Please unset --disable_taskset to use taskset instead of numactl."
+                    )
+                    sys.exit(-1)
+
+        if not args.disable_taskset:
+            enable_taskset = True
+
+        self.set_multi_thread_and_allocator(
+            args.ncores_per_instance,
+            args.disable_iomp,
+            set_kmp_affinity,
+            args.enable_tcmalloc,
+            args.enable_jemalloc,
+            args.use_default_allocator,
+        )
+        entrypoint = ""
+        launch_args = {}
+        launch_envs: dict[int, dict] = {}
+        launch_tee = {}
+        # check whether is launched from torchrun with --nproc-per-node 
+        local_size = int(os.environ.get("LOCAL_WORLD_SIZE", 1))
+        local_rank = int(os.environ.get("LOCAL_RANK", 0))
+        for i in range(args.ninstances):
+            cmd = []
+            cur_process_cores = ""
+            if not args.disable_numactl or enable_taskset:
+                if not args.disable_numactl:
+                    cmd = ["numactl"]
+                elif enable_taskset:
+                    cmd = ["taskset"]
+                cores = sorted(cores)
+                if (
+                    args.rank == -1
+                ):  # sequentially assign ncores_per_instance to ninstances
+                    core_list = cores[
+                        i * args.ncores_per_instance : (i + 1)
+                        * args.ncores_per_instance
+                    ]
+                else:  # assign ncores_per_instance from rank
+                    core_list = cores[
+                        args.rank * args.ncores_per_instance : (args.rank + 1)
+                        * args.ncores_per_instance
+                    ]
+
+                core_ranges: list[dict] = []
+                if local_size > 1:
+                    total_num_cores = len(core_list)
+                    cores_per_rank = total_num_cores // local_size
+                    assert cores_per_rank >= 1, (
+                        "At least one core needs to be assigned to each rank"
+                    )
+                    core_list = core_list[
+                        cores_per_rank * local_rank : cores_per_rank * (local_rank + 1)
+                    ]
+                for core in core_list:
+                    if len(core_ranges) == 0:
+                        range_elem = {"start": core, "end": core}
+                        core_ranges.append(range_elem)
+                    else:
+                        if core - core_ranges[-1]["end"] == 1:
+                            core_ranges[-1]["end"] = core
+                        else:
+                            range_elem = {"start": core, "end": core}
+                            core_ranges.append(range_elem)
+                for r in core_ranges:
+                    cur_process_cores = f"{cur_process_cores}{r['start']}-{r['end']},"
+                cur_process_cores = cur_process_cores[:-1]
+                if not args.disable_numactl:
+                    numa_params = f"-C {cur_process_cores} "
+                    numa_ids = ",".join(
+                        [
+                            str(numa_id)
+                            for numa_id in self.cpuinfo.numa_aware_check(core_list)
+                        ]
+                    )
+                    numa_params += f"-m {numa_ids}"
+                    cmd.extend(numa_params.split())
+                elif enable_taskset:
+                    taskset_params = f"-c {cur_process_cores} "
+                    cmd.extend(taskset_params.split())
+            with_python = not args.no_python
+            if with_python:
+                cmd.append(sys.executable)
+                cmd.append("-u")
+            if args.module:
+                cmd.append("-m")
+            cmd.append(args.program)
+            cmd.extend(args.program_args)
+            cmd_s = " ".join(cmd)
+            logger.info(cmd_s)
+            if entrypoint == "":
+                entrypoint = cmd[0]
+            del cmd[0]
+            launch_args[i] = tuple(cmd)
+            launch_envs[i] = {}
+            launch_tee[i] = Std.ALL
+
+            if args.rank != -1:  # launches single instance, rank, only
+                break
+
+        ctx = start_processes(
+            name=args.log_file_prefix,
+            entrypoint=entrypoint,
+            args=launch_args,
+            envs=launch_envs,
+            logs_specs=_DefaultLogsSpecs(log_dir=args.log_path, tee=launch_tee),
+        )
+        ctx.wait()
+
+
+def _add_memory_allocator_params(parser):
+    group = parser.add_argument_group("Memory Allocator Parameters")
+    # allocator control
+    group.add_argument(
+        "--enable-tcmalloc",
+        "--enable_tcmalloc",
+        action="store_true",
+        default=False,
+        help="Enable tcmalloc allocator",
+    )
+    group.add_argument(
+        "--enable-jemalloc",
+        "--enable_jemalloc",
+        action="store_true",
+        default=False,
+        help="Enable jemalloc allocator",
+    )
+    group.add_argument(
+        "--use-default-allocator",
+        "--use_default_allocator",
+        action="store_true",
+        default=False,
+        help="Use default memory allocator",
+    )
+
+
+def _add_multi_instance_params(parser):
+    group = parser.add_argument_group("Multi-instance Parameters")
+    # multi-instance control
+    group.add_argument(
+        "--ncores-per-instance",
+        "--ncores_per_instance",
+        metavar="\b",
+        default=-1,
+        type=int,
+        help="Cores per instance",
+    )
+    group.add_argument(
+        "--ninstances",
+        metavar="\b",
+        default=-1,
+        type=int,
+        help="For multi-instance, you should give the cores number you used for per instance.",
+    )
+    group.add_argument(
+        "--skip-cross-node-cores",
+        "--skip_cross_node_cores",
+        action="store_true",
+        default=False,
+        help="If specified --ncores-per-instance, skips cross-node cores.",
+    )
+    group.add_argument(
+        "--rank",
+        metavar="\b",
+        default="-1",
+        type=int,
+        help="Specify instance index to assign ncores_per_instance for rank; \
+otherwise ncores_per_instance will be assigned sequentially to ninstances. Please refer to \
+https://github.com/intel/intel-extension-for-pytorch/blob/master/docs/tutorials/performance_tuning/launch_script.md",
+    )
+    group.add_argument(
+        "--latency-mode",
+        "--latency_mode",
+        action="store_true",
+        default=False,
+        help="By default 4 core per instance and use all physical cores",
+    )
+    group.add_argument(
+        "--throughput-mode",
+        "--throughput_mode",
+        action="store_true",
+        default=False,
+        help="By default one instance per node and use all physical cores",
+    )
+    group.add_argument(
+        "--node-id",
+        "--node_id",
+        metavar="\b",
+        default=-1,
+        type=int,
+        help="node id for multi-instance, by default all nodes will be used",
+    )
+    group.add_argument(
+        "--use-logical-core",
+        "--use_logical_core",
+        action="store_true",
+        default=False,
+        help="Whether only use physical cores",
+    )
+    group.add_argument(
+        "--disable-numactl",
+        "--disable_numactl",
+        action="store_true",
+        default=False,
+        help="Disable numactl",
+    )
+    group.add_argument(
+        "--disable-taskset",
+        "--disable_taskset",
+        action="store_true",
+        default=False,
+        help="Disable taskset",
+    )
+    group.add_argument(
+        "--core-list",
+        "--core_list",
+        metavar="\b",
+        default=None,
+        type=str,
+        help='Specify the core list as "core_id, core_id, ....", otherwise, all the cores will be used.',
+    )
+    group.add_argument(
+        "--log-path",
+        "--log_path",
+        metavar="\b",
+        default="",
+        type=str,
+        help="The log file directory. Default path is "
+        ", which means disable logging to files.",
+    )
+    group.add_argument(
+        "--log-file-prefix",
+        "--log_file_prefix",
+        metavar="\b",
+        default="run",
+        type=str,
+        help="log file prefix",
+    )
+
+
+def _add_kmp_iomp_params(parser):
+    group = parser.add_argument_group("IOMP Parameters")
+    group.add_argument(
+        "--disable-iomp",
+        "--disable_iomp",
+        action="store_true",
+        default=False,
+        help="By default, we use Intel OpenMP and libiomp5.so will be add to LD_PRELOAD",
+    )
+
+
+def create_args(parser=None):
+    """
+    Parse the command line options.
+
+    @retval ArgumentParser
+    """
+    # pyrefly: ignore [missing-attribute]
+    parser.add_argument(
+        "--multi-instance",
+        "--multi_instance",
+        action="store_true",
+        default=False,
+        help="Enable multi-instance, by default one instance per node",
+    )
+
+    # pyrefly: ignore [missing-attribute]
+    parser.add_argument(
+        "-m",
+        "--module",
+        default=False,
+        action="store_true",
+        help="Changes each process to interpret the launch script "
+        "as a python module, executing with the same behavior as"
+        '"python -m".',
+    )
+
+    # pyrefly: ignore [missing-attribute]
+    parser.add_argument(
+        "--no-python",
+        "--no_python",
+        default=False,
+        action="store_true",
+        help='Do not prepend the --program script with "python" - just exec '
+        "it directly. Useful when the script is not a Python script.",
+    )
+
+    _add_memory_allocator_params(parser)
+    _add_kmp_iomp_params(parser)
+
+    _add_multi_instance_params(parser)
+    # positional
+    # pyrefly: ignore [missing-attribute]
+    parser.add_argument(
+        "program",
+        type=str,
+        help="The full path to the program/script to be launched. "
+        "followed by all the arguments for the script",
+    )
+
+    # rest from the training program
+    # pyrefly: ignore [missing-attribute]
+    parser.add_argument("program_args", nargs=REMAINDER)
+
+
+def main(args):
+    env_before = set(os.environ.keys())
+    if platform.system() in ["Windows", "Darwin"]:
+        raise RuntimeError(f"{platform.system()} is not supported!!!")
+
+    if args.log_path:
+        os.makedirs(args.log_path, exist_ok=True)
+    else:
+        args.log_path = os.devnull
+
+    if args.latency_mode and args.throughput_mode:
+        raise RuntimeError(
+            "Either args.latency_mode or args.throughput_mode should be set"
+        )
+
+    if not args.no_python and not args.program.endswith(".py"):
+        raise RuntimeError(
+            'For non Python script, you should use "--no-python" parameter.'
+        )
+
+    # Verify LD_PRELOAD
+    if "LD_PRELOAD" in os.environ:
+        lst_valid = []
+        tmp_ldpreload = os.environ["LD_PRELOAD"]
+        for item in tmp_ldpreload.split(":"):
+            matches = glob.glob(item)
+            if len(matches) > 0:
+                lst_valid.append(item)
+            else:
+                logger.warning("%s doesn't exist. Removing it from LD_PRELOAD.", item)
+        if len(lst_valid) > 0:
+            os.environ["LD_PRELOAD"] = ":".join(lst_valid)
+        else:
+            os.environ["LD_PRELOAD"] = ""
+
+    launcher = _Launcher()
+    launcher.launch(args)
+    for x in sorted(set(os.environ.keys()) - env_before):
+        logger.debug("%s=%s", x, os.environ[x])
+
+
+if __name__ == "__main__":
+    parser = ArgumentParser(
+        description="This is a script for launching PyTorch inference on Intel(R) Xeon(R) Scalable "
+        "Processors with optimal configurations. Single instance inference, "
+        "multi-instance inference are enable. To get the peak performance on Intel(R) "
+        "Xeon(R) Scalable Processors, the script optimizes the configuration "
+        "of thread and memory management. For thread management, the script configures thread "
+        "affinity and the preload of Intel OMP library. For memory management, it configures "
+        "NUMA binding and preload optimized memory allocation library (e.g. tcmalloc, jemalloc) "
+        "\n################################# Basic usage ############################# \n"
+        "\n 1. single instance\n"
+        "\n   >>> python -m torch.backends.xeon.run_cpu python_script args \n"
+        "\n2. multi-instance \n"
+        "\n   >>> python -m torch.backends.xeon.run_cpu --ninstances xxx "
+        "--ncores-per-instance xx python_script args\n"
+        "\n############################################################################# \n",
+        formatter_class=RawTextHelpFormatter,
+    )
+    create_args(parser)
+    args = parser.parse_args()
+    main(args)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/xnnpack/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/xnnpack/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..31e69876927d01878a9d1cb836d72fd14adf95e9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/backends/xnnpack/__init__.py
@@ -0,0 +1,29 @@
+# mypy: allow-untyped-defs
+import sys
+import types
+
+import torch
+
+
+class _XNNPACKEnabled:
+    def __get__(self, obj, objtype):
+        return torch._C._is_xnnpack_enabled()
+
+    def __set__(self, obj, val):
+        raise RuntimeError("Assignment not supported")
+
+
+class XNNPACKEngine(types.ModuleType):
+    def __init__(self, m, name):
+        super().__init__(name)
+        self.m = m
+
+    def __getattr__(self, attr):
+        return self.m.__getattribute__(attr)
+
+    enabled = _XNNPACKEnabled()
+
+
+# This is the sys.modules replacement trick, see
+# https://stackoverflow.com/questions/2447353/getattr-on-a-module/7668273#7668273
+sys.modules[__name__] = XNNPACKEngine(sys.modules[__name__], __name__)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/bin/torch_shm_manager b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/bin/torch_shm_manager
new file mode 100644
index 0000000000000000000000000000000000000000..7870cec348094e49fe3341f835b4fda177de810b
Binary files /dev/null and b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/bin/torch_shm_manager differ
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/compiler/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/compiler/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..442cd7d765b89136483e733f010c2a82d0fff18f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/compiler/__init__.py
@@ -0,0 +1,691 @@
+# mypy: allow-untyped-defs
+import io
+from collections.abc import Callable
+from typing import Any, Optional, TYPE_CHECKING, TypeVar, Union
+from typing_extensions import ParamSpec
+
+import torch
+
+from . import config
+
+
+if TYPE_CHECKING:
+    from ._cache import CacheInfo
+
+
+__all__ = [
+    "compile",
+    "config",
+    "assume_constant_result",
+    "reset",
+    "allow_in_graph",
+    "substitute_in_graph",
+    "list_backends",
+    "disable",
+    "set_stance",
+    "set_enable_guard_collectives",
+    "cudagraph_mark_step_begin",
+    "load_compiled_function",
+    "wrap_numpy",
+    "is_compiling",
+    "is_dynamo_compiling",
+    "is_exporting",
+    "save_cache_artifacts",
+    "load_cache_artifacts",
+    "skip_guard_on_inbuilt_nn_modules_unsafe",
+    "skip_guard_on_all_nn_modules_unsafe",
+    "keep_tensor_guards_unsafe",
+    "skip_guard_on_globals_unsafe",
+    "skip_all_guards_unsafe",
+    "nested_compile_region",
+]
+
+
+_P = ParamSpec("_P")
+_R = TypeVar("_R")
+FuncType = Callable[..., Any]
+F = TypeVar("F", bound=FuncType)
+
+
+def compile(*args, **kwargs):
+    """
+    See :func:`torch.compile` for details on the arguments for this function.
+    """
+    # pyrefly: ignore [not-iterable]
+    return torch.compile(*args, **kwargs)
+
+
+def reset() -> None:
+    """
+    This function clears all compilation caches and restores the system to its initial state.
+    It is recommended to call this function, especially after using operations like `torch.compile(...)`
+    to ensure a clean state before another unrelated compilation
+    """
+    import torch._dynamo
+
+    torch._dynamo.reset()
+
+
+def allow_in_graph(fn):
+    """
+    Tells the compiler frontend (Dynamo) to skip symbolic introspection of the function
+    and instead directly write it to the graph when encountered.
+
+    If you are using :func:`torch.compile` (with backend="inductor" (the default)), or
+    :func:`torch.export.export`, and trying to black-box a Python function throughout
+    all tracing, do not use this API.
+    Instead, please create a custom operator (see `PyTorch Custom Operators Landing Page
+    `_)
+
+    .. warning::
+
+        If you're a typical torch.compile user (e.g. you're applying torch.compile to
+        a model to make it run faster), you probably don't want to use this function.
+        :func:`allow_in_graph` is a footgun because it skips the compiler frontend
+        (Dynamo) that is responsible for doing safety checks (graph breaks, handling
+        closures, etc). Incorrect usage will lead to difficult-to-debug silent
+        incorrectness issues.
+
+    Given a Python function with no allow_in_graph decorator, regular execution
+    of torch.compile traces through the function. :func:`allow_in_graph` changes
+    it so that the frontend does not trace inside the function, but the compiler
+    backend still traces through it. Compare this to custom operators, which
+    treats a function as a black box throughout the torch.compile stack. The following
+    table compares these mechanisms.
+
+    +------------------------+-----------------------+--------------------------------+
+    | Mechanism              | Frontend (Dynamo)     | Backend (AOTAutograd+Inductor) |
+    +========================+=======================+================================+
+    | no decorator           | trace inside          | trace inside                   |
+    +------------------------+-----------------------+--------------------------------+
+    | allow_in_graph         | opaque callable       | trace inside                   |
+    +------------------------+-----------------------+--------------------------------+
+    | custom op              | opaque callable       | opaque callable                |
+    +------------------------+-----------------------+--------------------------------+
+
+    One common use case for :func:`allow_in_graph()` is as an escape hatch for the compiler
+    frontend: if you know the function works w.r.t. to the downstream components of the
+    compilation stack (AOTAutograd and Inductor) but there is a Dynamo bug that prevents it from
+    symbolically introspecting the function properly (or if your code is in C/C++ and
+    therefore cannot be introspected with Dynamo), then one can decorate said function
+    with :func:`allow_in_graph` to bypass Dynamo.
+
+    We require that ``fn`` adhere to the following restrictions. Failure to adhere
+    results in undefined behavior:
+
+    - The inputs to ``fn`` must be Proxy-able types in the FX graph. Valid types include:
+      Tensor/int/bool/float/None/List[Tensor?]/List[int?]/List[float?]
+      Tuple[Tensor?, ...]/Tuple[int?, ...]/Tuple[float?, ...]/torch.dtype/torch.device
+    - The outputs to ``fn`` must be Proxy-able types in the FX graph (see previous bullet)
+    - all Tensors used inside of ``fn`` must be passed directly as inputs to ``fn``
+      (as opposed to being captured variables).
+
+    Args:
+        fn: A callable representing the function to be included in the graph.
+            If ``fn`` is a list or tuple of callables it recursively applies
+            :func:`allow_in_graph()` to each function and returns a new list or
+            tuple containing the modified functions.
+
+    Example::
+
+        torch.compiler.allow_in_graph(my_custom_function)
+
+
+        @torch.compile(...)
+        def fn(x):
+            x = torch.add(x, 1)
+            x = my_custom_function(x)
+            x = torch.add(x, 1)
+            return x
+
+
+        fn(...)
+
+    Will capture a single graph containing ``my_custom_function()``.
+
+    """
+    import torch._dynamo
+
+    return torch._dynamo.allow_in_graph(fn)
+
+
+def substitute_in_graph(
+    original_fn: Callable[_P, _R],
+    *,
+    can_constant_fold_through: bool = False,
+    skip_signature_check: bool = False,
+) -> Callable[[Callable[_P, _R]], Callable[_P, _R]]:
+    """
+    Register a polyfill handler for a function, usually a C function from the C extension, to be
+    used in place of the original function when inlining the original function in the graph.
+
+    .. note::
+
+        The polyfill handler is only used when inlining the original function. It is not used when
+        the original function is called directly. In the eager mode, the decorated function calls
+        the performant C function rather than the polyfill handler.
+
+    The polyfill handler is a function that will be called in place of the original function when
+    inlining the original function. The polyfill handler should have the same signature and the same
+    behavior as the original function.
+
+    Args:
+        original_fn (callable): The original function, usually a C function, to register a polyfill
+            handler for.
+        can_constant_fold_through (bool, optional): Whether the polyfill handler can be constant
+            folded through. That is, if the polyfill handler is a pure function and its arguments
+            are constant, the result of the polyfill handler can be constant folded during the
+            compilation. Defaults to ``False``.
+        skip_signature_check (bool, optional): Whether to skip the signature check between the
+            original function and the polyfill handler. Defaults to ``False``.
+
+    Returns:
+        A decorator that registers the polyfill handler for the original function.
+
+    Example::
+
+        >>> import operator
+        >>> operator.indexOf([1, 2, 3, 4, 5], 3)
+        2
+        >>> torch.compile(operator.indexOf, fullgraph=True)([1, 2, 3, 4, 5], 3)
+        ... # xdoctest: +SKIP("Long tracebacks")
+        Traceback (most recent call last):
+        ...
+        torch._dynamo.exc.Unsupported: ...
+
+        >>> @torch.compiler.substitute_in_graph(operator.indexOf)
+        ... def indexOf(a, b, /):
+        ...     for i, item in enumerate(a):
+        ...         if item is b or item == b:
+        ...             return i
+        ...     raise ValueError("sequence.index(x): x not in sequence")
+        >>>
+        >>> torch.compile(operator.indexOf, fullgraph=True)([1, 2, 3, 4, 5], 3)
+        2
+    """
+    import torch._dynamo
+
+    return torch._dynamo.substitute_in_graph(
+        original_fn,
+        can_constant_fold_through=can_constant_fold_through,
+        skip_signature_check=skip_signature_check,
+    )
+
+
+def list_backends(exclude_tags=("debug", "experimental")) -> list[str]:
+    """
+    Return valid strings that can be passed to `torch.compile(..., backend="name")`.
+
+    Args:
+        exclude_tags(optional): A tuple of strings representing tags to exclude.
+    """
+    import torch._dynamo
+
+    return torch._dynamo.list_backends(exclude_tags)
+
+
+def assume_constant_result(fn):
+    """
+    This function is used to mark a function `fn` as having a constant result.
+    This allows the compiler to optimize away your function.
+    Returns The same function `fn`
+
+    Args:
+        fn: The function to be marked as having a constant result.
+
+    .. warning::
+        `assume_constant_result` can if invalid cause safety and soundness issues, :func:`torch.compile`
+        will not attempt to validate whether the constant assumption is true or not
+
+    """
+    import torch._dynamo
+
+    return torch._dynamo.assume_constant_result(fn)
+
+
+def disable(fn=None, recursive=True, *, reason=None):
+    """
+    This function provides a decorator to disable compilation on a function.
+    It also provides the option of recursively disabling called functions.
+
+    Args:
+        fn (optional): The function to disable
+        recursive (optional): A boolean value indicating whether the disabling should be recursive.
+        reason (optional): A string value indicating the reason for disabling the function.
+    """
+    import torch._dynamo
+
+    return torch._dynamo.disable(fn, recursive, reason=reason)
+
+
+def set_stance(
+    stance: str = "default",
+    *,
+    skip_guard_eval_unsafe: bool = False,
+    force_backend: Union[str, Callable[..., Any], None] = None,
+):
+    """
+    Set the current stance of the compiler.
+    Can be used as a function, context manager, or decorator.
+    Do not use this function inside a `torch.compile` region - an error will be raised otherwise.
+
+    .. code-block:: python
+
+        @torch.compile
+        def foo(x): ...
+
+
+        @torch.compiler.set_stance("force_eager")
+        def bar():
+            # will not be compiled
+            foo(...)
+
+
+        bar()
+
+        with torch.compiler.set_stance("force_eager"):
+            # will also not be compiled
+            foo(...)
+
+        torch.compiler.set_stance("force_eager")
+        # will also not be compiled
+        foo(...)
+        torch.compiler.set_stance("default")
+
+        # will be compiled
+        foo(...)
+
+    Args:
+        stance: The stance to set the compiler to. Valid values are:
+
+            - "default": The default stance, used for normal compilation.
+            - "force_eager": Ignore all `torch.compile` directives.
+            - "eager_on_recompile": Run code eagerly when a recompile is necessary.
+              If there is cached compiled code valid for the input, it will still be used.
+            - "fail_on_recompile": Raise an error when recompiling a function.
+            - "eager_then_compile": Run the first invocation in eager mode, then compile on
+              subsequent calls. This is beneficial for dynamic shapes as it allows inferring
+              dynamism from the first two invocations instead of wasting a static compile on
+              the first invocation.
+            - "aot_eager_then_compile": Run the first invocation with AOT eager to get memory
+              benefits from activation checkpointing, then compile on subsequent calls. Like
+              eager_then_compile, this improves handling of dynamic shapes by avoiding an
+              initial static compile.
+
+
+        skip_guard_eval_unsafe: A flag to run only differentiating guards.
+            CAUTION - This flag is unsafe and should only be used if your setup
+            meets the following conditions.
+
+            torch.compile uses a guard system to support recompilations and
+            choose which compiled artifact to run at runtime.  These guards,
+            though efficient, add some overhead, which may impact performance in
+            scenarios where you need to optimize for minimal guard processing
+            time.  This API enables you to disable guard evaluation, assuming
+            that you have warmed up the compiled model with a sufficient variety
+            of inputs. This assumption means that, after the warmup phase, no
+            further recompilations will be necessary.  If this assumption fails,
+            there is a risk of silently producing incorrect results (hence the
+            term "unsafe" in the API name).
+
+        force_backend: If `stance` is "default", this argument can be used to force `torch.compile`
+            to use a specific backend. Otherwise, an error is raised.
+    """
+    import torch._dynamo
+
+    return torch._dynamo.set_stance(
+        stance,
+        skip_guard_eval_unsafe=skip_guard_eval_unsafe,
+        force_backend=force_backend,
+    )
+
+
+# forbid in graph
+set_stance._dynamo_forbidden = True  # type: ignore[attr-defined]
+
+
+def set_enable_guard_collectives(enabled: bool):
+    """
+    Enables use of collectives *during* guard evaluation to synchronize behavior
+    across ranks.  This is expensive: we have to issue a collective every time
+    we enter a compiled code region, even if no rank actually would need to
+    compile.  This can help prevent NCCL hangs by ensuring that we never have a
+    situation where one rank starts recompiling while other ranks don't compile;
+    it is especially useful in conjunction with enable_compiler_collectives
+    where such a situation would immediately cause a hang (as it is necessary
+    for all ranks to compile at the same time to run compiler collectives).  Like
+    compiler collectives, you can only run this on SPMD programs; you will hang
+    otherwise.  Note that a guard collective is only issued if there is any
+    compiled code to guard on; if this the first time we encounter a frame or
+    the frame is skipped, we don't issue collectives.
+
+    Returns the previous setting of enabled.
+    """
+    from torch._C._dynamo.eval_frame import set_guard_complete_hook  # noqa: F401
+    from torch._dynamo.eval_frame import guard_collectives_hook
+
+    if enabled:
+        return set_guard_complete_hook(guard_collectives_hook) is not None  # type: ignore[arg-type]
+    else:
+        return set_guard_complete_hook(None) is not None
+
+
+set_enable_guard_collectives._dynamo_forbidden = True  # type: ignore[attr-defined]
+
+
+def cudagraph_mark_step_begin():
+    """
+    Indicates that a new iteration of inference or training is about to begin.
+
+    CUDA Graphs will free tensors of a prior iteration. A new iteration is started on each invocation of
+    torch.compile, so long as there is not a pending backward that has not been called.
+
+    If that heuristic is wrong, such as in the following example, manually mark it with this api.
+
+    .. code-block:: python
+
+        @torch.compile(mode="reduce-overhead")
+        def rand_foo():
+            return torch.rand([4], device="cuda")
+
+
+        for _ in range(5):
+            torch.compiler.cudagraph_mark_step_begin()
+            rand_foo() + rand_foo()
+
+    For more details, see `torch.compiler_cudagraph_trees `__
+    """
+    from torch._inductor import cudagraph_trees
+
+    cudagraph_trees.mark_step_begin()
+
+
+def wrap_numpy(fn):
+    r"""Decorator that turns a function from ``np.ndarray``s to ``np.ndarray``s into a function
+    from ``torch.Tensor``s to ``torch.Tensor``s.
+
+    It is designed to be used with :func:`torch.compile` with ``fullgraph=True``. It allows to
+    compile a NumPy function as if it were a PyTorch function. This allows you to run NumPy code
+    on CUDA or compute its gradients.
+
+    .. note::
+
+        This decorator does not work without :func:`torch.compile`.
+
+    Example::
+
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
+        >>> # Compile a NumPy function as a Tensor -> Tensor function
+        >>> @torch.compile(fullgraph=True)
+        >>> @torch.compiler.wrap_numpy
+        >>> def fn(a: np.ndarray):
+        >>>     return np.sum(a * a)
+        >>> # Execute the NumPy function using Tensors on CUDA and compute the gradients
+        >>> x = torch.arange(6, dtype=torch.float32, device="cuda", requires_grad=True)
+        >>> out = fn(x)
+        >>> out.backward()
+        >>> print(x.grad)
+        tensor([ 0.,  2.,  4.,  6.,  8., 10.], device='cuda:0')
+    """
+    from torch._dynamo.external_utils import wrap_numpy as wrap
+
+    return wrap(fn)
+
+
+_is_compiling_flag: bool = False
+_is_exporting_flag: bool = False
+
+
+def is_compiling() -> bool:
+    """
+    Indicates whether a graph is executed/traced as part of torch.compile() or torch.export().
+
+    Note that there are 2 other related flags that should deprecated eventually:
+      * torch._dynamo.external_utils.is_compiling()
+      * torch._utils.is_compiling()
+
+    Example::
+
+        >>> def forward(self, x):
+        >>>     if not torch.compiler.is_compiling():
+        >>>        pass # ...logic that is not needed in a compiled/traced graph...
+        >>>
+        >>>     # ...rest of the function...
+    """
+    if torch.jit.is_scripting():
+        return False
+    else:
+        return _is_compiling_flag
+
+
+def is_dynamo_compiling() -> bool:
+    """
+    Indicates whether a graph is traced via TorchDynamo.
+
+    It's stricter than is_compiling() flag, as it would only be set to True when
+    TorchDynamo is used.
+
+    Example::
+
+        >>> def forward(self, x):
+        >>>     if not torch.compiler.is_dynamo_compiling():
+        >>>        pass # ...logic that is not needed in a TorchDynamo-traced graph...
+        >>>
+        >>>     # ...rest of the function...
+    """
+    return False
+
+
+def is_exporting() -> bool:
+    """
+    Indicated whether we're under exporting.
+
+    It's stricter than is_compiling() flag, as it would only be set to True when
+    torch.export is used.
+
+    Example::
+
+        >>> def forward(self, x):
+        >>>     if not torch.compiler.is_exporting():
+        >>>        pass # ...logic that is not needed in export...
+        >>>
+        >>>     # ...rest of the function...
+    """
+    return _is_exporting_flag
+
+
+def save_cache_artifacts() -> Optional[tuple[bytes, "CacheInfo"]]:
+    """
+    Serializes all the cache artifacts that were created during the compilation
+
+    Example:
+
+    - Execute torch.compile
+    - Call torch.compiler.save_cache_artifacts()
+    """
+    from ._cache import CacheArtifactManager
+
+    if torch._dynamo.config.caching_precompile:
+        from torch._dynamo.precompile_context import PrecompileContext
+
+        PrecompileContext.save_to_dynamo_cache()
+
+    return CacheArtifactManager.serialize()
+
+
+def load_cache_artifacts(serialized_artifacts: bytes) -> Optional["CacheInfo"]:
+    """
+    Hot loads cache artifacts that were previously serialized via
+    save_cache_artifacts
+
+    Example:
+
+    # From a previous invocation
+    artifacts = torch.compiler.save_cache_artifacts()
+
+    torch.compiler.load_cache_artifacts(artifacts[0])
+    """
+    from ._cache import CacheArtifactManager, CacheInfo
+
+    artifacts = CacheArtifactManager.deserialize(serialized_artifacts)
+    if artifacts is not None:
+        return CacheArtifactManager.populate_caches(artifacts)
+    return None
+
+
+def skip_guard_on_inbuilt_nn_modules_unsafe(guard_entries):
+    """
+    A common function to skip guards on the inbuilt nn modules like
+    torch.nn.Linear. This is unsafe to use by default. But for majority of
+    torch.compile users, the model code does not modify the inbuilt nn module
+    attributes. They can benefit from reduction in guard latency overhead using
+    this API.
+
+    To use this API, use guard_filter_fn argument while calling torch.compile
+
+    >> opt_mod = torch.compile(
+    >>     mod,
+    >>     options={"guard_filter_fn": torch.compiler.skip_guard_on_all_nn_modules_unsafe},
+    >> )
+    """
+    return [
+        not entry.orig_guard.source.is_unspecialized_builtin_nn_module()
+        for entry in guard_entries
+    ]
+
+
+def skip_guard_on_all_nn_modules_unsafe(guard_entries):
+    """
+    A common function to skip guards on all nn modules, both user defined as
+    well inbuilt nn modules (like torch.nn.Linear). This is unsafe to use by
+    default. But for majority of torch.compile users, the model code does not
+    modify the nn module attributes. They can benefit from reduction in guard
+    latency overhead using this API.
+
+    To use this API, use guard_filter_fn argument while calling torch.compile
+
+    >> opt_mod = torch.compile(
+    >>     mod,
+    >>     options={"guard_filter_fn": torch.compiler.skip_guard_on_all_nn_modules_unsafe},
+    >> )
+    """
+
+    return [
+        not entry.orig_guard.source.is_unspecialized_nn_module()
+        for entry in guard_entries
+    ]
+
+
+def keep_tensor_guards_unsafe(guard_entries, keep_parameters=False):
+    """
+    A common function to keep tensor guards on all tensors. This is unsafe to
+    use by default. But if you don't expect any changes in the model code, you
+    can just keep the tensor guards.
+
+
+    >> opt_mod = torch.compile(
+    >>     mod,
+    >>     options={"guard_filter_fn": torch.compiler.keep_tensor_guards},
+    >> )
+    """
+
+    keep_flags = []
+    for entry in guard_entries:
+        if entry.guard_type == "TENSOR_MATCH":
+            if not isinstance(entry.value, torch.nn.Parameter):
+                keep_flags.append(True)
+            elif keep_parameters:
+                keep_flags.append(True)
+            else:
+                keep_flags.append(False)
+        else:
+            keep_flags.append(False)
+    return keep_flags
+
+
+def skip_guard_on_globals_unsafe(guard_entries):
+    """
+    A common function to skip guards on all globals. This is unsafe to use by
+    default. But if you don't expect any changes in the globals, you can just
+    keep the tensor guards.
+
+    >> opt_mod = torch.compile(
+    >>     mod,
+    >>     options={"guard_filter_fn": torch.compiler.skip_guard_on_globals},
+    >> )
+    """
+
+    return [not entry.is_global for entry in guard_entries]
+
+
+def skip_all_guards_unsafe(guard_entries):
+    """
+    A function for skipping all guards on a compiled function.
+
+    WARNING: This function will drop all the safety guarantees from Dynamo
+             compiled function. Use this with caution.
+
+    To use this API, use guard_filter_fn argument while calling torch.compile
+
+    >> opt_mod = torch.compile(
+    >>     mod,
+    >>     options={"guard_filter_fn": torch.compiler.skip_all_guards_unsafe},
+    >> )
+    """
+    return [False for entry in guard_entries]
+
+
+def nested_compile_region(fn=None):
+    """
+    Tells **``torch.compile``** that the marked set of operations forms a nested
+    compile region (which is often repeated in the full model) whose code can be
+    compiled once and safely reused.  ``nested_compile_region`` can also be used
+    as a decorator.
+
+    During **``torch.compile``** tracing, the compiler applies *hierarchical
+    compilation* with ``nested_compile_region``: it emits optimized code for the
+    marked region the first time it is encountered and re-emits (or “stamps
+    out”) the previously compiled code on every subsequent invocation.  This can
+    substantially reduce overall compile time for deeply-stacked,
+    structurally-identical components such as the transformer layers of a
+    large-language-model (LLM).
+
+    Outside a ``torch.compile`` context—i.e., in standard eager execution—the
+    call is a no-op, so existing workflows remain unaffected.
+
+    Note that ``nested_compile_region`` **does not** promise that a region will
+    be compiled exactly once.  If the compiler detects that new input conditions
+    (shape, dtype, device, stride, globals etc.) make the cached version invalid
+    to reuse, it will transparently re-compile the region.  Using it is
+    therefore *safe*: correctness is always preserved, and you pay the extra
+    compilation cost only when required.
+    """
+
+    from torch._higher_order_ops.invoke_subgraph import (
+        mark_compile_region as _mark_compile_region,
+    )
+
+    return _mark_compile_region(fn)
+
+
+def load_compiled_function(
+    file: io.IOBase, *, f_globals: Optional[dict[str, object]] = None
+) -> Callable[..., Any]:
+    """
+    Load an aot-compiled function from a file.
+
+    .. warning::
+
+        This API is currently experimental and subject to change.
+
+    Args:
+        file: A file-like object containing the serialized compiled function.
+        f_globals: Optional globals to be loaded into the compiled function.
+
+    Returns:
+        A torch-compiled function with compilation preloaded from disk.
+    """
+    from torch._dynamo.aot_compile import AOTCompiledFunction
+
+    data = file.read()
+    return AOTCompiledFunction.deserialize(data, f_globals)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/compiler/_cache.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/compiler/_cache.py
new file mode 100644
index 0000000000000000000000000000000000000000..b525438d1bb5b74b7c6fe3c2e2df06366ff4bd7e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/compiler/_cache.py
@@ -0,0 +1,322 @@
+import copy
+import dataclasses
+import logging
+from abc import ABC, abstractmethod
+from collections import defaultdict
+from collections.abc import Generator
+from contextlib import contextmanager
+from itertools import chain
+from typing import Any, Optional
+
+from torch.utils._appending_byte_serializer import (
+    AppendingByteSerializer,
+    BytesReader,
+    BytesWriter,
+)
+from torch.utils._ordered_set import OrderedSet
+
+
+log = logging.getLogger(__name__)
+
+
+@dataclasses.dataclass(frozen=True)
+class CacheArtifact(ABC):
+    """
+    Data for each cache artifact that will be serialized and deserialized
+    """
+
+    key: str
+    content: bytes = dataclasses.field(repr=False)  # Do not display potential binary
+
+    @staticmethod
+    def serialize(writer: BytesWriter, cls: "CacheArtifact") -> None:
+        writer.write_str(cls.key)
+        writer.write_bytes(cls.content)
+
+    @staticmethod
+    def deserialize(artifact_type: str, reader: BytesReader) -> "CacheArtifact":
+        key = reader.read_str()
+        content = reader.read_bytes()
+        return CacheArtifactFactory.create(artifact_type, key, content)
+
+    @staticmethod
+    def encode(content: Any) -> bytes:
+        assert isinstance(content, bytes), f"Expected bytes, got {type(content)}"
+        return content
+
+    @abstractmethod
+    def populate_cache(self) -> None:
+        pass
+
+    @staticmethod
+    def type() -> str:
+        """
+        Returns the type of the artifact. Must be unique across all CacheArtifact classes.
+
+        CacheArtifactFactory.register will add property method to CacheInfo based on this (def {type}_artifacts)
+        that returns all artifacts for specific cache.
+        """
+        raise RuntimeError("CacheArtifact is an abstract class, please use a subclass")
+
+
+class CacheArtifactFactory:
+    """
+    Factory for creating CacheArtifact objects based on their type
+    """
+
+    _artifact_types: dict[str, type[CacheArtifact]] = {}
+
+    @classmethod
+    def register(cls, artifact_cls: type[CacheArtifact]) -> type[CacheArtifact]:
+        artifact_type_key = artifact_cls.type()
+        assert artifact_cls.type() not in cls._artifact_types, (
+            f"Artifact of type={artifact_type_key} already registered in mega-cache artifact factory"
+        )
+        cls._artifact_types[artifact_type_key] = artifact_cls
+        setattr(
+            CacheInfo,
+            f"{artifact_type_key}_artifacts",
+            property(lambda self: self.artifacts[artifact_type_key]),
+        )
+        return artifact_cls
+
+    @classmethod
+    def _get_artifact_type(cls, artifact_type_key: str) -> type[CacheArtifact]:
+        assert artifact_type_key in cls._artifact_types, (
+            f"Artifact of type={artifact_type_key} not registered in mega-cache artifact factory"
+        )
+        return cls._artifact_types[artifact_type_key]
+
+    @classmethod
+    def create(cls, artifact_type_key: str, key: str, content: bytes) -> CacheArtifact:
+        artifact_cls = cls._get_artifact_type(artifact_type_key)
+        # pyrefly: ignore [bad-instantiation]
+        return artifact_cls(key, content)
+
+    @classmethod
+    def encode_create(
+        cls, artifact_type_key: str, key: str, content: Any
+    ) -> CacheArtifact:
+        artifact_cls = cls._get_artifact_type(artifact_type_key)
+        # pyrefly: ignore [bad-instantiation]
+        return artifact_cls(key, artifact_cls.encode(content))
+
+
+@dataclasses.dataclass
+class CacheInfo:
+    """
+    Return value of serialization and deserialization for the purpose of
+    instrumentation
+    """
+
+    artifacts: defaultdict[str, list[str]] = dataclasses.field(
+        default_factory=lambda: defaultdict(list)
+    )
+
+    # Methods set by CacheArtifactFactory.register based on CacheArtifact.type()
+    @property
+    def inductor_artifacts(self) -> list[str]:  # type: ignore[empty-body]
+        ...
+
+    @property
+    def autotune_artifacts(self) -> list[str]:  # type: ignore[empty-body]
+        ...
+
+    @property
+    def aot_autograd_artifacts(self) -> list[str]:  # type: ignore[empty-body]
+        ...
+
+    @property
+    def pgo_artifacts(self) -> list[str]:  # type: ignore[empty-body]
+        ...
+
+    @property
+    def precompile_artifacts(self) -> list[str]:  # type: ignore[empty-body]
+        ...
+
+    def add(self, artifact: CacheArtifact) -> None:
+        self.artifacts[artifact.type()].append(artifact.key)
+
+    def clear(self) -> None:
+        self.artifacts.clear()
+
+    def empty(self) -> bool:
+        return not self.artifacts
+
+
+def _serialize_single_cache(
+    writer: BytesWriter, cls: "tuple[str, list[CacheArtifact]]"
+) -> None:
+    writer.write_str(cls[0])
+    writer.write_uint64(len(cls[1]))
+    for artifact in cls[1]:
+        CacheArtifact.serialize(writer, artifact)
+
+
+def _deserialize_single_cache(
+    reader: BytesReader,
+) -> "tuple[str, list[CacheArtifact]]":
+    artifacts = []
+    artifact_type_key = reader.read_str()
+    num_artifacts = reader.read_uint64()
+    for _ in range(num_artifacts):
+        artifacts.append(CacheArtifact.deserialize(artifact_type_key, reader))
+
+    return artifact_type_key, artifacts
+
+
+CacheArtifactsResult = dict[str, list[CacheArtifact]]
+
+
+class CacheArtifactManager:
+    """
+    Lightweight manager class for collecting and processing cache artifacts for
+    hot loading
+
+    Intended Lifecycle:
+    - Execute code via torch.compile, this will call
+        CacheArtifactManager.record_artifact on each cache artifact
+    - Call CacheArtifactManager.serialize to convert all the cache artifacts
+        to portable format
+    - Call CacheArtifactManager.deserialize to hot load the cache artifacts on
+        a potentially different process
+
+    NOTE: There's no FB/FC guarantees, results of cache artifacts will not be
+          used unless code version matches.
+    """
+
+    # Protected by the compile_lock
+    _new_cache_artifacts: CacheArtifactsResult = defaultdict(list)
+    # Keep a separate seen artifacts list to make avoid unnecessary duplicates
+    # This list will not be cleared between serialize() calls
+    _seen_artifacts: OrderedSet[CacheArtifact] = OrderedSet()
+    # When serialize() is called, artifacts are transferred from _cache_artifacts to
+    # internal data structure of the _serializer
+    # This allows us to only pay the cost of serialization if serialize() is called
+    _serializer: AppendingByteSerializer[tuple[str, list[CacheArtifact]]] = (
+        AppendingByteSerializer(serialize_fn=_serialize_single_cache)
+    )
+    _cache_info: CacheInfo = CacheInfo()
+
+    @classmethod
+    def clear(cls) -> None:
+        cls._new_cache_artifacts.clear()
+        cls._seen_artifacts.clear()
+        cls._serializer.clear()
+        cls._cache_info.clear()
+
+    @classmethod
+    @contextmanager
+    def with_fresh_cache(cls) -> Generator[None, None, None]:
+        original_new_cache_artifacts = cls._new_cache_artifacts
+        original_seen_artifacts = cls._seen_artifacts
+        original_serializer = cls._serializer
+        original_cache_info = cls._cache_info
+
+        cls._new_cache_artifacts = defaultdict(list)
+        cls._seen_artifacts = OrderedSet()
+        cls._serializer = AppendingByteSerializer(serialize_fn=_serialize_single_cache)
+        cls._cache_info = cls._cache_info.__class__()
+        try:
+            yield
+        finally:
+            cls._new_cache_artifacts = original_new_cache_artifacts
+            cls._seen_artifacts = original_seen_artifacts
+            cls._serializer = original_serializer
+            cls._cache_info = original_cache_info
+
+    @classmethod
+    def record_artifact(
+        cls,
+        artifact_type: str,
+        key: str,
+        content: Any,
+    ) -> None:
+        """
+        Called from each caching operation to record the artifact in this
+        "mega" list
+        """
+        artifact = CacheArtifactFactory.encode_create(artifact_type, key, content)
+        if artifact in cls._seen_artifacts:
+            return
+        log.debug("Recording %s", str(artifact))
+        cls._new_cache_artifacts[artifact_type].append(artifact)
+        cls._seen_artifacts.add(artifact)
+
+    @classmethod
+    def need_serialize(cls) -> bool:
+        """
+        Have we seen new artifacts since last serialize call?
+        """
+        return len(cls._new_cache_artifacts) != 0
+
+    @classmethod
+    def serialize(cls) -> Optional[tuple[bytes, CacheInfo]]:
+        """
+        Converts the "mega" list into portable format
+        """
+        for artifact in chain(*cls._new_cache_artifacts.values()):
+            log.debug("saving: %s", artifact)
+            cls._cache_info.add(artifact)
+
+        if cls._cache_info.empty():
+            # If there are not artifacts, dont just return bytes with
+            # version.
+            return None
+
+        try:
+            # We deep copy cls._cache_info since later compilations
+            # can keep adding to cache_info
+            info = copy.deepcopy(cls._cache_info)
+            cls._serializer.extend(cls._new_cache_artifacts.items())
+            artifact_bytes = cls._serializer.to_bytes()
+            cls._new_cache_artifacts.clear()
+            return artifact_bytes, info
+        except Exception:
+            log.warning("Failed to pickle cache artifacts", exc_info=True)
+        return None
+
+    @staticmethod
+    def deserialize(serialized_artifacts: bytes) -> Optional[CacheArtifactsResult]:
+        """
+        Converts the portable format back into CacheArtifacts
+        """
+        try:
+            CacheArtifactManager._ensure_cache_artifacts_registered()
+            artifacts = dict(
+                AppendingByteSerializer.to_list(
+                    serialized_artifacts,
+                    deserialize_fn=_deserialize_single_cache,
+                )
+            )
+        except Exception:
+            log.warning("Failed to un-pickle cache artifacts", exc_info=True)
+            return None
+
+        return artifacts
+
+    @staticmethod
+    def populate_caches(artifacts: CacheArtifactsResult) -> CacheInfo:
+        info = CacheInfo()
+        for artifact in chain(*artifacts.values()):
+            log.debug("writing: %s", artifact)
+            info.add(artifact)
+            artifact.populate_cache()
+
+        return info
+
+    @classmethod
+    def _ensure_cache_artifacts_registered(cls) -> None:
+        """When deserializing caches in fresh process, we need to ensure that all
+        cache artifacts are registered in the cache registry. This is done by
+        simply importing all the cache artifacts already wrapped with register call.
+        """
+        from torch._dynamo.package import PrecompileCacheArtifact  # noqa: F401
+        from torch._dynamo.pgo import PGOCacheArtifact  # noqa: F401
+        from torch._functorch._aot_autograd.autograd_cache import (  # noqa: F401
+            AOTAutogradCacheArtifact,
+        )
+        from torch._inductor.codecache import InductorCacheArtifact  # noqa: F401
+        from torch._inductor.runtime.autotune_cache import (  # noqa: F401
+            AutotuneCacheArtifact,
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/compiler/config.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/compiler/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..e507ddc18052e2e9b53bd6de3a6001a1d17a1be0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/compiler/config.py
@@ -0,0 +1,281 @@
+"""
+This is the top-level configuration module for the compiler, containing
+cross-cutting configuration options that affect all parts of the compiler
+stack.
+
+You may also be interested in the per-component configuration modules, which
+contain configuration options that affect only a specific part of the compiler:
+
+* :mod:`torch._dynamo.config`
+* :mod:`torch._inductor.config`
+* :mod:`torch._functorch.config`
+* :mod:`torch.fx.experimental.config`
+"""
+
+import sys
+from typing import Optional
+
+from torch.utils._config_module import Config, install_config_module
+
+
+__all__ = [
+    "job_id",
+    "dynamic_shapes",
+    "assume_static_by_default",
+    "automatic_dynamic_shapes",
+    "recompile_limit",
+    "accumulated_recompile_limit",
+    "verbose",
+    "capture_scalar_outputs",
+    "capture_dynamic_output_shape_ops",
+    "log_file_name",
+    "fail_on_recompile_limit_hit",
+    "allow_unspec_int_on_nn_module",
+    "skip_tensor_guards_with_matching_dict_tags",
+    "enable_cpp_symbolic_shape_guards",
+    "wrap_top_frame",
+    "reorderable_logging_functions",
+    "force_disable_caches",
+]
+
+
+# NB: Docblocks go UNDER variable definitions!  Use spacing to make the
+# grouping clear.
+
+# FB-internal note: you do NOT have to specify this explicitly specify this if
+# you run on MAST, we will automatically default this to
+# mast:MAST_JOB_NAME:MAST_JOB_VERSION.
+job_id: Optional[str] = Config(
+    env_name_default=["TORCH_COMPILE_JOB_ID", "TORCH_COMPILE_STICKY_PGO_KEY"],
+    default=None,
+)
+"""
+Semantically, this should be an identifier that uniquely identifies, e.g., a
+training job.  You might have multiple attempts of the same job, e.g., if it was
+preempted or needed to be restarted, but each attempt should be running
+substantially the same workload with the same distributed topology.  You can
+set this by environment variable with :envvar:`TORCH_COMPILE_JOB_ID`.
+
+Operationally, this controls the effect of profile-guided optimization related
+persistent state.  PGO state can affect how we perform compilation across
+multiple invocations of PyTorch, e.g., the first time you run your program we
+may compile twice as we discover what inputs are dynamic, and then PGO will
+save this state so subsequent invocations only need to compile once, because
+they remember it is dynamic.  This profile information, however, is sensitive
+to what workload you are running, so we require you to tell us that two jobs
+are *related* (i.e., are the same workload) before we are willing to reuse
+this information.  Notably, PGO does nothing (even if explicitly enabled)
+unless a valid ``job_id`` is available.  In some situations, PyTorch can
+configured to automatically compute a ``job_id`` based on the environment it
+is running in.
+
+Profiles are always collected on a per rank basis, so different ranks may have
+different profiles.  If you know your workload is truly SPMD, you can run with
+:data:`torch._dynamo.config.enable_compiler_collectives` to ensure nodes get
+consistent profiles across all ranks.
+"""
+
+pgo_extra_read_key: Optional[str] = Config(
+    env_name_default="TORCH_COMPILE_STICKY_PGO_READ", default=None
+)
+pgo_extra_write_key: Optional[str] = Config(
+    env_name_default="TORCH_COMPILE_STICKY_PGO_WRITE", default=None
+)
+"""
+Additional read/write keys for PGO.
+Write key: Besides writing to the default local/remote PGO state, this also writes to the specified key.
+Read key: Besides reading from the default state, this also reads from the specified key (if written to before)
+and merges it with the default state.
+"""
+
+
+cache_key_tag: str = Config(env_name_default="TORCH_COMPILE_CACHE_KEY_TAG", default="")
+"""
+Tag to be included in the cache key generation for all torch compile caching.
+A common use case for such a tag is to break caches.
+"""
+
+force_disable_caches: bool = Config(
+    justknob="pytorch/remote_cache:force_disable_caches",
+    env_name_force=[
+        "TORCHINDUCTOR_FORCE_DISABLE_CACHES",
+        "TORCH_COMPILE_FORCE_DISABLE_CACHES",
+    ],
+    default=False,
+)
+"""
+Force disables all caching -- This will take precedence over and override any other caching flag
+"""
+
+dynamic_sources: str = Config(
+    env_name_default="TORCH_COMPILE_DYNAMIC_SOURCES", default=""
+)
+"""
+Comma delimited list of sources that should be marked as dynamic. Primarily useful for large
+models with graph breaks where you need intermediate tensors and ints to be marked dynamic.
+
+This whitelist is dominant over all other flags dynamic=False, force_nn_module_property_static_shapes
+and force_parameter_static_shapes.
+"""
+
+unbacked_sources: str = Config(
+    env_name_default="TORCH_COMPILE_UNBACKED_SOURCES", default=""
+)
+"""
+Comma delimited list of sources that should be marked as unbacked. Primarily useful for large
+models with graph breaks where you need intermediate tensors marked unbacked.
+
+This whitelist is dominant over all other flags dynamic=False, force_nn_module_property_static_shapes
+and force_parameter_static_shapes.
+"""
+
+# force a python GC before recording cudagraphs
+force_cudagraph_gc: bool = Config(env_name_default="TORCH_CUDAGRAPH_GC", default=False)
+"""
+If True (the backward-compatible behavior) then gc.collect() before recording
+any cudagraph.
+"""
+
+
+# Cross-cutting configuration options that affect the entire compilation pipeline
+
+dynamic_shapes: bool = Config(alias="torch._dynamo.config.dynamic_shapes")
+"""
+Controls whether the compilation pipeline supports dynamic tensor shapes.
+When enabled, the compiler can handle tensors with varying dimensions across
+different invocations. This is a cross-cutting setting that affects shape
+inference, guard generation, and code generation across the entire compilation
+stack.
+"""
+
+assume_static_by_default: bool = Config(
+    alias="torch._dynamo.config.assume_static_by_default"
+)
+"""
+When enabled, all tensor dimensions are assumed to be static unless explicitly
+marked as dynamic or detected as changing. This compilation-wide behavior affects
+how the entire stack handles shape specialization and can improve performance
+for static workloads.
+"""
+
+automatic_dynamic_shapes: bool = Config(
+    alias="torch._dynamo.config.automatic_dynamic_shapes"
+)
+"""
+Enables automatic detection and handling of dynamic shapes. When a tensor's
+shape changes between compilations, the system automatically marks those
+dimensions as dynamic rather than requiring manual specification. This
+cross-cutting optimization improves the user experience by reducing recompilations.
+"""
+
+recompile_limit: int = Config(alias="torch._dynamo.config.recompile_limit")
+"""
+Maximum number of recompilations allowed for a single function before falling
+back to eager execution. This compilation performance control prevents excessive
+recompilation overhead that can degrade overall performance.
+"""
+
+accumulated_recompile_limit: int = Config(
+    alias="torch._dynamo.config.accumulated_recompile_limit"
+)
+"""
+Global limit on total recompilations across all compiled functions to prevent
+runaway recompilation scenarios. This safeguard protects against compilation
+performance issues that could affect the entire program.
+"""
+
+verbose: bool = Config(alias="torch._dynamo.config.verbose")
+"""
+Enables verbose debugging output for Dynamo. When enabled, provides detailed
+information about Dynamo's compilation decisions, optimizations, and potential
+issues.
+"""
+
+
+# TorchDynamo-specific configuration options
+
+capture_scalar_outputs: bool = Config(
+    alias="torch._dynamo.config.capture_scalar_outputs"
+)
+"""
+Controls whether TorchDynamo captures operations that return scalar values (like .item())
+into the FX graph. When disabled, these operations cause graph breaks. This is a
+TorchDynamo-specific tracing behavior that affects how the tracer handles
+scalar-returning operations.
+"""
+
+capture_dynamic_output_shape_ops: bool = Config(
+    alias="torch._dynamo.config.capture_dynamic_output_shape_ops"
+)
+"""
+Controls whether TorchDynamo captures operations with dynamic output shapes (like
+nonzero, unique) into the FX graph. When disabled, these operations cause graph breaks.
+This is a TorchDynamo-specific setting for handling operations with unpredictable
+output shapes during tracing.
+"""
+
+log_file_name: Optional[str] = Config(alias="torch._dynamo.config.log_file_name")
+"""
+Specifies a file path for TorchDynamo-specific logging output. When set, internal
+TorchDynamo debug information is written to this file rather than stdout. This is
+useful for debugging TorchDynamo's internal tracing behavior.
+"""
+
+fail_on_recompile_limit_hit: bool = Config(
+    alias="torch._dynamo.config.fail_on_recompile_limit_hit"
+)
+"""
+Raises a hard error when recompile limits are exceeded instead of falling back
+to eager execution. This is useful for detecting excessive recompilation in
+performance-critical deployments where you want to ensure compilation overhead
+is kept under control.
+"""
+
+allow_unspec_int_on_nn_module: bool = Config(
+    alias="torch._dynamo.config.allow_unspec_int_on_nn_module"
+)
+"""
+Allows integer attributes of nn.Module instances to be unspecialized through
+the dynamic shape mechanism. By default, TorchDynamo specializes on all integer
+module attributes, but this can cause excessive recompilation when integers
+like step counters change frequently.
+"""
+
+skip_tensor_guards_with_matching_dict_tags: bool = Config(
+    alias="torch._dynamo.config.skip_tensor_guards_with_matching_dict_tags"
+)
+"""
+Optimizes guard generation by treating tensors as immutable when they are
+dictionary values with consistent dictionary tags across invocations. This
+reduces guard overhead for tensors stored in persistent data structures.
+"""
+
+enable_cpp_symbolic_shape_guards: bool = Config(
+    alias="torch._dynamo.config.enable_cpp_symbolic_shape_guards"
+)
+"""
+Uses C++ implementation for symbolic shape guard evaluation to improve performance.
+The C++ guard manager can significantly speed up guard checking for symbolic shapes
+in shape-polymorphic compilations.
+"""
+
+wrap_top_frame: bool = Config(alias="torch._dynamo.config.wrap_top_frame")
+"""
+Wraps the top-level decorated function/module in a frame wrapper to ensure
+nn.Module hooks are compiled within the same frame as the main function. This
+improves compilation coverage for models that rely on hooks.
+"""
+
+reorderable_logging_functions: set = Config(
+    alias="torch._dynamo.config.reorderable_logging_functions"
+)
+"""
+A set of logging functions that can be reordered to execute after the compiled
+portion of the graph, allowing larger graphs to be captured. Functions in this
+set will have their execution deferred to avoid graph breaks, though this may
+affect the timing of log output. In particular, mutated values will not be logged
+at the right time, leading to incorrect logging.
+"""
+
+
+install_config_module(sys.modules[__name__])
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/contrib/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/contrib/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/contrib/_tensorboard_vis.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/contrib/_tensorboard_vis.py
new file mode 100644
index 0000000000000000000000000000000000000000..fd54513d76e390f80d0079e88e399658832d0eb9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/contrib/_tensorboard_vis.py
@@ -0,0 +1,152 @@
+# mypy: allow-untyped-defs
+import time
+from collections import defaultdict
+from functools import partial
+
+import torch
+
+
+# Unfortunately it doesn't seem as if there was any way to get TensorBoard to do
+# anything without having TF installed, and so this file has a hard dependency on it
+# as well. It really is a debugging tool, so it doesn't matter.
+try:
+    from tensorflow.core.framework import graph_pb2
+    from tensorflow.core.util import event_pb2
+    from tensorflow.python.summary.writer.writer import FileWriter
+except ImportError:
+    raise ImportError(
+        "TensorBoard visualization of GraphExecutors requires having "
+        "TensorFlow installed"
+    ) from None
+
+
+def dump_tensorboard_summary(graph_executor, logdir):
+    with FileWriter(logdir) as w:
+        pb_graph = visualize(graph_executor)
+        evt = event_pb2.Event(
+            wall_time=time.time(), graph_def=pb_graph.SerializeToString()
+        )
+        w.add_event(evt)
+
+
+def visualize(graph, name_prefix="", pb_graph=None, executors_it=None):
+    """Visualizes an independent graph, or a graph executor."""
+    value_map = {}
+    pb_graph = pb_graph or graph_pb2.GraphDef()
+
+    if isinstance(graph, torch._C.GraphExecutorState):
+        visualize_graph_executor(
+            graph, name_prefix, pb_graph, partial(visualize, pb_graph=pb_graph)
+        )
+        return pb_graph
+
+    # Set up an input node
+    pb_graph.node.add(op="input", name=name_prefix + "input")
+    for i, value in enumerate(graph.param_node().outputs()):
+        value_map[value.unique()] = name_prefix + "input:" + str(i)
+
+    visualize_rec(graph, value_map, name_prefix, pb_graph, executors_it)
+
+    # Gather all outputs
+    return_node = pb_graph.node.add(op="output", name=name_prefix + "output")
+    for value in graph.return_node().inputs():
+        return_node.input.append(value_map[value.unique()])
+
+    return pb_graph
+
+
+def visualize_graph_executor(state, name_prefix, pb_graph, inline_graph):
+    """Append the state of a given GraphExecutor to the graph protobuf.
+
+    Args:
+        state (GraphExecutor or GraphExecutorState): GraphExecutor to display.
+        name_prefix (str): Name prefix of the containing subgraph.
+        pb_graph (GraphDef): graph to append to.
+        inline_graph (Callable): a function that handles setting up a value_map,
+            so that some graphs in here can be inlined. This is necessary, because
+            this will simply be `visualize` for the top-level GraphExecutor,
+            or `inline_graph` for all nested ones.
+
+            The signature should look like (Graph, name_prefix) -> ().
+            It will be called exactly once.
+
+    The strategy is to embed all different configurations as independent subgraphs,
+    while inlining the original graph as the one that actually produces the values.
+    """
+    if state.autograd_fallback_graph is not None:
+        visualize(
+            graph=state.autograd_fallback_graph,
+            name_prefix=name_prefix + "autograd_fallback/",
+            pb_graph=pb_graph,
+            executors_it=iter(state.autograd_fallback.executors()),
+        )
+
+    for i, (arg_spec, plan) in enumerate(state.execution_plans.items()):
+        subgraph_name = name_prefix + f"plan{i}/"
+
+        # Create a disconnected node that will keep information regarding the input
+        # types of this trace. This is unfortunately a bit too verbose to be included
+        # in the subgraph name.
+        input_kinds = pb_graph.node.add(op="INPUT_KIND", name=subgraph_name)
+        input_kinds.attr["inputs"].s = repr(arg_spec).encode("ascii")
+
+        visualize(plan.graph, subgraph_name, pb_graph, iter(plan.code.executors()))
+
+        # Show gradient as an independent subgraph of this plan
+        if plan.grad_executor is not None:
+            grad_subgraph_name = subgraph_name + "grad/"
+            visualize(plan.grad_executor, grad_subgraph_name, pb_graph)
+
+    return inline_graph(state.graph, name_prefix + "original/")
+
+
+def visualize_rec(graph, value_map, name_prefix, pb_graph, executors_it=None):
+    """Recursive part of visualize (basically skips setting up the input and output nodes)."""
+
+    def inline_graph(subgraph, name, node):
+        rec_value_map = {
+            inp.unique(): value_map[val.unique()]
+            for inp, val in zip(subgraph.inputs(), node.inputs())
+        }
+        visualize_rec(
+            graph=subgraph, value_map=rec_value_map, name_prefix=name, pb_graph=pb_graph
+        )
+        for out, val in zip(subgraph.outputs(), node.outputs()):
+            value_map[val.unique()] = rec_value_map[out.unique()]
+
+    op_id_counter: defaultdict[str, int] = defaultdict(int)
+
+    def name_for(node):
+        kind = node.kind()[node.kind().index("::") + 2 :]
+        op_id_counter[kind] += 1
+        return kind, name_prefix + kind + "_" + str(op_id_counter[kind])
+
+    def add_fusion_group(node):
+        op, name = name_for(node)
+        inline_graph(node.g("Subgraph"), name + "/", node)
+
+    def add_graph_executor(node):
+        op, name = name_for(node)
+        if executors_it is None:
+            add_node(node)
+        else:
+            ge = next(executors_it)
+            visualize_graph_executor(
+                ge, name + "/", pb_graph, partial(inline_graph, node=node)
+            )
+
+    def add_node(node):
+        if node.kind() == "prim::FusionGroup":
+            return add_fusion_group(node)
+        elif node.kind() == "prim::GraphExecutor":
+            return add_graph_executor(node)
+        op, name = name_for(node)
+        pb_node = pb_graph.node.add(op=op, name=name)
+        for value in node.inputs():
+            pb_node.input.append(value_map[value.unique()])
+        # TODO: handle attrs
+        for i, value in enumerate(node.outputs()):
+            value_map[value.unique()] = name + ":" + str(i)
+
+    for node in graph.nodes():
+        add_node(node)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cpu/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cpu/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b42b7f0ff54bd7dafda3fb72cffe93a4e4e23645
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cpu/__init__.py
@@ -0,0 +1,202 @@
+# mypy: allow-untyped-defs
+r"""
+This package implements abstractions found in ``torch.cuda``
+to facilitate writing device-agnostic code.
+"""
+
+from contextlib import AbstractContextManager
+from typing import Any, Optional, Union
+
+import torch
+
+from .. import device as _device
+from . import amp
+
+
+__all__ = [
+    "is_available",
+    "is_initialized",
+    "synchronize",
+    "current_device",
+    "current_stream",
+    "stream",
+    "set_device",
+    "device_count",
+    "Stream",
+    "StreamContext",
+    "Event",
+]
+
+
+def _is_avx2_supported() -> bool:
+    r"""Returns a bool indicating if CPU supports AVX2."""
+    return torch._C._cpu._is_avx2_supported()
+
+
+def _is_avx512_supported() -> bool:
+    r"""Returns a bool indicating if CPU supports AVX512."""
+    return torch._C._cpu._is_avx512_supported()
+
+
+def _is_avx512_bf16_supported() -> bool:
+    r"""Returns a bool indicating if CPU supports AVX512_BF16."""
+    return torch._C._cpu._is_avx512_bf16_supported()
+
+
+def _is_vnni_supported() -> bool:
+    r"""Returns a bool indicating if CPU supports VNNI."""
+    # Note: Currently, it only checks avx512_vnni, will add the support of avx2_vnni later.
+    return torch._C._cpu._is_avx512_vnni_supported()
+
+
+def _is_amx_tile_supported() -> bool:
+    r"""Returns a bool indicating if CPU supports AMX_TILE."""
+    return torch._C._cpu._is_amx_tile_supported()
+
+
+def _is_amx_fp16_supported() -> bool:
+    r"""Returns a bool indicating if CPU supports AMX FP16."""
+    return torch._C._cpu._is_amx_fp16_supported()
+
+
+def _init_amx() -> bool:
+    r"""Initializes AMX instructions."""
+    return torch._C._cpu._init_amx()
+
+
+def is_available() -> bool:
+    r"""Returns a bool indicating if CPU is currently available.
+
+    N.B. This function only exists to facilitate device-agnostic code
+
+    """
+    return True
+
+
+def synchronize(device: torch.types.Device = None) -> None:
+    r"""Waits for all kernels in all streams on the CPU device to complete.
+
+    Args:
+        device (torch.device or int, optional): ignored, there's only one CPU device.
+
+    N.B. This function only exists to facilitate device-agnostic code.
+    """
+
+
+class Stream:
+    """
+    N.B. This class only exists to facilitate device-agnostic code
+    """
+
+    def __init__(self, priority: int = -1) -> None:
+        pass
+
+    def wait_stream(self, stream) -> None:
+        pass
+
+    def record_event(self) -> None:
+        pass
+
+    def wait_event(self, event) -> None:
+        pass
+
+
+class Event:
+    def query(self) -> bool:
+        return True
+
+    def record(self, stream=None) -> None:
+        pass
+
+    def synchronize(self) -> None:
+        pass
+
+    def wait(self, stream=None) -> None:
+        pass
+
+
+_default_cpu_stream = Stream()
+_current_stream = _default_cpu_stream
+
+
+def current_stream(device: torch.types.Device = None) -> Stream:
+    r"""Returns the currently selected :class:`Stream` for a given device.
+
+    Args:
+        device (torch.device or int, optional): Ignored.
+
+    N.B. This function only exists to facilitate device-agnostic code
+
+    """
+    return _current_stream
+
+
+class StreamContext(AbstractContextManager):
+    r"""Context-manager that selects a given stream.
+
+    N.B. This class only exists to facilitate device-agnostic code
+
+    """
+
+    cur_stream: Optional[Stream]
+
+    def __init__(self, stream):
+        self.stream = stream
+        self.prev_stream = _default_cpu_stream
+
+    def __enter__(self):
+        cur_stream = self.stream
+        if cur_stream is None:
+            return
+
+        global _current_stream
+        self.prev_stream = _current_stream
+        _current_stream = cur_stream
+
+    def __exit__(self, type: Any, value: Any, traceback: Any) -> None:
+        cur_stream = self.stream
+        if cur_stream is None:
+            return
+
+        global _current_stream
+        _current_stream = self.prev_stream
+
+
+def stream(stream: Stream) -> AbstractContextManager:
+    r"""Wrapper around the Context-manager StreamContext that
+    selects a given stream.
+
+    N.B. This function only exists to facilitate device-agnostic code
+    """
+    return StreamContext(stream)
+
+
+def device_count() -> int:
+    r"""Returns number of CPU devices (not cores). Always 1.
+
+    N.B. This function only exists to facilitate device-agnostic code
+    """
+    return 1
+
+
+def set_device(device: torch.types.Device) -> None:
+    r"""Sets the current device, in CPU we do nothing.
+
+    N.B. This function only exists to facilitate device-agnostic code
+    """
+
+
+def current_device() -> str:
+    r"""Returns current device for cpu. Always 'cpu'.
+
+    N.B. This function only exists to facilitate device-agnostic code
+    """
+    return "cpu"
+
+
+def is_initialized() -> bool:
+    r"""Returns True if the CPU is initialized. Always True.
+
+    N.B. This function only exists to facilitate device-agnostic code
+    """
+    return True
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cpu/amp/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cpu/amp/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..dae673c7b2313480a940a9cc19517dba21d20d3a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cpu/amp/__init__.py
@@ -0,0 +1,3 @@
+# pyrefly: ignore [deprecated]
+from .autocast_mode import autocast
+from .grad_scaler import GradScaler
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cpu/amp/autocast_mode.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cpu/amp/autocast_mode.py
new file mode 100644
index 0000000000000000000000000000000000000000..f0f81060d4a01fc6857138c49ec8276bee59b90d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cpu/amp/autocast_mode.py
@@ -0,0 +1,71 @@
+# mypy: allow-untyped-defs
+import sys
+from typing import Any
+from typing_extensions import deprecated
+
+import torch
+
+
+__all__ = ["autocast"]
+
+
+@deprecated(
+    "`torch.cpu.amp.autocast(args...)` is deprecated. "
+    "Please use `torch.amp.autocast('cpu', args...)` instead.",
+    category=FutureWarning,
+)
+class autocast(torch.amp.autocast_mode.autocast):
+    r"""
+    See :class:`torch.autocast`.
+    ``torch.cpu.amp.autocast(args...)`` is deprecated. Please use ``torch.amp.autocast("cpu", args...)`` instead.
+    """
+
+    # TODO: remove this conditional once we stop supporting Python < 3.13
+    # Prior to Python 3.13, inspect.signature could not retrieve the correct
+    # signature information for classes decorated with @deprecated (unless
+    # the __new__ static method was explicitly defined);
+    #
+    # However, this issue has been fixed in Python 3.13 and later versions.
+    if sys.version_info < (3, 13):
+
+        def __new__(
+            cls,
+            enabled: bool = True,
+            dtype: torch.dtype = torch.bfloat16,
+            cache_enabled: bool = True,
+        ):
+            return super().__new__(cls)
+
+        def __init_subclass__(cls):
+            pass
+
+    def __init__(
+        self,
+        enabled: bool = True,
+        dtype: torch.dtype = torch.bfloat16,
+        cache_enabled: bool = True,
+    ):
+        if torch._jit_internal.is_scripting():
+            self._enabled = enabled
+            self.device = "cpu"
+            self.fast_dtype = dtype
+            return
+        super().__init__(
+            "cpu", enabled=enabled, dtype=dtype, cache_enabled=cache_enabled
+        )
+
+    def __enter__(self):
+        if torch._jit_internal.is_scripting():
+            return self
+        return super().__enter__()
+
+    # TODO: discuss a unified TorchScript-friendly API for autocast
+    def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any):  # type: ignore[override]
+        if torch._jit_internal.is_scripting():
+            return
+        return super().__exit__(exc_type, exc_val, exc_tb)
+
+    def __call__(self, func):
+        if torch._jit_internal.is_scripting():
+            return func
+        return super().__call__(func)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cpu/amp/grad_scaler.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cpu/amp/grad_scaler.py
new file mode 100644
index 0000000000000000000000000000000000000000..aefaa1c323f5ff9089fc69c7a7aabbb380cc7233
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cpu/amp/grad_scaler.py
@@ -0,0 +1,35 @@
+from typing_extensions import deprecated
+
+import torch
+
+
+__all__ = ["GradScaler"]
+
+
+class GradScaler(torch.amp.GradScaler):
+    r"""
+    See :class:`torch.amp.GradScaler`.
+    ``torch.cpu.amp.GradScaler(args...)`` is deprecated. Please use ``torch.amp.GradScaler("cpu", args...)`` instead.
+    """
+
+    @deprecated(
+        "`torch.cpu.amp.GradScaler(args...)` is deprecated. "
+        "Please use `torch.amp.GradScaler('cpu', args...)` instead.",
+        category=FutureWarning,
+    )
+    def __init__(
+        self,
+        init_scale: float = 2.0**16,
+        growth_factor: float = 2.0,
+        backoff_factor: float = 0.5,
+        growth_interval: int = 2000,
+        enabled: bool = True,
+    ) -> None:
+        super().__init__(
+            "cpu",
+            init_scale=init_scale,
+            growth_factor=growth_factor,
+            backoff_factor=backoff_factor,
+            growth_interval=growth_interval,
+            enabled=enabled,
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/csrc/inductor/aoti_runtime/model.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/csrc/inductor/aoti_runtime/model.h
new file mode 100644
index 0000000000000000000000000000000000000000..253c5e917e76bdc8a2adc669404fc8d5c40b6b27
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/csrc/inductor/aoti_runtime/model.h
@@ -0,0 +1,62 @@
+#pragma once
+
+// WARNING: Be careful when adding new includes here. This header will be used
+// in model.so, and should not refer to any aten/c10 headers except the stable
+// C ABI defined in torch/csrc/inductor/aoti_torch/c/shim.h. The same rule
+// applies to other files under torch/csrc/inductor/aoti_runtime/.
+#include 
+
+namespace torch::aot_inductor {
+
+class AOTInductorModel : public AOTInductorModelBase {
+ public:
+  AOTInductorModel(
+      std::shared_ptr constants_map,
+      std::shared_ptr> constants_array,
+      const std::string& device_str,
+      std::optional cubin_dir);
+
+  std::unordered_map const_run_impl(
+      DeviceStreamType stream,
+      AOTIProxyExecutorHandle proxy_executor,
+      bool initialization = false);
+
+  void _const_run_impl(
+      std::vector& output_handles,
+      DeviceStreamType stream,
+      AOTIProxyExecutorHandle proxy_executor);
+
+  void run_impl(
+      AtenTensorHandle*
+          input_handles, // array of input AtenTensorHandle; handles
+                         // are stolen; the array itself is borrowed
+      AtenTensorHandle*
+          output_handles, // array for writing output AtenTensorHandle; handles
+                          // will be stolen by the caller; the array itself is
+                          // borrowed
+      DeviceStreamType stream,
+      AOTIProxyExecutorHandle proxy_executor);
+
+  template 
+  Outputs run_impl_minimal_arrayref_interface(
+      const Inputs& inputs,
+      DeviceStreamType stream,
+      AOTIProxyExecutorHandle proxy_executor);
+
+  static std::unique_ptr Create(
+      std::shared_ptr constants_map,
+      std::shared_ptr> constants_array,
+      const std::string& device_str,
+      std::optional cubin_dir) {
+    return std::make_unique(
+        std::move(constants_map),
+        std::move(constants_array),
+        device_str,
+        std::move(cubin_dir));
+  }
+
+ private:
+  std::unique_ptr kernels_;
+};
+
+} // namespace torch::aot_inductor
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..23d297b6d95e03064187b191c8849920e65a4ad7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/__init__.py
@@ -0,0 +1,1956 @@
+# mypy: allow-untyped-defs
+r"""
+This package adds support for CUDA tensor types.
+
+It implements the same function as CPU tensors, but they utilize
+GPUs for computation.
+
+It is lazily initialized, so you can always import it, and use
+:func:`is_available()` to determine if your system supports CUDA.
+
+:ref:`cuda-semantics` has more details about working with CUDA.
+"""
+
+import importlib
+import os
+import sys
+import threading
+import traceback
+import warnings
+from collections.abc import Callable
+from functools import lru_cache
+from typing import Any, cast, NewType, Optional, TYPE_CHECKING, Union
+
+import torch
+import torch._C
+from torch import device as _device
+from torch._utils import _dummy_type, _LazySeedTracker, classproperty
+
+from . import _device_limits, gds
+from ._utils import _get_device_index
+from .graphs import (
+    CUDAGraph,
+    graph,
+    graph_pool_handle,
+    is_current_stream_capturing,
+    make_graphed_callables,
+)
+from .green_contexts import GreenContext
+from .streams import Event, ExternalStream, Stream
+
+
+if TYPE_CHECKING:
+    from torch.types import Device
+
+try:
+    from torch._C import _cudart  # type: ignore[attr-defined]
+except ImportError:
+    _cudart = None
+
+_initialized = False
+_tls = threading.local()
+_initialization_lock = threading.Lock()
+_queued_calls: list[
+    tuple[Callable[[], None], list[str]]
+] = []  # don't invoke these until initialization occurs
+_is_in_bad_fork = getattr(torch._C, "_cuda_isInBadFork", lambda: False)
+
+_HAS_PYNVML = False
+_PYNVML_ERR = None
+try:
+    from torch import version as _version
+
+    try:
+        if not _version.hip:
+            import pynvml  # type: ignore[import]
+        else:
+            import ctypes
+            from pathlib import Path
+
+            # In ROCm (at least up through 6.3.2) there're 2 copies of libamd_smi.so:
+            # - One at lib/libamd_smi.so
+            # - One at share/amd_smi/amdsmi/libamd_smi.so
+            #
+            # The amdsmi python module hardcodes loading the second one in share-
+            # https://github.com/ROCm/amdsmi/blob/1d305dc9708e87080f64f668402887794cd46584/py-interface/amdsmi_wrapper.py#L174
+            #
+            # See also https://github.com/ROCm/amdsmi/issues/72.
+            #
+            # This creates an ODR violation if the copy of libamd_smi.so from lib
+            # is also loaded (via `ld` linking, `LD_LIBRARY_PATH` or `rpath`).
+            #
+            # In order to avoid the violation we hook CDLL and try using the
+            # already loaded version of amdsmi, or any version in the processes
+            # rpath/LD_LIBRARY_PATH first, so that we only load a single copy
+            # of the .so.
+            class _amdsmi_cdll_hook:
+                def __init__(self) -> None:
+                    self.original_CDLL = ctypes.CDLL  # type: ignore[misc,assignment]
+                    paths = ["libamd_smi.so"]
+                    if rocm_home := os.getenv("ROCM_HOME", os.getenv("ROCM_PATH")):
+                        paths = [os.path.join(rocm_home, "lib/libamd_smi.so")] + paths
+                    self.paths: list[str] = paths
+
+                def hooked_CDLL(
+                    self, name: Union[str, Path, None], *args: Any, **kwargs: Any
+                ) -> ctypes.CDLL:
+                    if name and Path(name).name == "libamd_smi.so":
+                        for path in self.paths:
+                            try:
+                                return self.original_CDLL(path, *args, **kwargs)
+                            except OSError:
+                                pass
+                    return self.original_CDLL(name, *args, **kwargs)  # type: ignore[arg-type]
+
+                def __enter__(self) -> None:
+                    ctypes.CDLL = self.hooked_CDLL  # type: ignore[misc,assignment]
+
+                def __exit__(self, type: Any, value: Any, traceback: Any) -> None:
+                    ctypes.CDLL = self.original_CDLL  # type: ignore[misc]
+
+            with _amdsmi_cdll_hook():
+                import amdsmi  # type: ignore[import]
+
+        _HAS_PYNVML = True
+    except ModuleNotFoundError:
+        pass
+    finally:
+        del _version
+except ImportError as err:
+    _PYNVML_ERR = err  # sometimes a lib is installed but the import fails for some other reason, so we log the error for later
+
+_lazy_seed_tracker = _LazySeedTracker()
+
+# Define dummy _CudaDeviceProperties type if PyTorch was compiled without CUDA
+if hasattr(torch._C, "_CudaDeviceProperties"):
+    _CudaDeviceProperties = torch._C._CudaDeviceProperties
+else:
+    _CudaDeviceProperties = _dummy_type("_CudaDeviceProperties")  # type: ignore[assignment, misc]
+
+if hasattr(torch._C, "_cuda_exchangeDevice"):
+    _exchange_device = torch._C._cuda_exchangeDevice
+else:
+
+    def _exchange_device(device: int) -> int:
+        if device < 0:
+            return -1
+        raise RuntimeError("PyTorch was compiled without CUDA support")
+
+
+if hasattr(torch._C, "_cuda_maybeExchangeDevice"):
+    _maybe_exchange_device = torch._C._cuda_maybeExchangeDevice
+else:
+
+    def _maybe_exchange_device(device: int) -> int:
+        if device < 0:
+            return -1
+        raise RuntimeError("PyTorch was compiled without CUDA support")
+
+
+has_half: bool = True
+has_magma: bool = torch._C._has_magma
+
+default_generators: tuple[torch._C.Generator] = ()  # type: ignore[assignment]
+
+
+def _is_compiled() -> bool:
+    r"""Return true if compile with CUDA support."""
+    return hasattr(torch._C, "_cuda_getDeviceCount")
+
+
+def _nvml_based_avail() -> bool:
+    return os.getenv("PYTORCH_NVML_BASED_CUDA_CHECK") == "1"
+
+
+def is_available() -> bool:
+    r"""
+    Return a bool indicating if CUDA is currently available.
+
+    .. note:: This function will NOT poison fork if the environment variable
+        ``PYTORCH_NVML_BASED_CUDA_CHECK=1`` is set. For more details, see
+        :ref:`multiprocessing-poison-fork-note`.
+    """
+    if not _is_compiled():
+        return False
+    if _nvml_based_avail():
+        # The user has set an env variable to request this availability check that attempts to avoid fork poisoning by
+        # using NVML at the cost of a weaker CUDA availability assessment. Note that if NVML discovery/initialization
+        # fails, this assessment falls back to the default CUDA Runtime API assessment (`cudaGetDeviceCount`)
+        return device_count() > 0
+    else:
+        # The default availability inspection never throws and returns 0 if the driver is missing or can't
+        # be initialized. This uses the CUDA Runtime API `cudaGetDeviceCount` which in turn initializes the CUDA Driver
+        # API via `cuInit`
+        return torch._C._cuda_getDeviceCount() > 0
+
+
+def is_bf16_supported(including_emulation: bool = True):
+    r"""Return a bool indicating if the current CUDA/ROCm device supports dtype bfloat16."""
+    # Check for ROCm, if true return true, no ROCM_VERSION check required,
+    # since it is supported on AMD GPU archs.
+    if torch.version.hip:
+        return True
+
+    # If CUDA is not available, than it does not support bf16 either
+    if not is_available():
+        return False
+
+    device = torch.cuda.current_device()
+
+    # Check for CUDA version and device compute capability.
+    # This is a fast way to check for it.
+    cuda_version = torch.version.cuda
+    if cuda_version is not None and torch.cuda.get_device_properties(device).major >= 8:
+        return True
+
+    if not including_emulation:
+        return False
+
+    # Finally try to create a bfloat16 device.
+    return _check_bf16_tensor_supported(device)
+
+
+@lru_cache(maxsize=16)
+def _check_bf16_tensor_supported(device: "Device"):
+    try:
+        torch.tensor([1.0], dtype=torch.bfloat16, device=device)
+        return True
+    except Exception:
+        return False
+
+
+def is_tf32_supported() -> bool:
+    r"""Return a bool indicating if the current CUDA/ROCm device supports dtype tf32."""
+    if torch.version.hip:
+        prop_name = torch.cuda.get_device_properties().gcnArchName
+        archs = ("gfx94", "gfx95")
+        for arch in archs:
+            if arch in prop_name:
+                return True
+        return False
+
+    # Otherwise, tf32 is supported on CUDA platforms that natively (i.e. no emulation)
+    # support bfloat16.
+    return is_bf16_supported(including_emulation=False)
+
+
+def _sleep(cycles):
+    torch._C._cuda_sleep(cycles)
+
+
+def _busy_wait_for_flag():
+    torch._C._cuda_busy_wait_for_flag()
+
+
+def _clear_flag():
+    torch._C._cuda_clear_flag()
+
+
+def _extract_arch_version(arch_string: str) -> int:
+    """Extracts the architecture string from a CUDA version"""
+    base = arch_string.split("_", maxsplit=2)[1]
+    base = base.removesuffix("a").removesuffix("f")
+    return int(base)
+
+
+def _check_capability():
+    incompatible_gpu_warn = """
+    Found GPU%d %s which is of cuda capability %d.%d.
+    Minimum and Maximum cuda capability supported by this version of PyTorch is
+    (%d.%d) - (%d.%d)
+    """
+    matched_cuda_warn = """
+    Please install PyTorch with a following CUDA
+    configurations: {} following instructions at
+    https://pytorch.org/get-started/locally/
+    """
+
+    # Binary CUDA_ARCHES SUPPORTED by PyTorch
+    CUDA_ARCHES_SUPPORTED = {
+        "12.6": {"min": 50, "max": 90},
+        "12.8": {"min": 70, "max": 120},
+        "13.0": {"min": 75, "max": 120},
+    }
+
+    if (
+        torch.version.cuda is not None and torch.cuda.get_arch_list()
+    ):  # on ROCm we don't want this check
+        for d in range(device_count()):
+            capability = get_device_capability(d)
+            major = capability[0]
+            minor = capability[1]
+            name = get_device_name(d)
+            current_arch = major * 10 + minor
+            min_arch = min(
+                (_extract_arch_version(arch) for arch in torch.cuda.get_arch_list()),
+                default=50,
+            )
+            max_arch = max(
+                (_extract_arch_version(arch) for arch in torch.cuda.get_arch_list()),
+                default=50,
+            )
+            if current_arch < min_arch or current_arch > max_arch:
+                warnings.warn(
+                    incompatible_gpu_warn
+                    % (
+                        d,
+                        name,
+                        major,
+                        minor,
+                        min_arch // 10,
+                        min_arch % 10,
+                        max_arch // 10,
+                        max_arch % 10,
+                    ),
+                    stacklevel=2,
+                )
+                matched_arches = ""
+                for arch, arch_info in CUDA_ARCHES_SUPPORTED.items():
+                    if (
+                        current_arch >= arch_info["min"]
+                        and current_arch <= arch_info["max"]
+                    ):
+                        matched_arches += f" {arch}"
+                if matched_arches != "":
+                    warnings.warn(
+                        matched_cuda_warn.format(matched_arches), stacklevel=2
+                    )
+
+
+def _check_cubins():
+    incompatible_device_warn = """
+{} with CUDA capability sm_{} is not compatible with the current PyTorch installation.
+The current PyTorch install supports CUDA capabilities {}.
+If you want to use the {} GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/
+"""
+    if torch.version.cuda is None:  # on ROCm we don't want this check
+        return
+    arch_list = get_arch_list()
+    if len(arch_list) == 0:
+        return
+    supported_sm = [_extract_arch_version(arch) for arch in arch_list if "sm_" in arch]
+    for idx in range(device_count()):
+        cap_major, cap_minor = get_device_capability(idx)
+        # NVIDIA GPU compute architectures are backward compatible within major version
+        supported = any(sm // 10 == cap_major for sm in supported_sm)
+        if not supported:
+            device_name = get_device_name(idx)
+            capability = cap_major * 10 + cap_minor
+            warnings.warn(
+                incompatible_device_warn.format(
+                    device_name, capability, " ".join(arch_list), device_name
+                ),
+                stacklevel=2,
+            )
+
+
+def is_initialized():
+    r"""Return whether PyTorch's CUDA state has been initialized."""
+    return _initialized and not _is_in_bad_fork()
+
+
+def _lazy_call(callable, **kwargs):
+    with _initialization_lock:
+        if is_initialized():
+            callable()
+        else:
+            # TODO(torch_deploy): this accesses linecache, which attempts to read the
+            # file system to get traceback info. Patch linecache or do something
+            # else here if this ends up being important.
+            global _lazy_seed_tracker
+            if kwargs.get("seed_all", False):
+                _lazy_seed_tracker.queue_seed_all(callable, traceback.format_stack())
+            elif kwargs.get("seed", False):
+                _lazy_seed_tracker.queue_seed(callable, traceback.format_stack())
+            else:
+                # Don't store the actual traceback to avoid memory cycle
+                _queued_calls.append((callable, traceback.format_stack()))
+
+
+_lazy_call(_check_capability)
+_lazy_call(_check_cubins)
+
+
+class DeferredCudaCallError(Exception):
+    pass
+
+
+AcceleratorError = torch._C.AcceleratorError
+OutOfMemoryError = torch._C.OutOfMemoryError
+
+
+def init():
+    r"""Initialize PyTorch's CUDA state.
+
+    You may need to call this explicitly if you are interacting with
+    PyTorch via its C API, as Python bindings for CUDA functionality
+    will not be available until this initialization takes place.
+    Ordinary users should not need this, as all of PyTorch's CUDA methods
+    automatically initialize CUDA state on-demand.
+
+    Does nothing if the CUDA state is already initialized.
+    """
+    _lazy_init()
+
+
+def _lazy_init():
+    global _initialized, _queued_calls
+    if is_initialized() or hasattr(_tls, "is_initializing"):
+        return
+    with _initialization_lock:
+        # We be double-checked locking, boys!  This is OK because
+        # the above test was GIL protected anyway.  The inner test
+        # is for when a thread blocked on some other thread which was
+        # doing the initialization; when they get the lock, they will
+        # find there is nothing left to do.
+        if is_initialized():
+            return
+        # It is important to prevent other threads from entering _lazy_init
+        # immediately, while we are still guaranteed to have the GIL, because some
+        # of the C calls we make below will release the GIL
+        if _is_in_bad_fork():
+            raise RuntimeError(
+                "Cannot re-initialize CUDA in forked subprocess. To use CUDA with "
+                "multiprocessing, you must use the 'spawn' start method"
+            )
+        if not hasattr(torch._C, "_cuda_getDeviceCount"):
+            raise AssertionError("Torch not compiled with CUDA enabled")
+        if _cudart is None:
+            raise AssertionError(
+                "libcudart functions unavailable. It looks like you have a broken build?"
+            )
+        # This function throws if there's a driver initialization error, no GPUs
+        # are found or any other error occurs
+        torch._C._cuda_init()
+        # Some of the queued calls may reentrantly call _lazy_init();
+        # we need to just return without initializing in that case.
+        # However, we must not let any *other* threads in!
+        _tls.is_initializing = True
+
+        _queued_calls.extend(calls for calls in _lazy_seed_tracker.get_calls() if calls)
+
+        try:
+            for queued_call, orig_traceback in _queued_calls:
+                try:
+                    queued_call()
+                except Exception as e:
+                    msg = (
+                        f"CUDA call failed lazily at initialization with error: {str(e)}\n\n"
+                        f"CUDA call was originally invoked at:\n\n{''.join(orig_traceback)}"
+                    )
+                    raise DeferredCudaCallError(msg) from e
+        finally:
+            delattr(_tls, "is_initializing")
+        _initialized = True
+
+
+def cudart():
+    r"""Retrieves the CUDA runtime API module.
+
+
+    This function initializes the CUDA runtime environment if it is not already
+    initialized and returns the CUDA runtime API module (_cudart). The CUDA
+    runtime API module provides access to various CUDA runtime functions.
+
+    Args:
+        ``None``
+
+    Returns:
+        module: The CUDA runtime API module (_cudart).
+
+    Raises:
+        RuntimeError: If CUDA cannot be re-initialized in a forked subprocess.
+        AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable.
+
+    Example of CUDA operations with profiling:
+        >>> import torch
+        >>> from torch.cuda import cudart, check_error
+        >>> import os
+        >>>
+        >>> os.environ["CUDA_PROFILE"] = "1"
+        >>>
+        >>> def perform_cuda_operations_with_streams():
+        >>>     stream = torch.cuda.Stream()
+        >>>     with torch.cuda.stream(stream):
+        >>>         x = torch.randn(100, 100, device='cuda')
+        >>>         y = torch.randn(100, 100, device='cuda')
+        >>>         z = torch.mul(x, y)
+        >>>     return z
+        >>>
+        >>> torch.cuda.synchronize()
+        >>> print("====== Start nsys profiling ======")
+        >>> check_error(cudart().cudaProfilerStart())
+        >>> with torch.autograd.profiler.emit_nvtx():
+        >>>     result = perform_cuda_operations_with_streams()
+        >>>     print("CUDA operations completed.")
+        >>> check_error(torch.cuda.cudart().cudaProfilerStop())
+        >>> print("====== End nsys profiling ======")
+
+    To run this example and save the profiling information, execute:
+        >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py
+
+    This command profiles the CUDA operations in the provided script and saves
+    the profiling information to a file named `trace_name.prof`.
+    The `--profile-from-start off` option ensures that profiling starts only
+    after the `cudaProfilerStart` call in the script.
+    The `--csv` and `--print-summary` options format the profiling output as a
+    CSV file and print a summary, respectively.
+    The `-o` option specifies the output file name, and the `-f` option forces the
+    overwrite of the output file if it already exists.
+    """
+    _lazy_init()
+    return _cudart
+
+
+class cudaStatus:
+    SUCCESS: int = 0
+    ERROR_NOT_READY: int = 34
+
+
+class CudaError(RuntimeError):
+    def __init__(self, code: int) -> None:
+        # pyrefly: ignore [missing-attribute]
+        msg = _cudart.cudaGetErrorString(_cudart.cudaError(code))
+        super().__init__(f"{msg} ({code})")
+
+
+def check_error(res: int) -> None:
+    r"""Raise an error if the result of a CUDA runtime API call is not success."""
+    # pyrefly: ignore [missing-attribute]
+    if res != _cudart.cudaError.success:
+        raise CudaError(res)
+
+
+class _DeviceGuard:
+    def __init__(self, index: int):
+        self.idx = index
+        self.prev_idx = -1
+
+    def __enter__(self):
+        self.prev_idx = torch.cuda._exchange_device(self.idx)
+
+    def __exit__(self, type: Any, value: Any, traceback: Any):
+        self.idx = torch.cuda._maybe_exchange_device(self.prev_idx)
+        return False
+
+
+class device:
+    r"""Context-manager that changes the selected device.
+
+    Args:
+        device (torch.device or int): device index to select. It's a no-op if
+            this argument is a negative integer or ``None``.
+    """
+
+    def __init__(self, device: Any):
+        self.idx = _get_device_index(device, optional=True)
+        self.prev_idx = -1
+
+    def __enter__(self):
+        self.prev_idx = torch.cuda._exchange_device(self.idx)
+
+    def __exit__(self, type: Any, value: Any, traceback: Any):
+        self.idx = torch.cuda._maybe_exchange_device(self.prev_idx)
+        return False
+
+
+class device_of(device):
+    r"""Context-manager that changes the current device to that of given object.
+
+    You can use both tensors and storages as arguments. If a given object is
+    not allocated on a GPU, this is a no-op.
+
+    Args:
+        obj (Tensor or Storage): object allocated on the selected device.
+    """
+
+    def __init__(self, obj):
+        idx = obj.get_device() if obj.is_cuda else -1
+        super().__init__(idx)
+
+
+def set_device(device: "Device") -> None:
+    r"""Set the current device.
+
+    Usage of this function is discouraged in favor of :any:`device`. In most
+    cases it's better to use ``CUDA_VISIBLE_DEVICES`` environmental variable.
+
+    Args:
+        device (torch.device or int): selected device. This function is a no-op
+            if this argument is negative.
+    """
+    device = _get_device_index(device)
+    if device >= 0:
+        torch._C._cuda_setDevice(device)
+
+
+def get_device_name(device: "Device" = None) -> str:
+    r"""Get the name of a device.
+
+    Args:
+        device (torch.device or int or str, optional): device for which to return the
+            name. This function is a no-op if this argument is a negative
+            integer. It uses the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    Returns:
+        str: the name of the device
+    """
+    return get_device_properties(device).name
+
+
+def get_device_capability(device: "Device" = None) -> tuple[int, int]:
+    r"""Get the cuda capability of a device.
+
+    Args:
+        device (torch.device or int or str, optional): device for which to return the
+            device capability. This function is a no-op if this argument is
+            a negative integer. It uses the current device, given by
+            :func:`~torch.cuda.current_device`, if :attr:`device` is ``None``
+            (default).
+
+    Returns:
+        tuple(int, int): the major and minor cuda capability of the device
+    """
+    prop = get_device_properties(device)
+    return prop.major, prop.minor
+
+
+# pyrefly: ignore [not-a-type]
+def get_device_properties(device: "Device" = None) -> _CudaDeviceProperties:
+    r"""Get the properties of a device.
+
+    Args:
+        device (torch.device or int or str, optional): device for which to return the
+            properties of the device.  It uses the current device, given by
+            :func:`~torch.cuda.current_device`, if :attr:`device` is ``None``
+            (default).
+
+    Returns:
+        _CudaDeviceProperties: the properties of the device
+    """
+    _lazy_init()  # will define _get_device_properties
+    device = _get_device_index(device, optional=True)
+    if device < 0 or device >= device_count():
+        raise AssertionError("Invalid device id")
+    return _get_device_properties(device)  # type: ignore[name-defined]
+
+
+def can_device_access_peer(device: "Device", peer_device: "Device") -> bool:
+    r"""Check if peer access between two devices is possible."""
+    _lazy_init()
+    device = _get_device_index(device, optional=True)
+    peer_device = _get_device_index(peer_device)
+    if device < 0 or device >= device_count():
+        raise AssertionError("Invalid device id")
+    if peer_device < 0 or peer_device >= device_count():
+        raise AssertionError("Invalid peer device id")
+    return torch._C._cuda_canDeviceAccessPeer(device, peer_device)
+
+
+class StreamContext:
+    r"""Context-manager that selects a given stream.
+
+    All CUDA kernels queued within its context will be enqueued on a selected
+    stream.
+
+    Args:
+        Stream (Stream): selected stream. This manager is a no-op if it's
+            ``None``.
+    .. note:: Streams are per-device.
+    """
+
+    cur_stream: Optional["torch.cuda.Stream"]
+
+    def __init__(self, stream: Optional["torch.cuda.Stream"]):
+        self.stream = stream
+        self.idx = _get_device_index(None, True)
+        if not torch.jit.is_scripting():
+            if self.idx is None:
+                # pyrefly: ignore [bad-assignment]
+                self.idx = -1
+
+        self.src_prev_stream = (
+            None if not torch.jit.is_scripting() else torch.cuda.default_stream(None)
+        )
+        self.dst_prev_stream = (
+            None if not torch.jit.is_scripting() else torch.cuda.default_stream(None)
+        )
+
+    def __enter__(self):
+        # Local cur_stream variable for type refinement
+        cur_stream = self.stream
+        # Return if stream is None or CUDA device not available
+        if cur_stream is None or self.idx == -1:
+            return
+        self.src_prev_stream = torch.cuda.current_stream(None)
+
+        # If the stream is not on the current device, then
+        # set the current stream on the device
+        if self.src_prev_stream.device != cur_stream.device:
+            with device(cur_stream.device):
+                self.dst_prev_stream = torch.cuda.current_stream(cur_stream.device)
+        torch.cuda.set_stream(cur_stream)
+
+    def __exit__(self, type: Any, value: Any, traceback: Any):
+        # Local cur_stream variable for type refinement
+        cur_stream = self.stream
+        # If stream is None or no CUDA device available, return
+        if cur_stream is None or self.idx == -1:
+            return
+
+        # Reset the stream on the original device
+        # and destination device
+        if self.src_prev_stream.device != cur_stream.device:  # type: ignore[union-attr]
+            torch.cuda.set_stream(self.dst_prev_stream)  # type: ignore[arg-type]
+        torch.cuda.set_stream(self.src_prev_stream)  # type: ignore[arg-type]
+
+
+def stream(stream: Optional["torch.cuda.Stream"]) -> StreamContext:
+    r"""Wrap around the Context-manager StreamContext that selects a given stream.
+
+    Arguments:
+        stream (Stream): selected stream. This manager is a no-op if it's
+            ``None``.
+    .. note::
+        In eager mode stream is of type Stream class while in JIT it is
+        an object of the custom class ``torch.classes.cuda.Stream``.
+    """
+    return StreamContext(stream)
+
+
+def _set_stream_by_id(stream_id, device_index, device_type):
+    r"""set stream specified by the stream id, device index and
+        device type
+
+    Args: stream_id (int): stream id in stream pool
+          device_index (int): device index in topo
+          device_type (int): enum device type
+    """
+    torch._C._cuda_setStream(
+        stream_id=stream_id,
+        device_index=device_index,
+        device_type=device_type,
+    )
+
+
+def set_stream(stream: Stream):
+    r"""Set the current stream.This is a wrapper API to set the stream.
+        Usage of this function is discouraged in favor of the ``stream``
+        context manager.
+
+    Args:
+        stream (Stream): selected stream. This function is a no-op
+            if this argument is ``None``.
+    """
+    if stream is None:
+        return
+    _set_stream_by_id(
+        stream_id=stream.stream_id,
+        device_index=stream.device_index,
+        device_type=stream.device_type,
+    )
+
+
+def _parse_visible_devices() -> Union[list[int], list[str]]:
+    r"""Parse CUDA_VISIBLE_DEVICES environment variable."""
+    var = os.getenv("CUDA_VISIBLE_DEVICES")
+
+    if torch.version.hip:
+        hip_devices = os.getenv("HIP_VISIBLE_DEVICES")
+        rocr_devices = os.getenv("ROCR_VISIBLE_DEVICES")
+
+        # You must take care if both HIP and ROCR env vars are set as they have
+        # different meanings. Both env vars accept either a list of ints or a
+        # list of UUIDs. The ROCR env var is processed first which then reduces
+        # the number of GPUs that HIP can select from.
+        if rocr_devices is not None:
+            rocr_count = len(rocr_devices.split(","))
+            if hip_devices is not None:
+                # sanity check if both env vars are set
+                if len(hip_devices.split(",")) > rocr_count:
+                    raise RuntimeError(
+                        "HIP_VISIBLE_DEVICES contains more devices than ROCR_VISIBLE_DEVICES"
+                    )
+                # HIP_VISIBLE_DEVICES is preferred over ROCR_VISIBLE_DEVICES
+                var = hip_devices
+            else:
+                return list(range(rocr_count))
+        elif hip_devices is not None:
+            var = hip_devices
+
+    if var is None:
+        return list(range(64))
+
+    def _strtoul(s: str) -> int:
+        """Return -1 or positive integer sequence string starts with."""
+        if not s:
+            return -1
+        for idx, c in enumerate(s):
+            if not (c.isdigit() or (idx == 0 and c in "+-")):
+                break
+            if idx + 1 == len(s):
+                idx += 1
+        return int(s[:idx]) if idx > 0 else -1
+
+    def parse_list_with_prefix(lst: str, prefix: str) -> list[str]:
+        rcs: list[str] = []
+        for elem in lst.split(","):
+            # Repeated id results in empty set
+            if elem in rcs:
+                return cast(list[str], [])
+            # Anything other but prefix is ignored
+            if not elem.startswith(prefix):
+                break
+            rcs.append(elem)
+        return rcs
+
+    if var.startswith("GPU-"):
+        return parse_list_with_prefix(var, "GPU-")
+    if var.startswith("MIG-"):
+        return parse_list_with_prefix(var, "MIG-")
+    # CUDA_VISIBLE_DEVICES uses something like strtoul
+    # which makes `1gpu2,2ampere` is equivalent to `1,2`
+    rc: list[int] = []
+    for elem in var.split(","):
+        x = _strtoul(elem.strip())
+        # Repeated ordinal results in empty set
+        if x in rc:
+            return cast(list[int], [])
+        # Negative value aborts the sequence
+        if x < 0:
+            break
+        rc.append(x)
+    return rc
+
+
+def _raw_device_count_amdsmi() -> int:
+    if not _HAS_PYNVML:  # If amdsmi is not available
+        return -1
+    try:
+        amdsmi.amdsmi_init()
+    except amdsmi.AmdSmiException as e:
+        warnings.warn(
+            f"Can't initialize amdsmi - Error code: {e.err_code}", stacklevel=2
+        )
+        return -1
+    socket_handles = amdsmi.amdsmi_get_processor_handles()
+    return len(socket_handles)
+
+
+def _raw_device_count_nvml() -> int:
+    r"""Return number of devices as reported by NVML or negative value if NVML discovery/initialization failed."""
+    from ctypes import byref, c_int, CDLL
+
+    nvml_h = CDLL("libnvidia-ml.so.1")
+    rc = nvml_h.nvmlInit()
+    if rc != 0:
+        warnings.warn("Can't initialize NVML", stacklevel=2)
+        return -1
+    dev_count = c_int(-1)
+    rc = nvml_h.nvmlDeviceGetCount_v2(byref(dev_count))
+    if rc != 0:
+        warnings.warn("Can't get nvml device count", stacklevel=2)
+        return -1
+    del nvml_h
+    return dev_count.value
+
+
+def _raw_device_uuid_amdsmi() -> Optional[list[str]]:
+    from ctypes import byref, c_int, c_void_p, CDLL, create_string_buffer
+
+    if not _HAS_PYNVML:  # If amdsmi is not available
+        return None
+    try:
+        amdsmi.amdsmi_init()
+    except amdsmi.AmdSmiException:
+        warnings.warn("Can't initialize amdsmi", stacklevel=2)
+        return None
+    try:
+        socket_handles = amdsmi.amdsmi_get_processor_handles()
+        dev_count = len(socket_handles)
+    except amdsmi.AmdSmiException:
+        warnings.warn("Can't get amdsmi device count", stacklevel=2)
+        return None
+    uuids: list[str] = []
+    for idx in range(dev_count):
+        try:
+            handler = amdsmi.amdsmi_get_processor_handles()[idx]
+        except amdsmi.AmdSmiException:
+            warnings.warn("Cannot get amd device handler", stacklevel=2)
+            return None
+        try:
+            uuid = amdsmi.amdsmi_get_gpu_asic_info(handler)["asic_serial"][
+                2:
+            ]  # Removes 0x prefix from serial
+        except amdsmi.AmdSmiException:
+            warnings.warn("Cannot get uuid for amd device", stacklevel=2)
+            return None
+        uuids.append(
+            str(uuid).lower()
+        )  # Lower-case to match expected HIP_VISIBLE_DEVICES uuid input
+    return uuids
+
+
+def _raw_device_uuid_nvml() -> Optional[list[str]]:
+    r"""Return list of device UUID as reported by NVML or None if NVM discovery/initialization failed."""
+    from ctypes import byref, c_int, c_void_p, CDLL, create_string_buffer
+
+    nvml_h = CDLL("libnvidia-ml.so.1")
+    rc = nvml_h.nvmlInit()
+    if rc != 0:
+        warnings.warn("Can't initialize NVML", stacklevel=2)
+        return None
+    dev_count = c_int(-1)
+    rc = nvml_h.nvmlDeviceGetCount_v2(byref(dev_count))
+    if rc != 0:
+        warnings.warn("Can't get nvml device count", stacklevel=2)
+        return None
+    uuids: list[str] = []
+    for idx in range(dev_count.value):
+        dev_id = c_void_p()
+        rc = nvml_h.nvmlDeviceGetHandleByIndex_v2(idx, byref(dev_id))
+        if rc != 0:
+            warnings.warn("Can't get device handle", stacklevel=2)
+            return None
+        buf_len = 96
+        buf = create_string_buffer(buf_len)
+        rc = nvml_h.nvmlDeviceGetUUID(dev_id, buf, buf_len)
+        if rc != 0:
+            warnings.warn("Can't get device UUID", stacklevel=2)
+            return None
+        uuids.append(buf.raw.decode("ascii").strip("\0"))
+    del nvml_h
+    return uuids
+
+
+def _transform_uuid_to_ordinals(candidates: list[str], uuids: list[str]) -> list[int]:
+    r"""Given the set of partial uuids and list of known uuids builds a set of ordinals excluding ambiguous partials IDs."""
+
+    def uuid_to_ordinal(candidate: str, uuids: list[str]) -> int:
+        best_match = -1
+        for idx, uuid in enumerate(uuids):
+            if not uuid.startswith(candidate):
+                continue
+            # Ambiguous candidate
+            if best_match != -1:
+                return -1
+            best_match = idx
+        return best_match
+
+    rc: list[int] = []
+    for candidate in candidates:
+        if torch.version.hip:
+            candidate = candidate.replace(
+                "GPU-", "", 1
+            )  # Remove GPU-prefix to match amdsmi asic serial
+        idx = uuid_to_ordinal(candidate, uuids)
+        # First invalid ordinal stops parsing
+        if idx < 0:
+            break
+        # Duplicates result in empty set
+        if idx in rc:
+            return cast(list[int], [])
+        rc.append(idx)
+    return rc
+
+
+def _device_count_amdsmi() -> int:
+    visible_devices = _parse_visible_devices()
+    if not visible_devices:
+        return 0
+    try:
+        if type(visible_devices[0]) is str:
+            uuids = _raw_device_uuid_amdsmi()
+            if uuids is None:
+                return -1
+            # Create string version of visible devices to avoid mypy warnings
+            visible_device_str = cast(list[str], visible_devices)
+            visible_devices = _transform_uuid_to_ordinals(visible_device_str, uuids)
+        else:
+            raw_cnt = _raw_device_count_amdsmi()
+            if raw_cnt <= 0:
+                return raw_cnt
+            # Trim the list up to a maximum available device
+            # pyrefly: ignore [bad-argument-type]
+            for idx, val in enumerate(visible_devices):
+                # pyrefly: ignore [redundant-cast]
+                if cast(int, val) >= raw_cnt:
+                    return idx
+    except OSError:
+        return -1
+    except AttributeError:
+        return -1
+    return len(visible_devices)
+
+
+def _device_count_nvml() -> int:
+    r"""Return number of devices as reported by NVML taking CUDA_VISIBLE_DEVICES into account.
+
+    Negative value is returned if NVML discovery or initialization has failed.
+    """
+    visible_devices = _parse_visible_devices()
+    if not visible_devices:
+        return 0
+    try:
+        if type(visible_devices[0]) is str:
+            # Skip MIG parsing
+            if visible_devices[0].startswith("MIG-"):
+                return -1
+            uuids = _raw_device_uuid_nvml()
+            if uuids is None:
+                return -1
+            visible_devices = _transform_uuid_to_ordinals(
+                cast(list[str], visible_devices), uuids
+            )
+        else:
+            raw_cnt = _raw_device_count_nvml()
+            if raw_cnt <= 0:
+                return raw_cnt
+            # Trim the list up to a maximum available device
+            # pyrefly: ignore [bad-argument-type]
+            for idx, val in enumerate(visible_devices):
+                # pyrefly: ignore [redundant-cast]
+                if cast(int, val) >= raw_cnt:
+                    return idx
+    except OSError:
+        return -1
+    except AttributeError:
+        return -1
+    return len(visible_devices)
+
+
+def _get_nvml_device_index(device: "Device") -> int:
+    r"""Return the NVML index of the device, taking CUDA_VISIBLE_DEVICES into account."""
+    idx = _get_device_index(device, optional=True)
+    visible_devices = _parse_visible_devices()
+    if type(visible_devices[0]) is str:
+        uuids = _raw_device_uuid_nvml()
+        if uuids is None:
+            raise RuntimeError("Can't get device UUIDs")
+        visible_devices = _transform_uuid_to_ordinals(
+            cast(list[str], visible_devices), uuids
+        )
+    visible_devices = cast(list[int], visible_devices)
+    if idx < 0 or idx >= len(visible_devices):
+        raise RuntimeError(
+            f"device {idx} is not visible (CUDA_VISIBLE_DEVICES={visible_devices})"
+        )
+    return visible_devices[idx]
+
+
+_cached_device_count: Optional[int] = None
+
+
+def device_count() -> int:
+    r"""
+    Return the number of GPUs available.
+
+    .. note:: This API will NOT poison fork if NVML discovery succeeds.
+        See :ref:`multiprocessing-poison-fork-note` for more details.
+    """
+    global _cached_device_count
+    if not _is_compiled():
+        return 0
+    if _cached_device_count is not None:
+        return _cached_device_count
+    # bypass _device_count_nvml() if rocm (not supported)
+    nvml_count = _device_count_amdsmi() if torch.version.hip else _device_count_nvml()
+    r = torch._C._cuda_getDeviceCount() if nvml_count < 0 else nvml_count
+    # NB: Do not cache the device count prior to CUDA initialization, because
+    # the number of devices can change due to changes to CUDA_VISIBLE_DEVICES
+    # setting prior to CUDA initialization.
+    if _initialized:
+        _cached_device_count = r
+    return r
+
+
+def get_arch_list() -> list[str]:
+    r"""Return list CUDA architectures this library was compiled for."""
+    if not is_available():
+        return []
+    arch_flags = torch._C._cuda_getArchFlags()
+    if arch_flags is None:
+        return []
+    return arch_flags.split()
+
+
+def get_gencode_flags() -> str:
+    r"""Return NVCC gencode flags this library was compiled with."""
+    arch_list = get_arch_list()
+    if len(arch_list) == 0:
+        return ""
+    arch_list_ = [arch.split("_") for arch in arch_list]
+    return " ".join(
+        [
+            f"-gencode compute=compute_{arch},code={kind}_{arch}"
+            for (kind, arch) in arch_list_
+        ]
+    )
+
+
+def current_device() -> int:
+    r"""Return the index of a currently selected device."""
+    _lazy_init()
+    return torch._C._cuda_getDevice()
+
+
+def synchronize(device: "Device" = None) -> None:
+    r"""Wait for all kernels in all streams on a CUDA device to complete.
+
+    Args:
+        device (torch.device or int, optional): device for which to synchronize.
+            It uses the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+    """
+    _lazy_init()
+    with torch.cuda.device(device):
+        return torch._C._cuda_synchronize()
+
+
+def ipc_collect():
+    r"""Force collects GPU memory after it has been released by CUDA IPC.
+
+    .. note::
+        Checks if any sent CUDA tensors could be cleaned from the memory. Force
+        closes shared memory file used for reference counting if there is no
+        active counters. Useful when the producer process stopped actively sending
+        tensors and want to release unused memory.
+    """
+    _lazy_init()
+    return torch._C._cuda_ipc_collect()
+
+
+def current_stream(device: "Device" = None) -> Stream:
+    r"""Return the currently selected :class:`Stream` for a given device.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            the currently selected :class:`Stream` for the current device, given
+            by :func:`~torch.cuda.current_device`, if :attr:`device` is ``None``
+            (default).
+    """
+    _lazy_init()
+    streamdata = torch._C._cuda_getCurrentStream(
+        _get_device_index(device, optional=True)
+    )
+    return Stream(
+        stream_id=streamdata[0], device_index=streamdata[1], device_type=streamdata[2]
+    )
+
+
+def default_stream(device: "Device" = None) -> Stream:
+    r"""Return the default :class:`Stream` for a given device.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            the default :class:`Stream` for the current device, given by
+            :func:`~torch.cuda.current_device`, if :attr:`device` is ``None``
+            (default).
+    """
+    _lazy_init()
+    streamdata = torch._C._cuda_getDefaultStream(
+        _get_device_index(device, optional=True)
+    )
+    return Stream(
+        stream_id=streamdata[0], device_index=streamdata[1], device_type=streamdata[2]
+    )
+
+
+def get_stream_from_external(data_ptr: int, device: "Device" = None) -> Stream:
+    r"""Return a :class:`Stream` from an externally allocated CUDA stream.
+
+    This function is used to wrap streams allocated in other libraries in order
+    to facilitate data exchange and multi-library interactions.
+
+    .. note:: This function doesn't manage the stream life-cycle, it is the user
+       responsibility to keep the referenced stream alive while this returned
+       stream is being used.
+
+    Args:
+        data_ptr(int): Integer representation of the `cudaStream_t` value that
+            is allocated externally.
+        device(torch.device or int, optional): the device where the stream
+            was originally allocated. If device is specified incorrectly,
+            subsequent launches using this stream may fail.
+    """
+    _lazy_init()
+    streamdata = torch._C._cuda_getStreamFromExternal(
+        data_ptr, _get_device_index(device, optional=True)
+    )
+    return Stream(
+        stream_id=streamdata[0], device_index=streamdata[1], device_type=streamdata[2]
+    )
+
+
+def current_blas_handle():
+    r"""Return cublasHandle_t pointer to current cuBLAS handle"""
+    _lazy_init()
+    return torch._C._cuda_getCurrentBlasHandle()
+
+
+def set_sync_debug_mode(debug_mode: Union[int, str]) -> None:
+    r"""Set the debug mode for cuda synchronizing operations.
+
+    Args:
+        debug_mode(str or int): if "default" or 0, don't error or warn on synchronizing operations,
+            if "warn" or 1, warn on synchronizing operations, if "error" or 2, error out synchronizing operations.
+
+    Warning:
+        This is an experimental feature, and not all synchronizing operations will trigger warning or error. In
+        particular, operations in torch.distributed and torch.sparse namespaces are not covered yet.
+    """
+    _lazy_init()
+    if isinstance(debug_mode, str):
+        if debug_mode == "default":
+            debug_mode = 0
+        elif debug_mode == "warn":
+            debug_mode = 1
+        elif debug_mode == "error":
+            debug_mode = 2
+        else:
+            raise RuntimeError(
+                "invalid value of debug_mode, expected one of `default`, `warn`, `error`"
+            )
+
+    torch._C._cuda_set_sync_debug_mode(debug_mode)
+
+
+def get_sync_debug_mode() -> int:
+    r"""Return current value of debug mode for cuda synchronizing operations."""
+    _lazy_init()
+    return torch._C._cuda_get_sync_debug_mode()
+
+
+def _get_pynvml_handler(device: "Device" = None):
+    if not _HAS_PYNVML:
+        raise ModuleNotFoundError(
+            "nvidia-ml-py does not seem to be installed or it can't be imported."
+            # pyrefly: ignore [invalid-inheritance]
+        ) from _PYNVML_ERR
+    # pyrefly: ignore [import-error,missing-module-attribute]
+    from pynvml import NVMLError_DriverNotLoaded
+
+    try:
+        pynvml.nvmlInit()
+    except NVMLError_DriverNotLoaded as e:
+        raise RuntimeError("cuda driver can't be loaded, is cuda enabled?") from e
+
+    device = _get_nvml_device_index(device)
+    handle = pynvml.nvmlDeviceGetHandleByIndex(device)
+    return handle
+
+
+def _get_amdsmi_handler(device: "Device" = None):
+    if not _HAS_PYNVML:
+        raise ModuleNotFoundError(
+            "amdsmi does not seem to be installed or it can't be imported."
+            # pyrefly: ignore [invalid-inheritance]
+        ) from _PYNVML_ERR
+    try:
+        amdsmi.amdsmi_init()
+    except amdsmi.AmdSmiException as e:
+        raise RuntimeError(
+            "amdsmi driver can't be loaded, requires >=ROCm5.6 installation"
+        ) from e
+    device = _get_amdsmi_device_index(device)
+    handle = amdsmi.amdsmi_get_processor_handles()[device]
+    return handle
+
+
+def _get_amdsmi_device_index(device: "Device") -> int:
+    r"""Return the amdsmi index of the device, taking visible_devices into account."""
+    idx = _get_device_index(device, optional=True)
+    visible_devices = _parse_visible_devices()
+    if type(visible_devices[0]) is str:
+        uuids = _raw_device_uuid_amdsmi()
+        if uuids is None:
+            raise RuntimeError("Can't get device UUIDs")
+        visible_devices_str = cast(
+            list[str], visible_devices
+        )  # Create str variable for mypy
+        visible_devices = _transform_uuid_to_ordinals(visible_devices_str, uuids)
+    idx_map = dict(enumerate(cast(list[int], visible_devices)))
+    if idx not in idx_map:
+        raise RuntimeError(
+            f"device {idx} is not visible (HIP_VISIBLE_DEVICES={visible_devices})"
+        )
+    return idx_map[idx]
+
+
+def _get_amdsmi_device_memory_used(device: "Device" = None) -> int:
+    handle = _get_amdsmi_handler(device)
+    # amdsmi_get_gpu_vram_usage returns mem usage in megabytes
+    mem_mega_bytes = amdsmi.amdsmi_get_gpu_vram_usage(handle)["vram_used"]
+    mem_bytes = mem_mega_bytes * 1024 * 1024
+    return mem_bytes
+
+
+def _get_amdsmi_memory_usage(device: "Device" = None) -> int:
+    handle = _get_amdsmi_handler(device)
+    return amdsmi.amdsmi_get_gpu_activity(handle)["umc_activity"]
+
+
+def _get_amdsmi_utilization(device: "Device" = None) -> int:
+    handle = _get_amdsmi_handler(device)
+    return amdsmi.amdsmi_get_gpu_activity(handle)["gfx_activity"]
+
+
+def _get_amdsmi_temperature(device: "Device" = None) -> int:
+    handle = _get_amdsmi_handler(device)
+    return amdsmi.amdsmi_get_temp_metric(
+        handle,
+        amdsmi.AmdSmiTemperatureType.JUNCTION,
+        amdsmi.AmdSmiTemperatureMetric.CURRENT,
+    )
+
+
+def _get_amdsmi_power_draw(device: "Device" = None) -> int:
+    handle = _get_amdsmi_handler(device)
+    socket_power = amdsmi.amdsmi_get_power_info(handle)["average_socket_power"]
+    if socket_power != "N/A":
+        return socket_power
+    else:
+        socket_power = amdsmi.amdsmi_get_power_info(handle)["current_socket_power"]
+        if socket_power != "N/A":
+            return socket_power
+        else:
+            return 0
+
+
+def _get_amdsmi_clock_rate(device: "Device" = None) -> int:
+    handle = _get_amdsmi_handler(device)
+    clock_info = amdsmi.amdsmi_get_clock_info(handle, amdsmi.AmdSmiClkType.GFX)
+    if "cur_clk" in clock_info:  # ROCm 6.2 deprecation
+        clock_rate = clock_info["cur_clk"]
+    else:
+        clock_rate = clock_info["clk"]
+    if clock_rate != "N/A":
+        return clock_rate
+    else:
+        return 0
+
+
+def device_memory_used(device: "Device" = None) -> int:
+    r"""Return used global (device) memory in bytes as given by `nvidia-smi` or `amd-smi`.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    """
+    if not torch.version.hip:
+        handle = _get_pynvml_handler()
+        device = _get_nvml_device_index(device)
+        handle = pynvml.nvmlDeviceGetHandleByIndex(device)
+        return pynvml.nvmlDeviceGetMemoryInfo(handle).used
+    else:
+        return _get_amdsmi_device_memory_used(device)
+
+
+def memory_usage(device: "Device" = None) -> int:
+    r"""Return the percent of time over the past sample period during which global (device)
+    memory was being read or written as given by `nvidia-smi`.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    Warning: Each sample period may be between 1 second and 1/6 second,
+    depending on the product being queried.
+    """
+    if not torch.version.hip:
+        handle = _get_pynvml_handler()
+        device = _get_nvml_device_index(device)
+        handle = pynvml.nvmlDeviceGetHandleByIndex(device)
+        return pynvml.nvmlDeviceGetUtilizationRates(handle).memory
+    else:
+        return _get_amdsmi_memory_usage(device)
+
+
+def utilization(device: "Device" = None) -> int:
+    r"""Return the percent of time over the past sample period during which one or
+    more kernels was executing on the GPU as given by `nvidia-smi`.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    Warning: Each sample period may be between 1 second and 1/6 second,
+    depending on the product being queried.
+    """
+    if not torch.version.hip:
+        handle = _get_pynvml_handler(device)
+        device = _get_nvml_device_index(device)
+        handle = pynvml.nvmlDeviceGetHandleByIndex(device)
+        return pynvml.nvmlDeviceGetUtilizationRates(handle).gpu
+    else:
+        return _get_amdsmi_utilization(device)
+
+
+def temperature(device: "Device" = None) -> int:
+    r"""Return the average temperature of the GPU sensor in Degrees C (Centigrades).
+
+    The average temperature is computed based on past sample period as given by `nvidia-smi`.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    Warning: Each sample period may be between 1 second and 1/6 second,
+    depending on the product being queried.
+    """
+    if not torch.version.hip:
+        handle = _get_pynvml_handler(device)
+        # 0 refers to the temperature sensor for the GPU die.
+        return pynvml.nvmlDeviceGetTemperature(handle, 0)
+    else:
+        return _get_amdsmi_temperature(device)
+
+
+def power_draw(device: "Device" = None) -> int:
+    r"""Return the average power draw of the GPU sensor in mW (MilliWatts)
+        over the past sample period as given by `nvidia-smi` for Fermi or newer fully supported devices.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    Warning: Each sample period may be between 1 second and 1/6 second,
+    depending on the product being queried.
+    """
+    if not torch.version.hip:
+        handle = _get_pynvml_handler(device)
+        return pynvml.nvmlDeviceGetPowerUsage(handle)
+    else:
+        return _get_amdsmi_power_draw(device)
+
+
+def clock_rate(device: "Device" = None) -> int:
+    r"""Return the clock speed of the GPU SM in MHz (megahertz) over the past sample period as given by `nvidia-smi`.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    Warning: Each sample period may be between 1 second and 1/6 second,
+    depending on the product being queried.
+    """
+    if not torch.version.hip:
+        handle = _get_pynvml_handler(device)
+        return pynvml.nvmlDeviceGetClockInfo(handle, 1)
+    else:
+        return _get_amdsmi_clock_rate(device)
+
+
+def _get_device(device: Union[int, str, torch.device]) -> torch.device:
+    r"""Return the torch.device type object from the passed in device.
+
+    Args:
+        device (torch.device or int): selected device.
+    """
+    if isinstance(device, str):
+        device = torch.device(device)
+    elif isinstance(device, int):
+        device = torch.device("cuda", device)
+    return device
+
+
+def _get_generator(device: torch.device) -> torch._C.Generator:
+    r"""Return the CUDA Generator object for the given device.
+
+    Args:
+        device (torch.device): selected device.
+    """
+    idx = device.index
+    if idx is None:
+        idx = current_device()
+    return torch.cuda.default_generators[idx]
+
+
+def _set_rng_state_offset(
+    offset: int, device: Union[int, str, torch.device] = "cuda"
+) -> None:
+    r"""Set the random number generator state offset of the specified GPU.
+
+    Args:
+        offset (int): The desired offset
+        device (torch.device or int, optional): The device to set the RNG state.
+            Default: ``'cuda'`` (i.e., ``torch.device('cuda')``, the current CUDA device).
+    """
+    final_device = _get_device(device)
+
+    def cb():
+        default_generator = _get_generator(final_device)
+        default_generator.set_offset(offset)
+
+    _lazy_call(cb)
+
+
+def _get_rng_state_offset(device: Union[int, str, torch.device] = "cuda") -> int:
+    r"""Return the random number generator state offset of the specified GPU.
+
+    Args:
+        device (torch.device or int, optional): The device to return the RNG state offset of.
+            Default: ``'cuda'`` (i.e., ``torch.device('cuda')``, the current CUDA device).
+
+    .. warning::
+        This function eagerly initializes CUDA.
+    """
+    _lazy_init()
+    final_device = _get_device(device)
+    default_generator = _get_generator(final_device)
+    return default_generator.get_offset()
+
+
+# pyrefly: ignore [deprecated]
+from .memory import *  # noqa: F403
+from .random import *  # noqa: F403
+
+
+################################################################################
+# Define Storage and Tensor classes
+################################################################################
+
+
+@staticmethod  # type: ignore[misc]
+def _lazy_new(cls, *args, **kwargs):
+    _lazy_init()
+    # We may need to call lazy init again if we are a forked child
+    # del _CudaBase.__new__
+    return super(_CudaBase, cls).__new__(cls, *args, **kwargs)
+
+
+class _CudaBase:
+    is_cuda = True
+    is_sparse = False
+
+    def type(self, *args, **kwargs):
+        # We could use a Protocol here to tell mypy that self has `get_device` method
+        # but it is only available in the typing module on Python >= 3.8
+        # or on typing_extensions module on Python >= 3.6
+        with device(self.get_device()):  # type: ignore[attr-defined]
+            return super().type(*args, **kwargs)  # type: ignore[misc]
+
+    __new__ = _lazy_new
+
+
+from torch.storage import _LegacyStorage, _warn_typed_storage_removal
+
+
+class _CudaLegacyStorage(_LegacyStorage):
+    @classmethod
+    def from_buffer(cls, *args, **kwargs):
+        _warn_typed_storage_removal()
+        raise RuntimeError("from_buffer: Not available for CUDA storage")
+
+    @classmethod
+    def _new_with_weak_ptr(cls, *args, **kwargs):
+        raise RuntimeError("_new_with_weak_ptr: Not available for CUDA storage")
+
+    @classmethod
+    def _new_shared_filename(cls, manager, obj, size, *, device=None, dtype=None):
+        raise RuntimeError("_new_shared_filename: Not available for CUDA storage")
+
+
+class ByteStorage(_CudaLegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal()
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.uint8
+
+
+class DoubleStorage(_CudaLegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal()
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.double
+
+
+class FloatStorage(_CudaLegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal()
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.float
+
+
+class HalfStorage(_CudaLegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal()
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.half
+
+
+class LongStorage(_CudaLegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal()
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.long
+
+
+class IntStorage(_CudaLegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal()
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.int
+
+
+class ShortStorage(_CudaLegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal()
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.short
+
+
+class CharStorage(_CudaLegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal()
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.int8
+
+
+class BoolStorage(_CudaLegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal()
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.bool
+
+
+class BFloat16Storage(_CudaLegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal()
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.bfloat16
+
+
+class ComplexDoubleStorage(_CudaLegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal()
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.cdouble
+
+
+class ComplexFloatStorage(_CudaLegacyStorage):
+    @classproperty
+    def dtype(self):
+        _warn_typed_storage_removal()
+        return self._dtype
+
+    @classproperty
+    def _dtype(self):
+        return torch.cfloat
+
+
+del _LegacyStorage
+del _CudaLegacyStorage
+
+torch._storage_classes.add(DoubleStorage)
+torch._storage_classes.add(FloatStorage)
+torch._storage_classes.add(LongStorage)
+torch._storage_classes.add(IntStorage)
+torch._storage_classes.add(ShortStorage)
+torch._storage_classes.add(CharStorage)
+torch._storage_classes.add(ByteStorage)
+torch._storage_classes.add(HalfStorage)
+torch._storage_classes.add(BoolStorage)
+torch._storage_classes.add(BFloat16Storage)
+torch._storage_classes.add(ComplexDoubleStorage)
+torch._storage_classes.add(ComplexFloatStorage)
+
+
+class _WrappedTritonKernel:
+    """Just a simple wrapper to store some metadata for testing purposes."""
+
+    def __init__(self, kernel):
+        self.kernel = kernel
+        self.kernel_invoked = False
+
+    def __call__(self, *args, **kwargs):
+        res = self.kernel(*args, **kwargs)
+        self.kernel_invoked = True
+        return res
+
+
+def _register_triton_kernels():
+    @_WrappedTritonKernel
+    def kernel_impl(*args, **kwargs):
+        from torch.sparse._triton_ops import bsr_dense_mm
+
+        # pyrefly: ignore [not-callable]
+        return bsr_dense_mm(*args, skip_checks=True, **kwargs)
+
+    @_WrappedTritonKernel
+    def addmm_kernel_impl(*args, **kwargs):
+        from torch.sparse._triton_ops import bsr_dense_addmm
+
+        return bsr_dense_addmm(*args, skip_checks=True, **kwargs)
+
+    has_triton = importlib.util.find_spec("triton") is not None
+    if has_triton:
+        torch._TritonLibrary.registerOp(
+            "_triton_bsr_dense_mm_out",
+            "_triton_bsr_dense_mm_out(Tensor bsr, Tensor dense, *, Tensor(a!) out) -> Tensor(a!)",
+            kernel_impl,
+            "SparseCsrCUDA",
+        )
+
+        torch._TritonLibrary.registerOp(
+            "_triton_bsr_dense_addmm_out",
+            (
+                "_triton_bsr_dense_addmm_out(Tensor input, Tensor bsr, Tensor dense,"
+                " *, Scalar beta, Scalar alpha, Tensor(a!) out) -> Tensor(a!)"
+            ),
+            addmm_kernel_impl,
+            "SparseCsrCUDA",
+        )
+
+
+_lazy_call(_register_triton_kernels)
+
+
+def _compile_kernel(
+    kernel_source: str,
+    kernel_name: str,
+    compute_capability: Optional[str] = None,
+    cuda_include_dirs: Optional[list] = None,
+    nvcc_options: Optional[list] = None,
+):
+    """
+    Compiles a CUDA kernel using NVRTC and returns a callable function.
+
+    This function is a wrapper for NVRTC that enables runtime compilation of CUDA kernels.
+    Note that this returns a raw CUDA kernel that operates on raw memory pointers.
+    To use this kernel as a proper PyTorch operator, you should wrap it following the guide at:
+    pytorch.org/tutorials/advanced/python_custom_ops.html
+
+    Args:
+        kernel_source (str): The CUDA kernel source code as a string
+        kernel_name (str): The name of the kernel function to compile
+        compute_capability (str, optional): The compute capability to target (e.g., "86").
+                                           If None, will detect from current device.
+        cuda_include_dirs (list, optional): List of directories containing CUDA headers
+        nvcc_options (list, optional): Additional options to pass to NVRTC
+
+    Returns:
+        callable: A Python function that can be called with PyTorch tensor arguments to execute the kernel
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> kernel_code = '''
+        extern "C"
+        __global__ void add_tensors(const float* a, const float* b, float* c, int n) {
+            int i = threadIdx.x + blockIdx.x * blockDim.x;
+            if (i < n)
+                c[i] = a[i] + b[i];
+        }
+        '''
+        >>> add_kernel = torch.cuda.compile_kernel(kernel_code, "add_tensors")
+        >>> a = torch.randn(1024, device="cuda")
+        >>> b = torch.randn(1024, device="cuda")
+        >>> c = torch.empty_like(a)
+        >>> add_kernel(grid=(4, 1, 1), block=(256, 1, 1), args=[a, b, c, a.numel()])
+    """
+    from torch.cuda._utils import _cuda_load_module, _nvrtc_compile
+
+    # Compile the kernel to PTX
+    ptx, mangled_name = _nvrtc_compile(
+        kernel_source,
+        kernel_name,
+        compute_capability,
+        cuda_include_dirs,
+        nvcc_options,
+    )
+
+    # Load the module and get the kernel
+    result = _cuda_load_module(ptx, [mangled_name])
+
+    if isinstance(result, dict):
+        return result[mangled_name]
+    else:
+        # This branch shouldn't be executed if kernel_names is provided,
+        # but MyPy needs this to understand type narrowing
+        return getattr(result, mangled_name)
+
+
+from . import amp, jiterator, nvtx, profiler, sparse, tunable
+
+
+_POOL_HANDLE = NewType("_POOL_HANDLE", tuple[int, int])
+
+
+__all__ = [
+    # Typed storage and tensors
+    "BFloat16Storage",
+    "BFloat16Tensor",
+    "BoolStorage",
+    "BoolTensor",
+    "ByteStorage",
+    "ByteTensor",
+    "CharStorage",
+    "CharTensor",
+    "ComplexDoubleStorage",
+    "ComplexFloatStorage",
+    "DoubleStorage",
+    "DoubleTensor",
+    "FloatStorage",
+    "FloatTensor",
+    "HalfStorage",
+    "HalfTensor",
+    "IntStorage",
+    "IntTensor",
+    "LongStorage",
+    "LongTensor",
+    "ShortStorage",
+    "ShortTensor",
+    "CUDAGraph",
+    "CudaError",
+    "DeferredCudaCallError",
+    "Event",
+    "ExternalStream",
+    "Stream",
+    "StreamContext",
+    "GreenContext",
+    "amp",
+    "caching_allocator_alloc",
+    "caching_allocator_delete",
+    "caching_allocator_enable",
+    "can_device_access_peer",
+    "check_error",
+    "cudaStatus",
+    "cudart",
+    "current_blas_handle",
+    "current_device",
+    "current_stream",
+    "default_generators",
+    "default_stream",
+    "device",
+    "device_count",
+    "device_memory_used",
+    "device_of",
+    "empty_cache",
+    "get_allocator_backend",
+    "CUDAPluggableAllocator",
+    "change_current_allocator",
+    "get_arch_list",
+    "get_device_capability",
+    "get_device_name",
+    "get_device_properties",
+    "get_gencode_flags",
+    "get_per_process_memory_fraction",
+    "get_rng_state",
+    "get_rng_state_all",
+    "get_stream_from_external",
+    "get_sync_debug_mode",
+    "graph",
+    "graph_pool_handle",
+    "graphs",
+    "has_half",
+    "has_magma",
+    "host_memory_stats",
+    "host_memory_stats_as_nested_dict",
+    "init",
+    "initial_seed",
+    "ipc_collect",
+    "is_available",
+    "is_bf16_supported",
+    "is_current_stream_capturing",
+    "is_initialized",
+    "is_tf32_supported",
+    "jiterator",
+    "list_gpu_processes",
+    "make_graphed_callables",
+    "manual_seed",
+    "manual_seed_all",
+    "max_memory_allocated",
+    "max_memory_cached",
+    "max_memory_reserved",
+    "mem_get_info",
+    "memory",
+    "memory_allocated",
+    "memory_cached",
+    "memory_reserved",
+    "memory_snapshot",
+    "memory_stats",
+    "memory_stats_as_nested_dict",
+    "memory_summary",
+    "memory_usage",
+    "MemPool",
+    "use_mem_pool",
+    "temperature",
+    "power_draw",
+    "clock_rate",
+    "nccl",
+    "nvtx",
+    "profiler",
+    "random",
+    "reset_accumulated_host_memory_stats",
+    "reset_accumulated_memory_stats",
+    "reset_max_memory_allocated",
+    "reset_max_memory_cached",
+    "reset_peak_host_memory_stats",
+    "reset_peak_memory_stats",
+    "seed",
+    "seed_all",
+    "set_device",
+    "set_per_process_memory_fraction",
+    "set_rng_state",
+    "set_rng_state_all",
+    "set_stream",
+    "set_sync_debug_mode",
+    "sparse",
+    "stream",
+    "streams",
+    "synchronize",
+    "tunable",
+    "utilization",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/_device_limits.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/_device_limits.py
new file mode 100644
index 0000000000000000000000000000000000000000..60aeedc8053abfeb3d6772c6e1408eebe7affbf7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/_device_limits.py
@@ -0,0 +1,140 @@
+import torch
+from torch._C import dtype
+
+
+__all__ = ["GPULimits"]
+
+
+class GPULimits:
+    r"""Utility class that provides the theoretical limits of Nvidia GPU devices. The
+    limits don't take into account thermal throttling (assume that the GPU run at its
+    peak rated frequency). This is because user hardware configuration may influence
+    power behavior.
+    """
+
+    def __init__(self, target_device: torch.device):
+        # The device properties object is obtained by calling 'cudaGetDeviceProperties' CUDA
+        # runtime function. We need the total memory bus width and the memory clock rate to
+        # calculate the memory bandwidth.
+        self.device_properties = torch.cuda.get_device_properties(target_device)
+
+        # The compute capability is needed to determine the number of FLOPs per cycle per SM
+        self.compute_capability = int(
+            f"{self.device_properties.major}{self.device_properties.minor}"
+        )
+
+    # FLOPs per cycle information derived from Table 2 in:
+    # https://resources.nvidia.com/en-us-hopper-architecture/nvidia-h100-tensor-c
+
+    # Returns the number of FMA instructions retired per cycle per SM for a given
+    # data type, when tensor cores are NOT used
+    def get_fma_per_cycle_per_sm_cuda_cores(self, data_type: dtype) -> int:
+        hardcoded_device_values = {
+            # Ampere Architecture
+            "fp16_80": 256,
+            "fp32_80": 64,
+            "fp64_80": 32,
+            # Hopper Architecture
+            "fp16_90": 64,
+            "fp32_90": 128,
+            "fp64_90": 64,
+            # Blackwell Architecture
+            "fp16_100": 256,
+            "fp32_100": 128,
+            "fp64_100": 64,
+        }
+        dict_key = ""
+        if data_type is torch.float16:
+            dict_key = f"fp16_{self.compute_capability}"
+        elif data_type is torch.float32:
+            dict_key = f"fp32_{self.compute_capability}"
+        elif data_type is torch.float64:
+            dict_key = f"fp64_{self.compute_capability}"
+        else:
+            dict_key = "unknown"
+
+        if dict_key not in hardcoded_device_values:
+            raise RuntimeError(
+                f"No data for sm_{self.compute_capability} and {data_type}."
+            )
+
+        return hardcoded_device_values[dict_key]
+
+    # Returns the number of FMA instructions retired per cycle per SM for a given
+    # data type, when tensor cores ARE used
+    def get_fma_per_cycle_per_sm_tensor_cores(self, data_type: dtype) -> int:
+        hardcoded_device_values = {
+            # Ampere Architecture
+            "int8_80": 2048,
+            "fp16_80": 1024,
+            "fp32_80": 512,
+            "fp64_80": 64,
+            # Hopper Architecture
+            "int8_90": 4096,
+            "fp8_90": 4096,
+            "fp16_90": 2048,
+            "fp32_90": 1024,
+            "fp64_90": 128,
+            # Blackwell Architecture
+            "int8_100": 8192,
+            "fp8_100": 8192,
+            "fp16_100": 4096,
+            "fp32_100": 2048,
+        }
+        dict_key = ""
+        if data_type is torch.float16:
+            dict_key = f"fp16_{self.compute_capability}"
+        elif data_type is torch.bfloat16:
+            # FP16 and BF16 are equivalent in terms of FLOPs per cycle per SM
+            dict_key = f"fp16_{self.compute_capability}"
+        elif data_type is torch.float32:
+            dict_key = f"fp32_{self.compute_capability}"
+        elif data_type is torch.int8:
+            dict_key = f"int8_{self.compute_capability}"
+        elif data_type is torch.float64:
+            dict_key = f"fp64_{self.compute_capability}"
+        else:
+            dict_key = "unknown"
+
+        if dict_key not in hardcoded_device_values:
+            raise RuntimeError(
+                f"No data for sm_{self.compute_capability} and {data_type}."
+            )
+
+        return hardcoded_device_values[dict_key]
+
+    def get_tflops_per_second(
+        self, data_type: dtype, use_tensor_cores: bool = True
+    ) -> float:
+        num_sms = self.device_properties.multi_processor_count
+        clock_rate = self.device_properties.clock_rate  # KHz
+
+        fma_per_cycle = 0
+        if use_tensor_cores:
+            fma_per_cycle = self.get_fma_per_cycle_per_sm_tensor_cores(data_type)
+        else:
+            fma_per_cycle = self.get_fma_per_cycle_per_sm_cuda_cores(data_type)
+
+        # 1 FMA counts as 2 floating point operations
+        # Clock rate is in KHz
+        tflops_per_second = num_sms * fma_per_cycle * 2 * clock_rate / 1e9
+        return tflops_per_second
+
+    def get_memory_bandwidth_Bps(self) -> int:
+        # DRAM devices are Double-Data which means they provide an output at both fronts of
+        # a clock beat
+        bus_bytes_per_cycle = int(2 * self.device_properties.memory_bus_width / 8)
+        mem_clock_rate_Hz = self.device_properties.memory_clock_rate * 1000
+        bytes_per_second = bus_bytes_per_cycle * mem_clock_rate_Hz * 2
+        return bytes_per_second
+
+    def get_shared_memory_bandwidth_Bps(self) -> int:
+        # Each warp can LD or ST 32 x 4 bytes per cycle. To calculate the
+        # device's throughput we need to multiply with frequency and number of SMs.
+        num_sms = self.device_properties.multi_processor_count
+        bytes_per_cycle_per_sm = 128
+        bytes_per_cycle_per_device = num_sms * bytes_per_cycle_per_sm
+        bytes_per_second = (
+            bytes_per_cycle_per_device * self.device_properties.clock_rate * 1000
+        )
+        return bytes_per_second
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/_gpu_trace.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/_gpu_trace.py
new file mode 100644
index 0000000000000000000000000000000000000000..d3b8f7e4626f9bec478a7c74292f78894d62ee6f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/_gpu_trace.py
@@ -0,0 +1,73 @@
+from collections.abc import Callable
+
+from torch._utils import CallbackRegistry
+
+
+EventCreationCallbacks: "CallbackRegistry[int]" = CallbackRegistry(
+    "CUDA event creation"
+)
+EventDeletionCallbacks: "CallbackRegistry[int]" = CallbackRegistry(
+    "CUDA event deletion"
+)
+EventRecordCallbacks: "CallbackRegistry[int, int]" = CallbackRegistry(
+    "CUDA event record"
+)
+EventWaitCallbacks: "CallbackRegistry[int, int]" = CallbackRegistry("CUDA event wait")
+MemoryAllocationCallbacks: "CallbackRegistry[int]" = CallbackRegistry(
+    "CUDA memory allocation"
+)
+MemoryDeallocationCallbacks: "CallbackRegistry[int]" = CallbackRegistry(
+    "CUDA memory deallocation"
+)
+StreamCreationCallbacks: "CallbackRegistry[int]" = CallbackRegistry(
+    "CUDA stream creation"
+)
+DeviceSynchronizationCallbacks: "CallbackRegistry[[]]" = CallbackRegistry(
+    "CUDA device synchronization"
+)
+StreamSynchronizationCallbacks: "CallbackRegistry[int]" = CallbackRegistry(
+    "CUDA stream synchronization"
+)
+EventSynchronizationCallbacks: "CallbackRegistry[int]" = CallbackRegistry(
+    "CUDA event synchronization"
+)
+
+
+def register_callback_for_event_creation(cb: Callable[[int], None]) -> None:
+    EventCreationCallbacks.add_callback(cb)
+
+
+def register_callback_for_event_deletion(cb: Callable[[int], None]) -> None:
+    EventDeletionCallbacks.add_callback(cb)
+
+
+def register_callback_for_event_record(cb: Callable[[int, int], None]) -> None:
+    EventRecordCallbacks.add_callback(cb)
+
+
+def register_callback_for_event_wait(cb: Callable[[int, int], None]) -> None:
+    EventWaitCallbacks.add_callback(cb)
+
+
+def register_callback_for_memory_allocation(cb: Callable[[int], None]) -> None:
+    MemoryAllocationCallbacks.add_callback(cb)
+
+
+def register_callback_for_memory_deallocation(cb: Callable[[int], None]) -> None:
+    MemoryDeallocationCallbacks.add_callback(cb)
+
+
+def register_callback_for_stream_creation(cb: Callable[[int], None]) -> None:
+    StreamCreationCallbacks.add_callback(cb)
+
+
+def register_callback_for_device_synchronization(cb: Callable[[], None]) -> None:
+    DeviceSynchronizationCallbacks.add_callback(cb)
+
+
+def register_callback_for_stream_synchronization(cb: Callable[[int], None]) -> None:
+    StreamSynchronizationCallbacks.add_callback(cb)
+
+
+def register_callback_for_event_synchronization(cb: Callable[[int], None]) -> None:
+    EventSynchronizationCallbacks.add_callback(cb)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/_memory_viz.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/_memory_viz.py
new file mode 100644
index 0000000000000000000000000000000000000000..56da01b202d62cc4968a252482dbb43df984915d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/_memory_viz.py
@@ -0,0 +1,795 @@
+# mypy: allow-untyped-defs
+import base64
+import io
+import json
+import operator
+import os
+import pickle
+import subprocess
+import sys
+import warnings
+from functools import lru_cache
+from itertools import groupby
+from typing import Any
+
+
+cache = lru_cache(None)
+
+__all__ = ["format_flamegraph", "segments", "memory", "compare"]
+
+
+def _frame_fmt(f, full_filename=False):
+    i = f["line"]
+    fname = f["filename"]
+    if not full_filename:
+        fname = fname.split("/")[-1]
+    func = f["name"]
+    return f"{fname}:{i}:{func}"
+
+
+@cache
+def _frame_filter(name, filename):
+    omit_functions = [
+        "unwind::unwind",
+        "CapturedTraceback::gather",
+        "gather_with_cpp",
+        "_start",
+        "__libc_start_main",
+        "PyEval_",
+        "PyObject_",
+        "PyFunction_",
+    ]
+    omit_filenames = [
+        "core/boxing",
+        "/Register",
+        "/Redispatch",
+        "pythonrun.c",
+        "Modules/main.c",
+        "Objects/call.c",
+        "Objects/methodobject.c",
+        "pycore_ceval.h",
+        "ceval.c",
+        "cpython/abstract.h",
+    ]
+    for of in omit_functions:
+        if of in name:
+            return False
+    for of in omit_filenames:
+        if of in filename:
+            return False
+    return True
+
+
+def _frames_fmt(frames, full_filename=False, reverse=False):
+    if reverse:
+        frames = reversed(frames)
+    return [
+        _frame_fmt(f, full_filename)
+        for f in frames
+        if _frame_filter(f["name"], f["filename"])
+    ]
+
+
+def _block_extra_legacy(b):
+    if "history" in b:
+        frames = b["history"][0].get("frames", [])
+        real_size = b["history"][0]["real_size"]
+    else:
+        real_size = b.get("requested_size", b["size"])
+        frames = []
+    return frames, real_size
+
+
+def _block_extra(b):
+    if "frames" not in b:
+        # old snapshot format made it more complicated to get frames/allocated size
+        return _block_extra_legacy(b)
+    return b["frames"], b["requested_size"]
+
+
+def format_flamegraph(flamegraph_lines, flamegraph_script=None):
+    if flamegraph_script is None:
+        cache_dir = os.path.expanduser("~/.cache/")
+        os.makedirs(cache_dir, exist_ok=True)
+        flamegraph_script = f"{cache_dir}/flamegraph.pl"
+    if not os.path.exists(flamegraph_script):
+        import tempfile
+        import urllib.request
+
+        print(f"Downloading flamegraph.pl to: {flamegraph_script}")
+        with tempfile.NamedTemporaryFile(mode="wb", suffix=".pl") as f:
+            urllib.request.urlretrieve(
+                "https://raw.githubusercontent.com/brendangregg/FlameGraph/master/flamegraph.pl",
+                f.name,
+            )
+            try:
+                os.chmod(f.name, 0o755)
+                os.rename(f.name, flamegraph_script)
+            except OSError:  # noqa: B001,E722
+                # Ok to skip, the file will be removed by tempfile
+                pass
+    args = [flamegraph_script, "--countname", "bytes"]
+    with subprocess.Popen(
+        args, stdin=subprocess.PIPE, stdout=subprocess.PIPE, encoding="utf-8"
+    ) as p:
+        assert p.stdin is not None
+        assert p.stdout is not None
+        p.stdin.write(flamegraph_lines)
+        p.stdin.close()
+        result = p.stdout.read()
+        p.stdout.close()
+        p.wait()
+        assert p.wait() == 0
+        return result
+
+
+def _write_blocks(f, prefix, blocks):
+    def frames_fragment(frames):
+        if not frames:
+            return ""
+        return ";".join(_frames_fmt(frames, reverse=True))
+
+    for b in blocks:
+        if "history" not in b:
+            frames, accounted_for_size = _block_extra(b)
+            f.write(
+                f"{prefix};{b['state']};{frames_fragment(frames)} {accounted_for_size}\n"
+            )
+        else:
+            accounted_for_size = 0
+            for h in b["history"]:
+                sz = h["real_size"]
+                accounted_for_size += sz
+                if "frames" in h:
+                    frames = h["frames"]
+                    f.write(f"{prefix};{b['state']};{frames_fragment(frames)} {sz}\n")
+                else:
+                    f.write(f"{prefix};{b['state']}; {sz}\n")
+        gaps = b["size"] - accounted_for_size
+        if gaps:
+            f.write(f"{prefix};{b['state']}; {gaps}\n")
+
+
+def segments(snapshot, format_flamegraph=format_flamegraph):
+    f = io.StringIO()
+    for seg in snapshot["segments"]:
+        prefix = f"stream_{seg['stream']};seg_{seg['address']}"
+        _write_blocks(f, prefix, seg["blocks"])
+    return format_flamegraph(f.getvalue())
+
+
+def memory(snapshot, format_flamegraph=format_flamegraph):
+    f = io.StringIO()
+    for seg in snapshot["segments"]:
+        prefix = f"stream_{seg['stream']}"
+        _write_blocks(f, prefix, seg["blocks"])
+    return format_flamegraph(f.getvalue())
+
+
+def compare(before, after, format_flamegraph=format_flamegraph):
+    def _seg_key(seg):
+        return (seg["address"], seg["total_size"])
+
+    def _seg_info(seg):
+        return f"stream_{seg['stream']};seg_{seg['address']}"
+
+    f = io.StringIO()
+
+    before_segs = {_seg_key(seg) for seg in before}
+    after_segs = {_seg_key(seg) for seg in after}
+
+    print(f"only_before = {[a for a, _ in (before_segs - after_segs)]}")
+    print(f"only_after = {[a for a, _ in (after_segs - before_segs)]}")
+
+    for seg in before:
+        if _seg_key(seg) not in after_segs:
+            _write_blocks(f, f"only_before;{_seg_info(seg)}", seg["blocks"])
+
+    for seg in after:
+        if _seg_key(seg) not in before_segs:
+            _write_blocks(f, f"only_after;{_seg_info(seg)}", seg["blocks"])
+
+    return format_flamegraph(f.getvalue())
+
+
+def _format_size(num):
+    # https://stackoverflow.com/questions/1094841/get-human-readable-version-of-file-size
+    for unit in ["", "Ki", "Mi", "Gi", "Ti", "Pi", "Ei", "Zi"]:
+        if abs(num) < 1024.0:
+            return f"{num:3.1f}{unit}B"
+        num /= 1024.0
+    return f"{num:.1f}YiB"
+
+
+class Bytes:
+    def __init__(self, value):
+        self.value = value
+
+    def __add__(self, rhs):
+        return Bytes(self.value + rhs)
+
+    def __repr__(self):
+        return _format_size(self.value)
+
+
+def calc_active(seg):
+    return sum(b["size"] for b in seg["blocks"] if b["state"] == "active_allocated")
+
+
+def _report_free(free_external, free_internal):
+    total = free_external + free_internal
+    suffix = ""
+    if total != 0:
+        pct = (free_internal / total) * 100
+        suffix = f" ({pct:.1f}% internal)"
+    return f"{Bytes(total)}{suffix}"
+
+
+PAGE_SIZE = 1024 * 1024 * 20
+legend = f"""\
+
+Legend:
+    [a     ] - a segment in the allocator
+     ^-- a page {Bytes(PAGE_SIZE)} of memory in the segment
+    a-z: pages filled with a single block's content
+    ' ': page is completely free
+    *: page if completely full with multiple blocks
+    0-9: page is partially full with tensors of multiple blocks (9 == 90% full)
+    (X% internal) - of the free memory, X% is free because we rounded the size of the allocation.
+"""
+
+
+def segsum(data):
+    r"""Visually reports how the allocator has filled its segments.
+
+    This printout can help debug fragmentation issues since free fragments
+    will appear as gaps in this printout.  The amount of free space is reported
+    for each segment.
+    We distinguish between internal free memory which occurs because the
+    allocator rounds the allocation size, and external free memory, which are
+    the gaps between allocations in a segment.
+    Args:
+        data: snapshot dictionary created from _snapshot()
+    """
+    out = io.StringIO()
+    out.write(f"Summary of segments >= {Bytes(PAGE_SIZE)} in size\n")
+    total_reserved = 0
+    total_allocated = 0
+    free_external = 0
+    free_internal = 0
+    for seg in sorted(
+        data["segments"], key=lambda x: (x["total_size"], calc_active(x))
+    ):
+        total_reserved += seg["total_size"]
+
+        seg_free_external = 0
+        seg_free_internal = 0
+        seg_allocated = 0
+        all_ranges = []
+        boffset = 0
+        for b in seg["blocks"]:
+            active = b["state"] == "active_allocated"
+            if active:
+                _, allocated_size = _block_extra(b)
+                all_ranges.append((boffset, allocated_size, True))
+                seg_allocated += allocated_size
+                seg_free_internal += b["size"] - allocated_size
+            else:
+                seg_free_external += b["size"]
+
+            boffset += b["size"]
+
+        total_allocated += seg_allocated
+        free_external += seg_free_external
+        free_internal += seg_free_internal
+
+        nseg = (seg["total_size"] - 1) // PAGE_SIZE + 1
+        occupied = [" " for _ in range(nseg)]
+        frac = [0.0 for _ in range(nseg)]
+        active_size = 0
+        for i, (start_, size, active) in enumerate(all_ranges):
+            active_size += size
+            finish_ = start_ + size
+            start = start_ // PAGE_SIZE
+            finish = (finish_ - 1) // PAGE_SIZE + 1
+            m = chr(ord("a" if active else "A") + (i % 26))
+            for j in range(start, finish):
+                s = max(start_, j * PAGE_SIZE)
+                e = min(finish_, (j + 1) * PAGE_SIZE)
+                frac[j] += (e - s) / PAGE_SIZE
+                if occupied[j] != " ":
+                    occupied[j] = "0123456789*"[int(frac[j] * 10)]
+                else:
+                    occupied[j] = m
+        stream = "" if seg["stream"] == 0 else f", stream_{seg['stream']}"
+        body = "".join(occupied)
+        assert (
+            seg_free_external + seg_free_internal + seg_allocated == seg["total_size"]
+        )
+        stream = f" stream_{seg['stream']}" if seg["stream"] != 0 else ""
+        if seg["total_size"] >= PAGE_SIZE:
+            out.write(
+                f"[{body}] {Bytes(seg['total_size'])} allocated, "
+                f"{_report_free(seg_free_external, seg_free_internal)} free{stream}\n"
+            )
+    out.write(f"segments: {len(data['segments'])}\n")
+    out.write(f"total_reserved: {Bytes(total_reserved)}\n")
+    out.write(f"total_allocated: {Bytes(total_allocated)}\n")
+    out.write(f"total_free: {_report_free(free_external, free_internal)}\n")
+    out.write(legend)
+    assert free_internal + free_external + total_allocated == total_reserved
+    return out.getvalue()
+
+
+def trace(data):
+    out = io.StringIO()
+
+    def format(entries):
+        segment_intervals: list = []
+        segment_addr_to_name = {}
+        allocation_addr_to_name = {}
+
+        free_names: list = []
+        next_name = 0
+
+        def _name():
+            nonlocal next_name
+            if free_names:
+                return free_names.pop()
+            r, m = next_name // 26, next_name % 26
+            next_name += 1
+            return f"{chr(ord('a') + m)}{'' if r == 0 else r}"
+
+        def find_segment(addr):
+            for name, saddr, size in segment_intervals:
+                if addr >= saddr and addr < saddr + size:
+                    return name, saddr
+            for i, seg in enumerate(data["segments"]):
+                saddr = seg["address"]
+                size = seg["allocated_size"]
+                if addr >= saddr and addr < saddr + size:
+                    return f"seg_{i}", saddr
+            return None, None
+
+        count = 0
+        out.write(f"{len(entries)} entries\n")
+
+        total_reserved = 0
+        for seg in data["segments"]:
+            total_reserved += seg["total_size"]
+
+        for count, e in enumerate(entries):
+            if e["action"] == "alloc":
+                addr, size = e["addr"], e["size"]
+                n = _name()
+                seg_name, seg_addr = find_segment(addr)
+                if seg_name is None:
+                    seg_name = "MEM"
+                    offset = addr
+                else:
+                    offset = addr - seg_addr
+                out.write(f"{n} = {seg_name}[{offset}:{Bytes(size)}]\n")
+                allocation_addr_to_name[addr] = (n, size, count)
+                count += size
+            elif e["action"] == "free_requested":
+                addr, size = e["addr"], e["size"]
+                name, _, _ = allocation_addr_to_name.get(addr, (addr, None, None))
+                out.write(f"del {name} # {Bytes(size)}\n")
+            elif e["action"] == "free_completed":
+                addr, size = e["addr"], e["size"]
+                count -= size
+                name, _, _ = allocation_addr_to_name.get(addr, (addr, None, None))
+                out.write(f"# free completed for {name} {Bytes(size)}\n")
+                if name in allocation_addr_to_name:
+                    free_names.append(name)
+                    del allocation_addr_to_name[name]
+            elif e["action"] == "segment_alloc":
+                addr, size = e["addr"], e["size"]
+                name = _name()
+                out.write(f"{name} = cudaMalloc({addr}, {Bytes(size)})\n")
+                segment_intervals.append((name, addr, size))
+                segment_addr_to_name[addr] = name
+            elif e["action"] == "segment_free":
+                addr, size = e["addr"], e["size"]
+                name = segment_addr_to_name.get(addr, addr)
+                out.write(f"cudaFree({name}) # {Bytes(size)}\n")
+                if name in segment_addr_to_name:
+                    free_names.append(name)
+                    del segment_addr_to_name[name]
+            elif e["action"] == "oom":
+                size = e["size"]
+                free = e["device_free"]
+                out.write(
+                    f"raise OutOfMemoryError # {Bytes(size)} requested, {Bytes(free)} free in CUDA\n"
+                )
+            else:
+                out.write(f"{e}\n")
+        out.write(f"TOTAL MEM: {Bytes(count)}")
+
+    for i, d in enumerate(data["device_traces"]):
+        if d:
+            out.write(f"Device {i} ----------------\n")
+            format(d)
+    return out.getvalue()
+
+
+_memory_viz_template = r"""
+
+
+
+
+
+
+
+"""
+
+
+def _format_viz(data, viz_kind, device):
+    if device is not None:
+        warnings.warn(
+            "device argument is deprecated, plots now contain all device",
+            FutureWarning,
+            stacklevel=3,
+        )
+    buffer = pickle.dumps(data)
+    buffer += b"\x00" * (3 - len(buffer) % 3)
+    # Encode the buffer with base64
+    encoded_buffer = base64.b64encode(buffer).decode("utf-8")
+
+    json_format = json.dumps([{"name": "snapshot.pickle", "base64": encoded_buffer}])
+    return _memory_viz_template.replace("$VIZ_KIND", repr(viz_kind)).replace(
+        "$SNAPSHOT", json_format
+    )
+
+
+def filter_alloc_free_pairs(data):
+    for dev_id in range(len(data["device_traces"])):
+        # set of indexes of trace events for alloc-free pairs
+        filterSet = set()
+        # map from addr to index of alloc event
+        allocMap = {}
+        # set of addrs from free_requested events
+        freeRequested = set()
+        for idx, event in enumerate(data["device_traces"][dev_id]):
+            if event["action"] == "alloc":
+                allocMap[event["addr"]] = idx
+            elif event["action"] == "free_requested":
+                freeRequested.add(event["addr"])
+                if allocMap.get(event["addr"]) is not None:
+                    filterSet.add(idx)
+                    filterSet.add(allocMap[event["addr"]])
+                    allocMap.pop(event["addr"])
+            elif event["action"] == "free_completed":
+                if event["addr"] in freeRequested:
+                    freeRequested.remove(event["addr"])
+                    filterSet.add(idx)
+                else:
+                    print(f"free_completed without free_requested: {event}")
+
+        # Remove events whose index is in filterSet
+        if filterSet:
+            # Create a new list excluding events with indices in filterSet
+            data["device_traces"][dev_id] = [
+                event
+                for idx, event in enumerate(data["device_traces"][dev_id])
+                if idx not in filterSet
+            ]
+
+    return data
+
+
+def trace_plot(data, device=None, plot_segments=False, filter_freed=False):
+    """Generate a visualization over time of the memory usage recorded by the trace as an html file.
+
+    Args:
+        data: Memory snapshot as generated from torch.cuda.memory._snapshot()
+        device (torch.device, optional): Generate the trace for this device, needed if multiple devices have allocations.
+        plot_segments (bool, optional): Plots memory returned from cudaMalloc, rather than individual allocations.
+                                        Defaults to False.
+        filter_freed (bool, optional): Filter out alloc-free paired events to only plot allocations that are not freed yet.
+                                        Defaults to False to plot all trace events.
+
+    Returns:
+        str: HTML of visualization
+    """
+    if filter_freed:
+        data = filter_alloc_free_pairs(data)
+
+    return _format_viz(
+        data,
+        "Active Memory Timeline"
+        if not plot_segments
+        else "Active Cached Memory Timeline",
+        device,
+    )
+
+
+def _profile_to_snapshot(profile):
+    import torch
+    from torch._C._profiler import _EventType
+    from torch.profiler._memory_profiler import Action, TensorKey
+
+    memory_profile = profile._memory_profile()
+
+    allocation_stacks = {}
+    for event in memory_profile._op_tree.sorted_nodes:
+        if event.tag == _EventType.Allocation:
+            parent = event.parent
+            python_parents = []
+            while parent:
+                if parent.tag in (_EventType.PyCall, _EventType.PyCCall):
+                    python_parents.append(parent)
+                parent = parent.parent
+            key = TensorKey.from_allocation(event.extra_fields)
+
+            # Corner case: If allocation doesn't have an ID (can't prove it was used as a Tensor)
+            #              key will be None. I should add some way to identify these, I just haven't yet.
+            if key and event.extra_fields.alloc_size > 0:
+                allocation_stacks[key] = python_parents
+
+    device_count = torch.cuda.device_count()
+    snapshot: dict[str, list[Any]] = {
+        "device_traces": [[] for _ in range(device_count + 1)],
+        "segments": [
+            {
+                "device": device,
+                "address": None,
+                "total_size": 0,
+                "stream": 0,
+                "blocks": [],
+            }
+            for device in range(device_count + 1)
+        ],
+    }
+
+    def to_device(device):
+        if device.type == "cuda":
+            return device.index
+        else:
+            return device_count
+
+    def allocate(size, tensor_key, version, during_trace=True):
+        device = to_device(tensor_key.device)
+        addr = tensor_key.storage.ptr
+
+        seg = snapshot["segments"][device]  # type: ignore[index]
+        if seg["address"] is None or seg["address"] > addr:
+            seg["address"] = addr
+        seg["total_size"] = max(
+            seg["total_size"], addr + size
+        )  # record max addr for now, we will make it the size later
+        category = memory_profile._categories.get(tensor_key, version)
+        category = category.name.lower() if category is not None else "unknown"
+        stack = allocation_stacks.get(tensor_key, ())
+        stack = [{"filename": "none", "line": 0, "name": p.name} for p in stack]
+        r = {
+            "action": "alloc",
+            "addr": addr,
+            "size": size,
+            "stream": 0,
+            "frames": stack,
+            "category": category,
+        }
+        if during_trace:
+            snapshot["device_traces"][device].append(r)
+        return r
+
+    def free(alloc, device):
+        for e in ("free_requested", "free_completed"):
+            snapshot["device_traces"][device].append(
+                {
+                    "action": e,
+                    "addr": alloc["addr"],
+                    "size": alloc["size"],
+                    "stream": 0,
+                    "frames": alloc["frames"],
+                }
+            )
+
+    kv_to_elem = {}
+
+    # create the device trace
+    for _time, action, (tensor_key, version), size in memory_profile.timeline:
+        if not isinstance(tensor_key, TensorKey):
+            continue
+        if action == Action.CREATE:
+            kv_to_elem[(tensor_key, version)] = allocate(size, tensor_key, version)
+        elif action == Action.DESTROY:
+            free(kv_to_elem.pop((tensor_key, version)), to_device(tensor_key.device))
+        elif action == Action.INCREMENT_VERSION:
+            free(kv_to_elem.pop((tensor_key, version)), to_device(tensor_key.device))
+            kv_to_elem[(tensor_key, version + 1)] = allocate(
+                size, tensor_key, version + 1
+            )
+        elif action == Action.PREEXISTING:
+            kv_to_elem[(tensor_key, version)] = allocate(
+                size, tensor_key, version, during_trace=False
+            )
+
+    # create the final snapshot state
+    blocks_at_end = [
+        (to_device(tensor_key.device), event["addr"], event["size"], event["frames"])
+        for (tensor_key, version), event in kv_to_elem.items()
+    ]
+    for device, blocks in groupby(sorted(blocks_at_end), key=operator.itemgetter(0)):
+        seg = snapshot["segments"][device]  # type: ignore[index]
+        last_addr = seg["address"]
+        for _, addr, size, frames in blocks:
+            if last_addr < addr:
+                seg["blocks"].append({"size": addr - last_addr, "state": "inactive"})
+            seg["blocks"].append(
+                {
+                    "size": size,
+                    "state": "active_allocated",
+                    "requested_size": size,
+                    "frames": frames,
+                }
+            )
+            last_addr = addr + size
+        if last_addr < seg["total_size"]:
+            seg["blocks"].append(
+                {"size": seg["total_size"] - last_addr, "state": "inactive"}
+            )
+
+    snapshot["segments"] = [seg for seg in snapshot["segments"] if seg["blocks"]]  # type: ignore[attr-defined]
+    for seg in snapshot["segments"]:  # type: ignore[attr-defined, name-defined, no-redef]
+        seg["total_size"] -= seg["address"]
+        if not seg["blocks"]:
+            seg["blocks"].append({"size": seg["total_size"], "state": "inactive"})
+
+    return snapshot
+
+
+def profile_plot(profile, device=None):
+    """Generate a visualization over time of the memory usage recorded by kineto memory profiling as an html file.
+
+    Args:
+        profile: profile as generated by `torch.profiler.profile(profile_memory=True)`
+        device (torch.device, optional): Generate the trace for this device, needed if multiple devices have allocations.
+
+    Returns:
+        str: HTML of visualization
+    """
+    snapshot = _profile_to_snapshot(profile)
+    return _format_viz(snapshot, "Active Memory Timeline", device)
+
+
+def segment_plot(data: Any, device=None):
+    return _format_viz(data, "Allocator State History", device)
+
+
+if __name__ == "__main__":
+    import os.path
+
+    thedir = os.path.realpath(os.path.dirname(__file__))
+    if thedir in sys.path:
+        # otherwise we find cuda/random.py as random...
+        sys.path.remove(thedir)
+    import argparse
+
+    fn_name = "torch.cuda.memory._snapshot()"
+    pickled = f"pickled memory statistics from {fn_name}"
+    parser = argparse.ArgumentParser(
+        description=f"Visualize memory dumps produced by {fn_name}"
+    )
+
+    subparsers = parser.add_subparsers(dest="action")
+
+    def _output(p):
+        p.add_argument(
+            "-o",
+            "--output",
+            default="output.svg",
+            help="flamegraph svg (default: output.svg)",
+        )
+
+    description = "Prints overall allocation statistics and a visualization of how the allocators segments are currently filled."
+    stats_a = subparsers.add_parser("stats", description=description)
+    stats_a.add_argument("input", help=pickled)
+
+    description = "Prints buffer of the most recent allocation events embedded in the snapshot in a Pythonic style."
+    trace_a = subparsers.add_parser("trace", description=description)
+    trace_a.add_argument("input", help=pickled)
+
+    description = "Generate a flamegraph that visualizes what memory is stored in each allocator segment (aka block)"
+    segments_a = subparsers.add_parser("segments", description=description)
+    segments_a.add_argument("input", help=pickled)
+    _output(segments_a)
+
+    description = (
+        "Generate a flamegraph the program locations contributing to CUDA memory usage."
+    )
+    memory_a = subparsers.add_parser("memory", description=description)
+    memory_a.add_argument("input", help=pickled)
+    _output(memory_a)
+
+    description = (
+        "Generate a flamegraph that shows segments (aka blocks) that have been added "
+        "or removed between two different memorys snapshots."
+    )
+    compare_a = subparsers.add_parser("compare", description=description)
+    compare_a.add_argument("before", help=pickled)
+    compare_a.add_argument("after", help=pickled)
+    _output(compare_a)
+
+    plots = (
+        (
+            "trace_plot",
+            "Generate a visualization over time of the memory usage recorded by the trace as an html file.",
+        ),
+        (
+            "segment_plot",
+            "Visualize how allocations are packed into allocator segments at each point in a trace as an html file.",
+        ),
+    )
+    for cmd, description in plots:
+        trace_plot_a = subparsers.add_parser(cmd, description=description)
+        trace_plot_a.add_argument("input", help=pickled)
+        help = "visualize trace from this device (default: chooses the only device with trace info or errors)"
+        trace_plot_a.add_argument("-d", "--device", type=int, default=None, help=help)
+        help = "path to save the visualization(default: output.html)"
+        trace_plot_a.add_argument("-o", "--output", default="output.html", help=help)
+        if cmd == "trace_plot":
+            help = "visualize change to segments rather than individual allocations"
+            trace_plot_a.add_argument(
+                "-s", "--segments", action="store_true", help=help
+            )
+
+            help = (
+                "filter out allocation-free pairs to only visualize the allocations that are not freed yet;"
+                "useful to reduce the number of events for large traces for debugging OOM"
+            )
+            trace_plot_a.add_argument(
+                "-f", "--filter_freed", action="store_true", help=help
+            )
+
+    args = parser.parse_args()
+
+    def _read(name):
+        if name == "-":
+            data = pickle.load(sys.stdin.buffer)
+        else:
+            with open(name, "rb") as f:
+                data = pickle.load(f)
+        if isinstance(data, list):  # segments only...
+            data = {"segments": data, "traces": []}
+        return data
+
+    def _write(name, data):
+        with open(name, "w") as f:
+            f.write(data)
+
+    if args.action == "segments":
+        data = _read(args.input)
+        _write(args.output, segments(data))
+    elif args.action == "memory":
+        data = _read(args.input)
+        _write(args.output, memory(data))
+    elif args.action == "stats":
+        data = _read(args.input)
+        print(segsum(data))
+    elif args.action == "trace":
+        data = _read(args.input)
+        print(trace(data))
+    elif args.action == "compare":
+        before = _read(args.before)
+        after = _read(args.after)
+        _write(args.output, compare(before, after))
+    elif args.action == "trace_plot":
+        data = _read(args.input)
+        _write(
+            args.output,
+            trace_plot(
+                data,
+                device=args.device,
+                plot_segments=args.segments,
+                filter_freed=args.filter_freed,
+            ),
+        )
+    elif args.action == "segment_plot":
+        data = _read(args.input)
+        _write(args.output, segment_plot(data, device=args.device))
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/_pin_memory_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/_pin_memory_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..d3c01f3293f7e35cc5381aee7f6bb2b25957924c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/_pin_memory_utils.py
@@ -0,0 +1,24 @@
+import torch
+
+
+def pin_memory(data_ptr: int, size: int) -> None:
+    cudart = torch.cuda.cudart()
+    succ = int(
+        cudart.cudaHostRegister(
+            data_ptr,
+            size,
+            1,  # lines up with 'cudaHostRegisterPortable'
+        )
+    )
+
+    if succ != 0:
+        raise RuntimeError(
+            f"Registering memory failed with cudaError: {succ}."
+            " It's possible that this is an asynchronous error raised from a previous cuda operation."
+            " Consider launching with CUDA_LAUNCH_BLOCKING=1 to debug."
+        )
+
+
+def unpin_memory(data_ptr: int) -> None:
+    succ = int(torch.cuda.cudart().cudaHostUnregister(data_ptr))
+    assert succ == 0, f"Unpinning shared memory failed with error-code: {succ}"
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/_sanitizer.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/_sanitizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f215a730923bf144405f5827efdf4197b1ce09f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/_sanitizer.py
@@ -0,0 +1,662 @@
+# mypy: allow-untyped-defs
+r"""
+This module introduces CUDA Sanitizer, a tool for detecting synchronization errors between kernels ran on different streams.
+
+It stores information on accesses to tensors to determine if they are synchronized
+or not. When enabled in a python program and a possible data race is detected, a
+detailed warning will be printed and the program will exit.
+
+It can be enabled either by importing this module and calling
+:func:`enable_cuda_sanitizer()` or by exporting the ``TORCH_CUDA_SANITIZER``
+environment variable.
+"""
+
+import enum
+import functools
+import inspect
+import io
+import logging
+import re
+import sys
+import textwrap
+import traceback
+from collections.abc import Iterator
+from dataclasses import dataclass, field
+from typing import Any, Optional, TypeVar
+
+import torch
+import torch.cuda._gpu_trace as gpu_trace
+from torch.utils import _pytree as pytree
+from torch.utils._python_dispatch import TorchDispatchMode
+
+
+DEFAULT_STREAM_ID = 0
+
+TK = TypeVar("TK")
+TVa = TypeVar("TVa")
+TVb = TypeVar("TVb")
+
+DataPtr = int
+StreamId = int
+EventId = int
+SeqNum = int
+
+logger = logging.getLogger(__name__)
+
+# Note that this is only factories that take Tensor as input as they are
+# the ones we care about.
+FACTORY_FUNCTION_REGEX = re.compile("(new_.*|.*_like)")
+
+
+class AccessType(enum.Enum):
+    READ = enum.auto()
+    WRITE = enum.auto()
+
+    def __str__(self):
+        return "reading from" if self is AccessType.READ else "writing to"
+
+
+@dataclass
+class Access:
+    r"""Stores information about a single access to a tensor by a kernel.
+
+    Args:
+        type: either AccessType.READ or AccessType.Write.
+        seq_num: the sequential number of the kernel performing the access.
+        stream: the stream id of the stream executing the kernel.
+        operator: the schema of the launched kernel, which lists the
+            arguments and return type.
+        aliases: the arguments in the schema this access corresponds to.
+        is_output: Whether the tensor was an output of the kernel.
+        stack_trace: the stack summary object captured during access.
+    """
+
+    type: AccessType
+    seq_num: SeqNum
+    stream: StreamId
+    operator: str
+    aliases: list[str]
+    is_output: bool
+    stack_trace: traceback.StackSummary
+
+
+class SynchronizationError(Exception):
+    """Base class for errors detected by CUDA Sanitizer."""
+
+
+class UnsynchronizedAccessError(SynchronizationError):
+    """Stores information about two unsynchronized accesses to one data pointer."""
+
+    def __init__(
+        self,
+        data_ptr: DataPtr,
+        allocation_stack_trace: Optional[traceback.StackSummary],
+        current_access: Access,
+        previous_access: Access,
+    ):
+        self.data_ptr = data_ptr
+        self.allocation_stack_trace = allocation_stack_trace
+        self.current_access = current_access
+        self.previous_access = previous_access
+
+    def __str__(self):
+        def format_access(access: Access):
+            message.write(f"{access.operator}\n{access.type}")
+            if access.aliases:
+                message.write(" argument(s) " + ", ".join(access.aliases))
+                if access.is_output:
+                    message.write(", and to")
+            if access.is_output:
+                message.write(" the output")
+            message.write(
+                f"\nWith stack trace:\n{''.join(access.stack_trace.format())}\n"
+            )
+
+        with io.StringIO() as message:
+            message.write(
+                textwrap.dedent(
+                    f"""\
+                    ============================
+                    CSAN detected a possible data race on tensor with data pointer {self.data_ptr}
+                    Access by stream {self.current_access.stream} during kernel:
+                    """
+                )
+            )
+            format_access(self.current_access)
+
+            message.write(
+                f"Previous access by stream {self.previous_access.stream} during kernel:\n"
+            )
+            format_access(self.previous_access)
+
+            if self.allocation_stack_trace:
+                message.write(
+                    "Tensor was allocated with stack trace:\n"
+                    f"{''.join(self.allocation_stack_trace.format())}"
+                )
+            else:
+                message.write("Trace for tensor allocation not found.")
+            return message.getvalue()
+
+
+class CUDASanitizerErrors(Exception):
+    """Wrapper class for errors reported by CUDA Sanitizer."""
+
+    def __init__(self, errors: list[SynchronizationError]):
+        self.errors = errors
+
+    def __str__(self):
+        return f"detected {len(self.errors)} errors"
+
+
+@dataclass
+class TensorInfo:
+    r"""Stores information about a single tensor and recent accesses to it.
+
+    Args:
+        allocation_stack_trace: the stack summary object captured during tensor
+            allocation. Can be ``None`` if the allocation wasn't caught by CSAN.
+        reads: list of read accesses to the tensor that were performed since
+            the last write.
+        write: the last write access to the tensor.
+    """
+
+    allocation_stack_trace: Optional[traceback.StackSummary]
+    reads: list[Access] = field(default_factory=list)
+    write: Optional[Access] = None
+
+
+class _TensorsAccessed:
+    def __init__(self) -> None:
+        self.accesses: dict[DataPtr, TensorInfo] = {}
+
+    def ensure_tensor_exists(self, data_ptr: DataPtr) -> None:
+        if data_ptr not in self.accesses:
+            logger.info(
+                "Found tensor with pointer: %s, but no matching tensor "
+                "allocation in the trace. Backfilling the trace now. "
+                "Perhaps the sanitizer was enabled after some torch operations?",
+                data_ptr,
+            )
+            self.create_tensor(data_ptr, None)
+
+    def ensure_tensor_does_not_exist(self, data_ptr: DataPtr) -> None:
+        if data_ptr in self.accesses:
+            logger.info(
+                "Found duplicate tensor allocation in the trace for tensor with "
+                "pointer: %s. Assuming the trace for tensor deallocation "
+                "wasn't caught and backfilling it now. "
+                "Perhaps the sanitizer was enabled after some torch operations?",
+                data_ptr,
+            )
+            self.delete_tensor(data_ptr)
+
+    def create_tensor(
+        self, data_ptr: DataPtr, stack_trace: Optional[traceback.StackSummary]
+    ) -> None:
+        self.accesses[data_ptr] = TensorInfo(stack_trace)
+
+    def delete_tensor(self, data_ptr: DataPtr) -> None:
+        del self.accesses[data_ptr]
+
+    def were_there_reads_since_last_write(self, data_ptr: DataPtr) -> bool:
+        return bool(self.accesses[data_ptr].reads)
+
+    def get_allocation_stack_trace(
+        self, data_ptr: DataPtr
+    ) -> Optional[traceback.StackSummary]:
+        return self.accesses[data_ptr].allocation_stack_trace
+
+    def get_write(self, data_ptr: DataPtr) -> Optional[Access]:
+        return self.accesses[data_ptr].write
+
+    def get_reads(self, data_ptr: DataPtr) -> list[Access]:
+        return self.accesses[data_ptr].reads
+
+    def add_read(self, data_ptr: DataPtr, access: Access) -> None:
+        self.accesses[data_ptr].reads.append(access)
+
+    def set_write(self, data_ptr: DataPtr, access: Access) -> None:
+        self.accesses[data_ptr].write = access
+        self.accesses[data_ptr].reads = []
+
+
+class StreamSynchronizations:
+    def __init__(self) -> None:
+        self.current_sync_states: dict[StreamId, dict[StreamId, SeqNum]] = {}
+        self.recorded_sync_states: dict[EventId, dict[StreamId, SeqNum]] = {}
+        self.host_sync_state: dict[StreamId, SeqNum] = {}
+        self.create_stream(DEFAULT_STREAM_ID)
+
+    def _ensure_stream_exists(self, stream: StreamId) -> None:
+        if stream not in self.current_sync_states:
+            logger.info(
+                "Found Stream with id: %s, but no matching stream "
+                "creation in the trace. Backfilling the trace now. "
+                "Perhaps the sanitizer was enabled after some torch operations?",
+                stream,
+            )
+            self.create_stream(stream)
+
+    def _ensure_event_exists(self, event: EventId) -> None:
+        if event not in self.recorded_sync_states:
+            logger.info(
+                "Found Event with id: %s, but no matching event "
+                "creation in the trace. Backfilling the trace now. "
+                "Perhaps the sanitizer was enabled after some torch operations?",
+                event,
+            )
+            self.create_event(event)
+
+    def _ensure_event_does_not_exist(self, event: EventId) -> None:
+        if event in self.recorded_sync_states:
+            logger.info(
+                "Found duplicate event creation in the trace for event with "
+                "id: %s. Assuming the trace for event deletion wasn't caught "
+                "and backfilling it now. "
+                "Perhaps the sanitizer was enabled after some torch operations?",
+                event,
+            )
+            self.delete_event(event)
+
+    def create_stream(self, stream: StreamId) -> None:
+        if stream in self.current_sync_states:
+            logger.info(
+                "Found duplicate Stream creation in the trace for Stream with "
+                "id: %s. PyTorch Streams are only created once, so this "
+                "trace entry is ignored.",
+                stream,
+            )
+        else:
+            self.host_sync_state[stream] = 0
+            self.current_sync_states[stream] = self.host_sync_state.copy()
+
+    def create_event(self, event: EventId) -> None:
+        self._ensure_event_does_not_exist(event)
+        self.recorded_sync_states[event] = {}
+
+    def delete_event(self, event: EventId) -> None:
+        self._ensure_event_exists(event)
+        del self.recorded_sync_states[event]
+
+    def update_seq_num(self, stream: StreamId, seq_num: SeqNum) -> None:
+        self._ensure_stream_exists(stream)
+        self.current_sync_states[stream][stream] = seq_num
+
+    def record_state(self, event: EventId, stream: StreamId) -> None:
+        self._ensure_event_exists(event)
+        self._ensure_stream_exists(stream)
+        self.recorded_sync_states[event] = self.current_sync_states[stream].copy()
+
+    def _state_wait_for_other(
+        self, state: dict[StreamId, SeqNum], other: dict[StreamId, SeqNum]
+    ) -> None:
+        for stream, seq_num in other.items():
+            state[stream] = max(state.get(stream, -1), seq_num)
+
+    def stream_wait_for_event(self, stream: StreamId, event: EventId) -> None:
+        self._ensure_stream_exists(stream)
+        self._ensure_event_exists(event)
+        self._state_wait_for_other(
+            self.current_sync_states[stream], self.recorded_sync_states[event]
+        )
+
+    def all_streams_wait_for_event(self, event: EventId) -> None:
+        self._ensure_event_exists(event)
+        for stream in self.current_sync_states:
+            self.stream_wait_for_event(stream, event)
+
+        self._state_wait_for_other(
+            self.host_sync_state, self.recorded_sync_states[event]
+        )
+
+    def all_streams_wait_for_stream(self, stream: StreamId) -> None:
+        self._ensure_stream_exists(stream)
+        for state in self.current_sync_states.values():
+            self._state_wait_for_other(state, self.current_sync_states[stream])
+
+        self._state_wait_for_other(
+            self.host_sync_state, self.current_sync_states[stream]
+        )
+
+    def sync_all_streams(self) -> None:
+        for stream, state in self.current_sync_states.items():
+            self.host_sync_state[stream] = state[stream]
+
+        for state in self.current_sync_states.values():
+            self._state_wait_for_other(state, self.host_sync_state)
+
+    def is_ordered_after(
+        self, current_stream: StreamId, seq_num: SeqNum, other_stream: StreamId
+    ) -> bool:
+        self._ensure_stream_exists(current_stream)
+        self._ensure_stream_exists(other_stream)
+        return seq_num <= self.current_sync_states[current_stream].get(other_stream, -1)
+
+
+class EventHandler:
+    """Analyzes CSAN trace for synchronization errors.
+
+    Stores information on each stream's synchronizations with other streams as well
+    as tensor accesses to determine whether a given kernel launch might cause a
+    data race.
+    """
+
+    def __init__(self) -> None:
+        self.tensors_accessed = _TensorsAccessed()
+        self.syncs = StreamSynchronizations()
+        self.seq_num: SeqNum = 0
+
+    def _handle_kernel_launch(
+        self,
+        stream: StreamId,
+        read_only: set[DataPtr],
+        read_write: set[DataPtr],
+        outputs: set[DataPtr],
+        operator: str,
+        tensor_aliases: dict[int, list[str]],
+    ) -> list[SynchronizationError]:
+        def check_conflict(
+            data_ptr: DataPtr, current_access: Access, previous_access: Optional[Access]
+        ) -> None:
+            if previous_access is None:
+                return
+            if not self.syncs.is_ordered_after(
+                current_access.stream, previous_access.seq_num, previous_access.stream
+            ):
+                error_list.append(
+                    UnsynchronizedAccessError(
+                        data_ptr,
+                        self.tensors_accessed.get_allocation_stack_trace(data_ptr),
+                        current_access,
+                        previous_access,
+                    )
+                )
+
+        error_list: list[SynchronizationError] = []
+        self.seq_num += 1
+        self.syncs.update_seq_num(stream, self.seq_num)
+        stack_trace = traceback.StackSummary.extract(
+            traceback.walk_stack(inspect.currentframe()), lookup_lines=False
+        )
+        # The stack trace generated in this way is in the inverse order, so it must be
+        # reversed.
+        stack_trace.reverse()
+
+        for data_ptr in read_only:
+            self.tensors_accessed.ensure_tensor_exists(data_ptr)
+            current_access = Access(
+                AccessType.READ,
+                self.seq_num,
+                stream,
+                operator,
+                tensor_aliases[data_ptr],
+                data_ptr in outputs,
+                stack_trace,
+            )
+            check_conflict(
+                data_ptr, current_access, self.tensors_accessed.get_write(data_ptr)
+            )
+            self.tensors_accessed.add_read(data_ptr, current_access)
+
+        for data_ptr in read_write:
+            self.tensors_accessed.ensure_tensor_exists(data_ptr)
+            current_access = Access(
+                AccessType.WRITE,
+                self.seq_num,
+                stream,
+                operator,
+                tensor_aliases[data_ptr],
+                data_ptr in outputs,
+                stack_trace,
+            )
+            if self.tensors_accessed.were_there_reads_since_last_write(data_ptr):
+                for previous_access in self.tensors_accessed.get_reads(data_ptr):
+                    check_conflict(data_ptr, current_access, previous_access)
+            else:
+                check_conflict(
+                    data_ptr, current_access, self.tensors_accessed.get_write(data_ptr)
+                )
+            self.tensors_accessed.set_write(data_ptr, current_access)
+
+        return error_list
+
+    def _handle_event_creation(self, event: EventId) -> None:
+        self.syncs.create_event(event)
+
+    def _handle_event_deletion(self, event: EventId) -> None:
+        self.syncs.delete_event(event)
+
+    def _handle_event_record(self, event: EventId, stream: StreamId) -> None:
+        self.syncs.record_state(event, stream)
+
+    def _handle_event_wait(self, event: EventId, stream: StreamId) -> None:
+        self.syncs.stream_wait_for_event(stream, event)
+
+    def _handle_memory_allocation(self, data_ptr: DataPtr) -> None:
+        self.tensors_accessed.ensure_tensor_does_not_exist(data_ptr)
+        stack_trace = traceback.StackSummary.extract(
+            traceback.walk_stack(inspect.currentframe()), lookup_lines=False
+        )
+        # The stack trace generated in this way is in the inverse order, so it must be
+        # reversed.
+        stack_trace.reverse()
+        self.tensors_accessed.create_tensor(
+            data_ptr,
+            stack_trace,
+        )
+
+    def _handle_memory_deallocation(self, data_ptr: DataPtr) -> None:
+        self.tensors_accessed.ensure_tensor_exists(data_ptr)
+        self.tensors_accessed.delete_tensor(data_ptr)
+
+    def _handle_stream_creation(self, stream: StreamId) -> None:
+        self.syncs.create_stream(stream)
+
+    def _handle_device_synchronization(self) -> None:
+        self.syncs.sync_all_streams()
+
+    def _handle_stream_synchronization(self, stream: StreamId) -> None:
+        self.syncs.all_streams_wait_for_stream(stream)
+
+    def _handle_event_synchronization(self, event: EventId) -> None:
+        self.syncs.all_streams_wait_for_event(event)
+
+
+def zip_by_key(a: dict[TK, TVa], b: dict[TK, TVb]) -> Iterator[tuple[TK, TVa, TVb]]:
+    for arg, value in a.items():
+        if arg in b:
+            yield arg, value, b[arg]
+
+
+def zip_arguments(
+    schema: torch.FunctionSchema, args: tuple[Any, ...], kwargs: dict[str, Any]
+) -> Iterator[tuple[torch.Argument, Any]]:
+    schema_args = schema.arguments[: len(args)]
+    schema_kwargs = {arg.name: arg for arg in schema.arguments[len(args) :]}
+
+    yield from zip(schema_args, args)
+
+    for _, argument, value in zip_by_key(schema_kwargs, kwargs):
+        yield (argument, value)
+
+
+class ArgumentHandler:
+    def __init__(self) -> None:
+        self.dataptrs_read: set[DataPtr] = set()
+        self.dataptrs_written: set[DataPtr] = set()
+        self.tensor_aliases: dict[DataPtr, list[str]] = {}
+        self.outputs: set[DataPtr] = set()
+
+    def _handle_argument(
+        self,
+        value: Any,
+        is_write: bool,
+        metadata_only: bool,
+        name: Optional[str] = None,
+        is_output: bool = False,
+    ) -> None:
+        if isinstance(value, torch.Tensor) and value.is_cuda:
+            data_ptr = value.data_ptr()
+            if is_write:
+                self.dataptrs_written.add(data_ptr)
+            elif not metadata_only:
+                self.dataptrs_read.add(data_ptr)
+
+            self.tensor_aliases.setdefault(data_ptr, [])
+            if name is not None:
+                self.tensor_aliases[data_ptr].append(name)
+            if is_output:
+                self.outputs.add(data_ptr)
+
+    def parse_inputs(
+        self,
+        schema: torch.FunctionSchema,
+        args: tuple[Any, ...],
+        kwargs: dict[str, Any],
+        *,
+        is_factory: bool,
+    ) -> None:
+        for argument, value in zip_arguments(schema, args, kwargs):
+            is_write = argument.alias_info is not None and argument.alias_info.is_write
+            # A change is metadata only if it is a view or a factory function that
+            # reads only metadata
+            metadata_only = is_factory or (
+                argument.alias_info is not None and not argument.alias_info.is_write
+            )
+            pytree.tree_map_(
+                functools.partial(
+                    self._handle_argument,
+                    is_write=is_write,
+                    name=argument.name,
+                    metadata_only=metadata_only,
+                ),
+                value,
+            )
+
+    def parse_outputs(
+        self, schema: torch.FunctionSchema, outputs: Any, *, is_factory: bool
+    ) -> None:
+        for res, value in zip(schema.returns, (outputs,)):
+            metadata_only = is_factory or (
+                res.alias_info is not None and not res.alias_info.is_write
+            )
+            pytree.tree_map_(
+                functools.partial(
+                    self._handle_argument,
+                    is_write=not metadata_only,
+                    is_output=True,
+                    metadata_only=metadata_only,
+                ),
+                value,
+            )
+
+
+class CUDASanitizerDispatchMode(TorchDispatchMode):
+    def __init__(self) -> None:
+        self.event_handler = EventHandler()
+        torch._C._activate_gpu_trace()
+        gpu_trace.register_callback_for_event_creation(
+            self.event_handler._handle_event_creation
+        )
+        gpu_trace.register_callback_for_event_deletion(
+            self.event_handler._handle_event_deletion
+        )
+        gpu_trace.register_callback_for_event_record(
+            self.event_handler._handle_event_record
+        )
+        gpu_trace.register_callback_for_event_wait(
+            self.event_handler._handle_event_wait
+        )
+        gpu_trace.register_callback_for_memory_allocation(
+            self.event_handler._handle_memory_allocation
+        )
+        gpu_trace.register_callback_for_memory_deallocation(
+            self.event_handler._handle_memory_deallocation
+        )
+        gpu_trace.register_callback_for_stream_creation(
+            self.event_handler._handle_stream_creation
+        )
+        gpu_trace.register_callback_for_device_synchronization(
+            self.event_handler._handle_device_synchronization
+        )
+        gpu_trace.register_callback_for_stream_synchronization(
+            self.event_handler._handle_stream_synchronization
+        )
+        gpu_trace.register_callback_for_event_synchronization(
+            self.event_handler._handle_event_synchronization
+        )
+
+    def __torch_dispatch__(self, func, types, args=(), kwargs=None):
+        if kwargs is None:
+            kwargs = {}
+
+        is_factory = bool(FACTORY_FUNCTION_REGEX.match(func._schema.name))
+
+        argument_handler = ArgumentHandler()
+        argument_handler.parse_inputs(func._schema, args, kwargs, is_factory=is_factory)
+
+        outputs = func(*args, **kwargs)
+
+        argument_handler.parse_outputs(func._schema, outputs, is_factory=is_factory)
+        errors = self.event_handler._handle_kernel_launch(
+            torch.cuda.current_stream().cuda_stream,
+            argument_handler.dataptrs_read - argument_handler.dataptrs_written,
+            argument_handler.dataptrs_written,
+            argument_handler.outputs,
+            func._schema,
+            argument_handler.tensor_aliases,
+        )
+        if errors:
+            for error in errors:
+                print(error, file=sys.stderr)
+            raise CUDASanitizerErrors(errors)
+
+        return outputs
+
+
+class CUDASanitizer:
+    """Manages the lifetime of a CUDASanitizer dispatch mode object.
+
+    The CUDASanitizer class wraps the entering/exiting functions of the dispatch mode
+    context manager in the enable function/destructor, respectively. This is to
+    explicitly set the lifetime of the dispatch mode object to that of the application.
+    This approach was deemed more elegant than using the atexit module.
+    """
+
+    def __init__(self) -> None:
+        self.dispatch = CUDASanitizerDispatchMode()
+        self.enabled = False
+
+    def enable(self):
+        self.dispatch.__enter__()
+        self.enabled = True
+
+    def disable(self):
+        self.dispatch.__exit__(None, None, None)
+        self.enabled = False
+
+    def __del__(self):
+        # Since this object lifetime is linked to the `torch.cuda._sanitizer` python
+        # module, it often gets deleted as part of the overall `torch` module cleanup
+        # At that time, depending on CPython version, the torch.* module might be in
+        # different states of being already cleaned up.
+        # Similarly other imports might already have been cleaned up so `sys` might
+        # be already gone as well.
+        # Skip exiting the mode if it outlived the runtime.
+        if (sys is not None) and (not sys.is_finalizing()) and self.enabled:
+            self.disable()
+
+
+def enable_cuda_sanitizer():
+    """Enable CUDA Sanitizer.
+
+    The sanitizer will begin to analyze low-level CUDA calls invoked by torch functions
+    for synchronization errors. All data races found will be printed to the standard
+    error output along with stack traces of suspected causes. For best results, the
+    sanitizer should be enabled at the very beginning of the program.
+    """
+    cuda_sanitizer.enable()
+
+
+cuda_sanitizer = CUDASanitizer()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..24b6aeeef0d8c5632282786494c19d3664763d14
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/_utils.py
@@ -0,0 +1,529 @@
+import ctypes
+import sys
+from typing import Any, Optional, Union
+
+import torch
+
+# The _get_device_index has been moved to torch.utils._get_device_index
+from torch._utils import _get_device_index as _torch_get_device_index
+
+
+def _get_hip_runtime_library() -> ctypes.CDLL:
+    if sys.platform == "win32":
+        lib = ctypes.CDLL(f"amdhip64_{torch.version.hip[0]}.dll")
+    else:  # Unix-based systems
+        lib = ctypes.CDLL("libamdhip64.so")
+    lib.cuGetErrorString = lib.hipGetErrorString  # type: ignore[attr-defined]
+    lib.cuModuleLoadData = lib.hipModuleLoadData  # type: ignore[attr-defined]
+    lib.cuModuleGetFunction = lib.hipModuleGetFunction  # type: ignore[attr-defined]
+    lib.cuLaunchKernel = lib.hipModuleLaunchKernel  # type: ignore[attr-defined]
+    lib.cuFuncSetAttribute = lib.hipFuncSetAttribute  # type: ignore[attr-defined]
+    return lib
+
+
+def _get_cuda_library() -> ctypes.CDLL:
+    if sys.platform == "win32":
+        return ctypes.CDLL("nvcuda.dll")
+    else:  # Unix-based systems
+        return ctypes.CDLL("libcuda.so.1")
+
+
+# Load GPU driver runtime
+def _get_gpu_runtime_library() -> ctypes.CDLL:
+    if torch.version.hip:
+        return _get_hip_runtime_library()
+    else:
+        return _get_cuda_library()
+
+
+# Helper: check CUDA errors
+def _check_cuda(result: int) -> None:
+    if result == 0:
+        return
+    err_str = ctypes.c_char_p()
+    libcuda = _get_gpu_runtime_library()  # Get reference to CUDA library
+    libcuda.cuGetErrorString(result, ctypes.byref(err_str))
+    error_message = (
+        err_str.value.decode() if err_str.value is not None else "Unknown CUDA error"
+    )
+    raise RuntimeError(f"CUDA error: {error_message}")
+
+
+def _get_hiprtc_library() -> ctypes.CDLL:
+    if sys.platform == "win32":
+        version_str = "".join(["0", torch.version.hip[0], "0", torch.version.hip[2]])
+        lib = ctypes.CDLL(f"hiprtc{version_str}.dll")
+    else:
+        lib = ctypes.CDLL("libhiprtc.so")
+
+    # Provide aliases for HIP RTC functions to match NVRTC API
+    lib.nvrtcGetErrorString = lib.hiprtcGetErrorString  # type: ignore[attr-defined]
+    lib.nvrtcCreateProgram = lib.hiprtcCreateProgram  # type: ignore[attr-defined]
+    lib.nvrtcDestroyProgram = lib.hiprtcDestroyProgram  # type: ignore[attr-defined]
+    lib.nvrtcCompileProgram = lib.hiprtcCompileProgram  # type: ignore[attr-defined]
+    lib.nvrtcGetPTXSize = lib.hiprtcGetCodeSize  # type: ignore[attr-defined]
+    lib.nvrtcGetPTX = lib.hiprtcGetCode  # type: ignore[attr-defined]
+    lib.nvrtcGetProgramLogSize = lib.hiprtcGetProgramLogSize  # type: ignore[attr-defined]
+    lib.nvrtcGetProgramLog = lib.hiprtcGetProgramLog  # type: ignore[attr-defined]
+    lib.nvrtcAddNameExpression = lib.hiprtcAddNameExpression  # type: ignore[attr-defined]
+    lib.nvrtcGetLoweredName = lib.hiprtcGetLoweredName  # type: ignore[attr-defined]
+    return lib
+
+
+def _get_nvrtc_library() -> ctypes.CDLL:
+    major_version = int(torch.version.cuda.split(".")[0])  # type: ignore[union-attr]
+    if sys.platform == "win32":
+        nvrtc_libs = [
+            f"nvrtc64_{major_version}0_0.dll",
+        ]
+    else:
+        nvrtc_libs = [
+            f"libnvrtc.so.{major_version}",
+            "libnvrtc.so",  # Fallback to unversioned
+        ]
+    for lib_name in nvrtc_libs:
+        try:
+            return ctypes.CDLL(lib_name)
+        except OSError:
+            continue
+    raise OSError("Could not find any NVRTC library")
+
+
+def _get_gpu_rtc_library() -> ctypes.CDLL:
+    # Since PyTorch already loads the GPU RTC library, we can use the system library
+    # which should be compatible with PyTorch's version
+    if torch.version.hip:
+        return _get_hiprtc_library()
+    else:
+        return _get_nvrtc_library()
+
+
+def _get_gpu_rtc_compatible_flags() -> list[str]:
+    """
+    Get HIPCC/NVCC flags that are compatible with NVRTC compilation.
+
+    Returns:
+        List of HIPCC/NVCC flags that can be safely used with NVRTC.
+    """
+    from torch.utils.cpp_extension import COMMON_HIPCC_FLAGS, COMMON_NVCC_FLAGS
+
+    nvrtc_unsupported_flags = {
+        "--expt-relaxed-constexpr",
+    }
+
+    # Filter out unsupported flags
+    compatible_flags = [
+        flag for flag in COMMON_NVCC_FLAGS if flag not in nvrtc_unsupported_flags
+    ]
+
+    if torch.version.hip:
+        compatible_flags.extend(COMMON_HIPCC_FLAGS)
+
+    return compatible_flags
+
+
+def _nvrtc_compile(
+    kernel_source: str,
+    kernel_name: str,
+    compute_capability: Optional[str] = None,
+    cuda_include_dirs: Optional[list] = None,
+    nvcc_options: Optional[list] = None,
+    auto_pch: bool = False,
+) -> tuple[bytes, str]:
+    """
+    Compiles a CUDA kernel using NVRTC and returns the PTX code.
+
+    Args:
+        kernel_source (str): The CUDA kernel source code as a string
+        kernel_name (str): The name of the kernel function to compile
+        compute_capability (str, None): The compute capability to target (e.g., "86").
+                                           If None, will detect from current device.
+        cuda_include_dirs (list, None): List of directories containing CUDA headers
+        nvcc_options (list, None): Additional options to pass to NVRTC
+        auto_pch (bool): Enable automatic precompiled headers (CUDA 12.8+)
+
+    Returns:
+        Tuple[bytes, str]: The compiled PTX code and mangled kernel name
+    """
+    # Ensure CUDA is initialized
+    import torch.cuda
+
+    # Load NVRTC library
+    libnvrtc = _get_gpu_rtc_library()
+
+    # NVRTC constants
+    NVRTC_SUCCESS = 0
+
+    # Helper: check NVRTC errors
+    def check_nvrtc(result: int) -> None:
+        if result != NVRTC_SUCCESS:
+            err_str = ctypes.c_char_p()
+            libnvrtc.nvrtcGetErrorString(result, ctypes.byref(err_str))
+            error_message = (
+                err_str.value.decode()
+                if err_str.value is not None
+                else "Unknown CUDA error"
+            )
+            raise RuntimeError(f"CUDA error: {error_message}")
+
+    # Convert source to bytes
+    source_bytes = kernel_source.encode("utf-8")
+
+    # Get compute capability if not provided
+    if compute_capability is None:
+        props = torch.cuda.get_device_properties(torch.cuda.current_device())
+        if torch.version.hip:
+            compute_capability = f"{props.gcnArchName}"
+        else:
+            compute_capability = f"{props.major}{props.minor}"
+
+    # Prepare compilation options
+    options = []
+    if torch.version.hip:
+        options.append(f"--offload-arch={compute_capability}".encode())
+    else:
+        options.append(f"--gpu-architecture=sm_{compute_capability}".encode())
+
+    # Auto-detect and add CUDA include paths
+    from torch.utils.cpp_extension import include_paths
+
+    cuda_include_paths = include_paths("cuda")
+    for cuda_path in cuda_include_paths:
+        options.append(f"-I{cuda_path}".encode())
+
+    # Add custom include directories
+    if cuda_include_dirs:
+        for directory in cuda_include_dirs:
+            options.append(f"-I{directory}".encode())
+
+    # Enable automatic precompiled headers (CUDA 12.8+)
+    if auto_pch:
+        assert str(torch.version.cuda) >= "12.8", "PCH requires CUDA 12.8+"
+        if nvcc_options is None:
+            nvcc_options = []
+        nvcc_options.append("--pch")
+
+    # Add custom NVCC options
+    if nvcc_options:
+        for option in nvcc_options:
+            options.append(option.encode("utf-8"))
+
+    nvrtc_compatible_flags = _get_gpu_rtc_compatible_flags()
+    options.extend([flag.encode("utf-8") for flag in nvrtc_compatible_flags])
+
+    # Convert options to C array
+    num_options = len(options)
+    options_array = (ctypes.c_char_p * num_options)(*options)
+
+    # Create program
+    prog = ctypes.c_void_p()
+    check_nvrtc(
+        libnvrtc.nvrtcCreateProgram(
+            ctypes.byref(prog),
+            source_bytes,
+            f"{kernel_name}.cu".encode(),
+            0,
+            None,
+            None,
+        )
+    )
+
+    # Add kernel name, which can be a template expression
+    c_kernel_name = kernel_name.encode("utf-8")
+    check_nvrtc(libnvrtc.nvrtcAddNameExpression(prog, c_kernel_name))
+
+    # Compile program
+    res = libnvrtc.nvrtcCompileProgram(prog, num_options, options_array)
+
+    # Handle compilation errors
+    if res != NVRTC_SUCCESS:
+        # Get log
+        log_size = ctypes.c_size_t()
+        libnvrtc.nvrtcGetProgramLogSize(prog, ctypes.byref(log_size))
+        log = ctypes.create_string_buffer(log_size.value)
+        libnvrtc.nvrtcGetProgramLog(prog, log)
+        raise RuntimeError(f"Kernel compilation failed:\n{log.value.decode()}")
+
+    # Get PTX
+    ptx_size = ctypes.c_size_t()
+    check_nvrtc(libnvrtc.nvrtcGetPTXSize(prog, ctypes.byref(ptx_size)))
+    ptx = ctypes.create_string_buffer(ptx_size.value)
+    check_nvrtc(libnvrtc.nvrtcGetPTX(prog, ptx))
+
+    # Get mangled name
+    c_mangled_name = ctypes.c_char_p()
+    check_nvrtc(
+        libnvrtc.nvrtcGetLoweredName(prog, c_kernel_name, ctypes.byref(c_mangled_name))
+    )
+    if c_mangled_name.value is not None:
+        mangled_name = c_mangled_name.value.decode()  # make a copy
+    else:
+        mangled_name = ""
+
+    libnvrtc.nvrtcDestroyProgram(ctypes.byref(prog))
+
+    # For HIP, hipRTC generates raw CO binaries instead of PTX,
+    # and for some reason, ".value" causes the string to be truncated,
+    # likely due to the presence of '\0' in the string. So we use .raw instead.
+    ptx_bytes = ptx.raw if torch.version.hip else ptx.value
+    return ptx_bytes, mangled_name
+
+
+class _CudaModule:
+    def __init__(self, module: ctypes.c_void_p) -> None:
+        self._module = module
+        self._kernels: dict[str, _CudaKernel] = {}
+
+    def __getattr__(self, name: str) -> "_CudaKernel":
+        if name in self._kernels:
+            return self._kernels[name]
+
+        # Import the CUDA library inside the method
+        # pyrefly: ignore [missing-module-attribute]
+        from torch.cuda._utils import _get_gpu_runtime_library
+
+        libcuda = _get_gpu_runtime_library()
+
+        func = ctypes.c_void_p()
+        try:
+            _check_cuda(
+                libcuda.cuModuleGetFunction(
+                    ctypes.byref(func), self._module, name.encode("utf-8")
+                )
+            )
+            kernel = _CudaKernel(func, self._module)
+            self._kernels[name] = kernel
+            return kernel
+
+        except RuntimeError as err:
+            raise AttributeError(f"No kernel named '{name}' in this module") from err
+
+
+class _CudaKernel:
+    """
+    Represents a compiled CUDA kernel that can be called with PyTorch tensors.
+    """
+
+    def __init__(self, func: ctypes.c_void_p, module: ctypes.c_void_p) -> None:
+        self.func = func
+        self.module = module
+        self._max_shared_mem_bytes = 0
+
+    def __call__(
+        self,
+        grid: tuple[int, int, int] = (1, 1, 1),
+        block: tuple[int, int, int] = (1, 1, 1),
+        args: Optional[list] = None,
+        shared_mem: int = 0,
+        stream: Optional[Any] = None,
+    ) -> None:
+        """
+        Call the compiled CUDA kernel
+
+        Args:
+            grid (tuple): Grid dimensions (grid_x, grid_y, grid_z)
+            block (tuple): Block dimensions (block_x, block_y, block_z)
+            args (list): List of arguments to pass to the kernel.
+                         PyTorch tensor arguments will be automatically converted to pointers.
+            shared_mem (int): Shared memory size in bytes
+            stream (torch.cuda.Stream): CUDA stream to use. If None, uses current stream.
+        """
+        import torch
+
+        libcuda = torch.cuda._utils._get_gpu_runtime_library()
+
+        if not args:
+            args = []
+
+        # Process arguments and convert tensors to pointers
+        processed_args: list[ctypes.c_void_p] = []
+        c_args = []
+
+        for arg in args:
+            if isinstance(arg, torch.Tensor):
+                if not arg.is_cuda and not (arg.is_cpu and arg.is_pinned()):
+                    raise ValueError(
+                        "All tensor arguments must be CUDA tensors or pinned CPU tensors"
+                    )
+                # Get pointer to tensor data
+                ptr = ctypes.c_void_p(arg.data_ptr())
+                processed_args.append(ptr)
+                c_args.append(ctypes.byref(ptr))
+            elif isinstance(arg, int):
+                # Convert integers to C int
+                c_int = ctypes.c_int(arg)
+                # Store the C int for reference keeping, not in processed_args
+                c_args.append(ctypes.byref(c_int))
+            elif isinstance(arg, float):
+                # Python floats are doubles - use double by default
+                c_double = ctypes.c_double(arg)
+                # Store the C double for reference keeping, not in processed_args
+                c_args.append(ctypes.byref(c_double))
+            else:
+                raise TypeError(f"Unsupported argument type: {type(arg)}")
+
+        # Convert to array of void pointers
+        c_args_array = (ctypes.c_void_p * len(c_args))()
+        for i, arg in enumerate(c_args):
+            c_args_array[i] = ctypes.cast(arg, ctypes.c_void_p)
+
+        # Get the stream
+        if stream is None:
+            # Defer import to avoid circular imports
+            import torch.cuda
+
+            stream = torch.cuda.current_stream()
+
+        # Check if kernel requires large shared memory but hasn't been configured
+        if shared_mem >= 48 * 1024 and (
+            self._max_shared_mem_bytes == 0 or shared_mem > self._max_shared_mem_bytes
+        ):
+            configured_msg = (
+                "not configured"
+                if self._max_shared_mem_bytes == 0
+                else f"only {self._max_shared_mem_bytes} bytes configured"
+            )
+            raise RuntimeError(
+                f"Kernel requires {shared_mem} bytes of shared memory (>= 48KB), "
+                f"but {configured_msg}. "
+                "Call kernel.set_shared_memory_config(shared_mem) after compilation "
+                "and before launching the kernel."
+            )
+
+        _check_cuda(
+            libcuda.cuLaunchKernel(
+                self.func,
+                grid[0],
+                grid[1],
+                grid[2],
+                block[0],
+                block[1],
+                block[2],
+                shared_mem,
+                stream._as_parameter_,
+                c_args_array,
+                None,
+            )
+        )
+
+    def set_shared_memory_config(self, shared_mem_bytes: int) -> None:
+        if shared_mem_bytes < 48 * 1024:
+            # No configuration needed for <= 48KB, just update the value
+            self._max_shared_mem_bytes = shared_mem_bytes
+            return
+
+        libcuda = _get_gpu_runtime_library()
+
+        # Get device properties to validate against limits
+        device_props = torch.cuda.get_device_properties()
+        # HIP doesn't have shared_memory_per_block_optin in device properties, so we hard-code it here
+        if torch.version.hip:
+            # navi, CDNA1-CDNA3 allows a max of 64KB shared memory
+            # CDNA4 allows a max of 160KB shared memory
+            max_shared_mem = (
+                65536 if device_props.gcnArchName != "gfx950" else 160 * 1024
+            )
+        else:
+            max_shared_mem = getattr(
+                device_props, "shared_memory_per_block_optin", 49152
+            )
+
+        if shared_mem_bytes > max_shared_mem:
+            raise RuntimeError(
+                f"Requested shared memory ({shared_mem_bytes} bytes) exceeds "
+                f"device limit ({max_shared_mem} bytes). "
+                "Consider reducing block size or shared memory usage."
+            )
+
+        # Set the function attribute once
+        # https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html
+        cudaFuncAttributeMaxDynamicSharedMemorySize = 8
+        _check_cuda(
+            libcuda.cuFuncSetAttribute(
+                self.func,
+                cudaFuncAttributeMaxDynamicSharedMemorySize,
+                shared_mem_bytes,
+            )
+        )
+
+        self._max_shared_mem_bytes = shared_mem_bytes
+
+
+def _cuda_load_module(
+    ptx: Union[str, bytes], kernel_names: Optional[list[str]] = None
+) -> Union[_CudaModule, dict[str, "_CudaKernel"]]:
+    """
+    Loads a CUDA module from PTX code and returns a module object that can access kernels.
+
+    Args:
+        ptx (bytes or str): The PTX code to load
+        kernel_names (list, optional): List of kernel names to extract from the module.
+                                      If None, will return a module object with __getattr__.
+
+    Returns:
+        object: If kernel_names is None, returns a module object with __getattr__ to access kernels.
+               If kernel_names is provided, returns a dict mapping kernel names to _CudaKernel objects.
+    """
+    # Ensure CUDA is initialized
+    import torch.cuda
+
+    # Load CUDA driver library
+    libcuda = _get_gpu_runtime_library()
+
+    # Convert PTX to bytes if it's a string
+    if isinstance(ptx, str):
+        ptx = ptx.encode("utf-8")
+
+    # Load PTX module
+    module = ctypes.c_void_p()
+    # Get the current stream without directly importing torch.cuda at module level
+    stream = torch.cuda.current_stream()
+    with stream:
+        _check_cuda(libcuda.cuModuleLoadData(ctypes.byref(module), ptx))
+
+    if not kernel_names:
+        return _CudaModule(module)
+
+    # Return specific kernels
+    kernels = {}
+    for name in kernel_names:
+        func = ctypes.c_void_p()
+        _check_cuda(
+            libcuda.cuModuleGetFunction(
+                ctypes.byref(func), module, name.encode("utf-8")
+            )
+        )
+        kernels[name] = _CudaKernel(func, module)
+    return kernels
+
+
+def _get_device_index(
+    device: Any, optional: bool = False, allow_cpu: bool = False
+) -> int:
+    r"""Get the device index from :attr:`device`, which can be a torch.device object, a Python integer, or ``None``.
+
+    If :attr:`device` is a torch.device object, returns the device index if it
+    is a CUDA device. Note that for a CUDA device without a specified index,
+    i.e., ``torch.device('cuda')``, this will return the current default CUDA
+    device if :attr:`optional` is ``True``. If :attr:`allow_cpu` is ``True``,
+    CPU devices will be accepted and ``-1`` will be returned in this case.
+
+    If :attr:`device` is a Python integer, it is returned as is.
+
+    If :attr:`device` is ``None``, this will return the current default CUDA
+    device if :attr:`optional` is ``True``.
+    """
+    if isinstance(device, int):
+        return device
+    if isinstance(device, str):
+        device = torch.device(device)
+    if isinstance(device, torch.device):
+        if allow_cpu:
+            if device.type not in ["cuda", "cpu"]:
+                raise ValueError(f"Expected a cuda or cpu device, but got: {device}")
+        elif device.type != "cuda":
+            raise ValueError(f"Expected a cuda device, but got: {device}")
+    if not torch.jit.is_scripting():
+        if isinstance(device, torch.cuda.device):
+            return device.idx
+    return _torch_get_device_index(device, optional, allow_cpu)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/amp/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/amp/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..88ef0b5acac5e5bdeb034169052bcf5aa7456e33
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/amp/__init__.py
@@ -0,0 +1,13 @@
+# pyrefly: ignore [deprecated]
+from .autocast_mode import autocast, custom_bwd, custom_fwd
+from .common import amp_definitely_not_available
+from .grad_scaler import GradScaler
+
+
+__all__ = [
+    "amp_definitely_not_available",
+    "autocast",
+    "custom_bwd",
+    "custom_fwd",
+    "GradScaler",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/amp/autocast_mode.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/amp/autocast_mode.py
new file mode 100644
index 0000000000000000000000000000000000000000..e6b63c708d3f2ddfc162a4431e114a2bcf47e9eb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/amp/autocast_mode.py
@@ -0,0 +1,110 @@
+# mypy: allow-untyped-defs
+import functools
+import sys
+from typing import Any
+from typing_extensions import deprecated
+
+import torch
+
+
+__all__ = ["autocast", "custom_fwd", "custom_bwd"]
+
+
+@deprecated(
+    "`torch.cuda.amp.autocast(args...)` is deprecated. "
+    "Please use `torch.amp.autocast('cuda', args...)` instead.",
+    category=FutureWarning,
+)
+class autocast(torch.amp.autocast_mode.autocast):
+    r"""See :class:`torch.autocast`.
+
+    ``torch.cuda.amp.autocast(args...)`` is deprecated. Please use ``torch.amp.autocast("cuda", args...)`` instead.
+    """
+
+    # TODO: remove this conditional once we stop supporting Python < 3.13
+    # Prior to Python 3.13, inspect.signature could not retrieve the correct
+    # signature information for classes decorated with @deprecated (unless
+    # the __new__ static method was explicitly defined);
+    #
+    # However, this issue has been fixed in Python 3.13 and later versions.
+    if sys.version_info < (3, 13):
+
+        def __new__(
+            cls,
+            enabled: bool = True,
+            dtype: torch.dtype = torch.float16,
+            cache_enabled: bool = True,
+        ):
+            return super().__new__(cls)
+
+        def __init_subclass__(cls):
+            pass
+
+    def __init__(
+        self,
+        enabled: bool = True,
+        dtype: torch.dtype = torch.float16,
+        cache_enabled: bool = True,
+    ):
+        if torch._jit_internal.is_scripting():
+            self._enabled = enabled
+            self.device = "cuda"
+            self.fast_dtype = dtype
+            return
+        super().__init__(
+            "cuda", enabled=enabled, dtype=dtype, cache_enabled=cache_enabled
+        )
+
+    def __enter__(self):
+        if torch._jit_internal.is_scripting():
+            return self
+        return super().__enter__()
+
+    # TODO: discuss a unified TorchScript-friendly API for autocast
+    def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any):  # type: ignore[override]
+        if torch._jit_internal.is_scripting():
+            return
+        return super().__exit__(exc_type, exc_val, exc_tb)
+
+    def __call__(self, func):
+        if torch._jit_internal.is_scripting():
+            return func
+        return super().__call__(func)
+
+
+# Preserved only for BC reasons
+@deprecated(
+    "`torch.cuda.amp.autocast_mode._cast(value, dtype)` is deprecated. "
+    "Please use `torch.amp.autocast_mode._cast(value, 'cuda', dtype)` instead.",
+    category=FutureWarning,
+)
+def _cast(value, dtype):
+    return torch.amp.autocast_mode._cast(value, "cuda", dtype)
+
+
+@deprecated(
+    "`torch.cuda.amp.custom_fwd(args...)` is deprecated. "
+    "Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.",
+    category=FutureWarning,
+)
+def custom_fwd(fwd=None, *, cast_inputs=None):
+    """
+    ``torch.cuda.amp.custom_fwd(args...)`` is deprecated. Please use
+    ``torch.amp.custom_fwd(args..., device_type='cuda')`` instead.
+    """
+    return functools.partial(torch.amp.custom_fwd, device_type="cuda")(
+        fwd=fwd, cast_inputs=cast_inputs
+    )
+
+
+@deprecated(
+    "`torch.cuda.amp.custom_bwd(args...)` is deprecated. "
+    "Please use `torch.amp.custom_bwd(args..., device_type='cuda')` instead.",
+    category=FutureWarning,
+)
+def custom_bwd(bwd):
+    """
+    ``torch.cuda.amp.custom_bwd(args...)`` is deprecated. Please use
+    ``torch.amp.custom_bwd(args..., device_type='cuda')`` instead.
+    """
+    return functools.partial(torch.amp.custom_bwd, device_type="cuda")(bwd)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/amp/common.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/amp/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..915a9b4f4a9ca6c147abefd7c8ab1891ee5a8179
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/amp/common.py
@@ -0,0 +1,11 @@
+# mypy: allow-untyped-defs
+from importlib.util import find_spec
+
+import torch
+
+
+__all__ = ["amp_definitely_not_available"]
+
+
+def amp_definitely_not_available():
+    return not (torch.cuda.is_available() or find_spec("torch_xla"))
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/amp/grad_scaler.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/amp/grad_scaler.py
new file mode 100644
index 0000000000000000000000000000000000000000..62e2020073c8ed99f7295edd1aaea4c54d815f63
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/amp/grad_scaler.py
@@ -0,0 +1,38 @@
+from typing_extensions import deprecated
+
+import torch
+
+# We need to keep this unused import for BC reasons
+from torch.amp.grad_scaler import OptState  # noqa: F401
+
+
+__all__ = ["GradScaler"]
+
+
+class GradScaler(torch.amp.GradScaler):
+    r"""
+    See :class:`torch.amp.GradScaler`.
+    ``torch.cuda.amp.GradScaler(args...)`` is deprecated. Please use ``torch.amp.GradScaler("cuda", args...)`` instead.
+    """
+
+    @deprecated(
+        "`torch.cuda.amp.GradScaler(args...)` is deprecated. "
+        "Please use `torch.amp.GradScaler('cuda', args...)` instead.",
+        category=FutureWarning,
+    )
+    def __init__(
+        self,
+        init_scale: float = 2.0**16,
+        growth_factor: float = 2.0,
+        backoff_factor: float = 0.5,
+        growth_interval: int = 2000,
+        enabled: bool = True,
+    ) -> None:
+        super().__init__(
+            "cuda",
+            init_scale=init_scale,
+            growth_factor=growth_factor,
+            backoff_factor=backoff_factor,
+            growth_interval=growth_interval,
+            enabled=enabled,
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/comm.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/comm.py
new file mode 100644
index 0000000000000000000000000000000000000000..2915de5d090fd18c82540beedb9971a0b7b6cc3e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/comm.py
@@ -0,0 +1,19 @@
+# The functions here have been moved to torch.nn.parallel.comm
+from torch.nn.parallel.comm import (
+    broadcast,
+    broadcast_coalesced,
+    gather,
+    reduce_add,
+    reduce_add_coalesced,
+    scatter,
+)
+
+
+__all__ = [
+    "broadcast",
+    "broadcast_coalesced",
+    "reduce_add",
+    "reduce_add_coalesced",
+    "scatter",
+    "gather",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/gds.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/gds.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a7dfa388caa0420900408edf70afde7798b30a2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/gds.py
@@ -0,0 +1,167 @@
+import os
+import sys
+from collections.abc import Callable
+from typing import Optional
+
+import torch
+from torch.types import Storage
+
+
+__all__: list[str] = [
+    "gds_register_buffer",
+    "gds_deregister_buffer",
+    "GdsFile",
+]
+
+
+def _dummy_fn(name: str) -> Callable:
+    def fn(*args, **kwargs):  # type: ignore[no-untyped-def]
+        raise RuntimeError(f"torch._C.{name} is not supported on this platform")
+
+    return fn
+
+
+if not hasattr(torch._C, "_gds_register_buffer"):
+    assert not hasattr(torch._C, "_gds_deregister_buffer")
+    assert not hasattr(torch._C, "_gds_register_handle")
+    assert not hasattr(torch._C, "_gds_deregister_handle")
+    assert not hasattr(torch._C, "_gds_load_storage")
+    assert not hasattr(torch._C, "_gds_save_storage")
+    # Define functions
+    torch._C.__dict__["_gds_register_buffer"] = _dummy_fn("_gds_register_buffer")
+    torch._C.__dict__["_gds_deregister_buffer"] = _dummy_fn("_gds_deregister_buffer")
+    torch._C.__dict__["_gds_register_handle"] = _dummy_fn("_gds_register_handle")
+    torch._C.__dict__["_gds_deregister_handle"] = _dummy_fn("_gds_deregister_handle")
+    torch._C.__dict__["_gds_load_storage"] = _dummy_fn("_gds_load_storage")
+    torch._C.__dict__["_gds_save_storage"] = _dummy_fn("_gds_save_storage")
+
+
+def gds_register_buffer(s: Storage) -> None:
+    """Registers a storage on a CUDA device as a cufile buffer.
+
+    Example::
+
+        >>> # xdoctest: +SKIP("gds filesystem requirements")
+        >>> src = torch.randn(1024, device="cuda")
+        >>> s = src.untyped_storage()
+        >>> gds_register_buffer(s)
+
+    Args:
+        s (Storage): Buffer to register.
+    """
+    torch._C._gds_register_buffer(s)
+
+
+def gds_deregister_buffer(s: Storage) -> None:
+    """Deregisters a previously registered storage on a CUDA device as a cufile buffer.
+
+    Example::
+
+        >>> # xdoctest: +SKIP("gds filesystem requirements")
+        >>> src = torch.randn(1024, device="cuda")
+        >>> s = src.untyped_storage()
+        >>> gds_register_buffer(s)
+        >>> gds_deregister_buffer(s)
+
+    Args:
+        s (Storage): Buffer to register.
+    """
+    torch._C._gds_deregister_buffer(s)
+
+
+class GdsFile:
+    r"""Wrapper around cuFile.
+
+    cuFile is a file-like interface to the GPUDirect Storage (GDS) API.
+
+    See the `cufile docs `_
+    for more details.
+
+    Args:
+        filename (str): Name of the file to open.
+        flags (int): Flags to pass to ``os.open`` when opening the file. ``os.O_DIRECT`` will
+            be added automatically.
+
+    Example::
+
+        >>> # xdoctest: +SKIP("gds filesystem requirements")
+        >>> src1 = torch.randn(1024, device="cuda")
+        >>> src2 = torch.randn(2, 1024, device="cuda")
+        >>> file = torch.cuda.gds.GdsFile(f, os.O_CREAT | os.O_RDWR)
+        >>> file.save_storage(src1.untyped_storage(), offset=0)
+        >>> file.save_storage(src2.untyped_storage(), offset=src1.nbytes)
+        >>> dest1 = torch.empty(1024, device="cuda")
+        >>> dest2 = torch.empty(2, 1024, device="cuda")
+        >>> file.load_storage(dest1.untyped_storage(), offset=0)
+        >>> file.load_storage(dest2.untyped_storage(), offset=src1.nbytes)
+        >>> torch.equal(src1, dest1)
+        True
+        >>> torch.equal(src2, dest2)
+        True
+
+    """
+
+    def __init__(self, filename: str, flags: int):
+        if sys.platform == "win32":
+            raise RuntimeError("GdsFile is not supported on this platform.")
+        self.filename = filename
+        self.flags = flags
+        self.fd = os.open(filename, flags | os.O_DIRECT)  # type: ignore[attr-defined]
+        self.handle: Optional[int] = None
+        self.register_handle()
+
+    def __del__(self) -> None:
+        if self.handle is not None:
+            self.deregister_handle()
+        os.close(self.fd)
+
+    def register_handle(self) -> None:
+        """Registers file descriptor to cuFile Driver.
+
+        This is a wrapper around ``cuFileHandleRegister``.
+        """
+        assert self.handle is None, (
+            "Cannot register a handle that is already registered."
+        )
+        self.handle = torch._C._gds_register_handle(self.fd)
+
+    def deregister_handle(self) -> None:
+        """Deregisters file descriptor from cuFile Driver.
+
+        This is a wrapper around ``cuFileHandleDeregister``.
+        """
+        assert self.handle is not None, (
+            "Cannot deregister a handle that is not registered."
+        )
+        torch._C._gds_deregister_handle(self.handle)
+        self.handle = None
+
+    def load_storage(self, storage: Storage, offset: int = 0) -> None:
+        """Loads data from the file into the storage.
+
+        This is a wrapper around ``cuFileRead``. ``storage.nbytes()`` of data
+        will be loaded from the file at ``offset`` into the storage.
+
+        Args:
+            storage (Storage): Storage to load data into.
+            offset (int, optional): Offset into the file to start loading from. (Default: 0)
+        """
+        assert self.handle is not None, (
+            "Cannot load data from a file that is not registered."
+        )
+        torch._C._gds_load_storage(self.handle, storage, offset)
+
+    def save_storage(self, storage: Storage, offset: int = 0) -> None:
+        """Saves data from the storage into the file.
+
+        This is a wrapper around ``cuFileWrite``. All bytes of the storage
+        will be written to the file at ``offset``.
+
+        Args:
+            storage (Storage): Storage to save data from.
+            offset (int, optional): Offset into the file to start saving to. (Default: 0)
+        """
+        assert self.handle is not None, (
+            "Cannot save data to a file that is not registered."
+        )
+        torch._C._gds_save_storage(self.handle, storage, offset)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/graphs.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/graphs.py
new file mode 100644
index 0000000000000000000000000000000000000000..b4d2cb43f8bc3d964ea620606127b5f7a661de4c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/graphs.py
@@ -0,0 +1,613 @@
+# pylint: disable=useless-parent-delegation
+from __future__ import annotations
+
+import gc
+import typing
+from collections.abc import Callable
+from typing import Optional, overload, TYPE_CHECKING, TypeAlias, Union
+from typing_extensions import ParamSpec, Self, TypeVar
+
+import torch
+from torch import Tensor
+
+
+if TYPE_CHECKING:
+    # importing _POOL_HANDLE at runtime toplevel causes an import cycle
+    from torch.cuda import _POOL_HANDLE
+
+from .._utils import _dummy_type
+
+
+__all__ = [
+    "is_current_stream_capturing",
+    "graph_pool_handle",
+    "CUDAGraph",
+    "graph",
+    "make_graphed_callables",
+]
+
+
+_R = TypeVar("_R")
+_P = ParamSpec("_P")
+
+
+if not hasattr(torch._C, "_CudaStreamBase"):
+    # Define dummy base classes
+    torch._C.__dict__["_CUDAGraph"] = _dummy_type("_CUDAGraph")
+    torch._C.__dict__["_graph_pool_handle"] = _dummy_type("_graph_pool_handle")
+    torch._C.__dict__["_cuda_isCurrentStreamCapturing"] = _dummy_type(
+        "_cuda_isCurrentStreamCapturing"
+    )
+
+from torch._C import (  # noqa: F401
+    _cuda_isCurrentStreamCapturing,
+    _CUDAGraph,
+    _graph_pool_handle,
+)
+
+
+def is_current_stream_capturing() -> bool:
+    r"""Return True if CUDA graph capture is underway on the current CUDA stream, False otherwise.
+
+    If a CUDA context does not exist on the current device, returns False without initializing the context.
+    """
+    return _cuda_isCurrentStreamCapturing()
+
+
+# Python shim helps Sphinx process docstrings more reliably.
+def graph_pool_handle() -> _POOL_HANDLE:
+    r"""Return an opaque token representing the id of a graph memory pool.
+
+    See :ref:`Graph memory management`.
+
+    .. warning::
+        This API is in beta and may change in future releases.
+    """
+    return torch.cuda._POOL_HANDLE(_graph_pool_handle())
+
+
+# Python shim helps Sphinx process docstrings more reliably.
+class CUDAGraph(torch._C._CUDAGraph):
+    r"""Wrapper around a CUDA graph.
+
+    Arguments:
+        keep_graph (bool, optional): If ``keep_graph=False``, the
+            cudaGraphExec_t will be instantiated on GPU at the end of
+            ``capture_end`` and the underlying cudaGraph_t will be
+            destroyed. Users who want to query or otherwise modify the
+            underlying cudaGraph_t before instantiation can set
+            ``keep_graph=True`` and access it via ``raw_cuda_graph`` after
+            ``capture_end``. Note that the cudaGraphExec_t will not be
+            instantiated at the end of ``capture_end`` in this
+            case. Instead, it will be instantiated via an explicit called
+            to ``instantiate`` or automatically on the first call to
+            ``replay`` if ``instantiate`` was not already called. Calling
+            ``instantiate`` manually before ``replay`` is recommended to
+            prevent increased latency on the first call to ``replay``. It
+            is allowed to modify the raw cudaGraph_t after first calling
+            ``instantiate``, but the user must call ``instantiate`` again
+            manually to make sure the instantiated graph has these
+            changes. Pytorch has no means of tracking these changes.
+
+    .. warning::
+        This API is in beta and may change in future releases.
+
+    """
+
+    def __new__(cls, keep_graph: bool = False) -> Self:
+        return super().__new__(cls, keep_graph)
+
+    def capture_begin(
+        self, pool: Optional[_POOL_HANDLE] = None, capture_error_mode: str = "global"
+    ) -> None:
+        r"""Begin capturing CUDA work on the current stream.
+
+        Typically, you shouldn't call ``capture_begin`` yourself.
+        Use :class:`~torch.cuda.graph` or :func:`~torch.cuda.make_graphed_callables`,
+        which call ``capture_begin`` internally.
+
+        Arguments:
+            pool (optional): Token (returned by :func:`~torch.cuda.graph_pool_handle` or
+                :meth:`other_Graph_instance.pool()`) that hints this graph may share memory
+                with the indicated pool.  See :ref:`Graph memory management`.
+            capture_error_mode (str, optional): specifies the cudaStreamCaptureMode for the graph capture stream.
+                Can be "global", "thread_local" or "relaxed". During cuda graph capture, some actions, such as cudaMalloc,
+                may be unsafe. "global" will error on actions in other threads, "thread_local" will only error for
+                actions in the current thread, and "relaxed" will not error on these actions. Do NOT change this setting
+                unless you're familiar with `cudaStreamCaptureMode `_
+        """  # noqa: B950
+        super().capture_begin(pool=pool, capture_error_mode=capture_error_mode)
+
+    def capture_end(self) -> None:
+        r"""End CUDA graph capture on the current stream.
+
+        After ``capture_end``, ``replay`` may be called on this instance.
+
+        Typically, you shouldn't call ``capture_end`` yourself.
+        Use :class:`~torch.cuda.graph` or :func:`~torch.cuda.make_graphed_callables`,
+        which call ``capture_end`` internally.
+        """
+        super().capture_end()
+
+    def instantiate(self) -> None:
+        r"""Instantiate the CUDA graph. Will be called by
+        ``capture_end`` if ``keep_graph=False``, or by ``replay`` if
+        ``keep_graph=True`` and ``instantiate`` has not already been
+        explicitly called. Does not destroy the cudaGraph_t returned
+        by ``raw_cuda_graph``.
+        """
+        super().instantiate()
+
+    def replay(self) -> None:
+        r"""Replay the CUDA work captured by this graph."""
+        super().replay()
+
+    def reset(self) -> None:
+        r"""Delete the graph currently held by this instance."""
+        super().reset()
+
+    def pool(self) -> _POOL_HANDLE:
+        r"""Return an opaque token representing the id of this graph's memory pool.
+
+        This id can optionally be passed to another graph's ``capture_begin``,
+        which hints the other graph may share the same memory pool.
+        """
+        return super().pool()
+
+    def enable_debug_mode(self) -> None:
+        r"""Enable debugging mode for CUDAGraph.debug_dump."""
+        return super().enable_debug_mode()
+
+    def debug_dump(self, debug_path: str) -> None:
+        r"""
+        Arguments:
+            debug_path (required): Path to dump the graph to.
+
+        Calls a debugging function to dump the graph if the debugging is
+        enabled via CUDAGraph.enable_debug_mode()
+        """
+        return super().debug_dump(debug_path)
+
+    def raw_cuda_graph(self) -> int:
+        r"""Returns the underlying cudaGraph_t. ``keep_graph`` must be True.
+
+        See the following for APIs for how to manipulate this object: `Graph Managmement `_ and `cuda-python Graph Management bindings `_
+        """  # noqa: B950
+        return super().raw_cuda_graph()
+
+    def raw_cuda_graph_exec(self) -> int:
+        r"""Returns the underlying cudaGraphExec_t. ``instantiate`` must have been called if ``keep_graph`` is True, or ``capture_end`` must have been called if ``keep_graph`` is False. If you call ``instantiate()`` after ``raw_cuda_graph_exec()``, the previously returned cudaGraphExec_t will be destroyed. It is your responsibility not to use this object after destruction.
+
+        See the following for APIs for how to manipulate this object: `Graph Execution `_ and `cuda-python Graph Execution bindings `_
+        """  # noqa: B950
+        return super().raw_cuda_graph_exec()
+
+
+class graph:
+    r"""Context-manager that captures CUDA work into a :class:`torch.cuda.CUDAGraph` object for later replay.
+
+    See :ref:`CUDA Graphs ` for a general introduction,
+    detailed use, and constraints.
+
+    Arguments:
+        cuda_graph (torch.cuda.CUDAGraph): Graph object used for capture.
+        pool (optional): Opaque token (returned by a call to :func:`~torch.cuda.graph_pool_handle()` or
+            :meth:`other_Graph_instance.pool()`) hinting this graph's capture
+            may share memory from the specified pool. See :ref:`Graph memory management`.
+        stream (torch.cuda.Stream, optional): If supplied, will be set as the current stream in the context.
+            If not supplied, ``graph`` sets its own internal side stream as the current stream in the context.
+        capture_error_mode (str, optional): specifies the cudaStreamCaptureMode for the graph capture stream.
+            Can be "global", "thread_local" or "relaxed". During cuda graph capture, some actions, such as cudaMalloc,
+            may be unsafe. "global" will error on actions in other threads, "thread_local" will only error for
+            actions in the current thread, and "relaxed" will not error on actions. Do NOT change this setting
+            unless you're familiar with `cudaStreamCaptureMode `_
+
+    .. note::
+        For effective memory sharing, if you pass a ``pool`` used by a previous capture and the previous capture
+        used an explicit ``stream`` argument, you should pass the same ``stream`` argument to this capture.
+
+    .. warning::
+        This API is in beta and may change in future releases.
+
+    .. _cudaStreamCaptureMode:
+        https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__STREAM.html#group__CUDART__STREAM_1g9d0535d93a214cbf126835257b16ba85
+    """  # noqa: B950
+
+    default_capture_stream: Optional[torch.cuda.Stream] = None
+
+    def __init__(
+        self,
+        cuda_graph: CUDAGraph,
+        pool: Optional[_POOL_HANDLE] = None,
+        stream: Optional[torch.cuda.Stream] = None,
+        capture_error_mode: str = "global",
+    ):
+        # Lazy-init of default_capture_stream helps avoid circular-import errors.
+        # Not thread safe, but graphs already have the general (explicitly documented)
+        # restriction that only one capture may be underway at a time in the process.
+        if self.__class__.default_capture_stream is None:
+            self.__class__.default_capture_stream = torch.cuda.Stream()
+
+        self.pool: Union[tuple[()], tuple[_POOL_HANDLE]] = (
+            () if pool is None else (pool,)
+        )
+        self.capture_stream = (
+            stream if stream is not None else self.__class__.default_capture_stream
+        )
+        assert self.capture_stream is not None
+        self.stream_ctx = torch.cuda.stream(self.capture_stream)
+        self.cuda_graph = cuda_graph
+        self.capture_error_mode = capture_error_mode
+
+    def __enter__(self) -> None:
+        # Free as much memory as we can for the graph
+        torch.cuda.synchronize()
+
+        if torch.compiler.config.force_cudagraph_gc:
+            # Originally we unconditionally garbage collected here. On one hand
+            # that's nice because we have a chance to collect more memory, but
+            # on the other hand it is REALLY expensive, especially for doing
+            # multiple cudagraph captures in a row. In theory it will only help
+            # when a dead python cycle is holding onto CUDA memory.
+            gc.collect()
+
+        torch.cuda.empty_cache()
+
+        # Stackoverflow seems comfortable with this pattern
+        # https://stackoverflow.com/questions/26635684/calling-enter-and-exit-manually#39172487
+        self.stream_ctx.__enter__()
+
+        self.cuda_graph.capture_begin(
+            # type: ignore[misc]
+            *self.pool,
+            # pyrefly: ignore [bad-keyword-argument]
+            capture_error_mode=self.capture_error_mode,
+        )
+
+    def __exit__(self, *args: object) -> None:
+        self.cuda_graph.capture_end()
+        self.stream_ctx.__exit__(*args)
+        # returning None should propagate exceptions from either capture_end or stream_ctx.__exit__()
+
+
+_ModuleOrCallable: TypeAlias = Union["torch.nn.Module", Callable[..., object]]
+
+
+@overload
+def make_graphed_callables(
+    callables: _ModuleOrCallable,
+    sample_args: tuple[Tensor, ...],
+    num_warmup_iters: int = 3,
+    allow_unused_input: bool = False,
+    pool: Optional[_POOL_HANDLE] = None,
+) -> _ModuleOrCallable: ...
+
+
+@overload
+def make_graphed_callables(
+    callables: tuple[_ModuleOrCallable, ...],
+    sample_args: tuple[tuple[Tensor, ...], ...],
+    num_warmup_iters: int = 3,
+    allow_unused_input: bool = False,
+    pool: Optional[_POOL_HANDLE] = None,
+) -> tuple[_ModuleOrCallable, ...]: ...
+
+
+def make_graphed_callables(
+    callables: Union[_ModuleOrCallable, tuple[_ModuleOrCallable, ...]],
+    sample_args: Union[tuple[Tensor, ...], tuple[tuple[Tensor, ...], ...]],
+    num_warmup_iters: int = 3,
+    allow_unused_input: bool = False,
+    pool: Optional[_POOL_HANDLE] = None,
+) -> Union[_ModuleOrCallable, tuple[_ModuleOrCallable, ...]]:
+    r"""Accept callables (functions or :class:`nn.Module`\ s) and returns graphed versions.
+
+    Each graphed callable's forward pass runs its source callable's
+    forward CUDA work as a CUDA graph inside a single autograd node.
+
+    The graphed callable's forward pass also appends
+    a backward node to the autograd graph. During backward, this node runs the
+    callable's backward work as a CUDA graph.
+
+    Therefore, each graphed callable should be a drop-in replacement for its source callable
+    in an autograd-enabled training loop.
+
+    See :ref:`Partial-network capture` for detailed use and constraints.
+
+    If you pass a tuple of several callables, their captures will use the same memory pool.
+    See :ref:`Graph memory management` for when this is appropriate.
+
+    Arguments:
+        callables (torch.nn.Module or Python function, or tuple of these): Callable or callables to graph.
+            See :ref:`Graph memory management` for when passing a tuple of callables
+            is appropriate.  If you pass a tuple of callables, their order in the tuple must be the same order
+            they'll run in the live workload.
+        sample_args (tuple of Tensors, or tuple of tuples of Tensors): Samples args for each callable.
+            If a single callable was passed, ``sample_args`` must be a single tuple of argument Tensors.
+            If a tuple of callables was passed, ``sample_args`` must be tuple of tuples of argument Tensors.
+        num_warmup_iters (int): The number of warmup iterations. Currently, ``DataDistributedParallel`` needs
+            11 iterations for warm up. Default: ``3``.
+        allow_unused_input (bool): If False, specifying inputs that were not used when computing outputs
+            (and therefore their grad is always zero) is an error. Defaults to False.
+        pool (optional): Token (returned by :func:`~torch.cuda.graph_pool_handle` or
+            :meth:`other_Graph_instance.pool()`) that hints this graph may share memory
+            with the indicated pool.  See :ref:`Graph memory management`.
+    .. note::
+        The ``requires_grad`` state of each Tensor in ``sample_args`` must match the state
+        that's expected for the corresponding real input in the training loop.
+
+    .. warning::
+        This API is in beta and may change in future releases.
+
+    .. warning::
+        ``sample_args`` for each callable must contain only Tensors. Other types are not allowed.
+
+    .. warning::
+        Returned callables do not support higher order differentiation (e.g., double backward).
+
+    .. warning::
+        In any :class:`~torch.nn.Module` passed to :func:`~make_graphed_callables`, only parameters
+        may be trainable. Buffers must have ``requires_grad=False``.
+
+    .. warning::
+        After you pass a :class:`torch.nn.Module` through :func:`~make_graphed_callables`,
+        you may not add or remove any of that Module's parameters or buffers.
+
+    .. warning::
+        :class:`torch.nn.Module`\s passed to :func:`~torch.cuda.make_graphed_callables` must not have module hooks
+        registered on them at the time they are passed. However, registering hooks on modules *after* passing them
+        through :func:`~torch.cuda.make_graphed_callables` is allowed.
+
+    .. warning::
+        When running a graphed callable, you must pass its arguments in the same order and format
+        they appeared in that callable's ``sample_args``.
+
+    .. warning::
+        The automatic mixed precision is supported in :func:`~torch.cuda.make_graphed_callables` only with disabled
+        caching. The context manager `torch.cuda.amp.autocast()` must have `cache_enabled=False`.
+    """
+    if torch.is_autocast_enabled() and torch.is_autocast_cache_enabled():
+        raise RuntimeError(
+            "make_graphed_callables does not support the autocast caching. Please set `cache_enabled=False`."
+        )
+
+    just_one_callable = False
+
+    _sample_args: tuple[tuple[Tensor, ...], ...]
+    if not isinstance(callables, tuple):
+        just_one_callable = True
+        callables = (callables,)
+        _sample_args = (typing.cast(tuple[Tensor, ...], sample_args),)
+    else:
+        _sample_args = typing.cast(tuple[tuple[Tensor, ...], ...], sample_args)
+
+    flatten_sample_args = []
+
+    for c, args in zip(callables, _sample_args):
+        if isinstance(c, torch.nn.Module):
+            assert (
+                len(c._backward_hooks) == 0
+                and len(c._forward_hooks) == 0
+                and len(c._forward_pre_hooks) == 0
+            ), (
+                "Modules must not have hooks registered at the time they are passed. However, registering hooks "
+                + "on modules after passing them through make_graphed_callables is allowed."
+            )
+            assert all(b.requires_grad is False for b in c.buffers()), (
+                "In any :class:`~torch.nn.Module` passed to "
+                + ":func:`~make_graphed_callables`, only parameters may be trainable. All buffers must have "
+                + "``requires_grad=False``."
+            )
+        flatten_arg = torch.utils._pytree.arg_tree_leaves(*args)
+        flatten_sample_args.append(tuple(flatten_arg))
+        assert all(isinstance(arg, torch.Tensor) for arg in flatten_arg), (
+            "In the beta API, sample_args "
+            + "for each callable must contain only Tensors. Other types are not allowed."
+        )
+
+    # If a callable is an nn.Module, its graph's full input surface is the args the user explicitly
+    # passes to forward (ie, its sample_args) AND the module's parameter attributes.
+    per_callable_len_user_args = [len(args) for args in flatten_sample_args]
+    per_callable_module_params = [
+        tuple(c.parameters()) if isinstance(c, torch.nn.Module) else ()
+        for c in callables
+    ]
+    per_callable_static_input_surfaces = [
+        flatten_sample_args[i] + per_callable_module_params[i]
+        for i in range(len(callables))
+    ]
+
+    fwd_graphs = [torch.cuda.CUDAGraph() for _ in range(len(callables))]
+    bwd_graphs = [torch.cuda.CUDAGraph() for _ in range(len(callables))]
+
+    mempool = graph_pool_handle() if pool is None else pool
+
+    # Warmup
+    # Hopefully prevents cudnn benchmarking and other lazy-initialization cuda work
+    # from ending up in any captures.
+    torch.cuda.synchronize()
+    with torch.cuda.stream(torch.cuda.Stream()):
+        for func, args, static_input_surface in zip(
+            callables, _sample_args, per_callable_static_input_surfaces
+        ):
+            grad_inputs, outputs, outputs_grad = None, None, None
+            for _ in range(num_warmup_iters):
+                outputs = torch.utils._pytree.tree_leaves(func(*args))
+                outputs_grad = tuple(o for o in outputs if o.requires_grad)
+                if len(outputs_grad) > 0:
+                    grad_inputs = torch.autograd.grad(
+                        outputs=outputs_grad,
+                        inputs=tuple(
+                            i for i in static_input_surface if i.requires_grad
+                        ),
+                        grad_outputs=tuple(
+                            torch.empty_like(o) for o in outputs if o.requires_grad
+                        ),
+                        only_inputs=True,
+                        allow_unused=allow_unused_input,
+                    )
+            for v in [outputs, outputs_grad, grad_inputs]:
+                del v
+
+    torch.cuda.synchronize()
+
+    # All captures here share a mempool. To avoid replays corrupting each other's memory,
+    # the safest approach is to capture all passes in the same order they'll run:
+    # fwd 1, fwd 2, ... fwd N, then bwd N, bwd N-1, ... bwd 1.
+
+    # Capture forward graphs
+    per_callable_static_outputs = []
+    per_callable_output_unflatten_spec = []
+    for func, args, fwd_graph in zip(callables, _sample_args, fwd_graphs):
+        with torch.cuda.graph(fwd_graph, pool=mempool):
+            func_outputs = func(*args)
+
+        flatten_outputs, spec = torch.utils._pytree.tree_flatten(func_outputs)
+        per_callable_static_outputs.append(tuple(flatten_outputs))
+        per_callable_output_unflatten_spec.append(spec)
+
+    # Capture backward graphs in reverse order
+    per_callable_static_grad_outputs = []
+    per_callable_static_grad_inputs = []
+    for static_input_surface, static_outputs, bwd_graph in zip(
+        reversed(per_callable_static_input_surfaces),
+        reversed(per_callable_static_outputs),
+        reversed(bwd_graphs),
+    ):
+        # For now, assumes all static_outputs require grad
+        # assert all(o.requires_grad for o in static_outputs), "Outputs of graphed callables must require grad."
+        static_grad_outputs = tuple(
+            torch.empty_like(o) if o.requires_grad else None for o in static_outputs
+        )
+
+        outputs_grad = tuple(o for o in static_outputs if o.requires_grad)
+        grad_inputs = None
+        if len(outputs_grad) > 0:
+            with torch.cuda.graph(bwd_graph, pool=mempool):
+                grad_inputs = torch.autograd.grad(
+                    outputs=outputs_grad,
+                    inputs=tuple(i for i in static_input_surface if i.requires_grad),
+                    grad_outputs=tuple(o for o in static_grad_outputs if o is not None),
+                    only_inputs=True,
+                    allow_unused=allow_unused_input,
+                )
+
+        # Constructs a tuple suitable for returning from Graphed.backward:
+        # Pads out the actually-needed grads with Nones in gradient slots for inputs that don't require grad.
+        # I couldn't think of a slick one-liner for this pattern.
+        static_grad_inputs = []
+        grad_idx = 0
+        for arg in static_input_surface:
+            if arg.requires_grad and grad_inputs is not None:
+                static_grad_inputs.append(grad_inputs[grad_idx])
+                grad_idx += 1
+            else:
+                static_grad_inputs.append(None)  # type: ignore[arg-type]
+        static_grad_inputs = tuple(static_grad_inputs)  # type: ignore[assignment]
+
+        per_callable_static_grad_outputs.append(static_grad_outputs)
+        per_callable_static_grad_inputs.append(static_grad_inputs)
+
+    # Reverses the most recent two lists
+    per_callable_static_grad_outputs.reverse()
+    per_callable_static_grad_inputs.reverse()
+    # Now for every per_callable list, per_callable_*[i] holds the stuff for the ith callable.
+
+    def make_graphed_autograd_function(
+        fwd_graph: CUDAGraph,
+        bwd_graph: CUDAGraph,
+        module_params: tuple[torch.nn.Parameter, ...],
+        len_user_args: int,
+        output_unflatten_spec: torch.utils._pytree.TreeSpec,
+        static_input_surface: tuple[Tensor, ...],
+        static_outputs: tuple[Tensor, ...],
+        static_grad_outputs: tuple[Optional[Tensor], ...],
+        static_grad_inputs: tuple[Tensor, ...],
+    ) -> Callable[..., object]:
+        class Graphed(torch.autograd.Function):
+            @staticmethod
+            # pyrefly: ignore [bad-override]
+            def forward(ctx: object, *inputs: Tensor) -> tuple[Tensor, ...]:
+                # At this stage, only the user args may (potentially) be new tensors.
+                for i in range(len_user_args):
+                    if static_input_surface[i].data_ptr() != inputs[i].data_ptr():
+                        static_input_surface[i].copy_(inputs[i])
+                fwd_graph.replay()
+                assert isinstance(static_outputs, tuple)
+                return tuple(o.detach() for o in static_outputs)
+
+            @staticmethod
+            @torch.autograd.function.once_differentiable
+            # pyrefly: ignore [bad-override]
+            def backward(ctx: object, *grads: Tensor) -> tuple[Tensor, ...]:
+                assert len(grads) == len(static_grad_outputs)
+                for g, grad in zip(static_grad_outputs, grads):
+                    if g is not None:
+                        # don't copy if autograd gods have been kind and the
+                        # incoming grad is already in the right place
+                        if g.data_ptr() != grad.data_ptr():
+                            g.copy_(grad)
+                bwd_graph.replay()
+
+                # Input args that didn't require grad expect a None gradient.
+                assert isinstance(static_grad_inputs, tuple)
+                return tuple(
+                    # pyrefly: ignore [bad-argument-type]
+                    b.detach() if b is not None else b
+                    for b in static_grad_inputs
+                )
+
+        def functionalized(*user_args: object) -> object:
+            # Runs the autograd function with inputs == all inputs to the graph that might require grad
+            # (explicit user args + module parameters)
+            # Assumes module params didn't change since capture.
+            flatten_user_args = torch.utils._pytree.arg_tree_leaves(*user_args)
+            out = Graphed.apply(*(tuple(flatten_user_args) + module_params))
+            return torch.utils._pytree.tree_unflatten(out, output_unflatten_spec)
+
+        return functionalized
+
+    # Put together the final graphed callables
+    ret: list[_ModuleOrCallable] = []
+    for i, func in enumerate(callables):
+        graphed = make_graphed_autograd_function(
+            fwd_graphs[i],
+            bwd_graphs[i],
+            per_callable_module_params[i],
+            per_callable_len_user_args[i],
+            per_callable_output_unflatten_spec[i],
+            per_callable_static_input_surfaces[i],
+            per_callable_static_outputs[i],
+            per_callable_static_grad_outputs[i],
+            per_callable_static_grad_inputs[i],
+        )
+
+        if isinstance(func, torch.nn.Module):
+
+            def make_graphed_forward(
+                func: torch.nn.Module,
+                graph_training_state: bool,
+                graphed: Callable[_P, _R],
+                orig_fwd: Callable[_P, _R],
+            ) -> Callable[_P, _R]:
+                def new_fwd(*user_args: _P.args, **user_kwargs: _P.kwargs) -> _R:
+                    # If the module's training-or-eval state matches what we graphed,
+                    # run the graph, otherwise run the original forward method
+                    if func.training == graph_training_state:
+                        return graphed(*user_args, **user_kwargs)
+                    else:
+                        return orig_fwd(*user_args, **user_kwargs)
+
+                return new_fwd
+
+            func.forward = make_graphed_forward(
+                func, func.training, graphed, func.forward
+            )
+            ret.append(func)
+        else:
+            ret.append(graphed)
+
+    if just_one_callable:
+        return ret[0]
+
+    return tuple(ret)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/green_contexts.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/green_contexts.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b8f7f176499818c3cdc842683b825771031eea5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/green_contexts.py
@@ -0,0 +1,43 @@
+import torch
+
+
+_GreenContext = object
+SUPPORTED = False
+
+if hasattr(torch._C, "_CUDAGreenContext"):
+    _GreenContext = torch._C._CUDAGreenContext  # type: ignore[misc]
+    SUPPORTED = True
+
+
+# Python shim helps Sphinx process docstrings more reliably.
+# pyrefly: ignore [invalid-inheritance]
+class GreenContext(_GreenContext):
+    r"""Wrapper around a CUDA green context.
+
+    .. warning::
+       This API is in beta and may change in future releases.
+    """
+
+    @staticmethod
+    def create(num_sms: int, device_id: int = 0) -> _GreenContext:
+        r"""Create a CUDA green context.
+
+        Arguments:
+            num_sms (int): The number of SMs to use in the green context.
+            device_id (int, optional): The device index of green context.
+        """
+        if not SUPPORTED:
+            raise RuntimeError("PyTorch was not built with Green Context support!")
+        return _GreenContext.create(num_sms, device_id)  # type: ignore[attr-defined]
+
+    # Note that these functions are bypassed by we define them here
+    # for Sphinx documentation purposes
+    def set_context(self) -> None:  # pylint: disable=useless-parent-delegation
+        r"""Make the green context the current context."""
+        return super().set_context()  # type: ignore[misc]
+
+    def pop_context(self) -> None:  # pylint: disable=useless-parent-delegation
+        r"""Assuming the green context is the current context, pop it from the
+        context stack and restore the previous context.
+        """
+        return super().pop_context()  # type: ignore[misc]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/jiterator.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/jiterator.py
new file mode 100644
index 0000000000000000000000000000000000000000..6eaa54b5b795c78ed97bb25e335d3bf978410474
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/jiterator.py
@@ -0,0 +1,189 @@
+# mypy: allow-untyped-defs
+import re
+from collections.abc import Callable
+
+import torch
+from torch import Tensor
+
+
+__all__: list[str] = []
+
+
+class _CodeParser:
+    def __init__(self, code_string: str):
+        optional_ws = r"\s*"
+        required_ws = r"\s+"
+        template_params = r"(?P\<.+\>)"
+        return_type = r"(?P\w+)"
+        function_name = r"(?P\w+)"
+        function_params = r"(?P\(.+\))"
+        function_body = r"(?P\{.+\})"
+
+        pattern = (
+            optional_ws
+            + "template"
+            + optional_ws
+            + template_params
+            + optional_ws
+            + return_type
+            + required_ws
+            + function_name
+            + optional_ws
+            + function_params
+            + optional_ws
+            + function_body
+            + optional_ws
+        )
+
+        result = re.match(
+            pattern, code_string, re.DOTALL
+        )  # DOTALL for matching multiline
+
+        if result is None:
+            raise Exception(  # noqa: TRY002
+                f"Couldn't parse code, please check correctness:\n {code_string}"
+            )
+
+        self.template_params = result["template_params"]
+        self.return_type = result["return_type"]
+        self.function_name = result["function_name"]
+        self.function_params = result["function_params"]
+        self.function_body = result["function_body"]
+
+
+class _JittedFunction:
+    def __init__(
+        self, code_string: str, return_by_ref: bool, num_outputs: int, **kwargs
+    ):
+        self.code_string = code_string
+
+        assert return_by_ref or num_outputs == 1, (
+            "Return by value only works for single output. "
+        )
+        self.return_by_ref = return_by_ref
+        self.num_outputs = num_outputs
+
+        parsed_code = _CodeParser(code_string)
+        self.kernel_name = parsed_code.function_name
+
+        self.kwargs_dict = kwargs
+        self.is_cuda_available = torch.cuda.is_available()
+
+    def __call__(self, *tensors: Tensor, **kwargs):
+        # Jiterator follow torch.cuda's lazy initialization behavior
+        # Defer checking cuda's availability at the function invocation time
+        assert self.is_cuda_available, (
+            "Jiterator is only supported on CUDA and ROCm GPUs, none are available."
+        )
+
+        assert len(tensors) <= 8, "jiterator only supports up to 8 tensor inputs."
+
+        expanded_kwargs = self.kwargs_dict.copy()
+        for key, value in kwargs.items():
+            if key in self.kwargs_dict:
+                expanded_kwargs[key] = value
+            else:
+                raise KeyError(f"{key} is not declared in function definition")
+
+        return torch._C._cuda_jiterator_compile_and_launch_kernel(
+            self.code_string,
+            self.kernel_name,
+            self.return_by_ref,
+            self.num_outputs,
+            tensors,
+            expanded_kwargs,
+        )
+
+
+def _create_jit_fn(code_string: str, **kwargs) -> Callable:
+    """
+    Create a jiterator-generated cuda kernel for an elementwise op.
+
+    The code string has to be a valid CUDA function that describes the computation for a single element. The code
+    string has to follow the c++ template pattern, as shown in the example below. This function will be inlined
+    into elementwise kernel template, and compiled on the fly. Compiled kernel will be cached in memory, as well as
+    local temp dir.
+
+    Jiterator-generated kernels accepts noncontiguous tensors, and supports broadcasting and type promotion.
+
+    Args:
+        code_string (str): CUDA code string to be compiled by jiterator. The entry functor must return by value.
+        kwargs (Dict, optional): Keyword arguments for generated function
+
+    Example::
+
+        code_string = "template  T my_kernel(T x, T y, T alpha) { return -x + alpha * y; }"
+        jitted_fn = create_jit_fn(code_string, alpha=1.0)
+        a = torch.rand(3, device="cuda")
+        b = torch.rand(3, device="cuda")
+        # invoke jitted function like a regular python function
+        result = jitted_fn(a, b, alpha=3.14)
+
+    code_string also allows multiple function definitions, and the last function will be treated as the entry function.
+
+    Example::
+
+        code_string = (
+            "template  T util_fn(T x, T y) { return ::sin(x) + ::cos(y); }"
+        )
+        code_string += "template  T my_kernel(T x, T y, T val) { return ::min(val, util_fn(x, y)); }"
+        jitted_fn = create_jit_fn(code_string, val=0.0)
+        a = torch.rand(3, device="cuda")
+        b = torch.rand(3, device="cuda")
+        # invoke jitted function like a regular python function
+        result = jitted_fn(a, b)  # using default val=0.0
+
+    Jiterator can be used together with python registration to override an operator's cuda kernel.
+    Following example is overriding gelu's cuda kernel with relu.
+
+    Example::
+
+        code_string = "template  T my_gelu(T a) { return a > 0 ? a : 0; }"
+        my_gelu = create_jit_fn(code_string)
+        my_lib = torch.library.Library("aten", "IMPL")
+        my_lib.impl("aten::gelu", my_gelu, "CUDA")
+        # torch.nn.GELU and torch.nn.function.gelu are now overridden
+        a = torch.rand(3, device="cuda")
+        torch.allclose(torch.nn.functional.gelu(a), torch.nn.functional.relu(a))
+
+    .. warning::
+        This API is in beta and may change in future releases.
+
+    .. warning::
+        This API only supports up to 8 inputs and 1 output
+
+    .. warning::
+        All input tensors must live in CUDA device
+    """
+    return _JittedFunction(code_string, return_by_ref=False, num_outputs=1, **kwargs)
+
+
+def _create_multi_output_jit_fn(
+    code_string: str, num_outputs: int, **kwargs
+) -> Callable:
+    """
+    Create a jiterator-generated cuda kernel for an elementwise op that supports returning one or more outputs.
+
+    Args:
+        code_string (str): CUDA code string to be compiled by jiterator. The entry functor must return value by reference.
+        num_outputs(int): number of outputs return by the kernel
+        kwargs (Dict, optional): Keyword arguments for generated function
+
+    Example::
+
+        code_string = "template  void my_kernel(T x, T y, T alpha, T& out) { out = -x + alpha * y; }"
+        jitted_fn = create_jit_fn(code_string, alpha=1.0)
+        a = torch.rand(3, device="cuda")
+        b = torch.rand(3, device="cuda")
+        # invoke jitted function like a regular python function
+        result = jitted_fn(a, b, alpha=3.14)
+
+    .. warning::
+        This API is in beta and may change in future releases.
+
+    .. warning::
+        This API only supports up to 8 inputs and 8 outputs
+    """
+    return _JittedFunction(
+        code_string, return_by_ref=True, num_outputs=num_outputs, **kwargs
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/memory.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/memory.py
new file mode 100644
index 0000000000000000000000000000000000000000..de5fd7baa8cd20a311530323edfcb7acfcf6e22b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/memory.py
@@ -0,0 +1,1463 @@
+# mypy: allow-untyped-defs
+r"""This package adds support for device memory management implemented in CUDA."""
+
+import collections
+import contextlib
+import ctypes
+import os
+import pickle
+import re
+import sys
+import warnings
+from inspect import signature
+from typing import Any, cast, Literal, Optional, TYPE_CHECKING, TypedDict
+from typing_extensions import deprecated, NotRequired
+
+import torch
+from torch import _C
+from torch._utils import _dummy_type
+
+from . import (
+    _get_amdsmi_device_index,
+    _get_device_index,
+    _get_nvml_device_index,
+    _lazy_init,
+    is_initialized,
+)
+from ._memory_viz import memory as _memory, segments as _segments
+
+
+if TYPE_CHECKING:
+    from torch.types import Device
+
+
+# Type definitions for memory profiler
+class _Frame(TypedDict):
+    """Frame information from memory profiler snapshots."""
+
+    filename: str
+    line: int
+    name: str
+    # Fields added by FX augmentation (optional)
+    fx_node_op: NotRequired[str]
+    fx_node_name: NotRequired[str]
+    fx_node_target: NotRequired[str]
+    fx_original_trace: NotRequired[str]
+
+
+class _Block(TypedDict):
+    """Memory block information."""
+
+    size: int
+    requested_size: int
+    address: int
+    state: str
+    frames: list[_Frame]
+
+
+class _Segment(TypedDict):
+    """Memory segment information."""
+
+    address: int
+    total_size: int
+    stream: int
+    segment_type: str
+    allocated_size: int
+    active_size: int
+    blocks: list[_Block]
+
+
+class _TraceEntry(TypedDict):
+    """Memory trace entry information."""
+
+    action: str
+    addr: NotRequired[int]
+    frames: list[_Frame]
+    size: int
+    stream: int
+    device_free: NotRequired[int]
+
+
+class _Snapshot(TypedDict):
+    """Memory snapshot structure."""
+
+    segments: list[_Segment]
+    device_traces: NotRequired[list[list[_TraceEntry]]]
+
+
+__all__ = [
+    "caching_allocator_alloc",
+    "caching_allocator_delete",
+    "caching_allocator_enable",
+    "get_per_process_memory_fraction",
+    "set_per_process_memory_fraction",
+    "empty_cache",
+    "memory_stats",
+    "memory_stats_as_nested_dict",
+    "reset_accumulated_memory_stats",
+    "reset_peak_memory_stats",
+    "reset_max_memory_allocated",
+    "reset_max_memory_cached",
+    "host_memory_stats",
+    "host_memory_stats_as_nested_dict",
+    "reset_accumulated_host_memory_stats",
+    "reset_peak_host_memory_stats",
+    "memory_allocated",
+    "max_memory_allocated",
+    "memory_reserved",
+    "max_memory_reserved",
+    "memory_cached",
+    "max_memory_cached",
+    "memory_snapshot",
+    "memory_summary",
+    "list_gpu_processes",
+    "mem_get_info",
+    "get_allocator_backend",
+    "CUDAPluggableAllocator",
+    "change_current_allocator",
+    "MemPool",
+    "use_mem_pool",
+]
+
+
+if not hasattr(torch._C, "_cuda_CUDAAllocator"):
+    # Define dummy base classes
+    torch._C.__dict__["_cuda_CUDAAllocator"] = _dummy_type("_cuda_CUDAAllocator")
+
+
+if not hasattr(torch._C, "_MemPool"):
+    # Define dummy base classes
+    torch._C.__dict__["_MemPool"] = _dummy_type("_MemPool")
+    torch._C.__dict__["_cuda_beginAllocateToPool"] = _dummy_type(
+        "_cuda_beginAllocateToPool"
+    )
+    torch._C.__dict__["_cuda_beginAllocateCurrentThreadToPool"] = _dummy_type(
+        "_cuda_beginAllocateCurrentThreadToPool"
+    )
+    torch._C.__dict__["_cuda_endAllocateToPool"] = _dummy_type(
+        "_cuda_endAllocateToPool"
+    )
+    torch._C.__dict__["_cuda_releasePool"] = _dummy_type("_cuda_releasePool")
+
+from torch._C import (  # noqa: F401
+    _cuda_beginAllocateCurrentThreadToPool,
+    _cuda_beginAllocateToPool,
+    _cuda_CUDAAllocator,
+    _cuda_endAllocateToPool,
+    _cuda_releasePool,
+    _MemPool,
+)
+
+
+def _host_allocator():
+    _lazy_init()
+    return torch._C._cuda_cudaHostAllocator()
+
+
+@contextlib.contextmanager
+def _free_mutex():
+    torch._C._cuda_lock_mutex()
+    try:
+        yield
+    finally:
+        torch._C._cuda_unlock_mutex()
+
+
+def caching_allocator_alloc(size, device: "Device" = None, stream=None):
+    r"""Perform a memory allocation using the CUDA memory allocator.
+
+    Memory is allocated for a given device and a stream, this
+    function is intended to be used for interoperability with other
+    frameworks. Allocated memory is released through
+    :func:`~torch.cuda.caching_allocator_delete`.
+
+    Args:
+        size (int): number of bytes to be allocated.
+        device (torch.device or int, optional): selected device. If it is
+            ``None`` the default CUDA device is used.
+        stream (torch.cuda.Stream or int, optional): selected stream. If is ``None`` then
+            the default stream for the selected device is used.
+
+    .. note::
+        See :ref:`cuda-memory-management` for more details about GPU memory
+        management.
+    """
+    if device is None:
+        device = torch.cuda.current_device()
+    device = _get_device_index(device)
+    if stream is None:
+        stream = torch.cuda.current_stream(device)
+    if isinstance(stream, torch.cuda.streams.Stream):
+        stream = stream.cuda_stream
+    if not isinstance(stream, int):
+        raise TypeError(
+            "Invalid type for stream argument, must be "
+            "`torch.cuda.Stream` or `int` representing a pointer "
+            "to a existing stream"
+        )
+    with torch.cuda.device(device):
+        return torch._C._cuda_cudaCachingAllocator_raw_alloc(size, stream)
+
+
+def caching_allocator_delete(mem_ptr):
+    r"""Delete memory allocated using the CUDA memory allocator.
+
+    Memory allocated with :func:`~torch.cuda.caching_allocator_alloc`.
+    is freed here. The associated device and stream are tracked inside
+    the allocator.
+
+    Args:
+        mem_ptr (int): memory address to be freed by the allocator.
+
+    .. note::
+        See :ref:`cuda-memory-management` for more details about GPU memory
+        management.
+    """
+    torch._C._cuda_cudaCachingAllocator_raw_delete(mem_ptr)
+
+
+def caching_allocator_enable(value: bool = True) -> None:
+    r"""Enable or disable the CUDA memory allocator. On by default."""
+    if is_initialized():
+        torch._C._cuda_cudaCachingAllocator_enable(value)
+
+
+def set_per_process_memory_fraction(fraction, device: "Device" = None) -> None:
+    r"""Set memory fraction for a process.
+
+    The fraction is used to limit an caching allocator to allocated memory on a CUDA device.
+    The allowed value equals the total visible memory multiplied fraction.
+    If trying to allocate more than the allowed value in a process, will raise an out of
+    memory error in allocator.
+
+    Args:
+        fraction(float): Range: 0~1. Allowed memory equals total_memory * fraction.
+        device (torch.device or int, optional): selected device. If it is
+            ``None`` the default CUDA device is used.
+    .. note::
+        In general, the total available free memory is less than the total capacity.
+    """
+    _lazy_init()
+    if device is None:
+        device = torch.cuda.current_device()
+    device = _get_device_index(device)
+    if not isinstance(fraction, float):
+        raise TypeError("Invalid type for fraction argument, must be `float`")
+    if fraction < 0 or fraction > 1:
+        raise ValueError(f"Invalid fraction value: {fraction}. Allowed range: 0~1")
+
+    torch._C._cuda_setMemoryFraction(fraction, device)
+
+
+def get_per_process_memory_fraction(device: "Device" = None) -> float:
+    r"""Get memory fraction for a process.
+
+    Args:
+        device (torch.device or int, optional): selected device. If it is
+            ``None`` the default CUDA device is used.
+    Returns:
+        memory fraction, in range 0~1. Allowed memory equals total_memory * fraction.
+    """
+    _lazy_init()
+    if device is None:
+        device = torch.cuda.current_device()
+    device = _get_device_index(device)
+    return torch._C._cuda_getMemoryFraction(device)
+
+
+def empty_cache() -> None:
+    r"""Release all unoccupied cached memory currently held by the caching
+    allocator so that those can be used in other GPU application and visible in
+    `nvidia-smi`.
+
+    .. note::
+        :func:`~torch.cuda.empty_cache` doesn't increase the amount of GPU
+        memory available for PyTorch. However, it may help reduce fragmentation
+        of GPU memory in certain cases. See :ref:`cuda-memory-management` for
+        more details about GPU memory management.
+    """
+    if is_initialized():
+        torch._C._cuda_emptyCache()
+
+
+def memory_stats(device: "Device" = None) -> dict[str, Any]:
+    r"""Return a dictionary of CUDA memory allocator statistics for a given device.
+
+    The return value of this function is a dictionary of statistics, each of
+    which is a non-negative integer.
+
+    Core statistics:
+
+    - ``"allocated.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
+      number of allocation requests received by the memory allocator.
+    - ``"allocated_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
+      amount of allocated memory.
+    - ``"segment.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
+      number of reserved segments from ``cudaMalloc()``.
+    - ``"reserved_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
+      amount of reserved memory.
+    - ``"active.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
+      number of active memory blocks.
+    - ``"active_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
+      amount of active memory.
+    - ``"inactive_split.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
+      number of inactive, non-releasable memory blocks.
+    - ``"inactive_split_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
+      amount of inactive, non-releasable memory.
+
+    For these core statistics, values are broken down as follows.
+
+    Pool type:
+
+    - ``all``: combined statistics across all memory pools.
+    - ``large_pool``: statistics for the large allocation pool
+      (as of June 2025, for size >= 1MB allocations).
+    - ``small_pool``: statistics for the small allocation pool
+      (as of June 2025, for size < 1MB allocations).
+
+    Metric type:
+
+    - ``current``: current value of this metric.
+    - ``peak``: maximum value of this metric.
+    - ``allocated``: historical total increase in this metric.
+    - ``freed``: historical total decrease in this metric.
+
+    In addition to the core statistics, we also provide some simple event
+    counters:
+
+    - ``"num_alloc_retries"``: number of failed ``cudaMalloc`` calls that
+      result in a cache flush and retry.
+    - ``"num_ooms"``: number of out-of-memory errors thrown.
+    - ``"num_sync_all_streams"``: number of ``synchronize_and_free_events`` calls.
+    - ``"num_device_alloc"``: number of CUDA allocation calls. This includes both
+      cuMemMap and cudaMalloc.
+    - ``"num_device_free"``: number of CUDA free calls. This includes both cuMemUnmap
+      and cudaFree.
+
+    The caching allocator can be configured via ENV to not split blocks larger than a
+    defined size (see Memory Management section of the Cuda Semantics documentation).
+    This helps avoid memory fragmentation but may have a performance
+    penalty. Additional outputs to assist with tuning and evaluating impact:
+
+    - ``"max_split_size"``: blocks above this size will not be split.
+    - ``"oversize_allocations.{current,peak,allocated,freed}"``:
+      number of over-size allocation requests received by the memory allocator.
+    - ``"oversize_segments.{current,peak,allocated,freed}"``:
+      number of over-size reserved segments from ``cudaMalloc()``.
+
+    The caching allocator can be configured via ENV to round memory allocations in order
+    to reduce fragmentation. Sometimes the overhead from rounding can be higher than
+    the fragmentation it helps reduce. The following stat can be used to check if
+    rounding adds too much overhead:
+
+    - ``"requested_bytes.{all,large_pool,small_pool}.{current,peak,allocated,freed}"``:
+      memory requested by client code, compare this with allocated_bytes to check if
+      allocation rounding adds too much overhead.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            statistics for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    .. note::
+        See :ref:`cuda-memory-management` for more details about GPU memory
+        management.
+
+    .. note::
+        With :ref:`backend:cudaMallocAsync`, some stats are not
+        meaningful, and are always reported as zero.
+    """
+    result = []
+
+    def _recurse_add_to_result(prefix, obj):
+        if isinstance(obj, dict):
+            if len(prefix) > 0:
+                prefix += "."
+            for k, v in obj.items():
+                _recurse_add_to_result(prefix + k, v)
+        else:
+            result.append((prefix, obj))
+
+    stats = memory_stats_as_nested_dict(device=device)
+    _recurse_add_to_result("", stats)
+    result.sort()
+
+    return collections.OrderedDict(result)
+
+
+def memory_stats_as_nested_dict(device: "Device" = None) -> dict[str, Any]:
+    r"""Return the result of :func:`~torch.cuda.memory_stats` as a nested dictionary."""
+    if not is_initialized():
+        return {}
+    device = _get_device_index(device, optional=True)
+    return torch._C._cuda_memoryStats(device)
+
+
+def reset_accumulated_memory_stats(device: "Device" = None) -> None:
+    r"""Reset the "accumulated" (historical) stats tracked by the CUDA memory allocator.
+
+    See :func:`~torch.cuda.memory_stats` for details. Accumulated stats correspond to
+    the `"allocated"` and `"freed"` keys in each individual stat dict, as well as
+    `"num_alloc_retries"` and `"num_ooms"`.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    .. note::
+        See :ref:`cuda-memory-management` for more details about GPU memory
+        management.
+    """
+    device = _get_device_index(device, optional=True)
+    return torch._C._cuda_resetAccumulatedMemoryStats(device)
+
+
+def reset_peak_memory_stats(device: "Device" = None) -> None:
+    r"""Reset the "peak" stats tracked by the CUDA memory allocator.
+
+    See :func:`~torch.cuda.memory_stats` for details. Peak stats correspond to the
+    `"peak"` key in each individual stat dict.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    .. note::
+        See :ref:`cuda-memory-management` for more details about GPU memory
+        management.
+    """
+    device = _get_device_index(device, optional=True)
+    return torch._C._cuda_resetPeakMemoryStats(device)
+
+
+def host_memory_stats() -> dict[str, Any]:
+    r"""Return a dictionary of CUDA memory allocator statistics for a given device.
+
+     The return value of this function is a dictionary of statistics, each of
+     which is a non-negative integer.
+
+     Core statistics:
+
+     - ``"allocated.{current,peak,allocated,freed}"``:
+       number of allocation requests received by the memory allocator.
+     - ``"allocated_bytes.{current,peak,allocated,freed}"``:
+       amount of allocated memory.
+     - ``"segment.{current,peak,allocated,freed}"``:
+       number of reserved segments from ``cudaMalloc()``.
+     - ``"reserved_bytes.{current,peak,allocated,freed}"``:
+       amount of reserved memory.
+
+     For these core statistics, values are broken down as follows.
+
+     Metric type:
+
+     - ``current``: current value of this metric.
+     - ``peak``: maximum value of this metric.
+     - ``allocated``: historical total increase in this metric.
+     - ``freed``: historical total decrease in this metric.
+
+     In addition to the core statistics, we also provide some simple event
+     counters:
+
+     - ``"num_host_alloc"``: number of CUDA allocation calls. This includes both
+       cudaHostAlloc and cudaHostRegister.
+     - ``"num_host_free"``: number of CUDA free calls. This includes both cudaHostFree
+       and cudaHostUnregister.
+
+     Finally, we also provide some simple timing counters:
+
+     - ``"host_alloc_time.{total,max,min,count,avg}"``:
+       timing of allocation requests going through CUDA calls.
+     - ``"host_free_time.{total,max,min,count,avg}"``:
+       timing of free requests going through CUDA calls.
+
+    For these timing statistics, values are broken down as follows.
+
+     Metric type:
+
+     - ``total``: total time spent.
+     - ``max``: maximum value per call.
+     - ``min``: minimum value per call.
+     - ``count``: number of times it was called.
+     - ``avg``: average time per call.
+    """
+    result = []
+
+    def _recurse_add_to_result(prefix, obj):
+        if isinstance(obj, dict):
+            if len(prefix) > 0:
+                prefix += "."
+            for k, v in obj.items():
+                _recurse_add_to_result(prefix + k, v)
+        else:
+            result.append((prefix, obj))
+
+    stats = host_memory_stats_as_nested_dict()
+    _recurse_add_to_result("", stats)
+    result.sort()
+
+    return collections.OrderedDict(result)
+
+
+def host_memory_stats_as_nested_dict() -> dict[str, Any]:
+    r"""Return the result of :func:`~torch.cuda.host_memory_stats` as a nested dictionary."""
+    if not is_initialized():
+        return {}
+    return torch._C._cuda_hostMemoryStats()
+
+
+def reset_accumulated_host_memory_stats() -> None:
+    r"""Reset the "accumulated" (historical) stats tracked by the host memory allocator.
+
+    See :func:`~torch.cuda.host_memory_stats` for details. Accumulated stats correspond to
+    the `"allocated"` and `"freed"` keys in each individual stat dict.
+    """
+    return torch._C._cuda_resetAccumulatedHostMemoryStats()
+
+
+def reset_peak_host_memory_stats() -> None:
+    r"""Reset the "peak" stats tracked by the host memory allocator.
+
+    See :func:`~torch.cuda.host_memory_stats` for details. Peak stats correspond to the
+    `"peak"` key in each individual stat dict.
+    """
+    return torch._C._cuda_resetPeakHostMemoryStats()
+
+
+def reset_max_memory_allocated(device: "Device" = None) -> None:
+    r"""Reset the starting point in tracking maximum GPU memory occupied by tensors for a given device.
+
+    See :func:`~torch.cuda.max_memory_allocated` for details.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    .. warning::
+        This function now calls :func:`~torch.cuda.reset_peak_memory_stats`, which resets
+        /all/ peak memory stats.
+
+    .. note::
+        See :ref:`cuda-memory-management` for more details about GPU memory
+        management.
+    """
+    warnings.warn(
+        "torch.cuda.reset_max_memory_allocated now calls torch.cuda.reset_peak_memory_stats, "
+        "which resets /all/ peak memory stats.",
+        FutureWarning,
+        stacklevel=2,
+    )
+    return reset_peak_memory_stats(device=device)
+
+
+def reset_max_memory_cached(device: "Device" = None) -> None:
+    r"""Reset the starting point in tracking maximum GPU memory managed by the caching allocator for a given device.
+
+    See :func:`~torch.cuda.max_memory_cached` for details.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    .. warning::
+        This function now calls :func:`~torch.cuda.reset_peak_memory_stats`, which resets
+        /all/ peak memory stats.
+
+    .. note::
+        See :ref:`cuda-memory-management` for more details about GPU memory
+        management.
+    """
+    warnings.warn(
+        "torch.cuda.reset_max_memory_cached now calls torch.cuda.reset_peak_memory_stats, "
+        "which resets /all/ peak memory stats.",
+        FutureWarning,
+        stacklevel=2,
+    )
+    return reset_peak_memory_stats(device=device)
+
+
+def memory_allocated(device: "Device" = None) -> int:
+    r"""Return the current GPU memory occupied by tensors in bytes for a given device.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    .. note::
+        This is likely less than the amount shown in `nvidia-smi` since some
+        unused memory can be held by the caching allocator and some context
+        needs to be created on GPU. See :ref:`cuda-memory-management` for more
+        details about GPU memory management.
+    """
+    return memory_stats(device=device).get("allocated_bytes.all.current", 0)
+
+
+def max_memory_allocated(device: "Device" = None) -> int:
+    r"""Return the maximum GPU memory occupied by tensors in bytes for a given device.
+
+    By default, this returns the peak allocated memory since the beginning of
+    this program. :func:`~torch.cuda.reset_peak_memory_stats` can be used to
+    reset the starting point in tracking this metric. For example, these two
+    functions can measure the peak allocated memory usage of each iteration in a
+    training loop.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    .. note::
+        See :ref:`cuda-memory-management` for more details about GPU memory
+        management.
+    """
+    return memory_stats(device=device).get("allocated_bytes.all.peak", 0)
+
+
+def memory_reserved(device: "Device" = None) -> int:
+    r"""Return the current GPU memory managed by the caching allocator in bytes for a given device.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    .. note::
+        See :ref:`cuda-memory-management` for more details about GPU memory
+        management.
+    """
+    return memory_stats(device=device).get("reserved_bytes.all.current", 0)
+
+
+def max_memory_reserved(device: "Device" = None) -> int:
+    r"""Return the maximum GPU memory managed by the caching allocator in bytes for a given device.
+
+    By default, this returns the peak cached memory since the beginning of this
+    program. :func:`~torch.cuda.reset_peak_memory_stats` can be used to reset
+    the starting point in tracking this metric. For example, these two functions
+    can measure the peak cached memory amount of each iteration in a training
+    loop.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    .. note::
+        See :ref:`cuda-memory-management` for more details about GPU memory
+        management.
+    """
+    return memory_stats(device=device).get("reserved_bytes.all.peak", 0)
+
+
+@deprecated(
+    "`torch.cuda.memory_cached` has been renamed to `torch.cuda.memory_reserved`",
+    category=FutureWarning,
+)
+def memory_cached(device: "Device" = None) -> int:
+    r"""Deprecated; see :func:`~torch.cuda.memory_reserved`."""
+    return memory_reserved(device=device)
+
+
+@deprecated(
+    "`torch.cuda.max_memory_cached` has been renamed to `torch.cuda.max_memory_reserved`",
+    category=FutureWarning,
+)
+def max_memory_cached(device: "Device" = None) -> int:
+    r"""Deprecated; see :func:`~torch.cuda.max_memory_reserved`."""
+    return max_memory_reserved(device=device)
+
+
+def memory_snapshot(mempool_id=None):
+    r"""Return a snapshot of the CUDA memory allocator state across all devices.
+
+    Interpreting the output of this function requires familiarity with the
+    memory allocator internals.
+
+    .. note::
+        See :ref:`cuda-memory-management` for more details about GPU memory
+        management.
+    """
+    return torch._C._cuda_memorySnapshot(mempool_id)["segments"]
+
+
+def memory_summary(device: "Device" = None, abbreviated: bool = False) -> str:
+    r"""Return a human-readable printout of the current memory allocator statistics for a given device.
+
+    This can be useful to display periodically during training, or when
+    handling out-of-memory exceptions.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            printout for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+        abbreviated (bool, optional): whether to return an abbreviated summary
+            (default: False).
+
+    .. note::
+        See :ref:`cuda-memory-management` for more details about GPU memory
+        management.
+    """
+    device = _get_device_index(device, optional=True)
+    stats = memory_stats(device=device)
+
+    def _format_size(sz, pref_sz):
+        prefixes = ["B  ", "KiB", "MiB", "GiB", "TiB", "PiB"]
+        prefix = prefixes[0]
+        for new_prefix in prefixes[1:]:
+            if pref_sz < 768 * 1024:
+                break
+            prefix = new_prefix
+            sz //= 1024
+            pref_sz /= 1024
+        return f"{sz:6d} {prefix}"
+
+    def _format_count(cnt, pref_cnt):
+        prefixes = [" ", "K", "M"]
+        prefix = prefixes[0]
+        for new_prefix in prefixes[1:]:
+            if pref_cnt < 750 * 1000:
+                break
+            prefix = new_prefix
+            cnt //= 1000
+            pref_cnt /= 1000
+        return f"{cnt:7d} {prefix} "
+
+    metrics_to_display = [
+        ("allocated_bytes", "Allocated memory", _format_size),
+        ("active_bytes", "Active memory", _format_size),
+        ("requested_bytes", "Requested memory", _format_size),
+        ("reserved_bytes", "GPU reserved memory", _format_size),
+        ("inactive_split_bytes", "Non-releasable memory", _format_size),
+        ("allocation", "Allocations", _format_count),
+        ("active", "Active allocs", _format_count),
+        ("segment", "GPU reserved segments", _format_count),
+        ("inactive_split", "Non-releasable allocs", _format_count),
+    ]
+
+    lines = []
+    lines.append("=" * 75)
+    lines.append(" {_:16} PyTorch CUDA memory summary, device ID {device:<17d} ")
+    lines.append("-" * 75)
+    lines.append(
+        "  {_:9} CUDA OOMs: {num_ooms:<12d} | {_:6} cudaMalloc retries: {num_alloc_retries:<8d}  "
+    )
+    lines.append("=" * 75)
+    lines.append(
+        "        Metric         | Cur Usage  | Peak Usage | Tot Alloc  | Tot Freed  "
+    )
+
+    for metric_key, metric_name, formatter in metrics_to_display:
+        lines.append("-" * 75)
+        submetrics = [("all", metric_name)]
+        if not abbreviated:
+            submetrics.append(("large_pool", "      from large pool"))
+            submetrics.append(("small_pool", "      from small pool"))
+
+        current_prefval, peak_prefval, allocated_prefval, freed_prefval = (
+            None,
+            None,
+            None,
+            None,
+        )
+
+        for submetric_key, submetric_name in submetrics:
+            prefix = metric_key + "." + submetric_key + "."
+
+            current = stats[prefix + "current"]
+            peak = stats[prefix + "peak"]
+            allocated = stats[prefix + "allocated"]
+            freed = stats[prefix + "freed"]
+
+            if current_prefval is None:
+                current_prefval = current
+                peak_prefval = peak
+                allocated_prefval = allocated
+                freed_prefval = freed
+
+            lines.append(
+                f" {submetric_name:<21} | {formatter(current, current_prefval)} | {formatter(peak, peak_prefval)} | "
+                f"{formatter(allocated, allocated_prefval)} | {formatter(freed, freed_prefval)} ",
+            )
+
+    metrics_to_display = [
+        ("oversize_allocations", "Oversize allocations", _format_count),
+        ("oversize_segments", "Oversize GPU segments", _format_count),
+    ]
+
+    for metric_key, metric_name, formatter in metrics_to_display:
+        lines.append("-" * 75)
+
+        prefix = metric_key + "."
+
+        current = stats[prefix + "current"]
+        peak = stats[prefix + "peak"]
+        allocated = stats[prefix + "allocated"]
+        freed = stats[prefix + "freed"]
+
+        lines.append(
+            f" {metric_name:<21} | {formatter(current, current)} | {formatter(peak, peak)} | "
+            f"{formatter(allocated, allocated)} | {formatter(freed, freed)} ",
+        )
+
+    lines.append("=" * 75)
+
+    fmt_dict = {"_": "", "device": device}
+    for k, v in stats.items():
+        fmt_dict[k.replace(".", "-")] = v
+    return "|" + "|\n|".join(lines).format(**fmt_dict) + "|\n"
+
+
+def list_gpu_processes(device: "Device" = None) -> str:
+    r"""Return a human-readable printout of the running processes and their GPU memory use for a given device.
+
+    This can be useful to display periodically during training, or when
+    handling out-of-memory exceptions.
+
+    Args:
+        device (torch.device or int, optional): selected device. Returns
+            printout for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+    """
+    if not torch.version.hip:
+        try:
+            import pynvml  # type: ignore[import]
+        except ModuleNotFoundError:
+            return "pynvml module not found, please install nvidia-ml-py"
+        # pyrefly: ignore [import-error,missing-module-attribute]
+        from pynvml import NVMLError_DriverNotLoaded
+
+        try:
+            pynvml.nvmlInit()
+        except NVMLError_DriverNotLoaded:
+            return "cuda driver can't be loaded, is cuda enabled?"
+
+        device = _get_nvml_device_index(device)
+        handle = pynvml.nvmlDeviceGetHandleByIndex(device)
+        procs = pynvml.nvmlDeviceGetComputeRunningProcesses(handle)
+    else:
+        try:
+            import amdsmi  # type: ignore[import]
+        except ModuleNotFoundError:
+            return "amdsmi module not found, please install amdsmi"
+        try:
+            amdsmi.amdsmi_init()  # type: ignore[attr-defined]
+        except amdsmi.AmdSmiException:  # type: ignore[attr-defined]
+            return "amdsmi driver can't be loaded, is ROCm installed?"
+
+        device = _get_amdsmi_device_index(device)
+
+        try:
+            handle = amdsmi.amdsmi_get_processor_handles()[device]  # type: ignore[attr-defined]
+            procs = amdsmi.amdsmi_get_gpu_process_list(handle)  # type: ignore[attr-defined]
+        except amdsmi.AmdSmiException:  # type: ignore[attr-defined]
+            return "amdsmi cannot list processes from other users"
+
+    lines = []
+    lines.append(f"GPU:{device}")
+    if len(procs) == 0:
+        lines.append("no processes are running")
+    for p in procs:
+        if not torch.version.hip:
+            mem = p.usedGpuMemory / (1024 * 1024)
+            pid = p.pid
+        else:
+            try:
+                proc_info = amdsmi.amdsmi_get_gpu_process_info(handle, p)  # type: ignore[possibly-undefined]
+            except AttributeError:
+                # https://github.com/ROCm/amdsmi/commit/c551c3caedbd903ba828e7fdffa5b56d475a15e7
+                # is a BC-breaking change that removes amdsmi_get_gpu_process_info API from amdsmi
+                proc_info = p
+            mem = proc_info["memory_usage"]["vram_mem"] / (1024 * 1024)
+            pid = proc_info["pid"]
+        lines.append(f"process {pid:>10d} uses {mem:>12.3f} MB GPU memory")
+    return "\n".join(lines)
+
+
+def mem_get_info(device: "Device" = None) -> tuple[int, int]:
+    r"""Return the global free and total GPU memory for a given device using cudaMemGetInfo.
+
+    Args:
+        device (torch.device or int or str, optional): selected device. Returns
+            statistic for the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default) or if the device index is not specified.
+
+    .. note::
+        See :ref:`cuda-memory-management` for more
+        details about GPU memory management.
+    """
+    if device is None:
+        device = torch.cuda.current_device()
+    # optional=True allows `device = torch.device('cuda')` for which device.index is None
+    device = _get_device_index(device, optional=True)
+    return torch.cuda.cudart().cudaMemGetInfo(device)
+
+
+def _record_memory_history_legacy(
+    enabled: bool,
+    record_context=True,
+    trace_alloc_max_entries=1,
+    trace_alloc_record_context=False,
+    device: "Device" = None,
+    record_context_cpp=False,
+    clear_history=False,
+    compile_context=False,
+    global_record_annotations=False,
+):
+    _C._cuda_record_memory_history_legacy(  # type: ignore[call-arg]
+        enabled,
+        record_context,
+        # pyrefly: ignore [bad-argument-type]
+        trace_alloc_max_entries,
+        trace_alloc_record_context,
+        record_context_cpp,
+        clear_history,
+        compile_context,
+        global_record_annotations,
+    )
+
+
+def _record_memory_history(
+    enabled: Optional[Literal["state", "all"]] = "all", *args, **kwargs
+) -> None:
+    """Enable recording of stack traces associated with memory
+    allocations, so you can tell what allocated any piece of memory in
+    :func:`torch.cuda.memory._snapshot()`.
+
+    In addition to keeping stack traces with each current allocation and free,
+    this will also enable recording of a history of all alloc/free events.
+
+    Use :func:`torch.cuda.memory._snapshot()` to retrieve this information,
+    and the tools in `_memory_viz.py` to visualize snapshots.
+
+    Buffer behavior
+    ---------------
+
+    This will store up to `max_entries` instances of `TraceEntry` when enabled.
+    Python trace collection defaults to `sys.maxsize`, meaning long-running
+    or indefinitely running jobs should set a reasonable limit to avoid excessive
+    memory use. Expect each entry to be several KB.
+
+    Longer running workflows or those with smaller `max_entries` values will only
+    store the last accumulated `max_entries` entries, meaning new entries overwrite
+    older entries.
+
+    C++ implementation for reference to ring buffer implementation:
+
+    .. code-block:: cpp
+
+        if (record_history) {
+          if (alloc_trace->size() < alloc_trace_max_entries_) {
+            alloc_trace->emplace_back(te);
+          } else {
+            (*alloc_trace)[alloc_trace_next++] = te;
+            if (alloc_trace_next == alloc_trace_max_entries_) {
+              alloc_trace_next = 0;
+            }
+          }
+        }
+
+    Latency impact
+    --------------
+
+    The Python trace collection is fast (2us per trace), so you may consider
+    enabling this on production jobs if you anticipate ever having to debug
+    memory issues.
+
+    C++ trace collection is also fast (~50ns/frame), which for many typical programs
+    works out to ~2us per trace, but can vary depending on stack depth.
+
+    Args:
+        enabled (Literal[None, "state", "all"], optional):
+            `None`, disable recording memory history.
+            `"state"`, keep information for currently allocated memory.
+            `"all"`, additionally keep a history of all alloc/free calls.
+            Defaults to "all".
+        context (Literal[None, "state", "alloc", "all"], optional):
+            `None`, Do not record any tracebacks.
+            `"state"`, Record tracebacks for currently allocated memory.
+            `"alloc"`, additionally keep tracebacks for alloc calls.
+            `"all"`, additionally keep tracebacks for free calls.
+            Defaults to "all".
+        stacks (Literal["python", "all"], optional):
+            `"python"`, include Python, TorchScript, and inductor frames in tracebacks
+            `"all"`, additionally include C++ frames
+            Defaults to "all".
+        max_entries (int, optional): Keep a maximum of `max_entries`
+            alloc/free events in the recorded history recorded.
+    """
+    if isinstance(enabled, bool):
+        return _record_memory_history_legacy(enabled, *args, **kwargs)
+    else:
+        return _record_memory_history_impl(enabled, *args, **kwargs)
+
+
+def _record_memory_history_impl(
+    enabled: Optional[str] = "all",
+    context: Optional[str] = "all",
+    stacks: str = "all",
+    max_entries: int = sys.maxsize,
+    device: "Device" = None,
+    clear_history: bool = False,
+    compile_context: bool = False,
+    global_record_annotations: bool = False,
+):
+    _C._cuda_record_memory_history(  # type: ignore[call-arg]
+        enabled,
+        context,
+        stacks,
+        max_entries,
+        clear_history,
+        compile_context,
+        global_record_annotations,
+    )
+
+
+_record_memory_history.__signature__ = signature(_record_memory_history_impl)  # type: ignore[attr-defined]
+
+
+def _augment_frames(frames: list[_Frame]) -> int:
+    """
+    Augment a list of frames with FX debug information.
+
+    Args:
+        frames: List of frame dictionaries to augment
+
+    Returns:
+        The count of frames that were augmented.
+    """
+    from torch.fx.graph_module import FX_GRAPH_MODULE_FILE_PREFIX
+
+    # Regex pattern to match FX generated files
+    _FX_GENERATED_PATTERN = re.compile(
+        rf"{re.escape(FX_GRAPH_MODULE_FILE_PREFIX)}.*\.py$"
+    )
+
+    count = 0
+    if not frames:
+        return count
+
+    for frame in frames:
+        if "filename" in frame and "line" in frame:
+            filename = frame["filename"]
+            lineno = frame["line"]
+
+            # Check if this looks like an FX generated file
+            if not _FX_GENERATED_PATTERN.search(os.path.basename(filename)):
+                continue
+
+            # Look up metadata from the global registry
+            from torch.fx.traceback import _FX_METADATA_REGISTRY
+
+            metadata = _FX_METADATA_REGISTRY.get(filename)
+            if metadata is None:
+                continue
+
+            lineno_map = metadata.get("lineno_map", {})
+            node_metadata = metadata.get("node_metadata", {})
+            prologue_start = metadata.get("prologue_start", 0)
+
+            # Get the node index for this line
+            node_idx = lineno_map.get(lineno - prologue_start)
+
+            if node_idx is not None and node_idx in node_metadata:
+                node_info = node_metadata[node_idx]
+                original_trace = node_info.get("stack_trace")
+                node_op = node_info.get("op")
+                node_name = node_info.get("name")
+                node_target = node_info.get("target")
+
+                # Always add node metadata
+                frame["fx_node_op"] = node_op
+                frame["fx_node_name"] = node_name
+                frame["fx_node_target"] = str(node_target)
+
+                # Add original trace if available
+                if original_trace:
+                    frame["fx_original_trace"] = original_trace
+
+                count += 1
+
+    return count
+
+
+def _augment_memory_snapshot_stack_traces(
+    snapshot: str | _Snapshot,
+) -> _Snapshot:
+    """
+    Augment a memory snapshot with original source stack traces from FX metadata.
+
+    IMPORTANT: This function reads from a global in-memory registry (_FX_METADATA_REGISTRY)
+    that is populated during graph module compilation. It must be called in the same
+    Python process where the FX graphs were compiled. It cannot be used to augment
+    snapshots loaded from disk in a different process.
+
+    Args:
+        snapshot: Either a memory snapshot dict or path to a snapshot pickle file
+
+    Returns:
+        The augmented snapshot dictionary with fx_node_op, fx_node_name,
+        fx_original_trace, and fx_node_info fields added to frames
+    """
+
+    snapshot_dict: _Snapshot
+    if isinstance(snapshot, str):
+        # Load the memory snapshot
+        with open(snapshot, "rb") as f:
+            snapshot_dict = cast(_Snapshot, pickle.load(f))
+    else:
+        snapshot_dict = snapshot
+
+    # Process stack traces in the snapshot
+    augmented_count = 0
+
+    # Process blocks in segments (for regular allocations)
+    if "segments" in snapshot_dict:
+        for segment in snapshot_dict["segments"]:
+            if "blocks" in segment:
+                for block in segment["blocks"]:
+                    if "frames" in block:
+                        augmented_count += _augment_frames(block["frames"])
+
+    # Process device traces (for memory history)
+    if "device_traces" in snapshot_dict:
+        for trace_list in snapshot_dict["device_traces"]:
+            for trace_entry in trace_list:
+                if isinstance(trace_entry, dict) and "frames" in trace_entry:
+                    augmented_count += _augment_frames(trace_entry["frames"])
+
+    return snapshot_dict
+
+
+def _snapshot(device: "Device" = None, augment_with_fx_traces=False):
+    """Save a snapshot of CUDA memory state at the time it was called.
+
+    The state is represented as a dictionary with the following structure.
+
+    .. code-block:: python
+
+        class Snapshot(TypedDict):
+            segments: List[Segment]
+            device_traces: List[List[TraceEntry]]
+
+
+        class Segment(TypedDict):
+            # Segments are memory returned from a cudaMalloc call.
+            # The size of reserved memory is the sum of all Segments.
+            # Segments are cached and reused for future allocations.
+            # If the reuse is smaller than the segment, the segment
+            # is split into more then one Block.
+            # empty_cache() frees Segments that are entirely inactive.
+            address: int
+            total_size: int  #  cudaMalloc'd size of segment
+            stream: int
+            segment_type: Literal["small", "large"]  # 'large' (>1MB)
+            allocated_size: int  # size of memory in use
+            active_size: int  # size of memory in use or in active_awaiting_free state
+            blocks: List[Block]
+
+
+        class Block(TypedDict):
+            # A piece of memory returned from the allocator, or
+            # current cached but inactive.
+            size: int
+            requested_size: int  # size requested during malloc, may be smaller than
+            # size due to rounding
+            address: int
+            state: Literal[
+                "active_allocated",  # used by a tensor
+                "active_awaiting_free",  # waiting for another stream to finish using
+                # this, then it will become free
+                "inactive",
+            ]  # free for reuse
+            frames: List[Frame]  # stack trace from where the allocation occurred
+
+
+        class Frame(TypedDict):
+            filename: str
+            line: int
+            name: str
+            # Optional FX debug fields (present when augment_with_fx_traces=True
+            # and the frame corresponds to FX-generated code)
+            fx_node_op: str  # FX node operation type (e.g., 'call_function', 'output')
+            fx_node_name: str  # FX node name (e.g., 'linear', 'relu_1')
+            fx_original_trace: str  # Original model source code stack trace
+
+
+        class TraceEntry(TypedDict):
+            # When `torch.cuda.memory._record_memory_history()` is enabled,
+            # the snapshot will contain TraceEntry objects that record each
+            # action the allocator took.
+            action: Literal[
+                "alloc"  # memory allocated
+                "free_requested",  # the allocated received a call to free memory
+                "free_completed",  # the memory that was requested to be freed is now
+                # able to be used in future allocation calls
+                "segment_alloc",  # the caching allocator ask cudaMalloc for more memory
+                # and added it as a segment in its cache
+                "segment_free",  # the caching allocator called cudaFree to return memory
+                # to cuda possibly trying free up memory to
+                # allocate more segments or because empty_caches was called
+                "oom",  # the allocator threw an OOM exception. 'size' is
+                # the requested number of bytes that did not succeed
+                "snapshot",  # the allocator generated a memory snapshot
+                # useful to coorelate a previously taken
+                # snapshot with this trace
+            ]
+            addr: int  # not present for OOM
+            frames: List[Frame]
+            size: int
+            stream: int
+            device_free: int  # only present for OOM, the amount of
+            # memory cuda still reports to be free
+
+    Args:
+        device: Device to capture snapshot for. If None, captures for current device.
+        augment_with_fx_traces: If True, augment stack trace frames with FX debug information
+                                that maps generated FX code back to original model source code.
+                                This adds fx_node_op, fx_node_name, fx_original_trace, and
+                                fx_node_info fields to Frame objects. Default: False.
+
+    Returns:
+        The Snapshot dictionary object
+    """
+    s = _C._cuda_memorySnapshot(None)
+    if augment_with_fx_traces:
+        s = _augment_memory_snapshot_stack_traces(s)  # type: ignore[assignment, arg-type]
+    return s
+
+
+def _dump_snapshot(filename="dump_snapshot.pickle", augment_with_fx_traces=False):
+    """
+    Save a pickled version of the `torch.memory._snapshot()` dictionary to a file.
+
+    This file can be opened by the interactive snapshot viewer at pytorch.org/memory_viz
+
+    Snapshot file sizes scale with `max_entries` and stack trace depth per entry,
+    with several KB per entry. These can easily be in the GB range for longer running
+    workflows with large `max_entries`.
+
+    Args:
+        filename (str, optional): Name of the file to create. Defaults to "dump_snapshot.pickle".
+        augment_with_fx_traces (bool, optional): If True, augment the snapshot with FX debug information
+                                                  before dumping. This maps generated FX code stack traces
+                                                  back to original model source code. Defaults to False.
+        verbose (bool, optional): If True and augment_with_fx_traces is True, print verbose debug output
+                                  during augmentation. Defaults to False.
+    """
+    s = _snapshot(augment_with_fx_traces=augment_with_fx_traces)
+
+    with open(filename, "wb") as f:
+        pickle.dump(s, f)
+
+
+def _set_memory_metadata(metadata: str):
+    """
+    Set custom metadata that will be attached to all subsequent CUDA memory allocations.
+
+    This metadata will be recorded in the memory snapshot for all allocations made
+    after this call until the metadata is cleared or changed.
+
+    Args:
+        metadata (str): Custom metadata string to attach to allocations.
+                       Pass an empty string to clear the metadata.
+    """
+    # pyrefly: ignore [missing-attribute]
+    torch._C._cuda_setMemoryMetadata(metadata)
+
+
+def _get_memory_metadata() -> str:
+    """
+    Get the current custom metadata that is being attached to CUDA memory allocations.
+
+    Returns:
+        str: The current metadata string, or empty string if no metadata is set.
+    """
+    # pyrefly: ignore [missing-attribute]
+    return torch._C._cuda_getMemoryMetadata()
+
+
+def _save_segment_usage(filename="output.svg", snapshot=None):
+    if snapshot is None:
+        snapshot = _snapshot()
+    with open(filename, "w") as f:
+        f.write(_segments(snapshot))
+
+
+def _save_memory_usage(filename="output.svg", snapshot=None):
+    if snapshot is None:
+        snapshot = _snapshot()
+    with open(filename, "w") as f:
+        f.write(_memory(snapshot))
+
+
+@deprecated(
+    "torch.cuda._set_allocator_settings is deprecated. Use torch._C._accelerator_setAllocatorSettings instead.",
+    category=FutureWarning,
+)
+def _set_allocator_settings(env: str):
+    # pyrefly: ignore [missing-attribute]
+    return torch._C._accelerator_setAllocatorSettings(env)
+
+
+def get_allocator_backend() -> str:
+    r"""Return a string describing the active allocator backend as set by
+    ``PYTORCH_ALLOC_CONF``. Currently available backends are
+    ``native`` (PyTorch's native caching allocator) and `cudaMallocAsync``
+    (CUDA's built-in asynchronous allocator).
+
+    .. note::
+        See :ref:`cuda-memory-management` for details on choosing the allocator backend.
+    """
+    return torch._C._cuda_getAllocatorBackend()
+
+
+class _CUDAAllocator:
+    r"""Wrapper over internal CUDA memory allocators."""
+
+    def __init__(self, allocator: torch._C._cuda_CUDAAllocator):
+        self._allocator = allocator
+
+    def allocator(self):
+        return self._allocator
+
+
+class CUDAPluggableAllocator(_CUDAAllocator):
+    r"""CUDA memory allocator loaded from a so file."""
+
+    def __init__(self, path_to_so_file: str, alloc_fn_name: str, free_fn_name: str):
+        r"""Memory allocators are compiled in .so files and loaded dynamically using ctypes.
+
+        To change the active allocator use the :func:`torch.memory.cuda.change_current_allocator` function.
+
+        Args:
+            path_to_so_file(str): Path in the filesystem to the `.so` file containing
+                the allocator functions
+            alloc_fn_name(str): Name of the function to perform the memory allocation
+                in the so file. The signature must be:
+                void* alloc_fn_name(ssize_t size, int device, cudaStream_t stream);
+            free_fn_name(str): Name of the function to perform the memory release
+                in the so file. The signature must be:
+                void free_fn_name(void* ptr, size_t size, cudaStream_t stream);
+
+        .. warning::
+            This is currently supported only in unix OSs
+
+        .. note::
+            See :ref:`cuda-memory-management` for details on creating and using a custom allocator
+        """
+        allocator = ctypes.CDLL(path_to_so_file)
+        alloc_fn = ctypes.cast(getattr(allocator, alloc_fn_name), ctypes.c_void_p).value
+        free_fn = ctypes.cast(getattr(allocator, free_fn_name), ctypes.c_void_p).value
+        assert alloc_fn is not None
+        assert free_fn is not None
+        self._allocator = torch._C._cuda_customAllocator(alloc_fn, free_fn)
+
+
+def change_current_allocator(allocator: _CUDAAllocator) -> None:
+    r"""Change the currently used memory allocator to be the one provided.
+
+    If the current allocator has already been used/initialized, this function will error.
+
+
+    Args:
+        allocator (torch.cuda.memory._CUDAAllocator): allocator to be set as the active one.
+    .. note::
+        See :ref:`cuda-memory-management` for details on creating and using a custom allocator
+    """
+    torch._C._cuda_changeCurrentAllocator(allocator.allocator())
+
+
+def _get_current_allocator() -> _CUDAAllocator:
+    r"""Return the allocator being currently used.
+
+    .. note::
+        See :ref:`cuda-memory-management` for details on creating and using a custom allocator
+    """
+    return _CUDAAllocator(torch._C._cuda_getAllocator())
+
+
+class MemPool(_MemPool):
+    r"""MemPool represents a pool of memory in a caching allocator. Currently,
+    it's just the ID of the pool object maintained in the CUDACachingAllocator.
+
+    Args:
+        allocator(torch._C._cuda_CUDAAllocator, optional): a
+            torch._C._cuda_CUDAAllocator object that can be used to
+            define how memory gets allocated in the pool. If :attr:`allocator`
+            is ``None`` (default), memory allocation follows the default/
+            current configuration of the CUDACachingAllocator.
+        use_on_oom(bool): a bool that indicates if this pool can be used
+            as a last resort if a memory allocation outside of the pool fails due
+            to Out Of Memory. This is False by default.
+        no_split(bool): a bool that indicates if this pool should not split a segment.
+            This is False by default.
+    """
+
+    def __init__(
+        self,
+        allocator: Optional[_cuda_CUDAAllocator] = None,
+        use_on_oom: bool = False,
+        no_split: bool = False,
+    ):
+        super().__init__(allocator, True, use_on_oom, no_split)
+
+    @property
+    def id(self) -> tuple[int, int]:
+        r"""Returns the ID of this pool as a tuple of two ints."""
+        return super().id
+
+    @property
+    def allocator(self) -> Optional[_cuda_CUDAAllocator]:
+        r"""Returns the allocator this MemPool routes allocations to."""
+        return super().allocator
+
+    def use_count(self) -> int:  # pylint: disable=useless-parent-delegation
+        r"""Returns the reference count of this pool."""
+        return super().use_count()
+
+    def snapshot(self):
+        r"""Return a snapshot of the CUDA memory allocator pool state across all
+        devices.
+
+        Interpreting the output of this function requires familiarity with the
+        memory allocator internals.
+
+        .. note::
+            See :ref:`cuda-memory-management` for more details about GPU memory
+            management.
+        """
+        snapshot = torch.cuda.memory_snapshot(self.id)
+        return snapshot
+
+
+@contextlib.contextmanager
+def use_mem_pool(pool: MemPool, device: "Device" = None):
+    r"""A context manager that routes allocations to a given pool.
+
+    Args:
+        pool(torch.cuda.MemPool): a MemPool object to be made active so that
+            allocations route to this pool.
+        device (torch.device or int, optional): selected device. Uses MemPool on
+            the current device, given by :func:`~torch.cuda.current_device`,
+            if :attr:`device` is ``None`` (default).
+
+    .. note::
+        This context manager makes only current thread's allocations route to
+        the given pool. If a new thread is spawned inside the context manager
+        (e.g. by calling backward) the allocations in that thread will not
+        route to the given pool.
+    """
+    device_index = (
+        torch.cuda.current_device() if device is None else _get_device_index(device)
+    )
+    _cuda_beginAllocateCurrentThreadToPool(device_index, pool.id)
+    try:
+        yield
+    finally:
+        _cuda_endAllocateToPool(device_index, pool.id)
+        _cuda_releasePool(device_index, pool.id)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/nccl.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/nccl.py
new file mode 100644
index 0000000000000000000000000000000000000000..bef781c19a0e92cc8658c1503312ec4580756d1f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/nccl.py
@@ -0,0 +1,152 @@
+# mypy: allow-untyped-defs
+import collections
+import warnings
+from collections.abc import Sequence
+from typing import Optional, Union
+
+import torch.cuda
+
+
+__all__ = ["all_reduce", "reduce", "broadcast", "all_gather", "reduce_scatter"]
+
+SUM = 0  # ncclRedOp_t
+
+
+def is_available(tensors):
+    if not hasattr(torch._C, "_nccl_all_reduce"):
+        warnings.warn("PyTorch is not compiled with NCCL support", stacklevel=2)
+        return False
+
+    devices = set()
+    for tensor in tensors:
+        if tensor.is_sparse:
+            return False
+        if not tensor.is_contiguous():
+            return False
+        if not tensor.is_cuda:
+            return False
+        device = tensor.get_device()
+        if device in devices:
+            return False
+        devices.add(device)
+
+    return True
+
+
+def version():
+    """
+    Returns the version of the NCCL.
+
+
+    This function returns a tuple containing the major, minor, and patch version numbers of the NCCL.
+    The suffix is also included in the tuple if a version suffix exists.
+    Returns:
+        tuple: The version information of the NCCL.
+    """
+    ver = torch._C._nccl_version()
+    major = ver >> 32
+    minor = (ver >> 16) & 65535
+    patch = ver & 65535
+    suffix = torch._C._nccl_version_suffix().decode("utf-8")
+    if suffix == "":
+        return (major, minor, patch)
+    else:
+        return (major, minor, patch, suffix)
+
+
+def unique_id():
+    return torch._C._nccl_unique_id()
+
+
+def init_rank(num_ranks, uid, rank):
+    return torch._C._nccl_init_rank(num_ranks, uid, rank)
+
+
+def _check_sequence_type(inputs: Union[torch.Tensor, Sequence[torch.Tensor]]) -> None:
+    if not isinstance(inputs, collections.abc.Container) or isinstance(
+        inputs, torch.Tensor
+    ):
+        raise TypeError("Inputs should be a collection of tensors")
+
+
+def all_reduce(inputs, outputs=None, op=SUM, streams=None, comms=None):
+    _check_sequence_type(inputs)
+    if outputs is None:
+        outputs = inputs
+    _check_sequence_type(outputs)
+    torch._C._nccl_all_reduce(inputs, outputs, op, streams, comms)
+
+
+# `output` used to be `outputs`, taking in a list of tensors. So we have two
+# arguments for BC reasons.
+def reduce(
+    inputs: Sequence[torch.Tensor],
+    output: Optional[Union[torch.Tensor, Sequence[torch.Tensor]]] = None,
+    root: int = 0,
+    op: int = SUM,
+    streams: Optional[Sequence[torch.cuda.Stream]] = None,
+    comms=None,
+    *,
+    outputs: Optional[Sequence[torch.Tensor]] = None,
+) -> None:
+    _check_sequence_type(inputs)
+    _output: torch.Tensor
+    if outputs is not None:
+        if output is not None:
+            raise ValueError(
+                "'output' and 'outputs' can not be both specified. 'outputs' is deprecated in "
+                "favor of 'output', taking in a single output tensor. The signature of reduce is: "
+                "reduce(inputs, output=None, root=0, op=SUM, streams=None, comms=None)."
+            )
+        else:
+            warnings.warn(
+                "`nccl.reduce` with an output tensor list is deprecated. "
+                "Please specify a single output tensor with argument 'output' instead instead.",
+                FutureWarning,
+                stacklevel=2,
+            )
+            _output = outputs[root]
+    elif not isinstance(output, torch.Tensor) and isinstance(
+        output, collections.abc.Sequence
+    ):
+        # User called old API with positional arguments of list of output tensors.
+        warnings.warn(
+            "nccl.reduce with an output tensor list is deprecated. "
+            "Please specify a single output tensor.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        _output = output[root]
+    else:
+        _output = inputs[root] if output is None else output
+    torch._C._nccl_reduce(inputs, _output, root, op, streams, comms)
+
+
+def broadcast(
+    inputs: Sequence[torch.Tensor], root: int = 0, streams=None, comms=None
+) -> None:
+    _check_sequence_type(inputs)
+    torch._C._nccl_broadcast(inputs, root, streams, comms)
+
+
+def all_gather(
+    inputs: Sequence[torch.Tensor],
+    outputs: Sequence[torch.Tensor],
+    streams=None,
+    comms=None,
+) -> None:
+    _check_sequence_type(inputs)
+    _check_sequence_type(outputs)
+    torch._C._nccl_all_gather(inputs, outputs, streams, comms)
+
+
+def reduce_scatter(
+    inputs: Sequence[torch.Tensor],
+    outputs: Sequence[torch.Tensor],
+    op: int = SUM,
+    streams=None,
+    comms=None,
+) -> None:
+    _check_sequence_type(inputs)
+    _check_sequence_type(outputs)
+    torch._C._nccl_reduce_scatter(inputs, outputs, op, streams, comms)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/nvtx.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/nvtx.py
new file mode 100644
index 0000000000000000000000000000000000000000..f77f9a0c9fd73576a362be526e8b1396a1901233
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/nvtx.py
@@ -0,0 +1,129 @@
+# mypy: allow-untyped-defs
+r"""This package adds support for NVIDIA Tools Extension (NVTX) used in profiling."""
+
+from contextlib import contextmanager
+
+
+try:
+    from torch._C import _nvtx
+except ImportError:
+
+    class _NVTXStub:
+        @staticmethod
+        def _fail(*args, **kwargs):
+            raise RuntimeError(
+                "NVTX functions not installed. Are you sure you have a CUDA build?"
+            )
+
+        rangePushA = _fail
+        rangePop = _fail
+        markA = _fail
+
+    _nvtx = _NVTXStub()  # type: ignore[assignment]
+
+__all__ = ["range_push", "range_pop", "range_start", "range_end", "mark", "range"]
+
+
+def range_push(msg):
+    """
+    Push a range onto a stack of nested range span.  Returns zero-based depth of the range that is started.
+
+    Args:
+        msg (str): ASCII message to associate with range
+    """
+    return _nvtx.rangePushA(msg)
+
+
+def range_pop():
+    """Pop a range off of a stack of nested range spans.  Returns the  zero-based depth of the range that is ended."""
+    return _nvtx.rangePop()
+
+
+def range_start(msg) -> int:
+    """
+    Mark the start of a range with string message. It returns an unique handle
+    for this range to pass to the corresponding call to rangeEnd().
+
+    A key difference between this and range_push/range_pop is that the
+    range_start/range_end version supports range across threads (start on one
+    thread and end on another thread).
+
+    Returns: A range handle (uint64_t) that can be passed to range_end().
+
+    Args:
+        msg (str): ASCII message to associate with the range.
+    """
+    # pyrefly: ignore [missing-attribute]
+    return _nvtx.rangeStartA(msg)
+
+
+def range_end(range_id) -> None:
+    """
+    Mark the end of a range for a given range_id.
+
+    Args:
+        range_id (int): an unique handle for the start range.
+    """
+    # pyrefly: ignore [missing-attribute]
+    _nvtx.rangeEnd(range_id)
+
+
+def _device_range_start(msg: str, stream: int = 0) -> object:
+    """
+    Marks the start of a range with string message.
+    It returns an opaque heap-allocated handle for this range
+    to pass to the corresponding call to device_range_end().
+
+    A key difference between this and range_start is that the
+    range_start marks the range right away, while _device_range_start
+    marks the start of the range as soon as all the tasks on the
+    CUDA stream are completed.
+
+    Returns: An opaque heap-allocated handle that should be passed to _device_range_end().
+
+    Args:
+        msg (str): ASCII message to associate with the range.
+        stream (int): CUDA stream id.
+    """
+    # pyrefly: ignore [missing-attribute]
+    return _nvtx.deviceRangeStart(msg, stream)
+
+
+def _device_range_end(range_handle: object, stream: int = 0) -> None:
+    """
+    Mark the end of a range for a given range_handle as soon as all the tasks
+    on the CUDA stream are completed.
+
+    Args:
+        range_handle: an unique handle for the start range.
+        stream (int): CUDA stream id.
+    """
+    # pyrefly: ignore [missing-attribute]
+    _nvtx.deviceRangeEnd(range_handle, stream)
+
+
+def mark(msg):
+    """
+    Describe an instantaneous event that occurred at some point.
+
+    Args:
+        msg (str): ASCII message to associate with the event.
+    """
+    return _nvtx.markA(msg)
+
+
+@contextmanager
+def range(msg, *args, **kwargs):
+    """
+    Context manager / decorator that pushes an NVTX range at the beginning
+    of its scope, and pops it at the end. If extra arguments are given,
+    they are passed as arguments to msg.format().
+
+    Args:
+        msg (str): message to associate with the range
+    """
+    range_push(msg.format(*args, **kwargs))
+    try:
+        yield
+    finally:
+        range_pop()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/profiler.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/profiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae0674f4a4a1d9d3f20a759301606c8236b09828
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/profiler.py
@@ -0,0 +1,86 @@
+# mypy: allow-untyped-defs
+import contextlib
+import tempfile
+
+import torch
+
+from . import check_error, cudart
+
+
+__all__ = ["init", "start", "stop", "profile"]
+
+DEFAULT_FLAGS = [
+    "gpustarttimestamp",
+    "gpuendtimestamp",
+    "gridsize3d",
+    "threadblocksize",
+    "streamid",
+    "enableonstart 0",
+    "conckerneltrace",
+]
+
+
+def init(output_file, flags=None, output_mode="key_value"):
+    rt = cudart()
+    if not hasattr(rt, "cudaOutputMode"):
+        raise AssertionError("HIP does not support profiler initialization!")
+    if (
+        hasattr(torch.version, "cuda")
+        and torch.version.cuda is not None
+        and int(torch.version.cuda.split(".")[0]) >= 12
+    ):
+        # Check https://github.com/pytorch/pytorch/pull/91118
+        # cudaProfilerInitialize is no longer needed after CUDA 12
+        raise AssertionError("CUDA12+ does not need profiler initialization!")
+    flags = DEFAULT_FLAGS if flags is None else flags
+    if output_mode == "key_value":
+        output_mode_enum = rt.cudaOutputMode.KeyValuePair
+    elif output_mode == "csv":
+        output_mode_enum = rt.cudaOutputMode.CSV
+    else:
+        raise RuntimeError(
+            "supported CUDA profiler output modes are: key_value and csv"
+        )
+    with tempfile.NamedTemporaryFile(delete=True) as f:
+        f.write(b"\n".join(f.encode("ascii") for f in flags))
+        f.flush()
+        check_error(rt.cudaProfilerInitialize(f.name, output_file, output_mode_enum))
+
+
+def start():
+    r"""Starts cuda profiler data collection.
+
+    .. warning::
+        Raises CudaError in case of it is unable to start the profiler.
+    """
+    check_error(cudart().cudaProfilerStart())
+
+
+def stop():
+    r"""Stops cuda profiler data collection.
+
+    .. warning::
+        Raises CudaError in case of it is unable to stop the profiler.
+    """
+    check_error(cudart().cudaProfilerStop())
+
+
+@contextlib.contextmanager
+def profile():
+    """
+    Enable profiling.
+
+    Context Manager to enabling profile collection by the active profiling tool from CUDA backend.
+    Example:
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA)
+        >>> import torch
+        >>> model = torch.nn.Linear(20, 30).cuda()
+        >>> inputs = torch.randn(128, 20).cuda()
+        >>> with torch.cuda.profiler.profile() as prof:
+        ...     model(inputs)
+    """
+    try:
+        start()
+        yield
+    finally:
+        stop()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/random.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/random.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe7c53c3d7ebefc1e3b37864dc86a24f923f337b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/random.py
@@ -0,0 +1,184 @@
+# mypy: allow-untyped-defs
+from collections.abc import Iterable
+from typing import Union
+
+import torch
+from torch import Tensor
+
+from . import _lazy_call, _lazy_init, current_device, device_count, is_initialized
+
+
+__all__ = [
+    "get_rng_state",
+    "get_rng_state_all",
+    "set_rng_state",
+    "set_rng_state_all",
+    "manual_seed",
+    "manual_seed_all",
+    "seed",
+    "seed_all",
+    "initial_seed",
+]
+
+
+def get_rng_state(device: Union[int, str, torch.device] = "cuda") -> Tensor:
+    r"""Return the random number generator state of the specified GPU as a ByteTensor.
+
+    Args:
+        device (torch.device or int, optional): The device to return the RNG state of.
+            Default: ``'cuda'`` (i.e., ``torch.device('cuda')``, the current CUDA device).
+
+    .. warning::
+        This function eagerly initializes CUDA.
+    """
+    _lazy_init()
+    if isinstance(device, str):
+        device = torch.device(device)
+    elif isinstance(device, int):
+        device = torch.device("cuda", device)
+    idx = device.index
+    if idx is None:
+        idx = current_device()
+    default_generator = torch.cuda.default_generators[idx]
+    return default_generator.get_state()
+
+
+def get_rng_state_all() -> list[Tensor]:
+    r"""Return a list of ByteTensor representing the random number states of all devices."""
+    results = [get_rng_state(i) for i in range(device_count())]
+    return results
+
+
+def set_rng_state(
+    new_state: Tensor, device: Union[int, str, torch.device] = "cuda"
+) -> None:
+    r"""Set the random number generator state of the specified GPU.
+
+    Args:
+        new_state (torch.ByteTensor): The desired state
+        device (torch.device or int, optional): The device to set the RNG state.
+            Default: ``'cuda'`` (i.e., ``torch.device('cuda')``, the current CUDA device).
+    """
+    if not is_initialized():
+        with torch._C._DisableFuncTorch():
+            # Clone the state because the callback will be triggered
+            # later when CUDA is lazy initialized.
+            new_state = new_state.clone(memory_format=torch.contiguous_format)
+    if isinstance(device, str):
+        device = torch.device(device)
+    elif isinstance(device, int):
+        device = torch.device("cuda", device)
+
+    def cb():
+        idx = device.index
+        if idx is None:
+            idx = current_device()
+        default_generator = torch.cuda.default_generators[idx]
+        default_generator.set_state(new_state)
+
+    _lazy_call(cb)
+
+
+def set_rng_state_all(new_states: Iterable[Tensor]) -> None:
+    r"""Set the random number generator state of all devices.
+
+    Args:
+        new_states (Iterable of torch.ByteTensor): The desired state for each device.
+    """
+    for i, state in enumerate(new_states):
+        set_rng_state(state, i)
+
+
+def manual_seed(seed: int) -> None:
+    r"""Set the seed for generating random numbers for the current GPU.
+
+    It's safe to call this function if CUDA is not available; in that
+    case, it is silently ignored.
+
+    Args:
+        seed (int): The desired seed.
+
+    .. warning::
+        If you are working with a multi-GPU model, this function is insufficient
+        to get determinism.  To seed all GPUs, use :func:`manual_seed_all`.
+    """
+    seed = int(seed)
+
+    def cb():
+        idx = current_device()
+        default_generator = torch.cuda.default_generators[idx]
+        default_generator.manual_seed(seed)
+
+    _lazy_call(cb, seed=True)
+
+
+def manual_seed_all(seed: int) -> None:
+    r"""Set the seed for generating random numbers on all GPUs.
+
+    It's safe to call this function if CUDA is not available; in that
+    case, it is silently ignored.
+
+    Args:
+        seed (int): The desired seed.
+    """
+    seed = int(seed)
+
+    def cb():
+        for i in range(device_count()):
+            default_generator = torch.cuda.default_generators[i]
+            default_generator.manual_seed(seed)
+
+    _lazy_call(cb, seed_all=True)
+
+
+def seed() -> None:
+    r"""Set the seed for generating random numbers to a random number for the current GPU.
+
+    It's safe to call this function if CUDA is not available; in that
+    case, it is silently ignored.
+
+    .. warning::
+        If you are working with a multi-GPU model, this function will only initialize
+        the seed on one GPU.  To initialize all GPUs, use :func:`seed_all`.
+    """
+
+    def cb():
+        idx = current_device()
+        default_generator = torch.cuda.default_generators[idx]
+        default_generator.seed()
+
+    _lazy_call(cb)
+
+
+def seed_all() -> None:
+    r"""Set the seed for generating random numbers to a random number on all GPUs.
+
+    It's safe to call this function if CUDA is not available; in that
+    case, it is silently ignored.
+    """
+
+    def cb():
+        random_seed = 0
+        seeded = False
+        for i in range(device_count()):
+            default_generator = torch.cuda.default_generators[i]
+            if not seeded:
+                default_generator.seed()
+                random_seed = default_generator.initial_seed()
+                seeded = True
+            else:
+                default_generator.manual_seed(random_seed)
+
+    _lazy_call(cb)
+
+
+def initial_seed() -> int:
+    r"""Return the current random seed of the current GPU.
+
+    .. warning::
+        This function eagerly initializes CUDA.
+    """
+    _lazy_init()
+    idx = current_device()
+    default_generator = torch.cuda.default_generators[idx]
+    return default_generator.initial_seed()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/sparse.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/sparse.py
new file mode 100644
index 0000000000000000000000000000000000000000..f37a34118d2d8f73437dee54337a666df1b99a09
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/sparse.py
@@ -0,0 +1 @@
+# The Tensor classes are added to this module by python_tensor.cpp
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/streams.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/streams.py
new file mode 100644
index 0000000000000000000000000000000000000000..3670937cf9f75136201f2de68243ca905c367053
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/streams.py
@@ -0,0 +1,262 @@
+# mypy: allow-untyped-defs
+# pylint: disable=useless-parent-delegation
+import ctypes
+
+import torch
+from torch._utils import _dummy_type
+
+
+if not hasattr(torch._C, "_CudaStreamBase"):
+    # Define dummy base classes
+    torch._C.__dict__["_CudaStreamBase"] = _dummy_type("_CudaStreamBase")
+    torch._C.__dict__["_CudaEventBase"] = _dummy_type("_CudaEventBase")
+
+
+class Stream(torch._C._CudaStreamBase):
+    r"""Wrapper around a CUDA stream.
+
+    A CUDA stream is a linear sequence of execution that belongs to a specific
+    device, independent from other streams. It supports with statement as a
+    context manager to ensure the operators within the with block are running
+    on the corresponding stream.  See :ref:`cuda-semantics` for details.
+
+    Args:
+        device(torch.device or int, optional): a device on which to allocate
+            the stream. If :attr:`device` is ``None`` (default) or a negative
+            integer, this will use the current device.
+        priority(int, optional): priority of the stream, which can be positive, 0, or negative.
+            A lower number indicates a higher priority. By default, the priority is set to 0.
+            If the value falls outside of the allowed priority range, it will automatically be
+            mapped to the nearest valid priority (lowest for large positive numbers or
+            highest for large negative numbers).
+
+    """
+
+    def __new__(cls, device=None, priority=0, **kwargs):
+        # Check CUDA availability
+        if not torch.backends.cuda.is_built():
+            raise RuntimeError("torch.cuda.Stream requires CUDA support")
+        # setting device manager is expensive, so we avoid it unless necessary
+        if device is None or ("stream_id" in kwargs and "device_index" in kwargs):
+            return super().__new__(cls, priority=priority, **kwargs)
+        else:
+            with torch.cuda.device(device):
+                return super().__new__(cls, priority=priority, **kwargs)
+
+    def wait_event(self, event) -> None:
+        r"""Make all future work submitted to the stream wait for an event.
+
+        Args:
+            event (torch.cuda.Event): an event to wait for.
+
+        .. note:: This is a wrapper around ``cudaStreamWaitEvent()``: see
+           `CUDA Stream documentation`_ for more info.
+
+           This function returns without waiting for :attr:`event`: only future
+           operations are affected.
+
+        .. _CUDA Stream documentation:
+           https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__STREAM.html
+        """
+        event.wait(self)
+
+    def wait_stream(self, stream) -> None:
+        r"""Synchronize with another stream.
+
+        All future work submitted to this stream will wait until all kernels
+        submitted to a given stream at the time of call complete.
+
+        Args:
+            stream (Stream): a stream to synchronize.
+
+        .. note:: This function returns without waiting for currently enqueued
+           kernels in :attr:`stream`: only future operations are affected.
+        """
+        self.wait_event(stream.record_event())
+
+    def record_event(self, event=None):
+        r"""Record an event.
+
+        Args:
+            event (torch.cuda.Event, optional): event to record. If not given, a new one
+                will be allocated.
+
+        Returns:
+            Recorded event.
+        """
+        if event is None:
+            event = Event()
+        event.record(self)
+        return event
+
+    def query(self) -> bool:
+        r"""Check if all the work submitted has been completed.
+
+        Returns:
+            A boolean indicating if all kernels in this stream are completed.
+        """
+        return super().query()
+
+    def synchronize(self) -> None:
+        r"""Wait for all the kernels in this stream to complete.
+
+        .. note:: This is a wrapper around ``cudaStreamSynchronize()``: see
+           `CUDA Stream documentation`_ for more info.
+        """
+        super().synchronize()
+
+    @property
+    def _as_parameter_(self):
+        return ctypes.c_void_p(self.cuda_stream)
+
+    def __eq__(self, o) -> bool:
+        if isinstance(o, Stream):
+            return super().__eq__(o)
+        return False
+
+    def __hash__(self):
+        return hash((self.cuda_stream, self.device))
+
+    def __repr__(self):
+        return f""
+
+    def __cuda_stream__(self):
+        """Implements the CUDA Stream Protocol:
+        https://nvidia.github.io/cuda-python/cuda-core/latest/interoperability.html#cuda-stream-protocol
+
+        Returns:
+            tuple: A 2-tuple of (version, handle) where version is the protocol version
+                   and handle is the address of cudaStream_t (CUDA) or hipStream_t (ROCm) as a Python int.
+        """
+        return (0, self.cuda_stream)
+
+
+class ExternalStream(Stream):
+    r"""Wrapper around an externally allocated CUDA stream.
+
+    This class is used to wrap streams allocated in other libraries in order
+    to facilitate data exchange and multi-library interactions.
+
+    .. note:: This class doesn't manage the stream life-cycle, it is the user
+       responsibility to keep the referenced stream alive while this class is
+       being used.
+
+    Args:
+        stream_ptr(int): Integer representation of the `cudaStream_t` value.
+            allocated externally.
+        device(torch.device or int, optional): the device where the stream
+            was originally allocated. If device is specified incorrectly,
+            subsequent launches using this stream may fail.
+    """
+
+    def __new__(cls, stream_ptr, device=None, **kwargs):
+        with torch.cuda.device(device):
+            return super().__new__(cls, stream_ptr=stream_ptr, **kwargs)
+
+
+class Event(torch._C._CudaEventBase):
+    r"""Wrapper around a CUDA event.
+
+    CUDA events are synchronization markers that can be used to monitor the
+    device's progress, to accurately measure timing, and to synchronize CUDA
+    streams.
+
+    The underlying CUDA events are lazily initialized when the event is first
+    recorded or exported to another process. After creation, only streams on the
+    same device may record the event. However, streams on any device can wait on
+    the event.
+
+    Args:
+        enable_timing (bool, optional): indicates if the event should measure time
+            (default: ``False``)
+        blocking (bool, optional): if ``True``, :meth:`wait` will be blocking (default: ``False``)
+        interprocess (bool): if ``True``, the event can be shared between processes
+            (default: ``False``)
+        external (bool, optional): indicates whether this event should create event record and event wait nodes, or create an internal cross-stream dependency, when captured in a cuda graph. See `cross-stream dependencies `_, `cudaEventRecordExternal `_, and `cudaEventWaitExternal `_ for more information about internal vs. external events. (default: ``False``)
+
+    .. _CUDA Event Documentation:
+       https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__EVENT.html
+    """  # noqa: B950
+
+    def __new__(
+        cls, enable_timing=False, blocking=False, interprocess=False, external=False
+    ):
+        return super().__new__(
+            cls,
+            enable_timing=enable_timing,
+            blocking=blocking,
+            interprocess=interprocess,
+            external=external,
+        )
+
+    @classmethod
+    def from_ipc_handle(cls, device, handle):
+        r"""Reconstruct an event from an IPC handle on the given device."""
+        return super().from_ipc_handle(device, handle)
+
+    def record(self, stream=None):
+        r"""Record the event in a given stream.
+
+        Uses ``torch.cuda.current_stream()`` if no stream is specified. The
+        stream's device must match the event's device.
+        """
+        if stream is None:
+            stream = torch.cuda.current_stream()
+        super().record(stream)
+
+    def wait(self, stream=None) -> None:
+        r"""Make all future work submitted to the given stream wait for this event.
+
+        Use ``torch.cuda.current_stream()`` if no stream is specified.
+
+        .. note:: This is a wrapper around ``cudaStreamWaitEvent()``: see
+            `CUDA Event documentation`_ for more info.
+        """
+        if stream is None:
+            stream = torch.cuda.current_stream()
+        super().wait(stream)
+
+    def query(self):
+        r"""Check if all work currently captured by event has completed.
+
+        Returns:
+            A boolean indicating if all work currently captured by event has
+            completed.
+        """
+        return super().query()
+
+    def elapsed_time(self, end_event):
+        r"""Return the time elapsed.
+
+        Time reported in milliseconds after the event was recorded and
+        before the end_event was recorded.
+        """
+        return super().elapsed_time(end_event)
+
+    def synchronize(self) -> None:
+        r"""Wait for the event to complete.
+
+        Waits until the completion of all work currently captured in this event.
+        This prevents the CPU thread from proceeding until the event completes.
+
+         .. note:: This is a wrapper around ``cudaEventSynchronize()``: see
+            `CUDA Event documentation`_ for more info.
+        """
+        super().synchronize()
+
+    def ipc_handle(self):
+        r"""Return an IPC handle of this event.
+
+        If not recorded yet, the event will use the current device.
+        """
+        return super().ipc_handle()
+
+    @property
+    def _as_parameter_(self):
+        return ctypes.c_void_p(self.cuda_event)
+
+    def __repr__(self) -> str:
+        if self.cuda_event:
+            return f""
+        else:
+            return ""
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/tunable.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/tunable.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a5ee73cbddd39c05f6fe269c1e280b3f5624a64
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/cuda/tunable.py
@@ -0,0 +1,802 @@
+r"""
+This module exposes a TunableOp interface.
+
+Some operations, such as GEMMs, could be implemented using more than one library
+or more than one technique. For example, a GEMM could be implemented for CUDA or
+ROCm using either the blas or blasLt libraries. Further, ROCm's rocblas and
+hipblaslt libraries allow the user to query for all possible algorithms and then
+choose one. How does one know which implementation is the fastest and should be
+chosen? That's what TunableOp provides.
+
+Enabling TunableOp and Tuning Separately
+========================================
+
+The TunableOp feature is enabled separately from enabling the tuning phase
+itself. Enabling TunableOp means that PyTorch will replace any standard
+operators with their Tunable implementations. Any call to a TunableOp first
+checks whether it has already been tuned for the given operator inputs. If so,
+it will immediately call the tuned operation; no further tuning will take place
+even when the tuning setting is enabled. Instead if no tuning result is found,
+and tuning is enabled, the TunableOp will benchmark every registered
+implementation of that operator for the given set of inputs and select the
+fastest.
+
+File Input and Output
+=====================
+
+The first time any TunableOp is invoked, the internal database of tuned
+operations will be prepared by attempting to read the results from the given
+file. The default filename is 'tunableop_results.csv'. To support tuning when
+multiple GPUs are used across multiple processes, the GPU device ordinal is
+automatically inserted into the filename to avoid multiple processes overwriting
+the same file.
+
+If tuning is enabled and new tunings are discovered during the course of your
+workload, it will also write out to this same filename with all tunings, both
+the ones it read in at startup as well as the new ones found at runtime. This
+can be used, for example, to build up a tunings file across many workloads by
+reusing the same file. The output file is automatically created when the
+application terminates. This behavior can be controlled by the C++ and Python
+APIs but not the environment variables.
+
+Assuming you specified a filename, you'll end up with a CSV file with contents
+like so::
+
+  Validator,PT_VERSION,2.2.0
+  Validator,ROCM_VERSION,6.0.0.0-12969-1544e39
+  Validator,HIPBLASLT_VERSION,0.6.0-a9c5cc7
+  Validator,ROCBLAS_VERSION,4.0.0-72e57364-dirty
+  GemmTunableOp_float_NT,nt_25088_4096_64,Gemm_Hipblaslt_1219,1.262
+  GemmTunableOp_float_NT,nt_4096_4096_64,Gemm_Rocblas_1216,0.033
+
+Note the "Validator" lines. If you change a library version, or ROCm version, or
+PyTorch version, TunableOp will detect this and reject the tunings file because
+the prior tunings are likely affected by other software changes.
+
+The remaining lines are the tuned solutions for each TunableOp encountered
+during your execution. Each line consists of 4 comma-separated fields: operator
+name, operator parameters, solution name, and average execution time. The
+execution time is an optional field. The CSV file can be edited, but with
+caution. For example, the solution name (field 3) can be changed to "Default"
+and it will fall back to the original PyTorch untuned implementation. Or, in the
+case of ROCm's hipBLAS or hipBLASLt libraries, if you know the specific solution
+index you can override the solution that TunableOp selected by replacing the
+value. The operator name and parameters (fields 1 and 2) are internally named
+and should not be modified. In the case of GemmTunableOp, field 1 indicates the
+datatype and whether the inputs are transposed (T) or not (N) and field 2
+indicates the M, N, K input shapes.
+
+There is an option to enable verbose output but it is only recommended for
+debugging purposes. This will produce a lot of diagnostic messages but may be
+useful to see if TunableOp is being used at all. Otherwise, TunableOp is
+completely silent, besides file output, unless there is a warning or error
+during its use. The verbose option is only available by setting the environment
+variable PYTORCH_TUNABLEOP_VEROBSE=1.
+
+A Note on Tuning Behavior, Warmup, and Cache Effects
+====================================================
+
+Tuning an operator consists of iterating through the list or registered
+implementations and profiling each one. The profile is established by running a
+single implementation in a loop multiple times and taking the average execution
+time. There is also an optional warmup phase prior to tuning that can help with
+reaching stable power states by the hardware. During tuning of a workload the
+various hardware caches will more likely produce hits than when not tuning.
+There are options for flushing the instruction cache and rotate the input tensors
+which might help produce a more faithful profile of the tuned operator as if the
+operator were run within a larger workload instead of in a tight, repetitive loop.
+
+By default, each possible solution for a given operator will be run for either
+100 iterations or as many iterations that can be run within 30ms, whichever is
+smaller, and its average execution will be calculated. The fastest solution
+among all that were successfully profiled will be chosen. A profile might fail
+if the given solution doesn't achieve the same accuracy as the default
+implementation or if the solution returns an error code.
+
+Current Tunable Operators
+=========================
+
+TunableGemm for ROCm
+--------------------
+
+Currently only a TunableGemm for ROCm is implemented. Note that CUDA builds of
+PyTorch will function correctly when using TunableOp but the only solution
+available to CUDA builds is the 'Default' implementation i.e. the original
+cuBLAS default, now called through TunableOp. Any call to at::cuda::blas::gemm()
+or ::bgemm() will be routed through TunableOp when enabled. Calling gemm() for a
+given set of input arguments (transa, transb, m, n, k) will attempt to use the
+fastest available implementation across both rocblas and hipblaslt.
+
+Offline Tuning
+==============
+
+Motivation
+----------
+There are several use cases for offline tuning.
+
+One use case involves a workload with a high-memory utilization, where regular tuning might lead to running out of memory.
+
+Another use case is for compute-intensive workloads. In such cases, it is more resource-efficient to collect
+the GEMMs for the workload once and then tune repeatedly with different tuning parameters or libraries.
+
+Workflow
+--------
+There are basically two steps:
+1) Set the environment variables to collect the untuned GEMM and this will generate ``tunableop_untuned0.csv``:
+
+.. code-block:: bash
+
+   export PYTORCH_TUNABLEOP_ENABLED=1
+   export PYTORCH_TUNABLEOP_TUNING=0
+   export PYTORCH_TUNABLEOP_RECORD_UNTUNED=1
+   ...
+
+2) Run a Python script that reads the ``tunableop_untuned0.csv`` and generates the ``tunableop_results0.csv``, like this:
+
+.. code-block:: python
+
+   import torch.cuda.tunable as tunable
+   import os
+
+   os.putenv("PYTORCH_TUNABLEOP_ENABLED", "1")
+   os.putenv("PYTORCH_TUNABLEOP_TUNING", "1")
+   os.putenv("PYTORCH_TUNABLEOP_RECORD_UNTUNED", "0")
+   tunable.tune_gemm_in_file("tunableop_untuned0.csv")
+
+
+It is also possible to take multiple untuned files and distribute the GEMMs for tuning to multiple GPUs
+within a single node. In the first step, the GEMMs are first gathered and duplicate GEMMs are eliminated.
+Next, the GEMMs are distributed to different GPUs for tuning. After all GEMMs are tuned, the results from
+all the GPUs are then gathered into a single file whose base filename has ``_full0`` appended to it
+(for example ``tunableop_results_full0.csv``). Finally, this new file, containing the gathered results, will be
+duplicated N times, once for each GPU as convenience to the user will run the workload with the tuned
+configuration on N GPUs.
+
+.. code-block:: python
+
+   if __name__ == "__main__":
+       num_gpus = 8  # number of GPUs that will be used during the tuning process
+       tunable.mgpu_tune_gemm_in_file("tunableop_untuned?.csv", num_gpus)
+
+Note that the usage of the ``mgpu_tune_gemm_in_file`` API is different from its single GPU counterpart
+(``tune_gemm_in_file``). The body of the Python script that calls the API must be wrapped in ``main()`` as shown
+due to the use of concurrent futures module. The argument to ``mgpu_tune_gemm_in_file`` must contain a wild card
+expression (``?`` or ``*``) to generate the list of untuned files containing the GEMMs to be processed. The ``num_gpus``
+must between 1 and the total number of GPUs available.
+
+Tuning Context
+==============
+
+The behavior of TunableOp is currently manipulated through environment
+variables, the C++ interface of at::cuda::tunable::getTuningContext(), or the
+torch.cuda.tunable python interfaces. The environment variables take precedence
+over any setting you manipulate using the C++ or Python APIs.
+
+Environment Variable Interface
+------------------------------
+Environment variables are cached the first time they are read. You cannot use the
+environment variable interface programmatically since the settings become fixed.
+Use the C++ or Python APIs instead.
+
+"""
+
+import concurrent.futures
+import glob
+import multiprocessing as mp
+import os
+import shutil
+import warnings
+from typing import Optional
+
+import torch
+
+
+__all__ = [
+    "enable",
+    "is_enabled",
+    "tuning_enable",
+    "tuning_is_enabled",
+    "record_untuned_enable",
+    "record_untuned_is_enabled",
+    "set_max_tuning_duration",
+    "get_max_tuning_duration",
+    "set_max_tuning_iterations",
+    "get_max_tuning_iterations",
+    "set_filename",
+    "get_filename",
+    "get_results",
+    "get_validators",
+    "read_file",
+    "tune_gemm_in_file",
+    "mgpu_tune_gemm_in_file",
+    "set_rotating_buffer_size",
+    "get_rotating_buffer_size",
+    "set_numerical_check_tolerances",
+]
+
+
+def enable(val: bool = True) -> None:
+    r"""This is the big on/off switch for all TunableOp implementations."""
+    torch._C._cuda_tunableop_enable(val)  # type: ignore[attr-defined]
+
+
+def is_enabled() -> bool:
+    r"""Returns whether the TunableOp feature is enabled."""
+    return torch._C._cuda_tunableop_is_enabled()  # type: ignore[attr-defined]
+
+
+def tuning_enable(val: bool = True) -> None:
+    r"""Enable tuning of TunableOp implementations.
+
+    When enabled, if a tuned entry isn't found, run the tuning step and record
+    the entry.
+    """
+    torch._C._cuda_tunableop_tuning_enable(val)  # type: ignore[attr-defined]
+
+
+def tuning_is_enabled() -> bool:
+    r"""Returns whether TunableOp implementations can be tuned."""
+    return torch._C._cuda_tunableop_tuning_is_enabled()  # type: ignore[attr-defined]
+
+
+def record_untuned_enable(val: bool = True) -> None:
+    r"""Enable recording untuned of TunableOp perations for offline tuning.
+
+    When enabled, if a tuned entry isn't found, write it to the untuned file.
+    """
+    torch._C._cuda_record_untuned_enable(val)  # type: ignore[attr-defined]
+
+
+def record_untuned_is_enabled() -> bool:
+    r"""Returns whether TunableOp operations are recorded for offline tuning."""
+    return torch._C._cuda_record_untuned_is_enabled()  # type: ignore[attr-defined]
+
+
+def set_max_tuning_duration(duration: int) -> None:
+    r"""Set max time in milliseconds to spend tuning a given solution.
+
+    If both max tuning duration and iterations are set, the smaller of the two
+    will be honored. At minimum 1 tuning iteration will always be run.
+    """
+    torch._C._cuda_tunableop_set_max_tuning_duration(duration)  # type: ignore[attr-defined]
+
+
+def get_max_tuning_duration() -> int:
+    r"""Get max time to spend tuning a given solution."""
+    return torch._C._cuda_tunableop_get_max_tuning_duration()  # type: ignore[attr-defined]
+
+
+def set_max_tuning_iterations(iterations: int) -> None:
+    r"""Set max number of iterations to spend tuning a given solution.
+
+    If both max tuning duration and iterations are set, the smaller of the two
+    will be honored. At minimum 1 tuning iteration will always be run.
+    """
+    torch._C._cuda_tunableop_set_max_tuning_iterations(iterations)  # type: ignore[attr-defined]
+
+
+def get_max_tuning_iterations() -> int:
+    r"""Get max iterations to spend tuning a given solution."""
+    return torch._C._cuda_tunableop_get_max_tuning_iterations()  # type: ignore[attr-defined]
+
+
+def set_filename(filename: str, insert_device_ordinal: bool = False) -> None:
+    r"""Set the filename to use for input/output of tuning results.
+
+    If :attr:`insert_device_ordinal` is ``True`` then the current device ordinal
+    will be added to the given filename automatically. This can be used in a
+    1-process-per-gpu scenario to ensure all processes write to a separate file.
+    """
+    torch._C._cuda_tunableop_set_filename(filename, insert_device_ordinal)  # type: ignore[attr-defined]
+
+
+def get_filename() -> str:
+    r"""Get the results filename."""
+    return torch._C._cuda_tunableop_get_filename()  # type: ignore[attr-defined]
+
+
+def get_results() -> tuple[str, str, str, float]:
+    r"""Return all TunableOp results."""
+    return torch._C._cuda_tunableop_get_results()  # type: ignore[attr-defined]
+
+
+def get_validators() -> tuple[str, str]:
+    r"""Return the TunableOp validators."""
+    return torch._C._cuda_tunableop_get_validators()  # type: ignore[attr-defined]
+
+
+def read_file(filename: Optional[str] = None) -> bool:
+    r"""Read results from a TunableOp CSV file.
+
+    If :attr:`filename` is not given, ``get_filename()`` is called.
+    """
+    if filename is None:
+        filename = get_filename()
+    return torch._C._cuda_tunableop_read_file(filename)  # type: ignore[attr-defined]
+
+
+def set_rotating_buffer_size(buffer_size: int) -> None:
+    r"""Set rotating buffer size to this value in MB, if the buffer size is greater than zero.
+
+    If less than zero, query L2 cache size. If equal to zero, means deactivate rotating buffer.
+    """
+    return torch._C._cuda_tunableop_set_rotating_buffer_size(buffer_size)  # type: ignore[attr-defined]
+
+
+def get_rotating_buffer_size() -> int:
+    r"""Get the rotating buffer size in kilobytes."""
+    return torch._C._cuda_tunableop_get_rotating_buffer_size()  # type: ignore[attr-defined]
+
+
+def set_numerical_check_tolerances(
+    enable: bool, atol: float = 1e-5, rtol: float = 1e-5
+) -> None:
+    r"""Set the atol and rtol values in numeric check"""
+    return torch._C._cuda_tunableop_set_numerical_check_tolerances(enable, atol, rtol)  # type: ignore[attr-defined]
+
+
+def tune_gemm_in_file(filename: str) -> None:
+    r"""tune GEMM in file."""
+
+    assert is_enabled()
+    assert tuning_is_enabled()
+
+    deviceid = torch.cuda.current_device()
+
+    with open(filename) as file:
+        for line in file:
+            if line.startswith(("Gemm", "ScaledGemm")):
+                _process_single_offline_gemm(line, deviceid)
+
+
+def _gather_unique_untuned_gemm_from_files(filename_pattern: str) -> set[str]:
+    r"""Process multiple untuned results file and return a set with duplicates removed."""
+    unique_gemm_entries = set()  # set will avoid duplicates
+
+    for file_path in glob.glob(filename_pattern):
+        with open(file_path) as file:
+            for line in file:
+                if line.startswith(("Gemm", "ScaledGemm")):
+                    unique_gemm_entries.add(line)
+
+    return unique_gemm_entries
+
+
+def _gather_tunableop_results() -> None:
+    r"""Gather results from multiple tunableop results file and create a single file."""
+    gemm_lines = set()
+    validator_lines = []
+
+    # Need to allow for the possibility that results filename was
+    # set with the Python API instead of with environment variable.
+    # Also possible that results filename was not set at all.
+    # There are several test cases to check, but ultimately we
+    # need a glob-able expression
+    results_filename = get_filename()  # Note empty string could be returned here
+
+    if (
+        results_filename is not None and results_filename != ""
+    ):  # Case were the Python API was used to set the filename
+        dot_pos = results_filename.find(".")
+        if dot_pos != -1 and dot_pos > 0:
+            # Replace the character just to the left of the dot
+            filename_pattern = (
+                results_filename[: dot_pos - 1] + "?" + results_filename[dot_pos:]
+            )
+        else:
+            filename_pattern = ""  # Needed to make linter happy
+    else:  # Case where the environment variable was used to set the filename.
+        results_filename_env = os.getenv("PYTORCH_TUNABLEOP_FILENAME")
+        if results_filename_env is None or results_filename_env == "":
+            filename_pattern = "tunableop_results?.csv"
+        elif "%d" in results_filename_env:
+            filename_pattern = results_filename_env.replace("%d", "?")
+        else:
+            filename_pattern = results_filename_env.replace(".", "?.")
+
+    assert "?" in filename_pattern
+
+    FirstFile = False
+    matching_files = glob.glob(filename_pattern)
+    num_matching_files = len(matching_files)
+    for file_path in matching_files:
+        with open(file_path) as file:
+            for line in file:
+                if line.startswith("Validator"):
+                    if not (FirstFile):
+                        # Only read Validator from first file
+                        validator_lines.append(line)
+                else:
+                    gemm_lines.add(line)
+
+        FirstFile = True
+
+    output_file = filename_pattern.replace("?", "_full0")
+
+    with open(output_file, "w") as out_file:
+        for line in validator_lines:
+            out_file.write(line)
+        for line in gemm_lines:
+            out_file.write(line)
+
+    # Create num_matching_copies of the results file
+    for i in range(1, num_matching_files):
+        duplicate_file = output_file.replace("0", str(i))
+        shutil.copy(output_file, duplicate_file)
+
+
+def _create_matrices(
+    m: int,
+    n: int,
+    k: int,
+    lda: int,
+    ldb: int,
+    ldc: int,
+    transA: bool,
+    transB: bool,
+    dtypeA: torch.dtype,
+    deviceid: str,
+    dtypeB: Optional[torch.dtype] = None,
+    randn: bool = True,
+    subMatrix: bool = False,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    r"""Helper function for _process_single_offline_gemm.
+    Creates matrices that are then consumed by one of the Torch GEMM APIs.
+    """
+    # Fill parameters set for use with ScaledGEMM
+    fillA = 0.25
+    fillB = 0.75
+
+    if dtypeB is None:
+        dtypeB = dtypeA
+
+    if subMatrix:
+        # User reference for understanding leading dimension:
+        # https://github.com/Reference-LAPACK/lapack/blob/master/BLAS/SRC/dgemm.f
+        # TO DO: According to lines 108 - 133, there is no lower bound on rowsA,
+        # but there is a restriction on rowsB. Using this formula for now as it
+        # seems to work for all UTs.
+        rowsA = rowsB = max(ldc, k)
+
+        if randn:
+            matA = torch.randn(rowsA, lda, dtype=dtypeA, device=deviceid)
+            matB = torch.randn(rowsB, ldb, dtype=dtypeA, device=deviceid)
+        else:
+            matA = torch.full((rowsA, lda), fillA, dtype=dtypeB, device=deviceid)
+            matB = torch.full((rowsB, ldb), fillB, dtype=dtypeB, device=deviceid)
+
+        subA = matA[:k, :m].t() if transA else matA[:m, :k]
+        subB = matB[:n, :k].t() if transB else matB[:k, :n]
+        return subA, subB
+    else:
+        if randn:
+            matA = (
+                torch.rand(k, m, dtype=dtypeA, device=deviceid).t()
+                if transA
+                else torch.rand(m, k, dtype=dtypeA, device=deviceid)
+            )
+            matB = (
+                torch.rand(n, k, dtype=dtypeB, device=deviceid).t()
+                if transB
+                else torch.rand(k, n, dtype=dtypeB, device=deviceid)
+            )
+        else:
+            matA = (
+                torch.full((k, m), fillA, dtype=dtypeA, device=deviceid).t()
+                if transA
+                else torch.full((m, k), fillA, dtype=dtypeA, device=deviceid)
+            )
+            matB = (
+                torch.full((n, k), fillB, dtype=dtypeB, device=deviceid).t()
+                if transB
+                else torch.full((k, n), fillB, dtype=dtypeB, device=deviceid)
+            )
+        return matA, matB
+
+
+def _create_batch_matrices(
+    m: int,
+    n: int,
+    k: int,
+    b: int,
+    lda: int,
+    ldb: int,
+    ldc: int,
+    transA: bool,
+    transB: bool,
+    dtype: torch.dtype,
+    deviceid: str,
+    subMatrix: bool = False,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    r"""Helper function for _process_single_offline_gemm.
+    Creates batch matrices that are then consumed by one of the Torch GEMM APIs.
+    Similar to _create_matrices but for 3D batch matrices.
+    """
+    if subMatrix:
+        # User reference for understanding leading dimension:
+        # https://github.com/Reference-LAPACK/lapack/blob/master/BLAS/SRC/dgemm.f
+        # TO DO: According to lines 108 - 133, there is no lower bound on rowsA,
+        # but there is a restriction on rowsB. Using this formula for now as it
+        # seems to work for all UTs.
+        rowsA = rowsB = max(ldc, k)
+
+        matA = torch.randn(b, rowsA, lda, dtype=dtype, device=deviceid)
+        matB = torch.randn(b, rowsB, ldb, dtype=dtype, device=deviceid)
+
+        subA = matA[:b, :k, :m].transpose(1, 2) if transA else matA[:b, :m, :k]
+        subB = matB[:b, :n, :k].transpose(1, 2) if transB else matB[:b, :k, :n]
+        return subA, subB
+    else:
+        matA = (
+            torch.rand(b, k, m, dtype=dtype, device=deviceid)
+            if transA
+            else torch.rand(b, m, k, dtype=dtype, device=deviceid)
+        )
+        matB = (
+            torch.rand(b, n, k, dtype=dtype, device=deviceid)
+            if transB
+            else torch.rand(b, k, n, dtype=dtype, device=deviceid)
+        )
+        matA = matA.transpose(1, 2) if transA else matA
+        matB = matB.transpose(1, 2) if transB else matB
+        return matA, matB
+
+
+def _process_single_offline_gemm(untuned_gemm_line: str, gpu_id: int) -> None:
+    r"""Process a single untuned GEMM."""
+
+    deviceid = "cuda:" + str(gpu_id)
+
+    dtype_dict = {
+        "float": torch.float32,
+        "tf32": torch.float32,
+        "double": torch.float64,
+        "BFloat16": torch.bfloat16,
+        "Half": torch.half,
+        "c10::complex": torch.complex128,
+        "c10::complex": torch.complex64,
+        "Float8_e4m3fn": torch.float8_e4m3fn,
+        "Float8_e5m2": torch.float8_e5m2,
+        "Float8_e4m3fnuz": torch.float8_e4m3fnuz,
+        "Float8_e5m2fnuz": torch.float8_e5m2fnuz,
+    }
+
+    untuned_gemm = untuned_gemm_line.strip().split(",")[:]
+
+    underscore_count = untuned_gemm[0].count("_")
+
+    # Initialize dtype to make linter happy
+    dtype = None
+    dtypeA = None
+    dtypeB = None
+    dtypeC = None
+
+    # Extract BLAS parameters
+    if underscore_count == 2:
+        [op_sig, data_type, layout] = untuned_gemm[0].split("_")
+        transB = layout[0] == "T"
+        transA = layout[1] == "T"
+        dtype = dtype_dict.get(data_type)
+        if data_type == "tf32":
+            torch.backends.cuda.matmul.allow_tf32 = True
+        else:
+            torch.backends.cuda.matmul.allow_tf32 = False
+
+    else:  # ScaledGEMM
+        count = untuned_gemm[0].count("_")
+        assert count in [6, 7]
+        untuned_gemm_temp = untuned_gemm[0].split("_")
+        # dtypeC = might not be FP8 type, keep track
+        # of the number of underscores
+        op_sig = untuned_gemm_temp[0]
+        data_typeA = untuned_gemm_temp[1] + "_" + untuned_gemm_temp[2]
+        data_typeB = untuned_gemm_temp[3] + "_" + untuned_gemm_temp[4]
+        if count == 7:
+            data_typeC = untuned_gemm_temp[5] + "_" + untuned_gemm_temp[6]
+        else:
+            data_typeC = untuned_gemm_temp[5]
+        transB = untuned_gemm_temp[count][0] == "T"
+        transA = untuned_gemm_temp[count][1] == "T"
+        dtypeA = dtype_dict.get(data_typeA)
+        dtypeB = dtype_dict.get(data_typeB)
+        dtypeC = dtype_dict.get(data_typeC)
+
+    untuned_gemm_temp = untuned_gemm[1].split("_")
+    [n, m, k] = [int(g) for g in untuned_gemm_temp[1:4]]
+    if op_sig == "GemmStridedBatchedTunableOp":
+        assert untuned_gemm_temp[6] == "ld"
+        [ldb, lda, ldc] = [int(g) for g in untuned_gemm_temp[7:10]]
+    else:
+        assert untuned_gemm_temp[4] == "ld"
+        [ldb, lda, ldc] = [int(g) for g in untuned_gemm_temp[5:8]]
+
+    # Detect subMatrix case
+    if all(item in [n, m, k] for item in [lda, ldb, ldc]):
+        subMatrix = False
+    else:
+        subMatrix = True
+
+    if op_sig == "GemmTunableOp":
+        # Warnings for unsupported cases:
+        if m == 1 or n == 1 or k == 1:
+            if (not transA) and (not transB):
+                pass  # case is supported
+            elif transA and n == 1:
+                pass  # case is supported
+            else:
+                warnings.warn(
+                    "Offline tuning is not supported for this GEMM. Use online tuning instead. "
+                    + f"Skipped tuning for: {untuned_gemm[1]}",
+                    stacklevel=2,
+                )
+                return
+
+        # Resolve linter issue
+        if dtype is None or not isinstance(dtype, torch.dtype):
+            raise TypeError(f"dtype must be a torch.dtype, but got {dtype}")
+
+        matA, matB = _create_matrices(
+            m, n, k, lda, ldb, ldc, transA, transB, dtype, deviceid, subMatrix=subMatrix
+        )
+        torch.mm(matA, matB)
+
+    elif op_sig == "GemmStridedBatchedTunableOp":
+        # Warnings for unsupported cases:
+        if m == 1 or n == 1 or k == 1:
+            warnings.warn(
+                "Offline tuning is not support for this GEMM. Use online tuning instead. "
+                + f"Skipped tuning for: {untuned_gemm[1]}",
+                stacklevel=2,
+            )
+            return
+
+        [b] = [int(g) for g in untuned_gemm_temp[5:6]]
+
+        # Resolve linter issue
+        if dtype is None or not isinstance(dtype, torch.dtype):
+            raise TypeError(f"dtype must be a torch.dtype, but got {dtype}")
+
+        matA, matB = _create_batch_matrices(
+            m,
+            n,
+            k,
+            b,
+            lda,
+            ldb,
+            ldc,
+            transA,
+            transB,
+            dtype,
+            deviceid,
+            subMatrix=subMatrix,
+        )
+        torch.bmm(matA, matB)
+    elif op_sig == "ScaledGemmTunableOp":
+        # Only combination supported by PyTorch
+        assert transB is True
+        assert transA is False
+
+        # Resolve linter issue
+        if dtypeA is None or not isinstance(dtypeA, torch.dtype):
+            raise TypeError(f"dtype must be a torch.dtype, but got {dtypeA}")
+
+        matA, matB = _create_matrices(
+            m,
+            n,
+            k,
+            lda,
+            ldb,
+            ldc,
+            transA,
+            transB,
+            dtypeA,
+            deviceid,
+            dtypeB=dtypeB,
+            randn=False,
+            subMatrix=subMatrix,
+        )
+
+        assert untuned_gemm_temp[8] == "rw"
+        if untuned_gemm_temp[9] == "1":
+            rowwise = True
+        else:
+            rowwise = False
+        if rowwise:
+            scaleA = (
+                torch.ones((1, m), device=deviceid)
+                if transA
+                else torch.ones((m, 1), device=deviceid)
+            )
+            scaleB = (
+                torch.ones((1, n), device=deviceid)
+                if transB
+                else torch.ones((n, 1), device=deviceid)
+            )
+        else:
+            scaleA = torch.tensor(0.8, device=deviceid)
+            scaleB = torch.tensor(0.9, device=deviceid)
+
+        assert untuned_gemm_temp[10] == "bias"
+        if untuned_gemm_temp[11] == "None":  # no bias vector
+            torch._scaled_mm(
+                matA, matB, scale_a=scaleA, scale_b=scaleB, out_dtype=dtypeC
+            )
+        else:  # bias vector present
+            fillbias = 0.10
+            bias_dtype = dtype_dict.get(untuned_gemm_temp[11])
+            bias = (
+                torch.full((n,), fillbias, dtype=bias_dtype, device=deviceid)
+                if transB
+                else torch.full((m,), fillbias, dtype=bias_dtype, device=deviceid)
+            )
+            torch._scaled_mm(
+                matA, matB, scale_a=scaleA, scale_b=scaleB, out_dtype=dtypeC, bias=bias
+            )
+
+    elif op_sig == "GemmAndBiasTunableOp":
+        # y = x*A^T + b
+        assert transA != transB
+
+        # Resolve linter issue
+        if dtype is None or not isinstance(dtype, torch.dtype):
+            raise TypeError(f"dtype must be a torch.dtype, but got {dtype}")
+
+        bias = torch.rand(n, dtype=dtype, device=deviceid)
+
+        X, matA = _create_matrices(
+            m, n, k, lda, ldb, ldc, transA, transB, dtype, deviceid, subMatrix=subMatrix
+        )
+        matA = matA.t()
+        torch.nn.functional.linear(X, matA, bias)
+    else:
+        warnings.warn(f"error: unknown op {op_sig}", stacklevel=2)
+
+
+def _check_tuning_assertions() -> None:
+    r"""Helper function for multi-GPU tuning case. Need to check that TunableOp feature
+    is enabled and that tuning is enabled.
+    """
+
+    if is_enabled() is False:
+        warnings.warn("TunableOp was disabled. Trying to enable now.", stacklevel=2)
+        enable(True)
+    assert is_enabled() is True
+    assert tuning_is_enabled() is True
+    assert record_untuned_is_enabled() is False
+
+
+def mgpu_tune_gemm_in_file(filename_pattern: str, num_gpus: int) -> None:
+    r"""Process one or more files and distribute work over one or more GPUs."""
+    unique_gemm_entries = _gather_unique_untuned_gemm_from_files(filename_pattern)
+
+    total_gpus = torch.cuda.device_count()
+
+    assert 1 <= num_gpus <= total_gpus
+
+    mp_context = mp.get_context("spawn")
+
+    futures = []  # empty list to hold futures
+
+    # GEMM are assigned to GPUs in a round robin manner
+    h = 0
+    with concurrent.futures.ProcessPoolExecutor(
+        max_workers=num_gpus,
+        mp_context=mp_context,
+        initializer=_check_tuning_assertions,
+    ) as executor:
+        # The workers are a separate process. TunableOp will be
+        # enabled in the child processes if PYTORCH_TUNABLEOP_ENABLED=1
+        # In the initializer, we also try to enable TunableOP if th
+        # environment variable was NOT set.
+
+        for line in unique_gemm_entries:
+            future = executor.submit(_process_single_offline_gemm, line, h)
+            futures.append(future)
+            h = (h + 1) % num_gpus
+
+        for future in concurrent.futures.as_completed(futures):
+            future.result()
+
+    torch.cuda.synchronize()
+
+    _gather_tunableop_results()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..095e8e9bf2654e3e609554dec3fde496abe66a44
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/__init__.py
@@ -0,0 +1,168 @@
+# mypy: allow-untyped-defs
+import logging
+import pdb
+import sys
+import traceback
+import typing
+from datetime import timedelta
+
+import torch
+
+
+log = logging.getLogger(__name__)
+
+
+def is_available() -> bool:
+    """
+    Return ``True`` if the distributed package is available.
+
+    Otherwise,
+    ``torch.distributed`` does not expose any other APIs. Currently,
+    ``torch.distributed`` is available on Linux, MacOS and Windows. Set
+    ``USE_DISTRIBUTED=1`` to enable it when building PyTorch from source.
+    Currently, the default value is ``USE_DISTRIBUTED=1`` for Linux and Windows,
+    ``USE_DISTRIBUTED=0`` for MacOS.
+    """
+    return hasattr(torch._C, "_c10d_init")
+
+
+if is_available() and not torch._C._c10d_init():
+    raise RuntimeError("Failed to initialize torch.distributed")
+
+# Custom Runtime Errors thrown from the distributed package
+DistError = torch._C._DistError
+DistBackendError = torch._C._DistBackendError
+DistNetworkError = torch._C._DistNetworkError
+DistStoreError = torch._C._DistStoreError
+QueueEmptyError = torch._C._DistQueueEmptyError
+
+if is_available():
+    from torch._C._distributed_c10d import (
+        _broadcast_coalesced,
+        _compute_bucket_assignment_by_size,
+        _ControlCollectives,
+        _DEFAULT_FIRST_BUCKET_BYTES,
+        _make_nccl_premul_sum,
+        _register_builtin_comm_hook,
+        _register_comm_hook,
+        _StoreCollectives,
+        _test_python_store,
+        _verify_params_across_processes,
+        Backend as _Backend,
+        BuiltinCommHookType,
+        DebugLevel,
+        FileStore,
+        get_debug_level,
+        GradBucket,
+        Logger,
+        PrefixStore,
+        ProcessGroup as ProcessGroup,
+        Reducer,
+        set_debug_level,
+        set_debug_level_from_env,
+        Store,
+        TCPStore,
+        Work as _Work,
+    )
+
+    class _DistributedPdb(pdb.Pdb):
+        """
+        Supports using PDB from inside a multiprocessing child process.
+
+        Usage:
+        _DistributedPdb().set_trace()
+        """
+
+        def interaction(self, *args, **kwargs):
+            _stdin = sys.stdin
+            try:
+                with open("/dev/stdin") as sys.stdin:
+                    pdb.Pdb.interaction(self, *args, **kwargs)
+            finally:
+                sys.stdin = _stdin
+
+    _breakpoint_cache: dict[int, typing.Any] = {}
+
+    def breakpoint(rank: int = 0, skip: int = 0, timeout_s=3600):
+        """
+        Set a breakpoint, but only on a single rank.  All other ranks will wait for you to be
+        done with the breakpoint before continuing.
+
+        Args:
+            rank (int): Which rank to break on.  Default: ``0``
+            skip (int): Skip the first ``skip`` calls to this breakpoint. Default: ``0``.
+        """
+        if skip > 0:
+            key = hash(str(traceback.format_exc()))
+            counter = _breakpoint_cache.get(key, 0) + 1
+            _breakpoint_cache[key] = counter
+            if counter <= skip:
+                log.warning("Skip the breakpoint, counter=%d", counter)
+                return
+
+        # avoid having the default timeout (if short) interrupt your debug session
+        if timeout_s is not None:
+            for group in torch.distributed.distributed_c10d._pg_map:
+                torch.distributed.distributed_c10d._set_pg_timeout(
+                    timedelta(seconds=timeout_s), group
+                )
+
+        if get_rank() == rank:
+            pdb = _DistributedPdb()
+            pdb.message(
+                "\n!!! ATTENTION !!!\n\n"
+                f"Type 'up' to get to the frame that called dist.breakpoint(rank={rank})\n"
+            )
+            pdb.set_trace()
+        # If Meta/Python keys are in the TLS, we want to make sure that we ignore them
+        # and hit the (default) CPU/CUDA implementation of barrier.
+        meta_in_tls = torch._C._meta_in_tls_dispatch_include()
+        guard = torch._C._DisableTorchDispatch()  # type: ignore[attr-defined]
+        torch._C._set_meta_in_tls_dispatch_include(False)
+        try:
+            barrier()
+        finally:
+            torch._C._set_meta_in_tls_dispatch_include(meta_in_tls)
+            del guard
+
+    if sys.platform != "win32":
+        from torch._C._distributed_c10d import HashStore
+
+    from .device_mesh import DeviceMesh, init_device_mesh
+
+    # Variables prefixed with underscore are not auto imported
+    # See the comment in `distributed_c10d.py` above `_backend` on why we expose
+    # this.
+    # pyrefly: ignore [deprecated]
+    from .distributed_c10d import *  # noqa: F403
+    from .distributed_c10d import (  # pyrefly: ignore  # deprecated; pyrefly: ignore [deprecated]
+        _all_gather_base,
+        _coalescing_manager,
+        _CoalescingManager,
+        _create_process_group_wrapper,
+        _get_process_group_name,
+        _rank_not_in_group,
+        _reduce_scatter_base,
+        _time_estimator,
+        get_node_local_rank,
+    )
+    from .remote_device import _remote_device
+    from .rendezvous import (
+        _create_store_from_options,
+        register_rendezvous_handler,
+        rendezvous,
+    )
+
+    set_debug_level_from_env()
+
+else:
+    # This stub is sufficient to get
+    #   python test/test_public_bindings.py -k test_correct_module_names
+    # working even when USE_DISTRIBUTED=0.  Feel free to add more
+    # stubs as necessary.
+    # We cannot define stubs directly because they confuse pyre
+
+    class _ProcessGroupStub:
+        pass
+
+    sys.modules["torch.distributed"].ProcessGroup = _ProcessGroupStub  # type: ignore[attr-defined]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_checkpointable.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_checkpointable.py
new file mode 100644
index 0000000000000000000000000000000000000000..0594c20337b3bf1c73fb40e2218e0c71580b75c5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_checkpointable.py
@@ -0,0 +1,37 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+from typing_extensions import Protocol, runtime_checkable
+
+import torch
+
+
+@runtime_checkable
+class _Checkpointable(Protocol):  # noqa: PYI046
+    """
+    Interface for checkpointable objects.
+    Implemented as a protocol, implicit subtyping is supported so subclasses do not need to inherit this explicitly.
+    This is to allow arbitrary objects/tensor subclasses to hook into DCP seamlessly through implementing the interface.
+    """
+
+    def __create_write_items__(self, fqn: str, object: object) -> list[object]:
+        """
+        Return a list of WriteItems based on object's contents.
+        """
+        raise NotImplementedError(
+            "_Checkpointable._create_write_items is not implemented"
+        )
+
+    def __create_chunk_list__(self) -> list[object]:
+        """
+        Return a list of `ChunkStorageMetadata` based on object's contents.
+        """
+        raise NotImplementedError(
+            "_Checkpointable._create_chunk_list is not implemented"
+        )
+
+    def __get_tensor_shard__(self, index: int) -> torch.Tensor:
+        """
+        Return a 'torch.Tensor' shard based on 'MetadataIndex'.
+        """
+        raise NotImplementedError(
+            "_Checkpointable._get_tensor_shard is not implemented"
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_composable/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_composable/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e38281810696814a7eae148eff19b58c10e072b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_composable/__init__.py
@@ -0,0 +1,3 @@
+from .checkpoint_activation import checkpoint
+from .contract import _get_registry, contract
+from .replicate import replicate
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_composable/checkpoint_activation.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_composable/checkpoint_activation.py
new file mode 100644
index 0000000000000000000000000000000000000000..93ae14110ef79a3b9b065c4ca1e8af613bd90ff5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_composable/checkpoint_activation.py
@@ -0,0 +1,134 @@
+# mypy: allow-untyped-defs
+from collections.abc import Generator
+from contextlib import AbstractContextManager, contextmanager, nullcontext
+from typing import Any
+
+import torch
+import torch.nn as nn
+from torch.utils.checkpoint import (
+    _checkpoint_without_reentrant_generator,
+    _DEFAULT_DETERMINISM_MODE,
+)
+
+from .contract import _State, contract
+
+
+@contextmanager
+def _no_hook(module: nn.Module, user_ctx: AbstractContextManager | None = None):
+    r"""
+    Disable hooks installed by checkpoint to avoid unintentional recursion
+    during backward recomputation.
+    """
+
+    with user_ctx if user_ctx else nullcontext():
+        orig_enable_hook = checkpoint.state(module).enable_hook
+        checkpoint.state(module).enable_hook = False
+        try:
+            yield
+        finally:
+            checkpoint.state(module).enable_hook = orig_enable_hook
+
+
+class _CheckpointState(_State):
+    enable_hook: bool = False
+    _ac_generator: Generator[None, None, None] | None
+
+
+@contract(_CheckpointState)
+def checkpoint(module: nn.Module, **kwargs) -> nn.Module:
+    r"""
+    This is a composable activation checkpointing API. Unlike functional
+    activation checkpointing APIs, this one does not require changing model
+    source code. Unlike ``nn.Module`` wrapper activation checkpointing APIs,
+    this one does not modify model structure or fully-qualified names either.
+    Under the hood, it registers activation checkpointing logic as pre- and
+    post-forward hooks. Hence, this API can be easily applied to any model or
+    sub-modules in the model.
+
+    Args:
+        module (nn.Module): the target model or sub-module to apply activation
+            checkpointing.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> import torch.nn as nn
+        >>>
+        >>> class MyModel(nn.Module):
+        >>>     def __init__(self) -> None:
+        >>>         super().__init__()
+        >>>         self.l1 = nn.Linear(10, 10)
+        >>>         self.l2 = nn.Linear(10, 10)
+        >>>
+        >>>     def forward(self, x):
+        >>>         return self.l2(self.l1(x))
+        >>>
+        >>> model = MyModel()
+        >>> checkpoint(model.l1)  # apply activation checkpointing only to l1
+        >>> model(torch.zeros(2, 10)).sum().backward()
+
+    """
+    torch._C._log_api_usage_once("torch.distributed.checkpoint")
+
+    use_reentrant = kwargs.pop("use_reentrant", False)
+    if use_reentrant:
+        raise NotImplementedError(
+            "use_reentrant=True is not supported in composable checkpoint. "
+            "Please use torch.utils.checkpoint.checkpoint instead."
+        )
+    preserve_rng_state = kwargs.pop("preserve_rng_state", True)
+    user_context_fns = kwargs.pop("context_fn", None)
+    determinism_check = kwargs.pop("determinism_check", _DEFAULT_DETERMINISM_MODE)
+    debug = kwargs.pop("debug", False)
+    early_stop = kwargs.pop("early_stop", True)
+
+    if kwargs:
+        raise ValueError(
+            "Unexpected keyword arguments: " + ",".join(arg for arg in kwargs)
+        )
+
+    def forward_pre_hook(
+        module: nn.Module, args: tuple[Any, ...], kwargs: dict[str, Any]
+    ) -> None:
+        if checkpoint.state(module).enable_hook:
+
+            def context_fns():
+                if user_context_fns is not None:
+                    ctx1, ctx2 = user_context_fns()
+                    return ctx1, _no_hook(module, ctx2)
+                else:
+                    return nullcontext(), _no_hook(module)
+
+            gen = _checkpoint_without_reentrant_generator(
+                module,
+                preserve_rng_state,
+                context_fns,
+                determinism_check,
+                debug,
+                early_stop,
+                *args,
+                **kwargs,
+            )
+            checkpoint.state(module)._ac_generator = gen
+            next(gen)
+
+    def forward_hook(module: nn.Module, inputs: tuple[Any, ...], output: Any) -> Any:
+        if checkpoint.state(module).enable_hook:
+            try:
+                gen = checkpoint.state(module)._ac_generator
+                assert gen is not None
+                next(gen)
+            except StopIteration:
+                pass
+            else:
+                raise RuntimeError(
+                    "Expected non-reentrant activation checkpoint generator to be exhausted, but it was not!"
+                )
+
+        #  Ensure that we no longer hold on to the generator. always_call=True helps ensure we
+        # clear this even in the case of exception in fwd pass.
+        checkpoint.state(module)._ac_generator = None
+
+    checkpoint.state(module).enable_hook = True
+    module.register_forward_pre_hook(forward_pre_hook, with_kwargs=True)
+    module.register_forward_hook(forward_hook, prepend=True, always_call=True)
+    return module
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_composable/contract.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_composable/contract.py
new file mode 100644
index 0000000000000000000000000000000000000000..c810da8cb583c1199cda7087f7feb45b8ab6c443
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_composable/contract.py
@@ -0,0 +1,259 @@
+# mypy: allow-untyped-defs
+import uuid
+from collections import OrderedDict
+from collections.abc import Callable
+from functools import wraps
+from typing import Concatenate, Generic, Protocol
+from typing_extensions import ParamSpec, TypeVar
+
+import torch
+import torch.nn as nn
+from torch.distributed._composable_state import _State
+from torch.distributed.utils import _get_root_modules
+
+
+_T = TypeVar("_T", covariant=True)
+_P = ParamSpec("_P")
+
+
+def generate_state_key(string="__composable_api_state_key"):
+    return f"{string}_{str(uuid.uuid4())}"
+
+
+STATE_KEY = generate_state_key()
+REGISTRY_KEY = generate_state_key()
+
+
+# TODO: we can add additional info to RegistryItem to share across APIs. E.g.,
+# we can add args and kwargs here, and then we can detect whether fully_shard
+# is combined with reentrant activation checkpointing and error out with a clear
+# message.
+class RegistryItem:
+    pass
+
+
+_TState = TypeVar("_TState", bound="_State", covariant=True)
+_M = TypeVar("_M", nn.Module, list[nn.Module])
+
+
+class _ContractFn(Protocol, Generic[_P, _T, _TState]):
+    def __call__(self, *args: _P.args, **kwargs: _P.kwargs) -> _T: ...
+
+    def state(self, module: nn.Module) -> _TState: ...
+
+
+def contract(
+    state_cls: type[_TState] = _State,  # type: ignore[assignment]
+) -> Callable[
+    [Callable[Concatenate[_M, _P], _M]],
+    _ContractFn[Concatenate[_M, _P], _M, _TState],
+]:
+    r"""
+    Decorate a function as a composable distributed API, where the first
+    argument of the function must be an :class:`nn.Module` instance or sequence
+    of :class:`nn.Module` instances.
+
+    The decorator verifies that the decorated function does not modify
+    fully-qualified names (FQNs) for parameters, buffers, or modules. The
+    decorated function can return different module instances than the input
+    modules; the FQN invariant will be enforced following the input order.
+
+    When a function ``func`` is decorated by ``@contract()``, a
+    ``.state(module: nn.Module)`` method will be installed to the decorated
+    function. Then you can retrieve and modify the state on a module by calling
+    ``func.state(module)``.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> import torch.nn as nn
+        >>>
+        >>> class MyModel(nn.Module):
+        >>>     def __init__(self) -> None:
+        >>>         super().__init__()
+        >>>         self.l1 = nn.Linear(10, 10)
+        >>>         self.l2 = nn.Linear(10, 10)
+        >>>
+        >>>     def forward(self, x):
+        >>>         return self.l2(self.l1(x))
+        >>>
+        >>> @contract()
+        >>> def my_feature(module: nn.Module) -> nn.Module:
+        >>>     my_feature.state(module).some_state = "any value"
+        >>>     return module
+        >>>
+        >>> model = MyModel()
+        >>> my_feature(model.l1)
+        >>> assert my_feature.state(model.l1).some_state == "any value"
+        >>> my_feature(model.l2)
+        >>> model(torch.randn(2, 10)).sum().backward()
+    """
+
+    # wraps will make functions decorated with contract() pickleable - needed for integration with torch.package
+    @wraps(state_cls)  # type: ignore[arg-type]
+    def inner(
+        func: Callable[Concatenate[_M, _P], _M],
+    ) -> _ContractFn[Concatenate[_M, _P], _M, _TState]:
+        @wraps(func)
+        def wrapper(
+            module: _M,
+            *args: _P.args,
+            **kwargs: _P.kwargs,
+        ) -> _M:
+            inp_module = module
+            modules: list[nn.Module]
+            if isinstance(module, nn.Module):
+                modules = [module]
+            else:
+                # If the user passes a sequence of modules, then we assume that
+                # we only need to insert the state object on the root modules
+                # (i.e. those without a parent) among the passed-in modules.
+                # pyrefly: ignore [no-matching-overload]
+                modules = _get_root_modules(list(module))
+            state = state_cls()  # shared across all modules
+            registry_item = RegistryItem()  # shared across all modules
+
+            # `func` is allowed to return different module instances than the
+            # input modules as long as FQNs are preserved following the input
+            # module order
+            all_orig_named_params: list[dict[str, nn.Parameter]] = []
+            all_orig_named_buffers: list[dict[str, torch.Tensor]] = []
+            all_orig_named_modules: list[dict[str, nn.Module]] = []
+
+            # pyrefly: ignore [bad-assignment]
+            for module in modules:
+                default_all_state: dict[Callable, _State] = OrderedDict()
+                default_registry: dict[str, RegistryItem] = OrderedDict()
+                all_state: dict[Callable, _State] = module.__dict__.setdefault(  # type: ignore[call-overload]
+                    STATE_KEY, default_all_state
+                )
+                if not isinstance(all_state, dict):
+                    raise AssertionError(
+                        f"Distributed composable API states corrupted: {all_state}"
+                    )
+                registry: dict[str, RegistryItem] = module.__dict__.setdefault(  # type: ignore[call-overload]
+                    REGISTRY_KEY, default_registry
+                )
+                if not isinstance(registry, dict):
+                    raise AssertionError(
+                        f"Distributed composable API registry corrupted: {registry}"
+                    )
+                if func in all_state or func.__name__ in registry:
+                    raise AssertionError(
+                        "Each distinct composable distributed API can only be applied to a "
+                        f"module once. {func.__name__} has already been applied to the "
+                        f"following module:\n{module}"
+                    )
+                all_state.setdefault(func, state)
+                registry.setdefault(func.__name__, registry_item)
+
+                # pyrefly: ignore [missing-attribute]
+                all_orig_named_params.append(OrderedDict(module.named_parameters()))
+                # pyrefly: ignore [missing-attribute]
+                all_orig_named_buffers.append(OrderedDict(module.named_buffers()))
+                # pyrefly: ignore [missing-attribute]
+                all_orig_named_modules.append(OrderedDict(module.named_modules()))
+
+            updated = func(inp_module, *args, **kwargs)
+            if updated is None:
+                updated = inp_module  # type: ignore[assignment]
+            updated_modules: list[nn.Module]
+            if isinstance(updated, nn.Module):
+                updated_modules = [updated]
+            else:
+                updated_modules = _get_root_modules(list(inp_module))  # type: ignore[arg-type, call-overload]
+
+            all_new_named_params: list[dict[str, nn.Parameter]] = []
+            all_new_named_buffers: list[dict[str, torch.Tensor]] = []
+            all_new_named_modules: list[dict[str, nn.Module]] = []
+            # pyrefly: ignore [bad-assignment]
+            for module in updated_modules:
+                # pyrefly: ignore [missing-attribute]
+                all_new_named_params.append(OrderedDict(module.named_parameters()))
+                # pyrefly: ignore [missing-attribute]
+                all_new_named_buffers.append(OrderedDict(module.named_buffers()))
+                # pyrefly: ignore [missing-attribute]
+                all_new_named_modules.append(OrderedDict(module.named_modules()))
+
+            num_orig_modules = len(all_orig_named_modules)
+            num_new_modules = len(all_new_named_modules)
+            if num_orig_modules != num_new_modules:
+                raise AssertionError(
+                    f"{func.__name__} should return the same number of modules as input modules"
+                    f"Inputs: {num_orig_modules} modules\n"
+                    f"Outputs: {num_new_modules} modules"
+                )
+
+            def check_fqn(orig_fqns: list[str], new_fqns: list[str], check_key: str):
+                if orig_fqns == new_fqns:
+                    return
+
+                orig_fqn_set, new_fqn_set = set(orig_fqns), set(new_fqns)
+                orig_only = orig_fqn_set - new_fqn_set
+                new_only = new_fqn_set - orig_fqn_set
+                if len(orig_only) or len(new_only):
+                    raise RuntimeError(
+                        f"{check_key}"
+                        "Composable distributed API implementations cannot modify FQNs.\n"
+                        f"FQNs only in original: {orig_only}\n"
+                        f"FQNs only in new: {new_only}"
+                    )
+                else:
+                    raise RuntimeError(
+                        f"{check_key}"
+                        "Composable distributed API implementations cannot modify "
+                        "the order of FQNs.\n"
+                        f"Original FQNs: {orig_only}\n"
+                        f"New FQNs: {new_only}"
+                    )
+
+            for orig_named_params, new_named_params in zip(
+                all_orig_named_params, all_new_named_params
+            ):
+                check_fqn(
+                    list(orig_named_params.keys()),
+                    list(new_named_params.keys()),
+                    "Checking parameters: ",
+                )
+            for orig_named_buffers, new_named_buffers in zip(
+                all_orig_named_buffers, all_new_named_buffers
+            ):
+                check_fqn(
+                    list(orig_named_buffers.keys()),
+                    list(new_named_buffers.keys()),
+                    "Checking buffers: ",
+                )
+            for orig_named_modules, new_named_modules in zip(
+                all_orig_named_modules, all_new_named_modules
+            ):
+                check_fqn(
+                    list(orig_named_modules.keys()),
+                    list(new_named_modules.keys()),
+                    "Checking modules: ",
+                )
+
+            # TODO: verify that installed distributed paradigms are compatible with
+            # each other.
+
+            # pyrefly: ignore [bad-return]
+            return updated
+
+        def get_state(module: nn.Module) -> _State:
+            return module.__dict__.setdefault(  # type: ignore[call-overload]
+                STATE_KEY,
+                {},  # TODO(@yhcharles): this is a temporary fix, need a better way
+            ).get(func)  # type: ignore[call-overload]
+
+        wrapper.state = get_state  # type: ignore[attr-defined]
+
+        return wrapper  # type: ignore[return-value]
+
+    return inner  # type: ignore[return-value]
+
+
+def _get_registry(module: nn.Module) -> dict[str, RegistryItem] | None:
+    r"""
+    Get an ``OrderedDict`` of composable APIs that have been applied to the
+    ``module``, indexed by the API name. If no API has been applied, then this
+    returns ``None``.
+    """
+    return getattr(module, REGISTRY_KEY, None)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_composable/fsdp/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_composable/fsdp/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..108c765ba4766bf7d9110aa67e09ac02cab00410
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_composable/fsdp/__init__.py
@@ -0,0 +1,3 @@
+from torch.distributed.fsdp import CPUOffloadPolicy, MixedPrecisionPolicy, OffloadPolicy
+
+from .fully_shard import FSDPModule, fully_shard, register_fsdp_forward_method
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_composable/fsdp/fully_shard.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_composable/fsdp/fully_shard.py
new file mode 100644
index 0000000000000000000000000000000000000000..9e36c7b430fc89dd58cc5742f299ac607eb4367b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_composable/fsdp/fully_shard.py
@@ -0,0 +1,8 @@
+# TODO: For backward compatibility, we are importing the public objects
+# originally from this file.
+from torch.distributed.fsdp import (  # noqa: F401
+    FSDPModule,
+    fully_shard,
+    register_fsdp_forward_method,
+    UnshardHandle,
+)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_composable/replicate.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_composable/replicate.py
new file mode 100644
index 0000000000000000000000000000000000000000..8cdec49468703e53b0a125a0d3c71a92ec80d00c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_composable/replicate.py
@@ -0,0 +1,254 @@
+# mypy: allow-untyped-defs
+import weakref
+from collections.abc import Iterable
+from typing import Any, NoReturn
+
+import torch
+import torch.nn as nn
+from torch.distributed._composable_state import _State
+from torch.nn.parallel import DistributedDataParallel
+
+from .contract import _get_registry, contract
+
+
+_ROOT_MODULE_PREFIX = ""
+
+
+class _ReplicateState(_State):
+    _ddp_weakref: weakref.ref
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.module: nn.Module = nn.ParameterList()
+        self.has_initialized: bool = False
+        self._param_list: nn.ParameterList = nn.ParameterList()
+        # TODO(@fegin): this variable is originally create for testing, we
+        # should remove this if possible.
+        self._orig_module = self.module
+        self._param_names: list[str] = []
+        self._no_sync: bool = False
+        self._init_args: tuple[Any, ...] | None = None
+        self._init_kwargs: dict[str, Any] = {}
+        self._comm_hook_args: list[Any] = []
+
+    def _collect_params(
+        self,
+        module: nn.Module,
+        ignored_modules: set[nn.Module],
+        ignored_params: set[nn.Parameter],
+        prefix: str = _ROOT_MODULE_PREFIX,
+    ) -> None:
+        # skip if managed by fully_sharded API
+        if _is_fully_sharded(module):
+            return
+
+        # if a module is ignored, all descendants of the module are ignored.
+        if module in ignored_modules:
+            return
+
+        recurse_prefix = (
+            f"{prefix}." if prefix != _ROOT_MODULE_PREFIX else _ROOT_MODULE_PREFIX
+        )
+
+        for n, p in module.named_parameters(recurse=False):
+            if p not in ignored_params:
+                self._param_list.append(p)
+                self._param_names.append(f"{recurse_prefix}{n}")
+
+        for name, child_module in module.named_children():
+            self._collect_params(
+                child_module,
+                ignored_modules,
+                ignored_params,
+                prefix=f"{recurse_prefix}{name}",
+            )
+
+    def lazy_init(self) -> None:
+        @torch._disable_dynamo(recursive=True)
+        def _lazy_init():
+            assert self._init_args is not None
+            self.init(*self._init_args, **self._init_kwargs)
+            self.register_comm_hook()
+            self._init_args = ()
+            self._init_kwargs = {}
+
+        _lazy_init()
+
+    def init(
+        self,
+        module: nn.Module,
+        ignored_modules: set[nn.Module],
+        **kwargs,
+    ) -> None:
+        if self.has_initialized:
+            return
+
+        self.has_initialized = True
+        self.module = module
+        ignored_params = {p for m in ignored_modules for p in m.parameters()}
+        for submodule in module.modules():
+            if _is_fully_sharded(submodule):
+                ignored_params.update(submodule.parameters())
+        from torch.distributed.tensor.parallel.ddp import _localize_dtensor
+
+        _localize_dtensor(module, ignored_params=ignored_params)
+        self._collect_params(module, ignored_modules, ignored_params)
+
+        if "device_id" in kwargs:
+            # replicate() supports a small usability enhancement where
+            # user can pass in device_id as a Union[int, torch.device] even for
+            # CPU devices so users don't have to change code for CPU/GPU runs.
+            # We derive the right device_ids to feed into DDP to support this.
+            if kwargs["device_id"] is not None:
+                device_id = kwargs["device_id"]
+                # Convert to device_ids that DDP expects.
+                if isinstance(device_id, torch.device) and device_id.type == "cpu":
+                    # CPU modules receive device_ids None
+                    kwargs["device_ids"] = None
+                else:
+                    # GPU modules expect device_ids=[cuda_device]
+                    kwargs["device_ids"] = [device_id]
+            else:
+                kwargs["device_ids"] = None
+            kwargs.pop("device_id")
+
+        self._ddp = DistributedDataParallel(self._param_list, **kwargs)
+        # Weakref to the DDP instance is currently only used for testing.
+        replicate.state(self.module)._ddp_weakref = weakref.ref(self._ddp)
+
+    def register_comm_hook(self) -> None:
+        for comm_args, comm_kwargs in self._comm_hook_args:
+            self._ddp.register_comm_hook(*comm_args, **comm_kwargs)
+        self._comm_hook_args.clear()
+
+    def record_init_args(self, *args, **kwargs) -> None:
+        self._init_args = args
+        self._init_kwargs = kwargs
+
+    def forward_pre_hook(
+        self, module: nn.Module, args: tuple[Any, ...], kwargs: dict[str, Any]
+    ) -> Any:
+        if self._init_args or self._init_kwargs:
+            self.lazy_init()
+        self._ddp.require_backward_grad_sync = not self._no_sync
+        return self._ddp._pre_forward(*args, **kwargs)
+
+    def forward_post_hook(
+        self,
+        module: nn.Module,
+        input: tuple[torch.Tensor],
+        output: torch.Tensor,
+    ) -> torch.Tensor:
+        return self._ddp._post_forward(output)
+
+
+def unimplemented_deepcopy(*args: Any, **kwargs: Any) -> NoReturn:
+    raise AssertionError(
+        "DDP does not support deepcopy. Please use state dict for serialization."
+    )
+
+
+# Follow the same pattern as FSDP/fully_shard
+class DDP:
+    def __new__(cls, *args, **kwargs):
+        """
+        Override ``__new__`` to remove the DDP class and directly construct
+        the original class for cases like indexing into a container module.
+        """
+        # Use index 2 since 0 is the dynamically constructed `DDP<...>` class
+        # and index 1 is the `DDP` class itself
+        orig_cls = cls.__mro__[2]
+        return orig_cls.__new__(orig_cls, *args, **kwargs)
+
+    def set_requires_gradient_sync(self, requires_gradient_sync: bool) -> None:
+        """
+        Sets if the module should sync gradients. This can be used to implement
+        gradient accumulation without communication.
+
+        Args:
+            requires_gradient_sync (bool): Whether to reduce gradients for the
+                module's parameters.
+        """
+        replicate.state(self)._no_sync = not requires_gradient_sync  # type: ignore[arg-type]
+
+    def register_comm_hook(self, *args, **kwargs) -> None:
+        replicate.state(self)._comm_hook_args.append((args, kwargs))  # type: ignore[arg-type]
+
+
+@contract(state_cls=_ReplicateState)
+def replicate(
+    module: nn.Module,
+    ignored_modules: Iterable[torch.nn.Module] | None = None,
+    **kwargs,
+) -> nn.Module:
+    r"""Replicates a module
+
+    Args:
+        module (torch.nn.Module): module to replicate
+
+    Example::
+        >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d)
+        >>> module = nn.Linear(3, 3)
+        >>> replicate(module)
+    """
+    torch._C._log_api_usage_once("torch.distributed.replicate")
+
+    # TODO(fegin): using kwargs is not a good idea if we would like to make
+    # replicate a formal API to replace DDP.
+    if "device_id" in kwargs:
+        if not isinstance(kwargs["device_id"], (int, torch.device)):
+            raise RuntimeError(
+                "Expected device_id to be int or torch.device, "
+                f"but got {type(kwargs['device_id'])}"
+            )
+
+    if _is_fully_sharded(module):
+        raise RuntimeError(
+            "Cannot apply `replicate()` on a Module already managed by `fully_shard`"
+        )
+
+    if ignored_modules is None:
+        ignored_modules = {}
+    else:
+        ignored_modules = set(ignored_modules)
+
+    state = replicate.state(module)
+    module.register_forward_pre_hook(state.forward_pre_hook, with_kwargs=True)
+    device_mesh = kwargs.get("device_mesh")
+    if device_mesh is not None:
+        root_mesh = device_mesh._get_root_mesh()
+        # if a root mesh is not the same as device_mesh,
+        # meaning the device_mesh is sliced out from the root mesh.
+        if root_mesh != device_mesh:
+            # TODO: This is a temporary work around to enable DDP + TP.
+            # We should do the logic in DDP so that the 2D implementation is
+            # sound and the state_dict works out of the box.
+            #
+            # This won't conflict with what is done in DDP class as the module
+            # replicate is going to pass is NOT the original module.
+            from torch.distributed.tensor.parallel.ddp import (
+                _localize_dtensor,
+                _reconstruct_dtensor,
+            )
+
+            module.register_forward_pre_hook(_reconstruct_dtensor)
+            module.register_forward_hook(_localize_dtensor)
+
+    module.register_forward_hook(state.forward_post_hook)  # type: ignore[arg-type]
+
+    state.record_init_args(module, ignored_modules, **kwargs)
+
+    # Place DDP leftmost for highest priority in the method resolution order
+    cls = module.__class__
+    dct = {"__deepcopy__": unimplemented_deepcopy}
+    new_cls = type(f"DDP{cls.__name__}", (DDP, cls), dct)
+    module.__class__ = new_cls
+    return module
+
+
+def _is_fully_sharded(module: nn.Module) -> bool:
+    r"""Check if module is marked with fully_shard."""
+    registry = _get_registry(module)
+    if registry is None:
+        return False
+    return "fully_shard" in registry
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_composable/replicate_with_fsdp.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_composable/replicate_with_fsdp.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c242323bcac82a55198f6f768ff5bd60c01595f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_composable/replicate_with_fsdp.py
@@ -0,0 +1,408 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations
+
+import logging
+from typing import overload
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+from torch.distributed._composable_state import _get_module_state, _insert_module_state
+from torch.distributed.device_mesh import _get_device_handle
+from torch.distributed.fsdp._fully_shard._fsdp_api import (
+    MixedPrecisionPolicy,
+    OffloadPolicy,
+)
+from torch.distributed.fsdp._fully_shard._fsdp_common import (
+    DDPMeshInfo,
+    detect_compiled_autograd,
+)
+from torch.distributed.fsdp._fully_shard._fsdp_init import (
+    _get_device_from_mesh,
+    _get_managed_states,
+    _init_default_fully_shard_mesh,
+    _move_states_to_device,
+)
+from torch.distributed.fsdp._fully_shard._fsdp_param_group import FSDPParamGroup
+from torch.distributed.fsdp._fully_shard._fsdp_state import (
+    _register_group_forward_hooks,
+    FSDPState,
+)
+from torch.distributed.fsdp._fully_shard._fully_shard import (
+    _unimplemented_deepcopy,
+    FSDPModule,
+)
+from torch.distributed.tensor import DeviceMesh, init_device_mesh
+from torch.distributed.utils import _get_root_modules
+
+from .contract import _get_registry, contract
+
+
+cls_to_replicate_cls: dict[type, type] = {}
+
+_ROOT_MODULE_PREFIX = ""
+
+logger = logging.getLogger("torch.distributed._composable.replicate_with_fsdp")
+
+
+class _ReplicateStateContext:
+    """This has state shared across Replicate states."""
+
+    def __init__(self) -> None:
+        # All Replicate states in the root state's module tree
+        self.all_states: list[_ReplicateState] = []
+        # Iteration's forward root runs the once-per-forward logic; this root
+        # may not be the overall root set by lazy initialization in cases where
+        # only a submodule runs forward (e.g. encoder-only for eval)
+        self.iter_forward_root: _ReplicateState | None = None
+        # Final callback should only be queued once per backward
+        self.post_backward_final_callback_queued: bool = False
+        # Whether to finalize backward in this backward's final callback
+        self.is_last_backward: bool = True
+        # Optional user-provided event recorded after optimizer for the
+        # all-gather streams to wait on in the root pre-forward
+        self.post_optim_event: torch.Event | None = None
+
+
+def _get_module_replicate_state(module: nn.Module) -> _ReplicateState | None:
+    """Checks if module state is ReplicateState"""
+    state = _get_module_state(module)
+    if isinstance(state, _ReplicateState):
+        return state
+    return None
+
+
+class _ReplicateState(FSDPState):
+    """
+    Replicate state functionality is adapted from FSDP state.
+    In the future, could experiment with inheriting from it instead.
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+        self._state_ctx = _ReplicateStateContext()  # type: ignore[assignment]
+
+    # Define a separate init since `__init__` is called in the contract
+    def init(
+        self,
+        modules: tuple[nn.Module, ...],
+        device: torch.device,
+        mp_policy: MixedPrecisionPolicy,
+        auto_reshard_after_forward: bool = False,
+    ) -> None:
+        for module in modules:
+            _insert_module_state(module, self)
+        self._modules = modules
+        # pyrefly: ignore [read-only]
+        self._device = device
+        self._device_handle = _get_device_handle(device.type)
+        self._mp_policy = mp_policy
+        self._auto_reshard_after_forward = auto_reshard_after_forward
+        if len(modules) == 1:
+            self._pre_forward_hook_handle = modules[0].register_forward_pre_hook(
+                self._pre_forward, prepend=True, with_kwargs=True
+            )
+            self._post_forward_hook_handle = modules[0].register_forward_hook(
+                self._post_forward, prepend=False
+            )
+        else:
+            hook_handle = _register_group_forward_hooks(
+                modules,
+                self._pre_forward,
+                self._post_forward,
+                self._modules_to_run_forward,
+            )
+            self._pre_forward_hook_handle = hook_handle
+            self._post_forward_hook_handle = hook_handle
+
+    def _lazy_init(self) -> None:
+        """
+        Lazy initialization represents when all modules' parallelisms have
+        finalized (e.g. Replicate has been applied to all desired modules). This
+        means that we can determine which state is the root, and we do so by
+        the 1st state to run forward.
+        """
+        if self._is_root is not None:
+            return  # no-op: already initialized
+        self._is_root = True
+        if len(self._modules) > 1:
+            raise RuntimeError(
+                f"Replicate requires a single root module but got {self._modules}"
+            )
+        detect_compiled_autograd()
+        root_module = self._modules[0]
+        visited_states: set[_ReplicateState] = set()
+        for module_name, module in root_module.named_modules():
+            if (state := _get_module_replicate_state(module)) is None:
+                continue
+            if module is not root_module:
+                if state not in visited_states and state._is_root is not None:
+                    raise RuntimeError(
+                        "Replicate state has already been lazily initialized for "
+                        f"{module_name}\nReplicate requires running forward through "
+                        "the root module first"
+                    )
+                state._is_root = False
+            self._state_ctx.all_states.append(state)
+            # pyrefly: ignore [bad-argument-type]
+            visited_states.add(state)
+        if self._fsdp_param_group and self._auto_reshard_after_forward:
+            # For the root, do not reshard after forward since for training,
+            # the parameters would be freed and all-gathered immediately
+            self._fsdp_param_group.post_forward_mesh_info = None
+        self._init_fqns()
+        self._init_shared_state()
+        # Run parameter group lazy inits after initializing FQNs for improved
+        # error messages
+        for state in self._state_ctx.all_states:  # type: ignore[assignment]
+            if state._fsdp_param_group:  # type: ignore[union-attr]
+                state._fsdp_param_group.lazy_init()  # type: ignore[union-attr]
+
+
+def replicate_impl(
+    module,
+    mesh: DeviceMesh,
+    *,
+    device_id: int | torch.device | None = None,
+    mp_policy: MixedPrecisionPolicy = MixedPrecisionPolicy(),
+    offload_policy: OffloadPolicy = OffloadPolicy(),
+    ignored_params: set[nn.Parameter] | None = None,
+):
+    torch._C._log_api_usage_once("torch.distributed._composable.replicate_with_fsdp")
+    if isinstance(module, (nn.ModuleList, nn.ModuleDict)):
+        raise ValueError(
+            f"replicate does not support containers that do not implement forward: {module}"
+        )
+
+    mesh = mesh or _init_default_fully_shard_mesh()
+    if mesh.ndim != 1:
+        raise ValueError(f"replicate expects a 1D DeviceMesh but got {mesh}")
+
+    else:
+        if mesh.mesh_dim_names is None:
+            raise AssertionError(
+                "Please init the 2D mesh for HSDP with mesh_dim_names specified"
+            )
+        mesh_info = DDPMeshInfo(mesh, replicate_mesh_dim=0)
+    device = _get_device_from_mesh(mesh)
+
+    post_forward_mesh_info = None
+
+    arg_module = module
+    modules = (
+        (module,) if isinstance(module, nn.Module) else tuple(_get_root_modules(module))
+    )
+    state = replicate.state(modules[0])  # type: ignore[attr-defined] # see [1]
+    state.init(modules, device, mp_policy)
+
+    managed_modules = _get_managed_modules(modules, ignored_params)
+    params, buffers = _get_managed_states(managed_modules, ignored_params)
+
+    _move_states_to_device(params, buffers, device)
+    if params:
+        state._fsdp_param_group = FSDPParamGroup(
+            params,
+            modules,
+            mesh_info,  # type: ignore[arg-type]
+            post_forward_mesh_info,
+            device,
+            None,
+            mp_policy,
+            offload_policy,
+        )
+
+    # Place Replicate leftmost for highest priority in the method resolution order
+    for module in modules:
+        cls = module.__class__
+        new_cls = cls_to_replicate_cls.get(cls)
+        if not new_cls:
+            dct = {"__deepcopy__": _unimplemented_deepcopy}
+            new_cls = type(f"Replicate{cls.__name__}", (ReplicateModule, cls), dct)
+            cls_to_replicate_cls[cls] = new_cls
+        module.__class__ = new_cls
+    return arg_module
+
+
+@overload
+# pyrefly: ignore [inconsistent-overload]
+def replicate(
+    module: nn.Module,
+    *,
+    mesh: DeviceMesh | None = ...,
+    mp_policy: MixedPrecisionPolicy = ...,
+    offload_policy: OffloadPolicy = ...,
+    ignored_params: set[nn.Parameter] | None = ...,
+) -> ReplicateModule: ...
+
+
+@overload
+# pyrefly: ignore [inconsistent-overload]
+def replicate(
+    module: list[nn.Module],
+    *,
+    mesh: DeviceMesh | None = ...,
+    mp_policy: MixedPrecisionPolicy = ...,
+    offload_policy: OffloadPolicy = ...,
+    ignored_params: set[nn.Parameter] | None = ...,
+) -> list[ReplicateModule]: ...
+
+
+@contract(state_cls=_ReplicateState)  # type: ignore[misc]
+def replicate(
+    module: nn.Module,
+    *,
+    mesh: DeviceMesh | None = None,
+    mp_policy: MixedPrecisionPolicy = MixedPrecisionPolicy(),
+    offload_policy: OffloadPolicy = OffloadPolicy(),
+    ignored_params: set[nn.Parameter] | None = None,
+):
+    r"""Replicates a module
+
+    Args:
+        module (torch.nn.Module): module to replicate
+
+    Example::
+        >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d)
+        >>> module = nn.Linear(3, 3)
+        >>> replicate(module)
+    """
+
+    if not is_composable_with_replicate(module):
+        raise RuntimeError(
+            "Cannot apply `replicate()` on a Module already managed by `fully_shard`"
+        )
+
+    if mesh is None:
+        mesh = replicate_mesh()
+
+    return replicate_impl(
+        module,
+        mesh,
+        mp_policy=mp_policy,
+        offload_policy=offload_policy,
+        ignored_params=ignored_params,
+    )
+
+
+class ReplicateModule(FSDPModule):
+    def __new__(cls, *args, **kwargs):
+        """
+        Override ``__new__`` to remove the FSDP class and directly construct
+        the original class for cases like indexing into a container module.
+        """
+        # Use index 2 since 0 is the dynamically constructed `FSDP<...>` class
+        # and index 1 is the `FSDPModule` class itself
+        orig_cls = cls.__mro__[3]
+        self = orig_cls.__new__(orig_cls, *args, **kwargs)
+        self.__init__(*args, **kwargs)
+        return self
+
+
+def _get_managed_modules(
+    root_modules: tuple[nn.Module, ...],
+    ignored_params: set[nn.Parameter] | None = None,
+) -> list[nn.Module]:
+    modules: list[nn.Module] = []
+    root_modules_set = set(root_modules)
+    # Track visisted modules to avoid visiting shared modules multiple times
+    visited_modules: set[nn.Module] = set()
+
+    def dfs(module: nn.Module) -> None:
+        """
+        Runs a DFS to collect managed modules, not recursing into modules with
+        a non-composable API or ``replicate`` already applied.
+        """
+        if not is_composable_with_replicate(module):
+            return
+        elif (
+            module not in root_modules_set
+            and _get_module_replicate_state(module) is not None
+        ):
+            return  # nested `fully_shard` module
+        visited_modules.add(module)
+        for submodule in module.children():
+            if submodule not in visited_modules:
+                dfs(submodule)
+        modules.append(module)
+
+    for root_module in root_modules:
+        dfs(root_module)
+
+    if ignored_params is None:
+        return modules
+
+    adjusted_modules = _adjust_managed_modules(modules, ignored_params)
+    return adjusted_modules
+
+
+def is_composable_with_replicate(module: nn.Module) -> bool:
+    """Checks if replicate can be applied with module"""
+    registry = _get_registry(module)
+    if registry is None:
+        return True
+    # Registry keys by function name
+    return "fully_shard" not in registry
+
+
+def replicate_mesh():
+    """Creates a device mesh for replicate if the user doesn't provide one"""
+    if not dist.distributed_c10d.is_initialized():
+        dist.distributed_c10d.init_process_group()
+    default_pg = dist.distributed_c10d._get_default_group()
+    device = torch._C._get_accelerator()
+    mesh = init_device_mesh(
+        device.type,
+        mesh_shape=(default_pg.size(),),
+        mesh_dim_names=("replicate",),
+    )
+    return mesh
+
+
+def _adjust_managed_modules(
+    modules: list[nn.Module], ignored_params: set[nn.Parameter]
+) -> list[nn.Module]:
+    """
+    Adjust the given list of managed modules by removing those with all parameters ignored.
+    """
+    ignore_decision: dict[nn.Module, bool] = {}
+    new_modules = []
+    for module in modules:
+        ignored = _ignore_module(module, ignored_params, ignore_decision)
+        if not ignored:
+            new_modules.append(module)
+    return new_modules
+
+
+def _ignore_module(
+    module: nn.Module,
+    ignored_params: set[nn.Parameter],
+    ignore_decision: dict[nn.Module, bool],
+) -> bool:
+    """
+    Decide if it is safe to ignore a module for applying replicate.
+    """
+    if module in ignore_decision:
+        return ignore_decision[module]
+
+    if len(list(module.buffers(recurse=False))) > 0:
+        # Cannot ignore a module with any buffer
+        ignore_decision[module] = False
+        return False
+
+    for _, param in module.named_parameters(recurse=False):
+        if param not in ignored_params:
+            # at least one param is not ignored. So this module shouldn't be.
+            ignore_decision[module] = False
+            return False
+
+    # Need to consider descendants of module
+    for child in list(module.children()):
+        ignore_child = _ignore_module(child, ignored_params, ignore_decision)
+        if not ignore_child:
+            # Cannot ignore module if one of its children is not ignored
+            ignore_decision[module] = False
+            return False
+
+    # Safe to ignore module
+    ignore_decision[module] = True
+    return True
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_composable_state.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_composable_state.py
new file mode 100644
index 0000000000000000000000000000000000000000..b91797536ec7fb969e9ad2c57cbbe9b7e0bd181c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_composable_state.py
@@ -0,0 +1,46 @@
+import weakref
+from typing import cast
+
+import torch.nn as nn
+
+
+class _State:
+    pass
+
+
+_module_state_mapping: weakref.WeakKeyDictionary[
+    nn.Module, weakref.ReferenceType[_State]
+] = weakref.WeakKeyDictionary()
+
+
+def _insert_module_state(module: nn.Module, state: _State) -> None:
+    global _module_state_mapping
+    if module in _module_state_mapping:
+        raise AssertionError(f"Inserting {module} more than once.")
+    _module_state_mapping[module] = weakref.ref(state)
+
+
+def _get_module_state(module: nn.Module) -> _State | None:
+    """
+    Return the ``_State`` in ``model``.
+
+    Given a ``module``, this API finds out if the module is also a ``_State``
+    instance or if the module is managed by a composable API. If the module
+    is also a ``_State``, ``module`` will be casted to ``_State` and returned.
+    If it is managed by a composable API, the corresponding ``_State`` will
+    be returned.
+    """
+    global _module_state_mapping
+    if isinstance(module, _State):
+        # pyrefly: ignore [redundant-cast]
+        return cast(_State, module)
+    else:
+        # https://github.com/pytorch/pytorch/issues/107054
+        if module in _module_state_mapping:
+            state_ref = _module_state_mapping[module]
+            state = state_ref()
+            if state is None:
+                raise AssertionError("State has already been garbage collected")
+            return state
+        else:
+            return None
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_dist2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_dist2.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ec53372c4d62bd24de7956ef95e1c033c3b3bd7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_dist2.py
@@ -0,0 +1,183 @@
+"""
+This is an experimental new API for PyTorch Distributed. This is actively in development and subject to change or deletion entirely.
+
+This is intended as a proving ground for more flexible and object oriented distributed APIs.
+"""
+
+from collections.abc import Generator
+from contextlib import contextmanager
+from datetime import timedelta
+from typing import Protocol
+
+import torch
+from torch._C._distributed_c10d import (
+    _current_process_group,
+    _set_process_group,
+    ProcessGroup,
+    ReduceOp,
+    Store,
+)
+from torch.distributed.rendezvous import rendezvous
+
+
+_BACKENDS: dict[str, "ProcessGroupFactory"] = {}
+
+__all__ = [
+    "ProcessGroup",
+    "ReduceOp",
+    "ProcessGroupFactory",
+    "register_backend",
+    "new_group",
+    "current_process_group",
+    "process_group",
+]
+
+
+class ProcessGroupFactory(Protocol):
+    """Protocol for process group factories."""
+
+    def __call__(
+        self,
+        store: Store,
+        rank: int,
+        world_size: int,
+        timeout: timedelta,
+        device: torch.device,
+        **kwargs: object,
+    ) -> ProcessGroup: ...
+
+
+def register_backend(name: str, func: ProcessGroupFactory) -> None:
+    """
+    Register a new process group backend.
+
+    Args:
+        name: The name of the backend.
+        func: The function to create the process group.
+    """
+    if name in _BACKENDS:
+        raise ValueError(f"Backend {name} already registered")
+
+    _BACKENDS[name] = func
+
+
+def _gloo_factory(
+    store: Store,
+    rank: int,
+    world_size: int,
+    timeout: timedelta,
+    device: torch.device,
+    **kwargs: object,
+) -> ProcessGroup:
+    from torch.distributed import ProcessGroupGloo
+
+    if len(kwargs) != 0:
+        raise AssertionError("Gloo backend received unexpected kwargs")
+
+    backend_class = ProcessGroupGloo(store, rank, world_size, timeout)
+    backend_class._set_sequence_number_for_group()
+
+    pg = ProcessGroup(store, rank, world_size)
+    pg._set_default_backend(ProcessGroup.BackendType.GLOO)
+
+    # register devices
+    pg._register_backend(device, ProcessGroup.BackendType.GLOO, backend_class)
+    pg._register_backend(
+        torch.device("cpu"), ProcessGroup.BackendType.GLOO, backend_class
+    )
+    if torch.cuda.is_available():
+        pg._register_backend(
+            torch.device("cuda"), ProcessGroup.BackendType.GLOO, backend_class
+        )
+    return pg
+
+
+def _nccl_factory(
+    store: Store,
+    rank: int,
+    world_size: int,
+    timeout: timedelta,
+    device: torch.device,
+    **kwargs: object,
+) -> ProcessGroup:
+    from torch.distributed import ProcessGroupNCCL
+
+    opts = ProcessGroupNCCL.Options()
+    opts._timeout = timeout
+    for k, v in kwargs.items():
+        if not hasattr(opts, k):
+            raise KeyError(f"Unknown option {k}")
+        setattr(opts, k, v)
+
+    backend_class = ProcessGroupNCCL(store, rank, world_size, opts)
+    backend_class._set_sequence_number_for_group()
+    backend_class.eager_connect_single_device(device)
+
+    pg = ProcessGroup(store, rank, world_size)
+    pg._set_default_backend(ProcessGroup.BackendType.NCCL)
+    pg._register_backend(device, ProcessGroup.BackendType.NCCL, backend_class)
+
+    return pg
+
+
+register_backend("gloo", _gloo_factory)
+register_backend("nccl", _nccl_factory)
+
+
+def new_group(
+    backend: str,
+    timeout: timedelta,
+    device: str | torch.device,
+    **kwargs: object,
+) -> ProcessGroup:
+    """
+    Create a new process group with the given backend and options. This group is
+    independent and will not be globally registered and thus not usable via the
+    standard torch.distributed.* APIs.
+
+    Args:
+        backend: The backend to use for the process group.
+        timeout: The timeout for collective operations.
+        device: The device to use for the process group.
+        **kwargs: All remaining arguments are passed to the backend constructor.
+                  See the backend specific documentation for details.
+
+    Returns:
+        A new process group.
+    """
+    if backend not in _BACKENDS:
+        raise ValueError(f"Backend {backend} not registered")
+
+    device = torch.device(device)
+
+    store, rank, world_size = next(iter(rendezvous("env://")))
+    store.set_timeout(timeout)
+
+    return _BACKENDS[backend](store, rank, world_size, timeout, device, **kwargs)
+
+
+def current_process_group() -> ProcessGroup:
+    """
+    Get the current process group. Thread local method.
+
+    Returns:
+        The current process group.
+    """
+    return _current_process_group()
+
+
+@contextmanager
+def process_group(pg: ProcessGroup) -> Generator[None, None, None]:
+    """
+    Context manager for process groups. Thread local method.
+
+    Args:
+        pg: The process group to use.
+    """
+    prev_pg = current_process_group()
+
+    _set_process_group(pg)
+    try:
+        yield
+    finally:
+        _set_process_group(prev_pg)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_functional_collectives.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_functional_collectives.py
new file mode 100644
index 0000000000000000000000000000000000000000..24d7d5cf2748bd70b9df1a54895da03870185192
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_functional_collectives.py
@@ -0,0 +1,1251 @@
+# mypy: allow-untyped-defs
+import contextlib
+import sys
+import warnings
+from typing import Any, cast, TYPE_CHECKING, Union
+
+import torch
+import torch.distributed as dist
+import torch.distributed.distributed_c10d as c10d
+from torch.distributed.device_mesh import DeviceMesh
+from torch.fx.experimental.proxy_tensor import get_proxy_mode
+
+from . import _functional_collectives_impl as fun_col_impl
+
+
+try:
+    from torch.utils._cxx_pytree import tree_map_only
+except ImportError:
+    from torch.utils._pytree import tree_map_only  # type: ignore[no-redef]
+
+
+try:
+    from torch.compiler import is_dynamo_compiling as is_torchdynamo_compiling
+except Exception:
+    warnings.warn(
+        "Unable to import torchdynamo util `is_torchdynamo_compiling`, so won't support torchdynamo correctly",
+        stacklevel=2,
+    )
+
+    def is_torchdynamo_compiling():  # type: ignore[misc]
+        return False
+        return False
+
+
+"""
+New traceable, functional collectives.
+RFC: https://github.com/pytorch/pytorch/issues/93173
+
+  compiler: trace these ops with plain-old-data schemas, then choose how to lower them.
+  eager: execute these 'functional' ops which in eager return AsyncCollectiveTensor subclasses,
+         automatically calling .wait() on underlying/hidden async 'work' obj only when fed to
+         a downstream op.
+
+Issues:
+* Where should these ops live? Couldn't `import torch` if putting these ops in existing torch.distributed files
+* Proper support for eager requires inplace ops. We should explore having it as an option for the API.
+"""
+
+"""
+Functional collectives are asynchronous only and we perform implicit stream synchronization
+on behalf of the user.
+
+We use AsyncCollectiveTensor to wrap the result tensor of a collective and it lets us witness
+first usage of the tensor and insert cross stream sync at the right place.
+
+The above are the easy bits, the hard one is how we match the Work object returned by
+c10d and the tensor AsyncCollectiveTensor wraps. We alloc the tensor inside the collective
+op implementation (see ``clone()`` call in ``_all_reduce``) and then it's handled by the
+dispatcher which might call other implementations that are allowed to change the returned
+tensor - even return a tensor with a different shape (see ``torch.vmap``).
+
+This means the caller of our ops receives a Tensor that is not guaranteed to be the same
+allocated by our implementations and that makes pairing The AsyncTensor to the original
+tensor a lot harder. This pairing is needed so we can lookup the Work object to use.
+
+Originally, we tried WeakKeyDictionary to map from Tensor to Work, but because Tensor's
+identity is not stable across dispatch, the op caller would end up with a different Tensor
+instance that would not match any in the dictionary.
+
+With Tensor identity out of the question, we decided use the tensor data pointer, which
+should be stable across all the Tensor changes done during dispatch.
+
+We have a dictionary of tensor::data_ptr -> Work that we insert right after we call into c10d.
+
+We use this dictionary when AsyncCollectiveTensor is used to invoke Work::wait()
+
+Finally, we setup a finalizer against the tensor wrapper to observe it getting collected so we
+can clean up stale entries in the dictionary.
+
+To eliminate the possibility of races we have a global version counter that is used by the finalizer.
+
+As a wise man said once: Don't cross the streams (https://www.youtube.com/watch?v=wyKQe_i9yyo)
+
+"""
+
+"""
+Functional collectives can accept any of these types to describe the ranks participating in collectives.
+
+The different types will be desugared to a canonical format
+"""
+RANK_TYPES = Union[
+    list[int],
+    list[list[int]],
+    dist.ProcessGroup,
+    DeviceMesh,
+    tuple["dist.tensor.DeviceMesh", int],
+    c10d.GroupName,
+]
+
+
+"""
+User facing APIs for functional collectives
+-------------------------------------------
+
+These apis are called by user code and expected to work both in eager execution and compilation,
+but there are significant differences to how the two modes are implemented underneath.
+
+Eager execution is 'optimized' using a tensor subclass that schedules the synchronization (via wait_tensor() op)
+just before the tensor is first used.  Compiled tracing currently relies on the compiler to perform this optimization,
+and cannot yet correctly trace the AsyncTensor wrapper class.  In the future, these paths may be unified
+if sufficient subclass support is added in dynamo.
+
+Example: all_reduce is an entrypoint API, and other collectives follow a similar pattern.
+
+Here's how it works under torch.compile/dynamo:
+all_reduce(...)
+  |--> _expand_group(...)               - desugars processgroup into canonical/traceable format
+  |--> c10d_functional.all_reduce(...)  - dynamo captures this op call, doesn't trace deeper
+  |--> _maybe_wrap_tensor(...)          - wait_tensor() op is immediately called, no AsyncTensor subclass needed
+
+And under eager execution:
+all_reduce(...)
+  |--> _expand_group(...)               - same as above, but less critical for eager
+  |--> c10d_functional.all_reduce(...)  - dispatches to real kernel OR records op in trace
+  |--> _maybe_wrap_tensor(...)          - AsyncTensor wrapper applied to returned tensor,
+                                          which issues wait_tensor() at the time of first use
+"""
+
+
+def wait_tensor(tensor):
+    """
+    Wait on a tensor returned by the collectives ops.
+
+    Waiting follows device semantics, which means blocking on CPU and synchronizing streams on CUDA.
+    """
+    return torch.ops._c10d_functional.wait_tensor(tensor)  # type: ignore[attr-defined]
+
+
+def broadcast(self: torch.Tensor, src: int, group: RANK_TYPES, tag: str = ""):
+    """
+    Broadcasts the tensor to all processes in the given process group.
+
+    Args:
+        src (int): Source rank
+        group (ProcessGroup or List[int]): The process group to work on.
+        tag (str, optional): A unique identifier for the collective. Default: empty string
+    """
+    group_name = _resolve_group_name(group, tag)
+    tensor = torch.ops._c10d_functional.broadcast(self, src, group_name)
+    return _maybe_wrap_tensor(tensor)
+
+
+def all_reduce(self: torch.Tensor, reduceOp: str, group: RANK_TYPES, tag: str = ""):
+    """
+    Reduces the tensor data across all machines in such a way that all get
+    the final result.
+
+    The input tensor is left unmodified.
+
+    Group can be one of:
+        List[int]: ranks participating in the collective.
+        List[List[int]]: 2D mesh of ranks taking part of this collective in MPMD.
+        ProcessGroup: Will perform a collective using the ranks and tag of the PG.
+        DeviceMesh: Do a SPMD collective over all ranks of the mesh
+        (DeviceMesh, int): Do a MPMD collective over one dimension of the DeviceMesh
+
+    :: N.B. If you pass a PG or a 1D list to perform a MPMD collective, the compiler won't be able to recover
+    that information and perform collective algebraic optimization. Use other forms of input for that.
+    """
+    group_name = _resolve_group_name(group, tag)
+    tensor = torch.ops._c10d_functional.all_reduce(self, reduceOp.lower(), group_name)
+    return _maybe_wrap_tensor(tensor)
+
+
+def all_gather_tensor(
+    self: torch.Tensor,
+    gather_dim: int,
+    group: RANK_TYPES,
+    tag: str = "",
+) -> torch.Tensor:
+    """
+    Gather tensor data across from all machines and concatenate over ``gather_dim``.
+
+    Note that it currently only supports gather_dim = 0.
+
+    The input tensor is left unmodified.
+    Group can be one of:
+        List[int]: ranks participating in the collective.
+        List[List[int]]: 2D mesh of ranks taking part of this collective in MPMD.
+        ProcessGroup: Will perform a collective using the ranks and tag of the PG.
+        DeviceMesh: Do a SPMD collective over all ranks of the mesh
+        (DeviceMesh, int): Do a MPMD collective over one dimension of the DeviceMesh
+
+    :: N.B. If you pass a PG or a 1D list to perform a MPMD collective, the compiler won't be able to recover
+    that information and perform collective algebraic optimization. Use other forms of input for that.
+    """
+    if not self.is_contiguous():
+        raise AssertionError("Tensor must be contiguous for all_gather_tensor")
+    group_name = _resolve_group_name(group, tag)
+    group_size = c10d._get_group_size_by_name(group_name)
+    tensor = torch.ops._c10d_functional.all_gather_into_tensor(
+        self, group_size, group_name
+    )
+    res = _maybe_wrap_tensor(tensor)
+    # TODO this should be done inside AsyncCollectiveTensor to delay the wait() call
+    if gather_dim != 0:
+        # torch.cat access the data so we already need to wait here, first do wait
+        # and then chunk + cat avoid us going through ACT dispatching logic again
+        if isinstance(res, AsyncCollectiveTensor):
+            res = res.wait()  # type: ignore[attr-defined]
+        res = torch.cat(torch.chunk(res, group_size, dim=0), dim=gather_dim)
+    return res
+
+
+def all_gather_tensor_autograd(
+    self: torch.Tensor,
+    gather_dim: int,
+    group: RANK_TYPES,
+    tag: str = "",
+):
+    """
+    Gather tensor data across from all machines and concatenate over ``gather_dim``.
+
+    Note that it currently only supports gather_dim = 0.
+
+    This function is the same as all_gather_tensor but will propagate the
+    backwards gradient across workers.
+
+    See all_gather_tensor for more details on usage.
+    """
+    group_name = _resolve_group_name(group, tag)
+    group_size = c10d._get_group_size_by_name(group_name)
+
+    tensor = torch.ops._c10d_functional_autograd.all_gather_into_tensor(
+        self, group_size, group_name
+    )
+    res = _FromTorchTensor.apply(tensor)
+    # TODO this should be done inside AsyncCollectiveTensor to delay the wait() call
+    if gather_dim != 0:
+        # torch.cat access the data so we already need to wait here, first do wait
+        # and then chunk + cat avoid us going through ACT dispatching logic again
+        if isinstance(res, AsyncCollectiveTensor):
+            res = res.wait()  # type: ignore[attr-defined]
+        res = torch.cat(torch.chunk(res, group_size, dim=0), dim=gather_dim)
+    return res
+
+
+def reduce_scatter_tensor(
+    self: torch.Tensor,
+    reduceOp: str,
+    scatter_dim: int,
+    group: RANK_TYPES,
+    tag: str = "",
+):
+    """
+    Reduces the tensor data across all machines in such a way that all get
+    the final result, then scatter the results to corresponding ranks.
+
+
+    The input tensor is left unmodified.
+    Group can be one of:
+        List[int]: ranks participating in the collective.
+        List[List[int]]: 2D mesh of ranks taking part of this collective in MPMD.
+        ProcessGroup: Will perform a collective using the ranks and tag of the PG.
+        DeviceMesh: Do a SPMD collective over all ranks of the mesh
+        (DeviceMesh, int): Do a MPMD collective over one dimension of the DeviceMesh
+    :: N.B. If you pass a PG or a 1D list to perform a MPMD collective, the compiler won't be able to recover
+    that information and perform collective algebraic optimization. Use other forms of input for that.
+    """
+    group_name = _resolve_group_name(group, tag)
+    group_size = c10d._get_group_size_by_name(group_name)
+
+    if self.size(scatter_dim) % group_size != 0:
+        raise AssertionError(
+            f"input dimension 0 ({self.size(0)} must be a multiple of group_size {group_size})"
+        )
+    if scatter_dim != 0:
+        tensor_list = torch.chunk(self, group_size, dim=scatter_dim)
+        self = torch.cat(tensor_list)
+
+    tensor = torch.ops._c10d_functional.reduce_scatter_tensor(
+        self,
+        reduceOp.lower(),
+        group_size,
+        group_name,  # type: ignore[possibly-undefined]
+    )
+    res = _maybe_wrap_tensor(tensor)
+    return res
+
+
+def reduce_scatter_tensor_autograd(
+    self: torch.Tensor,
+    reduceOp: str,
+    scatter_dim: int,
+    group: RANK_TYPES,
+    tag: str = "",
+):
+    """
+    Reduces the tensor data across all machines in such a way that all get
+    the final result, then scatter the results to corresponding ranks.
+
+    This function is the same as reduce_scatter_tensor but will propagate the
+    backwards gradient across workers.
+
+    Currently only the "sum" reduceOp is supported.
+
+    See reduce_scatter_tensor for more details on usage.
+    """
+
+    group_name = _resolve_group_name(group, tag)
+    group_size = c10d._get_group_size_by_name(group_name)
+
+    if self.size(scatter_dim) % group_size != 0:
+        raise AssertionError(
+            f"input dimension 0 ({self.size(0)} must be a multiple of group_size {group_size}"
+        )
+    if scatter_dim != 0:
+        tensor_list = torch.chunk(self, group_size, dim=scatter_dim)
+        self = torch.cat(tensor_list)
+
+    tensor = torch.ops._c10d_functional_autograd.reduce_scatter_tensor(
+        self,
+        reduceOp.lower(),
+        group_size,
+        group_name,  # type: ignore[possibly-undefined]
+    )
+    res = _FromTorchTensor.apply(tensor)
+    return res
+
+
+def all_reduce_coalesced(
+    self: list[torch.Tensor], reduceOp: str, group: RANK_TYPES, tag: str = ""
+) -> list[torch.Tensor]:
+    """
+    Reduces a list of tensors across all machines in such a way that all get
+    the final result.
+
+    The all tensors in the input list are left unmodified.
+
+    Group can be one of:
+        List[int]: ranks participating in the collective.
+        List[List[int]]: 2D mesh of ranks taking part of this collective in MPMD.
+        ProcessGroup: Will perform a collective using the ranks and tag of the PG.
+        DeviceMesh: Do a SPMD collective over all ranks of the mesh
+        (DeviceMesh, int): Do a MPMD collective over one dimension of the DeviceMesh
+
+    :: N.B. If you pass a PG or a 1D list to perform a MPMD collective, the compiler won't be able to recover
+    that information and perform collective algebraic optimization. Use other forms of input for that.
+    """
+    group_name = _resolve_group_name(group, tag)
+    tensor_list = torch.ops._c10d_functional.all_reduce_coalesced(  # type: ignore[attr-defined]
+        self,
+        reduceOp.lower(),
+        group_name,
+    )
+    return list(map(_maybe_wrap_tensor, tensor_list))
+
+
+def all_gather_into_tensor_coalesced(
+    self: list[torch.Tensor], group: RANK_TYPES, tag: str = ""
+) -> list[torch.Tensor]:
+    """
+    Gather a list of tensors across from all machines.
+
+    Note that it currently only supports gather_dim = 0.
+
+    The input tensor is left unmodified.
+    Group can be one of:
+        List[int]: ranks participating in the collective.
+        List[List[int]]: 2D mesh of ranks taking part of this collective in MPMD.
+        ProcessGroup: Will perform a collective using the ranks and tag of the PG.
+        DeviceMesh: Do a SPMD collective over all ranks of the mesh
+        (DeviceMesh, int): Do a MPMD collective over one dimension of the DeviceMesh
+
+    :: N.B. If you pass a PG or a 1D list to perform a MPMD collective, the compiler won't be able to recover
+    that information and perform collective algebraic optimization. Use other forms of input for that.
+    """
+    group_name = _resolve_group_name(group, tag)
+    group_size = c10d._get_group_size_by_name(group_name)
+    tensor_list = torch.ops._c10d_functional.all_gather_into_tensor_coalesced(  # type: ignore[attr-defined]
+        self,
+        group_size,
+        group_name,
+    )
+    return list(map(_maybe_wrap_tensor, tensor_list))
+
+
+def reduce_scatter_tensor_coalesced(
+    inputs: list[torch.Tensor],
+    reduceOp: str,
+    scatter_dim: list[int],
+    group: RANK_TYPES,
+    tag: str = "",
+) -> list[torch.Tensor]:
+    """
+    Reduces a list of tensors across all machines in such a way that all get
+    the final result, then scatter the results to corresponding ranks.
+
+    The input tensors are left unmodified.
+    Group can be one of:
+        List[int]: ranks participating in the collective.
+        List[List[int]]: 2D mesh of ranks taking part of this collective in MPMD.
+        ProcessGroup: Will perform a collective using the ranks and tag of the PG.
+        DeviceMesh: Do a SPMD collective over all ranks of the mesh
+        (DeviceMesh, int): Do a MPMD collective over one dimension of the DeviceMesh
+
+    :: N.B. If you pass a PG or a 1D list to perform a MPMD collective, the compiler won't be able to recover
+    that information and perform collective algebraic optimization. Use other forms of input for that.
+    """
+    group_name = _resolve_group_name(group, tag)
+    group_size = c10d._get_group_size_by_name(group_name)
+
+    if len(scatter_dim) != len(inputs):
+        raise AssertionError(
+            f"Length of scatter_dim ({len(scatter_dim)}) must equal length of inputs ({len(inputs)})"
+        )
+    for idx, (dim, tensor) in enumerate(zip(scatter_dim, inputs)):
+        if tensor.size(dim) % group_size != 0:
+            raise AssertionError(
+                f"input dimension {dim} ({tensor.size(dim)} must be a multiple of group_size {group_size} for tensor at index {idx}"
+            )
+        if dim != 0:
+            tensor_list = torch.chunk(tensor, group_size, dim=dim)
+            inputs[idx] = torch.cat(tensor_list)
+
+    tensor_list = torch.ops._c10d_functional.reduce_scatter_tensor_coalesced(  # type: ignore[attr-defined]
+        inputs,
+        reduceOp.lower(),
+        group_size,
+        group_name,  # type: ignore[possibly-undefined]
+    )
+
+    return list(map(_maybe_wrap_tensor, tensor_list))
+
+
+# This is a bit unsafe: it checks if the first argument in the schema reports as a non-mutable alias.
+# Today, this maps 1:1 with "aten ops that are views".
+def _is_view_op(tgt):
+    if not isinstance(tgt, torch._ops.OpOverload):
+        raise AssertionError(f"Expected torch._ops.OpOverload, got {type(tgt)}")
+    # Don't apply the view optimization to any `CompositeImplicitAutograd` ops.
+    # See issue: https://github.com/pytorch/pytorch/issues/133421
+    if torch._C._dispatch_has_kernel_for_dispatch_key(
+        tgt.name(), torch.DispatchKey.CompositeImplicitAutograd
+    ):
+        return False
+    schema = tgt._schema
+    if len(schema.arguments) > 0:
+        first_arg = schema.arguments[0]
+        # check if op is a view
+        return first_arg.alias_info is not None and not first_arg.alias_info.is_write
+
+
+def all_to_all_single(
+    self: torch.Tensor,
+    output_split_sizes: list[int] | None,
+    input_split_sizes: list[int] | None,
+    group: RANK_TYPES,
+    tag: str = "",
+) -> torch.Tensor:
+    """
+    Each process splits input tensor and then scatters the split list
+    to all processes in a group. Then concatenate the received tensors from all
+    the processes in the group and return single output tensor.
+
+    Group can be one of:
+        List[int]: ranks participating in the collective.
+        List[List[int]]: 2D mesh of ranks taking part of this collective in MPMD.
+        ProcessGroup: Will perform a collective using the ranks and tag of the PG.
+        DeviceMesh: Do a SPMD collective over all ranks of the mesh
+        (DeviceMesh, int): Do a MPMD collective over one dimension of the DeviceMesh
+
+    :: N.B. If you pass a PG or a 1D list to perform a MPMD collective, the compiler won't be able to recover
+    that information and perform collective algebraic optimization. Use other forms of input for that.
+    """
+    if output_split_sizes is not None:
+        if not all(
+            isinstance(size, (int, torch.SymInt)) for size in output_split_sizes
+        ):
+            raise AssertionError(
+                f"All output_split_sizes must be int or SymInt, got {output_split_sizes}"
+            )
+    if input_split_sizes is not None:
+        if not all(isinstance(size, (int, torch.SymInt)) for size in input_split_sizes):
+            raise AssertionError(
+                f"All input_split_sizes must be int or SymInt, got {input_split_sizes}"
+            )
+    group_name = _resolve_group_name(group, tag)
+    group_size = c10d._get_group_size_by_name(group_name)
+    if output_split_sizes is None or input_split_sizes is None:
+        if not (output_split_sizes is None and input_split_sizes is None):
+            raise AssertionError(
+                "output_split_sizes and input_split_sizes must either be "
+                "specified together or both set to None"
+            )
+        output_split_sizes = [self.shape[0] // group_size] * group_size
+        input_split_sizes = output_split_sizes
+    tensor = torch.ops._c10d_functional.all_to_all_single(  # type: ignore[attr-defined]
+        self,
+        output_split_sizes,
+        input_split_sizes,
+        group_name,
+    )
+    return _maybe_wrap_tensor(tensor)
+
+
+def all_to_all_single_autograd(
+    self: torch.Tensor,
+    output_split_sizes: list[int] | None,
+    input_split_sizes: list[int] | None,
+    group: RANK_TYPES,
+    tag: str = "",
+) -> torch.Tensor:
+    """
+    Same as all_to_all_single but supports autograd.
+    """
+    if output_split_sizes is not None:
+        if not all(
+            isinstance(size, (int, torch.SymInt)) for size in output_split_sizes
+        ):
+            raise AssertionError(
+                f"All output_split_sizes must be int or SymInt, got {output_split_sizes}"
+            )
+    if input_split_sizes is not None:
+        if not all(isinstance(size, (int, torch.SymInt)) for size in input_split_sizes):
+            raise AssertionError(
+                f"All input_split_sizes must be int or SymInt, got {input_split_sizes}"
+            )
+
+    group_name = _resolve_group_name(group, tag)
+    group_size = c10d._get_group_size_by_name(group_name)
+    if output_split_sizes is None or input_split_sizes is None:
+        if not (output_split_sizes is None and input_split_sizes is None):
+            raise AssertionError(
+                "output_split_sizes and input_split_sizes must either be "
+                "specified together or both set to None"
+            )
+        output_split_sizes = [self.shape[0] // group_size] * group_size
+        input_split_sizes = output_split_sizes
+    tensor = torch.ops._c10d_functional_autograd.all_to_all_single(  # type: ignore[attr-defined]
+        self,
+        output_split_sizes,
+        input_split_sizes,
+        group_name,
+    )
+    return _FromTorchTensor.apply(tensor)
+
+
+def permute_tensor(
+    self: torch.Tensor,
+    src_dst: list[int],
+    group: RANK_TYPES,
+    tag: str = "",
+) -> torch.Tensor:
+    """
+    Permutes the elements of the tensor according to the given source/destination pairs. `src_dst` should
+    be defined such that src_dst[m] == n means m sends to n.
+
+    Group can be one of:
+        List[int]: ranks participating in the collective.
+        List[List[int]]: 2D mesh of ranks taking part of this collective in MPMD.
+        ProcessGroup: Will perform a collective using the ranks and tag of the PG.
+        DeviceMesh: Do a SPMD collective over all ranks of the mesh
+        (DeviceMesh, int): Do a MPMD collective over one
+    """
+    t, rankset, group_size = _expand_group(group, tag)
+    local_pg = c10d._find_or_create_pg_by_ranks_and_tag(t, rankset, group_size)
+
+    output_split_sizes = [0] * group_size
+    input_split_sizes = [0] * group_size
+    for src, dst in enumerate(src_dst):
+        if src == dist.get_rank(local_pg):
+            input_split_sizes[dst] = self.numel()
+        if dst == dist.get_rank(local_pg):
+            output_split_sizes[src] = self.numel()
+
+    return all_to_all_single(self, output_split_sizes, input_split_sizes, group, tag)
+
+
+class AsyncCollectiveTensor(torch.Tensor):
+    r"""
+    A Tensor wrapper subclass that is used to trigger a call to wait
+    prior to first use of the underlying tensor.
+    Use it inside functional collective pytorch wrappers like the following:
+    def functional_collective(self, group, tag):
+        tag, rankset, group_size = _expand_group(group, tag)
+        tensor = torch.ops.c10d_functional.{collective}(self, tag, rankset, group_size)
+        return _maybe_wrap_tensor(tensor)
+    """
+
+    elem: torch.Tensor
+    completed: bool
+
+    __slots__ = ["elem", "completed"]
+
+    @staticmethod
+    def __new__(cls, elem: torch.Tensor):
+        r = torch.Tensor._make_wrapper_subclass(
+            cls,
+            elem.size(),
+            strides=elem.stride(),
+            storage_offset=elem.storage_offset(),
+            dtype=elem.dtype,
+            layout=elem.layout,
+            device=elem.device,
+            requires_grad=elem.requires_grad,
+        )
+        r.elem = elem
+        r.completed = False
+        return r
+
+    def __tensor_flatten__(self):
+        return ["elem"], None
+
+    def tolist(self):
+        return self.trigger_wait().tolist()
+
+    @staticmethod
+    def __tensor_unflatten__(inner_tensors, meta, outer_size, outer_stride):
+        if meta is not None:
+            raise AssertionError(
+                "meta must be None for AsyncCollectiveTensor unflatten"
+            )
+        elem = inner_tensors["elem"]
+        return AsyncCollectiveTensor(elem)
+
+    def __coerce_same_metadata_as_tangent__(
+        self, expected_metadata: Any, expected_type: type | None = None
+    ):
+        if expected_type is not torch.Tensor:
+            return None
+
+        return self.trigger_wait()
+
+    def __repr__(self) -> str:  # type: ignore[override]
+        return f"AsyncCollectiveTensor({self.trigger_wait()})"
+
+    def trigger_wait(self):
+        if not self.completed:
+            out = wait_tensor(self.elem)
+            self.completed = True
+            return out
+        else:
+            return self.elem
+
+    def wait(self) -> torch.Tensor:
+        return wait_tensor(self.elem)
+
+    def _get_acs_underlying_tensor(self):
+        """This method enables  _functional_collectives_impl to test if a tensor is an ACS"""
+        return self.elem
+
+    @classmethod
+    def __torch_dispatch__(cls, func, types, args=(), kwargs=None):  # type: ignore[override]
+        if func is torch.ops.aten.view.default:
+            # Fast handle aten.view as a lot of view related op goes to aten.view
+            # eventually, this avoids pytree slowdown
+            # pyrefly: ignore [index-error]
+            res = func(args[0].elem, args[1])
+            wrapper_res = AsyncCollectiveTensor(res)
+            return wrapper_res
+
+        is_view_op = _is_view_op(func)
+
+        def unwrap(e: AsyncCollectiveTensor):
+            # wait_tensor is idepotent and will do stream sync only once
+            if not is_view_op:
+                return e.trigger_wait()
+            return e.elem
+
+        def wrap(e: torch.Tensor):
+            # wait_tensor is idepotent and will do stream sync only once
+            if isinstance(e, AsyncCollectiveTensor):
+                raise AssertionError(
+                    "Cannot wrap an AsyncCollectiveTensor inside another AsyncCollectiveTensor"
+                )
+            res = AsyncCollectiveTensor(e)
+            return res
+
+        unwrapped_args = tree_map_only(AsyncCollectiveTensor, unwrap, args)
+        unwrapped_kwargs = tree_map_only(AsyncCollectiveTensor, unwrap, kwargs)
+
+        # we don't wrap the result as it doesn't need to be waited on.
+        out = func(*unwrapped_args, **unwrapped_kwargs)
+
+        # View ops dont require a sync, so we should re-wrap the outputs.
+        if is_view_op:
+            out = tree_map_only(torch.Tensor, wrap, out)
+
+        return out
+
+    def numpy(self):  # type: ignore[override]
+        return self.wait().numpy()
+
+
+"""
+Utils and infrastructure for tracing support
+"""
+
+
+def _expand_group(group: RANK_TYPES, tag: str = "") -> tuple[str, list[int], int]:
+    """
+    _expand_group desugars the different RANK_TYPES types into a canonical format that is traceable.
+
+    By having this be part of the explicit eager codepath, we avoid having to specialize behavior inside
+    torchdynamo and can still interoperate with processgroup objects or other untraceable forms.
+    """
+    # had to define this hack _inside_ expand_group to avoid
+    # graph_break [('torch.* op returned non-Tensor int
+    # caused by 'cast_*` functions being treated as 'torch.*' ops (iiuc)
+    if TYPE_CHECKING:
+
+        def cast_listlistint(x):
+            return cast(list[list[int]], x)
+
+        def cast_listint(x):
+            return cast(list[int], x)
+
+    else:
+        # fake cast op for use at runtime since dynamo doesn't support real cast
+        # also, dynamo didn't like encountering 'typing' objects ()
+        # NotImplementedError: argument of type: 
+        def cast_listlistint(x):
+            return x
+
+        def cast_listint(x):
+            return x
+
+    rankset: list[int]
+    if isinstance(group, list):
+        if isinstance(group[0], list):
+            nested_list = cast_listlistint(group)
+            rankset = []
+            group_size = -1
+            for rs in nested_list:
+                rankset.extend(rs)
+                if group_size != -1 and group_size != len(rs):
+                    raise ValueError(
+                        f"group sizes must be identical found {group_size} and {len(rs)}"
+                    )
+                group_size = len(rs)
+        else:
+            rankset = cast_listint(group)
+            group_size = len(rankset)
+    elif isinstance(group, dist.ProcessGroup):
+        rankset = dist.get_process_group_ranks(group)
+        group_size = len(rankset)
+        tag = tag or c10d._get_group_tag(group)
+    elif isinstance(group, DeviceMesh):
+        if group.ndim != 1:
+            raise AssertionError(
+                "Only 1D mesh is supported, pass in (DeviceMesh, int) together if mesh > 1D"
+            )
+        # TODO: it should run collective in the whole mesh instead of dim 0
+        pg = group.get_group()
+        rankset = dist.get_process_group_ranks(pg)
+        group_size = len(rankset)
+        tag = tag or c10d._get_group_tag(pg)
+    elif isinstance(group, tuple):
+        if (
+            len(group) == 2
+            and isinstance(group[0], DeviceMesh)
+            and isinstance(group[1], int)
+        ):
+            dmesh = group[0]
+            dim = group[1]
+            pg = dmesh.get_group(dim)
+            rankset = dist.get_process_group_ranks(pg)
+            group_size = len(rankset)
+            tag = tag or c10d._get_group_tag(pg)
+        else:
+            raise ValueError("Invalid tuple for group must be (DeviceMesh, int)")
+    else:
+        raise ValueError(
+            "Invalid type for group, must be one of List, Processgroup, DeviceMesh or (DeviceMesh, int)."
+        )
+
+    return (tag, rankset, group_size)
+
+
+def _resolve_group_name(group: RANK_TYPES, tag: str = "") -> c10d.GroupName:
+    """
+    Given group in RANK_TYPES, return the group name.
+    """
+    # `tag` will be deprecated. See details in:
+    # https://github.com/pytorch/pytorch/issues/93173#issuecomment-1907095208
+    if isinstance(group, dist.ProcessGroup):
+        return group.group_name
+    elif isinstance(group, str):
+        # In some cases Dynamo doesn't like tracing through NewType constructors
+        # - so use a cast instead (the actual newtype representation is
+        # literally the underlying type so this is fine). I haven't been able to
+        # reproduce it in isolation (see T247631668).
+        return cast(c10d.GroupName, group)  # c10d.GroupName(group)
+    elif isinstance(group, DeviceMesh):
+        if group.ndim != 1:
+            raise AssertionError(
+                "Only 1D mesh is supported, pass in (DeviceMesh, int) together if mesh > 1D"
+            )
+        return group._dim_group_names[0]
+    elif isinstance(group, tuple):
+        if (
+            len(group) == 2
+            and isinstance(group[0], DeviceMesh)
+            and isinstance(group[1], int)
+        ):
+            dmesh = group[0]
+            dim = group[1]
+            return dmesh._dim_group_names[dim]
+        else:
+            raise ValueError("Invalid tuple for group must be (DeviceMesh, int)")
+    elif isinstance(group, list):
+        if not is_torchdynamo_compiling():
+            warnings.warn(
+                "The combination of ranks + tag as process group "
+                "identifier has been deprecated. Please switch to "
+                "using ProcessGroup, DeviceMesh, or group name instead.",
+                FutureWarning,
+                stacklevel=3,
+            )
+        # pyrefly: ignore [redundant-cast]
+        return c10d._resolve_group_name_by_ranks_and_tag(cast(list[int], group), tag)
+    else:
+        raise ValueError(f"Unsupported group type: {type(group)}, {group}")
+
+
+class _FromTorchTensor(torch.autograd.Function):
+    """
+    _FromTorchTensor allows autograd to propagate from a normal Tensor to an
+    AsyncCollectiveTensor.
+    """
+
+    @staticmethod
+    def forward(  # type: ignore[override]
+        ctx,  # pyre-ignore[2]: Parameter must be annotated.
+        input: torch.Tensor,
+    ) -> torch.Tensor:
+        return _maybe_wrap_tensor(input)
+
+    @staticmethod
+    def backward(ctx, grad_output: torch.Tensor) -> torch.Tensor:  # type: ignore[override]
+        return grad_output
+
+
+def _are_we_tracing() -> bool:
+    if is_torchdynamo_compiling():
+        return True
+    # If fake mode is turned on, we are almost definitely compiling/tracing.
+    if torch._C._get_dispatch_mode(torch._C._TorchDispatchModeKey.FAKE) is not None:
+        return True
+    # See Note [enable_python_dispatcher in dynamo]
+    if torch._C._dispatch_tls_is_dispatch_key_included(
+        torch._C.DispatchKey.PythonDispatcher
+    ):
+        return True
+    return get_proxy_mode() is not None
+
+
+def _maybe_wrap_tensor(self) -> torch.Tensor:
+    if _are_we_tracing():
+        return wait_tensor(self)
+    res = AsyncCollectiveTensor(self)
+    return cast(torch.Tensor, res)
+
+
+@contextlib.contextmanager
+def allow_inflight_collective_as_graph_input_ctx(value: bool = True):
+    """
+    Context manager to temporarily set whether inflight collectives are allowed as torch.compile graph inputs.
+    Common use case is when the collective is issued in eager (with `async_op=True`) but waited in compiled region:
+    ```
+    def all_reduce_eager(x):
+        y = x * x
+        req = dist.all_reduce(y, op=dist.ReduceOp.SUM, async_op=True)
+        return y
+
+
+    @torch.compile(fullgraph=True)
+    def all_reduce_wait_compiled(y):
+        torch.ops.c10d_functional.wait_tensor(y)
+        return y * y
+
+
+    x = torch.ones(1280, 1280, device="cuda") + self.rank
+    # the context manager ensures that `wait_tensor(y)` will wait on the correct work object
+    with allow_inflight_collective_as_graph_input_ctx():
+        y = all_reduce_eager(x)
+        z = all_reduce_wait_compiled(y)
+    ```
+    With this context manager, when a collective is called, under the hood the work object of the collective
+    will be registered in the work registry, and the wait_tensor() in compiled region called on
+    the output tensor of the collective will wait on the correct work object.
+    """
+    previous = torch._C._distributed_c10d._allow_inflight_collective_as_graph_input()
+
+    try:
+        torch._C._distributed_c10d._set_allow_inflight_collective_as_graph_input(value)
+        yield
+    finally:
+        torch._C._distributed_c10d._set_allow_inflight_collective_as_graph_input(
+            previous
+        )
+
+
+def _make_all_gather_out_tensor(input, group_size):
+    out_size = list(input.size())
+    if len(out_size) == 0:
+        out_size.append(group_size)
+    else:
+        out_size[0] *= group_size
+    out_tensor = input.new_empty(out_size)
+    return out_tensor
+
+
+def _all_gather_into_tensor_coalesced_meta(self, tag, rankset, group_size):
+    return [_make_all_gather_out_tensor(t, group_size) for t in self]
+
+
+# We now register meta kernels to deal with tracing
+def _broadcast_meta(self, *args):
+    return torch.empty_like(self)
+
+
+def _all_reduce_meta(self, *args):
+    return torch.empty_like(self)
+
+
+def _wait_tensor_meta(self, *args):
+    return torch.empty_like(self)
+
+
+def _all_gather_into_tensor_meta(shard, tag, rankset, group_size):
+    return _make_all_gather_out_tensor(shard, group_size)
+
+
+def _reduce_scatter_tensor_meta(input, reduce_op, tag, rankset, group_size):
+    out_size = list(input.size())
+    out_size[0] //= group_size
+    return input.new_empty(out_size)
+
+
+def _all_reduce_coalesced_meta(self, *args):
+    return [torch.empty_like(t) for t in self]
+
+
+def _all_reduce__meta(inp, *args):
+    return inp
+
+
+def _broadcast__meta(inp, *args):
+    return inp
+
+
+def _all_reduce_coalesced__meta(inputs, *args):
+    return inputs
+
+
+def _reduce_scatter_tensor_coalesced_meta(inputs, reduceOp, tag, rankset, group_size):
+    def mk_out_tensor(input):
+        out_size = list(input.size())
+        out_size[0] //= group_size
+        out_tensor = input.new_empty(out_size)
+        return out_tensor
+
+    return [mk_out_tensor(t) for t in inputs]
+
+
+# NB: We often say all_to_all has dynamic output size, but this is not
+# technically true: instead, what typically happens is you manually
+# communicate the output_split_sizes ahead of time (which is dynamic),
+# but then you pass those sizes explicitly, and the all to all itself
+# isn't dynamic, it just follows the specified output splits
+def _all_to_all_single_meta(
+    input, output_split_sizes, input_split_sizes, *args, **kwargs
+):
+    if output_split_sizes is None:
+        return input.new_empty(input.size())
+    else:
+        for s in output_split_sizes:
+            torch._check(s >= 0)
+        out_size = list(input.size())
+        out_size[0] = sum(output_split_sizes)
+        return input.new_empty(out_size)
+
+
+def _all_gather_into_tensor_out_native_meta(input, group_size, group_name, *, out):
+    return _make_all_gather_out_tensor(input, group_size)
+
+
+def _all_gather_into_tensor_native_meta(input, group_size, group_name):
+    return _make_all_gather_out_tensor(input, group_size)
+
+
+def _all_gather_into_tensor_coalesced_native_meta(inputs, group_size, group_name):
+    return [
+        _all_gather_into_tensor_native_meta(input, group_size, group_name)
+        for input in inputs
+    ]
+
+
+def _reduce_scatter_tensor_native_meta(inp, reduce_op, group_size, group_name):
+    shape = list(inp.size())
+    shape[0] //= group_size
+    return inp.new_empty(shape)
+
+
+def _reduce_scatter_tensor_out_native_meta(
+    inp, reduce_op, group_size, group_name, *, out
+):
+    shape = list(inp.size())
+    shape[0] //= group_size
+    return inp.new_empty(shape)
+
+
+def _reduce_scatter_tensor_coalesced_native_meta(
+    inputs, reduce_op, group_size, group_name
+):
+    return [
+        _reduce_scatter_tensor_native_meta(inp, reduce_op, group_size, group_name)
+        for inp in inputs
+    ]
+
+
+# Library MUST be defined at module scope or it doesn't work
+lib_impl = torch.library.Library("_c10d_functional", "IMPL")
+lib_impl.impl("all_reduce", _all_reduce_meta, "Meta")
+lib_impl.impl("all_reduce_", _all_reduce__meta, "Meta")
+lib_impl.impl("all_reduce_coalesced", _all_reduce_coalesced_meta, "Meta")
+lib_impl.impl("all_reduce_coalesced_", _all_reduce_coalesced__meta, "Meta")
+lib_impl.impl("wait_tensor", _wait_tensor_meta, "Meta")
+lib_impl.impl(
+    "all_gather_into_tensor_out", _all_gather_into_tensor_out_native_meta, "Meta"
+)
+lib_impl.impl("all_gather_into_tensor", _all_gather_into_tensor_native_meta, "Meta")
+lib_impl.impl(
+    "all_gather_into_tensor_coalesced",
+    _all_gather_into_tensor_coalesced_native_meta,
+    "Meta",
+)
+lib_impl.impl("reduce_scatter_tensor", _reduce_scatter_tensor_native_meta, "Meta")
+lib_impl.impl(
+    "reduce_scatter_tensor_out", _reduce_scatter_tensor_out_native_meta, "Meta"
+)
+lib_impl.impl(
+    "reduce_scatter_tensor_coalesced",
+    _reduce_scatter_tensor_coalesced_native_meta,
+    "Meta",
+)
+lib_impl.impl("all_to_all_single", _all_to_all_single_meta, "Meta")
+lib_impl.impl("broadcast", _broadcast_meta, "Meta")
+lib_impl.impl("broadcast_", _broadcast__meta, "Meta")
+
+# mark these ops has side effect so that they won't be removed by DCE
+torch.fx.node.has_side_effect(torch.ops._c10d_functional.wait_tensor.default)  # type: ignore[has-type]
+torch.fx.node.has_side_effect(torch.ops._c10d_functional.wait_tensor)  # type: ignore[has-type]
+
+# Register legacy ops for backward compatibility
+# TODO(yifu): remove these in functional collective beta release
+legacy_lib = torch.library.Library("c10d_functional", "DEF")
+legacy_lib_impl = torch.library.Library("c10d_functional", "IMPL")
+ops_defs = [
+    "broadcast(Tensor self, int src, str tag, int[] ranks, int group_size) -> Tensor",
+    "all_reduce(Tensor self, str reduceOp, str tag, int[] ranks, int group_size) -> Tensor",
+    "all_reduce_coalesced(Tensor[] self, str reduceOp, str tag, int[] ranks, int group_size) -> Tensor[]",
+    "wait_tensor(Tensor self) -> Tensor",
+    "all_gather_into_tensor(Tensor shard, str tag, int[] ranks, int group_size) -> Tensor",
+    "all_gather_into_tensor_coalesced(Tensor[] input, str tag, int[] ranks, int group_size) -> Tensor[]",
+    "reduce_scatter_tensor(Tensor input, str reduceOp, str tag, int[] ranks, int group_size) -> Tensor",
+    "reduce_scatter_tensor_coalesced(Tensor[] inputs, str reduceOp, str tag, int[] ranks, int group_size) -> Tensor[]",
+    "all_to_all_single(Tensor input, SymInt[]? output_split_sizes, SymInt[]? input_split_sizes, str tag, int[] ranks, int group_size) -> Tensor",  # noqa: B950
+]
+
+my_module = sys.modules[__name__]
+for op_def in ops_defs:
+    op_name = op_def[0 : op_def.index("(")]
+    backend_impl = getattr(fun_col_impl, f"_{op_name}")
+    legacy_lib.define(op_def, tags=torch.Tag.pt2_compliant_tag)
+    legacy_lib_impl.impl(op_name, backend_impl, "CompositeImplicitAutograd")
+
+
+"""
+Dynamo Remappings allow seamless translation from non-functional collectives of supportable form into
+functional collective calls followed by inplace copy ops, allowing them to be traced into a functional graph.
+
+We implement this by writing a decomposition and teaching dynamo how to associate it to a corresponding op via
+the mapping dict below.
+
+These schemas intentionally match torch.distributed.distributed_c10d.* ops that we are trying to remap from
+"""
+
+
+def all_gather_tensor_inplace(
+    output_tensor: torch.Tensor,
+    input_tensor: torch.Tensor,
+    group=None,  # TODO add a type,
+    async_op: bool = False,
+    tag: str = "",
+    gather_dim: int = 0,
+):
+    if async_op:
+        raise AssertionError(
+            "Can't remap async version of inplace op to functional collective"
+        )
+
+    group = group or dist.group.WORLD
+    if group is None:
+        raise AssertionError("group cannot be None")
+
+    return output_tensor.copy_(all_gather_tensor(input_tensor, gather_dim, group, tag))
+
+
+def reduce_scatter_tensor_inplace(
+    output: torch.Tensor,
+    input: torch.Tensor,
+    op: str = "sum",  # TODO type is actually c10d ReduceOp. is this ok?
+    group=None,  # TODO add a type
+    async_op: bool = False,
+    scatter_dim: int = 0,
+    tag: str = "",
+):
+    if async_op:
+        raise AssertionError(
+            "Can't remap async version of inplace op to functional collective"
+        )
+
+    group = group or dist.group.WORLD
+    if group is None:
+        raise AssertionError("group cannot be None")
+
+    return output.copy_(reduce_scatter_tensor(input, op, scatter_dim, group, tag))
+
+
+REDUCE_OP_TO_STR = {
+    dist.ReduceOp.SUM: "sum",
+    dist.ReduceOp.AVG: "avg",
+    dist.ReduceOp.PRODUCT: "product",
+    dist.ReduceOp.MIN: "min",
+    dist.ReduceOp.MAX: "max",
+    dist.ReduceOp.BAND: "band",
+    dist.ReduceOp.BOR: "bor",
+    dist.ReduceOp.BXOR: "bxor",
+}
+
+
+def all_reduce_inplace(
+    tensor: torch.Tensor,
+    op: str = "sum",
+    group=None,
+    async_op: bool = False,
+    tag: str = "",
+):
+    if async_op:
+        raise AssertionError(
+            "Can't remap async version of inplace op to functional collective"
+        )
+
+    group = group or dist.group.WORLD
+    if group is None:
+        raise AssertionError("group cannot be None")
+
+    return tensor.copy_(all_reduce(tensor, op, group, tag))
+
+
+def all_to_all_inplace(
+    output: torch.Tensor,
+    input: torch.Tensor,
+    output_split_sizes=None,
+    input_split_sizes=None,
+    group=None,
+    async_op=False,
+    tag: str = "",
+):
+    if async_op:
+        raise AssertionError(
+            "Can't remap async version of inplace op to functional collective"
+        )
+
+    group = group or dist.group.WORLD
+    if group is None:
+        raise AssertionError("group cannot be None")
+
+    return output.copy_(
+        all_to_all_single(
+            input,
+            output_split_sizes,
+            input_split_sizes,
+            group,
+            tag,
+        )
+    )
+
+
+def all_gather_inplace(
+    tensor_list: list[torch.Tensor],
+    tensor: torch.Tensor,
+    group=None,
+    async_op=False,
+    tag: str = "",
+):
+    if async_op:
+        raise AssertionError(
+            "Can't remap async version of inplace op to functional collective"
+        )
+    if tensor.dim() != 0 and not all(t.size(0) == tensor.size(0) for t in tensor_list):
+        raise AssertionError("Remapping variable size all_gather is not yet supported")
+
+    group = group or dist.group.WORLD
+    if group is None:
+        raise AssertionError("group cannot be None")
+
+    output = all_gather_tensor(tensor, 0, group, tag)
+
+    # Use aten.slice instead of aten.split because the latter causes
+    # tensor.shape(0) to be unnecessarily baked in when it's a SymInt.
+    output_splits = []
+    offset = 0
+    for t in tensor_list:
+        is_scalar = t.dim() == 0
+        t_offset = 1 if is_scalar else t.size(0)
+        # pyrefly: ignore [unsupported-operation]
+        out = output[offset] if is_scalar else output[offset : offset + t_offset]
+        output_splits.append(out)
+        # pyrefly: ignore [unsupported-operation]
+        offset += t_offset
+    for dst, src in zip(tensor_list, output_splits):
+        dst.copy_(src)
+    return tensor_list
+
+
+from torch.distributed.distributed_c10d import (  # pyrefly: ignore  # deprecated; pyrefly: ignore [deprecated]
+    _all_gather_base as legacy_all_gather_base,
+    _reduce_scatter_base as legacy_reduce_scatter_base,
+    all_gather as legacy_all_gather,
+    all_gather_into_tensor as legacy_allgather,
+    all_reduce as legacy_allreduce,
+    all_to_all_single as legacy_all_to_all_single,
+    reduce_scatter_tensor as legacy_reducescatter,
+)
+
+
+# This dict should contain sets of functions that dynamo is allowed to remap.
+# Functions in this set should accept the same args/kwargs 1:1 as their mapping.
+traceable_collective_remaps = {
+    legacy_allgather: all_gather_tensor_inplace,  # type: ignore[has-type]
+    legacy_reducescatter: reduce_scatter_tensor_inplace,  # type: ignore[has-type]
+    legacy_allreduce: all_reduce_inplace,  # type: ignore[has-type]
+    legacy_all_to_all_single: all_to_all_inplace,  # type: ignore[has-type]
+    legacy_all_gather: all_gather_inplace,  # type: ignore[has-type]
+    legacy_reduce_scatter_base: reduce_scatter_tensor_inplace,  # type: ignore[has-type]
+    legacy_all_gather_base: all_gather_tensor_inplace,  # type: ignore[has-type]
+}
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_functional_collectives_impl.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_functional_collectives_impl.py
new file mode 100644
index 0000000000000000000000000000000000000000..fcb659b74bc0537b36e447f1a69628e70933d3e9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_functional_collectives_impl.py
@@ -0,0 +1,117 @@
+# mypy: allow-untyped-defs
+
+import torch
+import torch.distributed.distributed_c10d as c10d
+
+
+"""
+This file contains the op impls for the legacy (c10d_functional) functional collectives.
+These impls simply call into the native (_c10d_functional) functional collectives.
+"""
+
+
+def _broadcast(input, src, tag, ranks, group_size):
+    group_name = c10d._resolve_group_name_by_ranks_and_tag(ranks, tag)
+    return torch.ops._c10d_functional.broadcast(
+        input,
+        src,
+        group_name,
+    )
+
+
+def _all_reduce(input, reduce_op, tag, ranks, group_size):
+    group_name = c10d._resolve_group_name_by_ranks_and_tag(ranks, tag)
+    return torch.ops._c10d_functional.all_reduce(
+        input,
+        reduce_op,
+        group_name,
+    )
+
+
+def _all_reduce_coalesced(inputs, reduce_op, tag, ranks, group_size):
+    group_name = c10d._resolve_group_name_by_ranks_and_tag(ranks, tag)
+    return torch.ops._c10d_functional.all_reduce_coalesced(
+        inputs,
+        reduce_op,
+        group_name,
+    )
+
+
+def _all_gather_into_tensor(input, tag, ranks, group_size):
+    group_name = c10d._resolve_group_name_by_ranks_and_tag(ranks, tag)
+    return torch.ops._c10d_functional.all_gather_into_tensor(
+        input,
+        group_size,
+        group_name,
+    )
+
+
+def _all_gather_into_tensor_coalesced(input, tag, ranks, group_size):
+    group_name = c10d._resolve_group_name_by_ranks_and_tag(ranks, tag)
+    return torch.ops._c10d_functional.all_gather_into_tensor_coalesced(
+        input,
+        group_size,
+        group_name,
+    )
+
+
+def _reduce_scatter_tensor(
+    input: torch.Tensor,
+    reduce_op: str,
+    tag: str,
+    ranks: list[int],
+    group_size: int,
+):
+    group_name = c10d._resolve_group_name_by_ranks_and_tag(ranks, tag)
+    return torch.ops._c10d_functional.reduce_scatter_tensor(
+        input,
+        reduce_op,
+        group_size,
+        group_name,
+    )
+
+
+def _reduce_scatter_tensor_coalesced(
+    inputs: list[torch.Tensor],
+    reduce_op: str,
+    tag: str,
+    ranks: list[int],
+    group_size: int,
+):
+    group_name = c10d._resolve_group_name_by_ranks_and_tag(ranks, tag)
+    return torch.ops._c10d_functional.reduce_scatter_tensor_coalesced(
+        inputs,
+        reduce_op,
+        group_size,
+        group_name,
+    )
+
+
+def _all_to_all_single(
+    input: torch.Tensor,
+    output_split_sizes: list[int] | None,
+    input_split_sizes: list[int] | None,
+    tag: str,
+    ranks: list[int],
+    group_size: int,
+):
+    if output_split_sizes is None or input_split_sizes is None:
+        if not (output_split_sizes is None and input_split_sizes is None):
+            raise AssertionError(
+                "output_split_sizes and input_split_sizes must either be "
+                "specified together or both set to None"
+            )
+        output_split_sizes = [input.shape[0] // group_size] * group_size
+        input_split_sizes = output_split_sizes
+
+    group_name = c10d._resolve_group_name_by_ranks_and_tag(ranks, tag)
+    return torch.ops._c10d_functional.all_to_all_single(
+        input,
+        output_split_sizes,
+        input_split_sizes,
+        group_name,
+    )
+
+
+def _wait_tensor(tensor: torch.Tensor) -> torch.Tensor:
+    return torch.ops._c10d_functional.wait_tensor(tensor)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_local_tensor/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_local_tensor/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..632f28224193de697b7eb96608a262f58dd6363d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_local_tensor/__init__.py
@@ -0,0 +1,1965 @@
+from ast import Call
+
+from torch._ops import OpOverload
+
+
+"""
+A LocalTensor is a tensor subclass which simulates a tensor that is
+distributed across SPMD ranks.  A LocalTensor might be size N, but in fact
+there are world_size shards/replicas of it stored internally.  When you do a
+plain PyTorch operation on it, we apply the operation to each shard; when you
+do a collective, we do the mathematically equivalent operation on the local
+shards.  A LocalTensor is associated with a list of ranks which specify
+which ranks it holds local tensors for.
+
+NB, this is NOT a DataParallel like abstraction where you can run operations
+on multiple different GPUs. It is intended purely for *debugging* purposes,
+the overhead is almost certainly too high to keep eight GPUs (even the C++
+autograd needs multithreading to keep up!)  (It might potentially be possible
+to trace through this with torch.compile and then compile it with CUDA graphs
+but this is currently a non-goal.)
+
+We do not directly handling MPMD. However in practice even in SPMD you may
+encounter divergence in behavior per rank (for example, uneven sharding
+across ranks). To support scenarios like this, we provide a helper decorator
+that allows you to run a function with no side effects for each LocalTensor
+shard and combine results back into LocalTensor or LocalIntNode.
+
+NB: This is a torch dispatch Tensor subclass, as we want to assume that autograd
+is SPMD, so we run it once, and dispatch the inner autograd calls to the individual
+local shards.
+
+NOTE ABOUT MESH:  This subclass requires collectives that are issued to it to
+respect a DeviceMesh like abstraction.  The reason for this is that when
+DTensor issues us a collective for a particular rank, you will be asked to do
+this on a specific process group which involves some ranks.  However, this
+will only be for the LOCAL PG that this particular rank is participating in;
+there will be a bunch of other PGs for other nodes that you don't get to see.
+We need to be able to reverse engineer all of the collectives that don't
+involve the current local rank here to actually issue them.  This can be done
+two ways: (1) looking at the participating local ranks in the PG and computing
+the complement which specifies all the other collectives you have to run, or
+(2) retrieving the device mesh axis corresponding to the PG for this rank, and
+then running all the fibers for this.
+"""
+
+import contextlib
+import copy
+import functools
+import operator
+import os
+import sys
+import threading
+from collections import defaultdict
+from collections.abc import Callable, Generator, Sequence
+from types import TracebackType
+from typing import Any, Optional, ParamSpec, TypeVar, Union
+
+
+try:
+    import numpy as np
+
+    HAS_NUMPY = True
+except ModuleNotFoundError:
+    HAS_NUMPY = False
+    np = None  # type: ignore[assignment]
+
+import torch
+import torch.distributed as dist
+from torch import Size, SymBool, SymInt, Tensor
+from torch._C import DispatchKey, DispatchKeySet, ScriptObject
+from torch._export.wrappers import mark_subclass_constructor_exportable_experimental
+from torch._subclasses.fake_tensor import FakeTensor, FakeTensorMode
+from torch.distributed import DeviceMesh, ProcessGroup
+from torch.distributed._functional_collectives import AsyncCollectiveTensor
+from torch.distributed.distributed_c10d import _get_default_group
+from torch.fx.experimental._constant_symnode import ConstantIntNode
+from torch.nested._internal.nested_int import NestedIntNode
+from torch.utils import _pytree as pytree
+from torch.utils._mode_utils import no_dispatch
+from torch.utils._python_dispatch import (
+    _get_current_dispatch_mode_stack,
+    return_and_correct_aliasing,
+    TorchDispatchMode,
+)
+from torch.utils.checkpoint import get_device_states, set_device_states
+
+
+_R = TypeVar("_R")
+_P = ParamSpec("_P")
+
+not_implemented_log = torch._logging.getArtifactLogger(__name__, "not_implemented")
+
+
+from . import _c10d
+
+
+def _is_in_fake_tensor_mode() -> bool:
+    return any(
+        isinstance(mode, FakeTensorMode) for mode in _get_current_dispatch_mode_stack()
+    )
+
+
+def _reduce_multidim_lists(
+    lists_to_reduce: list[Any], reduce_func: Callable[[list[Any]], Any]
+) -> Any:
+    """
+    Reduces a list of multi-dimensional lists, assuming they all have
+    the exact same shape.
+
+    Args:
+        lists_to_reduce (list): A list where each item is a multi-dimensional
+                                list (e.g., [md_list_1, md_list_2, ...]).
+                                All inner md_lists must have the same shape.
+        reduce_func (callable): A function that takes an iterable (list) of
+                                values and returns a single reduced value.
+                                For example: sum, max, min, or
+                                lambda x: sum(x) / len(x) for mean.
+
+    Returns:
+        A single multi-dimensional list of the same shape as the inputs,
+        where each value is the result of the reduce_func.
+
+    Raises:
+        ValueError: If the input list is empty or if shapes are inconsistent
+                    (which may also raise IndexError or TypeError).
+    """
+    if not lists_to_reduce:
+        raise ValueError("Input 'lists_to_reduce' cannot be empty.")
+
+    # Get the first list to inspect its structure (shape)
+    first_list = lists_to_reduce[0]
+
+    # Check if the first element of this list is *also* a list.
+    # This determines if we are at the base case or need to recurse.
+    if isinstance(first_list[0], list):
+        # --- RECURSIVE STEP ---
+        # The elements are lists, so we need to go one level deeper.
+
+        # We find the number of sub-lists from the first list.
+        # (e.g., for [[1,2], [3,4]], this is 2)
+        num_sublists = len(first_list)
+
+        result = []
+        # Iterate by the index of the sub-lists (e.g., i = 0, then i = 1)
+        for i in range(num_sublists):
+            # Build a new list to pass to the recursive call.
+            # This list will contain the i-th sublist from *each* of the
+            # input lists.
+            # e.g., if lists_to_reduce = [ L1, L2 ] and i = 0,
+            # this creates [ L1[0], L2[0] ]
+            sublists_to_reduce = [l[i] for l in lists_to_reduce]
+
+            # Recurse and append the result
+            result.append(_reduce_multidim_lists(sublists_to_reduce, reduce_func))
+        return result
+    else:
+        # --- BASE CASE ---
+        # The elements are values (int, float, etc.), not lists.
+        # We are at the innermost dimension.
+
+        # Find the number of values in the innermost list.
+        # (e.g., for [1, 2], this is 2)
+        num_values = len(first_list)
+
+        result = []
+        # Iterate by the index of the values (e.g., i = 0, then i = 1)
+        for i in range(num_values):
+            # Get the values at this specific position (i) from *all*
+            # input lists.
+            # e.g., if lists_to_reduce = [ [1,2], [10,20] ] and i = 0,
+            # this creates [ 1, 10 ]
+            values_at_pos = [l[i] for l in lists_to_reduce]
+
+            # Apply the user-provided reduction function to this list of values
+            # and append the single result.
+            result.append(reduce_func(values_at_pos))
+        return result
+
+
+def _is_inplace_op(op: OpOverload | Callable[..., Any]) -> bool:
+    return (
+        isinstance(op, OpOverload)
+        # Not precise heuristic to detect inplace operation
+        and op._schema.name[-1] == "_"
+        # Strengthen the heuristic to check that the first argument and return value are a write
+        and len(op._schema.arguments) > 0
+        and op._schema.arguments[0].is_write
+        and len(op._schema.returns) > 0
+        and op._schema.returns[0].is_write
+    )
+
+
+def _int_on_rank(i: "int | LocalIntNode | ConstantIntNode", r: int) -> int:
+    if isinstance(i, LocalIntNode):
+        return i._local_ints[r]
+    elif isinstance(i, ConstantIntNode):
+        return i.val
+    elif isinstance(i, int):
+        return i
+    else:
+        raise AssertionError(type(i))
+
+
+def _check_for_subclass(flat_args: Sequence[object]) -> bool:
+    return any(_check_for_subclass_arg(x) for x in flat_args)
+
+
+def _check_for_subclass_arg(x: object) -> bool:
+    return (
+        not isinstance(x, LocalTensor)
+        and isinstance(x, Tensor)
+        and type(x)
+        not in (
+            Tensor,
+            FakeTensor,
+            torch.nn.Parameter,
+            torch.nn.Buffer,
+        )
+    )
+
+
+def _map_to_rank_local_val(val: Any, rank: int) -> Any:
+    if isinstance(val, LocalTensor):
+        return val._local_tensors[rank]
+    if isinstance(val, SymInt):
+        if isinstance(val.node, LocalIntNode):
+            return val.node._local_ints[rank]
+        if isinstance(val.node, ConstantIntNode):
+            return val.node.val
+    return val
+
+
+def _collect_accelerator_rng_states() -> dict[int, torch.Tensor]:
+    """
+    Collects RNG state from all available acceleator devices.
+
+    Returns:
+        List of RNG state tensors, one for each accelerator device.
+        Returns empty list if accelerator is not available.
+    """
+    if not torch.accelerator.is_available():
+        return {}
+
+    if torch.accelerator.is_available():
+        device_idx = torch.accelerator.current_device_index()
+        with torch.accelerator.device_index(device_idx):
+            return {device_idx: torch.get_device_module().get_rng_state()}
+
+    return {}
+
+
+def _set_accelerator_rng_states(rng_states: dict[int, torch.Tensor]) -> None:
+    """
+    Sets RNG state for all accelerator devices from a list of states.
+
+    Args:
+        rng_states: List of RNG state tensors to restore.
+    """
+    if not torch.accelerator.is_available():
+        return
+
+    if torch.accelerator.is_available():
+        for device_idx, device_rng_state in rng_states.items():
+            with torch.accelerator.device_index(device_idx):
+                torch.get_device_module().set_rng_state(device_rng_state)
+
+
+def _get_rng_state() -> tuple[torch.Tensor, dict[int, torch.Tensor]]:
+    """
+    Gets CPU and accelerator (e.g., CUDA, XPU device) rng states from all devices.
+    """
+    return (torch.get_rng_state(), _collect_accelerator_rng_states())
+
+
+def _set_rng_state(
+    cpu_state: torch.Tensor, accelerator_states: dict[int, torch.Tensor]
+) -> None:
+    """
+    Sets CPU and accelerator (e.g., CUDA, XPU device) rng states for all devices. If
+    the list of accelerator states is shorter than the number of devices only the
+    first len(accelerator_states) devices will get their rng state set.
+    """
+    torch.set_rng_state(cpu_state)
+    _set_accelerator_rng_states(accelerator_states)
+
+
+def _combine_int_rank_results(rank_results: dict[int, int]) -> int | torch.SymInt:
+    any_v = next(iter(rank_results.values()))
+
+    if all(v == any_v for v in rank_results.values()):
+        return any_v
+
+    return torch.SymInt(LocalIntNode(rank_results))
+
+
+def _combine_any_rank_results(rank_results: dict[int, Any]) -> Any:
+    any_v = next(iter(rank_results.values()))
+
+    if isinstance(any_v, Tensor):
+        # pyrefly: ignore [bad-argument-type, bad-argument-count]
+        return LocalTensor(rank_results)
+
+    if isinstance(any_v, int):
+        return _combine_int_rank_results(rank_results)
+
+    if isinstance(any_v, torch.device):
+        assert all(v.type == any_v.type for v in rank_results.values()), (
+            "device type should be the same"
+        )
+        # Just use the first device - the device type is what matters,
+        # and LocalTensorMode runs on a single physical device anyway
+        return any_v
+
+    assert all(v == any_v for v in rank_results.values()), (
+        "Non Tensor or int rank results must be equal for all ranks"
+    )
+
+    return any_v
+
+
+def _combine_rank_results(rank_results: dict[int, Any], default: Any | None) -> Any:
+    rank_ids = rank_results.keys()
+    rank_value = rank_results[next(iter(rank_ids))]
+
+    if isinstance(rank_value, (list, tuple)):
+        max_rank_result_len = max(len(v) for v in rank_results.values())
+        ret_list = []
+        for i in range(max_rank_result_len):
+            rank_col_results = {
+                r: v[i] if i < len(v) else default for r, v in rank_results.items()
+            }
+            ret_list.append(_combine_any_rank_results(rank_col_results))
+        return type(rank_value)(ret_list)
+    else:
+        return _combine_any_rank_results(rank_results)
+
+
+def _zero_sized_like(tensor: torch.Tensor, dim: int) -> torch.Tensor:
+    tensor_size = list(tensor.size())
+    tensor_size[dim] = 0
+    empty_tensor = torch.empty(*tensor_size, dtype=tensor.dtype, device=tensor.device)
+    return empty_tensor
+
+
+def _for_each_rank_run_func(
+    func: OpOverload | Callable[..., Any],
+    ranks: frozenset[int],
+    args: Sequence[Any],
+    kwargs: dict[str, Any],
+    *,
+    alias: bool = True,
+) -> Any:
+    flat_args, args_spec = pytree.tree_flatten((args, kwargs))
+    flat_args = [
+        a.wait() if isinstance(a, AsyncCollectiveTensor) else a for a in flat_args
+    ]
+
+    lm = enabled_local_tensor_mode()
+    use_per_rank_rng = lm is not None and len(lm._per_rank_rng_states) > 0
+
+    global_rng_state = None if use_per_rank_rng else _get_rng_state()
+
+    flat_rank_rets = {}
+
+    default_value: Tensor | None = None
+    for r in sorted(ranks):
+        if use_per_rank_rng:
+            assert lm is not None
+            if r in lm._per_rank_rng_states:
+                _set_rng_state(*lm._per_rank_rng_states[r])
+        else:
+            assert global_rng_state is not None
+            _set_rng_state(*global_rng_state)
+
+        rank_flat_args = [_map_to_rank_local_val(a, r) for a in flat_args]
+        rank_args, rank_kwargs = pytree.tree_unflatten(rank_flat_args, args_spec)
+        if func is torch.ops.aten.hash_tensor.default and rank_args[0].numel() == 0:
+            # Special case for empty tensors, hash_tensor returns an empty tensor
+            rank_ret = torch.empty(0, dtype=torch.uint64, device=rank_args[0].device)
+        else:
+            rank_ret = func(*rank_args, **rank_kwargs)
+        flat_rank_rets[r] = rank_ret
+
+        if use_per_rank_rng:
+            assert lm is not None
+            lm._per_rank_rng_states[r] = _get_rng_state()
+
+        if default_value is None and func is torch.ops.aten.split.Tensor:
+            # If split happens over the dimension smaller than the number of chunks
+            # it is possible that some ranks will produce shorter lists of chunks.
+            # In order to make the result across all ranks of the same length we
+            # append empty tensors (zero size on the split dimension).
+            tensor = rank_flat_args[0]
+            split_dim = 0 if len(rank_flat_args) < 3 else rank_flat_args[2]
+            default_value = _zero_sized_like(tensor, split_dim)
+
+    if _is_inplace_op(func):
+        alias = False
+        # For the in-place ops return self
+        ret = args[0]
+        if isinstance(func, OpOverload) and torch.Tag.inplace_view in func.tags:
+            # Ensure that wrapper tensor size is synchronized with its local tensors
+            ret._sync_meta()
+    else:
+        ret = _combine_rank_results(flat_rank_rets, default_value)
+
+    if alias:
+        return return_and_correct_aliasing(func, args, kwargs, ret)
+    else:
+        return ret
+
+
+def _get_extra_dispatch_keys(t: torch.Tensor) -> DispatchKeySet:
+    extra_dispatch_keys = torch._C.DispatchKeySet.from_raw_repr(0)
+    if torch._C._dispatch_keys(t).has(torch._C.DispatchKey.Conjugate):
+        extra_dispatch_keys = extra_dispatch_keys.add(torch._C.DispatchKey.Conjugate)
+    if torch._C._dispatch_keys(t).has(torch._C.DispatchKey.Negative):
+        extra_dispatch_keys = extra_dispatch_keys.add(torch._C.DispatchKey.Negative)
+    return extra_dispatch_keys
+
+
+class LocalIntNode:
+    """
+    Like a LocalTensor, but for an int.  We can't use a 0D tensor to represent this
+    because often only a SymInt is accepted where we wish to use this.
+    """
+
+    def __new__(cls, local_ints: dict[int, int]) -> "ConstantIntNode | LocalIntNode":  # type: ignore[misc]
+        if len(set(local_ints.values())) == 1:
+            return ConstantIntNode(next(iter(local_ints.values())))
+        return super().__new__(cls)
+
+    def __init__(self, local_ints: dict[int, int]):
+        self._local_ints = local_ints
+
+    def maybe_as_int(self) -> int | None:
+        return None
+
+    def is_int(self) -> bool:
+        return True
+
+    def is_float(self) -> bool:
+        return False
+
+    def is_bool(self) -> bool:
+        return False
+
+    def is_nested_int(self) -> bool:
+        return False
+
+    def clone(self) -> "LocalIntNode":
+        return self
+
+    def _str(self) -> str:
+        return f"LocalIntNode({self._local_ints})"
+
+    def __str__(self) -> str:
+        return self._str()
+
+    def __repr__(self) -> str:
+        return self._str()
+
+    def _graph_repr(self) -> str:
+        return self._str()
+
+    def is_symbolic(self) -> bool:
+        return False
+
+    def is_constant(self) -> bool:
+        return False
+
+    def sym_max(
+        self, other: "int | LocalIntNode | ConstantIntNode"
+    ) -> "LocalIntNode | ConstantIntNode":
+        return LocalIntNode(
+            {
+                r: max(self._local_ints[r], _int_on_rank(other, r))
+                for r in self._local_ints
+            }
+        )
+
+    def sym_sum(self, other: Any) -> "LocalIntNode | ConstantIntNode":
+        t = LocalIntNode(dict.fromkeys(self._local_ints, 0))
+        for o in other:
+            t = t.add(o)
+        return t
+
+    def neg(self) -> "LocalIntNode | ConstantIntNode":
+        return LocalIntNode({r: -self._local_ints[r] for r in self._local_ints})
+
+    def add(
+        self, other: "int | LocalIntNode | ConstantIntNode"
+    ) -> "LocalIntNode | ConstantIntNode":
+        return LocalIntNode(
+            {r: self._local_ints[r] + _int_on_rank(other, r) for r in self._local_ints}
+        )
+
+    def sub(
+        self, other: "int | LocalIntNode | ConstantIntNode"
+    ) -> "LocalIntNode | ConstantIntNode":
+        return LocalIntNode(
+            {r: self._local_ints[r] - _int_on_rank(other, r) for r in self._local_ints}
+        )
+
+    def mul(
+        self, other: "int | LocalIntNode | ConstantIntNode"
+    ) -> "LocalIntNode | ConstantIntNode":
+        return LocalIntNode(
+            {r: self._local_ints[r] * _int_on_rank(other, r) for r in self._local_ints}
+        )
+
+    def floordiv(
+        self, other: "int | LocalIntNode | ConstantIntNode"
+    ) -> "LocalIntNode | ConstantIntNode":
+        return LocalIntNode(
+            {r: self._local_ints[r] // _int_on_rank(other, r) for r in self._local_ints}
+        )
+
+    def mod(
+        self, other: "int | LocalIntNode | ConstantIntNode"
+    ) -> "LocalIntNode | ConstantIntNode":
+        return LocalIntNode(
+            {r: self._local_ints[r] % _int_on_rank(other, r) for r in self._local_ints}
+        )
+
+    def int_floordiv(
+        self, other: "int | LocalIntNode | ConstantIntNode"
+    ) -> "LocalIntNode | ConstantIntNode":
+        return LocalIntNode(
+            {r: self._local_ints[r] // _int_on_rank(other, r) for r in self._local_ints}
+        )
+
+    def eq(self, other: "int | LocalIntNode | ConstantIntNode") -> bool | SymBool:
+        r = {self._local_ints[r] == _int_on_rank(other, r) for r in self._local_ints}
+        return torch._C._get_constant_bool_symnode(len(r) == 1 and next(iter(r)))
+
+    def ne(self, other: "int | LocalIntNode | ConstantIntNode") -> bool | SymBool:
+        r = {self._local_ints[r] != _int_on_rank(other, r) for r in self._local_ints}
+        return torch._C._get_constant_bool_symnode(len(r) > 1 or next(iter(r)))
+
+    def ge(self, other: "int | LocalIntNode | ConstantIntNode") -> bool | SymBool:
+        r = {self._local_ints[r] >= _int_on_rank(other, r) for r in self._local_ints}
+        assert len(r) == 1, (self, other)
+        return torch._C._get_constant_bool_symnode(next(iter(r)))
+
+    def gt(self, other: "int | LocalIntNode | ConstantIntNode") -> bool | SymBool:
+        r = {self._local_ints[r] > _int_on_rank(other, r) for r in self._local_ints}
+        assert len(r) == 1, (self, other)
+        return torch._C._get_constant_bool_symnode(next(iter(r)))
+
+    def lt(self, other: "int | LocalIntNode | ConstantIntNode") -> bool | SymBool:
+        r = {self._local_ints[r] < _int_on_rank(other, r) for r in self._local_ints}
+        assert len(r) == 1, (self, other)
+        return torch._C._get_constant_bool_symnode(next(iter(r)))
+
+    def wrap_int(self, num: int) -> "LocalIntNode | ConstantIntNode":
+        return ConstantIntNode(num)
+
+
+class _LocalDeviceHandle:
+    """
+    Wrapper around device module (e.g., torch.cuda) with automatic LocalTensor semantics.
+
+    This class wraps device modules and automatically handles per-rank operations in
+    LocalTensor mode:
+    - get_rng_state() returns a LocalTensor with per-rank states
+    - set_rng_state(LocalTensor) sets per-rank states
+
+    When not in LocalTensor mode, it delegates directly to the underlying device handle.
+    """
+
+    def __init__(self, device_handle, device_type: str):
+        """
+        Initialize the local device handle wrapper.
+
+        Args:
+            device_handle: The underlying device module (e.g., torch.cuda)
+            device_type: Device type string (e.g., "cuda", "cpu")
+        """
+        self._device_handle = device_handle
+        self._device_type = device_type
+
+    def get_rng_state(self):
+        """
+        Get RNG state, automatically returning LocalTensor in LocalTensor mode.
+
+        Returns:
+            LocalTensor in LocalTensor mode, regular Tensor otherwise
+        """
+        lm = enabled_local_tensor_mode()
+        if not lm:
+            return self._device_handle.get_rng_state()
+
+        original_state = _get_rng_state()
+        per_rank_states = {}
+
+        try:
+            for rank in lm.ranks:
+                # We need to set-then-get instead of directly copying lm._per_rank_rng_states[rank]
+                # because they have different structures:
+                # - lm._per_rank_rng_states[rank] is a tuple: (cpu_state, {device_idx: cuda_state})
+                # - self._device_handle.get_rng_state() returns just the device-specific tensor
+                # So we temporarily restore the full RNG state (CPU + all CUDA devices) for this rank,
+                # then extract only the specific device's state tensor that we need.
+                if rank in lm._per_rank_rng_states:
+                    _set_rng_state(*lm._per_rank_rng_states[rank])
+
+                per_rank_states[rank] = self._device_handle.get_rng_state()
+        finally:
+            _set_rng_state(*original_state)
+
+        # pyrefly: ignore [bad-argument-type, bad-argument-count]
+        return LocalTensor(per_rank_states)
+
+    def set_rng_state(self, state):
+        """
+        Set RNG state, automatically handling LocalTensor input.
+
+        Args:
+            state: Regular Tensor or LocalTensor with per-rank states
+        """
+        if isinstance(state, LocalTensor):
+            lm = enabled_local_tensor_mode()
+            assert lm is not None
+
+            # Similar to get_rng_state but in reverse: we need to convert from
+            # device-specific tensor format to full state tuple format.
+            # - state._local_tensors[rank] contains just the device-specific RNG state tensor
+            # - lm._per_rank_rng_states[rank] needs a tuple: (cpu_state, {device_idx: cuda_state})
+            # So we set the device's state with the rank-specific tensor, then _get_rng_state()
+            # captures both CPU and CUDA states into the tuple format that _per_rank_rng_states expects.
+            for rank, rank_state in state._local_tensors.items():
+                self._device_handle.set_rng_state(rank_state.to("cpu"))
+                lm._per_rank_rng_states[rank] = _get_rng_state()
+        else:
+            self._device_handle.set_rng_state(state.to("cpu"))
+
+    def __getattr__(self, name):
+        """Delegate all other attributes to the underlying device module."""
+        return getattr(self._device_handle, name)
+
+
+class _LocalOffsetBasedRNGTracker:
+    """
+    LocalTensor-specific RNG tracker for DTensor random operations.
+
+    This class manages per-rank RNG states when running in LocalTensor mode,
+    using _LocalPhiloxState to track different offsets for each virtual rank.
+    It is instantiated and used by OffsetBasedRNGTracker when in LocalTensor mode.
+
+    Much of this is derived from OffsetBasedRNGTracker:
+    https://github.com/pytorch/pytorch/blob/402c46503002f98ccfc023a733081fb0719223a1/torch/distributed/tensor/_random.py#L182
+    """
+
+    def __init__(self, device_type: str = "cuda"):
+        """Initialize the LocalTensor RNG tracker."""
+        from torch.distributed.device_mesh import _get_device_handle
+
+        self._device_type = device_type
+        self._device_handle = _LocalDeviceHandle(
+            _get_device_handle(device_type), device_type
+        )
+        self.distribute_region_enabled = True
+        self._device_mesh = None
+
+    @property
+    def _device(self):
+        return torch.device(self._device_type, torch.cuda.current_device())
+
+    def _set_pre_op_offset(self, state, spec) -> None:
+        """Compute and set per-rank offsets before the random operation."""
+        from torch.distributed.tensor._ops.utils import prod
+        from torch.distributed.tensor._utils import (
+            _compute_local_shape_and_global_offset,
+        )
+        from torch.distributed.tensor.placement_types import Shard
+
+        lm = enabled_local_tensor_mode()
+        assert lm is not None
+
+        state._per_rank_offsets = {}
+
+        for rank in lm.ranks:
+            # compute this rank's coordinate in the mesh
+            mesh_coords = []
+            for mesh_dim_idx in range(spec.mesh.ndim):
+                mesh_dim_size = spec.mesh.size(mesh_dim_idx)
+                # calculate rank's coordinate in this mesh dimension
+                num_chunks_after = 1
+                for j in range(mesh_dim_idx + 1, spec.mesh.ndim):
+                    num_chunks_after *= spec.mesh.size(j)
+                coord = (rank // num_chunks_after) % mesh_dim_size
+                mesh_coords.append(coord)
+
+            # compute shard offset based on placements
+            from torch.distributed.tensor._random import (
+                _calc_first_shard_size,
+                _calc_shard_info,
+                _calc_shard_linear_idx,
+            )
+
+            # Compute shard index and total number of shards on each tensor dim
+            shard_idx_by_dim, total_num_shards_by_dim = _calc_shard_info(
+                mesh_coords, spec
+            )
+
+            # compute shard linear index
+            shard_linear_idx = _calc_shard_linear_idx(
+                shard_idx_by_dim, total_num_shards_by_dim
+            )
+
+            # get current offset for this rank
+            current_offset = int(
+                state._per_rank_states[rank][8:].view(dtype=torch.int64).item()
+            )
+
+            local_shape = _calc_first_shard_size(spec)
+            # compute local size
+            local_size = prod(local_shape)
+
+            # compute new offset (must be multiple of 4)
+            offset_incr = (shard_linear_idx * local_size + 3) // 4 * 4
+            state._per_rank_offsets[rank] = current_offset + offset_incr
+
+    def _set_post_op_offset(self, state, spec, old_offset) -> None:
+        """Set per-rank offsets after the random operation."""
+        from torch.distributed.tensor._ops.utils import prod
+
+        lm = enabled_local_tensor_mode()
+        assert lm is not None
+
+        dtensor_shape = spec.shape
+        numel = prod(dtensor_shape)
+        # offset must be multiple of 4
+        numel = (numel + 3) // 4 * 4
+
+        if not hasattr(state, "_per_rank_offsets"):
+            state._per_rank_offsets = {}
+
+        # handle LocalIntNode old_offset (different values per rank)
+        if isinstance(old_offset, SymInt) and isinstance(old_offset.node, LocalIntNode):
+            for rank in lm.ranks:
+                rank_old_offset = old_offset.node._local_ints[rank]
+                state._per_rank_offsets[rank] = rank_old_offset + numel
+        else:
+            # same old_offset for all ranks
+            old_offset_int = (
+                int(old_offset) if isinstance(old_offset, SymInt) else old_offset
+            )
+            for rank in lm.ranks:
+                state._per_rank_offsets[rank] = old_offset_int + numel
+
+    @contextlib.contextmanager
+    def _distribute_region(self, spec, generator=None):
+        """Context manager for LocalTensor mode distribute region."""
+        lm = enabled_local_tensor_mode()
+        assert lm is not None
+
+        # get base state
+        if generator is not None:
+            base_state_tensor = generator.get_state()
+            per_rank_states = {rank: base_state_tensor.clone() for rank in lm.ranks}
+            # pyrefly: ignore [bad-argument-type, bad-argument-count]
+            base_state_tensor = LocalTensor(per_rank_states)
+        else:
+            base_state_tensor = self._device_handle.get_rng_state()
+
+        state = _LocalPhiloxState(base_state_tensor)
+
+        if self.distribute_region_enabled:
+            # sync to rank 0's state if no explicit generator
+            if generator is None:
+                any_rank_state = lm._any_local_rng_state()
+                any_rank_cpu, any_rank_cuda = any_rank_state
+
+                if self._device.type == "cuda":
+                    assert self._device.index in any_rank_cuda
+                    any_rank_device_state = any_rank_cuda[self._device.index]
+                else:
+                    any_rank_device_state = any_rank_cpu
+
+                from torch.distributed.tensor._random import _PhiloxState
+
+                any_rank_philox = _PhiloxState(any_rank_device_state)
+                state.seed = any_rank_philox.seed
+                state.offset = any_rank_philox.offset
+
+            old_offset = state.offset
+            self._set_pre_op_offset(state, spec)
+            state.apply_to_local_tensor_mode(self._device_handle)
+
+            try:
+                yield
+            finally:
+                self._set_post_op_offset(state, spec, old_offset)
+                state.apply_to_local_tensor_mode(self._device_handle)
+        else:
+            yield
+
+        # maybe reset generator to rank 0's state
+        if generator is not None:
+            rank_0_state = state._per_rank_states[0]
+            generator.set_state(rank_0_state)
+
+
+_LOCAL_TENSOR_ATTR_PREFIX = "_local_tensor_"
+
+
+def _is_local_tensor_attr(attr: str) -> bool:
+    return attr.startswith(_LOCAL_TENSOR_ATTR_PREFIX)
+
+
+def _to_local_tensor_attr(rank: int) -> str:
+    return f"{_LOCAL_TENSOR_ATTR_PREFIX}{rank}"
+
+
+def _from_local_tensor_attr(attr: str) -> int:
+    if not _is_local_tensor_attr(attr):
+        raise AssertionError(f"Invalid local tensor attr {attr}")
+    return int(attr[len(_LOCAL_TENSOR_ATTR_PREFIX) :])
+
+
+def _all_elements_same(values: list[Any]) -> bool:
+    if not values:
+        return True
+    first_value = values[0]
+    return all(value == first_value for value in values)
+
+
+def _compute_local_tensor_meta(
+    local_tensors: dict[int, torch.Tensor],
+) -> tuple[
+    list[torch.SymInt | int],
+    list[torch.SymInt | int],
+    torch.device,
+    torch.dtype,
+    torch.layout,
+    DispatchKeySet,
+]:
+    """
+    Computes the meta information for a LocalTensor from its local tensors.
+    """
+    it = iter(local_tensors.values())
+    first_local_tensor = next(it)
+
+    first_shape = first_local_tensor.shape
+    first_stride = first_local_tensor.stride()
+    dtype = first_local_tensor.dtype
+    device = first_local_tensor.device
+    layout = first_local_tensor.layout
+
+    extra_dispatch_keys = _get_extra_dispatch_keys(first_local_tensor)
+
+    # Assert that all tensors have the same dtype, layout and dispatch keys. Due
+    # to uneven sharding, it is possible that tensors will have different shapes.
+    for local_tensor in it:
+        assert dtype == local_tensor.dtype, (
+            "Tensors representing LocalTensor shards must have the same dtype"
+        )
+        assert layout == local_tensor.layout, (
+            "Tensors representing LocalTensor shards must have the same layout"
+        )
+        assert extra_dispatch_keys == _get_extra_dispatch_keys(local_tensor), (
+            "Tensors representing LocalTensor shards must have the same set of extra dispatch keys"
+        )
+
+    # Compute shape/stride.  We allow for non-SPMD'ness here
+    local_shapes: dict[int, dict[int, int]] = defaultdict(dict)  # dim => rank => size
+    local_strides: dict[int, dict[int, int]] = defaultdict(dict)  # dim => rank => size
+    for r, local_tensor in local_tensors.items():
+        for d, size in enumerate(local_tensor.shape):
+            local_shapes[d][r] = size
+            local_strides[d][r] = local_tensor.stride(d)
+    shape = [
+        (
+            first_shape[d]
+            if _all_elements_same(list(local_shapes[d].values()))
+            else torch.SymInt(LocalIntNode(local_shapes[d]))
+        )
+        for d in range(len(first_shape))
+    ]
+    strides = [
+        (
+            first_stride[d]
+            if _all_elements_same(list(local_strides[d].values()))
+            else torch.SymInt(LocalIntNode(local_strides[d]))
+        )
+        for d in range(len(first_shape))
+    ]
+    return shape, strides, device, dtype, layout, extra_dispatch_keys
+
+
+class LocalTensor(torch.Tensor):
+    """
+    LocalTensor is a Tensor subclass that simulates a tensor distributed across multiple SPMD
+    (Single Program, Multiple Data) ranks. Each LocalTensor instance internally holds a mapping from
+    global rank ids to their corresponding local Tensor shards.Operations performed on a LocalTensor
+    are applied independently to each local shard, mimicking distributed computation. Collectives
+    and other distributed operations are handled by mapping them to the local shards as appropriate.
+
+    Note:
+        This class is primarily intended for debugging and simulating distributed tensor computations
+        on a single process.
+
+    """
+
+    # Map from global rank to the local tensor.
+    _local_tensors: dict[int, torch.Tensor]
+    # Precomputed for speed set of keys from the local tensor map.
+    _ranks: frozenset[int]
+    _size: list[torch.SymInt | int]
+    __slots__ = ["_local_tensors", "_ranks", "_size"]
+
+    @staticmethod
+    @torch._disable_dynamo
+    def __new__(
+        cls,
+        local_tensors: dict[int, torch.Tensor],
+        requires_grad: bool = False,
+    ) -> "LocalTensor":
+        if any(t.requires_grad for t in local_tensors.values()):
+            raise AssertionError(
+                "Internal local_tensors require grad, but we will ignore those autograd graph. "
+                "Make a custom autograd function and make sure you detach the inner tensors."
+            )
+
+        (shape, strides, device, dtype, layout, extra_dispatch_keys) = (
+            _compute_local_tensor_meta(local_tensors)
+        )
+
+        r = torch.Tensor._make_wrapper_subclass(
+            cls,
+            shape,
+            strides=strides,
+            dtype=dtype,
+            device=device,
+            layout=layout,
+            # In place ops potentially change local tensor sizes (e.g. resize_). While
+            # executing an in-place op the return value must be the same as "self" input
+            # otherwise we can introduce errors due to tensor identity changes. Hence we
+            # need to be able to update wrapper subclass sizes after in-place ops. This
+            # dispatch policy allows us to do that.
+            dispatch_sizes_strides_policy="sizes",
+            requires_grad=requires_grad,
+            _extra_dispatch_keys=extra_dispatch_keys,
+        )
+
+        local_tensors = {
+            r: v if not isinstance(v, AsyncCollectiveTensor) else v.wait()
+            for r, v in local_tensors.items()
+        }
+        r._local_tensors = local_tensors
+        r._ranks = frozenset(local_tensors.keys())
+        r._size = shape
+        return r
+
+    @torch._disable_dynamo
+    @mark_subclass_constructor_exportable_experimental  # type: ignore[misc]
+    def __init__(self, *args: Any, **kwargs: Any):
+        super().__init__()
+
+    def __deepcopy__(self, memo: dict[Any, Any] | None) -> "LocalTensor":
+        local_tensors_copy = {
+            r: copy.deepcopy(t, memo) for r, t in self._local_tensors.items()
+        }
+        # pyrefly: ignore [bad-argument-type, bad-argument-count]
+        return LocalTensor(local_tensors_copy, self.requires_grad)
+
+    def __repr__(self) -> str:  # type: ignore[override]
+        parts = []
+        for k, v in self._local_tensors.items():
+            # pyrefly: ignore [bad-argument-type]
+            parts.append(f"  {k}: {v}")
+        tensors_str = ",\n".join(parts)
+        return f"LocalTensor(\n{tensors_str}\n)"
+
+    def __getattr__(self, name: str) -> Any:
+        if _is_local_tensor_attr(name):
+            rank = _from_local_tensor_attr(name)
+            if rank not in self._ranks:
+                raise AttributeError(f"Local tensor has no knowledge of rank {rank}")
+            return self._local_tensors[rank]
+        return object.__getattribute__(self, name)
+
+    def __tensor_flatten__(self) -> tuple[list[str], tuple[Any, ...]]:
+        """
+        protocol to inform how to flatten a DTensor to local tensor
+        for PT2 tracing
+        """
+        local_tensor_attrs = [_to_local_tensor_attr(r) for r in self._ranks]
+        return local_tensor_attrs, ()
+
+    @staticmethod
+    def __tensor_unflatten__(
+        inner_tensors: dict[str, Any],
+        flatten_spec: tuple[Any, ...],
+        outer_size: torch.Size,
+        outer_stride: tuple[int, ...],
+    ) -> "LocalTensor":
+        assert flatten_spec is not None, (
+            "Expecting spec to be not None from `__tensor_flatten__` return value!"
+        )
+        local_tensors = {
+            _from_local_tensor_attr(a): t for a, t in inner_tensors.items()
+        }
+        # pyrefly: ignore [bad-argument-type, bad-argument-count]
+        return LocalTensor(local_tensors)
+
+    @classmethod
+    @torch._disable_dynamo
+    def __torch_dispatch__(  # type: ignore[override]
+        cls,
+        func: Any,
+        types: tuple[Any, ...],
+        args: tuple[Any, ...] = (),
+        kwargs: dict[str, Any] | None = None,
+    ) -> Any:
+        if kwargs is None:
+            kwargs = {}
+
+        # This is horribly inefficient
+        flat_args, args_spec = pytree.tree_flatten((args, kwargs))
+        local_tensor = None
+        for arg in flat_args:
+            if isinstance(arg, LocalTensor):
+                local_tensor = arg
+                break
+
+        assert local_tensor is not None, (
+            "At least one of the arguments must be a LocalTensor"
+        )
+
+        # Check for unrecognized tensor subclasses (but allow regular tensors and scalars)
+        has_unrecognized_types = _check_for_subclass(flat_args)
+        if has_unrecognized_types:
+            unrecognized_types = [
+                type(x) for x in flat_args if _check_for_subclass_arg(x)
+            ]
+            not_implemented_log.debug(
+                "LocalTensor unrecognized subclass(es): %s", unrecognized_types
+            )
+            return NotImplemented
+
+        with LocalTensorMode(local_tensor._ranks):
+            return func(*args, **kwargs)
+
+    def numpy(self, *, force: bool = False) -> Any:
+        if HAS_NUMPY:
+            return self.reconcile().numpy(force=force)
+        else:
+            raise RuntimeError("Numpy is not available")
+
+    def contiguous(
+        self,
+        memory_format: torch.memory_format = torch.contiguous_format,
+    ) -> torch.Tensor:
+        # pyrefly: ignore [bad-argument-type]
+        return LocalTensor(
+            # pyrefly: ignore [bad-argument-count]
+            {
+                r: t.contiguous(memory_format=memory_format)
+                for r, t in self._local_tensors.items()
+            }
+        )
+
+    def is_contiguous(
+        self,
+        memory_format: torch.memory_format = torch.contiguous_format,
+    ) -> bool:
+        return all(
+            t.is_contiguous(memory_format=memory_format)
+            for t in self._local_tensors.values()
+        )
+
+    def tolist(self) -> list[Any]:
+        """
+        Try to reconcile, if successful convert to list, otherwise if dtype is integer,
+        convert to list of local integers.
+        """
+        equal_obj = self._equal_local_tensors()
+        if isinstance(equal_obj, torch.Tensor):
+            return equal_obj.tolist()
+        if isinstance(equal_obj, torch.Size):
+            if not self.dtype.is_floating_point and not self.dtype.is_complex:
+                ranks = sorted(self._ranks)
+                local_lists = [self._local_tensors[r].tolist() for r in ranks]
+                return _reduce_multidim_lists(
+                    local_lists,
+                    lambda values: torch.SymInt(
+                        LocalIntNode(dict(zip(ranks, values, strict=True)))
+                    ),
+                )
+
+        raise RuntimeError("Cannot convert local tensor to list")
+
+    def reconcile(self) -> torch.Tensor:
+        """
+        Reconciles the LocalTensor into a single torch.Tensor by ensuring all local
+        shards are identical and returning a detached clone of one of them.
+
+        Note:
+            This method is useful for extracting a representative tensor from a LocalTensor
+            when all shards are expected to be the same, such as after a collective operation
+            that synchronizes all ranks.
+        """
+
+        # Force all local tensor shards across ranks to be the same
+        equal_obj = self._equal_local_tensors()
+        assert isinstance(equal_obj, torch.Tensor), (
+            "LocalTensor shards must be the same to reconcile"
+        )
+        cl = equal_obj.clone().detach()
+        cl.requires_grad_(self.requires_grad)
+        return cl
+
+    def _equal_local_tensors(self) -> torch.Tensor | torch.Size | None:
+        it = iter(self._local_tensors.values())
+        t1 = next(it)
+        if all(t2.equal(t1) for t2 in it):
+            return t1
+        if all(t2.shape == t1.shape for t2 in it):
+            return t1.shape
+        return None
+
+    def _sync_meta(self) -> None:
+        with no_dispatch():
+            (shape, strides, device, dtype, layout, extra_dispatch_keys) = (
+                _compute_local_tensor_meta(self._local_tensors)
+            )
+            self._size = shape
+
+
+# If set to `True` the LocalTensorMode stack will be created for the whole process,
+# otherwise it will be created for each thread.
+_PROCESS_MODE: bool = True
+_PROCESS_LOCAL_TENSOR_MODE: list["LocalTensorMode"] = []
+# When running under local runner each thread must create its own local tensor mode
+# so that they do not interfere with each other.
+_THREAD_LOCAL_TENSOR_MODE: threading.local = threading.local()
+
+
+def get_local_tensor_mode_list() -> list["LocalTensorMode"]:
+    global _PROCESS_MODE
+    if _PROCESS_MODE:
+        global _PROCESS_LOCAL_TENSOR_MODE
+        return _PROCESS_LOCAL_TENSOR_MODE
+    global _THREAD_LOCAL_TENSOR_MODE
+    if not hasattr(_THREAD_LOCAL_TENSOR_MODE, "value"):
+        _THREAD_LOCAL_TENSOR_MODE.value = []
+    return _THREAD_LOCAL_TENSOR_MODE.value
+
+
+class LocalTensorMode(TorchDispatchMode):
+    """
+    A TorchDispatchMode that simulates SPMD (Single Program, Multiple Data) execution
+    for LocalTensor objects across a set of ranks.
+
+    LocalTensorMode enables PyTorch operations to be transparently applied to each
+    local shard of a LocalTensor, as if they were distributed across multiple ranks.
+    When active, this mode intercepts tensor operations and dispatches them to each
+    rank's local tensor, collecting and wrapping the results as LocalTensors. It also
+    handles collective operations by mapping them to local implementations.
+
+    This mode is primarily intended for debugging and simulating distributed tensor
+    computations on a single process, rather than for high-performance distributed
+    training. It maintains a stack of active modes, patches DeviceMesh coordinate
+    resolution, and provides utilities for temporarily disabling the mode or mapping
+    functions over ranks.
+    """
+
+    # What ranks this local tensor mode is operating over
+    def __init__(self, ranks: int | frozenset[int]):
+        if isinstance(ranks, int):
+            # assume is world size
+            self.ranks = frozenset(range(ranks))
+        else:
+            assert isinstance(ranks, frozenset)
+            self.ranks = ranks
+        self._disable = True
+        self._old_get_coordinate = None
+        self._old_get_rank = None
+        self._old_get_local_rank = None
+        self._old_torch_manual_seed: Any = None
+        self._old_torch_initial_seed: Any = None
+        self._per_rank_rng_states: dict[
+            int, tuple[torch.Tensor, dict[int, torch.Tensor]]
+        ] = {}
+
+        self.enable_()
+
+    def __enter__(self) -> "LocalTensorMode":
+        self.enable_()
+        get_local_tensor_mode_list().append(self)
+
+        # _distribute_region will compute correct per-shard offsets
+        # but we want all ranks to start with the same state
+        if not _is_in_fake_tensor_mode():
+            cpu_state, cuda_states = _get_rng_state()
+            for rank in self.ranks:
+                self._per_rank_rng_states[rank] = (
+                    cpu_state.clone(),
+                    {idx: state.clone() for idx, state in cuda_states.items()},
+                )
+
+        return super().__enter__()
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_val: BaseException | None,
+        exc_tb: TracebackType | None,
+    ) -> None:
+        self.disable_()
+        get_local_tensor_mode_list().pop()
+        super().__exit__(exc_type, exc_val, exc_tb)
+
+    def __torch_dispatch__(
+        self,
+        func: Any,
+        types: tuple[Any, ...],
+        args: tuple[Any, ...] = (),
+        kwargs: dict[str, Any] | None = None,
+    ) -> Any:
+        if kwargs is None:
+            kwargs = {}
+
+        flat_args, args_spec = pytree.tree_flatten((args, kwargs))
+
+        # Find all LocalTensor arguments to determine ranks
+        local_tensors = [a for a in flat_args if isinstance(a, LocalTensor)]
+
+        # Check for unrecognized tensor subclasses (but allow regular tensors and scalars)
+        has_unrecognized_types = _check_for_subclass(flat_args)
+        if has_unrecognized_types:
+            unrecognized_types = [
+                type(x) for x in flat_args if _check_for_subclass_arg(x)
+            ]
+            not_implemented_log.debug(
+                "LocalTensorMode unrecognized subclass(es): %s", unrecognized_types
+            )
+            return NotImplemented
+
+        # Factory functions convert into LocalTensor, so we don't have to
+        # transmute a Tensor into a LocalTensor if mutation happens...
+        # But if you do an operation on a Tensor, do NOT wrap it into a
+        # LocalTensor.  This helps prevent accidents when you're doing Tensor
+        # operations on the inner non-wrapped tensors.
+        if not local_tensors:
+            if self._disable or any(isinstance(a, Tensor) for a in flat_args):
+                return func(*args, **kwargs)
+
+        # For LocalTensors, verify they have compatible ranks
+        for a in flat_args:
+            if isinstance(a, LocalTensor):
+                assert a._ranks <= self.ranks, (
+                    f"Input LocalTensor {a} must be configured for a subset of the LocalTensorMode ranks {self.ranks}"
+                )
+
+        if func.overloadpacket == torch.ops.aten.dim:
+            return len(args[0]._size)
+        if func.overloadpacket == torch.ops.aten.sym_size:
+            return tuple(args[0]._size)
+
+        if func.namespace == "c10d":
+            if func is torch.ops.c10d.allreduce_.default:
+                return _c10d._local_all_reduce_(*args, **kwargs)
+            elif func is torch.ops.c10d.allreduce_coalesced_.default:
+                return _c10d._local_allreduce_coalesced_(*args, **kwargs)
+            elif func is torch.ops.c10d.reduce_scatter_tensor_coalesced_.default:
+                return _c10d._local_reduce_scatter_tensor_coalesced_(*args, **kwargs)
+            elif func is torch.ops.c10d.scatter_.default:
+                return _c10d._local_scatter_(*args, **kwargs)
+            elif func is torch.ops.c10d.broadcast_.default:
+                return _c10d._local_broadcast_(*args, **kwargs)
+            elif func is torch.ops.c10d.allgather_.default:
+                return _c10d._local_all_gather_(*args, **kwargs)
+            elif func is torch.ops.c10d.allgather_into_tensor_coalesced_.default:
+                return _c10d._local_allgather_into_tensor_coalesced_(*args, **kwargs)
+            elif func is torch.ops.c10d._allgather_base_.default:
+                return _c10d._local_allgather_base_(*args, **kwargs)
+            elif func is torch.ops.c10d._reduce_scatter_base_.default:
+                return _c10d._local_reduce_scatter_base_(*args, **kwargs)
+            elif func is torch.ops.c10d.gather_.default:
+                return _c10d._local_gather_(*args, **kwargs)
+            elif func is torch.ops.c10d.alltoall_.default:
+                return _c10d._local_alltoall_(*args, **kwargs)
+            elif func is torch.ops.c10d.alltoall_base_.default:
+                return _c10d._local_alltoall_base_(*args, **kwargs)
+            elif func is torch.ops.c10d.barrier.default:
+                return _c10d._local_barrier(*args, **kwargs)
+            elif func is torch.ops.c10d.monitored_barrier_.default:
+                return _c10d._local_monitored_barrier_(*args, **kwargs)
+            elif func is torch.ops.c10d.send.default:
+                return _c10d._local_send(*args, **kwargs)
+            elif func is torch.ops.c10d.recv_.default:
+                return _c10d._local_recv_(*args, **kwargs)
+            elif func is torch.ops.c10d.recv_any_source_.default:
+                return _c10d._local_recv_any_source_(*args, **kwargs)
+            raise NotImplementedError(f"{func} not implemented")
+
+        if func.namespace == "_c10d_functional" or func.namespace == "_dtensor":
+            if func is torch.ops._dtensor.shard_dim_alltoall.default:
+                return _c10d._local_functional_shard_dim_alltoall(*args, **kwargs)
+            elif func is torch.ops._c10d_functional.all_gather_into_tensor.default:
+                return _c10d._local_functional_all_gather_into_tensor(*args, **kwargs)
+            elif func is torch.ops._c10d_functional.reduce_scatter_tensor.default:
+                return _c10d._local_functional_reduce_scatter_tensor(*args, **kwargs)
+            elif func is torch.ops._c10d_functional.all_to_all_single.default:
+                return _c10d._local_functional_all_to_all_single(*args, **kwargs)
+            else:
+                with LocalTensorMode(self.ranks):
+                    return func._op_dk(
+                        DispatchKey.CompositeExplicitAutograd, *args, **kwargs
+                    )
+
+        if func.namespace == "profiler":
+            return func(*args, **kwargs)
+
+        if func.namespace == "_c10d_functional_autograd":
+            raise NotImplementedError(f"{func} not implemented")
+
+        if func.namespace == "symm_mem":
+            raise NotImplementedError(f"{func} not implemented")
+
+        return _for_each_rank_run_func(func, self.ranks, args, kwargs, alias=True)
+
+    def disable_(self):
+        if self._disable:
+            return
+
+        self._unpatch_device_mesh()
+        self._unpatch_random_functions()
+        self._disable = True
+
+    def enable_(self):
+        if not self._disable:
+            return
+
+        self._patch_device_mesh()
+        self._patch_random_functions()
+        self._disable = False
+
+    @contextlib.contextmanager
+    def disable(self) -> Generator[None, None, None]:
+        """
+        Disables LocalTensorMode temporarily. Primarily is intended to be used to perform
+        rank specific computations and merge results back before enabling LocalTensorMode back.
+        """
+
+        # don't unpatch again if already disabled
+        if self._disable:
+            try:
+                yield
+            finally:
+                # re-disable if the yield messed
+                # with the state
+                self.disable_()
+            return
+
+        self.disable_()
+        try:
+            yield
+        finally:
+            self.enable_()
+
+    def rank_map(self, cb: Callable[[int], Tensor]) -> LocalTensor:
+        """
+        Creates a LocalTensor instance by mapping rank id to ids local shard.
+        """
+
+        with self.disable():
+            # pyrefly: ignore [bad-argument-type, bad-argument-count]
+            return LocalTensor({r: cb(r) for r in self.ranks})
+
+    def tensor_map(
+        self, tensor: LocalTensor, cb: Callable[[int, Tensor], Tensor | None]
+    ) -> LocalTensor:
+        """
+        Creates a LocalTensor instance by mapping rank id to ids local shard.
+        """
+
+        with self.disable():
+            results = {}
+            for r in self.ranks:
+                if r in tensor._local_tensors:
+                    m = cb(r, tensor._local_tensors[r])
+                    if m is not None:
+                        results[r] = m
+            # pyrefly: ignore [bad-argument-type, bad-argument-count]
+            return LocalTensor(results)
+
+    def _any_local_rng_state(self) -> tuple[torch.Tensor, dict[int, torch.Tensor]]:
+        return self._per_rank_rng_states[next(iter(self.ranks))]
+
+    def _patch_device_mesh(self) -> None:
+        assert self._old_get_coordinate is None
+        assert self._old_get_rank is None
+        assert self._old_get_local_rank is None
+        self._old_get_coordinate = DeviceMesh.get_coordinate  # type: ignore[assignment]
+        self._old_get_rank = DeviceMesh.get_rank  # type: ignore[assignment]
+        self._old_get_local_rank = DeviceMesh.get_local_rank  # type: ignore[assignment]
+        DeviceMesh.get_coordinate = _LocalDeviceMesh.get_coordinate  # type: ignore[method-assign]
+        DeviceMesh.get_rank = _LocalDeviceMesh.get_rank  # type: ignore[method-assign]
+        DeviceMesh.get_local_rank = _LocalDeviceMesh.get_local_rank  # type: ignore[method-assign]
+
+    def _unpatch_device_mesh(self) -> None:
+        assert self._old_get_coordinate is not None
+        assert self._old_get_rank is not None
+        assert self._old_get_local_rank is not None
+        DeviceMesh.get_coordinate = self._old_get_coordinate
+        DeviceMesh.get_rank = self._old_get_rank
+        DeviceMesh.get_local_rank = self._old_get_local_rank
+        # pyrefly: ignore [bad-assignment]
+        self._old_get_coordinate = None
+        # pyrefly: ignore [bad-assignment]
+        self._old_get_rank = None
+        # pyrefly: ignore [bad-assignment]
+        self._old_get_local_rank = None
+
+    def _patch_random_functions(self) -> None:
+        import torch.random
+        from torch.distributed.tensor import _random as dtensor_random
+
+        if self._old_torch_manual_seed is None:
+            self._old_torch_manual_seed = torch.random.manual_seed
+            torch.random.manual_seed = _LocalRandom.torch_manual_seed
+            torch.manual_seed = _LocalRandom.torch_manual_seed
+
+        if self._old_torch_initial_seed is None:
+            self._old_torch_initial_seed = torch.random.initial_seed
+            torch.random.initial_seed = _LocalRandom.torch_initial_seed
+            torch.initial_seed = _LocalRandom.torch_initial_seed
+
+    def _unpatch_random_functions(self) -> None:
+        import torch.random
+        from torch.distributed.tensor import _random as dtensor_random
+
+        if self._old_torch_manual_seed is not None:
+            torch.random.manual_seed = self._old_torch_manual_seed
+            torch.manual_seed = self._old_torch_manual_seed
+            self._old_torch_manual_seed = None
+
+        if self._old_torch_initial_seed is not None:
+            torch.random.initial_seed = self._old_torch_initial_seed
+            torch.initial_seed = self._old_torch_initial_seed
+            self._old_torch_initial_seed = None
+
+
+class _LocalRandom:
+    """
+    Holds implementations of random functionality that must be patched while running
+    under LocalTensorMode.
+    """
+
+    @staticmethod
+    def torch_manual_seed(seed) -> torch._C.Generator:
+        """LocalTensor-aware version of torch.random.manual_seed."""
+        if (
+            (lm := enabled_local_tensor_mode())
+            and isinstance(seed, torch.SymInt)
+            and isinstance(seed.node, LocalIntNode)
+        ):
+            from torch.random import _manual_seed_impl
+
+            for rank in sorted(lm.ranks):
+                rank_seed = seed.node._local_ints[rank]
+                _manual_seed_impl(rank_seed)
+                lm._per_rank_rng_states[rank] = _get_rng_state()
+            return torch.random.default_generator
+        from torch.random import _manual_seed_impl
+
+        result = _manual_seed_impl(seed)
+
+        if lm is not None and len(lm._per_rank_rng_states) > 0:
+            cpu_state, cuda_states = _get_rng_state()
+            for rank in lm.ranks:
+                lm._per_rank_rng_states[rank] = (
+                    cpu_state.clone(),
+                    {idx: state.clone() for idx, state in cuda_states.items()},
+                )
+
+        return result
+
+    @staticmethod
+    def torch_initial_seed():
+        """LocalTensor-aware version of torch.random.initial_seed."""
+        if lm := enabled_local_tensor_mode():
+            if len(lm._per_rank_rng_states) == 0:
+                return torch.random.default_generator.initial_seed()
+            rank_seeds = {}
+
+            for rank in sorted(lm.ranks):
+                _set_rng_state(*lm._per_rank_rng_states[rank])
+                rank_seeds[rank] = torch.random.default_generator.initial_seed()
+
+            local_int_node = LocalIntNode(rank_seeds)
+            return torch.SymInt(local_int_node)
+
+        return torch.random.default_generator.initial_seed()
+
+
+# Save the original get_coordinate method before any patching
+
+
+class _LocalDeviceMesh:
+    """
+    Holds implementations of DeviceMesh functionality that must be patched while running
+    under LocalTensorMode.
+    """
+
+    @staticmethod
+    def get_coordinate(self: DeviceMesh) -> list[int] | None:
+        # NB: In order to support submeshes the code below recreates for each
+        # rank submesh with the same mesh dimensions as current mesh. We are
+        # doing this because when submesh is created it is created for a particular
+        # rank (therefore below we are patching get_rank method). We are trying to
+        # limit the invasiveness of local tensor.
+        lm = enabled_local_tensor_mode()
+        assert lm is not None, "Unexpectedly not in LocalTensorMode"
+
+        coords: list[dict[int, int]] = [{} for _ in range(self.ndim)]
+        for r in lm.ranks:
+            rank_tensor = self._layout.remap_to_tensor(self._rank_map)
+            rank_coords = (rank_tensor == r).nonzero().tolist()
+            assert len(rank_coords) == 1
+            for d, c in enumerate(rank_coords[0][1:]):
+                coords[d][r] = c
+
+        out = [torch.SymInt(LocalIntNode(c)) for c in coords]
+        # The output contains coordinates for each of the ranks with respect to
+        # their meshes formed from root mesh and selecting the same dimensions
+        # as the current mesh.
+        return out  # type: ignore[return-value]
+
+    @staticmethod
+    def get_rank(self) -> int | SymInt:
+        lm = enabled_local_tensor_mode()
+        assert lm is not None, "Unexpectedly not in LocalTensorMode"
+        return torch.SymInt(LocalIntNode(local_ints={r: r for r in lm.ranks}))
+
+    @staticmethod
+    def get_local_rank(self, mesh_dim: int | str | None = None) -> int | SymInt:
+        lm = enabled_local_tensor_mode()
+        assert lm is not None, "Unexpectedly not in LocalTensorMode"
+
+        if self.ndim > 1 and mesh_dim is None:
+            raise RuntimeError(
+                f"Found the DeviceMesh have {self.ndim} dimensions",
+                "Optional kwarg `mesh_dim` needs to be specified when device_mesh.ndim > 1.",
+            )
+        elif mesh_dim is None:
+            mesh_dim = 0
+
+        if isinstance(mesh_dim, str):
+            mesh_dim = self._mesh_dim_names.index(mesh_dim)
+
+        # Compute local rank for each global rank
+        # get_coordinate returns a list of SymInt, one per mesh dimension
+        # We need to extract the coordinate for the specified mesh_dim
+        coords = _LocalDeviceMesh.get_coordinate(self)
+        assert coords is not None
+        return coords[mesh_dim]
+
+
+def reconcile_args(args: Any, kwargs: dict[str, Any] | None = None) -> Any:
+    """
+    Reconciles arguments by converting any LocalTensor instances in the input
+    arguments to their underlying torch.Tensor representation.
+
+    This function is typically used to prepare arguments for functions that
+    expect standard torch.Tensor objects, by flattening the input arguments,
+    replacing LocalTensor instances with their reconciled (standard tensor)
+    versions, and then reconstructing the original argument structure.
+
+    Args:
+        args: Positional arguments, possibly containing LocalTensor instances.
+        kwargs: Keyword arguments, possibly containing LocalTensor instances.
+
+    Returns:
+        Any: The arguments with all LocalTensor instances replaced by their reconciled torch.Tensor equivalents,
+             preserving the original structure.
+    """
+    if kwargs is None:
+        kwargs = {}
+    flat_args, args_spec = pytree.tree_flatten((args, kwargs))
+    reconciled_args = [
+        a.reconcile() if isinstance(a, LocalTensor) else a for a in flat_args
+    ]
+    return pytree.tree_unflatten(reconciled_args, args_spec)
+
+
+def local_tensor_mode() -> LocalTensorMode | None:
+    """
+    Returns the current active LocalTensorMode if one exists.
+
+    This function checks the global stack of LocalTensorMode instance. If there
+    is at least one LocalTensorMode active, it returns the most recently entered
+    (top of the stack) LocalTensorMode. If no LocalTensorMode is active, it returns None.
+
+    Returns:
+        Optional[LocalTensorMode]: The current LocalTensorMode if active, else None.
+    """
+    local_tensor_mode_list = get_local_tensor_mode_list()
+    if len(local_tensor_mode_list) > 0:
+        return local_tensor_mode_list[-1]
+    return None
+
+
+def enabled_local_tensor_mode() -> LocalTensorMode | None:
+    """
+    Returns the current active LocalTensorMode only if it's enabled.
+
+    This is a convenience function that combines the common pattern of checking
+    if local_tensor_mode() is not None and not disabled.
+
+    Returns:
+        Optional[LocalTensorMode]: The current LocalTensorMode if active and enabled, else None.
+    """
+    lm = local_tensor_mode()
+    if lm is not None and not lm._disable:
+        return lm
+    return None
+
+
+def maybe_run_for_local_tensor(func: Callable[_P, _R]) -> Callable[_P, _R]:
+    """
+    Decorator that ensures a function is executed for each local tensor shard
+    when running under LocalTensorMode. If not in LocalTensorMode, the function
+    is executed normally. When in LocalTensorMode, the function is run for each
+    rank, and the results are collected appropriately.
+
+    This decorator is useful for functions that exhibit non-SPMD behavior, such
+    as those requiring rank specific actions. For example, a function that computes
+    offset into input tensor based on rank.
+
+    Note that the function being decorated must not have any side effects and
+    contain operations for a single rank only. For example, wrapping a function
+    that performs a collective operation will not work.
+
+    Args:
+        func (Callable[..., Any]): The function to be decorated.
+
+    Returns:
+        Callable[..., Any]: The wrapped function that handles LocalTensorMode logic.
+    """
+
+    @functools.wraps(func)
+    def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> _R:
+        if not (lm := enabled_local_tensor_mode()):
+            return func(*args, **kwargs)
+        ret = None
+        with lm.disable():
+            ret = _for_each_rank_run_func(func, lm.ranks, args, kwargs, alias=False)
+
+        return ret
+
+    return wrapper
+
+
+def maybe_disable_local_tensor_mode() -> contextlib.AbstractContextManager:
+    """
+    Context manager that disables LocalTensorMode for the duration of the context.
+    """
+    lm = local_tensor_mode()
+    return lm.disable() if lm is not None else contextlib.nullcontext()
+
+
+def maybe_enable_local_tracker(
+    device_type: str, distribute_region_enabled: bool, spec, generator
+):
+    """
+    Returns a context manager for LocalTensor-mode RNG tracking if local tensor mode is enabled.
+
+    Args:
+        device_type: The device type (e.g., "cuda", "cpu")
+        distribute_region_enabled: Whether distribute region is enabled
+        spec: The DTensorSpec
+        generator: Optional torch.Generator
+
+    Returns:
+        Context manager from local_tracker._distribute_region if local tensor mode is enabled,
+        otherwise None.
+    """
+    if enabled_local_tensor_mode():
+        local_tracker = _LocalOffsetBasedRNGTracker(device_type)
+        local_tracker.distribute_region_enabled = distribute_region_enabled
+        return local_tracker._distribute_region(spec, generator)
+
+    return None
+
+
+def get_generator_seed_for_device_type(device_type: str):
+    """
+    Gets the generator seed for a specific device type, handling LocalTensor mode appropriately.
+
+    Args:
+        device_type: The device type (e.g., "cuda", "cpu")
+
+    Returns:
+        If in LocalTensor mode with per-rank RNG states:
+            - Returns int if all ranks have the same seed
+            - Returns SymInt(LocalIntNode) if ranks have different seeds
+        Otherwise:
+              - Returns int seed from the device's RNG state
+    """
+    if lm := enabled_local_tensor_mode():
+        if len(lm._per_rank_rng_states) == 0:
+            device_module = torch.get_device_module(device_type)
+            return device_module.get_rng_state()[:8].view(torch.int64).item()
+        device_module = torch.get_device_module(device_type)
+
+        original_state = _get_rng_state()
+
+        rank_seeds = {}
+        try:
+            for rank in sorted(lm.ranks):
+                _set_rng_state(*lm._per_rank_rng_states[rank])
+                rank_seeds[rank] = int(
+                    device_module.get_rng_state()[:8].view(torch.int64).item()
+                )
+        finally:
+            # restore original state
+            _set_rng_state(*original_state)
+
+        unique_seeds = set(rank_seeds.values())
+        if len(unique_seeds) == 1:
+            return next(iter(unique_seeds))
+        local_int_node = LocalIntNode(rank_seeds)
+        return torch.SymInt(local_int_node)
+    else:
+        device_module = torch.get_device_module(device_type)
+        return device_module.get_rng_state()[:8].view(torch.int64).item()
+
+
+import threading
+from queue import Queue
+
+
+_LOCAL_RUNNER_MODE: "LocalRunnerMode | None" = None
+
+
+class LocalRunnerMode:
+    """
+    A class for running multiple SPMD functions concurrently, however at any point
+    in time only one function can be running. The main use case for the local runner
+    mode is to enable SPMD functions to be able to use send and recv to communicate
+    with each other. Without local runner mode send and recv are not supported.
+    """
+
+    runner_context = threading.local()
+
+    def __init__(
+        self, ranks: frozenset[int] | int, concurrency: int, fn: Callable[[int], None]
+    ):
+        if isinstance(ranks, int):
+            ranks = frozenset(range(ranks))
+        self._ranks = ranks
+        self._fn = fn
+        self._run_lock = threading.Lock()
+        self._run_id = -1
+        self._run_cond = threading.Condition(self._run_lock)
+
+        self._recv_objects: dict[int, dict[int, Queue]] = {
+            dst: {src: Queue() for src in ranks} for dst in ranks
+        }
+        self._runners = [
+            threading.Thread(target=self._run, args=(i,), name="LocalRunnerMode")
+            for i in range(concurrency)
+        ]
+        self._process_mode = True
+
+    def __enter__(self) -> "LocalRunnerMode":
+        global _LOCAL_RUNNER_MODE
+        assert _LOCAL_RUNNER_MODE is None, "LocalRunnerMode is already running"
+        _LOCAL_RUNNER_MODE = self
+
+        global _PROCESS_MODE
+        self._process_mode = _PROCESS_MODE
+        _PROCESS_MODE = False
+        for r in self._runners:
+            r.start()
+        return self
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_val: BaseException | None,
+        exc_tb: TracebackType | None,
+    ) -> None:
+        for r in self._runners:
+            r.join()
+        global _LOCAL_RUNNER_MODE
+        _LOCAL_RUNNER_MODE = None
+
+        global _PROCESS_MODE
+        _PROCESS_MODE = self._process_mode
+
+    def _run(self, id: int) -> None:
+        LocalRunnerMode.runner_context.id = id
+        # Only one thread can run at a time, hence must acquire the lock
+        try:
+            self._acquire_run_lock()
+            self._fn(id)
+        finally:
+            self._release_run_lock()
+
+    def _acquire_run_lock(self) -> None:
+        self._run_lock.acquire()
+        self._run_id = LocalRunnerMode.runner_context.id
+
+    def _release_run_lock(self) -> None:
+        self._run_id = -1
+        self._run_lock.release()
+
+    def _assert_holds_run_lock(self) -> None:
+        assert self._run_id == LocalRunnerMode.runner_context.id, (
+            "Calling thread does not hold the run lock"
+        )
+
+    def _get_recv_object(self, src: int, dst: int) -> object | None:
+        peers = [src] if src != -1 else list(self._ranks)
+        recv_objects = self._recv_objects[dst]
+
+        for p in peers:
+            if not recv_objects[p].empty():
+                return recv_objects[p].get()
+
+        return None
+
+    def _signal_send(self, src: int, dst: int, obj: object) -> None:
+        assert obj is not None, "Cannot signal None"
+        # Only a single thread a time executes so it is safe to mutate
+        # read objects queue (executing thread is already holding the lock)
+        self._recv_objects[dst][src].put(obj)
+        # Signal directly condition variable since the calling thread is already
+        # holding the lock
+        self._run_cond.notify_all()
+
+    def _wait_recv(self, src: int, dst: int, post: Callable[[object], None]) -> None:
+        # Wait for the object to be available
+        while True:
+            obj = self._get_recv_object(src, dst)
+            if obj is not None:
+                post(obj)
+                # Note that we are not releasing the lock here, since the thread
+                # will continue to run and therefore must hold the lock
+                return
+            self._run_cond.wait()
+
+    @staticmethod
+    def current() -> "LocalRunnerMode":
+        global _LOCAL_RUNNER_MODE
+        assert _LOCAL_RUNNER_MODE is not None, "LocalRunnerMode is not enabled"
+        return _LOCAL_RUNNER_MODE
+
+
+class _LocalPhiloxState:
+    """
+    LocalTensor-aware version of _PhiloxState that manages per-rank RNG states.
+    This class handles the case where the generator state is a LocalTensor, allowing
+    different offsets and seeds for different virtual ranks.
+
+    Note: This is designed to be used as a drop-in replacement for _PhiloxState
+    when working with LocalTensors in the DTensor random ops implementation.
+    """
+
+    def __init__(self, state: torch.Tensor):
+        assert isinstance(state, LocalTensor), (
+            "_LocalPhiloxState requires a LocalTensor"
+        )
+        self._local_tensor = state
+        self._per_rank_states = {
+            rank: local_state.to("cpu")
+            for rank, local_state in state._local_tensors.items()
+        }
+
+    @property
+    def state(self):
+        return LocalTensor(self._per_rank_states)  # type: ignore[name-defined]
+
+    @property
+    def offset(self) -> int | SymInt:
+        from torch.distributed.tensor._random import _PhiloxState
+
+        offsets = {}
+        for rank, state in self._per_rank_states.items():
+            rank_philox = _PhiloxState(state)
+            offsets[rank] = rank_philox.offset
+
+        if len(set(offsets.values())) == 1:
+            return next(iter(offsets.values()))
+        # pyrefly: ignore [bad-argument-type, bad-argument-count]
+        return SymInt(LocalIntNode(offsets))
+
+    @offset.setter
+    def offset(self, offset: int | SymInt) -> None:
+        from torch.distributed.tensor._random import _PhiloxState
+
+        if isinstance(offset, SymInt) and isinstance(offset.node, LocalIntNode):
+            for rank, state in self._per_rank_states.items():
+                rank_offset = offset.node._local_ints[rank]
+                rank_philox = _PhiloxState(state)
+                rank_philox.offset = rank_offset
+        else:
+            offset_int = int(offset) if isinstance(offset, SymInt) else offset
+            for state in self._per_rank_states.values():
+                rank_philox = _PhiloxState(state)
+                rank_philox.offset = offset_int
+
+    @property
+    def seed(self) -> int | SymInt:
+        from torch.distributed.tensor._random import _PhiloxState
+
+        seeds = {}
+        for rank, state in self._per_rank_states.items():
+            rank_philox = _PhiloxState(state)
+            seeds[rank] = rank_philox.seed
+
+        if len(set(seeds.values())) == 1:
+            return next(iter(seeds.values()))
+        return SymInt(LocalIntNode(seeds))
+
+    @seed.setter
+    def seed(self, seed: int | SymInt) -> None:
+        from torch.distributed.tensor._random import _PhiloxState
+
+        if isinstance(seed, SymInt) and isinstance(seed.node, LocalIntNode):
+            for rank, state in self._per_rank_states.items():
+                rank_seed = seed.node._local_ints[rank]
+                rank_philox = _PhiloxState(state)
+                rank_philox.seed = rank_seed
+        else:
+            seed_int = int(seed) if isinstance(seed, SymInt) else seed
+            for state in self._per_rank_states.values():
+                rank_philox = _PhiloxState(state)
+                rank_philox.seed = seed_int
+
+    def apply_to_local_tensor_mode(self, device_handle) -> None:
+        """
+        Apply per-rank RNG states to the LocalTensorMode's tracked states.
+        This updates both the device RNG state and the LocalTensorMode's _per_rank_rng_states.
+
+        Args:
+            device_handle: The device handle to use for setting RNG state (_LocalDeviceHandle)
+        """
+        if not enabled_local_tensor_mode():
+            return
+
+        assert hasattr(self, "_per_rank_offsets")
+
+        for rank in sorted(self._per_rank_states.keys()):
+            offset_value = self._per_rank_offsets[rank]
+            if isinstance(offset_value, SymInt):
+                if isinstance(offset_value.node, LocalIntNode):
+                    offset_value = offset_value.node._local_ints[rank]
+                else:
+                    offset_value = int(offset_value)
+
+            offset_tensor = torch.tensor(
+                [offset_value], dtype=torch.uint64, device="cpu"
+            ).view(torch.uint8)
+            self._per_rank_states[rank][8:] = offset_tensor
+
+        # pyrefly: ignore [bad-argument-type, bad-argument-count]
+        device_handle.set_rng_state(LocalTensor(self._per_rank_states))
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_local_tensor/_c10d.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_local_tensor/_c10d.py
new file mode 100644
index 0000000000000000000000000000000000000000..b3eca57402c56d8b5e9cdb216245ee2652f5250e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_local_tensor/_c10d.py
@@ -0,0 +1,1060 @@
+import functools
+import math
+import operator
+from collections.abc import Callable, Sequence
+from datetime import timedelta
+
+import torch
+from torch._C import ScriptObject
+from torch._C._distributed_c10d import FakeWork, PythonCallbackWork
+from torch.distributed._mesh_layout import _MeshLayout
+from torch.distributed.distributed_c10d import (
+    _check_op,
+    _get_default_group,
+    _resolve_process_group,
+    GroupName,
+    ProcessGroup,
+    ReduceOp,
+    Work,
+)
+
+
+# NOTE: Most of the c10d collectives often take a Tensor[] (or Tensor[][])
+# when you would expect Tensor (or Tensor[]).  In fact, there will only ever
+# be one Tensor in this case; the old signature was to support dispatching a
+# collective on multiple devices (ala DataParallel) but we don't support that
+# API anymore.  Note that we are not 100% consistent about this; some more
+# modern collectives like _allgather_base_ got rid of the unnecessary list.
+# When in doubt, consult the code that dispatches to the collective on the PG
+# in distributed_c10d.py e.g., work = group.allgather([tensor_list], [tensor],
+# opts) indicates its always a list.
+
+
+def _gcd_list(numbers: Sequence[int]) -> int:
+    return 0 if not numbers else functools.reduce(math.gcd, numbers)
+
+
+def _indices_to_layout(indices: list[int]) -> tuple[tuple[int, ...], tuple[int, ...]]:
+    # Base case: A single index represents a point, not a dimension.
+    if len(indices) <= 1:
+        return (), ()
+
+    # The smallest stride is likely the GCD of the differences between consecutive indices.
+    # For a sorted, unique list, all differences will be positive.
+    diffs = [indices[i] - indices[i - 1] for i in range(1, len(indices))]
+    last_stride = _gcd_list(diffs)
+
+    assert last_stride != 0, (
+        # This case should not be reached if indices are unique and sorted.
+        "Cannot determine stride; indices may not be unique."
+    )
+
+    # Identify the starting index of each "row" in the last dimension.
+    # An index starts a new row if the preceding index (index - stride) is not present.
+    indices_set = set(indices)
+    higher_dim_indices = [indices[0]]
+    for index in indices[1:]:
+        if (index - last_stride) not in indices_set:
+            higher_dim_indices.append(index)
+
+    # From the number of rows, we can deduce the shape of the last dimension.
+    assert len(indices) % len(higher_dim_indices) == 0, (
+        "Indices do not form a regular grid. "
+        f"Found {len(higher_dim_indices)} subgroups for {len(indices)} total elements."
+    )
+    last_shape = len(indices) // len(higher_dim_indices)
+
+    # Recurse on the higher-dimensional indices (the start of each row).
+    higher_shapes, higher_strides = _indices_to_layout(higher_dim_indices)
+
+    # Combine the results from the recursion with the current dimension's results.
+    final_shapes = higher_shapes + (last_shape,)
+    final_strides = higher_strides + (last_stride,)
+
+    return final_shapes, final_strides
+
+
+def _prepare_collective_groups(
+    process_group_so: ScriptObject | ProcessGroup,
+) -> tuple[list[int], list[int], int]:
+    process_group = (
+        ProcessGroup.unbox(process_group_so)
+        if isinstance(process_group_so, ScriptObject)
+        else process_group_so
+    )
+
+    ranks = torch.distributed.get_process_group_ranks(process_group)
+    assert ranks
+    # TODO: We can handle permutations but the layout inference algorithm will
+    # lose the permutation so we will have to reapply it
+    assert ranks == sorted(ranks), ranks
+    offset = ranks[0]
+    ranks = [r - offset for r in ranks]
+
+    shape, strides = _indices_to_layout(ranks)
+    layout = _MeshLayout(shape, strides)
+
+    global_pg = _get_default_group()
+    group_offsets = layout.complement(global_pg.size()).all_ranks_from_zero()
+
+    return ranks, group_offsets, offset
+
+
+# NB: There are two flavors of the collectives: regular and functional. Regular collectives
+# allocate outputs to write the result to, accept process group and support async ops (return
+# work object). Functional collectives expect the implementation to allocate outputs, accept
+# process group name that must be resolved and do not support async ops (return output).
+def _local_functional_all_gather_into_tensor(
+    tensor: torch.Tensor, group_size: int, group_name: GroupName
+) -> torch.Tensor:
+    # "all_gather_into_tensor(Tensor input, int group_size, str group_name) -> Tensor"
+    from . import LocalTensor
+
+    ranks, group_offsets, offset = _prepare_collective_groups(
+        _resolve_process_group(group_name)
+    )
+
+    assert isinstance(tensor, LocalTensor), "Input tensor must be a LocalTensor"
+    output_local_tensors: dict[int, torch.Tensor] = {}
+
+    for group_offset in group_offsets:
+        group_ranks = [group_offset + r for r in ranks]
+
+        group_tensors = []
+        if not all(rank in tensor._local_tensors for rank in group_ranks):
+            continue
+
+        for rank in group_ranks:
+            group_tensors.append(tensor._local_tensors[rank])
+
+        gathered_tensor = torch.cat(group_tensors, dim=0)
+
+        for rank in group_ranks:
+            output_local_tensors[rank] = gathered_tensor.clone()
+
+    # pyrefly: ignore [bad-argument-type, bad-argument-count]
+    output = LocalTensor(output_local_tensors)
+
+    return output
+
+
+def _local_functional_reduce_scatter_tensor(
+    tensor: torch.Tensor, reduce_op: str, group_size: int, group_name: GroupName
+) -> torch.Tensor:
+    #  "reduce_scatter_tensor(Tensor input, str reduce_op, int group_size, str group_name) -> Tensor"
+    from . import _zero_sized_like, LocalTensor
+
+    ranks, group_offsets, offset = _prepare_collective_groups(
+        _resolve_process_group(group_name)
+    )
+
+    assert isinstance(tensor, LocalTensor), "Input tensor must be a LocalTensor"
+    output_local_tensors: dict[int, torch.Tensor] = {}
+
+    for group_offset in group_offsets:
+        group_ranks = [group_offset + r for r in ranks]
+
+        group_tensors = []
+        if not all(rank in tensor._local_tensors for rank in group_ranks):
+            continue
+
+        for rank in group_ranks:
+            group_tensors.append(tensor._local_tensors[rank])
+
+        reduced_tensor = _local_reduce(reduce_op, group_tensors)
+
+        scattered_tensor = torch.split(
+            reduced_tensor,
+            reduced_tensor.size(0) // len(group_ranks),
+            dim=0,
+        )
+
+        for i, rank in enumerate(group_ranks):
+            if i < len(scattered_tensor):
+                output_local_tensors[rank] = scattered_tensor[i].clone()
+            else:
+                output_local_tensors[rank] = _zero_sized_like(reduced_tensor, 0)
+
+    # pyrefly: ignore [bad-argument-type, bad-argument-count]
+    output = LocalTensor(output_local_tensors)
+
+    return output
+
+
+def _local_functional_shard_dim_alltoall(
+    tensor: torch.Tensor, gather_dim: int, shard_dim: int, group_name: GroupName
+) -> torch.Tensor:
+    # "shard_dim_alltoall(Tensor input, int gather_dim, int shard_dim, str group_name) -> Tensor"
+    from . import _zero_sized_like, LocalTensor
+
+    ranks, group_offsets, offset = _prepare_collective_groups(
+        _resolve_process_group(group_name)
+    )
+
+    assert isinstance(tensor, LocalTensor), "Input tensor must be a LocalTensor"
+    output_local_tensors: dict[int, torch.Tensor] = {}
+
+    for group_offset in group_offsets:
+        group_ranks = [group_offset + r for r in ranks]
+
+        group_tensors = []
+        if not all(rank in tensor._local_tensors for rank in group_ranks):
+            continue
+
+        for rank in group_ranks:
+            group_tensors.append(tensor._local_tensors[rank])
+
+        gathered_tensor = torch.cat(group_tensors, dim=gather_dim)
+
+        split_tensor = torch.split(
+            gathered_tensor,
+            gathered_tensor.size(shard_dim) // len(group_ranks),
+            dim=shard_dim,
+        )
+
+        for i, rank in enumerate(group_ranks):
+            if i < len(split_tensor):
+                output_local_tensors[rank] = split_tensor[i].clone()
+            else:
+                output_local_tensors[rank] = _zero_sized_like(
+                    gathered_tensor, shard_dim
+                )
+
+    # pyrefly: ignore [bad-argument-type, bad-argument-count]
+    output = LocalTensor(output_local_tensors)
+
+    return output
+
+
+def _local_functional_all_to_all_single(
+    tensor: torch.Tensor,
+    output_split_sizes: list[torch.SymInt],
+    input_split_sizes: list[torch.SymInt],
+    group_name: GroupName,
+) -> torch.Tensor:
+    # "all_to_all_single(Tensor input, SymInt[] output_split_sizes, SymInt[] input_split_sizes, str group_name) -> Tensor"
+    from . import LocalIntNode, LocalTensor
+
+    ranks, group_offsets, offset = _prepare_collective_groups(
+        _resolve_process_group(group_name)
+    )
+
+    assert isinstance(tensor, LocalTensor), "Input tensor must be a LocalTensor"
+
+    split_local_sizes: dict[int, list[int]] = {}
+    for input_split_size in input_split_sizes:
+        if isinstance(input_split_size, torch.SymInt) and isinstance(
+            input_split_size.node, LocalIntNode
+        ):
+            local_ints = dict(input_split_size.node._local_ints.items())
+        else:
+            local_ints = {rank: int(input_split_size) for rank in tensor._local_tensors}
+        for rank, split_size in local_ints.items():
+            if rank not in split_local_sizes:
+                split_local_sizes[rank] = []
+            split_local_sizes[rank].append(split_size)
+
+    split_local_tensors: dict[int, list[torch.Tensor]] = {}
+
+    for rank, split_sizes in split_local_sizes.items():
+        split_local_tensors[rank] = list(
+            torch.split(tensor._local_tensors[rank], split_sizes)
+        )
+
+    output_local_tensors: dict[int, torch.Tensor] = {}
+
+    for group_offset in group_offsets:
+        group_ranks = [group_offset + r for r in ranks]
+
+        if not all(rank in split_local_tensors for rank in group_ranks):
+            continue
+
+        for i, dst in enumerate(group_ranks):
+            splits = []
+            for j, src in enumerate(group_ranks):
+                splits.append(split_local_tensors[src][i])
+            output_local_tensors[dst] = torch.cat(splits)
+
+    # pyrefly: ignore [bad-argument-type, bad-argument-count]
+    output = LocalTensor(output_local_tensors)
+
+    return output
+
+
+def _local_broadcast_(
+    tensors: list[torch.Tensor],
+    process_group_so: ScriptObject,
+    root_rank: int,
+    root_tensor: int,
+    async_op: bool = True,
+    timeout: int = -1,
+) -> tuple[list[torch.Tensor], ScriptObject]:
+    # "broadcast_(Tensor[] tensors, __torch__.torch.classes.c10d.ProcessGroup process_group, "
+    # "int root_rank, int root_tensor, bool async_op=True, int timeout=-1) -> (Tensor[], __torch__.torch.classes.c10d.Work)"
+    from . import LocalTensor
+
+    assert len(tensors) == 1
+    assert root_tensor == 0
+    tensor = tensors[0]
+
+    ranks, group_offsets, offset = _prepare_collective_groups(process_group_so)
+
+    # We're going to assume SPMD where for every rank group the root_rank is
+    # the same relative to others
+    relative_root_rank = root_rank - offset
+
+    assert isinstance(tensor, LocalTensor), "Input tensor must be a LocalTensor"
+
+    for group_offset in group_offsets:
+        # For the tensors in this group [group_offset + r for r in ranks]
+        # perform the broadcast on them
+        group_ranks = [group_offset + r for r in ranks]
+        if not all(rank in tensor._local_tensors for rank in group_ranks):
+            continue
+
+        source_rank = group_offset + relative_root_rank
+        source_tensor = tensor._local_tensors[source_rank]
+
+        # Broadcast the source tensor to all ranks in this group
+        for rank in group_ranks:
+            if source_rank != rank:
+                tensor._local_tensors[rank].copy_(source_tensor)
+
+    work = FakeWork()
+    work_so = Work.boxed(work)
+    return (tensors, work_so)
+
+
+def _local_reduce(
+    reduce_op: ReduceOp | str,
+    tensors: list[torch.Tensor],
+) -> torch.Tensor:
+    if reduce_op == ReduceOp.SUM or reduce_op == "sum":
+        op = operator.add
+    elif reduce_op == ReduceOp.AVG or reduce_op == "avg":
+        op = None
+    elif reduce_op == ReduceOp.PRODUCT or reduce_op == "product":
+        op = operator.mul
+    elif reduce_op == ReduceOp.MIN or reduce_op == "min":
+        op = torch.minimum
+    elif reduce_op == ReduceOp.MAX or reduce_op == "max":
+        op = torch.maximum
+    elif reduce_op == ReduceOp.BAND or reduce_op == "band":
+        op = torch.bitwise_and
+    elif reduce_op == ReduceOp.BOR or reduce_op == "bor":
+        op = torch.bitwise_or
+    elif reduce_op == ReduceOp.BXOR or reduce_op == "bxor":
+        op = torch.bitwise_xor
+    elif reduce_op == ReduceOp.PREMUL_SUM or reduce_op == "premul_sum":
+        raise NotImplementedError("PREMUL_SUM: need to add binding for scaling factor")
+    else:
+        raise NotImplementedError(f"ReduceOp {reduce_op} not implemented")
+
+    if reduce_op == ReduceOp.AVG or reduce_op == "avg":
+        return functools.reduce(operator.add, tensors) / len(tensors)
+    else:
+        assert op is not None
+        return functools.reduce(op, tensors)
+
+
+def _local_all_reduce_(
+    tensors: list[torch.Tensor],
+    process_group_so: ScriptObject,
+    reduce_op_so: ScriptObject,
+    sparse_indices: torch.Tensor | None = None,
+    async_op: bool = True,
+    timeout: int = -1,
+) -> tuple[list[torch.Tensor], ScriptObject]:
+    # "allreduce_(Tensor[] tensors, __torch__.torch.classes.c10d.ProcessGroup process_group, "
+    # "__torch__.torch.classes.c10d.ReduceOp reduce_op, Tensor? sparse_indices, bool async_op=True, "
+    # "int timeout=-1) -> (Tensor[], __torch__.torch.classes.c10d.Work)");
+    from . import LocalTensor
+
+    assert len(tensors) == 1
+    tensor = tensors[0]
+    reduce_op = reduce_op_so.op()  # type: ignore[attr-defined]
+
+    ranks, group_offsets, _offset = _prepare_collective_groups(process_group_so)
+
+    assert isinstance(tensor, LocalTensor), "Input tensor must be a LocalTensor"
+
+    for group_offset in group_offsets:
+        # For the tensors in this group [group_offset + r for r in ranks]
+        # perform the allreduce on them
+        group_ranks = [group_offset + r for r in ranks]
+        if not all(rank in tensor._local_tensors for rank in group_ranks):
+            continue
+
+        # Collect tensors from the specified ranks in this group
+        group_tensors = []
+        for rank in group_ranks:
+            group_tensors.append(tensor._local_tensors[rank])
+
+        # Perform the reduction operation
+        reduced_tensor = _local_reduce(reduce_op, group_tensors)
+
+        # Update all tensors in the group with the reduced result
+        for rank in group_ranks:
+            tensor._local_tensors[rank].copy_(reduced_tensor)
+
+    work = FakeWork()
+    work_so = Work.boxed(work)
+    return (tensors, work_so)
+
+
+def _local_allreduce_coalesced_(
+    tensors: list[torch.Tensor],
+    process_group_so: ScriptObject,
+    reduce_op_so: ScriptObject,
+    async_op: bool = True,
+    timeout: int = -1,
+) -> ScriptObject:
+    # "allreduce_coalesced_(Tensor[] tensors, __torch__.torch.classes.c10d.ProcessGroup process_group, "
+    # "__torch__.torch.classes.c10d.ReduceOp reduce_op, bool async_op=True, int timeout=-1) -> __torch__.torch.classes.c10d.Work"
+    from . import LocalTensor
+
+    reduce_op = reduce_op_so.op()  # type: ignore[attr-defined]
+    ranks, group_offsets, _offset = _prepare_collective_groups(process_group_so)
+
+    for group_offset in group_offsets:
+        # For the tensors in this group [group_offset + r for r in ranks]
+        # perform the allreduce on all tensors together
+        group_ranks = [group_offset + r for r in ranks]
+
+        # For each tensor, perform the reduction operation
+        for tensor in tensors:
+            assert isinstance(tensor, LocalTensor), "Input tensor must be a LocalTensor"
+            if not all(rank in tensor._local_tensors for rank in group_ranks):
+                continue
+            # Collect tensors from the specified ranks in this group
+            group_tensors = []
+            for rank in group_ranks:
+                group_tensors.append(tensor._local_tensors[rank])
+
+            # Perform the reduction operation
+            reduced_tensor = _local_reduce(reduce_op, group_tensors)
+
+            # Update all tensors in the group with the reduced result
+            for rank in group_ranks:
+                tensor._local_tensors[rank].copy_(reduced_tensor)
+
+    work = FakeWork()
+    work_so = Work.boxed(work)
+    return work_so
+
+
+def _local_reduce_scatter_tensor_coalesced_(
+    output_tensors: list[torch.Tensor],
+    input_tensors: list[torch.Tensor],
+    process_group_so: ScriptObject,
+    reduce_op_so: ScriptObject,
+    async_op: bool = True,
+    timeout: int = -1,
+) -> ScriptObject:
+    # "reduce_scatter_tensor_coalesced_(Tensor[] outputs, Tensor[] inputs, "
+    # "__torch__.torch.classes.c10d.ProcessGroup process_group, "
+    # "__torch__.torch.classes.c10d.ReduceOp reduce_op, bool async_op=True, "
+    # "int timeout=-1) -> __torch__.torch.classes.c10d.Work"
+
+    from . import LocalTensor
+
+    reduce_op = reduce_op_so.op()  # type: ignore[attr-defined]
+    ranks, group_offsets, _offset = _prepare_collective_groups(process_group_so)
+
+    for group_offset in group_offsets:
+        # For the tensors in this group [group_offset + r for r in ranks]
+        # perform the allreduce on all tensors together
+        group_ranks = [group_offset + r for r in ranks]
+
+        # For each tensor, perform the reduction operation
+        for input_tensor, output_tensor in zip(input_tensors, output_tensors):
+            assert isinstance(input_tensor, LocalTensor), (
+                "Input tensor must be a LocalTensor"
+            )
+            assert isinstance(output_tensor, LocalTensor), (
+                "Output tensor must be a LocalTensor"
+            )
+            if not all(rank in input_tensor._local_tensors for rank in group_ranks):
+                continue
+            if not all(rank in output_tensor._local_tensors for rank in group_ranks):
+                continue
+
+            # Collect tensors from the specified ranks in this group
+            group_inputs = []
+            for rank in group_ranks:
+                group_inputs.append(input_tensor._local_tensors[rank])
+
+            # Perform the reduction operation
+            reduced_input = _local_reduce(reduce_op, group_inputs)
+
+            reduced_input_splits = torch.split(
+                reduced_input, reduced_input.size(0) // len(group_ranks), dim=0
+            )
+
+            # Update all tensors in the group with the reduced result
+            for i, rank in enumerate(group_ranks):
+                output_tensor._local_tensors[rank].copy_(reduced_input_splits[i])
+
+    work = FakeWork()
+    work_so = Work.boxed(work)
+    return work_so
+
+
+def _local_allgather_base_(
+    output_tensor: torch.Tensor,
+    input_tensor: torch.Tensor,
+    process_group_so: ScriptObject,
+    async_op: bool = True,
+    timeout: int = -1,
+) -> tuple[torch.Tensor, ScriptObject]:
+    # "_allgather_base_(Tensor output_tensor, Tensor input_tensor, __torch__.torch.classes.c10d.ProcessGroup
+    # process_group, bool async_op=True, int timeout=-1) -> (Tensor, __torch__.torch.classes.c10d.Work)");
+    from . import LocalTensor
+
+    ranks, group_offsets, _offset = _prepare_collective_groups(process_group_so)
+
+    assert isinstance(output_tensor, LocalTensor), "Output tensor must be a LocalTensor"
+    assert isinstance(input_tensor, LocalTensor), "Input tensor must be a LocalTensor"
+
+    for group_offset in group_offsets:
+        group_ranks = [group_offset + r for r in ranks]
+
+        if not all(rank in input_tensor._local_tensors for rank in group_ranks):
+            continue
+        if not all(rank in output_tensor._local_tensors for rank in group_ranks):
+            continue
+
+        gathered_tensors = []
+        for rank_i in group_ranks:
+            gathered_tensors.append(input_tensor._local_tensors[rank_i])
+
+        gathered_tensor = torch.cat(gathered_tensors, dim=0)
+
+        for rank_i in group_ranks:
+            output_tensor._local_tensors[rank_i].copy_(gathered_tensor)
+
+    work = FakeWork()
+    work_so = Work.boxed(work)
+    return output_tensor, work_so
+
+
+def _local_reduce_scatter_base_(  # type: ignore[no-untyped-def]
+    output_tensor: torch.Tensor,
+    input_tensor: torch.Tensor,
+    process_group_so: ScriptObject,
+    reduce_op_so: ScriptObject,
+    async_op: bool = True,
+    timeout: int = -1,
+) -> tuple[torch.Tensor, ScriptObject]:
+    # "_reduce_scatter_base_(Tensor output_tensor, Tensor input_tensor,
+    # __torch__.torch.classes.c10d.ProcessGroup process_group, __torch__.torch.classes.c10d.ReduceOp reduce_op,
+    # bool async_op=True, int timeout=-1) -> (Tensor, __torch__.torch.classes.c10d.Work)"
+
+    from . import LocalTensor
+
+    reduce_op = reduce_op_so.op()  # type: ignore[attr-defined]
+    ranks, group_offsets, _offset = _prepare_collective_groups(process_group_so)
+
+    assert isinstance(output_tensor, LocalTensor), "Output tensor must be a LocalTensor"
+    assert isinstance(input_tensor, LocalTensor), "Input tensor must be a LocalTensor"
+
+    for group_offset in group_offsets:
+        group_ranks = [group_offset + r for r in ranks]
+        if not all(rank in input_tensor._local_tensors for rank in group_ranks):
+            continue
+        if not all(rank in output_tensor._local_tensors for rank in group_ranks):
+            continue
+
+        gathered_tensors = []
+        for rank_i in group_ranks:
+            gathered_tensors.append(input_tensor._local_tensors[rank_i])
+
+        reduced_tensor = _local_reduce(reduce_op, gathered_tensors)
+
+        scattered_tensor = torch.split(
+            reduced_tensor,
+            reduced_tensor.size(0) // len(group_ranks),
+            dim=0,
+        )
+
+        for i, rank_i in enumerate(group_ranks):
+            output_tensor._local_tensors[rank_i].copy_(scattered_tensor[i].clone())
+
+    work = FakeWork()
+    work_so = Work.boxed(work)
+    return output_tensor, work_so
+
+
+def _local_all_gather_(
+    output_tensors: list[list[torch.Tensor]],
+    input_tensors: list[torch.Tensor],
+    process_group_so: ScriptObject,
+    async_op: bool = True,
+    timeout: int = -1,
+) -> tuple[list[list[torch.Tensor]], ScriptObject]:
+    # "allgather_(Tensor[][] output_tensors, Tensor[] input_tensors, "
+    # "__torch__.torch.classes.c10d.ProcessGroup process_group, bool async_op=True, "
+    # "int timeout=-1) -> (Tensor[][], __torch__.torch.classes.c10d.Work)");
+
+    from . import LocalTensor
+
+    assert len(output_tensors) == 1
+    assert len(input_tensors) == 1
+
+    input_tensor = input_tensors[0]
+    # pyrefly: ignore [bad-assignment]
+    output_tensors = output_tensors[0]
+
+    ranks, group_offsets, _offset = _prepare_collective_groups(process_group_so)
+
+    for i in range(len(output_tensors)):
+        assert isinstance(output_tensors[i], LocalTensor), (
+            "Output tensor must be a LocalTensor"
+        )
+
+    for group_offset in group_offsets:
+        # For the tensors in this group [group_offset + r for r in ranks]
+        # perform the all_gather on them
+        group_ranks = [group_offset + r for r in ranks]
+
+        # For each rank in the group, gather from their input tensor
+        for i, rank_i in enumerate(group_ranks):
+            # allgather object happens to create pure tensor, so we special case it here
+            source_tensor = input_tensor
+            if isinstance(input_tensor, LocalTensor):
+                source_tensor = input_tensor._local_tensors[rank_i]
+            # pyrefly: ignore [missing-attribute]
+            output_tensors[i].copy_(source_tensor)
+
+    work = FakeWork()
+    work_so = Work.boxed(work)
+    # pyrefly: ignore [bad-return]
+    return ([output_tensors], work_so)
+
+
+def _local_allgather_into_tensor_coalesced_(
+    output_tensors: list[torch.Tensor],
+    input_tensors: list[torch.Tensor],
+    process_group_so: ScriptObject,
+    async_op: bool = True,
+) -> ScriptObject:
+    # "allgather_into_tensor_coalesced_(Tensor[] outputs, Tensor[] inputs, "
+    # "__torch__.torch.classes.c10d.ProcessGroup process_group, bool async_op=True) "
+    # "-> __torch__.torch.classes.c10d.Work"
+    from . import LocalTensor
+
+    ranks, group_offsets, _offset = _prepare_collective_groups(process_group_so)
+
+    # Each output tensor should be sized to hold all gathered inputs
+    # outputs[i] will contain all inputs[i] from all ranks
+    assert len(output_tensors) == len(input_tensors), (
+        f"Number of outputs ({len(output_tensors)}) must match number of inputs ({len(input_tensors)})"
+    )
+
+    for group_offset in group_offsets:
+        # For the tensors in this group [group_offset + r for r in ranks]
+        # perform the allgather_into_tensor on them
+        group_ranks = [group_offset + r for r in ranks]
+
+        # For each input/output pair
+        for input_tensor, output_tensor in zip(input_tensors, output_tensors):
+            assert isinstance(input_tensor, LocalTensor), (
+                "Input tensor must be a LocalTensor"
+            )
+            assert isinstance(output_tensor, LocalTensor), (
+                "Output tensor must be a LocalTensor"
+            )
+
+            if not all(rank in input_tensor._local_tensors for rank in group_ranks):
+                continue
+            if not all(rank in output_tensor._local_tensors for rank in group_ranks):
+                continue
+
+            # Gather input_tensor from all ranks into output_tensor
+            # The output should be a concatenation of all inputs along the first dimension
+            gathered_tensors = []
+            for rank in group_ranks:
+                gathered_tensors.append(input_tensor._local_tensors[rank])
+
+            # Concatenate along first dimension and copy to output
+            if gathered_tensors:
+                concatenated = torch.cat(gathered_tensors, dim=0)
+                for rank in group_ranks:
+                    output_tensor._local_tensors[rank].copy_(concatenated)
+
+    work = FakeWork()
+    work_so = Work.boxed(work)
+    return work_so
+
+
+def _local_gather_(
+    output_tensors: list[list[torch.Tensor]],
+    input_tensors: list[torch.Tensor],
+    process_group_so: ScriptObject,
+    root_rank: int,
+    async_op: bool = True,
+    timeout: int = -1,
+) -> ScriptObject:
+    # "gather_(Tensor[][] output_tensors, Tensor[] input_tensors, "
+    # "__torch__.torch.classes.c10d.ProcessGroup process_group, int root_rank, "
+    # "bool async_op=True, int timeout=-1) -> __torch__.torch.classes.c10d.Work"
+    raise NotImplementedError(
+        "LocalTensor does not support MPMD operations like gather "
+        "(only root rank receives data). Use SPMD collective operations like allgather instead."
+    )
+
+
+def _local_scatter_(
+    output_tensors: list[torch.Tensor],
+    input_tensors: list[list[torch.Tensor]],
+    process_group_so: ScriptObject,
+    root_rank: int,
+    async_op: bool = True,
+    timeout: int = -1,
+) -> tuple[list[torch.Tensor], ScriptObject]:
+    # "scatter_(Tensor[] output_tensors, Tensor[][] input_tensors, "
+    # "__torch__.torch.classes.c10d.ProcessGroup process_group, int root_rank, "
+    # "bool async_op=True, int timeout=-1) -> (Tensor[], __torch__.torch.classes.c10d.Work)");
+
+    from . import LocalTensor
+
+    assert len(output_tensors) == 1
+    assert len(input_tensors) == 1
+    output_tensor = output_tensors[0]
+    # pyrefly: ignore [bad-assignment]
+    input_tensors = input_tensors[0]
+
+    ranks, group_offsets, offset = _prepare_collective_groups(process_group_so)
+
+    # We're going to assume SPMD where for every rank group the root_rank is
+    # the same relative to others
+    relative_root_rank = root_rank - offset
+
+    assert isinstance(output_tensor, LocalTensor), "Output tensor must be a LocalTensor"
+    assert len(ranks) == len(input_tensors), (ranks, input_tensors)
+
+    for group_offset in group_offsets:
+        # For the tensors in this group [group_offset + r for r in ranks]
+        # perform the scatter on them
+        group_ranks = [group_offset + r for r in ranks]
+        if not all(rank in output_tensor._local_tensors for rank in group_ranks):
+            continue
+
+        # Root rank scatters its input tensors to all ranks in this group
+        for i, rank in enumerate(group_ranks):
+            input_tensor = input_tensors[i]
+            assert isinstance(input_tensor, LocalTensor)
+            # Each rank i gets the i-th input tensor from the root
+            source_tensor = input_tensor._local_tensors[
+                group_offset + relative_root_rank
+            ]
+            output_tensor._local_tensors[rank].copy_(source_tensor)
+
+    work = FakeWork()
+    work_so = Work.boxed(work)
+    return (output_tensors, work_so)
+
+
+def _local_alltoall_(
+    output_tensors: list[torch.Tensor],
+    input_tensors: list[torch.Tensor],
+    process_group_so: ScriptObject,
+    async_op: bool = True,
+    timeout: int = -1,
+) -> tuple[list[torch.Tensor], ScriptObject]:
+    # "alltoall_(Tensor[] output_tensors, Tensor[] input_tensors, "
+    # "__torch__.torch.classes.c10d.ProcessGroup process_group, bool async_op=True, "
+    # "int timeout=-1) -> (Tensor[], __torch__.torch.classes.c10d.Work)";
+
+    from . import LocalTensor
+
+    ranks, group_offsets, _offset = _prepare_collective_groups(process_group_so)
+
+    assert len(input_tensors) == len(output_tensors) == len(ranks), (
+        f"Number of input tensors ({len(input_tensors)}), "
+        f"output tensors ({len(output_tensors)}), and ranks ({len(ranks)}) must match"
+    )
+
+    for group_offset in group_offsets:
+        # For the tensors in this group [group_offset + r for r in ranks]
+        # perform the alltoall on them
+        group_ranks = [group_offset + r for r in ranks]
+
+        # In alltoall, rank i sends input_tensors[j] to rank j and receives into output_tensors[i] from rank j
+        for i, rank_i in enumerate(group_ranks):
+            output_tensor = output_tensors[i]
+            assert isinstance(output_tensor, LocalTensor), (
+                "Output tensor must be a LocalTensor"
+            )
+
+            if not all(rank in output_tensor._local_tensors for rank in group_ranks):
+                continue
+
+            for j, rank_j in enumerate(group_ranks):
+                input_tensor = input_tensors[j]
+                assert isinstance(input_tensor, LocalTensor), (
+                    "Input tensor must be a LocalTensor"
+                )
+
+                if not all(rank in input_tensor._local_tensors for rank in group_ranks):
+                    continue
+
+                # Rank i's j-th input tensor goes to rank j's i-th output tensor
+                source_tensor = input_tensor._local_tensors[rank_i]
+                output_tensor._local_tensors[rank_j].copy_(source_tensor)
+
+    work = FakeWork()
+    work_so = Work.boxed(work)
+    return (output_tensors, work_so)
+
+
+def _local_alltoall_base_(
+    output_tensor: torch.Tensor,
+    input_tensor: torch.Tensor,
+    process_group_so: ScriptObject,
+    output_split_sizes: list[int],
+    input_split_sizes: list[int],
+    async_op: bool = True,
+    timeout: int = -1,
+) -> ScriptObject:
+    # "alltoall_base_(Tensor output, Tensor input, __torch__.torch.classes.c10d.ProcessGroup process_group, "
+    # "int[] output_split_sizes, int[] input_split_sizes, bool async_op=True, int timeout=-1) -> __torch__.torch.classes.c10d.Work";
+
+    from . import LocalTensor
+
+    ranks, group_offsets, _offset = _prepare_collective_groups(process_group_so)
+
+    assert isinstance(input_tensor, LocalTensor), "Input tensor must be a LocalTensor"
+    assert isinstance(output_tensor, LocalTensor), "Output tensor must be a LocalTensor"
+    # Convert split sizes to lists if they aren't already
+    if output_split_sizes is not None:
+        output_split_sizes = list(output_split_sizes)
+    if input_split_sizes is not None:
+        input_split_sizes = list(input_split_sizes)
+
+    for group_offset in group_offsets:
+        # For the tensors in this group [group_offset + r for r in ranks]
+        # perform the alltoall_base on them
+        group_ranks = [group_offset + r for r in ranks]
+
+        if not all(rank in input_tensor._local_tensors for rank in group_ranks):
+            continue
+        if not all(rank in output_tensor._local_tensors for rank in group_ranks):
+            continue
+
+        for i, rank_i in enumerate(group_ranks):
+            # Split input tensor from rank_i according to input_split_sizes
+            rank_tensor = input_tensor._local_tensors[rank_i]
+
+            if input_split_sizes is not None and len(input_split_sizes) > 0:
+                # Split the input tensor
+                input_splits = torch.split(rank_tensor, input_split_sizes, dim=0)
+            else:
+                # No split sizes specified, split evenly
+                split_size = rank_tensor.size(0) // len(group_ranks)
+                input_splits = torch.split(rank_tensor, split_size, dim=0)
+
+            # Send each split to the corresponding rank
+            for j, rank_j in enumerate(group_ranks):
+                if j < len(input_splits):
+                    split_tensor = input_splits[j]
+
+                    # Determine where to place this split in the output tensor
+                    if output_split_sizes is not None and len(output_split_sizes) > 0:
+                        # Calculate offset based on output split sizes
+                        output_offset = sum(output_split_sizes[:i]) if i > 0 else 0
+                        end_offset = (
+                            output_offset + output_split_sizes[i]
+                            if i < len(output_split_sizes)
+                            else output_tensor._local_tensors[rank_j].size(0)
+                        )
+                    else:
+                        # No output split sizes, use even splits
+                        split_size = output_tensor._local_tensors[rank_j].size(
+                            0
+                        ) // len(group_ranks)
+                        output_offset = i * split_size
+                        end_offset = min(
+                            (i + 1) * split_size,
+                            output_tensor._local_tensors[rank_j].size(0),
+                        )
+
+                    # Copy the split to the appropriate section of the output tensor
+                    output_section = output_tensor._local_tensors[rank_j][
+                        output_offset:end_offset
+                    ]
+                    if output_section.numel() > 0:
+                        # Reshape split_tensor to match output_section if necessary
+                        if split_tensor.size() != output_section.size():
+                            split_tensor = split_tensor.view(output_section.size())
+                        output_section.copy_(split_tensor)
+
+    work = FakeWork()
+    work_so = Work.boxed(work)
+    return work_so
+
+
+def _local_barrier(
+    tensor: torch.Tensor,
+    process_group_so: ScriptObject,
+    device_ids: list[int],
+    async_op: bool = True,
+    timeout: int = -1,
+) -> ScriptObject:
+    # "barrier(Tensor tensor, __torch__.torch.classes.c10d.ProcessGroup process_group, "
+    # "int[] device_ids, bool async_op=True, int timeout=-1) -> __torch__.torch.classes.c10d.Work";
+
+    from . import LocalTensor
+
+    # Barrier is a synchronization primitive - in local simulation,
+    # we don't need to do any actual work since all "ranks" are in the same process
+    # Just validate that the tensor is a LocalTensor
+    assert isinstance(tensor, LocalTensor)
+
+    # In a real distributed setting, barrier would synchronize all processes
+    # In local simulation, this is essentially a no-op since all ranks are local
+    work = FakeWork()
+    work_so = Work.boxed(work)
+    return work_so
+
+
+def _local_monitored_barrier_(
+    tensor: torch.Tensor,
+    process_group_so: ScriptObject,
+    device_ids: list[int],
+    timeout: int,
+    wait_all_ranks: bool,
+) -> None:
+    # "monitored_barrier_(Tensor tensor, __torch__.torch.classes.c10d.ProcessGroup process_group, "
+    # "int[] device_ids, int timeout, bool wait_all_ranks) -> ()";
+
+    from . import LocalTensor
+
+    # Monitored barrier is a synchronization primitive with monitoring - in local simulation,
+    # we don't need to do any actual work since all "ranks" are in the same process
+    # Just validate that the tensor is a LocalTensor
+    assert isinstance(tensor, LocalTensor)
+
+    # In a real distributed setting, monitored barrier would synchronize all processes
+    # and provide monitoring capabilities. In local simulation, this is essentially a no-op
+    # since all ranks are local and no actual synchronization is needed
+    return
+
+
+def _local_send(
+    tensors: list[torch.Tensor],
+    process_group_so: ScriptObject,
+    dst: int,
+    tag: int,
+) -> ScriptObject:
+    # "send(Tensor[] tensors, __torch__.torch.classes.c10d.ProcessGroup process_group, "
+    # "int dst, int tag) -> __torch__.torch.classes.c10d.Work";
+
+    from . import LocalRunnerMode, LocalTensor
+
+    assert len(tensors) == 1
+    tensor = tensors[0]
+
+    assert isinstance(tensor, LocalTensor), "Input tensor must be a Tensor"
+    src = int(tensor.__src_rank__)
+
+    LocalRunnerMode.current()._signal_send(src, dst, tensor._local_tensors[src])
+
+    work = FakeWork()
+    work_so = Work.boxed(work)
+    return work_so
+
+
+def _local_recv_(
+    tensors: list[torch.Tensor],
+    process_group_so: ScriptObject,
+    src: int,
+    tag: int,
+) -> ScriptObject:
+    # "recv_(Tensor[] tensors, __torch__.torch.classes.c10d.ProcessGroup process_group, "
+    # "int src, int tag) -> __torch__.torch.classes.c10d.Work";
+    from . import LocalRunnerMode, LocalTensor
+
+    assert len(tensors) == 1
+    tensor = tensors[0]
+
+    assert isinstance(tensor, LocalTensor), "Input tensor must be a Tensor"
+    dst = int(tensor.__src_rank__)
+
+    def _recv_and_store(timeout: timedelta) -> bool:
+        def _wait_and_store(obj: object) -> None:
+            assert isinstance(obj, torch.Tensor), "Expected to receive a Tensor"
+            assert isinstance(tensor, LocalTensor), "Input tensor must be a Tensor"
+            tensor._local_tensors[dst] = obj
+
+        LocalRunnerMode.current()._wait_recv(src, dst, _wait_and_store)
+        return True
+
+    work = PythonCallbackWork(_recv_and_store)
+    work_so = Work.boxed(work)
+    return work_so
+
+
+def _local_recv_any_source_(
+    tensors: list[torch.Tensor], process_group_so: ScriptObject, tag: int
+) -> ScriptObject:
+    # "recv_any_source_(Tensor[] tensors, __torch__.torch.classes.c10d.ProcessGroup process_group, "
+    # "int tag) -> __torch__.torch.classes.c10d.Work";
+
+    return _local_recv_(tensors, process_group_so, -1, tag)
+
+
+def _attach_rank(tensor: torch.Tensor, rank: int) -> torch.Tensor:
+    """
+    Attaches rank as an attribute to given tensor so that the send or recv implementation
+    knows which rank initiates the operation (note under local tensor mode ).
+    """
+    from torch.distributed.tensor import DTensor
+
+    if isinstance(tensor, DTensor):
+        tensor = tensor._local_tensor
+
+    tensor.__src_rank__ = rank  # type: ignore[attr-defined]
+    return tensor
+
+
+def local_p2p_op(
+    dst: torch.SymInt,
+    tensor: torch.Tensor,
+    op: Callable[[torch.Tensor, int], Work | None],
+) -> Work | None | list[Work | None]:
+    """
+    Runs a point-to-point (P2P) operation for all combinations of source and destination ranks.
+    """
+    _check_op(op)
+
+    from . import LocalIntNode
+
+    assert isinstance(dst.node, LocalIntNode), (
+        "Expected 'dst' to be a LocalIntNode where the value is the destination rank and key is the source rank"
+    )
+
+    w = []
+    for s, d in dst.node._local_ints.items():
+        tensor = _attach_rank(tensor, s)
+        w.append(op(tensor, d))
+    return w
+
+
+def wait_all(work: Work | None | list[Work | None]) -> None:
+    """
+    Waits for all work objects in the input to complete.
+
+    A single Work object, None, or a list of Work objects (possibly containing None).
+    If None, does nothing. If a single Work, waits for it to complete. If a list, waits
+    for each non-None Work in the list to complete.
+    """
+
+    if work is None:
+        return
+    if isinstance(work, Work):
+        work = [work]
+    for w in work:
+        if w is None:
+            continue
+        w.wait()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_mesh_layout.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_mesh_layout.py
new file mode 100644
index 0000000000000000000000000000000000000000..38026b7d3d5e1dc94325e6c26b4bf4c4841b4994
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_mesh_layout.py
@@ -0,0 +1,309 @@
+"""
+Definition of CuTe inspired Layouts for DeviceMesh internal bookkeeping and functions to manipulate them
+"""
+
+import math
+from collections.abc import Iterator
+from dataclasses import dataclass
+from itertools import product
+
+import torch
+from torch.distributed._pycute import (
+    as_tuple,
+    coalesce,
+    complement,
+    composition,
+    flatten,
+    IntTuple,
+    is_int,
+    is_tuple,
+    Layout,
+    match_structure,
+)
+
+
+@dataclass(frozen=True, init=True)
+class _MeshLayout(Layout):
+    """
+    Utility class for representing an integer layout by borrowing ideas from CuTe Layout Algebra.
+    See https://docs.nvidia.com/cutlass/media/docs/cpp/cute/02_layout_algebra.html for more details.
+
+    Each layout is represented as a list of sizes and strides. We use it as a way for mechanical bookkeeping
+    of the integers such as ranks in a SPMD mesh, and the transformation on top of it.
+
+    Lots of methods of layout like coalesce, composition, complement, etc. are borrowed from pycute.
+    https://github.com/NVIDIA/cutlass/blob/6dd13d42784ee5bfa232d2441e6b9a021c5c6290/python/pycute/layout.py#L137,L257
+
+    Note this is a CuTe-inspired layout, because CuTe uses co-lexicographic way in linearization while PyTorch
+    is using lexicographic. So even though the CuTe documentation can still be referenced, the implementation will be
+    different from that of PyCute's.
+    """
+
+    # pyrefly: ignore [bad-override]
+    shape: IntTuple
+    # pyrefly: ignore [bad-override]
+    stride: IntTuple
+
+    def __post_init__(self) -> None:
+        if not is_tuple(self.shape) and not is_int(self.shape):
+            raise TypeError(f"shape must be a tuple or int, got {type(self.shape)}")
+        if not is_tuple(self.stride) and not is_int(self.stride):
+            raise TypeError(f"stride must be a tuple or int, got {type(self.stride)}")
+        if not match_structure(self.shape, self.stride):
+            raise ValueError(
+                f"sizes {self.shape} and strides {self.stride} don't match"
+            )
+
+    @property
+    def sizes(self) -> IntTuple:
+        return self.shape
+
+    @property
+    def strides(self) -> IntTuple:
+        return self.stride
+
+    @property
+    def sizes_and_strides(self) -> Iterator[tuple[int, int]]:
+        return zip(flatten(self.shape), flatten(self.stride))
+
+    @property
+    def top_level_sizes(self) -> tuple[int, ...]:
+        return tuple(self[i].numel() for i in range(len(self)))
+
+    def numel(self) -> int:
+        return math.prod(flatten(self.shape))
+
+    # # operator []    (get-i like tuples)
+    def __getitem__(self, i: int) -> "_MeshLayout":
+        if i < -len(self) or i >= len(self):
+            raise IndexError(
+                f"Dim {i} is out of range for layout with {len(self)} dimensions. "
+                f"Expected dim to be in range [{-len(self)}, {len(self) - 1}]."
+            )
+        layout = super().__getitem__(i)
+        return _MeshLayout(layout.shape, layout.stride)
+
+    def nest(self) -> "_MeshLayout":
+        return _MeshLayout((self.shape,), (self.stride,))
+
+    def coalesce(self) -> "_MeshLayout":
+        """
+        A layout is represented by (sizes):(strides), e.g. (3,2):(4,2).
+        Two consecutive dimensions can be "merged" into one if their
+        strides are contiguous/multiplicative (i.e., the inner stride * inner size
+        equals the next stride), we perform this kind of merge inside coalesce.
+
+        Example 1 (simple): (3,2):(2,1)
+        - inner dimension: has stride=1, size=2
+        - outer dimension: stride = inner_stride * inner_size = 2
+        → coalesced = (6:1)    # acts like a flat 1D array of length 6
+
+        Example 2 (non-coalescible): (3,2):(4,1)
+        - inner dimension: stride=1, size=2 → 2*1 = 2
+        - outer dimension: stride=4, mismatch (≠ 2)
+        → cannot merge; result stays (3,2):(4,1)
+        """
+        layout = coalesce(self)
+        return _MeshLayout(layout.shape, layout.stride)
+
+    def composition(self, layout: "_MeshLayout") -> "_MeshLayout":
+        """
+        By-dimension composition allows one layout to "select from" or "filter through" another layout.
+        Think of it as function composition: (self ∘ layout)(input) = self(layout(input))
+        between two layouts. This function is a wrapper of pycute's composition.
+
+        Mental model about how to understand the composition logic:
+        - The LEFT layout (self) defines the "output space" - what indices are possible
+        - The RIGHT layout (layout parameter) acts as a "selector" - which specific indices to pick
+        - The composition only generates indices that the left layout could originally produce,
+          but the right layout determines which indices to be picked.
+        - The stride of the composition layout will not be smaller than the stride of the right layout,
+          because when picking the indices the composition will at least follow the the right layout's stride
+          to move forward.
+
+        Example:
+          self = (6,2):(2,1)      # sizes=(6,2), strides=(2,1)
+          layout = (3:2)          # sizes=(3,), stride=(2,)
+          self o layout = (3:2)
+
+        Returns:
+          Layout being composed.
+        """
+        result = composition(self, layout)
+        return _MeshLayout(result.shape, result.stride)
+
+    def complement(self, world_size: int) -> "_MeshLayout":
+        """
+        Compute the "complement layout" relative to a given world_size.
+        A complement layout fills in the "missing" factor so that: self repeat a layout of complement(self, world_size)
+        will get a complete world_size. We use ⊗ to denote the repeat operation.
+
+        Example:
+          self = (4:1)   # size=4, stride=1
+          world_size = 8
+          Then:
+            complete needed factor = 8 / 4 = 2
+            complement(self, 8) = (2:1)
+
+          Together they form:
+            (4:1) ⊗ (2:1) = (4,2):(2,1)
+          which has world_size = 4 * 2 = 8, as required.
+
+        In distributed terms, complement() is often used to derive the "other"
+        rank grouping when splitting processes into 2D meshes.
+
+        For a visualized explanation, see https://x.com/ezyang/status/1962364978393981433/
+        """
+        layout = complement(self, world_size)
+        return _MeshLayout(layout.shape, layout.stride)
+
+    def splice(self, start: int, end: int, layout: "_MeshLayout") -> "_MeshLayout":
+        sizes = list(as_tuple(self.sizes))
+        strides = list(as_tuple(self.strides))
+        sizes[start:end] = list(as_tuple(layout.sizes))
+        strides[start:end] = list(as_tuple(layout.strides))
+        return _MeshLayout(tuple(sizes), tuple(strides))
+
+    def all_ranks_from_zero(self) -> list[int]:
+        """
+        This function computes the all ranks specified by the layout staring from zero.
+
+        How it works:
+        1. we enumerates every possible coordinate (like a nested for-loop).
+        If sizes = (2, 3), we get the following coordinates:
+            (0,0), (0,1), (0,2), (1,0), (1,1), (1,2)
+
+        2. For each coordinate, we compute a linear rank index as:
+            all_ranks_from_zero = sum(coord[i] * strides[i] for i in range(ndim))
+
+        Example A:
+        sizes = (2, 3)        # 2 rows, 3 cols
+        strides = (3, 1)        # row-major layout
+        coords = (0,0) -> 0*3 + 0*1 = 0
+                 (0,1) -> 0*3 + 1*1 = 1
+                 (0,2) -> 0*3 + 2*1 = 2
+                 (1,0) -> 1*3 + 0*1 = 3
+                 (1,1) -> 1*3 + 1*1 = 4
+                 (1,2) -> 1*3 + 2*1 = 5
+        result = [0, 1, 2, 3, 4, 5]
+
+        Example B:
+        sizes = (2, 3)
+        strides = (1, 2)        # non-standard / strided layout
+        coords = (0,0) -> 0*1 + 0*2 = 0
+                 (0,1) -> 0*1 + 1*2 = 2
+                 (0,2) -> 0*1 + 2*2 = 4
+                 (1,0) -> 1*1 + 0*2 = 1
+                 (1,1) -> 1*1 + 1*2 = 3
+                 (1,2) -> 1*1 + 2*2 = 5
+        result = [0, 2, 4, 1, 3, 5]
+        """
+        return [
+            sum(c * s for c, s in zip(coord, flatten(self.strides)))
+            for coord in product(*(range(s) for s in flatten(self.sizes)))
+        ]
+
+    def global_ranks(self, world_size: int) -> list[list[int]]:
+        """
+        Build global ranks specified by the layout via two-level ranks composition.
+
+        The nested list forms the Cartesian product of all ranks for one layout and offset
+        regarding filling up the world_size with the layout.
+        The final global ranks are the addition of these two. The result is a
+        list of lists: one sublist per layout. This rank list will be used to build
+        the communicator underlying the layout and the given `world_size`.
+
+        Example:
+        world_size = 16
+        self.size = 4
+        self.stride = 1
+        ranks = [0, 1, 2, 3]
+        offsets = [0, 4, 8, 12]
+        result = [
+            [0+0, 0+1, 0+2, 0+3],  # → [0, 1, 2, 3]
+            [4+0, 4+1, 4+2, 4+3],  # → [4, 5, 6, 7]
+            [8+0, 8+1, 8+2, 8+3],  # → [8, 9, 10,11]
+            [12+0, 12+1, 12+2, 12+3],  # → [12,13,14,15]
+        ]
+        """
+        return [
+            [offset + rank for rank in self.all_ranks_from_zero()]
+            for offset in self.complement(world_size).all_ranks_from_zero()
+        ]
+
+    def check_non_overlap(self) -> bool:
+        """
+        Check if the layout has any overlap between the ranks it generates. If there is overlap,
+        we return False, otherwise True.
+
+        The layout is supposed to be injective i.e, aside from indice 0, indices from each
+        dim of the layout must be non-overlapping.
+
+        Example 1 - Valid (no overlap):
+        Layout: sizes=(2,3), strides=(6,1)
+        - Dim 1: stride=1, span=3*1=3, covers indices [0,1,2]
+        - Dim 0: stride=6, span=2*6=12, covers indices [0,6]
+        → No overlap since 6 > 3
+
+        Example 2 - Invalid (overlap):
+        Layout: sizes=(2,3), strides=(2,1)
+        - Dim 1: stride=1, span=3*1=3, covers indices [0,1,2]
+        - Dim 0: stride=2, span=2*2=4, covers indices [0,2]
+        → Overlap! stride=2 < span=3, so indices [0,2] are duplicated
+
+        Example 3 - Invalid (overlap):
+        Layout: sizes=(4,2), strides=(1,1)
+        - Dim 1: stride=1, span=4, covers indices [0,1,2,3]
+        - Dim 0: stride=1, span=2, covers indices [0,1]
+        → Overlap! stride is same for two dims, so indices [0,2] are duplicated
+
+        Returns:
+            bool: True if no overlap, False if overlap detected
+        """
+        ranks = self.all_ranks_from_zero()
+        return len(ranks) == len(set(ranks))
+
+    def remap_to_tensor(self, rank_map: torch.Tensor) -> torch.Tensor:
+        """
+        Leverage layout as an index for mesh tensor that re-maps the indexes after layout
+        transformation to actual device ranks.
+
+        With this method, the cute layout serves as the backend of indices bookkeeping for the
+        mesh tensor when it comes to flatten, unflatten and slicing operations. The actual mesh
+        tensor still represents the actual device assignment and ranks. We need this function
+        to specify device allocation and create backend for a mesh. Although any transform of mesh tensors
+        can be treated as a view or subset of mesh tensor, we do need to use the actual view or
+        sub-tensor for DeviceMesh and its backend creation.
+
+        The shape of the `rank_map` must be 1D and contiguous.
+
+        Examples:
+
+        Case 1 - Consecutive ranks, full world:
+            original_mesh_tensor = [[0,1],[2,3]]  # 2x2 mesh, ranks 0-3
+            world_size = 4
+            layout = Layout(2:2)
+            Return: [[0,2],[1,3]]
+
+        Case 2 - Non-consecutive ranks:
+            original_mesh_tensor = [[10,20],[30,40]]  # custom rank assignment
+            world_size = 4
+            layout = Layout(2:2)
+            Return: [[[10,30],[20,40]]]
+
+        Args:
+            rank_map: The concrete mesh tensor with actual device ranks
+
+        Returns:
+            torch.Tensor: A tensor representing the actual device allocation from rank_map
+        """
+        assert rank_map.ndim == 1
+        assert rank_map.is_contiguous()
+        assert rank_map.numel() >= self.cosize()
+
+        complement_layout = self.complement(rank_map.numel())
+
+        return rank_map.as_strided(
+            flatten(complement_layout.sizes) + flatten(self.sizes),
+            flatten(complement_layout.strides) + flatten(self.strides),
+        ).reshape(-1, *self.top_level_sizes)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_pycute/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_pycute/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e13bcc86e5095a0762417cf0c6cfdaa20951ee5d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_pycute/__init__.py
@@ -0,0 +1,74 @@
+#################################################################################################
+#
+# Copyright (c) 2023 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# 1. Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# 2. Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# 3. Neither the name of the copyright holder nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+#################################################################################################
+
+from .int_tuple import (
+    as_tuple,
+    crd2crd,
+    crd2idx,
+    elem_scale,
+    flatten,
+    has_none,
+    idx2crd,
+    inner_product,
+    IntTuple,
+    is_int,
+    is_tuple,
+    match_structure,
+    product,
+    shape_div,
+    signum,
+    slice_,
+    suffix_product,
+    tuple_max,
+)
+from .layout import (
+    coalesce,
+    complement,
+    composition,
+    cosize,
+    filter,
+    is_layout,
+    Layout,
+    LayoutBase,
+    left_inverse,
+    logical_divide,
+    logical_product,
+    make_layout,
+    right_inverse,
+    size,
+    slice_and_offset,
+    tiled_divide,
+    tiled_product,
+    zipped_divide,
+    zipped_product,
+)
+from .typing import Integer
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_pycute/int_tuple.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_pycute/int_tuple.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb3406a7399b1af1f892c17e2cf34755aeed244c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_pycute/int_tuple.py
@@ -0,0 +1,269 @@
+#################################################################################################
+#
+# Copyright (c) 2023 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# 1. Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# 2. Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# 3. Neither the name of the copyright holder nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+#################################################################################################
+
+"""
+Functions for manipulating IntTuples
+"""
+
+from functools import reduce
+from itertools import chain
+from typing import TypeAlias
+from typing_extensions import TypeIs
+
+from .typing import Integer
+
+
+# Type aliases for better readability
+IntTuple: TypeAlias = int | tuple["IntTuple", ...]
+
+
+def is_int(x: object) -> TypeIs[int]:
+    return isinstance(x, Integer)
+
+
+def is_tuple(x: object) -> TypeIs[tuple]:
+    return isinstance(x, tuple)
+
+
+def as_tuple(x: IntTuple) -> tuple[IntTuple, ...]:
+    if is_int(x):
+        return (x,)
+    return x
+
+
+def match_structure(a: IntTuple, b: IntTuple) -> bool:
+    if is_int(a) and is_int(b):
+        return True
+    if is_tuple(a) and is_tuple(b):
+        return len(a) == len(b) and all(match_structure(x, y) for x, y in zip(a, b))
+    return False
+
+
+def flatten(t: IntTuple) -> tuple[int, ...]:
+    if is_tuple(t):
+        if len(t) == 0:
+            return ()
+        else:
+            return tuple(i for a in t for i in flatten(a))
+    else:
+        return (t,)
+
+
+def signum(a: int) -> int:
+    return bool(a > 0) - bool(a < 0)
+
+
+def product(a: IntTuple) -> int:
+    if is_tuple(a):
+        return reduce(lambda val, elem: val * product(elem), a, 1)
+    else:
+        return a
+
+
+def inner_product(a: IntTuple, b: IntTuple) -> int:
+    if is_tuple(a) and is_tuple(b):  # tuple tuple
+        assert len(a) == len(b)
+        return sum(inner_product(x, y) for x, y in zip(a, b))
+    else:  # "int" "int"
+        assert not is_tuple(a) and not is_tuple(b)
+        return a * b
+
+
+def tuple_max(a: IntTuple) -> int:
+    if is_tuple(a):
+        return max(tuple_max(x) for x in a)
+    else:
+        return a
+
+
+def elem_scale(a: IntTuple, b: IntTuple) -> IntTuple:
+    if is_tuple(a):
+        if is_tuple(b):  # tuple tuple
+            assert len(a) == len(b)
+            return tuple(elem_scale(x, y) for x, y in zip(a, b))
+        else:  # tuple "int"
+            raise AssertionError("Invalid combination: tuple with int")
+    else:
+        if is_tuple(b):  # "int" tuple
+            return elem_scale(a, product(b))
+        else:  # "int" "int"
+            return a * b
+
+
+# Inclusive prefix ceil div with output congruent to input a
+def shape_div(a: IntTuple, b: IntTuple) -> IntTuple:
+    if is_tuple(a):
+        if is_tuple(b):  # tuple tuple
+            assert len(a) == len(b)
+            return tuple(shape_div(x, y) for x, y in zip(a, b))
+        else:  # tuple "int"
+            # r = [shape_div(a[0],b)] + [shape_div(a[i],b := shape_div(b, product(a[i-1]))) for i in range(1,len(a))]
+            r = []
+            for v in a:
+                r.append(shape_div(v, b))
+                b = shape_div(b, product(v))
+            return tuple(r)
+    else:
+        if is_tuple(b):  # "int" tuple
+            return shape_div(a, product(b))
+        else:  # "int" "int"
+            assert a % b == 0 or b % a == 0
+            return (a + b - 1) // b
+
+
+# Exclusive suffix product with output congruent to input a (lexicographic)
+def suffix_product(a: IntTuple, init: IntTuple = 1) -> IntTuple:
+    # TODO: With all these length asserts, may want to create a zip_strict wrapper.
+    if is_tuple(a):
+        if is_tuple(init):  # tuple tuple
+            assert len(a) == len(init)
+            return tuple(suffix_product(x, i) for x, i in zip(a, init))
+        else:  # tuple "int"
+            # Process from right to left for lexicographic ordering
+            # r = [prefix_product(a[len(a)-1],init)] +
+            # [prefix_product(a[i],init := init * product(a[i+1])) for i in range(len(a)-1,0)].reverse()
+            r = []
+
+            # Calculate products from right to left, appending to list
+            for i in range(len(a) - 1, -1, -1):
+                r.append(suffix_product(a[i], init))
+                init = init * product(a[i])
+
+            # Reverse to get correct lexicographic order
+            r.reverse()
+            return tuple(r)
+    else:
+        if is_tuple(init):  # "int" tuple
+            raise AssertionError("Invalid combination: int with tuple init")
+        else:  # "int" "int"
+            return init
+
+
+def idx2crd(idx: IntTuple, shape: IntTuple, stride: IntTuple | None = None) -> IntTuple:
+    if stride is None:
+        stride = suffix_product(shape)
+
+    if is_tuple(idx):
+        if is_tuple(shape) and is_tuple(stride):  # tuple tuple tuple
+            assert len(idx) == len(shape) and len(stride) == len(shape)
+            return tuple(idx2crd(i, s, d) for i, s, d in zip(idx, shape, stride))
+        else:  # tuple "int" "int"
+            raise AssertionError("Invalid combination: tuple with int stride")
+    else:
+        if is_tuple(shape) and is_tuple(stride):  # "int" tuple tuple
+            assert len(shape) == len(stride)
+            return tuple(idx2crd(idx, s, d) for s, d in zip(shape, stride))
+        else:  # "int" "int" "int"
+            assert not is_tuple(shape) and not is_tuple(stride)
+            return (idx // stride) % shape  # all are ints after type checks
+
+
+def crd2idx(
+    crd: IntTuple | None, shape: IntTuple, stride: IntTuple | None = None
+) -> int:
+    if stride is None:
+        stride = suffix_product(shape)
+
+    if is_tuple(crd):
+        if is_tuple(shape) and is_tuple(stride):  # tuple tuple tuple
+            assert len(crd) == len(shape) and len(stride) == len(shape)
+            return sum(crd2idx(c, s, d) for c, s, d in zip(crd, shape, stride))
+        else:  # tuple "int" "int"
+            raise AssertionError(f"Invalid combination: crd={crd}, shape={shape}")
+    else:
+        if crd is None:
+            crd = 0
+
+        if is_tuple(shape) and is_tuple(stride):  # "int" tuple tuple
+            assert len(shape) == len(stride)
+            result = 0
+            # Process from right to left for lexicographic ordering
+            for i in range(len(shape) - 1, 0, -1):
+                result += crd2idx(crd % product(shape[i]), shape[i], stride[i])
+                crd = crd // product(shape[i])
+            if len(shape) > 0:
+                result += crd2idx(crd, shape[0], stride[0])
+            return result
+        else:  # "int" "int" "int"
+            assert not is_tuple(shape) and not is_tuple(stride)
+            return crd * stride  # all are ints after type checks
+
+
+# Transform crd into the dst_shape's iteration space
+def crd2crd(
+    crd: IntTuple, dst_shape: IntTuple, src_shape: IntTuple | None = None
+) -> IntTuple:
+    if is_tuple(crd):
+        if is_tuple(dst_shape):  # tuple tuple
+            assert len(crd) == len(dst_shape)
+            return tuple(crd2crd(x, y) for x, y in zip(crd, dst_shape))
+        else:  # tuple "int"
+            # Ambiguous unless we have src_shape
+            assert src_shape is not None
+            return crd2idx(crd, src_shape)
+    else:
+        if is_tuple(dst_shape):  # "int" tuple
+            return idx2crd(crd, dst_shape)
+        else:  # "int" "int"
+            assert crd < dst_shape
+            return crd
+
+
+# Filter trg according to crd: keep only elements of trg that are paired with None
+def slice_(crd: None | tuple | int, trg: tuple | int) -> tuple | int:
+    if is_tuple(crd):
+        if is_tuple(trg):  # tuple tuple
+            assert len(crd) == len(trg)
+            # match C++ behavior of `filter_tuple` using `tuple_cat(...)`
+            return tuple(
+                chain(
+                    *filter(  # type: ignore[arg-type]  # filter returns Iterator which is compatible
+                        lambda x: x != (),
+                        [slice_(c, s) for c, s in zip(crd, trg)],
+                    )
+                )
+            )
+        else:
+            raise AssertionError("Invalid combination: tuple crd with int trg")
+    elif crd is None:
+        # match C++ behavior `return cute::tuple{b};`
+        return (trg,)
+    else:
+        return ()
+
+
+# Determine if None appears at any of an int_tuples' terminals
+def has_none(a: None | tuple | int) -> bool:
+    if is_tuple(a):
+        return any(has_none(v) for v in a)
+    else:
+        return a is None
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_pycute/layout.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_pycute/layout.py
new file mode 100644
index 0000000000000000000000000000000000000000..0adf94b5b142b925f7ab35dc82f46c4bf509001a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_pycute/layout.py
@@ -0,0 +1,470 @@
+#################################################################################################
+#
+# Copyright (c) 2023 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# 1. Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# 2. Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# 3. Neither the name of the copyright holder nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+#################################################################################################
+
+"""
+Definition of CuTe Layouts and functions to manipulate them which works with the order
+of lexicographic instead of co-lexicographic as implemented in the original layout.py
+"""
+
+from itertools import chain
+from typing import TypeAlias
+from typing_extensions import Self, TypeIs
+
+from .int_tuple import (
+    crd2idx,
+    flatten,
+    has_none,
+    IntTuple,
+    is_int,
+    is_tuple,
+    product,
+    slice_,
+    suffix_product,
+)
+
+
+# Type aliases
+CoordinateType: TypeAlias = (
+    int | IntTuple | tuple[object, ...] | None
+)  # Input for slice_ and crd2idx functions
+
+
+class LayoutBase:
+    pass
+
+
+def is_layout(x: object) -> TypeIs["Layout"]:
+    return isinstance(x, LayoutBase)
+
+
+class Layout(LayoutBase):
+    def __init__(self, _shape: IntTuple, _stride: IntTuple | None = None) -> None:
+        self.shape = _shape
+        if _stride is None:
+            self.stride = suffix_product(self.shape)
+        else:
+            self.stride = _stride
+
+    # operator ==
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, Layout):
+            return False
+        return self.shape == other.shape and self.stride == other.stride
+
+    # operator len(L)  (len [rank] like tuples)
+    def __len__(self) -> int:
+        if is_tuple(self.shape):
+            return len(self.shape)
+        else:
+            return 1
+
+    # operator ()    (map coord to idx)
+    def __call__(self, *args: CoordinateType) -> Self | int:
+        """
+        Map a logical coordinate to a linear index (Coord has no Underscore slice operators)
+        OR
+        Slice the layout and return the sublayout (Coord has an Underscore slice op)
+
+        Follow the same behavior of `Layout::operator(Coord const&)` in cute C++
+        """
+        if has_none(args):
+            if len(args) == 1:
+                return Layout(slice_(args[0], self.shape), slice_(args[0], self.stride))
+            else:
+                return Layout(slice_(args, self.shape), slice_(args, self.stride))
+        else:
+            if len(args) == 1:
+                return crd2idx(args[0], self.shape, self.stride)  # type: ignore[arg-type]
+            else:
+                return crd2idx(args, self.shape, self.stride)  # type: ignore[arg-type]
+
+    # operator []    (get-i like tuples)
+    def __getitem__(self, i: int) -> Self:
+        if is_tuple(self.shape):
+            return Layout(self.shape[i], self.stride[i])  # type: ignore[index]
+        else:
+            assert i == 0
+            return Layout(self.shape, self.stride)
+
+    # size(layout)   Size of the domain
+    def size(self) -> int:
+        return product(self.shape)
+
+    # cosize(layout)   Size of the codomain
+    def cosize(self) -> int:
+        return self(self.size() - 1) + 1  # type: ignore[operator]
+
+    # print and str
+    def __str__(self) -> str:
+        return f"{self.shape}:{self.stride}"
+
+    # error msgs and representation
+    def __repr__(self) -> str:
+        return f"Layout({self.shape},{self.stride})"
+
+
+# Type aliases
+LayoutOrIntTuple: TypeAlias = Layout | IntTuple
+LayoutProfile: TypeAlias = tuple[object, ...] | Layout | None
+LayoutInput: TypeAlias = Layout | IntTuple | tuple[object, ...] | None
+
+
+# Make Layout from a list of layouts (each layout it's own mode in the result)
+def make_layout(*layouts: Layout | tuple[Layout, ...]) -> Layout:
+    if len(layouts) == 1 and not is_layout(layouts[0]):
+        layouts = layouts[0]
+
+    shape, stride = zip(*((a.shape, a.stride) for a in layouts))  # type: ignore[union-attr]
+    return Layout(shape, stride)
+
+
+# Size of the domain
+def size(layout: LayoutOrIntTuple) -> int:
+    if is_layout(layout):
+        return layout.size()
+    return product(layout)
+
+
+# Size of the codomain
+def cosize(layout: Layout) -> int:
+    return layout.cosize()
+
+
+# Layout coalesce -- flatten and combine as many modes as possible while preserving the int-to-int function
+def coalesce(layout: Layout, profile: LayoutProfile = None) -> Layout:
+    if is_tuple(profile):
+        assert len(layout) >= len(profile)
+        return make_layout(
+            chain(
+                (coalesce(layout[i], profile[i]) for i in range(len(profile))),  # type: ignore[arg-type]
+                (layout[i] for i in range(len(profile), len(layout))),
+            )
+        )
+
+    result_shape = [1]
+    result_stride = [0]
+    # Since we now follow lexicographic order, we need to process from right to left.
+    # And to make implementation more efficient, we append to the end of list and reverse it in the end.
+    for shape, stride in zip(
+        reversed(flatten(layout.shape)), reversed(flatten(layout.stride))
+    ):
+        # skip their shape-1s
+        if shape == 1:
+            continue
+        # replace our shape-1 with anything
+        elif result_shape[-1] == 1:
+            result_shape[-1] = shape
+            result_stride[-1] = stride
+        # merge modes if the shape*stride match
+        elif result_shape[-1] * result_stride[-1] == stride:
+            result_shape[-1] = result_shape[-1] * shape
+        # append a new mode
+        else:
+            result_shape.append(shape)
+            result_stride.append(stride)
+
+    if len(result_shape) == 1:
+        return Layout(result_shape[0], result_stride[0])
+    else:
+        result_shape.reverse()
+        result_stride.reverse()
+        return Layout(tuple(result_shape), tuple(result_stride))
+
+
+# Layout filter -- replace all stride-0 modes with size-1 and then coalesce to remove them
+def filter(layout: Layout, profile: LayoutProfile = None) -> Layout:
+    if is_tuple(profile):
+        assert len(layout) >= len(profile)
+        return make_layout(
+            chain(
+                (filter(layout[i], profile[i]) for i in range(len(profile))),  # type: ignore[arg-type]
+                (layout[i] for i in range(len(profile), len(layout))),
+            )
+        )
+
+    result_shape = []
+    result_stride = []
+    for shape, stride in zip(flatten(layout.shape), flatten(layout.stride)):
+        # skip their shape-1s and stride-0s
+        if not (shape == 1 or stride == 0):
+            result_shape.append(shape)
+            result_stride.append(stride)
+
+    if len(result_shape) == 0:
+        return Layout(1, 0)
+    else:
+        return coalesce(Layout(tuple(result_shape), tuple(result_stride)))
+
+
+# Layout composition
+# Use tuples-of-layouts to perform this operation by-mode and None as no-op
+def composition(layoutA: Layout, layoutB: LayoutInput) -> Layout:
+    if layoutB is None:
+        return layoutA
+    elif is_int(layoutB):
+        return composition(layoutA, Layout(layoutB))
+    elif is_tuple(layoutB):
+        assert len(layoutA) >= len(layoutB)
+        return make_layout(
+            chain(
+                (composition(layoutA[i], layoutB[i]) for i in range(len(layoutB))),  # type: ignore[arg-type]
+                (layoutA[i] for i in range(len(layoutB), len(layoutA))),
+            )
+        )
+    elif is_tuple(layoutB.shape):
+        return make_layout(composition(layoutA, layoutB_i) for layoutB_i in layoutB)  # type: ignore[arg-type, attr-defined]
+
+    if layoutB.stride == 0:
+        return Layout(layoutB.shape, 0)
+    else:
+        result_shape = []
+        result_stride = []
+        rest_shape = layoutB.shape
+        rest_stride = layoutB.stride
+        flat_A = coalesce(layoutA)
+        # when left layout is multi-dimensional sublayout, aka, self = (a,b,...,c):(x,y,...,z), layout = s:d,
+        # for integral s and d means that we want:
+        # (1) “remove” the first d elements from left, starting from rightmost. (This will increase the stride.)
+        # (2) “keep” the first s of those strided elements. (This does not affect the stride.)
+        # For example, if self = (6,2):(2,1), layout = (3:2)
+        # Step 1: remove the first 2 elements from self with stride increase, i.e., (6,2):(2,1) -> (6,1):(2,2)
+        # Step 2: keep the first 3 of those strided elements, i.e., (6,1):(2,2) -> (3,1):(2,2)
+        # Because we are going lexicographically, we go through left layout from right to left.
+        for curr_shape, curr_stride in zip(
+            reversed(flatten(flat_A.shape)[1:]), reversed(flatten(flat_A.stride)[1:])
+        ):
+            assert curr_shape % rest_stride == 0 or rest_stride % curr_shape == 0  # type: ignore[operator]
+            new_shape = min(max(1, curr_shape // rest_stride), rest_shape)  # type: ignore[operator]
+
+            if new_shape != 1:
+                result_shape.append(new_shape)  # Append to end, will reverse later
+                result_stride.append(rest_stride * curr_stride)
+
+            rest_shape = rest_shape // new_shape  # type: ignore[operator]
+            rest_stride = -(
+                -rest_stride // curr_shape  # type: ignore[operator]
+            )  # Python exclusive impl: "//" is always floor div so == ceil_div(abs(rest_stride), curr_shape) * signum(rest_stride)
+
+        # When left has single-size sublayout or reach the last sublayout, aka, left = a:b, layout = s:d,
+        # the result is rather trivial: left o layout = a:b o s:d = s:(b*d).
+        # For example, if self = (6:2), layout = (3:2), the result is (3:(2*2)) = (3:4).
+        if rest_shape != 1 or len(result_shape) == 0:
+            result_shape.append(rest_shape)  # Append to end, will reverse later
+            result_stride.append(rest_stride * flatten(flat_A.stride)[0])
+
+        # Reverse the lists because we build lists in reverse order (append to end), this way it is more efficient.
+        result_shape.reverse()
+        result_stride.reverse()
+
+        if len(result_shape) == 1:
+            return Layout(result_shape[0], result_stride[0])  # type: ignore[arg-type]
+        else:
+            return Layout(tuple(result_shape), tuple(result_stride))  # type: ignore[arg-type]
+
+
+# Layout complement
+def complement(layout: LayoutOrIntTuple, max_idx: int = 1) -> Layout:
+    if is_int(layout):
+        return complement(Layout(layout))
+
+    result_shape = []
+    result_stride = []
+    current_idx = 1
+
+    sorted_DS = sorted(zip(flatten(layout.stride), flatten(layout.shape)))  # type: ignore[union-attr]
+    for stride, shape in sorted_DS:
+        if stride == 0 or shape == 1:
+            continue
+
+        in_bound = current_idx <= shape * stride
+        # To support symbolic value which can't be evaluated now
+        assert (type(in_bound) is not bool) or in_bound
+
+        result_shape.append(stride // current_idx)
+        result_stride.append(current_idx)
+        current_idx = shape * stride
+
+    result_shape.append((max_idx + current_idx - 1) // current_idx)  # ceil_div
+    result_stride.append(current_idx)
+    # This is different from original pycute implementation, because we want to follow the lexicographic order here
+    # where the right-most dimension is the innermost dimension (smallest stride).
+    result_shape.reverse()
+    result_stride.reverse()
+
+    return coalesce(Layout(tuple(result_shape), tuple(result_stride)))
+
+
+# Layout right inverse
+def right_inverse(layout: LayoutOrIntTuple | None) -> Layout | None:
+    if layout is None:
+        return None
+    elif is_int(layout):
+        return Layout(layout)
+
+    result_shape = []
+    result_stride = []
+    current_idx = 1
+
+    flat_shape = flatten(layout.shape)  # type: ignore[union-attr]
+    flat_stride = flatten(layout.stride)  # type: ignore[union-attr]
+    sorted_DSA = sorted(zip(flat_stride, flat_shape, suffix_product(flat_shape)))  # type: ignore[arg-type]
+    for stride, shape, rstride in sorted_DSA:
+        if shape == 1:
+            continue
+        if current_idx != stride:
+            break
+
+        result_shape.append(shape)
+        result_stride.append(rstride)
+        current_idx = shape * stride
+
+    result_shape.reverse()
+    result_stride.reverse()
+    return coalesce(Layout(tuple(result_shape), tuple(result_stride)))
+
+
+# Layout left inverse
+def left_inverse(layout: LayoutOrIntTuple | None) -> Layout | None:
+    if layout is None:
+        return None
+    elif is_int(layout):
+        return Layout(layout)
+    return right_inverse(make_layout(complement(layout), layout))  # type: ignore[arg-type]
+
+
+# Split a layout by the composition of B and the "rest"
+# Use tuples-of-layouts to perform this operation by-mode and None as no-op
+def logical_divide(layoutA: Layout, layoutB: LayoutInput) -> Layout:
+    if layoutB is None:
+        return layoutA
+    elif is_int(layoutB):
+        return logical_divide(layoutA, Layout(layoutB))
+    elif is_tuple(layoutB):
+        assert len(layoutA) >= len(layoutB)
+        return make_layout(
+            chain(
+                (
+                    logical_divide(layoutA[i], layoutB[i])  # type: ignore[arg-type]
+                    for i in range(len(layoutB))
+                ),
+                (layoutA[i] for i in range(len(layoutB), len(layoutA))),
+            )
+        )
+
+    return composition(
+        layoutA,
+        make_layout(layoutB, complement(layoutB, size(layoutA))),
+    )
+
+
+# Reproduce a layoutA over a layoutB
+# Use tuples-of-layouts to perform this operation by-mode and None as no-op
+def logical_product(layoutA: Layout, layoutB: LayoutInput) -> Layout:
+    if layoutB is None:
+        return layoutA
+    elif is_int(layoutB):
+        return logical_divide(layoutA, Layout(layoutB))
+    elif is_tuple(layoutB):
+        assert len(layoutA) >= len(layoutB)
+        return make_layout(
+            chain(
+                (
+                    logical_product(layoutA[i], layoutB[i])  # type: ignore[arg-type]
+                    for i in range(len(layoutB))
+                ),
+                (layoutA[i] for i in range(len(layoutB), len(layoutA))),
+            )
+        )
+
+    return make_layout(
+        layoutA,
+        composition(complement(layoutA, size(layoutA) * cosize(layoutB)), layoutB),
+    )
+
+
+# Gather the modes from a hierarchical logical_divide or logical_product
+def hier_unzip(
+    splitter: object,
+    layoutA: Layout,
+    layoutB: LayoutInput,
+) -> Layout:
+    if layoutB is None:
+        return make_layout(Layout(1, 0), layoutA)
+    elif is_tuple(layoutB):
+        assert len(layoutA) >= len(layoutB)
+        # A layout with shape ((A,a),(B,b),(C,c))
+        split = make_layout(
+            hier_unzip(splitter, layoutA[i], layoutB[i])  # type: ignore[arg-type]
+            for i in range(len(layoutB))
+        )
+        # Gather to shape ((A,B,C,...),(a,b,c,...,y,z))
+        return make_layout(
+            make_layout(split[i][0] for i in range(len(layoutB))),  # type: ignore[arg-type]
+            make_layout(
+                chain(  # type: ignore[arg-type]
+                    (split[i][1] for i in range(len(layoutB))),
+                    (layoutA[i] for i in range(len(layoutB), len(layoutA))),
+                )
+            ),
+        )
+
+    # splitter must return a rank-2 layout
+    return splitter(layoutA, layoutB)  # type: ignore[operator]
+
+
+# Apply logical divide hierarchically and gather the split modes into two modes
+def zipped_divide(layoutA: Layout, layoutB: LayoutInput) -> Layout:
+    return hier_unzip(logical_divide, layoutA, layoutB)
+
+
+# Perform logical divide hierarchically and gather tiles (B-layouts) into a new mode
+def tiled_divide(layoutA: Layout, layoutB: LayoutInput) -> Layout:
+    result = zipped_divide(layoutA, layoutB)
+    return make_layout([result[0]] + [result[1][i] for i in range(len(result[1]))])  # type: ignore[arg-type]
+
+
+# Apply logical product hierarchically and gather the split modes into two modes
+def zipped_product(layoutA: Layout, layoutB: LayoutInput) -> Layout:
+    return hier_unzip(logical_product, layoutA, layoutB)
+
+
+# Perform logical product hierarchically and gather tiles (B-layouts) into a new mode
+def tiled_product(layoutA: Layout, layoutB: LayoutInput) -> Layout:
+    result = zipped_product(layoutA, layoutB)
+    return make_layout([result[0]] + [result[1][i] for i in range(len(result[1]))])  # type: ignore[arg-type]
+
+
+def slice_and_offset(crd: tuple[object, ...], layout: Layout) -> tuple[Layout, int]:
+    return (
+        Layout(slice_(crd, layout.shape), slice_(crd, layout.stride)),
+        crd2idx(crd, layout.shape, layout.stride),  # type: ignore[arg-type]
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_pycute/typing.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_pycute/typing.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e6fe0a9c66e800186b4d84ef52cc12d6baeb1f6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_pycute/typing.py
@@ -0,0 +1,42 @@
+#################################################################################################
+#
+# Copyright (c) 2023 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# 1. Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# 2. Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# 3. Neither the name of the copyright holder nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+#################################################################################################
+
+from abc import ABC
+
+
+class Integer(ABC):  # noqa: B024  # Uses __subclasshook__ instead of abstract methods
+    @classmethod
+    def __subclasshook__(cls, c: type) -> bool:
+        if c in [bool, float]:
+            return False
+
+        return issubclass(c, int)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_serialization.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_serialization.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f7043453be769cccfb70cd391dac87348508016
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_serialization.py
@@ -0,0 +1,158 @@
+import pickle
+from dataclasses import dataclass
+from io import BufferedIOBase
+from typing import Any
+
+import torch
+import torch._weights_only_unpickler as _weights_only_unpickler
+from torch.serialization import _load, _save, DEFAULT_PROTOCOL, MAP_LOCATION
+
+
+__all__: list[str] = []
+
+
+@dataclass
+class _Entry:
+    key: str
+    is_storage: bool
+    length: int
+
+
+_weights_only_unpickler._add_safe_globals([_Entry])
+
+
+class _PseudoZipFile:
+    def __init__(self) -> None:
+        self.records: dict[str, tuple[object, int]] = {}
+
+    def write_record(self, key: str, data: object, length: int) -> None:
+        self.records[key] = (data, length)
+
+    def write_to(self, f: BufferedIOBase) -> None:
+        entries = []
+        for key, (data, length) in self.records.items():
+            entries.append(
+                _Entry(
+                    key=key,
+                    is_storage=isinstance(data, torch.UntypedStorage),
+                    length=length,
+                )
+            )
+
+        pickle.dump(entries, f, protocol=DEFAULT_PROTOCOL)
+
+        for data, _ in self.records.values():
+            if isinstance(data, bytes):
+                f.write(data)
+            elif isinstance(data, str):
+                f.write(data.encode("utf-8"))
+            elif isinstance(data, torch.UntypedStorage):
+                data._write_file(f, False, False, 1)
+            else:
+                raise TypeError(f"unknown type: {type(data)}")
+
+    def read_from(self, f: BufferedIOBase) -> None:
+        entries = _weights_only_unpickler.load(f)
+
+        for entry in entries:
+            data = f.read(entry.length)
+            if entry.is_storage:
+                if entry.length == 0:
+                    storage = torch.UntypedStorage(0)
+                else:
+                    storage = torch.frombuffer(
+                        data,
+                        dtype=torch.uint8,
+                    ).untyped_storage()
+
+                self.records[entry.key] = (
+                    storage,
+                    entry.length,
+                )
+            else:
+                self.records[entry.key] = (data, entry.length)
+
+    def has_record(self, key: str) -> bool:
+        return key in self.records
+
+    def get_record(self, key: str) -> object:
+        return self.records[key][0]
+
+    def get_storage_from_record(
+        self, key: str, _length: int, _type: int
+    ) -> torch.Tensor:
+        return torch.tensor(self.records[key][0], dtype=torch.uint8)
+
+    def serialization_id(self) -> str:
+        return "torchft"
+
+
+def _streaming_save(
+    obj: object,
+    f: BufferedIOBase,
+    pickle_module: Any = pickle,
+    pickle_protocol: int = DEFAULT_PROTOCOL,
+) -> None:
+    """
+    Save the object to a file-like object in a streaming fashion compatible with
+    network sockets.
+
+    This behaves similarly to :func:`torch.save` with a few notable differences:
+
+    * A non-seekable file like object can be used when loading.
+    * No forwards/backwards compatibility is provided for the serialization
+      format. This is only intended to be used with a single version of PyTorch
+      with transient storage (i.e. sockets or temp files).
+    * mmap is not supported
+
+    See :func:`torch.save` for more details on specific arguments.
+    """
+
+    zip_file = _PseudoZipFile()
+    _save(
+        obj,
+        zip_file=zip_file,
+        pickle_module=pickle_module,
+        pickle_protocol=pickle_protocol,
+        _disable_byteorder_record=False,
+    )
+    zip_file.write_to(f)
+
+
+def _streaming_load(
+    f: BufferedIOBase,
+    map_location: MAP_LOCATION = None,
+    pickle_module: Any = None,
+    *,
+    weights_only: bool = True,
+    **pickle_load_args: Any,
+) -> object:
+    """
+    Load the object from a file-like object in a streaming fashion compatible with
+    network sockets.
+
+    See :func:`_streaming_save` for more details about the streaming behavior.
+
+    See :func:`torch.load` for more details on specific arguments.
+    """
+    if weights_only:
+        if pickle_module is not None:
+            raise RuntimeError(
+                "Can not safely load weights when explicit pickle_module is specified"
+            )
+        pickle_module = _weights_only_unpickler
+    else:
+        if pickle_module is None:
+            pickle_module = pickle
+
+    if "encoding" not in pickle_load_args:
+        pickle_load_args["encoding"] = "utf-8"
+
+    zip_file = _PseudoZipFile()
+    zip_file.read_from(f)
+    return _load(
+        zip_file=zip_file,
+        map_location=map_location,
+        pickle_module=pickle_module,
+        **pickle_load_args,
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..85a313c779e7aa87726f425146048fcd37efd261
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/__init__.py
@@ -0,0 +1 @@
+from .api import _shard_tensor, load_with_process_group, shard_module, shard_parameter
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..6fd641b3f9443faa64b6b54c3ab209f8167a56f7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/_utils.py
@@ -0,0 +1,32 @@
+from collections.abc import Sequence
+
+import torch
+from torch.distributed._shard.metadata import ShardMetadata
+
+
+DEPRECATE_MSG = "Please use DTensor instead and we are deprecating ShardedTensor."
+
+
+def narrow_tensor_by_index(
+    tensor: torch.Tensor,
+    offsets: Sequence[int],
+    sizes: Sequence[int],
+) -> torch.Tensor:
+    """
+    Narrow the tensor according to ``offsets`` and ``sizes``.
+    """
+    narrowed_tensor = tensor
+    for idx, (offset, size) in enumerate(zip(offsets, sizes)):
+        if size < tensor.size(idx):
+            # Reshape to get shard for this rank and we don't want autograd
+            # recording here for the narrow op and 'local_shard' should be a
+            # leaf variable in the autograd graph.
+            narrowed_tensor = narrowed_tensor.narrow(idx, offset, size)
+    return narrowed_tensor
+
+
+def narrow_tensor(tensor: torch.Tensor, metadata: ShardMetadata) -> torch.Tensor:
+    """
+    Narrow the tensor according to the metadata
+    """
+    return narrow_tensor_by_index(tensor, metadata.shard_offsets, metadata.shard_sizes)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/api.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..82589119d7afa6086b6b6289954d88676516b620
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/api.py
@@ -0,0 +1,305 @@
+# mypy: allow-untyped-defs
+from contextlib import contextmanager
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+from torch.distributed import distributed_c10d
+from torch.distributed._shard.sharded_tensor import ShardedTensor
+
+from .sharder import Sharder
+from .sharding_plan import ShardingPlan
+from .sharding_spec import ChunkShardingSpec, ShardingSpec
+
+
+def _shard_tensor(
+    tensor: torch.Tensor, sharding_spec: ShardingSpec, src_rank=0, process_group=None
+) -> ShardedTensor:
+    """
+    Given a :class:`torch.Tensor`, it shards that tensor according to the provided
+    ``sharding_spec``. ``src_rank`` denotes the source rank which would be
+    used as the ground truth of the data which would be scattered as shards
+    across the rest of the ranks.
+
+    Args:
+        tensor (:class:`torch.Tensor`): Tensor needs to be sharded.
+        sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The specification
+            describing how to shard the Tensor.
+
+    Keyword args:
+        src_rank (int, optional): The source rank which is used as the ground truth of
+            the data for the parameter that would be sharded and scattered
+            across the rest of the ranks.
+            Default: 0.
+        process_group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+
+    Returns:
+        A :class:`ShardedTensor` sharded from the given tensor.
+
+    .. warning::
+        Only :class:`torch.distributed._shard.sharding_spec.ChunkShardingSpec` is
+        currently supported as the ``sharding_spec``.
+    """
+    if not tensor.is_contiguous():
+        raise ValueError("input tensor is not a contiguous Tensor")
+
+    pg = (
+        process_group
+        if process_group is not None
+        else distributed_c10d._get_default_group()
+    )
+    world_size = dist.get_world_size(pg)
+    current_rank = dist.get_rank(pg)
+
+    # Validate src_rank and sharding_spec are same across all ranks.
+    gathered_list = [None] * world_size
+    dist.all_gather_object(gathered_list, (src_rank, sharding_spec), group=pg)
+
+    for idx, entry in enumerate(gathered_list):
+        if src_rank != entry[0]:  # type: ignore[index]
+            raise ValueError(
+                f"src_rank={src_rank} on rank: {current_rank} does not "  # type: ignore[index]
+                f"match with src_rank={entry[0]} on rank: {idx}"  # type: ignore[index]
+            )
+        if sharding_spec != entry[1]:  # type: ignore[index]
+            raise ValueError(
+                f"sharding_spec={sharding_spec} on rank: {current_rank} does not "  # type: ignore[index]
+                f"match with sharding_spec={entry[1]} on rank: {idx}"  # type: ignore[index]
+            )
+
+    st = sharding_spec.shard(tensor, src_rank=src_rank, process_group=pg)
+
+    return st
+
+
+def shard_parameter(
+    module: torch.nn.Module,
+    param_name: str,
+    sharding_spec: ShardingSpec,
+    src_rank=0,
+    process_group=None,
+):
+    """
+    Given a :class:`torch.nn.Module`, a ``param_name`` for a parameter in that
+    module, it shards that parameter according to the provided
+    ``sharding_spec``. ``src_rank`` denotes the source rank which would be
+    used as the ground truth of the data which would be scattered as shards
+    across the rest of the ranks.
+
+    This method replaces ``module.param_name`` with a
+    :class:`torch.distributed._sharded_tensor.ShardedTensor`
+
+    Args:
+        module (:class:`torch.nn.Module`): Module whose parameter needs to be sharded.
+        param_name (str): Name of the parameter of ``module`` that needs to be sharded.
+        sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The specification
+            describing how to shard the Tensor.
+
+    Keyword args:
+        src_rank (int, optional): The source rank which is used as the ground truth of
+            the data for the parameter that would be sharded and scattered
+            across the rest of the ranks.
+            Default: 0.
+        process_group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+
+    .. warning::
+        Only :class:`torch.distributed._shard.sharding_spec.ChunkShardingSpec` is
+        currently supported as the ``sharding_spec``.
+    """
+    # Perform some validation first.
+    if not hasattr(module, param_name):
+        raise AttributeError(f"{module._get_name()} has no attribute `{param_name}`")
+
+    tensor = getattr(module, param_name)
+    if not isinstance(tensor, torch.Tensor):
+        raise ValueError(
+            f"Expected {type(module).__name__}.{param_name} to be a Tensor, but found {type(tensor).__name__}"
+        )
+
+    if not tensor.is_contiguous():
+        raise ValueError(f"param: {param_name} is not a contiguous Tensor")
+
+    st = _shard_tensor(tensor, sharding_spec, src_rank, process_group)
+
+    # Replace param with ShardedTensor.
+    module.register_parameter(param_name, nn.Parameter(st))
+
+
+# Tracks the current process group in the load context manager.
+_CURRENT_PROCESS_GROUP: dist.ProcessGroup | None = None
+
+
+@contextmanager
+def load_with_process_group(process_group):
+    """
+    Context manager to set the process group with which to load a ShardedTensor.
+    """
+    global _CURRENT_PROCESS_GROUP
+    if _CURRENT_PROCESS_GROUP is not None:
+        raise RuntimeError(
+            'ProcessGroup already set by previous "load_with_process_group" '
+            "context manager"
+        )
+    _CURRENT_PROCESS_GROUP = process_group
+    try:
+        yield process_group
+    finally:
+        _CURRENT_PROCESS_GROUP = None
+
+
+def _get_current_process_group():
+    """
+    Retrieves the current process group set by ``load_with_process_group``.
+    If not set, it just returns the default group.
+    """
+    global _CURRENT_PROCESS_GROUP
+    if _CURRENT_PROCESS_GROUP is None:
+        return distributed_c10d._get_default_group()
+    else:
+        return _CURRENT_PROCESS_GROUP
+
+
+def _reshard_output(
+    module: torch.nn.Module, resharding_spec: ShardingSpec
+) -> torch.nn.Module:
+    """
+    Hook a module with output resharding in the forward pass according
+    to the given ``resharding_spec``.
+
+    Args:
+        module (:class:`torch.nn.Module`): Module whose output needs to be resharded.
+        resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`):
+            The specification describing how the output of the module will be resharded.
+
+    Returns:
+        A :class:`torch.nn.Module` object with reshard API hooked.
+    """
+
+    def hook_func(_module, _input, output):
+        if isinstance(output, ShardedTensor):
+            return output.reshard(resharding_spec)
+        return output
+
+    module.register_forward_hook(hook_func)
+    return module
+
+
+def _collect_local_shard(module: torch.nn.Module) -> torch.nn.Module:
+    """
+    Hook a module with local shards collection in the forward pass.
+
+    This API is typically used to convert a sharded representation back to data parallel
+    representation. In particular, it returns the local tensor for this Shard. If the
+    size along the sharding dimension for the local tensor is 1, this dimension is removed
+    from the final result. For example a [4, 16] ShardedTensor across 4 ranks is typically
+    a local Tensor of size [16] across each rank and not [1, 16] across each rank.
+
+    Args:
+        module (:class:`torch.nn.Module`): Module whose output is ShardedTensor and the
+            local tensor value needs to be returned.
+
+    Returns:
+        A :class:`torch.nn.Module` object with collection API hooked.
+    """
+
+    def hook_func(_module, _input, output):
+        if isinstance(output, ShardedTensor):
+            local_tensor = output.local_tensor()
+            # Squeeze the # of dimensions manually, only applicable to ChunkShardingSpec
+            sharding_spec = output._sharding_spec
+            if (
+                isinstance(sharding_spec, ChunkShardingSpec)
+                and local_tensor.size(sharding_spec.dim) == 1  # type: ignore[attr-defined, arg-type]
+            ):
+                local_tensor = local_tensor.squeeze(
+                    output._sharding_spec.dim  # type: ignore[attr-defined]
+                )
+            return local_tensor
+
+    module.register_forward_hook(hook_func)
+    return module
+
+
+def shard_module(module: nn.Module, plan: ShardingPlan, src_rank=0, process_group=None):
+    """
+    Shards a given module according to the provided sharding `plan`. This method
+    first shards all the parameters according to the given sharding `plan`. Then if
+    `output_plan` and `return_local_tensor` are specified in the sharding `plan`, it
+    will tag the output of modules according `output_plan`, convert the module's
+    output back to data parallel according to `return_local_tensor`.
+
+    Needs to be called on all ranks in an SPMD fashion.
+
+    Args:
+        module (:class:`torch.nn.Module`): The module to apply sharding to
+        plan (:class:`torch.distributed._shard.sharding_plan.ShardingPlan`):
+            The ShardingPlan which specified param name to ShardingSpec to apply to
+            each parameter.
+
+    Keyword args:
+         src_rank (int, optional): The source rank which is used as the ground truth of
+            the data for the module that would be sharded and scattered across the rest
+            of the ranks.
+            Default: 0.
+        process_group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+    """
+    # record Sharder paths for sanity check on the plan to ensure items in the plan
+    # does not conflict with the submodule tree that the Sharder is working with
+    sharder_paths = []
+    for name, spec in plan.plan.items():
+        if isinstance(spec, Sharder):
+            sharder_paths.append(name)
+
+    # shard the parameter according to the ShardingPlan
+    for name, spec in plan.plan.items():
+        if isinstance(spec, ShardingSpec):
+            # if found a sharding spec, try to shard the parameter
+            module_path, _, param_name = name.rpartition(".")
+
+            for sharder_path in sharder_paths:
+                if module_path.startswith(sharder_path):
+                    raise RuntimeError(
+                        f"ShardingPlan is in-valid, trying to shard a parameter: {name},"
+                        f" but there's already a Sharder entry for module {sharder_path},"
+                        f" parameter sharding should not conflict with the submodule tree"
+                        f" that a Sharder is working with!"
+                    )
+
+            mod = module.get_submodule(module_path)
+            shard_parameter(
+                mod, param_name, spec, src_rank=src_rank, process_group=process_group
+            )
+        elif isinstance(spec, Sharder):
+            parent_mod_path, _, _mod_name = name.rpartition(".")
+            if name == "":
+                raise KeyError("Module path must not be empty for custom sharder!")
+            mod = module.get_submodule(name)
+            parent_mod = module.get_submodule(parent_mod_path)
+            sharded_mod = spec.shard(mod)
+            # swap this submodule with the sharded module
+            parent_mod.mod_name = sharded_mod
+        else:
+            raise TypeError(
+                f"Only `ShardingSpec` and `Sharder` are supported to shard '{name}'"
+            )
+
+    # reshard output if there's an entry in `reshard_output` for this module
+    if plan.output_plan is not None:
+        for module_path, output_spec in plan.output_plan.items():
+            if isinstance(output_spec, ShardingSpec):
+                mod = module.get_submodule(module_path)
+                _reshard_output(mod, output_spec)
+            else:
+                raise TypeError(
+                    f"Only `ShardingSpec` is supported as output_plan for '{module_path}'"
+                )
+    # convert the output back to data parallel for the modules appears in
+    # `return_local_tensor` of the plan, we will call `_collect_local_shard`
+    # to collect the local tensor for output of modules
+    if plan.return_local_tensor is not None:
+        for module_path in plan.return_local_tensor:
+            mod = module.get_submodule(module_path)
+            _collect_local_shard(mod)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/checkpoint/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/checkpoint/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..85915636a014640d8fff5a29db602c4a114f1b1d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/checkpoint/__init__.py
@@ -0,0 +1,19 @@
+# Keep old package for BC purposes, this file should be removed once
+# everything moves to the `torch.distributed.checkpoint` package.
+import sys
+import warnings
+
+import torch
+from torch.distributed.checkpoint import *  # noqa: F403
+
+
+with warnings.catch_warnings():
+    warnings.simplefilter("always")
+    warnings.warn(
+        "`torch.distributed._shard.checkpoint` will be deprecated, "
+        "use `torch.distributed.checkpoint` instead",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+
+sys.modules["torch.distributed._shard.checkpoint"] = torch.distributed.checkpoint
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/common_op_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/common_op_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..c98b8c87ca2c7ceb1608a59673738a7e57333035
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/common_op_utils.py
@@ -0,0 +1,64 @@
+# mypy: allow-untyped-defs
+
+import torch
+from torch.utils import _pytree as pytree
+
+
+def _basic_validation(op, args=(), kwargs=None):
+    """
+    Common validation across all ops go in here.
+    """
+    from torch.distributed._shard.sharded_tensor import ShardedTensor
+
+    if len(args) == 0 and (kwargs is None or len(kwargs) == 0):
+        raise ValueError(f" No input for '{op.__name__}'!")
+
+    # Validate types
+    has_distributed_tensor = False
+
+    def is_distributed_tensor(e):
+        nonlocal has_distributed_tensor
+        if isinstance(e, ShardedTensor):
+            has_distributed_tensor = True
+
+    pytree.tree_map_(is_distributed_tensor, args)
+    pytree.tree_map_(is_distributed_tensor, kwargs)
+
+    if not has_distributed_tensor:
+        raise TypeError(
+            f"torch function '{op.__name__}', with args: {args} and "
+            f"kwargs: {kwargs} are called without any distributed tensor!"
+        )
+
+    # Validate all distributed tensors use the same PG.
+    cur_pg: torch.distributed.ProcessGroup | None = None
+
+    def validate_pg(e):
+        nonlocal cur_pg
+        if isinstance(e, ShardedTensor):
+            if cur_pg is not None and e._process_group is not cur_pg:
+                raise RuntimeError(
+                    "All distributed tensors should use the "
+                    "same ProcessGroup if used together in an op."
+                )
+            cur_pg = e._process_group
+
+    pytree.tree_map_(validate_pg, args)
+    pytree.tree_map_(validate_pg, kwargs)
+
+
+def _register_default_op(op, decorator):
+    @decorator(op)
+    def tensor_default_op(types, args=(), kwargs=None, pg=None):
+        """
+        Handles ``__torch_function__`` dispatch for the default tensor ops that
+        behave the same as ``torch.Tensor`` such as ``torch.Tensor.shape`` or
+        ``torch.Tensor.dtype``. We simply lower to the real op call with
+        DisableTorchFunctionSubclass context like ``torch.Tensor.__torch_function__``
+        to avoid recursions.
+        """
+        if kwargs is None:
+            kwargs = {}
+
+        with torch._C.DisableTorchFunctionSubclass():
+            return op(*args, **kwargs)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/metadata.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/metadata.py
new file mode 100644
index 0000000000000000000000000000000000000000..63ef073b1c494ab450bca79c83f3867548140fd8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/metadata.py
@@ -0,0 +1,63 @@
+# mypy: allow-untyped-defs
+from dataclasses import dataclass
+from functools import reduce
+
+from torch.distributed.remote_device import _remote_device
+
+
+@dataclass
+class ShardMetadata:
+    """
+    Represents a shard of the overall Tensor including its
+    offsets, lengths and device placement.
+
+    Args:
+        shard_offsets(List[int]): Offsets in the original tensor indicating
+            the start offsets for this shard. Should have the same rank as
+            the original tensor.
+        shard_sizes(List[int]): Integers indicating the size of each
+            dimension for this shard. Should have the same rank as the
+            original tensor.
+        placement(:class:`torch.distributed._remote_device`):
+            Specifies the placement of this shard.
+    """
+
+    __slots__ = ["shard_offsets", "shard_sizes", "placement"]
+
+    shard_offsets: list[int]
+    shard_sizes: list[int]
+    placement: _remote_device | None
+
+    def __init__(
+        self,
+        shard_offsets: list[int],
+        shard_sizes: list[int],
+        placement: str | _remote_device | None = None,
+    ):
+        self.shard_offsets = shard_offsets
+        self.shard_sizes = shard_sizes
+        if isinstance(placement, str):
+            self.placement = _remote_device(placement)
+        else:
+            self.placement = placement
+        if len(self.shard_offsets) != len(self.shard_sizes):
+            raise ValueError(
+                f"shard_offsets and shard_sizes should have "
+                f"the same number of elements, found {len(self.shard_offsets)} "
+                f"and {self.shard_sizes} respectively"
+            )
+
+        for i in range(len(self.shard_offsets)):
+            if self.shard_offsets[i] < 0:
+                raise ValueError("shard_offsets should be >=0")
+            if self.shard_sizes[i] < 0:
+                raise ValueError("shard_sizes should be >= 0")
+
+    def __hash__(self):
+        def _hash_reduce(a, b):
+            return (a << 8) + hash(b)
+
+        res = reduce(_hash_reduce, self.shard_offsets, 37)
+        res = reduce(_hash_reduce, self.shard_sizes, res)
+        res = _hash_reduce(res, self.placement)
+        return res
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/op_registry_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/op_registry_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..12e0b1895e2f053e6c4a975cb6d3c0118baf50bb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/op_registry_utils.py
@@ -0,0 +1,41 @@
+# mypy: allow-untyped-defs
+import functools
+from inspect import signature
+
+from .common_op_utils import _basic_validation
+
+
+"""
+Common utilities to register ops on ShardedTensor
+and PartialTensor.
+"""
+
+
+def _register_op(op, func, op_table):
+    """
+    Performs basic validation and registers the provided op in the given
+    op_table.
+    """
+    if len(signature(func).parameters) != 4:
+        raise TypeError(
+            f"Custom sharded op function expects signature: "
+            f"(types, args, kwargs, process_group), but received "
+            f"signature: {signature(func)}"
+        )
+
+    op_table[op] = func
+
+
+def _decorator_func(wrapped_func, op, op_table):
+    """
+    Decorator function to register the given ``op`` in the provided
+    ``op_table``
+    """
+
+    @functools.wraps(wrapped_func)
+    def wrapper(types, args, kwargs, process_group):
+        _basic_validation(op, args, kwargs)
+        return wrapped_func(types, args, kwargs, process_group)
+
+    _register_op(op, wrapper, op_table)
+    return wrapper
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_optim/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_optim/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..effae2e3cd1b89027cf06bf6e603e6ca84551520
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_optim/__init__.py
@@ -0,0 +1,53 @@
+from collections.abc import Iterator
+from typing import Union
+
+import torch.nn as nn
+from torch.distributed._shard.sharded_tensor import ShardedTensor
+
+from .api import ShardedOptimizer
+
+
+def named_params_with_sharded_tensor(
+    module: nn.Module,
+    prefix: str = "",
+    recurse: bool = True,
+) -> Iterator[tuple[str, nn.Parameter | ShardedTensor]]:
+    r"""Returns an iterator over module parameters (together with the
+    ShardedTensor parameters), yielding both the name of the parameter
+    as well as the parameter itself. This is typically passed to a
+    :class:torch.distributed._shard.sharded_optim.ShardedOptimizer
+
+    Args:
+        prefix (str): prefix to prepend to all parameter names.
+        recurse (bool): if True, then yields parameters of this module
+            and all submodules. Otherwise, yields only parameters that
+            are direct members of this module.
+
+    Yields:
+        (str, Union[Tensor, ShardedTensor]): Tuple containing
+            the name and parameter (or ShardedTensor parameter)
+
+    Example::
+
+        >>> # xdoctest: +SKIP
+        >>> model = torch.nn.Linear(*linear_size)
+        >>> shard_parameter(model, "weight", spec)
+        >>> for name, param in named_params_with_sharded_tensor(model):
+        >>>    if name in ['weight']:
+        >>>        print(param.size())
+
+    """
+    modules = module.named_modules(prefix=prefix) if recurse else [(prefix, module)]
+
+    memo = set()
+    for mod_prefix, mod in modules:
+        # find all sharded tensor params
+        for name, val in vars(mod).items():
+            if isinstance(val, ShardedTensor) and val not in memo:
+                memo.add(val)
+                name = mod_prefix + ("." if mod_prefix else "") + name
+                yield name, val
+
+    # find all nn.Parameters
+    for name, val in module.named_parameters():
+        yield name, val
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_optim/api.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_optim/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..2989e85496090782fcdb39d0e6613b82155ea23c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_optim/api.py
@@ -0,0 +1,102 @@
+# mypy: allow-untyped-defs
+from collections.abc import Mapping
+from typing import Any
+
+import torch.optim as optim
+from torch import Tensor
+from torch.distributed._shard.sharded_tensor import ShardedTensor
+
+
+class ShardedOptimizer(optim.Optimizer):
+    def __init__(
+        self,
+        named_params: Mapping[str, Tensor | ShardedTensor],
+        optimizer_class,
+        *optimizer_args,
+        **optimizer_kwargs,
+    ):
+        """
+        ShardedOptimizer collects all tensors and local shard tensors of
+        ShardedTensor, then use these tensors as ``params`` for optimizers
+
+        Args:
+            named_params (Dict[str, Union[Tensor, ShardedTensor]]) : a Dict
+                of parameters, where key is the parameter key, value is either
+                Tensor or ShardedTensor parameter.
+            optimizer_class (torch.optim.Optimizer): the Optimizer to use
+                locally, i.e. torch.optim.SGD, torch.optim.Adagrad, etc.
+            *optimizer_args: the arguments to initialize the optimizer.
+            **optimizer_kwargs: the key-word arguments to initialize the optimizer.
+
+        """
+        tensors: list[Tensor] = []
+        for value in named_params.values():
+            if isinstance(value, ShardedTensor):
+                tensors.extend(
+                    local_shard.tensor for local_shard in value.local_shards()
+                )
+            else:
+                tensors.append(value)
+
+        self.named_params = named_params
+        self._optim = optimizer_class(tensors, *optimizer_args, **optimizer_kwargs)
+        self.param_groups = self._optim.param_groups
+        self.state = self._optim.state
+
+    def zero_grad(self, set_to_none: bool = True):  # type: ignore[override]
+        r"""Resets the gradients of all optimized :class:`torch.Tensor` s.
+
+        Args:
+            set_to_none (bool): instead of setting to zero, set the grads to None.
+                This will in general have lower memory footprint, and can modestly improve performance.
+                However, it changes certain behaviors. For example:
+                1. When the user tries to access a gradient and perform manual ops on it,
+                a None attribute or a Tensor full of 0s will behave differently.
+                2. If the user requests ``zero_grad(set_to_none=True)`` followed by a backward pass, ``.grad``\ s
+                are guaranteed to be None for params that did not receive a gradient.
+                3. ``torch.optim`` optimizers have a different behavior if the gradient is 0 or None
+                (in one case it does the step with a gradient of 0 and in the other it skips
+                the step altogether).
+        """
+        self._optim.zero_grad(set_to_none)
+
+    def step(self, closure=None):
+        r"""Performs a single optimization step (parameter update).
+
+        Args:
+            closure (Callable): A closure that reevaluates the model and
+                returns the loss. Optional for most optimizers.
+
+        .. note::
+            Unless otherwise specified, this function should not modify the
+            ``.grad`` field of the parameters.
+        """
+        self._optim.step(closure)
+
+    def state_dict(self) -> dict[str, Any]:
+        """
+        Returned state and param_groups will contain parameter keys
+        instead of parameter indices like torch.optim.Optimizer.
+        This allows for advanced functionality like optimizer re-sharding to be implemented.
+        """
+        # TODO: implement state_dict
+        raise NotImplementedError("ShardedOptimizer state_dict not implemented yet!")
+
+    def load_state_dict(self, state_dict: Mapping[str, Any]):
+        r"""Loads the ShardedOptimizer state.
+
+        Args:
+            state_dict (dict): ShardedOptimizer state. Should be an object returned
+                from a call to :meth:`state_dict`.
+        """
+        # TODO: implement load_state_dict
+        raise NotImplementedError(
+            "ShardedOptimizer load_state_dict not implemented yet!"
+        )
+
+    def add_param_group(self, param_group: Any):
+        r"""Add a new param group"""
+        # TODO: implement add_param_group
+        raise NotImplementedError(
+            "ShardedOptimizer add_param_group not implemented yet!"
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d3af3ed3595378ca8522384f295ef6ea9930ebf
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py
@@ -0,0 +1,490 @@
+# mypy: allow-untyped-defs
+import functools
+from typing import TYPE_CHECKING
+
+import torch
+from torch.distributed._shard.op_registry_utils import _decorator_func
+
+from .api import (
+    _CUSTOM_SHARDED_OPS,
+    _SHARDED_OPS,
+    Shard,
+    ShardedTensor,
+    ShardedTensorBase,
+    ShardedTensorMetadata,
+    TensorProperties,
+)
+from .metadata import ShardMetadata  # noqa: F401
+
+
+if TYPE_CHECKING:
+    from torch.distributed._shard.sharding_spec import ShardingSpec
+else:
+    ShardingSpec = "ShardingSpec"
+
+
+def empty(
+    sharding_spec: ShardingSpec,
+    *size,
+    dtype=None,
+    layout=torch.strided,
+    requires_grad=False,
+    pin_memory=False,
+    memory_format=torch.contiguous_format,
+    process_group=None,
+    init_rrefs=False,
+) -> ShardedTensor:
+    """
+    Returns a :class:`ShardedTensor` filled with uninitialized data.
+        Needs to be called on all ranks in an SPMD fashion.
+
+    Args:
+        sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The specification
+            describing how to shard the Tensor.
+        size (int...): a sequence of integers defining the shape of the output
+            tensor. Can be a variable number of arguments or a collection like a list or tuple.
+
+    Keyword args:
+        dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.
+            Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`).
+        layout (:class:`torch.layout`, optional): the desired layout of returned Tensor.
+            Default: ``torch.strided``.
+        requires_grad (bool, optional): If autograd should record operations on the
+            returned tensor. Default: ``False``.
+        pin_memory (bool, optional): If set, returned tensor would be allocated in
+            the pinned memory. Works only for CPU tensors. Default: ``False``.
+        memory_format (:class:`torch.memory_format`, optional): the desired memory format of
+            returned Tensor. Default: ``torch.contiguous_format``.
+        process_group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        init_rrefs (bool, optional): Whether or not to initialize
+            :class:`torch.distributed.rpc.RRef`s pointing to remote shards.
+            Need to initialize the RPC Framework if specified as ``True``.
+            Default: ``False``.
+
+    Returns:
+        A :class:`ShardedTensor` object on each rank
+    """
+    return ShardedTensor(
+        sharding_spec,
+        *size,
+        dtype=dtype,
+        layout=layout,
+        requires_grad=requires_grad,
+        pin_memory=pin_memory,
+        memory_format=memory_format,
+        process_group=process_group,
+        init_rrefs=init_rrefs,
+    )
+
+
+def ones(
+    sharding_spec: ShardingSpec,
+    *size,
+    dtype=None,
+    layout=torch.strided,
+    requires_grad=False,
+    pin_memory=False,
+    memory_format=torch.contiguous_format,
+    process_group=None,
+    init_rrefs=False,
+) -> ShardedTensor:
+    """
+    Returns a :class:`ShardedTensor` with the scalar value 1.
+        Needs to be called on all ranks in an SPMD fashion.
+
+    Args:
+        sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The specification
+            describing how to shard the Tensor.
+        size (int...): a sequence of integers defining the shape of the output
+            tensor. Can be a variable number of arguments or a collection like a list or tuple.
+
+    Keyword args:
+        dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.
+            Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`).
+        layout (:class:`torch.layout`, optional): the desired layout of returned Tensor.
+            Default: ``torch.strided``.
+        requires_grad (bool, optional): If autograd should record operations on the
+            returned tensor. Default: ``False``.
+        pin_memory (bool, optional): If set, returned tensor would be allocated in
+            the pinned memory. Works only for CPU tensors. Default: ``False``.
+        process_group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        init_rrefs (bool, optional): Whether or not to initialize
+            :class:`torch.distributed.rpc.RRef`s pointing to remote shards.
+            Need to initialize the RPC Framework if specified as ``True``.
+            Default: ``False``.
+
+    Returns:
+        A :class:`ShardedTensor` object on each rank
+    """
+    return full(
+        sharding_spec,
+        size,
+        fill_value=1,
+        dtype=dtype,
+        layout=layout,
+        requires_grad=requires_grad,
+        pin_memory=pin_memory,
+        memory_format=memory_format,
+        process_group=process_group,
+        init_rrefs=init_rrefs,
+    )
+
+
+def zeros(
+    sharding_spec: ShardingSpec,
+    *size,
+    dtype=None,
+    layout=torch.strided,
+    requires_grad=False,
+    pin_memory=False,
+    memory_format=torch.contiguous_format,
+    process_group=None,
+    init_rrefs=False,
+) -> ShardedTensor:
+    """
+    Returns a :class:`ShardedTensor` filled with the scalar value 0.
+        Needs to be called on all ranks in an SPMD fashion.
+
+    Args:
+        sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The specification
+            describing how to shard the Tensor.
+        size (int...): a sequence of integers defining the shape of the output
+            tensor. Can be a variable number of arguments or a collection like a list or tuple.
+
+    Keyword args:
+        dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.
+            Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`).
+        layout (:class:`torch.layout`, optional): the desired layout of returned Tensor.
+            Default: ``torch.strided``.
+        requires_grad (bool, optional): If autograd should record operations on the
+            returned tensor. Default: ``False``.
+        pin_memory (bool, optional): If set, returned tensor would be allocated in
+            the pinned memory. Works only for CPU tensors. Default: ``False``.
+        process_group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        init_rrefs (bool, optional): Whether or not to initialize
+            :class:`torch.distributed.rpc.RRef`s pointing to remote shards.
+            Need to initialize the RPC Framework if specified as ``True``.
+            Default: ``False``.
+
+    Returns:
+        A :class:`ShardedTensor` object on each rank
+    """
+    return full(
+        sharding_spec,
+        size,
+        fill_value=0,
+        dtype=dtype,
+        layout=layout,
+        requires_grad=requires_grad,
+        pin_memory=pin_memory,
+        memory_format=memory_format,
+        process_group=process_group,
+        init_rrefs=init_rrefs,
+    )
+
+
+def full(
+    sharding_spec: ShardingSpec,
+    size,
+    fill_value,
+    *,
+    dtype=None,
+    layout=torch.strided,
+    requires_grad=False,
+    pin_memory=False,
+    memory_format=torch.contiguous_format,
+    process_group=None,
+    init_rrefs=False,
+) -> ShardedTensor:
+    """
+    Creates a :class:`ShardedTensor` filled with fill_value. The tensor's dtype
+        is inferred from fill_value. If dtype is specified, it will override the
+        inferred type from fill_value. Needs to be called on all ranks in an SPMD fashion.
+    Args:
+        sharding_spec (:class:`torch.distributed._sharding_spec.ShardingSpec`): The specification
+            describing how to shard the Tensor.
+        size (int...):  a list, tuple, or `torch.Size` of integers defining the shape of the
+            output tensor.
+        fill_value (Scalar) - the value to fill the output tensor with.
+    Keyword args:
+        dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.
+            Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`).
+        layout (:class:`torch.layout`, optional): the desired layout of returned Tensor.
+            Default: ``torch.strided``.
+        requires_grad (bool, optional): If autograd should record operations on the
+            returned tensor. Default: ``False``.
+        pin_memory (bool, optional): If set, returned tensor would be allocated in
+            the pinned memory. Works only for CPU tensors. Default: ``False``.
+        process_group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        init_rrefs (bool, optional): Whether or not to initialize
+            :class:`torch.distributed.rpc.RRef`s pointing to remote shards.
+            Need to initialize the RPC Framework if specified as ``True``.
+            Default: ``False``.
+    Returns:
+        A :class:`ShardedTensor` object on each rank
+    """
+    sharded_tensor = ShardedTensor(
+        sharding_spec,
+        *size,
+        dtype=dtype,
+        layout=layout,
+        requires_grad=requires_grad,
+        pin_memory=pin_memory,
+        memory_format=memory_format,
+        process_group=process_group,
+        init_rrefs=init_rrefs,
+    )
+    torch.nn.init.constant_(sharded_tensor, fill_value)  # type: ignore[arg-type]
+    return sharded_tensor
+
+
+def rand(
+    sharding_spec: ShardingSpec,
+    *size,
+    dtype=None,
+    layout=torch.strided,
+    requires_grad=False,
+    pin_memory=False,
+    memory_format=torch.contiguous_format,
+    process_group=None,
+    init_rrefs=False,
+) -> ShardedTensor:
+    """
+    Creates a :class:`ShardedTensor` filled with random numbers from a uniform distribution
+        on the interval :math:`[0, 1)`. The shape of the tensor is defined by the
+        variable argument `size`. Needs to be called on all ranks in an SPMD fashion.
+
+    Args:
+        sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The specification
+            describing how to shard the Tensor.
+        size (int...):  a list, tuple, or `torch.Size` of integers defining the shape of the
+            output tensor.
+
+    Keyword args:
+        dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.
+            Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`).
+        layout (:class:`torch.layout`, optional): the desired layout of returned Tensor.
+            Default: ``torch.strided``.
+        requires_grad (bool, optional): If autograd should record operations on the
+            returned tensor. Default: ``False``.
+        pin_memory (bool, optional): If set, returned tensor would be allocated in
+            the pinned memory. Works only for CPU tensors. Default: ``False``.
+        process_group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        init_rrefs (bool, optional): Whether or not to initialize
+            :class:`torch.distributed.rpc.RRef`s pointing to remote shards.
+            Need to initialize the RPC Framework if specified as ``True``.
+            Default: ``False``.
+
+    Returns:
+        A :class:`ShardedTensor` object on each rank
+    """
+    sharded_tensor = ShardedTensor(
+        sharding_spec,
+        *size,
+        dtype=dtype,
+        layout=layout,
+        requires_grad=requires_grad,
+        pin_memory=pin_memory,
+        memory_format=memory_format,
+        process_group=process_group,
+        init_rrefs=init_rrefs,
+    )
+    torch.nn.init.uniform_(sharded_tensor, 0, 1)  # type: ignore[arg-type]
+    return sharded_tensor
+
+
+def randn(
+    sharding_spec: ShardingSpec,
+    *size,
+    dtype=None,
+    layout=torch.strided,
+    requires_grad=False,
+    pin_memory=False,
+    memory_format=torch.contiguous_format,
+    process_group=None,
+    init_rrefs=False,
+) -> ShardedTensor:
+    """
+    Creates a :class:`ShardedTensor` filled with random numbers from a uniform distribution
+        with mean `0` and variance `1` (also called standard normal distribution). The shape
+        of the tensor is defined by the variable argument `size`. Needs to be called on all ranks
+        in an SPMD fashion.
+
+    Args:
+        sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The specification
+            describing how to shard the Tensor.
+        size (int...):  a list, tuple, or `torch.Size` of integers defining the shape of the
+            output tensor.
+
+    Keyword args:
+        dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.
+            Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`).
+        layout (:class:`torch.layout`, optional): the desired layout of returned Tensor.
+            Default: ``torch.strided``.
+        requires_grad (bool, optional): If autograd should record operations on the
+            returned tensor. Default: ``False``.
+        pin_memory (bool, optional): If set, returned tensor would be allocated in
+            the pinned memory. Works only for CPU tensors. Default: ``False``.
+        process_group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        init_rrefs (bool, optional): Whether or not to initialize
+            :class:`torch.distributed.rpc.RRef`s pointing to remote shards.
+            Need to initialize the RPC Framework if specified as ``True``.
+            Default: ``False``.
+
+    Returns:
+        A :class:`ShardedTensor` object on each rank
+    """
+    sharded_tensor = ShardedTensor(
+        sharding_spec,
+        *size,
+        dtype=dtype,
+        layout=layout,
+        requires_grad=requires_grad,
+        pin_memory=pin_memory,
+        memory_format=memory_format,
+        process_group=process_group,
+        init_rrefs=init_rrefs,
+    )
+    torch.nn.init.normal_(sharded_tensor, 0, 1)  # type: ignore[arg-type]
+    return sharded_tensor
+
+
+def init_from_local_shards(
+    local_shards: list[Shard], *global_size, process_group=None, init_rrefs=False
+) -> ShardedTensor:
+    """
+    Creates an :class:`ShardedTensor` from local shards and the global metadata.
+    Needs to be called on all ranks in an SPMD fashion.
+
+    Args:
+        local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list
+            of shards that represent the local shards on this rank.
+        global_size (int...):  a list, tuple, or `torch.Size` of integers defining the
+            shape of the overall sharded tensor.
+
+    Keyword args:
+        process_group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        init_rrefs (bool, optional): Whether or not to initialize
+            :class:`torch.distributed.rpc.RRef`s pointing to remote shards.
+            Need to initialize the RPC Framework if specified as ``True``.
+            Default: ``False``.
+
+    Returns:
+        A :class:`ShardedTensor` object handle on this rank
+
+
+    Examples:
+        Suppose we want construct a sharded tensor on two ranks, global size = (10, 5),
+        each shard have a (5, 5) local tensor, we can do it like below:
+
+        on rank 0:
+        >>> # xdoctest: +SKIP("not distributed")
+        >>> local_shard_metadata = ShardMetadata(
+        >>>     shard_offsets=[0, 0],
+        >>>     shard_lengths=[5, 5],
+        >>>     placement="rank:0/cuda:0"
+        >>> )
+        >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)]
+        >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5])
+
+        on rank 1:
+        >>> # xdoctest: +SKIP("not distributed")
+        >>> local_shard_metadata = ShardMetadata(
+        >>>     shard_offsets=[5, 0],
+        >>>     shard_lengths=[5, 5],
+        >>>     placement="rank:1/cuda:1"
+        >>> )
+        >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)]
+        >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5])
+    """
+    return ShardedTensor._init_from_local_shards(
+        local_shards, *global_size, process_group=process_group, init_rrefs=init_rrefs
+    )
+
+
+def state_dict_hook(module, destination, prefix, local_metadata):
+    """
+    Hook to add ShardedTensor to Module's ``state_dict``. Needs to be
+    registered to the Module using
+    :meth:`torch.nn.Module._register_state_dict_hook`.
+    """
+    for submodule_name, submodule in module.named_modules():
+        for attr_name, attr in submodule.__dict__.items():
+            if isinstance(attr, ShardedTensor):
+                mod_prefix = prefix + submodule_name
+                key = mod_prefix + ("." if mod_prefix else "") + attr_name
+                destination[key] = attr
+
+
+def pre_load_state_dict_hook(
+    module,
+    state_dict,
+    prefix,
+    local_metadata,
+    strict,
+    missing_keys,
+    unexpected_keys,
+    error_msgs,
+):
+    """
+    Pre-load state dict hook to add ShardedTensor to the module.
+    """
+    for submodule_name, submodule in module.named_modules():
+        for attr_name in submodule.__dict__:
+            mod_prefix = prefix + submodule_name
+            key = mod_prefix + ("." if mod_prefix else "") + attr_name
+            if key in state_dict:
+                if isinstance(state_dict[key], ShardedTensor):
+                    setattr(submodule, attr_name, state_dict[key])
+
+
+def custom_sharded_op_impl(func):
+    """
+    Provides a way for users to write their own custom sharded operator. This
+    can be used to override existing ShardedTensor operators or write a new
+    one not supported by ShardedTensor. If the operator in question is covered
+    by ``__torch_function__`` dispatch and has a ShardedTensor as any of its
+    parameters, the function provided will be invoked for that operator.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> @custom_sharded_op_impl(torch.nn.functional.linear)
+        >>> def my_custom_sharded_linear(types, args, kwargs, process_group):
+        >>>     ...
+        >>> # xdoctest: +SKIP("Undefined variables")
+        >>> input = torch.rand(10, 32)
+        >>> weight = sharded_tensor.rand(32, 16)
+        >>> bias = torch.rand(16)
+        >>> # This will call 'my_custom_sharded_linear'
+        >>> torch.nn.functional.linear(input, weight, bias)
+
+    The types, args and kwargs parameters are the same parameters that are
+    passed to ``__torch_function__`` dispatch API
+    (https://pytorch.org/docs/stable/notes/extending.html#extending-torch).
+    There is an additional ``process_group`` parameter which is the
+    process_group used for the ShardedTensor and can be used by
+    implementations for communications within a sharded implementation.
+
+    Args:
+        func(Callable): Torch function for which we want to provide a sharded
+            implementation (ex: torch.nn.functional.linear)
+    """
+    return functools.partial(_decorator_func, op=func, op_table=_CUSTOM_SHARDED_OPS)
+
+
+def _sharded_op_impl(func):
+    """
+    Decorator to register a default sharded op.
+    """
+    return functools.partial(_decorator_func, op=func, op_table=_SHARDED_OPS)
+
+
+# Import all builtin sharded ops
+from ._ops import *  # noqa: F403
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/_ops/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/_ops/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..be6d01fc8e54ee214fafa847c9261db375d8b87e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/_ops/__init__.py
@@ -0,0 +1,13 @@
+import torch.distributed._shard.sharded_tensor._ops.misc_ops
+import torch.distributed._shard.sharded_tensor._ops.tensor_ops
+
+# Import all ChunkShardingSpec ops
+from torch.distributed._shard.sharding_spec.chunk_sharding_spec_ops.embedding import (
+    sharded_embedding,
+)
+from torch.distributed._shard.sharding_spec.chunk_sharding_spec_ops.embedding_bag import (
+    sharded_embedding_bag,
+)
+
+from .binary_cmp import allclose, equal
+from .init import constant_, kaiming_uniform_, normal_, uniform_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/_ops/_common.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/_ops/_common.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a356e524a47a6f1e73022a707f19d7ddb8c935d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/_ops/_common.py
@@ -0,0 +1,115 @@
+# mypy: allow-untyped-defs
+import functools
+
+from torch.distributed._shard.common_op_utils import _basic_validation
+from torch.distributed._shard.sharded_tensor import (
+    _sharded_op_impl,
+    Shard,
+    ShardedTensor,
+)
+
+
+def _sharded_op_common(op, early_stop_func, extra_check):
+    """
+    Inject sharded tensor op registration with common logics executed before
+    different behaviors are done on either local shards or a local tensor.
+
+    Example::
+        >>> # xdoctest: +SKIP("Undefined variables")
+        >>> op = torch.transpose
+        >>> @_sharded_op_impl(op)
+        >>> @_sharded_op_common(op, early_stop_func, extra_check)
+        >>> def sharded_tensor_op(types, args, kwargs, process_group):
+        >>>   ...
+        >>>
+        >>> st = sharded_tensor.rand(32, 16)
+        >>> st.transpose(1, 2)
+        >>> # This will call '_sharded_op_common'
+
+    Args:
+        op: The op to be registered and applied to all shards of the st.
+        early_stop_func (Callable, optional): the func for early stop.
+            Default: if ``None``, no early stop.
+        extra_check (Callable, optional): the func for extra condition check.
+            Default: if ``None``, no extra check.
+
+    Return:
+        func (Callable): Torch function for which we want to provide a sharded
+            implementation (ex: torch.transpose)
+    """
+
+    def decorator_sharded_func(wrapped_func):
+        @functools.wraps(wrapped_func)
+        def wrapper(types, args=(), kwargs=None, pg=None):
+            _basic_validation(op, args, kwargs)
+
+            # pyrefly: ignore [index-error]
+            st = args[0]
+            if kwargs is None:
+                kwargs = {}
+            if extra_check:
+                extra_check(*args, **kwargs)
+            if early_stop_func:
+                early_stop = early_stop_func(*args, **kwargs)
+                if early_stop:
+                    return st
+            return wrapped_func(types, args, kwargs, pg)
+
+        return wrapper
+
+    return decorator_sharded_func
+
+
+def _register_sharded_op_on_local_shards(
+    op, early_stop_func=None, extra_check=None, customized_func=None
+):
+    """
+    Handles ``__torch_function__`` dispatch for ops which are performed on
+    each shard of the sharded tensor such as elementwise op like
+    ``torch.nn.functional.gelu`` or ``torch.nn.functional.relu``.
+
+    For more complicated ops, a customized func can be used to generate
+    the new shards and sharded tensor size.
+
+    This function expects that the original ShardingSpec for the ShardedTensor
+    is preserved irrespective of whether or not a customized function is used.
+
+    Args:
+        op: The op to be registered and applied to all shards of the st.
+        early_stop_func (Callable, optional): the func for early stop.
+            Default: if ``None``, no early stop.
+        extra_check (Callable, optional): the func for extra condition check.
+            Default: if ``None``, no extra check.
+        customized_func (Callable, optional): the func for customized logic
+            to generate new shards and sharded tensor size.
+            Default: if ``None``, we simply lower to the real op call with
+                all local shards of the st.
+
+    Return:
+        func (Callable): registered implementation for sharded op for
+        ``__torch_function__`` dispatch.
+    """
+
+    @_sharded_op_impl(op)
+    @_sharded_op_common(op, early_stop_func, extra_check)
+    def sharded_tensor_op_on_local_shards(types, args=(), kwargs=None, pg=None):
+        # pyrefly: ignore [index-error]
+        st = args[0]
+        st_metadata = st.metadata()
+        local_shards = st.local_shards()
+        local_shards_new = []
+        if customized_func:
+            local_shards_new, st_metadata = customized_func(args, kwargs, pg)
+        else:
+            for local_shard in local_shards:
+                args = (local_shard.tensor, *args[1:])
+                local_shards_new.append(
+                    Shard(op(*args, **kwargs), local_shard.metadata)
+                )
+        return ShardedTensor._init_from_local_shards_and_global_metadata(
+            local_shards_new,
+            st_metadata,
+            process_group=pg,
+            init_rrefs=st._init_rrefs,
+            sharding_spec=st.sharding_spec(),
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/_ops/binary_cmp.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/_ops/binary_cmp.py
new file mode 100644
index 0000000000000000000000000000000000000000..0548b81fb90af087593d05695418664c6d109f2d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/_ops/binary_cmp.py
@@ -0,0 +1,78 @@
+# mypy: allow-untyped-defs
+import torch
+import torch.distributed as dist
+import torch.distributed.distributed_c10d as distributed_c10d
+from torch.distributed._shard.sharded_tensor import _sharded_op_impl, ShardedTensor
+
+
+def _communicate_result(result, pg):
+    # Gather results from all ranks.
+    if result:
+        result_tensor = torch.ones(1, device=torch.device(torch.cuda.current_device()))
+    else:
+        result_tensor = torch.zeros(1, device=torch.device(torch.cuda.current_device()))
+
+    dist.all_reduce(result_tensor, group=pg)
+
+    expected_result = torch.ones(
+        1, device=torch.device(torch.cuda.current_device())
+    ) * dist.get_world_size(pg)
+
+    return torch.equal(result_tensor, expected_result)
+
+
+def binary_cmp(cmp_fun, types, args, kwargs=None, process_group=None):
+    if len(args) != 2:
+        raise ValueError(f"Expected two arguments for torch.{cmp_fun.__name__}")
+
+    st1 = args[0]
+    st2 = args[1]
+    if not (isinstance(st1, ShardedTensor) and isinstance(st2, ShardedTensor)):
+        raise TypeError(
+            f"Both arguments to torch.{cmp_fun.__name__} need to be of type ShardedTensor"
+        )
+
+    # Verify same PG
+    if st1._process_group != st2._process_group:
+        return False
+
+    if distributed_c10d._rank_not_in_group(
+        st1._process_group
+    ) or distributed_c10d._rank_not_in_group(st2._process_group):
+        return distributed_c10d._rank_not_in_group(
+            st1._process_group
+        ) == distributed_c10d._rank_not_in_group(st2._process_group)
+
+    # Verify metadata
+    if st1.metadata() != st2.metadata():
+        return _communicate_result(False, st1._process_group)
+
+    # Verify number of local shards
+    st1_local_shards = st1.local_shards()
+    st2_local_shards = st2.local_shards()
+    if len(st1_local_shards) != len(st2_local_shards):
+        return _communicate_result(False, st1._process_group)
+
+    # kwargs must be dict-like
+    if kwargs is None:
+        kwargs = {}
+    # Verify each local shard
+    for idx in range(len(st1_local_shards)):
+        if st1_local_shards[idx].metadata != st2_local_shards[idx].metadata:
+            return _communicate_result(False, st1._process_group)
+        if not cmp_fun(
+            st1_local_shards[idx].tensor, st2_local_shards[idx].tensor, **kwargs
+        ):
+            return _communicate_result(False, st1._process_group)
+
+    return _communicate_result(True, st1._process_group)
+
+
+@_sharded_op_impl(torch.equal)
+def equal(types, args, kwargs, process_group):
+    return binary_cmp(torch.equal, types, args, kwargs, process_group)
+
+
+@_sharded_op_impl(torch.allclose)
+def allclose(types, args, kwargs, process_group):
+    return binary_cmp(torch.allclose, types, args, kwargs, process_group)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/_ops/init.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/_ops/init.py
new file mode 100644
index 0000000000000000000000000000000000000000..d0e576b45ebeeda7661e0011b6a100cd60d0f5f4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/_ops/init.py
@@ -0,0 +1,164 @@
+# mypy: allow-untyped-defs
+import torch
+import torch.distributed._shard.sharded_tensor as sharded_tensor
+from torch.distributed._shard.sharded_tensor import _sharded_op_impl
+
+
+def validate_param(param, param_name):
+    if param is None:
+        raise ValueError(f"param: {param_name} shouldn't be None!")
+
+
+@_sharded_op_impl(torch.nn.init.uniform_)
+def uniform_(types, args=(), kwargs=None, pg=None):
+    r"""
+    Fills the Tensor in tensor.local_shards with values drawn from the uniform
+    distribution :math:`\mathcal{U}(a, b)`.
+    Args:
+        tensor: tensor sharded across devices
+        a: the lower bound of the uniform distribution
+        b: the upper bound of the uniform distribution
+    """
+    validate_param(kwargs, "kwargs")
+    # pyrefly: ignore [unsupported-operation]
+    sharded_tensor = kwargs["tensor"]
+    validate_param(sharded_tensor, "tensor")
+    # pyrefly: ignore [unsupported-operation]
+    a = kwargs["a"]
+    validate_param(a, "a")
+    # pyrefly: ignore [unsupported-operation]
+    b = kwargs["b"]
+    validate_param(b, "b")
+
+    for shard in sharded_tensor.local_shards():
+        torch.nn.init.uniform_(shard.tensor, a=a, b=b)
+    return sharded_tensor
+
+
+@_sharded_op_impl(torch.nn.init.normal_)
+def normal_(types, args=(), kwargs=None, pg=None):
+    r"""
+    Fills the Tensors in tensor.local_shards with values drawn from the normal
+    distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`.
+    Args:
+        tensor: tensor sharded across devices
+        mean: the mean of the normal distribution
+        std: the standard deviation of the normal distribution
+    """
+    validate_param(kwargs, "kwargs")
+    # pyrefly: ignore [unsupported-operation]
+    sharded_tensor = kwargs["tensor"]
+    validate_param(sharded_tensor, "tensor")
+    # pyrefly: ignore [unsupported-operation]
+    mean = kwargs["mean"]
+    validate_param(mean, "mean")
+    # pyrefly: ignore [unsupported-operation]
+    std = kwargs["std"]
+    validate_param(std, "std")
+
+    for shard in sharded_tensor.local_shards():
+        torch.nn.init.normal_(shard.tensor, mean=mean, std=std)
+    return sharded_tensor
+
+
+@_sharded_op_impl(torch.nn.init.kaiming_uniform_)
+def kaiming_uniform_(types, args=(), kwargs=None, pg=None):
+    r"""
+    Fills the Tensors in tensor.local_shards with values according to the method
+    described in `Delving deep into rectifiers: Surpassing human-level
+    performance on ImageNet classification` - He, K. et al. (2015), using a
+    uniform distribution. The resulting tensor will have values sampled from
+    :math:`\mathcal{U}(-\text{bound}, \text{bound})` where
+    .. math::
+        \text{bound} = \text{gain} \times \sqrt{\frac{3}{\text{fan\_mode}}}
+    Also known as He initialization.
+    Args:
+        tensor: tensor sharded across devices
+        a: the negative slope of the rectifier used after this layer (only
+            used with ``'leaky_relu'``)
+        mode: either ``'fan_in'`` (default) or ``'fan_out'``. Choosing ``'fan_in'``
+            preserves the magnitude of the variance of the weights in the
+            forward pass. Choosing ``'fan_out'`` preserves the magnitudes in the
+            backwards pass.
+        nonlinearity: the non-linear function (`nn.functional` name),
+            recommended to use only with ``'relu'`` or ``'leaky_relu'`` (default).
+    """
+    validate_param(kwargs, "kwargs")
+    # pyrefly: ignore [unsupported-operation]
+    sharded_tensor = kwargs["tensor"]
+    validate_param(sharded_tensor, "tensor")
+    # pyrefly: ignore [unsupported-operation]
+    a = kwargs["a"]
+    validate_param(a, "a")
+    # pyrefly: ignore [unsupported-operation]
+    mode = kwargs["mode"]
+    validate_param(mode, "mode")
+    # pyrefly: ignore [unsupported-operation]
+    nonlinearity = kwargs["nonlinearity"]
+    validate_param(nonlinearity, "nonlinearity")
+
+    for shard in sharded_tensor.local_shards():
+        torch.nn.init.kaiming_uniform_(
+            shard.tensor, a=a, mode=mode, nonlinearity=nonlinearity
+        )
+    return sharded_tensor
+
+
+@_sharded_op_impl(torch.nn.init.constant_)
+def constant_(types, args=(), kwargs=None, pg=None):
+    r"""
+    Fills the input ShardedTensor with the value \text{val}val.
+    Args:
+        tensor: tensor sharded across devices
+        val: the value to fill the tensor with
+    """
+    validate_param(kwargs, "kwargs")
+    # pyrefly: ignore [unsupported-operation]
+    sharded_tensor = kwargs["tensor"]
+    validate_param(sharded_tensor, "tensor")
+    # pyrefly: ignore [unsupported-operation]
+    val = kwargs["val"]
+    validate_param(val, "val")
+    for shard in sharded_tensor.local_shards():
+        torch.nn.init.constant_(shard.tensor, val=val)
+    return sharded_tensor
+
+
+tensor_like_creation_op_map = {
+    torch.full_like: sharded_tensor.full,
+    torch.empty_like: sharded_tensor.empty,
+    torch.zeros_like: sharded_tensor.zeros,
+    torch.ones_like: sharded_tensor.ones,
+    torch.rand_like: sharded_tensor.rand,
+    torch.randn_like: sharded_tensor.randn,
+}
+
+
+# tensor ops that behave the same as the default tensor
+def register_tensor_creation_op(op):
+    @_sharded_op_impl(op)
+    def tensor_creation_op(types, args=(), kwargs=None, pg=None):
+        """
+        Handles ``__torch_function__`` dispatch for tensor creation ops that
+        takes a ShardedTensor as argument, such as ``torch.zeros_like`` or
+        ``torch.full_like``.
+        """
+        creation_op = tensor_like_creation_op_map.get(op)
+        if creation_op is None:
+            raise RuntimeError(f"Tensor creation {op} not supported!")
+        if kwargs is None:
+            kwargs = {}
+
+        # pyrefly: ignore [index-error]
+        st = args[0]
+
+        new_st = creation_op(st.sharding_spec(), st.size(), *args[1:], **kwargs)  # type: ignore[operator]
+        return new_st
+
+
+register_tensor_creation_op(torch.full_like)
+register_tensor_creation_op(torch.empty_like)
+register_tensor_creation_op(torch.zeros_like)
+register_tensor_creation_op(torch.ones_like)
+register_tensor_creation_op(torch.rand_like)
+register_tensor_creation_op(torch.randn_like)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/_ops/misc_ops.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/_ops/misc_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b84c1684c32456989e3998b3d4c30c34cb5dbf4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/_ops/misc_ops.py
@@ -0,0 +1,12 @@
+# mypy: allow-untyped-defs
+import torch
+from torch.distributed._shard.sharded_tensor import _sharded_op_impl
+
+
+# This is used by `_apply()` within module.py to set new
+# parameters after apply a certain method, we should follow
+# the future behavior of overwriting the existing tensor
+# instead of doing in-place change using `.data = `.
+@_sharded_op_impl(torch._has_compatible_shallow_copy_type)
+def tensor_has_compatible_shallow_copy_type(types, args=(), kwargs=None, pg=None):
+    return False
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/_ops/tensor_ops.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/_ops/tensor_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5b7ad7c77b1b7948f5464cde0bee0f703d738fb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/_ops/tensor_ops.py
@@ -0,0 +1,222 @@
+# mypy: allow-untyped-defs
+import copy
+
+import torch
+from torch.distributed._shard.common_op_utils import _register_default_op
+from torch.distributed._shard.sharded_tensor import (
+    _sharded_op_impl,
+    Shard,
+    ShardedTensor,
+)
+
+from ._common import _register_sharded_op_on_local_shards
+
+
+# Tensor properties access
+_register_default_op(torch.Tensor.shape.__get__, _sharded_op_impl)  # type: ignore[attr-defined]
+_register_default_op(torch.Tensor.dtype.__get__, _sharded_op_impl)  # type: ignore[attr-defined]
+_register_default_op(torch.Tensor.layout.__get__, _sharded_op_impl)  # type: ignore[attr-defined]
+_register_default_op(torch.Tensor.size, _sharded_op_impl)
+_register_default_op(torch.Tensor.dim, _sharded_op_impl)
+_register_default_op(torch.Tensor.ndim.__get__, _sharded_op_impl)  # type: ignore[attr-defined]
+_register_default_op(torch.Tensor.is_contiguous, _sharded_op_impl)
+_register_default_op(torch.Tensor.contiguous, _sharded_op_impl)
+_register_default_op(torch.Tensor.is_floating_point, _sharded_op_impl)
+
+# __reduce_ex__ to dispatch to get_state/set_state
+_register_default_op(torch.Tensor.__reduce_ex__, _sharded_op_impl)
+
+# autograd related properties
+_register_default_op(torch.Tensor.requires_grad.__get__, _sharded_op_impl)  # type: ignore[attr-defined]
+# TODO: set grad with a ShardedTensor that consists of all local grads
+_register_default_op(torch.Tensor.grad.__get__, _sharded_op_impl)  # type: ignore[union-attr]
+_register_default_op(torch.Tensor.grad_fn.__get__, _sharded_op_impl)  # type: ignore[union-attr]
+_register_default_op(torch.Tensor.is_leaf.__get__, _sharded_op_impl)  # type: ignore[attr-defined]
+
+
+# device property is ambiguous as from a global prospective,
+# ShardedTensor.device consists of multiple devices (might even across hosts)
+# We choose to return the current device of the local tensor to represent
+# the device property on each rank
+@_sharded_op_impl(torch.Tensor.device.__get__)
+def tensor_device(types, args=(), kwargs=None, pg=None):
+    # pyrefly: ignore [index-error]
+    self_st = args[0]
+    # Validate types
+    if not isinstance(self_st, ShardedTensor):
+        raise TypeError("input needs to be a ShardedTensor")
+    dev: torch.device
+    if self_st._local_shards:
+        dev = self_st._local_shards[0].tensor.device
+    elif pg and pg._get_backend_name() == "gloo":
+        dev = torch.device("cpu")
+    else:
+        dev = torch.device(torch.cuda.current_device())
+    return dev
+
+
+@_sharded_op_impl(torch.Tensor.is_meta.__get__)  # type: ignore[attr-defined]
+def st_is_meta(types, args=(), kwargs=None, pg=None):
+    # pyrefly: ignore [index-error]
+    return args[0].local_tensor().is_meta
+
+
+def sharded_type_as_check(*args, **kwargs):
+    """
+    Perform extra checks for the sharded_type_as op such as the input needs to
+    be either a Tensor or ShardedTensor.
+
+    Args: same as ``torch.Tensor.type_as``.
+
+    Return: None
+    """
+    if len(args) < 2:
+        raise ValueError("Needs to give a tensor to cast type as!")
+    if not isinstance(args[1], torch.Tensor) and not isinstance(args[1], ShardedTensor):
+        raise ValueError("Needs to give a Tensor or ShardedTensor to cast type as!")
+
+
+def same_dtype(*args, **kwargs):
+    """
+    When the dtype is the same, return the original ShardedTensor.
+
+    Args: same as ``torch.Tensor.type_as``.
+
+    Return (bool): Whether to return early or not.
+    """
+    return args[0].dtype == args[1].dtype
+
+
+def sharded_type_as(args, kwargs, pg):
+    """
+    Handles ``__torch_function__`` dispatch for the ``torch.Tensor.type_as`` op.
+
+    Args: same as ``torch.Tensor.type_as``.
+
+    Return:
+        new_local_shards (List[Shard]): Local shards for the new sharded tensor.
+        st_meta (ShardedTensorMetadata): Metadata of the new sharded tensor.
+    """
+    st = args[0]
+    tensor = args[1]
+    if isinstance(tensor, ShardedTensor):
+        tensor = tensor.local_tensor()
+    new_local_shards = [
+        Shard(shard.tensor.type_as(tensor), shard.metadata)
+        for shard in st.local_shards()
+    ]
+    st_meta = copy.deepcopy(st._metadata)
+    st_meta.tensor_properties.dtype = tensor.dtype
+    return new_local_shards, st_meta
+
+
+_register_sharded_op_on_local_shards(
+    torch.Tensor.type_as,
+    early_stop_func=same_dtype,
+    extra_check=sharded_type_as_check,
+    customized_func=sharded_type_as,
+)
+
+
+def sharded_deepcopy(args, kwargs, pg):
+    # NOTE: we directly implement deepcopy magic method
+    # instead of using the default tensor.__deepcopy__
+    # and implement clone(). This is because the default
+    # tensor deepcopy copies every attribute, but the
+    # process_group in ShardedTensor cannot be deep copied.
+    self_st = args[0]
+    new_local_shards = copy.deepcopy(self_st.local_shards())
+    new_metadata = copy.deepcopy(self_st.metadata())
+    return new_local_shards, new_metadata
+
+
+_register_sharded_op_on_local_shards(
+    torch.Tensor.__deepcopy__,
+    customized_func=sharded_deepcopy,
+)
+
+
+@_sharded_op_impl(torch.Tensor.copy_)
+def sharded_inplace_copy(types, args, kwargs, pg):
+    # NOTE: inplace op don't need to rewrap
+    kwargs = {} if kwargs is None else kwargs
+    self_st = args[0]
+    new_st = args[1]
+    nonblocking = kwargs.get("non_blocking", False)
+    for local_shard, new_shard in zip(self_st.local_shards(), new_st.local_shards()):
+        if local_shard.metadata != new_shard.metadata:
+            raise RuntimeError(
+                "inplace copy can only happen between two ShardedTensor with same metadata!"
+            )
+    for local_shard, new_shard in zip(self_st.local_shards(), new_st.local_shards()):
+        local_shard.tensor.copy_(new_shard.tensor, nonblocking)
+
+    return self_st
+
+
+def sharded_clone(args, kwargs, pg):
+    self_st = args[0]
+    desire_memory_format = kwargs.get("memory_format", None)
+    if desire_memory_format and desire_memory_format != torch.preserve_format:
+        raise RuntimeError("Only support torch.preserve_format for ShardedTensor!")
+    cloned_local_shards = [
+        Shard(
+            local_shard.tensor.clone(memory_format=desire_memory_format),
+            metadata=copy.deepcopy(local_shard.metadata),
+        )
+        for local_shard in self_st.local_shards()
+    ]
+    new_metadata = copy.deepcopy(self_st.metadata())
+    return cloned_local_shards, new_metadata
+
+
+_register_sharded_op_on_local_shards(
+    torch.Tensor.clone,
+    customized_func=sharded_clone,
+)
+
+
+def sharded_detach(args, kwargs, pg):
+    self_st = args[0]
+    detached_local_shards = [
+        Shard(
+            local_shard.tensor.detach(),
+            metadata=copy.deepcopy(local_shard.metadata),
+        )
+        for local_shard in self_st.local_shards()
+    ]
+    new_metadata = copy.deepcopy(self_st.metadata())
+    new_metadata.tensor_properties.requires_grad = False
+    return detached_local_shards, new_metadata
+
+
+_register_sharded_op_on_local_shards(
+    torch.Tensor.detach,
+    customized_func=sharded_detach,
+)
+
+
+@_sharded_op_impl(torch.Tensor.requires_grad_)
+def tensor_requires_grad_set(types, args=(), kwargs=None, pg=None):
+    # pyrefly: ignore [index-error]
+    self_st = args[0]
+    # Validate types
+    if not isinstance(self_st, ShardedTensor):
+        raise TypeError("input needs to be a ShardedTensor")
+
+    if kwargs is None:
+        kwargs = {}
+
+    requires_grad = args[1] if len(args) > 1 else kwargs.get("requires_grad", True)
+    if requires_grad == self_st.requires_grad:
+        return self_st
+
+    for local_shard in self_st.local_shards():
+        local_shard.tensor.requires_grad_(requires_grad)
+
+        # update the wrapper class property
+    with torch._C.DisableTorchFunctionSubclass():
+        self_st.requires_grad_(requires_grad)
+    # update the metadata in the meanwhile
+    self_st._metadata.tensor_properties.requires_grad = requires_grad
+    return self_st
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/api.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..a8e8677d6ae7c91cf8d871ff697e057b554b794c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/api.py
@@ -0,0 +1,1368 @@
+# mypy: allow-untyped-defs
+from __future__ import annotations  # type: ignore[attr-defined]
+
+import copy
+import operator
+import threading
+import warnings
+import weakref
+from dataclasses import dataclass
+from functools import reduce
+from typing import cast, TYPE_CHECKING
+from typing_extensions import deprecated
+
+import torch
+import torch.distributed as dist
+import torch.distributed._shard.sharding_spec as shard_spec
+from torch._utils import _get_device_module
+from torch.distributed import distributed_c10d, rpc
+from torch.distributed._shard._utils import DEPRECATE_MSG
+from torch.distributed._shard.sharding_spec._internals import (
+    check_tensor,
+    validate_non_overlapping_shards_metadata,
+)
+from torch.distributed._shard.sharding_spec.api import (
+    _dispatch_custom_op,
+    _has_custom_op,
+)
+from torch.distributed.remote_device import _remote_device
+from torch.utils import _pytree as pytree
+
+from .metadata import ShardedTensorMetadata, TensorProperties
+from .reshard import reshard_local_shard, reshuffle_local_shard
+from .shard import Shard
+from .utils import (
+    _flatten_tensor_size,
+    _parse_and_validate_remote_device,
+    _validate_output_tensor_for_gather,
+    build_global_metadata,
+    build_metadata_from_local_shards,
+)
+
+
+if TYPE_CHECKING:
+    from collections.abc import Callable, Sequence
+
+    from torch.distributed._shard.metadata import ShardMetadata
+
+
+# Tracking for sharded tensor objects.
+_sharded_tensor_lock = threading.Lock()
+_sharded_tensor_current_id = 0
+_sharded_tensor_map: dict[int, weakref.ReferenceType[ShardedTensor]] = {}
+
+# Default sharded ops
+_SHARDED_OPS: dict[Callable, Callable] = {}
+
+# Customized user ops
+_CUSTOM_SHARDED_OPS: dict[Callable, Callable] = {}
+
+
+def _register_remote_shards(
+    sharded_tensor_id: int, rrefs: list[rpc.RRef[Shard]], rpc_rank: int
+):
+    with _sharded_tensor_lock:
+        if sharded_tensor_id not in _sharded_tensor_map:
+            raise RuntimeError(
+                f"Could not find sharded_tensor_id: {sharded_tensor_id} in map: {_sharded_tensor_map.keys()}"
+            )
+
+        sharded_tensor = _sharded_tensor_map[sharded_tensor_id]()
+        if sharded_tensor is None:
+            raise RuntimeError("ShardedTensor weakref has been deallocated")
+        else:
+            sharded_tensor._register_remote_shards(rrefs, rpc_rank)
+
+
+class ShardedTensorBase(torch.Tensor):
+    _sharding_spec: shard_spec.ShardingSpec
+    _metadata: ShardedTensorMetadata
+    _local_shards: list[Shard]
+
+    def __new__(cls, sharding_spec: shard_spec.ShardingSpec, *size, **kwargs):
+        # Use __new__ to construct a wrapper tensor, for recording tensor
+        # properties and logging purposes.
+        torch._C._log_api_usage_once("torch.distributed._shard.sharded_tensor")
+
+        # check sharding spec and build sharded tensor metadata
+        if not isinstance(sharding_spec, shard_spec.ShardingSpec):
+            raise ValueError(f"Expecting ShardingSpec but got: {type(sharding_spec)}")
+
+        sizes = _flatten_tensor_size(size)
+        dtype = kwargs["dtype"]
+        layout = kwargs["layout"]
+        pin_memory = kwargs["pin_memory"]
+        requires_grad = kwargs["requires_grad"]
+
+        if dtype is None:
+            dtype = torch.get_default_dtype()
+
+        tensor_properties = TensorProperties(
+            dtype, layout, requires_grad, pin_memory=pin_memory
+        )
+        sharded_tensor_metadata = sharding_spec.build_metadata(
+            sizes, tensor_properties=tensor_properties
+        )
+
+        r = torch.Tensor._make_wrapper_subclass(
+            cls,
+            sizes,
+            dtype=dtype,
+            layout=layout,
+            pin_memory=pin_memory,
+            requires_grad=requires_grad,
+        )
+        # set sharding spec
+        r._sharding_spec = sharding_spec
+        # set metadata
+        r._metadata = sharded_tensor_metadata
+        # set local shards
+        r._local_shards = []
+        return r
+
+    def metadata(self) -> ShardedTensorMetadata:
+        """
+        Returns a :class:`ShardedTensorMetadata` object corresponding to the
+        metadata for the entire tensor.
+        """
+        return self._metadata
+
+    def local_shards(self) -> list[Shard]:
+        """
+        Returns a list of :class:`Shard' corresponding to the
+        local shards for this rank. Returns an empty list if the current rank
+        does not host any shards for this Tensor.
+        """
+        return self._local_shards
+
+    @classmethod
+    def _init_from_local_shards_and_global_metadata(
+        cls,
+        local_shards: list[Shard],
+        sharded_tensor_metadata: ShardedTensorMetadata,
+        sharding_spec=None,
+    ) -> ShardedTensorBase:
+        """
+        Initialize a ShardedTensorBase with local shards and a global
+        ShardedTensorMetadata built on each rank.
+        Warning: This API is experimental and subject to change. It does
+                 not do cross rank validations, and fully rely on the user
+                 for the correctness of sharded_tensor_metadata on each rank
+        """
+        shards_metadata = sharded_tensor_metadata.shards_metadata
+        tensor_properties = sharded_tensor_metadata.tensor_properties
+
+        if len(shards_metadata) == 0:
+            raise ValueError("shards_metadata must not be empty!")
+
+        if tensor_properties.layout != torch.strided:
+            raise ValueError("Only torch.strided layout is currently supported")
+
+        if sharding_spec is None:
+            spec = shard_spec._infer_sharding_spec_from_shards_metadata(shards_metadata)
+        else:
+            spec = sharding_spec
+
+        sharded_tensor_base = ShardedTensorBase.__new__(
+            ShardedTensor,
+            spec,
+            sharded_tensor_metadata.size,
+            dtype=tensor_properties.dtype,
+            layout=tensor_properties.layout,
+            pin_memory=tensor_properties.pin_memory,
+            requires_grad=tensor_properties.requires_grad,
+        )
+
+        # check if shards_metadata have overlap shards
+        validate_non_overlapping_shards_metadata(shards_metadata)
+
+        # check if the shards_metadata is compatible with overall size of the sharded tensor.
+        check_tensor(shards_metadata, list(sharded_tensor_metadata.size))
+
+        # done validation, add local_shards
+        sharded_tensor_base._local_shards = local_shards
+        return sharded_tensor_base
+
+    @classmethod
+    def __torch_dispatch__(cls, func, types, args=(), kwargs=None):  # type: ignore[override]
+        raise RuntimeError(
+            f"A {cls.__name__} object is being used from c++ while calling {func.__module__}.{func.__name__} "
+            "but the there is no custom __torch_dispatch__ implementation for it."
+        )
+
+
+class ShardedTensor(ShardedTensorBase):
+    """
+    ShardedTensor is an torch.Tensor subclass to represent Tensors that are sharded
+    across multiple devices and multiple processes.
+
+    ShardedTensor is initialized in an SPMD like fashion where each rank
+    initializes the ShardedTensor. The ShardedTensor object on each rank
+    then only stores the local shard for the Tensor and provides global
+    metadata for all the shards.
+
+    ShardedTensor doesn't provide any Tensor like operations but is a wrapper
+    providing the Tensor representing the local shard and the global metadata.
+    Using these, users can build their custom distributed._sharded computations
+    on top of this primitive. The local shards are all initialized using the
+    create_op specified by tensor_init_params.create_op, e.g., torch.ones, or
+    torch.empty
+
+    Args:
+        sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The specification
+            describing how to shard the Tensor.
+        size (int...): a sequence of integers defining the shape of the output
+            tensor. Can be a variable number of arguments or a collection like a list or tuple.
+
+    Keyword args:
+        dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.
+                Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`).
+        layout (:class:`torch.layout`, optional): the desired layout of returned Tensor.
+            Default: ``torch.strided``.
+        requires_grad (bool, optional): If autograd should record operations on the
+            returned tensor. Default: ``False``.
+        pin_memory (bool, optional): If set, returned tensor would be allocated in
+            the pinned memory. Works only for CPU tensors. Default: ``False``.
+        memory_format (:class:`torch.memory_format`, optional): the desired memory format of
+            returned Tensor. Default: ``torch.contiguous_format``.
+        init_rrefs (bool, optional): Whether or not to initialize
+            :class:`torch.distributed.rpc.RRef`s pointing to remote shards.
+            Need to initialize the RPC Framework if specified as ``True``.
+            Default: ``False``.
+
+    .. note:: ShardedTensor uses collectives to do various operations, i.e. it
+        uses all_gather to do cross rank validations. For NCCL-based process
+        groups, internal tensor representations of objects must be moved to the
+        GPU device before communication takes place. In this case, the device
+        used is given by ``torch.cuda.current_device()`` and it is the user's
+        responsibility to ensure that this is set so that each rank has an
+        individual GPU, via ``torch.cuda.set_device()``
+
+    """
+
+    def __new__(cls, sharding_spec: shard_spec.ShardingSpec, *size, **kwargs):
+        self = super().__new__(cls, sharding_spec, *size, **kwargs)
+        return self
+
+    def __init__(
+        self,
+        sharding_spec: shard_spec.ShardingSpec,
+        *size,
+        dtype=None,
+        layout=torch.strided,
+        requires_grad=False,
+        pin_memory=False,
+        memory_format=torch.contiguous_format,
+        process_group=None,
+        init_rrefs=False,
+    ):
+        # prepare initialization, initialize fields like
+        # _process_group, _local_shards, etc.
+        self._prepare_init(process_group=process_group, init_rrefs=init_rrefs)
+
+        if layout != torch.strided:
+            raise ValueError("Only torch.strided layout is currently supported")
+
+        if memory_format != torch.contiguous_format:
+            raise ValueError(
+                "Only torch.contiguous_format memory_format is currently supported"
+            )
+
+        self._metadata.tensor_properties.memory_format = memory_format
+
+        current_rank = dist.get_rank()  # global rank
+
+        for shard_metadata in self._metadata.shards_metadata:
+            rank, device = _parse_and_validate_remote_device(
+                self._process_group, shard_metadata.placement
+            )
+            if rank == current_rank:
+                local_tensor = _create_tensor_from_params(
+                    shard_metadata.shard_sizes,
+                    local_device=device,
+                    tensor_properties=self._metadata.tensor_properties,
+                )
+                self._local_shards.append(Shard(local_tensor, shard_metadata))
+
+        # do post initialization (i.e. register sharded_tensor_id, initialize_rpc)
+        self._post_init()
+
+    def _prepare_init(self, process_group=None, init_rrefs=False):
+        self._init_rrefs = init_rrefs
+        self._sharded_tensor_id = None
+
+        self._process_group = self._normalize_pg(process_group)
+        self._remote_shards: dict[int, list[rpc.RRef[Shard]]] = {}
+
+    def _post_init(self):
+        # Initialize RPC if available.
+        if self._init_rrefs:
+            with _sharded_tensor_lock:
+                global _sharded_tensor_current_id, _sharded_tensor_map
+                # pyrefly: ignore [bad-assignment]
+                self._sharded_tensor_id = _sharded_tensor_current_id
+                # pyrefly: ignore [unsupported-operation]
+                _sharded_tensor_map[self._sharded_tensor_id] = weakref.ref(self)
+                _sharded_tensor_current_id += 1
+
+            if not rpc._is_current_rpc_agent_set():
+                raise RuntimeError(
+                    "RPC Framework needs to be initialized using"
+                    " torch.distributed.rpc.init_rpc if init_rrefs is set to True"
+                )
+            self._init_rpc()
+
+    def __del__(self):
+        # Clean up the global map.
+        with _sharded_tensor_lock:
+            global _sharded_tensor_current_id, _sharded_tensor_map
+            if (
+                hasattr(self, "_sharded_tensor_id")
+                and self._sharded_tensor_id in _sharded_tensor_map
+            ):
+                _sharded_tensor_map.pop(self._sharded_tensor_id)  # type: ignore[call-overload]
+
+    def _init_rpc(self):
+        # Validate PG and RPC ranks match.
+        pg_rank = dist.get_rank()
+        rpc_rank = rpc.get_worker_info().id
+        if pg_rank != rpc_rank:
+            raise ValueError(
+                f"Default ProcessGroup and RPC ranks must be "
+                f"the same for ShardedTensor, found process group rank: "
+                f"{pg_rank} and RPC rank: {rpc_rank}"
+            )
+
+        self._remote_shards = {}
+
+        # Gather all the sharded tensor ids.
+        worker_infos = rpc._get_current_rpc_agent().get_worker_infos()
+        rank_to_name = {}
+        name_to_rank = {}
+
+        for worker_info in worker_infos:
+            rank_to_name[worker_info.id] = worker_info.name
+            name_to_rank[worker_info.name] = worker_info.id
+
+        all_tensor_ids = rpc.api._all_gather(self._sharded_tensor_id)
+
+        # Share the local shards to the entire world.
+        futs = []
+        rpc_rank = rpc.get_worker_info().id
+        for rank in range(dist.get_world_size()):
+            # Skip self.
+            if rank == dist.get_rank():
+                continue
+
+            if len(self.local_shards()) != 0:
+                rrefs: list[rpc.RRef[Shard]] = [
+                    rpc.RRef(shard) for shard in self.local_shards()
+                ]
+                fut = rpc.rpc_async(
+                    rank,
+                    _register_remote_shards,
+                    args=(all_tensor_ids[rank_to_name[rank]], rrefs, rpc_rank),
+                )
+                futs.append(fut)
+
+        torch.futures.wait_all(futs)
+
+        # Barrier for all RPCs to finish on all ranks.
+        rpc.api._all_gather(None)
+
+    def _get_preferred_device(self) -> torch.device:
+        """
+        Return the preferred device to be used when creating tensors for collectives.
+        This method takes into account the associated process group
+        """
+        backend = dist.get_backend(self._process_group)
+        if backend == dist.Backend.NCCL:
+            return torch.device(torch.cuda.current_device())
+        elif backend == dist.Backend.GLOO:
+            return torch.device("cpu")
+        else:
+            backend_config = dist.BackendConfig(backend)
+            for device, backend_str in backend_config.get_device_backend_map().items():
+                if backend_str == backend and device != "cpu":
+                    return torch.device(
+                        device, _get_device_module(device).current_device()
+                    )
+        return torch.device("cpu")
+
+    def gather(  # type: ignore[override]
+        self,
+        dst: int = 0,
+        out: torch.Tensor | None = None,
+        enforce_dtype: bool = False,
+        dtype: torch.dtype | None = None,
+    ) -> None:
+        """
+        Creates a full :class:`Tensor` on rank ``dst`` by gathering all shards of the
+        sharded tensor.
+
+        The API needs to be called on all ranks in SPMD fashion. All ranks should have
+        the same ``dst``. ``out`` should be a tensor of the same size as the overall
+        size of the sharded tensor on ``dst`` and ``None`` on all other ranks.
+
+        Args:
+            dst(int): The rank where full tensor is constructed.
+                Default: 0
+            out (:class `torch.Tensor`, optional): The output full tensor.
+                Must to be provided ONLY on ``dst`` rank.
+                Default: ``None``
+            enforce_dtype (bool): Deprecated, please use dtype instead.  Force the
+                gathered tensors to be the same type as input and output.
+            dtype (torch.dtype): Force the gathered tensors to be this dtype.
+                Default: ``None``
+        """
+
+        def shard_size(shard_md):
+            return reduce(operator.mul, shard_md.shard_sizes)  # type: ignore[attr-defined]
+
+        if enforce_dtype:
+            warnings.warn(
+                "`enforce_dtype` is deprecated. Please use `dtype` instead.",
+                FutureWarning,
+                stacklevel=2,
+            )
+
+        rank = dist.get_rank(self._process_group)
+        full_size = self.metadata().size
+        _validate_output_tensor_for_gather(rank, dst, full_size, out)
+
+        local_shards = self.local_shards()
+        world_size = dist.get_world_size(self._process_group)
+        rank_sizes = [0 for _ in range(world_size)]
+        max_rank_size = 0
+        shard_placement: dict[ShardMetadata, tuple[int, int]] = {}
+        # collect sizes
+        for shard_md in self.metadata().shards_metadata:
+            shard_rank = cast(_remote_device, shard_md.placement).rank()
+            assert shard_rank is not None
+
+            shard_placement[shard_md] = (shard_rank, rank_sizes[shard_rank])
+            rank_sizes[shard_rank] += shard_size(shard_md)
+            max_rank_size = max(max_rank_size, rank_sizes[shard_rank])
+
+        gather_list: list[torch.Tensor] | None
+        if rank == dst:
+            assert out is not None
+            if enforce_dtype:
+                # enforce_dtype is deprecated.  Do it for backward compatibility.
+                dtype = out.dtype
+            # TODO make it as a view of out tensor
+            gather_list = [
+                torch.empty((max_rank_size,), device=out.device, dtype=dtype)
+                for _ in range(world_size)
+            ]
+        else:
+            gather_list = None
+
+        with torch.no_grad():
+            if enforce_dtype and len(local_shards) > 0:
+                # enforce_dtype is deprecated.  Do it for backward compatibility.
+                dtype = local_shards[0].tensor.dtype
+            data = torch.empty(
+                max_rank_size, device=self._get_preferred_device(), dtype=dtype
+            )
+
+            for shard in local_shards:
+                src = shard.tensor.flatten()
+                if src.nelement() == 0:
+                    warnings.warn(
+                        "Gathering a tensor with zero elements on rank " + str(rank),
+                        stacklevel=2,
+                    )
+                    continue
+                shard_offset = shard_placement[shard.metadata][1]
+                data[shard_offset : shard_offset + src.numel()].copy_(src)
+
+        dist.gather(
+            tensor=data,
+            gather_list=gather_list,
+            dst=dst,
+            group=self._process_group,
+        )
+        if rank != dst:
+            return
+        # In _validate_output_tensor_for_gather, we raise if out == None and rank == dst
+        out = cast(torch.Tensor, out)
+        assert gather_list is not None
+
+        full_size = self.metadata().size
+        dims = len(full_size)
+        for shard_md in self.metadata().shards_metadata:
+            rank, rank_offset = shard_placement[shard_md]
+            tensor = gather_list[rank]
+            tensor = tensor[rank_offset : rank_offset + shard_size(shard_md)]
+            tensor = tensor.view(shard_md.shard_sizes)
+
+            out_narrow_view = out
+            for dim in range(dims):
+                out_narrow_view = out_narrow_view.narrow(
+                    dim,
+                    shard_md.shard_offsets[dim],
+                    shard_md.shard_sizes[dim],
+                )
+
+            out_narrow_view.copy_(tensor)
+
+    def cpu(
+        self, memory_format=torch.preserve_format, process_group=None
+    ) -> ShardedTensor:
+        """
+        Returns a copy of this object in CPU memory.
+
+        If this ShardedTensor is already on CPU memory, then no copy is
+        performed and original object is returned.
+
+        .. note:: When moving a ShardedTensor from GPU to CPU, the ShardedTensor might
+            need to be managed by a different type of ProcessGroup(i.e. ProcessGroupGloo),
+            it is the user's responsibility to explicitly pass in a new process_group that
+            is compatible with CPU.
+        """
+        # TODO: make this a __torch_function__ op once ShardedTensor becomes a
+        # torch.Tensor subclass, see https://github.com/pytorch/pytorch/issues/75402
+        if (
+            memory_format != torch.preserve_format
+            and memory_format != torch.contiguous_format
+        ):
+            raise RuntimeError(
+                "Only `torch.contiguous_format` or "
+                "`torch.preserve_format` is supported!"
+            )
+        all_on_cpu = True
+        for meta in self.metadata().shards_metadata:
+            all_on_cpu &= meta.placement.device().type == "cpu"  # type: ignore[union-attr]
+
+        # if every shard is already on CPU, return the original object
+        if all_on_cpu:
+            return self
+
+        # if not, returns a copy of this object on CPU
+        list_shards: list[Shard] = []
+        # move all local shards to cpu, and change metadata
+        for shard in self._local_shards:
+            cpu_tensor = shard.tensor.cpu(memory_format=memory_format)  # type: ignore[call-arg]
+            metadata = copy.deepcopy(shard.metadata)
+            metadata.placement._device = torch.device("cpu")  # type: ignore[union-attr]
+            list_shards.append(Shard(cpu_tensor, metadata))
+
+        st_meta = copy.deepcopy(self.metadata())
+        for meta in st_meta.shards_metadata:
+            if meta.placement.device().type != "cpu":  # type: ignore[union-attr]
+                meta.placement._device = torch.device("cpu")  # type: ignore[union-attr]
+
+        pg = self._process_group if process_group is None else process_group
+        st_cpu = ShardedTensor._init_from_local_shards_and_global_metadata(
+            list_shards,
+            sharded_tensor_metadata=st_meta,
+            process_group=pg,
+            init_rrefs=self._init_rrefs,
+        )
+        return st_cpu
+
+    def cuda(
+        self,
+        device=None,
+        non_blocking=False,
+        memory_format=torch.preserve_format,
+        process_group=None,
+    ) -> ShardedTensor:
+        """
+        Returns a copy of this object in CUDA memory, if the original ShardedTensor
+        is on CPU, we will move the local shard to the current GPU device of each
+        process in a SPMD fashion.
+        If this ShardedTensor is already on CUDA memory and local shards on each rank are
+        already on current device, we still returns a new ShardedTensor object with new
+        metadata, but no underlying data movements are performed.
+        .. note:: When moving a ShardedTensor from CPU to GPU, the ShardedTensor might
+            need to be managed by a different type of ProcessGroup(i.e. ProcessGroupNCCL),
+            it is the user's responsibility to explicitly pass in a new process_group that
+            is compatible with GPU.
+        """
+        if (
+            memory_format != torch.preserve_format
+            and memory_format != torch.contiguous_format
+        ):
+            raise RuntimeError(
+                "Only `torch.contiguous_format` or "
+                "`torch.preserve_format` is supported!"
+            )
+
+        if device is not None:
+            device = torch.device(device) if isinstance(device, str) else device
+            assert (
+                isinstance(device, torch.device)
+                and device.index == torch.cuda.current_device()
+            ), (
+                """Only device without device id (e.g. "cpu" or "cuda") is expected for ShardedTensor!"""
+            )
+
+        current_device = torch.device(torch.cuda.current_device())
+        # returns a copy of ShardedTensor on CUDA current device
+        list_shards: list[Shard] = []
+        # move all local shards to current device, and change metadata
+        # if local shards already on the current device, there's no
+        # real data movement, only the metadata are copied.
+        for shard in self._local_shards:
+            cuda_tensor = shard.tensor.cuda(
+                device=current_device,
+                non_blocking=non_blocking,
+                memory_format=memory_format,
+            )  # type: ignore[call-arg]
+            metadata = copy.deepcopy(shard.metadata)
+            metadata.placement._device = current_device  # type: ignore[union-attr]
+
+            list_shards.append(Shard(cuda_tensor, metadata))
+
+        st_meta = copy.deepcopy(self.metadata())
+        for meta in st_meta.shards_metadata:
+            if meta.placement.device().type != "cuda":  # type: ignore[union-attr]
+                meta.placement._device = current_device  # type: ignore[union-attr]
+
+        pg = self._process_group if process_group is None else process_group
+        # we need to use `init_from_local_shards` to communicate between ranks
+        # and update the sharding spec/shards metadata.
+        st_cuda = ShardedTensor._init_from_local_shards_and_global_metadata(
+            list_shards,
+            sharded_tensor_metadata=st_meta,
+            process_group=pg,
+            init_rrefs=self._init_rrefs,
+        )
+        return st_cuda
+
+    def to(self, *args, **kwargs) -> ShardedTensor:
+        current_device: torch.device
+        if self._local_shards:
+            current_device = self._local_shards[0].tensor.device
+        elif self._process_group._get_backend_name() == "gloo":
+            current_device = torch.device("cpu")
+        else:
+            current_device = torch.device(torch.cuda.current_device())
+        current_dtype = self.dtype
+        device_to = current_device
+        dtype_to = current_dtype
+        if len(args) == 1:
+            if isinstance(args[0], torch.dtype):
+                dtype_to = args[0]
+            elif isinstance(args[0], torch.device):
+                device_to = args[0]
+            elif isinstance(args[0], (str, int)):
+                device_to = torch.device(args[0])
+            elif isinstance(args[0], torch.Tensor):
+                dtype_to = args[0].dtype
+                device_to = args[0].device
+            else:
+                raise RuntimeError(f"ShardedTensor.to() have wrong arguments: {args}")
+        elif len(args) == 2:
+            device_to, dtype_to = args
+        else:
+            dtype_to = kwargs.get("dtype", current_dtype)
+            device_to = kwargs.get("device", current_device)
+
+        device_to = (
+            torch.device(device_to) if isinstance(device_to, (str, int)) else device_to
+        )
+
+        if device_to.type == "cuda":
+            # if device_to set to cuda, set to current device even
+            # if user specify the device index.
+            current_idx = torch.cuda.current_device()
+            if device_to.index != current_idx:
+                warnings.warn(
+                    "ShardedTensor.to only move tensor to its current device"
+                    "If you want to put to different device, use `reshard` instead.",
+                    stacklevel=2,
+                )
+            device_to = torch.device(current_idx)
+
+        copy_tensor = kwargs.get("copy", False)
+        non_blocking = kwargs.get("non_blocking", False)
+        memory_format = kwargs.get("memory_format", torch.preserve_format)
+        process_group = kwargs.get("process_group")
+
+        if (
+            not copy_tensor
+            and dtype_to == current_dtype
+            and device_to == current_device
+        ):
+            # already have correct dtype and device, return itself
+            return self
+
+        # returns a copy of ShardedTensor on CUDA current device
+        list_shards: list[Shard] = []
+
+        for shard in self._local_shards:
+            new_tensor = shard.tensor.to(  # type: ignore[call-overload]
+                device=device_to,
+                dtype=dtype_to,
+                non_blocking=non_blocking,
+                copy=copy_tensor,
+                memory_format=memory_format,
+            )
+            metadata = copy.deepcopy(shard.metadata)
+            if metadata.placement is not None:
+                metadata.placement._device = device_to
+            list_shards.append(Shard(new_tensor, metadata))
+
+        # update metadata
+        st_meta = copy.deepcopy(self.metadata())
+        st_meta.tensor_properties.dtype = dtype_to
+        for meta in st_meta.shards_metadata:
+            meta.placement._device = device_to  # type: ignore[union-attr]
+
+        pg = self._process_group if process_group is None else process_group
+        # we need to use `init_from_local_shards` to communicate between ranks
+        # and update the sharding spec/shards metadata.
+        st_to = ShardedTensor._init_from_local_shards_and_global_metadata(
+            list_shards,
+            sharded_tensor_metadata=st_meta,
+            process_group=pg,
+            init_rrefs=self._init_rrefs,
+        )
+        return st_to
+
+    @classmethod
+    def _normalize_pg(
+        cls, process_group: dist.ProcessGroup | None
+    ) -> dist.ProcessGroup:
+        if process_group is not None:
+            return process_group
+        return distributed_c10d._get_default_group()
+
+    @classmethod
+    def _init_from_local_shards(
+        cls,
+        local_shards: list[Shard],
+        *global_size,
+        process_group=None,
+        init_rrefs=False,
+    ):
+        # recalc metadata handles special ST creation cases like each rank only has tensor available
+        # caller need to provide None on the unknown dimension of the global size
+        # We will change None into zeros and go through the same amount of checks as before to create ST
+        # and use all_gather to calculate the offsets and global size for metadata
+        # It is compatible with the current use case since, conventionally we don't pass None as global size
+        # Therefore the old path won't trigger the new feature
+        recalc_metadata = False
+        for dim in global_size:
+            if dim is None:
+                recalc_metadata = True
+        if recalc_metadata:
+            global_size = tuple(
+                0 if dim_size is None else dim_size for dim_size in global_size
+            )
+        # STEP 1: Validate the Shardmetadatas locally
+        process_group = cls._normalize_pg(process_group)
+        current_rank = dist.get_rank()  # intentional to get global rank
+        world_size = dist.get_world_size(process_group)
+
+        local_sharded_tensor_metadata: ShardedTensorMetadata | None = None
+        global_tensor_size = _flatten_tensor_size(global_size)
+
+        if len(local_shards) > 0:
+            local_sharded_tensor_metadata = build_metadata_from_local_shards(
+                local_shards, global_tensor_size, current_rank, process_group
+            )
+
+        # STEP 2. Validate metadata across ranks, and build a global sharded tensor
+        # metadata by gathering local ShardedTensorMetadata
+        gathered_metadatas: list[ShardedTensorMetadata | None] = []
+        if world_size > 1:
+            gathered_metadatas = [None for _ in range(world_size)]
+
+            dist.all_gather_object(
+                gathered_metadatas, local_sharded_tensor_metadata, group=process_group
+            )
+        else:
+            gathered_metadatas = [local_sharded_tensor_metadata]
+
+        global_sharded_tensor_metadata = build_global_metadata(
+            gathered_metadatas, recalc_metadata=recalc_metadata
+        )
+        if recalc_metadata:
+            # for recalc use cases, we only support rw for now, limit the blast radius
+            # will modify here once we support more sharding type
+            assert (
+                len(local_shards) > 0
+                and len(global_sharded_tensor_metadata.shards_metadata) > current_rank
+            ), (
+                f"# for metadata recalculation, local_shards must be larger than 0 "
+                f"actual:{len(local_shards)}, # glb metadata must be greater than any rank id, "
+                f"# metadata:{len(global_sharded_tensor_metadata.shards_metadata)}, rank id:{current_rank}"
+            )
+            local_md = [
+                shard_md
+                for shard_md in global_sharded_tensor_metadata.shards_metadata
+                if shard_md.placement.rank() == current_rank
+            ]
+            assert len(local_md) == 1, (
+                f"should has and only has one metadata for local rank, actual:{local_md}"
+            )
+            local_shards[0].metadata = local_md[0]
+        tensor_properties = global_sharded_tensor_metadata.tensor_properties
+
+        # STEP 3: Validation done, create the actual ShardedTensor and populate fields
+        # prepare initialization
+        spec = shard_spec._infer_sharding_spec_from_shards_metadata(
+            global_sharded_tensor_metadata.shards_metadata
+        )
+        sharded_tensor = cls.__new__(
+            cls,
+            spec,
+            global_sharded_tensor_metadata.size,
+            dtype=tensor_properties.dtype,
+            layout=tensor_properties.layout,
+            pin_memory=tensor_properties.pin_memory,
+            requires_grad=tensor_properties.requires_grad,
+        )
+        sharded_tensor._prepare_init(process_group=process_group, init_rrefs=init_rrefs)
+
+        # attach local_shards to the ShardedTensor created
+        sharded_tensor._local_shards = local_shards
+
+        # run post initialization, i.e. map registration, rpc initialization
+        sharded_tensor._post_init()
+        return sharded_tensor
+
+    @classmethod
+    @deprecated(DEPRECATE_MSG, category=FutureWarning)
+    def _init_from_local_tensor(
+        cls,
+        local_tensor: torch.Tensor,
+        sharding_spec: shard_spec.ShardingSpec,
+        *global_size: Sequence[int],
+        process_group: dist.ProcessGroup | None = None,
+        init_rrefs=False,
+    ) -> ShardedTensor:
+        """
+        Initialize a ShardedTensor given only one local tensor, global sharded tensor
+        size and sharding spec on each rank.
+
+        Args:
+            local_tensor (Tensor): Single tensor of local shard stored in each rank.
+            sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`):
+                The specification describing how to shard the Tensor.
+            global_size (Sequence[int]): Size of the sharded tensor.
+            process_group (ProcessGroup, optional): The process group to aggregate on.
+                Default: None
+            init_rrefs (bool, optional): Whether or not to initialize
+                :class:`torch.distributed.rpc.RRef`s pointing to remote shards.
+                Need to initialize the RPC Framework if specified as ``True``.
+                Default: ``False``.
+
+        Returns:
+            A :class:`ShardedTensor` sharded based on the given sharding_spec with local
+                tensor stored in the current rank.
+
+        Examples:
+            >>> # xdoctest: +SKIP
+            >>> # All tensors below are of torch.int64 type.
+            >>> # We have 2 process groups, 2 ranks.
+            >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank
+            >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2]))
+            >>> local_tensor
+            tensor([[1, 2, 3, 4]]) # Rank 0
+            tensor([[3, 4, 5, 6]]) # Rank 1
+            >>> sharding_dim = 0
+            >>> sharding_spec = ChunkShardingSpec(
+                    dim=sharding_dim,
+                    placements=[
+                        "rank:0/cuda:0",
+                        "rank:1/cuda:1",
+                    ],
+                )
+            >>> st = ShardedTensor._init_from_local_tensor(
+            ...     local_tensor, sharding_spec, [2, 4]
+            ... )
+            >>> st
+            ShardedTensor(
+                ShardedTensorMetadata(
+                    shards_metadata=[
+                        ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0),
+                        ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1),
+                    ],
+                    size=torch.Size([2, 4])
+            )
+            >>> st.local_tensor()
+            tensor([1, 2, 3, 4]) # Rank 0
+            tensor([3, 4, 5, 6]) # Rank 1
+
+        Warning: This API is experimental and subject to change. It lacks of a fully across
+                 rank validations, and we only validate the local shard on the current rank.
+                 We fully rely on the user to ensure local tensor is sharded based on the
+                 sharding spec.
+        """
+        if not local_tensor.is_contiguous():
+            raise ValueError("local_tensor is not a contiguous Tensor.")
+
+        global_tensor_size = _flatten_tensor_size(global_size)
+        tensor_properties = TensorProperties(
+            dtype=local_tensor.dtype,
+            layout=local_tensor.layout,
+            requires_grad=local_tensor.requires_grad,
+            memory_format=torch.contiguous_format,
+            pin_memory=local_tensor.is_pinned(),
+        )
+        sharded_tensor_metadata = sharding_spec.build_metadata(
+            global_tensor_size, tensor_properties
+        )
+
+        process_group = cls._normalize_pg(process_group)
+        current_rank = dist.get_rank()  # intentional to get global rank
+
+        local_shards: list[Shard] = []
+        for shard_metadata in sharded_tensor_metadata.shards_metadata:
+            rank, _device = _parse_and_validate_remote_device(
+                process_group, shard_metadata.placement
+            )
+            if rank == current_rank:
+                local_shards.append(Shard(local_tensor, shard_metadata))
+
+        # TODO: figure out what the API should behave when some rank have no shard
+        # see https://github.com/pytorch/pytorch/issues/7313
+        return ShardedTensor._init_from_local_shards_and_global_metadata(
+            local_shards,
+            sharded_tensor_metadata,
+            process_group=process_group,
+            init_rrefs=init_rrefs,
+            sharding_spec=sharding_spec,
+        )
+
+    @classmethod
+    def _init_from_local_shards_and_global_metadata(  # type: ignore[override]
+        cls,
+        local_shards: list[Shard],
+        sharded_tensor_metadata: ShardedTensorMetadata,
+        process_group=None,
+        init_rrefs=False,
+        sharding_spec=None,
+    ) -> ShardedTensor:
+        """
+        Initialize a ShardedTensor with local shards and a global
+        ShardedTensorMetadata built on each rank.
+
+        Warning: This API is experimental and subject to change. It does
+                 not do cross rank validations, and fully rely on the user
+                 for the correctness of sharded_tensor_metadata on each rank
+        """
+        process_group = cls._normalize_pg(process_group)
+        current_rank = dist.get_rank()  # intentional to get global rank
+
+        shards_metadata = sharded_tensor_metadata.shards_metadata
+
+        local_shard_metadatas = []
+
+        # collect local shard metadatas from the global sharded_tensor_metadata
+        for shard_metadata in shards_metadata:  # type: ignore[attr-defined]
+            rank, local_device = _parse_and_validate_remote_device(
+                process_group, shard_metadata.placement
+            )
+
+            if current_rank == rank:
+                local_shard_metadatas.append(shard_metadata)
+
+        if len(local_shards) != len(local_shard_metadatas):
+            raise RuntimeError(
+                f"Number of local shards ({len(local_shards)}) does not match number of local "
+                f"shards metadata in sharded_tensor_metadata ({len(local_shard_metadatas)}) "
+                f"on rank ({current_rank}) "
+            )
+
+        shards_metadata = sharded_tensor_metadata.shards_metadata
+        tensor_properties = sharded_tensor_metadata.tensor_properties
+
+        if len(shards_metadata) == 0:
+            raise ValueError("shards_metadata must not be empty!")
+
+        if tensor_properties.layout != torch.strided:
+            raise ValueError("Only torch.strided layout is currently supported")
+
+        if sharding_spec is None:
+            spec = shard_spec._infer_sharding_spec_from_shards_metadata(shards_metadata)
+        else:
+            spec = sharding_spec
+
+        sharded_tensor = ShardedTensor.__new__(
+            ShardedTensor,
+            spec,
+            sharded_tensor_metadata.size,
+            dtype=tensor_properties.dtype,
+            layout=tensor_properties.layout,
+            pin_memory=tensor_properties.pin_memory,
+            requires_grad=tensor_properties.requires_grad,
+        )
+
+        def _raise_if_mismatch(expected, actual, prop_name, rank, is_property=False):
+            tensor_property_or_metadata = (
+                "tensor property" if is_property else "local ShardMetadata"
+            )
+            if expected != actual:
+                raise ValueError(
+                    f"Local shards' tensor {prop_name} property is incompatible with "
+                    f"{tensor_property_or_metadata} on rank {rank}: "
+                    f"{tensor_property_or_metadata} {prop_name}={expected}, "
+                    f"local shard tensor {prop_name}={actual}."
+                )
+
+        for shard in local_shards:
+            shard_meta = shard.metadata
+            local_shard_tensor = shard.tensor
+            placement = shard_meta.placement
+            assert placement is not None, "Must specify placement for `Shard`!"
+            rank = placement.rank()
+            local_device = placement.device()
+
+            _raise_if_mismatch(
+                tensor_properties.layout,
+                local_shard_tensor.layout,
+                "layout",
+                rank,
+                True,
+            )
+            if not local_shard_tensor.is_contiguous():
+                raise ValueError(
+                    "Only torch.contiguous_format memory_format is currently supported"
+                )
+
+            _raise_if_mismatch(
+                shard_meta.shard_sizes,
+                list(local_shard_tensor.size()),
+                "size",
+                rank,
+            )
+            _raise_if_mismatch(
+                tensor_properties.pin_memory,
+                local_shard_tensor.is_pinned(),
+                "pin_memory",
+                rank,
+                True,
+            )
+            _raise_if_mismatch(local_device, local_shard_tensor.device, "device", rank)
+            _raise_if_mismatch(
+                tensor_properties.dtype,
+                local_shard_tensor.dtype,
+                "dtype",
+                rank,
+                True,
+            )
+            _raise_if_mismatch(
+                tensor_properties.requires_grad,
+                local_shard_tensor.requires_grad,
+                "requires_grad",
+                rank,
+                True,
+            )
+
+        # check if shards_metadata have overlap shards
+        validate_non_overlapping_shards_metadata(shards_metadata)
+
+        # check if the shards_metadata is compatible with overall size of the sharded tensor.
+        check_tensor(shards_metadata, list(sharded_tensor_metadata.size))
+
+        # done validation, add local_shards
+        sharded_tensor._local_shards = local_shards
+        sharded_tensor._prepare_init(process_group=process_group, init_rrefs=init_rrefs)
+
+        # run post initialization, i.e. map registration, rpc initialization
+        sharded_tensor._post_init()
+        return sharded_tensor
+
+    def sharding_spec(self) -> shard_spec.ShardingSpec:
+        """
+        Returns the ShardingSpec for the tensor.
+        """
+        return self._sharding_spec
+
+    @deprecated(DEPRECATE_MSG, category=FutureWarning)
+    def reshard(self, resharding_spec: shard_spec.ShardingSpec) -> ShardedTensor:
+        """
+        Reshard a sharded tensor given the ``resharding_spec``. For now, we only support
+        single local shard.
+
+        If ``resharding_spec`` is same as the original one, this becomes a no-op.
+        If only ``resharding_spec`` shares the same sharding dim with the original one,
+        we swap local shards directly.
+        For more generic cases, we merge different shards across different ranks and split
+        the local shards based on the ``resharding_spec`` via `all_to_all` collective API.
+
+        Args:
+            resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The
+                specification describing how the tensor is sharded.
+
+        Returns:
+            A :class:`ShardedTensor` object whose local shards are resharded.
+
+        Examples:
+            >>> # xdoctest: +SKIP
+            >>> # We have 2 process groups, 2 ranks.
+            >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank
+            >>> tensor = torch.stack([tensor, tensor])
+            >>> tensor
+            tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0
+            tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1
+            tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2
+            tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3
+            >>> sharding_dim = 0
+            >>> spec = ChunkShardingSpec(
+                    dim=sharding_dim,
+                    placements=[
+                        "rank:0/cuda:0",
+                        "rank:1/cuda:1",
+                        "rank:2/cuda:2",
+                        "rank:3/cuda:3",
+                    ],
+                )
+            >>> current_offsets = [0] * 2
+            >>> current_offsets[0] = rank * 2
+            >>> shard_metadata = ShardMetadata(
+                    shard_offsets=copy.deepcopy(current_offsets),
+                    shard_sizes=tensor.size(),
+                    placement=spec.placements[rank],
+                )
+            >>> local_shards = [
+                    Shard(
+                        tensor=tensor,
+                        metadata=shard_metadata,
+                    )
+                ]
+            >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size())
+            >>> sharding_dim = 1
+            >>> resharding_spec = ChunkShardingSpec(
+                    dim=sharding_dim,
+                    placements=[
+                        "rank:0/cuda:0",
+                        "rank:1/cuda:1",
+                        "rank:2/cuda:2",
+                        "rank:3/cuda:3",
+                    ],
+                )
+            >>> st.reshard(resharding_spec)
+            >>> tensor = st.local_shards()[0].tensor
+            >>> tensor
+            tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0
+            tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1
+            tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2
+            tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3
+        """
+        if not isinstance(
+            resharding_spec, shard_spec.ChunkShardingSpec
+        ) or not isinstance(self._sharding_spec, shard_spec.ChunkShardingSpec):
+            raise NotImplementedError("Only ChunkShardingSpec supported for reshard.")
+
+        num_local_shards = len(self.local_shards())
+        if num_local_shards != 1:
+            raise NotImplementedError(
+                f"Only single local shard supported for reshard. Number of shards: {num_local_shards}"
+            )
+
+        if self._sharding_spec.dim == resharding_spec.dim:  # type: ignore[attr-defined]
+            if self._sharding_spec.placements == resharding_spec.placements:  # type: ignore[attr-defined]
+                return self
+            else:
+                local_shards, shards_metadata = reshuffle_local_shard(
+                    self.local_tensor(),
+                    self.size(),  # type: ignore[arg-type]
+                    self._sharding_spec,
+                    resharding_spec,
+                    self._process_group,
+                )
+        else:
+            local_shards, shards_metadata = reshard_local_shard(
+                self.local_tensor(),
+                self.size(),  # type: ignore[arg-type]
+                self._sharding_spec,
+                resharding_spec,
+                self._process_group,
+            )
+        self._local_shards = local_shards
+        self._metadata.shards_metadata = shards_metadata
+        self._sharding_spec = resharding_spec
+        return self
+
+    def local_tensor(self) -> torch.Tensor:
+        """
+        Return local tensor for a sharded_tensor. For now we only support single local shard.
+
+        Returns:
+            A :class:`torch.Tensor` of the local shard.
+        """
+        num_local_shards = len(self.local_shards())
+        if num_local_shards != 1:
+            raise NotImplementedError(
+                f"Only single local shard is supported. Number of shards: {num_local_shards}"
+            )
+        return self.local_shards()[0].tensor
+
+    @classmethod
+    @deprecated(DEPRECATE_MSG, category=FutureWarning)
+    def __torch_function__(cls, func, types, args=(), kwargs=None):
+        def dispatch(st: ShardedTensor, func: Callable):
+            # Dispatch to custom user provided op first if it exists.
+            if func in _CUSTOM_SHARDED_OPS:
+                return _CUSTOM_SHARDED_OPS[func](types, args, kwargs, st._process_group)
+
+            # Dispatch to custom sharding spec op if it has one.
+            if _has_custom_op(st._sharding_spec, func):
+                return _dispatch_custom_op(
+                    st._sharding_spec, func, types, args, kwargs, st._process_group
+                )
+
+            if func in _SHARDED_OPS:
+                return _SHARDED_OPS[func](types, args, kwargs, st._process_group)
+
+            raise RuntimeError(
+                f"torch function '{func.__name__}', with args: {args} and "
+                f"kwargs: {kwargs} not supported for ShardedTensor!"
+            )
+
+        # Find ShardedTensor instance to get process_group and sharding_spec.
+        st_instance = None
+
+        def find_sharded_tensor(e):
+            nonlocal st_instance
+            if st_instance is None and isinstance(e, ShardedTensor):
+                st_instance = e
+
+        pytree.tree_map_(find_sharded_tensor, args)
+        pytree.tree_map_(find_sharded_tensor, kwargs)
+
+        if st_instance is not None:
+            return dispatch(st_instance, func)
+
+        raise RuntimeError(
+            f"torch function '{func.__name__}', with args: {args} and "
+            f"kwargs: {kwargs} not supported for ShardedTensor!"
+        )
+
+    def is_pinned(self) -> bool:  # type: ignore[override]
+        """
+        Returns True if the sharded tensor (each local shard) resides in pinned memory.
+        """
+        return self._metadata.tensor_properties.pin_memory
+
+    def _register_remote_shards(
+        self, remote_shards: list[rpc.RRef[Shard]], rpc_rank: int
+    ):
+        self._remote_shards[rpc_rank] = remote_shards
+
+    def remote_shards(self) -> dict[int, list[rpc.RRef[Shard]]]:
+        """
+        Returns a Dict[int, RRef] with keys being the RPC rank and values
+        being RRefs to shards on that rank. Need to initialize the
+        RPC framework for this functionality.
+
+        Raises an exception if ShardedTensor was created with ``init_rrefs=False``
+        """
+        if not self._init_rrefs:
+            raise RuntimeError(
+                "ShardedTensor created with init_rrefs=False, no RRefs to remote shards available"
+            )
+        return self._remote_shards
+
+    def __hash__(self):
+        return id(self)
+
+    def __repr__(self) -> str:  # type: ignore[override]
+        return f"ShardedTensor({self._metadata})"
+
+    @dataclass
+    class ProcessGroupState:
+        """
+        State for ser-de of process group
+        """
+
+        local_rank: int
+        global_rank: int
+        local_world_size: int
+        global_world_size: int
+
+    def __getstate__(self):
+        pg_state = ShardedTensor.ProcessGroupState(
+            distributed_c10d.get_rank(self._process_group),
+            distributed_c10d.get_rank(),
+            distributed_c10d.get_world_size(self._process_group),
+            distributed_c10d.get_world_size(),
+        )
+
+        return (
+            self._local_shards,
+            self._metadata,
+            pg_state,
+            self._sharding_spec,
+            self._init_rrefs,
+        )
+
+    def __setstate__(self, state):
+        self._sharded_tensor_id = None
+        if not distributed_c10d.is_initialized():
+            raise RuntimeError(
+                "Need to initialize default process group using "
+                '"init_process_group" before loading ShardedTensor'
+            )
+
+        (
+            self._local_shards,
+            self._metadata,
+            pg_state,
+            self._sharding_spec,
+            self._init_rrefs,
+        ) = state
+
+        # Setup process group
+        from torch.distributed._shard.api import _get_current_process_group
+
+        self._process_group = _get_current_process_group()
+
+        # Validate process group.
+        local_rank = distributed_c10d.get_rank(self._process_group)
+        if pg_state.local_rank != local_rank:
+            raise RuntimeError(
+                f"Local rank at save time was {pg_state.local_rank}, but at "
+                f"load time was {local_rank}"
+            )
+
+        global_rank = distributed_c10d.get_rank()
+        if pg_state.global_rank != global_rank:
+            raise RuntimeError(
+                f"Global rank at save time was {pg_state.global_rank}, but at "
+                f"load time was {global_rank}"
+            )
+
+        local_world_size = distributed_c10d.get_world_size(self._process_group)
+        if pg_state.local_world_size != local_world_size:
+            raise RuntimeError(
+                f"Local world size at save time was {pg_state.local_world_size}, "
+                f"but at load time was {local_world_size}"
+            )
+
+        global_world_size = distributed_c10d.get_world_size()
+        if pg_state.global_world_size != global_world_size:
+            raise RuntimeError(
+                f"Global world size at save time was {pg_state.global_world_size}, "
+                f"but at load time was {global_world_size}"
+            )
+
+        self._post_init()
+
+
+def _create_tensor_from_params(
+    *size, local_device, tensor_properties: TensorProperties
+):
+    """Helper to construct tensor from size, device and common params."""
+    dtype = tensor_properties.dtype
+    layout = tensor_properties.layout
+    requires_grad = tensor_properties.requires_grad
+    memory_format = tensor_properties.memory_format
+    pin_memory = tensor_properties.pin_memory
+
+    return torch.empty(
+        *size,
+        dtype=dtype,
+        layout=layout,
+        device=local_device,
+        requires_grad=requires_grad,
+        memory_format=memory_format,
+        pin_memory=pin_memory,
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/logger.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/logger.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff8cb4d18fb180ea620dd8daad60b5771a9688be
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/logger.py
@@ -0,0 +1,35 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+
+from torch.distributed._shard.sharded_tensor.logging_handlers import _log_handlers
+
+
+__all__: list[str] = []
+
+
+def _get_or_create_logger() -> logging.Logger:
+    logging_handler, log_handler_name = _get_logging_handler()
+    logger = logging.getLogger(f"sharding-spec-{log_handler_name}")
+    logger.setLevel(logging.DEBUG)
+    formatter = logging.Formatter(
+        "%(asctime)s %(filename)s:%(lineno)s %(levelname)s p:%(processName)s t:%(threadName)s: %(message)s"
+    )
+    logging_handler.setFormatter(formatter)
+    logger.propagate = False
+    logger.addHandler(logging_handler)
+    return logger
+
+
+def _get_logging_handler(
+    destination: str = "default",
+) -> tuple[logging.Handler, str]:
+    log_handler = _log_handlers[destination]
+    log_handler_name = type(log_handler).__name__
+    return (log_handler, log_handler_name)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/logging_handlers.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/logging_handlers.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed6832fd1ae834b6365a6b005b07bbbfffe90726
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/logging_handlers.py
@@ -0,0 +1,16 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+
+
+__all__: list[str] = []
+
+_log_handlers: dict[str, logging.Handler] = {
+    "default": logging.NullHandler(),
+}
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/metadata.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/metadata.py
new file mode 100644
index 0000000000000000000000000000000000000000..466ca1a0c519ce4cc4ee24fae98ff4ddfbee300a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/metadata.py
@@ -0,0 +1,94 @@
+# mypy: allow-untyped-defs
+from dataclasses import dataclass, field
+from enum import Enum
+
+import torch
+from torch.distributed._shard.metadata import ShardMetadata
+
+
+class MEM_FORMAT_ENCODING(Enum):
+    TORCH_CONTIGUOUS_FORMAT = 0
+    TORCH_CHANNELS_LAST = 1
+    TORCH_PRESERVE_FORMAT = 2
+
+
+@dataclass
+class TensorProperties:
+    """Properties used to create :class:`Tensor`"""
+
+    # Regular tensor fields
+    dtype: torch.dtype = field(default=torch.get_default_dtype())
+    layout: torch.layout = field(default=torch.strided)
+    requires_grad: bool = False
+    memory_format: torch.memory_format = field(default=torch.contiguous_format)
+    pin_memory: bool = False
+
+    def __getstate__(self):
+        # Since torch.memory_format cannot be pickled!
+        memory_format = self.memory_format
+        if memory_format == torch.contiguous_format:
+            mem_format_encoding = MEM_FORMAT_ENCODING.TORCH_CONTIGUOUS_FORMAT
+        elif memory_format == torch.channels_last:
+            mem_format_encoding = MEM_FORMAT_ENCODING.TORCH_CHANNELS_LAST
+        elif memory_format == torch.preserve_format:
+            mem_format_encoding = MEM_FORMAT_ENCODING.TORCH_PRESERVE_FORMAT
+        else:
+            raise RuntimeError(f"Invalid torch.memory_format: {memory_format}")
+
+        return (
+            self.dtype,
+            self.layout,
+            self.requires_grad,
+            mem_format_encoding,
+            self.pin_memory,
+        )
+
+    def __setstate__(
+        self,
+        state,
+    ):
+        (
+            self.dtype,
+            self.layout,
+            self.requires_grad,
+            mem_format_encoding,
+            self.pin_memory,
+        ) = state
+
+        if mem_format_encoding == MEM_FORMAT_ENCODING.TORCH_CONTIGUOUS_FORMAT:
+            memory_format = torch.contiguous_format
+        elif mem_format_encoding == MEM_FORMAT_ENCODING.TORCH_CHANNELS_LAST:
+            memory_format = torch.channels_last
+        elif mem_format_encoding == MEM_FORMAT_ENCODING.TORCH_PRESERVE_FORMAT:
+            memory_format = torch.preserve_format
+        else:
+            raise RuntimeError(
+                f"Invalid torch.memory_format encoding: {mem_format_encoding}"
+            )
+
+        self.memory_format = memory_format
+
+    @staticmethod
+    def create_from_tensor(tensor: torch.Tensor) -> "TensorProperties":
+        return TensorProperties(
+            dtype=tensor.dtype,
+            layout=tensor.layout,
+            requires_grad=tensor.requires_grad,
+            memory_format=torch.contiguous_format,
+            pin_memory=tensor.is_pinned(),
+        )
+
+
+@dataclass
+class ShardedTensorMetadata:
+    """
+    Represents metadata for :class:`ShardedTensor`
+    """
+
+    # Metadata about each shard of the Tensor
+    shards_metadata: list[ShardMetadata] = field(default_factory=list)
+
+    # Size of each dim of the overall Tensor.
+    size: torch.Size = field(default=torch.Size([]))
+
+    tensor_properties: TensorProperties = field(default_factory=TensorProperties)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/reshard.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/reshard.py
new file mode 100644
index 0000000000000000000000000000000000000000..daef9c3586184e4e62b4a141ec2e43f5025bf454
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/reshard.py
@@ -0,0 +1,243 @@
+# mypy: allow-untyped-defs
+import copy
+
+import torch
+import torch.distributed as dist
+import torch.distributed._shard.sharding_spec as shard_spec
+from torch._C._distributed_c10d import ProcessGroup
+from torch.distributed._shard.metadata import ShardMetadata
+from torch.distributed._shard.sharding_spec._internals import (
+    get_chunked_dim_size,
+    get_split_size,
+)
+from torch.distributed.nn.functional import all_to_all, all_to_all_single
+
+from .shard import Shard
+
+
+def get_idx_from_placements(placements, current_rank) -> int:
+    """
+    Return the position of the current rank in the given placements.
+
+    Args:
+        placements(List[Union[_remote_device, str]]):
+            Specifies the placement of each shard of the Tensor. The size of
+            the list represents the number of shards to be created. This could
+            be a list of
+            :class:`torch.distributed._remote_device`'s. This list
+            could also contain a string which represents remote
+            device as accepted by
+            :class:`torch.distributed._remote_device`
+        current_rank (int): number of current device.
+
+    Returns:
+        A int which contains the position of current device in the placement list.
+    """
+    for idx, placement in enumerate(placements):  # type: ignore[attr-defined]
+        if current_rank == placement.rank():  # type: ignore[union-attr]
+            return idx
+    raise RuntimeError("current_rank not in the placement.")
+
+
+def build_reshard_metadata(
+    st_size: torch.Size,
+    sharding_spec: shard_spec.ShardingSpec,
+    world_size: int,
+) -> tuple[list[ShardMetadata], list[int]]:
+    """
+    Based the given sharding spec, we calculate the offset and local shard size.
+    We then build a ShardMetadata on top of the calculation result.
+
+    Args:
+        st_size (torch.Size): The size of the sharded tensor.
+        sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The
+            specification describing how the tensor is sharded.
+        world_size (int): number of ranks.
+
+    Returns:
+        A Tuple of the followings:
+            A List[`ShardMetadata`] which contains the metadata for the shard, including
+                offsets, lengths and device placement.
+            A List[int] which contains the ranks in the order of placement.
+    """
+    shard_dim = int(sharding_spec.dim)  # type: ignore[attr-defined]
+    shards_metadata = [None] * world_size
+    ranks = []
+    offsets = [0] * len(st_size)
+    split_size = get_split_size(st_size[shard_dim], world_size)
+    for idx, placement in enumerate(sharding_spec.placements):  # type: ignore[attr-defined]
+        ranks.append(placement.rank())
+        sharded_dim_size = get_chunked_dim_size(st_size[shard_dim], split_size, idx)
+        local_tensor_size = list(st_size)
+        local_tensor_size[shard_dim] = sharded_dim_size
+        shards_metadata[placement.rank()] = ShardMetadata(  # type: ignore[call-overload]
+            shard_offsets=copy.deepcopy(offsets),
+            shard_sizes=local_tensor_size,
+            placement=placement,
+        )
+        offsets[shard_dim] += sharded_dim_size
+    return shards_metadata, ranks  # type: ignore[return-value]
+
+
+def reshuffle_local_shard(
+    local_shard: torch.Tensor,
+    st_size: torch.Size,
+    sharding_spec: shard_spec.ShardingSpec,
+    resharding_spec: shard_spec.ShardingSpec,
+    pg: ProcessGroup,
+) -> tuple[list[Shard], list[ShardMetadata]]:
+    """
+    Reshuffle the local shard directly when the reshard dim is same as the original
+    sharding dim. Logically we do this in two step:
+    1. To collect all shards based on original sharding spec.
+    2. Reshard the tensor based on the given resharding spec.
+
+    In reality, we consolidate the two steps into one by sending the local tensor to
+    the new shard directly based on the resharding spec.
+
+    Args:
+        local_shard (Tensor): Local tensor stored in the current rank.
+        st_size (torch.Size): The size of the sharded tensor.
+        sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The
+            specification describing how the tensor is sharded originally.
+        resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The
+            specification describing how the tensor will be resharded.
+        pg (ProcessGroup): The process group to aggregate on.
+
+    Returns:
+        A Tuple of the followings:
+            A List[`Shard`] which contains the local tensor and its metadata.
+            A List[`ShardMetadata`] which contains the metadata for the shard, including
+                offsets, lengths and device placement.
+    """
+    current_rank = dist.get_rank(pg)
+    world_size = dist.get_world_size(pg)
+    # Build shards_metadata first.
+    shards_metadata, ranks = build_reshard_metadata(
+        st_size, resharding_spec, world_size
+    )
+    # Get input split size for all2all.
+    reshard_dim = int(resharding_spec.dim)  # type: ignore[attr-defined]
+    split_size = get_split_size(st_size[reshard_dim], world_size)
+    input_split_sizes = [0] * world_size
+    idx = get_idx_from_placements(sharding_spec.placements, current_rank)  # type: ignore[attr-defined]
+    new_rank = resharding_spec.placements[idx].rank()  # type: ignore[union-attr, attr-defined]
+    input_split_sizes[new_rank] = local_shard.size(reshard_dim)
+    # Get output split size for all2all.
+    output_split_sizes = [0] * world_size
+    new_idx = ranks.index(current_rank)
+    sharded_dim_size = get_chunked_dim_size(st_size[reshard_dim], split_size, new_idx)
+    output_split_sizes[new_rank] = sharded_dim_size
+    # Get gathered_input for all2all.
+    local_shard = local_shard.transpose(0, reshard_dim).contiguous()
+    gathered_input_size = list(local_shard.size())
+    gathered_input_size[0] = sharded_dim_size
+    gathered_input = torch.empty(
+        gathered_input_size, device=local_shard.device, dtype=local_shard.dtype
+    )
+    # all2all.
+    local_shard = all_to_all_single(
+        gathered_input,
+        local_shard,
+        input_split_sizes=input_split_sizes,
+        output_split_sizes=output_split_sizes,
+        group=pg,
+    )
+    local_tensor = local_shard.transpose(0, reshard_dim).contiguous()
+    local_shards = [Shard(local_tensor, shards_metadata[current_rank])]
+    return local_shards, shards_metadata
+
+
+def reshard_local_shard(
+    local_tensor: torch.Tensor,
+    st_size: torch.Size,
+    sharding_spec: shard_spec.ShardingSpec,
+    resharding_spec: shard_spec.ShardingSpec,
+    pg: ProcessGroup,
+) -> tuple[list[Shard], list[ShardMetadata]]:
+    """
+    Reshard a sharded tensor given the ``resharding_spec``. When the reshard dim is
+    different from the original sharding dim, we need to do two steps logically:
+    1. To collect all shards based on original sharding spec.
+    2. Reshard the tensor based on the given resharding spec.
+
+    In reality, we consolidate the two steps into one by sending each rank the new
+    shard based on the resharding spec.
+
+    Args:
+        local_tensor (Tensor): Local tensor stored in the current rank.
+        st_size (torch.Size): The size of the sharded tensor.
+        sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The
+            specification describing how the tensor is sharded originally.
+        resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The
+            specification describing how the tensor will be resharded.
+        pg (ProcessGroup): The process group to aggregate on.
+
+    Returns:
+        A Tuple of the followings:
+            A List[`Shard`] which contains the local tensor and its metadata.
+            A List[`ShardMetadata`] which contains the metadata for the shard, including
+                offsets, lengths and device placement.
+    """
+    current_rank = dist.get_rank(pg)
+    world_size = dist.get_world_size(pg)
+    current_sharding_dim = int(sharding_spec.dim)  # type: ignore[attr-defined]
+    reshard_dim = int(resharding_spec.dim)  # type: ignore[attr-defined]
+
+    # Build shards_metadata first.
+    shards_metadata, ranks = build_reshard_metadata(
+        st_size, resharding_spec, world_size
+    )
+
+    # Compute expected size
+    input_split_sizes = [
+        metadata.shard_sizes[reshard_dim] for metadata in shards_metadata
+    ]
+    rearrange_input = any(ranks[i] > ranks[i + 1] for i in range(len(ranks) - 1))
+
+    if rearrange_input:
+        # Need to re-arrange reshard_dim of local_tensor before all2all.
+        indices: list[int] = []
+        for metadata in shards_metadata:
+            offset_start_idx = metadata.shard_offsets[reshard_dim]
+            split_size = metadata.shard_sizes[reshard_dim]
+            indices += range(offset_start_idx, offset_start_idx + split_size)
+        local_tensor = local_tensor.index_select(
+            reshard_dim, torch.tensor(indices, device=local_tensor.device)
+        )
+
+    # Because reshard_dim != original shard_dim. We need to compute the
+    # size of tensor from each rank.
+    output_tensor_list = [torch.tensor(1)] * world_size
+    split_size = get_split_size(st_size[current_sharding_dim], world_size)
+    rearrange_output_list = False
+    indices = []
+    for idx, placement in enumerate(sharding_spec.placements):  # type: ignore[attr-defined]
+        sharded_dim_size = get_chunked_dim_size(
+            st_size[current_sharding_dim], split_size, idx
+        )
+        output_tensor_size = list(st_size)
+        output_tensor_size[current_sharding_dim] = sharded_dim_size
+        output_tensor_size[reshard_dim] = input_split_sizes[current_rank]
+        output_tensor_list[placement.rank()] = torch.empty(  # type: ignore[union-attr, index]
+            output_tensor_size, device=local_tensor.device, dtype=local_tensor.dtype
+        )
+        indices.append(placement.rank())  # type: ignore[union-attr, index, arg-type]
+        if idx != placement.rank():  # type: ignore[union-attr]
+            rearrange_output_list = True
+
+    # Perform autograd enabled all2all.
+    input_tensor_tuple = torch.split(local_tensor, input_split_sizes, dim=reshard_dim)
+    input_tensor_list = [tensor.contiguous() for tensor in input_tensor_tuple]
+    output_tensor_list = all_to_all(
+        output_tensor_list,
+        input_tensor_list,
+        group=pg,
+    )
+
+    if rearrange_output_list:
+        # Need to re-arrange original shard_dim of output_tensor_list.
+        output_tensor_list = [output_tensor_list[idx] for idx in indices]  # type: ignore[call-overload]
+    local_tensor = torch.cat(output_tensor_list, dim=current_sharding_dim)
+    local_shards = [Shard(local_tensor, shards_metadata[current_rank])]
+    return local_shards, shards_metadata
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/shard.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/shard.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d9d4357436a6c15f590a4db486d9d54b6d6ca57
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/shard.py
@@ -0,0 +1,61 @@
+from dataclasses import dataclass
+
+import torch
+from torch.distributed._shard.metadata import ShardMetadata
+from torch.distributed.remote_device import _remote_device
+
+
+@dataclass
+class Shard:
+    """
+    Container which holds the data for a shard as a Tensor and also
+    the associated metadata for that shard.
+
+    Args:
+        tensor(torch.Tensor): Local tensor for the shard.
+        metadata(:class `torch.distributed._shard.sharded_tensor.ShardMetadata`):
+            The metadata for the shard, including offsets, lengths and device placement.
+    """
+
+    __slots__ = ["tensor", "metadata"]
+    tensor: torch.Tensor
+    metadata: ShardMetadata
+
+    def __post_init__(self) -> None:
+        # verification between local tensor and metadata
+        if list(self.tensor.size()) != self.metadata.shard_sizes:
+            raise ValueError(
+                "Shard tensor size does not match with metadata.shard_lengths! "
+                f"Found shard tensor size: {list(self.tensor.size())}, "
+                f"metadata.shard_lengths: {self.metadata.shard_sizes}, "
+            )
+        placement_device = self.metadata.placement
+        if (
+            placement_device is not None
+            and placement_device.device() != self.tensor.device
+        ):
+            raise ValueError(
+                f"Local shard tensor device does not match with local Shard's placement! "
+                f"Found local shard tensor device: {self.tensor.device}, "
+                f"local shard metadata placement device: {placement_device.device()}"
+            )
+
+    @classmethod
+    def from_tensor_and_offsets(
+        cls, tensor: torch.Tensor, shard_offsets: list[int], rank: int
+    ) -> "Shard":
+        """
+        Creates a Shard of a ShardedTensor from a local torch.Tensor, shard_offsets and rank.
+
+        Args:
+            tensor(torch.Tensor): Local tensor for the shard.
+            shard_offsets(List[int]): List of integers specify the offset
+                of the shard on each dimension.
+            rank(int): Specify the rank for the shard.
+        """
+        shard_sizes = list(tensor.size())
+        placement = _remote_device(f"rank:{rank}/{str(tensor.device)}")
+        shard_meta = ShardMetadata(
+            shard_offsets=shard_offsets, shard_sizes=shard_sizes, placement=placement
+        )
+        return Shard(tensor, shard_meta)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..b323da4ecbfa3adcea51367dc42a6e54d2cd1624
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharded_tensor/utils.py
@@ -0,0 +1,325 @@
+# mypy: allow-untyped-defs
+import collections.abc
+import copy
+import itertools
+from collections.abc import Sequence
+from typing import TYPE_CHECKING
+
+import torch
+from torch.distributed import distributed_c10d as c10d, rpc
+from torch.distributed._shard.sharding_spec._internals import (
+    check_tensor,
+    validate_non_overlapping_shards_metadata,
+)
+
+from .metadata import ShardedTensorMetadata, TensorProperties
+from .shard import Shard
+
+
+if TYPE_CHECKING:
+    from torch.distributed._shard.metadata import ShardMetadata
+
+
+def _parse_and_validate_remote_device(pg, remote_device):
+    if remote_device is None:
+        raise ValueError("remote device is None")
+
+    worker_name = remote_device.worker_name()
+    rank = remote_device.rank()
+    device = remote_device.device()
+
+    # Validate rank, skip validation if rank is not part of process group.
+    if rank is not None and not c10d._rank_not_in_group(pg):
+        pg_global_ranks = c10d.get_process_group_ranks(pg)
+        if rank not in pg_global_ranks:
+            raise ValueError(
+                f"Global rank {rank} does not exist in input process group: {pg_global_ranks}"
+            )
+
+    if worker_name is not None:
+        if not rpc._is_current_rpc_agent_set():
+            raise RuntimeError(
+                f"RPC framework needs to be initialized for using worker names: {worker_name}"
+            )
+
+        workers = rpc._get_current_rpc_agent().get_worker_infos()
+        for worker in workers:
+            if worker.name == worker_name:
+                return worker.id, device
+
+        raise ValueError(f"Invalid worker name: {worker_name}")
+
+    return rank, device
+
+
+def _validate_output_tensor_for_gather(
+    my_rank: int,
+    dst_rank: int,
+    size: torch.Size,
+    dst_tensor: torch.Tensor | None,
+) -> None:
+    if dst_rank == my_rank:
+        if dst_tensor is None:
+            raise ValueError(
+                f"Argument ``dst_tensor`` must be specified on destination rank {dst_rank}"
+            )
+        if tuple(size) != (dst_tensor.size()):
+            raise ValueError(
+                f"Argument ``dst_tensor`` have size {tuple(dst_tensor.size())},"
+                f"but should be {tuple(size)}"
+            )
+    elif dst_tensor:
+        raise ValueError(
+            "Argument ``dst_tensor`` must NOT be specified on non-destination ranks."
+        )
+
+
+def _flatten_tensor_size(size) -> torch.Size:
+    """
+    Checks if tensor size is valid, then flatten/return a torch.Size object.
+    """
+    if len(size) == 1 and isinstance(size[0], collections.abc.Sequence):
+        # pyrefly: ignore [not-iterable]
+        dims = list(*size)
+    else:
+        dims = list(size)
+
+    for dim in dims:
+        if not isinstance(dim, int):
+            raise TypeError(f"size has to be a sequence of ints, found: {dims}")
+
+    return torch.Size(dims)
+
+
+def _raise_if_mismatch(expected, actual, prop_name, ranks, is_local=True):
+    if is_local:
+        assert isinstance(ranks, int)
+        if expected != actual:
+            raise ValueError(
+                f"Local shards' tensor {prop_name} property need to be the same on rank:{ranks}! "
+                f"Found one local shard tensor {prop_name}={expected}, "
+                f"the other local shard tensor {prop_name}={actual}."
+            )
+    else:
+        # compare failure check across ranks, ranks list should have two rank
+        assert len(ranks) == 2
+        if expected != actual:
+            raise ValueError(
+                f"ShardedTensor {prop_name} property does not match from different ranks! "
+                f"Found {prop_name}={expected} on rank:{ranks[0]}, "
+                f"and {prop_name}={actual} on rank:{ranks[1]}."
+            )
+
+
+def build_metadata_from_local_shards(
+    local_shards: list[Shard],
+    global_size: torch.Size,
+    current_rank: int,
+    pg: c10d.ProcessGroup,
+) -> ShardedTensorMetadata:
+    assert len(local_shards) > 0, "must have local shards!"
+    local_shard_metadatas: list[ShardMetadata] = []
+
+    first_shard_dtype = local_shards[0].tensor.dtype
+    first_shard_layout = local_shards[0].tensor.layout
+    first_shard_requires_grad = local_shards[0].tensor.requires_grad
+    first_shard_is_pinned = local_shards[0].tensor.is_pinned()
+
+    # 1). Validate local tensors and associated metadatas
+    for local_shard in local_shards:
+        local_shard_tensor = local_shard.tensor
+        local_shard_meta = local_shard.metadata
+        local_shard_metadatas.append(local_shard_meta)
+        rank, local_device = _parse_and_validate_remote_device(
+            pg, local_shard_meta.placement
+        )
+
+        if (
+            local_shard_tensor.layout != torch.strided
+            or local_shard_tensor.layout != first_shard_layout
+        ):
+            raise ValueError(
+                f"Only torch.strided layout is currently supported, but found "
+                f"{local_shard_tensor.layout} on rank:{current_rank}!"
+            )
+
+        if not local_shard_tensor.is_contiguous():
+            raise ValueError(
+                "Only torch.contiguous_format memory_format is currently supported!"
+            )
+
+        if rank != current_rank:
+            raise ValueError(
+                f"Local shard metadata's rank does not match with the rank in its process group! "
+                f"Found current rank in the process group: {current_rank}, "
+                f"local ShardMetadata placement's rank: {rank}"
+            )
+        if local_shard_tensor.device != local_device:
+            raise ValueError(
+                f"Local shard tensor device does not match with local Shard's placement! "
+                f"Found local shard tensor device: {local_shard_tensor.device}, "
+                f"local shard metadata placement device: {local_device}"
+            )
+
+        _raise_if_mismatch(
+            local_shard_meta.shard_sizes,
+            list(local_shard_tensor.size()),
+            "size",
+            current_rank,
+        )
+        _raise_if_mismatch(
+            local_shard_tensor.is_pinned(),
+            first_shard_is_pinned,
+            "pin_memory",
+            current_rank,
+        )
+        _raise_if_mismatch(
+            local_shard_tensor.dtype, first_shard_dtype, "dtype", current_rank
+        )
+        _raise_if_mismatch(
+            local_shard_tensor.requires_grad,
+            first_shard_requires_grad,
+            "requires_grad",
+            current_rank,
+        )
+
+    # 2). Build a "local" ShardedTensorMetadata with all local shards on this rank, then
+    #    do all_gather to collect local_sharded_tensor_metadata from all ranks
+    local_tensor_properties = TensorProperties(
+        dtype=first_shard_dtype,
+        layout=first_shard_layout,
+        requires_grad=first_shard_requires_grad,
+        memory_format=torch.contiguous_format,
+        pin_memory=first_shard_is_pinned,
+    )
+
+    local_sharded_tensor_metadata = ShardedTensorMetadata(
+        shards_metadata=local_shard_metadatas,
+        size=global_size,
+        tensor_properties=local_tensor_properties,
+    )
+
+    return local_sharded_tensor_metadata
+
+
+def build_global_metadata(
+    gathered_metadatas: Sequence[ShardedTensorMetadata | None],
+    recalc_metadata: bool = False,
+):
+    global_sharded_tensor_metadata = None
+    global_metadata_rank = 0
+
+    # pyrefly: ignore [bad-assignment]
+    for rank, rank_metadata in enumerate(gathered_metadatas):
+        if rank_metadata is None:
+            continue
+
+        if global_sharded_tensor_metadata is None:
+            global_sharded_tensor_metadata = copy.deepcopy(rank_metadata)
+            global_metadata_rank = rank
+        else:
+            _raise_if_mismatch(
+                global_sharded_tensor_metadata.size,
+                rank_metadata.size,
+                "global_size",
+                [global_metadata_rank, rank],
+                is_local=False,
+            )
+
+            # don't need to check layout and memory format as we already checked in local shards validation stage
+            _raise_if_mismatch(
+                global_sharded_tensor_metadata.tensor_properties.dtype,
+                rank_metadata.tensor_properties.dtype,
+                "dtype",
+                [global_metadata_rank, rank],
+                is_local=False,
+            )
+
+            _raise_if_mismatch(
+                global_sharded_tensor_metadata.tensor_properties.requires_grad,
+                rank_metadata.tensor_properties.requires_grad,
+                "requires_grad",
+                [global_metadata_rank, rank],
+                is_local=False,
+            )
+
+            _raise_if_mismatch(
+                global_sharded_tensor_metadata.tensor_properties.pin_memory,
+                rank_metadata.tensor_properties.pin_memory,
+                "pin_memory",
+                [global_metadata_rank, rank],
+                is_local=False,
+            )
+            # pass all validations, extend shards metadata
+            global_sharded_tensor_metadata.shards_metadata.extend(
+                rank_metadata.shards_metadata
+            )
+
+    if global_sharded_tensor_metadata is not None:
+        if recalc_metadata:
+            recalc_global_sharded_tensor_metadata(
+                global_sharded_tensor_metadata,
+                0,  # sharded on 0th dim
+            )
+
+        # check if shards_metadata have overlap shards
+        validate_non_overlapping_shards_metadata(
+            global_sharded_tensor_metadata.shards_metadata
+        )
+
+        # check if the shards_metadata is compatible with global size of the sharded tensor.
+        check_tensor(
+            global_sharded_tensor_metadata.shards_metadata,
+            global_sharded_tensor_metadata.size,
+        )
+    else:
+        raise ValueError("ShardedTensor have no local shards on all ranks!")
+
+    return global_sharded_tensor_metadata
+
+
+def recalc_global_sharded_tensor_metadata(
+    global_sharded_tensor_metadata: ShardedTensorMetadata, sharded_dim: int
+) -> None:
+    # recalculate global ShardedTensorMetadata
+
+    # reorder here in case shard metadata is not sorted on sharded_dim
+    placement_idx_pairs = []
+    for i, shard_metadata in enumerate(global_sharded_tensor_metadata.shards_metadata):
+        if shard_metadata.placement:
+            placement_idx_pairs.append((shard_metadata.placement.rank(), i))
+        else:
+            raise AssertionError(
+                "currently only support rw, it should always have valid rank info"
+            )
+    sorted_idx = sorted(placement_idx_pairs)
+    shard_sizes = [
+        global_sharded_tensor_metadata.shards_metadata[idx].shard_sizes[sharded_dim]
+        for _, idx in sorted_idx
+    ]
+    cum_sum = [0] + list(itertools.accumulate(shard_sizes))
+
+    for shard_id, shard_metadata in enumerate(
+        global_sharded_tensor_metadata.shards_metadata
+    ):
+        # update shard offset for each shard on the sharded dimension
+        shard_metadata.shard_offsets[sharded_dim] = cum_sum[shard_id]
+        for other_dim in range(
+            len(global_sharded_tensor_metadata.shards_metadata[0].shard_sizes)
+        ):
+            if other_dim != sharded_dim:
+                # shard offset for each shard on the unsharded dimension
+                shard_metadata.shard_offsets[other_dim] = 0
+
+    # update global size for ShardedTensorMetadata
+    global_size_list = []
+    for other_dim in range(
+        len(global_sharded_tensor_metadata.shards_metadata[0].shard_sizes)
+    ):
+        if other_dim != sharded_dim:
+            global_size_list.append(
+                global_sharded_tensor_metadata.shards_metadata[0].shard_sizes[other_dim]
+            )
+        else:
+            global_size_list.append(cum_sum[-1])
+    global_sharded_tensor_metadata.size = torch.Size(global_size_list)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharder.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharder.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d91ec15775bea870b81c4b10fb1443a3fba0977
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharder.py
@@ -0,0 +1,29 @@
+import abc
+
+import torch.nn as nn
+
+
+class Sharder(abc.ABC):
+    """
+    This is an interface which allows user to create more advanced
+    sharding strategies that are not easily be composed by the
+    `ShardingSpec`.
+
+    :class:`torch.distributed._shard.sharding_plan.ShardingPlan` could
+    take an object of the `Sharder` and call `shard` to shard the module,
+    then replace the original module with sharded module returned.
+    """
+
+    @abc.abstractmethod
+    def shard(self, module: nn.Module) -> nn.Module:
+        """
+        Shard a module base on the implementation of this method, and
+        return the sharded version of the module.
+
+        Args:
+            module (:class:`torch.nn.Module`):
+                The module to apply sharding to.
+        Returns:
+            A :class:`torch.nn.Module` object that represents a module
+            that's already been sharded.
+        """
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharding_plan/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharding_plan/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..325f7d7eb47b96a79fdc10cc2d1f072cdec9b4ce
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharding_plan/__init__.py
@@ -0,0 +1 @@
+from .api import ShardingPlan, ShardingPlanner
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharding_plan/api.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharding_plan/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..a94f4b54edf2b6c29fd9331ec5e662a793510102
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharding_plan/api.py
@@ -0,0 +1,86 @@
+import abc
+from dataclasses import dataclass
+
+import torch.nn as nn
+from torch.distributed._shard.sharder import Sharder
+from torch.distributed._shard.sharding_spec import ShardingSpec
+
+
+@dataclass
+class ShardingPlan:
+    """
+    Representation of a sharding plan, describes how to shard a module
+    across hosts. `plan` is used to shard module parameters according to the spec provided,
+    `output_plan` and `return_local_tensor` are optional, they are used to specify the output
+    layout of a module with a spec, and when to convert back to data parallel fashion.
+
+    Args:
+        plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`,
+              :class:`torch.distributed._shard.sharder.Sharder`]):
+            a dict describes how to shard a module, there're currently two ways to shard a module:
+                1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of
+                   a parameter to a `ShardingSpec`.
+                2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module
+                   to a `Sharder` object.
+        output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional):
+            a dict specifies the layout of a module's output which produces a ShardedTensor,
+            keyed by the name of module to ShardingSpec("" in key means the root module).
+            Default: `None`
+        return_local_tensor (List[str], optional): a list of string, each element enables
+            a module's sharded output to be returned as a Tensor from its local shards to
+            ensure further processing in a data parallel fashion. ("" in list means the
+            root module).
+            Default: None
+    Example:
+      Suppose we want to shard a module with two linear layers and then run it with DDP, we also
+      want to convert the output of the second linear layer back to DDP, we can do it as follows:
+
+        >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d)
+        >>> class MyModule(nn.Module):
+        >>>     def __init__(self) -> None:
+        >>>        super().__init__()
+        >>>        self.fc1 = nn.Linear()
+        >>>        self.gelu = nn.GELU()
+        >>>        self.fc2 = nn.Linear()
+        >>>        self.relu = nn.Linear()
+        >>>
+        >>>     def forward(self, input):
+        >>>         return self.relu(self.fc2(self.gelu(self.fc1(input))))
+
+
+        >>> # xdoctest: +SKIP("Undefined spec1, spec2)
+        >>> sharding_plan = ShardingPlan(
+        >>>    plan={
+        >>>        "fc1.weight": spec1,
+        >>>        "fc2.weight": spec2
+        >>>    },
+        >>>    output_plan={
+        >>>        "fc2": output_spec
+        >>>    },
+        >>>    return_local_tensor=["fc2"]
+        >>> )
+    """
+
+    plan: dict[str, ShardingSpec | Sharder]
+    output_plan: dict[str, ShardingSpec] | None = None
+    return_local_tensor: list[str] | None = None
+
+
+class ShardingPlanner(abc.ABC):
+    """
+    Default ShardingPlanner interface, can be extended and
+    implement advanced sharding strategies.
+    """
+
+    @abc.abstractmethod
+    def build_plan(self, module: nn.Module) -> ShardingPlan:
+        """
+        Given a nn.Module, define how to shard the module across
+        ranks, return a ShardingPlan
+        Args:
+            module (:class:`torch.nn.Module`):
+                The module to apply sharding to.
+        Returns:
+            A :class:`torch.distributed._shard.sharding_plan.ShardingPlan` object that
+            represents how to shard the module.
+        """
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharding_spec/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharding_spec/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..bfd3f0a7581e8c4352eba843af6d3751bee7f387
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharding_spec/__init__.py
@@ -0,0 +1,10 @@
+from torch.distributed._shard.metadata import ShardMetadata
+
+from .api import (
+    _infer_sharding_spec_from_shards_metadata,
+    DevicePlacementSpec,
+    EnumerableShardingSpec,
+    PlacementSpec,
+    ShardingSpec,
+)
+from .chunk_sharding_spec import ChunkShardingSpec as ChunkShardingSpec
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharding_spec/_internals.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharding_spec/_internals.py
new file mode 100644
index 0000000000000000000000000000000000000000..486c62a18cd7b91e30ad21891fb0c735e28d443f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharding_spec/_internals.py
@@ -0,0 +1,244 @@
+# mypy: allow-untyped-defs
+import math
+import sys
+from bisect import bisect_right, insort
+
+from torch.distributed._shard.metadata import ShardMetadata
+
+
+def _check_shard_metadata_pair_overlap(shard1: ShardMetadata, shard2: ShardMetadata):
+    """
+    Checks if two shards overlap.
+    """
+
+    # For each dim of each shard, check if one shard resides on the other
+    # end of second shard with respect to that dim. As an example for a 2D
+    # shard, we would check if one shard is above or on the left of the
+    # other shard.
+    ndims = len(shard1.shard_offsets)
+    for i in range(ndims):
+        if shard1.shard_offsets[i] >= shard2.shard_offsets[i] + shard2.shard_sizes[i]:
+            return False
+        if shard2.shard_offsets[i] >= shard1.shard_offsets[i] + shard1.shard_sizes[i]:
+            return False
+
+    return True
+
+
+def _find_nd_overlapping_shards(
+    shards: list[ShardMetadata], sharded_dims: list[int]
+) -> tuple[int, int] | None:
+    """Find overlapping shards using sweep-line algorithm."""
+    if len(shards) <= 1:
+        return None
+
+    dims = len(sharded_dims)
+    if dims == 0:
+        return None
+
+    sweep_dim_idx = 0
+    if dims > 1:
+        max_size = 0
+        for i, dim in enumerate(sharded_dims):
+            dim_size = shards[0].shard_offsets[dim] + shards[0].shard_sizes[dim]
+            if dim_size > max_size:
+                max_size = dim_size
+                sweep_dim_idx = i
+    sweep_dim = sharded_dims[sweep_dim_idx]
+
+    sorted_indices = sorted(
+        range(len(shards)),
+        key=lambda idx: (
+            shards[idx].shard_offsets[sweep_dim],
+            *(shards[idx].shard_offsets[d] for d in sharded_dims if d != sweep_dim),
+        ),
+    )
+    active: list[tuple[int, int]] = []
+
+    for idx in sorted_indices:
+        current = shards[idx]
+        start = current.shard_offsets[sweep_dim]
+        end = start + current.shard_sizes[sweep_dim]
+
+        cutoff = bisect_right(active, (start, sys.maxsize))
+        if cutoff:
+            del active[:cutoff]
+
+        for _, other_idx in active:
+            other = shards[other_idx]
+
+            if _check_shard_metadata_pair_overlap(current, other):
+                return (other_idx, idx)
+        insort(active, (end, idx))
+    return None
+
+
+def _find_1d_overlapping_shards(
+    shards: list[ShardMetadata], dim: int
+) -> tuple[int, int] | None:
+    # (begin, end, index_in_shards). Begin and end are inclusive.
+    intervals = [
+        (s.shard_offsets[dim], s.shard_offsets[dim] + s.shard_sizes[dim] - 1, i)
+        for i, s in enumerate(shards)
+    ]
+    intervals.sort()
+    for i in range(len(shards) - 1):
+        if intervals[i][1] >= intervals[i + 1][0]:
+            return (intervals[i][2], intervals[i + 1][2])
+    return None
+
+
+def validate_non_overlapping_shards_metadata(shards: list[ShardMetadata]):
+    """
+    Ensures none of the shards overlap with each other.
+
+    Args:
+        shards(List[ShardMetadata]): List of :class:`ShardMetadata` objects representing
+            each shard.
+    Raises:
+        ``ValueError`` if there's overlap in any two shards.
+    """
+    if not shards or len(shards) == 1:
+        return
+
+    sharded_dims: list[int] = []
+    for dim in range(len(shards[0].shard_offsets)):
+        for i in range(1, len(shards)):
+            if (
+                shards[i].shard_offsets[dim] != shards[0].shard_offsets[dim]
+                or shards[i].shard_sizes[dim] != shards[0].shard_sizes[dim]
+            ):
+                sharded_dims.append(dim)
+                break
+
+    pair: tuple[int, int] | None = None
+    if len(sharded_dims) == 0:
+        # if shard is all zeros, we should consider as pass
+        all_zeros: bool = all(
+            # strictly limited all offsets to be 0 to pass
+            # could loose it later on
+            shard.shard_offsets == [0] * len(shards[0].shard_offsets)
+            and math.prod(shard.shard_sizes) == 0  # one dimension is 0
+            for shard in shards
+        )
+        if all_zeros:
+            return
+        # All shards are the same, all dims are not partitioned. Choose any 2.
+        pair = (0, 1)
+    elif len(sharded_dims) == 1:
+        # Shards are partitioned over only one dimension. Overlap can be found
+        # using a O(nlogn) overlapping interval algorithm.
+        pair = _find_1d_overlapping_shards(shards, sharded_dims[0])
+    else:
+        # Shards are partitioned over more than one dimension.
+        # Use sweep-line algorithm for O(n log n) complexity.
+        pair = _find_nd_overlapping_shards(shards, sharded_dims)
+
+    if pair:
+        raise ValueError(f"Shards {shards[pair[0]]} and {shards[pair[1]]} overlap")
+
+
+def check_tensor(shards_metadata, tensor_dims) -> None:
+    """
+    Checks if the shards_metadata is compatible with the provided tensor dims.
+
+    Args:
+        shards_metadata(List[ShardMetadata]): List of :class:`ShardMetadata`
+            objects representing each shard of the tensor.
+        tensor_dims(Sequence of int): Dimensions of tensor to verify
+    Raises:
+        ``ValueError`` if not compatible.
+    """
+
+    # If the tensor's volume matches the total volume of all shards and
+    # all shard boundaries are within tensor dims, we have a compatible
+    # sharding spec for this tensor. Note that we have already verified
+    # we don't have overlapping shards.
+    tensor_rank = len(tensor_dims)
+    shards_rank = len(shards_metadata[0].shard_offsets)
+    if tensor_rank != shards_rank:
+        raise ValueError(
+            f"Rank of tensor is {tensor_rank}, but shards rank is {shards_rank}"
+        )
+
+    total_shard_volume = 0
+    for shard in shards_metadata:
+        shard_volume = 1
+        for i, shard_length in enumerate(shard.shard_sizes):
+            shard_volume *= shard_length
+            if shard.shard_offsets[i] + shard.shard_sizes[i] > tensor_dims[i]:
+                raise ValueError(
+                    f"Shard offset {shard.shard_offsets[i]} and length "
+                    f"{shard.shard_sizes[i]} exceeds tensor dim: {tensor_dims[i]} for shard {shard}"
+                )
+        total_shard_volume += shard_volume
+
+    tensor_volume = 1
+    for size in tensor_dims:
+        tensor_volume *= size
+
+    if total_shard_volume != tensor_volume:
+        # TODO: Can we improve this error message to point out the gaps?
+        raise ValueError(
+            f"Total volume of shards: {total_shard_volume} "
+            f"does not match tensor volume: {tensor_volume}, in other words "
+            f"all the individual shards do not cover the entire tensor"
+        )
+
+
+def get_split_size(dim_size, chunks):
+    """
+    Computes the split size inline with ``torch.chunk``
+
+    Args:
+        dim_size(int): Size of the dimension being chunked.
+        chunks(int): Number of chunks to create for ``dim_size``.
+
+    Returns:
+        An int indicating the split size to use.
+    """
+    return (dim_size + chunks - 1) // chunks
+
+
+def get_chunked_dim_size(dim_size, split_size, idx):
+    """
+    Computes the dim size of the chunk for provided ``idx`` given ``dim_size``
+    and ``split_size``.
+
+    Args:
+        dim_size(int): Size of the dimension being chunked.
+        split_size(int): The chunk size for each chunk of ``dim_size``.
+        idx(int): The index of chunk whose dim size is being requested.
+
+    Returns:
+        An int indicating the dim size of the chunk.
+    """
+    return max(min(dim_size, split_size * (idx + 1)) - split_size * idx, 0)
+
+
+def get_chunk_sharding_params(sharding_dim_size, world_size, spec, rank):
+    """
+    Generate the start pos and offset length for the current rank for
+    chunk sharding.
+
+    Args:
+        sharding_dim_size(int): The dimension length which we shard on.
+        world_size(int): number of ranks.
+        spec (:class:`torch.distributed._shard.sharding_spec.ChunkShardingSpec`):
+            sharding spec.
+        rank(int): # of cuda process.
+
+    Returns:
+        start_pos(int): start position of sharded tensor on the given rank.
+        chunk_size(int): chunk size of sharded tensor on the given rank.
+    """
+    split_size = get_split_size(sharding_dim_size, world_size)
+    current_offsets = 0
+    start_pos = current_offsets
+    for idx, placement in enumerate(spec.placements):
+        chunk_size = get_chunked_dim_size(sharding_dim_size, split_size, idx)
+        if rank == placement.rank():
+            start_pos = current_offsets
+            break
+        current_offsets += chunk_size
+    return start_pos, chunk_size  # type: ignore[possibly-undefined]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharding_spec/api.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharding_spec/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..87a49abdb5c05dcfe3db1fdf734dc9f3bef3b4bf
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharding_spec/api.py
@@ -0,0 +1,264 @@
+# mypy: allow-untyped-defs
+import functools
+import operator
+from abc import ABC, abstractmethod
+from collections.abc import Callable
+from dataclasses import dataclass
+from typing import TYPE_CHECKING
+
+import torch
+import torch.distributed._shard.sharded_tensor.metadata as sharded_tensor_meta
+from torch.distributed._shard.metadata import ShardMetadata
+from torch.distributed._shard.op_registry_utils import _decorator_func
+
+from ._internals import (
+    check_tensor,
+    get_chunked_dim_size,
+    get_split_size,
+    validate_non_overlapping_shards_metadata,
+)
+
+
+if TYPE_CHECKING:
+    # Only include ShardedTensor when do type checking, exclude it
+    # from run-time to resolve circular dependency.
+    from torch.distributed._shard.sharded_tensor import ShardedTensor
+
+
+class PlacementSpec(ABC):  # noqa: B024
+    """
+    Base class representing the placement of an entity. Subclasses of this
+    class can be used to specify customized placements which might not be
+    covered by existing APIs.
+    """
+
+
+@dataclass
+class DevicePlacementSpec(PlacementSpec):
+    """
+    Associates placement of an entity with a single device.
+
+    Args:
+        device(:class:`torch.distributed._remote_device`): The device to place the entity on.
+    """
+
+    device: torch.distributed._remote_device
+
+    def __post_init__(self):
+        if not isinstance(self.device, torch.distributed._remote_device):
+            self.device = torch.distributed._remote_device(self.device)
+
+
+class ShardingSpec(ABC):
+    """
+    Base class representing sharding specifications.
+    """
+
+    @abstractmethod
+    def build_metadata(
+        self,
+        tensor_sizes: torch.Size,
+        tensor_properties: sharded_tensor_meta.TensorProperties,
+    ) -> sharded_tensor_meta.ShardedTensorMetadata:
+        """
+        Given a global tensor size, define how to shard a tensor like this shape
+        across ranks, return ShardedTensorMetadata
+        Args:
+            tensor_sizes (:class:`torch.Size`):
+                The tensor shape to shard on, a `torch.Size` object that represents the
+                tensor shape to be sharded according to the ShardingSpec.
+            tensor_properties(:class:`torch.distributed._shard.sharded_tensor.TensorProperties):
+                Tensor properties used to create a ShardedTensor.
+        Returns:
+            A :class:`ShardedTensorMetadata` object that encodes the information about
+            the layout of the ShardedTensor and its properties.
+        """
+
+    @abstractmethod
+    def shard(
+        self, tensor: torch.Tensor, src_rank: int = 0, process_group=None
+    ) -> "ShardedTensor":
+        """
+        Given a global tensor on src_rank, shard this tensor
+        across ranks within the process group, return a ShardedTensor.
+        Args:
+            tensor (:class:`torch.Tensor`): Tensor needs to be sharded.
+        Keyword args:
+            src_rank (int, optional): The source rank which is used as the ground truth of
+                the data for the parameter that would be sharded and scattered
+                across the rest of the ranks.
+                Default: 0.
+            process_group (ProcessGroup, optional): The process group to work on. If None,
+                the default process group will be used.
+        Returns:
+            A :class:`ShardedTensor` sharded from the given tensor.
+        """
+
+
+# Ops customized for a particular ShardingSpec.
+_CUSTOM_SHARDING_SPEC_OPS: dict[str, dict[Callable, Callable]] = {}
+
+
+def _has_custom_op(sharding_spec, op):
+    """
+    Returns whether or not the ShardingSpec has a custom op implementation.
+    """
+    class_name = type(sharding_spec).__qualname__
+    return (
+        class_name in _CUSTOM_SHARDING_SPEC_OPS
+        and op in _CUSTOM_SHARDING_SPEC_OPS[class_name]
+    )
+
+
+def _dispatch_custom_op(
+    sharding_spec, op: Callable, types, args, kwargs, process_group
+):
+    """
+    Calls the custom op for this ShardingSpec if it exists.
+    """
+    class_name = type(sharding_spec).__qualname__
+    if not _has_custom_op(sharding_spec, op):
+        raise RuntimeError(f"Custom op: {op} not registered for {class_name}")
+    func = _CUSTOM_SHARDING_SPEC_OPS[class_name][op]
+    return func(types, args, kwargs, process_group)
+
+
+def custom_sharding_spec_op(sharding_spec_class, func):
+    """
+    Decorator to allow custom registration of ops.
+    Args:
+        sharding_spec_class(type): The ShardingSpec for which we need to add this custom op.
+        func(Callable): The op to override (ex: torch.bmm)
+    """
+    class_name = sharding_spec_class.__qualname__
+    if class_name not in _CUSTOM_SHARDING_SPEC_OPS:
+        _CUSTOM_SHARDING_SPEC_OPS[class_name] = {}
+    return functools.partial(
+        _decorator_func, op=func, op_table=_CUSTOM_SHARDING_SPEC_OPS[class_name]
+    )
+
+
+@dataclass
+class EnumerableShardingSpec(ShardingSpec):
+    """
+    This is a type of PlacementSpec that allows users to specify a generic
+    sharding scheme by enumerating exactly how each shard is laid out.
+
+    Args:
+        shards(List[ShardMetadata]): List of :class:`ShardMetadata` objects representing
+            each shard. Note that none of the shards should overlap.
+    """
+
+    shards: list[ShardMetadata]
+
+    def __post_init__(self):
+        if len(self.shards) == 0:
+            raise ValueError(f"Empty shard list provided: {self.shards}")
+
+        # Validate each shard has same rank.
+        rank = -1
+        for shard in self.shards:
+            if rank != -1 and rank != len(shard.shard_offsets):
+                raise ValueError(
+                    f"Found inconsistent ranks for shards: {rank} and {len(shard.shard_offsets)}"
+                )
+            rank = len(shard.shard_offsets)
+
+        validate_non_overlapping_shards_metadata(self.shards)
+
+    def build_metadata(
+        self,
+        tensor_sizes: torch.Size,
+        tensor_properties: sharded_tensor_meta.TensorProperties,
+    ) -> sharded_tensor_meta.ShardedTensorMetadata:
+        # check if shards form a valid tensor
+        check_tensor(self.shards, tensor_sizes)
+        return sharded_tensor_meta.ShardedTensorMetadata(
+            self.shards, tensor_sizes, tensor_properties
+        )
+
+    def shard(
+        self, tensor: torch.Tensor, src_rank: int = 0, process_group=None
+    ) -> "ShardedTensor":
+        # TODO: figure out a generic and efficient way to scatter the shards for EnumerableShardingSpec
+        raise NotImplementedError("EnumerableShardingSpec.shard not implemented yet!")
+
+
+def _infer_sharding_spec_from_shards_metadata(shards_metadata):
+    """
+    Infer the sharding spec from the metadata of each shard of a ShardedTensor.
+    If the tensor is sharded only on one dimension, we can then verify whether it's
+    a ChunkShardingSpec or not. The way to verify it is to first get the total length
+    and perform a chunk sharding with the given placements to see if we can have the
+    same chunk size as the given shards_metadata. If not, we assume it's enum sharded.
+
+    Args:
+        shards_metadata (List[ShardMetadata]): List of Metadata of local shards.
+
+    Returns:
+        A :class:`torch.distributed._shard.sharding_spec.ShardingSpec` object of sharding
+            spec for one sharded tensor.
+    """
+    placements = []
+    chunk_sharding_dim = None
+    chunk_offset_list = []
+    shard_size_list = []
+    shard_offset_list = []
+    # collect local shard metadatas from the global sharded_tensor_metadata
+    for shard_metadata in shards_metadata:  # type: ignore[attr-defined]
+        placements.append(shard_metadata.placement)
+        local_offsets = shard_metadata.shard_offsets
+        chunk_offset_list.append(sum(local_offsets))
+        shard_size_list.append(shard_metadata.shard_sizes)
+        shard_offset_list.append(shard_metadata.shard_offsets)
+        shard_dims = [idx for idx, e in enumerate(local_offsets) if e != 0]
+        # If the offset is [0, 0, ..., 0] (all zeros),
+        # we cannot decide whether how the tensor is sharded.
+        if len(shard_dims) == 0:
+            continue
+        # If the offset is [0, N, .,0, M, 0, .., 0],
+        # we are sure it's sharded by more than one dimension.
+        if len(shard_dims) != 1:
+            chunk_sharding_dim = None
+            break
+        # If the offset is [0, 0, .,0, M, 0, .., 0], aka, it's sharded by just
+        # one dimension, we need to make sure all ranks share the same dimension.
+        if not chunk_sharding_dim:
+            chunk_sharding_dim = shard_dims[0]
+        elif chunk_sharding_dim != shard_dims[0]:
+            chunk_sharding_dim = None
+            break
+
+    if chunk_sharding_dim is not None:
+        # Ensure we infer the correct placement order from offsets
+        placements = [
+            x
+            for _, x in sorted(
+                zip(chunk_offset_list, placements), key=operator.itemgetter(0)
+            )
+        ]
+
+        from .chunk_sharding_spec import ChunkShardingSpec
+
+        chunk_spec = ChunkShardingSpec(
+            dim=chunk_sharding_dim,
+            placements=placements,
+        )
+
+        shard_sizes = sorted([x[chunk_sharding_dim] for x in shard_size_list])
+        shard_total_length = sum(shard_sizes)
+        shard_offsets = sorted([x[chunk_sharding_dim] for x in shard_offset_list])
+
+        chunks = len(placements)
+        split_size = get_split_size(shard_total_length, chunks)
+        chunk_shard_sizes = sorted(
+            [
+                get_chunked_dim_size(shard_total_length, split_size, idx)
+                for idx in range(chunks)
+            ]
+        )
+        # Should match ChunkShardingSpec offsets calculation
+        chunk_shard_offsets = [split_size * idx for idx in range(chunks)]
+        if shard_sizes == chunk_shard_sizes and shard_offsets == chunk_shard_offsets:
+            return chunk_spec
+    return EnumerableShardingSpec(shards_metadata)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharding_spec/chunk_sharding_spec.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharding_spec/chunk_sharding_spec.py
new file mode 100644
index 0000000000000000000000000000000000000000..4d7b11b7c16c567b0b71fc6a0858dc58b7977ebf
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharding_spec/chunk_sharding_spec.py
@@ -0,0 +1,229 @@
+# mypy: allow-untyped-defs
+from dataclasses import dataclass
+from typing import cast, TYPE_CHECKING
+
+import torch
+import torch.distributed as dist
+import torch.distributed._shard.sharded_tensor.metadata as sharded_tensor_meta
+import torch.distributed.distributed_c10d as distributed_c10d
+from torch.distributed._shard._utils import narrow_tensor
+from torch.distributed._shard.metadata import ShardMetadata
+from torch.distributed._shard.sharded_tensor.shard import Shard
+from torch.distributed._shard.sharded_tensor.utils import (
+    _parse_and_validate_remote_device,
+)
+
+from ._internals import get_chunked_dim_size, get_split_size
+from .api import ShardingSpec
+
+
+if TYPE_CHECKING:
+    # Only include ShardedTensor when do type checking, exclude it
+    # from run-time to resolve circular dependency.
+    from torch.distributed._shard.sharded_tensor import ShardedTensor
+
+
+@dataclass
+class ChunkShardingSpec(ShardingSpec):
+    """
+    This is a type of PlacementSpec that defines the placement as being sharded
+    across multiple devices. In particular, it represents sharding a Tensor
+    along a single dimension into equal chunks (similar to :meth:`torch.chunk`).
+
+    The semantics of how a tensor is partitioned is inline with
+    :meth:`torch.chunk`, where ``dim`` in torch.chunk corresponds to the
+    specified ``dim`` and ``chunks`` in torch.chunk is the number of elements
+    in the placement specified.
+
+    Args:
+        dim (int or str):
+            The dimension to shard on, could be an integer representing the
+            dimension or a string in case of named tensors where dimensions are
+            named. Note that named tensor support is not added yet.
+        placement(List[Union[_remote_device, str]]):
+            Specifies the placement of each shard of the Tensor. The size of
+            the list represents the number of shards to be created. This could
+            be a list of
+            :class:`torch.distributed._remote_device`'s. This list
+            could also contain a string which represents remote
+            device as accepted by
+            :class:`torch.distributed._remote_device`
+    """
+
+    ShardingDim = int | str
+
+    dim: ShardingDim
+    placements: list[torch.distributed._remote_device | str]
+
+    def __post_init__(self):
+        self._verify_dim(self.dim)
+        for i, remote_device in enumerate(self.placements):
+            if not isinstance(remote_device, torch.distributed._remote_device):
+                self.placements[i] = torch.distributed._remote_device(remote_device)
+
+    @staticmethod
+    def _verify_dim(dim):
+        # Validate the sharding spec.
+        # TODO: support named dimension
+        if isinstance(dim, str):
+            raise NotImplementedError(
+                "ChunkShardingSpec does not support named dimension yet!"
+            )
+
+        if not isinstance(dim, int):
+            raise ValueError(f"Sharding dim needs to be an integer, found: {dim}")
+
+    def build_metadata(
+        self,
+        tensor_sizes: torch.Size,
+        tensor_properties: sharded_tensor_meta.TensorProperties,
+    ) -> sharded_tensor_meta.ShardedTensorMetadata:
+        tensor_num_dim = len(tensor_sizes)
+
+        self._verify_dim(self.dim)
+        if self.dim >= tensor_num_dim or self.dim < -tensor_num_dim:  # type: ignore[operator]
+            raise ValueError(f"Invalid sharding dim: {self.dim}")
+
+        shards_metadata = []
+        sharding_dim_size = tensor_sizes[self.dim]  # type: ignore[index]
+        chunks = len(self.placements)
+        split_size = get_split_size(sharding_dim_size, chunks)
+        for idx, placement in enumerate(self.placements):
+            # generate ShardMetadata for each placement device
+            chunked_dim_size = get_chunked_dim_size(sharding_dim_size, split_size, idx)
+            shard_size = list(tensor_sizes)
+            current_offsets = [0] * tensor_num_dim
+            current_offsets[self.dim] = split_size * idx  # type: ignore[index]
+            shard_size[self.dim] = chunked_dim_size  # type: ignore[index]
+
+            shard_metadata = ShardMetadata(
+                shard_offsets=current_offsets,
+                shard_sizes=shard_size,
+                placement=placement,
+            )
+            shards_metadata.append(shard_metadata)
+
+        return sharded_tensor_meta.ShardedTensorMetadata(
+            shards_metadata, tensor_sizes, tensor_properties
+        )
+
+    def shard(
+        self, tensor: torch.Tensor, src_rank: int = 0, process_group=None
+    ) -> "ShardedTensor":
+        """
+        Args:
+            src_rank: group rank relative to ``process_group``
+
+            N.B. If ``process_group`` is None, ``src_rank`` is a global rank.
+        """
+        # relative imports to avoid circular dependency
+        from torch.distributed._shard.sharded_tensor import ShardedTensor
+
+        tensor_properties = sharded_tensor_meta.TensorProperties(
+            dtype=tensor.dtype,
+            layout=tensor.layout,
+            requires_grad=tensor.requires_grad,
+            memory_format=torch.contiguous_format,
+            pin_memory=tensor.is_pinned(),
+        )
+        current_rank = dist.get_rank(process_group)
+        current_global_rank = dist.get_rank()
+        tensor_meta = self.build_metadata(tensor.size(), tensor_properties)
+        local_shards = []
+        local_tensor = None
+        local_metadata = None
+
+        tensors_to_scatter = cast(
+            list[torch.Tensor | None],
+            [None] * dist.get_world_size(process_group),
+        )
+
+        sharding_dim_size = tensor.size()[self.dim]  # type: ignore[index]
+        chunks = len(self.placements)
+        split_size = get_split_size(sharding_dim_size, chunks)
+        scatter_shape = list(tensor.size())
+        scatter_shape[self.dim] = split_size  # type: ignore[index]
+
+        for shard_meta in tensor_meta.shards_metadata:
+            remote_global_rank, device = _parse_and_validate_remote_device(
+                process_group, shard_meta.placement
+            )
+            if current_rank == src_rank:
+                # Reshape to get shard for this rank and we don't want autograd
+                # recording here for the narrow op and 'local_shard' should be a
+                # leaf variable in the autograd graph.
+                narrowed_tensor = narrow_tensor(tensor, shard_meta)
+                if shard_meta.shard_sizes[self.dim] < split_size:  # type: ignore[index]
+                    # for the last shard that might be smaller to other shards
+                    # resize the narrowed tensor to the same size and use it for
+                    # the scatter collective as dist.scatter requires same size
+                    # inputs on every rank
+                    tensor_to_scatter = (
+                        narrowed_tensor.detach().clone().resize_(scatter_shape)
+                    )
+                else:
+                    tensor_to_scatter = narrowed_tensor.detach().clone(
+                        memory_format=torch.contiguous_format
+                    )
+
+                tensors_to_scatter[
+                    # pyrefly: ignore [bad-argument-type]
+                    dist.get_group_rank(process_group, remote_global_rank)
+                ] = tensor_to_scatter
+
+            if current_global_rank == remote_global_rank:
+                local_tensor = torch.empty(
+                    scatter_shape,
+                    dtype=tensor.dtype,
+                    layout=tensor.layout,
+                    device=device,
+                )
+                local_metadata = shard_meta
+
+        # each rank should have local_tensor and local_metadata initialized if we build
+        # the metadata list in a correct way.
+        assert local_tensor is not None
+        assert local_metadata is not None
+
+        # Scatter the shards to all ranks in the pg
+        # scatter takes the global rank as ``src``
+        src_for_scatter = src_rank
+        if (
+            process_group is not None
+            and process_group is not distributed_c10d._get_default_group()
+        ):
+            src_for_scatter = distributed_c10d.get_global_rank(
+                process_group, src_for_scatter
+            )
+
+        tensors_to_scatter_: list[torch.Tensor] | None = None
+        if current_rank == src_rank:
+            tensors_to_scatter_ = []
+            for t in tensors_to_scatter:
+                assert isinstance(t, torch.Tensor)
+                tensors_to_scatter_.append(t)
+
+        dist.scatter(
+            local_tensor,
+            scatter_list=tensors_to_scatter_,
+            src=src_for_scatter,
+            group=process_group,
+        )
+
+        if list(local_tensor.size()) != local_metadata.shard_sizes:
+            # detach again after receiving to ensure local shards remain a leaf node
+            local_tensor = local_tensor.resize_(local_metadata.shard_sizes).detach()
+
+        # Sync requires_grad to local_shard.
+        local_tensor.requires_grad = tensor.requires_grad
+
+        local_shards.append(Shard(tensor=local_tensor, metadata=local_metadata))
+
+        st = ShardedTensor._init_from_local_shards_and_global_metadata(
+            local_shards, tensor_meta, process_group=process_group
+        )
+
+        # Manually set sharding_spec
+        st._sharding_spec = self
+
+        return st
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharding_spec/chunk_sharding_spec_ops/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharding_spec/chunk_sharding_spec_ops/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharding_spec/chunk_sharding_spec_ops/_common.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharding_spec/chunk_sharding_spec_ops/_common.py
new file mode 100644
index 0000000000000000000000000000000000000000..3a8a05fe79d19d2dc67e6ff535ae419e255192c1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharding_spec/chunk_sharding_spec_ops/_common.py
@@ -0,0 +1,350 @@
+# mypy: allow-untyped-defs
+
+import torch
+import torch.distributed as dist
+from torch.distributed._shard.sharded_tensor import ShardedTensor
+from torch.distributed._shard.sharded_tensor._ops._common import _sharded_op_common
+from torch.distributed._shard.sharding_spec import ChunkShardingSpec
+from torch.distributed._shard.sharding_spec._internals import (
+    get_chunk_sharding_params,
+    get_chunked_dim_size,
+    get_split_size,
+)
+from torch.distributed._shard.sharding_spec.api import custom_sharding_spec_op
+from torch.distributed.nn.functional import (
+    _all_gather_base,
+    all_reduce,
+    all_to_all_single,
+)
+
+
+def _chunk_sharding_spec_check(spec, op):
+    """
+    For the given op implementation check if the sharding spec is ChunkShardingSpec.
+    """
+    if not isinstance(spec, ChunkShardingSpec):
+        raise NotImplementedError(
+            f"Only ChunkShardingSpec supported for '{op.__name__}'."
+        )
+
+
+def _register_sharded_op_on_local_tensor(
+    op, early_stop_func=None, extra_check=None, customized_func=None
+):
+    """
+    Handles ``__torch_function__`` dispatch for ops which are performed on
+    the single local tensor of the sharded tensor such as op like
+    ``torch.nn.functional.softmax`` or ``torch.Tensor.view``.
+
+    For more complicated ops, a customized func can be used to generate
+    the new local tensor, sharding spec and sharded tensor size.
+
+    Args:
+        op: The op to be registered and applied to all shards of the st.
+        early_stop_func (Callable, optional): the func for early stop.
+            Default: if ``None``, no early stop.
+        extra_check (Callable, optional): the func for extra condition check.
+            Default: if ``None``, no extra check.
+        customized_func (Callable, optional): the func for customized logic
+            to generate the new local tensor, sharding spec and sharded tensor size.
+            Default: if ``None``, we simply lower to the real op call with
+                the single local tensor of the st.
+
+    Return:
+        func (Callable): registered implementation for sharded op for
+        ``__torch_function__`` dispatch.
+    """
+
+    @custom_sharding_spec_op(ChunkShardingSpec, op)
+    @_sharded_op_common(op, early_stop_func, extra_check)
+    def sharded_tensor_op_on_local_tensor(types, args=(), kwargs=None, pg=None):
+        # pyrefly: ignore [index-error]
+        st = args[0]
+        sharding_spec = st.sharding_spec()
+        if len(st.local_shards()) != 1:
+            raise TypeError(
+                f"torch function '{op.__name__}', with args: {args} and "
+                f"kwargs: {kwargs} only supported for single local tensor!"
+            )
+        st_size = st.size()
+        if customized_func:
+            local_tensor, sharding_spec, st_size = customized_func(args, kwargs, pg)
+        else:
+            args = (st.local_tensor(), *args[1:])
+            local_tensor = op(*args, **kwargs)
+        return ShardedTensor._init_from_local_tensor(
+            local_tensor.contiguous(),
+            sharding_spec,
+            st_size,  # type: ignore[arg-type]
+            process_group=pg,
+            init_rrefs=st._init_rrefs,
+        )
+
+
+def _handle_col_wise_sharding_base(
+    op_func,
+    col_dim,
+    input,
+    world_size,
+    weight,
+    local_shard,
+    pg,
+    gathered_inputs,
+    mode=None,
+    gathered_per_sample_weights=None,
+    gathered_offsets=None,
+    padding_idx=None,
+):
+    """
+    For col-wise sharding of weight, lots of logic are common.
+    So we extract the common logic and put in this function:
+    Step 1. To get input from each rank and
+    Step 2. To perform the op on the concatenated tensor.
+    Step 3. To distribute results to each rank with col rearrangement.
+    Step 4. To concatenate all results from all ranks.
+
+    Args:
+        op_func: operator which is applied to the input tensor.
+        col_dim: dim of result tensor after the operation.
+        input: tensor to be applied op on.
+        world_size: number of ranks.
+        weight: sharded weight tensor.
+        local_shard: col-wise sharded weight tensor.
+        pg: process group.
+        gathered_inputs: list of inputs from all ranks. If specified, we
+            don't need to communicate with each rank any more.
+        mode: aggregation mode of EmbeddingBag.
+        gathered_per_sample_weights: per_sample_weights across all ranks.
+        gathered_offsets: offsets across all ranks.
+        padding_idx: If specified, the entries at padding_idx do
+            not contribute to the gradient; therefore, the embedding
+            vector at padding_idx is not updated during training,
+            i.e. it remains as a fixed "pad".
+            Note that the embedding vector at padding_idx is
+            excluded from the reduction.
+
+    Return: final result of input being applied with the op.
+    """
+    # run the operator's function for all the inputs.
+    results = []
+    for i, inp in enumerate(gathered_inputs):
+        if op_func is torch.nn.functional.embedding_bag:
+            result = op_func(
+                inp,
+                local_shard,
+                offsets=gathered_offsets[i] if gathered_offsets is not None else None,
+                # pyrefly: ignore [bad-argument-type]
+                mode=mode,
+                per_sample_weights=gathered_per_sample_weights[i]
+                if gathered_per_sample_weights is not None
+                else None,
+                padding_idx=padding_idx,
+            )
+        elif op_func is torch.nn.functional.embedding:
+            result = op_func(
+                inp,
+                local_shard,
+                padding_idx=padding_idx,
+            )
+        else:
+            result = op_func(inp, local_shard)
+        results.append(torch.transpose(result, 0, col_dim))
+
+    # Distribute results to each rank with col rearrangement.
+    output = _result_distribute_with_col_rearrange(
+        results, input, world_size, weight, pg
+    )
+
+    # transpose the output and return result.
+    return torch.transpose(output, 0, col_dim)
+
+
+def _result_distribute_with_col_rearrange(results, input, world_size, weight, pg):
+    """
+    For col-wise sharding of weight, we need to distribute
+    results to each rank. We do them in this function.
+    Note that, if the index in the Sharding Spec is not equal to
+    the rank number, we need to do the rearrangement based on the
+    order given by the Sharding Spec (placement).
+
+    Args:
+        results: results from ops applied to inputs from all ranks.
+            We need to distribute them back to their original ranks.
+        input: tensor to be applied op to.
+        world_size: number of ranks.
+        weight: sharded weight tensor.
+        pg: process group.
+
+    Return: column rearranged result.
+    """
+    # Process results and outputs for all2all.
+    sharding_dim = weight._sharding_spec.dim
+    sharding_dim_size = weight.size(sharding_dim)
+    dims = list(results[0].size())
+    dims[0] = sharding_dim_size
+    combined_results = torch.cat(results)
+    output = torch.empty(
+        *dims, device=combined_results.device, dtype=combined_results.dtype
+    )
+
+    # Compute output splits
+    split_size = get_split_size(sharding_dim_size, world_size)
+    output_split_sizes = [0] * world_size
+    for idx, placement in enumerate(weight._sharding_spec.placements):
+        output_split_sizes[placement.rank()] = get_chunked_dim_size(
+            sharding_dim_size, split_size, idx
+        )
+
+    # distribute the outputs using all2all.
+    output = all_to_all_single(
+        output, combined_results, output_split_sizes=output_split_sizes, group=pg
+    )
+
+    # Check if we need to rearrange columns appropriately for output.
+    rearrange_columns = any(
+        idx != placement.rank()
+        for idx, placement in enumerate(weight._sharding_spec.placements)
+    )
+    if not rearrange_columns:
+        return output
+
+    indices = []
+    for placement in weight._sharding_spec.placements:
+        dim_size = output_split_sizes[placement.rank()]
+        start = sum(
+            split_size if i < placement.rank() else 0
+            for i, split_size in enumerate(output_split_sizes)
+        )
+        indices += list(range(start, start + dim_size))
+
+    return output.index_select(0, torch.tensor(indices, device=output.device))
+
+
+def _handle_max_norm_col_wise(
+    max_norm,
+    norm_type,
+    local_shard,
+    input,
+    world_size,
+    gathered_inputs,
+    pg,
+):
+    """
+    For col-wise sharding of weight, we need to aggregate the
+    norm across all ranks before we can perform the proper re-norm.
+    Note that, the max_norm logic is only applied to the embedding
+    indices that are looked up and not the whole shard.
+
+    Args:
+        max_norm: If given, each embedding vector with norm larger
+            than max_norm is renormalized to have norm max_norm.
+            Note: this will modify weight in-place.
+        norm_type: The p in the p-norm to compute for the max_norm option.
+        local_shard: col-wise shared local weight used for lookup.
+        input: tensor to be applied op to.
+        world_size: number of ranks.
+        gathered_inputs: list of inputs from all ranks.
+        pg: process group.
+
+    Return:
+        local_shard_norm_renormed: local_shard re-normed to max_norm if the norm is larger
+            than it.
+
+    """
+    norm_type = norm_type if norm_type is not None else 2.0
+    unique_inp = torch.unique(torch.cat(gathered_inputs))
+    local_shard_sum = torch.sum(
+        torch.pow(torch.abs(local_shard), norm_type), dim=1, dtype=local_shard.dtype
+    )
+    # For col-wise sharding, we need to first aggregate the powered sum
+    # from each rank first and then calculate the norm.
+    local_shard_sum = all_reduce(local_shard_sum, group=pg)
+    local_shard_norm = torch.pow(local_shard_sum, 1.0 / norm_type)
+    max_norm_tensor = torch.full(
+        (local_shard.size(0),),
+        float("inf"),
+        dtype=local_shard.dtype,
+        device=input.device,
+    )
+    max_norm_tensor[unique_inp] = max_norm
+    local_shard_t = local_shard.t().contiguous()
+    normalized_tensor = torch.where(
+        local_shard_norm > max_norm_tensor, max_norm_tensor, local_shard_norm
+    )
+    # Make sure divisor is not zero.
+    local_shard_norm[local_shard_norm == 0.0] = 1.0
+    local_shard_norm_renormed = (
+        torch.div(torch.mul(local_shard_t, normalized_tensor), local_shard_norm)
+        .t()
+        .contiguous()
+    )
+    return local_shard_norm_renormed
+
+
+def _all_gather_base_input(input, pg):
+    """
+    Use _all_gather_base to get a concatenated input from each rank.
+
+    Args:
+        input: tensor to be applied op on.
+        pg: process group.
+
+    Returns:
+        gathered_inputs: input gathered from each rank and concat by dim 0.
+    """
+    # allgather the inputs first.
+    gather_inp_size = list(input.size())
+    gather_inp_size[0] = input.size(0) * dist.get_world_size(pg)
+    gather_inp = torch.empty(gather_inp_size, device=input.device, dtype=input.dtype)
+    return _all_gather_base(gather_inp, input, group=pg)
+
+
+def _handle_row_wise_mask(gather_inp, padding_idx, weight, world_size, rank):
+    """
+    Mask the input for embedding look-up for IDs which are not stored
+    on the current rank. This function also adjust the ``padding_idx``
+    so that it is only used on the rank where the corresponding row is
+    stored.
+
+    Note that, with ``max_norm`` flag on, only weights of rows being
+    looked up will be re-normed. So we need an extra row for masked ID
+    so that it does not affect the final result and ``max_norm``.
+
+    Args:
+        gather_inp: tensor to be applied op on gathered from all ranks.
+        padding_idx: If specified, the entries at padding_idx do
+            not contribute to the gradient; therefore, the embedding
+            vector at padding_idx is not updated during training,
+            i.e. it remains as a fixed "pad".
+            Note that the embedding vector at padding_idx is
+            excluded from the reduction.
+        weight: weight tensor of Embedding look-up table.
+        world_size: number of ranks.
+        rank: # of cuda process.
+
+    Returns:
+        lookup_input: Tensor of masked input.
+        padding_idx: adjusted padding_idx.
+        padding_row: The extra row we used during lookup so that
+            looking up does not affect ``max_norm``.
+    """
+    (start_pos, chunk_size) = get_chunk_sharding_params(
+        weight.size(0), world_size, weight._sharding_spec, rank
+    )
+    mask = (gather_inp < start_pos) | (gather_inp >= start_pos + chunk_size)
+    lookup_input = gather_inp.clone() - start_pos
+    lookup_input[mask] = chunk_size
+    if (
+        padding_idx is not None
+        and padding_idx >= start_pos
+        and padding_idx < (start_pos + chunk_size)
+    ):
+        padding_idx = padding_idx - start_pos
+    else:
+        padding_idx = None
+
+    # When max_norm is set, it will only re-norm the row being looked up.
+    padding_row = torch.zeros(
+        1, weight.size(1), device=gather_inp.device, dtype=weight.dtype
+    )
+    return lookup_input, padding_idx, padding_row
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharding_spec/chunk_sharding_spec_ops/embedding.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharding_spec/chunk_sharding_spec_ops/embedding.py
new file mode 100644
index 0000000000000000000000000000000000000000..117aed79520d9ad78c10bdd2310fb6b032c2a024
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharding_spec/chunk_sharding_spec_ops/embedding.py
@@ -0,0 +1,294 @@
+# mypy: allow-untyped-defs
+
+import torch
+import torch.distributed as dist
+from torch.distributed._shard.sharded_tensor import ShardedTensor
+from torch.distributed._shard.sharding_spec import ChunkShardingSpec
+from torch.distributed._shard.sharding_spec.api import custom_sharding_spec_op
+from torch.distributed.nn.functional import all_gather, reduce_scatter
+
+from ._common import (
+    _all_gather_base_input,
+    _handle_col_wise_sharding_base,
+    _handle_max_norm_col_wise,
+    _handle_row_wise_mask,
+)
+
+
+@custom_sharding_spec_op(ChunkShardingSpec, torch.nn.functional.embedding)
+def sharded_embedding(types, args, kwargs, pg):
+    """
+    Handles ``__torch_function__`` dispatch for ``torch.nn.functional.embedding``.
+    This method computes a sharded embedding lookup and has the following limitations:
+
+    1. Supports only sharding of ``weight``.
+    2. Supports only ``ChunkShardingSpec``.
+    3. Supports only a single local shard per rank.
+    4. Supports all specs except for scale_grad_by_freq, sparse, etc.
+
+    Based on the dimension that the weight is sharded on, there are two
+    algorithms:
+
+    ROWWISE SHARDING
+    ================
+    For row-wise sharding the weight is sharded on dimension 0.
+
+    The overall algorithm can be best explained with an example. Let's assume
+    the dims for input are (4 x 6) and W are (10 x 17) and W is sharded across
+    4 GPUs creating 3 shard of (3 x 17) and 1 shard of (1 x 17).
+    The algorithm is as follows:
+
+    1. First the input is all gathered to all ranks, since this is SPMD and
+       input is actually sharded across all ranks. The inputs then become a
+       4 (4 x 6) tensor on each rank. For example if the given input is
+       tensor([[6, 5, 2, 9, 6, 3],
+               [3, 1, 2, 4, 7, 6],
+               [4, 0, 4, 9, 8, 9],
+               [8, 6, 6, 4, 6, 1]])
+       on rank 0.
+       Then on every rank, we will have this tensor.
+       If input itself is already replicated, no all-gather will be done.
+    2. Next, we mask the ID which are not stored on that rank.
+       For example on rank 0, we store ID [0, 1, 2]. We only keep the ID
+       inside the set of numbers. The rest of them will be masked to an extra row.
+       The masked matrix will be used for embedding look up and is like:
+       tensor([[4, 4, 2, 4, 4, 4],
+               [4, 1, 2, 4, 4, 4],
+               [4, 0, 4, 4, 4, 4],
+               [4, 4, 4, 4, 4, 1]])
+       The reason of having an extra row (aka, number 4 in the example) is
+       because when max_norm is specified only weight which has looked will
+       be re-normed so mask IDs whose embeddings are not stored in current
+       rank will to an extra row will ensure max_norm still works as expected.
+    3. If max_norm is specified, the extra row guarantees that the mask ID will
+       not affect the behavior of weigh re-norm.
+
+    COLWISE SHARDING
+    ================
+    For col-wise sharding the weight is sharded on dimension 1.
+
+    The overall algorithm can be best explained with an example. Let's assume
+    the dims for input are (4 x 6) and W are (16 x 17) and W is sharded across
+    4 GPUs creating 3 shards of (16 x 5) and 1 shard of (16 x 2).
+    The algorithm is as follows:
+
+    1. First the input is broadcasted to all ranks, since this is SPMD we
+       actually do an all_gather for all the inputs resulting in 4 (4 x 6)
+       inputs on each rank.
+    2. Next we perform local embedding lookup operation by apply each
+       input (4 x 6) with the local shard (16 x 5) ((16 x 2) for the last).
+       This results in 4 (5 x 6 x 4) ((2 x 6 x 4) for the last) matrices
+       on each rank. We transpose dim 0 and dim 2.
+    3. Next, we concat these 4 matrices and perform an all2all to share the
+       appropriate (5 x 6 x 4) or (2 x 6 x 4) matrices to each rank.
+    4. Now, each rank receives a (17 x 6 x 4) matrix which is basically the
+       size of the result we need.
+    5. If placements are not in order any appropriate rearrangement of columns
+       are done for the (17 x 6 x 4) matrix and finally we transpose the
+       dim 0 and dim 2 again.
+    6. If max_norm is specified, we manually sum up the norm and renorm. Because
+       the renorm must be in place, we need to override the local_shard to mimic
+       this behavior.
+    """
+    # Validate input params
+    _validate_embedding_param(args, kwargs)
+
+    input = args[0]
+    weight = args[1]
+    max_norm = kwargs.get("max_norm")
+    norm_type = kwargs.get("norm_type")
+    padding_idx = kwargs.get("padding_idx")
+
+    local_shard = weight.local_tensor().contiguous()
+    sharding_dim = weight._sharding_spec.dim
+    world_size = dist.get_world_size(pg)
+    rank = dist.get_rank(pg)
+
+    if sharding_dim == 1:
+        output, local_shard = _handle_col_wise_sharding(
+            input, world_size, weight, local_shard, max_norm, norm_type, padding_idx, pg
+        )
+        weight.local_shards()[0].tensor = local_shard
+        return output
+    elif sharding_dim == 0:
+        return _handle_row_wise_sharding(
+            input,
+            world_size,
+            weight,
+            local_shard,
+            max_norm,
+            norm_type,
+            padding_idx,
+            rank,
+            pg,
+        )
+    else:
+        raise RuntimeError(
+            f"nn.Embedding weight sharded on dim {sharding_dim} not supported!"
+        )
+
+
+def _validate_embedding_param(args, kwargs):
+    """
+    Validate input params of sharded embedding op.
+
+    Args:
+        input: list of ID used for lookup.
+        weight: sharded weight tensor.
+        kwargs: same as normal Embedding.
+
+    Return: None.
+    """
+
+    input = args[0]
+    weight = args[1]
+    max_norm = kwargs.get("max_norm")
+    scale_grad_by_freq = kwargs.get("scale_grad_by_freq")
+    sparse = kwargs.get("sparse")
+
+    # Validate types
+    if not isinstance(input, torch.Tensor):
+        raise TypeError("input need to be torch.Tensor")
+    if not isinstance(weight, ShardedTensor):
+        raise TypeError("weight needs to be ShardedTensor")
+    weight_size = weight.size()
+    if len(weight_size) != 2:
+        raise ValueError("Weight needs to have exactly 2 dims")
+    if int(torch.min(input).item()) < 0:
+        raise ValueError(
+            "Index out of range in Input %d %d",
+            int(torch.min(input).item()),
+            weight_size[1],
+        )
+    if int(torch.max(input).item()) >= weight_size[0]:
+        raise ValueError(
+            "Index out of range in Input %d %d",
+            int(torch.max(input).item()),
+            weight_size[1],
+        )
+    if scale_grad_by_freq:
+        raise RuntimeError(
+            'nn.Embedding weight sharded with flag on "scale_grad_by_freq" not supported!'
+        )
+    if sparse:
+        raise RuntimeError(
+            'nn.Embedding weight sharded with flag on "sparse" not supported!'
+        )
+    if max_norm and max_norm <= 0.0:
+        raise ValueError('"max_norm" must be larger than zero!')
+
+    if not isinstance(weight._sharding_spec, ChunkShardingSpec):
+        raise ValueError("Only ChunkShardingSpec supported for ShardedTensor ops!")
+    if len(weight.local_shards()) != 1:
+        raise ValueError("Only one local shard supported!")
+
+
+def _handle_col_wise_sharding(
+    input, world_size, weight, local_shard, max_norm, norm_type, padding_idx, pg
+):
+    """
+    Entry-point function to handle the logic of col-wise sharding of weight
+    for embedding. (Detailed explanations of the logic can be found in
+    the comment for sharded_embedding.)
+
+    Args:
+        input: list of ID used for lookup and aggregation.
+        world_size: number of ranks.
+        weight: sharded weight tensor.
+        local_shard: col-wise shared local weight used for lookup.
+        max_norm: If given, each embedding vector with norm larger
+            than max_norm is renormalized to have norm max_norm.
+            Note: this will modify weight in-place.
+        norm_type: The p in the p-norm to compute for the max_norm option.
+        padding_idx: If specified, the entries at padding_idx do
+            not contribute to the gradient; therefore, the embedding
+            vector at padding_idx is not updated during training,
+            i.e. it remains as a fixed "pad".
+        pg: process group.
+
+    Returns: final result of lookup.
+    """
+    # allgather the inputs first for non Replicated Tensor.
+    gathered_inputs = all_gather(input, group=pg)
+
+    if max_norm is not None:
+        # max_norm changes the weight in-place
+        local_shard = _handle_max_norm_col_wise(
+            max_norm, norm_type, local_shard, input, world_size, gathered_inputs, pg
+        )
+
+    output = _handle_col_wise_sharding_base(
+        torch.nn.functional.embedding,
+        len(input.size()),
+        input,
+        world_size,
+        weight,
+        local_shard,
+        pg,
+        gathered_inputs,
+        padding_idx=padding_idx,
+    )
+    return (output, local_shard)
+
+
+def _handle_row_wise_sharding(
+    input, world_size, weight, local_shard, max_norm, norm_type, padding_idx, rank, pg
+):
+    """
+    Entry-point function to handle the logic of row-wise sharding of weight
+    for embedding. (Detailed explanations of the logic can be found in
+    the comment for sharded_embedding.)
+
+    Args:
+        input: list of ID used for lookup and aggregation.
+        world_size: number of ranks.
+        weight: sharded weight tensor.
+        local_shard: row-wise shared local weight used for lookup.
+        max_norm: If given, each embedding vector with norm larger
+            than max_norm is renormalized to have norm max_norm.
+            Note: this will modify weight in-place.
+        norm_type: The p in the p-norm to compute for the max_norm option.
+        padding_idx: If specified, the entries at padding_idx do
+            not contribute to the gradient; therefore, the embedding
+            vector at padding_idx is not updated during training,
+            i.e. it remains as a fixed "pad".
+        rank: # of cuda process.
+        pg: process group.
+
+    Returns: final result of lookup.
+    """
+    # allgather the inputs first for non Replicated Tensor.
+    gather_inp = _all_gather_base_input(input, pg)
+
+    # Mask the input according to sharding spec.
+    lookup_input, padding_idx, padding_row = _handle_row_wise_mask(
+        gather_inp, padding_idx, weight, world_size, rank
+    )
+
+    # When input is a large tensor, the value of weight is changed.
+    # This is a walk-around for now. GH issue: #81717
+    if max_norm is not None:
+        torch.nn.functional.embedding(
+            torch.unique(lookup_input)[:-1],
+            local_shard,
+            padding_idx=padding_idx,
+            max_norm=max_norm,
+            norm_type=norm_type,
+        )
+        max_norm = None
+
+    local_input_embeddings = torch.nn.functional.embedding(
+        lookup_input,
+        torch.cat([local_shard, padding_row]),
+        padding_idx=padding_idx,
+        max_norm=max_norm,
+        norm_type=norm_type,
+    )
+
+    # TODO: Make the result a PartialTensor.
+    local_shards = local_input_embeddings.chunk(pg.size())
+    return reduce_scatter(
+        torch.empty_like(local_shards[0]),
+        list(local_shards),
+        group=pg,
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharding_spec/chunk_sharding_spec_ops/embedding_bag.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharding_spec/chunk_sharding_spec_ops/embedding_bag.py
new file mode 100644
index 0000000000000000000000000000000000000000..f1581575f5f47058325af51129fd0d9d4497b1d9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_shard/sharding_spec/chunk_sharding_spec_ops/embedding_bag.py
@@ -0,0 +1,479 @@
+# mypy: allow-untyped-defs
+
+from typing import cast
+
+import torch
+import torch.distributed as dist
+from torch._C._distributed_c10d import ReduceOp
+from torch.distributed._shard.sharded_tensor import ShardedTensor
+from torch.distributed._shard.sharding_spec import ChunkShardingSpec
+from torch.distributed._shard.sharding_spec.api import custom_sharding_spec_op
+from torch.distributed.nn.functional import all_gather, reduce_scatter
+
+from ._common import (
+    _all_gather_base_input,
+    _handle_col_wise_sharding_base,
+    _handle_max_norm_col_wise,
+    _handle_row_wise_mask,
+)
+
+
+@custom_sharding_spec_op(ChunkShardingSpec, torch.nn.functional.embedding_bag)
+def sharded_embedding_bag(types, args, kwargs, pg):
+    """
+    Handles ``__torch_function__`` dispatch for ``torch.nn.functional.embedding_bag``.
+    This method computes a sharded embedding bag aggregation and has the following limitations:
+
+    1. Supports only sharding of ``weight``.
+    2. Supports only ``ChunkShardingSpec``.
+    3. Supports only a single local shard per rank.
+    4. Supports all specs except for scale_grad_by_freq, sparse, etc.
+
+    Based on the dimension that the weight is sharded on, there are two
+    algorithms:
+
+    ROWWISE SHARDING
+    ================
+    For row-wise sharding the weight is sharded on dimension 0.
+
+    The overall algorithm can be best explained with an example. Let's assume
+    the dims for input are (4 x 6) and W are (16 x 17) and W is sharded across
+    4 GPUs creating 4 shard of (4 x 17).
+    The algorithm is as follows:
+
+    1. First the input is all gathered to all ranks, since this is SPMD and
+       input is actually sharded across all ranks. The inputs then become a
+       4 (4 x 6) tensor on each rank. For example if the given input is
+       tensor([[6, 5, 2, 9, 6, 3],
+               [3, 1, 2, 4, 7, 6],
+               [4, 0, 4, 9, 8, 9],
+               [8, 6, 6, 4, 6, 1]])
+       on rank 0.
+       Then on every rank, we will have this tensor.
+       If input itself is already replicated, no all-gather will be done.
+    2. Next, we mask the ID which are not stored on that rank.
+       For example on rank 0, we store ID [0, 1, 2]. We only keep the ID
+       inside the set of numbers. The rest of them will be masked to an extra row.
+       The masked matrix will be used for embedding look up and is like:
+       tensor([[4, 4, 2, 4, 4, 4],
+               [4, 1, 2, 4, 4, 4],
+               [4, 0, 4, 4, 4, 4],
+               [4, 4, 4, 4, 4, 1]])
+    3. If ``max_norm`` is specified, the extra row guarantees that the mask ID will
+       not affect the behavior of weigh re-norm.
+    4. The example above only happens in one rank and each rank does a very similar thing.
+       For "Mean" mode we need to divide by either column size (2D) or the interval length
+       defined by the offset (excluding the row specified in ``padding_idx``).
+       We also need to mask the unexisting row to neg Inf so that negative value does not
+       gets wiped out in the "Max" mode.
+
+    COLWISE SHARDING
+    ================
+    For col-wise sharding the weight is sharded on dimension 1.
+
+    The overall algorithm can be best explained with an example. Let's assume
+    the dims for input are (4 x 6) and W are (16 x 17) and W is sharded across
+    4 GPUs creating 3 shards of (16 x 5) and 1 shard of (16 x 2).
+    The algorithm is as follows:
+
+    1. First the input is broadcasted to all ranks, since this is SPMD we
+       actually do an all_gather for all the inputs resulting in 4 (4 x 6)
+       inputs on each rank.
+    2. Next we perform local embedding bag operation under the given mode by
+       apply each input (4 x 6) with the local shard (16 x 5) ((16 x 2) for the last).
+       This results in 4 (5 x 4) ((2 x 4) for the last) matrices on each rank.
+       We transpose the aggregation result.
+    3. Next, we concatenate these 4 matrices and perform an all2all to share the
+       appropriate (5 x 4) or (2 x 4) matrices to each rank.
+    4. Now, each rank receives a (17 x 4) matrix which is basically the
+       size of the result we need.
+    5. If placements are not in order any appropriate rearrangement of columns
+       are done for the (17 x 4) matrix and finally we transpose the output again.
+    6. If max_norm is specified, we manually sum up the norm and renorm. Because
+       the renorm must be in place, we need to override the local_shard to mimic
+       this behavior.
+    """
+    # Validate input params
+    _validate_embedding_bag_param(args, kwargs)
+
+    input = args[0]
+    weight = args[1]
+    offsets = kwargs.get("offsets")
+    per_sample_weights = kwargs.get("per_sample_weights")
+    mode = kwargs.get("mode")
+    max_norm = kwargs.get("max_norm")
+    norm_type = kwargs.get("norm_type")
+    include_last_offset = kwargs.get("include_last_offset")
+    padding_idx = kwargs.get("padding_idx")
+
+    local_shard = weight.local_tensor().contiguous()
+    sharding_dim = weight._sharding_spec.dim
+    world_size = dist.get_world_size(pg)
+    rank = dist.get_rank(pg)
+    if include_last_offset:
+        offsets = offsets[:-1]
+
+    if sharding_dim == 1:
+        output, local_shard = _handle_col_wise_sharding(
+            input,
+            world_size,
+            weight,
+            local_shard,
+            offsets,
+            per_sample_weights,
+            mode,
+            max_norm,
+            norm_type,
+            padding_idx,
+            pg,
+        )
+        weight.local_shards()[0].tensor = local_shard
+        return output
+    elif sharding_dim == 0:
+        return _handle_row_wise_sharding(
+            input,
+            world_size,
+            weight,
+            local_shard,
+            offsets,
+            per_sample_weights,
+            mode,
+            max_norm,
+            norm_type,
+            padding_idx,
+            rank,
+            pg,
+        )
+    else:
+        raise RuntimeError(
+            f"nn.EmbeddingBag weight sharded on dim {sharding_dim} not supported!"
+        )
+
+
+def _validate_embedding_bag_param(args, kwargs):
+    """
+    Validate input params of sharded embeddingBag op.
+
+    Args:
+        input: list of ID used for lookup and aggregation.
+        weight: sharded weight tensor.
+        kwargs: same as normal EmbeddingBag.
+
+    Return: None.
+    """
+
+    input = args[0]
+    weight = args[1]
+    offsets = kwargs.get("offsets")
+    per_sample_weights = kwargs.get("per_sample_weights")
+    mode = kwargs.get("mode")
+    max_norm = kwargs.get("max_norm")
+    scale_grad_by_freq = kwargs.get("scale_grad_by_freq")
+    sparse = kwargs.get("sparse")
+    include_last_offset = kwargs.get("include_last_offset")
+
+    # Validate types
+    if not isinstance(input, torch.Tensor):
+        raise TypeError("input need to be torch.Tensor")
+    if offsets is not None and not isinstance(offsets, torch.Tensor):
+        raise TypeError("offsets need to be torch.Tensor")
+    if per_sample_weights is not None and not isinstance(
+        per_sample_weights, torch.Tensor
+    ):
+        raise TypeError("per_sample_weights need to be torch.Tensor")
+    if not isinstance(weight, ShardedTensor):
+        raise TypeError("weight needs to be ShardedTensor")
+    if len(input.size()) > 2:
+        raise ValueError("Input more than 2 dims not supported")
+    weight_size = weight.size()
+    if len(weight_size) != 2:
+        raise ValueError("Weight needs to have exactly 2 dims")
+    if int(torch.min(input).item()) < 0:
+        raise ValueError(
+            "Index out of range in Input %d %d",
+            int(torch.min(input).item()),
+            weight_size[1],
+        )
+    if int(torch.max(input).item()) >= weight_size[0]:
+        raise ValueError(
+            "Index out of range in Input %d %d",
+            int(torch.max(input).item()),
+            weight_size[1],
+        )
+    if offsets is not None and len(input.size()) != 1:
+        raise ValueError("Input dimension needs to be exactly 1 dim")
+    if len(input.size()) == 1 and offsets is None:
+        raise ValueError("offsets is required for 1D input")
+    if per_sample_weights is not None and per_sample_weights.size() != input.size():
+        raise ValueError(
+            f"per_sample_weights size {per_sample_weights.size()} not equal to input size {input.size()}"
+        )
+    if mode is None:
+        mode = "mean"
+    if mode not in ["sum", "mean", "max"]:
+        raise ValueError(f"mode '{mode}' is not supported")
+    if scale_grad_by_freq:
+        raise RuntimeError(
+            'nn.Embedding weight sharded with flag on "scale_grad_by_freq" not supported!'
+        )
+    if sparse:
+        raise RuntimeError(
+            'nn.Embedding weight sharded with flag on "sparse" not supported!'
+        )
+    if include_last_offset and offsets is None:
+        raise ValueError('offsets is required for flag "include_last_offset"!')
+    if include_last_offset and cast(list[int], offsets)[-1] != input.size(0):
+        raise ValueError(
+            'offsets need to have the input size in the end when the flag "include_last_offset" is on!'
+        )
+
+    if max_norm and max_norm <= 0.0:
+        raise ValueError('"max_norm" must be larger than zero!')
+
+    if not isinstance(weight._sharding_spec, ChunkShardingSpec):
+        raise ValueError("Only ChunkShardingSpec supported for ShardedTensor ops!")
+    if len(weight.local_shards()) != 1:
+        raise ValueError("Only one local shard supported!")
+
+
+def _handle_col_wise_sharding(
+    input,
+    world_size,
+    weight,
+    local_shard,
+    offsets,
+    per_sample_weights,
+    mode,
+    max_norm,
+    norm_type,
+    padding_idx,
+    pg,
+):
+    """
+    Entry-point function to handle the logic of col-wise sharding of weight
+    for embeddingBag. (Detailed explanations of the logic can be found in
+    the comment for sharded_embedding_bag.)
+
+    Args:
+        input: list of ID used for lookup and aggregation.
+        world_size: number of ranks.
+        weight: sharded weight tensor.
+        local_shard: col-wise shared local weight used for lookup.
+        offsets: list of start positions of each bag for 1D input.
+        per_sample_weights: weights for weighted sum mode.
+        mode: aggregation method of each bag.
+        max_norm: If given, each embedding vector with norm larger
+            than max_norm is renormalized to have norm max_norm.
+            Note: this will modify weight in-place.
+        norm_type: The p in the p-norm to compute for the max_norm option.
+        padding_idx: If specified, the entries at padding_idx do
+            not contribute to the gradient; therefore, the embedding
+            vector at padding_idx is not updated during training,
+            i.e. it remains as a fixed "pad".
+            Note that the embedding vector at padding_idx is
+            excluded from the reduction.
+        pg: process group.
+
+    Return:
+        output: final result of lookup and aggregation.
+        local_shard: col-wise shared local weight used for lookup.
+            If max_norm, this will be the renormed weight.
+    """
+    # allgather the special input of embedding bag first.
+    (
+        gathered_inputs,
+        gathered_per_sample_weights,
+        gathered_offsets,
+    ) = _all_gather_embedding_bag_input(input, per_sample_weights, offsets, pg)
+
+    if max_norm is not None:
+        # max_norm changes the weight in-place
+        local_shard = _handle_max_norm_col_wise(
+            max_norm, norm_type, local_shard, input, world_size, gathered_inputs, pg
+        )
+
+    output = _handle_col_wise_sharding_base(
+        torch.nn.functional.embedding_bag,
+        1,
+        input,
+        world_size,
+        weight,
+        local_shard,
+        pg,
+        gathered_inputs,
+        mode=mode,
+        gathered_per_sample_weights=gathered_per_sample_weights,
+        gathered_offsets=gathered_offsets,
+        padding_idx=padding_idx,
+    )
+    return (output, local_shard)
+
+
+def _handle_row_wise_sharding(
+    input,
+    world_size,
+    weight,
+    local_shard,
+    offsets,
+    per_sample_weights,
+    mode,
+    max_norm,
+    norm_type,
+    padding_idx,
+    rank,
+    pg,
+):
+    """
+    Entry-point function to handle the logic of row-wise sharding of weight
+    for embeddingBag. (Detailed explanations of the logic can be found in
+    the comment for sharded_embedding_bag.)
+
+    Args:
+        input: list of ID used for lookup and aggregation.
+        world_size: number of ranks.
+        weight: sharded weight tensor.
+        local_shard: row-wise shared local weight used for lookup.
+        offsets: list of start positions of each bag for 1D input.
+        per_sample_weights: weights for weighted sum mode.
+        mode: aggregation method of each bag.
+        max_norm: If given, each embedding vector with norm larger
+            than max_norm is renormalized to have norm max_norm.
+            Note: this will modify weight in-place.
+        norm_type: The p in the p-norm to compute for the max_norm option.
+        padding_idx: If specified, the entries at padding_idx do
+            not contribute to the gradient; therefore, the embedding
+            vector at padding_idx is not updated during training,
+            i.e. it remains as a fixed "pad".
+            Note that the embedding vector at padding_idx is
+            excluded from the reduction.
+        rank: # of cuda process.
+        pg: process group.
+
+    Returns:
+        gathered_output: final result of lookup and aggregation.
+    """
+    if input.dim() > 1 and per_sample_weights is None:
+        # allgather the inputs first for non Replicated Tensor.
+        gather_inp = _all_gather_base_input(input, pg)
+    else:
+        (
+            gathered_inputs,
+            gathered_per_sample_weights,
+            gathered_offsets,
+        ) = _all_gather_embedding_bag_input(input, per_sample_weights, offsets, pg)
+        cat_dim = 0 if input.dim() != 1 else -1
+        gather_inp = torch.cat(gathered_inputs, dim=cat_dim)
+        if per_sample_weights is not None:
+            per_sample_weights = torch.cat(gathered_per_sample_weights, dim=cat_dim)
+        offset_add = 0 if input.dim() > 1 else input.size(0)
+        if offsets is not None:
+            offsets_list = torch.cat(
+                [gathered_offsets[i] + (offset_add * i) for i in range(pg.size())],
+                dim=cat_dim,
+            )
+
+    # Mask the input according to sharding spec.
+    lookup_input, padding_local, padding_row = _handle_row_wise_mask(
+        gather_inp, padding_idx, weight, world_size, rank
+    )
+    if mode == "max":
+        padding_row[:] = -float("Inf")
+
+    # When input is a large tensor, the value of weight is changed.
+    # This is a walk-around for now. GH issue: #81717.
+    if max_norm is not None:
+        torch.nn.functional.embedding_bag(
+            torch.unique(lookup_input)[:-1],
+            local_shard,
+            offsets=torch.tensor([0], device=local_shard.device, dtype=torch.long),
+            mode=mode,
+            per_sample_weights=None,
+            max_norm=max_norm,
+            norm_type=norm_type,
+            padding_idx=padding_local,
+        )
+        max_norm = None
+    result = torch.nn.functional.embedding_bag(
+        lookup_input,
+        torch.cat([local_shard, padding_row]),
+        offsets=offsets_list if offsets is not None else offsets,  # type: ignore[possibly-undefined]
+        mode=mode if mode != "mean" else "sum",
+        per_sample_weights=per_sample_weights,
+        max_norm=max_norm,
+        norm_type=norm_type,
+        padding_idx=padding_local,
+    )
+
+    op = ReduceOp.SUM if mode != "max" else ReduceOp.MAX
+    # TODO: Make the result a PartialTensor and move the logic below there.
+    local_shards = result.chunk(pg.size())
+    result = reduce_scatter(
+        torch.empty_like(local_shards[0]),
+        list(local_shards),
+        op=op,
+        group=pg,
+    )
+
+    # For Mean, we cannot do the division until very end because the sum of means
+    # not equal to the mean of sum. (Divisor is different)
+    if mode == "mean":
+        if input.dim() > 1:
+            padding_idx = padding_idx if padding_idx is not None else -1
+            split_sizes = torch.sum(
+                torch.ne(input, padding_idx), dim=-1, dtype=local_shard.dtype
+            )
+        else:
+            split_sizes = torch.cat(
+                (
+                    # pyrefly: ignore [unsupported-operation]
+                    offsets[1 : offsets.size(0)] - offsets[0:-1],
+                    # pyrefly: ignore [unsupported-operation]
+                    (input.size(0) - offsets[-1]).unsqueeze(0),
+                ),
+                dim=-1,
+            )
+        return torch.div(result, split_sizes.unsqueeze(1))
+
+    # Return the appropriate local result.
+    return result
+
+
+def _all_gather_embedding_bag_input(input, per_sample_weights, offsets, pg):
+    """
+    In case we need to gather input and all other parameters of embeddingBag
+    ops, we need to stack all input together to perform ``all_gather``
+    collective communication just once.
+
+    Note that since offsets does not share the same size as input and
+    is always smaller than input, we resize it during the communication.
+
+    Args:
+        input: tensor to be applied op on.
+        per_sample_weights: weights for weighted sum mode.
+        offsets: when input is 1D. offsets determines the starting
+            index position of each bag (sequence) in input.
+        pg: process group.
+
+    Returns:
+        gathered_inputs: list of input tensor gathered from each rank.
+        gathered_per_sample_weights: list of per_sample_weights from each rank.
+        gathered_offsets: list of offsets from each rank.
+    """
+    input_to_gather = [input]
+    if per_sample_weights is not None:
+        input_to_gather.append(per_sample_weights)
+    if offsets is not None:
+        input_to_gather.append(offsets.clone().resize_(input.size()))
+    gathered_inputs = all_gather(torch.stack(input_to_gather), group=pg)
+
+    gathered_per_sample_weights = None
+    if per_sample_weights is not None:
+        gathered_per_sample_weights = [t[1] for t in gathered_inputs]
+    gathered_offsets = None
+    if offsets is not None:
+        idx = 2 if per_sample_weights is not None else 1
+        gathered_offsets = [
+            t[idx].resize_(offsets.size()).to(offsets.dtype) for t in gathered_inputs
+        ]
+    gathered_inputs = [t[0].to(input.dtype) for t in gathered_inputs]
+    return gathered_inputs, gathered_per_sample_weights, gathered_offsets
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_sharded_tensor/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_sharded_tensor/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..24de2628c0ab9ceb89fa28b52753a421b58b56c2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_sharded_tensor/__init__.py
@@ -0,0 +1,21 @@
+# Keep old package for BC purposes, this file should be removed once
+# everything moves to the `torch.distributed._shard` package.
+import sys
+import warnings
+
+import torch
+from torch.distributed._shard.sharded_tensor import *  # noqa: F403
+
+
+with warnings.catch_warnings():
+    warnings.simplefilter("always")
+    warnings.warn(
+        "`torch.distributed._sharded_tensor` will be deprecated, "
+        "use `torch.distributed._shard.sharded_tensor` instead",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+
+sys.modules["torch.distributed._sharded_tensor"] = (
+    torch.distributed._shard.sharded_tensor
+)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_sharding_spec/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_sharding_spec/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c74dd3633e0f5e8436b844fd2d14f3bdb00635b7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_sharding_spec/__init__.py
@@ -0,0 +1,22 @@
+# Keep old package for BC purposes, this file should be removed once
+# everything moves to the `torch.distributed._shard` package.
+import sys
+import warnings
+
+import torch
+from torch.distributed._shard.sharding_spec import *  # noqa: F403
+
+
+with warnings.catch_warnings():
+    warnings.simplefilter("always")
+    warnings.warn(
+        "`torch.distributed._sharding_spec` will be deprecated, "
+        "use `torch.distributed._shard.sharding_spec` instead",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+
+import torch.distributed._shard.sharding_spec as _sharding_spec
+
+
+sys.modules["torch.distributed._sharding_spec"] = _sharding_spec
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_state_dict_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_state_dict_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..5cb614e89cf9c29c8ed9a07d36b60e7f867002a8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_state_dict_utils.py
@@ -0,0 +1,830 @@
+# mypy: allow-untyped-defs
+import copy
+import io
+import math
+import weakref
+from collections.abc import Callable, Mapping, MutableMapping
+from typing import Any, cast, NamedTuple, TYPE_CHECKING, Union
+
+import torch
+import torch.cuda._pin_memory_utils as pin_memory_utils
+import torch.distributed as dist
+import torch.nn.functional as F
+from torch.distributed._functional_collectives import AsyncCollectiveTensor
+
+
+if dist.is_available() or TYPE_CHECKING:
+    from torch.distributed import distributed_c10d
+    from torch.distributed._shard.sharded_tensor import ShardedTensor
+    from torch.distributed.tensor import distribute_tensor, DTensor, Replicate
+    from torch.distributed.tensor._utils import compute_local_shape_and_global_offset
+
+
+def _identity_func(
+    obj: torch.Tensor,
+    pg: dist.ProcessGroup | None,
+    device: torch.device | None,
+    companion_obj: Any,
+) -> torch.Tensor:
+    return obj
+
+
+def _all_gather_sharded_tensor(
+    sharded_tensor: "ShardedTensor",
+    pg: dist.ProcessGroup | None = None,
+    device: torch.device | None = None,
+) -> torch.Tensor:
+    if pg is None:
+        pg = distributed_c10d._get_default_group()
+    world_size = dist.get_world_size(pg)
+    shards = sharded_tensor.local_shards()
+    dim_0_size = sharded_tensor.size()[0]  # type: ignore[index]
+    tensor_numel = sharded_tensor.size().numel()  # type: ignore[union-attr]
+    chunk_size = math.ceil(dim_0_size / world_size) * tensor_numel // dim_0_size
+    pg_device = (
+        distributed_c10d._get_pg_default_device(pg) if device is None else device
+    )
+    if shards:
+        local_tensor = shards[0].tensor.flatten()
+        if local_tensor.device.type != pg_device.type:
+            local_tensor = local_tensor.to(pg_device)
+        num_padding = chunk_size - local_tensor.numel()
+        if num_padding > 0:
+            local_tensor = F.pad(local_tensor, [0, num_padding])
+    else:
+        local_tensor = torch.zeros(
+            chunk_size, dtype=sharded_tensor.dtype, device=pg_device
+        )
+
+    tensor = torch.empty(
+        chunk_size * world_size,
+        dtype=local_tensor.dtype,
+        device=pg_device,
+    )
+    dist.all_gather_into_tensor(tensor, local_tensor, group=pg)
+
+    tensor = tensor.narrow(0, 0, tensor_numel).reshape(sharded_tensor.size())
+    return tensor
+
+
+class CompanionMismatch(Exception):
+    pass
+
+
+def _iterate_state_dict(
+    iter_object: Any,
+    sharded_tensor_func: Callable,
+    dtensor_func: Callable,
+    tensor_func: Callable,
+    *,
+    pg: dist.ProcessGroup | None = None,
+    device: torch.device | None = None,
+    cpu_offload: bool = False,
+    companion_obj: Any = None,
+    ranks_only: tuple[int, ...] = (),
+    type_check: bool = True,
+    non_blocking: bool = True,
+) -> dict[str, Any]:
+    """Iterate through the state dict, applying the given functions to each tensor type.
+
+    Args:
+        iter_object (Any): the target state_dict.
+        sharded_tensor_func (Callable): the function to apply to ShardedTensor
+        dtensor_func (Callable): the function to apply to DTensor
+        tensor_func (Callable): the function to apply to Tensor
+        pg (Optional[dist.ProcessGroup]): process group passed to tensor functions
+        device (Optional[torch.device]): device passed to tensor functions
+        cpu_offload (bool): whether to offload the tensors to CPU memory. This option is ignored
+            if a companion_obj is supplied.
+        companion_obj (Any): A companion object to the state dict. If this object
+            is supplied, we attempt to copy the tensor to the companion object.
+        ranks_only (Tuple[int, ...]): if this tuple is empty, all ranks will
+            have the same state_dicts. Otherwise only ranks that in ``ranks_only``
+            have the same state_dicts. Other ranks will get empty state_dicts.
+        type_check (bool): check if the instance data type is a supported type
+            that can be saved by DCP.  The current supported data types are
+            torch.Tensor, DTensor, int, float, str, list, dict, None.
+        non_blocking (bool): whether to use non-blocking copy when copying to the companion object.
+    """
+    # TODO: should we use pytree?
+    cpu_device = torch.device("cpu")
+    if isinstance(iter_object, ShardedTensor):
+        ret = sharded_tensor_func(iter_object, pg, device, companion_obj)
+    elif isinstance(iter_object, DTensor):
+        ret = dtensor_func(iter_object, pg, device, companion_obj)
+    elif isinstance(iter_object, torch.Tensor):
+        ret = tensor_func(iter_object, pg, device, companion_obj)
+    elif (
+        isinstance(iter_object, (int, float, str, bytes, io.BytesIO))
+        or iter_object is None
+    ):
+        ret = iter_object
+    elif isinstance(iter_object, dict):
+        if companion_obj is not None and (
+            not isinstance(companion_obj, dict)
+            or set(companion_obj.keys()) != set(iter_object.keys())
+        ):
+            msg = (
+                ""
+                if isinstance(companion_obj, dict)
+                else f"{set(companion_obj.keys())=} {set(iter_object.keys())=}"
+            )
+            raise CompanionMismatch(msg)
+
+        ret = {
+            key: _iterate_state_dict(
+                value,
+                sharded_tensor_func,
+                dtensor_func,
+                tensor_func,
+                pg=pg,
+                device=device,
+                cpu_offload=cpu_offload,
+                companion_obj=companion_obj[key] if companion_obj is not None else None,
+                ranks_only=ranks_only,
+                type_check=type_check,
+                non_blocking=non_blocking,
+            )
+            for key, value in iter_object.items()
+        }
+    elif isinstance(iter_object, (list, tuple)):
+        if companion_obj is not None and (
+            not isinstance(companion_obj, (list, tuple))
+            or len(companion_obj) != len(iter_object)
+        ):
+            raise CompanionMismatch
+
+        ret = [
+            _iterate_state_dict(
+                v,
+                sharded_tensor_func,
+                dtensor_func,
+                tensor_func,
+                pg=pg,
+                device=device,
+                cpu_offload=cpu_offload,
+                companion_obj=companion_obj[idx] if companion_obj is not None else None,
+                ranks_only=ranks_only,
+                type_check=type_check,
+                non_blocking=non_blocking,
+            )
+            for idx, v in enumerate(iter_object)
+        ]
+        if isinstance(iter_object, tuple):
+            ret = tuple(ret)
+    elif not type_check:
+        ret = copy.deepcopy(iter_object)
+    else:
+        raise ValueError(f"Unexpected value type {type(iter_object)}")
+
+    if not ranks_only or dist.get_rank(pg) in ranks_only:
+        if isinstance(ret, torch.Tensor):
+            if cpu_offload and companion_obj is None:
+                ret = ret.to(cpu_device)
+
+            if companion_obj is not None:
+                if isinstance(companion_obj, DTensor):
+                    if not isinstance(ret, DTensor):
+                        raise AssertionError(
+                            "ret must be a DTensor when companion_obj is a DTensor"
+                        )
+                    companion_obj._local_tensor.copy_(
+                        ret._local_tensor, non_blocking=non_blocking
+                    )
+                elif isinstance(companion_obj, ShardedTensor):
+                    if not isinstance(ret, ShardedTensor):
+                        raise AssertionError(
+                            "ret must be a ShardedTensor when companion_obj is a ShardedTensor"
+                        )
+                    for idx, shard in enumerate(companion_obj.local_shards()):
+                        shard.tensor.copy_(
+                            ret.local_shards()[idx].tensor, non_blocking=non_blocking
+                        )
+                else:
+                    # pyrefly: ignore [missing-attribute]
+                    companion_obj.copy_(ret, non_blocking=non_blocking)
+                ret = companion_obj
+    else:
+        ret = {} if isinstance(ret, dict) else None
+
+    # pyrefly: ignore [bad-return]
+    return ret
+
+
+def _gather_state_dict(
+    state_dict: dict[str, Any],
+    *,
+    pg: dist.ProcessGroup | None = None,
+    device: torch.device | None = None,
+    cpu_offload: bool = False,
+    ranks_only: tuple[int, ...] = (),
+    type_check: bool = True,
+) -> dict[str, Any]:
+    """
+    Given a state_dict, this API gathers all the ShardedTensors or DTensors in
+    the state_dict.
+
+
+    Args:
+        state_dict (Dict[str, Any]): the target sharded state_dict.
+        pg (Optional[dist.ProcessGroup]): the process group that is used to
+            gather ShardedTensor. Note that gathering a DTensor will use
+            the DeviceMesh. So this argument will be ignored when gathering a
+            DTensor.
+        device: (Optional[torch.device]): the device that is used to
+            perform allgather for ShardedTensor. Note that gathering a DTensor
+            will use the DeviceMesh. So this argument will be ignored when
+            gathering a DTensor.
+        cpu_offload (bool): whether to offload the tensors to CPU memory. The
+            default value is False.
+        ranks_only: (Tuple[int, ...]): if this tuple is empty, all ranks will
+            have the same state_dicts. Otherwise only ranks that in ``ranks_only``
+            have the same state_dicts. Other ranks will get empty state_dicts.
+        type_check: (bool): check if the instance data type is a supported type
+            that can be saved by DCP.  The current supported data types are
+            torch.Tensor, DTensor, int, float, str, list, dict, None.
+
+    Returns:
+        The gathered state dictionary.
+    """
+
+    def sharded_tensor_func(value, pg, device, companion_obj):
+        # ShardedTensor does not seem to record the original device type.
+        # So if the tensor is moved to CPU, we won't know the original type.
+        # As a result, we have to rely on the user to tell us the correct one.
+        cpu_device = torch.device("cpu")
+        output_tensor = _all_gather_sharded_tensor(value, pg, device)
+        local_shard_device = (
+            value.local_shards()[0].tensor.device
+            if value.local_shards()
+            else cpu_device
+        )
+        if output_tensor.device != local_shard_device:
+            value = output_tensor.to(local_shard_device)
+        else:
+            value = output_tensor
+        return value
+
+    def dtensor_func(value, pg, device, companion_obj):
+        if value.device != value.device_mesh.device_type:
+            value = value.to(value.device_mesh.device_type)
+        # FSDP all_gather: [Shard(0)] -> [Replicate()]
+        # HSDP all_gather: [Replicate(), Shard(0)] -> [Replicate(), Replicate()]
+        # 2D FSDP + TP all_gather:
+        # - [Shard(0), Shard(n)] -> [Replicate(), Replicate()]
+        # - [Shard(0), Replicate()] -> [Replicate(), Replicate()]
+        placements = [Replicate() for _ in value.placements]
+        value = value.redistribute(
+            device_mesh=value.device_mesh,
+            placements=placements,
+        )
+        # Call `wait()` to force the tensor to be synchronous with respect
+        # to the main stream.
+        # See the discussion in https://github.com/pytorch/pytorch/pull/117799.
+        value = value.to_local()
+        if isinstance(value, AsyncCollectiveTensor):
+            value = value.wait()
+        return value
+
+    return _iterate_state_dict(
+        state_dict,
+        sharded_tensor_func,
+        dtensor_func,
+        _identity_func,
+        pg=pg,
+        device=device,
+        cpu_offload=cpu_offload,
+        ranks_only=ranks_only,
+        type_check=type_check,
+    )
+
+
+def _offload_state_dict_to_cpu(
+    state_dict: dict[str, Any],
+    *,
+    ranks_only: tuple[int, ...] = (),
+    type_check: bool = True,
+) -> dict[str, Any]:
+    """
+    Given a state_dict, this API offload all the tensors to CPU memory.
+
+    Args:
+        state_dict (Dict[str, Any]): the target state_dict.
+        pg (Optional[dist.ProcessGroup]): the process group that is used to
+            gather ShardedTensor. Note that gathering a DTensor will use
+            the DeviceMesh. So this argument will be ignored when gathering a
+            DTensor.
+        ranks_only: (Tuple[int, ...]): if this tuple is empty, all ranks will
+            have the same state_dicts. Otherwise only ranks that in ``ranks_only``
+            have the same state_dicts. Other ranks will get empty state_dicts.
+        type_check: (bool): check if the instance data type is a supported type
+            that can be saved by DCP.  The current supported data types are
+            torch.Tensor, DTensor, int, float, str, list, dict, None.
+
+    Returns:
+        The gathered state dictionary.
+    """
+
+    ret = _iterate_state_dict(
+        state_dict,
+        _identity_func,
+        _identity_func,
+        _identity_func,
+        pg=None,
+        device=None,
+        cpu_offload=True,
+        ranks_only=ranks_only,
+        type_check=type_check,
+    )
+    return ret
+
+
+@torch.no_grad()
+def _copy_state_dict(
+    state_dict: dict[str, Any],
+    copy_state_dict: dict[str, Any],
+    non_blocking: bool = False,
+    type_check: bool = True,
+) -> dict[str, Any]:
+    """
+    Copies all tensors in a given state dict into a different state_dict with the
+    same structure. Additionally, a copied state dict with the same value references
+    is returned. Editing the keys on this state dict will not affect the
+    passed in copy_state_dict (but the value references are the same).
+
+    .. warning::
+        It is expected by this function that state_dict and copy_state_dict share
+        the same structure and data types.
+
+    .. warning::
+        The current supported data types are
+            torch.Tensor, DTensor, int, float, str, list, dict, None.
+
+    Args:
+        state_dict (Dict[str, Any]): the target state_dict.
+        copy_state_dict (Dict[str, Any]):
+            The state dict we are copying into. This state_dict must have exactly
+             the same structure as the source `state_dict`.
+        non_blocking: (bool): Whether copy ops should be performed asynchronously
+        type_check (bool): check if the instance data type is a supported type
+            that can be saved by DCP. The current supported data types are
+            torch.Tensor, DTensor, int, float, str, list, dict, None.
+
+    Returns:
+        State Dict copy
+    """
+
+    return _iterate_state_dict(
+        state_dict,
+        _identity_func,
+        _identity_func,
+        _identity_func,
+        pg=None,
+        device=None,
+        cpu_offload=False,
+        ranks_only=(),
+        companion_obj=copy_state_dict,
+        type_check=type_check,
+        non_blocking=non_blocking,
+    )
+
+
+@torch.no_grad()
+def _create_cpu_state_dict(
+    state_dict: dict[str, Any], pin_memory: bool = False, share_memory: bool = False
+) -> dict[str, Any]:
+    """
+    Given a state_dict, create another state_dict with the same structure and elements.
+    However, all tensors in the returned state_dict are new tensors on CPU. These
+    tensors can be placed on pin_memory or share_memory based on the provided arguments.
+
+    .. warning::
+        Setting both `pin_memory` and `share_memory` to True significantly increases the
+        latency of this method because of the nuances which require us to register memory
+        as pinned directly as opposed to relying on the pin_memory cache allocator. This
+        option should only be used for long lived tensors which are required to be shared.
+        This is not the case as long as at least one of `pin_memory` or `share_memory` is
+         set to False.
+
+    """
+
+    def tensor_func(
+        obj: torch.Tensor,
+        pg: dist.ProcessGroup | None,
+        device: torch.device | None,
+        _: Any,
+    ) -> torch.Tensor:
+        if len(obj.size()) == 0:
+            return torch.tensor(0, dtype=obj.dtype)
+
+        # sometimes, a tensor might have non-zero size and 0 numel. In this case, pinning memory will fail
+        # so we take a best guess at how to replicate the tensor below to maintain symmetry in the returned
+        # state dict.
+        if obj.numel() == 0 or obj.data_ptr() == 0:
+            t = torch.zeros_like(obj, device="cpu")
+            if share_memory:
+                t = t.share_memory_()
+            return t
+
+        if share_memory:
+            t = torch.empty(*tuple(obj.size()), dtype=obj.dtype)
+            t = t.share_memory_()
+            if pin_memory:
+                pin_memory_utils.pin_memory(t.data_ptr(), t.numel() * t.element_size())
+                weakref.finalize(t, pin_memory_utils.unpin_memory, t.data_ptr())
+
+            return t
+        elif pin_memory:
+            return torch.empty(*tuple(obj.size()), dtype=obj.dtype).pin_memory()
+        else:
+            return torch.empty(*tuple(obj.size()), dtype=obj.dtype)
+
+    def dtensor_func(
+        obj: DTensor,
+        pg: dist.ProcessGroup | None,
+        device: torch.device | None,
+        _: Any,
+    ) -> DTensor:
+        if len(obj.size()) == 0:
+            return obj
+
+        if obj.device != torch.device("cpu"):
+            ret = cast(DTensor, obj.to(device="cpu"))
+        else:
+            ret = copy.deepcopy(obj)
+        ret._local_tensor = tensor_func(ret._local_tensor, pg, device, None)
+        return ret
+
+    def sharded_tensor_func(
+        obj: ShardedTensor,
+        pg: dist.ProcessGroup | None,
+        device: torch.device | None,
+        _: Any,
+    ) -> ShardedTensor:
+        if not obj.local_shards():
+            return obj
+
+        if obj.device != torch.device("cpu"):
+            ret = obj.to(device="cpu")
+        else:
+            ret = copy.deepcopy(obj)
+
+        for shards in ret.local_shards():
+            shards.tensor = tensor_func(shards.tensor, pg, device, None)
+
+        return ret
+
+    ret = _iterate_state_dict(
+        state_dict,
+        sharded_tensor_func,
+        dtensor_func,
+        tensor_func,
+        pg=None,
+        device=None,
+        cpu_offload=False,
+        ranks_only=(),
+        type_check=False,
+    )
+    return ret
+
+
+def _check_state_dict_similarity(
+    state_dict: dict[str, Any],
+    compared_state_dict: dict[str, Any],
+) -> bool:
+    """
+    Given two state_dicts, check if the structures are the same. And
+    if a [key, tensor] pair exist in one state_dict there must be
+    the a corresponding pait, [key, other_tensor], in the other state_dict,
+    where tensor and other_tensor have the same size and dtype.
+
+    Return the check result.
+    """
+
+    def tensor_func(
+        obj: torch.Tensor,
+        pg: dist.ProcessGroup | None,
+        device: torch.device | None,
+        companion_obj: Any,
+    ) -> torch.Tensor:
+        if companion_obj.dtype != obj.dtype or companion_obj.size() != obj.size():
+            raise CompanionMismatch
+        return obj
+
+    try:
+        _iterate_state_dict(
+            state_dict,
+            _identity_func,
+            _identity_func,
+            tensor_func,
+            pg=None,
+            device=None,
+            cpu_offload=False,
+            ranks_only=(),
+            companion_obj=compared_state_dict,
+            type_check=False,
+        )
+    except CompanionMismatch:
+        return False
+
+    return True
+
+
+class _TensorInfo(NamedTuple):
+    size: torch.Size
+    dtype: torch.dtype
+
+
+def _broadcast_tensors(
+    full_state_dict: dict[str, Any],
+    local_state_dict: dict[str, Any],
+    keys: list[str],
+    device: torch.device,
+    pg: dist.ProcessGroup | None = None,
+) -> None:
+    if pg is None:
+        pg = dist.distributed_c10d._get_default_group()
+    pg_device = (
+        device
+        if device.type in {pg_device.type for pg_device in pg._device_types}
+        else pg._device_types[0]
+    )
+
+    tensors: list[torch.Tensor] = []
+    for key in keys:
+        if dist.get_rank() == 0:
+            full_state = full_state_dict[key]
+            if not isinstance(full_state, torch.Tensor):
+                raise AssertionError("full_state must be a torch.Tensor")
+            full_tensor = full_state.detach().to(pg_device)
+        else:
+            tensor_info = full_state_dict[key]
+            full_tensor = torch.empty(
+                size=tensor_info.size,
+                device=pg_device,
+                dtype=tensor_info.dtype,
+            )
+
+        tensors.append(full_tensor)
+
+        if (local_state := local_state_dict.get(key)) is None:
+            continue
+
+        local_state_dict[key] = (
+            (local_state, full_tensor)
+            if isinstance(local_state, DTensor)
+            else full_tensor
+        )
+
+    if len(tensors) > 1:
+        dist._broadcast_coalesced(pg, tensors, 500, 0)
+    else:
+        dist.broadcast(tensors[0], src=0, group=pg)
+
+    if pg_device != device:
+        for key, full_tensor in zip(keys, tensors):
+            if (local_state := local_state_dict.get(key)) is not None:
+                local_state_dict[key] = (
+                    (local_state[0], full_tensor.to(device))
+                    if (
+                        isinstance(local_state, tuple)
+                        and isinstance(local_state[0], DTensor)
+                    )
+                    else full_tensor.to(device)
+                )
+
+    _distribute_tensors(local_state_dict, keys, device, pg)
+
+
+def _distribute_tensors(
+    local_state_dict: dict[str, Any],
+    keys: list[str],
+    device: torch.device,
+    pg: dist.ProcessGroup | None = None,
+) -> None:
+    if pg is None:
+        pg = dist.distributed_c10d._get_default_group()
+    for key in keys:
+        _local_state = local_state_dict.get(key)
+        if _local_state is None or torch.is_tensor(_local_state):
+            continue
+
+        local_state = _local_state[0]
+        full_tensor = _local_state[1]
+
+        shape, offset = compute_local_shape_and_global_offset(
+            full_tensor.shape, local_state.device_mesh, local_state.placements
+        )
+        slices = [
+            slice(cur_offset, cur_offset + cur_shape)
+            for cur_shape, cur_offset in zip(shape, offset)
+        ]
+        if local_state.is_meta:
+            # Use .clone() here rather than view to clone and return only the sliced portion, minimizing memory access and cost.
+            local_tensor = full_tensor[tuple(slices)].detach().clone()
+            # TODO: currently, we cannot handle strided sharding if the dp dimension is not even. For example,
+            # one of the case that is not yet supported is when placements = (Shard(0), _StridedShard(0, sf=2)).
+            ret = DTensor.from_local(
+                local_tensor,
+                local_state.device_mesh,
+                local_state.placements,
+                shape=local_state.shape,
+                stride=local_state.stride(),
+            )
+        else:
+            ret = local_state
+            # Copy full_tensor[slices] into local_state.to_local() to reduce memory footprint.
+            ret.to_local().copy_(full_tensor[tuple(slices)])
+        local_state_dict[key] = ret
+
+
+def _broadcast_state_dict(
+    full_state_dict: dict[str, Any],
+    local_state_dict: dict[str, Any],
+    device: torch.device,
+    pg: dist.ProcessGroup | None = None,
+    strict: bool = False,
+    cpu_offload: bool = False,
+) -> None:
+    # Broadcast from rank0's `full_state_dict` to all ranks' `local_state_dict`.
+    # If strict is True, any keys in `local_state_dict` but not in `full_state_dict`
+    # will be removed from `local_state_dict`.
+    ret = {}
+    if dist.get_rank() == 0:
+        for key, value in full_state_dict.items():
+            if not torch.is_tensor(value):
+                ret[key] = value
+            elif value.dim() == 0:
+                ret[key] = value.cpu()
+            else:
+                ret[key] = _TensorInfo(value.size(), value.dtype)
+
+    broadcast_list = [ret]
+    dist.broadcast_object_list(broadcast_list, src=0, group=pg)
+    ret = broadcast_list[0]
+    # Gather values
+    keys = []
+    local_state_dict_keys = set(local_state_dict.keys())
+    global_keys = set()
+    for key, value in ret.items():
+        global_keys.add(key)
+        if not isinstance(value, _TensorInfo):
+            if key in local_state_dict:
+                local_state_dict[key] = value
+            continue
+
+        if dist.get_rank() == 0:
+            ret[key] = full_state_dict[key]
+
+        keys.append(key)
+        # Broadcast every tensor to avoid OOM for now.
+        if len(keys) >= 1:
+            _broadcast_tensors(ret, local_state_dict, keys, device, pg)
+            if cpu_offload:
+                for key in keys:
+                    local_state_dict[key] = local_state_dict[key].cpu()
+            keys.clear()
+
+    if strict:
+        if missing_keys := (local_state_dict_keys - global_keys):
+            for key in missing_keys:
+                local_state_dict.pop(key)
+
+    if keys:
+        _broadcast_tensors(ret, local_state_dict, keys, device, pg)
+        if cpu_offload:
+            for key in keys:
+                local_state_dict[key] = local_state_dict[key].cpu()
+
+
+def _distribute_state_dict(
+    full_state_dict: dict[str, Any],
+    local_state_dict: dict[str, Any],
+    device: torch.device,
+    pg: dist.ProcessGroup | None = None,
+) -> None:
+    # Full_state_dict = True, broadcast_from_rank0 = False here. Each rank has
+    # full_state_dict. Skip the broadcast in ``_broadcast_state_dict`` and
+    # distribute tensors in each rank
+    for key, value in full_state_dict.items():
+        if key not in full_state_dict:
+            continue
+        if not torch.is_tensor(value):
+            local_state_dict[key] = value
+        elif value.dim() == 0:
+            local_state_dict[key] = value.cpu()
+        else:
+            if not isinstance(value, torch.Tensor):
+                raise AssertionError("value must be a torch.Tensor")
+            local_state = local_state_dict.get(key)
+            if local_state is None:
+                continue
+            elif isinstance(local_state, DTensor):
+                local_state_dict[key] = distribute_tensor(
+                    value.detach().to(device),
+                    local_state.device_mesh,
+                    local_state.placements,
+                )
+            else:
+                local_state_dict[key] = value.detach().to(device)
+
+
+# These APIs are from torch.distributed.checkpoint.
+# TODO: We should consolidate the code here as some not all modules can depend on
+# DCP.
+PATH_ITEM = Union[str, int]
+OBJ_PATH = tuple[PATH_ITEM, ...]
+FLATTEN_MAPPING = dict[str, OBJ_PATH]
+STATE_DICT_TYPE = dict[str, Any]
+CONTAINER_TYPE = MutableMapping[PATH_ITEM, Any]
+
+
+def _traverse_state_dict(
+    state_dict: STATE_DICT_TYPE,
+    visitor: Callable[[OBJ_PATH, Any], None],
+) -> None:
+    """
+    Invoke ``visitor`` for each value recursively in ``state_dict``.
+    Mapping, list, and tuple will be flattened and other value types are treated
+    as the terminal values and will invoke ``visitor``.
+    """
+
+    def _traverse_obj(path: OBJ_PATH, value: Any) -> None:
+        if isinstance(value, Mapping):
+            for k, v in value.items():
+                _traverse_obj(path + (str(k),), v)
+        elif isinstance(value, (list, tuple)):
+            for i, v in enumerate(value):
+                _traverse_obj(path + (i,), v)
+        else:
+            visitor(path, value)
+
+    for key, value in state_dict.items():
+        _traverse_obj((str(key),), value)
+
+
+def _flatten_state_dict(
+    state_dict: STATE_DICT_TYPE,
+) -> tuple[STATE_DICT_TYPE, FLATTEN_MAPPING]:
+    """
+    Flatten ``state_dict`` made of nested dicts and lists into a top level dictionary.
+
+    Use ``unflatten_state_dict`` to revert this process.
+    Returns:
+        A tuple with the flatten state_dict and a mapping from original to new state_dict.
+    N.B. The new keys are derived from the object paths, joined by dot.
+        For example: ``{ 'a': {'b':...}}`` results in the key `a.b`.
+    """
+    flattened: STATE_DICT_TYPE = {}
+    mappings: FLATTEN_MAPPING = {}
+
+    def flat_copy(path: OBJ_PATH, value: Any) -> None:
+        new_fqn = ".".join(map(str, path))
+        if new_fqn in flattened:
+            raise ValueError(f"duplicated flatten key {new_fqn}")
+        flattened[new_fqn] = value
+        mappings[new_fqn] = path
+
+    _traverse_state_dict(state_dict, flat_copy)
+    return flattened, mappings
+
+
+def _set_element(root_dict: STATE_DICT_TYPE, path: OBJ_PATH, value: Any) -> None:
+    """Set ``value`` in ``root_dict`` along the ``path`` object path."""
+    cur_container = cast(CONTAINER_TYPE, root_dict)
+
+    def extend_list(lst: list[Any], idx: int) -> None:
+        while len(lst) <= idx:
+            lst.append(None)
+
+    for i in range(1, len(path)):
+        prev_key = path[i - 1]
+        key = path[i]
+        def_val: CONTAINER_TYPE | list[Any] = {} if type(key) is str else []
+
+        if isinstance(cur_container, Mapping):
+            cur_container = cast(
+                CONTAINER_TYPE, cur_container.setdefault(prev_key, def_val)
+            )
+        else:
+            # pyrefly: ignore [bad-argument-type]
+            extend_list(cur_container, prev_key)
+            if cur_container[prev_key] is None:
+                cur_container[prev_key] = def_val
+            cur_container = cur_container[prev_key]
+
+    key = path[-1]
+    if type(key) is int:
+        extend_list(cast(list[Any], cur_container), key)
+
+    cur_container[key] = value
+
+
+def _unflatten_state_dict(
+    state_dict: STATE_DICT_TYPE, mapping: FLATTEN_MAPPING
+) -> STATE_DICT_TYPE:
+    """Restore the original nested state_dict according to ``mapping`` and the flattened ``state_dict``."""
+    nested: STATE_DICT_TYPE = {}
+    for key, value in state_dict.items():
+        _set_element(nested, mapping[key], value)
+    return nested
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_symmetric_memory/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_symmetric_memory/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ee29ea452143fce950421ade8803bf53776d397
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_symmetric_memory/__init__.py
@@ -0,0 +1,2121 @@
+from __future__ import annotations
+
+import math
+import os
+import socket
+import uuid
+from collections.abc import Callable, Generator
+from contextlib import contextmanager
+from datetime import timedelta
+from enum import Enum
+from functools import partial
+from typing import Any, Literal
+
+import torch
+import torch.distributed._functional_collectives as funcol
+import torch.distributed.distributed_c10d as c10d
+from torch._C._autograd import DeviceType
+from torch._C._distributed_c10d import _SymmetricMemory, Work as _Work
+
+
+_group_name_to_store: dict[str, c10d.Store] = {}
+
+
+def enable_symm_mem_for_group(group_name: c10d.GroupName) -> None:
+    """
+    Enables symmetric memory for a process group.
+
+    Args:
+        group_name (str): the name of the process group.
+    """
+    if group_name in _group_name_to_store:
+        return
+
+    group = c10d._resolve_process_group(group_name)
+    global_ranks = sorted(c10d._world.pg_group_ranks[group].keys())
+    # Different subgroups with the same name should use different stores
+    global_ranks_str = "_".join(map(str, global_ranks))
+    store = c10d.PrefixStore(
+        f"symmetric_memory-{global_ranks_str}",
+        c10d._get_process_group_store(group),
+    )
+    _group_name_to_store[group_name] = store
+    _SymmetricMemory.set_group_info(
+        group_name,
+        group.rank(),
+        group.size(),
+        store,
+    )
+
+
+_is_test_mode: bool = False
+_mocked_group_names: set[str] | None = None
+
+
+@contextmanager
+def _test_mode(group_names: set[str] | None = None) -> Generator[None, None, None]:
+    """
+    Forces ``is_symm_mem_enabled_for_group()`` to return ``True`` and the ops
+    defined in the ``symm_mem`` namespace to use fallback implementations.
+
+    The context manager is not thread safe.
+    """
+    global _is_test_mode
+    global _mocked_group_names
+    prev = _is_test_mode
+    prev_group_names = _mocked_group_names
+    try:
+        _is_test_mode = True
+        _mocked_group_names = group_names
+        yield
+    finally:
+        _is_test_mode = prev
+        _mocked_group_names = prev_group_names
+
+
+def is_symm_mem_enabled_for_group(group_name: c10d.GroupName) -> bool:
+    """
+    Check if symmetric memory is enabled for a process group.
+
+    Args:
+        group_name (str): the name of the process group.
+    """
+    if _is_test_mode:
+        return _mocked_group_names is None or group_name in _mocked_group_names
+    return group_name in _group_name_to_store
+
+
+_group_name_to_workspace_tensor: dict[str, torch.Tensor | None] = {}
+
+
+def get_symm_mem_workspace(
+    group_name: c10d.GroupName, min_size: int
+) -> _SymmetricMemory:
+    """
+    Get the symmetric memory workspace associated with the process group. If
+    ``min_size`` is greater than the workspace associated with ``group_name``,
+    the workspace will be re-allocated and re-rendezvous'd.
+
+    Args:
+        group_name (str): the name of the process group.
+        min_size (int): the size requirement for the workspace in bytes.
+
+    Returns:
+        _SymmetricMemory: the symmetric memory workspace associated with the
+        group.
+    """
+    enable_symm_mem_for_group(group_name)
+
+    tensor = _group_name_to_workspace_tensor.get(group_name)
+    size = tensor.numel() * tensor.element_size() if tensor is not None else 0
+    if tensor is None or size < min_size:
+        if torch.cuda.is_current_stream_capturing():
+            curr_size = 0 if tensor is None else tensor.numel() * tensor.element_size()
+            raise RuntimeError(
+                f"get_symm_mem_workspace(): the requested size ({min_size} bytes) "
+                "is greater than the size of the currently allocated workspace "
+                f"({curr_size} bytes). It's currently not possible to expand the "
+                "workspace size during graph capture. Please invoke "
+                f'`get_symm_mem_workspace(group_name="{group_name}", '
+                f'min_size="{min_size}")` before initiating the graph capture '
+                "and try again."
+            )
+        tensor = _SymmetricMemory.empty_strided_p2p(
+            (max(size, min_size),),
+            [1],
+            torch.uint8,
+            torch.device(f"cuda:{torch.cuda.current_device()}"),
+            group_name,
+        )
+        _group_name_to_workspace_tensor[group_name] = tensor
+    return _SymmetricMemory.rendezvous(tensor)
+
+
+_backend_streams: dict[int, torch.cuda.Stream] = {}
+
+
+def _get_backend_stream(priority: int = 0) -> torch.cuda.Stream:
+    if priority not in _backend_streams:
+        _backend_streams[priority] = torch.cuda.Stream(priority=priority)
+    return _backend_streams[priority]
+
+
+def _pipelined_multi_all_gather_and_consume(
+    shard: list[torch.Tensor],
+    shard_consumer: Callable[[list[torch.Tensor], int], None],
+    ag_out: list[torch.Tensor],
+    group_name: c10d.GroupName,
+    ag_out_needed: bool = True,
+) -> None:
+    """
+    Perform the following logic with micro-pipelined computation and
+    communication:
+
+        gathered = [
+            all_gather_tensor(x, gather_dim=0, group=group)
+            for x in shard
+        ]
+
+        shards = [[] for _ in range(group_size)]
+        for x in ag_out:
+            for i, y in enumerate(x.chunk(group_size)):
+                shards[i].append(y)
+
+        for src_rank, shard in enumerate(shards):
+            shard_consumer(shard, src_rank)
+    """
+    p2p_workspace_size_req = 0
+    for x in shard:
+        p2p_workspace_size_req += x.numel() * x.element_size()
+    symm_mem = get_symm_mem_workspace(group_name, min_size=p2p_workspace_size_req)
+    group_size = symm_mem.world_size
+    rank = symm_mem.rank
+
+    symm_mem.barrier(channel=0)
+    backend_stream = _get_backend_stream()
+    backend_stream.wait_stream(torch.cuda.current_stream())
+
+    for x, y in zip(shard, ag_out):
+        assert x.is_contiguous(), (
+            "_pipelined_all_gather_and_consume: all tensors "
+            "in `shard` must be contiguous"
+        )
+        assert y.is_contiguous(), (
+            "_pipelined_all_gather_and_consume: all tensors "
+            "in `ag_out` must be contiguous"
+        )
+        assert x.shape[0] * group_size == y.shape[0]
+        assert x.shape[1:] == y.shape[1:]
+
+    def copy_shard(dst: list[torch.Tensor], src: list[torch.Tensor]) -> None:
+        for d, s in zip(dst, src):
+            d.copy_(s)
+
+    def get_p2p_bufs(remote_rank: int) -> list[torch.Tensor]:
+        offset_bytes = 0
+        bufs = []
+        for x in shard:
+            buf = symm_mem.get_buffer(
+                remote_rank,
+                x.shape,
+                x.dtype,
+                storage_offset=offset_bytes // x.element_size(),
+            )
+            bufs.append(buf)
+            offset_bytes += buf.numel() * buf.element_size()
+        return bufs
+
+    local_p2p_bufs = get_p2p_bufs(rank)
+
+    # shards[i] => shard from rank i
+    shards: list[list[torch.Tensor]] = [[] for _ in range(group_size)]
+    for x in ag_out:
+        for i, y in enumerate(x.chunk(group_size)):
+            shards[i].append(y)
+
+    # Parallelization strategy: after each rank copies its shard into its local
+    # p2p buffer, every rank issues independent p2p copy -> shard_consumer
+    # sequences to two streams. In addition to computation/communication
+    # overlapping, the strategy allows for computation/computation overlapping,
+    # greatly reducing quantization inefficiency.
+    #
+    # Notation:
+    # - "mv" for the copy to local buffer
+    # - "cp" for p2p copies
+    # - "b" for barriers
+    #
+    # Constraints:
+    # - The GPU scheduler may or may not overlap "mv" with the first shard_consumer.
+    # - "cp" from different streams cannot overlap.
+    #
+    # Ideal scenario 0 - "mv" overlaps with the first shard_consumer:
+    #
+    # stream 0: [ shard_consumer ][ cp ][ shard_consumer ]
+    # stream 1: [ mv ][b][ cp ][ shard_consumer ]
+    #
+    # Ideal scenario 1 - "mv" is scheduled before the first shard_consumer:
+    #
+    # stream 0:       [ shard_consumer ][ cp ][ shard_consumer ]
+    # stream 1: [ mv ][b][ cp ][ shard_consumer ]
+    #
+    # Suboptimal scenario 0 - "mv" is scheduled after the first shard_consumer:
+    #
+    # stream 0: [ shard_consumer ]               [ cp ][ shard_consumer ]
+    # stream 1:                   [ mv ][b][ cp ][ shard_consumer ]
+    #
+    # Suboptimal scenario 0 - "b" is scheduled after the first shard_consumer:
+    #
+    # stream 0:       [ shard_consumer ]         [ cp ][ shard_consumer ]
+    # stream 1: [ mv ]                  [b][ cp ][ shard_consumer ]
+    #
+    # We haven't yet figured out a way to ensure "mv" and "b" are either
+    # overlapped with or scheduled before the first shard_consumer. Thus, to
+    # prevent suboptimal scenarios, we are giving up the chance to overlap "mv"
+    # and "b" with the first shard_consumer for now.
+    copy_shard(dst=local_p2p_bufs, src=shard)
+    symm_mem.barrier(channel=1)
+    backend_stream.wait_stream(torch.cuda.current_stream())
+
+    # At this point, all ranks have copied their local shard to
+    # their local p2p buffer. Each rank can now copy and consume
+    # remote shards.
+    shard_consumer(shard, rank)
+
+    for step in range(1, group_size):
+        if step % 2 == 0:
+            stream = torch.cuda.current_stream()
+        else:
+            stream = backend_stream
+        remote_rank = (step + rank) % group_size
+        remote_p2p_bufs = get_p2p_bufs(remote_rank)
+        with stream:
+            copy_shard(dst=shards[remote_rank], src=remote_p2p_bufs)
+            shard_consumer(shards[remote_rank], remote_rank)
+
+    if ag_out_needed:
+        # Copy from input to the all-gather output. Opportunistically overlap
+        # it with the last shard_consumer.
+        if group_size % 2 == 0:
+            stream = torch.cuda.current_stream()
+        else:
+            stream = backend_stream
+        with stream:
+            copy_shard(dst=shards[rank], src=shard)
+
+    torch.cuda.current_stream().wait_stream(backend_stream)
+    symm_mem.barrier(channel=0)
+
+
+def _pipelined_all_gather_and_consume(
+    shard: torch.Tensor,
+    shard_consumer: Callable[[torch.Tensor, int], None],
+    ag_out: torch.Tensor,
+    group_name: c10d.GroupName,
+    ag_out_needed: bool = True,
+) -> None:
+    """
+    Perform the following logic with micro-pipelined computation and
+    communication:
+
+        ag_out = all_gather_tensor(shard, gather_dim=0, group=group)
+        shards = ag_out.chunk(group.size())
+        for src_rank, shard in enumerate(shards):
+            shard_consumer(shard, src_rank)
+    """
+
+    def adapter(shard: list[torch.Tensor], rank: int) -> None:
+        shard_consumer(shard[0], rank)
+
+    _pipelined_multi_all_gather_and_consume(
+        [shard],
+        adapter,
+        [ag_out],
+        group_name,
+        ag_out_needed,
+    )
+
+
+def _pipelined_produce_and_all2all(
+    chunk_producer: Callable[[int, torch.Tensor], None],
+    output: torch.Tensor,
+    group_name: c10d.GroupName,
+    out_chunk_dim: int = 0,
+) -> None:
+    """
+    Perform the following logic with micro-pipelined computation and
+    communication:
+
+        chunks = [
+            chunk_producer(dst_rank, chunks[dst_rank])
+            for dst_rank in range(group_size):
+        ]
+        dist.all_to_all_single(output=output, input=torch.cat(chunks))
+    """
+    out_chunks = output.chunk(
+        c10d._get_group_size_by_name(group_name), dim=out_chunk_dim
+    )
+    p2p_workspace_size_req = out_chunks[0].numel() * out_chunks[0].element_size() * 2
+    symm_mem = get_symm_mem_workspace(group_name, min_size=p2p_workspace_size_req)
+    group_size = symm_mem.world_size
+    rank = symm_mem.rank
+
+    symm_mem.barrier(channel=0)
+    backend_stream = _get_backend_stream()
+    backend_stream.wait_stream(torch.cuda.current_stream())
+
+    def get_p2p_buf(rank: int, idx: int) -> torch.Tensor:
+        assert idx in (0, 1)
+        offset = 0 if idx == 0 else out_chunks[0].numel()
+        return symm_mem.get_buffer(
+            rank, out_chunks[0].shape, out_chunks[0].dtype, offset
+        )
+
+    # Prepare two local p2p buffers, so that a remote rank can pull the result
+    # of step [i] in one p2p buffer while the local rank can compute the
+    # result of step [i+1] and write it directly the other p2p buffer.
+    local_p2p_buf_0 = get_p2p_buf(rank, 0)
+    local_p2p_buf_1 = get_p2p_buf(rank, 1)
+
+    for step in range(1, group_size):
+        remote_rank = (rank - step) % group_size
+        if step % 2 == 0:
+            stream = torch.cuda.current_stream()
+            p2p_buf = local_p2p_buf_1
+            remote_p2p_buf = get_p2p_buf(remote_rank, 1)
+        else:
+            stream = backend_stream
+            p2p_buf = local_p2p_buf_0
+            remote_p2p_buf = get_p2p_buf(remote_rank, 0)
+        with stream:
+            # Parallelization strategy: every rank issues independent compute
+            # -> barrier -> p2p copy sequences on two streams. In addition to
+            # computation/communication overlapping, the strategy allows for
+            # computation/computation overlapping, greatly reducing
+            # quantization inefficiency.
+            #
+            # Ideally, stream activities would look like this ("b" for
+            # barriers, "cp" for p2p copies):
+            #
+            # [rank 0]
+            # stream 0:         [  chunk_producer  ][b][ cp ][  chunk_producer ][b][ cp ]
+            # stream 1: [  chunk_producer  ][b][ cp ][  chunk_producer  ][b][ cp ]
+            #
+            # [rank 1]
+            # stream 0:         [  chunk_producer  ][b][ cp ][  chunk_producer ][b][ cp ]
+            # stream 1: [  chunk_producer  ][b][ cp ][  chunk_producer  ][b][ cp ]
+            #
+            # Note that the barriers synchronize streams with the same ID
+            # across ranks. They don't synchronize streams on the same rank.
+            #
+            # Since the work on both streams is independent, there's no
+            # guarantee that the chunk_producer from stream 0 or stream 1 will
+            # be scheduled first. If there is a scheduling mismatch across
+            # ranks, the barrier forces all ranks to wait for the slowest.
+            #
+            # When scheduling mismatches occur among ranks, the stream
+            # activities might look like this (note that p2p copies from
+            # different streams cannot overlap with each other):
+            #
+            # [rank 0]
+            # stream 0: [  chunk_producer  ][b        ][ cp ][  chunk_producer ][b       ][ cp ]
+            # stream 1:         [  chunk_producer  ][b]      [ cp ][  chunk_producer  ][b]      [ cp ]
+            #
+            # [rank 1]
+            # stream 0:         [  chunk_producer  ][b]      [ cp ][  chunk_producer  ][b]      [ cp ]
+            # stream 1: [  chunk_producer  ][b        ][ cp ][  chunk_producer  ][b      ][ cp ]
+            #
+            # To prevent this, we need to ensure that the chunk_producer on
+            # stream 1 gets scheduled first on every rank. Without access to
+            # the underlying kernels, CUDA offers no API to control the
+            # scheduling order of two independent, overlapping kernels. Our
+            # solution is to issue a small sleep kernel in stream 0. The sleep
+            # duration is insignificant, but having an extra task in stream 0
+            # will almost guarantee that the chunk_producer on stream 1 gets
+            # scheduled first. Once the first chunk_producer is scheduled in
+            # the correct order, there's very little room for the scheduling
+            # order of subsequent kernels to be inconsistent across ranks.
+            if step == 2:
+                torch.cuda._sleep(100)
+            chunk_producer((rank + step) % group_size, p2p_buf)
+            symm_mem.barrier(channel=step % 2)
+            out_chunks[remote_rank].copy_(remote_p2p_buf)
+            # The local P2P buffer can only be overwritten by the next
+            # chunk_producer after all peers have finished reading from it.
+            symm_mem.barrier(channel=step % 2)
+
+    # If the sleep wasn't issued in the above loop, do it now.
+    if group_size == 2:
+        torch.cuda._sleep(100)
+
+    chunk_producer(rank, out_chunks[rank])
+    torch.cuda.current_stream().wait_stream(backend_stream)
+    symm_mem.barrier(channel=0)
+
+
+lib = torch.library.Library("symm_mem", "DEF")  # noqa: TOR901
+lib.define(
+    "fused_all_gather_matmul("
+    "Tensor A, Tensor[] Bs, int gather_dim, str group_name, *, bool return_A = True) -> (Tensor?, Tensor[])",
+    tags=[torch._C.Tag.needs_fixed_stride_order],
+)
+lib.define(
+    "fused_all_gather_scaled_matmul("
+    "Tensor A, Tensor[] Bs, Tensor A_scale, Tensor[] B_scales, "
+    "int gather_dim, str group_name, "
+    "Tensor?[] biases, "
+    "Tensor?[] result_scales, "
+    "ScalarType?[] out_dtypes, "
+    "bool[] use_fast_accum) -> (Tensor, Tensor[])",
+    tags=[torch._C.Tag.needs_fixed_stride_order],
+)
+lib.define(
+    "fused_matmul_reduce_scatter(Tensor A, Tensor B, str reduce_op, int scatter_dim, str group_name) -> Tensor",
+    tags=[torch._C.Tag.needs_fixed_stride_order],
+)
+lib.define(
+    "fused_scaled_matmul_reduce_scatter("
+    "Tensor A, Tensor B, Tensor A_scale, Tensor B_scale, "
+    "str reduce_op, int orig_scatter_dim, int scatter_dim_after_maybe_reshape, str group_name, SymInt[]? output_shape, "
+    "Tensor? bias = None, "
+    "Tensor? result_scale = None, "
+    "ScalarType? out_dtype = None, "
+    "bool use_fast_accum = False) -> Tensor",
+    tags=[torch._C.Tag.needs_fixed_stride_order],
+)
+lib.define("_low_contention_all_gather(Tensor tensor, str group_name) -> Tensor")
+lib.define(
+    "_low_contention_reduce_scatter(Tensor tensor, str reduce_op, str group_name) -> Tensor"
+)
+
+lib.define("get_remote_tensors(Tensor x, str group_name) -> Tensor[]")
+"""
+Given a local tensor and a group name, return a tuple of tensors that are
+symmetric on other devices. The returned tensors are ordered by rank IDs. The
+length of the tuple equals to the size of the group.
+
+Note: this API works only when `world_within_direct_access()` returns True, i.e.
+only when the group is within NVLink domain or similar. It does not work across
+network interfaces.
+"""
+
+
+@torch.library.impl(lib, "get_remote_tensors", "CUDA")
+def _get_remote_tensors_default(
+    local: torch.Tensor, group_name: c10d.GroupName
+) -> tuple[torch.Tensor, ...]:
+    hdl = rendezvous(local, group_name)
+    if hdl is None:
+        raise ValueError("Tensor is not allocated from Symmetric Memory")
+
+    return tuple(
+        hdl.get_remote_tensor(peer, local.size(), local.dtype)
+        for peer in range(hdl.world_size)
+    )
+
+
+@torch.library.impl(lib, "get_remote_tensors", "Meta")
+def _get_remote_tensors_meta(
+    local: torch.Tensor, group_name: c10d.GroupName
+) -> tuple[torch.Tensor, ...]:
+    group = c10d._resolve_process_group(group_name)
+    return tuple(torch.empty_like(local) for _ in range(group.size()))
+
+
+class _ScaleMode(Enum):
+    UNSCALED = "unscaled"
+    TENSOR_WISE = "tensor-wise"
+    ROW_WISE_SHARDED = "row-wise-sharded"
+    ROW_WISE_REPLICATED = "row-wise-replicated"
+
+
+def _check_and_verify_fp8_all_gather_scale_mode(
+    shard: torch.Tensor, scale: torch.Tensor | None, gather_dim: int, group_size: int
+) -> _ScaleMode:
+    full_shape = list(shard.shape)
+    full_shape[gather_dim] *= group_size
+
+    if scale is None:
+        return _ScaleMode.UNSCALED
+    elif scale.shape[:-1] == shard.shape[:-1] and scale.shape[-1] == 1:
+        # Row-wise scaling
+        #
+        # NOTE: when the last dim of both A_shard and A_scale is one, we can't
+        # tell if A_scale is replicated tensor-wise scale or sharded row-wise
+        # scale. Treating it as row-wise scaling for safety.
+        return _ScaleMode.ROW_WISE_SHARDED
+    elif scale.numel() == 1:
+        return _ScaleMode.TENSOR_WISE
+    elif list(scale.shape[:-1]) == full_shape[:-1]:
+        return _ScaleMode.ROW_WISE_REPLICATED
+    else:
+        raise ValueError(
+            "Invalid scale shape for fp8 all-gather "
+            f"(shard shape: {shard.shape}, scale shape: {scale.shape})"
+        )
+
+
+def _fused_all_gather_matmul_impl(
+    mm_out_op: torch._ops.OpOverload,
+    A_shard: torch.Tensor,
+    Bs: list[torch.Tensor],
+    A_scale: torch.Tensor | None,
+    kwargs_list: list[dict[str, Any]],
+    out_dtypes: list[torch.dtype | None],
+    gather_dim: int,
+    group_name: c10d.GroupName,
+    return_A: bool,
+) -> tuple[torch.Tensor | None, list[torch.Tensor]]:
+    if A_shard.dim() < 2:
+        raise ValueError("A_shard must be a matrix")
+    for B in Bs:
+        if B.dim() != 2:
+            raise ValueError("B must be a matrix")
+    if len(out_dtypes) != len(Bs):
+        raise ValueError("len(out_types) must be the same as len(Bs)")
+    if len(kwargs_list) != len(Bs):
+        raise ValueError("len(kwargs_list) must be the same as len(Bs)")
+    if gather_dim < 0 or gather_dim >= A_shard.dim():
+        raise ValueError("Invalid gather_dim")
+
+    group = c10d._resolve_process_group(group_name)
+
+    if gather_dim == A_shard.ndim - 1 or gather_dim == -1:
+        return _fused_all_gather_matmul_last_gather_dim_impl(
+            mm_out_op,
+            A_shard,
+            Bs,
+            A_scale,
+            kwargs_list,
+            out_dtypes,
+            gather_dim,
+            group_name,
+            return_A,
+        )
+
+    # Move the gather_dim to the front and flatten the tensor into a 2D matrix.
+    # The flattened tensor doesn't need to be contiguous (for computation
+    # efficiency), as _pipelined_all_gather_and_consume guarantees that shards
+    # passed to shard_consumer are contiguous.
+    A_shard_flat = A_shard.movedim(gather_dim, 0)
+    leading_dims = [group.size()] + list(A_shard_flat.shape[:-1])
+    A_shard_flat = A_shard_flat.flatten(0, -2)
+
+    # Helper function for reverting the above transformation
+    def unflatten(t: torch.Tensor) -> torch.Tensor:
+        return t.view(*leading_dims, -1).flatten(0, 1).movedim(0, gather_dim)
+
+    A_flat = A_shard_flat.new_empty(
+        A_shard_flat.shape[0] * group.size(),
+        A_shard_flat.shape[1],
+    )
+
+    outputs = [
+        A_flat.new_empty(A_flat.shape[0], B.shape[1], dtype=out_dtype or B.dtype)
+        for B, out_dtype in zip(Bs, out_dtypes)
+    ]
+    output_shards = [output.chunk(group.size()) for output in outputs]
+
+    scale_mode = _check_and_verify_fp8_all_gather_scale_mode(
+        shard=A_shard, scale=A_scale, gather_dim=gather_dim, group_size=group.size()
+    )
+
+    # Computing block-wise matmul along the first dim of A
+    if scale_mode == _ScaleMode.ROW_WISE_SHARDED:
+        assert A_scale is not None
+        A_scale_shard = A_scale.movedim(gather_dim, 0).flatten(0, -2)
+        A_scale_flat = A_scale_shard.new_empty(
+            A_scale_shard.shape[0] * group.size(),
+            A_scale_shard.shape[1],
+        )
+
+        def row_wise_sharded_consumer(shard: list[torch.Tensor], rank: int) -> None:
+            for idx, (B, kwargs) in enumerate(zip(Bs, kwargs_list)):
+                mm_out_op(
+                    shard[0],
+                    B,
+                    scale_a=shard[1],
+                    **kwargs,
+                    out=output_shards[idx][rank],
+                )
+
+        _pipelined_multi_all_gather_and_consume(
+            [A_shard_flat, A_scale_shard],
+            row_wise_sharded_consumer,
+            [A_flat, A_scale_flat],
+            group_name,
+            return_A,
+        )
+    elif scale_mode == _ScaleMode.ROW_WISE_REPLICATED:
+        assert A_scale is not None
+        A_scale_shards = (
+            A_scale.movedim(gather_dim, 0).flatten(0, -2).chunk(group.size())
+        )
+
+        def row_wise_replicated_consumer(shard: torch.Tensor, rank: int) -> None:
+            for idx, (B, kwargs) in enumerate(zip(Bs, kwargs_list)):
+                mm_out_op(
+                    shard,
+                    B,
+                    scale_a=A_scale_shards[rank],
+                    **kwargs,
+                    out=output_shards[idx][rank],
+                )
+
+        _pipelined_all_gather_and_consume(
+            A_shard_flat,
+            row_wise_replicated_consumer,
+            A_flat,
+            group_name,
+            return_A,
+        )
+    else:
+        if scale_mode == _ScaleMode.TENSOR_WISE:
+            assert A_scale is not None
+            for kwargs in kwargs_list:
+                kwargs["scale_a"] = A_scale
+        else:
+            assert scale_mode == _ScaleMode.UNSCALED
+
+        def default_consumer(shard: torch.Tensor, rank: int) -> None:
+            for idx, (B, kwargs) in enumerate(zip(Bs, kwargs_list)):
+                mm_out_op(shard, B, **kwargs, out=output_shards[idx][rank])
+
+        _pipelined_all_gather_and_consume(
+            A_shard_flat,
+            default_consumer,
+            A_flat,
+            group_name,
+            return_A,
+        )
+
+    A = unflatten(A_flat) if return_A else None
+    return A, [unflatten(output) for output in outputs]
+
+
+def _pipelined_all_gather_and_consume_last_dim(
+    shard: torch.Tensor,
+    shard_consumer: Callable[[torch.Tensor, int], None],
+    ag_out: torch.Tensor,
+    group_name: c10d.GroupName,
+    ag_out_needed: bool = True,
+) -> None:
+    p2p_workspace_size_req = 0
+    p2p_workspace_size_req = shard.numel() * shard.element_size()
+    symm_mem = get_symm_mem_workspace(group_name, min_size=p2p_workspace_size_req)
+    group_size = symm_mem.world_size
+    rank = symm_mem.rank
+
+    symm_mem.barrier(channel=0)
+    backend_stream = _get_backend_stream()
+    backend_stream.wait_stream(torch.cuda.current_stream())
+
+    def copy_shard(dst: torch.Tensor, src: torch.Tensor) -> None:
+        dst.copy_(src)
+
+    def get_p2p_buf(remote_rank: int) -> torch.Tensor:
+        buf = symm_mem.get_buffer(
+            remote_rank,
+            shard.shape,
+            shard.dtype,
+        )
+        return buf
+
+    local_p2p_buf = get_p2p_buf(rank)
+
+    shards = ag_out.chunk(group_size)
+
+    copy_shard(dst=local_p2p_buf, src=shard)
+    symm_mem.barrier(channel=1)
+    backend_stream.wait_stream(torch.cuda.current_stream())
+
+    # At this point, all ranks have copied their local shard to
+    # their local p2p buffer. Each rank can now copy and consume
+    # remote shards.
+    shard_consumer(shard, rank)
+
+    for step in range(1, group_size):
+        if step % 2 == 0:
+            stream = torch.cuda.current_stream()
+        else:
+            stream = backend_stream
+        remote_rank = (step + rank) % group_size
+        remote_p2p_buf = get_p2p_buf(remote_rank)
+        with stream:
+            copy_shard(dst=shards[remote_rank], src=remote_p2p_buf)
+            shard_consumer(shards[remote_rank], remote_rank)
+
+    if ag_out_needed:
+        # Copy from input to the all-gather output. Opportunistically overlap
+        # it with the last shard_consumer.
+        if group_size % 2 == 0:
+            stream = torch.cuda.current_stream()
+        else:
+            stream = backend_stream
+        with stream:
+            copy_shard(dst=shards[rank], src=shard)
+
+    torch.cuda.current_stream().wait_stream(backend_stream)
+    symm_mem.barrier(channel=0)
+
+
+def _fused_all_gather_matmul_last_gather_dim_impl(
+    mm_out_op: torch._ops.OpOverload,
+    A_shard: torch.Tensor,
+    Bs: list[torch.Tensor],
+    A_scale: torch.Tensor | None,
+    kwargs_list: list[dict[str, Any]],
+    out_dtypes: list[torch.dtype | None],
+    gather_dim: int,
+    group_name: c10d.GroupName,
+    return_A: bool,
+) -> tuple[torch.Tensor | None, list[torch.Tensor]]:
+    group = c10d._resolve_process_group(group_name)
+    group_size = group.size()
+
+    B_shards = [B.chunk(group.size()) for B in Bs]
+
+    leading_dims = list(A_shard.shape[:-1])
+    A_shard_flat = A_shard.flatten(0, -2)
+
+    def unflatten(t: torch.Tensor) -> torch.Tensor:
+        return t.view(*leading_dims, -1)
+
+    A_flat_out = A_shard_flat.new_empty(
+        A_shard_flat.shape[0] * group.size(),
+        A_shard_flat.shape[1],
+    )
+
+    outputs = [
+        torch.empty(
+            (A_shard_flat.shape[0], B.shape[1]),
+            dtype=out_dtype or B.dtype,
+            device=A_shard.device,
+        )
+        for B, out_dtype in zip(Bs, out_dtypes)
+    ]
+
+    first = True
+    events = [torch.cuda.Event() for _ in outputs]
+
+    def default_consumer(shard: torch.Tensor, rank: int) -> None:
+        nonlocal first
+        for out, event, B_shard, kwargs in zip(outputs, events, B_shards, kwargs_list):
+            event.wait()
+            if first:
+                torch.ops.aten.mm.out(shard, B_shard[rank], **kwargs, out=out)
+            else:
+                out.addmm_(shard, B_shard[rank])
+            event.record()
+
+        first = False
+
+    _pipelined_all_gather_and_consume_last_dim(
+        A_shard_flat,
+        default_consumer,
+        A_flat_out,
+        group_name,
+        return_A,
+    )
+    ret_A = None
+    if return_A:
+        # This path is inefficient and will be filtered out at passes stage
+        # Added only for completeness.
+        A_split_cat_out_flat = torch.cat(A_flat_out.chunk(group_size), dim=-1)
+        ret_A = unflatten(A_split_cat_out_flat)
+
+    return ret_A, [unflatten(output) for output in outputs]
+
+
+@torch.library.impl(lib, "fused_all_gather_matmul", "Meta")
+def _fused_all_gather_matmul_fallback(
+    A_shard: torch.Tensor,
+    Bs: list[torch.Tensor],
+    gather_dim: int,
+    group_name: c10d.GroupName,
+    *,
+    return_A: bool = True,
+) -> tuple[torch.Tensor | None, list[torch.Tensor]]:
+    group_size = c10d._get_group_size_by_name(group_name)
+    A = torch.ops._c10d_functional.all_gather_into_tensor(
+        A_shard.contiguous(), group_size, group_name
+    )
+    A = torch.ops._c10d_functional.wait_tensor(A)
+    if gather_dim == A.ndim - 1 or gather_dim == -1:
+        A_splits = A.chunk(group_size)
+        A_mm = torch.cat(A_splits, dim=-1)
+        res = [torch.matmul(A_mm, B) for B in Bs]
+        if return_A:
+            return A_mm, res
+        else:
+            return None, res
+
+    A = A.view(group_size, *A_shard.shape).movedim(gather_dim + 1, 1).flatten(0, 1)
+    res = [torch.matmul(A, B).movedim(0, gather_dim) for B in Bs]
+    if return_A:
+        return A.movedim(0, gather_dim), res
+    else:
+        return None, res
+
+
+@torch.library.impl(lib, "fused_all_gather_matmul", "CUDA")
+def _fused_all_gather_matmul(
+    A_shard: torch.Tensor,
+    Bs: list[torch.Tensor],
+    gather_dim: int,
+    group_name: c10d.GroupName,
+    *,
+    return_A: bool = True,
+) -> tuple[torch.Tensor | None, list[torch.Tensor]]:
+    """
+    Perform the following logic with micro-pipelined computation and
+    communication:
+
+        all_gather_tensor(A_shard, gather_dim, group_name) @ B
+
+    Optimal stride order for A_shard - if A_shard.movedim(gather_dim, 0) is
+    contiguous, no extra copy is required for input layout transformation.
+    Otherwise A_shard needs to be copied once.
+    """
+    if _is_test_mode:
+        return _fused_all_gather_matmul_fallback(
+            A_shard, Bs, gather_dim, group_name, return_A=return_A
+        )
+
+    if _should_use_fused_all_gather_matmul_native(A_shard, Bs, gather_dim, group_name):
+        group = c10d._resolve_process_group(group_name)
+        leading_dims = list(A_shard.shape[:-1])
+        leading_dims[0] *= group.size()
+        A, out = _fused_all_gather_matmul_native(
+            A_shard.flatten(0, -2), Bs[0], group_name
+        )
+        return A.view(*leading_dims, -1), [out.view(*leading_dims, -1)]
+
+    if _should_use_multimem_all_gather_matmul(
+        A_shard, gather_dim, group_name, return_A
+    ):
+        return None, _multimem_all_gather_matmul(A_shard, Bs, group_name)
+
+    with torch.profiler.record_function("fused_all_gather_matmul"):
+        return _fused_all_gather_matmul_impl(
+            torch.ops.aten.mm.out,
+            A_shard,
+            Bs,
+            None,
+            [{} for B in Bs],
+            [B.dtype for B in Bs],
+            gather_dim,
+            group_name,
+            return_A,
+        )
+
+
+def _should_use_fused_all_gather_matmul_native(
+    A_shard: torch.Tensor,
+    Bs: list[torch.Tensor],
+    gather_dim: int,
+    group_name: c10d.GroupName,
+) -> bool:
+    group = c10d._resolve_process_group(group_name)
+    local_M = math.prod(A_shard.shape[:-1])
+
+    return (
+        "TORCH_SYMM_MEM_ENABLE_NATIVE_ASYNC_TP" in os.environ
+        and A_shard.is_contiguous()
+        and gather_dim == 0
+        # _async_input_mm requires local_M to be divisible by world_size.
+        and local_M % group.size() == 0
+        # _async_input_mm outperforms the decomposition-based approach when the
+        # global M is small.
+        and 2048 < local_M * group.size() <= 4096
+        # _async_input_mm only supports a single B.
+        and len(Bs) == 1
+    )
+
+
+def _fused_all_gather_matmul_native(
+    A_shard: torch.Tensor,
+    B: torch.Tensor,
+    group_name: c10d.GroupName,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    symm_mem = rendezvous(A_shard, group_name)
+    if symm_mem is None:
+        symm_mem = get_symm_mem_workspace(
+            group_name, A_shard.numel() * A_shard.element_size()
+        )
+        symm_mem.barrier()
+        buf = symm_mem.get_buffer(symm_mem.rank, A_shard.shape, A_shard.dtype)
+        buf.copy_(A_shard)
+        A_shard = buf
+
+    rank = symm_mem.rank
+    world_size = symm_mem.world_size
+
+    current_stream = torch.cuda.current_stream()
+    backend_stream = _get_backend_stream(priority=-1)
+
+    symm_mem.barrier()
+    backend_stream.wait_stream(current_stream)
+    current_stream.wait_stream(backend_stream)
+
+    A = A_shard.new_empty(A_shard.shape[0] * world_size, A_shard.shape[1])
+    A_signals = torch.zeros(world_size, dtype=torch.uint32, device=A_shard.device)
+    A_shards = A.chunk(world_size)
+
+    A_shards[rank].copy_(A_shard)
+    if not torch.cuda.is_current_stream_capturing():
+        _SymmetricMemory.stream_write_value32(A_signals, rank, 1)
+    else:
+        _SymmetricMemory.memset32(A_signals, offset=rank, val=1, count=1)
+
+    out = torch.ops.symm_mem._async_input_mm(A, B, A_signals, rank)
+    for step in range(1, world_size):
+        src_rank = (rank + step) % world_size
+        src_buf = symm_mem.get_buffer(src_rank, A_shard.shape, A_shard.dtype)
+        with backend_stream:
+            A_shards[src_rank].copy_(src_buf)
+            if not torch.cuda.is_current_stream_capturing():
+                # cuStreamWriteValue32 issues a system level fence before the write
+                _SymmetricMemory.stream_write_value32(A_signals, src_rank, 1)
+            else:
+                _SymmetricMemory.memset32(A_signals, offset=src_rank, val=1, count=1)
+
+    current_stream.wait_stream(backend_stream)
+    backend_stream.wait_stream(current_stream)
+
+    symm_mem.barrier()
+    return A, out
+
+
+def _should_use_multimem_all_gather_matmul(
+    A_shard: torch.Tensor,
+    gather_dim: int,
+    group_name: c10d.GroupName,
+    return_A: bool,
+) -> bool:
+    group = c10d._resolve_process_group(group_name)
+    local_M = math.prod(A_shard.shape[:-1])
+    has_multicast_support = (
+        A_shard.device.type == "cuda"
+        and _SymmetricMemory.has_multicast_support(
+            DeviceType.CUDA, A_shard.device.index
+        )
+    )
+
+    return (
+        has_multicast_support
+        and not return_A
+        and A_shard.is_contiguous()
+        and gather_dim == 0
+        # The heuristic is empirical. We could refine it with a more
+        # sophisticated perf model.
+        and local_M * group.size() <= 2048
+    )
+
+
+def _multimem_all_gather_matmul(
+    A_shard: torch.Tensor,
+    Bs: list[torch.Tensor],
+    group_name: c10d.GroupName,
+) -> list[torch.Tensor]:
+    group = c10d._resolve_process_group(group_name)
+    A_shape = torch.Size((A_shard.shape[0] * group.size(), *A_shard.shape[1:]))
+    symm_mem = get_symm_mem_workspace(
+        group_name, A_shape.numel() * A_shard.element_size()
+    )
+    A = symm_mem.get_buffer(symm_mem.rank, A_shape, A_shard.dtype)
+    torch.ops.symm_mem.multimem_all_gather_out(A_shard, group_name, A)
+    return [torch.matmul(A, B) for B in Bs]
+
+
+@torch.library.impl(lib, "fused_all_gather_scaled_matmul", "Meta")
+def _fused_all_gather_scaled_matmul_fallback(
+    A_shard: torch.Tensor,
+    Bs: list[torch.Tensor],
+    A_scale: torch.Tensor,
+    B_scales: list[torch.Tensor],
+    gather_dim: int,
+    group_name: c10d.GroupName,
+    biases: list[torch.Tensor | None],
+    result_scales: list[torch.Tensor | None],
+    out_dtypes: list[torch.dtype | None],
+    use_fast_accum: list[bool],
+) -> tuple[torch.Tensor, list[torch.Tensor]]:
+    out_dtypes = _maybe_convert_scalar_types_to_dtypes(out_dtypes)
+
+    group_size = c10d._get_group_size_by_name(group_name)
+    A = torch.ops._c10d_functional.all_gather_into_tensor(
+        A_shard.contiguous(), group_size, group_name
+    )
+    A = torch.ops._c10d_functional.wait_tensor(A)
+    A = A.view(group_size, *A_shard.shape).movedim(gather_dim + 1, 1).flatten(0, 1)
+
+    scale_mode = _check_and_verify_fp8_all_gather_scale_mode(
+        shard=A_shard, scale=A_scale, gather_dim=gather_dim, group_size=group_size
+    )
+    if scale_mode == _ScaleMode.ROW_WISE_SHARDED:
+        A_scale_shard = A_scale
+        A_scale = torch.ops._c10d_functional.all_gather_into_tensor(
+            A_scale.contiguous(), group_size, group_name
+        )
+        A_scale = torch.ops._c10d_functional.wait_tensor(A_scale)
+        A_scale = (
+            A_scale.view(group_size, *A_scale_shard.shape)
+            .movedim(gather_dim + 1, 1)
+            .flatten(0, -2)
+        )
+    elif scale_mode == _ScaleMode.ROW_WISE_REPLICATED:
+        A_scale = A_scale.movedim(gather_dim, 0).flatten(0, -2)
+    else:
+        assert scale_mode == _ScaleMode.TENSOR_WISE
+
+    def scaled_matmul(
+        A: torch.Tensor,
+        B: torch.Tensor,
+        A_scale: torch.Tensor,
+        B_scale: torch.Tensor,
+        bias: torch.Tensor | None,
+        result_scale: torch.Tensor | None,
+        out_dtype: torch.dtype | None,
+        use_fast_accum: bool,
+    ) -> torch.Tensor:
+        leading_dims = A.shape[:-1]
+        res = torch.ops.aten._scaled_mm(
+            A.flatten(0, -2),
+            B,
+            A_scale,
+            B_scale,
+            bias,
+            result_scale,
+            out_dtype=out_dtype,
+            use_fast_accum=use_fast_accum,
+        )
+        return res.unflatten(0, leading_dims)
+
+    return A.movedim(0, gather_dim), [
+        scaled_matmul(
+            A, B, A_scale, B_scale, bias, result_scale, out_dtype, fast_accum
+        ).movedim(0, gather_dim)
+        for B, B_scale, bias, result_scale, out_dtype, fast_accum in zip(
+            Bs, B_scales, biases, result_scales, out_dtypes, use_fast_accum
+        )
+    ]
+
+
+@torch.library.impl(lib, "fused_all_gather_scaled_matmul", "CUDA")
+def _fused_all_gather_scaled_matmul(
+    A_shard: torch.Tensor,
+    Bs: list[torch.Tensor],
+    A_scale: torch.Tensor,
+    B_scales: list[torch.Tensor],
+    gather_dim: int,
+    group_name: c10d.GroupName,
+    biases: list[torch.Tensor | None],
+    result_scales: list[torch.Tensor | None],
+    out_dtypes: list[torch.dtype | None],
+    use_fast_accum: list[bool],
+) -> tuple[torch.Tensor, list[torch.Tensor]]:
+    """
+    Perform the following logic with micro-pipelined computation and
+    communication:
+
+        A = all_gather_tensor(A_shard, gather_dim, group_name)
+        leading_dims = A.shape[:-1]
+        res = torch.ops.aten._scaled_mm(A.flatten(0, -2), B, A_scale, B_scale)
+        res = res.unflatten(0, leading_dims)
+
+    The input `A_scale` can be tensor-wise, row-wise-sharded or
+    row-wise-replicated.
+
+    Optimal stride order for `A_shard` - if `A_shard.movedim(gather_dim, 0)` is
+    contiguous, no extra copy is required for input layout transformation.
+    Otherwise A_shard needs to be copied once.
+    """
+    out_dtypes = _maybe_convert_scalar_types_to_dtypes(out_dtypes)
+
+    if len(biases) != len(Bs):
+        raise ValueError("len(biases) must be the same as len(Bs)")
+    if len(result_scales) != len(Bs):
+        raise ValueError("len(result_scales) must be the same as len(Bs)")
+    if len(out_dtypes) != len(Bs):
+        raise ValueError("len(out_dtypes) must be the same as len(Bs)")
+    if len(use_fast_accum) != len(Bs):
+        raise ValueError("len(use_gast_accum_list) must be the same as len(Bs)")
+
+    if _is_test_mode:
+        return _fused_all_gather_scaled_matmul_fallback(
+            A_shard,
+            Bs,
+            A_scale,
+            B_scales,
+            gather_dim,
+            group_name,
+            biases,
+            result_scales,
+            out_dtypes,
+            use_fast_accum,
+        )
+
+    with torch.profiler.record_function("fused_all_gather_scaled_matmul"):
+        A, res = _fused_all_gather_matmul_impl(
+            torch.ops.aten._scaled_mm.out,
+            A_shard,
+            Bs,
+            A_scale,
+            [
+                {
+                    "scale_b": B_scale,
+                    "bias": bias,
+                    "scale_result": result_scale,
+                    "out_dtype": out_dtype,
+                    "use_fast_accum": fast_accum,
+                }
+                for B_scale, bias, result_scale, out_dtype, fast_accum in zip(
+                    B_scales, biases, result_scales, out_dtypes, use_fast_accum
+                )
+            ],
+            out_dtypes,
+            gather_dim,
+            group_name,
+            True,
+        )
+        assert A is not None
+        return A, res
+
+
+def make_contiguous_for_perm(
+    t: torch.Tensor,
+    perm: list[int],
+) -> torch.Tensor:
+    """
+    Restride `t` such that `t.permute(perm)` is contiguous.
+    """
+    inv_perm = [0] * len(perm)
+    for i, p in enumerate(perm):
+        inv_perm[p] = i
+    return t.permute(perm).contiguous().permute(inv_perm)
+
+
+def restride_A_shard_for_fused_all_gather_matmul(
+    t: torch.Tensor,
+    gather_dim: int,
+) -> torch.Tensor:
+    """
+    Restride the `A_shard` arg of `fused_all_gather_matmul` for optimal perf.
+    See the doc for `fused_all_gather_matmul` for detail.
+    """
+    perm = list(range(len(t.shape)))
+    perm.insert(0, perm.pop(gather_dim))
+    return make_contiguous_for_perm(t, perm)
+
+
+@torch.library.impl(lib, "fused_matmul_reduce_scatter", "CUDA")
+def _fused_matmul_reduce_scatter(
+    A: torch.Tensor,
+    B: torch.Tensor,
+    reduce_op: str,
+    scatter_dim: int,
+    group_name: c10d.GroupName,
+) -> torch.Tensor:
+    """
+    Perform the following logic with micro-pipelined computation and
+    communication:
+
+        reduce_scatter_tensor(A @ B, reduce_op, scatter_dim, group_name)
+
+    Optimal stride order for A - if A.movedim(scatter_dim, 0) is contiguous, no
+    extra copy is required for input layout transformation. Otherwise A needs
+    to be copied once.
+    """
+    if _is_test_mode:
+        return _fused_matmul_reduce_scatter_fallback(
+            A, B, reduce_op, scatter_dim, group_name
+        )
+
+    with torch.profiler.record_function("fused_matmul_reduce_scatter"):
+        return _fused_matmul_reduce_scatter_impl(
+            mm_out_op=torch.ops.aten.mm.out,
+            A=A,
+            B=B,
+            kwargs={},
+            out_dtype=A.dtype,
+            reduce_op=reduce_op,
+            scatter_dim=scatter_dim,
+            group_name=group_name,
+        )
+
+
+@torch.library.impl(lib, "fused_matmul_reduce_scatter", "Meta")
+def _fused_matmul_reduce_scatter_fallback(
+    A: torch.Tensor,
+    B: torch.Tensor,
+    reduce_op: str,
+    scatter_dim: int,
+    group_name: c10d.GroupName,
+) -> torch.Tensor:
+    res = funcol.reduce_scatter_tensor(A @ B, reduce_op, scatter_dim, group_name)
+    res = funcol.wait_tensor(res)
+    return res
+
+
+def _fused_matmul_reduce_scatter_impl(
+    mm_out_op: torch._ops.OpOverload,
+    A: torch.Tensor,
+    B: torch.Tensor,
+    kwargs: dict[str, Any],
+    out_dtype: torch.dtype | None,
+    reduce_op: str,
+    scatter_dim: int,
+    group_name: c10d.GroupName,
+) -> torch.Tensor:
+    if A.dim() < 2:
+        raise ValueError("A_shard must be a matrix")
+    if scatter_dim < 0 or scatter_dim >= A.dim():
+        raise ValueError("Invalid gather_dim")
+    if B.dim() != 2:
+        raise ValueError("B must be a matrix")
+    if reduce_op == "sum":
+        reduce_fn = partial(torch.sum, dim=0)
+    elif reduce_op == "avg":
+        reduce_fn = partial(torch.mean, dim=0)
+    else:
+        raise ValueError("reduce_op must be sum or avg")
+    group = c10d._resolve_process_group(group_name)
+    out_shape = [*A.shape[:-1], B.shape[1]]
+    out_shape[scatter_dim] //= group.size()
+
+    if scatter_dim == A.ndim - 1:
+        B_shards = B.chunk(group.size(), dim=B.ndim - 1)
+        A_flat = A.flatten(0, -2)
+
+        def _chunk_producer(rank: int, out: torch.Tensor) -> None:
+            mm_out_op(A_flat, B_shards[rank], **kwargs, out=out)
+
+        leading_dims = list(A.shape[:-1])
+
+        stacked_partials = torch.empty(
+            (A_flat.shape[0], B.shape[1]),
+            dtype=out_dtype or A.dtype,
+            device=A.device,
+        )
+
+        _pipelined_produce_and_all2all(
+            _chunk_producer,
+            stacked_partials,
+            group_name,
+            out_chunk_dim=1,
+        )
+
+        stacked_partials_view = stacked_partials.reshape(
+            *leading_dims, group.size(), -1
+        )
+        return reduce_fn(
+            stacked_partials_view,
+            dim=-2,
+        )
+
+    # Move the scatter_dim to the front and flatten the tensor into a 2D matrix
+    x = A.movedim(scatter_dim, 0)
+    leading_dims = [group.size()] + list(x.shape[:-1])
+    leading_dims[1] //= group.size()
+    x = x.flatten(0, -2)
+    A_shards = x.chunk(group.size())
+
+    # Computing block-wise matmul along the first dim of A
+    def chunk_producer(rank: int, out: torch.Tensor) -> None:
+        mm_out_op(A_shards[rank], B, **kwargs, out=out)
+
+    stacked_partials = x.new_empty(x.shape[0], B.shape[1], dtype=out_dtype or A.dtype)
+
+    _pipelined_produce_and_all2all(
+        chunk_producer,
+        stacked_partials,
+        group_name,
+    )
+
+    # Ensures that the transpose and reduction produce contiguous result
+    # in a single reduction kernel.
+    return reduce_fn(
+        stacked_partials.view(*leading_dims, -1)
+        .movedim(1, scatter_dim + 1)
+        .movedim(0, scatter_dim),
+        dim=scatter_dim,
+    )
+
+
+@torch.library.impl(lib, "fused_scaled_matmul_reduce_scatter", "CUDA")
+def _fused_scaled_matmul_reduce_scatter(
+    A: torch.Tensor,
+    B: torch.Tensor,
+    A_scale: torch.Tensor,
+    B_scale: torch.Tensor,
+    reduce_op: str,
+    orig_scatter_dim: int,
+    scatter_dim_after_maybe_reshape: int,
+    group_name: c10d.GroupName,
+    output_shape: list[int],
+    bias: torch.Tensor | None = None,
+    result_scale: torch.Tensor | None = None,
+    out_dtype: torch.dtype | None = None,
+    use_fast_accum: bool = False,
+) -> torch.Tensor:
+    if _is_test_mode:
+        return _fused_scaled_matmul_reduce_scatter_fallback(
+            A,
+            B,
+            A_scale,
+            B_scale,
+            reduce_op,
+            orig_scatter_dim,
+            scatter_dim_after_maybe_reshape,
+            group_name,
+            output_shape,
+            bias,
+            result_scale,
+            out_dtype,
+            use_fast_accum,
+        )
+    with torch.profiler.record_function("fused_scaled_matmul_reduce_scatter"):
+        return _fused_scaled_matmul_reduce_scatter_impl(
+            mm_out_op=torch.ops.aten._scaled_mm.out,
+            A=A,
+            B=B,
+            A_scale=A_scale,
+            kwargs={
+                "scale_b": B_scale,
+                "bias": bias,
+                "scale_result": result_scale,
+                "out_dtype": out_dtype,
+                "use_fast_accum": use_fast_accum,
+            },
+            out_dtype=out_dtype,
+            reduce_op=reduce_op,
+            orig_scatter_dim=orig_scatter_dim,
+            scatter_dim_after_maybe_reshape=scatter_dim_after_maybe_reshape,
+            group_name=group_name,
+            output_shape=output_shape,
+        )
+
+
+@torch.library.impl(lib, "fused_scaled_matmul_reduce_scatter", "Meta")
+def _fused_scaled_matmul_reduce_scatter_fallback(
+    A: torch.Tensor,
+    B: torch.Tensor,
+    A_scale: torch.Tensor,
+    B_scale: torch.Tensor,
+    reduce_op: str,
+    orig_scatter_dim: int,
+    scatter_dim_after_maybe_reshape: int,
+    group_name: c10d.GroupName,
+    output_shape: list[int],
+    bias: torch.Tensor | None = None,
+    result_scale: torch.Tensor | None = None,
+    out_dtype: torch.dtype | None = None,
+    use_fast_accum: bool = False,
+) -> torch.Tensor:
+    if A_scale.numel() > 1:
+        if A_scale.shape[:-1] != A.shape[:-1]:
+            raise ValueError(
+                "For row-wise scaling, the leading dims of A_scale "
+                "must match the leading dims of A "
+                f"(A shape: {A.shape}, A_scale shape: {A_scale.shape})"
+            )
+        A_scale = A_scale.flatten(0, -2).contiguous()
+    elif A_scale.numel() != 1:
+        raise ValueError(
+            "Invalid A_scale shape "
+            f"(A shape: {A.shape}, A_scale shape: {A_scale.shape})"
+        )
+
+    C = torch._scaled_mm(
+        A.flatten(0, -2).contiguous(),
+        B,
+        A_scale,
+        B_scale,
+        bias,
+        result_scale,
+        out_dtype,
+        use_fast_accum,
+    )
+    C = C.view(*output_shape[:-1], B.shape[1])
+    res = funcol.reduce_scatter_tensor(
+        C,
+        reduce_op,
+        orig_scatter_dim,  # need original scatter dim for 3D+ output tensor here
+        group_name,
+    )
+    res = funcol.wait_tensor(res)
+    return res
+
+
+def _fused_scaled_matmul_reduce_scatter_impl(
+    mm_out_op: torch._ops.OpOverload,
+    A: torch.Tensor,
+    B: torch.Tensor,
+    A_scale: torch.Tensor,
+    kwargs: dict[str, Any],
+    out_dtype: torch.dtype | None,
+    reduce_op: str,
+    orig_scatter_dim: int,
+    scatter_dim_after_maybe_reshape: int,
+    group_name: c10d.GroupName,
+    output_shape: list[int],
+) -> torch.Tensor:
+    if A.dim() < 2:
+        raise ValueError("A_shard must be a matrix")
+    if (
+        scatter_dim_after_maybe_reshape < 0
+        or scatter_dim_after_maybe_reshape >= A.dim()
+    ):
+        raise ValueError("Invalid scatter dim for 2D tensor input to scaled_mm")
+    if orig_scatter_dim < 0 or orig_scatter_dim >= len(output_shape):
+        raise ValueError("Invalid scatter dim for 3D+ output tensor")
+    if B.dim() != 2:
+        raise ValueError("B must be a matrix")
+    if reduce_op == "sum":
+        reduce_fn = partial(torch.sum, dim=0)
+    elif reduce_op == "avg":
+        reduce_fn = partial(torch.mean, dim=0)
+    else:
+        raise ValueError("reduce_op must be sum or avg")
+
+    group = c10d._resolve_process_group(group_name)
+
+    # Move scatter to first dim, then shard the tensor along the first dim, so the chunk producer
+    # can perform matmuls along the first dim.
+    A_with_scatter_dim_0 = A.movedim(scatter_dim_after_maybe_reshape, 0)
+
+    # To handle case where A is 3D+, reshape to 2D to prepare for mm which requires 2D inputs.
+    A_2D_with_scatter_dim_0 = A_with_scatter_dim_0.flatten(0, -2)
+
+    # Partition A along the first dim to prepare for sharding across TP process group.
+    A_shards = A_2D_with_scatter_dim_0.chunk(group.size())
+
+    # Now that 'A' is sharded along the first dim, we need to update its scale(s) accordingly.
+    # How we do this depends on if we are using tensorwise scaling, rowwise scaling, or no scaling.
+    tensorwise_scaling = A_scale is not None and A_scale.numel() == 1
+    rowwise_scaling = A_scale is not None and A_scale.numel() > 1
+
+    # For tensorwise scaling, the scale should be replicated so each shard has a copy.
+    if tensorwise_scaling:
+        A_scale_shards = [A_scale] * group.size()
+
+    # For rowwise scaling, we need to move the scatter dim to the first dim to match the
+    # dim swap of the 'A' tensor. Then we can shard the scales along the first dim, just like
+    # the 'A' tensor.
+    elif rowwise_scaling:
+        if A_scale.shape[:-1] != A.shape[:-1]:
+            raise ValueError(
+                "For row-wise scaling, the leading dims of A_scale "
+                "must match the leading dims of A "
+                f"(A shape: {A.shape}, A_scale shape: {A_scale.shape})"
+            )
+        A_scale = (
+            A_scale.movedim(scatter_dim_after_maybe_reshape, 0)
+            .contiguous()
+            .flatten(0, -2)
+        )
+        A_scale_shards = list(A_scale.chunk(group.size()))
+        # cuBLAS's row-wise kernel requires scales to be aligned to 16 bytes.
+        # When we slice them we might break this and need to reallocate them.
+        A_scale_shards = [
+            t if t.data_ptr() % 16 == 0 else t.clone() for t in A_scale_shards
+        ]
+    else:
+        raise ValueError("A_scale cannot be none for scaled_mm")
+
+    # Computing block-wise matmul along the first dim of A
+    def chunk_producer(rank: int, out: torch.Tensor) -> None:
+        mm_out_op(A_shards[rank], B, scale_a=A_scale_shards[rank], **kwargs, out=out)
+
+    # Stacked partials will be the 2D outputs of the pipelined scaled mm, and will
+    # have the shape (A_with_scatter_dim_0_tensor.shape[0], B.shape[1]) to align with the formula:
+    # (a*b,c) @ (c,d) = (a*b,d)
+    stacked_partials = A_with_scatter_dim_0.new_empty(
+        A_2D_with_scatter_dim_0.shape[0], B.shape[1], dtype=out_dtype or A.dtype
+    )
+
+    # Execute the pipelined mm/scaled_mm.
+    _pipelined_produce_and_all2all(
+        chunk_producer,
+        stacked_partials,
+        group_name,
+    )
+
+    # We now need to transform the *unreduced* stacked 2D partial mm outputs to an *unreduced* 3D+ output,
+    # then reduce-scatter. To do this, we first need to determine the shape of the unreduced 3D+ output,
+    # to reshape our stacked partials so we can apply the reduce-scatter.
+    #
+    # The *unreduced* 3D+ tensor will have dim 0 = `group_size`, as we have `group_size` instances of
+    # stacked partial outputs. The next dims will be A's leading dims (sharded along the original scatter dim),
+    # as it was the left operand of the mm op. We can use -1 as the final dim of the view to populate the rest.
+    stacked_partials_3D_leading_dims = [group.size()] + list(
+        # We use A from after the dim swap 0<=>scatter_dim, but before the flatten,
+        # to get the leading dims of the 3D+ view of stacked partials.
+        A_with_scatter_dim_0.shape[:-1]
+    )
+
+    # The `group_size` leading dim has been prepended to `stacked_partials_3D_leading_dims`,
+    # to capture the partial output from each rank. We need to divide the sharding/scatter dim
+    # by the group size. If the original scatter dim was 0, then it is now dim 1 in this
+    # tensor, since this new `group_size` dim was prepended.
+    stacked_partial_scatter_dim = orig_scatter_dim if orig_scatter_dim > 0 else 1
+    stacked_partials_3D_leading_dims[stacked_partial_scatter_dim] //= group.size()
+
+    # Ensures that the transpose and reduction produce contiguous result
+    # in a single reduction kernel.
+    reduced_out = reduce_fn(
+        # View 2D stacked partials as 3D+ tensor of shape (`group_size`, ...)
+        stacked_partials.view(*stacked_partials_3D_leading_dims, -1)
+        # We originally swapped 0<=>scatter_dim_after_maybe_reshape. Now after
+        # prepending the `group_size` dim, to undo this original swap, we
+        # must swap 1<=>scatter_dim_after_maybe_reshape+1.
+        .movedim(1, scatter_dim_after_maybe_reshape + 1),
+        # Reduce along the `group_size` dim (0).
+        dim=0,
+    )
+
+    # Output shape must be scattered along original scatter dim as well.
+    output_shape[orig_scatter_dim] //= group.size()
+    out = reduced_out.view(*output_shape)
+    return out
+
+
+def restride_A_for_fused_matmul_reduce_scatter(
+    t: torch.Tensor,
+    scatter_dim: int,
+) -> torch.Tensor:
+    """
+    Restride the `A_shard` arg of `fused_matmul_reduce_scatter` for optimal
+    perf. See the doc for `fused_matmul_reduce_scatter` for detail.
+    """
+    perm = list(range(len(t.shape)))
+    perm.insert(0, perm.pop(scatter_dim))
+    return make_contiguous_for_perm(t, perm)
+
+
+def _maybe_convert_scalar_types_to_dtypes(
+    scalar_types: list[Any],
+) -> list[torch.dtype | None]:
+    """
+    When a list of `torch.dtype`s is passed through the dispatcher as
+    `ScalarType[]`, it is converted to a list of scalar type enum values. This
+    function converts it back to a list of `torch.dtype`s.
+    """
+    # Order defined in https://github.com/pytorch/pytorch/blob/344defc9733a45fee8d0c4d3f5530f631e823196/c10/core/ScalarType.h
+    _SCALAR_TYPE_TO_DTYPE = {
+        0: torch.uint8,
+        1: torch.int8,
+        2: torch.short,
+        3: torch.int,
+        4: torch.int64,
+        5: torch.half,
+        6: torch.float,
+        7: torch.double,
+        8: torch.complex32,
+        9: torch.complex64,
+        10: torch.complex128,
+        11: torch.bool,
+        12: torch.qint8,
+        13: torch.quint8,
+        14: torch.qint32,
+        15: torch.bfloat16,
+        16: torch.float8_e5m2,
+        17: torch.float8_e4m3fn,
+        18: torch.float8_e5m2fnuz,
+        19: torch.float8_e4m3fnuz,
+    }
+    if any(not isinstance(x, (type(None), int)) for x in scalar_types):
+        return scalar_types
+
+    dtypes: list[torch.dtype | None] = []
+    for scalar_type in scalar_types:
+        if scalar_type is None:
+            dtypes.append(scalar_type)
+        elif scalar_type not in _SCALAR_TYPE_TO_DTYPE:
+            raise ValueError(f"Unrecognized scalar type {scalar_type}")
+        else:
+            dtypes.append(_SCALAR_TYPE_TO_DTYPE[scalar_type])
+    return dtypes
+
+
+class Work(_Work):
+    def __init__(self) -> None:
+        super().__init__()
+        self.event = torch.cuda.Event()
+        self.event.record()
+
+    def wait(self, timeout: timedelta = timedelta(seconds=0)) -> bool:
+        self.event.wait()
+        return True
+
+
+"""
+NOTE [low-contention collectives]
+When a collective is overlapped with abundant compute, it makes sense to
+prioritize reducing the contention between the collective and the overlapped
+compute, even at the cost of a slightly slower collective.
+
+Common collective implementations (e.g., NCCL without user buffer
+registration) optimize for throughput with no ambient compute. However, such
+implementations may not be optimal when they are overlapped with compute:
+- These implementations typically fuse the entire collective into a single
+kernel and reserve SM resources based on the most demanding portion of the
+collective, even when a large portion of the collective does not require this
+much resource.
+- These implementations often use SM-based P2P copy as opposed to copy
+engine-based P2P copy. Copy engine-based P2P copy may not have a significant
+advantage when there's no ambient compute. However, it may significantly
+improve overall resource utilization in the presence of ambient compute.
+
+When overlapped with intensive compute (e.g., persistent matmul kernels), the
+SM-usage of a collective can lead to inefficient overlapping.
+
+Low-contention collectives achieve their goals with the following strategies:
+- Use copy engine-based copy whenever possible.
+- Break down portions of a collective with different resource requirements
+into multiple kernels. This improves the overlapping efficiency at the cost
+of additional launching overhead.
+"""
+
+
+@torch.library.impl(lib, "_low_contention_all_gather", "Meta")
+def _low_contention_all_gather_meta(
+    tensor: torch.Tensor,
+    group_name: c10d.GroupName,
+) -> torch.Tensor:
+    group_size = c10d._get_group_size_by_name(group_name)
+    return tensor.new_empty(tensor.shape[0] * group_size, *tensor.shape[1:])
+
+
+@torch.library.impl(lib, "_low_contention_all_gather", "CUDA")
+def _low_contention_all_gather(
+    tensor: torch.Tensor,
+    group_name: c10d.GroupName,
+) -> torch.Tensor:
+    """
+    Performs all-gather with symmetric memory in a low-contention fashion.
+
+    When `tensor` is already in symmetric memory:
+        - The collective is carried out without using SMs.
+        - No symmetric memory workspace is required.
+
+    When `tensor` is not in symmetric memory:
+        - An extra SM-based copy is performed to copy the input data into the
+          symmetric memory workspace.
+        - Symmetric memory workspace size requirement: the size of `tensor`.
+    """
+    symm_mem = rendezvous(tensor, group_name)
+    if symm_mem is not None:
+        input_is_symm_mem = True
+    else:
+        symm_mem = get_symm_mem_workspace(
+            group_name, tensor.numel() * tensor.element_size()
+        )
+        input_is_symm_mem = False
+
+    rank = symm_mem.rank
+    world_size = symm_mem.world_size
+
+    output = tensor.new_empty(tensor.shape[0] * world_size, *tensor.shape[1:])
+    chunks = output.chunk(world_size)
+
+    _get_backend_stream().wait_stream(torch.cuda.current_stream())
+    with _get_backend_stream():
+        if not input_is_symm_mem:
+            local_buf = symm_mem.get_buffer(rank, tensor.shape, tensor.dtype)
+            local_buf.copy_(tensor)
+        # pull
+        symm_mem.barrier()
+        for step in range(world_size):
+            remote_rank = (rank - step) % world_size
+            src_buf = symm_mem.get_buffer(remote_rank, tensor.shape, tensor.dtype)
+            chunks[remote_rank].copy_(src_buf)
+        symm_mem.barrier()
+        torch._C._distributed_c10d._register_work(output, Work())
+        return output
+
+
+@torch.library.impl(lib, "_low_contention_reduce_scatter", "Meta")
+def _low_contention_reduce_scatter_meta(
+    tensor: torch.Tensor,
+    reduce_op: str,
+    group_name: c10d.GroupName,
+) -> torch.Tensor:
+    group_size = c10d._get_group_size_by_name(group_name)
+    return tensor.unflatten(0, (group_size, -1)).mean(dim=0)
+
+
+def _low_contention_reduce_scatter_with_symm_mem_input(
+    tensor: torch.Tensor,
+    reduce_op: str,
+    symm_mem: _SymmetricMemory,
+) -> torch.Tensor:
+    rank = symm_mem.rank
+    world_size = symm_mem.world_size
+
+    assert tensor.shape[0] % world_size == 0
+    a2a_res = torch.empty_like(tensor)
+    chunks = a2a_res.chunk(world_size)
+
+    _get_backend_stream().wait_stream(torch.cuda.current_stream())
+    with _get_backend_stream():
+        # pull + offline reduction
+        symm_mem.barrier()
+        for step in range(world_size):
+            remote_rank = (rank - step) % world_size
+            src_buf = symm_mem.get_buffer(
+                remote_rank,
+                chunks[0].shape,
+                chunks[0].dtype,
+                chunks[0].numel() * rank,
+            )
+            chunks[remote_rank].copy_(src_buf)
+        symm_mem.barrier()
+
+        ret = a2a_res.unflatten(0, (world_size, -1))
+        if reduce_op == "sum":
+            ret = ret.sum(dim=0)
+        elif reduce_op == "avg":
+            ret = ret.mean(dim=0)
+        else:
+            raise ValueError(f"reduce_op ({reduce_op}) is not supported")
+        torch._C._distributed_c10d._register_work(ret, Work())
+        return ret
+
+
+def _low_contention_reduce_scatter_with_workspace(
+    tensor: torch.Tensor,
+    reduce_op: str,
+    workspace: _SymmetricMemory,
+) -> torch.Tensor:
+    rank = workspace.rank
+    world_size = workspace.world_size
+
+    assert tensor.shape[0] % world_size == 0
+    chunks = tensor.chunk(world_size)
+
+    _get_backend_stream().wait_stream(torch.cuda.current_stream())
+    with _get_backend_stream():
+        # push + offline reduction
+        workspace.barrier()
+        for step in range(world_size):
+            remote_rank = (rank - step) % world_size
+            dst_buf = workspace.get_buffer(
+                remote_rank, chunks[0].shape, chunks[0].dtype, chunks[0].numel() * rank
+            )
+            dst_buf.copy_(chunks[remote_rank])
+        workspace.barrier()
+
+        buf = workspace.get_buffer(rank, tensor.shape, tensor.dtype)
+        ret = buf.unflatten(0, (world_size, -1))
+        if reduce_op == "sum":
+            ret = ret.sum(dim=0)
+        elif reduce_op == "avg":
+            ret = ret.mean(dim=0)
+        else:
+            raise ValueError(f"reduce_op ({reduce_op}) is not supported")
+        torch._C._distributed_c10d._register_work(ret, Work())
+        return ret
+
+
+@torch.library.impl(lib, "_low_contention_reduce_scatter", "CUDA")
+def _low_contention_reduce_scatter(
+    tensor: torch.Tensor,
+    reduce_op: str,
+    group_name: c10d.GroupName,
+) -> torch.Tensor:
+    """
+    Performs reduce-scatter with symmetric memory in a low-contention fashion.
+
+    This implementation performs a P2P-based all-to-all followed by an offline
+    reduction.
+
+    When `tensor` is already in symmetric memory:
+        - Pull-based all-to-all is used.
+        - No symmetric memory workspace is required.
+
+    When `tensor` is not in symmetric memory:
+        - Push-based all-to-all is used.
+        - Symmetric memory workspace size requirement: the size of `tensor`.
+
+    SM-usage:
+        - SM-based copy of the rank's own chunk for the all-to-all.
+        - Reduction on the all-to-all result.
+
+    TODO(yifu): the SM-based copy can be avoided with a list-based reduction
+    kernel.
+    """
+    symm_mem = rendezvous(tensor, group_name)
+    if symm_mem is not None:
+        return _low_contention_reduce_scatter_with_symm_mem_input(
+            tensor, reduce_op, symm_mem
+        )
+    else:
+        workspace = get_symm_mem_workspace(
+            group_name, tensor.numel() * tensor.element_size()
+        )
+        return _low_contention_reduce_scatter_with_workspace(
+            tensor, reduce_op, workspace
+        )
+
+
+@torch.library.impl(lib, "all_to_all_vdev_2d", "Meta")
+def _all_to_all_vdev_2d_meta(
+    input: torch.Tensor,
+    out: torch.Tensor,
+    in_splits: torch.Tensor,
+    out_splits_offsets: torch.Tensor,
+    group_name: c10d.GroupName,
+    major_align: int | None = None,
+) -> None:
+    return None
+
+
+@torch.library.impl(lib, "all_to_all_vdev_2d_offset", "Meta")
+def _all_to_all_vdev_2d_offset_meta(
+    input: torch.Tensor,
+    out: torch.Tensor,
+    in_splits_offsets: torch.Tensor,
+    out_splits_offsets: torch.Tensor,
+    group_name: c10d.GroupName,
+) -> None:
+    return None
+
+
+# =============================================================================
+# User-facing APIs
+# =============================================================================
+
+
+from collections.abc import Sequence
+from typing import overload, TYPE_CHECKING, Union
+
+
+if TYPE_CHECKING:
+    from torch._C._distributed_c10d import ProcessGroup
+    from torch.types import _device, _dtype, _int
+
+
+@overload
+def empty(
+    *size: _int, dtype: _dtype | None = None, device: _device | None = None
+) -> torch.Tensor: ...
+
+
+@overload
+# pyrefly: ignore [inconsistent-overload]
+def empty(
+    size: Sequence[_int],
+    *,
+    dtype: _dtype | None = None,
+    device: _device | None = None,
+) -> torch.Tensor: ...
+
+
+def empty(  # type: ignore[misc]
+    *size: Any,
+    dtype: _dtype | None = None,
+    device: _device | None = None,
+) -> torch.Tensor:
+    r"""
+    Similar to :func:`torch.empty()`. The returned tensor can be used by
+    :func:`torch._distributed._symmetric_memory.rendezvous()` to establish a
+    symmetric memory tensor among participating processes.
+
+    Args:
+        size (int...): a sequence of integers defining the shape of the output tensor.
+            Can be a variable number of arguments or a collection like a list or tuple.
+
+    Keyword args:
+        dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.
+            Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`).
+        device (:class:`torch.device`, optional): the desired device of returned tensor.
+            Default: if ``None``, uses the current device for the default tensor type
+            (see :func:`torch.set_default_device`). :attr:`device` will be the CPU
+            for CPU tensor types and the current CUDA device for CUDA tensor types.
+    """
+    if len(size) == 1 and isinstance(size[0], Sequence):
+        size = tuple(size[0])
+    else:
+        size = tuple(size)
+
+    if dtype is None:
+        dtype = torch.get_default_dtype()
+
+    if device is None:
+        device = torch.get_default_device()
+
+    return _SymmetricMemory.empty_strided_p2p(
+        size=size,
+        stride=torch._prims_common.make_contiguous_strides_for(size),
+        dtype=dtype,
+        device=torch.device(device),
+    )
+
+
+def rendezvous(
+    tensor: torch.Tensor, group: Union[c10d.GroupName, ProcessGroup]
+) -> _SymmetricMemory:
+    r"""
+    rendezvous(tensor, group) -> _SymmetricMemory
+
+    Establish a symmetric memory tensor among participating processes. This is
+    a collective operation.
+
+    Args:
+        tensor (:class:`torch.Tensor`): the local tensor used to establish the symmetric memory tensor.
+            It must be allocated via :func:`torch._distributed._symmetric_memory.empty()`. The shape,
+            dtype, and device type must be identical across all participating processes.
+        group (Union[str, :class:`torch.distributed.ProcessGroup`]): The group identifying the
+            participating processes. This can be either a group name or a process group object.
+    """
+    from torch._C._distributed_c10d import ProcessGroup
+
+    if isinstance(group, str):
+        group_name = c10d.GroupName(group)
+    elif isinstance(group, ProcessGroup):
+        group_name = group.group_name
+    else:
+        raise TypeError(f"rendezvous: unsupported group type: {type(group)}")
+
+    enable_symm_mem_for_group(group_name)
+    return _SymmetricMemory.rendezvous(tensor, group_name)
+
+
+def is_nvshmem_available() -> bool:
+    r"""
+    is_nvshmem_available() -> bool
+
+    Check if NVSHMEM is available in current build and on current system.
+    """
+    try:
+        from torch._C._distributed_c10d import _is_nvshmem_available
+    except ImportError:
+        # Not all builds have NVSHMEM support.
+        return False
+
+    # Check if NVSHMEM is available on current system.
+    return _is_nvshmem_available()
+
+
+def set_backend(name: Literal["NVSHMEM", "CUDA", "NCCL"]) -> None:
+    r"""
+    Set the backend for symmetric memory allocation. This is a global setting
+    and affects all subsequent calls to
+    :func:`torch._distributed._symmetric_memory.empty()`.  Note that the backend
+    cannot be changed once a symmetric memory tensor has been allocated.
+
+    Args:
+        backend (str): the backend for symmetric memory allocation. Currently,
+            only `"NVSHMEM"`, `"CUDA"`, `"NCCL"` are supported.
+    """
+    _SymmetricMemory.set_backend(name)
+
+
+def get_backend(device: _device) -> str | None:
+    r"""
+    Get the backend for symmetric memory allocation for a given device. If not
+    found, return None.
+
+    Args:
+        device (`torch.device` or str): the device for which to get the backend.
+    """
+    return _SymmetricMemory.get_backend(torch.device(device))
+
+
+def get_mempool_allocator(device: _device):  # type: ignore[no-untyped-def]
+    r"""
+    Get the MemPool allocator for symmetric memory for a given device.
+
+    Args:
+        device (`torch.device` or str): the device for which to get the MemPool
+            allocator.
+    """
+    return _SymmetricMemory.get_mempool_allocator(torch.device(device))
+
+
+def set_signal_pad_size(size: int) -> None:
+    r"""
+    Set the signal pad size for future symmetric memory allocations.
+
+    Signal pads are P2P-accessible memory regions used for synchronization in
+    symmetric memory. This function allows users to configure
+    the signal pad size to be proportional to their workload requirements.
+
+    .. warning::
+        This must be called before any symmetric memory allocations are made.
+        The size cannot be changed after allocations have been performed.
+
+    Args:
+        size (int): the signal pad size in bytes. The size should be
+            proportional to the number of blocks launched and the world size.
+
+    Example::
+
+        >>> # doctest: +SKIP
+        >>> # Set a larger signal pad size before any allocations
+        >>> torch.distributed._symmetric_memory.set_signal_pad_size(1024 * 1024)  # 1MB
+    """
+    _SymmetricMemory.signal_pad_size = size
+
+
+def get_signal_pad_size() -> int:
+    r"""
+    Get the current signal pad size for symmetric memory allocations.
+
+    Returns the user-configured size if set via :func:`set_signal_pad_size`,
+    otherwise returns the default size.
+
+    Returns:
+        int: the signal pad size in bytes.
+
+    Example::
+
+        >>> # doctest: +SKIP
+        >>> size = torch.distributed._symmetric_memory.get_signal_pad_size()
+        >>> print(f"Signal pad size: {size} bytes")
+    """
+    return _SymmetricMemory.signal_pad_size
+
+
+# An internal map from device to the symmetric memory pool for that device.
+_symm_mem_pools: dict[_device, torch.cuda.MemPool] = {}
+
+
+def get_mem_pool(device: _device) -> torch.cuda.MemPool:
+    """
+    Get the symmetric memory pool for a given device. If not found, create a new
+    pool.
+
+    The tensor allocations with this pool must be symmetric across ranks.  The
+    allocated tensors can be used with symmetric operations, for example,
+    operations defined under `torch.ops.symm_mem`.
+
+    Args:
+        device (`torch.device` or str): the device for which to get the symmetric memory pool.
+
+    Returns:
+        `torch.cuda.MemPool`: the symmetric memory pool for the given device.
+
+    Example::
+
+        >>> # doctest: +SKIP
+        >>> pool = torch.distributed._symmetric_memory.get_mem_pool("cuda:0")
+        >>> with torch.cuda.use_mem_pool(pool):
+        >>>     tensor = torch.randn(1000, device="cuda:0")
+        >>> tensor = torch.ops.symm_mem.one_shot_all_reduce(tensor, "sum", group_name)
+
+    """
+    # This function is a wrapper around the `torch.cuda.MemPool` constructor.
+    # Due to special requirements of SymmetricMemory, we preset certain options for the pool.
+    # - use_on_oom=False: we don't want to lend the space of the pool for
+    # non-symmetric allocations because this could desync the allocation state
+    # across ranks.
+    # - no_split=True: we don't want to split segments, because today a segment
+    # is associated with a signal pad, if two allocated tensors share a segment
+    # and their kernels concurrently use (the same) signal pad, this could cause
+    # undefined behaviors. We could consider relaxing this in the future if we
+    # establish stream tracking and implicit synchronization around an
+    # allocation.
+    if device not in _symm_mem_pools:
+        allocator = get_mempool_allocator(device)
+        # Create a new pool with the given allocator and the preset options.
+        _symm_mem_pools[device] = torch.cuda.MemPool(
+            allocator,
+            use_on_oom=False,
+            no_split=True,
+        )
+
+    return _symm_mem_pools[device]
+
+
+__all__ = [
+    "empty",
+    "rendezvous",
+    "is_nvshmem_available",
+    "set_backend",
+    "get_backend",
+    "set_signal_pad_size",
+    "get_signal_pad_size",
+    "get_mem_pool",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_symmetric_memory/_nvshmem_triton.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_symmetric_memory/_nvshmem_triton.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ca8bc95eae39ebefdd97f8805b97099a82e9a92
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_symmetric_memory/_nvshmem_triton.py
@@ -0,0 +1,1220 @@
+import logging
+import os
+import subprocess
+import sysconfig
+from typing import Any
+
+import torch.distributed as dist
+from torch.utils._triton import has_triton
+
+
+logger = logging.getLogger(__name__)
+
+
+class NvshmemLibFinder:
+    """
+    A class to find path to the NVSHMEM device library.
+
+    Environment variable:
+
+    `NVSHMEM_LIB_DIR` (Optional[str]): The directory where the NVSHMEM device
+    library is located. If not provided, it will use the default path where
+    NVSHMEM wheel is installed, or search for the library in common system
+    paths.
+    """
+
+    # Class variable to store the found library path for reuse
+    found_device_lib_path: str | None = None
+
+    @classmethod
+    def find_device_library(cls) -> str:
+        """
+        Find the path to the NVSHMEM device library.
+
+        Returns:
+            str: The path to libnvshmem_device.bc (included).
+        """
+        if cls.found_device_lib_path is not None:
+            # Return the cached path if it exists
+            return cls.found_device_lib_path
+
+        # First, check if the user has specified a custom library path
+        user_lib_dir = os.environ.get("NVSHMEM_LIB_DIR", None)
+        if user_lib_dir is not None:
+            lib_path = os.path.join(user_lib_dir, "libnvshmem_device.bc")
+            if not os.path.exists(lib_path):
+                raise RuntimeError(
+                    f"NVSHMEM device library not found at specified path: {user_lib_dir}"
+                )
+            cls.found_device_lib_path = lib_path
+            return lib_path
+
+        # Otherwise, search for the library in the default installation paths
+        paths = [
+            os.path.join(sysconfig.get_path("purelib"), "nvidia", "nvshmem", "lib")
+        ]
+
+        # Add common system installation paths
+        common_paths = [
+            "/usr/local/lib",
+            "/usr/lib",
+            "/opt/nvidia/nvshmem/lib",
+        ]
+        paths.extend(common_paths)
+
+        try:
+            import torch
+
+            torch_lib = os.path.join(os.path.dirname(torch.__file__), "lib")
+            so_path = os.path.join(torch_lib, "libtorch_nvshmem.so")
+
+            if os.path.exists(so_path):
+                try:
+                    result = subprocess.run(
+                        ["readelf", "-d", so_path],
+                        capture_output=True,
+                        text=True,
+                        check=True,
+                    )
+
+                    for line in result.stdout.splitlines():
+                        if ("RPATH" in line or "RUNPATH" in line) and "[" in line:
+                            rpath = line.split("[", 1)[1].split("]", 1)[0]
+                            for p in rpath.split(":"):
+                                p = p.strip().replace("$ORIGIN", torch_lib)
+                                if p and p not in paths:
+                                    paths.append(p)
+                except subprocess.CalledProcessError:
+                    pass
+
+        except ImportError:
+            pass
+
+        for path in paths:
+            device_lib = os.path.join(path, "libnvshmem_device.bc")
+            if os.path.exists(device_lib):
+                cls.found_device_lib_path = device_lib
+                return device_lib
+
+        raise RuntimeError(f"NVSHMEM device library not found. Searched: {paths}")
+
+
+def enable_triton(lib_dir: str | None = None) -> dict[str, str]:
+    raise NotImplementedError(
+        "`enable_triton` is deprecated. "
+        "If you need NVSHMEM device function support for Triton, "
+        "please use `@requires_nvshmem` to decorate your Triton kernel. ",
+    )
+
+
+class NvshmemKernelRegistry:
+    """
+    A class to register kernel functions that ** require NVSHMEM initialization **
+    """
+
+    # Class variable to store the functions to be initialized
+    _to_init: dict[str, Any] = {}
+
+    @classmethod
+    def register(cls, name: str) -> None:
+        """
+        Register a kernel function with the given name.
+
+        Args:
+            name (str): The name of the kernel function.
+        """
+        cls._to_init.setdefault(name)
+
+    @classmethod
+    def deregister(cls, name: str) -> None:
+        """
+        Deregister a kernel function with the given name.
+
+        Args:
+            name (str): The name of the kernel function.
+        """
+        cls._to_init.pop(name, None)
+
+    @classmethod
+    def has(cls, name: str) -> bool:
+        """
+        Check if a kernel function with the given name is registered.
+
+        Args:
+            name (str): The name of the kernel function.
+
+        Returns:
+            bool: True if the kernel function is registered, False otherwise.
+        """
+        return name in cls._to_init
+
+
+def _nvshmem_init_hook(*args, **kwargs) -> None:  # type: ignore[no-untyped-def]
+    """
+    A hook function to initialize the CUModule created by `triton.jit` with
+    NVSHMEM device context
+    """
+    from torch._C._distributed_c10d import _nvshmemx_cumodule_init
+
+    jit_function = kwargs["fn"].jit_function
+    fn_name = jit_function.fn.__name__
+
+    # Only initialize NVSHMEM module for kernels registered via @requires_nvshmem
+    if NvshmemKernelRegistry.has(fn_name):
+        key = kwargs["key"]
+        device = kwargs["compile"]["device"]
+        jit_function = kwargs["fn"].jit_function
+        kernel_cache = jit_function.device_caches[device][0]
+        kernel = kernel_cache.get(key, None)
+        if kernel is not None:
+            kernel.run
+            # Initialize NVSHMEM for the CU module
+            _nvshmemx_cumodule_init(kernel.module)
+        else:
+            logger.warning(
+                f"It seems Triton hasn't created a kernel for function {fn_name}. "  # noqa: G004
+                "Please report this issue to Triton."
+            )
+
+
+if has_triton():
+    from triton.runtime.jit import JITFunction, KernelInterface
+
+    # Create a new Callable class that follows the KernelInterface protocol so
+    # that the Callable works with the subscript operator, e.g. `foo[(1, 1)]`
+    class GridCallableWithExtern(KernelInterface):
+        """
+        `KernelInterface` invokes `self.run` in `__getitem__`, i.e. [].  We
+        implement a `run` method by directing the call to `JITFunction.run`,
+        with added extern_libs kwarg, so that users don't have to pass it
+        """
+
+        def __init__(self, jit_func: JITFunction, extern_libs: dict[str, str]) -> None:
+            self.jit_func = jit_func
+            self.extern_libs = extern_libs
+
+        def run(self, *args, **kwargs):  # type: ignore[no-untyped-def]
+            # Call the JITFunction.run with added extern_libs kwarg
+            return self.jit_func.run(*args, **kwargs, extern_libs=self.extern_libs)
+
+
+def requires_nvshmem(  # type: ignore[no-untyped-def]
+    jit_func,  # JITFunction created by triton.jit
+):
+    """
+    A decorator to register a Triton kernel function that requires NVSHMEM initialization.
+
+    Example usage:
+    ```
+        @requires_nvshmem
+        @triton.jit
+        def foo(...):
+            ...
+    ```
+
+    If you would like to specify a path to the NVSHMEM device library other
+    than standard search locations, you can use the following environment
+    variable:
+    ```
+        export NVSHMEM_LIB_DIR=/path/to/nvshmem/lib
+    ```
+    """
+
+    import triton
+    from triton.runtime.jit import JITFunction
+
+    if not isinstance(jit_func, JITFunction):
+        raise TypeError(f"Expected a JITFunction, but got {type(jit_func)}")
+
+    # Find the NVSHMEM device library
+    lib_path = NvshmemLibFinder.find_device_library()
+    extern_libs = {"libnvshmem_device": lib_path}
+
+    # Register the JITFunction with the kernel registry as "to be initialized"
+    NvshmemKernelRegistry.register(jit_func.fn.__name__)
+
+    # Register the NVSHMEM init function as a post-compile hook.
+    # [Note] This is a global setting (due to lack of Triton API exposure). To
+    # avoid initializing Triton kernels that do not require NVSHMEM, filtering
+    # is performed in the hook function itself by checking against
+    # NvshmemKernelRegistry.
+    triton.knobs.runtime.jit_post_compile_hook = _nvshmem_init_hook
+
+    return GridCallableWithExtern(jit_func, extern_libs)
+
+
+if has_triton():
+    import triton
+    import triton.language as tl
+    from triton.language import core
+
+    @triton.jit  # type: ignore[misc]
+    def put(dest, source, nelems, pe):  # type: ignore[no-untyped-def]
+        """
+        Put tensor data from local PE to a remote PE.
+
+        This high-level function provides a tensor-aware interface for NVSHMEM put
+        operations. It automatically handles type checking and size calculations, making
+        the API more ergonomic and type-safe.
+
+        Args:
+            dest: Destination tensor on the remote PE. Type must match source.
+            source: Source tensor on the local PE containing data to be copied.
+            nelems: Number of elements to transfer.
+            pe: PE number of the remote PE (0 ≤ pe < nvshmem_n_pes()).
+
+        Notes:
+            - Performs compile-time type checking between dest and source tensors.
+            - Automatically calculates byte size from tensor type and element count.
+            - This is a blocking operation that returns after data has been copied out
+              of the source array on the local PE.
+            - The operation does not guarantee delivery to the destination PE.
+              Use nvshmem_fence() for ordering or nvshmem_quiet() for completion.
+
+        Example:
+            ```
+            # Transfer 100 elements to PE 1
+            nvshmem.put(dest_tensor, src_tensor, 100, 1)
+            ```
+        """
+        tl.static_assert(dest.type == source.type)
+        nbytes = nelems * dest.type.element_ty.itemsize
+        return putmem_block_extern_wrapper(
+            dest.to(tl.int64), source.to(tl.int64), nbytes.to(tl.int64), pe
+        )
+
+    @core.extern
+    def putmem_block_extern_wrapper(dest, source, size_bytes, pe, _semantic=None):  # type: ignore[no-untyped-def]
+        """Low-level extern wrapper for NVSHMEM put"""
+        return core.extern_elementwise(
+            "",
+            "",
+            [dest, source, size_bytes, pe],
+            {
+                (
+                    core.dtype("int64"),  # dest ptr
+                    core.dtype("int64"),  # source ptr
+                    core.dtype("int64"),  # size in bytes
+                    core.dtype("int32"),  # pe number
+                ): ("nvshmemx_putmem_block", core.dtype("int32"))
+            },
+            is_pure=False,
+            _semantic=_semantic,
+        )
+
+    @triton.jit  # type: ignore[misc]
+    def get(dest, source, nelems, pe):  # type: ignore[no-untyped-def]
+        """
+        Get tensor data from a remote PE to local PE.
+
+        This high-level function provides a tensor-aware interface for NVSHMEM get
+        operations. It automatically handles type checking and size calculations, making
+        the API more ergonomic and type-safe.
+
+        Args:
+            dest: Destination tensor on the local PE. Type must match source.
+            source: Source tensor on the remote PE containing data to be copied.
+            nelems: Number of elements to transfer.
+            pe: PE number of the remote PE (0 ≤ pe < nvshmem_n_pes()).
+
+        Notes:
+            - Performs compile-time type checking between dest and source tensors.
+            - Automatically calculates byte size from tensor type and element count.
+            - This is a blocking operation that returns after data has been delivered
+              to the destination array on the local PE.
+            - The destination data is guaranteed to be available for use after the call returns.
+
+        Example:
+            ```
+            # Get 100 elements from PE 0
+            nvshmem.get(dest_tensor, src_tensor, 100, 0)
+            ```
+        """
+        tl.static_assert(dest.type == source.type)
+        nbytes = nelems * dest.type.element_ty.itemsize
+        return getmem_block_extern_wrapper(
+            dest.to(tl.int64), source.to(tl.int64), nbytes.to(tl.int64), pe
+        )
+
+    @core.extern
+    def getmem_block_extern_wrapper(dest, source, size_bytes, pe, _semantic=None):  # type: ignore[no-untyped-def]
+        """Low-level extern wrapper for NVSHMEM get"""
+        return core.extern_elementwise(
+            "",
+            "",
+            [dest, source, size_bytes, pe],
+            {
+                (
+                    core.dtype("int64"),  # dest ptr
+                    core.dtype("int64"),  # source ptr
+                    core.dtype("int64"),  # size in bytes
+                    core.dtype("int32"),  # pe number
+                ): ("nvshmemx_getmem_block", core.dtype("int32"))
+            },
+            is_pure=False,
+            _semantic=_semantic,
+        )
+
+    @triton.jit  # type: ignore[misc]
+    def get_nbi(dest, source, nelems, pe):  # type: ignore[no-untyped-def]
+        """
+        Get tensor data from a remote PE to local PE, non-blocking.
+
+        Different from the `get` function, this function returns after
+        initiating the operation. The operation is considered complete after a
+        subsequent call to `quiet`.
+
+        Args:
+            dest: Destination tensor on the local PE. Type must match source.
+            source: Source tensor on the remote PE containing data to be copied.
+            nelems: Number of elements to transfer.
+            pe: PE number of the remote PE (0 ≤ pe < nvshmem_n_pes()).
+
+        Notes:
+            - Performs compile-time type checking between dest and source tensors.
+            - Automatically calculates byte size from tensor type and element count.
+
+        Example:
+            ```
+            # Get 100 elements from PE 0
+            nvshmem.get_nbi(dest, src, 100, 0)
+            # Some independent computation which overlaps with the get operation
+            ...
+            # Wait for completion of the get operation
+            nvshmem.quiet()
+            ```
+        """
+        tl.static_assert(dest.type == source.type)
+        nbytes = nelems * dest.type.element_ty.itemsize
+        return getmem_block_extern_wrapper(
+            dest.to(tl.int64), source.to(tl.int64), nbytes.to(tl.int64), pe
+        )
+
+    @core.extern
+    def getmem_nbi_block_extern_wrapper(dest, source, size_bytes, pe, _semantic=None):  # type: ignore[no-untyped-def]
+        """Low-level extern wrapper for NVSHMEM get"""
+        return core.extern_elementwise(
+            "",
+            "",
+            [dest, source, size_bytes, pe],
+            {
+                (
+                    core.dtype("int64"),  # dest ptr
+                    core.dtype("int64"),  # source ptr
+                    core.dtype("int64"),  # size in bytes
+                    core.dtype("int32"),  # pe number
+                ): ("nvshmemx_getmem_nbi_block", core.dtype("int32"))
+            },
+            is_pure=False,
+            _semantic=_semantic,
+        )
+
+    @triton.jit  # type: ignore[misc]
+    def putmem_signal_block(  # type: ignore[no-untyped-def]
+        dst,
+        src,
+        size_bytes,
+        signal,
+        sig_val,
+        sig_op,
+        pe,
+    ):  # type: ignore[no-untyped-def]
+        """
+        Put data to remote PE with atomic signal operation using block-scoped operation.
+
+        This function copies data from the local PE to the remote PE and then
+        atomically updates a signal variable on the remote PE to indicate completion.
+        This enables efficient point-to-point synchronization between PEs.
+
+        Args:
+            dst (tensor): A tensor on calling PE symmetric to the destination tensor on remote PE.
+            src (tensor): Local tensor containing the source data.
+            size_bytes (int64): Number of bytes to transfer. Must be positive.
+            signal (tensor): Symmetric signal pad with remote PE.
+                             Must be 8-byte aligned symmetric memory.
+            signal (int64): Value to be used in the signal operation.
+            sig_op (int32): Signal operation type. Common values:
+                           - NVSHMEM_SIGNAL_SET (0): Atomic set operation
+                           - NVSHMEM_SIGNAL_ADD (5): Atomic add operation
+            pe (int32): PE number of the remote PE (0 ≤ pe < nvshmem_n_pes()).
+
+        Returns:
+            int32: Status code (0 for success).
+
+        Notes:
+            - This is a blocking operation that returns after data has been copied out
+              of the source array and the signal has been updated on the remote PE.
+            - The signal update is performed atomically with respect to other signal
+              operations and synchronization routines.
+            - The signal variable must be of type uint64_t in symmetric memory.
+            - Use with nvshmem_signal_wait_until() for synchronization.
+
+        Example:
+            ```
+            # Transfer data and set completion flag to 1
+            NVSHMEM_SIGNAL_SET = 0
+            nvshmem.putmem_signal_block(
+                dst_ptr, src_ptr, 1024, sig_ptr, 1, NVSHMEM_SIGNAL_SET, target_pe
+            )
+            ```
+        """
+        # Ensure sig_val is 64 bits
+        sig_val = 0 << 32 | sig_val
+        return putmem_signal_block_extern_wrapper(
+            dst.to(tl.int64),
+            src.to(tl.int64),
+            size_bytes.to(tl.int64),
+            signal.to(tl.int64),
+            sig_val.to(tl.uint64),
+            sig_op,
+            pe,
+        )
+
+    @core.extern
+    def putmem_signal_block_extern_wrapper(  # type: ignore[no-untyped-def]
+        dst,
+        src,
+        size_bytes,
+        signal,
+        sig_val,
+        sig_op,
+        pe,
+        _semantic=None,
+    ):  # type: ignore[no-untyped-def]
+        return core.extern_elementwise(
+            "",
+            "",
+            [dst, src, size_bytes, signal, sig_val, sig_op, pe],
+            {
+                (
+                    core.dtype("int64"),
+                    core.dtype("int64"),
+                    core.dtype("int64"),
+                    core.dtype("int64"),
+                    core.dtype("uint64"),
+                    core.dtype("int32"),
+                    core.dtype("int32"),
+                ): ("nvshmemx_putmem_signal_block", core.dtype("int32"))
+            },
+            is_pure=False,
+            _semantic=_semantic,
+        )
+
+    # Wait and Signal Operations
+
+    @triton.jit  # type: ignore[misc]
+    def wait_until(ivar, cmp_op, cmp_val):  # type: ignore[no-untyped-def]
+        """
+        Wait until a tensor variable meets a specified condition.
+
+        This high-level function provides a tensor-aware interface for NVSHMEM wait_until
+        operations. It automatically handles tensor address extraction, making
+        the API more ergonomic and type-safe.
+
+        Args:
+            ivar_tensor: Tensor to monitor (typically int64/uint64) in symmetric memory.
+            cmp: Comparison operator. Common values:
+                 - NVSHMEM_CMP_EQ (0): Wait until ivar == cmp_val
+                 - NVSHMEM_CMP_NE (1): Wait until ivar != cmp_val
+                 - NVSHMEM_CMP_GT (2): Wait until ivar > cmp_val
+                 - NVSHMEM_CMP_GE (3): Wait until ivar >= cmp_val
+                 - NVSHMEM_CMP_LT (4): Wait until ivar < cmp_val
+                 - NVSHMEM_CMP_LE (5): Wait until ivar <= cmp_val
+            cmp_val: Value to compare against.
+
+        Notes:
+            - This is a blocking operation that will wait indefinitely until the
+              condition is satisfied.
+            - The tensor must be in symmetric memory and accessible from other PEs.
+
+        Example:
+            ```
+            # Wait until flag tensor becomes 1 (set by another PE)
+            NVSHMEM_CMP_EQ = 0
+            nvshmem.wait_until_tensor(flag_tensor, NVSHMEM_CMP_EQ, 1)
+            ```
+        """
+        tl.static_assert(
+            ivar.type.element_ty.itemsize == 4,
+            "wait_until expects a 32-bit type for the synchronization variable",
+        )
+        return wait_until_extern_wrapper(ivar.to(tl.int64), cmp_op, cmp_val)
+
+    @core.extern
+    def wait_until_extern_wrapper(ivar, cmp, cmp_val, _semantic=None):  # type: ignore[no-untyped-def]
+        return core.extern_elementwise(
+            "",
+            "",
+            [ivar, cmp, cmp_val],
+            {
+                (
+                    core.dtype("int64"),
+                    core.dtype("int32"),
+                    core.dtype("int32"),
+                ): ("nvshmem_int_wait_until", core.dtype("int32"))
+            },
+            is_pure=False,
+            _semantic=_semantic,
+        )
+
+    @triton.jit  # type: ignore[misc]
+    def signal_wait_until(signal, cmp, cmp_val):  # type: ignore[no-untyped-def]
+        """
+        Wait until a signal variable meets a specified condition.
+
+        This function blocks the calling thread until the value at the specified
+        signal variable satisfies the given comparison condition. Signal variables
+        are special uint64_t symmetric objects used for efficient synchronization
+        with signal operations.
+
+        Args:
+            signal (tensor): Symmetric signal tensor with remote PE.
+                             Must be 8-byte aligned symmetric memory.
+            cmp (int32): Comparison operator. Common values:
+                        - NVSHMEM_CMP_EQ (0): Wait until signal == cmp_val
+                        - NVSHMEM_CMP_NE (1): Wait until signal != cmp_val
+                        - NVSHMEM_CMP_GT (2): Wait until signal > cmp_val
+                        - NVSHMEM_CMP_GE (3): Wait until signal >= cmp_val
+                        - NVSHMEM_CMP_LT (4): Wait until signal < cmp_val
+                        - NVSHMEM_CMP_LE (5): Wait until signal <= cmp_val
+            cmp_val (int64): Value to compare against.
+
+        Returns:
+            int32: Status code (0 for success).
+
+        Notes:
+            - This is a blocking operation designed specifically for signal variables.
+            - Signal variables are updated atomically by putmem_signal operations.
+            - More efficient than wait_until for signal-based synchronization patterns.
+            - Ensures the signal update is fully complete before returning.
+            - Commonly used with putmem_signal_block for producer-consumer patterns.
+
+        Example:
+            ```
+            # Wait for signal to be set to completion value
+            NVSHMEM_CMP_EQ = 0
+            nvshmem.signal_wait_until(signal_ptr, NVSHMEM_CMP_EQ, 42)
+            ```
+        """
+        cmp_val = 0 << 32 | cmp_val
+        return signal_wait_until_extern_wrapper(
+            signal.to(tl.int64), cmp, cmp_val.to(tl.uint64)
+        )
+
+    @core.extern
+    def signal_wait_until_extern_wrapper(signal, cmp, cmp_val, _semantic=None):  # type: ignore[no-untyped-def]
+        return core.extern_elementwise(
+            "",
+            "",
+            [signal, cmp, cmp_val],
+            {
+                (
+                    core.dtype("int64"),
+                    core.dtype("int32"),
+                    core.dtype("uint64"),
+                ): ("nvshmem_signal_wait_until", core.dtype("int32"))
+            },
+            is_pure=False,
+            _semantic=_semantic,
+        )
+
+    @core.extern
+    def signal_op(sig_addr, signal, sig_op, pe, _semantic=None):  # type: ignore[no-untyped-def]
+        """
+        Perform an atomic signal operation on a remote PE.
+
+        This function atomically updates a signal variable on the specified remote PE
+        using the given operation and value. This enables efficient point-to-point
+        synchronization and notification between PEs.
+
+        Args:
+            sig_addr (int64): Symmetric address of the signal variable (uint64_t) on the remote PE.
+                             Must be 8-byte aligned symmetric memory.
+            signal (int64): Value to be used in the signal operation.
+            sig_op (int32): Signal operation type. Common values:
+                           - NVSHMEM_SIGNAL_SET (0): Atomically set sig_addr = signal
+                           - NVSHMEM_SIGNAL_ADD (5): Atomically set sig_addr += signal
+            pe (int32): PE number of the remote PE (0 ≤ pe < nvshmem_n_pes()).
+            _semantic: Optional semantic information for Triton compilation.
+
+        Returns:
+            int32: Status code (0 for success).
+
+        Notes:
+            - This is a one-sided operation - the remote PE does not need to participate.
+            - The signal operation is performed atomically on the remote PE.
+            - Can be used with signal_wait_until() on the remote PE for synchronization.
+            - Provides low-overhead notification mechanism between PEs.
+            - The signal variable must be of type uint64_t in symmetric memory.
+
+        Example:
+            ```python
+            # Atomically set remote signal to 1 to notify completion
+            NVSHMEM_SIGNAL_SET = 0
+            nvshmem.signal_op(remote_signal_ptr, 1, NVSHMEM_SIGNAL_SET, target_pe)
+            ```
+        """
+        return core.extern_elementwise(
+            "",
+            "",
+            [sig_addr, signal, sig_op, pe],
+            {
+                (
+                    core.dtype("int64"),
+                    core.dtype("int64"),
+                    core.dtype("int32"),
+                    core.dtype("int32"),
+                ): ("nvshmemx_signal_op", core.dtype("int32"))
+            },
+            is_pure=False,
+            _semantic=_semantic,
+        )
+
+    # Memory Ordering Operations
+    @core.extern
+    def fence(_semantic=None):  # type: ignore[no-untyped-def]
+        """
+        Ensure ordering of put operations to each remote PE.
+
+        This function provides a memory fence that ensures point-to-point ordering
+        of remote memory operations. Put operations issued before the fence are
+        guaranteed to be ordered before put operations issued after the fence,
+        when targeting the same remote PE.
+
+        Args:
+            _semantic: Optional semantic information for Triton compilation.
+
+        Returns:
+            int32: Status code (0 for success).
+
+        Notes:
+            - This provides weaker ordering guarantees than quiet().
+            - Operations to each PE are ordered, but operations to different PEs
+              may still be reordered relative to each other.
+            - Does not guarantee completion of operations, only ordering.
+            - Non-blocking operations are not ordered by fence - use quiet() instead.
+            - Essential for ensuring correct ordering in communication patterns.
+
+        Memory Ordering Guarantees:
+            - Put operations before fence() → ordered before → Put operations after fence()
+            - Ordering is maintained per-destination-PE basis
+            - Remote PEs can observe the enforced ordering
+
+        Example:
+            ```
+            # Ensure first put completes before second put to same PE
+            nvshmem.put(dst, src, nelems, target_pe)
+            nvshmem.fence()  # Enforce ordering
+            nvshmem.put(dst2, src2, nelems, target_pe)
+            ```
+        """
+        return core.extern_elementwise(
+            "",
+            "",
+            [],
+            {
+                (): ("nvshmem_fence", core.dtype("int32")),
+            },
+            is_pure=False,
+            _semantic=_semantic,
+        )
+
+    @core.extern
+    def quiet(_semantic=None):  # type: ignore[no-untyped-def]
+        """
+        Wait for completion of all outstanding put operations.
+
+        This function blocks until all outstanding remote memory operations issued
+        by the calling PE have completed. It provides stronger guarantees than
+        fence() by ensuring both ordering and completion of all operations.
+
+        Args:
+            _semantic: Optional semantic information for Triton compilation.
+
+        Returns:
+            int32: Status code (0 for success).
+
+        Notes:
+            - This is a blocking operation that waits for completion.
+            - Ensures all previous put operations have been delivered to their destinations.
+            - Provides global ordering - operations to ALL PEs are ordered.
+            - Required to complete non-blocking operations.
+            - More expensive than fence() but provides stronger guarantees.
+
+        Memory Ordering Guarantees:
+            - All put operations before quiet() are completed before any operations after quiet()
+            - Operations are visible to all PEs as having occurred before subsequent operations
+            - Both blocking and non-blocking operations are completed
+
+        Example:
+            ```
+            # Ensure all data transfers complete before setting completion flag
+            nvshmem.putmem_block(data_ptr, src_ptr, data_size, target_pe)
+            nvshmem.quiet()  # Wait for data transfer completion
+            nvshmem.putmem_block(
+                flag_ptr, flag_src_ptr, 8, target_pe
+            )  # Signal completion
+            ```
+        """
+        return core.extern_elementwise(
+            "",
+            "",
+            [],
+            {
+                (): ("nvshmem_quiet", core.dtype("int32")),
+            },
+            is_pure=False,
+            _semantic=_semantic,
+        )
+
+    # PE Information Operations
+    @core.extern
+    def my_pe(_semantic=None):  # type: ignore[no-untyped-def]
+        """
+        Get the PE number of the calling PE.
+
+        This function returns the unique identifier (PE number) of the current
+        processing element within the NVSHMEM job. PE numbers range from 0 to
+        nvshmem_n_pes() - 1.
+
+        Args:
+            _semantic: Optional semantic information for Triton compilation.
+
+        Returns:
+            int32: PE number of the calling PE (0 ≤ pe < nvshmem_n_pes()).
+
+        Notes:
+            - This is a pure function that returns the same value throughout execution.
+            - PE numbering starts from 0 and is contiguous.
+            - Each PE has a unique identifier within the NVSHMEM job.
+            - Can be called from both host and device code.
+            - Essential for implementing PE-specific logic and communication patterns.
+
+        Example:
+            ```
+            # Get current PE number for conditional logic
+            pe = nvshmem.my_pe()
+            if pe == 0:
+                # Root PE logic
+                pass
+            else:
+                # Non-root PE logic
+                pass
+            ```
+        """
+        return core.extern_elementwise(
+            "",
+            "",
+            [],
+            {(): ("nvshmem_my_pe", core.dtype("int32"))},
+            is_pure=True,
+            _semantic=_semantic,
+        )
+
+    @core.extern
+    def n_pes(_semantic=None):  # type: ignore[no-untyped-def]
+        """
+        Get the total number of PEs in the NVSHMEM job.
+
+        This function returns the total count of processing elements (PEs)
+        participating in the current NVSHMEM job. This value remains constant
+        throughout the execution of the program.
+
+        Args:
+            _semantic: Optional semantic information for Triton compilation.
+
+        Returns:
+            int32: Total number of PEs in the job (always ≥ 1).
+
+        Notes:
+            - This is a pure function that returns the same value throughout execution.
+            - The value is determined at NVSHMEM initialization and never changes.
+            - Valid PE numbers range from 0 to n_pes() - 1.
+            - Can be called from both host and device code.
+            - Essential for implementing collective operations and communication patterns.
+
+        Example:
+            ```
+            # Broadcast from root to all other PEs
+            total_pes = nvshmem.n_pes()
+            my_rank = nvshmem.my_pe()
+
+            if my_rank == 0:
+                # Send to all other PEs
+                for peer in range(1, total_pes):
+                    nvshmem.putmem_block(dst_ptr, src_ptr, size, peer)
+            ```
+        """
+        return core.extern_elementwise(
+            "",
+            "",
+            [],
+            {(): ("nvshmem_n_pes", core.dtype("int32"))},
+            is_pure=True,
+            _semantic=_semantic,
+        )
+
+    # Synchronization Operations
+    @core.extern
+    def barrier_all(_semantic=None):  # type: ignore[no-untyped-def]
+        """
+        Synchronize all PEs with completion guarantee.
+
+        This function creates a barrier across all PEs in the NVSHMEM job. It ensures
+        that all local and remote memory updates issued before the barrier by any PE
+        are completed before any PE exits the barrier. This provides both
+        synchronization and memory consistency.
+
+        Args:
+            _semantic: Optional semantic information for Triton compilation.
+
+        Returns:
+            int32: Status code (0 for success).
+
+        Notes:
+            - This is a collective operation - all PEs must participate.
+            - Stronger guarantee than sync_all() - ensures completion of remote operations.
+            - Blocks until all PEs reach the barrier AND all memory operations complete.
+            - Must be called from kernels launched with cooperative launch.
+            - Provides full memory consistency across all PEs.
+            - More expensive than sync_all() due to completion guarantees.
+
+        Memory Consistency Guarantees:
+            - All memory updates before barrier_all() are visible to all PEs
+            - All remote memory operations are completed before any PE continues
+            - Provides a global synchronization point with memory ordering
+
+        Example:
+            ```
+            # Ensure all PEs complete their work before proceeding
+            # All PEs execute this - it's a collective operation
+            nvshmem.barrier_all()
+            # At this point, all previous operations are complete on all PEs
+            ```
+        """
+        return core.extern_elementwise(
+            "",
+            "",
+            [],
+            {(): ("nvshmem_barrier_all", core.dtype("int32"))},
+            is_pure=False,
+            _semantic=_semantic,
+        )
+
+    @core.extern
+    def sync_all(_semantic=None):  # type: ignore[no-untyped-def]
+        """
+        Synchronize all PEs with local completion guarantee.
+
+        This function creates a lightweight synchronization barrier across all PEs.
+        It ensures that all local store operations issued before the sync are
+        visible to other PEs, but does not guarantee completion of remote memory
+        operations initiated by the calling PE.
+
+        Args:
+            _semantic: Optional semantic information for Triton compilation.
+
+        Returns:
+            int32: Status code (0 for success).
+
+        Notes:
+            - This is a collective operation - all PEs must participate.
+            - Lighter weight than barrier_all() - only ensures local store visibility.
+            - Does not guarantee completion of remote memory updates initiated locally.
+            - Must be called from kernels launched with cooperative launch.
+            - Suitable when only synchronization (not completion) is needed.
+            - More efficient than barrier_all() for synchronization-only patterns.
+
+        Memory Consistency Guarantees:
+            - Local store operations are visible to other PEs
+            - Does NOT ensure completion of outgoing remote operations
+            - Provides synchronization point without full completion overhead
+
+        Example:
+            ```
+            # Lightweight synchronization between PEs
+            # All PEs execute this - it's a collective operation
+            nvshmem.sync_all()
+            # Local stores are visible, but remote ops may still be in flight
+            ```
+        """
+        return core.extern_elementwise(
+            "",
+            "",
+            [],
+            {(): ("nvshmem_sync_all", core.dtype("int32"))},
+            is_pure=False,
+            _semantic=_semantic,
+        )
+
+    # Collective Operations (mem-based APIs - sizes in bytes)
+    @triton.jit  # type: ignore[misc]
+    def alltoall(team, dest, source, nelems_per_pe):  # type: ignore[no-untyped-def]
+        """
+        All-to-all tensor exchange between PEs in a team.
+
+        This high-level function provides a tensor-aware interface for NVSHMEM alltoall
+        operations. Each PE sends nelems_per_pe elements to every other PE and receives
+        the same amount from every other PE.
+
+        Args:
+            team: Team handle for the collective operation. Use 0 for NVSHMEM_TEAM_WORLD.
+            dest: Destination tensor. Must be large enough for nelems_per_pe * n_pes elements.
+            source: Source tensor containing data for all PEs. Must contain nelems_per_pe * n_pes elements.
+            nelems_per_pe: Number of elements to exchange with each PE.
+
+        Notes:
+            - Performs compile-time type checking between dest and source tensors.
+            - Automatically calculates byte size from tensor type and element count.
+            - This is a collective operation - all PEs in the team must participate.
+            - Data layout: source=[data_for_pe0, data_for_pe1, ...], dest=[data_from_pe0, data_from_pe1, ...]
+
+        Example:
+            ```
+            # Each PE exchanges 10 elements with every other PE
+            nvshmem.alltoall(0, dest_tensor, src_tensor, 10)
+            ```
+        """
+        tl.static_assert(dest.type == source.type)
+        size_bytes_per_pe = nelems_per_pe * dest.type.element_ty.itemsize
+        return alltoallmem_block_extern_wrapper(
+            team, dest.to(tl.int64), source.to(tl.int64), size_bytes_per_pe.to(tl.int64)
+        )
+
+    @core.extern  # type: ignore[misc]
+    def alltoallmem_block_extern_wrapper(
+        team: Any, dest: Any, source: Any, size_bytes: Any, _semantic: Any = None
+    ) -> None:
+        """Low-level extern wrapper for NVSHMEM alltoall"""
+        return core.extern_elementwise(
+            "",
+            "",
+            [team, dest, source, size_bytes],
+            {
+                (
+                    core.dtype("int32"),  # team handle
+                    core.dtype("int64"),  # dest ptr
+                    core.dtype("int64"),  # source ptr
+                    core.dtype("int64"),  # size in bytes
+                ): ("nvshmemx_alltoallmem_block", core.dtype("int32"))
+            },
+            is_pure=False,
+            _semantic=_semantic,
+        )
+
+    @triton.jit  # type: ignore[misc]
+    def broadcast(team, dest, source, nelems, pe_root):  # type: ignore[no-untyped-def]
+        """
+        Broadcast tensor data from a root PE to all other PEs in a team.
+
+        This high-level function provides a tensor-aware interface for NVSHMEM broadcast
+        operations. It automatically handles type checking and size calculations, making
+        the API more ergonomic and type-safe.
+
+        Args:
+            team: Team handle for the collective operation. Use 0 for NVSHMEM_TEAM_WORLD.
+            dest: Destination tensor with type information. All PEs receive data here.
+            source: Source tensor on the root PE. Type must match dest.
+            nelems: Number of elements to broadcast.
+            pe_root: PE number of the root PE that provides the source data.
+
+        Notes:
+            - Performs compile-time type checking between dest and source tensors.
+            - Automatically calculates byte size from tensor type and element count.
+            - This is a collective operation - all PEs in the team must participate.
+            - Must be called from kernels launched with cooperative launch.
+
+        Example:
+            ```
+            # Broadcast 100 elements from PE 0 to all PEs
+            nvshmem.broadcast(0, dest_tensor, src_tensor, 100, 0)
+            ```
+        """
+        tl.static_assert(dest.type == source.type)
+        nbytes = nelems * dest.type.element_ty.itemsize
+        return broadcastmem_block_extern_wrapper(
+            team, dest.to(tl.int64), source.to(tl.int64), nbytes.to(tl.int64), pe_root
+        )
+
+    @core.extern  # type: ignore[misc]
+    def broadcastmem_block_extern_wrapper(
+        team: Any,
+        dest: Any,
+        source: Any,
+        size_bytes: Any,
+        pe_root: Any,
+        _semantic: Any = None,
+    ) -> None:
+        """Low-level extern wrapper for NVSHMEM broadcast"""
+        return core.extern_elementwise(
+            "",
+            "",
+            [team, dest, source, size_bytes, pe_root],
+            {
+                (
+                    core.dtype("int32"),  # team handle
+                    core.dtype("int64"),  # dest ptr
+                    core.dtype("int64"),  # source ptr
+                    core.dtype("int64"),  # size in bytes
+                    core.dtype("int32"),  # pe_root
+                ): ("nvshmemx_broadcastmem_block", core.dtype("int32"))
+            },
+            is_pure=False,
+            _semantic=_semantic,
+        )
+
+    # Reduction Operation
+    @triton.jit  # type: ignore[misc]
+    def reduce(team, dest, source, nreduce, operation: tl.constexpr):  # type: ignore[no-untyped-def]
+        """
+        Performs a collective reduction on tensors across a team of PEs.
+
+        This high-level function provides a tensor-aware interface for NVSHMEM
+        reduction operations. It automatically infers the data type from the
+        input tensors and calls the appropriate underlying NVSHMEM function.
+
+        Args:
+            team: The team handle for the collective (0 for NVSHMEM_TEAM_WORLD).
+            dest: Destination tensor for the reduction results.
+            source: Source tensor containing data to be reduced. Must be the same type as dest.
+            nreduce: The number of elements in the source tensor to reduce.
+            operation: The reduction operation to perform ("sum", "max", "min", "prod").
+
+        Notes:
+            - Performs compile-time type checking between dest and source tensors.
+            - This is a collective operation that must be called by all PEs in the team.
+            - Requires a cooperative grid launch.
+
+        Example:
+            ```
+            # Perform a sum reduction on two tensors
+            nvshmem.reduce(0, dest_tensor, src_tensor, 100, "sum")
+            ```
+        """
+        tl.static_assert(dest.type == source.type)
+        dtype = dest.type.element_ty
+        return reduce_extern_wrapper(
+            team,
+            dest.to(tl.int64),
+            source.to(tl.int64),
+            nreduce.to(tl.int64),
+            operation,
+            dtype,
+        )
+
+    @core.extern  # type: ignore[misc]
+    def reduce_extern_wrapper(
+        team: Any,
+        dest: Any,
+        source: Any,
+        nreduce: Any,
+        operation: str,
+        dtype: Any,
+        _semantic: Any = None,
+    ) -> None:
+        """
+        Low-level extern wrapper for NVSHMEM reduction operations.
+
+        This function provides a generic interface to NVSHMEM reduction operations,
+        automatically selecting the appropriate NVSHMEM function based on the data type
+        and operation specified.
+        Args:
+            team (int64): The team handle (0 for NVSHMEM_TEAM_WORLD).
+            dest (pointer): Destination pointer where reduction results are stored.
+            source (pointer): Source pointer containing data to be reduced.
+            nreduce (int64): Number of elements to reduce.
+            operation (str): Reduction operation ("sum", "max", "min", "prod").
+            dtype: Data type specification - accepts torch.dtype, tl.dtype, str, or constexpr.
+            _semantic: Optional semantic information for Triton compilation.
+
+        Raises:
+            ValueError: If the operation is not supported.
+            TypeError: If the data type is not supported.
+
+        Example:
+            nvshmem.reduce(0, dest_ptr, src_ptr, 100, "sum", torch.float32)
+        """
+        # Mapping from Triton dtype names to NVSHMEM typenames
+        DTYPE_TO_NVSHMEM_MAP = {
+            "int8": "int8",
+            "int16": "int16",
+            "int32": "int32",
+            "int64": "int64",
+            "uint8": "uint8",
+            "uint16": "uint16",
+            "uint32": "uint32",
+            "uint64": "uint64",
+            "fp16": "half",
+            "bf16": "bfloat16",
+            "fp32": "float",
+            "fp64": "double",
+        }
+
+        # Triton dtype names are standardized as fp16, bf16, fp32, etc.
+        dtype_name = str(dtype).replace("tl.", "")
+
+        if dtype_name not in DTYPE_TO_NVSHMEM_MAP:
+            raise TypeError(
+                f"Unsupported reduction dtype: {dtype_name}. Supported dtypes: {list(DTYPE_TO_NVSHMEM_MAP.keys())}"
+            )
+
+        # Extract operation name from constexpr if needed
+        op_name = operation.value if hasattr(operation, "value") else operation
+
+        # Validate operation is supported
+        supported_ops = {"sum", "max", "min", "prod"}
+        if op_name not in supported_ops:
+            raise ValueError(
+                f"Unsupported reduction operation: '{op_name}'. Supported ops are {supported_ops}"
+            )
+
+        # Map to NVSHMEM typename and validate dtype is supported
+        nvshmem_typename = DTYPE_TO_NVSHMEM_MAP.get(dtype_name)
+        if nvshmem_typename is None:
+            raise TypeError(
+                f"Unsupported reduction dtype: {dtype_name}. Supported dtypes are {list(DTYPE_TO_NVSHMEM_MAP.keys())}"
+            )
+
+        # Generate NVSHMEM function name
+        nvshmem_func = f"nvshmem_{nvshmem_typename}_{op_name}_reduce"
+
+        # Define function signature - all parameters are int64 in Triton (they are just ptrs)
+        signature = (
+            core.dtype("int32"),  # team handle
+            core.dtype("int64"),  # destination pointer
+            core.dtype("int64"),  # source pointer
+            core.dtype("int64"),  # number of elements
+        )
+
+        return core.extern_elementwise(
+            "",
+            "",
+            [team, dest, source, nreduce],
+            {signature: (nvshmem_func, core.dtype("int32"))},
+            is_pure=False,
+            _semantic=_semantic,
+        )
+
+    # Utility for inspecting Triton kernels
+
+    triton_kernels: dict = {}
+
+    def _log_triton_kernel(kernel) -> None:  # type: ignore[no-untyped-def]
+        import atexit
+        import tempfile
+
+        if dist.is_initialized() and dist.get_rank() != 0:
+            return
+
+        def on_exit() -> None:
+            logger.info("PTX files:")
+            for kernel in triton_kernels:
+                with tempfile.NamedTemporaryFile(dir="/tmp", delete=False) as f:
+                    f.write(kernel.asm["ptx"].encode("utf-8"))
+                    logger.info(f"+- {kernel.name}: {f.name}")  # noqa: G004
+
+        if len(triton_kernels) == 0:
+            atexit.register(on_exit)
+
+        if kernel not in triton_kernels:
+            triton_kernels[kernel] = None
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tensor/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tensor/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c5559cc10fabdc1172c9a3ac95ee48ca72b2d65f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tensor/__init__.py
@@ -0,0 +1,45 @@
+"""
+NOTICE: DTensor has moved to torch.distributed.tensor
+
+This file is a shim to redirect to the new location, and
+we keep the old import path starts with `_tensor` for
+backward compatibility. We will remove this folder once
+we resolve all the BC issues.
+"""
+
+import sys
+from importlib import import_module
+
+
+submodules = [
+    # TODO: _shards_wrapper/_utils here mainly for checkpoint BC, remove them
+    "_shards_wrapper",
+    "_utils",
+    "experimental",
+    "device_mesh",
+]
+
+# Redirect imports
+for submodule in submodules:
+    full_module_name = f"torch.distributed.tensor.{submodule}"
+    sys.modules[f"torch.distributed._tensor.{submodule}"] = import_module(
+        full_module_name
+    )
+
+from torch.distributed.tensor import (  # noqa: F401
+    DeviceMesh,
+    distribute_module,
+    distribute_tensor,
+    DTensor,
+    empty,
+    full,
+    init_device_mesh,
+    ones,
+    Partial,
+    Placement,
+    rand,
+    randn,
+    Replicate,
+    Shard,
+    zeros,
+)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tensor/api.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tensor/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..9e5742156a86ca511619360038a9028b0efeeaef
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tensor/api.py
@@ -0,0 +1,9 @@
+"""
+NOTE: torch.distributed._tensor has been moved to torch.distributed.tensor.
+The imports here are purely for backward compatibility. We will remove these
+imports in a few releases
+
+TODO: throw warnings when this module imported
+"""
+
+from torch.distributed.tensor._api import *  # noqa: F401, F403
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tensor/placement_types.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tensor/placement_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a4e70dbba455471feef2326cae8ba28b32d0304
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tensor/placement_types.py
@@ -0,0 +1,10 @@
+"""
+NOTE: torch.distributed._tensor has been moved to torch.distributed.tensor.
+The imports here are purely for backward compatibility. We will remove these
+imports in a few releases
+
+TODO: throw warnings when this module imported
+"""
+
+from torch.distributed.tensor._dtensor_spec import *  # noqa: F401, F403
+from torch.distributed.tensor.placement_types import *  # noqa: F401, F403
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tools/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tools/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..22e974cdd64f1082e7a89e441eb8c90163f56d3b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tools/__init__.py
@@ -0,0 +1,12 @@
+from .fsdp2_mem_tracker import FSDPMemTracker
+from .mem_tracker import MemTracker
+from .memory_tracker import MemoryTracker
+from .mod_tracker import ModTracker
+from .runtime_estimator import RuntimeEstimator
+from .sac_estimator import (
+    MSPS,
+    SACEstimator,
+    SACGreedyOrderMeta,
+    SACStats,
+    SACTradeOffStats,
+)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tools/common_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tools/common_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..0188a4aa08440e05bcdbbff8c9d14c05540a7909
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tools/common_utils.py
@@ -0,0 +1,33 @@
+import warnings
+
+import torch
+from torch.utils._python_dispatch import is_traceable_wrapper_subclass
+
+
+def get_untyped_storages(t: torch.Tensor) -> set[torch.UntypedStorage]:
+    """
+    Recursively extracts untyped storages from a tensor or its subclasses.
+
+    Args:
+        t (torch.Tensor): The tensor to extract storages from.
+
+    Returns:
+        Set[torch.UntypedStorage]: A set of untyped storages.
+    """
+    unflattened_tensors = [t]
+    flattened_tensor_storages = set()
+    while len(unflattened_tensors) > 0:
+        obj = unflattened_tensors.pop()
+        if is_traceable_wrapper_subclass(obj):
+            attrs, _ = obj.__tensor_flatten__()  # type: ignore[attr-defined]
+            unflattened_tensors.extend([getattr(obj, attr) for attr in attrs])
+        else:
+            if not hasattr(obj, "untyped_storage"):
+                warnings.warn(
+                    f"Expected a tensor or a traceable wrapper-subclass of tensor, but got {type(obj)}",
+                    category=UserWarning,
+                    stacklevel=2,
+                )
+            else:
+                flattened_tensor_storages.add(obj.untyped_storage())
+    return flattened_tensor_storages
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tools/fake_collectives.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tools/fake_collectives.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ac0f8a764d3eca836de98bd82d5495817eadf5b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tools/fake_collectives.py
@@ -0,0 +1,307 @@
+import random
+from typing import Any
+
+import torch
+from torch._C._distributed_c10d import (
+    _resolve_process_group,
+    FakeWork,
+    ProcessGroup,
+    Work,
+)
+from torch.utils._pytree import tree_map_only
+
+
+torch.distributed.batch_isend_irecv
+
+c10d = torch.ops.c10d
+_c10d_functional = torch.ops._c10d_functional
+_c10d_functional_autograd = torch.ops._c10d_functional_autograd
+_dtensor = torch.ops._dtensor
+used_ids: set[int] = set()
+
+
+def generate_unique_id() -> int:
+    while True:
+        new_id = random.randint(1, 10**9)
+        if new_id not in used_ids:
+            used_ids.add(new_id)
+            return new_id
+
+
+# Function to create and return FakeWork object
+def create_fakework(args, return_first_arg=True):  # type: ignore[no-untyped-def]
+    work = FakeWork()
+    work.seq_id = generate_unique_id()
+    fakework_script_obj = work.boxed()
+    return (args[0], fakework_script_obj) if return_first_arg else fakework_script_obj
+
+
+# Dictionary mapping collective operations to their meta functions
+# All 20 ops from torch.csrc.distributed.c10d.Ops.cpp are included
+# _DEPRECATED_META_FUNCTIONS = {
+#     "allreduce_coalesced_": lambda *args: create_fakework(args, return_first_arg=False),
+#     "allgather_coalesced_": lambda *args: create_fakework(args, return_first_arg=False),
+#     "allgather_into_tensor_coalesced_": lambda *args: create_fakework(args, return_first_arg=False),
+#     "reduce_scatter_tensor_coalesced_": lambda *args: create_fakework(args, return_first_arg=False),
+# }
+_META_FUNCTIONS = {
+    "broadcast_": lambda *args: create_fakework(args),
+    "allreduce_": lambda *args: create_fakework(args),
+    "allgather_": lambda *args: create_fakework(args),
+    "_allgather_base_": lambda *args: create_fakework(args),
+    "reduce_scatter_": lambda *args: create_fakework(args),
+    "_reduce_scatter_base_": lambda *args: create_fakework(args),
+    "reduce_": lambda *args: create_fakework(args, return_first_arg=False),
+    "gather_": lambda *args: create_fakework(args, return_first_arg=False),
+    "scatter_": lambda *args: create_fakework(args),
+    "alltoall_": lambda *args: create_fakework(args),
+    "alltoall_base_": lambda *args: create_fakework(args, return_first_arg=False),
+    "barrier": lambda *args: create_fakework(args, return_first_arg=False),
+    "monitored_barrier_": lambda *args: None,
+    "send": lambda *args: create_fakework(args, return_first_arg=False),
+    "recv_": lambda *args: create_fakework(args, return_first_arg=False),
+    "recv_any_source_": lambda *args: create_fakework(args, return_first_arg=False),
+}
+
+lib_impl = torch.library.Library("c10d", "IMPL")  # noqa: TOR901
+for op, meta_func in _META_FUNCTIONS.items():
+    lib_impl.impl(op, meta_func, "Meta")
+
+# List of collective operation functions including functional collectives
+# Note: The following collectives might be deprecated soon hence not adding them
+# depcreated_non_functional_collectives = [
+#     c10d.allreduce_coalesced_.default,
+#     c10d.reduce_scatter_tensor_coalesced_.default,
+#     c10d.allgather_into_tensor_coalesced_.default,
+#     c10d.allgather_coalesced_.default,
+# ]
+non_functional_collectives: set[torch._ops.OpOverload] = {
+    c10d.broadcast_.default,
+    c10d.allreduce_.default,
+    c10d.reduce_.default,
+    c10d.send.default,
+    c10d.recv_.default,
+    c10d.recv_any_source_.default,
+    c10d.allgather_.default,
+    c10d.reduce_scatter_.default,
+    c10d._reduce_scatter_base_.default,
+    c10d._allgather_base_.default,
+    c10d.gather_.default,
+    c10d.scatter_.default,
+    c10d.alltoall_.default,
+    c10d.alltoall_base_.default,
+    c10d.barrier.default,
+    c10d.monitored_barrier_.default,
+}
+functional_collectives: set[torch._ops.OpOverload] = {
+    _c10d_functional.broadcast.default,
+    _c10d_functional.all_reduce.default,
+    _c10d_functional.all_gather_into_tensor.default,
+    _c10d_functional.reduce_scatter_tensor.default,
+    _c10d_functional.reduce_scatter_tensor_out.default,
+    _c10d_functional.all_to_all_single.default,
+    _c10d_functional_autograd.all_to_all_single.default,
+    _c10d_functional.wait_tensor.default,
+    _c10d_functional.all_reduce_.default,
+    _c10d_functional.all_reduce_coalesced.default,
+    _c10d_functional.all_reduce_coalesced_.default,
+    _c10d_functional.all_gather_into_tensor_out.default,
+    _c10d_functional.all_gather_into_tensor_coalesced.default,
+    _c10d_functional_autograd.all_gather_into_tensor.default,
+    _c10d_functional.reduce_scatter_tensor_coalesced.default,
+    _c10d_functional_autograd.reduce_scatter_tensor.default,
+    _c10d_functional.broadcast_.default,
+    _dtensor.shard_dim_alltoall.default,
+}
+
+sync_ops: set[torch._ops.OpOverload] = {
+    c10d.barrier.default,
+    c10d.monitored_barrier_.default,
+    _c10d_functional.wait_tensor.default,
+}
+
+collective_ops = set.union(functional_collectives, non_functional_collectives)
+
+
+class CollectiveOp:
+    # Static sets for performance optimization
+    PG_ARG_1 = {
+        c10d.broadcast_.default,
+        c10d.allreduce_.default,
+        c10d.reduce_.default,
+        c10d.send.default,
+        c10d.recv_.default,
+        c10d.recv_any_source_.default,
+        c10d.barrier.default,
+        # c10d.allreduce_coalesced_.default
+    }
+
+    PG_ARG_2 = {
+        c10d.allgather_.default,
+        c10d._allgather_base_.default,
+        c10d.reduce_scatter_.default,
+        c10d._reduce_scatter_base_.default,
+        c10d.gather_.default,
+        c10d.scatter_.default,
+        c10d.alltoall_.default,
+        c10d.alltoall_base_.default,
+        # c10d.allgather_coalesced_.default,
+        # c10d.allgather_into_tensor_coalesced_.default
+        # c10d.reduce_scatter_tensor_coalesced_.default
+    }
+
+    PG_ARG_3 = {
+        _c10d_functional.broadcast.default,
+        _c10d_functional.broadcast_.default,
+        _c10d_functional.all_reduce.default,
+        _c10d_functional.all_reduce_.default,
+        _c10d_functional.all_reduce_coalesced.default,
+        _c10d_functional.all_reduce_coalesced_.default,
+        _c10d_functional.all_gather_into_tensor.default,
+        _c10d_functional.all_gather_into_tensor_out.default,
+        _c10d_functional_autograd.all_gather_into_tensor.default,
+        _c10d_functional.all_gather_into_tensor_coalesced.default,
+    }
+
+    PG_ARG_4 = {
+        _c10d_functional.reduce_scatter_tensor.default,
+        _c10d_functional.reduce_scatter_tensor_coalesced.default,
+        _c10d_functional_autograd.reduce_scatter_tensor.default,
+        _c10d_functional.all_to_all_single.default,
+        _c10d_functional_autograd.all_to_all_single.default,
+        _dtensor.shard_dim_alltoall.default,
+    }
+
+    WK_ARG_1 = {
+        c10d.broadcast_.default,
+        c10d.allreduce_.default,
+        c10d.allgather_.default,
+        c10d.reduce_scatter_.default,
+        c10d._reduce_scatter_base_.default,
+        c10d._allgather_base_.default,
+        c10d.scatter_.default,
+        c10d.alltoall_.default,
+    }
+
+    WK = {
+        c10d.send.default,
+        c10d.recv_.default,
+        c10d.recv_any_source_.default,
+        c10d.reduce_.default,
+        c10d.gather_.default,
+        c10d.alltoall_base_.default,
+        c10d.barrier.default,
+    }
+
+    COMM_TENSOR_ARG_0 = {
+        c10d.allreduce_.default,
+        c10d.send.default,
+        c10d.recv_.default,
+        c10d.recv_any_source_.default,
+        c10d.allgather_.default,
+        c10d.gather_.default,
+        c10d.reduce_.default,
+        c10d.broadcast_.default,
+        _c10d_functional.all_reduce_coalesced.default,
+        _c10d_functional.all_reduce_coalesced_.default,
+        # c10d.allreduce_coalesced_.default
+        # c10d.allgather_coalesced_.default
+        # c10d.allgather_into_tensor_coalesced_.default,
+    }
+
+    COMM_TENSOR_ARG_1 = {
+        c10d.reduce_scatter_.default,
+        c10d.scatter_.default,
+        # c10d.reduce_scatter_tensor_coalesced_.default,
+    }
+
+    COMM_TENSOR_ARG_RES = {
+        _c10d_functional.all_gather_into_tensor.default,
+        _c10d_functional_autograd.all_gather_into_tensor.default,
+    }
+
+    COMM_TENSOR_SINGLE_UNTYPED_STORAGE = {
+        c10d._allgather_base_.default,
+        _c10d_functional.broadcast.default,
+        _c10d_functional.broadcast_.default,
+        _c10d_functional.all_reduce.default,
+        _c10d_functional.all_reduce_.default,
+        _c10d_functional.reduce_scatter_tensor.default,
+        _c10d_functional_autograd.reduce_scatter_tensor.default,
+    }
+
+    COMM_TENSOR_ARG_0_AND_RES = {
+        _c10d_functional.all_to_all_single.default,
+        _c10d_functional_autograd.all_to_all_single.default,
+        _dtensor.shard_dim_alltoall.default,
+    }
+
+    COMM_TENSOR_RES_SUM = {
+        _c10d_functional.all_gather_into_tensor_coalesced.default,
+        _c10d_functional.reduce_scatter_tensor_coalesced.default,
+    }
+
+    @staticmethod
+    def sum_tensors(arg: Any) -> int:
+        """Calculate total memory consumed by the tensors in the argument."""
+        total_memory = 0
+
+        def sum_bytes(t: torch.Tensor) -> None:
+            nonlocal total_memory
+            total_memory += t.untyped_storage().nbytes()
+
+        tree_map_only(torch.Tensor, sum_bytes, arg)
+        return total_memory
+
+    @staticmethod
+    def get_process_group(func, args) -> ProcessGroup:  # type: ignore[no-untyped-def]
+        """Retrieve the process group for collective operations, except `wait_tensor`."""
+        if func in CollectiveOp.PG_ARG_1:
+            return ProcessGroup.unbox(args[1])
+        if func in CollectiveOp.PG_ARG_2:
+            return ProcessGroup.unbox(args[2])
+        if func in CollectiveOp.PG_ARG_3:
+            return _resolve_process_group(args[2])
+        if func in CollectiveOp.PG_ARG_4:
+            return _resolve_process_group(args[3])
+        raise TypeError(f"Func {func} not found in {collective_ops}")
+
+    @staticmethod
+    def get_comm_tensor_size(func, res, args, kwargs) -> int:  # type: ignore[no-untyped-def]
+        """Compute the communication tensor size, except for `wait_tensor`, `barrier`, and `monitored_barrier`."""
+        if func in CollectiveOp.COMM_TENSOR_ARG_0:
+            return CollectiveOp.sum_tensors(args[0])
+        if func in CollectiveOp.COMM_TENSOR_ARG_1:
+            return CollectiveOp.sum_tensors(args[1])
+        if func in CollectiveOp.COMM_TENSOR_ARG_RES:
+            return res.untyped_storage().nbytes()
+        if func in CollectiveOp.COMM_TENSOR_SINGLE_UNTYPED_STORAGE:
+            return args[0].untyped_storage().nbytes()
+        if func is c10d._reduce_scatter_base_.default:
+            return args[1].untyped_storage().nbytes()
+        if func is c10d.alltoall_.default:
+            # TODO(@sanketpurandare) - Confirm size computation
+            return max(
+                CollectiveOp.sum_tensors(args[0]), CollectiveOp.sum_tensors(args[1])
+            )
+        if func is c10d.alltoall_base_.default:
+            # TODO(@sanketpurandare) - Confirm size computation
+            return max(
+                args[0].untyped_storage().nbytes(), args[1].untyped_storage().nbytes()
+            )
+        if func == _c10d_functional.all_gather_into_tensor_out.default:
+            return args[-1].untyped_storage().nbytes()
+        if func in CollectiveOp.COMM_TENSOR_RES_SUM:
+            return CollectiveOp.sum_tensors(res)
+        if func in CollectiveOp.COMM_TENSOR_ARG_0_AND_RES:
+            # TODO(@sanketpurandare) - Confirm size computation
+            return args[0].untyped_storage().nbytes() + res.untyped_storage().nbytes()
+        raise TypeError(f"Unknown function: {func} in {collective_ops}")
+
+    @staticmethod
+    def get_work(func, res) -> Work:  # type: ignore[no-untyped-def]
+        if func in CollectiveOp.WK:
+            return FakeWork.unbox(res)
+        elif func in CollectiveOp.WK_ARG_1:
+            return FakeWork.unbox(res[1])
+        raise TypeError(f"Func {func} not found in {collective_ops}")
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tools/fsdp2_mem_tracker.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tools/fsdp2_mem_tracker.py
new file mode 100644
index 0000000000000000000000000000000000000000..7db24cad45b1a69a525efab736437fc48899a6d1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tools/fsdp2_mem_tracker.py
@@ -0,0 +1,578 @@
+from collections.abc import Callable
+from copy import deepcopy
+from enum import auto, Enum
+from functools import partial, wraps
+from typing import Any, NamedTuple, TYPE_CHECKING, TypeVar
+from typing_extensions import ParamSpec, TypeVarTuple, Unpack
+
+import torch
+import torch.distributed._tools.fake_collectives
+from torch import nn, optim
+from torch._guards import active_fake_mode
+from torch.distributed._tools.mem_tracker import _RefType, _State, MemTracker
+from torch.distributed.fsdp import FSDPModule
+from torch.distributed.fsdp._fully_shard._fsdp_param_group import FSDPParamGroup
+from torch.distributed.tensor import DTensor
+from torch.utils._python_dispatch import TorchDispatchMode
+from torch.utils._pytree import tree_map_only
+from torch.utils.weak import WeakIdKeyDictionary, weakref
+
+
+if TYPE_CHECKING:
+    from torch.utils.hooks import RemovableHandle
+
+_TOTAL_KEY = "Total"
+
+__all__ = ["FSDPMemTracker"]
+
+_P = ParamSpec("_P")
+_R = TypeVar("_R")
+_Ts = TypeVarTuple("_Ts")
+
+c10d = torch.ops.c10d
+
+
+class _FSDPRefType(_RefType):
+    """
+    Enumerates categories of memory usage in FSDP modules, including parameters, gradients, activations,
+    and optimizer states.
+
+    Attributes:
+        SHARDED_PARAM (str): Memory usage of sharded parameters.
+        UNSHARDED_PARAM (str): Memory usage of unsharded parameters.
+        SHARDED_GRAD (str): Memory usage of sharded gradients corresponding to the sharded parameters.
+        UNSHARDED_GRAD (str): Memory usage of unsharded gradients corresponding to the unsharded parameters.
+        ACT (str): Memory usage of activations and tensors from forward and AC recomputation.
+        TEMP (str): Memory usage of temporary tensors during the backward pass including gradients of activations.
+        ALL_GATHER (str): Memory usage of all_gather output tensor.
+        REDUCE_SCATTER (str): Memory usage of reduce_scatter input tensor.
+        OPT (str): Memory usage of tensors storing optimizer states.
+        INP (str): Memory usage of input tensors.
+    """
+
+    SHARDED_PARAM = "Sharded Param"
+    UNSHARDED_PARAM = "Unsharded Param"
+    BUFFER = "Buffer"
+    SHARDED_GRAD = "Sharded Grad"
+    UNSHARDED_GRAD = "Unsharded Grad"
+    ACT = "Activation"
+    TEMP = "Temp"
+    ALL_GATHER = "All Gather"
+    REDUCE_SCATTER = "Reduce Scatter"
+    OPT = "OptState"
+    INP = "Inputs"
+
+
+class _SavedFSDPMethods(NamedTuple):
+    pre_backward: Callable
+    post_backward: Callable
+
+
+class _FSDPModState(_State):
+    """
+    Enumerates the states of FSDP modules during the forward and backward passes.
+    """
+
+    BEF_PRE_FW = "Before Pre-Forward"
+    AFT_PRE_FW = "After Pre-Forward"
+    BEF_POST_FW = "Before Post-Forward"
+    AFT_POST_FW = "After Post-Forward"
+    BEF_PRE_BW = "Before Pre-Backward"
+    AFT_PRE_BW = "After Pre-Backward"
+    BEF_POST_BW = "Before Post-Backward"
+    AFT_POST_BW = "After Post-Backward"
+    PRE_FW_AC = "Pre-Forward AC"
+    POST_FW_AC = "Post-Forward AC"
+    PEAK_FW = "Peak Forward"
+    PEAK_BW = "Peak Backward"
+
+
+class _FSDPModMemStats:
+    """
+    A class to store the memory statistics of an FSDP module.
+
+    Args:
+        mod_fqn (str): The fully qualified name of the FSDP module.
+
+    Attributes:
+        snapshots (Dict[_FSDPModState, Dict[torch.device, Dict[str, int]]]): A dictionary of memory snapshots
+        of the module at different states as defined by ``_FSDPModState``. Each key is a device, and
+        each value is another dictionary with keys as memory reference types defined by ``_FSDPRefType`` and
+        values as the memory consumed in bytes.
+
+    """
+
+    def __init__(self, mod_fqn: str) -> None:
+        self.mod_fqn = mod_fqn
+        self.local_peak: dict[torch.device, int] = {}
+        self.snapshots: dict[
+            _FSDPModState, list[dict[torch.device, dict[str, int]]]
+        ] = {}
+
+
+class _FSDPState(Enum):
+    PRE_FW = auto()
+    FW = auto()
+    POST_FW = auto()
+    PRE_BW = auto()
+    BW = auto()
+    POST_BW = auto()
+
+
+class FSDPMemTracker(MemTracker):
+    """
+    A ``TorchDispatchMode`` based context manager that extends ``torch.distributed._tools.mem_tracker.MemTracker`` to track
+    and categorize the peak memory and module-wise memory usage of FSDP modules.
+
+    It tracks the peak memory usage across all the devices of all the FSDP modules in the module tree and categorizes
+    the tensor memory usage as defined by ``_FSDPRefType``. Further, it captures memory `snapshots` at different stages of
+    the module execution defined by ``_FSDPModState``.
+
+    Attributes:
+        memory_tracking: A weakref key dictionary to store the memory statistics of each module. Each key is a reference
+        to a module, and each value is a ``_FSDPModMemStats`` object that stores the memory statistics of the module.
+
+    Args:
+        mod (torch.nn.Module): The root FSDP module to be tracked.
+        optm (torch.optim.Optimizer, optional): The optimizer to be tracked.
+
+    Note: Please refer to ``torch.distributed._tools.mem_tracker.MemTracker`` to learn about the limitations.
+
+    Example usage
+
+    .. code-block:: python
+
+        module = ...
+        optimizer = ...
+        inp = ...
+        fmt = FSDPMemTracker(module, optimizer)
+        fmt.track_inputs((inp,))
+        with fmt:
+            optimizer.zero_grad()
+            loss = module(inp)
+            print("After Forward:")
+            fmt.display_snapshot("current")
+            loss.backward()
+            optimizer.step()
+        fmt.display_snapshot("peak")
+        fmt.display_modulewise_snapshots(depth=3, units="MB")
+
+    """
+
+    def __init__(
+        self,
+        mod: torch.nn.Module,
+        optm: torch.optim.Optimizer | None = None,
+    ) -> None:
+        super().__init__()
+        assert isinstance(mod, FSDPModule), "FSDPMemTracker only supports FSDP modules"
+        self._root_mod = mod
+        self._optm = optm
+        self._fsdp_mod_to_saved_methods: WeakIdKeyDictionary = WeakIdKeyDictionary()
+        self._fsdp_state: _FSDPState = _FSDPState.PRE_FW
+        self._ref_class: type[_RefType] = _FSDPRefType
+
+    def _instrument_fsdp_sharded_params_grads(
+        self, fsdp_param_group: FSDPParamGroup
+    ) -> None:
+        # Track sharded params and grads after initialization
+        for fsdp_param in fsdp_param_group.fsdp_params:
+            self._update_and_maybe_create_winfos(
+                fsdp_param.sharded_param,
+                _FSDPRefType.SHARDED_PARAM,
+            )
+            sharded_grad = fsdp_param.sharded_param.grad
+            if sharded_grad is not None:
+                self._update_and_maybe_create_winfos(
+                    sharded_grad,
+                    _FSDPRefType.SHARDED_GRAD,
+                )
+
+    def _fsdp_state_pre_forward(
+        self,
+        fsdp_mod: FSDPModule,
+        orig_fsdp_state_pre_fw: Callable[_P, tuple[tuple[Unpack[_Ts]], dict[str, Any]]],
+    ) -> Callable[_P, tuple[tuple[Unpack[_Ts]], dict[str, Any]]]:
+        # We capture memory snapshots before and after ``FSDPState._pre_forward`` to attribute the `unsharded` params
+        # and `all_gather` buffers.  There are three cases:
+        # Case 1: If the module is not in the ``memory_tracking`` dictionary, create a new ``_FSDPModMemStats``
+        #         instance for the module and add it to the ``memory_tracking`` dictionary.
+        # Case 2: If the module is already in the ``memory_tracking`` dictionary and we are in backward, this means
+        #         we are in the AC region. We check if this is the top most module in the AC region. If it is,
+        #         we store a weak reference and set the flag ``_in_ac`` to True.
+        # Case 3: If the module is already in the ``memory_tracking`` dictionary and we are in forward, this means
+        #         this module is called for the second time. If it is a root module, that means we are in the next
+        #         iteration and we error out. If it is not a root module, that means it's a submodule that is being
+        #         used multiple times in the same iteration, which we allow and track.
+        # For Case 1 and 3, we also initialize the ``local_peak`` and ``PEAK_FW`` snapshot for the module.
+        # For Case 2 we only capture 1 snapshot after ``FSDPState._pre_forward`` runs because it is a no-op.
+        @wraps(orig_fsdp_state_pre_fw)
+        def inner(
+            *args: _P.args, **kwargs: _P.kwargs
+        ) -> tuple[tuple[Unpack[_Ts]], dict[str, Any]]:
+            self._fsdp_state = _FSDPState.PRE_FW
+            mod_fqn = self._mod_tracker.get_known_fqn(fsdp_mod)
+            assert mod_fqn is not None
+            if fsdp_mod not in self.memory_tracking:
+                mod_stat = _FSDPModMemStats(mod_fqn)
+                self.memory_tracking[fsdp_mod] = mod_stat
+                snapshot = self.get_tracker_snapshot()
+                mod_stat.local_peak = {
+                    dev: dev_snap[_TOTAL_KEY] for dev, dev_snap in snapshot.items()
+                }
+                mod_stat.snapshots.setdefault(_FSDPModState.PEAK_FW, []).append(
+                    snapshot
+                )
+                mod_stat.snapshots.setdefault(_FSDPModState.BEF_PRE_FW, []).append(
+                    deepcopy(snapshot)
+                )
+            elif not self._mod_tracker.is_bw:
+                parents = self._mod_tracker.parents - {mod_fqn}
+                if len(parents) == 1 and "Global" in parents:
+                    raise NotImplementedError(
+                        "FSDPMemTracker does not support memory tracking for multiple iterative calls."
+                        " Either use ``reset_mod_stats`` to clear module memory stats for the previous iteration"
+                        " or file a github issue if you need this feature."
+                    )
+
+            # pyrefly: ignore [bad-assignment]
+            args, kwargs = orig_fsdp_state_pre_fw(*args, **kwargs)
+
+            fsdp_state = fsdp_mod._get_fsdp_state()
+            if fsdp_param_group := fsdp_state._fsdp_param_group:
+                for fsdp_param in fsdp_param_group.fsdp_params:
+                    self._update_and_maybe_create_winfos(
+                        fsdp_param.unsharded_param,
+                        _FSDPRefType.UNSHARDED_PARAM,
+                    )
+            mod_stat = self.memory_tracking[fsdp_mod]
+            if self._mod_tracker.is_bw:
+                state = _FSDPModState.PRE_FW_AC
+                if self._ac_mod is None:
+                    self._ac_mod = weakref.ref(fsdp_mod)
+                    self._in_ac = True
+            else:
+                state = _FSDPModState.AFT_PRE_FW
+            mod_stat.snapshots.setdefault(state, []).append(self.get_tracker_snapshot())
+            self._fsdp_state = _FSDPState.FW
+            return args, kwargs
+
+        return inner
+
+    def _fsdp_state_post_forward(
+        self,
+        fsdp_mod: FSDPModule,
+        orig_fsdp_state_post_fw: Callable[_P, _R],
+    ) -> Callable[_P, _R]:
+        # We capture memory snapshots before and after ``FSDPState._post_forward`` to capture the resharded state
+        # if ``reshard_after_forward`` is not ``False``. There are two cases:
+        # Case 1: This is called in backward, which means we are in the AC region. If this is the top most module
+        #         in the AC region, we set the flag ``_in_ac`` to False.
+        # Case 2: This is called in forward.
+        @wraps(orig_fsdp_state_post_fw)
+        def inner(*args: _P.args, **kwargs: _P.kwargs) -> _R:
+            mod_stat = self.memory_tracking[fsdp_mod]
+            if self._mod_tracker.is_bw:
+                state = _FSDPModState.POST_FW_AC
+                if self._ac_mod is not None and self._ac_mod() is fsdp_mod:
+                    self._ac_mod = None
+                    self._in_ac = False
+            else:
+                state = _FSDPModState.BEF_POST_FW
+            mod_stat.snapshots.setdefault(state, []).append(self.get_tracker_snapshot())
+            self._fsdp_state = _FSDPState.POST_FW
+
+            output = orig_fsdp_state_post_fw(*args, **kwargs)
+
+            if not self._mod_tracker.is_bw:
+                mod_stat.snapshots.setdefault(_FSDPModState.AFT_POST_FW, []).append(
+                    self.get_tracker_snapshot()
+                )
+            return output
+
+        return inner
+
+    def _fsdp_param_group_pre_backward(
+        self,
+        fsdp_mod: FSDPModule,
+        orig_fsdp_param_group_pre_backward: Callable[_P, Any],
+    ) -> Callable[_P, None]:
+        # We capture memory snapshots before and after ``FSDPParamGroup.pre_backward`` to capture the pre-fetching
+        # and unsharding of params. We also initialize ``local_peak`` and ``PEAK_BW`` snapshot for the module.
+        @wraps(orig_fsdp_param_group_pre_backward)
+        def inner(*args: _P.args, **kwargs: _P.kwargs) -> None:
+            self._fsdp_state = _FSDPState.PRE_BW
+            mod_stat = self.memory_tracking[fsdp_mod]
+            snapshot = self.get_tracker_snapshot()
+            mod_stat.local_peak = {
+                dev: dev_snap[_TOTAL_KEY] for dev, dev_snap in snapshot.items()
+            }
+            mod_stat.snapshots.setdefault(_FSDPModState.PEAK_BW, []).append(snapshot)
+            mod_stat.snapshots.setdefault(_FSDPModState.BEF_PRE_BW, []).append(
+                deepcopy(snapshot)
+            )
+            orig_fsdp_param_group_pre_backward(*args, **kwargs)
+
+            mod_stat.snapshots.setdefault(_FSDPModState.AFT_PRE_BW, []).append(
+                self.get_tracker_snapshot()
+            )
+            self._fsdp_state = _FSDPState.BW
+
+        return inner
+
+    def _fsdp_param_group_post_backward(
+        self,
+        fsdp_mod: FSDPModule,
+        orig_fsdp_param_group_post_backward: Callable[_P, Any],
+    ) -> Callable[_P, None]:
+        # We capture the memory snapshots before and after ``FSDPParamGroup.post_backward`` to track and attribute
+        # the `unsharded` grads before the post backward and then `sharded` grads and `reduce_scatter`  buffers
+        # after the post backward.
+        @wraps(orig_fsdp_param_group_post_backward)
+        def inner(*args: _P.args, **kwargs: _P.kwargs) -> None:
+            fsdp_state = fsdp_mod._get_fsdp_state()
+            if fsdp_param_group := fsdp_state._fsdp_param_group:
+                for fsdp_param in fsdp_param_group.fsdp_params:
+                    unsharded_grad = fsdp_param._unsharded_param.grad
+                    if unsharded_grad is not None:
+                        self._update_and_maybe_create_winfos(
+                            unsharded_grad,
+                            _FSDPRefType.UNSHARDED_GRAD,
+                            update_existing=True,
+                        )
+
+            mod_stat = self.memory_tracking[fsdp_mod]
+            mod_stat.snapshots.setdefault(_FSDPModState.BEF_POST_BW, []).append(
+                self.get_tracker_snapshot()
+            )
+            self._fsdp_state = _FSDPState.POST_BW
+            orig_fsdp_param_group_post_backward(*args, **kwargs)
+
+            if fsdp_param_group := fsdp_state._fsdp_param_group:
+                for fsdp_param in fsdp_param_group.fsdp_params:
+                    sharded_grad = fsdp_param.sharded_param.grad
+                    if sharded_grad is not None:
+                        self._update_and_maybe_create_winfos(
+                            sharded_grad,
+                            _FSDPRefType.SHARDED_GRAD,
+                        )
+
+            mod_stat.snapshots.setdefault(_FSDPModState.AFT_POST_BW, []).append(
+                self.get_tracker_snapshot()
+            )
+
+        return inner
+
+    def _instrument_fsdp_module(self) -> None:
+        # We uninstall the existing `FSDPState._pre_forward` and `FSDPState._post_forward` hooks and install
+        # our own hooks that wrap them. We choose this over monkey-patching `FSDPParamGroup.pre_forward` and
+        # `FSDPParamGroup.post_forward` because during AC these won't be called.
+        # TODO(@sanketpurandare): This will need to be modified after this PR (https://github.com/pytorch/pytorch/pull/127786)
+        # lands. For backward we monkey-patch the `FSDPParamGroup.pre_backward` and `FSDPParamGroup.post_backward`.
+
+        # get the unique _MultiHandlers/RemoveHandlers and store in dictionary
+        # the _MultiHandlers object will only need to be grabbed once.
+        unique_handlers: dict[RemovableHandle, bool] = {}
+        # pyrefly: ignore  # missing-attribute
+        for module in self._root_mod.modules():
+            if isinstance(module, FSDPModule):
+                fsdp_state = module._get_fsdp_state()
+                if fsdp_param_group := fsdp_state._fsdp_param_group:
+                    if not unique_handlers.get(fsdp_state._pre_forward_hook_handle):
+                        unique_handlers[fsdp_state._pre_forward_hook_handle] = True
+                    if not unique_handlers.get(fsdp_state._post_forward_hook_handle):
+                        unique_handlers[fsdp_state._post_forward_hook_handle] = True
+        # call remove on the handles once
+        for f_hook_handle in unique_handlers:
+            f_hook_handle.remove()
+        # pyrefly: ignore  # missing-attribute
+        for module in self._root_mod.modules():
+            if isinstance(module, FSDPModule):
+                fsdp_state = module._get_fsdp_state()
+                if fsdp_param_group := fsdp_state._fsdp_param_group:
+                    self._instrument_fsdp_sharded_params_grads(fsdp_param_group)
+                    fsdp_state._pre_forward_hook_handle = (
+                        # pyrefly: ignore [missing-attribute]
+                        module.register_forward_pre_hook(
+                            self._fsdp_state_pre_forward(
+                                module, fsdp_state._pre_forward
+                            ),
+                            prepend=True,
+                            with_kwargs=True,
+                        )
+                    )
+                    # pyrefly: ignore [missing-attribute]
+                    fsdp_state._post_forward_hook_handle = module.register_forward_hook(
+                        self._fsdp_state_post_forward(module, fsdp_state._post_forward),
+                        prepend=False,
+                        always_call=True,
+                    )
+                    self._fsdp_mod_to_saved_methods[module] = _SavedFSDPMethods(
+                        fsdp_param_group.pre_backward,
+                        fsdp_param_group.post_backward,
+                    )
+                    fsdp_param_group.pre_backward = self._fsdp_param_group_pre_backward(  # type: ignore[assignment]
+                        module, fsdp_param_group.pre_backward
+                    )
+                    fsdp_param_group.post_backward = (  # type: ignore[assignment]
+                        self._fsdp_param_group_post_backward(
+                            module, fsdp_param_group.post_backward
+                        )
+                    )
+
+        # pyrefly: ignore [missing-attribute]
+        for buffer in self._root_mod.buffers():
+            self._update_and_maybe_create_winfos(
+                buffer,
+                _FSDPRefType.BUFFER,
+            )
+
+    def _instrument_optimizer(self) -> None:
+        # Register a hook on the optimizer step to track the optimizer states.
+        # The pre-hook is to set the flag ``_in_opt`` to True. The post-hook unsets the flag,
+        # and also tracks any optimizer states that are created during the optimizer step.
+        if self._optm is not None:
+            self._track_optimizer_states(_FSDPRefType.OPT, self._optm)
+
+            def _opt_step_pre_hook(
+                optimizer: optim.Optimizer, args: Any, kwargs: Any
+            ) -> None:
+                self._in_opt = True
+
+            def _opt_step_post_hook(
+                optimizer: optim.Optimizer, args: Any, kwargs: Any
+            ) -> None:
+                self._track_optimizer_states(_FSDPRefType.OPT, optimizer)
+                self._in_opt = False
+
+            self._optimizer_hook_handles = (
+                self._optm.register_step_pre_hook(_opt_step_pre_hook),
+                self._optm.register_step_post_hook(_opt_step_post_hook),
+            )
+
+    def _register_module_and_optimizer_hooks(self) -> None:
+        self._instrument_fsdp_module()
+        self._instrument_optimizer()
+
+    def _deregister_module_and_optimizer_hooks(self) -> None:
+        for (
+            fsdp_mod,
+            saved_methods,
+        ) in self._fsdp_mod_to_saved_methods.items():
+            fsdp_state = fsdp_mod._get_fsdp_state()
+            fsdp_state._pre_forward_hook_handle.remove()
+            fsdp_state._post_forward_hook_handle.remove()
+            fsdp_state._pre_forward_hook_handle = fsdp_mod.register_forward_pre_hook(
+                fsdp_state._pre_forward, prepend=True, with_kwargs=True
+            )
+            fsdp_state._post_forward_hook_handle = fsdp_mod.register_forward_hook(
+                fsdp_state._post_forward, prepend=False
+            )
+            if fsdp_param_group := fsdp_state._fsdp_param_group:
+                fsdp_param_group.pre_backward = saved_methods.pre_backward
+                fsdp_param_group.post_backward = saved_methods.post_backward
+        self._fsdp_mod_to_saved_methods.clear()
+
+        if self._optimizer_hook_handles is not None:
+            for handle in self._optimizer_hook_handles:
+                handle.remove()
+            self._optimizer_hook_handles = None
+
+    def track_inputs(self, inputs: tuple[Any, ...]) -> None:
+        """
+        This is used to track the input tensors to the model and annotate them as ``Inputs``.
+        Args:
+            inputs (Tuple[Any]): A tuple containing the input data. This can include tensors
+                        as well as other data types. Only tensors will be tracked.
+        """
+
+        def _track_inputs(t: torch.Tensor) -> None:
+            self._update_and_maybe_create_winfos(
+                t,
+                _FSDPRefType.INP,
+            )
+
+        tree_map_only(torch.Tensor, _track_inputs, inputs)
+
+    def track_external(
+        self, *external: nn.Module | optim.Optimizer | torch.Tensor
+    ) -> None:
+        """This is no-op for ``FSDPMemTracker``"""
+
+    def __enter__(self) -> "FSDPMemTracker":
+        if self._depth == 0:
+            self._register_module_and_optimizer_hooks()
+            self._track_resize()
+            self._peak_mem_snap = self.get_tracker_snapshot()
+            self._peak_mem = {
+                dev: dev_snap[_TOTAL_KEY]
+                for dev, dev_snap in self._peak_mem_snap.items()
+            }
+            self._mod_tracker.__enter__()
+        TorchDispatchMode.__enter__(self)
+        self._depth += 1
+        return self
+
+    def __exit__(self, *args: Any) -> None:
+        self._depth -= 1
+        if self._depth == 0:
+            self._deregister_module_and_optimizer_hooks()
+            self._restore_resize()
+            self._mod_tracker.__exit__(*args)
+        TorchDispatchMode.__exit__(self, *args)
+
+    def __torch_dispatch__(self, func, types, args=..., kwargs=None):  # type: ignore[no-untyped-def]
+        # When running this mode with DTensor, ordinarily all modes will
+        # run **before** subclasses get a chance to run.
+        # Returning NotImplemented here gives us a chance to let DTensor
+        # run and desugar into local tensor ops, before `MemTracker` sees them.
+        if any(t == DTensor for t in types):
+            return NotImplemented
+        if (
+            func is torch.ops._c10d_functional.wait_tensor.default
+            and active_fake_mode()
+        ):
+            # N.B: This is a hacky way to override the Meta IMPL of wait_tensor. The original impl returns
+            # a new tensor which does not happen in eager mode, when a wait_tensor is called.
+            # pyrefly: ignore [unsupported-operation]
+            res = args[0]
+        else:
+            res = func(*args, **kwargs or {})
+        # If we are tracking an optimizer state, we use the optimizer reference type.
+        # If we are in backward region and not in AC region, we use the backward reference type.
+        # Else we use the forward reference type.
+        if self._in_opt:
+            reftype = _FSDPRefType.OPT
+        elif self._mod_tracker.is_bw and not self._in_ac:
+            reftype = _FSDPRefType.TEMP
+        else:
+            reftype = _FSDPRefType.ACT
+        if func is c10d._allgather_base_.default and self._fsdp_state in [
+            _FSDPState.PRE_FW,
+            _FSDPState.PRE_BW,
+        ]:
+            # pyrefly: ignore [unsupported-operation]
+            output_tensor = args[0]
+            self._update_and_maybe_create_winfos(
+                output_tensor,
+                _FSDPRefType.ALL_GATHER,
+                update_existing=True,
+            )
+        if (
+            func is c10d._reduce_scatter_base_.default
+            and self._fsdp_state == _FSDPState.POST_BW
+        ):
+            # pyrefly: ignore [unsupported-operation]
+            input_tensor = args[1]
+            self._update_and_maybe_create_winfos(
+                input_tensor,
+                _FSDPRefType.REDUCE_SCATTER,
+                update_existing=True,
+            )
+
+        tree_map_only(torch.Tensor, partial(self._track, reftype), res)
+        peak_state = (
+            _FSDPModState.PEAK_BW if self._mod_tracker.is_bw else _FSDPModState.PEAK_FW
+        )
+        self._update_peak_stats(peak_state)
+        return res
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tools/ilp_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tools/ilp_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e8ba4195ffd20323d419642159fe199549e3de1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tools/ilp_utils.py
@@ -0,0 +1,292 @@
+import copy
+from collections import OrderedDict
+from typing import cast, TypedDict
+
+import numpy as np
+
+import torch
+from torch.distributed._tools.mem_tracker import (
+    _MemRefType,
+    _ModMemStats,
+    _ModState,
+    MemTracker,
+)
+from torch.distributed._tools.runtime_estimator import RuntimeEstimator
+from torch.distributed._tools.sac_estimator import SACEstimator, SACTradeOffStats
+
+
+class ModOrder(TypedDict):
+    fw_pre_order: list[str]
+    bw_pre_order: list[str]
+    fw_post_order: list[str]
+    bw_post_order: list[str]
+
+
+class ModRuntime(TypedDict):
+    fw: float
+    bw: float
+
+
+class ModStats(TypedDict):
+    fqn: str
+    # per-module params
+    param_per_module: int
+    # per-module grads
+    grad_per_module: int
+    # total accumulated gradients up to and including this module
+    grad_total: int
+    # per module fw activation size (excluding input and output)
+    act_fw_per_module: int
+    # per module bw activation size during peak_bw
+    act_bw_per_module: int
+    # per module activation grad size during peak_bw
+    act_grad_per_module: int
+    # total activation size up to but excluding the current module
+    # includes input of the current module (i.e., output of previous module)
+    act_total: int
+    # Inputs to the module
+    input_per_module: int
+    # Outputs of the module
+    output_per_module: int
+    # Total fw run-time of the module
+    fw_runtime_per_module: float
+    # Total bw run-time of the module
+    bw_runtime_per_module: float
+    # Is this module a leaf module
+    is_leaf: bool
+    # Total ac run-time of the module
+    sac_runtime: float
+    # Total ac_memory for the module
+    sac_memory: int
+    # Number of piecewise-linear functions used for approximating ac tradeoff curve
+    n_segments: int
+    # Slopes of the of piecewise-linear functions
+    slopes: list[float]
+    # Intercepts of the of piecewise-linear functions
+    intercepts: list[float]
+    # X breakpoints of the of piecewise-linear functions
+    breakpoints: list[float]
+    # Original trade-off curves
+    tradeoff_curve: OrderedDict[float, float]
+
+
+class ModuleInfo(TypedDict):
+    mod_order: ModOrder
+    mod_stats: list[ModStats]
+
+
+def aggregate_stats(
+    model: torch.nn.Module,
+    mem_tracker: MemTracker,
+    runtime_estimator: RuntimeEstimator,
+    sac_estimator: SACEstimator,
+    dev: torch.device,
+) -> ModuleInfo:
+    """
+    Collect modulewise stats for a given model, including memory, runtime, and AC tradeoff stats.
+
+    Args:
+        model: nn.Module object
+        runtime_estimator: RuntimeEstimator object with runtime stats
+        mem_tracker: MemTracker object with memory stats
+        sac_estimator: SACEstimator object with AC tradeoff stats
+        dev: device the model was run on (used to extract memory stats from MemTracker)
+
+    Returns:
+        ModuleInfo: A dictionary with module order and module stats.
+    """
+
+    # Memory stats
+    mod_mem_stats: dict[torch.nn.Module, _ModMemStats] = dict(
+        copy.deepcopy(mem_tracker.memory_tracking)
+    )
+
+    # Runtime stats
+    mod_runtime_stats: dict[str, ModRuntime] = {
+        fqn: {"fw": v["fw"], "bw": v["bw"]}
+        for fqn, v in runtime_estimator.mod_runtimes.items()
+    }
+
+    # Module order
+    mod_order: ModOrder = {
+        "fw_pre_order": list(runtime_estimator.mod_fw_pre_order),
+        "bw_pre_order": list(runtime_estimator.mod_bw_pre_order),
+        "fw_post_order": list(runtime_estimator.mod_fw_post_order),
+        "bw_post_order": list(runtime_estimator.mod_bw_post_order),
+    }
+
+    # Selective Activation Checkpointing stats
+    sac_estimator.pwlf_sac_tradeoff_curve()
+    mod_sac_tradeoff_stats: dict[str, SACTradeOffStats] = copy.deepcopy(
+        sac_estimator.sac_mod_tradeoff_stats
+    )
+
+    module_info: ModuleInfo = {
+        "mod_order": mod_order,
+        "mod_stats": [],
+    }
+
+    for mod in model.modules():
+        if mod_mem_stat := mod_mem_stats.get(mod):
+            if tradeoff_stats := mod_sac_tradeoff_stats.get(mod_mem_stat.mod_fqn, None):
+                sac_runtime = tradeoff_stats.sac_runtime
+                sac_memory = tradeoff_stats.sac_memory
+                n_segments = tradeoff_stats.n_segments
+                slopes = tradeoff_stats.slopes
+                intercepts = tradeoff_stats.intercepts
+                breakpoints = tradeoff_stats.fit_breaks
+                tradeoff_curve = tradeoff_stats.tradeoff_curve
+                is_leaf = False
+            else:
+                sac_runtime = sac_memory = n_segments = 0
+                slopes = intercepts = breakpoints = []
+                tradeoff_curve: OrderedDict[float, float] = OrderedDict()  # type: ignore[no-redef]
+                is_leaf = True
+            mod_stat: ModStats = {
+                "fqn": mod_mem_stat.mod_fqn,
+                "param_per_module": mod_mem_stat.parameter_mem,
+                "grad_per_module": mod_mem_stat.parameter_mem,
+                "grad_total": mod_mem_stat.snapshots[_ModState.PRE_BW][-1][dev][
+                    _MemRefType.GRAD
+                ],
+                "act_fw_per_module": max(
+                    0,
+                    mod_mem_stat.snapshots[_ModState.POST_FW][-1][dev][_MemRefType.ACT]
+                    - mod_mem_stat.snapshots[_ModState.PRE_FW][-1][dev][_MemRefType.ACT]
+                    - mod_mem_stat.output_mem,
+                ),
+                "act_bw_per_module": max(
+                    0,
+                    mod_mem_stat.snapshots[_ModState.PEAK_BW][-1][dev][_MemRefType.ACT],
+                ),
+                "act_grad_per_module": (
+                    mod_mem_stat.snapshots[_ModState.PEAK_BW][-1][dev][_MemRefType.TEMP]
+                    - mod_mem_stat.snapshots[_ModState.PRE_BW][-1][dev][
+                        _MemRefType.TEMP
+                    ]
+                ),
+                "act_total": mod_mem_stat.snapshots[_ModState.POST_FW][-1][dev][
+                    _MemRefType.ACT
+                ],
+                "input_per_module": mod_mem_stat.input_mem,
+                "output_per_module": mod_mem_stat.output_mem,
+                "fw_runtime_per_module": mod_runtime_stats[mod_mem_stat.mod_fqn]["fw"],
+                "bw_runtime_per_module": mod_runtime_stats[mod_mem_stat.mod_fqn]["bw"],
+                "is_leaf": is_leaf,
+                "sac_runtime": sac_runtime,
+                "sac_memory": sac_memory,
+                "n_segments": n_segments,
+                "slopes": slopes,
+                "intercepts": intercepts,
+                "breakpoints": breakpoints,
+                "tradeoff_curve": tradeoff_curve,
+            }
+            module_info["mod_stats"].append(mod_stat)
+
+    return module_info
+
+
+class Node(ModStats):
+    index: int  # index according to forward pre-order
+    pos_fw_post_order: int  # index according to forward post-order
+
+
+class Graph:
+    def __init__(self, n: int) -> None:
+        self.nodes: list[Node] = []
+        self.name2node: dict[str, Node] = {}
+        self.ad_matrix = np.zeros((n, n))
+        self.fw_post_order: list[str] = []
+
+    def add_node(self, node: Node) -> None:
+        self.nodes.append(node)
+        self.name2node[node["fqn"]] = node
+
+
+def parse_module_info(module_info: ModuleInfo) -> Graph:
+    """
+    Parse module info and create a graph (tree) of modules. The graph will be
+    used by MILP solver to find optimal SAC and/or FSDP configurations.
+    """
+    mod_stats = module_info["mod_stats"]
+    fw_pre_order = module_info["mod_order"]["fw_pre_order"]
+    # assertion and number of nodes
+    assert len(mod_stats) == len(fw_pre_order)
+    n_nodes = len(mod_stats)
+
+    # create graph
+    g = Graph(n_nodes)
+    g.fw_post_order = module_info["mod_order"]["fw_post_order"]
+
+    # sort the modules by pre-order and add them to the graph
+    module_info["mod_stats"] = sorted(
+        mod_stats, key=lambda x: fw_pre_order.index(x["fqn"])
+    )
+    for i, one_mod_stats in enumerate(mod_stats):
+        node: Node = cast(Node, one_mod_stats)
+        node["index"] = i
+        node["pos_fw_post_order"] = g.fw_post_order.index(node["fqn"])
+        g.add_node(node)
+
+    # set up ancestor-descendant matrix
+    for i in range(n_nodes):
+        for j in range(i, n_nodes):
+            if is_self_or_submodule(g.nodes[j]["fqn"], g.nodes[i]["fqn"]):
+                g.ad_matrix[i][j] = 1
+            else:
+                break
+
+    return g
+
+
+def is_self_or_submodule(name_descendant: str, name_ancestor: str) -> bool:
+    """
+    check if name_descendant is a submodule of name_ancestor, or if they are the same
+    """
+    return name_descendant == name_ancestor or name_ancestor + "." in name_descendant
+
+
+def is_submodule(name_descendant: str, name_ancestor: str) -> bool:
+    """
+    if name_descendant is a submodule of name_ancestor, but not the same
+    """
+    return name_ancestor + "." in name_descendant
+
+
+def display_bytes(b: int, unit: str = "MiB") -> str:
+    """
+    return a string that represent the number of bytes in a desired unit
+    """
+    if unit == "KiB":
+        return f"{b / 2**10:.2f} KiB"
+    if unit == "MiB":
+        return f"{b / 2**20:.2f} MiB"
+    if unit == "GiB":
+        return f"{b / 2**30:.2f} GiB"
+    return f"{b:.2f} bytes"
+
+
+def get_peak_memory_runtime_baseline(graph: Graph) -> tuple[int, float]:
+    """
+    Get the baseline peak memory and runtime.
+    Baseline here means there is no FSDP or AC.
+    Memory includes the parameters, gradients, activations, and activation gradients.
+    Memory does not include e.g., optimizer states, embedding tables, etc.
+
+    Returns:
+        int: peak memory in bytes
+        float: compute time in ms
+    """
+    P_1 = graph.nodes[0]["param_per_module"]
+    num_nodes = len(graph.nodes)
+    peak_mem = 0
+    for i in range(num_nodes):
+        TG_i = graph.nodes[i]["grad_total"]
+        AG_i = graph.nodes[i]["act_grad_per_module"]
+        TA_i = graph.nodes[i]["act_total"]
+        peak_mem = max(peak_mem, P_1 + TG_i + AG_i + TA_i)
+    compute_time = (
+        graph.nodes[0]["fw_runtime_per_module"]
+        + graph.nodes[0]["bw_runtime_per_module"]
+    )
+    return (peak_mem, compute_time)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tools/mem_tracker.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tools/mem_tracker.py
new file mode 100644
index 0000000000000000000000000000000000000000..bcf03f132b1a7fbd7c03ed0b1a0b03e40ebebdb2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tools/mem_tracker.py
@@ -0,0 +1,938 @@
+import math
+import os
+import re
+import warnings
+from collections.abc import Callable
+from copy import deepcopy
+from enum import auto, Enum
+from functools import partial, wraps
+from typing import Any, TYPE_CHECKING
+from typing_extensions import Self
+
+import torch
+import torch.distributed._tools.fake_collectives
+from torch import nn, optim
+from torch._guards import active_fake_mode
+from torch.distributed._tools.common_utils import get_untyped_storages
+from torch.distributed._tools.mod_tracker import ModTracker
+from torch.distributed.tensor import DTensor
+from torch.optim.optimizer import (
+    register_optimizer_step_post_hook,
+    register_optimizer_step_pre_hook,
+)
+from torch.utils._python_dispatch import TorchDispatchMode
+from torch.utils._pytree import tree_flatten, tree_map_only
+from torch.utils.weak import WeakIdKeyDictionary, weakref
+
+
+if TYPE_CHECKING:
+    from torch.utils.hooks import RemovableHandle
+
+# This value is hard-coded here:
+# https://github.com/pytorch/pytorch/blob/5fba5d83f0703ff8077ab65448a998e9ad6598fd/c10/cuda/CUDACachingAllocator.cpp#L117
+_PYTORCH_MIN_ALLOCATE = (
+    2**9 if int(os.environ.get("PYTORCH_NO_CUDA_MEMORY_CACHING", 0)) == 0 else 1
+)
+_TOTAL_KEY = "Total"
+
+__all__ = ["MemTracker"]
+
+
+class _RefType(str, Enum):
+    """Base Class for defining memory reference types, categorizing tensors based on their usage within a model."""
+
+
+class _State(str, Enum):
+    """Base Class for defining module state to capture snapshots ."""
+
+
+class _MemRefType(_RefType):
+    """
+    An enum to define memory reference types, categorizing tensors based on their usage within a model.
+
+        - PARAM: Tensors registered as nn.Parameter within modules.
+        - BUFFER: Tensors registered as nn.Buffer within modules.
+        - GRAD: Gradients associated with parameters.
+        - ACT: Tensors produced during the forward pass and recomputation in activation checkpointing.
+        - TMP: Temporary memory used during the backward pass, including gradients of activations.
+        - OPT: Tensors holding optimizer states.
+        - OTH: Tensors registered via `track_external` that do not fit the above categories.
+    """
+
+    PARAM = "Parameter"
+    BUFFER = "Buffer"
+    GRAD = "Gradient"
+    ACT = "Activation"
+    TEMP = "Temp"
+    OPT = "Optstate"
+    OTH = "Other"
+
+
+class _ModState(_State):
+    """
+    An enum to define the state of a module.
+
+        - PRE_FW: The module is about to run the forward pass.
+        - POST_FW: The module has finished running the forward pass.
+        - PEAK_FW: The module has reached the peak memory usage during the forward pass.
+        - PRE_BW: The module is about to run the backward pass.
+        - PRE_FW_AC: The module is about to run the forward pass with activation checkpointing.
+        - POST_FW_AC: The module has finished running the forward pass with activation checkpointing.
+        - POST_BW: The module has finished running the backward pass.
+        - PEAK_BW: The module has reached the peak memory usage during the backward pass.
+    """
+
+    PRE_FW = "Pre-Forward"
+    POST_FW = "Post-Forward"
+    PEAK_FW = "Peak-Forward"
+    PRE_BW = "Pre-Backward"
+    PRE_FW_AC = "Pre-Forward-AC"
+    POST_FW_AC = "Post-Forward-AC"
+    POST_BW = "Post-Backward"
+    PEAK_BW = "Peak-Backward"
+
+
+class _ModMemStats:
+    """
+    A class to store the memory statistics of a module.
+
+    Args:
+        mod_fqn (str): The fully qualified name of the module.
+    Attributes:
+        mod_fqn (str): The fully qualified name of the module.
+        parameter_mem (int): The memory usage of the parameters of the module.
+        buffer_mem (int): The memory usage of the buffers of the module.
+        input_mem (int): The memory usage of the inputs to the module.
+        output_mem (int): The memory usage of the outputs from the module.
+        snapshots (Dict[_ModState, Dict[torch.device, Dict[str, int]]]): A dictionary of memory snapshots
+        of the module at different states defined by ``_ModState``.
+    Note:
+        The memory snapshot is stored as a dictionary - Dict[torch.device, Dict[str, int]], where each key is a device,
+         and each value is another dictionary with keys as memory reference types defined by `_MemRefType` and
+         values as the memory consumed in bytes.
+    """
+
+    def __init__(self, mod_fqn: str):
+        self.mod_fqn = mod_fqn
+        self.parameter_mem: int
+        self.buffer_mem: int
+        self.input_mem: int
+        self.output_mem: int
+        self.local_peak: dict[torch.device, int] = {}
+        self.snapshots: dict[_ModState, list[dict[torch.device, dict[str, int]]]] = {}
+
+
+class _WeakRefInfo:
+    """
+    Manages memory statistics and device attributes for tensor storages.
+    """
+
+    def __init__(
+        self, size: int, element_size: int, device: torch.device, reftype: _RefType
+    ) -> None:
+        """
+        Initializes the ``_WeakRefInfo`` object with tensor storage properties.
+
+        Args:
+            size (int): The number of elements in the tensor storage.
+            element_size (int): The size of each element in the tensor storage.
+            device (torch.device): The device on which the tensor is allocated.
+            reftype (_RefType): The reference type of the tensor.
+        """
+        self.size = size
+        self.element_size = element_size
+        self.reftype = reftype
+        # pyrefly: ignore [read-only]
+        self.device = device
+        self.mem_consumed = self._calculate_mem_consumed()
+
+    def _calculate_mem_consumed(self) -> int:
+        """
+        Calculates the memory consumed by the tensor storage, considering device-specific allocation rules.
+
+        Returns:
+            int: The memory consumed in bytes.
+        """
+        mem = self.size * self.element_size
+        if self.device.type == "cuda":
+            return math.ceil((mem) / _PYTORCH_MIN_ALLOCATE) * _PYTORCH_MIN_ALLOCATE
+        return mem
+
+    def update_mem_consumed(self, st: torch.UntypedStorage) -> int:
+        """
+        Updates and returns the memory consumed if the storage size has changed.
+
+        Args:
+            st (torch.UntypedStorage): The tensor storage to check for size updates.
+
+        Returns:
+            int: The updated memory consumed in bytes.
+        """
+        if st.size() != self.size:
+            self.size = st.size()
+            self.mem_consumed = self._calculate_mem_consumed()
+        return self.mem_consumed
+
+    @classmethod
+    def create_winfo(
+        cls,
+        st: torch.UntypedStorage,
+        device: torch.device,
+        reftype: _RefType,
+        callback: Callable[[Self, weakref.ref], Any] | None = None,
+    ) -> tuple[Self, weakref.ref]:
+        """
+        Creates a new ``_WeakRefInfo`` instance and a weak reference to a ``torch.UntypedStorage`` object,
+        optionally attaching a callback to the weak reference.
+
+        Args:
+            st (torch.UntypedStorage): The storage object for which to create the weak reference info.
+            device (torch.device): The device associated with the storage object.
+            reftype (_RefType): The type of reference, used to categorize the storage.
+            callback (Optional[Callable[[Self, weakref.ref]]]): A callback function that is called when
+                the storage object is about to be finalized (garbage collected). The callback function
+                should accept two arguments: the ``_WeakRefInfo`` instance and the weak reference to the storage.
+        Returns:
+            Tuple[Self, weakref.ref]: A tuple containing the newly created ``_WeakRefInfo`` instance and the
+            weak reference to the storage object. The weak reference may have an attached callback if provided.
+        """
+
+        winfo = cls(st.size(), st.element_size(), device, reftype)
+        w_st = weakref.ref(st, partial(callback, winfo) if callback else None)
+        return winfo, w_st
+
+
+def _get_mem_divisor(units: str) -> int:
+    unit_dict = {"B": 1, "KiB": 2**10, "MiB": 2**20, "GiB": 2**30}
+    if units in unit_dict:
+        return unit_dict[units]
+    else:
+        raise ValueError(
+            f"Unsupported unit: {units}. Supported units are: {', '.join(unit_dict.keys())}"
+        )
+
+
+def _rounding_fn(value: int, divisor: int, precision: int) -> float | int:
+    return value if divisor == 1 else round(value / divisor, precision)
+
+
+def _print_snapshot(snapshot: dict[torch.device, dict[str, int]], units: str) -> None:
+    if len(snapshot) == 0:
+        print("No memory tracked.")
+        return
+    divisor = _get_mem_divisor(units)
+    for dev, dev_snap in snapshot.items():
+        if _rounding_fn(dev_snap[_TOTAL_KEY], divisor, 2) <= 0:
+            continue
+        print(
+            f"Device: {dev}",
+            *(
+                f"\t{k.value}: {_rounding_fn(v, divisor, 2)} {units}"
+                if isinstance(k, _RefType)
+                else f"\t{k}: {_rounding_fn(v, divisor, 2)} {units}"
+                for k, v in dev_snap.items()
+            ),
+            sep="\n",
+        )
+
+
+def _print_snapshot_tabular(
+    snapshot: dict[torch.device, dict[str, int]], units: str
+) -> None:
+    if len(snapshot) == 0:
+        print("No memory tracked.")
+        return
+    try:
+        from tabulate import tabulate
+    except ImportError as err:
+        raise ImportError(
+            "Please install tabulate to use the tabulate option."
+        ) from err
+    divisor = _get_mem_divisor(units)
+    table_data = []
+    key_list = list(next(iter(snapshot.values())).keys())
+    headers = ["Device"] + [
+        f"{key.value}" if isinstance(key, _RefType) else f"{key}" for key in key_list
+    ]
+
+    for dev, dev_snap in snapshot.items():
+        if _rounding_fn(dev_snap[_TOTAL_KEY], divisor, 2) <= 0:
+            continue
+        row = [str(dev)]
+        row.extend(f"{_rounding_fn(v, divisor, 2)} {units}" for v in dev_snap.values())
+        table_data.append(row)
+    print(tabulate(table_data, headers=headers, tablefmt="rst"))
+
+
+def _print_state_snapshots(
+    snapshots: dict[_State, list[dict[torch.device, dict[str, int]]]], units: str
+) -> None:
+    for state, snapshot_list in snapshots.items():
+        print(f"{state.value}")
+        for i, snapshot in enumerate(snapshot_list):
+            print(f"# {i + 1}:")
+            _print_snapshot(snapshot, units)
+    print()
+
+
+def _print_state_snapshots_tabular(
+    snapshots: dict[_State, list[dict[torch.device, dict[str, int]]]], units: str
+) -> None:
+    try:
+        from tabulate import tabulate
+    except ImportError as err:
+        raise ImportError(
+            "Please install tabulate to use the tabulate option."
+        ) from err
+
+    table_data = []
+    last_state_call = None
+    divisor = _get_mem_divisor(units)
+    for state, snapshot_list in snapshots.items():
+        for i, snapshot in enumerate(snapshot_list):
+            state_call = f"{state.value} # {i + 1}"
+            for dev, dev_snap in snapshot.items():
+                if _rounding_fn(dev_snap[_TOTAL_KEY], divisor, 2) <= 0:
+                    continue
+                row = {
+                    "State & Call": (
+                        state_call if state_call != last_state_call else ""
+                    ),
+                    "Device": str(dev),
+                }
+                last_state_call = state_call
+                for k, v in dev_snap.items():
+                    row[f"{k.value}" if isinstance(k, _RefType) else f"{k}"] = (
+                        f"{_rounding_fn(v, divisor, 2)} {units}"
+                    )
+                table_data.append(row)
+    print(tabulate(table_data, headers="keys", tablefmt="rst"))
+
+
+class _UpdateType(Enum):
+    # These are used for tracking updates to the continuouly maintained memory snapshot.
+    # ADD - When a new tensor storage is tracked
+    # DEL - When a tensor storage is about to be finalized (garbage collected).
+    # REF - When a tensor reference is updated, for instance, the gradients are marked as
+    #       generic backward reference types until the grad_hook categorizes them as gradients.
+    # SIZE - When a tensor's storage is resized.
+    ADD = auto()
+    DEL = auto()
+    REF = auto()
+    SIZE = auto()
+
+
+class MemTracker(TorchDispatchMode):
+    """
+    A TorchDispatchMode to track, categorize and attribute the tensor memory created or accessed within its context.
+
+    It categorizes the tracked tensors as parameters, buffers, activations, gradients, temporary memory and optimizer states
+    as defined by ``_MemRefType`` within its context. It captures memory `snapshots` for the modules, called within its context,
+    at various states defined by ``_ModState``.
+
+    Attributes:
+        memory_tracking: A weakref key dictionary to store the memory statistics of each module. Each key
+        is a reference to a module, and each value is a ``_ModMemStats`` object that stores the memory
+        statistics of the module.
+
+    Note:
+        The MemTracker should be used as a context manager. The modules, optimizers, and any other tensors created within
+        the context of MemTracker will be tracked by default. Any tensors or stateful objects such as modules, optimizers etc.
+        that need to be tracked but are created outside the MemTracker should be registered using the `track_external` method.
+        The `track_external` method should be called before the MemTracker is used. Any tensors created outside the ``MemTracker``
+        and not supplied to the `track_external` method will not be tracked by the ``MemTracker``.
+
+    Example usage:
+
+        .. code-block:: python
+
+            module = ...
+            optimizer = ...
+            inp = ...
+            mem_tracker = MemTracker()
+            mem_tracker.track_external(module, optimizer, inp)
+            with mem_tracker as mt:
+                loss = module(inp)
+                print("After Forward:")
+                mt.display_snapshot("current")
+                loss.backward()
+                optimizer.step()
+                optimizer.zero_grad()
+            mt.display_snapshot("peak")
+            mt.display_modulewise_snapshots(depth=3, units="MiB")
+
+    Known Limitations:
+        - The ``MemTracker`` does not track memory for tensors that bypass the ``TorchDispatchMode`` ex. under ``no_dispatch``.
+        - Resizing tensor storages directly by using non-Tensor methods other than using ``torch.Untyped_Storage.resize_``
+          is not tracked. File a Github issue if you have use-cases for this.
+        - If the tensors are not traceable or wrappable subclasses of ``torch.Tensor``, then the tracker does not know how to
+            track their storages. File a Github issue if you have use-cases for this.
+        - During AC in the backward pass there might be misattribution between activation and temp memory, but the peak memory
+          will be tracked accurately. This will be fixed in the next update by hooking intricately with ``torch.uitls.checkpoint``.
+    """
+
+    def __init__(self) -> None:
+        self.memory_tracking = WeakIdKeyDictionary()
+        self._curr_mem_snap: dict[torch.device, dict[str, int]] = {}
+        self._peak_mem: dict[torch.device, int] = {}
+        self._peak_mem_snap: dict[torch.device, dict[str, int]] = {}
+        self._param_to_grad_hook_handles = WeakIdKeyDictionary()
+        self._optimizer_hook_handles: tuple[RemovableHandle, RemovableHandle] | None = (
+            None
+        )
+        # Dictionary to store the ``_WeakRefInfo`` instances corresponding to each tensor's storage.
+        self._WINFO = WeakIdKeyDictionary()
+        self._mod_tracker = ModTracker()
+        # This is a general memory tracker which can be used with any ``_RefType`` subclass
+        self._ref_class: type[_RefType] = _MemRefType
+        # Flags to track if we are in the AC region or optimizer step region
+        self._in_opt: bool = False
+        self._in_ac: bool = False
+        # Weak references to the topmost AC module currently active
+        self._ac_mod: weakref.ref | None = None
+        self._orig_resize = torch.UntypedStorage.resize_
+        self._depth = 0
+
+    def _update_snap(
+        self,
+        u_type: _UpdateType,
+        winfo: _WeakRefInfo,
+        old_mem_consumed: int | None = None,
+        old_reftype: _RefType | None = None,
+    ) -> None:
+        # Initialize a flag to track if the total memory might drop to zero after updates.
+        maybe_zero = False
+        # Ensure the device entry exists in the current memory snapshot, initializing if necessary.
+        # pyrefly: ignore [no-matching-overload]
+        dev_snap = self._curr_mem_snap.setdefault(
+            winfo.device, dict.fromkeys(self._ref_class, 0)
+        )
+        dev_snap.setdefault(_TOTAL_KEY, 0)
+        # Handle different types of updates based on the update type (`u_type`).
+        if u_type == _UpdateType.ADD:
+            # Increase the memory consumed for the specific reference type and update the total.
+            dev_snap[winfo.reftype] += winfo.mem_consumed
+            dev_snap[_TOTAL_KEY] += winfo.mem_consumed
+        elif u_type == _UpdateType.DEL:
+            # Decrease the memory consumed for the specific reference type and reduce the total.
+            dev_snap[winfo.reftype] -= winfo.mem_consumed
+            dev_snap[_TOTAL_KEY] -= winfo.mem_consumed
+            maybe_zero = True
+        elif u_type == _UpdateType.REF:
+            assert old_reftype is not None
+            # Adjust memory consumption between two reference types within the same device.
+            dev_snap[old_reftype] -= winfo.mem_consumed
+            dev_snap[winfo.reftype] += winfo.mem_consumed
+        elif u_type == _UpdateType.SIZE:
+            assert old_mem_consumed is not None
+            # Adjust the memory consumed for a reference type due to a change in size.
+            change = winfo.mem_consumed - old_mem_consumed
+            dev_snap[winfo.reftype] += change
+            dev_snap[_TOTAL_KEY] += change
+            maybe_zero = True
+        else:
+            raise ValueError(f"Invalid update type: {u_type}")
+        # Check if the total memory for the device has dropped to zero.
+        if maybe_zero:
+            if self._curr_mem_snap[winfo.device][_TOTAL_KEY] == 0:
+                # Remove the device entry from the memory snapshot if the total memory is zero.
+                del self._curr_mem_snap[winfo.device]
+
+    def _update_and_maybe_create_winfos(
+        self,
+        t: torch.Tensor,
+        reftype: _RefType,
+        update_existing: bool = False,
+    ) -> set[_WeakRefInfo]:
+        sts = get_untyped_storages(t)
+        winfos = set()
+        for st in sts:
+            # Attempt to retrieve existing ``_WeakRefInfo`` and its weak reference from the tracking dictionary.
+            winfo, _ = self._WINFO.get(st, (None, None))
+            if winfo is not None:
+                # If ``_WeakRefInfo`` exists, check if the reference type needs to be updated.
+                old_reftype = winfo.reftype
+                if old_reftype != reftype:
+                    # Update the reference type and apply changes via ``_update_snap``.
+                    winfo.reftype = reftype
+                    self._update_snap(_UpdateType.REF, winfo, old_reftype=old_reftype)
+                winfos.add(winfo)
+            elif update_existing:
+                # If no existing ``_WeakRefInfo`` is found and update_existing is True, raise an error.
+                raise KeyError("No existing winfo found")
+            else:
+                # If no existing _WeakRefInfo is found and update_existing is False, create a new ``_WeakRefInfo``.
+                winfo, w_st = _WeakRefInfo.create_winfo(
+                    st, t.device, reftype, self._delete_callback
+                )
+                # Store the new ``_WeakRefInfo`` and its weak reference in the tracking dictionary.
+                self._WINFO[st] = (winfo, w_st)
+                # Update the snapshot for the newly added ``_WeakRefInfo``.
+                if winfo.mem_consumed > 0:
+                    self._update_snap(_UpdateType.ADD, winfo)
+                winfos.add(winfo)
+        return winfos
+
+    def _delete_callback(self, winfo: _WeakRefInfo, w_st: weakref.ref) -> None:
+        # Callback to be called when the storage object corresponding to the  ``_WeakRefInfo``
+        # instance is about to be finalized.
+        if winfo.mem_consumed > 0:
+            self._update_snap(_UpdateType.DEL, winfo)
+
+    def _track_resize(self) -> None:
+        # Need to monkey-patch this because ``torch.UntypedStorage.resize_`` is not captured
+        # by ``TorchDispatchMode``.
+        @wraps(self._orig_resize)
+        def resize_(st: torch.UntypedStorage, size: int) -> None:
+            self._orig_resize(st, size)
+            winfo, _ = self._WINFO.get(st, (None, None))
+            if winfo is not None and winfo.size != st.size():
+                old_mem_consumed = winfo.mem_consumed
+                winfo.update_mem_consumed(st)
+                self._update_snap(
+                    _UpdateType.SIZE, winfo, old_mem_consumed=old_mem_consumed
+                )
+
+        torch.UntypedStorage.resize_ = resize_  # type: ignore[method-assign, assignment]
+
+    def _restore_resize(self) -> None:
+        torch.UntypedStorage.resize_ = self._orig_resize  # type: ignore[method-assign]
+
+    def _update_peak_stats(self, peak_state: _State) -> None:
+        # We first capture the current memory snapshot of the current tracker state then,
+        # We step through each of the modules we have tracked so far in ``memory_tracking``
+        #  and check if it is currently active by querying ``_mod_tracker.parents``
+        # If it is active, we update the per device peak memory usage for the module
+        #  corresponding to the ``_State`` which can be ``PEAK_FW`` or ``PEAK_BW``.
+        curr_snap = self._curr_mem_snap
+
+        for mod_stats in self.memory_tracking.values():
+            if mod_stats.mod_fqn in self._mod_tracker.parents:
+                if peak_state in mod_stats.snapshots:
+                    for dev, dev_snap in curr_snap.items():
+                        if mod_stats.local_peak.get(dev, 0) < dev_snap[_TOTAL_KEY]:
+                            mod_stats.local_peak[dev] = dev_snap[_TOTAL_KEY]
+                            mod_stats.snapshots[peak_state][-1][dev] = deepcopy(
+                                dev_snap
+                            )
+
+        for dev, dev_snap in curr_snap.items():
+            if self._peak_mem.get(dev, 0) < dev_snap[_TOTAL_KEY]:
+                self._peak_mem[dev] = dev_snap[_TOTAL_KEY]
+                self._peak_mem_snap[dev] = deepcopy(dev_snap)
+
+    def _track(self, reftype: _RefType, t: torch.Tensor) -> None:
+        # Get the storages of the tensor and check if we have already tracked them.
+        # If yes, then check if the storage size has changed and update the current snapshot.
+        # Else create a new ``_WeakRefInfo`` instance and add it to the dictionary.
+        sts = get_untyped_storages(t)
+        for st in sts:
+            winfo, _ = self._WINFO.get(st, (None, None))
+            if winfo is not None:
+                if winfo.size != st.size():
+                    old_mem_consumed = winfo.mem_consumed
+                    winfo.update_mem_consumed(st)
+                    self._update_snap(
+                        _UpdateType.SIZE, winfo, old_mem_consumed=old_mem_consumed
+                    )
+                return
+            else:
+                winfo, w_st = _WeakRefInfo.create_winfo(
+                    st, t.device, reftype, self._delete_callback
+                )
+                self._WINFO[st] = (winfo, w_st)
+                # Update the current snapshot for the newly added ``_WeakRefInfo``.
+                if winfo.mem_consumed > 0:
+                    self._update_snap(_UpdateType.ADD, winfo)
+
+    def get_tracker_snapshot(
+        self, type: str = "current"
+    ) -> dict[torch.device, dict[str, int]]:
+        """
+        Capture a snapshot of the memory usage breakdown per device, based on the specified type.
+
+        Args:
+            type (str): The type of snapshot to capture. Can be "current" for the current memory usage or "peak" for the
+                        peak memory usage. Defaults to "current".
+        Returns:
+            Dict[torch.device, Dict[str, int]]: A dictionary where each key is a torch.device, and each value is another
+                                                dictionary. This inner dictionary has keys representing memory reference
+                                                types as defined in ``_MemRefType`` and values representing the amount of
+                                                memory consumed in bytes.
+        Raises:
+            ValueError: If an invalid type is specified.
+        """
+        if type == "current":
+            return deepcopy(self._curr_mem_snap)
+        elif type == "peak":
+            return deepcopy(self._peak_mem_snap)
+        else:
+            raise ValueError(f"Invalid type {type}")
+
+    def _track_module_params_and_buffers(
+        self, module: nn.Module, install_grad_hooks: bool = True
+    ) -> tuple[int, int]:
+        # Track the parameters and buffers of the module if not already tracked.
+        # If the parameters have gradients, track the gradients as well.
+        # If install_grad_hooks is True, install a gradient hook on the parameters
+        #  to track the gradients, if it has not already been installed.
+        # Return the total memory consumed by the parameters and buffers.
+        def _grad_hook(grad: torch.Tensor) -> None:
+            self._update_and_maybe_create_winfos(
+                grad,
+                _MemRefType.GRAD,
+            )
+
+        param_memory = 0
+        for param in module.parameters():
+            winfos = self._update_and_maybe_create_winfos(
+                param,
+                _MemRefType.PARAM,
+            )
+            param_memory += sum(winfo.mem_consumed for winfo in winfos)
+            if param.grad is not None:
+                self._update_and_maybe_create_winfos(
+                    param.grad,
+                    _MemRefType.GRAD,
+                )
+            if (
+                self._param_to_grad_hook_handles.get(param, None) is None
+                and install_grad_hooks
+            ):
+                grad_hook_handle = param.register_hook(_grad_hook)
+                post_acc_grad_hook_handle = param.register_post_accumulate_grad_hook(
+                    lambda p: (_grad_hook(p.grad))
+                )
+                self._param_to_grad_hook_handles[param] = (
+                    grad_hook_handle,
+                    post_acc_grad_hook_handle,
+                )
+        buffer_memory = 0
+        for buffer in module.buffers():
+            winfos = self._update_and_maybe_create_winfos(
+                buffer,
+                _MemRefType.BUFFER,
+            )
+            buffer_memory += sum(winfo.mem_consumed for winfo in winfos)
+        return (param_memory, buffer_memory)
+
+    def _track_inputs_or_outputs(self, args: Any) -> int:
+        # Calculate the memory consumed by the inputs or outputs of the module.
+        input_or_output_memory = 0
+
+        def add_inps_or_outs(t: torch.Tensor) -> None:
+            nonlocal input_or_output_memory
+            sts = get_untyped_storages(t)
+            for st in sts:
+                winfo, _ = self._WINFO.get(st, (None, None))
+                if winfo is not None:
+                    input_or_output_memory += winfo.mem_consumed
+
+        tree_map_only(torch.Tensor, add_inps_or_outs, args)
+        return input_or_output_memory
+
+    def _pre_fw_hook(self, module: nn.Module, inputs: Any) -> None:
+        # This is installed as a pre-fwd user hook with ``ModTracker.`` Based on the following cases we
+        # set the state and capture the memory snapshot for the module.
+        # Case 1: If the module is not in the ``memory_tracking`` dictionary, we track the parameters, buffers,
+        #         input and output memory of the module. Create a new ``_ModMemStats`` instance for the module
+        #         and add it to the ``memory_tracking`` dictionary.
+        # Case 2: If the module is already in the ``memory_tracking`` dictionary and we are in backward, this means
+        #         we are in the AC region. We check if this is the top most module in the AC region. If it is,
+        #         we store a weak reference and set the flag ``_in_ac`` to True.
+        # Case 3: If the module is already in the ``memory_tracking`` dictionary and we are in forward, this means
+        #         this module is called for the second time. If it is a root module, that means we are in the next
+        #         iteration and we error out. If it is not a root module, that means it's a submodule that is being
+        #         used multiple times in the same iteration, which we allow and track.
+        # For Case 1 and 3, we also initialize the ``local_peak`` and ``PEAK_FW`` snapshot for the module.
+        mod_name = self._mod_tracker.get_known_fqn(module)
+        assert mod_name is not None
+        if module not in self.memory_tracking:
+            mod_stats = _ModMemStats(mod_name)
+            param_mem, buffer_mem = self._track_module_params_and_buffers(
+                module, install_grad_hooks=True
+            )
+            input_mem = self._track_inputs_or_outputs(inputs)
+            mod_stats.parameter_mem = param_mem
+            mod_stats.buffer_mem = buffer_mem
+            mod_stats.input_mem = input_mem
+            self.memory_tracking[module] = mod_stats
+            state = _ModState.PRE_FW
+
+        elif self._mod_tracker.is_bw:
+            mod_stats = self.memory_tracking[module]
+            state = _ModState.PRE_FW_AC
+            if self._ac_mod is None:
+                self._ac_mod = weakref.ref(module)
+                self._in_ac = True
+        else:
+            parents = set(self._mod_tracker.parents) - {mod_name}
+            if len(parents) == 1 and "Global" in parents:
+                raise NotImplementedError(
+                    "MemTracker does not support memory tracking for multiple iterative calls."
+                    " Either use ``reset_mod_stats`` to clear module memory stats for the previous iteration"
+                    " or file a github issue if you need this feature."
+                )
+            mod_stats = self.memory_tracking[module]
+            state = _ModState.PRE_FW
+            input_mem = self._track_inputs_or_outputs(inputs)
+            mod_stats.mod_fqn = mod_name
+            mod_stats.input_mem = input_mem
+
+        mem_snapshot = self.get_tracker_snapshot()
+        if state == _ModState.PRE_FW:
+            mod_stats.local_peak = {
+                dev: dev_snap[_TOTAL_KEY] for dev, dev_snap in mem_snapshot.items()
+            }
+            mod_stats.snapshots.setdefault(_ModState.PEAK_FW, []).append(mem_snapshot)
+        mod_stats.snapshots.setdefault(state, []).append(deepcopy(mem_snapshot))
+
+    def _post_fw_hook(self, module: nn.Module, inputs: Any, outputs: Any) -> None:
+        # This is installed as a post-fwd user hook with ``ModTracker``. Based on the following cases we
+        # set the state and capture the memory snapshot for the module.
+        # Case 1: This is called in backward, which means we are in the AC region. If this is the top most module
+        #         in the AC region, we set the flag ``_in_ac`` to False.
+        # Case 2: This is called in forward so we calculate the output memory
+        #         of the module and update its mod_stats.
+        mod_stats = self.memory_tracking[module]
+        if self._mod_tracker.is_bw:
+            state = _ModState.POST_FW_AC
+            if self._ac_mod is not None and self._ac_mod() is module:
+                self._ac_mod = None
+                self._in_ac = False
+        else:
+            state = _ModState.POST_FW
+            output_mem = self._track_inputs_or_outputs(outputs)
+            mod_stats.output_mem = output_mem
+        mod_stats.snapshots.setdefault(state, []).append(self.get_tracker_snapshot())
+
+    def _pre_bw_hook(self, module: nn.Module, args: Any) -> None:
+        # This is installed as a pre-bwd user hook with ``ModTracker``. We set the state and capture the
+        # snapshot for the module. We also initialize the ``local_peak`` and ``PEAK_BW`` snapshot for it.
+        # If the module is None, we skip the hook.
+        # This can happen since this installed inside a multi-grad hook on the module's output tensors
+        # and the module itself may not be alive during backward.
+        if module is None:
+            warnings.warn("Module is None. Skipping PRE_BW hook.", stacklevel=2)
+            return
+        mod_stats = self.memory_tracking[module]
+        mem_snapshot = self.get_tracker_snapshot()
+        mod_stats.local_peak = {
+            dev: dev_snap[_TOTAL_KEY] for dev, dev_snap in mem_snapshot.items()
+        }
+        mod_stats.snapshots.setdefault(_ModState.PEAK_BW, []).append(mem_snapshot)
+        mod_stats.snapshots.setdefault(_ModState.PRE_BW, []).append(
+            deepcopy(mem_snapshot)
+        )
+
+    def _post_bw_hook(self, module: nn.Module, args: Any) -> None:
+        # This is installed as a post-bwd user hook with ``ModTracker``. We set the state and capture the
+        # snapshot for the module if it is not None.
+        # This can happen since this installed inside a multi-grad hook on the module's input tensors
+        # and the module itself may not be alive during backward.
+        if module is None:
+            warnings.warn("Module is None. Skipping POST_BW hook.", stacklevel=2)
+            return
+        mod_stats = self.memory_tracking[module]
+        mod_stats.snapshots.setdefault(_ModState.POST_BW, []).append(
+            self.get_tracker_snapshot()
+        )
+
+    def _track_optimizer_states(
+        self, reftype: _RefType, optimizer: optim.Optimizer
+    ) -> None:
+        for states in optimizer.state.values():
+            for val in states.values():
+                if isinstance(val, torch.Tensor):
+                    self._update_and_maybe_create_winfos(
+                        val,
+                        reftype,
+                    )
+
+    def _register_global_optimizer_hook(self) -> None:
+        # Register a hook on the optimizer step to track the optimizer states.
+        # The pre-hook is to set the flag ``_in_opt`` to True. The post-hook unsets the flag,
+        # and also tracks any optimizer states that are created during the optimizer step.
+        def _opt_step_pre_hook(
+            optimizer: optim.Optimizer, args: Any, kwargs: Any
+        ) -> None:
+            self._in_opt = True
+
+        def _opt_step_post_hook(
+            optimizer: optim.Optimizer, args: Any, kwargs: Any
+        ) -> None:
+            self._track_optimizer_states(_MemRefType.OPT, optimizer)
+            self._in_opt = False
+
+        self._optimizer_hook_handles = (
+            register_optimizer_step_pre_hook(_opt_step_pre_hook),
+            register_optimizer_step_post_hook(_opt_step_post_hook),
+        )
+
+    def _deregister_param_and_optimizer_hooks(self) -> None:
+        for (
+            grad_hook_handle,
+            post_acc_grad_hook_handle,
+        ) in self._param_to_grad_hook_handles.values():
+            grad_hook_handle.remove()
+            post_acc_grad_hook_handle.remove()
+        self._param_to_grad_hook_handles.clear()
+
+        if self._optimizer_hook_handles is not None:
+            for handle in self._optimizer_hook_handles:
+                handle.remove()
+            self._optimizer_hook_handles = None
+
+    def track_external(
+        self, *external: nn.Module | optim.Optimizer | torch.Tensor
+    ) -> None:
+        """
+        Track tensors and stateful objects like modules, optimizers etc. that are created outside the MemTracker.
+
+        This method should be called before the ``MemTracker`` is used. Any tensors that are not module parameters, buffers,
+        gradients activations, or optimizer states will be categorized as ``Other``. If you want them categorized with a
+        custom name, please file a GitHub issue. Any tensors created outside the MemTracker and not supplied to this
+        method will not be be tracked by ``MemTracker``.
+
+        Args:
+            *external (Union[nn.Module, optim.Optimizer, torch.Tensor]): The external modules, optimizers, and
+                                                                         tensors to be tracked.
+        """
+        flat_external, _ = tree_flatten(external)
+        for obj in flat_external:
+            if isinstance(obj, torch.Tensor):
+                self._update_and_maybe_create_winfos(
+                    obj,
+                    _MemRefType.OTH,
+                )
+            elif isinstance(obj, torch.nn.Module):
+                self._track_module_params_and_buffers(obj, install_grad_hooks=False)
+            elif isinstance(obj, optim.Optimizer):
+                self._track_optimizer_states(_MemRefType.OPT, obj)
+            elif obj is None:
+                continue
+            else:
+                raise TypeError(
+                    f"Object of type {type(obj)} is not supported for tracking. "
+                    f"Only stateful objects like modules, optimizers, and tensors are supported."
+                )
+
+    def display_snapshot(
+        self, type: str = "current", units: str = "B", tabulate: bool = False
+    ) -> None:
+        """
+        Display the memory usage breakdown snapshot of the tracker based on the specified type and units.
+
+        Keyword args:
+            type (str): The type of snapshot to display. Can be "current" for the current memory usage or "peak" for the
+                        peak memory usage. Defaults to "current".
+            units (str): The units to use for displaying memory usage. Defaults to "B". Supports ["B", "KiB", "MiB", "GiB"].
+            tabulate (bool): Whether to display the snapshot in a tabular format. Defaults to False.
+        """
+        snapshot = self.get_tracker_snapshot(type)
+        if tabulate:
+            _print_snapshot_tabular(snapshot, units)
+        else:
+            _print_snapshot(snapshot, units)
+
+    def display_modulewise_snapshots(
+        self, depth: int = 2, units: str = "B", tabulate: bool = False
+    ) -> None:
+        """
+        Print per device memory breakdown snapshot for each module called within MemTracker.
+
+        Snapshots are displayed for the states defined by ``_ModState``.
+        The module hierarchy is displayed up to the specified depth.
+
+        Keyword Args:
+            depth (int, optional): The depth of the module hierarchy to display. Defaults to 2.
+            units (str, optional): The units to use for memory tracking. Defaults to "B". Supports ["B", "KiB", "MiB", "GiB"].
+            tabulate (bool, optional): Whether to display the snapshot in a tabular format. Defaults to False.
+        """
+
+        def natural_sort_key(s: str) -> list[int | str]:
+            return [
+                int(text) if text.isdigit() else text.lower()
+                for text in re.split("([0-9]+)", s)
+            ]
+
+        for mod_stats in sorted(
+            self.memory_tracking.values(),
+            key=lambda m_stats: natural_sort_key(m_stats.mod_fqn),
+        ):
+            mod_fqn = mod_stats.mod_fqn
+            mod_depth = mod_fqn.count(".") + 1
+            if mod_depth > depth:
+                continue
+            print(f"Module:  {mod_fqn}")
+            if tabulate:
+                _print_state_snapshots_tabular(mod_stats.snapshots, units)
+            else:
+                _print_state_snapshots(mod_stats.snapshots, units)
+
+    def reset_mod_stats(self) -> None:
+        """
+        Reset all the module memory stats. Clears ``memory_tracking`` dictionary.
+        """
+        self.memory_tracking.clear()
+
+    def __enter__(self) -> "MemTracker":
+        if self._depth == 0:
+            self._register_global_optimizer_hook()
+            self._mod_tracker.register_user_hooks(
+                self._pre_fw_hook,
+                self._post_fw_hook,
+                self._pre_bw_hook,
+                self._post_bw_hook,
+            )
+            self._track_resize()
+            self._peak_mem_snap = self.get_tracker_snapshot()
+            self._peak_mem = {
+                dev: dev_snap[_TOTAL_KEY]
+                for dev, dev_snap in self._peak_mem_snap.items()
+            }
+            self._mod_tracker.__enter__()
+        super().__enter__()
+        self._depth += 1
+        return self
+
+    # pyrefly: ignore [bad-override]
+    def __exit__(self, *args: Any) -> None:
+        self._depth -= 1
+        if self._depth == 0:
+            self._deregister_param_and_optimizer_hooks()
+            self._mod_tracker.clear_user_hooks()
+            self._restore_resize()
+            self._mod_tracker.__exit__(*args)
+        super().__exit__(*args)
+
+    def __torch_dispatch__(self, func, types, args=(), kwargs=None):  # type: ignore[no-untyped-def]
+        # When running this mode with DTensor, ordinarily all modes will
+        # run **before** subclasses get a chance to run.
+        # Returning NotImplemented here gives us a chance to let DTensor
+        # run and desugar into local tensor ops, before `MemTracker` sees them.
+        if any(t == DTensor for t in types):
+            return NotImplemented
+        if (
+            func is torch.ops._c10d_functional.wait_tensor.default
+            and active_fake_mode()
+        ):
+            # N.B: This is a hacky way to override the Meta IMPL of wait_tensor. The original impl returns
+            # a new tensor which does not happen in eager mode, when a wait_tensor is called.
+            # pyrefly: ignore [index-error]
+            res = args[0]
+        else:
+            res = func(*args, **kwargs or {})
+        # If we are tracking an optimizer state, we use the optimizer reference type.
+        # If we are in backward region and not in AC region, we use the backward reference type.
+        # Else we use the forward reference type.
+        if self._in_opt:
+            reftype = _MemRefType.OPT
+        elif self._mod_tracker.is_bw and not self._in_ac:
+            reftype = _MemRefType.TEMP
+        else:
+            reftype = _MemRefType.ACT
+        tree_map_only(torch.Tensor, partial(self._track, reftype), res)
+        peak_state = _ModState.PEAK_BW if self._mod_tracker.is_bw else _ModState.PEAK_FW
+        self._update_peak_stats(peak_state)
+        return res
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tools/memory_tracker.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tools/memory_tracker.py
new file mode 100644
index 0000000000000000000000000000000000000000..890d2be2794a4e570085a91da1440842473c9f49
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tools/memory_tracker.py
@@ -0,0 +1,304 @@
+# mypy: allow-untyped-defs
+import operator
+import pickle
+from collections import defaultdict
+from collections.abc import Callable, Sequence
+from itertools import chain
+from typing import Any, no_type_check, TYPE_CHECKING
+
+import torch
+import torch.nn as nn
+from torch.utils._python_dispatch import TorchDispatchMode
+
+
+if TYPE_CHECKING:
+    from torch.utils.hooks import RemovableHandle
+
+
+BYTES_PER_MB = 1024 * 1024.0
+
+
+class MemoryProfileDispatchMode(TorchDispatchMode):
+    """Run in ``TorchDispatchMode`` to get memory stats at operator level."""
+
+    def __init__(self, memory_tracker) -> None:
+        self.memory_tracker = memory_tracker
+
+    def __torch_dispatch__(self, func, types, args=..., kwargs=None):
+        rs = func(*args, **kwargs)
+        if func is torch.ops.aten.detach.default:
+            return rs
+        func_name: str = (
+            self.memory_tracker._cur_module_name
+            + "."
+            + func.__name__
+            + "_"
+            + str(self.memory_tracker._operator_names[func.__name__])
+        )
+        self.memory_tracker._operator_names[func.__name__] = (
+            self.memory_tracker._operator_names[func.__name__] + 1
+        )
+        self.memory_tracker._record_memory_stats(func_name)
+
+        return rs
+
+
+class MemoryTracker:
+    """
+    Collect and plot the memory stats at operator level.
+
+    Includes ``memories_allocated``, ``memories_active`` and ``memories_reserved``.
+    It also prints a summary for the top 20 operators that generate the most memories.
+
+    Example usage:
+
+        >>> # xdoctest: +SKIP(failing)
+        >>> net.cuda()
+        >>> input = input.cuda()
+
+        >>> mem_tracker = MemoryTracker()
+        >>> mem_tracker.start_monitor(net)
+
+        >>> net.zero_grad(True)
+        >>> loss = net(input)
+        >>> if isinstance(loss, dict):
+        >>>    loss = loss['out']
+        >>> loss.sum().backward()
+        >>> net.zero_grad(set_to_none=True)
+
+        >>> mem_tracker.stop()
+        >>> mem_tracker.summary()
+        >>> mem_tracker.show_traces()
+    """
+
+    def __init__(self) -> None:
+        torch._C._log_api_usage_once("torch.distributed.memory_tracker")
+        self._hooks: list[RemovableHandle] = []
+        self._operator_names: dict[str, int] = defaultdict(int)
+        self.memories_allocated: dict[int, dict[str, float]] = defaultdict()
+        self.memories_active: dict[int, dict[str, float]] = defaultdict()
+        self.memories_reserved: dict[int, dict[str, float]] = defaultdict()
+        self._markers: dict[str, int] = defaultdict(int)
+        self._cur_module_name: str = ""
+        self._op_index: int = 0
+        self._num_alloc_retries: int = 0
+        self._device_module = torch.get_device_module()
+
+    @no_type_check
+    def start_monitor(self, root_module: nn.Module) -> None:
+        """
+        Register module hooks and entering ``MemoryProfileDispatchMode``.
+
+        This enables operator level memory stats can be tracked during module runtime.
+        """
+        self._clear_state()
+        root_module.__setattr__("_memory_tracker_is_root", True)
+        for name, m in root_module.named_modules():
+            if m is not root_module:
+                m.__setattr__("_memory_tracker_is_root", False)
+            # fused_proxy_group does not support hooks
+            if ".fused_proxy_grouped_embedding_bag" in name:
+                continue
+            # hook ordering with other hooks added by users is not managed, so
+            # the memory stats tracked here may not completely accurate.
+            h1 = m.register_forward_pre_hook(self._create_pre_forward_hook(name))
+            h2 = m.register_forward_hook(self._create_post_forward_hook(name))
+            # it does not work well with jagged tensor somehow, the root cause is not
+            # clear and remove it for now as it does not really capture important info.
+            # h3 = m.register_backward_hook(self._create_backward_hook(name))
+            self._hooks.extend([h1, h2])
+        self._device_module.empty_cache()
+        assert getattr(self, "profile_mode", None) is None
+        self.profile_mode = MemoryProfileDispatchMode(self)
+        self.profile_mode.__enter__()
+
+    @no_type_check
+    def stop(self) -> None:
+        """
+        Remove module hooks and exit ``MemoryProfileDispatchMode`` to stop tracking memory stats at operator level.
+
+        Get some aggregated stats when the memory_tracker() is enabled, like ``num_alloc_retries``.
+        """
+        self._num_alloc_retries = self._device_module.memory_stats().get(
+            "num_alloc_retries", 0
+        )
+
+        for h in self._hooks:
+            h.remove()
+        self._hooks.clear()
+        assert getattr(self, "profile_mode", None) is not None
+        self.profile_mode.__exit__(None, None, None)
+        self.profile_mode = None
+
+    @no_type_check
+    def summary(self, top: int = 20) -> None:
+        """
+        Print out the top operators that generate the most memories.
+
+        The number of the top operators can be configured.
+        """
+        op_diff: dict[str, float] = defaultdict(float)
+        op_name, previous_allocated_memory = self.memories_allocated[0]
+        for i in range(1, self._op_index):
+            op_name, current_allocated_memory = self.memories_allocated[i]
+            op_diff[op_name] = current_allocated_memory - previous_allocated_memory
+            previous_allocated_memory = current_allocated_memory
+
+        print("------------------------------------------------")
+        print(f"The number of alloc retries are: {self._num_alloc_retries}")
+        print(f"Top {top} ops that generates memory are:")
+        for k, v in sorted(op_diff.items(), key=operator.itemgetter(1), reverse=True)[
+            :top
+        ]:
+            print(f"{k}: {v}MB")
+        print("------------------------------------------------")
+
+    @no_type_check
+    def show_traces(self, path: str = "") -> None:
+        import matplotlib.pyplot as plt
+
+        def _plot_figure(x, y_values, labels):
+            min_val = min(chain.from_iterable(y_values)) * 0.999
+            max_val = max(chain.from_iterable(y_values)) * 1.001
+            plt.figure()
+            for y, label in zip(y_values, labels):
+                plt.plot(x, y, label=label)
+            plt.xlabel("# Operator Calls")
+            plt.ylabel("Memory (MB)")
+            plt.legend()
+            for marker_name, marker in self._markers.items():
+                if marker_name == "fw_bw_boundary":
+                    plt.plot(
+                        [marker, marker],
+                        [min_val, max_val],
+                        "r",
+                        lw=2,
+                        label=marker_name,
+                    )
+                else:
+                    plt.plot(
+                        [marker, marker],
+                        [min_val, max_val],
+                        "k-",
+                        lw=2,
+                        label=marker_name,
+                    )
+
+        if path != "":
+            self.load(path)
+
+        y_1 = [gb for (name, gb) in self.memories_allocated.values()]
+        y_2 = [gb for (name, gb) in self.memories_active.values()]
+        y_3 = [gb for (name, gb) in self.memories_reserved.values()]
+        x = list(range(len(y_1)))
+        # Split figures when there is big difference between
+        # "reserved_memory" and "allocated_memory" or "active_memory".
+        _plot_figure(
+            x,
+            [list(y_1), list(y_2), list(y_3)],
+            ["allocated_memory", "active_memory", "reserved_memory"],
+        )
+        _plot_figure(x, [list(y_1)], ["allocated_memory"])
+        _plot_figure(x, [list(y_2)], ["active_memory"])
+        _plot_figure(x, [list(y_3)], ["reserved_memory"])
+
+    def save_stats(self, path: str) -> None:
+        """Save the stats using pickle during runtime if users want to plot the traces in other places like notebook."""
+        stats = {
+            "memories_allocated": self.memories_allocated,
+            "memories_active": self.memories_active,
+            "memories_reserved": self.memories_reserved,
+            "markers": self._markers,
+            "num_alloc_retries": self._num_alloc_retries,
+        }
+
+        with open(path, "wb") as f:
+            pickle.dump(stats, f, pickle.HIGHEST_PROTOCOL)
+
+    def load(self, path: str) -> None:
+        """Load the pickled memory stats to plot the traces or print the summary."""
+        with open(path, "rb") as f:
+            stats = pickle.load(f)
+
+        self.memories_allocated = stats["memories_allocated"]
+        self.memories_active = stats["memories_active"]
+        self.memories_reserved = stats["memories_reserved"]
+        self._markers = stats["markers"]
+        self._num_alloc_retries = stats["num_alloc_retries"]
+
+    def _create_pre_forward_hook(self, name: str) -> Callable:
+        """Prefix operator name with current module and 'forward', and insert 'fw_start' marker at forward pass start."""
+
+        def _pre_forward_hook(module: nn.Module, inputs: Any) -> None:
+            self._cur_module_name = f"{name}.forward"
+            if (
+                # pyrefly: ignore [invalid-argument]
+                hasattr(module, "_memory_tracker_is_root")
+                # pyrefly: ignore [not-callable]
+                and module._memory_tracker_is_root
+            ):
+                self._add_marker("fw_start")
+
+        return _pre_forward_hook
+
+    def _create_post_forward_hook(self, name: str) -> Callable:
+        """Insert the marker 'fw_bw_boundary' at the boundary of forward and backward pass."""
+
+        def _post_forward_hook(
+            module: nn.Module,
+            inputs: Sequence[torch.Tensor],
+            outputs: Sequence[torch.Tensor],
+        ) -> None:
+            if (
+                # pyrefly: ignore [invalid-argument]
+                hasattr(module, "_memory_tracker_is_root")
+                # pyrefly: ignore [not-callable]
+                and module._memory_tracker_is_root
+            ):
+                self._add_marker("fw_bw_boundary")
+
+        return _post_forward_hook
+
+    def _create_backward_hook(self, name: str) -> Callable:
+        """Insert the current module name with backward prefix for the operator name."""
+
+        def _backward_hook(
+            module: nn.Module, grad_input: torch.Tensor, grad_output: torch.Tensor
+        ) -> None:
+            self._cur_module_name = f"{name}.backward"
+
+        return _backward_hook
+
+    @no_type_check
+    def _record_memory_stats(self, fn_name: str) -> None:
+        """
+        Record current memory allocated, current memory active and current memory reserved.
+
+        The memory stats dict is indexed with ``self._op_index``.
+        """
+        memory_allocated: float = self._device_module.memory_allocated() / BYTES_PER_MB
+        memory_reserved: float = self._device_module.memory_reserved() / BYTES_PER_MB
+        memory_active: float = (
+            self._device_module.memory_stats().get("active_bytes.all.current", 0)
+            / BYTES_PER_MB
+        )
+        self.memories_allocated[self._op_index] = (fn_name, memory_allocated)
+        self.memories_reserved[self._op_index] = (fn_name, memory_reserved)
+        self.memories_active[self._op_index] = (fn_name, memory_active)
+        self._op_index += 1
+
+    def _add_marker(self, marker_name: str) -> None:
+        """Set the marker's x-axis value."""
+        marker_val = len(self.memories_allocated.values())
+        self._markers[marker_name] = marker_val
+
+    def _clear_state(self) -> None:
+        """Clear states when start_monitor() is called."""
+        self._operator_names.clear()
+        self.memories_allocated.clear()
+        self.memories_active.clear()
+        self.memories_reserved.clear()
+        self._markers.clear()
+        self._cur_module_name = ""
+        self._op_index = 0
+        self._num_alloc_retries = 0
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tools/mod_tracker.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tools/mod_tracker.py
new file mode 100644
index 0000000000000000000000000000000000000000..bae745bcc58040dd14a1dfbd0c2f116554870689
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tools/mod_tracker.py
@@ -0,0 +1,259 @@
+# mypy: allow-untyped-defs
+import warnings
+import weakref
+from collections.abc import Callable
+
+import torch
+from torch.autograd.graph import register_multi_grad_hook
+from torch.nn.modules.module import (
+    register_module_forward_hook,
+    register_module_forward_pre_hook,
+)
+from torch.utils._pytree import tree_flatten
+
+
+__all__ = ["ModTracker"]
+
+
+class ModTracker:
+    """
+    ``ModTracker`` is a context manager that tracks the nn.Module hierarchy during execution
+    so that other system can query which Module is currently being executed (or its backward is being
+    executed).
+
+    You can access the ``parents`` attribute on this context manager to get the set of all the
+    Modules currently being executed via their fqn (fully qualified name, also used as the key within
+    the state_dict).
+    You can access the ``is_bw`` attribute to know if you are currently running in backward or not.
+
+    Note that ``parents`` is never empty and always contains the "Global" key. The ``is_bw`` flag
+    will remain ``True`` after the forward until another Module is executed. If you need it to be
+    more accurate, please submit an issue requesting this. Adding a map from fqn to the module instance
+    is possible but not done yet, please submit an issue requesting this if you need it.
+
+    Example usage
+
+    .. code-block:: python
+
+        mod = torch.nn.Linear(2, 2)
+
+        with ModTracker() as tracker:
+            # Access anything during the forward pass
+            def my_linear(m1, m2, bias):
+                print(f"Current modules: {tracker.parents}")
+                return torch.mm(m1, m2.t()) + bias
+
+            torch.nn.functional.linear = my_linear
+
+            mod(torch.rand(2, 2))
+
+    """
+
+    parents: set[str]
+    """
+    A Set containing the fqn for each module currently running their forward
+    """
+
+    def __init__(self):
+        self.parents = {"Global"}
+        self._active_module_cnt = {}
+        self._known_modules: weakref.WeakKeyDictionary = weakref.WeakKeyDictionary()
+        self._seen_modules: weakref.WeakSet = weakref.WeakSet()
+        self._has_callback = False
+        self._post_bw_callbacks_to_enqueue: list[Callable] = []
+        self._user_pre_fw_hook = None
+        self._user_post_fw_hook = None
+        self._user_pre_bw_hook = None
+        self._user_post_bw_hook = None
+
+    def _maybe_set_engine_callback(self):
+        # This assumes no concurrent calls to backward
+        if self._has_callback:
+            return
+
+        for post_bw_callback in reversed(self._post_bw_callbacks_to_enqueue):
+            torch.autograd.Variable._execution_engine.queue_callback(post_bw_callback)
+        self._post_bw_callbacks_to_enqueue.clear()
+
+        def callback():
+            self.parents = {"Global"}
+            self._has_callback = False
+
+        torch.autograd.Variable._execution_engine.queue_callback(callback)
+        self._has_callback = True
+
+    @property
+    def is_bw(self):
+        """
+        A boolean marking if this is currently running during the backward pass or not
+        """
+        return torch._C._current_graph_task_id() != -1
+
+    def get_known_fqn(self, mod):
+        """
+        Return the fqn for the given module if it is known to the ``ModTracker``, otherwise ``None``.
+        """
+        return self._known_modules.get(mod, None)
+
+    def register_user_hooks(
+        self,
+        pre_fw_hook: Callable | None = None,
+        post_fw_hook: Callable | None = None,
+        pre_bw_hook: Callable | None = None,
+        post_bw_hook: Callable | None = None,
+    ):
+        """
+        Registers user-specified hooks to be called before/after the forward/backward pass for each
+        module tracked by the ``ModTracker``. One or more can be ``None``.
+        Args:
+            pre_fw_hook (Callable, optional): A hook to be called before the forward pass for the
+                module. It should have the following signature:
+                pre_fw_hook (module, input) -> None
+            post_fw_hook (Callable, optional): A hook to be called after the forward pass for the
+                module. It should have the following signature:
+                post_fw_hook (module, input, output) -> None
+            pre_bw_hook (Callable, optional): A multi-grad hook to be called on all the outputs of
+                the module that require gradients. It should have the following signature:
+                pre_bw_hook (module, grad_output) -> None
+            post_bw_hook (Callable, optional): A multi-grad hook to be called on all the inputs of
+                the module that require gradients. It should have the following signature:
+                post_bw_hook (module, grad_input) -> None
+        Raises:
+            AssertionError: If a new hook is provided when one is already registered.
+        Note:
+            If the module is not alive during the backward pass, the pre_bw_hook and post_bw_hook will
+            will receive None as the module argument.
+            The module fqn will be present in the ``parents`` attribute when each of the hooks is called.
+            Hooks are intended to be used as markers only not to modify the inputs/outputs.
+        """
+
+        def set_hook(hook, user_hook, hook_name):
+            if hook is not None and user_hook is not None:
+                raise AssertionError(
+                    f"Only one {hook_name} can be registered at a time"
+                    f" Clear the existing hook by calling ``clear_user_hooks`` before registering a new one"
+                )
+            return hook
+
+        self._user_pre_fw_hook = set_hook(
+            pre_fw_hook, self._user_pre_fw_hook, "pre_fw_hook"
+        )
+        self._user_post_fw_hook = set_hook(
+            post_fw_hook, self._user_post_fw_hook, "post_fw_hook"
+        )
+        self._user_pre_bw_hook = set_hook(
+            pre_bw_hook, self._user_pre_bw_hook, "pre_bw_hook"
+        )
+        self._user_post_bw_hook = set_hook(
+            post_bw_hook, self._user_post_bw_hook, "post_bw_hook"
+        )
+
+    def clear_user_hooks(self):
+        """
+        Clears the user specified hooks registered with ``register_user_hooks``
+        """
+        self._user_pre_fw_hook = None
+        self._user_post_fw_hook = None
+        self._user_pre_bw_hook = None
+        self._user_post_bw_hook = None
+
+    def _get_mod_name(self, mod):
+        if mod not in self._known_modules:
+            self._known_modules[mod] = type(mod).__name__
+        mod_name = self._known_modules[mod]
+        if mod not in self._seen_modules:
+            for name, submod in mod.named_children():
+                self._known_modules[submod] = f"{mod_name}.{name}"
+                self._get_mod_name(submod)
+            self._seen_modules.add(mod)
+        return mod_name
+
+    def _get_append_fn(self, w_mod, name, is_bw):
+        def fn(*args):
+            if is_bw:
+                self._maybe_set_engine_callback()
+            if name in self.parents and not self.is_bw:
+
+                def custom_formatwarning(msg, category, filename, lineno, line=None):
+                    return f"{filename}:{lineno}: {category.__name__}: {msg} \n"
+
+                # pyrefly: ignore [bad-assignment]
+                warnings.formatwarning = custom_formatwarning
+                warnings.warn(
+                    "The module hierarchy tracking maybe be messed up."
+                    " Please file a bug to PyTorch, if it is the case.",
+                    stacklevel=2,
+                )
+            if name not in self.parents:
+                self._active_module_cnt[name] = 1
+                self.parents.add(name)
+            else:
+                self._active_module_cnt[name] += 1
+
+            if self._user_pre_bw_hook is not None and is_bw:
+                self._user_pre_bw_hook(w_mod(), args)
+
+        return fn
+
+    def _get_pop_fn(self, w_mod, name, is_bw):
+        def fn(*args):
+            if self._user_post_bw_hook is not None and is_bw:
+                self._user_post_bw_hook(w_mod(), args)
+            if name in self.parents:
+                self._active_module_cnt[name] -= 1
+                if self._active_module_cnt[name] == 0:
+                    self.parents.remove(name)
+            elif not self.is_bw:
+                # Due to some input/output not requiring gradients, we cannot enforce
+                # proper nesting in backward
+                raise RuntimeError(
+                    "The Module hierarchy tracking is wrong. Report a bug to PyTorch"
+                )
+
+        return fn
+
+    def _fw_pre_hook(self, mod, input):
+        if torch._dynamo.eval_frame._is_in_optimized_module():
+            return
+
+        name = self._get_mod_name(mod)
+        w_mod = weakref.ref(mod)
+        self._get_append_fn(w_mod, name, False)()
+        if self._user_pre_fw_hook is not None:
+            self._user_pre_fw_hook(mod, input)
+        args, _ = tree_flatten(input)
+        tensors = [a for a in args if isinstance(a, torch.Tensor) and a.requires_grad]
+        if not self.is_bw:
+            if tensors:
+                register_multi_grad_hook(tensors, self._get_pop_fn(w_mod, name, True))
+            else:
+                self._post_bw_callbacks_to_enqueue.append(
+                    self._get_pop_fn(w_mod, name, True)
+                )
+
+    def _fw_post_hook(self, mod, input, output):
+        if torch._dynamo.eval_frame._is_in_optimized_module():
+            return
+
+        name = self._get_mod_name(mod)
+        w_mod = weakref.ref(mod)
+        if self._user_post_fw_hook is not None:
+            self._user_post_fw_hook(mod, input, output)
+        self._get_pop_fn(w_mod, name, False)()
+        args, _ = tree_flatten(output)
+        tensors = [a for a in args if isinstance(a, torch.Tensor) and a.requires_grad]
+        if not self.is_bw and tensors:
+            register_multi_grad_hook(
+                tensors, self._get_append_fn(w_mod, name, True), mode="any"
+            )
+
+    def __enter__(self):
+        self._fw_pre_handle = register_module_forward_pre_hook(self._fw_pre_hook)
+        self._fw_post_handle = register_module_forward_hook(
+            self._fw_post_hook, always_call=True
+        )
+        return self
+
+    def __exit__(self, *args):
+        self._fw_pre_handle.remove()
+        self._fw_post_handle.remove()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tools/runtime_estimator.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tools/runtime_estimator.py
new file mode 100644
index 0000000000000000000000000000000000000000..caf399cf6a802677f754084d2d867a743036520f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tools/runtime_estimator.py
@@ -0,0 +1,398 @@
+# Owner(s): ["module: unknown"]
+from collections import defaultdict
+from typing import Any, TYPE_CHECKING
+from typing_extensions import Self
+
+import torch
+import torch.utils._pytree as pytree
+from torch._guards import active_fake_mode
+from torch._subclasses.fake_tensor import FakeTensorMode
+from torch.distributed._tools.mod_tracker import ModTracker
+from torch.utils._mode_utils import no_dispatch
+from torch.utils._python_dispatch import TorchDispatchMode
+from torch.utils._runtime_estimation import (
+    _FLOAT_TYPES,
+    _IGNORE_OPS,
+    _VIEW_OPS,
+    get_compute_time,
+    get_transfer_time,
+)
+
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+__all__ = ["RuntimeEstimator"]
+
+
+class RuntimeEstimator(TorchDispatchMode):
+    """
+    Estimates the GPU runtime in milliseconds using various estimation methods under the ``FakeTensorMode``.
+
+    This class provides a ``TorchDispatchMode`` based context manager that can be used to estimate the eager
+    runtime of PyTorch functions. It supports two estimation modes, benchmarking (`operator-level-benchmark`) and
+    roofline cost modeling (`operator-level-cost-model`).
+    For modules executed under this context manager, it aggregates the forward and backward operation runtimes
+    and also records their execution orders.
+
+    Attributes:
+        mod_runtimes (Dict[str, Dict[str, float]]): A dictionary of module runtimes. The key to the outer dictionary
+            is the fully qualified name (FQN) of the module. For each module the forward and backward runtimes of the
+            operations are aggregated in the inner dictionary keyed by 'fw' and 'bw'.
+        mod_fw_pre_order (List[str]): List of module FQNs in pre-forward execution order.
+        mod_bw_pre_order (List[str]): List of module FQNs in pre-backward execution order.
+        mod_fw_post_order (List[str]): List of module FQNs in post-forward execution order.
+        mod_bw_post_order (List[str]): List of module FQNs in post-backward execution order.
+        total_runtime (float): The total estimated runtime in milliseconds.
+
+    Note:
+        1) The benchmarking estimate mode will execute kernels on GPU and assumes that every operation can run in
+            isolation without causing an OOM error. It is also designed to be used only under ``FakeTensorMode``.
+        2) Currently wrapper tensor sub-classes such as ``DTensor`` won't produce correct estimates. We plan to support
+            them in future PRs.
+        3) We only estimate the compute time, if your code has communication, it will not be considered. Again, we will
+            support this in future PRs.
+
+    Example usage:
+
+        .. code-block:: python
+
+            runtime_estimator = RuntimeEstimator()
+            with FakeTensorMode():
+                module = ...
+                optimizer = ...
+                inp = ...
+                with runtime_estimator(estimate_mode_type="operator-level-cost-model"):
+                    loss = module(inp)
+                    loss.backward()
+                    optimizer.step()
+                    optimizer.zero_grad()
+                runtime_estimator.display_modulewise_stats()
+    """
+
+    _no_fallback_kernel: set[torch._ops._OpNamespace] = set()
+    fake_mode: FakeTensorMode
+
+    def __init__(self) -> None:
+        super().__init__()
+        self._estimate: Callable
+        self._estimate_mode_type: str
+        self._mod_tracker = ModTracker()
+        self.mod_runtimes: dict[str, dict[str, float]] = defaultdict(
+            lambda: defaultdict(lambda: 0.0)
+        )
+        self.mod_fw_pre_order: list[str] = []
+        self.mod_bw_pre_order: list[str] = []
+        self.mod_fw_post_order: list[str] = []
+        self.mod_bw_post_order: list[str] = []
+        self.total_runtime: float = 0.0
+
+    # Adapted from: https://github.com/pytorch/pytorch/blob/9b902b3ee3bd608a19543362b66bf06c373dd374/torch/_subclasses/fake_tensor.py#L1969  # noqa: PGH004,B950
+    # NB: returns fake tensors
+    @classmethod
+    def _maybe_run_and_benchmark_fallback_kernel(  # type: ignore[no-untyped-def]
+        cls,
+        func,
+        args,
+        kwargs,
+        orig_not_implemented_exception,
+    ):
+        """
+        Runs and benchmarks a fallback kernel for a given function.
+
+        Args:
+            func (Callable): The function to benchmark.
+            args (Tuple): The arguments to pass to the function.
+            kwargs (Dict[str, Any]): The keyword arguments to pass to the function.
+            orig_not_implemented_exception (Exception): The original exception to raise if the fallback kernel
+                is not implemented.
+
+        Returns:
+            Tuple[Any, float]: A tuple containing the result of the function and
+                the mean operation time in milliseconds.
+        """
+        # these should all be supported, just to be safe
+        # avoid fallback for operators which inplace modify metadata
+        # because the input fake tensors would be umodified
+        if torch.Tag.inplace_view in func.tags:  # type: ignore[attr-defined]
+            raise orig_not_implemented_exception
+
+        inp_impls = {}
+        flat_args, args_spec = pytree.tree_flatten((args, kwargs))
+        # Don't use in_kernel_invocation_manager(fake_mode) as we want to do
+        # REAL compute (not with meta device)
+        with no_dispatch():
+
+            def to_real_tensor(e):  # type: ignore[no-untyped-def]
+                if cls.fake_mode.is_our_fake(e):
+                    if e.dtype in _FLOAT_TYPES:
+                        out = torch.rand_like(e, device=e.fake_device)
+                    else:
+                        out = torch.ones_like(e, device=e.fake_device)
+                    if e.is_sparse:
+                        out._coalesced_(e.is_coalesced())
+                    inp_impls[id(out)] = e
+                    return out
+                return e
+
+            flat_args = [to_real_tensor(a) for a in flat_args]
+            args, kwargs = pytree.tree_unflatten(flat_args, args_spec)
+            r = func(*args, **kwargs)
+            warmup_iters, actual_iters = 2, 3
+            for _ in range(warmup_iters):
+                func(*args, **kwargs)
+            start_event = torch.cuda.Event(enable_timing=True)
+            end_event = torch.cuda.Event(enable_timing=True)
+            start_event.record(torch.cuda.current_stream())
+            for _ in range(actual_iters):
+                func(*args, **kwargs)
+            end_event.record(torch.cuda.current_stream())
+            torch.cuda.synchronize()
+            cuda_time = start_event.elapsed_time(end_event)
+            mean_op_time = cuda_time / actual_iters
+
+        storages = set()
+
+        for e in flat_args:
+            if isinstance(e, torch.Tensor):
+                if not e.is_sparse:
+                    storages.add(e._typed_storage()._cdata)
+
+        # TODO: also check metadata change on inputs
+        # proper aliasing/metadata relationship between outputs and inputs will
+        # not be set up, bc of conversion to device, unless we can reuse an
+        # input impl
+
+        def map_out(e):  # type: ignore[no-untyped-def]
+            if id(e) not in inp_impls and (
+                isinstance(e, torch.Tensor)
+                and not e.is_sparse
+                and e._typed_storage()._cdata in storages
+            ):
+                raise orig_not_implemented_exception
+
+            if isinstance(e, torch.Tensor):
+                if id(e) in inp_impls:
+                    return inp_impls[id(e)]
+                else:
+                    return cls.fake_mode.fake_tensor_converter.from_real_tensor(
+                        cls.fake_mode, e
+                    )
+            else:
+                return e
+
+        return (pytree.tree_map(map_out, r), mean_op_time)
+
+    @classmethod
+    def _benchmark_estimate(cls, func, args, kwargs) -> tuple[Any, float]:  # type: ignore[no-untyped-def]
+        """
+        Estimates the runtime of a function using benchmarking.
+
+        Args:
+            func: The function to estimate.
+            args: The arguments to pass to the function.
+            kwargs: The keyword arguments to pass to the function.
+            res: The result of the function.
+
+        Returns:
+            Tuple[Any, float]: A tuple containing the result of the function and
+                the mean operation time in milliseconds.
+        """
+        assert isinstance(cls.fake_mode, FakeTensorMode), (
+            "Initialize/Assign FakeTensorMode before using this function"
+        )
+        mean_op_time = 0.0
+        if func._overloadpacket not in _VIEW_OPS:
+            try:
+                res, mean_op_time = cls._maybe_run_and_benchmark_fallback_kernel(
+                    func,
+                    args,
+                    kwargs,
+                    NotImplementedError,
+                )
+                return (res, mean_op_time)
+            except NotImplementedError:
+                cls._no_fallback_kernel.add(func._overloadpacket)
+        res = func(*args, **kwargs or {})
+        return (res, mean_op_time)
+
+    # Adapted from: https://github.com/pytorch/pytorch/blob/9b902b3ee3bd608a19543362b66bf06c373dd374/torch/_inductor/scheduler.py#L589  # noqa: PGH004,B950
+    @classmethod
+    def _roofline_estimate(cls, func, args, kwargs) -> tuple[Any, float]:  # type: ignore[no-untyped-def]
+        """
+        Estimates the runtime of a function using a roofline cost model.
+
+        Args:
+            func: The function to estimate.
+            args: The arguments to pass to the function.
+            kwargs: The keyword arguments to pass to the function.
+            out: The output of the function.
+
+        Returns:
+            Tuple[Any, float]: A tuple containing the result of the function and
+                the mean operation time in milliseconds.
+        """
+        assert torch.cuda.is_available(), (
+            "Roofline estimation needs to access CUDA capabilities to make estimations"
+        )
+
+        # Roofline Cost Model Explanation
+
+        # The roofline cost model estimates the execution time of an operator based on
+        # the device's empirical maximum FLOPs/sec (pi) and device DRAM bandwidth (beta).
+
+        # Variables:
+        # - pi: Maximum empirical FLOPs/sec of the device
+        # - beta: Maximum empirical device DRAM bandwidth (bytes/sec) of the device
+        # - I: Arithmetic intensity of the operator (FLOPs/bytes)
+        # - op_flops: FLOPs required by the operator
+        # - op_bytes: Bytes transferred to and from DRAM for the operator
+
+        # Calculation Steps:
+        # 1. Calculate arithmetic intensity: I = op_flops / op_bytes
+        # 2. Calculate estimated FLOPs/sec: est_flops_sec = min(pi, beta * I)
+        # 3. Calculate estimated operator time: estimated_op_time = op_flops / est_flops_sec
+        #    This simplifies to: estimated_op_time = max(op_flops / pi, op_flops / (beta * I))
+        #    Further simplifying: estimated_op_time = max(op_flops / pi, op_bytes / beta)
+
+        # Simplified Formulas:
+        # - compute_time = op_flops / pi
+        # - transfer_time = op_bytes / beta
+        # - estimated_op_time = max(compute_time, transfer_time)
+
+        kwargs = kwargs if kwargs else {}
+        out = func(*args, **kwargs)
+        op_time = 0.0
+        func_packet = func._overloadpacket
+        if func_packet not in _IGNORE_OPS:
+            flat_args_kwargs, args_spec = pytree.tree_flatten((args, kwargs))
+            flat_outs, out_spec = pytree.tree_flatten(out)
+            transfer_time = get_transfer_time(flat_args_kwargs, flat_outs)
+
+            out_dtypes = {
+                t.dtype
+                for t in flat_outs
+                if isinstance(t, torch.Tensor) and t.dtype in _FLOAT_TYPES
+            }
+
+            args, kwargs = pytree.tree_unflatten(flat_args_kwargs, args_spec)
+            out = pytree.tree_unflatten(flat_outs, out_spec)
+
+            compute_time = get_compute_time(func_packet, args, kwargs, out, out_dtypes)
+            # We get the estimated time as the max of the transfer time and
+            # compute time. We divide by 1e6 to get the time in ms
+            op_time = max(transfer_time, compute_time) / 1e6
+
+        return (out, op_time)
+
+    def display_modulewise_stats(self, depth: int = 2) -> None:
+        """
+        Displays module-wise statistics collected by ``RuntimeEstimator``.
+
+        Prints the pre-forward and pre-backward execution orders.
+        Displays the module-wise forward and backward runtimes in milliseconds.
+
+        Args:
+            depth (int): The maximum depth of module hierarchy to display (default to 2).
+        """
+        print("Pre-Forward Execution Order: ")
+        for mod_fqn in self.mod_fw_pre_order:
+            mod_depth = mod_fqn.count(".") + 1
+            if mod_depth > depth:
+                continue
+            print(mod_fqn)
+        print("Pre-Backward Execution Order: ")
+        for mod_fqn in self.mod_bw_pre_order:
+            mod_depth = mod_fqn.count(".") + 1
+            if mod_depth > depth:
+                continue
+            print(mod_fqn)
+        for mod_fqn, runtimes in self.mod_runtimes.items():
+            mod_depth = mod_fqn.count(".") + 1
+            if mod_depth > depth:
+                continue
+            print(
+                f"{mod_fqn} fw: {runtimes.get('fw', 0.0):.3f}ms bw: {runtimes.get('bw', 0.0):.3f}ms"
+            )
+
+    def __torch_dispatch__(self, func, types, args=..., kwargs=None):  # type: ignore[no-untyped-def]
+        # TODO: @sanketpurandare: Flatten tensors by desugaring the tensor subclasses
+        # TODO: @sanketpurandare: Add logic for incorporating communication time
+        res, op_time = self._estimate(func, args, kwargs)
+        for par in self._mod_tracker.parents:
+            if self._mod_tracker.is_bw:
+                self.mod_runtimes[par]["bw"] += op_time
+            else:
+                self.mod_runtimes[par]["fw"] += op_time
+        self.total_runtime += op_time
+        return res
+
+    def __call__(self, estimate_mode_type: str) -> Self:
+        """
+        Sets the estimate mode type.
+
+        Currently supported modes:
+            - "operator-level-benchmark": Estimates runtime using operator benchmarking.
+            - "operator-level-cost-model": Estimates runtime using roofline cost model.
+
+        Args:
+            estimate_mode_type (str): The type of estimate mode to use.
+
+        Returns:
+            RuntimeEstimator: The runtime estimator instance.
+
+        Raises:
+            NotImplementedError: If the estimate mode type is not supported.
+        """
+        if estimate_mode_type == "operator-level-benchmark":
+            self._estimate = RuntimeEstimator._benchmark_estimate
+        elif estimate_mode_type == "operator-level-cost-model":
+            self._estimate = RuntimeEstimator._roofline_estimate
+        else:
+            raise NotImplementedError(
+                f"estimate_mode_type {estimate_mode_type} not supported"
+            )
+        self._estimate_mode_type = estimate_mode_type
+        return self
+
+    def __enter__(self) -> Self:
+        fake_mode = active_fake_mode()
+        assert isinstance(fake_mode, FakeTensorMode), (
+            "No FakeTensorMode found, designed to used under FakeTensorMode"
+        )
+        RuntimeEstimator.fake_mode = fake_mode
+        self.total_runtime = 0.0
+        self.mod_runtimes = defaultdict(lambda: defaultdict(lambda: 0.0))
+        self.mod_fw_pre_order.clear()
+        self.mod_bw_pre_order.clear()
+        self.mod_fw_post_order.clear()
+        self.mod_bw_post_order.clear()
+        self._mod_tracker.register_user_hooks(
+            pre_fw_hook=lambda mod, inp: self.mod_fw_pre_order.append(
+                self._mod_tracker.get_known_fqn(mod)
+            ),
+            pre_bw_hook=lambda mod, g_out: self.mod_bw_pre_order.append(
+                self._mod_tracker.get_known_fqn(mod)
+            ),
+            post_fw_hook=lambda mod, inp, out: self.mod_fw_post_order.append(
+                self._mod_tracker.get_known_fqn(mod)
+            ),
+            post_bw_hook=lambda mod, g_inp: self.mod_bw_post_order.append(
+                self._mod_tracker.get_known_fqn(mod)
+            ),
+        )
+        self._mod_tracker.__enter__()
+        super().__enter__()
+        return self
+
+    # pyrefly: ignore [bad-override]
+    def __exit__(self, *args: Any) -> None:
+        print(
+            f"Estimated ({self._estimate_mode_type})"
+            f"total_time: {self.total_runtime:.3f} ms"
+        )
+        if len(self._no_fallback_kernel) > 0:
+            print("no_fallback_kernel: ", list(self._no_fallback_kernel))
+        super().__exit__(*args)
+        self._mod_tracker.clear_user_hooks()
+        self._mod_tracker.__exit__()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tools/sac_estimator.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tools/sac_estimator.py
new file mode 100644
index 0000000000000000000000000000000000000000..c43de8c2b916742cf131b1761e801b41fc689ba6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tools/sac_estimator.py
@@ -0,0 +1,961 @@
+import math
+import os
+import sys
+from collections import OrderedDict
+from dataclasses import astuple, dataclass
+from typing import Any, NamedTuple
+from typing_extensions import Self
+
+import torch
+from torch import nan, nn, UntypedStorage
+from torch._guards import active_fake_mode
+from torch._subclasses.fake_tensor import FakeTensorMode
+from torch.distributed._tools.common_utils import get_untyped_storages
+from torch.distributed._tools.mod_tracker import ModTracker
+from torch.distributed._tools.runtime_estimator import RuntimeEstimator
+from torch.testing._internal.composite_compliance import (
+    is_inplace,
+    is_inplace_view_fn,
+    is_view_fn,
+)
+from torch.utils._python_dispatch import TorchDispatchMode
+from torch.utils._pytree import tree_flatten
+from torch.utils.checkpoint import SAC_IGNORED_OPS
+
+
+__all__ = ["SACEstimator", "SACStats", "MSPS", "SACTradeOffStats", "SACGreedyOrderMeta"]
+aten = torch.ops.aten
+
+_ADDITIONAL_IGNORED_OPS = {
+    aten.lift_fresh.default,  # type: ignore[attr-defined]
+    torch.ops.profiler._record_function_exit._RecordFunction,  # type: ignore[attr-defined]
+    aten.clone.default,  # type: ignore[attr-defined] # seems needed for torch.compile
+}
+OPS_TO_ALWAYS_SKIP = SAC_IGNORED_OPS | _ADDITIONAL_IGNORED_OPS
+# This value is hard-coded here:
+# https://github.com/pytorch/pytorch/blob/5fba5d83f0703ff8077ab65448a998e9ad6598fd/c10/cuda/CUDACachingAllocator.cpp#L117
+_PYTORCH_MIN_ALLOCATE = (
+    2**9 if int(os.environ.get("PYTORCH_NO_CUDA_MEMORY_CACHING", 0)) == 0 else 1
+)
+
+
+def _display_stats_tabular(headers: list[str], table_data: list[list[Any]]) -> None:
+    try:
+        from tabulate import tabulate
+    except ImportError as err:
+        raise ImportError("Please install tabulate.") from err
+
+    # Use tabulate to print the table
+    print(tabulate(table_data, headers=headers, tablefmt="rst"))
+
+
+# Based on:
+# https://github.com/facebookresearch/xformers/blob/main/xformers/checkpoint.py#L71
+@dataclass
+class _SACMetadata:
+    """
+    Stores metadata for a single operator for SAC.
+
+    Attributes:
+        func (Any): The operator function.
+        time_taken (float): The time taken by the operator.
+        memory_used (float): The memory used by the operator.
+        curr_idx (int): The current operator index.
+        output_ids (Tuple[int, ...]): The storage IDs of the operator's outputs.
+        inplace_info (Tuple[int, ...]): Tuple of self and parent operator for in-place operator.
+        is_view_like (bool): Whether the operator is view-like.
+        is_rand_op (bool): Whether the operator is a random operator.
+    """
+
+    func: Any
+    time_taken: float
+    memory_used: float
+    curr_idx: int
+    output_ids: tuple[int, ...]
+    inplace_info: tuple[int, ...]
+    is_view_like: bool
+    is_rand_op: bool
+
+
+@dataclass
+class _SACModMetadata:
+    """
+    Stores metadata for a module for SAC.
+
+    Attributes:
+        start_idx (int): The starting index of the module's operators.
+        force_store_random (bool): Whether to force store random operators in the module.
+        sac_metadata (List[_SACMetadata]): List of metadata for each operator in the module.
+    """
+
+    start_idx: int
+    force_store_random: bool
+    sac_metadata: list[_SACMetadata]
+
+
+@dataclass
+class SACStats:
+    """
+    A class for storing Activation Checkpointing statistics corresponding to a module.
+
+    Attributes:
+        func_names (List[str]): List of operator names.
+        runtimes (List[float]): List of operator runtimes in millliseconds.
+        memory (List[int]): List of operator memory usage in bytes.
+        view_like_ops (List[int]): Indices of view-like operators.
+        rand_ops (List[int]): Indices of random operators.
+        saved_autograd_ops (List[int]): Indices of operator results saved by autograd engine.
+        inplace_ops (List[Tuple[int, int]]): Tuple of indices of op and its first parent for Inplace operators.
+        force_store_random (bool): Whether to force store random operator results.
+    """
+
+    func_names: list[str]
+    runtimes: list[float]
+    memory: list[int]
+    view_like_ops: list[int]
+    rand_ops: list[int]
+    saved_autograd_ops: list[int]
+    inplace_ops: list[tuple[int, int]]
+    force_store_random: bool
+
+
+class MSPS(NamedTuple):
+    """
+    Represents Memory and Runtime Statistics for an operator/operator group.
+
+    Attributes:
+        func_names (set[str]): Set of operator/operator group names.
+        op_idx (int): Operator index (group head index in case of operator groups).
+        memory (int): Memory usage in bytes.
+        runtime (float): Runtime in milliseconds.
+        msps (float): Memory per second calculated as memory/runtime.
+    """
+
+    func_names: set[str]
+    op_idx: int
+    memory: int
+    runtime: float
+    msps: float
+
+
+@dataclass
+class SACTradeOffStats:
+    """
+    Stores statistics for activation-checkpointing trade-off.
+
+    Attributes:
+        n_segments (int): Number of piecewise linear segments fitted to the trade-off curve.
+        slopes (List[float]): Slopes of the pieces of linear segments fitted to the trade-off curve.
+        intercepts (List[float]): Intercepts of the of the pieces of linear segments fitted to the trade-off curve.
+        fit_breaks (List[float]): Breakpoints of the of the pieces of linear segments fitted to the trade-off curve.
+        tradeoff_curve (OrderedDict[float, float]): Trade-off curve data of memory discarded vs recomputation time.
+        sac_memory (int): Total memory of operations available for activation checkpointing in bytes.
+        sac_runtime (float): Total runtime of operations available for activation checkpointing in milliseconds.
+    """
+
+    n_segments: int
+    slopes: list[float]
+    intercepts: list[float]
+    fit_breaks: list[float]
+    tradeoff_curve: OrderedDict[float, float]
+    sac_memory: int
+    sac_runtime: float
+
+
+@dataclass
+class SACGreedyOrderMeta:
+    """
+    Stores metadata for Greedy-order SAC.
+
+    Attributes:
+        recomputed_ops (set[int]): Set of operator indices to be recomputed.
+        stored_ops (set[int]): Set of operator indices to be stored.
+        inplace_op_groups (dict[int, set[int]]): Dictionary of inplace operator groups from group-head to operators.
+        random_ops_group (dict[int, set[int]]): Dictionary of random op group head to random ops.
+        msps_meta (list[MSPS]): List of Memory and Runtime Statistics for operators.
+    """
+
+    recomputed_ops: set[int]
+    stored_ops: set[int]
+    inplace_op_groups: dict[int, set[int]]
+    random_ops_group: dict[int, set[int]]
+    msps_meta: list[MSPS]
+
+
+class SACEstimator(TorchDispatchMode):
+    """
+    Estimates the memory and recomputation time trade-offs for applying Selective Activation Checkpointing (SAC).
+
+    This class provides a ``TorchDispatchMode`` based context manager that can be used to estimate the memory and
+    runtime trade-offs of functions or ``torch.nn.Module``s for Selective Activation Checkpointing (SAC). It provides
+    detailed statistics and metadata information for operators of each module and provides a greedy order for selecting
+    the operators to be recomputed/checkpointed.  It also constructs the per-module trade-off graph of discarded memory
+    vs recomputation time for the obtained greedy order. Using ``RuntimeEstimator`` under the hood, it supports two
+    estimation modes, `operator-level-benchmark` and (`operator-level-cost-model` (roofline model).
+
+    Attributes:
+        sac_mod_stats (Dict[str, SACStats]): Dictionary from module FQN (fully qualified name) to ``SACStats``.
+        sac_mod_tradeoff_stats (Dict[str, SACTradeOffStats]): Dictionary from module FQN to ``SACTradeOffStats``.
+        sac_mod_greedy_order_meta (Dict[str, SACGreedyOrderMeta]): Dictionary from module FQN to ``SACGreedyOrderMeta``.
+
+    Note:
+        1) This class is designed to be used under ``FakeTensorMode``.
+        2) Currently, it only supports estimation of compute time and memory usage, and does not consider communication.
+
+    Example usage:
+
+        .. code-block:: python
+
+            sac_estimator = SACEstimator()
+            with FakeTensorMode():
+                module = ...
+                inp = ...
+                with sac_estimator("operator-level-cost-model"):
+                    output = module(inp)
+                sac_estimator.display_modulewise_sac_stats(depth=4, print_tabular=True)
+    """
+
+    def __init__(self) -> None:
+        self.sac_mod_stats: dict[str, SACStats] = {}
+        self.sac_mod_tradeoff_stats: dict[str, SACTradeOffStats] = {}
+        self.sac_mod_greedy_order_meta: dict[str, SACGreedyOrderMeta] = {}
+        self._mod_tracker = ModTracker()
+        self._sac_metadata: list[_SACMetadata] = []
+        self._sac_mod_metadata: dict[str, _SACModMetadata] = {}
+        self._leaf_modules: set[str] = set()
+        self._saved_tensor_hook_ctx = torch.autograd.graph.saved_tensors_hooks(
+            self._pack_hook, lambda x: x
+        )
+        self._saved_tensor_ids: set[int] = set()
+        self._estimate_runtime = RuntimeEstimator._roofline_estimate
+
+    def _pack_hook(self, x: torch.Tensor) -> torch.Tensor:
+        # Hook function to track underlying storage IDs of tensors
+        # Updates the _saved_tensor_ids set with the IDs of the tensor's storages
+        # Used in conjunction with torch.autograd.graph.saved_tensors_hooks
+        untyped_storages = get_untyped_storages(x)
+        storage_ids = (hash(st) for st in untyped_storages)
+        self._saved_tensor_ids.update(storage_ids)
+        return x
+
+    def _pre_fw_hook(self, mod: nn.Module, inputs: Any) -> None:
+        # Pre-forward hook function to prepare module metadata
+        # Tracks module FQN, force store random flag, and ``SACModMetadata``
+        # Initializes metadata for non-leaf modules, marks leaf modules
+        mod_fqn = self._mod_tracker.get_known_fqn(mod)
+        assert mod_fqn is not None
+        num_children = sum(1 for _ in mod.children())
+        if num_children > 0:
+            force_store_random = self._get_force_store_random(inputs)
+            self._sac_mod_metadata[mod_fqn] = _SACModMetadata(
+                start_idx=len(self._sac_metadata),
+                force_store_random=force_store_random,
+                sac_metadata=[],
+            )
+        else:
+            self._leaf_modules.add(mod_fqn)
+
+    def _post_fw_hook(self, mod: nn.Module, inputs: Any, outputs: Any) -> None:
+        # 1. Retrieves the module's FQN and checks if it's a leaf module
+        # 2. If not a leaf module, computes:
+        #    - ``SACStats`` using the module's metadata and force store random flag
+        #    - ``SACGreedyOrderMeta`` using the computed SAC statistics
+        mod_fqn = self._mod_tracker.get_known_fqn(mod)
+        assert mod_fqn is not None
+        if mod_fqn in self._leaf_modules:
+            return
+        else:
+            self.sac_mod_stats[mod_fqn] = self._get_sac_stats(
+                data=self._sac_mod_metadata[mod_fqn].sac_metadata,
+                force_store_random=self._sac_mod_metadata[mod_fqn].force_store_random,
+            )
+            self.sac_mod_greedy_order_meta[mod_fqn] = self._get_greedy_order_meta(
+                self.sac_mod_stats[mod_fqn]
+            )
+
+    def _get_force_store_random(self, inputs: Any) -> bool:
+        flat_inputs, _ = tree_flatten(inputs)
+        return all(not isinstance(x, torch.Tensor) for x in flat_inputs)
+
+    def _get_sac_stats(
+        self, data: list[_SACMetadata], force_store_random: bool
+    ) -> SACStats:
+        # 1. Ignore the operations that should be skipped by SAC such as aten.detach.default because autograd
+        # inserts those during backward and it breaks the fwd-bwd alignment
+        filtered_data = [x for x in data if x.func not in OPS_TO_ALWAYS_SKIP]
+
+        (
+            ops,
+            runtimes_,
+            memory_,
+            new_ids,
+            output_ids,
+            inplace_ops_,
+            view_like_ops_,
+            rand_ops_,
+        ) = zip(*[astuple(x) for x in filtered_data], strict=True)
+
+        # 2. Extract the metadata information
+        runtimes = list(runtimes_)
+        memory = list(memory_)
+        func_names = [op._overloadpacket.__name__ for op in ops]
+        view_like_ops = [i for i, x in enumerate(view_like_ops_) if x]
+        rand_ops = [i for i, x in enumerate(rand_ops_) if x]
+        saved_autograd_ops = [
+            i
+            for i, out_ids in enumerate(output_ids)
+            if set(out_ids).issubset(self._saved_tensor_ids)
+        ]
+
+        # 3. Remap the inplace indices as we have removed OPS_TO_ALWAYS_SKIP
+        # FIXME @sanketpurandare: Fix this by changing the parent of the inplace-op
+        # to itself if the original parent is in OPS_TO_ALWAYS_SKIP.
+        try:
+            inplace_ops = [tuple(map(new_ids.index, x)) for x in inplace_ops_ if x]
+        except ValueError as err:
+            raise ValueError(
+                f"The remapping of inplace ops failed since one of the inplace op parents"
+                f" must have been present in {OPS_TO_ALWAYS_SKIP}"
+            ) from err
+
+        # 4. The last operation is always stored as the output of the checkpoint
+        # block, so we can avoid recomputing it. We set the memory to zero
+        # instead of adding a new constraint because we want both the 0 and 1
+        # endpoints for memory_budget to be valid
+        # FIXME @sanketpurandare: this heuristic for finding the last non-view non-inplace op
+        # might not always be correct, which would yield suboptimal policies
+        last_op = len(ops) - 1
+        skip_ops_ = set(view_like_ops) | set({x[0] for x in inplace_ops})
+        reversed_skip_ops = sorted(skip_ops_, reverse=True)
+        for op in reversed_skip_ops:
+            if op == last_op:
+                last_op -= 1
+
+        memory[last_op] = 0
+
+        # 5. Create a single ``SACStats`` object for the entire block of ``_SACMetadata``.
+        return SACStats(
+            func_names=func_names,
+            runtimes=runtimes,
+            memory=memory,
+            view_like_ops=view_like_ops,
+            rand_ops=rand_ops,
+            saved_autograd_ops=saved_autograd_ops,
+            inplace_ops=inplace_ops,  # type: ignore[arg-type]
+            force_store_random=force_store_random,
+        )
+
+    def _get_inplace_metadata(
+        self, func: Any, out_storages: set[UntypedStorage]
+    ) -> tuple[int, tuple[int, ...], dict[str, tuple[int, ...]]]:
+        # 1. Get the current index of the metadata obtained so far
+        curr_idx = len(self._sac_metadata)
+        # 2. Get the set of active modules that are not leaf
+        active_mod_fqns: set[str] = {
+            par for par in self._mod_tracker.parents if par not in self._leaf_modules
+        }
+        # 3. Output ids are the identifies of the storage objects corresponding to the tensors
+        output_ids = tuple(hash(st) for st in out_storages)
+        # 4. If the function is not inplace, return
+        if not is_inplace(func):
+            return curr_idx, output_ids, dict.fromkeys(active_mod_fqns, ())
+
+        op_idx = curr_idx
+        # 5. Initialize the parent op ids of the inplace op for each of the active modules
+        mod_op_parent_idxs: dict[str, int] = dict.fromkeys(active_mod_fqns, -1)
+        for i, d in enumerate(self._sac_metadata):
+            # 6. Find the first occurrence of a tensor corresponding to each module that
+            # shares the same storage as the current tensor
+            past_output_ids = d.output_ids
+            if set(output_ids).issubset(set(past_output_ids)):
+                for mod_fqn, op_parent_idx in mod_op_parent_idxs.items():
+                    if op_parent_idx == -1:
+                        if acm_stats := self._sac_mod_metadata.get(mod_fqn, None):
+                            if i >= acm_stats.start_idx:
+                                mod_op_parent_idxs[mod_fqn] = i
+                        else:
+                            assert mod_fqn == "Global"
+                            mod_op_parent_idxs[mod_fqn] = i
+        # 7. If no parent tensor is found, then it's probably an inplace op on the arguments
+        # so one can just store the current-op idx as parent idx
+        for mod_fqn, op_parent_idx in mod_op_parent_idxs.items():
+            if op_parent_idx < 0:
+                mod_op_parent_idxs[mod_fqn] = op_idx
+        mod_inplace_info = {
+            mod_fqn: (op_idx, mod_op_parent_idxs[mod_fqn])
+            for mod_fqn in active_mod_fqns
+        }
+        return curr_idx, output_ids, mod_inplace_info  # type: ignore[return-value]
+
+    def __torch_dispatch__(  # type: ignore[no-untyped-def]
+        self, func, types, args=..., kwargs=None
+    ):
+        # 1. Get the runtime estimate
+        out, op_time = self._estimate_runtime(func, args, kwargs)
+        flat_outs, _ = tree_flatten(out)
+        out_storages_cuda: set[UntypedStorage] = set()
+        out_storages_cpu: set[UntypedStorage] = set()
+        cuda_devices: set[torch.device] = set()
+        for o in flat_outs:
+            if isinstance(o, torch.Tensor):
+                if o.device.type == "cuda":
+                    out_storages_cuda.update(get_untyped_storages(o))
+                    cuda_devices.add(o.device)
+                else:
+                    out_storages_cpu.update(get_untyped_storages(o))
+
+        # Check if there's more than 1 CUDA device
+        assert len(cuda_devices) <= 1, (
+            f"{func.__name__}'s output has more than 1 CUDA devices {cuda_devices}"
+        )
+
+        # 2. Get the memory consumed by output
+        nbytes_cuda = sum(
+            math.ceil(st.nbytes() / _PYTORCH_MIN_ALLOCATE) * _PYTORCH_MIN_ALLOCATE
+            for st in out_storages_cuda
+        )
+        nbytes_cpu = sum(st.nbytes() for st in out_storages_cpu)
+        nbytes = nbytes_cuda + nbytes_cpu
+        # 3. Get the current operator index, output storage identifiers and inplace metadata
+        out_storages = out_storages_cuda | out_storages_cpu
+        curr_idx, output_ids, mod_inplace_info = self._get_inplace_metadata(
+            func, out_storages
+        )
+        # 4. Determine if the function is in-place, random-op or a view-like
+        is_view_like = is_view_fn(func) or is_inplace_view_fn(func)
+        is_rand_op = torch.Tag.nondeterministic_seeded in func.tags
+        if is_view_like:
+            nbytes = 0
+        # sdpa has non-deterministic seed, but might be deterministic
+        # if no dropout is applied
+        if func.overloadpacket.__name__ == "_scaled_dot_product_flash_attention":
+            # pyrefly: ignore [missing-attribute]
+            is_rand_op = kwargs.get("dropout_p", 0) != 0
+        # 5. Create metadata information per active non-leaf module
+        for mod_fqn in self._mod_tracker.parents:
+            if mod_fqn in self._leaf_modules:
+                continue
+            acm = _SACMetadata(
+                func=func,
+                time_taken=op_time,
+                memory_used=nbytes,
+                curr_idx=curr_idx,
+                output_ids=output_ids,
+                inplace_info=mod_inplace_info[mod_fqn],
+                is_view_like=is_view_like,
+                is_rand_op=is_rand_op,
+            )
+            if acm_stats := self._sac_mod_metadata.get(mod_fqn, None):
+                acm_stats.sac_metadata.append(acm)
+            else:
+                assert mod_fqn == "Global", (
+                    f"Module {mod_fqn} not found in AC Mod Stats"
+                )
+                self._sac_metadata.append(acm)
+
+        return out
+
+    def _get_greedy_order_meta(self, sac_stats: SACStats) -> SACGreedyOrderMeta:
+        # An inplace-op group is a set of inplace-ops that operate on the same underlying tensor storage.
+        # 1. inplace_op_groups: A dictionary from the top-most parent of inplace-ops to the inplace-ops in the group
+        #   The top-most op can itself be an inplace-op or can be a non-inplace op.
+        # 2. inplace_op_to_group_head: A dictionary that maps all the inplace-ops to their respective group heads.
+        inplace_op_groups: dict[int, set[int]] = {}
+        inplace_op_to_group_head: dict[int, int] = dict(sac_stats.inplace_ops)
+
+        # Initialize inplace_op_groups using inplace_op_to_group_head
+        for op_idx, group_head_idx in inplace_op_to_group_head.items():
+            op_group = inplace_op_groups.setdefault(group_head_idx, {group_head_idx})
+            op_group.add(op_idx)
+
+        # Like inplace ops, all of the random ops in the function/module should all be either recomputed or saved
+        # as a group. This is because, they affect the ranom seed generator. If force_store_random is set True,
+        # all of the random ops will be stored by default. For easy of manageability, we store the top-most random op
+        # as the leader of the random_ops_group.
+        random_ops_group: dict[int, set[int]] = {}
+        random_group_head_idx = min(sac_stats.rand_ops, default=-1)
+        has_rand_ops = bool(sac_stats.rand_ops)
+        if has_rand_ops:
+            random_ops_group[random_group_head_idx] = set(sac_stats.rand_ops)
+
+        # 1. Random ops are stored if force_store_random is set
+        # 2. View-like ops are recomputed by default
+        # 3. For inplace_op_groups:
+        #   a) If the head of this group is an inplace op, then we have to store the entire group.
+        #   b) If any op in the group is random and force_store_random is set, then entire group will be stored.
+        #   c) If none of ops in the group are random and the head of the group is not an in-place op, then
+        #       this group can be considered for recomputation in its entirety
+        stored_ops: set[int] = set()
+        recomputed_ops: set[int] = set()
+        # Case 1:
+        if has_rand_ops and sac_stats.force_store_random:
+            stored_ops.add(random_group_head_idx)
+        # Case 2:
+        recomputed_ops.update(set(sac_stats.view_like_ops))
+
+        for group_head_idx, op_group in inplace_op_groups.items():
+            # Case 3a:
+            if group_head_idx in inplace_op_to_group_head:
+                stored_ops.add(group_head_idx)
+            # Case 3b:
+            if (
+                sac_stats.force_store_random & len(op_group & set(sac_stats.rand_ops))
+                > 0
+            ):
+                stored_ops.add(group_head_idx)
+
+        # The potential recompute candidates are populated as:
+        recompute_candidates: set[int] = set()
+        # 1) The random group head if it is not stored
+        if has_rand_ops and random_group_head_idx not in stored_ops:
+            recompute_candidates.add(random_group_head_idx)
+        # 2) The in-place op group heads that are not stored
+        recompute_candidates.update(set(inplace_op_groups.keys()) - stored_ops)
+        # 3) The non-inplace and non-random ops that are neither stored nor recomputed by default
+        recompute_candidates.update(
+            set(range(len(sac_stats.memory)))
+            - recomputed_ops
+            - stored_ops
+            - set(inplace_op_to_group_head.keys())
+            - set(sac_stats.rand_ops)
+        )
+
+        # We define msps for a recomp candidate as the ratio of memory/runtime aka memory savings per second
+        msps_meta: list[MSPS] = []
+        for cand_idx in recompute_candidates:
+            op_indices = {cand_idx}
+            if cand_idx in inplace_op_groups:
+                op_indices.update(inplace_op_groups[cand_idx])
+            if has_rand_ops and cand_idx == random_group_head_idx:
+                op_indices.update(sac_stats.rand_ops)
+
+            mem = sum(sac_stats.memory[op_idx] for op_idx in op_indices)
+            runtime = sum(sac_stats.runtimes[op_idx] for op_idx in op_indices)
+            func_names = {sac_stats.func_names[op_idx] for op_idx in op_indices}
+            msps = (mem / runtime) if runtime > 0 else sys.float_info.max
+            msps_meta.append(MSPS(func_names, cand_idx, mem, runtime, msps))
+        # We choose candidates to be recomputed based on increasing msps
+        msps_meta.sort(key=lambda x: x.msps, reverse=True)
+        return SACGreedyOrderMeta(
+            recomputed_ops, stored_ops, inplace_op_groups, random_ops_group, msps_meta
+        )
+
+    def _get_sac_tradeoff_pwlf_stats(
+        self,
+        sac_stats: SACStats,
+        greedy_order_meta: SACGreedyOrderMeta,
+        n_segments: int = 2,
+        save_tradeoff_graph: bool = False,
+        filename: str = "ac_tradeoff",
+    ) -> SACTradeOffStats:
+        try:
+            import numpy as np  # type: ignore[import-not-found]
+            import pwlf  # type: ignore[import-untyped, import-not-found]
+        except ImportError as err:
+            raise ImportError("Please install pwlf and numpy package.") from err
+
+        stored_ops, recomputed_ops, inplace_op_groups, random_ops_group, msps_meta = (
+            greedy_order_meta.stored_ops,
+            greedy_order_meta.recomputed_ops,
+            greedy_order_meta.inplace_op_groups,
+            greedy_order_meta.random_ops_group,
+            greedy_order_meta.msps_meta,
+        )
+        # 1. Initialize the discarded memory and recomputation runtime to sum of already chosen recomputed_ops
+        recomp_indices: set[int] = set()
+        for r_idx in recomputed_ops:
+            recomp_indices.add(r_idx)
+            if r_idx in inplace_op_groups:
+                recomp_indices.update(inplace_op_groups[r_idx])
+            if r_idx in random_ops_group:
+                recomp_indices.update(random_ops_group[r_idx])
+
+        discarded_mem = sum(sac_stats.memory[op_idx] for op_idx in recomp_indices)
+        recomp_runtime = sum(sac_stats.runtimes[op_idx] for op_idx in recomp_indices)
+        # 2. Initialize the max recomputation time and total recomputation memory
+        sac_runtime = sum(sac_stats.runtimes)
+        sac_memory = sum(sac_stats.memory)
+        # 3. Tradeoff curve stores the KV pair of the discarded memory to total memory and,
+        # recomputation time to total runtime incurred.
+        delta = 1e-2
+        tradeoff_curve = OrderedDict()
+        # 4. Initialize the trade-off curve with the stats of of already chosen recomputed_ops
+        tradeoff_curve[(discarded_mem / sac_memory) + delta] = (
+            recomp_runtime / sac_runtime
+        )
+        # 5. Update the trade-off curve with memory and runtime stats of SAC candidates in the
+        # greedy order of their ``MSPS``.
+        for cand in msps_meta:
+            discarded_mem += cand.memory
+            recomp_runtime += cand.runtime
+            tradeoff_curve[(discarded_mem / sac_memory) + delta] = (
+                recomp_runtime / sac_runtime
+            )
+        # 6. Finally, we add the memory and recomputation time of the always stored ops.
+        stored_indices: set[int] = set()
+        for s_idx in stored_ops:
+            stored_indices.add(s_idx)
+            if s_idx in inplace_op_groups:
+                stored_indices.update(inplace_op_groups[s_idx])
+            if s_idx in random_ops_group:
+                stored_indices.update(random_ops_group[s_idx])
+        discarded_mem += sum(sac_stats.memory[op_idx] for op_idx in stored_indices)
+        recomp_runtime += sum(sac_stats.runtimes[op_idx] for op_idx in stored_indices)
+        tradeoff_curve[(discarded_mem / sac_memory) + delta] = (
+            recomp_runtime / sac_runtime
+        )
+        x_ = list(tradeoff_curve.keys())
+        y_ = list(tradeoff_curve.values())
+        # 7. We shift the y values to left and x values to right to upperbound the trade-off function
+        # TODO: Write a better explanation why this needs to be done
+        x = x_[: len(x_) - 1]
+        y = y_[1:]
+        tradeoff_pwlf = pwlf.PiecewiseLinFit(x, y)
+        # 8. Fit a piecewise linear function with the specified number of segments to the trade-off curve.
+        n_segments = max(min(len(x) - 2, n_segments), 1)
+        tradeoff_pwlf.fit(n_segments=n_segments)
+
+        # save prediction graph
+        def save_prediction_graph(
+            pwlf_: pwlf.PiecewiseLinFit, x: list[float], y: list[float], filename: str
+        ) -> None:
+            try:
+                import matplotlib.pyplot as plt  # type: ignore[import-not-found]
+                import numpy as np  # type: ignore[import-not-found]
+            except ImportError as err:
+                raise ImportError(
+                    "Install matplotlib and numpy using pip: pip install matplotlib numpy"
+                ) from err
+            # predict for the determined points
+            xHat = np.linspace(min(x), max(x), num=10000)
+            yHat = pwlf_.predict(xHat)
+
+            # plot the results
+            plt.figure()
+            plt.plot(x, y, "o", label="Shifted")
+            plt.plot(xHat, yHat, "-", label="Predicted")
+            plt.plot(x_, y_, "x", label="Original")
+            plt.ylabel("Recomp time / Total recomp time")
+            plt.xlabel("Memory discarded / Total memory")
+            plt.legend()
+            plt.title(f"{filename}")
+            plt.suptitle(
+                f"Total Memory = {sac_memory} B Total Runtime = {sac_runtime:.4f} ms",
+                fontsize=10,
+            )
+            folder_name = "tradeoff_graphs"
+            if not os.path.exists(folder_name):
+                os.makedirs(folder_name)
+            # Save the plots in the folder
+            plt.savefig(os.path.join(folder_name, f"{filename}.png"))
+
+        if save_tradeoff_graph:
+            save_prediction_graph(tradeoff_pwlf, x, y, filename)
+        # 9. Obtain the slopes, intercepts and breakpoints of the fitted piecewise linear functions
+        slopes = tradeoff_pwlf.calc_slopes().tolist()
+        assert isinstance(tradeoff_pwlf.intercepts, np.ndarray) and isinstance(
+            tradeoff_pwlf.fit_breaks, np.ndarray
+        )
+        intercepts = tradeoff_pwlf.intercepts.tolist()
+        fit_breaks = tradeoff_pwlf.fit_breaks.tolist()
+        return SACTradeOffStats(
+            n_segments=n_segments,
+            slopes=slopes,
+            intercepts=intercepts,  # type: ignore[arg-type]
+            fit_breaks=fit_breaks,  # type: ignore[arg-type]
+            tradeoff_curve=tradeoff_curve,
+            sac_memory=sac_memory,
+            sac_runtime=sac_runtime,
+        )
+
+    def display_sac_stats(
+        self, sac_stats: SACStats, print_tabular: bool = False
+    ) -> None:
+        """
+        Displays the SAC statistics.
+
+        Args:
+            sac_stats (SACStats): The SAC statistics to display.
+            print_tabular (bool, optional): Whether to print the statistics in a tabular format. Defaults to False.
+
+        Prints:
+            1. Total Memory: The total memory usage in bytes.
+            2. Total Runtime: The total runtime in milliseconds.
+            3. Store Random: A flag indicating whether to force store random operator results.
+
+            Followed by a table with the following columns:
+            1. Op Idx: The operator index.
+            2. Op Name: The operator name.
+            3. Runtimes (ms): The operator runtime in milliseconds.
+            4. Memory (B): The operator memory usage in bytes.
+            5. View-like: A flag indicating whether the operator is view-like.
+            6. Random: A flag indicating whether the operator is random.
+            7. Saved Autograd: A flag indicating whether the operator's result is saved by autograd engine.
+            8. In-place: The index of the operator's first parent, or None if not in-place.
+
+        If print_tabular is True, the table is printed in a tabular format.
+        Otherwise, the table is printed in a plain text format.
+        """
+        print(
+            f"Total Memory: {sum(sac_stats.memory)} B Total Runtime: {sum(sac_stats.runtimes)} ms"
+            f" Store Random: {sac_stats.force_store_random}"
+        )
+        table_data = []
+        op_parent = dict(sac_stats.inplace_ops)
+        for i, fn_name in enumerate(sac_stats.func_names):
+            row = [
+                str(i),
+                fn_name,
+                f"{sac_stats.runtimes[i]:.4f}",
+                str(sac_stats.memory[i]),
+                str(i in sac_stats.view_like_ops),
+                str(i in sac_stats.rand_ops),
+                str(i in sac_stats.saved_autograd_ops),
+                str(op_parent.get(i)),
+            ]
+            table_data.append(row)
+        # Define headers
+        headers = [
+            "Op Idx",
+            "Op Name",
+            "Runtimes(ms)",
+            "Memory (B)",
+            "View-like",
+            "Random",
+            "Saved Autograd",
+            "In-place",
+        ]
+        if print_tabular:
+            _display_stats_tabular(headers, table_data)
+        else:
+            max_widths = [0 for _ in range(len(headers))]
+            table_data.insert(0, headers)
+            for row in table_data:
+                for i, elem in enumerate(row):
+                    max_widths[i] = max(max_widths[i], len(elem))
+            for row in table_data:
+                print(
+                    "\t".join(
+                        [f"{elem:<{max_widths[i]}}" for i, elem in enumerate(row)]
+                    )
+                )
+
+    def display_sac_tradeoff_stats(
+        self,
+        greedy_order_meta: SACGreedyOrderMeta,
+        sac_stats: SACStats,
+        print_tabular: bool = False,
+    ) -> None:
+        """
+        Displays the SAC trade-off statistics.
+
+        Args:
+            greedy_order_meta (SACGreedyOrderMeta): The SAC greedy order metadata.
+            sac_stats (SACStats): The SAC statistics.
+            print_tabular (bool, optional): Whether to print the statistics in a tabular format. Defaults to False.
+
+        Prints:
+            A table with the following columns:
+            1. Op Id(s): The operator index(es).
+            2. Op Name(s): The operator name(s).
+            3. Discarded Mem (%): The percentage of discarded memory.
+            4. Discarded Mem (B): The discarded memory in bytes.
+            5. Recomp time (%): The percentage of recomputed time.
+            6. Recomp time (ms): The recomputed time in milliseconds.
+            7. MSPS: The memory per second.
+            8. Always Stored: A flag indicating whether the operator is always stored.
+            9. Always Recomputed: A flag indicating whether the operator is always recomputed.
+
+        If print_tabular is True, the table is printed in a tabular format.
+        Otherwise, the table is printed in a plain text format.
+        """
+        table_data = []
+        total_memory, total_runtime = sum(sac_stats.memory), sum(sac_stats.runtimes)
+        discarded_mem: int = 0
+        recomp_runtime: float = 0.0
+
+        def append_row(
+            op_indices: set[int],
+            func_names: set[str],
+            msps: float | None = None,
+            stored: bool | None = False,
+            recomputed: bool | None = False,
+        ) -> None:
+            row = [
+                str(op_indices),
+                str(func_names),
+                f"{discarded_mem / total_memory:.4f}",
+                str(discarded_mem),
+                f"{recomp_runtime / total_runtime:.4f}",
+                str(recomp_runtime),
+                f"{msps:.2e}" if msps is not None else str(nan),
+                str(stored),
+                str(recomputed),
+            ]
+            table_data.append(row)
+
+        stored_ops, recomputed_ops, inplace_op_groups, random_ops_group, msps_meta = (
+            greedy_order_meta.stored_ops,
+            greedy_order_meta.recomputed_ops,
+            greedy_order_meta.inplace_op_groups,
+            greedy_order_meta.random_ops_group,
+            greedy_order_meta.msps_meta,
+        )
+
+        for op_idx in recomputed_ops:
+            op_indices: set[int] = {op_idx}
+            if op_idx in inplace_op_groups:
+                op_indices.update(inplace_op_groups[op_idx])
+            if op_idx in random_ops_group:
+                op_indices.update(random_ops_group[op_idx])
+            discarded_mem += sum(sac_stats.memory[i] for i in op_indices)
+            recomp_runtime += sum(sac_stats.runtimes[i] for i in op_indices)
+            func_names = {sac_stats.func_names[i] for i in op_indices}
+            append_row(op_indices, func_names, recomputed=True)
+
+        for cand in msps_meta:
+            discarded_mem += cand.memory
+            recomp_runtime += cand.runtime
+            op_indices = {cand.op_idx}
+            if cand.op_idx in inplace_op_groups:
+                op_indices.update(inplace_op_groups[cand.op_idx])
+            if cand.op_idx in random_ops_group:
+                op_indices.update(random_ops_group[cand.op_idx])
+            append_row(op_indices, cand.func_names, msps=cand.msps)
+
+        for op_idx in stored_ops:
+            op_indices = {op_idx}
+            if op_idx in inplace_op_groups:
+                op_indices.update(inplace_op_groups[op_idx])
+            if op_idx in random_ops_group:
+                op_indices.update(random_ops_group[op_idx])
+            discarded_mem += sum(sac_stats.memory[i] for i in op_indices)
+            recomp_runtime += sum(sac_stats.runtimes[i] for i in op_indices)
+            func_names = {sac_stats.func_names[i] for i in op_indices}
+            append_row(op_indices, func_names, stored=True)
+
+        headers = [
+            "Op Id(s)",
+            "Op Name(s)",
+            "Discarded Mem (%)",
+            "Discarded Mem (B)",
+            "Recomp time (%)",
+            "Recomp time (ms)",
+            "MSPS",
+            "Always Stored",
+            "Always Recomputed",
+        ]
+        if print_tabular:
+            _display_stats_tabular(headers, table_data)
+        else:
+            max_widths = [0 for _ in range(len(headers))]
+            table_data.insert(0, headers)
+            for row in table_data:
+                for i, elem in enumerate(row):
+                    max_widths[i] = max(max_widths[i], len(elem))
+            for row in table_data:
+                print(
+                    "\t".join(
+                        [f"{elem:<{max_widths[i]}}" for i, elem in enumerate(row)]
+                    )
+                )
+
+    def pwlf_sac_tradeoff_curve(
+        self,
+        n_segments: int = 2,
+        save_tradeoff_graphs: bool = False,
+    ) -> None:
+        """
+        Fits a piecewise linear function with the specified sumber of segments to the SAC trade-off curve of
+        discarded memory vs recomputation time.
+
+        Args:
+            n_segments (int, optional): The number of segments to be used for fitting the piecewise linear function to
+                the trade-off curve. Defaults to 2.
+            save_tradeoff_graphs (bool, optional): Whether to save the trade-off graphs to file. Defaults to False.
+
+        If save_tradeoff_graphs is True, the trade-off graphs are saved to file using the module FQN as the filename.
+        """
+        for mod_fqn, sac_stats in self.sac_mod_stats.items():
+            self.sac_mod_tradeoff_stats[mod_fqn] = self._get_sac_tradeoff_pwlf_stats(
+                sac_stats=sac_stats,
+                greedy_order_meta=self.sac_mod_greedy_order_meta[mod_fqn],
+                n_segments=n_segments,
+                save_tradeoff_graph=save_tradeoff_graphs,
+                filename=mod_fqn,
+            )
+
+    def display_modulewise_sac_stats(
+        self, depth: int = 2, print_tabular: bool = False
+    ) -> None:
+        """
+        Displays the SAC and trade-off statistics for each module.
+
+        Args:
+            depth (int, optional): The maximum depth of modules to display. Defaults to 2.
+            print_tabular (bool, optional): Whether to print the statistics in a tabular format. Defaults to False.
+
+        Prints:
+            For each module with depth less than or equal to the specified depth:
+            1. The SAC statistics for the module (using display_sac_stats).
+            2. The SAC trade-off statistics for the module (using display_sac_tradeoff_stats).
+
+        If print_tabular is True, the statistics are printed in a tabular format.
+        Otherwise, the statistics are printed in a plain text format.
+        """
+        for mod_fqn, sac_stats in self.sac_mod_stats.items():
+            mod_depth = mod_fqn.count(".") + 1
+            if mod_depth > depth:
+                continue
+            print(f"Module: {mod_fqn}")
+            self.display_sac_stats(sac_stats, print_tabular)
+            print(f"AC Trade-off for Module: {mod_fqn} MSPS = Memory/Runtime")
+            self.display_sac_tradeoff_stats(
+                self.sac_mod_greedy_order_meta[mod_fqn], sac_stats, print_tabular
+            )
+
+    def __call__(self, estimate_mode_type: str) -> Self:
+        """
+        Sets the estimate mode type.
+
+        Currently supported modes:
+            - "operator-level-benchmark": Estimates runtime using operator benchmarking.
+            - "operator-level-cost-model": Estimates runtime using roofline cost model.
+
+        Args:
+            estimate_mode_type (str): The type of estimate mode to use.
+
+        Returns:
+            SACEstimator: The SAC estimator instance.
+
+        Raises:
+            NotImplementedError: If the estimate mode type is not supported.
+        """
+        if estimate_mode_type == "operator-level-benchmark":
+            self._estimate_runtime = RuntimeEstimator._benchmark_estimate
+        elif estimate_mode_type == "operator-level-cost-model":
+            self._estimate_runtime = RuntimeEstimator._roofline_estimate
+        else:
+            raise NotImplementedError(
+                f"estimate_mode_type {estimate_mode_type} not supported"
+            )
+        return self
+
+    def __enter__(self) -> Self:  # type: ignore[no-untyped-def]
+        fake_mode = active_fake_mode()
+        assert isinstance(fake_mode, FakeTensorMode), (
+            "SAC Estimator should be called in FakeTensorMode"
+        )
+        RuntimeEstimator.fake_mode = fake_mode
+        self._mod_tracker.register_user_hooks(
+            pre_fw_hook=self._pre_fw_hook,
+            post_fw_hook=self._post_fw_hook,
+        )
+        self._mod_tracker.__enter__()
+        self._saved_tensor_hook_ctx.__enter__()
+        return super().__enter__()
+
+    def __exit__(self, *args: Any) -> None:  # type: ignore[no-untyped-def]
+        self._saved_tensor_hook_ctx.__exit__()
+        self._mod_tracker.__exit__(*args)
+        super().__exit__(*args)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tools/sac_ilp.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tools/sac_ilp.py
new file mode 100644
index 0000000000000000000000000000000000000000..8799493f260a5967c8086aa3d24e64132cc4102d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/_tools/sac_ilp.py
@@ -0,0 +1,294 @@
+import logging
+import math
+from enum import IntEnum
+
+from torch.distributed._tools.ilp_utils import Graph, is_submodule
+from torch.distributed._tools.sac_estimator import SACStats
+
+
+try:
+    from pulp import (  # type: ignore[import-untyped,import-not-found]
+        lpDot,
+        LpInteger,
+        LpMaximize,
+        LpMinimize,
+        LpProblem,
+        LpStatus,
+        lpSum,
+        LpVariable,
+        PULP_CBC_CMD,
+        value,
+    )
+except ImportError as err:
+    raise ImportError(
+        "Please install pulp package. See: https://github.com/coin-or/pulp."
+    ) from err
+
+# Create a logger object
+logger = logging.getLogger(__name__)
+
+# Set the logging level to INFO
+logger.setLevel(logging.INFO)
+
+
+def sac_milp(
+    graph: Graph,
+    memory_budget: float,
+    world_size: int = 1,
+    ac_units: list[str] | None = None,
+    fsdp_units: list[str] | None = None,
+) -> tuple[dict[str, float], float, int]:
+    """
+    MILP to decide which modules to AC and how much memory to discard.
+    The objective is to minimize recomputation time.
+    The constraint is to ensure peak memory is under budget.
+
+    Args:
+        graph: graph representation of the model as a module submodule tree
+            where each node is a submodule with memory & runtime stats
+        memory_budget: memory budget in GiB
+        world_size: number of GPUs. In the case of FSDP, world_size will be
+            used to compute the amount of parameter and gradient memory on each rank
+        ac_units: a list of user-specified AC units.
+        fsdp_units: a list of FSDP units. AC units cannot be supermodules of FSDP units.
+
+    Returns:
+        Dict[str, float]: the optimal SAC solution, mapping from module fqn to
+            the percentage of activation memory to **discard**
+        float: the recomputation time of the optimal SAC solution
+        int: upper bound on the peak memory of the optimal SAC solution.
+            note that value of -1 means that the ILP solver failed to find a solution.
+
+    """
+    num_nodes = len(graph.nodes)
+    M = 10**2  # note: numerical issue may occur if M is too big
+    MEM_MULTIPLIER = 2**30
+
+    # Create a MILP problem
+    prob = LpProblem("SAC", LpMinimize)
+
+    # Create decision variables
+    # y_i: indicator for if module i is AC'ed
+    y = LpVariable.matrix("y", list(range(num_nodes)), 0, 1, LpInteger)
+    # r_i: percentage of discarded activation memory
+    r = LpVariable.matrix("r", list(range(num_nodes)), 0, 1)
+    # d_i: discarded activation memory for module i
+    d = LpVariable.matrix("d", list(range(num_nodes)), 0)
+    # a_i: total activation memory at module i
+    a = LpVariable.matrix("a", list(range(num_nodes)), 0)
+    # m_i: memory at module i, combining parameters, gradients, and activations
+    m = LpVariable.matrix("m", list(range(num_nodes)), 0)
+    # rcp_i: percentage of recomputation time
+    rcp = LpVariable.matrix("rcp", list(range(num_nodes)), 0)
+    # rct_i: recomputation time for module i (in ms)
+    rct = LpVariable.matrix("rct", list(range(num_nodes)), 0)
+    # max_m: peak memory
+    max_m = LpVariable("max_m", 0)
+
+    # Add constraints
+    # [Constraint] User specified AC units
+    if ac_units:
+        ac_units_set = set(ac_units)
+        for i in range(num_nodes):
+            if graph.nodes[i]["fqn"] not in ac_units_set:
+                prob += y[i] == 0
+
+    # [Constraint] AC units cannot be supmodules of user specified FSDP units
+    if fsdp_units:
+        for i in range(num_nodes):
+            if any(
+                is_submodule(fsdp_unit, graph.nodes[i]["fqn"])
+                for fsdp_unit in fsdp_units
+            ):
+                prob += y[i] == 0
+
+    # [Constraint] No nested AC units
+    for i in range(num_nodes):
+        for j in range(i + 1, num_nodes):
+            if graph.ad_matrix[i][j] == 1:
+                prob += y[i] + y[j] <= 1
+
+    # [Constraint] Do not AC leaf modules
+    for i in range(num_nodes):
+        if graph.nodes[i]["is_leaf"]:
+            prob += y[i] == 0
+
+    # [Constraint] Express amount of discarded activation memory
+    for i in range(num_nodes):
+        # There are two measures for activation memory: ACM and IA
+        # 1. IA is the activation memory saved when not using AC
+        # 2. ACM is the total activation memory, including those
+        #    that are not typically saved when not using AC
+        # Note: ACM >= IA
+        if (not graph.nodes[i]["is_leaf"]) and graph.nodes[i][
+            "sac_memory"
+        ] < graph.nodes[i]["act_fw_per_module"]:
+            logger.warning("For module {%s}: ", graph.nodes[i]["fqn"])
+            logger.warning(
+                "activation memory from memory tracker is {%d},",
+                graph.nodes[i]["act_fw_per_module"],
+            )
+            logger.warning(
+                "activation memory from SAC estimator is {%d}.",
+                graph.nodes[i]["sac_memory"],
+            )
+            logger.warning("Something is wrong. Please check!")
+            logger.warning("Overriding the latter with the former.")
+            graph.nodes[i]["sac_memory"] = graph.nodes[i]["act_fw_per_module"]
+        ACM_i = graph.nodes[i]["sac_memory"] / MEM_MULTIPLIER
+        IA_i = graph.nodes[i]["act_fw_per_module"] / MEM_MULTIPLIER
+        prob += d[i] == ACM_i * r[i] - (ACM_i - IA_i) * y[i]
+
+    # [Constraint] Ensure correctness of r_i
+    # There are two parts to its correctness
+    # 1. r_i > 0 only if y_i == 1 (discard only if it is an AC unit)
+    # 2. r_i needs to be large enough to cover the difference between
+    #    ACM and IA. Otherwise, we are not saving any memory
+    for i in range(num_nodes):
+        prob += y[i] >= r[i]
+        if graph.nodes[i]["is_leaf"]:
+            continue
+        ACM_i = graph.nodes[i]["sac_memory"] / MEM_MULTIPLIER
+        IA_i = graph.nodes[i]["act_fw_per_module"] / MEM_MULTIPLIER
+        prob += r[i] >= (ACM_i - IA_i) / ACM_i * y[i]
+
+    # [Constraint] Express total activation memory in the backward pass
+    for i in range(num_nodes):
+        AG_i = graph.nodes[i]["act_grad_per_module"] / MEM_MULTIPLIER
+        TA_i = graph.nodes[i]["act_total"] / MEM_MULTIPLIER
+        # related to discarded amount of memory
+        pos = graph.nodes[i]["pos_fw_post_order"]
+        coeff = [0] * num_nodes
+        for p in range(pos):
+            j = graph.name2node[graph.fw_post_order[p]]["index"]
+            coeff[j] = 1
+        prob += a[i] == TA_i + AG_i - lpDot(coeff, d)
+
+    # [Constraint] Express the total amount of memory at each module
+    # Note that unsharded parameters and gradients are not included here
+    P_1 = graph.nodes[0]["param_per_module"] / MEM_MULTIPLIER
+    for i in range(num_nodes):
+        TG_i = graph.nodes[i]["grad_total"] / MEM_MULTIPLIER
+        prob += m[i] == a[i] + (P_1 + TG_i) / world_size
+
+    # [Constraint] Express peak memory
+    for i in range(num_nodes):
+        prob += max_m >= m[i]
+
+    # [Constraint] Express percentage of recomputation time
+    for i in range(num_nodes):
+        for s in range(graph.nodes[i]["n_segments"]):
+            slope = graph.nodes[i]["slopes"][s]
+            intercept = graph.nodes[i]["intercepts"][s]
+            prob += rcp[i] >= slope * r[i] + intercept
+
+    # [Constraint] Express recomputation time
+    # rct_i = (rcp_i * ACT_i) if y_i == 1 else 0
+    for i in range(num_nodes):
+        ACT_i = graph.nodes[i]["sac_runtime"]
+        prob += rct[i] <= M * y[i]
+        prob += rct[i] <= ACT_i * rcp[i]
+        prob += rct[i] >= ACT_i * rcp[i] - M * (1 - y[i])
+
+    # [Constraint] Peak memory should be below budget
+    prob += max_m <= memory_budget
+
+    # Set Objeictive
+    prob += lpSum(rct)
+
+    # Solve
+    solver = PULP_CBC_CMD(gapRel=0.05, timeLimit=180, msg=0)
+    status = prob.solve(solver)
+
+    # If solver fails, print status and return empty solution
+    if status != 1:
+        logger.error("Solver failed to find a solution: %s", LpStatus[status])
+        return {}, 0, -1
+
+    # Gather and return solution if optimal solution is found
+    ac_decisions = {}
+    for i in range(num_nodes):
+        if round(y[i].varValue) == 1:
+            ac_decisions[graph.nodes[i]["fqn"]] = round(r[i].varValue, 4)
+    recomputation_time = round(value(prob.objective), 2)
+    peak_mem = round(max_m.varValue * MEM_MULTIPLIER)
+
+    return ac_decisions, recomputation_time, peak_mem
+
+
+class SACDecision(IntEnum):
+    RECOMPUTE = 0
+    SAVE = 1
+
+
+def get_optimal_checkpointing_policy_per_module(
+    sac_stats: SACStats, memory_budget: float
+) -> list[int]:
+    """
+    This is adapted from --
+    https://github.com/facebookresearch/xformers/blob/c6c0ac31f1b08542a0bc27278c6ed10f825f6963/xformers/checkpoint.py#L375
+
+    Given the SACStats of a module, including list of operators, their memory, runtimes, and metadata,
+    decide via MILP an optimal set of operators to checkpoint under a given ``memory_budget``.
+
+    Args:
+        sac_stats: the SACStats object of the module
+        memory_budget: a float between zero and one
+
+    Returns:
+        List[int]: the decision whether each operator should be saved (1) or recomptued (0).
+    """
+    if not (0 <= memory_budget <= 1):
+        raise ValueError(
+            f"`memory_budget` must be a float between 0 and 1. Got {memory_budget}."
+        )
+    num_ops = len(sac_stats.func_names)
+
+    # Create a MILP problem
+    prob = LpProblem("SAC-per-module", LpMaximize)
+
+    # Create decision variables
+    # x[i] = 1 means the i-th operator should be saved, otherwise it should be recomputed
+    x = LpVariable.matrix("x", list(range(num_ops)), 0, 1, LpInteger)
+
+    # Add constraints
+    # [Constraint] random ops should be saved if ``force_store_random`` is True
+    # otherwise, random ops should either be all recomputed or all saved
+    if sac_stats.force_store_random:
+        for i in sac_stats.rand_ops:
+            prob += x[i] == SACDecision.SAVE.value
+    else:
+        for i1, i2 in zip(sac_stats.rand_ops[:-1], sac_stats.rand_ops[1:]):
+            prob += x[i1] == x[i2]
+
+    # [Constraint] view-like ops should always be recomputed
+    for i in sac_stats.view_like_ops:
+        prob += x[i] == SACDecision.RECOMPUTE.value
+
+    # [Constraint] inplace ops should always be done in conjunction with its parent op
+    for op, op_parent in sac_stats.inplace_ops:
+        if op != op_parent:
+            prob += x[op] == x[op_parent]
+        else:
+            prob += x[op] == SACDecision.SAVE.value
+
+    # [Constraint] saved memory should be under the ``memory_budget``
+    max_memory = math.ceil(memory_budget * sum(sac_stats.memory))
+    prob += lpDot(x, sac_stats.memory) <= max_memory
+
+    # [Objective] minimize recomputation time, note the ILP is a maximization problem
+    # because x[i] == 1 means the op is saved (not recomputed), and thus recomputation
+    # time is sum(sac_stats.runtimes) - lpDot(x, sac_stats.runtimes)
+    prob += lpDot(x, sac_stats.runtimes)
+
+    # Solve
+    solver = PULP_CBC_CMD(gapRel=0.05, timeLimit=10, msg=0)
+    status = prob.solve(solver)
+
+    # If solver fails, print status and return empty solution
+    if status != 1:
+        logger.error("Solver failed to find a solution: %s", LpStatus[status])
+        return []
+
+    # Gather and return solution if optimal solution is found
+    return [round(x[i].varValue) for i in range(num_ops)]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..06c814295699405de9a8f8cf7f6a861b07b63a05
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/__init__.py
@@ -0,0 +1 @@
+from .join import Join, Joinable, JoinHook
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/_checkpoint/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/_checkpoint/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/_checkpoint/checkpoint_wrapper.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/_checkpoint/checkpoint_wrapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..081d397a9c1f11e332f95649d362e1f3c27abe8a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/_checkpoint/checkpoint_wrapper.py
@@ -0,0 +1,321 @@
+# mypy: allow-untyped-defs
+import warnings
+from abc import ABC, abstractmethod
+from collections.abc import Callable, Iterator
+from enum import auto, Enum
+from functools import partial
+from typing import Any
+
+import torch
+import torch.nn as nn
+from torch.autograd.graph import save_on_cpu
+from torch.distributed.utils import _pack_kwargs, _replace_by_prefix, _unpack_kwargs
+from torch.utils.checkpoint import checkpoint as torch_utils_checkpoint
+
+
+_CHECKPOINT_WRAPPED_MODULE = "_checkpoint_wrapped_module"
+_CHECKPOINT_PREFIX = _CHECKPOINT_WRAPPED_MODULE + "."
+
+
+class CheckpointImpl(Enum):
+    REENTRANT = auto()
+    NO_REENTRANT = auto()
+
+
+class ActivationWrapper(torch.nn.Module, ABC):
+    """
+    Base class for Activation Checkpoint and Activation Offload.
+
+    Not meant to be instantiated directly.
+    """
+
+    def __init__(self, mod):
+        super().__init__()
+        self._checkpoint_wrapped_module = mod
+        # state_dict post hook to remove prefix to allow loading into a
+        # non-checkpoint wrapped module.
+        self._register_state_dict_hook(self._post_state_dict_hook)
+        # load_state_dict pre-hook to allow loading back into
+        # checkpoint-wrapped module.
+        self.register_load_state_dict_pre_hook(self._pre_load_state_dict_hook)
+
+    @abstractmethod
+    def forward(self, *args, **kwargs):
+        raise ValueError("Subclasses should implement forward().")
+
+    def __getattr__(self, name: str) -> Any:
+        """Forward missing attributes to wrapped module."""
+        try:
+            return super().__getattr__(name)  # defer to nn.Module's logic
+        except AttributeError:
+            return getattr(self._checkpoint_wrapped_module, name)
+
+    def __getitem__(self, key: int) -> Any:
+        """Forward indexing calls in case the module is a nn.Sequential."""
+        return self._checkpoint_wrapped_module.__getitem__(key)  # type: ignore[operator]
+
+    def named_parameters(
+        self,
+        *args,
+        **kwargs,
+    ) -> Iterator[tuple[str, torch.nn.Parameter]]:
+        """
+        Override :meth:`named_parameters()` to intercept parameter names.
+
+        remove all occurrences of ``_CHECKPOINT_PREFIX``.
+        """
+        for param_name, param in super().named_parameters(*args, **kwargs):
+            yield param_name.replace(_CHECKPOINT_PREFIX, ""), param
+
+    @staticmethod
+    def _post_state_dict_hook(
+        module: nn.Module,
+        state_dict: dict[str, Any],
+        prefix: str,
+        *args: Any,
+    ) -> dict[str, Any]:
+        """
+        _post_state_dict_hook() is called after the state_dict() of this FSDP module is executed.
+
+        For ``checkpoint_wrapper``, it will strip checkpoint-wrapped module prefix,
+        so that this module can be loaded into non-checkpointed modules.
+        It would still be able to be loaded into checkpoint-wrapped modules as this class,
+        adds the prefix back before loading the state_dict.
+        """
+        _replace_by_prefix(state_dict, f"{prefix}{_CHECKPOINT_PREFIX}", prefix)
+        return state_dict
+
+    @staticmethod
+    def _pre_load_state_dict_hook(
+        module: nn.Module,
+        state_dict: dict[str, Any],
+        prefix: str,
+        *args: Any,
+    ) -> None:
+        """
+        ``_pre_state_dict_hook` is called before ``self._load_from_state_dict()`` is called.
+
+        For ``checkpoint_wrapper``, it will add back the module
+        prefix so that non-checkpointed modules can be loaded into
+        checkpoint_wrapper modules properly.
+        """
+        _replace_by_prefix(state_dict, prefix, prefix + f"{_CHECKPOINT_PREFIX}")
+
+
+class OffloadWrapper(ActivationWrapper):
+    def forward(self, *args, **kwargs):
+        with save_on_cpu(pin_memory=True):
+            return self._checkpoint_wrapped_module(*args, **kwargs)
+
+
+class CheckpointWrapper(ActivationWrapper):
+    """
+    An ``nn.Module`` that wraps another ``nn.Module`` with checkpointing.
+
+    Note that this module is not meant to be used directly but instead,
+    it is to be used through the ``checkpoint_wrapper`` function.
+    """
+
+    def __init__(
+        self,
+        mod: torch.nn.Module,
+        checkpoint_impl: CheckpointImpl = CheckpointImpl.NO_REENTRANT,
+        checkpoint_fn=None,
+        **checkpoint_fn_kwargs,
+    ):
+        super().__init__(mod)
+        self.checkpoint_impl = checkpoint_impl
+        if checkpoint_fn is None:
+            # use torch.utils.checkpoint
+            self.checkpoint_fn = partial(
+                torch_utils_checkpoint,
+                use_reentrant=(self.checkpoint_impl == CheckpointImpl.REENTRANT),
+                **checkpoint_fn_kwargs,
+            )
+        else:
+            # Construct user-specified checkpoint function.
+            self.checkpoint_fn = partial(
+                checkpoint_fn,
+                **checkpoint_fn_kwargs,
+            )
+
+    def forward(self, *args, **kwargs):
+        # Support keyword arguments for reentrant checkpoint. Note that this
+        # only works if user has specified self.checkpoint_impl and is not
+        # using their own custom checkpoint_fn.
+        if self.checkpoint_impl == CheckpointImpl.REENTRANT and kwargs != {}:
+            # Pack the args and kwargs
+            flat_args, kwarg_keys = _pack_kwargs(*args, **kwargs)
+
+            # Function that only takes (packed) args, but can unpack them
+            # into the original args and kwargs for the checkpointed
+            # function, and runs that function.
+            def my_function(*inputs):
+                # unpack back into args and kwargs
+                unpacked_args, unpacked_kwargs = _unpack_kwargs(inputs, kwarg_keys)
+                # run original module
+                return self._checkpoint_wrapped_module(
+                    *unpacked_args, **unpacked_kwargs
+                )
+
+            # Pass the function that only takes packed args into reentrant
+            # checkpoint API.
+            return self.checkpoint_fn(  # type: ignore[misc]
+                my_function,
+                *flat_args,
+            )
+        else:
+            return self.checkpoint_fn(  # type: ignore[misc]
+                self._checkpoint_wrapped_module, *args, **kwargs
+            )
+
+
+def offload_wrapper(module: torch.nn.Module) -> torch.nn.Module:
+    """
+    Wrap a module for activation offloading to CPU.
+
+    Offloads intermediate activations to the CPU for modules wrapped with this function.
+    Wrappers with activation offload can be composed with ones that do recomputation-based
+    checkpoint to trade off increased compute versus increased CPU
+    memory usage and additional H2D transfers.
+
+    Usage::
+        offloaded_module = offload_wrapper(module)
+        outputs = checkpointed_module(inputs)
+    Args:
+        module (nn.Module):
+            The module to be wrapped
+    Returns:
+        (nn.Module):
+            Wrapped module
+    """
+    return OffloadWrapper(module)
+
+
+def checkpoint_wrapper(
+    module: torch.nn.Module,
+    checkpoint_impl: CheckpointImpl = CheckpointImpl.NO_REENTRANT,
+    checkpoint_fn=None,
+    **checkpoint_fn_kwargs,
+) -> torch.nn.Module:
+    """
+    Wrap a module for activation checkpointing.
+
+    If the module is wrapped with this function, all subsequent calls to the module will,
+    automatically perform checkpointing without the user having to explicitly call ``checkpoint`` function.
+
+    Usage::
+        checkpointed_module = checkpoint_wrapper(module)
+        outputs = checkpointed_module(inputs)
+    Args:
+        module (nn.Module):
+            The module to be wrapped
+        checkpoint_impl (Optional[CheckpointImpl]):
+            The checkpointing implementation to use. Note that this will only
+            be passed into the ``torch.utils.checkpoint.checkpoint``
+            implementation, and is ignored if a custom ``checkpoint_fn`` is
+            specified. Note that for implementations using reentrant checkpoint
+            from ``torch.utils.checkpoint``, keyword arguments will only be
+            supported if ``checkpoint_impl`` is passed as ``CheckpointImpl.REENTRANT`.
+        checkpoint_fn (Optional[Callable]):
+            Functional checkpoint implementation to use. If this is specified,
+            it will be used over the default ``torch.utils.checkpoint.checkpoint``
+            implementation and the `checkpoint_impl` argument will be ignored.
+        **checkpoint_fn_kwargs: (Dict[str, Any]): Keyword arguments to pass into `checkpoint_fn`.
+
+    Returns:
+        (nn.Module):
+            Wrapped module
+    """
+
+    if checkpoint_impl == CheckpointImpl.REENTRANT:
+        warnings.warn(
+            f"Please specify {CheckpointImpl.NO_REENTRANT} as "
+            f"{CheckpointImpl.REENTRANT} will soon be removed as "
+            "the default and eventually deprecated.",
+            FutureWarning,
+            stacklevel=2,
+        )
+    return CheckpointWrapper(
+        module,
+        checkpoint_impl,
+        checkpoint_fn,
+        **checkpoint_fn_kwargs,
+    )
+
+
+def apply_activation_checkpointing(
+    model,
+    checkpoint_wrapper_fn=checkpoint_wrapper,
+    check_fn=lambda _: True,
+    auto_wrap_policy: Callable[[nn.Module, bool, int], bool] | None = None,
+):
+    """
+    Apply :func:`checkpoint_wrapper` to modules within `model` based on a user-defined configuration.
+
+    For each module within `model`, the `check_fn` is used to decide
+    whether `module` should be wrapped with :func:`checkpoint_wrapper` or not.
+
+    Note::
+        This function modifies `model` in place and replaces appropriate layers with
+        their checkpoint-wrapped modules.
+    Note::
+        This function will not wrap the overall root module. If this is needed, please directly use
+        :func:`checkpoint_wrapper` or :func:`offload_wrapper`.
+    Usage::
+        model = nn.Sequential(
+            nn.Linear(10, 10), nn.Linear(10, 10), nn.Linear(10, 10)
+        )
+        check_fn = lambda l: isinstance(l, nn.Linear)
+        # checkpoint activations
+        apply_activation_checkpointing(model, checkpoint_wrapper_fn=checkpoint_wrapper, check_fn=check_fn)
+        # Or offload activations to CPU
+        apply_activation_checkpointing(model, checkpoint_wrapper_fn=offload_wrapper, check_fn=check_fn)
+    Args:
+        model (nn.Module):
+            The model whose submodules should be wrapped with activation checkpointing.
+        checkpoint_wrapper_fn (Optional[Callable[nn.Module]])
+            A ``Callable`` which will wrap modules
+        check_fn (Optional[Callable[nn.Module, nn.Module]])
+            A lambda function which will be passed each child submodule of ``model`` and returns
+            ``True`` or ``False`` depending on whether the submodule should be wrapped.
+        auto_wrap_policy (Optional[Callable[[nn.Module, bool, int], bool]]): A policy to wrap model's
+            submodules with AC. Note that if this is specified, it takes precedence over ``check_fn``.
+    Returns: None (`model` is modified inplace)
+    """
+    # TODO: Importing inside function to avoid circular import issue between FSDP and
+    # checkpoint_wrapper. This can be resolved once wrap() APIs are decoupled from FSDP code.
+    from torch.distributed.fsdp._wrap_utils import _construct_wrap_fn, _post_order_apply
+    from torch.distributed.fsdp.wrap import (
+        _Policy,
+        _recursive_wrap,
+        lambda_auto_wrap_policy,
+    )
+
+    policy = (
+        auto_wrap_policy
+        if auto_wrap_policy is not None
+        else partial(lambda_auto_wrap_policy, lambda_fn=check_fn)
+    )
+    if not callable(policy):
+        if not isinstance(policy, _Policy):
+            raise ValueError(
+                f"Expected {policy} to be callable or be a pre-defined wrap policy"
+            )
+        target_module_to_kwargs = policy._run_policy(
+            model, ignored_modules=set(), root_kwargs={}
+        )
+        wrap_fn = _construct_wrap_fn(
+            model, target_module_to_kwargs, checkpoint_wrapper_fn
+        )
+        _post_order_apply(model, wrap_fn)
+        return
+
+    _recursive_wrap(
+        module=model,
+        auto_wrap_policy=policy,  # type: ignore[arg-type]
+        wrapper_cls=checkpoint_wrapper_fn,
+        ignored_modules=set(),
+        ignored_params=set(),
+        only_wrap_children=True,
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/_comm_hooks/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/_comm_hooks/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7b57a075ad729d0ae3004dc15585250b04810f43
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/_comm_hooks/__init__.py
@@ -0,0 +1,7 @@
+from . import default_hooks as default
+
+
+LOW_PRECISION_HOOKS = [
+    default.fp16_compress_hook,
+    default.bf16_compress_hook,
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/_comm_hooks/default_hooks.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/_comm_hooks/default_hooks.py
new file mode 100644
index 0000000000000000000000000000000000000000..76cd01c2265b1d7e5739d79b406cb94a0b0a9893
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/_comm_hooks/default_hooks.py
@@ -0,0 +1,191 @@
+# mypy: allow-untyped-defs
+import functools
+
+import torch
+import torch.distributed as dist
+
+
+class DefaultState:
+    r"""
+    Stores state needed to perform the default communication algorithm within a communication hook.
+
+    Args:
+        process_group (ProcessGroup): The process group to be used.
+    """
+
+    __slots__ = [
+        "process_group",
+        "world_size",
+        "gradient_predivide_factor",
+        "gradient_postdivide_factor",
+    ]
+
+    def __init__(self, process_group: dist.ProcessGroup):
+        if process_group is None:
+            raise ValueError(f"Expected to pass in an explicit ProcessGroup to {self}.")
+        self.process_group = process_group
+        self.world_size = dist.get_world_size(process_group)
+        # Setting two factors `self.gradient_predivide_factor`
+        # and `self.gradient_postdivide_factor` to avoid underflow and overflow
+        self.gradient_predivide_factor = self._get_gradient_predivide_factor(
+            self.world_size
+        )
+        self.gradient_postdivide_factor = (
+            self.world_size / self.gradient_predivide_factor
+        )
+
+    @staticmethod
+    def _get_gradient_predivide_factor(world_size: int) -> float:
+        factor: int = 1
+        while world_size % factor == 0 and world_size / factor > factor:
+            factor *= 2
+        return float(factor)
+
+
+class LowPrecisionState(DefaultState):
+    r"""
+    Stores state needed to perform gradient communication in a lower precision within a communication hook.
+
+    Communication hook will cast gradients back to the original
+    parameter precision specified by ``parameter_type`` (default: torch.float32).
+    Builds on top of the :class:`DefaultState`.
+
+    Args:
+        parameter_type (torch.dtype): The precision of model's parameters.
+        Required for a hook to cast gradients back to a parameter's precision.
+    """
+
+    __slots__ = [
+        "parameter_type",
+    ]
+
+    def __init__(
+        self,
+        process_group,
+        parameter_type=torch.float32,
+    ):
+        super().__init__(process_group)
+        self.parameter_type = parameter_type
+
+
+def _decompress(state: LowPrecisionState, grad: torch.Tensor):
+    """
+    Casts gradients back to full parameter precision so that further computation happens in full precision.
+    """
+    orig_grad_data = grad.data
+    grad.data = grad.data.to(state.parameter_type)
+    device_type = ""
+    try:
+        if grad.device.type == "privateuse1":
+            device_type = torch._C._get_privateuse1_backend_name()
+        else:
+            device_type = grad.device.type
+        backend = getattr(torch, device_type)
+    except AttributeError as e:
+        raise AttributeError(
+            f"Device {grad.device}  does not have a \
+                corresponding backend registered as 'torch.device_type'."
+        ) from e
+
+    # Don't let this memory get reused until after the transfer.
+    orig_grad_data.record_stream(backend.current_stream())  # type: ignore[arg-type]
+
+
+def allreduce_hook(state: DefaultState, grad: torch.Tensor):
+    r"""
+    Implement the  FSDP communication hook for ``all_reduce`` algorithm and a necessary pre- and post-division of gradients.
+
+    Args:
+        state (DefaultState): State information, configures pre- and post-division factors.
+        grad (torch.Tensor): A gradient for the local batch that needs to be communicated across ranks.
+    """
+    # Average grad by pre-division factor. Together pre- and post-division factors
+    # lead to an overall averaging by world_size, required for consistency with PyTorch DDP.
+    # This is a two-step process to avoid potential underflow and overflow.
+    if state.gradient_predivide_factor > 1:
+        grad.div_(state.gradient_predivide_factor)
+    dist.all_reduce(grad, group=state.process_group)
+    # Average grad by post-division factor.
+    if state.gradient_postdivide_factor > 1:
+        grad.div_(state.gradient_postdivide_factor)
+
+
+def reduce_scatter_hook(state: DefaultState, grad: torch.Tensor, output: torch.Tensor):
+    r"""
+    Implement the  FSDP communication hook for ``reduce_scatter`` algorithm.
+
+    For sharded FSDP strategies and a necessary pre- and post-division of gradients.
+
+    Args:
+        state (DefaultState): State information, configures pre- and post-division factors.
+        grad (torch.Tensor): An unsharded gradient for the local batch that needs to be
+        communicated across ranks.
+        output (torch.Tensor): Stores a single shard of the gradient after ``reduce_scatter``.
+    """
+    # Average grad by pre-division factor.
+    if state.gradient_predivide_factor > 1:
+        grad.div_(state.gradient_predivide_factor)
+    dist.reduce_scatter_tensor(output, grad, group=state.process_group)
+    # Average grad's shard by post-division factor.
+    if state.gradient_postdivide_factor > 1:
+        output.div_(state.gradient_postdivide_factor)
+
+
+def _low_precision_hook(
+    prec: torch.dtype,
+    state: LowPrecisionState,
+    grad: torch.Tensor,
+    output: torch.Tensor | None,
+):
+    if grad.dtype != prec:
+        grad.data = grad.data.to(prec)
+    if output is not None:
+        if output.dtype != prec:
+            output.data = output.data.to(prec)
+        reduce_scatter_hook(state, grad, output)
+        _decompress(state, output)
+    else:
+        allreduce_hook(state, grad)
+        _decompress(state, grad)
+
+
+def fp16_compress_hook(
+    state: LowPrecisionState, grad: torch.Tensor, output: torch.Tensor | None = None
+):
+    r"""
+    Implement FSDP communication hook for a simple gradient compression approach.
+    Casts ``grad`` to half-precision floating-point format (``torch.float16``).
+
+    It also averages gradients by ``world_size`` in two steps: first it pre-divides gradients by a
+    ``state.gradient_predivide_factor``, and after a communication step (``all_reduce`` or ``reduce_scatter``)
+    gradients are averaged by a ``state.gradient_postdivide_factor``.
+    Once post-division is done, compressed gradients are casted back to parameters' precision.
+
+    Args:
+        state (LowPrecisionState): State information, configures pre- and post-division factors, parameters' precision.
+        grad (torch.Tensor): A gradient for the local batch that needs to be communicated across ranks in a lower precision.
+        output (torch.Tensor): Stores a single shard of the gradient after ``reduce_scatter``.
+    """
+    fp16_hook = functools.partial(_low_precision_hook, torch.float16)
+    return fp16_hook(state, grad, output)
+
+
+def bf16_compress_hook(
+    state: LowPrecisionState, grad: torch.Tensor, output: torch.Tensor | None = None
+):
+    r"""
+    Implement FSDP communication hook for a simple gradient compression approach .
+    Casts ``grad`` to half-precision floating-point format.
+
+    It also averages gradients by ``world_size`` in two steps: first it pre-divides gradients by a
+    ``state.gradient_predivide_factor``, and after a communication step (``all_reduce`` or ``reduce_scatter``)
+    gradients are averaged by a ``state.gradient_postdivide_factor``.
+    Once post-division is done, compressed gradients are casted back to parameters' precision.
+
+    Args:
+        state (LowPrecisionState): State information, configures pre- and post-division factors, parameters' precision.
+        grad (torch.Tensor): A gradient for the local batch that needs to be communicated across ranks in a lower precision.
+        output (torch.Tensor): Stores a single shard of the gradient after ``reduce_scatter``.
+    """
+    bf16_hook = functools.partial(_low_precision_hook, torch.bfloat16)
+    return bf16_hook(state, grad, output)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/_optimizer_overlap/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/_optimizer_overlap/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba62bfb68f42a136dcfa27bcf378d3892cf6751a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/_optimizer_overlap/__init__.py
@@ -0,0 +1 @@
+from .optimizer_overlap import _as_overlapped_optim
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/_optimizer_overlap/optimizer_overlap.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/_optimizer_overlap/optimizer_overlap.py
new file mode 100644
index 0000000000000000000000000000000000000000..569a42ffe7643bb6b6403dfb323a4dfd28493e1b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/_optimizer_overlap/optimizer_overlap.py
@@ -0,0 +1,96 @@
+# mypy: allow-untyped-defs
+import inspect
+from abc import ABC, abstractmethod
+
+from torch.distributed.algorithms.ddp_comm_hooks.default_hooks import allreduce_hook
+from torch.distributed.algorithms.ddp_comm_hooks.optimizer_overlap_hooks import (
+    _hook_then_optimizer,
+    _OptimizerHookState,
+)
+from torch.distributed.fsdp import FullyShardedDataParallel
+from torch.distributed.optim import as_functional_optim
+from torch.nn.parallel import DistributedDataParallel
+from torch.optim import Optimizer
+
+
+# Contains the mappings between the regular and overlapped optimizer types.
+_registered_overlapped_optims: dict[type, type] = {}
+
+
+def register_overlapped(optim_cls):
+    def decorator(target_overlapped_optim_cls):
+        if target_overlapped_optim_cls in _registered_overlapped_optims:
+            raise ValueError(
+                f"{target_overlapped_optim_cls} already registered with optim_cls "
+                f"{_registered_overlapped_optims[optim_cls]} {optim_cls}, trying to"
+                f"re-register it for {optim_cls} is not supported."
+            )
+        _registered_overlapped_optims[optim_cls] = target_overlapped_optim_cls
+        return target_overlapped_optim_cls
+
+    return decorator
+
+
+class OverlappedOptimizer(ABC):
+    def __init__(self, optim_cls: type) -> None:
+        """
+        Initialize the OverlappedOptimizer.
+
+        Overlappedoptimizer is a base class that child classes can implement to
+        specify how different optimizers will register themselves with DDP.
+        """
+        self.optim_cls = optim_cls
+
+    @abstractmethod
+    def register_ddp(self, ddp: DistributedDataParallel) -> None:
+        """Registers the overlapped optimizer with DDP."""
+        raise NotImplementedError(
+            f"{self.__class__.__name__} does not support overlapped DDP."
+        )
+
+    @abstractmethod
+    def register_fsdp(self, fsdp: FullyShardedDataParallel) -> None:
+        """Registers the overlapped optimizer with FSDP."""
+        raise NotImplementedError(
+            f"{self.__class__.__name__} does not support overlapped FSDP."
+        )
+
+
+@register_overlapped(Optimizer)
+class _OverlappedStandardOptimizer(OverlappedOptimizer):
+    """Overlaps a regular ``Optimizer``."""
+
+    def __init__(self, optim_cls: type, params, *optim_args, **optim_kwargs) -> None:
+        super().__init__(optim_cls)
+        f_optim = as_functional_optim(self.optim_cls, *optim_args, **optim_kwargs)
+        self._opt_hook_state = _OptimizerHookState(f_optim, params)
+
+    def register_ddp(self, ddp_inst: DistributedDataParallel):
+        # NOTE: using a custom communication hook and fused optimizer is not
+        # yet supported.
+        ddp_inst.register_comm_hook(  # type: ignore[operator]
+            None,  # wrapped hook state
+            _hook_then_optimizer(allreduce_hook, self._opt_hook_state),
+        )
+
+    # TODO: register_fsdp once FSDP supports communication hook.
+    def register_fsdp(self, fsdp: FullyShardedDataParallel) -> None:
+        """Register the overlapped optimizer with FSDP."""
+        raise NotImplementedError(
+            f"{self.__class__.__name__} does not support overlapped FSDP."
+        )
+
+
+def _as_overlapped_optim(optim_cls: type, params, *args, **kwargs):
+    """Return a new ``OverlappedOptimizer`` instance that supports ``optim_cls``."""
+    for clz in inspect.getmro(optim_cls):
+        try:
+            return _registered_overlapped_optims[clz](
+                optim_cls, params, *args, **kwargs
+            )
+        except KeyError:
+            pass
+
+    # Fallback to standard overlapped optimizer, which will raise errors if user
+    # is attempting to use an unsupported optimizer.
+    return _OverlappedStandardOptimizer(optim_cls, params, *args, **kwargs)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/_quantization/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/_quantization/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/_quantization/quantization.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/_quantization/quantization.py
new file mode 100644
index 0000000000000000000000000000000000000000..69d88604561355b344b43129108d276e398e0f9f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/_quantization/quantization.py
@@ -0,0 +1,151 @@
+# mypy: allow-untyped-defs
+import functools
+from enum import Enum
+
+import torch
+import torch.distributed as dist
+
+
+TORCH_HALF_MIN = torch.finfo(torch.float16).min
+TORCH_HALF_MAX = torch.finfo(torch.float16).max
+
+
+class DQuantType(Enum):
+    """
+    Different quantization methods for auto_quantize API are identified here.
+
+    auto_quantize API currently supports fp16 and bfp16 methods.
+    """
+
+    FP16 = ("fp16",)
+    BFP16 = "bfp16"
+
+    def __str__(self) -> str:
+        return self.value
+
+
+def _fp32_to_fp16_with_clamp(tensor: torch.Tensor) -> torch.Tensor:
+    return torch.clamp(tensor, TORCH_HALF_MIN, TORCH_HALF_MAX).half()
+
+
+def _quantize_tensor(tensor, qtype):
+    if not isinstance(tensor, torch.Tensor):
+        raise RuntimeError(
+            f"_quantize_tensor expecting torch.Tensor as input but found {type(tensor)}"
+        )
+    if qtype == DQuantType.FP16:
+        return _fp32_to_fp16_with_clamp(tensor)
+    elif qtype == DQuantType.BFP16:
+        return torch.ops.quantization._FloatToBfloat16Quantized(tensor)
+    else:
+        raise RuntimeError(f"Quantization type {qtype} is not supported")
+
+
+def _quantize_tensor_list(tensor_list, qtype):
+    if not isinstance(tensor_list, list) or not all(
+        isinstance(p, torch.Tensor) for p in tensor_list
+    ):
+        raise RuntimeError(
+            f"_quantize_tensor_list expecting list of torch.Tensor as input but found {type(tensor_list)}"
+        )
+    quantized_tensor_list = [_quantize_tensor(t, qtype) for t in tensor_list]
+    return quantized_tensor_list
+
+
+def _dequantize_tensor(tensor, qtype, quant_loss=None):
+    if not isinstance(tensor, torch.Tensor):
+        raise RuntimeError(
+            f"_dequantize_tensor expecting torch.Tensor as input but found {type(tensor)}"
+        )
+    if qtype == DQuantType.FP16:
+        if tensor.dtype != torch.float16:
+            raise RuntimeError(
+                f"tensor dtype is {tensor.dtype} while expected to be FP16."
+            )
+        elif tensor.dtype == torch.float16 and quant_loss is None:
+            return tensor.float()
+        else:
+            # pyrefly: ignore [unsupported-operation]
+            return tensor.float() / quant_loss
+    elif qtype == DQuantType.BFP16:
+        if tensor.dtype != torch.float16:
+            raise RuntimeError(
+                f"tensor dtype is {tensor.dtype} while expected to be FP16."
+            )
+        else:
+            return torch.ops.quantization._Bfloat16QuantizedToFloat(tensor)
+    else:
+        raise RuntimeError(f"Quantization type {qtype} is not supported")
+
+
+def _dequantize_tensor_list(tensor_list, qtype, quant_loss=None):
+    if not isinstance(tensor_list, list) or not all(
+        isinstance(p, torch.Tensor) for p in tensor_list
+    ):
+        raise RuntimeError(
+            f"_dequantize_tensor_list expecting list of torch.Tensor as input but found {type(tensor_list)}"
+        )
+    dequantized_tensor_list = [_dequantize_tensor(t, qtype) for t in tensor_list]
+    return dequantized_tensor_list
+
+
+def auto_quantize(func, qtype, quant_loss=None):
+    """
+    Quantize the input tensors, choose the precision types, and pass other necessary arguments and then dequantizes the output.
+
+    Currently it only supports:
+        . FP16 and BFP16 quantization method supported for gloo and nccl backends
+        . all_gather, all_to_all collective ops
+    Note: BFP16 only supports 2D tensors.
+    Args:
+        func (Callable): A function representing collective operations.
+        qtype (QuantType): Quantization method
+        quant_loss (float, optional): This can be used to improve accuracy in the dequantization.
+    Returns:
+        (Callable): the same collective as func but enables automatic quantization/dequantization.
+    """
+
+    @functools.wraps(func)
+    def wrapper(*args, **kwargs):
+        group = kwargs.get("group")
+        async_op = kwargs.get("async_op", False)
+        if async_op is True:
+            raise RuntimeError("The async_op=True mode is not supported yet.")
+        if func is dist.all_gather:
+            tensors = args[0]
+            input_tensors = _quantize_tensor(args[1], qtype)
+            out_tensors = _quantize_tensor_list(tensors, qtype)
+            dist.all_gather(out_tensors, input_tensors, group=group, async_op=async_op)
+            for i, t in enumerate(
+                _dequantize_tensor_list(out_tensors, qtype, quant_loss=quant_loss)
+            ):
+                tensors[i] = t
+
+        elif func is dist.all_to_all:
+            tensors = args[0]
+            input_tensors = _quantize_tensor_list(args[1], qtype)
+            out_tensors = _quantize_tensor_list(tensors, qtype)
+            dist.all_to_all(out_tensors, input_tensors, group=group, async_op=async_op)
+            for i, t in enumerate(
+                _dequantize_tensor_list(out_tensors, qtype, quant_loss=quant_loss)
+            ):
+                tensors[i] = t
+
+        elif func is dist.all_to_all_single:
+            tensors = args[0]
+            out_splits = kwargs.get("out_splits")
+            in_splits = kwargs.get("in_splits")
+            # Quantizing the input/output tensor
+            input_tensors = _quantize_tensor(args[1], qtype)
+            out_tensors = _quantize_tensor(tensors, qtype)
+            dist.all_to_all_single(
+                out_tensors, input_tensors, out_splits, in_splits, group=group
+            )
+            for i, t in enumerate(
+                _dequantize_tensor(out_tensors, qtype, quant_loss=quant_loss)
+            ):
+                tensors[i] = t
+        else:
+            raise RuntimeError(f"The collective op {func} is not supported yet")
+
+    return wrapper
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9cc6d12785cc760ae77039d5403bd36c94fcdb8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/__init__.py
@@ -0,0 +1,140 @@
+# mypy: allow-untyped-defs
+import sys
+from enum import Enum
+from functools import partial
+
+
+# To suppress FutureWarning from partial since 3.13
+if sys.version_info >= (3, 13):
+    from enum import member
+
+    def _enum_member(x):
+        return member(x)
+else:
+
+    def _enum_member(x):
+        return x
+
+
+import torch.distributed as dist
+
+from . import (
+    debugging_hooks as debugging,
+    default_hooks as default,
+    optimizer_overlap_hooks as optimizer_overlap,
+    powerSGD_hook as powerSGD,
+    quantization_hooks as quantization,
+)
+
+
+__all__ = ["DDPCommHookType", "register_ddp_comm_hook"]
+
+
+def _ddp_comm_hook_wrapper(comm_hook, model, state):
+    model.register_comm_hook(state, comm_hook)
+
+
+def _powerSGD_comm_hook_wrapper(
+    comm_hook,
+    model,
+    state,
+    matrix_approximation_rank,
+    start_powerSGD_iter=1_000,
+):
+    """
+    Wrap PowerSGD communication hook.
+
+    To be consistent with the wrappers of other DDP comm hooks, the input state only needs to be a process group,
+    which will be wrapped up with other state info.
+    """
+    powerSGD_state = powerSGD.PowerSGDState(
+        process_group=state,
+        matrix_approximation_rank=matrix_approximation_rank,
+        start_powerSGD_iter=start_powerSGD_iter,
+    )
+    model.register_comm_hook(powerSGD_state, comm_hook)
+
+
+class DDPCommHookType(Enum):
+    """
+    Enumerate ``ddp_comm_hooks`` and ``ddp_comm_hook_wrapper`` communucation hook types.
+
+    DDPCommHookType enumerates the hooks of ``torch.distributed.algorithms.ddp_comm_hooks``
+    as names and ``ddp_comm_hook_wrapper`` partials with hook specified. As an example,
+    you can register allreduce hook by
+    ``DDPCommHookType.ALLREDUCE.value(model=model, state=process_group)``.
+    """
+
+    ALLREDUCE = _enum_member(
+        partial(_ddp_comm_hook_wrapper, comm_hook=default.allreduce_hook)
+    )
+    FP16_COMPRESS = _enum_member(
+        partial(_ddp_comm_hook_wrapper, comm_hook=default.fp16_compress_hook)
+    )
+    BF16_COMPRESS = _enum_member(
+        partial(_ddp_comm_hook_wrapper, comm_hook=default.bf16_compress_hook)
+    )
+    QUANTIZE_PER_TENSOR = _enum_member(
+        partial(
+            _ddp_comm_hook_wrapper, comm_hook=quantization.quantization_pertensor_hook
+        )
+    )
+    QUANTIZE_PER_CHANNEL = _enum_member(
+        partial(
+            _ddp_comm_hook_wrapper, comm_hook=quantization.quantization_perchannel_hook
+        )
+    )
+    POWER_SGD = _enum_member(
+        partial(
+            _powerSGD_comm_hook_wrapper,
+            comm_hook=powerSGD.powerSGD_hook,
+            matrix_approximation_rank=1,
+        )
+    )
+    # Rank-2 PowerSGD can give a higher accuracy than the default rank-1 version,
+    # but it runs slower and consumes more memory.
+    POWER_SGD_RANK2 = _enum_member(
+        partial(
+            _powerSGD_comm_hook_wrapper,
+            comm_hook=powerSGD.powerSGD_hook,
+            matrix_approximation_rank=2,
+        )
+    )
+    # Batching can lead to a faster training at the cost of accuracy.
+    BATCHED_POWER_SGD = _enum_member(
+        partial(
+            _powerSGD_comm_hook_wrapper,
+            comm_hook=powerSGD.batched_powerSGD_hook,
+            matrix_approximation_rank=1,
+        )
+    )
+    BATCHED_POWER_SGD_RANK2 = _enum_member(
+        partial(
+            _powerSGD_comm_hook_wrapper,
+            comm_hook=powerSGD.batched_powerSGD_hook,
+            matrix_approximation_rank=2,
+        )
+    )
+    NOOP = _enum_member(
+        partial(
+            _ddp_comm_hook_wrapper,
+            comm_hook=debugging.noop_hook,
+        )
+    )
+
+
+def register_ddp_comm_hook(comm_hook_type: DDPCommHookType, model, state=None):
+    """
+    Register ``ddp_comm_hooks`` to DDP model.
+
+    Registers the hooks of ``torch.distributed.algorithms.ddp_comm_hooks``
+    to the DDP model. User can specify the type of hook as an enum
+    ``DDPCommHookType`` type using ``comm_hook_type`` input. State input will
+    be passed to the model.
+    Uses Python comm hook implementations.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> register_ddp_comm_hook(DDPCommHookType.FP16_COMPRESS, model, state)
+    """
+    comm_hook_type.value(model=model, state=state)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/ddp_zero_hook.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/ddp_zero_hook.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa8c865c89151033b379d6cd4785fd15e002cd66
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/ddp_zero_hook.py
@@ -0,0 +1,457 @@
+# mypy: allow-untyped-defs
+import weakref
+from collections.abc import Callable
+from typing import Any
+
+import torch
+import torch.distributed as dist
+from torch.distributed.optim import ZeroRedundancyOptimizer
+from torch.distributed.optim.zero_redundancy_optimizer import _OverlapStatus
+from torch.nn.parallel.distributed import DistributedDataParallel
+
+
+__all__ = ["hook_with_zero_step", "hook_with_zero_step_interleaved"]
+
+# Functional optimizers require passing a list of gradients to their `step()`
+# method, and ZeRO requires a functional optimizer to overlap with DDP
+# Passing a `None` instead of an actual gradient indicates to the optimizer
+# to not update the corresponding parameter
+_NO_PARAM_UPDATE: None = None
+
+
+def _perform_local_step(
+    bucket: dist.GradBucket,
+    zero: ZeroRedundancyOptimizer,
+    rank: int,
+):
+    r"""
+    Perform a local optimizer step using the gradients provided by ``bucket``.
+
+    Arguments:
+        bucket (dist.GradBucket): the bucket providing the gradients.
+        zero (ZeroRedundancyOptimizer): the :class:`ZeroRedundancyOptimizer`
+            instance to perform the :meth:`_local_step`.
+        rank (int): the calling process's rank.
+
+    .. warning::
+        This function assumes that appropriate synchronization has taken place
+        so that the bucket's gradients can be used.
+    """
+    overlap_info = zero._overlap_info
+    bucket_index = bucket.index()
+    assert len(zero.optim.param_groups) == 1, (
+        "Overlapping DDP with ZeRO only supports a single parameter group"
+    )
+
+    # Construct the `gradients` input for the local optimizer step, which
+    # expects `None` in a list position to indicate that the corresponding
+    # parameter should not be updated
+    num_local_optim_params = len(zero.optim.param_groups[0]["params"])
+    gradients: list[torch.Tensor | None] = [
+        _NO_PARAM_UPDATE for _ in range(num_local_optim_params)
+    ]
+    assert bucket_index in overlap_info.offsets, (
+        f"Bucket index {bucket_index} was not assigned to rank {rank}"
+    )
+    gradients_offset = overlap_info.offsets[bucket_index]
+    bucket_assignment = zero._bucket_assignments_per_rank[rank][bucket_index]
+    bucket_offset = bucket_assignment.offset
+    length = len(bucket_assignment.parameters)
+    bucket_gradients = bucket.gradients()[bucket_offset : bucket_offset + length]
+    for i, grad in enumerate(bucket_gradients):
+        gradients[gradients_offset + i] = grad
+
+    zero._local_step(gradients)
+
+
+def _broadcast_bucket(
+    bucket_index: int,
+    zero: ZeroRedundancyOptimizer,
+):
+    r"""
+    Broadcasts a bucket's parameters.
+
+    Arguments:
+        bucket_index (int): the index of the bucket corresponding to the
+            parameters to broadcast.
+        zero (ZeroRedundancyOptimizer): the calling process's
+            :class:`ZeroRedundancyOptimizer` instance.
+    """
+    overlap_info = zero._overlap_info
+    assert len(overlap_info.assigned_ranks_per_bucket) > bucket_index, (
+        "`assigned_ranks_per_bucket` is not fully constructed"
+    )
+    # Sort to ensure the same ordering across ranks
+    assigned_ranks = sorted(overlap_info.assigned_ranks_per_bucket[bucket_index])
+    assert len(assigned_ranks) > 0, (
+        f"Bucket {bucket_index} should be assigned to at least one rank"
+    )
+    for assigned_rank in assigned_ranks:
+        bucket_assignments = zero._bucket_assignments_per_rank[assigned_rank]
+        if bucket_index in bucket_assignments:
+            send_tensor = bucket_assignments[bucket_index].tensor
+            assert send_tensor is not None
+            overlap_info.broadcast_handles.append(
+                dist.broadcast(
+                    send_tensor,
+                    src=dist.get_global_rank(zero.process_group, assigned_rank),
+                    group=zero.process_group,
+                    async_op=True,
+                )
+            )
+
+
+def _save_ddp_bucket_info(
+    bucket: dist.GradBucket,
+    zero: ZeroRedundancyOptimizer,
+):
+    r"""
+    Save :class:`DistributedDataParallel` gradient bucket information for :class:`ZeroRedundancyOptimizer` instance ``zero``.
+
+    In particular, this function is meant to be called upon seeing each
+    gradient bucket to use when overlapping, meaning it does not save or compute any global
+    information.
+
+    Arguments:
+        bucket (dist.GradBucket): the current gradient bucket.
+        zero (ZeroRedundancyOptimizer): the calling process's
+            :class:`ZeroRedundancyOptimizer` instance.
+    """
+    overlap_info = zero._overlap_info
+    bucket_params = bucket.parameters()
+    assert len(bucket_params) > 0, "Empty bucket"
+
+    # Save the parameters in the bucket
+    overlap_info.params_per_bucket.append(bucket_params)
+    if overlap_info.shard_buckets:
+        # Additionally save the bucket size for the assignment heuristic to use
+        bucket_size = 0
+        for param in bucket_params:
+            bucket_size += param.numel()
+        assert overlap_info.total_size is not None
+        overlap_info.total_size += bucket_size
+
+
+def _hook_with_zero_step_setup(
+    ddp_ref: weakref.ReferenceType,
+    zero: ZeroRedundancyOptimizer,
+    bucket: dist.GradBucket,
+):
+    r"""
+    Encapsulate the setup logic for :func:`hook_with_zero_step` and :func:`hook_with_zero_step_interleaved`.
+
+    This means the logic to run in the
+    hook before the backward pass and optimizer step can actually be
+    overlapped. This is factored out since it is common to both
+    :func:`hook_with_zero_step` and :func:`hook_with_zero_step_interleaved`.
+
+    Arguments:
+        ddp_ref (weakref.ReferenceType): weak reference to the process's
+            :class:`DistributedDataParallel` instance.
+        zero (ZeroRedundancyOptimizer): the calling process's
+            :class:`ZeroRedundancyOptimizer` instance.
+        bucket (dist.GradBucket): the current gradient bucket.
+    """
+    # Proceed as normal until the DDP buckets have been rebuilt
+    if not ddp_ref()._has_rebuilt_buckets:  # type: ignore[union-attr]
+        assert zero._overlap_info.status == _OverlapStatus.UNINITIALIZED
+        return
+
+    bucket_index = bucket.index()
+    overlap_info = zero._overlap_info
+    if overlap_info.status == _OverlapStatus.UNINITIALIZED:
+        overlap_info.status = _OverlapStatus.DDP_HAS_REBUILT_BUCKETS
+
+    if overlap_info.status == _OverlapStatus.DDP_HAS_REBUILT_BUCKETS:
+        if bucket_index == 0 and len(overlap_info.params_per_bucket) > 0:
+            # This corresponds to the first bucket of the backward pass
+            # immediately after all information has been saved, so we
+            # can perform the delayed ZeRO initialization
+            zero._init_zero_for_overlap()
+        else:
+            # Once DDP buckets have been rebuilt but ZeRO has not been
+            # properly initialized yet, save the information needed
+            _save_ddp_bucket_info(bucket, zero)
+
+
+def hook_with_zero_step(
+    hook: Callable[[Any, dist.GradBucket], torch.futures.Future],
+    ddp: DistributedDataParallel,
+    zero: ZeroRedundancyOptimizer,
+    shard_buckets: bool = False,
+) -> Callable[[Any, dist.GradBucket], torch.futures.Future[torch.Tensor]]:
+    r"""
+    Modify ``hook`` to overlap :class:`ZeroRedundancyOptimizer` optimizer step with :class:`DistributedDataParallel` backward pass.
+
+    This approach overlaps the optimizer computation and communication with the
+    backward communication. In particular, the backward computation proceeds
+    contiguously, and the optimizer computation follows, overlapping with
+    outstanding backward communication (i.e. all-reduces) and possibly other
+    optimizer communication (i.e. broadcasts).
+    The optimizer step computation begins after the last gradient bucket computation has finished.
+
+    This approach may be preferred over :meth:`hook_with_zero_step_interleaved`
+    if communication is relatively slow compared to computation.
+
+    Arguments:
+        hook (Callable[[Any, dist.GradBucket], torch.futures.Future]): the hook
+            to modify.
+        ddp (DistributedDataParallel): the :class:`DistributedDataParallel`
+            instance to use.
+        zero (ZeroRedundancyOptimizer): the :class:`ZeroRedundancyOptimizer`
+            instance to use.
+        shard_buckets (bool): if ``True``, then the assignment of each
+            :class:`DistributedDataParallel` bucket is partitioned across
+            possibly multiple :class:`ZeroRedundancyOptimizer` instances (i.e.
+            across possibly multiple ranks) to approximate uniformity; if
+            ``False``, then each bucket is wholly assigned to a single
+            :class:`ZeroRedundancyOptimizer` instance (i.e. to a single rank).
+
+    Returns:
+        The modified hook.
+
+    Raises:
+        ValueError: if ``zero`` was constructed with ``overlap_with_ddp=False``.
+        RuntimeError: if using any backend other than NCCL/HCCL since currently
+            Gloo may hang.
+
+    .. warning::
+        Given the way that overlapping :class:`DistributedDataParallel` with
+        :class:`ZeroRedundancyOptimizer` is currently implemented, the first
+        two or three training iterations do not perform parameter updates in
+        the optimizer step, depending on if ``static_graph=False`` or
+        ``static_graph=True``, respectively. This is because it needs
+        information about the gradient bucketing strategy used by
+        :class:`DistributedDataParallel`, which is not finalized until the
+        second forward pass if ``static_graph=False`` or until the third
+        forward pass if ``static_graph=True``.
+    """
+    if not zero._overlap_with_ddp:
+        raise ValueError(
+            "ZeroRedundancyOptimizer must be constructed with "
+            "`overlap_with_ddp=True` to use this hook properly"
+        )
+    ddp_ref = weakref.ref(ddp)
+
+    # NOTE: Gloo may hang with this overlapping approach; see https://github.com/pytorch/pytorch/issues/62300
+    pg = dist.get_backend(ddp_ref().process_group)  # type: ignore[union-attr]
+    if pg == dist.Backend.GLOO:
+        raise RuntimeError(
+            "Gloo backend using Overlapping DDP with ZeRO may meet hangs"
+        )
+
+    if shard_buckets:
+        zero._overlap_info.shard_buckets = True
+        zero._overlap_info.total_size = 0
+
+    def hook_with_zero_fn(
+        state: Any,
+        bucket: dist.GradBucket,
+    ) -> torch.futures.Future[torch.Tensor]:
+        r"""
+        Return :class:`Future` that runs the optimizer step if this corresponds to the last gradient bucket.
+
+        Perform equivalent of :class:`ZeroRedundancyOptimizer` :meth:`step` if ``bucket`` is last gradient bucket.
+        The function gives a gradient bucket tensor and
+        performs additional computation on the iteration that
+        the :class:`DistributedDataParallel` buckets are rebuilt to collect
+        information used to implement the modified hook.
+
+        Arguments:
+            state (Any): any state for the hook.
+            bucket (dist.GradBucket): the :class:`DistributedDataParallel`
+                gradient bucket.
+        """
+        fut = hook(state, bucket)
+        _hook_with_zero_step_setup(ddp_ref, zero, bucket)
+        if zero._overlap_info.status != _OverlapStatus.INITIALIZED:
+            return fut
+
+        overlap_info = zero._overlap_info
+        bucket_index = bucket.index()
+        rank = zero.global_rank
+
+        assert overlap_info.status == _OverlapStatus.INITIALIZED
+        assert len(overlap_info.assigned_ranks_per_bucket) > bucket_index, (
+            "`assigned_ranks_per_bucket` is not fully constructed"
+        )
+        assigned_to_bucket = (
+            rank in overlap_info.assigned_ranks_per_bucket[bucket_index]
+        )
+
+        # Save the bucket reference and all-reduce future for the final bucket
+        if assigned_to_bucket:
+            overlap_info.bucket_index_to_bucket[bucket_index] = bucket
+            overlap_info.bucket_index_to_future[bucket_index] = fut
+
+        # Check that buckets are indexed incrementally starting from 0 in the
+        # order of their autograd hooks firing
+        if len(overlap_info.bucket_indices_seen) > 0:
+            assert overlap_info.bucket_indices_seen[-1] == bucket_index - 1, (
+                "Bucket indices are not in incremental order"
+            )
+        else:
+            assert bucket_index == 0, "Bucket indices do not start from 0"
+        overlap_info.bucket_indices_seen.append(bucket_index)
+
+        # Directly return the future without any optimizer computation if this
+        # is not the last bucket
+        num_buckets = len(overlap_info.params_per_bucket)
+        is_last_bucket = bucket_index == num_buckets - 1
+        if not is_last_bucket:
+            return fut
+
+        # Perform partial optimizer step on all buckets after the final
+        # bucket has been computed
+        # NOTE: This should not be chained as a callback to the last bucket's
+        # all-reduce future since that would add synchronization that delays
+        # all optimizer computation to wait for that last all-reduce
+        for bucket_index in range(num_buckets):
+            assigned_ranks = overlap_info.assigned_ranks_per_bucket[bucket_index]
+            if rank in assigned_ranks:
+                # Wait on the bucket's all-reduce future to ensure correct
+                # gradients
+                assert bucket_index in overlap_info.bucket_index_to_future, (
+                    f"All-reduce future for bucket {bucket_index} not saved "
+                    f"on rank {rank}"
+                )
+                allreduce_future = overlap_info.bucket_index_to_future[bucket_index]
+                allreduce_future.wait()
+
+                # Perform the partial optimizer step
+                curr_bucket = overlap_info.bucket_index_to_bucket[bucket_index]
+                _perform_local_step(curr_bucket, zero, rank)
+
+            _broadcast_bucket(bucket_index, zero)
+
+        # Ensure that all parameter updates are finished before the
+        # next forward pass
+        overlap_info.wait_for_broadcasts()
+        overlap_info.clear_per_iter_info()
+
+        return fut
+
+    return hook_with_zero_fn
+
+
+def hook_with_zero_step_interleaved(
+    hook: Callable[[Any, dist.GradBucket], torch.futures.Future],
+    ddp: DistributedDataParallel,
+    zero: ZeroRedundancyOptimizer,
+    shard_buckets: bool = False,
+) -> Callable[[Any, dist.GradBucket], torch.futures.Future[torch.Tensor]]:
+    r"""
+    Modify ``hook`` to overlap :class:`ZeroRedundancyOptimizer` optimizer step with :class:`DistributedDataParallel` backward pass
+
+    This approach overlaps the optimizer computation and communication with the
+    backward computation and communication. In particular, once a bucket's
+    gradients have been computed, the optimizer computation using those
+    gradients is launched (though the actual computation must wait for the
+    bucket's all-reduce to complete). This yields an interleaving of all-
+    reduces and broadcasts in the communication stream.
+
+    This approach may be preferred over :meth:`hook_with_zero_step` if
+    communication is relatively fast compared to computation.
+
+    Arguments:
+        hook (Any * dist.GradBucket -> torch.futures.Future): the hook to
+            modify.
+        ddp (DistributedDataParallel): the :class:`DistributedDataParallel`
+            instance to use.
+        zero (ZeroRedundancyOptimizer): the :class:`ZeroRedundancyOptimizer`
+            instance to use.
+        shard_buckets (bool): if ``True``, then the assignment of each
+            :class:`DistributedDataParallel` bucket is partitioned across
+            possibly multiple :class:`ZeroRedundancyOptimizer` instances (i.e.
+            across possibly multiple ranks) to approximate uniformity; if
+            ``False``, then each bucket is wholly assigned to a single
+            :class:`ZeroRedundancyOptimizer` instance (i.e. to a single rank).
+
+    Returns:
+        The modified hook.
+
+    Raises:
+        ValueError: if ``zero`` was constructed with ``overlap_with_ddp=False``.
+        RuntimeError: if using any backend other than NCCL since currently
+            Gloo may hang.
+
+    .. warning::
+        Given the way that overlapping :class:`DistributedDataParallel` with
+        :class:`ZeroRedundancyOptimizer` is currently implemented, the first
+        two or three training iterations do not perform parameter updates in
+        the optimizer step, depending on if ``static_graph=False`` or
+        ``static_graph=True``, respectively. This is because it needs
+        information about the gradient bucketing strategy used by
+        :class:`DistributedDataParallel`, which is not finalized until the
+        second forward pass if ``static_graph=False`` or until the third
+        forward pass if ``static_graph=True``.
+    """
+    if not zero._overlap_with_ddp:
+        raise ValueError(
+            "ZeroRedundancyOptimizer must be constructed with "
+            "`overlap_with_ddp=True` to use this hook properly"
+        )
+    ddp_ref = weakref.ref(ddp)
+
+    # NOTE: Gloo may hang with this overlapping approach; see https://github.com/pytorch/pytorch/issues/62300
+    pg = dist.get_backend(ddp_ref().process_group)  # type: ignore[union-attr]
+    if pg == dist.Backend.GLOO:
+        raise RuntimeError(
+            "Gloo backend using Overlapping DDP with ZeRO may meet hangs"
+        )
+
+    if shard_buckets:
+        zero._overlap_info.shard_buckets = True
+        zero._overlap_info.total_size = 0
+
+    def hook_with_zero_interleaved_fn(
+        state,
+        bucket: dist.GradBucket,
+    ) -> torch.futures.Future[torch.Tensor]:
+        r"""
+        Return :class:`Future` that gives gradient bucket tensor and performs partial :class:`ZeroRedundancyOptimizer` :meth:`step`.
+
+        This function uses the gradients in gradient in given bucket to perform a partial
+        :class:`ZeroRedundancyOptimizer` :meth:`step`
+
+        Arguments:
+            state: any state for the hook.
+            bucket (dist.GradBucket): the :class:`DistributedDataParallel`
+                gradient bucket.
+        """
+        fut = hook(state, bucket)
+        _hook_with_zero_step_setup(ddp_ref, zero, bucket)
+        if zero._overlap_info.status != _OverlapStatus.INITIALIZED:
+            return fut
+
+        def zero_step(fut: torch.futures.Future) -> torch.Tensor:
+            r"""
+            Perform partial :class:`ZeroRedundancyOptimizer` :meth:`step` using gradients in the :class:`DistributedDataParallel`.
+
+            Returns:
+                A :class:`torch.Tensor` representing the contents of the
+                gradient bucket.
+            """
+            overlap_info = zero._overlap_info
+            bucket_index = bucket.index()
+            rank = zero.global_rank
+
+            assigned_ranks = overlap_info.assigned_ranks_per_bucket[bucket_index]
+            overlap_info.bucket_indices_seen.append(bucket_index)
+            if rank in assigned_ranks:
+                _perform_local_step(bucket, zero, rank)
+
+            _broadcast_bucket(bucket_index, zero)
+
+            num_buckets = len(overlap_info.params_per_bucket)
+            if len(overlap_info.bucket_indices_seen) == num_buckets:
+                # Ensure that all parameter updates are finished before the
+                # next forward pass
+                overlap_info.wait_for_broadcasts()
+                overlap_info.clear_per_iter_info()
+
+            return bucket.buffer()
+
+        return fut.then(zero_step)
+
+    return hook_with_zero_interleaved_fn
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/debugging_hooks.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/debugging_hooks.py
new file mode 100644
index 0000000000000000000000000000000000000000..53a184839a06f4787471f14f48137f4aa344fd91
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/debugging_hooks.py
@@ -0,0 +1,29 @@
+from typing import Any
+
+import torch
+from torch.distributed import GradBucket
+
+
+__all__ = ["noop_hook"]
+
+
+def noop_hook(_: Any, bucket: GradBucket) -> torch.futures.Future[torch.Tensor]:
+    """
+    Return a future that wraps the input, so it is a no-op that does not incur any communication overheads.
+
+    This hook should **only** be used for headroom analysis of allreduce optimization,
+    instead of the normal gradient synchronization.
+    For example, if only less than 10% speedup of training time can be observed after this hook is registered,
+    it usually implies that allreduce is not a performance bottleneck for this case.
+    Such instrumentation can be particularly useful
+    if GPU traces cannot be easily retrieved or the trace analysis is complicated
+    some factors such as the overlap between allreduce and computation or the desynchronization across ranks.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> ddp_model.register_comm_hook(None, noop_hook)
+    """
+    fut: torch.futures.Future[torch.Tensor] = torch.futures.Future()
+    fut.set_result(bucket.buffer())
+
+    return fut
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py
new file mode 100644
index 0000000000000000000000000000000000000000..20a0de7ef318c10f3b5bbdaf98483d9fd19b2691
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py
@@ -0,0 +1,211 @@
+# mypy: allow-untyped-defs
+from collections.abc import Callable
+from typing import Any, cast
+
+import torch
+import torch.distributed as dist
+
+
+__all__ = [
+    "allreduce_hook",
+    "fp16_compress_hook",
+    "bf16_compress_hook",
+    "fp16_compress_wrapper",
+    "bf16_compress_wrapper",
+]
+
+
+def _allreduce_fut(
+    process_group: dist.ProcessGroup, tensor: torch.Tensor
+) -> torch.futures.Future[torch.Tensor]:
+    """Average the input gradient tensor by allreduce and returns a future."""
+    group_to_use = process_group if process_group is not None else dist.group.WORLD
+
+    # Apply the division first to avoid overflow, especially for FP16.
+    # pyrefly: ignore [missing-attribute]
+    tensor.div_(group_to_use.size())
+
+    return (
+        dist.all_reduce(tensor, group=group_to_use, async_op=True)
+        .get_future()
+        .then(lambda fut: fut.value()[0])
+    )
+
+
+def allreduce_hook(
+    process_group: dist.ProcessGroup, bucket: dist.GradBucket
+) -> torch.futures.Future[torch.Tensor]:
+    """
+    Call ``allreduce`` using ``GradBucket`` tensors.
+
+    Once gradient tensors are aggregated across all workers, its ``then``
+    callback takes the mean and returns the result.
+
+    If user registers this DDP communication hook,
+    DDP results is expected to be same as the case where no hook was registered.
+    Hence, this won't change behavior of DDP and user can use this as a reference
+    or modify this hook to log useful information or any other purposes while
+    unaffecting DDP behavior.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> ddp_model.register_comm_hook(process_group, allreduce_hook)
+    """
+    return _allreduce_fut(process_group, bucket.buffer())
+
+
+def _compress_hook(
+    dtype: torch.dtype,
+    process_group: dist.ProcessGroup,
+    bucket: dist.GradBucket,
+) -> torch.futures.Future[torch.Tensor]:
+    group_to_use = process_group if process_group is not None else dist.group.WORLD
+    # pyrefly: ignore [missing-attribute]
+    world_size = group_to_use.size()
+
+    buffer = (
+        cast(tuple[torch.Tensor, ...], bucket)[0]
+        if isinstance(bucket, tuple)
+        else bucket.buffer()
+    )
+    compressed_tensor = buffer.to(dtype).div_(world_size)
+
+    def decompress(fut):
+        decompressed_tensor = buffer
+        # Decompress in place to reduce the peak memory.
+        # See: https://github.com/pytorch/pytorch/issues/45968
+        value = fut if isinstance(fut, torch.Tensor) else fut.value()[0]
+        decompressed_tensor.copy_(value)
+        return decompressed_tensor
+
+    if torch.compiler.is_compiling():
+        grad = dist._functional_collectives.all_reduce(
+            compressed_tensor,
+            "sum",
+            # pyrefly: ignore [bad-argument-type]
+            group_to_use,
+        )
+        return decompress(grad)
+    else:
+        fut = dist.all_reduce(
+            compressed_tensor, group=group_to_use, async_op=True
+        ).get_future()
+        return fut.then(decompress)
+
+
+def fp16_compress_hook(
+    process_group: dist.ProcessGroup,
+    bucket: dist.GradBucket,
+) -> torch.futures.Future[torch.Tensor]:
+    """
+    Compress by casting ``GradBucket`` to ``torch.float16`` divided by process group size.
+
+    This DDP communication hook implements a simple gradient compression
+    approach that casts ``GradBucket`` tensor to half-precision floating-point format (``torch.float16``)
+    and then divides it by the process group size.
+    It allreduces those ``float16`` gradient tensors. Once compressed gradient
+    tensors are allreduced, the chained callback ``decompress`` casts it back to the input data type (such as ``float32``).
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> ddp_model.register_comm_hook(process_group, fp16_compress_hook)
+    """
+    return _compress_hook(torch.float16, process_group, bucket)
+
+
+def bf16_compress_hook(
+    process_group: dist.ProcessGroup,
+    bucket: dist.GradBucket,
+) -> torch.futures.Future[torch.Tensor]:
+    """
+    Warning: This API is experimental, and it requires NCCL version later than 2.9.6.
+
+    This DDP communication hook implements a simple gradient compression
+    approach that casts ``GradBucket`` tensor to half-precision
+    `Brain floating point format `_ (``torch.bfloat16``)
+    and then divides it by the process group size.
+    It allreduces those ``bfloat16`` gradient tensors. Once compressed gradient
+    tensors are allreduced, the chained callback ``decompress`` casts it back to the input data type (such as ``float32``).
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> ddp_model.register_comm_hook(process_group, bf16_compress_hook)
+    """
+    return _compress_hook(torch.bfloat16, process_group, bucket)
+
+
+def fp16_compress_wrapper(
+    hook: Callable[[Any, dist.GradBucket], torch.futures.Future[torch.Tensor]],
+) -> Callable[[Any, dist.GradBucket], torch.futures.Future[torch.Tensor]]:
+    """
+    Cast input tensor to ``torch.float16``, cast result of hook back to input dtype.
+
+    This wrapper casts the input gradient tensor of a given DDP communication hook to half-precision
+    floating point format (``torch.float16``), and casts the resulting tensor of the given hook back to
+    the input data type, such as ``float32``.
+    Therefore, ``fp16_compress_hook`` is equivalent to ``fp16_compress_wrapper(allreduce_hook)``.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, start_powerSGD_iter=10)
+        >>> ddp_model.register_comm_hook(state, fp16_compress_wrapper(powerSGD_hook))
+    """
+
+    def fp16_compress_wrapper_hook(
+        hook_state, bucket: dist.GradBucket
+    ) -> torch.futures.Future[torch.Tensor]:
+        # Cast bucket tensor to FP16.
+        bucket.set_buffer(bucket.buffer().to(torch.float16))
+
+        fut = hook(hook_state, bucket)
+
+        def decompress(fut):
+            decompressed_tensor = bucket.buffer()
+            # Decompress in place to reduce the peak memory.
+            # See: https://github.com/pytorch/pytorch/issues/45968
+            decompressed_tensor.copy_(fut.value())
+            return decompressed_tensor
+
+        # Decompress after hook has run.
+        return fut.then(decompress)
+
+    return fp16_compress_wrapper_hook
+
+
+def bf16_compress_wrapper(
+    hook: Callable[[Any, dist.GradBucket], torch.futures.Future[torch.Tensor]],
+) -> Callable[[Any, dist.GradBucket], torch.futures.Future[torch.Tensor]]:
+    """
+    Warning: This API is experimental, and it requires NCCL version later than 2.9.6.
+
+    This wrapper casts the input gradient tensor of a given DDP communication hook to half-precision
+    `Brain floating point format `_  (``torch.bfloat16``),
+    and casts the resulting tensor of the given hook back to the input data type, such as ``float32``.
+
+    Therefore, ``bf16_compress_hook`` is equivalent to ``bf16_compress_wrapper(allreduce_hook)``.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, start_powerSGD_iter=10)
+        >>> ddp_model.register_comm_hook(state, bf16_compress_wrapper(powerSGD_hook))
+    """
+
+    def bf16_compress_wrapper_hook(
+        hook_state, bucket: dist.GradBucket
+    ) -> torch.futures.Future[torch.Tensor]:
+        # Cast bucket tensor to BF16.
+        bucket.set_buffer(bucket.buffer().to(torch.bfloat16))
+
+        fut = hook(hook_state, bucket)
+
+        def decompress(fut):
+            decompressed_tensor = bucket.buffer()
+            # Decompress in place to reduce the peak memory.
+            # See: https://github.com/pytorch/pytorch/issues/45968
+            decompressed_tensor.copy_(fut.value())
+            return decompressed_tensor
+
+        # Decompress after hook has run.
+        return fut.then(decompress)
+
+    return bf16_compress_wrapper_hook
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/mixed_precision_hooks.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/mixed_precision_hooks.py
new file mode 100644
index 0000000000000000000000000000000000000000..f1968042e5e21aa1b6714f78356b43896cccdf60
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/mixed_precision_hooks.py
@@ -0,0 +1,86 @@
+from dataclasses import dataclass
+from typing import Any, no_type_check
+
+import torch
+import torch.distributed as dist
+from torch.autograd import Variable
+from torch.distributed.utils import _free_storage
+
+
+@dataclass
+class _AllreduceUpcastHookState:
+    """
+    State to manage DDP mixed precision in backward / gradient communication.
+
+    This contains a weakref to the DDP module for access to reducer and process
+    group, and a stream to run parameter and gradient upcasts.
+    """
+
+    ddp_weakref: Any
+    upcast_stream: torch.Stream
+    wait_for_stream_enqueued: bool = False
+
+
+@no_type_check
+def _reducer_allreduce_and_upcast_hook(
+    hook_state: _AllreduceUpcastHookState, bucket: dist.GradBucket
+) -> torch.futures.Future[torch.Tensor]:
+    """
+    Perform allreduce in precision ``reduce_dtype``, upcast to prepare for optimizer.
+
+    Performs allreduce in the reduced precision given by DDP's mixed precision
+    reduce_dtype, and upcasts parameters and gradients to fp32 in preparation
+    to run the optimizer.
+    """
+    ddp_weakref = hook_state.ddp_weakref
+    reducer, process_group = ddp_weakref().reducer, ddp_weakref().process_group
+    # Cast bucket if different than param_dtype.
+    if (
+        ddp_weakref().mixed_precision.param_dtype
+        != ddp_weakref().mixed_precision.reduce_dtype
+    ):
+        # Cast bucket tensor to reduce_dtype
+        bucket.set_buffer(
+            bucket.buffer().to(ddp_weakref().mixed_precision.reduce_dtype)
+        )
+    fut = reducer._run_allreduce_hook(bucket)
+    ret_fut = torch.futures.Future()
+    stream = hook_state.upcast_stream
+    with stream:
+        fut.wait()
+        bucket.buffer().div_(process_group.size())
+        ret_fut.set_result(bucket.buffer())
+
+        # Upcast parameters and gradients so optimizer step can run in fp32.
+        for p in bucket.parameters():
+            p.data = p._fp_param
+            # free storage for mp param as it will be allocated again in next
+            # forward pass.
+            _free_storage(p._mp_param)
+            p.grad.data = p.grad.to(p.data.dtype)
+
+    # enqueue a callback to wait for this stream at end of backward
+    def wait_for_stream_cb():
+        torch.accelerator.current_stream().wait_stream(stream)
+        # Remove post-backward hooks since they are re-installed in next
+        # iteration, similar to FSDP.
+        # Parameters that don't require grad still needed to be casted since
+        # they may participate in computation. However, they would not be recast
+        # by hook above as they don't have a grad hook installed, so cast them
+        # back here.
+        for _, p in ddp_weakref().module.named_parameters():
+            if hasattr(p, "_ddp_mp_hook_state"):
+                p._ddp_mp_hook_state[1].remove()
+                delattr(p, "_ddp_mp_hook_state")
+            if not p.requires_grad and not hasattr(p, "_ddp_ignored"):
+                p.data = p._fp_param
+
+        # reset for next backward pass
+        hook_state.wait_for_stream_enqueued = False
+
+    if not hook_state.wait_for_stream_enqueued:
+        Variable._execution_engine.queue_callback(wait_for_stream_cb)
+        # mark that the callback is enqueued
+        hook_state.wait_for_stream_enqueued = True
+
+    return ret_fut
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/optimizer_overlap_hooks.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/optimizer_overlap_hooks.py
new file mode 100644
index 0000000000000000000000000000000000000000..162160e394ad0b634365f941f3a9f216bf1aa2d8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/optimizer_overlap_hooks.py
@@ -0,0 +1,163 @@
+# mypy: allow-untyped-defs
+from collections.abc import Callable
+from dataclasses import dataclass
+from functools import partial
+from typing import Any, no_type_check
+
+import torch
+import torch.distributed as dist
+from torch.autograd import Variable
+
+
+__all__: list[str] = []
+
+_FUNCTIONAL_OPTIM_STEP_METHOD_NAME = "step_param"
+
+
+class _OptimizerHookState:
+    """
+    Holds state for running optimizer in-line after DDP communication hook.
+
+    Currently contains only optimizer class which must have a method `step_param`.
+    """
+
+    __slots__ = ["functional_optimizer", "params_to_optimize"]
+
+    def __init__(self, functional_optim, params=None):
+        self.functional_optimizer = functional_optim
+        self._check_valid_functional_optim()
+        self._set_params_to_optimize(params)
+
+    def _set_params_to_optimize(self, params):
+        if params is not None:
+            self.params_to_optimize = set(params)
+
+    def _check_valid_functional_optim(self):
+        if not hasattr(self.functional_optimizer, _FUNCTIONAL_OPTIM_STEP_METHOD_NAME):
+            raise ValueError(
+                f"Class {type(self.functional_optimizer)} must implement method "
+                f"{_FUNCTIONAL_OPTIM_STEP_METHOD_NAME}."
+            )
+
+
+@dataclass
+class _OptimInBackwardHookState:
+    optim_stream: torch.Stream
+    wait_for_optim_stream_enqueued: bool
+
+
+@no_type_check
+def _apply_optim_in_backward_hook(
+    gradient_is_bucket_view: bool,
+) -> Callable[[Any, dist.GradBucket], torch.futures.Future[torch.Tensor]]:
+    r"""
+    Register hook to apply the optimizer in backward.
+
+    If torch.distributed.optim._apply_optimizer_in_backward is used to overlap
+    optimizer with backward pass, DDP will run the below hook to run optimizer
+    step for parameters after gradient communication has taken place.
+    """
+    optim_in_bwd_state = _OptimInBackwardHookState(
+        optim_stream=torch.Stream(),
+        wait_for_optim_stream_enqueued=False,
+    )
+
+    def apply_optim_in_backward_hook(
+        hook_state: Any,
+        bucket: dist.GradBucket,
+        optim_stream_state,
+    ) -> torch.futures.Future[torch.Tensor]:
+        # Run original hook
+        ddp_weakref = hook_state
+        ddp_inst = ddp_weakref()
+        reducer, process_group = ddp_inst.reducer, ddp_inst.process_group
+        fut = reducer._run_allreduce_hook(bucket)
+        optimizer_stream = optim_stream_state.optim_stream
+        with optimizer_stream:
+            fut.wait()
+            # Apply gradient division since C++ side only allreduces and does
+            # not average. TODO: (rohan-varma) the div factor may be different
+            # when running with join hook
+            bucket.buffer().div_(process_group.size())
+            model_params = bucket.parameters()
+            grads = bucket.gradients()
+            # TODO (rohan-varma): upcast as needed for DDP mixed precision,
+            # once optimizer in backward + DDP mixed precision is supported.
+            for p, g in zip(model_params, grads):
+                if hasattr(p, "_in_backward_optimizers"):
+                    # Note: need to set grad to the bucket's grad, because
+                    # running allreduce results in the bucket's grad being
+                    # reduced, but not grad field.
+                    if not gradient_is_bucket_view:
+                        p.grad = g
+                    for optim in p._in_backward_optimizers:
+                        optim.step()
+
+        # Need to return a Future[Tensor] to obey comm hook API contract.
+        ret_fut = torch.futures.Future()
+        ret_fut.set_result(bucket.buffer())
+
+        # enqueue a callback to wait for this optimizer stream at the end of
+        # backward and set all DDP managed grads to None.
+        def wait_for_optim_stream_callback():
+            torch.accelerator.current_stream().wait_stream(
+                optim_stream_state.optim_stream
+            )
+            # Set DDP managed grads to None
+            for param in ddp_inst._get_data_parallel_params(ddp_inst.module):
+                if hasattr(param, "_in_backward_optimizers"):
+                    param.grad = None
+
+            # reset for the next backwards pass
+            optim_stream_state.wait_for_optim_stream_enqueued = False
+
+        if not optim_stream_state.wait_for_optim_stream_enqueued:
+            Variable._execution_engine.queue_callback(wait_for_optim_stream_callback)
+            # mark that the callback is enqueued
+            optim_stream_state.wait_for_optim_stream_enqueued = True
+
+        return ret_fut
+
+    comm_hook = partial(
+        apply_optim_in_backward_hook, optim_stream_state=optim_in_bwd_state
+    )
+    # These are needed for DDP's logging of comm hooks
+    comm_hook.__name__ = apply_optim_in_backward_hook.__name__
+    comm_hook.__qualname__ = apply_optim_in_backward_hook.__qualname__
+
+    return comm_hook
+
+
+def _hook_then_optimizer(
+    hook: Callable[[Any, dist.GradBucket], torch.futures.Future[torch.Tensor]],
+    optimizer_state: _OptimizerHookState,
+) -> Callable[[Any, dist.GradBucket], torch.futures.Future[torch.Tensor]]:
+    r"""Run optimizer in a functional fashion after DDP communication hook."""
+    has_set_params = (
+        hasattr(optimizer_state, "params_to_optimize")
+        and optimizer_state.params_to_optimize is not None
+    )
+
+    def hook_then_optimizer_wrapper(
+        hook_state, bucket: dist.GradBucket
+    ) -> torch.futures.Future[torch.Tensor]:
+        # Run original hook
+        fut = hook(hook_state, bucket)
+
+        def optimizer_step(fut):
+            gradient_tensors = bucket.gradients()
+            model_params = bucket.parameters()
+            for grad_tensor, model_param in zip(gradient_tensors, model_params):
+                if (
+                    not has_set_params
+                    or model_param in optimizer_state.params_to_optimize
+                ):
+                    optimizer_state.functional_optimizer.step_param(
+                        model_param,
+                        grad_tensor,
+                    )
+            return bucket.buffer()
+
+        return fut.then(optimizer_step)
+
+    return hook_then_optimizer_wrapper
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/post_localSGD_hook.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/post_localSGD_hook.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff513f62183c516b96c62ca89eee51d2b1793e85
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/post_localSGD_hook.py
@@ -0,0 +1,124 @@
+# mypy: allow-untyped-defs
+import logging
+
+import torch
+import torch.distributed as dist
+
+from . import default_hooks as default
+
+
+logger = logging.getLogger(__name__)
+
+
+class PostLocalSGDState:
+    r"""
+    Store state for all-reducing gradients globally until given step, then locally after.
+
+    Stores the state for all-reducing gradients globally using ``process_group`` until step ``start_localSGD_iter``,
+    and all-reducing gradients locally using ``subgroup`` afterwards.
+
+    If ``process_group`` is ``None``, the global process group will be used.
+    If ``subgroup`` is ``None``, the intra-node process group on each machine will be used.
+
+    Additionally, ``post_local_gradient_allreduce`` may be worth tuning,
+    because both true and false may give a faster convergence.
+    """
+
+    __slots__ = [
+        "process_group",
+        "subgroup",
+        "start_localSGD_iter",
+        "post_local_gradient_allreduce",
+        "iter",
+    ]
+
+    def __init__(
+        self,
+        process_group,
+        subgroup,
+        start_localSGD_iter,
+        post_local_gradient_allreduce=True,
+    ):
+        """Initialize state object with given parameters and log when localSGD start."""
+        logger.info(
+            "Local SGD will be started after %s iterations", start_localSGD_iter
+        )
+
+        # The group used for all-reducing gradients globally.
+        self.process_group = process_group
+        # The group used for all-reducing gradients locally.
+        self.subgroup = subgroup
+        self.start_localSGD_iter = start_localSGD_iter
+        # Allreduce gradients locally since iteration `start_localSGD_iter`.
+        # This may help with the convergence efficiency at the cost of relatively cheap intra-subgroup communication.
+        self.post_local_gradient_allreduce = post_local_gradient_allreduce
+        # Iteration/step in the training loop.
+        self.iter = 0
+
+    def maybe_increase_iter(self, bucket):
+        """Track iterations and trigger log message at start of local SGD."""
+        # Since bucket 0 is the last bucket to allreduce in an iteration.
+        # Only increase `iter` when bucket 0 is processed.
+        if bucket.is_last():
+            self.iter += 1
+
+        if self.iter == self.start_localSGD_iter:
+            logger.info("Start to apply local SGD after %s iterations.", self.iter)
+
+
+def post_localSGD_hook(
+    state: PostLocalSGDState, bucket: dist.GradBucket
+) -> torch.futures.Future[torch.Tensor]:
+    """
+    Run post-localSGD algorithm.
+
+    This DDP communication hook is used for running post-localSGD algorithm,
+    by combining with a model averaging component (e.g.,
+    :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`)
+    that runs after the optimizer step.
+
+    Args:
+        state (PostLocalSGDState): State information to run post-localSGD.
+            Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD.
+        bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors.
+            Note that since DDP comm hook only supports single process single device mode,
+            only exactly one tensor is stored in this bucket.
+
+    Returns:
+        Future handler of the communication, which updates the gradients in place.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup,
+                                  start_localSGD_iter=10)
+        >>> ddp_model.register_comm_hook(state, post_localSGD_hook)
+        >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``.
+        >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module.
+    """
+    global_group_to_use = (
+        state.process_group if state.process_group is not None else dist.group.WORLD
+    )
+
+    # The input tensor is a flattened 1D tensor.
+    input_tensor = bucket.buffer()
+
+    # Run allreduce using `global_group_to_use` in the first `start_localSGD_iter` iterations.
+    if state.iter < state.start_localSGD_iter:
+        state.maybe_increase_iter(bucket)
+        return default._allreduce_fut(global_group_to_use, input_tensor)  # type: ignore[arg-type]
+
+    # If `post_local_gradient_allreduce` is not set,
+    # then no gradient synchronization after the first `start_localSGD_iter` iterations.
+    if not state.post_local_gradient_allreduce:
+        fut: torch.futures.Future[torch.Tensor] = torch.futures.Future()
+        fut.set_result(input_tensor)
+        return fut
+
+    # Run allreduce using `subgroup` after the first `start_localSGD_iter` iterations.
+    # Note that by default, a separate subgroup for each node is created which
+    # causes an intra-node allreduce to be done at each training step.
+    # From this moment, model averaging should run after the optimizer step,
+    # to globally allreduce all the parameters.
+    if state.subgroup is None:
+        state.subgroup, _ = dist.new_subgroups()
+    return default._allreduce_fut(state.subgroup, input_tensor)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py
new file mode 100644
index 0000000000000000000000000000000000000000..f1e95d12514eda18b52ae07a44a68e1678bd27a9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py
@@ -0,0 +1,862 @@
+# mypy: allow-untyped-defs
+import logging
+import math
+from collections import defaultdict
+
+import torch
+import torch.distributed as dist
+from torch.distributed import distributed_c10d
+from torch.utils._typing_utils import not_none
+
+from . import default_hooks as default
+
+
+__all__ = ["PowerSGDState", "powerSGD_hook", "batched_powerSGD_hook"]
+
+logger = logging.getLogger(__name__)
+
+
+def _orthogonalize(matrices, epsilon=0):
+    """
+    Decide between Gram-Schmidt or QR factorization to orthogonalize a batch of matrices.
+
+    QR factorization doesn't work with half-precision, but it is usually faster with a rank > 2.
+    """
+    assert len(matrices.shape) == 3 and matrices.shape[2] <= matrices.shape[1]
+
+    num_matrices = matrices.shape[0]
+    rank = matrices.shape[2]
+    dtype = matrices.dtype
+    if rank <= 2 or dtype in [torch.float16, torch.bfloat16]:
+        _orthogonalize_gram_schmidt(matrices, epsilon=epsilon)
+    else:
+        torch.linalg.qr(
+            matrices,
+            out=(
+                matrices,
+                torch.empty(
+                    num_matrices, rank, rank, device=matrices.device, dtype=dtype
+                ),
+            ),
+        )
+
+
+def _orthogonalize_gram_schmidt(matrices, epsilon=0):
+    """
+    Apply Gram-Schmidt procedure to orthogonalize a batch of matrices.
+
+    If epsilon is 0, this is equivalent to `torch.qr(matrices, out=(matrices, _))`,
+    """
+    num_cols = matrices.shape[2]
+    for i in range(num_cols):
+        # Normalize the i'th column.
+        col = matrices[:, :, i : i + 1]
+        # If no epsilon is added here, division by zero may be caused by vanishing gradients.
+        # This epsilon is not needed if the input batch of matrices covers the gradients of at least one entire layer
+        # in the neural network.
+        if epsilon == 0:
+            # Note that col ** 2 can underflow/overflow if we use FP16.
+            # May need to consider multiplying a scaling factor and dividing it later, or using bfloat16 instead.
+            try:
+                col /= torch.norm(col, dim=1, keepdim=True)
+            except ZeroDivisionError:
+                logger.error(
+                    "The matrices to be orthogonalized has at least a column of all 0s. Please set a small value such as 1e-8 "
+                    "as `orthogonalization_epsilon` in PowerSGD state."
+                )
+                # Recover the values from NaNs to 0s.
+                col.fill_(0.0)
+        else:
+            col /= torch.norm(col, dim=1, keepdim=True) + epsilon
+        # Project it on the rest and remove it.
+        if i + 1 < num_cols:
+            rest = matrices[:, :, i + 1 :]
+            rest -= torch.sum(col * rest, dim=1, keepdim=True) * col
+
+
+def _should_compress(
+    num_rows, num_cols, matrix_approximation_rank, min_compression_rate
+):
+    """
+    Recommend if tensor given is worth compressing.
+
+    Returns a recommendation as to whether the 2D tensor described by the arguments is worth compressing,
+    including statistics describing the expected savings from compression.  We consider a tensor worth
+    compressing when ``min_compression_rate`` < uncompressed size / compressed size, where
+    uncompressed size = ``num_rows`` * ``num_cols``,
+    and compressed size = (``num_rows`` + ``num_cols``) * ``matrix_approximation_rank``.
+
+    The result of this function is a tuple of the form (compression_recommendation, uncompressed_el_count, compressed_el_count), where:
+
+    compression_recommendation is true if the tensor is worth compressing, and false otherwise (see above);
+
+    uncompressed_el_count is the uncompressed element count, i.e. ``num_rows`` * ``num_cols``; and,
+
+    compress_el_count is the element count after compression, i.e. (``num_rows`` + ``num_cols``) * ``matrix_approximation_rank``.
+    """  # noqa: B950
+    uncompressed_size = num_rows * num_cols
+    compressed_size = (num_rows + num_cols) * matrix_approximation_rank
+    return (
+        compressed_size * min_compression_rate < uncompressed_size,
+        uncompressed_size,
+        compressed_size,
+    )
+
+
+def _report_compression_stats(bucket, state):
+    """Report compression stats at frequency of ``compression_stats_logging_frequency`` specified in PowerSGD state."""
+    if bucket.is_last() and state.iter >= state.next_stats_report:
+        stats = state.compression_stats()
+        logger.info(
+            "Compression stats: iter %s, total before compression %s, total after compression %s, "
+            "rate %s",
+            state.iter,
+            stats[1],
+            stats[2],
+            stats[0],
+        )
+        state.next_stats_report = state.iter + state.compression_stats_logging_frequency
+
+
+class PowerSGDState:
+    r"""
+    Store both the algorithm's hyperparameters and internal state for all gradients during training.
+
+    Particularly, ``matrix_approximation_rank`` and ``start_powerSGD_iter`` are the main hyperparameters that should be tuned by the user.
+    For performance, we suggest to keep binary hyperparameters ``use_error_feedback`` and ``warm_start`` on.
+
+    1. ``matrix_approximation_rank`` controls the size of compressed low-rank tensors, which determines the compression rate. The lower the rank, the stronger the compression.
+
+        1.1. If ``matrix_approximation_rank`` is too low, the full model quality will need more training steps to reach or will never reach and yield loss in accuracy.
+
+        1.2. The increase of ``matrix_approximation_rank`` can substantially increase the computation costs of the compression, and the accuracy may not be further improved beyond a certain ``matrix_approximation_rank`` threshold.
+
+    To tune ``matrix_approximation_rank``, we suggest to start from 1 and increase by factors of 2 (like an exponential grid search, 1, 2, 4, ...), until a satisfactory accuracy is reached. Typically only a small value 1-4 is used. For some NLP tasks (as shown in Appendix D of the original paper), this value has been increased to 32.
+
+    2. ``start_powerSGD_iter`` defers PowerSGD compression until step ``start_powerSGD_iter``, and vanilla allreduce runs prior to step ``start_powerSGD_iter``. This hybrid scheme of **vanilla allreduce + PowerSGD** can effectively improve the accuracy, even a relatively small ``matrix_approximation_rank`` is used. This is because that, the beginning of training phase is usually very sensitive to inaccurate gradients, and compressing gradients too early may make the training quickly take a suboptimal trajectory, which can result in an irrecoverable impact on the accuracy.
+
+    To tune ``start_powerSGD_iter``, we suggest to start with 10% of total training steps, and increase it until a satisfactory accuracy is reached. If there is a warm-up stage in the training, ``start_powerSGD_iter`` typically should be no less than the number of warm-up steps.
+
+    3. ``min_compression_rate`` is the minimum compression rate required when a layer is compressed. Due to the computation overheads incurred by the compression, a tensor is worth compressing only if there can be sufficient saving in bandwidth, where ``(num_rows + num_cols) * matrix_approximation_rank * min_compression_rate < num_rows * num_cols``. If the specified compression rate threshold cannot be satisfied, the tensor will be directly allreduced without compression.
+
+    Compression statistics are logged every ``compression_stats_logging_frequency`` iterations once PowerSGD compression starts.
+
+    4. ``orthogonalization_epsilon`` can be a very small value (e.g., 1e-8) added to every normalized matrix column in orthogonalization step, to prevent div-by-zero error if any column has all 0s. If this can already be prevented (e.g., by batch normalization), an epsilon of 0 is recommended for accuracy.
+
+    5. ``batch_tensors_with_same_shape`` controls whether to compress and decompress tensors with same shape in a batched operation to achieve higher parallelism. Note that you should also increase the bucket size (i.e., ``bucket_cap_mb`` arg in DDP constructor) to make more same-shaped tensors appear in the same bucket, however this may reduce the overlap between computation and communication, and increase the memory footprint due to stacking the tensors of the same shape. Set to ``True`` if the compression / decompression computation is a bottleneck.
+
+    .. warning ::
+        If error feedback or warm-up is enabled, the minimum value of ``start_powerSGD_iter`` allowed in DDP is 2.
+        This is because there is another internal optimization that rebuilds buckets at iteration 1 in DDP,
+        and this can conflict with any tensor memorized before the rebuild process.
+    """  # noqa: B950
+
+    __slots__ = [
+        "process_group",
+        # The fields below are the hyperparameters that often need to be tuned by the user.
+        "matrix_approximation_rank",
+        "start_powerSGD_iter",
+        # The fields below are the hyperparameters that seldom need be tuned by the user.
+        "min_compression_rate",
+        "orthogonalization_epsilon",
+        # The fields below are the binary hyperparameters recommended to be turned on for performance and accuracy.
+        "use_error_feedback",
+        "warm_start",
+        "batch_tensors_with_same_shape",
+        # The fields below are internal state.
+        "rng",
+        "error_dict",
+        "p_memory_dict",
+        "q_memory_dict",
+        "iter",
+        # The fields below are for recording compression stats.
+        "total_numel_before_compression",
+        "total_numel_after_compression",
+        "compression_stats_logging_frequency",
+        "next_stats_report",
+    ]
+
+    def __init__(
+        self,
+        process_group,
+        matrix_approximation_rank=1,
+        start_powerSGD_iter=1_000,
+        min_compression_rate=2,
+        use_error_feedback=True,
+        warm_start=True,
+        orthogonalization_epsilon=0,
+        random_seed=0,
+        compression_stats_logging_frequency=10_000,
+        batch_tensors_with_same_shape: bool = False,
+    ):
+        logger.info(
+            "PowerSGD config: matrix_approximation_rank = %s; start_powerSGD_iter = %s; "
+            "min_compression_rate = %s; orthogonalization_epsilon = %s; use_error_feedback = %s; warm_start = %s; "
+            "random_seed = %s; compression_stats_logging_frequency = %s; batch_tensors_with_same_shape = %s",
+            matrix_approximation_rank,
+            start_powerSGD_iter,
+            min_compression_rate,
+            orthogonalization_epsilon,
+            use_error_feedback,
+            warm_start,
+            random_seed,
+            compression_stats_logging_frequency,
+            batch_tensors_with_same_shape,
+        )
+
+        self.process_group = process_group
+        self.matrix_approximation_rank = matrix_approximation_rank
+        # Deferring PowerSGD compression util step 'start_powerSGD_iter' can have two advantages:
+        # 1) It turns out that PowerSGD may lead to a non-trivial accuracy loss,
+        # even if the matrix approximation rank is increased to a large value.
+        # To mitigate the accuracy loss, a simple yet effective way is mixing vanilla allreduce
+        # (or a more conservative compression such as FP16 compression) with PowerSGD.
+        # 2) There is an internal optimization of rebuilding buckets process in DDP,
+        # in order to save the memory space.
+        # This step takes place after the first iteration.
+        # However, this means that the shape of input bucketized tensors is subject to change,
+        # which will complicate the implementations of error feedback and warm-up.
+        # Running vanilla allreduce in the first few iterations can avoid this complexity.
+        if (use_error_feedback or warm_start) and start_powerSGD_iter <= 1:
+            raise ValueError(
+                "Expect `start_powerSGD_iter` > 1 if `use_error_feedback` or `warm_start` is enabled, "
+                "because PowerSGD can only be applied after the first two iterations in DDP."
+            )
+        self.start_powerSGD_iter = start_powerSGD_iter
+        self.min_compression_rate = min_compression_rate
+        # Error feedback is usually crucial for both for convergence and generalization,
+        # because PowerSGD is a biased compressor,
+        # i.e., compressing and decompressing a random gradient does not yield the original in expectation.
+        # This mechanism requires a temporary copy of the input gradients,
+        # so it increases the peak memory consumption by the size of the gradient tensor.
+        # However, if the target matrices are known to be exactly low-ranked (instead of just low stable rank),
+        # sometimes it is possible to converge to the optima without error feedback.
+        # See: http://proceedings.mlr.press/v54/yurtsever17a/yurtsever17a.pdf
+        self.use_error_feedback = use_error_feedback
+        # Warm-start reuses P(s) and Q(s) from the previous iteration.
+        # This can improve the approximation quality and hence improve the accuracy.
+        # Additionally, by avoiding the initialization of these low-rank tensors at every step,
+        # this can also accelerate training.
+        # However, this is at the cost of extra memory.
+        self.warm_start = warm_start
+        # Can use a very small value to prevent div-by-zero error caused by orthogonalization of vanishing gradients.
+        self.orthogonalization_epsilon = orthogonalization_epsilon
+        # The purpose of this RNG is to generate different random seeds for initializing Q across iterations,
+        # but in the same order for all the DDP replicas.
+        # Different random seeds across iterations indicate different 'projections' of the gradients at different SGD steps.
+        # If the same random projection is used,
+        # there will be differences between the gradients that are never synchronized.
+        import numpy as np
+
+        self.rng = np.random.RandomState(random_seed)
+        # Since there is only a single state instance for all the input buckets,
+        # need to maintain a dictionary that maps each bucket index to the local error.
+        self.error_dict: dict[int, torch.Tensor] = {}
+        self.p_memory_dict: dict[int, torch.Tensor] = {}
+        self.q_memory_dict: dict[int, torch.Tensor] = {}
+        # Iteration/step in the training loop.
+        self.iter = 0
+        # Compression stats accumulators
+        self.total_numel_before_compression = 0
+        self.total_numel_after_compression = 0
+        # We'll report compression stats every 'compression_stats_logging_frequency' iterations
+        # Note that we always report compression stats at least once.
+        self.compression_stats_logging_frequency = max(
+            1, compression_stats_logging_frequency
+        )
+        self.next_stats_report = 0
+        # Batching tensors with same shape can increase parallelism in compression / decompression computation.
+        # This requires a larger bucket size to make more same-shaped tensor to appear in one bucket, however
+        # this may reduce the overlap between computation and communication, and increase the memory footprint
+        # due to stacking tensors.
+        # Turn on if compression / decompression computation is a bottleneck.
+        self.batch_tensors_with_same_shape = batch_tensors_with_same_shape
+
+    def __getstate__(self):
+        r"""
+        Return a ``Dict[str, Any]`` which will be pickled and saved.
+
+        ``process_group`` is not serializable and excluded from
+        a returned state.
+        """
+        logger.warning(
+            "NOTE: Process group is not serializable and excluded from a saved state."
+        )
+        return {
+            slot: getattr(self, slot)
+            for slot in self.__slots__
+            if slot != "process_group"
+        }
+
+    def __setstate__(self, state):
+        r"""
+        Take a provided ``state`` and set to this ``PowerSGDState`` instance.
+
+        ``process_group`` is set to default.
+        """
+        self.process_group = distributed_c10d._get_default_group()
+        logger.warning(
+            "NOTE: Process group will be set to a default group (i.e. the world size).\
+                If a different group is desired, please set `self.process_group` after PowerSGD state is loaded."
+        )
+        for slot, value in state.items():
+            setattr(self, slot, value)
+
+    def maybe_increase_iter(self, bucket):
+        """Track iterations and trigger log message at start of local SGD."""
+        # Since bucket 0 is the last bucket to allreduce in an iteration.
+        # Only increase `iter` when bucket 0 is processed.
+        if bucket.is_last():
+            self.iter += 1
+
+        if self.iter == self.start_powerSGD_iter:
+            logger.info("Start to apply PowerSGD after %s iterations.", self.iter)
+
+    def compression_stats(self):
+        r"""
+        Return latest compression statistics as tuple.
+
+        Returns tuple of form (compress_rate, numel_before_compression, numel_after_compression) where:
+
+        compress_rate is the effective compression rate i.e. (number of elements before compression) / (number of elements after compression);
+
+        numel_before_compression is the total number of elements before compression was applied; and,
+
+        numel_after_compression is the total number of elements after compression was applied.
+        """  # noqa: B950
+        compress_rate = (
+            self.total_numel_before_compression / self.total_numel_after_compression
+            if self.total_numel_after_compression > 0
+            else 0
+        )
+        return (
+            compress_rate,
+            self.total_numel_before_compression,
+            self.total_numel_after_compression,
+        )
+
+
+def powerSGD_hook(
+    state: PowerSGDState, bucket: dist.GradBucket
+) -> torch.futures.Future[torch.Tensor]:
+    r"""
+    Implement PowerSGD algorithm.
+
+    This DDP communication hook implements PowerSGD gradient compression
+    algorithm described in the `paper `_.
+    Once gradient tensors are aggregated across all workers, this hook applies
+    compression as follows:
+
+    1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups:
+
+        1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth.
+
+        1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases).
+
+    2. Handles uncompressed tensors:
+
+        2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression;
+
+        2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor.
+
+    3. Handles the tensors that should be compressed by PowerSGD compression:
+
+        3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M,
+        such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized;
+
+        3.2. Computes each P in Ps, which is equal to MQ;
+
+        3.3. Allreduces Ps as a batch;
+
+        3.4. Orthogonalizes each P in Ps;
+
+        3.5. Computes each Q in Qs, which is approximately equal to M^TP;
+
+        3.6. Allreduces Qs as a batch;
+
+        3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T.
+
+    Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations.
+    This not only gives the user more control over the tradeoff between speedup and accuracy,
+    but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers.
+
+    Args:
+        state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc.
+            To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter``
+            and ``min_compression_rate``.
+        bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors.
+            Note that since DDP comm hook only supports single process single device mode,
+            only exactly one tensor is stored in this bucket.
+
+    Returns:
+        Future handler of the communication, which updates the gradients in place.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1,
+                                  start_powerSGD_iter=10, min_compression_rate=0.5)
+        >>> ddp_model.register_comm_hook(state, powerSGD_hook)
+    """  # noqa: B950
+    process_group = state.process_group
+    group_to_use = (
+        process_group if process_group is not None else not_none(dist.group.WORLD)
+    )
+    world_size = group_to_use.size()
+
+    # The input tensor is a flattened 1D tensor.
+    input_tensor = bucket.buffer()
+
+    # Run vanilla allreduce in the first `start_powerSGD_iter` iterations.
+    if state.iter < state.start_powerSGD_iter:
+        state.maybe_increase_iter(bucket)
+        return default._allreduce_fut(group_to_use, input_tensor)
+
+    # Apply PowerSGD after `start_powerSGD_iter` iterations.
+    device = input_tensor.device
+    dtype = input_tensor.dtype
+
+    # Incorporate the error from the previous state into the gradients.
+    bucket_index = bucket.index()
+    input_tensor_cp = None
+    total_length = input_tensor.shape[0]
+    if state.use_error_feedback:
+        if bucket_index in state.error_dict:
+            input_tensor.add_(state.error_dict[bucket_index])
+        else:
+            logger.info(
+                "A zero tensor of length %s that represents local error is created.",
+                total_length,
+            )
+            state.error_dict[bucket_index] = torch.zeros(
+                total_length, device=device, dtype=dtype
+            )
+
+        # Keep a copy of the input tensor,
+        # so that we can compute the local error caused by compression later,
+        # by comparing this copy and the input tensor updated after decompression.
+        input_tensor_cp = input_tensor.detach().clone()
+
+    # Unflatten the input tensor into per-parameter tensors, for layer-wise compression.
+    tensors = bucket.gradients()
+
+    # Step I: Divide all the tensors into two groups,
+    # one will be compressed before allreduce and the other will be directly allreduced without compression.
+    tensors_to_compress, uncompressed_tensors = [], []
+    total_Ps_size = 0
+    total_Qs_size = 0
+    for tensor in tensors:
+        matrix = tensor.view(tensor.shape[0], -1)
+        n, m = matrix.shape
+        matrix_approximation_rank = min(n, m, state.matrix_approximation_rank)
+        compress_test = _should_compress(
+            n, m, matrix_approximation_rank, state.min_compression_rate
+        )
+        state.total_numel_before_compression += compress_test[1]
+        if compress_test[0]:
+            tensors_to_compress.append(matrix)
+            total_Ps_size += n * matrix_approximation_rank
+            total_Qs_size += m * matrix_approximation_rank
+            state.total_numel_after_compression += compress_test[2]
+        else:
+            uncompressed_tensors.append(tensor)
+            state.total_numel_after_compression += compress_test[1]
+
+    _report_compression_stats(bucket, state)
+
+    # Step II: Handle uncompressed tensors.
+    # Allocate contiguous memory for these tensors to allreduce efficiently.
+    uncompressed_tensors_memory = (
+        torch.cat([tensor.view(-1) for tensor in uncompressed_tensors])
+        if uncompressed_tensors
+        else torch.tensor([], device=device, dtype=dtype)
+    )
+
+    # Step III: Handle the tensors that should be compressed.
+    # Allocate contiguous memory for Ps and Qs to allreduce efficiently.
+    # If warm-start is enabled, reuse Ps and Qs from the previous iteration if possible.
+    # The memory spaces of Ps and Qs need to be allocated in the first iteration when PowerSGD is applied.
+    need_randomize_qs = False
+    if not state.warm_start or bucket_index not in state.p_memory_dict:
+        need_randomize_qs = True
+        # If warm-start is disabled, low-rank tensors will be initialized at every step.
+        # Only log this if warm-start to avoid spamming.
+        if state.warm_start:
+            logger.info(
+                "Allocating contiguous memory of length %s for Ps, and of length %s for Qs, respectively.",
+                total_Ps_size,
+                total_Qs_size,
+            )
+        state.p_memory_dict[bucket_index] = torch.empty(
+            total_Ps_size, device=device, dtype=dtype
+        )
+        state.q_memory_dict[bucket_index] = torch.empty(
+            total_Qs_size, device=device, dtype=dtype
+        )
+
+    # Batch tensors to compress by shape.
+    shape_to_tensors = defaultdict(list)
+    for tensor in tensors_to_compress:
+        shape_to_tensors[tensor.shape].append(tensor)
+
+    # This function decides whether to batch tensors with same shape or not according to the argument,
+    # so the following process could share the same code.
+    def maybe_batched_tensors_to_compress():
+        for tensors in shape_to_tensors.values():
+            if state.batch_tensors_with_same_shape:
+                batch_size = len(tensors)
+                if batch_size == 1:
+                    # Use the original tensor to avoid copy.
+                    yield tensors[0].unsqueeze(0)
+                else:
+                    yield torch.stack(tensors)
+            else:
+                for tensor in tensors:
+                    yield tensor.unsqueeze(0)
+
+    # Create Ps and Qs that point to the allocated memory.
+    tensors_to_compress = []
+    ps = []
+    qs = []
+    p_idx = 0
+    q_idx = 0
+    for tensor in maybe_batched_tensors_to_compress():
+        batch_size, n, m = tensor.shape
+        matrix_approximation_rank = min(n, m, state.matrix_approximation_rank)
+        tensors_to_compress.append(tensor)
+        ps.append(
+            state.p_memory_dict[bucket_index][
+                p_idx : p_idx + batch_size * n * matrix_approximation_rank
+            ].view(batch_size, n, matrix_approximation_rank)
+        )
+        qs.append(
+            state.q_memory_dict[bucket_index][
+                q_idx : q_idx + batch_size * m * matrix_approximation_rank
+            ].view(batch_size, m, matrix_approximation_rank)
+        )
+        p_idx += batch_size * n * matrix_approximation_rank
+        q_idx += batch_size * m * matrix_approximation_rank
+
+    # If warm-start is enabled, reuse Qs from the previous iteration if possible and skip filling random values.
+    # The exception is the first iteration when PowerSGD is applied.
+    if not need_randomize_qs:
+        for q in qs:
+            _orthogonalize(q, state.orthogonalization_epsilon)
+    else:
+        with torch.random.fork_rng(devices=[]):
+            # Fork this RNG to avoid changing the seed globally and affecting the random sampling anywhere else in the training.
+            # The seed makes sure that the initial random values are the same across all the DDP replicas.
+            # This seed should differ at every step.
+            # Since it is very slow to fork RNG state across all the CUDA devices,
+            # only fork on CPU and then move the generated tensor to the CUDA device (by overwriting q).
+            torch.manual_seed(state.rng.randint(1_000_000_000))
+            for q in qs:
+                q.copy_(
+                    torch.randn(
+                        *q.shape,
+                        device="cpu",
+                        dtype=dtype,
+                    )
+                )
+                _orthogonalize(q, state.orthogonalization_epsilon)
+
+    # Compute Ps.
+    for tensor, q, p in zip(tensors_to_compress, qs, ps):
+        torch.bmm(tensor, q, out=p)
+
+    # This allreduce is only applied to uncompressed tensors,
+    # so it should have been kicked off before the above computation on the compressed tensors to hide more communication costs.
+    # However, this somehow requires a separate future chain at this time.
+    allreduce_contiguous_uncompressed_tensors_fut = dist.all_reduce(
+        uncompressed_tensors_memory, group=group_to_use, async_op=True
+    ).get_future()
+
+    def unpack_uncompressed_tensors_and_allreduce_ps(fut):
+        uncompressed_tensors_memory = fut.value()[0].div_(world_size)
+        idx = 0
+        for tensor in uncompressed_tensors:
+            tensor.copy_(
+                uncompressed_tensors_memory[idx : idx + tensor.numel()].view_as(tensor)
+            )
+            idx += tensor.numel()
+
+        # Since these Ps will be orthogonalized later, no need to divide them by world size.
+        return (
+            dist.all_reduce(
+                state.p_memory_dict[bucket_index], group=group_to_use, async_op=True
+            )
+            .get_future()
+            .wait()[0]
+        )
+
+    def compute_qs(fut):
+        state.p_memory_dict[bucket_index] = fut.value()
+        for p in ps:
+            _orthogonalize(p, state.orthogonalization_epsilon)
+
+        # Compute Qs.
+        for tensor, p, q in zip(tensors_to_compress, ps, qs):
+            torch.bmm(tensor.transpose(1, 2), p, out=q)
+
+        # TODO: The above procedure does two matmul+allreduce steps per iteration --
+        # one left multiplication and one right multiplication.
+        # For warm-start, can take one such step at a time, and alternate between them.
+
+        # Allreduce Qs.
+        return (
+            dist.all_reduce(
+                state.q_memory_dict[bucket_index], group=group_to_use, async_op=True
+            )
+            .get_future()
+            .wait()[0]
+        )
+
+    def decompress(fut):
+        state.q_memory_dict[bucket_index] = fut.value().div_(world_size)
+
+        for p, q, tensor in zip(ps, qs, tensors_to_compress):
+            torch.bmm(p, q.transpose(1, 2), out=tensor)
+
+        # Copy batched tensors back to original buffer.
+        if state.batch_tensors_with_same_shape:
+            for tensor in tensors_to_compress:
+                if tensor.shape[0] == 1:
+                    # Skip tensor with batch_size == 1 since itself is the original tensor.
+                    continue
+                original_tensors = shape_to_tensors[tensor.shape[1:]]
+                for i, original_tensor in enumerate(original_tensors):
+                    original_tensor.copy_(tensor[i])
+
+        if torch.cuda.is_available():
+            torch.cuda.synchronize(device)
+
+        if state.use_error_feedback:
+            # Memorize the local errors.
+            assert input_tensor_cp is not None
+            state.error_dict[bucket_index] = input_tensor_cp - input_tensor
+        if not state.warm_start:
+            state.p_memory_dict.clear()
+            state.q_memory_dict.clear()
+
+        state.maybe_increase_iter(bucket)
+
+        return input_tensor
+
+    return (
+        allreduce_contiguous_uncompressed_tensors_fut.then(
+            unpack_uncompressed_tensors_and_allreduce_ps
+        )
+        .then(compute_qs)
+        .then(decompress)
+    )
+
+
+def batched_powerSGD_hook(
+    state: PowerSGDState, bucket: dist.GradBucket
+) -> torch.futures.Future[torch.Tensor]:
+    r"""
+    Implement simplified PowerSGD algorithm.
+
+    This DDP communication hook implements a simplified PowerSGD gradient compression
+    algorithm described in the `paper `_.
+    This variant does not compress the gradients layer by layer,
+    but instead compresses the flattened input tensor that batches all the gradients.
+    Therefore, it is **faster** than :meth:`powerSGD_hook`,
+    but usually results in a **much lower accuracy**, unless ``matrix_approximation_rank`` is 1.
+
+    .. warning ::
+        Increasing ``matrix_approximation_rank`` here may not necessarily increase the accuracy,
+        because batching per-parameter tensors without column/row alignment can destroy low-rank structure.
+        Therefore, the user should always consider :meth:`powerSGD_hook` first,
+        and only consider this variant when a satisfactory accuracy can be achieved when ``matrix_approximation_rank`` is 1.
+
+    Once gradient tensors are aggregated across all workers, this hook applies
+    compression as follows:
+
+    1. Views the input flattened 1D gradient tensor as a square-shaped tensor M with 0 paddings;
+
+    2. Creates two low-rank tensors P and Q for decomposing M, such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized;
+
+    3. Computes P, which is equal to MQ;
+
+    4. Allreduces P;
+
+    5. Orthogonalizes P;
+
+    6. Computes Q, which is approximately equal to M^TP;
+
+    7. Allreduces Q;
+
+    8. Computes M, which is approximately equal to PQ^T.
+
+    9. Truncates the input tensor to the original length.
+
+    Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations.
+    This not only gives the user more control over the tradeoff between speedup and accuracy,
+    but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers.
+
+    Args:
+        state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc.
+            To tune the compression configs, mainly need to tune ``matrix_approximation_rank`` and ``start_powerSGD_iter``.
+        bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors.
+            Note that since DDP comm hook only supports single process single device mode,
+            only exactly one tensor is stored in this bucket.
+
+    Returns:
+        Future handler of the communication, which updates the gradients in place.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1)
+        >>> ddp_model.register_comm_hook(state, batched_powerSGD_hook)
+    """  # noqa: B950
+    process_group = state.process_group
+    group_to_use = (
+        process_group if process_group is not None else not_none(dist.group.WORLD)
+    )
+    world_size = group_to_use.size()
+
+    # The input tensor is a flattened 1D tensor.
+    input_tensor = bucket.buffer()
+
+    # Run vanilla allreduce in the first `start_powerSGD_iter` iterations.
+    if state.iter < state.start_powerSGD_iter:
+        state.maybe_increase_iter(bucket)
+        return default._allreduce_fut(group_to_use, input_tensor)
+
+    # Apply PowerSGD after `start_powerSGD_iter` iterations.
+    device = input_tensor.device
+    total_length = input_tensor.shape[0]
+    state.total_numel_before_compression += total_length
+
+    # View the input tensor as a 2D square-shape tensor, and pad 0s if necessary.
+    square_side_length = math.ceil(math.sqrt(total_length))
+    state.total_numel_after_compression += (
+        square_side_length * state.matrix_approximation_rank * 2
+    )
+    padded_total_length = square_side_length**2
+    input_tensor.resize_(padded_total_length)
+    input_tensor[total_length:padded_total_length].fill_(0)
+
+    _report_compression_stats(bucket, state)
+
+    # Incorporate the error from the previous state into the gradients.
+    bucket_index = bucket.index()
+    input_tensor_cp = None
+    if state.use_error_feedback:
+        if bucket_index in state.error_dict:
+            input_tensor.add_(state.error_dict[bucket_index])
+        else:
+            logger.info(
+                "A zero tensor of length %s that represents local error is created.",
+                padded_total_length,
+            )
+            state.error_dict[bucket_index] = torch.zeros(
+                padded_total_length, device=device, dtype=input_tensor.dtype
+            )
+
+        # Keep a copy of the input tensor,
+        # so that we can compute the local error caused by compression later,
+        # by comparing this copy and the input tensor updated after decompression.
+        input_tensor_cp = input_tensor.detach().clone()
+    matrix = input_tensor.view(square_side_length, square_side_length)
+
+    # Reuse P and Q from the previous iteration if possible.
+    # The memory spaces of P and Q need to be allocated in the first iteration when PowerSGD is applied.
+    if not state.warm_start or bucket_index not in state.p_memory_dict:
+        # If warm-start is disabled, low-rank tensors will be initialized at every step.
+        # Only log this if warm-start to avoid spamming.
+        if state.warm_start:
+            logger.info(
+                "Initializing low-rank tensors P and Q, each of which has a shape of %s x %s.",
+                square_side_length,
+                state.matrix_approximation_rank,
+            )
+
+        def create_low_rank_tensor(fill_random_values, rng):
+            """Return a low-rank 2D tensor of square_side_length * matrix_approximation_rank."""
+            if fill_random_values:
+                with torch.random.fork_rng(devices=[]):
+                    # Fork this RNG to avoid changing the seed globally and affecting the random sampling
+                    # anywhere else in the training.
+                    # The seed makes sure that the initial random values are the same across all the DDP replicas.
+                    # This seed should differ at every step.
+                    # Since it is very slow to fork RNG state across all the CUDA devices,
+                    # only fork on CPU and then move the generated tensor to the CUDA device.
+                    torch.manual_seed(rng.randint(1_000_000_000))
+                    return torch.randn(
+                        square_side_length,
+                        state.matrix_approximation_rank,
+                        device="cpu",
+                        dtype=input_tensor.dtype,
+                    ).to(device)
+            else:
+                return torch.empty(
+                    square_side_length,
+                    state.matrix_approximation_rank,
+                    device=device,
+                    dtype=input_tensor.dtype,
+                )
+
+        state.p_memory_dict[bucket_index] = create_low_rank_tensor(
+            fill_random_values=False, rng=state.rng
+        )
+        state.q_memory_dict[bucket_index] = create_low_rank_tensor(
+            fill_random_values=True, rng=state.rng
+        )
+    _orthogonalize(state.q_memory_dict[bucket_index])
+
+    torch.matmul(
+        matrix, state.q_memory_dict[bucket_index], out=state.p_memory_dict[bucket_index]
+    )
+    allreduce_p_fut = dist.all_reduce(
+        state.p_memory_dict[bucket_index], group=group_to_use, async_op=True
+    ).get_future()
+
+    def compute_q(fut):
+        state.p_memory_dict[bucket_index] = fut.value()[0]
+        _orthogonalize(state.p_memory_dict[bucket_index])
+
+        torch.matmul(
+            matrix.t(),
+            state.p_memory_dict[bucket_index],
+            out=state.q_memory_dict[bucket_index],
+        )
+
+        # TODO: The above procedure does two matmul+allreduce steps per iteration --
+        # one left multiplication and one right multiplication.
+        # For warm-start, can take one such step at a time, and alternate between them.
+
+        return (
+            dist.all_reduce(
+                state.q_memory_dict[bucket_index], group=group_to_use, async_op=True
+            )
+            .get_future()
+            .wait()[0]
+        )
+
+    def decompress(fut):
+        state.q_memory_dict[bucket_index] = fut.value().div_(world_size)
+        torch.matmul(
+            state.p_memory_dict[bucket_index],
+            state.q_memory_dict[bucket_index].t(),
+            out=matrix,
+        )
+
+        if state.use_error_feedback:
+            # Memorize the local errors.
+            assert input_tensor_cp is not None
+            state.error_dict[bucket_index] = input_tensor_cp - input_tensor
+        # Removing this seemingly unnecessary sync somehow may cause failures.
+        # See: https://github.com/pytorch/pytorch/pull/54838
+        if torch.cuda.is_available():
+            torch.cuda.synchronize(device)
+        if not state.warm_start:
+            state.p_memory_dict.clear()
+            state.q_memory_dict.clear()
+        ret = input_tensor.resize_(total_length)
+
+        state.maybe_increase_iter(bucket)
+
+        return ret
+
+    return allreduce_p_fut.then(compute_q).then(decompress)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py
new file mode 100644
index 0000000000000000000000000000000000000000..886155908e1a702972184a550082c33c677eacdc
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py
@@ -0,0 +1,220 @@
+# mypy: allow-untyped-defs
+import torch
+import torch.distributed as dist
+from torch import nn
+
+
+def _quantize_per_tensor_backend(x, scale, zero_point):
+    y = torch.round(x / scale) + zero_point
+    y = torch.clamp(y, 0, 255).to(torch.uint8)
+    return y
+
+
+def _dequantize_per_tensor_backend(y, scale, zero_point):
+    x = scale * (y.to(torch.float32) - zero_point)
+    return x
+
+
+def _quantize_per_channel_backend(x, scale, zero_point):
+    y = torch.zeros(x.size(), device=x.device)
+    for i in range(x.size()[0]):
+        y[i, :] = torch.round(x[i, :] / scale[i]) + zero_point[i]
+    y = torch.clamp(y, 0, 255).to(torch.uint8)
+    return y
+
+
+def _dequantize_per_channel_backend(y, scale, zero_point):
+    y = y.to(torch.float32).to(y.device)
+    x = torch.zeros_like(y, device=y.device)
+    for i in range(x.size()[0]):
+        x[i, :] = scale[i] * (y[i, :] - zero_point[i])
+    return x
+
+
+def _get_allgather_out_list(all_gather_in_list, world_size):
+    out_list = [
+        torch.zeros_like(
+            all_gather_in_list,
+            device=all_gather_in_list.device,
+            dtype=all_gather_in_list.dtype,
+        )
+        for _ in range(world_size)
+    ]
+    return out_list
+
+
+def quantization_pertensor_hook(
+    process_group: dist.ProcessGroup, bucket: dist.GradBucket
+) -> torch.futures.Future[torch.Tensor]:
+    """
+    Apply ``torch.quantize_per_tensor`` logic to DDP using ``allgather`` protocol.
+
+    Workers first allgather the scale and zero point of their own
+    ``GradBucket`` prior to the quantization. After all workers have that information,
+    the first ``then`` callback called ``quantize_and_allgather`` quantizes worker's
+    own gradient tensor, and uses ``allgather`` to communicate these across all workers.
+    The final ``then`` callback called ``dequantize_and_aggregate``, dequantizes and
+    aggregates each quantized gradient tensor locally and returns the mean.
+
+    .. warning ::
+        This is experimental, and uses ``allgather`` protocol which is considerably slower than
+        ``allreduce`` protocol. It works only with flattened grads.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> ddp_model.register_comm_hook(process_group, quantization_pertensor_hook)
+    """
+    group_to_use = process_group if process_group is not None else dist.group.WORLD
+    rank = process_group.rank() if process_group is not None else dist.get_rank()
+    # pyrefly: ignore [missing-attribute]
+    world_size = group_to_use.size()
+
+    tensor = bucket.buffer()
+
+    myObserver = torch.ao.quantization.MinMaxObserver().to(tensor.device)
+    myObserver(tensor)
+
+    s, z = myObserver.calculate_qparams()
+    s_and_z = torch.FloatTensor([s, z]).to(tensor.device)
+
+    all_ranks_s_and_z = _get_allgather_out_list(s_and_z, world_size)
+
+    # First, allgather scale and zeros.
+    fut = dist.all_gather(
+        all_ranks_s_and_z, s_and_z, group=group_to_use, async_op=True
+    ).get_future()
+
+    def quantize_and_allgather(fut):
+        # Store scale and zeros across all workers.
+        all_ranks_s_and_z = fut.wait()[0]
+        # All workers quantize their own ``GradBucket`` tensors.
+        quantized_tensor = _quantize_per_tensor_backend(
+            tensor, all_ranks_s_and_z[rank][0], all_ranks_s_and_z[rank][1]
+        )
+        # Allgather quantized tensors.
+        fut = dist.all_gather(
+            _get_allgather_out_list(quantized_tensor, world_size),
+            quantized_tensor,
+            group=group_to_use,
+            async_op=True,
+        ).get_future()
+
+        return fut.wait()
+
+    def dequantize_and_aggregate(fut):
+        all_ranks_quantized_tensor = fut.wait()[0]
+
+        aggregated_dequantized_tensor = torch.zeros_like(
+            all_ranks_quantized_tensor[0], device=tensor.device, dtype=torch.float32
+        )
+        # Using previously allgathered scales and zeros, dequantize gradient tensors
+        # locally and then aggregate them.
+        for r, quantized_tensor in enumerate(all_ranks_quantized_tensor):
+            aggregated_dequantized_tensor += _dequantize_per_tensor_backend(
+                quantized_tensor, all_ranks_s_and_z[r][0], all_ranks_s_and_z[r][1]
+            )
+
+        return aggregated_dequantized_tensor / world_size
+
+    return fut.then(quantize_and_allgather).then(dequantize_and_aggregate)
+
+
+def quantization_perchannel_hook(
+    process_group: dist.ProcessGroup, bucket: dist.GradBucket, bucket_size=512
+) -> torch.futures.Future[torch.Tensor]:
+    """
+    Apply``torch.quantize_per_channel`` logic to DDP using ``allgather`` protocol.
+
+    Compared to per-tensor, the main motivation of per-channel is
+    for considerably large tensors such as a tensor that contains 6 million
+    elements quantizing per a bucket size of 512 (or 128) elements may significantly
+    increase the resolution.
+
+    It first splits ``GradBucket`` tensor into multiple chunks (channels) of ``bucket_size``
+    elements. Then, workers allgather the scales and zero points of their own
+    ``GradBucket`` prior to the quantization. After all workers have that information,
+    the first ``then`` callback called ``quantize_and_allgather`` quantizes worker's
+    own gradient tensor, and uses ``allgather`` to communicate these across all workers.
+    The final ``then`` callback called ``dequantize_and_aggregate``, dequantizes, flattens, and
+    aggregates each quantized gradient tensor locally and returns the mean.
+
+    .. warning ::
+        This is experimental, and uses ``allgather`` protocol which is considerably slower than
+        ``allreduce`` protocol. It works only with flattened grads.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> ddp_model.register_comm_hook(process_group, quantization_perchannel_hook)
+    """
+    group_to_use = process_group if process_group is not None else dist.group.WORLD
+    rank = process_group.rank() if process_group is not None else dist.get_rank()
+    # pyrefly: ignore [missing-attribute]
+    world_size = group_to_use.size()
+
+    tensor = bucket.buffer()
+
+    tensor_in_channels = (
+        nn.functional.pad(
+            input=tensor,
+            pad=(0, bucket_size - len(tensor) % bucket_size),
+            mode="constant",
+            value=0,
+        )
+        .view(-1, bucket_size)
+        .to(tensor.device)
+    )
+
+    myPerChannelObserver = torch.ao.quantization.PerChannelMinMaxObserver().to(
+        tensor.device
+    )
+    myPerChannelObserver(tensor_in_channels)
+
+    s_ch, z_ch = myPerChannelObserver.calculate_qparams()
+    s_and_z = torch.stack((s_ch, z_ch)).to(tensor.device)
+
+    all_ranks_s_and_z = _get_allgather_out_list(s_and_z, world_size)
+    # First, allgather scale and zeros.
+    fut = dist.all_gather(
+        all_ranks_s_and_z, s_and_z, group=group_to_use, async_op=True
+    ).get_future()
+
+    def quantize_and_allgather(fut):
+        # Store scale and zeros across all workers.
+        all_ranks_s_and_z = fut.wait()[0]
+        # All workers quantize their corresponding ``GradBucket`` tensors.
+        quantized_tensor = _quantize_per_channel_backend(
+            tensor_in_channels,
+            all_ranks_s_and_z[rank, 0, :],
+            all_ranks_s_and_z[rank, 1, :],
+        )
+        # Allgather quantized tensors.
+        fut = dist.all_gather(
+            _get_allgather_out_list(quantized_tensor, world_size),
+            quantized_tensor,
+            group=group_to_use,
+            async_op=True,
+        ).get_future()
+
+        return fut.wait()
+
+    def dequantize_and_aggregate(fut):
+        all_ranks_quantized_tensor = fut.wait()[0]
+
+        aggregated_dequantized_tensor = torch.zeros_like(
+            all_ranks_quantized_tensor[0], device=tensor.device, dtype=torch.float32
+        )
+        # Using previously allgathered scales and zeros, dequantize gradient tensors
+        # locally and then aggregate them.
+        for r, quantized_tensor in enumerate(all_ranks_quantized_tensor):
+            aggregated_dequantized_tensor += _dequantize_per_channel_backend(
+                quantized_tensor, all_ranks_s_and_z[r][0], all_ranks_s_and_z[r][1]
+            )
+
+        return (
+            torch.flatten(aggregated_dequantized_tensor).to(tensor.device)[
+                : tensor.size()[0]
+            ]
+            / world_size
+        )
+
+    return fut.then(quantize_and_allgather).then(dequantize_and_aggregate)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/join.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/join.py
new file mode 100644
index 0000000000000000000000000000000000000000..52d0c52fbfb59d3c906bd282db51a76886206c96
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/join.py
@@ -0,0 +1,350 @@
+# mypy: allow-untyped-defs
+import warnings
+from abc import ABC, abstractmethod
+from types import TracebackType
+from typing import Any, NamedTuple
+
+import torch
+import torch.distributed as dist
+
+
+__all__ = ["JoinHook", "Joinable", "Join"]
+
+
+class JoinHook:
+    r"""
+    This defines a join hook, which provides two entry points in the join context manager.
+
+    Entry points : a main hook, which is called repeatedly while there exists a non-joined
+    process, and a post-hook, which is called once all processes have joined.
+
+    To implement a join hook for the generic join context manager, define a
+    class that inherits from :class:`JoinHook` and override ``main_hook()`` and
+    ``post_hook()`` as appropriate.
+    """
+
+    def main_hook(self) -> None:
+        r"""Call this hook while there exists a non-joined process to shadow collective communications in a training iteration.
+
+        Training iteration i.e., in one forward pass, backward pass, and optimizer step.
+        """
+
+    def post_hook(self, is_last_joiner: bool) -> None:
+        r"""
+        Call hook after all processes have joined.
+
+        It is passed an additional ``bool`` argument ``is_last_joiner``, which indicates if the rank is one of the last to join.
+
+        Arguments:
+            is_last_joiner (bool): ``True`` if the rank is one of the last to
+                join; ``False`` otherwise.
+        """
+
+
+class Joinable(ABC):
+    r"""
+    This defines an abstract base class for joinable classes.
+
+    A joinable class
+    (inheriting from :class:`Joinable`) should implement :meth:`join_hook`,
+    which returns a :class:`JoinHook` instance, in addition to
+    :meth:`join_device` and :meth:`join_process_group` that return device and
+    process group information, respectively.
+    """
+
+    @abstractmethod
+    def __init__(self) -> None:
+        super().__init__()
+        self._join_config = _JoinConfig.construct_disabled_join_config()
+
+    @abstractmethod
+    def join_hook(self, **kwargs) -> JoinHook:
+        r"""
+        Return a :class:`JoinHook` instance for the given :class:`Joinable`.
+
+        Arguments:
+            kwargs (dict): a :class:`dict` containing any keyword arguments
+                to modify the behavior of the join hook at run time; all
+                :class:`Joinable` instances sharing the same join context
+                manager are forwarded the same value for ``kwargs``.
+        """
+        ...
+
+    @property
+    @abstractmethod
+    def join_device(self) -> torch.device:
+        r"""Return the device from which to perform collective communications needed by the join context manager."""
+        ...
+
+    @property
+    @abstractmethod
+    def join_process_group(self) -> Any:
+        r"""Returns the process group for the collective communications needed by the join context manager itself."""
+        ...
+
+
+class _JoinConfig(NamedTuple):
+    r"""This includes all fields needed from a :class:`Joinable` instance for the join context manager side."""
+
+    enable: bool
+    throw_on_early_termination: bool
+    is_first_joinable: bool
+
+    @staticmethod
+    def construct_disabled_join_config():
+        r"""Return a :class:`_JoinConfig` instance indicating that join-related logic should be disabled.
+
+        e.g. if the caller is not in a join context manager.
+        """
+        return _JoinConfig(
+            enable=False, throw_on_early_termination=False, is_first_joinable=False
+        )
+
+
+class Join:
+    r"""
+    This class defines the generic join context manager, which allows custom hooks to be called after a process joins.
+
+    These hooks should shadow the
+    collective communications of non-joined processes to prevent hanging and
+    erroring and to ensure algorithmic correctness. Refer to :class:`JoinHook`
+    for details about the hook definition.
+
+    .. warning::
+        The context manager requires each participating :class:`Joinable` to
+        call the method :meth:`notify_join_context()` before its own per-
+        iteration collective communications to ensure correctness.
+
+    .. warning::
+        The context manager requires that all ``process_group`` attributes in
+        the :class:`JoinHook` objects are the same. If there are multiple
+        :class:`JoinHook` objects, then the ``device`` of the first is used.
+        The process group and device information is used for checking for non-
+        joined processes and for notifying processes to throw an exception if
+        ``throw_on_early_termination`` is enabled, both of which using an all-
+        reduce.
+
+    Arguments:
+        joinables (List[Joinable]): a list of the participating
+            :class:`Joinable` s; their hooks are iterated over in the given
+            order.
+
+        enable (bool): a flag enabling uneven input detection; setting to
+            ``False`` disables the context manager's functionality and should
+            only be set when the user knows the inputs will not be uneven
+            (default: ``True``).
+
+        throw_on_early_termination (bool): a flag controlling whether to throw an
+            exception upon detecting uneven inputs (default: ``False``).
+
+    Example::
+
+        >>> import os
+        >>> import torch
+        >>> import torch.distributed as dist
+        >>> import torch.multiprocessing as mp
+        >>> # xdoctest: +SKIP
+        >>> import torch.nn.parallel.DistributedDataParallel as DDP
+        >>> import torch.distributed.optim.ZeroRedundancyOptimizer as ZeRO
+        >>> from torch.distributed.algorithms.join import Join
+        >>>
+        >>> # On each spawned worker
+        >>> def worker(rank):
+        >>>     dist.init_process_group("nccl", rank=rank, world_size=2)
+        >>>     model = DDP(torch.nn.Linear(1, 1).to(rank), device_ids=[rank])
+        >>>     optim = ZeRO(model.parameters(), torch.optim.Adam, lr=0.01)
+        >>>     # Rank 1 gets one more input than rank 0
+        >>>     inputs = [torch.tensor([1.]).to(rank) for _ in range(10 + rank)]
+        >>>     with Join([model, optim]):
+        >>>         for input in inputs:
+        >>>             loss = model(input).sum()
+        >>>             loss.backward()
+        >>>             optim.step()
+        >>>     # All ranks reach here without hanging/erroring
+    """
+
+    def __init__(
+        self,
+        joinables: list[Joinable],
+        enable: bool = True,
+        throw_on_early_termination: bool = False,
+        **kwargs,
+    ):
+        if len(joinables) == 0:
+            raise ValueError("The join context manager requires at least one joinable")
+        self._joinables = joinables
+        self._join_hooks = [
+            joinable.join_hook(**kwargs) for joinable in self._joinables
+        ]
+        self._enable = enable
+        self._throw_on_early_termination = throw_on_early_termination
+        self._set_joinable_configs()
+        self._extract_dist_info()
+
+    def _set_joinable_configs(self) -> None:
+        r"""Set the :class:`_JoinConfig` of each participating :class:`Joinable`."""
+        assert len(self._joinables) > 0
+        is_first_joinable = True
+        for joinable in self._joinables:
+            joinable._join_config = _JoinConfig(
+                enable=self._enable,
+                throw_on_early_termination=self._throw_on_early_termination,
+                is_first_joinable=is_first_joinable,
+            )
+            is_first_joinable = False
+
+    def _extract_dist_info(self) -> None:
+        r"""
+        Extract the process group and device information from the joinables.
+
+        If there are multiple joinables, then the context manager uses the
+        first specified device.
+
+        Preconditions:
+            ``self._joinables`` is not ``None`` and is non-empty.
+
+        Raises:
+            ValueError
+                If there are multiple conflicting ``process_group`` attributes
+                among the ``Joinable`` objects.
+        """
+        process_group = None
+        device = None
+        # pyrefly: ignore [bad-assignment]
+        for joinable in self._joinables:
+            if process_group is None:
+                process_group = joinable.join_process_group
+            elif process_group != joinable.join_process_group:
+                raise ValueError(
+                    "Using join context manager with multiple process groups"
+                )
+            if device is None:
+                device = joinable.join_device
+        self._process_group = process_group
+        self._rank = dist.get_rank(self._process_group)
+        self._device = device
+
+    def __enter__(self): ...
+
+    def __exit__(
+        self,
+        type: type[BaseException] | None,
+        value: BaseException | None,
+        traceback: TracebackType | None,
+    ):
+        r"""
+        Repeatedly runs the main hooks until all processes join; then, runs the post-hooks.
+
+        Raises:
+            RuntimeError
+                If ``throw_on_early_termination=True``.
+        """
+        if not self._enable or type:
+            return  # propagate the exception directly if one was raised
+
+        all_procs_joined = False
+        is_last_joiner = True
+
+        i = 0
+        WARN_THRESHOLD = 1000
+        warnings.simplefilter("once")
+
+        while not all_procs_joined:
+            if i > WARN_THRESHOLD:
+                warnings.warn(
+                    "Detected uneven input skew of greater than "
+                    f"{WARN_THRESHOLD}. This means that rank "
+                    f"{self._rank} has at least {WARN_THRESHOLD} "
+                    f"fewer inputs than other currently-active ranks. "
+                    "This level of skew could lead to performance "
+                    "degradation during training.",
+                    stacklevel=2,
+                )
+            # Shadow the all-reduce in non-joined processes
+            num_nonjoined_procs = self._get_num_nonjoined_procs()
+            if num_nonjoined_procs == 0:
+                all_procs_joined = True
+            else:
+                if self._throw_on_early_termination:
+                    self._notify_procs_to_terminate()
+
+                # Run main hooks
+                for join_hook in self._join_hooks:
+                    join_hook.main_hook()
+
+                is_last_joiner = False
+                i += 1
+
+        # Run post-hooks
+        for join_hook in self._join_hooks:
+            join_hook.post_hook(is_last_joiner)
+
+    def _get_num_nonjoined_procs(self):
+        r"""Return the number of non-joined processes by shadowing an all-reduce in the non-joined processes."""
+        num_nonjoined_procs = torch.zeros(1, device=self._device)
+        dist.all_reduce(num_nonjoined_procs, group=self._process_group)
+        return num_nonjoined_procs.item()
+
+    def _notify_procs_to_terminate(self):
+        r"""Schedule an all-reduce to notify non-joined processes to terminate.
+
+        Also raise a ``RuntimeError`` indicating that the current process has exhausted its inputs.
+        """
+        ones = torch.ones(1, device=self._device)
+        dist.all_reduce(ones, group=self._process_group)
+        raise RuntimeError(f"Rank {self._rank} exhausted all inputs.")
+
+    @staticmethod
+    def notify_join_context(joinable: Joinable):
+        r"""
+        Notifies the join context manager that the calling process has not yet joined.
+
+        Then, if ``throw_on_early_termination=True``, checks if uneven inputs have been detected
+        (i.e. if one process has already joined) and throws an exception if so.
+
+        This method should be called from a :class:`Joinable` object before
+        its per-iteration collective communications. For example, this should
+        be called at the beginning of the forward pass in
+        :class:`DistributedDataParallel`.
+
+        Only the first :class:`Joinable` object passed into the context
+        manager performs the collective communications in this method, and
+        for the others, this method is vacuous.
+
+        Arguments:
+            joinable (Joinable): the :class:`Joinable` object calling this
+                method.
+
+        Returns:
+            An async work handle for the all-reduce meant to notify the context
+            manager that the process has not yet joined if ``joinable`` is the
+            first one passed into the context manager; ``None`` otherwise.
+        """
+        assert hasattr(joinable, "_join_config"), (
+            f"Check that the {type(joinable)} constructor calls the "
+            "``Joinable`` constructor"
+        )
+
+        join_config = joinable._join_config
+        # First joinable is responsible for the collective communications
+        if not join_config.is_first_joinable or not join_config.enable:
+            return None
+
+        device = joinable.join_device
+        process_group = joinable.join_process_group
+
+        # Schedule an all-reduce to indicate that the caller has not yet joined
+        ones = torch.ones(1, device=device)
+        work = dist.all_reduce(ones, group=process_group, async_op=True)
+
+        if join_config.throw_on_early_termination:
+            # Check if uneven inputs have been detected
+            zeros = torch.zeros(1, device=device)
+            dist.all_reduce(zeros, group=process_group)
+            should_throw = zeros.item()
+            if should_throw:
+                raise RuntimeError(
+                    "Detected at least one rank that exhausted inputs. "
+                    "Throwing across all ranks."
+                )
+        return work
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/model_averaging/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/model_averaging/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/model_averaging/averagers.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/model_averaging/averagers.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d669d4ea592250556ed5188b21ae265bb3b2c9c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/model_averaging/averagers.py
@@ -0,0 +1,128 @@
+# mypy: allow-untyped-defs
+import warnings
+from abc import ABC, abstractmethod
+from collections.abc import Iterable
+
+import torch
+import torch.distributed as dist
+import torch.distributed.algorithms.model_averaging.utils as utils
+from torch.utils._typing_utils import not_none as _not_none
+
+
+__all__ = ["ModelAverager", "PeriodicModelAverager"]
+
+
+class ModelAverager(ABC):
+    r"""Base class for all model averagers.
+
+    Args:
+        process_group: The process group to be used for all-reduce.
+                       If ``None``, the default process group, which
+                       is created by :func:`torch.distributed.init_process_group`,
+                       will be used. (default: ``None``)
+    """
+
+    def __init__(self, process_group: dist.ProcessGroup | None = None):
+        self.process_group = (
+            process_group if process_group is not None else _not_none(dist.group.WORLD)
+        )
+        self.step = 0
+
+    @abstractmethod
+    def average_parameters(self, params):
+        raise NotImplementedError
+
+
+class PeriodicModelAverager(ModelAverager):
+    r"""
+    Averages parameters periodically after the warm-up stage.
+
+    This can be used for running `post-local SGD `_,
+    by running :class:`~torch.nn.DistributedDataParallel` (DDP)
+    using the subgroups created by :meth:`~torch.distributed.new_subgroups`.
+
+    Args:
+        period (int): The number of steps per model averaging.
+                      Usually the period should be greater than ``1`` to reduce the communication cost.
+                      Otherwise, only DDP needs to be used.
+        warmup_steps (int): The number of warm-up steps. During this stage,
+                            model averaging is skipped.
+        process_group: The process group to be used for all-reduce.
+                       If ``None``, the default process group, which
+                       is created by :func:`torch.distributed.init_process_group`,
+                       will be used. (default: ``None``)
+
+    Example::
+
+        >>> # xdoctest: +SKIP("undefined variables")
+        >>> import torch
+        >>> import torch.distributed as dist
+        >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD
+        >>> import torch.distributed.algorithms.model_averaging.averagers as averagers
+        >>> import torch.nn as nn
+        >>>
+        >>> dist.init_process_group("nccl", rank=rank, world_size=16)
+        >>> torch.cuda.set_device(rank)
+        >>> module = nn.Linear(1, 1, bias=False).cuda()
+        >>> model = nn.parallel.DistributedDataParallel(
+        >>>    module, device_ids=[rank], output_device=rank
+        >>> )
+        >>> # Register a post-localSGD communication hook.
+        >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100)
+        >>> model.register_comm_hook(state, post_localSGD_hook)
+        >>>
+        >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step.
+        >>> # After 100 steps, run model averaging every 4 steps.
+        >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``.
+        >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100)
+        >>> for step in range(0, 200):
+        >>>    optimizer.zero_grad()
+        >>>    loss = loss_fn(output, labels)
+        >>>    loss.backward()
+        >>>    optimizer.step()
+        >>>    # Will average model parameters globally every 4 steps. Thus,
+        >>>    # inter-node communication only occurs every 4 iterations after
+        >>>    # the initial ``warmup_steps`` period.
+        >>>    averager.average_parameters(model.parameters())
+    """
+
+    def __init__(
+        self, period, warmup_steps=0, process_group: dist.ProcessGroup | None = None
+    ):
+        super().__init__(process_group)
+        if warmup_steps < 0:
+            raise ValueError("Arg ``warmup_steps`` must be a non-negative number.")
+        self.warmup_steps = warmup_steps
+        if period < 1:
+            raise ValueError("Arg ``period`` must be a positive value.")
+        elif period == 1:
+            warnings.warn(
+                "When period is 1, no need to use model averaging because the communication cost "
+                "of all-reducing parameters will be no less than the cost of all-reducing gradients "
+                "by DistributedDataParallel in the backward pass. Therefore, only "
+                "DistributedDataParallel should be used for this case.",
+                stacklevel=2,
+            )
+        self.period = period
+
+    def average_parameters(
+        self,
+        params: Iterable[torch.nn.Parameter] | Iterable[dict[str, torch.nn.Parameter]],
+    ):
+        """
+        Averages parameters or parameter groups of an optimizer if ``step`` is no less than ``warmup_steps``.
+
+        Can be divided by ``period``, where ``step`` is increased by 1
+        at each iteration in the training loop.
+        Args:
+            params: The parameters of a model or parameter groups of an optimizer.
+
+        """
+        if (
+            self.step >= self.warmup_steps
+            and (self.step - self.warmup_steps) % self.period == 0
+        ):
+            utils.average_parameters_or_parameter_groups(
+                params, _not_none(self.process_group)
+            )
+        self.step += 1
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/model_averaging/hierarchical_model_averager.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/model_averaging/hierarchical_model_averager.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f7edc447d1089e2c09ba10764bc0fbfce9a1770
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/model_averaging/hierarchical_model_averager.py
@@ -0,0 +1,179 @@
+# mypy: allow-untyped-defs
+# Copyright 2022 Cruise LLC
+import logging
+import warnings
+from collections import OrderedDict
+from collections.abc import Iterable
+
+import torch
+import torch.distributed as dist
+import torch.distributed.algorithms.model_averaging.averagers as averagers
+import torch.distributed.algorithms.model_averaging.utils as utils
+
+
+logger = logging.getLogger(__name__)
+
+
+class HierarchicalModelAverager(averagers.ModelAverager):
+    r"""
+    Runs hierarchical model averaging (`hierarchical SGD `_).
+
+    Process groups of different sizes are organized in a hierarchy, and they average parameters
+    by using different periods concurrently after the warm-up stage.
+    This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`
+    that supports `post-local SGD `_, which essentially only supports
+    a two-level hierarchy: the intra-machine level and the global level, where the intra-machine
+    level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`.
+    Similarly, the process groups within this class do not have such an intra-machine process
+    subgroup, which should be embedded by the post-local SGD communication hook instead.
+
+    Args:
+        period_group_size_dict: An ordered dict mapping keys of model averaging period to
+                                process group size, used for initializing process groups of
+                                different sizes in a hierarchy to average parameters concurrently.
+                                Particularly, at each iteration, there will be at most a single
+                                process group that runs averaging -- the period of such group should
+                                have the largest period which the current step can be divided by.
+                                For example, if the dict has three keys: 2, 4, and 8,
+                                then this means totally three process groups will be created to
+                                average parameters every 2, 4, and 8 iterations, respectively.
+                                At the 4th iteration, only the second process group will run
+                                averaging, because the first process group should be a
+                                subset of the second process group, and no need to execute the first
+                                process group redundantly.
+                                On the other hand, the third process group can only be triggered
+                                every 8 iterations, so it will not be triggered at the 4th iteration.
+        warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped.
+        process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging.
+                                                If ``None``, the default process group, which is created
+                                                by :func:`torch.distributed.init_process_group`, will be used.
+                                                (default: ``None``)
+
+    Example::
+        >>> # xdoctest: +SKIP('undefined rank')
+        >>> from collections import OrderedDict
+        >>> import torch
+        >>> import torch.distributed as dist
+        >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import (
+        >>>     PostLocalSGDState,
+        >>>     post_localSGD_hook,
+        >>> )
+        >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD
+        >>> import torch.nn as nn
+        >>>
+        >>> dist.init_process_group("nccl", rank=rank, world_size=16)
+        >>> torch.cuda.set_device(rank)
+        >>> module = nn.Linear(1, 1, bias=False).to(rank)
+        >>> model = nn.parallel.DistributedDataParallel(
+        >>>    module, device_ids=[rank], output_device=rank
+        >>> )
+        >>> # Register a post-localSGD communication hook.
+        >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4.
+        >>> subgroup, _ = dist.new_subgroups()
+        >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100)
+        >>> model.register_comm_hook(state, post_localSGD_hook)
+        >>>
+        >>> # Average parameters among each group of 8 processes every 4 iterations, and among all
+        >>> # the 16 processes every 16 iterations.
+        >>> averager = hierarchicalSGD.HierarchicalModelAverager(
+        >>>     period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100)
+        >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``.
+        >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step.
+        >>> # After 100 steps, run model averaging at two levels.
+        >>> for step in range(0, 200):
+        >>>    optimizer.zero_grad()
+        >>>    loss = loss_fn(output, labels)
+        >>>    loss.backward()
+        >>>    optimizer.step()
+        >>>    # Average parameters after ``optimizer.step()``.
+        >>>    # Thus, the inter-node communication only occurs periodically after ``warmup_steps``.
+        >>>    averager.average_parameters(model.parameters())
+
+    .. warning ::
+        The last group size in the dict must be the size of the provided ``process_group``,
+        which indicates model averaging at the highest level of the hierarchy.
+        If ``process_group`` is not provided, then the last group size should be equal to the world size.
+
+    .. warning ::
+        `HierarchicalModelAverager` is experimental and subject to change.
+    """
+
+    def __init__(self, period_group_size_dict=None, warmup_steps=0, process_group=None):
+        super().__init__(process_group)
+        if not period_group_size_dict:
+            raise ValueError("Arg ``period_group_size_dict`` must not be empty.")
+        self._periods = list(period_group_size_dict.keys())
+        if self._periods[0] <= 0:
+            raise ValueError(
+                "The minimum period in arg ``period_group_size_dict`` must be a positive value."
+            )
+        elif self._periods[-1] == 1:
+            warnings.warn(
+                "When the maximum period in arg ``period_group_size_dict`` is 1, "
+                "no need to use model averaging because the communication cost "
+                "of all-reducing parameters will be no less than the cost of all-reducing gradients "
+                "by DistributedDataParallel in the backward pass. Therefore, only "
+                "DistributedDataParallel should be used for this case.",
+                stacklevel=2,
+            )
+        overall_group_size = dist.get_world_size(group=self.process_group)
+        if list(period_group_size_dict.values())[-1] != overall_group_size:
+            raise ValueError(
+                f"The last value in arg ``period_process_group_dict`` {list(period_group_size_dict.values())[-1]} "
+                f"must be equal to the size of arg ``process_group`` {overall_group_size}."
+            )
+
+        self.period_process_group_dict = OrderedDict()
+        logger.info("Model averaging hierarchy:")
+        for period, group_size in period_group_size_dict.items():
+            logger.info(
+                "\tEach group that has %s processes average parameters every %s iterations, "
+                "if no higher-level averaging.",
+                group_size,
+                period,
+            )
+            if group_size != overall_group_size:
+                self.period_process_group_dict[period], _ = dist.new_subgroups(
+                    group_size=group_size, group=self.process_group
+                )
+            else:
+                self.period_process_group_dict[period] = self.process_group
+
+        if warmup_steps < 0:
+            raise ValueError("Arg ``warmup_steps`` must be a non-negative number.")
+        self.warmup_steps = warmup_steps
+
+    def _find_process_group(self):
+        """
+        Return a process group as the value of an ``period_process_group_dict`` entry.
+
+        If ``step`` can be divided by multiple periods in the keys of ``period_process_group_dict``,
+        then the returned process group is the one corresponding to the largest period,
+        since this process group will be used for averaging parameters at this ``step``.
+        Returns ``None`` if not found.
+        """
+        for period in reversed(self._periods):
+            if self.step % period == 0:
+                return self.period_process_group_dict[period]
+        return None
+
+    def average_parameters(
+        self,
+        params: Iterable[torch.nn.Parameter] | Iterable[dict[str, torch.nn.Parameter]],
+    ):
+        """
+        Averages parameters or parameter groups of an optimizer.
+
+        Averaging only occurs if ``step`` is no less than ``warmup_steps``
+        and it can be divided by a period in the keys of ``period_process_group_dict``,
+        where ``step`` is increased by 1 at each iteration in the training loop.
+        If ``step`` can be divided by multiple periods in the keys of ``period_process_group_dict``,
+        only the largest period is used, and the corresponding process group is used for averaging parameters.
+        Args:
+            params: The parameters of a model or parameter groups of an optimizer.
+        """
+        if self.step >= self.warmup_steps:
+            group = self._find_process_group()
+            if group is not None:
+                utils.average_parameters_or_parameter_groups(params, group)
+        self.step += 1
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/model_averaging/utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/model_averaging/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a61c036913edd6cf7fbcde6b77bc6ee5970065e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/algorithms/model_averaging/utils.py
@@ -0,0 +1,86 @@
+# mypy: allow-untyped-defs
+import itertools
+from collections.abc import Iterable, Iterator
+
+import torch
+import torch.distributed as dist
+
+# The two imports below are not always available depending on the
+# USE_DISTRIBUTED compile flag. Make sure they raise import error
+# if we're trying to use them.
+from torch.distributed import group, ProcessGroup
+
+
+__all__ = [
+    "average_parameters",
+    "get_params_to_average",
+    "average_parameters_or_parameter_groups",
+]
+
+
+def average_parameters(
+    params: Iterator[torch.nn.Parameter], process_group: ProcessGroup
+):
+    """
+    Averages all the given parameters.
+
+    For allreduce efficiency, all the parameters are flattened into a contiguous buffer.
+    Thus, it requires extra memory of the same size as the given parameters.
+    """
+    group_to_use = process_group if process_group is not None else group.WORLD
+    # Do not update any parameter if not in the process group.
+    if dist._rank_not_in_group(group_to_use):
+        return
+
+    params_it1, params_it2 = itertools.tee(params)
+    # If the input parameters have different data types,
+    # packing these parameters will trigger an implicit type up-casting.
+    # The original parameter data types will be restored during the subsequent unpacking.
+    flat_params = torch.cat([p.data.reshape(-1) for p in params_it1])
+    flat_params /= dist.get_world_size(group_to_use)
+    # Make sure the allreduce will not conflict with any other ongoing process group.
+    if torch.accelerator.is_available():
+        torch.accelerator.synchronize()
+    dist.all_reduce(flat_params, group=group_to_use)
+
+    offset = 0
+    for p in params_it2:
+        p.data = flat_params[offset : offset + p.numel()].view_as(p).type_as(p)
+        offset += p.numel()
+
+
+def get_params_to_average(
+    params: Iterable[torch.nn.Parameter] | Iterable[dict[str, torch.nn.Parameter]],
+):
+    """
+    Return a list of parameters that need to average.
+
+    This filters out the parameters that do not contain any gradients.
+    Args:
+        params: The parameters of a model or parameter groups of an optimizer.
+    """
+    filtered_params = []
+    for param in params:
+        if isinstance(param, torch.nn.Parameter):
+            # model.parameters() input
+            param_data = param
+            if param_data.grad is not None:
+                filtered_params.append(param_data)
+        elif isinstance(param, dict):
+            # optimizer.param_groups input
+            for param_data in param["params"]:
+                if param_data.grad is not None:
+                    filtered_params.append(param_data)
+        else:
+            raise NotImplementedError(
+                f"Parameter input of type {type(param)} is not supported"
+            )
+    return filtered_params
+
+
+def average_parameters_or_parameter_groups(
+    params: Iterable[torch.nn.Parameter] | Iterable[dict[str, torch.nn.Parameter]],
+    process_group: ProcessGroup,
+):
+    """Averages parameters of a model or parameter groups of an optimizer."""
+    average_parameters(iter(get_params_to_average(params)), process_group)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/argparse_util.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/argparse_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..c475eebf21273abb53ab99e3edcbdef18e9f0c8f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/argparse_util.py
@@ -0,0 +1,104 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+import os
+from argparse import Action
+
+
+class env(Action):
+    """
+    Get argument values from ``PET_{dest}`` before defaulting to the given ``default`` value.
+
+    For flags (e.g. ``--standalone``)
+    use ``check_env`` instead.
+
+    .. note:: when multiple option strings are specified, ``dest`` is
+              the longest option string (e.g. for ``"-f", "--foo"``
+              the env var to set is ``PET_FOO`` not ``PET_F``)
+
+    Example:
+    ::
+
+     parser.add_argument("-f", "--foo", action=env, default="bar")
+
+     ./program                                      -> args.foo="bar"
+     ./program -f baz                               -> args.foo="baz"
+     ./program --foo baz                            -> args.foo="baz"
+     PET_FOO="env_bar" ./program -f baz    -> args.foo="baz"
+     PET_FOO="env_bar" ./program --foo baz -> args.foo="baz"
+     PET_FOO="env_bar" ./program           -> args.foo="env_bar"
+
+     parser.add_argument("-f", "--foo", action=env, required=True)
+
+     ./program                                      -> fails
+     ./program -f baz                               -> args.foo="baz"
+     PET_FOO="env_bar" ./program           -> args.foo="env_bar"
+     PET_FOO="env_bar" ./program -f baz    -> args.foo="baz"
+    """
+
+    def __init__(self, dest, default=None, required=False, **kwargs) -> None:
+        env_name = f"PET_{dest.upper()}"
+        default = os.environ.get(env_name, default)
+
+        # ``required`` means that it NEEDS to be present  in the command-line args
+        # rather than "this option requires a value (either set explicitly or default"
+        # so if we found default then we don't "require" it to be in the command-line
+        # so set it to False
+        if default:
+            required = False
+
+        super().__init__(dest=dest, default=default, required=required, **kwargs)
+
+    def __call__(self, parser, namespace, values, option_string=None):
+        setattr(namespace, self.dest, values)
+
+
+class check_env(Action):
+    """
+    Check whether the env var ``PET_{dest}`` exists before defaulting to the given ``default`` value.
+
+    Equivalent to
+    ``store_true`` argparse built-in action except that the argument can
+    be omitted from the commandline if the env var is present and has a
+    non-zero value.
+
+    .. note:: it is redundant to pass ``default=True`` for arguments
+              that use this action because a flag should be ``True``
+              when present and ``False`` otherwise.
+
+    Example:
+    ::
+
+     parser.add_argument("--verbose", action=check_env)
+
+     ./program                                  -> args.verbose=False
+     ./program --verbose                        -> args.verbose=True
+     PET_VERBOSE=1 ./program           -> args.verbose=True
+     PET_VERBOSE=0 ./program           -> args.verbose=False
+     PET_VERBOSE=0 ./program --verbose -> args.verbose=True
+
+    Anti-pattern (don't do this):
+
+    ::
+
+     parser.add_argument("--verbose", action=check_env, default=True)
+
+     ./program                                  -> args.verbose=True
+     ./program --verbose                        -> args.verbose=True
+     PET_VERBOSE=1 ./program           -> args.verbose=True
+     PET_VERBOSE=0 ./program           -> args.verbose=False
+
+    """
+
+    def __init__(self, dest, default=False, **kwargs) -> None:
+        env_name = f"PET_{dest.upper()}"
+        default = bool(int(os.environ.get(env_name, "1" if default else "0")))
+        super().__init__(dest=dest, const=True, default=default, nargs=0, **kwargs)
+
+    def __call__(self, parser, namespace, values, option_string=None):
+        setattr(namespace, self.dest, self.const)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/autograd/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/autograd/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a52c36942e48e389a7e344abeb929febdb62c6c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/autograd/__init__.py
@@ -0,0 +1,66 @@
+from __future__ import annotations
+
+from typing import Any, TYPE_CHECKING
+
+import torch
+
+
+if TYPE_CHECKING:
+    from types import TracebackType
+
+
+def is_available() -> bool:
+    return hasattr(torch._C, "_dist_autograd_init")
+
+
+if is_available() and not torch._C._dist_autograd_init():
+    raise RuntimeError("Failed to initialize torch.distributed.autograd")
+
+if is_available():
+    from torch._C._distributed_autograd import (
+        _current_context,
+        _get_debug_info,
+        _get_max_id,
+        _init,
+        _is_valid_context,
+        _new_context,
+        _release_context,
+        _retrieve_context,
+        backward,
+        DistAutogradContext,
+        get_gradients,
+    )
+
+__all__ = ["context", "is_available"]
+
+
+class context:
+    """
+    Context object to wrap forward and backward passes when using
+    distributed autograd. The ``context_id`` generated in the ``with``
+    statement  is required to uniquely identify a distributed backward pass
+    on all workers. Each worker stores metadata associated with this
+    ``context_id``, which is required to correctly execute a distributed
+    autograd pass.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> import torch.distributed.autograd as dist_autograd
+        >>> with dist_autograd.context() as context_id:
+        >>>     t1 = torch.rand((3, 3), requires_grad=True)
+        >>>     t2 = torch.rand((3, 3), requires_grad=True)
+        >>>     loss = rpc.rpc_sync("worker1", torch.add, args=(t1, t2)).sum()
+        >>>     dist_autograd.backward(context_id, [loss])
+    """
+
+    def __enter__(self) -> int:
+        self.autograd_context = _new_context()
+        return self.autograd_context._context_id()
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ) -> None:
+        _release_context(self.autograd_context._context_id())
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/c10d_logger.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/c10d_logger.py
new file mode 100644
index 0000000000000000000000000000000000000000..1dfae5b92962f44f4dea3a3393cbcb6ae752999b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/c10d_logger.py
@@ -0,0 +1,100 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import functools
+import logging
+from collections.abc import Callable
+from typing import Any, TypeVar
+from typing_extensions import ParamSpec
+
+import torch
+import torch.distributed as dist
+from torch.distributed.logging_handlers import _log_handlers
+from torch.monitor import _WaitCounter
+
+
+__all__: list[str] = []
+
+_DEFAULT_DESTINATION = "default"
+
+
+def _get_or_create_logger(destination: str = _DEFAULT_DESTINATION) -> logging.Logger:
+    logging_handler, log_handler_name = _get_logging_handler(destination)
+    logger = logging.getLogger(f"c10d-{log_handler_name}")
+    logger.setLevel(logging.DEBUG)
+    formatter = logging.Formatter(
+        "%(asctime)s %(filename)s:%(lineno)s %(levelname)s p:%(processName)s t:%(threadName)s: %(message)s"
+    )
+    logging_handler.setFormatter(formatter)
+    logger.propagate = False
+    logger.addHandler(logging_handler)
+    return logger
+
+
+def _get_logging_handler(
+    destination: str = _DEFAULT_DESTINATION,
+) -> tuple[logging.Handler, str]:
+    log_handler = _log_handlers[destination]
+    log_handler_name = f"{type(log_handler).__name__}-{destination}"
+    return (log_handler, log_handler_name)
+
+
+# pyrefly: ignore [unknown-name]
+global _c10d_logger
+_c10d_logger = _get_or_create_logger()
+
+
+def _get_msg_dict(func_name, *args, **kwargs) -> dict[str, Any]:
+    if dist.is_initialized():
+        group = kwargs.get("group") or kwargs.get("process_group")
+        msg_dict = {
+            "func_name": f"{func_name}",
+            "pg_name": f"{dist._get_process_group_name(kwargs.get('pg'))}",  # type: ignore[arg-type]
+            "backend": f"{dist.get_backend(group)}",
+            "world_size": f"{dist.get_world_size()}",
+            "group_size": f"{dist.get_world_size(group)}",
+            "global_rank": f"{dist.get_rank()}",
+            "local_rank": f"{dist.get_rank(group)}",
+        }
+        if msg_dict["backend"] == "nccl":
+            nccl_version = torch.cuda.nccl.version()
+            msg_dict["nccl_version"] = ".".join(str(v) for v in nccl_version)
+    else:
+        msg_dict = {
+            "func_name": f"{func_name}",
+        }
+    return msg_dict
+
+
+_T = TypeVar("_T")
+_P = ParamSpec("_P")
+
+
+def _exception_logger(func: Callable[_P, _T]) -> Callable[_P, _T]:
+    @functools.wraps(func)
+    def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> _T:
+        try:
+            return func(*args, **kwargs)
+        except Exception as error:
+            msg_dict = _get_msg_dict(func.__name__, *args, **kwargs)
+            msg_dict["error"] = f"{error}"
+            _c10d_logger.debug(msg_dict)
+            raise
+
+    return wrapper
+
+
+def _time_logger(func: Callable[_P, _T]) -> Callable[_P, _T]:
+    @functools.wraps(func)
+    def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> _T:
+        with _WaitCounter(f"pytorch.wait_counter.c10d.{func.__name__}").guard():
+            func_return = func(*args, **kwargs)
+        return func_return
+
+    return wrapper
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8104a8df99f0b5c4a4f1db57ac98602a61666626
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/__init__.py
@@ -0,0 +1,21 @@
+from . import _extension
+from .api import CheckpointException
+from .default_planner import DefaultLoadPlanner, DefaultSavePlanner
+from .filesystem import FileSystemReader, FileSystemWriter
+from .hf_storage import HuggingFaceStorageReader, HuggingFaceStorageWriter
+from .metadata import (
+    BytesStorageMetadata,
+    ChunkStorageMetadata,
+    Metadata,
+    TensorStorageMetadata,
+)
+from .optimizer import load_sharded_optimizer_state_dict
+from .planner import LoadPlan, LoadPlanner, ReadItem, SavePlan, SavePlanner, WriteItem
+from .quantized_hf_storage import QuantizedHuggingFaceStorageReader
+
+# pyrefly: ignore [deprecated]
+from .state_dict_loader import load, load_state_dict
+
+# pyrefly: ignore [deprecated]
+from .state_dict_saver import async_save, save, save_state_dict
+from .storage import StorageReader, StorageWriter
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_async_executor.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_async_executor.py
new file mode 100644
index 0000000000000000000000000000000000000000..428c697b91e9b567e99d52714a8248d322798073
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_async_executor.py
@@ -0,0 +1,34 @@
+# pyre-strict
+# mypy: allow-untyped-defs
+import abc
+import os
+from concurrent.futures import Future
+from typing import Optional, Union
+
+import torch.distributed as dist
+from torch.distributed.checkpoint.metadata import STATE_DICT_TYPE
+from torch.distributed.checkpoint.planner import SavePlanner
+from torch.distributed.checkpoint.storage import StorageWriter
+
+
+class _AsyncCheckpointExecutor(abc.ABC):
+    @abc.abstractmethod
+    def execute_save(
+        self,
+        staging_future_or_state_dict: Union[STATE_DICT_TYPE, Future[STATE_DICT_TYPE]],
+        *,
+        checkpoint_id: Union[str, os.PathLike, None] = None,
+        storage_writer: Optional[StorageWriter] = None,
+        planner: Optional[SavePlanner] = None,
+        process_group: Optional[dist.ProcessGroup] = None,
+        no_dist: bool = False,
+        use_collectives: bool = True,
+    ) -> Future:
+        """
+        Execute the checkpoint save request asynchronously.
+
+        This method is intended to be used as an abstraction for
+        implementing async checkpointing. The actual checkpoint save
+        operation is executed in a separate thread or process depending
+        on the implementation of this interface.
+        """
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_async_process_executor.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_async_process_executor.py
new file mode 100644
index 0000000000000000000000000000000000000000..48390253c302a5acc9806ecac587a24022262565
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_async_process_executor.py
@@ -0,0 +1,455 @@
+# pyre-strict
+# mypy: allow-untyped-defs
+import gc
+import logging
+import os
+from concurrent.futures import Future, ThreadPoolExecutor
+from dataclasses import dataclass
+from enum import Enum
+from typing import Any, Optional, Union
+from uuid import uuid4
+
+import torch.distributed as dist
+import torch.multiprocessing as mp
+from torch.distributed import PrefixStore, TCPStore
+from torch.distributed.checkpoint._async_executor import _AsyncCheckpointExecutor
+from torch.distributed.checkpoint.logger import _dcp_method_logger, _init_logger
+from torch.distributed.checkpoint.metadata import Metadata, STATE_DICT_TYPE
+from torch.distributed.checkpoint.planner import SavePlanner
+from torch.distributed.checkpoint.storage import StorageWriter
+from torch.distributed.checkpoint.utils import _DistWrapper
+from torch.distributed.elastic.agent.server.api import _get_fq_hostname
+from torch.distributed.elastic.utils.distributed import get_free_port
+
+
+logger = logging.getLogger()
+
+
+class _CheckpointSaveProcessControlOpts(Enum):
+    INIT_COMPLETE = "init_complete"
+    TERMINATE = "terminate"
+
+
+@dataclass(init=False, unsafe_hash=True)
+class _CheckpointRequestIdentifier:
+    checkpoint_id: Union[str, os.PathLike, None]
+    uuid: str
+
+    def __init__(self, checkpoint_id: Union[str, os.PathLike, None]):
+        self.checkpoint_id = checkpoint_id
+        self.uuid = str(uuid4())
+
+
+@dataclass
+class _AsyncCheckpointRequest:
+    staged_state_dict: STATE_DICT_TYPE
+    checkpoint_request_id: _CheckpointRequestIdentifier
+    storage_writer: Optional[StorageWriter] = None
+    planner: Optional[SavePlanner] = None
+    no_dist: bool = False
+    use_collectives: bool = True
+
+
+@dataclass(init=False)
+class _ProcessGroupInitInfo:
+    local_rank: int
+    global_rank: int
+    world_size: int
+    tcp_store_master_addr: str
+    tcp_store_master_port: int
+    use_prefix_store: bool
+    disable_automatic_gc: bool
+    disable_manual_gc: bool
+
+    def __init__(self, process_group: Optional[dist.ProcessGroup] = None):
+        self.local_rank = dist.get_node_local_rank(fallback_rank=0)
+        self.global_rank = dist.get_rank(process_group)
+        self.world_size = dist.get_world_size(process_group)
+        self.use_prefix_store = os.environ.get("DCP_USE_PREFIX_STORE", "0") == "1"
+        self.disable_automatic_gc = (
+            os.environ.get("DCP_DISABLE_AUTOMATIC_GC", "0") == "1"
+        )
+        self.disable_manual_gc = os.environ.get("DCP_DISABLE_MANUAL_GC", "0") == "1"
+
+        # Let coordinator rank find a port on the localhost.
+        # Broadcast the (master_addr, port) to all ranks; each rank in the
+        # checkpoint daemon process will use TCPStore (master_addr, port)
+        # for collective communication.
+        dist_wrapper: _DistWrapper = _DistWrapper(
+            group=process_group,
+            use_dist=True,
+            coordinator_rank=0,
+        )
+
+        def get_master_addr_and_port() -> tuple[str, int]:
+            if self.use_prefix_store:
+                master_addr = os.environ.get("MASTER_ADDR")
+                master_port = os.environ.get("MASTER_PORT")
+                assert master_addr is not None, (
+                    "DCP needs MASTER_ADDR to use prefix store"
+                )
+                assert master_port is not None, (
+                    "DCP needs MASTER_PORT to use prefix store"
+                )
+                master_port = int(master_port)
+            else:
+                master_addr = os.environ.get("MASTER_ADDR")
+                if master_addr is None:
+                    master_addr = _get_fq_hostname()
+                master_port = get_free_port()
+
+            return master_addr, master_port
+
+        self.tcp_store_master_addr, self.tcp_store_master_port = dist_wrapper.broadcast(
+            step="get_master_addr_and_port",
+            map_fun=get_master_addr_and_port,
+        )
+
+
+class _AsyncCheckpointProcess:
+    def __init__(
+        self,
+        pg_init_info: _ProcessGroupInitInfo,
+    ):
+        self.ctx = mp.get_context("spawn")
+        self._process_pipe, child_end = self.ctx.Pipe()
+
+        self._save_process = self.ctx.Process(
+            target=self._checkpointing_subprocess,
+            args=(
+                pg_init_info,
+                child_end,
+            ),
+            daemon=True,
+        )
+
+        self._save_process.start()
+
+        # Close the parent's copy of child end after we pass it into the child,
+        # so the recv()s on it will fail-fast if the child process dies.
+        child_end.close()
+
+        # Wait for the checkpoint background process to initialize.
+        # Using default GLOO init timeout.
+        response = self._wait_for_response(timeout=1800)
+        if not response == _CheckpointSaveProcessControlOpts.INIT_COMPLETE:
+            raise AssertionError(f"Expected INIT_COMPLETE response, got {response}")
+
+    def __del__(self) -> None:
+        if self._save_process.is_alive():
+            try:
+                logger.info("Terminating the checkpoint background process.")
+                self._send(_CheckpointSaveProcessControlOpts.TERMINATE)
+                self._save_process.join(timeout=5)
+            finally:
+                if self._save_process.is_alive():
+                    logger.warning(
+                        "Checkpoint background process is still alive after termination request. Sending SIGTERM."
+                    )
+                    self._save_process.terminate()
+
+    def _send(self, data: Any) -> None:
+        self._process_pipe.send(data)
+
+    def _wait_for_response(self, timeout: Optional[float] = None) -> Any:
+        if not self._save_process.is_alive():
+            logger.info("Checkpoint background process is dead calling join()...")
+            self._save_process.join()
+            raise RuntimeError(
+                f"Checkpoint background process is dead. Exit code: {self._save_process.exitcode}"
+            )
+
+        if timeout is not None and not self._process_pipe.poll(timeout=timeout):
+            raise RuntimeError(
+                f"Timed out after {timeout}s while waiting for response from checkpointer process pid: {self._save_process.pid}"
+            )
+
+        try:
+            response = self._process_pipe.recv()
+        except EOFError:
+            raise RuntimeError(  # noqa: B904
+                f"Checkpoint background process is dead. Exit code: {self._save_process.exitcode}"
+            )
+
+        if isinstance(response, BaseException):
+            raise response
+
+        return response
+
+    def save(
+        self,
+        staged_state_dict: STATE_DICT_TYPE,
+        *,
+        checkpoint_id: Union[str, os.PathLike, None] = None,
+        storage_writer: Optional[StorageWriter] = None,
+        planner: Optional[SavePlanner] = None,
+        no_dist: bool = False,
+        use_collectives: bool = True,
+    ) -> Metadata:
+        # Create a unique identifier to locate requests/responses
+        # from the checkpoint daemon process.
+        checkpoint_request_id = _CheckpointRequestIdentifier(checkpoint_id)
+        async_cp_request = _AsyncCheckpointRequest(
+            staged_state_dict=staged_state_dict,
+            checkpoint_request_id=checkpoint_request_id,
+            storage_writer=storage_writer,
+            planner=planner,
+            no_dist=no_dist,
+            use_collectives=use_collectives,
+        )
+        self._send(async_cp_request)
+        result = self._wait_for_response()
+        if not isinstance(result, Metadata):
+            raise AssertionError(f"Expected Metadata response, got {type(result)}")
+        return result
+
+    @staticmethod
+    def _execute_save(
+        state_dict: STATE_DICT_TYPE,
+        *,
+        checkpoint_request_id: _CheckpointRequestIdentifier,
+        storage_writer: Optional[StorageWriter] = None,
+        planner: Optional[SavePlanner] = None,
+        no_dist: bool = False,
+        use_collectives: bool = True,
+    ) -> Metadata:
+        from torch.distributed.checkpoint.state_dict_saver import save
+
+        metadata = save(
+            state_dict,
+            checkpoint_id=checkpoint_request_id.checkpoint_id,
+            storage_writer=storage_writer,
+            planner=planner,
+            no_dist=no_dist,
+            use_collectives=use_collectives,
+        )
+        return metadata
+
+    @staticmethod
+    def _checkpointing_subprocess(
+        pg_init_info: _ProcessGroupInitInfo,
+        parent_conn,
+    ) -> None:
+        # Phase 1: Process Group Initialization
+        # Only needs to execute once during the lifetime of the checkpoint background process.
+        try:
+            _init_logger(pg_init_info.global_rank)
+
+            # Setup environment variables for process group initialization.
+            os.environ["TORCHELASTIC_USE_AGENT_STORE"] = "False"
+            os.environ["MASTER_ADDR"] = pg_init_info.tcp_store_master_addr
+            os.environ["MASTER_PORT"] = str(pg_init_info.tcp_store_master_port)
+            os.environ["LOCAL_RANK"] = str(pg_init_info.local_rank)
+            os.environ["RANK"] = str(pg_init_info.global_rank)
+            os.environ["WORLD_SIZE"] = str(pg_init_info.world_size)
+
+            logger.info(
+                "Initializing dist.ProcessGroup in checkpoint background process on port %s",
+                pg_init_info.tcp_store_master_port,
+            )
+            # NOTE: GLOO backend is enforced here.
+            if pg_init_info.use_prefix_store:
+                logger.info(
+                    "Initializing dist.ProcessGroup in checkpoint background process with prefix store"
+                )
+                store = PrefixStore(
+                    "AsyncCheckpointProcess/",
+                    TCPStore(
+                        pg_init_info.tcp_store_master_addr,
+                        pg_init_info.tcp_store_master_port,
+                    ),
+                )
+                dist.init_process_group(
+                    backend=dist.Backend.GLOO,
+                    store=store,
+                    world_size=pg_init_info.world_size,
+                    rank=pg_init_info.global_rank,
+                )
+            else:
+                dist.init_process_group(backend=dist.Backend.GLOO)
+            dist.barrier()
+
+            logger.info("Checkpoint background process is running...")
+            parent_conn.send(_CheckpointSaveProcessControlOpts.INIT_COMPLETE)
+
+            if pg_init_info.disable_automatic_gc:
+                # Disable automatic garbage collection
+                # GC can optionally be called manually after each checkpoint
+                gc.disable()
+                logger.info("Disabled automatic garbage collection")
+        except BaseException as e:  # noqa: B036
+            logger.error(
+                f"Checkpoint background process failed during initialization: {e}"  # noqa: G004
+            )
+            parent_conn.send(e)
+            return
+
+        # Phase 2: Serving Loop
+        try:
+            first_request = True
+            while True:
+                logger.info("Waiting for checkpoint save request...")
+                obj = parent_conn.recv()
+                if (
+                    isinstance(obj, _CheckpointSaveProcessControlOpts)
+                    and obj == _CheckpointSaveProcessControlOpts.TERMINATE
+                ):
+                    logger.info("Terminating the checkpoint background process.")
+                    return
+                if not isinstance(obj, _AsyncCheckpointRequest):
+                    raise AssertionError(
+                        f"Expected _AsyncCheckpointRequest, got {type(obj)}"
+                    )
+                logger.info(
+                    f"Received async checkpoint request with id={obj.checkpoint_request_id.checkpoint_id}"  # noqa: G004
+                )
+
+                try:
+                    response = _AsyncCheckpointProcess._execute_save(
+                        obj.staged_state_dict,
+                        checkpoint_request_id=obj.checkpoint_request_id,
+                        storage_writer=obj.storage_writer,
+                        planner=obj.planner,
+                        no_dist=obj.no_dist,
+                        use_collectives=obj.use_collectives,
+                    )
+                    parent_conn.send(response)
+                    logger.info(
+                        f"Completed checkpoint save request for checkpoint_id={obj.checkpoint_request_id}"  # noqa: G004
+                    )
+
+                    # in theory this manual gc should not be needed as we shouldn't be leaking anything from checkpointing process
+                    if (
+                        pg_init_info.disable_automatic_gc
+                        and not pg_init_info.disable_manual_gc
+                    ):
+                        del obj
+
+                        collected_objects = gc.collect()
+
+                        logger.info(
+                            f"Manual garbage collection completed - collected {collected_objects} objects."  # noqa: G004
+                        )
+                        if first_request:
+                            # Freeze GC to not check GC for large checkpoint save plans
+                            # After freezing, subsequent gc.collect() calls will only scan
+                            # NEW objects created after this point, not the frozen save plan
+                            logger.info(
+                                "First checkpoint request completed - freezing gc"
+                            )
+                            gc.freeze()
+                    first_request = False
+                except BaseException as e:  # noqa: B036
+                    logger.error(
+                        f"Checkpoint save failed for checkpoint_id={obj.checkpoint_request_id.checkpoint_id}: {e}"  # noqa: G004
+                    )
+                    parent_conn.send(e)
+                    # Continue serving loop - don't exit process
+        finally:
+            logger.info("Checkpoint background process is shutting down...")
+            dist.destroy_process_group()
+            parent_conn.close()
+
+
+_CHECKPOINT_PROCESS: Optional[_AsyncCheckpointProcess] = None
+
+
+class _ProcessBasedAsyncCheckpointExecutor(_AsyncCheckpointExecutor):
+    def __init__(self) -> None:
+        self._executor = ThreadPoolExecutor(max_workers=1)
+
+    @staticmethod
+    def _execute_save_impl(
+        *,
+        pg_init_info: Optional[_ProcessGroupInitInfo],
+        staging_future_or_state_dict: Union[Future[STATE_DICT_TYPE], STATE_DICT_TYPE],
+        checkpoint_id: Union[str, os.PathLike, None] = None,
+        storage_writer: Optional[StorageWriter] = None,
+        planner: Optional[SavePlanner] = None,
+        process_group: Optional[dist.ProcessGroup] = None,
+        no_dist: bool = False,
+        use_collectives: bool = True,
+    ) -> Metadata:
+        global _CHECKPOINT_PROCESS
+        if _CHECKPOINT_PROCESS is None:
+            if pg_init_info is None:
+                raise AssertionError(
+                    "pg_init_info must not be None when _CHECKPOINT_PROCESS is None"
+                )
+            ckpt_kwargs = {}
+            if (ckpt_id := getattr(storage_writer, "checkpoint_id", None)) is not None:
+                ckpt_kwargs["checkpoint_id"] = ckpt_id
+                ckpt_kwargs["process_group"] = process_group
+
+            @_dcp_method_logger(**ckpt_kwargs)
+            def create_checkpoint_daemon_process() -> None:
+                global _CHECKPOINT_PROCESS
+                # pyrefly: ignore [bad-argument-type]
+                _CHECKPOINT_PROCESS = _AsyncCheckpointProcess(pg_init_info=pg_init_info)
+
+            create_checkpoint_daemon_process()
+
+        if _CHECKPOINT_PROCESS is None:
+            raise AssertionError(
+                "_CHECKPOINT_PROCESS must not be None after initialization"
+            )
+        staged_state_dict = (
+            staging_future_or_state_dict.result()
+            if isinstance(staging_future_or_state_dict, Future)
+            else staging_future_or_state_dict
+        )
+        return _CHECKPOINT_PROCESS.save(
+            staged_state_dict=staged_state_dict,
+            checkpoint_id=checkpoint_id,
+            storage_writer=storage_writer,
+            planner=planner,
+            no_dist=no_dist,
+            use_collectives=use_collectives,
+        )
+
+    def execute_save(
+        self,
+        staging_future_or_state_dict: Union[Future[STATE_DICT_TYPE], STATE_DICT_TYPE],
+        *,
+        checkpoint_id: Union[str, os.PathLike, None] = None,
+        storage_writer: Optional[StorageWriter] = None,
+        planner: Optional[SavePlanner] = None,
+        process_group: Optional[dist.ProcessGroup] = None,
+        no_dist: bool = False,
+        use_collectives: bool = True,
+    ) -> Future:
+        """
+        NOTE:
+
+        - Checkpoint process is implemented as a daemon process.
+        The AsyncCheckpointProcess' lifetime is tied to the lifetime of the
+        main process (e.g. trainer process).
+
+        - The first call to execute_save_in_process() will initialize the checkpoint
+        daemon process. Subsequent async checkpoint requests will not need process
+        initialization. Therefore, the first async checkpoint request will take longer to complete.
+
+        - Process initialization can have significant overhead, dominated by latency for all ranks to spawn
+        a background process + process group initialization in the background process.
+        """
+
+        global _CHECKPOINT_PROCESS
+        pg_init_info: Optional[_ProcessGroupInitInfo] = None
+        if _CHECKPOINT_PROCESS is None:
+            # Find a port on coordinator rank and broadcast
+            # to all ranks.
+            pg_init_info = _ProcessGroupInitInfo(process_group)
+
+        f: Future = self._executor.submit(
+            self._execute_save_impl,
+            pg_init_info=pg_init_info,
+            staging_future_or_state_dict=staging_future_or_state_dict,
+            checkpoint_id=checkpoint_id,
+            storage_writer=storage_writer,
+            planner=planner,
+            no_dist=no_dist,
+            use_collectives=use_collectives,
+        )
+        f.add_done_callback(lambda f: self._executor.shutdown(wait=False))
+
+        return f
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_async_thread_executor.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_async_thread_executor.py
new file mode 100644
index 0000000000000000000000000000000000000000..8dfe63413d433c75a012916f65628f2bd4e57f20
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_async_thread_executor.py
@@ -0,0 +1,71 @@
+# pyre-strict
+# mypy: allow-untyped-defs
+import os
+from concurrent.futures import Future, ThreadPoolExecutor
+from typing import Optional, Union
+
+import torch.distributed as dist
+from torch.distributed.checkpoint._async_executor import _AsyncCheckpointExecutor
+from torch.distributed.checkpoint.metadata import STATE_DICT_TYPE
+from torch.distributed.checkpoint.planner import SavePlanner
+from torch.distributed.checkpoint.storage import StorageWriter
+
+
+def save_wrapper(
+    staging_future_or_state_dict: Union[Future[STATE_DICT_TYPE], STATE_DICT_TYPE],
+    *,
+    checkpoint_id: Union[str, os.PathLike, None] = None,
+    storage_writer: Optional[StorageWriter] = None,
+    planner: Optional[SavePlanner] = None,
+    process_group: Optional[dist.ProcessGroup] = None,
+    no_dist: bool = False,
+    use_collectives: bool = True,
+) -> Future:
+    from torch.distributed.checkpoint.state_dict_saver import save
+
+    staged_dict = (
+        staging_future_or_state_dict.result()
+        if isinstance(staging_future_or_state_dict, Future)
+        else staging_future_or_state_dict
+    )
+    return save(
+        staged_dict,
+        checkpoint_id=checkpoint_id,
+        storage_writer=storage_writer,
+        planner=planner,
+        process_group=process_group,
+        no_dist=no_dist,
+        use_collectives=use_collectives,
+    )
+
+
+class _ThreadBasedAsyncCheckpointExecutor(_AsyncCheckpointExecutor):
+    def __init__(self) -> None:
+        self._executor = ThreadPoolExecutor(
+            max_workers=1, thread_name_prefix="AsyncCheckpointExecutor"
+        )
+
+    def execute_save(
+        self,
+        staging_future_or_state_dict: Union[Future[STATE_DICT_TYPE], STATE_DICT_TYPE],
+        *,
+        checkpoint_id: Union[str, os.PathLike, None] = None,
+        storage_writer: Optional[StorageWriter] = None,
+        planner: Optional[SavePlanner] = None,
+        process_group: Optional[dist.ProcessGroup] = None,
+        no_dist: bool = False,
+        use_collectives: bool = True,
+    ) -> Future:
+        f: Future = self._executor.submit(
+            save_wrapper,
+            staging_future_or_state_dict=staging_future_or_state_dict,
+            checkpoint_id=checkpoint_id,
+            storage_writer=storage_writer,
+            planner=planner,
+            process_group=process_group,
+            no_dist=no_dist,
+            use_collectives=use_collectives,
+        )
+        f.add_done_callback(lambda f: self._executor.shutdown(wait=False))
+
+        return f
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_checkpointer.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_checkpointer.py
new file mode 100644
index 0000000000000000000000000000000000000000..13b0d627a36cc0fedc75695932260ecec718bcde
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_checkpointer.py
@@ -0,0 +1,103 @@
+from concurrent.futures import Future
+from typing import Any, Optional
+
+import torch.distributed as dist
+import torch.distributed.checkpoint.state_dict_loader as loader
+import torch.distributed.checkpoint.state_dict_saver as saver
+from torch.distributed.checkpoint.metadata import Metadata, STATE_DICT_TYPE
+from torch.distributed.checkpoint.storage import (
+    LoadPlanner,
+    SavePlanner,
+    StorageReader,
+    StorageWriter,
+)
+
+
+__all__: list[str] = []
+
+
+class _Checkpointer:
+    """This base class specifies a high level API for saving and loading
+    distributed `state_dict` 's. It provides an abstraction over the low-level APIs
+    provided by :py:mod:`torch.distributed.checkpoint.storage`, essentially calling
+    :py:meth: `torch.distributed.state_dict_saver.save` and
+    :py:meth: `torch.distributed.state_dict_loader.load` with the provided storage
+    readers and writers.
+
+    .. warning::
+        This feature is experimental and subject to removal/change.
+
+    """
+
+    def __init__(
+        self,
+        storage_writer: StorageWriter,
+        storage_reader: StorageReader,
+        *,
+        process_group: Optional[dist.ProcessGroup] = None,
+        coordinator_rank: int = 0,
+        no_dist: bool = False,
+        load_planner: Optional[LoadPlanner] = None,
+        save_planner: Optional[SavePlanner] = None,
+    ):
+        """Initializes the Checkpointer instance.
+
+        Args:
+            storage_writer: Instance of StorageWrite use to perform writes.
+            storage_reader: StorageReader used to load data from.
+            process_group: ProcessGroup to be used for cross-rank synchronization.
+            coordinator_rank: Rank to use to coordinate the checkpoint. rank0 is used by default.
+            no_dist: If ``True``, distributed checkpoint will not load in SPMD style. (Default: ``False``)
+            loader_planner: Instance of LoadPlanner to use when loading.
+            save_planner: Instance of SavePlanner to use when saving.
+        """
+        self.storage_writer = storage_writer
+        self.storage_reader = storage_reader
+        self.process_group = process_group
+        self.coordinator_rank = coordinator_rank
+        self.no_dist = no_dist
+        self.load_planner = load_planner
+        self.save_planner = save_planner
+
+    def save(
+        self,
+        state_dict: STATE_DICT_TYPE,
+    ) -> Metadata:
+        """Calls :py:meth: `torch.distributed.state_dict_saver.save`. Utilizing values passed during initialization."""
+        return saver.save(
+            state_dict,
+            self.storage_writer,
+            process_group=self.process_group,
+            coordinator_rank=self.coordinator_rank,
+            no_dist=self.no_dist,
+            planner=self.save_planner,
+        )
+
+    def async_save(
+        self,
+        state_dict: STATE_DICT_TYPE,
+    ) -> Future:
+        """
+        Calls :py:meth: `torch.distributed.state_dict_saver._async_save`. Utilizing values passed during initialization.
+
+        Returns:
+            Future: A future holding the resultant Metadata object from `save`.
+        """
+        response = saver.async_save(
+            state_dict,
+            storage_writer=self.storage_writer,
+            process_group=self.process_group,
+            planner=self.save_planner,
+        )
+        if not isinstance(response, Future):
+            raise AssertionError("response should be a Future instance")
+        return response
+
+    def load(self, state_dict: dict[str, Any]) -> None:
+        """Calls :py:meth: `torch.distributed.state_dict_loader.load`. Utilizing values passed during initialization."""
+        loader.load(
+            state_dict,
+            storage_reader=self.storage_reader,
+            process_group=self.process_group,
+            planner=self.load_planner,
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_consolidate_hf_safetensors.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_consolidate_hf_safetensors.py
new file mode 100644
index 0000000000000000000000000000000000000000..32d81fb1ea7213e7672a9e7fe23b030962a354f0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_consolidate_hf_safetensors.py
@@ -0,0 +1,716 @@
+# pyre-strict
+
+import concurrent.futures
+import glob
+import json
+import logging
+import math
+import mmap
+import os
+import struct
+import time
+from dataclasses import dataclass, field
+from typing import Any, Optional
+
+import torch
+from torch import distributed as dist
+from torch.distributed.checkpoint._hf_utils import (
+    _gen_file_name,
+    _get_dcp_custom_metadata,
+    _get_safetensors_file_metadata,
+    _metadata_fn,
+    DATA_OFFSETS_KEY,
+    DEFAULT_EXTRA_METADATA_KEY,
+    DTYPE_KEY,
+    SAVED_OFFSETS_KEY,
+    SHAPE_KEY,
+    SUFFIX,
+)
+
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+
+@dataclass
+class _FqnData:
+    """
+    Dataclass to store information about a tensor (identified by its fully qualified name).
+
+    Attributes:
+        offset_in_file: Byte offset where this tensor's data begins in the output file
+        shape_in_file: Shape of the tensor in the output file
+        dtype_size: Size of the tensor's data type in bytes
+        dtype_str: String representation of the tensor's data type
+    """
+
+    offset_in_file: int = 0
+    shape_in_file: list[int] = field(default_factory=list)
+    dtype_size: int = 0
+    dtype_str: str = ""
+
+
+@dataclass
+class _OutputFileData:
+    """
+    Dataclass to store information about an output safetensors file.
+
+    Attributes:
+        metadata_size: Size of the metadata section in bytes
+        fqn_data: Dictionary mapping tensor names to their metadata
+    """
+
+    metadata_size: int = 0
+    fqn_data: dict[str, _FqnData] = field(default_factory=dict)
+
+
+@dataclass
+class _InputFileData:
+    """
+    Dataclass to store information about an input safetensors file.
+
+    Attributes:
+        metadata_size: Size of the metadata section in bytes
+        metadata: Json metadata from the safetensors file
+    """
+
+    metadata_size: int = 0
+    metadata: Any = None
+
+
+def _parse_input_metadata(
+    input_files_data: dict[str, _InputFileData],
+    output_files_data: dict[str, _OutputFileData],
+) -> None:
+    """
+    Parse metadata from input safetensors files to determine the full tensor shapes and types.
+
+    This function analyzes the metadata from all input files to determine the complete shape
+    of each tensor after consolidation. It updates the output_files_data with this information.
+
+    Args:
+        input_files_data: dict of metadata from input safetensors files
+        output_files_data: Dictionary mapping output file paths to their metadata
+
+    Raises:
+        ValueError: If no DCP custom metadata is found in a safetensors file
+    """
+
+    from safetensors.torch import _getdtype  # type: ignore[import]
+
+    # Dictionary to track the full size of each tensor across all shards
+    fqn_to_size_mapping: dict[str, tuple[list[int], str]] = {}
+
+    for file_data in input_files_data.values():
+        safetensors_metadata = file_data.metadata
+        dcp_sharding_info = _get_dcp_custom_metadata(safetensors_metadata)
+        if not dcp_sharding_info:
+            raise ValueError(
+                "No DCP custom metadata found in safetensors file. The file must be saved with DCP to be consolidated."
+            )
+
+        for key, val in safetensors_metadata.items():
+            if key == DEFAULT_EXTRA_METADATA_KEY:
+                continue
+
+            # Get the shape of this tensor shard and its offset in the full tensor
+            sizes = val[SHAPE_KEY]
+            offsets = dcp_sharding_info[key][SAVED_OFFSETS_KEY]
+
+            if key not in fqn_to_size_mapping:
+                # First time seeing this tensor - calculate its full size by adding offsets to dimensions
+                cur_size = [size + offset for size, offset in zip(sizes, offsets)]
+                fqn_to_size_mapping[key] = (cur_size, val[DTYPE_KEY])
+            else:
+                # We've seen this tensor before - update its size if this shard extends beyond current known dimensions
+                cur_size = fqn_to_size_mapping[key][0]
+                for i in range(len(sizes)):
+                    cur_size[i] = max(cur_size[i], sizes[i] + offsets[i])
+
+    # Now that we know the full size of each tensor, populate the output file data
+    for fqn, tensor_info in fqn_to_size_mapping.items():
+        tensor_size = tensor_info[0]
+        dtype_str = tensor_info[1]
+        for output_data in output_files_data.values():
+            # Add this tensor to the output file if it's already assigned there
+            if fqn in output_data.fqn_data:
+                output_data.fqn_data[fqn] = _FqnData(
+                    shape_in_file=tensor_size,
+                    dtype_size=torch.finfo(_getdtype(dtype_str)).bits
+                    // 8,  # Convert bits to bytes
+                    dtype_str=dtype_str,
+                )
+
+
+def _write_metadata(
+    output_files_data: dict[str, _OutputFileData],
+) -> None:
+    """
+    Write metadata to the beginning of each output safetensors file.
+
+    This function writes the metadata section to each output file, including information
+    about tensor shapes, data types, and offsets. It also updates the offset_in_file
+    field for each tensor in the output_files_data.
+
+    Args:
+        output_files_data: Dictionary mapping output file paths to their metadata
+    """
+    # Process each output file
+    for file_path, output_data in output_files_data.items():
+        with open(file_path, "wb") as f:
+            metadata = {}
+            curr_offset = 0
+
+            # Calculate offsets for each tensor in the file
+            for fqn, fqn_data in output_data.fqn_data.items():
+                # Calculate the end offset by multiplying all dimensions and the data type size
+                end_offset = (
+                    curr_offset
+                    + math.prod(fqn_data.shape_in_file) * fqn_data.dtype_size
+                )
+
+                # Store metadata for this tensor
+                metadata[fqn] = {
+                    SHAPE_KEY: fqn_data.shape_in_file,
+                    DTYPE_KEY: fqn_data.dtype_str,
+                    DATA_OFFSETS_KEY: [
+                        curr_offset,
+                        end_offset,
+                    ],  # Start and end byte offsets
+                }
+                # Store the offset for later use when writing the actual tensor data
+                fqn_data.offset_in_file = curr_offset
+
+                # Update current offset for the next tensor
+                curr_offset = end_offset
+
+            # Convert metadata to JSON and encode as bytes
+            json_metadata = json.dumps(metadata)
+            json_bytes = json_metadata.encode("utf-8")
+
+            # Write the metadata size as an 8-byte unsigned integer (little-endian)
+            size_in_bytes = len(json_bytes)
+            header_len = struct.pack(" bytes:
+    """
+    Read tensor data from a safetensors file using memory mapping for efficiency.
+
+    Args:
+        file_path: Path to the safetensors file
+        start_offset: Start offset of tensor data within the data section
+        end_offset: End offset of tensor data within the data section
+        metadata_size: Size of the metadata header
+
+    Returns:
+        Raw tensor data as bytes
+    """
+    # Use mmap for efficient access
+    with open(file_path, "rb") as f:
+        with mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ) as mm:
+            absolute_start = metadata_size + start_offset
+            absolute_end = metadata_size + end_offset
+            return bytes(mm[absolute_start:absolute_end])
+
+
+def _process_output_file(
+    output_file: str,
+    output_data: _OutputFileData,
+    input_files_data: dict[str, _InputFileData],
+) -> None:
+    """
+    Process a single output file by writing tensor data from input files using memory mapping.
+
+    This function is designed to be run in parallel for different output files.
+
+    Args:
+        output_file: Path to the output file
+        output_data: Metadata for the output file
+        input_files_data: Dictionary mapping input file paths to their metadata
+    """
+
+    sorted_tensors = sorted(
+        output_data.fqn_data.items(), key=lambda x: x[1].offset_in_file
+    )
+
+    with open(output_file, "r+b") as output_stream:
+        output_stream.seek(0, os.SEEK_END)
+        # Process each tensor in sequential output order
+        for tensor_fqn, tensor_fqn_data in sorted_tensors:
+            full_tensor_mv = memoryview(
+                bytearray(
+                    math.prod(tensor_fqn_data.shape_in_file)
+                    * tensor_fqn_data.dtype_size
+                )
+            )
+
+            # Process each input safetensors file
+            for safetensors_file in input_files_data:
+                file_metadata = input_files_data[safetensors_file].metadata
+                input_metadata_size = input_files_data[safetensors_file].metadata_size
+
+                if tensor_fqn not in file_metadata:
+                    continue
+
+                metadata = file_metadata[tensor_fqn]
+
+                data_offsets = metadata[DATA_OFFSETS_KEY]
+
+                # Use memory mapping to read tensor data efficiently
+                data_to_write = _read_tensor_data_mmap(
+                    safetensors_file,
+                    data_offsets[0],
+                    data_offsets[1],
+                    input_metadata_size,
+                )
+
+                # Get the offsets of this tensor shard within the full tensor
+                fqn_custom_metadata = _get_dcp_custom_metadata(file_metadata)[
+                    tensor_fqn
+                ]  # type: ignore[index]
+                offsets_of_tensor_being_read = fqn_custom_metadata[SAVED_OFFSETS_KEY]  # type: ignore[index]
+
+                # Write this tensor shard to the appropriate position in the output file
+                _write_sub_tensor_to_file_optimized(
+                    full_tensor_mv,
+                    data_to_write,
+                    tensor_fqn_data.dtype_size,  # Size of each element in bytes
+                    tensor_fqn_data.shape_in_file,  # Full tensor shape
+                    offsets_of_tensor_being_read,  # Where this shard belongs in the full tensor
+                    metadata[SHAPE_KEY],  # Shape of this shard
+                )
+
+            output_stream.write(full_tensor_mv)
+
+
+def _write_data(
+    input_files_data: dict[str, _InputFileData],
+    output_files_data: dict[str, _OutputFileData],
+    num_threads: int = 1,
+) -> None:
+    """
+    Write tensor data from input files to the output files using memory mapping.
+
+    This function reads tensor data from each input file and writes it to the appropriate
+    position in the output files based on the tensor's offsets. When num_threads > 1,
+    the work is split across threads with each thread handling a different output file.
+
+    Args:
+        input_files_data: Dictionary mapping input file paths to their metadata
+        output_files_data: Dictionary mapping output file paths to their metadata
+        num_threads: Number of threads to use for parallel processing
+    """
+    if num_threads <= 1 or len(output_files_data) <= 1:
+        # Sequential processing
+        for output_file, output_data in output_files_data.items():
+            _process_output_file(output_file, output_data, input_files_data)
+    else:
+        # Parallel processing with ThreadPoolExecutor
+        with concurrent.futures.ThreadPoolExecutor(
+            max_workers=min(num_threads, len(output_files_data))
+        ) as executor:
+            futures = []
+            for output_file, output_data in output_files_data.items():
+                futures.append(
+                    executor.submit(
+                        _process_output_file,
+                        output_file,
+                        output_data,
+                        input_files_data,
+                    )
+                )
+
+            # Wait for all futures to complete
+            for future in concurrent.futures.as_completed(futures):
+                # Handle any exceptions that might have occurred
+                try:
+                    future.result()
+                except Exception as e:
+                    print(f"Error processing output file: {e}")
+                    raise
+
+
+def _write_sub_tensor_to_file_optimized(
+    full_tensor_mv: memoryview,
+    sub_tensor_bytes: bytes,
+    element_size: int,
+    tensor_shape: list[int],
+    sub_tensor_offsets: list[int],
+    sub_tensor_shape: list[int],
+) -> None:
+    """
+    Optimized version that writes the maximum number of contiguous bytes possible.
+
+    Uses a unified algorithm that calculates the maximum contiguous bytes that can be
+    written in each iteration and continues until the entire subtensor is written.
+    Handles all sharding patterns efficiently:
+    - Full sub-tensor at once for row-wise sharding
+    - Row-by-row for column-wise sharding
+    - Optimized chunks for other patterns
+
+    Args:
+        full_tensor_mv: Buffer to write the full tensor to
+        sub_tensor_bytes: Raw tensor data as bytes
+        element_size: Size of each element in bytes
+        tensor_shape: Shape of the full tensor
+        sub_tensor_offsets: Starting offsets of the sub-tensor within the full tensor
+        sub_tensor_shape: Shape of the sub-tensor
+    """
+    # Handle empty tensors
+    if not tensor_shape or not sub_tensor_shape:
+        return
+
+    # Calculate tensor strides for efficient indexing
+    tensor_strides = [1]
+    for i in range(len(tensor_shape) - 1, 0, -1):
+        tensor_strides.insert(0, tensor_strides[0] * tensor_shape[i])
+
+    sub_tensor_strides = [1]
+    for i in range(len(sub_tensor_shape) - 1, 0, -1):
+        sub_tensor_strides.insert(0, sub_tensor_strides[0] * sub_tensor_shape[i])
+
+    total_elements = math.prod(sub_tensor_shape)
+
+    elements_written = 0
+    while elements_written < total_elements:
+        # Convert linear index to multi-dimensional indices
+        temp_idx = elements_written
+        indices = []
+        for dim_size in reversed(sub_tensor_shape):
+            indices.append(temp_idx % dim_size)
+            temp_idx //= dim_size
+        indices.reverse()
+
+        # Calculate maximum contiguous elements we can write from this position
+        max_contiguous = _calculate_max_contiguous_elements(
+            indices, sub_tensor_shape, tensor_shape
+        )
+
+        # Calculate source position in bytes
+        src_pos = sum(idx * stride for idx, stride in zip(indices, sub_tensor_strides))
+        src_byte_offset = src_pos * element_size
+
+        # Calculate destination position in bytes
+        dest_indices = [
+            idx + offset for idx, offset in zip(indices, sub_tensor_offsets)
+        ]
+        dest_pos = sum(
+            idx * stride for idx, stride in zip(dest_indices, tensor_strides)
+        )
+        dest_byte_offset = dest_pos * element_size
+
+        # Write the contiguous chunk
+        bytes_to_write = max_contiguous * element_size
+        chunk_data = sub_tensor_bytes[
+            src_byte_offset : src_byte_offset + bytes_to_write
+        ]
+        full_tensor_mv[dest_byte_offset : dest_byte_offset + bytes_to_write] = (
+            chunk_data
+        )
+
+        elements_written += max_contiguous
+
+
+def _calculate_max_contiguous_elements(
+    indices: list[int],
+    sub_tensor_shape: list[int],
+    tensor_shape: list[int],
+) -> int:
+    """
+    Calculate the maximum number of contiguous elements that can be written from current position.
+
+    This determines the largest chunk by checking how elements are laid out in memory
+    and finding natural boundaries where contiguity breaks.
+
+    Args:
+        indices: Current position indices in the sub-tensor
+        sub_tensor_shape: Shape of the sub-tensor being written
+        tensor_shape: Shape of the full tensor
+
+    Raises:
+        ValueError: If input lists are empty, have mismatched lengths, or contain invalid values
+    """
+    # Validate input lists are not empty
+    if not indices or not sub_tensor_shape or not tensor_shape:
+        raise ValueError("Input lists cannot be empty")
+
+    # Validate all lists have the same length (same number of dimensions)
+    if not (len(indices) == len(sub_tensor_shape) == len(tensor_shape)):
+        raise ValueError(
+            f"All input lists must have the same length. Got indices: {len(indices)}, "
+            f"sub_tensor_shape: {len(sub_tensor_shape)}, tensor_shape: {len(tensor_shape)}"
+        )
+
+    # Validate indices are within bounds of sub_tensor_shape
+    for i, (idx, sub_dim) in enumerate(zip(indices, sub_tensor_shape)):
+        if idx >= sub_dim:
+            raise ValueError(
+                f"Index {idx} at dimension {i} is out of bounds for sub-tensor shape {sub_tensor_shape}"
+            )
+
+    # Validate sub_tensor dimensions don't exceed tensor dimensions
+    for i, (sub_dim, tensor_dim) in enumerate(zip(sub_tensor_shape, tensor_shape)):
+        if sub_dim > tensor_dim:
+            raise ValueError(
+                f"Sub-tensor dimension {sub_dim} at position {i} exceeds tensor dimension {tensor_dim}"
+            )
+
+    # Start with elements remaining in the last dimension
+    max_contiguous = sub_tensor_shape[-1] - indices[-1]
+
+    # Check if we can extend across multiple dimensions
+    # We can write across dimension boundaries if we're writing complete "rows"
+    # and the layout in destination tensor maintains contiguity
+
+    # For 2D case: check if we can write multiple complete rows
+    if len(sub_tensor_shape) >= 2:
+        # If we're at the start of a row and can write complete rows
+        if indices[-1] == 0:  # At start of last dimension (column)
+            rows_remaining = sub_tensor_shape[-2] - indices[-2]  # Rows left to write
+
+            # Check if writing complete rows maintains contiguity in destination
+            # This is true for row-wise sharding or when sub-tensor spans full width
+            if sub_tensor_shape[-1] == tensor_shape[-1]:  # Full width
+                max_contiguous = rows_remaining * sub_tensor_shape[-1]
+
+            # For higher dimensions, check if we can extend further
+            if len(sub_tensor_shape) >= 3 and indices[-2] == 0:
+                # Check if we can write complete 2D slices
+                remaining_in_dim = sub_tensor_shape[-3] - indices[-3]
+                if (
+                    sub_tensor_shape[-1] == tensor_shape[-1]
+                    and sub_tensor_shape[-2] == tensor_shape[-2]
+                ):
+                    max_contiguous = (
+                        remaining_in_dim * sub_tensor_shape[-2] * sub_tensor_shape[-1]
+                    )
+
+    return max_contiguous
+
+
+def _write_overall_metadata_file(
+    output_dir: str,
+    output_files_data: dict[str, _OutputFileData],
+) -> None:
+    """
+    Write the overall metadata file that maps tensor names to their file locations.
+
+    This creates a model.safetensors.index.json file that HuggingFace models use
+    to locate tensors across multiple files.
+
+    Args:
+        output_dir: Directory where the metadata file will be written
+        output_files_data: Dictionary mapping output file paths to their metadata
+    """
+    total_size = 0
+    weight_map = {}
+    for output_path, value in output_files_data.items():
+        for fqn, fqn_data in value.fqn_data.items():
+            total_size += math.prod(fqn_data.shape_in_file) * fqn_data.dtype_size
+            weight_map[fqn] = os.path.basename(output_path)
+
+    metadata_to_write: dict[str, Any] = {}
+    metadata_to_write["metadata"] = {"total_size": total_size}
+    metadata_to_write["weight_map"] = weight_map
+
+    metadata_path = os.path.join(output_dir, f"{_metadata_fn}")
+    with open(metadata_path, "w") as metadata_file:
+        json.dump(metadata_to_write, metadata_file, indent=2)
+
+
+def _consolidate_safetensors_files(
+    input_dir: str,
+    output_dir: str,
+    fqn_to_file_mapping: dict[str, str],
+    num_threads: int,
+) -> dict[str, _OutputFileData]:
+    output_files_data: dict[str, _OutputFileData] = {}
+    # Create multiple output files based on the provided mapping
+    for fqn, filename in fqn_to_file_mapping.items():
+        output_path = os.path.join(output_dir, filename)
+
+        if output_path not in output_files_data:
+            output_files_data[output_path] = _OutputFileData(fqn_data={fqn: _FqnData()})
+        else:
+            output_files_data[output_path].fqn_data[fqn] = _FqnData()
+
+    # Find all safetensors files in the input directory
+    safetensors_files = glob.glob(os.path.join(input_dir, f"*{SUFFIX}"))
+
+    # Read metadata from all input files
+    input_files_data: dict[str, _InputFileData] = {}
+    for safetensor_file in safetensors_files:
+        with open(safetensor_file, "rb") as f:
+            metadata, size = _get_safetensors_file_metadata(f)
+            input_files_data[safetensor_file] = _InputFileData(
+                metadata_size=size, metadata=metadata
+            )
+    # Step 1: Parse metadata to determine tensor shapes and types
+    _parse_input_metadata(input_files_data, output_files_data)
+
+    # Step 2: Write metadata headers to output files
+    _write_metadata(output_files_data)
+    # Step 3: Write actual tensor data from input files to output files
+    _write_data(input_files_data, output_files_data, num_threads)
+
+    return output_files_data
+
+
+def consolidate_safetensors_files(
+    input_dir: str,
+    output_dir: str,
+    fqn_to_index_mapping: dict[str, int],
+    num_threads: int = 1,
+) -> None:
+    """
+    Main function to consolidate sharded safetensors files into one or more output files.
+
+    This function orchestrates the entire consolidation process:
+    1. Sets up the output file structure based on the fqn_to_index_mapping
+    2. Finds all safetensors files in the input directory
+    3. Parses metadata from all input files
+    4. Writes metadata to the output files
+    5. Writes tensor data from input files to output files
+    6. Writes overall model.index.safetensors.json file with weight map
+
+    Args:
+        input_dir: Directory containing sharded safetensors files
+        output_dir: Directory where consolidated files will be written
+        fqn_to_index_mapping: Optional mapping of tensor names to output file indices.
+                             If None, all tensors will be consolidated into a single file.
+        num_threads: Number of threads to use for parallel processing of saving data to output files.
+    """
+    start_time = time.time()
+    logger.info(
+        "Consolidating safetensors files from %s to %s. Beginning at time %f",
+        input_dir,
+        output_dir,
+        start_time,
+    )
+
+    max_index = max(fqn_to_index_mapping.values())
+    fqn_to_file_mapping = {
+        fqn: _gen_file_name(idx, max_index) for fqn, idx in fqn_to_index_mapping.items()
+    }
+
+    output_files_data = _consolidate_safetensors_files(
+        input_dir, output_dir, fqn_to_file_mapping, num_threads
+    )
+
+    # Step 4: Write overall model.index.safetensors.json file with weight map
+    _write_overall_metadata_file(output_dir, output_files_data)
+
+    logger.info("Done consolidating. Took %.2f secs.", time.time() - start_time)
+
+
+def consolidate_safetensors_files_on_every_rank(
+    input_dir: str,
+    output_dir: str,
+    fqn_to_index_mapping: dict[str, int],
+    num_threads: int = 1,
+    process_group: Optional[dist.ProcessGroup] = None,
+) -> None:
+    """
+    Consolidate sharded safetensors files across multiple ranks, with each rank handling a subset of output files.
+
+    This function distributes the consolidation work by assigning output files to different ranks.
+    All tensors with the same index in fqn_to_index_mapping are processed by the same rank,
+    as they belong to the same output file.
+
+    If process_group is provided, rank and world_size will be derived from it. Otherwise,
+    they will be automatically detected from the distributed environment if available.
+
+    Args:
+        input_dir: Directory containing sharded safetensors files
+        output_dir: Directory where consolidated files will be written
+        fqn_to_index_mapping: Mapping of tensor names to output file indices
+        num_threads: Number of threads to use for parallel processing on each rank
+        process_group: PyTorch distributed process group (default: None, will use default group)
+    """
+
+    start_time = time.time()
+    # Derive rank and world_size from process_group or default distributed environment
+    if dist.is_available() and dist.is_initialized():
+        rank = dist.get_rank(group=process_group)
+        world_size = dist.get_world_size(group=process_group)
+    else:
+        # Default to single process mode if distributed is not initialized
+        rank = 0
+        world_size = 1
+        logger.warning(
+            "Distributed environment not initialized. Running in single process mode."
+        )
+    logger.info(
+        "Rank %d/%d: Consolidating safetensors files from %s to %s",
+        rank,
+        world_size,
+        input_dir,
+        output_dir,
+    )
+
+    # Find all unique indices in the mapping
+    unique_indices = set(fqn_to_index_mapping.values())
+
+    # Distribute indices across ranks
+    indices_for_this_rank = []
+    for idx in unique_indices:
+        # Simple distribution: index % world_size == rank
+        if idx % world_size == rank:
+            indices_for_this_rank.append(idx)
+
+    logger.info(
+        "Rank %d: Assigned %d output files out of %d total files",
+        rank,
+        len(indices_for_this_rank),
+        len(unique_indices),
+    )
+
+    # Filter the fqn_to_index_mapping to only include tensors for this rank
+    filtered_mapping = {
+        fqn: idx
+        for fqn, idx in fqn_to_index_mapping.items()
+        if idx in indices_for_this_rank
+    }
+
+    if filtered_mapping:
+        # Convert index mapping to filename mapping
+        max_index = max(unique_indices)
+        filtered_filename_mapping = {}
+        for fqn, idx in filtered_mapping.items():
+            filename = _gen_file_name(idx, max_index)
+            filtered_filename_mapping[fqn] = filename
+
+        # Call the existing consolidation function with the filtered mapping
+        _consolidate_safetensors_files(
+            input_dir=input_dir,
+            output_dir=output_dir,
+            fqn_to_file_mapping=filtered_filename_mapping,
+            num_threads=num_threads,
+        )
+
+    logger.info(
+        "Rank %d: Done consolidating. Processed %d unique indices in %.2f secs.",
+        rank,
+        len(indices_for_this_rank),
+        time.time() - start_time,
+    )
+
+    # Wait for all ranks to complete
+    if dist.is_available() and dist.is_initialized():
+        logger.info("Rank %d: Waiting for all ranks to complete...", rank)
+        dist.barrier()
+        logger.info("Rank %d: All ranks have completed.", rank)
+        if rank == 0:
+            logger.info("Total time taken: %.2f secs.", time.time() - start_time)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_dedup_save_plans.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_dedup_save_plans.py
new file mode 100644
index 0000000000000000000000000000000000000000..acb81c41862852320cdc1d412ddaffdd48e73841
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_dedup_save_plans.py
@@ -0,0 +1,65 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+import dataclasses
+from collections import defaultdict
+from typing import TYPE_CHECKING
+
+from torch.distributed.checkpoint.planner import SavePlan, WriteItem
+
+
+if TYPE_CHECKING:
+    from torch.distributed.checkpoint.metadata import MetadataIndex
+
+__all__ = ["dedup_save_plans"]
+
+
+def dedup_save_plans(
+    all_plans: list[SavePlan],
+    save_to_lowest_rank: bool = False,
+) -> list[SavePlan]:
+    """
+    Removes duplicate entries from appearing on multiple SavePlans. For each duplicate across
+    a set of SavePlans, only the smallest SavePlan in terms of planned storage keeps the entry.
+
+    Please note that this function does not modify the original SavePlans, but rather returns
+    """
+
+    # Map to query the plan indices that a write item is duplicated in
+    write_item_to_plan_indices: dict[MetadataIndex, set[int]] = defaultdict(set)
+    # Map to query the write item from its index
+    write_item_idx_to_write_item: dict[MetadataIndex, WriteItem] = {}
+    # Set of write item indices that are present in each plan
+    # After deduplication, this will be the set of write item indices that are present in the final plans
+    plan_to_item_indices: list[set[MetadataIndex]] = [
+        {item.index for item in plan.items} for plan in all_plans
+    ]
+
+    for plan_idx, plan in enumerate(all_plans):
+        for write_item in plan.items:
+            # map each write item to its plan
+            write_item_to_plan_indices[write_item.index].add(plan_idx)
+            write_item_idx_to_write_item[write_item.index] = write_item
+    plan_to_size = [0] * len(all_plans)
+    for write_item_idx, plan_indices in write_item_to_plan_indices.items():
+        if save_to_lowest_rank:
+            select_plan_idx = min(plan_indices)
+        else:
+            select_plan_idx = min(
+                plan_indices, key=lambda plan_idx: plan_to_size[plan_idx]
+            )
+
+        write_item = write_item_idx_to_write_item[write_item_idx]
+        # Ignore the storage size of anything that is not a tensor, since
+        # we don't know how much storage they represent
+        plan_to_size[select_plan_idx] += write_item.tensor_storage_size() or 1
+        for plan_idx in plan_indices - {select_plan_idx}:
+            plan_to_item_indices[plan_idx].discard(write_item_idx)
+    # Sanity check
+    if len(all_plans) != len(plan_to_item_indices):
+        raise AssertionError("len(all_plans) != len(plan_to_item_indices)")
+    # Create new plans with the updated write items post deduplication
+    return [
+        dataclasses.replace(
+            plan, items=[item for item in plan.items if item.index in item_indexes]
+        )
+        for plan, item_indexes in zip(all_plans, plan_to_item_indices)
+    ]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_dedup_tensors.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_dedup_tensors.py
new file mode 100644
index 0000000000000000000000000000000000000000..c57b2e149106abbac66522aa571d1a462db4157d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_dedup_tensors.py
@@ -0,0 +1,62 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+import dataclasses
+import logging
+from typing import TYPE_CHECKING
+
+from torch.distributed.checkpoint.planner import SavePlan
+
+
+if TYPE_CHECKING:
+    from torch.distributed.checkpoint.metadata import MetadataIndex
+
+__all__ = ["dedup_tensors"]
+
+
+def init_logger() -> logging.Logger:
+    logger = logging.getLogger(__name__)
+    level = logging.INFO
+    logger.setLevel(level)
+    console = logging.StreamHandler()
+    formatter = logging.Formatter(
+        "%(asctime)s %(filename)s:%(lineno)s %(levelname)s p:%(processName)s t:%(threadName)s: %(message)s"
+    )
+    console.setFormatter(formatter)
+    console.setLevel(level)
+    logger.addHandler(console)
+    logger.propagate = False
+    return logger
+
+
+logger = init_logger()
+
+
+# TODO add docstring for dedup_tensors
+def dedup_tensors(all_plans: list[SavePlan]) -> list[SavePlan]:
+    all_plans = list(all_plans)
+    key_to_plan: dict[MetadataIndex, list[int]] = {}
+    for plan_idx, plan in enumerate(all_plans):
+        for write_item in plan.items:
+            key_to_plan.setdefault(write_item.index, []).append(plan_idx)
+
+    replicated_items = {k: v for k, v in key_to_plan.items() if len(v) > 1}
+
+    # Remove duplicates by always keeping the first entry.
+    # Compute the per-rank remove set.
+    plan_to_keys: dict[int, list[MetadataIndex]] = {}
+    for key, plans in replicated_items.items():
+        for plan_idx in plans[1:]:
+            plan_to_keys.setdefault(plan_idx, []).append(key)
+    if len(plan_to_keys) > 0:
+        logger.info("Duplicate keys to remove: %s", plan_to_keys)
+
+    for plan_idx, keys in plan_to_keys.items():
+        key_set = set(keys)
+        # rewrite items and remove elements
+        new_items = [
+            write_item
+            for write_item in all_plans[plan_idx].items
+            if write_item.index not in key_set
+        ]
+        all_plans[plan_idx] = dataclasses.replace(all_plans[plan_idx], items=new_items)
+
+    return all_plans
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8361362eb3a5ed5abae10d39c1db54c3e8739b46
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_experimental/__init__.py
@@ -0,0 +1,53 @@
+"""
+Checkpoint functionality for machine learning models.
+
+This module provides classes for saving and loading model checkpoints in a distributed
+training environment. It includes functionality for coordinating checkpoint operations
+across multiple processes and customizing the checkpoint process through hooks.
+
+Key components:
+- Checkpointer: Main class for orchestrating checkpoint operations (save, load)
+- CheckpointWriter: Handles writing state dictionaries to storage
+- CheckpointReader: Handles reading state dictionaries from storage read
+- Barrier: Synchronization mechanism for distributed checkpointing
+- RankInfo: Information about the current rank in a distributed environment
+"""
+
+from .barriers import (
+    Barrier,
+    BarrierConfig,
+    create_barrier_from_config,
+    TCPStoreBarrier,
+)
+from .builder import make_async_checkpointer, make_sync_checkpointer
+from .checkpoint_reader import CheckpointReader
+from .checkpoint_writer import CheckpointWriter, CheckpointWriterConfig, WriterHook
+from .checkpointer import AsyncCheckpointer, Checkpointer, SyncCheckpointer
+from .config import CheckpointerConfig
+from .staging import CheckpointStager, CheckpointStagerConfig, DefaultStager
+from .types import RankInfo, STATE_DICT
+from .utils import wrap_future
+
+
+__all__ = [
+    "Barrier",
+    "TCPStoreBarrier",
+    "CheckpointReader",
+    "CheckpointWriter",
+    "CheckpointWriterConfig",
+    "WriterHook",
+    "Checkpointer",
+    "SyncCheckpointer",
+    "AsyncCheckpointer",
+    "CheckpointerConfig",
+    "BarrierConfig",
+    "create_barrier_from_config",
+    "CheckpointStager",
+    "CheckpointStagerConfig",
+    "DefaultStager",
+    "RankInfo",
+    "STATE_DICT",
+    "wrap_future",
+    "make_sync_checkpointer",
+    "make_async_checkpointer",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_experimental/barriers.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_experimental/barriers.py
new file mode 100644
index 0000000000000000000000000000000000000000..bcea8ad91401e50f4e9f39ace06f0bafe4f0d6a2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_experimental/barriers.py
@@ -0,0 +1,267 @@
+"""
+Barrier implementations for synchronizing distributed checkpoint operations.
+
+This module provides abstract and concrete barrier implementations that ensure
+all ranks in a distributed training environment complete their checkpoint operations
+before proceeding, which is essential for data consistency.
+"""
+
+import abc
+import logging
+from collections import Counter
+from dataclasses import dataclass, field
+from datetime import timedelta
+from typing import Any, Optional
+
+import torch.distributed as dist
+import torch.distributed.elastic.utils.store as store_util
+
+
+logger = logging.getLogger()
+
+
+# Registry of barrier types
+BARRIER_REGISTRY: dict[str, type] = {}
+
+
+def register_barrier(barrier_class: type) -> type:
+    """Register a barrier class in the global registry."""
+    if hasattr(barrier_class, "barrier_type"):
+        BARRIER_REGISTRY[barrier_class.barrier_type] = barrier_class
+    return barrier_class
+
+
+@dataclass
+class BarrierConfig:
+    """
+    Configuration for barrier construction.
+
+    This class provides a flexible way to configure different barrier implementations
+    with their specific constructor arguments. The barrier type will be looked up
+    from a registry and instantiated with rank_info and barrier_args.
+
+    Attributes:
+        barrier_type: A string identifying the barrier type (e.g., "tcp_store").
+                     If None, no barrier will be used.
+        barrier_args: Dictionary of arguments to pass to the barrier constructor.
+                     rank_info will be automatically injected as the first argument.
+
+    Examples:
+        # No barrier
+        BarrierConfig()
+
+        # TCPStore barrier
+        BarrierConfig(
+            barrier_type="tcp_store",
+            barrier_args={
+                'timeout_barrier_init_secs': 30,
+                'barrier_prefix_list': ['checkpoint'],
+                'use_checkpoint_barrier_tcpstore_libuv': False,
+                'tcpstore_port': 12345,
+                'master_address': 'localhost'
+            }
+        )
+    """
+
+    barrier_type: Optional[str] = None
+    barrier_args: dict[str, Any] = field(default_factory=dict)
+
+
+def create_barrier_from_config(
+    barrier_config: BarrierConfig,
+) -> Optional["Barrier"]:
+    """
+    Create a barrier instance from BarrierConfig.
+
+    Args:
+        barrier_config: Configuration for barrier construction.
+
+    Returns:
+        Barrier instance or None if no barrier type is configured.
+
+    Raises:
+        ValueError: If the barrier_type is not found in the registry.
+    """
+    if barrier_config.barrier_type is None:
+        return None
+
+    if barrier_config.barrier_type not in BARRIER_REGISTRY:
+        raise ValueError(
+            f"Unknown barrier type: {barrier_config.barrier_type}. "
+            f"Available types: {list(BARRIER_REGISTRY.keys())}"
+        )
+
+    barrier_class = BARRIER_REGISTRY[barrier_config.barrier_type]
+    return barrier_class(**barrier_config.barrier_args)
+
+
+class Barrier(abc.ABC):
+    """
+    Abstract base class for synchronization barriers.
+
+    A barrier ensures that all ranks in a distributed environment reach a certain
+    point in execution before any rank proceeds further, which is essential for
+    coordinating operations like checkpointing across multiple processes.
+    """
+
+    @abc.abstractmethod
+    def __init__(self, **kwargs: dict[str, Any]):
+        """
+        Initialize a barrier.
+
+        Args:
+            **kwargs: Keyword arguments for specific barrier implementations.
+                     Common arguments may include rank information, barrier prefixes,
+                     timeout settings, and other barrier-specific configuration.
+        """
+        # No implementation needed in the abstract base class
+
+    @abc.abstractmethod
+    def execute_barrier(self) -> None:
+        """
+        Execute a synchronization barrier.
+
+        This method uses the barrier_prefix provided during initialization to
+        coordinate synchronization across processes.
+        """
+
+
+@register_barrier
+class DistBarrier(Barrier):
+    """
+    A barrier implementation using PyTorch's distributed barrier for synchronization.
+
+    This barrier uses the built-in torch.distributed.barrier() function to coordinate
+    synchronization across multiple processes. It's simpler than TCPStoreBarrier but
+    requires an initialized process group.
+    """
+
+    barrier_type = "dist_barrier"
+
+    def __init__(
+        self,
+    ) -> None:
+        """
+        Initialize a DistBarrier.
+
+        This barrier requires an initialized PyTorch distributed process group.
+        No additional arguments are needed as it uses the current process group.
+
+        Raises:
+            AssertionError: If the distributed process group is not initialized.
+        """
+        if not dist.is_initialized():
+            raise AssertionError("DistBarrier requires an initialized process group.")
+
+    def execute_barrier(self) -> None:
+        """
+        Execute a synchronization barrier using the prefix provided during initialization.
+        """
+        # Note: dist.barrier() doesn't support explicit timeouts
+        # The timeout is handled by the underlying implementation
+        dist.barrier()
+
+
+@register_barrier
+class TCPStoreBarrier(Barrier):
+    """
+    A barrier implementation using PyTorch's TCPStore for synchronization.
+
+    This barrier uses a TCP-based distributed key-value store to coordinate
+    synchronization across multiple processes. It uses a single TCP store
+    for all barrier operations, with different prefixes to distinguish between
+    different barrier types.
+    """
+
+    barrier_type = "tcp_store"
+
+    def __init__(
+        self,
+        global_rank: int,
+        global_world_size: int,
+        barrier_prefix: str,
+        timeout_barrier_init_secs: int,
+        use_checkpoint_barrier_tcpstore_libuv: bool,
+        tcpstore_port: int,
+        master_address: str,
+        timeout_secs: int,
+    ):
+        """
+        Initialize a TCPStoreBarrier.
+
+        Args:
+            global_rank: The rank of the current process in the distributed environment.
+            global_world_size: The total number of processes in the distributed environment.
+            barrier_prefix: A string prefix to identify this specific barrier.
+            timeout_barrier_init_secs: Timeout in seconds for initializing the TCPStore.
+            use_checkpoint_barrier_tcpstore_libuv: Whether to use libuv for the TCPStore.
+            tcpstore_port: Port number for the TCPStore.
+            master_address: Address of the master node for the TCPStore.
+            timeout_secs: Maximum time in seconds to wait for all ranks to reach the barrier.
+        """
+        logger.info(
+            "Initializing TCPStore master_address=%s tcpstore_port=%s rank=%s "
+            "world_size=%s barrier_prefix=%s timeout_barrier_init_secs=%s "
+            "use_checkpoint_barrier_tcpstore_libuv=%s timeout_secs=%s",
+            master_address,
+            tcpstore_port,
+            global_rank,
+            global_world_size,
+            barrier_prefix,
+            timeout_barrier_init_secs,
+            use_checkpoint_barrier_tcpstore_libuv,
+            timeout_secs,
+        )
+
+        # Counter collection to track barrier seq on a per barrier prefix basis.
+        self._tcp_store_barrier_seq: Counter = Counter()
+        self._barrier_prefix = barrier_prefix
+
+        # Store rank and world size for barrier operations
+        self._global_rank = global_rank
+        self._global_world_size = global_world_size
+        self._timeout_secs = timeout_secs
+
+        # Create a single TCP store for all barrier operations
+        self._tcp_store = dist.TCPStore(
+            master_address,
+            int(tcpstore_port),
+            world_size=self._global_world_size,
+            timeout=timedelta(seconds=timeout_barrier_init_secs),
+            is_master=(self._global_rank == 0),
+        )
+
+    def execute_barrier(self) -> None:
+        """
+        Execute a synchronization barrier using the prefix provided during initialization.
+
+        The implementation uses a sequence number that is incremented every time
+        a barrier is reached. The sequence number is per barrier prefix to allow
+        different barriers to operate concurrently.
+        """
+        barrier_prefix = self._barrier_prefix
+
+        logger.info(
+            "Executing barrier barrier_prefix=%s timeout_secs=%s",
+            barrier_prefix,
+            self._timeout_secs,
+        )
+
+        def _rank_key(rank: int) -> str:
+            return f"rank{rank}"
+
+        # Track which barrier sequence this rank is joining.
+        self._tcp_store.set(
+            _rank_key(self._global_rank),
+            str(self._tcp_store_barrier_seq[barrier_prefix]),
+        )
+
+        # Execute barrier for that sequence number (for the specific prefix).
+        store_util.barrier(
+            store=self._tcp_store,
+            world_size=self._global_world_size,
+            key_prefix=(
+                barrier_prefix + str(self._tcp_store_barrier_seq[barrier_prefix])
+            ),
+        )
+        self._tcp_store_barrier_seq[barrier_prefix] += 1
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_experimental/builder.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_experimental/builder.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a7f5fa9e71268b665f0863e85c6775b2391b6c3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_experimental/builder.py
@@ -0,0 +1,174 @@
+"""
+Factory functions for creating checkpointer instances with sensible defaults.
+
+This module provides high-level factory functions that simplify the creation
+of checkpointer instances by automatically handling component initialization
+and configuration with reasonable defaults.
+"""
+
+from collections.abc import Callable
+from typing import Any, Optional
+
+import torch.distributed as dist
+
+from .barriers import create_barrier_from_config
+from .checkpoint_process import CheckpointProcess
+from .checkpoint_reader import CheckpointReader
+from .checkpoint_writer import CheckpointWriter, CheckpointWriterConfig, WriterHook
+from .checkpointer import AsyncCheckpointer, SyncCheckpointer
+from .config import CheckpointerConfig
+from .staging import DefaultStager
+from .types import RankInfo
+
+
+def _get_default_rank_info() -> RankInfo:
+    """
+    Get default rank information from the current distributed environment.
+
+    Returns:
+        RankInfo: Rank information from the default process group if initialized,
+                 otherwise single-rank fallback.
+    """
+    if dist.is_initialized():
+        return RankInfo(
+            global_world_size=dist.get_world_size(),
+            global_rank=dist.get_rank(),
+        )
+    else:
+        # Single-rank fallback
+        return RankInfo(global_world_size=1, global_rank=0)
+
+
+def default_subprocess_init_fn(*_: Any) -> None:
+    """Default subprocess initialization function (no-op)."""
+
+
+def default_writer_init_fn(rank_info: RankInfo) -> CheckpointWriter:
+    """Default checkpoint writer initialization function."""
+    return CheckpointWriter(
+        config=CheckpointWriterConfig(),
+        rank_info=rank_info,
+    )
+
+
+def make_sync_checkpointer(
+    config: CheckpointerConfig = CheckpointerConfig(),
+    rank_info: Optional[RankInfo] = None,
+    commit_hook: Optional[WriterHook] = None,
+) -> SyncCheckpointer:
+    """
+    Factory function to create a SyncCheckpointer instance with sensible defaults.
+
+    This function creates a synchronous checkpointer with default components, automatically
+    detecting rank information from the default process group if available, and using the
+    provided component configurations.
+
+    Args:
+        config: CheckpointerConfig containing component-specific configurations
+               (writer_config, staging_config, process_config). Defaults to CheckpointerConfig().
+        rank_info: RankInfo for distributed training. Defaults to auto-detection from
+                  the default PyTorch distributed process group if initialized, otherwise
+                  falls back to single-rank (world_size=1, rank=0).
+        commit_hook: Optional hook for custom actions before and after checkpoint commits.
+
+    Returns:
+        SyncCheckpointer: A configured synchronous checkpointer instance.
+
+    Examples:
+        # Simplest usage - auto-detect rank, default config
+        checkpointer = make_sync_checkpointer()
+
+        # Explicit rank configuration
+        checkpointer = make_sync_checkpointer(
+            rank_info=RankInfo(global_world_size=4, global_rank=0)
+        )
+
+        # Disable barrier
+        from .barriers import BarrierConfig
+        config = CheckpointerConfig(barrier_config=BarrierConfig(barrier_type=None))
+        checkpointer = make_sync_checkpointer(config=config)
+    """
+    if rank_info is None:
+        rank_info = _get_default_rank_info()
+
+    reader = CheckpointReader(
+        rank_info=rank_info,
+    )
+
+    barrier = create_barrier_from_config(config.barrier_config)
+
+    writer = CheckpointWriter(
+        config=config.writer_config,
+        rank_info=rank_info,
+        barrier=barrier,
+        commit_hook=commit_hook,
+    )
+
+    return SyncCheckpointer(
+        writer=writer,
+        reader=reader,
+    )
+
+
+def make_async_checkpointer(
+    config: CheckpointerConfig = CheckpointerConfig(),
+    rank_info: Optional[RankInfo] = None,
+    subprocess_init_fn: Callable[..., None] = default_subprocess_init_fn,
+    subprocess_init_args: tuple[Any, ...] = (),
+    checkpoint_writer_init_fn: Callable[..., CheckpointWriter] = default_writer_init_fn,
+    checkpoint_writer_init_args: Optional[dict[str, Any]] = None,
+) -> AsyncCheckpointer:
+    """
+    Factory function to create an AsyncCheckpointer instance with sensible defaults.
+
+    This function creates an asynchronous checkpointer using the provided configuration,
+    automatically detecting rank information if not provided.
+
+    Args:
+        config: CheckpointerConfig containing component-specific configurations.
+        rank_info: RankInfo for distributed training. Defaults to auto-detection.
+        subprocess_init_fn: Function to initialize the subprocess. Defaults to no-op.
+        subprocess_init_args: Arguments to pass to subprocess_init_fn.
+        checkpoint_writer_init_fn: Function to create CheckpointWriter instance.
+        checkpoint_writer_init_args: Arguments to pass to checkpoint_writer_init_fn.
+
+    Returns:
+        AsyncCheckpointer: A configured asynchronous checkpointer instance.
+
+    Examples:
+        # Create with default config
+        checkpointer = make_async_checkpointer()
+
+        # Create with custom init functions
+        checkpointer = make_async_checkpointer(
+            subprocess_init_fn=my_subprocess_init_fn,
+            checkpoint_writer_init_fn=my_writer_init_fn
+        )
+    """
+    if rank_info is None:
+        rank_info = _get_default_rank_info()
+
+    reader = CheckpointReader(
+        rank_info=rank_info,
+    )
+
+    checkpoint_stager = DefaultStager(
+        config=config.staging_config,
+    )
+
+    checkpoint_writer_init_args = checkpoint_writer_init_args or {}
+
+    checkpoint_process = CheckpointProcess(
+        rank_info=rank_info,
+        config=config.process_config,
+        subprocess_init_fn=subprocess_init_fn,
+        subprocess_init_args=subprocess_init_args,
+        checkpoint_writer_init_fn=checkpoint_writer_init_fn,
+        checkpoint_writer_init_args=checkpoint_writer_init_args,
+    )
+
+    return AsyncCheckpointer(
+        checkpoint_stager=checkpoint_stager,
+        checkpoint_process=checkpoint_process,
+        reader=reader,
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_experimental/checkpoint_process.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_experimental/checkpoint_process.py
new file mode 100644
index 0000000000000000000000000000000000000000..c71210aaa54690ca894c9ded0fd54c335b7c2f0b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_experimental/checkpoint_process.py
@@ -0,0 +1,361 @@
+import logging
+import os
+import traceback
+from collections.abc import Callable
+from concurrent.futures import Future, ThreadPoolExecutor
+from dataclasses import dataclass
+from enum import Enum
+from multiprocessing.connection import Connection
+from typing import Any, Optional, Union
+
+import torch.multiprocessing as mp
+from torch.multiprocessing.spawn import ProcessExitedException
+
+from .checkpoint_writer import CheckpointWriter
+from .types import RankInfo, STATE_DICT
+
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class CheckpointProcessConfig:
+    """
+    Configuration options for the CheckpointProcess.
+
+    This class provides configuration options for the checkpoint process,
+    including initialization functions, timeouts, and writer configuration.
+
+    Attributes:
+        subprocess_init_timeout_secs: Maximum time in seconds to wait for subprocess initialization.
+        subprocess_shutdown_timeout_secs: Maximum time in seconds to wait for subprocess shutdown.
+    """
+
+    subprocess_init_timeout_secs: int = 30
+    subprocess_shutdown_timeout_secs: int = 60
+
+
+class RequestType(Enum):
+    PING = "ping"
+    WRITE_CHECKPOINT = "write_checkpoint"
+    TERMINATE_PROCESS = "exit"
+
+
+@dataclass
+class WorkerRequest:
+    """
+    A dataclass for storing the command to be sent to the worker process.
+    Note: This relies on pickling to send the command to the worker process. Handle
+    backward compatibility accordingly.
+    """
+
+    request_type: RequestType
+    payload: dict[str, Any]
+
+
+@dataclass
+class WorkerResponse:
+    request_type: RequestType
+    success: bool
+    error_msg: Optional[str] = None
+    payload: Optional[dict[str, Any]] = None
+
+
+class CheckpointProcess:
+    """
+    A checkpoint writer that writes checkpoints to a remote process.
+    """
+
+    def __init__(
+        self,
+        rank_info: RankInfo,
+        config: CheckpointProcessConfig,
+        subprocess_init_fn: Callable[[Any], None],
+        subprocess_init_args: tuple[Any, ...],
+        checkpoint_writer_init_fn: Callable[..., CheckpointWriter],
+        checkpoint_writer_init_args: dict[str, Any],
+    ):
+        self._executor = ThreadPoolExecutor(max_workers=1)
+        self._rank_info = rank_info
+        self._config = config
+        self._subprocess_init_fn = subprocess_init_fn
+        self._subprocess_init_args = subprocess_init_args
+        self._checkpoint_writer_init_fn = checkpoint_writer_init_fn
+        self._checkpoint_writer_init_args = checkpoint_writer_init_args
+        self.process = None
+        self._parent_end: Optional[Connection] = None
+        self._child_end: Optional[Connection] = None
+
+        self.process_creation_future = self._executor.submit(
+            self._create_subprocess,
+            config,
+        )
+
+    def _create_subprocess(
+        self,
+        config: CheckpointProcessConfig,
+    ) -> None:
+        logger.info(
+            "Creating checkpoint subprocess for rank %d", self._rank_info.global_rank
+        )
+
+        spawn_context = mp.get_context("spawn")
+        self._parent_end, child_end = spawn_context.Pipe()
+
+        # Known workaround for https://github.com/pytorch/pytorch/issues/37377
+        os.environ["MKL_SERVICE_FORCE_INTEL"] = "GNU"
+
+        logger.debug("Spawning subprocess for rank_info=%s", self._rank_info)
+        self.process = mp.spawn(
+            fn=CheckpointProcess._subprocess,
+            args=(
+                self._rank_info,
+                child_end,
+                self._subprocess_init_fn,
+                self._subprocess_init_args,
+                self._checkpoint_writer_init_fn,
+                self._checkpoint_writer_init_args,
+            ),
+            nprocs=1,
+            join=False,
+            daemon=True,
+        )
+
+        # close the child end of the pipe so recv on it will fail
+        # fast when the child process is terminated unexpectedly.
+        child_end.close()
+        self._send(
+            request_type=RequestType.PING,
+            payload={},
+        )
+
+        logger.debug(
+            "Waiting for checkpoint subprocess to initialize (timeout: %ds)",
+            config.subprocess_init_timeout_secs,
+        )
+
+        # wait for the timeout or a response from subprocess
+        if self._parent_end is None:
+            raise AssertionError("Parent end of pipe should be initialized")
+        if not self._parent_end.poll(timeout=config.subprocess_init_timeout_secs):
+            msg = f"Timed out after {config.subprocess_init_timeout_secs}s waiting for checkpoint subprocess to initialize"
+            logger.error(msg)
+            raise TimeoutError(msg)
+
+        self._recv()
+        logger.info("Checkpoint subprocess initialized successfully")
+
+    @staticmethod
+    def _subprocess(
+        sub_rank: int,
+        rank_info: RankInfo,
+        parent_pipe: Connection,
+        subprocess_init_fn: Callable[[Any], None],
+        subprocess_init_args: tuple[Any, ...],
+        checkpoint_writer_init_fn: Callable[..., CheckpointWriter],
+        checkpoint_writer_init_args: dict[str, Any],
+    ) -> None:
+        logger.debug(
+            "Checkpoint subprocess started for rank %d/%d (PID: %d)",
+            rank_info.global_rank,
+            rank_info.global_world_size,
+            os.getpid(),
+        )
+
+        if sub_rank != 0:
+            raise AssertionError("We need only one checkpointer per parent training")
+        request = WorkerRequest(request_type=RequestType.PING, payload={})
+
+        try:
+            # Calling initialize callback, so we can perform app-specific initialization of the subprocess.
+            subprocess_init_fn(*subprocess_init_args)
+
+            # Initialize checkpoint writer - automatically include rank_info in init_args
+            writer_init_args = dict(checkpoint_writer_init_args)
+            if "rank_info" not in writer_init_args:
+                writer_init_args["rank_info"] = rank_info
+            checkpoint_writer = checkpoint_writer_init_fn(**writer_init_args)
+
+            while True:
+                request = parent_pipe.recv()
+
+                if request.request_type == RequestType.PING:
+                    parent_pipe.send(
+                        WorkerResponse(request_type=RequestType.PING, success=True)
+                    )
+                elif request.request_type == RequestType.WRITE_CHECKPOINT:
+                    path = request.payload["path"]
+                    logger.info("Writing checkpoint to %s", path)
+
+                    checkpoint_writer.write(
+                        path=path,
+                        state_dict=request.payload["state_dict"],
+                        **request.payload["kwargs"],
+                    )
+
+                    logger.info("Checkpoint written successfully to %s", path)
+                    parent_pipe.send(
+                        WorkerResponse(RequestType.WRITE_CHECKPOINT, success=True)
+                    )
+                elif request.request_type == RequestType.TERMINATE_PROCESS:
+                    logger.debug("Received termination request.")
+                    parent_pipe.send(
+                        WorkerResponse(RequestType.TERMINATE_PROCESS, success=True)
+                    )
+                    logger.info("Subprocess terminated gracefully")
+                    break
+                else:
+                    error_msg = f"Unknown request type: {request.request_type}"
+                    logger.error(error_msg)
+                    raise ValueError(error_msg)
+
+        except Exception as e:
+            error_text = traceback.format_exc()
+            logger.error(
+                "Exception in subprocess  (%s): %s", type(e).__name__, error_text
+            )
+
+            # Communicating exception via the queue to the main process
+            parent_pipe.send(
+                WorkerResponse(
+                    request_type=request.request_type,
+                    success=False,
+                    error_msg=error_text,
+                )
+            )
+            parent_pipe.close()
+            logger.exception("Subprocess terminated due to exception")
+
+    def _send(self, request_type: RequestType, payload: dict[str, Any]) -> None:
+        try:
+            if self._parent_end is None:
+                raise AssertionError("Parent end of pipe should be initialized")
+            self._parent_end.send(
+                WorkerRequest(
+                    request_type=request_type,
+                    payload=payload,
+                )
+            )
+        except OSError as e:
+            error_msg = "Child process terminated unexpectedly"
+            logger.exception(
+                "Communication failed during %s request", request_type.value
+            )
+            raise RuntimeError(error_msg) from e
+
+    def _recv(self) -> Optional[dict[str, Any]]:
+        try:
+            if self._parent_end is None:
+                raise AssertionError("Parent end of pipe should be initialized")
+            response = self._parent_end.recv()
+            if response.success is False:
+                error_msg = (
+                    f"Unexpected response from worker process: {response.error_msg}"
+                )
+                logger.error(error_msg)
+                raise RuntimeError(error_msg)
+            return response.payload
+        except (EOFError, BrokenPipeError, ConnectionResetError) as e:
+            error_msg = f"Child process terminated unexpectedly: {e}"
+            logger.error(error_msg)
+            raise RuntimeError(error_msg) from e
+
+    def write(
+        self,
+        state_dict: Union[STATE_DICT, Future[STATE_DICT]],
+        path: str,
+        **kwargs: Any,
+    ) -> Optional[Future[None]]:
+        logger.debug("Waiting for subprocess initialization to complete")
+
+        # wait until the process is started
+        self.process_creation_future.result()
+
+        return self._executor.submit(
+            self._write,
+            state_dict,
+            path,
+            **kwargs,
+        )
+
+    def _write(
+        self,
+        state_dict: Union[STATE_DICT, Future[STATE_DICT]],
+        path: str,
+        **kwargs: Any,
+    ) -> None:
+        logger.debug("Starting checkpoint write to %s", path)
+
+        # wait for staging state_dict to be available
+        if isinstance(state_dict, Future):
+            logger.debug("Waiting for state_dict Future to resolve")
+            sd = state_dict.result()
+        else:
+            sd = state_dict
+
+        # Log state_dict info only if debug logging is enabled (performance-conscious)
+        if logger.isEnabledFor(logging.DEBUG):
+            if hasattr(sd, "keys"):
+                logger.debug("State_dict contains %d keys", len(sd.keys()))
+
+        self._send(
+            request_type=RequestType.WRITE_CHECKPOINT,
+            payload={
+                "state_dict": sd,
+                "path": path,
+                "kwargs": kwargs,
+            },
+        )
+
+        logger.debug("Waiting for write completion response")
+        # wait for response
+        self._recv()
+        logger.debug("Checkpoint write to %s completed successfully", path)
+
+    def close(self) -> None:
+        logger.debug(
+            "Closing CheckpointProcess for rank %d", self._rank_info.global_rank
+        )
+        self._executor.shutdown(wait=True, cancel_futures=True)
+
+        if self.process and self.process.processes[0].is_alive():
+            subprocess_pid = self.process.processes[0].pid
+            # send graceful termination to sub process
+            try:
+                # pyrefly: ignore [missing-attribute]
+                self._parent_end.send(
+                    WorkerRequest(
+                        request_type=RequestType.TERMINATE_PROCESS,
+                        payload={},
+                    )
+                )
+            except BrokenPipeError:
+                logger.warning(
+                    "BrokenPipeError when sending termination request - subprocess (PID: %d) may have already terminated",
+                    subprocess_pid,
+                )
+                # subprocess terminated unexpectedly and below code will raise a
+                # ProcessExitedException.
+
+            logger.debug(
+                "Waiting for subprocess to terminate gracefully (timeout: %ds)",
+                self._config.subprocess_shutdown_timeout_secs,
+            )
+
+            try:
+                if not self.process.join(
+                    timeout=self._config.subprocess_shutdown_timeout_secs
+                ):
+                    # graceful shutdown failed, kill the process.
+                    logger.warning(
+                        "Subprocess (PID: %d) did not terminate gracefully within %ds, killing it",
+                        subprocess_pid,
+                        self._config.subprocess_shutdown_timeout_secs,
+                    )
+                    self.process.processes[0].kill()
+                    logger.info("Subprocess killed forcefully")
+            except ProcessExitedException:
+                logger.exception("ProcessExitedException during subprocess termination")
+                raise
+
+        logger.debug("CheckpointProcess closed successfully")
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_experimental/checkpoint_reader.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_experimental/checkpoint_reader.py
new file mode 100644
index 0000000000000000000000000000000000000000..7be55938cfde162028c201476a5834533de41009
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_experimental/checkpoint_reader.py
@@ -0,0 +1,223 @@
+"""
+Checkpoint reader functionality for machine learning models.
+
+This module provides classes for reading checkpoints from storage, including
+determining checkpoint layout and configuring the reader.
+"""
+
+import logging
+import os
+from itertools import zip_longest
+from pathlib import Path
+from typing import Any, Optional
+
+import torch
+from torch._subclasses.fake_tensor import FakeTensorMode
+
+from .types import RankInfo, STATE_DICT
+
+
+logger = logging.getLogger(__name__)
+
+
+class CheckpointReader:
+    """
+    Handles reading state dictionaries from storage.
+
+    This class is responsible for reading model state dictionaries from storage according
+    to the specified checkpoint layout. It supports synchronization barriers to ensure
+    all ranks in a distributed setting complete their checkpoint operations.
+    """
+
+    def __init__(
+        self,
+        rank_info: RankInfo,
+    ):
+        """
+        Initialize a CheckpointReader.
+
+        Args:
+            rank_info: Information about the current rank in a distributed setting.
+        """
+
+        self._rank_info = rank_info
+
+    def read(
+        self,
+        path: str,
+        state_dict: Optional[STATE_DICT] = None,
+        *,
+        map_location: Any = None,
+        **kwargs: dict[str, Any],
+    ) -> tuple[STATE_DICT, list[str]]:
+        """
+        Reads a state dictionary from storage.
+
+        Args:
+            path (str): The path from which to read the checkpoint.
+            map_location (Any): Device mapping function or device name for relocating tensors.
+            **kwargs: Additional keyword arguments passed to torch.load.
+
+        Returns:
+            STATE_DICT: The loaded state dictionary.
+            list[str]: List of missing keys.
+        """
+        logger.debug(
+            "Reading checkpoint from %s for rank %s",
+            path,
+            self._rank_info.global_rank,
+        )
+
+        dir_path = Path(path)
+        file_path = dir_path / f"checkpoint_{self._rank_info.global_rank}.pt"
+
+        # Check if the file exists
+        if not os.path.exists(file_path):
+            logger.error("Checkpoint file not found at %s", file_path)
+            raise FileNotFoundError(f"Checkpoint file not found at {file_path}")
+
+        if state_dict is None:
+            result: tuple[STATE_DICT, list[str]] = (
+                torch.load(file_path, map_location=map_location),
+                [],
+            )
+        else:
+            result = self._partial_read(
+                file_path, state_dict, map_location=map_location, **kwargs
+            )
+        logger.debug("Successfully read checkpoint file from %s", file_path)
+        return result
+
+    def _partial_read(
+        self,
+        file_path: Path,
+        state_dict: STATE_DICT,
+        *,
+        map_location: Any = None,
+        **kwargs: dict[str, Any],
+    ) -> tuple[STATE_DICT, list[str]]:
+        """
+        Reads only the keys present in state_dict from the checkpoint file.
+
+        This method optimizes checkpoint loading by only loading the tensors that
+        are actually needed, based on the keys present in the input state_dict.
+        This can significantly reduce memory usage and loading time for large checkpoints
+        when only a subset of the model needs to be loaded.
+
+        Args:
+            file_path (str): The path to the checkpoint file.
+            state_dict (STATE_DICT): The state dictionary containing keys to load.
+            map_location (Any): Device mapping function or device name for relocating tensors.
+            **kwargs: Additional keyword arguments passed to torch.load.
+
+        Returns:
+            tuple[STATE_DICT, list[str]]: The updated state dictionary with loaded values and a list of missing keys.
+        """
+
+        with FakeTensorMode():
+            metadata_dict = torch.load(file_path, map_location=map_location)
+
+        missing_keys = []
+
+        with open(file_path, "rb") as file:
+            # Helper function to load tensor data from file
+            def load_tensor(
+                target: Optional[torch.Tensor], source: torch.Tensor, full_key: str
+            ) -> torch.Tensor:
+                if target is not None and (
+                    target.size() != source.size() or target.dtype != source.dtype
+                ):
+                    raise RuntimeError(
+                        f"Target tensor size={target.size()} dtype={target.dtype} does not match "
+                        f"source tensor size={source.size()} dtype={source.dtype} for key {full_key}"
+                    )
+
+                tensor_offset = source.untyped_storage()._checkpoint_offset
+
+                if tensor_offset is None:
+                    raise AssertionError(
+                        "checkpoint_offset for tensor in torch serialized file is not set. This could "
+                        "happen if the checkpoint was saved with a older version of Pytorch. "
+                        "Please make sure that the checkpoint was saved with Pytorch 2.7 or later."
+                    )
+
+                tensor_len = source.nelement() * source.element_size()
+                file.seek(
+                    tensor_offset + source.element_size() * int(source.storage_offset())
+                )
+                if target is None:
+                    target = torch.empty(
+                        source.size(), dtype=source.dtype, device=source.device
+                    )
+
+                buffer = file.read(tensor_len)
+                cpu_tensor = torch.frombuffer(buffer, dtype=source.dtype)
+                tensor = cpu_tensor.view(source.size())
+                target.copy_(tensor)
+                return target
+
+            # Helper function to recursively process nested structures
+            def process_value(
+                target_value: Any, source_value: Any, key_path: str
+            ) -> Any:
+                source_type = type(source_value)
+                if source_type is torch._subclasses.fake_tensor.FakeTensor:
+                    source_type = torch.Tensor
+                if target_value is not None and not isinstance(
+                    target_value, source_type
+                ):
+                    raise RuntimeError(
+                        f"Target value {key_path} is set to {type(target_value)}, but source value is {type(source_value)}"
+                    )
+                if isinstance(source_value, torch.Tensor):
+                    return load_tensor(target_value, source_value, key_path)
+                elif isinstance(source_value, dict):
+                    if target_value is None:
+                        # create a new map with all the keys present in source_value
+                        target_value = dict.fromkeys(source_value.keys())
+
+                    # pyrefly: ignore [missing-attribute]
+                    for key in list(target_value.keys()):
+                        current_path = f"{key_path}.{key}" if key_path else key
+                        if key in source_value:
+                            target_value[key] = process_value(
+                                target_value[key], source_value[key], current_path
+                            )
+                        else:
+                            missing_keys.append(current_path)
+
+                    return target_value
+                elif isinstance(source_value, list):
+                    if target_value is None:
+                        target_value = [None] * len(source_value)
+                    result = []
+                    for i, (target_item, source_item) in enumerate(
+                        zip_longest(target_value, source_value, fillvalue=None)
+                    ):
+                        current_path = f"{key_path}[{i}]" if key_path else f"[{i}]"
+                        result.append(
+                            process_value(target_item, source_item, current_path)
+                        )
+                    return result
+                else:
+                    return source_value
+
+            # Start recursive processing from the root of the state dictionary
+            updated_state_dict = process_value(state_dict, metadata_dict, "")
+
+        if missing_keys:
+            if len(missing_keys) > 10:
+                logger.warning(
+                    "Missing %s keys from checkpoint: %s... (and %s more)",
+                    len(missing_keys),
+                    missing_keys[:10],
+                    len(missing_keys) - 10,
+                )
+            else:
+                logger.warning(
+                    "Missing %s keys from checkpoint: %s",
+                    len(missing_keys),
+                    missing_keys,
+                )
+
+        return updated_state_dict, missing_keys
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_experimental/checkpoint_writer.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_experimental/checkpoint_writer.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b0041fbf292bd8b9c38fc2e395b17251fb67089
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_experimental/checkpoint_writer.py
@@ -0,0 +1,163 @@
+"""
+Checkpoint writer functionality for machine learning models.
+
+This module provides classes for writing checkpoints to storage, including
+determining checkpoint layout, configuring the writer, and defining hooks
+for custom actions during the checkpoint writing process.
+"""
+
+import abc
+import logging
+import os
+from concurrent.futures import Future
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any, Optional
+
+import torch
+
+from .barriers import Barrier
+from .types import RankInfo, STATE_DICT
+
+
+logger = logging.getLogger(__name__)
+
+
+class WriterHook(abc.ABC):
+    """
+    Abstract base class for checkpoint commit hooks.
+
+    A commit hook provides callbacks that are executed before and after a checkpoint
+    is committed to storage. This allows for custom actions to be performed at specific
+    points in the checkpoint writing process, such as metadata updates, cleanup operations,
+    or notifications.
+    """
+
+    @abc.abstractmethod
+    def pre_commit(self, path: str, **kwargs: dict[str, Any]) -> None:
+        """
+        Performs actions before committing the checkpoint.
+        """
+
+    @abc.abstractmethod
+    def post_commit(self, path: str, **kwargs: dict[str, Any]) -> None:
+        """
+        Performs actions after committing the checkpoint.
+        """
+
+
+@dataclass
+class CheckpointWriterConfig:
+    """
+    Configuration options for the CheckpointWriter.
+
+    Attributes:
+        write_barrier_timeout_secs: Maximum time in seconds to wait for all ranks
+            to reach the checkpoint barrier before timing out. Default is 600 seconds.
+    """
+
+    write_barrier_timeout_secs: int = 600
+
+
+class CheckpointWriter:
+    """
+    Handles writing state dictionaries to storage.
+
+    This class is responsible for writing model state dictionaries to storage according
+    to the specified checkpoint layout. It supports synchronization barriers to ensure
+    all ranks in a distributed setting complete their checkpoint operations.
+    """
+
+    def __init__(
+        self,
+        config: CheckpointWriterConfig,
+        rank_info: RankInfo,
+        barrier: Optional[Barrier] = None,
+        commit_hook: Optional[WriterHook] = None,
+    ):
+        """
+        Initialize a CheckpointWriter.
+
+        Args:
+            config: Configuration options for the checkpoint writer.
+            rank_info: Information about the current rank in a distributed setting.
+            barrier: Optional synchronization barrier for distributed checkpointing.
+                    Note: The barrier should be initialized with the appropriate barrier_prefix
+                    and timeout_secs parameters.
+            commit_hook: Optional hook for custom actions before and after checkpoint commits.
+        """
+
+        self._config = config
+        self._rank_info = rank_info
+        self._commit_hook = commit_hook
+        self._barrier = barrier
+
+    def write(
+        self,
+        path: str,
+        state_dict: STATE_DICT,
+        **kwargs: dict[str, Any],
+    ) -> Optional[Future[None]]:
+        """
+        Writes the state_dict to storage.
+
+        Args:
+            path (str): The path to write the checkpoint to.
+            state_dict (STATE_DICT): The state_dict to write.
+            **kwargs: Additional keyword arguments passed to hooks.
+
+        Returns:
+            Optional[Future[None]]: A future for tracking the write operation, if applicable.
+        """
+        logger.debug(
+            "Writing checkpoint to %s for rank %s",
+            path,
+            self._rank_info.global_rank,
+        )
+        dir_path = Path(path)
+        full_path = dir_path / f"checkpoint_{self._rank_info.global_rank}.pt"
+        os.makedirs(
+            os.path.dirname(full_path),
+            exist_ok=True,
+        )
+        torch.save(state_dict, full_path)
+        logger.debug("Successfully saved checkpoint file to %s", full_path)
+
+        # Execute pre-commit hook if available
+        commit_hook = self._commit_hook
+        if commit_hook is not None:
+            logger.debug("Executing pre-commit hook for %s", path)
+            commit_hook.pre_commit(path, **kwargs)
+
+        # Wait for all ranks to finish writing if barrier is available
+        barrier = self._barrier
+        if barrier is not None:
+            logger.info(
+                "Waiting for all ranks at barrier with timeout %ss",
+                self._config.write_barrier_timeout_secs,
+            )
+            barrier.execute_barrier()
+            logger.info("All ranks passed barrier")
+        else:
+            logger.info("No barrier configured, skipping synchronization")
+
+        # Execute commit hook if available
+        if commit_hook is not None:
+            logger.debug("Executing commit hook for %s", path)
+            commit_hook.post_commit(path, **kwargs)
+
+        logger.info(
+            "Successfully wrote checkpoint to %s for rank %s",
+            path,
+            self._rank_info.global_rank,
+        )
+        return None
+
+    def close(self) -> None:
+        """
+        Close the writer and release any resources.
+
+        This is a no-op for the base CheckpointWriter but may be overridden
+        by subclasses that need to perform cleanup.
+        """
+        logger.debug("Closing checkpoint writer")
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_experimental/checkpointer.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_experimental/checkpointer.py
new file mode 100644
index 0000000000000000000000000000000000000000..2609bd9c4af428ecb3883435db4b738676b9b540
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_experimental/checkpointer.py
@@ -0,0 +1,341 @@
+import abc
+import logging
+from concurrent.futures import Future
+from typing import Any, Optional, TypeVar
+
+from .checkpoint_process import CheckpointProcess
+from .checkpoint_reader import CheckpointReader
+from .checkpoint_writer import CheckpointWriter
+from .staging import CheckpointStager
+from .types import STATE_DICT
+from .utils import wrap_future
+
+
+logger = logging.getLogger(__name__)
+
+LOG_INTERVAL = 60
+T = TypeVar("T")
+
+
+class Checkpointer(abc.ABC):
+    """
+    WARNING: This class is experimental, and is created to validate certain ideas,
+    and is subjected to change or deprecation and we strong discourage any usages at
+    this time.
+
+    Abstract base class that defines the API for checkpointing.
+
+    This class defines the interface for coordinating the writing and loading of model
+    state dictionaries to and from storage. It provides abstract methods to save and load model states
+    with support for both synchronous and asynchronous operations.
+
+    Concrete implementations of this class must implement all the abstract methods.
+    """
+
+    @abc.abstractmethod
+    def save(
+        self,
+        path: str,
+        state_dict: STATE_DICT,
+        **kwargs: dict[str, Any],
+    ) -> Optional[tuple[Future, Future]]:
+        """
+        Save a state dictionary to storage.
+
+        Args:
+            path: The path where the checkpoint should be saved.
+            state_dict: The state dictionary to save.
+            **kwargs: Additional keyword arguments to pass to the writer.
+
+        Returns:
+            For synchronous implementations: None
+            For asynchronous implementations: tuple of (stage_future, write_future)
+                                            representing the staging and writing operations.
+        """
+
+    @abc.abstractmethod
+    def load(
+        self,
+        path: str,
+        state_dict: Optional[STATE_DICT] = None,
+        *,
+        default_map_location: Any = None,
+        strict: bool = False,
+        **kwargs: dict[str, Any],
+    ) -> STATE_DICT:
+        """
+        Load a state dictionary from storage.
+
+        Args:
+            path: The path from which to load the checkpoint.
+            state_dict: Optional state dictionary to update with loaded values.
+                        If provided, only keys in this dictionary will be loaded.
+            default_map_location: Device mapping function or device name for relocating tensors.
+            strict: If True, raises an error when there are missing keys in the checkpoint.
+            **kwargs: Additional keyword arguments to pass to the reader.
+
+        Returns:
+            The loaded state dictionary.
+        """
+
+    @abc.abstractmethod
+    def close(self) -> None:
+        """
+        Close the checkpointer and release any resources.
+
+        This method should be called when the checkpointer is no longer needed to ensure
+        proper cleanup of resources.
+        """
+
+
+class SyncCheckpointer(Checkpointer):
+    """
+    Synchronous implementation of Checkpointer.
+
+    This class coordinates the writing and loading of model state dictionaries to and from storage
+    using only synchronous operations. It provides a simple, efficient interface for checkpoint
+    operations without async overhead.
+
+    Attributes:
+        _writer: CheckpointWriter for writing state dictionaries to storage.
+        _reader: CheckpointReader for reading state dictionaries from storage.
+
+    Example:
+        checkpointer = SyncCheckpointer(writer=writer, reader=reader)
+        checkpointer.save(state_dict, path)
+        loaded_state_dict = checkpointer.load(path)
+    """
+
+    def __init__(
+        self,
+        writer: CheckpointWriter,
+        reader: CheckpointReader,
+    ):
+        """
+        Initialize a synchronous checkpointer.
+
+        Args:
+            writer: CheckpointWriter for writing checkpoints to storage.
+            reader: CheckpointReader for reading checkpoints from storage.
+        """
+        self._writer = writer
+        self._reader = reader
+
+    def save(
+        self,
+        path: str,
+        state_dict: STATE_DICT,
+        **kwargs: dict[str, Any],
+    ) -> Optional[tuple[Future, Future]]:
+        """
+        Save a state dictionary to storage synchronously.
+
+        Args:
+            path: The path where the checkpoint should be saved.
+            state_dict: The state dictionary to save.
+            **kwargs: Additional keyword arguments to pass to the writer.
+
+        Returns:
+            Always returns None as operations are synchronous.
+
+        Example:
+            checkpointer.save("/path/to/checkpoint", state_dict)
+        """
+        logger.debug("Saving checkpoint synchronously to %s", path)
+        self._writer.write(path, state_dict, **kwargs)
+        return None
+
+    def load(
+        self,
+        path: str,
+        state_dict: Optional[STATE_DICT] = None,
+        *,
+        default_map_location: Any = None,
+        strict: bool = False,
+        **kwargs: dict[str, Any],
+    ) -> STATE_DICT:
+        """
+        Load a state dictionary from storage.
+
+        Args:
+            path: The path from which to load the checkpoint.
+            state_dict: Optional state dictionary to update with loaded values.
+                        If provided, only keys in this dictionary will be loaded.
+            default_map_location: Device mapping function or device name for relocating tensors.
+            strict: If True, raises an error when there are missing keys in the checkpoint.
+            **kwargs: Additional keyword arguments to pass to the reader.
+
+        Returns:
+            The loaded state dictionary.
+
+        Raises:
+            RuntimeError: If strict=True and there are missing keys in the checkpoint.
+            FileNotFoundError: If the checkpoint file is not found.
+        """
+        logger.info("Loading checkpoint from %s", path)
+
+        loaded_state_dict, missing_keys = self._reader.read(
+            path=path,
+            state_dict=state_dict,
+            map_location=default_map_location,
+            **kwargs,
+        )
+        if strict and missing_keys is not None and missing_keys != []:
+            raise RuntimeError(f"Checkpoint at {path} is missing keys: {missing_keys}")
+        return loaded_state_dict
+
+    def close(self) -> None:
+        """
+        Close the checkpointer and release any resources.
+
+        This method should be called when the checkpointer is no longer needed to ensure
+        proper cleanup of resources.
+        """
+        self._writer.close()
+        logger.info("SyncCheckpointer closed")
+
+
+class AsyncCheckpointer(Checkpointer):
+    """
+    Asynchronous implementation of Checkpointer.
+
+    This class coordinates the writing and loading of model state dictionaries to and from storage
+    using asynchronous operations for saving. It provides efficient async checkpoint operations
+    with staging and background writing capabilities.
+
+    Attributes:
+        _reader: CheckpointReader for reading state dictionaries from storage.
+        _checkpoint_stager: Stager for async operations.
+        _checkpoint_process: Process for async operations.
+        _write_future: Future representing the ongoing async write operation.
+
+    Example:
+        checkpointer = AsyncCheckpointer(
+            reader=reader,
+            checkpoint_stager=stager,
+            checkpoint_process=process
+        )
+        stage_future, write_future = checkpointer.save(state_dict, path)
+        # ... do other work ...
+        write_future.result()  # Wait for completion
+    """
+
+    def __init__(
+        self,
+        checkpoint_stager: CheckpointStager,
+        checkpoint_process: CheckpointProcess,
+        reader: CheckpointReader,
+    ):
+        """
+        Initialize an asynchronous checkpointer.
+
+        Args:
+            checkpoint_stager: Stager for async operations.
+            checkpoint_process: Process for async operations.
+            reader: CheckpointReader for reading checkpoints from storage.
+        """
+        self._reader = reader
+        self._checkpoint_stager = checkpoint_stager
+        self._checkpoint_process = checkpoint_process
+        self._write_future: Optional[Future[Any]] = None
+
+    def save(
+        self,
+        path: str,
+        state_dict: STATE_DICT,
+        **kwargs: Any,
+    ) -> Optional[tuple[Future, Future]]:
+        """
+        Save a state dictionary to storage asynchronously.
+
+        Args:
+            path: The path where the checkpoint should be saved.
+            state_dict: The state dictionary to save.
+            **kwargs: Additional keyword arguments to pass to the stager and writer.
+
+        Returns:
+            A tuple of (stage_future, write_future) representing the staging and writing operations.
+
+        Example:
+            stage_future, write_future = checkpointer.save("/path/to/checkpoint", state_dict)
+            # ... do other work ...
+            write_future.result()  # Wait for completion
+        """
+        logger.info(
+            "Initiating checkpoint save to %s. Will wait for prev checkpoints to complete.",
+            path,
+        )
+        # Wait for previous checkpoint ops to finish and verify they are successful
+        if self._write_future is not None:
+            self._write_future.result()
+
+        logger.debug("Starting state dictionary staging")
+        staging_result = self._checkpoint_stager.stage(
+            state_dict=state_dict,
+            **kwargs,
+        )
+
+        logger.debug("Starting checkpoint write to %s", path)
+        self._write_future = self._checkpoint_process.write(
+            staging_result, path, **kwargs
+        )
+        logger.info("Checkpoint save to %s initiated", path)
+
+        # Return futures for the staging and writing operations
+        if self._write_future is not None:
+            return wrap_future(staging_result), self._write_future
+        else:
+            # This should not happen since we just assigned _write_future above
+            raise RuntimeError("Write future is unexpectedly None")
+
+    def load(
+        self,
+        path: str,
+        state_dict: Optional[STATE_DICT] = None,
+        *,
+        default_map_location: Any = None,
+        strict: bool = False,
+        **kwargs: Any,
+    ) -> STATE_DICT:
+        """
+        Load a state dictionary from storage.
+
+        Loading is always performed synchronously, even in AsyncCheckpointer.
+
+        Args:
+            path: The path from which to load the checkpoint.
+            state_dict: Optional state dictionary to update with loaded values.
+                        If provided, only keys in this dictionary will be loaded.
+            default_map_location: Device mapping function or device name for relocating tensors.
+            strict: If True, raises an error when there are missing keys in the checkpoint.
+            **kwargs: Additional keyword arguments to pass to the reader.
+
+        Returns:
+            The loaded state dictionary.
+
+        Raises:
+            RuntimeError: If strict=True and there are missing keys in the checkpoint.
+            FileNotFoundError: If the checkpoint file is not found.
+        """
+        logger.info("Loading checkpoint from %s", path)
+
+        loaded_state_dict, missing_keys = self._reader.read(
+            path=path,
+            state_dict=state_dict,
+            map_location=default_map_location,
+            **kwargs,
+        )
+        if strict and missing_keys is not None and missing_keys != []:
+            raise RuntimeError(f"Checkpoint at {path} is missing keys: {missing_keys}")
+        return loaded_state_dict
+
+    def close(self) -> None:
+        """
+        Close the checkpointer and release any resources.
+
+        This method should be called when the checkpointer is no longer needed to ensure
+        proper cleanup of async resources.
+        """
+        self._checkpoint_stager.close()
+        self._checkpoint_process.close()
+        logger.info("AsyncCheckpointer closed")
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_experimental/config.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_experimental/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..a81156e3929cac9edb13135b925c8096dc4e702a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_experimental/config.py
@@ -0,0 +1,44 @@
+"""
+Configuration classes for checkpointer construction.
+
+This module provides configuration dataclasses that consolidate all
+configuration options needed to construct checkpointers.
+"""
+
+from dataclasses import dataclass, field
+
+from .barriers import BarrierConfig
+from .checkpoint_process import CheckpointProcessConfig
+from .checkpoint_writer import CheckpointWriterConfig
+from .staging import CheckpointStagerConfig
+
+
+@dataclass
+class CheckpointerConfig:
+    """
+    Configuration class for checkpointer construction.
+
+    This class consolidates the core component configuration options needed to construct
+    a checkpointer, providing a clean separation of concerns where each component
+    manages its own configuration.
+
+    Attributes:
+        writer_config: Configuration options for the checkpoint writer component.
+        barrier_config: Configuration for barrier construction and arguments.
+        staging_config: Configuration options for the async staging component.
+        process_config: Configuration options for the async checkpoint process component.
+
+    """
+
+    writer_config: CheckpointWriterConfig = field(
+        default_factory=CheckpointWriterConfig
+    )
+    barrier_config: BarrierConfig = field(default_factory=BarrierConfig)
+
+    # Below configs are used for async checkpointing
+    staging_config: CheckpointStagerConfig = field(
+        default_factory=CheckpointStagerConfig
+    )
+    process_config: CheckpointProcessConfig = field(
+        default_factory=CheckpointProcessConfig
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_experimental/staging.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_experimental/staging.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d83278e13197b40ff31807a71b442621ebe5f51
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_experimental/staging.py
@@ -0,0 +1,218 @@
+"""
+Experimental staging module for PyTorch Distributed Checkpointing.
+
+This module provides advanced staging capabilities for checkpoints including:
+- Asynchronous staging using ThreadPoolExecutor
+- Pinned memory allocation for faster CPU-GPU transfers
+- Shared memory support for multi-process scenarios
+- Non-blocking CUDA operations with stream synchronization
+- Caching of frequently used storages for efficient memory management
+- Automatic resource cleanup and memory management
+
+Classes:
+    CheckpointStager: Abstract base class defining the staging interface
+    StagingOptions: Configuration dataclass for staging behavior
+    DefaultStager: Default implementation with comprehensive staging features
+"""
+
+import abc
+from concurrent.futures import Future, ThreadPoolExecutor
+from dataclasses import dataclass
+from typing import Any, TypeVar, Union
+
+import torch
+from torch.distributed.checkpoint._state_dict_stager import StateDictStager
+
+from .types import STATE_DICT
+
+
+T = TypeVar("T")
+
+
+class CheckpointStager(abc.ABC):
+    """
+    Abstract base class for checkpoint staging implementations.
+
+    CheckpointStager defines the interface that all staging implementations
+    must follow. Staging is the process of offloading state dictionaries
+    for async checkpointing.
+    """
+
+    @abc.abstractmethod
+    def stage(
+        self,
+        state_dict: STATE_DICT,
+        **kwargs: Any,
+    ) -> Union[STATE_DICT, Future[STATE_DICT]]:
+        """
+        Stage a state dictionary for checkpointing.
+
+        Args:
+            state_dict: The state dictionary to stage
+            **kwargs: Additional staging parameters
+
+        Returns:
+            Either a staged state dictionary (synchronous) or a Future
+            that will resolve to the staged state dictionary (asynchronous)
+        """
+
+    @abc.abstractmethod
+    def close(self) -> None:
+        """
+        Clean up all resources used by the stager.
+        """
+
+
+@dataclass
+class CheckpointStagerConfig:
+    """
+    Configuration options for checkpoint staging behavior.
+
+    Attributes:
+        use_pinned_memory (bool): Enable pinned memory allocation for faster
+            CPU-GPU transfers. Requires CUDA to be available. Default: True
+        use_shared_memory (bool): Enable shared memory for multi-process
+            scenarios. Useful when multiple processes need access to the
+            same staged data. Default: True
+        use_async_staging (bool): Enable asynchronous staging using a
+            background thread pool. Allows overlapping computation with
+            staging operations. Requires CUDA. Default: True
+        use_non_blocking_copy (bool): Use non-blocking device memory
+            copies with stream synchronization. Improves performance by
+            allowing CPU work to continue during GPU transfers. Default: True
+
+    Note:
+        CUDA-dependent features will raise exception if CUDA is not available.
+    """
+
+    use_pinned_memory: bool = True
+    use_shared_memory: bool = True
+    use_async_staging: bool = True
+    use_non_blocking_copy: bool = True
+
+
+class DefaultStager(CheckpointStager):
+    """
+    DefaultStager provides a full-featured staging implementation that combines
+    multiple optimization techniques for efficient checkpoint preparation.
+
+    The staging process works as follows:
+    1. State dictionary is submitted for staging (sync or async)
+    2. Tensors are copied from GPU to optimized CPU storage
+    3. CUDA operations are synchronized if non-blocking copies are used
+    4. Staged state dictionary is returned or made available via Future
+
+    NOTE: state_dict should be deep-copyable object as staging will create a
+    copy of it.
+
+    Usage Patterns:
+        # Synchronous staging
+        stager = DefaultStager(CheckpointStagerConfig(use_async_staging=False))
+        staged_dict = stager.stage(state_dict)
+        stager.close()
+
+        # Asynchronous staging
+        stager = DefaultStager(CheckpointStagerConfig(use_async_staging=True))
+        future = stager.stage(state_dict)
+        # ... do other work ...
+        staged_dict = future.result()
+        stager.close()
+
+        # Context manager pattern (recommended)
+        with DefaultStager(config) as stager:
+            result = stager.stage(state_dict)
+            # Automatic cleanup on exit
+
+    Performance Considerations:
+        - Async staging provides best performance when model computation
+          can overlap with staging operations
+        - Pinned memory improves CPU-GPU transfer speeds but uses more memory
+        - Shared memory allows efficient IPC to checkpoint process
+        - Non-blocking copies reduce GPU idle time during memory transfers
+
+    Thread Safety:
+        DefaultStager is not thread-safe. Each thread should use its own
+        instance, or external synchronization should be provided.
+    """
+
+    def __init__(
+        self,
+        config: CheckpointStagerConfig = CheckpointStagerConfig(),
+    ):
+        self._config = config
+        self._state_dict_stager = StateDictStager(
+            pin_memory=config.use_pinned_memory, share_memory=config.use_shared_memory
+        )
+        self._staging_executor = None
+        self._staging_stream = None
+
+        if self._config.use_async_staging:
+            # pyrefly: ignore [bad-assignment]
+            self._staging_executor = ThreadPoolExecutor(max_workers=1)
+            if torch.accelerator.is_available():
+                # Note: stream needs to be initialized on the main thread after default cuda
+                # stream is setup/used to avoid the risk of accidentally reusing the main
+                # compute stream or in other cases kernels actually launching from the
+                # main thread.
+                # pyrefly: ignore [bad-assignment]
+                self._staging_stream = torch.Stream()
+
+        if self._config.use_non_blocking_copy:
+            if not torch.accelerator.is_available():
+                raise AssertionError(
+                    "Non-blocking copy requires that the current accelerator is available."
+                )
+
+    def stage(
+        self,
+        state_dict: STATE_DICT,
+        **kwargs: Any,
+    ) -> Union[STATE_DICT, Future[STATE_DICT]]:
+        if self._config.use_async_staging:
+            if self._staging_executor is None:
+                raise AssertionError(
+                    "Staging executor should be initialized for async staging"
+                )
+            return self._staging_executor.submit(
+                self._stage,
+                state_dict,
+                **kwargs,
+            )
+        else:
+            return self._stage(state_dict, **kwargs)
+
+    def _stage(self, state_dict: STATE_DICT, **kwargs: Any) -> STATE_DICT:
+        state_dict = self._state_dict_stager.stage(
+            state_dict, non_blocking=self._config.use_non_blocking_copy, **kwargs
+        )
+
+        if self._config.use_non_blocking_copy:
+            if not (self._staging_stream or not self._config.use_async_staging):
+                raise AssertionError(
+                    "Non-blocking copy in a background thread for async staging needs staging_stream to be initialized."
+                )
+
+            # waits for the enqued copy operations to finish.
+            self._staging_stream.synchronize() if self._staging_stream else torch.accelerator.synchronize()
+
+        return state_dict
+
+    def close(self) -> None:
+        """
+        Clean up all resources used by the DefaultStager. Shuts down the ThreadPoolExecutor
+        used for async staging operations and cleans up the underlying StateDictStager's
+        cached storages. Should be called when the stager is no longer needed to prevent
+        resource leaks, especially in long-running applications. After calling close(),
+        the stager should not be used for further staging operations.
+
+        state_dict should be deep-copyable object.
+
+        Example:
+            stager = DefaultStager(CheckpointStagerConfig(use_async_staging=True))
+            # ... do staging operations ...
+            stager.close()  # Clean up all resources
+        """
+        if self._staging_executor:
+            self._staging_executor.shutdown(wait=True)
+
+        self._state_dict_stager.close()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_experimental/types.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_experimental/types.py
new file mode 100644
index 0000000000000000000000000000000000000000..61268fd5b14a89e6ee8dc21fdac300a50a2dd21f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_experimental/types.py
@@ -0,0 +1,28 @@
+"""
+Type definitions for distributed training and checkpointing.
+
+This module provides type definitions and classes for managing rank information
+in distributed training environments, which is essential for proper checkpoint
+saving and loading.
+"""
+
+from dataclasses import dataclass
+from typing import Any, TypeAlias
+
+
+# Type alias for state dictionaries used in checkpointing
+STATE_DICT: TypeAlias = dict[str, Any]
+
+
+@dataclass
+class RankInfo:
+    """
+    Information about the current rank in a distributed training environment.
+
+    Attributes:
+        global_rank: The global rank ID of the current process.
+        global_world_size: The total number of processes in the distributed environment.
+    """
+
+    global_rank: int
+    global_world_size: int
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_experimental/utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_experimental/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..271e9aa112f682c8b56393c13db9eeefdeb37aa7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_experimental/utils.py
@@ -0,0 +1,42 @@
+"""
+Utility functions for the experimental checkpoint module.
+
+This module contains helper functions and utilities used across the experimental
+checkpoint functionality.
+"""
+
+from concurrent.futures import Future
+from typing import Any
+
+
+def wrap_future(original_result: Any) -> Future[None]:
+    """
+    Wraps a result (Future or not) to return a Future with None result.
+
+    If the input is a Future, returns a new Future that completes with None when
+    the original Future completes successfully, or propagates any exception.
+    If the input is not a Future, returns a completed Future with None result.
+
+    Args:
+        original_result: The result to wrap (Future or any other value).
+
+    Returns:
+        A Future that completes with None on success or propagates exceptions.
+    """
+    masked_future: Future[None] = Future()
+
+    if isinstance(original_result, Future):
+
+        def on_complete(_: Future[Any]) -> None:
+            try:
+                original_result.result()
+                masked_future.set_result(None)
+            except Exception as e:
+                masked_future.set_exception(e)
+
+        original_result.add_done_callback(on_complete)
+    else:
+        # Return a completed future with None result
+        masked_future.set_result(None)
+
+    return masked_future
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_extension.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_extension.py
new file mode 100644
index 0000000000000000000000000000000000000000..663caa8a857263e3fc2924e3e1ec80d13a9ae6b0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_extension.py
@@ -0,0 +1,223 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+import abc
+import io
+from collections.abc import Sequence
+from typing import cast, IO, Optional
+
+# introduced as collections.abc.Buffer in Python 3.12
+from typing_extensions import Buffer
+
+from torch._utils import try_import
+
+
+# NOTE: everything in this file is experimental, and subject to
+# change.  Feedback and bug fixes are always welcome.
+
+pyzstd_module_name = "pyzstd"
+pyzstd = try_import(pyzstd_module_name)
+zstandard_module_name = "zstandard"
+zstandard = try_import(zstandard_module_name)
+
+
+__all__ = [
+    "Extension",
+    "StreamTransformExtension",
+    "ZStandard",
+    "ExtensionRegistry",
+]
+
+
+class Extension(abc.ABC):
+    """
+    Extensions provide modular additions to functionality within distributed checkpointing,
+    which affect the layout or format of the written artifacts.  Extensions may be
+    built into pytorch, or provided externally.
+
+    When writing, the caller provides a list of extension instances of the appropriate
+    type.  Each extension can output a descriptor which is used to reconstitute the
+    extension at read-time.
+    """
+
+    @staticmethod
+    @abc.abstractmethod
+    def registry_name() -> str:
+        """
+        See ExtensionRegistry.from_descriptor_list
+        """
+
+    @staticmethod
+    @abc.abstractmethod
+    def from_descriptor(version: str) -> "Extension":
+        """
+        See ExtensionRegistry.from_descriptor_list
+        """
+
+    @abc.abstractmethod
+    def get_descriptor(self) -> str:
+        """
+        Return descriptor name to be included in metadata.  The form should be
+        "extension_name[@local-domain][/version]".
+        """
+
+
+class StreamTransformExtension(Extension):
+    """
+    An extension which performs transformation on a byte stream, such as compression
+    or encryption.
+
+    Implementations should try to be memory friendly and performant.  For example, don't
+    read the whole input, then transform it, and write it back.  If at all possible, do it in
+    chunks.  But, don't read/transform/write one byte at a time, either.
+    """
+
+    @abc.abstractmethod
+    def transform_to(self, output: IO[bytes]) -> IO[bytes]:
+        """
+        Takes a writeable output stream, and generates a new stream which implements the
+        output transform.  Input data written to the returned stream will be transformed
+        and written to the `output` argument stream.
+        """
+
+    @abc.abstractmethod
+    def transform_from(self, input: IO[bytes]) -> IO[bytes]:
+        """
+        Takes a readable input stream, and generates a new stream which implements the
+        input transform.  When the returned stream is read, data will be read from the
+        'input' stream, transformed, and returned.
+        """
+
+
+class ZStandard(StreamTransformExtension):
+    @staticmethod
+    def is_available() -> bool:
+        return zstandard is not None or pyzstd is not None
+
+    @staticmethod
+    # pyrefly: ignore [bad-override]
+    def from_descriptor(version: str) -> "ZStandard":
+        if version.partition(".")[0] != "1":
+            raise ValueError(f"Unknown extension {version=}")
+        if not ZStandard.is_available():
+            raise ValueError(
+                f"Stream with ZStandard compression cannot be processed because "
+                f"no module named '{zstandard_module_name}' or '{pyzstd_module_name}'"
+            )
+        return ZStandard()
+
+    @staticmethod
+    def registry_name() -> str:
+        return "stream.zstd"
+
+    def __init__(self) -> None:
+        super().__init__()
+        if not ZStandard.is_available():
+            raise ValueError(
+                f"ZStandard extension is unavailable because no module named '{zstandard_module_name}' or '{pyzstd_module_name}'"
+            )
+
+    def get_descriptor(self) -> str:
+        return f"{self.registry_name()}/1"
+
+    def transform_to(self, output: IO[bytes]) -> IO[bytes]:
+        if zstandard is not None:
+            compressor = zstandard.ZstdCompressor()  # type: ignore[union-attr]
+            return compressor.stream_writer(output)
+
+        class Writer(io.RawIOBase):
+            def __init__(self, output: IO[bytes]) -> None:
+                self.output = output
+                self.compressor = pyzstd.ZstdCompressor()  # type: ignore[union-attr]
+
+            def writeable(self) -> bool:
+                return True
+
+            def write(self, b: Buffer) -> Optional[int]:
+                outdata = self.compressor.compress(b)
+                if outdata:
+                    self.output.write(outdata)
+                return len(memoryview(b))
+
+            def flush(self) -> None:
+                outdata = self.compressor.flush()
+                if outdata:
+                    self.output.write(outdata)
+                self.output.flush()
+
+        return cast(IO[bytes], Writer(output))
+
+    def transform_from(self, input: IO[bytes]) -> IO[bytes]:
+        if zstandard is not None:
+            decompressor = zstandard.ZstdDecompressor()  # type: ignore[union-attr]
+            return decompressor.stream_reader(input)
+
+        class Reader(io.RawIOBase):
+            def __init__(self, input: IO[bytes]) -> None:
+                self.input = input
+                self.decompressor = pyzstd.EndlessZstdDecompressor()  # type: ignore[union-attr]
+
+            def readable(self) -> bool:
+                return True
+
+            def readinto(self, b: Buffer) -> Optional[int]:
+                # This needs to read enough so it can decompress
+                # something so the output doesn't look like EOF.  This
+                # means reading at least one block.  The max block
+                # size is 128KB, so we read that plus some
+                # overhead to be sure.
+
+                if self.decompressor.needs_input:
+                    indata = self.input.read((128 + 6) * 1024)
+                else:
+                    indata = b""
+
+                bview = memoryview(b)
+                blen = len(bview)
+                outdata = self.decompressor.decompress(indata, blen)
+                if outdata is None:
+                    return None
+
+                count = len(outdata)
+                bview[:count] = outdata
+                return count
+
+            def seekable(self) -> bool:
+                return False
+
+        return cast(IO[bytes], Reader(input))
+
+
+class ExtensionRegistry:
+    def __init__(self) -> None:
+        # Populate default registry contents
+        self.extensions: dict[str, type[Extension]] = {
+            cls.registry_name(): cls for cls in (ZStandard,)
+        }
+
+    def register(self, cls: type[Extension]) -> None:
+        self.extensions[cls.registry_name()] = cls
+
+    def from_descriptor_list(self, descriptors: Sequence[str]) -> Sequence[Extension]:
+        """
+        Given a seuquence of descriptor strings as returned by
+        Extension.get_descriptor at save time, creates a sequence of
+        Extension instances.  The name[@local-domain] preceding the
+        version number is used to look up an implementation class in
+        the registry, and the version is passed to the class's
+        from_descriptor static method.  If the registry contains no
+        match, this will throw ValueError.  If the from_descriptor
+        method raises an exception, that will pass through to the
+        caller.
+        """
+
+        def from_descriptor(desc: str) -> Extension:
+            name, _, version = desc.partition("/")
+            if version is None:
+                version = 0
+            ext = self.extensions.get(name)
+            if not ext:
+                raise ValueError(f"Unknown extension {name=}")
+            # pyrefly: ignore [bad-argument-type]
+            return ext.from_descriptor(version)
+
+        return [from_descriptor(desc) for desc in descriptors]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_fsspec_filesystem.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_fsspec_filesystem.py
new file mode 100644
index 0000000000000000000000000000000000000000..e239bbe891fb95b374479fdafaab9a0d16604147
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_fsspec_filesystem.py
@@ -0,0 +1,168 @@
+# Mypy will not try inferring the types of any 3rd party libraries installed.
+# mypy: ignore-errors
+
+import io
+import os
+from collections.abc import Generator, Sequence
+from contextlib import contextmanager
+from pathlib import Path
+from typing import Optional, TYPE_CHECKING, Union
+
+from fsspec.core import url_to_fs
+
+from torch.distributed.checkpoint._extension import StreamTransformExtension
+from torch.distributed.checkpoint.filesystem import (
+    FileSystemBase,
+    FileSystemReader,
+    FileSystemWriter,
+    SerializationFormat,
+)
+
+
+if TYPE_CHECKING:
+    from fsspec import AbstractFileSystem
+
+
+__all__ = [
+    "FsspecWriter",
+    "FsspecReader",
+]
+
+
+class FileSystem(FileSystemBase):
+    def __init__(self) -> None:
+        self.fs: Optional[AbstractFileSystem] = None
+
+    @contextmanager
+    def create_stream(
+        self, path: Union[str, os.PathLike], mode: str
+    ) -> Generator[io.IOBase, None, None]:
+        if self.fs is None:
+            raise AssertionError("fs should not be None")
+        path = os.fspath(path)
+
+        # fsspec does not support concurrent transactions, and not all
+        # AbstractFileSystem have working rollback implementations, so
+        # just manually delete the file if necessary on errors.
+        with self.fs.open(path, mode) as stream:
+            try:
+                yield stream
+            except:  # noqa: B001,E722
+                if any(ch in mode for ch in "w+a"):  # cleanup file if not read-only
+                    try:
+                        self.rm_file(path)
+                    except:  # noqa: B001,E722
+                        pass
+                raise
+
+    def concat_path(
+        self, path: Union[str, os.PathLike], suffix: str
+    ) -> Union[str, os.PathLike]:
+        return os.path.join(path, suffix)
+
+    def init_path(
+        self, path: Union[str, os.PathLike], **kwargs
+    ) -> Union[str, os.PathLike]:
+        self.fs, _ = url_to_fs(path, **kwargs)
+        return path
+
+    def rename(
+        self, path: Union[str, os.PathLike], new_path: Union[str, os.PathLike]
+    ) -> None:
+        self.fs.rename(path, new_path)
+
+    def mkdir(self, path: Union[str, os.PathLike]) -> None:
+        self.fs.makedirs(path, exist_ok=True)
+
+    @classmethod
+    def validate_checkpoint_id(cls, checkpoint_id: Union[str, os.PathLike]) -> bool:
+        if isinstance(checkpoint_id, Path):
+            return False
+
+        try:
+            url_to_fs(checkpoint_id)
+        except ValueError:
+            return False
+
+        return True
+
+    def exists(self, path: Union[str, os.PathLike]) -> bool:
+        return self.fs.exists(path)
+
+    def rm_file(self, path: Union[str, os.PathLike]) -> None:
+        self.fs.rm(path)
+
+    def ls(self, path: Union[str, os.PathLike]) -> list[str]:
+        # setting detail to False explicitly to keep the list[str] return type,
+        # instead of the list[Dict] return type when detail=True
+        return self.fs.ls(path, detail=False)
+
+
+# TODO: add the dcp.async_save mixin
+class FsspecWriter(FileSystemWriter):
+    """
+    Basic implementation of StorageWriter using FFspec.
+
+    This implementation makes the following assumptions and simplifications:
+
+    * The checkpoint path is an empty or non-existing directory.
+    * File creation is atomic
+
+    The checkpoint consist of one file per write request plus
+    a `.metadata` file with the serialized metadata.
+
+    """
+
+    def __init__(
+        self,
+        path: Union[str, os.PathLike],
+        single_file_per_rank: bool = True,
+        sync_files: bool = True,
+        thread_count: int = 1,
+        per_thread_copy_ahead: int = 10_000_000,
+        overwrite: bool = True,
+        _extensions: Optional[Sequence[StreamTransformExtension]] = None,
+        serialization_format: SerializationFormat = SerializationFormat.TORCH_SAVE,
+        **kwargs,
+    ) -> None:
+        """
+        Initialize the writer pointing to `path`.
+
+        Args:
+            path: directory where the checkpoint will be written to.
+            single_file_per_rank: Produce one file per rank instead of one file per tensor/blob. Default to True.
+            sync_files : force files to be synced to permanent storage. Default to True.
+            thread_count: Number of IO threads to use to write. Default to 1.
+            per_thread_copy_ahead: How many bytes to copy from the GPU ahead of saving then. Default 10Mb.
+            overwrite: Whether to allow overwriting existing checkpoints. Defaults to True.
+            _extensions: Extensions to apply to output streams (EXPERIMENTAL)
+
+        N. B. If sync_files is disabled, there's no guarantee that the checkpoint will be consistent in the case of a failure.
+        """
+        super().__init__(
+            path,
+            single_file_per_rank,
+            sync_files,
+            thread_count,
+            per_thread_copy_ahead,
+            overwrite=overwrite,
+            _extensions=_extensions,
+            serialization_format=serialization_format,
+        )
+        self.fs = FileSystem()
+        self.path = self.fs.init_path(path, **kwargs)
+
+    @classmethod
+    def validate_checkpoint_id(cls, checkpoint_id: Union[str, os.PathLike]) -> bool:
+        return FileSystem.validate_checkpoint_id(checkpoint_id)
+
+
+class FsspecReader(FileSystemReader):
+    def __init__(self, path: Union[str, os.PathLike], **kwargs) -> None:
+        super().__init__(path)
+        self.fs = FileSystem()
+        self.path = self.fs.init_path(path, **kwargs)
+
+    @classmethod
+    def validate_checkpoint_id(cls, checkpoint_id: Union[str, os.PathLike]) -> bool:
+        return FileSystem.validate_checkpoint_id(checkpoint_id)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_hf_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_hf_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d14229b7f8ccfe5a51211d0fb6a4c332af6066b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_hf_utils.py
@@ -0,0 +1,106 @@
+import io
+import json
+import struct
+from dataclasses import dataclass
+from typing import Any, Optional
+
+import torch
+
+
+_metadata_fn: str = "model.safetensors.index.json"
+
+FILE_NAME = "model-{cpt_idx}-of-{num_files}"
+SHARDED_FILE_NAME = "shard-{shard_idx}-model-{cpt_idx}-of-{num_files}"
+SUFFIX = ".safetensors"
+
+# metadata keys
+CUSTOM_METADATA_KEY = "DCP_SHARDING_INFO"
+DEFAULT_EXTRA_METADATA_KEY = "__metadata__"
+SAVED_OFFSETS_KEY = "saved_offsets"
+SHAPE_KEY = "shape"
+DATA_KEY = "data"
+DTYPE_KEY = "dtype"
+DATA_OFFSETS_KEY = "data_offsets"
+
+DTYPE_MAP = {
+    "F16": torch.float16,
+    "F32": torch.float32,
+    "F64": torch.float64,
+    "I8": torch.int8,
+    "U8": torch.uint8,
+    "I16": torch.int16,
+    "I32": torch.int32,
+    "I64": torch.int64,
+    "BF16": torch.bfloat16,
+}
+
+HF_DCP_VERSION: float = 1.0
+DCP_VERSION_KEY = "DCP_VERSION"
+DCP_SHARDING_INFO_KEY = "DCP_SHARDING_INFO"
+
+FORMAT_KEY = "format"
+FORMAT_VALUE = "pt"
+
+NUM_BYTES_FOR_HEADER_LEN = 8
+
+SHARDED_DIR_NAME = "sharded"
+
+
+@dataclass
+class _HFStorageInfo:
+    """This is the per entry storage info."""
+
+    relative_path: str
+    shape: torch.Size
+    dtype: torch.dtype
+
+
+def _gen_file_name(
+    index: int, largest_index: int, shard_index: Optional[int] = None
+) -> str:
+    if shard_index is not None:
+        return (
+            SHARDED_FILE_NAME.format(
+                shard_idx=f"{shard_index}".zfill(5),
+                cpt_idx=f"{index}".zfill(5),
+                num_files=f"{largest_index}".zfill(5),
+            )
+            + SUFFIX
+        )
+    else:
+        return (
+            FILE_NAME.format(
+                cpt_idx=f"{index}".zfill(5), num_files=f"{largest_index}".zfill(5)
+            )
+            + SUFFIX
+        )
+
+
+def _get_safetensors_file_metadata(file_bytes: io.IOBase) -> tuple[Any, int]:
+    # this uses the same logic that's done in HF code base
+    # https://github.com/2404589803/huggingface_hub/blob/main/src/huggingface_hub/hf_api.py#L5308
+    # and follows their documentation on how their files are serialized
+    # https://huggingface.co/docs/safetensors/index#format
+
+    header_len_bytes = file_bytes.read(NUM_BYTES_FOR_HEADER_LEN)
+    header_len = struct.unpack(" torch.dtype:
+    try:
+        dtype = DTYPE_MAP[dtype_str]
+    except KeyError:
+        dtype = torch.get_default_dtype()
+
+    return dtype
+
+
+def _get_dcp_custom_metadata(metadata: Any) -> Optional[Any]:
+    if DEFAULT_EXTRA_METADATA_KEY in metadata:
+        custom_metadata = metadata[DEFAULT_EXTRA_METADATA_KEY]
+        if CUSTOM_METADATA_KEY in custom_metadata:
+            return json.loads(custom_metadata[CUSTOM_METADATA_KEY])
+    return None
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_nested_dict.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_nested_dict.py
new file mode 100644
index 0000000000000000000000000000000000000000..eb26058370f766fbb96e4a5f1530577234eed62a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_nested_dict.py
@@ -0,0 +1,69 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+from torch.distributed.checkpoint.metadata import STATE_DICT_TYPE
+
+from . import _version
+from ._traverse import (
+    OBJ_PATH,
+    set_element,
+    STATE_DICT_ITEM,
+    traverse_state_dict,
+    traverse_state_dict_v_2_3,
+)
+
+
+"""
+TODO:
+Need to add ability to handle tuple, OrderedDict, NamedTuple.
+Update mappings from dict to a class.
+Change set_element to recreate the right type for tuple, OrderedDict, and NamedTuple.
+"""
+
+
+FLATTEN_MAPPING = dict[str, OBJ_PATH]
+
+
+# TODO: Update Docstring for nested_dict.py
+def flatten_state_dict(
+    state_dict: STATE_DICT_TYPE,
+) -> tuple[STATE_DICT_TYPE, FLATTEN_MAPPING]:
+    """
+    Flatten ``state_dict`` made of nested dicts and lists into a top level dictionary.
+
+    Use ``unflatten_state_dict`` to revert this process.
+    Returns:
+        A tuple with the flatten state_dict and a mapping from original to new state_dict.
+    N.B. The new keys are derived from the object paths, joined by dot.
+        For example: ``{ 'a': {'b':...}}`` results in the key `a.b`.
+    """
+    flattened: STATE_DICT_TYPE = {}
+    mappings: FLATTEN_MAPPING = {}
+
+    def flat_copy(path: OBJ_PATH, value: STATE_DICT_ITEM) -> None:
+        new_fqn = ".".join(map(str, path))
+        if new_fqn in flattened:
+            raise ValueError(f"duplicated flatten key {new_fqn}")
+        flattened[new_fqn] = value
+        mappings[new_fqn] = path
+
+    # We started to flatten dictionary since v2.4. But in order to not break
+    # the checkpoints that were saved before v2.4, we need to keep the old
+    # traversal so that we can reconstruct those checkpoints.
+    use_v_2_3 = (
+        _version._derived_version is not None and _version._derived_version == "2_3"
+    )
+    if use_v_2_3:
+        traverse_state_dict_v_2_3(state_dict, flat_copy)
+    else:
+        traverse_state_dict(state_dict, flat_copy)
+    return flattened, mappings
+
+
+def unflatten_state_dict(
+    state_dict: STATE_DICT_TYPE, mapping: FLATTEN_MAPPING
+) -> STATE_DICT_TYPE:
+    """Restore the original nested state_dict according to ``mapping`` and the flattened ``state_dict``."""
+    nested: STATE_DICT_TYPE = {}
+    for key, value in state_dict.items():
+        set_element(nested, mapping[key], value)
+    return nested
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_pg_transport.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_pg_transport.py
new file mode 100644
index 0000000000000000000000000000000000000000..b258517bdcebaa553c4acf7f5511b29432304ed9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_pg_transport.py
@@ -0,0 +1,387 @@
+import logging
+import pickle
+import time
+from collections.abc import Callable, Generator
+from contextlib import contextmanager
+from dataclasses import dataclass
+from datetime import timedelta
+from typing import cast, Optional, TypeVar, Union
+
+import torch
+from torch.distributed import ProcessGroup, Work
+from torch.distributed._shard.sharded_tensor import (
+    Shard as ShardedTensorShard,
+    ShardedTensor,
+    ShardMetadata,
+)
+from torch.distributed._shard.sharded_tensor.metadata import ShardedTensorMetadata
+from torch.distributed.tensor import _DTensorSpec, DTensor
+from torch.utils._pytree import (
+    KeyPath,
+    tree_flatten_with_path,
+    tree_unflatten,
+    TreeSpec,
+)
+
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+T = TypeVar("T")
+
+
+@dataclass
+class _TensorMeta:
+    """
+    This is the metadata for a tensor that is used to transfer checkpoints.
+    It contains the shape, the dtype, the storage offset and the stride of the
+    tensor.
+
+    This must be pickleable so that it can be sent over the wire.
+    """
+
+    shape: torch.Size
+    dtype: torch.dtype
+    storage_offset: int
+    stride: tuple[int, ...]
+    nbytes: int
+
+
+@dataclass
+class _DTensorMeta:
+    """
+    This is the metadata for a DTensor that is used to transfer checkpoints.
+    It contains the metadata for the local tensor and the spec of the DTensor.
+
+    This must be pickleable so that it can be sent over the wire.
+    """
+
+    local: _TensorMeta
+    spec: _DTensorSpec
+
+
+@dataclass
+class _ShardedTensorMeta:
+    """
+    This is the metadata for a ShardedTensor that is used to transfer checkpoints.
+    It contains the metadata for all local shards and the global tensor metadata.
+
+    This must be pickleable so that it can be sent over the wire.
+    """
+
+    local_shards_meta: list[_TensorMeta]
+    local_shards_shard_metadata: list[
+        ShardMetadata
+    ]  # Original shard metadata for each local shard
+    sharded_tensor_metadata: ShardedTensorMetadata
+
+
+@dataclass
+class _StateDictMeta:
+    """
+    This is the metadata for a state dict that is used to transfer checkpoints.
+    It contains the step, the pytree spec of the state dict and the metadata for
+    each tensor in the state dict.
+
+    This must be pickleable so that it can be sent over the wire.
+
+    Args:
+        step: the step of the checkpoint to verify consistency
+        treespec: the pytree spec of the state dict
+        paths: the path of each leaf in the state dict
+        non_tensor_leaves: the metadata for each tensor in the state dict and any
+            non-tensor leaves in the state dict
+    """
+
+    treespec: TreeSpec
+    paths: list[KeyPath]
+    non_tensor_leaves: list[
+        Union[object, _TensorMeta, _DTensorMeta, _ShardedTensorMeta]
+    ]
+
+
+@contextmanager
+def _timeit(name: str) -> Generator[None, None, None]:
+    start = time.perf_counter()
+    yield
+    dur = time.perf_counter() - start
+    logger.info("%s took %ss", name, dur)
+
+
+def _prepare_tensor(tensor: torch.Tensor) -> tuple[torch.Tensor, _TensorMeta]:
+    return (
+        _cast_tensor(tensor, torch.uint8),
+        _TensorMeta(
+            shape=tensor.shape,
+            dtype=tensor.dtype,
+            storage_offset=cast(int, tensor.storage_offset()),
+            stride=tensor.stride(),
+            nbytes=tensor.untyped_storage().nbytes(),
+        ),
+    )
+
+
+def _prepare_state_dict(
+    state_dict: object,
+    device: torch.device,
+) -> tuple[_StateDictMeta, list[torch.Tensor]]:
+    leaves: list[tuple[KeyPath, object]]
+    leaves, treespec = tree_flatten_with_path(state_dict)
+
+    paths: list[KeyPath] = []
+    non_tensor_leaves: list[
+        Union[object, _TensorMeta, _DTensorMeta, _ShardedTensorMeta]
+    ] = []
+    tensors: list[torch.Tensor] = []
+    for key_path, v in leaves:
+        paths.append(key_path)
+
+        if isinstance(v, DTensor):
+            tensor, tensor_meta = _prepare_tensor(v._local_tensor)
+
+            tensors.append(tensor)
+
+            non_tensor_leaves.append(
+                _DTensorMeta(
+                    local=tensor_meta,
+                    spec=v._spec,
+                )
+            )
+        elif isinstance(v, ShardedTensor):
+            # Handle ShardedTensor by extracting all local shards
+            local_shards = v.local_shards()
+
+            # Prepare metadata for all local shards
+            local_shards_meta = []
+            local_shards_shard_metadata = []
+            for shard in local_shards:
+                tensor, tensor_meta = _prepare_tensor(shard.tensor)
+                tensors.append(tensor)
+                local_shards_meta.append(tensor_meta)
+                local_shards_shard_metadata.append(shard.metadata)
+
+            non_tensor_leaves.append(
+                _ShardedTensorMeta(
+                    local_shards_meta=local_shards_meta,
+                    local_shards_shard_metadata=local_shards_shard_metadata,
+                    sharded_tensor_metadata=v.metadata(),  # Complete metadata
+                )
+            )
+        elif isinstance(v, torch.Tensor):
+            tensor, tensor_meta = _prepare_tensor(v)
+            tensors.append(tensor)
+            non_tensor_leaves.append(tensor_meta)
+        else:
+            non_tensor_leaves.append(v)
+
+    return (
+        _StateDictMeta(
+            treespec=treespec,
+            paths=paths,
+            non_tensor_leaves=non_tensor_leaves,
+        ),
+        tensors,
+    )
+
+
+def _cast_tensor(tensor: torch.Tensor, dtype: torch.dtype) -> torch.Tensor:
+    """
+    Casts the underlying storage to a tensor of the given dtype.
+
+    The returned tensor will be of size ``storage.nbytes``.
+
+    This works for all datatypes and supports strided/offset tensors with the
+    caveat that the cast tensor may be larger than the original tensor due to
+    the differences in striding.
+    """
+    if type(tensor) is not torch.Tensor:
+        raise AssertionError(f"can only cast standard tensors not {type(tensor)}")
+    storage = tensor.untyped_storage()
+    ret = torch.tensor(storage, dtype=dtype, device=tensor.device)
+    if ret.untyped_storage() is not storage:
+        raise AssertionError("storage should be the same")
+    return ret
+
+
+class PGTransport:
+    """
+    This is a checkpoint transport that uses the process group to transfer checkpoints.
+    This allows for fast recovery of workers by fetching the current weights
+    from an existing worker.
+
+    Args:
+        pg: the process group to use for communication
+        timeout: the timeout for communication
+        device: the device to use for tensors
+        state_dict: if specified this function will be called to do an inplace
+            receive into the returned state_dict. This is much faster than
+            having to allocate new tensors and transferring them to the CPU.
+    """
+
+    def __init__(
+        self,
+        pg: ProcessGroup,
+        timeout: timedelta,
+        device: torch.device,
+        state_dict: Optional[Callable[[], object]] = None,
+    ) -> None:
+        self._work: list[Work] = []
+        self._pg = pg
+        self._timeout = timeout
+        # pyrefly: ignore [read-only]
+        self._device = device
+        self._state_dict = state_dict
+
+    def send_checkpoint(self, dst_ranks: list[int], state_dict: object) -> None:
+        """
+        Send a checkpoint to multiple destination ranks.
+
+        The process:
+        1. Prepares the state dict by converting tensors to a serializable format
+        2. Sends metadata as pickled data
+        3. Sends each tensor sequentially to all destination ranks
+
+        Args:
+            dst_ranks: List of destination ranks to send the checkpoint to
+            state_dict: The state dictionary containing model parameters
+        """
+        with _timeit("preparing state_dict"):
+            meta, tensors = _prepare_state_dict(state_dict, device=self._device)
+
+        work = []
+
+        with _timeit("send meta"):
+            buf = pickle.dumps(meta)
+            len_t = torch.tensor([len(buf)], dtype=torch.int64, device=self._device)
+            buf_t = torch.frombuffer(buf, dtype=torch.uint8).to(self._device)
+            for dst_rank in dst_ranks:
+                work.append(self._pg.send([len_t], dst_rank, tag=1))
+                work.append(self._pg.send([buf_t], dst_rank, tag=2))
+
+        with _timeit("send tensors"):
+            for i, t in enumerate(tensors):
+                original_device = t.device
+                t = t.to(self._device)
+                for dst_rank in dst_ranks:
+                    work.append(self._pg.send([t], dst_rank, tag=3 + i))
+
+                # if we did a copy we should wait for the work to complete so we
+                # can free the memory to avoid OOMs
+                if original_device == torch.device("cpu"):
+                    for w in work:
+                        w.wait()
+                    work = []
+
+            for w in work:
+                w.wait()
+
+    def recv_checkpoint(self, src_rank: int) -> object:
+        """
+        Receive a checkpoint from a source rank.
+
+        The process:
+        1. Receives metadata about the checkpoint structure
+        2. Receives each tensor, potentially reusing existing tensors for in-place updates
+        3. Reconstructs the original state dict structure
+
+        Args:
+            src_rank: The source rank to receive the checkpoint from
+
+        Returns:
+            The reconstructed state dictionary with model parameters
+        """
+        state_dict = self._state_dict() if self._state_dict else {}
+        state_dict_leaves, _ = tree_flatten_with_path(state_dict)
+
+        dst_tensors: dict[KeyPath, object] = dict(state_dict_leaves)
+
+        len_t = torch.zeros(1, dtype=torch.int64, device=self._device)
+        self._pg.recv([len_t], src_rank, tag=1).wait()
+        length = cast(int, len_t.item())
+
+        buf = torch.empty(length, dtype=torch.uint8, device=self._device)
+        self._pg.recv([buf], src_rank, tag=2).wait()
+
+        meta: _StateDictMeta = pickle.loads(buf.cpu().numpy().tobytes())
+
+        i: int = 0
+        works: list[Work] = []
+
+        def recv(path: KeyPath, v: _TensorMeta) -> torch.Tensor:
+            nonlocal i
+
+            inplace = dst_tensors.get(path)
+            if (
+                isinstance(inplace, torch.Tensor)
+                and inplace.device.type == self._device.type
+            ):
+                if isinstance(inplace, DTensor):
+                    inplace = inplace._local_tensor
+                t = _cast_tensor(inplace, torch.uint8)
+                if t.nbytes != v.nbytes:
+                    raise AssertionError("inplace tensor storage must be the same size")
+            else:
+                t = torch.empty(v.nbytes, dtype=torch.uint8, device=self._device)
+
+            work = self._pg.recv([t], src_rank, tag=3 + i)
+            i += 1
+
+            if inplace is None:
+                # if not inplace we need to copy it to CPU to avoid OOMing
+                work.wait()
+                t = t.cpu()
+            else:
+                works.append(work)
+
+            return torch.as_strided(
+                t.view(v.dtype),
+                size=v.shape,
+                stride=v.stride,
+                storage_offset=v.storage_offset,
+            )
+
+        values: list[object] = []
+        for path, v in zip(meta.paths, meta.non_tensor_leaves):
+            if isinstance(v, _TensorMeta):
+                values.append(recv(path, v))
+            elif isinstance(v, _DTensorMeta):
+                tensor = recv(path, v.local)
+                # pyrefly: ignore [bad-argument-type, bad-argument-count, unexpected-keyword]
+                values.append(DTensor(tensor, v.spec, requires_grad=False))
+            elif isinstance(v, _ShardedTensorMeta):
+                # Receive all local shards that were sent to us
+                local_shards = []
+                current_rank = self._pg.rank()
+
+                # Receive tensors for each local shard that was sent
+                for j, shard_meta in enumerate(v.local_shards_meta):
+                    tensor = recv(path, shard_meta)
+
+                    # Use the original shard metadata that was stored during preparation
+                    # but update the placement to reflect the current rank/device
+                    original_shard_metadata = v.local_shards_shard_metadata[j]
+                    updated_shard_metadata = ShardMetadata(
+                        shard_offsets=original_shard_metadata.shard_offsets,
+                        shard_sizes=original_shard_metadata.shard_sizes,
+                        placement=f"rank:{current_rank}/{tensor.device.type}",
+                    )
+
+                    local_shard = ShardedTensorShard(
+                        tensor=tensor, metadata=updated_shard_metadata
+                    )
+                    local_shards.append(local_shard)
+
+                # Use complete metadata to reconstruct ShardedTensor
+                sharded_tensor = (
+                    ShardedTensor._init_from_local_shards_and_global_metadata(
+                        local_shards=local_shards,
+                        sharded_tensor_metadata=v.sharded_tensor_metadata,
+                    )
+                )
+                values.append(sharded_tensor)
+            else:
+                values.append(v)
+
+        for work in works:
+            work.wait()
+
+        return tree_unflatten(values, meta.treespec)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_sharded_tensor_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_sharded_tensor_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..a68bcddeb7f9d9ffe6f89056dfe1ccc30cc12eb5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_sharded_tensor_utils.py
@@ -0,0 +1,107 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+import copy
+from typing import TYPE_CHECKING
+
+import torch.distributed as dist
+from torch.distributed._shard.sharded_tensor import Shard, ShardedTensor, ShardMetadata
+from torch.distributed.checkpoint.metadata import STATE_DICT_TYPE
+from torch.distributed.remote_device import _remote_device
+
+from ._traverse import OBJ_PATH, set_element, STATE_DICT_ITEM, traverse_state_dict
+from .utils import _element_wise_add, _normalize_device_info
+
+
+if TYPE_CHECKING:
+    from torch.distributed._shard.sharded_tensor.metadata import ShardedTensorMetadata
+
+
+# TODO: We need to refactor this code.
+def _flatten_sharded_tensors(state_dict: STATE_DICT_TYPE) -> STATE_DICT_TYPE:
+    r"""
+    Transform ``state_dict`` by flattening all nested ShardedTensor instances found.
+
+    The resulting ShardedTensor instances are only correct regarding the local shard and
+    MUST not be used for any other purpose but checkpointing, as no operator will work with them.
+
+    This function should be used in conjunction with a state_dict produced by FSDP's
+    StateDictType.SHARDED_STATE_DICT methods.
+    """
+    new_state_dict: STATE_DICT_TYPE = {}
+
+    def rewrite_dict(path: OBJ_PATH, value: STATE_DICT_ITEM) -> None:
+        if not isinstance(value, ShardedTensor):
+            set_element(new_state_dict, path, value)
+            return
+        shards = value.local_shards()
+
+        if len(shards) == 0:
+            return
+        if len(shards) != 1:
+            set_element(new_state_dict, path, value)
+            return
+
+        outer_shard = shards[0]
+
+        inner_st = outer_shard.tensor
+        if not isinstance(inner_st, ShardedTensor):
+            set_element(new_state_dict, path, value)
+            return
+
+        if len(inner_st.local_shards()) != 1:
+            raise ValueError("Cannot handle inner tensor with more than 1 shard")
+        inner_shard = inner_st.local_shards()[0]
+
+        local_shards = [
+            Shard(
+                tensor=inner_shard.tensor,
+                metadata=ShardMetadata(
+                    shard_offsets=_element_wise_add(
+                        outer_shard.metadata.shard_offsets,
+                        inner_shard.metadata.shard_offsets,
+                    ),
+                    shard_sizes=inner_shard.metadata.shard_sizes,
+                    placement=f"rank:{dist.get_rank()}/{inner_shard.tensor.device}",
+                ),
+            )
+        ]
+
+        st_meta: ShardedTensorMetadata = copy.deepcopy(value.metadata())
+        other_rank = 0 if dist.get_rank() > 0 else 1
+        device_info = _normalize_device_info(inner_shard.tensor.device.type, 0)
+
+        # Remove the outer ST shard the inner ST covers
+        for i, shard_md in enumerate(st_meta.shards_metadata):
+            if shard_md.shard_offsets == outer_shard.metadata.shard_offsets:
+                st_meta.shards_metadata.pop(i)
+                break
+
+        # Attribute other rank for the other shards
+        for shard_md in st_meta.shards_metadata:
+            shard_md.placement = _remote_device(f"rank:{other_rank}/{device_info}")
+
+        # Add other inner shards from the inner tensor
+        for inner_md in inner_st.metadata().shards_metadata:
+            if inner_md.shard_offsets != inner_shard.metadata.shard_offsets:
+                st_meta.shards_metadata.append(
+                    ShardMetadata(
+                        shard_offsets=_element_wise_add(
+                            outer_shard.metadata.shard_offsets,
+                            inner_md.shard_offsets,
+                        ),
+                        shard_sizes=inner_md.shard_sizes,
+                        placement=f"rank:{other_rank}/{device_info}",
+                    )
+                )
+
+        # Finally add this shard
+        st_meta.shards_metadata.append(local_shards[0].metadata)
+
+        st = ShardedTensor._init_from_local_shards_and_global_metadata(
+            local_shards=local_shards,
+            sharded_tensor_metadata=st_meta,
+        )
+        set_element(new_state_dict, path, st)
+
+    traverse_state_dict(state_dict, rewrite_dict)
+    return new_state_dict
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_state_dict_stager.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_state_dict_stager.py
new file mode 100644
index 0000000000000000000000000000000000000000..155a87b9dec5bcd1f532d17ee2b8ef56454e37ab
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_state_dict_stager.py
@@ -0,0 +1,467 @@
+# mypy: allow-untyped-defs
+import types
+import warnings
+import weakref
+from copyreg import dispatch_table
+from typing import Any
+
+import torch
+import torch.cuda._pin_memory_utils as pin_memory_utils
+from torch.storage import UntypedStorage
+from torch.utils.weak import WeakIdKeyDictionary
+
+
+class StateDictStager:
+    """
+    A class for optimizing storage objects during staging for async checkpointing.
+
+    StateDictStager stages the state_dict to CPU DRAM while applying optimizations
+    like memory sharing and pinning to improve performance. It caches storage objects
+    to avoid redundant copies and can be configured to automatically share memory
+    (for multi-process usage) and pin memory (for faster CPU-GPU transfers).
+
+    Attributes:
+        pin_memory (bool): Whether to pin CPU memory for faster CPU-GPU transfers
+        share_memory (bool): Whether to share memory across processes
+        pin_memory_min_bytes (int): Minimum tensor size in bytes to pin memory (default: 5)
+        _cached_storage_mapping (WeakIdKeyDictionary): Maps storage objects to optimized CPU storages using weak references
+    """
+
+    def __init__(
+        self,
+        pin_memory: bool = False,
+        share_memory: bool = False,
+        pin_memory_min_bytes: int = 5,
+    ):
+        if pin_memory and not torch.cuda.is_available():
+            warnings.warn(
+                "Ignoring pin_memory flag for checkpoint staging as pinning memory"
+                "requires CUDA, but CUDA is not available. ",
+                stacklevel=2,
+            )
+            self.pin_memory = False
+        else:
+            self.pin_memory = pin_memory
+        self.share_memory = share_memory
+        # Mapping from original storage objects to CPU storages using weak references
+        self._cached_storage_mapping = WeakIdKeyDictionary()
+        self.pin_memory_min_bytes = pin_memory_min_bytes
+
+        def _deepcopy_atomic(x, _):
+            return x
+
+        def _deepcopy_list(x, memo, non_blocking=False):
+            y: list = []
+            memo[id(x)] = y
+            append = y.append
+            for a in x:
+                append(
+                    self.deepcopy_with_tensor_offload(
+                        a, memo, non_blocking=non_blocking
+                    )
+                )
+            return y
+
+        def _deepcopy_tuple(x, memo, non_blocking=False):
+            y = [
+                self.deepcopy_with_tensor_offload(a, memo, non_blocking=non_blocking)
+                for a in x
+            ]
+            # We're not going to put the tuple in the memo, but it's still important we
+            # check for it, in case the tuple contains recursive mutable structures.
+            try:
+                return memo[id(x)]
+            except KeyError:
+                pass
+
+            # Check if any elements changed during deepcopy
+            for k, j in zip(x, y):
+                if k is not j:
+                    # At least one element changed, create new tuple
+                    return tuple(y)
+
+            # No elements changed, return original tuple
+            return x
+
+        def _deepcopy_dict(x, memo, non_blocking=False):
+            y: dict = {}
+            memo[id(x)] = y
+            for key, value in x.items():
+                y[
+                    self.deepcopy_with_tensor_offload(
+                        key, memo, non_blocking=non_blocking
+                    )
+                ] = self.deepcopy_with_tensor_offload(
+                    value, memo, non_blocking=non_blocking
+                )
+            return y
+
+        def _deepcopy_method(x, memo, non_blocking=False):  # Copy instance methods
+            return type(x)(
+                x.__func__,
+                self.deepcopy_with_tensor_offload(
+                    x.__self__, memo, non_blocking=non_blocking
+                ),
+            )
+
+        d: dict[Any, Any] = {}
+        self._deepcopy_dispatch = d
+        d[type(None)] = _deepcopy_atomic
+        d[int] = _deepcopy_atomic
+        d[float] = _deepcopy_atomic
+        d[bool] = _deepcopy_atomic
+        d[complex] = _deepcopy_atomic
+        d[bytes] = _deepcopy_atomic
+        d[str] = _deepcopy_atomic
+        d[types.CodeType] = _deepcopy_atomic
+        d[type] = _deepcopy_atomic
+        d[range] = _deepcopy_atomic
+        d[types.BuiltinFunctionType] = _deepcopy_atomic
+        d[types.FunctionType] = _deepcopy_atomic
+        d[weakref.ref] = _deepcopy_atomic
+        d[property] = _deepcopy_atomic
+        d[types.MethodType] = _deepcopy_method
+        d[dict] = _deepcopy_dict
+        d[tuple] = _deepcopy_tuple
+        d[list] = _deepcopy_list
+
+    def _stage_untyped_storage(
+        self,
+        storage: UntypedStorage,
+        non_blocking: bool = False,
+    ):
+        """
+        Called from the hooked storage_deepcopy function in torch.Tensor.__deepcopy__.
+
+        This method handles the storage optimization logic for the StagingStateDict class.
+        It checks if the storage has already been cached, and if so, reuses it.
+        Otherwise, it creates a new CPU storage and applies memory optimizations.
+
+        Args:
+            storage: The storage to optimize
+
+        Returns:
+            The optimized storage
+        """
+        # Check if we've already cached this storage
+        if storage in self._cached_storage_mapping:
+            cached_storage = self._cached_storage_mapping[storage]
+            assert cached_storage.size() == storage.size(), (
+                "For async checkpointing,  We cache storages in DRAM and reuse them."
+                "Cached storage size does not match original storage size."
+                "This should never happen as we track the original storage weakref "
+                "and clean up the cache storage. Please report this to PyTorch Distributed Checkpointing."
+            )
+            # Reuse cached storage but update with new data
+            cached_storage.copy_(storage, non_blocking=non_blocking)
+            return cached_storage
+
+        # Create new CPU storage
+        if self.share_memory:
+            new_storage = type(storage)._new_shared(storage.size(), device="cpu")
+        else:
+            new_storage = type(storage)(storage.size(), device="cpu")
+
+        # Skip pinning for tensors below the minimum size threshold
+        # Small tensors (e.g., optimizer step counters, scalars) have negligible
+        # transfer time improvement from pinning, but pinning overhead is significant
+        if self.pin_memory and new_storage.nbytes() >= self.pin_memory_min_bytes:
+            pin_memory_utils.pin_memory(new_storage.data_ptr(), new_storage.nbytes())
+            # Set up a weak reference to unpin when cpu storage is garbage collected
+            f = weakref.finalize(
+                new_storage, pin_memory_utils.unpin_memory, new_storage.data_ptr()
+            )
+            # This makes sure that the finalizer is not called after
+            # cuda context is destroyed.
+            f.atexit = False
+
+        new_storage.copy_(storage, non_blocking=non_blocking)
+
+        # Cache the storage - WeakIdKeyDictionary will automatically clean up when storage is garbage collected
+        self._cached_storage_mapping[storage] = new_storage
+        return new_storage
+
+    @torch.no_grad()
+    def stage(
+        self,
+        state_dict: Any,
+        non_blocking: bool = False,
+    ) -> Any:
+        return self.deepcopy_with_tensor_offload(state_dict, None, [], non_blocking)
+
+    def _offload_tensor(self, x, memo, non_blocking=False):
+        """
+        Deep copy a PyTorch tensor with optimized storage handling.
+
+        This method creates a CPU copy of a tensor while applying memory optimizations
+        like sharing and pinning based on the StateDictStager configuration.
+
+        Args:
+            x: The tensor to copy
+            memo: Memo dictionary for tracking already copied objects
+            non_blocking: Whether to perform non-blocking copies where possible
+
+        Returns:
+            A CPU copy of the tensor with optimized storage
+        """
+        # if data_ptr is not 0, we allocate a new storage below. so we can skip
+        # memory allocation by using [] for size.
+        y = x.new_empty([] if x.data_ptr() != 0 else x.size(), device="cpu")
+
+        # Store in memo dict early to handle recursive references
+        d = id(x)
+        memo[d] = y
+
+        if type(x) is torch.Tensor or x.data_ptr() != 0:
+            # Get the untyped storage
+            untyped_storage = x.untyped_storage()
+            storage_id = id(untyped_storage)
+
+            # Check if this storage has already been staged in this deepcopy operation
+            # This handles the case where different tensors share the same storage
+            # (e.g., FSDP state_dict where norm.weight and norm_weight reference same storage)
+            # PyTorch caches untyped_storage() calls, so same storage -> same id
+            if storage_id in memo:
+                copied_storage = memo[storage_id]
+            else:
+                # Storage not seen before in this operation, stage it
+                copied_storage = self._stage_untyped_storage(
+                    untyped_storage, non_blocking=non_blocking
+                )
+                # Add to memo to avoid re-staging if we see this storage again
+                memo[storage_id] = copied_storage
+
+            # Set the tensor data using the staged storage
+            y.set_(copied_storage, x.storage_offset(), x.size(), x.stride())
+
+        # Copy any attributes the tensor might have
+        if hasattr(x, "__dict__"):
+            for attr_name, attr_value in x.__dict__.items():
+                setattr(
+                    y,
+                    attr_name,
+                    self.deepcopy_with_tensor_offload(
+                        attr_value, memo, non_blocking=non_blocking
+                    ),
+                )
+
+        if hasattr(x, "__slots__"):
+            for slot in x.__slots__:
+                if hasattr(x, slot):
+                    setattr(
+                        y,
+                        slot,
+                        self.deepcopy_with_tensor_offload(
+                            getattr(x, slot), memo, non_blocking=non_blocking
+                        ),
+                    )
+
+        return y
+
+    def close(self):
+        """
+        Clean up all cached storages and release associated resources.
+
+        This method clears the internal storage cache, allowing garbage collection
+        of cached CPU storages. Any pinned memory associated with cached storages
+        will be automatically unpinned through weak reference finalizers.
+        """
+        self._cached_storage_mapping.clear()
+
+    @torch.no_grad()
+    def deepcopy_with_tensor_offload(self, x, memo=None, _nil=[], non_blocking=False):  # noqa: B006
+        """Deep copy operation on arbitrary Python objects with special handling for PyTorch tensors.
+
+        This implementation extends the standard deepcopy functionality to handle PyTorch tensors
+        and their storages in a way that optimizes memory usage and performance, similar to the
+        stage method. It applies memory sharing and pinning optimizations based on the StateDictStager
+        configuration.
+
+        Args:
+            x: The object to deep copy
+            memo: Memo dictionary for tracking already copied objects
+            _nil: Sentinel value for memo dictionary
+            non_blocking: Whether to perform non-blocking copies where possible
+
+        Returns:
+            A deep copy of the input object with optimized tensor storage handling
+        """
+        if memo is None:
+            memo = {}
+
+        d = id(x)
+        y = memo.get(d, _nil)
+        if y is not _nil:
+            return y
+
+        cls = type(x)
+
+        # tensors and subclasses of tensors are handled separately
+        if isinstance(x, torch.Tensor):
+            y = self._offload_tensor(x, memo, non_blocking=non_blocking)
+        else:
+            # Use the dispatch table for standard types
+            copier = self._deepcopy_dispatch.get(cls)
+            if copier is not None:
+                # Check if this is an atomic copier (only accepts x and memo)
+                if copier.__name__ == "_deepcopy_atomic":
+                    y = copier(x, memo)
+                else:
+                    y = copier(x, memo, non_blocking=non_blocking)
+            else:
+                if issubclass(cls, type):
+                    # type copier is also atomic
+                    y = self._deepcopy_dispatch[type](x, memo)
+                else:
+                    copier = getattr(x, "__deepcopy__", None)
+                    if copier is not None:
+                        y = copier(memo)
+                    else:
+                        reductor = dispatch_table.get(cls)
+                        if reductor:
+                            rv = reductor(x)
+                        else:
+                            reductor = getattr(x, "__reduce_ex__", None)
+                            if reductor is not None:
+                                rv = reductor(4)
+                            else:
+                                reductor = getattr(x, "__reduce__", None)
+                                if reductor:
+                                    rv = reductor()
+                                else:
+                                    raise RuntimeError(
+                                        f"un(deep)copyable object of type {cls}"
+                                    )
+                        if isinstance(rv, str):
+                            y = x
+                        else:
+                            # Unpack rv tuple elements (up to 5 from pickle protocol)
+                            # and explicitly pass non_blocking as keyword arg
+                            if len(rv) == 2:
+                                func, args = rv
+                                y = self._reconstruct(
+                                    x, memo, func, args, non_blocking=non_blocking
+                                )
+                            elif len(rv) == 3:
+                                func, args, state = rv
+                                y = self._reconstruct(
+                                    x,
+                                    memo,
+                                    func,
+                                    args,
+                                    state,
+                                    non_blocking=non_blocking,
+                                )
+                            elif len(rv) == 4:
+                                func, args, state, listiter = rv
+                                y = self._reconstruct(
+                                    x,
+                                    memo,
+                                    func,
+                                    args,
+                                    state,
+                                    listiter,
+                                    non_blocking=non_blocking,
+                                )
+                            elif len(rv) == 5:
+                                func, args, state, listiter, dictiter = rv
+                                y = self._reconstruct(
+                                    x,
+                                    memo,
+                                    func,
+                                    args,
+                                    state,
+                                    listiter,
+                                    dictiter,
+                                    non_blocking=non_blocking,
+                                )
+                            else:
+                                raise RuntimeError(
+                                    f"Unexpected pickle protocol return value length: {len(rv)}"
+                                )
+
+        # If is its own copy, don't memoize.
+        if y is not x:
+            memo[d] = y
+            self._keep_alive(x, memo)  # Make sure x lives at least as long as d
+        return y
+
+    def _keep_alive(self, x, memo):
+        """Keeps a reference to the object x in the memo.
+
+        Because we remember objects by their id, we have
+        to assure that possibly temporary objects are kept
+        alive by referencing them.
+        We store a reference at the id of the memo, which should
+        normally not be used unless someone tries to deepcopy
+        the memo itself...
+        """
+        try:
+            memo[id(memo)].append(x)
+        except KeyError:
+            # aha, this is the first one :-)
+            memo[id(memo)] = [x]
+
+    def _reconstruct(
+        self,
+        x,
+        memo,
+        func,
+        args,
+        state=None,
+        listiter=None,
+        dictiter=None,
+        non_blocking=False,
+    ):
+        deep = memo is not None
+        if deep and args:
+            args = tuple(
+                self.deepcopy_with_tensor_offload(arg, memo, non_blocking=non_blocking)
+                for arg in args
+            )
+        y = func(*args)
+        if deep:
+            memo[id(x)] = y
+
+        if state is not None:
+            if deep:
+                state = self.deepcopy_with_tensor_offload(
+                    state, memo, non_blocking=non_blocking
+                )
+            if hasattr(y, "__setstate__"):
+                y.__setstate__(state)
+            else:
+                if isinstance(state, tuple) and len(state) == 2:
+                    state, slotstate = state
+                else:
+                    slotstate = None
+                if state is not None:
+                    y.__dict__.update(state)
+                if slotstate is not None:
+                    for key, value in slotstate.items():
+                        setattr(y, key, value)
+
+        if listiter is not None:
+            if deep:
+                for item in listiter:
+                    item = self.deepcopy_with_tensor_offload(
+                        item, memo, non_blocking=non_blocking
+                    )
+                    y.append(item)
+            else:
+                for item in listiter:
+                    y.append(item)
+        if dictiter is not None:
+            if deep:
+                for key, value in dictiter:
+                    key = self.deepcopy_with_tensor_offload(
+                        key, memo, non_blocking=non_blocking
+                    )
+                    value = self.deepcopy_with_tensor_offload(
+                        value, memo, non_blocking=non_blocking
+                    )
+                    y[key] = value
+            else:
+                for key, value in dictiter:
+                    y[key] = value
+        return y
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_storage_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_storage_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..73acc628342a058f659042b2d41c8245c86c2c42
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_storage_utils.py
@@ -0,0 +1,49 @@
+import os
+from typing import Union
+
+from .filesystem import FileSystemReader, FileSystemWriter
+from .storage import StorageReader, StorageWriter
+
+
+def _storage_setup(
+    storage: Union[StorageReader, StorageWriter, None],
+    checkpoint_id: Union[str, os.PathLike, None],
+    reader: bool = False,
+) -> Union[None, StorageReader, StorageWriter]:
+    if storage:
+        if checkpoint_id is not None:
+            storage.reset(checkpoint_id)
+        return storage
+
+    if not checkpoint_id:
+        raise RuntimeError(
+            "`checkpoint_id` must be specified if "
+            "storage_reader/storage_writer is None."
+        )
+
+    targets: list[type[Union[StorageReader, StorageWriter]]] = []
+    if reader:
+        targets = [
+            FileSystemReader,
+        ]
+    else:
+        targets = [
+            FileSystemWriter,
+        ]
+    try:
+        from ._fsspec_filesystem import FsspecReader, FsspecWriter
+
+        targets.append(FsspecReader if reader else FsspecWriter)
+    except Exception:
+        pass
+
+    for target in targets:
+        if target.validate_checkpoint_id(checkpoint_id):
+            storage = target(checkpoint_id)  # type: ignore[call-arg]
+            storage.reset(checkpoint_id)
+            return storage
+
+    raise RuntimeError(
+        "Cannot detect which StorageReader or StorageWriter to use. "
+        "Please specify the storage_reader/storage_writer."
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_traverse.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_traverse.py
new file mode 100644
index 0000000000000000000000000000000000000000..48eb67b4f7621b1aa3a4d6b2d7c56c5503337eb7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_traverse.py
@@ -0,0 +1,200 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+from collections.abc import Callable, Collection, Mapping, MutableMapping
+from typing import cast, Optional, TypeVar, Union
+
+import torch
+from torch.distributed._shard.sharded_tensor.api import ShardedTensor
+from torch.distributed.checkpoint.metadata import STATE_DICT_TYPE
+from torch.distributed.tensor import DTensor
+
+
+PATH_ITEM = Union[str, int]
+OBJ_PATH = tuple[PATH_ITEM, ...]
+T = TypeVar("T")
+
+STATE_DICT_ITEM = object
+CONTAINER_TYPE = MutableMapping[PATH_ITEM, STATE_DICT_ITEM]
+
+__all__ = ["traverse_state_dict", "set_element", "get_element", "print_tensor"]
+
+
+def _keep_visiting_tensors(value: STATE_DICT_ITEM) -> bool:
+    return isinstance(value, torch.Tensor)
+
+
+# TODO: update docstring for traverse.py
+def traverse_state_dict(
+    state_dict: STATE_DICT_TYPE,
+    visitor: Callable[[OBJ_PATH, STATE_DICT_ITEM], None],
+    keep_traversing: Callable[[STATE_DICT_ITEM], bool] = _keep_visiting_tensors,
+) -> None:
+    """
+    Invoke ``visitor`` for each value recursively in ``state_dict``.
+    Mapping will be traversed and ``visitor`` will be applied to the leaf elements.
+    ``visitor`` will only be applied to elements in a list or a tuple, if the
+    container contains tensors or mappings.
+    """
+
+    def _is_terminal(value: STATE_DICT_ITEM) -> bool:
+        values: Collection[STATE_DICT_ITEM]
+        if isinstance(value, Mapping):
+            return False
+        elif isinstance(value, list):
+            values = value
+        else:
+            return True
+
+        for entry in values:
+            if isinstance(entry, (Mapping, list)) and not _is_terminal(entry):
+                return False
+            if keep_traversing is not None and keep_traversing(entry):
+                return False
+        return True
+
+    def _traverse_obj(path: OBJ_PATH, value: STATE_DICT_ITEM) -> None:
+        if isinstance(value, Mapping):
+            for k, v in value.items():
+                _traverse_obj(path + (str(k),), v)
+        elif _is_terminal(value):
+            visitor(path, value)
+        elif isinstance(value, (list, tuple)):
+            for i, v in enumerate(value):
+                _traverse_obj(path + (i,), v)
+
+    for key, value in state_dict.items():
+        _traverse_obj((str(key),), value)
+
+
+def traverse_state_dict_v_2_3(
+    state_dict: STATE_DICT_TYPE,
+    visitor: Callable[[OBJ_PATH, STATE_DICT_ITEM], None],
+    keep_traversing: Callable[[STATE_DICT_ITEM], bool] = _keep_visiting_tensors,
+) -> None:
+    """
+    Traversal is short-circuited when if finds a collection for which ``keep_visiting_tensors`` evaluates
+    to false for all elements.
+    By default, all collections with at least one ``torch.Tensor`` element are traversed.
+    Visitor takes a path argument that is a tuple of the keys used to reach it.
+    """
+
+    # a value is terminal if it has no other containers values inside it
+    def _is_terminal(value: STATE_DICT_ITEM) -> bool:
+        values: Collection[STATE_DICT_ITEM]
+        if isinstance(value, Mapping):
+            values = value.values()
+        elif isinstance(value, list):
+            values = value
+        else:
+            return True
+
+        for entry in values:
+            if isinstance(entry, (Mapping, list)) and not _is_terminal(entry):
+                return False
+            if keep_traversing is not None and keep_traversing(entry):
+                return False
+        return True
+
+    def _traverse_obj(path: OBJ_PATH, value: STATE_DICT_ITEM) -> None:
+        if _is_terminal(value):
+            visitor(path, value)
+        elif isinstance(value, Mapping):
+            for k, v in value.items():
+                _traverse_obj(path + (str(k),), v)
+        elif isinstance(value, list):
+            for i, v in enumerate(value):
+                _traverse_obj(path + (i,), v)
+
+    for key, value in state_dict.items():
+        _traverse_obj((str(key),), value)
+
+
+def set_element(
+    root_dict: STATE_DICT_TYPE, path: OBJ_PATH, value: STATE_DICT_ITEM
+) -> None:
+    """Set ``value`` in ``root_dict`` along the ``path`` object path."""
+    cur_container = cast(CONTAINER_TYPE, root_dict)
+
+    def extend_list(lst: list[STATE_DICT_ITEM], idx: int) -> None:
+        while len(lst) <= idx:
+            lst.append(None)
+
+    for i in range(1, len(path)):
+        prev_key = path[i - 1]
+        key = path[i]
+        def_val = cast(STATE_DICT_ITEM, {} if type(key) is str else [])
+
+        if isinstance(cur_container, Mapping):
+            cur_container = cast(
+                CONTAINER_TYPE, cur_container.setdefault(prev_key, def_val)
+            )
+        else:
+            # pyrefly: ignore [bad-argument-type]
+            extend_list(cur_container, prev_key)
+            if cur_container[prev_key] is None:
+                cur_container[prev_key] = def_val
+            cur_container = cur_container[prev_key]
+
+    key = path[-1]
+    if type(key) is int:
+        extend_list(cast(list[STATE_DICT_ITEM], cur_container), key)
+
+    cur_container[key] = value
+
+
+def get_element(
+    root_dict: STATE_DICT_TYPE,
+    path: OBJ_PATH,
+    default_value: Optional[T] = None,
+) -> Optional[T]:
+    """Retrieve the value at ``path``from ``root_dict``, returning ``default_value`` if not found."""
+    cur_value = cast(CONTAINER_TYPE, root_dict)
+    for part in path:
+        if type(part) is int:
+            if not isinstance(cur_value, list) or len(cur_value) < part:
+                return default_value
+        elif not isinstance(cur_value, Mapping) or part not in cur_value:
+            return default_value
+
+        # pyrefly: ignore [index-error]
+        cur_value = cast(CONTAINER_TYPE, cur_value[part])
+    return cast(Optional[T], cur_value)
+
+
+def _print_nested(
+    value: STATE_DICT_ITEM,
+    prefix: str = "",
+    print_fun: Callable[[str], None] = print,
+) -> None:
+    if type(value) is ShardedTensor:
+        print_fun(f"{prefix} ShardedTensor size: {value.size()}")
+        for shard in value.local_shards():
+            _print_nested(
+                shard.tensor,
+                f"{shard.metadata.shard_offsets} ",
+                print_fun=print_fun,
+            )
+    elif type(value) is (DTensor):
+        print_fun(f"{prefix} DistributedTensor size: {value.size()}")
+        # TODO: add local offset for _local_tensor in print_nested.
+        _print_nested(
+            value._local_tensor,
+            print_fun=print_fun,
+        )
+    elif isinstance(value, torch.Tensor):
+        print_fun(f"{prefix} Tensor size: {value.size()}")
+    else:
+        print_fun(f"{prefix} Type: {type(value)}")
+
+
+def print_tensor(
+    path: OBJ_PATH,
+    value: STATE_DICT_ITEM,
+    print_fun: Callable[[str], None] = print,
+) -> None:
+    """
+    Use this callback with traverse_state_dict to print its content.
+
+    By default the content is printed using the builtin ``print`` but this can
+    be change by passing a different ``print_fun` callable.
+    """
+    _print_nested(value, prefix=str(path), print_fun=print_fun)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_version.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_version.py
new file mode 100644
index 0000000000000000000000000000000000000000..b3065bdfd6a2c141a959ef0ffe30aeafdc2dc54f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/_version.py
@@ -0,0 +1,6 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+from typing import Optional
+
+
+_derived_version: Optional[str] = None
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/api.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..4aa4854db2358ae4361403d37d59563ab8963fbd
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/api.py
@@ -0,0 +1,42 @@
+import traceback as tb
+from typing import Any
+
+
+WRAPPED_EXCEPTION = tuple[BaseException, tb.StackSummary]
+
+__all__ = ["CheckpointException"]
+
+
+def _wrap_exception(exc: BaseException) -> WRAPPED_EXCEPTION:
+    return (exc, tb.extract_tb(exc.__traceback__))
+
+
+def _is_wrapped_exception(obj: Any) -> bool:
+    if not isinstance(obj, tuple):
+        return False
+    if len(obj) != 2:
+        return False
+    return isinstance(obj[0], BaseException) and isinstance(obj[1], tb.StackSummary)
+
+
+class CheckpointException(BaseException):
+    """Exception raised if failure was detected as part of a checkpoint load or save."""
+
+    def __init__(self, msg: str, failures: dict[int, WRAPPED_EXCEPTION]):
+        super().__init__(msg, failures)
+        self._failures = failures
+
+    @property
+    def failures(self) -> dict[int, WRAPPED_EXCEPTION]:
+        """Return a dictionary mapping node ranks to their associated exceptions in case of failure."""
+        return self._failures
+
+    def __str__(self) -> str:
+        str = f"CheckpointException ranks:{self._failures.keys()}\n"
+        for rank, exc_pair in self._failures.items():
+            exc, trace = exc_pair
+            str += f"Traceback (most recent call last): (RANK {rank})\n"
+            if trace is not None:
+                str += "".join(tb.format_list(trace))
+            str += "".join(tb.format_exception_only(type(exc), value=exc))
+        return str
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/default_planner.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/default_planner.py
new file mode 100644
index 0000000000000000000000000000000000000000..716cb90a996534e4388a42545935ebee894eeb1a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/default_planner.py
@@ -0,0 +1,702 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+import dataclasses
+import io
+import logging
+import math
+import sys
+from bisect import bisect_right, insort
+from collections import ChainMap
+from typing import Any, cast, Optional, Union
+
+import torch
+from torch.distributed._shard._utils import narrow_tensor_by_index
+from torch.distributed.checkpoint._dedup_save_plans import dedup_save_plans
+from torch.distributed.checkpoint._nested_dict import (
+    FLATTEN_MAPPING,
+    flatten_state_dict,
+)
+from torch.distributed.checkpoint._sharded_tensor_utils import _flatten_sharded_tensors
+from torch.distributed.checkpoint._traverse import set_element
+from torch.distributed.checkpoint.metadata import (
+    BytesStorageMetadata,
+    ChunkStorageMetadata,
+    Metadata,
+    MetadataIndex,
+    STATE_DICT_TYPE,
+    STORAGE_TYPES,
+    StorageMeta,
+    TensorStorageMetadata,
+)
+from torch.distributed.checkpoint.planner import (
+    LoadPlan,
+    LoadPlanner,
+    ReadItem,
+    SavePlan,
+    SavePlanner,
+    WriteItem,
+    WriteItemType,
+)
+from torch.distributed.checkpoint.planner_helpers import (
+    _compare_save_plans,
+    _contains_usable_plan,
+    _create_default_metadata_only_plan,
+    _create_read_items,
+    _create_write_items,
+    _init_state_dict,
+    _merge_delta_local_plans,
+)
+from torch.distributed.checkpoint.utils import find_state_dict_object
+from torch.distributed.tensor import DTensor
+
+from . import _version
+
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+
+__all__ = [
+    "DefaultSavePlanner",
+    "DefaultLoadPlanner",
+    "create_default_local_load_plan",
+    "create_default_global_load_plan",
+    "create_default_local_save_plan",
+    "create_default_global_save_plan",
+]
+
+
+# TODO: Update docstrings for default_planner.py
+class DefaultSavePlanner(SavePlanner):
+    mappings: FLATTEN_MAPPING
+
+    def __init__(
+        self,
+        flatten_state_dict: bool = True,
+        flatten_sharded_tensors: bool = True,
+        dedup_replicated_tensors: Optional[bool] = None,
+        dedup_save_to_lowest_rank: bool = False,
+        enable_plan_caching: bool = False,
+    ) -> None:
+        self.flatten_state_dict = flatten_state_dict
+        self.flatten_sharded_tensors = flatten_sharded_tensors
+        self.mappings = {}
+        self.dedup_save_to_lowest_rank = dedup_save_to_lowest_rank
+        if dedup_replicated_tensors is not None:
+            logger.warning(
+                "DefaultSavePlanner's `dedup_replicated_tensors` argument is being "
+                "deprecated, and no longer has any effect. Please remove this argument "
+                "from your call."
+            )
+        self._cached_plans_key: str = self.__class__.__name__
+        self._enable_plan_caching = enable_plan_caching
+
+    def set_up_planner(
+        self,
+        state_dict: STATE_DICT_TYPE,
+        storage_meta: Optional[StorageMeta] = None,
+        is_coordinator: bool = False,
+    ) -> None:
+        if self.flatten_state_dict:
+            state_dict, self.mappings = flatten_state_dict(state_dict)
+        if self.flatten_sharded_tensors:
+            state_dict = _flatten_sharded_tensors(state_dict)
+        self.state_dict = state_dict
+        self.is_coordinator = is_coordinator
+
+    def create_local_plan(self) -> SavePlan:
+        plan = create_default_local_save_plan(self.state_dict, self.is_coordinator)
+        if self.flatten_state_dict:
+            plan = dataclasses.replace(plan, planner_data=self.mappings)
+        self.plan = plan
+
+        if self._enable_plan_caching:
+            # If plans are equal, we can skip sending the plan to the coordinator.
+            if (
+                self._cached_plans_key in SavePlanner._cached_save_plan
+                and _compare_save_plans(
+                    plan, SavePlanner._cached_save_plan[self._cached_plans_key]
+                )
+            ):
+                logger.info(
+                    "No change in the local plan. Skipping sending the plan to the coordinator"
+                )
+                return SavePlan([], usable=False)
+            else:
+                SavePlanner._cached_save_plan[self._cached_plans_key] = plan
+
+        return self.plan
+
+    def _dedup_save_plans(self, all_plans: list[SavePlan]) -> list[SavePlan]:
+        return dedup_save_plans(all_plans, self.dedup_save_to_lowest_rank)
+
+    def _create_global_plan(
+        self, all_plans: list[SavePlan]
+    ) -> tuple[list[SavePlan], Metadata]:
+        deduped_plans = self._dedup_save_plans(all_plans)
+
+        global_plan, metadata = create_default_global_save_plan(deduped_plans)
+
+        if self.flatten_state_dict:
+            # | does not work for Python 3.8 or older version.
+            # merged_mappings = reduce(
+            #     lambda x, y: x | y, (p.planner_data for p in global_plan)
+            # )
+            planner_data_dict = [p.planner_data for p in global_plan]
+            merged_mappings = dict(ChainMap(*planner_data_dict))
+            metadata = dataclasses.replace(metadata, planner_data=merged_mappings)
+
+        if not _validate_global_plan(global_plan, metadata):
+            raise ValueError("Failed to validate global plan")
+
+        return global_plan, metadata
+
+    def _create_global_plan_with_caching(
+        self, all_plans: list[SavePlan]
+    ) -> tuple[list[SavePlan], list[SavePlan], Metadata]:
+        """
+        Create global plan with caching.
+        Returns a tuple of global_plan_delta, global_plan, metadata.
+        """
+        global_plan_delta: list[SavePlan] = []
+
+        if self._cached_plans_key not in SavePlanner._cached_all_plans:
+            # Case 1: If the plans are not cached, the cache will be hydrated with the
+            # all_plans, global_plans (Deduped), and metadata.
+
+            # Cache the original all_plans
+            SavePlanner._cached_all_plans[self._cached_plans_key] = all_plans
+            global_plan, metadata = self._create_global_plan(all_plans)
+            # Cache the deduped and validated global_plan
+            SavePlanner._cached_global_plan[self._cached_plans_key] = global_plan
+            # Cache the metadata
+            SavePlanner._cached_metadata[self._cached_plans_key] = metadata
+            # If plans are not cached, global_plan delta will be the same as global plan.
+            return global_plan, global_plan, metadata
+
+        # Case 2: Plans are cached
+        if not _contains_usable_plan(all_plans):
+            # Case 2.1: Plans are cached and the local plans have NOT changed (No usable plans).
+            # Global plan delta will be empty plans to avoid the collective overhead.
+            # We can reuse the deduped global plan and metadata from the cache directly.
+            global_plan_delta = [SavePlan([], usable=False)] * len(all_plans)
+            global_plan = SavePlanner._cached_global_plan[self._cached_plans_key]
+            metadata = SavePlanner._cached_metadata[self._cached_plans_key]
+        else:
+            # Case 2.2: Plans are cached but the local plans have changed.
+            # We will merge the changed local plans with the cached local plans.
+            # Updated plans will overwrite the cached plans. New global plan and metadata will be created and cached.
+            # Global plan delta will be created by comparing the new global plan with the cached global plan.
+            # Only the global plan delta (updated ones) will be sent to the coordinator to avoid the collective overhead.
+            merged_plans = _merge_delta_local_plans(
+                SavePlanner._cached_all_plans[self._cached_plans_key], all_plans
+            )
+            # Cache the updated local plans
+            SavePlanner._cached_all_plans[self._cached_plans_key] = merged_plans
+            global_plan, metadata = self._create_global_plan(merged_plans)
+
+            if self._cached_plans_key in self._cached_global_plan:
+                for cached_plan, new_plan in zip(
+                    SavePlanner._cached_global_plan[self._cached_plans_key], global_plan
+                ):
+                    if _compare_save_plans(cached_plan, new_plan):
+                        global_plan_delta.append(SavePlan([], usable=False))
+                    else:
+                        global_plan_delta.append(new_plan)
+
+            # Cache the new global plan and the metadata
+            SavePlanner._cached_global_plan[self._cached_plans_key] = global_plan
+            SavePlanner._cached_metadata[self._cached_plans_key] = metadata
+
+        return global_plan_delta, global_plan, metadata
+
+    def create_global_plan(
+        self, all_plans: list[SavePlan]
+    ) -> tuple[list[SavePlan], Metadata]:
+        global_plan_delta: list[SavePlan] = []
+        if self._enable_plan_caching:
+            # If the plans are cached, we only need to send the global plan delta to be scattered
+            # across ranks. Ranks will use the cached final plans instead.
+            (
+                global_plan_delta,
+                global_plan,
+                metadata,
+            ) = self._create_global_plan_with_caching(all_plans)
+        else:
+            global_plan, metadata = self._create_global_plan(all_plans)
+            # If the caching is not enabled, global delta plan will always be same as the new global plan.
+            global_plan_delta = global_plan
+
+        self.global_plan = global_plan
+        self.metadata = metadata
+
+        return global_plan_delta, self.metadata
+
+    def _finish_plan_with_caching(self, new_plan: SavePlan) -> SavePlan:
+        finished_plan: SavePlan = new_plan
+
+        if not new_plan.usable:
+            finished_plan = SavePlanner._cached_final_save_plan[self._cached_plans_key]
+        else:
+            finished_plan = new_plan
+            SavePlanner._cached_final_save_plan[self._cached_plans_key] = new_plan
+        return finished_plan
+
+    def finish_plan(self, new_plan: SavePlan) -> SavePlan:
+        finished_plan: SavePlan = new_plan
+
+        if self._enable_plan_caching:
+            finished_plan = self._finish_plan_with_caching(new_plan)
+
+        self.plan = finished_plan
+        return self.plan
+
+    def resolve_data(self, write_item: WriteItem) -> Union[torch.Tensor, io.BytesIO]:
+        object = self.lookup_object(write_item.index)
+        return self.transform_object(write_item, object)
+
+    def lookup_object(self, index: MetadataIndex) -> Any:
+        """Extension from the planner interface to make it easy to extend the default planner."""
+        return find_state_dict_object(self.state_dict, index)
+
+    def transform_object(self, write_item: WriteItem, object: Any):
+        """Extension from the planner interface to make it easy to extend the default planner."""
+        if write_item.type == WriteItemType.BYTE_IO:
+            bytes = io.BytesIO()
+            torch.save(object, bytes)
+            object = bytes
+        return object
+
+
+class DefaultLoadPlanner(LoadPlanner):
+    """
+    DefaultLoadPlanner that adds multiple features on top of LoadPlanner.
+
+    In particular it adds the following:
+
+    flatten_state_dict: Handle state_dict with nested dicts
+    flatten_sharded_tensors: For FSDP in 2D parallel mode
+    allow_partial_load: If False, will raise a runtime error if a key is present in state_dict, but not in the checkpoint.
+    """
+
+    original_state_dict: STATE_DICT_TYPE
+    mappings: FLATTEN_MAPPING
+
+    def __init__(
+        self,
+        flatten_state_dict: bool = True,
+        flatten_sharded_tensors: bool = True,
+        allow_partial_load: bool = False,
+    ) -> None:
+        self.flatten_state_dict = flatten_state_dict
+        self.flatten_sharded_tensors = flatten_sharded_tensors
+        self.original_state_dict = {}
+        self.mappings = {}
+        self.allow_partial_load = allow_partial_load
+
+    def set_up_planner(
+        self,
+        state_dict: STATE_DICT_TYPE,
+        metadata: Optional[Metadata] = None,
+        is_coordinator: bool = False,
+    ) -> None:
+        _init_state_dict(state_dict)
+        self.original_state_dict = state_dict
+
+        if self.flatten_sharded_tensors:
+            state_dict = _flatten_sharded_tensors(state_dict)
+
+        if self.flatten_state_dict:
+            state_dict, self.mappings = flatten_state_dict(state_dict)
+
+        self.state_dict = state_dict
+        self.metadata = metadata
+        self.is_coordinator = is_coordinator
+
+    def create_local_plan(self) -> LoadPlan:
+        if self.metadata is None:
+            raise AssertionError("self.metadata is not None")
+        if self.flatten_state_dict:
+            # To support checkpoints that are saved before v2.4, we have to
+            # differentiate if the missing keys are due to old checkpoints.
+            # The contracts are:
+            # 1. There are 3 cases when we found a missing key.
+            #    1.1 Actual missing key, but allow_partial_load is False
+            #    1.2 Actual missing key, but allow_partial load is True
+            #    1.3 Old checkpoint, but allow_partial_load is False
+            #    1.4 Old checkpoint, but allow_partial_load is True
+            # 2. If we found a missing key, we first convert the keys back to
+            #    the key format of v2.3
+            # 3. If the previous missing keys are in the v2.3 keys, we assume
+            #    this is a old checkpoint.
+            # 4. Pass the state_dict to `create_default_local_load_plan()`,
+            #    which has the logic to check missing for allow_partial_load.
+            # So for 1.2 and 1.4 cases, we delegate allow_partial_load check to
+            # `create_default_local_load_plan()`. The logic here is to determine
+            # whether the checkpoint belong to 2.3 (or before) or 2.4 (or after).
+            current_keys = set(self.state_dict.keys())
+            load_keys = set(self.metadata.state_dict_metadata.keys())
+            missing_keys = load_keys - current_keys
+            if missing_keys:
+                _version._derived_version = "2_3"
+                old_state_dict, old_mappings = flatten_state_dict(
+                    self.original_state_dict
+                )
+                old_keys = set(old_state_dict.keys())
+                if old_keys & missing_keys:
+                    self.state_dict, self.mappings = old_state_dict, old_mappings
+                # _derived_version is only used by flatten_state_dict now.
+                # Set it back to None so that later we can save to a new version.
+                _version._derived_version = None
+
+        return create_default_local_load_plan(
+            self.state_dict, self.metadata, not self.allow_partial_load
+        )
+
+    def create_global_plan(self, global_plan: list[LoadPlan]) -> list[LoadPlan]:
+        return create_default_global_load_plan(global_plan)
+
+    def finish_plan(self, new_plan: LoadPlan) -> LoadPlan:
+        return new_plan
+
+    def load_bytes(self, read_item: ReadItem, value: io.BytesIO) -> None:
+        if self.flatten_state_dict:
+            set_element(
+                self.original_state_dict,
+                self.mappings[read_item.dest_index.fqn],
+                torch.load(value, weights_only=False),
+            )
+        else:
+            self.state_dict[read_item.dest_index.fqn] = torch.load(
+                value, weights_only=False
+            )
+
+    def resolve_tensor(self, read_item: ReadItem):
+        tensor = self.lookup_tensor(read_item.dest_index)
+        return self.transform_tensor(read_item, tensor)
+
+    def commit_tensor(self, read_item: ReadItem, tensor: torch.Tensor) -> None:
+        pass
+
+    def lookup_tensor(self, index: MetadataIndex) -> torch.Tensor:
+        """Extension from the planner interface to make it easy to extend the default planner."""
+        return find_state_dict_object(self.state_dict, index)
+
+    def transform_tensor(self, read_item: ReadItem, tensor: torch.Tensor):
+        """Extension from the planner interface to make it easy to extend the default planner."""
+        return narrow_tensor_by_index(tensor, read_item.dest_offsets, read_item.lengths)
+
+
+class _EmptyStateDictLoadPlanner(DefaultLoadPlanner):
+    """
+    Extension of DefaultLoadPlanner, which rebuilds state_dict from the saved metadata.
+    Useful for loading in state_dict without first initializing a model, such as
+    when converting a DCP checkpoint into a Torch save file.
+
+    . N.B. `state_dict` must be an empty dictionary when used with this LoadPlanner
+
+    .. warning::
+        Because the entire state dict is initialized, It's recommended to only utilize
+        this LoadPlanner on a single rank or process to avoid OOM.
+
+    """
+
+    def __init__(self, keys=None, *args, **kwargs):
+        self.keys = keys
+        super().__init__(*args, **kwargs)
+
+    def _should_include_key(self, key: str, metadata: Metadata) -> bool:
+        if self.keys is None:
+            return True
+
+        if key in self.keys:
+            return True
+
+        unflattened_keys: list[str] = []
+        planner_data = metadata.planner_data.get(key)
+        for unflattened_key in planner_data:
+            if unflattened_keys:
+                unflattened_keys.append(
+                    ".".join([unflattened_keys[-1], str(unflattened_key)])
+                )
+
+            else:
+                unflattened_keys.append(unflattened_key)
+
+        if any(unflattened_key in self.keys for unflattened_key in unflattened_keys):
+            return True
+
+        return False
+
+    def set_up_planner(
+        self,
+        state_dict: STATE_DICT_TYPE,
+        metadata: Optional[Metadata] = None,
+        is_coordinator: bool = False,
+    ) -> None:
+        if state_dict:
+            raise AssertionError("not state_dict")
+        if metadata is None:
+            raise AssertionError("metadata is not None")
+
+        # rebuild the state dict from the metadata
+        for k, v in metadata.state_dict_metadata.items():
+            if not self._should_include_key(k, metadata):
+                continue
+
+            if isinstance(v, TensorStorageMetadata):
+                v = torch.empty(v.size, dtype=v.properties.dtype)  # type: ignore[assignment]
+            if metadata.planner_data is not None and k in metadata.planner_data:
+                set_element(state_dict, metadata.planner_data[k], v)
+            else:
+                state_dict[k] = v
+
+        super().set_up_planner(state_dict, metadata, is_coordinator)
+
+
+def create_default_local_load_plan(
+    state_dict: dict[str, Any], metadata: Metadata, strict: bool = True
+) -> LoadPlan:
+    requests = []
+    """
+    Create the ``LoadPlan`` used by DefaultLoadPlanner.
+
+    It produces one read item per value in ``state_dict`` using the metadata in ``metadata``.
+
+    The default behavior is to match key exactly between state_dict and metadata.
+    It handles resharding by issuing multiple read requests against storage in order to match
+    load requirements.
+    """
+
+    for fqn, obj in state_dict.items():
+        # ignore state_dict keys which do not exist in `state_dict` if strict=False
+        if fqn not in metadata.state_dict_metadata:
+            if strict:
+                raise RuntimeError(f"Missing key in checkpoint state_dict: {fqn}.")
+            else:
+                continue
+
+        md = metadata.state_dict_metadata[fqn]
+        if (
+            isinstance(md, TensorStorageMetadata)
+            and getattr(obj, "size", None) is not None
+            and md.size != obj.size()
+        ):
+            raise ValueError(
+                f"Size mismatch between saved {md.size} and current: {obj.size()} for {fqn}",
+            )
+        # Since DTensor supports submesh, adding extra check to ensure _create_read_items()
+        # gets called only when the current rank is part of the mesh for the corresponding DTensor.
+        if isinstance(obj, DTensor):
+            if obj.device_mesh.get_coordinate() is not None:
+                requests += _create_read_items(fqn, md, obj)
+        else:
+            requests += _create_read_items(fqn, md, obj)
+
+    return LoadPlan(requests)
+
+
+def create_default_global_load_plan(
+    all_plans: list[LoadPlan],
+) -> list[LoadPlan]:
+    """
+    Create global load plan used by DefaultLoadPlanner.
+
+    The default load behavior involved no global coordination and this function
+    currently doesn't change the local plans.
+    """
+    return all_plans
+
+
+def create_default_local_save_plan(
+    state_dict: dict[str, Any], is_coordinator: bool
+) -> SavePlan:
+    """
+    Create the ``SavePlan`` used by DefaultSavePlanner.
+
+    On non-coordinator ranks, this function ignores tensors and non-tensor objects,
+    only producing writes for ShardedTensor objects.
+
+    On the coordinator rank, produce writes for all values.
+    """
+    requests = []
+    for fqn, obj in state_dict.items():
+        # Since DTensor supports submesh, adding extra check to ensure _create_write_items()
+        # gets called only when the current rank is part of the mesh for the corresponding DTensor.
+        if isinstance(obj, DTensor):
+            if obj.device_mesh.get_coordinate() is not None:
+                requests += _create_write_items(fqn, obj)
+        else:
+            # For the plain tensor and non-tensor values, add the request for all
+            # the ranks. Coordinator will decides whether to deduplicate the
+            # values based on the keys.
+            requests += _create_write_items(fqn, obj)
+
+    return SavePlan(requests)
+
+
+def create_default_global_save_plan(
+    all_plans: list[SavePlan],
+    rewrite_index_hints: bool = True,
+) -> tuple[list[SavePlan], Metadata]:
+    """
+    Create the global plan and metadata used by DefaultSavePlanner.
+
+    Metadata is produced by concatenating the metadata of all ``WriteItem`` from the supplied plans.
+
+    The only global planning change is to update index hints in all ``MetadataIndex`` objects if
+    ``rewrite_index_hints`` is True.
+    """
+    md: dict[str, STORAGE_TYPES] = {}
+    new_plans = []
+    for plan in all_plans:
+        new_items = []
+        for item in plan.items:
+            if item.type != WriteItemType.SHARD:
+                if item.index.fqn in md:
+                    raise AssertionError("item.index.fqn not in md")
+
+            if item.type == WriteItemType.BYTE_IO:
+                md[item.index.fqn] = BytesStorageMetadata()
+                new_items.append(item)
+            else:
+                if item.tensor_data is None:
+                    raise AssertionError("item.tensor_data is not None")
+                tensor_md = cast(
+                    TensorStorageMetadata,
+                    md.setdefault(
+                        item.index.fqn,
+                        TensorStorageMetadata(
+                            properties=item.tensor_data.properties,
+                            size=item.tensor_data.size,
+                            chunks=[],
+                        ),
+                    ),
+                )
+                new_item = item
+                if rewrite_index_hints:
+                    new_index = dataclasses.replace(
+                        item.index, index=len(tensor_md.chunks)
+                    )
+                    new_item = dataclasses.replace(item, index=new_index)
+                new_items.append(new_item)
+
+                if item.tensor_data.chunk is None:
+                    raise AssertionError(f"""
+                    Cannot create MD for tensor without bounds.
+                    FQN: {item.index.fqn}
+                """)
+                tensor_md.chunks.append(item.tensor_data.chunk)
+        new_plans.append(dataclasses.replace(plan, items=new_items))
+    return (new_plans, Metadata(md))
+
+
+def _create_default_local_metadata(state_dict: STATE_DICT_TYPE) -> Metadata:
+    """Return the ``Metadata`` if DefaultSavePlanner was used to checkpoint ``state_dict``."""
+    plan = _create_default_metadata_only_plan(state_dict)
+    _, md = create_default_global_save_plan([plan])
+    return md
+
+
+def _check_box_overlap(box0: ChunkStorageMetadata, box1: ChunkStorageMetadata) -> bool:
+    """Check if two boxes overlap. Tuples are (offset, lengths)."""
+    # For each dim of each shard, check if one shard resides on the other
+    # end of second shard with respect to that dim. As an example for a 2D
+    # shard, we would check if one shard is above or on the left of the
+    # other shard.
+    ndims = len(box0.offsets)
+    for i in range(ndims):
+        if box0.offsets[i] >= box1.offsets[i] + box1.sizes[i]:
+            return False
+        if box1.offsets[i] >= box0.offsets[i] + box0.sizes[i]:
+            return False
+
+    return True
+
+
+def _check_box_bounds(
+    outer_box_size: torch.Size, inner_box: ChunkStorageMetadata
+) -> bool:
+    for i in range(len(outer_box_size)):
+        if inner_box.offsets[i] < 0:
+            return False
+        if inner_box.sizes[i] < 0:
+            return False
+        if inner_box.offsets[i] + inner_box.sizes[i] > outer_box_size[i]:
+            return False
+
+    return True
+
+
+def _validate_global_plan(global_plan: list[SavePlan], metadata: Metadata) -> bool:
+    all_good = True
+    for key, value in metadata.state_dict_metadata.items():
+        if isinstance(value, BytesStorageMetadata):
+            continue
+        if len(value.size) == 0:
+            continue
+        chunks = value.chunks
+        chunks_volume = 0
+        for chunk in chunks:
+            # Compute the volume
+            if not _check_box_bounds(value.size, chunk):
+                logger.warning(
+                    """
+                        key:%s has out of bounds chunk:
+                        tensor-size:%s chunk: %s
+                    """,
+                    key,
+                    value.size,
+                    chunk,
+                )
+                all_good = False
+            chunks_volume += math.prod(chunk.sizes)
+
+        if len(chunks) > 1:
+            dims = len(value.size)
+            sweep_dim = max(range(dims), default=0, key=lambda d: value.size[d])
+            sorted_indices = sorted(
+                range(len(chunks)),
+                key=lambda idx: (
+                    chunks[idx].offsets[sweep_dim],
+                    *(chunks[idx].offsets[d] for d in range(dims)),
+                ),
+            )
+            active: list[tuple[int, int]] = []
+            for idx in sorted_indices:
+                current = chunks[idx]
+                start = current.offsets[sweep_dim]
+                end = start + current.sizes[sweep_dim]
+
+                cutoff = bisect_right(active, (start, sys.maxsize))
+                if cutoff:
+                    del active[:cutoff]
+
+                for _, other_idx in active:
+                    other = chunks[other_idx]
+                    if _check_box_overlap(current, other):
+                        logger.warning(
+                            "key:%s has overlapping chunks: %s %s",
+                            key,
+                            current,
+                            other,
+                        )
+                        all_good = False
+
+                insort(active, (end, idx))
+
+        # Check whether combined chunk cover the whole tensor
+        tensor_volume = math.prod(value.size)
+        if len(global_plan) > 1 and chunks_volume != tensor_volume:
+            logger.warning(
+                """
+                    key:%s invalid fill tensor-volume:
+                    %s chunks-volume: %s
+                """,
+                key,
+                tensor_volume,
+                chunks_volume,
+            )
+            all_good = False
+
+    return all_good
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/filesystem.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/filesystem.py
new file mode 100644
index 0000000000000000000000000000000000000000..b21cac12ff90522f075b7b32029eae01e7a92169
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/filesystem.py
@@ -0,0 +1,1035 @@
+# mypy: allow-untyped-defs
+import collections
+import dataclasses
+import io
+import json
+import operator
+import os
+import pickle
+import queue
+import threading
+import uuid
+import warnings
+from abc import ABC, abstractmethod
+from collections.abc import Callable, Generator, Iterable, Iterator, Sequence
+from contextlib import contextmanager
+from dataclasses import dataclass
+from enum import Enum
+from io import UnsupportedOperation
+from pathlib import Path
+from typing import Any, cast, Final, IO, Optional, Union
+
+# introduced as collections.abc.Buffer in Python 3.12
+from typing_extensions import Buffer
+
+import torch
+from torch import Tensor
+from torch._utils import _get_available_device_type, _get_device_module
+from torch.distributed._shard._utils import narrow_tensor_by_index
+from torch.distributed.checkpoint._extension import (
+    ExtensionRegistry,
+    StreamTransformExtension,
+)
+from torch.distributed.checkpoint._hf_utils import (
+    CUSTOM_METADATA_KEY,
+    DCP_VERSION_KEY,
+    FORMAT_KEY,
+    FORMAT_VALUE,
+    HF_DCP_VERSION,
+)
+from torch.distributed.checkpoint.metadata import Metadata, STATE_DICT_TYPE, StorageMeta
+from torch.distributed.checkpoint.planner import (
+    LoadItemType,
+    LoadPlan,
+    LoadPlanner,
+    ReadItem,
+    SavePlan,
+    SavePlanner,
+    WriteItem,
+    WriteItemType,
+)
+from torch.distributed.checkpoint.staging import BlockingAsyncStager
+from torch.distributed.checkpoint.storage import (
+    StorageReader,
+    StorageWriter,
+    WriteResult,
+)
+from torch.distributed.checkpoint.utils import _create_file_view
+from torch.futures import Future
+
+
+__all__ = [
+    "FileSystemWriter",
+    "FileSystemReader",
+    "FileSystem",
+    "FileSystemBase",
+    "SerializationFormat",
+]
+
+_metadata_fn: str = ".metadata"
+
+CURRENT_DCP_VERSION: Final[str] = "1.0.0"
+
+
+@dataclass
+class _StorageInfo:
+    """This is the per entry storage info."""
+
+    relative_path: str
+    offset: int
+    length: int
+    transform_descriptors: Optional[Sequence[str]] = None
+
+    def __getstate__(self):
+        return {k: v for k, v in self.__dict__.items() if v is not None}
+
+
+@dataclass
+class _StoragePrefix:
+    prefix: str
+
+
+class SerializationFormat(Enum):
+    TORCH_SAVE = "torch_save"
+    SAFETENSORS = "safetensors"
+
+
+DEFAULT_SUFFIX = ".distcp"
+
+
+def _generate_uuid() -> str:
+    return str(uuid.uuid4())
+
+
+class _TensorLoader(ABC):
+    @abstractmethod
+    def add(self, size: int, obj: object) -> None:
+        pass
+
+    @abstractmethod
+    def start_loading(self) -> None:
+        pass
+
+    @abstractmethod
+    def values(self) -> Iterator[tuple[torch.Tensor, object]]:
+        pass
+
+
+class _SerialCpuLoader(_TensorLoader):
+    def __init__(self, resolve_fun: Callable) -> None:
+        self.resolve_fun = resolve_fun
+        self.items: list[tuple[int, object]] = []
+
+    def add(self, size: int, obj: object) -> None:
+        self.items.append((size, obj))
+
+    def start_loading(self) -> None:
+        pass
+
+    def values(self) -> Iterator[tuple[torch.Tensor, object]]:
+        for _, obj in self.items:
+            tensor = self.resolve_fun(obj).detach()
+            tensor = tensor.cpu()
+            if tensor.storage().size() != tensor.numel():
+                tensor = tensor.clone()
+            yield (
+                tensor,
+                obj,
+            )
+
+
+class _OverlappingCpuLoader(_TensorLoader):
+    def __init__(
+        self,
+        resolve_fun: Callable,
+        stream: Optional[torch.Stream] = None,
+        inflight_threshhold: int = 1_000_000,
+    ) -> None:
+        self.resolve_fun = resolve_fun
+        self.items: list[tuple[int, object]] = []
+        self.inflight_threshhold = inflight_threshhold
+        self.in_flight_data = 0
+        self.current_items: collections.deque = collections.deque()
+        self.idx = 0
+        self.started = False
+        self.device_type = (
+            stream.device_type if stream else _get_available_device_type()
+        )
+        self.device_module = _get_device_module(self.device_type)
+        self.stream = cast(
+            torch.cuda.Stream, stream or self.device_module.current_stream()
+        )
+        if self.stream != self.device_module.current_stream():
+            self.stream.wait_stream(self.device_module.current_stream())
+
+    @property
+    def _done(self) -> bool:
+        return self.idx >= len(self.items)
+
+    def _drain(self) -> list[tuple[torch.Tensor, object]]:
+        drained = []
+        if self.in_flight_data >= self.inflight_threshhold:
+            self.stream.synchronize()
+        while self.in_flight_data >= self.inflight_threshhold:
+            val = self.current_items.popleft()
+            self.in_flight_data -= val[0].numel() * val[0].element_size()
+            drained.append(val)
+        return drained
+
+    def _refill(self) -> None:
+        with self.device_module.stream(self.stream):
+            while not self._done and self.in_flight_data < self.inflight_threshhold:
+                _, obj = self.items[self.idx]
+                self.idx += 1
+                tensor = self.resolve_fun(obj).detach()
+                if tensor.device.type == self.device_type:
+                    tensor = tensor.to(device="cpu", non_blocking=True)
+                elif tensor.device == torch.device("cpu"):
+                    if (
+                        tensor.untyped_storage().size()
+                        != tensor.numel() * tensor.itemsize
+                    ):
+                        # this forces the tensor to be both contiguous and with minimal storage
+                        tensor = tensor.clone()
+
+                self.current_items.append(
+                    (
+                        tensor,
+                        obj,
+                    )
+                )
+                self.in_flight_data += tensor.numel() * tensor.element_size()
+
+    def _finish(self) -> Iterable[tuple[torch.Tensor, object]]:
+        if not self._done:
+            raise AssertionError("_finish called before all items were processed")
+        if len(self.current_items) > 0:
+            self.stream.synchronize()
+        return self.current_items
+
+    def add(self, size: int, obj: object) -> None:
+        if self.started:
+            raise RuntimeError("cannot add items after loading started")
+        self.items.append((size, obj))
+
+    def start_loading(self) -> None:
+        if self.started:
+            return
+        self.started = True
+        self.items.sort(key=operator.itemgetter(0))
+        self._refill()
+
+    def values(self) -> Iterator[tuple[torch.Tensor, object]]:
+        self.start_loading()
+        while not self._done:
+            drained = self._drain()
+            self._refill()
+            yield from drained
+
+        yield from self._finish()
+
+
+class _StorageWriterTransforms:
+    """
+    This is experimental, and will likely move elsewhere in the
+    future.  It lives here to minimize changes while we are still
+    learning and gathering feedback.
+    """
+
+    def __init__(
+        self, extensions: Optional[Sequence[StreamTransformExtension]] = None
+    ) -> None:
+        """
+        If the extensions arg is None, this means the implementation
+        should provide whatever defaults it chooses.  An empty
+        sequence indicates no extensions should be used.  At this
+        time, the default extensions sequence is empty.
+        """
+        self.extensions = () if extensions is None else extensions
+
+    def transform_save_stream(
+        self, write_item: WriteItem, raw_stream: io.IOBase
+    ) -> tuple[IO[bytes], list[str]]:
+        # In order to avoid leaking fds, transformers' close must
+        # cascade to wrapped streams, but since this function can
+        # append to the raw stream, we can't close the actual stream.
+        # So, we use this to put a wrapper around the raw stream's
+        # close() to make it a noop, and it gets closed once all files
+        # are appended.
+
+        class NoCloseWriter(io.IOBase):
+            def __init__(self, raw: io.IOBase):
+                self.raw = raw
+
+            def writeable(self) -> bool:
+                return True
+
+            def write(self, b: Buffer) -> int:
+                return self.raw.write(b)
+
+            def close(self):
+                self.flush()
+                self.raw.flush()
+                # but not close.
+
+        transform_to = cast(IO[bytes], NoCloseWriter(raw_stream))
+
+        for ex in self.extensions:
+            transform_to = ex.transform_to(transform_to)
+
+        return (transform_to, [ex.get_descriptor() for ex in reversed(self.extensions)])
+
+
+def _item_size(item: WriteItem) -> int:
+    size = 1
+    if item.tensor_data is None:
+        raise AssertionError("WriteItem tensor_data must not be None")
+    # can't use math.prod as PT needs to support older python
+    for s in item.tensor_data.size:
+        size *= s
+
+    dtype = item.tensor_data.properties.dtype
+    return size * torch._utils._element_size(dtype)
+
+
+def _split_by_size_and_type(bins: int, items: list[WriteItem]) -> list[list[WriteItem]]:
+    if bins == 1:
+        return [items]
+
+    bytes_w = [wi for wi in items if wi.type == WriteItemType.BYTE_IO]
+    tensor_w = [wi for wi in items if wi.type != WriteItemType.BYTE_IO]
+
+    buckets: list[list[WriteItem]] = [[] for _ in range(bins)]
+    bucket_sizes = [0 for _ in range(bins)]
+
+    tensor_w.sort(key=_item_size, reverse=True)
+
+    for i, wi in enumerate(bytes_w):
+        buckets[i % bins].append(wi)
+
+    for wi in tensor_w:
+        # TODO replace with headq
+        idx = min(enumerate(bucket_sizes), key=operator.itemgetter(1))[0]
+        buckets[idx].append(wi)
+        bucket_sizes[idx] += _item_size(wi)
+
+    return buckets
+
+
+def _write_item(
+    transforms: _StorageWriterTransforms,
+    stream: io.IOBase,
+    data: Union[io.BytesIO, torch.Tensor],
+    write_item: WriteItem,
+    storage_key: str,
+    serialization_format: SerializationFormat,
+) -> WriteResult:
+    offset = stream.tell()
+
+    (transform_to, transform_descriptors) = transforms.transform_save_stream(
+        write_item, stream
+    )
+
+    if write_item.type == WriteItemType.BYTE_IO:
+        if not isinstance(data, io.BytesIO):
+            raise AssertionError("Data must be io.BytesIO for BYTE_IO write items")
+        transform_to.write(data.getbuffer())
+    else:
+        if not isinstance(data, torch.Tensor):
+            raise AssertionError(
+                "Data must be torch.Tensor for non-BYTE_IO write items"
+            )
+        if data.device != torch.device("cpu"):
+            raise AssertionError("Tensor must be on CPU device")
+        if serialization_format == SerializationFormat.TORCH_SAVE:
+            torch.save(data, transform_to)
+
+    transform_to.close()
+
+    if serialization_format == SerializationFormat.TORCH_SAVE or isinstance(
+        data, io.BytesIO
+    ):
+        length = stream.tell() - offset
+    else:
+        length = data.numel() * data.element_size()
+
+    # For consistency with earlier versions, leave this field out of the
+    # metadata if there are no extensions.
+    info_transform_descriptors = (
+        None if len(transform_descriptors) == 0 else transform_descriptors
+    )
+
+    return WriteResult(
+        index=write_item.index,
+        size_in_bytes=length,
+        storage_data=_StorageInfo(
+            storage_key,
+            offset,
+            length,
+            transform_descriptors=info_transform_descriptors,
+        ),
+    )
+
+
+def _write_files_from_queue(
+    create_stream: Callable,
+    file_queue: queue.Queue,
+    result_queue: queue.Queue,
+    planner: SavePlanner,
+    transforms: _StorageWriterTransforms,
+    inflight_threshhold: int,
+    use_fsync: bool,
+    thread_count: int,
+    serialization_format: SerializationFormat,
+) -> None:
+    try:
+        while True:
+            file_name, storage_key, write_items = file_queue.get_nowait()
+            loader: _TensorLoader
+
+            custom_backend_name = torch._C._get_privateuse1_backend_name()
+            custom_device_mod = getattr(torch, custom_backend_name, None)
+
+            # TODO: Using the OverlappingCpuLoader with multiple threads creates significant
+            # performance degradation, observed as being related to cuda stream syncs. We
+            # should try to fix this and use _OverlappingCpuLoader for all threaded cases
+            if (
+                thread_count == 1
+                and (
+                    torch.cuda.is_available()
+                    or (custom_device_mod and custom_device_mod.is_available())
+                )
+                and inflight_threshhold > 0
+            ):
+                loader = _OverlappingCpuLoader(
+                    planner.resolve_data,
+                    inflight_threshhold=inflight_threshhold,
+                )
+            else:
+                loader = _SerialCpuLoader(
+                    planner.resolve_data,
+                )
+
+            tensor_w = [wi for wi in write_items if wi.type != WriteItemType.BYTE_IO]
+            for write_item in tensor_w:
+                loader.add(_item_size(write_item), write_item)
+            loader.start_loading()
+
+            bytes_w = [wi for wi in write_items if wi.type == WriteItemType.BYTE_IO]
+            write_results = []
+
+            with create_stream(file_name, "wb") as stream:
+                for write_item in bytes_w:
+                    data = planner.resolve_data(write_item)
+                    write_results.append(
+                        _write_item(
+                            transforms,
+                            stream,
+                            data,
+                            write_item,
+                            storage_key,
+                            serialization_format,
+                        )
+                    )
+
+                tensor_dict = {}
+                metadata_dict = {}
+                for tensor, write_item in loader.values():
+                    if not tensor.is_cpu:
+                        raise AssertionError("Tensor must be on CPU")
+                    write_results.append(
+                        _write_item(
+                            transforms,
+                            stream,
+                            tensor,
+                            write_item,  # type: ignore[arg-type]
+                            storage_key,
+                            serialization_format,
+                        )
+                    )
+                    tensor_dict[write_item.index.fqn] = tensor  # type: ignore[attr-defined]
+                    metadata_dict[write_item.index.fqn] = {  # type: ignore[attr-defined]
+                        "saved_offsets": write_item.tensor_data.chunk.offsets  # type: ignore[attr-defined]
+                    }
+
+                if serialization_format == SerializationFormat.SAFETENSORS:
+                    from safetensors.torch import save  # type: ignore[import-not-found]
+
+                    stream.write(
+                        save(
+                            tensor_dict,
+                            metadata={
+                                CUSTOM_METADATA_KEY: json.dumps(metadata_dict),
+                                DCP_VERSION_KEY: str(HF_DCP_VERSION),
+                                FORMAT_KEY: FORMAT_VALUE,
+                            },
+                        )
+                    )
+
+                if use_fsync:
+                    try:
+                        os.fsync(stream.fileno())
+                    except (AttributeError, UnsupportedOperation):
+                        os.sync()
+                stream.close()
+            result_queue.put(write_results)
+    except queue.Empty:
+        pass
+
+
+class FileSystemBase(ABC):
+    @contextmanager
+    @abstractmethod
+    def create_stream(
+        self, path: Union[str, os.PathLike], mode: str
+    ) -> Generator[io.IOBase, None, None]: ...
+
+    @abstractmethod
+    def concat_path(
+        self, path: Union[str, os.PathLike], suffix: str
+    ) -> Union[str, os.PathLike]: ...
+
+    @abstractmethod
+    def rename(
+        self, path: Union[str, os.PathLike], new_path: Union[str, os.PathLike]
+    ) -> None: ...
+
+    @abstractmethod
+    def init_path(self, path: Union[str, os.PathLike]) -> Union[str, os.PathLike]: ...
+
+    @abstractmethod
+    def mkdir(self, path: Union[str, os.PathLike]) -> None: ...
+
+    @classmethod
+    @abstractmethod
+    def validate_checkpoint_id(cls, checkpoint_id: Union[str, os.PathLike]) -> bool: ...
+
+    @abstractmethod
+    def exists(self, path: Union[str, os.PathLike]) -> bool: ...
+
+    @abstractmethod
+    def rm_file(self, path: Union[str, os.PathLike]) -> None: ...
+
+
+class FileSystem(FileSystemBase):
+    @contextmanager
+    def create_stream(
+        self, path: Union[str, os.PathLike], mode: str
+    ) -> Generator[io.IOBase, None, None]:
+        if not isinstance(path, Path):
+            path = Path(path)
+        with path.open(mode) as stream:
+            yield cast(io.IOBase, stream)
+
+    def concat_path(
+        self, path: Union[str, os.PathLike], suffix: str
+    ) -> Union[str, os.PathLike]:
+        if not isinstance(path, Path):
+            path = Path(path)
+        return path / suffix
+
+    def init_path(self, path: Union[str, os.PathLike]) -> Union[str, os.PathLike]:
+        if not isinstance(path, Path):
+            path = Path(path)
+        return path
+
+    def rename(
+        self, path: Union[str, os.PathLike], new_path: Union[str, os.PathLike]
+    ) -> None:
+        if not isinstance(path, Path):
+            path = Path(path)
+
+        path.rename(cast(Path, new_path))
+
+    def mkdir(self, path: Union[str, os.PathLike]) -> None:
+        if not isinstance(path, Path):
+            path = Path(path)
+        path.mkdir(parents=True, exist_ok=True)
+
+    @classmethod
+    def validate_checkpoint_id(cls, checkpoint_id: Union[str, os.PathLike]) -> bool:
+        if isinstance(checkpoint_id, Path):
+            return True
+
+        if "://" in str(checkpoint_id):
+            return False
+
+        for p in Path(checkpoint_id).parents:
+            if p.exists() and os.access(str(p), os.W_OK):
+                return True
+
+        return False
+
+    def exists(self, path: Union[str, os.PathLike]) -> bool:
+        if not isinstance(path, Path):
+            path = Path(path)
+        return path.exists()
+
+    def rm_file(self, path: Union[str, os.PathLike]) -> None:
+        if not isinstance(path, Path):
+            path = Path(path)
+        path.unlink()
+
+    def ls(self, path: Union[str, os.PathLike]) -> list[str]:
+        if not isinstance(path, Path):
+            path = Path(path)
+        return [str(p) for p in path.iterdir()]
+
+
+class _FileSystemWriter(StorageWriter):
+    """
+    Basic implementation of StorageWriter using file IO.
+
+    This implementation makes the following assumptions and simplifications:
+
+    * The checkpoint path is an empty or non-existing directory.
+    * File creation is atomic
+
+    The checkpoint consist of one file per write request plus
+    a `.metadata` file with the serialized metadata.
+
+    """
+
+    def __init__(
+        self,
+        path: Union[str, os.PathLike],
+        single_file_per_rank: bool = True,
+        sync_files: bool = True,
+        thread_count: int = 1,
+        per_thread_copy_ahead: int = 10_000_000,
+        overwrite: bool = True,
+        _extensions: Optional[Sequence[StreamTransformExtension]] = None,
+        serialization_format: SerializationFormat = SerializationFormat.TORCH_SAVE,
+        *args: Any,
+        **kwargs: Any,
+    ) -> None:
+        """
+        Initialize the writer pointing to `path`.
+
+        Args:
+            path: directory where the checkpoint will be written to.
+            single_file_per_rank: Produce one file per rank instead of one file per tensor/blob. Default to True.
+            sync_files : force files to be synced to permanent storage. Default to True.
+            thread_count: Number of IO threads to use to write. Default to 1.
+            per_thread_copy_ahead: How many bytes to copy from the GPU ahead of saving then. Default 10Mb.
+            overwrite: Whether to allow overwriting existing checkpoints. Defaults to True.
+            _extensions: Extensions to apply to output streams (EXPERIMENTAL)
+
+        N. B. If sync_files is disabled, there's no guarantee that the checkpoint will be consistent in the case of a failure.
+        """
+        super().__init__()
+        self.fs = FileSystem()
+        self.path = self.fs.init_path(path)
+        self.single_file_per_rank = single_file_per_rank
+        self.sync_files = sync_files
+        self.thread_count = thread_count
+        self.per_thread_copy_ahead = per_thread_copy_ahead
+        self.save_id = _generate_uuid()
+        self.overwrite = overwrite
+        self.transforms = _StorageWriterTransforms(_extensions)
+        self.serialization_format = serialization_format
+        self.rank: Optional[int] = None
+        self.use_collectives: bool = True
+
+    def reset(self, checkpoint_id: Union[str, os.PathLike, None] = None) -> None:
+        if checkpoint_id:
+            self.path = self.fs.init_path(checkpoint_id)
+        self.save_id = _generate_uuid()
+
+    def set_up_storage_writer(
+        self, is_coordinator: bool, *args: Any, **kwargs: Any
+    ) -> None:
+        self.rank = kwargs.get("rank")
+        self.use_collectives = kwargs.get("use_collectives", True)
+
+    def _metadata_exists(self) -> bool:
+        if self.use_collectives:
+            # A global checkpoint metadata file
+            metadata_path = self._get_metadata_path(rank=None)
+        else:
+            # A rank 0 specific metadata file if every rank has written its own metadata
+            # Just looking for lowest rank metadata file is sufficient
+            metadata_path = self._get_metadata_path(rank=0)
+
+        return self.fs.exists(metadata_path)
+
+    def prepare_local_plan(self, plan: SavePlan) -> SavePlan:
+        self.fs.mkdir(self.path)
+        if self._metadata_exists():
+            if self.overwrite:
+                warnings.warn(
+                    f"Detected an existing checkpoint in {self.path}, overwriting since {self.overwrite=}."
+                    " Past version 2.5 of PyTorch, `overwrite` will default to False. Set this variable to True to"
+                    " maintain this functionality or False to raise when an existing checkpoint is found.",
+                    stacklevel=2,
+                )
+            else:
+                raise RuntimeError(f"Checkpoint already exists and {self.overwrite=}.")
+
+        if self.rank is not None and not self.use_collectives:
+            plan = dataclasses.replace(
+                plan, storage_data=_StoragePrefix(f"__{self.rank}_")
+            )
+
+        return plan
+
+    def prepare_global_plan(self, plans: list[SavePlan]) -> list[SavePlan]:
+        new_plans = [
+            dataclasses.replace(plan, storage_data=_StoragePrefix(f"__{i}_"))
+            if plan.storage_data is None
+            else plan
+            for i, plan in enumerate(plans)
+        ]
+        return new_plans
+
+    def write_data(
+        self,
+        plan: SavePlan,
+        planner: SavePlanner,
+    ) -> Future[list[WriteResult]]:
+        storage_plan: _StoragePrefix = plan.storage_data
+        file_count = 0
+
+        def gen_file():
+            nonlocal file_count
+            file_name = f"{storage_plan.prefix}{file_count}{DEFAULT_SUFFIX}"
+            file_count += 1
+            return file_name
+
+        file_queue: queue.Queue = queue.Queue()
+        if self.single_file_per_rank:
+            for bucket in _split_by_size_and_type(self.thread_count, plan.items):
+                file_name = gen_file()
+                path = self.fs.concat_path(self.path, file_name)
+                file_queue.put((path, file_name, bucket))
+        else:
+            for item in plan.items:
+                file_name = gen_file()
+                path = self.fs.concat_path(self.path, file_name)
+                file_queue.put((path, file_name, [item]))
+
+        return self._write_data(planner, file_queue)
+
+    def _write_data(
+        self,
+        planner: SavePlanner,
+        file_queue: queue.Queue,
+    ) -> Future[list[WriteResult]]:
+        result_queue: queue.Queue = queue.Queue()
+
+        threads = []
+        for _ in range(1, self.thread_count):
+            t = threading.Thread(
+                target=_write_files_from_queue,
+                args=(
+                    self.fs.create_stream,
+                    file_queue,
+                    result_queue,
+                    planner,
+                    self.transforms,
+                    self.per_thread_copy_ahead,
+                    self.sync_files,
+                    self.thread_count,
+                    self.serialization_format,
+                ),
+            )
+            t.start()
+            threads.append(t)
+
+        _write_files_from_queue(
+            create_stream=self.fs.create_stream,
+            file_queue=file_queue,
+            result_queue=result_queue,
+            planner=planner,
+            transforms=self.transforms,
+            inflight_threshhold=self.per_thread_copy_ahead,
+            use_fsync=self.sync_files,
+            thread_count=self.thread_count,
+            serialization_format=self.serialization_format,
+        )
+
+        for t in threads:
+            t.join()
+
+        res = []
+        try:
+            while True:
+                res += result_queue.get_nowait()
+        except queue.Empty:
+            fut: Future[list[WriteResult]] = Future()
+            fut.set_result(res)
+            return fut
+
+    def finish(self, metadata: Metadata, results: list[list[WriteResult]]) -> None:
+        metadata = dataclasses.replace(metadata, version=CURRENT_DCP_VERSION)
+
+        storage_md = {}
+        for wr_list in results:
+            storage_md.update({wr.index: wr.storage_data for wr in wr_list})
+        metadata.storage_data = storage_md
+
+        metadata.storage_meta = self.storage_meta()
+        tmp_filename = (
+            f"__{self.rank}{_metadata_fn}.tmp"
+            if not self.use_collectives and self.rank is not None
+            else f"{_metadata_fn}.tmp"
+        )
+        tmp_path = cast(Path, self.fs.concat_path(self.path, tmp_filename))
+        with self.fs.create_stream(tmp_path, "wb") as metadata_file:
+            pickle.dump(metadata, metadata_file)
+            if self.sync_files:
+                try:
+                    os.fsync(metadata_file.fileno())
+                except (AttributeError, UnsupportedOperation):
+                    os.sync()
+
+        # delete in-case other checkpoints were present.
+        if not self.use_collectives and self.rank is not None:
+            metadata_path = self._get_metadata_path(self.rank)
+        else:
+            metadata_path = self._get_metadata_path()
+
+        if self.fs.exists(metadata_path):
+            self.fs.rm_file(metadata_path)
+
+        self.fs.rename(tmp_path, metadata_path)
+
+    def storage_meta(self) -> Optional[StorageMeta]:
+        return StorageMeta(checkpoint_id=self.checkpoint_id, save_id=self.save_id)
+
+    def _get_metadata_path(self, rank: Optional[int] = None) -> os.PathLike:
+        filename = f"{_metadata_fn}" if rank is None else f"__{rank}{_metadata_fn}"
+        return cast(Path, self.fs.concat_path(self.path, filename))
+
+    @property
+    def checkpoint_id(self) -> Union[str, os.PathLike]:
+        """
+        return the checkpoint_id that will be used to save the checkpoint.
+        """
+        return self.path
+
+    @classmethod
+    def validate_checkpoint_id(cls, checkpoint_id: Union[str, os.PathLike]) -> bool:
+        return FileSystem.validate_checkpoint_id(checkpoint_id)
+
+
+class _StorageReaderTransforms:
+    """
+    This is experimental, and will likely move elsewhere in the
+    future.  It lives here to minimize changes while we are still
+    learning and gathering feedback.
+    """
+
+    def __init__(self, extension_registry: Optional[ExtensionRegistry] = None) -> None:
+        self.extension_registry = (
+            ExtensionRegistry() if extension_registry is None else extension_registry
+        )
+
+    def transform_load_stream(
+        self,
+        read_item: ReadItem,
+        transform_descriptors: Sequence[str],
+        raw_stream: IO[bytes],
+    ) -> IO[bytes]:
+        extensions = self.extension_registry.from_descriptor_list(transform_descriptors)
+        transform_from = raw_stream
+        for ex in extensions:
+            if isinstance(ex, StreamTransformExtension):
+                transform_from = ex.transform_from(transform_from)
+        return transform_from
+
+
+class FileSystemReader(StorageReader):
+    def __init__(
+        self,
+        path: Union[str, os.PathLike],
+        _extension_registry: Optional[ExtensionRegistry] = None,  # EXPERIMENTAL
+    ) -> None:
+        super().__init__()
+        self.fs = FileSystem()
+        self.path = self.fs.init_path(path)
+        self.storage_data: dict[Any, Any] = {}
+        self.load_id = _generate_uuid()
+        self.transforms = _StorageReaderTransforms(_extension_registry)
+        self.rank = None
+        self.use_collectives = True
+
+    def _slice_file(self, file, sinfo: _StorageInfo) -> IO[bytes]:
+        return cast(IO[bytes], _create_file_view(file, sinfo.offset, sinfo.length))
+
+    def reset(self, checkpoint_id: Union[str, os.PathLike, None] = None) -> None:
+        self.storage_data = {}
+        if checkpoint_id:
+            self.path = self.fs.init_path(checkpoint_id)
+        self.load_id = _generate_uuid()
+
+    def read_data(self, plan: LoadPlan, planner: LoadPlanner) -> Future[None]:
+        # group requests by file
+        per_file: dict[str, list[ReadItem]] = {}
+        for read_item in plan.items:
+            item_md: _StorageInfo = self.storage_data[read_item.storage_index]
+            path = item_md.relative_path
+            per_file.setdefault(path, []).append(read_item)
+
+        for relative_path, reqs in per_file.items():
+            new_path = self.fs.concat_path(self.path, relative_path)
+            with self.fs.create_stream(new_path, "rb") as stream:
+                # TODO sort by offset and cache the reading
+                for req in reqs:
+                    item_md = self.storage_data[req.storage_index]
+                    file_slice = self._slice_file(stream, item_md)
+                    transform_from = self.transforms.transform_load_stream(
+                        req,
+                        # This field wasn't present in older
+                        # implementations so provide a fallback.
+                        item_md.transform_descriptors or (),
+                        file_slice,
+                    )
+
+                    if req.type == LoadItemType.BYTE_IO:
+                        read_bytes = io.BytesIO(transform_from.read(-1))
+                        read_bytes.seek(0)
+                        planner.load_bytes(req, read_bytes)
+                    else:
+                        if transform_from.seekable():
+                            seekable = transform_from
+                        else:
+                            # torch.load requires a seekable input, so read the transform
+                            # stream now and store the output if needed
+                            seekable = io.BytesIO(transform_from.read(-1))
+                            seekable.seek(0)
+
+                        tensor = cast(
+                            Tensor,
+                            torch.load(
+                                seekable,
+                                map_location="cpu",
+                                weights_only=True,
+                            ),
+                        )
+                        tensor = narrow_tensor_by_index(
+                            tensor, req.storage_offsets, req.lengths
+                        )
+                        target_tensor = planner.resolve_tensor(req).detach()
+
+                        if target_tensor.size() != tensor.size():
+                            raise AssertionError(
+                                f"req {req.storage_index} mismatch sizes {target_tensor.size()} vs {tensor.size()}"
+                            )
+                        target_tensor.copy_(tensor)
+                        planner.commit_tensor(req, target_tensor)
+
+        fut: Future = Future()
+        fut.set_result(None)
+        return fut
+
+    def _get_metadata_path(self, rank: Optional[int] = None) -> os.PathLike:
+        filename = f"{_metadata_fn}" if rank is None else f"__{rank}{_metadata_fn}"
+        return cast(Path, self.fs.concat_path(self.path, filename))
+
+    # Implementing the abstract function in StorageReader
+    def read_metadata(self, *args: Any, **kwargs: Any) -> Metadata:
+        rank = kwargs.get("rank")
+        path = self._get_metadata_path(rank)
+        with self.fs.create_stream(path, "rb") as metadata_file:
+            metadata = pickle.load(metadata_file)
+
+        if getattr(metadata, "storage_meta", None) is None:
+            metadata.storage_meta = StorageMeta()
+        metadata.storage_meta.load_id = self.load_id
+
+        return metadata
+
+    def set_up_storage_reader(
+        self, metadata: Metadata, is_coordinator: bool, *args: Any, **kwargs: Any
+    ) -> None:
+        self.storage_data = metadata.storage_data
+        self.rank = kwargs.get("rank")
+        self.use_collectives = kwargs.get("use_collectives", True)
+        if self.storage_data is None:
+            raise AssertionError("storage_data must not be None in metadata")
+
+    def prepare_local_plan(self, plan: LoadPlan) -> LoadPlan:
+        return plan
+
+    def prepare_global_plan(self, plans: list[LoadPlan]) -> list[LoadPlan]:
+        return plans
+
+    @property
+    def checkpoint_id(self) -> Union[str, os.PathLike]:
+        """
+        return the checkpoint_id that will be used to load the checkpoint.
+        """
+        return self.path
+
+    @classmethod
+    def validate_checkpoint_id(cls, checkpoint_id: Union[str, os.PathLike]) -> bool:
+        return FileSystem.validate_checkpoint_id(checkpoint_id)
+
+
+class FileSystemWriter(_FileSystemWriter, BlockingAsyncStager):
+    """
+    Basic implementation of StorageWriter using file IO.
+
+    This implementation makes the following assumptions and simplifications:
+
+    * The checkpoint path is an empty or non-existing directory.
+    * File creation is atomic
+
+    The checkpoint consist of one file per write request plus
+    a global `.metadata` file with the serialized metadata if rank coordination is enabled.
+    a rank local `__{rank}.metadata` file with the serialized metadata if rank coordination is NOT enabled.
+
+    """
+
+    def __init__(
+        self,
+        path: Union[str, os.PathLike],
+        single_file_per_rank: bool = True,
+        sync_files: bool = True,
+        thread_count: int = 1,
+        per_thread_copy_ahead: int = 10_000_000,
+        cache_staged_state_dict: bool = False,
+        overwrite: bool = True,
+        _extensions: Optional[Sequence[StreamTransformExtension]] = None,
+        serialization_format: SerializationFormat = SerializationFormat.TORCH_SAVE,
+    ) -> None:
+        """
+        Initialize the writer pointing to `path`.
+
+        Args:
+            path: directory where the checkpoint will be written to.
+            single_file_per_rank: Produce one file per rank instead of one file per tensor/blob. Default to True.
+            sync_files : force files to be synced to permanent storage. Default to True.
+            thread_count: Number of IO threads to use to write. Default to 1.
+            per_thread_copy_ahead: How many bytes to copy from the GPU ahead of saving then. Default 10Mb.
+            cache_staged_state_dict: Whether to cache the staged state_dict. This option decreases staging latency
+                at the cost of increases memory usage. Additionally, if this parameter is set to True, it's the expectation
+                that the stager is maintained and reused for multiple dcp.async_save calls. Default to False.
+            overwrite: Whether to allow overwriting existing checkpoints. Defaults to True.
+            _extensions: Extensions to apply to output streams (EXPERIMENTAL)
+
+        N. B. If sync_files is disabled, there's no guarantee that the checkpoint will be consistent in the case of a failure.
+        """
+        _FileSystemWriter.__init__(
+            self,
+            path=path,
+            single_file_per_rank=single_file_per_rank,
+            sync_files=sync_files,
+            thread_count=thread_count,
+            per_thread_copy_ahead=per_thread_copy_ahead,
+            overwrite=overwrite,
+            _extensions=_extensions,
+            serialization_format=serialization_format,
+        )
+        BlockingAsyncStager.__init__(
+            self,
+            cache_staged_state_dict=cache_staged_state_dict,
+        )
+
+    def stage(self, state_dict: STATE_DICT_TYPE) -> STATE_DICT_TYPE:
+        """Override of AsyncStager.stage"""
+        # in the async case, the state dict is already on CPU, so maintaining this
+        # buffer makes no sense
+        self.per_thread_copy_ahead = 0
+        return super().stage(state_dict)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/format_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/format_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..912f983fe2a7ce9267ce74940d42f9bd2b3969ca
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/format_utils.py
@@ -0,0 +1,292 @@
+# mypy: allow-untyped-defs
+import argparse
+import os
+from enum import Enum
+from typing import cast, Optional, Union
+
+import torch
+import torch.distributed as dist
+from torch.distributed._shard._utils import narrow_tensor_by_index
+from torch.distributed.checkpoint import FileSystemReader, FileSystemWriter
+from torch.distributed.checkpoint._nested_dict import flatten_state_dict
+from torch.distributed.checkpoint.default_planner import (
+    _EmptyStateDictLoadPlanner,
+    DefaultLoadPlanner,
+)
+from torch.distributed.checkpoint.metadata import (
+    Metadata,
+    STATE_DICT_TYPE,
+    STORAGE_TYPES,
+    TensorProperties,
+    TensorStorageMetadata,
+)
+from torch.distributed.checkpoint.planner import LoadItemType, LoadPlan, LoadPlanner
+from torch.distributed.checkpoint.planner_helpers import _create_chunk_list
+from torch.distributed.checkpoint.state_dict_loader import _load_state_dict
+from torch.distributed.checkpoint.state_dict_saver import _save_state_dict
+from torch.distributed.checkpoint.storage import StorageReader
+from torch.futures import Future
+
+
+__all__ = [
+    "dcp_to_torch_save",
+    "torch_save_to_dcp",
+    "BroadcastingTorchSaveReader",
+    "DynamicMetaLoadPlanner",
+]
+
+
+class BroadcastingTorchSaveReader(StorageReader):
+    """
+    StorageReader for reading a Torch Save file. This reader will read the entire checkpoint
+    on the coordinator rank, and then broadcast and shard each tensor to all ranks.
+
+    . N.B. Intended to be used with DynamicMetaLoadPlanner
+
+    .. warning::
+        Current implementation only supports loading Tensors.
+
+    >>> # xdoctest: +SKIP("undefined vars")
+    >>> sd = {"mode": model}
+    >>> dcp.load(
+    >>>    sd,
+    >>>    storage_reader=BroadcastingTorchSaveReader(),
+    >>>    planner=DynamicMetaLoadPlanner(),
+    >>>    checkpoint_id="path_to_model.pt"
+    >>> )
+    """
+
+    def __init__(
+        self,
+        checkpoint_id: Optional[Union[str, os.PathLike]] = None,
+        coordinator_rank: int = 0,
+    ) -> None:
+        self.checkpoint_id = checkpoint_id
+        self.coordinator_rank = coordinator_rank
+
+    # pyrefly: ignore [bad-override]
+    def read_metadata(self) -> Metadata:
+        """Extends the default StorageReader to support building the metadata file"""
+        # Metadata is built in planner.set_up_planner, since we are not actually reading metadata from
+        # the disk
+        return Metadata(state_dict_metadata={})
+
+    def read_data(self, plan: LoadPlan, planner: LoadPlanner) -> Future[None]:
+        """
+        Reads torch save data on the coordinator rank, and broadcast afterwards
+        this incurrs a communication cost, but avoids having to load
+        the entire checkpoint on each rank, hopefully preventing OOM issues
+        """
+        planner = cast(DefaultLoadPlanner, planner)
+
+        # data is read in on the coordinator rank, and broadcast afterwards
+        # this incurs a communication cost, but it avoids having to load
+        # the entire checkpoint on each rank, hopefully preventing OOM issues
+        # TODO: read on each host, instead of only the coordinator
+        if self.is_coordinator:
+            if self.checkpoint_id is None:
+                raise AssertionError("checkpoint_id must be set before reading data")
+            torch_state_dict = torch.load(
+                self.checkpoint_id, map_location="cpu", weights_only=False
+            )
+            if planner.flatten_state_dict:
+                torch_state_dict, _ = flatten_state_dict(torch_state_dict)
+        else:
+            torch_state_dict = None
+
+        for req in plan.items:
+            if req.type == LoadItemType.BYTE_IO:
+                raise RuntimeError(
+                    f"Non-tensor value identified at {req.storage_index.fqn}. "
+                    f"At this time {type(self).__name__} only supports loading Tensors."
+                )
+
+            #  Broadcast the tensor from the coordinator rank
+            if self.is_coordinator:
+                pg_device = dist.distributed_c10d._get_pg_default_device()
+                # pyrefly: ignore [unsupported-operation]
+                tensor = torch_state_dict[req.storage_index.fqn].to(pg_device)
+            else:
+                tensor = torch.empty_like(planner.state_dict[req.storage_index.fqn])
+
+            dist.broadcast(tensor, src=self.coordinator_rank, async_op=False)
+
+            tensor = narrow_tensor_by_index(tensor, req.storage_offsets, req.lengths)
+            target_tensor = planner.resolve_tensor(req).detach()
+            if not target_tensor.size() == tensor.size():
+                raise AssertionError(
+                    f"req {req.storage_index} mismatch sizes, "
+                    f"{target_tensor.size()} vs {tensor.size()}"
+                )
+            target_tensor.copy_(tensor)
+            planner.commit_tensor(req, target_tensor)
+
+        fut: Future = Future()
+        fut.set_result(None)
+        return fut
+
+    # pyrefly: ignore [bad-override]
+    def set_up_storage_reader(self, metadata: Metadata, is_coordinator: bool) -> None:
+        """Implementation of the StorageReader method"""
+        self.is_coordinator = is_coordinator
+        if self.is_coordinator:
+            if not dist.get_rank() == self.coordinator_rank:
+                raise AssertionError(
+                    f"Coordinator rank mismatch: expected {self.coordinator_rank}, "
+                    f"got {dist.get_rank()}"
+                )
+
+        if self.checkpoint_id is None:
+            raise AssertionError(
+                "checkpoint_id must be set before setting up storage reader"
+            )
+
+    def prepare_local_plan(self, plan: LoadPlan) -> LoadPlan:
+        """Implementation of the StorageReader method"""
+        return plan
+
+    def prepare_global_plan(self, global_plan: list[LoadPlan]) -> list[LoadPlan]:
+        """Implementation of the StorageReader method"""
+        return global_plan
+
+    def reset(self, checkpoint_id: Union[str, os.PathLike, None] = None) -> None:
+        """Implementation of the StorageReader method"""
+        self.checkpoint_id = checkpoint_id
+
+    @classmethod
+    def validate_checkpoint_id(cls, checkpoint_id: Union[str, os.PathLike]) -> bool:
+        """Implementation of the StorageReader method"""
+        return os.path.isfile(checkpoint_id)
+
+
+class DynamicMetaLoadPlanner(DefaultLoadPlanner):
+    """
+    Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict,
+    avoiding the need to read metadata from disk. This is useful when reading formats which don't have a
+    metadata file, like Torch Save files.
+
+    . N.B. Intended to be used with BroadcastingTorchSaveReader
+
+    .. warning::
+        Current implementation only supports loading Tensors.
+
+    >>> # xdoctest: +SKIP("undefined vars")
+    >>> sd = {"mode": model}
+    >>> dcp.load(
+    >>>    sd,
+    >>>    storage_reader=BroadcastingTorchSaveReader(),
+    >>>    planner=DynamicMetaLoadPlanner(),
+    >>>    checkpoint_id="path_to_model.pt"
+    >>> )
+    """
+
+    def set_up_planner(
+        self,
+        state_dict: STATE_DICT_TYPE,
+        metadata: Optional[Metadata] = None,
+        is_coordinator: bool = False,
+    ) -> None:
+        """Setups of the planner, extnding default behavior by creating the Metadata object from the state dict"""
+        super().set_up_planner(state_dict, metadata, is_coordinator)
+
+        state_dict_metadata: dict[str, STORAGE_TYPES] = {}
+        for key, tensor in self.state_dict.items():
+            if not torch.is_tensor(tensor):
+                raise RuntimeError(
+                    f"Non-tensor value identified at {key}. "
+                    f"At this time {type(self).__name__} only supports loading Tensors."
+                )
+
+            state_dict_metadata[key] = TensorStorageMetadata(
+                TensorProperties(dtype=tensor.dtype),
+                tensor.size(),
+                _create_chunk_list(tensor),
+            )
+        self.metadata = Metadata(state_dict_metadata=state_dict_metadata)
+
+
+def dcp_to_torch_save(
+    dcp_checkpoint_dir: Union[str, os.PathLike],
+    torch_save_path: Union[str, os.PathLike],
+):
+    """
+    Given a directory containing a DCP checkpoint, this function will convert it into a
+    Torch save file.
+
+    Args:
+        dcp_checkpoint_dir: Directory containing the DCP checkpoint.
+        torch_save_path: Filename to store the converted Torch save file.
+
+    .. warning::
+        To avoid OOM, it's recommended to only run this function on a single rank.
+    """
+    sd: STATE_DICT_TYPE = {}
+    _load_state_dict(
+        sd,
+        storage_reader=FileSystemReader(dcp_checkpoint_dir),
+        planner=_EmptyStateDictLoadPlanner(),
+        no_dist=True,
+    )
+    torch.save(sd, torch_save_path)
+
+
+def torch_save_to_dcp(
+    torch_save_path: Union[str, os.PathLike],
+    dcp_checkpoint_dir: Union[str, os.PathLike],
+):
+    """
+    Given the location of a torch save file, converts it into a DCP checkpoint.
+
+    Args:
+        torch_save_path: Filename of the Torch save file.
+        dcp_checkpoint_dir: Directory to store the DCP checkpoint.
+
+    .. warning::
+        To avoid OOM, it's recommended to only run this function on a single rank.
+    """
+
+    state_dict = torch.load(torch_save_path, weights_only=False)
+    # we don't need stateful behavior here because the expectation is anything loaded by
+    # torch.load would not contain stateful objects.
+    _save_state_dict(
+        state_dict, storage_writer=FileSystemWriter(dcp_checkpoint_dir), no_dist=True
+    )
+
+
+if __name__ == "__main__":
+
+    class FormatMode(Enum):
+        TORCH_TO_DCP = "torch_to_dcp"
+        DCP_TO_TORCH = "dcp_to_torch"
+
+    # Parse command-line arguments
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "mode",
+        type=str,
+        help="Conversion mode",
+        choices=[m.value for m in FormatMode],
+        default=FormatMode.TORCH_TO_DCP,
+    )
+    parser.add_argument("src", type=str, help="Path to the source model")
+    parser.add_argument("dst", type=str, help="Path to the destination model")
+    args = parser.parse_args()
+
+    print(
+        f"Converting checkpoint from {args.src} to {args.dst} using method: '{args.mode}'"
+    )
+    checkpoint_missing_warning = (
+        f"No checkpoint found at {args.src}. Skipping conversion."
+    )
+    if args.mode == FormatMode.TORCH_TO_DCP.value:
+        if os.path.isfile(args.src):
+            torch_save_to_dcp(args.src, args.dst)
+        else:
+            print(checkpoint_missing_warning)
+    elif args.mode == FormatMode.DCP_TO_TORCH.value:
+        if os.path.isdir(args.src):
+            dcp_to_torch_save(args.src, args.dst)
+        else:
+            print(checkpoint_missing_warning)
+    else:
+        raise ValueError(f"Unknown conversion mode: {args.mode}")
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/hf_storage.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/hf_storage.py
new file mode 100644
index 0000000000000000000000000000000000000000..52f9209da0ec58826cfa3c445e2b2070c5dee60f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/hf_storage.py
@@ -0,0 +1,391 @@
+# mypy: allow-untyped-defs
+import dataclasses
+import json
+import logging
+import queue
+import threading
+from typing import Any, Optional
+
+import torch
+from torch.distributed.checkpoint import FileSystemReader, FileSystemWriter
+from torch.distributed.checkpoint._consolidate_hf_safetensors import (
+    consolidate_safetensors_files,
+)
+from torch.distributed.checkpoint._hf_utils import (
+    _gen_file_name,
+    _HFStorageInfo,
+    _metadata_fn,
+    CUSTOM_METADATA_KEY,
+    SAVED_OFFSETS_KEY,
+    SHARDED_DIR_NAME,
+    SUFFIX,
+)
+from torch.distributed.checkpoint.filesystem import SerializationFormat
+from torch.distributed.checkpoint.metadata import (
+    ChunkStorageMetadata,
+    Metadata,
+    MetadataIndex,
+    StorageMeta,
+    TensorProperties,
+    TensorStorageMetadata,
+)
+from torch.distributed.checkpoint.planner import (
+    LoadPlan,
+    LoadPlanner,
+    ReadItem,
+    SavePlan,
+    SavePlanner,
+    WriteItem,
+)
+from torch.distributed.checkpoint.storage import WriteResult
+from torch.futures import Future
+
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+__all__ = ["HuggingFaceStorageWriter", "HuggingFaceStorageReader"]
+
+
+class HuggingFaceStorageWriter(FileSystemWriter):
+    """
+    A writer that writes to storage in the huggingface safetensors format.
+    """
+
+    def __init__(
+        self,
+        path: str,
+        fqn_to_index_mapping: Optional[dict[str, int]] = None,
+        thread_count: int = 1,
+        save_distributed: bool = False,
+        enable_consolidation: bool = False,
+        thread_count_consolidation: int = 1,
+    ) -> None:
+        """
+        Initialize the huggingface writer pointing to path.
+
+        Args:
+            path: directory where the checkpoint will be read from.
+            fqn_to_index_mapping: A mapping from tensor FQN to the index of the file that the tensor should be written to.
+                              Indices are from 1 to N, where N is the number of files. If not provided,
+                              the tensors will be written to a single file. If none, then all the tensors on the
+                              same rank will be written to the same file.
+            thread_count: Number of threads to use to write distributed checkpoint. Default to 1.
+            save_distributed: If True, save the checkpoint using distributed APIs where every rank saves its own shard.
+                        Default is False which assumes rank-0 checkpointing of the full state_dict.
+            enable_consolidation: If True, consolidate the sharded checkpoint after saving. The sharded tensors will be
+                                saved to path/sharded and the full tensors will be saved to path. Default to False.
+            thread_count_consolidation: Number of threads to use for parallel processing of saving data
+                                to consolidated output files. Default to 1.
+        """
+
+        super().__init__(
+            path=path,
+            serialization_format=SerializationFormat.SAFETENSORS,
+            thread_count=thread_count,
+        )
+        self.fqn_to_index_mapping: Optional[dict[str, int]] = fqn_to_index_mapping
+        self.save_distributed: bool = save_distributed
+        self.enable_consolidation: bool = enable_consolidation
+        self.consolidated_output_path: Optional[str] = None
+        if self.enable_consolidation:
+            self.consolidated_output_path = str(self.path)
+            self.path = self.fs.concat_path(self.path, SHARDED_DIR_NAME)
+        self.thread_count_consolidation = thread_count_consolidation
+
+    def prepare_global_plan(self, plans: list[SavePlan]) -> list[SavePlan]:
+        new_plans = []
+        for i, plan in enumerate(plans, start=1):
+            storage_data: dict[str, Any] = {}
+            if self.fqn_to_index_mapping is not None:
+                storage_data["fqn_to_index_mapping"] = self.fqn_to_index_mapping
+            if self.save_distributed:
+                storage_data["shard_index"] = i
+
+            new_plans.append(dataclasses.replace(plan, storage_data=storage_data))
+
+        return new_plans
+
+    def write_data(
+        self,
+        plan: SavePlan,
+        planner: SavePlanner,
+    ) -> Future[list[WriteResult]]:
+        if len(plan.items) == 0:
+            fut: Future = Future()
+            fut.set_result([])
+            return fut
+
+        # storage_plan is a map from key to file index
+        storage_data: dict[str, Any] = plan.storage_data
+        storage_plan: Optional[dict[str, int]] = None
+        shard_index: Optional[int] = None
+        if "fqn_to_index_mapping" in storage_data:
+            storage_plan = storage_data["fqn_to_index_mapping"]
+        if "shard_index" in storage_data:
+            shard_index = storage_data["shard_index"]
+
+        buckets = self._split_by_storage_plan(storage_plan, plan.items)
+        highest_index = max(storage_plan.values()) if storage_plan is not None else 1
+
+        file_queue: queue.Queue = queue.Queue()
+        for file_index, write_items in buckets.items():
+            file_name = _gen_file_name(file_index, highest_index, shard_index)
+            file_queue.put(
+                (self.fs.concat_path(self.path, file_name), file_name, write_items)
+            )
+
+        return super()._write_data(planner, file_queue)
+
+    def finish(self, metadata: Metadata, results: list[list[WriteResult]]) -> None:
+        if self.save_distributed and not self.enable_consolidation:
+            # if we are saving distributed, without consolidating,
+            # then we have no metadata to write because a metadata
+            # file with fqn to file mapping doesn't make sense
+            # in this case, because fqns will be in multiple files
+            logger.info("Not consolidating sharded checkpoint in finish step.")
+            return
+        if self.save_distributed:
+            fqn_to_index_mapping: dict[str, int] = (
+                self.fqn_to_index_mapping
+                if self.fqn_to_index_mapping is not None
+                else dict.fromkeys(metadata.state_dict_metadata.keys(), 1)
+            )
+
+            return consolidate_safetensors_files(
+                input_dir=str(self.path),
+                output_dir=self.consolidated_output_path,  # type: ignore[arg-type]
+                num_threads=self.thread_count_consolidation,
+                fqn_to_index_mapping=fqn_to_index_mapping,
+            )
+
+        # writing a model.index.safetensors.json file with fqn to file mapping
+        # for the rank-0 checkpointing case
+        metadata_to_write = {}
+        storage_md = {}
+        total_size = 0
+        for wr_list in results:
+            storage_md.update(
+                {wr.index.fqn: wr.storage_data.relative_path for wr in wr_list}
+            )
+            total_size += sum([wr.storage_data.length for wr in wr_list])
+        metadata_to_write["metadata"] = {"total_size": total_size}
+        metadata_to_write["weight_map"] = storage_md
+
+        metadata_path = self.fs.concat_path(self.path, f"{_metadata_fn}")
+        with self.fs.create_stream(metadata_path, "w") as metadata_file:
+            json.dump(metadata_to_write, metadata_file, indent=2)
+
+    def _split_by_storage_plan(
+        self, storage_plan: Optional[dict[str, int]], items: list[WriteItem]
+    ) -> dict[int, list[WriteItem]]:
+        # storage_plan is a map from key to index
+        if storage_plan is None:
+            return {1: items}
+
+        buckets = {}
+        for item in items:
+            key = item.index.fqn
+
+            idx = storage_plan[key]
+            if idx not in buckets:
+                buckets[idx] = [item]
+            else:
+                buckets[idx].append(item)
+
+        return buckets
+
+    @property
+    def metadata_path(self) -> str:
+        return _metadata_fn
+
+
+class HuggingFaceStorageReader(FileSystemReader):
+    """
+    A reader that reads a checkpoint in the huggingface safetensors format.
+    """
+
+    def __init__(self, path: str, thread_count: int = 1) -> None:
+        """
+        Initialize the huggingface reader pointing to path.
+
+        Args:
+            path: directory where the checkpoint will be read from.
+            thread_count: Number of threads to use to read distributed checkpoint. Default to 1.
+        """
+
+        super().__init__(path=path)
+        self.thread_count = thread_count
+
+    def _process_read_request(self, f, req: ReadItem, planner: LoadPlanner) -> None:
+        """Helper function to process a single read request."""
+        # Create slices for each dimension based on offsets and lengths
+        slices = tuple(
+            slice(offset, offset + length)
+            for offset, length in zip(req.storage_offsets, req.lengths)
+        )
+        tensor = f.get_slice(req.storage_index.fqn)[slices]
+        target_tensor = planner.resolve_tensor(req).detach()
+
+        if target_tensor.size() != tensor.size():
+            raise AssertionError(
+                f"req {req.storage_index} mismatch sizes {target_tensor.size()} vs {tensor.size()}"
+            )
+
+        target_tensor.copy_(tensor)
+        planner.commit_tensor(req, target_tensor)
+
+    def _read_files_from_queue(
+        self,
+        file_queue: queue.Queue,
+        result_queue: queue.Queue,
+        planner: LoadPlanner,
+    ) -> None:
+        from safetensors import safe_open  # type: ignore[import]
+
+        try:
+            while True:
+                file_name, reqs = file_queue.get_nowait()
+                with safe_open(filename=file_name, framework="pt") as f:
+                    for req in reqs:
+                        self._process_read_request(f, req, planner)
+                result_queue.put(True)  # Signal that this file has been processed
+        except queue.Empty:
+            pass
+
+    def read_data(self, plan: LoadPlan, planner: LoadPlanner) -> Future[None]:
+        from safetensors import safe_open  # type: ignore[import]
+
+        per_file: dict[str, list[ReadItem]] = {}
+
+        for read_item in plan.items:
+            item_md: _HFStorageInfo = self.storage_data[read_item.storage_index]
+            file_name = item_md.relative_path
+            per_file.setdefault(file_name, []).append(read_item)
+
+        if self.thread_count <= 1 or len(per_file) <= 1:
+            for file_name, reqs in per_file.items():
+                with safe_open(filename=file_name, framework="pt") as f:
+                    for req in reqs:
+                        self._process_read_request(f, req, planner)
+        else:
+            # Use parallel implementation with thread pool
+            file_queue: queue.Queue = queue.Queue()
+            result_queue: queue.Queue = queue.Queue()
+
+            # Fill the queue with files to process
+            for file_name, reqs in per_file.items():
+                file_queue.put((file_name, reqs))
+
+            # Create and start worker threads
+            threads = []
+            num_threads = min(self.thread_count, len(per_file))
+            for _ in range(num_threads):
+                t = threading.Thread(
+                    target=self._read_files_from_queue,
+                    args=(file_queue, result_queue, planner),
+                )
+                t.start()
+                threads.append(t)
+
+            # Wait for all threads to complete
+            for t in threads:
+                t.join()
+
+            # Check if all files were processed
+            processed_count = 0
+            try:
+                while True:
+                    result_queue.get_nowait()
+                    processed_count += 1
+            except queue.Empty:
+                pass
+
+            if processed_count != len(per_file):
+                raise AssertionError(
+                    f"Not all files were processed: {processed_count} out of {len(per_file)}"
+                )
+
+        fut: Future = Future()
+        fut.set_result(None)
+        return fut
+
+    # pyrefly: ignore [bad-override]
+    def read_metadata(self) -> Metadata:
+        from safetensors import safe_open  # type: ignore[import]
+        from safetensors.torch import _getdtype  # type: ignore[import]
+
+        state_dict_metadata: dict[str, TensorStorageMetadata] = {}
+        storage_data: dict[MetadataIndex, _HFStorageInfo] = {}
+
+        safetensors_files = []
+        for file in self.fs.ls(self.path):
+            if file.endswith(SUFFIX):
+                safetensors_files.append(file)
+
+        for safetensor_file in safetensors_files:
+            with safe_open(safetensor_file, framework="pt") as f:
+                keys = f.keys()
+                extra_metadata = f.metadata()
+
+                dcp_sharding_info = None
+                if extra_metadata and extra_metadata.get(CUSTOM_METADATA_KEY):
+                    dcp_sharding_info = json.loads(
+                        extra_metadata.get(CUSTOM_METADATA_KEY)
+                    )
+
+                for key in keys:
+                    shape = f.get_slice(key).get_shape()
+                    dtype = f.get_slice(key).get_dtype()
+                    # construct state_dict_metadata
+                    if dcp_sharding_info is not None:
+                        offset = dcp_sharding_info[key][SAVED_OFFSETS_KEY]
+                    else:
+                        offset = [0] * len(shape)
+
+                    if key not in state_dict_metadata:
+                        state_dict_metadata[key] = TensorStorageMetadata(
+                            properties=TensorProperties(dtype=_getdtype(dtype)),
+                            size=torch.Size(
+                                [saved + offset for saved, offset in zip(shape, offset)]
+                            ),
+                            chunks=[
+                                ChunkStorageMetadata(
+                                    offsets=torch.Size(offset),
+                                    sizes=torch.Size(shape),
+                                )
+                            ],
+                        )
+                    else:
+                        state_dict_metadata[key].chunks.append(
+                            ChunkStorageMetadata(
+                                torch.Size(offset), sizes=torch.Size(shape)
+                            )
+                        )
+                        size = list(state_dict_metadata[key].size)
+                        for i in range(len(size)):
+                            size[i] = max(size[i], shape[i] + offset[i])
+                        state_dict_metadata[key].size = torch.Size(size)
+
+                    # construct storage data
+                    if dcp_sharding_info is not None:
+                        metadata_index = MetadataIndex(
+                            fqn=key, offset=dcp_sharding_info[key][SAVED_OFFSETS_KEY]
+                        )
+                    else:
+                        metadata_index = MetadataIndex(fqn=key, offset=[0] * len(shape))
+                    storage_data[metadata_index] = _HFStorageInfo(
+                        relative_path=safetensor_file,
+                        shape=torch.Size(shape),
+                        dtype=_getdtype(dtype),
+                    )
+
+        metadata = Metadata(
+            state_dict_metadata=state_dict_metadata,  # type: ignore[arg-type]
+            storage_data=storage_data,
+        )
+
+        if getattr(metadata, "storage_meta", None) is None:
+            metadata.storage_meta = StorageMeta()
+        metadata.storage_meta.load_id = self.load_id  # type: ignore[union-attr]
+
+        return metadata
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/logger.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/logger.py
new file mode 100644
index 0000000000000000000000000000000000000000..677cac0339cb9fab60c77f75da04bc7ef06504f3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/logger.py
@@ -0,0 +1,121 @@
+# mypy: allow-untyped-defs
+import functools
+import logging
+import time
+from collections.abc import Callable
+from typing import Any, TypeVar
+from typing_extensions import ParamSpec
+from uuid import uuid4
+
+import torch.distributed.c10d_logger as c10d_logger
+from torch.distributed.checkpoint.logging_handlers import DCP_LOGGER_NAME
+
+
+logger = logging.getLogger()
+
+
+__all__: list[str] = []
+
+# pyrefly: ignore [unknown-name]
+global _dcp_logger
+_dcp_logger = c10d_logger._get_or_create_logger(DCP_LOGGER_NAME)
+
+_T = TypeVar("_T")
+_P = ParamSpec("_P")
+
+
+def _msg_dict_from_dcp_method_args(*args, **kwargs) -> dict[str, Any]:
+    """
+    Extracts log data from dcp method args
+    """
+    msg_dict = {}
+
+    # checkpoint ID can be passed in through the serializer or through the checkpoint id directly
+    storage_writer = kwargs.get("storage_writer")
+    storage_reader = kwargs.get("storage_reader")
+    planner = kwargs.get("planner")
+
+    checkpoint_id = kwargs.get("checkpoint_id")
+    if not checkpoint_id and (serializer := storage_writer or storage_reader):
+        checkpoint_id = getattr(serializer, "checkpoint_id", None)
+
+    msg_dict["checkpoint_id"] = (
+        # pyrefly: ignore [unsupported-operation]
+        str(checkpoint_id) if checkpoint_id is not None else checkpoint_id
+    )
+
+    # Uniquely identify a _dcp_method_logger wrapped function call.
+    msg_dict["uuid"] = str(uuid4().int)
+
+    if storage_writer:
+        msg_dict["storage_writer"] = storage_writer.__class__.__name__
+
+    if storage_reader:
+        msg_dict["storage_reader"] = storage_reader.__class__.__name__
+
+    if planner:
+        msg_dict["planner"] = planner.__class__.__name__
+
+    return msg_dict
+
+
+def _get_msg_dict(func_name, *args, **kwargs) -> dict[str, Any]:
+    msg_dict = _msg_dict_from_dcp_method_args(*args, **kwargs)
+    msg_dict.update(c10d_logger._get_msg_dict(func_name, *args, **kwargs))
+
+    return msg_dict
+
+
+def _dcp_method_logger(
+    log_exceptions: bool = False, **wrapper_kwargs: Any
+) -> Callable[[Callable[_P, _T]], Callable[_P, _T]]:  # pyre-ignore
+    """This method decorator logs the start, end, and exception of wrapped events."""
+
+    def decorator(func: Callable[_P, _T]):
+        @functools.wraps(func)
+        def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> _T:
+            msg_dict = _get_msg_dict(
+                func.__name__, *args, **{**wrapper_kwargs, **kwargs}
+            )
+
+            # log start event
+            msg_dict["event"] = "start"
+            t0 = time.time_ns()
+            msg_dict["time"] = t0
+            msg_dict["log_exceptions"] = log_exceptions
+            _dcp_logger.debug(msg_dict)
+
+            # exceptions
+            try:
+                result = func(*args, **kwargs)
+            except BaseException as error:
+                if log_exceptions:
+                    msg_dict["event"] = "exception"
+                    msg_dict["error"] = f"{error}"
+                    msg_dict["time"] = time.time_ns()
+                    _dcp_logger.error(msg_dict)
+                raise
+
+            # end event
+            msg_dict["event"] = "end"
+            t1 = time.time_ns()
+            msg_dict["time"] = time.time_ns()
+            msg_dict["times_spent"] = t1 - t0
+            _dcp_logger.debug(msg_dict)
+
+            return result
+
+        return wrapper
+
+    return decorator
+
+
+def _init_logger(rank: int):
+    logger.setLevel(logging.INFO)
+    ch = logging.StreamHandler()
+    ch.setLevel(logging.INFO)
+    formatter = logging.Formatter(
+        f"[{rank}] %(asctime)s - %(name)s - %(levelname)s - %(message)s"
+    )
+    ch.setFormatter(formatter)
+    logger.addHandler(ch)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/logging_handlers.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/logging_handlers.py
new file mode 100644
index 0000000000000000000000000000000000000000..99c3ee4156ce340e37a2723106df5ea64b19170d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/logging_handlers.py
@@ -0,0 +1,14 @@
+import logging
+
+from torch.distributed.logging_handlers import _log_handlers
+
+
+__all__: list[str] = []
+
+DCP_LOGGER_NAME = "dcp_logger"
+
+_log_handlers.update(
+    {
+        DCP_LOGGER_NAME: logging.NullHandler(),
+    }
+)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/metadata.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/metadata.py
new file mode 100644
index 0000000000000000000000000000000000000000..36864b6bf3ad60778ad008fcbb4c10002933c4c6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/metadata.py
@@ -0,0 +1,185 @@
+# mypy: allow-untyped-defs
+import os
+from collections.abc import Sequence
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Any, Optional, Union
+
+import torch
+from torch.distributed.checkpoint.stateful import StatefulT
+
+
+__all__ = [
+    "ChunkStorageMetadata",
+    "TensorStorageMetadata",
+    "BytesStorageMetadata",
+    "Metadata",
+    "MetadataIndex",
+    "TensorProperties",
+    "StorageMeta",
+]
+
+
+@dataclass
+class ChunkStorageMetadata:
+    """
+    Each chunk is expected to have the same properties of the TensorStorageMetadata
+    that includes it.
+    """
+
+    offsets: torch.Size
+    sizes: torch.Size
+
+
+class _MEM_FORMAT_ENCODING(Enum):
+    """Describe the memory format of a tensor."""
+
+    TORCH_CONTIGUOUS_FORMAT = 0
+    TORCH_CHANNELS_LAST = 1
+    TORCH_PRESERVE_FORMAT = 2
+
+
+@dataclass
+class TensorProperties:
+    """Properties used to create :class:`Tensor`"""
+
+    # Regular tensor fields
+    dtype: torch.dtype = field(default_factory=torch.get_default_dtype)
+    # This field is deprecated.
+    layout: torch.layout = field(default=torch.strided)
+    # This field is deprecated.
+    requires_grad: bool = False
+    # This field is deprecated.
+    memory_format: torch.memory_format = field(default=torch.contiguous_format)
+    # This field is deprecated.
+    pin_memory: bool = False
+
+    def __getstate__(self):
+        # Since torch.memory_format cannot be pickled!
+        memory_format = self.memory_format
+        if memory_format == torch.contiguous_format:
+            mem_format_encoding = _MEM_FORMAT_ENCODING.TORCH_CONTIGUOUS_FORMAT
+        elif memory_format == torch.channels_last:
+            mem_format_encoding = _MEM_FORMAT_ENCODING.TORCH_CHANNELS_LAST
+        elif memory_format == torch.preserve_format:
+            mem_format_encoding = _MEM_FORMAT_ENCODING.TORCH_PRESERVE_FORMAT
+        else:
+            raise RuntimeError(f"Invalid torch.memory_format: {memory_format}")
+
+        return (
+            self.dtype,
+            self.layout,
+            self.requires_grad,
+            mem_format_encoding,
+            self.pin_memory,
+        )
+
+    def __setstate__(
+        self,
+        state,
+    ):
+        (
+            self.dtype,
+            self.layout,
+            self.requires_grad,
+            mem_format_encoding,
+            self.pin_memory,
+        ) = state
+
+        if mem_format_encoding == _MEM_FORMAT_ENCODING.TORCH_CONTIGUOUS_FORMAT:
+            memory_format = torch.contiguous_format
+        elif mem_format_encoding == _MEM_FORMAT_ENCODING.TORCH_CHANNELS_LAST:
+            memory_format = torch.channels_last
+        elif mem_format_encoding == _MEM_FORMAT_ENCODING.TORCH_PRESERVE_FORMAT:
+            memory_format = torch.preserve_format
+        else:
+            raise RuntimeError(
+                f"Invalid torch.memory_format encoding: {mem_format_encoding}"
+            )
+
+        self.memory_format = memory_format
+
+    @staticmethod
+    def create_from_tensor(tensor: torch.Tensor) -> "TensorProperties":
+        return TensorProperties(
+            dtype=tensor.dtype,
+            layout=tensor.layout,
+            requires_grad=tensor.requires_grad,
+            memory_format=torch.contiguous_format,
+            pin_memory=tensor.is_pinned(),
+        )
+
+
+@dataclass
+class TensorStorageMetadata:
+    properties: TensorProperties
+    size: torch.Size
+    chunks: list[ChunkStorageMetadata]
+
+
+@dataclass
+class BytesStorageMetadata:
+    pass
+
+
+STORAGE_TYPES = Union[TensorStorageMetadata, BytesStorageMetadata]
+STATE_DICT_TYPE = dict[str, Union[StatefulT, Any]]
+
+
+@dataclass
+class StorageMeta:
+    checkpoint_id: Union[str, os.PathLike, None] = None
+    save_id: Optional[str] = None
+    load_id: Optional[str] = None
+    modules: list[str] = field(default_factory=list)
+
+
+@dataclass
+class Metadata:
+    """This class represents the metadata of the checkpoint."""
+
+    # Keys are the same from the `state_dict` used.
+    state_dict_metadata: dict[str, STORAGE_TYPES]
+    # It is the responsibility of the planner and storage plugins to ensure
+    # backward compatibility of the planner_data and storage_data. DCP will
+    # also ensure the backward compatibility of the metadata in this file and
+    # the metadata of the built-in planner and storage plugins.
+    planner_data: Any = None
+    storage_data: Any = None
+    storage_meta: Optional[StorageMeta] = None
+    version: Optional[str] = None
+
+
+@dataclass(frozen=True)
+class MetadataIndex:
+    """This class represents a lookup key for items in a state dict or Metadata."""
+
+    fqn: str
+    """Fully Qualified Name of the object"""
+
+    offset: Optional[torch.Size] = None
+    """If the object is a tensor, offset into the tensor we're looking for"""
+
+    index: Optional[int] = field(hash=False, compare=False, default=None)
+    """
+    Index hint when searching for tensor chunk to speedup lookups (optional)
+
+    A common representation of a sharded tensor is as a list of chunks so to
+    find the index in such a list you need to linear search it.
+
+    When constructing an instance of MetadataIndex that points to that list,
+    one can provide the index as a hint and it will be probed first before
+    the linear search and thus making it significantly faster.
+    """
+
+    def __init__(
+        self,
+        fqn: str,
+        offset: Optional[Sequence[int]] = None,
+        index: Optional[int] = None,
+    ):
+        # We must use object.__setattr__ due to frozen=True
+        object.__setattr__(self, "fqn", fqn)
+        object.__setattr__(self, "index", index)
+        if offset is not None:
+            object.__setattr__(self, "offset", torch.Size(offset))
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/optimizer.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/optimizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..343497da0aa21f35a081a7ca9063d4dcbbf41ccc
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/optimizer.py
@@ -0,0 +1,360 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+import dataclasses
+from collections.abc import Sequence
+from typing import cast, Optional, Union
+
+import torch
+import torch.distributed as dist
+from torch._utils import _get_device_module
+from torch.distributed._shard.sharded_tensor.api import ShardedTensor
+from torch.distributed._shard.sharded_tensor.metadata import (
+    TensorProperties as ShardTensorProperties,
+)
+from torch.distributed._shard.sharded_tensor.shard import Shard
+from torch.distributed._shard.sharding_spec.chunk_sharding_spec import ChunkShardingSpec
+from torch.distributed.checkpoint._nested_dict import unflatten_state_dict
+from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner
+from torch.distributed.checkpoint.metadata import (
+    BytesStorageMetadata,
+    ChunkStorageMetadata,
+    Metadata,
+    MetadataIndex,
+    STATE_DICT_TYPE,
+    TensorProperties,
+    TensorStorageMetadata,
+)
+from torch.distributed.checkpoint.planner import LoadPlan, LoadPlanner
+from torch.distributed.checkpoint.planner_helpers import (
+    _create_read_items,
+    create_read_items_for_chunk_list,
+)
+
+# pyrefly: ignore [deprecated]
+from torch.distributed.checkpoint.state_dict_loader import load_state_dict
+from torch.distributed.checkpoint.storage import StorageReader
+from torch.distributed.checkpoint.utils import (
+    _element_wise_add,
+    _element_wise_sub,
+    _normalize_device_info,
+)
+from torch.distributed.distributed_c10d import _get_default_group
+from torch.distributed.fsdp._shard_utils import _create_chunk_sharded_tensor
+from torch.distributed.remote_device import _remote_device
+from torch.distributed.tensor import DTensor
+
+
+STATE_DICT_2D_LAYOUT = dict[str, tuple[Optional[Sequence[int]], Sequence[int]]]
+
+
+# TODO: Update docstrings for optimizer.py
+__all__ = [
+    "load_sharded_optimizer_state_dict",
+]
+
+
+def _gen_rank_device(global_rank: int, device_type: str = "cuda") -> str:
+    if device_type == "cpu":
+        return "cpu"
+    device_module = _get_device_module(device_type)
+    if device_module.is_available():
+        return _normalize_device_info(
+            device_type, global_rank % device_module.device_count()
+        )
+    return "cpu"
+
+
+def _create_colwise_spec(
+    pg: Optional[dist.ProcessGroup] = None,
+) -> ChunkShardingSpec:
+    pg_device_type = dist.distributed_c10d._get_pg_default_device(pg).type
+    if pg is None:
+        placements = [
+            f"rank:{idx}/{_gen_rank_device(idx, pg_device_type)}"
+            for idx in range(dist.get_world_size())
+        ]
+    else:
+        placements = [
+            f"rank:{idx}/{_gen_rank_device(dist.get_global_rank(pg, idx), pg_device_type)}"
+            for idx in range(pg.size())
+        ]
+    return ChunkShardingSpec(
+        dim=0,
+        placements=cast(list[Union[_remote_device, str]], placements),
+    )
+
+
+def _is_nested_tensor(val: torch.Tensor) -> bool:
+    if type(val) is ShardedTensor:
+        if len(val.local_shards()) == 0:
+            return False
+        if type(val.local_shards()[0].tensor) is ShardedTensor:
+            return True
+        if type(val.local_shards()[0].tensor) is DTensor:
+            raise ValueError("Cannot handle DTensor nested inside ShardedTensor")
+    elif type(val) is DTensor and (
+        type(val._local_tensor) is DTensor or type(val._local_tensor) is ShardedTensor
+    ):
+        raise ValueError("Cannot handle nested DTensor")
+    return False
+
+
+def _alloc_tensor(
+    props: TensorProperties, size: Sequence[int], device_type: str = "cuda"
+) -> torch.Tensor:
+    if device_type == "cpu":
+        device = cast(torch.device, _get_device_module(device_type).current_device())
+    else:
+        device = torch.device(
+            device_type, _get_device_module(device_type).current_device()
+        )
+
+    return torch.empty(
+        size=size,
+        dtype=props.dtype,
+        layout=props.layout,
+        requires_grad=props.requires_grad,
+        pin_memory=props.pin_memory,
+        device=device,
+    )
+
+
+def _get_state_dict_2d_layout(
+    state_dict: STATE_DICT_TYPE,
+) -> tuple[STATE_DICT_2D_LAYOUT, Optional[dist.ProcessGroup]]:
+    """
+    Load the right TP slice of the optimizer state.
+
+    This is not easy since the per-tensor slicing can't be inferred from checkpoint metadata.
+    We take advantage of the model state_dict producing a sliced ST to figure out what we need to load.
+    This is pretty fragile and it might be easier for FSDP to compute this info for us.
+    Returns a dictionary where keys are the same of the state_dict and the value is a tuple of
+    (offset, size) for the current rank TP slice.
+    N.B. The state_dict *MUST* come from FSDP.sharded_state_dict.
+    """
+    specs: STATE_DICT_2D_LAYOUT = {}
+    dp_pg: Optional[dist.ProcessGroup] = None
+    for key, value in state_dict.items():
+        specs[key] = (None, value.size())
+        if _is_nested_tensor(value):
+            if not len(value.local_shards()) == 1:
+                raise AssertionError("Cannot handle ST with multiple shards")
+            if not isinstance(value, ShardedTensor):
+                raise AssertionError("Can only handle nested ShardedTensor")
+            shard = value.local_shards()[0]
+            specs[key] = (
+                shard.metadata.shard_offsets,
+                shard.metadata.shard_sizes,
+            )
+            dp_pg = shard.tensor._process_group  # type: ignore[attr-defined]
+
+    return (
+        specs,
+        dp_pg,
+    )
+
+
+class _ReaderWithOffset(DefaultLoadPlanner):
+    translation: dict[MetadataIndex, MetadataIndex]
+    state_dict: STATE_DICT_TYPE
+    # pyrefly: ignore [bad-override]
+    metadata: Metadata
+
+    def __init__(self, fqn_to_offset: dict[str, Sequence[int]]) -> None:
+        super().__init__()
+        self.fqn_to_offset = fqn_to_offset
+        self.metadata = Metadata({})
+        self.state_dict = {}
+        self.translation = {}
+
+    def create_local_plan(self) -> LoadPlan:
+        requests = []
+        self.translation = {}
+        for fqn, obj in self.state_dict.items():
+            md = self.metadata.state_dict_metadata[fqn]
+            if not isinstance(obj, ShardedTensor):
+                requests += _create_read_items(fqn, md, obj)
+                continue
+
+            if fqn not in self.fqn_to_offset:
+                requests += _create_read_items(fqn, md, obj)
+                continue
+
+            offset = self.fqn_to_offset[fqn]
+
+            if not len(obj.local_shards()) == 1:
+                raise AssertionError("Expected exactly one local shard")
+            original_shard = obj.local_shards()[0]
+            local_chunks = [
+                ChunkStorageMetadata(
+                    offsets=torch.Size(
+                        _element_wise_add(original_shard.metadata.shard_offsets, offset)
+                    ),
+                    sizes=torch.Size(original_shard.metadata.shard_sizes),
+                )
+            ]
+
+            reqs = create_read_items_for_chunk_list(
+                fqn, cast(TensorStorageMetadata, md), local_chunks
+            )
+            # TODO: The ReadItems will have a displaced MetadataIndex, fix it.
+            # TODO: we should change _create_sharded_read_items to have more ergonomic API
+            for ri in reqs:
+                if ri.dest_index.offset is None:
+                    raise AssertionError("dest_index.offset must not be None")
+                original_offset = _element_wise_sub(ri.dest_index.offset, offset)
+                original_index = dataclasses.replace(
+                    ri.dest_index, offset=torch.Size(original_offset)
+                )
+                self.translation[ri.dest_index] = original_index
+
+            requests += reqs
+        return LoadPlan(requests)
+
+    def lookup_tensor(self, index: MetadataIndex) -> torch.Tensor:
+        return super().lookup_tensor(self.translation.get(index, index))
+
+
+def load_sharded_optimizer_state_dict(
+    model_state_dict: STATE_DICT_TYPE,
+    optimizer_key: str,
+    storage_reader: StorageReader,
+    planner: Optional[LoadPlanner] = None,
+) -> STATE_DICT_TYPE:
+    """
+    Load a state_dict in conjunction with FSDP sharded optimizer state.
+
+    This is the current recommended way to checkpoint FSDP.
+    >>> # xdoctest: +SKIP
+    >>> import torch.distributed.checkpoint as dist_cp
+    >>> # Save
+    >>> model: torch.nn.Model
+    >>> optim_params = model.parameters()
+    >>> optim = torch.optim.SGD(optim_params, lr=0.01)
+    >>> # Save
+    >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT):
+    >>>     state_dict = {
+    >>>         "optimizer": FSDP.optim_state_dict(model, optim),
+    >>>         "model": model.state_dict()
+    >>>     }
+    >>>     dist_cp.save_state_dict(
+    >>>         state_dict=optim_state,
+    >>>         storage_writer=dist_cp.FileSystemWriter("checkpoint"),
+    >>>         planner=dist_cp.DefaultSavePlanner(),
+    >>>     )
+    >>>
+    >>> # Load
+    >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT):
+    >>>     model_state_dict = model_tp.state_dict()
+    >>>     checkpoint = {
+    >>>         "model": model_state_dict
+    >>>     }
+    >>>     dist_cp.load_state_dict(
+    >>>         state_dict=checkpoint,
+    >>>         storage_reader=dist_cp.FileSystemReader(checkpoint_file),
+    >>>         planner=dist_cp.DefaultLoadPlanner(),
+    >>>     )
+    >>>     model.load_state_dict(checkpoint["model_state"])
+    >>>
+    >>>     optim_state = dist_cp.load_sharded_optimizer_state_dict(
+    >>>         model_state_dict,
+    >>>         optimizer_key="optimizer",
+    >>>         storage_reader=dist_cp.FileSystemReader("checkpoint"),
+    >>>     )
+    >>>
+    >>>     flattened_osd = FSDP.optim_state_dict_to_load(
+    >>>        model, optim, optim_state["optimizer"]
+    >>>     )
+    >>>
+    >>>     optim.load_state_dict(flattened_osd)
+    """
+    metadata = storage_reader.read_metadata()
+
+    layout_specs, dp_pg = _get_state_dict_2d_layout(model_state_dict)
+    dp_pg_device_type = dist.distributed_c10d._get_pg_default_device(dp_pg).type
+    device_module = _get_device_module(dp_pg_device_type)
+
+    if dp_pg is None:
+        placements = []
+        for i in range(dist.get_world_size()):
+            device_info = _normalize_device_info(
+                dp_pg_device_type, i % device_module.device_count()
+            )
+            placements.append(f"rank:{i}/{device_info}")
+        sharding_spec = ChunkShardingSpec(dim=0, placements=placements)  # type: ignore[arg-type]
+    else:
+        sharding_spec = _create_colwise_spec(dp_pg)
+
+    # Create a state_dict for optimizer state
+    state_dict: STATE_DICT_TYPE = {}
+
+    fqn_to_offset: dict[str, Sequence[int]] = {}
+    for key, value in metadata.state_dict_metadata.items():
+        key_path = metadata.planner_data[key]
+        if key_path[0] != optimizer_key:
+            continue
+
+        if isinstance(value, BytesStorageMetadata):
+            state_dict[key] = ""
+            continue
+
+        # value: TensorStorageMetadata
+        if value.size.numel() == 1:
+            state_dict[key] = _alloc_tensor(
+                value.properties, value.size, dp_pg_device_type
+            )
+        elif dp_pg is None:
+            state_dict[key] = _create_chunk_sharded_tensor(
+                _alloc_tensor(value.properties, value.size, dp_pg_device_type),
+                rank=dist.get_rank(),
+                world_size=dist.get_world_size(),
+                num_devices_per_node=device_module.device_count(),
+                pg=_get_default_group(),
+            )
+        else:
+            spec_key = key_path[2]
+            alloc_size = layout_specs.get(spec_key, (None, value.size))[1]
+
+            properties = ShardTensorProperties(
+                dtype=value.properties.dtype,
+                layout=value.properties.layout,
+                requires_grad=value.properties.requires_grad,
+                memory_format=value.properties.memory_format,
+                pin_memory=value.properties.pin_memory,
+            )
+
+            st_md = sharding_spec.build_metadata(torch.Size(alloc_size), properties)
+            local_shards = []
+            current_rank = dist.get_rank(dp_pg)
+            for shard_md in st_md.shards_metadata:
+                if cast(_remote_device, shard_md.placement).rank() != current_rank:
+                    continue
+                local_shards.append(
+                    Shard(
+                        tensor=_alloc_tensor(
+                            value.properties, shard_md.shard_sizes, dp_pg_device_type
+                        ),
+                        metadata=shard_md,
+                    )
+                )
+
+            st = ShardedTensor._init_from_local_shards_and_global_metadata(
+                local_shards, st_md, process_group=dp_pg
+            )
+
+            if spec_key in layout_specs and layout_specs[spec_key][0] is not None:
+                fqn_to_offset[key] = cast(Sequence[int], layout_specs[spec_key][0])
+
+            state_dict[key] = st
+
+    # Whether we unflatten before or after doesn't matter
+    load_state_dict(
+        state_dict=state_dict,
+        storage_reader=storage_reader,
+        # FIXME the type of planner is wrong in load_state_dict
+        planner=_ReaderWithOffset(fqn_to_offset) if dp_pg is not None else planner,
+    )
+
+    state_dict = unflatten_state_dict(state_dict, metadata.planner_data)
+
+    return state_dict
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/planner.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/planner.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c97dc0379b109dd3a9706176390720a88128851
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/planner.py
@@ -0,0 +1,450 @@
+import abc
+import io
+import operator
+from dataclasses import dataclass
+from enum import auto, Enum
+from functools import reduce
+from typing import Any, Optional, Union
+
+import torch
+from torch.distributed.checkpoint.metadata import (
+    ChunkStorageMetadata,
+    Metadata,
+    MetadataIndex,
+    STATE_DICT_TYPE,
+    StorageMeta,
+    TensorProperties,
+)
+
+
+__all__ = [
+    "WriteItemType",
+    "LoadItemType",
+    "BytesIOWriteData",
+    "TensorWriteData",
+    "WriteItem",
+    "ReadItem",
+    "SavePlan",
+    "LoadPlan",
+    "SavePlanner",
+    "LoadPlanner",
+]
+
+
+class WriteItemType(Enum):
+    TENSOR = auto()
+    SHARD = auto()
+    BYTE_IO = auto()
+
+
+class LoadItemType(Enum):
+    TENSOR = auto()
+    BYTE_IO = auto()
+
+
+@dataclass(frozen=True)
+class BytesIOWriteData:
+    nbytes: int
+
+
+@dataclass(frozen=True)
+class TensorWriteData:
+    chunk: ChunkStorageMetadata
+    properties: TensorProperties
+    size: torch.Size
+
+
+@dataclass(frozen=True)
+class WriteItem:
+    """Dataclass which holds information about what needs to be written to storage."""
+
+    index: MetadataIndex
+    type: WriteItemType
+
+    # Size of bytesIO data to be written.
+    bytes_io_data: Optional[BytesIOWriteData] = None
+
+    # Value present if it's a tensor write
+    tensor_data: Optional[TensorWriteData] = None
+
+    def tensor_storage_size(self) -> Optional[int]:
+        """
+        Calculates the storage size of the underlying tensor, or None if this is not a tensor write.
+
+        Returns:
+            Optional[int] storage size, in bytes of underlying tensor if any.
+        """
+        if self.tensor_data is None:
+            return None
+
+        numels = reduce(operator.mul, self.tensor_data.size, 1)
+        dtype_size = torch._utils._element_size(self.tensor_data.properties.dtype)
+        return numels * dtype_size
+
+
+@dataclass(frozen=True)
+class ReadItem:
+    # Read Item
+    type: LoadItemType
+
+    # Index into the state_dict
+    dest_index: MetadataIndex
+    # Offsets into destination tensor
+    dest_offsets: torch.Size
+
+    # Index into the checkpoint
+    storage_index: MetadataIndex
+    # Offset into the checkpoint data
+    storage_offsets: torch.Size
+
+    # Size of the hypercube to copy
+    lengths: torch.Size
+
+
+@dataclass(frozen=True)
+class SavePlan:
+    items: list[WriteItem]
+    storage_data: Any = None
+    planner_data: Any = None
+    # This is used to indicate that the ranks should
+    # use the cached plans to write data instead.
+    usable: bool = True
+
+
+@dataclass
+class LoadPlan:
+    items: list[ReadItem]
+    storage_data: Any = None
+    planner_data: Any = None
+
+
+class SavePlanner(abc.ABC):
+    """
+    Abstract class defining the protocol used by save_state_dict to plan the save process.
+
+    SavePlanners are stateful objects that can be used to customize the whole save process.
+
+    SavePlanner acts as an access proxy to the state_dict, so any transformation done to it
+    will be visible to the whole process.
+
+    A planner subclass can expect the following sequence of calls during save_state_dict:
+
+    1) set_up_planner - called on all ranks.
+        Signals the start of a checkpoint save.
+
+    2) create_local_plan - called on all ranks.
+        Process the state_dict and produces a `SavePlan` that will be sent for global planning.
+
+    3) create_global_plan - called on the coordinator rank only.
+        Takes the SavePlan from all ranks and make any global decision.
+
+    4) finish_plan - called on all ranks.
+        This gives each rank a chance to adjust to global planning decisions.
+
+    5) resolve_data - called multiple times on each rank
+        Lookups a value on the `state_dict` for the storage layer to write.
+
+    Users are recommended to extend DefaultSavePlanner instead of this interface directly as
+    most changes can be expressed by changes in a single method.
+
+    There are 3 usual patterns of extension:
+
+    Rewriting state_dict. This is the simplest way to extend the save process as it
+    doesn't requite understanding the intrincacies of how SavePlan works:
+
+    >>> # xdoctest: +SKIP("undefined vars")
+    >>> class RenamePlanner(DefaultSavePlanner):
+    >>>     def set_up_planner(
+    >>>         self,
+    >>>         state_dict: STATE_DICT_TYPE,
+    >>>         storage_meta: Optional[StorageMeta],
+    >>>         is_coordinator: bool,
+    >>>     ) -> None:
+    >>> # prefix all keys with `foo_``
+    >>>         super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator)
+
+    Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted
+
+    >>> # xdoctest: +SKIP("undefined vars")
+    >>> class FP16Planner(DefaultSavePlanner):
+    >>>     def create_local_plan(self):
+    >>>         plan = super().create_local_plan()
+    >>>         for p in plan:
+    >>>             if p.tensor_data is not None:
+    >>>                 p.tensor_data.properties.dtype = torch.float16
+    >>>         return plan
+    >>>
+    >>>     def resolve_data(self, write_item):
+    >>>         item = super().resolve_data(write_item)
+    >>>         return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16)
+
+    Using the global planning step to make central decisions that can't be made individually by each rank
+
+    >>> # xdoctest: +SKIP("undefined vars")
+    >>> from itertools import zip_longest
+    >>> from dataclasses import replace
+    >>> class DDPLoadBalancingPlanner(DefaultSavePlanner):
+    >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0
+    >>> # This sample doesn't handle ShardedTensors
+    >>>     def create_global_plan(self, all_plans):
+    >>>         iters = [iter(all_plans[0].items)] * len(all_plans)
+    >>>         items_per_rank = [
+    >>>             [item for item in items if item is not None]
+    >>>             for items in zip(*zip_longest(*iters), strict=True)
+    >>>         ]
+    >>>         all_plans = [
+    >>>             replace(plan, items=items)
+    >>>             for plan, items in zip(all_plans, items_per_rank, strict=True)
+    >>>         ]
+    >>>         return super().create_global_plan(all_plans)
+
+    Finally, some planners need to save additional metadata in the checkpoint, this is
+    accomplished by having each rank contribute their data items in the local plan and
+    the global planner aggregate them:
+
+    >>> # xdoctest: +SKIP("undefined vars")
+    >>> class SaveExtraDataPlanner(DefaultSavePlanner):
+    >>>     def create_local_plan(self) -> SavePlan:
+    >>>         plan = super().create_local_plan()
+    >>>         return replace(plan, planner_data="per-rank-data")
+    >>>
+    >>>     def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]:
+    >>>         global_plan, metadata = super().create_global_plan(all_plans)
+    >>>         merged_data = [p.planner_data for p in global_plan]
+    >>>         metadata = replace(metadata, planner_data=merged_data)
+    >>>         return global_plan, metadata
+    """
+
+    # Save plan for the current rank as computed by `create_local_plan` API
+    # Cached on the local rank.
+    _cached_save_plan: dict[str, SavePlan] = {}
+    # Final save plan for the current rank.
+    # This is created by merging the plan created by `create_local_plan` API
+    # and the result of `create_global_plan` for the given rank.
+    # This is the final plan computed by the `finish_plan` API that gets
+    # sent to the `write_data`.
+    # Cached on the local rank.
+    _cached_final_save_plan: dict[str, SavePlan] = {}
+    # Collection of all the local plans from all the ranks.
+    # This is the input to the `create_global_plan` API.
+    # Cached on the coordinator rank.
+    _cached_all_plans: dict[str, list[SavePlan]] = {}
+    # Global checkpoint plan as computed by `create_global_plan` API.
+    # Cached on the coordinator rank.
+    _cached_global_plan: dict[str, list[SavePlan]] = {}
+    # Metadata for the global checkpoint plan as computed by `create_global_plan` API.
+    # Cached on the coordinator rank.
+    _cached_metadata: dict[str, Metadata] = {}
+
+    @abc.abstractmethod
+    def set_up_planner(
+        self,
+        state_dict: STATE_DICT_TYPE,
+        storage_meta: Optional[StorageMeta] = None,
+        is_coordinator: bool = False,
+    ) -> None:
+        """
+        Initialize this planner to save ``state_dict``.
+
+        Implementations should save those values as they won't be provided lated in the save process.
+
+        This is called on all ranks.
+        """
+
+    @abc.abstractmethod
+    def create_local_plan(self) -> SavePlan:
+        """
+        Compute the save plan for the current rank.
+
+        This will be aggregated and passed to create_global_plan.
+        Planner specific data can be passed through SavePlan::planner_data.
+
+        This is called on all ranks.
+        """
+
+    @abc.abstractmethod
+    def create_global_plan(
+        self, all_plans: list[SavePlan]
+    ) -> tuple[list[SavePlan], Metadata]:
+        """
+        Compute the global checkpoint plan and return the local plan of each rank.
+
+        This is called on the coordinator rank only.
+        """
+
+    @abc.abstractmethod
+    def finish_plan(self, new_plan: SavePlan) -> SavePlan:
+        """
+        Merge the plan created by `create_local_plan` and the result of `create_global_plan`.
+
+        This is called on all ranks.
+        """
+
+    @abc.abstractmethod
+    def resolve_data(self, write_item: WriteItem) -> Union[torch.Tensor, io.BytesIO]:
+        """
+        Transform and prepare ``write_item`` from ``state_dict`` for storage, ensuring idempotency and thread-safety.
+
+        Lookup the object associated with ``write_item`` in ``state_dict`` and apply any
+        transformation (such as serialization) prior to the storage layer consuming it.
+
+        Called on each rank multiple times, at least once per WriteItem in the final SavePlan.
+
+        This method should be idempotent and thread-save. StorageWriter implementations
+        are free to call it as frequently as they need.
+
+        Any transformation that allocates memory should be lazily done when his method
+        is called in order to reduce peak memory required by checkpointing.
+
+        When returning tensors, they can be on any device or format, they can be views too.
+        It's the storage layer responsibility to figure out how to save them.
+        """
+
+
+class LoadPlanner:
+    """
+    Abstract class defining the protocol used by load_state_dict to plan the load process.
+
+    LoadPlanner are stateful objects that can be used to customize the whole load process.
+
+    LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it
+    will be visible to the whole process.
+
+    A planner subclass can expect the following sequence of calls during load_state_dict:
+
+    1) set_up_planner - called on all ranks.
+        Signals the start of loading a checkpoint.
+
+    2) create_local_plan - called on all ranks.
+        Process the state_dict and produces a `LoadPlan` that will be sent for global planning.
+
+    3) create_global_plan - called on the coordinator rank only.
+        Takes the LoadPlan from all ranks and make any global decision.
+
+    4) load_bytes - called multiple times on each rank
+        This is called once per non-tensor value in state_dict.
+
+    5) resolve_tensor and commit_tensor - called multiple times on each rank
+        They are called in pair for each Tensor value in state_dict.
+
+    Users are recommended to extend DefaultLoadPlanner instead of this interface directly as
+    most changes can be expressed by changes in a single method.
+
+    There are two usual patterns of extension:
+
+    Rewriting state_dict. This is the simplest way to extend the load process as it
+    doesn't requite understanding the intrincacies of how LoadPlan works. We need
+    to keep a reference to the original state_dict as load happens in place so
+    we need to be able to perform it in place
+
+    >>> # xdoctest: +SKIP("undefined vars")
+    >>> class RenamePlanner(DefaultLoadPlanner):
+    >>>     def set_up_planner(
+    >>>         self,
+    >>>         state_dict: STATE_DICT_TYPE,
+    >>>         metadata: Metadata,
+    >>>         is_coordinator: bool,
+    >>>     ) -> None:
+    >>>         self.original_state_dict = state_dict
+    >>>         state_dict = {"foo_" + k: v for k, v in state_dict.items()}
+    >>>
+    >>>         if self.flatten_sharded_tensors:
+    >>>             state_dict = _flatten_sharded_tensors(state_dict)
+    >>>
+    >>>         if self.flatten_state_dict:
+    >>>             state_dict, self.mappings = flatten_state_dict(state_dict)
+    >>>
+    >>>         self.state_dict = state_dict
+    >>>         self.metadata = metadata
+    >>>         self.is_coordinator = is_coordinator
+    >>>
+    >>>     def load_bytes(self, read_item, value):
+    >>> # Remove the "foo_" prefix
+    >>>         self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False)
+
+
+    Modifying resolve_tensor and commit_tensor to handle load time transformation.
+
+    >>> # xdoctest: +SKIP("undefined vars")
+    >>> class MetaModelMaterialize(DefaultSavePlanner):
+    >>>     def resolve_tensor(self, read_item):
+    >>>         tensor = super().resolve_tensor(read_item)
+    >>>         return torch.empty_like(tensor, device="cpu")
+    >>>
+    >>>     def commit_tensor(self, read_item, tensor):
+    >>>         self.state_dict[read_item.dest_index.fqn] = tensor
+    """
+
+    @abc.abstractmethod
+    def set_up_planner(
+        self,
+        state_dict: STATE_DICT_TYPE,
+        metadata: Optional[Metadata] = None,
+        is_coordinator: bool = False,
+    ) -> None:
+        """
+        Initialize this instance to load data into ``state_dict``.
+
+        . N.B. This is called on every rank.
+        """
+
+    @abc.abstractmethod
+    def create_local_plan(self) -> LoadPlan:
+        """
+        Create a LoadPlan based on state_dict and metadata provided by set_up_planner.
+
+        . N.B. This is called on every rank.
+        """
+
+    @abc.abstractmethod
+    def create_global_plan(self, global_plan: list[LoadPlan]) -> list[LoadPlan]:
+        """
+        Compute the global load plan and return plans for each rank.
+
+        . N.B. This is called on the coordinator rank only
+        """
+
+    @abc.abstractmethod
+    def finish_plan(self, central_plan: LoadPlan) -> LoadPlan:
+        """Accept the plan from coordinator and return final LoadPlan."""
+
+    @abc.abstractmethod
+    def load_bytes(self, read_item: ReadItem, value: io.BytesIO) -> None:
+        """
+        Load the item described by ``read_item``and ``value``.
+
+        This method is expected to modify in-place the underlying state_dict.
+
+        The contents of ``value`` are defined by the SavePlanner used to produce
+        the checkpoint being loaded.
+        """
+
+    def resolve_bytes(self, read_item: ReadItem) -> io.BytesIO:
+        """
+        Return the BytesIO to be used by the StorageReader to load `read_item`.
+
+        The BytesIO should alias with one on the underlying state_dict as StorageReader will replace its contents.
+        """
+        raise NotImplementedError("LoadPlanner.resolve_bytes is not implemented")
+
+    @abc.abstractmethod
+    def resolve_tensor(self, read_item: ReadItem) -> torch.Tensor:
+        """
+        Return the tensor described by ``read_item`` to be used by the StorageReader to load `read_item`.
+
+        The tensor should alias with one on the underlying state_dict as StorageReader will replace its contents.
+        If, for any reason, that's not possible, the planner can use the ``commit_tensor`` method to copy the data
+        back to the one in state_dict.
+        """
+
+    @abc.abstractmethod
+    def commit_tensor(self, read_item: ReadItem, tensor: torch.Tensor) -> None:
+        """
+        Call once the StorageReader finished loading data into ``tensor``.
+
+        The provided tensor is the same one returned by the call to ``resolve_tensor``.
+        This method is only needed if this LoadPlanner needs to post process ``tensor`` prior to
+        copying it back to the one in the state_dict.
+
+        The contents of tensor will follow its device synchronization model.
+        """
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/planner_helpers.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/planner_helpers.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d7af7d7a821b541cf66044a28d828d863624da2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/planner_helpers.py
@@ -0,0 +1,491 @@
+# mypy: allow-untyped-defs
+import io
+from collections.abc import Callable
+from typing import Any, cast
+
+import torch
+import torch.distributed as dist
+from torch._utils import _get_device_module
+from torch.distributed._shard.metadata import ShardMetadata
+from torch.distributed._shard.sharded_tensor import ShardedTensor
+from torch.distributed.tensor import DTensor
+from torch.distributed.tensor._utils import compute_local_shape_and_global_offset
+
+from .metadata import (
+    BytesStorageMetadata,
+    ChunkStorageMetadata,
+    MetadataIndex,
+    STATE_DICT_TYPE,
+    STORAGE_TYPES,
+    TensorProperties,
+    TensorStorageMetadata,
+)
+from .planner import (
+    LoadItemType,
+    ReadItem,
+    SavePlan,
+    TensorWriteData,
+    WriteItem,
+    WriteItemType,
+)
+from .resharding import (
+    _check_shard_metadata_pair_overlap,
+    _shards_get_overlap_region_wrt_saved_tensor,
+)
+
+
+__all__: list[str] = ["create_read_items_for_chunk_list"]
+
+
+def _compare_save_plans(plan: SavePlan, other_plan: SavePlan) -> bool:
+    """
+    Compare the two Save plans and return True if they are equal.
+
+    Args:
+        plan (SavePlan): First SavePlan to compare.
+        other_plan (SavePlan): Second SavePlan to compare.
+
+    Returns:
+       True if the two plans are equal, False otherwise.
+    """
+    if plan.usable != other_plan.usable:
+        return False
+
+    # Both the plans should have the same number of items
+    if len(plan.items) != len(other_plan.items):
+        return False
+
+    # Both the plans should have the same write items.
+    for plan_item, other_plan_item in zip(plan.items, other_plan.items):
+        # Write item type should be same
+        if plan_item.type != other_plan_item.type:
+            return False
+
+        plan_metadata_index = plan_item.index
+        other_plan_metadata_index = other_plan_item.index
+
+        # Write item metadata_index should be same
+        if (
+            plan_metadata_index.fqn != other_plan_metadata_index.fqn
+            or plan_metadata_index.offset != other_plan_metadata_index.offset
+            or plan_metadata_index.index != other_plan_metadata_index.index
+        ):
+            return False
+
+        # Write item tensor_data should be present in both the write items plans, if it exists in either of them.
+        tensor_data = plan_item.tensor_data
+        other_tensor_data = other_plan_item.tensor_data
+        if (tensor_data and not other_tensor_data) or (
+            not tensor_data and other_tensor_data
+        ):
+            return False
+
+        if tensor_data and other_tensor_data:
+            # Write item tensor_data size should be same
+            if tensor_data.size != other_tensor_data.size:
+                return False
+
+            # Write item tensor_data chunk should be present in both the write items, if it exists in either of them.
+            chunk = tensor_data.chunk
+            other_chunk = other_tensor_data.chunk
+            if (chunk and not other_chunk) or (not chunk and other_chunk):
+                return False
+
+            # Write item tensor_data chunk offsets and sizes should be same
+            if chunk and other_chunk:
+                if (
+                    chunk.offsets != other_chunk.offsets
+                    or chunk.sizes != other_chunk.sizes
+                ):
+                    return False
+
+    return True
+
+
+def _contains_usable_plan(delta_plans: list[SavePlan]) -> bool:
+    """
+    Check if any delta plan is usable, indicating the plan has changed.
+
+    Args:
+        delta_plans (List[SavePlan]): A list of delta plans to check.
+    Returns:
+        True if any delta plan is usable, False otherwise.
+    """
+    return any(delta_plan and delta_plan.usable for delta_plan in delta_plans)
+
+
+def _merge_delta_local_plans(
+    cached_plans: list[SavePlan],
+    delta_plans: list[SavePlan],
+) -> list[SavePlan]:
+    """
+    Merge a list of delta plans into a single plan.
+
+    Args:
+        cached_plans (List[SavePlan]): A list of cached plans.
+        delta_plans (List[SavePlan]): A list of delta plans to merge. It can contain empty plans
+
+    Returns:
+        A single merged plan. If a delta plan is not usable, use the cached plan. Otherwise, use the delta plan.
+    """
+    merged_plans = []
+
+    for cached_plan, delta_plan in zip(cached_plans, delta_plans):
+        if delta_plan and not delta_plan.usable:
+            merged_plans.append(cached_plan)
+        else:
+            merged_plans.append(delta_plan)
+
+    return merged_plans
+
+
+def _create_chunk_from_tensor(tensor: torch.Tensor) -> ChunkStorageMetadata:
+    return ChunkStorageMetadata(
+        offsets=torch.Size([0] * len(tensor.size())), sizes=tensor.size()
+    )
+
+
+def _chunk_for_shard(shard_md: ShardMetadata) -> ChunkStorageMetadata:
+    return ChunkStorageMetadata(
+        offsets=torch.Size(shard_md.shard_offsets),
+        sizes=torch.Size(shard_md.shard_sizes),
+    )
+
+
+def _sharded_tensor_metadata(
+    sharded_tensor: ShardedTensor, shard_md: ShardMetadata
+) -> TensorWriteData:
+    shard_properties = sharded_tensor.metadata().tensor_properties
+
+    properties = TensorProperties(
+        dtype=shard_properties.dtype,
+        layout=shard_properties.layout,
+        requires_grad=shard_properties.requires_grad,
+        memory_format=shard_properties.memory_format,
+        pin_memory=shard_properties.pin_memory,
+    )
+
+    return TensorWriteData(
+        chunk=_chunk_for_shard(shard_md),
+        properties=properties,
+        size=sharded_tensor.metadata().size,
+    )
+
+
+def _create_write_items_for_dtensor(fqn: str, tensor: DTensor) -> WriteItem:
+    sizes, offsets = compute_local_shape_and_global_offset(
+        tensor.shape, tensor.device_mesh, tensor.placements
+    )
+    sizes, offsets = torch.Size(sizes), torch.Size(offsets)
+
+    return WriteItem(
+        index=MetadataIndex(fqn, offsets),
+        type=WriteItemType.SHARD,
+        tensor_data=TensorWriteData(
+            chunk=ChunkStorageMetadata(
+                offsets=offsets,
+                sizes=sizes,
+            ),
+            properties=TensorProperties.create_from_tensor(tensor.to_local()),
+            size=tensor.size(),
+        ),
+    )
+
+
+def _create_write_item_for_shard(
+    fqn: str, sharded_tensor: ShardedTensor, shard_md: ShardMetadata
+) -> WriteItem:
+    offsets = torch.Size(shard_md.shard_offsets)
+    return WriteItem(
+        index=MetadataIndex(fqn, offsets),
+        type=WriteItemType.SHARD,
+        tensor_data=_sharded_tensor_metadata(sharded_tensor, shard_md),
+    )
+
+
+def _create_write_item_for_tensor(fqn: str, tensor: torch.Tensor) -> WriteItem:
+    offsets = torch.Size([0] * len(tensor.size()))
+    return WriteItem(
+        index=MetadataIndex(fqn, offsets),
+        type=WriteItemType.TENSOR,
+        tensor_data=TensorWriteData(
+            chunk=ChunkStorageMetadata(offsets=offsets, sizes=tensor.size()),
+            properties=TensorProperties.create_from_tensor(tensor),
+            size=tensor.size(),
+        ),
+    )
+
+
+def _create_write_item_for_bytesio(fqn: str, bytes: Any):
+    return WriteItem(
+        index=MetadataIndex(fqn),
+        type=WriteItemType.BYTE_IO,
+    )
+
+
+def _create_read_item_for_byteio(
+    dest_index, dest_offset, storage_index, storage_offset, length
+):
+    return ReadItem(
+        type=LoadItemType.BYTE_IO,
+        dest_index=dest_index,
+        dest_offsets=torch.Size((dest_offset,)),
+        storage_index=storage_index,
+        storage_offsets=torch.Size((storage_offset,)),
+        lengths=torch.Size((length,)),
+    )
+
+
+def _create_read_item_for_tensor(
+    dest_index, dest_offsets, storage_index, storage_offsets, lengths
+):
+    return ReadItem(
+        type=LoadItemType.TENSOR,
+        dest_index=dest_index,
+        dest_offsets=torch.Size(dest_offsets),
+        storage_index=storage_index,
+        storage_offsets=torch.Size(storage_offsets),
+        lengths=torch.Size(lengths),
+    )
+
+
+def create_read_items_for_chunk_list(
+    fqn: str,
+    checkpoint_md: TensorStorageMetadata,
+    local_chunks: list[ChunkStorageMetadata],
+) -> list[ReadItem]:
+    """
+    Create a list of ``ReadItem`` based on the checkpoint and local chunks.
+
+    This applies the resharding algorithm and computes the reads needed
+    to satisfy ``local_chunks`` with a checkpoint described by ``checkpoint_md``.
+
+    Args:
+        fqn (str) : The state_dict FQN to pass to ``ReadItem``.
+        checkpoint_md (TensorStorageMetadata): metadata for a given tensor
+            from a checkpoint.
+        local_chunks (List[ChunkStorageMetadata]): Local chunks that needs to be
+            loaded.
+
+    Returns:
+        A list of ``ReadItem`` that will satisfy all input chunks.
+    """
+    read_items = []
+    # this is a naive quadratic algo that can be optimized later
+    for idx, shard in enumerate(local_chunks):
+        for storage_idx, storage_md in enumerate(checkpoint_md.chunks):
+            if not _check_shard_metadata_pair_overlap(shard, storage_md):
+                continue
+
+            storage_offsets = []
+            dest_offsets = []
+            lengths = []
+            for (
+                _dim,
+                offset_for_saved_tensor,
+                offset_for_current_tensor,
+                length,
+            ) in _shards_get_overlap_region_wrt_saved_tensor(
+                saved_shard=storage_md, current_shard=shard
+            ):
+                storage_offsets.append(offset_for_saved_tensor)
+                dest_offsets.append(offset_for_current_tensor)
+                lengths.append(length)
+
+            read_items.append(
+                _create_read_item_for_tensor(
+                    dest_index=MetadataIndex(fqn, shard.offsets, idx),
+                    dest_offsets=dest_offsets,
+                    storage_index=MetadataIndex(fqn, storage_md.offsets, storage_idx),
+                    storage_offsets=storage_offsets,
+                    lengths=lengths,
+                )
+            )
+    return read_items
+
+
+def _create_default_metadata_only_plan(state_dict: STATE_DICT_TYPE) -> SavePlan:
+    requests = []
+    for fqn, obj in state_dict.items():
+        if isinstance(obj, DTensor):
+            requests.append(_create_write_items_for_dtensor(fqn, obj))
+        elif isinstance(obj, ShardedTensor):
+            requests.extend(
+                _create_write_item_for_shard(fqn, obj, shard_md)
+                for shard_md in obj.metadata().shards_metadata
+            )
+        elif isinstance(obj, torch.Tensor):
+            requests.append(_create_write_item_for_tensor(fqn, obj))
+        else:
+            requests.append(_create_write_item_for_bytesio(fqn, obj))
+    return SavePlan(requests)
+
+
+def _create_write_items(fqn: str, object: Any) -> list[WriteItem]:
+    if hasattr(object, "__create_write_items__"):
+        # DTensor implements _Checkpointable
+        return object.__create_write_items__(fqn, object)
+    elif isinstance(object, ShardedTensor):
+        return [
+            _create_write_item_for_shard(fqn, object, shard.metadata)
+            for shard in object.local_shards()
+        ]
+    elif isinstance(object, torch.Tensor):
+        return [_create_write_item_for_tensor(fqn, object)]
+    else:
+        return [_create_write_item_for_bytesio(fqn, object)]
+
+
+def _create_chunk_from_dtensor(tensor: DTensor) -> ChunkStorageMetadata:
+    sizes, offsets = compute_local_shape_and_global_offset(
+        tensor.shape, tensor.device_mesh, tensor.placements
+    )
+    sizes, offsets = torch.Size(sizes), torch.Size(offsets)
+    return ChunkStorageMetadata(
+        offsets=offsets,
+        sizes=sizes,
+    )
+
+
+def _create_chunk_list(tensor: torch.Tensor) -> list[ChunkStorageMetadata]:
+    if hasattr(tensor, "__create_chunk_list__"):
+        # DTensor implements _Checkpointable
+        local_chunks = tensor.__create_chunk_list__()  # type: ignore[attr-defined]
+    elif isinstance(tensor, ShardedTensor):
+        local_chunks = [
+            _chunk_for_shard(shard.metadata) for shard in tensor.local_shards()
+        ]
+    elif isinstance(tensor, torch.Tensor):
+        local_chunks = [_create_chunk_from_tensor(tensor)]
+    else:
+        raise ValueError(
+            "Unsupported Type, expecting one of [Tensor, DTensor, ShardedTensor] "
+            f",but got {type(tensor)}"
+        )
+
+    return local_chunks
+
+
+def _create_read_items(fqn: str, md: STORAGE_TYPES, obj: Any) -> list[ReadItem]:
+    if not isinstance(md, BytesStorageMetadata):
+        try:
+            local_chunks = _create_chunk_list(obj)
+        except ValueError as ex:
+            raise ValueError(
+                f"Invalid checkpoint metadata for {fqn}, "
+                + f"expected BytesStorageMetadata but found {type(md)}",
+            ) from ex
+
+        return create_read_items_for_chunk_list(fqn, md, local_chunks)
+    else:
+        return [
+            _create_read_item_for_byteio(
+                dest_index=MetadataIndex(fqn),
+                dest_offset=0,
+                storage_index=MetadataIndex(fqn),
+                storage_offset=0,
+                length=0,
+            )
+        ]
+
+
+def _init_state_dict(state_dict: dict[str, Any]) -> Any:
+    """
+    Initializes meta tensor if the meta tensor is DTensor or torch.Tensor.
+    """
+
+    def dtensor_func(value: DTensor):
+        device = getattr(value, "device", None)
+        if device == torch.device("meta"):
+            device_type = dist.distributed_c10d._get_pg_default_device().type
+            device = cast(
+                torch.device, _get_device_module(device_type).current_device()
+            )
+            new_local_tensor = torch.empty_like(value.to_local(), device=device)
+            # We need to pass shape and stride explicitly, since DTensor might be
+            # sharded unevenly.
+            dtensor = DTensor.from_local(
+                new_local_tensor,
+                device_mesh=value.device_mesh,
+                placements=value.placements,
+                shape=value.size(),
+                stride=value.stride(),
+            )
+            return dtensor
+        else:
+            return value
+
+    def sharded_tensor_func(value: Any):
+        device = getattr(value, "device", None)
+        if device == torch.device("meta"):
+            raise RuntimeError(
+                f"Found unsupported type {type(value)} for meta device loading."
+            )
+        else:
+            return value
+
+    def tensor_func(value: torch.Tensor):
+        device = getattr(value, "device", None)
+        if device == torch.device("meta"):
+            device_type = dist.distributed_c10d._get_pg_default_device().type
+            device = cast(
+                torch.device, _get_device_module(device_type).current_device()
+            )
+            tensor = torch.empty_like(value, device=device)
+            return tensor
+        else:
+            return value
+
+    _iterate_state_dict(
+        state_dict,
+        dtensor_func,
+        sharded_tensor_func,
+        tensor_func,
+    )
+
+
+def _iterate_state_dict(
+    iter_object: Any,
+    dtensor_func: Callable,
+    sharded_tensor_func: Callable,
+    tensor_func: Callable,
+):
+    """
+    Iterate through the state dict, applying the given functions to each tensor type
+    and update the state dict in place.
+
+    Args:
+        iter_object (Any): the target state_dict.
+        sharded_tensor_func (Callable): the function to apply to ShardedTensor
+        dtensor_func (Callable): the function to apply to DTensor
+        tensor_func (Callable): the function to apply to Tensor
+
+    # TODO: let state_dict_util._iterate_state_dict() to support in place option
+    so we don't need to have two versions of _iterate_state_dict.
+    """
+
+    if isinstance(iter_object, DTensor):
+        return dtensor_func(iter_object)
+    elif isinstance(iter_object, ShardedTensor):
+        return sharded_tensor_func(iter_object)
+    elif isinstance(iter_object, torch.Tensor):
+        return tensor_func(iter_object)
+    elif (
+        isinstance(iter_object, (int, float, str, bytes, io.BytesIO))
+        or iter_object is None
+    ):
+        return iter_object
+    elif isinstance(iter_object, dict):
+        for key, value in iter_object.items():
+            iter_object[key] = _iterate_state_dict(
+                value, dtensor_func, sharded_tensor_func, tensor_func
+            )
+        return iter_object
+    elif isinstance(iter_object, (list, tuple)):
+        ret = [
+            _iterate_state_dict(v, dtensor_func, sharded_tensor_func, tensor_func)
+            for v in iter_object
+        ]
+        if isinstance(iter_object, tuple):
+            ret = tuple(ret)  # type: ignore[assignment]
+        return ret
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/quantized_hf_storage.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/quantized_hf_storage.py
new file mode 100644
index 0000000000000000000000000000000000000000..464052d99062a9b7b4e4b156cbe7a25d0fedc017
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/quantized_hf_storage.py
@@ -0,0 +1,506 @@
+# mypy: allow-untyped-defs
+import json
+import logging
+import math
+from pathlib import Path
+from typing import Any
+
+import torch
+from torch.distributed.checkpoint._hf_utils import _metadata_fn
+from torch.distributed.checkpoint.metadata import TensorStorageMetadata
+from torch.distributed.checkpoint.planner import LoadPlanner, ReadItem
+
+from .hf_storage import HuggingFaceStorageReader
+
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+__all__ = ["QuantizedHuggingFaceStorageReader"]
+
+
+class QuantizedHuggingFaceStorageReader(HuggingFaceStorageReader):
+    """
+    Extension of HuggingFaceStorageReader that handles quantized tensors.
+    Checkpoint should have the full tensor in a SafeTensor file. The quantized
+    tensor should not be sharded across multiple files.
+
+    This reader handles the dequantization of tensors during the read process,
+    converting them from quantized blocks to full dequantized tensors before
+    copying to the target tensor.
+    """
+
+    def __init__(
+        self,
+        path: str,
+        thread_count: int = 1,
+        target_dtype: torch.dtype = torch.float32,
+        block_size: int = 128,
+    ):
+        """
+        Initialize the HuggingFace storage reader to load quantized checkpoints
+
+        Args:
+            path: directory where the checkpoint will be read from.
+            thread_count: Number of threads to use to read distributed checkpoint. Defaults to 1.
+            target_dtype: Target dtype for dequantized tensor. Defaults to torch.float32.
+            block_size: Fixed block size for dequantization. Defaults to 128.
+        """
+        super().__init__(path=path, thread_count=thread_count)
+
+        self.target_dtype: torch.dtype = target_dtype
+        self.block_size: int = block_size
+        self._weight_scale_mapping: dict[str, str] = {}
+        # Track which file contains each tensor
+        self._weight_map: dict[str, str] = {}
+        # Cache for full tensor shapes (fqn -> shape)
+        self._tensor_full_shapes: dict[str, torch.Size] = {}
+
+    def read_metadata(self) -> Any:
+        metadata = super().read_metadata()
+
+        # Load quantization metadata first.
+        self._load_quantization_metadata()
+
+        # Build a cache of FQN -> full tensor shape, correcting for quantized tensors.
+        for fqn, tensor_metadata in metadata.state_dict_metadata.items():
+            # Only process TensorStorageMetadata which has size attribute.
+            if isinstance(tensor_metadata, TensorStorageMetadata):
+                # Check if this is a MXFP4 quantized tensor that needs shape correction.
+                if fqn.endswith("_blocks"):
+                    # Save the quantized tensor shapes for lookup when dequantization.
+                    self._tensor_full_shapes[fqn + "_quantized"] = tensor_metadata.size
+                    *prefix_shape, G, B = tensor_metadata.size
+                    dequantized_size = torch.Size([*prefix_shape, G * B * 2])
+
+                    # Update the metadata with the size after dequantization.
+                    # Metadata used by planner to slice state dict.
+                    tensor_metadata.size = dequantized_size
+                    self._tensor_full_shapes[fqn] = dequantized_size
+                else:
+                    self._tensor_full_shapes[fqn] = tensor_metadata.size
+
+        return metadata
+
+    def _load_quantization_metadata(self):
+        """Load quantization metadata from the checkpoint."""
+        checkpoint_path = Path(self.path)
+        # Load weight mapping from index file
+        index_file = checkpoint_path / _metadata_fn
+
+        with open(index_file) as f:
+            index_data = json.load(f)
+            weight_map = index_data.get("weight_map", {})
+            self._build_weight_scale_mapping(weight_map)
+
+    def _build_weight_scale_mapping(self, weight_map: dict[str, str]):
+        """Analyze and build weight-scale tensor pairs from weight mapping."""
+        # Store the complete weight map for file location lookups.
+        self._weight_map = weight_map
+
+        for tensor_name in weight_map:
+            if tensor_name.endswith(".weight_scale_inv"):
+                weight_name = tensor_name.replace(".weight_scale_inv", ".weight")
+                if weight_name in weight_map:
+                    self._weight_scale_mapping[weight_name] = tensor_name
+            # Handle MXFP4 format: _blocks and _scales.
+            elif tensor_name.endswith("_scales"):
+                blocks_name = tensor_name.replace("_scales", "_blocks")
+                if blocks_name in weight_map:
+                    self._weight_scale_mapping[blocks_name] = tensor_name
+
+    def _process_read_request(
+        self, f: Any, req: ReadItem, planner: LoadPlanner
+    ) -> None:
+        """Override the Helper function that processes a single read request."""
+        tensor_fqn = req.storage_index.fqn
+
+        # Check if this is a quantized tensor that needs dequantization
+        if self._is_tensor_quantized(tensor_fqn):
+            tensor = self._read_quantized_tensor_with_block_alignment(req, f)
+        else:
+            # Standard tensor reading
+            slices = tuple(
+                slice(offset, offset + length)
+                for offset, length in zip(req.storage_offsets, req.lengths)
+            )
+            tensor = f.get_slice(tensor_fqn)[slices]
+
+        target_tensor = planner.resolve_tensor(req).detach()
+
+        if target_tensor.size() != tensor.size():
+            raise AssertionError(
+                f"req {req.storage_index} mismatch sizes {target_tensor.size()} vs {tensor.size()}"
+            )
+
+        target_tensor.copy_(tensor)
+        planner.commit_tensor(req, target_tensor)
+
+    def _get_slice_to_block_mapping(
+        self, req: ReadItem
+    ) -> tuple[tuple[int, int], tuple[int, int], slice, slice]:
+        """
+        Calculate which blocks correspond to the requested slice.
+
+        Args:
+            req: Read request containing tensor info and required slices
+
+        Returns:
+            Tuple of (row_block_range, col_block_range, row_slice, col_slice)
+        """
+        # Get the slice information
+        row_slice = slice(
+            req.storage_offsets[0], req.storage_offsets[0] + req.lengths[0]
+        )
+        col_slice = slice(
+            req.storage_offsets[1], req.storage_offsets[1] + req.lengths[1]
+        )
+
+        # Calculate which blocks this slice spans
+        row_start_block = row_slice.start // self.block_size
+        row_end_block = (row_slice.stop - 1) // self.block_size + 1  # Inclusive end
+
+        col_start_block = col_slice.start // self.block_size
+        col_end_block = (col_slice.stop - 1) // self.block_size + 1  # Inclusive end
+
+        return (
+            (row_start_block, row_end_block),
+            (col_start_block, col_end_block),
+            row_slice,
+            col_slice,
+        )
+
+    def _dequantize_tensor_mxfp4(
+        self,
+        blocks: torch.Tensor,
+        scales: torch.Tensor,
+        req: ReadItem,
+        group_start: int,
+        offset_in_first_group: int,
+    ) -> torch.Tensor:
+        """
+        Dequantize a 4D tensor using MXFP4 format.
+        Adapted from openai's implementation:
+        https://github.com/openai/gpt-oss/blob/8890e95919f975a490fc0ba09ffb10890ec7319d/gpt_oss/torch/weights.py#L68
+
+        Args:
+            blocks: Sliced quantized weight tensor of shape [a_slice, b_slice, groups_slice, B] in uint8
+            scales: FULL scale tensor of shape [a, b, c] in uint8 (will be converted to exponents)
+            req: Read request containing slice information
+            group_start: The starting group index in the checkpoint
+            offset_in_first_group: Offset in values within the first group
+
+        Returns:
+            Dequantized tensor matching the requested shape
+        """
+        # FP4 lookup table
+        FP4_VALUES = [
+            +0.0,
+            +0.5,
+            +1.0,
+            +1.5,
+            +2.0,
+            +3.0,
+            +4.0,
+            +6.0,
+            -0.0,
+            -0.5,
+            -1.0,
+            -1.5,
+            -2.0,
+            -3.0,
+            -4.0,
+            -6.0,
+        ]
+
+        # blocks: [a_slice, b_slice, groups_slice, B] uint8.
+        # Read slightly more groups than needed, and slice at the end.
+
+        # Slice the scales to match the blocks dimensions.
+        # [a_full, b_full, c_full] -> [a_slice, b_slice, groups_slice]
+        dim0_start = req.storage_offsets[0]
+        dim0_end = dim0_start + req.lengths[0]
+        dim1_start = req.storage_offsets[1]
+        dim1_end = dim1_start + req.lengths[1]
+        num_groups = blocks.shape[2]
+        scales = scales[
+            dim0_start:dim0_end,
+            dim1_start:dim1_end,
+            group_start : group_start + num_groups,
+        ]
+
+        scales = scales.to(torch.int32) - 127
+
+        assert blocks.shape[:-1] == scales.shape, (
+            f"{blocks.shape=} does not match {scales.shape=}"
+        )
+
+        lut = torch.tensor(FP4_VALUES, dtype=self.target_dtype, device=blocks.device)
+
+        *prefix_shape, G, B = blocks.shape
+        rows_total = math.prod(prefix_shape) * G
+
+        blocks = blocks.reshape(rows_total, B)
+        scales = scales.reshape(rows_total, 1)
+
+        out = torch.empty(
+            rows_total, B * 2, dtype=self.target_dtype, device=blocks.device
+        )
+
+        rows_per_chunk = 16384 * 512
+
+        for r0 in range(0, rows_total, rows_per_chunk):
+            r1 = min(r0 + rows_per_chunk, rows_total)
+
+            blk = blocks[r0:r1]
+            exp = scales[r0:r1]
+
+            # nibble indices -> int64
+            idx_lo = (blk & 0x0F).to(torch.long)
+            idx_hi = (blk >> 4).to(torch.long)
+
+            sub = out[r0:r1]
+            sub[:, 0::2] = lut[idx_lo]
+            sub[:, 1::2] = lut[idx_hi]
+
+            torch.ldexp(sub, exp, out=sub)
+
+            del idx_lo, idx_hi, blk, exp
+
+        result = out.reshape(*prefix_shape, G, B * 2).view(*prefix_shape, G * B * 2)
+
+        # Slice the last dimension to match the requested range.
+        if offset_in_first_group > 0 or result.shape[-1] > req.lengths[2]:
+            end_offset = offset_in_first_group + req.lengths[2]
+            result = result[..., offset_in_first_group:end_offset]
+
+        return result
+
+    def _dequantize_tensor(
+        self,
+        weight: torch.Tensor,
+        scale_inv: torch.Tensor,
+        full_tensor_shape: torch.Size,
+        slice_info: tuple[tuple[int, int], tuple[int, int], slice, slice],
+    ) -> torch.Tensor:
+        """
+        Dequantize a sliced tensor using the appropriate portion of the scale tensor.
+
+        Args:
+            weight: Sliced quantized weight tensor
+            scale_inv: Full scale inverse tensor for dequantization
+            full_tensor_shape: Shape of the original full tensor
+            slice_info: Block mapping information from _get_slice_to_block_mapping
+
+        Returns:
+            Dequantized tensor
+        """
+        (row_block_range, col_block_range, row_slice, col_slice) = slice_info
+
+        # Convert to float32 for computation
+        # Certain quantized dtypes like Float8_e4m3fn
+        # don't support multiplication on CPU yet in PyTorch.
+        upcasted_weight = weight.to(torch.float32)
+
+        # Create output tensor in target dtype
+        dequantized = weight.detach().to(dtype=self.target_dtype, copy=True)
+
+        # Get the actual slice boundaries
+        row_start_global = row_slice.start
+        row_end_global = row_slice.stop
+        col_start_global = col_slice.start
+        col_end_global = col_slice.stop
+
+        # Apply scaling factors to each block that intersects with our slice
+        for block_i in range(row_block_range[0], row_block_range[1]):
+            for block_j in range(col_block_range[0], col_block_range[1]):
+                # Calculate the block boundaries in global coordinates
+                block_row_start_global = block_i * self.block_size
+                block_row_end_global = min(
+                    block_row_start_global + self.block_size, full_tensor_shape[0]
+                )
+                block_col_start_global = block_j * self.block_size
+                block_col_end_global = min(
+                    block_col_start_global + self.block_size, full_tensor_shape[1]
+                )
+
+                # Find the intersection of the block with our slice
+                intersect_row_start = max(block_row_start_global, row_start_global)
+                intersect_row_end = min(block_row_end_global, row_end_global)
+                intersect_col_start = max(block_col_start_global, col_start_global)
+                intersect_col_end = min(block_col_end_global, col_end_global)
+
+                # Skip if no intersection
+                if (
+                    intersect_row_start >= intersect_row_end
+                    or intersect_col_start >= intersect_col_end
+                ):
+                    continue
+
+                # Convert global coordinates to local coordinates in the sliced tensor
+                local_row_start = intersect_row_start - row_start_global
+                local_row_end = intersect_row_end - row_start_global
+                local_col_start = intersect_col_start - col_start_global
+                local_col_end = intersect_col_end - col_start_global
+
+                # Get the block from the sliced tensor
+                block = upcasted_weight[
+                    local_row_start:local_row_end, local_col_start:local_col_end
+                ]
+
+                # Apply the scale factor
+                scale = scale_inv[block_i, block_j]
+                block = block * scale
+
+                # Convert block to target dtype and store
+                block_converted = block.to(dtype=self.target_dtype)
+                dequantized[
+                    local_row_start:local_row_end, local_col_start:local_col_end
+                ] = block_converted
+
+        return dequantized
+
+    def _is_tensor_quantized(self, tensor_fqn: str) -> bool:
+        """
+        Check if a tensor is a quantized.
+
+        Args:
+            tensor_fqn: Fully qualified name of the tensor
+
+        Returns:
+            True if tensor is quantized and has a corresponding scale tensor,
+            False otherwise
+        """
+        # Skip scale tensors themselves
+        if tensor_fqn.endswith((".weight_scale_inv", "_scales")):
+            return False
+
+        # Check if this weight tensor has a corresponding scale tensor
+        if tensor_fqn not in self._weight_scale_mapping:
+            return False
+
+        return True
+
+    def _read_quantized_tensor_with_block_alignment(
+        self, req: ReadItem, safetensor_file: Any
+    ) -> torch.Tensor:
+        """
+        Read a quantized tensor with block alignment.
+
+        Args:
+            req: Read request containing tensor info and required slices
+            safetensor_file: Open safetensors file handle
+
+        Returns:
+            Dequantized tensor ready for use
+        """
+        tensor_fqn = req.storage_index.fqn
+        scale_fqn = self._weight_scale_mapping[tensor_fqn]
+
+        try:
+            group_start = 0
+            offset_in_first_group = 0
+            if tensor_fqn.endswith("_blocks"):
+                # Full tensor is a 4D MXFP4 quantized tensor: [..., G, B].
+                # Each group G produces B * 2 dequantized values.
+                # Checkpoint [..., G, B] -> dequantized [..., G*B*2].
+
+                # The planner gives 3D requests based on the dequantized shape.
+                # Need to figure out which groups (dimension 2 in checkpoint) to read.
+
+                # Use the quantized checkpoint shape to get the correct B.
+                *prefix_shape, B = self._tensor_full_shapes[tensor_fqn + "_quantized"]
+                values_per_group = B * 2  # Each byte has 2 nibbles (4-bit values).
+
+                # Calculate which groups we need based on the requested range in dim 2.
+                # Ensure the reequest is in 3D.
+                assert len(req.storage_offsets) == 3
+
+                # Positions in dequantized space.
+                dim2_start_deq = req.storage_offsets[2]
+                dim2_length_deq = req.lengths[2]
+                dim2_end_deq = dim2_start_deq + dim2_length_deq
+
+                # Convert to group indices.
+                group_start = dim2_start_deq // values_per_group
+                group_end = (dim2_end_deq + values_per_group - 1) // values_per_group
+
+                # Read only the necessary groups from checkpoint.
+                weight_slices_4d = (
+                    slice(
+                        req.storage_offsets[0], req.storage_offsets[0] + req.lengths[0]
+                    ),
+                    slice(
+                        req.storage_offsets[1], req.storage_offsets[1] + req.lengths[1]
+                    ),
+                    slice(group_start, group_end),
+                    slice(None),  # Read all B values for each group.
+                )
+                quantized_tensor = safetensor_file.get_slice(tensor_fqn)[
+                    weight_slices_4d
+                ]
+
+                # Also track the offset within the first group
+                offset_in_first_group = dim2_start_deq - (
+                    group_start * values_per_group
+                )
+            else:
+                # 2D quantized tensor, use 2d block partition.
+                weight_slices = tuple(
+                    slice(offset, offset + length)
+                    for offset, length in zip(req.storage_offsets, req.lengths)
+                )
+                quantized_tensor = safetensor_file.get_slice(tensor_fqn)[weight_slices]
+
+            # Load the corresponding scale inverse tensor (full tensor)
+            scale_file_name = self._weight_map.get(scale_fqn)
+            if scale_file_name is None:
+                raise ValueError(f"Scale tensor {scale_fqn} not found in weight_map")
+
+            # Check if scale tensor is in the same file as the weight tensor
+            weight_file_name = self._weight_map.get(tensor_fqn)
+
+            if scale_file_name == weight_file_name:
+                # Scale tensor is in the same file, use current handle
+                scale_inv = safetensor_file.get_tensor(scale_fqn)
+            else:
+                # Scale tensor is in a different file, need to open it
+                from safetensors import safe_open  # type: ignore[import]
+
+                scale_file_path = Path(self.path) / scale_file_name
+                with safe_open(
+                    scale_file_path, framework="pt", device="cpu"
+                ) as scale_file:
+                    scale_inv = scale_file.get_tensor(scale_fqn)
+
+            # Get the full tensor shape from our O(1) lookup cache
+            full_tensor_shape = self._tensor_full_shapes.get(tensor_fqn)
+            if full_tensor_shape is None:
+                raise ValueError(f"Could not find full tensor shape for {tensor_fqn}")
+
+            # Determine which dequantization function to use.
+            if len(full_tensor_shape) == 2:
+                # 2D block-wise quantization, e.g., used in deepseek v3.1
+                slice_info = self._get_slice_to_block_mapping(req)
+                dequantized_tensor = self._dequantize_tensor(
+                    weight=quantized_tensor,
+                    scale_inv=scale_inv,
+                    full_tensor_shape=full_tensor_shape,
+                    slice_info=slice_info,
+                )
+            elif tensor_fqn.endswith("_blocks"):
+                # 4D with blocks along dimension 2, used in MXFP4, e.g. gpt-oss
+                dequantized_tensor = self._dequantize_tensor_mxfp4(
+                    blocks=quantized_tensor,
+                    scales=scale_inv,
+                    req=req,
+                    group_start=group_start,
+                    offset_in_first_group=offset_in_first_group,
+                )
+            else:
+                raise ValueError("Unsupported quantization types")
+
+            return dequantized_tensor
+
+        except Exception as e:
+            logger.error("Failed to read the quantized tensor!!")
+            raise e
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/resharding.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/resharding.py
new file mode 100644
index 0000000000000000000000000000000000000000..e6f24b891aa895d3a445908fe6d084e13f9b05da
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/resharding.py
@@ -0,0 +1,69 @@
+from torch.distributed.checkpoint.metadata import ChunkStorageMetadata
+
+
+__all__: list[str] = []
+
+
+def _check_shard_metadata_pair_overlap(
+    shard1: ChunkStorageMetadata, shard2: ChunkStorageMetadata
+) -> bool:
+    """Check if two shards overlap."""
+    # For each dim of each shard, check if one shard resides on the other
+    # end of second shard with respect to that dim. As an example for a 2D
+    # shard, we would check if one shard is above or on the left of the
+    # other shard.
+    ndims = len(shard1.offsets)
+    for i in range(ndims):
+        if shard1.offsets[i] >= shard2.offsets[i] + shard2.sizes[i]:
+            return False
+        if shard2.offsets[i] >= shard1.offsets[i] + shard1.sizes[i]:
+            return False
+
+    return True
+
+
+def _shards_get_overlap_region_wrt_saved_tensor(
+    saved_shard: ChunkStorageMetadata, current_shard: ChunkStorageMetadata
+) -> list[tuple[int, int, int, int]]:
+    """
+    Return the overlapping region between saved_shard and current_shard.
+
+    There returned list has the same number of elements as the tensor's dimension.
+    For each element, we produce a tuple with the following contents:
+        (dimension, `saved_shard` offset, `current_shard` offset, length)
+
+    Offsets are relative to each shard.
+    """
+    narrows = []
+    for dim, (
+        saved_shard_offset,
+        current_shard_offset,
+        saved_shard_size,
+        current_shard_size,
+    ) in enumerate(
+        zip(
+            saved_shard.offsets,
+            current_shard.offsets,
+            saved_shard.sizes,
+            current_shard.sizes,
+        )
+    ):
+        min_range_end = min(
+            saved_shard_offset + saved_shard_size,
+            current_shard_offset + current_shard_size,
+        )
+
+        length = min_range_end - max(current_shard_offset, saved_shard_offset)
+
+        if saved_shard_offset > current_shard_offset:
+            offset_for_saved_tensor = 0
+            offset_for_current_tensor = saved_shard_offset - current_shard_offset
+        else:
+            offset_for_saved_tensor = current_shard_offset - saved_shard_offset
+            offset_for_current_tensor = 0
+
+        narrows.append(
+            (dim, offset_for_saved_tensor, offset_for_current_tensor, length)
+        )
+
+    return narrows
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/staging.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/staging.py
new file mode 100644
index 0000000000000000000000000000000000000000..4bbacc66aaaffe038bced44b9ca7b466a2246f90
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/staging.py
@@ -0,0 +1,474 @@
+import os
+import tempfile
+from concurrent.futures import Future, ThreadPoolExecutor
+from contextlib import nullcontext
+from dataclasses import dataclass
+from datetime import timedelta
+from typing import Any, cast, Optional, Union
+from typing_extensions import deprecated, Protocol, runtime_checkable
+
+import torch
+import torch.distributed as dist
+from torch.distributed import ProcessGroup
+from torch.distributed._state_dict_utils import _copy_state_dict, _create_cpu_state_dict
+from torch.distributed.checkpoint._pg_transport import PGTransport
+from torch.distributed.checkpoint._state_dict_stager import StateDictStager
+from torch.distributed.checkpoint.metadata import STATE_DICT_TYPE
+
+
+__all__ = ["AsyncStager", "BlockingAsyncStager", "DefaultStager", "StagingOptions"]
+
+"""
+Experimental staging module for PyTorch Distributed Checkpointing.
+This module provides advanced staging capabilities for checkpoints including:
+- Asynchronous staging using ThreadPoolExecutor
+- Pinned memory allocation for faster CPU-GPU transfers
+- Shared memory support for multi-process scenarios
+- Non-blocking CUDA operations with stream synchronization
+- Caching of frequently used storages for efficient memory management
+- Automatic resource cleanup and memory management
+Classes:
+    AsyncStager: Protocol defining the staging interface
+    StagingOptions: Configuration dataclass for staging behavior
+    DefaultStager: Default implementation with comprehensive staging features
+    BlockingAsyncStager: Implementation of AsyncStager which stages the state_dict
+    on CPU RAM and blocks until the copy is complete. Please use DefaultStager instead.
+"""
+
+
+@runtime_checkable
+class AsyncStager(Protocol):
+    """
+    This protocol is meant to provide customization and extensibility for dcp.async_save, allowing users
+    to customize how data is staged previous to executing the usual dcp.save path in parallel.
+    The expected order of operations (concretely defined in `torch.distributed.state_dict_saver.async_save`)
+    is the following:
+
+    1. AsyncStager.stage_data(state_dict):
+        This call gives the AsyncStager the opportunity to 'stage'
+        the state_dict. The expectation and purpose of staging in this context is to create a "training-safe"
+        representation of the state dict, meaning that any updates to module data after staging is complete
+        should not be reflected in the state dict returned from this method. For example, in the default
+        case a copy of the entire state dict is created on CPU RAM and returned here, allowing users
+        to continue training without risking changes to data which is being serialized.
+
+    2. dcp.save is called on the state_dict returned from stage in parallel. This call is responsible
+        for serializing the state_dict and writing it to storage.
+
+    3. If AsyncStager.should_synchronize_after_execute is True, this method will be called immediately after
+        the serialization thread starts and before returning from dcp.async_save. If this is set to False,
+        the assumption is the user has defined a custom synchronization point for the purpose of further
+        optimizing save latency in the training loop (for example, by overlapping staging with the
+        forward/backward pass), and it is the respondsibility of the user to call `AsyncStager.synchronize_staging`
+        at the appropriate time.
+
+    """
+
+    # default to True since the common case is to stage synchronously
+    _synchronize_after_execute: bool = True
+
+    @property
+    def should_synchronize_after_execute(self) -> bool:
+        """
+        Whether to synchronize after executing the stage.
+        """
+        return self._synchronize_after_execute
+
+    def stage(
+        self, state_dict: STATE_DICT_TYPE
+    ) -> Union[Future[STATE_DICT_TYPE], STATE_DICT_TYPE]:
+        """
+        Returns a "staged" copy of `state_dict`. The expectation of the staged copy is that it is
+        inoculated from any updates incurred after the stage call is complete.
+        """
+        raise NotImplementedError(
+            f"{self.__class__.__name__} must implement stage method"
+        )
+
+    @deprecated(
+        "`synchronize_staging` is deprecated and will be removed in future versions."
+        "Please use staging_future from AsyncSaveResponse instead.",
+        category=FutureWarning,
+    )
+    def synchronize_staging(self) -> None:
+        """
+        In the case `stage` is async in some way, this method should be called to ensure staging
+        is complete and it is safe to begin modifying the original `state_dict`
+        """
+
+    def close(self) -> None:
+        """
+        Clean up all resources used by the stager.
+        """
+
+
+@dataclass
+class StagingOptions:
+    """
+    Configuration options for checkpoint staging behavior.
+
+    Attributes:
+        use_pinned_memory (bool): Enable pinned memory allocation for faster
+            CPU-GPU transfers. Requires CUDA to be available. Default: True
+        use_shared_memory (bool): Enable shared memory for multi-process
+            scenarios. Useful when multiple processes need access to the
+            same staged data. Default: True
+        use_async_staging (bool): Enable asynchronous staging using a
+            background thread pool. Allows overlapping computation with
+            staging operations. Requires CUDA. Default: True
+        use_non_blocking_copy (bool): Use non-blocking device memory
+            copies with stream synchronization. Improves performance by
+            allowing CPU work to continue during GPU transfers. Default: True
+
+    Note:
+        CUDA-dependent features will raise exception if CUDA is not available.
+    """
+
+    use_pinned_memory: bool = True
+    use_shared_memory: bool = True
+    use_async_staging: bool = True
+    use_non_blocking_copy: bool = True
+
+
+class DefaultStager(AsyncStager):
+    """
+    DefaultStager provides a full-featured staging implementation that combines
+    multiple optimization techniques for efficient checkpoint preparation.
+
+    The staging process works as follows:
+    1. State dictionary is submitted for staging (sync or async)
+    2. Tensors are copied from GPU to optimized CPU storage
+    3. CUDA operations are synchronized if non-blocking copies are used
+    4. Staged state dictionary is returned or made available via Future
+
+    Usage Patterns:
+        # Synchronous staging
+        stager = DefaultStager(StagingOptions(use_async_staging=False))
+        staged_dict = stager.stage(state_dict)
+        stager.close()
+
+        # Asynchronous staging
+        stager = DefaultStager(StagingOptions(use_async_staging=True))
+        future = stager.stage(state_dict)
+        # ... do other work ...
+        staged_dict = future.result()
+        stager.close()
+
+        # Context manager pattern (recommended)
+        stager = DefaultStager(config)
+        with stager:
+        result = stager.stage(state_dict)
+
+    Performance Considerations:
+        - Async staging provides best performance when model computation
+          can overlap with staging operations
+        - Pinned memory improves CPU-GPU transfer speeds but uses more memory
+        - Shared memory allows efficient IPC to checkpoint process
+        - Non-blocking copies reduce GPU idle time during memory transfers
+
+    Thread Safety:
+        DefaultStager is not thread-safe. Each thread should use its own
+        instance, or external synchronization should be provided.
+    """
+
+    def __init__(
+        self,
+        config: StagingOptions = StagingOptions(),
+    ):
+        self._config = config
+        self._state_dict_stager = StateDictStager(
+            pin_memory=config.use_pinned_memory, share_memory=config.use_shared_memory
+        )
+        self._staging_executor = None
+        self._staging_stream = None
+        if self._config.use_async_staging:
+            # pyrefly: ignore [bad-assignment]
+            self._staging_executor = ThreadPoolExecutor(max_workers=1)
+            if torch.accelerator.is_available():
+                # Note: stream needs to be initialized on the main thread after default cuda
+                # stream is setup/used to avoid the risk of accidentally reusing the main
+                # compute stream or in other cases kernels actually launching from the
+                # main thread.
+                # pyrefly: ignore [bad-assignment]
+                self._staging_stream = torch.Stream()
+
+        if self._config.use_non_blocking_copy:
+            if not torch.accelerator.is_available():
+                raise AssertionError(
+                    "Non-blocking copy requires that the current accelerator is available."
+                )
+
+        self._staging_future: Optional[Future[STATE_DICT_TYPE]] = None
+
+    def stage(
+        self,
+        state_dict: STATE_DICT_TYPE,
+        **kwargs: Any,
+    ) -> Union[STATE_DICT_TYPE, Future[STATE_DICT_TYPE]]:
+        """
+        This function is responsible for staging staging the state_dict.
+        See class docstring for more details on staging.
+        If use_async_staging is True, it will return a Future object that will be
+        fulfilled when staging is complete.
+        If use_async_staging is False, it will return the fully staged state_dict.
+
+        Args:
+            state_dict (STATE_DICT_TYPE): The state_dict to be staged.
+        """
+        if self._config.use_async_staging:
+            if self._staging_executor is None:
+                raise AssertionError(
+                    "staging_executor should not be None for async staging"
+                )
+            self._staging_future = self._staging_executor.submit(
+                self._stage,
+                state_dict,
+                **kwargs,
+            )
+            return self._staging_future
+        else:
+            return self._stage(state_dict, **kwargs)
+
+    def _stage(self, state_dict: STATE_DICT_TYPE, **kwargs: Any) -> STATE_DICT_TYPE:
+        if self._config.use_non_blocking_copy:
+            if not (self._staging_stream or not self._config.use_async_staging):
+                raise AssertionError(
+                    "Non-blocking copy in a background thread for async staging needs staging_stream to be initialized."
+                )
+            with (
+                self._staging_stream
+                if self._staging_stream is not None
+                else nullcontext()
+            ):
+                state_dict = self._state_dict_stager.stage(
+                    state_dict, non_blocking=self._config.use_non_blocking_copy
+                )
+            # waits for the enqued copy operations to finish.
+            self._staging_stream.synchronize() if self._staging_stream else torch.accelerator.synchronize()
+        else:
+            state_dict = self._state_dict_stager.stage(state_dict, non_blocking=False)
+        return state_dict
+
+    def close(self) -> None:
+        """
+        Clean up all resources used by the DefaultStager. Shuts down the ThreadPoolExecutor
+        used for async staging operations and cleans up the underlying StateDictStager's
+        cached storages. Should be called when the stager is no longer needed to prevent
+        resource leaks, especially in long-running applications. After calling close(),
+        the stager should not be used for further staging operations.
+
+        Example Usage:
+            stager = DefaultStager(StagingOptions(use_async_staging=True))
+            future = stager.stage(state_dict)
+            result = future.result()
+            stager.close()  # Clean up all resources
+        """
+        if self._staging_executor:
+            self._staging_executor.shutdown(wait=True)
+
+    def synchronize_staging(self) -> None:
+        """
+        When use_async_staging is True, this method will wait until staging is complete.
+        If use_async_staging is False, this method is a no-op.
+        """
+        if self._staging_future is not None:
+            self._staging_future.result()
+
+
+class BlockingAsyncStager(AsyncStager):
+    """
+    An implementation of AsyncStager which stages the state_dict on CPU RAM and blocks until the copy is complete.
+    This implementation also provides an option to optimize stage latency using pinned memory.
+
+    N.B. synchronize_staging is a no-op in this case.
+
+
+    """
+
+    # default to True since the common case is to stage synchronously
+    _synchronize_after_execute: bool = False
+
+    def __init__(
+        self,
+        cache_staged_state_dict: bool = False,
+        type_check: bool = False,
+    ):
+        """
+        Initializes the BlockingAsyncStager.
+
+        Args:
+            cache_staged_state_dict: Whether to cache the staged state_dict. This option decreases staging latency
+                at the cost of increases memory usage. Additionally, if this parameter is set to True, it's the expectation
+                that the stager is maintained and reused for multiple dcp.async_save calls. Default to False.
+            type_check: Whether to perform a type check during cpu_offload. Defaults to False.
+
+        """
+        self.cache_staged_state_dict = cache_staged_state_dict
+        self.type_check = type_check
+        self.state_dict_cache: Optional[STATE_DICT_TYPE] = None
+
+    def stage(self, state_dict: STATE_DICT_TYPE) -> STATE_DICT_TYPE:
+        """
+        Returns a copy of `state_dict` on the CPU.
+        """
+
+        if not self.cache_staged_state_dict:
+            staged_state_dict = _create_cpu_state_dict(state_dict)
+            _copy_state_dict(state_dict, staged_state_dict, type_check=self.type_check)
+            return staged_state_dict
+
+        if self.state_dict_cache is None:
+            self.state_dict_cache = _create_cpu_state_dict(state_dict, pin_memory=True)
+        return _copy_state_dict(state_dict, self.state_dict_cache)
+
+    def synchronize_staging(self) -> None:
+        """
+        No-op function, since staging is blocking.
+        """
+
+    def close(self) -> None:
+        pass
+
+
+class _ReplicationStager(AsyncStager):
+    """
+    An AsyncStager implementation that replicates state_dict across training ranks
+    using PGTransport.
+
+    Args:
+        pg: ProcessGroup for distributed communication
+        timeout: Timeout for communication operations
+        device: Device to use for tensor operations
+        storage_dir: Directory to store persisted state_dicts
+
+    Warning: This is experimental and subject to change.
+    """
+
+    _synchronize_after_execute: bool = False
+
+    def __init__(
+        self,
+        pg: ProcessGroup,
+        timeout: timedelta = timedelta(minutes=30),
+        device: torch.device = torch.device("cpu"),
+        storage_dir: Optional[str] = None,
+    ):
+        self._pg = pg
+        self._timeout = timeout
+        # pyrefly: ignore [read-only]
+        self._device = device
+        self._transport = PGTransport(pg, timeout, device, None)
+
+        # Set up storage directory for persisting exchanged state_dicts
+        if storage_dir is None:
+            self._storage_dir = tempfile.mkdtemp(prefix="replication_stager_")
+        else:
+            self._storage_dir = storage_dir
+        os.makedirs(self._storage_dir, exist_ok=True)
+
+    def stage(
+        self, state_dict: STATE_DICT_TYPE
+    ) -> Union[Future[STATE_DICT_TYPE], STATE_DICT_TYPE]:
+        """
+        Stage the state_dict by replicating it across ranks. Returns a state_dict representing
+        the received replica.
+
+        Perform the actual replication logic. Creates bidirectional pairs where each rank exchanges
+        state_dict with its partner at (rank + world_size//2) % world_size.
+        Uses simple rank-based ordering to prevent deadlocks.
+
+        Assumes world_size is always even.
+        """
+        if not dist.is_initialized():
+            return state_dict
+
+        world_size = dist.get_world_size()
+
+        current_rank = dist.get_rank()
+
+        # Calculate partner rank using half-world offset
+        # creates bidirectional pairs for replication.
+        offset = world_size // 2
+        partner_rank = (current_rank + offset) % world_size
+
+        # Use simple rank-based ordering to prevent deadlocks.
+        # Lower-numbered rank sends first, higher-numbered rank receives first.
+        if current_rank < partner_rank:
+            # Send first, then receive
+            self._transport.send_checkpoint([partner_rank], state_dict)
+            received_state_dict = self._transport.recv_checkpoint(partner_rank)
+        else:
+            # Receive first, then send
+            received_state_dict = self._transport.recv_checkpoint(partner_rank)
+            self._transport.send_checkpoint([partner_rank], state_dict)
+
+        # Persist the received state_dict for future discoverability
+        received_state_dict = cast(STATE_DICT_TYPE, received_state_dict)
+        self._persist_state_dict(received_state_dict, current_rank, partner_rank)
+
+        return received_state_dict
+
+    def _persist_state_dict(
+        self, state_dict: STATE_DICT_TYPE, current_rank: int, partner_rank: int
+    ) -> None:
+        """
+        Persist the received state_dict to disk for future discoverability.
+        Only keeps one replica per rank, overwriting any previous replica.
+        Uses atomic write pattern (temp file + rename).
+
+        Args:
+            state_dict: The state_dict received from partner rank
+            current_rank: Current rank that received the state_dict
+            partner_rank: Rank that sent the state_dict
+        """
+        final_path = self._get_persisted_path(current_rank, partner_rank)
+        temp_path = final_path + ".tmp"
+
+        try:
+            # Ensure parent directory exists and is writable
+            os.makedirs(os.path.dirname(final_path), exist_ok=True)
+
+            # Write to temporary file with explicit flushing
+            with open(temp_path, "wb") as f:
+                torch.save(state_dict, f)
+                # Flush application buffers to OS buffers
+                f.flush()
+                # Force OS buffers to disk for durability
+                os.fsync(f.fileno())
+
+            # Atomic rename to final location
+            os.rename(temp_path, final_path)
+        except Exception as e:
+            # Clean up temp file if it exists
+            try:
+                if os.path.exists(temp_path):
+                    os.remove(temp_path)
+            except Exception:
+                pass  # Ignore cleanup errors
+            # Re-raise the original exception with more context
+            raise RuntimeError(
+                f"Failed to persist state_dict from rank {partner_rank} to rank {current_rank}: {e}"
+            ) from e
+
+    def _get_persisted_path(self, current_rank: int, partner_rank: int) -> str:
+        """
+        Get the file path where a state_dict would be persisted.
+
+        Args:
+            current_rank: Current rank
+
+        Returns:
+            File path for the persisted state_dict
+        """
+        filename = f"rank_{current_rank}_replica_partner_{partner_rank}.pt"
+        return os.path.join(self._storage_dir, filename)
+
+    def synchronize_staging(self) -> None:
+        """
+        No-op function, since staging is blocking.
+        """
+
+    def close(self) -> None:
+        """
+        Clean up resources. Persisted files are intentionally left for future discovery.
+        """
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/state_dict.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/state_dict.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a31144348acb669e0a2f8e17805d5650d5d61d1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/state_dict.py
@@ -0,0 +1,1634 @@
+# mypy: allow-untyped-defs
+import contextlib
+import functools
+import gc
+import warnings
+from collections.abc import Callable, Generator, Iterable
+from dataclasses import asdict, dataclass, field
+from itertools import chain
+from typing import Any, cast, no_type_check, Optional, Union
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+from torch.distributed._shard.sharded_tensor import ShardedTensor
+from torch.distributed._state_dict_utils import (
+    _broadcast_state_dict,
+    _distribute_state_dict,
+    _flatten_state_dict,
+    _gather_state_dict,
+    _offload_state_dict_to_cpu,
+    _unflatten_state_dict,
+)
+from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
+    _CHECKPOINT_PREFIX,
+)
+from torch.distributed.fsdp import (
+    FullOptimStateDictConfig,
+    FullStateDictConfig,
+    FullyShardedDataParallel as FSDP,
+    OptimStateDictConfig,
+    ShardedOptimStateDictConfig,
+    ShardedStateDictConfig,
+    StateDictConfig,
+    StateDictType,
+)
+from torch.distributed.fsdp._common_utils import (
+    _get_module_fsdp_state_if_fully_sharded_module,
+    FSDP_WRAPPED_MODULE,
+)
+from torch.distributed.tensor import DTensor
+from torch.nn.modules.module import _IncompatibleKeys
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils._pytree import tree_map_only
+
+
+__all__ = [
+    "FQNS_T",
+    "PrimitiveType",
+    "ValueType",
+    "DictValueType",
+    "ListDictValueType",
+    "OptimizerStateType",
+    "StateDictOptions",
+    "get_model_state_dict",
+    "get_optimizer_state_dict",
+    "get_state_dict",
+    "set_model_state_dict",
+    "set_optimizer_state_dict",
+    "set_state_dict",
+]
+
+
+_FLAT_PARAM = "_flat_param"
+_PG = "param_groups"
+_PARAMS = "params"
+_STATE = "state"
+
+FQNS_T = set[str]
+PrimitiveType = Union[DTensor, ShardedTensor, torch.Tensor, int, float, str]
+ValueType = Union[
+    PrimitiveType, list[PrimitiveType], tuple[PrimitiveType], dict[str, "ValueType"]
+]
+DictValueType = dict[str, ValueType]
+ListDictValueType = list[DictValueType]
+OptimizerStateType = dict[str, Union[DictValueType, ListDictValueType]]
+
+
+_patched_state_dict: set[Callable] = set()
+
+
+@contextlib.contextmanager
+def _gc_context():
+    is_enabled = gc.isenabled()
+    gc.disable()
+    try:
+        yield
+    finally:
+        if is_enabled:
+            gc.enable()
+
+
+@dataclass
+class StateDictOptions:
+    """
+    This dataclass specifies how get_state_dict/set_state_dict will work.
+
+    - ``full_state_dict``: if this is set to True, all the tensors in the
+      returned state_dict will be gathered. No ShardedTensor and DTensor
+      will be in the returned state_dict.
+
+    - ``cpu_offload``: offload all the tensors to cpu. To prevent CPU OOM, if
+      ``full_state_dict`` is also true, then only the rank0 will get the
+      state_dict and all other ranks will get empty state_dict.
+
+    - ``ignore_frozen_params``: if the value is True, the returned state_dict
+      won't contain any frozen parameters -- the ``requires_grad`` is False.
+      The default value is False.
+
+    - ``keep_submodule_prefixes`` (deprecated): when ``submodules`` is not None, this option
+      indicates whether to keep the submodule prefixes from the state_dict keys.
+      or example, if the submodule is ``module.pretrain`` and the full FQN of
+      the parameter is ``pretrain.layer1.weight`` of the param. When this option
+      is True, the parameter's key in the returned state_dict will be
+      ``pretrain.layer1.weight``. If the options is False, the key will be
+      ``layer1.weight``.
+      Note that if ``keep_submodule_prefixes`` is False, there may be conflicted
+      FQNs, hence there should be only one submodule in ``submodules``.
+
+    - ``strict``: the ``strict`` option when ``set_state_dict`` calls
+      model.load_state_dict().
+
+    - ``broadcast_from_rank0``: when the option is True, rank0 should receive a
+       full state_dict and will broadcast the tensors in the state_dict/
+       optim_state_dict one by one to other ranks. Other ranks will receive
+       the tensors and shard according to the local shards in the model and
+       optimizer. ``full_state_dict`` must be set to True when using this option.
+       This option currently only supports DTensor, not the legacy ShardedTensor.
+    """
+
+    full_state_dict: bool = False
+    cpu_offload: bool = False
+    ignore_frozen_params: bool = False
+    keep_submodule_prefixes: bool = True
+    strict: bool = True
+    broadcast_from_rank0: bool = False
+    flatten_optimizer_state_dict: bool = False
+    dsd_fqn_modifiers: str = "_fqn_modifiers"
+
+
+@dataclass
+class _StateDictInfo(StateDictOptions):
+    fqn_param_mapping: dict[
+        Union[str, torch.Tensor],
+        Union[FQNS_T, torch.Tensor],
+    ] = field(default_factory=dict)
+    shared_params_mapping: dict[
+        Union[str, torch.Tensor],
+        Union[FQNS_T, torch.Tensor],
+    ] = field(default_factory=dict)
+    submodule_prefixes: set[str] = field(default_factory=set)
+    handle_model: bool = True
+    handle_optim: bool = True
+    fsdp_context: Callable = contextlib.nullcontext
+    fsdp_modules: list[nn.Module] = field(default_factory=list)
+
+
+def _get_fqns(
+    model: nn.Module,
+    name: str,
+    dsd_fqn_modifiers: str = "_fqn_modifiers",
+    skip_ddp_prefix: bool = True,
+    skip_compiler_prefix: bool = True,
+) -> FQNS_T:
+    """
+    This API is used to convert the name of a parameter to the FQNs. For FSDP
+    without `use_orig_params`, the name of FlatParameter can be mapped to
+    multiple original parameters. As a result, the return type of this function
+    is `set[str]`.
+
+    Args:
+        module (nn.Module): the root model.
+        name (str): the name
+        skip_ddp_prefix (bool): whether to skip DDP's `module` prefix
+
+    Returns:
+        The canonical FQNs based on the model traversal.
+    """
+
+    # Remove the checkpoint prefix, if it exists.
+    name = name.replace(_CHECKPOINT_PREFIX, "")
+    if "." not in name:
+        return {name}
+
+    obj_names = name.split(".")
+    fqn_obj_names = []
+    curr_obj = model
+    for i, curr_obj_name in enumerate(obj_names):
+        if isinstance(curr_obj, DDP):
+            if curr_obj_name != "module":
+                raise AssertionError(f"Expected 'module', got '{curr_obj_name}'")
+            curr_obj = curr_obj.module
+            if not skip_ddp_prefix:
+                fqn_obj_names.append(curr_obj_name)
+        elif isinstance(curr_obj, FSDP):
+            if i < len(obj_names) - 1 and obj_names[i + 1] == _FLAT_PARAM:
+                prefix = ".".join(fqn_obj_names)
+                flat_param = getattr(curr_obj, _FLAT_PARAM)
+                if prefix:
+                    prefix = f"{prefix}."
+                return {f"{prefix}{fqn}" for fqn in flat_param._fqns}
+            curr_obj = getattr(curr_obj, FSDP_WRAPPED_MODULE)
+            if curr_obj_name != FSDP_WRAPPED_MODULE:
+                # pyrefly: ignore [bad-argument-type]
+                fqn_obj_names.append(curr_obj_name)
+                curr_obj = getattr(curr_obj, curr_obj_name)
+        elif isinstance(curr_obj, torch._dynamo.eval_frame.OptimizedModule):
+            if curr_obj_name != "_orig_mod":
+                raise AssertionError(f"Expected '_orig_mod', got '{curr_obj_name}'")
+            curr_obj = curr_obj._orig_mod
+            if not skip_compiler_prefix:
+                fqn_obj_names.append(curr_obj_name)
+        else:
+            # In some modules, _fqn_modifiers would not shown in the state_dict keys,
+            # skip them in the fqn to ensure load stat dict successfully for them.
+            if hasattr(curr_obj, dsd_fqn_modifiers):
+                if removed_fqn := getattr(curr_obj, dsd_fqn_modifiers)().get(
+                    curr_obj_name
+                ):
+                    if hasattr(curr_obj, removed_fqn):
+                        curr_obj = getattr(curr_obj, removed_fqn)
+            # pyrefly: ignore [bad-argument-type]
+            fqn_obj_names.append(curr_obj_name)
+            if curr_obj_name == nn.modules.module._EXTRA_STATE_KEY_SUFFIX:
+                if i != len(obj_names) - 1:
+                    raise RuntimeError("Expect `_extra_state` to be the last obj name")
+            else:
+                curr_obj = getattr(curr_obj, curr_obj_name)
+
+    return {".".join(fqn_obj_names).replace(_CHECKPOINT_PREFIX, "")}
+
+
+class _EXTRA_STATE:
+    pass
+
+
+def _iterate_valid_model_state(model, dsd_fqn_modifiers="_fqn_modifiers"):
+    visited_modules: set[nn.Module] = set()
+
+    def recurse(module: nn.Module, curr_fqn: str) -> Generator:
+        visited_modules.add(module)
+
+        curr_fqn = f"{curr_fqn}." if curr_fqn else ""
+        for name, submodule in module.named_children():
+            if submodule in visited_modules:
+                continue
+            # if user have state_dict_hooks in their model, they can add the state_dict key changes
+            # at dsd_fqn_modifiers in input to align with the function of state_dict_hook
+            if (
+                hasattr(module, dsd_fqn_modifiers)
+                and name in getattr(module, dsd_fqn_modifiers)().values()
+            ):
+                # skip _fqn_modifiers here thus remove the last `.` added
+                new_fqn = curr_fqn[:-1]
+            else:
+                new_fqn = f"{curr_fqn}{name}"
+            yield from recurse(submodule, new_fqn)
+
+        for name, obj in chain(
+            module.named_buffers(recurse=False), module.named_parameters(recurse=False)
+        ):
+            if name in module._non_persistent_buffers_set:
+                continue
+            new_fqn = f"{curr_fqn}{name}"
+            yield new_fqn, obj
+
+        if (
+            getattr(module.__class__, "get_extra_state", nn.Module.get_extra_state)
+            != nn.Module.get_extra_state
+        ):
+            new_fqn = f"{curr_fqn}{nn.modules.module._EXTRA_STATE_KEY_SUFFIX}"
+            yield new_fqn, _EXTRA_STATE()
+
+    yield from recurse(model, "")
+
+
+def _verify_options(
+    model: nn.Module,
+    optims: tuple[torch.optim.Optimizer, ...],
+    optim_only: bool,
+    *,
+    submodules: Optional[set[nn.Module]] = None,
+    options: Optional[StateDictOptions] = None,
+) -> _StateDictInfo:
+    """
+    Verify the model and options passed by the user and generates _StateDictInfo.
+    """
+    if submodules:
+        warnings.warn(
+            "Getting submodules only model/optim state_dict is deprecated and "
+            "will be removed in 2.5. This feature can be achieved by manually "
+            "filtering out the state_dict returned from get_state_dict.",
+            FutureWarning,
+            stacklevel=2,
+        )
+    if optim_only and not optims:
+        raise RuntimeError(
+            "Optimizers are not passed in but optim_only is set to True."
+        )
+
+    options = options or StateDictOptions()
+
+    fqn_param_mapping: dict[
+        Union[str, torch.Tensor], Union[set[str], torch.Tensor]
+    ] = {}
+    shared_params_mapping: dict[
+        Union[str, torch.Tensor], Union[set[str], torch.Tensor]
+    ] = {}
+    for name, param in _iterate_valid_model_state(model):
+        if isinstance(param, _EXTRA_STATE):
+            continue
+
+        fqns = _get_fqns(model, name)
+        fqn = fqn_param_mapping.get(param)
+        if fqn is not None:
+            cast(set[str], fqn_param_mapping[param]).update(fqns)
+            shared_params_mapping[param] = fqn_param_mapping[param]
+        else:
+            # We need to do copy as _get_fqns is lru_cached
+            fqn_param_mapping[param] = fqns.copy()
+        for fqn in fqns:
+            if not isinstance(param, _EXTRA_STATE):
+                fqn_param_mapping[fqn] = param
+
+    for param_, fqns_ in list(shared_params_mapping.items()):
+        for fqn in fqns_:
+            shared_params_mapping[fqn] = cast(torch.Tensor, param_)
+
+    submodule_prefixes: set[str] = set()
+    if submodules:
+        submodules = set(submodules)
+        for name, module in model.named_modules():
+            if module not in submodules:
+                continue
+            fqns = _get_fqns(model, name)
+            if len(fqns) != 1:
+                raise AssertionError("Submodule FQN should only have 1 instance")
+            submodule_prefixes.update(f"{fqn}." for fqn in fqns)
+
+    if options.broadcast_from_rank0 and not options.full_state_dict:
+        raise ValueError(
+            "full_state_dict must be True when broadcast_from_rank0 is True."
+        )
+    fsdp_modules = FSDP.fsdp_modules(model)
+    state_dict_config: StateDictConfig
+    optim_state_dict_config: OptimStateDictConfig
+    fsdp_context: Callable
+    if fsdp_modules:
+        # FSDP API only work if at least one FSDP instance exists.
+        if options.full_state_dict:
+            state_dict_config = FullStateDictConfig(
+                offload_to_cpu=options.cpu_offload, rank0_only=options.cpu_offload
+            )
+            optim_state_dict_config = FullOptimStateDictConfig(
+                offload_to_cpu=options.cpu_offload,
+                rank0_only=(options.cpu_offload or options.broadcast_from_rank0),
+            )
+            state_dict_type = StateDictType.FULL_STATE_DICT
+        else:
+            state_dict_config = ShardedStateDictConfig(
+                offload_to_cpu=options.cpu_offload,
+            )
+            optim_state_dict_config = ShardedOptimStateDictConfig(
+                offload_to_cpu=options.cpu_offload,
+            )
+            state_dict_type = StateDictType.SHARDED_STATE_DICT
+
+        @contextlib.contextmanager
+        def fsdp_state_dict_type_without_warning(
+            module,
+            state_dict_type,
+            state_dict_config,
+            optim_state_dict_config,
+        ):
+            with warnings.catch_warnings():
+                warnings.filterwarnings(
+                    "ignore", message="FSDP.state_dict_type", category=FutureWarning
+                )
+                with FSDP.state_dict_type(
+                    module=module,
+                    state_dict_type=state_dict_type,
+                    state_dict_config=state_dict_config,
+                    optim_state_dict_config=optim_state_dict_config,
+                ):
+                    yield
+
+        fsdp_context = functools.partial(
+            fsdp_state_dict_type_without_warning,
+            module=model,
+            state_dict_type=state_dict_type,
+            state_dict_config=state_dict_config,
+            optim_state_dict_config=optim_state_dict_config,
+        )
+    else:
+        fsdp_context = contextlib.nullcontext
+
+    return _StateDictInfo(
+        **asdict(options),
+        fqn_param_mapping=fqn_param_mapping,
+        shared_params_mapping=shared_params_mapping,
+        submodule_prefixes=submodule_prefixes,
+        fsdp_context=fsdp_context,
+        fsdp_modules=cast(list[nn.Module], fsdp_modules),
+        handle_model=not optim_only,
+        handle_optim=(len(optims) > 0),
+    )
+
+
+def _verify_state_dict(
+    model_state_dict: dict[str, ValueType],
+    optim_state_dict: OptimizerStateType,
+    info: _StateDictInfo,
+) -> None:
+    for module in info.fsdp_modules:
+        fsdp_state = _get_module_fsdp_state_if_fully_sharded_module(module)
+        if fsdp_state is None:
+            raise AssertionError("Expected a fsdp_state with a fsdp module.")
+
+    # Verify if the model_state_dict and optim_state_dict are valid. This API
+    # should give the users an explicit error message to debug or report.
+    if (
+        info.handle_model
+        and not model_state_dict
+        and not info.submodule_prefixes
+        and not info.ignore_frozen_params
+        and not (info.cpu_offload and info.full_state_dict)
+        and info.strict
+        and not info.broadcast_from_rank0
+    ):
+        raise RuntimeError(
+            "The option indicates that model state_dict is required to save "
+            "or load, but model state_dict is empty."
+            f"rank = {dist.get_rank()=}."
+        )
+
+    if info.handle_optim:
+        if (
+            not optim_state_dict
+            and not (info.cpu_offload and info.full_state_dict)
+            and (not info.broadcast_from_rank0)
+        ):
+            raise RuntimeError(
+                "The option indicates that model state_dict is required to save, "
+                f"or load but optim state_dict is empty. {optim_state_dict}"
+            )
+
+    for key in model_state_dict:
+        if _FLAT_PARAM in key:
+            raise RuntimeError(
+                f"{key} contains {_FLAT_PARAM}. This can happen if the model "
+                "is not the root module."
+            )
+
+
+def _state_dict_fn(obj: Union[nn.Module, torch.optim.Optimizer], api: str) -> Callable:
+    call = getattr(obj, api)
+    if call in _patched_state_dict:
+        call = functools.partial(getattr(obj.__class__, api), self=obj)
+    return call
+
+
+def _maybe_full_or_cpu_state_dict(
+    state_dict: dict[str, Any], info: _StateDictInfo
+) -> dict[str, Any]:
+    if info.full_state_dict:
+        ranks_only = (
+            ()
+            if (not info.cpu_offload or not torch.distributed.is_initialized())
+            else (0,)
+        )
+        return _gather_state_dict(
+            state_dict, cpu_offload=info.cpu_offload, ranks_only=ranks_only
+        )
+    elif info.cpu_offload:
+        return _offload_state_dict_to_cpu(state_dict)
+    else:
+        return state_dict
+
+
+@torch.no_grad()
+def _get_model_state_dict(
+    model: nn.Module, info: _StateDictInfo
+) -> dict[str, ValueType]:
+    if not info.handle_model:
+        return {}
+
+    with info.fsdp_context():
+        state_dict = _state_dict_fn(model, "state_dict")()
+
+    for key in list(state_dict.keys()):
+        fqns = _get_fqns(model, key)
+        if len(fqns) != 1:
+            raise AssertionError(
+                f"Expected 1 FQN for key '{key}', got {len(fqns)}: {fqns}"
+            )
+        fqn = next(iter(fqns))
+        if fqn != key:
+            # As we only support FSDP, DDP, and TP, the only cases are
+            # wrapper-based DDP and compiler. Verify if the assumption
+            # is correct.
+            def verify(key, fqn) -> bool:
+                if len(fqn) >= len(key):
+                    return False
+                fqn_split = fqn.split(".")
+                key_split = key.split(".")
+                fqn_idx = 0
+                for key_idx, key_name in enumerate(key_split):
+                    if key_name == fqn_split[fqn_idx]:
+                        fqn_idx += 1
+                        if fqn_idx == len(fqn_split):
+                            return key_idx == len(key_split) - 1
+                    elif key_name in ("module", "_orig_mod"):
+                        continue
+                    else:
+                        return False
+                return True
+
+            if not verify(key, fqn):
+                raise RuntimeError(f"An unexpected key, {key}, exists. FQN is {fqn}")
+            state_dict[fqn] = state_dict.pop(key)
+
+    if info.submodule_prefixes:
+        new_state_dict: dict[str, ValueType] = {}
+        # TODO: make this faster.
+        for fqn in state_dict:
+            for prefix in info.submodule_prefixes:
+                if not fqn.startswith(prefix):
+                    continue
+                if info.keep_submodule_prefixes:
+                    new_state_dict[fqn] = state_dict[fqn]
+                else:
+                    new_fqn = fqn[len(prefix) :]
+                    new_state_dict[new_fqn] = state_dict[fqn]
+        state_dict = new_state_dict
+
+    if info.ignore_frozen_params:
+        for key, param in model.named_parameters():
+            if param.requires_grad:
+                continue
+            fqns = _get_fqns(model, key)
+            for fqn in fqns:
+                state_dict.pop(fqn)
+
+    return _maybe_full_or_cpu_state_dict(state_dict, info)
+
+
+@torch.no_grad()
+def _load_model_state_dict(
+    model: nn.Module,
+    state_dict: dict[str, ValueType],
+    info: _StateDictInfo,
+) -> _IncompatibleKeys:
+    if not info.handle_model or (not state_dict and not info.broadcast_from_rank0):
+        return _IncompatibleKeys({}, {})
+
+    local_state_dict = {}
+    for key, value in _iterate_valid_model_state(model, info.dsd_fqn_modifiers):
+        fqns = _get_fqns(model, key, info.dsd_fqn_modifiers)
+        fqns_with_prefix = _get_fqns(
+            model,
+            key,
+            info.dsd_fqn_modifiers,
+            skip_ddp_prefix=False,
+            skip_compiler_prefix=False,
+        )
+
+        for fqn, fqn_with_prefix in zip(fqns, fqns_with_prefix):
+            if (
+                not info.broadcast_from_rank0 or dist.get_rank() == 0
+            ) and fqn != fqn_with_prefix:
+                load_value = state_dict.pop(fqn, None)
+                if load_value is None:
+                    if info.strict:
+                        raise RuntimeError(f"Missing key: {fqn}.")
+                else:
+                    state_dict[fqn_with_prefix] = load_value
+            local_state_dict[fqn_with_prefix] = value
+
+    assign = False
+    if info.broadcast_from_rank0 or info.full_state_dict:
+        devices = set()
+        for value in local_state_dict.values():
+            if torch.is_tensor(value) and value.dim() > 0:
+                devices.add(value.device)
+        # In lora state_dict, there could be multiple devices, with meta device inside.
+        # Take the other device in the broadcast/distribtue, and set assign to True
+        if torch.device("meta") in devices:
+            devices.remove(torch.device("meta"))
+            assign = True
+        if len(devices) == 0:
+            devices.add(dist.distributed_c10d._get_pg_default_device())
+        elif len(devices) > 1:
+            raise ValueError("Multiple devices found")
+
+        if info.broadcast_from_rank0:
+            _broadcast_state_dict(
+                state_dict,
+                local_state_dict,
+                device=devices.pop(),
+                strict=info.strict,
+                cpu_offload=info.cpu_offload,
+            )
+        elif info.full_state_dict:
+            _distribute_state_dict(state_dict, local_state_dict, device=devices.pop())
+        state_dict.update(local_state_dict)
+
+    with info.fsdp_context():
+        return cast(
+            _IncompatibleKeys,
+            _state_dict_fn(model, "load_state_dict")(
+                state_dict=state_dict, strict=info.strict, assign=assign
+            ),
+        )
+
+
+def _init_optim_state(optim: torch.optim.Optimizer) -> None:
+    """
+    Initialize optim states by calling the step() with zero grads.
+    """
+    if optim.state:
+        # The optimizer state is initialized.
+        return
+
+    # There are some stateless optimizers like SGD. These optimizer will
+    # not return in the above condition. So if gradients exist, we should also
+    # return. If gradients do not exist, the following initialization should
+    # not disturb SGD because the gradients and lr are both zero.
+    for param_group in optim.param_groups:
+        for param in param_group[_PARAMS]:
+            if param.grad is not None:
+                return
+
+    for param_group in optim.param_groups:
+        for param in param_group[_PARAMS]:
+            if param.requires_grad:
+                param.grad = torch.zeros_like(param)
+
+    # Some optimizers will update parameters regardless of grads due to lr, so
+    # make lr to zero when calling `step()`.
+    lrs = []
+    for param_group in optim.param_groups:
+        if "lr" in param_group:
+            lrs.append(param_group["lr"])
+            param_group["lr"] = (
+                torch.tensor(0.0)
+                if isinstance(param_group["lr"], torch.Tensor)
+                else 0.0
+            )
+    optim.step(closure=None)
+    # Whether to recover the "lr" should not matter too much as we will
+    # restore checkpointing later.
+    for param_group in optim.param_groups:
+        if "lr" in param_group:
+            param_group["lr"] = lrs.pop(0)
+    optim.zero_grad(set_to_none=True)
+
+
+def _flatten_optim_state_dict(state_dict: OptimizerStateType) -> dict[str, ValueType]:
+    """
+    This API flattens the optimizer state_dict to support optimizer resharding for
+    MPMD, e.g., pipeline parallelism.
+
+    Without the API, the original optimizer state_dict looks like:
+    {
+        "state": {
+            "layer1.weight": {
+                "step": 10, "exp_avg": SomeTensor, "exp_avg_sq": SomeTensor
+            },
+            "layer2.weight": {
+                "step": 10, "exp_avg": SomeTensor, "exp_avg_sq": SomeTensor
+            },
+        },
+        "param_groups": [
+            {
+                "lr": 0.0,
+                "betas": (0.9, 0.95), ...,
+                "params": ["layer1.weight", "layer2.weight"]
+            }
+        ]
+    }
+
+    With this API, the optimizer state_dict looks like:
+    {
+        "state.layer1.weight.step": 10,
+        "state.layer2.weight.step": 10,
+        "state.layer1.weight.exp_avg": SomeTensor,
+        "state.layer2.weight.exp_avg": SomeTensor,
+        "state.layer1.weight.exp_avg_sq": SomeTensor,
+        "state.layer2.weight.exp_avg_sq": SomeTensor,
+        "param_groups.layer1.weight.lr": 0.1,
+        "param_groups.layer2.weight.lr": 0.1,
+        "param_groups.layer1.weight.betas": (0.9, 0.95),
+        "param_groups.layer2.weight.betas": (0.9, 0.95),
+    }
+
+    The "state" section supports arbitrary levels of nesting for optimizers like Shampoo.
+    """
+
+    def _flatten_state_nested_dict(
+        nested_dict: dict[str, Any], prefix: str
+    ) -> dict[str, ValueType]:
+        """
+        Recursively flatten a nested dictionary with dot-separated keys.
+
+        Args:
+            nested_dict: The dictionary to flatten
+            prefix: The prefix to prepend to all keys
+
+        Returns:
+            Flattened dictionary with dot-separated keys
+        """
+        flattened: dict[str, ValueType] = {}
+
+        for key, value in nested_dict.items():
+            # Convert all keys to strings for flattening
+            str_key = str(key)
+            full_key = f"{prefix}.{str_key}" if prefix else str_key
+
+            if isinstance(value, dict):
+                # Recursively flatten nested dictionaries
+                flattened.update(_flatten_state_nested_dict(value, full_key))
+            else:
+                # Base case: store the value with the flattened key
+                _raise_if_type_not_supported(value)
+                flattened[full_key] = value
+
+        return flattened
+
+    def _raise_if_type_not_supported(v):
+        if not isinstance(v, (torch.Tensor, int, float, dict)):
+            raise NotImplementedError(
+                "Flattening optimizer state_dict only supports "
+                "tensor, int, float, dict states now. "
+                f"Type is {type(v)}."
+            )
+
+    ret: dict[str, ValueType] = {}
+
+    # Handle the "state" section with recursive flattening
+    for fqn, state in cast(DictValueType, state_dict[_STATE]).items():
+        state_prefix = f"{_STATE}.{fqn}"
+        ret.update(
+            _flatten_state_nested_dict(cast(dict[str, Any], state), state_prefix)
+        )
+
+    # Handle the "param_groups" section with two-level flattening
+    for param_group in cast(ListDictValueType, state_dict[_PG]):
+        fqns = param_group.pop(_PARAMS)
+        for fqn in cast(list[str], fqns):
+            for k, v in param_group.items():
+                ret[f"{_PG}.{fqn}.{k}"] = v
+
+    return ret
+
+
+def _unflatten_optim_state_dict(
+    optim: torch.optim.Optimizer,
+    state_dict: dict[str, ValueType],
+    info: _StateDictInfo,
+) -> OptimizerStateType:
+    """
+    This API unflattens the state_dict generated by _flatten_optim_state_dict().
+    Supports arbitrary levels of nesting in the state section through recursive reconstruction.
+
+    See the docstring of _flatten_optim_state_dict() for more detail.
+    """
+
+    def _reconstruct_nested_dict(
+        flattened_key: str, flattened_dict: dict[str, ValueType]
+    ) -> dict[str, ValueType]:
+        """
+        Reconstructs a potentially nested value from flattened keys.
+        For non-nested values, returns the value directly.
+        For nested values, reconstructs the nested structure with string keys.
+        """
+
+        # Create the prefix to search for nested keys
+        # e.g., if flattened_key is "state.layer1.weight", prefix becomes "state.layer1.weight."
+        prefix = f"{flattened_key}."
+        # Initialize an empty dictionary to build our nested structure
+        nested_dict: dict[str, Any] = {}
+
+        # Iterate through all keys in the flattened dictionary
+        for key, value in flattened_dict.items():
+            # Check if this key is nested under our target key
+            # e.g., "state.layer1.weight.exp_avg" starts with "state.layer1.weight."
+            if not key.startswith(prefix):
+                # Skip keys that don't belong to this nested structure
+                continue
+
+            # Remove the prefix to get just the nested part
+            # e.g., "state.layer1.weight.exp_avg" -> "exp_avg"
+            remaining_key = key[len(prefix) :]
+            # Split the remaining key into parts to build the nested structure
+            # e.g., "step" -> ["step"] or "momentum_buffer" -> ["momentum_buffer"]
+            parts = remaining_key.split(".")
+            # Start at the root of our new nested dictionary
+            current = nested_dict
+
+            # Navigate through or create the nested dictionary structure
+            # For each part except the last one (which will hold the value)
+            for part in parts[:-1]:
+                # Create the nested dictionary if it doesn't exist yet
+                if part not in current:
+                    current[part] = {}
+                # Move deeper into the nested structure
+                assert isinstance(current[part], dict)
+                current = current[part]
+
+            # Set the value at the final level using the last part as the key
+            # e.g., current["exp_avg"] = tensor(...)
+            current[parts[-1]] = value
+
+        # Return the reconstructed nested dictionary (empty dict if no keys matched at all)
+        return nested_dict
+
+    state: DictValueType = {}
+    pg_state: ListDictValueType = []
+    return_osd: OptimizerStateType = {_STATE: state, _PG: pg_state}
+
+    for param_group in optim.param_groups:
+        pg_state.append({_PARAMS: []})
+        for param in param_group[_PARAMS]:
+            for fqn in info.fqn_param_mapping[param]:
+                # If a parameter is shared, only one of the FQN will be used.
+                # So we need to verify which if this fqn is actually used in
+                # the state_dict.
+                if fqn in info.shared_params_mapping:
+                    in_params = False
+                    for k in param_group:
+                        if k == _PARAMS:
+                            continue
+                        flatten_key = f"{_PG}.{fqn}.{k}"
+                        if flatten_key in state_dict:
+                            in_params = True
+                        break
+                else:
+                    in_params = True
+
+                if not in_params:
+                    continue
+
+                params = pg_state[-1][_PARAMS]
+                if not isinstance(params, list):
+                    raise AssertionError(f"Expected list, got {type(params)}")
+                params.append(fqn)
+
+                # Only add state if param requires grad
+                if not param.requires_grad:
+                    continue
+
+                # Reconstruct state for this parameter
+                state[fqn] = {}
+                for state_name in optim.state[param]:
+                    flattened_state_key = f"{_STATE}.{fqn}.{state_name}"
+
+                    if flattened_state_key not in state_dict:
+                        # Try to reconstruct the value
+                        reconstructed_value = _reconstruct_nested_dict(
+                            flattened_state_key, state_dict
+                        )
+                        cast(DictValueType, state[fqn])[state_name] = (
+                            reconstructed_value
+                        )
+                    else:
+                        # Existing keys mean no nesting, directly use the value.
+                        cast(DictValueType, state[fqn])[state_name] = state_dict[
+                            flattened_state_key
+                        ]
+
+        first_param_fqn = cast(list[str], pg_state[-1][_PARAMS])[0]
+        for k in param_group:
+            if k == _PARAMS:
+                continue
+            value = state_dict[f"{_PG}.{first_param_fqn}.{k}"]
+            if k not in pg_state[-1]:
+                pg_state[-1][k] = value
+            elif pg_state[-1][k] != value:
+                raise RuntimeError(
+                    "All the parameters in the same parameter group should have "
+                    f"the same saved param_group value. But {first_param_fqn}.{k} "
+                    f"is {value} while other(s) is {pg_state[-1][k]}."
+                )
+
+    return return_osd
+
+
+@torch.no_grad()
+def _get_optim_state_dict(
+    model: nn.Module,
+    optimizers: tuple[torch.optim.Optimizer, ...],
+    info: _StateDictInfo,
+) -> OptimizerStateType:
+    if not info.handle_optim:
+        return {}
+
+    optim_state_dict: OptimizerStateType = {_STATE: {}, _PG: []}
+    for optim in optimizers:
+        _init_optim_state(optim)
+        osd = _state_dict_fn(optim, "state_dict")()
+        if info.fsdp_modules:
+            with info.fsdp_context():
+                osd = FSDP.optim_state_dict(model, optim, osd)
+
+            # We need to specially handle FlatParameter FSDP as
+            # FlatParameter FSDP converts the FQNs.
+            # There are no easy ways to do this conversion systematically.
+            # We can only use a string replacement without correctness check.
+            if not osd:
+                continue
+            for k in list(osd[_STATE].keys()):
+                if "_orig_mod" in k:
+                    osd[_STATE][k.replace("_orig_mod.", "")] = osd[_STATE].pop(k)
+            for g in osd[_PG]:
+                params = [k.replace("_orig_mod.", "") for k in g[_PARAMS]]
+                g[_PARAMS] = params
+        else:
+            params = list(chain.from_iterable(g[_PARAMS] for g in optim.param_groups))
+            param_pid_mapping = dict(zip(params, range(len(params))))
+            fqn_pid_mapping = {}
+            for key, param in model.named_parameters():
+                fqns = _get_fqns(model, key)
+                if len(fqns) != 1:
+                    raise AssertionError(
+                        f"Expected 1 FQN for key '{key}', got {len(fqns)}"
+                    )
+                fqn = next(iter(fqns))
+                if param not in param_pid_mapping:
+                    continue
+                pid = param_pid_mapping[param]
+                fqn_pid_mapping[fqn] = pid
+                fqn_pid_mapping[pid] = fqn
+
+            # Only convert top-level parameter IDs to FQNs, preserve nested key types
+            for key in list(osd[_STATE].keys()):
+                fqn = fqn_pid_mapping[key]
+                # Move the entire state dict value (which may contain nested integer keys)
+                # without modifying its internal structure
+                osd[_STATE][fqn] = osd[_STATE].pop(key)
+
+            for group in osd[_PG]:
+                group[_PARAMS] = [fqn_pid_mapping[pid] for pid in group[_PARAMS]]
+
+        if not osd:
+            continue
+
+        cast(DictValueType, optim_state_dict[_STATE]).update(osd[_STATE])
+        cast(ListDictValueType, optim_state_dict[_PG]).extend(osd[_PG])
+
+    if info.flatten_optimizer_state_dict:
+        optim_state_dict = cast(
+            OptimizerStateType, _flatten_optim_state_dict(optim_state_dict)
+        )
+
+    return _maybe_full_or_cpu_state_dict(optim_state_dict, info)
+
+
+def _split_optim_state_dict(
+    model: nn.Module,
+    optim: torch.optim.Optimizer,
+    optim_state_dict: OptimizerStateType,
+    info: _StateDictInfo,
+) -> OptimizerStateType:
+    """
+    Extract the corresponding optim state_dict from ``optim_state_dict`` for
+    ``optim`` and return the result optim state_dict.
+
+    Args:
+        model (nn.Module): the root model.
+        optim (torch.optim.Optimizer): the optimizer.
+        optim_state_dict (Dict[str, ValueType]): the superset optim state_dict that
+            contains the optim state_dict of ``optim``.
+        info (_StateDictInfo): state dict information.
+
+    Returns:
+        The optim state_dict of ``optim``.
+    """
+
+    state: DictValueType = {}
+    pg_state: ListDictValueType = []
+    return_osd: OptimizerStateType = {_STATE: state, _PG: pg_state}
+    pg_mapping: dict[int, int] = {}
+
+    if all(isinstance(k, int) for k in cast(DictValueType, optim_state_dict[_STATE])):
+        return optim_state_dict
+
+    for param_group in optim.param_groups:
+        pg_state.append({_PARAMS: []})
+        for param in param_group[_PARAMS]:
+            for fqn in info.fqn_param_mapping[param]:
+                if fqn in info.shared_params_mapping:
+                    in_params = False
+                    for loaded_param_group in cast(
+                        ListDictValueType, optim_state_dict[_PG]
+                    ):
+                        if fqn in cast(list[str], loaded_param_group[_PARAMS]):
+                            in_params = True
+                            break
+                else:
+                    in_params = True
+                if not in_params:
+                    continue
+
+                params = pg_state[-1][_PARAMS]
+                if not isinstance(params, list):
+                    raise AssertionError(f"Expected list, got {type(params)}")
+                params.append(fqn)
+                if param.requires_grad:
+                    if fqn in cast(DictValueType, optim_state_dict[_STATE]):
+                        state[fqn] = cast(DictValueType, optim_state_dict[_STATE])[fqn]
+                    elif info.strict:
+                        raise RuntimeError(
+                            f"Missing optimizer state for parameter '{fqn}' in checkpoint. "
+                            "The parameter requires gradients but has no saved optimizer state. "
+                            "To load anyway, use StateDictOptions(strict=False)."
+                        )
+                for loaded_param_group in cast(
+                    ListDictValueType, optim_state_dict[_PG]
+                ):
+                    if fqn in cast(list[str], loaded_param_group[_PARAMS]):
+                        pg_mapping[id(loaded_param_group)] = len(return_osd[_PG]) - 1
+
+        if len(param_group[_PARAMS]) == 0:
+            # Param_group with empty params.
+            ret = []
+            for loaded_param_group in cast(ListDictValueType, optim_state_dict[_PG]):
+                if len(cast(list[str], loaded_param_group[_PARAMS])) == 0:
+                    ret.append(loaded_param_group)
+            if len(ret) != 1:
+                raise ValueError(
+                    "There are param groups that have zero parameters. "
+                    "In such a case, DSD only support exactly one param group "
+                    "with zero parameters."
+                    "But the loaded state_dict has zero or more than one param groups "
+                    "that have zero parameters."
+                )
+            if len(optim_state_dict[_PG]) != len(optim.param_groups):
+                raise ValueError(
+                    "When there is a parameter group that has zero parameters, "
+                    "multiple optimizers are not supported."
+                )
+            pg_mapping[id(loaded_param_group)] = len(return_osd[_PG]) - 1
+
+    for param_group in cast(ListDictValueType, optim_state_dict[_PG]):
+        pg_idx = pg_mapping.get(id(param_group), -1)
+        if pg_idx == -1:
+            continue
+
+        for key, value in param_group.items():
+            if key == _PARAMS:
+                continue
+            # TODO: check if value is the same if exists.
+            pg_state[pg_idx][key] = value
+
+    return return_osd
+
+
+@torch.no_grad()
+def _load_optim_state_dict(
+    model: nn.Module,
+    optimizers: tuple[torch.optim.Optimizer, ...],
+    state_dict: OptimizerStateType,
+    info: _StateDictInfo,
+) -> None:
+    if not info.handle_optim:
+        return
+
+    for optim in optimizers:
+        _init_optim_state(optim)
+        if state_dict:
+            if _STATE in state_dict:
+                optim_state_dict = _split_optim_state_dict(
+                    model, optim, state_dict, info
+                )
+            else:
+                optim_state_dict = _unflatten_optim_state_dict(
+                    optim, cast(dict[str, ValueType], state_dict), info
+                )
+        else:
+            optim_state_dict = {}
+        if info.fsdp_modules:
+            # We need to specially handle FlatParameter FSDP as
+            # FlatParameter FSDP converts the FQNs.
+            for original_fqn, _ in model.named_parameters():
+                fqns = _get_fqns(model, original_fqn)
+                fqns_with_compiler = _get_fqns(
+                    model, original_fqn, skip_compiler_prefix=False
+                )
+                if fqns == fqns_with_compiler:
+                    continue
+
+                if len(fqns) != 1:
+                    raise AssertionError(
+                        f"Expected 1 FQN for '{original_fqn}', got {len(fqns)}"
+                    )
+                fqn = fqns.pop()
+                fqn_with_compiler = fqns_with_compiler.pop()
+                for g in optim_state_dict[_PG]:
+                    val = cast(dict[str, Any], g)
+                    params = [
+                        key.replace(fqn, fqn_with_compiler) for key in val[_PARAMS]
+                    ]
+                    val[_PARAMS] = params
+                osd_state = cast(DictValueType, optim_state_dict[_STATE])
+                for k in list(osd_state.keys()):
+                    if fqn in k:
+                        osd_state[k.replace(fqn, fqn_with_compiler)] = osd_state.pop(k)
+
+            with info.fsdp_context():
+                optim_state_dict = FSDP.optim_state_dict_to_load(
+                    model, optim, optim_state_dict
+                )
+        elif info.full_state_dict:
+            info.full_state_dict = False
+            local_state_dict = _get_optim_state_dict(model, (optim,), info)
+            info.full_state_dict = True
+            device = None
+
+            def _device(t):
+                if t.dim() > 0:
+                    nonlocal device
+                    if device is None:
+                        device = t.device
+                    elif device != t.device:
+                        raise ValueError("Device mismatch")
+                return t
+
+            _ = tree_map_only(torch.Tensor, _device, local_state_dict)
+            if device is None:
+                raise AssertionError("Expected device to be set")
+            flatten_osd, osd_mapping = _flatten_state_dict(optim_state_dict)
+            flatten_local_osd, local_osd_mapping = _flatten_state_dict(local_state_dict)
+            if info.broadcast_from_rank0:
+                _broadcast_state_dict(flatten_osd, flatten_local_osd, device=device)
+            else:
+                _distribute_state_dict(flatten_osd, flatten_local_osd, device=device)
+            # The modifications listed seek to address the problem where optim might possess
+            # dissimilar parameters in comparison to optim_state_dict. This is achieved by
+            # incorporating differential parameters within local, which may result in optim
+            # having additional parameters ultimately.
+            for optim_key in flatten_osd:
+                if optim_key not in flatten_local_osd:
+                    if optim_key not in osd_mapping:
+                        raise AssertionError(
+                            f"Expected key '{optim_key}' in osd_mapping"
+                        )
+                    flatten_local_osd[optim_key] = flatten_osd[optim_key]
+                    local_osd_mapping[optim_key] = osd_mapping[optim_key]
+            optim_state_dict = _unflatten_state_dict(
+                flatten_local_osd, local_osd_mapping
+            )
+            for pg in optim_state_dict[_PG]:
+                if _PARAMS not in pg:
+                    cast(dict[str, ValueType], pg)[_PARAMS] = []
+
+        # Note that we do not have to convert the FQN back to param id here if
+        # order in optim.param_groups[idx][_PARAMS] is the same as the one in
+        # optim_state_dict[_PG][idx][_PARAMS].
+        _state_dict_fn(optim, "load_state_dict")(state_dict=optim_state_dict)
+
+
+def get_model_state_dict(
+    model: nn.Module,
+    *,
+    submodules: Optional[set[nn.Module]] = None,
+    options: Optional[StateDictOptions] = None,
+) -> dict[str, ValueType]:
+    """
+    Return the model state_dict of ``model``.
+
+    See ``get_state_dict`` for the detail usage.
+
+    Args:
+        model (nn.Module): the nn.Module to the model.
+        submodules (deprecated): Optional[set[nn.Module]]: only return the model parameters
+            that belong to the submodules.
+        options (StateDictOptions): the options to control how
+            model state_dict and optimizer state_dict should be returned. See
+            `StateDictOptions` for the details.
+
+    Returns:
+        The state_dict for ``model``.
+
+    :rtype: typing.Dict[str, ValueType]
+    """
+    with _gc_context():
+        info = _verify_options(
+            model,
+            (),
+            optim_only=False,
+            submodules=submodules,
+            options=options,
+        )
+        model_state_dict = _get_model_state_dict(model, info)
+        _verify_state_dict(model_state_dict, {}, info)
+        return model_state_dict
+
+
+def get_optimizer_state_dict(
+    model: nn.Module,
+    optimizers: Union[torch.optim.Optimizer, Iterable[torch.optim.Optimizer]],
+    *,
+    submodules: Optional[set[nn.Module]] = None,
+    options: Optional[StateDictOptions] = None,
+) -> OptimizerStateType:
+    """
+    Return the combined state_dict for optimizers.
+
+    See ``get_state_dict`` for the detail usage.
+
+    Args:
+        model (nn.Module): the nn.Module to the model.
+        optimizers (Union[None, Optimizer, Iterable[Optimizer]]):
+            The optimizers that are used to optimize ``model``.
+        submodules (deprecated): Optional[set[nn.Module]]: only return the model parameters
+            that belong to the submodules.
+        options (StateDictOptions): the options to control how
+            model state_dict and optimizer state_dict should be returned. See
+            `StateDictOptions` for the details.
+
+    Returns:
+        The state_dict for ``optimizers``.
+
+    :rtype: OptimizerStateType
+    """
+    with _gc_context():
+        optimizers = (
+            (optimizers,)
+            if isinstance(optimizers, torch.optim.Optimizer)
+            else tuple(optimizers)
+        )
+        info = _verify_options(
+            model,
+            optimizers,
+            optim_only=True,
+            submodules=submodules,
+            options=options,
+        )
+        optim_state_dict = _get_optim_state_dict(model, optimizers, info)
+        _verify_state_dict({}, optim_state_dict, info)
+        return optim_state_dict
+
+
+def get_state_dict(
+    model: nn.Module,
+    optimizers: Union[torch.optim.Optimizer, Iterable[torch.optim.Optimizer]],
+    *,
+    submodules: Optional[set[nn.Module]] = None,
+    options: Optional[StateDictOptions] = None,
+) -> tuple[dict[str, ValueType], OptimizerStateType]:
+    """
+    Return the model state_dict and optimizers state_dict.
+
+    ``get_state_dict`` can process any module that is parallelized by PyTorch
+    FSDP/fully_shard, DDP/replicate, tensor_parallel/parallelize_module, and any
+    combination of these parallelisms. The main functions of ``get_state_dict``
+    are: 1.) returning a model and optimizer state_dict that can be resharded
+    with a different number of trainers and/or different parallelisms.
+    2.) hiding the parallelism-specific state_dict APIs. Users don't have to call
+    these APIs.
+    3.) sanity checking the result state_dict.
+
+    The keys of the result state dictionary are the canonical FQNs (Fully
+    Qualified Names).  A canonical FQN refers to the FQN based on a parameter's
+    position in an nn.Module hierarchy. More specifically, a canonical FQN to a
+    parameter is the FQN returned by ``module.named_parameters()`` or
+    ``module.named_buffers()`` when the module is not distributed by any
+    parallelisms. Since the optimizer internally uses parameter IDs to represent
+    a parameter, there will be a conversion from the parameter IDs to the
+    canonical FQNs when calling this API.
+
+    ``get_state_dict`` can also process a module that is not parallelized. In
+    such a case, ``get_state_dict`` only performs one function -- converting the
+    optimizer parameter IDs to the canonical FQNs.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> import torch
+        >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+        >>> from torch.nn.parallel import DistributedDataParallel as DDP
+        >>> from torch.distributed.checkpoint.state_dict import get_state_dict
+
+        >>> fsdp_model = FSDP(copy.deepcopy(model))
+        >>> fsdp_optim = torch.optim.Adam(model.parameters(), lr=1e-3)
+        >>> ddp_model = DDP(copy.deepcopy(model))
+        >>> ddp_optim = torch.optim.Adam(model.parameters(), lr=1e-3)
+
+
+        >>> ddp_state_dict, ddp_optim_state_dict = get_state_dict(ddp_model, ddp_optim)
+        >>> fsdp_state_dict, fsdp_optim_state_dict = get_state_dict(
+        ...     fsdp_model, fsdp_optim
+        ... )
+
+        >>> # if we simply call ddp_model.state_dict() and fsdp_model.state_dict(),
+        >>> # the asserts will fail.
+        >>> assert ddp_state_dict == fsdp_state_dict
+        >>> assert ddp_optim_state == fsdp_optim_state_dict
+
+
+    Args:
+        model (nn.Module): the nn.Module to the model.
+        optimizers (Union[None, Optimizer, Iterable[Optimizer]]):
+            The optimizers that are used to optimize ``model``.
+        submodules (deprecated): Optional[set[nn.Module]]: only return the model parameters
+            that belong to the submodules.
+        options (StateDictOptions): the options to control how
+            model state_dict and optimizer state_dict should be returned. See
+            `StateDictOptions` for the details.
+
+    Returns:
+        ``Tuple`` that contain model state_dict and optimizer state_dict.
+
+    :rtype: typing.Tuple[typing.Dict[str, ValueType], OptimizerStateType]
+    """
+
+    with _gc_context():
+        optimizers = (
+            (optimizers,)
+            if isinstance(optimizers, torch.optim.Optimizer)
+            else tuple(optimizers)
+        )
+        info = _verify_options(
+            model,
+            optimizers,
+            optim_only=False,
+            submodules=submodules,
+            options=options,
+        )
+        model_state_dict = _get_model_state_dict(model, info)
+        optim_state_dict = _get_optim_state_dict(model, optimizers, info)
+        _verify_state_dict(model_state_dict, optim_state_dict, info)
+        return model_state_dict, optim_state_dict
+
+
+def _unflatten_model_state_dict(
+    model: nn.Module,
+    state_dict: Union[dict[nn.Module, dict[str, ValueType]], dict[str, ValueType]],
+) -> dict[str, ValueType]:
+    if not state_dict:
+        return {}
+
+    if isinstance(next(iter(state_dict.keys())), nn.Module):
+        warnings.warn(
+            "Passing model_state_dict as a ``Dict[nn.Module, Dict[str, Any]]``"
+            "is deprecated and will be removed in 2.5. If you need this "
+            "feature, please preprocessing the model_state_dict to achieve the "
+            "same functionality.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        cast_state_dict = cast(dict[nn.Module, dict[str, ValueType]], state_dict)
+        new_state_dict: dict[str, ValueType] = {}
+        for submodule, sub_state_dict in cast_state_dict.items():
+            for name, m in model.named_modules():
+                if m != submodule:
+                    continue
+
+                fqns = _get_fqns(model, name)
+                if len(fqns) != 1:
+                    raise AssertionError(
+                        "FQNs for a submodule should only have 1 element"
+                    )
+                prefix = f"{next(iter(fqns))}."
+                new_state_dict.update(
+                    {prefix + subfqn: value for subfqn, value in sub_state_dict.items()}
+                )
+        return new_state_dict
+    else:
+        return cast(dict[str, ValueType], state_dict)
+
+
+def set_model_state_dict(
+    model: nn.Module,
+    model_state_dict: dict[str, ValueType],
+    *,
+    options: Optional[StateDictOptions] = None,
+) -> _IncompatibleKeys:
+    """Load the model state_dict.
+
+    The counterpart of ``get_model_state_dict`` to set the state_dict to the
+    model. See ``set_state_dict`` for the detail usage.
+
+    Args:
+        model (nn.Module): the nn.Module to the model.
+        model_state_dict: (Dict[str, ValueType]):
+           the model state_dict to load. If the key of the ``model_state_dict``
+           is nn.Module, the key is a submodule of ``model`` and the value should
+           be the state_dict of the submodule. When loading the state_dict,
+           the prefix of the submodule will be append to the state_dict.
+        options (StateDictOptions): the options to control how
+            model state_dict and optimizer state_dict should be loaded. See
+            `StateDictOptions` for the details.
+
+    Returns:
+        ``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields:
+            * **missing_keys** is a list of str containing the missing keys
+            * **unexpected_keys** is a list of str containing the unexpected keys
+
+    :type model_state_dict: typing.Dict[str, ValueType]
+    """
+    model_state_dict: dict[str, ValueType] = _unflatten_model_state_dict(
+        model, model_state_dict
+    )
+    with _gc_context():
+        info = _verify_options(model, (), optim_only=False, options=options)
+
+        _verify_state_dict(model_state_dict, {}, info)
+        return _load_model_state_dict(model, model_state_dict, info)
+
+
+def set_optimizer_state_dict(
+    model: nn.Module,
+    optimizers: Union[torch.optim.Optimizer, Iterable[torch.optim.Optimizer]],
+    optim_state_dict: OptimizerStateType,
+    *,
+    options: Optional[StateDictOptions] = None,
+) -> None:
+    """Load the optimizers state_dict.
+
+    The counterpart of ``get_optimizer_state_dict`` to set the state_dict to the
+    optimizers. See ``set_state_dict`` for the detail usage.
+
+    WARN: ``set_optimizer_state_dict`` can only be called before ``backward()`` or after
+        ``step()`` is called on the optimizers. Otherwise, the optimizer states won't be
+        initialized correctly.
+
+    Args:
+        model (nn.Module): the nn.Module to the model.
+        optimizers (Union[Optimizer, Iterable[Optimizer]]):
+            The optimizers that are used to optimize ``model``.
+        optim_state_dict: OptimizerStateType:
+            the optimizer state_dict to load.
+        options (StateDictOptions): the options to control how
+            model state_dict and optimizer state_dict should be loaded. See
+            `StateDictOptions` for the details.
+
+    Returns:
+        None
+
+    :type optim_state_dict: typing.OptimizerStateType
+    """
+    with _gc_context():
+        optimizers = (
+            (optimizers,)
+            if isinstance(optimizers, torch.optim.Optimizer)
+            else tuple(optimizers)
+        )
+        info = _verify_options(model, optimizers, optim_only=True, options=options)
+
+        _verify_state_dict({}, optim_state_dict, info)
+        _load_optim_state_dict(model, optimizers, optim_state_dict, info)
+
+
+def set_state_dict(
+    model: nn.Module,
+    optimizers: Union[torch.optim.Optimizer, Iterable[torch.optim.Optimizer]],
+    *,
+    model_state_dict: dict[str, ValueType],
+    optim_state_dict: OptimizerStateType,
+    options: Optional[StateDictOptions] = None,
+) -> _IncompatibleKeys:
+    """Load the model state_dict and optimizers state_dict.
+
+    The counterpart of ``get_state_dict`` to set the state_dict to the model and
+    optimizers.  The given ``model_state_dict`` and ``optim_state_dict`` do not
+    have to be returned by ``get_state_dict`` but must meet the following
+    requirements: 1) all FQNs are canonical FQNs as defined in ``get_state_dict``,
+    2) if a tensor is sharded, it must be either a ShardedTensor or DTensor,
+    3) optimizer state_dict cannot contain the parameter IDs; the keys should be
+    the canonical FQNs.
+
+    WARN: ``set_state_dict`` can only be called before ``backward()`` or after ``step()``
+        is called on the optimizers. Otherwise, the optimizer states won't be initialized
+        correctly.
+
+    Args:
+        model (nn.Module): the nn.Module to the model.
+        optimizers (Union[Optimizer, Iterable[Optimizer]]):
+            The optimizers that are used to optimize ``model``.
+        model_state_dict: (Union[Dict[nn.Module, Dict[str, ValueType]], Dict[str, ValueType]]):
+           the model state_dict to load. If the key of the ``model_state_dict``
+           is nn.Module, the key is a submodule of ``model`` and the value should
+           be the state_dict of the submodule. When loading the state_dict,
+           the prefix of the submodule will be append to the state_dict.
+        optim_state_dict: OptimizerStateType:
+            the optimizer state_dict to load.
+        options (StateDictOptions): the options to control how
+            model state_dict and optimizer state_dict should be loaded. See
+            `StateDictOptions` for the details.
+
+    Returns:
+        ``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields:
+            * **missing_keys** is a list of str containing the missing keys of the model state_dict.
+            * **unexpected_keys** is a list of str containing the unexpected keys of the model state_dict.
+
+    :type model_state_dict: typing.Dict[str, ValueType]
+    :type optim_state_dict: typing.OptimizerStateType
+    """
+
+    model_state_dict: dict[str, ValueType] = _unflatten_model_state_dict(
+        model, model_state_dict
+    )
+    with _gc_context():
+        optimizers = (
+            (optimizers,)
+            if isinstance(optimizers, torch.optim.Optimizer)
+            else tuple(optimizers)
+        )
+        info = _verify_options(
+            model, optimizers, optim_only=not model_state_dict, options=options
+        )
+
+        _verify_state_dict(model_state_dict, optim_state_dict, info)
+        _load_optim_state_dict(model, optimizers, optim_state_dict, info)
+        return _load_model_state_dict(model, model_state_dict, info)
+
+
+# TODO: correct the state_dict function signature.
+# TODO: this API is not yet fully tested. Make it private
+@no_type_check
+def _patch_model_state_dict(
+    model: nn.Module,
+    *,
+    options: Optional[StateDictOptions] = None,
+) -> None:
+    """Patch the ``state_dict`` and ``load_state_dict`` attributes of ``model``.
+
+    Patch the ``state_dict`` and ``load_state_dict`` attributes of ``model`` to
+    be a partial function to call ``get_state_dict`` and ``set_state_dict``.
+
+    Example:
+        from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+        from torch.distributed.checkpoint.state_dict import patch_model_state_dict
+
+        model = fsdp(model)
+        patch_model_state_dict(model)
+
+    Args:
+        model (nn.Module): the nn.Module to the model.
+        options (StateDictOptions): the options to control how
+            model state_dict and optimizer state_dict should be loaded. See
+            `StateDictOptions` for the details.
+    Returns:
+        None
+    """
+
+    _state_dict_call = functools.partial(
+        get_model_state_dict,
+        model=model,
+        options=options,
+    )
+
+    def state_dict_call():
+        return _state_dict_call()
+
+    model.state_dict = state_dict_call
+
+    _load_state_dict_call = functools.partial(
+        set_model_state_dict,
+        model=model,
+        options=options,
+    )
+
+    def load_state_dict_call(state_dict: dict[str, Any]):
+        _load_state_dict_call(model_state_dict=state_dict)
+
+    model.load_state_dict = load_state_dict_call
+
+    _patched_state_dict.add(state_dict_call)
+    _patched_state_dict.add(load_state_dict_call)
+
+
+# TODO: correct the load_state_dict function signature.
+# TODO: this API is not yet fully tested. Make it private
+@no_type_check
+def _patch_optimizer_state_dict(
+    model: nn.Module,
+    *,
+    optimizers: tuple[torch.optim.Optimizer, ...],
+    options: Optional[StateDictOptions] = None,
+) -> None:
+    """Patch the ``state_dict`` and ``load_state_dict`` attributes of ``optimizers``.
+
+    Patch the ``state_dict`` and ``load_state_dict`` attributes of ``optimizers`` to
+    be a partial function to call ``get_state_dict`` and ``set_state_dict``.
+
+    Note that if there are multiple optimizers, all of the optimizers will be patched.
+    So users only need to call one of the state_dict() to get the full result.
+
+    Example:
+        from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+        from torch.distributed.checkpoint.state_dict import patch_model_state_dict
+
+        model = fsdp(model)
+        patch_model_state_dict(model)
+
+    Args:
+        model (nn.Module): the nn.Module to the model.
+        options (StateDictOptions): the options to control how
+            model state_dict and optimizer state_dict should be loaded. See
+            `StateDictOptions` for the details.
+    Returns:
+        None
+    """
+
+    _state_dict_call = functools.partial(
+        get_optimizer_state_dict,
+        model=model,
+        optimizers=optimizers,
+        options=options,
+    )
+
+    def state_dict_call():
+        return _state_dict_call()
+
+    _load_state_dict_call = functools.partial(
+        set_optimizer_state_dict,
+        model=model,
+        optimizers=optimizers,
+        options=options,
+    )
+
+    def load_state_dict_call(state_dict: dict[str, Any]):
+        _load_state_dict_call(optim_state_dict=state_dict)
+
+    _patched_state_dict.add(state_dict_call)
+    _patched_state_dict.add(load_state_dict_call)
+    optimizers = (
+        (optimizers,)
+        if isinstance(optimizers, torch.optim.Optimizer)
+        else tuple(optimizers)
+    )
+    for optim in optimizers:
+        optim.state_dict = state_dict_call
+        optim.load_state_dict = load_state_dict_call
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/state_dict_loader.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/state_dict_loader.py
new file mode 100644
index 0000000000000000000000000000000000000000..178e190e937fb5fab1aa582464e20f1cff8d7abf
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/state_dict_loader.py
@@ -0,0 +1,389 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+import inspect
+import logging
+import os
+import warnings
+from typing import Any, cast, Optional, TYPE_CHECKING, Union
+from typing_extensions import deprecated
+
+import torch
+import torch.distributed as dist
+from torch.distributed.checkpoint.default_planner import _EmptyStateDictLoadPlanner
+from torch.distributed.checkpoint.logger import _dcp_method_logger
+from torch.distributed.checkpoint.stateful import Stateful
+
+from ._storage_utils import _storage_setup
+from .default_planner import DefaultLoadPlanner
+from .planner import LoadPlan, LoadPlanner
+from .storage import StorageReader
+from .utils import _api_bc_check, _DistWrapper, _profile
+
+
+if TYPE_CHECKING:
+    from torch.distributed.checkpoint.metadata import Metadata
+
+__all__ = ["load_state_dict", "load"]
+
+logger = logging.getLogger()
+
+
+@deprecated(
+    "`load_state_dict` is deprecated and will be removed in future versions. "
+    "Please use `load` instead.",
+    category=FutureWarning,
+)
+def load_state_dict(
+    state_dict: dict[str, Any],
+    storage_reader: StorageReader,
+    process_group: Optional[dist.ProcessGroup] = None,
+    coordinator_rank: int = 0,
+    no_dist: bool = False,
+    planner: Optional[LoadPlanner] = None,
+) -> None:
+    """This method is deprecated. Please switch to 'load'."""
+    storage_reader.reset()
+    with _profile():
+        # TODO: test returning `load` here instead.
+        return _load_state_dict(
+            state_dict,
+            storage_reader,
+            process_group,
+            coordinator_rank,
+            no_dist,
+            planner,
+        )
+
+
+@_dcp_method_logger(log_exceptions=True)
+@_api_bc_check
+def load(
+    state_dict: dict[str, Any],
+    *,
+    checkpoint_id: Union[str, os.PathLike, None] = None,
+    storage_reader: Optional[StorageReader] = None,
+    planner: Optional[LoadPlanner] = None,
+    process_group: Optional[dist.ProcessGroup] = None,
+    no_dist: bool = False,
+) -> None:
+    """
+    Load a checkpoint into a distributed state dict in SPMD style.
+
+    Each rank must have the same keys in their ``state_dict`` provided to this
+    API. Mismatched keys may result in hangs or errors. If unsure, you can use
+    the ``utils._assert_same_keys`` API to check (but may incur communication
+    costs).
+
+    Each rank will try to read the least amount of data necessary
+    to fulfill the requested `state_dict`. When loading :class:`ShardedTensor`
+    or :class:`DTensor` instances, each rank only reads data for their local shards.
+
+    For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``),
+    load will first call ``state_dict`` before attempting deserialization, followed by
+    ``load_state_dict`` once the deserialization is complete.
+    For each non-``Stateful`` object, load will deserialize the object, and then replace
+    it in the ``state_dict`` with the deserialized object.
+
+    .. warning::
+        All tensors in ``state_dict`` must be allocated on their
+        destination device *prior to* calling this function.
+
+        All non-tensor data is loaded using `torch.load()` and modified in place
+        on state_dict.
+
+    .. warning::
+        Users must call `load_state_dict` on the root module to ensure load
+        pos-processing and non-tensor data properly propagates.
+
+    .. note:
+        If no process group is initialized, this function will assume the intent
+        is to load a checkpoint into the local process. This can be useful in the
+        case of local inference, and when using regular Tensors (as opposed to DTensor
+         or ShardedTensor)
+
+    .. note:
+        Rank 0 is assumed to be the coordinator rank.
+
+    Args:
+        state_dict (Dict[str, Any]): The state_dict to load the checkpoint into.
+        checkpoint_id (Union[str, os.PathLike, None]):
+            The ID of this checkpoint instance. The meaning of the checkpoint_id
+            depends on the storage. It can be a path to a folder or to a file.
+            It can also be a key if the storage is a key-value store.
+            (Default: ``None``)
+        storage_reader (Optional[StorageReader]):
+            Instance of StorageWriter used to perform reads. If this is not
+            specified, DCP will automatically infer the reader based on the
+            checkpoint_id. If checkpoint_id is also None, an exception will
+            be raised. (Default: ``None``)
+        planner (Optional[LoadPlanner]):
+            Instance of LoadPlanner. If this is not specified, the default
+            planner will be used. (Default: ``None``)
+        process_group (Optional[ProcessGroup]):
+            ProcessGroup to be used for cross-rank synchronization.
+            (Default: ``None``)
+        no_dist (bool): If ``True``, this function will assume the intent is to load
+            a checkpoint without using cross-rank synchronization. (Default: ``False``)
+    Returns:
+        None.
+
+    Examples
+        >>> # xdoctest: +SKIP
+        >>> my_model = MyModule()
+        >>> optimizer = Adagrad(my_model.parameters())
+        >>> model_state_dict = my_model.state_dict()
+        >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader(
+        ...     "/checkpoint/1"
+        ... )
+
+        >>> torch.distributed.checkpoint.load_state_dict(
+        >>>     state_dict=model_state_dict,
+        >>>     storage_reader=fs_storage_reader,
+        >>> )
+
+        >>> # module.load_state_dict() function might have customized steps
+        >>> # to flush the state_dict, must call it to
+        >>> # ensure correct behavior.
+        >>> my_model.load_state_dict(model_state_dict)
+
+    .. note::
+        load_state_dict uses collectives to coordinate reads across ranks.
+        For NCCL-based process groups, internal tensor representations of
+        objects must be moved to the GPU device before communication takes place.
+        In this case, the device used is given by ``torch.cuda.current_device()``
+        and it is the user's responsibility to ensure that this is set so that each
+        rank has an individual GPU, via ``torch.cuda.set_device()``.
+    """
+
+    no_dist = no_dist or (not dist.is_available()) or (not dist.is_initialized())
+    if no_dist:
+        warnings.warn(
+            "torch.distributed is disabled, unavailable or uninitialized, assuming the intent is to load in a single process.",
+            stacklevel=2,
+        )
+
+    with _profile():
+        storage_reader = cast(
+            StorageReader, _storage_setup(storage_reader, checkpoint_id, reader=True)
+        )
+
+        # All ranks must have the same keys in their `state_dict` provided to
+        # this API.  See documentation for more details.
+        # Here we simply sort the keys to ensure that all ranks load values in
+        # the same order.
+        keys = sorted(state_dict.keys())
+
+        statetful_sd = {}
+        for key in keys:
+            if key not in state_dict:
+                continue
+            elem = state_dict[key]
+            statetful_sd[key] = (
+                elem.state_dict() if isinstance(elem, Stateful) else elem
+            )
+
+        _load_state_dict(
+            state_dict=statetful_sd,
+            storage_reader=storage_reader,
+            process_group=process_group,
+            no_dist=no_dist,
+            planner=planner,
+        )
+        for key in keys:
+            if key not in state_dict:
+                continue
+            elem = state_dict[key]
+            if isinstance(elem, Stateful):
+                # If the state_dict is a Stateful object,
+                # DCP does an in-place load in the original state dict.
+                elem.load_state_dict(statetful_sd[key])
+            else:
+                # Otherwise, replace the state_dict with the loaded state_dict.
+                state_dict[key] = statetful_sd[key]
+
+
+def _load_state_dict(
+    state_dict: dict[str, Any],
+    storage_reader: StorageReader,
+    process_group: Optional[dist.ProcessGroup] = None,
+    coordinator_rank: int = 0,
+    no_dist: bool = False,
+    planner: Optional[LoadPlanner] = None,
+) -> None:
+    torch._C._log_api_usage_once("torch.distributed.checkpoint.load_state_dict")
+
+    distW = _DistWrapper(process_group, not no_dist, coordinator_rank)
+    if planner is None:
+        planner = DefaultLoadPlanner()
+
+    ckpt_kwargs = {}
+    if (ckpt_id := getattr(storage_reader, "checkpoint_id", None)) is not None:
+        ckpt_kwargs["checkpoint_id"] = ckpt_id
+        ckpt_kwargs["process_group"] = distW.group
+
+    use_collectives = True
+    metadata: Optional[Metadata] = None
+
+    @_dcp_method_logger(**ckpt_kwargs)
+    def local_step():
+        nonlocal use_collectives
+        nonlocal metadata
+
+        # Use global metadata if available, otherwise fallback to rank local metadata
+        try:
+            metadata = storage_reader.read_metadata()
+        except Exception:
+            logger.info(
+                "Global metadata is not found. Falling back to rank local metadata."
+            )
+
+        if (
+            not metadata
+            and "kwargs" in inspect.signature(storage_reader.read_metadata).parameters
+        ):
+            try:
+                metadata = storage_reader.read_metadata(rank=distW.rank)  # noqa: F841
+                use_collectives = False
+            except Exception:
+                logger.info("Rank local metadata is not found.")
+
+        if planner is None:
+            raise AssertionError("planner is None")
+        if metadata is None:
+            raise AssertionError("metadata is None")
+        planner.set_up_planner(state_dict, metadata, distW.is_coordinator)
+
+        if (
+            "kwargs"
+            in inspect.signature(storage_reader.set_up_storage_reader).parameters
+        ):
+            storage_reader.set_up_storage_reader(
+                metadata,
+                distW.is_coordinator,
+                rank=distW.rank,
+                use_collectives=use_collectives,
+            )
+        else:
+            storage_reader.set_up_storage_reader(metadata, distW.is_coordinator)
+
+        local_plan = planner.create_local_plan()
+        local_plan = storage_reader.prepare_local_plan(local_plan)
+        return local_plan
+
+    @_dcp_method_logger(**ckpt_kwargs)
+    def global_step(all_local_plans):
+        if planner is None:
+            raise AssertionError("planner is None")
+        all_local_plans = planner.create_global_plan(all_local_plans)
+        all_local_plans = storage_reader.prepare_global_plan(all_local_plans)
+        return all_local_plans
+
+    central_plan: Optional[LoadPlan] = None
+    if use_collectives:
+        central_plan = distW.reduce_scatter("plan", local_step, global_step)
+    else:
+        local_plan: LoadPlan = local_step()
+        global_plan: list[LoadPlan] = global_step([local_plan])
+        central_plan = global_plan[0]
+
+    @_dcp_method_logger(**ckpt_kwargs)
+    def read_data():
+        if planner is None:
+            raise AssertionError("planner is None")
+        if central_plan is None:
+            raise AssertionError("central_plan is None")
+        final_local_plan = planner.finish_plan(central_plan)
+        all_reads = storage_reader.read_data(final_local_plan, planner)
+
+        all_reads.wait()
+        return None
+
+    if use_collectives:
+        _ = distW.all_gather("read", read_data)
+    else:
+        read_data()
+        distW.barrier()
+
+
+def _load_state_dict_from_keys(
+    keys: Optional[Union[set[str], str]] = None,
+    *,
+    checkpoint_id: Union[str, os.PathLike, None] = None,
+    storage_reader: Optional[StorageReader] = None,
+    process_group: Optional[dist.ProcessGroup] = None,
+) -> dict[str, Any]:
+    """
+    Load only the specified keys from the checkpoint, if no keys are specified, the entire
+    checkpoint will be loaded. Note, this method completely loads the checkpoint into the
+    current process and is not distributed.
+
+    .. warning::
+
+
+    .. warning::
+
+        All non-tensor data is loaded using `torch.load()`
+
+    .. note:
+        As opposed to the usual pattern, this function does not take a state dict as input
+        and does not load inplace. Instead, a new state dict is directly initialized and read
+        from file.
+
+    .. note:
+        If no process group is initialized, this function will assume the intent
+        is to load a checkpoint into the local process. This can be useful in the
+        case of local inference, and when using regular Tensors (as opposed to DTensor
+         or ShardedTensor)
+
+    .. note:
+        Rank 0 is assumed to be the coordinator rank.
+
+    Args:
+        keys (Optional[Union[set[str], str]]):
+            Loads any key specified in this set. If no keys are specified, the entire checkpoint
+            is loaded.
+        checkpoint_id (Union[str, os.PathLike, None]):
+            The ID of this checkpoint instance. The meaning of the checkpoint_id
+            depends on the storage. It can be a path to a folder or to a file.
+            It can also be a key if the storage is a key-value store.
+            (Default: ``None``)
+        storage_reader (Optional[StorageReader]):
+            Instance of StorageWriter used to perform reads. If this is not
+            specified, DCP will automatically infer the reader based on the
+            checkpoint_id. If checkpoint_id is also None, an exception will
+            be raised. (Default: ``None``)
+        process_group (Optional[ProcessGroup]):
+            ProcessGroup to be used for cross-rank synchronization.
+            (Default: ``None``)
+
+    Returns:
+        State dict from specified keys
+    """
+    torch._C._log_api_usage_once(
+        "torch.distributed.checkpoint._load_state_dict_from_keys"
+    )
+
+    no_dist = not (dist.is_available() and dist.is_initialized())
+    if no_dist:
+        warnings.warn(
+            "torch.distributed is unavailable or uninitialized, assuming the intent is to load in a single process.",
+            stacklevel=2,
+        )
+
+    storage_reader = cast(
+        StorageReader, _storage_setup(storage_reader, checkpoint_id, reader=True)
+    )
+
+    if isinstance(keys, str):
+        keys = {keys}
+
+    sd: dict[str, Any] = {}
+    _load_state_dict(
+        state_dict=sd,
+        storage_reader=storage_reader,
+        process_group=process_group,
+        no_dist=no_dist,
+        planner=_EmptyStateDictLoadPlanner(keys=keys),
+    )
+
+    return sd
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/state_dict_saver.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/state_dict_saver.py
new file mode 100644
index 0000000000000000000000000000000000000000..370f97cd1cd013246563f021749b6537a327b235
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/state_dict_saver.py
@@ -0,0 +1,496 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+import inspect
+import os
+import warnings
+from concurrent.futures import Future
+from dataclasses import dataclass
+from enum import Enum
+from typing import cast, Optional, TYPE_CHECKING, Union
+from typing_extensions import deprecated
+
+import torch
+import torch.distributed as dist
+from torch.distributed._state_dict_utils import STATE_DICT_TYPE
+from torch.distributed.checkpoint._async_process_executor import (
+    _ProcessBasedAsyncCheckpointExecutor,
+)
+from torch.distributed.checkpoint._async_thread_executor import (
+    _ThreadBasedAsyncCheckpointExecutor,
+)
+from torch.distributed.checkpoint._storage_utils import _storage_setup
+from torch.distributed.checkpoint.default_planner import DefaultSavePlanner
+from torch.distributed.checkpoint.logger import _dcp_method_logger
+from torch.distributed.checkpoint.metadata import Metadata
+from torch.distributed.checkpoint.planner import SavePlan, SavePlanner
+from torch.distributed.checkpoint.staging import (
+    AsyncStager,
+    DefaultStager,
+    StagingOptions,
+)
+from torch.distributed.checkpoint.stateful import Stateful
+from torch.distributed.checkpoint.storage import StorageWriter, WriteResult
+from torch.distributed.distributed_c10d import _get_default_group
+
+from .utils import _api_bc_check, _DistWrapper, _profile
+
+
+if TYPE_CHECKING:
+    from torch.distributed.checkpoint._async_executor import _AsyncCheckpointExecutor
+
+
+__all__ = [
+    "save_state_dict",
+    "save",
+    "async_save",
+    "AsyncCheckpointerType",
+    "AsyncSaveResponse",
+]
+
+
+class AsyncCheckpointerType(Enum):
+    """Enum for async checkpointer type."""
+
+    THREAD = "thread"
+    PROCESS = "process"
+
+
+@deprecated(
+    "`save_state_dict` is deprecated and will be removed in future versions."
+    "Please use `save` instead.",
+    category=FutureWarning,
+)
+def save_state_dict(
+    state_dict: STATE_DICT_TYPE,
+    storage_writer: StorageWriter,
+    process_group: Optional[dist.ProcessGroup] = None,
+    coordinator_rank: int = 0,
+    no_dist: bool = False,
+    planner: Optional[SavePlanner] = None,
+) -> Metadata:
+    """This method is deprecated. Please switch to 'save'."""
+    storage_writer.reset()
+
+    # TODO: test returning `save` here instead.
+    with _profile():
+        return _save_state_dict(
+            state_dict,
+            storage_writer,
+            process_group,
+            coordinator_rank,
+            no_dist,
+            planner,
+        )
+
+
+@_dcp_method_logger(log_exceptions=True)  # type: ignore[arg-type]
+@_api_bc_check
+def save(
+    state_dict: STATE_DICT_TYPE,
+    *,
+    checkpoint_id: Union[str, os.PathLike, None] = None,
+    storage_writer: Optional[StorageWriter] = None,
+    planner: Optional[SavePlanner] = None,
+    process_group: Optional[dist.ProcessGroup] = None,
+    no_dist: bool = False,
+    use_collectives: bool = True,
+) -> Metadata:
+    """
+    Save a distributed model in SPMD style.
+
+    This function is different from ``torch.save()`` as it handles
+    ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards.
+
+    For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``),
+    save will call ``state_dict`` before serialization.
+
+    .. warning::
+        There is no guarantees of Backwards Compatibility across PyTorch versions
+        for saved state_dicts.
+
+    .. warning::
+        If using the `process_group` argument, make sure that only its ranks
+        call `save_state_dict` and that all data in state_dict belong to it.
+
+    .. note::
+        When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of
+        the shard_group should be calling `save_state_dict` and the corresponding process
+        group needs to be passed in.
+
+    .. note::
+        If no process group is available, this function assumes the intention is to save the
+         state_dict in the local process.
+
+    .. note:
+        Rank 0 is assumed to be the coordinator rank.
+
+
+    Args:
+        state_dict (Dict[str, Any]): The state_dict to save.
+        checkpoint_id (Union[str, os.PathLike, None]):
+            The ID of this checkpoint instance. The meaning of the checkpoint_id
+            depends on the storage. It can be a path to a folder or to a file.
+            It can also be a key if the storage is a key-value store.
+            (Default: ``None``)
+        storage_writer (Optional[StorageWriter]):
+            Instance of StorageWriter used to perform writes. If this is not
+            specified, DCP will automatically infer the writer based on the
+            checkpoint_id. If checkpoint_id is also None, an exception will
+            be raised. (Default: ``None``)
+        planner (Optional[SavePlanner]):
+            Instance of SavePlanner. If this is not specified, the default
+            planner will be used. (Default: ``None``)
+        process_group (Optional[ProcessGroup]):
+            ProcessGroup to be used for cross-rank synchronization.
+            (Default: ``None``)
+        no_dist (bool):
+            If ``True``, this function will assume the intent is to load
+            a checkpoint on a single rank/process.
+            (Default: ``False``)
+        use_collectives (bool): If ``False``, this function will assume the intent is to save
+            a checkpoint without using cross-rank synchronization.
+            (Default: ``True``)
+            This configuration is experimental and should be used with caution.
+            It will change the format of the saved checkpoint and may not be backward compatible.
+
+    Returns:
+        Metadata: Metadata object for the saved checkpoint.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> my_model = MyModule()
+
+        >>> state_dict = {"model": my_model}
+
+        >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter(
+        ...     "/checkpoint/1"
+        ... )
+        >>> torch.distributed.checkpoint.save(
+        >>>     state_dict=state_dict,
+        >>>     storage_writer=fs_storage_writer,
+        >>> )
+
+    .. note::
+        save_state_dict uses collectives to coordinate writes across ranks.
+        For NCCL-based process groups, internal tensor representations of
+        objects must be moved to the GPU device before communication takes place.
+        In this case, the device used is given by ``torch.cuda.current_device()``
+        and it is the user's responsibility to ensure that this is set so that
+        each rank has an individual GPU, via ``torch.cuda.set_device()``.
+    """
+    torch._C._log_api_usage_once("torch.distributed.checkpoint.save")
+
+    no_dist = no_dist or (not dist.is_available()) or (not dist.is_initialized())
+    if no_dist:
+        warnings.warn(
+            "torch.distributed is disabled, unavailable or uninitialized, assuming the intent is to save in a single process.",
+            stacklevel=2,
+        )
+
+    with _profile():
+        storage_writer = cast(
+            StorageWriter, _storage_setup(storage_writer, checkpoint_id, reader=False)
+        )
+
+        return _save_state_dict(
+            state_dict=_stateful_to_state_dict(state_dict),
+            storage_writer=storage_writer,
+            process_group=process_group,
+            no_dist=no_dist,
+            planner=planner,
+            use_collectives=use_collectives,
+        )
+
+
+@dataclass
+class AsyncSaveResponse:
+    """This class contains futures for staging and upload completion.
+    It is returned by async_save().
+    staging_completion is a future that indicates when local copy
+    of state_dict is complete.
+    upload_completion is a future that indicates when a checkpoint
+    completed saving.
+    """
+
+    staging_completion: Future[None]
+    upload_completion: Future[None]
+
+
+@_dcp_method_logger(log_exceptions=True)
+def async_save(
+    state_dict: STATE_DICT_TYPE,
+    *,
+    checkpoint_id: Union[str, os.PathLike, None] = None,
+    storage_writer: Optional[StorageWriter] = None,
+    planner: Optional[SavePlanner] = None,
+    process_group: Optional[dist.ProcessGroup] = None,
+    async_checkpointer_type: AsyncCheckpointerType = AsyncCheckpointerType.THREAD,
+    async_stager: Optional[AsyncStager] = None,
+    no_dist: bool = False,
+    use_collectives: bool = True,
+) -> Union[Future, AsyncSaveResponse]:
+    """Asynchronous version of ``save``. This code first de-stages the state_dict on to the
+    staging storage (defaults to CPU memory), and then calls the `save` in a separate thread.
+
+    .. warning::
+        This feature is experimental and subject to change.
+        MUST CALL CLOSE AFTER LAST CHECKPOINT IS SAVED
+
+    Args:
+        state_dict (Dict[str, Any]): The state_dict to save.
+        checkpoint_id (Union[str, os.PathLike, None]):
+            The ID of this checkpoint instance. The meaning of the checkpoint_id
+            depends on the storage. It can be a path to a folder or to a file.
+            It can also be a key if the storage is a key-value store.
+            (Default: ``None``)
+        storage_writer (Optional[StorageWriter]):
+            Instance of StorageWriter used to perform 'stage' and  'save'. If
+            this is not specified, DCP will automatically infer the writer based on the
+            checkpoint_id. If checkpoint_id is also None, an exception will
+            be raised. (Default: ``None``)
+        planner (Optional[SavePlanner]):
+            Instance of SavePlanner. If this is not specified, the default
+            planner will be used. (Default: ``None``)
+        process_group (Optional[ProcessGroup]):
+            ProcessGroup to be used for cross-rank synchronization.
+            (Default: ``None``)
+        async_checkpointer_type (AsyncCheckpointerType):
+            whether to do checkpoint in separate thread or process
+            (Default: ``AsyncCheckpointerType.THREAD``)
+        async_stager (AsyncStager):
+            provides staging implementation. If storage_writer implements AsyncStager
+            and async_stager is provided, async_stager will be used for staging
+        no_dist (bool):
+            If ``True``, this function will assume the intent is to save
+            a checkpoint on a single rank/process.
+            (Default: ``False``)
+        use_collectives: If False, Save the checkpoint without rank coordination. (Default: ``True``)
+            This configuration is experimental and should be used with caution.
+            It will change the format of the saved checkpoint and may not be backward compatible.
+
+    Returns:
+        Future: A future holding the resultant Metadata object from `save`.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> my_model = MyModule()
+
+        >>> state_dict = {"model": my_model}
+
+        >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter(
+        ...     "/checkpoint/1"
+        ... )
+        >>> checkpoint_future = torch.distributed.checkpoint.async_save(
+        >>>     state_dict=state_dict,
+        >>>     storage_writer=fs_storage_writer,
+        >>> )
+        >>>
+        >>> # ... do some work ...
+        >>>
+        >>> checkpoint_future.result()
+
+    """
+    torch._C._log_api_usage_once("torch.distributed.checkpoint.async_save")
+
+    if dist.is_available() and dist.is_initialized():
+        pg = process_group or _get_default_group()
+        if torch.device("cpu") not in pg._device_types:
+            raise AssertionError(
+                "A CPU backend must be enabled for async save; try initializing process group with 'cpu:gloo,cuda:nccl'"
+            )
+
+    if async_stager is None:
+        if storage_writer is not None and isinstance(storage_writer, AsyncStager):
+            # bwc with old storage_writers
+            async_stager = storage_writer
+        else:
+            async_stager = DefaultStager(
+                StagingOptions(
+                    False,
+                    False,
+                    False,
+                    False,
+                )
+            )
+
+    state_dict = _stateful_to_state_dict(state_dict)
+
+    @_dcp_method_logger(log_exceptions=True)
+    def stage_state_dict() -> Union[Future[STATE_DICT_TYPE], STATE_DICT_TYPE]:
+        return async_stager.stage(state_dict)
+
+    staging_future_or_state_dict = stage_state_dict()
+
+    upload_executor: _AsyncCheckpointExecutor = (
+        _ProcessBasedAsyncCheckpointExecutor()
+        if async_checkpointer_type == AsyncCheckpointerType.PROCESS
+        else _ThreadBasedAsyncCheckpointExecutor()
+    )
+
+    upload_future: Future = upload_executor.execute_save(
+        staging_future_or_state_dict,
+        checkpoint_id=checkpoint_id,
+        # pyrefly: ignore [bad-argument-type]
+        storage_writer=storage_writer,
+        planner=planner,
+        process_group=process_group,
+        no_dist=no_dist,
+        use_collectives=use_collectives,
+    )
+
+    if isinstance(staging_future_or_state_dict, Future):
+        staging_future = staging_future_or_state_dict
+        return_staging_future: Future[None] = Future()
+
+        def callback(
+            original_staging_future: Future[STATE_DICT_TYPE],
+            return_staging_future: Future[None] = return_staging_future,
+        ):
+            try:
+                original_staging_future.result()
+                return_staging_future.set_result(None)
+            except Exception as e:
+                return_staging_future.set_exception(e)
+
+        if not staging_future.done():
+            staging_future.add_done_callback(callback)
+        else:
+            return_staging_future.set_result(None)
+
+        # return new AsyncSaveResponse for users using new ZOC implementation
+        return AsyncSaveResponse(
+            staging_completion=return_staging_future, upload_completion=upload_future
+        )
+    else:
+
+        @_dcp_method_logger(log_exceptions=True)
+        def maybe_synchronize_staging():
+            if async_stager.should_synchronize_after_execute:
+                async_stager.synchronize_staging()
+
+        maybe_synchronize_staging()
+        return upload_future
+
+
+@_dcp_method_logger(log_exceptions=True)
+def _stateful_to_state_dict(state_dict: STATE_DICT_TYPE) -> STATE_DICT_TYPE:
+    """Creates a shallow copy of `state_dict` where `state_dict` is called for each Stateful object."""
+    stateful_state_dict = {}
+    for key, elem in state_dict.items():
+        # Apply _dcp_method_logger to each state_dict() call
+        def _elem_to_state_dict(elem):
+            return elem.state_dict() if isinstance(elem, Stateful) else elem
+
+        _elem_to_state_dict.__name__ = f"_stateful_to_state_dict.{key}"
+
+        stateful_state_dict[key] = _dcp_method_logger(log_exceptions=True)(
+            _elem_to_state_dict
+        )(elem)
+    return stateful_state_dict
+
+
+def _save_state_dict(
+    state_dict: STATE_DICT_TYPE,
+    storage_writer: StorageWriter,
+    process_group: Optional[dist.ProcessGroup] = None,
+    coordinator_rank: int = 0,
+    no_dist: bool = False,
+    planner: Optional[SavePlanner] = None,
+    use_collectives: bool = True,
+) -> Metadata:
+    torch._C._log_api_usage_once("torch.distributed.checkpoint.save_state_dict")
+
+    distW = _DistWrapper(process_group, not no_dist, coordinator_rank)
+    if planner is None:
+        planner = DefaultSavePlanner()
+    if planner is None:
+        raise AssertionError("planner is None")
+
+    global_metadata = None
+
+    ckpt_kwargs = {}
+    if (ckpt_id := getattr(storage_writer, "checkpoint_id", None)) is not None:
+        ckpt_kwargs["checkpoint_id"] = ckpt_id
+        ckpt_kwargs["process_group"] = distW.group
+
+    @_dcp_method_logger(**ckpt_kwargs)
+    def local_step():
+        if planner is None:
+            raise AssertionError("planner is None")
+        storage_meta = storage_writer.storage_meta()
+        if "storage_meta" not in inspect.signature(planner.set_up_planner).parameters:
+            warnings.warn(
+                "The function definition for SavePlanner.set_up_planner has been updated"
+                " to include the storage_meta argument. Please update your implementation"
+                " to include this parameter.",
+                stacklevel=2,
+            )
+            planner.set_up_planner(state_dict, distW.is_coordinator)  # type: ignore[call-arg, arg-type]
+        else:
+            planner.set_up_planner(
+                state_dict=state_dict,
+                storage_meta=storage_meta,
+                is_coordinator=distW.is_coordinator,
+            )
+
+        if (
+            "kwargs"
+            in inspect.signature(storage_writer.set_up_storage_writer).parameters
+        ):
+            storage_writer.set_up_storage_writer(
+                distW.is_coordinator,
+                rank=distW.rank,
+                use_collectives=use_collectives,
+            )
+        else:
+            storage_writer.set_up_storage_writer(distW.is_coordinator)
+
+        local_plan = planner.create_local_plan()
+        local_plan = storage_writer.prepare_local_plan(local_plan)
+        return local_plan
+
+    @_dcp_method_logger(**ckpt_kwargs)
+    def global_step(all_local_plans):
+        nonlocal global_metadata
+
+        if planner is None:
+            raise AssertionError("planner is None")
+        all_local_plans, global_metadata = planner.create_global_plan(all_local_plans)
+        all_local_plans = storage_writer.prepare_global_plan(all_local_plans)
+        return all_local_plans
+
+    central_plan: Optional[SavePlan] = None
+    if use_collectives:
+        central_plan = distW.reduce_scatter("plan", local_step, global_step)
+    else:
+        local_plan: SavePlan = local_step()
+        global_plan: list[SavePlan] = global_step([local_plan])
+        central_plan = global_plan[0]
+
+    @_dcp_method_logger(**ckpt_kwargs)
+    def write_data():
+        if planner is None:
+            raise AssertionError("planner is None")
+        if central_plan is None:
+            raise AssertionError("central_plan is None")
+        final_local_plan = planner.finish_plan(central_plan)
+        all_writes = storage_writer.write_data(final_local_plan, planner)
+
+        all_writes.wait()
+        return all_writes.value()
+
+    @_dcp_method_logger(**ckpt_kwargs)
+    def finish_checkpoint(all_results):
+        if global_metadata is None:
+            raise AssertionError("global_metadata is None")
+        storage_writer.finish(metadata=global_metadata, results=all_results)
+        return global_metadata
+
+    if use_collectives:
+        metadata = distW.all_reduce("write", write_data, finish_checkpoint)
+    else:
+        write_results: list[WriteResult] = write_data()
+        metadata = finish_checkpoint([write_results])
+        distW.barrier()
+
+    return metadata
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/stateful.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/stateful.py
new file mode 100644
index 0000000000000000000000000000000000000000..15e227d92fb5d29631b0316b3971c435120ad15b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/stateful.py
@@ -0,0 +1,42 @@
+from typing import Any, TypeVar
+from typing_extensions import Protocol, runtime_checkable
+
+
+__all__ = ["Stateful", "StatefulT"]
+
+
+@runtime_checkable
+class Stateful(Protocol):
+    """
+    Stateful protocol for objects that can be checkpointed and restored.
+    """
+
+    def state_dict(self) -> dict[str, Any]:
+        """
+        Objects should return their state_dict representation as a dictionary.
+        The output of this function will be checkpointed, and later restored in
+        `load_state_dict()`.
+
+        .. warning::
+            Because of the inplace nature of restoring a checkpoint, this function
+            is also called during `torch.distributed.checkpoint.load`.
+
+
+        Returns:
+            Dict: The objects state dict
+        """
+
+        ...
+
+    def load_state_dict(self, state_dict: dict[str, Any]) -> None:
+        """
+        Restore the object's state from the provided state_dict.
+
+        Args:
+            state_dict: The state dict to restore from
+        """
+
+        ...
+
+
+StatefulT = TypeVar("StatefulT", bound=Stateful)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/storage.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/storage.py
new file mode 100644
index 0000000000000000000000000000000000000000..b184d7b1700528ad22bc10726cb6619975e8d9e8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/storage.py
@@ -0,0 +1,288 @@
+import abc
+import os
+from dataclasses import dataclass
+from typing import Any, Optional, Union
+
+from torch.distributed.checkpoint.metadata import Metadata, MetadataIndex, StorageMeta
+from torch.distributed.checkpoint.planner import (
+    LoadPlan,
+    LoadPlanner,
+    SavePlan,
+    SavePlanner,
+)
+from torch.futures import Future
+
+
+__all__ = ["WriteResult", "StorageWriter", "StorageReader"]
+
+
+@dataclass(frozen=True)
+class WriteResult:
+    index: MetadataIndex
+
+    size_in_bytes: int
+    storage_data: Any
+
+
+class StorageWriter(abc.ABC):
+    """
+    Interface used by ``save_state_dict`` to write to storage.
+
+    One StorageWriter instance acts as both the coordinator and the follower
+    in a distributed checkpoint. As part of initialization, each instance
+    is told its role.
+
+    A subclass should expect the following sequence of calls.
+
+    0) (all ranks) set checkpoint_id if users pass a valid checkpoint_id.
+    1) (all ranks) set_up_storage_writer()
+    2) (all ranks) prepare_local_plan()
+    3) (coordinator) prepare_global_plan()
+    4) (all ranks) write_data()
+    5) (coordinator) finish()
+    """
+
+    @abc.abstractmethod
+    def reset(self, checkpoint_id: Union[str, os.PathLike, None] = None) -> None:
+        """
+        Calls to indicates a brand new checkpoint write is going to happen.
+        A checkpoint_id may be present if users set the checkpoint_id for
+        this checkpoint write. The meaning of the checkpiont_id is
+        storage-dependent. It can be a path to a folder/file or a key for
+        a key-value storage.
+
+        Args:
+            checkpoint_id (Union[str, os.PathLike, None]):
+                The ID of this checkpoint instance. The meaning of the checkpoint_id
+                depends on the storage. It can be a path to a folder or to a file.
+                It can also be a key if the storage is a key-value store.
+                (Default: ``None``)
+        """
+        ...
+
+    @abc.abstractmethod
+    def set_up_storage_writer(
+        self, is_coordinator: bool, *args: Any, **kwargs: Any
+    ) -> None:
+        """
+        Initialize this instance.
+
+        Args:
+            is_coordinator (bool): Whether this instance is responsible for coordinating
+              the checkpoint.
+        """
+
+    @abc.abstractmethod
+    def prepare_local_plan(self, plan: SavePlan) -> SavePlan:
+        """
+        Perform storage-specific local planning.
+
+        While this method can produce a completely different plan, the recommended
+        way is to store storage specific data in SavePlan::storage_data.
+
+        Args:
+            plan (SavePlan): The local plan from the ``SavePlanner`` in use.
+
+        Returns:
+            A transformed ``SavePlan`` after storage local planning
+        """
+
+    @abc.abstractmethod
+    def prepare_global_plan(self, plans: list[SavePlan]) -> list[SavePlan]:
+        """
+        Perform centralized planning of storage.
+
+        This method is only called on the coordinator instance.
+
+        While this method can produce a completely different plan, the preferred
+        way is to store storage specific data in SavePlan::storage_data.
+
+        Args:
+            plans: A list of ``SavePlan`` instances, one for each rank.
+
+        Returns:
+            A list of transformed ``SavePlan`` after storage global planning
+        """
+
+    @abc.abstractmethod
+    def write_data(
+        self, plan: SavePlan, planner: SavePlanner
+    ) -> Future[list[WriteResult]]:
+        """
+        Write all items from ``plan`` using ``planner`` to resolve the data.
+
+        A subclass should call ``SavePlanner::resolve_data`` on each item
+        from the plan to get access to the underlying object to write.
+
+        Subclasses should lazily call `resolve_data` as it can allocate memory.
+        In case of tensors, make following assumptions:
+
+        - They might be on any device, including not matching the one on ``WriteItem::tensor_data``
+        - They might be views or not contiguous. Only the projection needs to be saved.
+
+        Args:
+            plan (SavePlan): The save plan to execute.
+            planner (SavePlanner): Planner object to be used to resolve items to data.
+
+        Returns:
+            A future that completes to a list of WriteResult
+        """
+
+    @abc.abstractmethod
+    def finish(self, metadata: Metadata, results: list[list[WriteResult]]) -> None:
+        """
+        Write the metadata and marks the current checkpoint as successful.
+
+        The actual format/schema used for serializing `metadata` is an
+        implementation detail. The only requirement is that it's recoverable
+        in to the same object graph.
+
+        Args:
+            metadata (Metadata): metadata for the new checkpoint
+            results: A list of WriteResults from all ranks.
+
+        Returns:
+            None
+        """
+
+    @classmethod
+    @abc.abstractmethod
+    def validate_checkpoint_id(cls, checkpoint_id: Union[str, os.PathLike]) -> bool:
+        """
+        Check if the given checkpoint_id is supported by the storage. This allow
+        us to enable automatic storage selection.
+        """
+        ...
+
+    def storage_meta(self) -> Optional[StorageMeta]:
+        """
+        Return the storage-specific metadata. This is used to store additional information
+        in a checkpoint that can be useful for providing request-level observability. StorageMeta
+        is passed to the ``SavePlanner`` during save calls. Returns None by default.
+
+        TODO: provide an example
+        """
+        return None
+
+
+class StorageReader(abc.ABC):
+    """
+    Interface used by ``load_state_dict`` to read from storage.
+
+    One StorageReader instance acts as both the coordinator and the follower
+    in a distributed checkpoint. As part of initialization, each instance
+    is told its role.
+
+    A subclass should expected the following sequence of calls by ``load_state_dict``:
+
+    0) (all ranks) set checkpoint_id if users pass a valid checkpoint_id.
+    1) (all ranks) read_metadata()
+    2) (all ranks) set_up_storage_reader()
+    3) (all ranks) prepare_local_plan()
+    4) (coordinator) prepare_global_plan()
+    5) (all ranks) read_data()
+    """
+
+    @abc.abstractmethod
+    def reset(self, checkpoint_id: Union[str, os.PathLike, None] = None) -> None:
+        """
+        Calls to indicates a brand new checkpoint read is going to happen.
+        A checkpoint_id may be present if users set the checkpoint_id for
+        this checkpoint read. The meaning of the checkpiont_id is
+        storage-dependent. It can be a path to a folder/file or a key for
+        a key-value storage.
+
+        Args:
+            checkpoint_id (Union[str, os.PathLike, None]):
+                The ID of this checkpoint instance. The meaning of the checkpoint_id
+                depends on the storage. It can be a path to a folder or to a file.
+                It can also be a key if the storage is more like a key-value store.
+                (Default: ``None``)
+        """
+        ...
+
+    @abc.abstractmethod
+    def read_metadata(self, *args: Any, **kwargs: Any) -> Metadata:
+        """
+        Read the checkpoint metadata.
+
+        Returns:
+            The metadata object associated with the checkpoint being loaded.
+
+        """
+
+    @abc.abstractmethod
+    def set_up_storage_reader(
+        self, metadata: Metadata, is_coordinator: bool, *args: Any, **kwargs: Any
+    ) -> None:
+        """
+        Initialize this instance.
+
+        Args:
+            metadata (Metadata): The metadata schema to use.
+            is_coordinator (bool): Whether this instance is responsible for coordinating
+              the checkpoint.
+        """
+
+    @abc.abstractmethod
+    def prepare_local_plan(self, plan: LoadPlan) -> LoadPlan:
+        """
+        Perform storage-specific local planning.
+
+        While this method can produce a completely different plan, the recommended
+        way is to store storage specific data in LoadPlan::storage_data.
+
+        Args:
+            plan (LoadPlan): The local plan from the ``LoadPlan`` in use.
+
+        Returns:
+            A transformed ``LoadPlan`` after storage local planning
+        """
+
+    @abc.abstractmethod
+    def prepare_global_plan(self, plans: list[LoadPlan]) -> list[LoadPlan]:
+        """
+        Perform centralized planning of storage loading.
+
+        This method is only called on the coordinator instance.
+
+        While this method can produce a completely different plan, the preferred
+        way is to store storage specific data in LoadPlan::storage_data.
+
+        Args:
+            plans: A list of ``LoadPlan`` instances, one for each rank.
+
+        Returns:
+            A list of transformed ``LoadPlan`` after storage global planning
+        """
+
+    @abc.abstractmethod
+    def read_data(self, plan: LoadPlan, planner: LoadPlanner) -> Future[None]:
+        """
+        Read all items from ``plan`` using ``planner`` to resolve the data.
+
+        A subclass should call ``LoadPlanner::load_bytes`` to deserialize a BytesIO
+        object into the right place.
+
+        A subclass should call ``LoadPlanner::resolve_tensor`` to get access to the
+        tensors that in should load data into.
+
+        It's the StorageLayer responsibility to properly schedule any cross device copies
+        required.
+
+        Args:
+            plan (LoadPlan): The local plan to execute on
+            planner (LoadPlanner): The planner object to use to resolve items.
+
+        Returns:
+            A future that completes once all reads are finished.
+        """
+
+    @classmethod
+    @abc.abstractmethod
+    def validate_checkpoint_id(cls, checkpoint_id: Union[str, os.PathLike]) -> bool:
+        """
+        Check if the given checkpoint_id is supported by the storage. This allow
+        us to enable automatic storage selection.
+        """
+        ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..073649c5f124d1817af12d161d8a80b76ae3ceda
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/checkpoint/utils.py
@@ -0,0 +1,485 @@
+# mypy: allow-untyped-defs
+import cProfile
+import inspect
+import io
+import itertools
+import os
+import warnings
+from collections.abc import Callable, Sequence
+from contextlib import contextmanager
+from functools import wraps
+from pstats import Stats
+from typing import Any, cast, Optional, TypeVar, Union
+
+import torch
+import torch.distributed as dist
+from torch.distributed._shard.sharded_tensor import ShardedTensor
+from torch.distributed._shard.sharded_tensor.shard import Shard
+
+from .api import (
+    _is_wrapped_exception,
+    _wrap_exception,
+    CheckpointException,
+    WRAPPED_EXCEPTION,
+)
+from .metadata import MetadataIndex, STATE_DICT_TYPE
+
+
+__all__ = ["find_tensor_shard", "find_state_dict_object"]
+
+T = TypeVar("T")
+R = TypeVar("R")
+
+
+def _get_failure_dict(
+    results: list[Union[T, WRAPPED_EXCEPTION]],
+) -> dict[int, WRAPPED_EXCEPTION]:
+    return cast(
+        dict[int, WRAPPED_EXCEPTION],
+        {i: err for i, err in enumerate(results) if _is_wrapped_exception(err)},
+    )
+
+
+def _all_gather_keys(
+    local_dict: dict[str, Any], group: Optional[dist.ProcessGroup] = None
+) -> set[str]:
+    """Gathers all keys, and returns them sorted."""
+    keys = list(local_dict.keys())
+    gathered_keys: list[list[str]] = [None] * dist.get_world_size(group)  # type: ignore[list-item]
+
+    dist.all_gather_object(gathered_keys, keys, group=group)
+    return set(itertools.chain.from_iterable(gathered_keys))
+
+
+def _assert_same_keys(
+    state_dict: dict[str, Any], process_group: Optional[dist.ProcessGroup] = None
+) -> None:
+    """
+    Asserts that all ranks have the same keys in their state dict.
+    This is a collective call which requires all ranks in ``process_group`` to
+    join. It will also induce cross-rank communication and block CPU.
+    """
+
+    if dist.get_world_size(process_group) == 1:
+        return
+
+    all_keys = _all_gather_keys(state_dict, process_group)
+    my_keys = set(state_dict.keys())
+    diff = all_keys - my_keys
+    if len(diff) > 0:
+        raise AssertionError(
+            f"Key(s) present in other ranks but not this one, difference: {diff}"
+        )
+
+
+class _DistWrapper:
+    """
+    This is a wrapper around PG that provides a series of features around object collectives.
+
+    It works without distributed initialized, where most collectives turns into nops.
+
+    All variants that take functions are exception robust, meaning that if one or more
+    ranks raise errors, all ranks will observe those.
+    """
+
+    def __init__(
+        self,
+        group: Optional[dist.ProcessGroup],
+        use_dist: bool,
+        coordinator_rank: int,
+    ):
+        self.group = group
+        self.use_dist = use_dist
+        self.coordinator_rank = coordinator_rank
+        if self.use_dist:
+            self.global_coordinator_rank = (
+                dist.get_global_rank(group, coordinator_rank)
+                if group is not None
+                else coordinator_rank
+            )
+            self.rank = dist.get_rank(group)
+            self.is_coordinator = self.rank == coordinator_rank
+        else:
+            self.global_coordinator_rank = 0
+            self.rank = 0
+            self.is_coordinator = True
+
+    def get_rank(self) -> int:
+        return self.rank
+
+    def get_world_size(self) -> int:
+        if self.use_dist:
+            return dist.get_world_size(self.group)
+        return 1
+
+    def broadcast_object(self, object: Optional[T]) -> T:
+        """Implement functionality similar to c10d::broadcast_object_list but without distributed enabled."""
+        object_list = [object]
+        if self.use_dist:
+            dist.broadcast_object_list(
+                object_list=object_list,
+                group=self.group,
+                src=self.global_coordinator_rank,
+            )
+        return cast(T, object_list[0])
+
+    def gather_object(self, object: T) -> Optional[list[T]]:
+        """Implement functionality similar to c10d::gather_object but without distributed enabled."""
+        if self.use_dist:
+            gather_objs = (
+                cast(list[T], [None] * dist.get_world_size(self.group))
+                if self.is_coordinator
+                else None
+            )
+
+            dist.gather_object(
+                obj=object,
+                object_gather_list=gather_objs if self.is_coordinator else None,
+                dst=self.global_coordinator_rank,
+                group=self.group,
+            )
+            result = gather_objs
+        else:
+            result = [object]
+        return result
+
+    def all_gather_object(self, object: T) -> list[T]:
+        """Implement functionality similar to c10d::all_gather_object but without distributed enabled."""
+        if self.use_dist:
+            gather_objs = cast(list[T], [None] * dist.get_world_size(self.group))
+
+            dist.all_gather_object(
+                object_list=gather_objs, obj=object, group=self.group
+            )
+        else:
+            gather_objs = [object]
+        return gather_objs
+
+    def scatter_object(self, object_list: Optional[list[T]]) -> T:
+        """Implement functionality similar to c10d::scatter_object but without distributed enabled."""
+        if self.use_dist:
+            gather_result = cast(list[T], [None])
+            dist.scatter_object_list(
+                scatter_object_output_list=gather_result,
+                scatter_object_input_list=object_list if self.is_coordinator else None,
+                src=self.global_coordinator_rank,
+                group=self.group,
+            )
+
+            local_reply = gather_result[0]
+        else:
+            if object_list is None:
+                raise AssertionError("object_list is None")
+            local_reply = object_list[0]
+        return local_reply
+
+    def reduce_scatter(
+        self,
+        step: str,
+        map_fun: Callable[[], T],
+        reduce_fun: Callable[[list[T]], list[R]],
+    ) -> R:
+        """
+        Compute a value on each rank, then do centralized reduce on a single rank, followed by a scatter.
+
+        This method operates in the following way:
+            Run ``map_fun`` on all ranks
+            Gather results on rank 0
+            Call ``reduce_fun`` on all those values
+            Scatter to each rank part of the result.
+        """
+        local_data: Union[WRAPPED_EXCEPTION, T]
+        try:
+            local_data = map_fun()
+        except BaseException as e:  # noqa: B036
+            local_data = _wrap_exception(e)
+
+        all_data = self.gather_object(local_data)
+        all_results: Optional[list[Union[R, CheckpointException]]] = None
+        if self.is_coordinator:
+            if all_data is None:
+                raise AssertionError("all_data is None")
+            node_failures = _get_failure_dict(all_data)
+
+            if len(node_failures) == 0:
+                try:
+                    # N.B. why can't mypy cast List[R] to List[Union[R, WRAPPED_EXCEPTION]]?
+                    all_results = cast(
+                        list[Union[R, CheckpointException]],
+                        reduce_fun(cast(list[T], all_data)),
+                    )
+                except BaseException as e:  # noqa: B036
+                    node_failures[self.rank] = _wrap_exception(e)
+
+            if len(node_failures) > 0:
+                all_results = [
+                    CheckpointException(step, node_failures)
+                ] * self.get_world_size()
+
+        result = self.scatter_object(all_results)
+        if isinstance(result, CheckpointException):
+            raise result
+        return result
+
+    def all_reduce(
+        self,
+        step: str,
+        map_fun: Callable[[], T],
+        reduce_fun: Callable[[list[T]], R],
+    ) -> R:
+        """
+        Compute a value on each rank, then do centralized reduce on a single rank, followed by a broadcast.
+
+        This method operates in the following way:
+            Run ``map_fun`` on all ranks
+            Gather results on rank 0
+            Call ``reduce_fun`` on all those values
+            Broadcast the reduced value to all ranks.
+        """
+        local_data: Union[T, WRAPPED_EXCEPTION]
+        try:
+            local_data = map_fun()
+        except BaseException as e:  # noqa: B036
+            local_data = _wrap_exception(e)
+
+        all_data = self.gather_object(local_data)
+        result: Optional[Union[R, CheckpointException]] = None
+        if self.is_coordinator:
+            if all_data is None:
+                raise AssertionError("all_data is None")
+            node_failures = _get_failure_dict(all_data)
+            if len(node_failures) == 0:
+                try:
+                    result = reduce_fun(cast(list[T], all_data))
+                except BaseException as e:  # noqa: B036
+                    node_failures[self.rank] = _wrap_exception(e)
+
+            if len(node_failures) > 0:
+                result = CheckpointException(step, node_failures)
+
+        # pyrefly: ignore [bad-argument-type]
+        final_result = self.broadcast_object(result)
+        if isinstance(final_result, CheckpointException):
+            raise final_result
+        return cast(R, final_result)
+
+    def all_gather(
+        self,
+        step: str,
+        map_fun: Callable[[], T],
+    ) -> list[T]:
+        """
+        Compute a value on each rank, then all_gather them.
+
+        This method operates in the following way:
+            Run ``map_cp`` on all ranks
+            all_gather the values to all ranks
+        """
+        result: Union[T, WRAPPED_EXCEPTION]
+        try:
+            result = map_fun()
+        except BaseException as e:  # noqa: B036
+            result = _wrap_exception(e)
+
+        all_results = self.all_gather_object(result)
+
+        node_failures = _get_failure_dict(all_results)
+        if len(node_failures) > 0:
+            raise CheckpointException(step, node_failures)
+        return cast(list[T], all_results)
+
+    def broadcast(
+        self,
+        step: str,
+        map_fun: Callable[[], T],
+    ) -> T:
+        """
+        Compute a value on rank 0 and broadcast it.
+
+        This method operates in the following way:
+            Run ``map_cp`` on rank 0
+            broadcast the value
+        """
+        result: Optional[Union[T, CheckpointException]] = None
+        if self.is_coordinator:
+            try:
+                result = map_fun()
+            except BaseException as e:  # noqa: B036
+                result = CheckpointException(step, {self.rank: _wrap_exception(e)})
+        # pyrefly: ignore [bad-argument-type]
+        final_result = self.broadcast_object(result)
+        if isinstance(final_result, CheckpointException):
+            raise final_result
+        return cast(T, final_result)
+
+    def barrier(self) -> None:
+        """
+        Add a synchronization point across all processes when using distributed.
+        If torch.distributed is initialized, this function will invoke a barrier across the global process group.
+        If torch.distributed is not initialized, this function is a no-op.
+        """
+        if not self.use_dist:
+            return
+        dist.barrier(group=self.group)
+
+
+def _find_shard(tensor: ShardedTensor, index: MetadataIndex) -> Shard:
+    if index.offset is None:
+        raise ValueError(
+            f"Cannot lookup {index.fqn} since its a ShardedTensor and no offset was provided"
+        )
+
+    shards = tensor.local_shards()
+    # index fast path
+    if index.index is not None:
+        if (
+            len(shards) > index.index
+            and torch.Size(shards[index.index].metadata.shard_offsets) == index.offset
+        ):
+            return shards[index.index]
+
+    for shard in shards:
+        if torch.Size(shard.metadata.shard_offsets) == index.offset:
+            return shard
+    raise ValueError(f"Could not find shard at '{index.offset}' for FQN: '{index.fqn}'")
+
+
+def find_tensor_shard(tensor: torch.Tensor, index: MetadataIndex) -> torch.Tensor:
+    if hasattr(tensor, "__get_tensor_shard__"):
+        # DTensor implements _Checkpointable
+        return tensor.__get_tensor_shard__(index)  # type: ignore[attr-defined]
+    if isinstance(tensor, ShardedTensor):
+        return _find_shard(tensor, index).tensor
+    if index.offset is not None:
+        # special case looking up a tensor by origin
+        if index.offset == torch.Size([0] * len(tensor.size())):
+            return tensor
+        raise ValueError(
+            f"FQN: '{index.fqn}' is not a ShardedTensor, can't find by offset: '{index.offset}'"
+        )
+    return tensor
+
+
+def find_state_dict_object(state_dict: STATE_DICT_TYPE, index: MetadataIndex) -> Any:
+    if index.fqn not in state_dict:
+        raise ValueError(f"Could not find FQN: '{index.fqn}'")
+    obj = state_dict[index.fqn]
+
+    if isinstance(obj, torch.Tensor):
+        return find_tensor_shard(obj, index)
+    elif index.offset is not None:
+        raise ValueError(
+            f"FQN: '{index.fqn}' is not a ShardedTensor, can't find by offset: '{index.offset}'"
+        )
+    return obj
+
+
+def _element_wise_add(a: Sequence[int], b: Sequence[int]) -> list[int]:
+    return [i_a + i_b for i_a, i_b in zip(a, b)]
+
+
+def _element_wise_sub(a: Sequence[int], b: Sequence[int]) -> list[int]:
+    return [i_a - i_b for i_a, i_b in zip(a, b)]
+
+
+class _ReaderView(io.IOBase):
+    def __init__(self, base_stream: io.IOBase, offset: int, len: int):
+        super().__init__()
+        self.offset = offset
+        self.len = len
+        self.base_stream = base_stream
+        self.seek(0)
+
+    def seek(self, offset: int, whence: int = os.SEEK_SET, /) -> int:
+        if whence == os.SEEK_SET:
+            offset = self.offset + offset
+        elif whence == os.SEEK_END:
+            whence = os.SEEK_SET
+            offset = (self.offset + self.len) - offset
+        return self.base_stream.seek(offset, whence)
+
+    def tell(self) -> int:
+        return self.base_stream.tell() - self.offset
+
+    def readable(self) -> bool:
+        return self.base_stream.readable()
+
+    def seekable(self) -> bool:
+        return self.base_stream.seekable()
+
+    def readinto(self, b):
+        max_size = self.len - self.tell()
+        if max_size == 0:
+            return 0
+        if len(b) > max_size:
+            b = memoryview(b)[:max_size]
+        return self.base_stream.readinto(b)  # type: ignore[attr-defined]
+
+    def read(self, size=-1):
+        max_size = self.len - self.tell()
+        if size == -1 or size > max_size:
+            size = max_size
+        return self.base_stream.read(size)
+
+
+def _create_file_view(file: io.IOBase, offset: int, length: int) -> io.IOBase:
+    # FIXME (kumpera) torch.load fails if we wrap with io.BufferedReader
+    return _ReaderView(file, offset, length)
+
+
+def _normalize_device_info(device_type: str, device_id: int) -> str:
+    """Device info normalization."""
+    if device_type == "cpu":
+        return "cpu"
+    return f"{device_type}:{device_id}"
+
+
+# TODO: integrate with distributed logging flag
+ENABLE_PROFILE = False
+
+
+@contextmanager
+def _profile():
+    # Only log the profiling when it is enable and is on rank0  or dist is not
+    # available.
+    if ENABLE_PROFILE and (not dist.is_available() or dist.get_rank() == 0):
+        profiler = cProfile.Profile()
+        profiler.enable()
+        try:
+            yield
+        finally:
+            profiler.disable()
+            stats = Stats(profiler)
+            stats.sort_stats("time").print_stats(10)
+    else:
+        yield
+
+
+def _api_bc_check(func):
+    @wraps(func)
+    def inner_func(*args, **kwargs) -> Any:
+        if len(args) == 2:
+            warnings.warn(
+                f"The argument order of {func.__name__} has been changed. "
+                "Please check the document to avoid future breakages.",
+                stacklevel=2,
+            )
+            sig = inspect.signature(func)
+            kwonlyargs = [
+                p.name for p in sig.parameters.values() if p.kind == p.KEYWORD_ONLY
+            ]
+            if "storage_writer" in kwonlyargs:
+                if "storage_writer" in kwargs:
+                    raise AssertionError(f"storage_writer in kwargs: {(args, kwargs)}")
+                kwargs["storage_writer"] = args[1]
+            elif "storage_reader" in kwonlyargs:
+                if "storage_reader" in kwargs:
+                    raise AssertionError(f"storage_reader in kwargs: {(args, kwargs)}")
+                kwargs["storage_reader"] = args[1]
+            else:
+                raise RuntimeError(f"Unexpected kwonlyargs = {kwonlyargs}")
+            return func(args[0], **kwargs)
+        else:
+            return func(*args, **kwargs)
+
+    return inner_func
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/collective_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/collective_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb20c58f13309152c9e1cebaf38995bcc8b390fb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/collective_utils.py
@@ -0,0 +1,354 @@
+#!/usr/bin/env python3
+
+
+"""
+A set of primitive functions for performing collective ops.
+
+Each should also handle single rank scenario.
+"""
+
+from __future__ import annotations
+
+import importlib
+import logging
+from collections import defaultdict
+from dataclasses import dataclass
+from typing import Any, cast, Generic, TYPE_CHECKING, TypeVar
+
+
+if TYPE_CHECKING:
+    from collections.abc import Callable, Iterable
+
+import torch
+import torch.distributed as dist
+
+
+__all__: list[str] = [
+    "SyncPayload",
+    "broadcast",
+    "all_gather",
+    "all_gather_object_enforce_type",
+]
+
+logger = logging.getLogger(__name__)
+
+T = TypeVar("T")
+
+
+@dataclass
+class SyncPayload(Generic[T]):
+    stage_name: str | None
+    success: bool
+    payload: T
+    exception: Exception | None = None
+
+
+def broadcast(
+    data_or_fn: T | Callable[[], T],
+    *,
+    success: bool = True,
+    stage_name: str | None = None,
+    rank: int = 0,
+    pg: dist.ProcessGroup | None = None,
+) -> T:
+    """
+    Broadcasts the data payload from rank 0 to all other ranks.
+    Or if a function is passed, execute it in rank 0 and broadcast result to all other ranks.
+
+    Can be used to broadcast a failure signal to stop all ranks.
+
+    If the function raises an exception, all ranks will raise.
+
+    Args:
+        data_or_fn: the data to broadcast or function to execute and broadcast result.
+        success: False to stop all ranks.
+        stage_name: the name of the logical stage for synchronization and debugging
+        rank: rank to broadcast data or execute function and broadcast results.
+        pg: the process group for sync
+    Throws:
+        RuntimeError from original exception trace
+    Returns:
+        the value after synchronization
+
+    Example usage:
+    >> id = broadcast(data_or_fn=allocate_id, rank=0, pg=ext_pg.my_pg)
+    """
+
+    if not success and data_or_fn is not None:
+        raise AssertionError(
+            "Data or Function is expected to be None if not successful"
+        )
+
+    payload: T | None = None
+    exception: Exception | None = None
+    # if no pg is passed then execute if rank is 0
+    if (pg is None and rank == 0) or (pg is not None and pg.rank() == rank):
+        # determine if it is an executable function or data payload only
+        if callable(data_or_fn):
+            try:
+                payload = data_or_fn()
+            except Exception as e:
+                success = False
+                exception = e
+        else:
+            payload = data_or_fn
+
+    # broadcast the exception type if any to all ranks for failure categorization
+    sync_obj = SyncPayload(
+        stage_name=stage_name,
+        success=success,
+        payload=payload,
+        exception=exception,
+    )
+
+    if pg is not None:
+        broadcast_list = [sync_obj]
+        dist.broadcast_object_list(broadcast_list, src=rank, group=pg)
+        if len(broadcast_list) != 1:
+            raise AssertionError(
+                f"Expected broadcast_list to have exactly 1 element, got {len(broadcast_list)}"
+            )
+        sync_obj = broadcast_list[0]
+
+    # failure in any rank will trigger a throw in every rank.
+    if not sync_obj.success:
+        error_msg = f"Rank {rank} failed"
+        if stage_name is not None:
+            error_msg += f": stage {sync_obj.stage_name}"
+        if sync_obj.exception is not None:
+            error_msg += f": exception {sync_obj.exception}"
+        # pyrefly: ignore [invalid-inheritance]
+        raise RuntimeError(error_msg) from sync_obj.exception
+
+    return cast(T, sync_obj.payload)
+
+
+def all_gather(
+    data_or_fn: T | Callable[[], T],
+    stage_name: str | None = None,
+    pg: dist.ProcessGroup | None = None,
+) -> list[T]:
+    """
+    A simple all_gather primitive with basic synchronization guard logic,
+    by checking payload from all ranks has the same stage name.
+
+    Args:
+        data_or_fn: the data to be all gathered across ranks or function to be executed
+        stage_name: the sync stage name for out-of-sync protection
+        pg: the process group for sync
+    Throws:
+        RuntimeError from original exception trace
+    Returns:
+        a list of synced data from all ranks
+
+    Example usage:
+    >> all_ids = all_gather(data_or_fn=allocate_id, pg=ext_pg.my_pg)
+    """
+    payload: T | None = None
+    exception: Exception | None = None
+    success = True
+    # determine if it is an executable function or data payload only
+    if callable(data_or_fn):
+        try:
+            payload = data_or_fn()
+        except Exception as e:
+            success = False
+            exception = e
+    else:
+        payload = data_or_fn
+
+    sync_obj = SyncPayload(
+        stage_name=stage_name,
+        success=success,
+        payload=payload,
+        exception=exception,
+    )
+
+    if pg is not None:
+        # List of success/failure across all ranks.
+        total_list = [None] * dist.get_world_size(pg)
+        all_gather_object_enforce_type(pg, total_list, sync_obj)
+        # Each rank will throw RuntimeError in case of failure on any rank.
+        stage_name = cast(SyncPayload[T], total_list[0]).stage_name
+        exception_list: list[tuple[int, Exception]] = []
+        ret_list: list[T] = []
+        error_msg: str = ""
+
+        for i, sp in enumerate(cast(list[SyncPayload[T]], total_list)):
+            if sp.stage_name != stage_name:
+                error_msg += (
+                    f"Unexpected stage name received from rank {i}: {sp.stage_name} "
+                )
+                continue
+            if not sp.success and sp.exception is not None:
+                exception_list.append((i, sp.exception))
+                continue
+            ret_list.append(sp.payload)
+
+        if len(exception_list) > 0:
+            raise RuntimeError(  # type: ignore[misc]
+                error_msg,
+                exception_list,
+                # pyrefly: ignore [invalid-inheritance]
+            ) from exception_list[0]
+        return ret_list
+    else:
+        if not sync_obj.success:
+            raise RuntimeError(
+                f"all_gather failed with exception {sync_obj.exception}",
+                # pyrefly: ignore [invalid-inheritance]
+            ) from sync_obj.exception
+        return [sync_obj.payload]  # type: ignore[list-item]
+
+
+# Note: use Any for typing for now so users can pass in
+# either a list of None or target type placeholders
+# otherwise pyre would complain
+def all_gather_object_enforce_type(
+    pg: dist.ProcessGroup,
+    # pyre-fixme[2]: Parameter must have a type that does not contain `Any`
+    object_list: list[Any],
+    # pyre-fixme[2]: Parameter must have a type other than `Any`
+    obj: Any,
+    # pyre-fixme[2]: Parameter must have a type that does not contain `Any`
+    type_checker: Callable[[Any, Any], bool] = lambda x, y: type(x) is type(y),
+) -> None:
+    """
+    Similar to plain all_gather_object but with additional type checking
+    AFTER gather is done to ensure basic consistency.
+    If check does not pass, all ranks will fail with exception.
+
+    This is generally to prevent conditional logic leading to
+    unexpected messages being received. This is considered fatal code error,
+    but due to logic stacks this might happen implicitly in practice.
+
+    The default check does not check sub type (considered different)
+    or covariance (considered same) but users can pass in custom checker
+    if more complicated check is needed.
+    """
+    dist.all_gather_object(object_list, obj, group=pg)
+
+    # conservative check
+    list_len = len(object_list)
+    if list_len == 0:
+        return
+    first_obj = object_list[0]
+    for i in range(1, list_len):
+        if not type_checker(first_obj, object_list[i]):
+            raise TypeError(
+                f"Object type at index {i} is {type(object_list[i])}, "
+                f"while first object type is {type(first_obj)}"
+            )
+
+
+def _summarize_ranks(ranks: Iterable[int]) -> str:
+    ranks = sorted(ranks)
+    if min(ranks) < 0:
+        raise AssertionError("ranks should all be positive")
+    if len(set(ranks)) != len(ranks):
+        raise AssertionError("ranks should not contain duplicates")
+    curr: int | range | None = None
+    ranges = []
+    while ranks:
+        x = ranks.pop(0)
+        if curr is None:
+            curr = x
+        elif isinstance(curr, int):
+            if x == curr + 1:
+                curr = range(curr, x + 1, 1)
+            else:
+                step = x - curr
+                curr = range(curr, x + step, step)
+        else:
+            if not isinstance(curr, range):
+                raise AssertionError("curr must be an instance of range")
+            if x == curr.stop:
+                curr = range(curr.start, curr.stop + curr.step, curr.step)
+            else:
+                ranges.append(curr)
+                curr = x
+
+    if isinstance(curr, int):
+        ranges.append(range(curr, curr + 1, 1))
+    elif isinstance(curr, range):
+        ranges.append(curr)
+
+    result = []
+    for r in ranges:
+        if len(r) == 1:
+            # pyrefly: ignore [bad-argument-type]
+            result.append(f"{r.start}")
+        elif r.step == 1:
+            # pyrefly: ignore [bad-argument-type]
+            result.append(f"{r.start}:{r.stop}")
+        else:
+            # pyrefly: ignore [bad-argument-type]
+            result.append(f"{r.start}:{r.stop}:{r.step}")
+    return ",".join(result)
+
+
+def _check_philox_rng_sync(
+    generator: torch.Generator, group: dist.ProcessGroup
+) -> tuple[dict[Any, set], str]:
+    local_state = generator.get_state()
+    all_states = [torch.empty_like(local_state) for _ in range(group.size())]
+    torch.distributed.all_gather(all_states, local_state)
+    seeds_offsets = [
+        (state[:8].view(torch.uint64).item(), state[8:].view(torch.uint64).item())
+        for state in all_states
+    ]
+    seed_offset_ranks = defaultdict(set)
+    for rank, (seed, offset) in enumerate(seeds_offsets):
+        seed_offset_ranks[(seed, offset)].add(rank)
+    return seed_offset_ranks, "(Seed, Offset)"
+
+
+def _check_cpu_rng_sync(
+    generator: torch.Generator, group: dist.ProcessGroup
+) -> tuple[dict[Any, set], str]:
+    # seed is returned as uint64_t from C impl, so may not fit in torch int64 tensor directly.
+    state_tensor = generator.get_state()
+    all_state_tensors = [torch.empty_like(state_tensor) for _ in range(group.size())]
+    torch.distributed.all_gather(all_state_tensors, state_tensor)
+    state_ranks = defaultdict(set)
+    for rank, state_tensor in enumerate(all_state_tensors):
+        # Summarize the state vector of the CPU rng.
+        # The properties that matter most are (1) its different if there is a state difference, (2) its printable
+        # (see desync table- not viable to print whole state vector of size 5k)
+        state_ranks[torch.hash_tensor(state_tensor).item()].add(rank)
+    return state_ranks, "Generator state hash"
+
+
+def _check_rng_sync_internal(
+    generator: torch.Generator, group: dist.ProcessGroup
+) -> tuple[dict[Any, set], str]:
+    if generator.device.type == "cuda":
+        return _check_philox_rng_sync(generator, group)
+    elif generator.device.type == "cpu":
+        return _check_cpu_rng_sync(generator, group)
+    else:
+        raise NotImplementedError(
+            f"Unsupported generator device: {generator.device.type}"
+        )
+
+
+def _desync_table_str(tag: str, value_ranks: dict[Any, set[int]]) -> str:
+    headers = ["Ranks", f"{tag} values"]
+    rank_values = [
+        [_summarize_ranks(ranks), str(value)] for value, ranks in value_ranks.items()
+    ]
+    if importlib.util.find_spec("tabulate"):
+        from tabulate import tabulate
+
+        return tabulate(rank_values, headers=headers)
+    row_str = "\n".join([str(row) for row in rank_values])
+    return str(f"{headers}\n{row_str}")
+
+
+def _check_rng_sync(generator: torch.Generator, group: dist.ProcessGroup) -> str | None:
+    value_ranks, value_header = _check_rng_sync_internal(generator, group)
+    log_str = None
+    if len(value_ranks) > 1:
+        log_str = f"Generator desync detected:\n{_desync_table_str(value_header, value_ranks)}"
+        logger.error(log_str)
+    return log_str
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/constants.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a077bd6d4e5e5b614c3651e16286c41a814d983
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/constants.py
@@ -0,0 +1,25 @@
+from datetime import timedelta
+
+from torch._C._distributed_c10d import _DEFAULT_PG_TIMEOUT
+
+
+__all__ = ["default_pg_timeout", "default_pg_nccl_timeout"]
+
+# Default process group wide timeout, if applicable.
+# This only applies to the non-nccl backends
+# To make an attempt at backwards compatibility with THD, we use an
+# extraordinarily high default timeout, given that THD did not have timeouts.
+default_pg_timeout: timedelta = _DEFAULT_PG_TIMEOUT
+# Separate timeout for PGNCCL mainly because it's always been that way in the C++ layer, but until recently
+# there was one default that applied across all backends in the python layer.
+# Later, we could consider merging them back together at the c++ layer if we can align on a same value.
+# (only if TORCH_NCCL_BLOCKING_WAIT or TORCH_NCCL_ASYNC_ERROR_HANDLING is set to 1).
+
+try:
+    from torch._C._distributed_c10d import _DEFAULT_PG_NCCL_TIMEOUT
+
+    default_pg_nccl_timeout: timedelta | None = _DEFAULT_PG_NCCL_TIMEOUT
+except ImportError:
+    # if C++ NCCL support is not compiled, we don't have access to the default nccl value.
+    # if anyone is actually trying to use nccl in this state, it should error.
+    default_pg_nccl_timeout = None
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/debug/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/debug/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..93295802ae847cb939954e8c8918dfd2ce49cf4f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/debug/__init__.py
@@ -0,0 +1,88 @@
+import logging
+import multiprocessing
+import socket
+
+# import for registration side effect
+import torch.distributed.debug._handlers  # noqa: F401
+from torch._C._distributed_c10d import _WorkerServer
+from torch.distributed.debug._store import get_rank, tcpstore_client
+
+
+__all__ = [
+    "start_debug_server",
+    "stop_debug_server",
+]
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+_WORKER_SERVER: _WorkerServer | None = None
+_DEBUG_SERVER_PROC: multiprocessing.Process | None = None
+
+
+def start_debug_server(port: int = 25999, worker_port: int = 0) -> None:
+    """
+    Start the debug server stack on all workers. The frontend debug server is
+    only started on rank0 while the per rank worker servers are started on all
+    ranks.
+
+    This server provides an HTTP frontend that allows for debugging slow and
+    deadlocked distributed jobs across all ranks simultaneously. This collects
+    data such as stack traces, FlightRecorder events, and performance profiles.
+
+    This depends on dependencies which are not installed by default.
+
+    Dependencies:
+    - Jinja2
+    - aiohttp
+
+    WARNING: This is intended to only be used in trusted network environments.
+    The debug server is not designed to be secure and should not be exposed to
+    the public internet. See SECURITY.md for more details.
+
+    WARNING: This is an experimental feature and may change at any time.
+
+    Args:
+        port (int): The port to start the frontend debug server on.
+        worker_port (int): The port to start the worker server on. Defaults to 0, which
+            will cause the worker server to bind to an ephemeral port.
+    """
+    global _WORKER_SERVER, _DEBUG_SERVER_PROC
+
+    assert _WORKER_SERVER is None, "debug server already started"
+    assert _DEBUG_SERVER_PROC is None, "debug server already started"
+
+    logger.info("Starting debug server on port %d", port)
+
+    store = tcpstore_client()
+
+    _WORKER_SERVER = _WorkerServer("::", worker_port)
+
+    RANK = get_rank()
+    store.set(f"rank{RANK}", f"http://{socket.gethostname()}:{_WORKER_SERVER.port}")
+
+    from torch.distributed.debug._frontend import main
+
+    if RANK == 0:
+        _DEBUG_SERVER_PROC = multiprocessing.Process(
+            target=main, args=(port,), daemon=True
+        )
+        _DEBUG_SERVER_PROC.start()
+
+
+def stop_debug_server() -> None:
+    """
+    Shutdown the debug server and stop the frontend debug server process.
+    """
+    global _WORKER_SERVER, _DEBUG_SERVER_PROC
+
+    assert _DEBUG_SERVER_PROC is not None
+    assert _WORKER_SERVER is not None
+
+    logger.info("Stopping debug server")
+
+    _DEBUG_SERVER_PROC.terminate()
+    _WORKER_SERVER.shutdown()
+    _DEBUG_SERVER_PROC.join()
+
+    _WORKER_SERVER = None
+    _DEBUG_SERVER_PROC = None
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/debug/_frontend.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/debug/_frontend.py
new file mode 100644
index 0000000000000000000000000000000000000000..16cccb88632f0372bac132a01cc8b97f60223852
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/debug/_frontend.py
@@ -0,0 +1,553 @@
+import asyncio
+import json
+import logging
+import socket
+import threading
+from collections.abc import Iterable
+from concurrent.futures import ThreadPoolExecutor
+from dataclasses import dataclass
+from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
+from urllib.parse import parse_qs, urlparse
+
+from jinja2 import DictLoader, Environment
+from tabulate import tabulate
+
+from torch.distributed.debug._store import get_world_size, tcpstore_client
+from torch.distributed.flight_recorder.components.builder import build_db
+from torch.distributed.flight_recorder.components.config_manager import JobConfig
+from torch.distributed.flight_recorder.components.types import (
+    Collective,
+    Group,
+    Membership,
+    NCCLCall,
+)
+
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+
+@dataclass(slots=True)
+class Response:
+    status_code: int
+    text: str
+
+    def raise_for_status(self):
+        if self.status_code != 200:
+            raise RuntimeError(f"HTTP {self.status_code}: {self.text}")
+
+    def json(self):
+        return json.loads(self.text)
+
+
+def fetch_thread_pool(urls: list[str]) -> Iterable[Response]:
+    # late import for optional dependency
+    import requests
+
+    max_workers = 20
+
+    def get(url: str) -> Response:
+        resp = requests.post(url)
+        return Response(resp.status_code, resp.text)
+
+    with ThreadPoolExecutor(max_workers=max_workers) as executor:
+        resps = executor.map(get, urls)
+
+    return resps
+
+
+def fetch_aiohttp(urls: list[str]) -> Iterable[Response]:
+    # late import for optional dependency
+    import aiohttp
+
+    async def fetch(session: aiohttp.ClientSession, url: str) -> Response:
+        async with session.post(url) as resp:
+            text = await resp.text()
+            return Response(resp.status, text)
+
+    async def gather(urls: list[str]) -> Iterable[Response]:
+        async with aiohttp.ClientSession() as session:
+            return await asyncio.gather(*[fetch(session, url) for url in urls])
+
+    return asyncio.run(gather(urls))
+
+
+def fetch_all(endpoint: str, args: str = "") -> tuple[list[str], Iterable[Response]]:
+    store = tcpstore_client()
+    keys = [f"rank{r}" for r in range(get_world_size())]
+    addrs = store.multi_get(keys)
+    addrs = [f"{addr.decode()}/handler/{endpoint}?{args}" for addr in addrs]
+
+    try:
+        resps = fetch_aiohttp(addrs)
+    except ImportError:
+        resps = fetch_thread_pool(addrs)
+
+    return addrs, resps
+
+
+def format_json(blob: str):
+    parsed = json.loads(blob)
+    return json.dumps(parsed, indent=2)
+
+
+templates = {
+    "base.html": """
+
+
+    {% block title %}{% endblock %} - PyTorch Distributed
+    
+
+    
+
+
+
+
+

+  {% block header %}{% endblock %}
+  {% block content %}{% endblock %}
+

+    """,
+    "index.html": """
+{% extends "base.html" %}
+{% block header %}
+  
{% block title %}Index{% endblock %}

+{% endblock %}
+{% block content %}
+Hi
+{% endblock %}
+    """,
+    "raw_resp.html": """
+{% extends "base.html" %}
+{% block header %}
+    
{% block title %}{{title}}{% endblock %}

+{% endblock %}
+{% block content %}
+    {% for i, (addr, resp) in enumerate(zip(addrs, resps)) %}
+        
Rank {{ i }}: {{ addr }}

+        {% if resp.status_code != 200 %}
+            
Failed to fetch: status={{ resp.status_code }}

+            
{{ resp.text }}

+        {% else %}
+            
{{ resp.text }}

+        {% endif %}
+    {% endfor %}
+{% endblock %}
+    """,
+    "json_resp.html": """
+{% extends "base.html" %}
+{% block header %}
+    
{% block title %}{{ title }}{% endblock %}

+{% endblock %}
+{% block content %}
+    {% for i, (addr, resp) in enumerate(zip(addrs, resps)) %}
+        
Rank {{ i }}: {{ addr }}

+        {% if resp.status_code != 200 %}
+            
Failed to fetch: status={{ resp.status_code }}

+            
{{ resp.text }}

+        {% else %}
+            
{{ format_json(resp.text) }}

+        {% endif %}
+    {% endfor %}
+{% endblock %}
+    """,
+    "profile.html": """
+{% extends "base.html" %}
+{% block header %}
+    
{% block title %}torch.profiler{% endblock %}

+{% endblock %}
+
+{% block content %}
+    

+        
+        
+        
+    

+
+    
+
+    {% for i, (addr, resp) in enumerate(zip(addrs, resps)) %}
+        
Rank {{ i }}: {{ addr }}

+        {% if resp.status_code != 200 %}
+            
Failed to fetch: status={{ resp.status_code }}

+            
{{ resp.text }}

+        {% else %}
+            
+
+            
+        {% endif %}
+    {% endfor %}
+{% endblock %}
+    """,
+    "tcpstore.html": """
+{% extends "base.html" %}
+{% block header %}
+    
{% block title %}TCPStore Keys{% endblock %}

+{% endblock %}
+{% block content %}
+    
+    {% for k, v in zip(keys, values) -%}
+{{ k }}: {{ v | truncate(100) }}
+    {% endfor %}
+    

+{% endblock %}
+    """,
+    "fr_trace.html": """
+{% extends "base.html" %}
+{% block header %}
+    
{% block title %}{{ title }}{% endblock %}

+{% endblock %}
+{% block content %}
+    
Groups

+    {{ groups | safe }}
+    
Memberships

+    {{ memberships | safe }}
+    
Collectives

+    {{ collectives | safe }}
+    
NCCL Calls

+    {{ ncclcalls | safe }}
+{% endblock %}
+    """,
+    "pyspy_dump.html": """
+{% extends "base.html" %}
+{% block header %}
+    
{% block title %}py-spy Stack Traces{% endblock %}

+{% endblock %}
+{% block content %}
+    

+        
+        
+        
+        
+        
+    

+
+    {% for i, (addr, resp) in enumerate(zip(addrs, resps)) %}
+        
Rank {{ i }}: {{ addr }}

+        {% if resp.status_code != 200 %}
+            
Failed to fetch: status={{ resp.status_code }}

+            
{{ resp.text }}

+        {% else %}
+            
{{ resp.text }}

+        {% endif %}
+    {% endfor %}
+{% endblock %}
+    """,
+}
+
+
+class _IPv6HTTPServer(ThreadingHTTPServer):
+    address_family: socket.AddressFamily = socket.AF_INET6  # pyre-ignore
+    request_queue_size: int = 1024
+
+
+class HTTPRequestHandler(BaseHTTPRequestHandler):
+    frontend: "FrontendServer"
+
+    def log_message(self, format, *args):
+        logger.info(
+            "%s %s",
+            self.client_address[0],
+            format % args,
+        )
+
+    def do_GET(self):
+        self.frontend._handle_request(self)
+
+    def get_path(self) -> str:
+        return urlparse(self.path).path
+
+    def get_query(self) -> dict[str, list[str]]:
+        return parse_qs(self.get_raw_query())
+
+    def get_raw_query(self) -> str:
+        return urlparse(self.path).query
+
+    def get_query_arg(
+        self, name: str, default: object = None, type: type = str
+    ) -> object:
+        query = self.get_query()
+        if name not in query:
+            return default
+        return type(query[name][0])
+
+
+class FrontendServer:
+    def __init__(self, port: int):
+        # Setup templates
+        loader = DictLoader(templates)
+        self._jinja_env = Environment(loader=loader, enable_async=True)
+        self._jinja_env.globals.update(
+            zip=zip,
+            format_json=format_json,
+            enumerate=enumerate,
+        )
+
+        # Create routes
+        self._routes = {
+            "/": self._handle_index,
+            "/stacks": self._handle_stacks,
+            "/pyspy_dump": self._handle_pyspy_dump,
+            "/fr_trace": self._handle_fr_trace,
+            "/fr_trace_json": self._handle_fr_trace_json,
+            "/fr_trace_nccl": self._handle_fr_trace_nccl,
+            "/fr_trace_nccl_json": self._handle_fr_trace_nccl_json,
+            "/profile": self._handle_profiler,
+            "/wait_counters": self._handle_wait_counters,
+            "/tcpstore": self._handle_tcpstore,
+        }
+
+        # Create HTTP server
+        RequestHandlerClass = type(
+            "HTTPRequestHandler",
+            (HTTPRequestHandler,),
+            {"frontend": self},
+        )
+
+        server_address = ("", port)
+        self._server = _IPv6HTTPServer(server_address, RequestHandlerClass)
+
+        self._thread = threading.Thread(
+            target=self._serve,
+            args=(),
+            daemon=True,
+            name="distributed.debug.FrontendServer",
+        )
+        self._thread.start()
+
+    def _serve(self) -> None:
+        try:
+            self._server.serve_forever()
+        except Exception:
+            logger.exception("got exception in frontend server")
+
+    def join(self) -> None:
+        self._thread.join()
+
+    def _handle_request(self, req: HTTPRequestHandler) -> None:
+        path = req.get_path()
+        if path not in self._routes:
+            req.send_error(404, f"Handler not found: {path}")
+            return
+
+        handler = self._routes[path]
+        try:
+            resp = handler(req)
+        # Catch SystemExit to not crash when FlightRecorder errors.
+        except (Exception, SystemExit) as e:
+            logger.exception(
+                "Exception in frontend server when handling %s",
+                path,
+            )
+            req.send_error(500, f"Exception: {repr(e)}")
+            return
+
+        req.send_response(200)
+        req.send_header("Content-type", "text/html")
+        req.end_headers()
+        req.wfile.write(resp)
+
+    def _render_template(self, template: str, **kwargs: object) -> bytes:
+        return self._jinja_env.get_template(template).render(**kwargs).encode()
+
+    def _handle_index(self, req: HTTPRequestHandler) -> bytes:
+        return self._render_template("index.html")
+
+    def _handle_stacks(self, req: HTTPRequestHandler) -> bytes:
+        addrs, resps = fetch_all("dump_traceback")
+        return self._render_template(
+            "raw_resp.html", title="Stacks", addrs=addrs, resps=resps
+        )
+
+    def _handle_pyspy_dump(self, req: HTTPRequestHandler) -> bytes:
+        addrs, resps = fetch_all("pyspy_dump", req.get_raw_query())
+        return self._render_template(
+            "pyspy_dump.html",
+            addrs=addrs,
+            resps=resps,
+        )
+
+    def _render_fr_trace(self, addrs: list[str], resps: list[Response]) -> bytes:
+        config = JobConfig()
+        # pyrefly: ignore [bad-assignment]
+        args = config.parse_args(args=[])
+        args.allow_incomplete_ranks = True
+        args.verbose = True
+
+        details = {}
+        for rank, resp in enumerate(resps):
+            resp.raise_for_status()
+            dump = {
+                "rank": rank,
+                "host_name": addrs[rank],
+                **resp.json(),
+            }
+            if "entries" not in dump:
+                dump["entries"] = []
+            details[f"rank{rank}.json"] = dump
+
+        version = next(iter(details.values()))["version"]
+
+        db = build_db(details, args, version)
+
+        return self._render_template(
+            "fr_trace.html",
+            title="FlightRecorder",
+            groups=tabulate(db.groups, headers=Group._fields, tablefmt="html"),
+            memberships=tabulate(
+                db.memberships, headers=Membership._fields, tablefmt="html"
+            ),
+            collectives=tabulate(
+                db.collectives, headers=Collective._fields, tablefmt="html"
+            ),
+            ncclcalls=tabulate(db.ncclcalls, headers=NCCLCall._fields, tablefmt="html"),
+        )
+
+    def _handle_fr_trace(self, req: HTTPRequestHandler) -> bytes:
+        addrs, resps = fetch_all("fr_trace_json")
+
+        return self._render_fr_trace(addrs, list(resps))
+
+    def _handle_fr_trace_json(self, req: HTTPRequestHandler) -> bytes:
+        addrs, resps = fetch_all("fr_trace_json")
+
+        return self._render_template(
+            "json_resp.html",
+            title="FlightRecorder",
+            addrs=addrs,
+            resps=resps,
+        )
+
+    def _handle_fr_trace_nccl(self, req: HTTPRequestHandler) -> bytes:
+        addrs, resps = fetch_all("dump_nccl_trace_json", "onlyactive=true")
+
+        return self._render_fr_trace(addrs, list(resps))
+
+    def _handle_fr_trace_nccl_json(self, req: HTTPRequestHandler) -> bytes:
+        addrs, resps = fetch_all("dump_nccl_trace_json", "onlyactive=true")
+
+        return self._render_template(
+            "json_resp.html",
+            title="FlightRecorder NCCL",
+            addrs=addrs,
+            resps=resps,
+        )
+
+    def _handle_profiler(self, req: HTTPRequestHandler) -> bytes:
+        duration = req.get_query_arg("duration", default=1.0, type=float)
+
+        addrs, resps = fetch_all("torch_profile", f"duration={duration}")
+
+        return self._render_template("profile.html", addrs=addrs, resps=resps)
+
+    def _handle_wait_counters(self, req: HTTPRequestHandler) -> bytes:
+        addrs, resps = fetch_all("wait_counter_values")
+        return self._render_template(
+            "json_resp.html", title="Wait Counters", addrs=addrs, resps=resps
+        )
+
+    def _handle_tcpstore(self, req: HTTPRequestHandler) -> bytes:
+        store = tcpstore_client(prefix="")
+        keys = store.list_keys()
+        keys.sort()
+        values = [repr(v) for v in store.multi_get(keys)]
+        return self._render_template("tcpstore.html", keys=keys, values=values)
+
+
+def main(port: int) -> None:
+    logger.setLevel(logging.INFO)
+
+    server = FrontendServer(port=port)
+    logger.info("Frontend server started on port %d", server._server.server_port)
+    server.join()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/debug/_handlers.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/debug/_handlers.py
new file mode 100644
index 0000000000000000000000000000000000000000..b8095c5b34bea5d2408ed87b21b541bf8966f4ad
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/debug/_handlers.py
@@ -0,0 +1,23 @@
+import pathlib
+import tempfile
+import time
+
+from torch._C._distributed_c10d import _register_handler, _Request, _Response
+from torch.profiler import _ExperimentalConfig, profile
+
+
+def _torch_profile(req: _Request, resp: _Response) -> None:
+    experimental_config = _ExperimentalConfig(
+        profile_all_threads=True,
+    )
+    duration = float(req.get_param("duration"))
+    with profile(record_shapes=True, experimental_config=experimental_config) as prof:
+        time.sleep(duration)
+
+    with tempfile.NamedTemporaryFile(prefix="torch_debug", suffix=".json") as f:
+        prof.export_chrome_trace(f.name)
+        resp.set_content(pathlib.Path(f.name).read_bytes(), "application/json")
+        resp.set_status(200)
+
+
+_register_handler("torch_profile", _torch_profile)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/debug/_store.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/debug/_store.py
new file mode 100644
index 0000000000000000000000000000000000000000..487dd30abd6aff96d676ee3cf10d98490613e2a1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/debug/_store.py
@@ -0,0 +1,25 @@
+import os
+
+import torch.distributed as dist
+
+
+def get_rank() -> int:
+    return int(os.environ["RANK"])
+
+
+def get_world_size() -> int:
+    return int(os.environ["WORLD_SIZE"])
+
+
+def tcpstore_client(prefix: str = "debug_server") -> dist.Store:
+    MASTER_ADDR = os.environ["MASTER_ADDR"]
+    MASTER_PORT = int(os.environ["MASTER_PORT"])
+
+    store = dist.TCPStore(
+        host_name=MASTER_ADDR,
+        port=MASTER_PORT,
+        is_master=False,
+    )
+    if prefix:
+        store = dist.PrefixStore(prefix, store)
+    return store
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/device_mesh.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/device_mesh.py
new file mode 100644
index 0000000000000000000000000000000000000000..95d8fe8b8d2d0a16787baffc6ed43fb0771cc2c0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/device_mesh.py
@@ -0,0 +1,1370 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+import logging
+import os
+import threading
+import warnings
+from collections.abc import Iterator
+from itertools import zip_longest
+from typing import Optional, TYPE_CHECKING, Union
+
+import torch
+from torch.distributed import is_available
+from torch.distributed._mesh_layout import _MeshLayout
+from torch.distributed._pycute import IntTuple, is_int, suffix_product
+from torch.utils._typing_utils import not_none
+
+
+__all__ = ["init_device_mesh", "DeviceMesh"]
+
+
+if not is_available():
+    import sys
+
+    # We need to create the stubs when distributed is not available.
+    # Otherwise, we would fail the doc tests (```./.ci/pytorch/docs-test.sh```),
+    # since it would try to import ``torch.distributed.device_mesh`` or
+    # ``torch.distributed.init_device_mesh`` but cannot find them.
+
+    class _DeviceMeshStub:
+        pass
+
+    def _init_device_mesh_stub():
+        pass
+
+    sys.modules["torch.distributed.device_mesh"].DeviceMesh = _DeviceMeshStub  # type: ignore[attr-defined]
+    sys.modules[
+        "torch.distributed.device_mesh"
+    ].init_device_mesh = _init_device_mesh_stub  # type: ignore[attr-defined]
+
+
+else:
+    from torch._C._distributed_c10d import Backend as C10dBackend
+    from torch.distributed.distributed_c10d import (
+        _get_default_group,
+        _resolve_process_group,
+        get_backend,
+        get_process_group_ranks,
+        get_rank,
+        get_world_size,
+        GroupName,
+        init_process_group,
+        is_initialized,
+        new_group,
+        ProcessGroup,
+        split_group,
+    )
+
+    logger = logging.getLogger(__name__)
+
+    # only import numpy typing when type checking
+    if TYPE_CHECKING:
+        try:
+            from numpy.typing import ArrayLike
+        except ImportError:
+            logger.warning(
+                "DeviceMesh requires numpy >= 1.21 to be installed for type checking"
+            )
+
+    BackendConfig = tuple[str | None, C10dBackend.Options | None]
+    torch.serialization.add_safe_globals([_MeshLayout])
+
+    class _MeshEnv(threading.local):
+        def __init__(self) -> None:
+            self.mesh_stack: list[DeviceMesh] = []
+
+        def get_current_mesh(self) -> "DeviceMesh":
+            if len(self.mesh_stack) == 0:
+                raise RuntimeError("No device mesh is currently active!")
+            return self.mesh_stack[-1]
+
+        # TODO: to remove it once we move all use cases into new API.
+        def get_root_mesh(self, device_mesh: "DeviceMesh") -> "DeviceMesh":
+            # If a mesh could not be found in the child_to_root_mapping, it is a root mesh itself.
+            # A root mesh is not created through slicing.
+            # We considers the root mesh of a root mesh is itself.
+            # We keep this function for backward compatibility.
+            warnings.warn(
+                "This get_root_mesh API will be deprecated soon."
+                "Please use `get_root_mesh` inside DeviceMesh instead.",
+                stacklevel=2,
+            )
+            if not device_mesh:
+                return device_mesh
+            return device_mesh._get_root_mesh()
+
+        @staticmethod
+        def num_devices_per_host(device_type: str) -> int:
+            return _get_device_handle(device_type).device_count()
+
+        @staticmethod
+        def num_hosts(device_type: str) -> int:
+            # ProcessGroup can't tell us this info so we have to infer it, assume
+            # homogeneous hardware for now
+            return get_world_size() // _MeshEnv.num_devices_per_host(device_type)
+
+        # TODO: to remove it once we move all use cases into new API.
+        # We keep this API for backward compatibility.
+        def _get_all_submeshes(
+            self, device_mesh: "DeviceMesh", mesh_dim_name: str
+        ) -> list["DeviceMesh"]:
+            warnings.warn(
+                "This _get_all_submeshes API will be deprecated soon."
+                "Please use `_get_all_submeshes` inside DeviceMesh instead.",
+                stacklevel=2,
+            )
+            return device_mesh._get_all_submeshes(mesh_dim_name)
+
+    _mesh_resources: _MeshEnv = _MeshEnv()
+
+    def _get_device_handle(device_type: str = "cuda"):
+        """
+        Get the module corresponding to the device_type which is cuda or cuda-like device.
+        For example, when the device_type is cuda, the module `torch.cuda` is returned.
+        Return None when there is no corresponding module for device_type, otherwise
+        return the corresponding module.
+        """
+        return getattr(torch, device_type, None)
+
+    class DeviceMesh:
+        """
+        DeviceMesh represents a mesh of devices, where layout of devices could be
+        represented as a n-d dimension array, and each value of the n-d dimensional
+        array is the global id of the default process group ranks.
+
+        DeviceMesh could be used to setup the N dimensional device connections across the cluster,
+        and manage the ProcessGroups for N dimensional parallelisms. Communications could happen on
+        each dimension of the DeviceMesh separately. DeviceMesh respects the device that user selects
+        already (i.e. if user call `torch.cuda.set_device` before the DeviceMesh initialization),
+        and will select/set the device for the current process if user does not set the device
+        beforehand. Note that manual device selection should happen BEFORE the DeviceMesh initialization.
+
+        DeviceMesh can also be used as a context manager when using together with DTensor APIs.
+
+        .. note::
+            DeviceMesh follows SPMD programming model, which means the same PyTorch Python program
+            is running on all processes/ranks in the cluster. Therefore, users need to make sure the
+            `mesh` array (which describes the layout of devices) should be identical across all ranks.
+            Inconsistent `mesh` will lead to silent hang.
+
+        Args:
+            device_type (str): The device type of the mesh. Currently supports: "cpu", "cuda/cuda-like".
+            mesh (ndarray): A multi-dimensional array or an integer tensor describing the layout
+                of devices, where the IDs are global IDs of the default process group.
+            _rank (int): (experimental/internal)
+                The global rank of the current process. If not provided, it will
+                be inferred from the default process group.
+
+        Returns:
+            DeviceMesh: A :class:`DeviceMesh` object representing the device layout.
+
+        The following program runs on each process/rank in an SPMD manner. In this example, we have 2
+        hosts with 4 GPUs each.
+        A reduction over the first dimension of mesh will reduce across
+        columns (0, 4), .. and (3, 7), a reduction over the second dimension
+        of mesh reduces across rows (0, 1, 2, 3) and (4, 5, 6, 7).
+
+        Example::
+
+            >>> # xdoctest: +SKIP("no rank")
+            >>> from torch.distributed.device_mesh import DeviceMesh
+            >>>
+            >>> # Initialize device mesh as (2, 4) to represent the topology
+            >>> # of cross-host(dim 0), and within-host (dim 1).
+            >>> mesh = DeviceMesh(device_type="cuda", mesh=[[0, 1, 2, 3],[4, 5, 6, 7]])
+        """
+
+        _device_type: str
+        _rank_map: torch.Tensor
+        _mesh_dim_names: tuple[str, ...] | None
+        _layout: _MeshLayout
+        _root_mesh: Optional["DeviceMesh"] = None
+        # Record flatten mesh name to its flattened mesh in root mesh.
+        _flatten_mapping: dict[str, "DeviceMesh"]
+
+        def __init__(
+            self,
+            device_type: str,
+            mesh: Union[torch.Tensor, "ArrayLike"] | None = None,
+            *,
+            mesh_dim_names: tuple[str, ...] | None = None,
+            backend_override: tuple[BackendConfig, ...] | None = None,
+            _init_backend: bool = True,
+            _rank: int | None = None,
+            _layout: _MeshLayout | None = None,
+            _rank_map: torch.Tensor | None = None,
+            _root_mesh: Optional["DeviceMesh"] = None,
+        ) -> None:
+            # no-op in OSS, logs API usage metrics in meta-internal runs
+            torch._C._log_api_usage_once(
+                "torch.distributed.device_mesh.DeviceMesh.__init__"
+            )
+            if mesh is not None:
+                if _layout is not None or _rank_map is not None:
+                    raise TypeError(
+                        "Cannot provide _layout and/or _rank_map if passing explicit mesh"
+                    )
+                if isinstance(mesh, torch.Tensor) and mesh.device.type != "cpu":
+                    raise ValueError(f"`mesh` must be a CPU tensor, got {mesh}")
+                mesh_tensor = (
+                    mesh.detach().to(dtype=torch.int).contiguous()
+                    if isinstance(mesh, torch.Tensor)
+                    else torch.tensor(mesh, device="cpu", dtype=torch.int)
+                )
+                _layout = _MeshLayout(mesh_tensor.size(), mesh_tensor.stride())
+                _rank_map = mesh_tensor.flatten()
+            else:
+                if _layout is None or _rank_map is None:
+                    raise TypeError(
+                        "The mesh argument is required except for PRIVATE USAGE ONLY!"
+                    )
+
+            assert _layout.check_non_overlap(), (
+                "Please use a non-overlapping layout when creating a DeviceMesh."
+            )
+            assert _rank_map.ndim == 1, "The rank map must be 1-dimensional"
+            assert _rank_map.is_contiguous(), "The rank map must be contiguous"
+            assert _rank_map.numel() >= _layout.cosize(), (
+                f"The rank map contains {_rank_map.numel()} element, "
+                f"which isn't large enough for layout {_layout}"
+            )
+
+            self._device_type = device_type
+            self._layout = _layout
+            self._rank_map = _rank_map
+            self._mesh_dim_names = tuple(mesh_dim_names) if mesh_dim_names else None
+            self._root_mesh = _root_mesh
+
+            if backend_override is None:
+                backend_override = ((None, None),) * len(self._layout)
+            elif len(backend_override) != len(self._layout):
+                raise ValueError(
+                    f"backend_override should have the same length as the number of mesh dimensions, "
+                    f"but got {len(backend_override)} and {len(self._layout)}."
+                )
+            # Internal bookkeeping for the device mesh.
+            self._layout = (
+                _layout
+                if _layout
+                else _MeshLayout(self.mesh.size(), self.mesh.stride())
+            )
+            if not self._layout.check_non_overlap():
+                raise AssertionError(
+                    "Please use a non-overlapping layout when creating a DeviceMesh."
+                )
+            # Because we still need to support slicing of flattened dim from root mesh, so we don't check stride here.
+            if self._layout.numel() != self.mesh.numel():
+                raise AssertionError(
+                    "Please use a valid layout when creating a DeviceMesh."
+                    f"The layout {self._layout} is not consistent with the mesh size {self.mesh.size()}."
+                )
+
+            # private field to pre-generate DeviceMesh's hash
+            self._flatten_rank_map = tuple(self._rank_map.tolist())
+            self._thread_id = None
+            # Initialize instance-specific flatten mapping
+            self._flatten_mapping = {}
+
+            # Skip process group initialization if xla device or init backend is False
+            # TODO(yeounoh) implement DeviceMesh backend and register XLA backend.
+            if device_type != "xla":
+                # always try to create default (world) pg, even if it is not initialized
+                # already. The world pg is used for device mesh identity (rank) on each
+                # process (we need to know if the current global rank is in the mesh or not).
+                if _init_backend:
+                    self._setup_world_group_and_device()
+                    self._dim_group_names = self._init_process_groups(
+                        self._layout,
+                        self._rank_map,
+                        self._mesh_dim_names,
+                        backend_override,
+                    )
+
+                if is_initialized() and get_backend() == "threaded":
+                    # pyrefly: ignore [bad-assignment]
+                    self._thread_id = threading.get_ident()
+
+                if _rank is None:
+                    _rank = get_rank()
+
+                # calculate the coordinates of the current global rank on the mesh
+                rank_coords = (self.mesh == _rank).nonzero()
+                if rank_coords.size(0) not in (0, 1):
+                    raise AssertionError(
+                        f"rank_coords.size(0) must be 0 or 1, got {rank_coords.size(0)}"
+                    )
+                self._coordinate_on_dim: list[int] | None = (
+                    rank_coords[0].tolist() if rank_coords.size(0) > 0 else None
+                )
+
+        @property
+        def device_type(self) -> str:
+            """Returns the device type of the mesh."""
+            return self._device_type
+
+        @property
+        def mesh(self) -> torch.Tensor:
+            """Returns the tensor representing the layout of devices."""
+            full_mesh = self._layout.remap_to_tensor(self._rank_map)
+            if full_mesh.size(0) == 1:
+                return full_mesh[0]
+            my_coords = (full_mesh == get_rank()).nonzero()
+            if my_coords.size(0) > 0:
+                return full_mesh[my_coords[0, 0]]
+            raise RuntimeError(
+                "In order to get the mesh Tensor of a DeviceMesh it needs to "
+                "either have all its original dimensions (e.g., no slicing) "
+                "or it needs to contain the local rank"
+            )
+
+        @property
+        def mesh_dim_names(self) -> tuple[str, ...] | None:
+            """Returns the names of mesh dimensions."""
+            return self._mesh_dim_names
+
+        def _setup_world_group_and_device(self):
+            default_initialized = is_initialized()
+            # TODO: think about how to allow pg options to be passed to world group
+            # or mesh dimension groups
+            if not default_initialized:
+                init_process_group()
+
+            world_size = get_world_size()
+            if self._layout.numel() > world_size:
+                raise RuntimeError(
+                    f"Mesh should not be bigger than default world size {world_size}, but found {self._layout.numel()} ranks!"
+                )
+
+            # ONLY set the device if the current device is not initialized, if user already
+            # set the device before DeviceMesh init, we respect the user's choice.
+            device_handle = _get_device_handle(self._device_type)
+            if device_handle and not device_handle.is_initialized():
+                # auto set the cuda/cuda-like device only if user has not set it, if there's LOCAL_RANK
+                # env variable from launchers, we use it to set the device.
+                if "LOCAL_RANK" in os.environ:
+                    local_rank = int(os.environ["LOCAL_RANK"])
+                    logger.info(
+                        "Setting default device for the current process based on LOCAL_RANK=%s",
+                        local_rank,
+                    )
+                    device_handle.set_device(local_rank)
+                else:
+                    warnings.warn(
+                        "It seems like you did not set/select the default device for the current process before the DeviceMesh "
+                        "initialization or use a launcher (i.e. torchrun) which populates `LOCAL_RANK` environment variable. "
+                        "It is recommended to set the current device for the process BEFORE the DeviceMesh initialization so that "
+                        "the underlying communicator (i.e. NCCL) can be initialized properly. "
+                        "Given that the current process has no default device selected, DeviceMesh will use a heuristic to set the "
+                        "device_id via `global_rank % num_devices_per_host`, assuming homogeneous hardware cluster. ",
+                        stacklevel=2,
+                    )
+                    # heuristic to set the current cuda/cuda-like device base on num of gpu devices available in each host
+                    # NOTE: This device selection would only work for homogeneous hardware.
+                    num_devices_per_host = device_handle.device_count()
+                    if (
+                        world_size > num_devices_per_host
+                        and world_size % num_devices_per_host != 0
+                    ):
+                        raise RuntimeError(
+                            f"DeviceMesh only support homogeneous hardware, but found "
+                            f"{world_size} ranks and {num_devices_per_host} {self._device_type} devices!"
+                        )
+                    device_handle.set_device(get_rank() % num_devices_per_host)
+
+            return _get_default_group()
+
+        @staticmethod
+        def _init_one_process_group(
+            sub_layout: _MeshLayout,
+            rank_map: torch.Tensor,
+            dim_name: str,
+            backend_override: BackendConfig,
+        ) -> GroupName | None:
+            # Generate a 2D global mesh tensor for the current dim for PG creation.
+            pg_ranks_by_dim = sub_layout.nest().remap_to_tensor(rank_map)
+            backend, pg_options = backend_override
+            # We need to explicitly pass in timeout when specified in option, otherwise
+            # the default timeout will be used to override the timeout set in option.
+            # TODO: remove this once we have fixed inside c10d level.
+            timeout = pg_options._timeout if pg_options else None
+
+            # If we have a 2D mesh with mesh_dim_names ("dp", "tp"), the group description
+            # of the subgroups would be `mesh_dim_dp` and `mesh_name_tp`.
+            # If the mesh doesn't have a mesh_dim_names, then the group description of the
+            # subgroup would be `mesh_dim_0` and `mesh_dim_1`.
+            group_desc = f"mesh_{dim_name}"
+
+            dim_group = None
+            default_group = _get_default_group()
+
+            # Early return if there is only one sub_layout in the mesh layout.
+            if sub_layout.numel() == get_world_size() and backend_override == (
+                None,
+                None,
+            ):
+                # Append the default pg to the first dim groups only if the default pg is compatible with `self._device_type`.
+                # Otherwise, create new pg.
+                ranks = list(range(get_world_size()))
+                dim_group = (
+                    new_group(
+                        backend="cpu:gloo,cuda:nccl",
+                        ranks=ranks,
+                        group_desc="mesh_default",
+                    )
+                    if torch.cuda.is_available()
+                    and get_backend(default_group) == "gloo"
+                    else default_group
+                )
+                return dim_group.group_name  # type: ignore[union-attr]
+
+            # If bound_device_id exists, it means the nccl communicator has been eagerly initialized
+            # so that we can use `split_group` to create subgroups through `ncclCommSplit`.
+            # In this case, we only need to make one API call (`split_group``) for the subgroup creation
+            # for each mesh dimension. In a 2 * 4 mesh, we only need to make two API calls per ranks to create
+            # all the subgroups.
+            # Otherwise, we need to make more than one API call (`new_group`) for subgroup creations. The
+            # numbers of API calls are equal to the number of subgroups for each mesh dimension. In a 2 * 4
+            # mesh, we need to make two API calls per ranks to create all the subgroups.
+            if (
+                getattr(default_group, "bound_device_id", None) is not None
+                and torch.cuda.is_available()
+                and (
+                    backend is None
+                    or default_group._get_backend(torch.device("cuda")).name()
+                    == backend
+                )
+            ):
+                dim_group = split_group(
+                    parent_pg=default_group,
+                    timeout=timeout,
+                    pg_options=pg_options,
+                    split_ranks=pg_ranks_by_dim.tolist(),
+                    group_desc=group_desc,
+                )
+                return dim_group.group_name  # type: ignore[union-attr]
+
+            # If the subgroup has been already created through `split_group`, we simply loop over `pg_ranks_by_dim`
+            # and append the `group_name` to the `dim_group_names` list when the current rank is in the subgroup.
+            # Otherwise, we use `new_group` instead of `split_group` to create subgroups by looping over `pg_ranks_by_dim`
+            # along with appending information to the `dim_group_names` list whenever necessary.
+            pg_name = None
+            for dim_mesh in pg_ranks_by_dim:
+                subgroup_ranks = dim_mesh.tolist()
+                dim_group = new_group(
+                    ranks=subgroup_ranks,
+                    timeout=timeout,
+                    backend=backend,
+                    pg_options=pg_options,
+                    group_desc=group_desc,
+                )
+
+                # only add to dim_groups if the current rank in the subgroup
+                if get_rank() in subgroup_ranks:
+                    if pg_name is not None:
+                        raise RuntimeError(
+                            f"Each device mesh dimension should get only one process group, but got {get_rank()} "
+                            f"in {subgroup_ranks}!"
+                        )
+                    pg_name = dim_group.group_name
+            return pg_name
+
+        @staticmethod
+        def _init_process_groups(
+            layout: _MeshLayout,
+            rank_map: torch.Tensor,
+            mesh_dim_names: tuple[str, ...] | None,
+            backend_override: tuple[BackendConfig, ...],
+        ) -> list[GroupName]:
+            # group_name associated with each mesh dimension, each
+            # mesh dimension should have one sub-group per rank
+            dim_group_names: list[GroupName | None] = []
+            # create sub pgs base on the mesh argument specified
+            for dim in range(len(layout)):
+                dim_name = mesh_dim_names[dim] if mesh_dim_names else f"dim_{dim}"
+                dim_group_names.append(
+                    DeviceMesh._init_one_process_group(
+                        layout[dim], rank_map, dim_name, backend_override[dim]
+                    )
+                )
+            # Filter out None values. If any are None then they should all be None.
+            dim_non_none_group_names = [n for n in dim_group_names if n is not None]
+            assert not dim_non_none_group_names or len(dim_non_none_group_names) == len(
+                dim_group_names
+            )
+            return dim_non_none_group_names
+
+        def _get_root_mesh(self) -> "DeviceMesh":
+            return self._root_mesh if self._root_mesh else self
+
+        def __enter__(self) -> "DeviceMesh":
+            # set this mesh as the current mesh in mesh env
+            _mesh_resources.mesh_stack.append(self)
+            return self
+
+        # pyre-fixme[2]: Parameter must be annotated.
+        def __exit__(self, exc_type, exc_value, exc_traceback) -> None:
+            # pop this mesh from mesh env
+            _mesh_resources.mesh_stack.pop()
+
+        def __repr__(self) -> str:
+            device_mesh_repr = (
+                f"({', '.join(f'{k}={v}' for k, v in zip(self._mesh_dim_names, self._layout.top_level_sizes))})"
+                if self._mesh_dim_names
+                else f"{self._layout.top_level_sizes}"
+            )
+            device_mesh_repr = f"DeviceMesh({device_mesh_repr}, '{self.device_type}', stride={self._layout.strides}"
+            # We only print the mesh tensor if the debug mode is turned on.
+            if os.environ.get("TORCH_DISTRIBUTED_DEBUG", "") == "DETAIL":
+                device_mesh_repr += f", Mesh: {self.mesh.tolist()}"
+            return f"{device_mesh_repr})"
+
+        def __hash__(self):
+            # lazily compute hash
+            self._hash = getattr(self, "_hash", None)
+            if not self._hash:
+                self._hash = hash(
+                    (
+                        self._flatten_rank_map,
+                        self._layout,
+                        self._device_type,
+                        self._mesh_dim_names,
+                        self._thread_id,
+                    )
+                )
+            return self._hash
+
+        def __eq__(self, other: object) -> bool:
+            if self is other:
+                return True
+            if not isinstance(other, DeviceMesh):
+                return False
+            return (
+                self._flatten_rank_map == other._flatten_rank_map
+                and self._layout == other._layout
+                and self._device_type == other._device_type
+                and self._mesh_dim_names == other._mesh_dim_names
+                and self._thread_id == other._thread_id
+            )
+
+        def __getitem__(self, mesh_dim_names: str | tuple[str, ...]) -> "DeviceMesh":
+            """
+            Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh.
+            The submesh created consists of the dimensions and the communicators indicated by
+            ``mesh_dim_names``
+
+            Args:
+                mesh_dim_names (Union[str, tuple[str, ...]]): the name or the tuple of names of the
+                mesh dimension of the DeviceMesh to create the submesh for.
+            Returns:
+                A :class:`DeviceMesh` object
+
+            The following program runs on each process/rank in an SPMD manner in a world size of 8.
+            In the first example:
+                Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]).
+                Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of  DeviceMesh:([4, 5, 6, 7]).
+                Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of  DeviceMesh:([0, 4]).
+                Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of  DeviceMesh:([1, 5]).
+                Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of  DeviceMesh:([2, 6]).
+                Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of  DeviceMesh:([3, 7]).
+
+            In the second example:
+                Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]).
+                Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]).
+                Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]).
+                Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]).
+
+            Example::
+
+                >>> # xdoctest: +SKIP("no rank")
+                >>> from torch.distributed.device_mesh import DeviceMesh
+                >>>
+                >>> # Initialize a 2D device mesh as (2, 4) to represent the topology
+                >>> # of cross-host(dim 0), and within-host (dim 1).
+                >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))
+                >>> tp_mesh = mesh_2d["tp"]
+                >>> dp_mesh = mesh_2d["dp"]
+                >>>
+                >>> # Initialize a 3D mesh.
+                >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp"))
+                >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh.
+                >>> dp_cp_mesh = mesh_3d["dp", "cp"]
+                >>> cp_dp_mesh = mesh_3d["cp", "dp"]
+            """
+            if not self._mesh_dim_names:
+                raise RuntimeError("Cannot slice a DeviceMesh without mesh_dim_names!")
+
+            mesh_dim_names = (
+                (mesh_dim_names,) if isinstance(mesh_dim_names, str) else mesh_dim_names
+            )
+
+            if mesh_dim_names == self._mesh_dim_names:
+                return self
+            else:
+                sliced_mesh_layout = self._get_slice_mesh_layout(mesh_dim_names)
+                # When using FakeTensorMode to trace the model, `_create_sub_mesh()` will
+                # fail as it will require a real tensor to manipulate.
+                # `unset_fake_temporarily()` will allow us to materialize the tensors
+                # within `_create_sub_mesh`, which should not affect modling.
+                #
+                # Note that this should be orthogonal to torch.compile(). But whether
+                # we can compile device_mesh `slicing` (no graph break) is not verified
+                # yet and need a follow-up,
+                # TODO: compiler + device_mesh slicing.
+                with torch._subclasses.fake_tensor.unset_fake_temporarily():
+                    submesh = self._create_sub_mesh(sliced_mesh_layout, mesh_dim_names)
+                return submesh
+
+        def get_group(self, mesh_dim: int | str | None = None) -> ProcessGroup:
+            """
+            Returns the single ProcessGroup specified by mesh_dim, or, if mesh_dim is not specified and the
+            DeviceMesh is 1-dimensional, returns the only ProcessGroup in the mesh.
+
+            Args:
+                mesh_dim (str/int, optional): it can be the name of the mesh dimension or the index
+                of the mesh dimension. Default is None.
+
+            Returns:
+                A :class:`ProcessGroup` object.
+            """
+            if not hasattr(self, "_dim_group_names"):
+                raise RuntimeError("DeviceMesh process groups not initialized!")
+
+            if len(self._layout) > 1 and mesh_dim is None:
+                raise RuntimeError(
+                    f"Found the DeviceMesh have {len(self._layout)} dimensions",
+                    "Optional kwarg `mesh_dim` needs to be specified when device_mesh.ndim > 1.",
+                    "If you want to get the list of all the ProcessGroups in the DeviceMesh,"
+                    "please use `get_all_groups()` instead.",
+                )
+
+            # Quick return if the current device_mesh is a 1D mesh.
+            if len(self._layout) == 1 and mesh_dim is None:
+                return not_none(_resolve_process_group(self._dim_group_names[0]))
+
+            root_mesh = self._get_root_mesh()
+            root_to_flatten_mapping = root_mesh._flatten_mapping
+            if root_to_flatten_mapping and mesh_dim in root_to_flatten_mapping:
+                dim_group_name = root_to_flatten_mapping[
+                    mesh_dim  # type: ignore[index]
+                ]._dim_group_names[0]
+                return not_none(_resolve_process_group(dim_group_name))
+            else:
+                mesh_dim = (
+                    self._get_mesh_dim_by_name(mesh_dim)
+                    if isinstance(mesh_dim, str)
+                    else mesh_dim
+                )
+                if not isinstance(mesh_dim, int):
+                    raise AssertionError(
+                        f"mesh_dim must be an int, got {type(mesh_dim)}"
+                    )
+                return not_none(_resolve_process_group(self._dim_group_names[mesh_dim]))
+
+        def get_all_groups(self) -> list[ProcessGroup]:
+            """
+            Returns a list of ProcessGroups for all mesh dimensions.
+
+            Returns:
+                A list of :class:`ProcessGroup` object.
+            """
+            return [self.get_group(i) for i in range(len(self._layout))]
+
+        def _create_sub_mesh(
+            self,
+            layout: _MeshLayout,
+            submesh_dim_names: tuple[str, ...],
+        ) -> "DeviceMesh":
+            root_mesh = self._get_root_mesh()
+            slice_dim_group_name = []
+            for name in submesh_dim_names:
+                if name in not_none(self._mesh_dim_names):
+                    slice_dim_group_name.append(
+                        self._dim_group_names[  # type: ignore[has-type]
+                            not_none(self._mesh_dim_names).index(name)
+                        ]
+                    )
+                else:
+                    # If device_mesh is not root_mesh, we already throw error in _get_slice_mesh_layout
+                    # Since we will deprecate the slicing of flattened dim_name from root mesh soon,
+                    # we don't want to optimize the code furthermore.
+                    flatten_mesh = self._flatten_mapping[name]
+                    slice_dim_group_name.append(
+                        flatten_mesh._dim_group_names[  # type: ignore[has-type]
+                            not_none(flatten_mesh._mesh_dim_names).index(name)
+                        ]
+                    )
+            res_submesh = DeviceMesh(
+                self._device_type,
+                _layout=layout,
+                _rank_map=root_mesh._rank_map,
+                mesh_dim_names=submesh_dim_names,
+                _root_mesh=root_mesh,
+                _init_backend=False,
+            )
+            res_submesh._dim_group_names = slice_dim_group_name
+            return res_submesh
+
+        def _create_flatten_mesh(
+            self,
+            mesh_dim_name: str | None = None,
+            backend_override: BackendConfig = (None, None),
+        ) -> "DeviceMesh":
+            root_mesh = self._get_root_mesh()
+
+            if not mesh_dim_name:
+                mesh_dim_name = "_".join(not_none(self._mesh_dim_names))
+
+            # Flatten a 1D device mesh into its original mesh_dim_name will return itself.
+            if self.ndim == 1 and mesh_dim_name in not_none(self._mesh_dim_names):
+                return self
+
+            # Check whether the mesh_dim_name for flattened mesh is valid.
+            invalid_dim_names = not_none(root_mesh._mesh_dim_names)
+            if mesh_dim_name in invalid_dim_names:
+                raise ValueError(
+                    f"{mesh_dim_name} already exists for submesh of the {root_mesh}. ",
+                    f"The mesh_dim_names of submesh and flattened mesh are {invalid_dim_names}. "
+                    f"Please specify another valid mesh_dim_name.",
+                )
+
+            flattened_mesh_layout = self._layout.coalesce()
+            if len(flattened_mesh_layout) > 1:
+                flattened_mesh_layout = flattened_mesh_layout.nest()
+            # Quick return if the flatten mesh has been created before.
+            if mesh_dim_name in root_mesh._flatten_mapping:
+                if (
+                    flattened_mesh_layout
+                    == root_mesh._flatten_mapping[mesh_dim_name]._layout
+                ):
+                    return root_mesh._flatten_mapping[mesh_dim_name]
+                else:
+                    raise ValueError(
+                        f"Flatten mesh with mesh_dim_name {mesh_dim_name} has been created before, "
+                        f"Please specify another valid mesh_dim_name."
+                    )
+
+            res_flattened_mesh = DeviceMesh(
+                root_mesh._device_type,
+                _layout=flattened_mesh_layout,
+                _rank_map=root_mesh._rank_map,
+                mesh_dim_names=(mesh_dim_name,),
+                _root_mesh=root_mesh,
+                backend_override=(backend_override,),
+            )
+            root_mesh._flatten_mapping[mesh_dim_name] = res_flattened_mesh
+
+            return res_flattened_mesh
+
+        def _get_root_mesh_dim(self) -> int | None:
+            """
+            Returns the index of the mesh dim in the root mesh.
+            The device_mesh passed in needs to be sliced out from the root mesh
+            or submesh of the root mesh.
+            """
+            root_mesh = self._get_root_mesh()
+            child_mesh_dim_names = self._mesh_dim_names
+            if root_mesh and child_mesh_dim_names:
+                if len(child_mesh_dim_names) != 1:
+                    raise AssertionError("The submesh can only be a 1D mesh.")
+                child_mesh_dim_name = child_mesh_dim_names[0]
+                return root_mesh._get_mesh_dim_by_name(child_mesh_dim_name)
+            return None
+
+        def _get_mesh_dim_by_name(self, mesh_dim_name: str) -> int:
+            if self._mesh_dim_names is None or len(self._mesh_dim_names) == 0:
+                raise KeyError(
+                    "No `mesh_dim_names` found.",
+                )
+            if mesh_dim_name not in self._mesh_dim_names:
+                raise KeyError(
+                    f"Mesh dimension '{mesh_dim_name}' does not exist.",
+                    f"Available mesh dimensions are: mesh_dim_names={self._mesh_dim_names}",
+                )
+            return not_none(self._mesh_dim_names.index(mesh_dim_name))
+
+        def _get_slice_mesh_layout(
+            self, mesh_dim_names: tuple[str, ...]
+        ) -> _MeshLayout:
+            """
+            Validate whether the mesh_dim_names is valid for slicing the given device_mesh.
+            If valid, return dim indexes of the slice mesh in the device mesh.
+            """
+            slice_from_root = True
+            if self != self._get_root_mesh():
+                slice_from_root = False
+
+            # The slice mesh_dim_names should consist either the current device_mesh's mesh_dim_names
+            # or its flattened mesh's mesh_dim_names if it's root_mesh.
+            flatten_name_to_root_layout = (
+                {
+                    key: mesh._layout
+                    for key, mesh in self._get_root_mesh()._flatten_mapping.items()
+                }
+                if slice_from_root
+                else {}
+            )
+            valid_mesh_dim_names = [
+                *not_none(self._mesh_dim_names),
+                *flatten_name_to_root_layout,
+            ]
+
+            if not all(
+                mesh_dim_name in valid_mesh_dim_names
+                for mesh_dim_name in mesh_dim_names
+            ):
+                raise KeyError(
+                    f"Invalid mesh_dim_names {mesh_dim_names} specified. "
+                    f"Valid mesh_dim_names are {valid_mesh_dim_names}."
+                )
+
+            layout_sliced = []
+            for name in mesh_dim_names:
+                if name in not_none(self._mesh_dim_names):
+                    layout_sliced.append(
+                        self._layout[not_none(self._mesh_dim_names).index(name)]
+                    )
+                elif name in flatten_name_to_root_layout:
+                    warnings.warn(
+                        "Slicing a flattened dim from root mesh will be deprecated in PT 2.11. "
+                        "Users need to bookkeep the flattened mesh directly. ",
+                        stacklevel=2,
+                    )
+                    layout_sliced.append(flatten_name_to_root_layout[name])
+
+            sliced_sizes = tuple(l.sizes for l in layout_sliced)
+            sliced_strides = tuple(l.strides for l in layout_sliced)
+
+            # The check below is from DeviceMesh's implementation before adopting CuTe layout for internal
+            # bookkeeping and it can be removed but we need to define what is the expected behavior.
+            # TODO: Remove the below check and define the expected behavior.
+            # Validate the order of the slice mesh dim indices.
+            # This needs to be in ascending order.
+            pre_stride = -1
+            for stride in reversed(sliced_strides):
+                # Note that with CuTe layout, we can support slicing flattened non-contiguous mesh dims with no problem.
+                # But this will make this behavior complicated so we decided to not support it for now.
+                if not is_int(stride):
+                    raise NotImplementedError(
+                        "Currently, this only allows slicing out a contiguous flattened dim."
+                    )
+                if stride < pre_stride:
+                    raise KeyError(
+                        f"Invalid mesh_dim_names {mesh_dim_names} specified. "
+                        "Mesh dim indices should be in ascending order."
+                    )
+                pre_stride = stride
+
+            # When users sliced dim_names outside from current mesh, we will check whether
+            # there is layout overlap.
+            # TODO: Eventually we will just directly throw error here because
+            # we will deprecate the slicing of flattened dim_name from root mesh.
+            layout_sliced = _MeshLayout(sliced_sizes, sliced_strides)
+            if not layout_sliced.check_non_overlap():
+                raise RuntimeError(
+                    f"Slicing overlapping dim_names {mesh_dim_names} is not allowed."
+                )
+
+            return layout_sliced
+
+        # TODO: to make this use case by other components public API in the future.
+        def _get_all_submeshes(self, mesh_dim_name: str) -> list["DeviceMesh"]:
+            """
+            Return all the submeshes of a given mesh dimension of the device mesh.
+            """
+            mesh_dim = self._get_mesh_dim_by_name(mesh_dim_name)
+            layout = self._layout[mesh_dim]
+            pg_ranks_by_dim = layout.remap_to_tensor(self._rank_map)
+            cur_rank = self.get_rank()
+            res_submeshes = []
+            for mesh_1d in pg_ranks_by_dim:
+                submesh = DeviceMesh(
+                    self._device_type,
+                    mesh_1d,
+                    mesh_dim_names=(mesh_dim_name,),
+                    _init_backend=False,
+                )
+                submesh._dim_group_names = (  # type: ignore[has-type]
+                    [self._dim_group_names[mesh_dim]]  # type: ignore[has-type]
+                    if cur_rank in mesh_1d
+                    else []
+                )
+                res_submeshes.append(submesh)
+
+            return res_submeshes
+
+        @staticmethod
+        def from_group(
+            group: ProcessGroup | list[ProcessGroup],
+            device_type: str,
+            mesh: Union[torch.Tensor, "ArrayLike"] | None = None,
+            *,
+            mesh_dim_names: tuple[str, ...] | None = None,
+        ) -> "DeviceMesh":
+            """
+            Constructs a :class:`DeviceMesh` with ``device_type`` from an
+            existing :class:`ProcessGroup` or a list of existing :class:`ProcessGroup`.
+
+            The constructed device mesh has number of dimensions equal to the
+            number of groups passed. For example, if a single process group is passed in,
+            the resulted DeviceMesh is a 1D mesh. If a list of 2 process groups is passed in,
+            the resulted DeviceMesh is a 2D mesh.
+
+            If more than one group is passed, then the ``mesh`` and ``mesh_dim_names`` arguments
+            are required. The order of the process groups passed in determines the topology of
+            the mesh. For example, the first process group will be the 0th dimension of the DeviceMesh.
+            The `mesh` tensor passed in must have the same number of dimensions as the number of process
+            groups passed in, and the order of the dimensions in the `mesh` tensor must match the order
+            in the process groups passed in.
+
+            Args:
+                group (ProcessGroup or list[ProcessGroup]): the existing ProcessGroup
+                    or a list of existing ProcessGroups.
+                device_type (str): The device type of the mesh. Currently supports: "cpu",
+                    "cuda/cuda-like". Passing in a device type with a GPU index, such as "cuda:0",
+                    is not allowed.
+                mesh (torch.Tensor or ArrayLike, optional): A multi-dimensional array or an
+                    integer tensor describing the layout of devices, where the IDs are global IDs
+                    of the default process group. Default is None.
+                mesh_dim_names (tuple[str, ...], optional): A tuple of mesh dimension names to assign
+                    to each dimension of the multi-dimensional array describing the layout of devices.
+                    Its length must match the length of `mesh_shape`. Each string in `mesh_dim_names`
+                    must be unique. Default is None.
+
+            Returns:
+                DeviceMesh: A :class:`DeviceMesh` object representing the device layout.
+            """
+
+            # 1D scenario
+            if isinstance(group, ProcessGroup):
+                group_ranks = get_process_group_ranks(group)
+                if (
+                    isinstance(mesh, torch.Tensor) and mesh.tolist() != group_ranks
+                ) or (
+                    mesh is not None
+                    and not isinstance(mesh, torch.Tensor)
+                    and mesh != group_ranks
+                ):
+                    raise ValueError(
+                        f"Invalid mesh {str(mesh)} for ProcessGroup with ranks {group_ranks}"
+                    )
+                mesh = torch.tensor(group_ranks, device="cpu", dtype=torch.int)
+                device_mesh = DeviceMesh(
+                    device_type,
+                    mesh,
+                    mesh_dim_names=mesh_dim_names,
+                    _init_backend=False,
+                )
+                device_mesh._dim_group_names = [group.group_name]
+                return device_mesh
+
+            # nD scenario
+            groups = list(group)
+            if len(groups) == 0:
+                raise ValueError("Expects at least one ProcessGroup to be passed")
+            if mesh is None:
+                raise ValueError("Must pass mesh if passing multiple ProcessGroups")
+            if mesh_dim_names is None:
+                raise ValueError(
+                    "Must pass mesh_dim_names if passing multiple ProcessGroups"
+                )
+            # When init a DeviceMesh with multiple ProcessGroups directly, we need to make sure
+            # the mesh tensor is contiguous. Otherwise, the layout we inferred from the mesh tensor
+            # will have larger span than the actual tensor. This is just internal implementation detail
+            # and does not affect user facing behavior.
+            mesh = (
+                mesh.detach().to(dtype=torch.int, device="cpu")
+                if isinstance(mesh, torch.Tensor)
+                else torch.tensor(mesh, device="cpu", dtype=torch.int)
+            )
+            if mesh.ndim != len(groups):
+                raise ValueError(
+                    "Expects mesh with ndim equal to number of ProcessGroups but got "
+                    f"mesh {mesh.tolist()} and {len(groups)} ProcessGroups"
+                )
+            device_mesh = DeviceMesh(
+                device_type, mesh, mesh_dim_names=mesh_dim_names, _init_backend=False
+            )
+            device_mesh._dim_group_names = [group.group_name for group in groups]
+            return device_mesh
+
+        def size(self, mesh_dim: int | None = None) -> int:
+            if mesh_dim is not None:
+                return self._layout[mesh_dim].numel()
+            return self._layout.numel()
+
+        @property
+        def ndim(self) -> int:
+            return len(self._layout)
+
+        @property
+        def shape(self) -> tuple[int, ...]:
+            return self._layout.top_level_sizes
+
+        def get_rank(self) -> int:
+            """
+            Returns the current global rank.
+            """
+            return get_rank()
+
+        def get_local_rank(self, mesh_dim: int | str | None = None) -> int:
+            """
+            Returns the local rank of the given mesh_dim of the DeviceMesh.
+
+            Args:
+                mesh_dim (str/int, optional): it can be the name of the mesh dimension or the index
+                of the mesh dimension. Default is None.
+
+            Returns:
+                An integer denotes the local rank.
+
+            The following program runs on each process/rank in an SPMD manner. In this example, we have 2
+            hosts with 4 GPUs each.
+            Calling mesh_2d.get_local_rank(mesh_dim=0) on rank 0, 1, 2, 3 would return 0.
+            Calling mesh_2d.get_local_rank(mesh_dim=0) on rank 4, 5, 6, 7 would return 1.
+            Calling mesh_2d.get_local_rank(mesh_dim=1) on rank 0, 4 would return 0.
+            Calling mesh_2d.get_local_rank(mesh_dim=1) on rank 1, 5 would return 1.
+            Calling mesh_2d.get_local_rank(mesh_dim=1) on rank 2, 6 would return 2.
+            Calling mesh_2d.get_local_rank(mesh_dim=1) on rank 3, 7 would return 3.
+
+            Example::
+
+                >>> # xdoctest: +SKIP("no rank")
+                >>> from torch.distributed.device_mesh import DeviceMesh
+                >>>
+                >>> # Initialize device mesh as (2, 4) to represent the topology
+                >>> # of cross-host(dim 0), and within-host (dim 1).
+                >>> mesh = DeviceMesh(device_type="cuda", mesh=[[0, 1, 2, 3],[4, 5, 6, 7]])
+            """
+            if self.ndim > 1 and mesh_dim is None:
+                raise RuntimeError(
+                    f"Found the DeviceMesh have {len(self._layout)} dimensions",
+                    "Optional kwarg `mesh_dim` needs to be specified when device_mesh.ndim > 1.",
+                )
+            elif mesh_dim is None:
+                mesh_dim = 0
+
+            mesh_dim_group = not_none(self.get_group(mesh_dim))
+            if not isinstance(mesh_dim_group, ProcessGroup):
+                raise AssertionError(
+                    "We expect ProcessGroup before calling `get_rank`!"
+                )
+            return not_none(get_rank(mesh_dim_group))
+
+        def get_coordinate(self) -> list[int] | None:
+            """
+            Return the relative indices of this rank relative to all
+            dimensions of the mesh. If this rank is not part of the mesh, return None.
+            """
+            return self._coordinate_on_dim if self._coordinate_on_dim else None
+
+        def _flatten(
+            self,
+            mesh_dim_name: str | None = None,
+            backend_override: None
+            | str
+            | C10dBackend.Options
+            | tuple[str, C10dBackend.Options] = None,
+        ) -> "DeviceMesh":
+            """
+            Returns a 1D DeviceMesh by flattening the current DeviceMesh.
+
+            If no mesh_dim_name is provided, the default is a string concatenating the mesh_dim_names of the
+            given submesh with each mesh_dim_name separated by "_". For example, if we have a 3D mesh
+            DeviceMesh([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], mesh_dim_names=("dp", "cp", "tp")), calling
+            mesh_3d["dp", "cp"]._flatten() will create a 1D submesh DeviceMesh([0, 2, 4, 6], mesh_dim_names=("dp_cp",))
+            on rank 0, 2, 4, 6 and a 1D submesh DeviceMesh([1, 3, 5, 7], mesh_dim_names=("dp_cp",)) on rank 1, 3, 5, 7.
+
+            After the flattened dimension is created, to access the flattened dimension in mesh_3d, one can use the
+            existing slicing method to obtain the flattened mesh through calling mesh_3d["dp_cp"].
+            """
+            if not self._mesh_dim_names:
+                raise RuntimeError(
+                    "Cannot flatten a DeviceMesh without mesh_dim_names!"
+                )
+
+            if backend_override is not None:
+                (backend_override_tuple,) = _normalize_backend_override(
+                    {0: backend_override}, 1
+                )
+            else:
+                backend_override_tuple = (None, None)
+
+            return self._create_flatten_mesh(mesh_dim_name, backend_override_tuple)
+
+        def _create_unflatten_mesh(
+            self,
+            dim: int,
+            mesh_sizes: tuple[int, ...],
+            mesh_dim_names: tuple[str, ...],
+            backend_override: tuple[
+                tuple[str | None, C10dBackend.Options | None], ...
+            ] = ((None, None),),
+        ) -> "DeviceMesh":
+            inner_layout = _MeshLayout(tuple(mesh_sizes), suffix_product(mesh_sizes))
+
+            if inner_layout.numel() != self._layout[dim].numel():
+                raise ValueError(
+                    f"The product of {mesh_sizes=} is {inner_layout.numel()}, "
+                    f"but the original dimension at dim={dim} has size {self._layout[dim].numel()}. "
+                    f"These must be equal for unflatten to work correctly."
+                )
+
+            partial_layout = self._layout[dim].composition(inner_layout)
+            unflattened_layout = self._layout.splice(dim, dim + 1, partial_layout)
+            unflattened_mesh_dim_names = list(not_none(self.mesh_dim_names))
+            unflattened_mesh_dim_names[dim : dim + 1] = list(mesh_dim_names)
+
+            root_mesh = self._get_root_mesh()
+            res_mesh = DeviceMesh(
+                self.device_type,
+                _layout=unflattened_layout,
+                _rank_map=root_mesh._rank_map,
+                mesh_dim_names=tuple(unflattened_mesh_dim_names),
+                _root_mesh=root_mesh,
+                _init_backend=False,
+            )
+
+            # If original mesh has initiated its backend, we need to initialize the backend
+            # of unflatten dims as well.
+            # TODO: To make backend init more efficient with cute layout representation and support
+            # per dim backend init.
+            if hasattr(self, "_dim_group_names"):
+                dim_group_names = self._dim_group_names.copy()
+                dim_group_names[dim : dim + 1] = self._init_process_groups(
+                    partial_layout,
+                    root_mesh._rank_map,
+                    mesh_dim_names,
+                    backend_override,
+                )
+                res_mesh._dim_group_names = dim_group_names
+
+            return res_mesh
+
+        def _unflatten(
+            self,
+            dim: int | str,
+            mesh_sizes: tuple[int, ...],
+            mesh_dim_names: tuple[str, ...],
+            backend_override: dict[
+                str, str | C10dBackend.Options | tuple[str, C10dBackend.Options]
+            ]
+            | None = None,
+        ) -> "DeviceMesh":
+            """
+            Returns a DeviceMesh by unflatten the current DeviceMesh.
+
+            This api can be used to unflatten a N-D DeviceMesh into N-1+len(mesh_sizes)-D meshes or submeshes.
+            The dim is the dimension to be unflattened which can be either a string or an integer.
+
+            The mesh_sizes is a tuple which specifies the shape of the mesh unflatten into for the given dim.
+            The mesh_dim_names is a list of strings which specifies the names of the dimensions of the mesh unflatten into.
+            Its length must match the length of mesh_sizes.
+
+            For example, if we have a 1D mesh DeviceMesh([0, 1, 2, 3, 4, 5, 6, 7], mesh_dim_names=("world")),
+            calling mesh_1d._unflatten(0, (2, 2, 4), ["dp", "pp", "tp"]) will create a 3D mesh
+            DeviceMesh([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], mesh_dim_names=("dp", "cp", "tp")).
+
+            Note that after calling the unflatten, there is no access to the unflattened dimension in mesh_1d, one can only
+            use the newly unflattened mesh to slice out the unflattened mesh dims.
+            """
+            if isinstance(dim, int) and dim >= self.ndim:
+                raise ValueError(
+                    f"dim {dim} specified in `_unflatten` is out of range {self.ndim}"
+                )
+            elif isinstance(dim, str) and dim in not_none(self.mesh_dim_names):
+                raise ValueError(
+                    f"dim {dim} specified in `_unflatten` is not in {self.mesh_dim_names}"
+                )
+
+            if len(mesh_sizes) != len(mesh_dim_names):
+                raise RuntimeError(
+                    "mesh_dim_names must have same length as mesh_sizes in _unflatten!"
+                )
+
+            if isinstance(dim, str):
+                dim = not_none(self.mesh_dim_names).index(dim)
+
+            if backend_override is not None:
+                backend_override_tuple = tuple(
+                    _normalize_backend_override(
+                        backend_override,  # type: ignore[arg-type]
+                        len(mesh_sizes),
+                        mesh_dim_names,
+                    )
+                )
+            else:
+                backend_override_tuple = ((None, None),) * len(mesh_dim_names)
+
+            return self._create_unflatten_mesh(
+                dim,
+                mesh_sizes,
+                mesh_dim_names,
+                backend_override_tuple,
+            )
+
+        @staticmethod
+        def _concatenate(device_mesh_list: list["DeviceMesh"]) -> "DeviceMesh":
+            concat_dim_names: list[str] = []
+            concat_sizes: list[IntTuple] = []
+            concat_strides: list[IntTuple] = []
+            concat_dim_group_name: list[GroupName] = []
+            flatten_rank_map = device_mesh_list[0]._flatten_rank_map
+            for dm in device_mesh_list:
+                for i in range(len(dm._layout)):
+                    concat_sizes.append(dm._layout[i].sizes)
+                    concat_strides.append(dm._layout[i].strides)
+                concat_dim_names.extend(not_none(dm.mesh_dim_names))
+                concat_dim_group_name.extend(not_none(dm._dim_group_names))
+                # Concatenate device mesh having different root mesh tensors are meaningless
+                # because the concatenated indices should be indexed by the same root mesh tensor.
+                if dm._flatten_rank_map != flatten_rank_map:
+                    raise RuntimeError(
+                        "Cannot concatenate DeviceMeshes derived from different device meshs"
+                    )
+            concat_mesh_layout = _MeshLayout(tuple(concat_sizes), tuple(concat_strides))
+            if not concat_mesh_layout.check_non_overlap():
+                raise RuntimeError(
+                    f"Cannot concatenate overlapping meshes: {device_mesh_list}"
+                )
+            res_mesh = DeviceMesh(
+                device_mesh_list[0].device_type,
+                _layout=concat_mesh_layout,
+                _rank_map=device_mesh_list[0]._rank_map,
+                mesh_dim_names=tuple(concat_dim_names),
+                _root_mesh=device_mesh_list[0]._get_root_mesh(),
+                _init_backend=False,
+            )
+            res_mesh._dim_group_names = concat_dim_group_name
+            return res_mesh
+
+    def _normalize_backend_override(
+        backend_override: dict[
+            int | str,
+            str | C10dBackend.Options | tuple[str, C10dBackend.Options],
+        ],
+        ndim: int,
+        mesh_dim_names: tuple[str, ...] | None = None,
+    ) -> Iterator[BackendConfig]:
+        if mesh_dim_names is None:
+            mesh_dim_names = ()
+        for dim_idx, dim_name in zip_longest(range(ndim), mesh_dim_names):
+            if dim_name is not None and dim_name in backend_override:
+                if dim_idx in backend_override:
+                    raise RuntimeError(
+                        f"Found redundant dim index {dim_idx} and "
+                        f"name {dim_name} in backend_override"
+                    )
+                val = backend_override.pop(dim_name)
+            elif dim_idx in backend_override:
+                val = backend_override.pop(dim_idx)
+            else:
+                yield (None, None)
+                continue
+
+            if isinstance(val, str):
+                yield (val, None)
+            elif isinstance(val, C10dBackend.Options):
+                yield (None, val)
+            else:
+                yield val
+
+        if backend_override:
+            raise RuntimeError(
+                f"Found invalid keys in backend_override: got {list(backend_override.keys())}, "
+                f"expected integers in range [0, {ndim}) or one of {mesh_dim_names}"
+            )
+
+    def init_device_mesh(
+        device_type: str,
+        mesh_shape: tuple[int, ...],
+        *,
+        mesh_dim_names: tuple[str, ...] | None = None,
+        backend_override: dict[
+            int | str, str | C10dBackend.Options | tuple[str, C10dBackend.Options]
+        ]
+        | None = None,
+    ) -> DeviceMesh:
+        """
+        Initializes a `DeviceMesh` based on `device_type`, `mesh_shape`, and `mesh_dim_names` parameters.
+
+        This creates a DeviceMesh with an n-dimensional array layout, where `n` is the length of `mesh_shape`.
+        If `mesh_dim_names` is provided, each dimension is labeled as `mesh_dim_names[i]`.
+
+        .. note::
+            `init_device_mesh` follows SPMD programming model, meaning the same PyTorch Python program
+            runs on all processes/ranks in the cluster. Ensure `mesh_shape` (the dimensions of the nD array
+            describing device layout) is identical across all ranks. Inconsistent `mesh_shape` may lead to hanging.
+
+        .. note::
+            If no process group is found, init_device_mesh will initialize distributed process group/groups
+            required for distributed communications behind the scene.
+
+        Args:
+            device_type (str): The device type of the mesh. Currently supports: "cpu", "cuda/cuda-like", "xpu".
+                Passing in a device type with a GPU index, such as "cuda:0", is not allowed.
+            mesh_shape (Tuple[int]): A tuple defining the dimensions of the multi-dimensional array
+                describing the layout of devices.
+            mesh_dim_names (tuple[str, ...], optional): A tuple of mesh dimension names to assign to each dimension
+                of the multi-dimensional array describing the layout of devices. Its length must match the length
+                of `mesh_shape`. Each string in `mesh_dim_names` must be unique.
+            backend_override (Dict[int | str, tuple[str, Options] | str | Options], optional): Overrides for some or all of
+                the ProcessGroups that will be created for each mesh dimension. Each key can be either the index of a
+                dimension or its name (if mesh_dim_names is provided). Each value can be a tuple containing the name
+                of the backend and its options, or just one of these two components (in which case the other will be
+                set to its default value).
+
+        Returns:
+            DeviceMesh: A :class:`DeviceMesh` object representing the device layout.
+
+        Example::
+
+            >>> # xdoctest: +SKIP("no rank")
+            >>> from torch.distributed.device_mesh import init_device_mesh
+            >>>
+            >>> mesh_1d = init_device_mesh("cuda", mesh_shape=(8,))
+            >>> mesh_2d = init_device_mesh("cuda", mesh_shape=(2, 8), mesh_dim_names=("dp", "tp"))
+
+        """
+        if mesh_dim_names is not None:
+            if len(set(mesh_dim_names)) != len(mesh_dim_names):
+                raise RuntimeError(
+                    "Each mesh_dim_name must be unique.",
+                    f"Found repeated mesh_dim_name in mesh_dim_names {mesh_dim_names}",
+                )
+
+            if len(mesh_shape) != len(mesh_dim_names):
+                raise RuntimeError(
+                    "mesh_shape and mesh_dim_names should have same length!",
+                    f"Found len(mesh_dim_names): {len(mesh_dim_names)} and len(mesh_shape):{len(mesh_shape)}.",
+                )
+
+        if backend_override is not None:
+            backend_override_tuple = tuple(
+                _normalize_backend_override(
+                    backend_override, len(mesh_shape), mesh_dim_names
+                )
+            )
+        else:
+            backend_override_tuple = None
+
+        # assume valid device types are all letters
+        if device_type and not device_type.isalpha():
+            raise RuntimeError(
+                f"Device type with index is not supported but got {device_type}. ",
+                "If you maintained a 'torch.device' object, it's recommended to pass in 'device.type'.",
+            )
+
+        layout = _MeshLayout(tuple(mesh_shape), suffix_product(tuple(mesh_shape)))
+        # Always initialize the (identity) rank map on CPU, regardless of what the
+        # external device type has been set to be (e.g. meta)
+        with torch.device("cpu"):
+            rank_map = torch.arange(layout.numel(), dtype=torch.int)
+        device_mesh = DeviceMesh(
+            device_type=device_type,
+            _layout=layout,
+            _rank_map=rank_map,
+            mesh_dim_names=mesh_dim_names,
+            backend_override=backend_override_tuple,
+        )
+
+        return device_mesh
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py
new file mode 100644
index 0000000000000000000000000000000000000000..6d29e77da50a8c6eaff9af2eda317ff1ce5156d1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/distributed_c10d.py
@@ -0,0 +1,6286 @@
+# mypy: allow-untyped-defs
+"""Distributed Collective Communication (c10d)."""
+
+import collections.abc
+import contextlib
+import copy
+import ctypes
+import hashlib
+import io
+import itertools
+import logging
+import os
+import pickle
+import sys
+import time
+import warnings
+from collections import namedtuple
+from collections.abc import Callable
+from datetime import timedelta
+from typing import Any, NewType, TYPE_CHECKING
+from typing_extensions import deprecated
+
+import torch
+from torch._C import _DistStoreError as DistStoreError
+from torch._C._distributed_c10d import (
+    _DistributedBackendOptions,
+    _register_process_group,
+    _resolve_process_group,
+    _unregister_all_process_groups,
+    _unregister_process_group,
+    AllgatherOptions,
+    AllreduceCoalescedOptions,
+    AllreduceOptions,
+    AllToAllOptions,
+    BarrierOptions,
+    BroadcastOptions,
+    DebugLevel,
+    GatherOptions,
+    get_debug_level,
+    PrefixStore,
+    ProcessGroup,
+    ReduceOp,
+    ReduceOptions,
+    ReduceScatterOptions,
+    ScatterOptions,
+    Store,
+    Work,
+)
+from torch._utils_internal import set_pytorch_distributed_envs_from_justknobs
+from torch.monitor import _WaitCounter
+from torch.overrides import handle_torch_function, has_torch_function
+from torch.utils._typing_utils import not_none
+
+from .c10d_logger import _exception_logger, _time_logger
+from .constants import default_pg_nccl_timeout, default_pg_timeout
+from .rendezvous import register_rendezvous_handler, rendezvous  # noqa: F401
+
+
+__all__ = [
+    "Backend",
+    "BackendConfig",
+    "GroupMember",
+    "P2POp",
+    "all_gather",
+    "all_gather_coalesced",
+    "all_gather_object",
+    "all_reduce",
+    "all_reduce_coalesced",
+    "all_to_all",
+    "all_to_all_single",
+    "barrier",
+    "batch_isend_irecv",
+    "broadcast",
+    "send_object_list",
+    "recv_object_list",
+    "broadcast_object_list",
+    "destroy_process_group",
+    "gather",
+    "gather_object",
+    "get_backend_config",
+    "get_backend",
+    "get_default_backend_for_device",
+    "get_rank",
+    "get_world_size",
+    "get_pg_count",
+    "group",
+    "init_process_group",
+    "irecv",
+    "is_gloo_available",
+    "is_initialized",
+    "is_mpi_available",
+    "is_backend_available",
+    "is_nccl_available",
+    "is_torchelastic_launched",
+    "is_ucc_available",
+    "is_xccl_available",
+    "isend",
+    "monitored_barrier",
+    "new_group",
+    "new_subgroups",
+    "new_subgroups_by_enumeration",
+    "recv",
+    "reduce",
+    "reduce_scatter",
+    "scatter",
+    "scatter_object_list",
+    "send",
+    "supports_complex",
+    "AllreduceCoalescedOptions",
+    "AllreduceOptions",
+    "AllToAllOptions",
+    "BarrierOptions",
+    "BroadcastOptions",
+    "GatherOptions",
+    "GroupName",
+    "PrefixStore",
+    "ProcessGroup",
+    "ReduceOp",
+    "ReduceOptions",
+    "ReduceScatterOptions",
+    "ScatterOptions",
+    "Store",
+    "DebugLevel",
+    "get_debug_level",
+    "Work",
+    "default_pg_timeout",
+    "get_group_rank",
+    "get_global_rank",
+    "get_process_group_ranks",
+    "reduce_op",
+    "all_gather_into_tensor",
+    "reduce_scatter_tensor",
+    "get_node_local_rank",
+    "split_group",
+    "shrink_group",
+]
+
+_MPI_AVAILABLE = True
+_NCCL_AVAILABLE = True
+_GLOO_AVAILABLE = True
+_UCC_AVAILABLE = True
+_XCCL_AVAILABLE = True
+
+_pickler = pickle.Pickler
+_unpickler = pickle.Unpickler
+
+GroupName = NewType("GroupName", str)
+
+
+# Change __module__ of all imported types from torch._C._distributed_c10d that are public
+def _export_c_types() -> None:
+    _public_types_to_change_module = [
+        AllreduceCoalescedOptions,
+        AllreduceOptions,
+        AllToAllOptions,
+        BarrierOptions,
+        BroadcastOptions,
+        GatherOptions,
+        PrefixStore,
+        ProcessGroup,
+        ReduceOp,
+        ReduceOptions,
+        ReduceScatterOptions,
+        ScatterOptions,
+        Store,
+        DebugLevel,
+        get_debug_level,
+        Work,
+    ]
+    for type in _public_types_to_change_module:
+        type.__module__ = "torch.distributed.distributed_c10d"
+
+
+_export_c_types()
+
+try:
+    from torch._C._distributed_c10d import ProcessGroupMPI
+
+    ProcessGroupMPI.__module__ = "torch.distributed.distributed_c10d"
+    __all__ += ["ProcessGroupMPI"]
+except ImportError:
+    _MPI_AVAILABLE = False
+
+try:
+    from torch._C._distributed_c10d import ProcessGroupNCCL
+
+    ProcessGroupNCCL.__module__ = "torch.distributed.distributed_c10d"
+    __all__ += ["ProcessGroupNCCL"]
+except ImportError:
+    _NCCL_AVAILABLE = False
+
+try:
+    from torch._C._distributed_c10d import _ProcessGroupWrapper, ProcessGroupGloo
+
+    ProcessGroupGloo.__module__ = "torch.distributed.distributed_c10d"
+    __all__ += ["ProcessGroupGloo"]
+except ImportError:
+    _GLOO_AVAILABLE = False
+
+try:
+    from torch._C._distributed_c10d import ProcessGroupUCC
+
+    ProcessGroupUCC.__module__ = "torch.distributed.distributed_c10d"
+    __all__ += ["ProcessGroupUCC"]
+except ImportError:
+    _UCC_AVAILABLE = False
+
+try:
+    from torch._C._distributed_c10d import ProcessGroupXCCL
+
+    ProcessGroupXCCL.__module__ = "torch.distributed.distributed_c10d"
+    __all__ += ["ProcessGroupXCCL"]
+except ImportError:
+    _XCCL_AVAILABLE = False
+
+logger = logging.getLogger(__name__)
+
+PG_WRAPPER_STORE_PREFIX = "pg_wrapper"
+
+
+# Some reduce ops are not supported by complex numbers and will result in an error.
+# We currently provide complex support to the distributed API by viewing
+# complex tensors as real (torch.view_as_real), meaning that calling
+# these unsupported ops will return garbage values rather than error out.
+# (e.g. max(2+3i, 3+2i) = 3+3i)
+# We'd like calls to unsupported ops to error out accordingly,
+# rather than returning garbage values.
+def supports_complex(reduceOp: ReduceOp) -> bool:
+    """Return true if reduce ops is supported. False otherwise."""
+    denyList = [
+        ReduceOp.MAX,
+        ReduceOp.MIN,
+        ReduceOp.PRODUCT,
+        ReduceOp.BAND,
+        ReduceOp.BOR,
+        ReduceOp.BXOR,
+    ]
+    return reduceOp not in denyList
+
+
+# TODO refactor into enum/strenum
+class Backend(str):  # noqa: SLOT000
+    """
+    An enum-like class for backends.
+
+    Available backends: GLOO, NCCL, UCC, MPI, XCCL, and other registered backends.
+
+    The values of this class are lowercase strings, e.g., ``"gloo"``. They can
+    be accessed as attributes, e.g., ``Backend.NCCL``.
+
+    This class can be directly called to parse the string, e.g.,
+    ``Backend(backend_str)`` will check if ``backend_str`` is valid, and
+    return the parsed lowercase string if so. It also accepts uppercase strings,
+    e.g., ``Backend("GLOO")`` returns ``"gloo"``.
+
+    .. note:: The entry ``Backend.UNDEFINED`` is present but only used as
+              initial value of some fields. Users should neither use it directly
+              nor assume its existence.
+    """
+
+    UNDEFINED = "undefined"
+    GLOO = "gloo"
+    NCCL = "nccl"
+    UCC = "ucc"
+    MPI = "mpi"
+    XCCL = "xccl"
+
+    _BackendPlugin = namedtuple("_BackendPlugin", ["creator_fn", "extended_api"])
+
+    _plugins: dict[str, _BackendPlugin] = {}
+
+    backend_list = [UNDEFINED, GLOO, NCCL, XCCL, UCC, MPI]
+
+    # 3rd-party devices can register the default backend support here
+    default_device_backend_map: dict[str, str] = {
+        "cpu": GLOO,
+        "cuda": NCCL,
+        "xpu": XCCL,
+        "mps": GLOO,
+    }
+
+    backend_capability: dict[str, list[str]] = {
+        GLOO: ["cpu", "cuda"],
+        NCCL: ["cuda"],
+        XCCL: ["xpu"],
+        UCC: ["cpu", "cuda"],
+        MPI: ["cpu", "cuda"],
+    }
+
+    backend_type_map: dict[str, ProcessGroup.BackendType] = {
+        UNDEFINED: ProcessGroup.BackendType.UNDEFINED,
+        GLOO: ProcessGroup.BackendType.GLOO,
+        NCCL: ProcessGroup.BackendType.NCCL,
+        XCCL: ProcessGroup.BackendType.XCCL,
+        UCC: ProcessGroup.BackendType.UCC,
+        MPI: ProcessGroup.BackendType.MPI,
+    }
+
+    def __new__(cls, name: str):
+        """Create and return a new instance of the class."""
+        if not isinstance(name, str):
+            raise ValueError("Backend constructor parameter must be string-ish")
+        value = getattr(Backend, name.upper(), Backend.UNDEFINED)
+
+        if value == Backend.UNDEFINED:
+            value = name.lower()
+        return value
+
+    @classmethod
+    def register_backend(
+        cls,
+        name,
+        func,
+        extended_api: bool = False,
+        devices: str | list[str] | None = None,
+    ) -> None:
+        """
+        Register a new backend with the given name and instantiating function.
+
+        This class method is used by 3rd party ``ProcessGroup`` extension to
+        register new backends.
+
+        Args:
+            name (str): Backend name of the ``ProcessGroup`` extension. It
+                        should match the one in ``init_process_group()``.
+            func (function): Function handler that instantiates the backend.
+                             The function should be implemented in the backend
+                             extension and takes four arguments, including
+                             ``store``, ``rank``, ``world_size``, and ``timeout``.
+            extended_api (bool, optional): Whether the backend supports extended argument structure.
+                                           Default: ``False``. If set to ``True``, the backend
+                                           will get an instance of ``c10d::DistributedBackendOptions``, and
+                                           a process group options object as defined by the backend implementation.
+            device (str or list of str, optional): device type this backend
+                            supports, e.g. "cpu", "cuda", etc. If `None`,
+                            assuming both "cpu" and "cuda"
+
+        .. note:: This support of 3rd party backend is experimental and subject to change.
+
+        """
+        # This takes care of CUSTOM Out-of-tree backend types, update in backend_list indicates availability
+        if not hasattr(Backend, name.upper()):
+            setattr(Backend, name.upper(), name.lower())
+        if name.lower() not in Backend.backend_list:
+            Backend.backend_list.append(name.lower())
+
+        if devices is not None:
+            for device in devices:
+                if device not in Backend.default_device_backend_map:
+                    Backend.default_device_backend_map[device] = name.lower()
+        Backend.backend_type_map[name.lower()] = ProcessGroup.BackendType.CUSTOM
+
+        # Update device capability matrix in Backend class
+        if devices is None:
+            # This is more of a backward support for groups like `threaded`:
+            # assume default devices "cpu" and "cuda", but warn
+            warnings.warn(
+                f"Device capability of {name} unspecified, assuming `cpu` and "
+                "`cuda` or `xpu`. Please specify it via the `devices` argument of "
+                "`register_backend`.",
+                stacklevel=2,
+            )
+            Backend.backend_capability[name.lower()] = (
+                ["cpu", "cuda", "xpu"] if torch.xpu.is_available() else ["cpu", "cuda"]
+            )
+        elif isinstance(devices, str):
+            # Single device string specified. Simply convert to list.
+            Backend.backend_capability[name.lower()] = [devices]
+        else:
+            Backend.backend_capability[name.lower()] = devices
+
+        Backend._plugins[name.upper()] = Backend._BackendPlugin(func, extended_api)
+
+
+class BackendConfig:
+    """Backend configuration class."""
+
+    def __init__(self, backend: Backend):
+        """Init."""
+        self.device_backend_map: dict[str, Backend] = {}
+        # pyrefly: ignore [bad-assignment]
+        backend = str(backend)
+
+        if backend == Backend.UNDEFINED:
+            # Detect the accelerator on the machine. If no accelerator is
+            # available, it returns CPU.
+            device_type = torch._C._get_accelerator().type
+            try:
+                backend_str = Backend.default_device_backend_map[device_type]
+                self.device_backend_map[device_type] = Backend(backend_str)
+            except KeyError:
+                raise ValueError(
+                    f"We detected accelerator {device_type} on your machine. "
+                    f"But we don't know which communication backend to use for this accelerator. "
+                    f"Please specify the `backend` argument in the `init_process_group` call."
+                ) from None
+        elif backend.lower() in Backend.backend_list:
+            # Cases for when backend is a single string (without device types)
+            # e.g. "nccl", "gloo", "ucc", "mpi"
+            supported_devices = Backend.backend_capability[backend.lower()]
+            backend_val = Backend(backend)
+
+            self.device_backend_map = dict.fromkeys(supported_devices, backend_val)
+        elif ":" in backend.lower():
+            # Backend specified in "device:backend" format
+            # make sure the backend string is in the correct format
+            # "{device_type1}:{backend1},{device_type2}:{backend2}"
+            # e.g. "cpu:gloo,cuda:nccl"
+            backend_str_error_message = f"""The custom backend string argument is invalid: {backend}.
+                Custom backend string is an experimental feature where the backend string must be in the format:
+                ":,:...". e.g. 'cpu:gloo,cuda:nccl'"""
+
+            # parse the backend string and populate the device_backend_map
+            for device_backend_pair_str in backend.lower().split(","):
+                device_backend_pair = device_backend_pair_str.split(":")
+                if len(device_backend_pair) != 2:
+                    raise ValueError(
+                        f"Invalid device:backend pairing: \
+                                     {device_backend_pair_str}. {backend_str_error_message}"
+                    )
+                # pyrefly: ignore [bad-assignment]
+                device, backend = device_backend_pair
+                if device in self.device_backend_map:
+                    raise ValueError(
+                        f"Duplicate device type {device} \
+                                     in backend string: {backend}. {backend_str_error_message}"
+                    )
+                self.device_backend_map[device] = Backend(backend)
+        else:
+            # User specified a single backend name whose device capability is
+            # unknown, assuming it can support the default devices of PyTorch
+            # (cpu and cuda)
+            warnings.warn(
+                f"Device capability of {backend} unknown, assuming `cpu` and "
+                "`cuda`. You can specify it in `device:backend` format in "
+                "`init_process_group` call.",
+                stacklevel=2,
+            )
+            backend_val = Backend(backend)
+            self.device_backend_map = {
+                "cpu": backend_val,
+                "cuda": backend_val,
+                "xpu": backend_val,
+            }
+
+        logger.info("Using backend config: %s", self.device_backend_map)
+
+    def __repr__(self):
+        """Return all the device:backend pairs separated by commas."""
+        return ",".join(
+            f"{device}:{backend}" for device, backend in self.device_backend_map.items()
+        )
+
+    def get_device_backend_map(self) -> dict[str, Backend]:
+        """Return backend map of the device."""
+        return self.device_backend_map
+
+
+class _reduce_op:
+    r"""
+    Deprecated enum-like class.
+
+    For reduction operations: ``SUM``, ``PRODUCT``, ``MIN``, and ``MAX``.
+
+    :class:`~torch.distributed.ReduceOp` is recommended to use instead.
+    """
+
+    def __init__(self) -> None:
+        # __members__ is a dict storing key-value pairs for enum classes
+        for k, v in ReduceOp.RedOpType.__members__.items():
+            setattr(self, k, v)
+        self.__members__ = ReduceOp.RedOpType.__members__
+
+    @deprecated(
+        "`torch.distributed.reduce_op` is deprecated, "
+        "please use `torch.distributed.ReduceOp` instead",
+        category=FutureWarning,
+    )
+    def __getattribute__(self, key):
+        return object.__getattribute__(self, key)
+
+
+reduce_op = _reduce_op()
+
+
+class P2POp:
+    """
+    A class to build point-to-point operations for ``batch_isend_irecv``.
+
+    This class builds the type of P2P operation, communication buffer, peer rank,
+    Process Group, and tag. Instances of this class will be passed to
+    ``batch_isend_irecv`` for point-to-point communications.
+
+    Args:
+        op (Callable): A function to send data to or receive data from a peer process.
+            The type of ``op`` is either ``torch.distributed.isend`` or
+            ``torch.distributed.irecv``.
+        tensor (Tensor): Tensor to send or receive.
+        peer (int, optional): Destination or source rank.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        tag (int, optional): Tag to match send with recv.
+        group_peer (int, optional): Destination or source rank.
+    """
+
+    def __init__(
+        self,
+        op: Callable,
+        tensor: torch.Tensor,
+        peer: int | None = None,
+        group: ProcessGroup | None = None,
+        tag: int = 0,
+        group_peer: int | None = None,
+    ):
+        """Init."""
+        self.op = op
+        self.tensor = tensor
+        self.group = _group_or_default_group(group)
+        self.peer = _canonicalize_group_rank(
+            self.group, peer, group_peer, return_global=True
+        )
+        self.tag = tag
+        self.group_peer = _canonicalize_group_rank(self.group, peer, group_peer)
+
+    def __new__(
+        cls,
+        op: Callable,
+        tensor: torch.Tensor,
+        peer: int | None = None,
+        group: ProcessGroup | None = None,
+        tag: int = 0,
+        group_peer: int | None = None,
+    ):
+        """Create and return a new instance of the class."""
+        _check_op(op)
+        _check_single_tensor(tensor, "tensor")
+
+        return object.__new__(cls)
+
+    def __repr__(self):
+        my_group_rank = get_rank(self.group)
+        op_name = self.op.__name__
+        group_name = self.group.group_name if self.group else "default_pg"
+        if "send" in op_name:
+            s = my_group_rank
+            d = self.group_peer
+        elif "recv" in op_name:
+            s = self.group_peer
+            d = my_group_rank
+        else:
+            return super().__repr__()
+
+        return f"P2POp({op_name} pg={group_name}, group_src={s}, group_dst={d},  {self.tensor.shape}, {self.tensor.dtype})"
+
+
+class _CollOp:
+    """
+    A class to capture collective operations.
+
+    Args:
+        op (Callable): A collective function, e.g. ``torch.distributed.all_reduce``.
+        tensor (Tensor): Tensor to operate on.
+        dst_tensor (Tensor, optional): Provided when source and destination tensors are not the same.
+        redop (ReduceOp, optional): reduce operation.
+        root (int, optional): root of broadcast or reduce.
+    """
+
+    def __init__(
+        self,
+        op: Callable,
+        tensor: torch.Tensor,
+        dst_tensor: torch.Tensor | None = None,
+        redop: ReduceOp | None = None,
+        root: int | None = None,
+    ):
+        self.op = op
+        self.tensor = tensor
+        self.dst_tensor = dst_tensor
+        self.redop = redop
+        self.root = root
+
+
+# DO NOT USE THESE FIELDS DIRECTLY.
+# Use them through the _world object to make sure the _world override mechanism
+_pg_map: dict[ProcessGroup, tuple[str, Store]] = {}
+_pg_names: dict[ProcessGroup, GroupName] = {}
+_pg_group_ranks: dict[ProcessGroup, dict[int, int]] = {}
+# For a pg, it is a map from ProcessGroup to BackendConfig
+_pg_backend_config: dict[ProcessGroup, str] = {}
+_group_count = 0
+_tags_to_pg: dict[str, list[ProcessGroup]] = {}
+_pg_to_tag: dict[ProcessGroup, str] = {}
+_backend: str | None = None
+
+
+class _World:
+    """
+    Container class for c10d process group state.
+
+    This is used during registration and lookup of PG state.
+
+    .. warning:: This is an experimental API intended to expose the inner workings
+       of c10d and is subject to change..
+    """
+
+    def __init__(self) -> None:
+        self._default_pg = None
+        self._pg_coalesce_state: dict[ProcessGroup, list[_CollOp]] = {}
+
+    @property
+    def default_pg(self) -> ProcessGroup | None:
+        """
+        Process group that includes all ranks of the cluster.
+
+        This default ProcessGroup is used by c10d APIs when a ProcessGroup is needed
+        but None is provided.
+        """
+        return self._default_pg
+
+    @default_pg.setter
+    def default_pg(self, value) -> None:
+        self._default_pg = value
+
+    @property
+    def pg_map(self) -> dict[ProcessGroup, tuple[str, Store]]:
+        """
+        Provide Mapping from ProcessGroup to backend name and store.
+
+        For NCCL and GLOO pg, it is a map from ProcessGroup to (Backend, Store)
+        For MPI pg, it is a map from ProcessGroup to (Backend, None)
+
+        TODO don't expose the map, expose fine grained ops
+        """
+        global _pg_map
+        return _pg_map
+
+    @property
+    def pg_names(self) -> dict[ProcessGroup, GroupName]:
+        """
+        Process group's names, map from ProcessGroup to str.
+
+        TODO don't expose the map, expose fine grained ops
+        """
+        global _pg_names
+        return _pg_names
+
+    @property
+    def pg_group_ranks(self) -> dict[ProcessGroup, dict[int, int]]:
+        """
+        Process group's global rank to local rank mapping.
+
+        TODO don't expose the map, expose fine grained ops
+        """
+        global _pg_group_ranks
+        return _pg_group_ranks
+
+    @property
+    def pg_backend_config(self) -> dict[ProcessGroup, str]:
+        """
+        Process group's backend config.
+
+        TODO don't expose the map, expose fine grained ops
+        """
+        global _pg_backend_config
+        return _pg_backend_config
+
+    @property
+    def group_count(self) -> int:
+        """
+        Process group count for default naming.
+
+        TODO don't expose group_count, use something else instead
+        """
+        global _group_count
+        return _group_count
+
+    @group_count.setter
+    def group_count(self, value: int) -> None:
+        """Use to compute the name of ProcessGroups when using global synchronization."""
+        global _group_count
+        _group_count = value
+
+    @property
+    def tags_to_pg(self) -> dict[str, list[ProcessGroup]]:
+        global _tags_to_pg
+        return _tags_to_pg
+
+    @property
+    def pg_to_tag(self) -> dict[ProcessGroup, str]:
+        global _pg_to_tag
+        return _pg_to_tag
+
+    @property
+    def pg_coalesce_state(self) -> dict[ProcessGroup, list[_CollOp]]:
+        return self._pg_coalesce_state
+
+    @property
+    def pg_config_info(self) -> list[dict[str, Any]]:
+        """
+        Return a list of dict with process groups and backends.
+
+        Along with their unique IDs and configurations (types and ranks).
+        """
+        config_info: list[dict[str, Any]] = []
+        default_pg_size = _get_group_size(None)
+        for pg in self.pg_map:
+            ranks = self.pg_group_ranks[pg]
+            config_info.append(
+                {
+                    "pg_name": self.pg_names[pg],
+                    "pg_desc": pg.group_desc,
+                    "backend_config": self.pg_backend_config[pg],
+                    "ranks": (
+                        list(ranks.keys()) if len(ranks) != default_pg_size else []
+                    ),  # 'ranks' is an empty list when all ranks are involved in a pg
+                    "group_size": len(ranks),
+                    "group_count": self.group_count,
+                }
+            )
+        return config_info
+
+
+_world = _World()
+"""Holds the singleton instance of ``_World`` used by c10. Experimental extension point to override it"""
+
+
+class _WorldMeta(type):
+    """
+    Meta class of ``group`` and ``GroupMember``.
+
+    Allows them to have the class property ``WORLD``.
+    """
+
+    # Points to the default PG once initialized.
+    @property
+    def WORLD(cls) -> ProcessGroup | None:
+        return _world.default_pg
+
+    @WORLD.setter
+    def WORLD(cls, pg: ProcessGroup | None):
+        _world.default_pg = pg
+
+
+class group(metaclass=_WorldMeta):
+    """Group class. Placeholder."""
+
+
+class GroupMember(metaclass=_WorldMeta):
+    """Group member class."""
+
+    NON_GROUP_MEMBER = -100
+
+
+def _get_default_timeout(backend: Backend) -> timedelta:
+    # see note on nccl vs other backend timeout (constants.py)
+    if backend == Backend.NCCL:
+        if not isinstance(default_pg_nccl_timeout, timedelta):
+            # TODO moco benchmark on CPU initializes pgnccl backend today, triggered this assert in CI before it was
+            # changed to be a warning.  We should fix the moco model.
+            warnings.warn(
+                "Attempted to get default timeout for nccl backend, but NCCL support is not compiled",
+                stacklevel=2,
+            )
+            return default_pg_timeout
+        return default_pg_nccl_timeout
+    else:
+        return default_pg_timeout
+
+
+def _check_valid_timeout(timeout: Any) -> None:
+    if not isinstance(timeout, timedelta):
+        raise TypeError(
+            f"Expected timeout argument to be of type datetime.timedelta, got {timeout}"
+        )
+
+
+# Default process group state
+_default_pg_init_method: str | None = None
+
+STORE_BASED_BARRIER_PREFIX = "store_based_barrier_key"
+
+
+def _get_object_coll_device(group: ProcessGroup | None = None) -> str:
+    """
+    .. note:: This is an internal helper and does not have backward
+        compatibility, please use with caution.
+
+    Return the device type to use with ``group`` for object collectives or
+    barrier.
+
+    There are selection rules:
+        1. If user specifies exactly one backend in ``init_process_group`` call:
+            use that backend
+        2. Else if user specifies multiple "device:backend" pairs in init_process_group:
+            If "cpu" is among those pairs, use "cpu" (because the object is in cpu memory);
+            Otherwise, use the first backend (sort of a random pick).
+
+    Args:
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+
+    Returns:
+        str: The device type to use for object collective with ``group``.
+
+    """
+    group = group or _get_default_group()
+
+    if not isinstance(group, ProcessGroup):
+        warnings.warn(
+            f"You are using a Backend {type(group)} as a ProcessGroup. "
+            "This usage is deprecated since PyTorch 2.0. Please use a public API "
+            "of PyTorch Distributed instead.",
+            stacklevel=2,
+        )
+        # Provide backward compatibility to cases where `group` passed in is
+        # actually a Backend (like `ProcessGroupGloo`) rather than a
+        # `ProcessGroup` in PT 2.0 sense
+        if isinstance(group, ProcessGroupGloo):
+            # RPC uses Gloo for object collectives
+            return "cpu"
+        else:
+            raise ValueError(f"Expecting a ProcessGroup, but got a {type(group)}.")
+
+    """
+    ``group._device_types`` is a property pybind that returns the devices
+    ("cpu", "cuda", etc) supported by ``group``. Can be multiple if the
+    ``group`` supports multiple devices.
+    """
+    devices = group._device_types
+
+    if len(devices) == 1:
+        # User fixed exactly one backend in `init_process_group`
+        return devices[0].type
+    elif len(devices) == 0:
+        # No backend has been registered with this PG (maybe because no
+        # collective has been run?) We pick cpu as the default and hopefully
+        # this would lazily init Gloo or other available cpu backend.
+        return "cpu"
+    elif torch.device("cpu") in devices:
+        # There are multiple backends in this PG and cpu is among them.
+        # cpu is preferred as the object is in cpu memory. No need for device
+        # copy.
+        return "cpu"
+    else:
+        # No cpu in the backend list. Randomly pick the first backend
+        return devices[0].type
+
+
+def _get_pg_default_device(group: ProcessGroup | None = None) -> torch.device:
+    """
+    .. note:: This method will be deprecated, it only stays for
+        backward-compatiblity reason. Alternatives:
+
+        - If you need to find a device for object collectives, please use
+        `_get_object_coll_device(group)`.
+
+        - If you need to query the device types supported by group, please use
+        `_device_capability(group)`.
+
+    Return the device type registered with ``group``.
+
+    For example, if `init_process_group("nccl", ...)` was called, the returned
+    value would be `torch.device("cuda")`.
+
+    Errors out if no device has been registered.
+
+    Args:
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+
+    Returns:
+        torch.device: The device type registered with ``group``.
+    """
+
+    warnings.warn(
+        "`_get_pg_default_device` will be deprecated, it only stays for "
+        "backward-compatiblity reason. If you need to find a device for object "
+        "collectives, please use `_get_object_coll_device`. If you need to query "
+        "the device types supported by group, please use "
+        "`_device_capability(group)`. ",
+        stacklevel=2,
+    )
+    group = group or _get_default_group()
+
+    if not isinstance(group, ProcessGroup):
+        # Provide backward compatibility to cases where `group` passed in is
+        # actually a Backend (like `ProcessGroupGloo`) rather than a
+        # `ProcessGroup` in PT 2.0 sense
+        warnings.warn(
+            f"You are using a Backend {type(group)} as a ProcessGroup. "
+            "This usage is deprecated since PyTorch 2.0. Please use a public API "
+            "of PyTorch Distributed instead.",
+            FutureWarning,
+            stacklevel=3,
+        )
+        # Most users create Gloo with private API for object collectives
+        return torch.device("cpu")
+
+    """
+    ``group._device_types`` is a property pybind that returns the devices
+    ("cpu", "cuda", etc) supported by ``group``. Can be multiple if the
+    ``group`` supports multiple devices.
+    """
+    devices = group._device_types
+
+    if len(devices) == 1:
+        # User fixed exactly one backend in `init_process_group`
+        return devices[0]
+    elif len(devices) == 0:
+        raise RuntimeError(
+            "Default device not found, because no backend has been registered "
+            "with this ProcessGroup."
+        )
+    else:
+        # There are multiple backends in this PG.
+        if torch.device("cpu") in devices:
+            rv = torch.device("cpu")
+        else:
+            rv = devices[0]
+        warnings.warn(
+            "Multiple backends are registered with this ProcessGroup. We cannot "
+            f"determine which one is the default. Returning {rv}. "
+            "Please consider using other APIs.",
+            stacklevel=2,
+        )
+        return rv
+
+
+def _device_capability(group: ProcessGroup | None = None) -> list[str]:
+    """
+    Return the device type(s) supported by ``group``.
+
+    Args:
+        group (ProcessGroup, optional): The process group to query. If None,
+            the default process group will be used.
+
+    Returns:
+        List[str]: A list of device types supported by ``group``.
+    """
+    group = group or _get_default_group()
+    return [device.type for device in group._device_types]
+
+
+@_time_logger
+def _store_based_barrier(
+    rank,
+    store,
+    group_name: GroupName,
+    rendezvous_count,
+    timeout,
+    logging_interval=timedelta(seconds=10),
+) -> None:
+    """
+    Store based barrier for synchronizing processes.
+
+    Barrier based on store which is used for synchronizing processes after
+    ``init_process_group`` or ``new_group``. Intended to be used only with
+    those two methods and is not a generic alternative to ``barrier()``.
+    """
+    store_key = f"{STORE_BASED_BARRIER_PREFIX}:{group_name}"
+    store.add(store_key, 1)
+    logger.debug("Added key: %s to store for rank: %s", store_key, rank)
+
+    # Now wait for all workers to check in with the store.
+    world_size = rendezvous_count
+    worker_count = store.add(store_key, 0)
+
+    last_worker_key = f"{store_key}:last_worker"
+    if worker_count == world_size:
+        store.set(last_worker_key, "1")
+
+    # adjust the timeout to be at least 10secs + 1sec per thousand ranks to reduce the odds of timeout
+    # this value was empirically found while scale testing.
+    logging_interval = max(logging_interval, timedelta(seconds=10 + world_size / 1000))
+
+    start = time.time()
+    while True:
+        try:
+            # This will throw an exception after the logging_interval in which we print out
+            # the status of the group or time out officially, throwing runtime error
+            store.wait([last_worker_key], logging_interval)
+            break
+        except RuntimeError as e:
+            worker_count = store.add(store_key, 0)
+            # Print status periodically to keep track.
+            logger.debug(  # noqa: G200
+                "Waiting in store based barrier to initialize process group for %s seconds"
+                "rank: %s, key: %s (world_size=%s, num_workers_joined=%s, timeout=%s error=%s)",
+                time.time() - start,
+                rank,
+                store_key,
+                world_size,
+                worker_count,
+                timeout,
+                e,
+            )
+
+            if timedelta(seconds=(time.time() - start)) > timeout:
+                raise DistStoreError(  # noqa: B904
+                    "Timed out initializing process group in store based barrier on "
+                    f"rank {rank}, for key: {store_key} (world_size={world_size}, "
+                    f"num_workers_joined={worker_count}, timeout={timeout} error={e})"
+                )
+
+    logger.info(
+        "Rank %s: Completed store-based barrier for key:%s with %s nodes.",
+        rank,
+        store_key,
+        world_size,
+    )
+
+
+def _rank_not_in_group(group: ProcessGroup | None) -> bool:
+    """Check if the current process's rank is not in a given group."""
+    if group is None:
+        return False
+    return group == GroupMember.NON_GROUP_MEMBER
+
+
+def _warn_not_in_group(op_name) -> None:
+    global_rank = -1 if GroupMember.WORLD is None else GroupMember.WORLD.rank()
+    warnings.warn(
+        f"Running {op_name} on global rank {global_rank} which does not "
+        "belong to the given group.",
+        stacklevel=2,
+    )
+
+
+def get_group_rank(group: ProcessGroup, global_rank: int) -> int:
+    """
+    Translate a global rank into a group rank.
+
+    ``global_rank`` must be part of ``group`` otherwise this raises RuntimeError.
+
+    Args:
+        group (ProcessGroup): ProcessGroup to find the relative rank.
+        global_rank (int): Global rank to query.
+
+    Returns:
+        Group rank of ``global_rank`` relative to ``group``
+
+    N.B. calling this function on the default process group returns identity
+    """
+    if group is GroupMember.WORLD:
+        return global_rank
+    if group not in _world.pg_group_ranks:
+        raise ValueError(
+            f"Group {group} is not registered, please create group with torch.distributed.new_group API"
+        )
+    group_ranks = _world.pg_group_ranks[group]
+    if global_rank not in group_ranks:
+        raise ValueError(f"Global rank {global_rank} is not part of group {group}")
+
+    return group_ranks[global_rank]
+
+
+def get_global_rank(group: ProcessGroup, group_rank: int) -> int:
+    """
+    Translate a group rank into a global rank.
+
+    ``group_rank`` must be part of `group` otherwise this raises RuntimeError.
+
+    Args:
+        group (ProcessGroup): ProcessGroup to find the global rank from.
+        group_rank (int): Group rank to query.
+
+    Returns:
+        Global rank of ``group_rank`` relative to ``group``
+
+    N.B. calling this function on the default process group returns identity
+    """
+    if group is GroupMember.WORLD:
+        return group_rank
+    if group not in _world.pg_group_ranks:
+        raise ValueError(
+            f"Group {group} is not registered, please create group with torch.distributed.new_group API"
+        )
+    for rank, grp_rank in _world.pg_group_ranks[group].items():
+        if grp_rank == group_rank:
+            return rank
+    raise ValueError(f"Group rank {group_rank} is not part of group {group}")
+
+
+# TODO: remove this once the ecosystem moves away from it.
+@deprecated(
+    "`torch.distributed.distributed_c10d._get_global_rank` is deprecated, "
+    "please use `torch.distributed.distributed_c10d.get_global_rank` instead",
+    category=FutureWarning,
+)
+def _get_global_rank(group, rank) -> int:
+    """Use get_global_rank as this method is deprecated."""
+    return get_global_rank(group, rank)
+
+
+def get_process_group_ranks(group: ProcessGroup | None) -> list[int]:
+    """
+    Get all ranks associated with ``group``.
+
+    Args:
+        group (Optional[ProcessGroup]): ProcessGroup to get all ranks from.
+            If None, the default process group will be used.
+
+    Returns:
+        List of global ranks ordered by group rank.
+    """
+    return list(_world.pg_group_ranks[group or _get_default_group()].keys())
+
+
+def _get_group_size(group: ProcessGroup | None) -> int:
+    """Get a given group's world size."""
+    if group is GroupMember.WORLD or group is None:
+        default_pg = _get_default_group()
+        return default_pg.size()
+    return group.size()
+
+
+def _get_group_size_by_name(group_name: GroupName) -> int:
+    group = _resolve_process_group(group_name)
+    return group.size()
+
+
+def _resolve_group_name_by_ranks_and_tag(ranks: list[int], tag: str) -> GroupName:
+    # TODO(yifu): remove this function once ranks + tag is not a supported
+    # identifier for process group for functional collectives.
+    group = _find_pg_by_ranks_and_tag(tag, ranks)
+    if group is None:
+        raise ValueError("")
+    return group.group_name
+
+
+def _check_single_tensor(param, param_name: str) -> None:
+    """Check that the parameter ``param_name`` is a single tensor."""
+    if not isinstance(param, torch.Tensor):
+        raise TypeError(
+            f"""Invalid function argument. Expected parameter `{param_name}` of type torch.Tensor
+             but got {type(param)} instead."""
+        )
+
+
+def _check_tensor_list(param, param_name: str) -> None:
+    """Check that the parameter ``param_name`` is a list of tensors."""
+    if not isinstance(param, list):
+        raise TypeError(
+            f"""Invalid function argument. Expected parameter `{param_name}` of type List[torch.Tensor]
+             but got {type(param)} instead."""
+        )
+    elif not all(isinstance(p, torch.Tensor) for p in param):
+        raise TypeError(
+            f"""Invalid function argument. Expected parameter `{param_name}` of type List[torch.Tensor]
+             but got {type(param)} with elements of type {[type(p) for p in param]}."""
+        )
+
+
+def _group_or_default_group(group: ProcessGroup | None = None) -> ProcessGroup:
+    if group is None or group is GroupMember.WORLD:
+        group = _get_default_group()
+    return group
+
+
+def _canonicalize_group_rank(
+    group: ProcessGroup,
+    global_rank: int | None = None,
+    group_rank: int | None = None,
+    return_global: bool = False,
+) -> int:
+    """
+    Helper method to take _either_ a global rank or a group rank and produce a group rank.
+
+    If 'return_global' is true, produce a global rank instead of a group rank.
+    """
+
+    if group_rank is not None:
+        if global_rank is not None:
+            raise ValueError("Can't specify both group_rank and global_rank")
+        if return_global:
+            return get_global_rank(group, group_rank)
+    else:
+        if global_rank is None:
+            raise ValueError("Must specify global_rank or group_rank")
+        if return_global:
+            return global_rank
+        group_rank = get_group_rank(group, global_rank)
+    return group_rank
+
+
+def _check_not_self_rank(group: ProcessGroup, rank: int, rank_type: str):
+    if group.rank() == rank:
+        raise ValueError(
+            f"Invalid {rank_type} rank: {rank_type} rank should not be the same as "
+            "the rank of the current process."
+        )
+
+
+def _as_iterable(obj) -> collections.abc.Iterable:
+    return obj if isinstance(obj, list) else (obj,)
+
+
+def _ensure_all_tensors_same_dtype(*tensors) -> None:
+    last_dtype = None
+    # pyrefly: ignore [bad-assignment]
+    for tensor in itertools.chain.from_iterable(map(_as_iterable, tensors)):
+        tensor_dtype = tensor.dtype
+        # Mixing complex and its element type is allowed
+        if tensor_dtype.is_complex:
+            tensor_dtype = (
+                torch.float32 if tensor_dtype == torch.complex64 else torch.complex128
+            )
+
+        if last_dtype is None:
+            last_dtype = tensor_dtype
+        else:
+            if last_dtype != tensor_dtype:
+                raise ValueError(
+                    "Invalid usage of tensors with different dtypes"
+                    f"Found {last_dtype} and  {tensor.dtype}"
+                )
+
+
+def _check_op(op) -> None:
+    """Check that the ``op`` is either isend or irecv."""
+    if op not in [isend, irecv]:
+        raise ValueError(
+            "Invalid ``op``. Expected ``op`` "
+            "to be of type ``torch.distributed.isend`` or "
+            "``torch.distributed.irecv``."
+        )
+
+
+def _check_p2p_op_list(p2p_op_list) -> None:
+    """
+    Check that the ``p2p_op_list`` is a list of P2POp instances.
+
+    Also, check that all ops use the same group.
+    """
+    if not isinstance(p2p_op_list, list) or not all(
+        isinstance(p2p_op, P2POp) for p2p_op in p2p_op_list
+    ):
+        raise ValueError(
+            "Invalid ``p2p_op_list``. Each op is expected to "
+            "to be of type ``torch.distributed.P2POp``."
+        )
+
+    group = p2p_op_list[0].group
+    if not all(group == p2p_op.group for p2p_op in p2p_op_list):
+        raise ValueError("All ops need to use the same group.")
+
+
+def is_mpi_available() -> bool:
+    """Check if the MPI backend is available."""
+    return _MPI_AVAILABLE
+
+
+def is_nccl_available() -> bool:
+    """Check if the NCCL backend is available."""
+    return _NCCL_AVAILABLE
+
+
+def is_gloo_available() -> bool:
+    """Check if the Gloo backend is available."""
+    return _GLOO_AVAILABLE
+
+
+def is_ucc_available() -> bool:
+    """Check if the UCC backend is available."""
+    return _UCC_AVAILABLE
+
+
+def is_xccl_available() -> bool:
+    """Check if the XCCL backend is available."""
+    return _XCCL_AVAILABLE
+
+
+def _check_single_backend_availability(backend_name: str) -> bool:
+    """
+    Helper function to check if a single backend is available.
+    """
+    available_func = getattr(
+        torch.distributed, f"is_{str(backend_name).lower()}_available", None
+    )
+    if available_func:
+        return available_func()
+    return str(backend_name).lower() in Backend.backend_list
+
+
+def is_backend_available(backend: str) -> bool:
+    """
+    Check backend availability.
+
+    Checks if the given backend is available and supports the built-in backends or
+    third-party backends through function ``Backend.register_backend``.
+
+    Args:
+        backend (str): Backend name.
+    Returns:
+        bool: Returns true if the backend is available otherwise false.
+    """
+    # If the backend has an ``is_backend_available`` function, return the result of that function directly
+    if ":" in backend.lower():  # composite backend like "cpu:gloo"
+        backend_config = BackendConfig(Backend(backend))
+        device_backend_map = backend_config.get_device_backend_map()
+        return all(
+            _check_single_backend_availability(str(backend_name))
+            for backend_name in device_backend_map.values()
+        )
+    else:
+        # Handle simple backend strings like "nccl", "gloo"
+        return _check_single_backend_availability(backend)
+
+
+def is_initialized() -> bool:
+    """Check if the default process group has been initialized."""
+    return GroupMember.WORLD is not None
+
+
+def is_torchelastic_launched() -> bool:
+    """
+    Check whether this process was launched with ``torch.distributed.elastic`` (aka torchelastic).
+
+    The existence of ``TORCHELASTIC_RUN_ID`` environment
+    variable is used as a proxy to determine whether the current process
+    was launched with torchelastic. This is a reasonable proxy since
+    ``TORCHELASTIC_RUN_ID`` maps to the rendezvous id which is always a
+    non-null value indicating the job id for peer discovery purposes..
+    """
+    return os.getenv("TORCHELASTIC_RUN_ID") is not None
+
+
+def _is_barrier_after_init() -> int:
+    # Environment variable to control whether process group should perform a
+    # barrier after its init. Default value is 0, i.e. no barrier. If you
+    # experience issue with this setting, you may set
+    # `TORCH_DIST_INIT_BARRIER=1` to add the barrier.
+    return int(os.getenv("TORCH_DIST_INIT_BARRIER", "0"))
+
+
+def _get_default_group() -> ProcessGroup:
+    """Get the default process group created by init_process_group."""
+    if not is_initialized():
+        raise ValueError(
+            "Default process group has not been initialized, "
+            "please make sure to call init_process_group."
+        )
+    if TYPE_CHECKING:
+        return not_none(GroupMember.WORLD)
+    else:
+        return GroupMember.WORLD
+
+
+def _get_default_store() -> Store:
+    """Get the default store created by init_process_group."""
+    if not is_initialized():
+        raise ValueError(
+            "Default process group has not been initialized, "
+            "please make sure to call init_process_group."
+        )
+    default_pg = _get_default_group()
+    _, default_store = _world.pg_map[default_pg]
+    return default_store
+
+
+def _update_default_pg(pg: ProcessGroup | None) -> None:
+    _world.default_pg = pg
+    rank = pg.rank() if pg is not None and pg != GroupMember.NON_GROUP_MEMBER else -1
+    torch._C._distributed_c10d._set_global_rank(rank)
+
+
+def get_backend_config(group: ProcessGroup | None = None) -> str:
+    """
+    Return the backend configuration of the given process group.
+
+    Args:
+        group (ProcessGroup, optional): The process group to work on. The
+            default is the general main process group. If another specific group
+            is specified, the calling process must be part of :attr:`group`.
+
+    Returns:
+        The backend configuration of the given process group as a lower case string.
+
+    """
+    pg = group or _get_default_group()
+    if _rank_not_in_group(pg):
+        raise ValueError("Invalid process group specified")
+    backend_config = _world.pg_backend_config.get(pg)
+    return str(not_none(backend_config))
+
+
+def get_backend(group: ProcessGroup | None = None) -> Backend:
+    """
+    Return the backend of the given process group.
+
+    Args:
+        group (ProcessGroup, optional): The process group to work on. The
+            default is the general main process group. If another specific group
+            is specified, the calling process must be part of :attr:`group`.
+
+    Returns:
+        The backend of the given process group as a lower case string.
+
+    """
+    pg = group or _get_default_group()
+    if _rank_not_in_group(pg):
+        raise ValueError("Invalid process group specified")
+
+    pg_store = _world.pg_map.get(pg, None)
+    if pg_store is None:
+        raise ValueError(
+            f"Process group {pg} is not initialized in the world group map. Please initialize the group first."
+        )
+
+    return Backend(not_none(pg_store)[0])
+
+
+def get_default_backend_for_device(device: str | torch.device) -> str:
+    """
+    Return the default backend for the given device.
+
+    Args:
+        device (Union[str, torch.device]): The device to get the default backend for.
+
+    Returns:
+        The default backend for the given device as a lower case string.
+
+    """
+    if isinstance(device, torch.device):
+        device_str = device.type
+    else:
+        device_str = torch.device(device).type
+
+    backend = Backend.default_device_backend_map.get(device_str)
+    if backend is None:
+        raise ValueError(f"Default backend not registered for device : {device}")
+
+    return backend
+
+
+def _get_process_group_uid(pg: ProcessGroup) -> int:
+    backend = None
+    try:
+        backend = pg._get_backend(torch.device("cuda"))
+    except RuntimeError:
+        pass
+    if is_nccl_available() and isinstance(backend, ProcessGroupNCCL):
+        return backend.uid
+    return -1
+
+
+def _get_pg_config(group: ProcessGroup | None = None) -> dict[str, Any]:
+    """
+    Return the pg configuration of the given process group.
+
+    """
+    pg = group or _get_default_group()
+    return {
+        "pg_name": _get_process_group_name(pg),
+        "pg_desc": pg.group_desc,
+        "backend_config": get_backend_config(pg),
+        "pg_size": _get_group_size(pg),
+        "ranks": get_process_group_ranks(pg),
+    }
+
+
+def _get_all_pg_configs() -> list[dict[str, Any]]:
+    """
+    Return the pg configuration of all the process groups.
+
+    """
+    config_info: list[dict[str, Any]] = [_get_pg_config(pg) for pg in _world.pg_map]
+    return config_info
+
+
+def get_pg_count() -> int:
+    """
+    Return the number of process groups.
+
+    """
+    return _world.group_count
+
+
+def get_node_local_rank(fallback_rank: int | None = None) -> int:
+    """
+    Return the local rank of the current process relative to the node.
+
+    Semantically, this is a useful concept for mapping processes to devices.
+    For example, on a node with 8 accelerator you could use the node local rank to decide
+    which accelerator device to bind the process to.
+
+    In practice, the actual assignment of node local ranks is handled by the process launcher outside of pytorch,
+    and communicated via the `LOCAL_RANK` environment variable.
+
+    Torchrun will automatically populate `LOCAL_RANK`, but other launchers may not.  If `LOCAL_RANK` is unspecified,
+    this API will fall back to the provided kwarg 'fallback_rank' if specified, otherwise it will raise an error. The
+    intent is to allow writing an application that runs either in single or multi device contexts without error.
+
+    """
+    if "LOCAL_RANK" in os.environ:
+        return int(os.environ["LOCAL_RANK"])
+    elif fallback_rank is not None:
+        return int(fallback_rank)
+    raise RuntimeError(
+        "LOCAL_RANK is not in the environment. Consider passing fallback_rank to allow `get_node_local_rank` to work, "
+        "assuming you are not running in a multi-device context and want the code to run locally instead."
+    )
+
+
+def _add_ephemeral_timeout_for_all_pgs(timeout: timedelta) -> None:
+    """
+    This API adds an ephemeral timeout extension for all PGs locally
+    on one rank. The timeout gets reset when the first collective issued
+    after API called finished.
+    NOTE: We only support to set timeout for cuda backends for now.
+    NOTE: While this feature
+    provides flexibility in specific scenarios, it introduces statefulness
+    to timeout setting. Therefore, it is advisable to use this API sparingly
+    and consider alternative approaches, such as directly setting the timeout
+    or utilizing a barrier collective (one can set any timeout to the barrier),
+    whenever feasible.
+
+    Args:
+        timeout (timedelta): The delta of timeout to extend.
+
+    Returns:
+        None.
+    """
+    for pg in _world.pg_map:
+        devices = pg._device_types
+        if torch.device("cuda") in devices:
+            backend = pg._get_backend(torch.device("cuda"))
+            if is_nccl_available() and isinstance(backend, ProcessGroupNCCL):
+                backend._add_ephemeral_timeout(timeout)
+
+
+def _set_pg_timeout(timeout: timedelta, group: ProcessGroup | None = None) -> None:
+    """
+    Set the timeout for the given process group when users want to use a different timeout instead of
+    default values.
+
+    Args:
+        timeout (timedelta): Timeout for operations executed against the process group which
+            users want to set. Default value is 10 minutes for NCCL and 30 minutes for other backends.
+            This is the duration after which collectives will be aborted asynchronously and the process will crash.
+            This is done since CUDA execution is async and it is no longer safe to continue executing user code since
+            failed async NCCL operations might result in subsequent CUDA operations running on corrupted data.
+            When TORCH_NCCL_BLOCKING_WAIT is set, the process will block and wait for this timeout.
+
+        group (ProcessGroup, optional): The process group to work on. The
+            default is the general main process group. If another specific group
+            is specified, the calling process must be part of :attr:`group`.
+
+    Returns:
+        None
+    """
+    if group is None:
+        group = _get_default_group()
+    if _rank_not_in_group(group):
+        raise ValueError("Invalid process group specified")
+    if not isinstance(group, ProcessGroup):
+        raise AssertionError(f"Expected ProcessGroup, got {type(group)}")
+    devices = group._device_types
+    backends = set()
+    if torch.device("cpu") in devices and is_gloo_available():
+        backend = group._get_backend(torch.device("cpu"))
+        if isinstance(backend, ProcessGroupGloo):
+            backends.add(backend)
+    if torch.device("cuda") in devices:
+        backend = group._get_backend(torch.device("cuda"))
+        if is_nccl_available() and isinstance(backend, ProcessGroupNCCL):
+            backends.add(backend)  # type: ignore[arg-type]
+        elif is_gloo_available() and isinstance(backend, ProcessGroupGloo):
+            backends.add(backend)  # type: ignore[arg-type]
+    if len(backends) == 0:
+        warnings.warn(
+            "Set timeout is now only supported for either nccl or gloo.", stacklevel=2
+        )
+    for backend in backends:
+        backend._set_default_timeout(timeout)
+
+
+@_exception_logger
+@_time_logger
+def init_process_group(
+    backend: str | None = None,
+    init_method: str | None = None,
+    timeout: timedelta | None = None,
+    world_size: int = -1,
+    rank: int = -1,
+    store: Store | None = None,
+    group_name: str = "",
+    pg_options: Any | None = None,
+    device_id: torch.device | int | None = None,
+    _ranks: list[int] | None = None,
+) -> None:
+    """
+    Initialize the default distributed process group.
+
+    This will also initialize the distributed package.
+
+    There are 2 main ways to initialize a process group:
+        1. Specify ``store``, ``rank``, and ``world_size`` explicitly.
+        2. Specify ``init_method`` (a URL string) which indicates where/how
+           to discover peers. Optionally specify ``rank`` and ``world_size``,
+           or encode all required parameters in the URL and omit them.
+
+    If neither is specified, ``init_method`` is assumed to be "env://".
+
+
+    Args:
+        backend (str or Backend, optional): The backend to use. Depending on
+            build-time configurations, valid values include ``mpi``, ``gloo``,
+            ``nccl``, ``ucc``, ``xccl`` or one that is registered by a third-party
+            plugin.
+            Since 2.6, if ``backend`` is not provided, c10d will use a backend
+            registered for the device type indicated by the `device_id` kwarg
+            (if provided). The known default registrations today are: ``nccl``
+            for ``cuda``, ``gloo`` for ``cpu``, ``xccl`` for ``xpu``.
+            If neither ``backend`` nor ``device_id`` is provided, c10d will
+            detect the accelerator on the run-time machine and use a backend
+            registered for that detected accelerator (or ``cpu``).
+            This field can be given as a lowercase string (e.g., ``"gloo"``),
+            which can also be accessed via :class:`Backend` attributes (e.g.,
+            ``Backend.GLOO``).
+            If using multiple processes per machine with ``nccl`` backend, each
+            process must have exclusive access to every GPU it uses, as sharing
+            GPUs between processes can result in deadlock or NCCL invalid usage.
+            ``ucc`` backend is experimental.
+            Default backend for the device can be queried with
+            :func:`get_default_backend_for_device`.
+        init_method (str, optional): URL specifying how to initialize the
+                                     process group. Default is "env://" if no
+                                     ``init_method`` or ``store`` is specified.
+                                     Mutually exclusive with ``store``.
+        world_size (int, optional): Number of processes participating in
+                                    the job. Required if ``store`` is specified.
+        rank (int, optional): Rank of the current process (it should be a
+                              number between 0 and ``world_size``-1).
+                              Required if ``store`` is specified.
+        store(Store, optional): Key/value store accessible to all workers, used
+                                to exchange connection/address information.
+                                Mutually exclusive with ``init_method``.
+        timeout (timedelta, optional): Timeout for operations executed against
+            the process group. Default value is 10 minutes for NCCL and 30 minutes for other backends.
+            This is the duration after which collectives will be aborted asynchronously and the process will crash.
+            This is done since CUDA execution is async and it is no longer safe to continue executing user code since
+            failed async NCCL operations might result in subsequent CUDA operations running on corrupted data.
+            When TORCH_NCCL_BLOCKING_WAIT is set, the process will block and wait for this timeout.
+
+        group_name (str, optional, deprecated): Group name. This argument is ignored
+        pg_options (ProcessGroupOptions, optional): process group options
+            specifying what additional options need to be passed in during
+            the construction of specific process groups. As of now, the only
+            options we support is ``ProcessGroupNCCL.Options`` for the ``nccl``
+            backend, ``is_high_priority_stream`` can be specified so that
+            the nccl backend can pick up high priority cuda streams when
+            there're compute kernels waiting. For other available options to config nccl,
+            See https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/api/types.html#ncclconfig-t
+        device_id (torch.device | int, optional): a single, specific device
+            this process will work on, allowing for backend-specific
+            optimizations.  Currently this has two effects, only under
+            NCCL: the communicator is immediately formed (calling
+            ``ncclCommInit*`` immediately rather than the normal lazy
+            call) and sub-groups will use ``ncclCommSplit`` when
+            possible to avoid unnecessary overhead of group creation. If you
+            want to know NCCL initialization error early, you can also use this
+            field. If an `int` is provided, the API assumes that the accelerator
+            type at compile time will be used.
+        _ranks: The ranks in the process group. If provided, the process
+               group name will be the hash of all the ranks in the group.
+
+    .. note:: To enable ``backend == Backend.MPI``, PyTorch needs to be built from source
+        on a system that supports MPI.
+
+    .. note:: Support for multiple backends is experimental. Currently when no backend is
+        specified, both ``gloo`` and ``nccl`` backends will be created. The ``gloo`` backend
+        will be used for collectives with CPU tensors and the ``nccl`` backend will be used
+        for collectives with CUDA tensors. A custom backend can be specified by passing in
+        a string with format ":,:", e.g.
+        "cpu:gloo,cuda:custom_backend".
+
+    """
+
+    global _world
+
+    global _backend
+    global _default_pg_init_method
+
+    if GroupMember.WORLD is not None:
+        raise ValueError("trying to initialize the default process group twice!")
+
+    set_pytorch_distributed_envs_from_justknobs()
+
+    # Depending on the import order, some trace_rules functions may be evaluated
+    # during the import phase. In such a case, these functions may not correctly
+    # add the distributed related rules due to import circular dependency.
+    # We need to clear the lru_cache during the runtime to ensure the correctness
+    # of these trace_rules.
+    #
+    # Since this API must be called before all distributed code being compiled,
+    # clearing the cache here should be safe.
+    if "torch._dynamo" in sys.modules:
+        torch._dynamo.trace_rules.clear_lru_cache()
+
+    if not ((store is None) or (init_method is None)):
+        raise AssertionError("Cannot specify both init_method and store.")
+
+    if store is not None:
+        if not world_size > 0:
+            raise AssertionError("world_size must be positive if using store")
+        if not rank >= 0:
+            raise AssertionError("rank must be non-negative if using store")
+    elif init_method is None:
+        init_method = "env://"
+
+    # Get the compile-time accelerator type.
+    # None indicates no accelerator support.
+    acc = torch.accelerator.current_accelerator()
+
+    # Auto complete device id
+    if isinstance(device_id, int):
+        if acc is None:
+            raise ValueError(
+                "device_id is an int, but no accelerator support is found from the current compilation. "
+                "Please use a different compiled version that supports your accelerator."
+            )
+        device_id = torch.device(acc.type, device_id)
+
+    # Sanity check device_id
+    if device_id is not None and device_id.type != "cpu":
+        # Type
+        if acc is None or device_id.type != acc.type:
+            raise ValueError(
+                f"device_id {device_id} does not match the current compilation's accelerator support: {acc}. "
+                "Please use a different compiled version that supports your accelerator."
+            )
+        # Index
+        if device_id.index is None:
+            raise ValueError("Please use a device_id with index.")
+        # Range
+        if device_id.index >= torch.accelerator.device_count():
+            raise ValueError(
+                f"device_id {device_id} is out of range. Please use a device index less than "
+                f"the number of accelerators available: {torch.accelerator.device_count()}."
+            )
+
+    logger.info("Using device: %s", device_id)
+
+    # If user did not provide a backend string but provided a device id, e.g.
+    # >>> init_process_group(device_id=device)
+    # we try to figure out the backend name based on the device type.
+    if backend is None and device_id is not None:
+        # Note: 3rd-party devices can register default backend through the
+        # default map below.
+        backend = Backend.default_device_backend_map.get(device_id.type)
+
+    # If we still cannot figure it out, e.g.
+    # >>> init_process_group()
+    # we set it to `undefined` and rely on lazy init.
+    if backend is None:
+        backend = "undefined"
+
+    # Convert string into `Backend` type
+    backend = Backend(backend)
+
+    if timeout is None:
+        timeout = _get_default_timeout(backend)
+
+    _check_valid_timeout(timeout)
+
+    """
+    Group name is not visible to users unless they access
+    internals of c10d. This means we can ignore the value
+    they provide as it not exposed in a public way.
+    """
+    if _ranks is None or len(_ranks) == 0:
+        group_name = _process_group_name([], use_hashed_name=False)
+    else:
+        group_name = _process_group_name(_ranks, use_hashed_name=True)
+    if backend == Backend.MPI:
+        if world_size != -1 or rank != -1:
+            warnings.warn(
+                f"For MPI backend, world_size ({world_size}) and rank ({rank}) "
+                "are ignored since they are assigned by the "
+                "MPI runtime.",
+                stacklevel=2,
+            )
+
+        default_pg, _ = _new_process_group_helper(
+            -1,
+            -1,
+            [],
+            backend,
+            Store(),  # Placeholder value since store cannot be None
+            group_name,
+            timeout=timeout,
+            group_desc="default_pg",
+        )
+    else:
+        # backward compatible API
+        if store is None:
+            if backend == "fake":
+                from torch.testing._internal.distributed.fake_pg import FakeStore
+
+                store = FakeStore()
+            else:
+                rendezvous_iterator = rendezvous(
+                    not_none(init_method), rank, world_size, timeout=timeout
+                )
+                store, rank, world_size = next(rendezvous_iterator)
+                store.set_timeout(timeout)
+
+            # Use a PrefixStore to avoid accidental overrides of keys used by
+            # different systems (e.g. RPC) in case the store is multi-tenant.
+            store = PrefixStore("default_pg", store)
+
+        default_pg, _ = _new_process_group_helper(
+            world_size,
+            rank,
+            [],
+            backend,
+            store,
+            group_name,
+            backend_options=pg_options,
+            timeout=timeout,
+            device_id=device_id,
+            group_desc="default_pg",
+        )
+
+    _update_default_pg(default_pg)
+
+    _world.pg_group_ranks[GroupMember.WORLD] = {  # type: ignore[index]
+        i: i
+        for i in range(GroupMember.WORLD.size())  # type: ignore[attr-defined]
+    }
+    _backend = _world.pg_map[not_none(GroupMember.WORLD)][0]
+    _default_pg_init_method = init_method
+
+    old_hook = sys.excepthook
+    excepthook_prefix = f"[rank{get_rank()}]"
+
+    def _distributed_excepthook(*args):
+        old_stderr = sys.stderr
+        sys.stderr = buf = io.StringIO()
+        try:
+            old_hook(*args)
+        finally:
+            sys.stderr = old_stderr
+        msg = buf.getvalue()
+        msg = "\n".join(
+            f"{excepthook_prefix}: {s}" if s != "" else "" for s in msg.split("\n")
+        )
+        sys.stderr.write(msg)
+        sys.stderr.flush()
+
+    sys.excepthook = _distributed_excepthook
+
+    if _is_barrier_after_init() == 1:
+        # barrier at the end to ensure that once we return from this method, all
+        # process groups including global variables (if any) are updated
+        # correctly on all ranks.
+        # Update 04/2023: for large-scale runs, this barrier (esp. store-based
+        # barrier) may be costly and/or unscalable. Also, in a lot of cases,
+        # these barriers may be unnecessary, as proven by a green CI after
+        # removal. An environment variable `TORCH_DIST_INIT_BARRIER` has been
+        # added which enables this barrier only when set to 1.
+        logger.debug(
+            "Performing barrier after ProcessGroup initialization since "
+            "TORCH_DIST_INIT_BARRIER = 1"
+        )
+        if backend == Backend.MPI:
+            # MPI backend doesn't use store.
+            barrier()
+        else:
+            # Use store based barrier here since barrier() used a bunch of
+            # default devices and messes up NCCL internal state.
+            _store_based_barrier(rank, store, group_name, world_size, timeout)
+
+
+def _get_split_source(pg: ProcessGroup):
+    split_from = None
+    if pg.bound_device_id:
+        split_from = pg._get_backend(pg.bound_device_id)
+    elif pg is _world.default_pg:
+        try:
+            # pyrefly: ignore [missing-attribute]
+            split_from = pg._get_backend(torch.device("cuda"))
+        except RuntimeError:
+            # no cuda device associated with this backend
+            pass
+
+    if not split_from or not split_from.supports_splitting:
+        return None
+
+    # If necessary, find a backend to split from by peeling process
+    # group wrappers from our potentially wrapped process group.
+    while _GLOO_AVAILABLE and isinstance(split_from, _ProcessGroupWrapper):
+        split_from = split_from.wrapped_pg
+
+    return split_from
+
+
+def _new_process_group_helper(
+    group_size,
+    group_rank,
+    global_ranks_in_group,
+    backend,
+    store,
+    group_name: GroupName,
+    backend_options=None,
+    timeout=None,
+    pg_tag=None,
+    device_id=None,
+    group_desc=None,
+):
+    """
+    Create a new distributed process group.
+
+    This function must be called by ALL processes in the global group, even if
+    the calling process is not part of the newly created group. In that case,
+    this function returns GroupMember.NON_GROUP_MEMBER.
+
+    This function is called with ``global_ranks_in_group == []`` for the default group.
+    """
+    global _world
+
+    if group_name in _world.pg_names.values():
+        raise ValueError(
+            "The specified group name has already been "
+            "created, please use a different group name"
+        )
+
+    if device_id is not None and (device_id.index is None or device_id.type == "cpu"):
+        raise ValueError(
+            "init_process_group device_id parameter must be an accelerator with an index"
+        )
+
+    # Note: _new_process_group_helper is only called from init_process_group, which always provides a timeout value
+    _check_valid_timeout(timeout)
+
+    if pg_tag not in [None, ""]:
+        # creating with the same tag and rank set results in the same underlying PG
+        existing_group = _find_pg_by_ranks_and_tag(pg_tag, global_ranks_in_group)
+        if existing_group:
+            _, prefix_store = _world.pg_map[existing_group]
+            return existing_group, prefix_store
+
+    group_desc = "undefined" if group_desc is None else group_desc
+
+    # The list of group ranks is empty if we're creating the default group.
+    is_default_group = len(global_ranks_in_group) == 0
+
+    # nccl and potentially other backends allow creation of
+    # communicators based on pre-existing ones, which can save
+    # initialization time.  Due to lazy initialization of
+    # communicators in some backends, we have to be careful and only
+    # split when we *know* the default PG has already started communicator initialization.
+    # We know this if we have bound a device id to the default pg (eager initialized).
+    if is_initialized() and _get_default_group().bound_device_id:
+        split_from = _get_split_source(_get_default_group())
+    else:
+        split_from = None
+
+    # If this is a subgroup (which means group_ranks is specified),
+    # we check if the current process is a member of the new group.
+    if not is_default_group:
+        global_rank = _get_default_group().rank()
+        if global_rank not in global_ranks_in_group:
+            # If we are using `ncclCommSplit` (or similar split from
+            # other APIs) to create the communicator, we will need to
+            # call `ncclCommSplit` on *all* ranks in this new group's
+            # parent group, even those not in the new group.  This is
+            # a requirement of the NCCL API as otherwise we would get
+            # out of sync.
+            if split_from:
+                split_from.perform_nocolor_split(_get_default_group().bound_device_id)
+            return GroupMember.NON_GROUP_MEMBER, None
+
+    prefix_store = PrefixStore(f"{group_name}/", store)
+    # The backend for PG will be set later based on what's inside BackendConfig
+    # and timeout are set in each backend's option.
+    pg: ProcessGroup = ProcessGroup(
+        prefix_store,
+        group_rank,
+        group_size,
+    )
+    backend_config = BackendConfig(backend)
+    # Set the default backend when single backend is passed in.
+    if "," not in str(backend) and ":" not in str(backend):
+        if backend not in Backend.backend_type_map:
+            raise AssertionError(f"Unknown backend type {backend}")
+        if backend == Backend.UNDEFINED:
+            # Currently when backend is UNDEFINED, only one backend will be initialized
+            # we use nccl (if cuda is available) or gloo as default backend
+            # so we can correctly call getDefaultBackend which in ProcessGroup.
+            if Backend.NCCL in backend_config.get_device_backend_map().values():
+                pg._set_default_backend(ProcessGroup.BackendType.NCCL)
+            else:
+                pg._set_default_backend(ProcessGroup.BackendType.GLOO)
+        else:
+            pg._set_default_backend(Backend.backend_type_map[backend])
+    # In order to correctly call pg._has_hooks(), we should set the default backend
+    # when multi backend is passed in
+    else:
+        if Backend.NCCL in backend_config.device_backend_map.values():
+            pg._set_default_backend(ProcessGroup.BackendType.NCCL)
+        elif Backend._plugins.keys():
+            custom_backend = next(iter(Backend._plugins.keys()))
+            if custom_backend in backend_config.device_backend_map.values():
+                pg._set_default_backend(ProcessGroup.BackendType.CUSTOM)
+        else:
+            pg._set_default_backend(ProcessGroup.BackendType.GLOO)
+
+    if device_id:
+        pg.bound_device_id = device_id
+    backend_class: torch._C._distributed_c10d.Backend
+    for device, backend_str in backend_config.get_device_backend_map().items():
+        # Use the group name as prefix in the default store, such that
+        # a single store can be reused by multiple groups.
+        backend_prefix_store = PrefixStore(f"{device}/", prefix_store)
+
+        if backend_str == Backend.MPI:
+            if not is_mpi_available():
+                raise RuntimeError(
+                    "Distributed package doesn't have MPI built in."
+                    " MPI is only included if you build PyTorch from"
+                    " source on a host that has MPI installed."
+                )
+            backend_class = ProcessGroupMPI.create(global_ranks_in_group)
+            backend_type = ProcessGroup.BackendType.MPI
+            if not backend_class:
+                return GroupMember.NON_GROUP_MEMBER, None
+            # create new process group with accurate rank and size
+            if pg.rank() == -1 and pg.size() == -1:
+                pg = ProcessGroup(
+                    backend_prefix_store,
+                    backend_class.rank(),
+                    backend_class.size(),
+                )
+                pg._set_default_backend(backend_type)
+        elif backend_str == Backend.GLOO:
+            # TODO: remove this check after lazy initialization is supported
+            # if pg_options is not None:
+            #     raise RuntimeError("GLOO options not supported")
+            if not is_gloo_available():
+                raise RuntimeError("Distributed package doesn't have Gloo built in")
+            backend_class = ProcessGroupGloo(
+                backend_prefix_store,
+                group_rank,
+                group_size,
+                # pyrefly: ignore [bad-argument-type]
+                timeout=timeout,
+            )
+            backend_class.options.global_ranks_in_group = global_ranks_in_group
+            backend_class.options.group_name = group_name
+            backend_type = ProcessGroup.BackendType.GLOO
+        elif backend_str == Backend.NCCL:
+            if not is_nccl_available():
+                raise RuntimeError("Distributed package doesn't have NCCL built in")
+            if backend_options is not None:
+                if not isinstance(backend_options, ProcessGroupNCCL.Options):
+                    raise AssertionError(
+                        "Expected backend_options argument to be of type ProcessGroupNCCL.Options"
+                    )
+                if backend_options._timeout != timeout:
+                    warnings.warn(
+                        "backend_options._timeout was specified, "
+                        "but timeout kwarg has a default value that will always override it. ",
+                        stacklevel=2,
+                    )
+            else:
+                # default backend_options for NCCL
+                backend_options = ProcessGroupNCCL.Options()
+                backend_options.is_high_priority_stream = False
+            # pyrefly: ignore [bad-argument-type]
+            backend_options._timeout = timeout
+
+            if split_from:
+                backend_options.split_from = split_from
+                backend_options.split_color = _process_group_color(
+                    global_ranks_in_group
+                )
+            backend_options.global_ranks_in_group = global_ranks_in_group
+            backend_options.group_name = group_name
+            backend_class = ProcessGroupNCCL(
+                backend_prefix_store, group_rank, group_size, backend_options
+            )
+            backend_type = ProcessGroup.BackendType.NCCL
+        elif backend_str == Backend.UCC and is_ucc_available():
+            # TODO: once UCC plugin is fully deprecated, remove
+            # is_ucc_available() from above elif-condition and raise
+            # RuntimeError if is_ucc_available() returns false.
+
+            backend_class = ProcessGroupUCC(
+                backend_prefix_store,
+                group_rank,
+                group_size,
+                # pyrefly: ignore [bad-argument-type]
+                timeout=timeout,
+            )
+            backend_type = ProcessGroup.BackendType.UCC
+        elif backend_str == Backend.XCCL:
+            if not is_xccl_available():
+                raise RuntimeError("Distributed package doesn't have XCCL built in")
+            backend_options = ProcessGroupXCCL.Options()
+            backend_options.global_ranks_in_group = global_ranks_in_group
+            backend_options.group_name = group_name
+            # pyrefly: ignore [bad-argument-type]
+            backend_options._timeout = timeout
+            backend_class = ProcessGroupXCCL(
+                backend_prefix_store, group_rank, group_size, backend_options
+            )
+            backend_type = ProcessGroup.BackendType.XCCL
+        else:
+            if backend_str.upper() not in Backend._plugins:
+                raise AssertionError(f"Unknown c10d backend type {backend_str.upper()}")
+
+            backend_plugin = Backend._plugins[backend_str.upper()]
+            creator_fn = backend_plugin.creator_fn
+            extended_api = backend_plugin.extended_api
+            backend_type = ProcessGroup.BackendType.CUSTOM
+
+            if not extended_api:
+                backend_class = creator_fn(
+                    backend_prefix_store, group_rank, group_size, timeout
+                )
+            else:
+                dist_backend_opts = _DistributedBackendOptions()
+                dist_backend_opts.store = backend_prefix_store
+                dist_backend_opts.group_rank = group_rank
+                dist_backend_opts.group_size = group_size
+                # pyrefly: ignore [bad-argument-type]
+                dist_backend_opts.timeout = timeout
+                dist_backend_opts.group_id = group_name
+                dist_backend_opts.global_ranks_in_group = global_ranks_in_group
+
+                backend_class = creator_fn(dist_backend_opts, backend_options)
+
+        # Set sequence numbers for gloo and nccl backends.
+        if backend_str == Backend.GLOO:
+            if not isinstance(backend_class, ProcessGroupGloo):
+                raise AssertionError(
+                    f"Expected ProcessGroupGloo, got {type(backend_class)}"
+                )
+            backend_class._set_sequence_number_for_group()
+        elif backend_str == Backend.NCCL:
+            if not isinstance(backend_class, ProcessGroupNCCL):
+                raise AssertionError(
+                    f"Expected ProcessGroupNCCL, got {type(backend_class)}"
+                )
+            backend_class._set_sequence_number_for_group()
+
+        # If the type is a subclass of ProcessGroup then return this process group immediately
+        # TODO: This defaults to the old behavior for PythonProcessGroups which overwrites the
+        # ProcessGroup instance
+        if issubclass(type(backend_class), ProcessGroup):
+            pg = backend_class  # type: ignore[assignment]
+            break
+
+        # Process group wrapper initialization for supported PGs when TORCH_DISTRIBUTED_DEBUG is set
+        if (
+            backend_str in [Backend.GLOO, Backend.NCCL, Backend.UCC]
+            or backend_str.upper() in Backend._plugins
+        ):
+            # In debug mode and if GLOO is available, wrap in a wrapper PG that
+            # enables enhanced collective checking for debuggability.
+            if get_debug_level() == DebugLevel.DETAIL:
+                if not _GLOO_AVAILABLE:
+                    logger.info(
+                        """TORCH_DISTRIBUTED_DEBUG was set to DETAIL, but
+                                GLOO is not available. Build with Gloo to
+                                create a wrapper process group in debug mode
+                                to aid collective desynchronization debugging."""
+                    )
+                else:
+                    backend_class = _create_process_group_wrapper(
+                        wrapped_pg=backend_class,
+                        store_prefix=group_name,
+                        store=backend_prefix_store,
+                        rank=group_rank,
+                        world_size=group_size,
+                        # pyrefly: ignore [bad-argument-type]
+                        timeout=timeout,
+                    )
+
+        # register only a single backend when all get_device_backend_map values are the same
+        if len(set(backend_config.get_device_backend_map().values())) == 1:
+            for device in backend_config.get_device_backend_map():
+                pg._register_backend(torch.device(device), backend_type, backend_class)
+
+            # break out of outer loop to not create any more backends
+            break
+
+        pg._register_backend(torch.device(device), backend_type, backend_class)
+
+    # set group_name and group_dsec to backend
+    if group_name is None:
+        raise AssertionError("group_name must not be None")
+    if group_desc is None:
+        raise AssertionError("group_desc must not be None")
+    pg._set_group_name(group_name)
+    pg._set_group_desc(group_desc)
+
+    if device_id and pg._get_backend(device_id).supports_splitting:
+        eager_backend = pg._get_backend(device_id)
+        eager_backend.eager_connect_single_device(device_id)
+
+    # update global state
+    _world.pg_map[pg] = (backend, prefix_store)
+    _world.pg_names[pg] = group_name
+    _register_process_group(group_name, pg)
+
+    _world.pg_backend_config[pg] = str(backend_config)
+    # "" is the default tag for user PGs
+    if pg_tag in [None, ""]:
+        pg_tag = f"ptd:{group_name}"
+        _world.tags_to_pg.setdefault("", []).append(pg)
+    else:
+        pg_tag = f"user:{pg_tag}"
+
+    _world.tags_to_pg.setdefault(pg_tag, []).append(pg)
+    _world.pg_to_tag[pg] = pg_tag
+    return pg, prefix_store
+
+
+def destroy_process_group(group: ProcessGroup | None = None):
+    """
+    Destroy a given process group, and deinitialize the distributed package.
+
+    Args:
+        group (ProcessGroup, optional): The process group to be destroyed, if
+                                        group.WORLD is given, all process
+                                        groups including the default one will
+                                        be destroyed.
+    """
+    global _world
+
+    if group == GroupMember.NON_GROUP_MEMBER:
+        return
+
+    if group is None:
+        pg = GroupMember.WORLD
+    else:
+        pg = group
+
+    if pg is None:
+        raise AssertionError("Process group cannot be None")
+    if _world.pg_map.get(pg, None) is None:
+        raise ValueError("Invalid process group specified")
+
+    # When users register Python onCompletion hooks, those hooks will run on a
+    # different thread than the main thread. Today, the ProcessGroup dtor does
+    # wait for that thread. However, the dtor might finish after the Python
+    # Interpreter exits. After that grabbing the GIL for the Python hook will crash.
+    # We can either revive the interpreter when running hooks or keep the main one
+    # alive until all works and hooks are done. The current implementation does the
+    # latter. Therefore, we explicitly call _wait_for_pending_works() here to wait
+    # for the pending hooks to finish.
+    if type(pg) is ProcessGroup and pg._has_hooks():
+        pg._wait_for_pending_works()
+
+    if group is None or group == GroupMember.WORLD:
+        # shutdown all backends in the order of pg names. shutting down in order because
+        # ncclCommAbort() was a 'collective' call in some versions of NCCL.
+        for pg_to_shutdown in sorted(
+            _world.pg_names, key=lambda x: _world.pg_names[x], reverse=True
+        ):
+            pg_to_shutdown.shutdown()
+
+        _update_default_pg(None)
+        _world.pg_map.clear()
+        _world.pg_names.clear()
+        _world.pg_group_ranks.clear()
+        _world.pg_backend_config.clear()
+        _world.pg_to_tag.clear()
+        _world.tags_to_pg.clear()
+        _world.pg_coalesce_state.clear()
+        _unregister_all_process_groups()
+
+        # when process group doesn't have an explicit name (only WORLD (default)
+        # process group can have an explicit name), we use global _world.group_count
+        # to generate the name. We need to reset the counter on destruction to
+        # allow consistent value to be generated when we re-create process
+        # groups after some trainers recover from failure
+        #
+        # We only reset this when WORLD is being destroyed because if this
+        # process group is in good state, we aren't dealing with failures.
+        _world.group_count = 0
+    else:
+        pg.shutdown()
+        del _world.pg_map[pg]
+        del _world.pg_names[pg]
+        del _world.pg_group_ranks[pg]
+        del _world.pg_backend_config[pg]
+        if pg in _world.pg_coalesce_state:
+            warnings.warn(
+                "Some coalesced collectives haven't been launched when "
+                "ProcessGroup is destroyed. They will be cleaned.",
+                stacklevel=2,
+            )
+            del _world.pg_coalesce_state[pg]
+
+        tag = _world.pg_to_tag.get(pg)
+        del _world.pg_to_tag[pg]
+        if tag is not None:
+            try:
+                _world.tags_to_pg[tag].remove(pg)
+                if tag.startswith("ptd:"):
+                    _world.tags_to_pg[""].remove(pg)
+            except Exception:
+                pass
+        _unregister_process_group(pg.group_name)
+
+
+def _abort_process_group(group: ProcessGroup | None = None):
+    """
+    Abort a given process group. If group.WORLD (i.e. `None`) is given, all
+    process groups including the default one will be aborted.
+
+    Args:
+        group (ProcessGroup, optional): The process group to be aborted.
+
+    .. note:: this API is experimental and currently only works with the NCCL
+        backend.
+
+    .. note:: this API should be used with `TORCH_NCCL_ASYNC_ERROR_HANDLING`
+        turned off (i.e. set to 0). Otherwise, ProcessGroupNCCL's watchdog may
+        automatically handle errors or timeouts for you including aborting the
+        ProcessGroup.
+    """
+    global _world
+
+    if group == GroupMember.NON_GROUP_MEMBER:
+        return
+
+    pg = group or GroupMember.WORLD
+
+    if pg is None:
+        raise AssertionError("Process group cannot be None")
+    if _world.pg_map.get(pg, None) is None:
+        raise ValueError("Invalid process group specified or has been destroyed.")
+
+    try:
+        backend = pg._get_backend(torch.device("cuda"))
+    except RuntimeError:
+        backend = None
+
+    if group is None or group == GroupMember.WORLD:
+        # Abort all backends within a ncclGroupStart|End semantic.
+        # This ensures that different NCCL communicators' abort calls won't
+        # deadlock each other.
+        # For details, please see: https://github.com/pytorch/pytorch/issues/119797
+        if is_nccl_available() and isinstance(backend, ProcessGroupNCCL):
+            backend._group_start()
+        for pg_to_abort in sorted(
+            _world.pg_names, key=lambda x: _world.pg_names[x], reverse=True
+        ):
+            pg_to_abort.abort()
+        if is_nccl_available() and isinstance(backend, ProcessGroupNCCL):
+            backend._group_end()
+
+        _update_default_pg(None)
+        _world.pg_map.clear()
+        _world.pg_names.clear()
+        _world.pg_group_ranks.clear()
+        _world.pg_backend_config.clear()
+        _world.pg_to_tag.clear()
+        _world.tags_to_pg.clear()
+        _world.pg_coalesce_state.clear()
+        _unregister_all_process_groups()
+
+        # when process group doesn't have an explicit name (only WORLD (default)
+        # process group can have an explicit name), we use global _world.group_count
+        # to generate the name. We need to reset the counter on destruction to
+        # allow consistent value to be generated when we re-create process
+        # groups after some trainers recover from failure
+        #
+        # We only reset this when WORLD is being destroyed because if this
+        # process group is in good state, we aren't dealing with failures.
+        _world.group_count = 0
+    else:
+        pg.abort()
+        del _world.pg_map[pg]
+        del _world.pg_names[pg]
+        del _world.pg_group_ranks[pg]
+        del _world.pg_backend_config[pg]
+        if pg in _world.pg_coalesce_state:
+            warnings.warn(
+                "Some coalesced collectives haven't been launched when "
+                "ProcessGroup is aborted. They will be cleaned.",
+                stacklevel=2,
+            )
+            del _world.pg_coalesce_state[pg]
+
+        tag = _world.pg_to_tag.get(pg)
+        del _world.pg_to_tag[pg]
+        if tag is not None:
+            try:
+                _world.tags_to_pg[tag].remove(pg)
+                if tag.startswith("ptd:"):
+                    _world.tags_to_pg[""].remove(pg)
+            except Exception:
+                pass
+        _unregister_process_group(pg.group_name)
+
+
+def get_rank(group: ProcessGroup | None = None) -> int:
+    """
+    Return the rank of the current process in the provided ``group``, default otherwise.
+
+    Rank is a unique identifier assigned to each process within a distributed
+    process group. They are always consecutive integers ranging from 0 to
+    ``world_size``.
+
+    Args:
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+
+    Returns:
+        The rank of the process group
+        -1, if not part of the group
+
+    """
+    if _rank_not_in_group(group):
+        return -1
+
+    default_pg = _get_default_group()
+    if group is None or group is GroupMember.WORLD:
+        return default_pg.rank()
+
+    return get_group_rank(group, default_pg.rank())
+
+
+def get_world_size(group: ProcessGroup | None = None) -> int:
+    """
+    Return the number of processes in the current process group.
+
+    Args:
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+
+    Returns:
+        The world size of the process group
+        -1, if not part of the group
+
+    """
+    if _rank_not_in_group(group):
+        return -1
+
+    return _get_group_size(group)
+
+
+def isend(
+    tensor: torch.Tensor,
+    dst: int | None = None,
+    group: ProcessGroup | None = None,
+    tag: int = 0,
+    group_dst: int | None = None,
+) -> Work | None:
+    """
+    Send a tensor asynchronously.
+
+    .. warning::
+        Modifying ``tensor`` before the request completes causes undefined
+        behavior.
+
+    .. warning::
+        ``tag`` is not supported with the NCCL backend.
+
+    Unlike send, which is blocking, isend allows src == dst rank, i.e. send to self.
+
+    Args:
+        tensor (Tensor): Tensor to send.
+        dst (int): Destination rank on global process group (regardless of ``group`` argument)
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        tag (int, optional): Tag to match send with remote recv
+        group_dst (int, optional): Destination rank on ``group``.  Invalid to specify both ``dst`` and ``group_dst``
+
+    Returns:
+        A distributed request object.
+        None, if not part of the group
+
+    """
+    group = _group_or_default_group(group)
+    group_dst = _canonicalize_group_rank(group, dst, group_dst)
+    _check_single_tensor(tensor, "tensor")
+    if _rank_not_in_group(group):
+        _warn_not_in_group("isend")
+        return None
+
+    if tensor.is_complex():
+        tensor = torch.view_as_real(tensor)
+
+    return group.send([tensor], group_dst, tag)
+
+
+def irecv(
+    tensor: torch.Tensor,
+    src: int | None = None,
+    group: ProcessGroup | None = None,
+    tag: int = 0,
+    group_src: int | None = None,
+) -> Work | None:
+    """
+    Receives a tensor asynchronously.
+
+    .. warning::
+        ``tag`` is not supported with the NCCL backend.
+
+    Unlike recv, which is blocking, irecv allows src == dst rank, i.e. recv from self.
+
+    Args:
+        tensor (Tensor): Tensor to fill with received data.
+        src (int, optional): Source rank on global process group (regardless of ``group`` argument).
+            Will receive from any process if unspecified.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        tag (int, optional): Tag to match recv with remote send
+        group_src (int, optional): Destination rank on ``group``.  Invalid to specify both ``src`` and ``group_src``.
+
+    Returns:
+        A distributed request object.
+        None, if not part of the group
+
+    """
+    _check_single_tensor(tensor, "tensor")
+    if _rank_not_in_group(group):
+        _warn_not_in_group("irecv")
+        return None
+
+    if tensor.is_complex():
+        tensor = torch.view_as_real(tensor)
+
+    group = _group_or_default_group(group)
+    if src is None and group_src is None:
+        return group.recv_anysource([tensor], tag)
+    else:
+        group_src = _canonicalize_group_rank(group, src, group_src)
+        return group.recv([tensor], group_src, tag)
+
+
+@_exception_logger
+def send(
+    tensor: torch.Tensor,
+    dst: int | None = None,
+    group: ProcessGroup | None = None,
+    tag: int = 0,
+    group_dst: int | None = None,
+) -> None:
+    """
+    Send a tensor synchronously.
+
+    .. warning::
+        ``tag`` is not supported with the NCCL backend.
+
+    Args:
+        tensor (Tensor): Tensor to send.
+        dst (int): Destination rank on global process group (regardless of ``group`` argument).
+            Destination rank should not be the same as the rank of the current process.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        tag (int, optional): Tag to match send with remote recv
+        group_dst (int, optional): Destination rank on ``group``.  Invalid to specify both ``dst`` and ``group_dst``.
+
+    """
+    group = _group_or_default_group(group)
+    group_dst = _canonicalize_group_rank(group, dst, group_dst)
+    _check_not_self_rank(group, group_dst, "destination")
+    work = isend(tensor, group=group, tag=tag, group_dst=group_dst)
+    if work is not None:
+        work.wait()
+
+
+@_exception_logger
+def recv(
+    tensor: torch.Tensor,
+    src: int | None = None,
+    group: ProcessGroup | None = None,
+    tag: int = 0,
+    group_src: int | None = None,
+) -> int:
+    """
+    Receives a tensor synchronously.
+
+    .. warning::
+        ``tag`` is not supported with the NCCL backend.
+
+    Args:
+        tensor (Tensor): Tensor to fill with received data.
+        src (int, optional): Source rank on global process group (regardless of ``group`` argument).
+            Will receive from any process if unspecified.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        tag (int, optional): Tag to match recv with remote send
+        group_src (int, optional): Destination rank on ``group``.  Invalid to specify both ``src`` and ``group_src``.
+    Returns:
+        Sender rank
+        -1, if not part of the group
+
+    """
+    work = irecv(tensor, src=src, group=group, tag=tag, group_src=group_src)
+    if work is None:
+        return -1
+    work.wait()
+    if src is None:
+        if group_src is None:
+            group_src = work._source_rank()
+        group = _group_or_default_group(group)
+        _check_not_self_rank(group, group_src, "source")
+        src = get_global_rank(group, group_src)
+    return src
+
+
+class _IllegalWork(Work):
+    def __getattribute__(self, name):
+        if name in [
+            "is_success",
+            "exception",
+            "wait",
+            "source_rank",
+            "_source_rank",
+            "result",
+            "synchronize",
+        ]:
+            raise ValueError(f"Illegal to call {name} on IllegalWork object")
+
+
+class _CoalescingManager:
+    def __init__(self) -> None:
+        self.works: list[Work] = []
+
+    def append(self, work: Work | None = None):
+        if work:
+            self.works.append(work)
+
+    def wait(self):
+        for work in self.works:
+            work.wait()
+
+
+@contextlib.contextmanager
+def _coalescing_manager(
+    group: ProcessGroup | None = None,
+    device: torch.device | None = None,
+    async_ops: bool = False,
+):
+    """
+    Context manager used to coalesce collectives or P2P operations when possible.
+
+    Args:
+        group (`ProcessGroup`, optional): The process group to work on. If None,
+            the default process group will be used.
+        device (`torch.device`, optional): Default is None, set to a device if
+            there isn't a `**_coalesced` implementation by the backend.
+        async_ops (`bool`, optional): whether the coalesced ops are async ops.
+
+    Examples:
+        >>> # xdoctest: +SKIP("no rank")
+        >>> # Synchronous ops
+        >>> with _coalescing_manager():
+        >>>     for i in range(num_colls):
+        >>>         dist.all_reduce(tensors[i])
+        >>> # Asynchronous ops
+        >>> with _coalescing_manager(async_ops=True) as cm:
+        >>>     for i in range(num_colls):
+        >>>         dist.all_reduce(tensors[i])
+        >>> cm.wait()
+
+    .. warning::
+       :func:`_coalescing_manager` currently do not support coalescing
+       all-reduces with different reduce operators, e.g.  `ReduceOp.SUM` mixed
+       with `ReduceOp.PRODUCT`.
+    """
+    group = group or _get_default_group()
+    op_list = _world.pg_coalesce_state.setdefault(group, [])
+    if op_list:
+        raise ValueError(
+            "ProcessGroup has non-empty op list at the start of coalescing"
+        )
+    if device:
+        group._start_coalescing(device)
+    cm = _CoalescingManager()
+    yield cm
+    work = None
+    op_list = _world.pg_coalesce_state.pop(group)
+    if op_list:
+        # Collectives supporting "Fast Path" coalescing are captured.
+        # See implementation in corresponding collective APIs.
+        # Currently supported:
+        # - coalesced `all_reduce`
+        # - coalesced `all_gather_into_tensor`
+        # - coalesced `reduce_scatter_tensor`
+        op0 = op_list[0].op
+        if op0 is all_reduce:
+            tensors = [op.tensor for op in op_list]
+            all_reduce_opts = AllreduceCoalescedOptions()
+            all_reduce_opts.reduceOp = not_none(op_list[0].redop)
+            all_reduce_opts.asyncOp = async_ops
+            work = group.allreduce_coalesced(tensors, all_reduce_opts)
+        elif op0 is all_gather_into_tensor:
+            inputs = []
+            outputs = []
+            for op in op_list:
+                inputs.append(op.tensor)
+                outputs.append(not_none(op.dst_tensor))
+            all_gather_opts = AllgatherOptions()
+            all_gather_opts.asyncOp = async_ops
+            work = group.allgather_into_tensor_coalesced(outputs, inputs)
+        elif op0 is reduce_scatter_tensor:
+            inputs = []
+            outputs = []
+            for op in op_list:
+                inputs.append(op.tensor)
+                outputs.append(not_none(op.dst_tensor))
+            reduce_opts = ReduceScatterOptions()
+            reduce_opts.reduceOp = not_none(op_list[0].redop)
+            reduce_opts.asyncOp = async_ops
+            work = group.reduce_scatter_tensor_coalesced(outputs, inputs, reduce_opts)
+        else:
+            raise AssertionError(
+                f"Coalescing manager does not support fast-path coalescing of {op0}, "
+                f"yet {op0} is still recorded in op list. This is an internal error of c10d."
+            )
+
+    if device:
+        # Old style of letting each coll inside the context manager to call into C++ counterpart via python binding
+        work = group._end_coalescing(device)
+
+    if async_ops:
+        cm.append(work)
+    elif (
+        work is not None
+    ):  # Backward compatible with backends that don't sync at CPP level
+        work.wait()
+    # Otherwise, the backend has sync'ed at CPP level
+
+
+class _TimeEstimator:
+    def __init__(self) -> None:
+        self.estimated_time: float | None = None
+
+
+@contextlib.contextmanager
+def _time_estimator(
+    group: ProcessGroup | None = None,
+    device: torch.device | None = None,
+):
+    """
+    Context manager used to estimate time of collectives.
+    Within the context manager, nothing is actually run and the backend just simulates
+    the collective time only.
+
+    Args:
+        group (`ProcessGroup`, optional): The process group to work on. If None,
+            the default process group will be used.
+        device (`torch.device`, optional): Default is None, set to a device if
+            there isn't a `**_coalesced` implementation by the backend.
+
+    Examples:
+        >>> # xdoctest: +SKIP("no rank")
+        >>> # Synchronous ops
+        >>> with _time_estimator() as cm:
+        >>>     for i in range(num_colls):
+        >>>         dist.all_reduce(tensors[i])
+        >>> # estimate time is stored in cm.estimated_time
+
+    .. warning::
+       :func:`_time_estimator` currently only support NCCL backend but it can
+       easily be extended to other backends.
+
+       Also a NCCL communicator needs to be created because only with a real communicator can we do accurate estimation.
+       The communicator internally has knowledge about the links it runs on
+       (e.g. intra-node or inter-node, whether the links are NVLink or PCI-e or IB).
+    """
+    # TODO: We need to also support torch inductor for the time estimator.
+    group = group or _get_default_group()
+    device = device or _get_pg_default_device(group)
+    backend = group._get_backend(device)
+    if not backend.supports_time_estimate:
+        raise NotImplementedError(
+            f"collective time estimator is not supported in the current version of backend {backend}"
+        )
+    backend._start_time_estimate()  # type: ignore[attr-defined]
+    cm = _TimeEstimator()
+    yield cm
+    cm.estimated_time = backend._end_time_estimate()  # type: ignore[attr-defined]
+
+
+def batch_isend_irecv(p2p_op_list: list[P2POp]) -> list[Work]:
+    """
+    Send or Receive a batch of tensors asynchronously and return a list of requests.
+
+    Process each of the operations in ``p2p_op_list`` and return the corresponding
+    requests. NCCL, Gloo, and UCC backend are currently supported.
+
+    Args:
+        p2p_op_list: A list of point-to-point operations(type of each operator is
+            ``torch.distributed.P2POp``). The order of the isend/irecv in the list
+            matters and it needs to match with corresponding isend/irecv on the
+            remote end.
+
+    Returns:
+        A list of distributed request objects returned by calling the corresponding
+        op in the op_list.
+
+    Examples:
+        >>> # xdoctest: +SKIP("no rank")
+        >>> send_tensor = torch.arange(2, dtype=torch.float32) + 2 * rank
+        >>> recv_tensor = torch.randn(2, dtype=torch.float32)
+        >>> send_op = dist.P2POp(dist.isend, send_tensor, (rank + 1) % world_size)
+        >>> recv_op = dist.P2POp(
+        ...     dist.irecv, recv_tensor, (rank - 1 + world_size) % world_size
+        ... )
+        >>> reqs = batch_isend_irecv([send_op, recv_op])
+        >>> for req in reqs:
+        >>>     req.wait()
+        >>> recv_tensor
+        tensor([2, 3])     # Rank 0
+        tensor([0, 1])     # Rank 1
+
+    .. note:: Note that when this API is used with the NCCL PG backend, users must set
+        the current GPU device with `torch.cuda.set_device`, otherwise it will
+        lead to unexpected hang issues.
+
+        In addition, if this API is the first collective call in the ``group``
+        passed to ``dist.P2POp``, all ranks of the ``group`` must participate in
+        this API call; otherwise, the behavior is undefined. If this API call is
+        not the first collective call in the ``group``, batched P2P operations
+        involving only a subset of ranks of the ``group`` are allowed.
+    """
+    _check_p2p_op_list(p2p_op_list)
+    group = p2p_op_list[0].group
+    if group is None:
+        group = _get_default_group()
+    device = p2p_op_list[0].tensor.device
+
+    def peer_kwarg(op: P2POp) -> dict[str, int]:
+        key = "group_dst" if op.op is isend else "group_src"
+        return {key: op.group_peer}
+
+    if type(group) is ProcessGroup and group._get_backend(device).supports_coalescing:
+        # NCCL style coalescing
+        with _coalescing_manager(group, device, async_ops=True) as cm:
+            for p2p_op in p2p_op_list:
+                p2p_op.op(
+                    p2p_op.tensor,
+                    group=p2p_op.group,
+                    tag=p2p_op.tag,
+                    **peer_kwarg(p2p_op),
+                )
+
+        return cm.works
+    else:
+        # backend not support coalescing
+        reqs = []
+        for p2p_op in p2p_op_list:
+            work = p2p_op.op(
+                p2p_op.tensor,
+                group=p2p_op.group,
+                tag=p2p_op.tag,
+                **peer_kwarg(p2p_op),
+            )
+            if work:
+                reqs.append(work)
+        return reqs
+
+
+@_exception_logger
+def broadcast(
+    tensor: torch.Tensor,
+    src: int | None = None,
+    group: ProcessGroup | None = None,
+    async_op: bool = False,
+    group_src: int | None = None,
+):
+    """
+    Broadcasts the tensor to the whole group.
+
+    ``tensor`` must have the same number of elements in all processes
+    participating in the collective.
+
+    Args:
+        tensor (Tensor): Data to be sent if ``src`` is the rank of current
+            process, and tensor to be used to save received data otherwise.
+        src (int): Source rank on global process group (regardless of ``group`` argument).
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op
+        group_src (int): Source rank on ``group``.  Must specify one of ``group_src``
+            and ``src`` but not both.
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group
+
+    """
+    group = _group_or_default_group(group)
+    group_src = _canonicalize_group_rank(group, src, group_src, return_global=False)
+    _check_single_tensor(tensor, "tensor")
+    if _rank_not_in_group(group):
+        _warn_not_in_group("broadcast")
+        return
+
+    opts = BroadcastOptions()
+    opts.rootRank = group_src
+    opts.rootTensor = 0
+    opts.asyncOp = async_op
+    if tensor.is_complex():
+        tensor = torch.view_as_real(tensor)
+    work = group.broadcast([tensor], opts)
+    if async_op:
+        return work
+    elif (
+        work is not None
+    ):  # Backward compatible with backends that don't sync at CPP level
+        work.wait()
+    # Otherwise, the backend has sync'ed at CPP level
+
+
+@_exception_logger
+def all_reduce(tensor, op=ReduceOp.SUM, group=None, async_op: bool = False):
+    """
+    Reduces the tensor data across all machines in a way that all get the final result.
+
+    After the call ``tensor`` is going to be bitwise identical in all processes.
+
+    Complex tensors are supported.
+
+    Args:
+        tensor (Tensor): Input and output of the collective. The function
+            operates in-place.
+        op (optional): One of the values from
+            ``torch.distributed.ReduceOp``
+            enum.  Specifies an operation used for element-wise reductions.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group
+
+    Examples:
+        >>> # xdoctest: +SKIP("no rank")
+        >>> # All tensors below are of torch.int64 type.
+        >>> # We have 2 process groups, 2 ranks.
+        >>> device = torch.device(f"cuda:{rank}")
+        >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank
+        >>> tensor
+        tensor([1, 2], device='cuda:0') # Rank 0
+        tensor([3, 4], device='cuda:1') # Rank 1
+        >>> dist.all_reduce(tensor, op=ReduceOp.SUM)
+        >>> tensor
+        tensor([4, 6], device='cuda:0') # Rank 0
+        tensor([4, 6], device='cuda:1') # Rank 1
+
+        >>> # All tensors below are of torch.cfloat type.
+        >>> # We have 2 process groups, 2 ranks.
+        >>> tensor = torch.tensor(
+        ...     [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device
+        ... ) + 2 * rank * (1 + 1j)
+        >>> tensor
+        tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0
+        tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1
+        >>> dist.all_reduce(tensor, op=ReduceOp.SUM)
+        >>> tensor
+        tensor([4.+4.j, 6.+6.j], device='cuda:0') # Rank 0
+        tensor([4.+4.j, 6.+6.j], device='cuda:1') # Rank 1
+
+    """
+    # Dynamo has built-in logic to map legacy distributed ops to functional collectives.
+    # Let's redirect to a torch function mode that can mimic this logic outside Dynamo
+    # (e.g., non-strict export implements such a torch function mode).
+    relevant_args = (tensor,)
+    if has_torch_function(relevant_args):
+        return handle_torch_function(
+            all_reduce,
+            relevant_args,
+            tensor,
+            op=op,
+            group=group,
+            async_op=async_op,
+        )
+
+    _check_single_tensor(tensor, "tensor")
+    if _rank_not_in_group(group):
+        _warn_not_in_group("all_reduce")
+        return
+
+    if tensor.is_complex():
+        if not supports_complex(op):
+            raise ValueError(f"all_reduce does not support {op} on complex tensors")
+        tensor = torch.view_as_real(tensor)
+
+    opts = AllreduceOptions()
+    opts.reduceOp = op
+    opts.asyncOp = async_op
+    if group is None:
+        group = _get_default_group()
+
+    if group in _world.pg_coalesce_state:
+        # We are in coalescing context, do not issue single operation, just append a collective representation
+        coll = _CollOp(all_reduce, tensor, None, op, None)
+        _world.pg_coalesce_state[group].append(coll)
+        if async_op:
+            return _IllegalWork()
+        else:
+            return None
+
+    work = group.allreduce([tensor], opts)
+
+    if async_op:
+        return work
+    elif (
+        work is not None
+    ):  # Backward compatible with backends that don't sync at CPP level
+        work.wait()
+    # Otherwise, the backend has sync'ed at CPP level
+
+
+@_exception_logger
+@deprecated(
+    "`torch.distributed.all_reduce_coalesced` will be deprecated. If you must "
+    "use it, please revisit our documentation later at "
+    "https://pytorch.org/docs/main/distributed.html#collective-functions",
+    category=FutureWarning,
+)
+def all_reduce_coalesced(tensors, op=ReduceOp.SUM, group=None, async_op: bool = False):
+    """
+    WARNING: at this time individual shape checking is not implemented across nodes.
+
+    For example, if the rank 0 node passes [torch.rand(4), torch.rand(2)] and the
+    rank 1 node passes [torch.rand(2), torch.rand(2), torch.rand(2)], the allreduce
+    operation will proceed without complaint and return erroneous outputs. This lack
+    of shape checking results in significant performance improvements but users of this
+    function should take extra care to ensure that each node passes in tensors whose
+    shapes match across nodes.
+
+    Reduces each tensor in tensors (residing on the same device) across all machines
+    in such a way that all get the final result.
+
+    After the call each tensor in tensors is going to bitwise identical
+    in all processes.
+
+    Complex tensors are supported.
+
+    Args:
+        tensors (Union[List[Tensor], Tensor]): Input and output of the collective.
+            The function operates in-place.
+        op (Optional[ReduceOp]): One of the values from
+            ``torch.distributed.ReduceOp`` enum. Specifies an operation used for
+            element-wise reductions.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (Optional[bool]): Whether this op should be an async op.
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group.
+
+    """
+    if isinstance(tensors, torch.Tensor):
+        tensors = [tensors]
+    _check_tensor_list(tensors, "tensor")
+    _ensure_all_tensors_same_dtype(tensors)
+    if _rank_not_in_group(group):
+        _warn_not_in_group("all_reduce_coalesced")
+        return
+
+    if any(t.is_complex() for t in tensors) and not supports_complex(op):
+        raise ValueError(f"all_reduce does not support {op} on complex tensors")
+
+    tensors = [t if not t.is_complex() else torch.view_as_real(t) for t in tensors]
+
+    opts = AllreduceCoalescedOptions()
+    opts.reduceOp = op
+    opts.asyncOp = async_op
+    group = group or _get_default_group()
+    work = group.allreduce_coalesced(tensors, opts)
+
+    if async_op:
+        return work.get_future()
+    elif (
+        work is not None
+    ):  # Backward compatible with backends that don't sync at CPP level
+        work.wait()
+    # Otherwise, the backend has sync'ed at CPP level
+
+
+@_exception_logger
+def reduce(
+    tensor: torch.Tensor,
+    dst: int | None = None,
+    op=ReduceOp.SUM,
+    group: ProcessGroup | None = None,
+    async_op: bool = False,
+    group_dst: int | None = None,
+):
+    """
+    Reduces the tensor data across all machines.
+
+    Only the process with rank ``dst`` is going to receive the final result.
+
+    Args:
+        tensor (Tensor): Input and output of the collective. The function
+            operates in-place.
+        dst (int): Destination rank on global process group (regardless of ``group`` argument)
+        op (optional): One of the values from
+            ``torch.distributed.ReduceOp``
+            enum.  Specifies an operation used for element-wise reductions.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op
+        group_dst (int): Destination rank on ``group``.  Must specify one of ``group_dst``
+            and ``dst`` but not both.
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group
+
+    """
+    group = _group_or_default_group(group)
+    group_dst = _canonicalize_group_rank(group, dst, group_dst, return_global=False)
+    _check_single_tensor(tensor, "tensor")
+    if _rank_not_in_group(group):
+        _warn_not_in_group("reduce")
+        return
+
+    opts = ReduceOptions()
+    opts.reduceOp = op
+    opts.rootRank = group_dst
+    opts.asyncOp = async_op
+    work = group.reduce([tensor], opts)
+    if async_op:
+        return work
+    elif (
+        work is not None
+    ):  # Backward compatible with backends that don't sync at CPP level
+        work.wait()
+    # Otherwise, the backend has sync'ed at CPP level
+
+
+def _object_to_tensor(obj, device, group):
+    with _WaitCounter("pytorch.wait_counter.c10d._object_to_tensor").guard():
+        f = io.BytesIO()
+        _pickler(f).dump(obj)
+        byte_storage = torch.ByteStorage._from_buffer(f.getvalue())  # type: ignore[attr-defined]
+        # Do not replace `torch.ByteTensor` or `torch.LongTensor` with torch.tensor and specifying dtype.
+        # Otherwise, it will cause 100X slowdown.
+        # See: https://github.com/pytorch/pytorch/issues/65696
+        byte_tensor = torch.ByteTensor(byte_storage).to(device)
+        if get_debug_level() == DebugLevel.DETAIL and is_nccl_available():
+            backend = get_backend(group)
+            if backend == Backend.NCCL:
+                hash = torch._C._distributed_c10d._hash_tensors([byte_tensor])
+                logger.warning(
+                    "_object_to_tensor size: %s hash value: %s",
+                    byte_tensor.numel(),
+                    hash,
+                )
+        local_size = torch.LongTensor([byte_tensor.numel()]).to(device)
+        return byte_tensor, local_size
+
+
+def _tensor_to_object(tensor, tensor_size, group):
+    with _WaitCounter("pytorch.wait_counter.c10d._tensor_to_object").guard():
+        if get_debug_level() == DebugLevel.DETAIL and is_nccl_available():
+            backend = get_backend(group)
+            if backend == Backend.NCCL:
+                hash = torch._C._distributed_c10d._hash_tensors([tensor])
+                logger.warning(
+                    "_tensor_to_object size: %s hash value: %s", tensor.numel(), hash
+                )
+        tensor = tensor.cpu()
+        buf = tensor.numpy().tobytes()[:tensor_size]
+        return _unpickler(io.BytesIO(buf)).load()
+
+
+@_exception_logger
+def all_gather_object(object_list, obj, group=None):
+    """
+    Gathers picklable objects from the whole group into a list.
+
+    Similar to :func:`all_gather`, but Python objects can be passed in.
+    Note that the object must be picklable in order to be gathered.
+
+    Args:
+        object_list (list[Any]): Output list. It should be correctly sized as the
+            size of the group for this collective and will contain the output.
+        obj (Any): Pickable Python object to be broadcast from current process.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used. Default is ``None``.
+
+    Returns:
+        None. If the calling rank is part of this group, the output of the
+        collective will be populated into the input ``object_list``. If the
+        calling rank is not part of the group, the passed in ``object_list`` will
+        be unmodified.
+
+    .. note:: Note that this API differs slightly from the :func:`all_gather`
+        collective since it does not provide an ``async_op`` handle and thus
+        will be a blocking call.
+
+    .. note:: For NCCL-based processed groups, internal tensor representations
+        of objects must be moved to the GPU device before communication takes
+        place. In this case, the device used is given by
+        ``torch.cuda.current_device()`` and it is the user's responsibility to
+        ensure that this is set so that each rank has an individual GPU, via
+        ``torch.cuda.set_device()``.
+
+    .. warning::
+        Object collectives have a number of serious performance and scalability
+        limitations.  See :ref:`object_collectives` for details.
+
+    .. warning::
+        :func:`all_gather_object` uses ``pickle`` module implicitly, which is
+        known to be insecure. It is possible to construct malicious pickle data
+        which will execute arbitrary code during unpickling. Only call this
+        function with data you trust.
+
+    .. warning::
+        Calling :func:`all_gather_object` with GPU tensors is not well supported
+        and inefficient as it incurs GPU -> CPU transfer since tensors would be
+        pickled. Please consider using :func:`all_gather` instead.
+
+    Example::
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> # Note: Process group initialization omitted on each rank.
+        >>> import torch.distributed as dist
+        >>> # Assumes world_size of 3.
+        >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object
+        >>> output = [None for _ in gather_objects]
+        >>> dist.all_gather_object(output, gather_objects[dist.get_rank()])
+        >>> output
+        ['foo', 12, {1: 2}]
+    """
+    if _rank_not_in_group(group):
+        _warn_not_in_group("all_gather_object")
+        return
+
+    current_device = _get_object_coll_device(group)
+    input_tensor, local_size = _object_to_tensor(obj, current_device, group)
+
+    # Gather all local sizes. This is so that we can find the max size, and index
+    # until the correct size when deserializing the tensors.
+    group_size = get_world_size(group=group)
+    object_sizes_tensor = torch.zeros(
+        group_size, dtype=torch.long, device=current_device
+    )
+    object_size_list = [
+        object_sizes_tensor[i].unsqueeze(dim=0) for i in range(group_size)
+    ]
+    # Allgather tensor sizes
+    all_gather(object_size_list, local_size, group=group)
+    max_object_size = int(max(object_size_list).item())  # type: ignore[type-var]
+    # Resize tensor to max size across all ranks.
+    input_tensor.resize_(max_object_size)
+    coalesced_output_tensor = torch.empty(
+        max_object_size * group_size, dtype=torch.uint8, device=current_device
+    )
+    # Output tensors are nonoverlapping views of coalesced_output_tensor
+    output_tensors = [
+        coalesced_output_tensor[max_object_size * i : max_object_size * (i + 1)]
+        for i in range(group_size)
+    ]
+    all_gather(output_tensors, input_tensor, group=group)
+    # Deserialize outputs back to object.
+    for i, tensor in enumerate(output_tensors):
+        tensor = tensor.type(torch.uint8)
+        tensor_size = object_size_list[i]
+        object_list[i] = _tensor_to_object(tensor, tensor_size, group)
+
+
+@_exception_logger
+def gather_object(
+    obj: Any,
+    object_gather_list: list[Any] | None = None,
+    dst: int | None = None,
+    group: ProcessGroup | None = None,
+    group_dst: int | None = None,
+):
+    """
+    Gathers picklable objects from the whole group in a single process.
+
+    Similar to :func:`gather`, but Python objects can be passed in. Note that the
+    object must be picklable in order to be gathered.
+
+    Args:
+        obj (Any): Input object. Must be picklable.
+        object_gather_list (list[Any]): Output list. On the ``dst`` rank, it
+            should be correctly sized as the size of the group for this
+            collective and will contain the output. Must be ``None`` on non-dst
+            ranks. (default is ``None``)
+        dst (int, optional): Destination rank on global process group (regardless of ``group`` argument).
+            (If both ``dst`` and ``group_dst`` are None, default is global rank 0)
+        group: (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used. Default is ``None``.
+        group_dst (int, optional): Destination rank on ``group``.  Invalid to specify both ``dst`` and ``group_dst``
+
+    Returns:
+        None. On the ``dst`` rank, ``object_gather_list`` will contain the
+        output of the collective.
+
+    .. note:: Note that this API differs slightly from the gather collective
+        since it does not provide an async_op handle and thus will be a blocking
+        call.
+
+    .. note:: For NCCL-based processed groups, internal tensor representations
+        of objects must be moved to the GPU device before communication takes
+        place. In this case, the device used is given by
+        ``torch.cuda.current_device()`` and it is the user's responsibility to
+        ensure that this is set so that each rank has an individual GPU, via
+        ``torch.cuda.set_device()``.
+
+    .. warning::
+        Object collectives have a number of serious performance and scalability
+        limitations.  See :ref:`object_collectives` for details.
+
+    .. warning::
+        :func:`gather_object` uses ``pickle`` module implicitly, which is
+        known to be insecure. It is possible to construct malicious pickle data
+        which will execute arbitrary code during unpickling. Only call this
+        function with data you trust.
+
+    .. warning::
+        Calling :func:`gather_object` with GPU tensors is not well supported
+        and inefficient as it incurs GPU -> CPU transfer since tensors would be
+        pickled. Please consider using :func:`gather` instead.
+
+    Example::
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> # Note: Process group initialization omitted on each rank.
+        >>> import torch.distributed as dist
+        >>> # Assumes world_size of 3.
+        >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object
+        >>> output = [None for _ in gather_objects]
+        >>> dist.gather_object(
+        ...     gather_objects[dist.get_rank()],
+        ...     output if dist.get_rank() == 0 else None,
+        ...     dst=0
+        ... )
+        >>> # On rank 0
+        >>> output
+        ['foo', 12, {1: 2}]
+    """
+    group = _group_or_default_group(group)
+    if dst is None and group_dst is None:
+        dst = 0
+    group_dst = _canonicalize_group_rank(group, dst, group_dst, return_global=False)
+    if _rank_not_in_group(group):
+        _warn_not_in_group("gather_object")
+        return
+
+    # Ensure object_gather_list is specified appropriately.
+    my_group_rank = group.rank()
+    _validate_output_list_for_rank(my_group_rank, group_dst, object_gather_list)
+    current_device = _get_object_coll_device(group)
+    input_tensor, local_size = _object_to_tensor(obj, current_device, group)
+
+    # Gather all local sizes. This is so that we can find the max size, and index
+    # until the correct size when deserializing the tensors.
+    group_size = get_world_size(group=group)
+    object_sizes_tensor = torch.zeros(
+        group_size, dtype=torch.long, device=current_device
+    )
+    object_size_list = [
+        object_sizes_tensor[i].unsqueeze(dim=0) for i in range(group_size)
+    ]
+    # Allgather tensor sizes. An all-gather is needed here despite this being a
+    # gather, since each rank needs to broadcast a tensor of the same (maximal)
+    # size.
+    all_gather(object_size_list, local_size, group=group)
+    max_object_size = int(max(object_size_list).item())  # type: ignore[type-var]
+    # Resize tensor to max size across all ranks.
+    input_tensor.resize_(max_object_size)
+    # Avoid populating output tensors if the result won't be gathered on this rank.
+    if my_group_rank == group_dst:
+        coalesced_output_tensor = torch.empty(
+            max_object_size * group_size, dtype=torch.uint8, device=current_device
+        )
+        # Output tensors are nonoverlapping views of coalesced_output_tensor
+        output_tensors = [
+            coalesced_output_tensor[max_object_size * i : max_object_size * (i + 1)]
+            for i in range(group_size)
+        ]
+    # All ranks call gather with equal-sized tensors.
+    gather(
+        input_tensor,
+        gather_list=output_tensors if my_group_rank == group_dst else None,  # type: ignore[possibly-undefined]
+        group_dst=group_dst,
+        group=group,
+    )
+    if my_group_rank != group_dst:
+        return
+
+    if object_gather_list is None:
+        raise AssertionError("Must provide object_gather_list on dst rank")
+    # pyrefly: ignore  # unbound-name
+    for i, tensor in enumerate(output_tensors):
+        tensor = tensor.type(torch.uint8)
+        tensor_size = object_size_list[i]
+        object_gather_list[i] = _tensor_to_object(tensor, tensor_size, group)
+
+
+@_exception_logger
+def send_object_list(
+    object_list: list[Any],
+    dst: int | None = None,
+    group: ProcessGroup | None = None,
+    device: torch.device | None = None,
+    group_dst: int | None = None,
+    use_batch: bool = False,
+):
+    """
+    Sends picklable objects in ``object_list`` synchronously.
+
+    Similar to :func:`send`, but Python objects can be passed in.
+    Note that all objects in ``object_list`` must be picklable in order to be
+    sent.
+
+    Args:
+        object_list (List[Any]): List of input objects to sent.
+            Each object must be picklable. Receiver must provide lists of equal sizes.
+        dst (int): Destination rank to send ``object_list`` to.
+            Destination rank is based on global process group (regardless of ``group`` argument)
+        group: (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used. Default is ``None``.
+        device (``torch.device``, optional): If not None, the objects are
+            serialized and converted to tensors which are moved to the
+            ``device`` before sending. Default is ``None``.
+        group_dst (int, optional): Destination rank on ``group``.
+            Must specify one of ``dst`` and ``group_dst`` but not both
+        use_batch (bool, optional): If True, use batch p2p operations instead of
+            regular send operations. This avoids initializing 2-rank communicators and
+            uses existing entire group communicators. See batch_isend_irecv for usage and
+            assumptions. Default is ``False``.
+    Returns:
+        ``None``.
+
+    .. note:: For NCCL-based process groups, internal tensor representations
+        of objects must be moved to the GPU device before communication takes
+        place. In this case, the device used is given by
+        ``torch.cuda.current_device()`` and it is the user's responsibility to
+        ensure that this is set so that each rank has an individual GPU, via
+        ``torch.cuda.set_device()``.
+
+    .. warning::
+        Object collectives have a number of serious performance and scalability
+        limitations.  See :ref:`object_collectives` for details.
+
+    .. warning::
+        :func:`send_object_list` uses ``pickle`` module implicitly, which
+        is known to be insecure. It is possible to construct malicious pickle
+        data which will execute arbitrary code during unpickling. Only call this
+        function with data you trust.
+
+    .. warning::
+        Calling :func:`send_object_list` with GPU tensors is not well supported
+        and inefficient as it incurs GPU -> CPU transfer since tensors would be
+        pickled. Please consider using :func:`send` instead.
+
+    Example::
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> # Note: Process group initialization omitted on each rank.
+        >>> import torch.distributed as dist
+        >>> # Assumes backend is not NCCL
+        >>> device = torch.device("cpu")
+        >>> if dist.get_rank() == 0:
+        >>>     # Assumes world_size of 2.
+        >>>     objects = ["foo", 12, {1: 2}] # any picklable object
+        >>>     dist.send_object_list(objects, dst=1, device=device)
+        >>> else:
+        >>>     objects = [None, None, None]
+        >>>     dist.recv_object_list(objects, src=0, device=device)
+        >>> objects
+        ['foo', 12, {1: 2}]
+    """
+    group = _group_or_default_group(group)
+    group_dst = _canonicalize_group_rank(group, dst, group_dst)
+    _check_not_self_rank(group, group_dst, "destination")
+
+    if _rank_not_in_group(group):
+        _warn_not_in_group("send_object_list")
+        return
+
+    # Current device selection.
+    # To preserve backwards compatibility, ``device`` is default to ``None``
+    # in which case we run current logic of device selection, i.e.
+    # ``current_device`` is CUDA if backend is NCCL otherwise CPU device. In the
+    # case it is not ``None`` we move the size and object tensors to be
+    # sent to this device.
+    current_device = device or _get_object_coll_device(group)
+    # Serialize object_list elements to tensors on src rank.
+    tensor_list, size_list = zip(
+        *[_object_to_tensor(obj, current_device, group) for obj in object_list]
+    )
+    object_sizes_tensor = torch.cat(size_list)
+
+    # Send object sizes
+    if use_batch:
+        batch_isend_irecv(
+            [P2POp(isend, object_sizes_tensor, group_peer=group_dst, group=group)]
+        ).pop().wait()
+    else:
+        send(object_sizes_tensor, group_dst=group_dst, group=group)
+
+    # Concatenate and send serialized object tensors
+    # Note: torch.cat will do an extra memory copy to the current device, if the tensor_list
+    # has only one element, we can skip the copy.
+    if len(tensor_list) == 1:  # type: ignore[possibly-undefined]
+        object_tensor = tensor_list[0]
+    else:
+        object_tensor = torch.cat(tensor_list)
+
+    if use_batch:
+        batch_isend_irecv(
+            [P2POp(isend, object_tensor, group_peer=group_dst, group=group)]
+        ).pop().wait()
+    else:
+        send(object_tensor, group_dst=group_dst, group=group)
+
+
+@_exception_logger
+def recv_object_list(
+    object_list: list[Any],
+    src: int | None = None,
+    group: ProcessGroup | None = None,
+    device: torch.device | None = None,
+    group_src: int | None = None,
+    use_batch: bool = False,
+):
+    """
+    Receives picklable objects in ``object_list`` synchronously.
+
+    Similar to :func:`recv`, but can receive Python objects.
+
+    Args:
+        object_list (List[Any]): List of objects to receive into.
+            Must provide a list of sizes equal to the size of the list being sent.
+        src (int, optional): Source rank from which to recv ``object_list``.
+            Source rank is based on global process group (regardless of ``group`` argument)
+            Will receive from any rank if set to None. Default is ``None``.
+        group: (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used. Default is ``None``.
+        device (``torch.device``, optional): If not None, receives on this device.
+            Default is ``None``.
+        group_src (int, optional): Destination rank on ``group``.  Invalid to specify both ``src`` and ``group_src``.
+        use_batch (bool, optional): If True, use batch p2p operations instead of
+            regular send operations. This avoids initializing 2-rank communicators and
+            uses existing entire group communicators. See batch_isend_irecv for usage and
+            assumptions. Default is ``False``.
+
+    Returns:
+        Sender rank. -1 if rank is not part of the group. If rank is part of the group,
+        ``object_list`` will contain the sent objects from ``src`` rank.
+
+    .. note:: For NCCL-based process groups, internal tensor representations
+        of objects must be moved to the GPU device before communication takes
+        place. In this case, the device used is given by
+        ``torch.cuda.current_device()`` and it is the user's responsibility to
+        ensure that this is set so that each rank has an individual GPU, via
+        ``torch.cuda.set_device()``.
+
+    .. warning::
+        Object collectives have a number of serious performance and scalability
+        limitations.  See :ref:`object_collectives` for details.
+
+    .. warning::
+        :func:`recv_object_list` uses ``pickle`` module implicitly, which
+        is known to be insecure. It is possible to construct malicious pickle
+        data which will execute arbitrary code during unpickling. Only call this
+        function with data you trust.
+
+    .. warning::
+        Calling :func:`recv_object_list` with GPU tensors is not well supported
+        and inefficient as it incurs GPU -> CPU transfer since tensors would be
+        pickled. Please consider using :func:`recv` instead.
+
+    Example::
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> # Note: Process group initialization omitted on each rank.
+        >>> import torch.distributed as dist
+        >>> # Assumes backend is not NCCL
+        >>> device = torch.device("cpu")
+        >>> if dist.get_rank() == 0:
+        >>>     # Assumes world_size of 2.
+        >>>     objects = ["foo", 12, {1: 2}] # any picklable object
+        >>>     dist.send_object_list(objects, dst=1, device=device)
+        >>> else:
+        >>>     objects = [None, None, None]
+        >>>     dist.recv_object_list(objects, src=0, device=device)
+        >>> objects
+        ['foo', 12, {1: 2}]
+    """
+    group = _group_or_default_group(group)
+    group_src = _canonicalize_group_rank(group, src, group_src)
+    _check_not_self_rank(group, group_src, "source")
+
+    if _rank_not_in_group(group):
+        _warn_not_in_group("recv_object_list")
+        return -1
+
+    # Current device selection.
+    # To preserve backwards compatibility, ``device`` is default to ``None``
+    # in which case we run current logic of device selection, i.e.
+    # ``current_device`` is CUDA if backend is NCCL otherwise CPU device. In the
+    # case it is not ``None`` we move the size and object tensors to be
+    # received to this device.
+    current_device = device or _get_object_coll_device(group)
+    object_sizes_tensor = torch.empty(
+        len(object_list), dtype=torch.long, device=current_device
+    )
+
+    # Receive object sizes
+    if use_batch:
+        work = batch_isend_irecv(
+            [
+                P2POp(
+                    irecv,
+                    object_sizes_tensor,
+                    group_peer=group_src,
+                    group=group,
+                )
+            ]
+        ).pop()
+        work.wait()
+        rank_sizes = get_global_rank(group, group_src)
+    else:
+        rank_sizes = recv(object_sizes_tensor, group=group, group_src=group_src)
+
+    # Tensor to receive serialized objects into.
+    object_tensor = torch.empty(  # type: ignore[call-overload]
+        torch.sum(object_sizes_tensor).item(),  # type: ignore[arg-type]
+        dtype=torch.uint8,
+        device=current_device,
+    )
+
+    if use_batch:
+        work = batch_isend_irecv(
+            [
+                P2POp(
+                    irecv,
+                    object_tensor,
+                    group_peer=group_src,
+                    group=group,
+                )
+            ]
+        ).pop()
+        work.wait()
+        rank_objects = get_global_rank(group, group_src)
+    else:
+        rank_objects = recv(object_tensor, group=group, group_src=group_src)
+    if rank_sizes != rank_objects:
+        raise AssertionError("Mismatch in return ranks for object sizes and objects.")
+    # Deserialize objects using their stored sizes.
+    offset = 0
+    for i, obj_size in enumerate(object_sizes_tensor):
+        obj_view = object_tensor[offset : offset + obj_size]
+        obj_view = obj_view.type(torch.uint8)
+        offset += obj_size
+        object_list[i] = _tensor_to_object(obj_view, obj_size, group)
+    return rank_objects
+
+
+@_exception_logger
+def broadcast_object_list(
+    object_list: list[Any],
+    src: int | None = None,
+    group: ProcessGroup | None = None,
+    device: torch.device | None = None,
+    group_src: int | None = None,
+):
+    """
+    Broadcasts picklable objects in ``object_list`` to the whole group.
+
+    Similar to :func:`broadcast`, but Python objects can be passed in.
+    Note that all objects in ``object_list`` must be picklable in order to be
+    broadcasted.
+
+    Args:
+        object_list (List[Any]): List of input objects to broadcast.
+            Each object must be picklable. Only objects on the ``src`` rank will
+            be broadcast, but each rank must provide lists of equal sizes.
+        src (int): Source rank from which to broadcast ``object_list``.
+            Source rank is based on global process group (regardless of ``group`` argument)
+        group: (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used. Default is ``None``.
+        device (``torch.device``, optional): If not None, the objects are
+            serialized and converted to tensors which are moved to the
+            ``device`` before broadcasting. Default is ``None``.
+        group_src (int): Source rank on ``group``.  Must not specify one of ``group_src``
+            and ``src`` but not both.
+
+    Returns:
+        ``None``. If rank is part of the group, ``object_list`` will contain the
+        broadcasted objects from ``src`` rank.
+
+    .. note:: For NCCL-based process groups, internal tensor representations
+        of objects must be moved to the GPU device before communication takes
+        place. In this case, the device used is given by
+        ``torch.cuda.current_device()`` and it is the user's responsibility to
+        ensure that this is set so that each rank has an individual GPU, via
+        ``torch.cuda.set_device()``.
+
+    .. note:: Note that this API differs slightly from the :func:`broadcast`
+        collective since it does not provide an ``async_op`` handle and thus
+        will be a blocking call.
+
+    .. warning::
+        Object collectives have a number of serious performance and scalability
+        limitations.  See :ref:`object_collectives` for details.
+
+    .. warning::
+        :func:`broadcast_object_list` uses ``pickle`` module implicitly, which
+        is known to be insecure. It is possible to construct malicious pickle
+        data which will execute arbitrary code during unpickling. Only call this
+        function with data you trust.
+
+    .. warning::
+        Calling :func:`broadcast_object_list` with GPU tensors is not well supported
+        and inefficient as it incurs GPU -> CPU transfer since tensors would be
+        pickled. Please consider using :func:`broadcast` instead.
+
+    Example::
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> # Note: Process group initialization omitted on each rank.
+        >>> import torch.distributed as dist
+        >>> if dist.get_rank() == 0:
+        >>>     # Assumes world_size of 3.
+        >>>     objects = ["foo", 12, {1: 2}] # any picklable object
+        >>> else:
+        >>>     objects = [None, None, None]
+        >>> # Assumes backend is not NCCL
+        >>> device = torch.device("cpu")
+        >>> dist.broadcast_object_list(objects, src=0, device=device)
+        >>> objects
+        ['foo', 12, {1: 2}]
+    """
+    group = _group_or_default_group(group)
+    if src is None and group_src is None:
+        src = 0
+    group_src = _canonicalize_group_rank(group, src, group_src, return_global=False)
+    if _rank_not_in_group(group):
+        _warn_not_in_group("broadcast_object_list")
+        return
+
+    # Current device selection.
+    # To preserve backwards compatibility, ``device`` is default to ``None``
+    # in which case we run current logic of device selection, i.e.
+    # ``current_device`` is CUDA if backend is NCCL otherwise CPU device. In the
+    # case it is not ``None`` we move the size and object tensors to be
+    # broadcasted to this device.
+    current_device = device or _get_object_coll_device(group)
+    my_group_rank = group.rank()
+    # Serialize object_list elements to tensors on src rank.
+    if my_group_rank == group_src:
+        tensor_list, size_list = zip(
+            *[_object_to_tensor(obj, current_device, group) for obj in object_list]
+        )
+        object_sizes_tensor = torch.cat(size_list)
+    else:
+        object_sizes_tensor = torch.empty(
+            len(object_list), dtype=torch.long, device=current_device
+        )
+
+    # Broadcast object sizes
+    broadcast(object_sizes_tensor, group_src=group_src, group=group)
+
+    # Concatenate and broadcast serialized object tensors
+    # Note: torch.cat will do an extra memory copy to the current device, if the tensor_list
+    # has only one element, we can skip the copy.
+    if my_group_rank == group_src:
+        if len(tensor_list) == 1:  # type: ignore[possibly-undefined]
+            # pyrefly: ignore [unbound-name]
+            object_tensor = tensor_list[0]
+        else:
+            # pyrefly: ignore [unbound-name]
+            object_tensor = torch.cat(tensor_list)
+    else:
+        object_tensor = torch.empty(  # type: ignore[call-overload]
+            torch.sum(object_sizes_tensor).item(),  # type: ignore[arg-type]
+            dtype=torch.uint8,
+            device=current_device,
+        )
+
+    broadcast(object_tensor, group_src=group_src, group=group)
+    # Deserialize objects using their stored sizes.
+    offset = 0
+    if my_group_rank != group_src:
+        for i, obj_size in enumerate(object_sizes_tensor):
+            obj_view = object_tensor[offset : offset + obj_size]
+            obj_view = obj_view.type(torch.uint8)
+            offset += obj_size
+            object_list[i] = _tensor_to_object(obj_view, obj_size, group)
+
+
+@_exception_logger
+def scatter_object_list(
+    scatter_object_output_list: list[Any],
+    scatter_object_input_list: list[Any] | None = None,
+    src: int | None = None,
+    group: ProcessGroup | None = None,
+    group_src: int | None = None,
+):
+    """
+    Scatters picklable objects in ``scatter_object_input_list`` to the whole group.
+
+    Similar to :func:`scatter`, but Python objects can be passed in. On
+    each rank, the scattered object will be stored as the first element of
+    ``scatter_object_output_list``. Note that all objects in
+    ``scatter_object_input_list`` must be picklable in order to be scattered.
+
+    Args:
+        scatter_object_output_list (List[Any]): Non-empty list whose first
+            element will store the object scattered to this rank.
+        scatter_object_input_list (List[Any], optional): List of input objects to scatter.
+            Each object must be picklable. Only objects on the ``src`` rank will
+            be scattered, and the argument can be ``None`` for non-src ranks.
+        src (int): Source rank from which to scatter ``scatter_object_input_list``.
+            Source rank is based on global process group (regardless of ``group`` argument).
+            (If both ``src`` and ``group_src`` are None, default is global rank 0)
+        group: (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used. Default is ``None``.
+        group_src (int, optional): Source rank on ``group``.  Invalid to specify both ``src`` and ``group_src``
+
+    Returns:
+        ``None``. If rank is part of the group, ``scatter_object_output_list``
+        will have its first element set to the scattered object for this rank.
+
+    .. note:: Note that this API differs slightly from the scatter collective
+        since it does not provide an ``async_op`` handle and thus will be a
+        blocking call.
+
+    .. warning::
+        Object collectives have a number of serious performance and scalability
+        limitations.  See :ref:`object_collectives` for details.
+
+    .. warning::
+        :func:`scatter_object_list` uses ``pickle`` module implicitly, which
+        is known to be insecure. It is possible to construct malicious pickle
+        data which will execute arbitrary code during unpickling. Only call this
+        function with data you trust.
+
+    .. warning::
+        Calling :func:`scatter_object_list` with GPU tensors is not well supported
+        and inefficient as it incurs GPU -> CPU transfer since tensors would be
+        pickled. Please consider using :func:`scatter` instead.
+
+    Example::
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> # Note: Process group initialization omitted on each rank.
+        >>> import torch.distributed as dist
+        >>> if dist.get_rank() == 0:
+        >>>     # Assumes world_size of 3.
+        >>>     objects = ["foo", 12, {1: 2}] # any picklable object
+        >>> else:
+        >>>     # Can be any list on non-src ranks, elements are not used.
+        >>>     objects = [None, None, None]
+        >>> output_list = [None]
+        >>> dist.scatter_object_list(output_list, objects, src=0)
+        >>> # Rank i gets objects[i]. For example, on rank 2:
+        >>> output_list
+        [{1: 2}]
+    """
+    group = _group_or_default_group(group)
+    if src is None and group_src is None:
+        src = 0
+    group_src = _canonicalize_group_rank(group, src, group_src, return_global=False)
+    if _rank_not_in_group(group):
+        _warn_not_in_group("scatter_object_list")
+        return
+
+    if (
+        not isinstance(scatter_object_output_list, list)
+        or len(scatter_object_output_list) < 1
+    ):
+        raise ValueError(
+            "Expected argument scatter_object_output_list to be a list of size at least 1."
+        )
+
+    my_group_rank = group.rank()
+    pg_device = _get_object_coll_device(group)
+    if my_group_rank == group_src:
+        if scatter_object_input_list is None:
+            raise ValueError(
+                "source rank must provide non-None scatter_object_input_list"
+            )
+        tensor_list, tensor_sizes = zip(
+            *[
+                _object_to_tensor(obj, pg_device, group)
+                for obj in scatter_object_input_list
+            ]
+        )
+        tensor_list, tensor_sizes = list(tensor_list), list(tensor_sizes)
+
+        # Src rank broadcasts the maximum tensor size. This is because all ranks are
+        # expected to call into scatter() with equal-sized tensors.
+        max_tensor_size = max(tensor_sizes)  # type: ignore[possibly-undefined]
+        for tensor in tensor_list:  # type: ignore[possibly-undefined]
+            tensor.resize_(max_tensor_size)
+    else:
+        max_tensor_size = torch.tensor([0], dtype=torch.long, device=pg_device)
+    broadcast(max_tensor_size, group_src=group_src, group=group)
+
+    # Scatter actual serialized objects
+    # pyrefly: ignore [no-matching-overload]
+    output_tensor = torch.empty(
+        max_tensor_size.item(), dtype=torch.uint8, device=pg_device
+    )
+    scatter(
+        output_tensor,
+        scatter_list=None if my_group_rank != group_src else tensor_list,  # type: ignore[possibly-undefined]
+        group_src=group_src,
+        group=group,
+    )
+
+    # Scatter per-object sizes to trim tensors when deserializing back to object
+    obj_tensor_size = torch.tensor([0], dtype=torch.long, device=pg_device)
+    scatter(
+        obj_tensor_size,
+        scatter_list=None if my_group_rank != group_src else tensor_sizes,  # type: ignore[possibly-undefined]
+        group_src=group_src,
+        group=group,
+    )
+
+    # Deserialize back to object
+    scatter_object_output_list[0] = _tensor_to_object(
+        output_tensor, obj_tensor_size, group
+    )
+
+
+@_exception_logger
+def all_gather(tensor_list, tensor, group=None, async_op=False):
+    """
+    Gathers tensors from the whole group in a list.
+
+    Complex and uneven sized tensors are supported.
+
+    Args:
+        tensor_list (list[Tensor]): Output list. It should contain
+            correctly-sized tensors to be used for output of the collective.
+            Uneven sized tensors are supported.
+        tensor (Tensor): Tensor to be broadcast from current process.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group
+
+    Examples:
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> # All tensors below are of torch.int64 dtype.
+        >>> # We have 2 process groups, 2 ranks.
+        >>> device = torch.device(f"cuda:{rank}")
+        >>> tensor_list = [
+        ...     torch.zeros(2, dtype=torch.int64, device=device) for _ in range(2)
+        ... ]
+        >>> tensor_list
+        [tensor([0, 0], device='cuda:0'), tensor([0, 0], device='cuda:0')] # Rank 0
+        [tensor([0, 0], device='cuda:1'), tensor([0, 0], device='cuda:1')] # Rank 1
+        >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank
+        >>> tensor
+        tensor([1, 2], device='cuda:0') # Rank 0
+        tensor([3, 4], device='cuda:1') # Rank 1
+        >>> dist.all_gather(tensor_list, tensor)
+        >>> tensor_list
+        [tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0')] # Rank 0
+        [tensor([1, 2], device='cuda:1'), tensor([3, 4], device='cuda:1')] # Rank 1
+
+        >>> # All tensors below are of torch.cfloat dtype.
+        >>> # We have 2 process groups, 2 ranks.
+        >>> tensor_list = [
+        ...     torch.zeros(2, dtype=torch.cfloat, device=device) for _ in range(2)
+        ... ]
+        >>> tensor_list
+        [tensor([0.+0.j, 0.+0.j], device='cuda:0'), tensor([0.+0.j, 0.+0.j], device='cuda:0')] # Rank 0
+        [tensor([0.+0.j, 0.+0.j], device='cuda:1'), tensor([0.+0.j, 0.+0.j], device='cuda:1')] # Rank 1
+        >>> tensor = torch.tensor(
+        ...     [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device
+        ... ) + 2 * rank * (1 + 1j)
+        >>> tensor
+        tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0
+        tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1
+        >>> dist.all_gather(tensor_list, tensor)
+        >>> tensor_list
+        [tensor([1.+1.j, 2.+2.j], device='cuda:0'), tensor([3.+3.j, 4.+4.j], device='cuda:0')] # Rank 0
+        [tensor([1.+1.j, 2.+2.j], device='cuda:1'), tensor([3.+3.j, 4.+4.j], device='cuda:1')] # Rank 1
+
+    """
+    # Dynamo has built-in logic to map legacy distributed ops to functional collectives.
+    # Let's redirect to a torch function mode that can mimic this logic outside Dynamo
+    # (e.g., non-strict export implements such a torch function mode).
+    relevant_args = (tensor,)
+    if has_torch_function(relevant_args):
+        return handle_torch_function(
+            all_gather,
+            relevant_args,
+            tensor_list,
+            tensor,
+            group=group,
+            async_op=async_op,
+        )
+
+    _check_tensor_list(tensor_list, "tensor_list")
+    _check_single_tensor(tensor, "tensor")
+    _ensure_all_tensors_same_dtype(tensor_list, tensor)
+    if _rank_not_in_group(group):
+        _warn_not_in_group("all_gather")
+        return
+
+    tensor_list = [
+        t if not t.is_complex() else torch.view_as_real(t) for t in tensor_list
+    ]
+    tensor = tensor if not tensor.is_complex() else torch.view_as_real(tensor)
+
+    group = group or _get_default_group()
+    opts = AllgatherOptions()
+    opts.asyncOp = async_op
+    work = group.allgather([tensor_list], [tensor], opts)
+
+    if async_op:
+        return work
+    elif (
+        work is not None
+    ):  # Backward compatible with backends that don't sync at CPP level
+        work.wait()
+    # Otherwise, the backend has sync'ed at CPP level
+
+
+@_exception_logger
+def all_gather_into_tensor(output_tensor, input_tensor, group=None, async_op=False):
+    """
+    Gather tensors from all ranks and put them in a single output tensor.
+
+    This function requires all tensors to be the same size on each process.
+
+    Args:
+        output_tensor (Tensor): Output tensor to accommodate tensor elements
+            from all ranks. It must be correctly sized to have one of the
+            following forms:
+            (i) a concatenation of all the input tensors along the primary
+            dimension; for definition of "concatenation", see ``torch.cat()``;
+            (ii) a stack of all the input tensors along the primary dimension;
+            for definition of "stack", see ``torch.stack()``.
+            Examples below may better explain the supported output forms.
+        input_tensor (Tensor): Tensor to be gathered from current rank.
+            Different from the ``all_gather`` API, the input tensors in this
+            API must have the same size across all ranks.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group
+
+    Examples:
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> # All tensors below are of torch.int64 dtype and on CUDA devices.
+        >>> # We have two ranks.
+        >>> device = torch.device(f"cuda:{rank}")
+        >>> tensor_in = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank
+        >>> tensor_in
+        tensor([1, 2], device='cuda:0') # Rank 0
+        tensor([3, 4], device='cuda:1') # Rank 1
+        >>> # Output in concatenation form
+        >>> tensor_out = torch.zeros(world_size * 2, dtype=torch.int64, device=device)
+        >>> dist.all_gather_into_tensor(tensor_out, tensor_in)
+        >>> tensor_out
+        tensor([1, 2, 3, 4], device='cuda:0') # Rank 0
+        tensor([1, 2, 3, 4], device='cuda:1') # Rank 1
+        >>> # Output in stack form
+        >>> tensor_out2 = torch.zeros(world_size, 2, dtype=torch.int64, device=device)
+        >>> dist.all_gather_into_tensor(tensor_out2, tensor_in)
+        >>> tensor_out2
+        tensor([[1, 2],
+                [3, 4]], device='cuda:0') # Rank 0
+        tensor([[1, 2],
+                [3, 4]], device='cuda:1') # Rank 1
+    """
+    # Dynamo has built-in logic to map legacy distributed ops to functional collectives.
+    # Let's redirect to a torch function mode that can mimic this logic outside Dynamo
+    # (e.g., non-strict export implements such a torch function mode).
+    relevant_args = (input_tensor,)
+    if has_torch_function(relevant_args):
+        return handle_torch_function(
+            all_gather_into_tensor,
+            relevant_args,
+            output_tensor,
+            input_tensor,
+            group=group,
+            async_op=async_op,
+        )
+
+    _check_single_tensor(input_tensor, "input_tensor")
+    _check_single_tensor(output_tensor, "output_tensor")
+    if _rank_not_in_group(group):
+        _warn_not_in_group("all_gather_into_tensor")
+        return
+
+    output_tensor = (
+        output_tensor
+        if not output_tensor.is_complex()
+        else torch.view_as_real(output_tensor)
+    )
+    input_tensor = (
+        input_tensor
+        if not input_tensor.is_complex()
+        else torch.view_as_real(input_tensor)
+    )
+
+    opts = AllgatherOptions()
+    opts.asyncOp = async_op
+
+    group = group or _get_default_group()
+
+    if group in _world.pg_coalesce_state:
+        # We are in coalescing context, do not issue single operation, just append a collective representation
+        coll = _CollOp(all_gather_into_tensor, input_tensor, output_tensor)
+        _world.pg_coalesce_state[group].append(coll)
+        if async_op:
+            return _IllegalWork()
+        else:
+            return None
+
+    work = group._allgather_base(output_tensor, input_tensor, opts)
+
+    if async_op:
+        return work
+    elif (
+        work is not None
+    ):  # Backward compatible with backends that don't sync at CPP level
+        work.wait()
+    # Otherwise, the backend has sync'ed at CPP level
+
+
+@_exception_logger
+@deprecated(
+    "`torch.distributed._all_gather_base` is a private function and will be deprecated. "
+    "Please use `torch.distributed.all_gather_into_tensor` instead.",
+    category=FutureWarning,
+)
+def _all_gather_base(output_tensor, input_tensor, group=None, async_op: bool = False):
+    """
+    Single tensor all gather. Gathers a single tensor from all ranks, and puts them in a single output tensor.
+
+    Args:
+        output_tensor (Tensor): Output tensor. It should contain
+            correctly-sized tensors to be used for output of the collective.
+        input_tensor (Tensor): Tensor to be broadcast from current process.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group
+
+    .. warning::
+        `_all_gather_base` is a private function. Users should use
+        `all_gather_into_tensor` instead.
+
+    """
+    return all_gather_into_tensor(output_tensor, input_tensor, group, async_op)
+
+
+@_exception_logger
+@deprecated(
+    "`torch.distributed.all_gather_coalesced` will be deprecated. If you must use it, "
+    "please revisit our documentation later at "
+    "https://pytorch.org/docs/main/distributed.html#collective-functions",
+    category=FutureWarning,
+)
+def all_gather_coalesced(
+    output_tensor_lists, input_tensor_list, group=None, async_op: bool = False
+):
+    """
+    Gathers input tensors from the whole group in a list in a coalesced manner.
+
+    Complex tensors are supported.
+
+    Args:
+        output_tensor_lists (list[list[Tensor]]): Output list. It should contain
+            correctly-sized tensors to be used for output of the collective.
+        input_tensor_list (list[Tensor]): Tensors to be broadcast from
+            current process. At least one tensor has to be non empty.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op.
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group
+
+    Example:
+        we have 2 process groups, 2 ranks.
+        rank 0 passes:
+            input_tensor_list = [[[1, 1], [1, 1]], [2], [3, 3]]
+            output_tensor_lists =
+               [[[[-1, -1], [-1, -1]], [-1], [-1, -1]],
+                [[[-1, -1], [-1, -1]], [-1], [-1, -1]]]
+        rank 1 passes:
+            input_tensor_list = [[[3, 3], [3, 3]], [5], [1, 1]]
+            output_tensor_lists =
+               [[[[-1, -1], [-1, -1]], [-1], [-1, -1]],
+                [[[-1, -1], [-1, -1]], [-1], [-1, -1]]]
+        both rank 0 and 1 get:
+            output_tensor_lists =
+               [[[1, 1], [1, 1]], [2], [3, 3]],
+                [[3, 3], [3, 3]], [5], [1, 1]]].
+
+    WARNING: at this time individual shape checking is not implemented across nodes.
+    For example, if the rank 0 node passes [torch.rand(4), torch.rand(2)] and the
+    rank 1 node passes [torch.rand(2), torch.rand(2), torch.rand(2)], the
+    all_gather_coalesced operation will proceed without complaint and return
+    erroneous outputs. This lack of shape checking results in significant
+    performance improvements but users of this function should take extra care
+    to ensure that each node passes in tensors whose shapes match across nodes.
+    """
+    # We only check basic compatibility with C++ params here, C++ code will
+    # do shape and type checking.
+    if _rank_not_in_group(group):
+        _warn_not_in_group("all_gather_coalesced")
+        return
+    _check_tensor_list(input_tensor_list, "input_tensor_list")
+    _ensure_all_tensors_same_dtype(input_tensor_list)
+    if not isinstance(output_tensor_lists, list):
+        raise TypeError(
+            "Invalid function argument: output_tensor_lists should be a list"
+        )
+    for output_tensor_list in output_tensor_lists:
+        _check_tensor_list(output_tensor_list, "output_tensor_lists")
+        _ensure_all_tensors_same_dtype(output_tensor_list)
+
+    output_tensor_lists = [
+        [t if not t.is_complex() else torch.view_as_real(t) for t in l]
+        for l in output_tensor_lists
+    ]
+    input_tensor_list = [
+        t if not t.is_complex() else torch.view_as_real(t) for t in input_tensor_list
+    ]
+
+    group = group or _get_default_group()
+    opts = AllgatherOptions()
+    opts.asyncOp = async_op
+    work = group.allgather_coalesced(output_tensor_lists, input_tensor_list, opts)
+
+    if async_op:
+        return work.get_future()
+    elif (
+        work is not None
+    ):  # Backward compatible with backends that don't sync at CPP level
+        work.wait()
+    # Otherwise, the backend has sync'ed at CPP level
+
+
+def _validate_output_list_for_rank(my_rank: int, dst: int, gather_list):
+    if dst == my_rank:
+        if not gather_list:
+            raise ValueError(
+                "Argument ``gather_list`` must be specified on destination rank."
+            )
+    elif gather_list:
+        raise ValueError(
+            "Argument ``gather_list`` must NOT be specified on non-destination ranks."
+        )
+
+
+@_exception_logger
+def gather(
+    tensor: torch.Tensor,
+    gather_list: list[torch.Tensor] | None = None,
+    dst: int | None = None,
+    group: ProcessGroup | None = None,
+    async_op: bool = False,
+    group_dst: int | None = None,
+):
+    """
+    Gathers a list of tensors in a single process.
+
+    This function requires all tensors to be the same size on each process.
+
+    Args:
+        tensor (Tensor): Input tensor.
+        gather_list (list[Tensor], optional): List of appropriately,
+            same-sized tensors to use for gathered data
+            (default is None, must be specified on the destination rank)
+        dst (int, optional): Destination rank on global process group (regardless of ``group`` argument).
+            (If both ``dst`` and ``group_dst`` are None, default is global rank 0)
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op
+        group_dst (int, optional): Destination rank on ``group``.  Invalid to specify both ``dst`` and ``group_dst``
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group
+
+    .. note:: Note that all Tensors in gather_list must have the same size.
+
+    Example::
+        >>> # xdoctest: +SKIP("no rank")
+        >>> # We have 2 process groups, 2 ranks.
+        >>> tensor_size = 2
+        >>> device = torch.device(f'cuda:{rank}')
+        >>> tensor = torch.ones(tensor_size, device=device) + rank
+        >>> if dist.get_rank() == 0:
+        >>>     gather_list = [torch.zeros_like(tensor, device=device) for i in range(2)]
+        >>> else:
+        >>>     gather_list = None
+        >>> dist.gather(tensor, gather_list, dst=0)
+        >>> # Rank 0 gets gathered data.
+        >>> gather_list
+        [tensor([1., 1.], device='cuda:0'), tensor([2., 2.], device='cuda:0')] # Rank 0
+        None                                                                   # Rank 1
+
+    """
+    _check_single_tensor(tensor, "tensor")
+
+    # Parameter ``gather_list`` may be left unspecified on non-dst ranks.
+    if gather_list:
+        _check_tensor_list(gather_list, "gather_list")
+    else:
+        gather_list = []
+    _ensure_all_tensors_same_dtype(tensor, gather_list)
+    group = _group_or_default_group(group)
+    if _rank_not_in_group(group):
+        _warn_not_in_group("gather")
+        return
+    if dst is None and group_dst is None:
+        dst = 0
+    group_dst = _canonicalize_group_rank(group, dst, group_dst, return_global=False)
+    my_group_rank = group.rank()
+    _validate_output_list_for_rank(my_group_rank, group_dst, gather_list)
+    output_tensors = [gather_list] if group_dst == my_group_rank else []
+    input_tensors = [tensor]
+
+    opts = GatherOptions()
+    opts.rootRank = group_dst
+    opts.asyncOp = async_op
+    work = group.gather(output_tensors, input_tensors, opts)
+
+    if async_op:
+        return work
+    elif (
+        work is not None
+    ):  # Backward compatible with backends that don't sync at CPP level
+        work.wait()
+    # Otherwise, the backend has sync'ed at CPP level
+
+
+@_exception_logger
+def scatter(
+    tensor: torch.Tensor,
+    scatter_list: list[torch.Tensor] | None = None,
+    src: int | None = None,
+    group: ProcessGroup | None = None,
+    async_op: bool = False,
+    group_src: int | None = None,
+):
+    """
+    Scatters a list of tensors to all processes in a group.
+
+    Each process will receive exactly one tensor and store its data in the
+    ``tensor`` argument.
+
+    Complex tensors are supported.
+
+    Args:
+        tensor (Tensor): Output tensor.
+        scatter_list (list[Tensor]): List of tensors to scatter (default is
+            None, must be specified on the source rank)
+        src (int): Source rank on global process group (regardless of ``group`` argument).
+            (If both ``src`` and ``group_src`` are None, default is global rank 0)
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op
+        group_src (int, optional): Source rank on ``group``.  Invalid to specify both ``src`` and ``group_src``
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group
+
+    .. note:: Note that all Tensors in scatter_list must have the same size.
+
+    Example::
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> # Note: Process group initialization omitted on each rank.
+        >>> import torch.distributed as dist
+        >>> tensor_size = 2
+        >>> device = torch.device(f'cuda:{rank}')
+        >>> output_tensor = torch.zeros(tensor_size, device=device)
+        >>> if dist.get_rank() == 0:
+        >>>     # Assumes world_size of 2.
+        >>>     # Only tensors, all of which must be the same size.
+        >>>     t_ones = torch.ones(tensor_size, device=device)
+        >>>     t_fives = torch.ones(tensor_size, device=device) * 5
+        >>>     scatter_list = [t_ones, t_fives]
+        >>> else:
+        >>>     scatter_list = None
+        >>> dist.scatter(output_tensor, scatter_list, src=0)
+        >>> # Rank i gets scatter_list[i].
+        >>> output_tensor
+        tensor([1., 1.], device='cuda:0') # Rank 0
+        tensor([5., 5.], device='cuda:1') # Rank 1
+
+    """
+    _check_single_tensor(tensor, "tensor")
+    # Parameter ``scatter_list`` may be left unspecified on non-src ranks.
+    if scatter_list:
+        _check_tensor_list(scatter_list, "scatter_list")
+    else:
+        scatter_list = []
+    _ensure_all_tensors_same_dtype(tensor, scatter_list)
+    group = _group_or_default_group(group)
+    if src is None and group_src is None:
+        src = 0
+    group_src = _canonicalize_group_rank(group, src, group_src, return_global=False)
+    if _rank_not_in_group(group):
+        _warn_not_in_group("scatter")
+        return
+    scatter_list = [
+        t if not t.is_complex() else torch.view_as_real(t) for t in scatter_list
+    ]
+    tensor = tensor if not tensor.is_complex() else torch.view_as_real(tensor)
+
+    my_group_rank = group.rank()
+    if group_src == my_group_rank:
+        if not scatter_list:
+            raise ValueError(
+                "Argument ``scatter_list`` must be specified on source rank."
+            )
+        input_tensors = [scatter_list]
+        output_tensors = [tensor]
+    else:
+        if scatter_list:
+            raise ValueError(
+                "Argument ``scatter_list`` must NOT be specified on non-source ranks."
+            )
+        input_tensors = []
+        output_tensors = [tensor]
+
+    opts = ScatterOptions()
+    opts.rootRank = group_src
+    opts.asyncOp = async_op
+    work = group.scatter(output_tensors, input_tensors, opts)
+
+    if async_op:
+        return work
+    elif (
+        work is not None
+    ):  # Backward compatible with backends that don't sync at CPP level
+        work.wait()
+    # Otherwise, the backend has sync'ed at CPP level
+
+
+@_exception_logger
+def reduce_scatter(
+    output, input_list, op=ReduceOp.SUM, group=None, async_op: bool = False
+):
+    """
+    Reduces, then scatters a list of tensors to all processes in a group.
+
+    Args:
+        output (Tensor): Output tensor.
+        input_list (list[Tensor]): List of tensors to reduce and scatter.
+        op (optional): One of the values from
+            ``torch.distributed.ReduceOp``
+            enum.  Specifies an operation used for element-wise reductions.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op.
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group.
+
+    """
+    _check_single_tensor(output, "output")
+    _check_tensor_list(input_list, "input_list")
+    _ensure_all_tensors_same_dtype(output, input_list)
+    if _rank_not_in_group(group):
+        _warn_not_in_group("reduce_scatter")
+        return
+
+    opts = ReduceScatterOptions()
+    opts.reduceOp = op
+    opts.asyncOp = async_op
+
+    group = group or _get_default_group()
+    work = group.reduce_scatter([output], [input_list], opts)
+
+    if async_op:
+        return work
+    elif (
+        work is not None
+    ):  # Backward compatible with backends that don't sync at CPP level
+        work.wait()
+    # Otherwise, the backend has sync'ed at CPP level
+
+
+@_exception_logger
+def reduce_scatter_tensor(output, input, op=ReduceOp.SUM, group=None, async_op=False):
+    """
+    Reduces, then scatters a tensor to all ranks in a group.
+
+    Args:
+        output (Tensor): Output tensor. It should have the same size across all
+            ranks.
+        input (Tensor): Input tensor to be reduced and scattered. Its size
+            should be output tensor size times the world size. The input tensor
+            can have one of the following shapes:
+            (i) a concatenation of the output tensors along the primary
+            dimension, or
+            (ii) a stack of the output tensors along the primary dimension.
+            For definition of "concatenation", see ``torch.cat()``.
+            For definition of "stack", see ``torch.stack()``.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op.
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group.
+
+    Examples:
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> # All tensors below are of torch.int64 dtype and on CUDA devices.
+        >>> # We have two ranks.
+        >>> device = torch.device(f"cuda:{rank}")
+        >>> tensor_out = torch.zeros(2, dtype=torch.int64, device=device)
+        >>> # Input in concatenation form
+        >>> tensor_in = torch.arange(world_size * 2, dtype=torch.int64, device=device)
+        >>> tensor_in
+        tensor([0, 1, 2, 3], device='cuda:0') # Rank 0
+        tensor([0, 1, 2, 3], device='cuda:1') # Rank 1
+        >>> dist.reduce_scatter_tensor(tensor_out, tensor_in)
+        >>> tensor_out
+        tensor([0, 2], device='cuda:0') # Rank 0
+        tensor([4, 6], device='cuda:1') # Rank 1
+        >>> # Input in stack form
+        >>> tensor_in = torch.reshape(tensor_in, (world_size, 2))
+        >>> tensor_in
+        tensor([[0, 1],
+                [2, 3]], device='cuda:0') # Rank 0
+        tensor([[0, 1],
+                [2, 3]], device='cuda:1') # Rank 1
+        >>> dist.reduce_scatter_tensor(tensor_out, tensor_in)
+        >>> tensor_out
+        tensor([0, 2], device='cuda:0') # Rank 0
+        tensor([4, 6], device='cuda:1') # Rank 1
+
+    """
+    # Dynamo has built-in logic to map legacy distributed ops to functional collectives.
+    # Let's redirect to a torch function mode that can mimic this logic outside Dynamo
+    # (e.g., non-strict export implements such a torch function mode).
+    relevant_args = (input,)
+    if has_torch_function(relevant_args):
+        return handle_torch_function(
+            reduce_scatter_tensor,
+            relevant_args,
+            output,
+            input,
+            op=op,
+            group=group,
+            async_op=async_op,
+        )
+
+    _check_single_tensor(output, "output")
+    _check_single_tensor(input, "input")
+
+    if _rank_not_in_group(group):
+        _warn_not_in_group("reduce_scatter_tensor")
+        return
+
+    opts = ReduceScatterOptions()
+    opts.reduceOp = op
+    opts.asyncOp = async_op
+
+    group = group or _get_default_group()
+
+    # Check if we are in coalescing context
+    # If we are, do not issue single operation, just append a collective representation
+    if group in _world.pg_coalesce_state:
+        coll = _CollOp(reduce_scatter_tensor, input, output, op, None)
+        _world.pg_coalesce_state[group].append(coll)
+        if async_op:
+            return _IllegalWork()
+        else:
+            return None
+
+    work = group._reduce_scatter_base(output, input, opts)
+
+    if async_op:
+        return work
+    elif (
+        work is not None
+    ):  # Backward compatible with backends that don't sync at CPP level
+        work.wait()
+    # Otherwise, the backend has sync'ed at CPP level
+
+
+@deprecated(
+    "`torch.distributed._reduce_scatter_base` is a private function and will be deprecated. "
+    "Please use `torch.distributed.reduce_scatter_tensor` instead.",
+    category=FutureWarning,
+)
+def _reduce_scatter_base(
+    output, input, op=ReduceOp.SUM, group=None, async_op: bool = False
+):
+    """
+    Reduces, then scatters a flattened tensor to all processes in a group.
+
+    Args:
+        output (Tensor): Output tensor.
+        input (Tensor): Input tensor that is of size output tensor size times world size
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op.
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group.
+
+    .. warning::
+        `_reduce_scatter_base` is a private function. Users should use
+        `reduce_scatter_tensor` instead.
+
+    """
+    return reduce_scatter_tensor(output, input, op, group, async_op)
+
+
+@_exception_logger
+def all_to_all_single(
+    output,
+    input,
+    output_split_sizes=None,
+    input_split_sizes=None,
+    group=None,
+    async_op: bool = False,
+):
+    """
+    Split input tensor and then scatter the split list to all processes in a group.
+
+    Later the received tensors are concatenated from all the processes in the group
+    and returned as a single output tensor.
+
+    Complex tensors are supported.
+
+    Args:
+        output (Tensor): Gathered concatenated output tensor.
+        input (Tensor): Input tensor to scatter.
+        output_split_sizes: (list[Int], optional): Output split sizes for dim 0
+            if specified None or empty, dim 0 of ``output`` tensor must divide
+            equally by ``world_size``.
+        input_split_sizes: (list[Int], optional): Input split sizes for dim 0
+            if specified None or empty, dim 0 of ``input`` tensor must divide
+            equally by ``world_size``.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op.
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group.
+
+    .. warning::
+        `all_to_all_single` is experimental and subject to change.
+
+    Examples:
+        >>> # xdoctest: +SKIP("Undefined rank")
+        >>> input = torch.arange(4) + rank * 4
+        >>> input
+        tensor([0, 1, 2, 3])     # Rank 0
+        tensor([4, 5, 6, 7])     # Rank 1
+        tensor([8, 9, 10, 11])   # Rank 2
+        tensor([12, 13, 14, 15]) # Rank 3
+        >>> output = torch.empty([4], dtype=torch.int64)
+        >>> dist.all_to_all_single(output, input)
+        >>> output
+        tensor([0, 4, 8, 12])    # Rank 0
+        tensor([1, 5, 9, 13])    # Rank 1
+        tensor([2, 6, 10, 14])   # Rank 2
+        tensor([3, 7, 11, 15])   # Rank 3
+
+        >>> # Essentially, it is similar to following operation:
+        >>> scatter_list = list(input.chunk(world_size))
+        >>> gather_list = list(output.chunk(world_size))
+        >>> for i in range(world_size):
+        >>>     dist.scatter(gather_list[i], scatter_list if i == rank else [], src = i)
+
+        >>> # Another example with uneven split
+        >>> input
+        tensor([0, 1, 2, 3, 4, 5])                                       # Rank 0
+        tensor([10, 11, 12, 13, 14, 15, 16, 17, 18])                     # Rank 1
+        tensor([20, 21, 22, 23, 24])                                     # Rank 2
+        tensor([30, 31, 32, 33, 34, 35, 36])                             # Rank 3
+        >>> input_splits
+        [2, 2, 1, 1]                                                     # Rank 0
+        [3, 2, 2, 2]                                                     # Rank 1
+        [2, 1, 1, 1]                                                     # Rank 2
+        [2, 2, 2, 1]                                                     # Rank 3
+        >>> output_splits
+        [2, 3, 2, 2]                                                     # Rank 0
+        [2, 2, 1, 2]                                                     # Rank 1
+        [1, 2, 1, 2]                                                     # Rank 2
+        [1, 2, 1, 1]                                                     # Rank 3
+        >>> output = ...
+        >>> dist.all_to_all_single(output, input, output_splits, input_splits)
+        >>> output
+        tensor([ 0,  1, 10, 11, 12, 20, 21, 30, 31])                     # Rank 0
+        tensor([ 2,  3, 13, 14, 22, 32, 33])                             # Rank 1
+        tensor([ 4, 15, 16, 23, 34, 35])                                 # Rank 2
+        tensor([ 5, 17, 18, 24, 36])                                     # Rank 3
+
+
+        >>> # Another example with tensors of torch.cfloat type.
+        >>> input = torch.tensor(
+        ...     [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat
+        ... ) + 4 * rank * (1 + 1j)
+        >>> input
+        tensor([1+1j, 2+2j, 3+3j, 4+4j])                                # Rank 0
+        tensor([5+5j, 6+6j, 7+7j, 8+8j])                                # Rank 1
+        tensor([9+9j, 10+10j, 11+11j, 12+12j])                          # Rank 2
+        tensor([13+13j, 14+14j, 15+15j, 16+16j])                        # Rank 3
+        >>> output = torch.empty([4], dtype=torch.int64)
+        >>> dist.all_to_all_single(output, input)
+        >>> output
+        tensor([1+1j, 5+5j, 9+9j, 13+13j])                              # Rank 0
+        tensor([2+2j, 6+6j, 10+10j, 14+14j])                            # Rank 1
+        tensor([3+3j, 7+7j, 11+11j, 15+15j])                            # Rank 2
+        tensor([4+4j, 8+8j, 12+12j, 16+16j])                            # Rank 3
+    """
+    # Dynamo has built-in logic to map legacy distributed ops to functional collectives.
+    # Let's redirect to a torch function mode that can mimic this logic outside Dynamo
+    # (e.g., non-strict export implements such a torch function mode).
+    relevant_args = (input,)
+    if has_torch_function(relevant_args):
+        return handle_torch_function(
+            all_to_all_single,
+            relevant_args,
+            output,
+            input,
+            output_split_sizes=output_split_sizes,
+            input_split_sizes=input_split_sizes,
+            group=group,
+            async_op=async_op,
+        )
+
+    if _rank_not_in_group(group):
+        _warn_not_in_group("all_to_all_single")
+        return
+
+    opts = AllToAllOptions()
+    opts.asyncOp = async_op
+    _check_single_tensor(output, "output")
+    _check_single_tensor(input, "input")
+    _ensure_all_tensors_same_dtype(output, input)
+
+    if input.is_complex():
+        input = torch.view_as_real(input)
+    if output.is_complex():
+        output = torch.view_as_real(output)
+
+    output_split_sizes = [] if output_split_sizes is None else output_split_sizes
+    input_split_sizes = [] if input_split_sizes is None else input_split_sizes
+
+    group = group or _get_default_group()
+    work = group.alltoall_base(
+        output, input, output_split_sizes, input_split_sizes, opts
+    )
+
+    if async_op:
+        return work
+    elif (
+        work is not None
+    ):  # Backward compatible with backends that don't sync at CPP level
+        work.wait()
+    # Otherwise, the backend has sync'ed at CPP level
+
+
+@_exception_logger
+def all_to_all(
+    output_tensor_list, input_tensor_list, group=None, async_op: bool = False
+):
+    """
+    Scatters list of input tensors to all processes in a group and return gathered list of tensors in output list.
+
+    Complex tensors are supported.
+
+    Args:
+        output_tensor_list (list[Tensor]): List of tensors to be gathered one
+            per rank.
+        input_tensor_list (list[Tensor]): List of tensors to scatter one per rank.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op.
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group.
+
+    .. warning::
+        `all_to_all` is experimental and subject to change.
+
+    Examples:
+        >>> # xdoctest: +SKIP("Undefined rank")
+        >>> input = torch.arange(4) + rank * 4
+        >>> input = list(input.chunk(4))
+        >>> input
+        [tensor([0]), tensor([1]), tensor([2]), tensor([3])]     # Rank 0
+        [tensor([4]), tensor([5]), tensor([6]), tensor([7])]     # Rank 1
+        [tensor([8]), tensor([9]), tensor([10]), tensor([11])]   # Rank 2
+        [tensor([12]), tensor([13]), tensor([14]), tensor([15])] # Rank 3
+        >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4))
+        >>> dist.all_to_all(output, input)
+        >>> output
+        [tensor([0]), tensor([4]), tensor([8]), tensor([12])]    # Rank 0
+        [tensor([1]), tensor([5]), tensor([9]), tensor([13])]    # Rank 1
+        [tensor([2]), tensor([6]), tensor([10]), tensor([14])]   # Rank 2
+        [tensor([3]), tensor([7]), tensor([11]), tensor([15])]   # Rank 3
+
+        >>> # Essentially, it is similar to following operation:
+        >>> scatter_list = input
+        >>> gather_list = output
+        >>> for i in range(world_size):
+        >>>     dist.scatter(gather_list[i], scatter_list if i == rank else [], src=i)
+
+        >>> input
+        tensor([0, 1, 2, 3, 4, 5])                                       # Rank 0
+        tensor([10, 11, 12, 13, 14, 15, 16, 17, 18])                     # Rank 1
+        tensor([20, 21, 22, 23, 24])                                     # Rank 2
+        tensor([30, 31, 32, 33, 34, 35, 36])                             # Rank 3
+        >>> input_splits
+        [2, 2, 1, 1]                                                     # Rank 0
+        [3, 2, 2, 2]                                                     # Rank 1
+        [2, 1, 1, 1]                                                     # Rank 2
+        [2, 2, 2, 1]                                                     # Rank 3
+        >>> output_splits
+        [2, 3, 2, 2]                                                     # Rank 0
+        [2, 2, 1, 2]                                                     # Rank 1
+        [1, 2, 1, 2]                                                     # Rank 2
+        [1, 2, 1, 1]                                                     # Rank 3
+        >>> input = list(input.split(input_splits))
+        >>> input
+        [tensor([0, 1]), tensor([2, 3]), tensor([4]), tensor([5])]                   # Rank 0
+        [tensor([10, 11, 12]), tensor([13, 14]), tensor([15, 16]), tensor([17, 18])] # Rank 1
+        [tensor([20, 21]), tensor([22]), tensor([23]), tensor([24])]                 # Rank 2
+        [tensor([30, 31]), tensor([32, 33]), tensor([34, 35]), tensor([36])]         # Rank 3
+        >>> output = ...
+        >>> dist.all_to_all(output, input)
+        >>> output
+        [tensor([0, 1]), tensor([10, 11, 12]), tensor([20, 21]), tensor([30, 31])]   # Rank 0
+        [tensor([2, 3]), tensor([13, 14]), tensor([22]), tensor([32, 33])]           # Rank 1
+        [tensor([4]), tensor([15, 16]), tensor([23]), tensor([34, 35])]              # Rank 2
+        [tensor([5]), tensor([17, 18]), tensor([24]), tensor([36])]                  # Rank 3
+
+        >>> # Another example with tensors of torch.cfloat type.
+        >>> input = torch.tensor(
+        ...     [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat
+        ... ) + 4 * rank * (1 + 1j)
+        >>> input = list(input.chunk(4))
+        >>> input
+        [tensor([1+1j]), tensor([2+2j]), tensor([3+3j]), tensor([4+4j])]            # Rank 0
+        [tensor([5+5j]), tensor([6+6j]), tensor([7+7j]), tensor([8+8j])]            # Rank 1
+        [tensor([9+9j]), tensor([10+10j]), tensor([11+11j]), tensor([12+12j])]      # Rank 2
+        [tensor([13+13j]), tensor([14+14j]), tensor([15+15j]), tensor([16+16j])]    # Rank 3
+        >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4))
+        >>> dist.all_to_all(output, input)
+        >>> output
+        [tensor([1+1j]), tensor([5+5j]), tensor([9+9j]), tensor([13+13j])]          # Rank 0
+        [tensor([2+2j]), tensor([6+6j]), tensor([10+10j]), tensor([14+14j])]        # Rank 1
+        [tensor([3+3j]), tensor([7+7j]), tensor([11+11j]), tensor([15+15j])]        # Rank 2
+        [tensor([4+4j]), tensor([8+8j]), tensor([12+12j]), tensor([16+16j])]        # Rank 3
+
+    """
+    if _rank_not_in_group(group):
+        _warn_not_in_group("all_to_all")
+        return
+
+    opts = AllToAllOptions()
+    opts.asyncOp = async_op
+    _check_tensor_list(output_tensor_list, "output_tensor_list")
+    _check_tensor_list(input_tensor_list, "input_tensor_list")
+    _ensure_all_tensors_same_dtype(output_tensor_list, input_tensor_list)
+
+    input_tensor_list = [
+        t if not t.is_complex() else torch.view_as_real(t) for t in input_tensor_list
+    ]
+    output_tensor_list = [
+        t if not t.is_complex() else torch.view_as_real(t) for t in output_tensor_list
+    ]
+
+    group = group or _get_default_group()
+    work = group.alltoall(output_tensor_list, input_tensor_list, opts)
+
+    if async_op:
+        return work
+    elif (
+        work is not None
+    ):  # Backward compatible with backends that don't sync at CPP level
+        work.wait()
+    # Otherwise, the backend has sync'ed at CPP level
+
+
+@_exception_logger
+def barrier(
+    group: ProcessGroup | None = GroupMember.WORLD,
+    async_op: bool = False,
+    device_ids=None,
+):
+    """
+    Synchronize all processes.
+
+    This collective blocks processes until the whole group enters this function,
+    if async_op is False, or if async work handle is called on wait().
+
+    Args:
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+        async_op (bool, optional): Whether this op should be an async op
+        device_ids ([int], optional): List of device/GPU ids. Only one id is expected.
+
+    Returns:
+        Async work handle, if async_op is set to True.
+        None, if not async_op or if not part of the group
+
+    .. note:: `ProcessGroupNCCL` now blocks the cpu thread till the completion of the barrier collective.
+    .. note:: `ProcessGroupNCCL` implements barrier as an all_reduce of a 1-element tensor. A device must be chosen
+       for allocating this tensor.  The device choice is made by checking in this order (1) the first device passed to
+       `device_ids` arg of barrier if not None, (2) the device passed to init_process_group if not None, (3) the device
+       that was first used with this process group, if another collective with tensor inputs has been performed, (4)
+       the device index indicated by the global rank mod local device count.
+    """
+    group = group or _get_default_group()
+
+    if _rank_not_in_group(group):
+        _warn_not_in_group("barrier")
+        return
+
+    opts = BarrierOptions()
+    opts.asyncOp = async_op
+    # Detect the accelerator on the machine. If no accelerator is available, it
+    # returns CPU.
+    device = torch._C._get_accelerator()
+    if isinstance(device_ids, list):
+        opts.device_ids = device_ids
+        # use only the first device id
+        # pyrefly: ignore [read-only]
+        opts.device = torch.device(device.type, device_ids[0])
+    elif getattr(group, "bound_device_id", None) is not None:
+        # Use device id from `init_process_group(device_id=...)`
+        opts.device = group.bound_device_id  # type: ignore[assignment]
+    elif device.type == "cpu" or _get_object_coll_device(group) == "cpu":
+        # pyrefly: ignore [read-only]
+        opts.device = torch.device("cpu")
+    else:
+        # Use the current device set by the user. If user did not set any, this
+        # may use default device 0, causing issues like hang or all processes
+        # creating context on device 0.
+        # pyrefly: ignore [read-only]
+        opts.device = device
+        if group.rank() == 0:
+            warnings.warn(  # warn only once
+                "barrier(): using the device under current context. "
+                "You can specify `device_id` in `init_process_group` to mute this warning.",
+                stacklevel=2,
+            )
+
+    work = group.barrier(opts=opts)
+
+    if async_op:
+        return work
+    elif (
+        work is not None
+    ):  # Backward compatible with backends that don't sync at CPP level
+        work.wait()
+    # Otherwise, the backend has sync'ed at CPP level
+
+
+def monitored_barrier(
+    group: ProcessGroup | None = GroupMember.WORLD,
+    timeout=None,
+    wait_all_ranks: bool = False,
+):
+    """
+    Synchronize processes similar to ``torch.distributed.barrier``, but consider a configurable timeout.
+
+    It is able to report ranks that did not pass this barrier within the provided timeout.
+    Specifically, for non-zero ranks, will block until a send/recv is processed from rank 0.
+    Rank 0 will block until all send /recv from other ranks are processed, and will report
+    failures for ranks that failed to respond in time. Note that if one rank does not reach the
+    monitored_barrier (for example due to a hang), all other ranks would fail in monitored_barrier.
+
+    This collective will block all processes/ranks in the group, until the
+    whole group exits the function successfully, making it useful for debugging
+    and synchronizing. However, it can have a performance impact and should only
+    be used for debugging or scenarios that require full synchronization points
+    on the host-side. For debugging purposes, this barrier can be inserted
+    before the application's collective calls to check if any ranks are
+    desynchronized.
+
+    .. note:: Note that this collective is only supported with the GLOO backend.
+
+    Args:
+        group (ProcessGroup, optional): The process group to work on. If
+            ``None``, the default process group will be used.
+        timeout (datetime.timedelta, optional): Timeout for monitored_barrier.
+            If ``None``, the default process group timeout will be used.
+        wait_all_ranks (bool, optional): Whether to collect all failed ranks or
+            not. By default, this is ``False`` and ``monitored_barrier`` on rank 0
+            will throw on the first failed rank it encounters in order to fail
+            fast. By setting ``wait_all_ranks=True`` ``monitored_barrier`` will
+            collect all failed ranks and throw an error containing information
+            about all failed ranks.
+
+    Returns:
+        ``None``.
+
+    Example::
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> # Note: Process group initialization omitted on each rank.
+        >>> import torch.distributed as dist
+        >>> if dist.get_rank() != 1:
+        >>>     dist.monitored_barrier() # Raises exception indicating that
+        >>> # rank 1 did not call into monitored_barrier.
+        >>> # Example with wait_all_ranks=True
+        >>> if dist.get_rank() == 0:
+        >>>     dist.monitored_barrier(wait_all_ranks=True) # Raises exception
+        >>> # indicating that ranks 1, 2, ... world_size - 1 did not call into
+        >>> # monitored_barrier.
+    """
+    # Need to call rank not in group before using the group, otherwise
+    # "Invalid process group" error is raised.
+    if _rank_not_in_group(group):
+        _warn_not_in_group("monitored_barrier")
+        return
+
+    if get_backend(group) != Backend.GLOO:
+        raise ValueError("monitored_barrier is only implemented for GLOO backend.")
+
+    if timeout is None:
+        timeout = _get_default_timeout(get_backend(group))
+    elif isinstance(timeout, float):
+        # TODO(whc) apparently some existing test case for monitored_barrier passes in a timeout in float format?
+        warnings.warn(
+            "Please specify timeout arg as a timedelta. "
+            f"Converting current value of {timeout} assuming it represents seconds",
+            stacklevel=2,
+        )
+        timeout = timedelta(seconds=timeout)
+
+    _check_valid_timeout(timeout)
+
+    group_to_use = _get_default_group() if group is None else group
+    return group_to_use.monitored_barrier(  # type:ignore[attr-defined]
+        timeout, wait_all_ranks=wait_all_ranks
+    )
+
+
+def _create_process_group_wrapper(
+    wrapped_pg: torch._C._distributed_c10d.Backend,
+    store_prefix: str,
+    store: Store,
+    rank: int,
+    world_size: int,
+    timeout: timedelta = default_pg_timeout,
+):
+    if not _GLOO_AVAILABLE:
+        raise AssertionError("ProcessGroupWrapper unsupported without GLOO backend.")
+
+    # (whc) this appears to be just for the gloo backend? if so, `default_pg_timeout` is appropriate...
+
+    # Create a separate prefix store for the helper process group.
+    prefix = f"{PG_WRAPPER_STORE_PREFIX}:{store_prefix}"
+    store = PrefixStore(prefix, store)
+    helper_pg = ProcessGroupGloo(store, rank, world_size, timeout=timeout)
+    # Wrap the underlying pg with ProcessGroupWrapper.
+    wrapped_pg = _ProcessGroupWrapper(wrapped_pg, helper_pg)
+    return wrapped_pg
+
+
+# helper function for deterministically hashing a list of ranks to a unique
+# string
+def _hash_ranks_to_str(ranks: list[int]) -> str:
+    rank_join: str = "_".join(map(str, ranks))
+    # In case there is already a PG with the same rank composition
+    unique_str = "_".join([rank_join, str(len(_world.pg_names))])
+    return hashlib.sha1(bytes(unique_str, "utf-8"), usedforsecurity=False).hexdigest()
+
+
+# Takes a list of ranks and computes an integer color
+def _process_group_color(ranks: list[int]) -> int:
+    # Convert list to tuple to make it hashable
+    # pyrefly: ignore [bad-assignment]
+    ranks = tuple(ranks)
+    hash_value = hash(ranks)
+    # Split color must be:
+    # - a non-negative integer;
+    # - a type compatible with C's int because we are pybinding to the latter.
+    # Thus, we limit the hash value within c_int's max value.
+    max_c_int = 2 ** (ctypes.sizeof(ctypes.c_int) * 8 - 1)
+    color = abs(hash_value) % max_c_int
+    return color
+
+
+def _process_group_name(ranks, use_hashed_name) -> GroupName:
+    # Create name for a process group.
+    global _world
+    if use_hashed_name:
+        pg_name = GroupName(_hash_ranks_to_str(ranks))
+    else:
+        pg_name = GroupName(str(_world.group_count))
+        _world.group_count += 1
+    # TODO: why is group count incremented only in the else path?
+    return pg_name
+
+
+def _get_backend_from_str(backend: str | None = None) -> Backend:
+    # Default to the same backend as the global process group
+    #  if backend is not specified.
+    if not backend:
+        backend = get_backend(_get_default_group())
+    return Backend(backend)
+
+
+def _is_safe_to_split() -> bool:
+    """
+    Checks if it is safe to split the any process group in the world.
+    This is only safe if the default pg has a bound device id, otherwise
+    users must be aware that a pg is only splittable after the first collective is
+    issued.
+    """
+    return _get_default_group().bound_device_id is not None
+
+
+@_time_logger
+def split_group(
+    parent_pg: ProcessGroup | None = None,
+    split_ranks: list | None = None,
+    timeout: timedelta | None = None,
+    pg_options: Any | None = None,
+    group_desc: str | None = None,
+) -> ProcessGroup | None:
+    """
+    Create a new process group split from the given parent process group.
+
+    warning:: This is an experimental API. Only the ``NCCL`` and custom plugin backends
+    are supported. Other backends will raise an error.
+    Users of this API must guarantee that all ranks in the parent group enter this API call,
+    and the split of the sub groups is the same across all ranks in the parent group.
+
+    Args:
+        parent_pg (ProcessGroup, optional): The parent process group. If None,
+            the default process group will be used. Users need to guarantee that
+            the parent group is fully initialized (e.g, communicators are initialized)
+        split_ranks (list[list[int]]): the split ranks, which is a list of list of ranks.
+            Users need to make sure the validity of the split ranks such that one
+            split (represented by one inner list of ints) does not overlap with any other split.
+            Note that the ranks in each split is the group rank (instead of global rank)
+            in the parent pg. For example, if the parent group has 4 ranks, and split_ranks can be
+            [[0, 1], [2, 3]]. Note [[0,1]] is also a valid split, in which case ranks 2, 3 would
+            return a non-group member.
+        timeout (timedelta, optional): see `init_process_group` for details and default value.
+        pg_options (ProcessGroupOptions, optional): Additional options need to be passed in during
+            the construction of specific process groups. i.e.``is_high_priority_stream``
+            can be specified so that process group can pick up high priority cuda streams.
+        group_desc (str, optional): a string to describe the process group.
+
+    Returns:
+        ProcessGroup if the current rank is within one split/subgroup given by split_ranks,
+        or None if the current rank is not part of any split_ranks`.
+
+    """
+    # check inputs
+    if split_ranks is None or len(split_ranks) == 0:
+        raise ValueError("split_ranks cannot be None or empty")
+
+    global _world
+    default_pg = _get_default_group()
+    device_id = default_pg.bound_device_id
+    if not device_id:
+        raise RuntimeError(
+            "No device associated with the default pg, not safe to split any process groups"
+        )
+    global_rank = default_pg.rank()
+    global_world_size = default_pg.size()
+
+    if not parent_pg:
+        parent_pg = default_pg
+    if parent_pg not in _world.pg_group_ranks:
+        raise ValueError(f"Group {parent_pg} is not registered")
+
+    parent_global_to_group_ranks = _world.pg_group_ranks[parent_pg]
+    parent_group_to_global_ranks = {
+        group_rank: global_rank
+        for global_rank, group_rank in parent_global_to_group_ranks.items()
+    }
+
+    if global_rank not in parent_global_to_group_ranks:
+        raise ValueError(
+            f"Global rank {global_rank} is not part of the parent group {parent_pg}"
+        )
+
+    parent_group_rank = parent_global_to_group_ranks[global_rank]
+    parent_backend = parent_pg._get_backend(torch.device("cuda"))
+
+    # if the parent backend does not support splitting, raise error
+    # currently this API only support NCCL backend
+    if not parent_backend or not parent_backend.supports_splitting:
+        raise RuntimeError(
+            "No backend for the parent process group or its backend does not support splitting"
+        )
+
+    # set the group_desc before the color or no_cloor split
+    if hasattr(parent_backend, "comm_split_count") and group_desc is None:
+        group_desc = f"{parent_pg.group_desc}:split:{parent_backend.comm_split_count()}"  # type: ignore[attr-defined]
+
+    parent_backend_str, _ = _world.pg_map[parent_pg]
+    # same type of backend as the parent process group
+    backend = Backend(parent_backend_str)
+    backend_config = BackendConfig(backend)
+
+    if pg_options is None:
+        # default pg_options same as the parent process group
+        # A deep copy is needed because if the option will be modified inside split
+        # and if we split parent pg multiple times, we will run into device out of bound error.
+        pg_options = copy.deepcopy(parent_backend.options)
+
+    # this timeout defaulting/validation is used for all the new_groups/new_subgroups variants,
+    # which may just pass their timeout value (or None)
+    if timeout is None:
+        timeout = _get_default_timeout(backend)
+    _check_valid_timeout(timeout)
+
+    # find my group of ranks and my group local rank in split_ranks
+    # for ranks which are not in any split PGs, we just pass in this the first split group
+    # and None will be returned.
+    my_group = split_ranks[0]
+
+    for split_group in split_ranks:
+        if len(split_group) == 0:
+            raise ValueError("the split group cannot be empty")
+        if len(split_group) > global_world_size:
+            raise ValueError(
+                "the split group's size should be less or equal to the world_size set by init_process_group"
+            )
+        if len(split_group) != len(set(split_group)):
+            raise ValueError("the split group cannot have duplicate ranks")
+        split_group = sorted(split_group)
+        if parent_group_rank in split_group:
+            my_group = split_group
+            break
+
+    # use_hashed_name is True to ensure that subgroups have unique names.
+    # This is needed as some backends (e.g. Gloo) use the group name as a
+    # PrefixStore prefix for initialization of splits. Thus, names have to be
+    # unique to avoid key collisions.
+    group_name = _process_group_name(my_group, use_hashed_name=True)
+    split_pg = parent_pg.split_group(
+        my_group,
+        timeout=timeout,
+        opts=pg_options,
+        group_name=group_name,
+        group_desc=group_desc,
+    )
+    if split_pg is None:
+        return None
+
+    global_ranks_in_my_group = [parent_group_to_global_ranks[rank] for rank in my_group]
+    split_pg.bound_device_id = device_id  # type: ignore[union-attr]
+    split_backend_class = split_pg._get_backend(torch.device("cuda"))
+    split_backend_class._set_sequence_number_for_group()
+    if split_pg.group_name != group_name:
+        raise AssertionError(
+            f"group name should be set to {group_name} but got {split_pg.group_name}"
+        )
+
+    # update global state
+    _world.pg_map[split_pg] = (backend, split_pg.get_group_store())
+    _world.pg_names[split_pg] = group_name
+    _register_process_group(group_name, split_pg)
+    _world.pg_backend_config[split_pg] = str(backend_config)
+    pg_tag = f"ptd:{group_name}"
+    _world.tags_to_pg.setdefault(pg_tag, []).append(split_pg)
+    _world.pg_to_tag[split_pg] = pg_tag
+
+    # Create the global rank to group rank mapping
+    _world.pg_group_ranks[split_pg] = {
+        global_rank: group_rank
+        for group_rank, global_rank in enumerate(global_ranks_in_my_group)
+    }
+
+    return split_pg
+
+
+@_time_logger
+def new_group(
+    ranks=None,
+    timeout=None,
+    backend=None,
+    pg_options=None,
+    use_local_synchronization: bool = False,
+    group_desc=None,
+    device_id: torch.device | None = None,
+):
+    """
+    Create a new distributed group.
+
+    This function requires that all processes in the main group (i.e. all
+    processes that are part of the distributed job) enter this function, even
+    if they are not going to be members of the group. Additionally, groups
+    should be created in the same order in all processes.
+
+    .. warning::
+        Safe concurrent usage:
+        When using multiple process groups with the ``NCCL`` backend, the user
+        must ensure a globally consistent execution order of collectives across
+        ranks.
+
+        If multiple threads within a process issue collectives, explicit
+        synchronization is necessary to ensure consistent ordering.
+
+        When using async variants of torch.distributed communication APIs,
+        a work object is returned and the communication kernel is
+        enqueued on a separate CUDA stream, allowing overlap of communication
+        and computation. Once one or more async ops have been issued on one process
+        group, they must be synchronized with other cuda streams by calling `work.wait()`
+        before using another process group.
+
+        See `Using multiple NCCL communicators concurrently
+        `
+        for more details.
+
+    Args:
+        ranks (list[int]): List of ranks of group members. If ``None``, will be
+            set to all ranks. Default is ``None``.
+        timeout (timedelta, optional): see `init_process_group` for details and default value.
+        backend (str or Backend, optional): The backend to use. Depending on
+            build-time configurations, valid values are ``gloo`` and ``nccl``.
+            By default uses the same backend as the global group. This field
+            should be given as a lowercase string (e.g., ``"gloo"``), which can
+            also be accessed via :class:`Backend` attributes (e.g.,
+            ``Backend.GLOO``). If ``None`` is passed in, the backend
+            corresponding to the default process group will be used. Default is
+            ``None``.
+        pg_options (ProcessGroupOptions, optional): process group options
+            specifying what additional options need to be passed in during
+            the construction of specific process groups. i.e. for the ``nccl``
+            backend, ``is_high_priority_stream`` can be specified so that
+            process group can pick up high priority cuda streams. For other available options to config nccl,
+            See https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/api/types.html#ncclconfig-tuse_local_synchronization
+            (bool, optional): perform a group-local barrier at the end of the process group creation.
+            This is different in that non-member ranks don't need to call into API and don't
+            join the barrier.
+        group_desc (str, optional): a string to describe the process group.
+        device_id (torch.device, optional): a single, specific device
+            to "bind" this process to,  The `new_group` call will try to initialize
+            a communication backend immediately for the device if this field is given.
+
+    Returns:
+        A handle of distributed group that can be given to collective calls or
+        GroupMember.NON_GROUP_MEMBER if the rank is not part of ``ranks``.
+
+    N.B. use_local_synchronization doesn't work with MPI.
+
+    N.B. While use_local_synchronization=True can be significantly faster with larger
+    clusters and small process groups, care must be taken since it changes cluster behavior
+    as non-member ranks don't join the group barrier().
+
+    N.B. use_local_synchronization=True can lead to deadlocks when each rank creates
+    multiple overlapping process groups. To avoid that, make sure all ranks follow the
+    same global creation order.
+    """
+    return _new_group_with_tag(
+        ranks,
+        timeout,
+        backend,
+        pg_options,
+        None,
+        use_local_synchronization=use_local_synchronization,
+        group_desc=group_desc,
+        device_id=device_id,
+    )
+
+
+def _new_group_with_tag(
+    ranks=None,
+    timeout=None,
+    backend=None,
+    backend_options=None,
+    pg_tag=None,
+    use_local_synchronization=False,
+    group_desc=None,
+    device_id: torch.device | None = None,
+):
+    """
+    Variant of ``new_group`` that exposes tag creation.
+
+    :: N.B. The mechanism is experimental and tied to the functional collectives effort, see
+    ``torch.distributed._functional_collectives`` for reference on how to use it.
+    """
+    global _world
+
+    default_pg = _get_default_group()
+    if device_id is None:
+        device_id = default_pg.bound_device_id
+    elif default_pg.bound_device_id is not None:
+        if device_id != default_pg.bound_device_id:
+            raise AssertionError(
+                "Mismatched bound device between new pg and the default pg."
+            )
+    default_backend, default_store = _world.pg_map[default_pg]
+    global_rank = default_pg.rank()
+    global_world_size = default_pg.size()
+
+    # Default to the same backend as the global process group
+    # if the backend is not specified.
+    if not backend:
+        backend = default_backend
+    backend = Backend(backend)
+
+    # this timeout defaulting/validation is used for all the new_groups/new_subgroups variants,
+    # which may just pass their timeout value (or None)
+    if timeout is None:
+        timeout = _get_default_timeout(backend)
+    _check_valid_timeout(timeout)
+
+    if use_local_synchronization:
+        # MPI backend doesn't have have a way for us to perform a partial sync
+        if backend == Backend.MPI:
+            raise ValueError(
+                "MPI backend doesn't support use_local_synchronization=True"
+            )
+        if ranks is not None and get_rank() not in ranks:
+            return None
+
+    # checks the input ranks
+    if ranks is not None:
+        ranks = sorted(ranks)
+        group_world_size = len(ranks)
+        if group_world_size > global_world_size:
+            raise ValueError(
+                "the new group's world size should be less or "
+                "equal to the world size set by "
+                "init_process_group"
+            )
+        # check ranks' sanity
+        for rank in ranks:
+            if rank < 0 or rank >= global_world_size:
+                raise ValueError(
+                    "The new group's rank should be within "
+                    "the world_size set by init_process_group"
+                )
+        if global_rank in ranks:
+            group_rank = ranks.index(global_rank)
+        else:
+            group_rank = None
+    else:
+        ranks = list(range(global_world_size))
+        group_world_size = global_world_size
+        group_rank = global_rank
+
+    group_name = _process_group_name(ranks, use_hashed_name=use_local_synchronization)
+
+    pg, pg_store = _new_process_group_helper(
+        group_world_size,
+        group_rank,
+        ranks,
+        backend,
+        default_store,
+        group_name,
+        backend_options=backend_options,
+        timeout=timeout,
+        pg_tag=pg_tag,
+        device_id=device_id,
+        group_desc=group_desc,
+    )
+
+    # Create the global rank to group rank mapping
+    _world.pg_group_ranks[pg] = {
+        global_rank: group_rank for group_rank, global_rank in enumerate(ranks)
+    }
+
+    if _is_barrier_after_init() == 1:
+        # barrier at the end to ensure that once we return from this method, all
+        # process groups including global variables (if any) are updated
+        # correctly on all ranks.
+        # Update 04/2023: for large-scale runs, this barrier (esp. store-based
+        # barrier) may be costly and/or unscalable. Also, in a lot of cases,
+        # these barriers may be unnecessary, as proven by a green CI after
+        # removal. An environment variable `TORCH_DIST_INIT_BARRIER` has been
+        # added which enables this barrier only when set to 1.
+        logger.info(
+            "Performing barrier after ProcessGroup initialization since "
+            "TORCH_DIST_INIT_BARRIER = 1"
+        )
+        if backend == Backend.MPI:
+            # MPI doesn't have store.
+            barrier()
+        else:
+            barrier_store = pg_store if use_local_synchronization else default_store
+            world_size = len(ranks) if use_local_synchronization else get_world_size()
+            # Use store based barrier here since barrier() used a bunch of
+            # default devices and messes up NCCL internal state.
+            _store_based_barrier(
+                global_rank, barrier_store, group_name, world_size, timeout
+            )
+
+    return pg
+
+
+def new_subgroups(
+    group_size=None,
+    group=None,
+    timeout=None,
+    backend=None,
+    pg_options=None,
+    group_desc=None,
+):
+    """
+    Create subgroups of equal size.
+
+    By default, it creates intra-machine subgroups,
+    where each of which contains all the ranks of a machine, based on the assumption
+    that each machine has the same number of devices.
+
+    This is a convenience API that calls ``new_group`` to generate multiple subgroups.
+    It requires that all processes in the main group (i.e. all
+    processes that are part of the distributed job) enter this function, even
+    if they are not going to be members of the group.
+
+    .. warning::
+        If ``group_size`` is passed in, the world size must be divisible by ``group_size``.
+        If no ``group_size`` is passed in, it believe that you are creating a group based
+        on CUDA and determining the group size by number of CUDA devices, and if not all
+        the machines have the same number of devices, the subgroup division will be
+        different across nodes and can cause unexpected behaviors. Therefore, if you are
+        creating a subgroup that does not depend on CUDA (such as Gloo on CPU), please
+        pass in ``group_size`` correctly.
+
+    .. warning::
+        See warning `Safe concurrent usage` for `new_group` API for important details about
+        using multiple process groups concurrently in a safe manner.
+
+    Args:
+        group_size (int, optional): The size of each subgroup. If ``None``,
+            the default subgroup size is equal to the number of devices on each machine,
+            based on the assumption that each machine has exactly the same
+            number of devices. Default is ``None``.
+        group (ProcessGroup, optional): The process group to work on. If
+            ``None``, the default process group will be used. Default is ``None``.
+        timeout (timedelta, optional): see `init_process_group` for details and default value.
+        backend (str or Backend, optional): The backend to use. Depending on
+            build-time configurations, valid values are ``gloo`` and ``nccl``.
+            By default uses the same backend as the global group. This field
+            should be given as a lowercase string (e.g., ``"gloo"``), which can
+            also be accessed via :class:`Backend` attributes (e.g.,
+            ``Backend.GLOO``). If ``None`` is passed in, the backend
+            corresponding to the default process group will be used. Default is
+            ``None``.
+        pg_options (ProcessGroupOptions, optional): process group options
+            specifying what additional options need to be passed in during
+            the construction of specific process groups. i.e. for the ``nccl``
+            backend, ``is_high_priority_stream`` can be specified so that
+            process group can pick up high priority cuda streams.
+        group_desc (str, optional): A string describing the group. Each subgroup will
+            inherit its group_desc
+
+    Returns:
+        The subgroup containing the current rank, and all the subgroups used for cleanup.
+
+    Examples:
+        >>> # Create intra-machine subgroups.
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> cur_subgroup, subgroups = dist.new_subgroups()
+        >>> # Allreduce within the machine.
+        >>> rank = dist.get_rank()
+        >>> tensor = torch.ones(1, device=rank) * rank
+        >>> dist.all_reduce(tensor, group=cur_subgroup)
+        >>> tensor
+        tensor([28])  # Assume 8 CUDA devices per machine.  28 is sum(range(8)).
+        >>> # Cleanup.
+        >>> for subgroup in subgroups:
+        >>>     dist.destroy_process_group(subgroup)
+    """
+    if group_size is None:
+        if not torch.cuda.is_available():
+            raise ValueError(
+                "Default group size only takes effect when CUDA is available."
+                "If your subgroup using a backend that does not depend on CUDA,"
+                "please pass in 'group_size' correctly."
+            )
+        group_size = torch.cuda.device_count()
+    if group_size <= 0:
+        raise ValueError(f"The arg 'group_size' ({group_size}) must be positive")
+
+    world_size = get_world_size(group=group)
+    if world_size < group_size:
+        raise ValueError(
+            f"The arg 'group_size' ({group_size}) must not exceed the world size ({world_size})"
+        )
+    if world_size % group_size != 0:
+        raise ValueError(
+            f"The world size ({world_size}) must be divisible by '{group_size=}'"
+        )
+
+    # TODO: Use itertools.batched(get_process_group_ranks(group=group), group_size) instead when Python 3.12 is supported.
+    ranks = get_process_group_ranks(group=group)
+    ranks_per_subgroup_list = [
+        ranks[i : i + group_size] for i in range(0, len(ranks), group_size)
+    ]
+    return new_subgroups_by_enumeration(
+        ranks_per_subgroup_list,
+        timeout=timeout,
+        backend=backend,
+        pg_options=pg_options,
+        group_desc=group_desc,
+    )
+
+
+def new_subgroups_by_enumeration(
+    ranks_per_subgroup_list,
+    timeout=None,
+    backend=None,
+    pg_options=None,
+    group_desc=None,
+):
+    """
+    Create subgroups by dividing the global world.
+
+    The division is specified by a nested list of ranks. The subgroups cannot have
+    overlap, and some ranks may not have to be in any subgroup.
+
+    This is a convenience API that calls ``new_group`` to generate multiple subgroups.
+    It requires that all processes in the main group (i.e. all
+    processes that are part of the distributed job) enter this function, even
+    if they are not going to be members of the group.
+
+    .. warning::
+        See warning `Safe concurrent usage` for `new_group` API for important details about
+        using multiple process groups concurrently in a safe manner.
+
+    Args:
+        ranks_per_subgroup_list (list[list[int]]): A nested list of ranks of
+            group members.
+        timeout (timedelta, optional): see `init_process_group` for details and default value.
+        backend (str or Backend, optional): The backend to use. Depending on
+             build-time configurations, valid values are ``gloo`` and ``nccl``.
+             By default uses the same backend as the global group. This field
+             should be given as a lowercase string (e.g., ``"gloo"``), which can
+             also be accessed via :class:`Backend` attributes (e.g.,
+             ``Backend.GLOO``). If ``None`` is passed in, the backend
+             corresponding to the default process group will be used. Default is
+             ``None``.
+        pg_options (ProcessGroupOptions, optional): process group options
+            specifying what additional options need to be passed in during
+            the construction of specific process groups. i.e. for the ``nccl``
+            backend, ``is_high_priority_stream`` can be specified so that
+            process group can pick up high priority cuda streams.
+        group_desc (str, optional): A string describing the group. Each subgroup will
+            inherit its group_desc.
+
+    Returns:
+        The subgroup containing the current rank, and all the subgroups used for cleanup.
+
+    Examples:
+        >>> # Create two subgroups, where each has 2 processes.
+        >>> # xdoctest: +SKIP("need process group init")
+        >>> cur_subgroup, subgroups = dist.new_subgroups(ranks=[[0, 2], [1, 3]])
+        >>> rank = dist.get_rank()
+        >>> tensor = torch.ones(1, device=rank) * rank
+        >>> dist.all_reduce(tensor, group=cur_subgroup)
+        >>> tensor
+        tensor([2])     # Subgroup 0: ranks 0 and 2
+        tensor([4])     # Subgroup 1: ranks 1 and 3
+    """
+    if ranks_per_subgroup_list is None or len(ranks_per_subgroup_list) == 0:
+        raise ValueError("The arg 'ranks_per_subgroup_list' cannot be empty")
+
+    subgroups = []
+    cur_subgroup = None
+    # Create a mapping from rank to subgroup to check if there is any subgroup overlap.
+    rank_to_ranks_dict = {}  # type: ignore[var-annotated]
+    for ranks in ranks_per_subgroup_list:
+        subgroup = new_group(
+            ranks=ranks,
+            timeout=timeout,
+            backend=backend,
+            pg_options=pg_options,
+            group_desc=group_desc,
+        )
+        subgroups.append(subgroup)
+        my_rank = get_rank()
+        for rank in ranks:
+            if rank in rank_to_ranks_dict:
+                raise ValueError(
+                    f"Rank {rank} has appeared in both subgroup {rank_to_ranks_dict[rank]} and {ranks}"
+                )
+            rank_to_ranks_dict[rank] = ranks
+            if my_rank == rank:
+                cur_subgroup = subgroup
+                logger.info("Rank %s is assigned to subgroup %s", rank, ranks)
+
+    return cur_subgroup, subgroups
+
+
+def _find_pg_by_ranks_and_tag(tag: str, ranks: list[int]) -> ProcessGroup | None:
+    if len(tag) > 0 and not tag.startswith("ptd:") and not tag.startswith("user:"):
+        tag = f"user:{tag}"
+
+    for group in _world.tags_to_pg.get(tag, []):
+        if group.size() != len(ranks):
+            continue
+
+        group_ranks = get_process_group_ranks(group)
+        good = all(r in group_ranks for r in ranks)
+        if good:
+            return group
+    return None
+
+
+def _find_or_create_pg_by_ranks_and_tag(
+    tag: str, ranks: list[int], stride: int
+) -> ProcessGroup:
+    if len(ranks) % stride != 0:
+        raise ValueError(
+            f"Ranks length ({len(ranks)}) must be divisible by stride ({stride})"
+        )
+
+    my_rank = get_rank()
+    my_ranks = None
+
+    if stride == len(ranks):
+        my_ranks = ranks.copy()
+        if my_rank not in my_ranks:
+            raise AssertionError("rankset doesn't include the current node")
+    else:
+        for i in range(0, len(ranks), stride):
+            rank_set = ranks[i : i + stride]
+            if my_rank in rank_set:
+                my_ranks = rank_set
+        if my_ranks is None:
+            raise AssertionError("rankset doesn't include the current node")
+
+    my_ranks = sorted(my_ranks)
+
+    pg = _find_pg_by_ranks_and_tag(tag, my_ranks)
+    if pg is not None:
+        return pg
+    if tag == "":
+        raise ValueError("Cannot automatically create PG with empty tag")
+    # TODO copy settings and timeout from default PG
+    return _new_group_with_tag(my_ranks, pg_tag=tag)
+
+
+def _get_group_tag(pg: ProcessGroup) -> str:
+    """Return the tag associated with ``pg``."""
+    tag = _world.pg_to_tag[pg]
+    tag = tag.removeprefix("user:")
+    return tag
+
+
+def _get_process_group_name(pg: ProcessGroup) -> str:
+    return _world.pg_names.get(pg, "None")
+
+
+def _get_process_group_store(pg: ProcessGroup) -> Store:
+    return _world.pg_map[pg][1]
+
+
+# Shrink flags for process group backends
+SHRINK_DEFAULT = 0x00
+SHRINK_ABORT = 0x01
+
+
+@_time_logger
+def shrink_group(
+    ranks_to_exclude: list[int],
+    group: ProcessGroup | None = None,
+    shrink_flags: int = SHRINK_DEFAULT,
+    pg_options: Any | None = None,
+) -> ProcessGroup:
+    """
+    Shrinks a process group by excluding specified ranks.
+
+    Creates and returns a new, smaller process group comprising only the ranks
+    from the original group that were not in the ``ranks_to_exclude`` list.
+
+    Args:
+        ranks_to_exclude (List[int]): A list of ranks from the original
+            ``group`` to exclude from the new group.
+        group (ProcessGroup, optional): The process group to shrink. If ``None``,
+            the default process group is used. Defaults to ``None``.
+        shrink_flags (int, optional): Flags to control the shrinking behavior.
+            Can be ``SHRINK_DEFAULT`` (default) or ``SHRINK_ABORT``.
+            ``SHRINK_ABORT`` will attempt to terminate ongoing operations
+            in the parent communicator before shrinking.
+            Defaults to ``SHRINK_DEFAULT``.
+        pg_options (ProcessGroupOptions, optional): Backend-specific options to apply
+            to the shrunken process group. If provided, the backend will use
+            these options when creating the new group. If omitted, the new group
+            inherits defaults from the parent.
+
+    Returns:
+        ProcessGroup: a new group comprised of the remaining ranks. If the
+        default group was shrunk, the returned group becomes the new default group.
+
+    Raises:
+        TypeError: if the group’s backend does not support shrinking.
+        ValueError: if ``ranks_to_exclude`` is invalid (empty, out of bounds,
+        duplicates, or excludes all ranks).
+        RuntimeError: if an excluded rank calls this function or the backend
+        fails the operation.
+
+    Notes:
+        - Only non-excluded ranks should call this function; excluded ranks
+          must not participate in the shrink operation.
+        - Shrinking the default group destroys all other process groups since
+          rank reassignment makes them inconsistent.
+    """
+    # Step 1: Validate input parameters with comprehensive error checking
+    _validate_shrink_inputs(ranks_to_exclude, shrink_flags)
+
+    # Step 2: Get target group and essential properties
+    target_group_info = _prepare_shrink_target_group(group)
+
+    # Step 3: Validate backend requirements and availability
+    backend_impl = _validate_shrink_backend_requirements(target_group_info)
+
+    # Step 4: Validate ranks against group and check for duplicates
+    excluded_ranks_set = _validate_and_process_excluded_ranks(
+        ranks_to_exclude, target_group_info
+    )
+
+    # Step 5: Execute the actual shrink operation (backend-specific)
+    new_backend = backend_impl.shrink(
+        sorted(excluded_ranks_set),
+        shrink_flags,
+        pg_options if pg_options is not None else None,
+    )
+
+    # Step 6: Handle cleanup and creation of new process group
+    target_group_info["pg_options_override"] = pg_options
+    return _finalize_shrunk_group(target_group_info, excluded_ranks_set, new_backend)
+
+
+def _validate_shrink_inputs(ranks_to_exclude: list[int], shrink_flags: int) -> None:
+    """Validate input parameters for shrink_group."""
+    if not isinstance(ranks_to_exclude, list):
+        raise TypeError(
+            f"ranks_to_exclude must be a list, but got {type(ranks_to_exclude).__name__}. "
+            f"Example: [1, 3, 5] to exclude ranks 1, 3, and 5."
+        )
+
+    if not ranks_to_exclude:
+        raise ValueError(
+            "ranks_to_exclude cannot be empty. To shrink a group, you must specify at least "
+            "one rank to exclude. Example: [failed_rank_id]"
+        )
+
+    # Validate shrink_flags with clear explanation of valid values
+    valid_flags = [SHRINK_DEFAULT, SHRINK_ABORT]
+    if not isinstance(shrink_flags, int) or shrink_flags not in valid_flags:
+        raise ValueError(
+            f"Invalid shrink_flags value: {shrink_flags}. Must be one of: "
+            f"SHRINK_DEFAULT ({SHRINK_DEFAULT}) or SHRINK_ABORT ({SHRINK_ABORT}). "
+            f"Use SHRINK_ABORT to abort ongoing operations before shrinking."
+        )
+
+
+def _prepare_shrink_target_group(group: ProcessGroup | None) -> dict:
+    """Prepare and validate the target group for shrinking."""
+    target_pg = group if group is not None else _get_default_group()
+
+    # Cache frequently accessed properties to avoid repeated calls
+    group_size = int(target_pg.size())
+    group_info = {
+        "process_group": target_pg,
+        "is_default_group": (target_pg == _get_default_group()),
+        "group_size": group_size,
+        "current_rank": target_pg.rank(),
+        "group_name": _get_process_group_name(target_pg),
+    }
+
+    # Validate that we have a valid process group
+    if group_size <= 1:
+        raise ValueError(
+            f"Cannot shrink a process group with size {group_size}. "
+            f"Group must have at least 2 ranks to support shrinking."
+        )
+
+    return group_info
+
+
+def _validate_shrink_backend_requirements(group_info: dict) -> Any:
+    """Return the backend implementation for the target group or raise if unsupported."""
+    target_pg = group_info["process_group"]
+    group_name = group_info["group_name"]
+
+    # Get the group's backend directly via ProcessGroup API. Prefer a bound device if present,
+    # otherwise try CUDA then fall back to CPU.
+    try:
+        preferred_device = getattr(target_pg, "bound_device_id", None)
+        if preferred_device is not None:
+            backend_impl = target_pg._get_backend(preferred_device)
+        else:
+            # Try CUDA first if available, else CPU
+            try:
+                backend_impl = target_pg._get_backend(torch.device("cuda"))
+            except Exception:
+                backend_impl = target_pg._get_backend(torch.device("cpu"))
+    except RuntimeError as e:
+        raise RuntimeError(
+            f"Cannot access device backend for process group '{group_name}'. "
+            f"Ensure the process group was initialized with a compatible device backend and devices are available."
+        ) from e
+
+    try:
+        supports = bool(backend_impl.supports_shrinking)
+    except Exception:
+        supports = False
+    if not supports:
+        raise TypeError(
+            f"Process group backend for '{group_name}' does not support shrinking operations."
+        )
+
+    return backend_impl
+
+
+def _validate_and_process_excluded_ranks(
+    ranks_to_exclude: list[int], group_info: dict
+) -> set:
+    """Validate excluded ranks and convert to set for efficient operations."""
+    group_size = group_info["group_size"]
+    current_rank = group_info["current_rank"]
+
+    # Use set for O(1) duplicate detection and membership testing
+    excluded_ranks_set = set()
+
+    # Validate each rank with detailed error messages
+    for i, rank in enumerate(ranks_to_exclude):
+        if not isinstance(rank, int):
+            raise TypeError(
+                f"All elements in ranks_to_exclude must be integers. "
+                f"Element at index {i} is {type(rank).__name__}: {rank}"
+            )
+
+        if not (0 <= rank < group_size):
+            raise ValueError(
+                f"Rank {rank} at index {i} is out of bounds for group size {group_size}. "
+                f"Valid ranks are in range [0, {group_size - 1}]."
+            )
+
+        if rank in excluded_ranks_set:
+            raise ValueError(
+                f"Duplicate rank {rank} found in ranks_to_exclude at index {i}. "
+                f"Each rank can only be excluded once."
+            )
+
+        excluded_ranks_set.add(rank)
+
+    # Ensure we don't exclude all ranks
+    if len(excluded_ranks_set) >= group_size:
+        raise ValueError(
+            f"Cannot exclude all {group_size} ranks from process group. "
+            f"At least one rank must remain. Excluding {len(excluded_ranks_set)} ranks."
+        )
+
+    # Critical check: current rank should not be in excluded list
+    if current_rank in excluded_ranks_set:
+        raise RuntimeError(
+            f"Current rank {current_rank} is in the exclusion list and should not call shrink_group(). "
+            f"Only non-excluded ranks should participate in the shrinking operation. "
+            f"Excluded ranks should terminate their processes instead."
+        )
+
+    return excluded_ranks_set
+
+
+def _finalize_shrunk_group(
+    group_info: dict, excluded_ranks_set: set, new_backend
+) -> ProcessGroup:
+    """Clean up old group and create new shrunk process group."""
+    target_pg = group_info["process_group"]
+    is_default_group = group_info["is_default_group"]
+
+    # Handle default group dependencies - destroy other groups first
+    if is_default_group:
+        _destroy_all_other_groups(exclude_group=target_pg)
+
+    # Gather original group metadata before cleanup
+    original_group_metadata = _extract_group_metadata(target_pg)
+
+    # Calculate remaining ranks efficiently
+    original_ranks = get_process_group_ranks(target_pg)
+    remaining_ranks = [
+        rank for rank in original_ranks if rank not in excluded_ranks_set
+    ]
+
+    # Clean up the original group
+    _cleanup_original_group(target_pg, is_default_group)
+
+    # Create and configure the new process group
+    new_pg = _create_shrunk_process_group(
+        new_backend, remaining_ranks, original_group_metadata, is_default_group
+    )
+
+    # Register the new group in global state
+    if is_default_group:
+        _update_default_pg(new_pg)
+
+    # Update global state with new group information
+    rank_mapping = {
+        global_rank: group_rank
+        for group_rank, global_rank in enumerate(remaining_ranks)
+    }
+    _update_process_group_global_state(
+        pg=new_pg,
+        backend_name=original_group_metadata["backend_name"],
+        store=original_group_metadata["store"],
+        group_name=original_group_metadata["new_group_name"],
+        backend_config=original_group_metadata["backend_config"],
+        rank_mapping=rank_mapping,
+    )
+
+    return new_pg
+
+
+def _extract_group_metadata(target_pg: ProcessGroup) -> dict:
+    """Extract metadata from the original group before cleanup."""
+    original_backend_name, original_store = _world.pg_map[target_pg]
+    original_backend_config = _world.pg_backend_config.get(target_pg, "")
+    original_group_name = _get_process_group_name(target_pg)
+
+    # Extract device binding information before cleanup to avoid accessing destroyed group
+    bound_device_id = None
+    if hasattr(target_pg, "bound_device_id"):
+        bound_device_id = target_pg.bound_device_id
+
+    # Generate new group name for the shrunk group; hash for uniqueness across backends
+    remaining_ranks = list(get_process_group_ranks(target_pg))
+    new_group_name = _process_group_name(remaining_ranks, use_hashed_name=True)
+
+    return {
+        "backend_name": original_backend_name,
+        "store": original_store,
+        "backend_config": original_backend_config,
+        "original_group_name": original_group_name,
+        "new_group_name": new_group_name,
+        "bound_device_id": bound_device_id,  # Safe to access after cleanup
+    }
+
+
+def _cleanup_original_group(target_pg: ProcessGroup, is_default_group: bool) -> None:
+    """Clean up the original process group safely."""
+    try:
+        destroy_process_group(target_pg)
+    except Exception:
+        group_type = "default" if is_default_group else "non-default"
+        logger.warning(
+            "Failed to destroy %s group during shrinking", group_type, exc_info=True
+        )
+
+    # Ensure global state cleanup even if destroy_process_group fails
+    _cleanup_process_group_global_state(target_pg)
+
+
+def _create_shrunk_process_group(
+    new_backend, remaining_ranks: list[int], metadata: dict, is_default_group: bool
+) -> ProcessGroup:
+    """Create and configure the new shrunk process group."""
+    # Create new group properties
+    new_group_rank = new_backend.rank()
+    new_group_size = new_backend.size()
+    group_name = metadata["new_group_name"]
+
+    # Generate descriptive group description
+    if is_default_group:
+        group_desc = "default:shrunken"
+    else:
+        group_desc = f"{metadata['original_group_name']}:shrunk"
+
+    # Create process group with new communicator (clone the parent store like split does)
+    prefix_store = PrefixStore(f"{group_name}/", metadata["store"].clone())
+    new_pg = ProcessGroup(prefix_store, new_group_rank, new_group_size)
+
+    # Configure backend using the device type of the new backend's bound device if available,
+    # otherwise derive from the original group's bound device or fall back to CPU.
+    backend_device = metadata.get("bound_device_id")
+    if backend_device is None:
+        # Default to CPU if no bound device is present
+        backend_device = torch.device("cpu")
+
+    # Choose backend enum based on device type
+    if backend_device.type == "cuda":
+        backend_type = ProcessGroup.BackendType.NCCL
+    else:
+        backend_type = ProcessGroup.BackendType.GLOO
+
+    new_pg._register_backend(backend_device, backend_type, new_backend)
+    new_pg._set_default_backend(backend_type)
+
+    # Inherit device binding from original group if it was bound
+    bound_device_id = metadata.get("bound_device_id")
+    if bound_device_id is not None:
+        new_pg.bound_device_id = bound_device_id
+
+    # Set group metadata
+    new_pg._set_group_name(group_name)
+    new_pg._set_group_desc(group_desc)
+
+    # Persist backend configuration overrides (if provided via shrink_group)
+    backend_config_override = metadata.get("backend_config")
+    if backend_config_override is not None:
+        # Store for introspection/debugging and potential backend hooks
+        _world.pg_backend_config[new_pg] = backend_config_override
+
+    return new_pg
+
+
+def _destroy_all_other_groups(exclude_group: ProcessGroup | None = None) -> None:
+    """
+    Destroy all process groups except the excluded group and clean up all global state.
+
+    This is necessary when shrinking the default group because global ranks
+    are reassigned by NCCL, making all existing process groups inconsistent.
+
+    Note: Uses abort for non-collective cleanup since excluded ranks may not
+    participate in collective operations. Backend cleanup is handled independently per group.
+
+    Args:
+        exclude_group (ProcessGroup, optional): Process group to exclude from destruction.
+            If None, destroys all process groups.
+    """
+    # Get list of groups to destroy (avoid modifying dict while iterating)
+    groups_to_destroy = []
+    for pg in list(_world.pg_group_ranks.keys()):
+        if exclude_group is not None and pg == exclude_group:
+            continue
+        groups_to_destroy.append(pg)
+
+    # Warn user about automatic destruction
+    if groups_to_destroy:
+        group_names = [_get_process_group_name(pg) for pg in groups_to_destroy]
+        logger.warning(
+            "Shrinking default group will destroy %d other process groups: %s. "
+            "This is necessary because shrinking the default group reassigns global ranks, "
+            "making existing groups inconsistent.",
+            len(groups_to_destroy),
+            ", ".join(group_names),
+        )
+
+    # Destroy each group and clean up global state
+    for pg in groups_to_destroy:
+        try:
+            # First call abort_process_group which handles the C++ cleanup non-collectively
+            _abort_process_group(pg)
+        except Exception:
+            # Log but don't fail - some groups might already be destroyed
+            logger.warning(
+                "Failed to abort process group %s",
+                _get_process_group_name(pg),
+                exc_info=True,
+            )
+
+        # Ensure all global state is cleaned up even if _abort_process_group fails
+        # or doesn't clean up everything
+        _cleanup_process_group_global_state(pg)
+
+
+def _cleanup_process_group_global_state(pg: ProcessGroup) -> None:
+    """
+    Clean up all global state associated with a process group.
+
+    This function ensures complete cleanup of process group state from all
+    global dictionaries and registries, even if destroy_process_group fails
+    or doesn't clean up everything. This is critical when destroying multiple
+    groups to prevent inconsistent state.
+
+    The cleanup removes the process group from:
+    - _world.pg_map (backend and store mapping)
+    - _world.pg_names (group name mapping)
+    - _world.pg_group_ranks (rank mappings)
+    - _world.pg_backend_config (backend configuration)
+    - _world.tags_to_pg and _world.pg_to_tag (tag mappings)
+    - _world.pg_coalesce_state (coalescing state)
+    - C++ internal registries via _unregister_process_group
+
+    Args:
+        pg (ProcessGroup): The process group to clean up.
+    """
+    try:
+        # Clean up main process group mappings
+        _world.pg_map.pop(pg, None)
+        _world.pg_group_ranks.pop(pg, None)
+        _world.pg_backend_config.pop(pg, None)
+
+        # Clean up process group name mapping
+        group_name = _world.pg_names.pop(pg, None)
+
+        # Clean up tag mappings
+        pg_tag = _world.pg_to_tag.pop(pg, None)
+        if pg_tag is not None and pg_tag in _world.tags_to_pg:
+            try:
+                _world.tags_to_pg[pg_tag].remove(pg)
+                # Remove the tag entry if list is empty
+                if not _world.tags_to_pg[pg_tag]:
+                    _world.tags_to_pg.pop(pg_tag, None)
+            except (ValueError, KeyError):
+                # Process group was already removed from the list
+                pass
+
+        # Clean up any registered process group names using C++ unregister function
+        if group_name is not None:
+            try:
+                _unregister_process_group(group_name)
+            except Exception:
+                # Process group name might not be registered or already unregistered
+                pass
+
+        # Clean up coalesce state if present
+        _world.pg_coalesce_state.pop(pg, None)
+
+    except Exception:
+        # Log cleanup failures but don't propagate - we want to continue with other cleanups
+        logger.warning(
+            "Failed to fully clean up global state for process group", exc_info=True
+        )
+
+
+def _update_process_group_global_state(
+    pg: ProcessGroup,
+    backend_name: str,
+    store: Store,
+    group_name: GroupName,
+    backend_config: str,
+    rank_mapping: dict[int, int] | None = None,
+    pg_tag: str | None = None,
+    user_tag: str | None = None,
+) -> None:
+    """
+    Update all global state dictionaries for a process group.
+
+    This helper function consolidates the common pattern of updating multiple
+    global state dictionaries when creating or modifying process groups.
+
+    Args:
+        pg (ProcessGroup): The process group to update state for.
+        backend_name (str): Backend name for pg_map.
+        store (Store): Store instance for pg_map.
+        group_name (str): Group name for pg_names and registration.
+        backend_config (str): Backend configuration string.
+        rank_mapping (Dict[int, int], optional): Global rank to group rank mapping.
+            If None, skips updating pg_group_ranks.
+        pg_tag (str, optional): Process group tag. If None, defaults to f"ptd:{group_name}".
+        user_tag (str, optional): User-provided tag for special tag handling.
+            If provided, creates "user:{user_tag}" tag and also adds to default "".
+    """
+    # Update main process group mappings
+    _world.pg_map[pg] = (backend_name, store)
+    _world.pg_names[pg] = group_name
+    _world.pg_backend_config[pg] = backend_config
+
+    # Register the process group name
+    _register_process_group(group_name, pg)
+
+    # Update rank mapping if provided
+    if rank_mapping is not None:
+        _world.pg_group_ranks[pg] = rank_mapping
+
+    # Handle tag management
+    if pg_tag is None:
+        pg_tag = f"ptd:{group_name}"
+
+    if user_tag is not None:
+        # Special handling for user-provided tags
+        # Add to default "" tag first
+        _world.tags_to_pg.setdefault("", []).append(pg)
+        # Then create user-specific tag
+        user_pg_tag = f"user:{user_tag}"
+        _world.tags_to_pg.setdefault(user_pg_tag, []).append(pg)
+        _world.pg_to_tag[pg] = user_pg_tag
+    else:
+        # Standard process group tag
+        _world.tags_to_pg.setdefault(pg_tag, []).append(pg)
+        _world.pg_to_tag[pg] = pg_tag
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a7c9b29a750593a812907ce2cf4c800d7d1435bb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/__init__.py
@@ -0,0 +1,77 @@
+#!/usr/bin/env/python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+
+Torchelastic agent and user worker failover contract:
+
+**TL;DR;**:
+
+* TE(torchelastic) expects user workers to finish with the 5 minutes drift
+* It is better to design DDP app to fail for all workers, rather than a single one.
+* TE does not synchronize number of restarts between agents
+* TE re-rendezvous does not trigger restart decrease
+* When a single agent finishes its job(successfully or not), it will close rendezvous.
+  If other agents still have workers in progress, they will be terminated.
+* Based on above, scale down does not work if at least single agent finishes the job.
+* When Scale up is detected by agents, it will not decrease ``max_restarts``
+
+
+In general TE(torchelastic) can launch arbitrary user code, but there is some
+clarifications need to be done around what failover mechanism torchelastic
+provides and what failover mechanism it expects from user workers.
+
+Torchelastic currently supports DDP style applications.  That means that
+TE expects *ALL* workers finish approximately at the same time. In practice,
+it is nearly to impossible to guarantee that all workers in arbitrary
+DDP application finish at the time, so TE provides a finalization barrier
+that waits for TIMEOUT(5 minutes) for worker finalization.
+
+**Worker Failure**
+
+When worker fails, TE will check the number of restarts
+available, if there is more than 0 restarts, TE will start a new rendezvous
+round and restart the worker process. New rendezvous round will other
+TE agents to terminate their workers.
+
+.. note:: The TE agent does not synchronize restarts between themselves.
+          When a single agent performs restart, it will trigger a local ``max_restarts``
+          decrease, other agent will not decrease their ``max_restarts``.
+          the user to run the distributed application locally on a dev host.
+
+A single worker failure can cause the whole cluster to fail:
+If a single worker is constantly failing, it will cause the TE agent
+``max_restarts``  to go to zero. This will cause an agent to finish its
+work and close rendezvous. If there are any other workers on different
+agents, they will be terminated.
+
+
+**Re-Rendezvous**
+
+Re-rendezvous occurs when TE agents detect a new node
+trying to joint a cluster. TE will not decrease ``max_restarts``. TE agents
+will terminate its workers and start a new rendezvous round.
+
+Note about DynamicRendezvous(etcd-v2, c10d-experimental): If the rendezvous
+has already max_nodes, the new node won't be added to the wait list right
+away since there is no need to tear down a rendezvous that is already fully
+utilized. The new node will wait until its timeout (600 secs by default)
+and periodically check the number of participants. If the number becomes
+less than max_nodes, it will be added to the wait list; otherwise, it will time out after 600 secs.
+
+*Scale up event*. When scale up event happens, torchelastic rendezvous
+will detect that there are new nodes trying to join. Torchelastic agent
+will stop all workers and perform re-rendezvous. Note: when scale up event
+happens, *``max_restarts``* will *not* decrease.
+
+*Scale down event*. When scale down event happens, rendezvous will not
+notify the torchelastic agent about it. If TE agent launched with ``max_restarts=0`` ,
+it relies on the underlying scheduler to handle job restart. If the ``max_restarts>0`` ,
+TE agent will terminate workers and start a new rdzv round, which is a *Scale up event*.
+
+"""
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/agent/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/agent/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c0d76131fe40d70945ffa8ff97431954151d50e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/__init__.py
@@ -0,0 +1,41 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+The elastic agent is the control plane of torchelastic.
+
+It is a process that launches and manages underlying worker processes.
+The agent is responsible for:
+
+1. Working with distributed torch: the workers are started with all the
+   necessary information to successfully and trivially call
+   ``torch.distributed.init_process_group()``.
+
+2. Fault tolerance: monitors workers and upon detecting worker failures
+   or unhealthiness, tears down all workers and restarts everyone.
+
+3. Elasticity: Reacts to membership changes and restarts workers with the new
+   members.
+
+The simplest agents are deployed per node and works with local processes.
+A more advanced agent can launch and manage workers remotely. Agents can
+be completely decentralized, making decisions based on the workers it manages.
+Or can be coordinated, communicating to other agents (that manage workers
+in the same job) to make a collective decision.
+"""
+
+from .api import (  # noqa: F401
+    ElasticAgent,
+    RunResult,
+    SimpleElasticAgent,
+    Worker,
+    WorkerGroup,
+    WorkerSpec,
+    WorkerState,
+)
+from .local_elastic_agent import TORCHELASTIC_ENABLE_FILE_TIMER, TORCHELASTIC_TIMER_FILE
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..2575aa137a58128213173dde681c313bb24fc5a2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/api.py
@@ -0,0 +1,995 @@
+# mypy: ignore-errors
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import abc
+import json
+import os
+import signal
+import socket
+import time
+import traceback
+import warnings
+from collections import defaultdict
+from collections.abc import Callable
+from contextlib import contextmanager
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Any
+
+import torch.distributed.elastic.rendezvous as rdzv
+import torch.distributed.elastic.utils.store as store_util
+from torch.distributed.elastic.events import Event, EventSource, record
+from torch.distributed.elastic.metrics import prof, put_metric
+from torch.distributed.elastic.multiprocessing import ProcessFailure, SignalException
+from torch.distributed.elastic.rendezvous import RendezvousGracefulExitError
+from torch.distributed.elastic.utils.logging import get_logger
+from torch.numa.binding import NumaOptions
+
+
+__all__ = [
+    "WorkerSpec",
+    "Worker",
+    "WorkerState",
+    "WorkerGroup",
+    "RunResult",
+    "ElasticAgent",
+    "SimpleElasticAgent",
+]
+_TERMINAL_STATE_SYNC_ID = "torchelastic/agent/terminal_state"
+
+DEFAULT_ROLE = "default"
+logger = get_logger(__name__)
+
+
+@dataclass
+class WorkerSpec:
+    """
+    Blueprint information about a particular type of worker.
+
+    For a given role, there must only exist a single worker spec.
+    Worker spec is expected to be homogeneous across all nodes (machine),
+    that is each node runs the same number of workers for a particular spec.
+
+    Args:
+        role: user-defined role for the workers with this spec
+        local_world_size: number local workers to run
+        fn: (deprecated use entrypoint instead)
+        entrypoint: worker function or command
+        args: arguments to pass to ``entrypoint``
+        rdzv_handler: handles rdzv for this set of workers
+        max_restarts: number of max retries for the workers
+        monitor_interval: monitor status of workers every ``n`` seconds
+        master_port: fixed port to run the c10d store on rank 0
+                     if not specified then will chose a random free port
+        master_addr: fixed master_addr to run the c10d store on rank 0
+                     if not specified then will chose hostname on agent rank 0
+        redirects: redirect std streams to a file,
+                   selectively redirect for a particular
+                   local rank by passing a map
+        tee: tees the specified std stream(s) to console + file,
+             selectively tee for a particular local rank by passing a map,
+             takes precedence over ``redirects`` settings.
+        event_log_handler: name of the event logging handler as registered in
+          `elastic/events/handlers.py `_.
+        duplicate_stdout_filters: If non-empty, duplicates stdout to a file containing only lines
+                                 that match _any_ of the filter strings.
+        duplicate_stderr_filters: If non-empty, duplicates stderr to a file containing only lines
+                                 that match _any_ of the filter strings.
+        virtual_local_rank: Enable virtual local rank mode for workers (defaults to False).
+                            When enabled, LOCAL_RANK is set to 0 for all workers and
+                            CUDA_VISIBLE_DEVICES is adjusted so each worker accesses its
+                            assigned GPU at device index 0.
+    """
+
+    role: str
+    local_world_size: int
+    rdzv_handler: rdzv.RendezvousHandler
+    fn: Callable | None = None
+    # TODO @kiuk - make entrypoint a required field
+    entrypoint: Callable | str | None = None
+    args: tuple = ()
+    max_restarts: int = 3
+    monitor_interval: float = 0.1
+    master_port: int | None = None
+    master_addr: str | None = None
+    local_addr: str | None = None
+    event_log_handler: str = "null"
+    numa_options: NumaOptions | None = None
+    duplicate_stdout_filters: list[str] | None = None
+    duplicate_stderr_filters: list[str] | None = None
+    virtual_local_rank: bool = False
+
+    def __post_init__(self):
+        assert self.local_world_size > 0
+        assert self.monitor_interval > 0
+
+        if self.fn:
+            warnings.warn(
+                "WorkerSpec.fn will be deprecated,"
+                " please use WorkerSpec.entrypoint instead",
+                stacklevel=2,
+                category=DeprecationWarning,
+            )
+            self.entrypoint = self.fn
+        assert self.entrypoint
+
+    def get_entrypoint_name(self):
+        """Get the entry point name.
+
+        If the entrypoint is a function (e.g. ``Callable``) returns its ``__qualname__``
+        else if the entrypoint is a binary (e.g. ``str``), returns the binary name.
+        """
+        if isinstance(self.entrypoint, str):
+            return os.path.basename(self.entrypoint)
+        else:
+            assert self.entrypoint is not None
+            return self.entrypoint.__qualname__
+
+
+class Worker:
+    """A worker instance.
+
+    Contrast this with ``WorkerSpec`` that represents the specifications of a
+    worker. A ``Worker`` is created from a ``WorkerSpec``. A ``Worker`` is to
+    a ``WorkerSpec`` as an object is to a class.
+
+    The ``id`` of the worker is interpreted
+    by the specific implementation of ``ElasticAgent``. For a local
+    agent, it could be the ``pid (int)`` of the worker, for a remote
+    agent it could be encoded as ``host:port (string)``.
+
+    Args:
+        id (Any): uniquely identifies a worker (interpreted by the agent)
+        local_rank (int): local rank of the worker
+        global_rank (int): global rank of the worker
+        role_rank (int): rank of the worker across all workers that have the same role
+        world_size (int): number of workers (globally)
+        role_world_size (int): number of workers that have the same role
+    """
+
+    __slots__ = [
+        "id",
+        "local_rank",
+        "global_rank",
+        "role_rank",
+        "world_size",
+        "role_world_size",
+    ]
+
+    def __init__(
+        self,
+        local_rank: int,
+        global_rank: int = -1,
+        role_rank: int = -1,
+        world_size: int = -1,
+        role_world_size: int = -1,
+    ):
+        # unique identifier for this worker
+        self.id: Any = None
+
+        # rank of the worker among workers with the same role being monitored
+        # by the same ``agent`` instance.
+        self.local_rank: int = local_rank
+
+        #  rank of the worker among all the workers across all roles
+        #  across all ``agent`` instances.
+        #  Global rank is not stable between re-rendezvous.
+        self.global_rank: int = global_rank
+
+        #  rank of the worker among all the workers with the same role
+        #  across all ``agent`` instances.
+        #  Role rank is not stable between re-rendezvous.
+        self.role_rank: int = role_rank
+
+        # total number of workers (globally). Due to elasticity
+        # the world size may change between re-rendezvous.
+        self.world_size: int = world_size
+
+        # total number of workers that share the same role. Due to elasticity
+        # the role world size may change between re-rendezvous.
+        self.role_world_size: int = role_world_size
+
+    def __str__(self):
+        return (
+            f"local_rank={self.local_rank},global_rank={self.global_rank}"
+            f",role_rank={self.role_rank},world_size={self.world_size}"
+            f",role_world_size={self.role_world_size}"
+        )
+
+    def __repr__(self):
+        return str(self)
+
+
+class WorkerState(str, Enum):
+    """A state of the ``WorkerGroup``.
+
+    Workers in a worker group change state as a unit. If a single worker
+    in a worker group fails the entire set is considered failed::
+
+      UNKNOWN - agent lost track of worker group state, unrecoverable
+      INIT - worker group object created not yet started
+      HEALTHY - workers running and healthy
+      UNHEALTHY - workers running and unhealthy
+      STOPPED - workers stopped (interrupted) by the agent
+      SUCCEEDED - workers finished running (exit 0)
+      FAILED - workers failed to successfully finish (exit !0)
+
+
+    A worker group starts from an initial ``INIT`` state,
+    then progresses to ``HEALTHY`` or ``UNHEALTHY`` states,
+    and finally reaches a terminal ``SUCCEEDED`` or ``FAILED`` state.
+
+    Worker groups can be interrupted and temporarily put into ``STOPPED`` state
+    by the agent. Workers in ``STOPPED`` state are scheduled to be restarted
+    in the near future by the agent. Some examples of workers being put into
+    ``STOPPED`` state are:
+
+    1. Worker group failure|unhealthy observed
+    2. Membership change detected
+
+    When actions (start, stop, rdzv, retry, etc) on worker group fails
+    and results in the action being partially applied to the worker group
+    the state will be ``UNKNOWN``. Typically this happens on uncaught/unhandled
+    exceptions during state change events on the agent. The agent is not
+    expected to recover worker groups in ``UNKNOWN`` state and is better off
+    self terminating and allowing the job manager to retry the node.
+    """
+
+    UNKNOWN = "UNKNOWN"
+    INIT = "INIT"
+    HEALTHY = "HEALTHY"
+    UNHEALTHY = "UNHEALTHY"
+    STOPPED = "STOPPED"
+    SUCCEEDED = "SUCCEEDED"
+    FAILED = "FAILED"
+
+    @staticmethod
+    def is_running(state: "WorkerState") -> bool:
+        """Return the state of the Worker.
+
+        Returns:
+             True if the worker state represents workers still running
+             (e.g. that the process exists but not necessarily healthy).
+        """
+        return state in {WorkerState.HEALTHY, WorkerState.UNHEALTHY}
+
+
+class WorkerGroup:
+    """A set of ``Worker`` instances.
+
+    The class defines a set of ``Worker`` instances for the given ``WorkerSpec`` managed by ``ElasticAgent``. Whether the worker
+    group contains cross instance workers or not depends on the implementation of the agent.
+    """
+
+    __slots__ = [
+        "spec",
+        "workers",
+        "store",
+        "group_rank",
+        "group_world_size",
+        "state",
+        "master_addr",
+        "master_port",
+    ]
+
+    def __init__(self, spec: WorkerSpec):
+        self.spec = spec
+        self.workers = [Worker(local_rank=i) for i in range(self.spec.local_world_size)]
+
+        # assigned after rdzv
+        self.store = None
+        self.group_rank = None
+        self.group_world_size = None
+        self.master_addr = None
+        self.master_port = None
+
+        self.state = WorkerState.INIT
+
+
+class _RoleInstanceInfo:
+    """The class is used by the agent to exchange the information with other agents.
+
+    The information is used to determine the rank of the workers that agent
+    manages in heterogeneous environments, where different agents can have
+    different number of workers.
+    """
+
+    __slots__ = ["role", "rank", "local_world_size"]
+
+    def __init__(self, role: str, rank: int, local_world_size: int):
+        r"""Initialize the agent class instance.
+
+        Args:
+            role (str): user-defined role for the workers with this spec
+            rank (int): the rank of the agent
+            local_world_size (int): number of local workers to run
+        """
+        self.role = role
+        self.rank = rank
+        self.local_world_size = local_world_size
+
+    def serialize(self) -> bytes:
+        dict_data = {
+            "role": self.role,
+            "rank": self.rank,
+            "local_world_size": self.local_world_size,
+        }
+        return json.dumps(dict_data).encode(encoding="UTF-8")
+
+    @staticmethod
+    def deserialize(data: bytes):
+        dict_data = json.loads(data.decode(encoding="UTF-8"))
+        return _RoleInstanceInfo(
+            dict_data["role"], dict_data["rank"], dict_data["local_world_size"]
+        )
+
+    @staticmethod
+    def compare(obj1, obj2) -> int:
+        if obj1.role == obj2.role:
+            return obj1.rank - obj2.rank
+        elif obj1.role > obj2.role:
+            return 1
+        else:
+            return -1
+
+    @staticmethod
+    def find_role_boundaries(roles_infos: list, role: str) -> tuple[int, int]:
+        start_idx, end_idx = -1, -1
+        for idx, role_info in enumerate(roles_infos):
+            if role_info.role == role:
+                if start_idx == -1:
+                    start_idx = idx
+                end_idx = idx
+        return (start_idx, end_idx)
+
+
+@dataclass
+class RunResult:
+    """Return results of the worker executions.
+
+    Run results follow an "all-or-nothing" policy where the run is successful if and
+    only if ALL local workers managed by this agent complete successfully.
+
+    If the result is successful (e.g. ``is_failed() = False``) then the ``return_values``
+    field contains the outputs (return values) of the workers managed by THIS agent mapped
+    by their GLOBAL ranks. That is ``result.return_values[0]`` is the return value of
+    global rank 0.
+
+    .. note:: ``return_values`` are only meaningful for when the worker entrypoint
+              is a function. Workers specified as a binary entrypoint do not canonically
+              have a return value and the ``return_values`` field is meaningless and
+              may be empty.
+
+    If ``is_failed()`` returns ``True`` then the ``failures`` field contains the
+    failure information, again, mapped by the GLOBAL rank of the worker that failed.
+
+    The keys in ``return_values`` and ``failures`` are mutually exclusive, that is,
+    a worker's final state can only be one of: succeeded, failed. Workers intentionally
+    terminated by the agent according to the agent's restart policy, are not represented
+    in either ``return_values`` nor ``failures``.
+    """
+
+    state: WorkerState
+    return_values: dict[int, Any] = field(default_factory=dict)
+    failures: dict[int, ProcessFailure] = field(default_factory=dict)
+
+    def is_failed(self) -> bool:
+        return self.state == WorkerState.FAILED
+
+
+def _get_fq_hostname() -> str:
+    return socket.getfqdn(socket.gethostname())
+
+
+class ElasticAgent(abc.ABC):
+    """An agent process responsible for managing one or more worker processes.
+
+    The worker processes are assumed to be regular distributed PyTorch scripts.
+    When the worker process is created by the agent, the agent provides the
+    necessary information for the worker processes to properly initialize
+    a torch process group.
+
+    The exact deployment topology and ratio of agent-to-worker is dependent
+    on the specific implementation of the agent and the user's job placement
+    preferences. For instance, to run a distributed training job on GPU with
+    8 trainers (one per GPU) one can:
+
+    1. Use 8 x single GPU instances, place an agent per instance, managing
+       1 worker per agent.
+    2. Use 4 x double GPU instances, place an agent per instance, managing
+       2 workers per agent.
+    3. Use 2 x quad GPU instances, place an agent per instance, managing
+       4 workers per agent.
+    4. Use 1 x 8 GPU instance, place an agent per instance, managing
+       8 workers per agent.
+
+    Usage
+    ::
+
+     group_result = agent.run()
+      if group_result.is_failed():
+        # workers failed
+        failure = group_result.failures[0]
+        logger.exception("worker 0 failed with exit code : %s", failure.exit_code)
+      else:
+        return group_result.return_values[0] # return rank 0's results
+
+    """
+
+    @abc.abstractmethod
+    def run(self, role: str = DEFAULT_ROLE) -> RunResult:
+        """Run the agent.
+
+        Supports retrying the worker group on failures up to ``max_restarts``.
+
+        Returns:
+            The result of the execution, containing the return values or
+            failure details for each worker mapped by the worker's global rank.
+
+        Raises:
+            Exception - any other failures NOT related to worker process
+        """
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def get_worker_group(self, role: str = DEFAULT_ROLE) -> WorkerGroup:
+        """Return the ``WorkerGroup`` for the given ``role``.
+
+        Note that the worker group is a mutable object and hence in a
+        multi-threaded/process environment it may change state.
+        Implementers are encouraged (but not required) to return
+        a defensive read-only copy.
+        """
+        raise NotImplementedError
+
+
+class SimpleElasticAgent(ElasticAgent):
+    """An ``ElasticAgent`` that manages one particular type of worker role.
+
+    An ``ElasticAgent`` that manages workers (``WorkerGroup``) for a single ``WorkerSpec``
+    such as one particular type of worker role.
+    """
+
+    def __init__(self, spec: WorkerSpec, exit_barrier_timeout: float = 300):
+        self._worker_group = WorkerGroup(spec)
+        self._remaining_restarts = self._worker_group.spec.max_restarts
+        self._store = None
+        self._exit_barrier_timeout = exit_barrier_timeout
+        self._total_execution_time = 0
+
+    def get_worker_group(self, role: str = DEFAULT_ROLE) -> WorkerGroup:
+        return self._worker_group
+
+    @abc.abstractmethod
+    def _start_workers(self, worker_group: WorkerGroup) -> dict[int, Any]:
+        r"""Start ``worker_group.spec.local_world_size`` number of workers.
+
+        This is according to worker spec for the worker group .
+        Returns a map of ``local_rank`` to worker ``id``.
+        """
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def _stop_workers(self, worker_group: WorkerGroup) -> None:
+        r"""Stop all workers in the given worker group.
+
+        Implementers must deal with workers in all states defined by
+        ``WorkerState``. That is, it must gracefully handle stopping
+        non-existent workers, unhealthy (stuck) workers, etc.
+        """
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def _monitor_workers(self, worker_group: WorkerGroup) -> RunResult:
+        r"""Check on the workers for the ``worker_group``.
+
+        This function also returns the new state of the worker group.
+        """
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def _shutdown(self, death_sig: signal.Signals = signal.SIGTERM) -> None:
+        """Clean up any resources that were allocated during the agent's work.
+
+        Args:
+            death_sig: Signal to send to the child process, SIGTERM is default
+        """
+        raise NotImplementedError
+
+    @prof
+    def _rendezvous(self, worker_group: WorkerGroup) -> None:
+        r"""Run rendezvous for the workers specified by the worker spec.
+
+        Assigns workers a new global rank and world size.
+        Updates the rendezvous store for the worker group.
+        """
+        spec = worker_group.spec
+
+        with self.record_duration("RENDEZVOUS"):
+            rdzv_info = spec.rdzv_handler.next_rendezvous()
+        store = rdzv_info.store
+        group_rank = rdzv_info.rank
+        group_world_size = rdzv_info.world_size
+
+        # master_addr/master_port could be explicitly overridden
+        # TODO: BC - specific to static rdzv and can be simplified further
+        master_addr = spec.master_addr or rdzv_info.bootstrap_store_info.master_addr
+        master_port = spec.master_port or rdzv_info.bootstrap_store_info.master_port
+
+        self._store = store
+
+        with self.record_duration("ASSIGN_WORKER_RANKS"):
+            workers = self._assign_worker_ranks(
+                store, group_rank, group_world_size, spec
+            )
+        worker_group.workers = workers
+        worker_group.store = store
+        worker_group.group_rank = group_rank
+        worker_group.group_world_size = group_world_size
+        worker_group.master_addr = master_addr
+        worker_group.master_port = master_port
+
+        restart_count = spec.max_restarts - self._remaining_restarts
+
+        logger.info(
+            "[%(role)s] Rendezvous complete for workers. Result:\n"
+            "  restart_count=%(restart_count)s\n"
+            "  master_addr=%(master_addr)s\n"
+            "  master_port=%(master_port)s\n"
+            "  group_rank=%(group_rank)s\n"
+            "  group_world_size=%(group_world_size)s\n"
+            "  local_ranks=%(local_ranks)s\n"
+            "  role_ranks=%(role_ranks)s\n"
+            "  global_ranks=%(global_ranks)s\n"
+            "  role_world_sizes=%(role_world_sizes)s\n"
+            "  global_world_sizes=%(global_world_sizes)s\n"
+            "  event_log_handler=%(event_log_handler)s\n",
+            {
+                "role": spec.role,
+                "restart_count": restart_count,
+                "master_addr": master_addr,
+                "master_port": master_port,
+                "group_rank": group_rank,
+                "group_world_size": group_world_size,
+                "local_ranks": [worker.local_rank for worker in workers],
+                "role_ranks": [worker.role_rank for worker in workers],
+                "global_ranks": [worker.global_rank for worker in workers],
+                "role_world_sizes": [worker.role_world_size for worker in workers],
+                "global_world_sizes": [worker.world_size for worker in workers],
+                "event_log_handler": spec.event_log_handler,
+            },
+        )
+
+    # pyre-fixme[56]: Pyre was not able to infer the type of the decorator
+    #  `torch.distributed.elastic.metrics.prof`.
+    @prof
+    def _assign_worker_ranks(
+        self, store, group_rank: int, group_world_size: int, spec: WorkerSpec
+    ) -> list[Worker]:
+        """Determine proper ranks for worker processes.
+
+        Fast Path: when all workers have the same role and world size. We calculate
+        the global rank to be group_rank * group_world_size + local_rank. And the
+        `role_world_size` is the same as `global_world_size`. No TCP store is used in
+        this case. This is only enabled when users set the environment variable
+        `TORCH_ELASTIC_WORKER_IDENTICAL` to 1.
+
+        Time complexity: each worker O(1), overall O(1)
+
+        Slow Path: when workers have different roles and world sizes. We use the
+        the following algorithm:
+
+        1. Each agent writes its configuration(group_rank, group_world_size
+           , num_workers) to the common store.
+        2. The rank 0 agent reads all the role_info from the store and
+           determines each agents worker ranks.
+        3. Determine the global rank: the global rank of the workers is computed
+           by cumulative sum of the local_world_size for all workers in front of it.
+           For efficiency reasons each worker is assigned a base global rank
+           such that it's workers are in the range [base_global_rank,
+           base_global_rank + local_world_size).
+        4. Determine the role rank: The role rank is determined using the algorithms
+           in the point 3 with the exception that the ranks are calculated with
+           respect to the role name.
+        5. The rank 0 agent writes the assigned ranks to the store.
+        6. Each agent reads the assigned ranks from the store.
+
+        Time complexity: each worker O(1), rank0 O(n), overall O(n)
+        """
+
+        if os.environ.get("TORCH_ELASTIC_WORKER_IDENTICAL", "0") == "1":
+            global_world_size = group_world_size * spec.local_world_size
+            base_global_rank = group_rank * spec.local_world_size
+            base_role_rank = base_global_rank
+            role_world_size = global_world_size
+        else:
+            ROLE_INFO_PREFIX = "torchelastic/role_info/"
+            ASSIGNED_RANKS_PREFIX = "torchelastic/assigned_ranks/"
+
+            agent_role_info = _RoleInstanceInfo(
+                spec.role, group_rank, spec.local_world_size
+            )
+            store.set(f"{ROLE_INFO_PREFIX}{group_rank}", agent_role_info.serialize())
+
+            # tcp store is collocated with rank 0 so we can use it to do extra compute to reduce overall # of operations.
+            if group_rank == 0:
+                role_infos_bytes = store.multi_get(
+                    [f"torchelastic/role_info/{i}" for i in range(group_world_size)]
+                )
+                role_infos = [
+                    _RoleInstanceInfo.deserialize(info_bytes)
+                    for info_bytes in role_infos_bytes
+                ]
+
+                role_sizes = defaultdict(lambda: 0)
+                global_size = 0
+                for role_info in role_infos:
+                    role_sizes[role_info.role] += role_info.local_world_size
+                    global_size += role_info.local_world_size
+
+                base_global_rank = 0
+                role_ranks = defaultdict(lambda: 0)
+
+                keys = []
+                values = []
+                for i, role_info in enumerate(role_infos):
+                    keys.append(f"{ASSIGNED_RANKS_PREFIX}{i}")
+                    values.append(
+                        json.dumps(
+                            [
+                                base_global_rank,
+                                global_size,
+                                role_ranks[role_info.role],
+                                role_sizes[role_info.role],
+                            ]
+                        )
+                    )
+
+                    base_global_rank += role_info.local_world_size
+                    role_ranks[role_info.role] += role_info.local_world_size
+
+                store.multi_set(keys, values)
+
+            # get will block until the data is available in the store.
+            (
+                base_global_rank,
+                global_world_size,
+                base_role_rank,
+                role_world_size,
+            ) = json.loads(store.get(f"{ASSIGNED_RANKS_PREFIX}{group_rank}"))
+
+        workers = []
+        for local_rank in range(spec.local_world_size):
+            worker = Worker(
+                local_rank=local_rank,
+                global_rank=base_global_rank + local_rank,
+                role_rank=base_role_rank + local_rank,
+                world_size=global_world_size,
+                role_world_size=role_world_size,
+            )
+            workers.append(worker)
+        return workers
+
+    # pyre-fixme[56]: Pyre was not able to infer the type of the decorator
+    #  `torch.distributed.elastic.metrics.prof`.
+    @prof
+    def _initialize_workers(self, worker_group: WorkerGroup) -> None:
+        r"""Start a fresh set of workers for the worker_group.
+
+        Essentially, a rendezvous followed by a ``start_workers``.
+        The caller should first call ``_stop_workers()`` to stop running workers
+        prior to calling this method.
+
+        Optimistically sets the state of the worker group that
+        just started as ``HEALTHY`` and delegates the actual monitoring
+        of state to ``_monitor_workers()`` method
+        """
+        role = worker_group.spec.role
+        logger.info("[%s] Rendezvous'ing worker group", role)
+
+        # TODO after stopping workers, wait at least monitor_interval*2 for
+        # workers on different nodes to fail on a collective op before waiting
+        # on the rdzv barrier, this way we ensure that nodes enter rdzv
+        # at around the same time and reduce false positive rdzv timeout errors
+        self._rendezvous(worker_group)
+
+        logger.info("[%s] Starting worker group", role)
+        worker_ids = self._start_workers(worker_group)
+        for local_rank, w_id in worker_ids.items():
+            worker = worker_group.workers[local_rank]
+            worker.id = w_id
+            record(
+                self._construct_event("START", EventSource.WORKER, worker),
+                worker_group.spec.event_log_handler,
+            )
+
+        worker_group.state = WorkerState.HEALTHY
+
+    # pyre-fixme[56]: Pyre was not able to infer the type of the decorator
+    #  `torch.distributed.elastic.metrics.prof`.
+    @prof
+    def _restart_workers(self, worker_group: WorkerGroup) -> None:
+        """Restart (stops, rendezvous, starts) all local workers in the group."""
+        role = worker_group.spec.role
+        logger.info("[%s] Stopping worker group", role)
+        self._stop_workers(worker_group)
+        worker_group.state = WorkerState.STOPPED
+        self._initialize_workers(worker_group)
+
+    # pyre-fixme[56]: Pyre was not able to infer the type of the decorator
+    #  `torch.distributed.elastic.metrics.prof`.
+    @prof
+    def run(self, role: str = DEFAULT_ROLE) -> RunResult:
+        start_time = time.monotonic()
+        shutdown_called: bool = False
+        try:
+            result = self._invoke_run(role)
+            self._total_execution_time = int(time.monotonic() - start_time)
+            self._record_metrics(result)
+            self._record_worker_events(result)
+            return result
+        except RendezvousGracefulExitError as e:
+            logger.info("Rendezvous gracefully exited: %s", e)  # noqa: G200
+        except SignalException as e:
+            logger.warning("Received %s death signal, shutting down workers", e.sigval)
+            self._shutdown(e.sigval)
+            shutdown_called = True
+            raise
+        finally:
+            if not shutdown_called:
+                self._shutdown()
+            # record the execution time in case there were any exceptions during run.
+            self._total_execution_time = int(time.monotonic() - start_time)
+
+    def get_event_failed(self) -> Event:
+        return self._construct_event(
+            state="FAILED",
+            source=EventSource.AGENT,
+            raw_error=traceback.format_exc(),
+        )
+
+    def get_event_succeeded(self) -> Event:
+        return self._construct_event(
+            state="SUCCEEDED",
+            source=EventSource.AGENT,
+        )
+
+    def _record_worker_events(self, result: RunResult) -> None:
+        for worker in self._worker_group.workers:
+            failure = result.failures.get(worker.global_rank)
+            state: str = self._get_worker_state(worker, result)
+            raw_error = json.dumps(failure.error_file_data) if failure else None
+            exit_code = failure.exitcode if failure else None
+            worker_pid = failure.pid if failure else None
+            record(
+                self._construct_event(
+                    state=state,
+                    source=EventSource.WORKER,
+                    worker=worker,
+                    raw_error=raw_error,
+                    exit_code=exit_code,
+                    worker_pid=worker_pid,
+                ),
+                self._worker_group.spec.event_log_handler,
+            )
+
+    def _get_worker_state(self, worker: Worker, result: RunResult) -> str:
+        failure = result.failures.get(worker.global_rank)
+        if result.state in {WorkerState.UNHEALTHY, WorkerState.FAILED} and not failure:
+            # The worker got terminated by the torchelastic agent via SIGTERM signal
+            return "TERMINATED"
+        elif failure or worker.global_rank in result.return_values:
+            return result.state.value
+        else:
+            raise ValueError(f"Unknown worker: {worker.global_rank}")
+
+    @contextmanager
+    def record_duration(self, state: str):
+        start_time = time.perf_counter()
+        try:
+            yield
+        finally:
+            end_time = time.perf_counter()
+            duration_ms = (end_time - start_time) * 1000
+            record(
+                self._construct_event(
+                    state=state, source=EventSource.AGENT, duration_ms=duration_ms
+                ),
+                self._worker_group.spec.event_log_handler,
+            )
+
+    def _construct_event(
+        self,
+        state: str,
+        source: EventSource,
+        worker: Worker | None = None,
+        raw_error: str | None = None,
+        duration_ms: float | None = None,
+        exit_code: int | None = None,
+        worker_pid: int | None = None,
+    ) -> Event:
+        wg = self._worker_group
+        spec = wg.spec
+        md = {
+            "group_world_size": wg.group_world_size,
+            "entry_point": spec.get_entrypoint_name(),
+        }
+        if worker:
+            md["local_rank"] = (worker.local_rank,)
+            md["role_rank"] = (worker.role_rank,)
+            md["role_world_size"] = (worker.role_world_size,)
+            md["exit_code"] = (exit_code,)
+            md["worker_pid"] = (worker_pid,)
+            global_rank = worker.global_rank
+            worker_id = str(worker.id)
+        else:
+            global_rank = None
+            worker_id = None
+        md_str = json.dumps(md)
+        metadata = {
+            "run_id": spec.rdzv_handler.get_run_id(),
+            "global_rank": global_rank,
+            "group_rank": wg.group_rank,
+            "worker_id": worker_id,
+            "role": spec.role,
+            "hostname": _get_fq_hostname(),
+            "state": state,
+            "total_run_time": self._total_execution_time,
+            "rdzv_backend": spec.rdzv_handler.get_backend(),
+            "raw_error": raw_error,
+            "metadata": md_str,
+            "agent_restarts": spec.max_restarts - self._remaining_restarts,
+            "duration_ms": duration_ms,
+        }
+
+        return Event(
+            f"torchelastic.worker.status.{state}", source=source, metadata=metadata
+        )
+
+    def _record_metrics(self, group_results: RunResult):
+        is_failed = group_results.is_failed()
+        self._record_flakiness_metric(is_failed)
+        spec = self._worker_group.spec
+        restarts_happened = self._remaining_restarts != spec.max_restarts
+        put_metric(f"workers.{spec.role}.run_total", 1)
+        self._record_metric_with_condition(
+            "run_success_with_retries", not is_failed and restarts_happened
+        )
+        self._record_metric_with_condition(
+            "run_success_no_retries", not is_failed and not restarts_happened
+        )
+        self._record_metric_with_condition(
+            "run_failed_with_retries", is_failed and restarts_happened
+        )
+        self._record_metric_with_condition(
+            "run_failed_no_retries", is_failed and not restarts_happened
+        )
+
+    def _record_metric_with_condition(self, metric_name, condition):
+        spec = self._worker_group.spec
+        if condition:
+            put_metric(f"workers.{spec.role}.{metric_name}", 1)
+        else:
+            put_metric(f"workers.{spec.role}.{metric_name}", 0)
+
+    def _record_flakiness_metric(self, is_failed: bool = False):
+        if is_failed:
+            flakiness = 100.0
+        else:
+            spec = self._worker_group.spec
+            flakiness = 100.0 - 100.0 * (self._remaining_restarts + 1) / (
+                spec.max_restarts + 1
+            )
+        spec = self._worker_group.spec
+
+        put_metric(f"workers.{spec.role}.flakiness", int(flakiness))
+
+    def _invoke_run(self, role: str = DEFAULT_ROLE) -> RunResult:
+        # NOTE: currently only works for a single role
+
+        spec = self._worker_group.spec
+        role = spec.role
+
+        logger.info(
+            "[%s] starting workers for entrypoint: %s", role, spec.get_entrypoint_name()
+        )
+
+        self._initialize_workers(self._worker_group)
+        monitor_interval = spec.monitor_interval
+        rdzv_handler = spec.rdzv_handler
+
+        while True:
+            assert self._worker_group.state != WorkerState.INIT
+            time.sleep(monitor_interval)
+            run_result = self._monitor_workers(self._worker_group)
+            state = run_result.state
+            self._worker_group.state = state
+
+            put_metric(f"workers.{role}.remaining_restarts", self._remaining_restarts)
+            put_metric(f"workers.{role}.{state.name.lower()}", 1)
+
+            if state == WorkerState.SUCCEEDED:
+                logger.info(
+                    "[%s] worker group successfully finished."
+                    " Waiting %s seconds for other agents to finish.",
+                    role,
+                    self._exit_barrier_timeout,
+                )
+                self._exit_barrier()
+                return run_result
+            elif state in {WorkerState.UNHEALTHY, WorkerState.FAILED}:
+                if self._remaining_restarts > 0:
+                    logger.info(
+                        "[%s] Worker group %s. "
+                        "%s/%s attempts left;"
+                        " will restart worker group",
+                        role,
+                        state.name,
+                        self._remaining_restarts,
+                        spec.max_restarts,
+                    )
+                    self._remaining_restarts -= 1
+                    self._restart_workers(self._worker_group)
+                else:
+                    self._stop_workers(self._worker_group)
+                    self._worker_group.state = WorkerState.FAILED
+                    return run_result
+            elif state == WorkerState.HEALTHY:
+                # membership changes do not count as retries
+                num_nodes_waiting = rdzv_handler.num_nodes_waiting()
+                group_rank = self._worker_group.group_rank
+                if num_nodes_waiting > 0:
+                    logger.info(
+                        "[%s] Detected %s "
+                        "new nodes from group_rank=%s; "
+                        "will restart worker group",
+                        role,
+                        num_nodes_waiting,
+                        group_rank,
+                    )
+                    self._restart_workers(self._worker_group)
+            else:
+                raise Exception(  # noqa: TRY002
+                    f"[{role}] Worker group in {state.name} state"
+                )
+
+    def _exit_barrier(self):
+        """
+        Define a barrier that keeps the agent process alive until all workers finish.
+
+        Wait for ``exit_barrier_timeout`` seconds for all agents to finish
+        executing their local workers (either successfully or not). This
+        acts as a safety guard against user scripts that terminate at different
+        times.
+        """
+        logger.info(
+            "Local worker group finished (%s). "
+            "Waiting %s seconds for other agents to finish",
+            self._worker_group.state,
+            self._exit_barrier_timeout,
+        )
+        start = time.time()
+        try:
+            store_util.barrier(
+                store=self._store,
+                world_size=self._worker_group.group_world_size,
+                key_prefix=_TERMINAL_STATE_SYNC_ID,
+                barrier_timeout=self._exit_barrier_timeout,
+            )
+            logger.info(
+                "Done waiting for other agents. Elapsed: %s seconds",
+                time.time() - start,
+            )
+        except SignalException as e:
+            logger.warning("Got termination signal: %s", e.sigval)
+            raise
+        except Exception:
+            logger.exception(
+                "Error waiting on exit barrier. Elapsed: %s seconds",
+                time.time() - start,
+            )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/health_check_server.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/health_check_server.py
new file mode 100644
index 0000000000000000000000000000000000000000..4815d86aa289c531a01bfcc8277b7ae9ffb2930e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/health_check_server.py
@@ -0,0 +1,65 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from collections.abc import Callable
+
+from torch.distributed.elastic.utils.logging import get_logger
+
+
+log = get_logger(__name__)
+
+__all__ = ["HealthCheckServer", "create_healthcheck_server"]
+
+
+class HealthCheckServer:
+    """
+    Interface for health check monitoring server, which can be extended
+    by starting tcp/http server on the specified port.
+
+    Args:
+
+        alive_callback: Callable[[], int], callback to last progress time of agent
+
+        port: int, port number to start tcp/http server
+
+        timeout: int, timeout seconds to decide agent is alive/dead
+    """
+
+    _alive_callback: Callable[[], int]
+    _port: int
+    _timeout: int
+
+    def __init__(
+        self, alive_callback: Callable[[], int], port: int, timeout: int
+    ) -> None:
+        self._alive_callback = alive_callback
+        self._port = port
+        self._timeout = timeout
+
+    def start(self) -> None:
+        """
+        Unsupported functionality for Pytorch, doesn't start any health check server
+        """
+        log.warning("No health check server started")
+
+    def stop(self) -> None:
+        """
+        Function to stop health check server
+        """
+        log.info("Stopping noop health check server.")
+
+
+def create_healthcheck_server(
+    alive_callback: Callable[[], int],
+    port: int,
+    timeout: int,
+) -> HealthCheckServer:
+    """
+    creates health check server object
+    """
+    return HealthCheckServer(alive_callback, port, timeout)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef281b6c58c318a06e2c97832ab43171313e56df
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/agent/server/local_elastic_agent.py
@@ -0,0 +1,456 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+import json
+import os
+import signal
+import socket
+import time
+import uuid
+from string import Template
+from typing import Any, TYPE_CHECKING
+
+import torch.distributed.elastic.timer as timer
+from torch.distributed.elastic import events
+from torch.distributed.elastic.agent.server.api import (
+    RunResult,
+    SimpleElasticAgent,
+    WorkerGroup,
+    WorkerSpec,
+    WorkerState,
+)
+from torch.distributed.elastic.agent.server.health_check_server import (
+    create_healthcheck_server,
+    HealthCheckServer,
+)
+from torch.distributed.elastic.metrics.api import prof
+from torch.distributed.elastic.multiprocessing import (
+    LogsSpecs,
+    PContext,
+    start_processes,
+)
+from torch.distributed.elastic.utils import macros
+from torch.distributed.elastic.utils.logging import get_logger
+
+
+if TYPE_CHECKING:
+    from torch.distributed.elastic.events.api import EventMetadataValue
+
+logger = get_logger(__name__)
+
+__all__ = [
+    "LocalElasticAgent",
+    "TORCHELASTIC_ENABLE_FILE_TIMER",
+    "TORCHELASTIC_TIMER_FILE",
+    "TORCHELASTIC_HEALTH_CHECK_PORT",
+]
+
+TORCHELASTIC_ENABLE_FILE_TIMER = "TORCHELASTIC_ENABLE_FILE_TIMER"
+TORCHELASTIC_HEALTH_CHECK_PORT = "TORCHELASTIC_HEALTH_CHECK_PORT"
+TORCHELASTIC_TIMER_FILE = "TORCHELASTIC_TIMER_FILE"
+
+
+class LocalElasticAgent(SimpleElasticAgent):
+    """An implementation of :py:class:`torchelastic.agent.server.ElasticAgent` that handles host-local workers.
+
+    This agent is deployed per host and is configured to spawn ``n`` workers.
+    When using GPUs, ``n`` maps to the number of GPUs available on the host.
+
+    The local agent does not communicate to other local agents deployed on
+    other hosts, even if the workers may communicate inter-host. The worker id
+    is interpreted to be a local process. The agent starts and stops all worker
+    processes as a single unit.
+
+
+    The worker function and argument passed to the worker function must be
+    python multiprocessing compatible. To pass multiprocessing data structures
+    to the workers you may create the data structure in the same multiprocessing
+    context as the specified ``start_method`` and pass it as a function argument.
+
+    The ``exit_barrier_timeout`` specifies the amount of time (in seconds) to wait
+    for other agents to finish. This acts as a safety net to handle cases where
+    workers finish at different times, to prevent agents from viewing workers
+    that finished early as a scale-down event. It is strongly advised that the
+    user code deal with ensuring that workers are terminated in a synchronous
+    manner rather than relying on the exit_barrier_timeout.
+
+    A named pipe based watchdog can be enabled in ```LocalElasticAgent``` if an
+    environment variable ``TORCHELASTIC_ENABLE_FILE_TIMER`` with value 1 has
+    been defined in the ```LocalElasticAgent``` process.
+    Optionally, another environment variable ```TORCHELASTIC_TIMER_FILE```
+    can be set with a unique file name for the named pipe. If the environment
+    variable ```TORCHELASTIC_TIMER_FILE``` is not set, ```LocalElasticAgent```
+    will internally create a unique file name and set it to the environment
+    variable ```TORCHELASTIC_TIMER_FILE```, and this environment variable will
+    be propagated to the worker processes to allow them to connect to the same
+    named pipe that ```LocalElasticAgent``` uses.
+
+    Logs are written to the specified log directory. Each log line will be by default
+    prefixed by ``[${role_name}${local_rank}]:`` (e.g. ``[trainer0]: foobar``).
+    Log prefixes can be customized by passing a `template string
+    `_ as the
+    ``log_line_prefix_template`` argument.
+    The following macros (identifiers) are substituted at runtime:
+    ``${role_name}, ${local_rank}, ${rank}``. For example, to prefix each log line with
+    global rank instead of the local rank, set ``log_line_prefix_template = "[${rank}]:``.
+
+
+    Example launching function
+
+    ::
+
+        def trainer(args) -> str:
+            return "do train"
+
+        def main():
+            start_method="spawn"
+            shared_queue= multiprocessing.get_context(start_method).Queue()
+            spec = WorkerSpec(
+                        role="trainer",
+                        local_world_size=nproc_per_process,
+                        entrypoint=trainer,
+                        args=("foobar",),
+                        ...)
+            agent = LocalElasticAgent(spec, start_method)
+            results = agent.run()
+
+            if results.is_failed():
+                print("trainer failed")
+            else:
+                print(f"rank 0 return value: {results.return_values[0]}")
+                # prints -> rank 0 return value: do train
+
+    Example launching binary
+
+    ::
+
+        def main():
+            spec = WorkerSpec(
+                        role="trainer",
+                        local_world_size=nproc_per_process,
+                        entrypoint="/usr/local/bin/trainer",
+                        args=("--trainer-args", "foobar"),
+                        ...)
+            agent = LocalElasticAgent(spec)
+            results = agent.run()
+
+            if not results.is_failed():
+                print("binary launches do not have return values")
+
+    """
+
+    def __init__(
+        self,
+        spec: WorkerSpec,
+        logs_specs: LogsSpecs,
+        start_method="spawn",
+        exit_barrier_timeout: float = 300,
+        log_line_prefix_template: str | None = None,
+    ):
+        super().__init__(spec, exit_barrier_timeout)
+        self._start_method = start_method
+        self._pcontext: PContext | None = None
+        self._rdzv_handler = spec.rdzv_handler
+        self._log_line_prefix_template = log_line_prefix_template
+        self._worker_watchdog: timer.FileTimerServer | None = None
+        self._logs_specs = logs_specs
+        self._health_check_server: HealthCheckServer | None = None
+
+    def _setup_local_watchdog(self, envs: dict[int, dict[str, str]]) -> None:
+        enable_watchdog_env_name = TORCHELASTIC_ENABLE_FILE_TIMER
+        watchdog_enabled = os.getenv(enable_watchdog_env_name)
+        watchdog_file_env_name = TORCHELASTIC_TIMER_FILE
+        watchdog_file_path = os.getenv(watchdog_file_env_name)
+        if watchdog_enabled is not None and str(watchdog_enabled) == "1":
+            if watchdog_file_path is None:
+                watchdog_file_path = "/tmp/watchdog_timer_" + str(uuid.uuid4())
+            logger.info("Starting a FileTimerServer with %s ...", watchdog_file_path)
+            if not envs:
+                logger.warning(
+                    "Empty envs variables, using empty run_id for FileTimerServer"
+                )
+                run_id = ""
+            else:
+                run_id = envs[0]["TORCHELASTIC_RUN_ID"]
+            self._worker_watchdog = timer.FileTimerServer(
+                file_path=watchdog_file_path,
+                run_id=run_id,
+                max_interval=0.1,
+                daemon=True,
+                log_event=self._log_watchdog_event,
+            )
+            self._worker_watchdog.start()
+            logger.info("FileTimerServer started")
+        else:
+            logger.info(
+                "Environment variable '%s' not found. Do not start FileTimerServer.",
+                enable_watchdog_env_name,
+            )
+        # Propagate the watchdog file env to worker processes
+        if watchdog_file_path is not None:
+            for worker_env in envs.values():
+                worker_env[watchdog_file_env_name] = watchdog_file_path
+
+    @staticmethod
+    def _get_current_time_secs() -> int:
+        return int(time.time())
+
+    def _setup_healthcheck(self) -> None:
+        healthcheck_port_env_name = TORCHELASTIC_HEALTH_CHECK_PORT
+        healthcheck_port = os.getenv(healthcheck_port_env_name)
+        if healthcheck_port is not None:
+            logger.info(
+                "Found healthcheck port %s: %s",
+                healthcheck_port_env_name,
+                healthcheck_port,
+            )
+            if self._worker_watchdog is None:
+                logger.info(
+                    "FileTimerServer doesn't exist, using current time as dummy callback"
+                )
+                alive_callback = LocalElasticAgent._get_current_time_secs
+            else:
+                alive_callback = self._worker_watchdog.get_last_progress_time
+
+            try:
+                healthcheck_port_as_int = int(healthcheck_port)
+                self._health_check_server = create_healthcheck_server(
+                    alive_callback=alive_callback,
+                    port=healthcheck_port_as_int,
+                    timeout=60,
+                )
+                self._health_check_server.start()
+            except ValueError:
+                logger.info(
+                    "Invalid healthcheck port value: '%s', expecting integer. Not starting healthcheck server.",
+                    healthcheck_port,
+                )
+        else:
+            logger.info(
+                "Environment variable '%s' not found. Do not start health check.",
+                healthcheck_port_env_name,
+            )
+
+    def _get_fq_hostname(self) -> str:
+        return socket.getfqdn(socket.gethostname())
+
+    def _log_watchdog_event(
+        self,
+        name: str,
+        request: timer.FileTimerRequest | None,
+    ) -> None:
+        wg = self._worker_group
+        spec = wg.spec
+        md = {"watchdog_event": name}
+        if request is not None:
+            md["worker_pid"] = str(request.worker_pid)
+            md["scope_id"] = request.scope_id
+            md["expiration_time"] = str(request.expiration_time)
+            md["signal"] = str(request.signal)
+        md_str = json.dumps(md)
+        state = "RUNNING"
+        metadata: dict[str, EventMetadataValue] = {
+            "run_id": spec.rdzv_handler.get_run_id(),
+            "global_rank": None,
+            "group_rank": wg.group_rank,
+            "worker_id": None,
+            "role": spec.role,
+            "hostname": self._get_fq_hostname(),
+            "state": state,
+            "total_run_time": self._total_execution_time,
+            "rdzv_backend": spec.rdzv_handler.get_backend(),
+            "raw_error": None,
+            "metadata": md_str,
+            "agent_restarts": spec.max_restarts - self._remaining_restarts,
+        }
+        # Note: The 'metadata' field of the Event is converted to a TorchelasticStatusLogEntry later.
+        #       The 'name' field of the Event is NOT used in the TorchelasticStatusLogEntry.
+        event = events.Event(
+            name=name, source=events.EventSource.AGENT, metadata=metadata
+        )
+        events.record(event, self._worker_group.spec.event_log_handler)
+
+    # pyre-fixme[56]: Pyre was not able to infer the type of the decorator
+    #  `torch.distributed.elastic.metrics.prof`.
+    @prof
+    def _stop_workers(self, worker_group: WorkerGroup) -> None:
+        self._shutdown()
+
+    # pyre-fixme[56]: Pyre was not able to infer the type of the decorator
+    #  `torch.distributed.elastic.metrics.prof`.
+    @prof
+    def _start_workers(self, worker_group: WorkerGroup) -> dict[int, Any]:
+        spec = worker_group.spec
+        store = worker_group.store
+        assert store is not None
+        restart_count = spec.max_restarts - self._remaining_restarts
+
+        use_agent_store: bool = spec.rdzv_handler.use_agent_store
+        logger.info("use_agent_store: %s", use_agent_store)
+
+        args: dict[int, tuple] = {}
+        envs: dict[int, dict[str, str]] = {}
+        log_line_prefixes: dict[int, str] | None = (
+            {} if self._log_line_prefix_template else None
+        )
+        for worker in worker_group.workers:
+            local_rank = worker.local_rank
+            worker_env = {
+                "RANK": str(worker.global_rank),
+                "GROUP_RANK": str(worker_group.group_rank),
+                "ROLE_RANK": str(worker.role_rank),
+                "ROLE_NAME": spec.role,
+                "LOCAL_WORLD_SIZE": str(spec.local_world_size),
+                "WORLD_SIZE": str(worker.world_size),
+                "GROUP_WORLD_SIZE": str(worker_group.group_world_size),
+                "ROLE_WORLD_SIZE": str(worker.role_world_size),
+                "MASTER_ADDR": worker_group.master_addr,
+                "MASTER_PORT": str(worker_group.master_port),
+                "TORCHELASTIC_RESTART_COUNT": str(restart_count),
+                "TORCHELASTIC_MAX_RESTARTS": str(spec.max_restarts),
+                "TORCHELASTIC_RUN_ID": spec.rdzv_handler.get_run_id(),
+                "TORCHELASTIC_USE_AGENT_STORE": str(use_agent_store),
+                "TORCH_NCCL_ASYNC_ERROR_HANDLING": os.getenv(
+                    "TORCH_NCCL_ASYNC_ERROR_HANDLING", str(1)
+                ),
+            }
+            self._set_local_rank_env(worker_env, local_rank, spec)
+            if "OMP_NUM_THREADS" in os.environ:
+                worker_env["OMP_NUM_THREADS"] = os.environ["OMP_NUM_THREADS"]
+
+            if self._log_line_prefix_template:
+                log_line_prefix = Template(
+                    self._log_line_prefix_template
+                ).safe_substitute(
+                    role_name=spec.role,
+                    rank=worker.global_rank,
+                    local_rank=local_rank,
+                )
+                # pyrefly: ignore [unsupported-operation]
+                log_line_prefixes[local_rank] = log_line_prefix
+
+            # pyrefly: ignore [unsupported-operation]
+            envs[local_rank] = worker_env
+            worker_args = list(spec.args)
+            worker_args = macros.substitute(worker_args, str(local_rank))
+            args[local_rank] = tuple(worker_args)
+
+        self._setup_local_watchdog(envs=envs)
+        self._setup_healthcheck()
+
+        assert spec.entrypoint is not None
+        assert self._logs_specs is not None
+        self._pcontext = start_processes(
+            name=spec.role,
+            entrypoint=spec.entrypoint,
+            args=args,
+            envs=envs,
+            logs_specs=self._logs_specs,
+            log_line_prefixes=log_line_prefixes,
+            start_method=self._start_method,
+            numa_options=spec.numa_options,
+            duplicate_stdout_filters=spec.duplicate_stdout_filters,
+            duplicate_stderr_filters=spec.duplicate_stderr_filters,
+        )
+
+        return self._pcontext.pids()
+
+    def _set_local_rank_env(
+        self, worker_env: dict[str, str | None], local_rank: int, spec: WorkerSpec
+    ) -> None:
+        # Set CUDA_VISIBLE_DEVICES and LOCAL_RANK based on virtual_local_rank mode.
+        # Virtual mode: Each worker sees only its assigned GPU as device 0, LOCAL_RANK=0
+        # Traditional mode: Workers see all GPUs, LOCAL_RANK matches actual local rank
+
+        if spec.virtual_local_rank:
+            # Set LOCAL_RANK=0 and use CUDA_VISIBLE_DEVICES to control the actual GPU access.
+
+            worker_env["LOCAL_RANK"] = "0"
+
+            # Map local_rank through existing CUDA_VISIBLE_DEVICES
+            # HIP uses CUDA_VISIBLE_DEVICES as a compatibility hack:
+            # https://rocm.docs.amd.com/en/latest/conceptual/gpu-isolation.html#cuda-visible-devices
+            parent_visible_devices = os.getenv("CUDA_VISIBLE_DEVICES")
+            if parent_visible_devices is not None:
+                # Parse comma-separated list of GPU IDs
+                available_gpus = parent_visible_devices.split(",")
+                if local_rank >= len(available_gpus):
+                    raise ValueError(
+                        f"local_rank {local_rank} exceeds available GPUs in "
+                        f"CUDA_VISIBLE_DEVICES={parent_visible_devices}"
+                    )
+
+                visible_gpu = available_gpus[local_rank].strip()
+            else:
+                # No restriction, use local_rank directly
+                visible_gpu = str(local_rank)
+
+            worker_env["CUDA_VISIBLE_DEVICES"] = visible_gpu
+            return
+
+        # In traditional mode, don't override CUDA_VISIBLE_DEVICES
+        # (inherit from parent environment)
+        worker_env["LOCAL_RANK"] = str(local_rank)
+
+        if "CUDA_VISIBLE_DEVICES" in os.environ:
+            worker_env["CUDA_VISIBLE_DEVICES"] = os.environ["CUDA_VISIBLE_DEVICES"]
+
+    def _shutdown(self, death_sig: signal.Signals = signal.SIGTERM) -> None:
+        if self._worker_watchdog is not None:
+            self._worker_watchdog.stop()
+            self._worker_watchdog = None
+        if self._health_check_server is not None:
+            self._health_check_server.stop()
+            self._health_check_server = None
+        if self._pcontext:
+            self._pcontext.close(death_sig)
+
+    # pyre-fixme[56]: Pyre was not able to infer the type of the decorator
+    #  `torch.distributed.elastic.metrics.prof`.
+    @prof
+    def _monitor_workers(self, worker_group: WorkerGroup) -> RunResult:
+        role = worker_group.spec.role
+        worker_pids = {w.id for w in worker_group.workers}
+        assert self._pcontext is not None
+        pc_pids = set(self._pcontext.pids().values())
+        if worker_pids != pc_pids:
+            logger.error(
+                "[%s] worker pids do not match process_context pids."
+                " Expected: %s, actual: %s",
+                role,
+                worker_pids,
+                pc_pids,
+            )
+            return RunResult(state=WorkerState.UNKNOWN)
+
+        result = self._pcontext.wait(0)
+        if result:
+            if result.is_failed():
+                # map local rank failure to global rank
+                worker_failures = {}
+                for local_rank, failure in result.failures.items():
+                    worker = worker_group.workers[local_rank]
+                    worker_failures[worker.global_rank] = failure
+                return RunResult(
+                    state=WorkerState.FAILED,
+                    failures=worker_failures,
+                )
+            else:
+                # copy ret_val_queue into a map with a global ranks
+                workers_ret_vals = {}
+                for local_rank, ret_val in result.return_values.items():
+                    worker = worker_group.workers[local_rank]
+                    workers_ret_vals[worker.global_rank] = ret_val
+                return RunResult(
+                    state=WorkerState.SUCCEEDED,
+                    return_values=workers_ret_vals,
+                )
+        else:
+            return RunResult(state=WorkerState.HEALTHY)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/control_plane.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/control_plane.py
new file mode 100644
index 0000000000000000000000000000000000000000..817255edd23dcee2deea8554ada3637d30f9885f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/control_plane.py
@@ -0,0 +1,53 @@
+import os
+from collections.abc import Generator
+from contextlib import contextmanager, ExitStack
+
+from torch.distributed.elastic.multiprocessing.errors import record
+
+
+__all__ = [
+    "worker_main",
+]
+
+TORCH_WORKER_SERVER_SOCKET = "TORCH_WORKER_SERVER_SOCKET"
+
+
+@contextmanager
+def _worker_server(socket_path: str) -> Generator[None, None, None]:
+    from torch._C._distributed_c10d import _WorkerServer
+
+    server = _WorkerServer(socket_path)
+    try:
+        yield
+    finally:
+        server.shutdown()
+
+
+@record
+@contextmanager
+def worker_main() -> Generator[None, None, None]:
+    """
+    This is a context manager that wraps your main entry function. This combines
+    the existing ``errors.record`` logic as well as a new ``_WorkerServer`` that
+    exposes handlers via a unix socket specified by
+    ``Torch_WORKER_SERVER_SOCKET``.
+
+    Example
+
+    ::
+
+     @worker_main()
+     def main():
+         pass
+
+
+     if __name__ == "__main__":
+         main()
+
+    """
+    with ExitStack() as stack:
+        socket_path = os.environ.get(TORCH_WORKER_SERVER_SOCKET)
+        if socket_path is not None:
+            stack.enter_context(_worker_server(socket_path))
+
+        yield
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/events/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/events/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..deea40f3899aee490a899cfa1dd6d3019512cb9e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/events/__init__.py
@@ -0,0 +1,173 @@
+#!/usr/bin/env/python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Module contains events processing mechanisms that are integrated with the standard python logging.
+
+Example of usage:
+
+::
+
+  from torch.distributed.elastic import events
+
+  event = events.Event(
+      name="test_event", source=events.EventSource.WORKER, metadata={...}
+  )
+  events.get_logging_handler(destination="console").info(event)
+
+"""
+
+import inspect
+import logging
+import os
+import socket
+import traceback
+from typing import Optional
+
+from torch.distributed.elastic.events.handlers import get_logging_handler
+
+from .api import (  # noqa: F401
+    Event,
+    EventMetadataValue,
+    EventSource,
+    NodeState,
+    RdzvEvent,
+)
+
+
+_events_loggers: dict[str, logging.Logger] = {}
+
+
+def _get_or_create_logger(destination: str = "null") -> logging.Logger:
+    """
+    Construct python logger based on the destination type or extends if provided.
+
+    Available destination could be found in ``handlers.py`` file.
+    The constructed logger does not propagate messages to the upper level loggers,
+    e.g. root logger. This makes sure that a single event can be processed once.
+
+    Args:
+        destination: The string representation of the event handler.
+            Available handlers found in ``handlers`` module
+    """
+    global _events_loggers
+
+    if destination not in _events_loggers:
+        _events_logger = logging.getLogger(f"torchelastic-events-{destination}")
+        _events_logger.setLevel(os.environ.get("LOGLEVEL", "INFO"))
+        # Do not propagate message to the root logger
+        _events_logger.propagate = False
+
+        logging_handler = get_logging_handler(destination)
+        _events_logger.addHandler(logging_handler)
+
+        # Add the logger to the global dictionary
+        _events_loggers[destination] = _events_logger
+
+    return _events_loggers[destination]
+
+
+def record(event: Event, destination: str = "null") -> None:
+    _get_or_create_logger(destination).info(event.serialize())
+
+
+def record_rdzv_event(event: RdzvEvent) -> None:
+    _get_or_create_logger("dynamic_rendezvous").info(event.serialize())
+
+
+def construct_and_record_rdzv_event(
+    run_id: str,
+    message: str,
+    node_state: NodeState,
+    name: str = "",
+    hostname: str = "",
+    pid: int | None = None,
+    master_endpoint: str = "",
+    local_id: int | None = None,
+    rank: int | None = None,
+) -> None:
+    """
+    Initialize rendezvous event object and record its operations.
+
+    Args:
+        run_id (str): The run id of the rendezvous.
+        message (str): The message describing the event.
+        node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED).
+        name (str): Event name. (E.g. Current action being performed).
+        hostname (str): Hostname of the node.
+        pid (Optional[int]): The process id of the node.
+        master_endpoint (str): The master endpoint for the rendezvous store, if known.
+        local_id (Optional[int]):  The local_id of the node, if defined in dynamic_rendezvous.py
+        rank (Optional[int]): The rank of the node, if known.
+    Returns:
+        None
+    Example:
+        >>> # See DynamicRendezvousHandler class
+        >>> def _record(
+        ...     self,
+        ...     message: str,
+        ...     node_state: NodeState = NodeState.RUNNING,
+        ...     rank: Optional[int] = None,
+        ... ) -> None:
+        ...     construct_and_record_rdzv_event(
+        ...         name=f"{self.__class__.__name__}.{get_method_name()}",
+        ...         run_id=self._settings.run_id,
+        ...         message=message,
+        ...         node_state=node_state,
+        ...         hostname=self._this_node.addr,
+        ...         pid=self._this_node.pid,
+        ...         local_id=self._this_node.local_id,
+        ...         rank=rank,
+        ...     )
+    """
+    # We don't want to perform an extra computation if not needed.
+    if isinstance(get_logging_handler("dynamic_rendezvous"), logging.NullHandler):
+        return
+
+    # Set up parameters.
+    if not hostname:
+        hostname = socket.getfqdn()
+    if not pid:
+        pid = os.getpid()
+
+    # Determines which file called this function.
+    callstack = inspect.stack()
+    filename = "no_file"
+    if len(callstack) > 1:
+        stack_depth_1 = callstack[1]
+        filename = os.path.basename(stack_depth_1.filename)
+        if not name:
+            name = stack_depth_1.function
+
+    # Delete the callstack variable. If kept, this can mess with python's
+    # garbage collector as we are holding on to stack frame information in
+    # the inspect module.
+    del callstack
+
+    # Set up error trace if this is an exception
+    if node_state == NodeState.FAILED:
+        error_trace = traceback.format_exc()
+    else:
+        error_trace = ""
+
+    # Initialize event object
+    event = RdzvEvent(
+        name=f"{filename}:{name}",
+        run_id=run_id,
+        message=message,
+        hostname=hostname,
+        pid=pid,
+        node_state=node_state,
+        master_endpoint=master_endpoint,
+        rank=rank,
+        local_id=local_id,
+        error_trace=error_trace,
+    )
+
+    # Finally, record the event.
+    record_rdzv_event(event)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/events/api.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/events/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..31afe29ff5f597b27b453e9993e1257e3f1f8d2a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/events/api.py
@@ -0,0 +1,116 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import json
+from dataclasses import asdict, dataclass, field
+from enum import Enum
+from typing import Union
+
+
+__all__ = ["EventSource", "Event", "NodeState", "RdzvEvent"]
+
+EventMetadataValue = Union[str, int, float, bool, None]
+
+
+class EventSource(str, Enum):
+    """Known identifiers of the event producers."""
+
+    AGENT = "AGENT"
+    WORKER = "WORKER"
+
+
+@dataclass
+class Event:
+    """
+    The class represents the generic event that occurs during the torchelastic job execution.
+
+    The event can be any kind of meaningful action.
+
+    Args:
+        name: event name.
+        source: the event producer, e.g. agent or worker
+        timestamp: timestamp in milliseconds when event occurred.
+        metadata: additional data that is associated with the event.
+    """
+
+    name: str
+    source: EventSource
+    timestamp: int = 0
+    metadata: dict[str, EventMetadataValue] = field(default_factory=dict)
+
+    def __str__(self):
+        return self.serialize()
+
+    @staticmethod
+    def deserialize(data: Union[str, "Event"]) -> "Event":
+        if isinstance(data, Event):
+            return data
+        if isinstance(data, str):
+            data_dict = json.loads(data)
+        data_dict["source"] = EventSource[data_dict["source"]]  # type: ignore[possibly-undefined]
+        # pyrefly: ignore [unbound-name]
+        return Event(**data_dict)
+
+    def serialize(self) -> str:
+        return json.dumps(asdict(self))
+
+
+class NodeState(str, Enum):
+    """The states that a node can be in rendezvous."""
+
+    INIT = "INIT"
+    RUNNING = "RUNNING"
+    SUCCEEDED = "SUCCEEDED"
+    FAILED = "FAILED"
+
+
+@dataclass
+class RdzvEvent:
+    """
+    Dataclass to represent any rendezvous event.
+
+    Args:
+        name: Event name. (E.g. Current action being performed)
+        run_id: The run id of the rendezvous
+        message: The message describing the event
+        hostname: Hostname of the node
+        pid: The process id of the node
+        node_state: The state of the node (INIT, RUNNING, SUCCEEDED, FAILED)
+        master_endpoint: The master endpoint for the rendezvous store, if known
+        rank: The rank of the node, if known
+        local_id: The local_id of the node, if defined in dynamic_rendezvous.py
+        error_trace: Error stack trace, if this is an error event.
+    """
+
+    name: str
+    run_id: str
+    message: str
+    hostname: str
+    pid: int
+    node_state: NodeState
+    master_endpoint: str = ""
+    rank: int | None = None
+    local_id: int | None = None
+    error_trace: str = ""
+
+    def __str__(self):
+        return self.serialize()
+
+    @staticmethod
+    def deserialize(data: Union[str, "RdzvEvent"]) -> "RdzvEvent":
+        if isinstance(data, RdzvEvent):
+            return data
+        if isinstance(data, str):
+            data_dict = json.loads(data)
+        data_dict["node_state"] = NodeState[data_dict["node_state"]]  # type: ignore[possibly-undefined]
+        # pyrefly: ignore [unbound-name]
+        return RdzvEvent(**data_dict)
+
+    def serialize(self) -> str:
+        return json.dumps(asdict(self))
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/events/handlers.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/events/handlers.py
new file mode 100644
index 0000000000000000000000000000000000000000..30d925353253d5bab4c4780f298e7fa68a4409e5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/events/handlers.py
@@ -0,0 +1,21 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+
+
+_log_handlers: dict[str, logging.Handler] = {
+    "console": logging.StreamHandler(),
+    "dynamic_rendezvous": logging.NullHandler(),
+    "null": logging.NullHandler(),
+}
+
+
+def get_logging_handler(destination: str = "null") -> logging.Handler:
+    global _log_handlers
+    return _log_handlers[destination]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/metrics/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/metrics/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b2c2330924879ddbe35629a82d94a9b0c4c9c339
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/metrics/__init__.py
@@ -0,0 +1,168 @@
+#!/usr/bin/env/python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""Metrics API.
+
+**Overview**:
+
+The metrics API in torchelastic is used to publish telemetry metrics.
+It is designed to be used by torchelastic's internal modules to
+publish metrics for the end user with the goal of increasing visibility
+and helping with debugging. However you may use the same API in your
+jobs to publish metrics to the same metrics ``sink``.
+
+A ``metric`` can be thought of as timeseries data
+and is uniquely identified by the string-valued tuple
+``(metric_group, metric_name)``.
+
+torchelastic makes no assumptions about what a ``metric_group`` is
+and what relationship it has with ``metric_name``. It is totally up
+to the user to use these two fields to uniquely identify a metric.
+
+.. note:: The metric group ``torchelastic`` is reserved by torchelastic for
+          platform level metrics that it produces.
+          For instance torchelastic may output the latency (in milliseconds)
+          of a re-rendezvous operation from the agent as
+          ``(torchelastic, agent.rendezvous.duration.ms)``
+
+A sensible way to use metric groups is to map them to a stage or module
+in your job. You may also encode certain high level properties
+the job such as the region or stage (dev vs prod).
+
+**Publish Metrics**:
+
+Using torchelastic's metrics API is similar to using python's logging
+framework. You first have to configure a metrics handler before
+trying to add metric data.
+
+The example below measures the latency for the ``calculate()`` function.
+
+::
+
+  import time
+  import torch.distributed.elastic.metrics as metrics
+
+  # makes all metrics other than the one from "my_module" to go /dev/null
+  metrics.configure(metrics.NullMetricsHandler())
+  metrics.configure(metrics.ConsoleMetricsHandler(), "my_module")
+
+
+  def my_method():
+      start = time.time()
+      calculate()
+      end = time.time()
+      metrics.put_metric("calculate_latency", int(end - start), "my_module")
+
+You may also use the torch.distributed.elastic.metrics.prof` decorator
+to conveniently and succinctly profile functions
+
+::
+
+  # -- in module examples.foobar --
+
+  import torch.distributed.elastic.metrics as metrics
+
+  metrics.configure(metrics.ConsoleMetricsHandler(), "foobar")
+  metrics.configure(metrics.ConsoleMetricsHandler(), "Bar")
+
+
+  @metrics.prof
+  def foo():
+      pass
+
+
+  class Bar:
+      @metrics.prof
+      def baz():
+          pass
+
+``@metrics.prof`` will publish the following metrics
+::
+
+  .success - 1 if the function finished successfully
+  .failure - 1 if the function threw an exception
+  .duration.ms - function duration in milliseconds
+
+**Configuring Metrics Handler**:
+
+`torch.distributed.elastic.metrics.MetricHandler` is responsible for emitting
+the added metric values to a particular destination. Metric groups can be
+configured with different metric handlers.
+
+By default torchelastic emits all metrics to ``/dev/null``.
+By adding the following configuration metrics,
+``torchelastic`` and ``my_app`` metric groups will be printed out to
+console.
+
+::
+
+  import torch.distributed.elastic.metrics as metrics
+
+  metrics.configure(metrics.ConsoleMetricHandler(), group="torchelastic")
+  metrics.configure(metrics.ConsoleMetricHandler(), group="my_app")
+
+**Writing a Custom Metric Handler**:
+
+If you want your metrics to be emitted to a custom location, implement
+the `torch.distributed.elastic.metrics.MetricHandler` interface
+and configure your job to use your custom metric handler.
+
+Below is a toy example that prints the metrics to ``stdout``
+
+::
+
+  import torch.distributed.elastic.metrics as metrics
+
+
+  class StdoutMetricHandler(metrics.MetricHandler):
+      def emit(self, metric_data):
+          ts = metric_data.timestamp
+          group = metric_data.group_name
+          name = metric_data.name
+          value = metric_data.value
+          print(f"[{ts}][{group}]: {name}={value}")
+
+
+  metrics.configure(StdoutMetricHandler(), group="my_app")
+
+Now all metrics in the group ``my_app`` will be printed to stdout as:
+
+::
+
+  [1574213883.4182858][my_app]: my_metric=
+  [1574213940.5237644][my_app]: my_metric=
+
+"""
+
+from typing import Optional
+
+from .api import (  # noqa: F401
+    configure,
+    ConsoleMetricHandler,
+    get_elapsed_time_ms,
+    getStream,
+    MetricData,
+    MetricHandler,
+    MetricsConfig,
+    NullMetricHandler,
+    prof,
+    profile,
+    publish_metric,
+    put_metric,
+)
+
+
+def initialize_metrics(cfg: MetricsConfig | None = None):
+    pass
+
+
+try:
+    from torch.distributed.elastic.metrics.static_init import *  # type: ignore[import] # noqa: F401 F403
+except ModuleNotFoundError:
+    pass
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/metrics/api.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/metrics/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..102049481538d15a7fe995a8602ba45d6842303e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/metrics/api.py
@@ -0,0 +1,216 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import abc
+import time
+from collections import namedtuple
+from functools import wraps
+from typing_extensions import deprecated
+
+
+__all__ = [
+    "MetricsConfig",
+    "MetricHandler",
+    "ConsoleMetricHandler",
+    "NullMetricHandler",
+    "MetricStream",
+    "configure",
+    "getStream",
+    "prof",
+    "profile",
+    "put_metric",
+    "publish_metric",
+    "get_elapsed_time_ms",
+    "MetricData",
+]
+
+MetricData = namedtuple("MetricData", ["timestamp", "group_name", "name", "value"])
+
+
+class MetricsConfig:
+    __slots__ = ["params"]
+
+    def __init__(self, params: dict[str, str] | None = None):
+        self.params = params
+        if self.params is None:
+            self.params = {}
+
+
+class MetricHandler(abc.ABC):
+    @abc.abstractmethod
+    def emit(self, metric_data: MetricData):
+        pass
+
+
+class ConsoleMetricHandler(MetricHandler):
+    def emit(self, metric_data: MetricData):
+        print(
+            f"[{metric_data.timestamp}][{metric_data.group_name}]: {metric_data.name}={metric_data.value}"
+        )
+
+
+class NullMetricHandler(MetricHandler):
+    def emit(self, metric_data: MetricData):
+        pass
+
+
+class MetricStream:
+    def __init__(self, group_name: str, handler: MetricHandler):
+        self.group_name = group_name
+        self.handler = handler
+
+    def add_value(self, metric_name: str, metric_value: int):
+        self.handler.emit(
+            MetricData(time.time(), self.group_name, metric_name, metric_value)
+        )
+
+
+_metrics_map: dict[str, MetricHandler] = {}
+_default_metrics_handler: MetricHandler = NullMetricHandler()
+
+
+# pyre-fixme[9]: group has type `str`; used as `None`.
+def configure(handler: MetricHandler, group: str | None = None):
+    if group is None:
+        global _default_metrics_handler
+        # pyre-fixme[9]: _default_metrics_handler has type `NullMetricHandler`; used
+        #  as `MetricHandler`.
+        _default_metrics_handler = handler
+    else:
+        _metrics_map[group] = handler
+
+
+def getStream(group: str):
+    handler = _metrics_map.get(group, _default_metrics_handler)
+    return MetricStream(group, handler)
+
+
+def _get_metric_name(fn):
+    qualname = fn.__qualname__
+    split = qualname.split(".")
+    if len(split) == 1:
+        module = fn.__module__
+        if module:
+            return module.split(".")[-1] + "." + split[0]
+        else:
+            return split[0]
+    else:
+        return qualname
+
+
+def prof(fn=None, group: str = "torchelastic"):
+    r"""
+    @profile decorator publishes duration.ms, count, success, failure metrics for the function that it decorates.
+
+    The metric name defaults to the qualified name (``class_name.def_name``) of the function.
+    If the function does not belong to a class, it uses the leaf module name instead.
+
+    Usage
+
+    ::
+
+     @metrics.prof
+     def x():
+         pass
+
+
+     @metrics.prof(group="agent")
+     def y():
+         pass
+    """
+
+    def wrap(f):
+        @wraps(f)
+        def wrapper(*args, **kwargs):
+            key = _get_metric_name(f)
+            try:
+                start = time.time()
+                result = f(*args, **kwargs)
+                put_metric(f"{key}.success", 1, group)
+            except Exception:
+                put_metric(f"{key}.failure", 1, group)
+                raise
+            finally:
+                put_metric(f"{key}.duration.ms", get_elapsed_time_ms(start), group)  # type: ignore[possibly-undefined]
+            return result
+
+        return wrapper
+
+    if fn:
+        return wrap(fn)
+    else:
+        return wrap
+
+
+@deprecated("Deprecated, use `@prof` instead", category=FutureWarning)
+def profile(group=None):
+    """
+    @profile decorator adds latency and success/failure metrics to any given function.
+
+    Usage
+
+    ::
+
+     @metrics.profile("my_metric_group")
+     def some_function():
+    """
+
+    def wrap(func):
+        @wraps(func)
+        def wrapper(*args, **kwargs):
+            try:
+                start_time = time.time()
+                result = func(*args, **kwargs)
+                # pyrefly: ignore [bad-argument-type]
+                publish_metric(group, f"{func.__name__}.success", 1)
+            except Exception:
+                # pyrefly: ignore [bad-argument-type]
+                publish_metric(group, f"{func.__name__}.failure", 1)
+                raise
+            finally:
+                publish_metric(
+                    # pyrefly: ignore [bad-argument-type]
+                    group,
+                    f"{func.__name__}.duration.ms",
+                    get_elapsed_time_ms(start_time),  # type: ignore[possibly-undefined]
+                )
+            return result
+
+        return wrapper
+
+    return wrap
+
+
+def put_metric(metric_name: str, metric_value: int, metric_group: str = "torchelastic"):
+    """
+    Publish a metric data point.
+
+    Usage
+
+    ::
+
+     put_metric("metric_name", 1)
+     put_metric("metric_name", 1, "metric_group_name")
+    """
+    getStream(metric_group).add_value(metric_name, metric_value)
+
+
+@deprecated(
+    "Deprecated, use `put_metric(metric_group)(metric_name, metric_value)` instead",
+    category=FutureWarning,
+)
+def publish_metric(metric_group: str, metric_name: str, metric_value: int):
+    metric_stream = getStream(metric_group)
+    metric_stream.add_value(metric_name, metric_value)
+
+
+def get_elapsed_time_ms(start_time_in_seconds: float):
+    """Return the elapsed time in millis from the given start time."""
+    end_time = time.time()
+    return int((end_time - start_time_in_seconds) * 1000)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..60b7cd32fd2531a3e3b04416b75a29767ba835fa
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/__init__.py
@@ -0,0 +1,256 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Library that launches and manages ``n`` copies of worker subprocesses either specified by a function or a binary.
+
+For functions, it uses ``torch.multiprocessing`` (and therefore python
+``multiprocessing``) to spawn/fork worker processes. For binaries it uses python
+``subprocessing.Popen`` to create worker processes.
+
+
+Usage 1: Launching two trainers as a function
+
+::
+
+ from torch.distributed.elastic.multiprocessing import Std, start_processes
+
+
+ def trainer(a, b, c):
+     pass  # train
+
+
+ # runs two trainers
+ # LOCAL_RANK=0 trainer(1,2,3)
+ # LOCAL_RANK=1 trainer(4,5,6)
+ ctx = start_processes(
+     name="trainer",
+     entrypoint=trainer,
+     args={0: (1, 2, 3), 1: (4, 5, 6)},
+     envs={0: {"LOCAL_RANK": 0}, 1: {"LOCAL_RANK": 1}},
+     log_dir="/tmp/foobar",
+     redirects=Std.ALL,  # write all worker stdout/stderr to a log file
+     tee={0: Std.ERR},  # tee only local rank 0's stderr to console
+ )
+
+ # waits for all copies of trainer to finish
+ ctx.wait()
+
+Usage 2: Launching 2 echo workers as a binary
+
+::
+
+ # same as invoking
+ # echo hello
+ # echo world > stdout.log
+ ctx = start_processes(
+         name="echo"
+         entrypoint="echo",
+         log_dir="/tmp/foobar",
+         args={0: "hello", 1: "world"},
+         redirects={1: Std.OUT},
+        )
+
+Just like ``torch.multiprocessing``, the return value of the function
+:func:`start_processes` is a process context (:class:`api.PContext`). If a function
+was launched, a :class:`api.MultiprocessContext` is returned and if a binary
+was launched a :class:`api.SubprocessContext` is returned. Both are specific
+implementations of the parent :class:`api.PContext` class.
+"""
+
+from collections.abc import Callable
+from typing import Optional, Union
+
+from torch.distributed.elastic.multiprocessing.api import (  # noqa: F401
+    _validate_full_rank,
+    DefaultLogsSpecs,
+    LogsDest,
+    LogsSpecs,
+    MultiprocessContext,
+    PContext,
+    ProcessFailure,
+    RunProcsResult,
+    SignalException,
+    Std,
+    SubprocessContext,
+    to_map,
+)
+from torch.distributed.elastic.utils.logging import get_logger
+from torch.numa.binding import NumaOptions
+
+
+__all__ = [
+    "start_processes",
+    "MultiprocessContext",
+    "PContext",
+    "ProcessFailure",
+    "RunProcsResult",
+    "SignalException",
+    "Std",
+    "LogsDest",
+    "LogsSpecs",
+    "DefaultLogsSpecs",
+    "SubprocessContext",
+    "to_map",
+]
+
+
+def start_processes(
+    name: str,
+    entrypoint: Callable | str,
+    args: dict[int, tuple],
+    envs: dict[int, dict[str, str]],
+    logs_specs: LogsSpecs,
+    log_line_prefixes: dict[int, str] | None = None,
+    start_method: str = "spawn",
+    numa_options: NumaOptions | None = None,
+    duplicate_stdout_filters: list[str] | None = None,
+    duplicate_stderr_filters: list[str] | None = None,
+) -> PContext:
+    """
+    Start ``n`` copies of ``entrypoint`` processes with the provided options.
+
+    ``entrypoint`` is either a ``Callable`` (function) or a ``str`` (binary).
+    The number of copies is determined by the number of entries for ``args`` and
+    ``envs`` arguments, which need to have the same key set.
+
+    ``args`` and ``env`` parameters are the arguments and environment variables
+    to pass down to the entrypoint mapped by the replica index (local rank).
+    All local ranks must be accounted for.
+    That is, the keyset should be ``{0,1,...,(nprocs-1)}``.
+
+    .. note:: When the ``entrypoint`` is a binary (``str``), ``args`` can only be strings.
+              If any other type is given, then it is casted to a string representation
+              (e.g. ``str(arg1)``). Furthermore, a binary failure will only write
+              an ``error.json`` error file if the main function is annotated with
+              ``torch.distributed.elastic.multiprocessing.errors.record``. For function launches,
+              this is done by default and there is no need to manually annotate
+              with the ``@record`` annotation.
+
+    Inside ``logs_specs``, ``redirects`` and ``tee`` are bitmasks specifying which std
+    stream(s) to redirect to a log file in the ``log_dir``. Valid mask values are defined
+    in ``Std``.  To redirect/tee only certain local ranks, pass ``redirects`` as a map
+    with the key as the local rank to specify the redirect behavior for.
+    Any missing local ranks will default to ``Std.NONE``.
+
+    ``duplicate_stdout_filters`` and ``duplicate_stderr_filters``, if non-empty,
+    duplicate stdouts and stderrs respectively specified in ``logs_specs``'s ``tee``
+    to a file containing only lines that match _any_ of the filter strings. The log
+    file is aggregated across all ranks selected by ``tee``.
+
+    ``tee`` acts like the unix "tee" command in that it redirects + prints to console.
+    To avoid worker stdout/stderr from printing to console, use the ``redirects`` parameter.
+
+    For each process, the ``log_dir`` will contain:
+
+    #. ``{local_rank}/error.json``: if the process failed, a file with the error info
+    #. ``{local_rank}/stdout.log``: if ``redirect & STDOUT == STDOUT``
+    #. ``{local_rank}/stderr.log``: if ``redirect & STDERR == STDERR``
+    #. ``filtered_stdout.log``: if ``duplicate_stdout_filters`` is non-empty
+    #. ``filtered_stderr.log``: if ``duplicate_stderr_filters`` is non-empty
+
+    .. note:: It is expected that the ``log_dir`` exists, is empty, and is a directory.
+
+    Example:
+    ::
+
+     log_dir = "/tmp/test"
+
+     # ok; two copies of foo: foo("bar0"), foo("bar1")
+     start_processes(
+        name="trainer",
+        entrypoint=foo,
+        args:{0:("bar0",), 1:("bar1",),
+        envs:{0:{}, 1:{}},
+        log_dir=log_dir
+     )
+
+     # invalid; envs missing for local rank 1
+     start_processes(
+        name="trainer",
+        entrypoint=foo,
+        args:{0:("bar0",), 1:("bar1",),
+        envs:{0:{}},
+        log_dir=log_dir
+     )
+
+     # ok; two copies of /usr/bin/touch: touch file1, touch file2
+     start_processes(
+        name="trainer",
+        entrypoint="/usr/bin/touch",
+        args:{0:("file1",), 1:("file2",),
+        envs:{0:{}, 1:{}},
+        log_dir=log_dir
+      )
+
+     # caution; arguments casted to string, runs:
+     # echo "1" "2" "3" and echo "[1, 2, 3]"
+     start_processes(
+        name="trainer",
+        entrypoint="/usr/bin/echo",
+        args:{0:(1,2,3), 1:([1,2,3],),
+        envs:{0:{}, 1:{}},
+        log_dir=log_dir
+      )
+
+    Args:
+        name: a human readable short name that describes what the processes are
+              (used as header when tee'ing stdout/stderr outputs)
+        entrypoint: either a ``Callable`` (function) or ``cmd`` (binary)
+        args: arguments to each replica
+        envs: env vars to each replica
+        log_dir: directory used to write log files
+        start_method: multiprocessing start method (spawn, fork, forkserver)
+                      ignored for binaries
+        logs_specs: defines ``log_dir``, ``redirects``, and ``tee``.
+                    inside ``logs_specs``:
+                    - redirects: which std streams to redirect to a log file
+                    - tee: which std streams to redirect + print to console
+        local_ranks_filter: which ranks' logs to print to console
+        duplicate_stdout_filters: filters for the duplicated stdout logs
+        duplicate_stderr_filters: filters for the duplicated stderr logs
+
+    """
+
+    nprocs = len(args)
+    _validate_full_rank(args, nprocs, "args")
+    _validate_full_rank(envs, nprocs, "envs")
+
+    context: PContext
+    if isinstance(entrypoint, str):
+        context = SubprocessContext(
+            name=name,
+            entrypoint=entrypoint,
+            args=args,
+            envs=envs,
+            duplicate_stdout_filters=duplicate_stdout_filters,
+            duplicate_stderr_filters=duplicate_stderr_filters,
+            logs_specs=logs_specs,
+            log_line_prefixes=log_line_prefixes,
+            numa_options=numa_options,
+        )
+    else:
+        context = MultiprocessContext(
+            name=name,
+            entrypoint=entrypoint,
+            args=args,
+            envs=envs,
+            duplicate_stdout_filters=duplicate_stdout_filters,
+            duplicate_stderr_filters=duplicate_stderr_filters,
+            log_line_prefixes=log_line_prefixes,
+            start_method=start_method,
+            logs_specs=logs_specs,
+            numa_options=numa_options,
+        )
+
+    try:
+        context.start()
+        return context
+    except Exception:
+        context.close()
+        raise
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/api.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..45351c380ca0db821149edd174cb588192619be6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/api.py
@@ -0,0 +1,1036 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import abc
+import logging
+import os
+import re
+import shutil
+import signal
+import subprocess
+import sys
+import tempfile
+import threading
+import time
+from abc import ABC, abstractmethod
+from collections.abc import Callable
+from contextlib import nullcontext
+from dataclasses import dataclass, field
+from enum import IntFlag
+from multiprocessing import synchronize
+from types import FrameType
+from typing import Any, TextIO, Union
+
+import torch.multiprocessing as mp
+from torch.distributed.elastic.multiprocessing.errors import ProcessFailure, record
+from torch.distributed.elastic.multiprocessing.redirects import (
+    redirect_stderr,
+    redirect_stdout,
+)
+from torch.distributed.elastic.multiprocessing.subprocess_handler import (
+    get_subprocess_handler,
+    SubprocessHandler,
+)
+from torch.distributed.elastic.multiprocessing.tail_log import TailLog
+from torch.numa.binding import maybe_wrap_with_numa_binding, NumaOptions
+
+
+IS_WINDOWS = sys.platform == "win32"
+IS_MACOS = sys.platform == "darwin"
+
+
+logger = logging.getLogger(__name__)
+
+__all__ = [
+    "DefaultLogsSpecs",
+    "SignalException",
+    "Std",
+    "to_map",
+    "RunProcsResult",
+    "PContext",
+    "get_std_cm",
+    "MultiprocessContext",
+    "SubprocessContext",
+    "LogsDest",
+    "LogsSpecs",
+]
+
+
+class SignalException(Exception):
+    """
+    Exception is raised inside the torchelastic agent process by the termination handler
+    if the death signal got received by the process.
+    """
+
+    def __init__(self, msg: str, sigval: signal.Signals) -> None:
+        super().__init__(msg)
+        self.sigval = sigval
+
+
+def _terminate_process_handler(signum: int, frame: FrameType | None) -> None:
+    """Termination handler that raises exceptions on the main process.
+
+    When the process receives death signal(SIGTERM, SIGINT), this termination handler will
+    be invoked. It raises the ``SignalException`` exception that should be processed by the
+    user code. Python does not terminate process after the termination handler is finished,
+    so the exception should not be silently ignored, otherwise the process will never
+    be terminated.
+    """
+    sigval = signal.Signals(signum)
+    raise SignalException(f"Process {os.getpid()} got signal: {sigval}", sigval=sigval)
+
+
+def _get_kill_signal() -> signal.Signals:
+    """Get the kill signal. SIGKILL for unix, CTRL_C_EVENT for windows."""
+    if IS_WINDOWS:
+        return signal.CTRL_C_EVENT  # type: ignore[attr-defined] # noqa: F821
+    else:
+        return signal.SIGKILL
+
+
+def _get_default_signal() -> signal.Signals:
+    """Get the default termination signal. SIGTERM for unix, CTRL_C_EVENT for windows."""
+    if IS_WINDOWS:
+        return signal.CTRL_C_EVENT  # type: ignore[attr-defined] # noqa: F821
+    else:
+        return signal.SIGTERM
+
+
+def _validate_full_rank(d: dict[int, Any], nprocs: int, what: str):
+    actual_keys = set(d.keys())
+    expected_keys = set(range(nprocs))
+
+    if actual_keys != expected_keys:
+        raise RuntimeError(
+            f"{what}, local rank mapping mismatch,"
+            f" expected: {expected_keys}, actual: {actual_keys}"
+        )
+
+
+_MAPPING_REGEX = r"^(\d:[0123],)*(\d:[0123])$"
+_VALUE_REGEX = r"^[0123]$"
+
+
+class Std(IntFlag):
+    NONE = 0
+    OUT = 1
+    ERR = 2
+    ALL = OUT | ERR
+
+    @classmethod
+    def from_str(cls, vm: str) -> Union["Std", dict[int, "Std"]]:
+        """
+        Example:
+        ::
+
+         from_str("0") -> Std.NONE
+         from_str("1") -> Std.OUT
+         from_str("0:3,1:0,2:1,3:2") -> {0: Std.ALL, 1: Std.NONE, 2: Std.OUT, 3: Std.ERR}
+
+        Any other input raises an exception
+        """
+
+        def to_std(v: str) -> Std:  # type: ignore[return]
+            s = Std(int(v))
+            if s in Std:
+                return s
+            # return None -> should NEVER reach here since we regex check input
+
+        if re.match(_VALUE_REGEX, vm):  # vm is a number (e.g. 0)
+            return to_std(vm)
+        elif re.match(_MAPPING_REGEX, vm):  # vm is a mapping (e.g. 0:1,1:2)
+            d: dict[int, Std] = {}
+            for m in vm.split(","):
+                i, v = m.split(":")
+                d[int(i)] = to_std(v)
+            return d
+        else:
+            raise ValueError(
+                f"{vm} does not match: <{_VALUE_REGEX}> or <{_MAPPING_REGEX}>"
+            )
+
+
+def to_map(val_or_map: Std | dict[int, Std], local_world_size: int) -> dict[int, Std]:
+    """
+    Certain APIs take redirect settings either as a single value (e.g. apply to all
+    local ranks) or as an explicit user-provided mapping. This method is a convenience
+    method that converts a value or mapping into a mapping.
+
+    Example:
+    ::
+
+     to_map(Std.OUT, local_world_size=2)  # returns: {0: Std.OUT, 1: Std.OUT}
+     to_map({1: Std.OUT}, local_world_size=2)  # returns: {0: Std.NONE, 1: Std.OUT}
+     to_map(
+         {0: Std.OUT, 1: Std.OUT}, local_world_size=2
+     )  # returns: {0: Std.OUT, 1: Std.OUT}
+    """
+    if isinstance(val_or_map, Std):
+        return dict.fromkeys(range(local_world_size), val_or_map)
+    else:
+        map = {}
+        for i in range(local_world_size):
+            map[i] = val_or_map.get(i, Std.NONE)
+        return map
+
+
+@dataclass
+class LogsDest:
+    """
+    For each log type, holds mapping of local rank ids to file paths.
+    """
+
+    stdouts: dict[int, str] = field(default_factory=dict)
+    stderrs: dict[int, str] = field(default_factory=dict)
+    tee_stdouts: dict[int, str] = field(default_factory=dict)
+    tee_stderrs: dict[int, str] = field(default_factory=dict)
+    error_files: dict[int, str] = field(default_factory=dict)
+    filtered_stdout: str = field(default_factory=str)
+    filtered_stderr: str = field(default_factory=str)
+
+
+class LogsSpecs(ABC):
+    """
+    Defines logs processing and redirection for each worker process.
+
+    Args:
+        log_dir:
+            Base directory where logs will be written.
+        redirects:
+            Streams to redirect to files. Pass a single ``Std``
+            enum to redirect for all workers, or a mapping keyed
+            by local_rank to selectively redirect.
+        tee:
+            Streams to duplicate to stdout/stderr.
+            Pass a single ``Std`` enum to duplicate streams for all workers,
+            or a mapping keyed by local_rank to selectively duplicate.
+    """
+
+    def __init__(
+        self,
+        log_dir: str | None = None,
+        redirects: Std | dict[int, Std] = Std.NONE,
+        tee: Std | dict[int, Std] = Std.NONE,
+        local_ranks_filter: set[int] | None = None,
+    ) -> None:
+        self._root_log_dir = log_dir
+        self._redirects = redirects
+        self._tee = tee
+        self._local_ranks_filter = local_ranks_filter
+
+    @abstractmethod
+    def reify(
+        self,
+        envs: dict[int, dict[str, str]],
+    ) -> LogsDest:
+        """
+        Given the environment variables, builds destination of log files for each of the local ranks.
+
+        Envs parameter contains env variables dict for each of the local ranks, where entries are defined in:
+        :func:`~torchelastic.distributed.elastic.agent.server.local_elastic_agent.LocalElasticAgent._start_workers`.
+        """
+
+    @property
+    @abstractmethod
+    def root_log_dir(self) -> str:
+        pass
+
+
+class DefaultLogsSpecs(LogsSpecs):
+    """
+    Default LogsSpecs implementation:
+
+    - `log_dir` will be created if it doesn't exist
+    - Generates nested folders for each attempt and rank.
+    """
+
+    def __init__(
+        self,
+        log_dir: str | None = None,
+        redirects: Std | dict[int, Std] = Std.NONE,
+        tee: Std | dict[int, Std] = Std.NONE,
+        local_ranks_filter: set[int] | None = None,
+    ) -> None:
+        if log_dir != os.devnull:
+            if not log_dir:
+                log_dir = tempfile.mkdtemp(prefix="torchelastic_")
+            elif not os.path.exists(log_dir):
+                os.makedirs(log_dir, exist_ok=True)
+            else:
+                if os.path.isfile(log_dir):
+                    raise NotADirectoryError(f"log_dir: {log_dir} is a file")
+        super().__init__(log_dir, redirects, tee, local_ranks_filter)
+        # initialized only once
+        self._run_log_dir = None
+
+    @property
+    def root_log_dir(self) -> str:
+        return str(self._root_log_dir)
+
+    def _make_log_dir(self, log_dir: str | None, rdzv_run_id: str):
+        base_log_dir = log_dir or tempfile.mkdtemp(prefix="torchelastic_")
+        os.makedirs(base_log_dir, exist_ok=True)
+        dir = tempfile.mkdtemp(prefix=f"{rdzv_run_id}_", dir=base_log_dir)
+        logger.info("log directory set to: %s", dir)
+        return dir
+
+    def reify(
+        self,
+        envs: dict[int, dict[str, str]],
+    ) -> LogsDest:
+        """
+        Uses following scheme to build log destination paths:
+
+        - `//attempt_//stdout.log`
+        - `//attempt_//stderr.log`
+        - `//attempt_//error.json`
+        - `//attempt_/filtered_stdout.log`
+        - `//attempt_/filtered_stderr.log`
+        """
+        nprocs = len(envs)
+        global_env = {}  # use only to query properties that are not dependent on a rank
+        if nprocs > 0:
+            global_env = envs[0]
+        else:
+            logger.warning(
+                "Empty envs map provided when defining logging destinations."
+            )
+        # Keys are always defined, but values can be missing in unit tests
+        run_id = global_env.get("TORCHELASTIC_RUN_ID", "test_run_id")
+        restart_count = global_env.get("TORCHELASTIC_RESTART_COUNT", "0")
+
+        attempt_log_dir: str = ""
+        if self._root_log_dir != os.devnull:
+            if not self._run_log_dir:
+                self._run_log_dir = self._make_log_dir(self._root_log_dir, run_id)
+
+            attempt_log_dir = os.path.join(
+                self._run_log_dir, f"attempt_{restart_count}"
+            )  # type: ignore[call-overload]
+            shutil.rmtree(attempt_log_dir, ignore_errors=True)
+            os.makedirs(attempt_log_dir)
+
+        if self._root_log_dir == os.devnull:
+            attempt_log_dir = os.devnull
+
+        # create subdirs for each local rank in the logs_dir
+        # logs_dir
+        #       |- 0
+        #          |- error.json
+        #          |- stdout.log
+        #          |- stderr.log
+        #       |- ...
+        #       |- (nprocs-1)
+        redirs = to_map(self._redirects, nprocs)
+        ts = to_map(self._tee, nprocs)
+
+        # to tee stdout/stderr we first redirect into a file
+        # then tail -f stdout.log/stderr.log so add tee settings to redirects
+        for local_rank, tee_std in ts.items():
+            redirect_std = redirs[local_rank]
+            redirs[local_rank] = redirect_std | tee_std
+
+        SYS_STREAM = ""  # special case to indicate to output to console
+        stdouts = dict.fromkeys(range(nprocs), SYS_STREAM)
+        stderrs = dict.fromkeys(range(nprocs), SYS_STREAM)
+        tee_stdouts: dict[int, str] = {}
+        tee_stderrs: dict[int, str] = {}
+        error_files = {}
+
+        for local_rank in range(nprocs):
+            if attempt_log_dir == os.devnull:
+                tee_stdouts[local_rank] = os.devnull
+                tee_stderrs[local_rank] = os.devnull
+                error_files[local_rank] = os.devnull
+                envs[local_rank]["TORCHELASTIC_ERROR_FILE"] = ""
+            else:
+                clogdir = os.path.join(attempt_log_dir, str(local_rank))
+                os.mkdir(clogdir)
+
+                rd = redirs[local_rank]
+                if (rd & Std.OUT) == Std.OUT:
+                    stdouts[local_rank] = os.path.join(clogdir, "stdout.log")
+                if (rd & Std.ERR) == Std.ERR:
+                    stderrs[local_rank] = os.path.join(clogdir, "stderr.log")
+
+                t = ts[local_rank]
+                if t & Std.OUT == Std.OUT:
+                    tee_stdouts[local_rank] = stdouts[local_rank]
+                if t & Std.ERR == Std.ERR:
+                    tee_stderrs[local_rank] = stderrs[local_rank]
+
+                if (
+                    self._local_ranks_filter
+                    and local_rank not in self._local_ranks_filter
+                ):
+                    # If stream is tee'd, only write to file, but don't tail
+                    if local_rank in tee_stdouts:
+                        tee_stdouts.pop(local_rank, None)
+                    if local_rank in tee_stderrs:
+                        tee_stderrs.pop(local_rank, None)
+
+                    # If stream is not redirected, don't print
+                    if stdouts[local_rank] == SYS_STREAM:
+                        stdouts[local_rank] = os.devnull
+                    if stderrs[local_rank] == SYS_STREAM:
+                        stderrs[local_rank] = os.devnull
+
+                error_file = os.path.join(clogdir, "error.json")
+                error_files[local_rank] = error_file
+                logger.info(
+                    "Setting worker%s reply file to: %s", local_rank, error_file
+                )
+                envs[local_rank]["TORCHELASTIC_ERROR_FILE"] = error_file
+
+        return LogsDest(
+            stdouts,
+            stderrs,
+            tee_stdouts,
+            tee_stderrs,
+            error_files,
+            os.path.join(attempt_log_dir, "filtered_stdout.log"),
+            os.path.join(attempt_log_dir, "filtered_stderr.log"),
+        )
+
+    def __repr__(self) -> str:
+        return (
+            f"DefaultLogsSpecs(root_log_dir={self._root_log_dir}, redirects={self._redirects}, "
+            f"tee={self._tee}, local_ranks_filter={self._local_ranks_filter})"
+        )
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, DefaultLogsSpecs):
+            return False
+
+        return (
+            self._root_log_dir == other._root_log_dir
+            and self._redirects == other._redirects
+            and self._tee == other._tee
+            and self._local_ranks_filter == other._local_ranks_filter
+        )
+
+
+@dataclass
+class RunProcsResult:
+    """
+    Results of a completed run of processes started with ``start_processes()``. Returned by ``PContext``.
+
+    Note the following:
+
+    1. All fields are mapped by local rank
+    2. ``return_values`` - only populated for functions (not the binaries).
+    3. ``stdouts`` - path to stdout.log (empty string if no redirect)
+    4. ``stderrs`` - path to stderr.log (empty string if no redirect)
+
+    """
+
+    return_values: dict[int, Any] = field(default_factory=dict)
+    failures: dict[int, ProcessFailure] = field(default_factory=dict)
+    stdouts: dict[int, str] = field(default_factory=dict)
+    stderrs: dict[int, str] = field(default_factory=dict)
+
+    def is_failed(self) -> bool:
+        return len(self.failures) > 0
+
+
+class PContext(abc.ABC):
+    """
+    The base class that standardizes operations over a set of processes that are launched via different mechanisms.
+
+    The name ``PContext`` is intentional to disambiguate with ``torch.multiprocessing.ProcessContext``.
+
+    .. warning:: stdouts and stderrs should ALWAYS be a superset of
+                 tee_stdouts and tee_stderrs (respectively) this is b/c
+                 tee is implemented as a redirect + tail -f 
+
+    Args:
+        duplicate_stdout_filters:
+            If non-empty, duplicates stdouts specified in ``logs_specs``'s ``tee``
+            to a file containing only lines that match _any_ of the filter strings.
+            The log file is aggregated across all ranks selected by ``tee``.
+        duplicate_stderr_filters:
+            If non-empty, duplicates stderrs specified in ``logs_specs``'s ``tee``
+            to a file containing only lines that match _any_ of the filter strings.
+            The log file is aggregated across all ranks selected by ``tee``.
+    """
+
+    def __init__(
+        self,
+        name: str,
+        entrypoint: Callable | str,
+        args: dict[int, tuple],
+        envs: dict[int, dict[str, str]],
+        logs_specs: LogsSpecs,
+        log_line_prefixes: dict[int, str] | None = None,
+        duplicate_stdout_filters: list[str] | None = None,
+        duplicate_stderr_filters: list[str] | None = None,
+    ):
+        self.name = name
+        # validate that all mappings have the same number of keys and
+        # all local ranks are accounted for
+        nprocs = len(args)
+
+        # TODO log_line_prefixes can be expanded too
+        logs_dest = logs_specs.reify(envs)
+
+        _validate_full_rank(logs_dest.stdouts, nprocs, "stdouts")
+        _validate_full_rank(logs_dest.stderrs, nprocs, "stderrs")
+
+        self.entrypoint = entrypoint
+        self.args = args
+        self.envs = envs
+        self.stdouts = logs_dest.stdouts
+        self.stderrs = logs_dest.stderrs
+        self.error_files = logs_dest.error_files
+        self.nprocs = nprocs
+        self.filtered_stdout: TextIO | None = None
+        self.filtered_stderr: TextIO | None = None
+
+        self._tail_logs = [
+            TailLog(name, logs_dest.tee_stdouts, sys.stdout, log_line_prefixes),
+            TailLog(name, logs_dest.tee_stderrs, sys.stderr, log_line_prefixes),
+        ]
+
+        if duplicate_stdout_filters:
+            self.filtered_stdout = open(  # noqa: SIM115
+                logs_dest.filtered_stdout, mode="w", errors="replace", buffering=1
+            )
+            self._tail_logs.append(
+                TailLog(
+                    name,
+                    logs_dest.tee_stdouts,
+                    self.filtered_stdout,
+                    log_line_prefixes,
+                    log_line_filter=lambda line: any(
+                        needle in line for needle in duplicate_stdout_filters
+                    ),
+                )
+            )
+
+        if duplicate_stderr_filters:
+            self.filtered_stderr = open(  # noqa: SIM115
+                logs_dest.filtered_stderr, mode="w", errors="replace", buffering=1
+            )
+            self._tail_logs.append(
+                TailLog(
+                    name,
+                    logs_dest.tee_stderrs,
+                    self.filtered_stderr,
+                    log_line_prefixes,
+                    log_line_filter=lambda line: any(
+                        needle in line for needle in duplicate_stderr_filters
+                    ),
+                )
+            )
+
+    def start(self) -> None:
+        """Start processes using parameters defined in the constructor."""
+        if threading.current_thread() is threading.main_thread():
+            # Register signal handlers for the signals specified in the environment variable
+            signals_to_handle = os.environ.get(
+                "TORCHELASTIC_SIGNALS_TO_HANDLE", "SIGTERM,SIGINT,SIGHUP,SIGQUIT"
+            )
+            signal_list = signals_to_handle.split(",")
+
+            for sig_name in signal_list:
+                try:
+                    sig = getattr(signal, sig_name.strip())
+                    signal.signal(sig, _terminate_process_handler)
+                    logger.info("Registered signal handler for %s", sig_name)
+                except (AttributeError, ValueError):
+                    logger.warning(
+                        "Failed to register signal handler for %s",
+                        sig_name,
+                        exc_info=True,
+                    )
+                except RuntimeError:
+                    if IS_WINDOWS and sig_name.strip() in [
+                        "SIGHUP",
+                        "SIGQUIT",
+                        "SIGUSR1",
+                        "SIGUSR2",
+                    ]:
+                        logger.info(
+                            "Signal %s is not supported on Windows, skipping", sig_name
+                        )
+                    else:
+                        logger.warning(
+                            "Failed to register signal handler for %s",
+                            sig_name,
+                            exc_info=True,
+                        )
+        else:
+            logger.warning(
+                "Failed to register signal handlers since torchelastic is running on a child thread. "
+                "This could lead to orphaned worker processes if the torchrun is terminated."
+            )
+        self._start()
+        for tail_log in self._tail_logs:
+            tail_log.start()
+
+    @abc.abstractmethod
+    def _start(self) -> None:
+        """Start processes using strategy defined in a particular context."""
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def _poll(self) -> RunProcsResult | None:
+        """
+        Poll the run status of the processes running under this context.
+        This method follows an "all-or-nothing" policy and returns
+        a ``RunProcessResults`` object if either all processes complete
+        successfully or any process fails. Returns ``None`` if
+        all processes are still running.
+        """
+        raise NotImplementedError
+
+    def wait(self, timeout: float = -1, period: float = 1) -> RunProcsResult | None:
+        """
+        Wait for the specified ``timeout`` seconds, polling every ``period`` seconds
+        for the processes to be done. Returns ``None`` if the processes are still running
+        on timeout expiry. Negative timeout values are interpreted as "wait-forever".
+        A timeout value of zero simply queries the status of the processes (e.g. equivalent
+        to a poll).
+
+        .. note::
+            Multiprocessing library registers SIGTERM and SIGINT signal handlers that raise
+            ``SignalException`` when the signals received. It is up to the consumer of the code
+            to properly handle the exception. It is important not to swallow the exception otherwise
+            the process would not terminate. Example of the typical workflow can be:
+
+        .. code-block:: python
+            pc = start_processes(...)
+            try:
+                pc.wait(1)
+                .. do some other work
+            except SignalException as e:
+                pc.shutdown(e.sigval, timeout=30)
+
+        If SIGTERM or SIGINT occurs, the code above will try to shutdown child processes by propagating
+        received signal. If child processes will not terminate in the timeout time, the process will send
+        the SIGKILL.
+        """
+        if timeout == 0:
+            return self._poll()
+
+        if timeout < 0:
+            timeout = sys.maxsize
+
+        expiry = time.time() + timeout
+        while time.time() < expiry:
+            pr = self._poll()
+            if pr:
+                return pr
+            time.sleep(period)
+
+        return None
+
+    @abc.abstractmethod
+    def pids(self) -> dict[int, int]:
+        """Return pids of processes mapped by their respective local_ranks."""
+        raise NotImplementedError
+
+    @abc.abstractmethod
+    def _close(self, death_sig: signal.Signals, timeout: int = 30) -> None:
+        r"""
+        Terminates all processes managed by this context and cleans up any
+        meta resources (e.g. redirect, error_file files).
+        """
+        raise NotImplementedError
+
+    def close(self, death_sig: signal.Signals | None = None, timeout: int = 30) -> None:
+        r"""
+        Terminates all processes managed by this context and cleans up any
+        meta resources (e.g. redirect, error_file files).
+
+        Args:
+            death_sig: Death signal to terminate processes.
+            timeout: Time to wait for processes to finish, if process is
+                still alive after this time, it will be terminated via SIGKILL.
+        """
+        if not death_sig:
+            death_sig = _get_default_signal()
+        self._close(death_sig=death_sig, timeout=timeout)
+        for tail_log in self._tail_logs:
+            tail_log.stop()
+        if self.filtered_stdout:
+            self.filtered_stdout.close()
+        if self.filtered_stderr:
+            self.filtered_stderr.close()
+
+
+def get_std_cm(std_rd: str, redirect_fn):
+    if IS_WINDOWS or IS_MACOS or not std_rd:
+        return nullcontext()
+    else:
+        return redirect_fn(std_rd)
+
+
+def _wrap(
+    local_rank: int,
+    fn: Callable,
+    args: dict[int, tuple],
+    envs: dict[int, dict[str, str]],
+    stdout_redirects: dict[int, str],  # redirect file for stdout (to console if None)
+    stderr_redirects: dict[int, str],  # redirect file for stderr (to console if None)
+    ret_vals: dict[int, mp.SimpleQueue],
+    queue_finished_reading_event: synchronize.Event,
+    numa_options: NumaOptions | None,
+) -> None:
+    # get the per-rank params up front so we fail fast if no mapping is found
+    args_ = args[local_rank]
+    env_ = envs[local_rank]
+    ret_val_ = ret_vals[local_rank]
+
+    stdout_rd = stdout_redirects[local_rank]
+    stderr_rd = stderr_redirects[local_rank]
+
+    stdout_cm = get_std_cm(stdout_rd, redirect_stdout)
+    stderr_cm = get_std_cm(stderr_rd, redirect_stderr)
+
+    for k, v in env_.items():
+        os.environ[k] = v
+
+    with stdout_cm, stderr_cm:
+        fn = maybe_wrap_with_numa_binding(
+            fn, gpu_index=local_rank, numa_options=numa_options
+        )
+        ret = record(fn)(*args_)
+    ret_val_.put(ret)
+    queue_finished_reading_event.wait()
+
+
+class MultiprocessContext(PContext):
+    """``PContext`` holding worker processes invoked as a function."""
+
+    def __init__(
+        self,
+        name: str,
+        entrypoint: Callable,
+        args: dict[int, tuple],
+        envs: dict[int, dict[str, str]],
+        start_method: str,
+        logs_specs: LogsSpecs,
+        log_line_prefixes: dict[int, str] | None = None,
+        numa_options: NumaOptions | None = None,
+        duplicate_stdout_filters: list[str] | None = None,
+        duplicate_stderr_filters: list[str] | None = None,
+    ):
+        super().__init__(
+            name,
+            entrypoint,
+            args,
+            envs,
+            logs_specs,
+            log_line_prefixes,
+            duplicate_stdout_filters,
+            duplicate_stderr_filters,
+        )
+
+        self.start_method = start_method
+        # each ret_val queue will always contain a single element.
+        self._ret_vals = {
+            local_rank: mp.get_context(self.start_method).SimpleQueue()
+            for local_rank in range(self.nprocs)
+        }
+
+        # see comments in ``join()`` for what this is
+        self._return_values: dict[int, Any] = {}
+        self._pc: mp.ProcessContext | None = None
+        # Note: set method should ONLY be invoked for the use case when all processes finished
+        # successfully. If any process died on event.wait() calling set() method will deadlock.
+        self._worker_finished_event = mp.get_context(self.start_method).Event()
+
+        self._numa_options: NumaOptions | None = numa_options
+
+    def _start(self):
+        if self._pc:
+            raise ValueError(
+                "The process context already initialized."
+                " Most likely the start method got called twice."
+            )
+        self._pc = mp.start_processes(
+            fn=_wrap,
+            args=(
+                self.entrypoint,
+                self.args,
+                self.envs,
+                self.stdouts,
+                self.stderrs,
+                self._ret_vals,
+                self._worker_finished_event,
+                self._numa_options,
+            ),
+            nprocs=self.nprocs,
+            join=False,
+            daemon=False,
+            start_method=self.start_method,
+        )
+
+    def _is_done(self) -> bool:
+        return len(self._return_values) == self.nprocs
+
+    def _poll(self) -> RunProcsResult | None:
+        assert self._pc is not None  # assertion for mypy type checker
+
+        try:
+            # torch.mp.ProcessContext Throws an Exception if some/all of
+            # worker processes failed
+            # timeout < 0 checks worker status and return immediately
+            # Join will never return success since we use synchronize.Event to wait
+            # for all processes to finish.
+            self._pc.join(-1)
+
+            # IMPORTANT: we use multiprocessing.Queue to carry worker return values
+            # back to the parent, the worker process will wait before terminating
+            # until all the buffered items are fed by the feeder thread to the underlying
+            # pipe. Hence to prevent deadlocks on large return values,
+            # we opportunistically try queue.get on each join call
+            # See: https://docs.python.org/2/library/multiprocessing.html#all-platforms
+            for local_rank in range(self.nprocs):
+                return_queue = self._ret_vals[local_rank]
+                if not return_queue.empty():
+                    # save the return values temporarily into a member var
+                    self._return_values[local_rank] = return_queue.get()
+
+            if self._is_done():
+                # we should ALWAYS have ALL the return values when all the processes are done
+                self._worker_finished_event.set()
+
+                # At this point workers finished running the user function
+                # But the child process might still have not exited. Wait for them.
+                # pc.join() blocks [forever] until "a" proc exits. Loop until all of them exits.
+                while not self._pc.join():
+                    logger.debug(
+                        "entrypoint fn finished, waiting for all child procs to exit..."
+                    )
+
+                _validate_full_rank(
+                    self._return_values, self.nprocs, "return_value queue"
+                )
+                self.close()
+                return RunProcsResult(
+                    return_values=self._return_values,
+                    stdouts=self.stdouts,
+                    stderrs=self.stderrs,
+                )
+            else:
+                return None
+        except (mp.ProcessRaisedException, mp.ProcessExitedException) as e:
+            failed_local_rank = e.error_index
+
+            # entrypoint for MultiprocessContext will always be a Callable
+            fn_name = self.entrypoint.__qualname__  # type: ignore[union-attr]
+            failed_proc = self._pc.processes[failed_local_rank]
+            error_filepath = self.error_files[failed_local_rank]
+
+            logger.exception(
+                "failed (exitcode: %s)"
+                " local_rank: %s (pid: %s)"
+                " of fn: %s (start_method: %s)",
+                failed_proc.exitcode,
+                failed_local_rank,
+                e.error_pid,
+                fn_name,
+                self.start_method,
+            )
+
+            self.close()
+            return RunProcsResult(
+                failures={
+                    failed_local_rank: ProcessFailure(
+                        local_rank=failed_local_rank,
+                        pid=e.error_pid,
+                        exitcode=failed_proc.exitcode,
+                        error_file=error_filepath,
+                    )
+                },
+                stdouts=self.stdouts,
+                stderrs=self.stderrs,
+            )
+
+    def pids(self) -> dict[int, int]:
+        assert self._pc is not None  # assertion for mypy type checking
+        return dict(enumerate(self._pc.pids()))
+
+    def _close(self, death_sig: signal.Signals, timeout: int = 30) -> None:
+        if not self._pc:
+            return
+        for proc in self._pc.processes:
+            if proc.is_alive():
+                logger.warning(
+                    "Closing process %s via signal %s", proc.pid, death_sig.name
+                )
+                try:
+                    os.kill(proc.pid, death_sig)
+                except ProcessLookupError:
+                    # If the process exited because of some reason,
+                    # `ProcessLookupError` will be raised, it is safe to ignore it.
+                    pass
+        end = time.monotonic() + timeout
+        for proc in self._pc.processes:
+            time_to_wait = end - time.monotonic()
+            if time_to_wait <= 0:
+                break
+            proc.join(time_to_wait)
+        for proc in self._pc.processes:
+            if proc.is_alive():
+                logger.warning(
+                    "Unable to shutdown process %s via %s, forcefully exiting via %s",
+                    proc.pid,
+                    death_sig,
+                    _get_kill_signal(),
+                )
+                try:
+                    os.kill(proc.pid, _get_kill_signal())
+                except ProcessLookupError:
+                    # If the process exited because of some reason,
+                    # `ProcessLookupError` will be raised, it is safe to ignore it.
+                    pass
+            proc.join()
+
+
+class SubprocessContext(PContext):
+    """``PContext`` holding worker processes invoked as a binary."""
+
+    def __init__(
+        self,
+        name: str,
+        entrypoint: str,
+        args: dict[int, tuple],
+        envs: dict[int, dict[str, str]],
+        logs_specs: LogsSpecs,
+        log_line_prefixes: dict[int, str] | None = None,
+        numa_options: NumaOptions | None = None,
+        duplicate_stdout_filters: list[str] | None = None,
+        duplicate_stderr_filters: list[str] | None = None,
+    ):
+        super().__init__(
+            name,
+            entrypoint,
+            args,
+            envs,
+            logs_specs,
+            log_line_prefixes,
+            duplicate_stdout_filters,
+            duplicate_stderr_filters,
+        )
+
+        # state vector; _vdone[local_rank] -> is local_rank finished or not
+        self._running_local_ranks: set[int] = set(range(self.nprocs))
+        self._failures: dict[int, ProcessFailure] = {}
+        self.subprocess_handlers: dict[int, SubprocessHandler] = {}
+        self._numa_options: NumaOptions | None = numa_options
+
+    def _start(self):
+        if self.subprocess_handlers:
+            raise ValueError(
+                "The subprocess handlers already initialized. Most likely the start method got called twice."
+            )
+        self.subprocess_handlers = {
+            local_rank: get_subprocess_handler(
+                entrypoint=self.entrypoint,  # type: ignore[arg-type] # entrypoint is always a str
+                args=self.args[local_rank],
+                env=self.envs[local_rank],
+                stdout=self.stdouts[local_rank],
+                stderr=self.stderrs[local_rank],
+                local_rank_id=local_rank,
+                numa_options=self._numa_options,
+            )
+            for local_rank in range(self.nprocs)
+        }
+
+    def _capture_process_failures(self, done_local_ranks: set[int]):
+        for local_rank in self._running_local_ranks:
+            handler = self.subprocess_handlers[local_rank]
+            exitcode = handler.proc.poll()
+            if exitcode is not None:
+                done_local_ranks.add(local_rank)
+                if exitcode != 0:  # failed or signaled
+                    self._failures[local_rank] = ProcessFailure(
+                        local_rank=local_rank,
+                        pid=handler.proc.pid,
+                        exitcode=exitcode,
+                        error_file=self.error_files[local_rank],
+                    )
+                # else: --> succeeded; nothing to do
+
+    def _poll(self) -> RunProcsResult | None:
+        done_local_ranks: set[int] = set()
+        self._capture_process_failures(done_local_ranks)
+
+        self._running_local_ranks.difference_update(done_local_ranks)
+
+        # if ALL procs are finished or ANY have failed
+        if not self._running_local_ranks or self._failures:
+            self.close()  # terminate all running procs
+            self._capture_process_failures(
+                done_local_ranks
+            )  # log sigterms and sigkill exit codes in the self._failures for bookkeeping purposes
+
+            result = RunProcsResult(
+                failures=self._failures,
+                stdouts=self.stdouts,
+                stderrs=self.stderrs,
+            )
+            if result.is_failed():
+                first_failure = min(result.failures.values(), key=lambda f: f.timestamp)
+                logger.error(
+                    "failed (exitcode: %s) local_rank: %s (pid: %s) of binary: %s",
+                    first_failure.exitcode,
+                    first_failure.local_rank,
+                    first_failure.pid,
+                    self.entrypoint,
+                )
+            else:
+                # Populate return with dummy values. This provides consistency with MultiprocessingHandler
+                result.return_values = dict.fromkeys(range(self.nprocs))
+
+            return result
+        else:  # there are no failures and procs still running
+            return None
+
+    def pids(self) -> dict[int, int]:
+        return {
+            local_rank: sh.proc.pid
+            for local_rank, sh in self.subprocess_handlers.items()
+        }
+
+    def _close(self, death_sig: signal.Signals, timeout: int = 30) -> None:
+        if not self.subprocess_handlers:
+            return
+        for handler in self.subprocess_handlers.values():
+            if handler.proc.poll() is None:
+                logger.warning(
+                    "Sending process %s closing signal %s",
+                    handler.proc.pid,
+                    death_sig.name,
+                )
+                handler.close(death_sig=death_sig)
+        end = time.monotonic() + timeout
+        for handler in self.subprocess_handlers.values():
+            time_to_wait = end - time.monotonic()
+            if time_to_wait <= 0:
+                break
+            try:
+                handler.proc.wait(time_to_wait)
+            except subprocess.TimeoutExpired:
+                # Ignore the timeout expired exception, since
+                # the child process will be forcefully terminated via SIGKILL
+                pass
+        for handler in self.subprocess_handlers.values():
+            if handler.proc.poll() is None:
+                logger.warning(
+                    "Unable to shutdown process %s via %s, forcefully exiting via %s",
+                    handler.proc.pid,
+                    death_sig,
+                    _get_kill_signal(),
+                )
+                handler.close(death_sig=_get_kill_signal())
+                handler.proc.wait()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f61c99dc5c7779a5d839ca5b0364616b55079286
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py
@@ -0,0 +1,390 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Each host in a distributed PyTorch job runs with a single TorchElastic agent,
+and multiple workers (as children processes of the TorchElastic agent).
+Since the workers are user-provided (your PyTorch script/job), TorchElastic
+has a way to propagate errors on the trainers through the agent and up to the
+scheduler, which ultimately informs the end-user about the state of the job
+and applies any retry policies.
+
+TorchElastic categorizes errors into 3 categories:
+
++----------------+----------------+--------------------------------------------------------------+
+| Category       | Sub-Category   |  Description                                                 |
++================+================+==============================================================+
+| User Error     | Input Error    | invalid inputs to TorchElastic APIs (e.g. min > max nodes)   |
+|                +----------------+--------------------------------------------------------------+
+|                | Worker Failure | any failures on the worker child process                     |
++----------------+----------------+--------------------------------------------------------------+
+| Platform Error |      n/a       | failures caused by the agent                                 |
++----------------+----------------+--------------------------------------------------------------+
+| Infra Error    |      n/a       | failures outside the domain of the agent and workers         |
+|                |                | (e.g. host failures)                                         |
++----------------+----------------+--------------------------------------------------------------+
+
+All errors other than "Worker Failure" are either raised canonically from the
+agent process or implicitly or explicitly crash the agent process. So the
+standard language (python) provided exception handling strategies apply.
+
+Worker Failures are special because the exception/failure originates on a different
+process from the agent so the error needs to be propagated inter-process
+(e.g. the agent cannot simply ``try-catch`` an exception raised on the worker process).
+
+TorchElastic agents use :func:`torch.distributed.elastic.multiprocessing.start_processes`
+to launch the workers which has a simple file based inter-process error propagation
+built-in.
+
+Any function or binary entrypoint decorated with :func:`record`
+will write uncaught exceptions (with the trace information) to a file specified by the
+environment variable ``TORCHELASTIC_ERROR_FILE``. The parent process (e.g. agent)
+sets this env var on each child it launches, then aggregates the error files for all
+children, and propagates the one with the **smallest** timestamp (e.g. the **first** error).
+"""
+
+import json
+import os
+import signal
+import socket
+import time
+from collections.abc import Callable
+from dataclasses import dataclass, field
+from datetime import datetime
+from functools import wraps
+from string import Template
+from typing import Any, Optional, TypeVar, Union
+from typing_extensions import ParamSpec
+
+from torch.distributed.elastic.utils.logging import get_logger
+
+from .error_handler import ErrorHandler  # noqa: F401
+from .handlers import get_error_handler  # noqa: F401
+
+
+__all__ = [
+    "ProcessFailure",
+    "ChildFailedError",
+    "record",
+    "ErrorHandler",
+    "get_error_handler",
+]
+
+logger = get_logger(__name__)
+
+
+JSON = dict[str, Any]
+
+_EMPTY_ERROR_DATA: dict[str, Any] = {"message": ""}
+_NOT_AVAILABLE = ""
+
+_R = TypeVar("_R")
+_P = ParamSpec("_P")
+
+
+@dataclass
+class ProcessFailure:
+    """
+    Represent the failed process result. When the worker process fails, it may record failure root cause into the file.
+
+    Tries to read the failure timestamp from the provided ``error_file``,
+    if the ``error_file`` does not exist, the timestamp is the current
+    timestamp (seconds since epoch).
+
+    The ``message`` field is a concise explanation of the failure. If
+    the error file exists then the message is obtained from the error file.
+    Otherwise one is generated based on the failure signature.
+
+    .. note:: It is assumed that the ``error_file`` is written by
+              ``torch.distributed.elastic.multiprocessing.errors.error_handler.ErrorHandler``.
+              Otherwise the behavior is undefined.
+
+    """
+
+    local_rank: int
+    pid: int
+    exitcode: int
+    error_file: str
+    error_file_data: JSON = field(init=False)
+    message: str = field(init=False)
+    timestamp: int = field(init=False)
+
+    def __post_init__(self):
+        self.error_file_data = _EMPTY_ERROR_DATA
+        if os.path.isfile(self.error_file):
+            try:
+                with open(self.error_file) as fp:
+                    self.error_file_data = json.load(fp)
+                    logger.debug(
+                        "User process failed with error data: %s",
+                        json.dumps(self.error_file_data, indent=2),
+                    )
+                    self.message, self.timestamp = self._get_error_data(
+                        self.error_file_data
+                    )
+            except Exception:
+                logger.exception("Failed to parse reply file: %s", self.error_file)
+                raise
+        else:
+            self._set_no_reply_file()
+
+        # make up an informative message if not already present
+        if not self.message:
+            # signals typically do not generate an error file message
+            if self.exitcode < 0:
+                self.message = (
+                    f"Signal {-self.exitcode} ({self.signal_name()})"
+                    f" received by PID {self.pid}"
+                )
+            else:
+                self.error_file_data["errorTraits"] = {
+                    "category": "system_terminated_error",
+                    "retryability": "False",
+                }
+                self.message = "To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html"
+
+    def _get_error_data(self, error_file_data: dict[str, Any]) -> tuple[str, int]:
+        message = error_file_data["message"]
+        if isinstance(message, str):
+            timestamp = int(error_file_data.get("timestamp", 0))
+        else:
+            timestamp = int(message["extraInfo"]["timestamp"])
+        return (message, timestamp)
+
+    def _set_no_reply_file(self):
+        self.error_file = _NOT_AVAILABLE
+        self.error_file_data = _EMPTY_ERROR_DATA
+        self.message = ""
+        self.timestamp = int(time.time())
+
+    def signal_name(self) -> str:
+        if self.exitcode < 0:
+            # We don't want to kill the parent process trying to find the signal name.
+            # if the signal doesn't map to a known name, use not available.
+            try:
+                return signal.Signals(-self.exitcode).name
+            except Exception:
+                return _NOT_AVAILABLE
+        else:
+            return _NOT_AVAILABLE
+
+    def timestamp_isoformat(self):
+        """Return timestamp in ISO format (YYYY-MM-DD_HH:MM:SS)."""
+        return datetime.fromtimestamp(self.timestamp).isoformat(sep="_")
+
+
+GlobalRank = int
+
+_FAILURE_FORMAT_TEMPLATE = """[${idx}]:
+  time      : ${time}
+  host      : ${hostname}
+  rank      : ${rank} (local_rank: ${local_rank})
+  exitcode  : ${exitcode} (pid: ${pid})
+  error_file: ${error_file}
+  traceback : ${message}"""
+
+# extra new lines before and after are intentional
+_MSG_FORMAT_TEMPLATE = """
+${boarder}
+${title}
+${section}
+Failures:
+${other_failures}
+${section}
+Root Cause (first observed failure):
+${root_failure}
+${boarder}"""
+
+
+class ChildFailedError(Exception):
+    """
+    Special exception type that can be raised from a function annotated with the
+    ``@record`` decorator to have the child process' (root exception) propagate
+    up the stack as-is (e.g. without being wrapped in the parent's traceback).
+
+    Useful in cases where the parent is a simple nanny process
+    and the child (worker) processes are actually doing meaningful compute.
+    In this case, errors typically occur on the child process as the parent
+    is not doing anything non-trivial, and child errors should be propagated
+    to the scheduler for accurate root cause diagnostics.
+
+    .. note:: The propagation relies on error files rather than exception handling to
+              support both function and binary launches.
+
+    Example:
+    ::
+
+     # process tree on a host (container)
+     0: scheduler-init-process:
+                |- 1: torchelastic_agent:
+                         |- 2: trainer_0 (ok)
+                         |- 3: trainer_1 (fail) -> error.json
+                         |- ...
+                         |- n+2: trainer_n (ok)
+                |- n+3: other processes
+                |- ...
+
+    In the example above, trainer 1's failure (written into error.json) is
+    the root cause and should be reported to the scheduler's init process.
+    The torchelastic agent raises a ``ChildFailedError("trainer", {1: "trainer_1/error.json"})``
+    upon detecting trainer 1's failure which would propagate the contents
+    of trainer 1's error file to the scheduler's init process.
+    """
+
+    def __init__(self, name: str, failures: dict[GlobalRank, ProcessFailure]):
+        self.name = name
+        self.failures = failures
+        assert (
+            self.failures
+        )  # does not make sense to create a ChildFaileError with no failures
+        super().__init__(self.format_msg())
+
+    def get_first_failure(self) -> tuple[GlobalRank, ProcessFailure]:
+        rank = min(self.failures.keys(), key=lambda r: self.failures[r].timestamp)
+        return rank, self.failures[rank]
+
+    def format_msg(self, boarder_delim="=", section_delim="-"):
+        title = f"{self.name} FAILED"
+        root_rank, _root_failure = self.get_first_failure()
+
+        root_failure_fmt: str = ""
+        other_failures_fmt: list[str] = []
+        width = len(title)
+        for idx, (rank, failure) in enumerate(self.failures.items()):
+            fmt, w = self._format_failure(idx, rank, failure)
+            width = max(width, w)
+            if rank == root_rank:
+                root_failure_fmt = fmt
+            else:
+                other_failures_fmt.append(fmt)
+
+        # upper boundary on width
+        width = min(width, 60)
+
+        return Template(_MSG_FORMAT_TEMPLATE).substitute(
+            boarder=boarder_delim * width,
+            title=title,
+            section=section_delim * width,
+            root_failure=root_failure_fmt,
+            other_failures="\n".join(other_failures_fmt or ["  "]),
+        )
+
+    def _format_failure(
+        self, idx: int, rank: int, failure: ProcessFailure
+    ) -> tuple[str, int]:
+        # failure.message is either a str (when the failure does not generate a traceback - e.g. signals)
+        # or a dict (json) of the form
+        # {"message": $ERROR_MSG, "extraInfo": {"py_callstack": $TRACEBACK, timestamp: $TS}}
+        # so the display logic is:
+        # 1. if failure.message is not a dict (it is a str) just show it as is
+        # 2. else try to get the traceback (py_callstack)
+        # 3.      if the traceback is not there, use the message
+        # 4.      if the message  is not there show 
+        msg = failure.message
+        if isinstance(failure.message, dict):
+            msg = (
+                failure.message.get("extraInfo", {})
+                .get("py_callstack", failure.message.get("message", ""))
+                .replace("\n", "\n  ")  # to properly indent the traceback
+            )
+
+        fmt = Template(_FAILURE_FORMAT_TEMPLATE).substitute(
+            idx=idx,
+            time=failure.timestamp_isoformat(),
+            hostname=socket.getfqdn(),
+            rank=rank,
+            local_rank=failure.local_rank,
+            exitcode=failure.exitcode,
+            pid=failure.pid,
+            error_file=failure.error_file,
+            message=msg,
+        )
+        width = 0
+        for line in fmt.split("\n"):
+            width = max(width, len(line))
+        return fmt, width
+
+
+def record(
+    fn: Callable[_P, _R], error_handler: ErrorHandler | None = None
+) -> Callable[_P, _R | None]:
+    """
+    Syntactic sugar to record errors/exceptions that happened in the decorated
+    function using the provided ``error_handler``.
+
+    Using this decorator is equivalent to:
+
+    ::
+
+     error_handler = get_error_handler()
+     error_handler.initialize()
+     try:
+         foobar()
+     except ChildFailedError as e:
+         _, failure = e.get_first_failure()
+         error_handler.dump_error_file(failure.error_file, failure.exitcode)
+         raise
+     except Exception as e:
+         error_handler.record_exception(e)
+         raise
+
+    .. important:: use this decorator once per process at the top level method,
+                   typically this is the main method.
+
+    Example
+
+    ::
+
+     @record
+     def main():
+         pass
+
+
+     if __name__ == "__main__":
+         main()
+
+    """
+    if not error_handler:
+        error_handler = get_error_handler()
+
+    def wrap(f: Callable[_P, _R]) -> Callable[_P, _R | None]:
+        @wraps(f)
+        def wrapper(*args: _P.args, **kwargs: _P.kwargs):
+            assert error_handler is not None  # assertion for mypy type checker
+            error_handler.initialize()
+            try:
+                return f(*args, **kwargs)
+            except SystemExit as se:
+                # For run_path based entrypoints, SystemExit with code = 0 will never exit.
+                # Handling it here by returning a value:
+                if se.code == 0:
+                    return None
+                else:
+                    raise
+            except ChildFailedError as e:
+                rank, failure = e.get_first_failure()
+                if failure.error_file != _NOT_AVAILABLE:
+                    error_handler.dump_error_file(failure.error_file, failure.exitcode)
+                else:
+                    logger.info(
+                        (
+                            "local_rank %s FAILED with no error file."
+                            " Decorate your entrypoint fn with @record for traceback info."
+                            " See: https://pytorch.org/docs/stable/elastic/errors.html",
+                            rank,
+                        )
+                    )
+                raise
+            except Exception as e:
+                error_handler.record_exception(e)
+                raise
+
+        return wrapper
+
+    return wrap(fn)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/error_handler.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/error_handler.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab6613e54dee10edbec54abcc5bc689b01676358
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/error_handler.py
@@ -0,0 +1,170 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+import faulthandler
+import json
+import logging
+import os
+import time
+import traceback
+import warnings
+from typing import Any
+
+
+__all__ = ["ErrorHandler"]
+
+logger = logging.getLogger(__name__)
+
+
+class ErrorHandler:
+    """
+    Write the provided exception object along with some other metadata about
+    the error in a structured way in JSON format to an error file specified by the
+    environment variable: ``TORCHELASTIC_ERROR_FILE``. If this environment
+    variable is not set, then simply logs the contents of what would have been
+    written to the error file.
+
+    This handler may be subclassed to customize the handling of the error.
+    Subclasses should override ``initialize()`` and ``record_exception()``.
+    """
+
+    def _get_error_file_path(self) -> str | None:
+        """
+        Return the error file path.
+
+        May return ``None`` to have the structured error be logged only.
+        """
+        return os.environ.get("TORCHELASTIC_ERROR_FILE", None)
+
+    def initialize(self) -> None:
+        """
+        Call prior to running code that we wish to capture errors/exceptions.
+
+        Typically registers signal/fault handlers. Users can override this
+        function to add custom initialization/registrations that aid in
+        propagation/information of errors/signals/exceptions/faults.
+        """
+        try:
+            faulthandler.enable(all_threads=True)
+        except Exception as e:
+            warnings.warn(
+                f"Unable to enable fault handler. {type(e).__name__}: {e}", stacklevel=2
+            )
+
+    def _write_error_file(self, file_path: str, error_msg: str) -> None:
+        """Write error message to the file."""
+        try:
+            with open(file_path, "w") as fp:
+                fp.write(error_msg)
+        except Exception as e:
+            warnings.warn(
+                f"Unable to write error to file. {type(e).__name__}: {e}", stacklevel=2
+            )
+
+    def record_exception(self, e: BaseException) -> None:
+        """
+        Write a structured information about the exception into an error file in JSON format.
+
+        If the error file cannot be determined, then logs the content
+        that would have been written to the error file.
+        """
+        file = self._get_error_file_path()
+        if file:
+            data = {
+                "message": {
+                    "message": f"{type(e).__name__}: {e}",
+                    "extraInfo": {
+                        "py_callstack": traceback.format_exc(),
+                        "timestamp": str(int(time.time())),
+                    },
+                }
+            }
+            with open(file, "w") as fp:
+                json.dump(data, fp)
+
+    def override_error_code_in_rootcause_data(
+        self,
+        rootcause_error_file: str,
+        rootcause_error: dict[str, Any],
+        error_code: int = 0,
+    ):
+        """Modify the rootcause_error read from the file, to correctly set the exit code."""
+        if "message" not in rootcause_error:
+            logger.warning(
+                "child error file (%s) does not have field `message`. \n"
+                "cannot override error code: %s",
+                rootcause_error_file,
+                error_code,
+            )
+        elif isinstance(rootcause_error["message"], str):
+            logger.warning(
+                "child error file (%s) has a new message format. \n"
+                "skipping error code override",
+                rootcause_error_file,
+            )
+        else:
+            rootcause_error["message"]["errorCode"] = error_code
+
+    def dump_error_file(self, rootcause_error_file: str, error_code: int = 0):
+        """Dump parent error file from child process's root cause error and error code."""
+        with open(rootcause_error_file) as fp:
+            rootcause_error = json.load(fp)
+            # Override error code since the child process cannot capture the error code if it
+            # is terminated by signals like SIGSEGV.
+            if error_code:
+                self.override_error_code_in_rootcause_data(
+                    rootcause_error_file, rootcause_error, error_code
+                )
+            logger.debug(
+                "child error file (%s) contents:\n%s",
+                rootcause_error_file,
+                json.dumps(rootcause_error, indent=2),
+            )
+
+        my_error_file = self._get_error_file_path()
+        if my_error_file:
+            # Guard against existing error files
+            # This can happen when the child is created using multiprocessing
+            # and the same env var (TORCHELASTIC_ERROR_FILE) is used on the
+            # parent and child to specify the error files (respectively)
+            # because the env vars on the child is set in the wrapper function
+            # and by default the child inherits the parent's env vars, if the child
+            # process receives a signal before the wrapper function kicks in
+            # and the signal handler writes to the error file, then the child
+            # will write to the parent's error file. In this case just log the
+            # original error file contents and overwrite the error file.
+            self._rm(my_error_file)
+            self._write_error_file(my_error_file, json.dumps(rootcause_error))
+            logger.info("dumped error file to parent's %s", my_error_file)
+        else:
+            logger.error(
+                "no error file defined for parent, to copy child error file (%s)",
+                rootcause_error_file,
+            )
+
+    def _rm(self, my_error_file):
+        if os.path.isfile(my_error_file):
+            # Log the contents of the original file.
+            with open(my_error_file) as fp:
+                try:
+                    original = json.dumps(json.load(fp), indent=2)
+                    logger.warning(
+                        "%s already exists"
+                        " and will be overwritten."
+                        " Original contents:\n%s",
+                        my_error_file,
+                        original,
+                    )
+                except json.decoder.JSONDecodeError:
+                    logger.warning(
+                        "%s already exists"
+                        " and will be overwritten."
+                        " Unable to load original contents:\n",
+                        my_error_file,
+                    )
+            os.remove(my_error_file)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/handlers.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/handlers.py
new file mode 100644
index 0000000000000000000000000000000000000000..6721217a41190c2bdd6bf2293540a33c893c145d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/handlers.py
@@ -0,0 +1,18 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+# Multiprocessing error-reporting module
+
+
+from torch.distributed.elastic.multiprocessing.errors.error_handler import ErrorHandler
+
+
+__all__ = ["get_error_handler"]
+
+
+def get_error_handler() -> ErrorHandler:
+    return ErrorHandler()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/redirects.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/redirects.py
new file mode 100644
index 0000000000000000000000000000000000000000..057013fbb9e5b8a2aeca69b41d7679cbe75c0e28
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/redirects.py
@@ -0,0 +1,104 @@
+# mypy: allow-untyped-defs
+# !/usr/bin/env python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# Taken and modified from original source:
+# https://eli.thegreenplace.net/2015/redirecting-all-kinds-of-stdout-in-python/
+import ctypes
+import logging
+import os
+import sys
+from contextlib import contextmanager
+from functools import partial
+
+
+IS_WINDOWS = sys.platform == "win32"
+IS_MACOS = sys.platform == "darwin"
+
+
+logger = logging.getLogger(__name__)
+
+
+def get_libc():
+    if IS_WINDOWS or IS_MACOS:
+        logger.warning(
+            "NOTE: Redirects are currently not supported in Windows or MacOs."
+        )
+        return None
+    else:
+        return ctypes.CDLL("libc.so.6")
+
+
+libc = get_libc()
+
+
+def _c_std(stream: str):
+    return ctypes.c_void_p.in_dll(libc, stream)
+
+
+def _python_std(stream: str):
+    return {"stdout": sys.stdout, "stderr": sys.stderr}[stream]
+
+
+_VALID_STD = {"stdout", "stderr"}
+
+
+@contextmanager
+def redirect(std: str, to_file: str):
+    """
+    Redirect ``std`` (one of ``"stdout"`` or ``"stderr"``) to a file in the path specified by ``to_file``.
+
+    This method redirects the underlying std file descriptor (not just python's ``sys.stdout|stderr``).
+    See usage for details.
+
+    Directory of ``dst_filename`` is assumed to exist and the destination file
+    is overwritten if it already exists.
+
+    .. note:: Due to buffering cross source writes are not guaranteed to
+              appear in wall-clock order. For instance in the example below
+              it is possible for the C-outputs to appear before the python
+              outputs in the log file.
+
+    Usage:
+
+    ::
+
+     # syntactic-sugar for redirect("stdout", "tmp/stdout.log")
+     with redirect_stdout("/tmp/stdout.log"):
+        print("python stdouts are redirected")
+        libc = ctypes.CDLL("libc.so.6")
+        libc.printf(b"c stdouts are also redirected"
+        os.system("echo system stdouts are also redirected")
+
+     print("stdout restored")
+
+    """
+    if std not in _VALID_STD:
+        raise ValueError(
+            f"unknown standard stream <{std}>, must be one of {_VALID_STD}"
+        )
+
+    c_std = _c_std(std)
+    python_std = _python_std(std)
+    std_fd = python_std.fileno()
+
+    def _redirect(dst):
+        libc.fflush(c_std)
+        python_std.flush()
+        os.dup2(dst.fileno(), std_fd)
+
+    with os.fdopen(os.dup(std_fd)) as orig_std, open(to_file, mode="w+b") as dst:
+        _redirect(dst)
+        try:
+            yield
+        finally:
+            _redirect(orig_std)
+
+
+redirect_stdout = partial(redirect, "stdout")
+redirect_stderr = partial(redirect, "stderr")
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/subprocess_handler/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/subprocess_handler/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f56d423ce080fd7c331dc9b43eda58e5370678fc
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/subprocess_handler/__init__.py
@@ -0,0 +1,16 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+from torch.distributed.elastic.multiprocessing.subprocess_handler.handlers import (
+    get_subprocess_handler,
+)
+from torch.distributed.elastic.multiprocessing.subprocess_handler.subprocess_handler import (
+    SubprocessHandler,
+)
+
+
+__all__ = ["SubprocessHandler", "get_subprocess_handler"]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/subprocess_handler/handlers.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/subprocess_handler/handlers.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea1742626e285838485c19911704792510d13fb4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/subprocess_handler/handlers.py
@@ -0,0 +1,33 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from torch.distributed.elastic.multiprocessing.subprocess_handler.subprocess_handler import (
+    SubprocessHandler,
+)
+from torch.numa.binding import NumaOptions
+
+
+__all__ = ["get_subprocess_handler"]
+
+
+def get_subprocess_handler(
+    entrypoint: str,
+    args: tuple,
+    env: dict[str, str],
+    stdout: str,
+    stderr: str,
+    local_rank_id: int,
+    numa_options: NumaOptions | None = None,
+) -> SubprocessHandler:
+    return SubprocessHandler(
+        entrypoint=entrypoint,
+        args=args,
+        env=env,
+        stdout=stdout,
+        stderr=stderr,
+        local_rank_id=local_rank_id,
+        numa_options=numa_options,
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/subprocess_handler/subprocess_handler.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/subprocess_handler/subprocess_handler.py
new file mode 100644
index 0000000000000000000000000000000000000000..268817108d8cd20f6ba0130818286d297da78c4e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/subprocess_handler/subprocess_handler.py
@@ -0,0 +1,89 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+import os
+import signal
+import sys
+from subprocess import Popen
+from typing import Any
+
+from torch.numa.binding import maybe_wrap_command_args_with_numa_binding, NumaOptions
+
+
+__all__ = ["SubprocessHandler"]
+
+IS_WINDOWS = sys.platform == "win32"
+
+
+def _get_default_signal() -> signal.Signals:
+    """Get the default termination signal. SIGTERM for unix, CTRL_C_EVENT for windows."""
+    if IS_WINDOWS:
+        return signal.CTRL_C_EVENT  # type: ignore[attr-defined] # noqa: F821
+    else:
+        return signal.SIGTERM
+
+
+class SubprocessHandler:
+    """
+    Convenience wrapper around python's ``subprocess.Popen``. Keeps track of
+    meta-objects associated to the process (e.g. stdout and stderr redirect fds).
+    """
+
+    def __init__(
+        self,
+        entrypoint: str,
+        args: tuple,
+        env: dict[str, str],
+        stdout: str | None,
+        stderr: str | None,
+        local_rank_id: int,
+        numa_options: NumaOptions | None,
+    ):
+        self._stdout = open(stdout, "w") if stdout else None  # noqa: SIM115
+        self._stderr = open(stderr, "w") if stderr else None  # noqa: SIM115
+        # inherit parent environment vars
+        env_vars = os.environ.copy()
+        env_vars.update(env)
+
+        args_str = (entrypoint, *[str(e) for e in args])
+        args_str = maybe_wrap_command_args_with_numa_binding(
+            args_str,
+            gpu_index=local_rank_id,
+            numa_options=numa_options,
+        )
+
+        self.local_rank_id = local_rank_id
+
+        self.proc: Popen = self._popen(args_str, env_vars)
+
+    def _popen(self, args: tuple, env: dict[str, str]) -> Popen:
+        kwargs: dict[str, Any] = {}
+        if not IS_WINDOWS:
+            kwargs["start_new_session"] = True
+
+        return Popen(
+            # pyre-fixme[6]: Expected `Union[typing.Sequence[Union[_PathLike[bytes],
+            #  _PathLike[str], bytes, str]], bytes, str]` for 1st param but got
+            #  `Tuple[str, *Tuple[Any, ...]]`.
+            args=args,
+            env=env,
+            stdout=self._stdout,
+            stderr=self._stderr,
+            **kwargs,
+        )
+
+    def close(self, death_sig: signal.Signals | None = None) -> None:
+        if not death_sig:
+            death_sig = _get_default_signal()
+        if IS_WINDOWS:
+            self.proc.send_signal(death_sig)
+        else:
+            os.killpg(self.proc.pid, death_sig)
+        if self._stdout:
+            self._stdout.close()
+        if self._stderr:
+            self._stderr.close()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/tail_log.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/tail_log.py
new file mode 100644
index 0000000000000000000000000000000000000000..77d410cce55c09b0acd79ebf4583028f5a7bb759
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/tail_log.py
@@ -0,0 +1,168 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+import os
+import time
+from collections.abc import Callable
+from concurrent.futures.thread import ThreadPoolExecutor
+from threading import Event
+from typing import TextIO, TYPE_CHECKING
+
+
+if TYPE_CHECKING:
+    from concurrent.futures._base import Future
+
+__all__ = ["tail_logfile", "TailLog"]
+
+logger = logging.getLogger(__name__)
+
+
+def tail_logfile(
+    header: str,
+    file: str,
+    dst: TextIO,
+    finished: Event,
+    interval_sec: float,
+    log_line_filter: Callable[[str], bool] | None = None,
+):
+    while not os.path.exists(file):
+        if finished.is_set():
+            return
+        time.sleep(interval_sec)
+
+    with open(file, errors="replace") as fp:
+        while True:
+            line = fp.readline()
+
+            if line:
+                if log_line_filter and log_line_filter(line):
+                    dst.write(f"{header}{line}")
+            else:  # reached EOF
+                if finished.is_set():
+                    # log line producer is finished
+                    break
+                else:
+                    # log line producer is still going
+                    # wait for a bit before looping again
+                    time.sleep(interval_sec)
+
+
+class TailLog:
+    """
+    Tail the given log files.
+
+    The log files do not have to exist when the ``start()`` method is called. The tail-er will gracefully wait until
+    the log files are created by the producer and will tail the contents of the
+    log files until the ``stop()`` method is called.
+
+    .. warning:: ``TailLog`` will wait indefinitely for the log file to be created!
+
+    Each log file's line will be suffixed with a header of the form: ``[{name}{idx}]:``,
+    where the ``name`` is user-provided and ``idx`` is the index of the log file
+    in the ``log_files`` mapping. ``log_line_prefixes`` can be used to override the
+    header for each log file.
+
+    Usage:
+
+    ::
+
+     log_files = {0: "/tmp/0_stdout.log", 1: "/tmp/1_stdout.log"}
+     tailer = TailLog("trainer", log_files, sys.stdout).start()
+     # actually run the trainers to produce 0_stdout.log and 1_stdout.log
+     run_trainers()
+     tailer.stop()
+
+     # once run_trainers() start writing the ##_stdout.log files
+     # the tailer will print to sys.stdout:
+     # >>> [trainer0]:log_line1
+     # >>> [trainer1]:log_line1
+     # >>> [trainer0]:log_line2
+     # >>> [trainer0]:log_line3
+     # >>> [trainer1]:log_line2
+
+    .. note:: Due to buffering log lines between files may not necessarily
+              be printed out in order. You should configure your application's
+              logger to suffix each log line with a proper timestamp.
+
+    """
+
+    def __init__(
+        self,
+        name: str,
+        log_files: dict[int, str],
+        dst: TextIO,
+        log_line_prefixes: dict[int, str] | None = None,
+        interval_sec: float = 0.1,
+        log_line_filter: Callable[[str], bool] = (lambda _: True),
+    ):
+        n = len(log_files)
+        self._threadpool = None
+        if n > 0:
+            # pyrefly: ignore [bad-assignment]
+            self._threadpool = ThreadPoolExecutor(
+                max_workers=n,
+                thread_name_prefix=f"{self.__class__.__qualname__}_{name}",
+            )
+
+        self._name = name
+        self._dst = dst
+        self._log_files = log_files
+        self._log_line_prefixes = log_line_prefixes
+        self._log_line_filter = log_line_filter
+        self._finished_events: dict[int, Event] = {
+            local_rank: Event() for local_rank in log_files
+        }
+        self._futs: list[Future] = []
+        self._interval_sec = interval_sec
+        self._stopped = False
+
+    def start(self) -> "TailLog":
+        if not self._threadpool or not self._dst:
+            return self
+
+        for local_rank, file in self._log_files.items():
+            header = f"[{self._name}{local_rank}]:"
+            if self._log_line_prefixes and local_rank in self._log_line_prefixes:
+                header = self._log_line_prefixes[local_rank]
+            self._futs.append(
+                self._threadpool.submit(
+                    tail_logfile,
+                    header=header,
+                    file=file,
+                    dst=self._dst,
+                    finished=self._finished_events[local_rank],
+                    interval_sec=self._interval_sec,
+                    log_line_filter=self._log_line_filter,
+                )
+            )
+        return self
+
+    def stop(self) -> None:
+        for finished in self._finished_events.values():
+            finished.set()
+
+        for local_rank, f in enumerate(self._futs):
+            try:
+                f.result()
+            except Exception as e:
+                logger.exception(
+                    "error in log tailor for %s%s. %s",
+                    self._name,
+                    local_rank,
+                    e.__class__.__qualname__,
+                )
+
+        if self._threadpool:
+            self._threadpool.shutdown(wait=True)
+
+        self._stopped = True
+
+    def stopped(self) -> bool:
+        return self._stopped
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c387a3ec2833ac643c571afa7a194a1dc0d3fbea
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/__init__.py
@@ -0,0 +1,163 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+In the context of Torch Distributed Elastic we use the term *rendezvous* to
+refer to a particular functionality that combines a **distributed
+synchronization** primitive with **peer discovery**.
+
+It is used by Torch Distributed Elastic to gather participants of a training
+job (i.e. nodes) such that they all agree on the same list of participants and
+everyone's roles, as well as make a consistent collective decision on when
+training can begin/resume.
+
+Torch Distributed Elastic rendezvous provides the following critical
+functionalities:
+
+**Barrier**:
+
+Nodes performing rendezvous will all block until the rendezvous is considered
+complete - this happens when at least ``min`` total number of nodes have joined
+the rendezvous barrier (for the same job). This also implies the barrier is not
+necessarily of fixed size.
+
+There's an additional small waiting time after reaching ``min`` number of
+nodes - this is used to ensure the rendezvous is not completed "too quickly"
+(which could potentially exclude additional nodes attempting to join at
+approximately the same time).
+
+If ``max`` number of nodes is gathered at the barrier, the rendezvous is
+completed immediately.
+
+There's also an overall timeout which causes the rendezvous to fail if ``min``
+number of nodes is never reached - this is meant to be a simple fail-safe to
+help release partially allocated job resources, in case there's a problem with
+the resource manager, and is meant to be interpreted as non-retryable.
+
+**Exclusivity**:
+
+A simple distributed barrier would not be sufficient, as we also need to ensure
+that only one group of nodes exists at any given time (for a given job). In
+other words, new nodes (i.e. joining late) should not be able to form a parallel
+independent group of workers for the same job.
+
+Torch Distributed Elastic rendezvous ensures that if a group of nodes has
+already completed a rendezvous (and hence might already be training), then
+additional "late" nodes attempting to rendezvous will only announce themselves
+as waiting, and will have to wait until the (previously completed) existing
+rendezvous is destroyed first.
+
+**Consistency**:
+
+When a rendezvous is completed, all its members will agree on the job membership
+and everyone's role in it. This role is represented using an integer, called
+rank, that is between between 0 and world size.
+
+Note that ranks are *not stable*, in the sense that the same node can be
+assigned a different rank in the next (re-)rendezvous.
+
+**Fault-tolerance**:
+
+Torch Distributed Elastic rendezvous is designed to tolerate node failures
+during the rendezvous process. Should a process crash (or lose network
+connectivity, etc), between joining the rendezvous and it being completed, then
+a re-rendezvous with remaining healthy nodes will happen automatically.
+
+A node can also fail *after* it has completed (or *has been observed* by other
+nodes to have completed) the rendezvous - this scenario will be handled by the
+Torch Distributed Elastic ``train_loop`` instead (where it will also trigger a
+re-rendezvous).
+
+**Shared key-value store**:
+
+When the rendezvous is completed, a shared key-value store is created and
+returned. This store implements a ``torch.distributed.Store`` API (see
+`distributed communication docs
+`__).
+
+This store is only shared by the members of the completed rendezvous. It
+is intended to be used by Torch Distributed Elastic to exchange information
+necessary to initialize job control and data-planes.
+
+**Waiting workers and rendezvous closing**:
+
+Torch Distributed Elastic rendezvous handler object provides additional
+functionalities, which are technically not part of the rendezvous process:
+
+1. Querying how many workers arrived late at the barrier, who can participate in
+   *next* rendezvous.
+
+2. Setting the rendezvous *closed* to signal all nodes not to participate in
+   next rendezvous.
+
+**DynamicRendezvousHandler**:
+
+Torch Distributed Elastic comes with the :py:class:`.DynamicRendezvousHandler`
+class that implements the rendezvous mechanism described above. It is a backend-
+agnostic type that expects a particular :py:class:`.RendezvousBackend` instance
+to be specified during construction.
+
+Torch distributed users can either implement their own backend type or use one
+of the following implementations that come with PyTorch:
+
+- :py:class:`.C10dRendezvousBackend`: Uses a C10d store (by default
+  ``TCPStore``) as the rendezvous backend. The main advantage of using a C10d
+  store is that it requires no 3rd-party dependency (such as etcd) to establish
+  a rendezvous.
+- :py:class:`.EtcdRendezvousBackend`: Supersedes the legacy
+  :py:class:`.EtcdRendezvousHandler` class. Passing an
+  :py:class:`.EtcdRendezvousBackend` instance to
+  :py:class:`.DynamicRendezvousHandler` is functionally equivalent to
+  instantiating an :py:class:`.EtcdRendezvousHandler`.
+
+  ::
+
+     store = TCPStore("localhost")
+
+     backend = C10dRendezvousBackend(store, "my_run_id")
+
+     rdzv_handler = DynamicRendezvousHandler.from_backend(
+         run_id="my_run_id", store=store, backend=backend, min_nodes=2, max_nodes=4
+     )
+"""
+
+from .api import (
+    rendezvous_handler_registry,
+    RendezvousClosedError,
+    RendezvousConnectionError,
+    RendezvousError,
+    RendezvousGracefulExitError,
+    RendezvousHandler,
+    RendezvousHandlerCreator,
+    RendezvousHandlerRegistry,
+    RendezvousInfo,
+    RendezvousParameters,
+    RendezvousStateError,
+    RendezvousStoreInfo,
+    RendezvousTimeoutError,
+)
+from .registry import _register_default_handlers, _register_out_of_tree_handlers
+
+
+_register_default_handlers()
+_register_out_of_tree_handlers()
+
+
+__all__ = [
+    "RendezvousClosedError",
+    "RendezvousConnectionError",
+    "RendezvousError",
+    "RendezvousGracefulExitError",
+    "RendezvousHandler",
+    "RendezvousHandlerCreator",
+    "RendezvousHandlerRegistry",
+    "RendezvousInfo",
+    "RendezvousParameters",
+    "RendezvousStateError",
+    "RendezvousStoreInfo",
+    "RendezvousTimeoutError",
+    "rendezvous_handler_registry",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/_etcd_stub.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/_etcd_stub.py
new file mode 100644
index 0000000000000000000000000000000000000000..5890a97c672a61b5678e66b006ba173fe7668286
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/_etcd_stub.py
@@ -0,0 +1,75 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Any
+
+
+"""
+This file is not meant to be used directly. It serves as a stub to allow
+other files to be safely imported without requiring the installation of
+the 'etcd' library. The classes and methods here raise exceptions to
+indicate that the real 'etcd' module is needed.
+"""
+
+
+class EtcdStubError(ImportError):
+    """Custom exception to indicate that the real etcd module is required."""
+
+    def __init__(self) -> None:
+        super().__init__("The 'etcd' module is required but not installed.")
+
+
+class EtcdAlreadyExist(Exception):
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        raise EtcdStubError
+
+
+class EtcdCompareFailed(Exception):
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        raise EtcdStubError
+
+
+class EtcdKeyNotFound(Exception):
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        raise EtcdStubError
+
+
+class EtcdWatchTimedOut(Exception):
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        raise EtcdStubError
+
+
+class EtcdEventIndexCleared(Exception):
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        raise EtcdStubError
+
+
+class EtcdException(Exception):
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        raise EtcdStubError
+
+
+class EtcdResult:
+    def __init__(self) -> None:
+        raise EtcdStubError
+
+
+class Client:
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        raise EtcdStubError
+
+    def read(self, key: str) -> None:
+        raise EtcdStubError
+
+    def write(
+        self, key: str, value: Any, ttl: int | None = None, **kwargs: Any
+    ) -> None:
+        raise EtcdStubError
+
+    def test_and_set(
+        self, key: str, value: Any, prev_value: Any, ttl: int | None = None
+    ) -> None:
+        raise EtcdStubError
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/api.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b3fa8183dfb81da2f0b675a5e1a5d1f6fee935f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/api.py
@@ -0,0 +1,391 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import socket
+from abc import ABC, abstractmethod
+from collections.abc import Callable
+from dataclasses import dataclass
+from typing import Any, ClassVar
+
+from torch.distributed import Store
+from torch.distributed.elastic.utils.distributed import get_free_port
+
+
+__all__ = [
+    "RendezvousClosedError",
+    "RendezvousConnectionError",
+    "RendezvousError",
+    "RendezvousGracefulExitError",
+    "RendezvousHandler",
+    "RendezvousHandlerCreator",
+    "RendezvousHandlerRegistry",
+    "RendezvousInfo",
+    "RendezvousParameters",
+    "RendezvousStateError",
+    "RendezvousStoreInfo",
+    "RendezvousTimeoutError",
+    "rendezvous_handler_registry",
+]
+
+
+class RendezvousError(Exception):
+    """Represents the base type for rendezvous errors."""
+
+
+class RendezvousClosedError(RendezvousError):
+    """Raised when a rendezvous is closed."""
+
+
+class RendezvousTimeoutError(RendezvousError):
+    """Raised when a rendezvous did not complete on time."""
+
+
+class RendezvousConnectionError(RendezvousError):
+    """Raised when the connection to a rendezvous backend has failed."""
+
+
+class RendezvousStateError(RendezvousError):
+    """Raised when the state of a rendezvous is corrupt."""
+
+
+class RendezvousGracefulExitError(RendezvousError):
+    """Raised when node wasn't not included in rendezvous and gracefully exits.
+
+    Exception is a mechanism to exit the stack, however does not mean a failure.
+    """
+
+
+@dataclass
+class RendezvousStoreInfo:
+    """Store address and port that can be used to bootstrap trainer distributed comms"""
+
+    MASTER_ADDR_KEY: ClassVar[str] = "MASTER_ADDR"
+    MASTER_PORT_KEY: ClassVar[str] = "MASTER_PORT"
+    master_addr: str
+    master_port: int
+
+    @staticmethod
+    def build(
+        rank: int,
+        store: Store,
+        local_addr: str | None,
+        server_port: int | None = None,
+    ) -> "RendezvousStoreInfo":
+        """Factory method, finds unused new port on rank0 host and addr/port info with all ranks.
+
+        If master_addr/master_port is knowns (useful when sharing existing tcp store server) use the constructor.
+
+        Args:
+            rank: rank of the current node
+            store: store to use for rendezvous
+            local_addr: address of the current node, if not provided will be resolved from hostname
+            server_port: port of the TCPStore server, when the TCPStore is shared.
+        """
+        # TODO swap to collectives comms API
+        if rank == 0:
+            addr = local_addr or socket.getfqdn()
+            # When TCPStore is not shared, we fallback to get_free_port.
+            port = server_port or get_free_port()
+            store.set(
+                RendezvousStoreInfo.MASTER_ADDR_KEY,
+                addr.encode(encoding="UTF-8"),  # type: ignore[arg-type]
+            )
+            store.set(
+                RendezvousStoreInfo.MASTER_PORT_KEY,
+                str(port).encode(encoding="UTF-8"),  # type: ignore[arg-type]
+            )
+
+        addr = store.get(RendezvousStoreInfo.MASTER_ADDR_KEY).decode(encoding="UTF-8")
+        port = int(
+            store.get(RendezvousStoreInfo.MASTER_PORT_KEY).decode(encoding="UTF-8")
+        )
+        return RendezvousStoreInfo(master_addr=addr, master_port=port)
+
+
+class RendezvousInfo:
+    """Holds the information about the rendezvous."""
+
+    def __init__(
+        self,
+        store: Store,
+        rank: int,
+        world_size: int,
+        bootstrap_store_info: RendezvousStoreInfo,
+    ):
+        self._store = store
+        self._rank = rank
+        self._world_size = world_size
+        self._bootstrap_store_info = bootstrap_store_info
+
+    @property
+    def store(self) -> Store:
+        """Store used by torchelastic control plane"""
+        return self._store
+
+    @property
+    def rank(self) -> int:
+        """Rank within a group"""
+        return self._rank
+
+    @property
+    def world_size(self) -> int:
+        """Global group size"""
+        return self._world_size
+
+    @property
+    def bootstrap_store_info(self) -> RendezvousStoreInfo | None:
+        """Store information that can used by trainer code to bootstrap distributed comms."""
+        return self._bootstrap_store_info
+
+
+class RendezvousHandler(ABC):
+    """Main rendezvous interface.
+
+    Note:
+        Distributed Torch users normally **do not** need to implement their own
+        ``RendezvousHandler``. An implementation based on C10d Store is already
+        provided, and is recommended for most users.
+    """
+
+    @abstractmethod
+    def get_backend(self) -> str:
+        """Return the name of the rendezvous backend."""
+
+    @property
+    def use_agent_store(self) -> bool:
+        """Indicates that store reference returned by :py:meth:`next_rendezvous` can be shared with user
+        applications and will be available during application lifecycle.
+
+        Rendezvous handler impl will share store details as instance of :py:class:`RendezvousStoreInfo`.
+        Applications as a convention use `MASTER_ADDR`/`MASTER_PORT` env variables to lookup the store.
+        """
+        return False
+
+    @abstractmethod
+    def next_rendezvous(self) -> RendezvousInfo:
+        """Main entry-point into the rendezvous barrier.
+
+        Blocks until the rendezvous is complete and the current process is
+        included in the formed worker group, or a timeout occurs, or the
+        rendezvous was marked closed.
+
+        Returns:
+            Instance of :py:class:`RendezvousInfo`.
+
+        Raises:
+            RendezvousClosedError:
+                The rendezvous is closed.
+            RendezvousConnectionError:
+                The connection to the rendezvous backend has failed.
+            RendezvousStateError:
+                The rendezvous state is corrupt.
+            RendezvousTimeoutError:
+                The rendezvous did not complete on time.
+        """
+
+    @abstractmethod
+    def is_closed(self) -> bool:
+        """Check whether the rendezvous has been closed.
+
+        A closed rendezvous means all future attempts to re-rendezvous within
+        same job will fail.
+
+        ``is_closed()`` and :py:meth:`set_closed` have semantics of eventual
+        propagation and should not be used for synchronization. The intention is
+        that if at least one node decides the job is finished, it will close the
+        rendezvous, and other nodes will soon observe this and stop running as
+        well.
+        """
+
+    @abstractmethod
+    def set_closed(self):
+        """Mark the rendezvous as closed."""
+
+    @abstractmethod
+    def num_nodes_waiting(self) -> int:
+        """Return the number of nodes who arrived late at the rendezvous
+        barrier, hence were not included in the current worker group.
+
+        Callers should periodically call this method to check whether new
+        nodes are waiting to join the job and if so admit them by calling
+        :py:meth:`next_rendezvous()` (re-rendezvous).
+        """
+
+    @abstractmethod
+    def get_run_id(self) -> str:
+        """Return the run id of the rendezvous.
+
+        The run id is a user-defined id that uniquely identifies an instance of
+        a distributed application. It typically maps to a job id and is used to
+        allow nodes to join the correct distributed application.
+        """
+
+    @abstractmethod
+    def shutdown(self) -> bool:
+        """Close all resources that were open for the rendezvous.
+
+        Example::
+
+            rdzv_handler = ...
+            try:
+                store, rank, world_size = rdzv_handler.next_rendezvous()
+            finally:
+                rdzv_handler.shutdown()
+        """
+
+
+class RendezvousParameters:
+    """Hold the parameters to construct a :py:class:`RendezvousHandler`.
+
+    Args:
+        backend:
+            The name of the backend to use to handle the rendezvous.
+        endpoint:
+            The endpoint of the rendezvous, usually in form [:].
+        run_id:
+            The id of the rendezvous.
+        min_nodes:
+            The minimum number of nodes to admit to the rendezvous.
+        max_nodes:
+            The maximum number of nodes to admit to the rendezvous.
+        local_addr:
+            The address of the local node.
+        **kwargs:
+            Additional parameters for the specified backend.
+    """
+
+    def __init__(
+        self,
+        backend: str,
+        endpoint: str,
+        run_id: str,
+        min_nodes: int,
+        max_nodes: int,
+        local_addr: str | None = None,
+        **kwargs,
+    ):
+        if not backend:
+            raise ValueError("The rendezvous backend name must be a non-empty string.")
+
+        if min_nodes < 1:
+            raise ValueError(
+                f"The minimum number of rendezvous nodes ({min_nodes}) must be greater than zero."
+            )
+        if max_nodes < min_nodes:
+            raise ValueError(
+                f"The maximum number of rendezvous nodes ({max_nodes}) must be greater than or "
+                f"equal to the minimum number of rendezvous nodes ({min_nodes})."
+            )
+
+        self.backend = backend
+        self.endpoint = endpoint
+        self.run_id = run_id
+        self.min_nodes = min_nodes
+        self.max_nodes = max_nodes
+        self.config = kwargs
+        self.local_addr = local_addr
+
+    def get(self, key: str, default: Any = None) -> Any:
+        """Return the value for ``key`` if ``key`` exists, else ``default``."""
+        return self.config.get(key, default)
+
+    def get_as_bool(self, key: str, default: bool | None = None) -> bool | None:
+        """Return the value for ``key`` as a ``bool``."""
+        value = self.get(key, default)
+        if value is None or isinstance(value, bool):
+            return value
+        if isinstance(value, int):
+            if value == 1:
+                return True
+            if value == 0:
+                return False
+        elif isinstance(value, str):
+            if value.lower() in ["1", "true", "t", "yes", "y"]:
+                return True
+            if value.lower() in ["0", "false", "f", "no", "n"]:
+                return False
+        raise ValueError(
+            f"The rendezvous configuration option '{key}' does not represent a valid boolean value."
+        )
+
+    def get_as_int(self, key: str, default: int | None = None) -> int | None:
+        """Return the value for ``key`` as an ``int``."""
+        value = self.get(key, default)
+        if value is None:
+            return value
+        try:
+            return int(value)
+        except ValueError as e:
+            raise ValueError(
+                f"The rendezvous configuration option '{key}' does not represent a valid integer "
+                "value."
+            ) from e
+
+
+RendezvousHandlerCreator = Callable[[RendezvousParameters], RendezvousHandler]
+
+
+class RendezvousHandlerRegistry:
+    """Represent a registry of :py:class:`RendezvousHandler` backends."""
+
+    _registry: dict[str, RendezvousHandlerCreator]
+
+    def __init__(self) -> None:
+        self._registry = {}
+
+    def register(self, backend: str, creator: RendezvousHandlerCreator) -> None:
+        """Register a new rendezvous backend.
+
+        Args:
+            backend:
+                The name of the backend.
+            creator:
+                The callback to invoke to construct the
+                :py:class:`RendezvousHandler`.
+        """
+        if not backend:
+            raise ValueError("The rendezvous backend name must be a non-empty string.")
+
+        current_creator: RendezvousHandlerCreator | None
+        try:
+            current_creator = self._registry[backend]
+        except KeyError:
+            current_creator = None
+
+        if current_creator is not None and current_creator != creator:
+            raise ValueError(
+                f"The rendezvous backend '{backend}' cannot be registered with '{creator}' as it "
+                f"is already registered with '{current_creator}'."
+            )
+
+        self._registry[backend] = creator
+
+    def create_handler(self, params: RendezvousParameters) -> RendezvousHandler:
+        """Create a new :py:class:`RendezvousHandler`."""
+        try:
+            creator = self._registry[params.backend]
+        except KeyError as e:
+            raise ValueError(
+                f"The rendezvous backend '{params.backend}' is not registered. Did you forget "
+                f"to call `{self.register.__name__}`?"
+            ) from e
+
+        handler = creator(params)
+
+        # Do some sanity check.
+        if handler.get_backend() != params.backend:
+            raise RuntimeError(
+                f"The rendezvous backend '{handler.get_backend()}' does not match the requested "
+                f"backend '{params.backend}'."
+            )
+
+        return handler
+
+
+# The default global registry instance used by launcher scripts to instantiate
+# rendezvous handlers.
+rendezvous_handler_registry = RendezvousHandlerRegistry()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py
new file mode 100644
index 0000000000000000000000000000000000000000..0296c4d45ddc13dadc9ee1d91f07a3950c277892
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py
@@ -0,0 +1,270 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import binascii
+import logging
+import os
+import tempfile
+from base64 import b64decode, b64encode
+from datetime import timedelta
+from typing import Any, cast
+
+from torch.distributed import FileStore, Store, TCPStore
+from torch.distributed.elastic.events import construct_and_record_rdzv_event, NodeState
+
+from .api import (
+    RendezvousConnectionError,
+    RendezvousError,
+    RendezvousParameters,
+    RendezvousStateError,
+)
+from .dynamic_rendezvous import RendezvousBackend, Token
+from .utils import _matches_machine_hostname, parse_rendezvous_endpoint
+
+
+logger = logging.getLogger(__name__)
+
+# default port for the TCP store
+DEFAULT_PORT = 29400
+
+
+class C10dRendezvousBackend(RendezvousBackend):
+    """Represents a C10d-backed rendezvous backend.
+
+    Args:
+        store:
+            The :py:class:`torch.distributed.Store` instance to use to
+            communicate with the C10d store.
+        run_id:
+            The run id of the rendezvous.
+    """
+
+    # See the explanation in the __init__ method.
+    _NULL_SENTINEL = "Y2FuaW1hZGFt"
+
+    _store: Store
+    _key: str
+
+    def __init__(self, store: Store, run_id: str) -> None:
+        if not run_id:
+            raise ValueError("The run id must be a non-empty string.")
+
+        self._store = store
+
+        self._key = "torch.rendezvous." + run_id
+
+        # The read operation of a store blocks the caller until the specified
+        # key becomes available. This behavior makes it tricky to use a store
+        # as a regular key-value dictionary.
+        #
+        # As a workaround we initially set a sentinel value as the rendezvous
+        # state. Whenever this value gets returned we treat it as a None.
+        self._call_store("compare_set", self._key, "", self._NULL_SENTINEL)
+
+    @property
+    def name(self) -> str:
+        """See base class."""
+        return "c10d"
+
+    def get_state(self) -> tuple[bytes, Token] | None:
+        """See base class."""
+        base64_state: bytes = self._call_store("get", self._key)
+
+        return self._decode_state(base64_state)
+
+    def set_state(
+        self, state: bytes, token: Token | None = None
+    ) -> tuple[bytes, Token, bool] | None:
+        """See base class."""
+        base64_state_str: str = b64encode(state).decode()
+
+        if token:
+            # Shortcut if we know for sure that the token is not valid.
+            if not isinstance(token, bytes):
+                result = self.get_state()
+                if result is not None:
+                    return *result, False
+                return None
+
+            token = token.decode()
+        else:
+            token = self._NULL_SENTINEL
+
+        base64_state: bytes = self._call_store(
+            "compare_set", self._key, token, base64_state_str
+        )
+
+        state_token_pair = self._decode_state(base64_state)
+        if state_token_pair is None:
+            return None
+
+        new_state, new_token = state_token_pair
+
+        # C10d Store's compare_set method does not offer an easy way to find out
+        # whether our write attempt was successful. As a brute-force solution we
+        # perform a bitwise comparison of our local state and the remote state.
+        return new_state, new_token, new_state == state
+
+    def _call_store(self, store_op: str, *args, **kwargs) -> Any:
+        try:
+            return getattr(self._store, store_op)(*args, **kwargs)
+        except (ValueError, RuntimeError, TimeoutError) as exc:
+            raise RendezvousConnectionError(
+                "The connection to the C10d store has failed. See inner exception for details."
+            ) from exc
+
+    def _decode_state(self, base64_state: bytes) -> tuple[bytes, Token] | None:
+        if base64_state == self._NULL_SENTINEL.encode():
+            return None
+
+        try:
+            state = b64decode(base64_state)
+        except binascii.Error as exc:
+            raise RendezvousStateError(
+                "The state object is corrupt. See inner exception for details."
+            ) from exc
+
+        return state, base64_state
+
+
+def _create_tcp_store(params: RendezvousParameters) -> TCPStore:
+    host, port = parse_rendezvous_endpoint(params.endpoint, default_port=DEFAULT_PORT)
+
+    cfg_is_host = params.get_as_bool("is_host")
+    # If the user has explicitly specified whether our process should host the
+    # the store, respect it.
+    if cfg_is_host is not None:
+        is_host = cfg_is_host
+    # Otherwise try to determine whether we are the host based on our hostname
+    # and IP address.
+    else:
+        is_host = _matches_machine_hostname(host)
+
+    # The timeout
+    read_timeout = cast(int, params.get_as_int("read_timeout", 60))
+    if read_timeout <= 0:
+        raise ValueError("The read timeout must be a positive integer.")
+
+    # In specific cases we attempt to instantiate the store twice. For details
+    # see the explanation in the except clause below.
+    for is_server in [is_host, False]:
+        try:
+            store = TCPStore(
+                host,
+                port,
+                is_master=is_server,
+                multi_tenant=True,
+                timeout=timedelta(seconds=read_timeout),
+            )
+
+            if is_server:
+                msg = f"Process {os.getpid()} hosts the TCP store for the C10d rendezvous backend."
+                construct_and_record_rdzv_event(
+                    run_id=params.run_id, message=msg, node_state=NodeState.INIT
+                )
+                logger.info(msg)
+
+            break
+        except (ValueError, RuntimeError, TimeoutError) as exc:
+            # If we heuristically inferred the value of is_host as True and our
+            # first attempt to instantiate the TCP store has failed, try it one
+            # more time with is_host set to False. As an edge case there can be
+            # more than one process that is part of the same rendezvous on this
+            # machine and only one of them will eventually host the store.
+
+            if not is_server or cfg_is_host is not None:
+                raise RendezvousConnectionError(
+                    "The connection to the C10d store has failed. See inner exception for details."
+                ) from exc
+
+    return store  # type: ignore[possibly-undefined]
+
+
+def _create_file_store(params: RendezvousParameters) -> FileStore:
+    # If a user specifies an endpoint, we treat it as a path to a file.
+    if params.endpoint:
+        path = params.endpoint
+    else:
+        try:
+            # The temporary file is readable and writable only by the user of
+            # this process.
+            _, path = tempfile.mkstemp()
+        except OSError as exc:
+            raise RendezvousError(
+                "The file creation for C10d store has failed. See inner exception for details."
+            ) from exc
+
+    try:
+        store = FileStore(path)
+    except (ValueError, RuntimeError) as exc:
+        raise RendezvousConnectionError(
+            "The connection to the C10d store has failed. See inner exception for details."
+        ) from exc
+
+    return store
+
+
+def create_backend(params: RendezvousParameters) -> tuple[C10dRendezvousBackend, Store]:
+    """Create a new :py:class:`C10dRendezvousBackend` from the specified parameters.
+
+    +--------------+-----------------------------------------------------------+
+    | Parameter    | Description                                               |
+    +==============+===========================================================+
+    | store_type   | The type of the C10d store. The currently supported types |
+    |              | are "tcp" and "file" which correspond to                  |
+    |              | :py:class:`torch.distributed.TCPStore` and                |
+    |              | :py:class:`torch.distributed.FileStore`, respectively.    |
+    |              | Defaults to "tcp".                                        |
+    +--------------+-----------------------------------------------------------+
+    | read_timeout | The read timeout, in seconds, for store operations.       |
+    |              | Defaults to 60 seconds.                                   |
+    |              |                                                           |
+    |              | Note this only applies to                                 |
+    |              | :py:class:`torch.distributed.TCPStore`. It is not relevant|
+    |              | to :py:class:`torch.distributed.FileStore` which does not |
+    |              | take in timeout as a parameter.                           |
+    +--------------+-----------------------------------------------------------+
+    | is_host      | A boolean value indicating whether this backend instance  |
+    |              | will host the C10d store. If not specified it will be     |
+    |              | inferred heuristically by matching the hostname or the IP |
+    |              | address of this machine against the specified rendezvous  |
+    |              | endpoint. Defaults to ``None``.                           |
+    |              |                                                           |
+    |              | Note that this configuration option only applies to       |
+    |              | :py:class:`torch.distributed.TCPStore`. In normal         |
+    |              | circumstances you can safely skip it; the only time when  |
+    |              | it is needed is if its value cannot be correctly          |
+    |              | determined (e.g. the rendezvous endpoint has a CNAME as   |
+    |              | the hostname or does not match the FQDN of the machine).  |
+    +--------------+-----------------------------------------------------------+
+    """
+    # As of today we only support TCPStore and FileStore. Other store types do
+    # not have the required functionality (e.g. compare_set) yet.
+    store_type = params.get("store_type", "tcp").strip().lower()
+    store: Store
+
+    try:
+        if store_type == "file":
+            store = _create_file_store(params)
+        elif store_type == "tcp":
+            store = _create_tcp_store(params)
+        else:
+            raise ValueError(
+                "Invalid store type given. Currently only supports file and tcp."
+            )
+
+        backend = C10dRendezvousBackend(store, params.run_id)
+
+    except Exception as e:
+        construct_and_record_rdzv_event(
+            message=f"{type(e).__name__}: {str(e)}",
+            run_id=params.run_id,
+            node_state=NodeState.FAILED,
+        )
+        raise
+
+    return backend, store
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py
new file mode 100644
index 0000000000000000000000000000000000000000..84adeea95573121e69f11c6faa52fe6601f271c7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py
@@ -0,0 +1,1453 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import inspect
+import logging
+import os
+import pickle
+import socket
+import threading
+import time
+import weakref
+from abc import ABC, abstractmethod
+from collections.abc import Callable
+from dataclasses import dataclass
+from datetime import datetime, timedelta, timezone
+from enum import Enum
+from typing import Any
+
+import torch.distributed as dist
+from torch.distributed import Store
+from torch.distributed.elastic.events import construct_and_record_rdzv_event, NodeState
+
+from .api import (
+    RendezvousClosedError,
+    RendezvousError,
+    RendezvousGracefulExitError,
+    RendezvousHandler,
+    RendezvousInfo,
+    RendezvousParameters,
+    RendezvousStateError,
+    RendezvousStoreInfo,
+    RendezvousTimeoutError,
+)
+from .utils import _delay, _PeriodicTimer
+
+
+__all__ = [
+    "RendezvousBackend",
+    "RendezvousTimeout",
+    "RendezvousSettings",
+    "DynamicRendezvousHandler",
+    "create_handler",
+]
+
+logger = logging.getLogger(__name__)
+
+
+def get_method_name(depth=2):
+    if len(inspect.stack()) > depth:
+        return inspect.stack()[depth].function
+    return "no_method_name"
+
+
+Token = Any
+"""Represent an opaque fencing token used by the rendezvous backend."""
+
+
+class RendezvousBackend(ABC):
+    """Represent a backend that holds the rendezvous state."""
+
+    @property
+    @abstractmethod
+    def name(self) -> str:
+        """Get the name of the backend."""
+
+    @abstractmethod
+    def get_state(self) -> tuple[bytes, Token] | None:
+        """Get the rendezvous state.
+
+        Returns:
+            A tuple of the encoded rendezvous state and its fencing token or
+            ``None`` if no state is found in the backend.
+
+        Raises:
+            RendezvousConnectionError:
+                The connection to the backend has failed.
+            RendezvousStateError:
+                The rendezvous state is corrupt.
+        """
+
+    @abstractmethod
+    def set_state(
+        self, state: bytes, token: Token | None = None
+    ) -> tuple[bytes, Token, bool] | None:
+        """Set the rendezvous state.
+
+        The new rendezvous state is set conditionally:
+
+          - If the specified ``token`` matches the fencing token stored in the
+            backend, the state will be updated. The new state will be returned
+            to the caller along with its fencing token.
+          - If the specified ``token`` does not match the fencing token stored
+            in the backend, the state won't be updated; instead the existing
+            state along with its fencing token will be returned to the caller.
+          - If the specified ``token`` is ``None``, the new state will be set
+            only if there is no existing state in the backend. Either the new
+            state or the existing state along with its fencing token will be
+            returned to the caller.
+
+        Args:
+            state:
+                The encoded rendezvous state.
+            token:
+                An optional fencing token that was retrieved by a previous call
+                to :py:meth:`get_state` or ``set_state()``.
+
+        Returns:
+            A tuple of the serialized rendezvous state, its fencing token, and
+            a boolean value indicating whether our set attempt succeeded.
+
+        Raises:
+            RendezvousConnectionError:
+                The connection to the backend has failed.
+            RendezvousStateError:
+                The rendezvous state is corrupt.
+        """
+
+
+class RendezvousTimeout:
+    """Hold the timeout configuration of a rendezvous.
+
+    Args:
+        join:
+            The time within which the rendezvous is expected to complete.
+        last_call:
+            An additional wait amount before completing the rendezvous once the
+            rendezvous has the minimum number of required participants.
+        close:
+            The time within which the rendezvous is expected to close after a
+            call to :py:meth:`RendezvousHandler.set_closed` or
+            :py:meth:`RendezvousHandler.shutdown`.
+        heartbeat:
+            The time within which a keep-alive heartbeat is expected to
+            complete.
+    """
+
+    _ZERO = timedelta(0)
+
+    _DEFAULT_TIMEOUTS = {
+        "join": timedelta(seconds=600),
+        "last_call": timedelta(seconds=30),
+        "close": timedelta(seconds=30),
+        "heartbeat": timedelta(seconds=5),
+    }
+
+    _join: timedelta
+    _last_call: timedelta
+    _close: timedelta
+    _heartbeat: timedelta
+
+    def __init__(
+        self,
+        join: timedelta | None = None,
+        last_call: timedelta | None = None,
+        close: timedelta | None = None,
+        heartbeat: timedelta | None = None,
+    ) -> None:
+        self._set_timeouts(
+            join=join, last_call=last_call, close=close, heartbeat=heartbeat
+        )
+
+    @property
+    def join(self) -> timedelta:
+        """Get the join timeout."""
+        return self._join
+
+    @property
+    def last_call(self) -> timedelta:
+        """Get the last call timeout."""
+        return self._last_call
+
+    @property
+    def close(self) -> timedelta:
+        """Get the close timeout."""
+        return self._close
+
+    @property
+    def heartbeat(self) -> timedelta:
+        """Get the keep-alive heartbeat timeout."""
+        return self._heartbeat
+
+    def _set_timeouts(self, **timeouts: timedelta | None):
+        for name, timeout in timeouts.items():
+            if timeout is None:
+                timeout = self._DEFAULT_TIMEOUTS[name]
+            if timeout <= self._ZERO:
+                raise ValueError(f"The {name} timeout ({timeout}) must be positive.")
+            setattr(self, "_" + name, timeout)
+
+
+@dataclass(repr=False, eq=False, frozen=True)
+class RendezvousSettings:
+    """Hold the settings of the rendezvous.
+
+    Attributes:
+        run_id:
+            The run id of the rendezvous.
+        min_nodes:
+            The minimum number of nodes to admit to the rendezvous.
+        max_nodes:
+            The maximum number of nodes to admit to the rendezvous.
+        timeout:
+            The timeout configuration of the rendezvous.
+        keep_alive_interval:
+            The amount of time a node waits before sending a heartbeat to keep
+            it alive in the rendezvous.
+        keep_alive_max_attempt:
+            The maximum number of failed heartbeat attempts after which a node
+            is considered dead.
+    """
+
+    run_id: str
+    min_nodes: int
+    max_nodes: int
+    timeout: RendezvousTimeout
+    keep_alive_interval: timedelta
+    keep_alive_max_attempt: int
+
+
+@dataclass(eq=True, order=True, frozen=True)
+class _NodeDesc:
+    """Describe a node in the rendezvous.
+
+    Attributes:
+        addr:
+            The FQDN of the node or user specified local node address.
+        pid:
+            The id of the process in which the rendezvous handler runs.
+        local_id:
+            A process-wide unique id.
+    """
+
+    addr: str
+    pid: int
+    local_id: int
+
+    def __repr__(self) -> str:
+        return f"{self.addr}_{self.pid}_{self.local_id}"
+
+
+class _NodeDescGenerator:
+    """Generate node descriptors.
+
+    A node descriptor is a combination of an FQDN, a process id, and an auto-
+    incremented integer that uniquely identifies a node in the rendezvous.
+    """
+
+    _lock: threading.Lock
+    _local_id: int
+
+    def __init__(self) -> None:
+        self._lock = threading.Lock()
+
+        # An integer that is incremented with each call to generate().
+        self._local_id = 0
+
+    def generate(self, local_addr: str | None = None) -> _NodeDesc:
+        # This method can be called by multiple threads concurrently; therefore,
+        # we must increment the integer atomically.
+        with self._lock:
+            local_id = self._local_id
+
+            self._local_id += 1
+
+        return _NodeDesc(local_addr or socket.getfqdn(), os.getpid(), local_id)
+
+
+class _RendezvousState:
+    """Hold the state of a rendezvous.
+
+    Attributes:
+        round:
+            The current round of the rendezvous.
+        complete:
+            A boolean value indicating whether the current round of the
+            rendezvous is complete.
+        deadline:
+            The time at which the current round of the rendezvous will be
+            considered complete if it is still waiting for nodes to join.
+        closed:
+            A boolean value indicating whether the rendezvous is closed.
+        participants:
+            A dictionary of the participants and their corresponding ranks.
+        wait_list:
+            A set of nodes that are waiting to participate in the next round of
+            the rendezvous.
+        redundancy_list:
+            A set of nodes that are redundant in the current round and can join
+            the next rendezvous without triggering re-rendezvous.
+        last_heartbeats:
+            A dictionary containing each node's last heartbeat time.
+    """
+
+    round: int
+    complete: bool
+    deadline: datetime | None
+    closed: bool
+    participants: dict[_NodeDesc, int]
+    wait_list: set[_NodeDesc]
+    redundancy_list: set[_NodeDesc]
+    last_heartbeats: dict[_NodeDesc, datetime]
+
+    def __init__(self) -> None:
+        self.round = 0
+        self.complete = False
+        self.deadline = None
+        self.closed = False
+        self.participants = {}
+        self.wait_list = set()
+        self.redundancy_list = set()
+        self.last_heartbeats = {}
+
+
+def _remove_participant_epilogue(
+    state: _RendezvousState, settings: RendezvousSettings
+) -> None:
+    if state.complete:
+        # If we do not have any participants left, move to the next round.
+        if not state.participants:
+            msg = "No participants left in the rendezvous, marking rendezvous as incomplete"
+            logger.debug(msg)
+            state.complete = False
+
+            state.round += 1
+    else:
+        if len(state.participants) < settings.min_nodes:
+            msg = (
+                f"Number of participants {len(state.participants)}) less than"
+                f"min_nodes {settings.min_nodes}, clearning deadline in state"
+            )
+            logger.debug(msg)
+            state.deadline = None
+
+
+class _RendezvousStateHolder(ABC):
+    """Hold the shared rendezvous state synced with other nodes."""
+
+    @property
+    @abstractmethod
+    def state(self) -> _RendezvousState:
+        """Get the local state."""
+
+    @abstractmethod
+    def sync(self) -> bool | None:
+        """Read or writes the latest state.
+
+        Returns:
+            A boolean value indicating whether the local state, in case marked
+            as dirty, was successfully synced with other nodes.
+        """
+
+    @abstractmethod
+    def mark_dirty(self) -> None:
+        """Mark the local state as dirty."""
+
+
+class _BackendRendezvousStateHolder(_RendezvousStateHolder):
+    """Hold the rendezvous state synced with other nodes via a backend.
+
+    Args:
+        backend:
+            The rendezvous backend to use.
+        settings:
+            The rendezvous settings.
+        cache_duration:
+            The amount of time, in seconds, to cache the last rendezvous state
+            before requesting it from the backend again.
+    """
+
+    _backend: RendezvousBackend
+    _state: _RendezvousState
+    _settings: RendezvousSettings
+    _cache_duration: int
+    _token: Token
+    _dirty: bool
+    _last_sync_time: float
+    _dead_nodes: list[_NodeDesc]
+
+    def __init__(
+        self,
+        backend: RendezvousBackend,
+        settings: RendezvousSettings,
+        cache_duration: int = 1,
+    ) -> None:
+        self._backend = backend
+        self._state = _RendezvousState()
+        self._settings = settings
+        self._cache_duration = cache_duration
+        self._token = None
+        self._dirty = False
+        self._last_sync_time = -1
+        self._dead_nodes = []
+
+    def _record(self, message: str, node_state: NodeState = NodeState.RUNNING):
+        construct_and_record_rdzv_event(
+            name=f"{self.__class__.__name__}.{get_method_name()}",
+            run_id=self._settings.run_id,
+            message=message,
+            node_state=node_state,
+        )
+
+    @property
+    def state(self) -> _RendezvousState:
+        """See base class."""
+        return self._state
+
+    def sync(self) -> bool | None:
+        """See base class."""
+        state_bits: bytes | None = None
+
+        token = None
+
+        has_set: bool | None
+
+        if self._dirty:
+            has_set = False
+
+            state_bits = pickle.dumps(self._state)
+
+            set_response = self._backend.set_state(state_bits, self._token)
+            if set_response is not None:
+                state_bits, token, has_set = set_response
+        else:
+            has_set = None
+
+            if self._cache_duration > 0:
+                # Avoid overloading the backend if we are asked to retrieve the
+                # state repeatedly. Try to serve the cached state.
+                if self._last_sync_time >= max(
+                    time.monotonic() - self._cache_duration, 0
+                ):
+                    return None
+
+            get_response = self._backend.get_state()
+            if get_response is not None:
+                state_bits, token = get_response
+
+        if state_bits is not None:
+            try:
+                self._state = pickle.loads(state_bits)
+            except pickle.PickleError as exc:
+                raise RendezvousStateError(
+                    "The rendezvous state is corrupt. See inner exception for details."
+                ) from exc
+        else:
+            self._state = _RendezvousState()
+
+        if has_set and self._dead_nodes and logger.isEnabledFor(logging.DEBUG):
+            node_list = ", ".join(f"'{dead_node}'" for dead_node in self._dead_nodes)
+
+            msg = (
+                f"As part of the sync operation the node(s) {node_list} have been removed from the "
+                f"rendezvous '{self._settings.run_id}' since they had no heartbeat."
+            )
+            self._record(message=msg)
+            logger.debug(msg)
+
+        self._token = token
+
+        self._dirty = False
+
+        self._last_sync_time = time.monotonic()
+
+        self._sanitize()
+
+        return has_set
+
+    def _sanitize(self) -> None:
+        state = self._state
+
+        expire_time = datetime.now(timezone.utc) - (
+            self._settings.keep_alive_interval * self._settings.keep_alive_max_attempt
+        )
+
+        # Filter out the dead nodes.
+        self._dead_nodes = [
+            node
+            for node, last_heartbeat in state.last_heartbeats.items()
+            if last_heartbeat < expire_time
+        ]
+
+        participant_removed = False
+
+        for dead_node in self._dead_nodes:
+            msg = f"Detected dead node '{dead_node}', removing it from the rendezvous"
+            logger.debug(msg)
+            del state.last_heartbeats[dead_node]
+
+            try:
+                del state.participants[dead_node]
+
+                participant_removed = True
+            except KeyError:
+                pass
+
+            try:
+                state.wait_list.remove(dead_node)
+            except KeyError:
+                pass
+
+            try:
+                state.redundancy_list.remove(dead_node)
+            except KeyError:
+                pass
+
+        if participant_removed:
+            # Common epilogue shared with the _remove_from_participants()
+            # function of _DistributedRendezvousOpExecutor.
+            _remove_participant_epilogue(state, self._settings)
+
+    def mark_dirty(self) -> None:
+        """See base class.
+
+        If the local rendezvous state is dirty, the next sync call will try to
+        write the changes back to the backend. However this attempt might fail
+        if another node, which had the same state, also made changes and wrote
+        them before us.
+        """
+        self._dirty = True
+
+
+class _Action(Enum):
+    """Specifies the possible actions based on the state of the rendezvous."""
+
+    KEEP_ALIVE = 1
+    ADD_TO_PARTICIPANTS = 2
+    ADD_TO_WAIT_LIST = 3
+    ADD_TO_REDUNDANCY_LIST = 4
+    REMOVE_FROM_PARTICIPANTS = 5
+    REMOVE_FROM_WAIT_LIST = 6
+    REMOVE_FROM_REDUNDANCY_LIST = 7
+    MARK_RENDEZVOUS_COMPLETE = 8
+    MARK_RENDEZVOUS_CLOSED = 9
+    SYNC = 10
+    ERROR_CLOSED = 11
+    ERROR_TIMEOUT = 12
+    FINISH = 13
+
+
+class _RendezvousContext:
+    """Holds the context of the rendezvous.
+
+    Attributes:
+        node:
+            The node descriptor associated with the current rendezvous handler
+            instance.
+        state:
+            The current state of the rendezvous.
+        settings:
+            The rendezvous settings.
+    """
+
+    node: _NodeDesc
+    state: _RendezvousState
+    settings: RendezvousSettings
+
+    def __init__(
+        self, node: _NodeDesc, state: _RendezvousState, settings: RendezvousSettings
+    ) -> None:
+        self.node = node
+        self.state = state
+        self.settings = settings
+
+
+class _RendezvousOpExecutor(ABC):
+    """Execute rendezvous operations."""
+
+    @abstractmethod
+    def run(
+        self,
+        state_handler: Callable[[_RendezvousContext, float], _Action],
+        deadline: float,
+        update_deadline: Callable[[timedelta], float] | None = None,
+    ) -> None:
+        """Execute a rendezvous operation.
+
+        An operation is run inside a state machine and is expected to transition
+        the rendezvous from one state to another.
+
+        Args:
+            state_handler:
+                A callable that is expected to return the next state transition
+                action based on the current state of the rendezvous.
+            deadline:
+                The time, in seconds, at which the operation will be considered
+                timed-out.
+            update_deadline:
+                Function to generate a new operation deadline if the current
+                node may participate in the next rendezvous.
+        """
+
+
+class _DistributedRendezvousOpExecutor(_RendezvousOpExecutor):
+    """Execute rendezvous operations using a shared state.
+
+    Args:
+        node:
+            The node descriptor associated with the current rendezvous handler
+            instance.
+        state_holder:
+            The ``RendezvousStateHolder`` to use to sync the rendezvous state
+            with other nodes.
+        settings:
+            The rendezvous settings.
+    """
+
+    _node: _NodeDesc
+    _state: _RendezvousState
+    _state_holder: _RendezvousStateHolder
+    _settings: RendezvousSettings
+
+    def __init__(
+        self,
+        node: _NodeDesc,
+        state_holder: _RendezvousStateHolder,
+        settings: RendezvousSettings,
+    ) -> None:
+        self._node = node
+        self._state_holder = state_holder
+        self._settings = settings
+
+    def _record(self, message: str, node_state: NodeState = NodeState.RUNNING) -> None:
+        construct_and_record_rdzv_event(
+            name=f"{self.__class__.__name__}.{get_method_name()}",
+            run_id=self._settings.run_id,
+            message=message,
+            node_state=node_state,
+            hostname=self._node.addr,
+            pid=self._node.pid,
+            local_id=self._node.local_id,
+        )
+
+    def run(
+        self,
+        state_handler: Callable[[_RendezvousContext, float], _Action],
+        deadline: float,
+        update_deadline: Callable[[timedelta], float] | None = None,
+    ) -> None:
+        """See base class."""
+        action = None
+        while action != _Action.FINISH:
+            # Reads or writes the latest rendezvous state shared by all nodes in
+            # the rendezvous. Note that our local changes might get overridden
+            # by another node if that node synced its changes before us.
+            has_set = self._state_holder.sync()
+            if has_set is not None:
+                if has_set:
+                    msg = (
+                        f"The node '{self._node}' has successfully synced its local changes with "
+                        f"other nodes in the rendezvous '{self._settings.run_id}'."
+                    )
+                else:
+                    msg = (
+                        f"The node '{self._node}' has a stale state and failed to sync its local "
+                        f"changes with other nodes in the rendezvous '{self._settings.run_id}'."
+                    )
+
+                self._record(message=msg)
+                logger.debug(msg)
+
+            self._state = self._state_holder.state
+
+            ctx = _RendezvousContext(self._node, self._state, self._settings)
+
+            # Determine the next action to take based on the current state of
+            # the rendezvous.
+            action = state_handler(ctx, deadline)
+
+            if action == _Action.FINISH:
+                continue
+
+            if action == _Action.ERROR_CLOSED:
+                raise RendezvousClosedError
+
+            if action == _Action.ERROR_TIMEOUT:
+                raise RendezvousTimeoutError
+
+            if action == _Action.SYNC:
+                # Delay the execution by one second to avoid overloading the
+                # backend if we are asked to poll for state changes.
+                _delay(seconds=1)
+            else:
+                if action == _Action.KEEP_ALIVE:
+                    self._keep_alive()
+                elif action == _Action.ADD_TO_PARTICIPANTS:
+                    self._add_to_participants()
+                elif action == _Action.ADD_TO_WAIT_LIST:
+                    self._add_to_wait_list()
+                elif action == _Action.ADD_TO_REDUNDANCY_LIST:
+                    self._add_to_redundancy_list()
+                elif action == _Action.REMOVE_FROM_PARTICIPANTS:
+                    self._remove_from_participants()
+                elif action == _Action.REMOVE_FROM_WAIT_LIST:
+                    self._remove_from_wait_list()
+                elif action == _Action.REMOVE_FROM_REDUNDANCY_LIST:
+                    self._remove_from_redundancy_list()
+                    # update deadline since the node may participate in rendezvous process
+                    if update_deadline:
+                        deadline = update_deadline(self._settings.timeout.join)
+                elif action == _Action.MARK_RENDEZVOUS_COMPLETE:
+                    self._mark_rendezvous_complete()
+                elif action == _Action.MARK_RENDEZVOUS_CLOSED:
+                    self._mark_rendezvous_closed()
+
+                # Attempt to sync our changes back to other nodes.
+                self._state_holder.mark_dirty()
+
+    def _keep_alive(self) -> None:
+        msg = (
+            f"The node '{self._node}' updated its keep-alive heartbeat time for the rendezvous "
+            f"'{self._settings.run_id}'. Pending sync."
+        )
+        self._record(message=msg)
+        logger.debug(msg)
+
+        self._state.last_heartbeats[self._node] = datetime.now(timezone.utc)
+
+    def _add_to_participants(self) -> None:
+        msg = (
+            f"The node '{self._node}' added itself to the participants of round "
+            f"{self._state.round} of the rendezvous '{self._settings.run_id}'. Pending sync."
+        )
+        self._record(message=msg)
+        logger.debug(msg)
+
+        state = self._state
+
+        try:
+            state.wait_list.remove(self._node)
+        except KeyError:
+            pass
+
+        # The ranks of the participants will be set once the rendezvous is
+        # complete.
+        state.participants[self._node] = 0
+
+        self._keep_alive()
+
+        if len(state.participants) == self._settings.min_nodes:
+            state.deadline = (
+                datetime.now(timezone.utc) + self._settings.timeout.last_call
+            )
+
+        if len(state.participants) == self._settings.max_nodes:
+            self._mark_rendezvous_complete()
+
+    def _add_to_wait_list(self) -> None:
+        msg = (
+            f"The node '{self._node}' added itself to the wait list of round "
+            f"{self._state.round + 1} of the rendezvous '{self._settings.run_id}'. Pending sync."
+        )
+        self._record(message=msg)
+        logger.debug(msg)
+
+        if self._node in self._state.redundancy_list:
+            self._state.redundancy_list.remove(self._node)
+        self._state.wait_list.add(self._node)
+
+        self._keep_alive()
+
+    def _add_to_redundancy_list(self) -> None:
+        msg = (
+            f"The node '{self._node}' added itself to the redundancy list of round "
+            f"{self._state.round + 1} of the rendezvous '{self._settings.run_id}'. Pending sync."
+        )
+        self._record(message=msg)
+        logger.debug(msg)
+
+        self._state.redundancy_list.add(self._node)
+
+        self._keep_alive()
+
+    def _remove_from_participants(self) -> None:
+        msg = (
+            f"The node '{self._node}' removed itself from the participants of round "
+            f"{self._state.round} of the rendezvous '{self._settings.run_id}'. Pending sync."
+        )
+        self._record(message=msg)
+        logger.debug(msg)
+
+        state = self._state
+
+        del state.participants[self._node]
+
+        del state.last_heartbeats[self._node]
+
+        # Common epilogue shared with the sanitizer() function of
+        # _BackendRendezvousStateHolder.
+        _remove_participant_epilogue(state, self._settings)
+
+    def _remove_from_wait_list(self) -> None:
+        msg = (
+            f"The node '{self._node}' removed itself from the wait list of round "
+            f"{self._state.round + 1} of the rendezvous '{self._settings.run_id}'. Pending sync."
+        )
+        self._record(message=msg)
+        logger.debug(msg)
+
+        self._state.wait_list.remove(self._node)
+
+        del self._state.last_heartbeats[self._node]
+
+    def _remove_from_redundancy_list(self) -> None:
+        msg = (
+            f"The node '{self._node}' removed itself from the redundant list of round "
+            f"{self._state.round + 1} of the rendezvous '{self._settings.run_id}'. Pending sync."
+        )
+        self._record(message=msg)
+        logger.debug(msg)
+
+        self._state.redundancy_list.remove(self._node)
+
+        del self._state.last_heartbeats[self._node]
+
+    def _mark_rendezvous_complete(self) -> None:
+        msg = (
+            f"The node '{self._node}' marked round {self._state.round} of the rendezvous "
+            f"'{self._settings.run_id}' as complete. Pending sync."
+        )
+        self._record(message=msg, node_state=NodeState.SUCCEEDED)
+        logger.debug(msg)
+
+        state = self._state
+
+        state.complete = True
+        state.deadline = None
+
+        # Assign the ranks.
+        for rank, node in enumerate(sorted(state.participants)):
+            state.participants[node] = rank
+
+    def _mark_rendezvous_closed(self) -> None:
+        msg = (
+            f"The node '{self._node}' marked the rendezvous '{self._settings.run_id}' as closed. "
+            "Pending sync."
+        )
+        self._record(message=msg, node_state=NodeState.SUCCEEDED)
+        logger.debug(msg)
+
+        self._state.closed = True
+
+
+def _should_keep_alive(ctx: _RendezvousContext) -> bool:
+    """Determine whether a keep-alive heartbeat should be sent."""
+    try:
+        last_heartbeat = ctx.state.last_heartbeats[ctx.node]
+    except KeyError:
+        return False
+
+    return (
+        last_heartbeat <= datetime.now(timezone.utc) - ctx.settings.keep_alive_interval
+    )
+
+
+class _RendezvousExitOp:
+    """Represent a rendezvous exit operation."""
+
+    def __call__(self, ctx: _RendezvousContext, deadline: float) -> _Action:
+        if ctx.node in ctx.state.participants:
+            if time.monotonic() > deadline:
+                return _Action.ERROR_TIMEOUT
+            return _Action.REMOVE_FROM_PARTICIPANTS
+        return _Action.FINISH
+
+
+class _RendezvousJoinOp:
+    """Represent a rendezvous join operation."""
+
+    def __call__(self, ctx: _RendezvousContext, deadline: float) -> _Action:
+        state = ctx.state
+
+        # A closed rendezvous means that it no longer accepts new nodes.
+        if state.closed:
+            if ctx.node in state.redundancy_list:
+                msg = f"The rendezvous '{ctx.settings.run_id}' is closed, terminating pending rendezvous."
+                raise RendezvousGracefulExitError(msg)
+            return _Action.ERROR_CLOSED
+
+        if ctx.node in state.redundancy_list:
+            msg = f"The node {ctx.node} is in redundancy list"
+            logger.debug(msg)
+            # don't apply the timeout logic here, since we want to allow the node to rejoin
+            if len(state.participants) == ctx.settings.max_nodes:
+                if _should_keep_alive(ctx):
+                    return _Action.KEEP_ALIVE
+                else:
+                    return _Action.SYNC
+            else:
+                # transition to waiting state that will respect timeouts.
+                msg = f"The node {ctx.node} is removed from redundancy list"
+                logger.debug(msg)
+                return _Action.REMOVE_FROM_REDUNDANCY_LIST
+
+        is_participant = ctx.node in state.participants
+
+        # If we are part of the rendezvous and it is already complete there is
+        # no further action to take.
+        if state.complete and is_participant:
+            return _Action.FINISH
+
+        now = time.monotonic()
+        if now > deadline:
+            rollback_period = 5  # 5 seconds
+
+            # If we still have time to rollback (a short period on top of the
+            # operation deadline), try to remove ourself from the rendezvous.
+            # It is okay if we can't though as our keep-alive will eventually
+            # expire.
+            if now <= deadline + rollback_period:
+                # If we are part of the rendezvous, it means we couldn't find
+                # enough participants to complete it on time.
+                if is_participant:
+                    return _Action.REMOVE_FROM_PARTICIPANTS
+                # If we are in the wait list, it means we couldn't wait till the
+                # next round of the rendezvous.
+                if ctx.node in state.wait_list:
+                    return _Action.REMOVE_FROM_WAIT_LIST
+            return _Action.ERROR_TIMEOUT
+
+        if state.complete:
+            # If we are here, it means we are not part of the rendezvous. In
+            # case the rendezvous has capacity for additional participants add
+            # ourself to the wait list for the next round.
+            if len(state.participants) < ctx.settings.max_nodes:
+                if ctx.node not in state.wait_list:
+                    return _Action.ADD_TO_WAIT_LIST
+            elif len(state.participants) >= ctx.settings.max_nodes:
+                if (
+                    ctx.node not in state.redundancy_list
+                    and ctx.node not in state.wait_list
+                ):
+                    return _Action.ADD_TO_REDUNDANCY_LIST
+        elif is_participant:
+            # If the rendezvous has enough number of participants including us,
+            # check whether we have passed the rendezvous deadline. If yes,
+            # complete it.
+            if (
+                len(state.participants) >= ctx.settings.min_nodes
+                and len(state.participants) <= ctx.settings.max_nodes
+                and state.deadline is not None
+            ):
+                if state.deadline < datetime.now(timezone.utc):
+                    msg = (
+                        f"The node '{ctx.node}' marking the rendezvous complete, "
+                        f"quorum established within deadline"
+                    )
+                    logger.debug(msg)
+                    return _Action.MARK_RENDEZVOUS_COMPLETE
+                else:
+                    msg = f"The node '{ctx.node}' can't complete rendezvous: deadline reached"
+                    logger.debug(msg)
+            else:
+                msg = f"The node '{ctx.node}' can't complete rendezvous: not enough participants"
+                logger.debug(msg)
+        else:
+            # The rendezvous is not complete yet and we are not part of it. Try
+            # to join.
+            return _Action.ADD_TO_PARTICIPANTS
+
+        if _should_keep_alive(ctx):
+            return _Action.KEEP_ALIVE
+
+        # At this point either the rendezvous is not complete, but we are part
+        # of it, which means we have to wait for other participants to join; or
+        # the rendezvous is complete, but we are not part of it, which means we
+        # have to wait for the next round.
+        return _Action.SYNC
+
+
+class _RendezvousCloseOp:
+    """Represent a rendezvous close operation."""
+
+    def __call__(self, ctx: _RendezvousContext, deadline: float) -> _Action:
+        if ctx.state.closed:
+            return _Action.FINISH
+        if time.monotonic() > deadline:
+            return _Action.ERROR_TIMEOUT
+        return _Action.MARK_RENDEZVOUS_CLOSED
+
+
+class _RendezvousKeepAliveOp:
+    """Represent a rendezvous keep-alive update operation."""
+
+    def __call__(self, ctx: _RendezvousContext, deadline: float) -> _Action:
+        if _should_keep_alive(ctx):
+            if time.monotonic() > deadline:
+                return _Action.ERROR_TIMEOUT
+            return _Action.KEEP_ALIVE
+        return _Action.FINISH
+
+
+class DynamicRendezvousHandler(RendezvousHandler):
+    """Represent a handler that sets up a rendezvous among a set of nodes."""
+
+    # Static
+    _node_desc_generator = _NodeDescGenerator()
+
+    _this_node: _NodeDesc
+    _settings: RendezvousSettings
+    _backend_name: str
+    _store: Store
+    _state_holder: _RendezvousStateHolder
+    _op_executor: _RendezvousOpExecutor
+    _heartbeat_lock: threading.Lock
+    _keep_alive_timer: _PeriodicTimer | None
+
+    @classmethod
+    def from_backend(
+        cls,
+        run_id: str,
+        store: Store,
+        backend: RendezvousBackend,
+        min_nodes: int,
+        max_nodes: int,
+        local_addr: str | None = None,
+        timeout: RendezvousTimeout | None = None,
+        keep_alive_interval: int = 5,
+        keep_alive_max_attempt: int = 3,
+    ):
+        """Create a new :py:class:`DynamicRendezvousHandler`.
+
+        Args:
+            run_id:
+                The run id of the rendezvous.
+            store:
+                The C10d store to return as part of the rendezvous.
+            backend:
+                The backend to use to hold the rendezvous state.
+            min_nodes:
+                The minimum number of nodes to admit to the rendezvous.
+            max_nodes:
+                The maximum number of nodes to admit to the rendezvous.
+            local_addr:
+                The local node address.
+            timeout:
+                The timeout configuration of the rendezvous.
+            keep_alive_interval:
+                The amount of time a node waits before sending a heartbeat to keep
+                it alive in the rendezvous.
+            keep_alive_max_attempt:
+                The maximum number of failed heartbeat attempts after which a node
+                is considered dead.
+        """
+        # We associate each handler instance with a unique node descriptor.
+        node = cls._node_desc_generator.generate(local_addr)
+
+        settings = RendezvousSettings(
+            run_id,
+            min_nodes,
+            max_nodes,
+            timeout or RendezvousTimeout(),
+            keep_alive_interval=timedelta(seconds=keep_alive_interval),
+            keep_alive_max_attempt=keep_alive_max_attempt,
+        )
+
+        state_holder = _BackendRendezvousStateHolder(backend, settings)
+
+        return cls(node, settings, backend.name, store, state_holder)
+
+    def __init__(
+        self,
+        node: _NodeDesc,
+        settings: RendezvousSettings,
+        backend_name: str,
+        store: Store,
+        state_holder: _RendezvousStateHolder,
+    ) -> None:
+        if not settings.run_id:
+            raise ValueError("The run id must be a non-empty string.")
+
+        if settings.min_nodes < 1:
+            raise ValueError(
+                f"The minimum number of nodes ({settings.min_nodes}) must be greater than zero."
+            )
+
+        if settings.max_nodes < settings.min_nodes:
+            raise ValueError(
+                f"The maximum number of nodes ({settings.max_nodes}) must be greater than or equal "
+                f"to the minimum number of nodes ({settings.min_nodes})."
+            )
+
+        self._this_node = node
+
+        self._settings = settings
+
+        self._backend_name = backend_name
+
+        self._store = store
+
+        self._state_holder = state_holder
+
+        self._op_executor = _DistributedRendezvousOpExecutor(
+            self._this_node, self._state_holder, self._settings
+        )
+
+        self._heartbeat_lock = threading.Lock()
+
+        self._keep_alive_timer = None
+
+        # Cached shared store server reference
+        self._shared_tcp_store_server: dist.Store | None = None
+
+        self._bootstrap_store_info: RendezvousStoreInfo | None = None
+
+    def _record(
+        self,
+        message: str,
+        node_state: NodeState = NodeState.RUNNING,
+        rank: int | None = None,
+    ) -> None:
+        construct_and_record_rdzv_event(
+            name=f"{self.__class__.__name__}.{get_method_name()}",
+            run_id=self._settings.run_id,
+            message=message,
+            node_state=node_state,
+            hostname=self._this_node.addr,
+            pid=self._this_node.pid,
+            local_id=self._this_node.local_id,
+            rank=rank,
+        )
+
+    def _create_tcp_store_server(self, master_addr, master_port) -> dist.TCPStore:
+        return dist.TCPStore(
+            host_name=master_addr,
+            port=master_port,
+            is_master=True,
+            multi_tenant=True,
+        )
+
+    @property
+    def settings(self) -> RendezvousSettings:
+        """Get the settings of the rendezvous."""
+        return self._settings
+
+    def get_backend(self) -> str:
+        """See base class."""
+        return self._backend_name
+
+    @property
+    def use_agent_store(self) -> bool:
+        """See base class."""
+        return os.getenv("TORCH_DISABLE_SHARE_RDZV_TCP_STORE", "0") != "1"
+
+    def next_rendezvous(self) -> RendezvousInfo:
+        """See base class."""
+        msg = (
+            f"The node '{self._this_node}' attempts to join the next round of the rendezvous "
+            f"'{self._settings.run_id}'."
+        )
+        self._record(message=msg)
+        logger.info(msg)
+
+        try:
+            self._stop_heartbeats()
+
+            # Delay the execution for a small random amount of time if this is our
+            # first run. This will slightly skew the rendezvous attempts across the
+            # nodes and reduce the load on the backend.
+            if self._state_holder.state.round == 0:
+                _delay(seconds=(0, 0.3))
+
+            exit_op = _RendezvousExitOp()
+            join_op = _RendezvousJoinOp()
+
+            deadline = self._get_deadline(self._settings.timeout.join)
+            self._op_executor.run(exit_op, deadline)
+            self._op_executor.run(join_op, deadline, self._get_deadline)
+
+            self._start_heartbeats()
+
+            rank, world_size = self._get_world()
+            store = self._get_store()
+
+        except Exception as e:
+            self._record(
+                message=f"{type(e).__name__}: {str(e)}",
+                node_state=NodeState.FAILED,
+            )
+            raise
+
+        msg = (
+            f"The node '{self._this_node}' has joined round {self._state_holder.state.round} of "
+            f"the rendezvous '{self._settings.run_id}' as rank {rank} in a world of size "
+            f"{world_size}."
+        )
+        self._record(message=msg, rank=rank)
+        logger.info(msg)
+
+        # opt-out option of TCPStore sharing
+        if os.getenv("TORCH_DISABLE_SHARE_RDZV_TCP_STORE", "0") == "1":
+            bootstrap_store_info = RendezvousStoreInfo.build(
+                rank, store, local_addr=self._this_node.addr
+            )
+            return RendezvousInfo(
+                store,
+                rank,
+                world_size,
+                bootstrap_store_info,
+            )
+
+        # This will only be hit when TCPStore sharing is enabled.
+        if self._bootstrap_store_info is None:
+            # To avoid race in get_free_port because we release the port after the call,
+            # we want to create a TCPStore server soon afterwards.
+            server_port = 0
+            if rank == 0:
+                self._shared_tcp_store_server = self._create_tcp_store_server(
+                    self._this_node.addr, server_port
+                )
+                server_port = self._shared_tcp_store_server.port
+            self._bootstrap_store_info = RendezvousStoreInfo.build(
+                rank,
+                store,
+                local_addr=self._this_node.addr,
+                server_port=server_port,  # For non-0 rank, this is a no-op
+            )
+
+        assert self._bootstrap_store_info is not None
+        if rank == 0:
+            assert self._shared_tcp_store_server is not None
+
+        return RendezvousInfo(
+            store,
+            rank,
+            world_size,
+            self._bootstrap_store_info,  # type: ignore[assignment]
+        )
+
+    def is_closed(self) -> bool:
+        """See base class."""
+        try:
+            with self._heartbeat_lock:
+                self._state_holder.sync()
+
+                return self._state_holder.state.closed
+
+        except Exception as e:
+            self._record(
+                message=f"{type(e).__name__}: {str(e)}",
+                node_state=NodeState.FAILED,
+            )
+            raise
+
+    def set_closed(self) -> None:
+        """See base class."""
+        try:
+            with self._heartbeat_lock:
+                self._close()
+        except Exception as e:
+            self._record(
+                message=f"{type(e).__name__}: {str(e)}",
+                node_state=NodeState.FAILED,
+            )
+            raise
+
+    def num_nodes_waiting(self) -> int:
+        """See base class."""
+        try:
+            with self._heartbeat_lock:
+                self._state_holder.sync()
+
+                return len(self._state_holder.state.wait_list)
+
+        except Exception as e:
+            self._record(
+                message=f"{type(e).__name__}: {str(e)}",
+                node_state=NodeState.FAILED,
+            )
+            raise
+
+    def get_run_id(self) -> str:
+        """See base class."""
+        return self._settings.run_id
+
+    def shutdown(self) -> bool:
+        """See base class."""
+        self._stop_heartbeats()
+
+        try:
+            self._close()
+
+            return True
+        except RendezvousError as ex:
+            msg = (
+                f"The node '{self._this_node}' has failed to shutdown the rendezvous "
+                f"'{self._settings.run_id}' due to an error of type {type(ex).__name__}."
+            )
+            self._record(message=msg, node_state=NodeState.FAILED)
+            logger.warning(msg)
+
+            return False
+        except Exception as e:
+            self._record(
+                message=f"{type(e).__name__}: {str(e)}",
+                node_state=NodeState.FAILED,
+            )
+            raise
+
+    def _close(self) -> None:
+        op = _RendezvousCloseOp()
+
+        deadline = self._get_deadline(self._settings.timeout.close)
+
+        self._op_executor.run(op, deadline)
+
+        msg = f"The node '{self._this_node}' has closed the rendezvous '{self._settings.run_id}'."
+        self._record(message=msg, node_state=NodeState.SUCCEEDED)
+        logger.info(msg)
+
+    @staticmethod
+    def _keep_alive_weak(weak_self) -> None:
+        self = weak_self()
+        if self is not None:
+            self._keep_alive()
+
+    def _keep_alive(self) -> None:
+        with self._heartbeat_lock:
+            op = _RendezvousKeepAliveOp()
+
+            deadline = self._get_deadline(self._settings.timeout.heartbeat)
+
+            try:
+                self._op_executor.run(op, deadline)
+
+                msg = (
+                    f"The node '{self._this_node}' has sent a keep-alive heartbeat to the rendezvous "
+                    f"'{self._settings.run_id}'."
+                )
+                self._record(message=msg)
+                logger.debug(msg)
+            except RendezvousError as ex:
+                msg = (
+                    f"The node '{self._this_node}' has failed to send a keep-alive heartbeat to the "
+                    f"rendezvous '{self._settings.run_id}' due to an error of type {type(ex).__name__}."
+                )
+                self._record(message=msg, node_state=NodeState.FAILED)
+                logger.warning(msg)
+
+    def _start_heartbeats(self) -> None:
+        self._keep_alive_timer = _PeriodicTimer(
+            self._settings.keep_alive_interval, self._keep_alive_weak, weakref.ref(self)
+        )
+
+        self._keep_alive_timer.set_name(
+            f"RendezvousKeepAliveTimer_{self._this_node.local_id}"
+        )
+
+        self._keep_alive_timer.start()
+
+    def _stop_heartbeats(self) -> None:
+        if self._keep_alive_timer is None:
+            return
+
+        self._keep_alive_timer.cancel()
+
+    def _get_world(self) -> tuple[int, int]:
+        state = self._state_holder.state
+
+        return state.participants[self._this_node], len(state.participants)
+
+    def _wrap_store(self, store: Store) -> Store:
+        key_prefix = (
+            f"torch.rendezvous.{self._settings.run_id}.{self._state_holder.state.round}"
+        )
+
+        return dist.PrefixStore(key_prefix, store)
+
+    def _get_store(self) -> Store:
+        return self._wrap_store(self._store)
+
+    def _get_deadline(self, timeout: timedelta) -> float:
+        return time.monotonic() + timeout.total_seconds()
+
+
+def _get_timeout(params: RendezvousParameters, key: str) -> timedelta | None:
+    timeout = params.get_as_int(key + "_timeout")
+    if timeout is None:
+        return None
+    return timedelta(seconds=timeout)
+
+
+def create_handler(
+    store: Store, backend: RendezvousBackend, params: RendezvousParameters
+) -> DynamicRendezvousHandler:
+    """Create a new :py:class:`DynamicRendezvousHandler` from the specified parameters.
+
+    Args:
+        store:
+            The C10d store to return as part of the rendezvous.
+        backend:
+            The backend to use to hold the rendezvous state.
+
+    +-------------------+------------------------------------------------------+
+    | Parameter         | Description                                          |
+    +===================+======================================================+
+    | join_timeout      | The total time, in seconds, within which the         |
+    |                   | rendezvous is expected to complete. Defaults to 600  |
+    |                   | seconds.                                             |
+    +-------------------+------------------------------------------------------+
+    | last_call_timeout | An additional wait amount, in seconds, before        |
+    |                   | completing the rendezvous once the minimum number of |
+    |                   | nodes has been reached. Defaults to 30 seconds.      |
+    +-------------------+------------------------------------------------------+
+    | close_timeout     | The time, in seconds, within which the rendezvous is |
+    |                   | expected to close after a call to                    |
+    |                   | :py:meth:`RendezvousHandler.set_closed` or           |
+    |                   | :py:meth:`RendezvousHandler.shutdown`. Defaults to   |
+    |                   | 30 seconds.                                          |
+    +-------------------+------------------------------------------------------+
+    | heartbeat         | The time, in seconds, within which a keep-alive      |
+    |                   | heartbeat is expected to complete                    |
+    +-------------------+------------------------------------------------------+
+    """
+    try:
+        timeout = RendezvousTimeout(
+            _get_timeout(params, "join"),
+            _get_timeout(params, "last_call"),
+            _get_timeout(params, "close"),
+            _get_timeout(params, "heartbeat"),
+        )
+        keep_alive_interval = params.get_as_int("keep_alive_interval", 5)
+        if keep_alive_interval is None:
+            raise TypeError(
+                "You passed 'keep_alive_interval=None' as a rendezvous configuration option"
+            )
+        keep_alive_max_attempt = params.get_as_int("keep_alive_max_attempt", 3)
+        if keep_alive_max_attempt is None:
+            raise TypeError(
+                "You passed 'keep_alive_max_attempt=None' as a rendezvous configuration option"
+            )
+
+        return DynamicRendezvousHandler.from_backend(
+            params.run_id,
+            store,
+            backend,
+            params.min_nodes,
+            params.max_nodes,
+            params.local_addr,
+            timeout,
+            keep_alive_interval=keep_alive_interval,
+            keep_alive_max_attempt=keep_alive_max_attempt,
+        )
+    except Exception as e:
+        construct_and_record_rdzv_event(
+            message=f"{type(e).__name__}: {str(e)}",
+            run_id=params.run_id,
+            node_state=NodeState.FAILED,
+        )
+        raise
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/etcd_rendezvous.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/etcd_rendezvous.py
new file mode 100644
index 0000000000000000000000000000000000000000..93a7073bed87a33a7f2ba0dfb64c7daa57b9d55f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/etcd_rendezvous.py
@@ -0,0 +1,1080 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import json
+import logging
+import sys
+import threading
+import time
+
+
+try:
+    import etcd  # type: ignore[import]
+except ModuleNotFoundError:
+    from . import _etcd_stub as etcd
+
+from torch.distributed.elastic.rendezvous import (
+    RendezvousClosedError,
+    RendezvousError,
+    RendezvousHandler,
+    RendezvousInfo,
+    RendezvousParameters,
+    RendezvousStoreInfo,
+    RendezvousTimeoutError,
+)
+
+from .etcd_store import cas_delay, EtcdStore
+from .utils import parse_rendezvous_endpoint
+
+
+__all__ = [
+    "EtcdRendezvousRetryableFailure",
+    "EtcdRendezvousRetryImmediately",
+    "EtcdRendezvousHandler",
+    "EtcdRendezvous",
+    "create_rdzv_handler",
+]
+
+_log_fmt = logging.Formatter("%(levelname)s %(asctime)s %(message)s")
+_log_handler = logging.StreamHandler(sys.stderr)
+_log_handler.setFormatter(_log_fmt)
+
+logger = logging.getLogger(__name__)
+logger.propagate = False
+logger.setLevel(logging.INFO)
+logger.addHandler(_log_handler)
+
+
+# Retryable failure exception means the we were too late to make
+# a desired state transition (e.g. because of a race condition),
+# and should now restart from the beginning.
+# A small delay is recommended to avoid spamming Etcd.
+class EtcdRendezvousRetryableFailure(Exception):
+    pass
+
+
+# Similar to retryable failure, but the new state we observed suggests we
+# can re-try immediately, i.e. without a need for "safety delay".
+class EtcdRendezvousRetryImmediately(Exception):
+    pass
+
+
+# Default timeout for the rendezvous.
+_DEFAULT_TIMEOUT: int = 600  # 10 minutes
+
+# Additional waiting time after reaching the minimum number of nodes
+# in case the rendezvous is elastic (min != max).
+_DEFAULT_LAST_CALL_TIMEOUT: int = 30  # 30 seconds
+
+# Various constants used internally in EtcdRendezvous
+CONST_ETCD_SETUP_TTL = 5
+CONST_ETCD_FROZEN_TTL = 10
+CONST_ETCD_JOINABLE_EPHEMERAL_TTL = 10
+
+# Ephemeral node TTL for worker's keep-alive key:
+CONST_WORKER_KEEPALIVE_TTL = 10
+
+# TTL for the ephemeral run_id-specific directory. All rendezvous state data
+# for a specific run_id (job instance) is contained within directory.
+# Its only role is to clean-up rendezvous data from old runs (for the case when
+# etcd server is persistent), and has no affect on correctness, but should be
+# larger than any timeouts that a worker process is expected to survive:
+CONST_RUNID_SUBROOT_TTL = 7200  # 2 hours
+
+
+class EtcdRendezvousHandler(RendezvousHandler):
+    """
+    Implements a
+    :py:class:`torch.distributed.elastic.rendezvous.RendezvousHandler` interface
+    backed by
+    :py:class:`torch.distributed.elastic.rendezvous.etcd_rendezvous.EtcdRendezvous`.
+    ``EtcdRendezvousHandler`` uses a URL to configure the type of rendezvous to
+    use and to pass implementation specific configurations to the rendezvous
+    module. The basic etcd rendezvous configuration URL looks like the following
+    ::
+
+     etcd://:/?min_workers=&max_workers=  # noqa: W605
+
+     -- example --
+
+     etcd://localhost:2379/1234?min_workers=1&max_workers=3
+
+    The URL above is interpreted as follows:
+
+    1. Use the rendezvous handler that is registered with the ``etcd``
+       scheme
+    2. The ``etcd`` endpoint to use is ``localhost:2379``
+    3. ``job_id == 1234`` is used as the prefix in etcd (this allows one to
+       share a common etcd server for multiple jobs so long as the
+       ``job_ids`` are guaranteed to be unique). Note that the job id can be
+       any string (e.g. does not need to be a number) as long as it is
+       unique.
+    4. ``min_workers=1`` and ``max_workers=3`` specifies a range for
+       membership size - Torch Distributed Elastic starts running the job as
+       long as the cluster size is greater than or equal to ``min_workers``
+       and admits up to ``max_workers`` into the cluster.
+
+    Below are a full list of the parameters that can be passed to etcd
+    rendezvous:
+
+    +--------------------------------------------+--------------------------+
+    | Parameter                                  | Description              |
+    +============================================+==========================+
+    | min_workers                                | minimum number of        |
+    |                                            | workers for the          |
+    |                                            | rendezvous to be valid   |
+    +--------------------------------------------+--------------------------+
+    | max_workers                                | maximum number of        |
+    |                                            | workers to admit         |
+    +--------------------------------------------+--------------------------+
+    | timeout                                    | total timeout within     |
+    |                                            | which next_rendezvous is |
+    |                                            | expected to succeed      |
+    |                                            | (default 600s)           |
+    +--------------------------------------------+--------------------------+
+    | last_call_timeout                          | additional wait amount   |
+    |                                            | ("last call") after min  |
+    |                                            | number of workers has    |
+    |                                            | been reached (defaults   |
+    |                                            | to 30s)                  |
+    +--------------------------------------------+--------------------------+
+    | etcd_prefix                                | path prefix (from etcd   |
+    |                                            | root), inside which all  |
+    |                                            | etcd nodes will be       |
+    |                                            | created (defaults to     |
+    |                                            | ``/torchelastic/p2p``)   |
+    +--------------------------------------------+--------------------------+
+    """
+
+    def __init__(self, rdzv_impl: "EtcdRendezvous", local_addr: str | None):
+        """
+        Args:
+            rdzv_impl: the implementation of the rendezvous
+            local_addr: the local address of the current node
+        """
+
+        self._rdzv_impl = rdzv_impl
+        self._local_addr = local_addr
+
+    def __del__(self):
+        # TODO: look into using weakref here instead.
+        del self._rdzv_impl
+
+    def get_backend(self) -> str:
+        return "etcd"
+
+    def next_rendezvous(self):
+        rdzv_version, rank, world_size = self._rdzv_impl.rendezvous_barrier()
+
+        logger.info("Creating EtcdStore as the c10d::Store implementation")
+        store = self._rdzv_impl.setup_kv_store(rdzv_version)
+
+        bootstrap_store_info = RendezvousStoreInfo.build(
+            rank, store, local_addr=self._local_addr
+        )
+        return RendezvousInfo(store, rank, world_size, bootstrap_store_info)
+
+    def is_closed(self):
+        try:
+            _, state = self._rdzv_impl.get_rdzv_state()
+            return state["status"] == "closed"
+        except etcd.EtcdKeyNotFound:
+            # No rendezvous state, so it cannot be closed.
+            return False
+
+    def set_closed(self):
+        self._rdzv_impl.set_closed()
+
+    def num_nodes_waiting(self):
+        try:
+            _, state = self._rdzv_impl.get_rdzv_state()
+            if state["status"] == "final":
+                return state["num_workers_waiting"]
+        except etcd.EtcdKeyNotFound:
+            pass
+        return 0
+
+    def get_run_id(self) -> str:
+        return self._rdzv_impl._run_id
+
+    def shutdown(self) -> bool:
+        try:
+            self.set_closed()
+            return True
+        except BaseException:  # noqa: B036
+            logger.warning("Shutdown failed", exc_info=True)
+            return False
+
+
+# TODO: we should probably handle a few additional errors,
+# like EtcdLeaderElectionInProgress and EtcdWatcherCleared. These are
+# only relevant for multi-node Etcd ensemble. A simple retry would work,
+# but is verbose to add everywhere. Consider wrapping the client calls
+# into auto-retry for these errors?
+#
+class EtcdRendezvous:
+    """A rendezvous implementation that uses `etcd `__ as the backend store."""
+
+    def __init__(
+        self,
+        client,
+        prefix,
+        run_id,
+        num_min_workers,
+        num_max_workers,
+        timeout,
+        last_call_timeout,
+    ):
+        self.client = client
+        logger.info("Etcd machines: %s", self.client.machines)
+
+        self._prefix = prefix
+        self._run_id = run_id
+        self._num_min_workers = num_min_workers
+        self._num_max_workers = num_max_workers
+        self._timeout = timeout
+        self._last_call_timeout = last_call_timeout
+
+        # For cleaning up TTL refresher threads (for ephemeral keys)
+        self._lease_run_id_stop = None
+        self._lease_this_rank_stop = None
+
+        if not self._prefix.endswith("/"):
+            self._prefix += "/"
+
+        # Setup a permanent prefix dir, if didn't exist
+        if self._prefix != "/":
+            self.create_path_if_not_exists(self._prefix)
+
+        # Lease a "sub-root" node specific to this job instance (run_id)
+        self.create_path_if_not_exists(self.get_path(""), ttl=CONST_RUNID_SUBROOT_TTL)
+        self._lease_run_id_stop = self.setup_lease_renewal(
+            self.get_path(""), ttl=CONST_RUNID_SUBROOT_TTL
+        )
+
+        # Subdir for all rendezvous work
+        self.create_path_if_not_exists(self.get_path("/rdzv"))
+
+        # Create a rendezvous version counter, if doesn't exist
+        try:
+            self.client.write(
+                key=self.get_path("/rdzv/version_counter"), value="0", prevExist=False
+            )
+        except etcd.EtcdAlreadyExist:
+            pass
+
+    def __del__(self):
+        # TODO: look into using weakref here instead.
+        if self._lease_run_id_stop is not None:
+            self._lease_run_id_stop.set()
+
+        if self._lease_this_rank_stop is not None:
+            self._lease_this_rank_stop.set()
+
+    def rendezvous_barrier(self):
+        """
+        Main entry point for next rendezvous.
+
+        This method is blocking until rendezvous succeeds or a timeout occurs.
+
+        Returns:
+             ``(rdzv_version, rank, world_size)``
+
+        Raises:
+            RendezvousTimeoutError - timeout waiting for rendezvous
+            RendezvousClosedError - rendezvous is or was closed while waiting
+            RendezvousError - other persistent errors that
+             render the rendezvous non-retryable
+        """
+        self._rendezvous_deadline = time.time() + self._timeout
+        while True:
+            if time.time() > self._rendezvous_deadline:
+                raise RendezvousTimeoutError
+
+            logger.info("Attempting to join next rendezvous")
+            try:
+                # Dis-own our lease in the previous rendezvous, if exists
+                if self._lease_this_rank_stop is not None:
+                    self._lease_this_rank_stop.set()
+
+                return self.init_phase()
+
+            except EtcdRendezvousRetryImmediately:
+                # The type of failure suggests we can retry without delay
+                pass
+
+            except EtcdRendezvousRetryableFailure:
+                # In case of retryable failure, wait a small delay
+                # to avoid spamming etcd
+                time.sleep(1)
+
+            except RendezvousTimeoutError:
+                logger.info("Rendezvous timeout occurred in EtcdRendezvousHandler")
+                raise
+
+            except RendezvousClosedError:
+                logger.info(
+                    "Rendezvous for run_id=%s was observed to be closed", self._run_id
+                )
+                raise
+
+            except RendezvousError:
+                raise
+
+            except Exception as e:
+                # In case of a general exception, wait a small delay
+                # to avoid spamming etcd
+                # FIXME: there are a few things that fall under this like
+                # etcd.EtcdKeyNotFound, etc, which could be handled more explicitly.
+                logger.info("Rendezvous attempt failed, will retry. Reason: %s", e)  # noqa: G200
+                time.sleep(1)
+
+    def init_phase(self):
+        """
+        Initially, the rendezvous state is expected to be one of:
+
+        1. empty (non-existent) - in this case we try to create a new one.
+        2. joinable - we try to join it.
+        3. final - we announce ourselves as waiting, and go into monitoring mode
+
+        Any other state is considered transitional, and will be retried after
+        a short delay.
+
+        Returns:
+            ``(rdzv_version, rank, world_size)``
+
+        Raises:
+            RendezvousClosedError - current rendezvous was/is closed
+            EtcdRendezvousRetryableFailure - observed some intermediate
+             state, which is best handled by retrying later
+        """
+        try:
+            active_version = self.try_create_rendezvous()
+            state = json.loads(active_version.value)
+            logger.info("New rendezvous state created: %s", state)
+        except etcd.EtcdAlreadyExist:
+            active_version, state = self.get_rdzv_state()
+            # Note: it is possible for above query to fail (etcd.EtcdKeyNotFound),
+            # but this is ok for us - just means we'll restart from beginning.
+            logger.info("Observed existing rendezvous state: %s", state)
+
+        if state["status"] == "closed":
+            raise RendezvousClosedError
+
+        if state["status"] == "joinable":
+            return self.join_phase(state["version"])
+
+        if state["status"] == "final":
+            self.handle_existing_rendezvous(state["version"])
+            raise EtcdRendezvousRetryImmediately
+
+        self.try_wait_for_state_change(etcd_index=active_version.etcd_index + 1)
+        raise EtcdRendezvousRetryableFailure
+
+    def join_phase(self, expected_version):
+        """
+        We observed a rendezvous state in 'joinable' state, and attempt to join this
+        particular version, and then wait for all other peers to join.
+        """
+        # Failure to join will propagate an exception, causing a re-entry.
+        active_version, this_rank = self.join_rendezvous(expected_version)
+        state = json.loads(active_version.value)
+        logger.info(
+            "Joined rendezvous version %s as rank %s. Full state: %s",
+            state["version"],
+            this_rank,
+            state,
+        )
+
+        # If this worker was first to reach num_min_workers requirement,
+        # and rendezvous is still joinable (therefore it is elastic),
+        # then this worker will be responsible for waiting out the "last call"
+        # timeout and closing (i.e. transitioning to 'frozen') the rendezvous
+        # afterwards.
+        # As a safety against a potential failure of this worker (during the
+        # last call timeout), the rendezvous state is made ephemeral
+        # when min_num_workers is reached.
+
+        if this_rank == self._num_min_workers - 1 and state["status"] == "joinable":
+            logger.info("Rank %s is responsible for join last call.", this_rank)
+            last_call_deadline = time.time() + self._last_call_timeout
+            self.handle_join_last_call(expected_version, last_call_deadline)
+            logger.info("Rank %s finished join last call.", this_rank)
+
+        # Wait for rendezvous state to be frozen, which means a fixed set of peers
+        logger.info("Waiting for remaining peers.")
+        active_version = self.wait_for_peers(expected_version)
+        state = json.loads(active_version.value)
+
+        assert state["version"] == expected_version, (
+            "Logic error: failed to observe version mismatch"
+        )
+
+        return self.confirm_phase(expected_version, this_rank)
+
+    def confirm_phase(self, expected_version, this_rank):
+        """
+        Once the rendezvous state transitions from 'joinable' to 'frozen',
+        we have every participant confirm their membership and setup per-member
+        keep-alive TTL keys, and then wait for all other participants to confirm,
+        which would then successfully conclude this rendezvous.
+        """
+        logger.info("All peers arrived. Confirming membership.")
+        self.confirm_membership(expected_version, this_rank)
+
+        logger.info("Waiting for confirmations from all peers.")
+        active_version = self.wait_for_final(expected_version)
+        state = json.loads(active_version.value)
+
+        logger.info(
+            "Rendezvous version %s is complete. Final state: %s",
+            state["version"],
+            state,
+        )
+
+        # Rendezvous version number; our rank in it; world size
+        return state["version"], this_rank, len(state["participants"])
+
+    def handle_existing_rendezvous(self, expected_version):
+        """
+        Handle the case when there's an existing (state 'final) rendezvous already
+        in place, and we have to announce ourselves waiting, and wait until
+        the next rendezvous opportunity.
+        """
+        # If state is 'final' -> increment num_workers_waiting
+        # Then, observe state changes:
+        #   1. if it's no longer final -> bail out and re-try
+        #   2. if keep alives are missing, destroy it and bail out.
+        active_state = self.announce_self_waiting(expected_version)
+        logger.info(
+            "Added self to waiting list. Rendezvous full state: %s", active_state.value
+        )
+
+        self.wait_for_rendezvous_to_free(expected_version)
+        logger.info(
+            "Previously existing rendezvous state changed. Will re-try joining."
+        )
+
+    def try_create_rendezvous(self):
+        """
+        Create new rendezvous state or raise an exception that indicates an unexpected state (e.g. already exists).
+
+        Raises:
+             RendezvousError - on unexpected state
+        """
+        # Initially active_version is ephemeral - this is to handle the
+        # possibility that might fail to complete the setup transaction,
+        # i.e. the transition "setup" -> "joinable".
+        active_version = self.client.write(
+            key=self.get_path("/rdzv/active_version"),
+            value=json.dumps({"status": "setup"}),
+            prevExist=False,
+            ttl=CONST_ETCD_SETUP_TTL,
+        )
+
+        try:
+            version_counter = self.client.get(self.get_path("/rdzv/version_counter"))
+            version_counter.value = str(int(version_counter.value) + 1)
+            self.client.update(version_counter)
+        except (etcd.EtcdKeyNotFound, etcd.EtcdCompareFailed) as e:
+            raise RendezvousError(
+                "Unexpected state of EtcdRendezvousHandler, worker needs to die."
+            ) from e
+
+        # Any failure below results in declaring a retryable rendezvous failure.
+        # The ephemeral /rdzv/active_version will expire and someone can then
+        # re-try the setup process.
+
+        # Create directory node for participant data
+        self.client.write(
+            key=self.get_path(f"/rdzv/v_{version_counter.value}"),
+            value=None,
+            dir=True,
+            prevExist=False,
+        )
+
+        # Publish rendezvous version and signal it is ready-to-be-joined.
+        # If rendezvous was set closed just before this, a retry will happen,
+        # where the closed condition will be handled.
+        return self.client.test_and_set(
+            key=self.get_path("/rdzv/active_version"),
+            value=json.dumps(
+                {
+                    "status": "joinable",
+                    "version": version_counter.value,
+                    "participants": [],
+                }
+            ),
+            prev_value=active_version.value,
+        )
+
+    def join_rendezvous(self, expected_version):
+        """Helper method for the join phase."""
+        # Use compare-and-swap to add self to rendezvous state:
+        while True:
+            cas_delay()
+            active_version, state = self.get_rdzv_state()
+
+            if state["status"] != "joinable":
+                raise EtcdRendezvousRetryableFailure(
+                    "Rendezvous state became non-joinable before we could join. "
+                    "Must join next one."
+                )
+
+            if state["version"] != expected_version:
+                raise EtcdRendezvousRetryImmediately(
+                    "Rendezvous version changed. Must try join the new one."
+                )
+
+            assert len(state["participants"]) < self._num_max_workers, (
+                "Logic error: joinable rendezvous should always have space left"
+            )
+
+            this_rank = len(state["participants"])
+            state["participants"].append(this_rank)
+
+            # When reaching min workers, or changing state to frozen, we'll set
+            # the active_version node to be ephemeral.
+            set_ttl: int | None = None
+            if len(state["participants"]) == self._num_max_workers:
+                state["status"] = "frozen"
+                state["keep_alives"] = []
+                set_ttl = CONST_ETCD_FROZEN_TTL
+            elif len(state["participants"]) >= self._num_min_workers:
+                set_ttl = CONST_ETCD_JOINABLE_EPHEMERAL_TTL
+
+            try:
+                # Compare-and-swap.
+                active_version = self.client.test_and_set(
+                    key=self.get_path("/rdzv/active_version"),
+                    value=json.dumps(state),
+                    prev_value=active_version.value,
+                    ttl=set_ttl,
+                )
+                # We succeeded joining.
+                return active_version, this_rank
+
+            except etcd.EtcdCompareFailed:
+                logger.info("Join rendezvous CAS unsuccessful, retrying")
+
+    def wait_for_peers(self, expected_version):
+        """Helper method for the join phase."""
+        active_version, state = self.get_rdzv_state()
+        while True:
+            if state["status"] == "frozen" and state["version"] == expected_version:
+                # Success, all peers arrived.
+                return active_version
+
+            elif state["status"] == "joinable" and state["version"] == expected_version:
+                # Continue waiting for any interesting events.
+                active_version, state = self.try_wait_for_state_change(
+                    etcd_index=active_version.etcd_index + 1
+                )
+
+            else:
+                # No valid transition possible at this point
+                raise EtcdRendezvousRetryableFailure(
+                    "Rendezvous state transition no longer possible. Must re-enter."
+                )
+
+    def confirm_membership(self, expected_version, this_rank):
+        """Helper method for the confirm phase."""
+        # Compare-and-swap loop
+        while True:
+            cas_delay()
+            active_version, state = self.get_rdzv_state()
+
+            if state["status"] != "frozen":
+                raise EtcdRendezvousRetryImmediately(
+                    "Rendezvous no longer frozen, before we confirmed. "
+                    "Must join next one"
+                )
+            if state["version"] != expected_version:
+                raise EtcdRendezvousRetryImmediately(
+                    "Rendezvous version changed. Must try join the new one."
+                )
+
+            this_lease_key = self.get_path(
+                f"/rdzv/v_{expected_version}/rank_{this_rank}"
+            )
+            self.client.set(this_lease_key, value=None, ttl=CONST_WORKER_KEEPALIVE_TTL)
+
+            state["keep_alives"].append(this_lease_key)
+            if len(state["keep_alives"]) == len(state["participants"]):
+                # Everyone confirmed (this rank is last to do so)
+                state["status"] = "final"
+                state["num_workers_waiting"] = 0
+                finalize = True
+            else:
+                finalize = False
+
+            try:
+                # Compare-and-swap. If new state is still frozen, keep it ephemeral.
+                active_version = self.client.test_and_set(
+                    key=self.get_path("/rdzv/active_version"),
+                    value=json.dumps(state),
+                    prev_value=active_version.value,
+                    ttl=None if finalize else CONST_ETCD_FROZEN_TTL,
+                )
+
+                self._lease_this_rank_stop = self.setup_lease_renewal(
+                    this_lease_key, ttl=CONST_WORKER_KEEPALIVE_TTL
+                )
+                return active_version
+
+            except etcd.EtcdCompareFailed:
+                logger.info("Confirm membership CAS unsuccessful, retrying")
+
+    def wait_for_final(self, expected_version):
+        """Helper method for the confirm phase."""
+        active_version, state = self.get_rdzv_state()
+        while True:
+            if state["status"] == "final" and state["version"] == expected_version:
+                # Success. This rendezvous is final, and we accept it.
+                return active_version
+
+            elif state["status"] == "frozen" and state["version"] == expected_version:
+                # Continue waiting for any interesting events.
+                active_version, state = self.try_wait_for_state_change(
+                    etcd_index=active_version.etcd_index + 1
+                )
+
+            else:
+                # No valid transition possible at this point
+                raise EtcdRendezvousRetryableFailure(
+                    "Rendezvous state transition no longer possible. Must re-enter."
+                )
+
+    def announce_self_waiting(self, expected_version):
+        """
+        Announce this worker is waiting (via num_workers_waiting counter) to join next
+        rendezvous, but only if state and version match.
+        """
+        while True:
+            cas_delay()
+            active_version, state = self.get_rdzv_state()
+
+            if state["status"] != "final" or state["version"] != expected_version:
+                raise EtcdRendezvousRetryImmediately
+
+            # Increment counter to signal an additional waiting worker.
+            state["num_workers_waiting"] += 1
+
+            try:
+                active_version = self.client.test_and_set(
+                    key=self.get_path("/rdzv/active_version"),
+                    value=json.dumps(state),
+                    prev_value=active_version.value,
+                )
+                return active_version
+
+            except etcd.EtcdCompareFailed:
+                logger.info("Announce self as waiting CAS unsuccessful, retrying")
+
+    def wait_for_rendezvous_to_free(self, expected_version):
+        """
+        When there's an existing valid rendezvous in state 'final', we have to wait until the next opportunity to join.
+
+        Such opportunity may come from:
+
+        1. rendezvous state changed by someone else, in which case we unblock and retry.
+        2. rendezvous becomes invalid because at least one member failed to renew their
+           leased keep_alive node. We detect this, and destroy the rendezvous.
+        """
+        active_version, state = self.get_rdzv_state()
+        while True:
+            if state["status"] != "final" or state["version"] != expected_version:
+                return
+
+            # Check if current rendezvous state is valid, in the sense that all
+            # its members are alive (renewing their lease).
+            # If not, try destroy this rendezvous, so a new one can be created.
+            alive_members = self.client.get(
+                self.get_path(f"/rdzv/v_{expected_version}")
+            )
+            keep_alive_keys = [ch.key for ch in alive_members.children]
+
+            for key in state["keep_alives"]:
+                if key not in keep_alive_keys:
+                    # This participant didn't renew their lease. We'll declare this
+                    # rendezvous version as dead (but only if it hadn't changed)
+                    logger.info("Keep-alive key %s is not renewed.", key)
+                    logger.info(
+                        "Rendezvous version %s is incomplete. ", expected_version
+                    )
+                    logger.info("Attempting to destroy it.")
+
+                    # Compare-and-delete operation. Throws if compare failed,
+                    # which means rendezvous was already destroyed/re-created/closed,
+                    # and we can try to re-enter the barrier.
+                    self.client.delete(
+                        key=self.get_path("/rdzv/active_version"),
+                        prevValue=active_version.value,
+                    )
+
+                    logger.info(
+                        "Destroyed rendezvous version %s successfully.",
+                        expected_version,
+                    )
+
+                    # We can return (and retry) immediately
+                    return
+
+            # Existing rendezvous seems valid, no reason to destroy it.
+            # We just have to wait until something changes and re-check.
+            try:
+                overall_timeout = (
+                    max(self._rendezvous_deadline - time.time(), 0.0) + 1.0
+                )
+                self.client.watch(
+                    key=self.get_path("/rdzv"),
+                    index=active_version.etcd_index + 1,
+                    recursive=True,
+                    timeout=overall_timeout,
+                )
+            except (etcd.EtcdEventIndexCleared, etcd.EtcdWatchTimedOut):
+                pass
+
+            if time.time() > self._rendezvous_deadline:
+                raise RendezvousTimeoutError
+            active_version, state = self.get_rdzv_state()
+
+    def handle_join_last_call(self, expected_version, deadline):
+        """
+        After we reach min number of workers, one particular worker takes on the
+        responsibility of waiting an additional timeout before closing the join window.
+        If the worker responsible for this fails, the rendezvous will be destroyed due
+        to expiring TTL, and the other participants will re-rendezvous.
+
+        Here we expect to see state 
+        Exit gracefully if either:
+
+        1. state becomes 
+        2. timeout happens (reaching deadline), in which case
+           we try the transition to 
+
+        Exit with exception otherwise.
+        """
+        active_version, state = self.get_rdzv_state()
+        while True:
+            if state["status"] == "frozen" and state["version"] == expected_version:
+                # Worker set became frozen before last-call timeout. This is possible
+                # when num_max_workers is reached before the timeout.
+                return
+
+            if state["status"] != "joinable" or state["version"] != expected_version:
+                raise EtcdRendezvousRetryableFailure(
+                    "Rendezvous state transition no longer possible. Must re-enter."
+                )
+
+            # If timeout occurred, attempt a state transition (joinable -> frozen)
+            if time.time() >= deadline:
+                state["status"] = "frozen"
+                state["keep_alives"] = []
+                try:
+                    active_version = self.client.test_and_set(
+                        key=self.get_path("/rdzv/active_version"),
+                        value=json.dumps(state),
+                        prev_value=active_version.value,
+                        ttl=CONST_ETCD_FROZEN_TTL,
+                    )
+                    # We successfully made this rendezvous frozen.
+                    return
+                except etcd.EtcdCompareFailed:
+                    logger.info(
+                        "Join last-call transition CAS unsuccessful. Will retry"
+                    )
+                    cas_delay()
+                    active_version, state = self.get_rdzv_state()
+                    continue
+
+            # Timeout did not occur, so we must refresh TTL, and wait for
+            # further changes. Note: we only want TTL to be refreshed if
+            # state is still joinable, hence we use CAS for that here,
+            # even though we don't change any of the data.
+            try:
+                active_version = self.client.test_and_set(
+                    key=self.get_path("/rdzv/active_version"),
+                    value=active_version.value,
+                    prev_value=active_version.value,
+                    ttl=CONST_ETCD_JOINABLE_EPHEMERAL_TTL,
+                )
+
+                # Minimize "oversleeping":
+                timeout = min(
+                    CONST_ETCD_JOINABLE_EPHEMERAL_TTL / 2,
+                    deadline - time.time() + 1.0,  # Oversleeping by 1s is ok.
+                )
+                active_version, state = self.try_wait_for_state_change(
+                    etcd_index=active_version.etcd_index + 1, timeout=timeout
+                )
+            except etcd.EtcdCompareFailed:
+                logger.info("Join last-call TTL refresh CAS unsuccessful, will retry")
+                cas_delay()
+                active_version, state = self.get_rdzv_state()
+
+    def set_closed(self):
+        """
+        Mark rendezvous 'closed' for current run_id, which is used to signal other
+        participants to not attempt to perform (re-)rendezvous. This is useful
+        when one of the workers decides the job is complete.
+        """
+        while True:
+            active_version, state = self.get_rdzv_state()
+
+            if state["status"] == "closed":
+                # Already closed by someone else.
+                return
+
+            state["status"] = "closed"
+            try:
+                self.client.test_and_set(
+                    key=self.get_path("/rdzv/active_version"),
+                    value=json.dumps(state),
+                    prev_value=active_version.value,
+                )
+                return
+
+            except etcd.EtcdCompareFailed:
+                logger.info("Set closed CAS unsuccessful, retrying")
+                cas_delay()
+
+    def get_rdzv_state(self):
+        active_version = self.client.get(key=self.get_path("/rdzv/active_version"))
+        return active_version, json.loads(active_version.value)
+
+    def try_wait_for_state_change(self, etcd_index, timeout=None):
+        # Don't sleep past the overall deadline (at least more than by 1s)
+        overall_timeout = max(self._rendezvous_deadline - time.time(), 0.0) + 1.0
+        timeout = overall_timeout if timeout is None else min(timeout, overall_timeout)
+
+        try:
+            self.client.watch(
+                self.get_path("/rdzv/active_version"), index=etcd_index, timeout=timeout
+            )
+        except (etcd.EtcdEventIndexCleared, etcd.EtcdWatchTimedOut):
+            pass
+
+        if time.time() > self._rendezvous_deadline:
+            raise RendezvousTimeoutError
+
+        # Unfortunately, we have to do another fetch in order to get last etcd_index.
+        return self.get_rdzv_state()
+
+    def get_path(self, path):
+        if not path.startswith("/"):
+            path = "/" + path
+
+        return f"{self._prefix}run_{self._run_id}{path}"
+
+    def create_path_if_not_exists(self, full_path, ttl=None):
+        try:
+            self.client.write(
+                key=full_path, value=None, dir=True, prevExist=False, ttl=ttl
+            )
+        except etcd.EtcdAlreadyExist:
+            pass
+
+    def setup_lease_renewal(self, full_path, ttl):
+        # NOTE: For ephemeral key TTL renewal (~lease) to work correctly,
+        # make sure you don't call any long-blocking methods that do not
+        # release the Python's GIL! An example of this is calling a pybind11
+        # extension function that is blocking / long-running, but is not
+        # doing a scoped release of the GIL.
+        def lease_worker(client, path, ttl, stop_event):
+            while True:
+                try:
+                    client.refresh(path, ttl=ttl)
+                except etcd.EtcdKeyNotFound:
+                    break
+                except ConnectionRefusedError:
+                    # This error usually occurs during test when the server already got terminated but the
+                    # python garbage collector have not yet invoked the __del__ method.
+                    break
+
+                if stop_event.wait(timeout=ttl / 2):
+                    break
+
+        lease_stop_event = threading.Event()
+        lease_thread = threading.Thread(
+            target=lease_worker, args=(self.client, full_path, ttl, lease_stop_event)
+        )
+
+        lease_thread.daemon = True
+        lease_thread.start()
+
+        return lease_stop_event
+
+    def store_extra_data(self, rdzv_version, key, value):
+        node = self.get_path(f"/rdzv/v_{rdzv_version}/extra_data")
+        try:
+            # If first time we are storing anything:
+            extra_data = self.client.write(
+                key=node, value=json.dumps({key: value}), prevExist=False
+            )
+            return
+        except etcd.EtcdAlreadyExist:
+            pass
+
+        # CAS loop, to make sure we don't lose concurrent stores.
+        while True:
+            # We never delete extra_data. Failure here should be fatal, no special handling.
+            extra_data = self.client.get(node)
+
+            new_extra_data_value = json.loads(extra_data.value)
+            new_extra_data_value[key] = value
+
+            try:
+                extra_data = self.client.test_and_set(
+                    key=node,
+                    value=json.dumps(new_extra_data_value),
+                    prev_value=extra_data.value,
+                )
+                return
+            except etcd.EtcdCompareFailed:
+                logger.info("Store extra_data CAS unsuccessful, retrying")
+                time.sleep(0.1)
+
+    def load_extra_data(self, rdzv_version, key, timeout=None):
+        # 'extra_data' node itself, and the directory it is located in:
+        node = self.get_path(f"/rdzv/v_{rdzv_version}/extra_data")
+        node_dir = self.get_path(f"/rdzv/v_{rdzv_version}")
+
+        # TODO: implement timeout
+        # https://github.com/pytorch/elastic/issues/12
+        while True:
+            # Combined wait for the node itself, and the key inside it.
+            root = self.client.get(node_dir)
+
+            # Find the extra_data node, if it exists
+            extra_data = [n for n in root.children if n.key == node]
+            assert len(extra_data) <= 1
+
+            # Node for extra_data exists, check the desired key inside it.
+            if len(extra_data) == 1:
+                extra_data_dict = json.loads(extra_data[0].value)
+                if key in extra_data_dict:
+                    return extra_data_dict[key]
+
+            # The 'extra_data' node doesn't exist, or they key isn't published yet.
+            # Wait for interesting events on the extra_data node and retry.
+            try:
+                self.client.watch(node, index=root.etcd_index + 1)
+            except (etcd.EtcdEventIndexCleared, etcd.EtcdWatchTimedOut):
+                pass
+
+    def setup_kv_store(self, rdzv_version):
+        store_path = self.get_path(f"/rdzv/v_{rdzv_version}/kv")
+        self.create_path_if_not_exists(store_path)
+        return EtcdStore(etcd_client=self.client, etcd_store_prefix=store_path)
+
+
+def _create_etcd_client(params: RendezvousParameters) -> etcd.Client:
+    """Create a new ``etcd.Client`` from the specified ``RendezvousParameters``."""
+    hostname, port = parse_rendezvous_endpoint(params.endpoint, 2379)
+
+    # The communication protocol
+    protocol = params.config.get("protocol")
+    if protocol is None:
+        protocol = "http"
+    else:
+        if protocol != "http" and protocol != "https":
+            raise ValueError("The etcd protocol must be HTTP or HTTPS.")
+
+    # The SSL client certificate
+    ssl_cert = params.config.get("cert")
+    if ssl_cert is not None:
+        cert_key = params.config.get("key")
+        if cert_key is not None:
+            # The etcd client expects the certificate key as the second element
+            # of the `cert` tuple.
+            ssl_cert = (ssl_cert, cert_key)
+
+    # The root certificate
+    ca_cert = params.config.get("cacert")
+
+    return etcd.Client(
+        hostname,
+        port,
+        protocol=protocol,
+        cert=ssl_cert,
+        ca_cert=ca_cert,
+        allow_reconnect=True,
+    )
+
+
+# Handler for torch.distributed "static" registration
+def create_rdzv_handler(params: RendezvousParameters) -> RendezvousHandler:
+    """
+    Usage:
+
+    ::
+
+    rdzv_params = RendezvousParameters(
+                        backend="etcd",
+                        endpoint="192.168.0.42:2379",
+                        run_id="123",
+                        min_nodes=4,
+                        max_nodes=8,
+                        timeout=300,
+                        last_call_timeout=30,
+                        etcd_prefix="custom_prefix",
+                        protocol="https",
+                        cacert="/etc/kubernetes/certs/ca.crt",
+                        cert="/etc/kubernetes/certs/client.crt",
+                        key="/etc/kubernetes/certs/client.key")
+    # -- or --
+    rdzv_params = RendezvousParameters(
+                        backend="etcd",
+                        endpoint="192.168.0.42:2379",
+                        run_id="123",
+                        min_nodes=4,
+                        max_nodes=8)
+
+    etcd_rdzv_handler = create_etcd_rendezvous_handler(rdzv_params)
+
+
+    Where:
+        run_id - unique id for this training job instance,
+        min_nodes - min number of workers expected to join the rendezvous,
+        max_nodes - max number of workers allowed to join the rendezvous,
+                        defaults to min_workers is not specified.
+        timeout - total timeout within which next_rendezvous is expected to
+                      succeed; a RendezvousTimeoutError is raised otherwise;
+                      Defaults is 600 (10 minutes).
+        last_call_timeout - additional wait amount ("last call") after
+                            min number of workers has been reached.
+                            Defaults to 30 seconds.
+        etcd_prefix - path prefix (from etcd root), inside which all
+                      etcd nodes will be created.
+                      Default is "/torchelastic/p2p".
+        protocol - http (default) or https to access etcd.
+        cacert - CA cert to access etcd, only makes sense with https.
+        cert - client cert to access etcd, only makes sense with https.
+        key - client key to access etcd, only makes sense with https.
+    """
+    client = _create_etcd_client(params)
+
+    etcd_prefix = params.get("etcd_prefix", "/torchelastic/p2p")
+
+    rdzv = EtcdRendezvous(
+        client=client,
+        prefix=etcd_prefix,
+        run_id=params.run_id,
+        num_min_workers=params.min_nodes,
+        num_max_workers=params.max_nodes,
+        timeout=params.get_as_int("timeout", _DEFAULT_TIMEOUT),
+        last_call_timeout=params.get_as_int(
+            "last_call_timeout", _DEFAULT_LAST_CALL_TIMEOUT
+        ),
+    )
+    return EtcdRendezvousHandler(
+        rdzv_impl=rdzv,
+        local_addr=params.local_addr,
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.py
new file mode 100644
index 0000000000000000000000000000000000000000..4cda28221ff4ec79fbd468a5067c91942b9b7be4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.py
@@ -0,0 +1,214 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import binascii
+from base64 import b64decode, b64encode
+from typing import cast
+
+import urllib3.exceptions  # type: ignore[import]
+
+
+try:
+    import etcd  # type: ignore[import]
+except ModuleNotFoundError:
+    from . import _etcd_stub as etcd
+
+from torch.distributed import Store
+
+from .api import RendezvousConnectionError, RendezvousParameters, RendezvousStateError
+from .dynamic_rendezvous import RendezvousBackend, Token
+from .etcd_store import EtcdStore
+from .utils import parse_rendezvous_endpoint
+
+
+class EtcdRendezvousBackend(RendezvousBackend):
+    """Represents an etcd-based rendezvous backend.
+
+    Args:
+        client:
+            The ``etcd.Client`` instance to use to communicate with etcd.
+        run_id:
+            The run id of the rendezvous.
+        key_prefix:
+            The path under which to store the rendezvous state in etcd.
+        ttl:
+            The TTL of the rendezvous state. If not specified, defaults to two hours.
+    """
+
+    _DEFAULT_TTL = 7200  # 2 hours
+
+    _client: etcd.Client
+    _key: str
+    _ttl: int
+
+    def __init__(
+        self,
+        client: etcd.Client,
+        run_id: str,
+        key_prefix: str | None = None,
+        ttl: int | None = None,
+    ) -> None:
+        if not run_id:
+            raise ValueError("The run id must be a non-empty string.")
+
+        self._client = client
+
+        if key_prefix:
+            self._key = key_prefix + "/" + run_id
+        else:
+            self._key = run_id
+
+        if ttl and ttl > 0:
+            self._ttl = ttl
+        else:
+            self._ttl = self._DEFAULT_TTL
+
+    @property
+    def name(self) -> str:
+        """See base class."""
+        return "etcd-v2"
+
+    def get_state(self) -> tuple[bytes, Token] | None:
+        """See base class."""
+        try:
+            result = self._client.read(self._key)
+        except etcd.EtcdKeyNotFound:
+            return None
+        except (etcd.EtcdException, urllib3.exceptions.TimeoutError) as exc:
+            raise RendezvousConnectionError(
+                "The connection to etcd has failed. See inner exception for details."
+            ) from exc
+
+        return self._decode_state(result)
+
+    def set_state(
+        self, state: bytes, token: Token | None = None
+    ) -> tuple[bytes, Token, bool] | None:
+        """See base class."""
+        base64_state = b64encode(state).decode()
+
+        kwargs = {}
+
+        def get_state():
+            result = self.get_state()
+            if result is not None:
+                return *result, False
+            return None
+
+        if token:
+            try:
+                token = int(token)
+            except ValueError:
+                return get_state()
+
+        if token:
+            kwargs["prevIndex"] = token
+        else:
+            kwargs["prevExist"] = False
+
+        try:
+            result = self._client.write(self._key, base64_state, self._ttl, **kwargs)
+        except (etcd.EtcdAlreadyExist, etcd.EtcdCompareFailed):
+            result = None
+        except (etcd.EtcdException, urllib3.exceptions.TimeoutError) as exc:
+            raise RendezvousConnectionError(
+                "The connection to etcd has failed. See inner exception for details."
+            ) from exc
+
+        if result is None:
+            return get_state()
+
+        tmp = *self._decode_state(result), True
+        return tmp
+
+    def _decode_state(self, result: etcd.EtcdResult) -> tuple[bytes, Token]:
+        # pyrefly: ignore [missing-attribute]
+        base64_state = result.value.encode()
+
+        try:
+            state = b64decode(base64_state)
+        except binascii.Error as exc:
+            raise RendezvousStateError(
+                "The state object is corrupt. See inner exception for details."
+            ) from exc
+
+        # pyrefly: ignore [missing-attribute]
+        return state, result.modifiedIndex
+
+
+def _create_etcd_client(params: RendezvousParameters) -> etcd.Client:
+    host, port = parse_rendezvous_endpoint(params.endpoint, default_port=2379)
+
+    # The timeout
+    read_timeout = cast(int, params.get_as_int("read_timeout", 60))
+    if read_timeout <= 0:
+        raise ValueError("The read timeout must be a positive integer.")
+
+    # The communication protocol
+    protocol = params.get("protocol", "http").strip().lower()
+    if protocol != "http" and protocol != "https":
+        raise ValueError("The protocol must be HTTP or HTTPS.")
+
+    # The SSL client certificate
+    ssl_cert = params.get("ssl_cert")
+    if ssl_cert:
+        ssl_cert_key = params.get("ssl_cert_key")
+        if ssl_cert_key:
+            # The etcd client expects the certificate key as the second element
+            # of the `cert` tuple.
+            ssl_cert = (ssl_cert, ssl_cert_key)
+
+    # The root certificate
+    ca_cert = params.get("ca_cert")
+
+    try:
+        return etcd.Client(
+            host,
+            port,
+            read_timeout=read_timeout,
+            protocol=protocol,
+            cert=ssl_cert,
+            ca_cert=ca_cert,
+            allow_reconnect=True,
+        )
+    except (etcd.EtcdException, urllib3.exceptions.TimeoutError) as exc:
+        raise RendezvousConnectionError(
+            "The connection to etcd has failed. See inner exception for details."
+        ) from exc
+
+
+def create_backend(params: RendezvousParameters) -> tuple[EtcdRendezvousBackend, Store]:
+    """Create a new :py:class:`EtcdRendezvousBackend` from the specified parameters.
+
+    +--------------+-----------------------------------------------------------+
+    | Parameter    | Description                                               |
+    +==============+===========================================================+
+    | read_timeout | The read timeout, in seconds, for etcd operations.        |
+    |              | Defaults to 60 seconds.                                   |
+    +--------------+-----------------------------------------------------------+
+    | protocol     | The protocol to use to communicate with etcd. Valid       |
+    |              | values are "http" and "https". Defaults to "http".        |
+    +--------------+-----------------------------------------------------------+
+    | ssl_cert     | The path to the SSL client certificate to use along with  |
+    |              | HTTPS. Defaults to ``None``.                              |
+    +--------------+-----------------------------------------------------------+
+    | ssl_cert_key | The path to the private key of the SSL client certificate |
+    |              | to use along with HTTPS. Defaults to ``None``.            |
+    +--------------+-----------------------------------------------------------+
+    | ca_cert      | The path to the rool SSL authority certificate. Defaults  |
+    |              | to ``None``.                                              |
+    +--------------+-----------------------------------------------------------+
+    """
+    client = _create_etcd_client(params)
+
+    backend = EtcdRendezvousBackend(
+        client, params.run_id, key_prefix="/torch/elastic/rendezvous"
+    )
+
+    store = EtcdStore(client, "/torch/elastic/store")
+
+    return backend, store
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/etcd_server.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/etcd_server.py
new file mode 100644
index 0000000000000000000000000000000000000000..347e7339d9a46a78c9edf20917eef6146672ffc8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/etcd_server.py
@@ -0,0 +1,248 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+import atexit
+import logging
+import os
+import shlex
+import shutil
+import socket
+import subprocess
+import tempfile
+import time
+from typing import TextIO
+
+
+try:
+    import etcd  # type: ignore[import]
+except ModuleNotFoundError:
+    pass
+
+
+logger = logging.getLogger(__name__)
+
+
+def find_free_port():
+    """
+    Find a free port and binds a temporary socket to it so that the port can be "reserved" until used.
+
+    .. note:: the returned socket must be closed before using the port,
+              otherwise a ``address already in use`` error will happen.
+              The socket should be held and closed as close to the
+              consumer of the port as possible since otherwise, there
+              is a greater chance of race-condition where a different
+              process may see the port as being free and take it.
+
+    Returns: a socket binded to the reserved free port
+
+    Usage::
+
+    sock = find_free_port()
+    port = sock.getsockname()[1]
+    sock.close()
+    use_port(port)
+    """
+    addrs = socket.getaddrinfo(
+        host="localhost", port=None, family=socket.AF_UNSPEC, type=socket.SOCK_STREAM
+    )
+
+    for addr in addrs:
+        family, type, proto, _, _ = addr
+        try:
+            s = socket.socket(family, type, proto)
+            s.bind(("localhost", 0))
+            s.listen(0)
+            return s
+        except OSError as e:
+            s.close()  # type: ignore[possibly-undefined]
+            print(f"Socket creation attempt failed: {e}")
+    raise RuntimeError("Failed to create a socket")
+
+
+def stop_etcd(subprocess, data_dir: str | None = None):
+    if subprocess and subprocess.poll() is None:
+        logger.info("stopping etcd server")
+        subprocess.terminate()
+        subprocess.wait()
+
+    if data_dir:
+        logger.info("deleting etcd data dir: %s", data_dir)
+        shutil.rmtree(data_dir, ignore_errors=True)
+
+
+class EtcdServer:
+    """
+    .. note:: tested on etcd server v3.4.3.
+
+    Starts and stops a local standalone etcd server on a random free
+    port. Useful for single node, multi-worker launches or testing,
+    where a sidecar etcd server is more convenient than having to
+    separately setup an etcd server.
+
+    This class registers a termination handler to shutdown the etcd
+    subprocess on exit. This termination handler is NOT a substitute for
+    calling the ``stop()`` method.
+
+    The following fallback mechanism is used to find the etcd binary:
+
+    1. Uses env var TORCHELASTIC_ETCD_BINARY_PATH
+    2. Uses ``/bin/etcd`` if one exists
+    3. Uses ``etcd`` from ``PATH``
+
+    Usage
+    ::
+
+     server = EtcdServer("/usr/bin/etcd", 2379, "/tmp/default.etcd")
+     server.start()
+     client = server.get_client()
+     # use client
+     server.stop()
+
+    Args:
+        etcd_binary_path: path of etcd server binary (see above for fallback path)
+    """
+
+    def __init__(self, data_dir: str | None = None):
+        self._port = -1
+        self._host = "localhost"
+
+        root = os.path.dirname(__file__)
+        default_etcd_bin = os.path.join(root, "bin/etcd")
+        self._etcd_binary_path = os.environ.get(
+            "TORCHELASTIC_ETCD_BINARY_PATH", default_etcd_bin
+        )
+        if not os.path.isfile(self._etcd_binary_path):
+            self._etcd_binary_path = "etcd"
+
+        self._base_data_dir = (
+            data_dir if data_dir else tempfile.mkdtemp(prefix="torchelastic_etcd_data")
+        )
+        self._etcd_cmd = None
+        self._etcd_proc: subprocess.Popen | None = None
+
+    def _get_etcd_server_process(self) -> subprocess.Popen:
+        if not self._etcd_proc:
+            raise RuntimeError(
+                "No etcd server process started. Call etcd_server.start() first"
+            )
+        else:
+            return self._etcd_proc
+
+    def get_port(self) -> int:
+        """Return the port the server is running on."""
+        return self._port
+
+    def get_host(self) -> str:
+        """Return the host the server is running on."""
+        return self._host
+
+    def get_endpoint(self) -> str:
+        """Return the etcd server endpoint (host:port)."""
+        return f"{self._host}:{self._port}"
+
+    def start(
+        self,
+        timeout: int = 60,
+        num_retries: int = 3,
+        stderr: int | TextIO | None = None,
+    ) -> None:
+        """
+        Start the server, and waits for it to be ready. When this function returns the sever is ready to take requests.
+
+        Args:
+            timeout: time (in seconds) to wait for the server to be ready
+                before giving up.
+            num_retries: number of retries to start the server. Each retry
+                will wait for max ``timeout`` before considering it as failed.
+            stderr: the standard error file handle. Valid values are
+                `subprocess.PIPE`, `subprocess.DEVNULL`, an existing file
+                descriptor (a positive integer), an existing file object, and
+                `None`.
+
+        Raises:
+            TimeoutError: if the server is not ready within the specified timeout
+        """
+        curr_retries = 0
+        while True:
+            try:
+                data_dir = os.path.join(self._base_data_dir, str(curr_retries))
+                os.makedirs(data_dir, exist_ok=True)
+                return self._start(data_dir, timeout, stderr)
+            except Exception as e:
+                curr_retries += 1
+                stop_etcd(self._etcd_proc)
+                logger.warning(  # noqa: G200
+                    "Failed to start etcd server, got error: %s, retrying", str(e)
+                )
+                if curr_retries >= num_retries:
+                    shutil.rmtree(self._base_data_dir, ignore_errors=True)
+                    raise
+        atexit.register(stop_etcd, self._etcd_proc, self._base_data_dir)
+
+    def _start(
+        self, data_dir: str, timeout: int = 60, stderr: int | TextIO | None = None
+    ) -> None:
+        sock = find_free_port()
+        sock_peer = find_free_port()
+        self._port = sock.getsockname()[1]
+        peer_port = sock_peer.getsockname()[1]
+
+        etcd_cmd = shlex.split(
+            " ".join(
+                [
+                    self._etcd_binary_path,
+                    "--enable-v2",
+                    "--data-dir",
+                    data_dir,
+                    "--listen-client-urls",
+                    f"http://{self._host}:{self._port}",
+                    "--advertise-client-urls",
+                    f"http://{self._host}:{self._port}",
+                    "--listen-peer-urls",
+                    f"http://{self._host}:{peer_port}",
+                ]
+            )
+        )
+
+        logger.info("Starting etcd server: [%s]", etcd_cmd)
+
+        sock.close()
+        sock_peer.close()
+        self._etcd_proc = subprocess.Popen(etcd_cmd, close_fds=True, stderr=stderr)
+        self._wait_for_ready(timeout)
+
+    def get_client(self):
+        """Return an etcd client object that can be used to make requests to this server."""
+        return etcd.Client(
+            host=self._host, port=self._port, version_prefix="/v2", read_timeout=10
+        )
+
+    def _wait_for_ready(self, timeout: int = 60) -> None:
+        client = etcd.Client(
+            host=f"{self._host}", port=self._port, version_prefix="/v2", read_timeout=5
+        )
+        max_time = time.time() + timeout
+
+        while time.time() < max_time:
+            if self._get_etcd_server_process().poll() is not None:
+                # etcd server process finished
+                exitcode = self._get_etcd_server_process().returncode
+                raise RuntimeError(
+                    f"Etcd server process exited with the code: {exitcode}"
+                )
+            try:
+                logger.info("etcd server ready. version: %s", client.version)
+                return
+            except Exception:
+                time.sleep(1)
+        raise TimeoutError("Timed out waiting for etcd server to be ready!")
+
+    def stop(self) -> None:
+        """Stop the server and cleans up auto generated resources (e.g. data dir)."""
+        logger.info("EtcdServer stop method called")
+        stop_etcd(self._etcd_proc, self._base_data_dir)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/etcd_store.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/etcd_store.py
new file mode 100644
index 0000000000000000000000000000000000000000..faaf77587bc9d66e42110f8b36c8c17e5aedec87
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/etcd_store.py
@@ -0,0 +1,215 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import datetime
+import random
+import time
+from base64 import b64decode, b64encode
+
+# pyre-ignore[21]: Could not find name `Store` in `torch.distributed`.
+from torch.distributed import Store
+
+
+try:
+    import etcd  # type: ignore[import]
+except ModuleNotFoundError:
+    from . import _etcd_stub as etcd
+
+
+# Delay (sleep) for a small random amount to reduce CAS failures.
+# This does not affect correctness, but will reduce requests to etcd server.
+def cas_delay():
+    time.sleep(random.uniform(0, 0.1))
+
+
+# pyre-fixme[11]: Annotation `Store` is not defined as a type.
+class EtcdStore(Store):
+    """
+    Implement a c10 Store interface by piggybacking on the rendezvous etcd instance.
+
+    This is the store object returned by ``EtcdRendezvous``.
+    """
+
+    def __init__(
+        self,
+        etcd_client,
+        etcd_store_prefix,
+        # Default timeout same as in c10d/Store.hpp
+        timeout: datetime.timedelta | None = None,
+    ):
+        super().__init__()  # required for pybind trampoline.
+
+        self.client = etcd_client
+        self.prefix = etcd_store_prefix
+
+        if timeout is not None:
+            self.set_timeout(timeout)
+
+        if not self.prefix.endswith("/"):
+            self.prefix += "/"
+
+    def set(self, key, value):
+        """
+        Write a key/value pair into ``EtcdStore``.
+
+        Both key and value may be either Python ``str`` or ``bytes``.
+        """
+        self.client.set(key=self.prefix + self._encode(key), value=self._encode(value))
+
+    def get(self, key) -> bytes:
+        """
+        Get a value by key, possibly doing a blocking wait.
+
+        If key is not immediately present, will do a blocking wait
+        for at most ``timeout`` duration or until the key is published.
+
+
+        Returns:
+            value ``(bytes)``
+
+        Raises:
+            LookupError - If key still not published after timeout
+        """
+        b64_key = self.prefix + self._encode(key)
+        kvs = self._try_wait_get([b64_key])
+
+        if kvs is None:
+            raise LookupError(f"Key {key} not found in EtcdStore")
+
+        return self._decode(kvs[b64_key])
+
+    def add(self, key, num: int) -> int:
+        """
+        Atomically increment a value by an integer amount.
+
+        The integer is represented as a string using base 10. If key is not present,
+        a default value of ``0`` will be assumed.
+
+        Returns:
+             the new (incremented) value
+
+
+        """
+        b64_key = self._encode(key)
+        # c10d Store assumes value is an integer represented as a decimal string
+        try:
+            # Assume default value "0", if this key didn't yet:
+            node = self.client.write(
+                key=self.prefix + b64_key,
+                value=self._encode(str(num)),  # i.e. 0 + num
+                prevExist=False,
+            )
+            return int(self._decode(node.value))
+        except etcd.EtcdAlreadyExist:
+            pass
+
+        while True:
+            # Note: c10d Store does not have a method to delete keys, so we
+            # can be sure it's still there.
+            node = self.client.get(key=self.prefix + b64_key)
+            new_value = self._encode(str(int(self._decode(node.value)) + num))
+            try:
+                node = self.client.test_and_set(
+                    key=node.key, value=new_value, prev_value=node.value
+                )
+                return int(self._decode(node.value))
+            except etcd.EtcdCompareFailed:
+                cas_delay()
+
+    def wait(self, keys, override_timeout: datetime.timedelta | None = None):
+        """
+        Wait until all of the keys are published, or until timeout.
+
+        Raises:
+            LookupError - if timeout occurs
+        """
+        b64_keys = [self.prefix + self._encode(key) for key in keys]
+        kvs = self._try_wait_get(b64_keys, override_timeout)
+        if kvs is None:
+            raise LookupError("Timeout while waiting for keys in EtcdStore")
+        # No return value on success
+
+    def check(self, keys) -> bool:
+        """Check if all of the keys are immediately present (without waiting)."""
+        b64_keys = [self.prefix + self._encode(key) for key in keys]
+        kvs = self._try_wait_get(
+            b64_keys,
+            override_timeout=datetime.timedelta(microseconds=1),  # as if no wait
+        )
+        return kvs is not None
+
+    #
+    # Encode key/value data in base64, so we can store arbitrary binary data
+    # in EtcdStore. Input can be `str` or `bytes`.
+    # In case of `str`, utf-8 encoding is assumed.
+    #
+    def _encode(self, value) -> str:
+        if type(value) is bytes:
+            return b64encode(value).decode()
+        elif type(value) is str:
+            return b64encode(value.encode()).decode()
+        raise ValueError("Value must be of type str or bytes")
+
+    #
+    # Decode a base64 string (of type `str` or `bytes`).
+    # Return type is `bytes`, which is more convenient with the Store interface.
+    #
+    def _decode(self, value) -> bytes:
+        if type(value) is bytes:
+            return b64decode(value)
+        elif type(value) is str:
+            return b64decode(value.encode())
+        raise ValueError("Value must be of type str or bytes")
+
+    #
+    # Get all of the (base64-encoded) etcd keys at once, or wait until all the keys
+    # are published or timeout occurs.
+    # This is a helper method for the public interface methods.
+    #
+    # On success, a dictionary of {etcd key -> etcd value} is returned.
+    # On timeout, None is returned.
+    #
+    def _try_wait_get(self, b64_keys, override_timeout=None):
+        timeout = self.timeout if override_timeout is None else override_timeout  # type: ignore[attr-defined]
+        deadline = time.time() + timeout.total_seconds()
+
+        while True:
+            # Read whole directory (of keys), filter only the ones waited for
+            all_nodes = None
+            try:
+                all_nodes = self.client.get(key=self.prefix)
+                req_nodes = {
+                    node.key: node.value
+                    for node in all_nodes.children
+                    if node.key in b64_keys
+                }
+
+                if len(req_nodes) == len(b64_keys):
+                    # All keys are available
+                    return req_nodes
+            except etcd.EtcdKeyNotFound:
+                pass
+
+            watch_timeout = deadline - time.time()
+            if watch_timeout <= 0:
+                return None
+
+            try:
+                index = all_nodes.etcd_index + 1 if all_nodes else 0
+                self.client.watch(
+                    key=self.prefix,
+                    recursive=True,
+                    timeout=watch_timeout,
+                    index=index,
+                )
+            except etcd.EtcdWatchTimedOut:
+                if time.time() >= deadline:
+                    return None
+                else:
+                    continue
+            except etcd.EtcdEventIndexCleared:
+                continue
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/registry.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/registry.py
new file mode 100644
index 0000000000000000000000000000000000000000..ebada4623a814c6b8a2b802d544e5926426e13fc
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/registry.py
@@ -0,0 +1,95 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+from importlib.metadata import entry_points
+
+from .api import (
+    rendezvous_handler_registry as handler_registry,
+    RendezvousHandler,
+    RendezvousParameters,
+)
+from .dynamic_rendezvous import create_handler
+
+
+log = logging.getLogger(__name__)
+
+__all__ = ["get_rendezvous_handler"]
+
+
+def _create_static_handler(params: RendezvousParameters) -> RendezvousHandler:
+    from . import static_tcp_rendezvous
+
+    return static_tcp_rendezvous.create_rdzv_handler(params)
+
+
+def _create_etcd_handler(params: RendezvousParameters) -> RendezvousHandler:
+    from . import etcd_rendezvous
+
+    return etcd_rendezvous.create_rdzv_handler(params)
+
+
+def _create_etcd_v2_handler(params: RendezvousParameters) -> RendezvousHandler:
+    from .etcd_rendezvous_backend import create_backend
+
+    backend, store = create_backend(params)
+
+    return create_handler(store, backend, params)
+
+
+def _create_c10d_handler(params: RendezvousParameters) -> RendezvousHandler:
+    from .c10d_rendezvous_backend import create_backend
+
+    backend, store = create_backend(params)
+
+    return create_handler(store, backend, params)
+
+
+def _register_default_handlers() -> None:
+    handler_registry.register("etcd", _create_etcd_handler)
+    handler_registry.register("etcd-v2", _create_etcd_v2_handler)
+    handler_registry.register("c10d", _create_c10d_handler)
+    handler_registry.register("static", _create_static_handler)
+
+
+def _register_out_of_tree_handlers() -> None:
+    discovered_handler_generators = entry_points(group="torchrun.handlers")
+
+    for handler_generator in discovered_handler_generators:
+        try:
+            get_handler = discovered_handler_generators[handler_generator.name].load()
+            handler_registry.register(handler_generator.name, get_handler())
+        except Exception:
+            log.warning(
+                "Exception while registering out of tree plugin %s: ",
+                handler_generator.name,
+                exc_info=True,
+            )
+
+
+def get_rendezvous_handler(params: RendezvousParameters) -> RendezvousHandler:
+    """
+    Obtain a reference to a :py:class`RendezvousHandler`.
+
+    Custom rendezvous handlers can be registered by
+
+    ::
+
+      from torch.distributed.elastic.rendezvous import rendezvous_handler_registry
+      from torch.distributed.elastic.rendezvous.registry import get_rendezvous_handler
+
+
+      def create_my_rdzv(params: RendezvousParameters):
+          return MyCustomRdzv(params)
+
+
+      rendezvous_handler_registry.register("my_rdzv_backend_name", create_my_rdzv)
+
+      my_rdzv_handler = get_rendezvous_handler(
+          "my_rdzv_backend_name", RendezvousParameters
+      )
+    """
+    return handler_registry.create_handler(params)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/static_tcp_rendezvous.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/static_tcp_rendezvous.py
new file mode 100644
index 0000000000000000000000000000000000000000..52b68000530889b6be1a8ec78ea762f6e5817975
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/static_tcp_rendezvous.py
@@ -0,0 +1,128 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import datetime
+import logging
+from typing import cast
+
+from torch.distributed import PrefixStore, Store, TCPStore
+from torch.distributed.elastic.rendezvous import (
+    RendezvousHandler,
+    RendezvousInfo,
+    RendezvousParameters,
+    RendezvousStoreInfo,
+)
+from torch.distributed.elastic.rendezvous.utils import parse_rendezvous_endpoint
+
+
+__all__ = ["StaticTCPRendezvous", "create_rdzv_handler"]
+
+logger = logging.getLogger(__name__)
+
+_default_timeout_seconds = 600
+
+
+class StaticTCPRendezvous(RendezvousHandler):
+    """
+    Static rendezvous that is a wrapper around the TCPStore.
+
+    Creates TCPStore based on the input parameters with the
+    listener on the agent with group_rank=0
+    """
+
+    def __init__(
+        self,
+        master_addr: str,
+        master_port: int,
+        rank: int,
+        world_size: int,
+        run_id: str,
+        timeout: int,
+    ):
+        self.master_addr = master_addr
+        self.master_port = master_port
+        self.rank = rank
+        self.world_size = world_size
+        self.run_id = run_id
+        self.timeout = datetime.timedelta(seconds=timeout)
+        self._store: Store | None = None
+
+    def get_backend(self) -> str:
+        return "static"
+
+    @property
+    def use_agent_store(self) -> bool:
+        return True
+
+    def next_rendezvous(self) -> RendezvousInfo:
+        logger.info("Creating TCPStore as the c10d::Store implementation")
+        is_master = self.rank == 0
+        if not self._store:
+            self._store = TCPStore(  # type: ignore[call-arg]
+                self.master_addr,
+                self.master_port,
+                self.world_size,
+                is_master,
+                self.timeout,
+                multi_tenant=True,
+            )
+        store = PrefixStore(self.run_id, self._store)
+        # TCPStore server instance is used by trainer code
+        bootstrap_store_info = RendezvousStoreInfo(self.master_addr, self.master_port)
+        return RendezvousInfo(
+            store,
+            self.rank,
+            self.world_size,
+            bootstrap_store_info,
+        )
+
+    def is_closed(self):
+        return False
+
+    def set_closed(self):
+        pass
+
+    def num_nodes_waiting(self):
+        return 0
+
+    def get_run_id(self) -> str:
+        return self.run_id
+
+    def shutdown(self) -> bool:
+        return True
+
+
+def create_rdzv_handler(params: RendezvousParameters) -> RendezvousHandler:
+    if "rank" not in params.config:
+        raise ValueError(
+            "rank is absent in RendezvousParameters."
+            "Try add --node-rank to the cmd request"
+        )
+    endpoint = params.endpoint.strip()
+    if not endpoint:
+        raise ValueError(
+            "endpoint is absent in RendezvousParameters"
+            "Try add --master-port and --master-addr to the cmd request"
+        )
+    master_addr, master_port = parse_rendezvous_endpoint(endpoint, -1)
+    if master_port == -1:
+        raise ValueError(
+            f"Port is absent in endpoint: {endpoint}. Try launching with --master-port"
+        )
+    world_size = params.max_nodes
+    rank = cast(int, params.config.get("rank"))
+    run_id = params.run_id
+    if "timeout" in params.config:
+        timeout = int(params.config["timeout"])
+    else:
+        timeout = _default_timeout_seconds
+
+    return StaticTCPRendezvous(
+        master_addr, master_port, rank, world_size, run_id, timeout
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..05ebbba55913fc4f7d9843420a68b4ae233f3e14
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/rendezvous/utils.py
@@ -0,0 +1,285 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import ipaddress
+import random
+import re
+import socket
+import time
+import weakref
+from collections.abc import Callable
+from datetime import timedelta
+from threading import Event, Thread
+from typing import Any
+
+
+__all__ = ["parse_rendezvous_endpoint"]
+
+
+def _parse_rendezvous_config(config_str: str) -> dict[str, str]:
+    """Extract key-value pairs from a rendezvous configuration string.
+
+    Args:
+        config_str:
+            A string in format =,...,=.
+    """
+    config: dict[str, str] = {}
+
+    config_str = config_str.strip()
+    if not config_str:
+        return config
+
+    key_values = config_str.split(",")
+    for kv in key_values:
+        key, *values = kv.split("=", 1)
+
+        key = key.strip()
+        if not key:
+            raise ValueError(
+                "The rendezvous configuration string must be in format "
+                "=,...,=."
+            )
+
+        value: str | None
+        if values:
+            value = values[0].strip()
+        else:
+            value = None
+        if not value:
+            raise ValueError(
+                f"The rendezvous configuration option '{key}' must have a value specified."
+            )
+
+        config[key] = value
+    return config
+
+
+def _try_parse_port(port_str: str) -> int | None:
+    """Try to extract the port number from ``port_str``."""
+    if port_str and re.match(r"^[0-9]{1,5}$", port_str):
+        return int(port_str)
+    return None
+
+
+def parse_rendezvous_endpoint(
+    endpoint: str | None, default_port: int
+) -> tuple[str, int]:
+    """Extract the hostname and the port number from a rendezvous endpoint.
+
+    Args:
+        endpoint:
+            A string in format [:].
+        default_port:
+            The port number to use if the endpoint does not include one.
+
+    Returns:
+        A tuple of hostname and port number.
+    """
+    if endpoint is not None:
+        endpoint = endpoint.strip()
+
+    if not endpoint:
+        return ("localhost", default_port)
+
+    # An endpoint that starts and ends with brackets represents an IPv6 address.
+    if endpoint[0] == "[" and endpoint[-1] == "]":
+        host, *rest = endpoint, *[]
+    else:
+        host, *rest = endpoint.rsplit(":", 1)
+
+    # Sanitize the IPv6 address.
+    if len(host) > 1 and host[0] == "[" and host[-1] == "]":
+        host = host[1:-1]
+
+    if len(rest) == 1:
+        port = _try_parse_port(rest[0])
+        if port is None or port >= 2**16:
+            raise ValueError(
+                f"The port number of the rendezvous endpoint '{endpoint}' must be an integer "
+                "between 0 and 65536."
+            )
+    else:
+        port = default_port
+
+    if not re.match(r"^[\w\.:-]+$", host):
+        raise ValueError(
+            f"The hostname of the rendezvous endpoint '{endpoint}' must be a dot-separated list of "
+            "labels, an IPv4 address, or an IPv6 address."
+        )
+
+    return host, port
+
+
+def _matches_machine_hostname(host: str) -> bool:
+    """Indicate whether ``host`` matches the hostname of this machine.
+
+    This function compares ``host`` to the hostname as well as to the IP
+    addresses of this machine. Note that it may return a false negative if this
+    machine has CNAME records beyond its FQDN or IP addresses assigned to
+    secondary NICs.
+    """
+    if host == "localhost":
+        return True
+
+    try:
+        addr = ipaddress.ip_address(host)
+    except ValueError:
+        addr = None
+
+    if addr and addr.is_loopback:
+        return True
+
+    try:
+        host_addr_list = socket.getaddrinfo(
+            host, None, proto=socket.IPPROTO_TCP, flags=socket.AI_CANONNAME
+        )
+    except (ValueError, socket.gaierror) as _:
+        host_addr_list = []
+
+    host_ip_list = [host_addr_info[4][0] for host_addr_info in host_addr_list]
+
+    this_host = socket.gethostname()
+    if host == this_host:
+        return True
+
+    addr_list = socket.getaddrinfo(
+        this_host, None, proto=socket.IPPROTO_TCP, flags=socket.AI_CANONNAME
+    )
+    for addr_info in addr_list:
+        # If we have an FQDN in the addr_info, compare it to `host`.
+        if addr_info[3] and addr_info[3] == host:
+            return True
+
+        # Otherwise if `host` represents an IP address, compare it to our IP
+        # address.
+        if addr and addr_info[4][0] == str(addr):
+            return True
+
+        # If the IP address matches one of the provided host's IP addresses
+        if addr_info[4][0] in host_ip_list:
+            return True
+
+    return False
+
+
+def _delay(seconds: float | tuple[float, float]) -> None:
+    """Suspend the current thread for ``seconds``.
+
+    Args:
+        seconds:
+            Either the delay, in seconds, or a tuple of a lower and an upper
+            bound within which a random delay will be picked.
+    """
+    if isinstance(seconds, tuple):
+        seconds = random.uniform(*seconds)
+    # Ignore delay requests that are less than 10 milliseconds.
+    if seconds >= 0.01:
+        time.sleep(seconds)
+
+
+class _PeriodicTimer:
+    """Represent a timer that periodically runs a specified function.
+
+    Args:
+        interval:
+            The interval, in seconds, between each run.
+        function:
+            The function to run.
+    """
+
+    # The state of the timer is hold in a separate context object to avoid a
+    # reference cycle between the timer and the background thread.
+    class _Context:
+        interval: float
+        function: Callable[..., None]
+        args: tuple[Any, ...]
+        kwargs: dict[str, Any]
+        stop_event: Event
+
+    _name: str | None
+    _thread: Thread | None
+    _finalizer: weakref.finalize | None
+
+    # The context that is shared between the timer and the background thread.
+    _ctx: _Context
+
+    def __init__(
+        self,
+        interval: timedelta,
+        function: Callable[..., None],
+        *args: Any,
+        **kwargs: Any,
+    ) -> None:
+        self._name = None
+
+        self._ctx = self._Context()
+        self._ctx.interval = interval.total_seconds()
+        self._ctx.function = function  # type: ignore[assignment]
+        self._ctx.args = args or ()
+        self._ctx.kwargs = kwargs or {}
+        self._ctx.stop_event = Event()
+
+        self._thread = None
+        self._finalizer = None
+
+    @property
+    def name(self) -> str | None:
+        """Get the name of the timer."""
+        return self._name
+
+    def set_name(self, name: str) -> None:
+        """Set the name of the timer.
+
+        The specified name will be assigned to the background thread and serves
+        for debugging and troubleshooting purposes.
+        """
+        if self._thread:
+            raise RuntimeError("The timer has already started.")
+
+        self._name = name
+
+    def start(self) -> None:
+        """Start the timer."""
+        if self._thread:
+            raise RuntimeError("The timer has already started.")
+
+        self._thread = Thread(
+            target=self._run,
+            name=self._name or "PeriodicTimer",
+            args=(self._ctx,),
+            daemon=True,
+        )
+
+        # We avoid using a regular finalizer (a.k.a. __del__) for stopping the
+        # timer as joining a daemon thread during the interpreter shutdown can
+        # cause deadlocks. The weakref.finalize is a superior alternative that
+        # provides a consistent behavior regardless of the GC implementation.
+        self._finalizer = weakref.finalize(
+            self, self._stop_thread, self._thread, self._ctx.stop_event
+        )
+
+        # We do not attempt to stop our background thread during the interpreter
+        # shutdown. At that point we do not even know whether it still exists.
+        self._finalizer.atexit = False
+
+        self._thread.start()
+
+    def cancel(self) -> None:
+        """Stop the timer at the next opportunity."""
+        if self._finalizer:
+            self._finalizer()
+
+    @staticmethod
+    def _run(ctx) -> None:
+        while not ctx.stop_event.wait(ctx.interval):
+            ctx.function(*ctx.args, **ctx.kwargs)
+
+    @staticmethod
+    def _stop_thread(thread, stop_event):
+        stop_event.set()
+
+        thread.join()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/timer/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/timer/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b9c2ea349cc67ff7175d5ef17ec63aecddbf52a7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/timer/__init__.py
@@ -0,0 +1,54 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Expiration timers are set up on the same process as the agent and
+used from your script to deal with stuck workers. When you go into
+a code-block that has the potential to get stuck you can acquire
+an expiration timer, which instructs the timer server to kill the
+process if it does not release the timer by the self-imposed expiration
+deadline.
+
+Usage::
+
+    import torchelastic.timer as timer
+    import torchelastic.agent.server as agent
+
+    def main():
+        start_method = "spawn"
+        message_queue = mp.get_context(start_method).Queue()
+        server = timer.LocalTimerServer(message, max_interval=0.01)
+        server.start() # non-blocking
+
+        spec = WorkerSpec(
+                    fn=trainer_func,
+                    args=(message_queue,),
+                    ...)
+        agent = agent.LocalElasticAgent(spec, start_method)
+        agent.run()
+
+    def trainer_func(message_queue):
+        timer.configure(timer.LocalTimerClient(message_queue))
+        with timer.expires(after=60): # 60 second expiry
+            # do some work
+
+In the example above if ``trainer_func`` takes more than 60 seconds to
+complete, then the worker process is killed and the agent retries the worker group.
+"""
+
+from .api import (  # noqa: F401
+    configure,
+    expires,
+    TimerClient,
+    TimerRequest,
+    TimerServer,
+)
+from .file_based_local_timer import (  # noqa: F401
+    FileTimerClient,
+    FileTimerRequest,
+    FileTimerServer,
+)
+from .local_timer import LocalTimerClient, LocalTimerServer  # noqa: F401
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/timer/api.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/timer/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..efe942022246e90c3b6b68fae59be012d9c8d56b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/timer/api.py
@@ -0,0 +1,281 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+import abc
+import logging
+import threading
+import time
+from contextlib import contextmanager
+from inspect import getframeinfo, stack
+from typing import Any
+
+
+__all__ = [
+    "TimerRequest",
+    "TimerClient",
+    "RequestQueue",
+    "TimerServer",
+    "configure",
+    "expires",
+]
+
+logger = logging.getLogger(__name__)
+
+
+class TimerRequest:
+    """
+    Data object representing a countdown timer acquisition and release
+    that is used between the ``TimerClient`` and ``TimerServer``.
+    A negative ``expiration_time`` should be interpreted as a "release"
+    request.
+
+    .. note:: the type of ``worker_id`` is implementation specific.
+              It is whatever the TimerServer and TimerClient implementations
+              have on to uniquely identify a worker.
+    """
+
+    __slots__ = ["worker_id", "scope_id", "expiration_time"]
+
+    def __init__(self, worker_id: Any, scope_id: str, expiration_time: float):
+        self.worker_id = worker_id
+        self.scope_id = scope_id
+        self.expiration_time = expiration_time
+
+    def __eq__(self, other):
+        if isinstance(other, TimerRequest):
+            return (
+                self.worker_id == other.worker_id
+                and self.scope_id == other.scope_id
+                and self.expiration_time == other.expiration_time
+            )
+        return False
+
+
+class TimerClient(abc.ABC):
+    """
+    Client library to acquire and release countdown timers by communicating
+    with the TimerServer.
+    """
+
+    @abc.abstractmethod
+    def acquire(self, scope_id: str, expiration_time: float) -> None:
+        """
+        Acquires a timer for the worker that holds this client object
+        given the scope_id and expiration_time. Typically registers
+        the timer with the TimerServer.
+        """
+
+    @abc.abstractmethod
+    def release(self, scope_id: str):
+        """
+        Releases the timer for the ``scope_id`` on the worker this
+        client represents. After this method is
+        called, the countdown timer on the scope is no longer in effect.
+        """
+
+
+class RequestQueue(abc.ABC):
+    """
+    Consumer queue holding timer acquisition/release requests
+    """
+
+    @abc.abstractmethod
+    def size(self) -> int:
+        """
+        Returns the size of the queue at the time this method is called.
+        Note that by the time ``get`` is called the size of the queue
+        may have increased. The size of the queue should not decrease
+        until the ``get`` method is called. That is, the following assertion
+        should hold:
+
+        size = q.size()
+        res = q.get(size, timeout=0)
+        assert size == len(res)
+
+        -- or --
+
+        size = q.size()
+        res = q.get(size * 2, timeout=1)
+        assert size <= len(res) <= size * 2
+        """
+
+    @abc.abstractmethod
+    def get(self, size: int, timeout: float) -> list[TimerRequest]:
+        """
+        Gets up to ``size`` number of timer requests in a blocking fashion
+        (no more than ``timeout`` seconds).
+        """
+
+
+class TimerServer(abc.ABC):
+    """
+    Entity that monitors active timers and expires them
+    in a timely fashion. This server is responsible for
+    reaping workers that have expired timers.
+    """
+
+    def __init__(
+        self, request_queue: RequestQueue, max_interval: float, daemon: bool = True
+    ):
+        """
+        :param request_queue: Consumer ``RequestQueue``
+        :param max_interval: max time (in seconds) to wait
+                             for an item in the request_queue
+        :param daemon: whether to run the watchdog thread as a daemon
+        """
+        super().__init__()
+        self._request_queue = request_queue
+        self._max_interval = max_interval
+        self._daemon = daemon
+        self._watchdog_thread: threading.Thread | None = None
+        self._stop_signaled = False
+
+    @abc.abstractmethod
+    def register_timers(self, timer_requests: list[TimerRequest]) -> None:
+        """
+        Processes the incoming timer requests and registers them with the server.
+        The timer request can either be a acquire-timer or release-timer request.
+        Timer requests with a negative expiration_time should be interpreted
+        as a release-timer request.
+        """
+
+    @abc.abstractmethod
+    def clear_timers(self, worker_ids: set[Any]) -> None:
+        """
+        Clears all timers for the given ``worker_ids``.
+        """
+
+    @abc.abstractmethod
+    def get_expired_timers(self, deadline: float) -> dict[str, list[TimerRequest]]:
+        """
+        Returns all expired timers for each worker_id. An expired timer
+        is a timer for which the expiration_time is less than or equal to
+        the provided deadline.
+        """
+
+    @abc.abstractmethod
+    def _reap_worker(self, worker_id: Any) -> bool:
+        """
+        Reaps the given worker. Returns True if the worker has been
+        successfully reaped, False otherwise. If any uncaught exception
+        is thrown from this method, the worker is considered reaped
+        and all associated timers will be removed.
+        """
+
+    def _reap_worker_no_throw(self, worker_id: Any) -> bool:
+        """
+        Wraps ``_reap_worker(worker_id)``, if an uncaught exception is
+        thrown, then it considers the worker as reaped.
+        """
+        try:
+            return self._reap_worker(worker_id)
+        except Exception:
+            logger.exception(
+                "Uncaught exception thrown from _reap_worker(), "
+                "check that the implementation correctly catches exceptions",
+            )
+            return True
+
+    def _watchdog_loop(self):
+        while not self._stop_signaled:
+            try:
+                self._run_watchdog()
+            except Exception:
+                logger.exception("Error running watchdog")
+
+    def _run_watchdog(self):
+        batch_size = max(1, self._request_queue.size())
+        timer_requests = self._request_queue.get(batch_size, self._max_interval)
+        self.register_timers(timer_requests)
+        now = time.time()
+        reaped_worker_ids = set()
+        for worker_id, expired_timers in self.get_expired_timers(now).items():
+            logger.info(
+                "Reaping worker_id=[%s]. Expired timers: %s",
+                worker_id,
+                self._get_scopes(expired_timers),
+            )
+            if self._reap_worker_no_throw(worker_id):
+                logger.info("Successfully reaped worker=[%s]", worker_id)
+                reaped_worker_ids.add(worker_id)
+            else:
+                logger.error(
+                    "Error reaping worker=[%s]. Will retry on next watchdog.", worker_id
+                )
+        self.clear_timers(reaped_worker_ids)
+
+    def _get_scopes(self, timer_requests):
+        return [r.scope_id for r in timer_requests]
+
+    def start(self) -> None:
+        logger.info(
+            "Starting %s... max_interval=%s, daemon=%s",
+            type(self).__name__,
+            self._max_interval,
+            self._daemon,
+        )
+        self._watchdog_thread = threading.Thread(
+            target=self._watchdog_loop, daemon=self._daemon
+        )
+        logger.info("Starting watchdog thread...")
+        self._watchdog_thread.start()
+
+    def stop(self) -> None:
+        logger.info("Stopping %s", type(self).__name__)
+        self._stop_signaled = True
+        if self._watchdog_thread:
+            logger.info("Stopping watchdog thread...")
+            self._watchdog_thread.join(self._max_interval)
+            self._watchdog_thread = None
+        else:
+            logger.info("No watchdog thread running, doing nothing")
+
+
+_timer_client: TimerClient | None = None
+
+
+def configure(timer_client: TimerClient):
+    """
+    Configures a timer client. Must be called before using ``expires``.
+    """
+    global _timer_client
+    _timer_client = timer_client
+    logger.info("Timer client configured to: %s", type(_timer_client).__name__)
+
+
+@contextmanager
+def expires(after: float, scope: str | None = None, client: TimerClient | None = None):
+    """
+    Acquires a countdown timer that expires in ``after`` seconds from now,
+    unless the code-block that it wraps is finished within the timeframe.
+    When the timer expires, this worker is eligible to be reaped. The
+    exact meaning of "reaped" depends on the client implementation. In
+    most cases, reaping means to terminate the worker process.
+    Note that the worker is NOT guaranteed to be reaped at exactly
+    ``time.now() + after``, but rather the worker is "eligible" for being
+    reaped and the ``TimerServer`` that the client talks to will ultimately
+    make the decision when and how to reap the workers with expired timers.
+
+    Usage::
+
+        torch.distributed.elastic.timer.configure(LocalTimerClient())
+        with expires(after=10):
+            torch.distributed.all_reduce(...)
+    """
+    if client is None:
+        if _timer_client is None:
+            raise RuntimeError("Configure timer client before using countdown timers.")
+        client = _timer_client
+    if scope is None:
+        # grab the caller file + lineno
+        caller = getframeinfo(stack()[1][0])
+        scope = f"{caller.filename}#{caller.lineno}"
+    expiration = time.time() + after
+    client.acquire(scope, expiration)
+    try:
+        yield
+    finally:
+        client.release(scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/timer/debug_info_logging.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/timer/debug_info_logging.py
new file mode 100644
index 0000000000000000000000000000000000000000..e385d91283a7b610f00397bfa4bc4800a89761ca
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/timer/debug_info_logging.py
@@ -0,0 +1,24 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+from torch.distributed.elastic.utils.logging import get_logger
+
+
+logger = get_logger(__name__)
+
+__all__ = ["log_debug_info_for_expired_timers"]
+
+
+def log_debug_info_for_expired_timers(
+    run_id: str,
+    expired_timers: dict[int, list[str]],
+):
+    if expired_timers:
+        logger.info("Timers expired for run:[%s] [%s].", run_id, expired_timers)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/timer/file_based_local_timer.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/timer/file_based_local_timer.py
new file mode 100644
index 0000000000000000000000000000000000000000..5855efefcc85342378c273657fed27b37160a6ba
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/timer/file_based_local_timer.py
@@ -0,0 +1,444 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import io
+import json
+import os
+import select
+import signal
+import sys
+import threading
+import time
+from collections.abc import Callable
+from typing import TypeVar
+from typing_extensions import ParamSpec
+
+from torch.distributed.elastic.timer.api import TimerClient, TimerRequest
+from torch.distributed.elastic.timer.debug_info_logging import (
+    log_debug_info_for_expired_timers,
+)
+from torch.distributed.elastic.utils.logging import get_logger
+
+
+_P = ParamSpec("_P")
+_R = TypeVar("_R")
+
+__all__ = ["FileTimerClient", "FileTimerRequest", "FileTimerServer"]
+
+logger = get_logger(__name__)
+
+
+def _retry(max_retries: int, sleep_time: float) -> Callable:
+    """
+    A simple retry wrapper.
+
+    Args:
+        max_retries: int, the maximum number of retries.
+        sleep_time: float, the time to sleep between retries.
+    """
+
+    def wrapper(func: Callable[_P, _R]) -> Callable[_P, _R]:
+        def wrapper(*args: _P.args, **kwargs: _P.kwargs):
+            for i in range(max_retries):
+                try:
+                    return func(*args, **kwargs)
+                except Exception:
+                    logger.exception("Error running %s. Retrying...", func.__name__)
+                    if i < max_retries - 1:
+                        time.sleep(sleep_time)
+                    else:
+                        raise
+
+        return wrapper
+
+    return wrapper
+
+
+class FileTimerRequest(TimerRequest):
+    """
+    Data object representing a countdown timer acquisition and release
+    that is used between the ``FileTimerClient`` and ``FileTimerServer``.
+    A negative ``expiration_time`` should be interpreted as a "release"
+    request.
+    ``signal`` is the signal to reap the worker process from the server
+    process.
+    """
+
+    __slots__ = ["version", "signal"]
+
+    def __init__(
+        self, worker_pid: int, scope_id: str, expiration_time: float, signal: int = 0
+    ) -> None:
+        super().__init__(
+            worker_id=worker_pid, scope_id=scope_id, expiration_time=expiration_time
+        )
+        self.version = 1
+        self.signal = signal
+
+    @property
+    def worker_pid(self) -> int:
+        return self.worker_id
+
+    def __eq__(self, other) -> bool:
+        if isinstance(other, FileTimerRequest):
+            return (
+                super().__eq__(other)
+                and self.version == other.version
+                and self.signal == other.signal
+            )
+        return False
+
+    def to_json(self) -> str:
+        return json.dumps(
+            {
+                "version": self.version,
+                "pid": self.worker_pid,
+                "scope_id": self.scope_id,
+                "expiration_time": self.expiration_time,
+                "signal": self.signal,
+            },
+        )
+
+
+class FileTimerClient(TimerClient):
+    """
+    Client side of ``FileTimerServer``. This client is meant to be used
+    on the same host that the ``FileTimerServer`` is running on and uses
+    pid to uniquely identify a worker.
+    This client uses a named_pipe to send timer requests to the
+    ``FileTimerServer``. This client is a producer while the
+    ``FileTimerServer`` is a consumer. Multiple clients can work with
+    the same ``FileTimerServer``.
+
+    Args:
+
+        file_path: str, the path of a FIFO special file. ``FileTimerServer``
+                        must have created it by calling os.mkfifo().
+
+        signal: signal, the signal to use to kill the process. Using a
+                        negative or zero signal will not kill the process.
+    """
+
+    def __init__(
+        self,
+        file_path: str,
+        signal=(signal.SIGKILL if sys.platform != "win32" else signal.CTRL_C_EVENT),  # type: ignore[attr-defined]
+    ) -> None:
+        super().__init__()
+        self._file_path = file_path
+        self.signal = signal
+
+    @_retry(max_retries=10, sleep_time=0.1)
+    def _open_non_blocking(self) -> io.TextIOWrapper | None:
+        # The server may have crashed or may haven't started yet.
+        # In such case, calling open() in blocking model blocks the client.
+        # To avoid such issue, open it in non-blocking mode, and an OSError will
+        # be raised if the server is not there.
+        fd = os.open(self._file_path, os.O_WRONLY | os.O_NONBLOCK)
+        return os.fdopen(fd, "wt")
+
+    def _send_request(self, request: FileTimerRequest) -> None:
+        try:
+            file = self._open_non_blocking()
+        except Exception as e:
+            raise BrokenPipeError(
+                "Could not send the FileTimerRequest because FileTimerServer is not available."
+            ) from e
+        with file:
+            json_request = request.to_json()
+            # Write request with no greater than select.PIPE_BUF is guarantee to be atomic.
+            if len(json_request) > select.PIPE_BUF:
+                raise RuntimeError(
+                    f"FileTimerRequest larger than {select.PIPE_BUF} bytes "
+                    f"is not supported: {json_request}"
+                )
+            file.write(json_request + "\n")
+
+    def acquire(self, scope_id: str, expiration_time: float) -> None:
+        self._send_request(
+            request=FileTimerRequest(
+                worker_pid=os.getpid(),
+                scope_id=scope_id,
+                expiration_time=expiration_time,
+                signal=self.signal,
+            ),
+        )
+
+    def release(self, scope_id: str) -> None:
+        self._send_request(
+            request=FileTimerRequest(
+                worker_pid=os.getpid(), scope_id=scope_id, expiration_time=-1, signal=0
+            ),
+        )
+
+
+class FileTimerServer:
+    """
+    Server that works with ``FileTimerClient``. Clients are expected to be
+    running on the same host as the process that is running this server.
+    Each host in the job is expected to start its own timer server locally
+    and each server instance manages timers for local workers (running on
+    processes on the same host).
+
+    Args:
+
+        file_path: str, the path of a FIFO special file to be created.
+
+        max_interval: float, max interval in seconds for each watchdog loop.
+
+        daemon: bool, running the watchdog thread in daemon mode or not.
+                      A daemon thread will not block a process to stop.
+        log_event: Callable[[Dict[str, str]], None], an optional callback for
+                logging the events in JSON format.
+    """
+
+    def __init__(
+        self,
+        file_path: str,
+        run_id: str,
+        max_interval: float = 10,
+        daemon: bool = True,
+        log_event: Callable[[str, FileTimerRequest | None], None] | None = None,
+    ) -> None:
+        self._file_path = file_path
+        self._run_id = run_id
+        self._max_interval = max_interval
+        self._daemon = daemon
+        self._timers: dict[tuple[int, str], FileTimerRequest] = {}
+        self._stop_signaled = False
+        self._watchdog_thread: threading.Thread | None = None
+
+        self._is_client_started = False
+        if os.path.exists(self._file_path):
+            os.remove(self._file_path)
+        os.mkfifo(self._file_path)
+        # For test only. Count the number of requests received.
+        self._request_count = 0
+        # For test only. Process all requests and stop the server.
+        self._run_once = False
+        self._log_event = (
+            log_event if log_event is not None else lambda name, request: None
+        )
+        self._last_progress_time = int(time.time())
+
+    def start(self) -> None:
+        logger.info(
+            "Starting %s... max_interval=%s, daemon=%s, file_path=%s",
+            type(self).__name__,
+            self._max_interval,
+            self._daemon,
+            self._file_path,
+        )
+        self._watchdog_thread = threading.Thread(
+            target=self._watchdog_loop, daemon=self._daemon
+        )
+        logger.info("Starting watchdog thread...")
+        self._watchdog_thread.start()
+        self._log_event("watchdog started", None)
+
+    def stop(self) -> None:
+        logger.info("Stopping %s", type(self).__name__)
+        self._stop_signaled = True
+        if self._watchdog_thread:
+            logger.info("Stopping watchdog thread...")
+            self._watchdog_thread.join(self._max_interval)
+            self._watchdog_thread = None
+        else:
+            logger.info("No watchdog thread running, doing nothing")
+        if os.path.exists(self._file_path):
+            os.remove(self._file_path)
+        self._log_event("watchdog stopped", None)
+
+    def run_once(self) -> None:
+        self._run_once = True
+        if self._watchdog_thread:
+            logger.info("Stopping watchdog thread...")
+            self._watchdog_thread.join()
+            self._watchdog_thread = None
+        else:
+            logger.info("No watchdog thread running, doing nothing")
+        if os.path.exists(self._file_path):
+            os.remove(self._file_path)
+
+    @staticmethod
+    def is_process_running(pid: int):
+        """
+        function to check process is running or not
+        """
+        try:
+            # Check if the process exists and we can send signals to it
+            os.kill(pid, 0)
+            return True
+        except OSError:
+            return False
+
+    def _watchdog_loop(self) -> None:
+        # Open the pipe in blocking mode blocks the server thread.
+        # This is fine for the following reasons:
+        #  1. No client case usually does not happen.
+        #  2. We are running the watchdog loop in a separate daemon
+        #     thread, which will not block the process to stop.
+        try:
+            with open(self._file_path) as fd:
+                self._is_client_started = True
+                while not self._stop_signaled:
+                    try:
+                        run_once = self._run_once
+                        self._run_watchdog(fd)
+                        if run_once:
+                            break
+                        self._last_progress_time = int(time.time())
+                    except Exception:
+                        logger.exception("Error running watchdog")
+
+        except Exception:
+            logger.exception("Could not open the FileTimerServer pipe")
+            raise
+
+    def _run_watchdog(self, fd: io.TextIOWrapper) -> None:
+        timer_requests = self._get_requests(fd, self._max_interval)
+        self.register_timers(timer_requests)
+        now = time.time()
+        reaped_worker_pids = set()
+        kill_process = False
+        reap_signal = 0
+
+        all_expired_timers = self.get_expired_timers(now)
+        log_debug_info_for_expired_timers(
+            self._run_id,
+            {
+                pid: [expired_timer.to_json() for expired_timer in expired_timers]
+                for pid, expired_timers in all_expired_timers.items()
+            },
+        )
+
+        for worker_pid, expired_timers in all_expired_timers.items():
+            logger.info(
+                "Reaping worker_pid=[%s]. Expired timers: %s",
+                worker_pid,
+                self._get_scopes(expired_timers),
+            )
+            reaped_worker_pids.add(worker_pid)
+            # In case we have multiple expired timers, we find the first timer
+            # with a valid signal (>0) in the expiration time order.
+            expired_timers.sort(key=lambda timer: timer.expiration_time)
+            signal = 0
+            expired_timer = None
+            for timer in expired_timers:
+                self._log_event("timer expired", timer)
+                if timer.signal > 0:
+                    signal = timer.signal
+                    expired_timer = timer
+                    break
+            if signal <= 0:
+                logger.info(
+                    "No signal specified with worker=[%s]. Do not reap it.", worker_pid
+                )
+                continue
+            if self._reap_worker(worker_pid, signal):
+                logger.info(
+                    "Successfully reaped worker=[%s] with signal=%s", worker_pid, signal
+                )
+                self._log_event("kill worker process", expired_timer)
+                kill_process = True
+                reap_signal = signal
+            else:
+                logger.error(
+                    "Error reaping worker=[%s]. Will retry on next watchdog.",
+                    worker_pid,
+                )
+        if kill_process and reap_signal > 0:
+            logger.info(
+                "Terminating the server process=[%s] because of expired timers",
+                os.getpid(),
+            )
+            self._reap_worker(os.getpid(), reap_signal)
+
+        self.clear_timers(reaped_worker_pids)
+
+    def _get_scopes(self, timer_requests: list[FileTimerRequest]) -> list[str]:
+        return [r.scope_id for r in timer_requests]
+
+    def _get_requests(
+        self, fd: io.TextIOWrapper, max_interval: float
+    ) -> list[FileTimerRequest]:
+        start = time.time()
+        requests = []
+        while not self._stop_signaled or self._run_once:
+            # For named pipe, readline() is blocking when at least one writer opens.
+            # It returns only when flush() is called at the writer side.
+            # Note that flush() is automatically called inside close().
+            # After the last writer closes, readline() is not blocking.
+            # It will return an empty string when it's at end-of-file.
+            # Since the client side always opens the pipe, writes a message and closes
+            # the pipe immediately, the readline() call below is not blocking for long.
+            json_request = fd.readline()
+            if len(json_request) == 0:
+                if self._run_once:
+                    break
+                time.sleep(min(max_interval, 1))
+            else:
+                request = json.loads(json_request)
+                pid = request["pid"]
+                scope_id = request["scope_id"]
+                expiration_time = request["expiration_time"]
+                signal = request["signal"]
+                requests.append(
+                    FileTimerRequest(
+                        worker_pid=pid,
+                        scope_id=scope_id,
+                        expiration_time=expiration_time,
+                        signal=signal,
+                    )
+                )
+            now = time.time()
+            if now - start > max_interval:
+                break
+        return requests
+
+    def register_timers(self, timer_requests: list[FileTimerRequest]) -> None:
+        for request in timer_requests:
+            pid = request.worker_pid
+            scope_id = request.scope_id
+            expiration_time = request.expiration_time
+            self._request_count += 1
+
+            key = (pid, scope_id)
+            # negative expiration is a proxy for a release call
+            if expiration_time < 0:
+                if key in self._timers:
+                    del self._timers[key]
+            else:
+                self._timers[key] = request
+
+    def clear_timers(self, worker_pids: set[int]) -> None:
+        for pid, scope_id in list(self._timers.keys()):
+            if pid in worker_pids or not FileTimerServer.is_process_running(pid):
+                del self._timers[(pid, scope_id)]
+
+    def get_expired_timers(self, deadline: float) -> dict[int, list[FileTimerRequest]]:
+        # pid -> [timer_requests...]
+        expired_timers: dict[int, list[FileTimerRequest]] = {}
+        for request in self._timers.values():
+            if request.expiration_time <= deadline:
+                expired_scopes = expired_timers.setdefault(request.worker_pid, [])
+                expired_scopes.append(request)
+        return expired_timers
+
+    def _reap_worker(self, worker_pid: int, signal: int) -> bool:
+        try:
+            os.kill(worker_pid, signal)
+            return True
+        except ProcessLookupError:
+            logger.info("Process with pid=%s does not exist. Skipping", worker_pid)
+            return True
+        except Exception:
+            logger.exception("Error terminating pid=%s", worker_pid)
+        return False
+
+    def get_last_progress_time(self) -> int:
+        return self._last_progress_time if self._is_client_started else int(time.time())
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/timer/local_timer.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/timer/local_timer.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e66ef3fae34958422c1160bfdc1994b13bf1553
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/timer/local_timer.py
@@ -0,0 +1,128 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+import logging
+import multiprocessing as mp
+import os
+import signal
+import time
+from queue import Empty
+from typing import Any
+
+from .api import RequestQueue, TimerClient, TimerRequest, TimerServer
+
+
+__all__ = ["LocalTimerClient", "MultiprocessingRequestQueue", "LocalTimerServer"]
+
+logger = logging.getLogger(__name__)
+
+
+class LocalTimerClient(TimerClient):
+    """
+    Client side of ``LocalTimerServer``. This client is meant to be used
+    on the same host that the ``LocalTimerServer`` is running on and uses
+    pid to uniquely identify a worker. This is particularly useful in situations
+    where one spawns a subprocess (trainer) per GPU on a host with multiple
+    GPU devices.
+    """
+
+    def __init__(self, mp_queue):
+        super().__init__()
+        self._mp_queue = mp_queue
+
+    def acquire(self, scope_id, expiration_time):
+        pid = os.getpid()
+        acquire_request = TimerRequest(pid, scope_id, expiration_time)
+        self._mp_queue.put(acquire_request)
+
+    def release(self, scope_id):
+        pid = os.getpid()
+        release_request = TimerRequest(pid, scope_id, -1)
+        self._mp_queue.put(release_request)
+
+
+class MultiprocessingRequestQueue(RequestQueue):
+    """
+    A ``RequestQueue`` backed by python ``multiprocessing.Queue``
+    """
+
+    def __init__(self, mp_queue: mp.Queue):
+        super().__init__()
+        self._mp_queue = mp_queue
+
+    def size(self) -> int:
+        return self._mp_queue.qsize()
+
+    def get(self, size, timeout: float) -> list[TimerRequest]:
+        requests = []
+        wait = timeout
+        for _ in range(size):
+            start = time.time()
+
+            try:
+                r = self._mp_queue.get(block=True, timeout=wait)
+            except Empty:
+                break
+
+            requests.append(r)
+            wait = wait - (time.time() - start)
+            if wait <= 0:
+                break
+
+        return requests
+
+
+class LocalTimerServer(TimerServer):
+    """
+    Server that works with ``LocalTimerClient``. Clients are expected to be
+    subprocesses to the parent process that is running this server. Each host
+    in the job is expected to start its own timer server locally and each
+    server instance manages timers for local workers (running on processes
+    on the same host).
+    """
+
+    def __init__(
+        self, mp_queue: mp.Queue, max_interval: float = 60, daemon: bool = True
+    ):
+        super().__init__(MultiprocessingRequestQueue(mp_queue), max_interval, daemon)
+        self._timers: dict[tuple[Any, str], TimerRequest] = {}
+
+    def register_timers(self, timer_requests: list[TimerRequest]) -> None:
+        for request in timer_requests:
+            pid = request.worker_id
+            scope_id = request.scope_id
+            expiration_time = request.expiration_time
+
+            # negative expiration is a proxy for a release call
+            if expiration_time < 0:
+                self._timers.pop((pid, scope_id), None)
+            else:
+                self._timers[(pid, scope_id)] = request
+
+    def clear_timers(self, worker_ids: set[int]) -> None:
+        for pid, scope_id in list(self._timers.keys()):
+            if pid in worker_ids:
+                self._timers.pop((pid, scope_id))
+
+    def get_expired_timers(self, deadline: float) -> dict[Any, list[TimerRequest]]:
+        # pid -> [timer_requests...]
+        expired_timers: dict[Any, list[TimerRequest]] = {}
+        for request in self._timers.values():
+            if request.expiration_time <= deadline:
+                expired_scopes = expired_timers.setdefault(request.worker_id, [])
+                expired_scopes.append(request)
+        return expired_timers
+
+    def _reap_worker(self, worker_id: int) -> bool:
+        try:
+            os.kill(worker_id, signal.SIGKILL)
+            return True
+        except ProcessLookupError:
+            logger.info("Process with pid=%s does not exist. Skipping", worker_id)
+            return True
+        except Exception:
+            logger.exception("Error terminating pid=%s", worker_id)
+        return False
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/utils/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce2bbf5bbe2348bb0eaa411a034710dd14f7648e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/utils/__init__.py
@@ -0,0 +1,9 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .api import get_env_variable_or_raise, get_socket_with_port, macros  # noqa: F401
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/utils/api.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/utils/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b881137047c23789a061a719437a43b1743959f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/utils/api.py
@@ -0,0 +1,62 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import socket
+from string import Template
+from typing import Any
+
+
+def get_env_variable_or_raise(env_name: str) -> str:
+    r"""
+    Tries to retrieve environment variable. Raises ``ValueError``
+    if no environment variable found.
+
+    Args:
+        env_name (str): Name of the env variable
+    """
+    value = os.environ.get(env_name, None)
+    if value is None:
+        msg = f"Environment variable {env_name} expected, but not set"
+        raise ValueError(msg)
+    return value
+
+
+def get_socket_with_port() -> socket.socket:
+    addrs = socket.getaddrinfo(
+        host="localhost", port=None, family=socket.AF_UNSPEC, type=socket.SOCK_STREAM
+    )
+    for addr in addrs:
+        family, type, proto, _, _ = addr
+        s = socket.socket(family, type, proto)
+        try:
+            s.bind(("localhost", 0))
+            s.listen(0)
+            return s
+        except OSError:
+            s.close()
+    raise RuntimeError("Failed to create a socket")
+
+
+class macros:
+    """
+    Defines simple macros for caffe2.distributed.launch cmd args substitution
+    """
+
+    local_rank = "${local_rank}"
+
+    @staticmethod
+    def substitute(args: list[Any], local_rank: str) -> list[str]:
+        args_sub = []
+        for arg in args:
+            if isinstance(arg, str):
+                sub = Template(arg).safe_substitute(local_rank=local_rank)
+                args_sub.append(sub)
+            else:
+                args_sub.append(arg)
+        return args_sub
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/utils/data/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/utils/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c39bca6f3c8a31f5f2d7115ad12c1fc4925fe1d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/utils/data/__init__.py
@@ -0,0 +1,10 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from .cycling_iterator import CyclingIterator  # noqa: F401
+from .elastic_distributed_sampler import ElasticDistributedSampler  # noqa: F401
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/utils/data/cycling_iterator.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/utils/data/cycling_iterator.py
new file mode 100644
index 0000000000000000000000000000000000000000..291a04226db79c77b3bde4cec239e45b31be81b5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/utils/data/cycling_iterator.py
@@ -0,0 +1,57 @@
+#!/usr/bin/env python3
+
+from collections.abc import Callable, Iterator
+from typing import TypeVar
+from typing_extensions import Self
+
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+_T = TypeVar("_T")
+
+__all__ = ["CyclingIterator"]
+
+
+class CyclingIterator(Iterator[_T]):
+    """
+    An iterator decorator that cycles through the
+    underlying iterator "n" times. Useful to "unroll"
+    the dataset across multiple training epochs.
+
+    The generator function is called as ``generator_fn(epoch)``
+    to obtain the underlying iterator, where ``epoch`` is a
+    number less than or equal to ``n`` representing the ``k``th cycle
+
+    For example if ``generator_fn`` always returns ``[1,2,3]``
+    then ``CyclingIterator(n=2, generator_fn)`` will iterate through
+    ``[1,2,3,1,2,3]``
+    """
+
+    def __init__(
+        self,
+        n: int,
+        generator_fn: Callable[[int], Iterator[_T]],
+        start_epoch: int = 0,
+    ):
+        self._n = n
+        self._epoch = start_epoch
+        self._generator_fn = generator_fn
+        self._iter = generator_fn(self._epoch)
+
+    def __iter__(self) -> Self:
+        return self
+
+    def __next__(self) -> _T:
+        try:
+            return next(self._iter)
+        except StopIteration as eod:  # eod == end of data
+            if self._epoch < self._n - 1:
+                self._epoch += 1
+                self._iter = self._generator_fn(self._epoch)
+                return self.__next__()
+            else:
+                raise eod
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/utils/data/elastic_distributed_sampler.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/utils/data/elastic_distributed_sampler.py
new file mode 100644
index 0000000000000000000000000000000000000000..c824cc2fd018c005a59d0927a53ca449bf99102d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/utils/data/elastic_distributed_sampler.py
@@ -0,0 +1,93 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math
+from collections.abc import Iterator, Sized
+from typing import cast, TypeVar
+
+import torch
+from torch.utils.data import Dataset
+from torch.utils.data.distributed import DistributedSampler
+
+
+T = TypeVar("T")
+
+__all__ = ["ElasticDistributedSampler"]
+
+
+class ElasticDistributedSampler(DistributedSampler[T]):
+    """
+    Sampler that restricts data loading to a subset of
+    the dataset for elastic training.
+
+    It is especially useful in conjunction with
+    :class:`torch.nn.parallel.DistributedDataParallel`. In such case, each
+    process can pass a DistributedSampler instance as a DataLoader sampler,
+    and load a subset of the original dataset that is exclusive to it.
+
+    .. note::
+        Dataset is assumed to be of constant size.
+
+    Args:
+        dataset: Dataset used for sampling.
+        num_replicas (optional): Number of processes participating in
+            distributed training.
+        rank (optional): Rank of the current process within num_replicas.
+        start_index (optional):  Which index of the dataset to start sampling from
+    """
+
+    def __init__(
+        self,
+        dataset: Dataset[T],
+        num_replicas: int | None = None,
+        rank: int | None = None,
+        start_index: int = 0,
+    ):
+        super().__init__(dataset=dataset, num_replicas=num_replicas, rank=rank)
+        if not isinstance(dataset, Sized):
+            raise TypeError("Dataset must be an instance of collections.abc.Sized")
+
+        # Cast to Sized for mypy
+        # pyrefly: ignore [redundant-cast]
+        sized_dataset = cast(Sized, dataset)
+
+        if start_index >= len(sized_dataset):
+            raise ValueError(
+                f"Start index {start_index} should be less than dataset size {len(sized_dataset)}"
+            )
+
+        self.start_index = start_index
+        sized_dataset = cast(Sized, self.dataset)
+        self.num_samples = math.ceil(
+            float(len(sized_dataset) - self.start_index) / self.num_replicas
+        )
+        self.total_size = self.num_samples * self.num_replicas
+
+    def __iter__(self) -> Iterator[T]:
+        # deterministically shuffle based on epoch
+        g = torch.Generator()
+        g.manual_seed(self.epoch)
+        sized_dataset = cast(Sized, self.dataset)
+        indices = (
+            torch.randperm(len(sized_dataset) - self.start_index, generator=g)
+            .add(self.start_index)
+            .tolist()
+        )
+
+        # add extra samples to make it evenly divisible
+        indices += indices[: (self.total_size - len(indices))]
+        assert len(indices) == self.total_size
+
+        # subsample
+        indices = indices[self.rank : self.total_size : self.num_replicas]
+        assert len(indices) == self.num_samples
+
+        return iter(indices)
+
+    def __len__(self) -> int:
+        return self.num_samples
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/utils/distributed.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/utils/distributed.py
new file mode 100644
index 0000000000000000000000000000000000000000..7b294d222ea7de5f0b7e91ac27ef876768d47eb6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/utils/distributed.py
@@ -0,0 +1,183 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+import datetime
+import os
+import socket
+from contextlib import closing
+
+import torch.distributed as dist
+from torch.distributed.elastic.utils.logging import get_logger
+from torch.distributed.elastic.utils.store import barrier
+
+
+__all__ = ["create_c10d_store", "get_free_port", "get_socket_with_port"]
+
+logger = get_logger(__name__)
+
+_ADDRESS_IN_USE = "Address already in use"
+_SOCKET_TIMEOUT = "Socket Timeout"
+
+_TCP_STORE_INIT = "_tcp_store/num_members"
+
+
+def create_c10d_store(
+    is_server: bool,
+    server_addr: str,
+    server_port: int = -1,
+    world_size: int = 1,
+    timeout: float = (60 * 10),  # 10 min
+    wait_for_workers: bool = True,
+    retries=3,
+    use_libuv: bool | None = None,
+):
+    if use_libuv is not None:
+        logger.warning(
+            "argument use_libuv is deprecated and ignored. Set USE_LIBUV environment "
+            'variable to "0" to disable libuv, or "1" to enable it. If the env var '
+            "is not set, libuv will be used by default."
+        )
+
+    # check os.environ for use_libuv
+    use_libuv = os.environ.get("USE_LIBUV", "1") == "1"  # libuv is the default option
+
+    if server_port == -1 and world_size > 1:
+        raise ValueError(
+            f"server_port must be specified when world_size > 1, got server_port={server_port}, world_size={world_size}"
+        )
+
+    if server_port != -1:
+        logger.info("sever_port: %s, specified, ignoring retries", server_port)
+
+    # only retry when server_port is NOT static
+    attempt = retries if server_port == -1 else 1
+    while True:
+        if server_port != -1:
+            port = server_port
+        else:
+            port = get_free_port()
+
+        logger.info(
+            "Creating c10d store on %s:%s\n"
+            "  world_size  : %s\n"
+            "  is_server   : %s\n"
+            "  timeout(sec): %s\n"
+            "  use_libuv   : %s\n",
+            server_addr,
+            port,
+            world_size,
+            is_server,
+            timeout,
+            use_libuv,
+        )
+
+        try:
+            store = dist.TCPStore(
+                host_name=server_addr,
+                port=port,
+                world_size=world_size,
+                is_master=is_server,
+                timeout=datetime.timedelta(seconds=timeout),
+                wait_for_workers=wait_for_workers,
+                use_libuv=use_libuv,
+            )
+            # skips full rank check when we don't have to wait for all workers
+            if wait_for_workers:
+                _check_full_rank(store, world_size, timeout=timeout)
+            logger.info("Successfully created c10d store")
+            return store
+        except RuntimeError as e:
+            # this is brittle, but the underlying exception type is not properly pybinded
+            # so we parse the error msg for now, interestingly this is how torch itself
+            # detects timeouts and port conflicts in their own unittests
+            # see - caffe2/torch/testing/_internal/common_utils.py
+            # TODO properly map the exceptions in pybind (c10d/init.cpp)
+            if str(e) == _ADDRESS_IN_USE:  # this will only happen on the server
+                if attempt < retries:
+                    logger.warning(
+                        "port: %s already in use, attempt: [%s/%s]",
+                        port,
+                        attempt,
+                        retries,
+                    )
+                    attempt += 1
+                else:
+                    raise RuntimeError(
+                        f"on {server_addr}, port: {port} already in use"
+                    ) from e
+            else:
+                raise
+
+
+def _check_full_rank(store, world_size, timeout):
+    try:
+        barrier(store, world_size, key_prefix=_TCP_STORE_INIT, barrier_timeout=timeout)
+    except RuntimeError as e:
+        if str(e) == _SOCKET_TIMEOUT:
+            raise TimeoutError(
+                f"timed out waiting for all {world_size} members to join"
+            ) from e
+        else:
+            raise
+
+
+def get_free_port():
+    """
+    Returns an unused port on localhost.
+
+    This function finds an unused port on localhost by opening to socket to bind
+    to a port and then closing it.
+
+    Returns:
+        int: an unused port on localhost
+
+    Example:
+        >>> # xdoctest: +SKIP("Nondeterministic")
+        >>> get_free_port()
+        63976
+
+    .. note::
+        The port returned by :func:`get_free_port` is not reserved and may be
+        taken by another process after this function returns.
+    """
+    sock = get_socket_with_port()
+    with closing(sock):
+        return sock.getsockname()[1]
+
+
+def get_socket_with_port() -> socket.socket:
+    """
+    Returns a free port on localhost that is "reserved" by binding a temporary
+    socket on it. Close the socket before passing the port to the entity
+    that requires it. Usage example
+
+    ::
+
+    sock = _get_socket_with_port()
+    with closing(sock):
+        port = sock.getsockname()[1]
+        sock.close()
+        # there is still a race-condition that some other process
+        # may grab this port before func() runs
+        func(port)
+    """
+
+    addrs = socket.getaddrinfo(
+        host="localhost", port=None, family=socket.AF_UNSPEC, type=socket.SOCK_STREAM
+    )
+    for addr in addrs:
+        family, type, proto, _, _ = addr
+        s = socket.socket(family, type, proto)
+        try:
+            s.bind(("localhost", 0))
+            s.listen(0)
+            return s
+        except OSError as e:
+            s.close()
+            logger.warning("Socket creation attempt failed.", exc_info=e)
+    raise RuntimeError("Failed to create a socket")
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/utils/log_level.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/utils/log_level.py
new file mode 100644
index 0000000000000000000000000000000000000000..87ea0f7d64182488b40fd7fed6965ce57ec475a0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/utils/log_level.py
@@ -0,0 +1,14 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+def get_log_level() -> str:
+    """
+    Return default log level for pytorch.
+    """
+    return "WARNING"
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/utils/logging.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/utils/logging.py
new file mode 100644
index 0000000000000000000000000000000000000000..aadf37eb16b8084486a537b18f399098cbcc4fb5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/utils/logging.py
@@ -0,0 +1,69 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import inspect
+import logging
+import os
+import warnings
+
+from torch.distributed.elastic.utils.log_level import get_log_level
+
+
+def get_logger(name: str | None = None) -> logging.Logger:
+    """
+    Util function to set up a simple logger that writes
+    into stderr. The loglevel is fetched from the LOGLEVEL
+    env. variable or WARNING as default. The function will use the
+    module name of the caller if no name is provided.
+
+    Args:
+        name: Name of the logger. If no name provided, the name will
+              be derived from the call stack.
+    """
+
+    # Derive the name of the caller, if none provided
+    # Use depth=2 since this function takes up one level in the call stack
+    return _setup_logger(name or _derive_module_name(depth=2))
+
+
+def _setup_logger(name: str | None = None) -> logging.Logger:
+    logger = logging.getLogger(name)
+    logger.setLevel(os.environ.get("LOGLEVEL", get_log_level()))
+    return logger
+
+
+def _derive_module_name(depth: int = 1) -> str | None:
+    """
+    Derives the name of the caller module from the stack frames.
+
+    Args:
+        depth: The position of the frame in the stack.
+    """
+    try:
+        stack = inspect.stack()
+        assert depth < len(stack)
+        # FrameInfo is just a named tuple: (frame, filename, lineno, function, code_context, index)
+        frame_info = stack[depth]
+
+        module = inspect.getmodule(frame_info[0])
+        if module:
+            module_name = module.__name__
+        else:
+            # inspect.getmodule(frame_info[0]) does NOT work (returns None) in
+            # binaries built with @mode/opt
+            # return the filename (minus the .py extension) as modulename
+            filename = frame_info[1]
+            module_name = os.path.splitext(os.path.basename(filename))[0]
+        return module_name
+    except Exception as e:
+        warnings.warn(
+            f"Error deriving logger module name, using . Exception: {e}",
+            RuntimeWarning,
+            stacklevel=2,
+        )
+        return None
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/utils/store.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/utils/store.py
new file mode 100644
index 0000000000000000000000000000000000000000..598899e936aa0c9a1c43dda38ef2479eec03f842
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/elastic/utils/store.py
@@ -0,0 +1,225 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from collections.abc import Callable, Iterable
+from contextlib import contextmanager
+from datetime import timedelta
+
+import torch
+
+
+DistStoreError = torch._C._DistStoreError
+
+_NUM_MEMBERS = "/num_members"
+_LAST_MEMBER_CHECKIN = "/last_member"
+_TRACE = "/TRACE"
+_TRACING_GATE = "/TRACING_GATE"
+_MAX_TRACE_MISSING_RANKS = 16
+
+
+__all__ = ["store_timeout", "get_all", "synchronize", "barrier"]
+
+
+@contextmanager
+def store_timeout(store, timeout: float):
+    """
+    This sets the timeout and then restores the old timeout when the context
+    manager exits.
+
+    Args:
+        store: the store to set the timeout on
+        timeout: the timeout to set
+    """
+
+    old_timeout = store.timeout
+    store.set_timeout(timedelta(seconds=timeout))
+    yield
+    store.set_timeout(old_timeout)
+
+
+def get_all(store, rank: int, prefix: str, world_size: int):
+    r"""
+    Given a store and a prefix, the method goes through the array of keys
+    of the following format: ``{prefix}{idx}``, where idx is in a range
+    from 0 to size, and tries to retrieve the data.
+
+    The Rank0 process waits at the end to make sure all other processes
+    finished the procedure before exiting.
+
+    Usage
+
+    ::
+
+     values = get_all(store, "torchelastic/data", 3)
+     value1 = values[0]  # retrieves the data for key torchelastic/data0
+     value2 = values[1]  # retrieves the data for key torchelastic/data1
+     value3 = values[2]  # retrieves the data for key torchelastic/data2
+
+    """
+    data_arr = store.multi_get([f"{prefix}{idx}" for idx in range(world_size)])
+
+    barrier_key = _barrier_nonblocking(
+        store=store,
+        world_size=world_size,
+        key_prefix=f"{prefix}/finished",
+    )
+    if rank == 0:
+        # Rank0 runs the TCPStore daemon, as a result it needs to exit last.
+        # Otherwise, the barrier may timeout if rank0 process finished the work
+        # before other processes finished `get_all` method
+        store.wait([barrier_key])
+
+    return data_arr
+
+
+def synchronize(
+    store,
+    data: bytes,
+    rank: int,
+    world_size: int,
+    key_prefix: str,
+    timeout: float = 300,
+) -> list[bytes]:
+    """
+    Synchronizes ``world_size`` agents between each other using the underlying c10d store.
+    The ``data`` will be available on each of the agents.
+
+    Note: The data on the path is not deleted, as a result there can be stale data if
+        you use the same key_prefix twice.
+
+    Time complexity: O(N) per worker, O(N^2) globally.
+    """
+    with store_timeout(store, timeout):
+        store.set(f"{key_prefix}{rank}", data)
+        agent_data = get_all(store, rank, key_prefix, world_size)
+        return agent_data
+
+
+def _try_detecting_missing_ranks(
+    store,
+    world_size: int,
+    key_prefix: str,
+    rank: int,
+    rank_decoder: Callable[[int], str],
+    trace_timeout: float,
+) -> Iterable[str] | None:
+    store.set(f"{key_prefix}{rank}{_TRACE}", "")
+
+    def _find_missing_ranks():
+        missing_rank_info = set()
+        ranks_missing = 0
+        for i in range(1, world_size):
+            # reduce noise, assuming in general 8 ranks per node
+            # It is valuable to know that 1 or >1 nodes have timed-out.
+            if ranks_missing >= _MAX_TRACE_MISSING_RANKS:
+                break
+            try:
+                if ranks_missing == 0:
+                    store.wait(
+                        [f"{key_prefix}{i}{_TRACE}"], timedelta(seconds=trace_timeout)
+                    )
+                else:
+                    # use a shortest timeout, some ranks have failed to check-in
+                    store.wait([f"{key_prefix}{i}{_TRACE}"], timedelta(milliseconds=1))
+            except DistStoreError:
+                ranks_missing += 1
+                missing_rank_info.add(rank_decoder(i))
+        return missing_rank_info
+
+    def _checkin():
+        try:
+            store.wait([f"{key_prefix}{_TRACING_GATE}"])
+            return [f"[]"]
+        except DistStoreError:
+            # in case rank0 is the source of the timeout, original exception will be raised
+            return None
+
+    if rank == 0:
+        missing_rank_info = _find_missing_ranks()
+        store.set(f"{key_prefix}{_TRACING_GATE}", "")
+        return missing_rank_info
+    else:
+        return _checkin()
+
+
+def _barrier_nonblocking(store, world_size: int, key_prefix: str) -> str:
+    """
+    Does all the non-blocking operations for a barrier and returns the final key
+    that can be waited on.
+    """
+    num_members_key = key_prefix + _NUM_MEMBERS
+    last_member_key = key_prefix + _LAST_MEMBER_CHECKIN
+
+    idx = store.add(num_members_key, 1)
+    if idx == world_size:
+        store.set(last_member_key, "")
+
+    return last_member_key
+
+
+def barrier(
+    store,
+    world_size: int,
+    key_prefix: str,
+    barrier_timeout: float = 300,
+    rank: int | None = None,
+    rank_tracing_decoder: Callable[[int], str] | None = None,
+    trace_timeout: float = 10,
+) -> None:
+    """
+    A global lock between agents. This will pause all workers until at least
+    ``world_size`` workers respond.
+
+    This uses a fast incrementing index to assign waiting ranks and a success
+    flag set by the last worker.
+
+    Time complexity: O(1) per worker, O(N) globally.
+
+    Optionally, passing rank will enable tracing of missing ranks on timeouts.
+    `rank_tracing_decoder` lambda arg can be used to convert rank data
+    into a more meaningful information at an app level (e.g. hostname).
+
+    Note: Since the data is not removed from the store, the barrier can be used
+        once per unique ``key_prefix``.
+    """
+
+    if rank is None:
+        assert rank_tracing_decoder is None, "Tracing requires rank information"
+
+    with store_timeout(store, barrier_timeout):
+        last_member_key = _barrier_nonblocking(
+            store=store, world_size=world_size, key_prefix=key_prefix
+        )
+        try:
+            store.wait([last_member_key])
+        except DistStoreError as e:
+            if rank is None:
+                raise e
+            else:
+                missing_ranks = _try_detecting_missing_ranks(
+                    store,
+                    world_size,
+                    key_prefix,
+                    rank,
+                    rank_tracing_decoder or (lambda x: str(x)),
+                    trace_timeout,
+                )
+                if missing_ranks is not None:
+                    raise DistStoreError(
+                        "Timed out waiting on barrier on "
+                        "rank {}, for key prefix: {} (world_size={}, missing_ranks={}, timeout={})".format(
+                            rank,
+                            key_prefix,
+                            world_size,
+                            f"[{', '.join(missing_ranks)}]",
+                            barrier_timeout,
+                        )
+                    ) from None
+                else:
+                    raise e
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/flight_recorder/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/flight_recorder/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/flight_recorder/components/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/flight_recorder/components/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/flight_recorder/components/builder.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/flight_recorder/components/builder.py
new file mode 100644
index 0000000000000000000000000000000000000000..56736450e3f2a8decdc6dfc11c929d8a1bdfb16f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/flight_recorder/components/builder.py
@@ -0,0 +1,457 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import argparse
+import ast
+import copy
+import os
+import sys
+from typing import Any  # type: ignore[attr-defined]
+
+from torch.distributed.flight_recorder.components.fr_logger import FlightRecorderLogger
+from torch.distributed.flight_recorder.components.types import (
+    Collective,
+    Database,
+    EntryState,
+    Group,
+    MatchStateRecord,
+    Membership,
+    NCCLCall,
+    Op,
+    Traceback,
+)
+from torch.distributed.flight_recorder.components.utils import (
+    add_stack_id_in_entries,
+    align_trace_from_beginning,
+    check_current_entry_match,
+    check_no_missing_dump_files,
+    check_version,
+    error_analysis,
+    find_coalesced_group as find_coalesced_group_p2p_only,
+    find_coalesced_group_with_non_p2p,
+    get_version_detail,
+    just_print_entries,
+    match_coalesced_groups as match_coalesced_groups_p2p_only,
+    match_coalesced_groups_with_non_p2p,
+)
+
+
+__all__ = [
+    "build_groups_memberships",
+    "build_collectives",
+    "transform_ft",
+    "build_db",
+]
+
+# Set up logging
+logger: FlightRecorderLogger = FlightRecorderLogger()
+
+
+try:
+    from tabulate import tabulate
+except ModuleNotFoundError:
+    logger.warning("tabulate is not installed. Proceeding without it.")
+
+    # Define a no-op tabulate function
+    def tabulate(data: Any, headers: Any = None) -> Any:  # type: ignore[misc]
+        return data
+
+
+"""
+Flat DB builder
+"""
+
+
+def build_groups_memberships(
+    pg_config: Any,
+) -> tuple[
+    list[Group],
+    dict[Any, Group],
+    list[Membership],
+    dict[str, set[Any]],
+    dict[tuple[str, int], str],
+]:
+    """
+    pg_config: {
+        global_rank: {
+            (pg_guid, desc, ranks)
+        }
+    }
+
+    `pg_guid` is a system generated id, but depending on the mode of PG creation it could be a globally incrementing int
+          or a hash of the ranks.  See `_process_group_name` in distributed_c10d.py.
+    `desc` is provided by the user (optionally) and should be 'meaningful' (e.g. TP/PP/DP group)
+    `ranks` is a list of the 'global ranks' that are members of the PG.
+
+    (pg_guid, desc, ranks) tuples are appended lazily to the flight buffer when `getNCCLComm` is called on a PG and
+    the `enabled_` flag is true for that PG.
+        - the order of calling (init_process_group, new_group, etc) does not affect the order of the tuples in the list
+
+    Returns:
+        `groups`: a groups table where each row is a Group namedtuple.
+        `_groups`: a dict that is indexed by pg_guid with Group namedtuple as value.
+        `memberships`: a membership table where each row is a Membership namedtuple.
+        `_memberships`: a dict that is indexed by pg_guid with set of ranks (int) as value.
+        `_pg_guids`: a dict that is indexed by (pg_uid, global_rank) with pg_guid as value.
+    """
+    # flat lists for return
+    groups = []
+    memberships = []
+
+    # dicts for faster cross-rank validation
+    _groups = {}
+    _memberships = {}
+    _pg_guids = {}
+    for global_rank in pg_config:
+        for pg_uid in pg_config[global_rank]:
+            desc = pg_config[global_rank][pg_uid]["desc"]
+            ranks = ast.literal_eval(pg_config[global_rank][pg_uid]["ranks"])
+            # With the adoption of the split_group API, we can have multiple PGs with the same pg_guid (PG Name)
+            # So we need to add the hash of all its ranks within the PG as well.
+            # Also guid must be a string because `_process_group_name` returns a string.
+            pg_guid = pg_uid + str(hash(frozenset(ranks)))
+            _pg_guids[(pg_uid, global_rank)] = pg_guid
+            if isinstance(ranks, str):
+                # TODO Bug in FR data format? ranks is '[0, 1,...]'
+                ranks = eval(ranks)
+
+            if pg_guid not in _groups:
+                groups.append(Group(id=pg_guid, desc=desc, size=len(ranks)))
+                for rank in ranks:
+                    memberships.append(Membership(group_id=pg_guid, global_rank=rank))
+                _groups[pg_guid] = groups[-1]
+                _memberships[pg_guid] = set(ranks)
+            else:
+                # validation across ranks
+                assert _groups[pg_guid].desc == desc, (
+                    f"mismatch in desc {_groups[pg_guid].desc} vs {desc} for group {pg_guid}"
+                )
+                assert _memberships[pg_guid] == set(ranks), (
+                    f"mismatch in membership for group {pg_guid} {_memberships[pg_guid]} vs {set(ranks)}"
+                )
+    return groups, _groups, memberships, _memberships, _pg_guids
+
+
+def build_collectives(
+    all_entries: dict[int, list[dict[str, Any]]],
+    _groups: dict[str, Group],
+    _memberships: dict[str, set[Any]],
+    _pg_guids: dict[tuple[str, int], str],
+    version: str,
+    mismatch_cap: int = 10,
+) -> tuple[list[Traceback], list[Collective], list[NCCLCall]]:
+    """
+    groups, memberships are the non-flat dicts that are indexable
+    all_entries is a raw dict from the original dumps:
+
+    all_entries: {
+        global_rank: [
+            {
+                record_id: ordered id of the event in the trace buffer
+                pg_id: ProcessGroupNCCL::uid_
+                    *note: `pg_id` corresponds to nothing in groups table
+                process_group: (pg_name, desc)
+                    *note: `pg_name`, `desc` corresponds to `pg_id`, `desc` in groups table
+                collective_seq_id: ordered id for collective operations and coalesced group operations
+                p2p_seq_id: ordered id for point-to-point operations
+                op_id: ordered id including individual ops inside coalescing group
+                profiling_name: descriptive name of the operation
+                'time_created_ns',
+                'input_sizes',
+                'output_sizes',
+                'state',
+                'time_discovered_started_ns',
+                'time_discovered_completed_ns',
+                'retired',
+                'frames',
+            }
+        ]
+    }
+    """
+    tracebacks: list[Traceback] = []
+
+    collectives: list[Collective] = []
+    nccl_calls: list[NCCLCall] = []
+
+    # once we find one mismatch, we stop pairing up collectives since the pairing is possibly incorrect
+    # instead, just record the remaining ops as NCCLCalls
+    mismatch = {_groups[g].id: 0 for g in _groups}
+
+    # For best effort partial analysis.
+    dumps_ranks = {int(key) for key in all_entries}
+    """
+    - it doesn't matter what order I put collectives/ncclops into their table. we can later on re-sort it by start time
+    - there could be multiple options for the "first" collective to pair up (rank 0,1 might do a bcast while rank 2,3 do a bcast)
+    - within a group, the first collective must be the same on all ranks in the group, then it can be marked as a
+    collective and removed
+    """
+    while all_entries:
+        # we greedily match collectives, starting arbitrarily with the trace from the first rank
+        # later, if we exhaust the first rank, we continue with the next 'first rank'
+        rank_iter = iter(all_entries)
+        first_rank = next(rank_iter)
+        other_ranks = list(rank_iter)
+
+        if len(all_entries[first_rank]) == 0:
+            all_entries.pop(first_rank)
+            continue
+
+        # lets match the first collective! we need to know which ranks are involved, and ensure that this same
+        # collective is also the first one on those ranks within that group
+        entries = all_entries[first_rank]
+        current_entry = entries[0]
+        desc = current_entry["process_group"][1]
+        # For db build and logs printing, we want to use the original pg_name, not the hash one.
+        original_pg_name = current_entry["process_group"][0]
+        pg_name = _pg_guids[(original_pg_name, first_rank)]
+        expected_ranks = set(_memberships[pg_name])
+        entry_state = EntryState(current_entry, expected_ranks)
+        match_record = MatchStateRecord(
+            expected_ranks=expected_ranks,
+            other_ranks=other_ranks,
+            entry_state=entry_state,
+            candidate_ranks={first_rank},
+            candidate_idx={},
+            found_ranks=set(),
+            found_idx={},
+            errors=set(),
+        )
+
+        major_v, minor_v = get_version_detail(version)
+        find_coalesced_group = (
+            find_coalesced_group_p2p_only
+            if major_v <= 2 and minor_v < 7
+            else find_coalesced_group_with_non_p2p
+        )
+        maybe_coalesced_group = find_coalesced_group(
+            pg_name, entries, _pg_guids, first_rank
+        )
+        if len(maybe_coalesced_group) > 1:
+            num_coalesced_entries = len(maybe_coalesced_group)
+            # We need a copy of the original expected ranks to avoid modifying it.
+            candidate_ranks = copy.deepcopy(expected_ranks)
+            done_ranks = set()
+            all_coalesced_entries = {}
+            while candidate_ranks:
+                curr = candidate_ranks.pop()
+                done_ranks.add(curr)
+                grp = (
+                    find_coalesced_group(pg_name, all_entries[curr], _pg_guids, curr)  # type: ignore[index]
+                    if curr in all_entries  # type: ignore[comparison-overlap]
+                    else []
+                )
+                all_coalesced_entries[curr] = grp
+                for _, entry in grp:
+                    op = Op(entry, _memberships, pg_name)
+                    peer = None
+                    if op.type == "send":
+                        assert op._src_g == curr, (
+                            f"Send src error: {curr} expected but {op._src_g} is set"
+                        )
+                        peer = op._dst_g
+                    elif op.type == "recv":
+                        assert op._dst_g == curr, (
+                            f"Recv dst error: {curr} expected but {op._dst_g} is set"
+                        )
+                        peer = op._src_g
+                    if peer and peer not in done_ranks:
+                        candidate_ranks.add(peer)
+
+            if major_v <= 2 and minor_v < 7:
+                match = match_coalesced_groups_p2p_only(
+                    all_coalesced_entries,
+                    group_size=_groups[pg_name].size,
+                    groups=_groups,
+                    memberships=_memberships,
+                    _pg_guids=_pg_guids,
+                )
+            else:
+                match = match_coalesced_groups_with_non_p2p(
+                    copy.deepcopy(
+                        all_coalesced_entries
+                    ),  # We want to keep a copy for cleanup.
+                    pg_info=(pg_name, desc),
+                    memberships=_memberships,
+                    _pg_guids=_pg_guids,
+                    mismatch=mismatch,
+                    dumps_ranks=dumps_ranks,
+                    version=version,
+                    collectives=collectives,
+                    match_record=match_record,
+                )
+
+            if match and mismatch[pg_name] == 0:
+                # We treat coalesced collectives as a single collective.
+                # TODO: we need to surface a merged collective info like input/output sizes to users.
+                collectives.append(
+                    match_record.entry_state.to_collective(len(collectives))
+                )
+            else:
+                mismatch[pg_name] += 1
+            for r in all_coalesced_entries:
+                idx_map = {r: i for i, _ in reversed(all_coalesced_entries[r])}  # noqa: B035
+                nccl_calls.extend(
+                    reversed(
+                        match_record.entry_state.to_nccl_call(
+                            all_entries,
+                            idx_map,
+                            len(nccl_calls),
+                            collectives[-1].id if match else None,
+                        )
+                    )
+                )
+                # This extra cleanup is needed because we need to pop all collectives within a coalesced collective.
+                for i, k in idx_map.items():
+                    for _ in range(1, num_coalesced_entries):
+                        all_entries[i].pop(k)
+        else:
+            # Iterate through all the ranks and check if there is a mismatch for the current entry.
+            check_current_entry_match(
+                all_entries,
+                _pg_guids,
+                (pg_name, desc),
+                current_entry,
+                _memberships,
+                mismatch,
+                match_record,
+            )
+
+            # Use heuristics to decide what type of errors and error messages we should print.
+            error_analysis(
+                all_entries,
+                match_record,
+                dumps_ranks,
+                first_rank,
+                current_entry,
+                mismatch,
+                get_version_detail(version),
+                pg_name,
+            )
+
+            # at this point there are 3 possibilities
+            # 1. we found a match on all the ranks that are members of the group
+            #  -> we create a Collective and remove the individual entries from their original lists
+            if match_record.found_ranks == expected_ranks and mismatch[pg_name] == 0:
+                collectives.append(
+                    match_record.entry_state.to_collective(len(collectives))
+                )
+                idx_map = {
+                    r: match_record.found_idx[r] if r != first_rank else 0
+                    for r in match_record.found_ranks
+                }
+                nccl_calls.extend(
+                    match_record.entry_state.to_nccl_call(
+                        all_entries, idx_map, len(nccl_calls), collectives[-1].id
+                    )
+                )
+
+            # 2. we found a partial match but some ranks are missing
+            # 3. we found no match
+            #  -> since its not a complete collective, no entry goes into collectives but we still record a nccl call
+            #     TODO should there be a way to mark 'mismatches'?
+            else:
+                logger.debug("appending a non-matching collective")
+                idx_map = {
+                    r: match_record.candidate_idx[r] if r != first_rank else 0
+                    for r in match_record.candidate_ranks
+                }
+                collectives.append(
+                    match_record.entry_state.to_collective(
+                        len(collectives),
+                        errors=match_record.errors,
+                        idx_map=idx_map,
+                        all_entries=all_entries,
+                    )
+                )
+                nccl_calls.extend(
+                    match_record.entry_state.to_nccl_call(
+                        all_entries, idx_map, len(nccl_calls), None
+                    )
+                )
+
+        if mismatch[pg_name] > mismatch_cap:
+            logger.error(
+                "Too many mismatches for process_group %s: %s aborting", pg_name, desc
+            )
+            break
+
+    return tracebacks, collectives, nccl_calls
+
+
+def transform_ft(
+    details: dict[str, dict[str, Any]], group_world_size: int
+) -> dict[str, dict[str, Any]]:
+    for dump_key, dump in details.items():
+        rank = dump["rank"]
+        for key, pg_config in dump["pg_config"].items():
+            if pg_config["desc"] == "default_pg":
+                ranks = eval(pg_config["ranks"])
+                replica_id = rank // group_world_size
+                first_rank = replica_id * group_world_size
+                new_ranks = [r + first_rank for r in ranks]
+                details[dump_key]["pg_config"][key]["ranks"] = f"{new_ranks}"
+
+    return details
+
+
+def build_db(
+    details: dict[str, dict[str, Any]], args: argparse.Namespace, version: str
+) -> Database:
+    if args.verbose:
+        os.environ["FR_TRACE_VERBOSE_OUTPUT"] = "1"
+    # temporary state used for building database
+    entries = {}
+    pg_config = {}
+    version_by_ranks = {}
+    for dump in details.values():
+        rank = dump["rank"]
+        entries[rank] = dump["entries"]
+        version_by_ranks[rank] = dump["version"]
+        pg_config[rank] = dump["pg_config"]
+
+    # Ensure version is consistent across all ranks.
+    check_version(version_by_ranks, version)
+    entries = align_trace_from_beginning(entries)
+    stack_id_trace_map: dict[str, int] = {}
+    if args.just_print_entries:
+        entries, stack_id_trace_map = add_stack_id_in_entries(entries)
+
+    # flattened database
+    groups, _groups, memberships, _memberships, _pg_guids = build_groups_memberships(
+        pg_config
+    )
+    logger.debug("built groups, memberships")
+
+    if args.just_print_entries:
+        just_print_entries(
+            entries, _groups, _memberships, _pg_guids, args, stack_id_trace_map
+        )
+        sys.exit(0)
+
+    if not args.allow_incomplete_ranks:
+        check_no_missing_dump_files(entries, memberships)
+
+    tracebacks, collectives, nccl_calls = build_collectives(
+        entries, _groups, _memberships, _pg_guids, version, args.mismatch_cap
+    )
+    logger.debug("built collectives, nccl_calls")
+    if args.verbose:
+        logger.debug("Groups")
+        logger.debug(tabulate(groups, headers=Group._fields))
+        logger.debug("Memberships")
+        logger.debug(tabulate(memberships, headers=Membership._fields))
+        logger.debug("Collectives")
+        logger.debug(tabulate(collectives, headers=Collective._fields))
+        logger.debug("NCCLCalls")
+        logger.debug(tabulate(nccl_calls, headers=NCCLCall._fields))
+    db = Database(
+        tracebacks=tracebacks,
+        collectives=collectives,
+        ncclcalls=nccl_calls,
+        groups=groups,
+        memberships=memberships,
+    )
+    return db
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/flight_recorder/components/config_manager.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/flight_recorder/components/config_manager.py
new file mode 100644
index 0000000000000000000000000000000000000000..d1b12966588215ce01118f9aea9f8bb771390c3c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/flight_recorder/components/config_manager.py
@@ -0,0 +1,110 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import argparse
+import logging
+from collections.abc import Sequence
+
+from torch.distributed.flight_recorder.components.fr_logger import FlightRecorderLogger
+
+
+__all__ = ["JobConfig"]
+
+
+logger: FlightRecorderLogger = FlightRecorderLogger()
+
+
+class JobConfig:
+    """
+    A helper class to manage the script configuration.
+    """
+
+    def __init__(self: "JobConfig"):
+        self.parser = argparse.ArgumentParser(
+            description="PyTorch Flight recorder analyzing script."
+        )
+        self.parser.add_argument(
+            "trace_dir",
+            nargs="?",
+            help="Directory containing one trace file per rank, named with _.",
+        )
+        self.parser.add_argument(
+            "--selected-ranks",
+            default=None,
+            nargs="+",
+            type=int,
+            help="List of ranks we want to show traces for.",
+        )
+        self.parser.add_argument(
+            "--allow-incomplete-ranks",
+            action="store_true",
+            help=(
+                "FR trace require all ranks to have dumps for analysis. "
+                "This flag allows best-effort partial analysis of results "
+                "and printing of collected data."
+            ),
+        )
+        self.parser.add_argument(
+            "--pg-filters",
+            default=None,
+            nargs="+",
+            type=str,
+            help=(
+                "List of filter strings, it could be pg name or pg desc. "
+                "If specified, only show traces for the given pg."
+            ),
+        )
+        self.parser.add_argument("-o", "--output", default=None)
+        self.parser.add_argument(
+            "-p",
+            "--prefix",
+            help=(
+                "Common filename prefix to strip such that rank can be extracted. "
+                "If not specified, will attempt to infer a common prefix."
+            ),
+            default=None,
+        )
+        self.parser.add_argument("-j", "--just_print_entries", action="store_true")
+        self.parser.add_argument("-v", "--verbose", action="store_true")
+        self.parser.add_argument("--print_stack_trace", action="store_true")
+        self.parser.add_argument(
+            "--mismatch_cap",
+            type=int,
+            default=10,
+            help="Maximum number of mismatches we print (from earliest).",
+        )
+        self.parser.add_argument(
+            "--transform-ft",
+            action="store_true",
+            help="Transform PG config to use global ranks to analyze traces produced by torchft",
+        )
+        self.parser.add_argument(
+            "--group-world-size",
+            type=int,
+            default=None,
+            help="The number of ranks in 1 torchft replica group. Must be specified if --transform-ft is True",
+        )
+
+    def parse_args(self: "JobConfig", args: Sequence[str] | None) -> argparse.Namespace:
+        # pyrefly: ignore [bad-assignment]
+        args = self.parser.parse_args(args)
+        # pyrefly: ignore [missing-attribute]
+        if args.selected_ranks is not None:
+            # pyrefly: ignore [missing-attribute]
+            assert args.just_print_entries, (
+                "Not support selecting ranks without printing entries"
+            )
+        # pyrefly: ignore [missing-attribute]
+        if args.pg_filters is not None:
+            # pyrefly: ignore [missing-attribute]
+            assert args.just_print_entries, (
+                "Not support selecting pg filters without printing entries"
+            )
+        # pyrefly: ignore [missing-attribute]
+        if args.verbose:
+            logger.set_log_level(logging.DEBUG)
+        # pyrefly: ignore [bad-return]
+        return args
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/flight_recorder/components/fr_logger.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/flight_recorder/components/fr_logger.py
new file mode 100644
index 0000000000000000000000000000000000000000..e56634397bff9d6d1ec38eab43f1856f52e02829
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/flight_recorder/components/fr_logger.py
@@ -0,0 +1,54 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+from collections.abc import Callable
+from typing import Any
+
+
+__all__ = ["FlightRecorderLogger"]
+
+
+class FlightRecorderLogger:
+    _instance: Any | None = None
+    logger: logging.Logger
+
+    def __init__(self) -> None:
+        self.logger: logging.Logger = logging.getLogger("Flight Recorder")
+
+    def __new__(cls) -> Any:
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+            cls._instance.logger = logging.getLogger("Flight Recorder")
+            cls._instance.logger.setLevel(logging.INFO)
+            formatter = logging.Formatter("%(message)s")
+            ch = logging.StreamHandler()
+            ch.setFormatter(formatter)
+            cls._instance.logger.addHandler(ch)
+        return cls._instance
+
+    def set_log_level(self, level: int) -> None:
+        self.logger.setLevel(level)
+
+    @property
+    def debug(self) -> Callable[..., None]:
+        return self.logger.debug
+
+    @property
+    def info(self) -> Callable[..., None]:
+        return self.logger.info
+
+    @property
+    def warning(self) -> Callable[..., None]:
+        return self.logger.warning
+
+    @property
+    def error(self) -> Callable[..., None]:
+        return self.logger.error
+
+    @property
+    def critical(self) -> Callable[..., None]:
+        return self.logger.critical
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/flight_recorder/components/loader.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/flight_recorder/components/loader.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce361b103fe04488d0390df1b898d27016f2b47b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/flight_recorder/components/loader.py
@@ -0,0 +1,98 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import argparse
+import gc
+import os
+import pickle
+import re
+import time
+from collections import defaultdict
+from typing import Any
+
+from torch.distributed.flight_recorder.components.fr_logger import FlightRecorderLogger
+
+
+__all__ = [
+    "read_dump",
+    "read_dir",
+]
+
+
+logger: FlightRecorderLogger = FlightRecorderLogger()
+
+
+def read_dump(prefix: str, filename: str) -> dict[str, str | int | list[Any]]:
+    basename = os.path.basename(filename)
+
+    rank = int(basename[len(prefix) :])
+    host_name = f"host_rank{rank}"
+
+    with open(filename, "rb") as infile:
+        dump = pickle.load(infile)
+
+    entries = dump["entries"]
+    version = dump["version"]
+    pg_config = dump["pg_config"]
+
+    return {
+        "host_name": host_name,
+        "rank": rank,
+        "entries": entries,
+        "version": version,
+        "pg_config": pg_config,
+    }
+
+
+exp = re.compile(r"([\w\-\_]*?)(\d+)$")
+
+
+def _determine_prefix(files: list[str]) -> str:
+    """If the user doesn't specify a prefix, but does pass a dir full of similarly-prefixed files, we should be able to
+    infer the common prefix most of the time.  But if we can't confidently infer, just fall back to requiring the user
+    to specify it
+    """
+    possible_prefixes: defaultdict[str, set[int]] = defaultdict(set)
+    for f in files:
+        m = exp.search(f)
+        if m:
+            p, r = m.groups()
+            possible_prefixes[p].add(int(r))
+    if len(possible_prefixes) == 1:
+        prefix = next(iter(possible_prefixes))
+        logger.debug("Inferred common prefix %s", prefix)
+        return prefix
+    else:
+        raise ValueError(
+            "Unable to automatically determine the common prefix for the trace file names. "
+            "Please specify --prefix argument manually"
+        )
+
+
+def read_dir(args: argparse.Namespace) -> tuple[dict[str, dict[str, Any]], str]:
+    gc.disable()
+    prefix = args.prefix
+    details = {}
+    t0 = time.time()
+    version = ""
+    filecount = 0
+    assert os.path.isdir(args.trace_dir), f"folder {args.trace_dir} does not exist"
+    for root, _, files in os.walk(args.trace_dir):
+        if prefix is None:
+            prefix = _determine_prefix(files)
+        for f in files:
+            if (offset := f.find(prefix)) == -1:
+                continue
+            details[f] = read_dump(f[:offset] + prefix, os.path.join(root, f))
+            filecount += 1
+            if not version:
+                version = str(details[f]["version"])
+    tb = time.time()
+    assert len(details) > 0, (
+        f"no files loaded from {args.trace_dir} with prefix {prefix}"
+    )
+    logger.debug("loaded %s files in %ss", filecount, tb - t0)
+    return details, version
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/flight_recorder/components/types.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/flight_recorder/components/types.py
new file mode 100644
index 0000000000000000000000000000000000000000..7fdfd9d8838b5e6d24c96501ba5556dd001b1a6a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/flight_recorder/components/types.py
@@ -0,0 +1,661 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math
+import os
+from enum import auto, Enum
+from typing import (  # type: ignore[attr-defined]
+    _eval_type,
+    Any,
+    Generic,
+    NamedTuple,
+    TypeVar,
+)
+
+from torch.distributed.flight_recorder.components.fr_logger import FlightRecorderLogger
+
+
+__all__ = [
+    "Ref",
+    "TypeInfo",
+    "MatchState",
+    "MatchInfo",
+    "Group",
+    "Membership",
+    "Traceback",
+    "Collective",
+    "NCCLCall",
+    "Database",
+    "EntryState",
+    "Op",
+    "MatchStateRecord",
+]
+
+
+T = TypeVar("T", bound=NamedTuple)
+
+
+class Ref(Generic[T]):
+    pass
+
+
+class TypeInfo(NamedTuple):
+    name: str
+    fields: list[tuple[str, type]]  # type: ignore[type-arg]
+
+    @classmethod
+    def from_type(cls, c: T) -> "TypeInfo":
+        if hasattr(c, "__name__"):
+            name = c.__name__
+        else:
+            name = str(c)
+        return cls(
+            name,
+            [(f, _eval_type(c.__annotations__[f], globals(), {})) for f in c._fields],
+        )
+
+
+class MatchState(Enum):
+    """
+    Enum representing the possible states of matching for collective operations.
+
+    - FULLY_MATCHED: Indicates that all aspects of the collective operations match.
+    - COLLECTIVE_TYPE_MISMATCH: The types of the collective operations differ.
+    - SIZE_OR_SYNTAX_MISMATCH: There is a mismatch in input/output sizes or violation of collective syntax.
+    - COLLECTIVE_STATE_MISMATCH:
+        The states of the collective not same, such as one finished while another just started or scheduled.
+    - COLLECTIVE_DTYPE_MISMATCH: The data types of the collective input/output differ.
+    - UNDECIDED:
+        The match status is ambiguous or cannot be determined, e.g., we might need to check all ranks for alltoall_base.
+    """
+
+    FULLY_MATCHED = auto()
+    COLLECTIVE_TYPE_MISMATCH = auto()
+    SIZE_OR_SYNTAX_MISMATCH = auto()
+    COLLECTIVE_STATE_MISMATCH = auto()
+    COLLECTIVE_DTYPE_MISMATCH = auto()
+    UNDECIDED = auto()
+
+
+class MatchInfo:
+    """
+    Aside from the match state, we also store some dynamic info for the match such as the culprit rank
+    or collective state that caused the mismatch.
+    """
+
+    def __init__(self, state: MatchState, culprit: str | None = None) -> None:
+        self._state = state
+        self.culprit = culprit
+
+    def __str__(self) -> str:
+        details = f", {self.culprit}" if getattr(self, "culprit", None) else ""
+        return f"Error type: {self._state.name}{details}"
+
+    @property
+    def state(self) -> MatchState:
+        return self._state
+
+
+"""
+Schema for flat DB
+
+TODO schemas not yet implemented
+# threads as recorded at termination of process
+Threads
+    id: int
+    traceback_id: int
+    process_id: int
+
+Process:
+    id: int # Same as world groups RANK
+    pid: int
+    hostname: str
+
+NCCLOp:
+    # nccl op implementation details (sends/recv)
+    id: int
+    nccl_call_id: int
+
+"""
+
+
+class Group(NamedTuple):
+    id: str
+    desc: str
+    size: int
+
+
+class Membership(NamedTuple):
+    group_id: str
+    global_rank: int
+
+
+class Traceback(NamedTuple):
+    id: int
+    frames: str
+
+
+class Collective(NamedTuple):
+    id: int
+    group_id: str
+    pass_check: bool
+    collective_seq_id: int
+    p2p_seq_id: int
+    record_id: int
+    pg_desc: str
+    collective_name: str
+    input_sizes: list[list[int]]
+    output_sizes: list[list[int]]
+    expected_ranks: set[int]
+    collective_state: str
+    collective_frames: list[dict[str, str]]
+    input_numel: int | None = None
+    output_numel: int | None = None
+    missing_ranks: set[int] | None = None
+    mismatch_collectives: dict[int, "Collective"] | None = None
+    type_of_mismatch: MatchInfo | None = None
+
+
+class NCCLCall(NamedTuple):
+    id: int
+    collective_id: Ref[Collective]
+    group_id: str
+    global_rank: int  # technically Ref[Process] once we have it
+    traceback_id: Ref[Traceback]
+    collective_type: str
+    sizes: list[list[int]]
+
+
+class Database(NamedTuple):
+    groups: list[Group]
+    memberships: list[Membership]
+    tracebacks: list[Traceback]
+    collectives: list[Collective]
+    ncclcalls: list[NCCLCall]
+
+
+# TODO: We need to add a schema for the following
+types = [
+    TypeInfo.from_type(t)  # type: ignore[type-var]
+    for t in [Database, NCCLCall, Collective, Traceback, Membership, Group]
+    if (
+        isinstance(t, type)
+        and issubclass(t, tuple)
+        and hasattr(t, "_fields")
+        and t is not TypeInfo
+    )
+]
+
+"""
+Stacktrace cache
+TODO
+"""
+
+
+"""
+Collective Matching logic
+
+NOTE: For now, these collectives need to be supported by NCCL,
+https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/overview.html.
+"""
+COLLECTIVES = {
+    "broadcast",
+    "_broadcast_oop",
+    "reduce",
+    "_reduce_oop",
+    "all_gather",
+    "all_reduce",
+    "_all_gather_base",
+    "all_gather_into_tensor_coalesced",
+    "reduce_scatter",
+    "reduce_scatter_tensor_coalesced",
+    "_reduce_scatter_base",
+    "gather",
+    "scatter",
+    "all_to_all",
+    "all_reduce_barrier",
+    "allreduce_coalesced",
+    "ALLGATHER_coalesced",
+    "REDUCE_SCATTER_coalesced",
+}
+
+P2P = {
+    "send",
+    "recv",
+}
+
+
+class EntryState:
+    """
+    Util class to keep track of the state of an entry and standardize the way we
+    log the error info during analysis.
+    """
+
+    def __init__(self, entry: dict[str, Any], expected_ranks: set[int]) -> None:
+        self.pg_name = entry["process_group"][0]
+        self.desc = entry["process_group"][1]
+        self.pg_desc = (
+            f"{self.pg_name}:{self.desc}" if self.desc != "undefined" else self.pg_name
+        )
+        self.profiling_name = entry["profiling_name"]
+        self.collective_seq_id = entry["collective_seq_id"]
+        self.p2p_seq_id = entry["p2p_seq_id"]
+        self.record_id = entry["record_id"]
+        self.input_sizes = entry["input_sizes"]
+        self.output_sizes = entry["output_sizes"]
+        self.collective_state = entry["state"]
+        self.collective_frames = entry.get("frames", [])
+        self.expected_ranks = expected_ranks
+        self.missing_ranks: set[int]
+        self.input_numel: int
+        self.output_numel: int
+        self.errors: set[tuple[int, MatchInfo]]
+
+    def log(
+        self,
+        logger: FlightRecorderLogger,
+        logger_msg: str,
+        frame_formatter: Any,
+        total_numel: tuple[int, int] | None = None,
+        errors: set[tuple[int, MatchInfo]] | None = None,
+        missing_ranks: set[int] | None = None,
+    ) -> None:
+        logger.info(
+            logger_msg,
+            self.collective_seq_id,
+        )
+        logger.info("internal record id: %s", self.record_id)
+        logger.info("group info: %s", self.pg_desc)
+        logger.info("collective: %s", self.profiling_name)
+        if missing_ranks:
+            self.missing_ranks = missing_ranks
+            logger.info("missing ranks: %s", missing_ranks)
+        if total_numel:
+            self.input_numel = total_numel[0]
+            self.output_numel = total_numel[1]
+            logger.info("total input numel: %d", total_numel[0])
+            logger.info("total output numel: %d", total_numel[1])
+        logger.info("input sizes: %s", self.input_sizes)
+        logger.info("output sizes: %s", self.output_sizes)
+        logger.info("world size: %d", len(self.expected_ranks))
+        logger.info("expected ranks: %s", str(self.expected_ranks))
+        logger.info("collective state: %s", self.collective_state)
+        if errors:
+            self.errors = errors
+            error_msg = ", ".join(
+                f"Culprit rank {error[0]}; {str(error[1])}" for error in errors
+            )
+            logger.info("error msg: %s", error_msg)
+        logger.info(
+            "collective stack trace: \n %s", frame_formatter(self.collective_frames)
+        )
+
+    def to_collective(
+        self,
+        id: int,
+        errors: set[tuple[int, MatchInfo]] | None = None,
+        idx_map: dict[int, int] | None = None,
+        all_entries: dict[int, list[dict[str, Any]]] | None = None,
+    ) -> Collective:
+        if not errors:
+            return Collective(
+                id=id,
+                group_id=self.pg_name,
+                record_id=self.record_id,
+                pg_desc=self.pg_desc,
+                pass_check=True,
+                collective_seq_id=self.collective_seq_id,
+                p2p_seq_id=self.p2p_seq_id,
+                collective_name=self.profiling_name,
+                input_sizes=self.input_sizes,
+                output_sizes=self.output_sizes,
+                expected_ranks=self.expected_ranks,
+                collective_state=self.collective_state,
+                collective_frames=self.collective_frames,
+                missing_ranks=getattr(self, "missing_ranks", None),
+            )
+        else:
+            assert idx_map is not None, "idx_map is None"
+            assert all_entries is not None, "all_entries is None"
+            mismatch_collectives = {}
+            for rank, error in errors:
+                idx = idx_map[rank]
+                entry = all_entries[rank][idx]
+                desc = entry["process_group"][1]
+                pg_name = entry["process_group"][0]
+                mismatch_collectives[rank] = Collective(
+                    id=id,
+                    group_id=entry["process_group"][0],
+                    record_id=entry["record_id"],
+                    pg_desc=f"{pg_name}:{desc}" if desc != "undefined" else pg_name,
+                    pass_check=False,
+                    collective_seq_id=entry["collective_seq_id"],
+                    p2p_seq_id=entry["p2p_seq_id"],
+                    collective_name=entry["profiling_name"],
+                    input_sizes=entry["input_sizes"],
+                    output_sizes=entry["output_sizes"],
+                    expected_ranks=self.expected_ranks,
+                    collective_state=entry["state"],
+                    collective_frames=entry.get("frames", []),
+                    type_of_mismatch=error,
+                )
+            return Collective(
+                id=id,
+                group_id=self.pg_name,
+                record_id=self.record_id,
+                pg_desc=self.pg_desc,
+                pass_check=False,
+                collective_seq_id=self.collective_seq_id,
+                p2p_seq_id=self.p2p_seq_id,
+                collective_name=self.profiling_name,
+                input_sizes=self.input_sizes,
+                output_sizes=self.output_sizes,
+                expected_ranks=self.expected_ranks,
+                collective_state=self.collective_state,
+                collective_frames=self.collective_frames,
+                input_numel=self.input_numel if hasattr(self, "input_numel") else None,
+                output_numel=self.output_numel
+                if hasattr(self, "output_numel")
+                else None,
+                missing_ranks=self.missing_ranks
+                if hasattr(self, "missing_ranks")
+                else None,
+                mismatch_collectives=mismatch_collectives,
+            )
+
+    def to_nccl_call(
+        self,
+        all_entries: dict[int, list[dict[str, Any]]],
+        idx_map: dict[int, int],
+        nccl_call_id: int,
+        collective_id: Any,
+    ) -> list[NCCLCall]:
+        result = []
+        for i, k in idx_map.items():
+            all_entries[i].pop(k)
+            result.append(
+                NCCLCall(
+                    id=nccl_call_id,
+                    collective_id=collective_id,
+                    group_id=self.pg_name,  # type: ignore[arg-type]
+                    global_rank=i,
+                    traceback_id=0,  # type: ignore[arg-type]
+                    collective_type=self.profiling_name,
+                    sizes=self.input_sizes,
+                )
+            )
+            nccl_call_id += 1
+        return result
+
+
+class Op:
+    """Parses relevant info about operation out of 'event' dict
+
+    examples of supported `profiling_name`s:
+        nccl:broadcast
+        nccl:send 1->2
+        nccl:recv 3<-0
+    """
+
+    def __init__(
+        self, event: dict[Any, Any], memberships: dict[str, set[Any]], pg_name: str
+    ):
+        self.profiling_name = event["profiling_name"]
+        comm_lib_backend, name = self.profiling_name.split(":")
+        assert comm_lib_backend in ["nccl", "xccl"], (
+            f"name formatting error? {comm_lib_backend} != 'nccl' or 'xccl'"
+        )
+        parts = name.split(" ")
+        type = parts[0]
+        meta = parts[1] if len(parts) == 2 else None
+        self.state = event["state"]
+        # Store the hashed pg_name for accessing memberships, and original pg info for display
+        self.pg_name = pg_name  # This is the hashed version used for memberships lookup
+        self.original_pg_name, self.pg_desc = event["process_group"]
+        assert type in COLLECTIVES | P2P | {"coalesced"}, (
+            f"{type} is not a supported operation"
+        )
+        self.type = type
+        if type == "send":
+            assert isinstance(meta, str)
+            s, d = meta.split("->")
+            self._src, self._dst = int(s), int(d)
+        elif type == "recv":
+            assert isinstance(meta, str)
+            d, s = meta.split("<-")
+            self._dst, self._src = int(d), int(s)
+        else:
+            self._src, self._dst = -1, -1
+        self._init_global_src_dst(memberships[pg_name])
+        self.pg_size = len(memberships[pg_name])
+        if type in P2P | COLLECTIVES:
+            self.input_sizes = event["input_sizes"]
+            self.output_sizes = event["output_sizes"]
+        else:
+            self.input_sizes, self.output_sizes = None, None
+        self.collective_seq_id = event["collective_seq_id"]
+        self.stack_id = event.get("stack_id", -1)
+        self.p2p_seq_id = event["p2p_seq_id"]
+        self.input_dtypes = event["input_dtypes"]
+        self.output_dtypes = event["output_dtypes"]
+        self.time_created_ns = event["time_created_ns"]
+        self.collective_frames = event.get("frames", [])
+        self.is_verbose = os.getenv("FR_TRACE_VERBOSE_OUTPUT", "0") == "1"
+
+    def _init_global_src_dst(self, pg_ranks: set[Any]) -> None:
+        pg_ranks_sorted = sorted(pg_ranks)
+        self._src_g = pg_ranks_sorted[self._src] if self._src is not None else None
+        self._dst_g = pg_ranks_sorted[self._dst] if self._dst is not None else None
+
+    @property
+    def src(self) -> int:
+        assert self.type in P2P, "can't get src of non-p2p op"
+        return self._src
+
+    @property
+    def dst(self) -> int:
+        assert self.type in P2P, "can't get dst of non-p2p op"
+        return self._dst
+
+    def __repr__(self) -> str:
+        p2p_info = ""
+        if self.type in P2P:
+            p2p_info = f"s={self._src_g} d={self._dst_g}"
+        if self.is_verbose:
+            verbose_info = (
+                f"timestamp_created={self.time_created_ns}",
+                p2p_info,
+                f"input_sizes={self.input_sizes}",
+                f"output_sizes={self.output_sizes}",
+                f"input_dtypes={self.input_dtypes}",
+                f"output_dtypes={self.output_dtypes}",
+                "collective_seq_id | p2p_seq_id="
+                f"{self.p2p_seq_id if self.type in P2P else self.collective_seq_id}",
+                f"pg_name={self.pg_name}",
+                f"pg_description={self.pg_desc}",
+                f"pg_size={self.pg_size}",
+                f"stack_id={self.stack_id}",
+                f"state={self.state}",
+            )
+            return f"{self.type}(%s)" % ", ".join(s for s in verbose_info if s)
+        return f"{self.type}(%sinput_sizes={self.input_sizes}, state={self.state})" % (
+            f"{p2p_info}, " if p2p_info else ""
+        )
+
+    def dtype_mismatch(self, other: "Op") -> bool:
+        if (
+            (
+                self.type not in ["scatter", "gather", "broadcast"]
+                and set(self.input_dtypes) != set(self.output_dtypes)
+                and self.input_sizes[0]
+                and self.output_sizes[0]
+            )
+            or (
+                self.type not in ["scatter", "broadcast"]
+                and set(self.input_dtypes) != set(other.input_dtypes)
+                and self.input_sizes[0]
+                and other.input_sizes[0]
+            )
+            or (
+                self.type not in ["gather"]
+                and set(self.output_dtypes) != set(other.output_dtypes)
+                and self.output_sizes[0]
+                and other.output_sizes[0]
+            )
+        ):
+            return True
+        return False
+
+    def match(self, other: "Op") -> MatchInfo:
+        # TODO: I think this can validly not match,
+        # e.g. if one PG was used for p2p ops between only some of the peers?
+        # if self.seq_id != other.seq_id:
+        # return False
+
+        if self.type == "send":
+            # TODO: We need more states for p2p ops.
+            return (
+                MatchInfo(MatchState.FULLY_MATCHED)
+                if (
+                    other.type == "recv"
+                    and self.src == other.src
+                    and self.dst == other.dst
+                    and self.input_sizes == other.output_sizes
+                )
+                else MatchInfo(MatchState.SIZE_OR_SYNTAX_MISMATCH)
+            )
+        elif self.type == "recv":
+            return (
+                MatchInfo(MatchState.FULLY_MATCHED)
+                if (
+                    other.type == "send"
+                    and self.src == other.src
+                    and self.dst == other.dst
+                    and self.output_sizes == other.input_sizes
+                )
+                else MatchInfo(MatchState.SIZE_OR_SYNTAX_MISMATCH)
+            )
+        elif self.type in COLLECTIVES:
+            if self.type != other.type:
+                return MatchInfo(
+                    MatchState.COLLECTIVE_TYPE_MISMATCH,
+                    f"Expected collective type: '{self.type}' does not match found collective type: '{other.type}'",
+                )
+            if (
+                self.type not in ["all_to_all", "scatter"]
+                and self.input_sizes != other.input_sizes
+            ):
+                return MatchInfo(
+                    MatchState.SIZE_OR_SYNTAX_MISMATCH,
+                    f"Expected input sizes: '{self.input_sizes}' does not match found input sizes: "
+                    f"'{other.input_sizes}'",
+                )
+            if (
+                self.type not in ["all_to_all", "gather"]
+                and self.output_sizes != other.output_sizes
+            ):
+                return MatchInfo(
+                    MatchState.SIZE_OR_SYNTAX_MISMATCH,
+                    f"Expected output sizes: '{self.output_sizes}' does not match found output sizes: "
+                    f"'{other.output_sizes}'",
+                )
+            if (
+                self.type in ["all_reduce", "allreduce_coalesced"]
+                and self.input_sizes != other.output_sizes
+            ):
+                return MatchInfo(
+                    MatchState.SIZE_OR_SYNTAX_MISMATCH,
+                    f"Expected input sizes: '{self.input_sizes}' does not match found output sizes: '{other.output_sizes}'",
+                )
+            if (
+                self.type
+                in [
+                    "all_gather",
+                    "all_gather_base",
+                    "all_gather_into_tensor_coalesced",
+                ]
+                and math.prod(other.output_sizes[0])
+                != math.prod(self.input_sizes[0]) * self.pg_size
+            ):
+                return MatchInfo(
+                    MatchState.SIZE_OR_SYNTAX_MISMATCH,
+                    f"Found input numel '{math.prod(other.input_sizes[0])} * pg size {self.pg_size}' "
+                    f"does not match output numel '{math.prod(other.output_sizes[0])}'",
+                )
+            if (
+                self.type
+                in [
+                    "reduce_scatter",
+                    "_reduce_scatter_base",
+                    "reduce_scatter_tensor_coalesced",
+                ]
+                and math.prod(other.input_sizes[0])
+                != math.prod(self.output_sizes[0]) * self.pg_size
+            ):
+                return MatchInfo(
+                    MatchState.SIZE_OR_SYNTAX_MISMATCH,
+                    f"Found input numel '{math.prod(other.input_sizes[0])}' does not match output numel "
+                    f"'{math.prod(other.output_sizes[0])} * pg size {self.pg_size}'",
+                )
+            if self.dtype_mismatch(other):
+                return MatchInfo(
+                    MatchState.COLLECTIVE_DTYPE_MISMATCH,
+                    f"Expected dtypes: '{set(self.input_dtypes)}' does not "
+                    f"match found dtype: '{set(self.output_dtypes)}/"
+                    f"{set(other.input_dtypes)}/{set(other.output_dtypes)}'",
+                )
+            if self.state != other.state:
+                # MatchState()
+                return MatchInfo(
+                    MatchState.COLLECTIVE_STATE_MISMATCH,
+                    f"Expected state: '{self.state}' does not match found state: '{other.state}'",
+                )
+            if self.type == "all_to_all":
+                return MatchInfo(MatchState.UNDECIDED)
+        elif self.type in [
+            "coalesced",
+            "ALLGATHER_coalesced",
+            "REDUCE_SCATTER_coalesced",
+        ]:
+            return (
+                MatchInfo(MatchState.FULLY_MATCHED)
+                if (other.type == self.type)
+                else MatchInfo(MatchState.SIZE_OR_SYNTAX_MISMATCH)
+            )
+        return MatchInfo(MatchState.FULLY_MATCHED)
+
+
+class MatchStateRecord:
+    def __init__(
+        self,
+        expected_ranks: set[int],
+        other_ranks: list[int],
+        entry_state: EntryState,
+        candidate_ranks: set[int],
+        candidate_idx: dict[int, int],
+        found_ranks: set[int],
+        found_idx: dict[int, int],
+        errors: set[tuple[int, MatchInfo]],
+    ) -> None:
+        self.expected_ranks = expected_ranks
+        self.other_ranks = other_ranks
+        self.entry_state = entry_state
+        self.candidate_ranks = candidate_ranks
+        self.candidate_idx = candidate_idx
+        self.found_ranks = found_ranks
+        self.found_idx = found_idx
+        self.errors = errors
+        self.has_undecided_case = False
+
+    def reset_for_coalesced(
+        self, entry_state: EntryState, candidate_ranks: set[int]
+    ) -> None:
+        self.entry_state = entry_state
+        self.candidate_ranks = candidate_ranks
+        self.candidate_idx = {}
+        self.found_ranks = set()
+        self.found_idx = {}
+        self.errors = set()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/flight_recorder/components/utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/flight_recorder/components/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..6ab7919a2a24d81e7be692bc8bb9b0c326a99b28
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/flight_recorder/components/utils.py
@@ -0,0 +1,789 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import argparse
+import math
+from typing import Any
+
+from torch.distributed.flight_recorder.components.fr_logger import FlightRecorderLogger
+from torch.distributed.flight_recorder.components.types import (
+    Collective,
+    EntryState,
+    Group,
+    MatchInfo,
+    MatchState,
+    MatchStateRecord,
+    Membership,
+    Op,
+    P2P,
+)
+
+
+__all__ = [
+    "add_stack_id_in_entries",
+    "align_trace_from_beginning",
+    "check_current_entry_match",
+    "check_no_missing_dump_files",
+    "check_version",
+    "error_analysis",
+    "find_coalesced_group",
+    "find_coalesced_group_with_non_p2p",
+    "get_version_detail",
+    "just_print_entries",
+    "match_coalesced_groups_with_non_p2p",
+    "match_coalesced_groups",
+    "format_frame",
+    "format_frames",
+    "match_one_event",
+    "check_size_alltoall",
+]
+
+logger: FlightRecorderLogger = FlightRecorderLogger()
+
+
+try:
+    from tabulate import tabulate
+except ModuleNotFoundError:
+    logger.debug("tabulate is not installed. Proceeding without it.")
+
+
+def format_frame(frame: dict[str, str]) -> str:
+    name = frame["name"]
+    filename = frame["filename"]
+    line = frame["line"]
+    return f"{name} at {filename}:{line}"
+
+
+def format_frames(frames: list[dict[str, str]]) -> str:
+    formatted_frames = []
+    for frame in frames:
+        # pyrefly: ignore [bad-argument-type]
+        formatted_frames.append(format_frame(frame))
+    return "\n".join(formatted_frames)
+
+
+def match_one_event(
+    event_a: dict[Any, Any],
+    event_b: dict[Any, Any],
+    memberships: dict[str, set[Any]],
+    pg_name: str,
+) -> MatchInfo:
+    op_a = Op(event_a, memberships, pg_name)
+    op_b = Op(event_b, memberships, pg_name)
+    return op_a.match(op_b)
+
+
+def match_coalesced_groups(
+    all_rank_events: dict[Any, Any],
+    group_size: int,
+    groups: dict[str, Group],
+    memberships: dict[str, set[Any]],
+    _pg_guids: dict[tuple[str, int], str],
+) -> bool:
+    """
+    all_rank_events: {
+        rank: [
+            (idx, event_dict)
+        ]
+    }
+
+    Note: it is possible for event dicts in a coalesced group to be asymmetric.
+        e.g. the following events lists form a valid coalescing group
+             events0 [send:1]
+             events1 [recv:0, send:2]
+             events2 [recv:1]
+
+    Rule 1: all ops should find a match
+    Rule 2: relative ordering of sends and recvs in one event list can be arbitrary
+        e.g.
+        events1 [recv:0, send:2]  —> okay
+        events1 [send:2, recv:0] —> also okay
+    Rule 3: sends to the same dest or recvs from the src should be in a consistent order
+        e.g.
+        rank0 [send:1 (100B), send:1 (1000B)]
+        rank1 [recv:0 (1000B), recv:0 (100B)]   —> not okay
+    """
+    all_ops = {
+        rank: [
+            Op(e, memberships, _pg_guids[(e["process_group"][0], rank)])
+            for i, e in all_rank_events[rank]
+        ]
+        for rank in all_rank_events
+    }
+
+    def visualize_ops(
+        match: bool,
+        _pg_guids: dict[tuple[str, int], str],
+    ) -> None:
+        all_ops = {
+            rank: [
+                Op(e, memberships, _pg_guids[(e["process_group"][0], rank)])
+                for i, e in all_rank_events[rank]
+            ]
+            for rank in all_rank_events
+        }
+
+        i = 0
+        row = []
+        progress = True
+        table = []
+        while progress:
+            progress = False
+            for r in all_ops:
+                if len(all_ops[r]) > i:
+                    rank, event = all_rank_events[r][i]
+                    # Check if the pg_guid exists for this rank and process group
+                    pg_key = (event["process_group"][0], rank)
+                    if pg_key in _pg_guids:
+                        row.append(
+                            Op(
+                                event,
+                                memberships,
+                                _pg_guids[pg_key],
+                            )
+                        )
+                    else:
+                        # Skip this entry if pg_guid mapping doesn't exist
+                        row.append(None)  # type: ignore[arg-type]
+                    progress = True
+                else:
+                    row.append(None)  # type: ignore[arg-type]
+            table.append(row)
+            row = []
+            i += 1
+        title = "Match" if match else "MISMATCH"
+        logger.info("%s \n", title)
+        logger.info("%s", tabulate(table))  # type: ignore[operator]
+
+    # TODO can't verify seq_id bc there might have been valid seq deltas between ranks even within a pg.
+    for op_list in all_ops.values():
+        if not op_list:
+            # print("TODO- not sure if its valid for only some ranks in a PG to participate in a coalesced op?")
+            return False
+        assert op_list[-1].type == "coalesced"
+        op_list.pop(-1)
+
+    while all_ops:
+        first_rank = next(iter(all_ops))
+        my_ops = all_ops[first_rank]
+
+        if len(all_ops[first_rank]) == 0:
+            all_ops.pop(first_rank)
+            continue
+
+        # lets match the first collective! we need to know which ranks are involved, and ensure that this same
+        # collective is also the first one on those ranks within that group
+        op = my_ops[0]
+        match_idx = -1
+        if op.type in P2P:
+            dst_global_rank = sorted(memberships[op.pg_name])[op.dst]
+            peer_ops = all_ops[dst_global_rank]
+            for i, other in enumerate(peer_ops):
+                if op.match(other).state == MatchState.FULLY_MATCHED:
+                    match_idx = i
+                    break
+                elif op.dst == other.src:
+                    # Rule 3
+                    break
+                else:
+                    # Rule 1
+                    continue
+        else:
+            raise NotImplementedError("coalesced collective ops")
+        if match_idx >= 0:
+            my_ops.pop(0)
+            peer_ops.pop(match_idx)
+        else:
+            visualize_ops(False, _pg_guids)
+            return False
+
+    visualize_ops(True, _pg_guids)
+    return True
+
+
+# We enabled the creating FR entry for non-P2P slow path collective ops in v2.7.
+def match_coalesced_groups_with_non_p2p(
+    all_rank_events: dict[Any, Any],
+    pg_info: tuple[str, str],
+    memberships: dict[str, set[Any]],
+    _pg_guids: dict[tuple[str, int], str],
+    mismatch: dict[str, int],
+    dumps_ranks: set[int],
+    version: str,
+    collectives: list[Collective],
+    match_record: MatchStateRecord,
+) -> bool:
+    """
+    all_rank_events: {
+        rank: [
+            (idx, event_dict)
+        ]
+    }
+
+    Note: it is possible for event dicts in a coalesced group to be asymmetric.
+        e.g. the following events lists form a valid coalescing group
+             events0 [send:1]
+             events1 [recv:0, send:2]
+             events2 [recv:1]
+
+    Rule 1: all ops should find a match
+    Rule 2: relative ordering of sends and recvs in one event list can be arbitrary
+        e.g.
+        events1 [recv:0, send:2]  —> okay
+        events1 [send:2, recv:0] —> also okay
+    Rule 3: sends to the same dest or recvs from the src should be in a consistent order
+        e.g.
+        rank0 [send:1 (100B), send:1 (1000B)]
+        rank1 [recv:0 (1000B), recv:0 (100B)]   —> not okay
+    """
+    all_ops = {
+        rank: [
+            Op(e, memberships, _pg_guids[(e["process_group"][0], rank)])
+            for _, e in all_rank_events[rank]
+        ]
+        for rank in all_rank_events
+    }
+    is_p2p = any(op.type in P2P for ops in all_ops.values() for op in ops)
+    pg_name = pg_info[0]
+
+    def visualize_ops(
+        match: bool,
+        _pg_guids: dict[tuple[str, int], str],
+    ) -> None:
+        all_ops = {
+            rank: [
+                Op(e, memberships, _pg_guids[(e["process_group"][0], rank)])
+                for _, e in all_rank_events[rank]
+            ]
+            for rank in all_rank_events
+        }
+
+        i = 0
+        row = []
+        progress = True
+        table = []
+        while progress:
+            progress = False
+            for r in all_ops:
+                if len(all_ops[r]) > i:
+                    rank, event = all_rank_events[r][i]
+                    # Check if the pg_guid exists for this rank and process group
+                    pg_key = (event["process_group"][0], rank)
+                    if pg_key in _pg_guids:
+                        row.append(
+                            Op(
+                                event,
+                                memberships,
+                                _pg_guids[pg_key],
+                            )
+                        )
+                    else:
+                        # Skip this entry if pg_guid mapping doesn't exist
+                        row.append(None)  # type: ignore[arg-type]
+                    progress = True
+                else:
+                    row.append(None)  # type: ignore[arg-type]
+            table.append(row)
+            row = []
+            i += 1
+        title = "Match" if match else "MISMATCH"
+        logger.info("%s \n", title)
+        logger.info("%s", tabulate(table))  # type: ignore[operator]
+
+    # TODO Need to verify no seq_id deltas for P2P ops.
+    for rank, op_list in all_ops.items():
+        if not op_list:
+            logger.error("Rank %s has an empty op list.", rank)
+            continue
+        if op_list[-1].type == "coalesced" and is_p2p:
+            op_list.pop(-1)
+
+    while all_ops:
+        first_rank = next(iter(all_ops))
+        my_ops = all_ops[first_rank]
+
+        if len(all_ops[first_rank]) == 0:
+            all_ops.pop(first_rank)
+            continue
+
+        # lets match the first collective! we need to know which ranks are involved, and ensure that this same
+        # collective is also the first one on those ranks within that group
+        op = my_ops[0]
+        match_idx = -1
+        if is_p2p:
+            dst_global_rank = sorted(memberships[op.pg_name])[op.dst]
+            peer_ops = all_ops[dst_global_rank]
+            for i, other in enumerate(peer_ops):
+                if op.match(other).state == MatchState.FULLY_MATCHED:
+                    match_idx = i
+                    break
+                elif op.dst == other.src:
+                    # Rule 3
+                    break
+                else:
+                    # Rule 1
+                    continue
+            if match_idx >= 0:
+                my_ops.pop(0)
+                peer_ops.pop(match_idx)
+            else:
+                visualize_ops(False, _pg_guids)
+                return False
+        else:
+            all_coalesced_entries = {
+                rank: [e for _, e in all_rank_events[rank]] for rank in all_rank_events
+            }
+            current_entry = all_coalesced_entries[first_rank][0]
+            my_ops.pop(0)
+
+            match_record.reset_for_coalesced(
+                EntryState(current_entry, match_record.expected_ranks),
+                {first_rank},
+            )
+
+            # Iterate through all the ranks and check if there is a mismatch for the current entry.
+            check_current_entry_match(
+                all_coalesced_entries,
+                _pg_guids,
+                pg_info,
+                current_entry,
+                memberships,
+                mismatch,
+                match_record,
+            )
+
+            # Use heuristics to decide what type of errors and error messages we should print.
+            error_analysis(
+                all_coalesced_entries,
+                match_record,
+                dumps_ranks,
+                first_rank,
+                current_entry,
+                mismatch,
+                get_version_detail(version),
+                pg_info[0],
+            )
+
+            # TODO: For now, we only check the correctness of individual collective within a coalesced one in
+            # this script. We need to merge  (e.g, input/output sizes) together
+            # for downstream consumer.
+
+            # at this point there are 3 possibilities
+            # 1. we found a match on all the ranks that are members of the group
+            #  -> we create a Collective and remove the individual entries from their original lists
+            if (
+                match_record.found_ranks == match_record.expected_ranks
+                and mismatch[pg_name] == 0
+            ):
+                # Just pop out this collective.
+                idx_map = {
+                    r: match_record.found_idx[r] if r != first_rank else 0
+                    for r in match_record.found_ranks
+                }
+                for i, k in idx_map.items():
+                    all_rank_events[i].pop(k)
+                for r in match_record.found_ranks:
+                    if r != first_rank:
+                        all_ops[r].pop(0)
+
+            # 2. we found a partial match but some ranks are missing
+            # 3. we found no match
+            #  -> since its not a complete collective, no entry goes into collectives but we still record a nccl call
+            else:
+                logger.debug("Non-matching collective inside coalesced group")
+                idx_map = {
+                    r: match_record.candidate_idx[r] if r != first_rank else 0
+                    for r in match_record.candidate_ranks
+                }
+                collectives.append(
+                    match_record.entry_state.to_collective(
+                        len(collectives),
+                        errors=match_record.errors,
+                        idx_map=idx_map,
+                        all_entries=all_coalesced_entries,
+                    )
+                )
+                return False
+
+    if is_p2p:
+        visualize_ops(True, _pg_guids)
+    return True
+
+
+def check_size_alltoall(alltoall_cases: list[dict[str, Any]]) -> tuple[bool, int, int]:
+    input_numel = 0
+    output_numel = 0
+    for e in alltoall_cases:
+        input_numel += math.prod(e["input_sizes"][0])
+        output_numel += math.prod(e["output_sizes"][0])
+    return input_numel != output_numel, input_numel, output_numel
+
+
+def check_current_entry_match(
+    all_entries: dict[int, list[dict[str, Any]]],
+    _pg_guids: dict[tuple[str, int], str],
+    pg_info: tuple[str, str],
+    current_entry: dict[str, Any],
+    _memberships: dict[str, set[Any]],
+    mismatch: dict[str, int],
+    match_record: MatchStateRecord,
+) -> None:
+    pg_name, desc = pg_info[0], pg_info[1]
+    for o in match_record.expected_ranks.intersection(set(match_record.other_ranks)):
+        for i, e in enumerate(all_entries[o]):  # type: ignore[index]
+            # step over ops from other PGs
+            # only check match state when seq_id matches
+            if (
+                _pg_guids[(e["process_group"][0], o)] == pg_name
+                and e["process_group"][1] == desc
+                and e["collective_seq_id"] == match_record.entry_state.collective_seq_id
+            ):
+                match_info = match_one_event(current_entry, e, _memberships, pg_name)
+                if (
+                    match_info.state in [MatchState.FULLY_MATCHED, MatchState.UNDECIDED]
+                    and mismatch[pg_name] == 0
+                ):
+                    match_record.found_ranks.add(o)
+                    match_record.found_idx[o] = i
+                    match_record.has_undecided_case = (
+                        match_info.state == MatchState.UNDECIDED
+                    )
+                else:
+                    match_record.candidate_ranks.add(o)
+                    match_record.candidate_idx[o] = i
+                    if match_info.state not in [
+                        MatchState.FULLY_MATCHED,
+                        MatchState.UNDECIDED,
+                    ]:
+                        # Here we assume the current rank is not the source of the error.
+                        # But it's possible that the current rank is the culprit, then users will
+                        # see lots of normal ranks reported as culprit.
+                        # TODO: we need to figure out a better way to handle the case mentioned above.
+                        match_record.errors.add((o, match_info))
+                break
+
+
+def error_analysis(
+    all_entries: dict[int, list[dict[str, Any]]],
+    match_record: MatchStateRecord,
+    dumps_ranks: set[int],
+    first_rank: int,
+    current_entry: dict[str, Any],
+    mismatch: dict[str, int],
+    version: tuple[int, int],
+    pg_name: str,
+) -> None:
+    major_v, minor_v = version[0], version[1]
+    # case one: not every rank join the collective or in the flight recorder.
+    if (
+        match_record.candidate_ranks | match_record.found_ranks
+    ) != match_record.expected_ranks and match_record.expected_ranks - (
+        match_record.candidate_ranks | match_record.found_ranks
+    ) <= dumps_ranks:
+        mismatch[pg_name] += 1
+        logger_msg = "Not all ranks joining collective, sequence number: %s"
+        missing_ranks = match_record.expected_ranks - (
+            match_record.candidate_ranks | match_record.found_ranks
+        )
+        match_record.entry_state.log(
+            logger, logger_msg, format_frames, missing_ranks=missing_ranks
+        )
+        match_record.candidate_ranks.update(match_record.found_ranks)
+        match_record.candidate_idx.update(match_record.found_idx)
+        match_record.found_idx.clear()
+        match_record.found_ranks.clear()
+    # We didn't see any mismatch and all expected ranks are in the dump.
+    elif len(
+        match_record.candidate_ranks
+    ) == 1 and match_record.expected_ranks.issubset(dumps_ranks):
+        # case two: alltoall or alltoall_base case.
+        if match_record.has_undecided_case:
+            alltoall_cases = [current_entry] + [
+                all_entries[o][match_record.found_idx[o]]
+                for o in match_record.found_ranks
+            ]
+            fail_check, total_input_numel, total_output_numel = check_size_alltoall(
+                alltoall_cases
+            )
+            if major_v <= 2 and minor_v <= 3:
+                # We don't log the input/output sizes for alltoall before v2.4,
+                # so we don't consider the size mismatch as an error for now.
+                fail_check = False
+            if fail_check:
+                # When we see errors in all_to_all, it's hard to tell which rank is the source of the error.
+                mismatch[pg_name] += 1
+                logger_msg = (
+                    "Input/output mismatch in the collective sequence number: %s"
+                )
+                match_record.entry_state.log(
+                    logger,
+                    logger_msg,
+                    format_frames,
+                    total_numel=(total_input_numel, total_output_numel),
+                )
+                match_record.candidate_ranks.update(match_record.found_ranks)
+                match_record.candidate_idx.update(match_record.found_idx)
+                match_record.found_idx.clear()
+                match_record.found_ranks.clear()
+                match_record.errors.add(
+                    (first_rank, MatchInfo(MatchState.SIZE_OR_SYNTAX_MISMATCH))
+                )
+            else:
+                match_record.found_ranks.update(match_record.candidate_ranks)
+                match_record.found_idx.update(match_record.candidate_idx)
+                match_record.candidate_idx.clear()
+                match_record.candidate_ranks.clear()
+        # case three: all joined and everything matches on all ranks.
+        else:
+            match_record.found_ranks.update(match_record.candidate_ranks)
+            match_record.found_idx.update(match_record.candidate_idx)
+            match_record.candidate_idx.clear()
+            match_record.candidate_ranks.clear()
+    # case four: mismatch cases due to not same type, size mismatch or state mismatch.
+    elif len(match_record.errors) > 0:
+        mismatch[pg_name] += 1
+        logger_msg = "Collective sequence number: %s has errors"
+        match_record.entry_state.log(
+            logger, logger_msg, format_frames, errors=match_record.errors
+        )
+        match_record.candidate_ranks.update(match_record.found_ranks)
+        match_record.candidate_idx.update(match_record.found_idx)
+        match_record.found_idx.clear()
+        match_record.found_ranks.clear()
+    # partial analysis case when we cannot decide what's wrong with this collective entry.
+    else:
+        match_record.candidate_ranks.update(match_record.found_ranks)
+        match_record.candidate_idx.update(match_record.found_idx)
+        match_record.found_idx.clear()
+        match_record.found_ranks.clear()
+        # if any element in expected_ranks not in dumps_ranks.
+        if match_record.expected_ranks - dumps_ranks:
+            mismatch[pg_name] += 1
+            logger.info(
+                "We cannot decide what's wrong with this collective entry "
+                "because we missed FR dumps from ranks (%s) so we don't have enough "
+                "information. If you want to debug further use -j to dump all raw trace",
+                str(match_record.expected_ranks - dumps_ranks),
+            )
+        else:
+            logger.info(
+                "No errors found for this collective entry, There could be some "
+                "other reasons why we see collective timeout."
+            )
+
+
+def find_coalesced_group(
+    pg_name: str,
+    entries: list[dict[str, Any]],
+    _pg_guids: dict[tuple[str, int], str],
+    rank: int,
+) -> list[tuple[int, dict[str, Any]]]:
+    """Given a list of entries, if the collective_seq_id of the first entry matches that of subsequent ones,
+    build an return a list of entries terminating in a 'coalesced' op entry all sharing a collective_seq_id
+    """
+    found = []
+    collective_seq_id = None
+    for i, e in enumerate(entries):
+        if _pg_guids[(e["process_group"][0], rank)] != pg_name:
+            continue
+        elif collective_seq_id is None:
+            collective_seq_id = (
+                e["p2p_seq_id"] if e["is_p2p"] else e["collective_seq_id"]
+            )
+            found.append((i, e))
+        elif not e["is_p2p"] and e["collective_seq_id"] == collective_seq_id:
+            found.append((i, e))
+        elif e["is_p2p"] and e["p2p_seq_id"] == collective_seq_id:
+            found.append((i, e))
+        else:
+            break
+
+    if len(found) > 1:
+        assert found[-1][1]["profiling_name"] == "nccl:coalesced"
+        return found
+    return []
+
+
+# We enabled the creating FR entry for non-P2P slow path collective ops in v2.7.
+def find_coalesced_group_with_non_p2p(
+    pg_name: str,
+    entries: list[dict[str, Any]],
+    _pg_guids: dict[tuple[str, int], str],
+    rank: int,
+) -> list[tuple[int, dict[str, Any]]]:
+    """Given a list of entries, if the collective_seq_id of the first entry matches that of subsequent ones,
+    build an return a list of entries terminating in a 'coalesced' op entry all sharing a collective_seq_id
+    """
+    found = []
+    collective_seq_id = None
+    for i, e in enumerate(entries):
+        if _pg_guids[(e["process_group"][0], rank)] != pg_name:
+            continue
+        elif collective_seq_id is None:
+            collective_seq_id = (
+                e["p2p_seq_id"] if e["is_p2p"] else e["collective_seq_id"]
+            )
+            found.append((i, e))
+        elif not e["is_p2p"] and e["collective_seq_id"] == collective_seq_id:
+            found.append((i, e))
+        elif e["is_p2p"] and e["p2p_seq_id"] == collective_seq_id:
+            found.append((i, e))
+        else:
+            break
+
+    if len(found) > 1:
+        name = found[-1][1]["profiling_name"]
+        if name.startswith("nccl:") and not name.endswith("_coalesced"):
+            logger.error("Rank %s does not have a coalesced end.", rank)
+        return found
+    return []
+
+
+def just_print_entries(
+    all_entries: dict[int, list[dict[str, Any]]],
+    _groups: dict[str, Group],
+    _memberships: dict[str, set[Any]],
+    _pg_guids: dict[tuple[str, int], str],
+    args: argparse.Namespace,
+    stack_id_trace_map: dict[str, int],
+) -> None:
+    rows = []
+    ranks = sorted(all_entries.keys())
+    headers = [
+        f"Rank {rank}"
+        for rank in ranks
+        if args.selected_ranks is None or rank in args.selected_ranks
+    ]
+    progress = True
+    while progress:
+        progress = False
+        row = []
+        for rank in ranks:
+            if args.selected_ranks is not None and rank not in args.selected_ranks:
+                continue
+            if len(all_entries[rank]) == 0:
+                row.append("")
+            else:
+                entry = all_entries[rank].pop(0)
+                pg_name = _pg_guids[(entry["process_group"][0], rank)]
+                if (
+                    args.pg_filters is None
+                    or entry["process_group"][1] in args.pg_filters
+                    or entry["process_group"][0] in args.pg_filters
+                ):
+                    row.append(str(Op(entry, _memberships, pg_name)))
+                else:
+                    row.append("")
+                progress = True
+        if progress:
+            rows.append(row)
+
+    logger.info(tabulate(rows, headers=headers))
+
+    if stack_id_trace_map and args.print_stack_trace:
+        headers = ["stack_id", "frame_stack"]
+        rows = []
+
+        for frame, stack_id in sorted(
+            stack_id_trace_map.items(), key=lambda item: item[1]
+        ):
+            rows.append([str(stack_id), frame])
+
+        logger.info(tabulate(rows, headers=headers))
+
+
+def check_no_missing_dump_files(
+    entries: dict[int, Any], memberships: list[Membership]
+) -> None:
+    all_ranks = set()
+    for membership in memberships:
+        all_ranks.add(int(membership.global_rank))
+    dumps_ranks = {int(key) for key in entries}
+    missing = all_ranks - dumps_ranks
+    assert len(missing) == 0, f"Missing dump files from ranks {missing}"
+
+
+def check_version(version_by_ranks: dict[str, str], version: str) -> None:
+    for rank, v in version_by_ranks.items():
+        assert v == version, (
+            f"Rank {rank} has different version {v} from the given version {version}"
+        )
+
+
+def get_version_detail(version: str) -> tuple[int, int]:
+    # pyrefly: ignore [bad-assignment]
+    version = version.split(".")
+    assert len(version) == 2, f"Invalid version {version}"
+    major, minor = map(int, version)
+    return major, minor
+
+
+def add_stack_id_in_entries(
+    entries: dict[int, list[dict[str, Any]]],
+) -> tuple[dict[int, list[dict[str, Any]]], dict[str, int]]:
+    stack_id = 0
+    stack_id_trace_map = {}
+    for rank in entries:
+        for dump in entries[rank]:
+            if dump.get("frames", []):
+                frames = str(dump["frames"])
+                if frames not in stack_id_trace_map:
+                    stack_id_trace_map[frames] = stack_id
+                    dump["stack_id"] = stack_id
+                    stack_id += 1
+                else:
+                    dump["stack_id"] = stack_id_trace_map[frames]
+            else:
+                dump["stack_id"] = -1
+
+    return entries, stack_id_trace_map
+
+
+def align_trace_from_beginning(
+    entries: dict[int, list[dict[str, Any]]],
+) -> dict[int, list[dict[str, Any]]]:
+    """
+    Align the trace entries by record ID for entries.
+    This function takes a dictionary of rank names to lists of trace entries as input.
+    Each trace entry is a dictionary containing information about a collective operation,
+    including its unique identifier (`record_id` is monotonically increasing as we write into the ring buffer).
+    The function finds the largest starting point across all ranks by taking the maximum
+    `record_id` value of the first entry in each rank. Finally, it filters out any
+    entries with `record_id` values less than the maximum starting point.
+    The function returns the updated dictionary of sorted and filtered trace entries.
+
+    Args:
+        entries (Dict[str, List[Dict[str, Any]]]): A dictionary of rank names to lists of trace entries.
+
+    Returns:
+        entries (Dict[str, List[Dict[str, Any]]]): Entries sorted by record ID and filtered by the maximum starting point.
+    """
+
+    maximum_starting_record_id = 0
+    for rank in entries:
+        # Although this is a ring buffer, we already sort the entries by `record_id` when dumping, we just
+        # need to find the largest starting point. For example, if the buffer has the following entries:
+        # Rank 0: [0, 1, 2, 3, 4, 5, 6]
+        # Rank 1: [1, 2, 3, 4, 5, 6, 7]
+        # Rank 2: [2, 3, 4, 5, 6, 7, 8]
+        # Rank 3: [0, 1, 2, 3, 4, 5, None]
+        # Then we should start from collective 2 not 0 because any collective before,
+        # we don't have complete records from all ranks so we need to ignore them.
+        # If we don't have any trace from some ranks, ignore them
+        # as well.
+        if len(entries[rank]) == 0:
+            continue
+        first_record_id = entries[rank][0]["record_id"]
+        maximum_starting_record_id = max(maximum_starting_record_id, first_record_id)
+
+    for rank in entries:
+        entries[rank] = [
+            entry
+            for entry in entries[rank]
+            if entry["record_id"] >= maximum_starting_record_id
+        ]
+
+    return entries
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/flight_recorder/fr_trace.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/flight_recorder/fr_trace.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab338d1503ae0ac4359728ba3a5983041e678f3d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/flight_recorder/fr_trace.py
@@ -0,0 +1,67 @@
+#!/usr/bin/env python3
+"""Flight Recorder Trace Analyzer
+
+This script primarily merges data from individual flight recorder buffers from individual ranks in a
+PyTorch Distributed program into a flattened database format that can be used for further analysis.
+
+However as part of the merging process, it is necessary to perform some analysis in order to match operators
+on one rank with corresponding operators on other ranks and register them as one 'collective' entry.  During this
+process, a significant amount of useful information can already be extracted such as where the first mismatch occurs
+in cases of desync (when not all ranks issue a compatible collective in a particular process group).
+
+
+Not Yet Implemented
+- TODO- tracebacks aren't implemented
+
+Known Issues
+- Flight Recorder buffer sequence_id information is not sufficient to match collectives and coalesced collectives
+  unless we have the trace data from the beginning of the program.  To enable confident analysis of trace buffers that
+  do not start from zero (and to simplify the script's matching logic) we need to add more information to the recorder.
+- Currently, the script omits checking the 'status' of collectives.  We can look for the first 'non completed'
+  collective easily enough and report that.
+
+Usage
+python fr_trace.py  [-o ]
+
+- Omitting the optional output file will still yield analysis information to stdout
+- The output file is a pickle of the flat DB, which may change in format in the future.
+- This script is versioned so that we can ensure our future changes to flight recorder are backwards compatible.
+"""
+
+import pickle
+from collections.abc import Sequence
+
+from torch.distributed.flight_recorder.components.builder import build_db, transform_ft
+from torch.distributed.flight_recorder.components.config_manager import JobConfig
+from torch.distributed.flight_recorder.components.loader import read_dir
+from torch.distributed.flight_recorder.components.types import types
+
+
+__all__ = ["main"]
+
+
+def main(args: Sequence[str] | None = None) -> None:
+    config = JobConfig()
+    # pyrefly: ignore [bad-assignment]
+    args = config.parse_args(args)
+    # pyrefly: ignore [missing-attribute]
+    assert args.trace_dir, "Trace directory trace_dir is required"
+    # pyrefly: ignore [bad-argument-type]
+    details, version = read_dir(args)
+    # pyrefly: ignore [missing-attribute]
+    if args.transform_ft:
+        # pyrefly: ignore [missing-attribute]
+        assert args.group_world_size, "World size is required for transform_ft"
+        # pyrefly: ignore [bad-argument-type]
+        details = transform_ft(details, args.group_world_size)
+    # pyrefly: ignore [bad-argument-type]
+    db = build_db(details, args, version)
+    # pyrefly: ignore [missing-attribute]
+    if args.output:
+        # pyrefly: ignore [no-matching-overload]
+        with open(args.output, "wb") as f:
+            pickle.dump((types, db), f)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1e4219250c39dc44dd0c1132e4e1b263de08f5c5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/__init__.py
@@ -0,0 +1,69 @@
+from ._flat_param import FlatParameter as FlatParameter
+from ._fully_shard import (
+    CPUOffloadPolicy,
+    FSDPModule,
+    fully_shard,
+    MixedPrecisionPolicy,
+    OffloadPolicy,
+    register_fsdp_forward_method,
+    share_comm_ctx,
+    UnshardHandle,
+)
+from .fully_sharded_data_parallel import (
+    BackwardPrefetch,
+    CPUOffload,
+    FullOptimStateDictConfig,
+    FullStateDictConfig,
+    FullyShardedDataParallel,
+    LocalOptimStateDictConfig,
+    LocalStateDictConfig,
+    MixedPrecision,
+    OptimStateDictConfig,
+    OptimStateKeyType,
+    ShardedOptimStateDictConfig,
+    ShardedStateDictConfig,
+    ShardingStrategy,
+    StateDictConfig,
+    StateDictSettings,
+    StateDictType,
+)
+
+
+__all__ = [
+    # FSDP1
+    "BackwardPrefetch",
+    "CPUOffload",
+    "FullOptimStateDictConfig",
+    "FullStateDictConfig",
+    "FullyShardedDataParallel",
+    "LocalOptimStateDictConfig",
+    "LocalStateDictConfig",
+    "MixedPrecision",
+    "OptimStateDictConfig",
+    "OptimStateKeyType",
+    "ShardedOptimStateDictConfig",
+    "ShardedStateDictConfig",
+    "ShardingStrategy",
+    "StateDictConfig",
+    "StateDictSettings",
+    "StateDictType",
+    # FSDP2
+    "CPUOffloadPolicy",
+    "FSDPModule",
+    "fully_shard",
+    "MixedPrecisionPolicy",
+    "OffloadPolicy",
+    "register_fsdp_forward_method",
+    "UnshardHandle",
+    "share_comm_ctx",
+]
+
+# Set namespace for exposed private names
+CPUOffloadPolicy.__module__ = "torch.distributed.fsdp"
+FSDPModule.__module__ = "torch.distributed.fsdp"
+fully_shard.__module__ = "torch.distributed.fsdp"
+MixedPrecisionPolicy.__module__ = "torch.distributed.fsdp"
+OffloadPolicy.__module__ = "torch.distributed.fsdp"
+register_fsdp_forward_method.__module__ = "torch.distributed.fsdp"
+UnshardHandle.__module__ = "torch.distributed.fsdp"
+share_comm_ctx.__module__ = "torch.distributed.fsdp"
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_common_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_common_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..54d6c974caedf83a473148b7eb85da267f2be070
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_common_utils.py
@@ -0,0 +1,550 @@
+# mypy: allow-untyped-defs
+"""
+This file includes private common utilities for FSDP.
+"""
+
+import logging
+import traceback
+import warnings
+import weakref
+from collections.abc import Callable, Generator, Iterable
+from enum import auto, Enum
+from functools import partial
+from itertools import chain
+from typing import Any, cast, no_type_check, Optional, TYPE_CHECKING
+
+import torch
+import torch.distributed as dist
+import torch.distributed.fsdp._flat_param as flat_param_file
+import torch.nn as nn
+from torch.distributed._composable_state import _get_module_state, _State
+from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
+    _CHECKPOINT_PREFIX,
+)
+from torch.distributed.utils import _apply_to_tensors
+from torch.utils._mode_utils import no_dispatch
+
+from .api import (
+    FullOptimStateDictConfig,
+    FullStateDictConfig,
+    OptimStateDictConfig,
+    ShardingStrategy,
+    StateDictConfig,
+    StateDictType,
+)
+
+
+if TYPE_CHECKING:
+    from torch.distributed.device_mesh import DeviceMesh
+    from torch.distributed.fsdp._fsdp_extensions import FSDPExtensions
+
+    from ._flat_param import FlatParamHandle
+
+FSDP_WRAPPED_MODULE = "_fsdp_wrapped_module"
+FSDP_PREFIX = FSDP_WRAPPED_MODULE + "."
+FSDP_FLATTENED = "_fsdp_flattened"
+
+# Save a global mapping from module to its input tensor dtype to be populated
+# during the forward pre-hook and consumed in the forward post-hook when
+# overriding a module's mixed precision
+# NOTE: We currently take the last input tensor's dtype in the case of multiple
+# floating-point input tensors, which may be incorrect. However, since there is
+# not a 1:1 correspondence between input and output tensors, we must use *some*
+# heuristic like this to predict the desired output dtype.
+_MODULE_TO_INP_DTYPE: weakref.WeakKeyDictionary = weakref.WeakKeyDictionary()
+
+
+class _FSDPDeviceHandle:
+    """
+    This is a simple abstraction for FSDP computing devices,
+    which enables custom backends that implement CUDA-like
+    semantics to be integrated with FSDP.
+    """
+
+    def __init__(self, device: torch.device, backend: Any = None):
+        if backend is None:
+            try:
+                self.__backend = getattr(torch, device.type)
+                # pyrefly: ignore [read-only]
+                self.__device = device
+            except AttributeError as exc:
+                raise AttributeError(
+                    f"Device '{device}' does not have a corresponding backend registered as 'torch.{device.type}'."
+                ) from exc
+        else:
+            self.__backend = backend
+
+    @classmethod
+    def from_device(cls, device: torch.device) -> "_FSDPDeviceHandle":
+        """
+        Return a device handle corresponding to the device, and through this handle,
+        operations with the same semantics as CUDA can be performed on the device.
+        Just return torch.cuda if the device is cuda to make attribute-access faster.
+        Custom backend must first register a module with the same name with {device.type} on torch.
+        """
+        if device.type == "cuda":
+            return cast(_FSDPDeviceHandle, torch.cuda)
+        elif device.type == "mtia":
+            return cast(_FSDPDeviceHandle, torch.mtia)
+        return cls(device)
+
+    def __getattr__(self, name: str, /) -> Any:
+        try:
+            return getattr(self.__backend, name)
+        except AttributeError as exc:
+            raise AttributeError(
+                f"Custom backend '{self.__device.type}' not implement 'torch.{self.__device.type}.{name}'"
+            ) from exc
+
+
+class _UninitializedDeviceHandle(_FSDPDeviceHandle):
+    def __init__(self) -> None:
+        pass
+
+    def __getattribute__(self, name: str, /) -> Any:
+        raise RuntimeError("Trying to use an uninitialized device handle.")
+
+
+class _FSDPState(_State):
+    def __init__(self) -> None:
+        # TODO: Move all the attributes to this class to enable typing for
+        # FSDP/fully_shard.
+        self._ignored_modules: set[nn.Module] = set()
+        self._ignored_params: set[nn.Parameter] = set()
+        # Buffer names are cleaned (without wrapper prefixes)
+        self._ignored_buffer_names: set[str] = set()
+        self.process_group: Optional[dist.ProcessGroup] = None
+        self.rank: int = -1
+        self.world_size: int = -1
+        self._device_mesh: Optional[DeviceMesh] = None
+        self.sharding_strategy = ShardingStrategy.FULL_SHARD
+        self._use_orig_params: bool = False
+        self.training_state = TrainingState.IDLE
+        self._unshard_params_ctx: dict[nn.Module, Generator] = {}
+        self._state_dict_type: StateDictType = StateDictType.FULL_STATE_DICT
+        self._state_dict_config: StateDictConfig = FullStateDictConfig()
+        self._optim_state_dict_config: OptimStateDictConfig = FullOptimStateDictConfig()
+        self._is_root: Optional[bool] = None
+        self._handle: Optional[flat_param_file.FlatParamHandle] = None
+        self._fully_sharded_module_to_handle: dict[
+            nn.Module, Optional[flat_param_file.FlatParamHandle]
+        ] = {}
+        self.compute_device: Optional[torch.device] = None
+        self._gradient_predivide_factor: int = 0
+        self._gradient_postdivide_factor: int = 0
+        self._comm_hook: Optional[Callable] = None
+        self._comm_hook_state: Optional[Any] = None
+        self._unshard_event: Optional[torch.Event] = None
+        # Abstract device handle for fsdp compute device. For now,
+        # the compute device must implement cuda semantics used by fsdp
+        self._device_handle: _FSDPDeviceHandle = _UninitializedDeviceHandle()
+        # All following attributes should only be used for root states:
+        # Save these static lists to avoid the repeated tree traversals
+        self._all_fsdp_states: list[_FSDPState] = []
+        self._all_handles: list[flat_param_file.FlatParamHandle] = []
+        self._fsdp_extension: Optional[FSDPExtensions] = None
+
+
+def _get_module_fsdp_state(module: nn.Module) -> Optional[_FSDPState]:
+    state = _get_module_state(module)
+    if state is None or not isinstance(state, _FSDPState):
+        return None
+    return state
+
+
+def _get_module_fsdp_state_if_fully_sharded_module(
+    module: nn.Module,
+) -> Optional[_FSDPState]:
+    state = _get_module_fsdp_state(module)
+    if state is None:
+        return None
+    if state == module:  # FullyShardedDataParallel module case.
+        return state
+    if module in state._fully_sharded_module_to_handle:  # fully_shard case.
+        return state
+    return None
+
+
+class TrainingState(Enum):
+    """
+    An enum that indicates the state of a ``FullyShardedDataParallel` instance.
+    """
+
+    IDLE = auto()
+    FORWARD_BACKWARD = auto()
+    SUMMON_FULL_PARAMS = auto()
+
+
+class HandleTrainingState(Enum):
+    """
+    An enum that indicates the state of a ``FlatParamHandle`.
+    """
+
+    IDLE = auto()
+    FORWARD = auto()
+    BACKWARD_PRE = auto()
+    BACKWARD_POST = auto()
+    SUMMON_FULL_PARAMS = auto()
+
+
+def _is_composable(state: _FSDPState):
+    # TODO: This is a temporary hack for differentiate between code paths.
+    return not isinstance(state, nn.Module)
+
+
+@no_type_check
+def _module_handle(state: _FSDPState, module: nn.Module) -> Optional["FlatParamHandle"]:
+    """
+    Returns the ``FlatParamHandle`` s corresponding to ``module``. This is
+    the handle that contains some parameter in ``module``.
+    """
+    if _is_composable(state):
+        # A valid FSDP state may have no managed parameters and hence no
+        # handles, meaning no entry in `_fully_sharded_module_to_handles`
+        if state._handle is None:
+            return None
+        if module not in state._fully_sharded_module_to_handle:
+            raise AssertionError(
+                f"Expects a fully sharded module but got {module} on rank {state.rank}"
+            )
+        return state._fully_sharded_module_to_handle[module]
+    else:
+        # NOTE: This assumes `module` is a `FullyShardedDataParallel` instance.
+        return module._handle
+
+
+@no_type_check
+def _has_fsdp_params(state: _FSDPState, module: nn.Module) -> bool:
+    """Returns if ``module`` has parameters managed by FSDP."""
+    return _module_handle(state, module) is not None
+
+
+def _get_sharding_strategy(handle):
+    """
+    Returns the sharding strategy of the handle.
+    """
+    return handle._sharding_strategy if handle else None
+
+
+def clean_tensor_name(tensor_name: str) -> str:
+    """
+    Cleans the parameter or buffer name by removing any module wrapper
+    prefixes.
+    """
+    tensor_name = tensor_name.replace(FSDP_PREFIX, "")
+    # TODO: Explicitly replacing the checkpoint wrapper prefix is not ideal as
+    # it couples `CheckpointWrapper` and FSDP and also does not scale for more
+    # module wrappers.
+    tensor_name = tensor_name.replace(_CHECKPOINT_PREFIX, "")
+    return tensor_name
+
+
+def _set_fsdp_flattened(tensor: torch.Tensor) -> None:
+    """
+    Sets an attribute on ``tensor`` to mark it as flattened by FSDP. This is to
+    avoid re-flattening it during nested construction.
+    """
+    setattr(tensor, FSDP_FLATTENED, True)
+
+
+def _is_fsdp_flattened(tensor: torch.Tensor) -> bool:
+    """Returns if ``tensor`` has been marked as flattened by FSDP."""
+    return getattr(tensor, FSDP_FLATTENED, False)
+
+
+def _named_parameters_with_duplicates(
+    module: nn.Module, **kwargs: Any
+) -> list[tuple[str, nn.Parameter]]:
+    """
+    This API is required as some modules overwrite `named_parameters()` but do not support
+    `remove_duplicate`.
+    """
+    if "remove_duplicate" in kwargs:
+        raise AssertionError(
+            "_named_parameters_with_duplicates cannot be used with `remove_duplicate` argument."
+        )
+    kwargs["remove_duplicate"] = False
+    try:
+        ret = list(module.named_parameters(**kwargs))
+    except AssertionError:
+        kwargs.pop("remove_duplicate")
+        ret = list(module.named_parameters(**kwargs))
+    return ret
+
+
+def _get_param_to_fqns(
+    model: torch.nn.Module,
+    dedup_shared_params: bool = True,
+) -> dict[nn.Parameter, list[str]]:
+    """
+    Constructs a mapping from parameter to a list of its \"canonical\" FQNs. Here,
+    we use canonical to mean the fully-qualified name assigned to the parameter
+    based on its position in the original nn.Module hierarchy before any wrapper
+    or parallelism has been applied to it. This is in contrast to FQNs that may be
+    generated after parallelisms or wrappers have been applied to the model.
+
+    Each normal parameter maps to a singleton list containing its FQN, while each
+    ``FlatParameter`` maps to a list of its original parameter FQNs, which may
+    have length greater than one.  All FQNs are prefixed starting from ``model``.
+
+    In the case where FSDP was applied with ``use_orig_params=True``, there should be no
+    ``FlatParameter`` s registered to the model's modules and this mapping will only
+    contain mappings from ``nn.Parameter`` s to singleton FQN lists.
+
+    It is only in the case where FSDP was applied with ``use_orig_params=False`` where
+    a ``FlatParameter`` will be registered in place of the original parameters and there
+    will be mappings from each ``FlatParameter`` to lists of FQNs corresponding to the
+    original parameters.
+
+    Args:
+        model (torch.nn.Module): Root module (which may or may not be a
+            :class:`FullyShardedDataParallel` instance).
+        dedup_shared_params (bool): For shared parameters, if ``True``, only
+            includes the FQNs corresponding to the first encounter of the
+            shared parameter in the module traversal; if ``False``, then
+            includes the FQNs across all encounters. (Default: ``True``)
+    """
+
+    def module_fn(module, prefix, tree_level, param_to_fqns):
+        for param_name, param in _named_parameters_with_duplicates(
+            module, recurse=False
+        ):
+            local_fqns = (
+                param._fqns
+                if isinstance(param, flat_param_file.FlatParameter)
+                else [param_name]
+            )  # prefixed from `module`
+            global_fqns = [
+                clean_tensor_name(prefix + name) for name in local_fqns
+            ]  # prefixed from the top level `model` (i.e. including `prefix`)
+            is_shared_param = param in param_to_fqns
+            if not is_shared_param:
+                param_to_fqns[param] = global_fqns
+            else:
+                if isinstance(param, flat_param_file.FlatParameter):
+                    # DMP overwrites `named_parameters` and skip (advance to
+                    # the next child module) the wrapped_module (e.g.,
+                    # _dmp_wrapped_module and _fsdp_wrapped_module). When a user
+                    # calls `named_child` to traverse the module recursively and
+                    # calls `named_parameters` with `recurse=False`, parameters
+                    # will be traversed more than once.
+                    # This hack is specified designed for DMP + FSDP. We
+                    # overwrite the flat_parameters traversal result to only obtain
+                    # the last one, which happens to be the correct one.
+                    #
+                    # TODO: Remove this hack once DMP + FSDP is not supported.
+                    warnings.warn(
+                        "FlatParameter is being traversed more than once. "
+                        "This case should only happen when using "
+                        "DistributedModelParallel with FullyShardedDataParallel.",
+                        stacklevel=2,
+                    )
+                    param_to_fqns[param] = global_fqns
+                elif not dedup_shared_params:
+                    param_to_fqns[param].extend(global_fqns)
+
+    def return_fn(param_to_fqns):
+        return param_to_fqns
+
+    param_to_unflat_param_names: dict[torch.nn.Parameter, list[str]] = {}
+    return _apply_to_modules(
+        model,
+        module_fn,
+        return_fn,
+        [key for key, _ in _named_parameters_with_duplicates(model)],
+        param_to_unflat_param_names,
+    )
+
+
+@no_type_check
+def _log_post_backward_hook(
+    state: _FSDPState, handle: "FlatParamHandle", logger: logging.Logger
+) -> None:
+    # Under TORCH_DISTRIBUTED_DEBUG=INFO, log the module names this hook fires for.
+    # Below logging of module names this post-bwd hook fires for can help debug certain
+    # cases where hooks don't fire, such as under certain activation checkpoint configs.
+    if state._use_orig_params and handle._debug_level == dist.DebugLevel.INFO:
+        param_fqns = _get_handle_fqns_from_root(state, handle)
+        logger.warning("FSDP firing post-backward hooks for parameters %s", param_fqns)
+
+
+@no_type_check
+def _get_handle_fqns_from_root(
+    state: _FSDPState, handle: "FlatParamHandle"
+) -> Optional[list[str]]:
+    if handle is None:
+        return None
+    param_to_fqn = state._exec_order_data.param_to_fqn
+    handle_params = handle.flat_param._params  # only populated for use_orig_params
+    param_fqns = [*chain.from_iterable(param_to_fqn[p] for p in handle_params)]
+    return param_fqns
+
+
+def _apply_to_modules(
+    root_module: torch.nn.Module,
+    module_fn: Callable,
+    return_fn: Callable,
+    filter_fqns: Optional[list[str]] = None,
+    *args,
+    **kwargs,
+):
+    """
+    Performs a pre-order traversal of the modules in the hierarchy rooted at
+    ``root_module``, applying ``module_fn`` at each module and finally
+    returning a value using ``return_fn``. The traversal constructs the full
+    module prefix name (e.g. "module.submodule." just like in model state dict)
+    and makes that available to ``module_fn``.
+
+    ``filter_fqns`` is used because some module may have its own prefix similar
+    to ``FullyShardedDataParallel`` and the ``named_parameters()`` is overwritten
+    to remove the prefix.
+    """
+
+    def f(module: torch.nn.Module, prefix: str, tree_level: int, *args, **kwargs):
+        # Call the module function before recursing over children (pre-order)
+        module_fn(module, prefix, tree_level, *args, **kwargs)
+        for submodule_name, submodule in module.named_children():
+            if submodule is None:
+                continue
+            new_prefix = prefix + submodule_name + "."
+            new_tree_level = tree_level + 1
+            if filter_fqns is not None:
+                for fqn in filter_fqns:
+                    if fqn.startswith(new_prefix):
+                        break
+                else:
+                    # DMP's named_parameter() will mess up the traversal with
+                    # ``named_children`` + `named_parameter(recurse=False)``.
+                    # This hack is a must to make the traversal work.
+                    # TODO: Remove this hack once DMP + FSDP is not supported.
+                    # It turns out that recursive wrapping may trigger this as
+                    # well.
+                    if (
+                        submodule_name == "_fsdp_wrapped_module"
+                        or submodule_name == "_dmp_wrapped_module"
+                    ):
+                        new_prefix = prefix
+                    elif submodule_name == "module":
+                        new_prefix = prefix
+            f(submodule, new_prefix, new_tree_level, *args, **kwargs)
+
+    f(root_module, "", 0, *args, **kwargs)
+    return return_fn(*args, **kwargs)
+
+
+@no_type_check
+def _assert_in_training_states(
+    state: _FSDPState,
+    training_states: list[TrainingState],
+) -> None:
+    """Asserts that FSDP is in the states ``_training_states``."""
+    # Raise a `ValueError` instead of using `assert` to ensure that these
+    # logical assertions run even if `assert`s are disabled
+    if state.training_state not in training_states:
+        msg = (
+            f"expected to be in states {training_states} but current state is "
+            f"{state.training_state}"
+        )
+        # Print the error on rank 0 in case this is called in the backward pass
+        if state.rank == 0:
+            if isinstance(state, nn.Module):
+                print(f"Asserting FSDP instance is: {state}")
+            print(f"ERROR: {msg}")
+            traceback.print_stack()
+        raise ValueError(msg)
+
+
+def _get_root_modules(modules: set[nn.Module]) -> set[nn.Module]:
+    """
+    Returns:
+        Set[nn.Module]: The subset of ``modules`` that are root modules (i.e.
+        parent-less) with respect to the modules in the set itself. In other
+        words, these are the modules in ``modules`` that are not the child of
+        any other module in ``modules``.
+    """
+    root_modules: set[nn.Module] = set()
+    module_to_submodules = {module: set(module.modules()) for module in modules}
+    for candidate_module in modules:
+        is_root_module = True
+        for module, submodules in module_to_submodules.items():
+            is_child_module = (
+                candidate_module is not module and candidate_module in submodules
+            )
+            if is_child_module:
+                is_root_module = False
+                break
+        if is_root_module:
+            root_modules.add(candidate_module)
+    return root_modules
+
+
+def _override_module_mixed_precision(
+    root: torch.nn.Module,
+    module_classes_to_override: Iterable[type[nn.Module]],
+    wrap_override_dict: dict[str, Any] = {"mixed_precision": None},  # noqa: B006
+) -> set[type[nn.Module]]:
+    module_classes_to_override = tuple(set(module_classes_to_override))
+    # Return a set of the actually overridden module classes
+    overridden_module_classes: set[type[nn.Module]] = set()
+    for mod in root.modules():
+        if isinstance(mod, module_classes_to_override):
+            overridden_module_classes.add(type(mod))
+            mod._wrap_overrides = wrap_override_dict  # type: ignore[assignment]
+            # TODO: We need to run this mixed precision ignored module in fp32,
+            # but ensure subsequent modules, that may possibly be running with
+            # mixed precision, still receive the appropriate precision inputs
+            # without user having to adjust mixed precision config too much.
+            # As a result, we attach pre and post forward hooks to up / down
+            # cast. We should revisit this design.
+
+            def cast_fn(
+                dtype: torch.dtype, module: nn.Module, x: torch.Tensor
+            ) -> torch.Tensor:
+                if not torch.is_floating_point(x) or x.dtype == dtype:
+                    return x
+                _MODULE_TO_INP_DTYPE[module] = x.dtype
+                return x.to(dtype)
+
+            def forward_pre_hook(module, args):
+                return _apply_to_tensors(partial(cast_fn, torch.float32, module), args)
+
+            def forward_post_hook(module, args, output):
+                # NOTE: If the forward did not have any floating-point tensors,
+                # then the dtype will not be set for this module, and we do not
+                # upcast the dtype.
+                if module in _MODULE_TO_INP_DTYPE:
+                    old_dtype = _MODULE_TO_INP_DTYPE[module]
+                    return _apply_to_tensors(
+                        partial(cast_fn, old_dtype, module), output
+                    )
+
+            # We intentionally append both of these hooks so that they run after
+            # all other hooks.
+            mod.register_forward_pre_hook(forward_pre_hook, prepend=False)
+            mod.register_forward_hook(forward_post_hook, prepend=False)
+    return overridden_module_classes
+
+
+def _no_dispatch_record_stream(tensor: torch.Tensor, stream: torch.Stream) -> None:
+    # FIXME record_stream doesn't work with non-cuda/mtia/xpu tensors
+    if tensor.device.type not in [
+        "cuda",
+        "mtia",
+        "xpu",
+        torch._C._get_privateuse1_backend_name(),
+    ]:
+        return
+
+    if torch.distributed._functional_collectives.is_torchdynamo_compiling():
+        return
+        # from @ezyang:
+        # The no_dispatch was added in https://github.com/pytorch/pytorch/pull/88014 cc @fegin
+        # Looking over the PR, it looks like this is because we don't actually support Stream arguments
+        # in torch dispatch, so it just chokes.
+        # If Dynamo is able to answer "are there any torch dispatch modes" active (it should answer False),
+        # a better version of this would just be to check if there are any modes before disabling dispatch.
+        # TODO(voz): Extend a dynamo util to answer the above, unify the codepaths here.
+        tensor.record_stream(stream)
+    else:
+        with no_dispatch():
+            tensor.record_stream(stream)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_debug_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_debug_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf5a411f8c556ff1922775514cb2361a87bb492d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_debug_utils.py
@@ -0,0 +1,159 @@
+# mypy: allow-untyped-defs
+import logging
+import time
+from collections import defaultdict
+from collections.abc import Iterator
+from contextlib import contextmanager
+from enum import Enum
+
+import torch
+import torch.distributed as dist
+import torch.distributed.fsdp._flat_param as flat_param_file
+from torch.distributed.fsdp._common_utils import (
+    _apply_to_modules,
+    _get_module_fsdp_state,
+    clean_tensor_name,
+)
+
+
+logger = logging.getLogger(__name__)
+
+
+class SimpleProfiler:
+    class Type(str, Enum):
+        ALL = "all"
+        ALLGATHER = "all_gather"
+        ALLGATHER_OBJ = "all_gather_object"
+        RESHARDING = "resharding"
+        H2D = "H2D"
+        D2H = "D2H"
+
+    results: dict[str, float] = defaultdict(float)
+    profiling: set[str] = set()
+
+    @classmethod
+    def reset(cls) -> None:
+        cls.results.clear()
+        cls.profiling.clear()
+
+    @classmethod
+    @contextmanager
+    def profile(cls, profile_type: str) -> Iterator[None]:
+        if profile_type in cls.profiling:
+            raise AssertionError(
+                f"{profile_type} is already being profiled. "
+                "SimpleProfiler does not support profiling multiple instances at "
+                "the same time. "
+            )
+
+        cls.profiling.add(profile_type)
+        begin = time.monotonic()
+        try:
+            yield
+        finally:
+            end = time.monotonic()
+            cls.results[profile_type] += end - begin
+            cls.profiling.remove(profile_type)
+
+    @classmethod
+    def dump_and_reset(cls, msg: str) -> None:
+        # This cannot be combined with DETAIL distributed log
+        # as the profiling will be very incorrect.
+        if dist.get_rank() == 0 and dist.get_debug_level() == dist.DebugLevel.INFO:
+            logger.info("%s %s", msg, cls.results)
+        cls.reset()
+
+
+def _get_sharded_module_tree_with_module_name_to_fqns(
+    model: torch.nn.Module,
+) -> tuple[str, dict[str, list[str]]]:
+    """
+    It is used for composable fully_shard() code path, it returns
+      1. sharded module tree info: each line represents a submodule name that contains the
+    submodule's FQN and its submodule class name, if the submodule is sharded by `fully_shard`,
+    the submodule name will add a postfix with ' FULLY SHARDED'. Each increased tree
+    level adds 4 spaces before the printed name. A printed sharded module tree info for a toy model
+    is like this:
+        [CompositeModel] FULLY SHARDED
+            l1[Linear]
+            u1[UnitModule] FULLY SHARDED
+                u1.l1[Linear]
+                u1.seq[Sequential]
+                    u1.seq.0[ReLU]
+                    u1.seq.1[Linear]
+                    u1.seq.2[ReLU]
+                u1.l2[Linear]
+            u2[UnitModule] FULLY SHARDED
+                u2.l1[Linear]
+                u2.seq[Sequential]
+                    u2.seq.0[ReLU]
+                    u2.seq.1[Linear]
+                    u2.seq.2[ReLU]
+                u2.l2[Linear]
+            l2[Linear]
+      2. a dict mapping from the concated module FQN and class name to a list of its managed
+    original parameters' FQNs. An example of the dict for the above toy sharded model is like this:
+            {'[CompositeModel]': ['l1.weight', 'l1.bias', 'l2.weight', 'l2.bias'],
+             'u1[UnitModule]': ['u1.l1.weight', 'u1.l1.bias', 'u1.seq.1.weight', 'u1.seq.1.bias', 'u1.l2.weight', 'u1.l2.bias'],
+             'u2[UnitModule]': ['u2.l1.weight', 'u2.l1.bias', 'u2.seq.1.weight', 'u2.seq.1.bias', 'u2.l2.weight', 'u2.l2.bias']
+            }
+    All FQNs are prefixed starting from ``model``.
+
+    Args:
+        model (torch.nn.Module): Root module (which may or may not be passed to
+                                 composable `fully_shard()`).
+    """
+
+    def module_fn(
+        module, prefix, tree_level, sharded_tree_info, sharded_module_name_to_fqns
+    ):
+        num_spaces = tree_level * 4
+        trimed_prefix = (
+            prefix[:-1] if (len(prefix) > 0 and prefix[-1] == ".") else prefix
+        )
+        prefixed_module_name = trimed_prefix + "[" + module.__class__.__name__ + "]"
+        printed_prefixed_module_name = " " * num_spaces + prefixed_module_name
+
+        state = _get_module_fsdp_state(module)
+        if state is None:
+            sharded_tree_info[0] += printed_prefixed_module_name + "\n"
+            return
+
+        handle = state._fully_sharded_module_to_handle.get(module, None)
+
+        if handle:
+            sharded_tree_info[0] += (
+                printed_prefixed_module_name + " FULLY SHARDED" + "\n"
+            )
+        else:
+            sharded_tree_info[0] += printed_prefixed_module_name + "\n"
+
+        if handle:
+            param = handle.flat_param
+            if not isinstance(param, flat_param_file.FlatParameter):
+                raise AssertionError(f"Expected FlatParameter, got {type(param)}")
+            global_fqns = [
+                clean_tensor_name(prefix + name) for name in param._fqns
+            ]  # prefixed from the top level `model` (i.e. including `prefix`)
+
+            if prefixed_module_name in sharded_module_name_to_fqns:
+                sharded_module_name_to_fqns[prefixed_module_name].extend(global_fqns)
+            else:
+                sharded_module_name_to_fqns[prefixed_module_name] = global_fqns
+
+    def return_fn(sharded_tree_info, sharded_module_name_to_fqns):
+        return sharded_tree_info[0], sharded_module_name_to_fqns
+
+    # Use List to mutate its value in place while running the recursive functions
+    sharded_tree_info: list[str] = [
+        "",
+    ]
+    sharded_module_name_to_fqns: dict[str, list[str]] = {}
+    return _apply_to_modules(
+        model,
+        module_fn,
+        return_fn,
+        [key for key, _ in model.named_parameters()],
+        sharded_tree_info,
+        sharded_module_name_to_fqns,
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_dynamo_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_dynamo_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..77bcd43b63be27da8e8b79f877ce7cb9d67c74b8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_dynamo_utils.py
@@ -0,0 +1,43 @@
+import torch.nn as nn
+
+
+def _annotate_modules_for_dynamo(
+    module: nn.Module,
+    ignored_modules: set[nn.Module],
+    use_orig_params: bool,
+) -> None:
+    """
+    Annotates the submodules in ``module`` 's tree, except those in
+    ``ignored_modules``, indicating that the submodules are FSDP-managed and
+    saving the ``use_orig_params`` setting passed to the FSDP constructor.
+    """
+    for submodule in module.modules():
+        if submodule not in ignored_modules:
+            """[note: Dynamo treats FSDP wrapped modules as UnspecializedNNModule]
+
+            Dynamo doesn't get to see this instance (FullyShardedDataParallel) during tracing, since
+            it skips tracing all the torch.distributed.fsdp code.
+                - Why? Running the FSDP code eagerly avoids lots of issues trying to trace complex hooks, and also
+                gets us graph-breaks on FSDP module boundaries which we want anyway for comm ops.
+                - However, we _also_ want dynamo to treat the wrapped module inside FSDP 'unspecially' (*),
+                and we need a way to indicate to dynamo which modules are wrapped by FSDP.
+
+            (*) UnspecializedNNModules in dynamo are traced-through without any assumptions, and with thorough
+            guards.  NNModules otherwise are 'specialized', meaning there is less overhead due to assuming
+            their code is well-behaved.
+
+            One particular issue with specialized NNModules for FSDP is that the
+            views created for orig_params are captured into the compiled graph on the first iteration, and while
+            they are always going to point to the correct flatparameter and give correct results, their order
+            of creation influences the order of backward execution, preventing overlap of comm and computation
+            during backward.  We need to _use_ the new parameter views created on each forward iteration, in
+            order for backward to interleave hooks with compute per layer.  UnspecializedNNModule lets us achieve
+            this by capturing the module code more 'functionally' and passing parameters in as inputs each time.
+            """
+            submodule._is_fsdp_managed_module = True  # type: ignore[assignment]
+
+            # Dynamo only supports FSDP with use_orig_params=True.
+            # This is hacky, but I could not think of another way to add an assertion to dynamo
+            # for this, since Dynamo skips all the FSDP code frames and thus can't inspect the
+            # FSDP module directly
+            submodule._fsdp_use_orig_params = use_orig_params  # type: ignore[assignment]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_exec_order_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_exec_order_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..db2ea7bfae0b92a6a103ac35655a6da627761e7e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_exec_order_utils.py
@@ -0,0 +1,366 @@
+# mypy: allow-untyped-defs
+import itertools
+import warnings
+from enum import auto, Enum
+from typing import Optional, Union
+
+import torch
+import torch.distributed as dist
+import torch.distributed.fsdp._traversal_utils as traversal_utils
+import torch.nn as nn
+from torch.distributed.fsdp._common_utils import _FSDPState, _get_param_to_fqns
+from torch.distributed.fsdp._flat_param import FlatParamHandle
+
+
+class _ExecOrderWarnStatus(Enum):
+    """Used internally for execution order validation."""
+
+    NONE = auto()  # no deviation yet
+    WARNING = auto()  # deviated this iteration; currently issuing warnings
+    WARNED = auto()  # deviated in a previous iteration
+
+
+class _ExecOrderData:
+    """
+    This contains the data structures to track the execution order. We track
+    the pre-forward order on the *first* iteration for forward prefetching
+    (which thus assumes static graph) and the post-forward order on *every*
+    iteration for backward prefetching (which thus does not assume static
+    graph but may be provide an incorrect order).
+    """
+
+    def __init__(
+        self,
+        debug_level: dist.DebugLevel,
+        backward_prefetch_limit: int,
+        forward_prefetch_limit: int,
+    ) -> None:
+        # Tracks the (static) pre-forward order for execution order validation
+        # and forward prefetching
+        self.handles_pre_forward_order: list[FlatParamHandle] = []
+        # Tracks the post-forward order for pre-backward prefetching
+        self.handles_post_forward_order: list[Optional[FlatParamHandle]] = []
+        self._iter = 0
+
+        # Gives the max number of backward/forward prefetched all-gathers by a
+        # single module
+        self._backward_prefetch_limit = backward_prefetch_limit
+        self._forward_prefetch_limit = forward_prefetch_limit
+
+        # Data structures for execution order validation
+        self._checking_order: bool = debug_level == dist.DebugLevel.DETAIL
+        self.process_group: Optional[dist.ProcessGroup] = None
+        self.world_size: Optional[int] = None
+        self.all_handles: list[FlatParamHandle] = []
+        # Names are prefixed from the root module
+        self.param_to_fqn: dict[nn.Parameter, list[str]] = {}
+        # Current index in the pre-forward execution order
+        self.current_order_index = 0
+        self.warn_status = _ExecOrderWarnStatus.NONE
+
+    def init(
+        self,
+        state: _FSDPState,
+        root_module: nn.Module,
+        process_group: dist.ProcessGroup,
+    ) -> None:
+        """
+        Initializes the data structures needed for checking the forward order.
+        This should be called after a root FSDP instance has been set during
+        lazy initialization.
+        """
+        self.process_group = process_group
+        self.rank = process_group.rank()
+        self.world_size = process_group.size()
+        # Fix an order over the handles, which should be the same across ranks
+        for handle in traversal_utils._get_fsdp_handles(root_module):
+            index = len(self.all_handles)
+            self.all_handles.append(handle)
+            handle._handle_index = index
+        self.param_to_fqn = _get_param_to_fqns(root_module)
+        # TODO (awgu): We can broadcast the metadata of rank 0's `all_handles`
+        # to check that all ranks have the same handles in the same order.
+        # https://github.com/pytorch/pytorch/issues/79620
+
+    @property
+    def is_first_iter(self) -> bool:
+        return self._iter == 0
+
+    def get_handle_to_backward_prefetch(
+        self,
+        current_handle: FlatParamHandle,
+    ) -> Optional[FlatParamHandle]:
+        """
+        Returns a :class:`list` of the handles keys of the handles to backward
+        prefetch given the current handles key. If there are no valid handles
+        keys to prefetch, then this returns an empty :class:`list`.
+        """
+        current_index = current_handle._post_forward_index
+        if current_index is None:
+            return None
+        target_index = current_index - 1
+        target_handle: Optional[FlatParamHandle] = None
+        for _ in range(self._backward_prefetch_limit):
+            if target_index < 0:
+                break
+            target_handle = self.handles_post_forward_order[target_index]
+            target_index -= 1
+        return target_handle
+
+    def get_handle_to_forward_prefetch(
+        self,
+        current_handle: FlatParamHandle,
+    ) -> Optional[FlatParamHandle]:
+        """
+        Returns a :class:`list` of the handles keys of the handles to forward
+        prefetch given the current handles key. If there are no valid handles
+        keys to prefetch, then this returns an empty :class:`list`.
+        """
+        current_index = current_handle._pre_forward_order_index
+        if current_index is None:
+            return None
+        target_index = current_index + 1
+        target_handle: Optional[FlatParamHandle] = None
+        for _ in range(self._forward_prefetch_limit):
+            if target_index >= len(self.handles_pre_forward_order):
+                break
+            target_handle = self.handles_pre_forward_order[target_index]
+            target_index += 1
+        return target_handle
+
+    def record_post_forward(self, handle: Optional[FlatParamHandle]) -> None:
+        """
+        Records ``handles`` in the post-forward order, where ``handles`` should
+        be a group of handles used in the same module's forward. If ``handles``
+        is empty, then it is omitted.
+
+        Unlike :meth:`record_pre_forward`, this records the order *every*
+        iteration with the expectation that the recorded order is reset in
+        :meth:`next_iter`.
+        """
+        if not handle:
+            return
+        # Only record the first usage of a handles key
+        if handle._post_forward_index:
+            self.handles_post_forward_order.append(handle)
+            return
+        index = len(self.handles_post_forward_order)
+        handle._post_forward_index = index
+        self.handles_post_forward_order.append(handle)
+
+    def record_pre_forward(
+        self, handle: Optional[FlatParamHandle], is_training: bool
+    ) -> None:
+        """
+        Records ``handles`` in the pre-forward order, where ``handles`` should
+        be a group of handles used in the same module's forward. If ``handles``
+        is empty, then it is omitted.
+
+        On the first iteration, this checks the execution order across ranks.
+        See :meth:`_check_order` for details.
+        """
+        if not handle:
+            return
+        self._check_order(handle, is_training)
+        # Fix the order after the first iteration and only record the first
+        # usage of a handles key
+        if not self.is_first_iter or handle._pre_forward_order_index is not None:
+            return
+        index = len(self.handles_pre_forward_order)
+        handle._pre_forward_order_index = index
+        self.handles_pre_forward_order.append(handle)
+
+    def _check_order(self, handle: FlatParamHandle, is_training: bool) -> None:
+        """
+        Checks the forward execution order as long as ``is_training`` is
+        ``True`` since checking in eval mode is not supported. This only checks
+        if the distributed debug level is DETAIL.
+
+        - On the first iteration, this uses all-gathers to check that all ranks
+        are all-gathering the same handles and hence ``FlatParameter`` s,
+        raising an error if not.
+        - On subsequent iterations, this checks that each rank is locally
+        consistent with its own forward order from the first iteration, issuing
+        a warning if not. This issues a warning on the first deviating
+        iteration and stops warning thereafter.
+        """
+        # Do not check order in eval mode since the post-backward callback does
+        # not run so it cannot be used to mark the end of an iteration
+        if not is_training or not self._checking_order:
+            return
+        if self.is_first_iter:
+            msg_prefix = "Forward order differs across ranks:"
+            optional_local_indices: tuple[Optional[int], ...] = (
+                self._get_handle_indices(handle)
+            )
+            device = handle.device  # guaranteed to be non-CPU
+            num_valid_indices = sum(
+                (index is not None) for index in optional_local_indices
+            )
+            tensor_kwargs: dict[str, Union[torch.dtype, torch.device]] = {
+                "dtype": torch.int32,
+                "device": device,
+            }
+            world_num_valid_indices = torch.zeros(self.world_size, **tensor_kwargs)  # type: ignore[arg-type, call-overload]
+            local_num_valid_indices = torch.tensor([num_valid_indices], **tensor_kwargs)  # type: ignore[arg-type, call-overload]
+            dist.all_gather_into_tensor(
+                world_num_valid_indices,
+                local_num_valid_indices,
+                group=self.process_group,
+            )
+            # Copy entire tensor from D2H once to avoid per element D2H copies
+            world_num_valid_indices = world_num_valid_indices.cpu()
+            # Check that all ranks plan to all-gather the same number of
+            # parameters
+            # TODO (awgu): Since every module has at most one handle in the
+            # current implementation, this should never raise the error.
+            if self.world_size is None:
+                raise AssertionError("Expected world_size to not be None")
+            if not torch.distributed._functional_collectives.is_torchdynamo_compiling():
+                # TODO(voz): Don't graph break on this - dynamo hates the n1 != n2
+                # tensor comparison control flow.
+                # https://github.com/pytorch/pytorch/issues/107055
+                for (r1, n1), (r2, n2) in itertools.combinations(
+                    (
+                        (rank, world_num_valid_indices[rank])
+                        for rank in range(self.world_size)
+                    ),
+                    2,
+                ):
+                    if n1 != n2:
+                        raise RuntimeError(
+                            f"{msg_prefix} rank {r1} is all-gathering {n1} parameters "
+                            f"while rank {r2} is all-gathering {n2} parameters"
+                        )
+            world_indices = torch.zeros(  # type: ignore[call-overload]
+                self.world_size * num_valid_indices, **tensor_kwargs
+            )
+            local_indices = torch.tensor(optional_local_indices, **tensor_kwargs)  # type: ignore[arg-type]
+            dist.all_gather_into_tensor(
+                world_indices, local_indices, group=self.process_group
+            )
+            # Copy entire tensor from D2H once to avoid per element D2H copies
+            world_indices = world_indices.cpu()
+            # Check that all ranks plan to all-gather the same index parameters
+            if not torch.distributed._functional_collectives.is_torchdynamo_compiling():
+                # TODO(voz): Don't graph break on this - dynamo hates the i1 != i2
+                # tensor comparison control flow.
+                # https://github.com/pytorch/pytorch/issues/107055
+                for (r1, i1), (r2, i2) in itertools.combinations(
+                    (
+                        (
+                            rank,
+                            world_indices[
+                                rank * num_valid_indices : (rank + 1)
+                                * num_valid_indices
+                            ],
+                        )
+                        for rank in range(self.world_size)
+                    ),
+                    2,
+                ):
+                    if i1 != i2:
+                        r1_param_names = self._get_names_from_handle_indices(i1)
+                        r2_param_names = self._get_names_from_handle_indices(i2)
+                        raise RuntimeError(
+                            f"{msg_prefix} rank {r1} is all-gathering parameters "
+                            f"for {r1_param_names} while rank {r2} is all-gathering "
+                            f"parameters for {r2_param_names}"
+                        )
+        else:
+            # Only issue warnings on the first deviating iteration and stop
+            # checking thereafter to avoid flooding the console
+            if self.warn_status == _ExecOrderWarnStatus.WARNED:
+                return
+            msg_prefix = None  # non-`None` means we should warn
+            if self.current_order_index >= len(self.handles_pre_forward_order):
+                # This iteration sees extra all-gather(s) compared to the first
+                msg_prefix = (
+                    "Expected to not all-gather any more parameters in the "
+                    "forward but trying to all-gather parameters for "
+                )
+            else:
+                expected_handle = self.handles_pre_forward_order[
+                    self.current_order_index
+                ]
+                if expected_handle != handle:
+                    expected_param_names = self._get_names_from_handles(expected_handle)
+                    msg_prefix = (
+                        f"Expected to all-gather for {expected_param_names} "
+                        "but trying to all-gather parameters for "
+                    )
+            if msg_prefix is not None:
+                param_names = self._get_names_from_handles(handle)
+                msg_suffix = (
+                    f"{param_names}"
+                    if param_names
+                    else "a newly-added parameter since construction time"
+                )
+                warnings.warn(
+                    "Forward order differs from that of the first iteration "
+                    f"on rank {self.rank}. Collectives are unchecked and may "
+                    f"give incorrect results or hang.\n{msg_prefix}{msg_suffix}",
+                    stacklevel=2,
+                )
+                self.warn_status = _ExecOrderWarnStatus.WARNING
+            self.current_order_index += 1
+
+    def _get_handle_indices(
+        self,
+        handle: FlatParamHandle,
+    ) -> tuple[Optional[int], ...]:
+        """
+        Returns the handle indices (i.e. indices into ``self.all_handles``)
+        corresponding to the handles in ``handle``. An entry in the
+        returned tuple is ``None`` if the handle is invalid.
+        """
+        indices: list[Optional[int]] = []
+        if handle:
+            indices.append(handle._handle_index)
+        return tuple(indices)
+
+    def _get_names_from_handle_indices(
+        self,
+        handle_indices: tuple[int, ...],
+    ) -> list[list[str]]:
+        """
+        Returns a list of FQNs for each handle in ``handle_indices``. If a
+        handle index is invalid, then its FQNs are omitted from the returned
+        list.
+        """
+        fqns: list[list[str]] = []
+        for index in handle_indices:
+            if index is None or index < 0 or index >= len(self.all_handles):
+                continue
+            handle = self.all_handles[index]
+            flat_param = handle.flat_param
+            fqns.append(self.param_to_fqn[flat_param])
+        return fqns
+
+    def _get_names_from_handles(
+        self,
+        handle: FlatParamHandle,
+    ) -> list[list[str]]:
+        """
+        Returns a list of FQNs for each handle in ``handles_key``. If a handle
+        is invalid, then its FQNs are omitted from the returned list.
+        """
+        fqns: list[list[str]] = []
+        if handle:
+            flat_param = handle.flat_param
+            if flat_param in self.param_to_fqn:
+                fqns.append(self.param_to_fqn[flat_param])
+        return fqns
+
+    def next_iter(self):
+        """
+        Advances the internal data structures per iteration. This should be
+        called in the post-backward callback since that marks the true end of
+        an iteration.
+        """
+        self._iter += 1
+        self.handles_post_forward_order.clear()
+        if self._checking_order:
+            self.current_order_index = 0
+            if self.warn_status == _ExecOrderWarnStatus.WARNING:
+                self.warn_status = _ExecOrderWarnStatus.WARNED
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_flat_param.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_flat_param.py
new file mode 100644
index 0000000000000000000000000000000000000000..85e4c23d509f8c8751ac60572a7a4a78da0fc9cf
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_flat_param.py
@@ -0,0 +1,2841 @@
+# mypy: allow-untyped-defs
+import contextlib
+import functools
+import logging
+import os
+import warnings
+from collections.abc import Callable, Generator, Iterator, Sequence
+from enum import auto, Enum
+from itertools import accumulate, chain
+from typing import Any, cast, NamedTuple, no_type_check, Optional, Union
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import Tensor
+from torch.distributed.fsdp._common_utils import (
+    _FSDPDeviceHandle,
+    _named_parameters_with_duplicates,
+    _no_dispatch_record_stream,
+    _set_fsdp_flattened,
+    HandleTrainingState,
+)
+from torch.distributed.utils import (
+    _alloc_storage,
+    _data_ptr_allocated,
+    _free_storage,
+    _p_assert,
+)
+from torch.nn.parameter import _ParameterMeta  # type: ignore[attr-defined]
+from torch.testing._internal.distributed.fake_pg import FakeProcessGroup
+
+from ._fsdp_extensions import (
+    _ext_post_unflatten_transform,
+    _ext_pre_flatten_transform,
+    FSDPExtensions,
+)
+
+
+__all__ = [
+    "FlatParameter",
+    "FlatParamHandle",
+    "FlatParamShardMetadata",
+    "ParamInfo",
+    "SharedParamInfo",
+    "HandleShardingStrategy",
+]
+
+logger = logging.getLogger(__name__)
+
+
+"""
+[Note: Fully Sharded Module]
+We define the "fully sharded module" to be the original ``nn.Module`` that owns
+a ``FlatParamHandle``. It is the *single* module logically responsible for the
+*single* unshard/reshard pair for the handle's ``FlatParameter`` for a given
+forward or backward pass. The fully sharded module should be passed to the
+``FlatParamHandle`` constructor.
+
+For the wrapper code path:
+- The ``FullyShardedDataParallel`` module wrapping the fully sharded module
+runs the unshard/reshard on behalf of the fully sharded module by overriding
+``nn.Module.forward``.
+- The fully sharded module is exactly the module passed to the
+``FullyShardedDataParallel`` constructor's ``module`` argument.
+
+For the non-wrapper code path:
+- Hooks registered on the fully sharded module run the unshard/reshard.
+- The fully sharded module may either be the direct argument to ``fully_shard``
+or a submodule chosen by the provided wrapping policy.
+"""
+
+# Environment variable toggling whether to use unsafe `setattr()` for view
+# setting in `_use_sharded_views()` and `_use_unsharded_views()`
+# We should use 'safe' by default since it respects method overrides, but for
+# special cases such as for high CPU overhead or for intentionally bypassing
+# checks in the overrides, we may use 'unsafe'.
+_FSDP_USE_UNSAFE_SETATTR = "FSDP_USE_UNSAFE_SETATTR"
+
+# Environment variable toggling whether to check for parameter/gradient
+# writeback in case their storages change after FSDP initialization
+# We should check by default since it prevents silent correctness errors, but
+# since such changes are atypical, we may want to skip the check to save CPU
+# overhead, especially since the check happens in the pre-forward and
+# pre-backward each iteration.
+_FSDP_SKIP_WRITEBACK_CHECK = "FSDP_SKIP_WRITEBACK_CHECK"
+
+# Env var toggling whether when model is in .eval() mode, should we run in fp32
+# or the reduced precision.
+_FSDP_USE_FULL_PREC_IN_EVAL = "FSDP_USE_FULL_PREC_IN_EVAL"
+
+# Some value to set padding in tensors to for debuggability
+_FLAT_PARAM_PADDING_VALUE = 42
+
+# Environment variables for disabling the all-gather and reduce-scatter
+# communication ops for ablation studies. Note that without these communication
+# ops the training won't converge, and you probably need to disable correctness
+# checks in your model.
+_FSDP_USE_FAKE_ALL_GATHER = "FSDP_USE_FAKE_ALL_GATHER"
+_FSDP_USE_FAKE_REDUCE = "FSDP_USE_FAKE_REDUCE"
+
+
+# TODO: Define this for now to avoid circular imports. See if we can remove.
+class HandleShardingStrategy(Enum):
+    FULL_SHARD = auto()
+    SHARD_GRAD_OP = auto()
+    NO_SHARD = auto()
+    HYBRID_SHARD = auto()
+    _HYBRID_SHARD_ZERO2 = auto()
+
+
+RESHARD_AFTER_FORWARD_HANDLE_STRATEGIES = (
+    HandleShardingStrategy.FULL_SHARD,
+    HandleShardingStrategy.HYBRID_SHARD,
+)
+NO_RESHARD_AFTER_FORWARD_HANDLE_STRATEGIES = (
+    HandleShardingStrategy.SHARD_GRAD_OP,
+    HandleShardingStrategy._HYBRID_SHARD_ZERO2,
+)
+
+
+class ParamInfo(NamedTuple):
+    """Information for an original parameter."""
+
+    param_name: str  # unprefixed
+    module: nn.Module
+    module_name: str
+
+
+class SharedParamInfo(NamedTuple):
+    """
+    Additional information for a shared parameter.
+
+    For each shared parameter, we designate one module and its parameter
+    variable to be the primary owner, determined as the first one encountered
+    in the parameter walk. These are prefixed with "prim". The primary module
+    and parameter do not have their own :class:`SharedParamInfo` instance.
+    """
+
+    param_name: str  # unprefixed
+    module: nn.Module
+    module_name: str
+    prim_param_name: str  # unprefixed
+    prim_module: nn.Module
+    prim_module_name: str
+
+
+class _ShardParamInfo(NamedTuple):
+    """Shard-related information for an original parameter."""
+
+    in_shard: bool
+    # Use to index into the sharded flat parameter, e.g.
+    # `flat_param[offset_in_shard : offset_in_shard + numel_in_shard]`
+    offset_in_shard: Optional[int]
+    numel_in_shard: Optional[int]
+    # Use to get part of the parameter in the local shard from a flattened
+    # version of the unsharded parameter, e.g. either
+    # `param.flatten()[intra_param_start_idx : intra_param_end_idx + 1]` or
+    # `param.as_strided((param.numel(),), (1,))[intra_param_start_idx : intra_param_end_idx + 1]`
+    intra_param_start_idx: Optional[int]
+    intra_param_end_idx: Optional[int]  # inclusive
+
+
+class FlatParamShardMetadata(NamedTuple):
+    """
+    This holds metadata specific to this rank's shard of the flat parameter.
+
+    Attributes:
+        param_names (Tuple[str, ...]): Prefixed parameter names of this rank's
+            shard of the parameters; see :class:`FlatParameter`.
+        param_shapes (Tuple[torch.Size, ...]): Parameter shapes of this rank's
+            shard of the parameters; see :class:`FlatParameter`.
+        param_strides (Tuple[torch.Size, ...]): Parameter strides of this rank's
+            shard of the parameters; see :class:`FlatParameter`.
+        param_contiguities (Tuple[bool, ...]): Parameter `.contiguous` call results
+            of this rank's shard of the parameters; see :class:`FlatParameter`.
+        param_numels (Tuple[int, ...]): Parameter numels of this rank's shard
+            of the parameters; see :class:`FlatParameter`.
+        param_offsets (Tuple[Tuple[int, int], ...]): [start, end] offsets (in
+            units of numels) giving this rank's part of each flattened
+            original parameter.
+    """
+
+    param_names: tuple[str, ...]
+    param_shapes: tuple[torch.Size, ...]
+    param_strides: tuple[tuple[int, ...], ...]
+    param_contiguities: tuple[bool, ...]
+    param_numels: tuple[int, ...]
+    param_offsets: tuple[tuple[int, int], ...]
+
+
+class _FlatParameterMeta(_ParameterMeta):
+    # Make `isinstance(t, FlatParameter)` return True for custom tensor
+    # instances that have the _is_flat_param flag for BC
+    def __instancecheck__(self, instance):
+        # NB: do NOT test the super implementation
+        return isinstance(instance, torch.Tensor) and getattr(
+            instance, "_is_flat_param", False
+        )
+
+
+class FlatParameter(nn.Parameter, metaclass=_FlatParameterMeta):
+    """
+    This is the flat parameter used by :class:`FullyShardedDataParallel`.
+
+    It is comprised of one or more original parameters, which are flattened and
+    concatenated to construct the flat parameter.
+
+    Under the current design, this parameter logically represents both the
+    unsharded and sharded flat parameter, and its data changes storages
+    dynamically.
+        - In the :class:`FullyShardedDataParallel` constructor, the parameter
+        is initialized as unsharded and then sharded in-place.
+        - At runtime, the parameter is lazily (re)-initialized. The sharded
+        parameter data is saved in ``self._local_shard``, and a new ``Tensor``
+        ``self._full_param_padded`` is created, which is the all-gather
+        destination and owns the unsharded parameter storage thereafter. (See
+        :meth:`FlatParamHandle.init_flat_param_attributes`.)
+        - Throughout runtime, the parameter data changes storages as needed,
+        e.g. to the sharded flat parameter, low precision sharded flat
+        parameter, or the unsharded flat parameter.
+
+    NOTE: Since ``use_orig_params=True`` supports intra-``FlatParameter``
+    padding, we have two versions of the per-parameter numels, one that
+    includes the padding (``_numels_with_padding``) and one that does not
+    (``_numels``). The former may have length longer than the other data
+    structures, while the latter has the same length as the number of actual
+    original parameters like the other per-parameter data structures.
+
+    NOTE: This is not a real class; instead, you will always get a Parameter
+    back out if you try to create one of these.  This is similar to the trick
+    we implemented for Parameter to get it to work with subclasses; this
+    is primarily so that FlatParameter supports combination with FakeTensor.
+
+    Attributes:
+        _unpadded_unsharded_size (torch.Size): Unsharded flat parameter's size
+            without right-hand-side padding for divisibility by the world size.
+            For ``use_orig_params=True``, this includes alignment padding.
+        _padded_unsharded_size (torch.Size): Unsharded flat parameter's size
+            with right-hand-side padding for divisibility by the world size.
+            For ``use_orig_params=True``, this includes alignment padding. This
+            is only set for sharded strategies since they require padding for
+            the all-gather.
+        _sharded_size (torch.Size): Sharded flat parameter's size with padding.
+            This is also set for ``NO_SHARD``, in which case it is the same as
+            the unsharded sizes. (We omit "padded" because there is no
+            analogous unpadded one.)
+
+        _num_params (int): Number of original parameters flattened into this
+            flat parameter. This is the length of the per-parameter data
+            structures.
+        _param_infos (Tuple[ParamInfo, ...]): Each parameter's parameter info
+            entry; see :class:`ParamInfo` for details.
+        _shapes (Tuple[torch.Size, ...]): Each parameter's original shape.
+        _strides (Tuple[torch.Size, ...]): Each parameter's original stride.
+        _contiguities (Tuple[bool, ...]): Each parameter's ``contiguous()``
+            call result.
+        _fqns (Tuple[str, ...]): Each parameter's fully-qualified name (FQN)
+            prefixed from the ``_fully_sharded_module``. The names are
+            guaranteed to be unique in the subtree rooted at that module.
+        _param_extensions (Tuple[Optional[Any], ...]): Each parameter's
+            extension (i.e. some per-parameter state) used to customize
+            pre-flatten and post-unflatten behavior or ``None``. This is
+            experimental, and users should not depend on its existence in the
+            future.
+        _numels_with_padding (Tuple[int, ...]): Each parameter's numel
+            including entries for the padding. This is used to construct views
+            into the flat parameter via ``torch.split()``. This may have length
+            longer than ``_num_params``.
+        _numels (Tuple[int, ...]): Each parameter's numel excluding entries for
+            padding. This has length equal to ``_num_params``.
+        _shard_param_infos (Tuple[_ShardParamInfo, ...]): Each parameter's
+            shard parameter info; see :class:`_ShardParamInfo` for details.
+        _shared_param_infos (Tuple[SharedParamInfo, ...]): Shared parameter
+            info entries; see :class:`SharedParamInfo` for details.
+        _modules (set[nn.Module]): Modules that contain some original parameter
+            that is flattened into the flat parameter.
+
+        _shard_numel_padded (int): Numel padded for this rank's sharded flat
+            parameter.
+        _local_shard (Tensor): Sharded flat parameter with padding if using a
+            sharded strategy. If using ``NO_SHARD``, then this is the unpadded
+            unsharded flat parameter, and there is no notion of a sharded flat
+            parameter or padded unsharded flat parameter.
+        _full_param_padded (Tensor): Unsharded flat parameter with padding.
+            This is not defined for ``NO_SHARD``. When using mixed precision
+            for parameters, this has the low precision.
+        _full_prec_full_param_padded (Tensor): Full precision unsharded flat
+            parameter with padding. This is used for unsharding outside of
+            computation when using mixed precision for parameters. This is
+            never defined for ``NO_SHARD``.
+        _post_backward_hook_handle (RemovableHandle):
+            Flat parameter's post-backward hook handle. (Compile only)
+        _post_backward_hook_state (Tuple[AccumulateGrad, RemovableHandle]):
+            Flat parameter's :class:`AccumulateGrad` object and post-backward
+            hook handle. (Eager only)
+        _mp_shard (Tensor): Low precision sharded flat parameter with padding.
+            This is only defined when parameter mixed precision is enabled. For
+            ``NO_SHARD``, this is used for computation.
+        _cpu_grad (Tensor): Sharded gradient with padding stored on CPU.
+            This is only defined when offloading parameters is enabled.
+        _saved_grad_shard (Tensor): Sharded gradient with padding from previous
+            iterations for gradient accumulation without :meth:`no_sync`.
+
+        _params (Optional[List[nn.Parameter]]): If ``use_orig_params=True``,
+            then each original parameter variable; otherwise, ``None``. This
+            does not include any padding tensors.
+        _shared_params (Optional[List[nn.Parameter]]): The original shared
+            parameter variables if ``use_orig_params=True`` and ``None``
+            otherwise.
+        _tensors (Optional[List[Optional[Tensor]]]): This saves the ``Tensor``
+            views created in the forward and tracked by autograd when
+            ``use_orig_params=True`` and is ``None`` otherwise. This is to
+            preserve those ``Tensor`` variables for the backward to ensure that
+            the ``FlatParameter`` 's ``AccumulateGrad`` object does not change
+            in which case the post-backward hook does not run. This is relevant
+            for cases like reentrant activation checkpointing.
+        _is_grad_none_mask (Optional[List[bool]]): If ``use_orig_params=True``,
+            a mask over the original parameters' gradients indicating if it is
+            logically ``None`` or not; otherwise, ``None``. This does not
+            include entries for padding. This mask is needed because only some
+            of the parameters may have ``None`` gradient, in which case the
+            flat gradient must be non-``None`` and must use zeros to
+            approximate those original ``None`` gradients. This mask informs
+            FSDP to set the original parameter gradients to ``None`` (instead
+            of zeros) as needed.
+    """
+
+    _unpadded_unsharded_size: torch.Size
+    _padded_unsharded_size: torch.Size
+    _sharded_size: torch.Size
+    _num_params: int
+    _param_infos: tuple[ParamInfo, ...]
+    _shapes: tuple[torch.Size, ...]
+    _strides: tuple[tuple[int, ...], ...]
+    _contiguities: tuple[bool, ...]
+    _fqns: tuple[str, ...]
+    _param_extensions: tuple[Optional[Any], ...]
+    _numels_with_padding: tuple[int, ...]
+    _numels: tuple[int, ...]
+    _shard_param_infos: tuple[_ShardParamInfo, ...]
+    _shared_param_infos: tuple[SharedParamInfo, ...]
+    _modules: set[nn.Module]
+    _shard_numel_padded: int
+    _local_shard: Tensor
+    _full_param_padded: Tensor
+    _full_prec_full_param_padded: Tensor
+    # Eager only
+    _post_backward_hook_state: tuple[Any, Any]
+    # Compile only
+    _post_backward_hook_handle: Any
+    _mp_shard: Tensor
+    _cpu_grad: Tensor
+    _saved_grad_shard: Tensor
+    _params: Optional[list[nn.Parameter]]
+    _shared_params: Optional[list[nn.Parameter]]
+    _tensors: Optional[list[Optional[Tensor]]]
+    _is_grad_none_mask: Optional[list[bool]]
+
+    _is_padding_mask: list[bool]
+
+    def __new__(cls, data=None, requires_grad=True):
+        if cls is not FlatParameter:
+            raise AssertionError("subclasses FlatParameter not supported")
+        r = nn.Parameter.__new__(nn.Parameter, data, requires_grad)  # type: ignore[call-arg]
+        r._is_flat_param = True  # type: ignore[attr-defined]
+        return r
+
+    # NB: This is not a regular method, because FlatParameters are not actually
+    # instances of this class (see __new__ above).  So you must indirectly
+    # call this directly through the classmethod.
+    @classmethod
+    def _init_metadata(
+        cls,
+        self,
+        param_infos: list[ParamInfo],
+        numels: list[int],
+        shapes: list[torch.Size],
+        strides: list[tuple[int, ...]],
+        contiguities: list[bool],
+        fqns: list[str],
+        shared_param_infos: list[SharedParamInfo],
+        param_extensions: list[Optional[Any]],
+        params: Optional[list[nn.Parameter]],
+        shared_params: Optional[list[nn.Parameter]],
+        is_padding_mask: list[bool],
+    ) -> None:
+        """
+        Initialize attributes holding metadata about the original parameters comprising the flat parameter.
+
+        We expose this method separate from the constructor to keep the
+        constructor only responsible for the flat parameter's tensor data. This
+        method should only be called once per model, while the constructor may
+        be called multiple times, e.g. when reloading from a checkpoint, in
+        which case only the tensor data needs to be passed to the constructor.
+        Since :meth:`load_state_dict` is implemented via :meth:`copy_`, the
+        metadata is correctly assumed to be unchanged.
+
+        Args:
+            See the Attributes in the class docstring.
+        """
+        if len(param_infos) != len(shapes):
+            raise AssertionError(
+                f"Expected param_infos length {len(param_infos)} to match shapes length {len(shapes)}"
+            )
+        if len(param_infos) != len(strides):
+            raise AssertionError(
+                f"Expected param_infos length {len(param_infos)} to match strides length {len(strides)}"
+            )
+        if len(param_infos) != len(contiguities):
+            raise AssertionError(
+                f"Expected param_infos length {len(param_infos)} to match contiguities length {len(contiguities)}"
+            )
+        if len(param_infos) != len(fqns):
+            raise AssertionError(
+                f"Expected param_infos length {len(param_infos)} to match fqns length {len(fqns)}"
+            )
+        if len(param_infos) != len(param_extensions):
+            raise AssertionError(
+                f"Expected param_infos length {len(param_infos)} to match param_extensions length {len(param_extensions)}"
+            )
+        self._num_params = len(param_infos)
+        self._param_infos = param_infos
+        self._shapes = shapes
+        self._strides = strides
+        self._contiguities = contiguities
+        self._fqns = fqns
+        self._param_extensions = param_extensions
+        self._is_padding_mask = is_padding_mask
+
+        numels_without_padding: list[int] = []
+        for numel, is_padding in zip(numels, is_padding_mask):
+            if not is_padding:
+                numels_without_padding.append(numel)
+        self._numels = tuple(numels_without_padding)
+        self._numels_with_padding = tuple(numels)
+        if len(self._numels) != self._num_params:
+            raise AssertionError(
+                f"Expected _numels length {len(self._numels)} to equal _num_params {self._num_params}"
+            )
+
+        self._shared_param_infos = tuple(shared_param_infos)
+        self._modules = {pi.module for pi in self._param_infos}.union(
+            {spi.module for spi in self._shared_param_infos}
+        )
+        if (params is None) != (shared_params is None):
+            raise AssertionError(
+                "Expected params and shared_params to both be None or both be not None"
+            )
+        if params is not None:
+            if shared_params is None or len(shared_params) != len(shared_param_infos):
+                raise AssertionError(
+                    f"Expected shared_params to be not None and have length {len(shared_param_infos)}, got {shared_params}"
+                )
+            self._params = []
+            for param, is_padding in zip(params, is_padding_mask):
+                if not is_padding:
+                    self._params.append(param)
+            if shared_params is not None:
+                self._shared_params = shared_params
+            else:
+                self._shared_params = []
+            # Mark the original parameters to avoid flattening them into
+            # another `FlatParameter` during recursive construction
+            for param in chain(self._params, self._shared_params):
+                _set_fsdp_flattened(param)
+            self._is_grad_none_mask = [False for _ in range(self._num_params)]
+            self._tensors = [None for _ in range(self._num_params)]
+        else:
+            self._params = None
+            self._shared_params = None
+            self._is_grad_none_mask = None
+            self._tensors = None
+        self._unpadded_unsharded_size = self.size()
+        _set_fsdp_flattened(self)
+        # Tracks whether the `FlatParameter`'s post-backward hook has been
+        # called to modify the behavior of the post-backward callback
+        self._post_backward_called = False
+
+
+class FlatParamHandle:
+    """
+    A handle that manages a flat parameter (:class:`FlatParameter`).
+
+    This includes sharding and view management.
+
+    Args:
+        params (Sequence[nn.Parameter]): The parameters to flatten into the
+            flat parameter.
+        fully_sharded_module (nn.Module): See [Note: Fully Sharded Module].
+        device (torch.device): The compute and communication device, which
+            should be a non-CPU device. We refer to it as the compute device.
+        sharding_strategy (ShardingStrategy): Sharding strategy to apply to
+            this handle's ``FlatParameter``.
+        offload_params (bool): Whether to offload the handle's
+            ``FlatParameter`` to CPU.
+        mp_param_dtype (Optional[torch.dtype]): Parameter mixed precision
+            setting passed to the FSDP constructor.
+        mp_reduce_dtype (Optional[torch.dtype]): Gradient reduction mixed
+            precision setting passed to the FSDP constructor.
+        keep_low_precision_grads (bool): Whether to keep gradients in low
+            precision.
+        use_orig_params (bool): If ``True``, then FSDP preserves the original
+            parameter variables and returns them from ``named_parameters()``
+            (e.g. to support different optimizer hyperparameters within one
+            :class:`FlatParameter`). If ``False``, then FSDP reconstructs the
+            parameters every iteration and returns the :class:`FlatParameter` s
+            from ``named_parameters()``.
+    """
+
+    ##################
+    # INITIALIZATION #
+    ##################
+    def __init__(
+        self,
+        params: Sequence[Union[nn.Parameter, Tensor]],
+        fully_sharded_module: nn.Module,
+        device: torch.device,
+        sharding_strategy: HandleShardingStrategy,
+        offload_params: bool,
+        mp_param_dtype: Optional[torch.dtype],
+        mp_reduce_dtype: Optional[torch.dtype],
+        keep_low_precision_grads: bool,
+        process_group: dist.ProcessGroup,
+        use_orig_params: bool,
+        *,
+        fsdp_extension: Optional[FSDPExtensions] = None,
+    ):
+        super().__init__()
+        params = list(params)
+        if len(params) == 0:
+            raise ValueError(
+                f"Cannot construct a {self.__class__.__name__} with an empty parameter list"
+            )
+        self._init_setattr_fns()
+        self._skip_writeback_check = (
+            os.environ.get(_FSDP_SKIP_WRITEBACK_CHECK, "") == "1"
+        )
+        self._use_full_prec_in_eval = (
+            os.environ.get(_FSDP_USE_FULL_PREC_IN_EVAL, "") == "1"
+        )
+        self._use_fake_all_gather = os.environ.get(_FSDP_USE_FAKE_ALL_GATHER, "") == "1"
+        self._use_fake_reduce = os.environ.get(_FSDP_USE_FAKE_REDUCE, "") == "1"
+        if self._skip_writeback_check:
+            _warn_skip_writeback_check(
+                logger,
+                f"Since {_FSDP_SKIP_WRITEBACK_CHECK}=1, FSDP will not check "
+                "for parameter or gradient writeback. Changing parameter or "
+                "gradient storages may lead to silent correctness errors.",
+            )
+        if self._use_fake_all_gather:
+            _warn_use_fake_all_gather(
+                logger,
+                f"Since {_FSDP_USE_FAKE_ALL_GATHER}=1, FSDP will not execute "
+                "all-gather ops. Your training will be incorrect, but "
+                "can reveal how much time spent on all-gather ops.",
+            )
+        if self._use_fake_reduce:
+            _warn_use_fake_reduce(
+                logger,
+                f"Since {_FSDP_USE_FAKE_REDUCE}=1, FSDP will not execute "
+                "reduce-scatter ops. Your training will be incorrect, but "
+                "can reveal how much time spent on reduce-scatter ops.",
+            )
+        # Only align addresses for `use_orig_params=True` (for now)
+        align_addresses = use_orig_params
+        self._init_get_unflat_views_fn(align_addresses)
+        # pyrefly: ignore [read-only]
+        self.device = device
+        self._device_handle = _FSDPDeviceHandle.from_device(self.device)
+        self.process_group = process_group
+        if self._use_fake_all_gather or self._use_fake_reduce:
+            self._fake_process_group = FakeProcessGroup._create_internal(
+                rank=process_group.rank(), world_size=process_group.size()
+            )
+        self.rank = process_group.rank()
+        self.world_size = process_group.size()
+        self._sharding_strategy = sharding_strategy
+        self._offload_params = offload_params
+        self._use_orig_params = use_orig_params
+        self._keep_low_precision_grads = keep_low_precision_grads
+        self._training_state = HandleTrainingState.IDLE
+        self._debug_level = dist.get_debug_level()
+        self._fully_sharded_module = fully_sharded_module
+        # For strategies that do not free after forward, we skip using sharded
+        # views after forward since the unsharded data exists. We still switch
+        # `self.flat_param` to point to the sharded flat parameter since what
+        # it points to parameterizes behavior. We use the following attribute
+        # to track which tensor data the parameters are unsharded views into.
+        self._unsharded_flat_param_for_skipped_views: Optional[Tensor] = None
+        # The index in the state's `all_handles`, which must be the
+        # same across ranks for the execution order validation to work
+        self._handle_index: Optional[int] = None
+        # Index in handles_to_pre_forward_order
+        self._pre_forward_order_index: Optional[int] = None
+        # Index in `handles_post_forward_order`
+        self._post_forward_index: Optional[int] = None
+        # Used for guarding against mistargeted forward prefetches
+        self._needs_pre_forward_unshard = False
+        # Used for guarding against mistargeted backward prefetches
+        self._needs_pre_backward_unshard = False
+        # Was the handle prefetched? Set on successful _prefetch_handle and unshard
+        self._prefetched = False
+        # Optimistically assume a valid input `params` and set dtype attributes
+        # before `_init_flat_param()`, which performs the actual validation
+        self._orig_param_dtype = params[0].dtype
+        self._init_param_reduce_dtypes(mp_param_dtype, mp_reduce_dtype)
+        if self._fwd_bwd_param_dtype is None:
+            raise AssertionError("Expected _fwd_bwd_param_dtype to be not None")  # mypy
+        self._aligned_numel = (
+            _get_aligned_numel(unsharded_dtype=self._fwd_bwd_param_dtype)
+            if align_addresses
+            else 0
+        )
+        self._fsdp_extension = fsdp_extension
+        self._init_flat_param_and_metadata(
+            params,
+            fully_sharded_module,
+            self._aligned_numel,
+            use_orig_params,  # type: ignore[arg-type]
+        )
+        self._use_unsharded_views(as_params=False)
+
+    def __repr__(self):
+        return f"FlatParamHandle(flat_param.fqns={self.flat_param._fqns})"
+
+    def _init_setattr_fns(self):
+        use_unsafe_setattr = os.environ.get(_FSDP_USE_UNSAFE_SETATTR, "") == "1"
+        self._setattr_tensor: Callable[[nn.Module, str, Tensor], None]
+        self._setattr_param: Callable[[nn.Module, str, nn.Parameter], None]
+        if use_unsafe_setattr:
+            self._setattr_tensor = _unsafe_setattr_tensor
+            self._setattr_param = _unsafe_setattr_param
+        else:
+            self._setattr_tensor = _safe_setattr_tensor_or_param
+            self._setattr_param = _safe_setattr_tensor_or_param
+
+    def _init_get_unflat_views_fn(self, align_addresses: bool):
+        self._get_unflat_views = (
+            self._get_unflat_views_aligned
+            if align_addresses
+            else self._get_unflat_views_unaligned
+        )
+
+    def _init_flat_param_and_metadata(
+        self,
+        params: list[Union[Tensor, nn.Parameter]],
+        module: nn.Module,
+        aligned_numel: int,
+        use_orig_params: bool,
+    ) -> None:
+        """
+        Initialize the ``FlatParameter`` and its metadata.
+
+        NOTE: This should only be called once at construction time, after which
+        the ``FlatParameter`` metadata is assumed to be static.
+
+        NOTE: The elements of ``params`` should only be ``Tensor`` s when
+        composing with ``DTensor`` -based tensor parallelism, in which case the
+        elements may be ``DTensor`` local shards.
+        """
+        if len(params) == 0:
+            raise ValueError("Expects non-empty `params`")
+        if aligned_numel < 0:
+            raise ValueError(
+                f"Expects non-negative `aligned_numel` but got {aligned_numel}"
+            )
+        (
+            dtype,
+            flat_param_requires_grad,
+            device,
+        ) = self._validate_tensors_to_flatten(params)
+        params_set = set(params)
+        # For alignment padding, only `numels` gets strictly non-`None`
+        # elements, and all other lists get `None` elements for padding.
+        param_infos: list[ParamInfo] = []
+        numels: list[int] = []
+        shapes: list[torch.Size] = []
+        strides: list[tuple[int, ...]] = []
+        contiguities: list[bool] = []
+        fqns: list[str] = []
+        shared_param_infos: list[SharedParamInfo] = []
+        shared_param_memo: dict[
+            Union[Tensor, nn.Parameter], tuple[nn.Module, str, str]
+        ] = {}
+        params_to_flatten: list[Union[Tensor, nn.Parameter]] = []
+        shared_params: list[Union[Tensor, nn.Parameter]] = []
+        param_extensions: list[Any] = []
+        is_padding_mask: list[bool] = []
+        total_numel = total_numel_without_padding = 0
+        for submodule_name, submodule in module.named_modules(remove_duplicate=False):
+            for param_name, param in _named_parameters_with_duplicates(
+                submodule, recurse=False
+            ):
+                if param not in params_set:
+                    continue
+                if param in shared_param_memo:  # shared reference
+                    prim_module, prim_module_name, prim_param_name = shared_param_memo[
+                        param
+                    ]
+                    shared_params.append(param)
+                    shared_param_infos.append(
+                        SharedParamInfo(
+                            param_name,
+                            submodule,
+                            submodule_name,
+                            prim_param_name,
+                            prim_module,
+                            prim_module_name,
+                        )
+                    )
+                else:
+                    if aligned_numel > 0:
+                        numel_to_pad = aligned_numel - (total_numel % aligned_numel)
+                        if numel_to_pad > 0 and numel_to_pad < aligned_numel:
+                            padding_tensor = _construct_padding_tensor(
+                                numel_to_pad, dtype, False, device
+                            )
+                            params_to_flatten.append(padding_tensor)
+                            is_padding_mask.append(True)
+                            numels.append(numel_to_pad)
+                            total_numel += numel_to_pad
+                    transform_t, extension = _ext_pre_flatten_transform(
+                        param,
+                        self._fsdp_extension,
+                    )
+                    param = cast(nn.Parameter, transform_t)
+                    param_extensions.append(extension)
+                    shared_param_memo[param] = (submodule, submodule_name, param_name)
+                    params_to_flatten.append(param)
+                    is_padding_mask.append(False)
+                    param_infos.append(ParamInfo(param_name, submodule, submodule_name))
+                    numels.append(param.numel())
+                    shapes.append(param.shape)
+                    strides.append(param.stride())
+                    contiguities.append(_is_truly_contiguous(param))
+                    fqn = (
+                        submodule_name + "." + param_name
+                        if submodule_name
+                        else param_name
+                    )
+                    fqns.append(fqn)
+                    total_numel += param.numel()
+                    total_numel_without_padding += param.numel()
+        if len(params_to_flatten) == 0:
+            raise ValueError(
+                f"`params` were not found in `module`'s tree"
+                f"params: {params}\nmodule: {module}"
+            )
+        if (
+            self.rank == 0
+            and aligned_numel > 0
+            and total_numel != total_numel_without_padding
+        ):
+            logger.debug(
+                "FSDP FlatParameter address alignment created "
+                "%s numel of padding (%s vs. %s)",
+                total_numel - total_numel_without_padding,
+                total_numel,
+                total_numel_without_padding,
+            )
+        if aligned_numel > 0:
+            # Pad to be divisible by world size to avoid a copy for the
+            # post-backward reduce-scatter
+            numel_to_pad = self.world_size - (total_numel % self.world_size)
+            if numel_to_pad > 0 and numel_to_pad < self.world_size:
+                if self.rank == 0:
+                    logger.info(
+                        "FSDP FlatParameter world size divisibility created "
+                        "%s numel of padding",
+                        numel_to_pad,
+                    )
+                padding_tensor = _construct_padding_tensor(
+                    numel_to_pad, dtype, False, device
+                )
+                params_to_flatten.append(padding_tensor)
+                is_padding_mask.append(True)
+                numels.append(numel_to_pad)
+                total_numel += numel_to_pad
+        # Pass `aligned_numel=0` since we already included padding tensors
+        self.flat_param: FlatParameter = self.flatten_tensors_into_flat_param(
+            params_to_flatten,
+            aligned_numel=0,
+            requires_grad=flat_param_requires_grad,
+        )
+        FlatParameter._init_metadata(
+            self.flat_param,
+            param_infos,
+            numels,
+            shapes,
+            strides,
+            contiguities,
+            fqns,
+            shared_param_infos,
+            param_extensions,
+            _convert_to_params(params_to_flatten) if use_orig_params else None,
+            _convert_to_params(shared_params) if use_orig_params else None,
+            is_padding_mask,
+        )
+
+    def _validate_tensors_to_flatten(
+        self, tensors: list[Union[Tensor, nn.Parameter]]
+    ) -> tuple:
+        """Validate the tensors to flatten and returns any necessary metadata."""
+        dtype: Optional[torch.dtype] = None
+        # Return as the logical OR over each tensor's value
+        flat_param_requires_grad: Optional[bool] = None
+        device: Optional[torch.device] = None
+        # For `use_orig_params=True`, permit non-uniform `requires_grad`
+        for tensor in tensors:
+            if isinstance(tensor, FlatParameter):
+                raise ValueError("Cannot flatten a `FlatParameter`")
+            if dtype is None and not tensor.is_floating_point():
+                raise ValueError("Cannot flatten integer dtype tensors")
+            if dtype is not None and tensor.dtype != dtype:
+                raise ValueError(
+                    f"Must flatten tensors with uniform dtype but got {dtype} "
+                    f"and {tensor.dtype}"
+                )
+            if (
+                not self._use_orig_params
+                and flat_param_requires_grad is not None
+                and tensor.requires_grad != flat_param_requires_grad
+            ):
+                raise ValueError(
+                    "Must flatten tensors with uniform `requires_grad` when "
+                    "`use_orig_params=False`"
+                )
+            if device is not None and tensor.device != device:
+                raise ValueError(
+                    "Must flatten tensors on the same device but got both "
+                    f"{device} and {tensor.device}"
+                )
+            dtype = tensor.dtype
+            flat_param_requires_grad = flat_param_requires_grad or tensor.requires_grad
+            device = tensor.device
+        if flat_param_requires_grad is None:
+            raise AssertionError("Requires non-empty `tensors` list")
+        return dtype, flat_param_requires_grad, device
+
+    def flatten_tensors(
+        self,
+        tensors: list[Tensor],
+        aligned_numel: int,
+    ) -> Tensor:
+        """
+        Flatten ``tensors`` into a single flat tensor.
+
+        The flattening optionally includes
+        padding if ``aligned_numel`` is greater than 0, where ``aligned_numel``
+        gives the numel required to have address alignment.
+
+        NOTE: The padding alignment algorithm must be kept in sync with
+        :meth:`_init_flat_param_metadata`. We separate the two methods because
+        the initialization happens once, whereas this method may be called
+        multiple times throughout training (e.g. for checkpointing).
+        """
+        if len(tensors) == 0:
+            raise ValueError("Expects non-empty `tensors`")
+        if aligned_numel < 0:
+            raise ValueError(
+                f"Expects non-negative `aligned_numel` but got {aligned_numel}"
+            )
+        dtype, _, device = self._validate_tensors_to_flatten(tensors)
+        flat_tensors: list[Tensor] = []
+        if aligned_numel > 0:
+            total_numel = 0
+            for tensor in tensors:
+                numel_to_pad = aligned_numel - (total_numel % aligned_numel)
+                if numel_to_pad > 0 and numel_to_pad < aligned_numel:
+                    padding_tensor = _construct_padding_tensor(
+                        numel_to_pad, dtype, False, device
+                    )
+                    flat_tensors.append(padding_tensor)
+                    total_numel += numel_to_pad
+                flat_tensors.append(
+                    torch.flatten(_detach_if_needed(tensor))
+                    if _is_truly_contiguous(tensor)
+                    else _detach_if_needed(tensor).as_strided((tensor.numel(),), (1,))
+                )
+                total_numel += tensor.numel()
+            numel_to_pad = self.world_size - (total_numel % self.world_size)
+            if numel_to_pad > 0 and numel_to_pad < self.world_size:
+                padding_tensor = _construct_padding_tensor(
+                    numel_to_pad, dtype, False, device
+                )
+                flat_tensors.append(padding_tensor)
+                total_numel += numel_to_pad
+        else:
+            flat_tensors = [
+                torch.flatten(_detach_if_needed(tensor))
+                if _is_truly_contiguous(tensor)
+                else _detach_if_needed(tensor).as_strided((tensor.numel(),), (1,))
+                for tensor in tensors
+            ]
+        return torch.cat(flat_tensors, dim=0)
+
+    def flatten_tensors_into_flat_param(
+        self,
+        tensors: list[Tensor],
+        aligned_numel: int,
+        requires_grad: bool,
+    ) -> FlatParameter:
+        flat_param_data = self.flatten_tensors(tensors, aligned_numel)
+        return FlatParameter(flat_param_data, requires_grad=requires_grad)
+
+    def _init_param_reduce_dtypes(
+        self,
+        mp_param_dtype: Optional[torch.dtype],
+        mp_reduce_dtype: Optional[torch.dtype],
+    ) -> None:
+        """
+        Initialize param and reduce dtypes.
+
+        Precondition: ``self.flat_param`` is set. This ensures that this
+        handle's parameters have a single dtype.
+
+        Postcondition: This sets ``self._fwd_bwd_param_dtype`` and
+        ``self._reduce_dtype``. If ``mp_param_dtype`` or ``mp_reduce_dtype``
+        is ``None``, then we assume the original parameter dtype. One special
+        case is if ``mp_param_dtype`` is not ``None`` and ``mp_reduce_dtype``
+        is ``None``, in which case we assume the gradient reduction dtype
+        matches the forward/backward parameter dtype.
+        """
+        # Save whether these dtypes were specified so that we permit the
+        # parameter dtype to change up until the lazy initialization
+        self._low_prec_param_dtype_specified = mp_param_dtype is not None
+        self._low_prec_reduce_dtype_specified = mp_reduce_dtype is not None
+        if (
+            self._low_prec_param_dtype_specified
+            and not self._low_prec_reduce_dtype_specified
+        ):
+            # Special case: infer gradient reduction mixed precision
+            self._fwd_bwd_param_dtype = mp_param_dtype
+            self._reduce_dtype = self._fwd_bwd_param_dtype
+        else:
+            self._fwd_bwd_param_dtype = mp_param_dtype or self._orig_param_dtype
+            self._reduce_dtype = mp_reduce_dtype or self._orig_param_dtype
+        if self._fwd_bwd_param_dtype is None:
+            raise AssertionError("Expected _fwd_bwd_param_dtype to be not None")
+        if self._reduce_dtype is None:
+            raise AssertionError("Expected _reduce_dtype to be not None")
+
+    ###################################
+    # SHARD INITIALIZATION & METADATA #
+    ###################################
+    @torch.no_grad()
+    def shard(self):
+        """
+        Shard the handle's ``FlatParameter``.
+
+        This allocates new memory for
+        the sharded flat parameter and frees the unsharded flat parameter's
+        storage.
+
+        Postcondition: ``self.flat_param`` is the sharded flat parameter. Shard
+        metadata attributes are set for all sharding strategies.
+        """
+        flat_param = self.flat_param
+        if not self.uses_sharded_strategy:
+            self._init_shard_metadata(0, 0, flat_param.numel() - 1)
+        else:
+            _p_assert(
+                flat_param.storage_offset() == 0,
+                "The `FlatParameter` is not the sole occupant of its storage",
+            )
+            sharded_flat_param, numel_padded = FlatParamHandle._get_shard(
+                flat_param, self.rank, self.world_size
+            )
+            if not torch.distributed._functional_collectives.is_torchdynamo_compiling():
+                allocated = flat_param._typed_storage()._size() > 0
+                if allocated:
+                    flat_param._typed_storage()._resize_(0)
+            flat_param.set_(sharded_flat_param)  # type: ignore[call-overload]
+            start_idx = sharded_flat_param.numel() * self.rank
+            end_idx = sharded_flat_param.numel() * (self.rank + 1) - 1  # inclusive
+            self._init_shard_metadata(numel_padded, start_idx, end_idx)
+        if self._use_orig_params:
+            self._use_sharded_views()
+
+    def _init_shard_metadata(
+        self,
+        numel_padded: int,
+        unsharded_start_idx: int,
+        unsharded_end_idx: int,
+    ) -> None:
+        """
+        Initialize shard-related metadata for this rank's shard of the flat parameter.
+
+        This includes ``_sharded_size``, ``_shard_param_infos``, and ``_shard_numel_padded``.
+
+        Args:
+            numel_padded (int): Numel padded for this rank's sharded flat
+                parameter.
+            unsharded_start_idx (int): Start index in the unsharded flat
+            parameter assigned to this rank.
+            unsharded_end_idx (int): End index (inclusive) in the unsharded
+                flat parameter assigned to this rank.
+
+        Precondition: ``self.flat_param`` 's data is the sharded flat
+        parameter.
+        """
+        flat_param = self.flat_param
+        flat_param._sharded_size = flat_param.size()  # type: ignore[attr-defined]
+        sharded_flat_param_numel = flat_param.numel()  # includes `numel_padded`
+        _p_assert(
+            unsharded_start_idx >= 0 and unsharded_start_idx <= unsharded_end_idx,
+            f"unsharded_start_idx: {unsharded_start_idx} unsharded_end_idx: {unsharded_end_idx}",
+        )
+        _p_assert(
+            numel_padded <= sharded_flat_param_numel,
+            f"numel_padded: {numel_padded} "
+            f"sharded_flat_param_numel: {sharded_flat_param_numel}",
+        )
+        shard_param_infos = self._get_shard_metadata(
+            unsharded_start_idx, unsharded_end_idx
+        )
+        if len(shard_param_infos) != flat_param._num_params:
+            raise AssertionError(
+                f"Expects length {flat_param._num_params} but got {len(shard_param_infos)}"
+            )
+        flat_param._shard_param_infos = shard_param_infos  # type: ignore[attr-defined]
+        flat_param._shard_numel_padded = numel_padded  # type: ignore[attr-defined]
+
+    def _get_shard_metadata(
+        self,
+        unsharded_start_idx: int,
+        unsharded_end_idx: int,
+    ) -> tuple[_ShardParamInfo, ...]:
+        """
+        Compute the shard metadata based on ``unsharded_start_idx`` and ``unsharded_end_idx`` (inclusive).
+
+        ``unsharded_start_idx`` and ``unsharded_end_idx`` give the interval of the
+        unsharded flat parameter specifying the shard.
+        """
+        flat_param_offsets = self._get_flat_param_offsets()
+        if len(flat_param_offsets) != len(self.flat_param._numels_with_padding):
+            raise AssertionError(
+                f"Expected {len(self.flat_param._numels_with_padding)} but got {len(flat_param_offsets)}"
+            )
+        shard_param_infos: list[_ShardParamInfo] = []
+        sharded_flat_param_numel = unsharded_end_idx - unsharded_start_idx + 1
+        # `unsharded_param_start_idx` and `unsharded_param_end_idx` are indices
+        # into the unsharded flat parameter (inclusive) of the given parameter
+        for (
+            (unsharded_param_start_idx, unsharded_param_end_idx),
+            is_padding,
+        ) in zip(flat_param_offsets, self.flat_param._is_padding_mask):
+            if is_padding:
+                continue
+            in_sharded_flat_param = (
+                unsharded_start_idx <= unsharded_param_end_idx
+                and unsharded_end_idx >= unsharded_param_start_idx
+            )
+            if not in_sharded_flat_param:
+                shard_param_info = _ShardParamInfo(False, None, None, None, None)
+            else:
+                if unsharded_start_idx <= unsharded_param_start_idx:
+                    # This branch can only happen once since the rank's
+                    # unsharded start index can only intersect one parameter
+                    intra_param_start_idx = 0
+                    offset_in_shard = unsharded_param_start_idx - unsharded_start_idx
+                else:
+                    intra_param_start_idx = (
+                        unsharded_start_idx - unsharded_param_start_idx
+                    )
+                    offset_in_shard = 0
+                if not (
+                    offset_in_shard >= 0 and offset_in_shard < sharded_flat_param_numel
+                ):
+                    raise AssertionError(
+                        f"Invalid `offset_in_shard` of {offset_in_shard} for "
+                        f"sharded flat parameter with {sharded_flat_param_numel} numel"
+                    )
+                intra_param_end_idx = (
+                    min(unsharded_param_end_idx, unsharded_end_idx)
+                    - unsharded_param_start_idx
+                )
+                numel_in_shard = intra_param_end_idx - intra_param_start_idx + 1
+                shard_param_info = _ShardParamInfo(
+                    True,
+                    offset_in_shard,
+                    numel_in_shard,
+                    intra_param_start_idx,
+                    intra_param_end_idx,
+                )
+            shard_param_infos.append(shard_param_info)
+        return tuple(shard_param_infos)
+
+    @staticmethod
+    def _get_unpadded_shard(
+        tensor: Tensor,
+        rank: int,
+        world_size: int,
+    ) -> tuple[Tensor, int]:
+        """
+        Return the unpadded shard of ``tensor`` for the given ``rank`` and ``world_size``.
+
+        The returned value is a tuple of the shard of ``tensor`` without any
+        padding and the numel to pad for that shard.
+
+        If ``tensor`` is already flattened or may be viewed in the flattened
+        shape (which is true in the expected usage), then this method does not
+        allocate any new tensor memory.
+        """
+        chunks = (
+            torch.flatten(tensor).chunk(world_size)
+            if _is_truly_contiguous(tensor)
+            else tensor.as_strided((tensor.numel(),), (1,)).chunk(world_size)
+        )
+        if len(chunks) < (rank + 1):
+            # This rank gets an empty chunk fully padded with zeros since there
+            # are not enough chunks across ranks
+            chunk = chunks[0].new_empty(0)
+        else:
+            chunk = chunks[rank]
+        numel_to_pad = chunks[0].numel() - chunk.numel()
+        if numel_to_pad < 0:
+            raise AssertionError(
+                "Chunk's size should be at most the first chunk's size"
+            )
+        return chunk, numel_to_pad
+
+    @staticmethod
+    def _get_shard(
+        tensor: Tensor,
+        rank: int,
+        world_size: int,
+    ) -> tuple[Tensor, int]:
+        """
+        Return the shard of ``tensor`` with padding for the given ``rank`` and ``world_size`` and the numel padded for that shard.
+
+        This method allocates new memory (via :meth:`clone`) since the
+        unsharded ``tensor`` may be deallocated after this method returns.
+        """
+        chunk, numel_to_pad = FlatParamHandle._get_unpadded_shard(
+            tensor, rank, world_size
+        )
+        shard = chunk.clone()
+        if numel_to_pad > 0:
+            shard = F.pad(shard, [0, numel_to_pad])
+        return shard, numel_to_pad
+
+    @staticmethod
+    def _get_sharded_size(tensor: Tensor, rank: int, world_size: int) -> torch.Size:
+        """
+        Return the shape of ``tensor`` after sharding including padding.
+
+        This requires ``tensor`` to have 1D shape and ensures that the returned
+        shape is 1D.
+        """
+        if len(tensor.shape) != 1:
+            raise AssertionError(f"Expected 1D tensor shape, got {tensor.shape}")
+        unpadded_sharded_tensor, numel_to_pad = FlatParamHandle._get_unpadded_shard(
+            tensor, rank, world_size
+        )
+        unpadded_sharded_size = unpadded_sharded_tensor.size()
+        if len(unpadded_sharded_size) != 1:
+            raise AssertionError(
+                f"Expected 1D unpadded_sharded_size, got {unpadded_sharded_size}"
+            )
+        return torch.Size([unpadded_sharded_size[0] + numel_to_pad])
+
+    def _get_flat_param_offsets(self) -> list[tuple[int, int]]:
+        """
+        Return [start, end] offsets of each original parameter's flattened data in the unsharded flat parameter (without padding).
+
+        NOTE: The returned list includes elements for alignment padding.
+        """
+        cumulative_sum = list(accumulate(self.flat_param._numels_with_padding))
+        starts = [0] + cumulative_sum[:-1]
+        ends = [end - 1 for end in cumulative_sum]  # inclusive
+        param_offsets = list(zip(starts, ends))
+        return param_offsets
+
+    @no_type_check
+    def shard_metadata(
+        self,
+    ) -> FlatParamShardMetadata:
+        """
+        Return the shard-related metadata specific to this rank's shard of the flat parameter.
+
+        NOTE: The returned tuple does not include elements for alignment
+        padding but does account for the padding.
+        """
+        fqns_list = []
+        shapes_list = []
+        strides_list = []
+        contiguities_list = []
+        numels_list = []
+        shard_param_offsets = []
+        for fqn, shape, stride, contiguous, numel, shard_param_info in zip(
+            self.flat_param._fqns,
+            self.flat_param._shapes,
+            self.flat_param._strides,
+            self.flat_param._contiguities,
+            self.flat_param._numels,
+            self.flat_param._shard_param_infos,
+        ):
+            if not shard_param_info.in_shard:
+                continue
+            fqns_list.append(fqn)
+            shapes_list.append(shape)
+            strides_list.append(stride)
+            contiguities_list.append(contiguous)
+            numels_list.append(numel)
+            shard_param_offsets.append(
+                (
+                    shard_param_info.intra_param_start_idx,
+                    shard_param_info.intra_param_end_idx,
+                )
+            )
+        return FlatParamShardMetadata(
+            tuple(fqns_list),
+            tuple(shapes_list),
+            tuple(strides_list),
+            tuple(contiguities_list),
+            tuple(numels_list),
+            tuple(shard_param_offsets),
+        )
+
+    @no_type_check
+    @torch.no_grad()
+    def init_flat_param_attributes(self) -> None:
+        """
+        This initializes some attributes on the handle's ``FlatParameter``.
+        This should be called during lazy initialization since it requires the
+        parameter to be on the compute device if not offloading to CPU and we
+        want to give users the chance to move the parameter appropriately after
+        the FSDP constructor.
+
+        For each tensor attribute on the ``FlatParameter``, see the unshard and
+        reshard methods in this class for the allocation and free pattern.
+        """
+        flat_param = self.flat_param
+        if flat_param.dtype != self._orig_param_dtype:
+            # Entering this branch means that the user changed the parameter
+            # dtype after FSDP initialization, in which case we may need to
+            # refresh some saved dtype attributes (dtypes specified as a part
+            # of mixed precision take precedence).
+            if not self._low_prec_param_dtype_specified:
+                self._fwd_bwd_param_dtype = flat_param.dtype
+            # For `reduce_dtype`, require `param_dtype` was not specified since
+            # then we infer the `reduce_dtype` from the specified `param_dtype`
+            if (
+                not self._low_prec_reduce_dtype_specified
+                and not self._low_prec_param_dtype_specified
+            ):
+                self._reduce_dtype = flat_param.dtype
+            self._orig_param_dtype = flat_param.dtype
+        cpu_device = torch.device("cpu")
+        if self._offload_params:
+            _p_assert(
+                flat_param.device == cpu_device,
+                f"Expects the `FlatParameter` to be on CPU when parameter CPU "
+                f"offloading is enabled, not {flat_param.device}",
+            )
+        else:
+            self._check_on_compute_device(self.flat_param)
+        flat_param._local_shard = flat_param.data
+        if self._offload_params:
+            # Pin the memory for faster H2D transfer
+            flat_param._local_shard = flat_param._local_shard.pin_memory()
+            # Pre-allocate the sharded gradient on CPU to enable non-blocking
+            # D2H transfer during the backward pass
+            flat_param._cpu_grad = torch.zeros_like(
+                flat_param._local_shard, device=cpu_device
+            ).pin_memory()
+        if self._uses_param_mixed_precision:
+            # For parameter mixed precision, we maintain a low precision
+            # sharded tensor on the compute device to be all-gathered (for
+            # sharded strategies) or directly used (for `NO_SHARD`) for
+            # computation.
+            flat_param._mp_shard = torch.empty_like(
+                flat_param._local_shard,
+                device=self.device,
+                dtype=self._fwd_bwd_param_dtype,
+            )
+            _free_storage(flat_param._mp_shard)
+        if self.uses_sharded_strategy:
+            # We maintain a padded unsharded tensor that serves as the
+            # all-gather destination and owns the original parameter storages.
+            unsharded_param_dtype = (
+                self._fwd_bwd_param_dtype
+                if self._uses_param_mixed_precision
+                else flat_param.dtype
+            )  # use low precision if parameter mixed precision is enabled
+            padded_unsharded_numel = flat_param.numel() * self.world_size
+            flat_param._full_param_padded = torch.empty(
+                padded_unsharded_numel,
+                device=self.device,
+                dtype=unsharded_param_dtype,
+            )
+            flat_param._padded_unsharded_size = flat_param._full_param_padded.size()
+            _free_storage(flat_param._full_param_padded)
+
+            if self._uses_param_mixed_precision:
+                # For parameter mixed precision, we maintain a full precision
+                # padded unsharded tensor for when we force full precision.
+                flat_param._full_prec_full_param_padded = torch.empty(
+                    padded_unsharded_numel,
+                    device=self.device,
+                    dtype=flat_param.dtype,  # full precision
+                )
+                _free_storage(flat_param._full_prec_full_param_padded)
+
+    ###################
+    # UNSHARD/RESHARD #
+    ###################
+    def pre_unshard(self) -> bool:
+        """
+        Return ``False`` if this is a no-op and ``True`` otherwise.
+
+        Postcondition: ``self.flat_param`` 's data is on the device for
+        communication and is what should be all-gathered. This means that it
+        matches the dtype of the expected unsharded parameter.
+        """
+        if (
+            self._training_state == HandleTrainingState.SUMMON_FULL_PARAMS
+            and self._skipped_use_sharded_views
+        ):
+            # Since this path imposes special semantics for the unsharded flat
+            # parameter (e.g. forcing full precision), use sharded views to
+            # reuse the existing logic for that special handling
+            self._use_sharded_views()
+        ret = False
+        if self._use_orig_params and not self._skip_writeback_check:
+            ret = self._writeback_orig_params()
+        if (
+            self.uses_sharded_strategy
+            and not self._offload_params
+            and not self.needs_unshard()
+        ):
+            pass  # no-op
+        elif self._uses_param_mixed_precision and not self._force_full_precision:
+            self._use_low_precision_shard()
+            ret = True
+        elif self._offload_params and self.flat_param.device != self.device:
+            # NOTE: This creates a new tensor distinct from any attributes.
+            self.flat_param_to(self.device, non_blocking=True)
+            ret = True
+        self._check_on_compute_device(self.flat_param)
+        return ret
+
+    def _use_low_precision_shard(self):
+        """Allocate on the compute device and switch to using the low precision sharded flat parameter."""
+        self._check_low_precision_shard()
+        flat_param = self.flat_param
+        _alloc_storage(
+            flat_param._mp_shard,
+            flat_param._local_shard.size(),  # type: ignore[attr-defined]
+        )
+        # `copy_()` implicitly casts to the low precision
+        flat_param._mp_shard.copy_(  # type: ignore[attr-defined]
+            flat_param._local_shard.to(  # type: ignore[attr-defined]
+                self.device, non_blocking=True
+            )
+        )
+        # Invariant: `_mp_shard` is always on the compute device.
+        flat_param.data = flat_param._mp_shard  # type: ignore[attr-defined]
+
+    def unshard(self):
+        """
+        Run the unshard logic.
+
+        This includes all-gathering the flat parameter
+        and switching to using the unsharded flat parameter. If the handle does
+        not need unsharding, then this only switches to using the unsharded
+        flat parameter. For ``NO_SHARD``, this is a no-op.
+
+        If FSDP is in :meth:`summon_full_params` and the handle uses parameter
+        mixed precision, then the parameter is forced to full precision.
+        """
+        if not self.needs_unshard():
+            # Even when not needing an unshard, we should switch to using
+            # the unsharded flat parameter
+            unsharded_flat_param = (
+                self._get_padded_unsharded_flat_param()
+                if self.uses_sharded_strategy
+                else self.flat_param
+            )
+            self._use_unsharded_flat_param(unsharded_flat_param)
+            return
+        unsharded_flat_param = self._alloc_padded_unsharded_flat_param()
+        padded_unsharded_flat_param = self._all_gather_flat_param(unsharded_flat_param)
+        self._use_unsharded_flat_param(padded_unsharded_flat_param)
+
+    def needs_unshard(self) -> bool:
+        """Return if the handle's flat parameter needs to be unsharded."""
+        if not self.uses_sharded_strategy:
+            return False
+        unsharded_flat_param = self._get_padded_unsharded_flat_param()
+        already_unsharded = _same_storage_size(
+            unsharded_flat_param, unsharded_flat_param.numel()
+        )
+        return not already_unsharded
+
+    def _alloc_padded_unsharded_flat_param(self):
+        """
+        Allocate the *padded* unsharded flat parameter.
+
+        The unpadded unsharded
+        flat parameter is always a view into the padded one. This padded
+        parameter is saved to a different attribute on the ``FlatParameter``
+        depending on if we force full precision.
+        """
+        self._check_sharded_strategy()
+        flat_param = self.flat_param
+        unsharded_flat_param = self._get_padded_unsharded_flat_param()
+        self._check_storage_freed(unsharded_flat_param)
+        _alloc_storage(unsharded_flat_param, flat_param._padded_unsharded_size)  # type: ignore[attr-defined]
+        return unsharded_flat_param
+
+    def _get_padded_unsharded_flat_param(self) -> torch.Tensor:
+        """
+        Return a reference to the padded unsharded flat parameter depending on the calling context.
+
+        This should only be called if using a sharded strategy.
+        """
+        self._check_sharded_strategy()
+        flat_param = self.flat_param
+        if self._force_full_precision and self._uses_param_mixed_precision:
+            # When parameter mixed precision is enabled, we use a different
+            # tensor as the all-gather destination to preserve the invariant
+            # that  `_full_param_padded` is in the low precision
+            unsharded_flat_param = flat_param._full_prec_full_param_padded  # type: ignore[attr-defined]
+            _p_assert(
+                unsharded_flat_param.dtype != self._fwd_bwd_param_dtype,
+                f"Expects full precision but got {self._fwd_bwd_param_dtype}",
+            )
+            # For no-reshard-after-forward strategies, `_full_param_padded` may
+            # still be allocated from a previous forward. As we are forcing
+            # full precision here, the full-precision unsharded copy may be
+            # modified, invalidating the existing low-precision unsharded copy,
+            # so we should free it here to ensure a new all-gather for the next
+            # forward/backward computation to persist the modifications.
+            if flat_param._full_param_padded.untyped_storage().size() > 0:
+                _free_storage(flat_param._full_param_padded)
+        else:
+            unsharded_flat_param = flat_param._full_param_padded  # type: ignore[attr-defined]
+        return unsharded_flat_param
+
+    def _all_gather_flat_param(
+        self,
+        padded_unsharded_flat_param: Tensor,
+    ) -> Tensor:
+        """
+        All-gather the handle's flat parameter to the destination ``padded_unsharded_flat_param``.
+
+        Then switch to use the all-gathered tensor.
+        """
+        _p_assert(
+            hasattr(self, "process_group") and hasattr(self, "world_size"),
+            "Expects a process group and world size to have been set via `shard()`",
+        )
+        sharded_flat_param = self.flat_param.data
+        expected_numel = sharded_flat_param.numel() * self.world_size
+        _p_assert(
+            padded_unsharded_flat_param.numel() == expected_numel,
+            f"Expects {expected_numel} numel but got {padded_unsharded_flat_param.numel()}",
+        )
+
+        pg = (
+            self._fake_process_group
+            if self._use_fake_all_gather
+            else self.process_group
+        )
+
+        # HACK this should be handled by C10D
+        if sharded_flat_param.is_cpu:  # type: ignore[attr-defined]
+            tensor_list = list(
+                torch.chunk(
+                    padded_unsharded_flat_param,
+                    dist.get_world_size(pg),  # type: ignore[arg-type]
+                )
+            )
+            dist.all_gather(tensor_list, sharded_flat_param, group=pg)
+        else:
+            dist.all_gather_into_tensor(
+                padded_unsharded_flat_param,
+                sharded_flat_param,
+                pg,
+            )
+
+        if self._offload_params:
+            # In case of offloading, `flat_param.data` (i.e. sharded param) is
+            # created on the pre-unshard stream. We need to hand it over to the
+            # unshard stream for all-gather
+            _no_dispatch_record_stream(
+                sharded_flat_param,
+                self._device_handle.current_stream(),  # unshard_stream
+            )
+        return padded_unsharded_flat_param
+
+    def _use_unsharded_flat_param(
+        self,
+        padded_unsharded_flat_param: torch.Tensor,
+    ) -> None:
+        """
+        Switch to use the *unpadded* unsharded flat parameter.
+
+        This is a view into the *padded* unsharded flat parameter.
+        """
+        unsharded_size = self.flat_param._unpadded_unsharded_size
+        flat_param_part = padded_unsharded_flat_param[: unsharded_size.numel()]
+        # slicing [:] is not visible to autograd because of .data
+        self.flat_param.data = flat_param_part
+        in_forward = self._training_state == HandleTrainingState.FORWARD
+        in_pre_backward = self._training_state == HandleTrainingState.BACKWARD_PRE
+        if self._use_orig_params:
+            if self._skipped_use_sharded_views and in_pre_backward:
+                # This call corresponds to the complementary pre-backward
+                # `_use_unsharded_views()` to the skipped pre-forward
+                # `_use_sharded_views()`, so we should skip this one too.
+                return
+            # We use `Tensor` views in the forward so that they are tracked by
+            # autograd. We use them in the pre-backward as well to support
+            # reentrant activation checkpointing, which needs the views to be
+            # tracked by autograd in the backward pass's recomputed forward.
+            self._use_unsharded_views(
+                as_params=(not in_forward and not in_pre_backward)
+            )
+        elif in_forward:
+            self._use_unsharded_views(as_params=False)
+
+    def post_unshard(self):
+        """
+        Run the post-unshard logic.
+
+        This includes freeing the low precision shard if needed.
+        """
+        if self._uses_param_mixed_precision and self.uses_sharded_strategy:
+            self._free_low_precision_sharded_param()
+        self._check_on_compute_device(self.flat_param)
+
+    def _free_low_precision_sharded_param(self):
+        """Frees the low precision sharded flat parameter."""
+        self._check_low_precision_shard()
+        # `_mp_shard` is allocated in the pre-unshard stream, consumed in the
+        # unshard stream for sharded strategies, and consumed in both the
+        # unshard and default streams for `NO_SHARD`. For sharded strategies,
+        # the current stream here is the unshard stream, and for `NO_SHARD`,
+        # it is the default stream. For `NO_SHARD`, only recording for the
+        # default stream suffices since the default stream waits for the
+        # unshard stream.
+        _no_dispatch_record_stream(
+            self.flat_param._mp_shard,
+            self._device_handle.current_stream(),  # type: ignore[attr-defined]
+        )
+        _free_storage(self.flat_param._mp_shard)  # type: ignore[attr-defined]
+
+    @torch.no_grad()
+    def unshard_grad(self):
+        """
+        Unshard the handle's ``FlatParameter``'s gradient.
+
+        If all ranks have
+        ``None`` gradient, then all original parameters will as well. This
+        method performs an all-reduce and an all-gather. The additional
+        all-reduce is tolerable since this method is not meant to be used on
+        the computation critical path.
+
+        Postcondition: ``_saved_grad_shard`` is defined and contains the value
+        to set ``flat_param.grad`` after gradients are resharded.
+        """
+        if not self.uses_sharded_strategy:
+            self._use_unsharded_grad_views()
+            return
+        flat_param = self.flat_param
+        self._check_unsharded(flat_param)
+
+        # Check if all ranks have a `None` gradient
+        num_grad_none = torch.zeros(1, dtype=torch.int32, device=self.device)
+        num_grad_none[0] = flat_param.grad is None
+        dist.all_reduce(num_grad_none, group=self.process_group)
+        if num_grad_none[0] == self.world_size:
+            flat_param._saved_grad_shard = None  # type: ignore[assignment]
+            self._use_unsharded_grad_views()
+            return
+
+        if flat_param.grad is None:
+            # In the case that only some ranks have `None` gradient, we use
+            # zeros to approximate as a best effort attempt
+            if self._debug_level == dist.DebugLevel.INFO:
+                warnings.warn(
+                    f"[Rank {self.rank}] Only some but not all ranks have a "
+                    "`None` `FlatParameter` gradient, so FSDP is using zeros to "
+                    "approximate those ranks' sharded gradients being `None`",
+                    stacklevel=2,
+                )
+            flat_param._saved_grad_shard = None  # type: ignore[assignment]
+            sharded_grad = torch.zeros(flat_param._sharded_size, device=self.device)  # type: ignore[attr-defined]
+        else:
+            self._check_sharded(flat_param.grad)
+            flat_param._saved_grad_shard = flat_param.grad  # type: ignore[attr-defined]
+            sharded_grad = flat_param._saved_grad_shard  # type: ignore[attr-defined]
+        padded_unsharded_grad = torch.empty(
+            flat_param._padded_unsharded_size,  # type: ignore[attr-defined]
+            device=self.device,
+            dtype=sharded_grad.dtype,
+        )
+        dist.all_gather_into_tensor(
+            padded_unsharded_grad, sharded_grad, self.process_group
+        )
+        unsharded_size = self.flat_param._unpadded_unsharded_size
+        flat_param.grad = padded_unsharded_grad[: unsharded_size.numel()].view(
+            unsharded_size
+        )
+        self._use_unsharded_grad_views()
+
+    def reshard_grad(self):
+        if self._use_orig_params:
+            self._use_sharded_grad_views()
+        if not self.uses_sharded_strategy:
+            return
+        self.flat_param.grad = self.flat_param._saved_grad_shard  # type: ignore[attr-defined]
+        delattr(self.flat_param, "_saved_grad_shard")
+
+    def prepare_gradient_for_backward(self):
+        """
+        Prepare the gradient for the backward computation.
+
+        This is done by saving and clearing any existing sharded gradient
+        in ``.grad`` to enable computing a new unsharded gradient.
+        """
+        _p_assert(
+            self._training_state
+            in (HandleTrainingState.BACKWARD_PRE, HandleTrainingState.IDLE),
+            "Expects to be in `BACKWARD_PRE` or `IDLE` (if prefetching)",
+        )
+        flat_param = self.flat_param
+        if flat_param.grad is not None and (
+            flat_param.grad.size() != flat_param._unpadded_unsharded_size
+            or flat_param.grad.device != flat_param.device  # grad on CPU
+        ):
+            self._check_on_compute_device(self.flat_param)
+            grad_offloaded = flat_param.grad.device != self.device
+            _p_assert(
+                not grad_offloaded or self._offload_params,
+                f"Expects the sharded gradient to be on {self.device} "
+                f"but got {flat_param.grad.device}",
+            )
+            prev_iter_synced_gradients = (
+                flat_param.grad.size() == flat_param._local_shard.size()  # type: ignore[attr-defined]
+            )
+            if prev_iter_synced_gradients:
+                # TODO (awgu): Gradient accumulation outside `no_sync()`
+                # does not work with CPU offloading. The issue should be
+                # that, in the post-backward hook, we cannot do an addition
+                # between a CPU tensor (the existing sharded gradient) and
+                # a GPU tensor (the new sharded gradient).
+                if not grad_offloaded:
+                    flat_param._saved_grad_shard = flat_param.grad.data  # type: ignore[attr-defined]
+                    sharded_grad = flat_param._saved_grad_shard  # type: ignore[attr-defined]
+                else:
+                    _p_assert(
+                        hasattr(flat_param, "_cpu_grad"),
+                        "`_cpu_grad` should be defined if the gradient is on CPU",
+                    )
+                    sharded_grad = flat_param._cpu_grad  # type: ignore[attr-defined]
+                # If user specified to keep the gradient in low precision, then
+                # the gradient may still be of the low precision dtype if the
+                # user did not set the gradient to `None` after the previous
+                # backward, in which case FSDP should cast back to the full
+                # precision dtype so that FSDP can accumulate in that dtype in
+                # the post-backward hook and assign to `.grad` in that dtype in
+                # the post-backward callback.
+                local_shard_dtype = flat_param._local_shard.dtype  # type: ignore[attr-defined]
+                if (
+                    self._keep_low_precision_grads
+                    and sharded_grad.dtype != local_shard_dtype
+                ):
+                    sharded_grad.data = sharded_grad.to(local_shard_dtype)
+            else:
+                padded_unsharded_size = flat_param._padded_unsharded_size  # type: ignore[attr-defined]
+                _p_assert(
+                    flat_param.grad.size() == padded_unsharded_size,
+                    "Expects `.grad` to be the unsharded gradient in "
+                    f"`no_sync()` with size {padded_unsharded_size} "
+                    f"but got size {flat_param.grad.size()}",
+                )
+            flat_param.grad = None
+
+    def prepare_gradient_for_optim(self):
+        """Prepare the gradient for optimizer computation by moving the sharded gradient to the ``.grad`` attribute."""
+
+        def cast_grad_to_param_dtype_if_needed(flat_param):
+            # TODO (rohan-varma): test for full precision with keep_low_precision_grads
+            if not self._force_full_precision and self._keep_low_precision_grads:
+                _p_assert(flat_param.grad is not None, "Unexpected None grad!")
+                if flat_param.grad.dtype != self._fwd_bwd_param_dtype:
+                    flat_param.grad.data = flat_param.grad.to(self._fwd_bwd_param_dtype)
+                    if self._use_orig_params:
+                        self._use_sharded_grad_views()
+
+        flat_param = self.flat_param
+        # TODO (awgu): We should replace these conditional checks to encode
+        # the logical intention more directly.
+        if hasattr(flat_param, "_cpu_grad"):
+            # NOTE: This branch includes `NO_SHARD`.
+            self._check_sharded(flat_param)
+            self._check_on_cpu(flat_param)
+            flat_param.grad = flat_param._cpu_grad  # type: ignore[attr-defined]
+            cast_grad_to_param_dtype_if_needed(flat_param)
+        elif hasattr(flat_param, "_saved_grad_shard"):
+            self._check_sharded(flat_param)
+            self._check_on_compute_device(flat_param)
+            if flat_param._saved_grad_shard is not None:
+                self._check_on_compute_device(flat_param._saved_grad_shard)  # type: ignore[attr-defined]
+            # If no sharded gradient was computed this iteration, then there is
+            # no need to forward `_saved_grad_shard` to `grad`
+            if flat_param._post_backward_called:  # type: ignore[attr-defined]
+                flat_param.grad = flat_param._saved_grad_shard  # type: ignore[attr-defined]
+                if flat_param.grad is not None:
+                    cast_grad_to_param_dtype_if_needed(flat_param)
+        else:
+            _p_assert(
+                not self.uses_sharded_strategy or not flat_param._post_backward_called,  # type: ignore[attr-defined]
+                "All sharded parameters that received a gradient in the "
+                "post-backward should use `_saved_grad_shard`",
+            )
+        # Delete `_saved_grad_shard` since its existence indicates a previous
+        # gradient to accumulate with in the post-backward hook
+        if hasattr(flat_param, "_saved_grad_shard"):
+            delattr(flat_param, "_saved_grad_shard")
+
+    @contextlib.contextmanager
+    def to_cpu(self):
+        """
+        Move the unpadded unsharded flat parameter to CPU while in the context and moves it back to the previous device upon exit.
+
+        For now, this assumes the ``FlatParameter`` is the unpadded unsharded flat parameter
+        since (1) there is no reason to include the padding in the copy and (2)
+        there is no use case for the sharded flat parameter.
+
+        Precondition: ``self.flat_param`` 's data is the unpadded unsharded
+        flat parameter on the compute device, and the handle uses a sharded
+        strategy.
+        Postcondition: Same as the precondition.
+        """
+        self._check_sharded_strategy()
+        _p_assert(
+            self.flat_param.size() == self.flat_param._unpadded_unsharded_size,
+            f"Expects size {self.flat_param._unpadded_unsharded_size} but got {self.flat_param.size()}",
+        )
+        self._check_on_compute_device(self.flat_param)
+        # Check that the unpadded unsharded flat parameter is a view into the
+        # padded unsharded flat parameter as expected
+        # NOTE: This check is not strictly needed for correctness but is a
+        # useful sanity check since the tensor should only be used internally.
+        _p_assert(
+            _same_storage(self.flat_param, self._get_padded_unsharded_flat_param()),
+            "Expects the unpadded parameter to be a view into the padded parameter",
+        )
+        self.flat_param_to(torch.device("cpu"))
+        self._free_unsharded_flat_param()
+        try:
+            yield
+        finally:
+            _p_assert(
+                self.flat_param.size() == self.flat_param._unpadded_unsharded_size,
+                f"Expects size {self.flat_param._unpadded_unsharded_size} but got {self.flat_param.size()}",
+            )
+            padded_unsharded_flat_param = self._alloc_padded_unsharded_flat_param()
+            # Copy from CPU to the compute device
+            padded_unsharded_flat_param[: self.flat_param.numel()].copy_(
+                self.flat_param
+            )
+            self._use_unsharded_flat_param(padded_unsharded_flat_param)
+
+    def reshard(self, free_unsharded_flat_param: bool):
+        """
+        Run the reshard logic.
+
+        This includes freeing the unsharded flat
+        parameter if ``free_unsharded_flat_param`` and switching to using the
+        sharded flat parameter. Note that this also implicitly offloads
+        the sharded flat parameter (if CPU offload is enabled) by pointing
+        it to the ``_local_shard`` attribute which resides on CPU.
+        """
+        # Switch to the sharded `FlatParameter` before freeing to prevent
+        # "use-after-free"-type bugs with external profiling tools, where for
+        # `use_orig_params=True`, the `param` does not point to valid memory
+        # when setting `param.data = ...` in `_use_sharded_views()`.
+        self._use_sharded_flat_param()
+        if free_unsharded_flat_param:
+            self._free_unsharded_flat_param()
+
+    def post_reshard(self):
+        """
+        Run the post-reshard logic.
+
+        This includes freeing any memory that
+        can now be freed given that the ``FlatParameter`` points to the full
+        precision sharded flat parameter.
+
+        Precondition: ``self.flat_param`` 's data points to the full precision
+        sharded flat parameter.
+        """
+        # For `NO_SHARD`, `_mp_shard` is not freed in the post-unshard since it
+        # is also the low precision *unsharded* flat parameter. Hence, we delay
+        # the free until the reshard.
+        if (
+            self._uses_param_mixed_precision
+            and not self.uses_sharded_strategy
+            and not self._force_full_precision  # did not use the low precision shard
+        ):
+            self._free_low_precision_sharded_param()
+
+    def _free_unsharded_flat_param(self):
+        """
+        Free the padded unsharded flat parameter. We allow this
+        function to be called even when storage is not allocated
+
+        The tensor to free depends
+        on the calling context since the unshard may have forced full
+        precision, in which case a different tensor is used.
+        """
+        self._check_sharded_strategy()
+        unsharded_flat_param = self._get_padded_unsharded_flat_param()
+        self._check_on_compute_device(unsharded_flat_param)
+        # Do not free the memory until all ops in the current stream finish
+        _no_dispatch_record_stream(
+            unsharded_flat_param, self._device_handle.current_stream()
+        )
+        _free_storage(unsharded_flat_param)
+
+    def _use_sharded_flat_param(self) -> None:
+        """Switches to using the sharded flat parameter."""
+        flat_param = self.flat_param
+        if self._use_orig_params:
+            in_forward = self._training_state == HandleTrainingState.FORWARD
+            skip_use_sharded_views = (
+                torch.is_grad_enabled()
+                and in_forward
+                and self._sharding_strategy
+                in NO_RESHARD_AFTER_FORWARD_HANDLE_STRATEGIES
+            )
+            # Only incur the extra `.data` call if needed
+            if skip_use_sharded_views:
+                unsharded_flat_param = flat_param.data
+        if self._offload_params:
+            device = flat_param._local_shard.device  # type: ignore[attr-defined]
+            _p_assert(
+                device == torch.device("cpu"),
+                f"Expects the local shard to be on CPU but got {device}",
+            )
+        flat_param.data = flat_param._local_shard  # type: ignore[attr-defined]
+        if self._use_orig_params:
+            if skip_use_sharded_views:  # type: ignore[possibly-undefined]
+                self._unsharded_flat_param_for_skipped_views = unsharded_flat_param  # type: ignore[possibly-undefined]
+            else:
+                self._use_sharded_views()
+            # For the post-forward reshard, we may try to use sharded gradient
+            # views (or unsharded gradient views if a gradient was accumulated
+            # in `no_sync()`), but for the post-backward reshard, we delay the
+            # call to after the reduce-scatter.
+            if (
+                in_forward  # type: ignore[possibly-undefined]
+                # Skip using gradient views if skipped using sharded views
+                # since exposing unsharded parameters with sharded gradients
+                # may be confusing to the user
+                and not self._skipped_use_sharded_views
+            ):
+                # TODO: Change `_unpadded_unsharded_size` if we change the
+                # gradient to be computed directly with padding.
+                accumulated_grad_in_no_sync = (
+                    flat_param.grad is not None
+                    and self.uses_sharded_strategy
+                    and flat_param.grad.shape == flat_param._unpadded_unsharded_size
+                )
+                if accumulated_grad_in_no_sync:
+                    self._use_unsharded_grad_views()
+                else:
+                    self._use_sharded_grad_views()
+
+    #########
+    # VIEWS #
+    #########
+    @no_type_check
+    def _get_unflat_views_unaligned(
+        self,
+        tensor: Optional[torch.Tensor] = None,
+    ) -> Iterator[Tensor]:
+        """
+        Return unflattened ``Tensor`` views into ``tensor``.
+
+        If `tensor`` is ``None``,  ``flat_param`` is used. The unflattening is based
+        on ``flat_param`` 's metadata.
+
+        Examples for ``tensor`` include ``flat_param.grad`` or unsharded
+        tensor optimizer state.
+        """
+        flat_param = self.flat_param
+        if tensor is None:
+            tensor = flat_param
+        views = (
+            _ext_post_unflatten_transform(
+                subtensor.view(shape)
+                if contiguous
+                else subtensor.as_strided(shape, stride),
+                param_extension,
+                self._fsdp_extension,
+            )
+            for (subtensor, shape, stride, contiguous, param_extension) in zip(
+                torch.split(tensor, flat_param._numels, dim=0),
+                flat_param._shapes,
+                flat_param._strides,
+                flat_param._contiguities,
+                flat_param._param_extensions,
+            )
+        )
+        return views
+
+    @no_type_check
+    def _get_unflat_views_aligned(
+        self,
+        tensor: Optional[Tensor] = None,
+    ) -> list[Tensor]:
+        """
+        Return unflattened ``Tensor`` views into ``tensor`` with handling for padding.
+
+        This method has the same contract as :meth:`_get_unflat_views_unaligned`
+        except it checks for ``None`` placeholders representing padding for
+        alignment, which may incur slightly more CPU overhead.
+        """
+        flat_param = self.flat_param
+        if tensor is None:
+            tensor = flat_param
+        splits: list[Tensor] = torch.split(
+            tensor, flat_param._numels_with_padding, dim=0
+        )
+        idx = 0
+        views: list[Tensor] = []
+        for split, is_padding in zip(splits, flat_param._is_padding_mask):
+            if is_padding:
+                continue
+            views.append(
+                _ext_post_unflatten_transform(
+                    split.view(flat_param._shapes[idx])
+                    if flat_param._contiguities[idx]
+                    else split.as_strided(
+                        flat_param._shapes[idx], flat_param._strides[idx]
+                    ),
+                    flat_param._param_extensions[idx],
+                    self._fsdp_extension,
+                )
+            )
+            idx += 1
+        return views
+
+    @no_type_check
+    @torch.enable_grad()
+    def _use_unsharded_views(self, as_params: bool) -> None:
+        """
+        Unflatten the unsharded flat parameter by setting the original parameter variables to be views into it.
+
+        Args:
+            as_params (bool): If ``True``, then registers the original
+                parameters as ``nn.Parameter`` s; if ``False``, then registers
+                the original parameters only as ``Tensor`` s. ``False`` should
+                be used during forward/backward computation and when hiding the
+                original parameters from :meth:`nn.Module.named_parameters`.
+
+        Note:
+            when prefetching for next forward, current forward may be
+            annotated with `@torch.no_grad()`
+            `@torch.enable_grad()` ensures non-empty `view.grad_fn`
+            otherwise `_post_backward_hook` will not get called
+        """
+        flat_param = self.flat_param
+        self._check_unsharded(flat_param)
+        views = self._get_unflat_views()
+        from torch.distributed.tensor import DTensor
+
+        for i, (view, (param_name, module, _)) in enumerate(
+            zip(views, flat_param._param_infos)
+        ):
+            if self._use_orig_params and as_params:
+                if type(view) is DTensor:
+                    # A `DTensor` `view` is not compatible with assigning
+                    # `param.data = view`, so we cannot preserve the parameter
+                    # variable.
+                    self._setattr_param(
+                        module,
+                        param_name,
+                        nn.Parameter(view, requires_grad=flat_param.requires_grad),
+                    )
+                    continue
+                param = self.flat_param._params[i]
+                self._setattr_param(module, param_name, param)
+                param.data = view
+            elif as_params:
+                self._setattr_param(
+                    module,
+                    param_name,
+                    nn.Parameter(view, requires_grad=flat_param.requires_grad),
+                )
+            else:  # `as_params=False`
+                param_var: Tensor = view
+                if self._use_orig_params:
+                    if self._training_state == HandleTrainingState.FORWARD:
+                        # Save the `Tensor` for the pre-backward
+                        self.flat_param._tensors[i] = view  # save for pre-backward
+                    elif self._training_state == HandleTrainingState.BACKWARD_PRE:
+                        # Use the saved `Tensor` variable from the forward to
+                        # preserve the autograd graph so that the post-backward
+                        # hook fires (e.g. for reentrant AC)
+                        tensor = self.flat_param._tensors[i]
+                        tensor.data = view
+                        param_var = tensor
+                self._setattr_tensor(module, param_name, param_var)
+                if (
+                    self._use_orig_params
+                    and self._training_state == HandleTrainingState.FORWARD
+                ):
+                    module._parameters[param_name] = param_var
+        for i, (
+            param_name,
+            module,
+            _,
+            prim_param_name,
+            prim_module,
+            _,
+        ) in enumerate(self.flat_param._shared_param_infos):
+            prim_param: Union[Tensor, nn.Parameter] = getattr(
+                prim_module, prim_param_name
+            )
+            _p_assert(
+                not as_params or isinstance(prim_param, nn.Parameter),
+                f"as_params={as_params} type(prim_param)={type(prim_param)}",
+            )
+            if self._use_orig_params and as_params:
+                shared_param = self.flat_param._shared_params[i]
+                self._setattr_param(module, param_name, shared_param)
+                shared_param.data = prim_param
+            elif as_params:
+                self._setattr_param(module, param_name, prim_param)
+            else:
+                self._setattr_tensor(module, param_name, prim_param)
+                if (
+                    self._use_orig_params
+                    and self._training_state == HandleTrainingState.FORWARD
+                ):
+                    module._parameters[param_name] = prim_param
+
+    @no_type_check
+    def _use_unsharded_grad_views(self) -> None:
+        """
+        Unflatten the unsharded flat parameter's gradient.
+
+        The original parameter variables' gradients are set to be views into
+        the unsharded flat parameter's gradient.
+        """
+        # Expects the gradient to be in `flat_param.grad`
+        if self.flat_param.grad is None:
+            for param in chain(self.flat_param._params, self.flat_param._shared_params):
+                param.grad = None
+            return
+        self._check_unsharded(self.flat_param.grad)
+        views = self._get_unflat_views(self.flat_param.grad)
+        for i, (view, (param_name, module, _)) in enumerate(
+            zip(views, self.flat_param._param_infos)
+        ):
+            _p_assert(
+                hasattr(module, param_name),
+                f"{self.flat_param._fqns[i]} is missing",
+            )
+            param = getattr(module, param_name)
+            if (
+                param.shape != view.shape
+                or param.dtype != view.dtype
+                or param.device != view.device
+            ):
+                # NOTE: This is a hack using `.data` to side step the check
+                # that parameter/gradient sizes/dtypes/devices match. From
+                # calling `reshard()`, `param` has the sharded size, has the
+                # full precision dtype, and if CPU offloading is enabled, is on
+                # CPU. Thus, one or more of the following cases can hold when
+                # in `no_sync()`, where `view` is the original parameter's
+                # gradient:
+                # 1. `view` can have the unsharded size.
+                # 2. `view` can have the parameter low precision dtype.
+                # 3. `view` can be on GPU.
+                if param.grad is None:
+                    param.grad = torch.empty_like(param)
+                param.grad.data = view
+            else:
+                param.grad = view
+        for (
+            param_name,
+            module,
+            module_name,
+            prim_param_name,
+            prim_module,
+            _,
+        ) in self.flat_param._shared_param_infos:
+            _p_assert(
+                hasattr(module, param_name),
+                f"{module_name + '.' + param_name if module_name else param_name} is missing",
+            )
+            param = getattr(module, param_name)
+            prim_param = getattr(prim_module, prim_param_name)
+            if (
+                param.shape != prim_param.grad.shape
+                or param.dtype != prim_param.grad.dtype
+                or param.device != prim_param.grad.device
+            ):
+                # NOTE: This is the same hack to use `.data` to side step the
+                # size check.
+                if param.grad is None:
+                    param.grad = torch.empty_like(param)
+                param.grad.data = prim_param.grad
+            else:
+                param.grad = prim_param.grad
+
+    @contextlib.contextmanager
+    def unflatten_as_params(self) -> Generator:
+        """
+        Unflatten the original parameters.
+
+        The function assumes that the flat parameter is unsharded. When in the context,
+        unflattens the original parameters as ``nn.Parameter`` views into the
+        flat parameter, and after the context, restores the original parameters
+        as ``Tensor`` views into the flat parameter.
+        """
+        self._use_unsharded_views(as_params=True)
+        try:
+            yield
+        finally:
+            self._use_unsharded_views(as_params=False)
+
+    @no_type_check
+    @torch.no_grad()
+    def _use_sharded_views(self) -> None:
+        """
+        Set the original parameter variables' data to be flattened views into the sharded flat parameter.
+
+        The views are kept as flattened to simplify the case where a parameter
+        is sharded across ranks. Parameters whose data is not present in the
+        sharded flat parameter have their data set to a size-0 empty tensor. We
+        do not delete them to ensure to preserve expected behaviors like model
+        printability. Parameters whose data is present must preserve their
+        variables to be passable to an optimizer.
+        """
+        self._unsharded_flat_param_for_skipped_views = None
+        if not self.uses_sharded_strategy:
+            # For `NO_SHARD`, use the *unflattened* unsharded views since we
+            # have the unsharded parameter
+            self._use_unsharded_views(as_params=True)
+            return
+        flat_param = self.flat_param
+        self._check_sharded(flat_param)
+        # Construct once and reuse for all parameters not in the local shard
+        size_0_empty_tensor = torch.empty(
+            0,
+            dtype=self.flat_param.dtype,  # in case `flat_param` changed dtype
+            device=self.flat_param.device,
+            requires_grad=False,
+        )
+        for param, shard_param_info, (param_name, module, _) in zip(
+            flat_param._params, flat_param._shard_param_infos, flat_param._param_infos
+        ):
+            self._setattr_param(module, param_name, param)
+            if not shard_param_info.in_shard:
+                # Allow the original data to be freed via garbage collection
+                param.data = size_0_empty_tensor
+            else:
+                offset = shard_param_info.offset_in_shard
+                numel_in_shard = shard_param_info.numel_in_shard
+                param.data = flat_param[offset : offset + numel_in_shard]
+        if self.flat_param._shared_params is None:
+            raise AssertionError("Expected _shared_params to be not None")
+        for param, (param_name, module, _, prim_param_name, prim_module, _) in zip(
+            self.flat_param._shared_params, self.flat_param._shared_param_infos
+        ):
+            self._setattr_param(module, param_name, param)
+            prim_param = getattr(prim_module, prim_param_name)
+            param.data = prim_param  # could be both empty and non-empty
+        if self._training_state == HandleTrainingState.BACKWARD_POST:
+            # Clear the saved `Tensor`s since they are unneeded now
+            for i in range(len(self.flat_param._tensors)):
+                self.flat_param._tensors[i] = None
+
+    @no_type_check
+    @torch.no_grad()
+    def _use_sharded_grad_views(self) -> None:
+        """
+        Set the original parameter variables' gradients to be flattened views into the sharded flat parameter's gradient.
+
+        This is a no-op if there is no gradient.
+
+        Parameters whose data is not present in the sharded flat parameter and
+        parameters with ``requires_grad=False`` have their gradients set to
+        ``None``. Since the gradient variables do not need to be preserved,
+        this method does not manipulate existing ``Tensor`` data directly and
+        creates new ``Tensor`` variables instead.
+        """
+        flat_param = self.flat_param
+        self._check_sharded(flat_param)
+        grad = self.sharded_grad
+        if grad is None:
+            for param in chain(flat_param._params, flat_param._shared_params):
+                param.grad = None
+            return
+        self._check_sharded(grad)
+        for param, shard_param_info, is_grad_none in zip(
+            flat_param._params,
+            flat_param._shard_param_infos,
+            flat_param._is_grad_none_mask,
+        ):
+            if not shard_param_info.in_shard:
+                param.grad = None
+            else:
+                numel_in_shard = shard_param_info.numel_in_shard
+                if param.requires_grad and not is_grad_none:
+                    offset = shard_param_info.offset_in_shard
+                    if self._keep_low_precision_grads or param.dtype != grad.dtype:
+                        # NOTE: This is a hack using `.data` to side step the
+                        # check that parameter/gradient dtypes match. Here,
+                        # `param` has full precision; `grad` has low precision.
+                        if param.grad is None:
+                            # `.grad` must have the same shape as `param`
+                            param.grad = torch.empty_like(param)
+                        param.grad.data = grad[
+                            offset : offset + numel_in_shard
+                        ].reshape(param.shape)
+                    else:
+                        param.grad = grad[offset : offset + numel_in_shard].reshape(
+                            param.shape
+                        )
+                else:
+                    param.grad = None
+        if flat_param._shared_params is None:
+            raise AssertionError("Expected _shared_params to be not None")
+        for param, (_, _, _, prim_param_name, prim_module, _) in zip(
+            flat_param._shared_params, flat_param._shared_param_infos
+        ):
+            in_sharded_flat_param = hasattr(prim_module, prim_param_name)
+            if in_sharded_flat_param and param.requires_grad:
+                prim_param = getattr(prim_module, prim_param_name)
+                param.grad = prim_param.grad  # share the same reference
+            else:
+                param.grad = None
+
+    @no_type_check
+    @torch.no_grad()
+    def _writeback_orig_params(self) -> bool:
+        """
+        Write back any parameters that changed storage to the handle's ``FlatParameter``.
+
+        Iterates over the original parameters and writes back any parameters
+        that changed storages (due to a non-inplace operator) to the handle's
+        ``FlatParameter``. This method preserves the ``FlatParameter` 's
+        device even if an original parameter's device changes.
+
+        Raises:
+            RuntimeError: If an original parameter or gradient changes storages
+            but no longer has the expected flattened shape.
+        Returns: ``True`` if some writeback happened, and ``False`` otherwise.
+        """
+        if (
+            self.uses_sharded_strategy
+            and not self.is_sharded(self.flat_param)
+            and not self._skipped_use_sharded_views
+        ):
+            # For `NO_SHARD`, we may still need to writeback
+            return False
+        flat_param = self.flat_param
+        wroteback = False
+        if self._skipped_use_sharded_views and self.uses_sharded_strategy:
+            # NOTE: We must use the unsharded flat parameter from which the
+            # unsharded views were computed, not the one from the current
+            # calling context (`_get_padded_unsharded_flat_param()`) since that
+            # may be different (e.g. the model changed from train to eval).
+            flat_param_tensor = self._unsharded_flat_param_for_skipped_views
+            _p_assert(
+                _data_ptr_allocated(flat_param_tensor),
+                "If skipped using sharded views, the unsharded flat parameter "
+                "should be allocated",
+            )
+        else:
+            flat_param_tensor = flat_param
+        # NOTE: Since this method is called in the pre-unshard, which is only
+        # called during computation in the pre-forward or pre-backward, the
+        # sharded gradient should be guaranteed to be in `.grad`, not in
+        # `._saved_grad_shard`.
+        flat_param_grad = (
+            flat_param.grad
+            if self.uses_sharded_strategy or not self._offload_params
+            else flat_param._cpu_grad
+        )
+        for i, (
+            param,
+            (in_shard, offset_in_shard, numel_in_shard, _, _),
+            (param_name, module, _),
+        ) in enumerate(
+            zip(
+                flat_param._params,
+                flat_param._shard_param_infos,
+                flat_param._param_infos,
+            )
+        ):
+            if not in_shard:
+                continue
+            if not hasattr(module, param_name):
+                # Do not writeback if original parameters are deregistered
+                # (e.g. during model checkpointing)
+                continue
+
+            # Check for parameter writeback
+            if self._skipped_use_sharded_views:
+                param = flat_param._tensors[i]
+                _p_assert(
+                    param is not None,
+                    f"Expects to have saved tensor for {flat_param._fqns[i]}",
+                )
+            param_changed = getattr(module, param_name) is not param
+            needs_param_writeback = (
+                param_changed  # changed parameter variable itself
+                or not _same_storage(param, flat_param_tensor)
+            )
+            if self._skipped_use_sharded_views and (
+                param_changed or needs_param_writeback
+            ):
+                raise AssertionError(
+                    "FSDP does not support changing the parameters between "
+                    f"forward and backward for {self._sharding_strategy}"
+                )
+            if param_changed:
+                # NOTE: The gradient is not preserved after a parameter change.
+                param = getattr(module, param_name)
+                flat_param._params[i] = param
+            if needs_param_writeback:
+                expected_shape = torch.Size([numel_in_shard])
+                src = param if self.uses_sharded_strategy else param.view(-1)
+                self._writeback_tensor(
+                    src, flat_param, i, expected_shape, offset_in_shard, True
+                )
+                wroteback = True
+
+            # Check for gradient writeback
+            if self._skipped_use_sharded_views:
+                # Skip the writeback check because we do not expose gradients
+                # when we skipped using sharded views
+                continue
+            if param.grad is None and flat_param.grad is not None:
+                expected_shape = torch.Size([numel_in_shard])
+                self._writeback_tensor(
+                    None, flat_param.grad, i, expected_shape, offset_in_shard, False
+                )
+            elif param.grad is not None:
+                # For `NO_SHARD` + CPU offloading, `_cpu_grad` is always in
+                # memory and owns the gradient storage, so it will never
+                # require gradient writeback.
+                if not self.uses_sharded_strategy and self._offload_params:
+                    # Explicitly continue to handle the case of `no_sync()`,
+                    # where `param.grad` is a view into the GPU gradient
+                    # referenced by `flat_param.grad`, while `flat_param_grad`
+                    # is `flat_param._cpu_grad`, which is on CPU
+                    continue
+
+                needs_grad_writeback = flat_param_grad is None or not _same_storage(
+                    param.grad, flat_param_grad
+                )
+                if needs_grad_writeback:
+                    if flat_param_grad is None:
+                        flat_param_grad = torch.zeros_like(flat_param)
+                    expected_shape = torch.Size([numel_in_shard])
+                    src = (
+                        param.grad
+                        if self.uses_sharded_strategy
+                        else param.grad.view(-1)
+                    )
+                    self._writeback_tensor(
+                        src,
+                        flat_param_grad,
+                        i,
+                        expected_shape,
+                        offset_in_shard,
+                        False,
+                    )
+                    flat_param.grad = flat_param_grad
+                    flat_param_grad = flat_param.grad
+
+        # TODO: If we want to handle shared parameters, we need to re-generate
+        # the shared parameter data structures in case sharedness changed.
+        for (
+            param_name,
+            module,
+            _,
+            prim_param_name,
+            prim_module,
+            _,
+        ) in flat_param._shared_param_infos:
+            if getattr(module, param_name) is not getattr(prim_module, prim_param_name):
+                raise NotImplementedError(
+                    "Changing shared parameters is not supported yet"
+                )
+        return wroteback
+
+    def _writeback_tensor(
+        self,
+        src_tensor: Optional[Tensor],
+        dst_tensor: Tensor,
+        tensor_index: int,
+        expected_shape: torch.Size,
+        offset: int,
+        is_param: bool,  # else gradient
+    ) -> None:
+        """
+        Write back ``src_tensor`` to ``dst_tensor`` at offset ``offset``, where ``src_tensor`` should have shape ``expected_shape``.
+
+        ``is_param`` indicates if the tensor is the parameter (if ``True``) or gradient (if
+        ``False``). If ``src_tensor`` is ``None``, then the effect is zeroing
+        instead of copying. ``tensor_index`` gives the index of ``src_tensor``
+        in the metadata structures.
+
+        Raises:
+            RuntimeError: If the ``src_tensor`` does not have the expected
+            shape.
+        """
+        _p_assert(
+            len(expected_shape) == 1,
+            f"Expects a 1D expected shape but got {expected_shape}",
+        )
+        if self._debug_level == dist.DebugLevel.INFO:
+            rank = self.rank if hasattr(self, "rank") else dist.get_rank()
+            src_shape = src_tensor.shape if src_tensor is not None else None
+            src_device = src_tensor.device if src_tensor is not None else None
+            warnings.warn(
+                f"[Rank {rank}] {'Parameter' if is_param else 'Gradient'} needs "
+                f"writeback in {self._training_state}\n"
+                f"expected shape={expected_shape} shape={src_shape} "
+                f"expected device={dst_tensor.device} device={src_device}",
+                stacklevel=2,
+            )
+        if src_tensor is not None and src_tensor.shape != expected_shape:
+            # NOTE: Gradient shape mismatch is not possible in practice since
+            # the gradient shape is enforced to match that of the parameter and
+            # we already check for parameter shape mismatch.
+            raise RuntimeError(
+                f"Cannot writeback when the {'parameter' if is_param else 'gradient'} "
+                f"shape changes\nExpects {expected_shape} but got {src_tensor.shape}"
+            )
+        if src_tensor is not None:
+            dst_tensor[offset : offset + expected_shape.numel()].copy_(src_tensor)
+        else:
+            dst_tensor[offset : offset + expected_shape.numel()].zero_()
+            if self.flat_param._is_grad_none_mask is None:
+                raise AssertionError("Expected _is_grad_none_mask to be not None")
+            self.flat_param._is_grad_none_mask[tensor_index] = True
+
+    def _reset_flat_param_grad_info_if_needed(self):
+        """
+        Reset ``flat_param.grad`` if needed.
+
+        When ``use_orig_params=True``:
+        (1) sets the underlying ``flat_param.grad`` to ``None`` if *all* of the
+        original parameters' ``.grad`` are ``None``, and
+        (2) sets ``flat_param.requires_grad=False`` if *none* of the original
+        parameters require gradient.
+        For (1), this is targeting ``optim.zero_grad(set_to_none=True)``, in
+        which case we want to free the gradients as soon after the
+        ``zero_grad()`` call as possible.
+        """
+        if not self._use_orig_params:
+            return
+        flat_param = self.flat_param
+        if flat_param._params is None:
+            raise AssertionError("Expected _params to be not None")  # mypy
+        all_grad_none = True
+        requires_grad = False
+        for param in flat_param._params:
+            all_grad_none &= param.grad is None
+            requires_grad |= param.requires_grad
+        if all_grad_none:
+            flat_param.grad = None
+        # As long as one parameter requires gradient, then the flat parameter
+        # must require gradient
+        flat_param.requires_grad = requires_grad
+
+    def _deregister_orig_params(self):
+        for param_info in self.flat_param._param_infos:
+            param_name, module, _ = param_info
+            if hasattr(module, param_name):
+                delattr(module, param_name)
+        for param_name, module, _, _, _, _ in self.flat_param._shared_param_infos:
+            if hasattr(module, param_name):
+                delattr(module, param_name)
+
+    ###########
+    # HELPERS #
+    ###########
+    def flat_param_to(self, *args, **kwargs):
+        """Wrap an in-place call to ``.to()`` for ``self.flat_param``."""
+        # pyrefly: ignore [not-iterable]
+        self.flat_param.data = self.flat_param.to(*args, **kwargs)
+        if self._use_orig_params:
+            # Refresh the views because their storage may have changed
+            if self.is_sharded(self.flat_param):
+                self._use_sharded_views()
+            else:
+                self._use_unsharded_views(as_params=True)
+
+    def _get_modules(self) -> set[nn.Module]:
+        """Return a :class:`set` of the modules whose parameters are included in this handle's flat parameter."""
+        return {pi.module for pi in self.flat_param._param_infos}.union(
+            {spi.module for spi in self.flat_param._shared_param_infos}
+        )
+
+    def is_sharded(self, tensor: Tensor) -> bool:
+        """
+        Return whether ``tensor`` is *currently* sharded.
+
+        For ``NO_SHARD``, we choose to have this always return ``False`` for clarity.
+        """
+        if (
+            not hasattr(self.flat_param, "_sharded_size")
+            or not self.uses_sharded_strategy
+        ):
+            # `_sharded_size` is defined iff `handle.shard()` has been called
+            return False
+        sharded_size = self.flat_param._sharded_size  # type: ignore[attr-defined]
+        return tensor.size() == sharded_size
+
+    def param_module_names(self) -> Iterator[tuple[str, str]]:
+        shared_param_infos = [
+            ParamInfo(param_name, module, module_name)
+            for (
+                param_name,
+                module,
+                module_name,
+                _,
+                _,
+                _,
+            ) in self.flat_param._shared_param_infos
+        ]
+        for param_info in chain(self.flat_param._param_infos, shared_param_infos):
+            param_name, _, module_name = param_info  # type: ignore[misc]
+            yield (param_name, module_name)
+
+    def shared_param_module_names(self) -> Iterator[tuple[str, str]]:
+        for param_name, _, module_name in [
+            ParamInfo(param_name, module, module_name)
+            for (
+                param_name,
+                module,
+                module_name,
+                _,
+                _,
+                _,
+            ) in self.flat_param._shared_param_infos
+        ]:
+            yield (param_name, module_name)
+
+    @property
+    def _fqns_in_shard(self) -> list[str]:
+        """Return the FQNs of the parameters present in this rank's shard."""
+        fqns_in_shard: list[str] = []
+        for fqn, shard_param_info in zip(
+            self.flat_param._fqns,
+            self.flat_param._shard_param_infos,  # type: ignore[attr-defined]
+        ):
+            if shard_param_info.in_shard:
+                fqns_in_shard.append(fqn)
+        return fqns_in_shard
+
+    @property
+    def sharded_grad(self) -> Optional[Tensor]:
+        """Return the handle's sharded gradient."""
+        flat_param = self.flat_param
+        # Priority for non-`None`: `_cpu_grad` > `_saved_grad_shard` > `grad`
+        # - CPU offloading: `_cpu_grad`
+        # - No CPU offloading + sharded strategies: `_saved_grad_shard`
+        # - No CPU offloading + `NO_SHARD`: `grad`
+        grad: Optional[Tensor]
+        if hasattr(flat_param, "_cpu_grad"):
+            grad = flat_param._cpu_grad  # type: ignore[attr-defined]
+        elif hasattr(flat_param, "_saved_grad_shard"):
+            # In the post-backward hook, the sharded gradient is still in
+            # `_saved_grad_shard`.
+            grad = flat_param._saved_grad_shard  # type: ignore[attr-defined]
+        else:
+            # If in IDLE or in FORWARD states, then there may be an
+            # (accumulated) gradient. If accessed in IDLE, then this should
+            # be due to re-registering the original parameters (e.g. in state
+            # dict load).
+            _p_assert(
+                flat_param.grad is None
+                or not self.uses_sharded_strategy
+                or self._training_state
+                in (HandleTrainingState.FORWARD, HandleTrainingState.IDLE),
+                "Sharded strategies should use `_cpu_grad` or `_saved_grad_shard` "
+                "unless in IDLE or FORWARD",
+            )
+            grad = flat_param.grad
+        return grad
+
+    def _reset_is_grad_none(self) -> None:
+        """
+        Reset ``_is_grad_none_mask`` as needed.
+
+        This method should only be
+        called in the post-backward after gradient computation, in which case
+        if a parameter requires gradient, then it will surely receive a
+        gradient and we may reset its mask entry to ``False``.
+        """
+        if not self._use_orig_params:
+            return
+        _p_assert(
+            self._training_state == HandleTrainingState.BACKWARD_POST,
+            "Expects to only be called in the post-backward after gradient computation",
+        )
+        flat_param = self.flat_param
+        if flat_param._params is None:
+            raise AssertionError("Expected _params to be not None")  # mypy
+        for i, param in enumerate(flat_param._params):  # type: ignore[arg-type]
+            # As long as the parameter requires gradient, it should receive a
+            # meaningful gradient (even if the gradient happens to be zeros)
+            if param.requires_grad:
+                if flat_param._is_grad_none_mask is None:
+                    raise AssertionError(
+                        "Expected _is_grad_none_mask to be not None"
+                    )  # mypy
+                flat_param._is_grad_none_mask[i] = False
+
+    #######################
+    # CHECKS & INVARIANTS #
+    #######################
+    def _check_sharded_strategy(self):
+        _p_assert(self.uses_sharded_strategy, "Expects sharded strategy")
+
+    def _check_on_compute_device(self, tensor: Tensor):
+        _p_assert(
+            tensor.device == self.device,
+            f"Expects tensor to be on the compute device {self.device}, was on {tensor.device}",
+        )
+
+    def _check_on_cpu(self, tensor: Tensor):
+        _p_assert(
+            tensor.device == torch.device("cpu"),
+            f"Expects tensor to be on CPU but got {tensor.device}",
+        )
+
+    @staticmethod
+    def _check_storage_freed(tensor: Tensor):
+        # Compile does not resize during trace
+        if not torch.distributed._functional_collectives.is_torchdynamo_compiling():
+            _p_assert(
+                _same_storage_size(tensor, 0),
+                "Expects storage to be freed but got storage with size > 0",
+            )
+
+    @staticmethod
+    def _check_storage_allocated(tensor: Tensor):
+        _p_assert(_storage_size_allocated(tensor), "Expects storage to be allocated")
+
+    def _check_low_precision_shard(self):
+        _p_assert(
+            self._uses_param_mixed_precision,
+            "Not using low precision for parameters",
+        )
+        _p_assert(
+            getattr(self.flat_param, "_mp_shard", None) is not None,
+            "Expects `_mp_shard` to exist",
+        )
+        device = self.flat_param._mp_shard.device  # type: ignore[attr-defined]
+        _p_assert(
+            device == self.device,
+            f"Expects the low precision shard to be on {self.device} but got {device}",
+        )
+
+    def _check_unsharded(self, tensor: Tensor):
+        msg_prefix = "Expects tensor to be unsharded "
+        _p_assert(tensor is not None, msg_prefix + "but got `None`")
+        unsharded_size = self.flat_param._unpadded_unsharded_size
+        _p_assert(
+            tensor.size() == unsharded_size,
+            msg_prefix + f"with size {unsharded_size} but got {tensor.size()}",
+        )
+
+    def _check_sharded(self, tensor: Tensor):
+        msg_prefix = "Expects tensor to be sharded "
+        _p_assert(tensor is not None, msg_prefix + "but got `None`")
+        sharded_size = self.flat_param._sharded_size  # type: ignore[attr-defined]
+        _p_assert(
+            tensor.size() == sharded_size,
+            msg_prefix + f"with size {sharded_size} but got {tensor.size()}",
+        )
+
+    ##############
+    # PROPERTIES #
+    ##############
+    @property
+    def uses_sharded_strategy(self) -> bool:
+        return self._sharding_strategy != HandleShardingStrategy.NO_SHARD
+
+    @property
+    def _uses_param_mixed_precision(self) -> bool:
+        return self._fwd_bwd_param_dtype != self._orig_param_dtype
+
+    @property
+    def _uses_reduce_mixed_precision(self) -> bool:
+        return self._reduce_dtype != self._orig_param_dtype
+
+    @property
+    def _force_full_precision(self) -> bool:
+        return (
+            self._uses_param_mixed_precision or self._uses_reduce_mixed_precision
+        ) and (
+            self._training_state == HandleTrainingState.SUMMON_FULL_PARAMS
+            or
+            # Also disable mixed precision in model eval mode, if configured
+            (not self._fully_sharded_module.training and self._use_full_prec_in_eval)
+        )
+
+    @property
+    def _skipped_use_sharded_views(self) -> bool:
+        """
+        This property is used for sharding strategies that do not free after forward with ``use_orig_params=True``.
+
+        This returns if this handle is
+        currently in a state where it has skipped using sharded views, in which
+        case it can restore view invariants via ``_use_sharded_views()``.
+        """
+        return self._unsharded_flat_param_for_skipped_views is not None
+
+
+# NOTE: These are hacks to bypass `nn.Module.__setattr__` checks.
+def _unsafe_setattr_param(
+    module: nn.Module, param_name: str, param: nn.Parameter
+) -> None:
+    module._parameters[param_name] = param
+    # This bypasses any overrides in case `module` is an instance of an
+    # `nn.Module` subclass
+    super(nn.Module, module).__setattr__(param_name, param)
+
+
+def _unsafe_setattr_tensor(module: nn.Module, param_name: str, tensor: Tensor) -> None:
+    module._parameters.pop(param_name, None)
+    # This bypasses any overrides in case `module` is an instance of an
+    # `nn.Module` subclass
+    super(nn.Module, module).__setattr__(param_name, tensor)
+
+
+def _safe_setattr_tensor_or_param(
+    module: nn.Module, param_name: str, tensor_or_param: Union[Tensor, nn.Parameter]
+):
+    # Call `delattr()` and `setattr()` to go through `nn.Module` checks
+    if hasattr(module, param_name):
+        delattr(module, param_name)
+    setattr(module, param_name, tensor_or_param)
+
+
+def _convert_to_params(
+    tensors: list[Union[torch.Tensor, nn.Parameter]],
+) -> list[nn.Parameter]:
+    return [t if isinstance(t, nn.Parameter) else nn.Parameter(t) for t in tensors]
+
+
+def _is_truly_contiguous(x: Tensor) -> bool:
+    # Special case: Pytorch thinks that 1x1 channels_last convolution weights are
+    # both contiguous and channels_last contiguous at the same time.
+    # CuDNN does not agree though and refuses to select faster kernels.
+    # It is the reason of having the extra check here.
+    return x.stride(-1) == 1 and x.is_contiguous()
+
+
+def _detach_if_needed(param_or_tensor: Union[nn.Parameter, Tensor]) -> Tensor:
+    return (
+        param_or_tensor.detach()
+        if isinstance(param_or_tensor, nn.Parameter)
+        else param_or_tensor
+    )
+
+
+def _get_aligned_numel(unsharded_dtype: torch.dtype):
+    # NOTE: This alignment constraint comes from TorchInductor.
+    ALIGNMENT = 16  # bytes
+    unsharded_dtype_size = _get_dtype_size(unsharded_dtype)
+    aligned_numel = ALIGNMENT // unsharded_dtype_size
+    return aligned_numel
+
+
+@functools.lru_cache(8)
+def _get_dtype_size(dtype):
+    return torch.empty((), dtype=dtype).element_size()
+
+
+def _construct_padding_tensor(
+    padding_numel: int, dtype: torch.dtype, requires_grad: bool, device: torch.device
+):
+    # NOTE: Set the padding value as a magic number for debuggability. The
+    # value itself should never be used in any user-facing computation.
+    return (
+        torch.ones(
+            (padding_numel,), dtype=dtype, requires_grad=requires_grad, device=device
+        )
+        * _FLAT_PARAM_PADDING_VALUE
+    )
+
+
+# Use `lru_cache(1)` to only log the warning once (assuming the fixed warning
+# message is passed in)
+@functools.lru_cache(1)
+def _warn_skip_writeback_check(log: logging.Logger, warning: str):
+    logger.warning(warning)
+
+
+# Use `lru_cache(1)` to only log the warning once
+@functools.lru_cache(1)
+def _warn_use_fake_all_gather(log: logging.Logger, warning: str):
+    logger.warning(warning)
+
+
+# Use `lru_cache(1)` to only log the warning once
+@functools.lru_cache(1)
+def _warn_use_fake_reduce(log: logging.Logger, warning: str):
+    logger.warning(warning)
+
+
+def _same_storage(a, b):
+    # Params are DTensors in backward
+    # with SHARD_GRAD_OP + TP
+    from torch.distributed.tensor import DTensor
+
+    if isinstance(a, DTensor):
+        a = a._local_tensor
+    if isinstance(b, DTensor):
+        b = b._local_tensor
+    return a.untyped_storage().data_ptr() == b.untyped_storage().data_ptr()
+
+
+def _same_storage_size(a: torch.Tensor, b: int):
+    return a.untyped_storage().size() // a.element_size() == b
+
+
+def _storage_size_allocated(tensor: Tensor):
+    storage_size: int = tensor.untyped_storage().size()
+    return storage_size > 0
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_fsdp_extensions.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_fsdp_extensions.py
new file mode 100644
index 0000000000000000000000000000000000000000..699274ba50f9a57f26120bd15f5c49b4679f0e9e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_fsdp_extensions.py
@@ -0,0 +1,180 @@
+from abc import ABC, abstractmethod
+from typing import Any, Optional
+
+import torch
+import torch.distributed as dist
+from torch.distributed._shard.sharded_tensor.api import ShardedTensor
+from torch.distributed._shard.sharded_tensor.shard import Shard
+from torch.distributed.fsdp._shard_utils import (
+    _all_gather_dtensor,
+    _create_chunk_dtensor,
+    _create_chunk_sharded_tensor,
+)
+from torch.distributed.tensor import DeviceMesh, DTensor
+
+
+class FSDPExtensions(ABC):
+    """
+    This enables some customizable hooks to enable composability with tensor
+    parallelism. To activate these hooks, use :func:`_set_fsdp_extensions` to
+    set a custom :class:`FSDPExtensions` that implements the hooks.
+    """
+
+    @abstractmethod
+    def pre_flatten_transform(
+        self,
+        tensor: torch.Tensor,
+    ) -> tuple[torch.Tensor, Optional[Any]]:
+        """E.g. converting ``DistributedTensor`` to local tensor."""
+        ...
+
+    @abstractmethod
+    def post_unflatten_transform(
+        self,
+        tensor: torch.Tensor,
+        param_extension: Any,
+    ) -> torch.Tensor:
+        """E.g. converting local tensor to ``DistributedTensor``."""
+        ...
+
+    @abstractmethod
+    def chunk_tensor(
+        self,
+        tensor: torch.Tensor,
+        rank: int,
+        world_size: int,
+        num_devices_per_node: int,
+        pg: dist.ProcessGroup,
+        device: Optional[torch.device] = None,
+    ) -> torch.Tensor:
+        """Shards a tensor to chunks and returns the local chunk."""
+        ...
+
+    @abstractmethod
+    def chunk_dtensor(
+        self,
+        tensor: torch.Tensor,
+        rank: int,
+        device_mesh: DeviceMesh,
+    ) -> torch.Tensor:
+        """Shards a tensor/DTensor to DTensor and returns the local DTensor."""
+        ...
+
+    @abstractmethod
+    def pre_load_state_dict_transform(
+        self,
+        tensor: torch.Tensor,
+    ) -> tuple[torch.Tensor, list[Shard]]:
+        """
+        This is to be called before loading a *sharded* model state dict and
+        should return the tensor and list of shards from which to load data.
+        """
+        ...
+
+    @abstractmethod
+    def all_gather_dtensor(
+        self,
+        tensor: DTensor,
+        parent_mesh: Optional[DeviceMesh],
+    ) -> torch.Tensor:
+        """
+        This is to be called before loading a *sharded* DTensor state dict.
+        This gathers tensor in FSDP dimension and returns local tensor of
+        TP DTensor.
+        """
+        ...
+
+
+_extensions: Optional[FSDPExtensions] = None
+
+
+def _set_fsdp_extensions(flattener: FSDPExtensions) -> None:
+    global _extensions
+    _extensions = flattener
+
+
+def _ext_pre_flatten_transform(
+    tensor: torch.Tensor,
+    fsdp_extension: Optional[FSDPExtensions] = None,
+) -> tuple[torch.Tensor, Optional[Any]]:
+    if fsdp_extension is not None:
+        new_tensor, param_extension = fsdp_extension.pre_flatten_transform(tensor)
+        if param_extension is not None:
+            return new_tensor, param_extension
+    return tensor, None
+
+
+def _ext_post_unflatten_transform(
+    tensor: torch.Tensor,
+    param_extension: Any,
+    fsdp_extension: Optional[FSDPExtensions] = None,
+) -> torch.Tensor:
+    if fsdp_extension is not None and param_extension is not None:
+        return fsdp_extension.post_unflatten_transform(tensor, param_extension)
+    return tensor
+
+
+def _ext_chunk_tensor(
+    tensor: torch.Tensor,
+    rank: int,
+    world_size: int,
+    num_devices_per_node: int,
+    pg: dist.ProcessGroup,
+    fsdp_extension: Optional[FSDPExtensions] = None,
+) -> torch.Tensor:
+    chunk_tensor_fn = (
+        fsdp_extension.chunk_tensor
+        if fsdp_extension is not None
+        else _create_chunk_sharded_tensor
+    )
+    return chunk_tensor_fn(
+        tensor,
+        rank,
+        world_size,
+        num_devices_per_node,
+        pg,
+    )
+
+
+def _ext_chunk_dtensor(
+    tensor: torch.Tensor,
+    rank: int,
+    device_mesh: DeviceMesh,
+    fsdp_extension: Optional[FSDPExtensions] = None,
+) -> torch.Tensor:
+    chunk_dtensor_fn = (
+        fsdp_extension.chunk_dtensor
+        if fsdp_extension is not None
+        else _create_chunk_dtensor
+    )
+    return chunk_dtensor_fn(
+        tensor,
+        rank,
+        device_mesh,
+    )
+
+
+def _ext_pre_load_state_dict_transform(
+    tensor: torch.Tensor,
+    fsdp_extension: Optional[FSDPExtensions] = None,
+) -> tuple[torch.Tensor, list[Shard]]:
+    if fsdp_extension is not None:
+        return fsdp_extension.pre_load_state_dict_transform(tensor)
+
+    if type(tensor) is not ShardedTensor:
+        raise AssertionError(f"Expected ShardedTensor, got {type(tensor)}")
+    shards = tensor.local_shards()
+    return (tensor, shards)
+
+
+def _ext_all_gather_dtensor(
+    tensor: DTensor,
+    parent_mesh: Optional[DeviceMesh],
+    fsdp_extension: Optional[FSDPExtensions] = None,
+) -> torch.Tensor:
+    all_gather_dtensor_fn = (
+        fsdp_extension.all_gather_dtensor
+        if fsdp_extension is not None
+        else _all_gather_dtensor
+    )
+    return all_gather_dtensor_fn(tensor, parent_mesh)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4d0b341a3f82b35fc903ccffd5208d8fdade399
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/__init__.py
@@ -0,0 +1,20 @@
+from ._fsdp_api import CPUOffloadPolicy, MixedPrecisionPolicy, OffloadPolicy
+from ._fully_shard import (
+    FSDPModule,
+    fully_shard,
+    register_fsdp_forward_method,
+    share_comm_ctx,
+    UnshardHandle,
+)
+
+
+__all__ = [
+    "CPUOffloadPolicy",
+    "FSDPModule",
+    "fully_shard",
+    "MixedPrecisionPolicy",
+    "OffloadPolicy",
+    "register_fsdp_forward_method",
+    "UnshardHandle",
+    "share_comm_ctx",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_api.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_api.py
new file mode 100644
index 0000000000000000000000000000000000000000..38650323f5e99727f04964ca59fb268ca8e7b65c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_api.py
@@ -0,0 +1,155 @@
+# mypy: allow-untyped-defs
+from abc import ABC, abstractmethod
+from collections.abc import Sequence
+from dataclasses import dataclass
+from typing import Optional, Union
+
+import torch
+import torch.distributed as dist
+
+
+_ReduceOp = Union[dist.ReduceOp, dist.ReduceOp.RedOpType]
+
+
+@dataclass(frozen=True)
+class MixedPrecisionPolicy:
+    """
+    This configures FSDP's mixed precision. Unlike autocast, this applies mixed
+    precision at the module level, not op level, which means low-precision
+    activations are saved for backward and high-to-low-precision casts are
+    incurred only at module boundaries.
+
+    FSDP works well with module-level mixed precision since it keeps the
+    high-precision sharded parameters in memory anyway. In other words, FSDP
+    does not require any extra memory to keep a high-precision copy of the
+    parameters for the optimizer step.
+
+    Attributes:
+        param_dtype (Optional[torch.dtype]): This specifies the dtype for
+            the unsharded parameter and hence the dtype for forward/backward
+            computation and the parameter all-gather. If this is ``None``, then
+            the unsharded parameter uses the original dtype. The optimizer step
+            uses the sharded parameter in the original dtype. (Default:
+            ``None``)
+        reduce_dtype (Optional[torch.dtype]): This specifies the dtype for
+            gradient reduction (i.e. reduce-scatter or all-reduce). If this is
+            ``None`` but ``param_dtype`` is not ``None``, then the reduction
+            uses the compute dtype. This can be used to run gradient reduction
+            in full precision while using low precision for compute. If also
+            gradient reduction is disabled via :meth:`set_requires_gradient_sync`,
+            then FSDP will accumulate gradients using ``reduce_dtype``.
+            (Default: ``None``)
+        output_dtype (Optional[torch.dtype]): This specifies the dtype for
+            casting floating-point forward outputs. This can be used to
+            help implement cases where different modules have different mixed
+            precision policies. (Default: ``None``)
+        cast_forward_inputs (bool): This specifies whether FSDP should cast the
+            forward's floating-point input tensors to ``param_dtype`` or not.
+    """
+
+    param_dtype: Optional[torch.dtype] = None
+    reduce_dtype: Optional[torch.dtype] = None
+    output_dtype: Optional[torch.dtype] = None
+    cast_forward_inputs: bool = True
+
+
+class Comm(ABC):
+    """
+    Interface for communication primitives.
+    A primitive primarily needs to handle 3 tasks, namely:
+
+    1. How to allocate memory for communication
+       Depending on the goal, an implementation can choose to:
+       a. associate each call to a temporary buffer
+          (best for flexibility and simplicity)
+       b. reuse an persistent buffer for efficiency reasons
+
+    2. Where to allocate memory
+       (e.g. NCCL mem pool or regular cuda caching allocator)
+
+    3. What to do/call upon the comm is called
+       (see `AllGather` interface as an example)
+    """
+
+    @abstractmethod
+    def allocate(
+        self,
+        size: Sequence[Union[int, torch.SymInt]],
+        *,
+        dtype: torch.dtype,
+        device: torch.device,
+    ) -> torch.Tensor:
+        """
+        This handles the "how to allocate memory" part.
+
+        A default implementation could be simply:
+
+        .. code-block:: python
+            with self.mem_pool:
+                torch.empty(...)
+
+        Args:
+            size (Sequence[Union[int, torch.SymInt]]): size of the tensor buffer
+            dtype (torch.dtype): dtype of the tensor buffer
+            device (torch.device): which device to allocate the tensor onto
+        """
+        ...
+
+
+class AllGather(Comm):
+    """
+    Interface for all_gather comm primitive
+    """
+
+    @abstractmethod
+    def __call__(
+        self,
+        output_tensor: torch.Tensor,
+        input_tensor: torch.Tensor,
+        group: dist.ProcessGroup,
+        async_op: bool = False,
+    ) -> Optional[dist.Work]: ...
+
+
+class ReduceScatter(Comm):
+    """
+    Interface for reduce_scatter comm primitive
+    """
+
+    @abstractmethod
+    def __call__(
+        self,
+        output_tensor: torch.Tensor,
+        input_tensor: torch.Tensor,
+        group: dist.ProcessGroup,
+        op: _ReduceOp,
+        async_op: bool = False,
+    ) -> Optional[dist.Work]: ...
+
+
+@dataclass
+class OffloadPolicy:
+    """
+    This base class represents the policy of no offloading and is only used as
+    the default value for the ``offload_policy`` arg.
+    """
+
+
+@dataclass
+class CPUOffloadPolicy(OffloadPolicy):
+    """
+    This offload policy offloads parameters, gradients, and optimizer states to
+    CPU. Sharded parameters are copied host-to-device before all-gather. The
+    all-gathered parameters are freed according to ``reshard_after_forward``.
+    Sharded gradients are copied device-to-host in backward, and the optimizer
+    step runs on CPU with CPU optimizer states.
+
+    Attributes:
+        pin_memory (bool): Whether to pin sharded parameter and gradient
+            memory. Pinning memory allows both more efficient H2D/D2H copies
+            and for the copies to overlap with compute. However, the pinned
+            memory cannot be used by other processes. Set this to ``False`` if
+            you have insufficient CPU memory. (Default: ``True``)
+    """
+
+    pin_memory: bool = True
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_collectives.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_collectives.py
new file mode 100644
index 0000000000000000000000000000000000000000..2bd7d24cd7d3f2fc24b634d72197f8e51c4839e6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_collectives.py
@@ -0,0 +1,762 @@
+import math
+from collections.abc import Callable, Sequence
+from itertools import chain
+from typing import Any, cast, NamedTuple, Optional, Union
+
+import torch
+import torch.distributed as dist
+from torch.distributed.device_mesh import _get_device_handle
+from torch.distributed.distributed_c10d import ReduceOp
+from torch.distributed.fsdp._fully_shard._fsdp_api import AllGather, ReduceScatter
+from torch.distributed.tensor import DTensor
+
+from ._fsdp_api import _ReduceOp
+from ._fsdp_common import (
+    _get_dim0_padded_size,
+    _raise_assert_with_print,
+    _to_dtype_if_needed,
+    compiled_autograd_enabled,
+)
+from ._fsdp_param import FSDPParam, ShardedState
+
+
+class AllGatherResult(NamedTuple):
+    all_gather_output: torch.Tensor
+    all_gather_event: Optional[torch.Event]
+    all_gather_work: Optional[dist.distributed_c10d.Work]
+    # For each parameter, the all-gather input dtype for each input
+    param_all_gather_input_dtypes: list[list[torch.dtype]]
+    # For each parameter, the all-gather input numel for each input
+    param_all_gather_input_numels: list[list[int]]
+    # 1D flattened version of `param_all_gather_input_numels` saved to avoid
+    # CPU overhead from recomputing
+    all_gather_input_split_sizes: list[int]
+
+
+lib = torch.library.Library("fsdp", "FRAGMENT")  # noqa: TOR901
+
+lib.define(
+    """
+    all_gather_copy_in(
+        Tensor[] all_gather_inputs,
+        Tensor all_gather_output,
+        SymInt[] inp_split_sizes,
+        SymInt all_gather_input_numel,
+        SymInt rank
+    ) -> (Tensor, Tensor)
+    """
+)
+
+
+class DefaultAllocMixin:
+    def allocate(
+        self,
+        size: Sequence[Union[int, torch.SymInt]],
+        *,
+        dtype: torch.dtype,
+        device: torch.device,
+    ) -> torch.Tensor:
+        return torch.empty(*size, dtype=dtype, device=device)
+
+
+class ProcessGroupAllocMixin:
+    def __init__(self, group: dist.ProcessGroup, *args: Any, **kwargs: Any):
+        self._group = group
+        super().__init__(*args, **kwargs)
+
+    def allocate(
+        self,
+        size: Sequence[Union[int, torch.SymInt]],
+        *,
+        dtype: torch.dtype,
+        device: torch.device,
+    ) -> torch.Tensor:
+        backend = self._group._get_backend(device)
+        if backend.supports_tensor_alloc(device):
+            size_1d = math.prod(int(s) for s in size)
+            return backend.allocate_tensor(size_1d, dtype=dtype, device=device)
+        return torch.empty(*size, dtype=dtype, device=device)
+
+
+class DefaultAllGather(DefaultAllocMixin, AllGather):
+    def __call__(
+        self,
+        output_tensor: torch.Tensor,
+        input_tensor: torch.Tensor,
+        group: dist.ProcessGroup,
+        async_op: bool = False,
+    ) -> Optional[dist.Work]:
+        return dist.all_gather_into_tensor(
+            output_tensor,
+            input_tensor,
+            group=group,
+            async_op=async_op,
+        )
+
+
+class ProcessGroupAllocAllGather(ProcessGroupAllocMixin, AllGather):
+    def __init__(self, group: dist.ProcessGroup) -> None:
+        super().__init__(group)
+
+    def __call__(
+        self,
+        output_tensor: torch.Tensor,
+        input_tensor: torch.Tensor,
+        group: dist.ProcessGroup,
+        async_op: bool = False,
+    ) -> Optional[dist.Work]:
+        return dist.all_gather_into_tensor(
+            output_tensor,
+            input_tensor,
+            group=group,
+            async_op=async_op,
+        )
+
+
+class DefaultReduceScatter(DefaultAllocMixin, ReduceScatter):
+    def __call__(
+        self,
+        output_tensor: torch.Tensor,
+        input_tensor: torch.Tensor,
+        group: dist.ProcessGroup,
+        op: _ReduceOp,
+        async_op: bool = False,
+    ) -> dist.Work:
+        return dist.reduce_scatter_tensor(
+            output=output_tensor,
+            input=input_tensor,
+            group=group,
+            op=op,
+            async_op=async_op,
+        )
+
+
+class ProcessGroupAllocReduceScatter(ProcessGroupAllocMixin, ReduceScatter):
+    def __init__(self, group: dist.ProcessGroup) -> None:
+        super().__init__(group)
+
+    def __call__(
+        self,
+        output_tensor: torch.Tensor,
+        input_tensor: torch.Tensor,
+        group: dist.ProcessGroup,
+        op: _ReduceOp,
+        async_op: bool = False,
+    ) -> dist.Work:
+        return dist.reduce_scatter_tensor(
+            output=output_tensor,
+            input=input_tensor,
+            group=group,
+            op=op,
+            async_op=async_op,
+        )
+
+
+@torch.library.impl(lib, "all_gather_copy_in", "Meta")
+def all_gather_copy_in_meta(
+    all_gather_inputs: list[torch.Tensor],
+    all_gather_output: torch.Tensor,
+    inp_split_sizes: list[int],
+    all_gather_input_numel: int,
+    rank: int,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    all_gather_input = all_gather_output.narrow(
+        0, all_gather_input_numel * rank, all_gather_input_numel
+    )
+    return all_gather_input, all_gather_output
+
+
+@torch.library.impl(lib, "all_gather_copy_in", "CUDA")
+@torch.library.impl(lib, "all_gather_copy_in", "XPU")
+@torch.library.impl(lib, "all_gather_copy_in", "HPU")
+@torch.library.impl(lib, "all_gather_copy_in", "CPU")
+@torch.library.impl(lib, "all_gather_copy_in", "MTIA")
+@torch.library.impl(lib, "all_gather_copy_in", "PrivateUse1")
+def all_gather_copy_in_cuda(
+    all_gather_inputs: list[torch.Tensor],
+    all_gather_output: torch.Tensor,
+    inp_split_sizes: list[int],
+    all_gather_input_numel: int,
+    rank: int,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    all_gather_input = all_gather_output.narrow(
+        0, all_gather_input_numel * rank, all_gather_input_numel
+    )
+    foreach_copy_dsts = torch.split(all_gather_input, inp_split_sizes)
+    with torch.no_grad():
+        torch._foreach_copy_(foreach_copy_dsts, all_gather_inputs)
+    return all_gather_input, all_gather_output
+
+
+lib.define(
+    "split_with_sizes_copy(Tensor all_gather_output, SymInt[] all_gather_input_split_sizes, int dim=0, *, Tensor(a!)[] out) -> ()"
+)
+
+
+@torch.library.impl(lib, "split_with_sizes_copy", "Meta")
+@torch.library.impl(lib, "split_with_sizes_copy", "CUDA")
+@torch.library.impl(lib, "split_with_sizes_copy", "XPU")
+@torch.library.impl(lib, "split_with_sizes_copy", "HPU")
+@torch.library.impl(lib, "split_with_sizes_copy", "CPU")
+@torch.library.impl(lib, "split_with_sizes_copy", "MTIA")
+@torch.library.impl(lib, "split_with_sizes_copy", "PrivateUse1")
+def split_with_sizes_copy(
+    all_gather_output: torch.Tensor,
+    all_gather_input_split_sizes: list[int],
+    dim: int,
+    out: list[torch.Tensor],
+) -> None:
+    torch.split_with_sizes_copy(
+        all_gather_output, all_gather_input_split_sizes, dim=dim, out=out
+    )
+
+
+lib.define(
+    "chunk_cat(Tensor[] tensors, int dim, int num_chunks, *, Tensor(a!) out) -> ()"
+)
+
+
+@torch.library.impl(lib, "chunk_cat", "Meta")
+@torch.library.impl(lib, "chunk_cat", "CUDA")
+@torch.library.impl(lib, "chunk_cat", "XPU")
+@torch.library.impl(lib, "chunk_cat", "HPU")
+@torch.library.impl(lib, "chunk_cat", "CPU")
+@torch.library.impl(lib, "chunk_cat", "MTIA")
+@torch.library.impl(lib, "chunk_cat", "PrivateUse1")
+def chunk_cat(
+    tensors: list[torch.Tensor],
+    dim: int,
+    num_chunks: int,
+    out: torch.Tensor,
+) -> None:
+    torch._chunk_cat(tensors, dim, num_chunks, out=out)
+
+
+@torch.no_grad()
+def foreach_all_gather(
+    fsdp_params: list[FSDPParam],
+    group: dist.ProcessGroup,
+    async_op: bool,
+    all_gather_copy_in_stream: torch.Stream,
+    all_gather_stream: torch.Stream,
+    device: torch.device,
+    all_gather_comm: AllGather,
+) -> Optional[AllGatherResult]:
+    world_size, rank = group.size(), group.rank()
+    device_handle = _get_device_handle(device.type)
+    with device_handle.stream(all_gather_copy_in_stream):
+        param_all_gather_inputs = _get_param_all_gather_inputs(fsdp_params)
+        (
+            param_all_gather_input_dtypes,
+            param_all_gather_input_numels,
+            dtype,
+        ) = _get_all_gather_input_metadatas(param_all_gather_inputs)
+        if dtype == torch.uint8:
+            all_gather_inputs = [
+                t.view(torch.uint8) for ts in param_all_gather_inputs for t in ts
+            ]
+        else:
+            all_gather_inputs = [*chain.from_iterable(param_all_gather_inputs)]
+        inp_split_sizes = [t.numel() for t in all_gather_inputs]
+        all_gather_input_numel = sum(inp_split_sizes)
+        all_gather_output = all_gather_comm.allocate(
+            (all_gather_input_numel * world_size,), dtype=dtype, device=device
+        )
+        all_gather_input, all_gather_output = torch.ops.fsdp.all_gather_copy_in(
+            all_gather_inputs,
+            all_gather_output,
+            inp_split_sizes,
+            all_gather_input_numel,
+            rank,
+        )
+        del param_all_gather_inputs
+    all_gather_stream.wait_stream(all_gather_copy_in_stream)
+    with device_handle.stream(all_gather_stream):
+        all_gather_work = all_gather_comm(
+            output_tensor=all_gather_output,
+            input_tensor=all_gather_input,
+            group=group,
+            async_op=async_op,
+        )
+        all_gather_event = all_gather_stream.record_event()
+        return AllGatherResult(
+            all_gather_output,
+            all_gather_event,
+            all_gather_work,
+            param_all_gather_input_dtypes,
+            param_all_gather_input_numels,
+            inp_split_sizes,
+        )
+
+
+@torch.no_grad()
+def _get_param_all_gather_inputs(
+    fsdp_params: list[FSDPParam],
+) -> list[list[torch.Tensor]]:
+    if compiled_autograd_enabled():
+        return [fsdp_param.all_gather_inputs for fsdp_param in fsdp_params]
+
+    # Intentionally try to run a fast-path that bypasses abstractions for the
+    # common FSDP case of bf16/fp32 mixed precision in order to use foreach
+    # copy for lower CPU overhead and more efficient copying in eager
+    def use_foreach_copy(fsdp_param: FSDPParam) -> bool:
+        return (
+            fsdp_param.param_dtype is not None
+            and not fsdp_param.offload_to_cpu
+            and not hasattr(fsdp_param._sharded_local_tensor, "fsdp_pre_all_gather")
+        )
+
+    param_all_gather_inputs: list[list[torch.Tensor]] = [[] for _ in fsdp_params]
+    foreach_copy_indices: list[int] = []
+    foreach_copy_inputs: list[torch.Tensor] = []
+    foreach_copy_input_numels: list[int] = []
+
+    # 1st pass: for foreach-copy parameters, get inputs and metadata for the
+    # foreach copy, and for the others, actually get their all-gather inputs
+    for i, fsdp_param in enumerate(fsdp_params):
+        if use_foreach_copy(fsdp_param):
+            foreach_copy_indices.append(i)
+            all_gather_input = (
+                fsdp_param._sharded_param_data
+                if fsdp_param.sharded_state == ShardedState.SHARDED
+                else cast(torch.Tensor, fsdp_param._sharded_post_forward_param_data)
+            )
+            foreach_copy_inputs.append(all_gather_input)
+            foreach_copy_input_numels.append(all_gather_input.numel())
+        else:
+            param_all_gather_inputs[i] = fsdp_param.all_gather_inputs
+
+    # 2nd pass: use foreach copy to compute the remaining all-gather inputs
+    if foreach_copy_inputs:
+        fsdp_param_0 = fsdp_params[foreach_copy_indices[0]]
+        param_dtype, device = fsdp_param_0.param_dtype, fsdp_param_0.device
+        flat_foreach_copy_input = torch.empty(
+            (sum(foreach_copy_input_numels),), device=device, dtype=param_dtype
+        )
+        splits = torch.split(flat_foreach_copy_input, foreach_copy_input_numels)
+        torch._foreach_copy_(splits, foreach_copy_inputs)
+        for i, split in zip(foreach_copy_indices, splits):
+            param_all_gather_inputs[i] = [split]
+
+    return param_all_gather_inputs
+
+
+@torch.no_grad()
+def foreach_all_gather_copy_out(
+    all_gather_result: AllGatherResult,
+    fsdp_params: list[FSDPParam],
+    group: dist.ProcessGroup,
+) -> None:
+    (
+        all_gather_output,
+        all_gather_event,
+        all_gather_work,
+        param_all_gather_input_dtypes,
+        param_all_gather_input_numels,
+        all_gather_input_split_sizes,
+    ) = all_gather_result
+    _dtype, device = all_gather_output.dtype, all_gather_output.device
+    device_handle = _get_device_handle(device.type)
+    if all_gather_event is not None:  # sync op
+        device_handle.current_stream().wait_event(all_gather_event)
+    if isinstance(all_gather_work, dist.distributed_c10d.Work):  # async op
+        all_gather_work.wait()
+    world_size, device = group.size(), all_gather_output.device
+
+    split_with_sizes_out: list[torch.Tensor] = []
+    shard_i_copy_infos: list[tuple[FSDPParam, list[torch.Tensor]]] = []
+    for all_gather_input_numels, all_gather_input_dtypes, fsdp_param in zip(
+        param_all_gather_input_numels, param_all_gather_input_dtypes, fsdp_params
+    ):
+        # NOTE: Under compile, make sure we always recreate all_gather_outputs
+        # per AllGather. See [Note: Invariants for torch.compile Traceable FSDP2].
+        force_recreate = compiled_autograd_enabled()
+        fsdp_param.init_all_gather_outputs(
+            all_gather_input_numels,
+            all_gather_input_dtypes,
+            world_size,
+            device,
+            force_recreate=force_recreate,
+        )
+        if not force_recreate:
+            fsdp_param.alloc_all_gather_outputs()
+        param_all_gather_outputs = fsdp_param.all_gather_outputs
+        if fsdp_param.fsdp_placement.dim != 0:
+            # Copy to a temporary and then chunk-cat into the final all-gather
+            # output tensors
+            param_all_gather_outputs = [
+                torch.empty_like(t) for t in param_all_gather_outputs
+            ]
+            shard_i_copy_infos.append((fsdp_param, param_all_gather_outputs))
+        split_with_sizes_out.extend(param_all_gather_outputs)
+
+    all_gather_output = all_gather_output.view(world_size, -1)
+    if all_gather_output.dtype == torch.uint8:
+        out = [t.view(world_size, -1).view(torch.uint8) for t in split_with_sizes_out]
+    else:
+        out = [t.view(world_size, -1) for t in split_with_sizes_out]
+
+    # only avoid VC bump if we are not in inference mode
+    if torch._dynamo.is_compiling():
+        # For torch.compile, we turn off inference_mode for fake tensor
+        # propagation, and therefore graph break on is_inference. For `compile`,
+        # we don't care about VCs, so just skip the optimization.
+        non_inference_outs = []
+    else:
+        non_inference_outs = [o for o in out if not o.is_inference()]
+
+    if len(non_inference_outs) > 0:
+        with torch.autograd._unsafe_preserve_version_counter(tuple(non_inference_outs)):
+            torch.ops.fsdp.split_with_sizes_copy(
+                all_gather_output, all_gather_input_split_sizes, dim=1, out=out
+            )
+    else:
+        torch.ops.fsdp.split_with_sizes_copy(
+            all_gather_output, all_gather_input_split_sizes, dim=1, out=out
+        )
+
+    for fsdp_param, param_all_gather_outputs in shard_i_copy_infos:
+        # Chunk-cat from the temporary to the final all-gather output tensors
+        shard_dim = fsdp_param.fsdp_placement.dim
+
+        with torch.autograd._unsafe_preserve_version_counter(
+            tuple(fsdp_param.all_gather_outputs)
+        ):
+            for param_all_gather_output, target_all_gather_output in zip(
+                param_all_gather_outputs, fsdp_param.all_gather_outputs
+            ):
+                padded_sharded_size = (
+                    fsdp_param.padded_sharded_param_size
+                    if fsdp_param.sharded_state == ShardedState.SHARDED
+                    else cast(
+                        torch.Tensor, fsdp_param._sharded_post_forward_param_data
+                    ).size()
+                )
+                pre_param_size = list(padded_sharded_size)
+                pre_param_size[0] *= world_size
+                chunks = torch.chunk(
+                    param_all_gather_output.view(pre_param_size), world_size, dim=0
+                )
+                post_param_size = list(padded_sharded_size)
+                post_param_size[shard_dim] *= world_size
+                cat_out = target_all_gather_output.view(post_param_size)
+                torch.cat(chunks, dim=shard_dim, out=cat_out)
+
+
+@torch.no_grad()
+def foreach_reduce(
+    fsdp_params: list[FSDPParam],
+    unsharded_grads: list[torch.Tensor],
+    reduce_scatter_group: dist.ProcessGroup,
+    reduce_scatter_stream: torch.Stream,
+    reduce_scatter_comm: ReduceScatter,
+    orig_dtype: Optional[torch.dtype],
+    reduce_dtype: Optional[torch.dtype],
+    device: torch.device,
+    gradient_divide_factor: Optional[float],
+    all_reduce_group: Optional[dist.ProcessGroup],  # not `None` iff HSDP
+    all_reduce_stream: torch.Stream,
+    all_reduce_grads: bool,
+    partial_reduce_output: Optional[torch.Tensor],  # only used for HSDP
+    all_reduce_hook: Optional[Callable[[torch.Tensor], None]],
+    force_sum_reduction_for_comms: bool = False,
+) -> tuple[
+    torch.Tensor,
+    torch.Event,
+    torch.Event,
+    Optional[torch.Tensor],
+    Optional[torch.Event],
+    Optional[torch.Tensor],
+]:
+    """
+    ``unsharded_grads`` owns the references to the gradients computed by
+    autograd, so clearing the list frees the gradients.
+    """
+
+    grad_dtypes = {grad.dtype for grad in unsharded_grads}
+    if len(grad_dtypes) != 1:
+        # Check this at runtime since it could be a real runtime error if e.g.
+        # fp8 weights do not produce the correct higher precision gradients
+        _raise_assert_with_print(
+            f"FSDP reduce-scatter expects uniform gradient dtype but got {grad_dtypes}"
+        )
+    grad_dtype = unsharded_grads[0].dtype
+    reduce_dtype = reduce_dtype or grad_dtype
+    (predivide_factor, postdivide_factor, reduce_scatter_op, all_reduce_op) = (
+        _get_gradient_divide_factors(
+            reduce_scatter_group,
+            all_reduce_group,
+            reduce_dtype,
+            device.type,
+            gradient_divide_factor,
+            force_sum_reduction_for_comms,
+        )
+    )
+
+    if reduce_scatter_group is None:
+        world_size = 1
+    else:
+        world_size = reduce_scatter_group.size()
+    device_handle = _get_device_handle(device.type)
+    current_stream = device_handle.current_stream()
+
+    if world_size > 1:
+        for i, (fsdp_param, unsharded_grad) in enumerate(
+            zip(fsdp_params, unsharded_grads)
+        ):
+            if (shard_dim := fsdp_param.fsdp_placement.dim) == 0:
+                continue
+            if unsharded_grad.size(shard_dim) % world_size != 0:
+                raise AssertionError(
+                    f"Shard({shard_dim}) requires even sharding: {unsharded_grad.size()=} {world_size=}"
+                )
+            chunks = torch.chunk(unsharded_grad, world_size, dim=shard_dim)
+            unsharded_grads[i] = torch.cat(chunks, dim=0)
+
+    padded_unsharded_sizes = tuple(
+        _get_dim0_padded_size(grad.size(), world_size) for grad in unsharded_grads
+    )
+    reduce_scatter_input_numel = sum(s.numel() for s in padded_unsharded_sizes)
+    reduce_scatter_output_numel = reduce_scatter_input_numel // world_size
+    reduce_scatter_input = reduce_scatter_comm.allocate(
+        (reduce_scatter_input_numel,),
+        dtype=reduce_dtype,
+        device=device,
+    )
+
+    foreach_reduce_scatter_copy_in(unsharded_grads, reduce_scatter_input, world_size)
+
+    # Only after the copy-in finishes can we free the gradients
+    unsharded_grads.clear()
+    reduce_scatter_stream.wait_stream(current_stream)
+    all_reduce_input = None
+    all_reduce_event = None
+
+    with device_handle.stream(reduce_scatter_stream):
+        reduce_output = reduce_scatter_comm.allocate(
+            (reduce_scatter_output_numel,),
+            dtype=reduce_dtype,
+            device=device,
+        )
+        _div_if_needed(reduce_scatter_input, predivide_factor)
+        if world_size > 1:
+            reduce_scatter_comm(
+                output_tensor=reduce_output,
+                input_tensor=reduce_scatter_input,
+                group=reduce_scatter_group,
+                op=reduce_scatter_op,
+            )
+        else:
+            # For single GPU, just copy the input to output (no actual reduce-scatter needed), and
+            # account for a possible gradient_divide_factor.
+            if gradient_divide_factor is not None:
+                reduce_output.copy_(reduce_scatter_input / gradient_divide_factor)
+            else:
+                reduce_output.copy_(reduce_scatter_input)
+        reduce_scatter_event = reduce_scatter_stream.record_event()
+        post_reduce_stream = reduce_scatter_stream
+        if all_reduce_group is not None:  # HSDP or DDP/replicate
+            # Accumulations must run in the reduce-scatter stream
+            if not all_reduce_grads:
+                if partial_reduce_output is not None:
+                    partial_reduce_output += reduce_output
+                else:
+                    partial_reduce_output = reduce_output
+                return (
+                    reduce_scatter_input,
+                    reduce_scatter_event,
+                    post_reduce_stream.record_event(),
+                    all_reduce_input,
+                    all_reduce_event,
+                    partial_reduce_output,
+                )
+            if partial_reduce_output is not None:
+                reduce_output += partial_reduce_output
+            post_reduce_stream = all_reduce_stream
+            if world_size >= 1:
+                all_reduce_stream.wait_stream(reduce_scatter_stream)
+            else:
+                all_reduce_stream.wait_stream(current_stream)
+            with device_handle.stream(all_reduce_stream):
+                dist.all_reduce(
+                    reduce_output,
+                    group=all_reduce_group,
+                    op=all_reduce_op,
+                )
+                all_reduce_input = reduce_output
+                all_reduce_event = all_reduce_stream.record_event()
+    # -- END: ops in reduce_scatter stream
+
+    if all_reduce_hook is not None:
+        # Execute user-specified all reduce hook.
+        # If native HSDP is used, this is executed after the HSDP all reduce.
+        # If 1-d FSDP is used, this is executed post reduce-scatter.
+        post_reduce_stream = all_reduce_stream
+        all_reduce_stream.wait_stream(reduce_scatter_stream)
+        with device_handle.stream(all_reduce_stream):
+            all_reduce_hook(reduce_output)
+    # -- END: ops post reduce_scatter
+
+    with device_handle.stream(post_reduce_stream):
+        _div_if_needed(reduce_output, postdivide_factor)
+        reduce_output = _to_dtype_if_needed(reduce_output, orig_dtype)
+        # View out and accumulate sharded gradients
+        flat_grad_offset = 0  # [0, reduce_scatter_output_numel - 1]
+        for padded_unsharded_size, fsdp_param in zip(
+            padded_unsharded_sizes, fsdp_params
+        ):
+            # Assume even sharding for Shard(i), i > 0; otherwise would require
+            # copy-out for contiguous strides
+            new_sharded_grad = torch.as_strided(
+                reduce_output,
+                size=fsdp_param.sharded_size,
+                stride=fsdp_param.contiguous_sharded_stride,
+                storage_offset=flat_grad_offset,
+            )
+            to_accumulate_grad = fsdp_param.sharded_param.grad is not None
+            if fsdp_param.offload_to_cpu:
+                # Only overlap the D2H copy (copying to pinned memory) if not
+                # accumulating gradients since the CPU add kernel depends on
+                # the copy result and we cannot run the add as a callback
+                non_blocking = fsdp_param.pin_memory and not to_accumulate_grad
+                # Since the GPU sharded gradient is allocated in the RS stream,
+                # we can free it here by not keeping a ref without waiting for
+                # the D2H copy since future RS-stream ops run after the copy
+                new_sharded_grad = new_sharded_grad.to(
+                    torch.device("cpu"), non_blocking=non_blocking
+                )
+                if non_blocking:
+                    # Record an event on which to block the CPU thread to
+                    # ensure that the D2H copy finishes before the optimizer
+                    fsdp_param.grad_offload_event = post_reduce_stream.record_event()
+            if to_accumulate_grad:
+                if not isinstance(fsdp_param.sharded_param.grad, DTensor):
+                    raise AssertionError(
+                        f"Expected fsdp_param.sharded_param.grad to be DTensor, got {type(fsdp_param.sharded_param.grad)}"
+                    )
+                fsdp_param.sharded_param.grad._local_tensor += new_sharded_grad
+            else:
+                new_sharded_dtensor_grad = fsdp_param.to_sharded_dtensor(
+                    new_sharded_grad
+                )
+                fsdp_param.sharded_param.grad = new_sharded_dtensor_grad
+            if not compiled_autograd_enabled():
+                for hook in (
+                    getattr(fsdp_param.sharded_param, "_post_accumulate_grad_hooks", {})
+                    or {}
+                ).values():
+                    hook(fsdp_param.sharded_param)
+            padded_sharded_numel = padded_unsharded_size.numel() // world_size
+            flat_grad_offset += padded_sharded_numel
+        post_reduce_event = post_reduce_stream.record_event()
+    # The RS output is allocated in the RS stream and used in the default
+    # stream (for optimizer). To ensure its memory is not reused for later
+    # RSs, we do not need extra synchronization since the sharded parameters
+    # hold refs through the end of backward.
+    return (
+        reduce_scatter_input,
+        reduce_scatter_event,
+        post_reduce_event,
+        all_reduce_input,
+        all_reduce_event,
+        None,
+    )
+
+
+def foreach_reduce_scatter_copy_in(
+    unsharded_grads: list[torch.Tensor],
+    reduce_scatter_input: torch.Tensor,
+    world_size: int,
+) -> None:
+    reduce_scatter_input = reduce_scatter_input.view(world_size, -1)
+    torch.ops.fsdp.chunk_cat(
+        unsharded_grads, dim=0, num_chunks=world_size, out=reduce_scatter_input
+    )
+
+
+def _get_all_gather_input_metadatas(
+    param_all_gather_inputs: list[list[torch.Tensor]],
+) -> tuple[list[list[torch.dtype]], list[list[int]], torch.dtype]:
+    param_all_gather_input_dtypes: list[list[torch.dtype]] = []
+    param_all_gather_input_numels: list[list[int]] = []
+    all_gather_dtype = param_all_gather_inputs[0][0].dtype
+    for all_gather_inputs in param_all_gather_inputs:
+        input_dtypes: list[torch.dtype] = []
+        input_numels: list[int] = []
+        for all_gather_input in all_gather_inputs:
+            if all_gather_input.dtype != all_gather_dtype:
+                all_gather_dtype = torch.uint8
+            input_dtypes.append(all_gather_input.dtype)
+            input_numels.append(all_gather_input.numel())
+        param_all_gather_input_dtypes.append(input_dtypes)
+        param_all_gather_input_numels.append(input_numels)
+    return (
+        param_all_gather_input_dtypes,
+        param_all_gather_input_numels,
+        all_gather_dtype,
+    )
+
+
+def _get_gradient_divide_factors(
+    reduce_scatter_group: Optional[dist.ProcessGroup],
+    all_reduce_group: Optional[dist.ProcessGroup],
+    reduce_dtype: torch.dtype,
+    device_type: str = "",
+    factor: Optional[float] = None,
+    force_sum_reduction_for_comms: bool = False,
+) -> tuple[
+    Optional[float],
+    Optional[float],
+    Union[dist.ReduceOp, dist.ReduceOp.RedOpType],
+    Union[dist.ReduceOp, dist.ReduceOp.RedOpType],
+]:
+    # MTIA appears to only support SUM reduction, hence we force it implicitly
+    if device_type == "mtia":
+        force_sum_reduction_for_comms = True
+
+    # For fp32/bf16, we do not need to worry about overflow/underflow, so we
+    # use NCCL's built-in division to avoid separate div kernels
+    overflow_risk = reduce_dtype not in (torch.float32, torch.bfloat16)
+    if reduce_scatter_group is not None:
+        data_parallel_size = reduce_scatter_group.size()
+    else:
+        data_parallel_size = 1
+
+    if all_reduce_group is not None:
+        data_parallel_size *= all_reduce_group.size()
+
+    if not overflow_risk and not force_sum_reduction_for_comms:
+        if factor is None:
+            # Warning: NCCL ReduceOp.AVG may produce incorrect results with
+            # world size 1.
+            if data_parallel_size == 1:
+                return None, None, ReduceOp.SUM, ReduceOp.SUM
+            return None, None, ReduceOp.AVG, ReduceOp.AVG
+        if reduce_scatter_group is not None and factor == reduce_scatter_group.size():
+            reduce_scatter_op = ReduceOp.AVG
+        else:
+            reduce_scatter_op = torch.distributed._make_nccl_premul_sum(1 / factor)
+        return None, None, reduce_scatter_op, ReduceOp.SUM
+
+    if factor is None:
+        factor = float(data_parallel_size)
+    pre_factor: Optional[float]
+    if overflow_risk:
+        # Since fp16 has smaller dynamic range than fp32/bf16, we want to avoid
+        # overflow/underflow. For N data parallel workers, each worker computes
+        # g_i, and they collectively reduce (g_1 + ... + g_N) / N. To avoid
+        # overflow/underflow, we divide by ~sqrt(N) before/after the reduction.
+        pre_factor = 1
+        while factor % pre_factor == 0 and factor / pre_factor > pre_factor:
+            pre_factor *= 2
+        post_factor = factor / pre_factor
+    else:
+        # Prefer post-multiplying as it operates on less data and is thus faster
+        pre_factor, post_factor = None, factor
+
+    return pre_factor, post_factor, ReduceOp.SUM, ReduceOp.SUM
+
+
+def _div_if_needed(tensor: torch.Tensor, div_factor: Optional[float]) -> None:
+    if div_factor is not None and div_factor != 1:
+        tensor.div_(div_factor)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_common.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_common.py
new file mode 100644
index 0000000000000000000000000000000000000000..85addad83b3b08cbed358f3eb31b2bf4f2a2c9e8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_common.py
@@ -0,0 +1,181 @@
+# mypy: allow-untyped-defs
+import math
+import traceback
+from dataclasses import dataclass
+from enum import auto, Enum
+from typing import Any, Optional
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+from torch.distributed._composable.contract import _get_registry
+from torch.distributed.tensor import DeviceMesh, DTensor
+from torch.distributed.tensor._dtensor_spec import DTensorSpec
+
+
+_compiled_autograd_enabled: bool = False
+
+
+def detect_compiled_autograd():
+    if torch.compiler.is_compiling():
+        raise AssertionError(
+            "`detect_compiled_autograd()` is designed to be called in eager mode"
+        )
+    global _compiled_autograd_enabled
+    import torch._dynamo.compiled_autograd as ca
+
+    _compiled_autograd_enabled = (
+        ca.compiled_autograd_enabled
+        or ca.compiled_autograd_enabled_force_eager
+        or ca.in_compiled_autograd_region
+    )
+
+
+def compiled_autograd_enabled():
+    global _compiled_autograd_enabled
+    return _compiled_autograd_enabled
+
+
+@dataclass
+class DataParallelMeshInfo:
+    mesh: DeviceMesh
+    shard_mesh_dim: Optional[int] = None
+    replicate_mesh_dim: Optional[int] = None
+
+    def __post_init__(self):
+        if self.shard_mesh_dim is None and self.replicate_mesh_dim is None:
+            raise AssertionError(
+                "At least one of shard_mesh_dim and replicate_mesh_dim must not be None"
+            )
+
+
+@dataclass
+class FSDPMeshInfo(DataParallelMeshInfo):
+    def __post_init__(self):
+        super().__post_init__()
+        if self.shard_mesh_dim is None:
+            raise AssertionError("Expects non-None shard_mesh_dim")
+        self.shard_mesh_size: int = self.mesh.size(self.shard_mesh_dim)
+        self.shard_process_group = self.mesh.get_group(self.shard_mesh_dim)
+        self.shard_mesh_rank: int = self.shard_process_group.rank()
+
+
+@dataclass
+class DDPMeshInfo(DataParallelMeshInfo):
+    def __post_init__(self):
+        super().__post_init__()
+        if self.replicate_mesh_dim is None:
+            raise AssertionError("Expects non-None replicate_mesh_dim")
+        self.replicate_mesh_size: int = self.mesh.size(self.replicate_mesh_dim)
+        self.replicate_process_group = self.mesh.get_group(self.replicate_mesh_dim)
+        self.replicate_mesh_rank: int = self.replicate_process_group.rank()
+
+
+@dataclass
+class HSDPMeshInfo(FSDPMeshInfo, DDPMeshInfo):
+    def __post_init__(self):  # pylint:disable=useless-parent-delegation
+        # Calls `FSDPMeshInfo` -> `DDPMeshInfo` -> `DataParallelMeshInfo`
+        super().__post_init__()
+
+
+class TrainingState(Enum):
+    """Describes the training state of one FSDP state / parameter group."""
+
+    # Transition to forward starting pre-forward until post-forward
+    FORWARD = auto()
+    # Transition to pre-backward when unsharding in backward
+    PRE_BACKWARD = auto()
+    # Transition to post-backward when resharding and reducing gradients
+    POST_BACKWARD = auto()
+    # Idle before/after forward or before pre-backward/after post-backward
+    IDLE = auto()
+
+
+def _raise_assert_with_print(*args: Any, **kwargs: Any):
+    print(f"[Rank {dist.get_rank()}] ", end="")
+    print(*args, **kwargs)
+    traceback.print_stack()
+    raise AssertionError(*args, **kwargs)
+
+
+def _is_composable_with_fsdp(module: nn.Module) -> bool:
+    registry = _get_registry(module)
+    if registry is None:
+        return True
+    # Registry keys by function name
+    return "replicate" not in registry
+
+
+def _get_dim0_padded_size(tensor_size: torch.Size, dim0_factor: int) -> torch.Size:
+    padded_dim0 = math.ceil(tensor_size[0] / dim0_factor) * dim0_factor
+    return torch.Size([padded_dim0]) + tensor_size[1:]
+
+
+def _chunk_with_empty(
+    tensor: torch.Tensor, num_chunks: int, dim: int
+) -> list[torch.Tensor]:
+    chunks = list(torch.chunk(tensor, num_chunks, dim=dim))
+    while len(chunks) < num_chunks:
+        chunks.append(chunks[0].new_empty(0))
+    return chunks
+
+
+def _get_dim_chunked_size(
+    chunk: torch.Tensor, unchunked_size: torch.Size, dim: int
+) -> torch.Size:
+    if chunk.numel() > 0:
+        return chunk.size()
+    # For 0 numel, we need to preserve nonzero-sized dims for DTensor APIs
+    return unchunked_size[:dim] + torch.Size([0]) + unchunked_size[dim + 1 :]
+
+
+def _from_local_no_grad(
+    local_tensor: torch.Tensor,
+    sharding_spec: DTensorSpec,
+) -> DTensor:
+    """
+    This method is similar to ``DTensor.from_local()`` except that in eager mode
+    it avoids some CPU overhead by avoiding default args and not being differentiable.
+    """
+
+    if not compiled_autograd_enabled():
+        # pyrefly: ignore [bad-argument-type]
+        return DTensor(
+            # Use the local tensor directly instead of constructing a new tensor
+            # variable, e.g. with `view_as()`, since this is not differentiable
+            # pyrefly: ignore [bad-argument-count]
+            local_tensor,
+            sharding_spec,
+            # pyrefly: ignore [unexpected-keyword]
+            requires_grad=local_tensor.requires_grad,
+        )
+    else:
+        return DTensor.from_local(
+            local_tensor,
+            sharding_spec.mesh,
+            sharding_spec.placements,
+            shape=sharding_spec.shape,
+            stride=sharding_spec.stride,
+        )
+
+
+def _to_dtype_if_needed(
+    tensor: torch.Tensor, dtype: Optional[torch.dtype]
+) -> torch.Tensor:
+    if dtype is not None and tensor.dtype != dtype:
+        return tensor.to(dtype)
+    return tensor
+
+
+def _cast_fp_tensor(dtype: torch.dtype, x: torch.Tensor) -> torch.Tensor:
+    if (
+        not isinstance(x, torch.Tensor)
+        or not torch.is_floating_point(x)
+        or x.dtype == dtype
+    ):
+        return x
+    return x.to(dtype)
+
+
+def is_bw() -> bool:
+    return torch._C._current_graph_task_id() != -1
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_init.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_init.py
new file mode 100644
index 0000000000000000000000000000000000000000..01d196795c3d8f9270138f757b3e7f3de9e10f11
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_init.py
@@ -0,0 +1,243 @@
+import itertools
+import logging
+from typing import Optional, Union
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+from torch._logging import warning_once
+from torch.distributed.device_mesh import _get_device_handle
+from torch.distributed.tensor import DeviceMesh, DTensor, init_device_mesh
+from torch.utils._python_dispatch import is_traceable_wrapper_subclass
+
+from ._fsdp_common import _is_composable_with_fsdp, FSDPMeshInfo, HSDPMeshInfo
+from ._fsdp_state import _get_module_fsdp_state
+
+
+logger = logging.getLogger("torch.distributed.fsdp.fully_shard")
+
+
+def _get_post_forward_mesh_info(
+    reshard_after_forward: Union[bool, int], mesh_info: FSDPMeshInfo
+) -> Optional[FSDPMeshInfo]:
+    shard_mesh_size = mesh_info.shard_mesh_size
+    if not isinstance(reshard_after_forward, (bool, int)):
+        raise ValueError(
+            "reshard_after_forward should be a bool or an int representing the "
+            f"group size to reshard to, not {reshard_after_forward}"
+        )
+    # NOTE: `isinstance(False, int)` returns `True`.
+    if not isinstance(reshard_after_forward, bool) and isinstance(
+        reshard_after_forward, int
+    ):
+        if (
+            reshard_after_forward < 1
+            or reshard_after_forward > shard_mesh_size
+            or shard_mesh_size % reshard_after_forward != 0
+        ):
+            raise ValueError(
+                "If passing reshard_after_forward as an int, it should be a "
+                f"factor of {shard_mesh_size}, not {reshard_after_forward}"
+            )
+        elif reshard_after_forward == 1:
+            msg = (
+                "reshard_after_forward=1 (int) means resharding parameters to world size 1, "
+                "instead of reshard_after_forward=True (bool)"
+            )
+            warning_once(logger, msg, stacklevel=2)
+            reshard_after_forward = False
+        elif reshard_after_forward == shard_mesh_size:
+            reshard_after_forward = True
+    post_forward_mesh_info = None
+    if reshard_after_forward is True:
+        post_forward_mesh_info = mesh_info
+    elif reshard_after_forward is not False:  # int case
+        # For HSDP, we can flatten the two replicate dims into the 0th dim
+        post_forward_mesh_tensor = mesh_info.mesh.mesh.view(-1, reshard_after_forward)
+        post_forward_mesh = DeviceMesh(
+            mesh_info.mesh.device_type, post_forward_mesh_tensor
+        )
+        post_forward_mesh_info = HSDPMeshInfo(
+            post_forward_mesh, shard_mesh_dim=1, replicate_mesh_dim=0
+        )
+    return post_forward_mesh_info
+
+
+def _init_default_fully_shard_mesh() -> DeviceMesh:
+    """Default to global CUDA mesh if possible else global CPU mesh."""
+    if not dist.distributed_c10d.is_initialized():
+        dist.distributed_c10d.init_process_group()
+    default_pg = dist.distributed_c10d._get_default_group()
+    device = torch._C._get_accelerator()
+    mesh = init_device_mesh(device.type, mesh_shape=(default_pg.size(),))
+    return mesh
+
+
+def _get_device_from_mesh(mesh: DeviceMesh) -> torch.device:
+    if mesh.device_type == "cpu":
+        return torch.device("cpu")
+    device_handle = _get_device_handle(mesh.device_type)
+    return torch.device(mesh.device_type, device_handle.current_device())
+
+
+def _ignore_module(
+    module: nn.Module,
+    ignored_params: set[nn.Parameter],
+    ignore_decision: dict[nn.Module, bool],
+) -> bool:
+    """
+    Decide if it is safe to ignore a module for applying fully_shard.
+    """
+    if module in ignore_decision:
+        return ignore_decision[module]
+
+    if len(list(module.buffers(recurse=False))) > 0:
+        # Cannot ignore a module with any buffer
+        ignore_decision[module] = False
+        return False
+
+    for _, param in module.named_parameters(recurse=False):
+        if param not in ignored_params:
+            # at least one param is not ignored. So this module shouldn't be.
+            ignore_decision[module] = False
+            return False
+
+    # Need to consider descendants of module
+    for child in list(module.children()):
+        ignore_child = _ignore_module(child, ignored_params, ignore_decision)
+        if not ignore_child:
+            # Cannot ignore module if one of its children is not ignored
+            ignore_decision[module] = False
+            return False
+
+    # Safe to ignore module
+    ignore_decision[module] = True
+    return True
+
+
+def _adjust_managed_modules(
+    modules: list[nn.Module], ignored_params: set[nn.Parameter]
+) -> list[nn.Module]:
+    """
+    Adjust the given list of managed modules by removing those with all parameters ignored.
+    """
+    ignore_decision: dict[nn.Module, bool] = {}
+    new_modules = []
+    for module in modules:
+        ignored = _ignore_module(module, ignored_params, ignore_decision)
+        if not ignored:
+            new_modules.append(module)
+    return new_modules
+
+
+def _get_managed_modules(
+    root_modules: tuple[nn.Module, ...],
+    ignored_params: Optional[set[nn.Parameter]] = None,
+) -> list[nn.Module]:
+    modules: list[nn.Module] = []
+    root_modules_set = set(root_modules)
+    # Track visisted modules to avoid visiting shared modules multiple times
+    visited_modules: set[nn.Module] = set()
+
+    def dfs(module: nn.Module) -> None:
+        """
+        Runs a DFS to collect managed modules, not recursing into modules with
+        a non-composable API or ``fully_shard`` already applied.
+        """
+        if not _is_composable_with_fsdp(module):
+            return
+        elif (
+            module not in root_modules_set
+            and _get_module_fsdp_state(module) is not None
+        ):
+            return  # nested `fully_shard` module
+        visited_modules.add(module)
+        for submodule in module.children():
+            if submodule not in visited_modules:
+                dfs(submodule)
+        modules.append(module)
+
+    for root_module in root_modules:
+        dfs(root_module)
+
+    if ignored_params is None:
+        return modules
+
+    adjusted_modules = _adjust_managed_modules(modules, ignored_params)
+    return adjusted_modules
+
+
+def _verify_managed_param(name: str, param: nn.Parameter) -> None:
+    """
+    Verify if the parameter is accepted by fully_shard. The only restriction now
+    is that the parameter cannot be a scalar tensor (param.numel == 0) since we
+    need at least one dim to shard.
+    """
+    if len(param.shape) == 0:
+        raise ValueError(
+            "fully_shard doesn't support scalar parameters. "
+            f"Change {name} to a 1D tensor with numel equal to 1."
+        )
+
+
+def _get_managed_states(
+    modules: list[nn.Module], ignored_params: Optional[set[nn.Parameter]] = None
+) -> tuple[list[nn.Parameter], list[torch.Tensor]]:
+    params: list[nn.Parameter] = []
+    buffers: list[torch.Tensor] = []
+    # Track visited parameters/buffers to avoid visiting shared parameters and
+    # buffers multiple times
+    visited_params: set[nn.Parameter] = set()
+    visited_buffers: set[torch.Tensor] = set()
+    if ignored_params is None:
+        ignored_params = set()
+
+    for module in modules:
+        for name, param in module.named_parameters(recurse=False):
+            if param in ignored_params:
+                # do not include an ignored parameters
+                continue
+            if param not in visited_params:
+                _verify_managed_param(name, param)
+                params.append(param)
+                visited_params.add(param)
+        for buffer in module.buffers(recurse=False):
+            if buffer not in visited_buffers:
+                buffers.append(buffer)
+                visited_buffers.add(buffer)
+    return params, buffers
+
+
+def _move_states_to_device(
+    params: list[nn.Parameter],
+    buffers: list[torch.Tensor],
+    device: torch.device,
+) -> None:
+    """
+    We have FSDP move states to device for simpler and faster initialization
+    since FSDP almost always uses CUDA for training. We move parameters/buffers
+    rather than modules since modules to support ignoring parameters/buffers in
+    the future.
+    """
+    # Follow the logic in `nn.Module._apply`
+    # pyrefly: ignore [bad-argument-type]
+    for tensor in itertools.chain(params, buffers):
+        if tensor.device == device or tensor.device.type == "meta":
+            # Keep meta-device tensors on meta device for deferred init
+            continue
+        if isinstance(tensor, DTensor):
+            if (dtensor_mesh_type := tensor.device_mesh.device_type) != device.type:
+                raise ValueError(
+                    "Requires DTensor to have mesh of the same type as the FSDP mesh "
+                    f"but got {dtensor_mesh_type} for DTensor and {device.type} for FSDP"
+                )
+            raise AssertionError(
+                f"Expects DTensor to be moved to {dtensor_mesh_type} but got {tensor.device}"
+            )
+        tensor_ = tensor
+        if is_traceable_wrapper_subclass(tensor_):
+            with torch.no_grad():  # avoid autograd increasing C++ refcount by 1
+                tensor_on_device = nn.Parameter(tensor.to(device))
+            torch.utils.swap_tensors(tensor, tensor_on_device)
+        else:
+            tensor.data = tensor.to(device)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_param.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_param.py
new file mode 100644
index 0000000000000000000000000000000000000000..476fbd94928947bc95cf13eab10b85d76e554164
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_param.py
@@ -0,0 +1,966 @@
+# mypy: allow-untyped-defs
+import inspect
+import itertools
+from collections.abc import Callable, Sequence
+from dataclasses import dataclass, field
+from enum import auto, Enum
+from typing import Any, cast, Optional
+
+import torch
+import torch.nn as nn
+from torch._prims_common import make_contiguous_strides_for
+from torch.distributed._functional_collectives import AsyncCollectiveTensor
+from torch.distributed.device_mesh import DeviceMesh
+from torch.distributed.fsdp._fully_shard._fsdp_common import DDPMeshInfo
+from torch.distributed.tensor import DTensor, Replicate, Shard
+from torch.distributed.tensor._dtensor_spec import DTensorSpec, TensorMeta
+from torch.distributed.tensor.placement_types import _StridedShard, Placement
+
+from ._fsdp_api import CPUOffloadPolicy, MixedPrecisionPolicy, OffloadPolicy
+from ._fsdp_common import (
+    _chunk_with_empty,
+    _from_local_no_grad,
+    _get_dim_chunked_size,
+    _raise_assert_with_print,
+    _to_dtype_if_needed,
+    compiled_autograd_enabled,
+    FSDPMeshInfo,
+    HSDPMeshInfo,
+)
+
+
+"""
+[Note: FSDP tensors]
+FSDP considers the following tensors:
+- Original parameter: parameter passed to :class:`FSDPParam`, i.e. the one
+  on the module when applying FSDP
+- Sharded parameter: sharding the original parameter on dim-0 (or a
+  user-specified dim) as a DTensor over the main mesh
+- All-gather inputs: the ``torch.Tensor`` or ``Tensor`` s passed to all-gather,
+  derived from the sharded parameter
+- All-gather output: the ``torch.Tensor`` or ``Tensor`` s resulting from
+  all-gathering the all-gather inputs
+- Unsharded parameter: parameter used for forward/backward computation, derived
+  from the all-gather output; autograd leaf
+
+We define these tensors to describe the general framework that can accommodate
+extensions, where:
+- all-gather-inputs = pre-all-gather-transform(sharded-parameter)
+- unsharded-parameter = post-all-gather-transform(all-gather-outputs)
+
+For the default ``torch.Tensor`` case, there is only one all-gather input, and
+it shares the same underlying tensor data as the sharded parameter, meaning
+that they can be thought of as the same tensors. The same applies for the
+all-gather output and unsharded parameter. For non-``torch.Tensor`` extensions,
+these equivalences may no longer hold due to the pre/post-all-gather
+transforms, and some may have multiple all-gather inputs/outputs (e.g.
+quantized data and scales).
+
+[Note: FSDP and autograd]
+FSDP dynamically frees and allocates the unsharded parameter. Since autograd
+can pack a reference to it or a view to save for backward, we use storage
+resizing to implement the freeing/allocation since that preserves the aliasing.
+This implies that we construct the unsharded parameter object once and write to
+it in-place thereafter. For the default ``torch.Tensor` original parameter
+case, the all-gather output and unsharded parameter share the same
+data, so we use storage resizing on the all-gather output.
+"""
+
+lib = torch.library.Library("fsdp", "FRAGMENT")  # noqa: TOR901
+
+lib.define("copy_(Tensor(a!) tensor, Tensor data) -> ()")
+
+
+@torch.library.impl(lib, "copy_", "Meta")
+@torch.library.impl(lib, "copy_", "CUDA")
+@torch.library.impl(lib, "copy_", "XPU")
+@torch.library.impl(lib, "copy_", "HPU")
+@torch.library.impl(lib, "copy_", "CPU")
+@torch.library.impl(lib, "copy_", "MTIA")
+def copy_(tensor, data):
+    tensor.copy_(data)
+
+
+"""
+[Note: Avoiding functionalization for fsdp.copy_ and inductor.resize_storage_bytes_]
+
+Currently we don't functionalize `fsdp.copy_` op or `inductor.resize_storage_bytes_` op
+(i.e. they show up as a mutation op in the middle of the AOT joint graph).
+
+Reason:
+Traceable FSDP2 compiled autograd BWD graph have the following traits:
+(1) Two inputs of the graph were aliased to each other (one from hook closed-over tensors, one from FWD saved tensors).
+(2) One of them is mutated (copy_ and resize_ to handle the all-gathered param).
+(3) They are both subclasses.
+The combination of these traits is not supported by AOTAutograd (it's difficult to reason about subclass aliasing).
+So this doesn't work at all for Traceable FSDP2.
+
+The compromise we use is to avoid functionalization for the FSDP2 copy_ and resize_ ops.
+This avoids the problem above, because from AOTAutograd point-of-view there are no mutations
+that functionalization needs to handle. (Although we need to be careful not to DCE those mutable ops.)
+
+We can avoid this functionalization because:
+(1) The nn.Parameter is never used before its .copy_() is called in eager code (i.e. no alias of it is created),
+so it's safe to call .copy_() in the middle of the graph to update its content and start using the nn.Parameter downstream.
+(2) We always re-allocate the buffer for nn.Parameter to store the AllGather output and to be used in downstream user ops.
+So calling resize-to-0 in the middle of the graph to free nn.Parameter memory after use should always be okay
+(since we always allocate anew next time we need it, we strictly don't need to keep the old tensor storage around anymore).
+
+Q: Wouldn't the extra resize_ and copy_ ops hurt both memory usage and performance?
+A: Yes it would. As an optimization, we have an Inductor post-grad FX pass to remove those resize_ and copy_ ops
+for unsharded params that have this pattern: resize_(full) -> copy_ -> resize_(0).
+
+TODO:
+Now that we are maintaining the invariant of "no aliased + mutated graph inputs" in both the forward and backward,
+it is now more feasible to functionalize all of the mutable FSDP ops. Some of the pros and cons are:
+
+Cons (of functionalizing those ops):
+(1) By not functionalizing them as we are today, we are making it more likely that they will run at the "correct" time
+in the generated code. If we start to functionalize them, we will need to make sure that Inductor reinplaces them
+in a way where it properly moves the mutations back to exactly where they should have run, or we risk suffering worse
+peak memory than eager. (We probably already need to do something similar in Inductor's reinplacing for copy_:
+https://github.com/pytorch/pytorch/issues/135305#issuecomment-2334888089)
+
+Pros (of functionalizing):
+(1) Better safety, we don't need to worry about the graph passes in inductor/partitioning handling input mutations
+mid-graph quite as much (to be fair we've already done some amount of auditing, but we might have to do some more).
+(2) Better perf: each mutation midway through the graph prevents Inductor from pattern matching across it.
+But maybe there are few enough mutations induced by FSDP for this to matter.
+"""
+
+
+@torch.library.impl(lib, "copy_", "Functionalize")
+def copy__functionalize(tensor, data):
+    torch._sync(tensor)
+    torch._sync(data)
+    tensor_inner = torch._from_functional_tensor(tensor)
+    data_inner = torch._from_functional_tensor(data)
+    with torch._C._ExcludeDispatchKeyGuard(
+        torch._C.DispatchKeySet(torch._C.DispatchKey.Functionalize)
+    ):
+        torch.ops.fsdp.copy_.default(tensor_inner, data_inner)
+
+
+torch.fx.node.has_side_effect(torch.ops.fsdp.copy_.default)
+
+
+class ShardedState(Enum):
+    """
+    - ``SHARDED``: The sharded parameter is registered to the module. It is the
+      only contributor to parameter memory.
+    - ``SHARDED_POST_FORWARD``: The unsharded parameter is resharded to a
+      smaller world size. Since this data should not be used for computation,
+      we do not register it to the module. Users should reshard the module
+      before any in-place modifications. Both it and the sharded parameter
+      contribute to parameter memory.
+    - ``UNSHARDED``: The unsharded parameter is registered to the module. Both
+      it and the sharded parameter contribute to parameter memory.
+    """
+
+    SHARDED = auto()
+    SHARDED_POST_FORWARD = auto()
+    UNSHARDED = auto()
+
+
+@dataclass
+class ParamModuleInfo:
+    """
+    For a parameter, this stores the module and the parameter name to be able
+    to do a parameter swap via ``setattr(module, param_name, ...)`` or to get
+    the parameter via ``getattr(module, param_name)``. We additionally save
+    shared modules and shared parameter names to update them accordingly.
+    """
+
+    # Parameter names are unprefixed, e.g. "weight", not "lin.weight"
+    module: nn.Module
+    param_name: str
+    shared_modules: list[nn.Module] = field(default_factory=list)
+    shared_param_names: list[str] = field(default_factory=list)
+
+
+@dataclass
+class ExtensionsData:
+    # User-defined metadata passed from pre to post-all-gather
+    all_gather_metadata: Optional[Any] = None
+    # Save the all-gather input sizes to unflatten the all-gather outputs to ND
+    all_gather_input_sizes: Sequence[torch.Size] = ()  # ND
+
+    def clear(self):
+        self.all_gather_metadata = None
+        self.all_gather_input_sizes = ()
+
+
+class FSDPParam:
+    """
+    This class manages a parameter with FSDP or FSDP variants applied,
+    implementing dim-0 per-parameter sharding.
+    """
+
+    orig_dtype: torch.dtype
+    param_dtype: Optional[torch.dtype]
+    reduce_dtype: Optional[torch.dtype]
+    _orig_size: torch.Size  # ND
+    sharded_size: torch.Size  # ND
+    contiguous_sharded_stride: tuple[int, ...]
+    padded_sharded_param_size: torch.Size  # ND
+    sharded_post_forward_size: torch.Size  # ND
+    contiguous_sharded_post_forward_stride: tuple[int, ...]
+    _sharded_param_data: torch.Tensor  # 1D
+    sharded_param: nn.Parameter  # ND
+    _sharded_post_forward_param_data: Optional[torch.Tensor]  # 1D
+    _sharded_post_forward_param: Optional[nn.Parameter]  # ND
+    _unsharded_param: nn.Parameter  # ND
+    unsharded_accumulated_grad: Optional[torch.Tensor]  # ND
+    _sharding_spec: DTensorSpec
+    # DTensor attributes (only defined for DTensor `param`):
+    _tp_spec: DTensorSpec
+    all_gather_outputs: list[torch.Tensor]  # 1D
+    # All-gather extension attributes
+    _extensions_data: ExtensionsData
+    _unsharded_inner_tensors: list[torch.Tensor]
+
+    def __init__(
+        self,
+        param: nn.Parameter,
+        module_info: ParamModuleInfo,
+        mesh_info: FSDPMeshInfo,
+        post_forward_mesh_info: Optional[FSDPMeshInfo],
+        device: torch.device,
+        shard_placement_fn: Optional[Callable[[nn.Parameter], Optional[Shard]]],
+        mp_policy: MixedPrecisionPolicy,
+        offload_policy: OffloadPolicy,
+    ):
+        self._module_info: ParamModuleInfo = module_info
+        self.mesh_info = mesh_info
+        self.post_forward_mesh_info = post_forward_mesh_info
+        # pyrefly: ignore [read-only]
+        self.device = device
+        self.mp_policy = mp_policy
+        self.offload_to_cpu: bool = isinstance(offload_policy, CPUOffloadPolicy)
+        self.pin_memory = (
+            self.offload_to_cpu and cast(CPUOffloadPolicy, offload_policy).pin_memory
+        )
+        self.grad_offload_event: Optional[torch.Event] = None
+        self._init_sharded_param(param, device, shard_placement_fn)
+        if self.post_forward_mesh_info:
+            self._init_sharded_post_forward_param_metadata(param)
+        self._init_extensions()
+        self.all_gather_outputs: list[torch.Tensor] = []
+        self.unsharded_accumulated_grad = None
+        self._param_fqn: Optional[str] = None  # prefixed from root module
+        # TODO: Remove this padding logic once DTensor pads the local tensor:
+        # https://github.com/pytorch/pytorch/issues/113045
+        self._post_load_hook_handle = (
+            module_info.module.register_load_state_dict_post_hook(
+                lambda *args, **kwargs: self.reset_sharded_param()
+            )
+        )
+
+    @torch.no_grad()
+    def _init_sharded_param(
+        self,
+        param: nn.Parameter,
+        device: torch.device,
+        shard_placement_fn: Optional[Callable],
+    ):
+        if param.device != device and param.device.type != "meta":
+            raise AssertionError(
+                f"Expects the parameter to already be moved to device {device} but got {param.device}"
+            )
+        if not param.is_contiguous():
+            raise NotImplementedError(
+                f"FSDP does not support non-contiguous parameters yet: {param.shape=} {param.stride()=}"
+            )
+        fsdp_placement = shard_placement_fn(param) if shard_placement_fn else None
+        if fsdp_placement is None:
+            fsdp_placement = Shard(0)
+        elif fsdp_placement.dim < 0:
+            fsdp_placement = Shard(fsdp_placement.dim + param.ndim)
+        if not isinstance(fsdp_placement, Shard):
+            raise AssertionError(
+                f"Expected Shard, got {type(fsdp_placement)}: {fsdp_placement}"
+            )
+        self.fsdp_placement = fsdp_placement
+        shard_dim = fsdp_placement.dim
+        # TODO: Replace the sharded DTensor parameter construction logic with
+        # `distribute_tensor` after https://github.com/pytorch/pytorch/issues/116101
+        # TODO: Simplify the following sharded parameter padding logic after
+        # https://github.com/pytorch/pytorch/issues/113045
+        self.is_dtensor = isinstance(param, DTensor)
+        if self.is_dtensor:
+            self._tp_spec = cast(DTensor, param)._spec
+            dp_mesh, tp_mesh = (self.mesh_info.mesh, self._tp_spec.mesh)
+            if dp_mesh is None or tp_mesh is None:
+                raise AssertionError(
+                    "FSDP requires the DP and model parallel TP/EP mesh to be not None but got: \n"
+                    f"DP's mesh: {dp_mesh}\nTP/EP's mesh: {tp_mesh}"
+                )
+            self._spmd_mesh = DeviceMesh._concatenate([dp_mesh, tp_mesh])
+            if len(self._tp_spec.placements) > 2:
+                raise NotImplementedError(
+                    f"FSDP only supports 1D TP/EP or 2D EP+TP, not {self._tp_spec.placements}"
+                )
+            split_factor = self._tp_spec.num_shards_map[shard_dim]
+            if not (2 <= self._spmd_mesh.ndim <= 4):
+                raise AssertionError(
+                    "_spmd_mesh.ndim can only be 2 (FSDP+TP/EP), 3 (FSDP+EP+TP, HSDP+TP/EP), "
+                    f"or 4 (HSDP+EP+TP) but got {self._spmd_mesh.ndim}."
+                )
+            self._spmd_placements: tuple[Placement, ...]
+            if isinstance(self.mesh_info, FSDPMeshInfo):  # FSDP or HSDP
+                dp_shard_tp_placement = (
+                    (
+                        _StridedShard(shard_dim, split_factor=split_factor)
+                        if split_factor > 1
+                        else fsdp_placement
+                    ),
+                    *self._tp_spec.placements,
+                )
+            else:  # DDP
+                dp_shard_tp_placement = (
+                    (Replicate()),
+                    *self._tp_spec.placements,
+                )
+            if isinstance(self.mesh_info, HSDPMeshInfo):  # HSDP
+                if self.mesh_info.replicate_mesh_dim != 0:
+                    raise AssertionError(
+                        f"Expected replicate_mesh_dim to be 0, got {self.mesh_info.replicate_mesh_dim}"
+                    )
+                self._spmd_placements = (Replicate(),) + dp_shard_tp_placement
+            else:  # FSDP or DDP
+                self._spmd_placements = dp_shard_tp_placement
+
+            self._sharding_spec = DTensorSpec(
+                self._spmd_mesh,
+                self._spmd_placements,
+                tensor_meta=self._tp_spec.tensor_meta,
+            )
+            param_data = cast(DTensor, param)._local_tensor
+        else:
+            self._spmd_mesh = self.mesh_info.mesh
+            if isinstance(self.mesh_info, HSDPMeshInfo):  # HSDP
+                self._spmd_placements = (Replicate(), fsdp_placement)
+            elif isinstance(self.mesh_info, FSDPMeshInfo):  # FSDP
+                self._spmd_placements = (fsdp_placement,)
+            elif isinstance(self.mesh_info, DDPMeshInfo):  # DDP
+                self._spmd_placements = (Replicate(),)
+            self._sharding_spec = DTensorSpec(
+                self._spmd_mesh,
+                self._spmd_placements,
+                tensor_meta=TensorMeta(param.size(), param.stride(), param.dtype),
+            )
+            param_data = param
+        if not param_data.is_contiguous():
+            raise AssertionError(
+                f"Expected contiguous tensor, got {param_data.shape=} {param_data.stride()=}"
+            )
+        shard_dim = fsdp_placement.dim
+        if shard_dim >= param_data.ndim:
+            raise AssertionError(
+                f"Shard dim {shard_dim} is invalid for {param_data.ndim}D tensor: {param.shape}"
+            )
+        self._orig_size = param_data.size()
+        self._contiguous_orig_stride = make_contiguous_strides_for(self._orig_size)
+        if isinstance(self.mesh_info, FSDPMeshInfo):  # FSDP or HSDP
+            shard_rank = self.mesh_info.shard_mesh_rank
+            shard_world_size = self.mesh_info.shard_mesh_size
+        else:  # DDP
+            shard_rank = 0
+            shard_world_size = 1
+
+        if shard_dim > 0 and param_data.size(shard_dim) % shard_world_size != 0:
+            # If sharding on nonzero dim, require even sharding for now because
+            # the uneven sharding (1) requires extra copies before/after FSDP
+            # collectives and (2) introduces extra complexity to handle padding
+            # and unpadding
+            raise NotImplementedError(
+                f"FSDP does not support uneven sharding on dim {shard_dim}: "
+                f"{param_data.size()} (world size: {shard_world_size})"
+            )
+        chunks = _chunk_with_empty(param_data, shard_world_size, dim=shard_dim)
+        sharded_param = chunks[shard_rank]
+        self.sharded_size = _get_dim_chunked_size(
+            sharded_param, param_data.size(), dim=shard_dim
+        )
+        self.contiguous_sharded_stride = make_contiguous_strides_for(self.sharded_size)
+        padded_sharded_size = chunks[0].size()  # 0th always padded
+        self.padded_sharded_param_size = padded_sharded_size
+        # Pre-pad the sharded parameter to avoid padding before all-gather
+        padded_sharded_param = param_data.new_zeros(padded_sharded_size)
+        if sharded_param.numel() > 0:
+            padded_sharded_param.narrow(
+                dim=shard_dim, start=0, length=sharded_param.size(shard_dim)
+            ).copy_(sharded_param)
+        if self.offload_to_cpu and not padded_sharded_param.is_meta:
+            padded_sharded_param = padded_sharded_param.cpu()
+            if self.pin_memory:
+                padded_sharded_param = padded_sharded_param.pin_memory()
+        self._sharded_param_data = padded_sharded_param.view(-1)
+        length = sharded_param.size(shard_dim) if sharded_param.numel() > 0 else 0
+        sharded_param = padded_sharded_param.narrow(
+            dim=shard_dim, start=0, length=length
+        )
+        if not sharded_param.is_contiguous():
+            raise AssertionError(
+                f"Expected contiguous tensor with {self.fsdp_placement=}"
+            )
+        self.sharded_param = nn.Parameter(self.to_sharded_dtensor(sharded_param))
+        self.sharded_param.requires_grad_(param.requires_grad)
+        # Let `param_data` be freed normally when its ref count reaches 0 when
+        # the `fully_shard` call returns to allow provided parameters to alias
+        self._setattr_on_modules(self.sharded_param)
+        self.sharded_state = ShardedState.SHARDED
+
+    def _init_sharded_post_forward_param_metadata(self, param: torch.Tensor) -> None:
+        mesh_info = self.post_forward_mesh_info
+        if mesh_info is None:
+            raise AssertionError("Expected post_forward_mesh_info to not be None")
+        param_data = param._local_tensor if isinstance(param, DTensor) else param
+        if isinstance(mesh_info, FSDPMeshInfo):
+            chunks = _chunk_with_empty(param_data, mesh_info.shard_mesh_size, dim=0)
+            self.sharded_post_forward_size = _get_dim_chunked_size(
+                chunks[mesh_info.shard_mesh_rank],
+                param_data.size(),
+                dim=self.fsdp_placement.dim,
+            )
+        else:  # DDP
+            chunks = _chunk_with_empty(param_data, 1, dim=0)
+            self.sharded_post_forward_size = _get_dim_chunked_size(
+                chunks[0],
+                param_data.size(),
+                dim=self.fsdp_placement.dim,
+            )
+        self.contiguous_sharded_post_forward_stride = make_contiguous_strides_for(
+            self.sharded_post_forward_size
+        )
+
+    def init_dtype_attrs(self, mp_policy: MixedPrecisionPolicy):
+        param_dtype, reduce_dtype = (mp_policy.param_dtype, mp_policy.reduce_dtype)
+        self.orig_dtype = self.sharded_param.dtype
+        # Clamp `reduce_dtype` to `None` if no casting is required: since
+        # gradients are computed in `param_dtype`, if `reduce_dtype` matches,
+        # then we do not need extra casting
+        if reduce_dtype == param_dtype:
+            reduce_dtype = None
+        # Clamp `param_dtype` to `None` if no casting is required
+        if param_dtype == self.orig_dtype:
+            param_dtype = None
+        self.param_dtype = param_dtype
+        self.reduce_dtype = reduce_dtype
+        # None indicates that the mixed precision is not enabled
+
+    def _init_extensions(self) -> None:
+        inner_tensor = self._sharded_local_tensor
+        has_fsdp_pre_all_gather = hasattr(inner_tensor, "fsdp_pre_all_gather")
+        has_fsdp_post_all_gather = hasattr(inner_tensor, "fsdp_post_all_gather")
+        if has_fsdp_pre_all_gather != has_fsdp_post_all_gather:
+            raise AssertionError(
+                "Both fsdp_pre_all_gather and fsdp_post_all_gather should be defined "
+                f"if using all-gather extensions: {inner_tensor}"
+            )
+        if has_fsdp_pre_all_gather:
+            self._extensions_data = ExtensionsData()
+        self._unsharded_inner_tensors: list[torch.Tensor] = []
+
+    def init_all_gather_outputs(
+        self,
+        all_gather_input_numels: list[int],
+        all_gather_input_dtypes: list[torch.dtype],
+        world_size: int,
+        device: torch.device,
+        force_recreate: bool = False,
+    ):
+        if not force_recreate and len(self.all_gather_outputs) > 0:
+            return  # already initialized
+        self.all_gather_outputs = [
+            torch.empty(torch.Size([numel * world_size]), dtype=dtype, device=device)
+            for numel, dtype in zip(all_gather_input_numels, all_gather_input_dtypes)
+        ]
+
+    def init_unsharded_param(self):
+        """
+        [Note: Invariants for torch.compile Traceable FSDP2]
+        1. Under compile, we always re-populate the content of `self._unsharded_param`
+           per AllGather using the slow path.
+        2. Under compile, we always recreate `self.all_gather_outputs` per AllGather.
+           This is to ensure the buffer creation is internal to the graph and
+           avoid `self.all_gather_outputs` being captured as a graph input.
+        3. Under compile, at the end of `free_unsharded_param()`, we always clean up
+           `self.all_gather_outputs` and `self._unsharded_inner_tensors`,
+           to avoid them being captured as graph output.
+
+        With these invariants, only these tensors will be inputs to the graph:
+        - Sharded parameters
+        - Placeholders for the `self._unsharded_param` nn.Parameter
+        """
+        if not compiled_autograd_enabled() and hasattr(
+            self, "_unsharded_param"
+        ):  # after the 1st all-gather
+            inner_tensor = self._sharded_local_tensor
+            if not hasattr(inner_tensor, "fsdp_post_all_gather"):
+                return  # already initialized
+            for tensor in self._unsharded_inner_tensors:
+                alloc_storage(tensor)
+            all_gather_outputs = self._unflatten_all_gather_outputs()
+            inner_tensor.fsdp_post_all_gather(
+                all_gather_outputs,
+                self._extensions_data.all_gather_metadata,
+                self.param_dtype or self.orig_dtype,
+                out=self._unsharded_param,
+            )
+            self._extensions_data.clear()
+            return
+        inner_tensor = self._sharded_local_tensor
+        if not compiled_autograd_enabled() and hasattr(
+            inner_tensor, "fsdp_post_all_gather"
+        ):
+            all_gather_outputs = self._unflatten_all_gather_outputs()
+            (
+                unsharded_tensor,
+                self._unsharded_inner_tensors,
+            ) = inner_tensor.fsdp_post_all_gather(
+                all_gather_outputs,
+                self._extensions_data.all_gather_metadata,
+                self.param_dtype or self.orig_dtype,
+            )
+            self._extensions_data.clear()
+        else:
+            # For the default path (no post-all-gather), the all-gather output
+            # gives the unsharded parameter data directly
+            if len(self.all_gather_outputs) != 1:
+                raise AssertionError(
+                    f"Expected 1 all_gather_output, got {len(self.all_gather_outputs)}"
+                )
+            unsharded_tensor = self.all_gather_outputs[0]
+        unsharded_param = torch.as_strided(
+            unsharded_tensor,
+            self._orig_size,
+            self._contiguous_orig_stride,
+            storage_offset=0,
+        )
+        if self.is_dtensor:
+            unsharded_param = _from_local_no_grad(unsharded_param, self._tp_spec)
+        if hasattr(self, "_unsharded_param"):
+            if not compiled_autograd_enabled():
+                raise AssertionError("Expected compiled_autograd to be enabled")
+            with (
+                torch.no_grad(),
+                torch.autograd._unsafe_preserve_version_counter(self._unsharded_param),
+            ):
+                # NOTE: Under compile, if an unsharded param goes through
+                # resize_(full) -> copy_ -> resize_(0) pattern, we will remove those
+                # resize_ and copy_ ops in a compiler graph pass
+                # `remove_fsdp2_unsharded_param_graph_input_usage` to recover performance.
+                self._unsharded_param.untyped_storage().resize_(
+                    self._unsharded_param.numel() * self._unsharded_param.itemsize
+                )
+                torch.ops.fsdp.copy_(self._unsharded_param, unsharded_param)
+        else:
+            self._unsharded_param = nn.Parameter(
+                unsharded_param, requires_grad=self.sharded_param.requires_grad
+            )
+
+    def _unflatten_all_gather_outputs(self) -> tuple[torch.Tensor, ...]:
+        return tuple(
+            t.view(-1, *s[1:])
+            for t, s in zip(
+                self.all_gather_outputs, self._extensions_data.all_gather_input_sizes
+            )
+        )
+
+    def to_sharded(self) -> None:
+        self._setattr_on_modules(self.sharded_param)
+        self.free_unsharded_param()
+        self.sharded_state = ShardedState.SHARDED
+
+    def to_sharded_post_forward(self) -> None:
+        if self.is_dtensor:
+            raise NotImplementedError(
+                "Resharding to smaller mesh with TP is not supported yet"
+            )
+        self._assert_in_states(ShardedState.UNSHARDED)
+        if self.post_forward_mesh_info is None:
+            raise AssertionError("Expected post_forward_mesh_info to not be None")
+        if len(self.all_gather_outputs) != 1:
+            raise AssertionError(
+                f"Expected 1 all_gather_output, got {len(self.all_gather_outputs)}"
+            )
+        shard_world_size = self.post_forward_mesh_info.shard_mesh_size
+        if (numel := self.all_gather_outputs[0].numel()) % shard_world_size != 0:
+            _raise_assert_with_print(
+                f"All-gather output size ({numel}) must be divisible by the shard "
+                f"world size ({shard_world_size})"
+            )
+        shard_rank = self.post_forward_mesh_info.shard_mesh_rank
+        # pyrefly: ignore [unbound-name]
+        sharded_numel = numel // shard_world_size
+        self._sharded_post_forward_param_data = (
+            self.all_gather_outputs[0].narrow(
+                0, sharded_numel * shard_rank, sharded_numel
+            )
+        ).clone()  # clone to be able to free all-gather output
+        sharded_post_forward_tensor = torch.as_strided(
+            self._sharded_post_forward_param_data,
+            size=self.sharded_post_forward_size,
+            stride=self.contiguous_sharded_post_forward_stride,
+            storage_offset=0,
+        )
+        self._sharded_post_forward_param = nn.Parameter(
+            self.to_sharded_post_forward_dtensor(sharded_post_forward_tensor)
+        )
+        self._setattr_on_modules(self._sharded_post_forward_param)
+        self.free_unsharded_param()
+        self.sharded_state = ShardedState.SHARDED_POST_FORWARD
+
+    def to_unsharded(self) -> None:
+        # Assume that the data has been allocated and all-gathered
+        set_requires_grad_if_needed(self.sharded_param, self._unsharded_param)
+        self._setattr_on_modules(self._unsharded_param)
+        if self.sharded_state == ShardedState.SHARDED_POST_FORWARD:
+            # The data is allocated in the default stream via the post-forward
+            # reshard and must be kept alive for the next all-gather copy-in.
+            # Since we call this method after the copy-out, the data's lifetime
+            # is ensured without further synchronization.
+            self._sharded_post_forward_param = None
+            self._sharded_post_forward_param_data = None  # free
+        self.sharded_state = ShardedState.UNSHARDED
+
+    def _setattr_on_modules(self, param: nn.Parameter) -> None:
+        unsafe_setattr_param(
+            self._module_info.module, self._module_info.param_name, param
+        )
+        for shared_module, shared_param_name in zip(
+            self._module_info.shared_modules, self._module_info.shared_param_names
+        ):
+            unsafe_setattr_param(shared_module, shared_param_name, param)
+
+    def to_sharded_dtensor(self, tensor: torch.Tensor) -> DTensor:
+        """
+        Converts a local tensor representing either the sharded parameter or
+        sharded gradient to DTensor.
+        """
+        if tensor.shape != self.sharded_size:
+            _raise_assert_with_print(
+                f"Expects size {self.sharded_size} but got {tensor.shape}"
+            )
+        return _from_local_no_grad(
+            tensor,
+            self._sharding_spec,
+        )
+
+    def to_sharded_post_forward_dtensor(self, tensor: torch.Tensor) -> DTensor:
+        if tensor.shape != self.sharded_post_forward_size:
+            _raise_assert_with_print(
+                f"Expects size {self.sharded_post_forward_size} but got {tensor.shape}"
+            )
+        if not isinstance(self.post_forward_mesh_info, HSDPMeshInfo):
+            raise AssertionError(
+                f"Expected HSDPMeshInfo, got {type(self.post_forward_mesh_info)}"
+            )
+        # TODO: Prefer this DTensor to be read-only and generalize the
+        # placement once we support TP.
+        post_forward_sharding_spec = DTensorSpec(
+            self.post_forward_mesh_info.mesh,
+            (Replicate(), Shard(0)),
+            tensor_meta=self._sharding_spec.tensor_meta,
+        )
+        return _from_local_no_grad(tensor, post_forward_sharding_spec)
+
+    def to_accumulated_grad_if_needed(self) -> None:
+        # Access `_unsharded_param` to bypass the sharded state check since we
+        # prefer to reshard before upcasting the gradient to save memory
+        if (
+            self.reduce_dtype is None
+            or self._unsharded_param.grad is None
+            or self._unsharded_param.grad.dtype == self.reduce_dtype
+        ):
+            return
+        unsharded_grad = self._unsharded_param.grad
+        self._unsharded_param.grad = None
+        self.unsharded_accumulated_grad = unsharded_grad.to(self.reduce_dtype)
+
+    def accumulate_unsharded_grad_if_needed(self) -> None:
+        if (
+            self.unsharded_accumulated_grad is not None
+            and self.unsharded_param.grad is not None
+        ):
+            self.unsharded_accumulated_grad += self.unsharded_param.grad
+            self.unsharded_param.grad = None
+
+    def alloc_all_gather_outputs(self) -> None:
+        for tensor in self.all_gather_outputs:
+            alloc_storage(tensor)
+
+    def free_unsharded_param(self) -> None:
+        if compiled_autograd_enabled():
+            """
+            Assumptions under compile:
+            - `self._unsharded_param` is NOT an alias of `self.all_gather_outputs`.
+            Instead, we resize `self._unsharded_param` storage size to full and then
+            explicitly *copy* the data from `self.all_gather_outputs` to `self._unsharded_param`
+            in `init_unsharded_param()`. (For full-graph FSDP2 case, we will then remove
+            the resize_ and copy_ ops in a compiler graph pass to recover performance.)
+            - `self.all_gather_outputs` and `self._unsharded_inner_tensors` are NOT
+            graph inputs. They are created within the graph and is guaranteed to be freed
+            by the end of the graph. They don't leak outside of the graph.
+            """
+            self._unsharded_param.untyped_storage().resize_(0)
+            self.all_gather_outputs = []
+            self._unsharded_inner_tensors = []
+        else:
+            for tensor in itertools.chain(
+                self.all_gather_outputs, self._unsharded_inner_tensors
+            ):
+                free_storage(tensor)
+
+    @property
+    def all_gather_inputs(self) -> list[torch.Tensor]:  # 1D
+        self._assert_in_states(ShardedState.SHARDED, ShardedState.SHARDED_POST_FORWARD)
+        if self.sharded_state == ShardedState.SHARDED:
+            if not compiled_autograd_enabled() and hasattr(
+                self._sharded_local_tensor, "fsdp_pre_all_gather"
+            ):
+                sharded_local_tensor = self._sharded_local_tensor
+                if self.offload_to_cpu:
+                    sharded_local_tensor = sharded_local_tensor.to(
+                        self.device, non_blocking=True
+                    )
+                pre_all_gather_signature = inspect.signature(
+                    # pyrefly: ignore [missing-attribute]
+                    sharded_local_tensor.fsdp_pre_all_gather
+                )
+                num_fn_params = len(pre_all_gather_signature.parameters)
+                # Old signature only passes mesh; keep for BC for now
+                if num_fn_params not in (1, 5):
+                    raise AssertionError(
+                        f"Invalid fsdp_pre_all_gather: {pre_all_gather_signature}\n"
+                        "Expects fsdp_pre_all_gather(self, mesh: DeviceMesh, "
+                        "outer_size: torch.Size, outer_stride: tuple[int, ...], "
+                        "module: nn.Module, mp_policy: MixedPrecisionPolicy)"
+                    )
+                if num_fn_params == 1:
+                    (
+                        all_gather_inputs,
+                        self._extensions_data.all_gather_metadata,
+                        # pyrefly: ignore [missing-attribute]
+                    ) = sharded_local_tensor.fsdp_pre_all_gather(
+                        self.shard_mesh_from_root
+                    )
+                else:
+                    (
+                        all_gather_inputs,
+                        self._extensions_data.all_gather_metadata,
+                        # pyrefly: ignore [missing-attribute]
+                    ) = sharded_local_tensor.fsdp_pre_all_gather(
+                        self.shard_mesh_from_root,
+                        self._orig_size,
+                        self._contiguous_orig_stride,
+                        self._module_info.module,
+                        self.mp_policy,
+                    )
+                    if (
+                        sharded_local_tensor.size() != self.padded_sharded_param_size
+                        and any(
+                            all_gather_input.size() != self.padded_sharded_param_size
+                            for all_gather_input in all_gather_inputs
+                        )
+                    ):
+                        # NOTE: Since this error can only be raised on the
+                        # ranks that have padding, this can manifest as a NCCL
+                        # watchdog timeout, as the other ranks will not error.
+                        raise AssertionError(
+                            "When a parameter is unevenly sharded by FSDP "
+                            f"(orig size={self._orig_size}, FSDP world size={self.mesh_info.mesh.size()}), "
+                            "fsdp_pre_all_gather must return all-gather inputs with the padded sharded size "
+                            f"{self.padded_sharded_param_size} but got {[t.size() for t in all_gather_inputs]}"
+                        )
+                self._extensions_data.all_gather_input_sizes = [
+                    t.size() for t in all_gather_inputs
+                ]
+                return [t.view(-1) for t in all_gather_inputs]
+            sharded_param_data = self._sharded_param_data
+            if self.offload_to_cpu:
+                sharded_param_data = sharded_param_data.to(
+                    self.device, non_blocking=True
+                )
+            return [_to_dtype_if_needed(sharded_param_data, self.param_dtype)]
+        elif self.sharded_state == ShardedState.SHARDED_POST_FORWARD:
+            if not compiled_autograd_enabled() and hasattr(
+                self._sharded_local_tensor, "fsdp_pre_all_gather"
+            ):
+                raise NotImplementedError
+            all_gather_input = _to_dtype_if_needed(
+                cast(torch.Tensor, self._sharded_post_forward_param_data),
+                self.param_dtype,
+            )
+            return [all_gather_input]
+        return [torch.empty(0)]  # mypy
+
+    @property
+    def unsharded_param(self) -> nn.Parameter:  # ND
+        return self._unsharded_param
+
+    @property
+    def unsharded_grad_data(self) -> torch.Tensor:
+        grad = self.unsharded_param.grad
+        if grad is None:
+            raise AssertionError("Expects unsharded_param.grad to not be None")
+        return self._get_grad_inner_tensor(grad)
+
+    @property
+    def unsharded_accumulated_grad_data(self) -> torch.Tensor:
+        grad = self.unsharded_accumulated_grad
+        if grad is None:
+            raise AssertionError("Expects unsharded_accumulated_grad to not be None")
+        return self._get_grad_inner_tensor(grad)
+
+    def _get_grad_inner_tensor(self, grad: torch.Tensor) -> torch.Tensor:
+        if self.is_dtensor:
+            if isinstance(grad, AsyncCollectiveTensor):
+                grad = grad.wait()
+            if not isinstance(grad, DTensor):
+                raise AssertionError(f"Expected DTensor, got {type(grad)}")
+            placements = self._tp_spec.placements
+            if placements != grad.placements:
+                if len(self._tp_spec.placements) != len(grad.placements):
+                    raise AssertionError(
+                        f"Expected same placement length: {self._tp_spec=} {grad.placements=}"
+                    )
+                grad = grad.redistribute(placements=placements)
+            grad = grad._local_tensor
+        return grad
+
+    @property
+    def _sharded_local_tensor(self) -> torch.Tensor:
+        return cast(DTensor, self.sharded_param)._local_tensor
+
+    @property
+    def shard_mesh(self):
+        mesh = self.mesh_info.mesh
+        if mesh.ndim == 1:
+            return mesh
+        elif mesh.ndim == 2:
+            if mesh.mesh_dim_names is None:
+                raise AssertionError("Expected mesh_dim_names to not be None")
+            return mesh[mesh.mesh_dim_names[-1]]
+        raise ValueError(f"Invalid mesh: {mesh}")
+
+    @property
+    def shard_mesh_from_root(self):
+        mesh = self.mesh_info.mesh
+
+        if mesh.ndim == 1:
+            return mesh
+        else:
+            if mesh.mesh_dim_names is None:
+                raise AssertionError("Expected mesh_dim_names to not be None")
+            shard_dim_name = mesh.mesh_dim_names[-1]
+            return mesh[shard_dim_name]
+
+    def _assert_in_states(self, *states: ShardedState) -> None:
+        if self.sharded_state not in states:
+            _raise_assert_with_print(
+                f"Expects to be in one of {states}, not {self.sharded_state}"
+            )
+
+    def reset_sharded_param(self):
+        # For ops like `nn.Module._apply` or `load_state_dict(assign=True)`
+        # that change the sharded parameter tensor, we may need to re-pad the
+        # sharded local tensor and re-save the reference.
+        module_info = self._module_info
+        new_param = getattr(module_info.module, module_info.param_name)
+        if new_param is not self.sharded_param:
+            if torch.__future__.get_swap_module_params_on_conversion():
+                raise AssertionError(
+                    f"Expects swap_tensors to preserve object but got {new_param} "
+                    f"instead of {self.sharded_param}"
+                )
+            self.sharded_param = new_param
+        # pyrefly: ignore [missing-attribute]
+        local_tensor = new_param._local_tensor
+        if local_tensor.is_meta:
+            return
+        updated_local_tensor = False
+        # local_tensor can be padded twice
+        # 1st time in fully_shard(model)
+        # 2nd time in model(input) lazy_init
+        # 2nd time should be no-op if parameters remain unchanged
+        # 2nd time shouldn't be no-op if people call model.load_state_dict(...) before lazy_init
+        # this makes it possible for trainer to call `sd = model.state_dict()` before the training loop
+        # and use `sd` without calling .state_dict() per iteration
+        same_local_tensor = False
+        # TODO: need to support tensor subclass
+        if type(self._sharded_param_data) is torch.Tensor:
+            same_local_tensor = (
+                # when sharding param with shape (1, ...) over 2 ranks
+                # local_tensor on rank 1 can be size 0, data_ptr() can be 0
+                self._sharded_param_data.untyped_storage().data_ptr() > 0
+                and self._sharded_param_data.untyped_storage().data_ptr()
+                == local_tensor.untyped_storage().data_ptr()
+            )
+        padded_sharded_size = self.padded_sharded_param_size
+        shard_dim = self.fsdp_placement.dim
+        length = local_tensor.size(shard_dim) if local_tensor.numel() > 0 else 0
+        if local_tensor.size() != padded_sharded_size and not same_local_tensor:
+            if shard_dim != 0:
+                raise AssertionError(
+                    f"Shard({shard_dim}) requires even sharding: {local_tensor.size()=}"
+                )
+            padded_local_tensor = local_tensor.new_zeros(padded_sharded_size)
+            padded_local_tensor.narrow(dim=shard_dim, start=0, length=length).copy_(
+                local_tensor
+            )
+            local_tensor = padded_local_tensor
+            updated_local_tensor = True
+        if self.pin_memory and not local_tensor.is_pinned():
+            local_tensor = local_tensor.cpu().pin_memory()
+            updated_local_tensor = True
+        if not same_local_tensor:
+            self._sharded_param_data = local_tensor.view(-1)
+        if not isinstance(self.sharded_param, DTensor):
+            raise AssertionError(f"Expected DTensor, got {type(self.sharded_param)}")
+        if updated_local_tensor:
+            # Only change the local tensor object if needed
+            self.sharded_param._local_tensor = local_tensor.narrow(
+                dim=shard_dim, start=0, length=length
+            )
+            if not self.sharded_param._local_tensor.is_contiguous():
+                raise AssertionError(
+                    "Expected sharded_param._local_tensor to be contiguous"
+                )
+        self._sharding_spec = self.sharded_param._spec
+
+    def __repr__(self):
+        return f"FSDPParam(fqn={self._param_fqn}, orig_size={self._orig_size})"
+
+
+def alloc_storage(tensor: torch.Tensor) -> None:
+    size = tensor.numel() * tensor.itemsize
+    if (storage := tensor.untyped_storage()).size() != size:
+        storage.resize_(size)
+
+
+def free_storage(tensor: torch.Tensor) -> None:
+    if (storage := tensor.untyped_storage()).size() != 0:
+        storage.resize_(0)
+
+
+# NOTE: These bypass `nn.Module.__setattr__` checks, which incur non-trivial
+# CPU overhead, if the module did not override it. For FSDP, we know we do not
+# need those checks when transitioning between sharded/unsharded parameters.
+def unsafe_setattr_param(
+    module: nn.Module, param_name: str, param: nn.Parameter
+) -> None:
+    if getattr(module.__setattr__, "__func__", None) is nn.Module.__setattr__:
+        module._parameters[param_name] = param
+    else:  # slow path
+        setattr(module, param_name, param)
+
+
+def set_requires_grad_if_needed(
+    src_tensor: torch.Tensor, dst_tensor: torch.Tensor
+) -> None:
+    # Only call `requires_grad_` if needed to avoid the Python <> C++ context
+    # switch overhead
+    if src_tensor.requires_grad != dst_tensor.requires_grad:
+        dst_tensor.requires_grad_(src_tensor.requires_grad)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_param_group.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_param_group.py
new file mode 100644
index 0000000000000000000000000000000000000000..b70a5f06f4ae9b982b0f8e3a486573f79176c30b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_param_group.py
@@ -0,0 +1,901 @@
+# mypy: allow-untyped-defs
+import contextlib
+import logging
+from collections.abc import Callable
+from typing import Any, cast, NamedTuple, Optional
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+from torch.distributed.device_mesh import _get_device_handle
+from torch.distributed.fsdp._common_utils import _named_parameters_with_duplicates
+from torch.distributed.tensor import Shard
+from torch.profiler import record_function
+from torch.utils._pytree import tree_flatten, tree_unflatten
+from torch.utils.hooks import RemovableHandle
+
+from ._fsdp_api import CPUOffloadPolicy, MixedPrecisionPolicy, OffloadPolicy
+from ._fsdp_collectives import (
+    AllGather,
+    AllGatherResult,
+    DefaultAllGather,
+    DefaultReduceScatter,
+    foreach_all_gather,
+    foreach_all_gather_copy_out,
+    foreach_reduce,
+    ProcessGroupAllocAllGather,
+    ProcessGroupAllocReduceScatter,
+    ReduceScatter,
+)
+from ._fsdp_common import (
+    compiled_autograd_enabled,
+    DDPMeshInfo,
+    FSDPMeshInfo,
+    HSDPMeshInfo,
+    is_bw,
+    TrainingState,
+)
+from ._fsdp_param import alloc_storage, FSDPParam, ParamModuleInfo, ShardedState
+
+
+logger = logging.getLogger("torch.distributed.fsdp.fully_shard")
+
+_ModuleToHandleDict = dict[nn.Module, RemovableHandle]  # for state dict
+
+
+"""
+[Note: Overlapping all-gather copy-in and all-gather]
+For implicit forward prefetching, we want to overlap the next copy-in with the
+current all-gather. We do so using a separate copy-in stream. However, since
+we have the all-gather input as a view into the output, we must make sure to
+copy into different memory from the current all-gather's output. Thus, we keep
+a reference to the current all-gather's output and have the next FSDP parameter
+group free it after its copy-in. Finally, we have the last FSDP state flush the
+reference to avoid holding onto memory after forward.
+"""
+
+
+class FSDPCommContext:
+    """This has the communication state shared across FSDP states/parameter groups."""
+
+    def lazy_init(self, device: torch.device):
+        self.device_handle = _get_device_handle(device.type)
+        # Setting the all-gather/reduce-scatter streams to be higher priority
+        # can help avoid some issues where their copies in/out are delayed and
+        # block computation (this is different from high-pri NCCL streams)
+        high_priority = -1
+        # All-gather state and copy-in stream allow overlapping the next
+        # copy-in with the current all-gather in forward; copy-in overlaps with
+        # reduce-scatter in backward without the separate copy-in stream
+        self.all_gather_copy_in_stream = self.device_handle.Stream(
+            priority=high_priority
+        )
+        # All-gather stream allows overlapping next all-gather with current
+        # forward compute
+        self.all_gather_stream = self.device_handle.Stream(priority=high_priority)
+        # Reduce-scatter stream gives separate execution "thread" for post-
+        # backward logic like pre/post-gradient division and reduce-scatter
+        self.reduce_scatter_stream = self.device_handle.Stream(priority=high_priority)
+        # Run the HSDP all-reduces concurrently with all-gather/reduce-scatter
+        # since collectives use different network resources and can overlap
+        # in the typical intra-node sharding / inter-node replication case
+        self.all_reduce_stream = self.device_handle.Stream()
+        # All-gather/reduce-scatter states keep references to collective
+        # tensors produced in one stream and used in another and accompanying
+        # CUDA events for synchronization
+        self.all_gather_state: Optional[AllGatherState] = None
+        self.reduce_scatter_state: Optional[ReduceScatterState] = None
+        # Post-forward order for explicit backward prefetching
+        self.post_forward_order: list[FSDPParamGroup] = []  # will cause ref cycles
+
+    def get_all_gather_streams(
+        self, async_op: bool, training_state: TrainingState
+    ) -> tuple[torch.Stream, torch.Stream]:
+        if not async_op and training_state in (
+            TrainingState.FORWARD,
+            TrainingState.PRE_BACKWARD,
+        ):
+            # Use separate streams for implicit prefetching
+            return self.all_gather_copy_in_stream, self.all_gather_stream
+        current_stream = self.device_handle.current_stream()
+        return current_stream, current_stream
+
+
+# See [Note: Overlapping all-gather copy-in and all-gather]
+class AllGatherState(NamedTuple):
+    all_gather_result: AllGatherResult
+    event: Optional[torch.Event]  # all-gather copy-out
+
+
+class ReduceScatterState(NamedTuple):
+    reduce_scatter_input: torch.Tensor
+    event: Optional[torch.Event]  # reduce-scatter event
+
+
+class AllReduceState(NamedTuple):
+    all_reduce_input: torch.Tensor
+    event: Optional[torch.Event]  # all-reduce event
+
+
+class FSDPParamGroup:
+    """This class represents a parameter group to communicate together."""
+
+    _orig_dtype: Optional[torch.dtype]
+    _reduce_dtype: Optional[torch.dtype]
+
+    def __init__(
+        self,
+        params: list[nn.Parameter],
+        modules: tuple[nn.Module, ...],
+        mesh_info: FSDPMeshInfo,
+        post_forward_mesh_info: Optional[FSDPMeshInfo],
+        device: torch.device,
+        shard_placement_fn: Optional[Callable[[nn.Parameter], Optional[Shard]]],
+        mp_policy: MixedPrecisionPolicy,
+        offload_policy: OffloadPolicy,
+    ):
+        self.modules = modules  # permit ref cycle because 1:1 lifetime
+        param_module_infos = _get_param_module_infos(params, modules)
+
+        self.fsdp_params = [
+            FSDPParam(
+                param,
+                module_info,
+                mesh_info,
+                post_forward_mesh_info,
+                device,
+                shard_placement_fn,
+                mp_policy,
+                offload_policy,
+            )
+            for param, module_info in zip(params, param_module_infos)
+        ]
+        self.mesh_info = mesh_info
+        self.post_forward_mesh_info = post_forward_mesh_info
+        # pyrefly: ignore [read-only]
+        self.device = device
+        self.device_handle = _get_device_handle(device.type)
+        self.mp_policy = mp_policy
+        self.offload_policy = offload_policy
+        self._training_state = TrainingState.IDLE
+        # Group's sharded state always matches its parameters' sharded states
+        self._sharded_state = ShardedState.SHARDED
+        self._module_fqn: Optional[str] = None  # prefixed from root module
+        # Only consider resetting sharded parameters once in lazy init since it
+        # can incur nontrivial overhead to reset them
+        self._reset_sharded_params: bool = False
+
+        # - Hook state
+        self._module_to_pre_save_state_dict_hook_handle: _ModuleToHandleDict = {}
+        self._module_to_pre_load_state_dict_hook_handle: _ModuleToHandleDict = {}
+        self._all_reduce_hook: Optional[Callable[[torch.Tensor], None]] = None
+        self._all_gather_comm: AllGather = DefaultAllGather()
+        self._all_gather_output = torch.empty(0, device=self.device)
+        self._reduce_scatter_comm: ReduceScatter = DefaultReduceScatter()
+        # Optional stream to run the user-defined all-reduce hook in
+        # Saved here and not in the comm. context because we allow the user to
+        # specify it, possibly at construction time before lazy init
+        self._all_reduce_hook_stream: Optional[torch.cuda.Stream] = None
+
+        # - Communication and communication/computation overlap
+        self.comm_ctx = FSDPCommContext()
+        # Group's indices in the shared post-forward order
+        self._post_forward_indices: list[int] = []
+        # Whether to reduce gradients at all (whether for FSDP or HSDP)
+        self.reduce_grads: bool = True
+        # Whether to all-reduce gradients for HSDP; only used if
+        # `self.reduce_grads` is true, in which case setting this to false
+        # means reduce-scatter but no all-reduce
+        self.all_reduce_grads: bool = True
+        # Whether to reshard parameters after backward (only useful for
+        # gradient accumulation)
+        self.reshard_after_backward: bool = True
+        # Optional custom factor for the gradient reduction op (e.g. to divide
+        # by a factor other than the world size)
+        self.gradient_divide_factor: Optional[float] = None
+        # Whether reduce-scatter and all-reduce should be issued using only
+        # summations, potentially with separate pre-/post-scaling.
+        self.force_sum_reduction_for_comms: bool = False
+        # `async_op` arg used for pre-forward/pre-backward unshard; can be
+        # overridden to only do explicit prefetching and avoid inter-stream
+        # fragmentation from using separate unshard streams
+        self.unshard_async_op: bool = False
+        # Whether to unshard in backward: can be overridden by the user if the
+        # parameters in this group are not needed for backward (e.g. embedding)
+        self.unshard_in_backward: bool = True
+
+        # - CUDA events for stream synchronization
+        # Holds the all-gather output buffer, sync objects, and metadata
+        self._all_gather_result: Optional[AllGatherResult] = None
+        # Holds the reduce-scatter/all-reduce view-out CUDA event that marks the end of
+        # the group's post-backward (e.g. reduce-scatter, all-reduce and div), which
+        # should be waited on at the end of backward
+        self._post_reduce_event: Optional[torch.Event] = None
+        # Holds the reshard-after-forward CUDA event when resharding to a
+        # different world size, which should be waited on in the next unshard
+        self._reshard_after_forward_event: Optional[torch.Event] = None
+
+        # Only for HSDP, if accumulating gradients without all-reduce, save the
+        # partial reduce output (only reduce-scattered but not all-reduced)
+        self._partial_reduce_output: Optional[torch.Tensor] = None
+        # Holds the all-reduce input and all-reduce event to keep it alive
+        # until the end of backward (critical when doing bf16 reduction with
+        # fp32 parameters since the all-reduce input is allocated in the RS
+        # stream and will have no refs to it after being upcast to fp32)
+        self._all_reduce_state: Optional[AllReduceState] = None
+
+    # Initialization #
+    def _init_mp_dtypes(self) -> None:
+        for fsdp_param in self.fsdp_params:
+            fsdp_param.init_dtype_attrs(self.mp_policy)
+        trainable_params: list[FSDPParam] = [
+            p for p in self.fsdp_params if p.sharded_param.requires_grad
+        ]
+        orig_dtypes = {p.orig_dtype for p in trainable_params}
+        reduce_dtypes = {p.reduce_dtype for p in trainable_params}
+        if len(trainable_params) > 0 and len(orig_dtypes) != 1:
+            # Models may have no grad params
+            raise AssertionError(
+                f"FSDP expects uniform original parameter dtype but got {orig_dtypes}"
+            )
+        self._orig_dtype = next(iter(orig_dtypes)) if trainable_params else None
+        if len(trainable_params) > 0 and len(reduce_dtypes) != 1:
+            # This can be relaxed if we issue one reduce-scatter per reduce
+            # dtype (but we would need a way for users to specify multiple
+            # reduce dtypes)
+            raise AssertionError(
+                f"FSDP expects uniform reduce dtype but got {reduce_dtypes}"
+            )
+        self._reduce_dtype = next(iter(reduce_dtypes)) if trainable_params else None
+
+    def lazy_init(self):
+        # Lazy init should be idempotent
+        # Users may change or register parameters after construction time.
+        # For example, DoRA (https://arxiv.org/abs/2402.09353) initializes linear magnitudes based on
+        # other parameters (e.g. loaded from the state dict).
+        if not hasattr(self.comm_ctx, "device_handle"):
+            self.comm_ctx.device_handle = _get_device_handle(self.device.type)
+        if self.is_sharded and not self._reset_sharded_params:
+            for fsdp_param in self.fsdp_params:
+                fsdp_param.reset_sharded_param()
+                fsdp_param._init_extensions()  # allow monkey patch after init
+            self._reset_sharded_params = True
+        self._validate_no_meta_params()
+        self._validate_cpu_offload_params()
+        # Initialize mixed precision attributes lazily in case the user changes
+        # the parameter dtypes after construction time but before forward
+        self._init_mp_dtypes()
+        self._register_state_dict_hooks()
+
+    def set_allocate_memory_from_process_group(self, enable: bool) -> None:
+        """
+        Whether to (try to) use the ProcessGroup's allocate_tensor method for
+        the staging buffers for collective comms.
+        """
+        if not isinstance(
+            self._all_gather_comm, (DefaultAllGather | ProcessGroupAllocAllGather)
+        ):
+            raise AssertionError(
+                "cannot call set_allocate_memory_from_process_group() "
+                f"when all gather comm is custom: {self._all_gather_comm.__class__.__name__}"
+            )
+        self._all_gather_comm = (
+            ProcessGroupAllocAllGather(self._all_gather_process_group)
+            if enable
+            else DefaultAllGather()
+        )
+
+        if not isinstance(
+            self._reduce_scatter_comm,
+            (DefaultReduceScatter | ProcessGroupAllocReduceScatter),
+        ):
+            raise AssertionError(
+                "cannot call set_allocate_memory_from_process_group() "
+                f"when reduce scatter comm is custom: {self._reduce_scatter_comm.__class__.__name__}"
+            )
+        self._reduce_scatter_comm = (
+            ProcessGroupAllocReduceScatter(self._reduce_scatter_process_group)
+            if enable
+            else DefaultReduceScatter()
+        )
+
+    # Runtime #
+    def unshard(self, async_op: bool = False):
+        if self._all_gather_result is not None:  # already called, pending wait
+            return
+        if self.is_unsharded:
+            return  # no-op
+        if (
+            not self.unshard_in_backward
+            and self._training_state == TrainingState.PRE_BACKWARD
+        ):
+            return
+        if self._reshard_after_forward_event is not None:
+            # Resharded parameter data is allocated in the default stream and
+            # used in the all-gather streams
+            self._wait_all_gather_streams_on_event(self._reshard_after_forward_event)
+            self._reshard_after_forward_event = None
+
+        if isinstance(self.mesh_info, FSDPMeshInfo):
+            world_size = self._all_gather_process_group.size()
+        else:
+            world_size = 1
+        if world_size == 1:
+            # can't skip due to early return in wait_for_unshard if
+            # no self._all_gather_result
+            self._all_gather_result = AllGatherResult(
+                all_gather_output=self._all_gather_output,
+                all_gather_event=self.device_handle.Event().record(),
+                all_gather_work=None,
+                param_all_gather_input_dtypes=[],
+                param_all_gather_input_numels=[],
+                all_gather_input_split_sizes=[],
+            )
+
+            return
+
+        with record_function(self._with_fqn("FSDP::all_gather")):
+            self._all_gather_result = foreach_all_gather(
+                self.fsdp_params,
+                self._all_gather_process_group,
+                async_op,
+                *self.comm_ctx.get_all_gather_streams(async_op, self._training_state),
+                self.device,
+                self._all_gather_comm,
+            )
+
+    def wait_for_unshard(self):
+        """
+        1. In forward with implicit prefetching, to overlap the current copy-out
+        with the next all-gather, we save a reference to the current all-gather
+        result to free after the next copy-out.
+        2. Otherwise (explicit prefetching or in backward), we free the
+        all-gather result immediately after the current copy-out since we can
+        already overlap the current copy-out with the previous reduce-scatter.
+        """
+        if not self._all_gather_result:
+            return  # no preceding unshard
+        async_op = self._all_gather_result.all_gather_work is not None
+        if self._training_state == TrainingState.FORWARD:  # implicit prefetch
+            if prev_all_gather_state := self.comm_ctx.all_gather_state:
+                self._wait_all_gather_streams_on_event(prev_all_gather_state.event)
+                self.comm_ctx.all_gather_state = None  # free the all-gather result
+        if isinstance(self.mesh_info, FSDPMeshInfo):
+            world_size = self._all_gather_process_group.size()
+        else:
+            world_size = 1
+        if world_size == 1:
+            # directly initialize unsharded parameters from sharded parameters
+
+            for fsdp_param in self.fsdp_params:
+                # Use all_gather_inputs which already handles conversion to param_dtype
+                # This is consistent with the world_size > 1 path
+                all_gather_input = fsdp_param.all_gather_inputs[0]
+
+                # Make sure the all_gather_outputs has proper storage size before using it
+                # First ensure we have at least one tensor in all_gather_outputs
+                fsdp_param.init_all_gather_outputs(
+                    [all_gather_input.numel()],
+                    [all_gather_input.dtype],
+                    world_size,
+                    self.device,
+                    force_recreate=False,
+                )
+
+                tensor = fsdp_param.all_gather_outputs[0]
+                alloc_storage(tensor)
+
+                # find alternative way to check if tensor.is_inference
+                with torch.autograd._unsafe_preserve_version_counter(tensor):
+                    tensor.copy_(all_gather_input)
+
+        else:
+            with record_function(self._with_fqn("FSDP::all_gather_copy_out")):
+                foreach_all_gather_copy_out(
+                    self._all_gather_result,
+                    self.fsdp_params,
+                    self._all_gather_process_group,
+                )
+
+        for fsdp_param in self.fsdp_params:
+            fsdp_param.init_unsharded_param()
+
+        self._to_unsharded()
+        all_gather_copy_out_event = self.device_handle.Event()
+        all_gather_copy_out_event.record()
+
+        if (
+            not async_op
+            and self._training_state == TrainingState.FORWARD
+            and world_size > 1
+        ):
+            # Defer free to allow for overlap of this copy-out with next
+            # all-gather collective
+            self.comm_ctx.all_gather_state = AllGatherState(
+                self._all_gather_result, all_gather_copy_out_event
+            )
+        else:
+            self._wait_all_gather_streams_on_event(all_gather_copy_out_event)
+
+        self._all_gather_result = None  # free unless saved in `all_gather_state`
+
+    def _wait_all_gather_streams_on_event(self, event: Optional[torch.Event]):
+        # Calling `unshard` before lazy init means streams are not initialized
+        if hasattr(self.comm_ctx, "all_gather_copy_in_stream") and event is not None:
+            self.comm_ctx.all_gather_copy_in_stream.wait_event(event)
+        if hasattr(self.comm_ctx, "all_gather_stream") and event is not None:
+            self.comm_ctx.all_gather_stream.wait_event(event)
+
+    def reshard(self):
+        if self._training_state == TrainingState.FORWARD:
+            if not self._reshard_after_forward:
+                return
+            if self._use_post_forward_mesh:
+                self._to_sharded_post_forward()
+                self._reshard_after_forward_event = self.device_handle.Event()
+                if self._reshard_after_forward_event is not None:
+                    self._reshard_after_forward_event.record()
+                return
+        self._to_sharded()
+
+    def pre_forward(
+        self, module: nn.Module, args: tuple[Any, ...], kwargs: dict[str, Any]
+    ) -> tuple[tuple[Any, ...], dict[str, Any]]:
+        if not compiled_autograd_enabled():
+            logger.debug("%s", self._with_fqn("FSDP::pre_forward"))
+        with record_function(self._with_fqn("FSDP::pre_forward")):
+            self._training_state = TrainingState.FORWARD
+            self.unshard(self.unshard_async_op)
+            self.wait_for_unshard()
+            args, kwargs = self._register_post_backward_hook(args, kwargs)
+            return args, kwargs
+
+    def post_forward(self, module: nn.Module, input: Any, output: Any):
+        if not compiled_autograd_enabled():
+            logger.debug("%s", self._with_fqn("FSDP::post_forward"))
+        with record_function(self._with_fqn("FSDP::post_forward")):
+            if not compiled_autograd_enabled():
+                # for AC(fully_shard(model)), AC runs fsdp's _pre_forward
+                # it shouldn't change post_forward_order
+                if not is_bw():
+                    self.reshard()
+                    self._record_post_forward()
+            else:
+                self.reshard()
+                self._record_post_forward()
+            self._training_state = TrainingState.IDLE
+            return output
+
+    def _record_post_forward(self) -> None:
+        # Since a group has one pre-backward unshard for each forward call
+        # before the backward, we record each usage (with multiplicity)
+        post_forward_index = len(self.comm_ctx.post_forward_order)
+        self.comm_ctx.post_forward_order.append(self)
+        self._post_forward_indices.append(post_forward_index)
+
+    def pre_backward(self, default_prefetch: bool, *unused: Any):
+        if (
+            compiled_autograd_enabled()
+            and self._training_state == TrainingState.PRE_BACKWARD
+        ):
+            # Traceable FSDP2 cannot trigger the param group's `post_backward` immediately after param usage;
+            # instead it relies on this to trigger the previously unexecuted `post_backward`.
+            self.post_backward()
+        if self._training_state == TrainingState.PRE_BACKWARD:
+            return
+        if not compiled_autograd_enabled():
+            logger.debug("%s", self._with_fqn("FSDP::pre_backward"))
+        with record_function(self._with_fqn("FSDP::pre_backward")):
+            self._training_state = TrainingState.PRE_BACKWARD
+            self.unshard(self.unshard_async_op)  # no-op if prefetched
+            self.wait_for_unshard()
+            if default_prefetch and not compiled_autograd_enabled():
+                self._backward_prefetch()
+
+    def post_backward(self, *unused: Any):
+        # This method should be idempotent and safe to call even when this
+        # FSDP parameter group was not used in backward (should be a no-op)
+        if not compiled_autograd_enabled():
+            logger.debug("%s", self._with_fqn("FSDP::post_backward"))
+        self._training_state = TrainingState.POST_BACKWARD
+        with record_function(self._with_fqn("FSDP::post_backward_accumulate")):
+            for fsdp_param in self.fsdp_params:
+                fsdp_param.accumulate_unsharded_grad_if_needed()
+        with record_function(self._with_fqn("FSDP::post_backward_reshard")):
+            if not self.reduce_grads:
+                if self.reshard_after_backward:
+                    self.reshard()
+                for fsdp_param in self.fsdp_params:
+                    fsdp_param.to_accumulated_grad_if_needed()
+                return
+            # Save the autograd-computed gradients before resharding to only
+            # access the unsharded parameters when their data is present
+            fsdp_params_with_grad: list[FSDPParam] = []
+            unsharded_grads: list[torch.Tensor] = []
+            for fsdp_param in self.fsdp_params:
+                if not hasattr(fsdp_param, "_unsharded_param"):
+                    continue
+                # May have an accumulated gradient of the reduce dtype if the
+                # previous backward did not reduce-scatter
+                if fsdp_param.unsharded_accumulated_grad is not None:
+                    fsdp_params_with_grad.append(fsdp_param)
+                    unsharded_grads.append(fsdp_param.unsharded_accumulated_grad_data)
+                    fsdp_param.unsharded_accumulated_grad = None
+                elif fsdp_param.unsharded_param.grad is not None:
+                    fsdp_params_with_grad.append(fsdp_param)
+                    unsharded_grads.append(fsdp_param.unsharded_grad_data)
+                    fsdp_param.unsharded_param.grad = None
+            if self.reshard_after_backward:
+                self.reshard()
+        if len(fsdp_params_with_grad) == 0:
+            return
+        with record_function(self._with_fqn("FSDP::post_backward_reduce")):
+            if (
+                self.comm_ctx.reduce_scatter_state is not None
+                and self.comm_ctx.reduce_scatter_state.event is not None
+            ):
+                self.device_handle.current_stream().wait_event(
+                    self.comm_ctx.reduce_scatter_state.event
+                )
+            self.comm_ctx.reduce_scatter_state = None
+            all_reduce_pg = (
+                self._all_reduce_process_group
+                if isinstance(self.mesh_info, DDPMeshInfo)
+                else None
+            )
+            all_reduce_stream: torch.cuda.Stream
+            if all_reduce_pg is None and self._all_reduce_hook_stream is not None:
+                # this means the native HSDP is not enabled,
+                # but user may want to have a custom HSDP setup
+                if self._all_reduce_hook is None:
+                    raise AssertionError(
+                        "all reduce hook stream is specified but hook itself is missing."
+                    )
+                all_reduce_stream = self._all_reduce_hook_stream
+            else:
+                all_reduce_stream = self.comm_ctx.all_reduce_stream
+
+            self._wait_for_post_backward()
+            (
+                reduce_scatter_input,
+                reduce_scatter_event,
+                self._post_reduce_event,
+                all_reduce_input,
+                all_reduce_event,
+                self._partial_reduce_output,
+            ) = foreach_reduce(
+                fsdp_params_with_grad,
+                unsharded_grads,
+                (
+                    self._reduce_scatter_process_group
+                    if isinstance(self.mesh_info, FSDPMeshInfo)
+                    else None  # pyre-fixme[6]
+                ),
+                self.comm_ctx.reduce_scatter_stream,
+                self._reduce_scatter_comm,
+                self._orig_dtype,
+                self._reduce_dtype,
+                self.device,
+                self.gradient_divide_factor,
+                (
+                    self._all_reduce_process_group
+                    if isinstance(self.mesh_info, DDPMeshInfo)
+                    else None
+                ),
+                all_reduce_stream,
+                self.all_reduce_grads,
+                self._partial_reduce_output,
+                self._all_reduce_hook,
+                self.force_sum_reduction_for_comms,
+            )
+            self.comm_ctx.reduce_scatter_state = ReduceScatterState(
+                reduce_scatter_input, reduce_scatter_event
+            )
+            if all_reduce_input is not None:
+                if self.device.type != "cpu":
+                    if all_reduce_event is None:
+                        raise AssertionError(
+                            "Expected all_reduce_event to be set for non-CPU device"
+                        )
+                self._all_reduce_state = AllReduceState(
+                    all_reduce_input, all_reduce_event
+                )
+
+    def finalize_backward(self):
+        self._wait_for_post_backward()
+        for fsdp_param in self.fsdp_params:
+            if fsdp_param.grad_offload_event is not None:
+                fsdp_param.grad_offload_event.synchronize()
+                fsdp_param.grad_offload_event = None
+        if self._all_gather_result is not None:
+            # If there was a mistargeted unshard without a corresponding wait,
+            # then we wait here and clear the unshard
+            if (event := self._all_gather_result.all_gather_event) is not None:
+                torch.accelerator.current_stream().wait_event(event)
+            work = self._all_gather_result.all_gather_work
+            if isinstance(work, dist.distributed_c10d.Work):
+                work.wait()
+            self._all_gather_result = None
+        self._post_forward_indices.clear()
+
+    def _wait_for_post_backward(self):
+        if self._post_reduce_event is not None:
+            self.device_handle.current_stream().wait_event(self._post_reduce_event)
+            self._post_reduce_event = None
+        if (
+            self._all_reduce_state is not None
+            and self._all_reduce_state.event is not None
+        ):
+            self.device_handle.current_stream().wait_event(self._all_reduce_state.event)
+        self._all_reduce_state = None
+
+    def _backward_prefetch(self) -> None:
+        if self._training_state == TrainingState.PRE_BACKWARD:
+            if not self._post_forward_indices:
+                # Can be cleared if running multiple `backward`s
+                return
+            curr_index = self._post_forward_indices.pop()
+            if (target_index := curr_index - 1) < 0:
+                return
+            # Prefetch naively using the reverse post-forward order, which may
+            # have mistargeted prefetches if not all modules used in forward
+            # are used in this backward
+            # pyrefly: ignore [unbound-name]
+            target_fsdp_param_group = self.comm_ctx.post_forward_order[target_index]
+            self._prefetch_unshard(target_fsdp_param_group, "backward")
+
+    @staticmethod
+    def _prefetch_unshard(
+        target_fsdp_param_group: "FSDPParamGroup", pass_type: str
+    ) -> None:
+        if pass_type == "backward":
+            training_state = TrainingState.PRE_BACKWARD
+        elif pass_type == "forward":
+            training_state = TrainingState.FORWARD
+        else:
+            raise ValueError(f"Unknown pass type: {pass_type}")
+        target_fqn = target_fsdp_param_group._module_fqn
+        with (
+            record_function(f"FSDP::{pass_type}_prefetch for {target_fqn}"),
+            target_fsdp_param_group.use_training_state(training_state),
+        ):
+            async_op = target_fsdp_param_group.unshard_async_op
+            target_fsdp_param_group.unshard(async_op)
+
+    # Utilities #
+    def _to_sharded(self):
+        if not self.is_sharded:
+            for fsdp_param in self.fsdp_params:
+                fsdp_param.to_sharded()
+            self._sharded_state = ShardedState.SHARDED
+
+    def _to_sharded_post_forward(self):
+        if not self.is_sharded_post_forward:
+            for fsdp_param in self.fsdp_params:
+                fsdp_param.to_sharded_post_forward()
+            self._sharded_state = ShardedState.SHARDED_POST_FORWARD
+
+    def _to_unsharded(self):
+        if not self.is_unsharded:
+            for fsdp_param in self.fsdp_params:
+                fsdp_param.to_unsharded()
+            self._sharded_state = ShardedState.UNSHARDED
+
+    @property
+    def is_sharded(self) -> bool:
+        return self._sharded_state == ShardedState.SHARDED
+
+    @property
+    def is_sharded_post_forward(self) -> bool:
+        return self._sharded_state == ShardedState.SHARDED_POST_FORWARD
+
+    @property
+    def is_unsharded(self) -> bool:
+        return self._sharded_state == ShardedState.UNSHARDED
+
+    @contextlib.contextmanager
+    def use_training_state(self, training_state: TrainingState):
+        old_training_state = self._training_state
+        self._training_state = training_state
+        try:
+            yield
+        finally:
+            self._training_state = old_training_state
+
+    # Hook Registration #
+    def _register_post_backward_hook(
+        self, args: tuple[Any, ...], kwargs: dict[str, Any]
+    ) -> tuple[tuple[Any, ...], dict[str, Any]]:
+        # Traceable FSDP2 relies on `root_post_backward_callback` to call each
+        # `FSDPParamGroup.post_backward`
+        if (not torch._dynamo.config.skip_fsdp_hooks) or compiled_autograd_enabled():
+            return args, kwargs
+        if not torch.is_grad_enabled():
+            return args, kwargs
+        args_list, args_spec = tree_flatten(args)
+        kwargs_list, kwargs_spec = tree_flatten(kwargs)
+        args_kwargs_list = list(args_list) + list(kwargs_list)
+        inp_tensor_indices: list[int] = []
+        inp_tensors: list[torch.Tensor] = []
+        for i, obj in enumerate(args_kwargs_list):
+            if torch.is_tensor(obj) and obj.requires_grad:
+                inp_tensor_indices.append(i)
+                inp_tensors.append(obj)
+        if len(inp_tensors) == 0:
+            return args, kwargs  # no tensors that require gradients
+        inp_tensors = RegisterPostBackwardFunction.apply(self, *inp_tensors)
+        for inp_tensor_idx, inp_tensor in zip(inp_tensor_indices, inp_tensors):
+            args_kwargs_list[inp_tensor_idx] = inp_tensor
+        args_list = args_kwargs_list[: len(args_list)]
+        kwargs_list = args_kwargs_list[len(args_list) :]
+        args = tree_unflatten(args_list, args_spec)
+        kwargs = tree_unflatten(kwargs_list, kwargs_spec)
+        return args, kwargs
+
+    def _register_state_dict_hooks(self) -> None:
+        num_pre_save_hooks = len(self._module_to_pre_save_state_dict_hook_handle)
+        num_pre_load_hooks = len(self._module_to_pre_load_state_dict_hook_handle)
+        if num_pre_save_hooks != num_pre_load_hooks:
+            raise AssertionError(
+                f"Pre-save: {num_pre_save_hooks} pre-load: {num_pre_load_hooks}"
+            )
+        if num_pre_save_hooks > 0:
+            return  # already registered
+        modules_with_fsdp_params: set[nn.Module] = {
+            fsdp_param._module_info.module for fsdp_param in self.fsdp_params
+        }
+
+        def to_sharded_hook(*args: Any, **kwargs: Any) -> None:
+            self._to_sharded()
+
+        for module in modules_with_fsdp_params:
+            self._module_to_pre_save_state_dict_hook_handle[module] = (
+                module.register_state_dict_pre_hook(to_sharded_hook)
+            )
+            self._module_to_pre_load_state_dict_hook_handle[module] = (
+                module._register_load_state_dict_pre_hook(to_sharded_hook)
+            )
+
+    # Properties #
+    @property
+    def _reshard_after_forward(self) -> bool:
+        return self.post_forward_mesh_info is not None
+
+    @property
+    def _use_post_forward_mesh(self) -> bool:
+        return (
+            self._reshard_after_forward
+            and self.mesh_info != self.post_forward_mesh_info
+        )
+
+    @property
+    def _is_hsdp(self) -> bool:
+        return isinstance(self.mesh_info, HSDPMeshInfo)
+
+    @property
+    def _all_gather_process_group(self) -> dist.ProcessGroup:
+        mesh_info = (
+            cast(FSDPMeshInfo, self.post_forward_mesh_info)
+            if self.is_sharded_post_forward
+            else self.mesh_info
+        )
+        if not isinstance(mesh_info, FSDPMeshInfo):
+            raise AssertionError(
+                f"Expected mesh_info to be FSDPMeshInfo, got {type(mesh_info)}"
+            )
+        return mesh_info.shard_process_group
+
+    @property
+    def _reduce_scatter_process_group(self) -> dist.ProcessGroup:
+        if not isinstance(self.mesh_info, FSDPMeshInfo):
+            raise AssertionError(
+                f"Expected mesh_info to be FSDPMeshInfo, got {type(self.mesh_info)}"
+            )
+        return self.mesh_info.shard_process_group
+
+    @property
+    def _all_reduce_process_group(self) -> dist.ProcessGroup:
+        if not isinstance(self.mesh_info, DDPMeshInfo):
+            raise AssertionError(
+                f"Expected mesh_info to be DDPMeshInfo or HSDPMeshInfo, got {type(self.mesh_info)}"
+            )
+        return self.mesh_info.replicate_process_group
+
+    def _with_fqn(self, label: str) -> str:
+        if self._module_fqn:
+            return f"{label} ({self._module_fqn})"
+        return label
+
+    def __repr__(self):
+        return f"FSDPParamGroup(fqn={self._module_fqn})"
+
+    def _validate_no_meta_params(self):
+        param_names_on_meta = [
+            fsdp_param._param_fqn
+            for fsdp_param in self.fsdp_params
+            if fsdp_param.sharded_param.device.type == "meta"
+        ]
+        if param_names_on_meta:
+            raise RuntimeError(
+                "FSDP parameters should be materialized from meta device before training, "
+                f"but the following were still on meta device: {param_names_on_meta}\n"
+                "For example, call module.to_empty(device) to materialize to device and "
+                "call module.reset_parameters() on each module to initialize values."
+            )
+
+    def _validate_cpu_offload_params(self):
+        if not isinstance(self.offload_policy, CPUOffloadPolicy):
+            return
+        fsdp_params_not_on_cpu = [
+            fsdp_param
+            for fsdp_param in self.fsdp_params
+            if fsdp_param.sharded_param.device.type != "cpu"
+        ]
+        if fsdp_params_not_on_cpu:
+            raise RuntimeError(
+                "FSDP parameters should be materialized on CPU when enabling CPU offloading. "
+                'For example, load a CPU state dict or call module.to_empty(device="cpu"). '
+                "Found following parameters on non-CPU device: "
+                f"{[(fsdp_param._param_fqn, fsdp_param.sharded_param.device) for fsdp_param in fsdp_params_not_on_cpu]}\n"
+            )
+
+
+def _get_param_module_infos(
+    params: list[nn.Parameter], modules: tuple[nn.Module, ...]
+) -> list[ParamModuleInfo]:
+    """
+    Shared parameter: lin1.weight = lin2.weight
+    Shared module: mlp.lin1 = mlp.lin2
+    We do not remove duplicates when traversing both modules and parameters to
+    find shared modules' parameters and shared parameters within a module.
+    """
+    params_set = set(params)
+    param_to_module_info: dict[nn.Parameter, ParamModuleInfo] = {}
+    for module in modules:
+        for _, submodule in module.named_modules(remove_duplicate=False):
+            for param_name, param in _named_parameters_with_duplicates(
+                submodule, recurse=False
+            ):
+                if param in params_set:
+                    if param not in param_to_module_info:
+                        param_to_module_info[param] = ParamModuleInfo(
+                            submodule, param_name
+                        )
+                    else:
+                        param_to_module_info[param].shared_modules.append(submodule)
+                        param_to_module_info[param].shared_param_names.append(
+                            param_name
+                        )
+    if len(param_to_module_info) != len(params):
+        raise AssertionError(f"Some parameters are not in the module tree of {modules}")
+    return [param_to_module_info[param] for param in params]
+
+
+class RegisterPostBackwardFunction(torch.autograd.Function):
+    @staticmethod
+    def _assert_not_tracing_fsdp():
+        if compiled_autograd_enabled():
+            # TODO: Find a way to print the offending FSDP2 module.
+            msg = """\
+When Traceable FSDP2 is enabled, we should not be calling into `RegisterPostBackwardFunction`.
+Instead, we rely on the param group's next `pre_backward` hook to trigger its previously unexecuted
+`post_backward`, and we rely on FSDPState's `root_post_backward_callback` to trigger the resharding
+of any leftover unsharded param groups.
+If you are here, it means the forward part of this FSDP2 instance is not compiled, and you must also
+compile the forward part if you want to use Traceable FSDP2."""
+            torch._dynamo.comptime.comptime.print(msg)
+            raise RuntimeError(msg)
+
+    @staticmethod
+    # pyrefly: ignore [bad-override]
+    def forward(ctx, param_group: FSDPParamGroup, *inputs: torch.Tensor):
+        # All tensors in `inputs` should require gradient
+        RegisterPostBackwardFunction._assert_not_tracing_fsdp()
+        ctx.param_group = param_group
+        return inputs
+
+    @staticmethod
+    def backward(ctx, *grads: torch.Tensor):
+        RegisterPostBackwardFunction._assert_not_tracing_fsdp()
+        ctx.param_group.post_backward()
+        return (None,) + grads
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_state.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_state.py
new file mode 100644
index 0000000000000000000000000000000000000000..d68dfbf2ddcb0faaf1888fc912ba09bc599e2c5c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fsdp_state.py
@@ -0,0 +1,408 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+import functools
+import logging
+from collections.abc import Callable, Sequence
+from typing import Any, Optional, TYPE_CHECKING
+
+import torch
+import torch.nn as nn
+from torch._logging import warning_once
+from torch.autograd import Variable
+from torch.autograd.graph import _MultiHandle
+from torch.distributed._composable_state import (
+    _get_module_state,
+    _insert_module_state,
+    _State,
+)
+from torch.distributed.device_mesh import _get_device_handle
+from torch.distributed.utils import _apply_to_tensors, _to_kwargs
+from torch.utils._pytree import tree_flatten
+
+from ._fsdp_api import MixedPrecisionPolicy
+from ._fsdp_common import (
+    _cast_fp_tensor,
+    compiled_autograd_enabled,
+    detect_compiled_autograd,
+    TrainingState,
+)
+from ._fsdp_param_group import FSDPCommContext, FSDPParamGroup
+
+
+if TYPE_CHECKING:
+    from ._fsdp_param import FSDPParam
+
+
+logger = logging.getLogger("torch.distributed.fsdp.fully_shard")
+
+
+class FSDPStateContext:
+    """This has state shared across FSDP states."""
+
+    def __init__(self) -> None:
+        # All FSDP states in the root state's module tree
+        self.all_states: list[FSDPState] = []
+        # Iteration's forward root runs the once-per-forward logic; this root
+        # may not be the overall root set by lazy initialization in cases where
+        # only a submodule runs forward (e.g. encoder-only for eval)
+        self.iter_forward_root: Optional[FSDPState] = None
+        # Final callback should only be queued once per backward
+        self.post_backward_final_callback_queued: bool = False
+        # Whether to finalize backward in this backward's final callback
+        self.is_last_backward: bool = True
+        # Optional user-provided event recorded after optimizer for the
+        # all-gather streams to wait on in the root pre-forward
+        self.post_optim_event: Optional[torch.Event] = None
+
+
+def disable_if_config_true(func):
+    @functools.wraps(func)
+    def fsdp_hook_wrapper(*args, **kwargs):
+        if torch._dynamo.config.skip_fsdp_hooks:
+            return torch._dynamo.disable(
+                func,
+                recursive=True,
+                reason="skipping FSDP hooks since torch._dynamo.config.skip_fsdp_hooks is set",
+            )(*args, **kwargs)
+        else:
+            return func(*args, **kwargs)
+
+    return fsdp_hook_wrapper
+
+
+class FSDPState(_State):
+    def __init__(self) -> None:
+        super().__init__()
+        self._fsdp_param_group: Optional[FSDPParamGroup] = None
+        self._is_root: Optional[bool] = None  # root set during lazy init
+        self._state_ctx = FSDPStateContext()
+        self._comm_ctx = FSDPCommContext()
+        self._training_state: TrainingState = TrainingState.IDLE
+        self._states_to_forward_prefetch: list[FSDPState] = []
+        self._states_to_backward_prefetch: list[FSDPState] = []
+        self._modules_to_run_forward: set[nn.Module] = set()
+        # ``False`` when user set reshard_after_forward
+        # through ``fully_shard`` or ``set_reshard_after_forward``
+        self._auto_reshard_after_forward: Optional[bool] = True
+
+    # Define a separate init since `__init__` is called in the contract
+    def init(
+        self,
+        modules: tuple[nn.Module, ...],
+        device: torch.device,
+        mp_policy: MixedPrecisionPolicy,
+        auto_reshard_after_forward: bool,
+    ) -> None:
+        for module in modules:
+            _insert_module_state(module, self)
+        self._modules = modules
+        # pyrefly: ignore [read-only]
+        self._device = device
+        self._device_handle = _get_device_handle(device.type)
+        self._mp_policy = mp_policy
+        self._auto_reshard_after_forward = auto_reshard_after_forward
+        if len(modules) == 1:
+            self._pre_forward_hook_handle = modules[0].register_forward_pre_hook(
+                self._pre_forward, prepend=True, with_kwargs=True
+            )
+            self._post_forward_hook_handle = modules[0].register_forward_hook(
+                self._post_forward, prepend=False
+            )
+        else:
+            hook_handle = _register_group_forward_hooks(
+                modules,
+                self._pre_forward,
+                self._post_forward,
+                self._modules_to_run_forward,
+            )
+            self._pre_forward_hook_handle = hook_handle
+            self._post_forward_hook_handle = hook_handle
+
+    def _root_pre_forward(
+        self, module: nn.Module, args: tuple[Any, ...], kwargs: dict[str, Any]
+    ) -> tuple[tuple[Any, ...], dict[str, Any]]:
+        self._lazy_init()
+        if self._state_ctx.iter_forward_root is not None:
+            return args, kwargs
+        if not compiled_autograd_enabled():
+            logger.debug("FSDP::root_pre_forward")
+        self._state_ctx.iter_forward_root = self
+        with torch.profiler.record_function("FSDP::root_pre_forward"):
+            # Wait for optimizer before implicitly prefetched all-gathers
+            if (event := self._state_ctx.post_optim_event) is not None:
+                self._comm_ctx.all_gather_copy_in_stream.wait_event(event)
+                self._comm_ctx.all_gather_stream.wait_event(event)
+                self._state_ctx.post_optim_event = None
+            else:
+                current_stream = self._device_handle.current_stream()
+                self._comm_ctx.all_gather_copy_in_stream.wait_stream(current_stream)
+                self._comm_ctx.all_gather_stream.wait_stream(current_stream)
+            if self._device.type in [
+                "cuda",
+                "hpu",
+                "xpu",
+                "mtia",
+                torch._C._get_privateuse1_backend_name(),
+            ]:
+                with torch.profiler.record_function("FSDP::inputs_to_device"):
+                    args_tuple, kwargs_tuple = _to_kwargs(
+                        args, kwargs, self._device, False
+                    )  # same as DDP
+                args, kwargs = args_tuple[0], kwargs_tuple[0]
+        return args, kwargs
+
+    def _lazy_init(self) -> None:
+        """
+        Lazy initialization represents when all modules' parallelisms have
+        finalized (e.g. FSDP has been applied to all desired modules). This
+        means that we can determine which state is the root, and we do so by
+        the 1st state to run forward.
+        """
+        if self._is_root is not None:
+            return  # no-op: already initialized
+        self._is_root = True
+        if len(self._modules) > 1:
+            raise RuntimeError(
+                f"FSDP requires a single root module but got {self._modules}"
+            )
+        detect_compiled_autograd()
+        root_module = self._modules[0]
+        visited_states: set[FSDPState] = set()
+        for module_name, module in root_module.named_modules():
+            if (state := _get_module_fsdp_state(module)) is None:
+                continue
+            if module is not root_module:
+                if state not in visited_states and state._is_root is not None:
+                    raise RuntimeError(
+                        "FSDP state has already been lazily initialized for "
+                        f"{module_name}\nFSDP requires running forward through "
+                        "the root module first"
+                    )
+                state._is_root = False
+            self._state_ctx.all_states.append(state)
+            visited_states.add(state)
+        if self._fsdp_param_group and self._auto_reshard_after_forward:
+            # For the root, do not reshard after forward since for training,
+            # the parameters would be freed and all-gathered immediately
+            self._fsdp_param_group.post_forward_mesh_info = None
+        self._init_fqns()
+        self._init_shared_state()
+        # Run parameter group lazy inits after initializing FQNs for improved
+        # error messages
+        for state in self._state_ctx.all_states:
+            if state._fsdp_param_group:
+                state._fsdp_param_group.lazy_init()
+
+    def _init_shared_state(self) -> None:
+        self._comm_ctx.lazy_init(self._device)
+        for state in self._state_ctx.all_states:
+            state._state_ctx = self._state_ctx
+            state._comm_ctx = self._comm_ctx
+            if fsdp_param_group := state._fsdp_param_group:
+                fsdp_param_group.comm_ctx = self._comm_ctx
+
+    def _init_fqns(self) -> None:
+        """Sets module and parameter FQN attributes for debugging."""
+        if not self._is_root:
+            raise AssertionError("Expected _is_root to be True")
+        root_module = self._modules[0]
+        param_to_fsdp_param: dict[nn.Parameter, FSDPParam] = {}
+        module_to_fsdp_param_group: dict[nn.Module, FSDPParamGroup] = {}
+        for state in self._state_ctx.all_states:
+            if fsdp_param_group := state._fsdp_param_group:
+                for fsdp_param in fsdp_param_group.fsdp_params:
+                    param_to_fsdp_param[fsdp_param.sharded_param] = fsdp_param
+                for module in fsdp_param_group.modules:
+                    module_to_fsdp_param_group[module] = fsdp_param_group
+        for param_name, param in root_module.named_parameters():
+            if param in param_to_fsdp_param:
+                param_to_fsdp_param[param]._param_fqn = param_name
+        for module_name, module in root_module.named_modules():
+            if module in module_to_fsdp_param_group:
+                module_fqn = module_to_fsdp_param_group[module]._module_fqn
+                if module_fqn is None:
+                    module_to_fsdp_param_group[module]._module_fqn = module_name
+                else:
+                    if not isinstance(module_fqn, str):
+                        raise AssertionError(
+                            f"Expected module_fqn to be str, got {type(module_fqn)}: {module_fqn}"
+                        )
+                    module_fqn += f", {module_name}"
+                    module_to_fsdp_param_group[module]._module_fqn = module_fqn
+
+    @disable_if_config_true
+    def _pre_forward(
+        self, module: nn.Module, args: tuple[Any, ...], kwargs: dict[str, Any]
+    ) -> tuple[tuple[Any, ...], dict[str, Any]]:
+        # When composing with module-hook-based activation checkpointing, the
+        # pre-backward hook is responsible for the unshard
+        if self._training_state == TrainingState.PRE_BACKWARD:
+            return args, kwargs
+        self._training_state = TrainingState.FORWARD
+        args, kwargs = self._root_pre_forward(module, args, kwargs)
+        if self._mp_policy.cast_forward_inputs and self._mp_policy.param_dtype:
+            with torch.profiler.record_function("FSDP::cast_forward_inputs"):
+                cast_fn = functools.partial(
+                    _cast_fp_tensor, self._mp_policy.param_dtype
+                )
+                args, kwargs = (
+                    _apply_to_tensors(cast_fn, args),
+                    _apply_to_tensors(cast_fn, kwargs),
+                )
+        if self._fsdp_param_group:
+            args, kwargs = self._fsdp_param_group.pre_forward(module, args, kwargs)
+        for fsdp_state in self._states_to_forward_prefetch:
+            if (target_param_group := fsdp_state._fsdp_param_group) is not None:
+                FSDPParamGroup._prefetch_unshard(target_param_group, "forward")
+        return args, kwargs
+
+    @disable_if_config_true
+    def _post_forward(self, module: nn.Module, input: Any, output: Any) -> Any:
+        # When composing with module-hook-based activation checkpointing, the
+        # post-backward hook is responsible for the reshard
+        if self._training_state == TrainingState.PRE_BACKWARD:
+            return output
+        if self._fsdp_param_group:
+            output = self._fsdp_param_group.post_forward(module, input, output)
+        output = self._register_pre_backward_hook(output)
+        self._training_state = TrainingState.IDLE
+        if self._state_ctx.iter_forward_root is self:
+            if all_gather_state := self._comm_ctx.all_gather_state:
+                # Free the last all-gather result if needed; refer to
+                # [Note: Overlapping all-gather copy-in and all-gather]
+                self._comm_ctx.all_gather_copy_in_stream.wait_event(
+                    all_gather_state.event
+                )
+                self._comm_ctx.all_gather_stream.wait_event(all_gather_state.event)
+                self._comm_ctx.all_gather_state = None  # free the all-gather result
+            self._state_ctx.iter_forward_root = None
+        if self._mp_policy.output_dtype is not None:
+            with torch.profiler.record_function("FSDP::cast_forward_outputs"):
+                output = _apply_to_tensors(
+                    functools.partial(_cast_fp_tensor, self._mp_policy.output_dtype),
+                    output,
+                )
+        return output
+
+    def _pre_backward(self, grad: torch.Tensor) -> torch.Tensor:
+        self._training_state = TrainingState.PRE_BACKWARD
+        self._register_root_post_backward_final_callback()
+        if self._fsdp_param_group:
+            default_prefetch = len(self._states_to_backward_prefetch) == 0
+            self._fsdp_param_group.pre_backward(default_prefetch)
+        for fsdp_state in self._states_to_backward_prefetch:
+            if (target_param_group := fsdp_state._fsdp_param_group) is not None:
+                FSDPParamGroup._prefetch_unshard(target_param_group, "backward")
+        return grad
+
+    def _root_post_backward_final_callback(self) -> None:
+        if not compiled_autograd_enabled():
+            logger.debug("FSDP::root_post_backward")
+        with torch.profiler.record_function("FSDP::root_post_backward_callback"):
+            for state in self._state_ctx.all_states:
+                fsdp_param_group = state._fsdp_param_group
+                if (
+                    fsdp_param_group
+                    and fsdp_param_group._training_state != TrainingState.POST_BACKWARD
+                ):
+                    # Run post-backward in case forward inputs did not require
+                    # gradient so the autograd backward did not run
+                    fsdp_param_group.post_backward()
+                state._training_state = TrainingState.IDLE
+                if fsdp_param_group:
+                    fsdp_param_group._training_state = TrainingState.IDLE
+                if self._state_ctx.is_last_backward:
+                    state._finalize_backward()
+            if self._state_ctx.is_last_backward:
+                self._comm_ctx.post_forward_order.clear()
+                if self._comm_ctx.reduce_scatter_state is not None:
+                    self._device_handle.current_stream().wait_event(
+                        self._comm_ctx.reduce_scatter_state.event
+                    )
+                    self._comm_ctx.reduce_scatter_state = None
+            self._state_ctx.post_backward_final_callback_queued = False
+
+    def _finalize_backward(self) -> None:
+        if self._modules_to_run_forward:
+            msg = (
+                f"{len(self._modules_to_run_forward)} of the {len(self._modules)} "
+                f"modules passed to fully_shard did not run forward before backward, "
+                "which is error-prone since FSDP post-forward/pre-backward logic "
+                "will not run for these modules. We recommend passing only modules "
+                "that run forward together. Modules that did not run forward: "
+                f"{list(self._modules_to_run_forward)}"
+            )
+            warning_once(logger, msg, stacklevel=2)
+            # Clear since we want the next forward to run
+            self._modules_to_run_forward.clear()
+        if self._fsdp_param_group:
+            self._fsdp_param_group.finalize_backward()
+
+    def _register_pre_backward_hook(self, output: Any) -> Any:
+        if not torch.is_grad_enabled():
+            return output
+        flat_outputs, _ = tree_flatten(output)
+        for t in flat_outputs:
+            if torch.is_tensor(t) and t.requires_grad:
+                t.register_hook(self._pre_backward)
+        return output
+
+    def _register_root_post_backward_final_callback(self):
+        if self._state_ctx.post_backward_final_callback_queued:
+            return
+        self._state_ctx.post_backward_final_callback_queued = True
+        Variable._execution_engine.queue_callback(
+            self._root_post_backward_final_callback
+        )
+
+
+def _get_module_fsdp_state(module: nn.Module) -> Optional[FSDPState]:
+    state = _get_module_state(module)
+    if isinstance(state, FSDPState):
+        return state
+    return None
+
+
+def _register_group_forward_hooks(
+    modules: Sequence[nn.Module],
+    pre_hook: Callable,
+    post_hook: Callable,
+    modules_to_run: set[nn.Module],
+):
+    """
+    Registers group forward pre and post-hooks. The pre-hook runs upon the
+    first module pre-forward, and the post-hook runs upon the last. If at least
+    one module does not run forward, then the post-hook does not run.
+    """
+    modules_set = set(modules)
+
+    @disable_if_config_true
+    @functools.wraps(pre_hook)
+    def wrapped_pre_hook(*args: Any, **kwargs: Any):
+        if len(modules_to_run) == 0:  # first to run
+            modules_to_run.update(modules_set)
+            return pre_hook(*args, **kwargs)
+
+    @disable_if_config_true
+    def get_wrapped_post_hook(module: nn.Module):
+        @functools.wraps(post_hook)
+        def wrapped_post_hook(*args: Any, **kwargs: Any):
+            modules_to_run.discard(module)
+            if len(modules_to_run) == 0:
+                return post_hook(*args, **kwargs)
+
+        return wrapped_post_hook
+
+    pre_handles = [
+        module.register_forward_pre_hook(
+            wrapped_pre_hook, prepend=True, with_kwargs=True
+        )
+        for module in modules
+    ]
+    post_handles = [
+        module.register_forward_hook(
+            get_wrapped_post_hook(module), prepend=False, always_call=True
+        )
+        for module in modules
+    ]
+    return _MultiHandle(tuple(pre_handles + post_handles))
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fully_shard.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fully_shard.py
new file mode 100644
index 0000000000000000000000000000000000000000..998a33746f961fbf65f43b2c4245a6f12a9d3893
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_fully_shard/_fully_shard.py
@@ -0,0 +1,746 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+
+from __future__ import annotations
+
+import functools
+from contextlib import contextmanager
+from typing import Any, cast, NoReturn, Optional, overload, TYPE_CHECKING, Union
+from typing_extensions import deprecated
+
+import torch
+import torch.nn as nn
+from torch.distributed._composable import contract
+from torch.distributed.utils import _get_root_modules
+
+from ._fsdp_api import AllGather, MixedPrecisionPolicy, OffloadPolicy, ReduceScatter
+from ._fsdp_common import FSDPMeshInfo, HSDPMeshInfo
+from ._fsdp_init import (
+    _get_device_from_mesh,
+    _get_managed_modules,
+    _get_managed_states,
+    _get_post_forward_mesh_info,
+    _init_default_fully_shard_mesh,
+    _move_states_to_device,
+)
+from ._fsdp_param_group import FSDPParamGroup
+from ._fsdp_state import _get_module_fsdp_state, FSDPState
+
+
+if TYPE_CHECKING:
+    from collections.abc import Callable, Iterable, Iterator
+
+    from torch.distributed.tensor import DeviceMesh, Shard
+
+__all__ = [
+    "fully_shard",
+    "FSDPModule",
+    "UnshardHandle",
+    "register_fsdp_forward_method",
+    "get_cls_to_fsdp_cls",
+    "disable_fsdp_module_new_init",
+    "share_comm_ctx",
+]
+
+
+cls_to_fsdp_cls: dict[type, type] = {}
+
+
+def get_cls_to_fsdp_cls() -> dict[type, type]:
+    return cls_to_fsdp_cls
+
+
+@overload
+# pyrefly: ignore [inconsistent-overload]
+def fully_shard(
+    module: nn.Module,
+    *,
+    mesh: Optional[DeviceMesh] = ...,
+    reshard_after_forward: Union[bool, int] = ...,
+    shard_placement_fn: Optional[Callable[[nn.Parameter], Optional[Shard]]] = ...,
+    mp_policy: MixedPrecisionPolicy = ...,
+    offload_policy: OffloadPolicy = ...,
+    ignored_params: Optional[set[nn.Parameter]] = ...,
+) -> FSDPModule: ...
+
+
+@overload
+# pyrefly: ignore [inconsistent-overload]
+def fully_shard(
+    module: list[nn.Module],
+    *,
+    mesh: Optional[DeviceMesh] = ...,
+    reshard_after_forward: Union[bool, int] = ...,
+    shard_placement_fn: Optional[Callable[[nn.Parameter], Optional[Shard]]] = ...,
+    mp_policy: MixedPrecisionPolicy = ...,
+    offload_policy: OffloadPolicy = ...,
+    ignored_params: Optional[set[nn.Parameter]] = ...,
+) -> list[FSDPModule]: ...
+
+
+# The decorator adds a state object to `module` that can be accessed via
+# `fully_shard.state(module)`. The state object and module are 1:1.
+# [1] Python runtime decorator does not play well with static type checking
+# so suppressing some type checks to support type overloads
+# such that caller can still get correct return types based on input type
+@contract(state_cls=FSDPState)  # type: ignore[misc] # see [1]
+def fully_shard(
+    module,
+    *,
+    mesh: Optional[DeviceMesh] = None,
+    reshard_after_forward: Optional[Union[bool, int]] = None,
+    shard_placement_fn: Optional[Callable[[nn.Parameter], Optional[Shard]]] = None,
+    mp_policy: MixedPrecisionPolicy = MixedPrecisionPolicy(),
+    offload_policy: OffloadPolicy = OffloadPolicy(),
+    ignored_params: Optional[set[nn.Parameter]] = None,
+):
+    """
+    Apply fully sharded data parallelism (FSDP) to ``module``, where FSDP
+    shards module parameters, gradients, and optimizer states across data
+    parallel workers to save memory at the cost of communication.
+
+    At initialization, FSDP shards the module's parameters across the data
+    parallel workers given by ``mesh``. Before forward, FSDP all-gathers the
+    sharded parameters across the data-parallel workers to get the unsharded
+    parameters for forward computation. If ``reshard_after_forward`` is
+    ``True``, then FSDP frees the unsharded parameters after forward and
+    re-all-gathers them in backward before gradient computation. After gradient
+    computation, FSDP frees the unsharded parameters and reduce-scatters the
+    unsharded gradients across data-parallel workers.
+
+    This implementation represents the sharded parameters as :class:`DTensor` s
+    sharded on dim-0, while the unsharded parameters will be like the original
+    parameters on ``module`` (e.g. :class:`torch.Tensor` if originally
+    :class:`torch.Tensor`). A module
+    `forward pre-hook `_
+    on ``module`` all-gathers the parameters, and a module
+    `forward hook `_
+    on ``module`` frees them (if needed). Similar backward hooks all-gather
+    parameters and later free parameters and reduce-scatter gradients.
+
+    Since grouping multiple tensors together for one collective is critical for
+    communication efficiency, this implementation makes this grouping first
+    class. Calling :meth:`fully_shard` on ``module`` constructs one group that
+    includes the parameters in ``module.parameters()`` except those already
+    assigned to a group from an earlier call on a submodule. This means that
+    :meth:`fully_shard` should be called bottom-up on your model. Each group's
+    parameters are all-gathered in one collective, and its gradients are
+    reduce-scattered in one collective. Partitioning the model into multiple
+    groups ("layer by layer") allows for peak memory savings and communication/computation
+    overlap. Users generally should *not* call :meth:`fully_shard` only on the
+    topmost root module.
+
+    Args:
+        module (Union[nn.Module, List[nn.Module]): The module or modules to
+            shard with FSDP and group together for communication.
+        mesh (Optional[DeviceMesh]): This data parallel mesh defines the
+            sharding and device. If 1D, then parameters are fully sharded
+            across the 1D mesh (FSDP) with ``(Shard(0),)`` placement. If 2D,
+            then parameters are sharded across the 1st dim and replicated
+            across the 0th dim (HSDP) with ``(Replicate(), Shard(0))``
+            placement. The mesh's device type gives the device type used for
+            communication; if a CUDA or CUDA-like device type, then we use the
+            current device.
+        reshard_after_forward (Optional[Union[bool, int]]): This controls the parameter
+            behavior after forward and can trade off memory and communication:
+
+            - If ``True``, then this reshards parameters after forward and
+              re-all-gathers in backward.
+            - If ``False``, then this keeps the unsharded parameters in memory
+              after forward and avoids the all-gather in backward. For best performance,
+              we usually set ``False`` for the root module, because the root module
+              is typically required immediately when the backward pass begins.
+            - If ``None``, it is set to ``True`` for non-root modules and ``False``
+              for root modules.
+            - If an ``int``, then this represents the world size to reshard to
+              after forward. It should be a non-trivial divisor of the ``mesh``
+              shard dim size (i.e. excluding 1 and the dim size itself). A
+              choice may be the intra-node size (e.g. ``torch.cuda.device_count()``).
+              This allows the all-gather in backward to be over a smaller world
+              size at the cost of higher memory usage than setting to ``True``.
+            - After forward, the parameters registered to the module depend on
+              to this: The registered parameters are the sharded parameters if
+              ``True``; unsharded parameters if ``False``; and the parameters
+              resharded to the smaller mesh otherwise. To modify the parameters
+              between forward and backward, the registered parameters must be
+              the sharded parameters. For ``False`` or an ``int``, this can be
+              done by manually resharding via :meth:`reshard`.
+        shard_placement_fn (Optional[Callable[[nn.Parameter], Optional[Shard]]]):
+            This callable can be used to override the sharding placement for a
+            parameter to shard a parameter on a dimension other than dim-0. If
+            this callable returns a :class:`Shard` placement (not ``None``),
+            then FSDP will shard according to that placement (e.g. ``Shard(1)``).
+            If sharding on a nonzero dim, we currently require even sharding,
+            i.e. the tensor dim size on that dim must be divisible by the FSDP
+            shard mesh size.
+        mp_policy (MixedPrecisionPolicy): This controls the mixed precision
+            policy, which offers parameter/reduction mixed precision for this
+            module. See :class:`MixedPrecisionPolicy` for details.
+        offload_policy (OffloadPolicy): This controls the offloading policy,
+            which offers parameter/gradient/optimizer state offloading. See
+            :class:`OffloadPolicy` and its subclasses for details.
+        ignored_params: Optional(Set[nn.Parameter]): The set of parameters to be
+            ignored by FSDP. They will not be sharded, nor moved to the device
+            during init, nor have their gradients reduced in backward.
+
+    Returns:
+        FSDPModule: The module with FSDP applied (in-place).
+    """
+    torch._C._log_api_usage_once("torch.distributed.fsdp.fully_shard")
+    if isinstance(module, (nn.ModuleList, nn.ModuleDict)):
+        raise ValueError(
+            f"fully_shard does not support containers that do not implement forward: {module}"
+        )
+    mesh = mesh or _init_default_fully_shard_mesh()
+    if mesh.ndim not in (1, 2):
+        raise ValueError(f"fully_shard expects a 1D or 2D DeviceMesh but got {mesh}")
+    elif mesh.ndim == 1:
+        mesh_info = FSDPMeshInfo(mesh, shard_mesh_dim=0)
+    else:
+        if mesh.mesh_dim_names is None:
+            raise AssertionError(
+                "Please init the 2D mesh for HSDP with mesh_dim_names specified"
+            )
+        mesh_info = HSDPMeshInfo(mesh, shard_mesh_dim=1, replicate_mesh_dim=0)
+    device = _get_device_from_mesh(mesh)
+    auto_reshard_after_forward = reshard_after_forward is None
+    # If the user does not provide ``reshard_after_forward``, we set it to True.
+    # During lazy_init, we identify which module is the root and override its value to False
+    post_forward_mesh_info = _get_post_forward_mesh_info(
+        reshard_after_forward if not auto_reshard_after_forward else True,  # type: ignore[arg-type]
+        mesh_info,
+    )
+
+    arg_module = module
+    modules = (
+        (module,) if isinstance(module, nn.Module) else tuple(_get_root_modules(module))
+    )
+    state = fully_shard.state(modules[0])  # type: ignore[attr-defined] # see [1]
+    state.init(modules, device, mp_policy, auto_reshard_after_forward)
+
+    managed_modules = _get_managed_modules(modules, ignored_params)
+    params, buffers = _get_managed_states(managed_modules, ignored_params)
+
+    _move_states_to_device(params, buffers, device)
+    if params:
+        state._fsdp_param_group = FSDPParamGroup(
+            params,
+            modules,
+            mesh_info,
+            post_forward_mesh_info,
+            device,
+            shard_placement_fn,
+            mp_policy,
+            offload_policy,
+        )
+
+    # For Dynamo
+    for managed_module in managed_modules:
+        managed_module._is_fsdp_managed_module = True  # type: ignore[assignment]
+        managed_module._fsdp_use_orig_params = True  # type: ignore[assignment]
+
+    # Place FSDP leftmost for highest priority in the method resolution order
+    for module in modules:
+        cls = module.__class__
+        new_cls = cls_to_fsdp_cls.get(cls)
+        if not new_cls:
+            dct = {"__deepcopy__": _unimplemented_deepcopy}
+            new_cls = type(f"FSDP{cls.__name__}", (FSDPModule, cls), dct)
+            cls_to_fsdp_cls[cls] = new_cls
+        module.__class__ = new_cls
+    return arg_module
+
+
+def _unimplemented_deepcopy(*args: Any, **kwargs: Any) -> NoReturn:
+    raise AssertionError(
+        "FSDP does not support deepcopy. Please use state dict for serialization."
+    )
+
+
+_enable_fsdp_module_new_init: bool = True
+
+
+@contextmanager
+def disable_fsdp_module_new_init() -> Iterator[None]:
+    global _enable_fsdp_module_new_init
+    prev, _enable_fsdp_module_new_init = _enable_fsdp_module_new_init, False
+    try:
+        yield
+    finally:
+        _enable_fsdp_module_new_init = prev
+
+
+class FSDPModule:
+    def __new__(cls, *args, **kwargs):
+        """
+        Override ``__new__`` to remove the FSDP class and directly construct
+        the original class for cases like indexing into a container module.
+        """
+        # Use index 2 since 0 is the dynamically constructed `FSDP<...>` class
+        # and index 1 is the `FSDPModule` class itself
+        orig_cls = cls.__mro__[2]
+        self = orig_cls.__new__(orig_cls, *args, **kwargs)
+        if _enable_fsdp_module_new_init:
+            self.__init__(*args, **kwargs)
+        return self
+
+    def reshard(self) -> None:
+        """
+        Reshards the module's parameters, freeing the unsharded parameters if
+        they are allocated and registering the sharded parameters to the
+        module. This method is *not* recursive.
+        """
+        state = self._get_fsdp_state()
+        if fsdp_param_group := state._fsdp_param_group:
+            fsdp_param_group.reshard()
+
+    def unshard(self, async_op: bool = False) -> Optional[UnshardHandle]:
+        """
+        Unshards the module's parameters by allocating memory and all-gathering
+        the parameters. This method is *not* recursive. The unshard follows the
+        :class:`MixedPrecisionPolicy`, so it will all-gather following
+        ``param_dtype`` if set.
+
+        Args:
+            async_op (bool): If ``True``, then returns a :class:`UnshardHandle`
+                that has a :meth:`wait` method to wait on the unshard op. If
+                ``False``, then returns ``None`` and waits on the handle inside
+                this function.
+
+        .. note:: If ``async_op=True``, then FSDP will wait on the pending
+            unshard in the module's pre-forward for the user. The user only
+            needs to call :meth:`wait` explicitly if the wait should happen
+            before pre-forward.
+        """
+        state = self._get_fsdp_state()
+        fsdp_param_group = state._fsdp_param_group
+        if fsdp_param_group is not None:
+            fsdp_param_group.lazy_init()
+            fsdp_param_group.unshard(async_op=async_op)
+        handle = _UnshardHandleImpl(fsdp_param_group)
+        if async_op:
+            return handle
+        handle.wait()
+        return None
+
+    def set_is_last_backward(self, is_last_backward: bool) -> None:
+        """
+        Sets whether the next backward is the last one. On the last backward,
+        FSDP waits on pending gradient reduction and clears internal data
+        data structures for backward prefetching. This can be useful for
+        microbatching.
+        """
+        state = self._get_fsdp_state()
+        state._state_ctx.is_last_backward = is_last_backward
+
+    def set_requires_gradient_sync(
+        self, requires_gradient_sync: bool, *, recurse: bool = True
+    ) -> None:
+        """
+        Sets if the module should sync gradients. This can be used to implement
+        gradient accumulation *without communication*. For HSDP, this controls
+        both reduce-scatter and all-reduce together. This is the equivalence of
+        `no_sync` in FSDP1.
+
+        Args:
+            requires_gradient_sync (bool): Whether to reduce gradients for the
+                module's parameters.
+            recurse (bool): Whether to set for all FSDP submodules or just the
+                passed-in module.
+        """
+        self_module = cast(nn.Module, self)
+        modules = list(self_module.modules()) if recurse else [self_module]
+        for module in modules:
+            if isinstance(module, FSDPModule):
+                state = module._get_fsdp_state()
+                if fsdp_param_group := state._fsdp_param_group:
+                    fsdp_param_group.reduce_grads = requires_gradient_sync
+                    fsdp_param_group.all_reduce_grads = requires_gradient_sync
+
+    def set_requires_all_reduce(
+        self, requires_all_reduce: bool, *, recurse: bool = True
+    ) -> None:
+        """
+        Sets if the module should all-reduce gradients. This can be used to
+        implement gradient accumulation with only reduce-scatter but not
+        all-reduce for HSDP.
+        """
+        self_module = cast(nn.Module, self)
+        modules = list(self_module.modules()) if recurse else [self_module]
+        for module in modules:
+            if isinstance(module, FSDPModule):
+                state = module._get_fsdp_state()
+                if fsdp_param_group := state._fsdp_param_group:
+                    fsdp_param_group.all_reduce_grads = requires_all_reduce
+
+    def set_reshard_after_forward(
+        self, reshard_after_forward: bool, recurse: bool = True
+    ) -> None:
+        """
+        Sets if the module should reshard parameters after forward. This can be
+        used to change the ``reshard_after_forward`` FSDP arg at runtime. For
+        example, this can be used to set the FSDP root module's value to
+        ``True`` (since it is otherwise specially set to ``False``), or it can
+        set an FSDP module's value to ``False`` for running evals and set back
+        to ``True`` for training.
+
+        Args:
+            reshard_after_forward (bool): Whether to reshard parameters after
+                forward.
+            recurse (bool): Whether to set for all FSDP submodules or just the
+                passed-in module.
+        """
+        if not isinstance(reshard_after_forward, bool):
+            raise ValueError(
+                f"reshard_after_forward should be a bool, got {type(reshard_after_forward)}"
+            )
+        self_module = cast(nn.Module, self)
+        modules = list(self_module.modules()) if recurse else [self_module]
+        for module in modules:
+            if isinstance(module, FSDPModule):
+                state = module._get_fsdp_state()
+                state._auto_reshard_after_forward = False
+                if fsdp_param_group := state._fsdp_param_group:
+                    fsdp_param_group.post_forward_mesh_info = (
+                        _get_post_forward_mesh_info(
+                            reshard_after_forward, fsdp_param_group.mesh_info
+                        )
+                    )
+
+    def set_reshard_after_backward(
+        self, reshard_after_backward: bool, *, recurse: bool = True
+    ) -> None:
+        """
+        Sets if the module should reshard parameters after backward. This can
+        be used during gradient accumulation to trade off higher memory for
+        reduced communication since the unsharded parameters do not need to be
+        re-all-gathered before the next forward.
+
+        Args:
+            reshard_after_backward (bool): Whether to reshard parameters after
+                backward.
+            recurse (bool): Whether to set for all FSDP submodules or just the
+                passed-in module.
+        """
+        self_module = cast(nn.Module, self)
+        modules = list(self_module.modules()) if recurse else [self_module]
+        for module in modules:
+            if isinstance(module, FSDPModule):
+                state = module._get_fsdp_state()
+                if fsdp_param_group := state._fsdp_param_group:
+                    fsdp_param_group.reshard_after_backward = reshard_after_backward
+
+    def set_modules_to_forward_prefetch(self, modules: list[FSDPModule]) -> None:
+        """
+        Sets the FSDP modules for which this FSDP module should explicitly
+        prefetch all-gathers in forward. The prefetching runs after this
+        module's all-gather copy-out.
+
+        Passing a singleton list containing the next FSDP module gives the same
+        all-gather overlap behavior as the default overlap behavior, except the
+        prefetched all-gather is issued earlier from the CPU. Passing a list
+        with at least length two is required for more aggressive overlap and
+        will use more reserved memory.
+
+        Args:
+            modules (List[FSDPModule]): FSDP modules to prefetch.
+        """
+        _assert_all_fsdp_modules(modules)
+        self._get_fsdp_state()._states_to_forward_prefetch = [
+            module._get_fsdp_state() for module in modules
+        ]
+
+    def set_modules_to_backward_prefetch(self, modules: list[FSDPModule]) -> None:
+        """
+        Sets the FSDP modules for which this FSDP module should explicitly
+        prefetch all-gathers in backward. This overrides the default backward
+        pretching implementation that prefetches the next FSDP module based on
+        the reverse post-forward order.
+
+        Passing a singleton list containing the previous FSDP module gives the
+        same all-gather overlap behavior as the default overlap behavior.
+        Passing a list with at least length two is required for more aggressive
+        overlap and will use more reserved memory.
+
+        Args:
+            modules (List[FSDPModule]): FSDP modules to prefetch.
+        """
+        _assert_all_fsdp_modules(modules)
+        self._get_fsdp_state()._states_to_backward_prefetch = [
+            module._get_fsdp_state() for module in modules
+        ]
+
+    def set_custom_all_gather(self, comm: AllGather) -> None:
+        """
+        Overrides the default ``all_gather`` communication behavior,
+        to have better control over the communication and memory usage.
+        See `Comm` and `ReduceScatter` for details.
+
+        Args:
+            comm (AllGather): Custom all-gather communication.
+        """
+        state = self._get_fsdp_state()
+        if (fsdp_param_group := state._fsdp_param_group) is not None:
+            fsdp_param_group._all_gather_comm = comm
+
+    def set_custom_reduce_scatter(self, comm: ReduceScatter) -> None:
+        """
+        Overrides the default ``reduce_scatter`` communication behavior,
+        to have better control over the communication and memory usage.
+        See `Comm` and `ReduceScatter` for details.
+
+        Args:
+            comm (ReduceScatter): Custom reduce_scatter communication.
+        """
+        state = self._get_fsdp_state()
+        if (fsdp_param_group := state._fsdp_param_group) is not None:
+            fsdp_param_group._reduce_scatter_comm = comm
+
+    def set_all_reduce_hook(
+        self,
+        hook: Callable[[torch.Tensor], None],
+        *,
+        stream: Optional[torch.cuda.Stream] = None,
+    ):
+        """
+        Args:
+            hook (Callable[[torch.Tensor], None]): User-defined all-reduce hook
+                with expected signature ``hook(reduce_output: torch.Tensor) -> None``
+                where ``reduce_output`` is the reduce-scatter output if only
+                using FSDP or the all-reduce output if using native HSDP.
+            stream (Optional[torch.cuda.Stream]): Stream to run the all-reduce
+                hook in. This should only be set if not using native HSDP. If
+                using native HSDP, the hook will run in the internally defined
+                all-reduce stream used by the native HSDP all-reduce.
+        """
+        state = self._get_fsdp_state()
+        if (fsdp_param_group := state._fsdp_param_group) is not None:
+            fsdp_param_group._all_reduce_hook = hook
+            if stream is not None:
+                if fsdp_param_group._is_hsdp:
+                    raise ValueError("stream cannot be set when using native HSDP")
+                fsdp_param_group._all_reduce_hook_stream = stream
+
+    def set_post_optim_event(self, event: torch.Event) -> None:
+        """
+        Sets a post-optimizer-step event for the root FSDP module to wait the
+        all-gather streams on.
+
+        By default, the root FSDP module waits the all-gather streams on the
+        current stream to ensure that the optimizer step has finished before
+        all-gathering. However, this may introduce false dependencies if
+        there is unrelated computation after the optimizer step. This API
+        allows the user to provide their own event to wait on. After the root
+        waits on the event, the event is discarded, so this API should be
+        called with a new event each iteration.
+
+        Args:
+            event (torch.Event): Event recorded after the optimizer step
+                to wait all-gather streams on.
+        """
+        self._get_fsdp_state()._state_ctx.post_optim_event = event
+
+    @deprecated("Use `set_gradient_divide_factor` instead")
+    def set_reduce_scatter_divide_factor(self, factor: float) -> None:
+        """Use :py:meth:`set_gradient_divide_factor` instead"""
+        self.set_gradient_divide_factor(factor)
+
+    def set_gradient_divide_factor(self, factor: float) -> None:
+        """
+        Sets a custom divide factor for the gradient reduction. This might use
+        a custom reduce op using NCCL's PreMulSum, which allows multiplying by
+        the factor before reduction.
+
+        Args:
+            factor (float): Custom divide factor.
+        """
+        state = self._get_fsdp_state()
+        if (fsdp_param_group := state._fsdp_param_group) is not None:
+            fsdp_param_group.gradient_divide_factor = factor
+
+    def set_force_sum_reduction_for_comms(self, enable: bool) -> None:
+        """
+        Sets whether to require the low-level collective communication
+        primitives to exclusively use "sum"-type reductions, even if it comes
+        at the cost of separate additional pre- or post-scaling operations.
+        This is needed for example because NCCL currently supports zero-copy
+        transfers only for this kind of collectives.
+
+        NB: for MTIA devices, this is always implicitly enabled.
+
+        NB: if `set_all_reduce_hook` is used under FSDP setup, the caller needs
+        to ensure the custom all-reduce across FSDP units follow this strategy
+        as well, as FSDP can no longer automatically handle that.
+
+        Args:
+            enable (bool): Whether to only ever use ReduceOp.SUM for comms.
+        """
+        state = self._get_fsdp_state()
+        if (fsdp_param_group := state._fsdp_param_group) is not None:
+            fsdp_param_group.force_sum_reduction_for_comms = enable
+
+    def set_unshard_in_backward(self, unshard_in_backward: bool) -> None:
+        """
+        Sets whether the FSDP module's parameters need to be unsharded in
+        backward. This can be used in expert cases when the user knows that all
+        parameters in this FSDP module's parameter group are not needed for
+        backward computation (e.g. embedding).
+        """
+        state = self._get_fsdp_state()
+        if (fsdp_param_group := state._fsdp_param_group) is not None:
+            fsdp_param_group.unshard_in_backward = unshard_in_backward
+
+    def set_allocate_memory_from_process_group_for_comm(self, enable: bool) -> None:
+        """
+        Sets whether the temporary staging buffers used to send and receive data
+        over collective communications should be allocated using the custom
+        optimized allocator provided by the ProcessGroup itself (if any). This
+        might allow the ProcessGroup to be more efficient. For example, when
+        using NCCL, this enables it to leverage zero-copy transfers over SHARP
+        (for NVLink and/or InfiniBand).
+
+        This cannot be used together with :meth:`set_custom_all_gather` or
+        :meth:`set_custom_reduce_scatter` as those APIs allow for
+        finer-grained control over each communication, and this method cannot
+        determine their staging buffer allocation strategy.
+
+        Args:
+            enable (bool): Whether to turn on ProcessGroup allocation.
+        """
+        state = self._get_fsdp_state()
+        if (fsdp_param_group := state._fsdp_param_group) is not None:
+            fsdp_param_group.set_allocate_memory_from_process_group(enable)
+
+    def _set_unshard_async_op(self, async_op: bool):
+        """
+        Sets whether to use ``async_op=True`` or ``False`` for the pre-forward
+        and pre-backward unshard op. This defaults to ``False`` but can be set
+        to ``True`` with this method.
+
+        Setting this to ``True`` allows the all-gather allocations to happen in
+        the default stream, avoiding inter-stream memory fragmentation.
+        However, you must use explicit prefetching (e.g. via :meth:`unshard`)
+        in forward to still get overlap, and the pre-all-gather ops like dtype
+        casting and copy-in will not overlap with compute.
+        """
+        self_module = cast(nn.Module, self)
+        for module in self_module.modules():
+            if isinstance(module, FSDPModule):
+                state = module._get_fsdp_state()
+                if fsdp_param_group := state._fsdp_param_group:
+                    fsdp_param_group.unshard_async_op = async_op
+
+    def _get_fsdp_state(self) -> FSDPState:
+        if (state := _get_module_fsdp_state(cast(nn.Module, self))) is None:
+            raise AssertionError(f"No FSDP state found on {self}")
+        return state
+
+    def _apply(self, *args: Any, **kwargs: Any) -> Any:
+        # Reshard to ensure that sharded parameters are registered
+        self.reshard()
+        ret = super()._apply(*args, **kwargs)  # type: ignore[misc]
+        state = self._get_fsdp_state()
+        if not (fsdp_param_group := state._fsdp_param_group):
+            return ret
+        # TODO: Remove this padding logic once DTensor pads the local tensor:
+        # https://github.com/pytorch/pytorch/issues/113045
+        with torch.no_grad():
+            for fsdp_param in fsdp_param_group.fsdp_params:
+                fsdp_param.reset_sharded_param()
+        return ret
+
+
+class UnshardHandle:
+    """
+    A handle to wait on a :meth:`FSDPModule.unshard` op.
+    """
+
+    def wait(self) -> None:
+        """
+        Waits on the unshard op. This ensures that the current stream can use
+        the unsharded parameters, which are now registered to the module.
+        """
+        return
+
+
+class _UnshardHandleImpl(UnshardHandle):
+    def __init__(self, fsdp_param_group: Optional[FSDPParamGroup]):
+        self._fsdp_param_group = fsdp_param_group
+
+    def wait(self):
+        if self._fsdp_param_group is not None:
+            self._fsdp_param_group.wait_for_unshard()
+            # Avoid keeping a reference
+            self._fsdp_param_group = None
+
+
+def register_fsdp_forward_method(module: nn.Module, method_name: str) -> None:
+    """
+    Registers a method on ``module`` to be considered a forward method for
+    FSDP.
+
+    FSDP all-gathers parameters pre-forward and optionally frees parameters
+    post-forward (depending on ``reshard_after_forward``). FSDP only knows to
+    do this for :meth:`nn.Module.forward` by default. This function patches a
+    user-specified method to run the pre/post-forward hooks before/after the
+    method, respectively. If ``module`` is not an :class:`FSDPModule`, then
+    this is a no-op.
+
+    Args:
+        module (nn.Module): Module to register the forward method on.
+        method_name (str): Name of the forward method.
+    """
+    if not isinstance(module, FSDPModule):
+        # Make no-op to allow including both when using/not using FSDP
+        return
+    if not hasattr(module, method_name):
+        raise ValueError(f"{type(module)} does not have a method {method_name}")
+    orig_method = getattr(module, method_name)
+
+    @functools.wraps(orig_method)
+    def wrapped_method(self, *args, **kwargs):
+        fsdp_state = self._get_fsdp_state()
+        args, kwargs = fsdp_state._pre_forward(self, args, kwargs)
+        out = orig_method(*args, **kwargs)
+        return fsdp_state._post_forward(self, args, out)
+
+    # Use `__get__` to make `wrapped_method` an instance method
+    setattr(
+        module,
+        method_name,
+        wrapped_method.__get__(module, type(module)),  # type:ignore[attr-defined]
+    )
+
+
+def share_comm_ctx(modules: list[FSDPModule]) -> None:
+    """
+    Share cuda streams for multiple FSDPModules
+
+    Example usage:
+        from torch.distributed.fsdp import share_comm_ctx
+        share_comm_ctx([fsdp_model_1, fsdp_model_2, ...])
+
+    For Pipeline Parallelism (PP), each model chunk is a FSDP root. We want
+    to share cuda streams for all-gather, reduce-scatter, and all-reduce.
+    This avoids allocating inter-stream memory framgmentation
+
+    Args:
+        modules (List[FSDPModule]): modules to share cuda streams
+    """
+    if len(modules) == 0:
+        return
+    for module in modules:
+        if not isinstance(module, FSDPModule):
+            raise ValueError(f"Expects list of FSDPModules but got {module}")
+    fsdp_states = [module._get_fsdp_state() for module in modules]
+    comm_ctx = fsdp_states[0]._comm_ctx
+    for fsdp_state in fsdp_states[1:]:
+        fsdp_state._comm_ctx = comm_ctx
+        if fsdp_param_group := fsdp_state._fsdp_param_group:
+            fsdp_param_group.comm_ctx = comm_ctx
+
+
+def _assert_all_fsdp_modules(modules: Iterable[Any]) -> None:
+    for module in modules:
+        if not isinstance(module, FSDPModule):
+            raise ValueError(f"Expects FSDPModule but got {type(module)}: {module}")
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_init_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_init_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..36bdc23e741c0bbee64d4c79e8b1b5e0c553263c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_init_utils.py
@@ -0,0 +1,1206 @@
+# mypy: allow-untyped-defs
+import collections
+import itertools
+import os
+import warnings
+from collections.abc import Callable, Generator, Iterable, Iterator
+from typing import Any, no_type_check, Optional, TYPE_CHECKING, Union
+
+import torch
+import torch.distributed as dist
+import torch.distributed.fsdp._exec_order_utils as exec_order_utils
+import torch.distributed.fsdp._traversal_utils as traversal_utils
+import torch.distributed.fsdp.fully_sharded_data_parallel as fsdp_file
+import torch.nn as nn
+from torch.distributed.algorithms._comm_hooks import default_hooks
+from torch.distributed.device_mesh import DeviceMesh
+from torch.distributed.distributed_c10d import _get_default_group
+from torch.distributed.fsdp._common_utils import (
+    _FSDPDeviceHandle,
+    _FSDPState,
+    _get_module_fsdp_state,
+    _is_fsdp_flattened,
+    _named_parameters_with_duplicates,
+    clean_tensor_name,
+    TrainingState,
+)
+from torch.distributed.fsdp._flat_param import (
+    _FSDP_USE_FULL_PREC_IN_EVAL,
+    FlatParameter,
+    FlatParamHandle,
+    HandleShardingStrategy,
+)
+from torch.distributed.fsdp._limiter_utils import _FreeEventQueue
+from torch.distributed.fsdp.api import (
+    BackwardPrefetch,
+    CPUOffload,
+    FullOptimStateDictConfig,
+    FullStateDictConfig,
+    MixedPrecision,
+    ShardingStrategy,
+    StateDictConfig,
+    StateDictType,
+)
+from torch.distributed.fsdp.wrap import _Policy
+from torch.distributed.tensor.parallel.fsdp import DTensorExtensions
+from torch.distributed.utils import _sync_params_and_buffers
+from torch.utils._python_dispatch import is_traceable_wrapper_subclass
+
+
+if TYPE_CHECKING:
+    from torch.utils.hooks import RemovableHandle
+
+_TORCHDISTX_AVAIL = True
+try:
+    from torchdistx import deferred_init, fake  # type: ignore[import]
+except ImportError:
+    _TORCHDISTX_AVAIL = False
+
+PARAM_BROADCAST_BUCKET_SIZE = 250 * 1024 * 1024
+FSDP_SYNCED = "_fsdp_synced"
+# Specification of process groups for hybrid sharding strategies.
+HybridShardProcessGroupType = tuple[dist.ProcessGroup, dist.ProcessGroup]
+# Overall specification of process group.
+ProcessGroupType = Optional[Union[dist.ProcessGroup, HybridShardProcessGroupType]]
+
+
+# TODO (awgu): Refactor this later
+SHARDING_STRATEGY_MAP = {
+    ShardingStrategy.NO_SHARD: HandleShardingStrategy.NO_SHARD,
+    ShardingStrategy.FULL_SHARD: HandleShardingStrategy.FULL_SHARD,
+    ShardingStrategy.SHARD_GRAD_OP: HandleShardingStrategy.SHARD_GRAD_OP,
+    ShardingStrategy.HYBRID_SHARD: HandleShardingStrategy.HYBRID_SHARD,
+    ShardingStrategy._HYBRID_SHARD_ZERO2: HandleShardingStrategy._HYBRID_SHARD_ZERO2,
+}
+HYBRID_SHARDING_STRATEGIES = [
+    ShardingStrategy.HYBRID_SHARD,
+    ShardingStrategy._HYBRID_SHARD_ZERO2,
+]
+NO_RESHARD_AFTER_FORWARD_STRATEGIES = (
+    ShardingStrategy.SHARD_GRAD_OP,
+    ShardingStrategy._HYBRID_SHARD_ZERO2,
+)
+
+
+# NOTE: Since non-self attributes cannot be type annotated, several attributes
+# on `state` are defined first as local variables before being assigned.
+
+
+@no_type_check
+def _init_process_group_state(
+    state: _FSDPState,
+    process_group: ProcessGroupType,
+    sharding_strategy: ShardingStrategy,
+    policy: Optional[_Policy],
+    device_mesh: Optional[DeviceMesh] = None,
+) -> _FSDPState:
+    if process_group is not None and device_mesh is not None:
+        raise ValueError(
+            "Cannot pass both process_group and device_mesh at the "
+            "same time. Please just pass only one of them."
+        )
+    is_hybrid_strategy = sharding_strategy in HYBRID_SHARDING_STRATEGIES
+    if is_hybrid_strategy:
+        if process_group is None and policy is None and device_mesh is None:
+            # Raise an error here, since this is manual wrapping with no process group
+            # passed in, there is no way to ensure all wrapped FSDP instances use the same
+            # process groups.
+            raise ValueError(
+                f"Manual wrapping with {sharding_strategy} "
+                "requires explicit specification of process group or device_mesh."
+            )
+        else:
+            state = _init_process_group_state_for_hybrid_shard(
+                state, process_group, device_mesh
+            )
+    else:
+        if device_mesh:
+            state._device_mesh = device_mesh
+            state.process_group = device_mesh.get_group(mesh_dim=0)
+        else:
+            state.process_group = (
+                process_group if process_group is not None else _get_default_group()
+            )
+
+    state.rank = state.process_group.rank()
+    state.world_size = state.process_group.size()
+    data_parallel_world_size = state.world_size
+    if is_hybrid_strategy:
+        data_parallel_world_size *= state._inter_node_pg.size()
+    state._gradient_predivide_factor = (
+        default_hooks.DefaultState._get_gradient_predivide_factor(
+            data_parallel_world_size
+        )
+    )
+    state._gradient_postdivide_factor = (
+        data_parallel_world_size / state._gradient_predivide_factor
+    )
+    return state
+
+
+@no_type_check
+def _init_process_group_state_for_hybrid_shard(
+    state: _FSDPState,
+    process_group: ProcessGroupType,
+    device_mesh: DeviceMesh,
+) -> _FSDPState:
+    if device_mesh:
+        if _is_valid_hybrid_shard_device_mesh(device_mesh):
+            state._device_mesh = device_mesh
+            # We currently only allow _inter_node_pg to be the outermost dimension, and the
+            # process_group(intra_node) to be the innermost dimension.
+            state._inter_node_pg = device_mesh.get_group(mesh_dim=0)
+            state.process_group = device_mesh.get_group(mesh_dim=1)
+        else:
+            raise ValueError(
+                f"Expected device_mesh to have ndim=2 but got {device_mesh.ndim}"
+            )
+    elif process_group is None:
+        default_group = _get_default_group()
+        intra_node_group, inter_node_group = _init_intra_and_inter_node_groups(
+            default_group, state._device_handle.device_count()
+        )
+        # we shard across intra-node
+        state.process_group = intra_node_group
+        # save _inter_node_pg to allreduce across.
+        state._inter_node_pg = inter_node_group
+    else:
+        # Check type and assign state.process_group and state._inter_node_pg.
+        if _is_valid_hybrid_shard_pg_type(process_group):
+            # Assuming that user passed in as intra node group and inter node group
+            # as documented.
+            state.process_group, state._inter_node_pg = process_group
+        else:
+            raise ValueError(
+                "Expected process_group to be passed in as either None or "
+                f"Tuple[dist.ProcessGroup, dist.ProcessGroup] but got {type(process_group)}"
+            )
+    # Create state for allreduce
+    state._inter_node_state = _get_default_comm_hook_state(
+        process_group=state._inter_node_pg,
+    )
+    return state
+
+
+@no_type_check
+def _is_valid_hybrid_shard_pg_type(process_group: Any) -> bool:
+    return (
+        isinstance(process_group, tuple)
+        and len(process_group) == 2
+        and all(isinstance(pg, dist.ProcessGroup) for pg in process_group)
+    )
+
+
+@no_type_check
+def _is_valid_hybrid_shard_device_mesh(device_mesh: DeviceMesh) -> bool:
+    return isinstance(device_mesh, DeviceMesh) and device_mesh.ndim == 2
+
+
+@no_type_check
+def _init_intra_node_process_group(num_devices_per_node: int) -> dist.ProcessGroup:
+    """
+    Return a process group across the current node.
+
+    For example, given each row is a distinct node:
+    0  1  2  3  4  5  6  7
+    8  9 10 11 12 13 14 15
+    This API would return an intra-node subgroup across
+    [0, 1, ..., 7] or [8, 9, ..., 15] depending on the process's rank.
+    For example, rank 3 would get [0, 1, ..., 7].
+    """
+    intra_node_subgroup, _ = dist.new_subgroups(num_devices_per_node)
+    return intra_node_subgroup
+
+
+@no_type_check
+def _init_inter_node_process_group(
+    global_process_group: dist.ProcessGroup,
+    num_devices_per_node: int,
+) -> dist.ProcessGroup:
+    """
+    Return an inter-node process group where each contained rank has the same local rank.
+
+    For example, given each row is a distinct node:
+    0  1  2  3  4  5  6  7
+    8  9 10 11 12 13 14 15
+    This API would return inter-node process group [0, 8], [1, 9], [2, 10], and so forth
+    depending on the process's rank. For example, rank 1 would get [1, 9], rank 5
+    would get [5, 13].
+    """
+    # the inter-node pg that is returned
+    inter_node_pg = None
+    sharding_backend = dist.get_backend(global_process_group)
+    world_size = dist.get_world_size(global_process_group)
+    # Assuming fully homogeneous setup
+    num_nodes = world_size // num_devices_per_node
+    my_local_rank = dist.get_rank(global_process_group) % num_devices_per_node
+    for local_rank in range(num_devices_per_node):
+        ranks_for_inter_group = [
+            local_rank + (i * num_devices_per_node) for i in range(num_nodes)
+        ]
+        # every rank always needs to call dist.new_group
+        grp = dist.new_group(ranks=ranks_for_inter_group, backend=sharding_backend)
+        if local_rank == my_local_rank:
+            inter_node_pg = grp
+
+    if inter_node_pg is None:
+        raise AssertionError(
+            f"{my_local_rank} expected to assign inter-node pg, but did not"
+        )
+    return inter_node_pg
+
+
+def _init_intra_and_inter_node_groups(
+    global_process_group: dist.ProcessGroup,
+    num_devices_per_node: int,
+) -> tuple[dist.ProcessGroup, dist.ProcessGroup]:
+    """
+    Initialize intra and inter-node process groups and return the ones corresponding to this process's rank.
+
+    This function can be used to initialize process groups for ``HYBRID_SHARD`` or
+    ``_HYBRID_SHARD_ZERO2`` in FSDP.
+    This function assumes each node has an equal number of CUDA-enabled devices.
+    Returns:
+        Tuple[dist.ProcessGroup, dist.ProcessGroup]: Intra and inter-node process group.
+    """
+    return (
+        _init_intra_node_process_group(num_devices_per_node),
+        _init_inter_node_process_group(global_process_group, num_devices_per_node),
+    )
+
+
+@no_type_check
+def _init_ignored_module_states(
+    state: _FSDPState,
+    module: nn.Module,
+    ignored_modules: Optional[Iterable[torch.nn.Module]],
+    ignored_states: Union[
+        Optional[Iterable[torch.nn.Parameter]], Optional[Iterable[torch.nn.Module]]
+    ] = None,
+) -> _FSDPState:
+    if ignored_modules is not None and ignored_states is not None:
+        raise ValueError(
+            "Cannot pass both ignored_modules and ignored_states at the "
+            "same time. Please just pass ignored_states."
+        )
+    ignored_parameters = None
+    passed_as_ignored_states = ignored_states is not None
+    if passed_as_ignored_states:
+        ignored_states_list = list(ignored_states)
+        _check_ignored_states(ignored_states_list, True)
+    else:
+        ignored_states_list = []
+        _check_ignored_states(
+            list(ignored_modules) if ignored_modules is not None else [], False
+        )
+    if len(ignored_states_list) > 0:
+        if isinstance(ignored_states_list[0], nn.Parameter):
+            ignored_parameters = ignored_states_list
+        else:
+            ignored_modules = ignored_states_list
+    state._ignored_modules = _get_ignored_modules(module, ignored_modules)
+    state._ignored_params = _get_ignored_params(
+        module,
+        state._ignored_modules,
+        ignored_parameters,
+    )
+    state._ignored_buffer_names = _get_ignored_buffer_names(
+        module,
+        state._ignored_modules,
+    )
+    # TODO: FSDP's contract for buffers is not well-defined. They are
+    # implicitly ignored for most functionality since they are not sharded;
+    # however, FSDP still imposes some semantics on buffers (e.g. buffer mixed
+    # precision). We should formalize this contract and decide if we need to
+    # compute and store `_ignored_buffers`.
+    return state
+
+
+def _check_ignored_states(
+    ignored_states: list[Any], passed_as_ignored_states: bool
+) -> None:
+    """
+    Check that the ignored states are uniformly parameters or uniformly modules.
+
+    We may remove this check in the future if we permit mixing.
+    """
+    if len(ignored_states) == 0:
+        return
+    if passed_as_ignored_states:
+        all_params = all(isinstance(state, nn.Parameter) for state in ignored_states)
+        all_modules = all(isinstance(state, nn.Module) for state in ignored_states)
+        if not all_params and not all_modules:
+            # Sort for consistent ordering for unit test regex matching
+            sorted_types = sorted({type(state) for state in ignored_states}, key=repr)
+            raise ValueError(
+                "ignored_states expects all nn.Parameter or all nn.Module list "
+                f"elements but got types {sorted_types}"
+            )
+    else:
+        if not all(isinstance(state, nn.Module) for state in ignored_states):
+            sorted_types = sorted({type(state) for state in ignored_states}, key=repr)
+            raise ValueError(
+                "ignored_modules expects nn.Module list elements but got "
+                f"types {sorted_types}"
+            )
+
+
+@no_type_check
+def _init_device_handle(
+    state: _FSDPState,
+    module: nn.Module,
+    ignored_params: set[nn.Parameter],
+    device_id: Optional[Union[int, torch.device]],
+) -> _FSDPState:
+    """
+    Determine device handle used for initializing FSDP.
+
+    If a device is specified by ``device_id``,
+    then returns device handle corresponds to that device type. Otherwise, If the
+    module is already on a non-CPU device, then the device type is that non-CPU device type.
+    If the module is on CPU or meta, then the device type is the current accelerator device.
+    See the :ref:`Accelerators` for details.
+
+
+    This method will be called once ignored parameters was determined, as the device handle maybe needed
+    for other initialization.
+    """
+    determined_device = None
+    if device_id is not None:
+        determined_device = (
+            device_id
+            if isinstance(device_id, torch.device)
+            else torch.device(device_id)
+        )
+    if determined_device is None:
+        for param in _get_orig_params(module, ignored_params):
+            if param.device.type in {"cpu", "meta"}:
+                continue
+            if determined_device is None:
+                determined_device = param.device
+            else:
+                if param.device.type != determined_device.type:
+                    raise RuntimeError(
+                        f"FSDP does not support modules with different device types "
+                        f"but got params on {determined_device.type} and {param.device.type}"
+                    )
+        determined_device = determined_device or torch._C._get_accelerator()
+        if determined_device.type == "cpu":
+            raise RuntimeError(
+                "FSDP needs a non-CPU accelerator device, but no accelerator device is detected."
+            )
+
+    state._device_handle = _FSDPDeviceHandle.from_device(determined_device)
+    return state
+
+
+@no_type_check
+def _init_buffer_state(
+    state: _FSDPState,
+    module: nn.Module,
+) -> _FSDPState:
+    state._buffer_names = _get_buffer_names(module)
+    # Save a mapping from clean fully-qualified buffer name (starting from
+    # `module`) to its original dtype for restoring that dtype during model
+    # checkpointing when buffer mixed precision is enabled. The names should
+    # be clean since the casting happens in a `summon_full_params()` context.
+    _buffer_name_to_orig_dtype: dict[str, torch.dtype] = {}
+    for buffer_name, buffer in module.named_buffers():
+        buffer_name = clean_tensor_name(buffer_name)
+        _buffer_name_to_orig_dtype[buffer_name] = buffer.dtype
+    state._buffer_name_to_orig_dtype = _buffer_name_to_orig_dtype
+    return state
+
+
+@no_type_check
+def _init_core_state(
+    state: _FSDPState,
+    sharding_strategy: Optional[ShardingStrategy],
+    mixed_precision: Optional[MixedPrecision],
+    cpu_offload: Optional[CPUOffload],
+    limit_all_gathers: bool,
+    use_orig_params: bool,
+    backward_prefetch_limit: int,
+    forward_prefetch_limit: int,
+) -> _FSDPState:
+    # We clamp the strategy to `NO_SHARD` for world size of 1 since they are
+    # currently functionally equivalent. This may change if/when we integrate
+    # FSDP with MoE.
+    if state.world_size == 1:
+        if sharding_strategy != ShardingStrategy.NO_SHARD:
+            warnings.warn(
+                "FSDP is switching to use `NO_SHARD` instead of "
+                f"{sharding_strategy or ShardingStrategy.FULL_SHARD} since "
+                "the world size is 1.",
+                stacklevel=2,
+            )
+        sharding_strategy = ShardingStrategy.NO_SHARD
+    elif sharding_strategy == ShardingStrategy.NO_SHARD:
+        warnings.warn(
+            "The `NO_SHARD` sharding strategy is deprecated. If having issues, "
+            "please use `DistributedDataParallel` instead.",
+            FutureWarning,
+            # Level 1 is here, level 2 is from `FullyShardedDataParallel`, and
+            # level 3 is from the true caller
+            stacklevel=3,
+        )
+    state.sharding_strategy = sharding_strategy or ShardingStrategy.FULL_SHARD
+    state.mixed_precision = mixed_precision or MixedPrecision()
+    if mixed_precision is not None:
+        torch._C._log_api_usage_once(
+            f"torch.distributed.fsdp.mixed_precision.{str(state.mixed_precision)}"
+        )
+    state._use_full_prec_in_eval = (
+        os.environ.get(_FSDP_USE_FULL_PREC_IN_EVAL, "") == "1"
+    )
+    state.cpu_offload = cpu_offload or CPUOffload()
+    state.limit_all_gathers = limit_all_gathers
+    state._use_orig_params = use_orig_params
+    state.training_state = TrainingState.IDLE
+    state._is_root = None
+    state._free_event_queue = _FreeEventQueue()
+    state._debug_level = dist.get_debug_level()
+    state._exec_order_data = exec_order_utils._ExecOrderData(
+        state._debug_level,
+        backward_prefetch_limit,
+        forward_prefetch_limit,
+    )
+    state._unshard_event = None
+    # Mapping from fully sharded module to the handles it is responsible to
+    # unshard and reshard (see [Note: Fully Sharded Module])
+    _fully_sharded_module_to_handle: dict[nn.Module, FlatParamHandle] = {}
+    state._fully_sharded_module_to_handle = _fully_sharded_module_to_handle
+    # Invariant: `state.params` contains exactly the `FlatParameter`s of the
+    # handles in `state._handle`
+    _handle: Optional[FlatParamHandle] = None
+    state._handle = _handle
+    params: list[FlatParameter] = []
+    state.params = params
+    return state
+
+
+@no_type_check
+def _init_runtime_state(
+    state: _FSDPState,
+) -> _FSDPState:
+    _root_pre_forward_handles: list[RemovableHandle] = []
+    state._root_pre_forward_handles = _root_pre_forward_handles
+    _pre_forward_handles: list[RemovableHandle] = []
+    state._pre_forward_handles = _pre_forward_handles
+    _post_forward_handles: list[RemovableHandle] = []
+    state._post_forward_handles = _post_forward_handles
+    state._sync_gradients = True
+    state._comm_hook = None
+    state._comm_hook_state = None
+    # Used to prevent running the pre-backward hook multiple times
+    return state
+
+
+@no_type_check
+def _init_prefetching_state(
+    state: _FSDPState,
+    backward_prefetch: BackwardPrefetch,
+    forward_prefetch: bool,
+) -> _FSDPState:
+    state.backward_prefetch = backward_prefetch
+    state.forward_prefetch = forward_prefetch
+    # The data structures use tuples of handles to generalize over the case
+    # where a module's forward involves multiple handles.
+    return state
+
+
+@no_type_check
+# pyrefly: ignore [bad-function-definition]
+def _init_extension(state: _FSDPState, device_mesh: DeviceMesh = None) -> _FSDPState:
+    # TODO: we need to add additional check once we support FSDP + PiPPy.
+    # This check is currently sufficient, since we only support FSDP + TP.
+    root_mesh = device_mesh._get_root_mesh() if device_mesh is not None else None
+    # if a root mesh is not the same as device_mesh,
+    # meaning the device_mesh is sliced out from the root mesh.
+    if device_mesh and root_mesh != state._device_mesh:
+        state._fsdp_extension = DTensorExtensions(state._device_handle)
+    else:
+        # We need to explicitly set _fsdp_extension to None.
+        # Otherwise, we will run into an infinite recursion when getting the attribute.
+        state._fsdp_extension = None
+    return state
+
+
+@no_type_check
+def _init_state_dict_state(state: _FSDPState) -> _FSDPState:
+    state._state_dict_type = StateDictType.FULL_STATE_DICT
+    state_dict_config: StateDictConfig = FullStateDictConfig()
+    state._optim_state_dict_config = FullOptimStateDictConfig()
+    state._state_dict_config = state_dict_config
+    unshard_params_ctx: dict[nn.Module, Generator] = {}
+    state._unshard_params_ctx = unshard_params_ctx
+
+    return state
+
+
+def _verify_managed_params(module: nn.Module, params: list[nn.Parameter]) -> None:
+    """
+    Verify if the parameters are accepted by FSDP. The only restriction now
+    is that the parameter cannot be a scalar tensor (param.shape == []).
+    """
+    for param in params:
+        if len(param.shape) == 0:
+            param_name = ""
+            for name, param_ in module.named_parameters():
+                if param is param_:
+                    param_name = name
+                    break
+            if not param_name:
+                raise AssertionError("Expected param_name to be set")
+            raise ValueError(
+                "FSDP doesn't support scalar parameters. "
+                f"Change {param_name} to a 1D tensor with numel equal to 1."
+            )
+
+
+@no_type_check
+def _init_param_handle_from_module(
+    state: _FSDPState,
+    fully_sharded_module: nn.Module,
+    device_id: Optional[Union[int, torch.device]],
+    param_init_fn: Optional[Callable[[nn.Module], None]],
+    sync_module_states: bool,
+) -> _FSDPState:
+    """Initialize a ``FlatParamHandle`` from a module ``fully_sharded_module``."""
+    _check_single_device_module(fully_sharded_module, state._ignored_params, device_id)
+    device_from_device_id = _get_device_from_device_id(
+        device_id, state.rank, state._device_handle
+    )
+    is_meta_module, is_torchdistX_deferred_init = _need_to_materialize_module(
+        fully_sharded_module, state._ignored_params, state._ignored_modules
+    )
+    # Materialize the module if needed
+    if (is_meta_module or is_torchdistX_deferred_init) and param_init_fn is not None:
+        _materialize_with_param_init_fn(
+            fully_sharded_module, param_init_fn, state._ignored_modules
+        )
+    elif is_meta_module:
+        _materialize_meta_module(
+            fully_sharded_module,
+            device_id,
+            state._ignored_modules,
+            state._device_handle,
+        )
+    elif is_torchdistX_deferred_init:
+        deferred_init.materialize_module(
+            fully_sharded_module,
+            check_fn=lambda submodule: _get_module_fsdp_state(submodule) is None
+            and submodule not in state._ignored_modules,
+        )
+
+    ignored_buffers = {
+        buffer
+        for ignored_module in state._ignored_modules
+        for buffer in ignored_module.buffers()
+    }
+
+    _move_module_to_device(
+        fully_sharded_module,
+        state._ignored_params,
+        ignored_buffers,
+        device_from_device_id,
+    )
+    state.compute_device = _get_compute_device(
+        fully_sharded_module,
+        state._ignored_params,
+        device_from_device_id,
+        state.rank,
+        state._device_handle,
+    )
+
+    managed_params = list(_get_orig_params(fully_sharded_module, state._ignored_params))
+    _verify_managed_params(fully_sharded_module, managed_params)
+    if sync_module_states:
+        _sync_module_params_and_buffers(
+            fully_sharded_module, managed_params, state.process_group
+        )
+        if state.sharding_strategy in HYBRID_SHARDING_STRATEGIES:
+            _sync_module_params_and_buffers(
+                fully_sharded_module, managed_params, state._inter_node_pg
+            )
+    _init_param_handle_from_params(state, managed_params, fully_sharded_module)
+    return state
+
+
+@no_type_check
+def _init_param_handle_from_params(
+    state: _FSDPState,
+    params: list[nn.Parameter],
+    fully_sharded_module: nn.Module,
+):
+    if len(params) == 0:
+        return
+    handle = FlatParamHandle(
+        params,
+        fully_sharded_module,
+        state.compute_device,
+        SHARDING_STRATEGY_MAP[state.sharding_strategy],
+        state.cpu_offload.offload_params,
+        state.mixed_precision.param_dtype,
+        state.mixed_precision.reduce_dtype,
+        state.mixed_precision.keep_low_precision_grads,
+        state.process_group,
+        state._use_orig_params,
+        fsdp_extension=state._fsdp_extension,
+    )
+    handle.shard()
+    if state._handle:
+        raise AssertionError("Expected state._handle to be None")
+    state.params.append(handle.flat_param)
+    state._handle = handle
+    state._fully_sharded_module_to_handle[handle._fully_sharded_module] = handle
+    cpu_device = torch.device("cpu")
+    if state.cpu_offload.offload_params and handle.flat_param.device != cpu_device:
+        handle.flat_param_to(cpu_device)
+
+
+def _get_ignored_modules(
+    root_module: nn.Module,
+    _ignored_modules: Optional[Iterable[torch.nn.Module]],
+) -> set[nn.Module]:
+    """
+    Check that ``_ignored_modules`` is an iterable of ``nn.Module`` s without any FSDP instances.
+
+    Return the modules contained in their module
+    subtrees as a :class:`set`. Nested FSDP instances are excluded, but their
+    already-computed ignored modules are included.
+
+    ``_ignored_modules`` represents the argument passed by the user to FSDP.
+    """
+    msg_prefix = "`ignored_modules` should be an iterable of `torch.nn.Module`s "
+    try:
+        ignored_root_modules = (
+            set(_ignored_modules) if _ignored_modules is not None else set()
+        )
+    except TypeError as e:
+        raise TypeError(msg_prefix + f"but got {type(_ignored_modules)}") from e
+    for module in ignored_root_modules:
+        if not isinstance(module, torch.nn.Module):
+            raise TypeError(msg_prefix + f"but got an iterable with {type(module)}")
+        if _get_module_fsdp_state(module):
+            # TODO: We may relax this by taking the FSDP instance's wrapped
+            # module to provide more flexibility to the user.
+            raise ValueError("`ignored_modules` should not include FSDP modules")
+    # Treat modules that cannot compose with `fully_shard` as ignored modules,
+    # meaning that their subtrees are ignored
+    for module in root_module.modules():
+        if not traversal_utils._composable(module):
+            ignored_root_modules.add(module)
+    # NOTE: Even if `ignored_root_modules` is empty, do not return early so
+    # that this FSDP instance can get any ignored modules from its children.
+
+    # Include child modules and exclude nested FSDP modules themselves
+    ignored_modules = {
+        child
+        for module in ignored_root_modules
+        for child in module.modules()
+        if not isinstance(child, fsdp_file.FullyShardedDataParallel)
+    }
+    if root_module in ignored_modules:
+        warnings.warn(
+            "Trying to ignore the top-level module passed into the FSDP "
+            "constructor itself will result in all parameters being "
+            f"ignored and is not well-supported: {module}",
+            stacklevel=2,
+        )
+    # Include nested FSDP modules' ignored modules
+    for submodule in root_module.modules():
+        optional_fsdp_state = _get_module_fsdp_state(submodule)
+        if optional_fsdp_state is not None:
+            if not hasattr(optional_fsdp_state, "_ignored_modules"):
+                raise AssertionError(
+                    "Expected optional_fsdp_state to have _ignored_modules attribute"
+                )
+            ignored_modules.update(optional_fsdp_state._ignored_modules)
+    return ignored_modules
+
+
+def _get_ignored_params(
+    root_module: torch.nn.Module,
+    ignored_modules: set[torch.nn.Module],
+    ignored_parameters: Optional[Iterable[torch.nn.Parameter]] = None,
+) -> set[torch.nn.Parameter]:
+    """
+    Return the parameters of the modules in ``ignored_modules`` and the parameters in ``ignored_parameters``.
+
+    :class:`FlatParameter` s are excluded from the result.
+    """
+    all_ignored_params: set[torch.nn.Parameter] = set()
+
+    params_in_ignored_modules = {
+        p for m in ignored_modules for p in m.parameters() if not _is_fsdp_flattened(p)
+    }
+
+    all_ignored_params.update(params_in_ignored_modules)
+
+    if ignored_parameters is not None:
+        params_in_ignored_parameters = {
+            p for p in ignored_parameters if not _is_fsdp_flattened(p)
+        }
+        all_ignored_params.update(params_in_ignored_parameters)
+
+    # Always include nested FSDP modules' ignored parameters
+    for submodule in root_module.modules():
+        optional_fsdp_state = _get_module_fsdp_state(submodule)
+        if optional_fsdp_state is not None:
+            if not hasattr(optional_fsdp_state, "_ignored_params"):
+                raise AssertionError(
+                    "Expected optional_fsdp_state to have _ignored_params attribute"
+                )
+            all_ignored_params.update(optional_fsdp_state._ignored_params)
+
+    return all_ignored_params
+
+
+def _get_ignored_buffer_names(
+    root_module: torch.nn.Module,
+    ignored_modules: set[torch.nn.Module],
+) -> set[str]:
+    """Return the cleaned buffer FQNs in ``ignored_modules``."""
+    all_ignored_buffer_names: set[str] = set()
+
+    buffers_in_ignored_modules = {
+        buffer for m in ignored_modules for buffer in m.buffers()
+    }
+
+    all_ignored_buffer_names.update(
+        {
+            clean_tensor_name(buffer_name)
+            for buffer_name, buffer in root_module.named_buffers()
+            if buffer in buffers_in_ignored_modules
+        }
+    )
+
+    # Always include nested FSDP modules' ignored buffer names
+    for submodule in root_module.modules():
+        optional_fsdp_state = _get_module_fsdp_state(submodule)
+        if optional_fsdp_state is not None:
+            if not hasattr(optional_fsdp_state, "_ignored_buffer_names"):
+                raise AssertionError(
+                    "Expected optional_fsdp_state to have _ignored_buffer_names attribute"
+                )
+            all_ignored_buffer_names.update(optional_fsdp_state._ignored_buffer_names)
+
+    return all_ignored_buffer_names
+
+
+def _get_buffer_names(root_module: nn.Module) -> set[str]:
+    """Return the fully prefixed names of all buffers in the module hierarchy rooted at ``root_module`` as a class:`set`."""
+    return {
+        clean_tensor_name(buffer_name) for buffer_name, _ in root_module.named_buffers()
+    }
+
+
+def _check_single_device_module(
+    module: nn.Module,
+    ignored_params: set[nn.Parameter],
+    device_id: Optional[Union[int, torch.device]],
+) -> None:
+    """
+    Raise an error if ``module`` has original parameters on multiple devices, ignoring the parameters in ``ignored_params``.
+
+    Thus, after this method, the
+    module must be either fully on the CPU or fully on a non-CPU device.
+    """
+    devices = {param.device for param in _get_orig_params(module, ignored_params)}
+    # We allow module to be partially on CPU and partially on GPU if device_id is not
+    # None, since the device_id arg will result in the CPU portion being moved to
+    # GPU. This is useful in cases where part of the module may be parallelized
+    # by another algorithm and may already be on GPU. We'd like to enforce device_id
+    # to not be None, otherwise we'd flatten parameters in a mixed module which is
+    # not supported.
+    if len(devices) == 2 and torch.device("cpu") in devices:
+        if device_id is None:
+            raise RuntimeError(
+                "To support a module with both CPU and GPU params, "
+                "please pass in device_id argument."
+            )
+    elif len(devices) > 1:
+        raise RuntimeError(
+            f"FSDP only supports single device modules but got params on {devices}"
+        )
+
+
+def _get_device_from_device_id(
+    device_id: Optional[Union[int, torch.device]],
+    rank: int,
+    device_handle: _FSDPDeviceHandle,
+) -> Optional[torch.device]:
+    """
+    Return a ``torch.device`` for the specified ``device_id``.
+
+    Processes ``device_id`` and returns either the corresponding device or
+    ``None`` if ``device_id`` is ``None``.
+    """
+    if device_id is None:
+        return None
+    device = (
+        device_id if isinstance(device_id, torch.device) else torch.device(device_id)
+    )
+    if device.type != "cpu" and device.index is None:
+        warnings.warn(
+            f"FSDP got the argument `device_id` {device_id} on rank "
+            f"{rank}, which does not have an explicit index. "
+            f"FSDP will use the current device {device_handle.current_device()}. "
+            f"If this is incorrect, please explicitly call `torch.{device.type}.set_device()` "
+            "before FSDP initialization or pass in the explicit device "
+            "index as the `device_id` argument.",
+            stacklevel=2,
+        )
+        device = torch.device(device_handle.current_device())
+    return device
+
+
+def _need_to_materialize_module(
+    module: nn.Module,
+    ignored_params: set[nn.Parameter],
+    ignored_modules: set[nn.Module],
+) -> tuple[bool, bool]:
+    """
+    Return if ``module`` has parameters on meta device and if ``module`` is using torchdistX deferred initialization.
+
+    At most of the returned bools can
+    be ``True``. If either is ``True``, then ``module`` needs to be
+    materialized.
+    """
+    managed_params = list(_get_orig_params(module, ignored_params))
+    is_meta_module = any(param.is_meta for param in managed_params)
+    # TODO: We need to establish a contract for FSDP and buffers. For now, we
+    # skip checking for meta buffers from ignored modules. We should consider
+    # refactoring the initialization holistically to avoid so many traversals.
+    for submodule in module.modules():
+        if submodule in ignored_modules:
+            continue
+        for buf in submodule.buffers(recurse=False):
+            is_meta_module |= buf.is_meta
+    is_torchdistX_deferred_init = (
+        not is_meta_module
+        and _TORCHDISTX_AVAIL
+        and any(fake.is_fake(param) for param in managed_params)
+    )
+    return is_meta_module, is_torchdistX_deferred_init
+
+
+def _materialize_with_param_init_fn(
+    root_module: nn.Module,
+    param_init_fn: Callable[[nn.Module], None],
+    ignored_modules: set[nn.Module],
+) -> None:
+    if not callable(param_init_fn):
+        raise ValueError(
+            f"Expected {param_init_fn} to be callable but got {type(param_init_fn)}"
+        )
+    modules_to_materialize = _get_modules_to_materialize(root_module, ignored_modules)
+    for module in modules_to_materialize:
+        param_init_fn(module)
+
+
+def _materialize_meta_module(
+    root_module: nn.Module,
+    device_from_device_id: Optional[torch.device],
+    ignored_modules: set[nn.Module],
+    device_handle: _FSDPDeviceHandle,
+):
+    # Run default meta device initialization
+    materialization_device = device_from_device_id or torch.device(
+        device_handle.current_device()
+    )
+    modules_to_materialize = _get_modules_to_materialize(root_module, ignored_modules)
+    module = None
+    try:
+        # Assume that each module's `reset_parameters()` only initializes its
+        # own parameters and not those of its children
+        with torch.no_grad():
+            for module in modules_to_materialize:
+                # As a contract to the user, only call `reset_parameters()` if
+                # the module has directly managed parameters/buffers
+                module_state_iter = itertools.chain(
+                    module.parameters(recurse=False),
+                    # pyrefly: ignore [bad-argument-type]
+                    module.buffers(recurse=False),
+                )
+                has_module_states = len(list(module_state_iter)) > 0
+                if has_module_states:
+                    module.to_empty(device=materialization_device, recurse=False)
+                    module.reset_parameters()  # type: ignore[operator]
+    except BaseException as e:
+        warnings.warn(
+            "Unable to call `reset_parameters()` for module on meta "
+            f"device with error {str(e)}. Please ensure that your module of"
+            f"type {type(module)} implements a `reset_parameters()` method.",
+            stacklevel=2,  # type: ignore[possibly-undefined]
+        )
+        raise e
+
+
+def _get_modules_to_materialize(
+    root_module: nn.Module, ignored_modules: set[nn.Module]
+) -> list[nn.Module]:
+    # Run BFS to collect the modules to materialize via `reset_parameters()`,
+    # stopping at any module with FSDP already applied or at ignored modules.
+    modules_to_materialize: list[nn.Module] = []
+    queue = collections.deque([root_module])
+    visited_modules: set[nn.Module] = {root_module}
+    while queue:
+        module = queue.popleft()
+        modules_to_materialize.append(module)
+        for child_module in module.children():
+            if (
+                child_module not in visited_modules
+                and _get_module_fsdp_state(child_module) is None
+                and child_module not in ignored_modules
+            ):
+                visited_modules.add(child_module)
+                queue.append(child_module)
+    return modules_to_materialize
+
+
+def _move_module_to_device(
+    module: nn.Module,
+    ignored_params: set[nn.Parameter],
+    ignored_buffers: set[torch.Tensor],
+    device_from_device_id: Optional[torch.device],
+) -> None:
+    """
+    Move ``module`` depending on ``device_from_device_id`` and its current device.
+
+    This includes moving ignored modules' parameters.
+
+    - If ``device_from_device_id`` is not ``None``, then this moves
+    ``module`` to the device.
+    - If ``device_from_device_id`` is ``None``, then this does not move
+    ``module`` but warns the user if it is on CPU.
+
+    Precondition: ``_check_single_device_module()``.
+    """
+    cpu_device = torch.device("cpu")
+    if device_from_device_id is not None:
+        # BFS from `module` without traversing any nested FSDP instances to
+        # collect the parameters/buffers that have not yet been managed
+        queue: collections.deque[nn.Module] = collections.deque()
+        queue.append(module)
+        params: list[nn.Parameter] = []
+        buffers: list[torch.Tensor] = []
+        while queue:
+            curr_module = queue.popleft()
+            # NOTE: We include a check to only move parameters/buffers that are
+            # on CPU device. If they are on a CUDA device different from the
+            # one specified by `device_id`, then this does NOT move them. This
+            # is so that we can raise an error in `_get_compute_device()`.
+            params.extend(
+                param
+                for param in curr_module.parameters(recurse=False)
+                if param.device == cpu_device
+            )
+            buffers.extend(
+                buffer
+                for buffer in curr_module.buffers(recurse=False)
+                if buffer.device == cpu_device
+            )
+            for submodule in curr_module.children():
+                if not isinstance(submodule, fsdp_file.FullyShardedDataParallel):
+                    queue.append(submodule)
+        params_to_move = [p for p in params if p not in ignored_params]
+        bufs_to_move = [p for p in buffers if p not in ignored_buffers]
+        _move_states_to_device(params_to_move, bufs_to_move, device_from_device_id)
+        return
+    param = next(_get_orig_params(module, ignored_params), None)
+    if param is not None and param.device == cpu_device:
+        _warn_cpu_init()
+
+
+def _move_states_to_device(
+    params: list[nn.Parameter],
+    buffers: list[torch.Tensor],
+    device_from_device_id: Optional[torch.device],
+) -> None:
+    """
+    Move states to the specified device.
+
+    Precondition: ``_check_single_device_module()`` and module's parameters and
+    buffers have been materialized if needed.
+    """
+    if len(params) == 0 and len(buffers) == 0:
+        return
+    if len(params) > 0:
+        current_device = params[0].device
+    elif len(buffers) > 0:
+        current_device = buffers[0].device
+    cpu_device = torch.device("cpu")
+    if device_from_device_id is not None:
+        # Move the parameters and buffers like the `.data` code path in
+        # `nn.Module._apply()`, which underlies `nn.Module.to()`
+        for param in params:
+            with torch.no_grad():
+                param.data = param.to(device_from_device_id)
+                if param.grad is not None:
+                    param.grad.data = param.grad.to(device_from_device_id)
+        for buffer in buffers:
+            buffer.data = buffer.to(device_from_device_id)
+    elif current_device == cpu_device:  # type: ignore[possibly-undefined]
+        _warn_cpu_init()
+
+
+def _warn_cpu_init():
+    warnings.warn(
+        "The passed-in `module` is on CPU and will thus have FSDP's sharding "
+        "initialization run on CPU, which may be slower than on GPU. We "
+        "recommend passing in the `device_id` argument for FSDP to move "
+        "`module` to GPU for the sharding initialization. `module` must also "
+        "be on GPU device to work with the `sync_module_states=True` flag "
+        "since that requires GPU communication.",
+        stacklevel=2,
+    )
+
+
+def _get_compute_device(
+    module: nn.Module,
+    ignored_params: set[nn.Parameter],
+    device_from_device_id: Optional[torch.device],
+    rank: int,
+    device_handle: _FSDPDeviceHandle,
+) -> torch.device:
+    """
+    Determine and return this FSDP instance's compute device.
+
+    If the module is already on a non-CPU device, then the compute device is that non-CPU
+    device. If the module is on CPU, then the compute device is the current
+    device.
+
+    Since this method should be called after materializing the module, any
+    non-CPU device should not be meta device. For now, the compute device is
+    always a CUDA or CUDA-like device with its explicit index.
+
+    Precondition: ``_check_single_device_module()`` and
+    ``_move_module_to_device()``.
+    """
+    param = next(_get_orig_params(module, ignored_params), None)
+    if param is not None and param.device.type != "cpu":
+        compute_device = param.device  # Determined by model param placement
+    else:
+        compute_device = torch.device(device_handle.current_device())
+    if device_from_device_id is not None and compute_device != device_from_device_id:
+        raise ValueError(
+            f"Inconsistent compute device and `device_id` on rank {rank}: "
+            f"{compute_device} vs {device_from_device_id}"
+        )
+    return compute_device
+
+
+# TODO: See how to deprecate!
+def _sync_module_params_and_buffers(
+    module: nn.Module,
+    params: list[nn.Parameter],
+    process_group: dist.ProcessGroup,
+) -> None:
+    """
+    Synchronize module states (i.e. parameters ``params`` and all not-yet-synced buffers) by broadcasting from rank 0 to all ranks.
+
+    Precondition: ``sync_module_states == True`` and ``self.process_group`` has
+    been set.
+    """
+    module_states: list[torch.Tensor] = []
+    for buffer in module.buffers():
+        # Avoid re-synchronizing buffers in case of nested wrapping
+        if not getattr(buffer, FSDP_SYNCED, False):
+            setattr(buffer, FSDP_SYNCED, True)
+            detached_buffer = buffer.detach()
+            if is_traceable_wrapper_subclass(detached_buffer):
+                # NOTE: Here we assume no nested subclasses, at most one level of subclass
+                # in both model's buffers and params
+                attrs, _ = detached_buffer.__tensor_flatten__()  # type: ignore[attr-defined]
+                inner_buffers = [getattr(detached_buffer, attr) for attr in attrs]
+                module_states.extend(inner_buffers)
+            else:
+                module_states.append(detached_buffer)
+
+    for param in params:
+        detached_param = param.detach()
+        if is_traceable_wrapper_subclass(detached_param):
+            attrs, _ = detached_param.__tensor_flatten__()  # type: ignore[attr-defined]
+            inner_params = [getattr(detached_param, attr) for attr in attrs]
+            module_states.extend(inner_params)
+        else:
+            module_states.append(detached_param)
+
+    _check_module_states_for_sync_module_states(module_states)
+    _sync_params_and_buffers(
+        process_group,
+        module_states,
+        PARAM_BROADCAST_BUCKET_SIZE,
+        src=0,
+    )
+
+
+def _check_module_states_for_sync_module_states(
+    module_states: list[torch.Tensor],
+) -> None:
+    if module_states and any(
+        tensor.device == torch.device("cpu") for tensor in module_states
+    ):
+        raise ValueError(
+            "The module has CPU parameters or buffers when `sync_module_states=True`, "
+            "which requires them to be on GPU. Please specify the `device_id` argument "
+            "or move the module to GPU before passing it to FSDP."
+        )
+
+
+def _get_orig_params(
+    module: nn.Module,
+    ignored_params: set[nn.Parameter],
+) -> Iterator[nn.Parameter]:
+    """
+    Return an iterator over the original parameters in ``module``.
+
+    The iterator does not return
+    the parameters in ``ignored_params``, any ``FlatParameter`` s (which may be
+    present due to nested FSDP wrapping), or any original parameters already
+    flattened (only relevant when ``use_orig_params=True``).
+    """
+    param_gen = module.parameters()
+    try:
+        while True:
+            param = next(param_gen)
+            if param not in ignored_params and not _is_fsdp_flattened(param):
+                yield param
+    except StopIteration:
+        pass
+
+
+def _check_orig_params_flattened(
+    fsdp_module,
+    ignored_params: set[nn.Parameter],
+) -> None:
+    """
+    Check that original parameters in ``fsdp_module`` have been flattened.
+
+    The flattened parameters are made
+    invisible to ``named_parameters()`` for the module hierarchy rooted at
+    ``fsdp_module``. This should be called as a sanity check after flattening
+    the wrapped module's parameters.
+    """
+    for param_name, param in _named_parameters_with_duplicates(fsdp_module):
+        if param not in ignored_params and not _is_fsdp_flattened(param):
+            raise RuntimeError(
+                f"Found an unflattened parameter: {param_name}; "
+                f"{param.size()} {param.__class__}"
+            )
+
+
+def _get_default_comm_hook(sharding_strategy: ShardingStrategy):
+    return (
+        default_hooks.allreduce_hook
+        if sharding_strategy == ShardingStrategy.NO_SHARD
+        else default_hooks.reduce_scatter_hook
+    )
+
+
+def _get_default_comm_hook_state(
+    process_group: dist.ProcessGroup,
+) -> default_hooks.DefaultState:
+    return default_hooks.DefaultState(process_group=process_group)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_limiter_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_limiter_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9b190585342ee267716abace19add022b4d6b3e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_limiter_utils.py
@@ -0,0 +1,33 @@
+import collections
+from typing import Optional
+
+import torch
+
+
+class _FreeEventQueue:
+    """
+    This tracks all pending frees corresponding to inflight all-gathers. The
+    queueing pattern is iterative enqueues with a single dequeue per iteration
+    once the limit ``_max_num_inflight_all_gathers`` is reached.
+    """
+
+    def __init__(self) -> None:
+        self._queue: collections.deque[torch.Event] = collections.deque()
+        self._max_num_inflight_all_gathers = 2  # empirically chosen
+
+    def enqueue(self, free_event: torch.Event) -> None:
+        """Enqueues a free event."""
+        self._queue.append(free_event)
+
+    def dequeue_if_needed(self) -> Optional[torch.Event]:
+        """Dequeues a single event if the limit is reached."""
+        if len(self._queue) >= self._max_num_inflight_all_gathers:
+            return self._dequeue()
+        return None
+
+    def _dequeue(self) -> Optional[torch.Event]:
+        """Dequeues a free event if possible."""
+        if self._queue:
+            event = self._queue.popleft()
+            return event
+        return None
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_optim_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_optim_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..564cfeece48ee1e656ea4e06628c36c0d01c0af8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_optim_utils.py
@@ -0,0 +1,2139 @@
+# mypy: allow-untyped-defs
+import copy
+import functools
+import logging
+import warnings
+from collections.abc import Iterable, Iterator, Sequence
+from contextlib import ExitStack
+from dataclasses import dataclass, field
+from itertools import chain
+from typing import Any, cast, NamedTuple, no_type_check, Optional, TYPE_CHECKING, Union
+
+import torch
+import torch.distributed as dist
+import torch.distributed.fsdp._traversal_utils as traversal_utils
+import torch.nn as nn
+from torch.distributed._state_dict_utils import _gather_state_dict
+from torch.distributed.distributed_c10d import _get_pg_default_device
+from torch.distributed.fsdp._common_utils import (
+    _apply_to_modules,
+    _FSDPState,
+    _get_module_fsdp_state_if_fully_sharded_module,
+    _get_param_to_fqns,
+    _module_handle,
+    _named_parameters_with_duplicates,
+    clean_tensor_name,
+)
+from torch.distributed.fsdp._debug_utils import SimpleProfiler
+from torch.distributed.fsdp._flat_param import FlatParameter, FlatParamHandle
+from torch.distributed.fsdp._fsdp_extensions import (
+    _ext_chunk_dtensor,
+    _ext_chunk_tensor,
+)
+from torch.distributed.fsdp._runtime_utils import (
+    _lazy_init,
+    _reset_flat_param_grad_info_if_needed,
+)
+from torch.distributed.fsdp.api import (
+    ShardingStrategy,
+    StateDictSettings,
+    StateDictType,
+)
+from torch.distributed.tensor import DTensor, Replicate
+from torch.utils._pytree import tree_map_only
+
+
+if TYPE_CHECKING:
+    from torch.distributed._shard.sharded_tensor import ShardedTensor
+
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class FSDPParamInfo:
+    state: _FSDPState
+    handle: FlatParamHandle
+    param_indices: dict[str, int]
+    param_requires_grad: list[bool]
+
+
+def sorted_items(dictionary: dict[str, Any]) -> Iterator[tuple[str, Any]]:
+    keys = sorted(dictionary.keys())
+    for k in keys:
+        yield k, dictionary[k]
+
+
+@dataclass
+class _ConsolidatedOptimState:
+    """
+    This holds the consolidated optimizer state on the target rank. Positive-
+    dimension tensor state is communicated across ranks, while zero-dimension
+    tensor state and non-tensor state is taken directly from the target rank.
+
+    PyTorch version 1.12 moved to using zero-dimension tensors for scalar
+    values, but user implemented optimizers may still use float (i.e. a
+    non-tensor). Thus, we support both and handle them identically.
+
+    Attributes:
+        tensor_state (Dict[str, torch.Tensor]): Mapping from positive-dimension
+            tensor state name to the unsharded flat tensor representing the
+            state.
+        zero_dim_tensor_state (Dict[str, torch.Tensor]): Mapping from zero-
+            dimension tensor state name to its value.
+        non_tensor_state (Dict[str, Any]): Mapping from non-tensor state
+            name to its value.
+    """
+
+    tensor_state: dict[str, torch.Tensor] = field(default_factory=dict)
+    zero_dim_tensor_state: dict[str, torch.Tensor] = field(default_factory=dict)
+    non_tensor_state: dict[str, Any] = field(default_factory=dict)
+
+
+class _PosDimTensorInfo(NamedTuple):
+    """
+    Metadata for positive-dimension tensors used internally for
+    :meth:`scatter_full_optim_state_dict`.
+
+    Attributes:
+        shape (torch.Size): Sharded tensor shape (which is equal to the
+            unsharded tensor shape if the tensor is optimizer state for a
+            non-FSDP parameter and is hence not sharded).
+        dtype (torch.dtype): Data type of the tensor.
+    """
+
+    shape: torch.Size
+    dtype: torch.dtype
+
+
+class _OptimStateKey(NamedTuple):
+    """
+    This represents an optimizer state key that may be used commonly across
+    ranks. It is based on the unflattened parameter names rather than parameter
+    IDs to make it independent of each rank's own optimizer construction.
+    """
+
+    unflat_param_names: tuple[str, ...]
+    is_fsdp_managed: bool
+
+
+def _unflatten_optim_state(
+    fsdp_param_info: FSDPParamInfo,
+    flat_param_state: dict[str, Any],
+    to_save: bool,
+    shard_state: bool,
+    cpu_offload: bool,
+) -> list[dict[str, Any]]:
+    """
+    Unflattens the optimizer state, consisting of the "state" part and the
+    "param_groups" part. Unflattening the "state" part involves consolidating
+    the state on the target rank and remapping from flattened to unflattened
+    parameter IDs, and the "param_groups" part only involves remapping from
+    flattened to unflattened parameter IDs.
+
+    Args:
+        fsdp_param_info (FSDPParamInfo): The FSDP state, the handle, and a
+            mapping from FQN to original parameter index.
+        flat_param_state (Dict[str, Any]): Entry for the flat parameter in the
+            "state" part of the optimizer state dict.
+        to_save (bool): Whether to save the state on this rank.
+
+    Returns:
+        List[Dict[str, Any]]: A :class:`list` holding the entries in the
+        "state" part of the optimizer state dict corresponding to the
+        unflattened parameters comprising the flat parameter if on the target
+        rank or an empty :class:`list` otherwise. The final optimizer state
+        dict will need to map these entries using the proper unflattened
+        parameter IDs.
+    """
+    if shard_state and not to_save:
+        raise AssertionError("If ``shard_state`` is True, ``to_save`` has to be True.")
+    consolidated_state = _communicate_optim_state(
+        fsdp_param_info,
+        flat_param_state,
+    )
+    if to_save:
+        unflat_param_state = _unflatten_communicated_optim_state(
+            fsdp_param_info,
+            consolidated_state,
+            shard_state,
+        )
+        for optim_state in unflat_param_state:
+            # We can't use .items() below cuz we'd run into a concurrent modification error
+            if cpu_offload:
+                for key in list(optim_state.keys()):
+                    state = optim_state[key]
+                    if not isinstance(state, torch.Tensor):
+                        continue
+                    optim_state[key] = state.cpu()
+        return unflat_param_state
+    else:
+        return []
+
+
+def _is_zero_dim_tensor(x: Any) -> bool:
+    return torch.is_tensor(x) and x.dim() == 0
+
+
+def _communicate_optim_state(
+    fsdp_param_info: FSDPParamInfo,
+    flat_param_state: dict[str, Any],
+) -> _ConsolidatedOptimState:
+    """
+    Communicates the optimizer state for a flat parameter across ranks. All
+    ranks will hold the entire non-sharded optimizer state on GPU.
+
+    If ``N`` is the number of tensor optimizer states in the optimizer state
+    dict, then the communication complexity is 0 if ``N = 0`` and ``N + 1``
+    otherwise (where the plus 1 comes from all-gathering the padding per rank).
+
+    Args:
+        fsdp_param_info (FSDPParamInfo): The FSDP state, the handle, and a
+            mapping from FQN to original parameter index.
+        flat_param_state (Dict[str, Any]): The entry in the "state" part of the
+            optimizer state dict corresponding to the flat parameter.
+
+    Returns:
+        ConsolidatedOptimState: Consolidated optimizer state for the target
+        flat parameter.
+    """
+    fsdp_state = fsdp_param_info.state
+    flat_param = fsdp_param_info.handle.flat_param
+    state = _ConsolidatedOptimState()
+    tensor_state, zero_dim_tensor_state, non_tensor_state = (
+        state.tensor_state,
+        state.zero_dim_tensor_state,
+        state.non_tensor_state,
+    )
+
+    for state_name, value in sorted_items(flat_param_state):
+        # Positive-dimension tensor state: communicate across ranks
+        if torch.is_tensor(value) and value.dim() > 0:
+            # If the parameter is not sharded, then neither is the
+            # positive-dimension tensor state, so no need to communicate it --
+            # we take the target rank's value
+            if (
+                fsdp_state.world_size == 1
+                or fsdp_state.sharding_strategy == ShardingStrategy.NO_SHARD
+            ):
+                tensor_state[state_name] = value
+                continue
+            if fsdp_state.compute_device is None:
+                raise AssertionError("compute_device has not been initialized")
+            if value.device.type != fsdp_state.compute_device.type:
+                value = value.to(fsdp_state.compute_device)
+            # Assume that positive-dimension tensor optimizer state
+            # has the same shape as the sharded flat parameter
+            buffer_size = flat_param._full_param_padded.size()  # type: ignore[attr-defined]
+            tensor_buffer = value.new_zeros(*buffer_size)
+            dist.all_gather_into_tensor(
+                tensor_buffer, value, group=fsdp_state.process_group
+            )
+            fsdp_state._device_handle.synchronize()
+            unpadded_numel = cast(
+                nn.Parameter, flat_param._unpadded_unsharded_size
+            ).numel()
+            tensor_state[state_name] = tensor_buffer[:unpadded_numel]
+        # Zero-dimension tensor state and non-tensor state: take this rank's
+        # value directly
+        else:
+            if _is_zero_dim_tensor(value):
+                zero_dim_tensor_state[state_name] = value.detach().clone()
+            else:
+                non_tensor_state[state_name] = value
+    return state
+
+
+def _unflatten_communicated_optim_state(
+    fsdp_param_info: FSDPParamInfo,
+    state: _ConsolidatedOptimState,
+    shard_state: bool,
+) -> list[dict[str, Any]]:
+    """
+    Unflattens the communicated optimizer state (given by ``tensor_state``,
+    ``non_tensor_state``, and ``zero_dim_tensor_state``) for a single flat
+    parameter. This should only be called on the target rank.
+
+    Args:
+        fsdp_param_info (FSDPParamInfo): The FSDP state, the handle, and a
+            mapping from FQN to original parameter index.
+        state (_ConsolidatedOptimState): Consolidated optimizer state.
+
+    Returns:
+        List[Dict[str, Any]]: A :class:`list` holding the entries in the
+        "state" part of the optimizer state dict corresponding to the
+        unflattened parameters comprising the flat parameter. The final
+        optimizer state dict will need to map these entries using the proper
+        unflattened parameter IDs.
+    """
+    fsdp_state = fsdp_param_info.state
+    handle = fsdp_param_info.handle
+    flat_param = handle.flat_param
+    unflat_param_state: list[dict[str, Any]] = []
+    flat_param_views: dict[str, Iterator] = {}
+    num_unflat_params = flat_param._num_params
+    tensor_state, zero_dim_tensor_state, non_tensor_state = (
+        state.tensor_state,
+        state.zero_dim_tensor_state,
+        state.non_tensor_state,
+    )
+
+    for _ in range(num_unflat_params):
+        unflat_state_param = {}
+        # Add positive-dimension tensor state: unflatten with views
+        for state_name, flat_tensor in sorted_items(tensor_state):
+            views_generated = state_name in flat_param_views
+            if not views_generated:
+                views = handle._get_unflat_views(flat_tensor)
+                flat_param_views[state_name] = views
+            else:
+                views = flat_param_views[state_name]
+            optim_state: Union[torch.Tensor, ShardedTensor, DTensor] = next(views)
+            if shard_state:
+                osd_config = fsdp_state._optim_state_dict_config
+                if getattr(osd_config, "_use_dtensor", False):
+                    if fsdp_state._device_mesh is None:
+                        raise AssertionError(
+                            f"Expected _device_mesh to be not None, got {fsdp_state._device_mesh}"
+                        )
+                    optim_state = _ext_chunk_dtensor(
+                        optim_state,
+                        fsdp_state.rank,
+                        fsdp_state._device_mesh,
+                        fsdp_state._fsdp_extension,
+                    )
+                else:
+                    if fsdp_state.process_group is None:
+                        raise AssertionError(
+                            f"Expected process_group to be not None, got {fsdp_state.process_group}"
+                        )
+                    optim_state = _ext_chunk_tensor(
+                        optim_state,
+                        fsdp_state.rank,
+                        fsdp_state.world_size,
+                        fsdp_state._device_handle.device_count(),
+                        fsdp_state.process_group,
+                        fsdp_state._fsdp_extension,
+                    )
+            unflat_state_param[state_name] = optim_state
+
+        # Add zero-dimension tensor state: take the target rank's value
+        unflat_state_param.update(sorted_items(zero_dim_tensor_state))
+        # Add non-tensor state: take the target rank's value
+        unflat_state_param.update(sorted_items(non_tensor_state))
+        unflat_param_state.append(unflat_state_param)
+    return unflat_param_state
+
+
+def _broadcast_processed_state(
+    fsdp_state: _FSDPState,
+    optim_state: dict[str, Any],
+    group: Optional[dist.ProcessGroup],
+) -> dict[str, Any]:
+    objects: list[Any] = [None]
+    if dist.get_rank(group) == 0:
+        objects[0] = tree_map_only(
+            torch.Tensor,
+            lambda v: v.cpu() if v.dim() == 0 else _PosDimTensorInfo(v.shape, v.dtype),  # type: ignore[union-attr]
+            optim_state,
+        )
+    dist.broadcast_object_list(objects, src=0, group=group)
+    if dist.get_rank(group) == 0:
+        return optim_state
+    else:
+        return objects[0]
+
+
+def _broadcast_state(
+    fsdp_state: _FSDPState, state: Any, group: Optional[dist.ProcessGroup]
+) -> Any:
+    if dist.get_rank(group) == 0:
+        if not isinstance(state, torch.Tensor) or state.dim() == 0:
+            return state
+        tensor = state.to(fsdp_state.compute_device)
+    else:
+        if isinstance(state, torch.Tensor):
+            if state.dim() != 0:
+                raise AssertionError(
+                    "For non-zero ranks, a tensor state should have zero dimension, "
+                    f"but got the state with shape {state.shape}."
+                )
+            return state
+        elif not isinstance(state, _PosDimTensorInfo):
+            return state
+        tensor = torch.zeros(
+            state.shape, dtype=state.dtype, device=fsdp_state.compute_device
+        )
+    dist.broadcast(tensor, src=0, group=group)
+    return tensor
+
+
+def _shard_orig_param_state(
+    fsdp_param_info: FSDPParamInfo,
+    fqn: str,
+    optim_state: dict[str, Any],
+) -> dict[str, Any]:
+    """
+    Shard the optimizer state for the original parameter with the name ``fqn``.
+    This API should only be used when ``use_orig_params`` is True.
+    """
+    if not optim_state:
+        return {}
+    fsdp_state = fsdp_param_info.state
+    flat_param = fsdp_param_info.handle.flat_param
+    param_idx = fsdp_param_info.param_indices[fqn]
+    shard_param_info = flat_param._shard_param_infos[param_idx]  # type: ignore[attr-defined]
+    optim_state = _gather_state_dict(
+        optim_state, pg=fsdp_state.process_group, device=fsdp_state.compute_device
+    )
+    if not shard_param_info.in_shard:
+        return {}
+    # Flatten and shard the state.
+    new_optim_state: dict[str, Any] = {}
+    intra_param_start_idx = shard_param_info.intra_param_start_idx
+    intra_param_end_idx = shard_param_info.intra_param_end_idx
+    for state_name, value in optim_state.items():
+        if (
+            torch.is_tensor(value)
+            and value.dim() > 0
+            and fsdp_state.sharding_strategy != ShardingStrategy.NO_SHARD
+        ):
+            value = value.flatten()[
+                intra_param_start_idx : intra_param_end_idx  # type: ignore[operator]
+                + 1
+            ].clone()
+        new_optim_state[state_name] = value
+    return new_optim_state
+
+
+def _flatten_optim_state_dict(
+    optim_state_dict: dict[str, Any],
+    model: nn.Module,
+    use_orig_params: bool = False,
+    optim: Optional[torch.optim.Optimizer] = None,
+    rank0_only: bool = False,
+    group: Optional[dist.ProcessGroup] = None,
+) -> dict[str, Any]:
+    """
+    Flattens the full optimizer state dict, still keying by unflattened parameter
+    names.
+
+    If ``use_orig_params`` is True, each rank will have all FSDP-managed
+    parameters but some of these parameters may be empty due to the sharding.
+    For a regular optim.Optimizer, states for those empty parameters will
+    not be initialized. So, when aggregating the FQNs across ranks, no assert
+    will be raised on a rank even if it does not have all the states -- it is
+    valid and FSDP know how to aggregate them. However, FSDP has to ignore
+    handling those parameters that are not managed by FSDP and do not exist on
+    the local rank -- it is managed by other parallelism and FSDP does not
+    know ho to handle/aggregate them.
+
+    Note that ``_flatten_tensor_optim_state`` does not need ``optim`` to
+    flatten/shard the state. However, NamedOptimizer and KeyedOptimizer require
+    all the states even if the corresponding parameters are empty. To this end,
+    ``optim`` will be used to get the initial state of the empty parameters.
+    ``optim`` should only be non-None if the ``optim` is KeyedOptimizer or
+    NamedOptimizer.
+
+    Returns:
+        Dict[str, Any]: The flattened optimizer state dict.
+    """
+    SimpleProfiler.reset()
+
+    unflat_osd = optim_state_dict
+    if "state" not in unflat_osd and not rank0_only:
+        raise ValueError(
+            '`optim_state_dict` must have the keys "state"'
+            "to be a valid optimizer state dict"
+        )
+    param_to_fqns = _get_param_to_fqns(model)
+    fqn_to_fsdp_param_info = _get_fqn_to_fsdp_param_info(model)
+    fsdp_state = next(iter(fqn_to_fsdp_param_info.values())).state
+
+    # Broadcast unflat_osd without non-scalar tensor if rank0_only is True.
+    if rank0_only:
+        unflat_osd = _broadcast_processed_state(fsdp_state, unflat_osd, group=group)
+
+    # Construct the "state" part
+    flat_osd_state: dict[Union[_OptimStateKey, str], Any] = {}
+    unflat_osd_state = unflat_osd["state"]
+    all_state_keys = set(unflat_osd_state.keys())
+
+    for param, fqns in param_to_fqns.items():
+        fqn = fqns[0]
+        if fqn not in unflat_osd_state:
+            continue
+        all_state_keys.difference_update(fqns)
+
+        if rank0_only:
+            for fqn in fqns:
+                if not unflat_osd_state[fqn]:
+                    continue
+                for state_name in unflat_osd_state[fqn]:
+                    unflat_osd_state[fqn][state_name] = _broadcast_state(
+                        fsdp_state, unflat_osd_state[fqn][state_name], group=group
+                    )
+            fqn = fqns[0]
+        if fqn in fqn_to_fsdp_param_info:
+            fsdp_param_info = fqn_to_fsdp_param_info[fqn]
+            if use_orig_params:
+                with SimpleProfiler.profile(SimpleProfiler.Type.RESHARDING):
+                    flat_state = _shard_orig_param_state(
+                        fsdp_param_info,
+                        fqn,
+                        unflat_osd_state[fqn],
+                    )
+            else:
+                flat_state = _flatten_optim_state(
+                    fsdp_param_info,
+                    unflat_osd_state,
+                    fqns,
+                )
+            key = _OptimStateKey(tuple(fqns), True)
+            # Only include non-empty states since as expected by
+            # `torch.optim.Optimizer` s unless the optimizer is KeyedOptimizer
+            # or NamedOptimizer.
+            if flat_state:
+                flat_osd_state[key] = flat_state
+            elif use_orig_params:
+                if len(fqns) != 1:
+                    raise AssertionError(
+                        f"use_orig_params is True but there are multiple FQNs, {fqns}."
+                    )
+                if optim is not None:  # NamedOptimizer or KeyedOptimizer case.
+                    state = optim.state.get(param, None)  # type: ignore[call-overload]
+                    if state is not None:
+                        flat_osd_state[key] = copy.deepcopy(state)
+                    else:
+                        warnings.warn(
+                            f"optim_state[{key}] is not on rank{fsdp_state.rank}.",
+                            stacklevel=2,
+                        )
+
+            else:
+                raise RuntimeError(
+                    f"The state of {key} is empty. This should happen when "
+                    "use_orig_params=True."
+                )
+        else:  # do not flatten non-FSDP parameters' states
+            if len(fqns) != 1:
+                raise AssertionError(f"Expected len(fqns) == 1, got {len(fqns)}")
+            key = _OptimStateKey(tuple(fqns), False)
+            flat_osd_state[key] = copy.copy(unflat_osd_state[fqn])
+
+        if rank0_only:
+            for fqn in fqns:
+                if not unflat_osd_state[fqn]:
+                    continue
+                for state_name, param_state in list(unflat_osd_state[fqn].items()):
+                    if fsdp_state.rank > 0:
+                        # Deference the tensor so that PyTorch can collect the memory.
+                        del unflat_osd_state[fqn][state_name]
+                    else:
+                        # Move the tensor in the original osd back to CPU to make the
+                        # original osd unaffected.
+                        unflat_osd_state[fqn][state_name] = param_state.cpu()
+
+    # Handle user-defined state, states that are not associated with parameters.
+    for key in all_state_keys:
+        user_state = unflat_osd_state[key]
+        if isinstance(user_state, torch.Tensor) and rank0_only and use_orig_params:
+            user_state = _broadcast_state(fsdp_state, user_state, group=group)
+        flat_osd_state[key] = copy.copy(user_state)
+
+    SimpleProfiler.dump_and_reset("FSDP _flatten_optim_state_dict() profiling: ")
+    # Construct the "param_groups" part -- copy as is since it will be
+    # rekeyed later according to the target rank's optimizer
+    # Only copy param_groups if it exists in unflat_osd
+    if "param_groups" in unflat_osd:
+        flat_osd_param_groups = copy.deepcopy(unflat_osd["param_groups"])
+        return {"state": flat_osd_state, "param_groups": flat_osd_param_groups}
+    else:
+        return {"state": flat_osd_state}
+
+
+def _flatten_optim_state(
+    fsdp_param_info: FSDPParamInfo,
+    unflat_osd_state: dict[str, dict[str, Any]],
+    unflat_param_names: list[str],
+) -> dict[str, Any]:
+    """
+    Flattens the optimizer state in ``full_optim_state_dict`` for a single
+    flat parameter in ``fsdp_param_info`` corresponding to the unflattened
+    parameter names in ``unflat_param_names``.
+
+    Args:
+        fsdp_param_info (FSDPParamInfo): The FSDP state, the handle, and a
+            mapping from FQN to original parameter index.
+        unflat_osd_state (Dict[str, Dict[str, Any]]): The "state" part of the
+            optimizer state dict corresponding to the unflattened parameters.
+        unflat_param_names (List[str]): A :class:`list` of unflattened
+            parameter names corresponding to the flat parameter ``flat_param``.
+
+    Returns:
+        Dict[str, Any]: A :class:`dict` mapping state names to their values for
+        a particular flat parameter. The sharded optimizer state dict's "state"
+        part will map a key to this returned value.
+    """
+    fsdp_state = fsdp_param_info.state
+    handle = fsdp_param_info.handle
+    flat_param = handle.flat_param
+    num_unflat_params = len(unflat_param_names)
+    if num_unflat_params <= 0:
+        raise AssertionError(
+            "Expects at least one unflattened parameter corresponding to the flat parameter"
+        )
+    unflat_param_shapes = flat_param._shapes
+    num_unflat_param_shapes = len(unflat_param_shapes)
+    if num_unflat_params != num_unflat_param_shapes:
+        raise AssertionError(
+            f"Expects {num_unflat_params} shapes but got {num_unflat_param_shapes}"
+        )
+
+    # Check if these unflattened parameters have any optimizer state
+    has_state = [
+        bool(unflat_param_name in unflat_osd_state)
+        for unflat_param_name in unflat_param_names
+    ]
+    # If none of the unflattened parameters comprising this flat parameter have
+    # any state, then we do not want an entry in the optimizer state dict
+    if not any(has_state):
+        return {}  # no need to flatten any state
+    # There may still be some unflattened parameters with state and some
+    # without
+    unflat_param_states = [
+        _gather_state_dict(
+            unflat_osd_state[unflat_param_name],
+            pg=fsdp_state.process_group,
+            device=fsdp_state.compute_device,
+        )
+        if unflat_param_name in unflat_osd_state
+        else None
+        for unflat_param_name in unflat_param_names
+    ]
+    # Check that the unflattened parameters have the same state names
+    state_names = None
+    # pyrefly: ignore [bad-assignment]
+    for unflat_param_state in unflat_param_states:
+        if unflat_param_state is None:
+            continue
+        if state_names is None:
+            state_names = set(unflat_param_state.keys())
+        else:
+            if state_names != set(unflat_param_state.keys()):
+                raise ValueError(
+                    "Differing optimizer state names for the unflattened "
+                    f"parameters: {unflat_param_names}"
+                )
+    if state_names is None:
+        raise AssertionError(f"Expected state_names to be not None, got {state_names}")
+
+    # Flatten the state
+    flat_state: dict[str, Optional[torch.Tensor]] = {}
+    for state_name in state_names:
+        state_values = [
+            unflat_param_state[state_name] if unflat_param_state is not None else None
+            for unflat_param_state in unflat_param_states
+        ]
+        non_none_state_values = [v for v in state_values if v is not None]
+        # If all ranks have None, this is a None value
+        if not non_none_state_values:
+            flat_state[state_name] = None
+            continue
+        are_pos_dim_tensors = are_zero_dim_tensors = are_non_tensors = True
+        for v in non_none_state_values:
+            are_pos_dim_tensors &= torch.is_tensor(v) and v.dim() > 0
+            are_zero_dim_tensors &= _is_zero_dim_tensor(v)
+            are_non_tensors &= not torch.is_tensor(v)
+        types = {type(v) for v in non_none_state_values}
+        if len(types) != 1 or not (
+            are_pos_dim_tensors or are_zero_dim_tensors or are_non_tensors
+        ):
+            raise ValueError(
+                f"Differing optimizer state types for state {state_name}, "
+                f"values {non_none_state_values}, and unflattened parameter "
+                f"names {unflat_param_names}"
+            )
+        if are_pos_dim_tensors:
+            flat_tensor = _flatten_tensor_optim_state(
+                state_name,
+                state_values,  # type: ignore[arg-type]
+                unflat_param_names,
+                unflat_param_shapes,
+                handle,
+            )
+            # Shard the flattened tensor immediately to minimize max memory
+            # usage
+            if (
+                fsdp_state.world_size != 1
+                and fsdp_state.sharding_strategy != ShardingStrategy.NO_SHARD
+            ):
+                sharded_flat_tensor, _ = FlatParamHandle._get_shard(
+                    flat_tensor,
+                    fsdp_state.rank,
+                    fsdp_state.world_size,
+                )
+            else:
+                sharded_flat_tensor = flat_tensor
+            flat_state[state_name] = sharded_flat_tensor
+        elif are_zero_dim_tensors:
+            flat_state[state_name] = _flatten_zero_dim_tensor_optim_state(
+                state_name,
+                state_values,  # type: ignore[arg-type]
+                unflat_param_names,
+            )
+        else:
+            if not are_non_tensors:
+                raise AssertionError(
+                    f"Expected are_non_tensors to be True, got {are_non_tensors}"
+                )
+            flat_state[state_name] = _flatten_non_tensor_optim_state(
+                state_name,
+                state_values,
+                unflat_param_names,
+            )
+
+    return flat_state
+
+
+def _flatten_tensor_optim_state(
+    state_name: str,
+    pos_dim_tensors: list[torch.Tensor],
+    unflat_param_names: list[str],
+    unflat_param_shapes: Sequence[torch.Size],
+    handle: FlatParamHandle,
+) -> torch.Tensor:
+    """
+    Flattens the positive-dimension tensor optimizer state given by the values
+    ``tensors`` for the state ``state_name`` for a single flat parameter
+    from ``handle`` corresponding to the unflattened parameter names
+    ``unflat_param_names`` and unflatted parameter shapes
+    ``unflat_param_shapes``. This flattens each unflattened parameter's tensor
+    state into one tensor.
+
+    NOTE: We use zero tensors for any unflattened parameters without state
+    since some value is required to fill those entries. This assumes that the
+    zero tensor is mathematically equivalent to having no state, which is true
+    for Adam's "exp_avg" and "exp_avg_sq" but may not be true for all
+    optimizers.
+
+    Args:
+        state_name (str): Optimizer state name.
+        pos_dim_tensors (List[torch.Tensor]): Positive-dimension tensor
+            optimizer state values for the unflattened parameters corresponding
+            to the single flat parameter.
+        unflat_param_names (List[str]): A :class:`list` of unflattened
+            parameter names corresponding to the single flat parameter.
+        unflat_param_shapes (List[torch.Size]): Unflattened parameter shapes
+            corresponding to the single flat parameter.
+        handle (FlatParamHandle): The flat parameter's handle.
+
+    Returns:
+        torch.Tensor: A flat tensor containing the optimizer state
+        corresponding to ``state_name`` constructed by concatenating the
+        unflattened parameter tensor states in ``pos_dim_tensors`` (using zero
+        tensors for any unflattened parameters without the state).
+    """
+    flat_param = handle.flat_param
+    non_none_tensors = [t for t in pos_dim_tensors if t is not None]
+    # Check that all are tensors with the same dtype
+    dtypes = {t.dtype for t in non_none_tensors}
+    if len(dtypes) != 1:
+        raise ValueError(
+            "All unflattened parameters comprising a single flat "
+            "parameter must have positive-dimension tensor state with the "
+            f"same dtype but got dtypes {dtypes} for state {state_name} and "
+            f"unflattened parameter names {unflat_param_names}"
+        )
+    dtype = next(iter(dtypes))
+    # Check that each tensor state matches its parameter's shape
+    for tensor, shape in zip(pos_dim_tensors, unflat_param_shapes):
+        if tensor is None and len(shape) == 0:
+            raise ValueError("Flattening a zero-dimension parameter is not supported")
+        elif tensor is not None and tensor.shape != shape:
+            raise ValueError(
+                "Tensor optimizer state does not have same shape as its "
+                f"parameter: {tensor.shape} {shape}"
+            )
+    # Flatten the tensor states: we do not need to add any right-hand-side
+    # padding since the flat optimizer state tensor is sharded via
+    # `_get_shard()`, which pads the shard as needed (just like for the flat
+    # parameter)
+    cpu_device = torch.device("cpu")
+    tensors_to_flatten = [
+        torch.flatten(state_value.to(cpu_device))
+        if state_value is not None
+        else torch.flatten(
+            torch.zeros(
+                size=shape,
+                dtype=dtype,
+                device=cpu_device,
+            )
+        )
+        for state_value, shape in zip(pos_dim_tensors, unflat_param_shapes)
+    ]
+    flat_tensor = handle.flatten_tensors(tensors_to_flatten, handle._aligned_numel)
+    flat_param_shape = flat_param._unpadded_unsharded_size  # type: ignore[attr-defined]
+    if flat_tensor.shape != flat_param_shape:
+        raise AssertionError(
+            f"tensor optim state: {flat_tensor.shape} flat parameter: {flat_param_shape}"
+        )
+    return flat_tensor
+
+
+def _flatten_zero_dim_tensor_optim_state(
+    state_name: str,
+    zero_dim_tensors: list[torch.Tensor],
+    unflat_param_names: list[str],
+) -> torch.Tensor:
+    """
+    Flattens the zero-dimension tensor optimizer state given by the values
+    ``zero_dim_tensors`` for the state ``state_name`` for a single flat
+    parameter corresponding to the unflattened parameter names
+    ``unflat_param_names`` by enforcing that all tensors are the same and using
+    that common value.
+
+    NOTE: The requirement that the tensors are the same across all unflattened
+    parameters comprising the flat parameter is needed to maintain the
+    invariant that FSDP performs the same computation as its non-sharded
+    equivalent. This means that none of the unflattened parameters can be
+    missing this state since imposing a value may differ from having no value.
+    For example, for Adam's "step", no value means maximum bias correction,
+    while having some positive value means less bias correction.
+
+    Args:
+        state_name (str): Optimizer state name.
+        zero_dim_tensors (List[torch.Tensor]): Zero-dimension optimizer state
+            for the unflattened parameters corresponding to the single
+            flat parameter.
+        unflat_param_names (List[str]): A :class:`list` of unflattened
+            parameter names corresponding to the single flat parameter.
+
+    Returns:
+        torch.Tensor: A zero-dimensional tensor giving the value of the state
+        ``state_name`` for all unflattened parameters corresponding to the
+        names ``unflat_param_names``.
+    """
+    non_none_tensors = [t for t in zero_dim_tensors if t is not None]
+    # Enforce that all have the same value and dtype
+    values_set = {t.item() if t is not None else None for t in zero_dim_tensors}
+    dtypes = {t.dtype if t is not None else None for t in zero_dim_tensors}
+    if (
+        len(non_none_tensors) != len(zero_dim_tensors)
+        or len(values_set) != 1
+        or len(dtypes) != 1
+    ):
+        raise ValueError(
+            "All unflattened parameters comprising a single flat "
+            "parameter must have scalar state with the same value and dtype "
+            f"but got values {values_set} and dtypes {dtypes} for state "
+            f"{state_name} and unflattened parameter names "
+            f"{unflat_param_names}"
+        )
+    value = next(iter(values_set))
+    dtype = next(iter(dtypes))
+    return torch.tensor(value, dtype=dtype, device=torch.device("cpu"))
+
+
+def _flatten_non_tensor_optim_state(
+    state_name: str,
+    non_tensors: list[Any],
+    unflat_param_names: list[str],
+) -> Any:
+    """
+    Flattens the non-tensor optimizer state given by the values ``non_tensors``
+    for the state ``state_name`` for a single flat parameter corresponding
+    to the unflattened parameter names ``unflat_param_names`` by enforcing that
+    all values are the same and using that common value.
+
+    See the note in :func:`_flatten_zero_dim_tensor_optim_state`.
+
+    Args:
+        state_name (str): Optimizer state name.
+        non_tensors (List[Any]): Non-tensor optimizer state for the unflattened
+            parameters corresponding to the single flat parameter.
+        unflat_param_names (List[str]): A :class:`list` of unflattened
+            parameter names corresponding to the single flat parameter.
+
+    Returns:
+        Any: A non-tensor giving the value of the state ``state_name`` for all
+        unflattened parameters corresponding to the names
+        ``unflat_param_names``.
+    """
+    non_none_non_tensors = [nt for nt in non_tensors if nt is not None]
+    # Enforce that all have the same value (same type already checked)
+    non_tensor_set = set(non_tensors)
+    if len(non_none_non_tensors) != len(non_tensors) or len(non_tensor_set) != 1:
+        raise ValueError(
+            "All unflattened parameters comprising a single flat "
+            "parameter must have scalar state with the same value and dtype "
+            f"but got values {non_tensor_set} for state {state_name} and  "
+            f"unflattened parameter names {unflat_param_names}"
+        )
+    non_tensor = next(iter(non_tensor_set))
+    return non_tensor
+
+
+def _rekey_sharded_optim_state_dict(
+    sharded_osd: dict[str, Any],
+    model: nn.Module,
+    optim: torch.optim.Optimizer,
+    optim_input: Optional[
+        Union[
+            list[dict[str, Any]],
+            Iterable[nn.Parameter],
+        ]
+    ],
+    using_optim_input: bool,
+    is_named_optimizer: bool = False,
+) -> dict[str, Any]:
+    """
+    Rekeys the optimizer state dict from unflattened parameter names to flat
+    parameter IDs according to the calling rank's ``optim``, which may be
+    different across ranks. In particular, the unflattened parameter names are
+    represented as :class:`_OptimStateKey` s.
+    """
+    param_to_fqns = _get_param_to_fqns(model)
+    flat_param_to_fqn = _get_flat_param_to_fqn(model)
+    param_to_param_key: dict[nn.Parameter, Union[int, str]] = cast(
+        dict[nn.Parameter, Union[int, str]],
+        (
+            _get_param_to_param_id_from_optim_input(model, optim_input)
+            if using_optim_input
+            else _get_param_to_param_key(
+                optim, model, is_named_optimizer, param_to_fqns, flat_param_to_fqn
+            )
+        ),
+    )
+    # All parameter keys in `param_to_param_key` should be in
+    # `param_to_fqns` -- strict inequality follows when not all parameters are
+    # passed to the optimizer
+    if len(param_to_param_key) > len(param_to_fqns):
+        raise AssertionError(
+            f"Expected len(param_to_param_key) <= len(param_to_fqns), got {len(param_to_param_key)} > {len(param_to_fqns)}"
+        )
+
+    unflat_param_names_to_flat_param_key: dict[
+        tuple[str, ...], Union[int, str]
+    ] = {}  # for "state"
+    unflat_param_name_to_flat_param_key: dict[
+        str, Union[int, str]
+    ] = {}  # for "param_groups"
+    for param, unflat_param_names in param_to_fqns.items():
+        if param not in param_to_param_key:
+            # This parameter was not passed to the optimizer
+            continue
+        flat_param_key = param_to_param_key[param]
+        unflat_param_names_to_flat_param_key[tuple(unflat_param_names)] = flat_param_key
+        for unflat_param_name in unflat_param_names:
+            unflat_param_name_to_flat_param_key[unflat_param_name] = flat_param_key
+
+    sharded_osd_state = sharded_osd["state"]
+    rekeyed_osd_state: dict[Union[str, int], Any] = {}
+    for key, param_state in sharded_osd_state.items():
+        if isinstance(key, str):
+            rekeyed_osd_state[key] = param_state
+            continue
+        flat_param_key = unflat_param_names_to_flat_param_key.get(
+            key.unflat_param_names, key.unflat_param_names
+        )
+        # pyrefly: ignore [unsupported-operation]
+        rekeyed_osd_state[flat_param_key] = param_state
+
+    # Only process param_groups if it exists in sharded_osd
+    if "param_groups" in sharded_osd:
+        rekeyed_osd_param_groups: list[dict[str, Any]] = []
+        for unflat_param_group in sharded_osd["param_groups"]:
+            flat_param_group = copy.deepcopy(unflat_param_group)
+            flat_param_keys = sorted(
+                {
+                    unflat_param_name_to_flat_param_key[unflat_param_name]
+                    for unflat_param_name in unflat_param_group["params"]
+                }
+            )
+            flat_param_group["params"] = flat_param_keys
+            rekeyed_osd_param_groups.append(flat_param_group)
+        return {"state": rekeyed_osd_state, "param_groups": rekeyed_osd_param_groups}
+    else:
+        return {"state": rekeyed_osd_state}
+
+
+def _get_param_id_to_param_from_optim_input(
+    model: nn.Module,
+    optim_input: Optional[
+        Union[
+            list[dict[str, Any]],
+            Iterable[nn.Parameter],
+        ]
+    ] = None,
+) -> dict[int, nn.Parameter]:
+    """
+    Constructs a mapping from parameter IDs to parameters. This may be used
+    both for models with ``FlatParameter`` s and without.
+
+    NOTE: This method is only preserved for backward compatibility. The method
+    :meth:`_get_param_key_to_param` is the preferred code path that does not
+    rely on ``optim_input``.
+
+    NOTE: We critically assume that, whether the optimizer input is a list of
+    parameters or a list of parameter groups, :class:`torch.optim.Optimizer`
+    enumerates the parameter IDs in order. In other words, for a parameter list
+    input, the parameter IDs should be in that list order, and for a parameter
+    groups input, the parameter IDs should be in order within each parameter
+    group and in order across parameter groups.
+
+    Args:
+        model (nn.Module): Model whose parameters are passed into the
+            optimizer.
+        optim_input (Optional[Union[List[Dict[str, Any]],
+        Iterable[nn.Parameter]]]): Input passed into the optimizer
+            representing either a :class:`list` of parameter groups or an
+            iterable of parameters; if ``None``, then this method assumes the
+            input was ``model.parameters()``. (Default: ``None``)
+
+    Returns:
+        List[nn.Parameter]: Mapping from parameter IDs to parameters,
+        where the parameter ID is implicitly the index in the :class:`list`.
+    """
+    # Assume the standard case of passing `model.parameters()` to the optimizer
+    # if `optim_input` is not specified
+    if optim_input is None:
+        return dict(enumerate(model.parameters()))
+    try:
+        # pyrefly: ignore [no-matching-overload]
+        # pyrefly: ignore [redundant-cast]
+        params = cast(list[nn.Parameter], list(optim_input))
+    except TypeError as e:
+        raise TypeError(
+            "Optimizer input should be an iterable of Tensors or dicts, "
+            f"but got {optim_input}"
+        ) from e
+    if len(params) == 0:
+        raise ValueError("Optimizer input should not be empty")
+
+    # Check if the optimizer input represents tensors or parameter groups
+    all_tensors = True
+    all_dicts = True
+    for param in params:
+        all_tensors &= isinstance(param, torch.Tensor)
+        all_dicts &= isinstance(param, dict)
+    if not all_tensors and not all_dicts:
+        raise TypeError("Optimizer input should be an iterable of Tensors or dicts")
+    if all_tensors:
+        return dict(enumerate(params))
+    if not all_dicts:
+        raise AssertionError(f"Expected all_dicts to be True, got {all_dicts}")
+    param_id_to_param: list[nn.Parameter] = []
+    for param_group in params:
+        has_params_key = "params" in param_group  # type: ignore[operator]
+        if not has_params_key:
+            raise AssertionError(
+                'A parameter group should map "params" to a list of the parameters in the group'
+            )
+        # Implicitly map `flat_param_id` (current length of the list) to
+        # `param`
+        param_id_to_param.extend(param_group["params"])  # type: ignore[index]
+    return dict(enumerate(param_id_to_param))
+
+
+def _get_flat_param_to_fqn(model: torch.nn.Module) -> dict[FlatParameter, str]:
+    """
+    Constructs a mapping from ``FlatParameter`` to a cleaned (devoid of prefixes
+    from wrappers) fully qualified name (FQN). Note that this FQN is "non-canonical"
+    because ``FlatParameter``  s do not come from the original module but are
+    registered only after FSDP has been applied. This function returns the FSDP-given
+    name for the ``FlatParameter`` (usually module._flat_param) as opposed to the
+    canonical FQNs returned for ``FlatParameter`` s in ``_common_utils._get_param_to_fqns(...)``).
+
+    Consequently, this function will only return a non-empty mapping if FSDP was
+    applied with ``use_orig_params=False`` as, otherwise, the original parameters
+    are used within the module and there would be no ``FlatParameter`` s in the module.
+
+    """
+
+    def module_fn(module, prefix, tree_level, flat_param_to_fqn):
+        for param_name, param in _named_parameters_with_duplicates(
+            module, recurse=False
+        ):
+            if not isinstance(param, FlatParameter):
+                continue
+            fqn = clean_tensor_name(prefix + param_name)
+            flat_param_to_fqn[param] = fqn
+
+    def return_fn(flat_param_to_fqn):
+        return flat_param_to_fqn
+
+    flat_param_to_fqn_ret: dict[FlatParameter, str] = {}
+    return _apply_to_modules(
+        model,
+        module_fn,
+        return_fn,
+        [fqn for fqn, _ in _named_parameters_with_duplicates(model)],
+        flat_param_to_fqn_ret,
+    )
+
+
+def _get_param_key_to_param(
+    optim: torch.optim.Optimizer,
+    model: Optional[nn.Module] = None,
+    is_named_optimizer: bool = False,
+    param_to_fqns: Optional[dict[nn.Parameter, list[str]]] = None,
+    flat_param_to_fqn: Optional[dict[FlatParameter, str]] = None,
+) -> dict[Union[int, str], nn.Parameter]:
+    """
+    Constructs a mapping from parameter keys to parameters. For the regular
+    optimizers, the keys are parameter IDs. For NamedOptimizer, the keys
+    are FQNs. This API may be used both for models with ``FlatParameter`` s and
+    without.
+    """
+    clean_fqn_to_curr_fqn: dict[str, str] = {}
+    if is_named_optimizer:
+        if param_to_fqns is None or flat_param_to_fqn is None:
+            raise AssertionError(
+                "The optimizer is a NamedOptimizer, `param_to_fqns` must not be None."
+            )
+        if model is None:
+            raise AssertionError(f"Expected model to be not None, got {model}")
+        for key, _ in _named_parameters_with_duplicates(model):
+            clean_fqn_to_curr_fqn[clean_tensor_name(key)] = key
+
+    param_key_to_param: dict[Union[str, int], nn.Parameter] = {}
+    pid = 0
+    for param_group in optim.param_groups:
+        if is_named_optimizer:
+            for param in param_group["params"]:
+                if flat_param_to_fqn is None:
+                    raise AssertionError(
+                        f"Expected flat_param_to_fqn to be not None, got {flat_param_to_fqn}"
+                    )
+                if param in flat_param_to_fqn:
+                    # FlatParameter case
+                    key = flat_param_to_fqn[param]
+                else:
+                    if param_to_fqns is None:
+                        raise AssertionError(
+                            f"Expected param_to_fqns to be not None, got {param_to_fqns}"
+                        )
+                    # use_orig_params case
+                    if len(param_to_fqns[param]) != 1:
+                        raise AssertionError(
+                            f"Expected len(param_to_fqns[param]) == 1, got {len(param_to_fqns[param])}"
+                        )
+                    key = param_to_fqns[param][0]
+                try:
+                    key = clean_fqn_to_curr_fqn[key]
+                except KeyError as e:
+                    raise KeyError(
+                        f"Can't find {key} from {list(clean_fqn_to_curr_fqn.keys())}."
+                    ) from e
+                param_key_to_param[key] = param
+        else:
+            for param in param_group["params"]:
+                param_key_to_param[pid] = param
+                pid += 1
+
+    return param_key_to_param
+
+
+def _get_param_to_param_key(
+    optim: torch.optim.Optimizer,
+    model: Optional[nn.Module] = None,
+    is_named_optimizer: bool = False,
+    param_to_fqns: Optional[dict[nn.Parameter, list[str]]] = None,
+    flat_param_to_fqn: Optional[dict[FlatParameter, str]] = None,
+) -> dict[nn.Parameter, Union[int, str]]:
+    """
+    Constructs the inverse mapping of :func:`_get_param_key_to_param`. This API
+    only supports the case where `optim` is a regular optimizer, not NamedOptimizer.
+    So the parameter keys will be parameter ids.
+    """
+    param_id_to_param = _get_param_key_to_param(
+        optim, model, is_named_optimizer, param_to_fqns, flat_param_to_fqn
+    )
+    return {param: param_id for param_id, param in param_id_to_param.items()}
+
+
+def _get_param_to_param_id_from_optim_input(
+    model: nn.Module,
+    optim_input: Optional[
+        Union[
+            list[dict[str, Any]],
+            Iterable[nn.Parameter],
+        ]
+    ] = None,
+) -> dict[nn.Parameter, int]:
+    """Constructs the inverse mapping of :func:`_get_param_id_to_param_from_optim_input`."""
+    param_id_to_param = _get_param_id_to_param_from_optim_input(model, optim_input)
+    return {param: param_id for param_id, param in param_id_to_param.items()}
+
+
+def _check_missing_keys_on_rank(
+    r0_optim_state_keys: list[_OptimStateKey],
+    optim_state_key_to_param_key: dict[_OptimStateKey, Union[str, int]],
+    param_key_to_param: dict[Union[str, int], nn.Parameter],
+    group: Optional[dist.ProcessGroup],
+) -> None:
+    # Ensure that all ranks have at least the optimizer states needed by
+    # rank 0's optimizer
+    missing_keys: list[_OptimStateKey] = []
+    for r0_optim_state_key in r0_optim_state_keys:
+        if r0_optim_state_key not in optim_state_key_to_param_key:
+            # A parameter from rank 0's optimizer does not exist for this
+            # rank's optimizer
+            missing_keys.append(r0_optim_state_key)
+            continue
+        param_key = optim_state_key_to_param_key[r0_optim_state_key]
+        if isinstance(param_key, int):
+            if not (param_key >= 0 and param_key < len(param_key_to_param)):
+                raise AssertionError("Check the `param_key_to_param` construction")
+    # We cannot use FSDPState.compute_device as this API is a global view.
+    device = _get_pg_default_device(group)
+    num_missing = torch.tensor([len(missing_keys)], dtype=torch.int32, device=device)
+    dist.all_reduce(num_missing, group=group)
+    if num_missing.item() > 0:
+        obj_list = [None for _ in range(dist.get_world_size(group))]
+        dist.all_gather_object(obj_list, missing_keys, group=group)
+        error_msg = (
+            "FSDP currently requires each rank to have at least the "
+            "optimizer states needed by rank 0's optimizer but some ranks "
+            "are missing some of those states"
+        )
+        for rank, keys in enumerate(obj_list):
+            keys = cast(list[_OptimStateKey], keys)
+            if len(keys) > 0:
+                error_msg += (
+                    f"\nRank {rank} is missing states for the parameters: "
+                    f"{[key.unflat_param_names for key in keys]}"
+                )
+        raise RuntimeError(error_msg)
+
+
+def _map_param_key_to_optim_keys(
+    optim_state_dict: dict[str, Any],
+    group: Optional[dist.ProcessGroup],
+    param_key_to_param: dict[Union[int, str], nn.Parameter],
+    param_to_fqns: dict[nn.Parameter, list[str]],
+    fqn_to_fsdp_param_info: dict[str, FSDPParamInfo],
+    merge_keys: bool = False,
+) -> tuple[list[_OptimStateKey], dict[_OptimStateKey, Union[int, str]]]:
+    """
+    Construct the local mapping between the ``_OptimStateKey`` and parameter keys
+    and all the ``_OptimStateKey`` across ranks. If ``merge_keys`` is False, rank0
+    must contain all the ``_OptimStateKey``, an exception will be raised otherwise.
+    Note that ``merge_keys`` should equal to ``use_orig_params``.
+    """
+    rank = dist.get_rank(group)
+    optim_state_key_to_param_key: dict[_OptimStateKey, Union[int, str]] = {}  # local
+    all_optim_state_keys: list[_OptimStateKey] = []
+
+    for param_key, param in param_key_to_param.items():
+        # Do not include parameters without state to avoid empty mappings
+        # just like in normal `torch.optim.Optimizer.state_dict()`
+        if param_key not in optim_state_dict["state"]:
+            continue
+        fqns = param_to_fqns[param]
+        is_fsdp_managed = isinstance(param, FlatParameter)
+        if is_fsdp_managed:
+            if fqns[0] not in fqn_to_fsdp_param_info:
+                raise AssertionError(
+                    f"Expected {fqns[0]} to be in fqn_to_fsdp_param_info, got keys: {list(fqn_to_fsdp_param_info.keys())}"
+                )
+        is_fsdp_managed = fqns[0] in fqn_to_fsdp_param_info
+        optim_state_key = _OptimStateKey(
+            unflat_param_names=tuple(fqns),
+            is_fsdp_managed=is_fsdp_managed,
+        )
+        if rank == 0 or merge_keys:
+            all_optim_state_keys.append(optim_state_key)
+        optim_state_key_to_param_key[optim_state_key] = param_key
+
+    if merge_keys:
+        all_keys: list[list[_OptimStateKey]] = [
+            [] for _ in range(dist.get_world_size(group))
+        ]
+        dist.all_gather_object(all_keys, all_optim_state_keys, group=group)
+        merge_all_optim_state_keys = [*chain.from_iterable(all_keys)]
+        all_optim_state_keys = sorted(set(merge_all_optim_state_keys))
+    else:
+        key_obj_list: list[Optional[list[_OptimStateKey]]] = (
+            [all_optim_state_keys] if rank == 0 else [None]
+        )
+        dist.broadcast_object_list(key_obj_list, src=0, group=group)
+        if key_obj_list[0] is None:
+            raise AssertionError(
+                f"Expected key_obj_list[0] to be not None, got {key_obj_list[0]}"
+            )
+        all_optim_state_keys = key_obj_list[0]
+        _check_missing_keys_on_rank(
+            all_optim_state_keys,
+            optim_state_key_to_param_key,
+            param_key_to_param,
+            group,
+        )
+
+    return all_optim_state_keys, optim_state_key_to_param_key
+
+
+def _unflatten_param_groups(
+    state_dict: dict[str, Any],
+    param_key_to_param: dict[Union[int, str], nn.Parameter],
+    param_to_fqns: dict[nn.Parameter, list[str]],
+) -> list[dict[str, Any]]:
+    param_groups: list[dict[str, Any]] = []
+    for flat_param_group in state_dict["param_groups"]:
+        unflat_param_group = copy.deepcopy(flat_param_group)
+        param_group_params = [
+            param_key_to_param[flat_param_key]
+            for flat_param_key in flat_param_group["params"]
+        ]
+        nested_unflat_param_names = [
+            param_to_fqns[param] for param in param_group_params
+        ]
+        unflat_param_group["params"] = [
+            *chain.from_iterable(nested_unflat_param_names)
+        ]  # flatten the list of lists
+        param_groups.append(unflat_param_group)
+    return param_groups
+
+
+def _is_named_optimizer(optim_state_dict: dict[str, Any]) -> bool:
+    """
+    Returns whether the state_dict is from a NamedOptimizer.
+    This function checks that the keys in the state_dict['state'] are strings
+    (which usually are FQNs) versus integers (which usually refer to param_ids
+    from a vanilla torch.optim.Optimizer).
+    """
+    state = optim_state_dict.get("state")
+    if not state:
+        # If we cannot find a state, assume it is not NamedOptimizer as
+        # NamedOptimizer has eager initialization.
+        return False
+    try:
+        key = next(iter(state.keys()))
+    except Exception as e:
+        raise Exception(optim_state_dict) from e  # noqa: TRY002
+    return isinstance(key, str)
+
+
+@dataclass
+class StateInfo:
+    # The key of these dictionaries are the state name, e.g., `exp_avg`.
+    tensors: dict[str, _PosDimTensorInfo]
+    scalar_tensors: dict[str, torch.Tensor]
+    non_tensors: dict[str, Any]
+
+
+def _allgather_state_info(
+    fsdp_state: _FSDPState,
+    input_states: dict[str, Any],
+) -> list[dict[str, StateInfo]]:
+    """
+    Given the ``input_states``, allgather StateInfo for each state. The function
+    uses all_gather_object to gather StateInfo so no GPU tensors are sent.
+    """
+
+    processed_state_dict: dict[str, StateInfo] = {}
+    gathered_state_info: list[dict[str, StateInfo]] = [
+        {} for _ in range(fsdp_state.world_size)
+    ]
+
+    for fqn, optim_state in input_states.items():
+        # Allgather the scalar tensor state, non-tensor states and tensors metadata.
+        processed_state = StateInfo({}, {}, {})
+        for state_name, value in sorted_items(optim_state):
+            if torch.is_tensor(value):
+                if value.dim() == 0:
+                    # Ensure that `step` is on CPU.
+                    processed_state.scalar_tensors[state_name] = value.cpu()
+                else:
+                    processed_state.tensors[state_name] = _PosDimTensorInfo(
+                        value.shape, value.dtype
+                    )
+            else:
+                processed_state.non_tensors[state_name] = value
+        processed_state_dict[fqn] = processed_state
+    dist.all_gather_object(
+        gathered_state_info,
+        processed_state_dict,
+        group=fsdp_state.process_group,
+    )
+    return gathered_state_info
+
+
+def _convert_all_state_info(
+    fsdp_param_info: FSDPParamInfo,
+    gathered_state_info: list[dict[str, StateInfo]],
+    input_states: dict[str, Any],
+    output_states: dict[str, dict[str, Any]],
+) -> tuple[Optional[torch.dtype], dict[str, list[Optional[torch.Tensor]]]]:
+    """
+    Given the ``gathered_state_info`` and ``input_states``, the API converted
+    the StateInfo into the original state if the state is not a non-scalar
+    tensor. For a multi-dimensional tensor, the local state will be stored in
+    ``state_buffer`` in a correct order for later allgather purpose.
+    """
+
+    state_buffers: dict[str, list[Optional[torch.Tensor]]] = {}
+
+    for fqn, gathered_state in output_states.items():
+        state_info = [s[fqn] for s in gathered_state_info]
+        all_tensor_states = sorted({n for state in state_info for n in state.tensors})
+        empty_ranks: set[int] = set()
+        dtype: Optional[torch.dtype] = None
+        # First check all the non-scalar states and get the information of
+        # states on each rank.
+        for state_name in all_tensor_states:
+            numels = []
+            _empty_ranks: set[int] = set()
+            for rank, object_state in enumerate(state_info):
+                numels.append(0)
+                info = object_state.tensors.get(state_name, None)
+                if info is not None:
+                    numels[-1] = info.shape.numel()
+                    if not dtype:
+                        dtype = info.dtype
+                    else:
+                        if dtype != info.dtype:
+                            raise AssertionError(
+                                f"Expected dtype == info.dtype, got {dtype} != {info.dtype}"
+                            )
+                if numels[-1] == 0:
+                    _empty_ranks.add(rank)
+
+            if not (not empty_ranks or empty_ranks == _empty_ranks):
+                raise AssertionError(
+                    f"Expected empty_ranks to be empty or equal to _empty_ranks, got {empty_ranks} vs {_empty_ranks}"
+                )
+            empty_ranks = _empty_ranks
+            if state_name not in state_buffers:
+                state_buffers[state_name] = [
+                    None for _ in fsdp_param_info.param_indices
+                ]
+            local_state = input_states[fqn].get(state_name, None)
+            # N.B. We need to move the state to compute_device. The reason is
+            # not yet clear and we need to figure out why the state may be on a
+            # different device.
+            if local_state is not None:
+                local_state = local_state.to(fsdp_param_info.state.compute_device)
+            state_buffers[state_name][fsdp_param_info.param_indices[fqn]] = local_state
+
+        # Restoring the scalar and non-tensor states. If the corresponding
+        # non-scalar states do not exist on the rank, we also skip the scalar
+        # non-tensor states on that rank.
+        for rank, object_state in enumerate(state_info):
+            if rank in empty_ranks:
+                continue
+            for name, non_tensor_value in object_state.non_tensors.items():
+                curr_non_tensor_value = gathered_state.get(name, None)
+                if not (
+                    curr_non_tensor_value is None
+                    or curr_non_tensor_value == non_tensor_value
+                ):
+                    raise AssertionError(
+                        f"Rank {rank} has different values for {name}: {non_tensor_value}."
+                        + f" Other ranks: {curr_non_tensor_value}"
+                    )
+                gathered_state[name] = non_tensor_value
+
+            for name, scalar_tensor_value in object_state.scalar_tensors.items():
+                curr_scalar_tensor_value = gathered_state.get(name, None)
+                if not (
+                    curr_scalar_tensor_value is None
+                    or torch.equal(scalar_tensor_value, curr_scalar_tensor_value)
+                ):
+                    raise AssertionError(
+                        f"Rank {rank} has different values for {name}: {scalar_tensor_value}."
+                        + f" Other ranks: {curr_scalar_tensor_value}"
+                    )
+                gathered_state[name] = scalar_tensor_value
+
+    return dtype, state_buffers  # type: ignore[possibly-undefined]
+
+
+def _unflatten_orig_param_states(
+    fsdp_param_info: FSDPParamInfo,
+    output_states: dict[str, dict[str, Any]],
+    state_name: str,
+    shard_state: bool,
+    to_save: bool,
+    cpu_offload: bool,
+) -> None:
+    """
+    Given a output state dict, ``output_states``, which the keys are FQNs to the
+    original parameters (not FlatParameters nor parameter ID), and the values
+    are gathered states, unflatten the states to the original dimensions.
+
+    This function performs the unflattening process in-place.
+    """
+    if not to_save:
+        return
+    flat_param = fsdp_param_info.handle.flat_param
+    fsdp_state = fsdp_param_info.state
+    for fqn, gathered_state in output_states.items():
+        value = gathered_state[state_name]
+        param_idx = fsdp_param_info.param_indices[fqn]
+
+        # TODO: This solution is not general and only apply to PTD TP solution.
+        if isinstance(value, DTensor):
+            placement = value.placements[0]
+            # If gathered state is a DTensor and its TP placement is not Replicate(), we need to
+            # gather the tensor on its TP dimension before chunking them into DTensor again.
+            if placement != Replicate():
+                placement_dim = placement.dim  # type: ignore[attr-defined]
+                value.redistribute(placements=(Replicate(),))
+                reshape_size = list(flat_param._shapes[param_idx])
+                reshape_size[placement_dim] *= value.device_mesh.size(0)
+                reshape_size = torch.Size(reshape_size)
+                value = value.reshape(reshape_size)
+            # If gathered state is a replicate DTensor, we directly reshape it.
+            else:
+                value = value.reshape(flat_param._shapes[param_idx])
+        else:
+            # If gathered state is a tensor, we directly reshape it into unflatten state.
+            value = value.reshape(flat_param._shapes[param_idx])
+
+        if shard_state:
+            osd_config = fsdp_state._optim_state_dict_config
+            if getattr(osd_config, "_use_dtensor", False):
+                if fsdp_state._device_mesh is None:
+                    raise AssertionError(
+                        f"Expected _device_mesh to be not None, got {fsdp_state._device_mesh}"
+                    )
+                value = _ext_chunk_dtensor(
+                    value,
+                    fsdp_state.rank,
+                    fsdp_state._device_mesh,
+                    fsdp_state._fsdp_extension,
+                )
+            else:
+                if fsdp_state.process_group is None:
+                    raise AssertionError(
+                        f"Expected process_group to be not None, got {fsdp_state.process_group}"
+                    )
+                value = _ext_chunk_tensor(
+                    value,
+                    fsdp_state.rank,
+                    fsdp_state.world_size,
+                    fsdp_state._device_handle.device_count(),
+                    fsdp_state.process_group,
+                    fsdp_state._fsdp_extension,
+                )
+        elif not cpu_offload:
+            with SimpleProfiler.profile("clone"):
+                value = value.detach().clone()
+
+        if cpu_offload:
+            with SimpleProfiler.profile(SimpleProfiler.Type.D2H):
+                value = value.cpu()
+        gathered_state[state_name] = value
+
+
+def _allgather_orig_param_states(
+    fsdp_param_info: FSDPParamInfo,
+    gathered_state_info: list[dict[str, StateInfo]],
+    input_states: dict[str, Any],
+    shard_state: bool,
+    to_save: bool,
+    cpu_offload: bool,
+) -> dict[str, dict[str, Any]]:
+    """
+    Given the ``gathered_state_info`` and ``input_states``, the API allgathers
+    all tensor states and restore non-tensor states from ``gathered_state_info``.
+    """
+    fsdp_state = fsdp_param_info.state
+    if fsdp_state.rank == 0 and dist.get_debug_level() == dist.DebugLevel.DETAIL:
+        logger.info(
+            "Memory Summary before calling to _allgather_orig_param_states %s",
+            fsdp_state._device_handle.memory_summary(),
+        )
+
+    output_states: dict[str, dict[str, Any]] = {fqn: {} for fqn in input_states}
+
+    dtype, state_buffers = _convert_all_state_info(
+        fsdp_param_info, gathered_state_info, input_states, output_states
+    )
+
+    if len(state_buffers) == 0:
+        return output_states
+
+    has_state_params: list[bool] = [
+        fqn in output_states for fqn, idx in fsdp_param_info.param_indices.items()
+    ]
+
+    # Loop through the ``state_buffers`` and construct the flattened, concatenated,
+    # sharded states. The size of the constructed state will be the same size as
+    # flat_param (also sharded).
+    # Then we perform an allgather_into_tensor to get the full flat_param state.
+    # The full flat_param state is the result of concatenation of multiple states
+    # the order of of flat_param._fqns.
+    # The final step is to split the flat_param state into original param states
+    # and return the result.
+    flat_param = fsdp_param_info.handle.flat_param
+    empty_func = functools.partial(
+        torch.empty, dtype=dtype, device=fsdp_state.compute_device
+    )
+    gathered_tensor = empty_func(flat_param._padded_unsharded_size)
+    # Synchronize can be slow but this will be easier for us to debug.
+    fsdp_state._device_handle.synchronize()
+    for state_name, buffers in state_buffers.items():
+        local_buffers: list[torch.Tensor] = []
+        begin = fsdp_state.rank * flat_param._sharded_size.numel()
+        # End is inclusive.
+        end = begin + flat_param._sharded_size.numel() - 1
+        # param_idx corresponds to the parameter index in the FlatParameter.
+        mem_offset, param_idx = 0, 0
+        for numel, is_padding in zip(
+            flat_param._numels_with_padding, flat_param._is_padding_mask
+        ):
+            frozen_and_no_state = not is_padding and (
+                not fsdp_param_info.param_requires_grad[param_idx]
+                and not has_state_params[param_idx]
+            )
+
+            if is_padding or frozen_and_no_state:
+                # This memory range is a padding or the param is frozen and does
+                # not require gradient. For the later case, we treat it as a
+                # padding and add empty values to the local_buffers.
+
+                padding_begin, padding_end = mem_offset, mem_offset + numel - 1
+                if padding_begin <= begin <= padding_end:
+                    # The range is an align padding before the first parameter in
+                    # the shard. The shard includes parts of this align padding.
+                    padding_len = (
+                        padding_end - begin + 1
+                        if end >= padding_end
+                        else end - begin + 1
+                    )
+                elif padding_begin <= end <= padding_end:
+                    # The range is an align padding after the last parameter in
+                    # the shard. The shard includes parts of this align padding.
+                    padding_len = (
+                        end - padding_begin + 1
+                        if begin <= padding_begin
+                        else end - begin + 1
+                    )
+                elif begin < padding_begin <= padding_end < end:
+                    # The range is an align padding that is completely in the
+                    # shard.
+                    padding_len = numel
+                else:
+                    padding_len = 0
+                if padding_len:
+                    local_buffers.append(empty_func(padding_len))
+
+            if not is_padding:
+                # This memory range is a parameter in FlatParameter. So there
+                # should be an corresponding state in the optimizer unless the
+                # parameter is frozen, which we treat it as a padding above.
+
+                # We need to check if this rank owns the buffer. If this is None:
+                # 1.) the rank does not own any part of the original parameter.
+                #     As a result, there is no corresponding optimizer state on
+                #     the rank as well.
+                # 2.) the parameter is frozen AND no optimizer state for the
+                #     parameter. If a parameter is frozen, there can still be
+                #     optimizer state if the parameter is not frozen in the
+                #     previous steps.
+                if buffers[param_idx] is not None:
+                    local_buffers.append(cast(torch.Tensor, buffers[param_idx]))
+                param_idx += 1
+
+            mem_offset += numel
+
+        shard_numel_padded = flat_param._sharded_size.numel() - (
+            sum(t.numel() for t in local_buffers)
+        )
+
+        if flat_param._shard_numel_padded != shard_numel_padded:
+            raise AssertionError(
+                "Manually calculated _sharded_numel_padded is incorrect. "
+                f"_shard_numel_padded={flat_param._shard_numel_padded}, "
+                f"shard_numel_padded={shard_numel_padded}, "
+                f"_sharded_size.numel={flat_param._sharded_size.numel()}, "
+                f"_numels_with_padding={flat_param._numels_with_padding}, "
+                f"begin={begin}, end={end},"
+            )
+        if shard_numel_padded > 0:
+            # Add right-handed padding.
+            local_buffers.append(empty_func(shard_numel_padded))
+        local_shard = torch.cat(local_buffers)
+        if local_shard.numel() * fsdp_state.world_size != gathered_tensor.numel():
+            raise AssertionError(
+                "The size of local shard times the world size should equal to the "
+                "gathered tensor size. The inconsistency may be from a bug of "
+                "FlatParameter's metadata or the reconstruction logic in optimizer "
+                "state dict."
+            )
+        fsdp_state._device_handle.synchronize()
+        with SimpleProfiler.profile(SimpleProfiler.Type.ALLGATHER):
+            dist.all_gather_into_tensor(
+                gathered_tensor, local_shard, group=fsdp_state.process_group
+            )
+            # Synchronize can be slow but this will be easier for us to debug.
+            fsdp_state._device_handle.synchronize()
+
+        unpadded_tensor = gathered_tensor[: flat_param._unpadded_unsharded_size.numel()]
+        flat_param_handle = fsdp_param_info.handle
+        orig_states = flat_param_handle._get_unflat_views_aligned(unpadded_tensor)
+        if len(orig_states) != len(fsdp_param_info.param_indices):
+            raise AssertionError(
+                "The number of parameters from FlatParameter is not consistent to "
+                "the number of states used by optimizer state dict reconstruction "
+                "logic."
+            )
+        for fqn, idx in fsdp_param_info.param_indices.items():
+            if fsdp_param_info.param_requires_grad[idx] or fqn in output_states:
+                output_states[fqn][state_name] = orig_states[idx]
+
+        _unflatten_orig_param_states(
+            fsdp_param_info,
+            output_states,
+            state_name,
+            shard_state,
+            to_save,
+            cpu_offload,
+        )
+
+    del gathered_tensor
+    return output_states
+
+
+def _gather_all_orig_param_state(
+    fsdp_param_info: FSDPParamInfo,
+    input_states: dict[str, Any],
+    shard_state: bool,
+    to_save: bool,
+    cpu_offload: bool,
+) -> dict[str, Any]:
+    """
+    Given a optimizer state dict, ``input_states``, which the keys are FQNs to the
+    original parameters (not FlatParameters nor parameter ID), gather all the
+    states and unflatten them to the original dimensions. Note that all the
+    params referred by the ``input_states`` must be managed by FSDP.
+    """
+    fsdp_state = fsdp_param_info.state
+    if (
+        fsdp_state.world_size == 1
+        or fsdp_state.sharding_strategy == ShardingStrategy.NO_SHARD
+    ):
+        return input_states if to_save else {}
+
+    with SimpleProfiler.profile(SimpleProfiler.Type.RESHARDING):
+        with SimpleProfiler.profile(SimpleProfiler.Type.ALLGATHER_OBJ):
+            gathered_state_info = _allgather_state_info(fsdp_state, input_states)
+        output_states = _allgather_orig_param_states(
+            fsdp_param_info,
+            gathered_state_info,
+            input_states,
+            shard_state,
+            to_save,
+            cpu_offload,
+        )
+    if to_save:
+        for key, idx in fsdp_param_info.param_indices.items():
+            if key in output_states:
+                continue
+            if not fsdp_param_info.param_requires_grad[idx]:
+                continue
+
+            raise RuntimeError(
+                f"{key} is not in the output state. "
+                "The FSDPParamInfo has the param keys "
+                f"{sorted(fsdp_param_info.param_indices.keys())} while "
+                "the output_states has the param keys "
+                f"{sorted(output_states.keys())}."
+            )
+        return output_states
+    else:
+        return {}
+
+
+def _convert_state_with_orig_params(
+    all_optim_state_keys: list[_OptimStateKey],
+    optim_state_key_to_param_key: dict[_OptimStateKey, Union[int, str]],
+    fqn_to_fsdp_param_info: dict[str, FSDPParamInfo],
+    optim_state_dict: dict[Union[str, int], Any],
+    to_save: bool,
+    shard_state: bool,
+    cpu_offload: bool = True,
+) -> dict[str, Any]:
+    fsdp_osd_state: dict[str, Any] = {}
+    # This variable is used to deduplicate the FSDPParamInfo as one FSDPParamInfo
+    # usually corresponds to multiple parameters. We could not use FSDPParamInfo
+    # as the key because FSDPParamInfo is not hashable. As a result, we fall back
+    # to `id(FSDPParamInfo)`, which the type is an integer.
+    all_states: dict[int, dict[str, Any]] = {}
+    # Iterate in rank 0's flat parameter ID order to ensure aligned all-gathers
+    # across ranks
+    for optim_state_key in all_optim_state_keys:
+        param_key: Union[str, int, None] = optim_state_key_to_param_key.get(
+            optim_state_key
+        )
+
+        if param_key is None and not optim_state_key.is_fsdp_managed:
+            continue
+
+        if optim_state_key.is_fsdp_managed:
+            fqn = optim_state_key.unflat_param_names[0]
+            fsdp_param_info = fqn_to_fsdp_param_info.get(fqn)
+            if fsdp_param_info is None:
+                # This can happen if the not all FSDP instances have all the
+                # parameters. This can happen with FSDP + some MPMD style
+                # parallelism.
+
+                # TODO: it is unclear if we need to do the same check with
+                # non-FSDP managed keys.
+                continue
+            state = {} if param_key is None else optim_state_dict[param_key]
+            if id(fsdp_param_info) not in all_states:
+                all_states[id(fsdp_param_info)] = {}
+            all_states[id(fsdp_param_info)][fqn] = state
+
+        elif to_save:
+            if len(optim_state_key.unflat_param_names) != 1:
+                raise AssertionError(
+                    f"Expected len(optim_state_key.unflat_param_names) == 1, got {len(optim_state_key.unflat_param_names)}"
+                )
+            unflat_param_name = optim_state_key.unflat_param_names[0]
+            with SimpleProfiler.profile("none_fsdp_managed_copy"):
+                param_key = cast(Union[str, int], param_key)
+                fsdp_osd_state[unflat_param_name] = copy.copy(
+                    optim_state_dict[param_key]
+                )
+                if cpu_offload:
+                    for state_name, value in sorted_items(
+                        fsdp_osd_state[unflat_param_name]
+                    ):
+                        if not torch.is_tensor(value):
+                            continue
+                        fsdp_osd_state[unflat_param_name][state_name] = value.cpu()
+
+    # Instead of gathering the state of each parameter individually, we perform
+    # the gathering  all at once to speed up the process.
+    for _all_states in all_states.values():
+        fqn = next(iter(_all_states.keys()))
+        fsdp_param_info = fqn_to_fsdp_param_info[fqn]
+        if len(fsdp_param_info.param_requires_grad) <= 0:
+            raise AssertionError(
+                "With use_orig_params, FSDPParamInfo should have requires_grad "
+                "information. However, the length is zero."
+            )
+        for key, idx in fsdp_param_info.param_indices.items():
+            if key in _all_states:
+                continue
+            if not fsdp_param_info.param_requires_grad[idx]:
+                continue
+            raise RuntimeError(
+                f"{key} is not in the optimizer state. "
+                "The FSDPParamInfo has the param keys "
+                f"{sorted(fsdp_param_info.param_indices.keys())} while "
+                "the optimizer has the param keys "
+                f"{sorted(_all_states.keys())}."
+            )
+        fsdp_osd_state.update(
+            _gather_all_orig_param_state(
+                fsdp_param_info,
+                _all_states,
+                shard_state,
+                to_save,
+                cpu_offload,
+            )
+        )
+
+    return fsdp_osd_state
+
+
+def _convert_state_with_flat_params(
+    all_optim_state_keys: list[_OptimStateKey],
+    optim_state_key_to_param_key: dict[_OptimStateKey, Union[int, str]],
+    fqn_to_fsdp_param_info: dict[str, FSDPParamInfo],
+    optim_state_dict: dict[Union[str, int], Any],
+    to_save: bool,
+    shard_state: bool,
+    cpu_offload: bool = True,
+) -> dict[str, Any]:
+    fsdp_osd_state: dict[str, Any] = {}
+    # Iterate in rank 0's flat parameter ID order to ensure aligned all-gathers
+    # across ranks
+    for optim_state_key in all_optim_state_keys:
+        param_key: Union[str, int, None] = optim_state_key_to_param_key.get(
+            optim_state_key
+        )
+
+        if param_key is None:
+            raise AssertionError(
+                "If use_orig_params is False, we must be able to find the "
+                f"corresponding param id. {optim_state_key} {param_key}"
+            )
+
+        if optim_state_key.is_fsdp_managed:
+            # If there are multiple unflat_param_names (not use_orig_params),
+            # they share the same FSDPParamInfo. So the first unflat_param_name
+            # is sufficient to fetch the FSDPParamInfo.
+            fqn = optim_state_key.unflat_param_names[0]
+            fsdp_param_info = fqn_to_fsdp_param_info[fqn]
+            unflat_state = _unflatten_optim_state(
+                fsdp_param_info,
+                optim_state_dict[param_key],
+                to_save,
+                shard_state,
+                cpu_offload,
+            )
+            if to_save:
+                if len(unflat_state) != len(optim_state_key.unflat_param_names):
+                    raise AssertionError(
+                        f"Expected len(unflat_state) == len(optim_state_key.unflat_param_names), "
+                        f"got {len(unflat_state)} != {len(optim_state_key.unflat_param_names)}"
+                    )
+                fsdp_osd_state.update(
+                    zip(
+                        optim_state_key.unflat_param_names,
+                        unflat_state,
+                    )
+                )
+        elif to_save:
+            if len(optim_state_key.unflat_param_names) != 1:
+                raise AssertionError(
+                    f"Expected len(optim_state_key.unflat_param_names) == 1, got {len(optim_state_key.unflat_param_names)}"
+                )
+            unflat_param_name = optim_state_key.unflat_param_names[0]
+            fsdp_osd_state[unflat_param_name] = copy.copy(optim_state_dict[param_key])
+            if cpu_offload:
+                for state_name, value in sorted_items(
+                    fsdp_osd_state[unflat_param_name]
+                ):
+                    if not torch.is_tensor(value):
+                        continue
+                    fsdp_osd_state[unflat_param_name][state_name] = value.cpu()
+
+    return fsdp_osd_state
+
+
+@torch.no_grad()
+def _optim_state_dict(
+    model: nn.Module,
+    optim: torch.optim.Optimizer,
+    optim_state_dict: dict[str, Any],
+    optim_input: Optional[
+        Union[
+            list[dict[str, Any]],
+            Iterable[nn.Parameter],
+        ]
+    ],
+    rank0_only: bool,
+    shard_state: bool,
+    group: Optional[dist.ProcessGroup],
+    using_optim_input: bool,
+    use_orig_params: bool = False,
+    cpu_offload: bool = True,
+) -> dict[str, Any]:
+    """
+    Consolidates the optimizer state and returns it as a :class:`dict`
+    following the convention of :meth:`torch.optim.Optimizer.state_dict`,
+    i.e. with keys ``"state"`` and ``"param_groups"``.
+    The flat parameters in ``FSDP`` modules contained in ``model`` are mapped
+    back to their unflattened parameters.
+
+    Parameter keys are not well-defined. For a regular optimizer, the optimizer
+    state_dict contains a mapping from parameter IDs to parameter states.
+    Parameter IDs are the order of parameters in ``optim.param_groups()`` across
+    all the groups. This API also allows user to pass ``optim_input`` for the
+    mapping between parameters and parameter IDs. Using ``optim_input`` is being
+    deprecated.
+
+    If the optimizer is a ``NamedOptimizer``, the optimizer state_dict does not
+    contain parameter IDs mapping but a mapping from parameter FQNs to parameter
+    states. This API finds the mapping from FQNs to parameters if the optimizer
+    is a ``NamedOptimizer``.
+
+    If ``use_orig_params`` is True, each rank will have all FSDP-managed
+    parameters but some of these parameters may be empty due to the sharding.
+    For a regular optim.Optimizer, states for those empty parameters will
+    not be initialized. So, when aggregating the FQNs across ranks, no assert
+    will be raised on a rank even if it does not have all the states -- it is
+    valid and FSDP knows how to aggregate them. However, FSDP has to ignore
+    handling those parameters that are not managed by FSDP and do not exist on
+    the local rank -- those are managed by other parallelisms and FSDP does not
+    know how to handle/aggregate them.
+
+    Args:
+        model (nn.Module): Root module (which may or may not be a
+            :class:`FullyShardedDataParallel` instance) whose parameters
+            were passed into the optimizer ``optim``.
+        optim (torch.optim.Optimizer): Optimizer for ``model`` 's
+            parameters.
+        rank0_only (bool): If ``True``, saves the populated :class:`dict`
+            only on rank 0; if ``False``, saves it on all ranks. (Default:
+            ``True``)
+        shard_state (bool): If ``True``, shard and distribute all
+            non-zero-dimension states.
+
+    Returns:
+        Dict[str, Any]: A :class:`dict` containing the optimizer state for
+        ``model`` 's original unflattened parameters and including keys
+        "state" and "param_groups" following the convention of
+        :meth:`torch.optim.Optimizer.state_dict`. If ``rank0_only=False``,
+        then nonzero ranks return an empty :class:`dict`.
+    """
+    SimpleProfiler.reset()
+    cm = ExitStack()
+    cm.enter_context(SimpleProfiler.profile(SimpleProfiler.Type.ALL))
+    _reset_flat_param_grad_info_if_needed(traversal_utils._get_fsdp_handles(model))
+    to_save = not rank0_only or dist.get_rank(group) == 0 or shard_state
+
+    with SimpleProfiler.profile("preprocessing"):
+        param_to_fqns = _get_param_to_fqns(model)
+        flat_param_to_fqn = _get_flat_param_to_fqn(model)
+        is_named_optimizer = _is_named_optimizer(optim_state_dict)
+
+        param_key_to_param = cast(
+            dict[Union[int, str], nn.Parameter],
+            (
+                _get_param_id_to_param_from_optim_input(model, optim_input)
+                if using_optim_input
+                else _get_param_key_to_param(
+                    optim, model, is_named_optimizer, param_to_fqns, flat_param_to_fqn
+                )
+            ),
+        )
+        fqn_to_fsdp_param_info = _get_fqn_to_fsdp_param_info(model)
+
+    with SimpleProfiler.profile("preprocessing_with_comm"):
+        (
+            all_optim_state_keys,
+            optim_state_key_to_param_key,
+        ) = _map_param_key_to_optim_keys(
+            optim_state_dict,
+            group,
+            param_key_to_param,
+            param_to_fqns,
+            fqn_to_fsdp_param_info,
+            merge_keys=use_orig_params,
+        )
+
+    with SimpleProfiler.profile("state_converting"):
+        convert_fn = (
+            _convert_state_with_orig_params
+            if use_orig_params
+            else _convert_state_with_flat_params
+        )
+        fsdp_osd_state = convert_fn(
+            all_optim_state_keys,
+            optim_state_key_to_param_key,
+            fqn_to_fsdp_param_info,
+            optim_state_dict["state"],
+            to_save,
+            shard_state,
+            cpu_offload,
+        )
+
+    # At this point, communication is complete and ranks can return early if nothing
+    # will be saved on that rank.
+    if not to_save:
+        return {}
+
+    fsdp_osd: dict[str, Any] = {"state": fsdp_osd_state}
+
+    flat_param_fqns = set(flat_param_to_fqn.values())
+    for key, value in optim_state_dict["state"].items():
+        if key in fsdp_osd_state:
+            continue
+        if key in flat_param_fqns:
+            continue
+        if key in param_key_to_param:
+            continue
+        # This key is not recognized by FSDP. It may be a user-defined state
+        # or some parameters state that FSDP is unable to map from
+        # ``optim.param_groups``.
+        warnings.warn(
+            f"Found a optim state, {key}, that FSDP cannot process. FSDP "
+            "will directly copy everything to the returned state_dict. In "
+            "most cases, this is a user-defined state that is not "
+            "associated with any particular parameter. Another possible "
+            "case is this state is managed by TorchRec. Otherwise, there may "
+            " be a mismatched assumption of optim_state_dict of this mode.",
+            stacklevel=2,
+        )
+        fsdp_osd_state[key] = value
+
+    if "param_groups" in optim_state_dict:
+        fsdp_osd["param_groups"] = _unflatten_param_groups(
+            optim_state_dict, param_key_to_param, param_to_fqns
+        )
+
+    cm.close()
+    SimpleProfiler.dump_and_reset("FSDP _optim_state_dict() profiling: ")
+
+    return fsdp_osd
+
+
+def _get_fqn_to_fsdp_param_info(model: nn.Module) -> dict[str, FSDPParamInfo]:
+    """
+    Construct the mapping from a param's fqn to its corresponding ``FSDPParamInfo``
+    if the param is managed by FSDP. Shared parameters, or original parameters that
+    are shared across multiple nn.Modules, are required to belong to one and only
+    one FSDP instance and thus correspond to one ``FlatParameter``. Within the one
+    ``FlatParameter``, ``FlatParameter._fqns`` only stores the first FQN of a shared
+    parameter. Thus, the keys in the mapping are guaranteed to map to unique parameters.
+    """
+
+    def module_fn(module, prefix, tree_level, fqn_to_param_info):
+        fsdp_state = _get_module_fsdp_state_if_fully_sharded_module(module)
+        if fsdp_state is None:
+            return
+        _lazy_init(fsdp_state, module)
+        handle = _module_handle(fsdp_state, module)
+        if not handle:
+            return
+        flat_param = handle.flat_param
+        fsdp_param_info = FSDPParamInfo(fsdp_state, handle, {}, [])
+        # NOTE: `idx` indexes into the data structures *without* padding
+        # elements
+        for idx, local_fqn in enumerate(flat_param._fqns):
+            fqn = clean_tensor_name(prefix + local_fqn)
+            if fqn in fqn_to_param_info:
+                if fqn_to_param_info[fqn].handle.flat_param is not flat_param:
+                    raise AssertionError(
+                        f"Expected fqn_to_param_info[fqn].handle.flat_param is flat_param for {fqn}"
+                    )
+            fqn_to_param_info[fqn] = fsdp_param_info
+            fsdp_param_info.param_indices[fqn] = idx
+            if flat_param._params is not None:
+                fsdp_param_info.param_requires_grad.append(
+                    flat_param._params[idx].requires_grad
+                )
+
+    def return_fn(fqn_to_param_info):
+        return fqn_to_param_info
+
+    fqn_to_param_info: dict[str, FSDPParamInfo] = {}
+    # FlatParameter._fqns stores the local fqn, starting from the root of the
+    # FSDP. Using _apply_to_modules() with model (may not be the FSDP root
+    # module) allows us to construct the global fqn.
+    return _apply_to_modules(
+        model,
+        module_fn,
+        return_fn,
+        [fqn for fqn, _ in _named_parameters_with_duplicates(model)],
+        fqn_to_param_info,
+    )
+
+
+@no_type_check
+def _set_optim_use_dtensor(
+    fsdp_state: _FSDPState,
+    state_dict_settings: StateDictSettings,
+) -> None:
+    # If device_mesh is passed in when initializing FSDP, we automatically turn the
+    # _use_dtensor flag to be true for ShardedOptimStateDictConfig() if state_dict_type
+    # has to be set to SHARDED_STATE_DICT.
+    if getattr(fsdp_state, "_device_mesh", None):
+        state_dict_type = state_dict_settings.state_dict_type
+        if state_dict_type == StateDictType.LOCAL_STATE_DICT:
+            raise RuntimeError(
+                "Found state_dict_type LOCAL_STATE_DICT.",
+                "DeviceMesh is not compatible with LOCAL_STATE_DICT.",
+                "Please set state_dict_type to SHARDED_STATE_DICT to get DTensor state_dict.",
+            )
+        else:
+            state_dict_settings.optim_state_dict_config._use_dtensor = True
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_runtime_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_runtime_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..eab47412f5d25a3c8a3472141208d6833ec633d1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_runtime_utils.py
@@ -0,0 +1,1654 @@
+# mypy: allow-untyped-defs
+import functools
+import logging
+from collections.abc import Callable
+from enum import auto, Enum
+from typing import Any, no_type_check, Optional
+
+import torch
+import torch.distributed as dist
+import torch.distributed.fsdp._traversal_utils as traversal_utils
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+from torch.autograd.graph import register_multi_grad_hook
+from torch.distributed.algorithms._comm_hooks import LOW_PRECISION_HOOKS
+from torch.distributed.fsdp._common_utils import (
+    _assert_in_training_states,
+    _FSDPState,
+    _get_module_fsdp_state,
+    _is_composable,
+    _log_post_backward_hook,
+    _no_dispatch_record_stream,
+    clean_tensor_name,
+    TrainingState,
+)
+from torch.distributed.fsdp._flat_param import (
+    FlatParameter,
+    FlatParamHandle,
+    HandleShardingStrategy,
+    HandleTrainingState,
+    RESHARD_AFTER_FORWARD_HANDLE_STRATEGIES,
+)
+from torch.distributed.fsdp._init_utils import HYBRID_SHARDING_STRATEGIES
+from torch.distributed.fsdp.api import BackwardPrefetch
+from torch.distributed.utils import (
+    _apply_to_tensors,
+    _cast_forward_inputs,
+    _p_assert,
+    _to_kwargs,
+)
+from torch.utils import _pytree as pytree
+
+
+logger = logging.getLogger(__name__)
+
+# Do not include "process_group" to enable hybrid shard and MoE cases
+HOMOGENEOUS_ATTR_NAMES = (
+    "_use_orig_params",
+    "limit_all_gathers",
+    "_use_full_prec_in_eval",
+)
+
+
+class _PrefetchMode(Enum):
+    BACKWARD = auto()
+    FORWARD = auto()
+
+
+def _get_fsdp_root_states_with_modules(
+    module: nn.Module,
+) -> tuple[list[_FSDPState], list[nn.Module]]:
+    """
+    Returns a tuple containing:
+    1. A list of the root ``_FSDPState`` instances in the module tree rooted at
+    ``module`` without any duplicates and following the ``module.modules()``
+    traversal order (which is assumed to be depth-first).
+    2. A corresponding list of the root modules owning the states in the first
+    list.
+
+    This is similar to :func:`_get_fsdp_states_with_modules` except that we
+    must call :func:`_is_fsdp_root` to force a lazy initialization to determine
+    the FSDP root in case lazy initialization has not yet happened.
+    """
+    fsdp_root_states: list[_FSDPState] = []
+    fsdp_root_modules: list[nn.Module] = []
+    visited_fsdp_states: set[_FSDPState] = set()
+    # NOTE: This function assumes that `module.modules()` proceeds top-down.
+    for submodule in module.modules():
+        optional_state = _get_module_fsdp_state(submodule)
+        if (
+            optional_state is not None
+            and optional_state not in visited_fsdp_states
+            and _is_fsdp_root(optional_state, submodule)
+        ):
+            visited_fsdp_states.add(optional_state)
+            fsdp_root_states.append(optional_state)
+            fsdp_root_modules.append(submodule)
+    return fsdp_root_states, fsdp_root_modules
+
+
+def _get_fsdp_root_states(module: nn.Module) -> list[_FSDPState]:
+    """See :func:`_get_fsdp_root_states_with_modules`."""
+    fsdp_root_states, _ = _get_fsdp_root_states_with_modules(module)
+    return fsdp_root_states
+
+
+def _is_fsdp_root(state: _FSDPState, module: nn.Module) -> bool:
+    """
+    Returns if ``state`` corresponds to that of an FSDP root.
+
+    For the wrapper code path, ``state`` and ``module`` should be the same. For
+    the non-wrapper code path, ``state`` should be ``module`` 's state.
+    """
+    # Force a lazy initialization to determine the FSDP root
+    _lazy_init(state, module)
+    if state._is_root is None:
+        raise AssertionError("Expected _is_root to be set after lazy init")
+    return state._is_root
+
+
+@no_type_check
+def _lazy_init(
+    state: _FSDPState,
+    root_module: nn.Module,
+) -> _FSDPState:
+    """
+    Performs initialization lazily, typically right before the first forward
+    pass. The laziness is needed to ensure that the parameter device/dtype and
+    the FSDP hierarchy have finalized. This method's actual logic only runs on
+    the root FSDP instance, which performs initialization for all non-root FSDP
+    instances to avoid partial initialization.
+
+    For the non-composable code path, ``state`` and ``root_module`` should be
+    the same, namely the FSDP instance itself.
+    """
+    if state._is_root is not None:
+        return  # no-op: already lazily initialized
+    if not state._device_handle.is_available():
+        # Allow the FSDP constructor to run even without CUDA but check this
+        # once we start real execution
+        raise RuntimeError("FSDP does not support CPU only execution")
+    # The following logic is only run on the root FSDP instance since it will
+    # set `_is_root=False` for the non-root instances
+    state._is_root = True
+    _assert_in_training_states(state, [TrainingState.IDLE])
+    _check_flat_params_on_expected_device(state, root_module)
+    state._all_fsdp_states = traversal_utils._get_fsdp_states(root_module)
+    _init_streams(state)
+    buffers, buffer_dtypes = _get_buffers_and_dtypes_for_computation(state, root_module)
+    _cast_buffers_to_dtype_and_device(buffers, buffer_dtypes, state.compute_device)
+    state._exec_order_data.init(state, root_module, state.process_group)
+    _share_state_and_init_handle_attrs(state, root_module)
+    return state
+
+
+def _check_flat_params_on_expected_device(state: _FSDPState, module: nn.Module):
+    """
+    Checks that all ``FlatParameter``s in ``module`` 's tree managed by
+    ``state`` are on the expected device for *lazy initialization*.
+    """
+    cpu_device = torch.device("cpu")
+    for handle in traversal_utils._get_fsdp_handles(module):
+        if (
+            not handle._offload_params
+            and handle.flat_param.device != state.compute_device
+        ):
+            raise RuntimeError(
+                "An FSDP-managed module unexpectedly has parameters on "
+                f"{handle.flat_param.device}. Make sure to move the module to "
+                f"{state.compute_device} before training."
+            )
+        elif handle._offload_params and handle.flat_param.device != cpu_device:
+            raise RuntimeError(
+                "An FSDP-managed module with parameter CPU offloading enabled "
+                f"has parameters on {handle.flat_param.device}. Make sure to "
+                f"not move the module from CPU when offloading parameters."
+            )
+
+
+@no_type_check
+def _share_state_and_init_handle_attrs(
+    root_state: _FSDPState,
+    root_module: nn.Module,
+) -> None:
+    """
+    Shares data structure state from the ``root_state`` to all FSDP states in
+    ``root_module`` 's module tree, and initializes handle attributes. These
+    are done together to require a single loop over the states.
+    """
+    handle = root_state._handle
+    if handle:
+        handle.init_flat_param_attributes()
+    attr_name_to_values: dict[str, set[Any]] = {}
+    for attr_name in HOMOGENEOUS_ATTR_NAMES:
+        attr_name_to_values[attr_name] = set()
+    root_state._all_handles = root_state._exec_order_data.all_handles  # share reference
+    # Update _has_optim_in_backward for each handle.
+    for handle in root_state._all_handles:
+        flat_param = handle.flat_param
+        if hasattr(flat_param, "_in_backward_optimizers"):
+            raise RuntimeError(
+                "FSDP optimizer in backward only supported with use_orig_params=True!"
+            )
+        handle._has_optim_in_backward = flat_param._params is not None and any(
+            hasattr(param, "_in_backward_optimizers") for param in flat_param._params
+        )
+        if handle._has_optim_in_backward:
+            torch._C._log_api_usage_once("fsdp.optimizer_in_backward")
+    for fsdp_state in root_state._all_fsdp_states:
+        for attr_name in HOMOGENEOUS_ATTR_NAMES:
+            _p_assert(
+                hasattr(fsdp_state, attr_name),
+                f"FSDP state missing attribute {attr_name}",
+            )
+            attr_name_to_values[attr_name].add(getattr(fsdp_state, attr_name))
+        if fsdp_state is root_state:
+            continue
+        # Relax the assert for non-root FSDP instances in case the nested
+        # initialized module is wrapped again in FSDP later (e.g. after
+        # training to run inference)
+        _p_assert(
+            fsdp_state._is_root is None or not fsdp_state._is_root,
+            "Non-root FSDP instance's `_is_root` should not have been "
+            "set yet or should have been set to `False`",
+        )
+        fsdp_state._is_root = False
+        fsdp_state._unshard_stream = root_state._unshard_stream
+        fsdp_state._post_backward_stream = root_state._post_backward_stream
+        fsdp_state._pre_unshard_stream = root_state._pre_unshard_stream
+        fsdp_state._all_reduce_stream = root_state._all_reduce_stream
+        fsdp_state._default_stream = root_state._default_stream
+        fsdp_state._exec_order_data = root_state._exec_order_data
+        fsdp_state._free_event_queue = root_state._free_event_queue
+        if fsdp_state._fsdp_extension is not None:
+            fsdp_state._fsdp_extension.compute_stream = root_state._default_stream
+        handle = fsdp_state._handle
+        if handle:
+            handle.init_flat_param_attributes()
+    for attr_name, attr_values in attr_name_to_values.items():
+        if len(attr_values) != 1:
+            raise ValueError(
+                f"Expects one homogeneous value for {attr_name} but got {attr_values}"
+            )
+
+
+@no_type_check
+def _init_streams(
+    state: _FSDPState,
+) -> None:
+    """
+    Initializes CUDA streams for overlapping communication, computation, and
+    data transfers. The streams should be shared across FSDP instances.
+    """
+    if not state._is_root:
+        raise AssertionError("Expected state to be root")
+    if not state._device_handle.is_available():
+        raise AssertionError("Expected device handle to be available")
+    uses_hybrid_sharding = any(
+        fsdp_state.sharding_strategy in HYBRID_SHARDING_STRATEGIES
+        for fsdp_state in state._all_fsdp_states
+    )
+    # Prioritize all-gathers/reduce-scatters over async all-reduce for HSDP and
+    # preserve the default priority of 0 otherwise
+    high_priority = -1 if state.limit_all_gathers and uses_hybrid_sharding else 0
+    # Default stream for computation
+    state._default_stream = state._device_handle.current_stream()
+    if state._fsdp_extension is not None:
+        # set the compute stream to the FSDP extension
+        state._fsdp_extension.compute_stream = state._default_stream
+
+    # Stream for unshard logic, including allocating the all-gather destination
+    # tensors and the all-gathers themselves
+    state._unshard_stream = state._device_handle.Stream(priority=high_priority)
+    # Stream for overlapping gradient reduction with the backward pass gradient
+    # computation
+    state._post_backward_stream = state._device_handle.Stream(priority=high_priority)
+    # Stream for pre-unshard logic, namely allocations and writes for CPU
+    # offloading (H2D copy) and mixed precision (low precision cast)
+    state._pre_unshard_stream = state._device_handle.Stream(priority=high_priority)
+    # Stream to run HSDP's all-reduce as async (if using HSDP)
+    state._all_reduce_stream = (
+        state._device_handle.Stream() if uses_hybrid_sharding else state._default_stream
+    )
+
+
+@no_type_check
+def _unshard(
+    state: _FSDPState,
+    handle: FlatParamHandle,
+    unshard_stream: torch.Stream,
+    pre_unshard_stream: torch.Stream,
+) -> None:
+    """
+    Unshards the handles in ``handles``. If the handles are in
+    :meth:`summon_full_params` and are using mixed precision, then they are
+    forced to full precision.
+
+    Postcondition: handle's ``FlatParameter`` 's data is the padded
+    unsharded flat parameter on the compute device.
+    """
+    if not handle:
+        return
+    with state._device_handle.stream(pre_unshard_stream):
+        ran_pre_unshard = handle.pre_unshard()
+    if ran_pre_unshard:
+        unshard_stream.wait_stream(pre_unshard_stream)
+    if state.limit_all_gathers:
+        event = state._free_event_queue.dequeue_if_needed()
+        if event:
+            with torch.profiler.record_function(
+                "FullyShardedDataParallel.rate_limiter"
+            ):
+                event.synchronize()
+    with state._device_handle.stream(unshard_stream):
+        handle.unshard()
+        handle.post_unshard()
+
+
+@no_type_check
+def _reshard(
+    state: _FSDPState,
+    handle: FlatParamHandle,
+    free_unsharded_flat_param: bool,
+):
+    """
+    Reshards the handle. ``free_unsharded_flat_param`` indicates whether to
+    free the handle's padded unsharded flat parameter.
+    """
+    handle.reshard(free_unsharded_flat_param)
+    if state.limit_all_gathers and free_unsharded_flat_param:
+        if not torch.distributed._functional_collectives.is_torchdynamo_compiling():
+            # We don't run a even queue for freeing under torch compile atm
+            # But maybe we need to? TODO(voz): Look into this
+            free_event = state._device_handle.Event()
+            free_event.record()
+            state._free_event_queue.enqueue(free_event)
+    handle.post_reshard()
+    # Flat parameter freed or not, we always have to "unshard" the parameter
+    # upon next access to get its shape correct.
+    handle._prefetched = False
+
+
+def _unshard_grads(
+    handle: Optional[FlatParamHandle],
+) -> None:
+    if handle:
+        handle.unshard_grad()
+
+
+def _reshard_grads(
+    handle: Optional[FlatParamHandle],
+) -> None:
+    if handle:
+        handle.reshard_grad()
+
+
+@no_type_check
+def _pre_forward(
+    state: _FSDPState,
+    handle: Optional[FlatParamHandle],
+    unshard_fn: Callable,
+    module: nn.Module,
+    args: tuple[Any, ...],
+    kwargs: dict[str, Any],
+) -> tuple[tuple[Any, ...], dict[str, Any]]:
+    """
+    Runs the pre-forward logic. This includes an opportunity to unshard
+    currently sharded parameters such as those for the current forward and
+    registering post-backward hooks for these current parameters. This function
+    also converts forward ``args`` and ``kwargs`` to the given precision.
+
+    Args:
+        handles (List[FlatParamHandle]): Handles giving the parameters used in
+            the current forward.
+        unshard_fn (Optional[Callable]): A callable to unshard any currently
+            sharded parameters or ``None`` to not do any unsharding.
+        module (nn.Module): Module whose forward this method runs right before;
+            expected by the hook signature.
+        args (Tuple[Any, ...]): Module forward ``args``.
+        kwargs (Dict[str, Any]): Module forward ``kwargs``.
+    """
+    with torch.profiler.record_function("FullyShardedDataParallel._pre_forward"):
+        # For `fully_shard` + `checkpoint`, skip pre-forward logic in the
+        # recomputed forward
+        if handle and handle._training_state == HandleTrainingState.BACKWARD_PRE:
+            # For both checkpoint implementations, we do not need to re-cast
+            # inputs here since they will be checkpointed in the low precision
+            # either by AC or normally by autograd as long as the AC region is
+            # nested within FSDP
+            return args, kwargs
+        state.training_state = TrainingState.FORWARD_BACKWARD
+        state._exec_order_data.record_pre_forward(handle, module.training)
+        if handle:
+            handle._training_state = HandleTrainingState.FORWARD
+        if unshard_fn is not None:
+            unshard_fn(state, handle)
+        # Register post-backward hooks to reshard the parameters and reduce-scatter
+        # their gradients. They must be re-registered every forward pass in case
+        # the `grad_fn` is mutated.
+        _register_post_backward_hook(state, handle)
+        # We have to reallocate the _cpu_grad if optimizer overlap
+        # set the grad to None in the backward pass.
+        if handle and handle._offload_params and handle.flat_param._cpu_grad is None:
+            handle.flat_param._cpu_grad = torch.zeros_like(
+                handle.flat_param._local_shard, device=torch.device("cpu")
+            ).pin_memory()
+
+        should_cast_forward_inputs = (
+            state._handle and not state._handle._force_full_precision
+        )
+
+        if should_cast_forward_inputs and state.mixed_precision.cast_forward_inputs:
+            # Recursively convert args and kwargs to specified precision.
+            input_dtype: Optional[torch.dtype] = state.mixed_precision.param_dtype
+            args, kwargs = _cast_forward_inputs(input_dtype, *args, **kwargs)
+        _register_post_backward_reshard_only_hook(state, handle, args, kwargs)
+        return args, kwargs
+
+
+@no_type_check
+def _pre_forward_unshard(
+    state: _FSDPState,
+    handle: Optional[FlatParamHandle],
+) -> None:
+    """Unshards parameters in the pre-forward."""
+    if not handle:
+        return
+    # If the handles have been prefetched, then there is no need to call
+    # `_unshard()` again
+    if not handle._prefetched:
+        _unshard(state, handle, state._unshard_stream, state._pre_unshard_stream)
+    handle._needs_pre_forward_unshard = False
+    # Don't wait during trace
+    if not torch.distributed._functional_collectives.is_torchdynamo_compiling():
+        current_stream = state._device_handle.current_stream()
+        if state._unshard_event is not None:
+            current_stream.wait_event(state._unshard_event)
+            state._unshard_event = None
+        else:
+            current_stream.wait_stream(state._unshard_stream)
+    with torch.profiler.record_function(
+        "FullyShardedDataParallel._pre_forward_prefetch"
+    ):
+        _prefetch_handle(state, handle, _PrefetchMode.FORWARD)
+
+
+@no_type_check
+def _post_forward(
+    state: _FSDPState,
+    handle: Optional[FlatParamHandle],
+    reshard_fn: Callable,
+    module: nn.Module,
+    input: Any,
+    output: Any,
+) -> Any:
+    """
+    Runs the post-forward logic. This includes an opportunity to reshard
+    currently unsharded parameters such as those used in the current forward
+    and registering pre-backward hooks on the forward outputs.
+
+    Args:
+        handles (List[FlatParamHandle]): Handles giving the parameters used in
+            the current forward.
+        reshard_fn (Optional[Callable]): A callable to reshard any currently
+            unsharded parameters (e.g. from the current forward) or ``None`` to
+            not do any resharding.
+        module (nn.Module): Module whose forward just ran, which should be a
+            fully sharded module (see [Note: Fully Sharded Module]); expected
+            by the hook signature.
+        input (Any): Unused; expected by the hook signature.
+        output (Any): Forward pass output; pre-backward hooks are registered on
+            the tensors that require gradients in this output.
+
+    Postcondition: Each ``FlatParameter`` 's data points to the sharded flat
+    parameter.
+    """
+    with torch.profiler.record_function("FullyShardedDataParallel._post_forward"):
+        # For `fully_shard` + `checkpoint`, skip post-forward logic in the
+        # recomputed forward
+        if handle and handle._training_state == HandleTrainingState.BACKWARD_PRE:
+            return output
+
+        state._exec_order_data.record_post_forward(handle)
+        if reshard_fn is not None:
+            reshard_fn(state, handle)
+        # Register pre-backward hooks to unshard the flat parameters for the
+        # gradient computation (if needed)
+        output = _register_pre_backward_hooks(state, module, output, handle)
+        state.training_state = TrainingState.IDLE
+        if handle:
+            handle._training_state = HandleTrainingState.IDLE
+        return output
+
+
+@no_type_check
+def _post_forward_reshard(
+    state: _FSDPState,
+    handle: FlatParamHandle,
+) -> None:
+    """Reshards parameters in the post-forward."""
+    if not handle:
+        return
+    # Do not free the root's parameters in the post-forward for `FULL_SHARD`
+    # with the intention that they are immediately used for backward
+    # computation (though this may not be true)
+    free_unsharded_flat_param = (
+        not state._is_root
+        and handle._sharding_strategy in RESHARD_AFTER_FORWARD_HANDLE_STRATEGIES
+    )
+    _reshard(state, handle, free_unsharded_flat_param)
+
+
+@no_type_check
+def _root_pre_forward(
+    state: _FSDPState,
+    module: nn.Module,
+    args,
+    kwargs,
+) -> None:
+    """
+    Runs pre-forward logic specific to the root FSDP instance, which should run
+    before any individual module's pre-forward. This starts with an attempt at
+    lazy initialization (which only runs non-vacuously once). Otherwise, if
+    this is called on a non-root FSDP instance, then it returns directly.
+
+    Args:
+        module (nn.Module): Module for which this logic tries to run. It may or
+            may not be the root. If not, then this method does not do anything.
+    """
+    with torch.profiler.record_function("FullyShardedDataParallel._root_pre_forward"):
+        _lazy_init(state, module)
+        _p_assert(state._is_root is not None, "Expects a root FSDP to have been set")
+        if not state._is_root:
+            # Always cast forward inputs in the root of this local FSDP unit for mixed
+            # precision, as this is where mixed precision could be configured.
+            # This is more useful for auto wrapping that is recommended in composable path.
+            # For manual wrapping, cast forward inputs on each local FSDP unit root will
+            # increase some overhead, so not turned on for model wrapper path right now where
+            # manual wrapping is more broadly used.
+            if _is_composable(state):
+                return _root_cast_forward_input(state, module, args, kwargs)
+            return args, kwargs
+
+        # We cast buffers back to full precision if we're forcing full precision. Disjointly, we check if buffers
+        # are in full precision and if we should cast them back to lower precision, which happens when
+        # exiting eval() mode.
+        handle = state._handle
+        if handle:
+            should_cast_buffers_to_full_prec = handle._force_full_precision
+        else:
+            # If the root has no handle (no managed parameters), then we fall
+            # back to checking if any child wants to force full precision as a
+            # workaround
+            handles = traversal_utils._get_fsdp_handles(module)
+            should_cast_buffers_to_full_prec = any(
+                handle._force_full_precision for handle in handles
+            )
+
+        if should_cast_buffers_to_full_prec:
+            _cast_buffers_to_dtype_and_device(
+                buffers=dict(module.named_buffers()).values(),
+                buffer_dtypes=list(state._buffer_name_to_orig_dtype.values()),
+                device=state.compute_device,
+            )
+            # This flag is only set when we cast buffers to full precision, to avoid the
+            # CPU overhead that can stem from retrieving all buffers and their types in the
+            # following else branch.
+            state._needs_buffer_dtype_restore_check = True
+        elif getattr(state, "_needs_buffer_dtype_restore_check", False):
+            # Check if buffers are in full precision and we need to cast them
+            # back down.
+            (
+                buffers,
+                buffer_dtypes_for_computation,
+            ) = _get_buffers_and_dtypes_for_computation(state, module)
+            if len(buffers) > 0 and len(buffer_dtypes_for_computation) > 0:
+                if any(
+                    buffer.dtype != buffer_dtype_for_computation
+                    for buffer, buffer_dtype_for_computation in zip(
+                        buffers, buffer_dtypes_for_computation
+                    )
+                ):
+                    # Assume we have to cast everything if there is one mismatch
+                    _cast_buffers_to_dtype_and_device(
+                        buffers, buffer_dtypes_for_computation, state.compute_device
+                    )
+            # We don't have to check this again until we cast buffers to full precision again.
+            state._needs_buffer_dtype_restore_check = False
+
+        if state.forward_prefetch:
+            handles = [
+                fsdp_state._handle
+                for fsdp_state in state._all_fsdp_states
+                if fsdp_state._handle
+            ]
+            for handle in handles:
+                handle._needs_pre_forward_unshard = True
+                handle._prefetched = False
+        _wait_for_computation_stream(
+            state._device_handle.current_stream(),
+            state._unshard_stream,
+            state._pre_unshard_stream,
+        )
+        _reset_flat_param_grad_info_if_needed(state._all_handles)
+
+        # Prepares the forward inputs by moving them to ``compute_device``
+        # TODO: Do not use the side stream for tensor copies for now; investigate
+        # the perf with/without it.
+        with torch.profiler.record_function("FullyShardedDataParallel._to_kwargs"):
+            args_tuple, kwargs_tuple = _to_kwargs(
+                args, kwargs, state.compute_device, False
+            )
+        args = args_tuple[0] if args_tuple else tuple()
+        kwargs = kwargs_tuple[0] if kwargs_tuple else {}
+
+        return _root_cast_forward_input(state, module, args, kwargs)
+
+
+@no_type_check
+def _root_cast_forward_input(
+    state: _FSDPState, module: torch.nn.Module, args, kwargs
+) -> tuple[Any, Any]:
+    if state._handle:
+        force_full_precision = not state._handle._force_full_precision
+    else:
+        force_full_precision = True
+
+    should_cast_forward_inputs = (
+        (module.training or not state._use_full_prec_in_eval) and force_full_precision
+    ) and state.mixed_precision.cast_root_forward_inputs
+
+    if should_cast_forward_inputs:
+        input_dtype: Optional[torch.dtype] = state.mixed_precision.param_dtype
+        args, kwargs = _cast_forward_inputs(input_dtype, *args, **kwargs)
+
+    return args, kwargs
+
+
+@no_type_check
+def _pre_backward_hook(
+    state: _FSDPState,
+    module: nn.Module,
+    handle: FlatParamHandle,
+    grad,
+    *unused: Any,
+) -> Any:
+    """
+    Prepares ``_handle`` 's ``FlatParameter`` s for gradient computation.
+
+    Args:
+        module (nn.Module): Fully sharded module (see [Note: Fully Sharded
+            Module]).
+    """
+    # Only run the pre-backward hook once per group of handles involved in the
+    # same module forward computation
+    if (
+        handle
+        and hasattr(handle, "_ran_pre_backward_hook")
+        and handle._ran_pre_backward_hook
+    ):
+        return grad
+
+    with torch.profiler.record_function("FullyShardedDataParallel._pre_backward_hook"):
+        # Queue the post-backward callback once for the root FSDP instance to
+        # attach it to the outermost backward graph task so that it is called
+        # after all backward calls complete
+        if state._is_root and not state._post_backward_callback_queued:
+            _register_post_backward_final_callback(state, module)
+            _reset_flat_param_grad_info_if_needed(state._all_handles)
+        elif handle:
+            allowed_states = [TrainingState.IDLE]
+            if _is_composable(state):
+                allowed_states.append(TrainingState.FORWARD_BACKWARD)
+            _assert_in_training_states(state, allowed_states)
+        state.training_state = TrainingState.FORWARD_BACKWARD
+        # Queueing the post-backward callback is the only logic that is not
+        # per-handle in the pre-backward hook, so we can return early here if
+        # there are no handles.
+        if not handle:
+            return grad
+        handle._training_state = HandleTrainingState.BACKWARD_PRE
+
+        if handle._needs_pre_backward_unshard:
+            # If the handles have been prefetched, then there is no need to
+            # call `_unshard()` again
+            if not handle._prefetched:
+                _unshard(
+                    state,
+                    handle,
+                    state._unshard_stream,
+                    state._pre_unshard_stream,
+                )
+            # Don't wait during trace
+            if not torch.distributed._functional_collectives.is_torchdynamo_compiling():
+                state._device_handle.current_stream().wait_stream(state._unshard_stream)
+
+        # Set this to `False` to ensure that a mistargeted prefetch does not
+        # actually unshard these handles
+        handle._needs_pre_backward_unshard = False
+        with torch.profiler.record_function(
+            "FullyShardedDataParallel._pre_backward_prefetch"
+        ):
+            _prefetch_handle(state, handle, _PrefetchMode.BACKWARD)
+        handle.prepare_gradient_for_backward()
+        handle._ran_pre_backward_hook = True
+        return grad
+
+
+@no_type_check
+@torch.no_grad()
+def _post_backward_hook(
+    state: _FSDPState,
+    handle: FlatParamHandle,
+    flat_param,
+    *unused: Any,
+):
+    """
+    Reduce-scatters the gradient of ``handle`` 's ``FlatParameter``.
+
+    Precondition: The ``FlatParameter`` 's ``.grad`` attribute contains the
+    unsharded gradient for the local batch.
+
+    Postcondition:
+    - If using ``NO_SHARD``, then the ``.grad`` attribute is the reduced
+    unsharded gradient.
+    - Otherwise, the ``_saved_grad_shard`` attribute is the reduced sharded
+    gradient (accumulating with any existing gradient).
+    """
+    _log_post_backward_hook(state, handle, logger)
+    flat_param = handle.flat_param
+    flat_param._post_backward_called = True
+    with torch.autograd.profiler.record_function(
+        "FullyShardedDataParallel._post_backward_hook"
+    ):
+        _assert_in_training_states(state, [TrainingState.FORWARD_BACKWARD])
+        # For multiple applications of reentrant AC across submodules sharing
+        # the same `FlatParameter`, the post-backward hook may run multiple
+        # times in one backward, in which case we permit the state to already
+        # be in `BACKWARD_POST`.
+        _p_assert(
+            handle._training_state
+            in (HandleTrainingState.BACKWARD_PRE, HandleTrainingState.BACKWARD_POST),
+            f"Expects `BACKWARD_PRE` or `BACKWARD_POST` state but got {handle._training_state}",
+        )
+        handle._training_state = HandleTrainingState.BACKWARD_POST
+
+        if flat_param.grad is None:
+            return
+        if flat_param.grad.requires_grad:
+            raise RuntimeError("FSDP does not support gradients of gradients")
+
+        _post_backward_reshard(state, handle)
+        if not state._sync_gradients:
+            if handle._use_orig_params:
+                handle._use_unsharded_grad_views()
+            return
+
+        # Wait for all ops in the current stream (e.g. gradient computation) to
+        # finish before reduce-scattering the gradient
+        if not torch.distributed._functional_collectives.is_torchdynamo_compiling():
+            state._post_backward_stream.wait_stream(
+                state._device_handle.current_stream()
+            )
+
+        with state._device_handle.stream(state._post_backward_stream):
+            autograd_computed_grad = flat_param.grad.data
+            if (
+                not _low_precision_hook_enabled(state)
+                and flat_param.grad.dtype != handle._reduce_dtype
+                # If we are forcing full precision but communicating grads
+                # (i.e. model.eval() + full precision in eval was configured), don't downcast gradient.
+                and not handle._force_full_precision
+            ):
+                flat_param.grad.data = flat_param.grad.to(handle._reduce_dtype)
+            if handle.uses_sharded_strategy:
+                _reduce_grad(state, handle)
+            else:
+                _reduce_grad_no_shard(state, handle)
+            # Since the unsharded gradient is produced in the computation
+            # stream and consumed in the post-backward stream, inform the
+            # caching allocator (before it goes out of scope)
+            _no_dispatch_record_stream(
+                autograd_computed_grad, state._post_backward_stream
+            )
+
+
+def _post_backward_reshard_only_hook(
+    state: _FSDPState,
+    handle: FlatParamHandle,
+    *unused: Any,
+) -> None:
+    with torch.profiler.record_function(
+        "FullyShardedDataParallel._post_backward_hook_reshard_only"
+    ):
+        # `_pre_backward_hook` may not get executed
+        # if forward output does not require grad
+        # overwrite IDLE state for post-backward prefetching
+        state.training_state = TrainingState.FORWARD_BACKWARD
+        handle._training_state = HandleTrainingState.BACKWARD_POST
+        _post_backward_reshard(state, handle)
+
+
+def _post_backward_reshard(
+    state: _FSDPState,
+    handle: FlatParamHandle,
+    *unused: Any,
+) -> None:
+    free_unsharded_flat_param = _should_free_in_backward(state, handle)
+    _reshard(state, handle, free_unsharded_flat_param)
+
+    # TODO: Post-backward prefetching does not support the multiple handles
+    # per module case since the post-backward hook runs per handle, not per
+    # group of handles.
+    with torch.profiler.record_function(
+        "FullyShardedDataParallel._post_backward_prefetch"
+    ):
+        _prefetch_handle(state, handle, _PrefetchMode.BACKWARD)
+
+
+@no_type_check
+def _should_free_in_backward(
+    state: _FSDPState,
+    handle: FlatParamHandle,
+) -> bool:
+    """
+    Returns whether FSDP should free the unsharded flat parameter in the
+    post-backward or not.
+    """
+    if not handle.uses_sharded_strategy:
+        return False
+    # If not syncing gradients, then we do not free for strategies that do not
+    # reshard after forward as a *heuristic* to tradeoff higher memory for
+    # higher throughput.
+    return (
+        state._sync_gradients
+        or handle._sharding_strategy in RESHARD_AFTER_FORWARD_HANDLE_STRATEGIES
+    )
+
+
+@no_type_check
+def _reduce_grad(state: _FSDPState, handle: FlatParamHandle) -> None:
+    """
+    For sharded strategies, this runs gradient reduction, sharded gradient
+    accumulation if needed, and the post-reduction callback.
+    """
+    flat_param = handle.flat_param
+    uses_hybrid_sharded_strategy = handle._sharding_strategy in (
+        HandleShardingStrategy.HYBRID_SHARD,
+        HandleShardingStrategy._HYBRID_SHARD_ZERO2,
+    )
+    # We clear `.grad` to permit multiple backwards. This avoids a race where
+    # the second backward pass computation precedes ahead of the first backward
+    # pass reduction, which is possible since the reduction is issued in a
+    # separate stream and is async and would result in reducing the wrong
+    # gradient.
+    unsharded_grad = flat_param.grad.data
+    flat_param.grad = None
+    padded_unsharded_grad, new_sharded_grad = _get_reduce_scatter_tensors(
+        state, unsharded_grad
+    )
+    if state._comm_hook is None:  # default path
+        _div_if_needed(padded_unsharded_grad, state._gradient_predivide_factor)
+        pg = (
+            handle._fake_process_group
+            if handle._use_fake_reduce
+            else state.process_group
+        )
+        dist.reduce_scatter_tensor(
+            new_sharded_grad,
+            padded_unsharded_grad,
+            group=pg,
+        )
+        if uses_hybrid_sharded_strategy:
+            # Don't wait during trace
+            if not torch.distributed._functional_collectives.is_torchdynamo_compiling():
+                state._all_reduce_stream.wait_stream(state._post_backward_stream)
+            with state._device_handle.stream(state._all_reduce_stream):
+                # Since the new sharded gradient is produced in the post-
+                # backward stream and consumed in the all-reduce stream,
+                # inform the caching allocator
+                _no_dispatch_record_stream(new_sharded_grad, state._all_reduce_stream)
+                dist.all_reduce(new_sharded_grad, group=state._inter_node_pg)
+                _div_if_needed(new_sharded_grad, state._gradient_postdivide_factor)
+                grad_to_offload = _accumulate_sharded_grad(
+                    state, handle, new_sharded_grad
+                )
+                _post_reduce_grad_callback(state, handle, grad_to_offload)
+                return
+        _div_if_needed(new_sharded_grad, state._gradient_postdivide_factor)
+    else:
+        state._comm_hook(
+            state._comm_hook_state, padded_unsharded_grad, new_sharded_grad
+        )
+        # NOTE: HSDP variants do not support communication hook.
+    grad_to_offload = _accumulate_sharded_grad(state, handle, new_sharded_grad)
+    _post_reduce_grad_callback(state, handle, grad_to_offload)
+
+
+@no_type_check
+def _get_reduce_scatter_tensors(
+    state: _FSDPState, unsharded_grad: torch.Tensor
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """
+    Returns the input and output tensors to reduce-scatter, respectively.
+    """
+    chunks = list(unsharded_grad.chunk(state.world_size))
+    numel_to_pad = state.world_size * chunks[0].numel() - unsharded_grad.numel()
+    padded_unsharded_grad = (
+        F.pad(unsharded_grad, [0, numel_to_pad]) if numel_to_pad > 0 else unsharded_grad
+    )
+    new_sharded_grad = torch.empty_like(chunks[0])  # padded
+    return padded_unsharded_grad, new_sharded_grad
+
+
+@no_type_check
+def _accumulate_sharded_grad(
+    state: _FSDPState,
+    handle: FlatParamHandle,
+    sharded_grad: torch.Tensor,
+) -> torch.Tensor:
+    """
+    Accumulates the reduce-scattered sharded gradient with any existing sharded
+    gradient if needed, returning the gradient to offload (if CPU offloading is
+    enabled).
+    """
+    flat_param = handle.flat_param
+    _cast_grad_to_param_dtype(state, sharded_grad, flat_param)
+    # Save the sharded gradient in `_saved_grad_shard` to support gradient
+    # accumulation -- for multiple backwards, the gradient reductions may
+    # happen in arbitrary order
+    accumulate_grad = hasattr(flat_param, "_saved_grad_shard")
+    if accumulate_grad:
+        _check_grad_to_accumulate(sharded_grad, flat_param._saved_grad_shard)
+        flat_param._saved_grad_shard += sharded_grad
+    else:
+        flat_param._saved_grad_shard = sharded_grad
+    grad_to_offload = flat_param._saved_grad_shard
+    return grad_to_offload
+
+
+@no_type_check
+def _reduce_grad_no_shard(state: _FSDPState, handle: FlatParamHandle) -> None:
+    """
+    For no-shard, this runs gradient reduction (which directly covers any
+    gradient accumulation implicitly) and the post-reduction callback.
+    """
+    flat_param = handle.flat_param
+    if state._comm_hook is None:  # default path
+        _div_if_needed(flat_param.grad, state._gradient_predivide_factor)
+        dist.all_reduce(flat_param.grad, group=state.process_group)
+        _div_if_needed(flat_param.grad, state._gradient_postdivide_factor)
+    else:
+        state._comm_hook(state._comm_hook_state, flat_param.grad)
+    # For `NO_SHARD`, we can keep the low precision gradients by simply
+    # omitting the cast altogether
+    if not handle._keep_low_precision_grads:
+        _cast_grad_to_param_dtype(state, flat_param.grad, flat_param)
+    grad_to_offload = flat_param.grad.data
+    _post_reduce_grad_callback(state, handle, grad_to_offload)
+
+
+@no_type_check
+def _post_reduce_grad_callback(
+    state: _FSDPState,
+    handle: FlatParamHandle,
+    # Additional arguments needed for the callback logic
+    grad_to_offload: torch.Tensor,
+):
+    """
+    This callback captures any logic to run after the gradient reduction
+    finishes. Currently, this offloads the gradient to CPU if CPU offloading is
+    enabled and uses sharded gradient views if ``use_orig_params=True``.
+    """
+    _offload_grad(state, handle, grad_to_offload)
+    _post_backward_use_sharded_grad_views(handle)
+
+
+@no_type_check
+def _offload_grad(
+    state: _FSDPState,
+    handle: FlatParamHandle,
+    grad_to_offload: torch.Tensor,
+):
+    if not handle._offload_params:
+        return
+    # Offload the gradient to CPU to ensure parameters and gradients are on the
+    # same device as required by the optimizer
+    # TODO: Investigate why `NO_SHARD` breaks correctness when using
+    # `non_blocking=True` here.
+    # TODO (rohan-varma): When CPU offload and optimizer overlap,
+    # non_blocking=True won't work since the copy may have not finished before
+    # the optimizer step executes on CPU. If we want to use non-blocking=True
+    # here, we'll have to synchronize before using result on CPU.
+    non_blocking = handle.uses_sharded_strategy and not handle._has_optim_in_backward
+    handle.flat_param._cpu_grad.copy_(
+        grad_to_offload.detach(), non_blocking=non_blocking
+    )  # synchronized in the post-backward callback
+    # Since the gradient being offloaded may have been produced in the
+    # computation stream and is being consumed here in the post-backward
+    # stream, inform the caching allocator
+    _no_dispatch_record_stream(grad_to_offload.data, state._post_backward_stream)
+
+
+@no_type_check
+def _post_backward_use_sharded_grad_views(handle: FlatParamHandle):
+    if not handle._use_orig_params:
+        return
+    # Since the handle's `FlatParameter` completed its gradient computation, we
+    # should reset the gradient noneness mask
+    handle._reset_is_grad_none()
+    # Delay using sharded gradient views until after the reduce-scatter instead
+    # of immediately after resharding
+    handle._use_sharded_grad_views()
+    if handle._has_optim_in_backward:
+        handle.prepare_gradient_for_optim()
+        for orig_param in handle.flat_param._params:
+            # Check for `None` gradient to filter parameters not in the rank
+            if orig_param.grad is not None and hasattr(
+                orig_param, "_in_backward_optimizers"
+            ):
+                # TODO (rohan-varma): For CPU offload, this unfortunately
+                # operates on CPU because the parameters and gradients have
+                # already been offloaded. We should run this on GPU after
+                # refactoring.
+                for optim in orig_param._in_backward_optimizers:
+                    optim.step()
+
+                optim.zero_grad(set_to_none=True)
+        handle._reset_flat_param_grad_info_if_needed()
+        if handle._offload_params:
+            handle.flat_param._cpu_grad = None
+
+
+def _div_if_needed(tensor: torch.Tensor, div_factor: float) -> None:
+    if div_factor > 1:
+        tensor.div_(div_factor)
+
+
+@no_type_check
+def _cast_grad_to_param_dtype(
+    state: _FSDPState,
+    sharded_grad: torch.Tensor,
+    param: FlatParameter,
+):
+    """
+    Casts ``sharded_grad`` back to the full parameter dtype so that the
+    optimizer step runs with that dtype. This performs an actual cast if
+    1. parameters were in reduced precision during the forward since then
+    gradients would be in that reduced precision, or
+    2. parameters were not in reduced precision but gradients were in
+    reduced precision for communication.
+    However, if a low precision communication hook is registered, then this
+    dtype cast happens in the hook instead.
+    """
+    _assert_in_training_states(state, [TrainingState.FORWARD_BACKWARD])
+    if not _low_precision_hook_enabled(state) and sharded_grad.dtype != param.dtype:
+        low_prec_grad_data = sharded_grad.data
+        sharded_grad.data = sharded_grad.data.to(dtype=param.dtype)
+        # Since for `NO_SHARD`, the gradient is produced in the computation
+        # stream and consumed here in the post-backward stream, inform the
+        # caching allocator; for the sharded strategies, the gradient is
+        # produced in the post-backward stream, so this `record_stream()`
+        # should be a no-op
+        _no_dispatch_record_stream(
+            low_prec_grad_data, state._device_handle.current_stream()
+        )
+
+
+def _check_grad_to_accumulate(
+    new_sharded_grad: torch.Tensor,
+    accumulated_grad: torch.Tensor,
+) -> None:
+    _p_assert(
+        accumulated_grad.shape == new_sharded_grad.shape,
+        "Shape mismatch when accumulating gradients: "
+        f"existing gradient shape={accumulated_grad.shape} "
+        f"new gradient shape={new_sharded_grad.shape}",
+    )
+    _p_assert(
+        accumulated_grad.device == new_sharded_grad.device,
+        "Device mismatch when accumulating gradients: "
+        f"existing gradient device={accumulated_grad.device} "
+        f"new gradient device={new_sharded_grad.device}",
+    )
+
+
+@no_type_check
+def _low_precision_hook_enabled(state: _FSDPState) -> bool:
+    return state._comm_hook in LOW_PRECISION_HOOKS
+
+
+@no_type_check
+@torch.no_grad()
+def _post_backward_final_callback(
+    state: _FSDPState,
+    module: nn.Module,
+):
+    """
+    This waits for the post-backward to finish and performs some final cleanup.
+    This runs at the end of the entire backward pass and should only be called
+    on the root FSDP instance.
+    """
+    _p_assert(
+        state._is_root,
+        "The post-backward callback should only be called on the root FSDP instance",
+    )
+    root_state = state
+
+    if root_state._sync_gradients:
+        current_stream = state._device_handle.current_stream()
+        # TODO (rohan-varma): this also waits for the overlapped optimizer step to finish
+        # since it currently runs in the post-backward stream. That can be
+        # pushed to the next forward if run in a different stream
+        current_stream.wait_stream(root_state._post_backward_stream)
+        if root_state._all_reduce_stream is not current_stream:  # uses HSDP
+            current_stream.wait_stream(root_state._all_reduce_stream)
+        if root_state.cpu_offload.offload_params:
+            # Wait for non-blocking GPU -> CPU sharded gradient copies from the
+            # post-backward hooks to finish explicitly since CPU gradients do
+            # not automatically synchronize with the GPU
+            state._device_handle.current_stream().synchronize()
+    root_state._exec_order_data.next_iter()
+
+    for fsdp_state in state._all_fsdp_states:
+        _catch_all_reshard(fsdp_state)
+        _finalize_params(fsdp_state)
+        fsdp_state.training_state = TrainingState.IDLE
+        handle = fsdp_state._handle
+        if handle:
+            handle._ran_pre_backward_hook = False
+            handle._needs_pre_backward_unshard = False
+            handle._post_forward_index = None
+            handle._training_state = HandleTrainingState.IDLE
+            handle._prefetched = False
+    # Reset for cases like one forward and multiple backwards
+    root_state._post_backward_callback_queued = False
+
+
+@no_type_check
+def _catch_all_reshard(
+    state: _FSDPState,
+) -> None:
+    """
+    Reshards the parameters that may not have been resharded in the
+    post-backward hook. This can happen when a module's output is used in the
+    forward pass, meaning that its pre-backward hook runs (unsharding the
+    parameter), but the post-backward hook does not run because the output was
+    not jused in the loss computation corresponding to this backward pass.
+    """
+    # Wrap with a try-except to provide a more informative traceback if an
+    # error is raised
+    try:
+        if state._handle:
+            # TODO: This already-resharded check is brittle:
+            # https://github.com/pytorch/pytorch/issues/83956
+            already_resharded = (
+                state._handle.flat_param.data_ptr()
+                == state._handle.flat_param._local_shard.data_ptr()
+                # If FSDP skipped using sharded views, then the flat parameter
+                # still points to the sharded data, so we need to reshard to
+                # use sharded views
+                and not state._handle._skipped_use_sharded_views
+            )
+            if already_resharded:
+                return
+            free_unsharded_flat_param = _should_free_in_backward(state, state._handle)
+            _reshard(state, state._handle, free_unsharded_flat_param)
+    except Exception as e:
+        _p_assert(
+            False,
+            f"Got exception in the catch-all reshard for {state}: {str(e)}",
+            raise_assertion_error=False,
+        )
+        raise e
+
+
+@no_type_check
+def _finalize_params(
+    state: _FSDPState,
+) -> None:
+    """Finalizes the parameters before the next iteration."""
+    handle = state._handle
+    if not handle:
+        return
+    flat_param = handle.flat_param
+    if torch.distributed._functional_collectives.is_torchdynamo_compiling():
+        if hasattr(flat_param, "_post_backward_hook_handle"):
+            pbhs_handle = flat_param._post_backward_hook_handle
+            pbhs_handle.remove()
+            del flat_param._post_backward_hook_handle
+    else:
+        if hasattr(flat_param, "_post_backward_hook_state"):
+            post_backward_hook_state_len = len(flat_param._post_backward_hook_state)
+            expected_post_backward_hook_state_len = int(flat_param.requires_grad) + 1
+            _p_assert(
+                post_backward_hook_state_len == expected_post_backward_hook_state_len,
+                f"Invalid: ``_post_backward_hook_state``: {flat_param._post_backward_hook_state}",
+            )
+            flat_param._post_backward_hook_state[-1].remove()
+            delattr(flat_param, "_post_backward_hook_state")
+    if flat_param.requires_grad:
+        if not state._sync_gradients:
+            # Preserve the gradient accumulation state if not synchronizing
+            # gradients: `.grad` remains the unsharded gradient  from prior
+            # `no_sync()` iterations, and `_saved_grad_shard` remains the
+            # sharded gradient from the last synchronized iteration
+            return
+        if not handle._has_optim_in_backward:
+            handle.prepare_gradient_for_optim()
+        _p_assert(
+            hasattr(flat_param, "_post_backward_called"),
+            "Expects `_post_backward_called` to be set on the `FlatParameter`",
+        )
+        flat_param._post_backward_called = False
+
+
+@no_type_check
+def _prefetch_handle(
+    state: _FSDPState,
+    current_handle: Optional[FlatParamHandle],
+    prefetch_mode: _PrefetchMode,
+) -> None:
+    """
+    Prefetches the next handles if needed (without synchronization). An empty
+    handles key cannot prefetch.
+    """
+    if not current_handle:
+        return
+    handle = _get_handle_to_prefetch(state, current_handle)
+    if not handle:
+        return
+    # Temporarily emulate the training state while calling `_unshard` to
+    # ensure the correct `as_params` for `_use_unsharded_views()`
+    prev_training_state = handle._training_state
+    if prefetch_mode == _PrefetchMode.BACKWARD:
+        handle._training_state = HandleTrainingState.BACKWARD_PRE
+    elif prefetch_mode == _PrefetchMode.FORWARD:
+        handle._training_state = HandleTrainingState.FORWARD
+    else:
+        raise ValueError(f"Invalid prefetch mode on rank {state.rank}: {prefetch_mode}")
+    # Prefetch the next set of handles without synchronizing to allow
+    # the sync to happen as late as possible to maximize overlap
+    _unshard(state, handle, state._unshard_stream, state._pre_unshard_stream)
+    handle._training_state = prev_training_state
+    handle._prefetched = True
+
+
+@no_type_check
+def _get_handle_to_prefetch(
+    state: _FSDPState,
+    current_handle: FlatParamHandle,
+) -> FlatParamHandle:
+    """
+    Returns a :class:`list` of the handles keys to prefetch for the next
+    module(s), where ``current_handle`` represents the current module.
+
+    "Prefetching" refers to running the unshard logic early (without
+    synchronization), and the "next" modules depend on the recorded execution
+    order and the current training state.
+    """
+    training_state = _get_training_state(current_handle)
+    valid_training_states = (
+        HandleTrainingState.BACKWARD_PRE,
+        HandleTrainingState.BACKWARD_POST,
+        HandleTrainingState.FORWARD,
+    )
+    _p_assert(
+        training_state in valid_training_states,
+        f"Prefetching is only supported in {valid_training_states} but "
+        f"currently in {training_state}",
+    )
+    eod = state._exec_order_data
+    target_handle: Optional[FlatParamHandle] = None
+    if (
+        training_state == HandleTrainingState.BACKWARD_PRE
+        and state.backward_prefetch == BackwardPrefetch.BACKWARD_PRE
+    ) or (
+        training_state == HandleTrainingState.BACKWARD_POST
+        and state.backward_prefetch == BackwardPrefetch.BACKWARD_POST
+    ):
+        target_handle_candidate = eod.get_handle_to_backward_prefetch(current_handle)
+        if (
+            target_handle_candidate
+            and target_handle_candidate._needs_pre_backward_unshard
+            and not target_handle_candidate._prefetched
+        ):
+            target_handle = target_handle_candidate
+        else:
+            target_handle = None
+    elif training_state == HandleTrainingState.FORWARD and state.forward_prefetch:
+        target_handle_candidate = eod.get_handle_to_forward_prefetch(current_handle)
+        if (
+            target_handle_candidate
+            and target_handle_candidate._needs_pre_forward_unshard
+            and not target_handle_candidate._prefetched
+        ):
+            target_handle = target_handle_candidate
+        else:
+            target_handle = None
+
+    return target_handle
+
+
+def _get_training_state(
+    handle: FlatParamHandle,
+) -> HandleTrainingState:
+    """Returns the training state of the handles in ``handle``."""
+    _p_assert(handle, "Expects a non-empty handle")
+    return handle._training_state
+
+
+@no_type_check
+def _register_pre_forward_hook(
+    state: _FSDPState,
+    module: nn.Module,
+) -> None:
+    """
+    Registers a pre-forward hook on ``module``.
+    """
+    for forward_handle in state._pre_forward_handles:
+        forward_handle.remove()
+    state._pre_forward_handles.clear()
+    module_param_handle = state._fully_sharded_module_to_handle.get(module, None)
+    hook = functools.partial(
+        _pre_forward, state, module_param_handle, _pre_forward_unshard
+    )
+    state._pre_forward_handles.append(
+        module.register_forward_pre_hook(hook, prepend=True, with_kwargs=True)
+    )
+
+
+@no_type_check
+def _register_post_forward_hook(
+    state: _FSDPState,
+    module: nn.Module,
+) -> None:
+    """
+    Registers a post-forward hook on ``module``. Even if the module has no
+    handles, we should register the hook since it will register the module's
+    pre-backward hook.
+    """
+    for forward_handle in state._post_forward_handles:
+        forward_handle.remove()
+    state._post_forward_handles.clear()
+    module_param_handle = state._fully_sharded_module_to_handle.get(module, None)
+    hook = functools.partial(
+        _post_forward,
+        state,
+        module_param_handle,
+        _post_forward_reshard,
+    )
+    state._post_forward_handles.append(module.register_forward_hook(hook))
+
+
+@no_type_check
+def _register_root_pre_forward_hook(
+    state: _FSDPState,
+    module: nn.Module,
+):
+    """
+    Registers root pre-forward hook on ``module``, which should be the local
+    FSDP root.
+
+    NOTE: For the current composable FSDP design, we have each application of
+    ``fully_shard()`` to a module to indicate that that module is the local
+    FSDP root. We may remove this assumption in the future, in which case we
+    will need to register this root pre-forward hook on any candidate module
+    that may be the local FSDP root.
+    """
+    for forward_handle in state._root_pre_forward_handles:
+        forward_handle.remove()
+    state._root_pre_forward_handles.clear()
+    hook = functools.partial(_root_pre_forward, state)
+    state._root_pre_forward_handles.append(
+        module.register_forward_pre_hook(hook, prepend=True, with_kwargs=True)
+    )
+
+
+@no_type_check
+def _register_pre_backward_hooks(
+    state: _FSDPState,
+    module: nn.Module,
+    outputs: Any,
+    handle: FlatParamHandle,
+) -> None:
+    """
+    Registers pre-backward hooks on the tensors that require gradients in the
+    forward pass outputs ``outputs``, which were computed using the
+    ``FlatParameter`` s of ``handles``.
+
+    Args:
+        module (nn.Module): Fully sharded module (see [Note: Fully Sharded
+            Module]).
+
+    Returns:
+        Forward pass outputs with pre-backward hooks registered to tensors that
+        require gradients.
+    """
+    # If there is no gradient computation, then there is no need for
+    # pre-backward logic
+    if not torch.is_grad_enabled():
+        return outputs
+    if state._is_root:
+        state._post_backward_callback_queued = False  # only defined on the root
+
+    if handle:
+        handle._needs_pre_backward_unshard = False
+        # Since these handles' `FlatParameter`s participated in a forward, we
+        # conservatively assume that they will be used in the backward
+        handle._ran_pre_backward_hook = False
+
+    def _register_hook(t: torch.Tensor) -> torch.Tensor:
+        if t.requires_grad:
+            t.register_hook(
+                torch.utils.hooks.unserializable_hook(
+                    functools.partial(_pre_backward_hook, state, module, handle)
+                )
+            )
+            if handle:
+                handle._needs_pre_backward_unshard = True
+        return t
+
+    return _apply_to_tensors(_register_hook, outputs)
+
+
+def _register_post_backward_hook(
+    state: _FSDPState,
+    handle: Optional[FlatParamHandle],
+) -> None:
+    """
+    Registers post-backward hooks on the ``FlatParameter`` s'
+    ``AccumulateGrad`` objects to reshard and to reduce-scatter gradients.
+
+    The ``AccumulateGrad`` object represents the last function that finalizes
+    the ``FlatParameter`` 's gradient, so it only runs after its entire
+    gradient computation has finished.
+
+    We register the post-backward hook only once in the *first* forward that a
+    ``FlatParameter`` participates in. This relies on the ``AccumulateGrad``
+    object being preserved through multiple forwards.
+
+    NOTE: We follow this heuristic to prefer the *first* forward to target the
+    parameter mixed precision case, where there are *separate*
+    ``AccumulateGrad`` objects across the different forwards. (Without
+    parameter mixed precision, the ``AccumulateGrad`` objects are the same.) If
+    we instead prefer the *last* forward, then the hook runs early.
+    """
+    # If there is no gradient computation, then there is no need for
+    # post-backward logic
+    if not torch.is_grad_enabled():
+        return
+    if not handle:
+        return
+    flat_param = handle.flat_param
+
+    if torch.distributed._functional_collectives.is_torchdynamo_compiling():
+        already_registered = hasattr(flat_param, "_post_backward_hook_handle")
+        if already_registered or not flat_param.requires_grad:
+            return
+        hook = functools.partial(_post_backward_hook, state, handle)
+        hook_handle = flat_param.register_post_accumulate_grad_hook(hook)
+        flat_param._post_backward_hook_handle = hook_handle  # type: ignore[attr-defined]
+    else:
+        already_registered = hasattr(flat_param, "_post_backward_hook_state")
+        if already_registered or not flat_param.requires_grad:
+            return
+        # Get the `AccumulateGrad` object
+        temp_flat_param = flat_param.expand_as(flat_param)
+        _p_assert(
+            temp_flat_param.grad_fn is not None,
+            "The `grad_fn` is needed to access the `AccumulateGrad` and "
+            "register the post-backward hook",
+        )
+        acc_grad = temp_flat_param.grad_fn.next_functions[0][0]  # type: ignore[union-attr]
+        if acc_grad is None:
+            raise AssertionError("Expected acc_grad to be set")
+        hook_handle = acc_grad.register_hook(
+            functools.partial(_post_backward_hook, state, handle)
+        )
+        flat_param._post_backward_hook_state = (acc_grad, hook_handle)  # type: ignore[attr-defined]
+
+
+def _register_post_backward_reshard_only_hook(
+    state: _FSDPState,
+    handle: Optional[FlatParamHandle],
+    args: tuple[Any, ...],
+    kwargs: dict[str, Any],
+) -> None:
+    """
+    Registers post-backward hooks to reshard flat parameters that do not
+    require gradient. We register these using multi-post-grad hooks on the
+    input activations to ensure that all gradients that may depend on the
+    parameters have been computed before resharding.
+    """
+    # If there is no gradient computation, then there is no need for
+    # post-backward logic
+    if not torch.is_grad_enabled():
+        return
+    # Construct `inp_tensors` lazily to avoid CPU overhead in typical case
+    # where each flat parameter requires gradient
+    inp_tensors: Optional[list[torch.Tensor]] = None
+    if not handle:
+        return
+    flat_param = handle.flat_param
+
+    if torch.distributed._functional_collectives.is_torchdynamo_compiling():
+        already_registered = hasattr(flat_param, "_post_backward_hook_handle")
+    else:
+        already_registered = hasattr(flat_param, "_post_backward_hook_state")
+
+    if already_registered or flat_param.requires_grad:
+        return
+    if inp_tensors is None:
+        args_flat = pytree.arg_tree_leaves(*args, **kwargs)
+        inp_tensors = [
+            obj for obj in args_flat if torch.is_tensor(obj) and obj.requires_grad
+        ]
+    if inp_tensors is None:
+        raise AssertionError("Expected inp_tensors to be set")
+    hook_handle = register_multi_grad_hook(
+        inp_tensors, functools.partial(_post_backward_reshard_only_hook, state, handle)
+    )
+    if torch.distributed._functional_collectives.is_torchdynamo_compiling():
+        flat_param._post_backward_hook_handle = hook_handle  # type: ignore[attr-defined, assignment]
+    else:
+        flat_param._post_backward_hook_state = (hook_handle,)  # type: ignore[attr-defined, assignment]
+
+
+@no_type_check
+def _register_post_backward_final_callback(
+    state: _FSDPState, module: nn.Module
+) -> None:
+    """
+    Registers the post-backward final callback that runs at the end of the
+    backward pass. This should be called from the root FSDP instance at the
+    beginning of the pre-backward.
+    """
+    _p_assert(
+        state._is_root,
+        "Only the root FSDP instance should register the post-backward callback",
+    )
+    if state._post_backward_callback_queued:
+        return
+    _assert_in_training_states(state, [TrainingState.IDLE])
+    # Trace does not need this callback
+    if not torch.distributed._functional_collectives.is_torchdynamo_compiling():
+        state._post_backward_callback_queued = True
+        Variable._execution_engine.queue_callback(
+            functools.partial(_post_backward_final_callback, state, module)
+        )
+
+
+def _wait_for_computation_stream(
+    computation_stream: torch.Stream,
+    unshard_stream: torch.Stream,
+    pre_unshard_stream: torch.Stream,
+):
+    """
+    Has the unshard and pre-unshard streams wait for the computation stream.
+    For example, this should be called in the FSDP root's pre-forward to
+    respect optimizer step computation.
+    """
+    # Tracing does not need to wait
+    if torch.distributed._functional_collectives.is_torchdynamo_compiling():
+        return
+    unshard_stream.wait_stream(computation_stream)  # type: ignore[attr-defined]
+    # Having the pre-all-gather stream wait for the current stream even if we
+    # do not leverage the pre-all-gather stream is tolerable since this only
+    # runs once per iteration
+    pre_unshard_stream.wait_stream(computation_stream)  # type: ignore[attr-defined]
+
+
+def _reset_flat_param_grad_info_if_needed(
+    handles: list[FlatParamHandle],
+):
+    """
+    Clears the original parameters' gradients if needed. This method's CPU
+    overhead is minimal, so we may call it throughout FSDP methods, which serve
+    as callsites to free the gradient memory earlier.
+    """
+    if not isinstance(handles, list):
+        handles = [handles]
+    for handle in handles:
+        if handle._use_orig_params:
+            handle._reset_flat_param_grad_info_if_needed()
+
+
+@no_type_check
+def _get_buffers_and_dtypes_for_computation(
+    state: _FSDPState,
+    root_module: nn.Module,
+) -> tuple[list[torch.Tensor], list[Optional[torch.dtype]]]:
+    """
+    Returns all buffers in the module tree rooted at ``root_module`` and a
+    corresponding list of the buffer dtypes for computation. Each buffer dtype
+    is either ``None`` if buffer mixed precision is not enabled or the buffer
+    low precision dtype otherwise.
+    """
+    _p_assert(state._is_root, "Expects the root to cast buffers")
+    buffers: list[torch.Tensor] = []
+    buffer_dtypes: list[Optional[torch.dtype]] = []
+    visited_buffers: set[torch.Tensor] = set()
+    # Traverse the FSDP states bottom-up so that we prefer the owning FSDP
+    # instance's mixed precision setting for each buffer
+    fsdp_states, fsdp_modules = traversal_utils._get_fsdp_states_with_modules(
+        root_module
+    )
+    for fsdp_state, fsdp_module in zip(reversed(fsdp_states), reversed(fsdp_modules)):
+        for buffer_name, buffer in fsdp_module.named_buffers():
+            if buffer in visited_buffers:
+                continue
+            visited_buffers.add(buffer)
+            if clean_tensor_name(buffer_name) in fsdp_state._ignored_buffer_names:
+                continue
+            buffers.append(buffer)
+            buffer_dtypes.append(fsdp_state.mixed_precision.buffer_dtype)
+    if len(buffers) != len(buffer_dtypes):
+        raise AssertionError(
+            f"Expected buffers and buffer_dtypes to have the same length, got {len(buffers)} and {len(buffer_dtypes)}"
+        )
+    return buffers, buffer_dtypes
+
+
+@no_type_check
+def _get_orig_buffer_dtypes(
+    state: _FSDPState,
+    buffer_names: list[str],
+) -> list[torch.dtype]:
+    """
+    Returns the original buffer types of the given buffer names.
+    """
+    buffer_dtypes: list[torch.dtype] = []
+    for buffer_name in buffer_names:
+        _p_assert(
+            buffer_name in state._buffer_name_to_orig_dtype,
+            f"{buffer_name} is missing from pre-computed dict on rank "
+            f"{state.rank}, which only has keys "
+            f"{state._buffer_name_to_orig_dtype.keys()}",
+        )
+        buffer_dtypes.append(state._buffer_name_to_orig_dtype[buffer_name])
+    return buffer_dtypes
+
+
+def _cast_buffers_to_dtype_and_device(
+    buffers: list[torch.Tensor],
+    buffer_dtypes: list[Optional[torch.dtype]],
+    device: torch.device,
+) -> None:
+    """
+    Casts ``buffers`` to the dtypes given by ``buffer_dtypes`` and moves them
+    to ``device``. If an element in ``buffer_dtypes`` is ``None``, then the
+    corresponding buffer is only moved to ``device``.
+    """
+    _p_assert(
+        buffer_dtypes is None or len(buffers) == len(buffer_dtypes),
+        f"Expects `buffers` and `buffer_dtypes` to have the same length if "
+        f"`buffer_dtypes` is specified but got {len(buffers)} and "
+        f"{len(buffer_dtypes)}",
+    )
+    for buffer, buffer_dtype in zip(buffers, buffer_dtypes):
+        if not torch.is_floating_point(buffer) or buffer_dtype is None:
+            buffer.data = buffer.to(device=device)
+        else:
+            buffer.data = buffer.to(device=device, dtype=buffer_dtype)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_shard_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_shard_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..eca5b9bd398749f1f38f50a48969cfbc3758352a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_shard_utils.py
@@ -0,0 +1,140 @@
+# mypy: allow-untyped-defs
+import copy
+import itertools
+import math
+from typing import Optional
+
+import torch
+import torch.distributed as dist
+from torch._utils import _get_device_module
+from torch.distributed import distributed_c10d
+from torch.distributed._shard.sharded_tensor import (
+    Shard,
+    ShardedTensor,
+    ShardedTensorMetadata,
+    TensorProperties,
+)
+from torch.distributed._shard.sharding_spec import ShardMetadata
+from torch.distributed.tensor import DeviceMesh, DTensor, Replicate, Shard as DShard
+
+
+def _get_remote_device_str(rank, device_type, num_devices_per_node):
+    if device_type.lower() == "cpu":
+        return f"rank:{rank}/{device_type}"
+    elif device_type.lower() == "hpu":
+        return f"rank:{rank}/{device_type}:{_get_device_module(device_type).current_device()}"
+    else:
+        return f"rank:{rank}/{device_type}:{rank % num_devices_per_node}"
+
+
+def _create_chunk_sharded_tensor(
+    tensor: torch.Tensor,
+    rank: int,
+    world_size: int,
+    num_devices_per_node: int,
+    pg: dist.ProcessGroup,
+    device: Optional[torch.device] = None,
+) -> ShardedTensor:
+    """
+    Shard a tensor to chunks along the first dimension. The local rank will gets its
+    corresponding chunk as the local shard to create a ShardedTensor.
+    """
+    chunks = tensor.chunk(world_size, dim=0)
+    if len(chunks) > rank:
+        local_shard = chunks[rank].clone()
+        offsets = [0 for _ in tensor.size()]
+        offsets[0] = math.ceil(tensor.size()[0] / world_size) * rank
+        local_shards = [Shard.from_tensor_and_offsets(local_shard, offsets, rank)]
+    else:
+        local_shards = []
+
+    # Create a ShardedTensor without invoking communication.
+    chunk_sizes = [list(chunk.size()) for chunk in chunks]
+    dim0_offsets = [0] + list(
+        itertools.accumulate([chunk_size[0] for chunk_size in chunk_sizes])
+    )[:-1]
+    offsets = [0] * (len(chunk_sizes[0]) - 1)
+    chunk_offsets = [[d0] + offsets for d0 in dim0_offsets]
+    device_type = (
+        distributed_c10d._get_pg_default_device(pg).type
+        if device is None
+        else device.type
+    )
+    placements = [
+        _get_remote_device_str(
+            dist.get_global_rank(pg, r),
+            device_type,
+            num_devices_per_node,
+        )
+        for r in range(len(chunk_sizes))
+    ]
+    if len(chunk_sizes) != len(chunk_offsets) or len(chunk_sizes) != len(placements):
+        raise AssertionError(
+            f"Expected chunk_sizes, chunk_offsets, and placements to have the same length, "
+            f"got {len(chunk_sizes)}, {len(chunk_offsets)}, {len(placements)}"
+        )
+    shard_metadata = [
+        ShardMetadata(offset, size, placement)
+        for offset, size, placement in zip(chunk_offsets, chunk_sizes, placements)
+    ]
+    sharded_tensor_metadata = ShardedTensorMetadata(
+        shards_metadata=shard_metadata,
+        size=tensor.size(),
+        tensor_properties=TensorProperties(
+            dtype=tensor.dtype,
+            layout=tensor.layout,
+            requires_grad=False,
+            memory_format=torch.contiguous_format,
+            pin_memory=tensor.is_pinned(),
+        ),
+    )
+    return ShardedTensor._init_from_local_shards_and_global_metadata(
+        local_shards, sharded_tensor_metadata=sharded_tensor_metadata, process_group=pg
+    )
+
+
+def _create_chunk_dtensor(
+    tensor: torch.Tensor,
+    rank: int,
+    device_mesh: DeviceMesh,
+) -> DTensor:
+    """
+    Shard a tensor to chunks along the first dimension. The local rank will gets its
+    corresponding chunk as the local tensor to create a DTensor.
+    """
+    # We need to explicitly call .detach() to return a new tensor detached from the current graph.
+    tensor = tensor.detach().clone()
+
+    # FSDP placements: [Shard(0)]
+    # HSDP placements: [Replicate(), Shard(0)]
+    replicate_placements = [Replicate() for _ in range(device_mesh.ndim)]
+    shard_placements = [Replicate() for _ in range(device_mesh.ndim)]
+    shard_placements[-1] = DShard(0)  # type: ignore[call-overload]
+
+    return DTensor.from_local(
+        tensor, device_mesh, replicate_placements, run_check=False
+    ).redistribute(
+        placements=shard_placements,
+    )
+
+
+def _all_gather_dtensor(
+    tensor: DTensor,
+    root_mesh: Optional[DeviceMesh],
+) -> torch.Tensor:
+    """
+    All gather a DTensor in its sharded dimension and return the local tensor.
+    """
+    if root_mesh != tensor.device_mesh:
+        raise AssertionError("The device mesh of a tensor should be a root mesh.")
+
+    placements = list(copy.deepcopy(tensor.placements))
+    # FSDP placements: [Shard(0)] -> [Replicate()]
+    # HSDP placements: [Replicate(), Shard(0)] -> [Replicate(), Replicate()]
+    placements[-1] = Replicate()
+    tensor = tensor.redistribute(
+        device_mesh=tensor.device_mesh,
+        placements=placements,
+    )
+
+    return tensor.to_local()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_state_dict_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_state_dict_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec648ced837e155018c7002560bb7e297b163c78
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_state_dict_utils.py
@@ -0,0 +1,932 @@
+# mypy: allow-untyped-defs
+import contextlib
+import logging
+import math
+import warnings
+from collections.abc import Callable, Generator, Iterator
+from typing import Any, cast, no_type_check
+
+import torch
+import torch.distributed as dist
+import torch.distributed.algorithms._checkpoint.checkpoint_wrapper as checkpoint_wrapper
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.distributed._shard.sharded_tensor import (
+    init_from_local_shards,
+    Shard,
+    ShardedTensor,
+)
+from torch.distributed.fsdp._common_utils import (
+    _FSDPState,
+    _get_module_fsdp_state_if_fully_sharded_module,
+    _has_fsdp_params,
+    _is_composable,
+    _module_handle,
+    clean_tensor_name,
+    FSDP_PREFIX,
+    FSDP_WRAPPED_MODULE,
+)
+from torch.distributed.fsdp._debug_utils import SimpleProfiler
+from torch.distributed.fsdp._runtime_utils import (
+    _cast_buffers_to_dtype_and_device,
+    _get_orig_buffer_dtypes,
+    _lazy_init,
+    _reset_flat_param_grad_info_if_needed,
+)
+from torch.distributed.fsdp.api import (
+    FullStateDictConfig,
+    ShardingStrategy,
+    StateDictType,
+)
+from torch.distributed.tensor import DTensor
+from torch.distributed.utils import _replace_by_prefix
+
+from ._fsdp_extensions import (
+    _ext_all_gather_dtensor,
+    _ext_chunk_dtensor,
+    _ext_chunk_tensor,
+    _ext_post_unflatten_transform,
+    _ext_pre_load_state_dict_transform,
+)
+from ._unshard_param_utils import _unshard_fsdp_state_params, FLAT_PARAM
+
+
+logger = logging.getLogger(__name__)
+
+
+def _should_unshard_params(fsdp_state: _FSDPState) -> bool:
+    return not (
+        fsdp_state.sharding_strategy == ShardingStrategy.NO_SHARD
+        and (_is_composable(fsdp_state) or fsdp_state._use_orig_params)
+    )
+
+
+def _convert_to_wrapped_module_name(module_name: str) -> str:
+    module_name = module_name.replace(f"{FSDP_PREFIX}", "")
+    module_name = module_name.replace(f"{FSDP_WRAPPED_MODULE}", "")
+    if module_name:
+        module_name = f"{module_name}."
+    # `CheckpointWrapper` adds a prefix that has to be removed as well.
+    module_name = module_name.replace(checkpoint_wrapper._CHECKPOINT_PREFIX, "")
+    return module_name
+
+
+def _param_name_infos(
+    module: nn.Module, fsdp_state: _FSDPState
+) -> Iterator[tuple[str, str, str]]:
+    if not _has_fsdp_params(fsdp_state, module):
+        return
+    for param_name, module_name in _module_handle(
+        fsdp_state, module
+    ).param_module_names():
+        module_name = _convert_to_wrapped_module_name(module_name)
+        fqn = f"{module_name}{param_name}"
+        yield fqn, param_name, module_name
+
+
+def _shared_param_name_infos(
+    module: nn.Module, fsdp_state
+) -> Iterator[tuple[str, str, str]]:
+    for param_name, module_name in _module_handle(
+        fsdp_state, module
+    ).shared_param_module_names():
+        module_name = _convert_to_wrapped_module_name(module_name)
+        fqn = f"{module_name}{param_name}"
+        yield fqn, param_name, module_name
+
+
+@no_type_check
+def _enter_unshard_params_ctx(
+    module: nn.Module,
+    fsdp_state: _FSDPState,
+    writeback: bool = False,
+    rank0_only: bool = False,
+    offload_to_cpu: bool = False,
+    with_grads: bool = False,
+) -> None:
+    """
+    state_dict hooks cannot use the pure context call as the checkpoint flow
+    requires to enter the context in the pre-hook but leave the context in the
+    post-hook. This API enters the context of ``_unshard_fsdp_state_params``.
+    """
+    if module in fsdp_state._unshard_params_ctx:
+        raise AssertionError(
+            "Entering the ``_unshard_fsdp_state_params`` context but _unshard_params_ctx[module] "
+            "is not None."
+        )
+    fsdp_state._unshard_params_ctx[module] = _unshard_fsdp_state_params(
+        module,
+        fsdp_state,
+        writeback=writeback,
+        rank0_only=rank0_only,
+        offload_to_cpu=offload_to_cpu,
+        with_grads=with_grads,
+    )
+    fsdp_state._unshard_params_ctx[module].__enter__()
+
+
+@no_type_check
+def _exit_unshard_params_ctx(module: nn.Module, fsdp_state: _FSDPState) -> None:
+    """A helper function to exit ``_unshard_fsdp_state_params`` context."""
+    fsdp_state._unshard_params_ctx[module].__exit__(None, None, None)
+    fsdp_state._unshard_params_ctx.pop(module)
+
+
+def _common_pre_state_dict_hook(
+    module: nn.Module,
+    fsdp_state: _FSDPState,
+) -> None:
+    """Performs the pre-state_dict tasks shared by all state_dict types."""
+    if fsdp_state._device_handle.is_available():
+        fsdp_state._device_handle.synchronize()
+    # TODO: need to check if this is always correct for composable FSDP.
+    _lazy_init(fsdp_state, module)
+    if fsdp_state._is_root:
+        _reset_flat_param_grad_info_if_needed(fsdp_state._all_handles)
+
+
+def _common_unshard_pre_state_dict_hook(
+    module: nn.Module,
+    fsdp_state: _FSDPState,
+    offload_to_cpu: bool,
+    rank0_only: bool,
+) -> None:
+    """
+    Performs the pre-state_dict tasks shared by all state_dict types that require
+    ``_unshard_fsdp_state_params()``. FULL_STATE_DICT and SHARDED_STATE_DICT use this hook.
+    """
+    # For composable `fully_shard`, it does not need to unshard parameters for `NO_SHARD` cases.
+    if not _should_unshard_params(fsdp_state):
+        return
+    _enter_unshard_params_ctx(
+        module,
+        fsdp_state,
+        writeback=False,
+        offload_to_cpu=offload_to_cpu,
+        rank0_only=rank0_only,
+    )
+
+
+@no_type_check
+def _common_unshard_post_state_dict_hook(
+    module: nn.Module,
+    fsdp_state: _FSDPState,
+    state_dict: dict[str, Any],
+    prefix: str,
+    param_hook: Callable,
+) -> dict[str, Any]:
+    """
+    The post-state_dict flow that shared by all state_dict types that require
+    ``_unshard_fsdp_state_params()``. FULL_STATE_DICT and SHARDED_STATE_DICT use this
+    hook.
+    """
+    _replace_by_prefix(state_dict, prefix + f"{FSDP_PREFIX}", prefix)
+    # Return early for trivial cases
+    if not state_dict or not _has_fsdp_params(fsdp_state, module):
+        if _should_unshard_params(fsdp_state):
+            _exit_unshard_params_ctx(module, fsdp_state)
+        return state_dict
+
+    # If a rank does not have unsharded parameters(when `rank0_only=True`
+    # and `rank != 0`), then the rank only needed to participate in the
+    # all-gather and does not need to save the # state dict. We simply check
+    # rank0_only to ensure this issue.
+    rank0_only = (
+        fsdp_state._state_dict_type == StateDictType.FULL_STATE_DICT
+        and cast(FullStateDictConfig, fsdp_state._state_dict_config).rank0_only
+    )
+    # no_fsdp_return means the state_dict returned by this rank should contain
+    # only non-FSDP controlled parameters and buffers.
+    no_fsdp_return = rank0_only and fsdp_state.rank != 0
+    if no_fsdp_return and not fsdp_state._use_orig_params:
+        for clean_key in fsdp_state._buffer_names:
+            # This is a hack to support activation checkpoint.
+            clean_key = clean_key.replace(
+                f"{checkpoint_wrapper._CHECKPOINT_PREFIX}.", ""
+            )
+            state_dict.pop(f"{prefix}{clean_key}", None)
+        # Non-zero ranks have flat_param key when rank0_only=True, because rank0_only=True is
+        # passed in to unshard context, but nonzero ranks reshard early, causing this flat_param
+        # to appear in state_dict.
+        state_dict.pop(f"{prefix}{FLAT_PARAM}")
+        _exit_unshard_params_ctx(module, fsdp_state)
+        return state_dict
+
+    # Loop only the parameters saved in this instance's wrapped module to
+    # avoid processing buffers.
+    for fqn, param_name, module_name in _param_name_infos(module, fsdp_state):
+        fqn = f"{prefix}{fqn}"
+        if no_fsdp_return:
+            state_dict.pop(fqn)
+            continue
+        if fqn not in state_dict:
+            raise AssertionError(
+                f"FSDP assumes {fqn} is in the state_dict but the state_dict only "
+                f"has {state_dict.keys()}. "
+                f"prefix={prefix}, module_name={module_name}, "
+                f"param_name={param_name} rank={fsdp_state.rank}."
+            )
+
+        param_hook(state_dict, prefix, fqn)
+
+    if _should_unshard_params(fsdp_state):
+        _exit_unshard_params_ctx(module, fsdp_state)
+
+    cpu_device = torch.device("cpu")
+    buffer_clean_fqns = []
+    buffers = []
+    for clean_key in fsdp_state._buffer_names:
+        # This is a hack to support activation checkpoint.
+        clean_key = clean_tensor_name(clean_key)
+        fqn = f"{prefix}{clean_key}"
+        if fqn not in state_dict:
+            # A buffer can be registered as non-persistent.
+            continue
+        if no_fsdp_return:
+            state_dict.pop(fqn)
+        else:
+            buffer = state_dict[fqn]
+            if (
+                fsdp_state._state_dict_config.offload_to_cpu
+                and buffer.device != cpu_device
+            ):
+                state_dict[fqn] = buffer.to(cpu_device)
+            # skip upcasting for ignored buffers
+            if clean_key not in fsdp_state._ignored_buffer_names:
+                buffer_clean_fqns.append(clean_key)
+                buffers.append(state_dict[fqn])
+
+    if buffers:
+        mixed_precision_enabled_for_buffers = (
+            fsdp_state._mixed_precision_enabled_for_buffers()
+            if not _is_composable(fsdp_state)
+            else (fsdp_state.mixed_precision.buffer_dtype is not None)
+        )
+        if mixed_precision_enabled_for_buffers:
+            buffer_dtypes = _get_orig_buffer_dtypes(fsdp_state, buffer_clean_fqns)
+            _cast_buffers_to_dtype_and_device(
+                buffers, buffer_dtypes, fsdp_state.compute_device
+            )
+            for buffer, clean_fqn in zip(buffers, buffer_clean_fqns):
+                fqn = f"{prefix}{clean_fqn}"
+                logger.info("FSDP is casting the dtype of %s to %s", fqn, buffer.dtype)
+                state_dict[fqn] = buffer.clone()
+    return state_dict
+
+
+@no_type_check
+def _full_pre_state_dict_hook(
+    fsdp_state: _FSDPState,
+    module: nn.Module,
+    *args,
+    **kwargs,
+) -> None:
+    """
+    Hook that runs before model.state_dict() is called. pre-state_dict hook is
+    not actually supported by ``nn.Module``. As a result, this API is called
+    from ``_full_post_state_dict_hook()`` to simulate the case. Once pre-state_dict
+    is supported in ``nn.Module``, this hook will be registered as a hook in
+    ``nn.Module``.
+    """
+    if getattr(fsdp_state, "_device_mesh", False):
+        fsdp_state._device_mesh._get_root_mesh()
+
+    _common_pre_state_dict_hook(module, fsdp_state)
+    _common_unshard_pre_state_dict_hook(
+        module,
+        fsdp_state,
+        offload_to_cpu=fsdp_state._state_dict_config.offload_to_cpu,
+        rank0_only=cast(FullStateDictConfig, fsdp_state._state_dict_config).rank0_only,
+    )
+
+
+@no_type_check
+def _full_post_state_dict_hook(
+    module: nn.Module,
+    fsdp_state: _FSDPState,
+    state_dict: dict[str, Any],
+    prefix: str,
+) -> dict[str, Any]:
+    """
+    Hook that runs after model.state_dict() is called before returning result to
+    user. For FSDP, we may have to clone the tensors in state_dict as params go
+    back to sharded version after _unshard_fsdp_state_params ends, and also remove
+    the ``FSDP_WRAPPED_MODULE`` prefix.
+    """
+
+    def param_hook(
+        state_dict: dict[str, Any],
+        prefix: str,
+        fqn: str,
+    ) -> None:
+        clean_key = fqn
+        clean_prefix = clean_tensor_name(prefix)
+        # Strip prefix out of key if needed as buffer names and param names
+        # do not have prefix considered as they are not computed in `state_dict`
+        # call.
+        clean_key = clean_key.removeprefix(clean_prefix)
+
+        # Clone parameters before exiting the `_unshard_fsdp_state_params()` context.
+        if not getattr(state_dict[fqn], "_has_been_cloned", False):
+            try:
+                state_dict[fqn] = state_dict[fqn].detach().clone()
+                state_dict[fqn]._has_been_cloned = True  # type: ignore[attr-defined]
+            except BaseException as e:  # noqa: B036
+                warnings.warn(
+                    f"Failed to clone() tensor with name {fqn} on rank {fsdp_state.rank}. "
+                    "This may mean that this state_dict entry could point to invalid "
+                    "memory regions after returning from state_dict() call if this "
+                    "parameter is managed by FSDP. Please check clone "
+                    f"implementation of {fqn}. Error: {str(e)}",
+                    stacklevel=2,
+                )
+
+    return _common_unshard_post_state_dict_hook(
+        module, fsdp_state, state_dict, prefix, param_hook
+    )
+
+
+def _full_pre_load_state_dict_hook(
+    module: nn.Module,
+    fsdp_state: _FSDPState,
+    state_dict: dict[str, Any],
+    prefix: str,
+) -> None:
+    _lazy_init(fsdp_state, module)
+    if _should_unshard_params(fsdp_state):
+        with SimpleProfiler.profile("_enter_unshard_params_ctx"):
+            _enter_unshard_params_ctx(module, fsdp_state, writeback=True)
+    # Add FSDP_PREFIX only for wrapper-based FSDP.
+    if not _is_composable(fsdp_state):
+        _replace_by_prefix(state_dict, prefix, prefix + f"{FSDP_PREFIX}")
+
+
+def _full_post_load_state_dict_hook(
+    module: nn.Module, fsdp_state: _FSDPState, *args, **kwargs
+) -> None:
+    if _should_unshard_params(fsdp_state):
+        with SimpleProfiler.profile("_exit_unshard_params_ctx"):
+            _exit_unshard_params_ctx(module, fsdp_state)
+
+
+def _local_pre_state_dict_hook(
+    fsdp_state: _FSDPState,
+    module: nn.Module,
+    *args,
+    **kwargs,
+) -> None:
+    """
+    Hook that runs before model.state_dict() is called. Right now, pre-state_dict
+    hook is not supported by the PyTorch core. So this API is called from
+    `_local_post_state_dict_hook()` to simulate the case.
+    """
+    if (
+        _has_fsdp_params(fsdp_state, module)
+        and not _module_handle(fsdp_state, module).uses_sharded_strategy
+    ):
+        raise RuntimeError(
+            "``local_state_dict`` can only be used when parameters are flatten "
+            "and sharded."
+        )
+    _common_pre_state_dict_hook(module, fsdp_state)
+
+
+@no_type_check
+def _local_post_state_dict_hook(
+    module: nn.Module,
+    fsdp_state: _FSDPState,
+    state_dict: dict[str, Any],
+    prefix: str,
+) -> dict[str, Any]:
+    """
+    This hook create a ShardedTensor from the local flat_param and replace
+    the state_dict[f"{prefix}{FLAT_PARAM}] with the ShardedTensor. No copy
+    will happen. The underlying storage is the same.
+    """
+
+    _replace_by_prefix(state_dict, f"{prefix}{FSDP_PREFIX}", prefix)
+    if not _has_fsdp_params(fsdp_state, module):
+        return state_dict
+
+    # state_dict[f"{prefix}{FLAT_PARAM}"] exists and has the same tensor
+    # value as the flat_param but it is a pure Tensor because
+    # nn.Module.state_dict() will detach the parameter. Therefore, we need
+    # to get flat_param to get the metadata.
+    if not _module_handle(fsdp_state, module):
+        raise AssertionError("Should have returned early")
+    flat_param = _module_handle(fsdp_state, module).flat_param
+    # Constructs a ShardedTensor from the flat_param "without" padding.
+    # Removing the padding allows users to change the number of ranks
+    # when loading the local_state_dict.
+    full_numel = flat_param._unpadded_unsharded_size.numel()  # type: ignore[attr-defined]
+    shard_offset = flat_param.numel() * fsdp_state.rank
+    valid_data_size = flat_param.numel() - flat_param._shard_numel_padded
+    if valid_data_size > 0:
+        # If FlatParameter is returned, FlatParameter._local_shard cause a
+        # pickling issue (can be torch.save but not torch.load). Since there
+        # is no benefit for state_dict to return the actual FlatParameter class,
+        # a view (which is a tensor) of the FlatParameter will be returned.
+        flat_param = flat_param[:valid_data_size].view(valid_data_size)
+        local_shards = [
+            Shard.from_tensor_and_offsets(flat_param, [shard_offset], fsdp_state.rank)
+        ]
+    else:
+        local_shards = []
+    sharded_tensor = init_from_local_shards(
+        local_shards, full_numel, process_group=fsdp_state.process_group
+    )  # type: ignore[assignment]
+    # TODO: Add DTensor state_dict support for LOCAL_STATE_DICT.
+    if fsdp_state._state_dict_config.offload_to_cpu:
+        sharded_tensor = sharded_tensor.cpu()
+    state_dict[f"{prefix}{FLAT_PARAM}"] = sharded_tensor
+    return state_dict
+
+
+def _local_post_load_state_dict_hook(
+    module: nn.Module, fsdp_state: _FSDPState, *args, **kwargs
+) -> None:
+    pass
+
+
+def _local_pre_load_state_dict_hook(
+    module: nn.Module,
+    fsdp_state: _FSDPState,
+    state_dict: dict[str, Any],
+    prefix: str,
+) -> None:
+    """
+    This hook finds the local flat_param for this FSDP module from the
+    state_dict. The flat_param should be a ShardedTensor. This hook converts
+    the ShardedTensor to a tensor. No copy happen unless padding is required.
+    """
+    _lazy_init(fsdp_state, module)
+    _replace_by_prefix(state_dict, prefix, f"{prefix}{FSDP_PREFIX}")
+    fqn = f"{prefix}{FSDP_PREFIX}{FLAT_PARAM}"
+    if fqn not in state_dict:
+        if _has_fsdp_params(fsdp_state, module):
+            raise AssertionError(
+                "No `FlatParameter` in `state_dict` for this FSDP instance "
+                "but it has parameters"
+            )
+        return
+    load_tensor = state_dict[fqn]
+    if not isinstance(load_tensor, ShardedTensor):
+        raise AssertionError("Tensors in local_state_dict should be ShardedTensor.")
+
+    # Convert the ShardedTensor to a Tensor.
+    flat_param = _module_handle(fsdp_state, module).flat_param
+    if flat_param is None:
+        raise AssertionError("Expected flat_param to be set")
+    valid_data_size = flat_param.numel() - flat_param._shard_numel_padded
+    shards = load_tensor.local_shards()
+    if valid_data_size > 0:
+        if not len(shards):
+            raise AssertionError(
+                "load_local_state_dict assume one shard per ShardedTensor."
+            )
+        load_tensor = shards[0].tensor
+
+        # Get the metadata of the flat_param to decide whether to pad the loaded
+        # tensor.
+        if flat_param._shard_numel_padded > 0:
+            if load_tensor.numel() >= flat_param.numel():
+                raise AssertionError(
+                    f"Local shard size = {flat_param.numel()} and the tensor in "
+                    f"the state_dict is {load_tensor.numel()}."
+                )
+            load_tensor = F.pad(load_tensor, [0, flat_param._shard_numel_padded])
+    else:
+        load_tensor = flat_param
+    # TODO: Add DTensor state_dict support for LOCAL_STATE_DICT.
+    state_dict[fqn] = load_tensor
+
+
+def _sharded_pre_state_dict_hook(
+    fsdp_state: _FSDPState,
+    module: nn.Module,
+    *args,
+    **kwargs,
+) -> None:
+    """
+    Hook that runs before model.state_dict() is called. Check
+    ``_full_pre_load_state_dict_hook`` for the detail.
+    """
+    if (
+        _has_fsdp_params(fsdp_state, module)
+        and not _module_handle(fsdp_state, module).uses_sharded_strategy
+    ):
+        raise RuntimeError(
+            "``sharded_state_dict`` can only be used when parameters are flatten "
+            "and sharded."
+        )
+    _common_pre_state_dict_hook(module, fsdp_state)
+    # Setting offload_to_cpu here does not work even if offload_to_cpu is True.
+    # We have to create ShardedTensor first then move it to CPU.
+    _common_unshard_pre_state_dict_hook(
+        module,
+        fsdp_state,
+        offload_to_cpu=False,
+        rank0_only=False,
+    )
+
+
+@no_type_check
+def _sharded_post_state_dict_hook(
+    module: nn.Module,
+    fsdp_state: _FSDPState,
+    state_dict: dict[str, Any],
+    prefix: str,
+) -> dict[str, Any]:
+    """
+    The hook replaces the unflattened, unsharded parameter in the state_dict
+    with a unflattened, sharded parameter (a ShardedTensor).
+    """
+
+    def param_hook(state_dict: dict[str, Any], prefix: str, fqn: str):
+        param = state_dict[fqn]
+        if not fsdp_state._state_dict_config._use_dtensor:
+            sharded_tensor = _ext_chunk_tensor(
+                tensor=param,
+                rank=fsdp_state.rank,
+                world_size=fsdp_state.world_size,
+                num_devices_per_node=fsdp_state._device_handle.device_count(),
+                pg=fsdp_state.process_group,
+                fsdp_extension=fsdp_state._fsdp_extension,
+            )
+        else:
+            sharded_tensor = _ext_chunk_dtensor(
+                tensor=param,
+                rank=fsdp_state.rank,
+                device_mesh=fsdp_state._device_mesh,
+                fsdp_extension=fsdp_state._fsdp_extension,
+            )
+        if fsdp_state._state_dict_config.offload_to_cpu:
+            sharded_tensor = sharded_tensor.cpu()
+        state_dict[fqn] = sharded_tensor
+
+    return _common_unshard_post_state_dict_hook(
+        module, fsdp_state, state_dict, prefix, param_hook
+    )
+
+
+@no_type_check
+def _sharded_post_load_state_dict_hook(
+    module: nn.Module, fsdp_state: _FSDPState, *args, **kwargs
+) -> None:
+    if _has_fsdp_params(fsdp_state, module):
+        with SimpleProfiler.profile("_exit_unshard_params_ctx"):
+            _exit_unshard_params_ctx(module, fsdp_state)
+
+
+@no_type_check
+def _sharded_pre_load_state_dict_hook(
+    module: nn.Module,
+    fsdp_state: _FSDPState,
+    state_dict: dict[str, Any],
+    prefix: str,
+) -> None:
+    """
+    The hook combines the unflattened, sharded parameters (ShardedTensor) to
+    a new FlatParameter and shards the new FlatParameter to the local chunk.
+    """
+    _lazy_init(fsdp_state, module)
+    if not _is_composable(fsdp_state):
+        _replace_by_prefix(state_dict, prefix, prefix + f"{FSDP_PREFIX}")
+    if not _has_fsdp_params(fsdp_state, module):
+        return
+
+    handle = _module_handle(fsdp_state, module)
+    if not handle.uses_sharded_strategy:
+        raise RuntimeError(
+            "load_sharded_state_dict can only be called when parameters "
+            "are flattened and sharded."
+        )
+    fqn_to_param_ext = dict(
+        zip(handle.flat_param._fqns, handle.flat_param._param_extensions)
+    )
+
+    for fqn, _, _ in _param_name_infos(module, fsdp_state):
+        if not _is_composable(fsdp_state):
+            fqn_from_global_root = f"{prefix}{FSDP_PREFIX}{fqn}"
+        else:
+            fqn_from_global_root = f"{prefix}{fqn}"
+        try:
+            param = state_dict.pop(fqn_from_global_root)
+        except KeyError:
+            logger.warning(
+                f"Did not find param with FQN {fqn_from_global_root}, skipping it. "  # noqa: G004
+                "The weight will not be filled if you expect it to be."
+            )
+            continue  # TODO: Improve unittesting for state_dict finetuning
+            # cases: https://github.com/pytorch/pytorch/issues/109134
+
+        if not fsdp_state._state_dict_config._use_dtensor:
+            # All-gather the param (ShardedTensor)
+            param, shards = _ext_pre_load_state_dict_transform(
+                param, fsdp_state._fsdp_extension
+            )
+
+            if len(shards) >= 2:
+                raise AssertionError(
+                    "Expects 0 or 1 shard per rank "
+                    f"but got {len(shards)} shards on rank {fsdp_state.rank}."
+                )
+            param_numel = param.size().numel()
+            dim_0_size = param.size()[0]
+            chunk_size = (
+                math.ceil(dim_0_size / fsdp_state.world_size)
+                * param_numel
+                // dim_0_size
+            )
+            if len(shards) == 1:
+                local_tensor = shards[0].tensor.flatten()
+                with SimpleProfiler.profile(SimpleProfiler.Type.H2D):
+                    local_tensor = local_tensor.to(fsdp_state.compute_device)
+                num_padding = chunk_size - local_tensor.numel()
+                if num_padding > 0:
+                    local_tensor = F.pad(local_tensor, [0, num_padding])
+            else:
+                local_tensor = torch.zeros(
+                    chunk_size, dtype=param.dtype, device=fsdp_state.compute_device
+                )
+            tensor = torch.empty(
+                chunk_size * fsdp_state.world_size,
+                dtype=local_tensor.dtype,
+                device=fsdp_state.compute_device,
+            )
+            with SimpleProfiler.profile(SimpleProfiler.Type.ALLGATHER):
+                dist.all_gather_into_tensor(
+                    tensor, local_tensor, group=fsdp_state.process_group
+                )
+            tensor = tensor.narrow(0, 0, param_numel).reshape(param.size())
+            state_dict[fqn_from_global_root] = tensor
+        else:
+            if param.device != fsdp_state._device_mesh.device_type:
+                param = param.to(fsdp_state._device_mesh.device_type)
+
+            root_mesh = fsdp_state._device_mesh._get_root_mesh()
+            local_tensor = _ext_all_gather_dtensor(
+                param, root_mesh, fsdp_state._fsdp_extension
+            )
+
+            if fqn_to_param_ext.get(fqn) is not None:
+                ext = fqn_to_param_ext[fqn]
+                local_tensor = _ext_post_unflatten_transform(
+                    local_tensor, ext, fsdp_state._fsdp_extension
+                )
+            state_dict[fqn_from_global_root] = local_tensor
+
+    with SimpleProfiler.profile("_enter_unshard_params_ctx"):
+        _enter_unshard_params_ctx(module, fsdp_state, writeback=True)
+
+
+@contextlib.contextmanager
+def _replace_with_full_state_dict_type(fsdp_state: _FSDPState) -> Generator:
+    old_state_dict_config = fsdp_state._state_dict_config
+    old_state_dict_type = fsdp_state._state_dict_type
+    fsdp_state._state_dict_config = FullStateDictConfig()
+    fsdp_state._state_dict_type = StateDictType.FULL_STATE_DICT
+    yield
+    fsdp_state._state_dict_config = old_state_dict_config
+    fsdp_state._state_dict_type = old_state_dict_type
+
+
+@no_type_check
+@torch.no_grad()
+def _post_state_dict_hook(
+    module: nn.Module,
+    state_dict: dict[str, Any],
+    prefix: str,
+    *args: Any,
+) -> dict[str, Any]:
+    """
+    _post_state_dict_hook() is called after the state_dict() of this
+    FSDP module is executed. ``fsdp_state._state_dict_type`` is used to decide
+    what postprocessing will be done.
+    """
+    fsdp_state = _get_module_fsdp_state_if_fully_sharded_module(module)
+    if fsdp_state.sharding_strategy == ShardingStrategy.NO_SHARD:
+        context = _replace_with_full_state_dict_type(fsdp_state)
+        warnings.warn(
+            "When using ``NO_SHARD`` for ``ShardingStrategy``, full_state_dict will "
+            "be returned.",
+            stacklevel=2,
+        )
+    else:
+        context = contextlib.nullcontext()
+
+    with context:
+        _post_state_dict_hook_fn = {
+            StateDictType.FULL_STATE_DICT: _full_post_state_dict_hook,
+            StateDictType.LOCAL_STATE_DICT: _local_post_state_dict_hook,
+            StateDictType.SHARDED_STATE_DICT: _sharded_post_state_dict_hook,
+        }
+        processed_state_dict = _post_state_dict_hook_fn[fsdp_state._state_dict_type](
+            module, fsdp_state, state_dict, prefix
+        )
+
+    if fsdp_state._is_root:
+        logger.info("FSDP finished processing state_dict(), prefix=%s", prefix)
+        for key, tensor in sorted(processed_state_dict.items()):
+            if key.startswith(prefix) and isinstance(tensor, torch.Tensor):
+                local_shape = tensor.shape
+                device = None
+                if isinstance(tensor, ShardedTensor):
+                    local_shape = None
+                    shards = tensor.local_shards()
+                    if shards:
+                        local_shape = shards[0].tensor.shape
+                        device = shards[0].tensor.device
+                elif isinstance(tensor, DTensor):
+                    local_shape = tensor.to_local().shape
+                    device = tensor.device
+                else:
+                    device = tensor.device
+                logger.info(
+                    "FQN=%s: type=%s, shape=%s, local_shape=%s, dtype=%s, device=%s",
+                    key,
+                    type(tensor),
+                    tensor.shape,
+                    local_shape,
+                    tensor.dtype,
+                    device,
+                )
+
+    return processed_state_dict
+
+
+@no_type_check
+@torch.no_grad()
+def _pre_state_dict_hook(
+    module: nn.Module,
+    *args,
+    **kwargs,
+) -> None:
+    """
+    This is called before the core state dict saving logic of ``module``.
+    ``fsdp_state._state_dict_type`` is used to decide what postprocessing will
+    be done.
+    """
+    fsdp_state = _get_module_fsdp_state_if_fully_sharded_module(module)
+    if fsdp_state.sharding_strategy == ShardingStrategy.NO_SHARD:
+        context = _replace_with_full_state_dict_type(fsdp_state)
+        warnings.warn(
+            "When using ``NO_SHARD`` for ``ShardingStrategy``, full_state_dict will "
+            "be returned.",
+            stacklevel=2,
+        )
+    else:
+        _set_use_dtensor(fsdp_state)
+        context = contextlib.nullcontext()
+
+    with context:
+        _pre_state_dict_hook_fn = {
+            StateDictType.FULL_STATE_DICT: _full_pre_state_dict_hook,
+            StateDictType.LOCAL_STATE_DICT: _local_pre_state_dict_hook,
+            StateDictType.SHARDED_STATE_DICT: _sharded_pre_state_dict_hook,
+        }
+        _pre_state_dict_hook_fn[fsdp_state._state_dict_type](
+            fsdp_state,
+            module,
+            *args,
+            **kwargs,
+        )
+
+
+@no_type_check
+def _set_use_dtensor(fsdp_state: _FSDPState) -> None:
+    # If device_mesh is passed in when initializing FSDP, we automatically turn the
+    # _use_dtensor flag to be true for ShardedStateDictConfig().
+    if getattr(fsdp_state, "_device_mesh", None):
+        state_dict_type = fsdp_state._state_dict_type
+        if state_dict_type == StateDictType.LOCAL_STATE_DICT:
+            raise RuntimeError(
+                "Found state_dict_type LOCAL_STATE_DICT",
+                "DeviceMesh is not compatible with LOCAL_STATE_DICT.",
+                "Please set state_dict_type to SHARDED_STATE_DICT to get DTensor state_dict.",
+            )
+        else:
+            fsdp_state._state_dict_config._use_dtensor = True
+
+
+@no_type_check
+@torch.no_grad()
+def _pre_load_state_dict_hook(
+    module: nn.Module,
+    state_dict: dict[str, Any],
+    prefix: str,
+    *args: Any,
+) -> None:
+    """
+    This is called before ``module._load_from_state_dict()``.
+    ``fsdp_state._state_dict_type`` is used to decide what preprocessing will
+    be done.
+    """
+    fsdp_state = _get_module_fsdp_state_if_fully_sharded_module(module)
+    if fsdp_state.sharding_strategy == ShardingStrategy.NO_SHARD:
+        context = _replace_with_full_state_dict_type(fsdp_state)
+        warnings.warn(
+            "When using ``NO_SHARD`` for ``ShardingStrategy``, full_state_dict will"
+            "be returned.",
+            stacklevel=2,
+        )
+    else:
+        _set_use_dtensor(fsdp_state)
+        context = contextlib.nullcontext()
+
+    _lazy_init(fsdp_state, module)
+    if fsdp_state._is_root:
+        SimpleProfiler.reset()
+
+    with context:
+        _pre_load_state_dict_hook_fn = {
+            StateDictType.FULL_STATE_DICT: _full_pre_load_state_dict_hook,
+            StateDictType.LOCAL_STATE_DICT: _local_pre_load_state_dict_hook,
+            StateDictType.SHARDED_STATE_DICT: _sharded_pre_load_state_dict_hook,
+        }
+        # Code that is common for all state_dict impls
+        if fsdp_state._device_handle.is_available():
+            fsdp_state._device_handle.synchronize()
+        # Dispatch into state_dict specific implementation of pre-hook.
+        _pre_load_state_dict_hook_fn[fsdp_state._state_dict_type](
+            module, fsdp_state, state_dict, prefix
+        )
+
+
+@no_type_check
+@torch.no_grad()
+def _post_load_state_dict_hook(
+    module: nn.Module,
+    incompatible_keys: tuple[list[str], list[str]],
+    *args: Any,
+) -> None:
+    fsdp_state = _get_module_fsdp_state_if_fully_sharded_module(module)
+    if fsdp_state.sharding_strategy == ShardingStrategy.NO_SHARD:
+        context = _replace_with_full_state_dict_type(fsdp_state)
+        warnings.warn(
+            "When using ``NO_SHARD`` for ``ShardingStrategy``, full_state_dict will"
+            "be returned.",
+            stacklevel=2,
+        )
+    else:
+        context = contextlib.nullcontext()
+
+    with context:
+        _post_load_state_dict_hook_fn = {
+            StateDictType.FULL_STATE_DICT: _full_post_load_state_dict_hook,
+            StateDictType.LOCAL_STATE_DICT: _local_post_load_state_dict_hook,
+            StateDictType.SHARDED_STATE_DICT: _sharded_post_load_state_dict_hook,
+        }
+        # Code that is common for all state_dict impls
+        # Dispatch into state_dict type specific implementation of post-hook for
+        # loading state_dict.
+        _post_load_state_dict_hook_fn[fsdp_state._state_dict_type](module, fsdp_state)
+
+    # When reporting incompatible keys, trim FSDP prefixes.
+    missing_keys = incompatible_keys[0]
+    unexpected_keys = incompatible_keys[1]
+    for i in range(len(missing_keys)):
+        missing_keys[i] = clean_tensor_name(missing_keys[i])
+
+    for i in range(len(unexpected_keys)):
+        unexpected_keys[i] = clean_tensor_name(unexpected_keys[i])
+
+    if fsdp_state._is_root:
+        SimpleProfiler.dump_and_reset("FSDP model load_state_dict profiling: ")
+
+
+def _register_all_state_dict_hooks(state: _FSDPState):
+    """
+    Registers pre-save, post-save, pre-load, and post-load state dict hooks.
+    """
+    for hook_registration_fn_str, hook, hook_registration_fn_kwargs in (
+        ("register_state_dict_pre_hook", _pre_state_dict_hook, {}),
+        ("_register_state_dict_hook", _post_state_dict_hook, {}),
+        (
+            "_register_load_state_dict_pre_hook",
+            _pre_load_state_dict_hook,
+            {"with_module": True},
+        ),
+        ("register_load_state_dict_post_hook", _post_load_state_dict_hook, {}),
+    ):
+        _register_state_dict_hooks_base(
+            state, hook_registration_fn_str, hook, hook_registration_fn_kwargs
+        )
+
+
+@no_type_check
+def _register_state_dict_hooks_base(
+    state: _FSDPState,
+    hook_registration_fn_name: str,
+    hook: Callable,
+    hook_registration_fn_kwargs: dict[str, Any],
+) -> None:
+    """Registers ``hook`` using ``hook_registration_fn``."""
+    if not _is_composable(state):
+        getattr(state, hook_registration_fn_name)(hook, **hook_registration_fn_kwargs)
+    else:
+        handle = state._handle
+        if handle:
+            getattr(handle._fully_sharded_module, hook_registration_fn_name)(
+                hook, **hook_registration_fn_kwargs
+            )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_trace_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_trace_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4d514c5c6474b3a984424b1cd7563e1656f3f2a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_trace_utils.py
@@ -0,0 +1,240 @@
+# mypy: allow-untyped-defs
+import functools
+from collections.abc import Callable
+from contextlib import contextmanager
+from dataclasses import dataclass, field
+from typing import Any, NamedTuple, Optional
+
+import torch
+import torch.nn as nn
+
+
+@dataclass
+class TracingConfig:
+    """
+    This represents a symbolic tracing configuration.
+
+    Args:
+        tracer (torch.fx.Tracer): An instance of :class:`torch.fx.Tracer` to
+            use for symbolic tracing. The default value is the native
+            :class:`torch.fx.Tracer` constructed with default arguments.
+            However, the user may want to pass a different value such as the
+            ``HFTracer`` for models in the HuggingFace Transformers_ library.
+            .. _Transformers: https://huggingface.co/docs/transformers/index
+        concrete_args (Optional[Dict[str, Any]]): Concrete arguments that
+            should not be treated as ``torch.fx.Proxy`` when tracing the
+            module ``forward()``. Passing ``concrete_args`` allows partially
+            specializing the forward, e.g. to remove control flow or data
+            structures. This ``concrete_args`` here is the same argument used
+            in :meth:`~torch.fx.Tracer.trace`.
+    """
+
+    tracer: torch.fx.Tracer = field(default_factory=torch.fx.Tracer)
+    concrete_args: Optional[dict[str, Any]] = None
+
+
+class _ParamUsageInfo(NamedTuple):
+    """
+    This is used for ``_ExecutionInfo.module_to_param_usage_infos`` to record
+    execution information. The ``dict`` maps modules to a list of these
+    ``_ParamUsageInfo`` instances, where each instance represents a group of
+    parameters used together.
+
+    Specifically, for each module key in the ``dict``, each instance of this
+    class represents either:
+    (1) the module and some sublist of its ``named_parameters()`` used
+    together in execution (see ``_patched_create_proxy()``), or
+    (2) a submodule and all of ``submodule.named_parameters()`` (see
+    ``_patched_call_module()``).
+
+    Type (1) corresponds to directly using parameters in ops without calling
+    ``forward()``, and type (2) corresponds to calling ``forward()``. The
+    mapped-to lists in the ``dict`` follow the execution order.
+    """
+
+    module: nn.Module
+    named_params: list[tuple[str, nn.Parameter]]
+
+
+class _ExecutionInfo:
+    """
+    This represents the execution order information from the forward pass.
+
+    Attributes:
+        curr_module (nn.Module): Current module being traced.
+        module_forward_order (List[nn.Module]): The modules in (pre-)forward
+            order, i.e. the order in which their ``forward()`` methods are
+            called. Each call to a module's ``forward()`` corresponds to one
+            element in the list.
+        module_to_param_usage_infos (Dict[nn.Module, List[_ParamUsageInfo]]):
+            Maps a module to a list of module execution infos. See
+            :class:`_ParamUsageInfo` for details.
+        param_forward_order (List[nn.Parameter]): The parameters in forward
+            execution order, where only a parameter's first participation is
+            included.
+        visited_params (Set[nn.Parameter]): The parameters visited so far
+            during the trace. This is only used during tracing for fast
+            membership check. Invariant: The parameters in
+            ``param_forward_order`` are exactly those in ``visited_params``.
+    """
+
+    def __init__(self, root_module: nn.Module) -> None:
+        self.curr_module: nn.Module = root_module
+        self.module_forward_order: list[nn.Module] = [root_module]
+        self.module_to_param_usage_infos: dict[nn.Module, list[_ParamUsageInfo]] = {
+            root_module: []
+        }
+        self.param_forward_order: list[nn.Parameter] = []
+        self.visited_params: set[nn.Parameter] = set()
+
+
+class _ExecOrderTracer:
+    def __init__(self) -> None:
+        self.exec_info: Optional[_ExecutionInfo] = None
+
+    @contextmanager
+    def patch_tracer(self, tracer: torch.fx.Tracer, root_module: nn.Module):
+        self.exec_info = _ExecutionInfo(root_module)
+        orig_call_module = tracer.call_module
+        orig_create_proxy = tracer.create_proxy
+        tracer.call_module = functools.partial(  # type: ignore[method-assign]
+            self._patched_call_module, orig_call_module, self.exec_info
+        )
+        fqn_to_param = dict(root_module.named_parameters())
+        tracer.create_proxy = functools.partial(  # type: ignore[method-assign]
+            self._patched_create_proxy,
+            orig_create_proxy,
+            self.exec_info,
+            fqn_to_param,
+        )
+        try:
+            yield
+        finally:
+            tracer.call_module = orig_call_module  # type: ignore[method-assign]
+            tracer.create_proxy = orig_create_proxy  # type: ignore[method-assign]
+
+    def _patched_call_module(
+        self,
+        call_module: Callable,
+        exec_info: _ExecutionInfo,
+        # Below are the expected arguments to `call_module()`
+        module: nn.Module,
+        forward: Callable,
+        args: tuple[Any, ...],
+        kwargs: dict[str, Any],
+    ) -> Any:
+        """
+        Overrides ``call_module`` to save execution information to
+        ``exec_info``. Note that ``call_module`` is called during symbolic
+        tracing for each non-root module.
+
+        Args:
+            call_module (Callable): Original ``call_module`` to override.
+            exec_info (_ExecutionInfo): Used to record execution information.
+            module (nn.Module): Module corresponding to this ``call_module``.
+            forward (Callable): ``forward()`` method of ``module`` to be called
+                for this ``call_module``.
+            args (Tuple[Any, ...]): Positional arguments for ``forward``.
+            kwargs (Dict[str, Any]): Keyword arguments for ``forward``.
+
+        Returns:
+            Same return value as ``call_module``.
+        """
+        exec_info.module_forward_order.append(module)
+        named_params = list(module.named_parameters())
+        curr_module = exec_info.curr_module
+        if named_params:
+            if curr_module not in exec_info.module_to_param_usage_infos:
+                raise AssertionError(
+                    "The current module should have already been processed by a patched `call_module`"
+                )
+            exec_info.module_to_param_usage_infos[exec_info.curr_module].append(
+                _ParamUsageInfo(module, named_params)
+            )
+        prev_curr_module = curr_module
+        exec_info.curr_module = module
+        exec_info.module_to_param_usage_infos[module] = []
+        output = call_module(module, forward, args, kwargs)
+        exec_info.curr_module = prev_curr_module
+        return output
+
+    def _patched_create_proxy(
+        self,
+        create_proxy: Callable,
+        exec_info: _ExecutionInfo,
+        fqn_to_param: dict[str, nn.Parameter],
+        # Below are the expected arguments to `create_proxy()`
+        kind: str,
+        target: torch.fx.node.Target,
+        args: tuple[Any, ...],
+        kwargs: dict[str, Any],
+        name: Optional[str] = None,
+        type_expr: Optional[Any] = None,
+        proxy_factory_fn: Optional[Callable[[torch.fx.Node], torch.fx.Proxy]] = None,
+    ) -> torch.fx.Proxy:
+        """
+        Overrides ``create_proxy`` to save execution information to
+        ``exec_info``. Note that ``create_proxy`` is called during symbolic
+        tracing for each leaf function/method/module.
+
+        Args:
+            create_proxy (Callable): Original ``create_proxy`` to override.
+            exec_info (_ExecutionInfo): Used to record execution information.
+            fqn_to_param (Dict[str, nn.Parameter]): ``dict`` version of the
+                root module's ``named_parameters()`` with FQN as key and
+                parameter as value.
+            kind (str): Kind of the target method ('call_function',
+                'call_method', 'get_attr', 'call_module', 'placeholder', or
+                'output'). See :class:`torch.fx.Graph` for details. This is
+                passed to ``create_proxy``.
+            target (torch.fx.node.Target): Contains the string name of the
+                function/method/module. This is passed to ``create_proxy``.
+            args (Tuple[Any, ...]): Positional arguments for the function/
+                method/module. This is passed to ``create_proxy``.
+            kwargs (Dict[str, Any]): Keyword arguments for the function/method/
+                module. This is passed to ``create_proxy``
+            name (Optional[str]): An optional string name for the ``Node``
+                created in ``create_proxy``. This is passed to
+                ``create_proxy``.
+            type_expr (Optional[Any]): An optional type annotation representing
+                the Python type that the output of the node has. This is passed
+                to ``create_proxy``.
+            proxy_factory_fn (Callable[[torch.fx.Node], torch.fx.Proxy]):
+                An alternative proxy constructor used in ``create_proxy``. This
+                is passed to ``create_proxy``.
+
+        Returns:
+            torch.fx.Proxy: Created ``Node`` wrapped in a ``Proxy`` object.
+        """
+        proxy = create_proxy(
+            kind, target, args, kwargs, name, type_expr, proxy_factory_fn
+        )
+        curr_module = exec_info.curr_module
+        if kind in ("call_function", "call_method"):
+            if args is not None:
+                named_params: list[tuple[str, nn.Parameter]] = []
+                for arg in args:
+                    if (
+                        isinstance(arg, torch.fx.Proxy)
+                        and arg.node.target in fqn_to_param
+                    ):
+                        param = fqn_to_param[arg.node.target]  # type: ignore[index]
+                        named_params.append((arg.node.target, param))  # type: ignore[arg-type]
+                        if param not in exec_info.visited_params:
+                            exec_info.visited_params.add(param)
+                            exec_info.param_forward_order.append(param)
+                if named_params:
+                    exec_info.module_to_param_usage_infos[curr_module].append(
+                        _ParamUsageInfo(curr_module, named_params)
+                    )
+        elif kind == "call_module":
+            named_params = list(curr_module.named_parameters())
+            if named_params:
+                exec_info.module_to_param_usage_infos[curr_module].append(
+                    _ParamUsageInfo(curr_module, named_params)
+                )
+            for _, param in named_params:
+                if param not in exec_info.visited_params:
+                    exec_info.visited_params.add(param)
+                    exec_info.param_forward_order.append(param)
+        return proxy
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_traversal_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_traversal_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..51140d3b0a8d3d16ab50226b414e651f22772648
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_traversal_utils.py
@@ -0,0 +1,112 @@
+"""
+NOTE: This file must be imported like
+``import torch.distributed.fsdp._traversal_utils`` and not like
+``from torch.distributed.fsdp._traversal_utils import ...`` to avoid circular
+imports. For brevity, we may import the file as ``traversal_utils``.
+"""
+
+import collections
+
+import torch.nn as nn
+from torch.distributed._composable.contract import _get_registry
+from torch.distributed.fsdp._common_utils import _FSDPState, _get_module_fsdp_state
+
+
+"""
+[Note: FSDP State Traversal]
+For the wrapper code path, ``_FSDPState`` is the ``FullyShardedDataParallel``
+module wrapping a fully sharded module, and for the non-wrapper code path,
+``_FSDPState`` is an object that gets embedded on a fully sharded module.
+See [Note: Fully Sharded Module] for the definition.
+
+There are three common traversal idioms: Given a root module,
+- ``_get_fsdp_states()`` returns all ``_FSDPState`` s in the tree.
+- ``get_fsdp_root_states()`` returns all local root ``_FSDPState`` s in the
+tree (i.e. those with ``_is_root == True``).
+- ``_get_fsdp_handles()``returns all ``FlatParamHandle`` s in the tree.
+
+All of these methods must take in the root module (i.e. an ``nn.Module``) and
+not a general ``_FSDPState`` because ``_FSDPState`` does not support a graph
+traversal, whereas ``nn.Module`` has ``nn.Module.modules()`` for traversal.
+"""
+
+
+def _composable(module: nn.Module) -> bool:
+    """
+    Returns if ``module`` can compose with ``fully_shard``.
+    """
+    # TODO: Add any other composable APIs that are mutually exclusive.
+    registry = _get_registry(module)
+    if registry is None:
+        return True
+    return "replicate" not in registry
+
+
+# TODO (awgu): We may be able to remove this function if we retired the
+# `use_orig_params=False` code path since so far we only need the module for
+# `FlatParameter` registration, which is not needed for `use_orig_params=True`.
+def _get_fsdp_states_with_modules(
+    module: nn.Module,
+) -> tuple[list[_FSDPState], list[nn.Module]]:
+    """
+    Returns a tuple containing:
+    1. A list of the ``_FSDPState`` instances in the module tree rooted at
+    ``module`` without any duplicates and following the ``module.modules()``
+    traversal order (which is assumed to be depth-first).
+    2. A corresponding list of the modules owning the states in the first list.
+
+    For the wrapper code path, both returned lists are the same, each
+    containing all ``FullyShardedDataParallel`` instances. For the composable
+    code path, this returns a list of all composable state instances and a list
+    of the corresponding fully sharded modules. See [Note: Fully Sharded
+    Module].
+
+    NOTE: The traversal does not proceed into any module annotated by an
+    incompatible API (e.g. ``replicate``).
+    """
+    fsdp_states: list[_FSDPState] = []
+    fsdp_modules: list[nn.Module] = []
+    # Track the visited FSDP states since multiple modules may share the same
+    # one and we want to return a de-duplicated list
+    visited_fsdp_states: set[_FSDPState] = set()
+    # Track the visited modules in case of shared modules, which implies the
+    # module graph is no longer a tree
+    visited_modules: set[nn.Module] = set()
+
+    # Perform depth-first search from `module` to ensure that we do not
+    # traverse into an incompatible API's subtree (use DFS instead of BFS to
+    # match `.modules()` order)
+    deque: collections.deque[nn.Module] = collections.deque([module])
+    while deque:
+        submodule = deque.popleft()
+        visited_modules.add(submodule)
+        if not _composable(submodule):
+            continue
+        for child_module in reversed(list(submodule.children())):
+            if child_module not in visited_modules:
+                deque.appendleft(child_module)
+        optional_state = _get_module_fsdp_state(submodule)
+        if optional_state is not None and optional_state not in visited_fsdp_states:
+            visited_fsdp_states.add(optional_state)
+            fsdp_states.append(optional_state)
+            fsdp_modules.append(submodule)
+    return fsdp_states, fsdp_modules
+
+
+def _get_fsdp_states(module: nn.Module) -> list[_FSDPState]:
+    """See :func:`_get_fsdp_states_with_modules`."""
+    fsdp_states, _ = _get_fsdp_states_with_modules(module)
+    return fsdp_states
+
+
+def _get_fsdp_handles(module: nn.Module) -> list:
+    """
+    Returns all ``FlatParamHandle`` s in the module tree rooted at ``module``
+    following the rules in :func:`_get_fsdp_state`.
+    """
+    handles = [
+        fsdp_state._handle
+        for fsdp_state in _get_fsdp_states(module)
+        if fsdp_state._handle is not None
+    ]
+    return handles
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_unshard_param_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_unshard_param_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..71dc1a9f4e28c7101fc0acdae2582be89e954013
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_unshard_param_utils.py
@@ -0,0 +1,340 @@
+# mypy: allow-untyped-defs
+import contextlib
+import warnings
+from collections.abc import Generator
+from typing import cast
+
+import torch
+import torch.distributed.fsdp._traversal_utils as traversal_utils
+import torch.nn as nn
+from torch.distributed.fsdp._common_utils import (
+    _FSDPState,
+    _get_module_fsdp_state,
+    _has_fsdp_params,
+    _module_handle,
+    HandleTrainingState,
+    TrainingState,
+)
+from torch.distributed.fsdp._runtime_utils import (
+    _lazy_init,
+    _reset_flat_param_grad_info_if_needed,
+    _reshard,
+    _reshard_grads,
+    _unshard,
+    _unshard_grads,
+)
+from torch.distributed.utils import _p_assert
+
+from ._flat_param import FlatParamHandle
+
+
+FLAT_PARAM = "_flat_param"
+
+
+@torch.no_grad()
+def _writeback_to_local_shard(
+    handle: FlatParamHandle,
+    writeback_grad: bool,
+):
+    """
+    For the handle, writes back the this rank's shard of the unsharded
+    flattened parameter to the sharded flattened parameter. If
+    ``writeback_grad=True``, then writes back to the sharded gradient as
+    well.
+
+    Precondition: The handle's ``FlatParameter`` 's data points to the
+    padded unsharded flattened parameter.
+    """
+
+    def _get_shard(flat_param_or_grad: torch.Tensor) -> torch.Tensor:
+        if handle.uses_sharded_strategy:
+            # For sharded strategies, get the *unpadded* shard instead of
+            # the *padded* shard to persist user changes to the padding
+            # (though FSDP does not explicitly support this)
+            shard, _ = FlatParamHandle._get_unpadded_shard(
+                flat_param_or_grad,
+                handle.rank,
+                handle.world_size,
+            )
+            return shard
+        # For `NO_SHARD`, the `flat_param` or its gradient may be modified,
+        # so we write it back directly
+        return flat_param_or_grad
+
+    param_shard = _get_shard(handle.flat_param)
+    handle.flat_param._local_shard[: param_shard.numel()].copy_(param_shard)  # type: ignore[attr-defined]
+    if writeback_grad:
+        existing_grad = handle.sharded_grad
+        if existing_grad is not None:
+            if handle.flat_param.grad is None:
+                raise AssertionError("Expected handle.flat_param.grad to not be None")
+            grad_shard = _get_shard(handle.flat_param.grad)
+            existing_grad[: grad_shard.numel()].copy_(grad_shard)
+
+
+def _deregister_flat_param(state: _FSDPState, module: nn.Module) -> None:
+    """
+    De-registers the flattened parameter from the wrapped module, hiding it
+    from ``nn.Module`` methods.
+
+    We do not use ``del`` because we want ``FLAT_PARAM`` to always be an
+    attribute but dynamically change whether it is visible to ``nn.Module``
+    methods.
+    """
+    if _has_fsdp_params(state, module):
+        # TODO: figure out the case for the composable APIs.
+        cast(nn.Module, module.module)._parameters.pop(FLAT_PARAM, None)
+
+
+def _register_flat_param(state: _FSDPState, module: nn.Module) -> None:
+    """
+    Registers the flattened parameter to the wrapped module, making it
+    visible to ``nn.Module`` methods.
+
+    We do not use :meth:`nn.Module.register_parameter` because we want
+    ``FLAT_PARAM`` to always be an attribute but dynamically change whether
+    it is visible to ``nn.Module`` methods.
+    """
+    handle = _module_handle(state, module)
+    if _has_fsdp_params(state, module):
+        # TODO: figure out the case for the composable APIs.
+        cast(nn.Module, module.module)._parameters[FLAT_PARAM] = handle.flat_param
+
+
+@contextlib.contextmanager
+def _unflatten_as_params(state: _FSDPState, module: nn.Module) -> Generator:
+    """
+    Assumes that the flattened parameter is unsharded. When in the context,
+    de-registers the flattened parameter and unflattens the original
+    parameters as ``nn.Parameter`` views into the flattened parameter.
+    After the context, re-registers the flattened parameter and restores
+    the original parameters as ``Tensor`` views into the flattened
+    parameter.
+    """
+    handle = _module_handle(state, module)
+    if not handle:
+        yield
+    else:
+        _deregister_flat_param(state, module)
+        try:
+            with handle.unflatten_as_params():
+                yield
+        finally:
+            if not handle._use_orig_params:
+                _register_flat_param(state, module)
+
+
+def _validate_unshard_params_args(
+    state: _FSDPState,
+    writeback: bool,
+    rank0_only: bool,
+    offload_to_cpu: bool,
+    with_grads: bool,
+) -> None:
+    if with_grads and (offload_to_cpu or not state._use_orig_params):
+        raise NotImplementedError(
+            f"with_grads={with_grads}, "
+            f"use_orig_params={state._use_orig_params}, "
+            f"offload_to_cpu={offload_to_cpu} "
+            f"is not supported yet"
+        )
+    if offload_to_cpu and state._handle and (not state._handle.uses_sharded_strategy):
+        raise NotImplementedError(
+            "offload_to_cpu=True and NO_SHARD is not supported yet"
+        )
+    if writeback and rank0_only:
+        # TODO: Rank 0 can broadcast the `FlatParameter` to allow all ranks to
+        # persist the changes.
+        raise NotImplementedError(
+            "writeback=True and rank0_only=True is not supported yet"
+        )
+    if offload_to_cpu and not rank0_only:
+        warnings.warn(
+            "offload_to_cpu=True and rank0_only=False may result in the"
+            "unsharded parameters being redundantly copied to CPU memory for "
+            "GPUs sharing the same CPU memory, which risks CPU OOM. We "
+            "recommend using offload_to_cpu=True with rank0_only=True.",
+            stacklevel=2,
+        )
+
+
+@contextlib.contextmanager
+def _unshard_fsdp_state_params(
+    module: nn.Module,
+    state: _FSDPState,
+    writeback: bool,
+    rank0_only: bool,
+    offload_to_cpu: bool,
+    with_grads: bool,
+):
+    """
+    This unshards the parameters for a single FSDP state ``state`` that
+    corresponds to ``module``.
+    """
+    _validate_unshard_params_args(
+        state, writeback, rank0_only, offload_to_cpu, with_grads
+    )
+    state._device_handle.synchronize()
+    # If handles are shared by other module(s), the handle may be already unsharded.
+    maybe_handle = _module_handle(state, module)
+    handle = None
+    if (
+        maybe_handle
+        and maybe_handle._training_state != HandleTrainingState.SUMMON_FULL_PARAMS
+    ):
+        handle = maybe_handle
+    if not handle:
+        yield
+        return
+
+    if handle._training_state != HandleTrainingState.IDLE:
+        raise AssertionError(
+            f"Expects the handle training to be IDLE but got {handle._training_state}"
+        )
+
+    handle._training_state = HandleTrainingState.SUMMON_FULL_PARAMS
+
+    _reset_flat_param_grad_info_if_needed(handle)
+    free_unsharded_flat_param = handle.needs_unshard()
+    # No need to call `wait_stream()` since we unshard in the computation
+    # stream directly
+    computation_stream = state._device_handle.current_stream()
+    _unshard(state, handle, computation_stream, computation_stream)
+    if with_grads:
+        _unshard_grads(handle)
+
+    if rank0_only and state.rank != 0:
+        # Free the unsharded flattened parameter early
+        _reshard(state, handle, free_unsharded_flat_param)
+        if with_grads:
+            _reshard_grads(handle)
+        try:
+            yield
+        finally:
+            handle._training_state = HandleTrainingState.IDLE
+    else:
+        # Unflatten the unsharded flattened parameters
+        with contextlib.ExitStack() as stack:
+            # Invariant: rank == 0 or !rank0_only
+            if offload_to_cpu and handle.uses_sharded_strategy:
+                stack.enter_context(handle.to_cpu())
+                # NOTE: Since PyTorch enforces that a parameter and its
+                # gradients need to match metadata (e.g. device), we must
+                # move gradients to CPU *after* we move parameters.
+            # NOTE: This assumes 1 `FlatParameter`
+            if not state._use_orig_params:
+                stack.enter_context(_unflatten_as_params(state, module))
+            try:
+                yield
+            finally:
+                stack.close()
+                if writeback:
+                    _writeback_to_local_shard(handle, with_grads)
+                _reshard(state, handle, free_unsharded_flat_param)
+                if with_grads:
+                    _reshard_grads(handle)
+                handle._training_state = HandleTrainingState.IDLE
+
+
+@contextlib.contextmanager
+def _unshard_params_for_summon(
+    module: nn.Module,
+    state: _FSDPState,
+    writeback: bool,
+    rank0_only: bool,
+    offload_to_cpu: bool,
+    with_grads: bool,
+):
+    _validate_unshard_params_args(
+        state, writeback, rank0_only, offload_to_cpu, with_grads
+    )
+    _lazy_init(state, module)
+    if state.training_state == TrainingState.FORWARD_BACKWARD:
+        raise AssertionError(
+            "Cannot manually unshard parameters during forward/backward"
+        )
+    elif state.training_state == TrainingState.SUMMON_FULL_PARAMS:
+        raise AssertionError(
+            "Cannot manually unshard parameters when already unsharding parameters"
+        )
+    with _unshard_fsdp_state_params(
+        module=module,
+        state=state,
+        writeback=writeback,
+        rank0_only=rank0_only,
+        offload_to_cpu=offload_to_cpu,
+        with_grads=with_grads,
+    ):
+        try:
+            state.training_state = TrainingState.SUMMON_FULL_PARAMS
+            yield
+        finally:
+            state.training_state = TrainingState.IDLE
+
+
+@contextlib.contextmanager
+def _unshard_params(
+    module: nn.Module,
+    recurse: bool,
+    writeback: bool,
+    rank0_only: bool,
+    offload_to_cpu: bool,
+    with_grads: bool,
+):
+    """
+    This unshards FSDP-managed parameters for all modules with FSDP applied in
+    the module tree rooted at ``module``.
+    """
+    if not recurse:
+        optional_state = _get_module_fsdp_state(module)
+        if optional_state is None:
+            with contextlib.nullcontext():
+                yield
+            return
+        states_and_modules = ([optional_state], [module])
+    else:
+        states_and_modules = traversal_utils._get_fsdp_states_with_modules(module)
+    with contextlib.ExitStack() as stack:
+        for state, module in zip(*states_and_modules):
+            stack.enter_context(
+                _unshard_params_for_summon(
+                    module=module,
+                    state=state,
+                    writeback=writeback,
+                    rank0_only=rank0_only,
+                    offload_to_cpu=offload_to_cpu,
+                    with_grads=with_grads,
+                )
+            )
+        yield
+
+
+def _deregister_orig_params(state: _FSDPState, module: nn.Module) -> None:
+    """
+    Deregisters the original parameters; registers the ``FlatParameter``.
+    """
+    handle = _module_handle(state, module)
+    if not handle:
+        return
+    _p_assert(
+        handle._use_orig_params,
+        f"Inconsistent `_use_orig_params` -- FSDP: {state._use_orig_params} "
+        f"handle: {handle._use_orig_params}",
+    )
+    handle._deregister_orig_params()
+    _register_flat_param(state, module)
+
+
+def _register_orig_params(state: _FSDPState, module: nn.Module) -> None:
+    """
+    Deregisters the ``FlatParameter``; registers the original parameters.
+    """
+    handle = _module_handle(state, module)
+    if not handle:
+        return
+    _deregister_flat_param(state, module)
+    if handle.is_sharded(handle.flat_param):
+        handle._use_sharded_views()
+        handle._use_sharded_grad_views()
+    else:
+        handle._use_unsharded_views(as_params=True)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_wrap_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_wrap_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..41dc4d8575198875b8403c2a41c7b2f547a1b742
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/_wrap_utils.py
@@ -0,0 +1,264 @@
+# mypy: allow-untyped-defs
+import collections
+import functools
+import inspect
+import warnings
+from collections.abc import Callable
+from functools import partial
+from typing import Any, Union
+
+import torch.nn as nn
+from torch.distributed.fsdp._common_utils import (
+    _get_module_fsdp_state,
+    _override_module_mixed_precision,
+)
+from torch.distributed.fsdp.wrap import (
+    _construct_wrap_fn,
+    _or_policy,
+    _Policy,
+    _post_order_apply,
+    _recursive_wrap,
+    _run_mixed_precision_override_policy,
+    _wrap_module_cls_individually,
+)
+
+
+def _auto_wrap(
+    root_module: nn.Module,
+    policy: Union[Callable, _Policy],
+    ignored_modules: set[nn.Module],
+    ignored_params: set[nn.Parameter],
+    root_kwargs: dict[str, Any],
+    fsdp_fn: Callable,  # e.g. `FullyShardedDataParallel` or `fully_shard`
+):
+    """
+    Auto wraps modules in ``root_module`` 's tree according to ``policy``
+    following a post-order traversal.
+
+    Precondition: ``root_kwargs`` should contain all arguments except
+    ``module``. This function accepts the kwargs dict directly since it gets
+    forwarded into the post-order traversal function.
+    """
+    mixed_precision = root_kwargs["mixed_precision"]
+    is_wrapper = inspect.isclass(fsdp_fn)
+    # TODO: We may relax this no-nested-wrapping constraint to support manual
+    # wrapping followed by auto wrapping.
+    _check_nested_wrapping(root_module)
+
+    if isinstance(policy, _Policy):
+        root_kwargs["auto_wrap_policy" if is_wrapper else "policy"] = None
+        target_module_to_kwargs = policy._run_policy(
+            root_module, ignored_modules, root_kwargs
+        )
+        if mixed_precision is not None:
+            target_module_to_kwargs = _run_mixed_precision_override_policy(
+                root_module,
+                mixed_precision._module_classes_to_ignore,
+                ignored_modules,
+                root_kwargs,
+                target_module_to_kwargs,
+            )
+            overridden_module_classes = _override_module_mixed_precision(
+                root_module, mixed_precision._module_classes_to_ignore
+            )
+            _warn_on_overridden_mixed_precision(overridden_module_classes)
+        use_orig_params = root_kwargs.get("use_orig_params", False)
+        _validate_frozen_params(
+            root_module,
+            set(target_module_to_kwargs.keys()),
+            ignored_params,
+            use_orig_params,
+        )
+        wrap_fn = _construct_wrap_fn(root_module, target_module_to_kwargs, fsdp_fn)
+        _post_order_apply(root_module, wrap_fn)
+        return
+
+    recursive_wrap_kwargs = {
+        "module": root_module,
+        "auto_wrap_policy": policy,
+        "wrapper_cls": fsdp_fn,
+        "ignored_modules": ignored_modules,
+        "ignored_params": ignored_params,
+        "only_wrap_children": True,
+    }
+    if mixed_precision is not None:
+        # Wrap modules of the ignored types separately and register forward
+        # hooks to cast to fp32 and back to the original dtype, respectively
+        overridden_module_classes = _override_module_mixed_precision(
+            root_module, mixed_precision._module_classes_to_ignore
+        )
+        policy = functools.partial(
+            _or_policy,
+            policies=[
+                policy,
+                partial(
+                    _wrap_module_cls_individually,
+                    module_classes=mixed_precision._module_classes_to_ignore,
+                ),
+            ],
+        )
+        recursive_wrap_kwargs["auto_wrap_policy"] = policy
+        _warn_on_overridden_mixed_precision(overridden_module_classes)
+    _recursive_wrap(**recursive_wrap_kwargs, **root_kwargs)  # type: ignore[arg-type]
+
+
+def _check_nested_wrapping(root_module: nn.Module):
+    for module_name, module in root_module.named_modules():
+        if _get_module_fsdp_state(module) is not None:
+            raise ValueError(
+                "FSDP auto wrapping requires modules to not already have "
+                f"FSDP applied but found {module_name} in\n{root_module}"
+            )
+
+
+def _warn_on_overridden_mixed_precision(
+    overridden_module_classes: set[type[nn.Module]],
+):
+    if len(overridden_module_classes) == 0:
+        return
+    warnings.warn(
+        "Both mixed precision and an auto_wrap_policy were specified to FSDP, "
+        f"where the wrapped module has submodules of type:\n{overridden_module_classes}\n"
+        "These modules will be wrapped as separate FSDP instacnes with mixed "
+        "precision disabled.",
+        stacklevel=2,
+    )
+
+
+def _validate_frozen_params(
+    root_module: nn.Module,
+    modules_to_wrap: set[nn.Module],
+    ignored_params: set[nn.Parameter],
+    use_orig_params: bool,
+):
+    """
+    This checks that, given ``modules_to_wrap``, each module would manage
+    parameters that are uniformly frozen or non-frozen. This uniformity
+    requirement is strict for ``use_orig_params=False`` (hard error) and highly
+    recommended for ``use_orig_params=True`` (user warning).
+    """
+    post_order_named_modules = _get_post_order_named_modules(root_module)
+    visited_modules: set[nn.Module] = set()
+    for module_name, module in post_order_named_modules:
+        if module in modules_to_wrap:
+            param_to_fqn = _get_managed_param_to_fqn(
+                module, ignored_params, visited_modules, module_name
+            )
+            frozen_param_fqns: list[str] = []
+            frozen_param_numel = 0
+            nonfrozen_param_fqns: list[str] = []
+            nonfrozen_param_numel = 0
+            for param, fqn in param_to_fqn.items():
+                if param.requires_grad:
+                    nonfrozen_param_fqns.append(fqn)
+                    nonfrozen_param_numel += param.numel()
+                else:
+                    frozen_param_fqns.append(fqn)
+                    frozen_param_numel += param.numel()
+            if len(frozen_param_fqns) > 0 and len(nonfrozen_param_fqns) > 0:
+                msg = f"{module_name} has both parameters with requires_grad=True and False."
+                if use_orig_params:
+                    total_param_numel = frozen_param_numel + nonfrozen_param_numel
+                    msg += (
+                        " We do not recommend wrapping such modules since "
+                        "the gradient memory usage will be higher than expected "
+                        f"({total_param_numel} numel instead of {nonfrozen_param_numel} numel "
+                        "before sharding via reduce-scatter). "
+                    )
+                else:
+                    msg += " FSDP does not support wrapping such modules when use_orig_params=False. "
+                msg += "If possible, wrap the frozen parameters with FSDP separately.\n"
+                msg += (
+                    f"The following parameters have requires_grad=True:\n{nonfrozen_param_fqns}\n"
+                    f"The following parameters have requires_grad=False:\n{frozen_param_fqns}"
+                )
+                if use_orig_params:
+                    warnings.warn(msg, stacklevel=2)
+                else:
+                    raise ValueError(msg)
+
+
+def _get_post_order_named_modules(
+    root_module: nn.Module,
+) -> list[tuple[str, nn.Module]]:
+    """
+    This returns the named modules following a post-order traversal, which is a
+    valid reverse topological sort. We achieve this using the reverse of a
+    stack-based DFS order instead of reversing ``root_module.named_modules()``
+    since the former gives the modules in registration order at each level in
+    the module tree (as opposed to the reverse), which allows us to error/warn
+    on the first registered module that violates the condition.
+
+    For example, consider the following module structure:
+        M(
+          S1(),
+          S2(
+            SS1(),
+            SS2(),
+          ),
+          S3(),
+        )
+    The reverse DFS order is [S1, SS1, SS2, S2, S3, M], while the reverse
+    ``named_modules()`` order is [S3, SS2, SS1, S2, S1, M].
+    """
+    visited_modules = {root_module}
+    stack = [("", root_module)]
+    # Append and reverse at the end for linear-time algorithm
+    reverse_post_order_named_modules: list[tuple[str, nn.Module]] = []
+    while stack:
+        module_name, module = stack.pop()
+        reverse_post_order_named_modules.append((module_name, module))
+        for child_module_name, child_module in module.named_children():
+            if child_module is None:  # only for overrides of `named_children()`
+                continue
+            if child_module not in visited_modules:
+                visited_modules.add(child_module)
+                if module_name != "":
+                    child_module_name = module_name + "." + child_module_name
+                stack.append((child_module_name, child_module))
+    post_order_named_modules = list(reversed(reverse_post_order_named_modules))
+    return post_order_named_modules
+
+
+def _get_managed_param_to_fqn(
+    module_to_wrap: nn.Module,
+    ignored_params: set[nn.Parameter],
+    visited_modules: set[nn.Module],
+    root_prefix: str,
+) -> dict[nn.Parameter, str]:
+    """
+    This returns a dict that maps managed parameter to its FQN for the given
+    ``module_to_wrap``. The dict's keys are exactly the parameters that would
+    be managed by the module, where this is achieved by calling this function
+    on the modules to wrap in reverse topological order, destructively updating
+    ``visited_modules``, and not traversing into those modules. The FQNs are
+    prefixed from the root (via ``root_prefix``) to be more informative.
+
+    NOTE: This function is meant to be called pre-wrapping and iteratively in
+    reverse topological order to cover the full module tree. This differs from
+    the ``_get_param_to_fqn()`` function meant to be called post-wrapping and
+    on the full module tree in one shot. Given those differences, we do not try
+    to unify the two.
+    """
+    param_to_fqn: dict[nn.Parameter, str] = {}
+    # Run BFS (or any tree traversal works)
+    queue = collections.deque([(module_to_wrap, root_prefix)])
+    visited_modules.add(module_to_wrap)
+    while queue:
+        module, prefix = queue.popleft()
+        for param_name, param in module.named_parameters(recurse=False):
+            if param not in ignored_params:
+                fqn = param_name if prefix == "" else prefix + "." + param_name
+                param_to_fqn[param] = fqn
+        for child_module_name, child_module in module.named_children():
+            if child_module is None:  # only for overrides of `named_children()`
+                continue
+            if child_module not in visited_modules:
+                visited_modules.add(child_module)
+                child_prefix = (
+                    child_module_name
+                    if prefix == ""
+                    else prefix + "." + child_module_name
+                )
+                queue.append((child_module, child_prefix))
+    return param_to_fqn
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/api.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..17ed0483f1c26248673fe888bc5489e099b1313b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/api.py
@@ -0,0 +1,417 @@
+"""
+This file includes public APIs for FSDP such as the classes used for the
+constructor arguments.
+"""
+
+from collections.abc import Sequence
+from dataclasses import dataclass
+from enum import auto, Enum
+from typing import Optional
+
+import torch
+from torch.nn.modules.batchnorm import _BatchNorm
+
+
+__all__ = [
+    "ShardingStrategy",
+    "BackwardPrefetch",
+    "MixedPrecision",
+    "CPUOffload",
+    "StateDictType",
+    "StateDictConfig",
+    "FullStateDictConfig",
+    "LocalStateDictConfig",
+    "ShardedStateDictConfig",
+    "OptimStateDictConfig",
+    "FullOptimStateDictConfig",
+    "LocalOptimStateDictConfig",
+    "ShardedOptimStateDictConfig",
+    "StateDictSettings",
+]
+
+
+class ShardingStrategy(Enum):
+    """
+    This specifies the sharding strategy to be used for distributed training by
+    :class:`FullyShardedDataParallel`.
+
+    - ``FULL_SHARD``: Parameters, gradients, and optimizer states are sharded.
+      For the parameters, this strategy unshards (via all-gather) before the
+      forward, reshards after the forward, unshards before the backward
+      computation, and reshards after the backward computation. For gradients,
+      it synchronizes and shards them (via reduce-scatter) after the backward
+      computation. The sharded optimizer states are updated locally per rank.
+    - ``SHARD_GRAD_OP``: Gradients and optimizer states are sharded during
+      computation, and additionally, parameters are sharded outside
+      computation. For the parameters, this strategy unshards before the
+      forward, does not reshard them after the forward, and only reshards them
+      after the backward computation. The sharded optimizer states are updated
+      locally per rank. Inside ``no_sync()``, the parameters are not resharded
+      after the backward computation.
+    - ``NO_SHARD``: Parameters, gradients, and optimizer states are not sharded
+      but instead replicated across ranks similar to PyTorch's
+      :class:`DistributedDataParallel` API. For gradients, this strategy
+      synchronizes them (via all-reduce) after the backward computation. The
+      unsharded optimizer states are updated locally per rank.
+    - ``HYBRID_SHARD``: Apply ``FULL_SHARD`` within a node, and replicate parameters across
+      nodes. This results in reduced communication volume as expensive all-gathers and
+      reduce-scatters are only done within a node, which can be more performant for medium
+      -sized models.
+    - ``_HYBRID_SHARD_ZERO2``: Apply ``SHARD_GRAD_OP`` within a node, and replicate parameters across
+      nodes. This is like ``HYBRID_SHARD``, except this may provide even higher throughput
+      since the unsharded parameters are not freed after the forward pass, saving the
+      all-gathers in the pre-backward.
+    """
+
+    FULL_SHARD = auto()
+    SHARD_GRAD_OP = auto()
+    NO_SHARD = auto()
+    HYBRID_SHARD = auto()
+    _HYBRID_SHARD_ZERO2 = auto()
+
+
+class BackwardPrefetch(Enum):
+    """
+    This configures explicit backward prefetching, which improves throughput by
+    enabling communication and computation overlap in the backward pass at the
+    cost of slightly increased memory usage.
+
+    - ``BACKWARD_PRE``: This enables the most overlap but increases memory
+      usage the most. This prefetches the next set of parameters *before* the
+      current set of parameters' gradient computation. This overlaps the *next
+      all-gather* and the *current gradient computation*, and at the peak, it
+      holds the current set of parameters, next set of parameters, and current
+      set of gradients in memory.
+    - ``BACKWARD_POST``: This enables less overlap but requires less memory
+      usage. This prefetches the next set of parameters *after* the current
+      set of parameters' gradient computation. This overlaps the *current
+      reduce-scatter* and the *next gradient computation*, and it frees the
+      current set of parameters before allocating memory for the next set of
+      parameters, only holding the next set of parameters and current set of
+      gradients in memory at the peak.
+    - FSDP's ``backward_prefetch`` argument accepts ``None``, which disables
+      the backward prefetching altogether. This has no overlap and does not
+      increase memory usage. In general, we do not recommend this setting since
+      it may degrade throughput significantly.
+
+    For more technical context: For a single process group using NCCL backend,
+    any collectives, even if issued from different streams, contend for the
+    same per-device NCCL stream, which implies that the relative order in which
+    the collectives are issued matters for overlapping. The two backward
+    prefetching values correspond to different issue orders.
+    """
+
+    # NOTE: For both modes, the ordering that defines "current" and "next" is
+    # not always exact in the current implementation. A mistargeted prefetch
+    # simply means that the parameter memory is allocated earlier than needed,
+    # possibly increasing peak memory usage, but does not affect correctness.
+    BACKWARD_PRE = auto()
+    BACKWARD_POST = auto()
+
+
+@dataclass
+class MixedPrecision:
+    """
+    This configures FSDP-native mixed precision training.
+
+    Attributes:
+        param_dtype (Optional[torch.dtype]): This specifies the dtype for model
+            parameters during forward and backward and thus the dtype for
+            forward and backward computation. Outside forward and backward, the
+            *sharded* parameters are kept in full precision (e.g. for the
+            optimizer step), and for model checkpointing, the parameters are
+            always saved in full precision. (Default: ``None``)
+        reduce_dtype (Optional[torch.dtype]): This specifies the dtype for
+            gradient reduction (i.e. reduce-scatter or all-reduce). If this is
+            ``None`` but ``param_dtype`` is not ``None``, then this takes on
+            the ``param_dtype`` value, still running gradient reduction in low
+            precision. This is permitted to differ from ``param_dtype``, e.g.
+            to force gradient reduction to run in full precision. (Default:
+            ``None``)
+        buffer_dtype (Optional[torch.dtype]): This specifies the dtype for
+            buffers. FSDP does not shard buffers. Rather, FSDP casts them to
+            ``buffer_dtype`` in the first forward pass and keeps them in that
+            dtype thereafter. For model checkpointing, the buffers are saved
+            in full precision except for ``LOCAL_STATE_DICT``. (Default:
+            ``None``)
+        keep_low_precision_grads (bool): If ``False``, then FSDP upcasts
+            gradients to full precision after the backward pass in preparation
+            for the optimizer step. If ``True``, then FSDP keeps the gradients
+            in the dtype used for gradient reduction, which can save memory if
+            using a custom optimizer that supports running in low precision.
+            (Default: ``False``)
+        cast_forward_inputs (bool): If ``True``, then this FSDP module casts
+            its forward args and kwargs to ``param_dtype``. This is to ensure
+            that parameter and input dtypes match for forward computation, as
+            required by many ops. This may need to be set to ``True`` when only
+            applying mixed precision to some but not all FSDP modules, in which
+            case a mixed-precision FSDP submodule needs to recast its inputs.
+            (Default: ``False``)
+        cast_root_forward_inputs (bool): If ``True``, then the root FSDP module
+            casts its forward args and kwargs to ``param_dtype``, overriding
+            the value of ``cast_forward_inputs``. For non-root FSDP modules,
+            this does not do anything. (Default: ``True``)
+        _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies
+            module classes to ignore for mixed precision when using an
+            ``auto_wrap_policy``: Modules of these classes will have FSDP
+            applied to them separately with mixed precision disabled (meaning
+            that the final FSDP construction would deviate from the specified
+            policy). If ``auto_wrap_policy`` is not specified, then this does
+            not do anything. This API is experimental and subject to change.
+            (Default: ``(_BatchNorm,)``)
+
+    .. note:: This API is experimental and subject to change.
+
+    .. note:: Only floating point tensors are cast to their specified dtypes.
+
+    .. note:: In ``summon_full_params``, parameters are forced to full
+        precision, but buffers are not.
+
+    .. note:: Layer norm and batch norm accumulate in ``float32`` even when
+        their inputs are in a low precision like ``float16`` or ``bfloat16``.
+        Disabling FSDP's mixed precision for those norm modules only means that
+        the affine parameters are kept in ``float32``. However, this incurs
+        separate all-gathers and reduce-scatters for those norm modules, which
+        may be inefficient, so if the workload permits, the user should prefer
+        to still apply mixed precision to those modules.
+
+    .. note:: By default, if the user passes a model with any ``_BatchNorm``
+        modules and specifies an ``auto_wrap_policy``, then the batch norm
+        modules will have FSDP applied to them separately with mixed precision
+        disabled. See the ``_module_classes_to_ignore`` argument.
+
+    .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and
+        ``cast_forward_inputs=False`` by default. For the root FSDP instance,
+        its ``cast_root_forward_inputs`` takes precedence over its
+        ``cast_forward_inputs``. For non-root FSDP instances, their
+        ``cast_root_forward_inputs`` values are ignored. The default setting is
+        sufficient for the typical case where each FSDP instance has the same
+        ``MixedPrecision`` configuration and only needs to cast inputs to the
+        ``param_dtype`` at the beginning of the model's forward pass.
+
+    .. note:: For nested FSDP instances with different ``MixedPrecision``
+        configurations, we recommend setting individual ``cast_forward_inputs``
+        values to configure casting inputs or not before each instance's
+        forward. In such a case, since the casts happen before each FSDP
+        instance's forward, a parent FSDP instance should have its non-FSDP
+        submodules run before its FSDP submodules to avoid the activation dtype
+        being changed due to a different ``MixedPrecision`` configuration.
+
+        Example::
+
+            >>> # xdoctest: +SKIP("undefined variables")
+            >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3))
+            >>> model[1] = FSDP(
+            >>>     model[1],
+            >>>     mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True),
+            >>> )
+            >>> model = FSDP(
+            >>>     model,
+            >>>     mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True),
+            >>> )
+
+        The above shows a working example. On the other hand, if ``model[1]``
+        were replaced with ``model[0]``, meaning that the submodule using
+        different ``MixedPrecision`` ran its forward first, then ``model[1]``
+        would incorrectly see ``float16`` activations instead of ``bfloat16``
+        ones.
+
+    """
+
+    param_dtype: Optional[torch.dtype] = None
+    reduce_dtype: Optional[torch.dtype] = None
+    buffer_dtype: Optional[torch.dtype] = None
+    keep_low_precision_grads: bool = False
+    cast_forward_inputs: bool = False
+    cast_root_forward_inputs: bool = True
+    _module_classes_to_ignore: Sequence[type[torch.nn.Module]] = (_BatchNorm,)
+
+
+@dataclass
+class CPUOffload:
+    """
+    This configures CPU offloading.
+
+    Attributes:
+        offload_params (bool): This specifies whether to offload parameters to
+            CPU when not involved in computation. If ``True``, then this
+            offloads gradients to CPU as well, meaning that the optimizer step
+            runs on CPU.
+    """
+
+    offload_params: bool = False
+
+
+class StateDictType(Enum):
+    """
+    This enum indicates that which type of ``state_dict`` the FSDP module is
+    currently processing (returning or loading).
+    The default value is FULL_STATE_DICT to comply the PyTorch convention.
+
+    .. note::
+        FSDP currently supports three types of ``state_dict``:
+            1. ``state_dict/load_state_dict`: this pair of APIs return and load
+               the non-sharded, unflattened parameters. The semantics is the
+               same as using DDP.
+            2. ``_local_state_dict/_load_local_state_dict``: this pair of APIs return
+               and load local sharded, flattened parameters. The values returned
+               by ``_local_state_dict`` can be directly used by FSDP and is only
+               meaningful to FSDP (because parameters are flattened). Note that
+               these APIs are meant for use via the :func:`state_dict_type`
+               context manager as follows:
+                   >>> # xdoctest: +SKIP("undefined variables")
+                   >>> with fsdp.state_dict_type(StateDictType.LOCAL_STATE_DICT):
+                   ...     state = fsdp.state_dict()  # loads local state dict
+            3. ``_sharded_state_dict/_load_sharded_state_dict``: this pair of APIs
+               return and load sharded, unflattened parameters. The ``state_dict``
+               return by ``sharded_state_dict`` can be used by all other parallel
+               schemes (resharding may be required).
+    """
+
+    FULL_STATE_DICT = auto()
+    LOCAL_STATE_DICT = auto()
+    SHARDED_STATE_DICT = auto()
+
+
+@dataclass
+class StateDictConfig:
+    """
+    ``StateDictConfig`` is the base class for all ``state_dict`` configuration
+    classes. Users should instantiate a child class (e.g.
+    ``FullStateDictConfig``) in order to configure settings for the
+    corresponding ``state_dict`` type supported by FSDP.
+
+    Attributes:
+        offload_to_cpu (bool): If ``True``, then FSDP offloads the state dict
+            values to CPU, and if ``False``, then FSDP keeps them on GPU.
+            (Default: ``False``)
+    """
+
+    offload_to_cpu: bool = False
+
+
+@dataclass
+class FullStateDictConfig(StateDictConfig):
+    """
+    ``FullStateDictConfig`` is a config class meant to be used with
+    ``StateDictType.FULL_STATE_DICT``. We recommend enabling both
+    ``offload_to_cpu=True`` and ``rank0_only=True`` when saving full state
+    dicts to save GPU memory and CPU memory, respectively. This config class
+    is meant to be used via the :func:`state_dict_type` context manager as
+    follows:
+
+        >>> # xdoctest: +SKIP("undefined variables")
+        >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+        >>> fsdp = FSDP(model, auto_wrap_policy=...)
+        >>> cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
+        >>> with FSDP.state_dict_type(fsdp, StateDictType.FULL_STATE_DICT, cfg):
+        >>>     state = fsdp.state_dict()
+        >>> # `state` will be empty on non rank 0 and contain CPU tensors on rank 0.
+        >>> # To reload checkpoint for inference, finetuning, transfer learning, etc:
+        >>> model = model_fn()  # Initialize model in preparation for wrapping with FSDP
+        >>> if dist.get_rank() == 0:
+        >>> # Load checkpoint only on rank 0 to avoid memory redundancy
+        >>>     state_dict = torch.load("my_checkpoint.pt")
+        >>>     model.load_state_dict(state_dict)
+        >>> # All ranks initialize FSDP module as usual. `sync_module_states` argument
+        >>> # communicates loaded checkpoint states from rank 0 to rest of the world.
+        >>> fsdp = FSDP(
+        ...     model,
+        ...     device_id=torch.cuda.current_device(),
+        ...     auto_wrap_policy=...,
+        ...     sync_module_states=True,
+        ... )
+        >>> # After this point, all ranks have FSDP model with loaded checkpoint.
+
+    Attributes:
+        rank0_only (bool): If ``True``, then only rank 0 saves the full state
+            dict, and nonzero ranks save an empty dict. If ``False``, then all
+            ranks save the full state dict. (Default: ``False``)
+    """
+
+    rank0_only: bool = False
+
+
+@dataclass
+class LocalStateDictConfig(StateDictConfig):
+    pass
+
+
+@dataclass
+class ShardedStateDictConfig(StateDictConfig):
+    """
+    ``ShardedStateDictConfig`` is a config class meant to be used with
+    ``StateDictType.SHARDED_STATE_DICT``.
+
+    Attributes:
+        _use_dtensor (bool): If ``True``, then FSDP saves the state dict values
+            as ``DTensor``, and if ``False``, then FSDP saves them as
+            ``ShardedTensor``. (Default: ``False``)
+
+    .. warning:: ``_use_dtensor`` is a private field of :class:`ShardedStateDictConfig`
+      and it is used by FSDP to determine the type of state dict values. Users should not
+      manually modify ``_use_dtensor``.
+    """
+
+    _use_dtensor: bool = False
+
+
+@dataclass
+class OptimStateDictConfig:
+    """
+    ``OptimStateDictConfig`` is the base class for all ``optim_state_dict``
+    configuration classes.  Users should instantiate a child class (e.g.
+    ``FullOptimStateDictConfig``) in order to configure settings for the
+    corresponding ``optim_state_dict`` type supported by FSDP.
+
+    Attributes:
+        offload_to_cpu (bool): If ``True``, then FSDP offloads the state dict's
+            tensor values to CPU, and if ``False``, then FSDP keeps them on the
+            original device (which is GPU unless parameter CPU offloading is
+            enabled). (Default: ``True``)
+    """
+
+    offload_to_cpu: bool = True
+
+
+@dataclass
+class FullOptimStateDictConfig(OptimStateDictConfig):
+    """
+    Attributes:
+        rank0_only (bool): If ``True``, then only rank 0 saves the full state
+            dict, and nonzero ranks save an empty dict. If ``False``, then all
+            ranks save the full state dict. (Default: ``False``)
+    """
+
+    rank0_only: bool = False
+
+
+@dataclass
+class LocalOptimStateDictConfig(OptimStateDictConfig):
+    offload_to_cpu: bool = False
+
+
+@dataclass
+class ShardedOptimStateDictConfig(OptimStateDictConfig):
+    """
+    ``ShardedOptimStateDictConfig`` is a config class meant to be used with
+    ``StateDictType.SHARDED_STATE_DICT``.
+
+    Attributes:
+        _use_dtensor (bool): If ``True``, then FSDP saves the state dict values
+            as ``DTensor``, and if ``False``, then FSDP saves them as
+            ``ShardedTensor``. (Default: ``False``)
+
+    .. warning:: ``_use_dtensor`` is a private field of :class:`ShardedOptimStateDictConfig`
+      and it is used by FSDP to determine the type of state dict values. Users should not
+      manually modify ``_use_dtensor``.
+    """
+
+    _use_dtensor: bool = False
+
+
+@dataclass
+class StateDictSettings:
+    state_dict_type: StateDictType
+    state_dict_config: StateDictConfig
+    optim_state_dict_config: OptimStateDictConfig
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py
new file mode 100644
index 0000000000000000000000000000000000000000..cdc5ef424e7052a41ddb986da07e1edb389bed27
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py
@@ -0,0 +1,2199 @@
+# mypy: ignore-errors
+
+import contextlib
+import copy
+import functools
+import math
+import traceback
+import warnings
+from collections.abc import Callable, Generator, Iterable, Iterator
+from contextlib import contextmanager
+from enum import auto, Enum
+from typing import Any, Optional, Union
+
+import torch
+import torch.distributed as dist
+import torch.distributed.fsdp._traversal_utils as traversal_utils
+import torch.nn as nn
+from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
+    _CHECKPOINT_WRAPPED_MODULE,
+    ActivationWrapper,
+)
+from torch.distributed.algorithms._comm_hooks import LOW_PRECISION_HOOKS
+from torch.distributed.fsdp._common_utils import (
+    _FSDPState,
+    _get_param_to_fqns,
+    FSDP_PREFIX,
+    FSDP_WRAPPED_MODULE,
+    HandleTrainingState,
+    TrainingState,
+)
+from torch.distributed.fsdp._dynamo_utils import _annotate_modules_for_dynamo
+from torch.distributed.fsdp._init_utils import (
+    _check_orig_params_flattened,
+    _init_buffer_state,
+    _init_core_state,
+    _init_device_handle,
+    _init_extension,
+    _init_ignored_module_states,
+    _init_param_handle_from_module,
+    _init_prefetching_state,
+    _init_process_group_state,
+    _init_runtime_state,
+    _init_state_dict_state,
+    HYBRID_SHARDING_STRATEGIES,
+    ProcessGroupType,
+)
+from torch.distributed.fsdp._runtime_utils import (
+    _get_fsdp_root_states,
+    _is_fsdp_root,
+    _lazy_init,
+    _post_forward,
+    _post_forward_reshard,
+    _pre_forward,
+    _pre_forward_unshard,
+    _root_pre_forward,
+    _unshard,
+    _wait_for_computation_stream,
+)
+from torch.distributed.fsdp._wrap_utils import _auto_wrap
+from torch.distributed.fsdp.api import (
+    BackwardPrefetch,
+    CPUOffload,
+    FullOptimStateDictConfig,
+    FullStateDictConfig,
+    LocalOptimStateDictConfig,
+    LocalStateDictConfig,
+    MixedPrecision,
+    OptimStateDictConfig,
+    ShardedOptimStateDictConfig,
+    ShardedStateDictConfig,
+    ShardingStrategy,
+    StateDictConfig,
+    StateDictSettings,
+    StateDictType,
+)
+from torch.distributed.tensor import DeviceMesh
+from torch.distributed.utils import _p_assert
+
+from ._flat_param import FlatParameter, FlatParamHandle
+from ._optim_utils import (
+    _flatten_optim_state_dict,
+    _get_param_id_to_param_from_optim_input,
+    _get_param_key_to_param,
+    _get_param_to_param_id_from_optim_input,
+    _get_param_to_param_key,
+    _optim_state_dict,
+    _rekey_sharded_optim_state_dict,
+    _set_optim_use_dtensor,
+)
+from ._state_dict_utils import _register_all_state_dict_hooks
+from ._unshard_param_utils import (
+    _deregister_orig_params,
+    _register_flat_param,
+    _register_orig_params,
+    _unshard_params,
+    _unshard_params_for_summon,
+)
+from .wrap import CustomPolicy, ModuleWrapPolicy
+
+
+__all__ = [
+    "FullyShardedDataParallel",
+    "OptimStateKeyType",
+]
+
+
+FLAT_PARAM = "_flat_param"
+
+
+class OptimStateKeyType(Enum):
+    """Represents the type of key in an optimizer state-dict."""
+
+    PARAM_NAME = auto()
+    PARAM_ID = auto()
+
+
+class FullyShardedDataParallel(nn.Module, _FSDPState):
+    """A wrapper for sharding module parameters across data parallel workers.
+
+    This is inspired by `Xu et al. `_ as
+    well as the ZeRO Stage 3 from `DeepSpeed `_.
+    FullyShardedDataParallel is commonly shortened to FSDP.
+
+    Example::
+
+        >>> # xdoctest: +SKIP("undefined variables")
+        >>> import torch
+        >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+        >>> torch.cuda.set_device(device_id)
+        >>> sharded_module = FSDP(my_module)
+        >>> optim = torch.optim.Adam(sharded_module.parameters(), lr=0.0001)
+        >>> x = sharded_module(x, y=3, z=torch.Tensor([1]))
+        >>> loss = x.sum()
+        >>> loss.backward()
+        >>> optim.step()
+
+    Using FSDP involves wrapping your module and then initializing your
+    optimizer after. This is required since FSDP changes the parameter
+    variables.
+
+    When setting up FSDP, you need to consider the destination CUDA
+    device. If the device has an ID (``dev_id``), you have three options:
+
+    * Place the module on that device
+    * Set the device using ``torch.cuda.set_device(dev_id)``
+    * Pass ``dev_id`` into the ``device_id`` constructor argument.
+
+    This ensures that the FSDP instance's compute device is the
+    destination device. For option 1 and 3, the FSDP initialization
+    always occurs on GPU. For option 2, the FSDP initialization
+    happens on module's current device, which may be a CPU.
+
+    If you're using the ``sync_module_states=True`` flag, you need to
+    ensure that the module is on a GPU or use the ``device_id``
+    argument to specify a CUDA device that FSDP will move the module
+    to in the FSDP constructor. This is necessary because
+    ``sync_module_states=True`` requires GPU communication.
+
+    FSDP also takes care of moving input tensors to the forward method
+    to the GPU compute device, so you don't need to manually move them
+    from CPU.
+
+    For ``use_orig_params=True``,
+    ``ShardingStrategy.SHARD_GRAD_OP`` exposes the unsharded
+    parameters, not the sharded parameters after forward, unlike
+    ``ShardingStrategy.FULL_SHARD``. If you want
+    to inspect the gradients, you can use the ``summon_full_params``
+    method with ``with_grads=True``.
+
+    With ``limit_all_gathers=True``, you may see a gap in the FSDP
+    pre-forward where the CPU thread is not issuing any kernels. This is
+    intentional and shows the rate limiter in effect. Synchronizing the CPU
+    thread in that way prevents over-allocating memory for subsequent
+    all-gathers, and it should not actually delay GPU kernel execution.
+
+    FSDP replaces managed modules' parameters with ``torch.Tensor``
+    views during forward and backward computation for autograd-related
+    reasons. If your module's forward relies on saved references to
+    the parameters instead of reacquiring the references each
+    iteration, then it will not see FSDP's newly created views,
+    and autograd will not work correctly.
+
+    Finally, when using ``sharding_strategy=ShardingStrategy.HYBRID_SHARD``
+    with the sharding process group being intra-node and the
+    replication process group being inter-node, setting
+    ``NCCL_CROSS_NIC=1`` can help improve the all-reduce times over
+    the replication process group for some cluster setups.
+
+    **Limitations**
+
+    There are several limitations to be aware of when using FSDP:
+
+    * FSDP currently does not support gradient accumulation outside
+      ``no_sync()`` when using CPU offloading. This is because FSDP
+      uses the newly-reduced gradient instead of accumulating with any
+      existing gradient, which can lead to incorrect results.
+
+    * FSDP does not support running the forward pass of a submodule
+      that is contained in an FSDP instance. This is because the
+      submodule's parameters will be sharded, but the submodule itself
+      is not an FSDP instance, so its forward pass will not all-gather
+      the full parameters appropriately.
+
+    * FSDP does not work with double backwards due to the way it
+      registers backward hooks.
+
+    * FSDP has some constraints when freezing parameters.
+      For ``use_orig_params=False``, each FSDP instance must manage
+      parameters that are all frozen or all non-frozen. For
+      ``use_orig_params=True``, FSDP supports mixing frozen and
+      non-frozen parameters, but it's recommended to avoid doing so to
+      prevent higher than expected gradient memory usage.
+
+    * As of PyTorch 1.12, FSDP offers limited support for shared
+      parameters. If enhanced shared parameter support is needed for
+      your use case, please post in
+      `this issue `__.
+
+    * You should avoid modifying the parameters between forward and
+      backward without using the ``summon_full_params`` context, as
+      the modifications may not persist.
+
+    Args:
+        module (nn.Module):
+            This is the module to be wrapped with FSDP.
+        process_group (Optional[Union[ProcessGroup, Tuple[ProcessGroup, ProcessGroup]]]):
+            This is the process group over which the model is sharded and thus
+            the one used for FSDP's all-gather and reduce-scatter collective
+            communications. If ``None``, then FSDP uses the default process
+            group. For hybrid sharding strategies such as
+            ``ShardingStrategy.HYBRID_SHARD``, users can pass in a tuple of
+            process groups, representing the groups over which to shard and
+            replicate, respectively. If ``None``, then FSDP constructs process
+            groups for the user to shard intra-node and replicate inter-node.
+            (Default: ``None``)
+        sharding_strategy (Optional[ShardingStrategy]):
+            This configures the sharding strategy, which may trade off memory
+            saving and communication overhead. See :class:`ShardingStrategy`
+            for details. (Default: ``FULL_SHARD``)
+        cpu_offload (Optional[CPUOffload]):
+            This configures CPU offloading. If this is set to ``None``, then
+            no CPU offloading happens. See :class:`CPUOffload` for details.
+            (Default: ``None``)
+        auto_wrap_policy (Optional[Union[Callable[[nn.Module, bool, int], bool], ModuleWrapPolicy, CustomPolicy]]):
+            This specifies a policy to apply FSDP to submodules of ``module``,
+            which is needed for communication and computation overlap and thus
+            affects performance. If ``None``, then FSDP only applies to
+            ``module``, and users should manually apply FSDP to parent modules
+            themselves (proceeding bottom-up). For convenience, this accepts
+            ``ModuleWrapPolicy`` directly, which allows users to specify the
+            module classes to wrap (e.g. the transformer block). Otherwise,
+            this should be a callable that takes in three arguments
+            ``module: nn.Module``, ``recurse: bool``, and
+            ``nonwrapped_numel: int`` and should return a ``bool`` specifying
+            whether the passed-in ``module`` should have FSDP applied if
+            ``recurse=False`` or if the traversal should continue into the
+            module's subtree if ``recurse=True``. Users may add additional
+            arguments to the callable. The ``size_based_auto_wrap_policy`` in
+            ``torch.distributed.fsdp.wrap.py`` gives an example callable that
+            applies FSDP to a module if the parameters in its subtree exceed
+            100M numel. We recommend printing the model after applying FSDP
+            and adjusting as needed.
+
+            Example::
+
+                >>> def custom_auto_wrap_policy(
+                >>>     module: nn.Module,
+                >>>     recurse: bool,
+                >>>     nonwrapped_numel: int,
+                >>>     # Additional custom arguments
+                >>>     min_num_params: int = int(1e8),
+                >>> ) -> bool:
+                >>>     return nonwrapped_numel >= min_num_params
+                >>> # Configure a custom `min_num_params`
+                >>> my_auto_wrap_policy = functools.partial(custom_auto_wrap_policy, min_num_params=int(1e5))
+
+        backward_prefetch (Optional[BackwardPrefetch]):
+            This configures explicit backward prefetching of all-gathers. If
+            ``None``, then FSDP does not backward prefetch, and there is no
+            communication and computation overlap in the backward pass. See
+            :class:`BackwardPrefetch` for details. (Default: ``BACKWARD_PRE``)
+        mixed_precision (Optional[MixedPrecision]):
+            This configures native mixed precision for FSDP. If this is set to
+            ``None``, then no mixed precision is used. Otherwise, parameter,
+            buffer, and gradient reduction dtypes can be set. See
+            :class:`MixedPrecision` for details. (Default: ``None``)
+        ignored_modules (Optional[Iterable[torch.nn.Module]]): Modules whose
+            own parameters and child modules' parameters and buffers are
+            ignored by this instance. None of the modules directly in
+            ``ignored_modules`` should be :class:`FullyShardedDataParallel`
+            instances, and any child modules that are already-constructed
+            :class:`FullyShardedDataParallel` instances will not be ignored if
+            they are nested under this instance. This argument may be used to
+            avoid sharding specific parameters at module granularity when using an
+            ``auto_wrap_policy`` or if parameters' sharding is not managed by
+            FSDP. (Default: ``None``)
+        param_init_fn (Optional[Callable[[nn.Module], None]]):
+            A ``Callable[torch.nn.Module] -> None`` that
+            specifies how modules that are currently on the meta device should
+            be initialized onto an actual device. As of v1.12, FSDP detects
+            modules with parameters or buffers on meta device via ``is_meta``
+            and either applies ``param_init_fn`` if specified or calls
+            ``nn.Module.reset_parameters()`` otherwise. For both cases, the
+            implementation should *only* initialize the parameters/buffers of
+            the module, not those of its submodules. This is to avoid
+            re-initialization. In addition, FSDP also supports deferred
+            initialization via torchdistX's (https://github.com/pytorch/torchdistX)
+            ``deferred_init()`` API, where the deferred modules are initialized
+            by calling ``param_init_fn`` if specified or torchdistX's default
+            ``materialize_module()`` otherwise. If ``param_init_fn`` is
+            specified, then it is applied to all meta-device modules, meaning
+            that it should probably case on the module type. FSDP calls the
+            initialization function before parameter flattening and sharding.
+
+            Example::
+
+                >>> # xdoctest: +SKIP("undefined variables")
+                >>> module = MyModule(device="meta")
+                >>> def my_init_fn(module: nn.Module):
+                >>>     # E.g. initialize depending on the module type
+                >>>     ...
+                >>> fsdp_model = FSDP(module, param_init_fn=my_init_fn, auto_wrap_policy=size_based_auto_wrap_policy)
+                >>> print(next(fsdp_model.parameters()).device) # current CUDA device
+                >>> # With torchdistX
+                >>> module = deferred_init.deferred_init(MyModule, device="cuda")
+                >>> # Will initialize via deferred_init.materialize_module().
+                >>> fsdp_model = FSDP(module, auto_wrap_policy=size_based_auto_wrap_policy)
+
+        device_id (Optional[Union[int, torch.device]]): An ``int`` or
+            ``torch.device`` giving the CUDA device on which FSDP
+            initialization takes place, including the module initialization
+            if needed and the parameter sharding. This should be specified to
+            improve initialization speed if ``module`` is on CPU. If the
+            default CUDA device was set (e.g. via ``torch.cuda.set_device``),
+            then the user may pass ``torch.cuda.current_device`` to this.
+            (Default: ``None``)
+        sync_module_states (bool): If ``True``, then each FSDP module will
+            broadcast module parameters and buffers from rank 0 to ensure that
+            they are replicated across ranks (adding communication overhead to
+            this constructor). This can help load ``state_dict`` checkpoints
+            via ``load_state_dict`` in a memory efficient way. See
+            :class:`FullStateDictConfig` for an example of this. (Default:
+            ``False``)
+        forward_prefetch (bool): If ``True``, then FSDP *explicitly* prefetches
+            the next forward-pass all-gather before the current forward
+            computation. This is only useful for CPU-bound workloads, in which
+            case issuing the next all-gather earlier may improve overlap. This
+            should only be used for static-graph models since the prefetching
+            follows the first iteration's execution order. (Default: ``False``)
+        limit_all_gathers (bool): If ``True``, then FSDP explicitly
+            synchronizes the CPU thread to ensure GPU memory usage from only
+            *two* consecutive FSDP instances (the current instance running
+            computation and the next instance whose all-gather is prefetched).
+            If ``False``, then FSDP allows the CPU thread to issue all-gathers
+            without any extra synchronization. (Default: ``True``) We often
+            refer to this feature as the "rate limiter". This flag should only
+            be set to ``False`` for specific CPU-bound workloads with low
+            memory pressure in which case the CPU thread can aggressively issue
+            all kernels without concern for the GPU memory usage.
+        use_orig_params (bool): Setting this to ``True`` has FSDP use
+            ``module`` 's original parameters. FSDP exposes those original
+            parameters to the user via :meth:`nn.Module.named_parameters`
+            instead of FSDP's internal :class:`FlatParameter` s. This means
+            that the optimizer step runs on the original parameters, enabling
+            per-original-parameter hyperparameters. FSDP preserves the original
+            parameter variables and manipulates their data between unsharded
+            and sharded forms, where they are always views into the underlying
+            unsharded or sharded :class:`FlatParameter`, respectively. With the
+            current algorithm, the sharded form is always 1D, losing the
+            original tensor structure. An original parameter may have all,
+            some, or none of its data present for a given rank. In the none
+            case, its data will be like a size-0 empty tensor. Users should not
+            author programs relying on what data is present for a given
+            original parameter in its sharded form. ``True`` is required to
+            use ``torch.compile()``. Setting this to ``False`` exposes FSDP's
+            internal :class:`FlatParameter` s to the user via
+            :meth:`nn.Module.named_parameters`. (Default: ``False``)
+        ignored_states (Optional[Iterable[torch.nn.Parameter]], Optional[Iterable[torch.nn.Module]]):
+            Ignored parameters or modules that will not be managed by this FSDP
+            instance, meaning that the parameters are not sharded and their
+            gradients are not reduced across ranks. This argument unifies with
+            the existing ``ignored_modules`` argument, and we may deprecate
+            ``ignored_modules`` soon. For backward compatibility, we keep both
+            ``ignored_states`` and `ignored_modules``, but FSDP only allows one
+            of them to be specified as not ``None``.
+        device_mesh (Optional[DeviceMesh]): DeviceMesh can be used as an alternative to
+            process_group. When device_mesh is passed, FSDP will use the underlying process
+            groups for all-gather and reduce-scatter collective communications. Therefore,
+            these two args need to be mutually exclusive. For hybrid sharding strategies such as
+            ``ShardingStrategy.HYBRID_SHARD``, users can pass in a 2D DeviceMesh instead
+            of a tuple of process groups. For 2D FSDP + TP, users are required to pass in
+            device_mesh instead of process_group. For more DeviceMesh info, please visit:
+            https://pytorch.org/tutorials/recipes/distributed_device_mesh.html
+    """
+
+    def __init__(
+        self,
+        module: nn.Module,
+        process_group: ProcessGroupType = None,
+        sharding_strategy: Optional[ShardingStrategy] = None,
+        cpu_offload: Optional[CPUOffload] = None,
+        auto_wrap_policy: Optional[
+            Union[Callable, ModuleWrapPolicy, CustomPolicy]
+        ] = None,
+        backward_prefetch: Optional[BackwardPrefetch] = BackwardPrefetch.BACKWARD_PRE,
+        mixed_precision: Optional[MixedPrecision] = None,
+        ignored_modules: Optional[Iterable[torch.nn.Module]] = None,
+        param_init_fn: Optional[Callable[[nn.Module], None]] = None,
+        device_id: Optional[Union[int, torch.device]] = None,
+        sync_module_states: bool = False,
+        forward_prefetch: bool = False,
+        limit_all_gathers: bool = True,
+        use_orig_params: bool = False,
+        ignored_states: Union[
+            Optional[Iterable[torch.nn.Parameter]], Optional[Iterable[torch.nn.Module]]
+        ] = None,
+        device_mesh: Optional[DeviceMesh] = None,
+    ):
+        torch._C._log_api_usage_once("torch.distributed.fsdp")
+        super().__init__()
+        if isinstance(module, (nn.ModuleList, nn.ModuleDict)):
+            warnings.warn(
+                "FSDP will not all-gather parameters for containers that do "
+                f"not implement forward: {module}",
+                stacklevel=2,
+            )
+        _init_ignored_module_states(self, module, ignored_modules, ignored_states)
+        _init_device_handle(self, module, self._ignored_params, device_id)
+
+        # Add module annotations for Dynamo support (see function for details)
+        _annotate_modules_for_dynamo(module, self._ignored_modules, use_orig_params)
+
+        # Initializes self.process_group, along with rank and world size. This will
+        # also set another attribute, _inter_node_pg, to control the process group
+        # over which sharding occurs, if sharding_strategy is {HYBRID_SHARD, _HYBRID_SHARD_ZERO2}.
+        # Note that this is done before auto_wrapping, so that child FSDP modules simply pick up
+        # the same process group state as the root FSDP module.
+        self._device_mesh = device_mesh
+        _init_process_group_state(
+            self,
+            process_group,
+            sharding_strategy,
+            auto_wrap_policy,
+            device_mesh,
+        )
+        if auto_wrap_policy is not None:
+            root_kwargs = {
+                "process_group": process_group,
+                "sharding_strategy": sharding_strategy,
+                "cpu_offload": cpu_offload,
+                "backward_prefetch": backward_prefetch,
+                "mixed_precision": mixed_precision,
+                "param_init_fn": param_init_fn,
+                "device_id": device_id,
+                "sync_module_states": sync_module_states,
+                "forward_prefetch": forward_prefetch,
+                "limit_all_gathers": limit_all_gathers,
+                "use_orig_params": use_orig_params,
+                "ignored_states": self._ignored_params,
+                "device_mesh": device_mesh,
+            }
+            if sharding_strategy in HYBRID_SHARDING_STRATEGIES and device_mesh is None:
+                # Share root process groups with children to maintain
+                # the invariant that all FSDP modules will have the same
+                # process groups.
+                root_kwargs["process_group"] = (self.process_group, self._inter_node_pg)
+
+            _auto_wrap(
+                module,
+                auto_wrap_policy,
+                self._ignored_modules,
+                self._ignored_params,
+                root_kwargs,
+                FullyShardedDataParallel,
+            )
+
+        backward_prefetch_limit = 1
+        forward_prefetch_limit = 1
+        _init_core_state(
+            self,
+            sharding_strategy,
+            mixed_precision,
+            cpu_offload,
+            limit_all_gathers,
+            use_orig_params,
+            backward_prefetch_limit,
+            forward_prefetch_limit,
+        )
+        _init_runtime_state(self)
+        _init_prefetching_state(self, backward_prefetch, forward_prefetch)
+        _init_buffer_state(self, module)
+        # extension needs to be set before `_init_param_handle_from_module()`
+        _init_extension(self, device_mesh)
+        _init_param_handle_from_module(
+            self,
+            module,
+            device_id,
+            param_init_fn,
+            sync_module_states,
+        )
+        self._fsdp_wrapped_module = module
+        if not use_orig_params:
+            _check_orig_params_flattened(self, self._ignored_params)
+            _register_flat_param(self, self)
+
+        # `_state_dict_type` controls the `state_dict()` behavior, which is
+        # implemented using post-save and pre-load hooks
+        _init_state_dict_state(self)
+        _register_all_state_dict_hooks(self)
+        self._zero_scalar = None
+
+    @property
+    def module(self) -> nn.Module:
+        """Return the wrapped module."""
+        # FSDP's `.module` must refer to the innermost wrapped module when
+        # composing with other module wrappers in order for state dict to work
+        if isinstance(self._fsdp_wrapped_module, ActivationWrapper):
+            return getattr(self._fsdp_wrapped_module, _CHECKPOINT_WRAPPED_MODULE)
+        return self._fsdp_wrapped_module
+
+    @property
+    def _has_params(self) -> bool:
+        """Returns whether this FSDP instance manages any parameters."""
+        return hasattr(self, "_handle") and self._handle is not None
+
+    @property
+    def _flat_param(self) -> Optional[FlatParameter]:
+        return self._handle.flat_param if self._handle else None
+
+    def __getattr__(self, name: str) -> Any:
+        """Forward missing attributes to the wrapped module."""
+        try:
+            return super().__getattr__(name)  # defer to nn.Module's logic
+        except AttributeError:
+            return getattr(self._fsdp_wrapped_module, name)
+
+    def __getitem__(self, key: int) -> Any:
+        """Forward indexing calls in case the module is an ``nn.Sequential``."""
+        if hasattr(self, FSDP_WRAPPED_MODULE):
+            return self._fsdp_wrapped_module.__getitem__(key)  # type: ignore[operator]
+        return super().__getitem__(key)
+
+    def check_is_root(self) -> bool:
+        """Check if this instance is a root FSDP module."""
+        return _is_fsdp_root(self, self)
+
+    @staticmethod
+    def fsdp_modules(
+        module: nn.Module,
+        root_only: bool = False,
+    ) -> list["FullyShardedDataParallel"]:
+        """Return all nested FSDP instances.
+
+        This possibly includes ``module`` itself and only includes FSDP root modules if ``root_only=True``.
+
+        Args:
+            module (torch.nn.Module): Root module, which may or may not be an
+                ``FSDP`` module.
+            root_only (bool): Whether to return only FSDP root modules.
+                (Default: ``False``)
+
+        Returns:
+            List[FullyShardedDataParallel]: FSDP modules that are nested in
+            the input ``module``.
+        """
+        if root_only:
+            return _get_fsdp_root_states(module)
+        return traversal_utils._get_fsdp_states(module)
+
+    def apply(self, fn: Callable[[nn.Module], None]) -> "FullyShardedDataParallel":
+        r"""Apply ``fn`` recursively to every submodule (as returned by ``.children()``) as well as self.
+
+        Typical use includes initializing the parameters of a model (see also :ref:`nn-init-doc`).
+
+        Compared to ``torch.nn.Module.apply``, this version additionally gathers
+        the full parameters before applying ``fn``. It should not be called from
+        within another ``summon_full_params`` context.
+
+        Args:
+            fn (:class:`Module` -> None): function to be applied to each submodule
+
+        Returns:
+            Module: self
+        """
+        uninitialized = self._is_root is None
+        self._assert_state(TrainingState.IDLE)
+        # Use `_unshard_params_for_summon()` with `recurse=False` instead of
+        # `_unshard_fsdp_state_params()` directly to perform lazy
+        # initialization, which is needed to initialize `FlatParameter`
+        # parameter attributes as required by the unshard logic
+        with _unshard_params_for_summon(
+            self,
+            self,
+            writeback=True,
+            rank0_only=False,
+            offload_to_cpu=False,
+            with_grads=False,
+        ):
+            ret = super().apply(fn)
+
+        # Reset lazy init called in `_unshard_params_for_summon()` since
+        # `apply()` may have been called on FSDP instance that is not truly a
+        # root, in which case it will be incorrectly marked as one.
+        if uninitialized and self._is_root:
+            for module in traversal_utils._get_fsdp_states(self):
+                module._reset_lazy_init()
+
+        return ret
+
+    def _mixed_precision_enabled_for_buffers(self) -> bool:
+        """Return whether the user explicitly enabled buffer mixed precision.
+
+        NOTE: Unlike parameters and gradient reduction, buffer mixed precision
+        is applied at the FSDP instance level, not the ``FlatParameter`` level,
+        which may be different for the composable code path.
+        """
+        return self.mixed_precision.buffer_dtype is not None
+
+    def _low_precision_hook_enabled(self) -> bool:
+        """Whether a low precision hook is registered or not."""
+        return self._comm_hook is not None and self._comm_hook in LOW_PRECISION_HOOKS
+
+    def _reset_lazy_init(self) -> None:
+        """Reset instance so :func:`_lazy_init` will run on the next forward."""
+        self._is_root: Optional[bool] = None
+
+    @staticmethod
+    def set_state_dict_type(
+        module: nn.Module,
+        state_dict_type: StateDictType,
+        state_dict_config: Optional[StateDictConfig] = None,
+        optim_state_dict_config: Optional[OptimStateDictConfig] = None,
+    ) -> StateDictSettings:
+        """Set the ``state_dict_type`` of all the descendant FSDP modules of the target module.
+
+        Also takes (optional) configuration for the model's and optimizer's state dict.
+        The target module does not have to be a FSDP module. If the target
+        module is a FSDP module, its ``state_dict_type`` will also be changed.
+
+        .. note:: This API should be called for only the top-level (root)
+            module.
+
+        .. note:: This API enables users to transparently use the conventional
+            ``state_dict`` API to take model checkpoints in cases where the
+            root FSDP module is wrapped by another ``nn.Module``. For example,
+            the following will ensure ``state_dict`` is called on all non-FSDP
+            instances, while dispatching into `sharded_state_dict` implementation
+            for FSDP:
+
+        Example::
+
+            >>> # xdoctest: +SKIP("undefined variables")
+            >>> model = DDP(FSDP(...))
+            >>> FSDP.set_state_dict_type(
+            >>>     model,
+            >>>     StateDictType.SHARDED_STATE_DICT,
+            >>>     state_dict_config = ShardedStateDictConfig(offload_to_cpu=True),
+            >>>     optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True),
+            >>> )
+            >>> param_state_dict = model.state_dict()
+            >>> optim_state_dict = FSDP.optim_state_dict(model, optim)
+
+        Args:
+            module (torch.nn.Module): Root module.
+            state_dict_type (StateDictType): the desired ``state_dict_type`` to set.
+            state_dict_config (Optional[StateDictConfig]): the configuration for the
+                target ``state_dict_type``.
+            optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration
+                for the optimizer state dict.
+
+        Returns:
+            A StateDictSettings that include the previous state_dict type and
+            configuration for the module.
+        """
+        warnings.warn(
+            "FSDP.state_dict_type() and FSDP.set_state_dict_type() are being "
+            "deprecated. Please use APIs, get_state_dict() and set_state_dict(), "
+            "which can support different parallelisms, FSDP1, FSDP2, DDP. "
+            "API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html"
+            "#torch.distributed.checkpoint.state_dict.get_state_dict ."
+            "Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .",
+            FutureWarning,
+            stacklevel=2,
+        )
+        _state_dict_type_to_config = {
+            StateDictType.FULL_STATE_DICT: FullStateDictConfig,
+            StateDictType.LOCAL_STATE_DICT: LocalStateDictConfig,
+            StateDictType.SHARDED_STATE_DICT: ShardedStateDictConfig,
+        }
+        _optim_state_dict_type_to_config = {
+            StateDictType.FULL_STATE_DICT: FullOptimStateDictConfig,
+            StateDictType.LOCAL_STATE_DICT: LocalOptimStateDictConfig,
+            StateDictType.SHARDED_STATE_DICT: ShardedOptimStateDictConfig,
+        }
+
+        # Use the default config if a state_dict config is not set.
+        state_dict_config_type = _state_dict_type_to_config[state_dict_type]
+        optim_state_dict_config_type = _optim_state_dict_type_to_config[state_dict_type]
+        if state_dict_config is None:
+            state_dict_config = state_dict_config_type()
+        if optim_state_dict_config is None:
+            optim_state_dict_config = optim_state_dict_config_type()
+        if state_dict_config_type is not type(state_dict_config):
+            raise RuntimeError(
+                f"Expected state_dict_config of type {state_dict_config_type} "
+                f"but got {type(state_dict_config)}"
+            )
+        if optim_state_dict_config_type is not type(optim_state_dict_config):
+            raise RuntimeError(
+                f"Expected optim_state_dict_config of type {optim_state_dict_config_type} "
+                f"but got {type(optim_state_dict_config)}"
+            )
+
+        # Set the state_dict type and configurations.
+        prev_state_dict_type = None
+        prev_state_dict_config = None
+        prev_optim_state_dict_config = None
+        for submodule in traversal_utils._get_fsdp_states(module):
+            if prev_state_dict_type is None:
+                prev_state_dict_type = submodule._state_dict_type
+            else:
+                if prev_state_dict_type != submodule._state_dict_type:
+                    raise AssertionError(
+                        "All FSDP modules should have the same state_dict_type."
+                    )
+            if prev_state_dict_config is None:
+                prev_state_dict_config = submodule._state_dict_config
+            else:
+                if not isinstance(
+                    submodule._state_dict_config, type(prev_state_dict_config)
+                ):
+                    raise AssertionError(
+                        "All FSDP modules must have the same type of state_dict_config."
+                    )
+            if prev_optim_state_dict_config is None:
+                prev_optim_state_dict_config = submodule._optim_state_dict_config
+            else:
+                if not isinstance(
+                    submodule._optim_state_dict_config,
+                    type(prev_optim_state_dict_config),
+                ):
+                    raise AssertionError(
+                        "All FSDP modules must have the same type of optim_state_dict_config."
+                    )
+
+            submodule._state_dict_type = state_dict_type
+            submodule._state_dict_config = state_dict_config
+            submodule._optim_state_dict_config = optim_state_dict_config
+
+        return StateDictSettings(
+            prev_state_dict_type, prev_state_dict_config, prev_optim_state_dict_config
+        )
+
+    @staticmethod
+    def get_state_dict_type(module: nn.Module) -> StateDictSettings:
+        """Get the state_dict_type and the corresponding configurations for the FSDP modules rooted at ``module``.
+
+        The target module does not have to be an FSDP module.
+
+        Returns:
+            A ``StateDictSettings`` containing the state_dict_type and
+            state_dict / optim_state_dict configs that are currently set.
+
+        Raises:
+            ``AssertionError`` if the ``StateDictSettings`` for different
+            FSDP submodules differ.
+        """
+        state_dict_settings: Optional[StateDictSettings] = None
+        for submodule in FullyShardedDataParallel.fsdp_modules(module):
+            if state_dict_settings is None:
+                state_dict_settings = StateDictSettings(
+                    state_dict_type=submodule._state_dict_type,
+                    state_dict_config=submodule._state_dict_config,
+                    optim_state_dict_config=submodule._optim_state_dict_config,
+                )
+                _set_optim_use_dtensor(submodule, state_dict_settings)
+            else:
+                submodule_settings = StateDictSettings(
+                    submodule._state_dict_type,
+                    submodule._state_dict_config,
+                    submodule._optim_state_dict_config,
+                )
+                if state_dict_settings != submodule_settings:
+                    raise AssertionError(
+                        "All FSDP modules must have the same state dict settings."
+                        f"Got {submodule_settings} and {state_dict_settings}."
+                    )
+                _set_optim_use_dtensor(submodule, submodule_settings)
+        return state_dict_settings
+
+    @staticmethod
+    @contextlib.contextmanager
+    def state_dict_type(
+        module: nn.Module,
+        state_dict_type: StateDictType,
+        state_dict_config: Optional[StateDictConfig] = None,
+        optim_state_dict_config: Optional[OptimStateDictConfig] = None,
+    ) -> Generator:
+        """Set the ``state_dict_type`` of all the descendant FSDP modules of the target module.
+
+        This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of
+        :meth:`set_state_dict_type` for the detail.
+
+        Example::
+
+            >>> # xdoctest: +SKIP("undefined variables")
+            >>> model = DDP(FSDP(...))
+            >>> with FSDP.state_dict_type(
+            >>>     model,
+            >>>     StateDictType.SHARDED_STATE_DICT,
+            >>> ):
+            >>>     checkpoint = model.state_dict()
+
+        Args:
+            module (torch.nn.Module): Root module.
+            state_dict_type (StateDictType): the desired ``state_dict_type`` to set.
+            state_dict_config (Optional[StateDictConfig]): the model ``state_dict``
+                configuration for the target ``state_dict_type``.
+            optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer
+               ``state_dict`` configuration for the target ``state_dict_type``.
+        """
+        prev_state_dict_settings = FullyShardedDataParallel.set_state_dict_type(
+            module,
+            state_dict_type,
+            state_dict_config,
+            optim_state_dict_config,
+        )
+        yield
+        FullyShardedDataParallel.set_state_dict_type(
+            module,
+            prev_state_dict_settings.state_dict_type,
+            prev_state_dict_settings.state_dict_config,
+            prev_state_dict_settings.optim_state_dict_config,
+        )
+
+    def forward(self, *args: Any, **kwargs: Any) -> Any:
+        """Run the forward pass for the wrapped module, inserting FSDP-specific pre- and post-forward sharding logic."""
+        handle = self._handle
+        with torch.autograd.profiler.record_function(
+            "FullyShardedDataParallel.forward"
+        ):
+            args, kwargs = _root_pre_forward(self, self, args, kwargs)
+            unused = None
+            args, kwargs = _pre_forward(
+                self,
+                handle,
+                _pre_forward_unshard,
+                self._fsdp_wrapped_module,
+                args,
+                kwargs,
+            )
+            if handle:
+                _p_assert(
+                    handle.flat_param.device == self.compute_device,
+                    "Expected `FlatParameter` to be on the compute device "
+                    f"{self.compute_device} but got {handle.flat_param.device}",
+                )
+            output = self._fsdp_wrapped_module(*args, **kwargs)
+            return _post_forward(
+                self, handle, _post_forward_reshard, self, unused, output
+            )
+
+    @staticmethod
+    @contextlib.contextmanager
+    def summon_full_params(
+        module: nn.Module,
+        recurse: bool = True,
+        writeback: bool = True,
+        rank0_only: bool = False,
+        offload_to_cpu: bool = False,
+        with_grads: bool = False,
+    ) -> Generator:
+        r"""Expose full params for FSDP instances with this context manager.
+
+        Can be useful *after* forward/backward for a model to get
+        the params for additional processing or checking. It can take a non-FSDP
+        module and will summon full params for all contained FSDP modules as
+        well as their children, depending on the ``recurse`` argument.
+
+        .. note:: This can be used on inner FSDPs.
+        .. note:: This can *not* be used within a forward or backward pass. Nor
+            can forward and backward be started from within this context.
+        .. note:: Parameters will revert to their local shards after the context
+            manager exits, storage behavior is the same as forward.
+        .. note:: The full parameters can be modified, but only the portion
+            corresponding to the local param shard will persist after the
+            context manager exits (unless ``writeback=False``, in which case
+            changes will be discarded). In the case where FSDP does not shard
+            the parameters, currently only when ``world_size == 1``, or ``NO_SHARD``
+            config, the modification is persisted regardless of ``writeback``.
+        .. note:: This method works on modules which are not FSDP themselves but
+            may contain multiple independent FSDP units. In that case, the given
+            arguments will apply to all contained FSDP units.
+
+        .. warning:: Note that ``rank0_only=True`` in conjunction with
+            ``writeback=True`` is not currently supported and will raise an
+            error. This is because model parameter shapes would be different
+            across ranks within the context, and writing to them can lead to
+            inconsistency across ranks when the context is exited.
+
+        .. warning:: Note that ``offload_to_cpu`` and ``rank0_only=False`` will
+            result in full parameters being redundantly copied to CPU memory for
+            GPUs that reside on the same machine, which may incur the risk of
+            CPU OOM. It is recommended to use ``offload_to_cpu`` with
+            ``rank0_only=True``.
+
+        Args:
+            recurse (bool, Optional): recursively summon all params for nested
+                FSDP instances (default: True).
+            writeback (bool, Optional): if ``False``, modifications to params are
+                discarded after the context manager exits;
+                disabling this can be slightly more efficient (default: True)
+            rank0_only (bool, Optional): if ``True``, full parameters are
+                materialized on only global rank 0. This means that within the
+                context, only rank 0 will have full parameters and the other
+                ranks will have sharded parameters. Note that setting
+                ``rank0_only=True`` with ``writeback=True`` is not supported,
+                as model parameter shapes will be different across ranks
+                within the context, and writing to them can lead to
+                inconsistency across ranks when the context is exited.
+            offload_to_cpu (bool, Optional): If ``True``, full parameters are
+                offloaded to CPU. Note that this offloading currently only
+                occurs if the parameter is sharded (which is only not the case
+                for world_size = 1 or ``NO_SHARD`` config). It is recommended
+                to use ``offload_to_cpu`` with ``rank0_only=True`` to avoid
+                redundant copies of model parameters being offloaded to the same CPU memory.
+            with_grads (bool, Optional): If ``True``, gradients are also
+                unsharded with the parameters. Currently, this is only
+                supported when passing ``use_orig_params=True`` to the FSDP
+                constructor and ``offload_to_cpu=False`` to this method.
+                (Default: ``False``)
+        """
+        with _unshard_params(
+            module, recurse, writeback, rank0_only, offload_to_cpu, with_grads
+        ):
+            yield
+
+    @contextlib.contextmanager
+    def _deregister_orig_params_ctx(self):
+        """Deregister the original parameters and expose the :class:`FlatParameter`.
+
+        If a :class:`FlatParameter` is sharded, then
+        this refreshes the sharded views before exiting. This method should
+        only be called when using the original parameters.
+        """
+        _p_assert(
+            self._use_orig_params,
+            "`_deregister_orig_params_ctx()` should only be called when "
+            "`_use_orig_params=True`",
+        )
+        for fsdp_module in traversal_utils._get_fsdp_states(self):
+            _deregister_orig_params(fsdp_module, fsdp_module)
+        try:
+            yield
+        finally:
+            for fsdp_module in traversal_utils._get_fsdp_states(self):
+                _register_orig_params(fsdp_module, fsdp_module)
+
+    def _apply(self, *args, **kwargs):
+        """Deregister the original parameters and expose the :class:`FlatParameter` s before calling ``_apply()``."""
+        # When using the original parameters: Since (1) the `FlatParameter`s
+        # own the storage and (2) `_apply()` is the subroutine underlying the
+        # most common storage-changing ops like `to()` and `cuda()`, we
+        # override `_apply()` to have the storage change directly performed on
+        # the `FlatParameter`s instead of applying to the original parameters
+        # and then writing back to the `FlatParameter`s.
+        context = (
+            self._deregister_orig_params_ctx()
+            if self._use_orig_params
+            else contextlib.nullcontext()
+        )
+        with context:
+            return super()._apply(*args, **kwargs)
+
+    def named_buffers(
+        self,
+        *args,
+        **kwargs,
+    ) -> Iterator[tuple[str, torch.Tensor]]:
+        """Return an iterator over module buffers, yielding both the name of the buffer and the buffer itself.
+
+        Intercepts buffer names and removes all occurrences of the FSDP-specific flattened buffer prefix
+        when inside the :meth:`summon_full_params` context manager.
+        """
+        should_clean_name = self.training_state == TrainingState.SUMMON_FULL_PARAMS
+        for buffer_name, buffer in super().named_buffers(*args, **kwargs):
+            if should_clean_name:
+                # Remove any instances of the FSDP-specific prefix; there can
+                # be multiple in the case of nested FSDP modules
+                buffer_name = buffer_name.replace(FSDP_PREFIX, "")
+            yield (buffer_name, buffer)
+
+    def named_parameters(
+        self,
+        *args,
+        **kwargs,
+    ) -> Iterator[tuple[str, torch.nn.Parameter]]:
+        """Return an iterator over module parameters, yielding both the name of the parameter and the parameter itself.
+
+        Intercepts parameter names and removes all occurrences of the FSDP-specific flattened parameter prefix
+        when inside the :meth:`summon_full_params` context manager.
+        """
+        should_clean_name = self.training_state == TrainingState.SUMMON_FULL_PARAMS
+        for param_name, param in super().named_parameters(*args, **kwargs):
+            if should_clean_name:
+                # Remove any instances of the FSDP-specific prefix; there can
+                # be multiple in the case of nested FSDP modules
+                param_name = param_name.replace(FSDP_PREFIX, "")
+            yield (param_name, param)
+
+    def _assert_state(self, state: Union[TrainingState, list[TrainingState]]) -> None:
+        """Assert we are in the given state."""
+        # Since assert can be turned off and this error checking
+        # is really important, we use explicit error checking
+        # and raise a ValueError if needed.
+        if isinstance(state, TrainingState):
+            state = [state]
+        if self.training_state not in state:
+            msg = (
+                f"expected to be in states {state} but current state "
+                f"is {self.training_state}"
+            )
+            # In case we are failing in the context of autograd hook, asserting
+            # may not generate useful msg. So, let's print it to be sure.
+            if self.rank == 0:
+                print(f"Asserting FSDP instance is: {self}")
+                print(f"ERROR: {msg}")
+                traceback.print_stack()
+            raise ValueError(msg)
+
+    @contextmanager
+    def no_sync(self) -> Generator:
+        """Disable gradient synchronizations across FSDP instances.
+
+        Within this context, gradients will be accumulated in module
+        variables, which will later be synchronized in the first
+        forward-backward pass after exiting the context. This should only be
+        used on the root FSDP instance and will recursively apply to all
+        children FSDP instances.
+
+        .. note:: This likely results in higher memory usage because FSDP will
+            accumulate the full model gradients (instead of gradient shards)
+            until the eventual sync.
+
+        .. note:: When used with CPU offloading, the gradients will not be
+            offloaded to CPU when inside the context manager. Instead, they
+            will only be offloaded right after the eventual sync.
+        """
+        _lazy_init(self, self)
+        if not self._is_root:
+            raise RuntimeError(
+                "`no_sync()` on inner FSDP instances is not supported. Please call `no_sync()` on root FSDP module."
+            )
+        self._assert_state(TrainingState.IDLE)
+        old_flags = []
+        for m in self.modules():
+            if isinstance(m, FullyShardedDataParallel):
+                old_flags.append((m, m._sync_gradients))
+                m._sync_gradients = False
+        try:
+            yield
+        finally:
+            for m, old_flag in old_flags:
+                if m._sync_gradients:
+                    raise AssertionError(
+                        "`_sync_gradients` was incorrectly set to "
+                        "`True` while in the `no_sync()` context manager"
+                    )
+                m._sync_gradients = old_flag
+
+    @torch.no_grad()
+    def clip_grad_norm_(
+        self, max_norm: Union[float, int], norm_type: Union[float, int] = 2.0
+    ) -> torch.Tensor:
+        """Clip the gradient norm of all parameters.
+
+        The norm is computed over all parameters' gradients as viewed as a single vector, and the
+        gradients are modified in-place.
+
+        Args:
+            max_norm (float or int): max norm of the gradients
+            norm_type (float or int): type of the used p-norm. Can be ``'inf'``
+                for infinity norm.
+
+        Returns:
+            Total norm of the parameters (viewed as a single vector).
+
+        If every FSDP instance uses ``NO_SHARD``, meaning that no
+        gradients are sharded across ranks, then you may directly use
+        :func:`torch.nn.utils.clip_grad_norm_`.
+
+        If at least some FSDP instance uses a sharded strategy (i.e.
+        one other than ``NO_SHARD``), then you should use this method
+        instead of :func:`torch.nn.utils.clip_grad_norm_` since this method
+        handles the fact that gradients are sharded across ranks.
+
+        The total norm returned will have the "largest" dtype across
+        all parameters/gradients as defined by PyTorch's type promotion
+        semantics. For example, if *all* parameters/gradients use a low
+        precision dtype, then the returned norm's dtype will be that low
+        precision dtype, but if there exists at least one parameter/
+        gradient using FP32, then the returned norm's dtype will be FP32.
+
+        .. warning:: This needs to be called on all ranks since it uses
+            collective communications.
+        """
+        _lazy_init(self, self)
+        if not self._is_root:
+            raise RuntimeError(
+                "`clip_grad_norm_()` should only be called on the root FSDP instance"
+            )
+        if self._zero_scalar is None:
+            self._zero_scalar = torch.tensor(0.0, device=self.compute_device)
+        self._assert_state(TrainingState.IDLE)
+        # If every FSDP instance uses `NO_SHARD`, then we can directly use
+        # the normal `nn.utils` one targeting local gradients
+        all_no_shard = all(
+            not handle.uses_sharded_strategy for handle in self._all_handles
+        )
+        if all_no_shard:
+            return torch.nn.utils.clip_grad_norm_(
+                self.parameters(), max_norm, norm_type
+            )
+        # Otherwise, there exists some FSDP instance using a sharded strategy,
+        # where sharded and non-sharded parameters must be handled separately
+        max_norm = float(max_norm)
+        norm_type = float(norm_type)
+        sharded_params_set = set()
+        nonsharded_params_set = set()  # `NO_SHARD` or not FSDP-managed
+        # Make sure to compute the local norm using lists for deterministic
+        # iteration order and hence deterministic total norm computation
+        sharded_params = []
+        nonsharded_params = []
+        grads: list[torch.Tensor] = []
+        for handle in self._all_handles:
+            if handle.uses_sharded_strategy:
+                target_set = sharded_params_set
+                target_list = sharded_params
+            else:
+                target_set = nonsharded_params_set
+                target_list = nonsharded_params
+            if handle._use_orig_params:
+                for param in handle.flat_param._params:
+                    if param not in target_set:
+                        target_set.add(param)
+                        target_list.append(param)
+                        if param.grad is not None:
+                            grads.append(param.grad)
+            else:
+                if handle.flat_param not in target_set:
+                    target_set.add(handle.flat_param)
+                    target_list.append(handle.flat_param)
+                    if handle.flat_param.grad is not None:
+                        grads.append(handle.flat_param.grad)
+        for param in self.parameters():
+            not_fsdp_managed = (
+                param not in sharded_params_set and param not in nonsharded_params_set
+            )
+            if not_fsdp_managed:
+                nonsharded_params_set.add(param)
+                nonsharded_params.append(param)
+                if param.grad is not None:
+                    grads.append(param.grad)
+        # Compute local norms (forced to be in FP32)
+        local_sharded_norm = _get_grad_norm(
+            sharded_params, norm_type, self._zero_scalar, self.compute_device
+        )
+        local_nonsharded_norm = (
+            _get_grad_norm(
+                nonsharded_params, norm_type, self._zero_scalar, self.compute_device
+            )
+            if nonsharded_params
+            else None
+        )
+        # Reconstruct the total gradient norm depending on the norm type
+        if norm_type == math.inf:
+            total_norm = (
+                torch.maximum(local_sharded_norm, local_nonsharded_norm)
+                if local_nonsharded_norm is not None
+                else local_sharded_norm
+            )
+            dist.all_reduce(
+                total_norm, op=torch.distributed.ReduceOp.MAX, group=self.process_group
+            )
+        else:
+            total_norm = local_sharded_norm**norm_type
+            dist.all_reduce(total_norm, group=self.process_group)
+            # All-reducing the local non-sharded norm would count it an extra
+            # world-size-many times
+            if local_nonsharded_norm is not None:
+                total_norm += local_nonsharded_norm**norm_type
+            total_norm = total_norm ** (1.0 / norm_type)
+        if self.cpu_offload.offload_params:
+            total_norm = total_norm.cpu()
+
+        clip_coef = max_norm / (total_norm + 1e-6)
+        # Multiplying by the clamped coefficient is meaningless when it is
+        # equal to 1, but it avoids the host-device sync that would result from
+        # `if clip_coef < 1`
+        clip_coef_clamped = torch.clamp(clip_coef, max=1.0)
+        for grad in grads:
+            grad.mul_(clip_coef_clamped.to(grad.device, grad.dtype))
+        # Use the "largest" dtype by type promotion semantics to use the same
+        # dtype as if we did not force local norm computation to be in FP32
+        if len(grads) == 0:
+            # If this rank has no gradients, then we must default to FP32
+            # unless we use additional communication, which we prefer to avoid
+            # since `clip_grad_norm_()` is called in the training loop
+            warnings.warn(
+                f"Called FSDP.clip_grad_norm_() on rank {self.rank} with no "
+                "gradients -- returning the total norm in the default dtype "
+                f"{total_norm.dtype}",
+                stacklevel=2,
+            )  # warn since this is generally unexpected
+            return total_norm
+        total_norm_dtype = functools.reduce(
+            torch.promote_types,
+            [grad.dtype for grad in grads],
+        )
+        return total_norm.to(total_norm_dtype)
+
+    @staticmethod
+    def _warn_optim_input(optim_input, *, stacklevel: int = 1):
+        if optim_input is not None:
+            warnings.warn(
+                "The `optim_input` argument is deprecated and will be removed after PyTorch 1.13. "
+                "You may remove it from your code without changing its functionality.",
+                FutureWarning,
+                stacklevel=stacklevel + 1,
+            )
+
+    @staticmethod
+    def _is_using_optim_input(optim_input, optim) -> bool:
+        if optim_input is None and optim is None:
+            # Use the default behavior of `optim_input``
+            return True
+        if optim_input is not None:
+            # Use the `optim_input` code path
+            return True
+        # Use the `optim` code path
+        return False
+
+    @staticmethod
+    def _warn_legacy_optim_state_dict(curr: str, new: str, *, stacklevel: int = 1):
+        warnings.warn(
+            f"``FullyShardedDataParallel.{curr}``is being deprecated and is "
+            f"replaced by ``FullyShardedDataParallel.{new}``. "
+            f"``FullyShardedDataParallel.{curr}`` may be removed after PyTorch 2.2.",
+            FutureWarning,
+            stacklevel=stacklevel + 1,
+        )
+
+    @staticmethod
+    def _optim_state_dict_impl(
+        model: torch.nn.Module,
+        optim: torch.optim.Optimizer,
+        optim_state_dict: dict[str, Any],
+        optim_input: Optional[
+            Union[
+                list[dict[str, Any]],
+                Iterable[torch.nn.Parameter],
+            ]
+        ] = None,
+        rank0_only: bool = True,
+        full_state_dict: bool = True,
+        group: Optional[dist.ProcessGroup] = None,
+        cpu_offload: bool = True,
+        *,
+        _stacklevel: int = 1,
+    ) -> dict[str, Any]:
+        """Transform the state-dict of an optimizer corresponding to a sharded model.
+
+        This is the internal API that is used by all the optim_state_dict implementations.
+        Given model, optim, the original optim_state_dict, this API removes the
+        FSDP internal information and internal sharding from the optim_state_dict.
+        """
+        if full_state_dict:
+            FullyShardedDataParallel._warn_optim_input(
+                optim_input, stacklevel=_stacklevel + 1
+            )
+            using_optim_input = FullyShardedDataParallel._is_using_optim_input(
+                optim_input,
+                optim,
+            )
+        else:
+            using_optim_input = False
+            if optim_input is not None or rank0_only:
+                raise AssertionError(
+                    f"Expected optim_input to be None and rank0_only to be False, "
+                    f"got optim_input={optim_input}, rank0_only={rank0_only}"
+                )
+
+        use_orig_params = FullyShardedDataParallel.fsdp_modules(model)[
+            0
+        ]._use_orig_params
+        if not all(
+            use_orig_params == m._use_orig_params
+            for m in FullyShardedDataParallel.fsdp_modules(model)
+        ):
+            raise AssertionError(
+                "Not all FSDP modules have the same _use_orig_params value"
+            )
+
+        return _optim_state_dict(
+            model=model,
+            optim=optim,
+            optim_state_dict=optim_state_dict,
+            optim_input=optim_input,
+            rank0_only=rank0_only,
+            shard_state=not full_state_dict,
+            group=group,
+            using_optim_input=using_optim_input,
+            use_orig_params=use_orig_params,
+            cpu_offload=cpu_offload,
+        )
+
+    @staticmethod
+    def _optim_state_dict_to_load_impl(
+        optim_state_dict: dict[str, Any],
+        model: torch.nn.Module,
+        optim_input: Optional[
+            Union[
+                list[dict[str, Any]],
+                Iterable[torch.nn.Parameter],
+            ]
+        ] = None,
+        optim: Optional[torch.optim.Optimizer] = None,
+        full_state_dict: bool = True,
+        rank0_only: bool = False,
+        is_named_optimizer: bool = False,
+        group: Optional[dist.ProcessGroup] = None,
+    ) -> dict[str, Any]:
+        """
+        Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model.
+
+        This is the internal API that is used by all the load optim_state_dict implementations.
+        Given model, optim, and the saved optim_state_dict, this API adds the FSDP
+        internal information and internal sharding to the optim_state_dict.
+        """
+        if full_state_dict:
+            FullyShardedDataParallel._warn_optim_input(optim_input)
+            using_optim_input = FullyShardedDataParallel._is_using_optim_input(
+                optim_input,
+                optim,
+            )
+        else:
+            using_optim_input = False
+            if optim_input is not None or rank0_only:
+                raise AssertionError(
+                    f"Expected optim_input to be None and rank0_only to be False, "
+                    f"got optim_input={optim_input}, rank0_only={rank0_only}"
+                )
+
+        use_orig_params = FullyShardedDataParallel.fsdp_modules(model)[
+            0
+        ]._use_orig_params
+        if not all(
+            use_orig_params == m._use_orig_params
+            for m in FullyShardedDataParallel.fsdp_modules(model)
+        ):
+            raise AssertionError(
+                "Not all FSDP modules have the same _use_orig_params value"
+            )
+
+        if rank0_only and dist.get_rank(group) > 0:
+            optim_state_dict = {}
+        sharded_osd = _flatten_optim_state_dict(
+            optim_state_dict,
+            model=model,
+            use_orig_params=use_orig_params,
+            optim=(optim if is_named_optimizer else None),
+            rank0_only=rank0_only,
+            group=group,
+        )
+        return _rekey_sharded_optim_state_dict(
+            sharded_osd,
+            model=model,
+            optim=optim,
+            optim_input=optim_input,
+            using_optim_input=using_optim_input,
+            is_named_optimizer=is_named_optimizer,
+        )
+
+    @staticmethod
+    def full_optim_state_dict(
+        model: torch.nn.Module,
+        optim: torch.optim.Optimizer,
+        optim_input: Optional[
+            Union[
+                list[dict[str, Any]],
+                Iterable[torch.nn.Parameter],
+            ]
+        ] = None,
+        rank0_only: bool = True,
+        group: Optional[dist.ProcessGroup] = None,
+    ) -> dict[str, Any]:
+        """Return the full optimizer state-dict.
+
+        Consolidates the full optimizer state on rank 0 and returns it
+        as a :class:`dict` following the convention of
+        :meth:`torch.optim.Optimizer.state_dict`, i.e. with keys ``"state"``
+        and ``"param_groups"``. The flattened parameters in ``FSDP`` modules
+        contained in ``model`` are mapped back to their unflattened parameters.
+
+        This needs to be called on all ranks since it uses
+        collective communications. However, if ``rank0_only=True``, then
+        the state dict is only populated on rank 0, and all other ranks
+        return an empty :class:`dict`.
+
+        Unlike ``torch.optim.Optimizer.state_dict()``, this method
+        uses full parameter names as keys instead of parameter IDs.
+
+        Like in :meth:`torch.optim.Optimizer.state_dict`, the tensors
+        contained in the optimizer state dict are not cloned, so there may
+        be aliasing surprises. For best practices, consider saving the
+        returned optimizer state dict immediately, e.g. using
+        ``torch.save()``.
+
+        Args:
+            model (torch.nn.Module): Root module (which may or may not be a
+                :class:`FullyShardedDataParallel` instance) whose parameters
+                were passed into the optimizer ``optim``.
+            optim (torch.optim.Optimizer): Optimizer for ``model`` 's
+                parameters.
+            optim_input (Optional[Union[List[Dict[str, Any]], Iterable[torch.nn.Parameter]]]):
+                Input passed into the optimizer ``optim`` representing either a
+                :class:`list` of parameter groups or an iterable of parameters;
+                if ``None``, then this method assumes the input was
+                ``model.parameters()``. This argument is deprecated, and there
+                is no need to pass it in anymore. (Default: ``None``)
+            rank0_only (bool): If ``True``, saves the populated :class:`dict`
+                only on rank 0; if ``False``, saves it on all ranks. (Default:
+                ``True``)
+            group (dist.ProcessGroup): Model's process group or ``None`` if using
+                the default process group. (Default: ``None``)
+
+        Returns:
+            Dict[str, Any]: A :class:`dict` containing the optimizer state for
+            ``model`` 's original unflattened parameters and including keys
+            "state" and "param_groups" following the convention of
+            :meth:`torch.optim.Optimizer.state_dict`. If ``rank0_only=True``,
+            then nonzero ranks return an empty :class:`dict`.
+        """
+        FullyShardedDataParallel._warn_legacy_optim_state_dict(
+            "full_optim_state_dict",
+            "optim_state_dict",
+            stacklevel=2,
+        )
+        return FullyShardedDataParallel._optim_state_dict_impl(
+            model=model,
+            optim=optim,
+            optim_state_dict=optim.state_dict(),
+            optim_input=optim_input,
+            rank0_only=rank0_only,
+            group=group,
+            full_state_dict=True,
+            _stacklevel=2,
+        )
+
+    @staticmethod
+    def sharded_optim_state_dict(
+        model: torch.nn.Module,
+        optim: torch.optim.Optimizer,
+        group: Optional[dist.ProcessGroup] = None,
+    ) -> dict[str, Any]:
+        """Return the optimizer state-dict in its sharded form.
+
+        The API is similar to :meth:`full_optim_state_dict` but this API chunks
+        all non-zero-dimension states to :class:`ShardedTensor` to save memory.
+        This API should only be used when the model ``state_dict`` is derived
+        with the context manager ``with state_dict_type(SHARDED_STATE_DICT):``.
+
+        For the detailed usage, refer to :meth:`full_optim_state_dict`.
+
+        .. warning:: The returned state dict contains ``ShardedTensor`` and
+            cannot be directly used by the regular ``optim.load_state_dict``.
+        """
+        FullyShardedDataParallel._warn_legacy_optim_state_dict(
+            "sharded_optim_state_dict",
+            "optim_state_dict",
+            stacklevel=2,
+        )
+        return FullyShardedDataParallel._optim_state_dict_impl(
+            model=model,
+            optim=optim,
+            optim_state_dict=optim.state_dict(),
+            optim_input=None,
+            rank0_only=False,
+            full_state_dict=False,
+            group=group,
+            _stacklevel=2,
+        )
+
+    @staticmethod
+    def shard_full_optim_state_dict(
+        full_optim_state_dict: dict[str, Any],
+        model: torch.nn.Module,
+        optim_input: Optional[
+            Union[
+                list[dict[str, Any]],
+                Iterable[torch.nn.Parameter],
+            ]
+        ] = None,
+        optim: Optional[torch.optim.Optimizer] = None,
+    ) -> dict[str, Any]:
+        """Shard a full optimizer state-dict.
+
+        Remaps the state in ``full_optim_state_dict`` to flattened parameters instead of unflattened
+        parameters and restricts to only this rank's part of the optimizer state.
+        The first argument should be the return value of :meth:`full_optim_state_dict`.
+
+        Example::
+
+            >>> # xdoctest: +SKIP("undefined variables")
+            >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+            >>> model, optim = ...
+            >>> full_osd = FSDP.full_optim_state_dict(model, optim)
+            >>> torch.save(full_osd, PATH)
+            >>> # Define new model with possibly different world size
+            >>> new_model, new_optim = ...
+            >>> full_osd = torch.load(PATH)
+            >>> sharded_osd = FSDP.shard_full_optim_state_dict(full_osd, new_model)
+            >>> new_optim.load_state_dict(sharded_osd)
+
+        .. note:: Both :meth:`shard_full_optim_state_dict` and
+            :meth:`scatter_full_optim_state_dict` may be used to get the
+            sharded optimizer state dict to load. Assuming that the full
+            optimizer state dict resides in CPU memory, the former requires
+            each rank to have the full dict in CPU memory, where each rank
+            individually shards the dict without any communication, while the
+            latter requires only rank 0 to have the full dict in CPU memory,
+            where rank 0 moves each shard to GPU memory (for NCCL) and
+            communicates it to ranks appropriately. Hence, the former has
+            higher aggregate CPU memory cost, while the latter has higher
+            communication cost.
+
+        Args:
+            full_optim_state_dict (Dict[str, Any]): Optimizer state dict
+                corresponding to the unflattened parameters and holding the
+                full non-sharded optimizer state.
+            model (torch.nn.Module): Root module (which may or may not be a
+                :class:`FullyShardedDataParallel` instance) whose parameters
+                correspond to the optimizer state in ``full_optim_state_dict``.
+            optim_input (Optional[Union[List[Dict[str, Any]], Iterable[torch.nn.Parameter]]]):
+                Input passed into the optimizer representing either a
+                :class:`list` of parameter groups or an iterable of parameters;
+                if ``None``, then this method assumes the input was
+                ``model.parameters()``. This argument is deprecated, and there
+                is no need to pass it in anymore. (Default: ``None``)
+            optim (Optional[torch.optim.Optimizer]): Optimizer that will load
+                the state dict returned by this method. This is the preferred
+                argument to use over ``optim_input``. (Default: ``None``)
+
+        Returns:
+            Dict[str, Any]: The full optimizer state dict now remapped to
+            flattened parameters instead of unflattened parameters and
+            restricted to only include this rank's part of the optimizer state.
+        """
+        FullyShardedDataParallel._warn_legacy_optim_state_dict(
+            "shard_full_optim_state_dict",
+            "optim_state_dict_to_load",
+            stacklevel=2,
+        )
+        return FullyShardedDataParallel._optim_state_dict_to_load_impl(
+            optim_state_dict=full_optim_state_dict,
+            model=model,
+            optim_input=optim_input,
+            optim=optim,
+            full_state_dict=True,
+            is_named_optimizer=False,
+        )
+
+    @staticmethod
+    def flatten_sharded_optim_state_dict(
+        sharded_optim_state_dict: dict[str, Any],
+        model: torch.nn.Module,
+        optim: torch.optim.Optimizer,
+    ) -> dict[str, Any]:
+        """Flatten a sharded optimizer state-dict.
+
+        The API is similar to :meth:`shard_full_optim_state_dict`. The only
+        difference is that the input ``sharded_optim_state_dict`` should be
+        returned from :meth:`sharded_optim_state_dict`. Therefore, there will
+        be all-gather calls on each rank to gather ``ShardedTensor`` s.
+
+        Args:
+            sharded_optim_state_dict (Dict[str, Any]): Optimizer state dict
+                corresponding to the unflattened parameters and holding the
+                sharded optimizer state.
+            model (torch.nn.Module):
+                Refer to :meth:`shard_full_optim_state_dict`.
+            optim (torch.optim.Optimizer): Optimizer for ``model`` 's
+                parameters.
+
+        Returns:
+            Refer to :meth:`shard_full_optim_state_dict`.
+        """
+        FullyShardedDataParallel._warn_legacy_optim_state_dict(
+            "flatten_sharded_optim_state_dict",
+            "optim_state_dict_to_load",
+            stacklevel=2,
+        )
+        return FullyShardedDataParallel._optim_state_dict_to_load_impl(
+            optim_state_dict=sharded_optim_state_dict,
+            model=model,
+            optim_input=None,
+            optim=optim,
+            full_state_dict=False,
+            is_named_optimizer=False,
+        )
+
+    @staticmethod
+    def scatter_full_optim_state_dict(
+        full_optim_state_dict: Optional[dict[str, Any]],
+        model: torch.nn.Module,
+        optim_input: Optional[
+            Union[
+                list[dict[str, Any]],
+                Iterable[torch.nn.Parameter],
+            ]
+        ] = None,
+        optim: Optional[torch.optim.Optimizer] = None,
+        group: Optional[Any] = None,
+    ) -> dict[str, Any]:
+        """Scatter the full optimizer state dict from rank 0 to all other ranks.
+
+        Returns the sharded optimizer state dict on each rank.
+        The return value is the same as :meth:`shard_full_optim_state_dict`, and on rank
+        0, the first argument should be the return value of
+        :meth:`full_optim_state_dict`.
+
+        Example::
+
+            >>> # xdoctest: +SKIP("undefined variables")
+            >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+            >>> model, optim = ...
+            >>> full_osd = FSDP.full_optim_state_dict(model, optim)  # only non-empty on rank 0
+            >>> # Define new model with possibly different world size
+            >>> new_model, new_optim, new_group = ...
+            >>> sharded_osd = FSDP.scatter_full_optim_state_dict(full_osd, new_model, group=new_group)
+            >>> new_optim.load_state_dict(sharded_osd)
+
+        .. note:: Both :meth:`shard_full_optim_state_dict` and
+            :meth:`scatter_full_optim_state_dict` may be used to get the
+            sharded optimizer state dict to load. Assuming that the full
+            optimizer state dict resides in CPU memory, the former requires
+            each rank to have the full dict in CPU memory, where each rank
+            individually shards the dict without any communication, while the
+            latter requires only rank 0 to have the full dict in CPU memory,
+            where rank 0 moves each shard to GPU memory (for NCCL) and
+            communicates it to ranks appropriately. Hence, the former has
+            higher aggregate CPU memory cost, while the latter has higher
+            communication cost.
+
+        Args:
+            full_optim_state_dict (Optional[Dict[str, Any]]): Optimizer state
+                dict corresponding to the unflattened parameters and holding
+                the full non-sharded optimizer state if on rank 0; the argument
+                is ignored on nonzero ranks.
+            model (torch.nn.Module): Root module (which may or may not be a
+                :class:`FullyShardedDataParallel` instance) whose parameters
+                correspond to the optimizer state in ``full_optim_state_dict``.
+            optim_input (Optional[Union[List[Dict[str, Any]], Iterable[torch.nn.Parameter]]]):
+                Input passed into the optimizer representing either a
+                :class:`list` of parameter groups or an iterable of parameters;
+                if ``None``, then this method assumes the input was
+                ``model.parameters()``. This argument is deprecated, and there
+                is no need to pass it in anymore. (Default: ``None``)
+            optim (Optional[torch.optim.Optimizer]): Optimizer that will load
+                the state dict returned by this method. This is the preferred
+                argument to use over ``optim_input``. (Default: ``None``)
+            group (dist.ProcessGroup): Model's process group or ``None`` if
+                using the default process group. (Default: ``None``)
+
+        Returns:
+            Dict[str, Any]: The full optimizer state dict now remapped to
+            flattened parameters instead of unflattened parameters and
+            restricted to only include this rank's part of the optimizer state.
+        """
+        FullyShardedDataParallel._warn_legacy_optim_state_dict(
+            "scatter_full_optim_state_dict",
+            "optim_state_dict_to_load",
+            stacklevel=2,
+        )
+        return FullyShardedDataParallel._optim_state_dict_to_load_impl(
+            optim_state_dict=full_optim_state_dict,
+            model=model,
+            optim_input=optim_input,
+            optim=optim,
+            full_state_dict=True,
+            rank0_only=True,
+            is_named_optimizer=False,
+            group=group,
+        )
+
+    @staticmethod
+    def rekey_optim_state_dict(
+        optim_state_dict: dict[str, Any],
+        optim_state_key_type: OptimStateKeyType,
+        model: torch.nn.Module,
+        optim_input: Optional[
+            Union[
+                list[dict[str, Any]],
+                Iterable[torch.nn.Parameter],
+            ]
+        ] = None,
+        optim: Optional[torch.optim.Optimizer] = None,
+    ) -> dict[str, Any]:
+        """Re-keys the optimizer state dict ``optim_state_dict`` to use the key type ``optim_state_key_type``.
+
+        This can be used to achieve compatibility between optimizer state dicts from models with FSDP
+        instances and ones without.
+
+        To re-key an FSDP full optimizer state dict (i.e. from
+        :meth:`full_optim_state_dict`) to use parameter IDs and be loadable to
+        a non-wrapped model::
+
+            >>> # xdoctest: +SKIP("undefined variables")
+            >>> wrapped_model, wrapped_optim = ...
+            >>> full_osd = FSDP.full_optim_state_dict(wrapped_model, wrapped_optim)
+            >>> nonwrapped_model, nonwrapped_optim = ...
+            >>> rekeyed_osd = FSDP.rekey_optim_state_dict(full_osd, OptimStateKeyType.PARAM_ID, nonwrapped_model)
+            >>> nonwrapped_optim.load_state_dict(rekeyed_osd)
+
+        To re-key a normal optimizer state dict from a non-wrapped model to be
+        loadable to a wrapped model::
+
+            >>> # xdoctest: +SKIP("undefined variables")
+            >>> nonwrapped_model, nonwrapped_optim = ...
+            >>> osd = nonwrapped_optim.state_dict()
+            >>> rekeyed_osd = FSDP.rekey_optim_state_dict(osd, OptimStateKeyType.PARAM_NAME, nonwrapped_model)
+            >>> wrapped_model, wrapped_optim = ...
+            >>> sharded_osd = FSDP.shard_full_optim_state_dict(rekeyed_osd, wrapped_model)
+            >>> wrapped_optim.load_state_dict(sharded_osd)
+
+        Returns:
+            Dict[str, Any]: The optimizer state dict re-keyed using the
+            parameter keys specified by ``optim_state_key_type``.
+        """
+        FullyShardedDataParallel._warn_optim_input(optim_input)
+        using_optim_input = FullyShardedDataParallel._is_using_optim_input(
+            optim_input,
+            optim,
+        )
+        if optim_state_key_type not in (
+            OptimStateKeyType.PARAM_NAME,
+            OptimStateKeyType.PARAM_ID,
+        ):
+            raise AssertionError(
+                f"Expected optim_state_key_type to be PARAM_NAME or PARAM_ID, got {optim_state_key_type}"
+            )
+        osd = optim_state_dict  # alias
+        # Validate that the existing parameter keys are uniformly typed
+        uses_param_name_mask = [type(param_key) is str for param_key in osd["state"]]
+        uses_param_id_mask = [type(param_key) is int for param_key in osd["state"]]
+        if (any(uses_param_name_mask) and not all(uses_param_name_mask)) or (
+            any(uses_param_id_mask) and not all(uses_param_id_mask)
+        ):
+            error_msg = f"Invalid parameter keys: {osd['state'].keys()}"
+            raise ValueError(error_msg)
+        # Return directly if the existing key type matches the target key type
+        if (
+            optim_state_key_type == OptimStateKeyType.PARAM_NAME
+            and all(uses_param_name_mask)
+        ) or (
+            optim_state_key_type == OptimStateKeyType.PARAM_ID
+            and all(uses_param_id_mask)
+        ):
+            return osd
+        # Otherwise, actually perform the re-keying
+        new_osd = {}
+        if optim_state_key_type == OptimStateKeyType.PARAM_NAME:  # ID -> name
+            param_id_to_param = (
+                _get_param_id_to_param_from_optim_input(model, optim_input)
+                if using_optim_input
+                else _get_param_key_to_param(optim)
+            )
+            param_to_param_name = _get_param_to_fqn(model)
+            param_id_to_param_name: list[str] = [
+                param_to_param_name[param] for param in param_id_to_param.values()
+            ]
+            new_osd["state"] = {
+                param_id_to_param_name[param_id]: param_state
+                for param_id, param_state in osd["state"].items()
+            }
+            new_osd["param_groups"] = copy.deepcopy(osd["param_groups"])
+            for param_group in new_osd["param_groups"]:
+                param_group["params"] = sorted(
+                    [
+                        param_id_to_param_name[param_id]
+                        for param_id in param_group["params"]
+                    ]
+                )
+            return new_osd
+        elif optim_state_key_type == OptimStateKeyType.PARAM_ID:  # name -> ID
+            param_name_to_param = _get_fqn_to_param(model)
+            param_to_param_id = (
+                _get_param_to_param_id_from_optim_input(model, optim_input)
+                if using_optim_input
+                else _get_param_to_param_key(optim)
+            )
+            # Because not all model parameters may be passed as the optimizer
+            # input, we may need to drop some parameters from this mapping
+            param_name_to_param_id = {
+                param_name: param_to_param_id[param]
+                for param_name, param in param_name_to_param.items()
+                if param in param_to_param_id
+            }
+            new_osd["state"] = {
+                param_name_to_param_id[param_name]: param_state
+                for param_name, param_state in osd["state"].items()
+            }
+            new_osd["param_groups"] = copy.deepcopy(osd["param_groups"])
+            for param_group in new_osd["param_groups"]:
+                param_group["params"] = sorted(
+                    [
+                        param_name_to_param_id[param_name]
+                        for param_name in param_group["params"]
+                    ]
+                )
+            return new_osd
+        return new_osd  # should never reach here
+
+    @staticmethod
+    def optim_state_dict(
+        model: torch.nn.Module,
+        optim: torch.optim.Optimizer,
+        optim_state_dict: Optional[dict[str, Any]] = None,
+        group: Optional[dist.ProcessGroup] = None,
+    ) -> dict[str, Any]:
+        """
+        Transform the state-dict of an optimizer corresponding to a sharded model.
+
+        The given state-dict can be transformed to one of three types:
+        1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict.
+
+        For full optimizer state_dict, all states are unflattened and not sharded.
+        Rank0 only and CPU only can be specified via :meth:`state_dict_type` to
+        avoid OOM.
+
+        For sharded optimizer state_dict, all states are unflattened but sharded.
+        CPU only can be specified via :meth:`state_dict_type` to further save
+        memory.
+
+        For local state_dict, no transformation will be performed. But a state
+        will be converted from nn.Tensor to ShardedTensor to represent its sharding
+        nature (this is not supported yet).
+
+        Example::
+
+            >>> # xdoctest: +SKIP("undefined variables")
+            >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+            >>> from torch.distributed.fsdp import StateDictType
+            >>> from torch.distributed.fsdp import FullStateDictConfig
+            >>> from torch.distributed.fsdp import FullOptimStateDictConfig
+            >>> # Save a checkpoint
+            >>> model, optim = ...
+            >>> FSDP.set_state_dict_type(
+            >>>     model,
+            >>>     StateDictType.FULL_STATE_DICT,
+            >>>     FullStateDictConfig(rank0_only=False),
+            >>>     FullOptimStateDictConfig(rank0_only=False),
+            >>> )
+            >>> state_dict = model.state_dict()
+            >>> optim_state_dict = FSDP.optim_state_dict(model, optim)
+            >>> save_a_checkpoint(state_dict, optim_state_dict)
+            >>> # Load a checkpoint
+            >>> model, optim = ...
+            >>> state_dict, optim_state_dict = load_a_checkpoint()
+            >>> FSDP.set_state_dict_type(
+            >>>     model,
+            >>>     StateDictType.FULL_STATE_DICT,
+            >>>     FullStateDictConfig(rank0_only=False),
+            >>>     FullOptimStateDictConfig(rank0_only=False),
+            >>> )
+            >>> model.load_state_dict(state_dict)
+            >>> optim_state_dict = FSDP.optim_state_dict_to_load(
+            >>>     model, optim, optim_state_dict
+            >>> )
+            >>> optim.load_state_dict(optim_state_dict)
+
+        Args:
+            model (torch.nn.Module): Root module (which may or may not be a
+                :class:`FullyShardedDataParallel` instance) whose parameters
+                were passed into the optimizer ``optim``.
+            optim (torch.optim.Optimizer): Optimizer for ``model`` 's
+                parameters.
+            optim_state_dict (Dict[str, Any]): the target optimizer state_dict to
+                transform. If the value is None, optim.state_dict() will be used. (
+                Default: ``None``)
+            group (dist.ProcessGroup): Model's process group across which parameters
+                are sharded or ``None`` if using the default process group. (
+                Default: ``None``)
+
+        Returns:
+            Dict[str, Any]: A :class:`dict` containing the optimizer state for
+            ``model``. The sharding of the optimizer state is based on
+            ``state_dict_type``.
+        """
+        state_dict_settings = FullyShardedDataParallel.get_state_dict_type(model)
+        if optim_state_dict is None:
+            optim_state_dict = optim.state_dict()
+        return FullyShardedDataParallel._optim_state_dict_impl(
+            model=model,
+            optim=optim,
+            optim_state_dict=optim_state_dict,
+            optim_input=None,
+            rank0_only=getattr(
+                state_dict_settings.optim_state_dict_config, "rank0_only", False
+            ),
+            full_state_dict=state_dict_settings.state_dict_type
+            == StateDictType.FULL_STATE_DICT,
+            group=group,
+            cpu_offload=getattr(
+                state_dict_settings.optim_state_dict_config, "offload_to_cpu", True
+            ),
+            _stacklevel=2,
+        )
+
+    @staticmethod
+    def optim_state_dict_to_load(
+        model: torch.nn.Module,
+        optim: torch.optim.Optimizer,
+        optim_state_dict: dict[str, Any],
+        is_named_optimizer: bool = False,
+        load_directly: bool = False,
+        group: Optional[dist.ProcessGroup] = None,
+    ) -> dict[str, Any]:
+        """
+        Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model.
+
+        Given a ``optim_state_dict`` that is transformed through
+        :meth:`optim_state_dict`, it gets converted to the flattened optimizer
+        state_dict that can be loaded to ``optim`` which is the optimizer for
+        ``model``. ``model`` must be sharded by FullyShardedDataParallel.
+
+            >>> # xdoctest: +SKIP("undefined variables")
+            >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+            >>> from torch.distributed.fsdp import StateDictType
+            >>> from torch.distributed.fsdp import FullStateDictConfig
+            >>> from torch.distributed.fsdp import FullOptimStateDictConfig
+            >>> # Save a checkpoint
+            >>> model, optim = ...
+            >>> FSDP.set_state_dict_type(
+            >>>     model,
+            >>>     StateDictType.FULL_STATE_DICT,
+            >>>     FullStateDictConfig(rank0_only=False),
+            >>>     FullOptimStateDictConfig(rank0_only=False),
+            >>> )
+            >>> state_dict = model.state_dict()
+            >>> original_osd = optim.state_dict()
+            >>> optim_state_dict = FSDP.optim_state_dict(
+            >>>     model,
+            >>>     optim,
+            >>>     optim_state_dict=original_osd
+            >>> )
+            >>> save_a_checkpoint(state_dict, optim_state_dict)
+            >>> # Load a checkpoint
+            >>> model, optim = ...
+            >>> state_dict, optim_state_dict = load_a_checkpoint()
+            >>> FSDP.set_state_dict_type(
+            >>>     model,
+            >>>     StateDictType.FULL_STATE_DICT,
+            >>>     FullStateDictConfig(rank0_only=False),
+            >>>     FullOptimStateDictConfig(rank0_only=False),
+            >>> )
+            >>> model.load_state_dict(state_dict)
+            >>> optim_state_dict = FSDP.optim_state_dict_to_load(
+            >>>     model, optim, optim_state_dict
+            >>> )
+            >>> optim.load_state_dict(optim_state_dict)
+
+        Args:
+            model (torch.nn.Module): Root module (which may or may not be a
+                :class:`FullyShardedDataParallel` instance) whose parameters
+                were passed into the optimizer ``optim``.
+            optim (torch.optim.Optimizer): Optimizer for ``model`` 's
+                parameters.
+            optim_state_dict (Dict[str, Any]): The optimizer states to be loaded.
+            is_named_optimizer (bool): Is this optimizer a NamedOptimizer or
+                KeyedOptimizer. Only set to True if ``optim`` is TorchRec's
+                KeyedOptimizer or torch.distributed's NamedOptimizer.
+            load_directly (bool): If this is set to True, this API will also
+                call optim.load_state_dict(result) before returning the result.
+                Otherwise, users are responsible to call ``optim.load_state_dict()``
+                (Default: ``False``)
+            group (dist.ProcessGroup): Model's process group across which parameters
+                are sharded or ``None`` if using the default process group. (
+                Default: ``None``)
+        """
+        state_dict_settings = FullyShardedDataParallel.get_state_dict_type(model)
+        result = FullyShardedDataParallel._optim_state_dict_to_load_impl(
+            optim_state_dict=optim_state_dict,
+            model=model,
+            optim_input=None,
+            optim=optim,
+            full_state_dict=(
+                state_dict_settings.state_dict_type == StateDictType.FULL_STATE_DICT
+            ),
+            rank0_only=getattr(
+                state_dict_settings.optim_state_dict_config, "rank0_only", False
+            ),
+            is_named_optimizer=is_named_optimizer,
+            group=group,
+        )
+        if load_directly:
+            optim.load_state_dict(result)
+        return result
+
+    def register_comm_hook(self, state: object, hook: callable):
+        """Register a communication hook.
+
+        This is an enhancement that provides a flexible hook to users where they can specify how FSDP aggregates
+        gradients across multiple workers.
+        This hook can be used to implement several algorithms like
+        `GossipGrad `_ and gradient compression
+        which involve different communication strategies for
+        parameter syncs while training with :class:`FullyShardedDataParallel`.
+
+        .. warning ::
+            FSDP communication hook should be registered before running an initial forward pass
+            and only once.
+
+        Args:
+            state (object): Passed to the hook to maintain any state information during the training process.
+                            Examples include error feedback in gradient compression,
+                            peers to communicate with next in `GossipGrad `_, etc.
+                            It is locally stored by each worker
+                            and shared by all the gradient tensors on the worker.
+            hook (Callable): Callable, which has one of the following signatures:
+                            1) ``hook: Callable[torch.Tensor] -> None``:
+                            This function takes in a Python tensor, which represents
+                            the full, flattened, unsharded gradient with respect to all variables
+                            corresponding to the model this FSDP unit is wrapping
+                            (that are not wrapped by other FSDP sub-units).
+                            It then performs all necessary processing and returns ``None``;
+                            2) ``hook: Callable[torch.Tensor, torch.Tensor] -> None``:
+                            This function takes in two Python tensors, the first one represents
+                            the full, flattened, unsharded gradient with respect to all variables
+                            corresponding to the model this FSDP unit is wrapping
+                            (that are not wrapped by other FSDP sub-units). The latter
+                            represents a pre-sized tensor to store a chunk of a sharded gradient after
+                            reduction.
+                            In both cases, callable performs all necessary processing and returns ``None``.
+                            Callables with signature 1 are expected to handle gradient communication for a `NO_SHARD` case.
+                            Callables with signature 2 are expected to handle gradient communication for sharded cases.
+
+        """
+        if not self.check_is_root():
+            raise AssertionError(
+                "register_comm_hook can only be called on a root instance."
+            )
+        for fsdp_state in traversal_utils._get_fsdp_states(self):
+            if fsdp_state.sharding_strategy in HYBRID_SHARDING_STRATEGIES:
+                raise AssertionError(
+                    f"Communication hook is not supported for hybrid strategies: {fsdp_state.sharding_strategy}"
+                )
+            if fsdp_state._comm_hook is not None:
+                raise AssertionError("A communication hook is already registered")
+            if not callable(hook):
+                raise ValueError(
+                    f"The communication hook must be callable but got {hook}"
+                )
+            fsdp_state._comm_hook = hook
+            fsdp_state._comm_hook_state = state
+
+    def _unshard(self, async_op: bool = False):
+        class UnshardHandle:
+            def __init__(
+                self,
+                flat_param_handle: Optional[FlatParamHandle],
+                unshard_event: torch.Event,
+            ):
+                self._flat_param_handle = flat_param_handle
+                self._unshard_event = unshard_event
+
+            def wait(self):
+                if self._flat_param_handle is not None:
+                    current_stream = (
+                        self._flat_param_handle._device_handle.current_stream()
+                    )
+                    current_stream.wait_event(self._unshard_event)
+                    self._flat_param_handle = None
+
+        if self._handle:
+            with self._use_training_state(
+                TrainingState.FORWARD_BACKWARD, HandleTrainingState.FORWARD
+            ):
+                _unshard(
+                    self, self._handle, self._unshard_stream, self._pre_unshard_stream
+                )
+                self._unshard_event = self._unshard_stream.record_event()
+            self._handle._prefetched = True
+        unshard_handle = UnshardHandle(self._handle, self._unshard_stream)
+        if async_op:
+            return unshard_handle
+        unshard_handle.wait()
+        return None
+
+    def _wait_unshard_streams_on_current_stream(self):
+        _wait_for_computation_stream(
+            self._device_handle.current_stream(),
+            self._unshard_stream,
+            self._pre_unshard_stream,
+        )
+
+    @contextlib.contextmanager
+    def _use_training_state(
+        self, training_state: TrainingState, handle_training_state: HandleTrainingState
+    ):
+        prev_training_state = self.training_state
+        self.training_state = training_state
+        if self._handle:
+            prev_handle_training_state = self._handle._training_state
+            self._handle._training_state = handle_training_state
+        try:
+            yield
+        finally:
+            self.training_state = prev_training_state
+            if self._handle:
+                self._handle._training_state = prev_handle_training_state
+
+
+def _get_grad_norm(
+    params: Iterable[nn.Parameter],
+    norm_type: float,
+    zero: torch.Tensor,
+    device: torch.device,
+) -> torch.Tensor:
+    """
+    Return the gradient norm of parameters ``param`` s, where the gradients are viewed as a single vector.
+
+    The returned norm is in FP32 even if parameters/gradients are in a low precision. This is because the downstream
+    use of this return value is a reduction across ranks.
+    """
+    params_with_grad = [param for param in params if param.grad is not None]
+    if len(params_with_grad) == 0:
+        # Reuse a tensor for zero to avoid a GPU sync
+        return zero
+    grads = [param.grad for param in params_with_grad]
+    grad_dtypes = {grad.dtype for grad in grads}
+    if len(grad_dtypes) != 1:
+        raise ValueError(
+            f"Requires uniform dtype across all gradients but got {grad_dtypes}"
+        )
+    # Compute the gradient norm in FP32, where we treat the gradients as a
+    # single vector
+    grad_norm = torch.linalg.vector_norm(
+        torch.stack(
+            [
+                torch.linalg.vector_norm(grad.detach(), norm_type, dtype=torch.float32)
+                for grad in grads
+            ],
+        ),
+        norm_type,
+        dtype=torch.float32,
+    )
+    return grad_norm.to(device=device)
+
+
+def _get_param_to_fqn(
+    model: torch.nn.Module,
+) -> dict[torch.nn.Parameter, str]:
+    """
+    Construct a mapping from parameters to their parameter names.
+
+    The ``model`` should not contain any :class:`FullyShardedDataParallel` instances, which
+    means that none of the parameters should be ``FlatParameter`` s. As a
+    result, compared to :meth:`_get_param_to_fqns`, the mapped
+    values may be flattened from singleton :class:`list` s to the contained
+    names themselves.
+
+    Args:
+        model (torch.nn.Module): Root module, which should not contain any
+            :class:`FullyShardedDataParallel` instances.
+    """
+    param_to_param_names = _get_param_to_fqns(model)
+    for param_names in param_to_param_names.values():
+        if len(param_names) == 0:
+            raise AssertionError(
+                "`_get_param_to_fqns()` should not construct empty lists"
+            )
+        if len(param_names) > 1:
+            raise RuntimeError(
+                "Each parameter should only map to one parameter name but got "
+                f"{len(param_names)}: {param_names}"
+            )
+    param_to_param_name = {
+        param: param_names[0] for param, param_names in param_to_param_names.items()
+    }
+    return param_to_param_name
+
+
+def _get_fqn_to_param(
+    model: torch.nn.Module,
+) -> dict[str, torch.nn.Parameter]:
+    """Construct the inverse mapping of :meth:`_get_param_to_fqn`."""
+    param_to_param_name = _get_param_to_fqn(model)
+    return dict(zip(param_to_param_name.values(), param_to_param_name.keys()))
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/sharded_grad_scaler.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/sharded_grad_scaler.py
new file mode 100644
index 0000000000000000000000000000000000000000..3986d733328c80f12e6eed138386a9e8aafe6a3a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/sharded_grad_scaler.py
@@ -0,0 +1,377 @@
+# mypy: allow-untyped-defs
+import logging
+from collections import abc, defaultdict
+from collections.abc import Iterable
+from typing import Any, Optional, overload, Union
+
+import torch
+import torch.distributed as dist
+from torch.amp.grad_scaler import _MultiDeviceReplicator, GradScaler, OptState
+from torch.distributed.distributed_c10d import ProcessGroup
+
+
+logger = logging.getLogger(__name__)
+
+
+def _refresh_per_optimizer_state() -> dict[str, Any]:
+    return {"stage": OptState.READY, "found_inf_per_device": {}}
+
+
+def _is_supported_device(tensor: torch.Tensor) -> bool:
+    return tensor.is_cuda or tensor.device.type in (
+        "xla",
+        "cpu",
+        "hpu",
+        "mtia",
+        "xpu",
+        torch._C._get_privateuse1_backend_name(),
+    )
+
+
+class _GeneralMultiDeviceReplicator(_MultiDeviceReplicator):
+    """
+    Lazily serves tensor to request device. This class extends
+    _MultiDeviceReplicator to allow support for "cpu" as a device.
+    """
+
+    def __init__(self, master_tensor: torch.Tensor) -> None:
+        if not _is_supported_device(master_tensor):
+            raise AssertionError(
+                f"Expected supported device, got {master_tensor.device}"
+            )
+        self.master = master_tensor
+        self._per_device_tensors: dict[torch.device, torch.Tensor] = {}
+
+
+class ShardedGradScaler(GradScaler):
+    """
+    ShardedGradScaler helps perform gradient scaling in a shard aware manner. It extends
+    functionality from GradScaler:
+    * Supports Pytorch DDP and FSDP implementations
+    * Support CPU offloaded tensors (as used in fully sharded data parallel[FSDP])
+    * Supports the custom Mixed Precision loss dtype (fp16, bf16) that FSDP returns
+    * Sync inf/nan for scaled gradient tensors on any torch.device (where tensors are placed) across
+    nodes
+
+    Example::
+
+        # Creates a ShardedGradScaler once at the beginning of training.
+        scaler = ShardedGradScaler()
+
+        for epoch in epochs:
+            for input, target in data:
+                optimizer.zero_grad()
+                output = model(input)
+                loss = loss_fn(output, target)
+
+                # Scales loss.  Calls backward() on scaled loss to create scaled gradients.
+                scaler.scale(loss).backward()
+
+                # scaler.step() first unscales gradients of the optimizer's params.
+                # If gradients don't contain infs/NaNs, optimizer.step() is then called,
+                # otherwise, optimizer.step() is skipped.
+                scaler.step(optimizer)
+
+                # Updates the scale for next iteration.
+                scaler.update()
+
+    See :class:`GradScaler` for explanation of scaling/unscaling and more use cases.
+
+    Args:
+        init_scale (float, optional, default=2.**16):  Initial scale factor.
+        growth_factor (float, optional, default=2.0):  Factor by which the scale is multiplied during
+            :meth:`update` if no inf/NaN gradients occur for ``growth_interval`` consecutive iterations.
+        backoff_factor (float, optional, default=0.5):  Factor by which the scale is multiplied during
+            :meth:`update` if inf/NaN gradients occur in an iteration.
+        growth_interval (int, optional, default=2000):  Number of consecutive iterations without inf/NaN gradients
+            that must occur for the scale to be multiplied by ``growth_factor``.
+        enabled (bool, optional):  If ``False``, disables gradient scaling. :meth:`step` simply
+            invokes the underlying ``optimizer.step()``, and other methods become no-ops.
+            Default: ``True``
+        process_group (ProcessGroup, optional, default=torch.distributed.group.WORLD):
+            process group for sharding
+    """
+
+    def __init__(
+        self,
+        device: str = "cuda",
+        init_scale: float = 2.0**16,
+        backoff_factor: float = 0.5,
+        growth_factor: float = 2.0,
+        growth_interval: int = 2000,
+        enabled: bool = True,
+        process_group: Optional[ProcessGroup] = dist.group.WORLD,
+    ) -> None:
+        super().__init__(
+            device,
+            init_scale=init_scale,
+            backoff_factor=backoff_factor,
+            growth_factor=growth_factor,
+            growth_interval=growth_interval,
+            enabled=enabled,
+        )
+        if self._enabled:
+            self.process_group = process_group
+            self._per_optimizer_states = defaultdict(_refresh_per_optimizer_state)
+
+    @overload
+    def scale(self, outputs: torch.Tensor) -> torch.Tensor: ...
+
+    @overload
+    def scale(self, outputs: list[torch.Tensor]) -> list[torch.Tensor]: ...
+
+    @overload
+    def scale(self, outputs: tuple[torch.Tensor, ...]) -> tuple[torch.Tensor, ...]: ...
+
+    @overload
+    def scale(self, outputs: Iterable[torch.Tensor]) -> Iterable[torch.Tensor]: ...
+
+    def scale(
+        self, outputs: Union[torch.Tensor, Iterable[torch.Tensor]]
+    ) -> Union[torch.Tensor, Iterable[torch.Tensor]]:
+        if not self._enabled:
+            return outputs
+
+        if isinstance(outputs, torch.Tensor):
+            if not _is_supported_device(outputs):
+                raise AssertionError(f"Expected supported device, got {outputs.device}")
+            if self._scale is None:
+                self._lazy_init_scale_growth_tracker(outputs.device)
+            if self._scale is None:
+                raise AssertionError("Expected _scale to be initialized, got None")
+            scaled_output = outputs * self._scale.to(
+                device=outputs.device, non_blocking=True
+            )
+            # Here we ensure the return dtype is the same as the outputs dtype.
+            # For the FSDP + Mixed Precision use case, the loss output is in the Mixed Precision
+            # format (fp16, bf16) and so the scaled loss should be of the same dtype.
+            return scaled_output.type(outputs.dtype)
+
+        stash: list[_GeneralMultiDeviceReplicator] = []
+
+        def apply_scale(val: Union[torch.Tensor, Iterable[torch.Tensor]]):
+            if isinstance(val, torch.Tensor):
+                if not _is_supported_device(val):
+                    raise AssertionError(f"Expected supported device, got {val.device}")
+                if len(stash) == 0:
+                    if self._scale is None:
+                        self._lazy_init_scale_growth_tracker(val.device)
+                    if self._scale is None:
+                        raise AssertionError(
+                            "Expected _scale to be initialized, got None"
+                        )
+                    stash.append(_GeneralMultiDeviceReplicator(self._scale))
+                scaled_val = val * stash[0].get(val.device)
+                # Here we ensure the return dtype is the same as the outputs dtype.
+                # For the FSDP + Mixed Precision use case, the loss output is in the Mixed Precision
+                # format (fp16, bf16) and so the scaled loss should be of the same dtype.
+                return scaled_val.type(val.dtype)
+            if isinstance(val, abc.Iterable):
+                iterator = map(apply_scale, val)
+                if isinstance(val, (list, tuple)):
+                    return type(val)(iterator)
+                return iterator
+            raise ValueError("outputs must be a Tensor or an iterable of Tensors")
+
+        return apply_scale(outputs)
+
+    def _unscale_grads_(
+        self,
+        optimizer: torch.optim.Optimizer,
+        inv_scale: torch.Tensor,
+        found_inf: torch.Tensor,
+        allow_fp16: bool = True,
+    ) -> dict[torch.device, torch.Tensor]:
+        per_device_inv_scale = _GeneralMultiDeviceReplicator(inv_scale)
+        per_device_found_inf = _GeneralMultiDeviceReplicator(found_inf)
+
+        # To set up _amp_foreach_non_finite_check_and_unscale_, split grads by device and dtype.
+        # There could be thousands of grads, so we'd like to iterate through them just once.
+        # However, we don't know their devices or dtypes in advance.
+
+        # https://stackoverflow.com/questions/5029934/defaultdict-of-defaultdict
+        # Google says mypy struggles with defaultdicts type annotations.
+        per_device_and_dtype_grads = defaultdict(lambda: defaultdict(list))  # type: ignore[var-annotated]
+        with torch.no_grad():
+            for group in optimizer.param_groups:
+                for param in group["params"]:
+                    if param.grad is None:
+                        continue
+                    if (not allow_fp16) and param.grad.dtype == torch.float16:
+                        raise ValueError("Attempting to unscale FP16 gradients.")
+                    if param.grad.is_sparse:
+                        # is_coalesced() == False means the sparse grad has values with duplicate indices.
+                        # coalesce() deduplicates indices and adds all values that have the same index.
+                        # For scaled fp16 values, there's a good chance coalescing will cause overflow,
+                        # so we should check the coalesced _values().
+                        if param.grad.dtype is torch.float16:
+                            # coalesce is not supported in torch.float16
+                            param_grad_fp32 = param.grad.type(torch.float32).coalesce()
+                            param.grad = param_grad_fp32.type(torch.float16)
+                        to_unscale = param.grad._values()
+                    else:
+                        to_unscale = param.grad
+
+                    per_device_and_dtype_grads[to_unscale.device][
+                        to_unscale.dtype
+                    ].append(to_unscale)
+
+            for device, per_dtype_grads in per_device_and_dtype_grads.items():
+                for grads in per_dtype_grads.values():
+                    torch._amp_foreach_non_finite_check_and_unscale_(
+                        grads,
+                        per_device_found_inf.get(device),
+                        per_device_inv_scale.get(device),
+                    )
+        # There exist contexts (e.g. w/ `use_orig_params=True`) wherein some
+        # ranks may have no (non-zero sized) parameter shards, necessitating the
+        # initialization of `per_device_found_inf._per_device_tensors` here
+        if not per_device_found_inf._per_device_tensors:
+            if self._scale is None:
+                raise AssertionError("Expected _scale to be initialized, got None")
+            per_device_found_inf.get(self._scale.device)
+        return per_device_found_inf._per_device_tensors
+
+    def unscale_(self, optimizer: torch.optim.Optimizer) -> None:
+        if not self._enabled:
+            return
+
+        self._check_scale_growth_tracker("unscale_")
+
+        optimizer_state = self._per_optimizer_states[id(optimizer)]
+
+        if optimizer_state["stage"] is OptState.UNSCALED:
+            raise RuntimeError(
+                "unscale_() has already been called on this optimizer since the last update()."
+            )
+        elif optimizer_state["stage"] is OptState.STEPPED:
+            raise RuntimeError("unscale_() is being called after step().")
+
+        # FP32 division can be imprecise for certain compile options, so we carry out the reciprocal in FP64.
+        if self._scale is None:
+            raise AssertionError("Expected _scale to be initialized, got None")
+        inv_scale = self._scale.double().reciprocal().float()
+        found_inf = torch.full(
+            (1,), 0.0, dtype=torch.float32, device=self._scale.device
+        )
+
+        optimizer_state["found_inf_per_device"] = self._unscale_grads_(
+            optimizer, inv_scale, found_inf, True
+        )
+        optimizer_state["stage"] = OptState.UNSCALED
+
+        # Synchronize the detected inf across the ranks
+        optimizer_state = self._per_optimizer_states[id(optimizer)]
+        works = []
+        found_inf_on_cpus = []
+        found_inf_on_devices = []
+
+        for found_inf in optimizer_state["found_inf_per_device"].values():
+            if self._device != "cpu" and found_inf.device.type == "cpu":
+                found_inf_on_cpus.append(found_inf)
+                found_inf_on_device = found_inf.to(self._device)
+                found_inf_on_devices.append(found_inf_on_device)
+                works.append(
+                    dist.all_reduce(
+                        found_inf_on_device, async_op=True, group=self.process_group
+                    )
+                )
+            else:
+                works.append(
+                    dist.all_reduce(found_inf, async_op=True, group=self.process_group)
+                )
+        for work in works:
+            work.wait()
+        if found_inf_on_cpus:
+            torch._foreach_copy_(found_inf_on_cpus, found_inf_on_devices)
+
+    def _amp_update_scale_cpu_(self, found_inf: torch.Tensor) -> None:
+        """
+        If found_inf is 1.0 (True), then scale is multiplied by backoff_factor and growth_tracker is set to zero.
+        Otherwise, scale is multiplied by the growth factor when the growth interval is reached.
+        """
+        if self._scale is None or self._growth_tracker is None:
+            raise AssertionError(
+                "Expected _scale and _growth_tracker to be initialized, got None"
+            )
+
+        if found_inf.item() >= 1.0:
+            self._scale *= self._backoff_factor
+            self._growth_tracker.fill_(0)
+        else:
+            successful = self._growth_tracker + 1
+            if successful == self._growth_interval:
+                self._scale *= self._growth_factor
+                self._growth_tracker.fill_(0)
+            else:
+                self._growth_tracker = successful
+
+    def update(self, new_scale: Optional[Union[float, torch.Tensor]] = None) -> None:
+        """
+        Updates the scale factor.
+        If any optimizer steps were skipped the scale is multiplied by ``backoff_factor``
+        to reduce it. If ``growth_interval`` unskipped iterations occurred consecutively,
+        the scale is multiplied by ``growth_factor`` to increase it.
+        Passing ``new_scale`` sets the new scale value manually. (``new_scale`` is not
+        used directly, it's used to fill GradScaler's internal scale tensor. So if
+        ``new_scale`` was a tensor, later in-place changes to that tensor will not further
+        affect the scale GradScaler uses internally.)
+        Args:
+            new_scale (float or :class:`torch.Tensor`, optional, default=None):  New scale factor.
+        .. warning::
+            :meth:`update` should only be called at the end of the iteration, after ``scaler.step(optimizer)`` has
+            been invoked for all optimizers used this iteration.
+        """
+
+        if not self._enabled:
+            return
+
+        _scale, _growth_tracker = self._check_scale_growth_tracker("update")  # type: ignore[var-annotated]
+
+        if new_scale is not None:
+            # Accept a new user-defined scale.
+            if isinstance(new_scale, float):
+                self._scale.fill_(new_scale)  # type: ignore[union-attr]
+            else:
+                reason = (
+                    "new_scale should be a float or a 1-element torch.cuda.FloatTensor or "
+                    "torch.FloatTensor with requires_grad=False."
+                )
+                if new_scale.device.type != self._device:
+                    raise AssertionError(reason)
+                if new_scale.numel() != 1:
+                    raise AssertionError(reason)
+                if new_scale.requires_grad is not False:
+                    raise AssertionError(reason)
+                self._scale.copy_(new_scale)  # type: ignore[union-attr]
+        else:
+            # Consume shared inf/nan data collected from optimizers to update the scale.
+            # If all found_inf tensors are on the same device as self._scale, this operation is asynchronous.
+            found_infs = [
+                found_inf.to(device=_scale.device, non_blocking=True)
+                for state in self._per_optimizer_states.values()
+                for found_inf in state["found_inf_per_device"].values()
+            ]
+
+            if len(found_infs) == 0:
+                raise AssertionError("No inf checks were recorded prior to update.")
+
+            found_inf_combined = found_infs[0]
+            if len(found_infs) > 1:
+                for i in range(1, len(found_infs)):
+                    found_inf_combined += found_infs[i]
+
+            if _scale.device.type == "cpu":
+                self._amp_update_scale_cpu_(found_inf_combined)
+            else:
+                torch._amp_update_scale_(
+                    self._scale,  # type: ignore[arg-type]
+                    self._growth_tracker,  # type: ignore[arg-type]
+                    found_inf_combined,
+                    self._growth_factor,  # type: ignore[arg-type]
+                    self._backoff_factor,  # type: ignore[arg-type]
+                    self._growth_interval,  # type: ignore[arg-type]
+                )
+
+        # To prepare for next iteration, clear the data collected from optimizers this iteration.
+        self._per_optimizer_states = defaultdict(_refresh_per_optimizer_state)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/wrap.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/wrap.py
new file mode 100644
index 0000000000000000000000000000000000000000..f731854dab2eb475e7c8321738552fed205db70d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/fsdp/wrap.py
@@ -0,0 +1,608 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
+import contextlib
+import copy
+from abc import ABC, abstractmethod
+from collections.abc import Callable, Generator, Iterable, Sequence
+from typing import Any, cast, Optional, Union
+
+import torch.nn as nn
+
+
+__all__ = [
+    "always_wrap_policy",
+    "lambda_auto_wrap_policy",
+    "transformer_auto_wrap_policy",
+    "size_based_auto_wrap_policy",
+    "enable_wrap",
+    "wrap",
+    "CustomPolicy",
+    "ModuleWrapPolicy",
+]
+
+
+# NOTE: We intentionally keep this function simple and isolate the complexity
+# to `fn` to enable using this function generically. We may move this to a
+# non-FSDP-specific folder and/or make it public in the future.
+def _post_order_apply(
+    root_module: nn.Module,
+    fn: Callable[[nn.Module], Optional[nn.Module]],
+):
+    """
+    This applies ``fn`` to every module in the module tree of ``root_module``
+    following a post-order traversal. If ``fn`` returns an :class:`nn.Module`,
+    then this replaces the original module with the newly returned one in the
+    tree. Otherwise, ``fn`` should return ``None``, in which case the module is
+    not changed.
+    """
+    # Track visited modules to avoid visiting shared modules multiple times
+    visited_modules: set[nn.Module] = {root_module}
+
+    def _post_order_apply_inner(
+        module: nn.Module,
+        module_name: str,
+        parent_module: Optional[nn.Module],
+    ):
+        for child_module_name, child_module in module.named_children():
+            if child_module not in visited_modules:
+                visited_modules.add(child_module)
+                _post_order_apply_inner(child_module, child_module_name, module)
+        optional_module = fn(module)
+        if optional_module is not None:
+            if not isinstance(parent_module, nn.Module):
+                raise AssertionError(
+                    "Non-root modules should have their parent module set but got "
+                    f"{parent_module} for {module}"
+                )
+            if not module_name:
+                raise AssertionError(
+                    "Non-root modules should have their module name set but got "
+                    f"an empty module name for {module}"
+                )
+            if not isinstance(optional_module, nn.Module):
+                raise AssertionError(
+                    f"fn should return None or an nn.Module but got {optional_module}"
+                )
+            setattr(parent_module, module_name, optional_module)
+
+    _post_order_apply_inner(root_module, "", None)
+
+
+def _construct_wrap_fn(
+    root_module: nn.Module,
+    target_module_to_kwargs: dict[nn.Module, dict[str, Any]],
+    fsdp_fn: Callable,
+) -> Callable[[nn.Module], Optional[nn.Module]]:
+    """
+    This constructs the "wrap" function to pass to :func:`_post_order_apply`
+    based on ``target_module_to_kwargs``, which should be constructed from the
+    wrapping policy.
+    """
+
+    def fn(module: nn.Module) -> Optional[nn.Module]:
+        # Explicitly avoid wrapping the root module since for FSDP, it is
+        # handled by the caller
+        if module in target_module_to_kwargs and module is not root_module:
+            kwargs = target_module_to_kwargs[module]
+            return fsdp_fn(module, **kwargs)
+        return None
+
+    return fn
+
+
+def _run_mixed_precision_override_policy(
+    root_module: nn.Module,
+    module_classes: Iterable[type[nn.Module]],
+    ignored_modules: set[nn.Module],
+    root_kwargs: dict[str, Any],
+    target_module_to_kwargs: dict[nn.Module, dict[str, Any]],
+):
+    module_classes_tuple = tuple(set(module_classes))
+    for module in root_module.modules():
+        if module in ignored_modules:
+            continue
+        elif isinstance(module, module_classes_tuple):
+            # This policy overrides any existing policy
+            if module not in target_module_to_kwargs:
+                # Only inherit from the root kwargs if not already specified
+                target_module_to_kwargs[module] = root_kwargs
+            target_module_to_kwargs[module]["mixed_precision"] = None
+    return target_module_to_kwargs
+
+
+def always_wrap_policy(*args, **kwargs) -> bool:
+    """
+    A simple recursive wrap policy that always returns ``True``. This means
+    that every submodule is wrapped by the wrapper class in
+    :func:`_recursive_wrap`.
+    """
+    return True
+
+
+class _Policy(ABC):
+    """
+    This defines an abstract base class that represents a policy for applying
+    a module-level API.
+    """
+
+    @abstractmethod
+    def _run_policy(
+        self,
+        root_module: nn.Module,
+        ignored_modules: set[nn.Module],
+        root_kwargs: dict[str, Any],
+    ) -> dict[nn.Module, dict[str, Any]]:
+        """
+        This should return a dict ``target_module_to_kwargs`` that maps from
+        each target module to wrap to its kwargs.
+        """
+        ...
+
+
+def _module_wrap_policy(
+    module: nn.Module,
+    recurse: bool,
+    nonwrapped_numel: int,
+    module_classes: set[type[nn.Module]],
+) -> bool:
+    """
+    This auto wrap policy wraps every module that is an instance of any type in
+    ``module_classes`` as its own FSDP instance. The root module given by
+    ``module`` is always wrapped as an FSDP instance regardless. Since the
+    wrapping proceeds bottom up, each FSDP instance manages the parameters in
+    its subtree excluding any already managed by a child FSDP instance.
+
+    Args:
+        module (nn.Module): Current module being considered.
+        recurse (bool): If ``False``, then this function must decide whether
+            ``module`` should be wrapped as an FSDP instance or not. If
+            ``True``, then the function is still recursing down the module
+            tree as a part of the DFS.
+        nonwrapped_numel (int): Parameter numel not yet wrapped.
+        module_classes (Set[Type[nn.Module]]): Set of module classes that are
+            wrapped as FSDP instances.
+
+    Returns:
+        ``True`` if ``recurse=True``, and whether ``module`` should be wrapped
+        if ``recurse=False``.
+    """
+    if recurse:
+        return True  # always recurse
+    return isinstance(module, tuple(module_classes))
+
+
+class ModuleWrapPolicy(_Policy):
+    """
+    This policy applies to every module of the specified module classes,
+    passing in the kwargs given to the root.
+    """
+
+    def __init__(self, module_classes: Iterable[type[nn.Module]]):
+        module_classes_set = set(module_classes)
+        self._module_classes = module_classes_set
+        self._module_classes_str = str(module_classes_set)
+
+    def _run_policy(
+        self,
+        root_module: nn.Module,
+        ignored_modules: set[nn.Module],
+        root_kwargs: dict[str, Any],
+    ) -> dict[nn.Module, dict[str, Any]]:
+        module_classes = tuple(self._module_classes)
+        target_module_to_kwargs: dict[nn.Module, dict[str, Any]] = {}
+        for module in root_module.modules():
+            if module in ignored_modules:
+                continue
+            elif isinstance(module, module_classes):
+                # Shallow copy to avoid coupling changes across modules
+                target_module_to_kwargs[module] = copy.copy(root_kwargs)
+        return target_module_to_kwargs
+
+    def __call__(self, module, recurse, *args, **kwargs):
+        # nonwrapped_numel is not used.
+        return _module_wrap_policy(
+            module, recurse, nonwrapped_numel=-1, module_classes=self._module_classes
+        )
+
+    def __repr__(self) -> str:
+        return super().__repr__() + f"({self._module_classes_str})"
+
+
+class CustomPolicy(_Policy):
+    """
+    This policy takes in a lambda function that maps a given ``nn.Module`` to
+    either ``False``, ``True``, or a kwarg dictionary.
+    - If the function returns ``False`` or an empty dictionary, then the module
+      does not have the API applied.
+    - If the function returns ``True``, then the module has the API applied
+      with the root's kwargs.
+    - If the function returns a non-empty dictionary, then the module has the
+      API applied, and the dictionary overrides the root's kwargs.
+
+    Example::
+
+        >>> # xdoctest: +SKIP("undefined variables")
+        >>> model = init_transformer_model(...)
+        >>> def lambda_fn(module: nn.Module):
+        >>>     if module is model.lm_head:
+        >>>         return {"sharding_strategy": ShardingStrategy.SHARD_GRAD_OP}
+        >>>     elif isinstance(module, TransformerBlock):
+        >>>         return True
+        >>>     return False
+        >>> policy = CustomPolicy(lambda_fn)
+        >>> fsdp_model = FSDP(model, auto_wrap_policy=policy)
+    """
+
+    def __init__(self, lambda_fn: Callable[[nn.Module], Union[bool, dict[str, Any]]]):
+        self._lambda_fn = lambda_fn
+
+    def _run_policy(
+        self,
+        root_module: nn.Module,
+        ignored_modules: set[nn.Module],
+        root_kwargs: dict[str, Any],
+    ) -> dict[nn.Module, dict[str, Any]]:
+        target_module_to_kwargs: dict[nn.Module, dict[str, Any]] = {}
+        for module in root_module.modules():
+            if module in ignored_modules:
+                continue
+            res = self._lambda_fn(module)
+            if not isinstance(res, (dict, bool)):
+                raise ValueError(
+                    "The lambda_fn passed to CustomPolicy should return "
+                    f"False/True or a kwarg dict, but it returned {res}"
+                )
+            if not res:
+                continue
+            kwargs = copy.copy(root_kwargs)
+            if isinstance(res, dict):
+                # Override the root kwargs with the ones specified by the
+                # lambda function
+                kwargs.update(res)
+            target_module_to_kwargs[module] = kwargs
+        return target_module_to_kwargs
+
+
+def lambda_auto_wrap_policy(
+    module: nn.Module, recurse: bool, nonwrapped_numel: int, lambda_fn: Callable
+) -> bool:
+    """
+    A convenient auto wrap policy to wrap submodules based on an arbitrary user
+    function. If `lambda_fn(submodule) == True``, the submodule will be wrapped as
+    a `wrapper_cls` unit.
+
+    Return if a module should be wrapped during auto wrapping.
+
+    The first three parameters are required by :func:`_recursive_wrap`.
+
+    Args:
+        module (nn.Module): Current module being considered.
+        recurse (bool): If ``False``, then this function must decide whether
+            ``module`` should be wrapped as an FSDP instance or not. If
+            ``True``, then the function is still recursing down the module
+            tree as a part of the DFS.
+        nonwrapped_numel (int): Parameter numel not yet wrapped.
+
+        lambda_fn (Callable[[nn.Module], bool]): If this returns ``True``, then
+            this module will be wrapped.
+    """
+    if recurse:
+        return True  # always recurse
+    return lambda_fn(module)
+
+
+def transformer_auto_wrap_policy(
+    module: nn.Module,
+    recurse: bool,
+    nonwrapped_numel: int,
+    transformer_layer_cls: set[type[nn.Module]],
+) -> bool:
+    """
+    See :func:`_module_wrap_policy`, where ``transformer_layer_cls`` is the
+    same as ``module_classes``. Note that shared parameters must be wrapped in
+    the same FSDP instance, so this auto wrap policy can help wrap shared
+    embeddings into the same FSDP instance for transformer models.
+    """
+    return _module_wrap_policy(module, recurse, nonwrapped_numel, transformer_layer_cls)
+
+
+def _wrap_module_cls_individually(
+    module: nn.Module, module_classes: Sequence[type], recurse: bool, *args, **kwargs
+):
+    if recurse:
+        # always recurse
+        return True
+    else:
+        # if not recursing, decide whether we should wrap based on whether the type of module
+        # is in `module_classes`.
+        return isinstance(module, tuple(module_classes))
+
+
+def _or_policy(
+    module: nn.Module,
+    recurse: bool,
+    nonwrapped_numel: int,
+    policies,
+) -> bool:
+    """
+    A policy that wraps ``module`` if any policy in the passed in iterable of
+    ``policies`` returns ``True``.
+    """
+    return any(
+        policy(module=module, recurse=recurse, nonwrapped_numel=nonwrapped_numel)
+        for policy in policies
+    )
+
+
+def size_based_auto_wrap_policy(
+    module: nn.Module,
+    recurse: bool,
+    nonwrapped_numel: int,
+    # Additional custom arguments
+    min_num_params: int = int(1e8),
+    force_leaf_modules: Optional[set[type[nn.Module]]] = None,
+    exclude_wrap_modules: Optional[set[type[nn.Module]]] = None,
+) -> bool:
+    """
+    A size-based auto wrap policy.
+
+    Args:
+        module (nn.Module): Current module being considered.
+        recurse (bool): If ``False``, then this function must decide whether
+            ``module`` should be wrapped as an FSDP instance or not. If
+            ``True``, then the function is still recursing down the module
+            tree as a part of the DFS.
+        nonwrapped_numel (int): Parameter numel not yet wrapped.
+
+        min_num_params (int): Customizable policy input that controls the size
+            threshold over which a module is ready to be wrapped. This is in
+            units of numel.
+        force_leaf_modules (Optional[set[type[nn.Module]]]): Set of module types to keep
+            as leaves, i.e. their children will never be wrapped.
+        exclude_wrap_modules (Optional[set[type[nn.Module]]]): Set of module types to be
+            excluded in wrapping.
+
+    Returns:
+        Whether ``module`` should be wrapped.
+    """
+    force_leaf_modules = (
+        size_based_auto_wrap_policy.FORCE_LEAF_MODULES  # type: ignore[attr-defined]
+        if force_leaf_modules is None
+        else force_leaf_modules
+    )
+    exclude_wrap_modules = (
+        size_based_auto_wrap_policy.EXCLUDE_WRAP_MODULES  # type: ignore[attr-defined]
+        if exclude_wrap_modules is None
+        else exclude_wrap_modules
+    )
+
+    # Keep the argument `min_num_params` for BC for now, but it represents the
+    # minimum non-wrapped *numel* before triggering a wrapping
+    min_nonwrapped_numel = min_num_params
+    is_large = nonwrapped_numel >= min_nonwrapped_numel
+    if recurse:
+        # We should recurse if the module is big enough but not in force_leaf_modules list.
+        return is_large and not isinstance(module, tuple(force_leaf_modules))
+    else:
+        # If we are not recursing, determine if we should wrap.
+        return is_large and not isinstance(module, tuple(exclude_wrap_modules))
+
+
+# Set those defaults to the size_based_auto_wrap_policy function. Make them easy to be imported.
+size_based_auto_wrap_policy.EXCLUDE_WRAP_MODULES = {nn.ModuleList, nn.ModuleDict}  # type: ignore[attr-defined]
+size_based_auto_wrap_policy.FORCE_LEAF_MODULES = {nn.MultiheadAttention}  # type: ignore[attr-defined]
+
+
+@contextlib.contextmanager
+def enable_wrap(
+    *, wrapper_cls: Any, **wrapper_kwargs: Any
+) -> Generator[None, None, None]:
+    """
+    Context manager to wrap modules using a wrapper.
+
+    Useful for when you'd like to apply the same configuration arguments to all
+    child modules that you wrap. A particularly important use case is wrapping
+    large layers so that they get sharded (in-place) during initialization, to
+    avoid running out of system memory. Large layers can indicate that they
+    should be sharded via the ``wrap`` annotation and this context manager can
+    provide the exact configuration for these nested instances.
+
+    Usage::
+
+        with enable_wrap(wrapper_cls, **params):
+            # Wraps layer in FSDP by default if within context
+            self.l1 = wrap(torch.nn.Linear(5, 5))
+
+    Args:
+        wrapper_cls:
+            Class that `wrap` annotation will `wrap` modules with, such as
+            `FullyShardedDataParallel`.
+        **wrapper_kwargs:
+            Configuration settings that will be passed to all ``wrap``
+            instances inside the context
+    """
+    kwargs = {
+        "wrapper_cls": wrapper_cls,
+        **wrapper_kwargs,
+    }
+    with _ConfigAutoWrap(**kwargs):
+        yield
+
+
+def wrap(module: nn.Module, **wrap_overrides: Any) -> nn.Module:
+    """
+    Annotate that a module should be wrapped. Annotated modules will only be
+    wrapped if inside of an :func:`enable_wrap` context manager. This allows
+    a module to be initialized both with and without a wrapper without code
+    change.
+
+    The class that this function wraps the passed in ``nn.Module`` with is the
+    passed in ``wrapper_cls`` argument into ``enable_wrap``. Both
+    ``enable_wrap`` and ``wrap`` can take in kwargs specifying how to construct
+    the ``wrapper_cls`` instance. In the case of duplicate kwargs in
+    ``enable_wrap`` and ``wrap``, the argument passed into ``wrap`` will be
+    respected.
+
+    Usage::
+
+        with enable_wrap(wrapper_cls=FSDP, **fsdp_config):
+            # Wraps layer in FSDP by default if within context
+            self.l1 = wrap(torch.nn.Linear(5, 5))
+
+    Args:
+        module (nn.Module): module to wrap (if in :func:`enable_wrap` context)
+        **wrap_overrides: configuration overrides that will take priority over
+            the values provided by the :func:`enable_wrap` context
+    """
+    if _ConfigAutoWrap.in_autowrap_context:
+        if _ConfigAutoWrap.wrapper_cls is None:
+            raise AssertionError("Expected _ConfigAutoWrap.wrapper_cls to be set")
+
+        wrap_overrides = {**_ConfigAutoWrap.kwargs, **wrap_overrides}
+        return _wrap(
+            module,
+            _ConfigAutoWrap.wrapper_cls,
+            **wrap_overrides,
+        )
+    return module
+
+
+def _wrap(module: nn.Module, wrapper_cls: Callable, **kwargs) -> nn.Module:
+    if wrapper_cls is None:
+        raise AssertionError("Expected wrapper_cls to be set")
+    if hasattr(module, "_wrap_overrides"):
+        # If module has a _wrap_overrides attribute, we force overriding the
+        # FSDP config with these attributes for this module. Currently this
+        # is only used to disable mixed precision for BatchNorm when
+        # auto_wrapping.
+        overrides = {**kwargs, **module._wrap_overrides}  # type: ignore[arg-type, dict-item]
+        return wrapper_cls(module, **overrides)
+
+    return wrapper_cls(module, **kwargs)
+
+
+def _recursive_wrap(
+    module: nn.Module,
+    auto_wrap_policy: Callable,
+    wrapper_cls: Callable,
+    ignored_modules: set[nn.Module],
+    ignored_params: set[nn.Parameter],
+    only_wrap_children: bool = False,
+    **kwargs: Any,
+) -> tuple[nn.Module, int]:
+    """
+    Wraps submodules of ``module`` for which ``auto_wrap_policy`` returns
+    ``True`` with ``wrapper_cls``.
+
+    Args:
+        module (nn.Module): Module to recursively wrap.
+        auto_wrap_policy (Callable): A callable representing a policy that
+            determines which modules to recursively wrap with ``wrapper_cls``.
+        ignored_modules (set[torch.nn.Module]): Modules to ignore when
+            wrapping.
+        ignored_params (set[torch.nn.Parameter]): Parameters to ignore when
+            wrapping; these should be the parameters contained in the modules
+            in ``ignored_modules``.
+    Returns:
+        (nn.Module, int):
+            ``module`` after wrapping and the numel recursively wrapped.
+    """
+    if auto_wrap_policy is None:
+        raise AssertionError("Must specify auto_wrap_policy.")
+    if wrapper_cls is None:
+        raise AssertionError("Must specify wrapper_cls")
+    # Make sure no child is already wrapped.
+    for _, child in module.named_modules():
+        if child in ignored_modules:
+            continue
+        try:
+            if isinstance(child, cast(type, wrapper_cls)):
+                raise AssertionError(
+                    f"Child module {child} is already wrapped by {wrapper_cls}"
+                )
+        except TypeError:
+            # wrapper_cls is a function as opposed to a class type, just bypass above check.
+            pass
+
+    # We count all params, assuming none of them are already wrapped.
+    nonwrapped_numel = sum(
+        p.numel() for p in module.parameters() if p not in ignored_params
+    )
+
+    if auto_wrap_policy is None:
+        raise AssertionError("Expected auto_wrap_policy to be set")
+    if auto_wrap_policy(module=module, recurse=True, nonwrapped_numel=nonwrapped_numel):
+        total_wrapped_numel = 0
+        # Iterate through the children, recursively wrap if necessary
+        for name, child in module.named_children():
+            if child in ignored_modules:
+                continue
+            wrapped_child, num_wrapped_params = _recursive_wrap(
+                module=child,
+                auto_wrap_policy=auto_wrap_policy,
+                wrapper_cls=wrapper_cls,
+                ignored_modules=ignored_modules,
+                ignored_params=ignored_params,
+                **kwargs,
+            )
+            setattr(module, name, wrapped_child)
+            # Keep track of how many parameters have been wrapped
+            total_wrapped_numel += num_wrapped_params
+        # decide if we need to wrap the current module,
+        # since the left over parameters exceed the number of params to wrap
+        remainder = nonwrapped_numel - total_wrapped_numel
+        if not only_wrap_children and auto_wrap_policy(
+            module=module, recurse=False, nonwrapped_numel=remainder
+        ):
+            # Leaf node or final wrapping of the remainder both happen here.
+            return _wrap(module, wrapper_cls, **kwargs), nonwrapped_numel
+        else:
+            return module, total_wrapped_numel
+    return module, 0
+
+
+class _ConfigAutoWrap:
+    """
+    Helper class to wrap modules based on default config args via a context manager.
+    See :func:`enable_wrap` for more information.
+    """
+
+    in_autowrap_context: bool = False  # Context flag
+    wrapper_cls: Optional[Callable] = None  # The wrapper class
+    kwargs: dict[str, Any] = {}  # Wrapper's args
+
+    def __init__(self, **kwargs: dict[str, Any]):
+        self.kwargs = kwargs
+
+    @staticmethod
+    def enable_autowrap_context(kwargs: Any) -> None:
+        if _ConfigAutoWrap.in_autowrap_context:
+            raise NotImplementedError(
+                "You are already within an autowrap context and we currently do not supported nested autowrap."
+            )
+        _ConfigAutoWrap.in_autowrap_context = True
+        # Get and save the wrapper cls for the context.
+        if "wrapper_cls" not in kwargs:
+            raise AssertionError(
+                "Expected to pass in wrapper_cls arg into _ConfigAutoWrap."
+            )
+        _ConfigAutoWrap.wrapper_cls = cast(Callable, kwargs["wrapper_cls"])
+        del kwargs["wrapper_cls"]
+        # Save the rest.
+        _ConfigAutoWrap.kwargs = kwargs
+
+    @staticmethod
+    def disable_autowrap_context() -> None:
+        _ConfigAutoWrap.in_autowrap_context = False
+        _ConfigAutoWrap.wrapper_cls = None
+        _ConfigAutoWrap.kwargs = {}
+
+    def __enter__(self) -> None:
+        self.enable_autowrap_context(self.kwargs)
+
+    def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any) -> None:
+        self.disable_autowrap_context()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/launch.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/launch.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad3307c13303d0319af710923669d119b4cff30c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/launch.py
@@ -0,0 +1,207 @@
+# mypy: allow-untyped-defs
+r"""
+Module ``torch.distributed.launch``.
+
+``torch.distributed.launch`` is a module that spawns up multiple distributed
+training processes on each of the training nodes.
+
+.. warning::
+
+    This module is going to be deprecated in favor of :ref:`torchrun `.
+
+The utility can be used for single-node distributed training, in which one or
+more processes per node will be spawned. The utility can be used for either
+CPU training or GPU training. If the utility is used for GPU training,
+each distributed process will be operating on a single GPU. This can achieve
+well-improved single-node training performance. It can also be used in
+multi-node distributed training, by spawning up multiple processes on each node
+for well-improved multi-node distributed training performance as well.
+This will especially be beneficial for systems with multiple Infiniband
+interfaces that have direct-GPU support, since all of them can be utilized for
+aggregated communication bandwidth.
+
+In both cases of single-node distributed training or multi-node distributed
+training, this utility will launch the given number of processes per node
+(``--nproc-per-node``). If used for GPU training, this number needs to be less
+or equal to the number of GPUs on the current system (``nproc_per_node``),
+and each process will be operating on a single GPU from *GPU 0 to
+GPU (nproc_per_node - 1)*.
+
+**How to use this module:**
+
+1. Single-Node multi-process distributed training
+
+::
+
+    python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE
+               YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other
+               arguments of your training script)
+
+2. Multi-Node multi-process distributed training: (e.g. two nodes)
+
+
+Node 1: *(IP: 192.168.1.1, and has a free port: 1234)*
+
+::
+
+    python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE
+               --nnodes=2 --node-rank=0 --master-addr="192.168.1.1"
+               --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3
+               and all other arguments of your training script)
+
+Node 2:
+
+::
+
+    python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE
+               --nnodes=2 --node-rank=1 --master-addr="192.168.1.1"
+               --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3
+               and all other arguments of your training script)
+
+3. To look up what optional arguments this module offers:
+
+::
+
+    python -m torch.distributed.launch --help
+
+
+**Important Notices:**
+
+1. This utility and multi-process distributed (single-node or
+multi-node) GPU training currently only achieves the best performance using
+the NCCL distributed backend. Thus NCCL backend is the recommended backend to
+use for GPU training.
+
+2. In your training program, you must parse the command-line argument:
+``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module.
+If your training program uses GPUs, you should ensure that your code only
+runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by:
+
+Parsing the local_rank argument
+
+::
+
+    >>> # xdoctest: +SKIP
+    >>> import argparse
+    >>> parser = argparse.ArgumentParser()
+    >>> parser.add_argument("--local-rank", "--local_rank", type=int)
+    >>> args = parser.parse_args()
+
+Set your device to local rank using either
+
+::
+
+    >>> torch.cuda.set_device(args.local_rank)  # before your code runs
+
+or
+
+::
+
+    >>> with torch.cuda.device(args.local_rank):
+    >>>    # your code to run
+    >>>    ...
+
+.. versionchanged:: 2.0.0
+
+    The launcher will passes the ``--local-rank=`` argument to your script.
+    From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the
+    previously used underscored ``--local_rank``.
+
+    For backward compatibility, it may be necessary for users to handle both
+    cases in their argument parsing code. This means including both ``"--local-rank"``
+    and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is
+    provided, the launcher will trigger an error: "error: unrecognized arguments:
+    --local-rank=". For training code that only supports PyTorch 2.0.0+,
+    including ``"--local-rank"`` should be sufficient.
+
+3. In your training program, you are supposed to call the following function
+at the beginning to start the distributed backend. It is strongly recommended
+that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work,
+but ``env://`` is the one that is officially supported by this module.
+
+::
+
+    >>> torch.distributed.init_process_group(backend='YOUR BACKEND',
+    >>>                                      init_method='env://')
+
+4. In your training program, you can either use regular distributed functions
+or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your
+training program uses GPUs for training and you would like to use
+:func:`torch.nn.parallel.DistributedDataParallel` module,
+here is how to configure it.
+
+::
+
+    >>> model = torch.nn.parallel.DistributedDataParallel(model,
+    >>>                                                   device_ids=[args.local_rank],
+    >>>                                                   output_device=args.local_rank)
+
+Please ensure that ``device_ids`` argument is set to be the only GPU device id
+that your code will be operating on. This is generally the local rank of the
+process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``,
+and ``output_device`` needs to be ``args.local_rank`` in order to use this
+utility
+
+5. Another way to pass ``local_rank`` to the subprocesses via environment variable
+``LOCAL_RANK``. This behavior is enabled when you launch the script with
+``--use-env=True``. You must adjust the subprocess example above to replace
+``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher
+will not pass ``--local-rank`` when you specify this flag.
+
+.. warning::
+
+    ``local_rank`` is NOT globally unique: it is only unique per process
+    on a machine.  Thus, don't use it to decide if you should, e.g.,
+    write to a networked filesystem.  See
+    https://github.com/pytorch/pytorch/issues/12042 for an example of
+    how things can go wrong if you don't do this correctly.
+
+
+
+"""
+
+from typing_extensions import deprecated as _deprecated
+
+from torch.distributed.run import get_args_parser, run
+
+
+def parse_args(args):
+    parser = get_args_parser()
+    parser.add_argument(
+        "--use-env",
+        "--use_env",
+        default=False,
+        action="store_true",
+        help="Use environment variable to pass "
+        "'local rank'. For legacy reasons, the default value is False. "
+        "If set to True, the script will not pass "
+        "--local-rank as argument, and will instead set LOCAL_RANK.",
+    )
+    return parser.parse_args(args)
+
+
+def launch(args):
+    if args.no_python and not args.use_env:
+        raise ValueError(
+            "When using the '--no-python' flag, you must also set the '--use-env' flag."
+        )
+    run(args)
+
+
+@_deprecated(
+    "The module torch.distributed.launch is deprecated\n"
+    "and will be removed in future. Use torchrun.\n"
+    "Note that --use-env is set by default in torchrun.\n"
+    "If your script expects `--local-rank` argument to be set, please\n"
+    "change it to read from `os.environ['LOCAL_RANK']` instead. See \n"
+    "https://pytorch.org/docs/stable/distributed.html#launch-utility for \n"
+    "further instructions\n",
+    category=FutureWarning,
+)
+def main(args=None):
+    args = parse_args(args)
+    launch(args)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/launcher/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/launcher/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb744a2b93615b703eb0dafb7c8e6c71bc1ad5d2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/launcher/__init__.py
@@ -0,0 +1,14 @@
+#!/usr/bin/env/python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+from torch.distributed.launcher.api import (  # noqa: F401
+    elastic_launch,
+    launch_agent,
+    LaunchConfig,
+)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/launcher/api.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/launcher/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..2adf5549fecf13560d0c8637085872688c9454a4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/launcher/api.py
@@ -0,0 +1,337 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+import os
+import sys
+import uuid
+from collections.abc import Callable
+from dataclasses import dataclass, field
+from typing import Any
+
+import torch
+import torch.distributed.elastic.rendezvous.registry as rdzv_registry
+from torch._utils_internal import get_default_numa_options
+from torch.distributed.elastic import events, metrics
+from torch.distributed.elastic.agent.server.api import WorkerSpec
+from torch.distributed.elastic.agent.server.local_elastic_agent import LocalElasticAgent
+from torch.distributed.elastic.multiprocessing import (
+    DefaultLogsSpecs,
+    LogsSpecs,
+    SignalException,
+)
+from torch.distributed.elastic.multiprocessing.errors import ChildFailedError
+from torch.distributed.elastic.rendezvous import RendezvousParameters
+from torch.distributed.elastic.rendezvous.utils import parse_rendezvous_endpoint
+from torch.distributed.elastic.utils.logging import get_logger
+from torch.numa.binding import NumaOptions
+
+
+__all__ = ["LaunchConfig", "elastic_launch", "launch_agent"]
+
+logger = get_logger(__name__)
+
+
+@dataclass
+class LaunchConfig:
+    """
+    Creates a rendezvous config.
+
+    Args:
+        min_nodes: Minimum amount of nodes that the user function will
+                        be launched on. Elastic agent ensures that the user
+                        function start only when the min_nodes amount enters
+                        the rendezvous.
+        max_nodes: Maximum amount of nodes that the user function
+                        will be launched on.
+        nproc_per_node: On each node the elastic agent will launch
+                            this amount of workers that will execute user
+                            defined function.
+        rdzv_backend: rdzv_backend to use in the rendezvous (zeus-adapter, etcd).
+        rdzv_endpoint: The endpoint of the rdzv sync. storage.
+        rdzv_configs: Key, value pair that specifies rendezvous specific configuration.
+        rdzv_timeout: Legacy argument that specifies timeout for the rendezvous. It is going
+            to be removed in future versions, see the note below. The default timeout is 900 seconds.
+        run_id: The unique run id of the job (if not passed a unique one will be
+                deduced from run environment - flow workflow id in flow - or auto generated).
+        role: User defined role of the worker (defaults to "trainer").
+        max_restarts: The maximum amount of restarts that elastic agent will conduct
+                    on workers before failure.
+        monitor_interval: The interval in seconds that is used by the elastic_agent
+                        as a period of monitoring workers.
+        start_method: The method is used by the elastic agent to start the
+                    workers (spawn, fork, forkserver).
+        metrics_cfg: configuration to initialize metrics.
+        local_addr: address of the local node if any. If not set, a lookup on the local
+                machine's FQDN will be performed.
+        local_ranks_filter: ranks for which to show logs in console. If not set, show from all.
+        event_log_handler: name of the event logging handler as registered in
+          `elastic/events/handlers.py `_.
+        duplicate_stdout_filters: If non-empty, duplicates stdout to a file containing only lines
+                                that match _any_ of the filter strings.
+        duplicate_stderr_filters: If non-empty, duplicates stderr to a file containing only lines
+                                that match _any_ of the filter strings.
+        virtual_local_rank: Enable virtual local rank mode for workers (defaults to False).
+                           When enabled, LOCAL_RANK is set to 0 for all workers and
+                           CUDA_VISIBLE_DEVICES is adjusted so each worker accesses its
+                           assigned GPU at device index 0.
+
+
+    .. note::
+        `rdzv_timeout` is a legacy argument that will be removed in future.
+        Set the timeout via `rdzv_configs['timeout']`
+
+    """
+
+    min_nodes: int
+    max_nodes: int
+    nproc_per_node: int
+    logs_specs: LogsSpecs | None = None
+    run_id: str = ""
+    role: str = "default_role"
+    rdzv_endpoint: str = ""
+    rdzv_backend: str = "etcd"
+    rdzv_configs: dict[str, Any] = field(default_factory=dict)
+    rdzv_timeout: int = -1
+    max_restarts: int = 3
+    monitor_interval: float = 0.1
+    start_method: str = "spawn"
+    log_line_prefix_template: str | None = None
+    metrics_cfg: dict[str, str] = field(default_factory=dict)
+    local_addr: str | None = None
+    event_log_handler: str = "null"
+    numa_options: NumaOptions | None = None
+    signals_to_handle: str = "SIGTERM,SIGINT,SIGHUP,SIGQUIT"
+    duplicate_stdout_filters: list[str] | None = None
+    duplicate_stderr_filters: list[str] | None = None
+    virtual_local_rank: bool = False
+
+    def __post_init__(self):
+        default_timeout = 900
+        if self.rdzv_timeout != -1:
+            self.rdzv_configs["timeout"] = self.rdzv_timeout
+        elif "timeout" not in self.rdzv_configs:
+            self.rdzv_configs["timeout"] = default_timeout
+
+        # Post-processing to enable refactoring to introduce logs_specs due to non-torchrun API usage
+        if self.logs_specs is None:
+            self.logs_specs = DefaultLogsSpecs()
+
+        if (
+            self.numa_options is None
+            and torch.cuda.is_available()
+            # We assume local_rank n uses cuda device n.
+            and torch.cuda.device_count() == self.nproc_per_node
+        ):
+            self.numa_options = get_default_numa_options()
+            logger.info("Using default numa options = %r", self.numa_options)
+
+
+class elastic_launch:
+    """
+    Launches an torchelastic agent on the container that invoked the entrypoint.
+
+        1. Pass the ``entrypoint`` arguments as non ``kwargs`` (e.g. no named parameters)/
+           ``entrypoint`` can be a function or a command.
+        2. The return value is a map of each worker's output mapped
+           by their respective global rank.
+
+    Usage
+
+    ::
+
+    def worker_fn(foo):
+        # ...
+
+    def main():
+        # entrypoint is a function.
+        outputs = elastic_launch(LaunchConfig, worker_fn)(foo)
+        # return rank 0's output
+        return outputs[0]
+
+        # entrypoint is a command and ``script.py`` is the python module.
+        outputs = elastic_launch(LaunchConfig, "script.py")(args)
+        outputs = elastic_launch(LaunchConfig, "python")("script.py")
+    """
+
+    def __init__(
+        self,
+        config: LaunchConfig,
+        entrypoint: Callable | str | None,
+    ):
+        self._config = config
+        self._entrypoint = entrypoint
+
+    def __call__(self, *args):
+        return launch_agent(self._config, self._entrypoint, list(args))
+
+
+def _get_entrypoint_name(entrypoint: Callable | str | None, args: list[Any]) -> str:
+    """Retrieve entrypoint name with the rule:
+    1. If entrypoint is a function, use ``entrypoint.__qualname__``.
+    2. If entrypoint is a string, check its value:
+        2.1 if entrypoint equals to ``sys.executable`` (like "python"), use the first element from ``args``
+            which does not start with hifen letter (for example, "-u" will be skipped).
+        2.2 otherwise, use ``entrypoint`` value.
+    3. Otherwise, return empty string.
+    """
+    if isinstance(entrypoint, Callable):  # type: ignore[arg-type]
+        return entrypoint.__name__  # type: ignore[union-attr]
+    elif isinstance(entrypoint, str):
+        if entrypoint == sys.executable:
+            return next((arg for arg in args if arg[0] != "-"), "")
+        else:
+            return entrypoint
+    else:
+        return ""
+
+
+def _get_addr_and_port(
+    rdzv_parameters: RendezvousParameters,
+) -> tuple[str | None, int | None]:
+    if rdzv_parameters.backend != "static":
+        return (None, None)
+    endpoint = rdzv_parameters.endpoint
+    endpoint = endpoint.strip()
+    if not endpoint:
+        raise ValueError(
+            "Endpoint is missing in endpoint. Try to add --master-addr and --master-port"
+        )
+    master_addr, master_port = parse_rendezvous_endpoint(endpoint, default_port=-1)
+    if master_port == -1:
+        raise ValueError(
+            f"port is missing in endpoint: {endpoint}. Try to specify --master-port"
+        )
+    return (master_addr, master_port)
+
+
+def launch_agent(
+    config: LaunchConfig,
+    entrypoint: Callable | str | None,
+    args: list[Any],
+) -> dict[int, Any]:
+    if not config.run_id:
+        run_id = str(uuid.uuid4().int)
+        logger.warning("config has no run_id, generated a random run_id: %s", run_id)
+        config.run_id = run_id
+
+    entrypoint_name = _get_entrypoint_name(entrypoint, args)
+
+    logger.info(
+        "Starting elastic_operator with launch configs:\n"
+        "  entrypoint               : %(entrypoint)s\n"
+        "  min_nodes                : %(min_nodes)s\n"
+        "  max_nodes                : %(max_nodes)s\n"
+        "  nproc_per_node           : %(nproc_per_node)s\n"
+        "  run_id                   : %(run_id)s\n"
+        "  rdzv_backend             : %(rdzv_backend)s\n"
+        "  rdzv_endpoint            : %(rdzv_endpoint)s\n"
+        "  rdzv_configs             : %(rdzv_configs)s\n"
+        "  max_restarts             : %(max_restarts)s\n"
+        "  monitor_interval         : %(monitor_interval)s\n"
+        "  log_dir                  : %(log_dir)s\n"
+        "  metrics_cfg              : %(metrics_cfg)s\n"
+        "  event_log_handler        : %(event_log_handler)s\n"
+        "  numa_options             : %(numa_options)s\n"
+        "  signals_to_handle        : %(signals_to_handle)s\n"
+        "  duplicate_stdout_filters : %(duplicate_stdout_filters)s\n"
+        "  duplicate_stderr_filters : %(duplicate_stderr_filters)s\n",
+        {
+            "entrypoint": entrypoint_name,
+            "min_nodes": config.min_nodes,
+            "max_nodes": config.max_nodes,
+            "nproc_per_node": config.nproc_per_node,
+            "run_id": config.run_id,
+            "rdzv_backend": config.rdzv_backend,
+            "rdzv_endpoint": config.rdzv_endpoint,
+            "rdzv_configs": config.rdzv_configs,
+            "max_restarts": config.max_restarts,
+            "monitor_interval": config.monitor_interval,
+            "log_dir": config.logs_specs.root_log_dir,  # type: ignore[union-attr]
+            "metrics_cfg": config.metrics_cfg,
+            "event_log_handler": config.event_log_handler,
+            "numa_options": config.numa_options,
+            "signals_to_handle": config.signals_to_handle,
+            "duplicate_stdout_filters": config.duplicate_stdout_filters,
+            "duplicate_stderr_filters": config.duplicate_stderr_filters,
+        },
+    )
+
+    rdzv_parameters = RendezvousParameters(
+        backend=config.rdzv_backend,
+        endpoint=config.rdzv_endpoint,
+        run_id=config.run_id,
+        min_nodes=config.min_nodes,
+        max_nodes=config.max_nodes,
+        local_addr=config.local_addr,
+        **config.rdzv_configs,
+    )
+
+    master_addr, master_port = _get_addr_and_port(rdzv_parameters)
+
+    # Set the signals to handle in the environment variable
+    os.environ["TORCHELASTIC_SIGNALS_TO_HANDLE"] = config.signals_to_handle
+
+    spec = WorkerSpec(
+        role=config.role,
+        local_world_size=config.nproc_per_node,
+        entrypoint=entrypoint,
+        args=tuple(args),
+        rdzv_handler=rdzv_registry.get_rendezvous_handler(rdzv_parameters),
+        max_restarts=config.max_restarts,
+        monitor_interval=config.monitor_interval,
+        master_addr=master_addr,
+        master_port=master_port,
+        local_addr=config.local_addr,
+        event_log_handler=config.event_log_handler,
+        numa_options=config.numa_options,
+        duplicate_stdout_filters=config.duplicate_stdout_filters,
+        duplicate_stderr_filters=config.duplicate_stderr_filters,
+        virtual_local_rank=config.virtual_local_rank,
+    )
+
+    agent = LocalElasticAgent(
+        spec=spec,
+        logs_specs=config.logs_specs,  # type: ignore[arg-type]
+        start_method=config.start_method,
+        log_line_prefix_template=config.log_line_prefix_template,
+    )
+
+    shutdown_rdzv = True
+    try:
+        metrics.initialize_metrics(metrics.MetricsConfig(config.metrics_cfg))
+
+        result = agent.run()
+        # records that agent.run() has succeeded NOT that workers have succeeded
+        events.record(agent.get_event_succeeded(), config.event_log_handler)
+
+        if result.is_failed():
+            # ChildFailedError is treated specially by @record
+            # if the error files for the failed children exist
+            # @record will copy the first error (root cause)
+            # to the error file of the launcher process.
+            raise ChildFailedError(
+                name=entrypoint_name,
+                failures=result.failures,
+            )
+
+        return result.return_values
+    except ChildFailedError:
+        raise
+    except SignalException:
+        # when the agent dies with a signal do NOT shutdown the rdzv_handler
+        # since this closes the rendezvous on this rdzv_id permanently and
+        # prevents any additional scaling events
+        shutdown_rdzv = False
+        events.record(agent.get_event_failed(), config.event_log_handler)
+        raise
+    except Exception:
+        events.record(agent.get_event_failed(), config.event_log_handler)
+        raise
+    finally:
+        if shutdown_rdzv:
+            spec.rdzv_handler.shutdown()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/logging_handlers.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/logging_handlers.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed6832fd1ae834b6365a6b005b07bbbfffe90726
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/logging_handlers.py
@@ -0,0 +1,16 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+
+
+__all__: list[str] = []
+
+_log_handlers: dict[str, logging.Handler] = {
+    "default": logging.NullHandler(),
+}
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/nn/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/nn/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e15fb517052e4aefeb7377d1f0ca63cf2b2da753
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/nn/__init__.py
@@ -0,0 +1,7 @@
+import torch
+
+from .functional import *  # noqa: F403
+
+
+if torch.distributed.rpc.is_available():
+    from .api.remote_module import RemoteModule
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/nn/api/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/nn/api/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/nn/api/remote_module.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/nn/api/remote_module.py
new file mode 100644
index 0000000000000000000000000000000000000000..728bf9c0288a2002c6c81d5347cc0c44d27957da
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/nn/api/remote_module.py
@@ -0,0 +1,771 @@
+#!/usr/bin/python3
+# mypy: allow-untyped-defs
+import collections
+import io
+import sys
+import types
+from collections.abc import Callable, Iterator, Mapping
+from typing import Any, TypeVar, Union
+from typing_extensions import Self
+
+import torch
+import torch.distributed.rpc as rpc
+from torch import device, dtype, nn, Tensor
+from torch.distributed import _remote_device
+from torch.distributed.nn.jit import instantiator
+from torch.distributed.rpc.internal import _internal_rpc_pickler
+from torch.nn import Module
+from torch.nn.parameter import Parameter
+from torch.utils.hooks import RemovableHandle
+
+
+__all__ = ["RemoteModule"]
+
+_grad_t = Union[tuple[Tensor, ...], Tensor]
+# See https://mypy.readthedocs.io/en/latest/generics.html#generic-methods-and-generic-self for the use
+# of `T` to annotate `self`. Many methods of `Module` return `self` and we want those return values to be
+# the type of the subclass, not the looser type of `Module`.
+T = TypeVar("T", bound="Module")
+
+_NON_SCRIPTABLE_REMOTE_MODULE_MODULE = (
+    instantiator.instantiate_non_scriptable_remote_module_template()
+)
+
+_REMOTE_MODULE_PICKLED_ATTRIBUTES = (
+    "on",
+    "device",
+    "is_device_map_set",
+    "is_scriptable",
+    "generated_methods",
+    "module_rref",
+)
+
+_SerializedRemoteModule = collections.namedtuple(  # type: ignore[misc]
+    "_SerializedRemoteModule",
+    _REMOTE_MODULE_PICKLED_ATTRIBUTES,
+)
+
+# These attributes are mostly from RemoteModule's parent class and are intentionally not pickled.
+# A new attribute of RemoteModule should be either in _REMOTE_MODULE_PICKLED_ATTRIBUTES
+# or _REMOTE_MODULE_ATTRIBUTES_IGNORE_FOR_PICKLING.
+# Otherwise, it will not be pickled.
+_REMOTE_MODULE_ATTRIBUTES_IGNORE_FOR_PICKLING = (
+    "training",
+    "_parameters",
+    "_buffers",
+    "_non_persistent_buffers_set",
+    "_backward_hooks",
+    "_backward_pre_hooks",
+    "_is_full_backward_hook",
+    "_forward_hooks",
+    "_forward_hooks_with_kwargs",
+    "_forward_hooks_always_called",
+    "_forward_pre_hooks",
+    "_forward_pre_hooks_with_kwargs",
+    "_state_dict_hooks",
+    "_state_dict_pre_hooks",
+    "_load_state_dict_pre_hooks",
+    "_load_state_dict_post_hooks",
+    "_state_dict_pre_hooks",
+    "_modules",
+    # The two attributes below are generated methods, not available at pickling time.
+    "forward_async",
+    "forward",
+)
+
+
+# RPC handler.
+def _instantiate_template(module_interface_cls, enable_moving_cpu_tensors_to_cuda):
+    instantiator.instantiate_scriptable_remote_module_template(
+        module_interface_cls, enable_moving_cpu_tensors_to_cuda
+    )
+
+
+def _create_module(module_cls, args, kwargs, device):
+    module = module_cls(*args, **kwargs)
+    if not isinstance(module, nn.Module):
+        raise ValueError(
+            "Expect `module_cls(*args, **kwargs)` returns an instance of , "
+            f"but it returns an instance of {type(module)}."
+        )
+    module.to(device)
+    return module
+
+
+def _create_module_with_interface(
+    module_cls, args, kwargs, device, module_interface_cls
+):
+    module = _create_module(module_cls, args, kwargs, device)
+    if module_interface_cls is not None:
+        module = torch.jit.script(module)
+    return rpc.RRef(module, module_interface_cls)
+
+
+def _param_rrefs(module_rref, recurse) -> list[rpc.RRef[Parameter]]:
+    ret: list[rpc.RRef[Parameter]] = [
+        rpc.RRef(param) for param in module_rref.local_value().parameters(recurse)
+    ]
+    return ret
+
+
+def _raise_not_supported(name: str) -> None:
+    raise ValueError(f"Method ``{name}`` not supported for RemoteModule")
+
+
+class _RemoteModule(nn.Module):
+    def __new__(cls, *args, **kwargs):
+        # Use __new__ for logging purposes.
+        torch._C._log_api_usage_once("torch.distributed.nn.api.remote_module")
+        return super().__new__(cls)
+
+    def __init__(
+        self,
+        remote_device: str,
+        module_cls: type[nn.Module],
+        args: tuple | None = None,
+        kwargs: dict[str, Any] | None = None,
+        _module_interface_cls: Any = None,
+    ):
+        """
+        RemoteModule instance can only be created after RPC initialization.
+
+        It creates a user-specified module on a specified remote node.
+        It behaves like a regular ``nn.Module`` except that the ``forward`` method is
+        executed on the remote node.
+        It takes care of autograd recording to ensure the backward pass propagates
+        gradients back to the corresponding remote module.
+        It can be shared across processors using `RPC framework `__,
+        without incurring any overheads of copying the actual module,
+        which is equivalent to an :class:`~torch.distributed.rpc.RRef`
+        pointing to the remote module.
+
+        The arguments of ``forward_async`` and ``forward`` are the same as
+        the ``forward`` method of the module returned by the ``module_cls``.
+
+        Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now.
+
+        Particularly, to create a hybrid model, typically the local modules should be
+        created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``).
+        Hybrid Example:
+                >>> class HybridModel(nn.Module):
+                >>>     def __init__(self) -> None:
+                >>>         nn.Module.__init__(self)
+                >>>         self.remote_embedding = RemoteModule(...)
+                >>>         self.local_linear = nn.Linear(...)
+
+        For example, if ``module_cls`` returns an instance of ``nn.Linear``,
+        that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``,
+        the generated ``RemoteModule`` will have 2 methods in signature of
+        ``def forward(input: Tensor) -> Tensor:`` and
+        ``def forward_async(input: Tensor) -> Future[Tensor]:``.
+
+        .. note::
+            If the remote module is placed on a cuda device,
+            any input CPU tensors will be automatically moved to the same cuda device,
+            and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend.
+
+        Args:
+            remote_device (str): Device on the destination worker where we'd like to place this module.
+                The device can be a local device or a remote device specified by one of the following remote
+                formats:
+
+                    1. "rank:/" (ex: "rank:0/cuda:0").
+                    2. "/" (ex: "trainer0/cuda:0").
+
+                In addition, the device field can be optional and the default value is "cpu".
+            module_cls (nn.Module): For example,
+                >>> class MyModule(nn.Module):
+                >>>     def forward(input):
+                >>>         return input + 1
+                >>>
+                >>> module_cls = MyModule
+            args (Sequence, optional): args to be passed to ``module_cls``.
+            kwargs (Dict, optional): kwargs to be passed to ``module_cls``.
+            _module_interface_cls (type, optional): The TorchScript interface type for the module
+                to be created. The type object should be decorated by @torch.jit.interface.
+                If not provided, the generated RemoteModule is not torchscript-able.
+                Warning, this is an experimental API and susceptible to frequent changes.
+
+        Returns:
+            A remote module instance which wraps the :class:`~nn.Module` created by the
+            user-provided ``module_cls``, it has a blocking ``forward`` method and an
+            asynchronous ``forward_async`` method that returns a future of the ``forward`` call
+            on the user-provided module on the remote side.
+
+        Example::
+            Run the following code in two different processes:
+
+            >>> # xdoctest: +SKIP("distributed")
+            >>> # On worker 0:
+            >>> import torch
+            >>> import torch.distributed.rpc as rpc
+            >>> from torch import nn, Tensor
+            >>> from torch.distributed.nn.api.remote_module import RemoteModule
+            >>>
+            >>> rpc.init_rpc("worker0", rank=0, world_size=2)
+            >>> remote_linear_module = RemoteModule(
+            >>>     "worker1/cpu", nn.Linear, args=(20, 30),
+            >>> )
+            >>> input = torch.randn(128, 20)
+            >>> ret_fut = remote_linear_module.forward_async(input)
+            >>> ret = ret_fut.wait()
+            >>> rpc.shutdown()
+
+            >>> # On worker 1:
+            >>> import torch
+            >>> import torch.distributed.rpc as rpc
+            >>>
+            >>> rpc.init_rpc("worker1", rank=1, world_size=2)
+            >>> rpc.shutdown()
+        """
+        super().__init__()
+
+        enable_moving_cpu_tensors_to_cuda = self._prepare_init(remote_device)
+
+        # Default arguments preparation.
+        args = args if args is not None else ()
+        kwargs = kwargs if kwargs is not None else {}
+
+        if _module_interface_cls is not None:
+            # Users reply on this field to know if this generated RemoteModule is TorchScript-able.
+            self.is_scriptable = True
+
+            # Instantiate template on remote side.
+            fut = rpc.rpc_async(
+                self.on,
+                _instantiate_template,
+                (_module_interface_cls, enable_moving_cpu_tensors_to_cuda),
+            )
+
+            self._init_template(
+                _module_interface_cls, enable_moving_cpu_tensors_to_cuda
+            )
+
+            # Instantiate template on remote side.
+            fut = rpc.rpc_async(
+                self.on,
+                _instantiate_template,
+                (_module_interface_cls, enable_moving_cpu_tensors_to_cuda),
+            )
+
+            # Create the module on the remote side.
+            fut.wait()  # Ensure remote_module_cls is available on remote side.
+
+            # TODO: We need to change this to rpc.remote, and make it async (see the else branch below).
+            # For that we need to be able to apply _module_interface_cls to the RRef returned by rpc.remote
+            # See https://github.com/pytorch/pytorch/issues/58098 for more context.
+            self.module_rref = rpc.rpc_sync(
+                self.on,
+                _create_module_with_interface,
+                (module_cls, args, kwargs, self.device, _module_interface_cls),
+            )
+        else:
+            self.is_scriptable = False
+            self.generated_methods = (
+                _NON_SCRIPTABLE_REMOTE_MODULE_MODULE._generated_methods
+            )
+            # Create the module on the remote side.
+            self.module_rref = rpc.remote(
+                self.on,
+                _create_module,
+                (module_cls, args, kwargs, self.device),
+            )
+
+        self._install_generated_methods()
+        self._check_attribute_picklability()
+
+    def remote_parameters(self, recurse: bool = True) -> list[rpc.RRef[Parameter]]:
+        """
+        Return a list of :class:`~torch.distributed.rpc.RRef` pointing to the remote module's parameters.
+
+        This can typically be used in conjunction
+        with :class:`~torch.distributed.optim.DistributedOptimizer`.
+
+        Args:
+            recurse (bool): if True, then returns parameters of the remote
+                module and all submodules of the remote module. Otherwise,
+                returns only parameters that are direct members of the
+                remote module.
+
+        Returns:
+            A list of :class:`~torch.distributed.rpc.RRef` (``List[RRef[nn.Parameter]]``)
+            to remote module's parameters.
+        """
+        return rpc.rpc_sync(self.on, _param_rrefs, args=(self.module_rref, recurse))
+
+    def get_module_rref(self) -> rpc.RRef[nn.Module]:
+        """Return an :class:`~torch.distributed.rpc.RRef` (``RRef[nn.Module]``) pointing to the remote module."""
+        return self.module_rref
+
+    @torch.jit.export
+    def __getstate__(self):
+        raise RuntimeError(
+            "Cannot pickle RemoteModule in python pickler. RemoteModule can only be pickled when using RPC"
+        )
+
+    @torch.jit.export
+    def __setstate__(self, state):
+        raise RuntimeError(
+            "Cannot unpickle RemoteModule in python pickler. RemoteModule can only be unpickled when using RPC"
+        )
+
+    def register_buffer(
+        self, name: str, tensor: Tensor | None, persistent: bool = True
+    ) -> None:
+        _raise_not_supported(self.register_buffer.__name__)
+
+    def register_parameter(self, name: str, param: Parameter | None) -> None:
+        _raise_not_supported(self.register_parameter.__name__)
+
+    def add_module(self, name: str, module: Module | None) -> None:
+        _raise_not_supported(self.add_module.__name__)
+
+    def apply(self, fn: Callable[[Module], None]) -> Self:  # type: ignore[return]
+        _raise_not_supported(self.apply.__name__)
+
+    def cuda(self, device: int | device | None = None) -> Self:  # type: ignore[return]
+        _raise_not_supported(self.cuda.__name__)
+
+    def ipu(self, device: int | device | None = None) -> Self:  # type: ignore[return]
+        _raise_not_supported(self.ipu.__name__)
+
+    def xpu(self, device: int | device | None = None) -> Self:  # type: ignore[return]
+        _raise_not_supported(self.xpu.__name__)
+
+    def cpu(self) -> Self:  # type: ignore[return]
+        _raise_not_supported(self.cpu.__name__)
+
+    def type(self, dst_type: dtype | str) -> Self:  # type: ignore[return]
+        _raise_not_supported(self.type.__name__)
+
+    def float(self) -> Self:  # type: ignore[return]
+        _raise_not_supported(self.float.__name__)
+
+    def double(self) -> Self:  # type: ignore[return]
+        _raise_not_supported(self.double.__name__)
+
+    def half(self) -> Self:  # type: ignore[return]
+        _raise_not_supported(self.half.__name__)
+
+    def bfloat16(self) -> Self:  # type: ignore[return]
+        _raise_not_supported(self.bfloat16.__name__)
+
+    def to(self, *args, **kwargs) -> T:  # type: ignore[misc, return, type-var]
+        _raise_not_supported(self.to.__name__)
+
+    def register_backward_hook(  # type: ignore[return]
+        self,
+        hook: Callable[[Module, _grad_t, _grad_t], None | _grad_t],
+        # pyrefly: ignore [bad-return]
+    ) -> RemovableHandle:
+        _raise_not_supported(self.register_backward_hook.__name__)
+
+    def register_forward_pre_hook(  # type: ignore[return]
+        self,
+        hook: Callable[[T, tuple[Any, ...]], Any | None]
+        | Callable[
+            [T, tuple[Any, ...], dict[str, Any]], tuple[Any, dict[str, Any]] | None
+        ],
+        prepend: bool = False,
+        with_kwargs: bool = False,
+        # pyrefly: ignore [bad-return]
+    ) -> RemovableHandle:
+        _raise_not_supported(self.register_forward_pre_hook.__name__)
+
+    def register_forward_hook(  # type: ignore[return, override]
+        self,
+        hook: Callable[[T, tuple[Any, ...], Any], Any | None]
+        | Callable[[T, tuple[Any, ...], dict[str, Any], Any], Any | None],
+        prepend: bool = False,
+        with_kwargs: bool = False,
+        # pyrefly: ignore [bad-return]
+    ) -> RemovableHandle:
+        _raise_not_supported(self.register_forward_hook.__name__)
+
+    def state_dict(self, *args, **kwargs):
+        _raise_not_supported(self.state_dict.__name__)
+
+    def load_state_dict(
+        self,
+        state_dict: Mapping[str, Any],
+        strict: bool = True,
+        assign: bool = False,
+    ):
+        _raise_not_supported(self.load_state_dict.__name__)
+
+    def parameters(self, recurse: bool = True) -> Iterator[Parameter]:
+        raise ValueError(
+            "Method ``parameters`` not supported for RemoteModule. Please use ``remote_parameters`` instead."
+        )
+
+    def named_parameters(  # type: ignore[return]
+        self,
+        prefix: str = "",
+        recurse: bool = True,
+        remove_duplicate: bool = True,
+        # pyrefly: ignore [bad-return]
+    ) -> Iterator[tuple[str, Parameter]]:
+        _raise_not_supported(self.named_parameters.__name__)
+
+    def buffers(self, recurse: bool = True) -> Iterator[Tensor]:  # type: ignore[return]
+        _raise_not_supported(self.buffers.__name__)
+
+    def named_buffers(  # type: ignore[return]
+        self,
+        prefix: str = "",
+        recurse: bool = True,
+        remove_duplicate: bool = True,
+        # pyrefly: ignore [bad-return]
+    ) -> Iterator[tuple[str, Tensor]]:
+        _raise_not_supported(self.named_buffers.__name__)
+
+    def children(self) -> Iterator[Module]:  # type: ignore[return]
+        _raise_not_supported(self.children.__name__)
+
+    def named_children(self) -> Iterator[tuple[str, Module]]:  # type: ignore[return]
+        _raise_not_supported(self.named_children.__name__)
+
+    def modules(self) -> Iterator[Module]:  # type: ignore[return]
+        _raise_not_supported(self.modules.__name__)
+
+    def named_modules(
+        self,
+        memo: set[Module] | None = None,
+        prefix: str = "",
+        remove_duplicate: bool = True,
+    ):
+        _raise_not_supported(self.named_modules.__name__)
+
+    def train(self, mode: bool = True) -> Self:
+        return self.module_rref.rpc_sync().train()  # type: ignore[operator, union-attr]
+
+    def eval(self) -> Self:
+        return self.module_rref.rpc_sync().eval()  # type: ignore[operator, union-attr]
+
+    def requires_grad_(self, requires_grad: bool = True) -> Self:  # type: ignore[return]
+        _raise_not_supported(self.requires_grad_.__name__)
+
+    def zero_grad(self, set_to_none: bool = True) -> None:
+        _raise_not_supported(self.zero_grad.__name__)
+
+    def share_memory(self) -> Self:  # type: ignore[return]
+        _raise_not_supported(self.share_memory.__name__)
+
+    def extra_repr(self) -> str:  # type: ignore[return]
+        _raise_not_supported(self.extra_repr.__name__)
+
+    def _prepare_init(self, remote_device_str: str) -> bool:
+        """Prepare the initialization and returns whether to enable automatically moving CPU tensors to CUDA devices."""
+        # Sanity check.
+        assert rpc._is_current_rpc_agent_set(), "RemoteModule only works in RPC."
+
+        remote_device = _remote_device(remote_device_str)
+        self.on = (
+            remote_device.worker_name()
+            if remote_device.worker_name() is not None
+            else remote_device.rank()
+        )
+        self.device = str(remote_device.device())
+        agent = rpc._get_current_rpc_agent()
+        # If the device map of the remote worker is set,
+        # then enable moving any input CPU tensors to the same cuda device.
+        self.is_device_map_set = bool(
+            agent._get_device_map(agent.get_worker_info(self.on))  # type: ignore[arg-type]
+        )
+        # ``enable_moving_cpu_tensors_to_cuda`` is less strict than ``is_device_map_set``:
+        # If ``enable_moving_cpu_tensors_to_cuda`` is true, but the device map is not set,
+        # then any CPU tensors can still be moved to a cuda device to run forward,
+        # but the output must be moved back to CPU before being sent over the wire.
+        enable_moving_cpu_tensors_to_cuda = torch.device(self.device).type == "cuda"
+        return enable_moving_cpu_tensors_to_cuda
+
+    def _init_template(self, module_interface_cls, enable_moving_cpu_tensors_to_cuda):
+        """Instantiate template on local side."""
+        generated_module = instantiator.instantiate_scriptable_remote_module_template(
+            module_interface_cls, enable_moving_cpu_tensors_to_cuda
+        )
+        self.generated_methods = generated_module._generated_methods
+
+    def _check_attribute_picklability(self):
+        """Check if all the attribute has explicitly defined whether to be pickled (i.e., picklability)."""
+        for k in self.__dict__:
+            if (
+                k not in _REMOTE_MODULE_PICKLED_ATTRIBUTES
+                and k not in _REMOTE_MODULE_ATTRIBUTES_IGNORE_FOR_PICKLING
+            ):
+                raise AttributeError(
+                    f"Attribute {k} must be either in ``_REMOTE_MODULE_PICKLED_ATTRIBUTES`` or "
+                    "``_REMOTE_MODULE_ATTRIBUTES_IGNORE_FOR_PICKLING``."
+                )
+
+    def _install_generated_methods(self):
+        for method in self.generated_methods:
+            method_name = method.__name__
+            method = torch.jit.export(method)
+            setattr(self, method_name, types.MethodType(method, self))
+
+    @staticmethod
+    def init_from_module_rref(
+        remote_device: str,
+        module_rref: rpc.RRef[nn.Module],
+        _module_interface_cls: Any = None,
+    ):
+        """
+        Besides the constructor, a RemoteModule instance can also be initialized given a module RRef.
+
+        This alternate initialization method can be particularly useful if we want to create multiple
+        RemoteModule instances that share the same underlying module and reduce memory consumption.
+
+        Moreover, this also provides a workaround for passing script RemoteModule over RPC,
+        which is not supported. The recommended way is as follows:
+
+            1. the sender creates a RemoteModule;
+            2. the sender sends its ``module_rref`` over RPC;
+            3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``.
+
+        Example::
+            Run the following code in two different processes:
+
+            >>> # xdoctest: +SKIP("distributed")
+            >>> # On worker 0:
+            >>> import torch
+            >>> import torch.distributed.rpc as rpc
+            >>> from torch import nn, Tensor
+            >>> from torch.distributed.nn.api.remote_module import RemoteModule
+            >>>
+            >>> rpc.init_rpc("worker0", rank=0, world_size=2)
+            >>> remote_module = RemoteModule(
+            >>>     "worker1/cpu", nn.Linear, args=(20, 30),
+            >>> )
+            >>>
+            >>> remote_module1 = rpc.rpc_sync(
+            >>>     "worker1/cpu",
+            >>>     RemoteModule.init_from_module_rref,
+            >>>     ("worker1/cpu", remote_module1.get_module_rref()),
+            >>> )
+            >>> rpc.shutdown()
+
+            >>> # On worker 1:
+            >>> import torch
+            >>> import torch.distributed.rpc as rpc
+            >>>
+            >>> rpc.init_rpc("worker1", rank=1, world_size=2)
+            >>> rpc.shutdown()
+
+        Args:
+            remote_device (str): Device on the destination worker where we'd like to place this module.
+                The device can be a local device or a remote device specified by one of the following remote
+                formats:
+
+                    1. "rank:/" (ex: "rank:0/cuda:0").
+                    2. "/" (ex: "trainer0/cuda:0").
+
+                In addition, the device field can be optional and the default value is "cpu".
+            module_rref (RRef[nn.Module]): The module reference shared by both the caller and
+                the created remote module.
+            _module_interface_cls (type, optional): The TorchScript interface type for the module
+                to be created. The type object should be decorated by @torch.jit.interface.
+                If not provided, the generated RemoteModule is not torchscript-able.
+                Warning, this is an experimental API and susceptible to frequent changes.
+
+        Returns:
+            A remote module instance which wraps the :class:`~nn.Module` created by the
+            user-provided ``module_rref``, it has a blocking ``forward`` method and an
+            asynchronous ``forward_async`` method that returns a future of the ``forward`` call
+            on the user-provided module on the remote side.
+        """
+        # NOTE: if a new attribute is added to this class, also need to add it
+        # to ``_REMOTE_MODULE_PICKLED_ATTRIBUTES`` for pickling/unpickling.
+
+        remote_module = object.__new__(RemoteModule)
+
+        # pyrefly: ignore [missing-attribute]
+        enable_moving_cpu_tensors_to_cuda = remote_module._prepare_init(remote_device)
+
+        if _module_interface_cls is not None:
+            # Users reply on this field to know if this generated RemoteModule is TorchScript-able.
+            # pyrefly: ignore [missing-attribute]
+            remote_module.is_scriptable = True
+
+            # pyrefly: ignore [missing-attribute]
+            remote_module._init_template(
+                _module_interface_cls, enable_moving_cpu_tensors_to_cuda
+            )
+        else:
+            # pyrefly: ignore [missing-attribute]
+            remote_module.is_scriptable = False
+            # pyrefly: ignore [missing-attribute]
+            remote_module.generated_methods = (
+                _NON_SCRIPTABLE_REMOTE_MODULE_MODULE._generated_methods
+            )
+        # pyrefly: ignore [missing-attribute]
+        remote_module.module_rref = module_rref
+
+        # pyrefly: ignore [missing-attribute]
+        remote_module._install_generated_methods()
+        # pyrefly: ignore [missing-attribute]
+        remote_module._check_attribute_picklability()
+
+        return remote_module
+
+
+class RemoteModule(_RemoteModule):
+    """
+        A RemoteModule instance can only be created after RPC initialization.
+
+        It creates a user-specified module on a specified remote node.
+        It behaves like a regular ``nn.Module`` except that the ``forward`` method is
+        executed on the remote node.
+        It takes care of autograd recording to ensure the backward pass propagates
+        gradients back to the corresponding remote module.
+
+        It generates two methods ``forward_async`` and ``forward`` based on the
+        signature of the ``forward`` method of ``module_cls``. ``forward_async``
+        runs asynchronously and returns a Future. The arguments of ``forward_async``
+        and ``forward`` are the same as the ``forward`` method of the module
+        returned by the ``module_cls``.
+
+        For example, if ``module_cls`` returns an instance of ``nn.Linear``,
+        that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``,
+        the generated ``RemoteModule`` will have 2 methods with the signatures:
+
+        | ``def forward(input: Tensor) -> Tensor:``
+        | ``def forward_async(input: Tensor) -> Future[Tensor]:``
+
+    Args:
+        remote_device (str): Device on the destination worker where we'd like to place this module.
+            The format should be "/", where the device field can be parsed as torch.device type.
+            E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0".
+            In addition, the device field can be optional and the default value is "cpu".
+        module_cls (nn.Module): Class for the module to be created remotely. For example,
+
+            >>> class MyModule(nn.Module):
+            >>>     def forward(input):
+            >>>         return input + 1
+            >>>
+            >>> module_cls = MyModule
+
+        args (Sequence, optional): args to be passed to ``module_cls``.
+        kwargs (Dict, optional): kwargs to be passed to ``module_cls``.
+
+    Returns:
+        A remote module instance which wraps the :class:`~nn.Module` created by the
+        user-provided ``module_cls``, it has a blocking ``forward`` method and an
+        asynchronous ``forward_async`` method that returns a future of the ``forward`` call
+        on the user-provided module on the remote side.
+
+    Example::
+        Run the following code in two different processes:
+
+        >>> # xdoctest: +SKIP("distributed")
+        >>> # On worker 0:
+        >>> import torch
+        >>> import torch.distributed.rpc as rpc
+        >>> from torch import nn, Tensor
+        >>> from torch.distributed.nn.api.remote_module import RemoteModule
+        >>>
+        >>> rpc.init_rpc("worker0", rank=0, world_size=2)
+        >>> remote_linear_module = RemoteModule(
+        >>>     "worker1/cpu", nn.Linear, args=(20, 30),
+        >>> )
+        >>> input = torch.randn(128, 20)
+        >>> ret_fut = remote_linear_module.forward_async(input)
+        >>> ret = ret_fut.wait()
+        >>> rpc.shutdown()
+
+        >>> # On worker 1:
+        >>> import torch
+        >>> import torch.distributed.rpc as rpc
+        >>>
+        >>> rpc.init_rpc("worker1", rank=1, world_size=2)
+        >>> rpc.shutdown()
+
+        Furthermore, a more practical example that is combined with
+        `DistributedDataParallel `__ (DDP)
+        can be found in this `tutorial `__.
+    """
+
+    def __init__(
+        self,
+        remote_device: str,
+        module_cls: type[nn.Module],
+        args: tuple | None = None,
+        kwargs: dict[str, Any] | None = None,
+    ):
+        super().__init__(remote_device, module_cls, args, kwargs)
+
+
+def _remote_module_receiver(
+    *remote_module_pickled_attrs,
+):
+    """Deserializes a RemoteModule."""
+    serialized_remote_module = _SerializedRemoteModule._make(
+        remote_module_pickled_attrs
+    )
+    m = object.__new__(RemoteModule)
+    m.__dict__.update(serialized_remote_module._asdict())
+
+    # Unpickling the attribute `module_rref` must invoke RRef's `_deserialize()` method.
+    # pyrefly: ignore [missing-attribute]
+    m.module_rref = rpc.PyRRef._deserialize(m.module_rref)
+
+    # Install generated methods when unpickled.
+    # pyrefly: ignore [missing-attribute]
+    for method in m.generated_methods:
+        method_name = method.__name__
+        method = torch.jit.export(method)
+        setattr(m, method_name, types.MethodType(method, m))
+
+    return m
+
+
+def _remote_module_reducer(remote_module):
+    """Serialize a RemoteModule."""
+    pickled_attrs = {}
+    for k, v in remote_module.__dict__.items():
+        # Pickling the attribute `module_rref` must invoke RRef's `_serialize()` method.
+        if k == "module_rref":
+            pickled_attrs[k] = v._serialize()
+        elif k in _REMOTE_MODULE_PICKLED_ATTRIBUTES:
+            pickled_attrs[k] = v
+        # Check if unpickled attributes are all in _REMOTE_MODULE_ATTRIBUTES_IGNORE_FOR_PICKLING.
+        elif k not in _REMOTE_MODULE_ATTRIBUTES_IGNORE_FOR_PICKLING:
+            print(
+                f"The new attribute ``{k}`` of RemoteModule is ignored during RPC pickling. "
+                "To pickle this attribute, please add it to ``_REMOTE_MODULE_PICKLED_ATTRIBUTES``. "
+                "Otherwise, please explicitly add it to ``_REMOTE_MODULE_ATTRIBUTES_IGNORE_FOR_PICKLING``.",
+                file=sys.stderr,
+            )
+
+    return (
+        _remote_module_receiver,
+        tuple(pickled_attrs.values()),
+    )
+
+
+def _recursive_script_module_receiver(
+    recursive_script_module_serialized,
+):
+    """Deserializes a RecursiveScriptModule that does not contain a script RemoteModule."""
+    f = io.BytesIO(recursive_script_module_serialized)
+    m = torch.jit.load(f)
+    return m
+
+
+def _recursive_script_module_reducer(recursive_script_module):
+    """Serialize a RecursiveScriptModule that does not contain a script RemoteModule, and raises an error otherwise."""
+    if hasattr(recursive_script_module._c, "module_rref"):
+        raise RuntimeError(
+            "Passing a script RemoteModule over RPC is not supported. Please create a RemoteModule in the sender, "
+            "send the `module_rref` to the receiver, and create a new instance on the receiver end by passing this `module_rref`."
+        )
+
+    f = io.BytesIO()
+    torch.jit.save(recursive_script_module, f)
+    return (_recursive_script_module_receiver, (f.getvalue(),))
+
+
+_internal_rpc_pickler._register_reducer(RemoteModule, _remote_module_reducer)
+_internal_rpc_pickler._register_reducer(
+    torch.jit.RecursiveScriptModule, _recursive_script_module_reducer
+)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/nn/functional.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/nn/functional.py
new file mode 100644
index 0000000000000000000000000000000000000000..287775be924a399aff01fcda66f6ebc838c62873
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/nn/functional.py
@@ -0,0 +1,469 @@
+# mypy: allow-untyped-defs
+import torch
+import torch.distributed as dist
+from torch.autograd import Function
+
+# The two imports below are not always available depending on the
+# USE_DISTRIBUTED compile flag. Make sure they raise import error
+# if we're trying to use them.
+from torch.distributed import group, ReduceOp
+
+
+def broadcast(tensor, src, group=group.WORLD):
+    """
+    Broadcasts the tensor to the whole group.
+
+    ``tensor`` must have the same number of elements in all processes
+    participating in the collective.
+
+    Arguments:
+        tensor (Tensor): Data to be sent if ``src`` is the rank of current
+            process.
+        src (int): Source rank.
+        group (ProcessGroup, optional): The process group to work on.
+
+    Returns:
+        Tensor: Received tensor from the broadcast op.
+
+    """
+    return _Broadcast.apply(src, group, tensor)
+
+
+def gather(tensor, dst=0, group=group.WORLD):
+    """
+    Gathers a list of tensors in a single process.
+
+    Arguments:
+        tensor (Tensor): Input tensor.
+        dst (int, optional): Destination rank (default is 0).
+        group (ProcessGroup, optional): The process group to work on.
+
+    Returns:
+        tuple[Tensor]: List of appropriately-sized tensors with the gathered data.
+    """
+    return _Gather.apply(dst, group, tensor)
+
+
+def scatter(tensors, src=0, group=group.WORLD):
+    """
+    Scatters a list of tensors to all processes in a group.
+
+    Each process will receive exactly one tensor and store its data in the
+    ``tensor`` argument.
+
+    Arguments:
+        tensors (list[Tensor]): List of tensors to scatter on the source rank.
+            Receivers must pass ``None`.
+        src (int, optional): Source rank (default is 0).
+        group (ProcessGroup, optional): The process group to work on.
+
+    Returns:
+        Tensor: Output tensor from the scatter operation.
+
+    """
+    return _Scatter.apply(src, group, *tensors)
+
+
+def reduce(tensor, dst, op=ReduceOp.SUM, group=group.WORLD):
+    """
+    Reduces the tensor data across all machines.
+
+    Only the process with rank ``dst`` is going to receive the final result.
+
+    Arguments:
+        tensor (Tensor): Input of the collective.
+        dst (int): Destination rank.
+        op (optional): One of the values from
+            ``torch.distributed.ReduceOp``
+            enum.  Specifies an operation used for element-wise reductions.
+        group (ProcessGroup, optional): The process group to work on.
+
+    Returns:
+        Tensor: Output of the collective.
+
+    """
+    return _Reduce.apply(dst, op, group, tensor)
+
+
+def reduce_scatter(output, input_list, op=ReduceOp.SUM, group=group.WORLD):
+    """
+    Reduces, then scatters a list of tensors to all processes in a group.
+
+    Arguments:
+        output (Tensor): Output tensor.
+        input_list (list[Tensor]): List of tensors to reduce and scatter.
+        op (optional): One of the values from
+            ``torch.distributed.ReduceOp``
+            enum.  Specifies an operation used for element-wise reductions.
+        group (ProcessGroup, optional): The process group to work on.
+
+    Returns:
+        Tensor: Output of the collective.
+
+    """
+    return _Reduce_Scatter.apply(op, group, output, *input_list)
+
+
+def all_gather(tensor, group=group.WORLD):
+    """
+    Gathers tensors from the whole group in a list.
+
+    Arguments:
+        tensor (Tensor): Tensor to be broadcast from current process.
+        group (ProcessGroup, optional): The process group to work on.
+
+    Returns:
+        tuple([Tensor]): Output of the collective.
+
+    """
+    return _AllGather.apply(group, tensor)
+
+
+def _all_gather_base(output_tensor, input_tensor, group=group.WORLD):
+    """
+    Single tensor all gather. Gathers a single tensor from all ranks, and puts them in a single output tensor.
+
+    Args:
+        output_tensor (Tensor): Output tensor. It should contain
+            correctly-sized tensors to be used for output of the collective.
+        input_tensor (Tensor): Tensor to be broadcast from current process.
+        group (ProcessGroup, optional): The process group to work on. If None,
+            the default process group will be used.
+
+    Examples:
+        >>> # All tensors below are of torch.int64 dtype.
+        >>> # We have 2 process groups, 2 ranks.
+        >>> # xdoctest: +SKIP("incorrect want text")
+        >>> output_tensor = torch.zeros(2, dtype=torch.int64)
+        >>> output_tensor
+        [tensor([0, 0])] # Rank 0 and 1
+        >>> tensor = torch.arange(1, dtype=torch.int64) + 1 + rank
+        >>> tensor
+        tensor([1]) # Rank 0
+        tensor([2]) # Rank 1
+        >>> dist.all_gather_base(output_tensor, tensor)
+        >>> output_tensor
+        tensor([1,2]) # Rank 0
+        tensor([1,2]) # Rank 1
+
+    .. warning::
+        `_all_gather_base` is experimental and subject to change.
+        It is the caller's responsibility to ensure the output_tensor
+        is correctly sized.
+
+    """
+    return _AllGatherBase.apply(output_tensor, input_tensor, group)
+
+
+def all_to_all(output_tensor_list, input_tensor_list, group=group.WORLD):
+    """
+    Each process scatters list of input tensors to all processes in a group and return gathered list of tensors in output list.
+
+    Arguments:
+        output_tensor_list (list[Tensor]): list of tensors to gather one per rank.
+        input_tensor_list (list[Tensor]): List of tensors to scatter one per rank.
+        group (ProcessGroup, optional): The process group to work on.
+
+    Returns:
+        tuple([Tensor]): Output of the collective.
+
+    """
+    return _AlltoAll.apply(group, output_tensor_list, *input_tensor_list)
+
+
+def all_to_all_single(
+    output,
+    input,
+    output_split_sizes=None,
+    input_split_sizes=None,
+    group=group.WORLD,
+):
+    """
+    Each process splits input tensor and then scatters the split list to all processes in a group.
+
+    Then concatenate the received tensors from all the processes in the group and return single output tensor.
+
+    Arguments:
+        output (Tensor): Gathered concatenated output tensor.
+        input (Tensor): Input tensor to scatter.
+        output_split_sizes: (list[Int], optional): Output split sizes for dim 0
+            if specified None or empty, dim 0 of ``output`` tensor must divide
+            equally by ``world_size``.
+        input_split_sizes: (list[Int], optional): Input split sizes for dim 0
+            if specified None or empty, dim 0 of ``input`` tensor must divide
+            equally by ``world_size``.
+
+    Returns:
+        Tensor: Output of the collective.
+
+    """
+    return _AlltoAllSingle.apply(
+        group, output, output_split_sizes, input_split_sizes, input
+    )
+
+
+def all_reduce(tensor, op=ReduceOp.SUM, group=group.WORLD):
+    """
+    Reduces the tensor data across all machines in such a way that all get the final result.
+
+    After the call the returned tensor is going to be bitwise
+    identical in all processes.
+
+    Arguments:
+        tensor (Tensor): Input of the collective.
+        op (optional): One of the values from
+            ``torch.distributed.ReduceOp``
+            enum.  Specifies an operation used for element-wise reductions.
+        group (ProcessGroup, optional): The process group to work on.
+
+    Returns:
+        Tensor: Output of the collective
+
+    """
+    return _AllReduce.apply(op, group, tensor)
+
+
+class _Broadcast(Function):
+    @staticmethod
+    # pyrefly: ignore [bad-override]
+    def forward(ctx, src, group, tensor):
+        ctx.src = src
+        ctx.group = group
+        ctx.rank = dist.get_rank(group=group)
+        # torch.distributed makes all the calls in place
+        # we allocate new tensors to avoid this
+        tensor = tensor.clone()
+        dist.broadcast(tensor, src, group=group)
+        return tensor
+
+    @staticmethod
+    # pyrefly: ignore [bad-override]
+    def backward(ctx, grad_output):
+        gx = _Reduce.apply(ctx.src, ReduceOp.SUM, ctx.group, grad_output)
+        if ctx.src != ctx.rank:
+            gx.zero_()
+        return (None, None, gx)
+
+
+class _Gather(Function):
+    @staticmethod
+    # pyrefly: ignore [bad-override]
+    def forward(ctx, dst, group, tensor):
+        ctx.dst = dst
+        ctx.group = group
+        # Need to create a list of tensors here to do the
+        # aggregation, get it from the group size
+        # tensor should be correctly sized for the method
+        # gathering
+        tensor_list = [
+            torch.zeros_like(tensor) for i in range(dist.get_world_size(group=group))
+        ]
+
+        tensor = tensor.contiguous()
+        if dist.get_rank(group=group) == dst:
+            dist.gather(tensor, tensor_list, dst, group=group)
+        else:
+            dist.gather(tensor, None, dst, group=group)
+        return tuple(tensor_list)
+
+    @staticmethod
+    def backward(ctx, *grad_outputs):
+        return (None, None) + (_Scatter.apply(ctx.dst, ctx.group, *grad_outputs),)
+
+
+class _Scatter(Function):
+    @staticmethod
+    # pyrefly: ignore [bad-override]
+    def forward(ctx, src, group, *tensors):
+        ctx.src = src
+        ctx.group = group
+        assert all(t.size() == tensors[0].size() for t in tensors)
+        output = torch.zeros_like(tensors[0])
+        if dist.get_rank(group=group) == src:
+            dist.scatter(output, list(tensors), src, group=group)
+        else:
+            dist.scatter(output, None, src, group=group)
+        return output
+
+    @staticmethod
+    # pyrefly: ignore [bad-override]
+    def backward(ctx, grad_output):
+        return (None, None) + _Gather.apply(ctx.src, ctx.group, grad_output)
+
+
+class _Reduce(Function):
+    @staticmethod
+    # pyrefly: ignore [bad-override]
+    def forward(ctx, src, op, group, tensor):
+        ctx.src = src
+        ctx.group = group
+        tensor = tensor.clone()
+        dist.reduce(tensor, src, op=op, group=group)
+        return tensor
+
+    @staticmethod
+    # pyrefly: ignore [bad-override]
+    def backward(ctx, grad_output):
+        return (None, None, None) + (_Broadcast.apply(ctx.src, ctx.group, grad_output),)
+
+
+class _Reduce_Scatter(Function):
+    @staticmethod
+    # pyrefly: ignore [bad-override]
+    def forward(ctx, op, group, tensor, *input_tensor_list):
+        ctx.group = group
+        # Need contiguous tensors for collectives.
+        tensor = tensor.contiguous()
+        input_tensor_list = tuple(t.contiguous() for t in input_tensor_list)
+        dist.reduce_scatter(tensor, list(input_tensor_list), op=op, group=group)
+        return tensor
+
+    @staticmethod
+    # pyrefly: ignore [bad-override]
+    def backward(ctx, grad_output):
+        return (None, None, None) + _AllGather.apply(ctx.group, grad_output)
+
+
+class _AllGather(Function):
+    @staticmethod
+    # pyrefly: ignore [bad-override]
+    def forward(ctx, group, tensor):
+        # Need contiguous tensors for collectives.
+        tensor = tensor.contiguous()
+
+        ctx.group = group
+        out_tensor_list = [
+            torch.empty_like(tensor) for _ in range(dist.get_world_size(group=group))
+        ]
+
+        dist.all_gather(out_tensor_list, tensor, group=group)
+        return tuple(out_tensor_list)
+
+    @staticmethod
+    def backward(ctx, *grad_outputs):
+        if dist.get_backend(group=ctx.group) is dist.Backend.NCCL:
+            rank = dist.get_rank(group=ctx.group)
+            gx = torch.empty_like(grad_outputs[rank])
+            gx = _Reduce_Scatter.apply(ReduceOp.SUM, ctx.group, gx, *grad_outputs)
+        else:
+            # As many backends doesn't support ReduceScatter, we use AlltoAll with .sum()
+            # to emulate the ReduceScatter behavior
+            tensor_list = [torch.empty_like(tensor) for tensor in grad_outputs]
+            gxs = _AlltoAll.apply(ctx.group, tensor_list, *grad_outputs)
+            gx = torch.sum(torch.stack(gxs), dim=0)
+        return (None, gx)
+
+
+class _AllGatherBase(Function):
+    @staticmethod
+    # pyrefly: ignore [bad-override]
+    def forward(ctx, output_tensor, input_tensor, group):
+        ctx.group = group
+        dist._all_gather_base(output_tensor, input_tensor.contiguous(), group=group)
+        return output_tensor
+
+    @staticmethod
+    # pyrefly: ignore [bad-override]
+    def backward(ctx, grad_output):
+        if dist.get_backend(group=ctx.group) is dist.Backend.NCCL:
+            world_size = dist.get_world_size(group=ctx.group)
+            out_size = list(grad_output.size())
+            if out_size[0] % world_size != 0:
+                raise RuntimeError(
+                    f"Tensor with dimensions: {out_size} does "
+                    f"not have first dimension divisible by world_size: {world_size}"
+                )
+            out_size[0] = out_size[0] // dist.get_world_size(group=ctx.group)
+            gx = torch.empty(
+                out_size, device=grad_output.device, dtype=grad_output.dtype
+            )
+            dist._reduce_scatter_base(gx, grad_output, ReduceOp.SUM, ctx.group)
+        else:
+            raise RuntimeError("Backend not supported!")
+        return (None, gx, None)
+
+
+class _AlltoAll(Function):
+    @staticmethod
+    # pyrefly: ignore [bad-override]
+    def forward(ctx, group, out_tensor_list, *tensors):
+        ctx.group = group
+        ctx.input_tensor_size_list = [
+            tensors[i].size() for i in range(dist.get_world_size(group=group))
+        ]
+        my_rank = dist.get_rank(group=group)
+        tensors = tuple(t.contiguous() for t in tensors)
+        # Implement it on means of scatter/gather, send/recv async operations have issues
+        if dist.get_backend(group=group) is dist.Backend.GLOO:
+            for i in range(dist.get_world_size(group=group)):
+                to_send = None
+                if i == my_rank:
+                    to_send = list(tensors)
+                dist.scatter(out_tensor_list[i], to_send, i, group=group)
+        else:
+            dist.all_to_all(
+                out_tensor_list,
+                list(tensors),
+                group=group,
+            )
+        return tuple(out_tensor_list)
+
+    @staticmethod
+    def backward(ctx, *grad_outputs):
+        tensor_list = [
+            torch.empty(
+                size, device=grad_outputs[0].device, dtype=grad_outputs[0].dtype
+            )
+            for size in ctx.input_tensor_size_list
+        ]
+        return (None, None) + _AlltoAll.apply(ctx.group, tensor_list, *grad_outputs)
+
+
+class _AlltoAllSingle(Function):
+    @staticmethod
+    # pyrefly: ignore [bad-override]
+    def forward(ctx, group, output, output_split_sizes, input_split_sizes, input):
+        ctx.group = group
+        ctx.input_size = input.size()
+        ctx.output_split_sizes = input_split_sizes
+        ctx.input_split_sizes = output_split_sizes
+        dist.all_to_all_single(
+            output,
+            input,
+            output_split_sizes=output_split_sizes,
+            input_split_sizes=input_split_sizes,
+            group=group,
+        )
+        return output
+
+    @staticmethod
+    # pyrefly: ignore [bad-override]
+    def backward(ctx, grad_output):
+        tensor = torch.empty(
+            ctx.input_size, device=grad_output.device, dtype=grad_output.dtype
+        )
+        return (None, None, None, None) + (
+            _AlltoAllSingle.apply(
+                ctx.group,
+                tensor,
+                ctx.output_split_sizes,
+                ctx.input_split_sizes,
+                grad_output.contiguous(),
+            ),
+        )
+
+
+class _AllReduce(Function):
+    @staticmethod
+    # pyrefly: ignore [bad-override]
+    def forward(ctx, op, group, tensor):
+        ctx.group = group
+        ctx.op = op
+        tensor = tensor.clone(memory_format=torch.contiguous_format)
+        dist.all_reduce(tensor, op=op, group=group)
+        return tensor
+
+    @staticmethod
+    # pyrefly: ignore [bad-override]
+    def backward(ctx, grad_output):
+        return (None, None) + (_AllReduce.apply(ctx.op, ctx.group, grad_output),)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/nn/jit/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/nn/jit/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/nn/jit/instantiator.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/nn/jit/instantiator.py
new file mode 100644
index 0000000000000000000000000000000000000000..a6dee7e61ef5731f35915f704d2ffaf0444d168b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/nn/jit/instantiator.py
@@ -0,0 +1,141 @@
+#!/usr/bin/python3
+# mypy: allow-untyped-defs
+import importlib.abc
+import importlib.util
+import sys
+
+import torch
+from torch.distributed.nn.jit.templates.remote_module_template import (
+    get_remote_module_template,
+)
+
+
+_FILE_PREFIX = "_remote_module_"
+
+
+def get_arg_return_types_from_interface(module_interface):
+    assert getattr(module_interface, "__torch_script_interface__", False), (
+        "Expect a TorchScript class interface decorated by @torch.jit.interface."
+    )
+    qualified_name = torch._jit_internal._qualified_name(module_interface)
+    cu = torch.jit._state._python_cu
+    module_interface_c = cu.get_interface(qualified_name)
+    assert "forward" in module_interface_c.getMethodNames(), (
+        f"Expect forward in interface methods, while it has {module_interface_c.getMethodNames()}"
+    )
+    method_schema = module_interface_c.getMethod("forward")
+
+    arg_str_list = []
+    arg_type_str_list = []
+    assert method_schema is not None
+    for argument in method_schema.arguments:
+        arg_str_list.append(argument.name)
+
+        if argument.has_default_value():
+            default_value_str = f" = {argument.default_value}"
+        else:
+            default_value_str = ""
+        arg_type_str = f"{argument.name}: {argument.type}{default_value_str}"
+        arg_type_str_list.append(arg_type_str)
+
+    arg_str_list = arg_str_list[1:]  # Remove "self".
+    args_str = ", ".join(arg_str_list)
+
+    arg_type_str_list = arg_type_str_list[1:]  # Remove "self".
+    arg_types_str = ", ".join(arg_type_str_list)
+
+    assert len(method_schema.returns) == 1
+    argument = method_schema.returns[0]
+    return_type_str = str(argument.type)
+
+    return args_str, arg_types_str, return_type_str
+
+
+class _StringLoader(importlib.abc.SourceLoader):
+    def __init__(self, data):
+        self.data = data
+
+    def get_source(self, fullname):
+        return self.data
+
+    def get_data(self, path):
+        return self.data.encode("utf-8")
+
+    def get_filename(self, fullname):
+        return fullname
+
+
+def _do_instantiate_remote_module_template(
+    generated_module_name, str_dict, enable_moving_cpu_tensors_to_cuda
+):
+    if generated_module_name in sys.modules:
+        return sys.modules[generated_module_name]
+
+    loader = _StringLoader(
+        get_remote_module_template(enable_moving_cpu_tensors_to_cuda).format(**str_dict)
+    )
+    spec = importlib.util.spec_from_loader(
+        generated_module_name, loader, origin="torch-git"
+    )
+    assert spec is not None
+    module = importlib.util.module_from_spec(spec)
+    sys.modules[generated_module_name] = module
+    loader.exec_module(module)
+    return module
+
+
+def instantiate_scriptable_remote_module_template(
+    module_interface_cls, enable_moving_cpu_tensors_to_cuda=True
+):
+    if not getattr(module_interface_cls, "__torch_script_interface__", False):
+        raise ValueError(
+            f"module_interface_cls {module_interface_cls} must be a type object decorated by "
+            "@torch.jit.interface"
+        )
+
+    # Generate the template instance name.
+    module_interface_cls_name = torch._jit_internal._qualified_name(
+        module_interface_cls
+    ).replace(".", "_")
+    generated_module_name = f"{_FILE_PREFIX}{module_interface_cls_name}"
+
+    # Generate type annotation strs.
+    assign_module_interface_cls_str = (
+        f"from {module_interface_cls.__module__} import "
+        f"{module_interface_cls.__name__} as module_interface_cls"
+    )
+    args_str, arg_types_str, return_type_str = get_arg_return_types_from_interface(
+        module_interface_cls
+    )
+    kwargs_str = ""
+    arrow_and_return_type_str = f" -> {return_type_str}"
+    arrow_and_future_return_type_str = f" -> Future[{return_type_str}]"
+
+    str_dict = dict(
+        assign_module_interface_cls=assign_module_interface_cls_str,
+        arg_types=arg_types_str,
+        arrow_and_return_type=arrow_and_return_type_str,
+        arrow_and_future_return_type=arrow_and_future_return_type_str,
+        args=args_str,
+        kwargs=kwargs_str,
+        jit_script_decorator="@torch.jit.script",
+    )
+    return _do_instantiate_remote_module_template(
+        generated_module_name, str_dict, enable_moving_cpu_tensors_to_cuda
+    )
+
+
+def instantiate_non_scriptable_remote_module_template():
+    generated_module_name = f"{_FILE_PREFIX}non_scriptable"
+    str_dict = dict(
+        assign_module_interface_cls="module_interface_cls = None",
+        args="*args",
+        kwargs="**kwargs",
+        arg_types="*args, **kwargs",
+        arrow_and_return_type="",
+        arrow_and_future_return_type="",
+        jit_script_decorator="",
+    )
+    # For a non-scriptable template, always enable moving CPU tensors to a cuda device,
+    # because there is no syntax limitation on the extra handling caused by the script.
+    return _do_instantiate_remote_module_template(generated_module_name, str_dict, True)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/nn/jit/templates/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/nn/jit/templates/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/nn/jit/templates/remote_module_template.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/nn/jit/templates/remote_module_template.py
new file mode 100644
index 0000000000000000000000000000000000000000..07b055774b36af4835e308c8a1f85afd0ab35f0f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/nn/jit/templates/remote_module_template.py
@@ -0,0 +1,108 @@
+#!/usr/bin/python3
+# mypy: allow-untyped-defs
+
+
+def get_remote_module_template(enable_moving_cpu_tensors_to_cuda: bool):
+    return _TEMPLATE_PREFIX + (
+        _REMOTE_FORWARD_TEMPLATE_ENABLE_MOVING_CPU_TENSORS_TO_CUDA
+        if enable_moving_cpu_tensors_to_cuda
+        else _REMOTE_FORWARD_TEMPLATE
+    )
+
+
+_TEMPLATE_PREFIX = """from typing import *
+
+import torch
+import torch.distributed.rpc as rpc
+from torch import Tensor
+from torch._jit_internal import Future
+from torch.distributed.rpc import RRef
+from typing import Tuple  # pyre-ignore: unused import
+
+
+{assign_module_interface_cls}
+
+
+def forward_async(self, {arg_types}){arrow_and_future_return_type}:
+    args = (self.module_rref, self.device, self.is_device_map_set, {args})
+    kwargs = {{{kwargs}}}
+    return rpc.rpc_async(
+        self.module_rref.owner(),
+        _remote_forward,
+        args,
+        kwargs,
+    )
+
+
+def forward(self, {arg_types}){arrow_and_return_type}:
+    args = (self.module_rref, self.device, self.is_device_map_set, {args})
+    kwargs = {{{kwargs}}}
+    ret_fut = rpc.rpc_async(
+        self.module_rref.owner(),
+        _remote_forward,
+        args,
+        kwargs,
+    )
+    return ret_fut.wait()
+
+
+_generated_methods = [
+    forward_async,
+    forward,
+]
+
+
+{jit_script_decorator}
+"""
+
+# This template may cause typing error (the mismatch between ``Tuple[()]`` and ``Tuple[Any]``)
+# even if the code is only used for instantiation but not execution.
+# Therefore, only include handling moving CPU tensors to a cuda device if necessary.
+# TODO: Merge these two templates together in the future once TorchScript syntax is improved.
+_REMOTE_FORWARD_TEMPLATE_ENABLE_MOVING_CPU_TENSORS_TO_CUDA = """
+def _remote_forward(
+    module_rref: RRef[module_interface_cls], device: str, is_device_map_set: bool, {arg_types}){arrow_and_return_type}:
+    module = module_rref.local_value()
+    device = torch.device(device)
+
+    if device.type != "cuda":
+        return module.forward({args}, {kwargs})
+
+    # If the module is on a cuda device,
+    # move any CPU tensor in args or kwargs to the same cuda device.
+    # Since torch script does not support generator expression,
+    # have to use concatenation instead of
+    # ``tuple(i.to(device) if isinstance(i, Tensor) else i for i in *args)``.
+    args = ({args},)
+    out_args: Tuple[()] = ()
+    for arg in args:
+        arg = (arg.to(device),) if isinstance(arg, Tensor) else (arg,)
+        out_args = out_args + arg
+
+    kwargs = {{{kwargs}}}
+    for k, v in kwargs.items():
+        if isinstance(v, Tensor):
+            kwargs[k] = kwargs[k].to(device)
+
+    if is_device_map_set:
+        return module.forward(*out_args, {kwargs})
+
+    # If the device map is empty, then only CPU tensors are allowed to send over wire,
+    # so have to move any GPU tensor to CPU in the output.
+    # Since torch script does not support generator expression,
+    # have to use concatenation instead of
+    # ``tuple(i.cpu() if isinstance(i, Tensor) else i for i in module.forward(*out_args, {kwargs}))``.
+    ret: Tuple[()] = ()
+    for i in module.forward(*out_args, {kwargs}):
+        i = (i.cpu(),) if isinstance(i, Tensor) else (i,)
+        ret = ret + i
+    return ret
+"""
+
+_REMOTE_FORWARD_TEMPLATE = """
+def _remote_forward(
+    module_rref: RRef[module_interface_cls], device: str, is_device_map_set: bool, {arg_types}){arrow_and_return_type}:
+    module = module_rref.local_value()
+
+    return module.forward({args}, {kwargs})
+"""
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..faac68bb632934ba730ba7c5ce3cf7fe934a58cf
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/__init__.py
@@ -0,0 +1,44 @@
+"""
+:mod:`torch.distributed.optim` exposes DistributedOptimizer, which takes a list
+of remote parameters (:class:`~torch.distributed.rpc.RRef`) and runs the
+optimizer locally on the workers where the parameters live.  The distributed
+optimizer can use any of the local optimizer :ref:`optimizer-algorithms` to
+apply the gradients on each worker.
+"""
+
+import warnings
+
+import torch
+from torch import optim
+
+from .apply_optimizer_in_backward import (
+    _apply_optimizer_in_backward,
+    _get_in_backward_optimizers,
+)
+from .functional_adadelta import _FunctionalAdadelta
+from .functional_adagrad import _FunctionalAdagrad
+from .functional_adam import _FunctionalAdam
+from .functional_adamax import _FunctionalAdamax
+from .functional_adamw import _FunctionalAdamW
+from .functional_rmsprop import _FunctionalRMSprop
+from .functional_rprop import _FunctionalRprop
+from .functional_sgd import _FunctionalSGD
+from .named_optimizer import _NamedOptimizer
+from .utils import as_functional_optim
+
+
+# DistributedOptimizer imports torch.distributed.rpc names, so gate availability
+# based on RPC being available.
+if hasattr(torch._C, "_rpc_init"):
+    from .optimizer import DistributedOptimizer
+
+from .post_localSGD_optimizer import PostLocalSGDOptimizer
+from .zero_redundancy_optimizer import ZeroRedundancyOptimizer
+
+
+__all__ = [
+    "as_functional_optim",
+    "DistributedOptimizer",
+    "PostLocalSGDOptimizer",
+    "ZeroRedundancyOptimizer",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/_deprecation_warning.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/_deprecation_warning.py
new file mode 100644
index 0000000000000000000000000000000000000000..c3434a4cd4f081843295e488c18a67a5c297fcbf
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/_deprecation_warning.py
@@ -0,0 +1,16 @@
+import warnings
+
+import torch
+
+
+@torch.jit.ignore  # type: ignore[misc]
+def _scripted_functional_optimizer_deprecation_warning(stacklevel: int = 0) -> None:
+    with warnings.catch_warnings():
+        warnings.simplefilter("always")
+        warnings.warn(
+            "`TorchScript` support for functional optimizers is deprecated "
+            "and will be removed in a future PyTorch release. "
+            "Consider using the `torch.compile` optimizer instead.",
+            DeprecationWarning,
+            stacklevel=stacklevel + 2,
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ff9854793df1aa96a27cb105a1afd1190df942a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/apply_optimizer_in_backward.py
@@ -0,0 +1,121 @@
+from collections.abc import Iterable
+from typing import Any, no_type_check
+
+import torch
+
+
+__all__: list[str] = []
+
+# WeakTensorKeyDictionary to store relevant meta-data for the Tensor/Parameter
+# without changing it's life-time.
+# NOTE: Alternative is to add the meta-data as an attribute to the tensor,
+#       but that will serialize the meta-data if Tensor is serialized.
+param_to_optim_hook_handle_map = torch.utils.weak.WeakTensorKeyDictionary()
+param_to_acc_grad_map = torch.utils.weak.WeakTensorKeyDictionary()
+
+
+@no_type_check
+def _apply_optimizer_in_backward(
+    optimizer_class: type[torch.optim.Optimizer],
+    params: Iterable[torch.nn.Parameter],
+    optimizer_kwargs: dict[str, Any],
+    register_hook: bool = True,
+) -> None:
+    """
+    Upon ``backward()``, the optimizer specified for each parameter will fire after
+    the gradient has been accumulated into the parameter.
+
+    Note - gradients for these parameters will be set to None after ``backward()``.
+    This means that any other optimizer not specified via `_apply_optimizer_in_backward`
+    over this parameter will be a no-op.
+
+    Args:
+        optimizer_class: (Type[torch.optim.Optimizer]): Optimizer to apply to parameter
+        params: (Iterator[nn.Parameter]): parameters to apply optimizer state to
+        optimizer_kwargs: (Dict[str, Any]): kwargs to pass to optimizer constructor
+        register_hook: (bool): whether to register a hook that runs the optimizer
+            after gradient for this parameter is accumulated. This is the default
+            way that optimizer in backward is implemented, but specific use cases
+            (such as DDP) may wish to override this to implement custom behavior.
+            (Default = True)
+
+    Example::
+        params_generator = model.parameters()
+        param_1 = next(params_generator)
+        remainder_params = list(params_generator)
+
+        apply_optimizer_in_backward(torch.optim.SGD, [param_1], {"lr": 0.02})
+        apply_optimizer_in_backward(torch.optim.Adam, remainder_params, {"lr": 0.04})
+
+        model(...).sum().backward()  # after backward, parameters will already
+        # have their registered optimizer(s) applied.
+
+    """
+    torch._C._log_api_usage_once("torch.distributed.optim.apply_optimizer_in_backward")
+
+    @no_type_check
+    def _apply_optimizer_in_backward_to_param(param: torch.nn.Parameter) -> None:
+        # view_as creates a node in autograd graph that allows us access to the
+        # parameter's AccumulateGrad autograd function object. We register a
+        # hook on this object to fire the optimizer when the gradient for
+        # this parameter is ready (has been accumulated into .grad field)
+
+        # Don't create a new acc_grad if we already have one
+        # i.e. for shared parameters or attaching multiple optimizers to a param.
+        if param not in param_to_acc_grad_map:
+            param_to_acc_grad_map[param] = param.view_as(param).grad_fn.next_functions[
+                0
+            ][0]
+
+        optimizer = optimizer_class([param], **optimizer_kwargs)
+
+        if not hasattr(param, "_in_backward_optimizers"):
+            param._in_backward_optimizers = []  # type: ignore[attr-defined]
+            # TODO: Remove these attributes once we have a better way of accessing
+            # optimizer classes and kwargs for a parameter.
+            param._optimizer_classes = []  # type: ignore[attr-defined]
+            param._optimizer_kwargs = []  # type: ignore[attr-defined]
+
+        param._in_backward_optimizers.append(optimizer)  # type: ignore[attr-defined]
+        param._optimizer_classes.append(optimizer_class)  # type: ignore[attr-defined]
+        param._optimizer_kwargs.append(optimizer_kwargs)  # type: ignore[attr-defined]
+
+        if not register_hook:
+            return
+
+        def optimizer_hook(*_unused) -> None:
+            for opt in param._in_backward_optimizers:  # type: ignore[attr-defined]
+                opt.step()
+
+            param.grad = None
+
+        handle = param_to_acc_grad_map[param].register_hook(optimizer_hook)  # type: ignore[attr-defined]
+        if param not in param_to_optim_hook_handle_map:
+            param_to_optim_hook_handle_map[param] = []
+        param_to_optim_hook_handle_map[param].append(handle)
+
+    for param in params:
+        _apply_optimizer_in_backward_to_param(param)
+
+
+def _get_in_backward_optimizers(module: torch.nn.Module) -> list[torch.optim.Optimizer]:
+    """
+    Return a list of in-backward optimizers applied to ``module``'s parameters. Note that these
+    optimizers are not intended to directly have their ``step`` or ``zero_grad`` methods called
+    by the user and are intended to be used for things like checkpointing.
+
+    Args:
+        module: (torch.nn.Module): model to retrieve in-backward optimizers for
+
+    Returns:
+        List[torch.optim.Optimizer]: the in-backward optimizers.
+
+    Example::
+        _apply_optimizer_in_backward(torch.optim.SGD, model.parameters(), {"lr": 0.01})
+        optims = _get_optimizers_in_backward(model)
+    """
+    optims: list[torch.optim.Optimizer] = []
+    for param in module.parameters():
+        optims.extend(getattr(param, "_in_backward_optimizers", []))
+
+    return optims
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/functional_adadelta.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/functional_adadelta.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8455c5ef5a41613dc15140b6c562ceb3134ca4e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/functional_adadelta.py
@@ -0,0 +1,110 @@
+# mypy: allow-untyped-defs
+
+import torch
+import torch.optim._functional as F
+from torch import Tensor
+from torch.distributed.optim._deprecation_warning import (
+    _scripted_functional_optimizer_deprecation_warning,
+)
+
+
+__all__: list[str] = []
+
+
+# Define a TorchScript compatible Functional Adadelta Optimizer
+# where we use these optimizer in a functional way.
+# Instead of using the `param.grad` when updating parameters,
+# we explicitly allow the distributed optimizer pass gradients to
+# the `step` function. In this way, we could separate the gradients
+# and parameters and allow multithreaded trainer to update the
+# parameters without data traces on accumulating to the same .grad.
+# NOTE: This should be only used by distributed optimizer internals
+# and not meant to expose to the user.
+@torch.jit.script
+class _FunctionalAdadelta:
+    def __init__(
+        self,
+        params: list[Tensor],
+        lr: float = 1.0,
+        rho: float = 0.9,
+        eps: float = 1e-6,
+        weight_decay: float = 0.0,
+        foreach: bool = False,
+        maximize: bool = False,
+        _allow_empty_param_list: bool = False,
+    ):
+        _scripted_functional_optimizer_deprecation_warning(stacklevel=2)
+        self.defaults = {
+            "lr": lr,
+            "rho": rho,
+            "eps": eps,
+            "weight_decay": weight_decay,
+        }
+        self.foreach = foreach
+        self.maximize = maximize
+
+        if len(params) == 0 and not _allow_empty_param_list:
+            raise ValueError("optimizer got an empty parameter list")
+
+        # NOTE: we only have one param_group and don't allow user to add additional
+        # param group as it's not a common use case.
+        self.param_group = {"params": params}
+
+        self.state = torch.jit.annotate(dict[torch.Tensor, dict[str, torch.Tensor]], {})
+
+    def step(self, gradients: list[Tensor | None]):
+        params = self.param_group["params"]
+        params_with_grad = []
+        grads = []
+        square_avgs = []
+        acc_deltas = []
+        state_steps = []
+        lr = self.defaults["lr"]
+        rho = self.defaults["rho"]
+        eps = self.defaults["eps"]
+        weight_decay = self.defaults["weight_decay"]
+
+        if len(params) != len(gradients):
+            raise ValueError(
+                "the gradients passed in does not equal to the size of the parameters!"
+                + f"Params length: {len(params)}. "
+                + f"Gradients length: {len(gradients)}"
+            )
+        has_complex = False
+        for param, gradient in zip(params, gradients):
+            if gradient is not None:
+                has_complex |= torch.is_complex(param)
+                params_with_grad.append(param)
+                grads.append(gradient)
+                # Lazy state initialization
+                if param not in self.state:
+                    self.state[param] = {}
+                    state = self.state[param]
+                    state["step"] = torch.tensor(0.0)
+                    state["square_avg"] = torch.zeros_like(
+                        param, memory_format=torch.preserve_format
+                    )
+                    state["acc_delta"] = torch.zeros_like(
+                        param, memory_format=torch.preserve_format
+                    )
+
+                state = self.state[param]
+                square_avgs.append(state["square_avg"])
+                acc_deltas.append(state["acc_delta"])
+                state_steps.append(state["step"])
+
+        with torch.no_grad():
+            F.adadelta(
+                params_with_grad,
+                grads,
+                square_avgs,
+                acc_deltas,
+                state_steps,
+                lr=lr,
+                rho=rho,
+                eps=eps,
+                weight_decay=weight_decay,
+                foreach=self.foreach,
+                maximize=self.maximize,
+                has_complex=has_complex,
+            )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/functional_adagrad.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/functional_adagrad.py
new file mode 100644
index 0000000000000000000000000000000000000000..3da4e29b3f0154ab58206c835f80a24ae208a05c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/functional_adagrad.py
@@ -0,0 +1,114 @@
+# mypy: allow-untyped-defs
+
+import torch
+import torch.optim._functional as F
+from torch import Tensor
+from torch.distributed.optim._deprecation_warning import (
+    _scripted_functional_optimizer_deprecation_warning,
+)
+
+
+__all__: list[str] = []
+
+
+# Define a TorchScript compatible Functional Adagrad Optimizer
+# where we use these optimizer in a functional way.
+# Instead of using the `param.grad` when updating parameters,
+# we explicitly let the user pass gradients to the `step` function
+# this is so that we could separate the gradients and parameters
+# and allow multithreaded trainer to update the parameters
+# without data traces on accumulating to the same .grad.
+# NOTE: This should be only used by distributed optimizer internals
+# and not meant to expose to the user.
+@torch.jit.script
+class _FunctionalAdagrad:
+    def __init__(
+        self,
+        params: list[Tensor],
+        lr: float = 1e-2,
+        lr_decay: float = 0.0,
+        weight_decay: float = 0.0,
+        initial_accumulator_value: float = 0.0,
+        warmup_lr_multiplier: float = 1.0,
+        warmup_num_iters: float = 0.0,
+        eps: float = 1e-10,
+        coalesce_grad: bool = True,
+        foreach: bool = False,
+        fused: bool = False,
+        maximize: bool = False,
+        _allow_empty_param_list: bool = False,
+    ):
+        _scripted_functional_optimizer_deprecation_warning(stacklevel=2)
+        self.defaults = {
+            "lr": lr,
+            "lr_decay": lr_decay,
+            "eps": eps,
+            "weight_decay": weight_decay,
+            "initial_accumulator_value": initial_accumulator_value,
+            "warmup_lr_multiplier": warmup_lr_multiplier,
+            "warmup_num_iters": warmup_num_iters,
+        }
+        self.coalesce_grad = coalesce_grad
+        self.foreach = foreach
+        self.fused = fused
+        self.maximize = maximize
+        self.state = torch.jit.annotate(dict[torch.Tensor, dict[str, torch.Tensor]], {})
+
+        if len(params) == 0 and not _allow_empty_param_list:
+            raise ValueError("optimizer got an empty parameter list")
+
+        # NOTE: we only have one param_group and don't allow user to add additional
+        # param group as it's not a common use case.
+        self.param_group = {"params": params}
+
+        # TODO: no union or any types in TorchScript, make step a scalar tensor instead
+        # This is also needed by if we want to share_memory on the step across processes
+        for p in self.param_group["params"]:
+            self.state[p] = {
+                "sum": torch.full_like(p.data, initial_accumulator_value),
+                "step": torch.tensor(0.0),
+            }
+
+    def step(self, gradients: list[Tensor | None]):
+        params = self.param_group["params"]
+        params_with_grad = []
+        grads = []
+        state_sums = []
+        state_steps: list[Tensor] = []
+
+        if len(params) != len(gradients):
+            raise ValueError(
+                "the gradients passed in does not equal to the size of the parameters!"
+                + f"Params length: {len(params)}. "
+                + f"Gradients length: {len(gradients)}"
+            )
+
+        has_sparse_grad, has_complex = False, False
+        for param, gradient in zip(self.param_group["params"], gradients):
+            if gradient is not None:
+                has_sparse_grad |= gradient.is_sparse
+                has_complex |= torch.is_complex(param)
+                params_with_grad.append(param)
+                grads.append(gradient)
+                state = self.state[param]
+                state_sums.append(state["sum"])
+                state_steps.append(state["step"])
+
+        with torch.no_grad():
+            F.adagrad(
+                params,
+                grads,
+                state_sums,
+                state_steps,
+                lr=self.defaults["lr"],
+                weight_decay=self.defaults["weight_decay"],
+                lr_decay=self.defaults["lr_decay"],
+                eps=self.defaults["eps"],
+                has_sparse_grad=has_sparse_grad,
+                foreach=self.foreach,
+                maximize=self.maximize,
+                has_complex=has_complex,
+                fused=self.fused,
+                grad_scale=None,
+                found_inf=None,
+            )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/functional_adam.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/functional_adam.py
new file mode 100644
index 0000000000000000000000000000000000000000..1763edd14c9da1c19081fcc1334e267c889472c1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/functional_adam.py
@@ -0,0 +1,201 @@
+# mypy: allow-untyped-defs
+
+import torch
+import torch.optim._functional as F
+from torch import Tensor
+from torch.distributed.optim._deprecation_warning import (
+    _scripted_functional_optimizer_deprecation_warning,
+)
+
+
+__all__: list[str] = []
+
+
+# Define a TorchScript compatible Functional Adam Optimizer
+# where we use these optimizer in a functional way.
+# Instead of using the `param.grad` when updating parameters,
+# we explicitly allow the distributed optimizer pass gradients to
+# the `step` function. In this way, we could separate the gradients
+# and parameters and allow multithreaded trainer to update the
+# parameters without data traces on accumulating to the same .grad.
+# NOTE: This should be only used by distributed optimizer internals
+# and not meant to expose to the user.
+@torch.jit.script
+class _FunctionalAdam:
+    def __init__(
+        self,
+        params: list[Tensor],
+        lr: float = 1e-3,
+        betas: tuple[float, float] = (0.9, 0.999),
+        eps: float = 1e-8,
+        weight_decay: float = 0.0,
+        amsgrad: bool = False,
+        maximize: bool = False,
+        foreach: bool = False,
+        fused: bool = False,
+        _allow_empty_param_list: bool = False,
+    ):
+        _scripted_functional_optimizer_deprecation_warning(stacklevel=2)
+        if not 0.0 <= lr:
+            raise ValueError(f"Invalid learning rate: {lr}")
+        if not 0.0 <= eps:
+            raise ValueError(f"Invalid epsilon value: {eps}")
+        if not 0.0 <= betas[0] < 1.0:
+            raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}")
+        if not 0.0 <= betas[1] < 1.0:
+            raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}")
+        if not 0.0 <= weight_decay:
+            raise ValueError(f"Invalid weight_decay value: {weight_decay}")
+
+        self.defaults = {
+            "lr": lr,
+            "eps": eps,
+            "beta1": betas[0],
+            "beta2": betas[1],
+            "weight_decay": weight_decay,
+        }
+        self.amsgrad = amsgrad
+        self.maximize = maximize
+        self.foreach = foreach
+        self.fused = fused
+        self.state = torch.jit.annotate(dict[torch.Tensor, dict[str, torch.Tensor]], {})
+
+        if len(params) == 0 and not _allow_empty_param_list:
+            raise ValueError("optimizer got an empty parameter list")
+
+        # NOTE: we only have one param_group and don't allow user to add additional
+        # param group as it's not a common use case.
+        self.param_group = {"params": params}
+
+    def step_param(self, param: Tensor, grad: Tensor | None):
+        """
+        Similar to step, but operates on a single parameter and optionally a
+        gradient tensor.
+        """
+        params_with_grad = []
+        grads = []
+        exp_avgs = []
+        exp_avg_sqs = []
+        max_exp_avg_sqs = []
+        state_steps: list[Tensor] = []
+        has_complex = torch.is_complex(param)
+        if grad is not None:
+            params_with_grad.append(param)
+            grads.append(grad)
+        if param not in self.state:
+            self.state[param] = {}
+            state = self.state[param]
+            state["step"] = torch.tensor(0.0)
+            state["exp_avg"] = torch.zeros_like(
+                param, memory_format=torch.preserve_format
+            )
+            state["exp_avg_sq"] = torch.zeros_like(
+                param, memory_format=torch.preserve_format
+            )
+            if self.amsgrad:
+                state["max_exp_avg_sq"] = torch.zeros_like(
+                    param, memory_format=torch.preserve_format
+                )
+
+        state = self.state[param]
+        exp_avgs.append(state["exp_avg"])
+        exp_avg_sqs.append(state["exp_avg_sq"])
+
+        if self.amsgrad:
+            max_exp_avg_sqs.append(state["max_exp_avg_sq"])
+
+        state_steps.append(state["step"])
+        with torch.no_grad():
+            F.adam(
+                params_with_grad,
+                grads,
+                exp_avgs,
+                exp_avg_sqs,
+                max_exp_avg_sqs,
+                state_steps,
+                amsgrad=self.amsgrad,
+                has_complex=has_complex,
+                maximize=self.maximize,
+                beta1=self.defaults["beta1"],
+                beta2=self.defaults["beta2"],
+                lr=self.defaults["lr"],
+                weight_decay=self.defaults["weight_decay"],
+                eps=self.defaults["eps"],
+                foreach=self.foreach,
+                fused=self.fused,
+                grad_scale=None,
+                found_inf=None,
+            )
+
+    def step(self, gradients: list[Tensor | None]):
+        params = self.param_group["params"]
+        params_with_grad = []
+        grads = []
+        exp_avgs = []
+        exp_avg_sqs = []
+        max_exp_avg_sqs = []
+        state_steps: list[Tensor] = []
+        has_complex = False
+
+        if len(params) != len(gradients):
+            raise ValueError(
+                "the gradients passed in does not equal to the size of the parameters!"
+                + f"Params length: {len(params)}. "
+                + f"Gradients length: {len(gradients)}"
+            )
+
+        for param, gradient in zip(self.param_group["params"], gradients):
+            if gradient is not None:
+                has_complex |= torch.is_complex(param)
+                params_with_grad.append(param)
+                grads.append(gradient)
+                # Lazy state initialization
+                if param not in self.state:
+                    self.state[param] = {}
+                    state = self.state[param]
+                    state["step"] = torch.tensor(0.0)
+                    # Exponential moving average of gradient values
+                    state["exp_avg"] = torch.zeros_like(
+                        param, memory_format=torch.preserve_format
+                    )
+                    # Exponential moving average of squared gradient values
+                    state["exp_avg_sq"] = torch.zeros_like(
+                        param, memory_format=torch.preserve_format
+                    )
+                    if self.amsgrad:
+                        # Maintains max of all exp. moving avg. of sq. grad. values
+                        state["max_exp_avg_sq"] = torch.zeros_like(
+                            param, memory_format=torch.preserve_format
+                        )
+
+                state = self.state[param]
+
+                exp_avgs.append(state["exp_avg"])
+                exp_avg_sqs.append(state["exp_avg_sq"])
+
+                if self.amsgrad:
+                    max_exp_avg_sqs.append(state["max_exp_avg_sq"])
+
+                state_steps.append(state["step"])
+
+        with torch.no_grad():
+            F.adam(
+                params_with_grad,
+                grads,
+                exp_avgs,
+                exp_avg_sqs,
+                max_exp_avg_sqs,
+                state_steps,
+                amsgrad=self.amsgrad,
+                has_complex=has_complex,
+                maximize=self.maximize,
+                beta1=self.defaults["beta1"],
+                beta2=self.defaults["beta2"],
+                lr=self.defaults["lr"],
+                weight_decay=self.defaults["weight_decay"],
+                eps=self.defaults["eps"],
+                foreach=self.foreach,
+                fused=self.fused,
+                grad_scale=None,
+                found_inf=None,
+            )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/functional_adamax.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/functional_adamax.py
new file mode 100644
index 0000000000000000000000000000000000000000..595a5668a78fc0f8451fa9e2a81c03d049bb4b82
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/functional_adamax.py
@@ -0,0 +1,122 @@
+# mypy: allow-untyped-defs
+
+import torch
+import torch.optim._functional as F
+from torch import Tensor
+from torch.distributed.optim._deprecation_warning import (
+    _scripted_functional_optimizer_deprecation_warning,
+)
+
+
+__all__: list[str] = []
+
+
+# Define a TorchScript compatible Functional Adamax Optimizer
+# where we use these optimizer in a functional way.
+# Instead of using the `param.grad` when updating parameters,
+# we explicitly allow the distributed optimizer pass gradients to
+# the `step` function. In this way, we could separate the gradients
+# and parameters and allow multithreaded trainer to update the
+# parameters without data traces on accumulating to the same .grad.
+# NOTE: This should be only used by distributed optimizer internals
+# and not meant to expose to the user.
+@torch.jit.script
+class _FunctionalAdamax:
+    def __init__(
+        self,
+        params: list[Tensor],
+        lr: float = 1e-3,
+        betas: tuple[float, float] = (0.9, 0.999),
+        eps: float = 1e-8,
+        weight_decay: float = 0.0,
+        foreach: bool = False,
+        maximize: bool = False,
+        _allow_empty_param_list: bool = False,
+    ):
+        _scripted_functional_optimizer_deprecation_warning(stacklevel=2)
+        if not 0.0 <= lr:
+            raise ValueError(f"Invalid learning rate: {lr}")
+        if not 0.0 <= eps:
+            raise ValueError(f"Invalid epsilon value: {eps}")
+        if not 0.0 <= betas[0] < 1.0:
+            raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}")
+        if not 0.0 <= betas[1] < 1.0:
+            raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}")
+        if not 0.0 <= weight_decay:
+            raise ValueError(f"Invalid weight_decay value: {weight_decay}")
+
+        self.defaults = {
+            "lr": lr,
+            "eps": eps,
+            "beta1": betas[0],
+            "beta2": betas[1],
+            "weight_decay": weight_decay,
+        }
+        self.foreach = foreach
+        self.maximize = maximize
+        self.state = torch.jit.annotate(dict[torch.Tensor, dict[str, torch.Tensor]], {})
+
+        if len(params) == 0 and not _allow_empty_param_list:
+            raise ValueError("optimizer got an empty parameter list")
+
+        # NOTE: we only have one param_group and don't allow user to add additional
+        # param group as it's not a common use case.
+        self.param_group = {"params": params}
+
+    def step(self, gradients: list[Tensor | None]):
+        params = self.param_group["params"]
+        params_with_grad = []
+        grads = []
+        exp_avgs = []
+        exp_infs = []
+        state_steps: list[Tensor] = []
+
+        if len(params) != len(gradients):
+            raise ValueError(
+                "the gradients passed in does not equal to the size of the parameters!"
+                + f"Params length: {len(params)}. "
+                + f"Gradients length: {len(gradients)}"
+            )
+
+        has_complex = False
+        for param, gradient in zip(self.param_group["params"], gradients):
+            if gradient is not None:
+                has_complex |= torch.is_complex(param)
+                params_with_grad.append(param)
+                grads.append(gradient)
+                # Lazy state initialization
+                if param not in self.state:
+                    self.state[param] = {}
+                    state = self.state[param]
+                    state["step"] = torch.tensor(0.0)
+                    # Exponential moving average of gradient values
+                    state["exp_avg"] = torch.zeros_like(
+                        param, memory_format=torch.preserve_format
+                    )
+                    # Exponential moving average of squared gradient values
+                    state["exp_inf"] = torch.zeros_like(
+                        param, memory_format=torch.preserve_format
+                    )
+
+                state = self.state[param]
+
+                exp_avgs.append(state["exp_avg"])
+                exp_infs.append(state["exp_inf"])
+                state_steps.append(state["step"])
+
+        with torch.no_grad():
+            F.adamax(
+                params_with_grad,
+                grads,
+                exp_avgs,
+                exp_infs,
+                state_steps,
+                eps=self.defaults["eps"],
+                beta1=self.defaults["beta1"],
+                beta2=self.defaults["beta2"],
+                lr=self.defaults["lr"],
+                weight_decay=self.defaults["weight_decay"],
+                foreach=self.foreach,
+                maximize=self.maximize,
+                has_complex=has_complex,
+            )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/functional_adamw.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/functional_adamw.py
new file mode 100644
index 0000000000000000000000000000000000000000..d695ce8b473af8fbf1bde28293e576ff69fe6f04
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/functional_adamw.py
@@ -0,0 +1,202 @@
+# mypy: allow-untyped-defs
+
+import torch
+import torch.optim._functional as F
+from torch import Tensor
+from torch.distributed.optim._deprecation_warning import (
+    _scripted_functional_optimizer_deprecation_warning,
+)
+
+
+__all__: list[str] = []
+
+
+# Define a TorchScript compatible Functional AdamW Optimizer
+# where we use these optimizer in a functional way.
+# Instead of using the `param.grad` when updating parameters,
+# we explicitly allow the distributed optimizer pass gradients to
+# the `step` function. In this way, we could separate the gradients
+# and parameters and allow multithreaded trainer to update the
+# parameters without data traces on accumulating to the same .grad.
+# NOTE: This should be only used by distributed optimizer internals
+# and not meant to expose to the user.
+@torch.jit.script
+class _FunctionalAdamW:
+    def __init__(
+        self,
+        params: list[Tensor],
+        lr: float = 1e-3,
+        betas: tuple[float, float] = (0.9, 0.999),
+        eps: float = 1e-8,
+        weight_decay: float = 1e-2,
+        amsgrad: bool = False,
+        maximize: bool = False,
+        foreach: bool = False,
+        fused: bool = False,
+        _allow_empty_param_list: bool = False,
+    ):
+        _scripted_functional_optimizer_deprecation_warning(stacklevel=2)
+        if not 0.0 <= lr:
+            raise ValueError(f"Invalid learning rate: {lr}")
+        if not 0.0 <= eps:
+            raise ValueError(f"Invalid epsilon value: {eps}")
+        if not 0.0 <= betas[0] < 1.0:
+            raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}")
+        if not 0.0 <= betas[1] < 1.0:
+            raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}")
+        if not 0.0 <= weight_decay:
+            raise ValueError(f"Invalid weight_decay value: {weight_decay}")
+
+        self.defaults = {
+            "lr": lr,
+            "eps": eps,
+            "beta1": betas[0],
+            "beta2": betas[1],
+            "weight_decay": weight_decay,
+        }
+        self.amsgrad = amsgrad
+        self.maximize = maximize
+        self.foreach = foreach
+        self.fused = fused
+        self.state = torch.jit.annotate(dict[torch.Tensor, dict[str, torch.Tensor]], {})
+
+        if len(params) == 0 and not _allow_empty_param_list:
+            raise ValueError("optimizer got an empty parameter list")
+
+        # NOTE: we only have one param_group and don't allow user to add additional
+        # param group as it's not a common use case.
+        self.param_group = {"params": params}
+
+    def step_param(self, param: Tensor, grad: Tensor | None):
+        params_with_grad = []
+        grads = []
+        exp_avgs = []
+        exp_avg_sqs = []
+        max_exp_avg_sqs = []
+        state_steps: list[Tensor] = []
+        has_complex = torch.is_complex(param)
+        if grad is not None:
+            params_with_grad.append(param)
+            grads.append(grad)
+        # Lazy state initialization
+        if param not in self.state:
+            self.state[param] = {}
+            state = self.state[param]
+            state["step"] = torch.tensor(0.0)
+            # Exponential moving average of gradient values
+            state["exp_avg"] = torch.zeros_like(
+                param, memory_format=torch.preserve_format
+            )
+            # Exponential moving average of squared gradient values
+            state["exp_avg_sq"] = torch.zeros_like(
+                param, memory_format=torch.preserve_format
+            )
+            if self.amsgrad:
+                # Maintains max of all exp. moving avg. of sq. grad. values
+                state["max_exp_avg_sq"] = torch.zeros_like(
+                    param, memory_format=torch.preserve_format
+                )
+
+        state = self.state[param]
+
+        exp_avgs.append(state["exp_avg"])
+        exp_avg_sqs.append(state["exp_avg_sq"])
+
+        if self.amsgrad:
+            max_exp_avg_sqs.append(state["max_exp_avg_sq"])
+
+        state_steps.append(state["step"])
+        with torch.no_grad():
+            F.adamw(
+                params_with_grad,
+                grads,
+                exp_avgs,
+                exp_avg_sqs,
+                max_exp_avg_sqs,
+                state_steps,
+                amsgrad=self.amsgrad,
+                maximize=self.maximize,
+                beta1=self.defaults["beta1"],
+                beta2=self.defaults["beta2"],
+                lr=self.defaults["lr"],
+                weight_decay=self.defaults["weight_decay"],
+                eps=self.defaults["eps"],
+                foreach=self.foreach,
+                fused=self.fused,
+                grad_scale=None,
+                found_inf=None,
+                has_complex=has_complex,
+            )
+
+    def step(self, gradients: list[Tensor | None]):
+        params = self.param_group["params"]
+        params_with_grad = []
+        grads = []
+        exp_avgs = []
+        exp_avg_sqs = []
+        max_exp_avg_sqs = []
+        state_steps: list[Tensor] = []
+
+        if len(params) != len(gradients):
+            raise ValueError(
+                "the gradients passed in does not equal to the size of the parameters!"
+                + f"Params length: {len(params)}. "
+                + f"Gradients length: {len(gradients)}"
+            )
+
+        has_complex = False
+        for param, gradient in zip(self.param_group["params"], gradients):
+            if gradient is not None:
+                has_complex |= torch.is_complex(param)
+                params_with_grad.append(param)
+                grads.append(gradient)
+                # Lazy state initialization
+                if param not in self.state:
+                    self.state[param] = {}
+                    state = self.state[param]
+                    state["step"] = torch.tensor(0.0)
+                    # Exponential moving average of gradient values
+                    state["exp_avg"] = torch.zeros_like(
+                        param, memory_format=torch.preserve_format
+                    )
+                    # Exponential moving average of squared gradient values
+                    state["exp_avg_sq"] = torch.zeros_like(
+                        param, memory_format=torch.preserve_format
+                    )
+                    if self.amsgrad:
+                        # Maintains max of all exp. moving avg. of sq. grad. values
+                        state["max_exp_avg_sq"] = torch.zeros_like(
+                            param, memory_format=torch.preserve_format
+                        )
+
+                state = self.state[param]
+
+                exp_avgs.append(state["exp_avg"])
+                exp_avg_sqs.append(state["exp_avg_sq"])
+
+                if self.amsgrad:
+                    max_exp_avg_sqs.append(state["max_exp_avg_sq"])
+
+                state_steps.append(state["step"])
+
+        with torch.no_grad():
+            F.adamw(
+                params_with_grad,
+                grads,
+                exp_avgs,
+                exp_avg_sqs,
+                max_exp_avg_sqs,
+                state_steps,
+                amsgrad=self.amsgrad,
+                maximize=self.maximize,
+                beta1=self.defaults["beta1"],
+                beta2=self.defaults["beta2"],
+                lr=self.defaults["lr"],
+                weight_decay=self.defaults["weight_decay"],
+                eps=self.defaults["eps"],
+                foreach=self.foreach,
+                fused=self.fused,
+                grad_scale=None,
+                found_inf=None,
+                has_complex=has_complex,
+            )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/functional_rmsprop.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/functional_rmsprop.py
new file mode 100644
index 0000000000000000000000000000000000000000..45341b03237b456419ec181ae8b771dec081d3cb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/functional_rmsprop.py
@@ -0,0 +1,129 @@
+# mypy: allow-untyped-defs
+
+import torch
+import torch.optim._functional as F
+from torch import Tensor
+from torch.distributed.optim._deprecation_warning import (
+    _scripted_functional_optimizer_deprecation_warning,
+)
+
+
+__all__: list[str] = []
+
+
+# Define a TorchScript compatible Functional RMSprop Optimizer
+# where we use these optimizer in a functional way.
+# Instead of using the `param.grad` when updating parameters,
+# we explicitly allow the distributed optimizer pass gradients to
+# the `step` function. In this way, we could separate the gradients
+# and parameters and allow multithreaded trainer to update the
+# parameters without data traces on accumulating to the same .grad.
+# NOTE: This should be only used by distributed optimizer internals
+# and not meant to expose to the user.
+@torch.jit.script
+class _FunctionalRMSprop:
+    def __init__(
+        self,
+        params: list[Tensor],
+        lr: float = 1e-2,
+        alpha: float = 0.99,
+        eps: float = 1e-8,
+        weight_decay: float = 0.0,
+        momentum: float = 0.0,
+        centered: bool = False,
+        foreach: bool = False,
+        maximize: bool = False,
+        _allow_empty_param_list: bool = False,
+    ):
+        _scripted_functional_optimizer_deprecation_warning(stacklevel=2)
+        self.defaults = {
+            "lr": lr,
+            "alpha": alpha,
+            "eps": eps,
+            "weight_decay": weight_decay,
+            "momentum": momentum,
+        }
+        self.centered = centered
+        self.foreach = foreach
+        self.maximize = maximize
+
+        if len(params) == 0 and not _allow_empty_param_list:
+            raise ValueError("optimizer got an empty parameter list")
+
+        # NOTE: we only have one param_group and don't allow user to add additional
+        # param group as it's not a common use case.
+        self.param_group = {"params": params}
+
+        self.state = torch.jit.annotate(dict[torch.Tensor, dict[str, torch.Tensor]], {})
+
+    def step(self, gradients: list[Tensor | None]):
+        params = self.param_group["params"]
+        params_with_grad = []
+        grads = []
+        square_avgs = []
+        grad_avgs = []
+        momentum_buffer_list = []
+        state_steps = []
+        lr = self.defaults["lr"]
+        alpha = self.defaults["alpha"]
+        eps = self.defaults["eps"]
+        momentum = self.defaults["momentum"]
+        weight_decay = self.defaults["weight_decay"]
+
+        if len(params) != len(gradients):
+            raise ValueError(
+                "the gradients passed in does not equal to the size of the parameters!"
+                + f"Params length: {len(params)}. "
+                + f"Gradients length: {len(gradients)}"
+            )
+
+        has_complex = False
+        for param, gradient in zip(params, gradients):
+            if gradient is not None:
+                has_complex |= torch.is_complex(param)
+                params_with_grad.append(param)
+                grads.append(gradient)
+                # Lazy state initialization
+                if param not in self.state:
+                    self.state[param] = {}
+                    state = self.state[param]
+                    state["step"] = torch.tensor(0.0)
+                    state["square_avg"] = torch.zeros_like(
+                        param, memory_format=torch.preserve_format
+                    )
+                    if momentum > 0:
+                        state["momentum_buffer"] = torch.zeros_like(
+                            param, memory_format=torch.preserve_format
+                        )
+                    if self.centered:
+                        state["grad_avg"] = torch.zeros_like(
+                            param, memory_format=torch.preserve_format
+                        )
+
+                state = self.state[param]
+                square_avgs.append(state["square_avg"])
+                if momentum > 0:
+                    momentum_buffer_list.append(state["momentum_buffer"])
+                if self.centered:
+                    grad_avgs.append(state["grad_avg"])
+
+                state_steps.append(state["step"])
+
+        with torch.no_grad():
+            F.rmsprop(
+                params_with_grad,
+                grads,
+                square_avgs,
+                grad_avgs,
+                momentum_buffer_list,
+                state_steps,
+                lr=lr,
+                alpha=alpha,
+                eps=eps,
+                weight_decay=weight_decay,
+                momentum=momentum,
+                centered=self.centered,
+                foreach=self.foreach,
+                maximize=self.maximize,
+                has_complex=has_complex,
+            )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/functional_rprop.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/functional_rprop.py
new file mode 100644
index 0000000000000000000000000000000000000000..ffc9c510dabca7871d19890c5e52e0f5eeafcd49
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/functional_rprop.py
@@ -0,0 +1,106 @@
+# mypy: allow-untyped-defs
+
+import torch
+import torch.optim._functional as F
+from torch import Tensor
+from torch.distributed.optim._deprecation_warning import (
+    _scripted_functional_optimizer_deprecation_warning,
+)
+
+
+__all__: list[str] = []
+
+
+# Define a TorchScript compatible Functional Rprop Optimizer
+# where we use these optimizer in a functional way.
+# Instead of using the `param.grad` when updating parameters,
+# we explicitly allow the distributed optimizer pass gradients to
+# the `step` function. In this way, we could separate the gradients
+# and parameters and allow multithreaded trainer to update the
+# parameters without data traces on accumulating to the same .grad.
+# NOTE: This should be only used by distributed optimizer internals
+# and not meant to expose to the user.
+@torch.jit.script
+class _FunctionalRprop:
+    def __init__(
+        self,
+        params: list[Tensor],
+        lr: float = 1e-2,
+        etas: tuple[float, float] = (0.5, 1.2),
+        step_sizes: tuple[float, float] = (1e-6, 50),
+        foreach: bool = False,
+        maximize: bool = False,
+        _allow_empty_param_list: bool = False,
+    ):
+        _scripted_functional_optimizer_deprecation_warning(stacklevel=2)
+        self.defaults = {
+            "lr": lr,
+        }
+        self.etas = etas
+        self.step_sizes = step_sizes
+        self.foreach = foreach
+        self.maximize = maximize
+
+        if len(params) == 0 and not _allow_empty_param_list:
+            raise ValueError("optimizer got an empty parameter list")
+
+        # NOTE: we only have one param_group and don't allow user to add additional
+        # param group as it's not a common use case.
+        self.param_group = {"params": params}
+
+        self.state = torch.jit.annotate(dict[torch.Tensor, dict[str, torch.Tensor]], {})
+
+    def step(self, gradients: list[Tensor | None]):
+        params = self.param_group["params"]
+        params_with_grad = []
+        grads = []
+        prevs = []
+        step_sizes = []
+        state_steps = []
+        lr = self.defaults["lr"]
+        etaminus, etaplus = self.etas
+        step_size_min, step_size_max = self.step_sizes
+
+        if len(params) != len(gradients):
+            raise ValueError(
+                "the gradients passed in does not equal to the size of the parameters!"
+                + f"Params length: {len(params)}. "
+                + f"Gradients length: {len(gradients)}"
+            )
+
+        has_complex = False
+        for param, gradient in zip(params, gradients):
+            if gradient is not None:
+                has_complex |= torch.is_complex(param)
+                params_with_grad.append(param)
+                grads.append(gradient)
+                # Lazy state initialization
+                if param not in self.state:
+                    self.state[param] = {}
+                    state = self.state[param]
+                    state["step"] = torch.tensor(0.0)
+                    state["prev"] = torch.zeros_like(
+                        param, memory_format=torch.preserve_format
+                    )
+                    state["step_size"] = torch.full_like(gradient, lr)
+
+                state = self.state[param]
+                prevs.append(state["prev"])
+                step_sizes.append(state["step_size"])
+                state_steps.append(state["step"])
+
+        with torch.no_grad():
+            F.rprop(
+                params_with_grad,
+                grads,
+                prevs,
+                step_sizes,
+                state_steps,
+                step_size_min=step_size_min,
+                step_size_max=step_size_max,
+                etaminus=etaminus,
+                etaplus=etaplus,
+                foreach=self.foreach,
+                maximize=self.maximize,
+                has_complex=has_complex,
+            )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/functional_sgd.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/functional_sgd.py
new file mode 100644
index 0000000000000000000000000000000000000000..aed92403e6fb62394e2f755fffbe5b7f323200ff
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/functional_sgd.py
@@ -0,0 +1,165 @@
+# mypy: allow-untyped-defs
+
+import torch
+import torch.optim._functional as F
+from torch import Tensor
+from torch.distributed.optim._deprecation_warning import (
+    _scripted_functional_optimizer_deprecation_warning,
+)
+
+
+__all__: list[str] = []
+
+
+# Define a TorchScript compatible Functional SGD Optimizer
+# where we use these optimizer in a functional way.
+# Instead of using the `param.grad` when updating parameters,
+# we explicitly allow the distributed optimizer pass gradients to
+# the `step` function. In this way, we could separate the gradients
+# and parameters and allow multithreaded trainer to update the
+# parameters without data traces on accumulating to the same .grad.
+# NOTE: This should be only used by distributed optimizer internals
+# and not meant to expose to the user.
+@torch.jit.script
+class _FunctionalSGD:
+    def __init__(
+        self,
+        params: list[Tensor],
+        lr: float = 1e-2,
+        momentum: float = 0.0,
+        dampening: float = 0.0,
+        weight_decay: float = 0.0,
+        nesterov: bool = False,
+        maximize: bool = False,
+        foreach: bool = False,
+        fused: bool = False,
+        _allow_empty_param_list: bool = False,
+    ):
+        _scripted_functional_optimizer_deprecation_warning(stacklevel=2)
+        self.defaults = {
+            "lr": lr,
+            "momentum": momentum,
+            "dampening": dampening,
+            "weight_decay": weight_decay,
+        }
+        self.nesterov = nesterov
+        self.maximize = maximize
+        self.foreach = foreach
+        self.fused = fused
+        self.state = torch.jit.annotate(dict[torch.Tensor, dict[str, torch.Tensor]], {})
+
+        if len(params) == 0 and not _allow_empty_param_list:
+            raise ValueError("optimizer got an empty parameter list")
+
+        # NOTE: we only have one param_group and don't allow user to add additional
+        # param group as it's not a common use case.
+        self.param_group = {"params": params}
+
+    def step_param(self, param: Tensor, grad: Tensor | None):
+        """Similar to self.step, but operates on a single parameter and
+        its gradient.
+        """
+        # TODO: Once step_param interface is robust, refactor step to call
+        # step param on each param.
+        weight_decay = self.defaults["weight_decay"]
+        momentum = self.defaults["momentum"]
+        dampening = self.defaults["dampening"]
+        lr = self.defaults["lr"]
+        params = [param]
+        momentum_buffer_list: list[Tensor | None] = []
+        grads = []
+
+        has_sparse_grad = False
+        if grad is not None:
+            grads.append(grad)
+            if grad.is_sparse:
+                has_sparse_grad = True
+            if param not in self.state:
+                self.state[param] = {}
+            state = self.state[param]
+            if "momentum_buffer" not in state:
+                momentum_buffer_list.append(None)
+            else:
+                momentum_buffer_list.append(state["momentum_buffer"])
+
+        with torch.no_grad():
+            F.sgd(
+                params,
+                grads,
+                momentum_buffer_list,
+                weight_decay=weight_decay,
+                momentum=momentum,
+                lr=lr,
+                dampening=dampening,
+                nesterov=self.nesterov,
+                maximize=self.maximize,
+                has_sparse_grad=has_sparse_grad,
+                foreach=self.foreach,
+                fused=self.fused,
+                grad_scale=None,
+                found_inf=None,
+            )
+        # update momentum_buffer in state
+        state = self.state[param]
+        momentum_buffer = momentum_buffer_list[0]
+        if momentum_buffer is not None:
+            state["momentum_buffer"] = momentum_buffer
+
+    def step(self, gradients: list[Tensor | None]):
+        params = self.param_group["params"]
+        params_with_grad = []
+        grads = []
+        momentum_buffer_list: list[Tensor | None] = []
+        lr = self.defaults["lr"]
+        weight_decay = self.defaults["weight_decay"]
+        momentum = self.defaults["momentum"]
+        dampening = self.defaults["dampening"]
+
+        if len(params) != len(gradients):
+            raise ValueError(
+                "the gradients passed in does not equal to the size of the parameters!"
+                + f"Params length: {len(params)}. "
+                + f"Gradients length: {len(gradients)}"
+            )
+
+        has_sparse_grad = False
+        for param, gradient in zip(params, gradients):
+            if gradient is not None:
+                params_with_grad.append(param)
+                grads.append(gradient)
+                if gradient.is_sparse:
+                    has_sparse_grad = True
+
+                if param not in self.state:
+                    self.state[param] = {}
+
+                state = self.state[param]
+                if "momentum_buffer" not in state:
+                    momentum_buffer_list.append(None)
+                else:
+                    momentum_buffer_list.append(state["momentum_buffer"])
+
+        with torch.no_grad():
+            F.sgd(
+                params_with_grad,
+                grads,
+                momentum_buffer_list,
+                weight_decay=weight_decay,
+                momentum=momentum,
+                lr=lr,
+                dampening=dampening,
+                nesterov=self.nesterov,
+                maximize=self.maximize,
+                has_sparse_grad=has_sparse_grad,
+                foreach=self.foreach,
+                fused=self.fused,
+                grad_scale=None,
+                found_inf=None,
+            )
+
+        # update momentum_buffers in state
+        for i, p in enumerate(params_with_grad):
+            state = self.state[p]
+            momentum_buffer = momentum_buffer_list[i]
+            if momentum_buffer is not None:
+                state["momentum_buffer"] = momentum_buffer
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/named_optimizer.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/named_optimizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..a8432e198a083e194a1e48bf8c0af76ffa6b83a1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/named_optimizer.py
@@ -0,0 +1,328 @@
+import logging
+import warnings
+from collections.abc import Callable, Collection, Mapping
+from copy import deepcopy
+from typing import Any, overload
+
+import torch
+import torch.nn as nn
+from torch import optim
+from torch.distributed._shard.sharded_tensor import ShardedTensor
+from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+
+
+__all__: list[str] = []
+
+logger = logging.getLogger(__name__)
+
+
+class _NamedOptimizer(optim.Optimizer):
+    """
+    ``_NamedOptimizer`` takes a dict of parameters and exposes ``state_dict`` by parameter key.
+
+    We replace the original key (number) in an optim to the
+    fully qualified name (FQN) string. User can initialize the optim as they
+    initialize a PyTorch optim, the only difference is that they also need to
+    pass in the FQN of each parameters.
+
+    Args:
+        named_parameters (Mapping[str, Union[torch.Tensor, ShardedTensor]]):
+            Mapping from FQN to parameter.
+        optimizer_class (optim.Optimizer):
+            The class of optimizer to instantiate.
+        param_groups (Collection[Mapping[str, Any]]):
+            `param_groups` to pass to optimizer if specified.
+            The key of the inner map needs to be FQNs.
+            Default: None
+        module (nn.Module): the module whose parameters to updated
+            by the optimizer.
+        args: arguments to pass to the optimizer constructor.
+        kwargs: arguments to pass to the optimizer constructor.
+
+    Example::
+        >>> # xdoctest: +SKIP("distributed")
+        >>> from torch import optim
+        >>> from torch.distributed.optim import _NamedOptimizer
+        >>>
+        >>> # Define the named optimizer.
+        >>> m = Model(...)
+        >>> named_optim = _NamedOptimizer(m.named_parameters(), optim.SGD)
+        >>> # Forward pass + backward pass.
+        >>> named_optim.step()
+        >>> ...
+        >>> # Call state_dict for the named optimizer returns a FQN state_dict.
+        >>> named_optim.state_dict()
+
+    Warning: This API is still in development and subject to change.
+
+    TODO: Add tutorial for _NamedOptimizer.
+    TODO: Add documentation in the docstring for the public attributes
+          like self.param_groups and self.named_parameters.
+    """
+
+    def __init__(
+        self,
+        named_parameters: Mapping[str, torch.Tensor | ShardedTensor],
+        optimizer_class: optim.Optimizer,
+        param_groups: Collection[Mapping[str, Any]] | None = None,
+        module: nn.Module | None = None,
+        *args: tuple[Any, ...],
+        **kwargs: dict[str, Any],
+    ) -> None:
+        torch._C._log_api_usage_once("torch.distributed.optim._NamedOptimizer")
+        self.param_groups: Collection[Mapping[str, Any]] = param_groups  # type: ignore[assignment]
+        self._param_groups_check()
+        self.named_parameters = dict(named_parameters)
+        params_for_optimizer = (
+            self.named_parameters.values() if param_groups is None else param_groups
+        )
+        self._optimizer = optimizer_class(  # type: ignore[operator]
+            params_for_optimizer,
+            *args,
+            **kwargs,
+        )
+        self.module = module
+        if param_groups is None:
+            self.ordered_param_keys = list(self.named_parameters.keys())
+        else:
+            warnings.warn(
+                "Since we pass in param_groups, we will use param_groups to "
+                "initialize the optimizer, not all parameters of the module.",
+                stacklevel=2,
+            )
+            param_to_key = {param: key for key, param in self.named_parameters.items()}  # type: ignore[misc, has-type]
+            ordered_param_keys = []
+            for group in param_groups:
+                for param in group["params"]:
+                    if param not in param_to_key:
+                        raise ValueError(
+                            f"Expect param name {param} found in param group but is missing."
+                        )
+                    ordered_param_keys.append(param_to_key[param])
+            self.ordered_param_keys = ordered_param_keys
+        # Update param_groups from optimizer.
+        self.param_groups = self._optimizer.param_groups
+
+    def _param_groups_check(self) -> None:
+        if self.param_groups is not None:
+            for param_group in self.param_groups:
+                assert isinstance(param_group, dict), "param group must be a dict"
+                assert "params" in param_group, "param group must contain key params"
+                params = param_group["params"]
+                if isinstance(params, torch.Tensor):
+                    params = [params]
+                params = list(params)
+                for param in params:
+                    if not isinstance(param, torch.Tensor):
+                        raise TypeError(
+                            "optimizer can only optimize Tensors, "
+                            "but one of the params is " + torch.typename(param)
+                        )
+                param_group["params"] = params
+
+    def state_dict(self) -> dict[str, Any]:
+        """
+        Return the ``state_dict`` of the optimizer.
+
+        Instead of using number to index
+        parameters, we will use module fully qualified name (FQN) as the key.
+        """
+        state_dict = self._optimizer.state_dict()
+        param_groups = state_dict["param_groups"]
+
+        ret_state = {
+            self.ordered_param_keys[st_key]: state_val
+            for st_key, state_val in state_dict["state"].items()
+        }
+
+        ret_groups = []
+        for group in param_groups:
+            param_keys = [self.ordered_param_keys[param] for param in group["params"]]
+            ret_group = {"params": sorted(param_keys)}
+            for k, v in group.items():
+                if k != "params":
+                    ret_group[k] = deepcopy(v)
+            ret_groups.append(ret_group)
+
+        return self._post_state_dict({"state": ret_state, "param_groups": ret_groups})
+
+    @overload
+    def step(self, closure: None = None) -> None: ...
+
+    @overload
+    def step(self, closure: Callable[[], float]) -> float: ...
+
+    def step(self, closure: Callable[[], float] | None = None) -> float | None:
+        """
+        Perform a single optimization step.
+
+        This will call :meth:`torch.optim.Optimizer.step` on the wrapped
+        optimizer.
+        """
+        return self._optimizer.step(closure=closure)
+
+    @property
+    def state(self) -> Mapping[torch.Tensor, Any]:  # type: ignore[override]
+        return self._optimizer.state
+
+    def load_state_dict(self, state_dict: dict[str, Any]) -> None:
+        """
+        Define the default behavior to load a state_dict for ``_NamedOptimizer``.
+
+        Sample Code
+        ```
+            my_model = MyModule()
+            optimizer = _NamedOptimizer(my_model.named_parameters(), Adagrad)
+            ...
+
+            optim_state_dict = optimizer.state_dict()
+            ...
+            ...
+
+            optimizer.load_state_dict(optim_state_dict)
+            ...
+        ```
+        Args:
+            state_dict (dict[str, Any]) : A ``state_dict`` to load into the optimizer.
+                Note that this state dict update is performed in place.
+
+        .. note:: PyTorch is using lazy init to initialize the optim states.
+            So it is possible that there is no optim state when user call
+            ``load_state_dict`` and for ``_NamedOptimizer`` we make it stricter
+            that users can only call ``load_state_dict`` after the state is initialized.
+            By doing this, we can validate the optim ``state_dict`` to be loaded.
+        """
+        new_state_dict = self._optimizer.state_dict()
+        state_dict = self._pre_load_state_dict(state_dict)
+        state = state_dict["state"]
+        new_state = new_state_dict["state"]
+        if len(new_state) == 0:
+            raise ValueError(
+                "Expects the optim to be initialized before load but found not initialized."
+            )
+
+        for idx, param_key in enumerate(self.ordered_param_keys):
+            # When the conditional training is performed, not all parameters are updated in the optim.
+            if param_key not in state:
+                continue
+            if len(state[param_key]) != len(new_state[idx]):
+                raise ValueError(
+                    f"Expects equal length as {len(new_state[idx])} for parameter {param_key} but found: {len(state[param_key])}"
+                )
+            # Iterate through all optimizer states.
+            for state_key, state_val in new_state[idx].items():
+                if state_key not in state[param_key]:
+                    raise ValueError(
+                        f"Expects state {state_key} for parameter {param_key} but not found."
+                    )
+
+                src_state_val = state[param_key][state_key]
+                if isinstance(state_val, ShardedTensor):
+                    assert isinstance(src_state_val, ShardedTensor)
+                    num_shards = len(state_val.local_shards())
+                    num_new_shards = len(src_state_val.local_shards())
+                    if num_shards != num_new_shards:
+                        raise ValueError(
+                            f"Expects equal number of shards as {num_new_shards} but found {num_shards} for {param_key}/{state_key}"
+                        )
+                    for shard, src_shard in zip(
+                        state_val.local_shards(), src_state_val.local_shards()
+                    ):
+                        shard.tensor.detach().copy_(src_shard.tensor)
+                elif isinstance(state_val, torch.Tensor):
+                    assert isinstance(src_state_val, torch.Tensor)
+                    state_val.detach().copy_(src_state_val)
+                else:
+                    new_state[idx][state_key] = deepcopy(src_state_val)
+
+        # Load param_groups of state_dict
+        src_param_groups = state_dict["param_groups"]
+        new_param_groups = new_state_dict["param_groups"]
+
+        src_group_map = {}
+        for group in src_param_groups:
+            param_keys = list(group["params"])
+            src_group_map[_gen_param_group_key(param_keys)] = group
+        new_group_map = {}
+        for new_group in new_param_groups:
+            param_keys = []
+            for param_key in new_group["params"]:
+                param_keys.append(self.ordered_param_keys[param_key])  # type: ignore[call-overload]
+            new_group_map[_gen_param_group_key(param_keys)] = new_group
+        for group_key, new_group in new_group_map.items():
+            # When not all parameters are used in training or receive gradient, aka., not all parameters
+            # would be in the param_group. Thus we skip the group_key here.
+            if group_key not in src_group_map:
+                continue
+            src_group = src_group_map[group_key]
+            if len(src_group) != len(new_group):
+                raise ValueError(
+                    f"Expects equal param_group size as {len(new_group)} for group {group_key} but found {len(src_group)}."
+                )
+            for k in src_group:
+                if k not in new_group:
+                    raise ValueError(
+                        f"Expects group key {k} to be in group {group_key} in `state_dict` but is missing."
+                    )
+                if k != "params":
+                    new_group[k] = deepcopy(src_group[k])
+
+        self._optimizer.load_state_dict(new_state_dict)
+
+    def add_param_group(self, param_group: Mapping[str, Any]) -> None:
+        """
+        Add a param group to the :class:`_NamedOptimizer` s `param_groups`.
+
+        Warning: This API is still in development and subject to change.
+        """
+        assert isinstance(param_group, dict), "param group must be a dict"
+
+        params = param_group["params"]
+        if isinstance(params, torch.Tensor):
+            param_group["params"] = [params]
+        else:
+            param_group["params"] = list(params)
+
+        param_to_key = {param: key for key, param in self.named_parameters.items()}  # type: ignore[misc, has-type]
+        for param in param_group["params"]:
+            if param not in param_to_key:
+                raise ValueError("some parameters are not in the module")
+            self.ordered_param_keys.append(param_to_key[param])
+
+        self._optimizer.add_param_group(param_group)
+        # Update param_groups from optimizer.
+        self.param_groups = self._optimizer.param_groups
+
+    def init_state(self) -> None:
+        """
+        Run a dummy optimizer step, which allows to initialize optimizer state because we do lazy init for most optimizers.
+
+        This allows doing in-place loading of optimizer state from a checkpoint.
+        """
+        for param in self.named_parameters.values():
+            if param.requires_grad:
+                t = torch.zeros_like(param)
+                param.grad = torch.autograd.Variable(t)
+        # Calling ``step`` will load the initial state for optimizer states.
+        self.step(closure=None)
+
+    def _pre_load_state_dict(self, state_dict: dict[str, Any]) -> dict[str, Any]:
+        # TODO(chienchin): This API should be FSDP agnostic and should support
+        # general user hooks.
+        if isinstance(self.module, FSDP):
+            return FSDP.optim_state_dict_to_load(
+                self.module, self._optimizer, state_dict, is_named_optimizer=True
+            )
+        return state_dict
+
+    def _post_state_dict(self, state_dict: dict[str, Any]) -> dict[str, Any]:
+        # TODO(chienchin): This API should be FSDP agnostic and should support
+        # general user hooks.
+        if isinstance(self.module, FSDP):
+            FSDP.optim_state_dict(self.module, self._optimizer, state_dict)
+        return state_dict
+
+
+def _gen_param_group_key(param_keys: list[str]) -> str:
+    """Concatenate all param keys as a unique identifier for one param group."""
+    return "/".join(sorted(param_keys))
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/optimizer.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/optimizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9477aa414b429e4cb4ca8bf1d1fedf9788d4eaa
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/optimizer.py
@@ -0,0 +1,254 @@
+# mypy: allow-untyped-defs
+import logging
+from collections import defaultdict
+from threading import Lock
+
+import torch
+import torch.distributed.autograd as dist_autograd
+import torch.distributed.rpc as rpc
+import torch.jit as jit
+import torch.nn as nn
+from torch import Tensor
+from torch.distributed.rpc import RRef
+
+from .utils import functional_optim_map
+
+
+__all__ = ["DistributedOptimizer"]
+
+logger = logging.getLogger(__name__)
+
+
+# XXX: we define a _ScriptModuleOptimizer here to explicitly
+# compile the FunctionalOptimizer class into TorchScript
+# This is because ScriptClass instance still lives in
+# python unless you explicitly compile it as an attribute
+# in ScriptModule or pass it to a ScriptFunction
+# _ScriptLocalOptimizerInterface serves as a common
+# interface type for Optimizer ScriptModules.
+#
+# TODO (wanchaol): remove this once we added TorchScript
+# class reference semantics
+@jit.interface
+class _ScriptLocalOptimizerInterface:
+    def step(self, autograd_ctx_id: int) -> None:
+        pass
+
+
+class _ScriptLocalOptimizer(nn.Module):
+    # TorchScript does not support multithread concurrent compiling.
+    # request_callback might invoke concurrent compiling, so we
+    # serialize the compiling with a lock
+    compile_lock = Lock()
+
+    def __init__(self, optim_cls, local_params_rref, *args, **kwargs):
+        super().__init__()
+        self._local_params = [rref.local_value() for rref in local_params_rref]
+        self.optim = optim_cls(self._local_params, *args, **kwargs)
+
+    @jit.export
+    def step(self, autograd_ctx_id: int):
+        all_local_grads = dist_autograd.get_gradients(autograd_ctx_id)
+        # apply functional optimizer step with a list of gradients
+        grads: list[Tensor | None] = [
+            all_local_grads[p] if p in all_local_grads else None  # noqa: SIM401
+            for p in self._local_params
+        ]
+
+        self.optim.step(grads)
+
+
+# TODO (wanchaol): remove/merge this with ScriptLocalOptimizer once
+# we have converted all to functional optimizer in distributed.optim
+class _LocalOptimizer:
+    # Ideally we would only need to share a lock for instances of
+    # _LocalOptimizer that deal with the same parameters. We are
+    # making a simplifying assumption here that if there is more
+    # than one instance of _LocalOptimizer per worker, they will
+    # be optimizing the same parameters (e.g. each data parallel
+    # trainer will create its own instance of _LocalOptimizer but
+    # they will all optimize the same parameters on each worker)
+    global_lock = Lock()
+
+    def __init__(self, optim_cls, local_params_rref, *args, **kwargs):
+        self._local_params = [rref.local_value() for rref in local_params_rref]
+        self.optim = optim_cls(self._local_params, *args, **kwargs)
+
+    def step(self, autograd_ctx_id):
+        all_local_grads = dist_autograd.get_gradients(autograd_ctx_id)
+
+        with _LocalOptimizer.global_lock:
+            for param, grad in all_local_grads.items():
+                param.grad = grad
+            self.optim.step()
+
+
+def _new_local_optimizer(optim_cls, local_params_rref, *args, **kwargs):
+    return rpc.RRef(_LocalOptimizer(optim_cls, local_params_rref, *args, **kwargs))
+
+
+def _local_optimizer_step(local_optim_rref, autograd_ctx_id):
+    local_optim = local_optim_rref.local_value()
+    local_optim.step(autograd_ctx_id)
+
+
+# new/step functions combined with _ScriptLocalOptimizer to provide GIL-free optimizer
+def _new_script_local_optimizer(optim_cls, local_params_rref, *args, **kwargs):
+    optim = _ScriptLocalOptimizer(optim_cls, local_params_rref, *args, **kwargs)
+
+    with _ScriptLocalOptimizer.compile_lock:
+        script_optim = jit.script(optim)
+        return rpc.RRef(script_optim, _ScriptLocalOptimizerInterface)
+
+
+@jit.script
+def _script_local_optimizer_step(
+    local_optim_rref: RRef[_ScriptLocalOptimizerInterface], autograd_ctx_id: int
+) -> None:
+    local_optim = local_optim_rref.local_value()
+    local_optim.step(autograd_ctx_id)
+
+
+def _wait_for_all(rpc_futs):
+    # TODO: improve error propagation
+    exception = None
+    results = []
+    for fut in rpc_futs:
+        try:
+            results.append(fut.wait())
+        except Exception as e:
+            results.append(e)
+            exception = e
+    if exception is not None:
+        raise exception
+    return results
+
+
+class DistributedOptimizer:
+    """
+    DistributedOptimizer takes remote references to parameters scattered
+    across workers and applies the given optimizer locally for each parameter.
+
+    This class uses :meth:`~torch.distributed.autograd.get_gradients` in order
+    to retrieve the gradients for specific parameters.
+
+    Concurrent calls to
+    :meth:`~torch.distributed.optim.DistributedOptimizer.step`,
+    either from the same or different clients, will
+    be serialized on each worker -- as each worker's optimizer can only work
+    on one set of gradients at a time. However, there is no guarantee that
+    the full forward-backward-optimizer sequence will execute for one client
+    at a time. This means that the gradients being applied may not correspond
+    to the latest forward pass executed on a given worker. Also, there is no
+    guaranteed ordering across workers.
+
+    `DistributedOptimizer` creates the local optimizer with TorchScript enabled
+    by default, so that optimizer updates are not blocked by the Python Global
+    Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed
+    Model Parallel). This feature is currently enabled for most optimizers. You
+    can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support
+    for your own custom optimizers.
+
+    Args:
+        optimizer_class (optim.Optimizer): the class of optimizer to
+            instantiate on each worker.
+        params_rref (list[RRef]): list of RRefs to local or remote parameters
+            to optimize.
+        args: arguments to pass to the optimizer constructor on each worker.
+        kwargs: arguments to pass to the optimizer constructor on each worker.
+
+    Example::
+        >>> # xdoctest: +SKIP("distributed")
+        >>> import torch.distributed.autograd as dist_autograd
+        >>> import torch.distributed.rpc as rpc
+        >>> from torch import optim
+        >>> from torch.distributed.optim import DistributedOptimizer
+        >>>
+        >>> with dist_autograd.context() as context_id:
+        >>>   # Forward pass.
+        >>>   rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3))
+        >>>   rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1))
+        >>>   loss = rref1.to_here() + rref2.to_here()
+        >>>
+        >>>   # Backward pass.
+        >>>   dist_autograd.backward(context_id, [loss.sum()])
+        >>>
+        >>>   # Optimizer.
+        >>>   dist_optim = DistributedOptimizer(
+        >>>      optim.SGD,
+        >>>      [rref1, rref2],
+        >>>      lr=0.05,
+        >>>   )
+        >>>   dist_optim.step(context_id)
+
+    __ https://github.com/pytorch/tutorials/pull/1465
+    """
+
+    def __init__(self, optimizer_class, params_rref, *args, **kwargs):
+        torch._C._log_api_usage_once("torch.distributed.optim.DistributedOptimizer")
+        per_worker_params_rref = defaultdict(list)
+        for param in params_rref:
+            per_worker_params_rref[param.owner()].append(param)
+
+        if optimizer_class in functional_optim_map and jit._state._enabled:
+            optim_ctor = functional_optim_map.get(optimizer_class)
+        else:
+            optim_ctor = optimizer_class
+        self.is_functional_optim = optim_ctor != optimizer_class
+
+        if self.is_functional_optim:
+            optimizer_new_func = _new_script_local_optimizer
+        else:
+            logger.warning(
+                "Creating the optimizer %s without TorchScript support, "
+                "this might result in slow computation time in multithreading environment"
+                "(i.e. Distributed Model Parallel training on CPU) due to the Python's "
+                "Global Interpreter Lock (GIL). Please file an issue if you need this "
+                "optimizer in TorchScript. ",
+                optimizer_class,
+            )
+            optimizer_new_func = _new_local_optimizer
+
+        remote_optim_futs = []
+        for worker, param_rrefs in per_worker_params_rref.items():
+            remote_optim_rref_fut = rpc.rpc_async(
+                worker,
+                optimizer_new_func,
+                args=(optim_ctor, param_rrefs) + args,
+                kwargs=kwargs,
+            )
+            remote_optim_futs.append(remote_optim_rref_fut)
+
+        self.remote_optimizers = _wait_for_all(remote_optim_futs)
+
+    def step(self, context_id):
+        """
+        Performs a single optimization step.
+
+        This will call :meth:`torch.optim.Optimizer.step` on each worker
+        containing parameters to be optimized, and will block until all workers
+        return. The provided ``context_id`` will be used to retrieve the
+        corresponding :class:`~torch.distributed.autograd.context` that
+        contains the gradients that should be applied to the parameters.
+
+        Args:
+            context_id: the autograd context id for which we should run the
+                optimizer step.
+        """
+        dist_autograd._is_valid_context(context_id)
+
+        optimizer_step_func = (
+            _script_local_optimizer_step
+            if self.is_functional_optim
+            else _local_optimizer_step
+        )
+
+        rpc_futs = [
+            rpc.rpc_async(
+                optimizer.owner(),
+                optimizer_step_func,
+                args=(optimizer, context_id),
+            )
+            for optimizer in self.remote_optimizers
+        ]
+        _wait_for_all(rpc_futs)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/post_localSGD_optimizer.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/post_localSGD_optimizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7b78510ed1a111998a4eda21546b003eedbcce7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/post_localSGD_optimizer.py
@@ -0,0 +1,111 @@
+# mypy: allow-untyped-defs
+import warnings
+
+import torch
+import torch.distributed.algorithms.model_averaging.averagers as averagers
+
+
+class PostLocalSGDOptimizer(torch.optim.Optimizer):
+    r"""
+    Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_,
+    This optimizer runs local optimizer at every step.
+    After the warm-up stage, it averages parameters periodically after the local optimizer is applied.
+
+    Args:
+        optim: The local optimizer.
+        averager: A model averager instance to run post-localSGD algorithm.
+
+    Example::
+
+        >>> # xdoctest: +SKIP("undefined variables")
+        >>> import torch
+        >>> import torch.distributed as dist
+        >>> import torch.distributed.algorithms.model_averaging.averagers as averagers
+        >>> import torch.nn as nn
+        >>> from torch.distributed.optim import PostLocalSGDOptimizer
+        >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import (
+        >>>   PostLocalSGDState,
+        >>>   post_localSGD_hook,
+        >>> )
+        >>>
+        >>> model = nn.parallel.DistributedDataParallel(
+        >>>    module, device_ids=[rank], output_device=rank
+        >>> )
+        >>>
+        >>> # Register a post-localSGD communication hook.
+        >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100)
+        >>> model.register_comm_hook(state, post_localSGD_hook)
+        >>>
+        >>> # Create a post-localSGD optimizer that wraps a local optimizer.
+        >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as
+        >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``.
+        >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01)
+        >>> opt = PostLocalSGDOptimizer(
+        >>>     optim=local_optim,
+        >>>     averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100)
+        >>> )
+        >>>
+        >>> # In the first 100 steps, DDP runs global gradient averaging at every step.
+        >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default),
+        >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer.
+        >>> for step in range(0, 200):
+        >>>    opt.zero_grad()
+        >>>    loss = loss_fn(output, labels)
+        >>>    loss.backward()
+        >>>    opt.step()
+    """
+
+    def __init__(self, optim: torch.optim.Optimizer, averager: averagers.ModelAverager):
+        self.optim = optim
+        self.param_groups = self.optim.param_groups
+        self.averager = averager
+
+    @property
+    def state(self):  # type: ignore[override]
+        return self.optim.state
+
+    def __repr__(self):
+        return self.optim.__repr__()
+
+    def state_dict(self):
+        r"""
+        This is the same as :class:`torch.optim.Optimizer` :meth:`state_dict`,
+        but adds an extra entry to record model averager's step to the checkpoint
+        to ensure reload does not cause unnecessary warm up again.
+        """
+        optim_state_dict = self.optim.state_dict()
+        optim_state_dict["step"] = self.averager.step
+        return optim_state_dict
+
+    def load_state_dict(self, state_dict):
+        r"""
+        This is the same as :class:`torch.optim.Optimizer` :meth:`load_state_dict`,
+        but also restores model averager's step value to the one
+        saved in the provided ``state_dict``.
+
+        If there is no ``"step"`` entry in ``state_dict``,
+        it will raise a warning and initialize the model averager's step to 0.
+        """
+        self.optim.load_state_dict(state_dict)
+        if "step" in state_dict:
+            self.averager.step = state_dict["step"]
+        else:
+            warnings.warn(
+                "Loaded state dict does not contain a step counter for an averager. "
+                "Setting step counter to 0.",
+                stacklevel=2,
+            )
+            self.averager.step = 0
+
+    def step(self):  # type: ignore[override]
+        r"""
+        Performs a single optimization step (parameter update).
+        """
+        self.optim.step()
+        self.averager.average_parameters(params=self.param_groups)
+
+    def zero_grad(self, set_to_none: bool = True):  # type: ignore[override]
+        self.optim.zero_grad(set_to_none=set_to_none)
+
+    def add_param_group(self, param_group):
+        self.optim.add_param_group(param_group)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7075edd2e5210f1dc3d50aaa09688a4a4e1d09c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/utils.py
@@ -0,0 +1,65 @@
+# mypy: allow-untyped-defs
+
+from torch import optim
+
+from .functional_adadelta import _FunctionalAdadelta
+from .functional_adagrad import _FunctionalAdagrad
+from .functional_adam import _FunctionalAdam
+from .functional_adamax import _FunctionalAdamax
+from .functional_adamw import _FunctionalAdamW
+from .functional_rmsprop import _FunctionalRMSprop
+from .functional_rprop import _FunctionalRprop
+from .functional_sgd import _FunctionalSGD
+
+
+# dict to map a user passed in optimizer_class to a functional
+# optimizer class if we have already defined inside the
+# distributed.optim package, this is so that we hide the
+# functional optimizer to user and still provide the same API.
+functional_optim_map = {
+    optim.Adagrad: _FunctionalAdagrad,
+    optim.Adam: _FunctionalAdam,
+    optim.AdamW: _FunctionalAdamW,
+    optim.SGD: _FunctionalSGD,
+    optim.Adadelta: _FunctionalAdadelta,
+    optim.RMSprop: _FunctionalRMSprop,
+    optim.Rprop: _FunctionalRprop,
+    optim.Adamax: _FunctionalAdamax,
+}
+
+
+def register_functional_optim(key, optim):
+    """
+    Interface to insert a new functional optimizer to functional_optim_map
+    ``fn_optim_key`` and ``fn_optimizer`` are user defined. The optimizer and key
+    need not be of :class:`torch.optim.Optimizer` (e.g. for custom optimizers)
+    Example::
+        >>> # import the new functional optimizer
+        >>> # xdoctest: +SKIP
+        >>> from xyz import fn_optimizer
+        >>> from torch.distributed.optim.utils import register_functional_optim
+        >>> fn_optim_key = "XYZ_optim"
+        >>> register_functional_optim(fn_optim_key, fn_optimizer)
+    """
+    if key not in functional_optim_map:
+        functional_optim_map[key] = optim
+
+
+def as_functional_optim(optim_cls: type, *args, **kwargs):
+    try:
+        functional_cls = functional_optim_map[optim_cls]
+    except KeyError as e:
+        raise ValueError(
+            f"Optimizer {optim_cls} does not have a functional counterpart!"
+        ) from e
+
+    return _create_functional_optim(functional_cls, *args, **kwargs)
+
+
+def _create_functional_optim(functional_optim_cls: type, *args, **kwargs):
+    return functional_optim_cls(
+        [],
+        *args,
+        **kwargs,
+        _allow_empty_param_list=True,
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..3183299a48347b4444cfe7b5105c1a1aadc8b4fd
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py
@@ -0,0 +1,1679 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
+r"""Zero Redundancy Optimizer."""
+
+import collections
+import copy
+import enum
+import inspect
+import io
+import logging
+from collections.abc import Callable
+from itertools import chain
+from typing import Any
+
+import torch
+import torch.distributed as dist
+from torch.distributed.algorithms.join import Join, Joinable, JoinHook
+from torch.distributed.optim.utils import functional_optim_map
+from torch.optim import Optimizer
+
+
+__all__ = ["ZeroRedundancyOptimizer"]
+
+
+logger = logging.getLogger(__name__)
+
+
+# Credits:  classy_vision/generic/distributed_util.py
+def _recursive_copy_to_device(
+    value: Any,
+    non_blocking: bool,
+    device: torch.device,
+) -> Any:
+    r"""
+    Recursively searches lists, tuples, dicts and copies tensors to device if possible.
+
+    Non-tensor values are passed as-is in the result.
+
+    .. note::
+        These are all copies, so if there are two objects that reference
+        the same object, then after this call, there will be two different objects
+        referenced on the device.
+    """
+    if isinstance(value, torch.Tensor):
+        return value.to(device, non_blocking=non_blocking)
+
+    if isinstance(value, (list, tuple)):
+        values = [
+            _recursive_copy_to_device(val, non_blocking=non_blocking, device=device)
+            for val in value
+        ]
+        return values if isinstance(value, list) else tuple(values)
+
+    if isinstance(value, collections.abc.Mapping):
+        return {
+            key: _recursive_copy_to_device(
+                val, non_blocking=non_blocking, device=device
+            )
+            for key, val in value.items()
+        }
+
+    return value
+
+
+def _is_trainable(param: torch.Tensor) -> bool:
+    r"""Return if a parameter is trainable, where trainability is equivalent to requiring a gradient."""
+    return param.requires_grad
+
+
+def _broadcast_object(
+    obj: Any,
+    src_rank: int,
+    group: object = dist.group.WORLD,
+    device: torch.device = torch.device("cpu"),
+) -> Any:
+    r"""
+    Broadcasts an object to the given group.
+
+    It will be sending the object if called from the source rank and receiving
+    the object otherwise.
+
+    Arguments:
+        obj: object to broadcast; only used if called on the source rank.
+        src_rank (int): source rank.
+        group (``ProcessGroup``, optional): group used for the broadcast
+            (default: ``dist.group.WORLD``).
+        device (``torch.device``, optional): device to send from or receive
+            to (default: ``torch.device("cpu")``).
+
+    Returns:
+        The broadcasted object.
+    """
+    if dist.get_rank() == src_rank:
+        # Send the object
+        buffer = io.BytesIO()
+        torch.save(obj, buffer)
+        data = bytearray(buffer.getbuffer())
+        length_tensor = torch.LongTensor([len(data)]).to(device)
+        data_send_tensor = torch.ByteTensor(data).to(device)
+        # pyrefly: ignore [bad-argument-type]
+        dist.broadcast(length_tensor, src=src_rank, group=group, async_op=False)
+        # pyrefly: ignore [bad-argument-type]
+        dist.broadcast(data_send_tensor, src=src_rank, group=group, async_op=False)
+    else:
+        # Receive the object
+        length_tensor = torch.LongTensor([0]).to(device)
+        # pyrefly: ignore [bad-argument-type]
+        dist.broadcast(length_tensor, src=src_rank, group=group, async_op=False)
+        data_recv_tensor = torch.empty(
+            [int(length_tensor.item())], dtype=torch.uint8, device=device
+        )
+        # pyrefly: ignore [bad-argument-type]
+        dist.broadcast(data_recv_tensor, src=src_rank, group=group, async_op=False)
+        buffer = io.BytesIO(data_recv_tensor.cpu().numpy())
+        obj = torch.load(buffer, map_location=device, weights_only=False)
+    return obj
+
+
+class _ZeROJoinHook(JoinHook):
+    def __init__(self, zero):
+        assert isinstance(zero, ZeroRedundancyOptimizer), (
+            "ZeRO join hook requires passing in a ZeroRedundancyOptimizer "
+            "instance as the state"
+        )
+        self.zero = zero
+        super().__init__()
+
+    def main_hook(self):
+        """
+        Perform an optimizer step.
+
+        This step updates the joined process's shard of
+        the parameters and broadcasts those parameters.
+        """
+        self.zero.step()
+
+
+class _DDPBucketAssignment:
+    r"""
+    Represent a :class:`DistributedDataParallel` bucket assignment.
+
+    This means that a (possibly non-strict) subset of the parameters corresponding to
+    a DDP bucket assigned to a rank to update.
+
+    Attributes:
+        bucket_index (int): index of the bucket determined by the DDP gradient
+            bucket all-reduce order.
+        parameters (List[torch.Tensor]): model parameters in the bucket
+            assigned to this rank.
+        offset (int): offset into the :class:`GradBucket` 's :meth:`parameters`
+            giving the index of the first element in the passed-in
+            ``parameters``; this equivalently indexes into the
+            :class:`GradBucket` 's :meth:`gradients`.
+        device (torch.device): device on which the parameters are stored.
+        tensor (torch.Tensor): flattened tensor giving the data of the
+            parameter subset assigned to the rank.
+    """
+
+    def __init__(
+        self,
+        bucket_index: int,
+        parameters: list[torch.Tensor],
+        offset: int,
+    ):
+        self.bucket_index = bucket_index
+        self.parameters = parameters
+        self.offset = offset
+        if len(self.parameters) == 0:
+            raise ValueError("Empty bucket assignment")
+        # DDP guarantees all parameters in the bucket have the same device
+        # pyrefly: ignore [read-only]
+        self.device: torch.device = self.parameters[0].device
+        self.tensor: torch.Tensor | None = None
+
+
+class _OverlapStatus(enum.IntEnum):
+    r"""
+    Define possible statuses that :class:`ZeroRedundancyOptimizer` can be in when overlapping with :class:`DistributedDataParallel`.
+
+    Attributes:
+        ``UNINITIALIZED``: The ZeRO instance is effectively uninitialized and
+            is waiting for DDP to finalize its bucketing.
+        ``DDP_HAS_REBUILT_BUCKETS``: DDP has rebuilt its buckets, meaning that
+            its bucketing is finalized. The ZeRO instance can now collect the
+            necessary information about the DDP bucketing.
+        ``INITIALIZED``: The ZeRO instance is fully initialized and can now
+            optimize parameters.
+    """
+
+    UNINITIALIZED = 0
+    DDP_HAS_REBUILT_BUCKETS = 1
+    INITIALIZED = 2
+
+
+class _OverlapInfo:
+    r"""
+    Information needed by :class:`ZeroRedundancyOptimizer` to overlap with :class:`DistributedDataParallel`.
+
+    Arguments:
+        world_size (int): world size of the process group being used.
+
+    Attributes:
+        shard_buckets (bool): if ``True``, then the assignment of each
+            :class:`DistributedDataParallel` bucket is partitioned across
+            possibly multiple :class:`ZeroRedundancyOptimizer` instances (i.e.
+            across possibly multiple ranks) to approximate uniformity following
+            a threshold given by the total parameter size divided by the world
+            size; if ``False``, then each bucket is wholly assigned to a single
+            :class:`ZeroRedundancyOptimizer` instance (i.e. to a single rank);
+            this should be set to the value passed into the hook constructor.
+        status (_OverlapStatus): current status; see :class:`_OverlapStatus`
+            for more information.
+        params_per_bucket (List[List[torch.Tensor]]): ``params_per_bucket[i]``
+            gives the model parameters in the ``i``th bucket.
+        params_per_rank (List[List[torch.Tensor]]): ``params_per_rank[i]``
+            gives the model parameters assigned to the ``i``th rank, where the
+            parameters are grouped by increasing bucket indices.
+        offsets (Dict[int, int]): maps from bucket index to the offset in
+            ``self.params_per_rank[rank]`` giving the index of the first
+            parameter in that bucket, where ``rank`` is this process's own
+            rank; the keys of this :class:`dict` are the bucket indices
+            assigned to this rank.
+        num_bucket_assignments (int): total number of bucket assignments across
+            all ranks; this is equal to the number of
+            :class:`DistributedDataParallel` gradient buckets if
+            ``shard_buckets=False`` and possibly greater otherwise.
+        total_size (int, optional): total size of all buckets (i.e. sum of
+            ``param.numel()`` for all ``param`` across all buckets) if
+            ``shard_buckets=True``; otherwise, ``None``.
+        broadcast_handles (List[Work]): :class:`list` of async work handles for
+            the parameter broadcasts.
+        bucket_index_to_future (Dict[int, torch.futures.Future]):
+            :class:`dict` mapping bucket index to the corresponding all-reduce
+            future.
+        bucket_index_to_bucket (Dict[int, dist.GradBucket]): :class:`dict`
+            mapping bucket index to the corresponding bucket.
+        bucket_indices_seen (List[int]): :class:`list` of the bucket indices
+            seen on this iteration.
+    """
+
+    def __init__(self, world_size) -> None:
+        self.status: _OverlapStatus = _OverlapStatus.UNINITIALIZED
+        self.shard_buckets: bool = False
+
+        # Modified per bucket reconstruction
+        self.params_per_bucket: list[list[torch.Tensor]] = []
+        self.params_per_rank: list[list[torch.Tensor]] = [[] for _ in range(world_size)]
+        self.offsets: dict[int, int] = {}
+        # Group Ranks
+        self.assigned_ranks_per_bucket: list[set[int]] = []
+        self.num_bucket_assignments: int = 0
+        self.total_size: int | None = None
+
+        # Modified per iteration
+        self.broadcast_handles: list[Any] = []
+        self.bucket_indices_seen: list[int] = []
+        # Used by `hook_with_zero_step()`
+        self.bucket_index_to_future: dict[int, torch.futures.Future] = {}
+        self.bucket_index_to_bucket: dict[int, dist.GradBucket] = {}
+
+    def wait_for_broadcasts(self) -> None:
+        r"""
+        Wait for all parameter broadcasts.
+
+        This function should be called once all broadcasts have been scheduled,
+        meaning ``self.broadcast_handles`` is filled. This clears ``self.broadcast_handles``
+        in preparation for the next iteration.
+        """
+        assert len(self.broadcast_handles) == self.num_bucket_assignments, (
+            f"Missing at least one broadcast handle on rank {dist.get_rank()}"
+        )
+        _ = [x.wait() for x in self.broadcast_handles]
+        self.broadcast_handles.clear()
+
+    def clear_per_iter_info(self) -> None:
+        r"""
+        Clear the data structures that are modified per-iteration.
+
+        This function should be called at the end of an iteration.
+        """
+        self.bucket_indices_seen.clear()
+        self.bucket_index_to_future.clear()
+        self.bucket_index_to_bucket.clear()
+
+
+class ZeroRedundancyOptimizer(Optimizer, Joinable):
+    r"""
+    Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group.
+
+    The sharing is done as described by `ZeRO `_.
+
+    The local optimizer instance in each rank is only
+    responsible for updating approximately ``1 / world_size`` parameters and
+    hence only needs to keep ``1 / world_size`` optimizer states. After
+    parameters are updated locally, each rank will broadcast its parameters to
+    all other peers to keep all model replicas in the same state.
+    ``ZeroRedundancyOptimizer`` can be used in conjunction with
+    :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak
+    memory consumption.
+
+    ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number
+    of parameters at each rank. Each parameter belongs to a single rank and is
+    not divided among ranks. The partition is arbitrary and might not match the
+    the parameter registration or usage order.
+
+    Arguments:
+        params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s
+            or :class:`dict` s giving all parameters, which will be sharded
+            across ranks.
+
+    Keyword Args:
+        optimizer_class (:class:`torch.nn.Optimizer`): the class of the local
+            optimizer.
+        process_group (``ProcessGroup``, optional): ``torch.distributed``
+            ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by
+            :meth:`torch.distributed.init_process_group`).
+        parameters_as_bucket_view (bool, optional): if ``True``, parameters are
+            packed into buckets to speed up communication, and ``param.data``
+            fields point to bucket views at different offsets; if ``False``,
+            each individual parameter is communicated separately, and each
+            ``params.data`` stays intact (default: ``False``).
+        overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is
+            overlapped with :class:`DistributedDataParallel` 's gradient
+            synchronization; this requires (1) either a functional optimizer
+            for the ``optimizer_class`` argument or one with a functional
+            equivalent and (2) registering a DDP communication hook
+            constructed from one of the functions in ``ddp_zero_hook.py``;
+            parameters are packed into buckets matching those in
+            :class:`DistributedDataParallel`, meaning that the
+            ``parameters_as_bucket_view`` argument is ignored.
+            If ``False``, :meth:`step` runs disjointly after the backward pass
+            (per normal).
+            (default: ``False``)
+        **defaults: any trailing arguments, which are forwarded to the local
+            optimizer.
+
+    Example::
+
+        >>> # xdoctest: +SKIP
+        >>> import torch.nn as nn
+        >>> from torch.distributed.optim import ZeroRedundancyOptimizer
+        >>> from torch.nn.parallel import DistributedDataParallel as DDP
+        >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)])
+        >>> ddp = DDP(model, device_ids=[rank])
+        >>> opt = ZeroRedundancyOptimizer(
+        >>>     ddp.parameters(),
+        >>>     optimizer_class=torch.optim.Adam,
+        >>>     lr=0.01
+        >>> )
+        >>> ddp(inputs).sum().backward()
+        >>> opt.step()
+
+    .. warning::
+        Currently, ``ZeroRedundancyOptimizer`` requires that all of the
+        passed-in parameters are the same dense type.
+
+    .. warning::
+        If you pass ``overlap_with_ddp=True``, be wary of the following: Given
+        the way that overlapping :class:`DistributedDataParallel` with
+        :class:`ZeroRedundancyOptimizer` is currently implemented, the first
+        two or three training iterations do not perform parameter updates in
+        the optimizer step, depending on if ``static_graph=False`` or
+        ``static_graph=True``, respectively. This is because it needs
+        information about the gradient bucketing strategy used by
+        :class:`DistributedDataParallel`, which is not finalized until the
+        second forward pass if ``static_graph=False`` or until the third
+        forward pass if ``static_graph=True``. To adjust for this, one option
+        is to prepend dummy inputs.
+
+    .. warning:: ZeroRedundancyOptimizer is experimental and subject to change.
+    """
+
+    def __init__(
+        self,
+        params,
+        optimizer_class: type[Optimizer],
+        process_group: Any | None = None,
+        parameters_as_bucket_view: bool = False,
+        overlap_with_ddp: bool = False,
+        **defaults: Any,
+    ):
+        r"""Init."""
+        # Perform type and assumption checks on the input parameters
+        params = self._verify_and_init_params(params)
+        self._verify_same_dense_param_type()
+
+        # NOTE: The parent constructor uses `add_param_group()` which is
+        # partially overloaded in ZeroRedundancyOptimizer, so we use the
+        # `initialized` flag to dissociate the behaviour of `add_param_group()`
+        # between the parent and child.
+        self.initialized = False
+
+        Optimizer.__init__(self, params, defaults)
+        Joinable.__init__(self)
+        # Now, all parameters are held in both `self._all_params` and
+        # `self.param_groups`
+
+        # Internal data structures (`_cache` indicates lazily evaluated)
+        self._param_to_rank_cache: dict[torch.Tensor, int] = {}
+        self._param_to_index_cache: dict[torch.Tensor, int] = {}
+        self._partition_parameters_cache: list[list[dict]] = []
+        self._index_to_param_cache: list[torch.Tensor] = []
+        self._device_to_params_per_rank_cache: dict[
+            torch.device, list[list[torch.Tensor]]
+        ] = {}
+        self._bucket_assignments_per_rank_cache: list[
+            dict[int, _DDPBucketAssignment]
+        ] = []
+        self._is_trainable_mask = self._get_is_trainable_mask()
+
+        # Default device for collective communication and buckets
+        self._default_device = self._all_params[0].device
+
+        self.process_group = (
+            process_group if process_group is not None else dist.group.WORLD
+        )
+        self.world_size: int = dist.get_world_size(self.process_group)
+        self.rank: int = dist.get_rank(self.process_group)
+        self.global_rank: int = dist.distributed_c10d.get_global_rank(
+            # pyrefly: ignore [bad-argument-type]
+            self.process_group,
+            self.rank,
+        )
+
+        self._overlap_with_ddp: bool = overlap_with_ddp
+        self._optim_defaults = defaults
+        self._optim_constructor = self._get_optimizer_constructor(optimizer_class)
+
+        # If `overlap_with_ddp=True`, local optimizer initialization is delayed
+        # to run time after the necessary information has been collected
+        if not overlap_with_ddp:
+            self._init_local_optimizer()
+        else:
+            self._overlap_info: _OverlapInfo = _OverlapInfo(self.world_size)
+            if parameters_as_bucket_view:
+                logger.warning(
+                    "`parameters_as_bucket_view=True` will be ignored since "
+                    "`overlap_with_ddp=True`; instead, a different bucketing "
+                    "strategy will be used"
+                )
+
+        # `self._buckets` is used if `parameters_as_bucket_view=True`, in
+        # which case parameter data is flattened into contiguous bucket tensors
+        self.parameters_as_bucket_view = parameters_as_bucket_view
+        self._buckets: list[list[torch.Tensor]] = []
+        self._build_param_buckets()
+
+        # Optional consolidated optimizer state, only populated if this rank
+        # is the target in `consolidate_state_dict()`
+        self._all_state_dicts: list[dict[str, Any]] = []
+
+        self.initialized = True
+
+    def _clear_cache(self) -> None:
+        r"""Clear the cached data structures giving partition information."""
+        self._partition_parameters_cache.clear()
+        self._param_to_rank_cache.clear()
+        self._index_to_param_cache.clear()
+        self._param_to_index_cache.clear()
+        self._device_to_params_per_rank_cache.clear()
+        self._bucket_assignments_per_rank_cache.clear()
+
+    def add_param_group(self, param_group: dict[str, Any]) -> None:
+        r"""
+        Add a parameter group to the :class:`Optimizer` 's ``param_groups``.
+
+        This can be useful when fine tuning a pre-trained network, as frozen
+        layers can be made trainable and added to the :class:`Optimizer` as
+        training progresses.
+
+        Arguments:
+            param_group (dict): specifies the parameters to be optimized and
+                group-specific optimization options.
+
+        .. warning:: This method handles updating the shards on all partitions
+            but needs to be called on all ranks. Calling this on a subset of
+            the ranks will cause the training to hang because communication
+            primitives are called depending on the managed parameters and
+            expect all the ranks to participate on the same set of parameters.
+        """
+        if self.initialized and self._overlap_with_ddp:
+            raise RuntimeError(
+                "ZeroRedundancyOptimizer with `overlap_with_ddp=True` only "
+                "supports a single parameter group"
+            )
+
+        super().add_param_group(param_group)
+        # NOTE: The rest of the method assumes that the call to the parent's
+        # `add_param_group()` appends the new parameter group and preserves
+        # the previous parameter-group ordering
+
+        if self.initialized:
+            # Force a re-partitioning of the parameters
+            self._clear_cache()
+            param_groups = self._partition_parameters()[self.rank]
+            # NOTE: All parameters in the old parameter groups should be
+            # assigned to the same ranks so that the local optimizers do not
+            # need to be reinitialized
+
+            # Add the parameters assigned to this rank from the new parameter
+            # group to the local optimizer, if any
+            if len(param_groups) == len(self.optim.param_groups) + 1:
+                self.optim.add_param_group(param_groups[-1])
+
+            # Update the bucketing strategy accordingly
+            if self.parameters_as_bucket_view:
+                self._build_param_buckets()
+
+    def consolidate_state_dict(self, to: int = 0) -> None:
+        r"""
+        Consolidate a list of ``state_dict`` s (one per rank) on the target rank.
+
+        Arguments:
+            to (int): the rank that receives the optimizer states (default: 0).
+
+        Raises:
+            RuntimeError: if ``overlap_with_ddp=True`` and this method is
+                called before this :class:`ZeroRedundancyOptimizer` instance
+                has been fully initialized, which happens once
+                :class:`DistributedDataParallel` gradient buckets have been
+                rebuilt.
+
+        .. warning:: This needs to be called on all ranks.
+        """
+        self._check_overlap_initialized()
+
+        # Sync the exposed `param_groups` attributes to the local optimizer in
+        # case they have been updated
+        self._sync_param_groups(self.param_groups, self.optim.param_groups)
+
+        # Pull the sharded state from all ranks and store them in rank order
+        empty_messenger = torch.tensor(
+            [0], dtype=torch.uint8, device=self._default_device
+        )
+
+        # NOTE: We wastefully use `broadcast()` (e.g. instead of `gather()`)
+        # due to compatibility issues with NCCL backend; a possible follow-up
+        # is to move all sharded state management to RPC RRef
+        self._all_state_dicts = []
+        for rank in range(self.world_size):
+            global_rank = dist.distributed_c10d.get_global_rank(
+                # pyrefly: ignore [bad-argument-type]
+                self.process_group,
+                rank,
+            )
+            if self.rank == to:
+                # Consolidate all local `state_dict`s on this rank, storing on
+                # CPU to save GPU memory
+                if rank == self.rank:
+                    # Directly append own optimizer state
+                    self._all_state_dicts.append(
+                        _recursive_copy_to_device(
+                            self.optim.state_dict(),
+                            non_blocking=True,
+                            device=torch.device("cpu"),
+                        )
+                    )
+                else:
+                    # Receive the optimizer state from the source rank
+                    local_state_dict = _broadcast_object(
+                        empty_messenger,
+                        src_rank=global_rank,
+                        group=self.process_group,
+                        device=self._default_device,
+                    )
+                    self._all_state_dicts.append(
+                        _recursive_copy_to_device(
+                            local_state_dict,
+                            non_blocking=True,
+                            device=torch.device("cpu"),
+                        )
+                    )
+            else:
+                if rank == self.rank:
+                    # Send the optimizer state to the target rank
+                    _ = _broadcast_object(
+                        self.optim.state_dict(),
+                        src_rank=self.global_rank,
+                        group=self.process_group,
+                        device=self._default_device,
+                    )
+                elif rank != to:
+                    # Discard the received object; `broadcast()` is used for
+                    # compatibility reasons
+                    _ = _broadcast_object(
+                        empty_messenger,
+                        src_rank=global_rank,
+                        group=self.process_group,
+                        device=self._default_device,
+                    )
+
+    def _verify_params_per_rank(
+        self,
+        params_per_rank: list[list[torch.Tensor]],
+    ) -> None:
+        r"""
+        Verify ``params_per_rank`` for :meth:`_partition_parameters`.
+
+        The verification is done by checking that ``params_per_rank`` has length equal
+        to the world size and that it does not contain any parameters not passed into the
+        :class:`ZeroRedundancyOptimizer` constructor.
+
+        The parameters in ``params_per_rank`` being a strict subset of those
+        passed into the constructor is valid since some parameters may be
+        frozen.
+
+        Raises:
+            ValueError: if ``params_per_rank`` does not have length equal to
+                the world size or if it contains a parameter that was not
+                passed into the :class:`ZeroRedundancyOptimizer` constructor.
+        """
+        if len(params_per_rank) != self.world_size:
+            raise ValueError(
+                "`params_per_rank` must have length equal to the world size"
+            )
+        all_params_set = set(self._all_params)
+        for params in params_per_rank:
+            for param in params:
+                if param not in all_params_set:
+                    raise ValueError(
+                        "Passing a new parameter in `params_per_rank` that "
+                        "was not passed into the ZeroRedundancyOptimizer "
+                        "constructor"
+                    )
+
+    def _partition_param_group(
+        self, param_group: dict[str, Any], params_per_rank: list[list[torch.Tensor]]
+    ) -> None:
+        r"""
+        Partition the parameter group ``param_group`` according to ``params_per_rank``.
+
+        The partition will modify the ``self._partition_parameters_cache``. This method should
+        only be used as a subroutine for :meth:`_partition_parameters`.
+
+        Arguments:
+            param_group (dict[str, Any]): a parameter group as normally defined
+                in an optimizer state.
+            params_per_rank (list[list[torch.Tensor]]): a :class:`list` of
+                length world size containing :class:`list` s of parameters to
+                assign to each rank.
+        """
+        for rank, params in enumerate(params_per_rank):
+            rank_param_group = copy.copy(param_group)
+            rank_param_group["params"] = params
+            self._partition_parameters_cache[rank].append(rank_param_group)
+
+    def _partition_parameters(
+        self,
+        params_per_rank: list[list[torch.Tensor]] | None = None,
+    ) -> list[list[dict]]:
+        r"""
+        Partitions parameters across distributed data parallel ranks.
+
+        Arguments:
+            params_per_rank (list[list[torch.Tensor]], optional): a
+                :class:`list` of length world size containing :class:`list` s
+                of parameters to assign to each rank; this provides a way to
+                specify a partition manually.
+                If ``None``, the parameters are partitioned according to an
+                internal algorithm.
+                (default: ``None``)
+
+        Returns:
+            A :class:`list` where each element of the list contains the
+            ``param_groups`` for a rank (which itself is a :class:`list` of
+            :class:`dict`); element 0 corresponds to rank 0, etc.; each rank
+            stores the ``param_groups`` for all ranks for the collective
+            communication in :meth:`step`.
+
+        Raises:
+            ValueError: see :meth:`_validate_params_per_rank`.
+            RuntimeError: if ``params_per_rank`` is not ``None`` and this
+                :class:`ZeroRedundancyOptimizer` instance is using more than
+                one parameter group.
+        """
+        if params_per_rank is None:
+            # Partition the parameters optimizing for uniformity
+            if len(self._partition_parameters_cache) == 0:
+                self._partition_parameters_cache = [[] for _ in range(self.world_size)]
+                sizes = [0] * self.world_size
+                for param_group in self.param_groups:
+                    param_group_params_per_rank: list[list] = [
+                        [] for _ in range(self.world_size)
+                    ]
+                    # Sort the parameters by size (largest first)
+                    params_sorted = sorted(
+                        param_group["params"], key=lambda t: t.numel(), reverse=True
+                    )
+                    for param in params_sorted:
+                        # Greedily add the parameter to rank with smallest size so far
+                        rank = self._get_min_index(sizes)
+                        param_group_params_per_rank[rank].append(param)
+                        sizes[rank] += param.numel()
+                    # Apply the constructed partition of the parameter group
+                    self._partition_param_group(
+                        param_group, param_group_params_per_rank
+                    )
+
+            return self._partition_parameters_cache
+
+        # Partition the parameters according to `params_per_rank`
+        assert len(self._partition_parameters_cache) == 0, (
+            "Specifying `params_per_rank` should only be done when the "
+            "parameters have not been partitioned yet"
+        )
+        if len(self.param_groups) != 1:
+            raise RuntimeError(
+                "Specifying `params_per_rank` only supports a single parameter group"
+            )
+        self._verify_params_per_rank(params_per_rank)
+        self._partition_parameters_cache = [[] for _ in range(self.world_size)]
+
+        # Apply the passed-in partition of the parameter group
+        param_group = self.param_groups[0]
+        self._partition_param_group(param_group, params_per_rank)
+
+        return self._partition_parameters_cache
+
+    @property
+    def _param_to_rank(self) -> dict[torch.Tensor, int]:
+        r""":class:`dict` mapping parameters to their assigned data parallel rank in the partition."""
+        if len(self._param_to_rank_cache) == 0:
+            for rank, param_groups in enumerate(self._partition_parameters()):
+                for param_group in param_groups:
+                    for param in param_group["params"]:
+                        self._param_to_rank_cache[param] = rank
+        return self._param_to_rank_cache
+
+    @property
+    def _param_to_index(self) -> dict[torch.Tensor, int]:
+        r"""
+        :class:`dict` mapping parameters to their indices in the global optimizer state.
+
+        NOTE: This assumes that the global optimizer state's indexing (in
+        ``state_dict``) follows a linear ordering over the parameter groups.
+        """
+        if len(self._param_to_index_cache) == 0:
+            self._param_to_index_cache = {
+                p: i
+                for i, p in enumerate(
+                    chain.from_iterable(g["params"] for g in self.param_groups)
+                )
+            }
+        return self._param_to_index_cache
+
+    @property
+    def _index_to_param(self) -> list[torch.Tensor]:
+        r"""List mapping parameter indices in the global optimizer scheme to the actual params."""
+        if len(self._index_to_param_cache) == 0:
+            self._index_to_param_cache = list(
+                chain.from_iterable(g["params"] for g in self.param_groups)
+            )
+        return self._index_to_param_cache
+
+    def _broadcast_params_from_rank(self, rank: int):
+        r"""
+        Broadcast the shard of parameters from a given rank to all other ranks asynchronously.
+
+        Arguments:
+            rank (int): the source rank.
+
+        Returns:
+            A :class:`list` of async work handles for the ``broadcast()`` s
+            performed to synchronize the parameters.
+        """
+        assert not self._overlap_with_ddp, (
+            "`_broadcast_params_from_rank()` should not be used if "
+            "`overlap_with_ddp=True`; instead, the broadcasting should "
+            "happen in the DDP communication hook"
+        )
+        handles = []
+        if self.parameters_as_bucket_view:
+            for dev_i_buckets in self._buckets:
+                bucket = dev_i_buckets[rank]
+                global_rank = dist.distributed_c10d.get_global_rank(
+                    # pyrefly: ignore [bad-argument-type]
+                    self.process_group,
+                    rank,
+                )
+                handles.append(
+                    dist.broadcast(
+                        tensor=bucket,
+                        src=global_rank,
+                        group=self.process_group,
+                        async_op=True,
+                    )
+                )
+        else:
+            param_groups = self._partition_parameters()[rank]
+            global_rank = dist.distributed_c10d.get_global_rank(
+                # pyrefly: ignore [bad-argument-type]
+                self.process_group,
+                rank,
+            )
+            for param_group in param_groups:
+                handles.extend(
+                    dist.broadcast(
+                        tensor=param.data,
+                        src=global_rank,
+                        group=self.process_group,
+                        async_op=True,
+                    )
+                    for param in param_group["params"]
+                )
+        return handles
+
+    def _sync_params(self):
+        r"""
+        Sync all parameter shards across the ranks.
+
+        This rank sends its shard of the parameters to all other ranks and
+        receives a shard from each other rank. This is done using
+        ``broadcast()``. Parameters are sent bucket-by-bucket if
+        ``parameters_as_bucket_view=True``and sent parameter-by-parameter
+        otherwise.
+        """
+        handles = []
+        for rank in range(self.world_size):
+            handles.extend(self._broadcast_params_from_rank(rank))
+        _ = [x.wait() for x in handles]
+
+    @property
+    def _device_to_params_per_rank(
+        self,
+    ) -> dict[torch.device, list[list[torch.Tensor]]]:
+        r"""
+        Return device parameters assigned per rank.
+
+        :class:`dict` mapping each device to a :class:`list` of the per-rank parameter
+        lists filtered to only include the parameters stored on that device.
+        Each per-rank parameter list gives the parameters assigned to that rank
+        to update.
+
+        This is used for constructing the parameter buckets if
+        ``parameters_as_bucket_view=True``.
+
+        Let ``dev_i`` denote the ``i``th device for this rank. Then:
+        ``dev_0`` maps to a list containing:
+            rank 0's assigned parameters stored on ``dev_0``,
+            rank 1's assigned parameters stored on ``dev_0``,
+            ...
+        ``dev_1`` maps to a list containing:
+            rank 0's assigned parameters stored on ``dev_1``,
+            rank 1's assigned parameters stored on ``dev_1``,
+            ...
+        ...
+        """
+        assert self.parameters_as_bucket_view, (
+            "`_device_to_params_per_rank` should only be used if "
+            "`parameters_as_bucket_view=True`"
+        )
+        if len(self._device_to_params_per_rank_cache) == 0:
+            for rank, param_groups in enumerate(self._partition_parameters()):
+                for param_group in param_groups:
+                    for param in param_group["params"]:
+                        device = param.device
+                        if device not in self._device_to_params_per_rank_cache:
+                            self._device_to_params_per_rank_cache[device] = [
+                                [] for _ in range(self.world_size)
+                            ]
+                        self._device_to_params_per_rank_cache[device][rank].append(
+                            param
+                        )
+        return self._device_to_params_per_rank_cache
+
+    def _get_min_index(
+        self,
+        values: list[int],
+        disallowed_indices: set[int] | None = None,
+    ) -> int:
+        r"""
+        Return ``values.index(min(values))``, except only uses one pass.
+
+        It also excludes any indices in ``disallowed_indices`` if provided.
+
+        Arguments:
+            values: (List[int]): :class:`list` of values.
+            disallowed_indices (Optional[set[int]]): indices that are
+                disallowed from being the returned min index.
+        """
+        min_index = -1
+        min_value = float("inf")
+        for i, value in enumerate(values):
+            if disallowed_indices and i in disallowed_indices:
+                continue
+            if value < min_value:
+                min_value = value
+                min_index = i
+        assert min_index >= 0, "All indices are disallowed"
+        return min_index
+
+    def _assign_bucket_subset_to_rank(
+        self,
+        bucket_index: int,
+        bucket_params: list[torch.Tensor],
+        bucket_offset: int,
+        assigned_rank: int,
+        assigned_ranks_per_bucket: list[set[int]],
+    ) -> None:
+        r"""
+        Assign ``bucket_params`` to the rank with the least size assigned so far and collects relevant information.
+
+        The model parameters given by ``bucket_params`` represents a (possibly non-strict)
+        subset of the parameters corresponding to a :class:`DistributedDataParallel` bucket.
+
+        Arguments:
+            bucket_index (int): index of the :class:`DistributedDataParallel`
+                gradient bucket.
+            bucket_params (List[torch.Tensor]): subset of the parameters
+                corresponding to the bucket to assign.
+            bucket_offset (int): offset giving the index of the first element
+                in ``bucket_params`` in the bucket's full parameter list.
+            assigned_rank (int): group rank to assign to.
+            assigned_ranks_per_bucket (list[set[int]]): :class:`set` of group ranks
+                assigned to each bucket.
+        """
+        overlap_info = self._overlap_info
+        if len(bucket_params) == 0:
+            raise ValueError("Empty bucket assignment")
+        params_per_rank = overlap_info.params_per_rank
+        offsets = overlap_info.offsets
+
+        self._bucket_assignments_per_rank_cache[assigned_rank][bucket_index] = (
+            _DDPBucketAssignment(bucket_index, bucket_params, bucket_offset)
+        )
+        if self.global_rank == assigned_rank:
+            offsets[bucket_index] = len(params_per_rank[assigned_rank])
+        params_per_rank[assigned_rank].extend(bucket_params)
+        assigned_ranks_per_bucket[bucket_index].add(assigned_rank)
+        self._overlap_info.num_bucket_assignments += 1
+
+    @property
+    def _bucket_assignments_per_rank(self) -> list[dict[int, _DDPBucketAssignment]]:
+        r"""
+        Return DDP bucket parameters assigned per rank.
+
+        :class:`list` of length world size consisting of :class:`dict` s
+        mapping bucket indices to :class:`_DDPBucketAssignment` s for each
+        rank.
+        """
+        assert self._overlap_with_ddp, (
+            "`_bucket_assignments_per_rank` only be used if `overlap_with_ddp=True`"
+        )
+        if len(self._bucket_assignments_per_rank_cache) > 0:
+            return self._bucket_assignments_per_rank_cache
+
+        overlap_info = self._overlap_info
+        assert overlap_info.status == _OverlapStatus.INITIALIZED
+
+        self._bucket_assignments_per_rank_cache = [{} for _ in range(self.world_size)]
+        params_per_bucket = overlap_info.params_per_bucket
+
+        if overlap_info.shard_buckets:
+            # Define the assignment threshold to approximate uniformity
+            assert overlap_info.total_size is not None, "`total_size` was not computed"
+            threshold = overlap_info.total_size / self.world_size  # type: ignore[operator]
+            size_per_rank = [0 for _ in range(self.world_size)]
+
+        num_buckets = len(params_per_bucket)
+        overlap_info.assigned_ranks_per_bucket = [set() for _ in range(num_buckets)]
+        assigned_ranks_per_bucket = overlap_info.assigned_ranks_per_bucket
+        if not overlap_info.shard_buckets:
+            # Assign each DDP bucket entirely to a single rank
+            for bucket_index, bucket_params in enumerate(params_per_bucket):
+                assert len(bucket_params) > 0, "Empty bucket"
+                assigned_rank = self._get_assigned_rank(bucket_index)
+                self._assign_bucket_subset_to_rank(
+                    bucket_index,
+                    bucket_params,
+                    0,
+                    assigned_rank,
+                    assigned_ranks_per_bucket,
+                )
+        else:
+            # Assign each DDP bucket to possibly multiple ranks
+            # Specifically, sort the DDP buckets by increasing size, and for
+            # each bucket, iteratively assign the maximal unassigned subset
+            # with size less than `threshold` to the rank with the least total
+            # size so far -- each such assignment is represented by a
+            # `_DDPBucketAssignment` instance and only contains parameters from
+            # a single DDP bucket
+            params_per_bucket_enum = sorted(
+                enumerate(params_per_bucket), key=lambda x: sum(p.numel() for p in x[1])
+            )
+            for bucket_index, bucket_params in params_per_bucket_enum:
+                assert len(bucket_params) > 0, "Empty bucket"
+                bucket_offset = 0
+                assignment_size = 0
+                for param_index, param in enumerate(bucket_params):
+                    param_numel = param.numel()
+                    if (
+                        # pyrefly: ignore [unbound-name]
+                        assignment_size + param_numel >= threshold
+                        and param_index > bucket_offset
+                    ):
+                        assigned_rank = self._get_min_index(
+                            # pyrefly: ignore [unbound-name]
+                            size_per_rank,
+                            assigned_ranks_per_bucket[bucket_index],
+                        )
+                        # Include up to but not including the parameter that
+                        # exceeded the threshold
+                        self._assign_bucket_subset_to_rank(
+                            bucket_index,
+                            bucket_params[bucket_offset:param_index],
+                            bucket_offset,
+                            assigned_rank,
+                            assigned_ranks_per_bucket,
+                        )
+                        # pyrefly: ignore [unbound-name]
+                        size_per_rank[assigned_rank] += assignment_size
+                        bucket_offset = param_index
+                        assignment_size = 0
+                    assignment_size += param_numel
+                # Assign the remainder of the bucket so that no assignment
+                # spans across two buckets
+                assigned_rank = self._get_min_index(
+                    # pyrefly: ignore [unbound-name]
+                    size_per_rank,
+                    assigned_ranks_per_bucket[bucket_index],
+                )
+                self._assign_bucket_subset_to_rank(
+                    bucket_index,
+                    bucket_params[bucket_offset:],
+                    bucket_offset,
+                    assigned_rank,
+                    assigned_ranks_per_bucket,
+                )
+                # pyrefly: ignore [unbound-name]
+                size_per_rank[assigned_rank] += assignment_size
+
+        return self._bucket_assignments_per_rank_cache
+
+    def _local_step(
+        self,
+        gradients: list[torch.Tensor | None] | None = None,
+        closure: Callable[[], float] | None = None,
+        **kwargs: Any,
+    ) -> float | None:
+        r"""
+        Perform a single optimizer step without syncing parameters across ranks.
+
+        Arguments:
+            gradients (list[Optional[torch.Tensor]], optional): a :class:`list`
+                of length equal to the number of parameters assigned to this
+                rank containing gradient tensors or ``None`` as its elements;
+                a ``None`` in the :class:`list` indicates that the
+                corresponding parameter should not be updated.
+                If the argument itself is ``None``, then all parameters are
+                updated, and the gradients are assumed to be already populated.
+                (default: ``None``)
+            closure (Callable): a closure that re-evaluates the model and
+                returns the loss; optional for most optimizers and should be
+                ``None`` if ``gradients`` is not ``None``; (default: ``None``)
+        Returns:
+            Optional loss depending on the underlying local optimizer.
+
+        .. warning::
+            The argument ``gradients`` should only be specified (i.e. not
+            ``None``) if ``overlap_with_ddp=True``, in which case
+            :class:`ZeroRedundancyOptimizer` wraps a functional optimizer.
+        """
+        Join.notify_join_context(self)
+        # Check if the model trainability has changed
+        is_trainable_mask = self._get_is_trainable_mask()
+        if is_trainable_mask != self._is_trainable_mask:
+            if self._overlap_with_ddp:
+                raise RuntimeError(
+                    "ZeroRedundancyOptimizer with `overlap_with_ddp=True` "
+                    "does not support changing parameter trainability at run "
+                    "time"
+                )
+            logger.warning(
+                "ZeroRedundancyOptimizer detected that the trainable "
+                "parameters changed; rebuilding the parameter buckets if "
+                "enabled"
+            )
+            self._build_param_buckets()
+            self._is_trainable_mask = is_trainable_mask
+
+        # Sync the exposed `param_groups` attributes to the local optimizer in
+        # case they have been updated
+        self._sync_param_groups(self.param_groups, self.optim.param_groups)
+
+        # Run the optimizer step on this shard only
+        if gradients is None:
+            loss = (
+                self.optim.step(**kwargs)
+                if closure is None
+                else self.optim.step(closure=closure, **kwargs)
+            )
+        else:
+            assert self._overlap_with_ddp, (
+                "Specifying `gradients` should not "
+                "be used when `overlap_with_ddp=False`"
+            )
+            assert closure is None, (
+                "`closure` is not supported when using a local functional optimizer"
+            )
+            loss = self.optim.step(gradients=gradients)
+
+        # Sync any updated attributes in the local optimizer to the exposed
+        # `param_groups`
+        self._sync_param_groups(self.optim.param_groups, self.param_groups)
+
+        return loss
+
+    # pyrefly: ignore [bad-override]
+    def step(
+        self,
+        closure: Callable[[], float] | None = None,
+        **kwargs: Any,
+    ) -> float | None:
+        r"""
+        Perform a single optimizer step and syncs parameters across all ranks.
+
+        Arguments:
+            closure (Callable): a closure that re-evaluates the model and
+                returns the loss; optional for most optimizers.
+        Returns:
+            Optional loss depending on the underlying local optimizer.
+
+        .. note:: Any extra parameters are passed to the base optimizer as-is.
+        """
+        if self._overlap_with_ddp:
+            logger.warning(
+                "`step()` should not be included in the training loop when "
+                "`overlap_with_ddp=True`"
+            )
+            return None
+
+        # Perform the local optimizer step
+        loss = self._local_step(closure=closure, **kwargs)
+
+        # Sync all of the updated parameter shards across the ranks
+        self._sync_params()
+
+        return loss
+
+    def join_hook(self, **kwargs):
+        r"""
+        Return the ZeRO join hook.
+
+        It enables training on uneven inputs by
+        shadowing the collective communications in the optimizer step.
+
+        Gradients must be properly set before this hook is called.
+
+        Arguments:
+            kwargs (dict): a :class:`dict` containing any keyword arguments
+                to modify the behavior of the join hook at run time; all
+                :class:`Joinable` instances sharing the same join context
+                manager are forwarded the same value for ``kwargs``.
+
+        This hook does not support any keyword arguments; i.e. ``kwargs`` is
+        unused.
+        """
+        return _ZeROJoinHook(self)
+
+    @property
+    def join_device(self) -> torch.device:
+        r"""Return default device."""
+        return self._default_device
+
+    @property
+    def join_process_group(self) -> Any:
+        r"""Return process group."""
+        return self.process_group
+
+    def load_state_dict(self, state_dict: dict[str, Any]) -> None:
+        r"""
+        Load the state pertaining to the given rank from the input ``state_dict``, updating the local optimizer as needed.
+
+        Arguments:
+            state_dict (dict): optimizer state; should be an object returned
+                from a call to :meth:`state_dict`.
+
+        Raises:
+            RuntimeError: if ``overlap_with_ddp=True`` and this method is
+                called before this :class:`ZeroRedundancyOptimizer` instance
+                has been fully initialized, which happens once
+                :class:`DistributedDataParallel` gradient buckets have been
+                rebuilt.
+        """
+        self._check_overlap_initialized()
+
+        for index, value in state_dict["state"].items():
+            param = self._index_to_param[index]
+            if self._param_to_rank[param] != self.rank:
+                # Clear any state irrelevant to this rank
+                state_dict["state"][index] = None
+            else:
+                # Load the parameter state to the local optimizer
+                self.optim.state[param] = _recursive_copy_to_device(
+                    value, non_blocking=True, device=param.device
+                )
+                # Force zero-dimensional tensors (like Adam "step") on CPU
+                for state_name, state_value in self.optim.state[param].items():
+                    if torch.is_tensor(state_value) and state_value.dim() == 0:
+                        self.optim.state[param][state_name] = state_value.cpu()
+
+        super().load_state_dict(state_dict)
+
+        # Sync the input state with the exposed and local optimizer states
+        self._sync_param_groups(state_dict["param_groups"], self.param_groups)
+        self._sync_param_groups(self.param_groups, self.optim.param_groups)
+
+    def state_dict(self) -> dict[str, Any]:
+        r"""
+        Return the last global optimizer state known to this rank.
+
+        .. warning:
+            If the state has not been consolidated to this rank, this raises a
+            runtime error, and even if it has, the state may not be up-to-date,
+            depending on when :meth:`consolidate_state_dict` was last called.
+
+        Raises:
+            RuntimeError: if ``overlap_with_ddp=True`` and this method is
+                called before this :class:`ZeroRedundancyOptimizer` instance
+                has been fully initialized, which happens once
+                :class:`DistributedDataParallel` gradient buckets have been
+                rebuilt; or if this method is called without a preceding call
+                to :meth:`consolidate_state_dict`.
+        """
+        self._check_overlap_initialized()
+
+        if len(self._all_state_dicts) == 0:
+            raise RuntimeError(
+                "Optimizer state has not been consolidated on this rank. "
+                f"Please call `consolidate_state_dict(to={self.rank})` on "
+                "all ranks beforehand if you meant to save the global state."
+            )
+
+        # Get the possibly-stale global optimizer state that uses global
+        # parameter indexing
+        state_dict = super().state_dict()
+
+        # Update the global optimizer state with local state information,
+        # factoring in the translation from local to global indexing
+        for rank, local_state_dict in enumerate(self._all_state_dicts):
+            local_param_groups = local_state_dict["param_groups"]
+            global_param_groups = self._partition_parameters()[rank]
+            assert len(local_param_groups) == len(global_param_groups), (
+                "Mismatch between number of local and global parameter groups"
+            )
+
+            for local_param_group, global_param_group in zip(
+                local_param_groups, global_param_groups
+            ):
+                # `local_param_group` stores local indices, while
+                # `global_param_group` stores the tensors directly
+                local_param_indices = local_param_group["params"]
+                global_params = global_param_group["params"]
+
+                assert len(local_param_indices) == len(global_params), (
+                    "Mismatch between number of local and global parameters in parameter group"
+                )
+                for local_param_index, global_param in zip(
+                    local_param_indices, global_params
+                ):
+                    # Update the global parameter state, if any
+                    if local_param_index in local_state_dict["state"]:
+                        global_param_index = self._param_to_index[global_param]
+                        state_dict["state"][global_param_index] = local_state_dict[
+                            "state"
+                        ][local_param_index]
+
+        # Sort the parameters in the state
+        state_dict["state"] = dict(sorted(state_dict["state"].items()))
+        return state_dict
+
+    @staticmethod
+    def _sync_param_groups(
+        src_param_groups: list[dict[Any, Any]],
+        dst_param_groups: list[dict[Any, Any]],
+    ) -> None:
+        r"""
+        Sync the attributes from the source parameter groups to the destination parameter groups.
+
+        Example attributes include learning rate or scheduler attributes. The
+        two parameter groups should have the same length (i.e. same number of
+        parameter groups).
+
+        Arguments:
+            src_param_groups (list[dict]): parameter groups giving the
+                attribute settings to copy.
+            dst_param_groups (list[dict]): parameter groups giving the
+                attribute settings to set.
+        """
+        assert len(src_param_groups) == len(dst_param_groups), (
+            "Mismatch between number of source and destination parameter groups"
+        )
+        for src_param_group, dst_param_group in zip(src_param_groups, dst_param_groups):
+            # Sync all attributes except the parameters
+            for attr in filter(lambda x: x != "params", src_param_group.keys()):
+                dst_param_group[attr] = src_param_group[attr]
+
+    def _build_param_buckets(self) -> None:
+        r"""
+        Build parameter buckets if ``parameters_as_bucket_view=True``.
+
+        For each device that stores this rank's parameters, there is a
+        bucket (represented as a tensor) containing all of the parameters on
+        that device that are assigned to a given rank in the parameter update
+        partition.
+
+        This method is called in the constructor and any time parameter
+        trainability is changed.
+
+        .. warning::
+            The current implementation assumes that all of the parameters in a
+            bucket are of the same dense type when allocating the bucket's
+            tensor.
+
+        .. warning::
+            If the model parameters are stored across more than one device,
+            then the storage partitioning must be the same across all
+            processes in order for parameter synchronization to work.
+        """
+        if not self.parameters_as_bucket_view or self._overlap_with_ddp:
+            return
+
+        # `self._buckets[i][j]` are the parameters stored on device i and
+        # assigned to rank j
+        num_devices = len(self._device_to_params_per_rank)
+        self._buckets = [[] for _ in range(num_devices)]  # type: ignore[assignment]
+
+        for dev_i, (device, params_per_rank) in enumerate(
+            self._device_to_params_per_rank.items()
+        ):
+            for params in params_per_rank:
+                bucket_size = 0
+                dtype = None
+                trainable_params = []
+                for param in params:
+                    if not _is_trainable(param):
+                        # Clone in case the parameter was previously part of
+                        # a bucket to avoid the data from being destroyed
+                        param.data = param.data.detach().clone()
+                    else:
+                        bucket_size += param.numel()
+                        trainable_params.append(param)
+                    dtype = param.dtype  # assumes all same dtype
+
+                if bucket_size == 0:
+                    # Create a dummy bucket if there are no parameters
+                    bucket = torch.zeros(1, device=device)
+                else:
+                    # Construct the bucket (assuming all dense and same dtype)
+                    bucket = torch.empty(bucket_size, dtype=dtype, device=device)
+                    offset = 0
+                    for param in trainable_params:
+                        offset_next = offset + param.numel()
+                        bucket[offset:offset_next].copy_(param.data.flatten())
+                        param.data = bucket[offset:offset_next].view_as(param.data)
+                        offset = offset_next
+                self._buckets[dev_i].append(bucket)  # type: ignore[arg-type]
+
+    def _build_ddp_param_buckets(self) -> None:
+        r"""
+        Build the DDP bucket with parameters assigned to this rank.
+
+        For each DDP bucket with parameters assigned to this rank, flattens the
+        data of those parameters into a single tensor and saves the tensor to
+        the ``tensor`` attribute in the corresponding
+        :class:`_DDPBucketAssignment` instance stored in
+        ``self._bucket_assignments_per_rank``.
+
+        :class:`DistributedDataParallel` guarantees that the parameters
+        corresponding to a gradient bucket have the same device and the same
+        dtype.
+        """
+        for bucket_assignments in self._bucket_assignments_per_rank:
+            for bucket_assignment in bucket_assignments.values():
+                params = bucket_assignment.parameters
+                bucket_size = 0
+                dtype = None
+                for param in params:
+                    assert _is_trainable(param), (
+                        "Model parameter "
+                        "corresponding to a gradient in a DDP bucket should "
+                        "require a gradient"
+                    )
+                    bucket_size += param.numel()
+                    dtype = param.dtype  # assumes all same dtype
+                assert bucket_size > 0, "Empty bucket"
+
+                # Construct the bucket tensor (assuming all dense and same dtype)
+                tensor = torch.empty(
+                    bucket_size, dtype=dtype, device=bucket_assignment.device
+                )
+                offset = 0
+                for param in params:
+                    offset_next = offset + param.numel()
+                    tensor[offset:offset_next].copy_(param.data.flatten())
+                    param.data = tensor[offset:offset_next].view_as(param.data)
+                    offset = offset_next
+                bucket_assignment.tensor = tensor
+
+    def _verify_and_init_params(
+        self,
+        params: Any,
+    ) -> list[torch.Tensor] | list[dict]:
+        r"""
+        Verify the type of ``params`` and initializes ``self._all_params`` as a :class:`list` of all parameters.
+
+        The initializagtion will first make sure that provided ``params`` is valid.
+
+        Arguments:
+            params (Any): Candidate parameter list or parameter groups to verify.
+
+        Raises:
+            TypeError: ``params`` has an invalid type.
+            ValueError: ``params`` is empty.
+
+        Returns:
+            The persistent form of ``params`` to be passed into the parent
+            :class:`Optimizer` constructor -- i.e. returns ``params`` as a
+            :class:`list` to ensure that it can be iterated over again.
+        """
+        if isinstance(params, torch.Tensor):
+            raise TypeError(
+                "`params` argument should be an iterable of "
+                f"Tensors, but got {torch.typename(params)}"
+            )
+        try:
+            all_params = list(params)
+        except TypeError as e:
+            raise TypeError(
+                "`params` argument should be an iterable of Tensors"
+                f" or dicts, but got {torch.typename(params)}"
+            ) from e
+        if len(all_params) == 0:
+            raise ValueError("ZeroRedundancyOptimizer got an empty parameter list")
+        all_tensors = True
+        all_dicts = True
+        for param in all_params:
+            all_tensors &= isinstance(param, torch.Tensor)
+            all_dicts &= isinstance(param, dict)
+        if not all_tensors and not all_dicts:
+            raise TypeError(
+                "`params` argument should be an iterable of Tensors or dicts"
+            )
+        # Ensure that `self._all_params` contains a list of all parameters
+        if all_tensors:
+            self._all_params = all_params
+        elif all_dicts:
+            self._all_params = []
+            # `all_params` contains parameter groups (not parameters)
+            for param_group in all_params:
+                if "params" not in param_group:
+                    raise ValueError(
+                        "Each parameter group passed-in via `params` must "
+                        "have a 'params' key mapping to the parameters in "
+                        "the group"
+                    )
+                self._all_params.extend(param_group["params"])
+        return all_params
+
+    def _verify_same_dense_param_type(self) -> None:
+        r"""
+        Verify that all parameters are of the same dense type.
+
+        The method assumes that ``self._all_params`` has been initialized
+        and is non-empty.
+
+        Raises:
+            ValueError: ``params`` contains sparse parameters or parameters
+            of varying dense types.
+
+        NOTE: This method can be removed once support for sparse parameters
+        and varying parameter types is added.
+        """
+        typename = torch.typename(self._all_params[0])
+        if self._all_params[0].is_sparse:
+            raise ValueError(
+                "ZeroRedundancyOptimizer only supports using "
+                "the same dense type for all parameters but got "
+                f"{typename}"
+            )
+        for param in self._all_params[1:]:
+            other_typename = torch.typename(param)
+            if other_typename != typename:
+                raise ValueError(
+                    "ZeroRedundancyOptimizer only supports "
+                    "using the same dense type for all "
+                    f"parameters but got both {typename} and "
+                    f"{other_typename}"
+                )
+
+    def _get_is_trainable_mask(self) -> list[bool]:
+        r"""Return a boolean mask indicating if each parameter is trainable (``requires_grad``) or not."""
+        return list(map(_is_trainable, self._all_params))
+
+    def _init_local_optimizer(self) -> None:
+        r"""
+        Initialize this rank's local optimizer, responsible for its subset of the parameters.
+
+        The local optimizer is saved in ``self.optim``.
+        """
+        assert self._optim_constructor is not None, (
+            "The local optimizer class has not been set"
+        )
+
+        param_groups = self._partition_parameters()[self.rank]
+        # `overlap_with_ddp=True` requires a local functional optimizer
+        if self._overlap_with_ddp:
+            # Functional optimizers only support a single parameter group and
+            # require passing in the parameters as a list
+            assert len(param_groups) == 1, (
+                "Initializing the local "
+                "functional optimizer with more than one parameter group"
+            )
+            params = param_groups[0]["params"]
+            # Try to pass `_allow_empty_param_list=True` to avoid erroring
+            if (
+                "_allow_empty_param_list"
+                in inspect.signature(self._optim_constructor).parameters
+            ):
+                self.optim: Any = self._optim_constructor(
+                    params, **self._optim_defaults, _allow_empty_param_list=True
+                )
+            else:
+                logger.warning(
+                    "%s does not support the argument "
+                    "`_allow_empty_param_list`; ZeroRedundancyOptimizer may "
+                    "error due to an empty parameter list",
+                    self._optim_constructor,
+                )
+                self.optim: Any = self._optim_constructor(
+                    params, **self._optim_defaults
+                )  # type: ignore[no-redef]
+
+            # Log information about the DDP and ZeRO bucketing
+            if dist.get_debug_level() != dist.DebugLevel.OFF:
+                local_numel = sum(p.numel() for p in params)
+                num_assigned_buckets = len(
+                    self._bucket_assignments_per_rank[self.global_rank]
+                )
+                logger.info(
+                    "rank %s with %s parameters across %s buckets",
+                    self.global_rank,
+                    local_numel,
+                    num_assigned_buckets,
+                )
+                if self.global_rank == 0:
+                    logger.info(
+                        "%s DDP buckets and %s bucket assignments",
+                        len(self._overlap_info.params_per_bucket),
+                        self._overlap_info.num_bucket_assignments,
+                    )
+        else:
+            # NOTE: Passing `param_groups` into the local optimizer constructor
+            # bypasses the empty parameter list check
+            self.optim: Optimizer = self._optim_constructor(
+                param_groups, **self._optim_defaults
+            )  # type: ignore[no-redef]
+
+        # TODO: Manually add `self.param_groups` if using a functional
+        # optimizer; remove this if/when the functional optimizers support
+        # multiple parameter groups
+        if self._overlap_with_ddp and not hasattr(self.optim, "param_groups"):
+            assert hasattr(self.optim, "param_group"), (
+                "The functional optimizer should set at least one of the "
+                "attributes `param_group` or `param_groups`"
+            )
+            self.optim.param_groups = [self.optim.param_group]  # type: ignore[attr-defined]
+
+        self._sync_param_groups(self.optim.param_groups, self.param_groups)
+
+    def _init_zero_for_overlap(self) -> None:
+        r"""Perform a delayed initialization of the local optimizer and the supporting data structures."""
+        assert self._overlap_with_ddp, (
+            "`_init_zero_for_overlap()` should only be called when "
+            "`overlap_with_ddp=True`"
+        )
+        self._overlap_info.status = _OverlapStatus.INITIALIZED
+        self._clear_cache()
+        self._partition_parameters(self._overlap_info.params_per_rank)
+        self._build_ddp_param_buckets()
+        self._init_local_optimizer()
+
+    def _get_assigned_rank(self, bucket_index: int) -> int:
+        r"""
+        Return the single rank assigned to a :class:`DistributedDataParallel` gradient bucket.
+
+        Arguments:
+            bucket_index (int): index of the :class:`DistributedDataParallel`
+                bucket for which to get the assigned rank.
+        """
+        assert not self._overlap_info.shard_buckets, (
+            "The bucket assignment requires global bucket information and "
+            "will be computed later; there should be no need to use this "
+            "method"
+        )
+        return bucket_index % self.world_size
+
+    def _check_overlap_initialized(self):
+        r"""
+        Check the delayed initialization depending on the value of ``overlap_with_ddp``.
+
+        The delayed initialization has occurred (see
+        :meth:`_init_zero_for_overlap`) if ``overlap_with_ddp=True``, and
+        raises a ``RuntimeError`` if not. This should preface methods that
+        should not be run before that delayed initialization.
+
+        Raises:
+            RuntimeError: if ``overlap_with_ddp=True`` and
+                :meth:`_init_zero_for_overlap` has not been called.
+        """
+        if (
+            self._overlap_with_ddp
+            and self._overlap_info.status != _OverlapStatus.INITIALIZED
+        ):
+            raise RuntimeError(
+                "This method should not be called until this "
+                "ZeroRedundancyOptimizer instance has been fully "
+                "initialized"
+            )
+
+    def _get_optimizer_constructor(self, optimizer_class: Any) -> Any:
+        r"""
+        Return the optimizer constructor using validation and transformation depending on ``overlap_with_ddp``.
+
+        Returns:
+            - ``optimizer_class`` if ``overlap_with_ddp=False`` and
+                ``optimizer_class`` is not a functional optimizer.
+            - ``optimizer_class`` if ``overlap_with_ddp=True`` and
+                ``optimizer_class`` is already a functional optimizer.
+            - The functional equivalent of ``optimizer_class`` if
+                ``overlap_with_ddp=True`` and ``optimizer_class`` is not
+                already a functional optimizer (assuming the equivalent
+                exists).
+
+        Raises:
+            ValueError:
+
+                - if ``overlap_with_ddp=True`` but ``optimizer_class`` is
+                    neither a functional optimizer nor translatable to a
+                    functional optimizer.
+                - if ``overlap_with_ddp=False`` and ``optimizer_class`` is a
+                    functional optimizer.
+        """
+        functional_optims = functional_optim_map.values()
+        if not self._overlap_with_ddp:
+            if optimizer_class in functional_optims:
+                # Using a functional optimizer is only supported when
+                # `overlap_with_ddp=True`
+                raise ValueError(
+                    f"Passing in a functional optimizer {optimizer_class} "
+                    "when `overlap_with_ddp=False`"
+                )
+            else:
+                return optimizer_class
+        else:
+            if optimizer_class in functional_optims:
+                # Already a functional optimizer
+                return optimizer_class
+            elif optimizer_class in functional_optim_map:
+                # Translate the passed-in optimizer class to its functional
+                # equivalent if `overlap_with_ddp=True`
+                optim_constructor = functional_optim_map[optimizer_class]
+                logger.info(
+                    "Using the functional optimizer %s "
+                    "instead of %s since "
+                    "`overlap_with_ddp=True`",
+                    optim_constructor,
+                    optimizer_class,
+                )
+                return optim_constructor
+            else:
+                raise ValueError(
+                    "Using `ddp_with_overlap=True` requires using a "
+                    "functional optimizer, but there is no supported functional "
+                    f"optimizer equivalent for {optimizer_class}"
+                )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/zero_redundancy_optimizer.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/zero_redundancy_optimizer.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..8ffbb04f13ffcfdba07589eac0594c80cc28968d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/optim/zero_redundancy_optimizer.pyi
@@ -0,0 +1,85 @@
+# mypy: allow-untyped-defs
+import enum
+from collections.abc import Callable
+from typing import Any, overload
+
+import torch
+from torch.distributed.algorithms.join import Joinable, JoinHook
+from torch.optim import Optimizer
+
+class _ZeROJoinHook(JoinHook):
+    zero: Any = ...
+    def __init__(self, zero: Any) -> None: ...
+    def main_hook(self) -> None: ...
+
+class _DDPBucketAssignment:
+    bucket_index: int
+    parameters: list[torch.Tensor]
+    offset: int
+    device: torch.device
+    tensor: torch.Tensor | None
+
+class _OverlapStatus(enum.IntEnum):
+    UNINITIALIZED = ...
+    DDP_HAS_REBUILT_BUCKETS = ...
+    INITIALIZED = ...
+
+class _OverlapInfo:
+    status: Any = ...
+    params_per_bucket: Any = ...
+    params_per_rank: Any = ...
+    offsets: Any = ...
+    broadcast_handles: Any = ...
+    bucket_index_to_future: Any = ...
+    bucket_index_to_bucket: Any = ...
+    bucket_indices_seen: Any = ...
+    assigned_ranks_per_bucket: list[set[int]] = ...
+    total_size: int = ...
+    shard_buckets: bool = ...
+    def __init__(self) -> None: ...
+    def wait_for_broadcasts(self) -> None: ...
+    def clear_per_iter_info(self) -> None: ...
+
+class ZeroRedundancyOptimizer(Optimizer, Joinable):
+    functional_optim_map: Any = ...
+    initialized: bool = ...
+    process_group: Any = ...
+    world_size: int = ...
+    rank: int = ...
+    global_rank: int = ...
+    parameters_as_bucket_view: bool = ...
+    optim: Any = ...
+    _device_to_device_index: dict[torch.device, int] = ...
+    _overlap_with_ddp: bool = ...
+    _overlap_info: _OverlapInfo = ...
+    _buckets: list[list[torch.Tensor]] = ...
+    _bucket_assignments_per_rank: list[dict[int, _DDPBucketAssignment]] = ...
+    def __init__(
+        self,
+        params: Any,
+        optimizer_class: type[Optimizer],
+        process_group: Any | None = ...,
+        parameters_as_bucket_view: bool = ...,
+        overlap_with_ddp: bool = ...,
+        **defaults: Any,
+    ) -> None: ...
+    def add_param_group(self, param_group: dict[str, Any]) -> None: ...
+    def consolidate_state_dict(self, to: int = ...) -> None: ...
+    @overload
+    def step(self, closure: None = None, **kwargs: Any) -> None: ...
+    @overload
+    def step(self, closure: Callable[[], float], **kwargs: Any) -> float: ...
+    def load_state_dict(self, state_dict: dict[str, Any]) -> None: ...
+    def state_dict(self) -> dict[str, Any]: ...
+    def _local_step(
+        self,
+        gradients: list[torch.Tensor | None] | None = None,
+        closure: Callable[[], float] | None = None,
+        **kwargs: Any,
+    ) -> float | None: ...
+    def _get_assigned_rank(self, bucket_index: int) -> int: ...
+    def _init_zero_for_overlap(self) -> None: ...
+    def join_hook(self, **kwargs): ...
+    @property
+    def join_device(self) -> torch.device: ...
+    def join_process_group(self) -> Any: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/pipelining/_IR.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/pipelining/_IR.py
new file mode 100644
index 0000000000000000000000000000000000000000..eae7def75d5faf1b9427e7a477b866b8229b1651
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/pipelining/_IR.py
@@ -0,0 +1,1257 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+import copy
+import logging
+import operator
+from collections import defaultdict
+from collections.abc import Callable
+from enum import Enum
+from inspect import Parameter, Signature, signature
+from types import MethodType
+from typing import Any, Union
+
+import torch
+import torch.fx as fx
+from torch.distributed import ProcessGroup
+from torch.export import ExportedProgram
+from torch.export.unflatten import (
+    _assign_attr,
+    _AttrKind,
+    _sink_params,
+    InterpreterModule,
+)
+from torch.fx.node import map_aggregate
+from torch.fx.passes.split_module import split_module
+
+from ._backward import _null_coalesce_accumulate, stage_backward
+from ._unflatten import _outline_submodules
+from ._utils import PipeInfo
+from .stage import _PipelineStage
+
+
+logger = logging.getLogger(__name__)
+
+# TODO:
+# 1. investigate gradient sync for shared parameters. how does DDP do it?
+# 2. Add parameter movement to split_module
+
+
+PP_SUBMOD_PREFIX = "submod_pp"
+
+
+def get_submod_name(stage_idx: int):
+    """Returns the name of the submod for a given stage index.
+    For example, "submod_pp_0", "submod_pp_1", etc.
+    """
+    return "_".join([PP_SUBMOD_PREFIX, str(stage_idx)])
+
+
+def _find_loss_from_output_and_spec(output_val, spec_val):
+    if spec_val is False:
+        return None
+    if spec_val is True:
+        if not isinstance(output_val, fx.Node):
+            raise RuntimeError(
+                f"Loss spec must specify a dynamic value but got {output_val}"
+            )
+        return output_val
+
+    if isinstance(spec_val, (tuple, list)):
+        if not isinstance(output_val, (tuple, list)):
+            raise RuntimeError(
+                f"Output value {output_val} must match type of loss specification "
+                f"{spec_val}"
+            )
+        if len(output_val) != len(spec_val):
+            raise RuntimeError(
+                f"Output value {output_val} must match length of loss specification "
+                f"{spec_val}"
+            )
+        for out, spec in zip(output_val, spec_val):
+            loss_val = _find_loss_from_output_and_spec(out, spec)
+            if loss_val is not None:
+                return loss_val
+        raise RuntimeError(f"Did not find loss value in specification {spec_val}")
+
+    if isinstance(spec_val, dict):
+        if not isinstance(output_val, dict):
+            raise RuntimeError(
+                f"Output value {output_val} must match type of loss specification "
+                f"{spec_val}"
+            )
+        if set(output_val.keys()) != set(spec_val.keys()):
+            raise RuntimeError(
+                f"Output value {output_val} must match keys of loss specification "
+                f"{spec_val}"
+            )
+        for k in spec_val:
+            loss_val = _find_loss_from_output_and_spec(output_val[k], spec_val[k])
+            if loss_val is not None:
+                return loss_val
+        raise RuntimeError(f"Did not find loss value in specification {spec_val}")
+
+    raise RuntimeError(f"Unsupported type {type(spec_val)} in loss specification")
+
+
+def _find_loss_output(mod: torch.nn.Module, g: fx.Graph, output_loss_value_spec):
+    output_nodes = [n for n in g.nodes if n.op == "output"]
+    assert len(output_nodes) == 1
+    output_node = output_nodes[0]
+    output_val = output_node.args[0]
+    generated_spec: Any = None
+
+    if isinstance(mod, TrivialLossWrapper):
+        # TrivialLossWrapper is pre-defined by PiPPy.
+        # It has loss as the only output so we can safely assume the first output arg is the loss.
+        assert len(output_node.args) == 1
+        loss_node = output_val
+        generated_spec = TrivialLossWrapper.loss_spec
+    elif output_loss_value_spec is None:
+        # Use default spec, i.e. search for "loss" in output values
+        if isinstance(output_val, dict) and "loss" in output_val:
+            loss_node = output_val["loss"]
+            generated_spec = {k: k == "loss" for k in output_val}
+        else:
+            loss_node = None
+            generated_spec = None
+    else:
+        loss_node = _find_loss_from_output_and_spec(output_val, output_loss_value_spec)
+        generated_spec = output_loss_value_spec
+
+    return loss_node, output_node, generated_spec
+
+
+def _insert_stage_symbolic_backward(
+    g: fx.Graph,
+    loss_node: fx.Node,
+    output_node: fx.Node,
+):
+    # Collect metadata about tuple output values. TODO: move this to split_module or FX IR
+    tuples: dict[fx.Node, tuple] = {}
+    for node in reversed(g.nodes):
+        if node.op == "call_function":
+            # In the forward pass, only emit placeholder, module calls, and
+            # getitem calls. If we have a target other than getitem in this
+            # (forward-only) code, there is a bug.
+            assert node.target is operator.getitem, (
+                "Found non-getitem call in forward pass. Please report a bug to PiPPy"
+            )
+            assert len(node.args) == 2, (
+                "Found malformed getitem call. Please report a bug to PiPPy"
+            )
+            indexed_value, node_idx = tuple(node.args)
+
+            # indexed_value is a collection that we are indexing into. It could
+            # exist in the tuples map if we've processed another `getitem`
+            # already.
+            existing_list_size = (
+                len(tuples[indexed_value]) if indexed_value in tuples else -1
+            )
+            new_list_size = max(node_idx + 1, existing_list_size)
+
+            reconstructed_list = [None for _ in range(new_list_size)]
+
+            # Copy over existing elements if present
+            if indexed_value in tuples:
+                for i, val in enumerate(tuples[indexed_value]):
+                    reconstructed_list[i] = val
+
+            # Populate value represented by this node
+            reconstructed_list[node_idx] = node
+
+            tuples[indexed_value] = tuple(reconstructed_list)
+
+    # Keep track of nodes that dominate the loss node.
+    # We will only emit backward operations for nodes that can contribute
+    # to the specified loss value.
+    live_nodes = {loss_node: None}
+    val_to_grad: dict[fx.Node, fx.Node | None] = {loss_node: None}
+
+    def assign_or_accumulate_grad(forward_node, grad_value):
+        if forward_node in val_to_grad and forward_node.op != "placeholder":
+            grad_value = g.call_function(
+                _null_coalesce_accumulate,
+                (val_to_grad[forward_node], grad_value),
+            )
+        val_to_grad[forward_node] = grad_value
+
+    with g.inserting_before(output_node):
+        for node in reversed(g.nodes):
+            if node not in live_nodes:
+                continue
+
+            def add_to_live_nodes(n):
+                live_nodes.setdefault(n, None)
+
+            fx.node.map_arg(node.args, add_to_live_nodes)
+            fx.node.map_arg(node.kwargs, add_to_live_nodes)
+            if node.op == "call_module":
+                output_grads: tuple[fx.Node | None, ...] | fx.Node | None
+                if node in tuples:
+                    stage_output = tuples[node]
+                    output_grads = tuple(val_to_grad.get(n) for n in tuples[node])
+                    outputs_with_grads_idxs = [
+                        i for i, n in enumerate(tuples[node]) if n in live_nodes
+                    ]
+                else:
+                    stage_output = (node,)
+                    output_grads = val_to_grad[node]
+                    outputs_with_grads_idxs = [0]
+
+                output_grads = (
+                    (output_grads,)
+                    if not isinstance(output_grads, tuple)
+                    else output_grads
+                )
+
+                grad_call = g.call_function(
+                    stage_backward,
+                    kwargs={
+                        "stage_output": stage_output,
+                        "output_grads": output_grads,
+                        "input_values": list(node.all_input_nodes),
+                        "outputs_with_grads_idxs": outputs_with_grads_idxs,
+                    },
+                )
+                # Insert backward stage debug info
+                kwargs_copy = dict(grad_call.kwargs)
+                grad_call.kwargs = kwargs_copy
+
+                grad_call_proxy = fx.Proxy(grad_call)
+                grads = grad_call_proxy.node
+
+                input_nodes = list(node.all_input_nodes)
+                grads_proxy = fx.Proxy(grads)
+                for i, input_node in enumerate(input_nodes):
+                    assign_or_accumulate_grad(input_node, grads_proxy[i].node)  # type: ignore[index]
+
+    return g
+
+
+class PipeSequential(torch.nn.Sequential):
+    @staticmethod
+    def from_sequential(sequential_instance: torch.nn.Sequential):
+        return PipeSequential(*[copy.copy(m) for m in sequential_instance])
+
+    def forward(self, input):
+        for i, module in enumerate(self):
+            input = module(input)
+            if i != len(self) - 1:
+                pipe_split()
+        return input
+
+
+class LossWrapper(torch.nn.Module):
+    """
+    LossWrapper is a convenient abstract class that allows you to wrap up both
+    your model as well as its loss function and specify the connectivity between
+    the inputs, model, loss function, and output value. Example::
+
+        class MyModelWrapper(LossWrapper):
+            def forward(self, x, targets):
+                model_out = self.module(x)
+                loss_value = self.loss_fn(model_out, targets)
+                return loss_value
+
+    The above example defines a connectivity where we expect the forward/loss/backward
+    training procedure to take two arguments (x and targets), pass x into the module
+    to get the output of the feedforward computation, pass the model output and the
+    targets value into the loss function, and get and return the loss value, which will
+    be backpropagated by PiPPy. The above class would then be instantiated like::
+
+        model = ...  # instantiate the model
+        loss_fn = torch.nn.MSELoss()  # for the sake of demonstration
+
+        wrapper = MyModelWrapper(model, loss_fn)
+        pipe = Pipe.from_tracing(wrapper, ...)
+
+    """
+
+    def __init__(self, module, loss_fn):
+        super().__init__()
+        self.module = module
+        self.loss_fn = loss_fn
+
+    def forward(self, *args, **kwargs):
+        raise NotImplementedError(
+            "This instance of LossWrapper does not have an overridden"
+            "forward(). Please implement forward() to specify the arguments, "
+            "connection between the module and loss, and loss output "
+            "value."
+        )
+
+
+class TrivialLossWrapper(LossWrapper):
+    # pyrefly: ignore [bad-override]
+    def forward(self, x, targets):
+        model_out = self.module(x)
+        return self.loss_fn(model_out, targets)
+
+    loss_spec = True
+
+
+# Pipe model representation
+#
+# Pipe can be thought of as an `nn.Sequential++`. That is to say: it specifies
+# a single topological ordering of pipeline "stages" that, when run in series,
+# constitutes all of the operations of the program. However, unlike `nn.Sequential`,
+# Pipe allows non-local usages of values, so long as those uses still respect
+# topological ordering. In particular:
+#
+# 1. Non-local activations. This type of usage can appear in, for example, skip
+#    connections. These values will be directly transmitted from the "def" stage
+#    to all stages that use them skipping intermediate stages. During autograd,
+#    gradients will be propagated back through this skip connection reverse
+#    to how activations propagated in the forward pass.
+# 2. Non-local parameter/module invocations. This occurs when a parameter is used
+#    in a stage downstream of where it is resident. These values can be carried
+#    forward similarly to (1), but in addition one might want to replicate the
+#    value on multiple stages. Gradients for these shared parameters will be
+#    accumulated separately on each stage, but there will be an additional
+#    gradient accumulation before the optimizer step.
+
+
+# Register `_pipe_split()` as an ATen operator. This is required for Export to
+# preserve this marker in the graph.
+torch.library.define("pippy::_pipe_split", "() -> ()")
+
+
+@torch.library.impl("pippy::_pipe_split", "BackendSelect")
+def _pipe_split():
+    return None
+
+
+@torch.library.register_fake("pippy::_pipe_split")  # type: ignore[no-redef]
+def _pipe_split():  # noqa: F811
+    return None
+
+
+# Add an alias for convenience
+aten_pipe_split_alias = torch.ops.pippy._pipe_split.default
+
+# Ask Export to preserve the `_pipe_split` op.
+# See examples in pytorch/torch/fx/node.py
+fx.node._side_effectful_functions.add(aten_pipe_split_alias)
+
+
+# User facing API
+def pipe_split():
+    """
+    pipe_split is a special operator that is used to mark the boundary between
+    stages in a module. It is used to split the module into stages. It is a
+    no-op if your annotated module is run eagerly.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> def forward(self, x):
+        >>>     x = torch.mm(x, self.mm_param)
+        >>>     x = torch.relu(x)
+        >>>     pipe_split()
+        >>>     x = self.lin(x)
+        >>>     return x
+
+    The above example will be split into two stages.
+    """
+    return torch.ops.pippy._pipe_split()
+
+
+class MultiUseParameterConfig(Enum):
+    TRANSMIT = 1
+    REPLICATE = 2
+
+
+MultiUseParamSpec = Union[MultiUseParameterConfig, dict[str, MultiUseParameterConfig]]
+
+
+class DetachExecutor(fx.Interpreter):
+    """
+    Special interpreter to run the split_gm in testing that detaches all inputs to
+    a module invocation. This is needed so that the values at the boundary are
+    leaf modules in autograd execution.
+    """
+
+    def __init__(self, module, garbage_collect_values=True):
+        garbage_collect_values = False
+        super().__init__(module, garbage_collect_values)
+        self.value_remap = {}
+
+    def run(self, *args, initial_env=None):  # type: ignore[override]
+        self.value_remap = {}
+        return super().run(*args, initial_env=initial_env)
+
+    def call_module(self, target, args, kwargs):
+        def detach_tensors(a):
+            if isinstance(a, torch.Tensor) and a.requires_grad:
+                if a not in self.value_remap:
+                    new_val = a.detach().requires_grad_(True)
+                    self.value_remap[a] = new_val
+                return self.value_remap[a]
+            else:
+                return a
+
+        """
+        def dont_traverse_size(a):
+            return type(a) is not torch.Size
+        """
+
+        args = map_aggregate(
+            args,
+            detach_tensors,  # dont_traverse_size
+        )
+        kwargs = map_aggregate(
+            kwargs,
+            detach_tensors,  # dont_traverse_size
+        )
+
+        return super().call_module(target, args, kwargs)
+
+    def call_function(self, target, args, kwargs):
+        # HACK to reroute saved input tensors to point to the detach()ed version
+        if target is stage_backward:
+            kwargs = dict(kwargs)
+            kwargs["input_values"] = [
+                self.value_remap.get(v, v) for v in kwargs["input_values"]
+            ]
+        return super().call_function(target, args, kwargs)
+
+
+class _NodeReference:
+    def __init__(self, name):
+        self.name = name
+
+    name: str
+
+
+class _LinearNodeList:
+    def __init__(self, node_list):
+        self.serialize_node_list = []
+        for node in node_list:
+            node_args = fx.node.map_arg(node.args, lambda n: _NodeReference(n.name))  # type: ignore[arg-type,return-value]
+            node_kwargs = fx.node.map_arg(node.kwargs, lambda n: _NodeReference(n.name))  # type: ignore[arg-type,return-value]
+            serialize_node = fx.Node(
+                graph=None,  # type: ignore[arg-type]
+                name=node.name,
+                op=node.op,
+                target=node.target,
+                args=node_args,  # type: ignore[arg-type]
+                kwargs=node_kwargs,  # type: ignore[arg-type]
+                return_type=node.type,
+            )
+            serialize_node.meta = copy.copy(node.meta)
+            self.serialize_node_list.append(serialize_node)
+
+    def to_graph(self):
+        graph = fx.Graph()
+
+        ref_str_to_node: dict[str, fx.Node] = {}
+
+        def ref_to_node(arg):
+            if isinstance(arg, _NodeReference):
+                return ref_str_to_node[arg.name]
+            else:
+                return arg
+
+        for node in self.serialize_node_list:
+            node_args = map_aggregate(node.args, ref_to_node)
+            node_kwargs = map_aggregate(node.kwargs, ref_to_node)
+            deser_node = graph.create_node(
+                op=node.op,
+                target=node.target,
+                args=node_args,  # type: ignore[arg-type]
+                kwargs=node_kwargs,  # type: ignore[arg-type]
+                name=node.name,
+                type_expr=node.type,
+            )
+            ref_str_to_node[node.name] = deser_node
+
+        return graph
+
+
+def _direct_serialization_deserialize(body, nodes):
+    """
+    Custom `__reduce__` method for serialization.
+    DO AS I SAY -- NOT AS I DO. This violates the principle that
+    GraphModules serialize via code export & re-tracing. We allow
+    for this here because **PIPE STAGES SHOULD NOT BE PERSISTED
+    TO DISK -- THIS IS ONLY FOR TRANSMISSION VIA RPC**. Persisting
+    these instances to disk will expose internal implementation
+    details of `fx.Graph` and related data structures and is
+    NOT advised.
+    """
+
+    class DummyModule(torch.nn.Module):
+        def __init__(self, body):
+            super().__init__()
+            self.__dict__.update(body)
+
+    dummy = DummyModule(body)
+
+    return fx.GraphModule(dummy, nodes.to_graph())
+
+
+def _direct_serialization_reduce(self):
+    serialization_dict = dict(self.__dict__)
+    serialization_dict.pop("_graph")
+    return (
+        _direct_serialization_deserialize,
+        (serialization_dict, _LinearNodeList(self.graph.nodes)),
+    )
+
+
+def _modify_graph_op_device(
+    gm: torch.fx.GraphModule,
+    new_device: torch.device,
+):
+    """
+    Modify the device argument of all "call_function" nodes in the graph.  This
+    is useful for moving the graph to a different device. In particular for
+    generator ops, like torch.ones.
+    """
+    modified = False
+    for node in gm.graph.nodes:
+        if node.op == "call_function":
+            if "device" in node.kwargs and node.kwargs["device"] != new_device:
+                logger.debug(
+                    f"Changing device of Node {node.name} from {node.kwargs['device']} to {new_device}"  # noqa: G004
+                )
+                node.update_kwarg("device", new_device)
+                modified = True
+        elif node.op == "call_module":
+            # Recursively modify "device" in submodules
+            submod = gm.get_submodule(node.target)
+            if isinstance(submod, torch.fx.GraphModule):
+                _modify_graph_op_device(submod, new_device)
+            elif isinstance(submod, InterpreterModule):
+                # If unflattening has been performed, we need to access its graph module by `.graph_module`
+                _modify_graph_op_device(submod.graph_module, new_device)  # type: ignore[arg-type]
+            else:
+                logger.warning(
+                    f"Skipping device modification for submodule {node.target} because it is a {type(submod)}"  # noqa: G004
+                )
+
+    if modified:
+        gm.recompile()
+
+
+class Pipe(torch.nn.Module):
+    def __init__(
+        self,
+        split_gm: fx.GraphModule,
+        num_stages: int,
+        has_loss_and_backward: bool,
+        loss_spec,
+    ):
+        # TODO: is there a way not to hard wire init?
+        torch.nn.Module.__init__(self)
+        self.split_gm: fx.GraphModule = split_gm
+        self.executor: DetachExecutor = DetachExecutor(self.split_gm)
+        self.num_stages: int = num_stages
+        self.has_loss_and_backward = has_loss_and_backward
+        self.loss_spec = loss_spec
+
+        for node in split_gm.graph.nodes:
+            assert (
+                node.op in {"call_module", "placeholder", "output"}
+                or (node.op, node.target) == ("call_function", operator.getitem)
+                or (node.op, node.target) == ("call_method", "backward")
+                or (node.op, node.target) == ("call_function", stage_backward)
+                or (node.op, node.target)
+                == ("call_function", _null_coalesce_accumulate)
+            ), node
+
+        # Detect replicated parameters so we know that we have to do an additional allreduce
+        # before applying the optimizer
+        #
+        # Note that this also handles the case where there were multiple calls to a single
+        # module from different stages, regardless of whether that module invocation
+        # was handled by the logic above.
+
+        # Map parameter value to a dictionary that maps the user pipeline module
+        # to the local qualname within that module
+        params_to_users: dict[torch.nn.Parameter, dict[str, str]] = {}
+
+        for m_qualname, mod in self.split_gm.named_children():
+            for p_qualname, param in mod.named_parameters():
+                params_to_users.setdefault(param, {})
+                params_to_users[param][m_qualname] = p_qualname
+
+        self.replicated_params: list[dict[str, str]] = [
+            use_mapping
+            for _, use_mapping in params_to_users.items()
+            if len(use_mapping) > 1
+        ]
+
+        # We must break the aliasing relationship between the replicated parameters for correct
+        # numerics in reference runs. If we do not do this, the autograd tape in separate stages
+        # will have a reference to the same tensor value and will erroneously apply gradient
+        # updates multiple times. Therefore, for each replicated parameter set, we deepcopy the
+        # values so that we have separate instances.
+        for param_mapping in self.replicated_params:
+            for submod_name, param_qualname in param_mapping.items():
+                submod = getattr(self.split_gm, submod_name)
+                atoms = param_qualname.split(".")
+                for atom in atoms[:-1]:
+                    submod = getattr(submod, atom)
+                setattr(submod, atoms[-1], copy.deepcopy(getattr(submod, atoms[-1])))
+
+        def throw(self, *args, **kwargs):
+            raise RuntimeError(
+                "To run pipeline locally, invoke the Pipe object directly, not `split_gm`"
+            )
+
+        self.split_gm.forward = throw
+
+        # Make submodules use custom direct-serialized GraphModule
+        i = 0
+        while True:
+            try:
+                name = get_submod_name(i)
+                submod = getattr(self.split_gm, name)
+                submod.__class__.__reduce__ = _direct_serialization_reduce
+                i += 1
+            except AttributeError:
+                break
+
+    def forward(self, *args, **kwargs):
+        executor_args = args
+        if len(kwargs) > 0:
+            parameters = []
+            for node in self.split_gm.graph.nodes:
+                if node.op == "placeholder":
+                    if node.args and len(node.args) > 0:
+                        parameters.append(
+                            Parameter(
+                                node.target,
+                                Parameter.POSITIONAL_OR_KEYWORD,
+                                default=node.args[0],
+                            )
+                        )
+                    else:
+                        parameter_kind = Parameter.POSITIONAL_OR_KEYWORD
+                        param_name = node.target
+                        if node.target.startswith("**"):
+                            parameter_kind = Parameter.VAR_KEYWORD  # type: ignore[assignment]
+                            param_name = param_name[2:]
+                        elif node.target.startswith("*"):
+                            parameter_kind = Parameter.VAR_POSITIONAL  # type: ignore[assignment]
+                            param_name = param_name[1:]
+                        parameters.append(Parameter(param_name, parameter_kind))
+            signature = Signature(parameters)
+            ba = signature.bind(*args, **kwargs)
+            ba.apply_defaults()
+            executor_args = ba.arguments.values()  # type: ignore[assignment]
+
+        res = self.executor.run(*executor_args)
+
+        return res
+
+    def get_stage_module(self, stage_idx: int) -> torch.nn.Module:
+        """
+        Return a stage module corresponding to `stage_idx` of the `pipe`.
+        """
+        if stage_idx < 0 or stage_idx >= self.num_stages:
+            raise ValueError(f"Invalid stage index {stage_idx}!")
+
+        submod_name = get_submod_name(stage_idx)
+        return getattr(self.split_gm, submod_name)
+
+    @staticmethod
+    def _number_and_count_forward_stages(gm: fx.GraphModule):
+        num_stages = 0
+        found_idxs: dict[int, None] = {}
+        for node in gm.graph.nodes:
+            if node.op == "call_module" and node.target.startswith(PP_SUBMOD_PREFIX):
+                node.meta["stage_idx"] = int(node.target[len(PP_SUBMOD_PREFIX) + 1 :])
+                found_idxs.setdefault(node.meta["stage_idx"])
+                num_stages += 1
+
+        # this assert will fail if a split point is inserted before the first layer, which creates empty first submodule
+        # Update: the following assert may fail against some torch versions >=
+        # 2.2.0, as:
+        # submod_0, submod_1, submod_2, ...
+        # may be named as
+        # submod_0, submod_2, submod_4, ...
+        # TODO: investigate
+        # assert all(i in found_idxs for i in range(num_stages))
+
+        return num_stages
+
+    @staticmethod
+    def _from_traced(
+        mod: torch.nn.Module,
+        exported_program: ExportedProgram,
+        multi_use_param_spec: MultiUseParamSpec | None = None,
+        output_loss_value_spec=None,
+        split_policy: Callable[[torch.fx.GraphModule], torch.fx.GraphModule]
+        | None = None,
+    ):
+        """
+        Additionally, the ``output_loss_value_spec`` value can be specified to disambiguate
+        which value in the output of `forward` is the loss value on which PiPPy should apply
+        backpropagation. For example, if your ``forward`` returns a tuple ``(loss, model_out)``,
+        you can specify ``output_loss_value_spec=(True, False)``. Or, if your ``forward`` returns
+        a dict ``{'loss': loss_value, 'model_out': model_out}``, you can specify
+        ``output_loss_value_spec={'loss': True, 'model_out': False}``
+        """
+
+        traced = exported_program.module(check_guards=False)
+
+        if split_policy is not None:
+            logger.info("Auto-splitting model")
+            traced = split_policy(traced)  # type: ignore[arg-type]
+
+        logger.debug(traced.print_readable(print_output=False))  # type: ignore[operator]
+
+        # Deduplicate `get_attr` nodes that refer to the same parameter . Downstream code for moving
+        # parameters relies on the invariant that parameter accesses happen once. This is not necessarily
+        # the case (especially with custom tracers), so fix that up here.
+        get_attr_nodes: dict[str, fx.Node] = {}
+        for node in traced.graph.nodes:  # type: ignore[union-attr]
+            if node.op == "get_attr":
+                get_attr_nodes.setdefault(node.target, node)
+
+                if get_attr_nodes[node.target] != node:
+                    node.replace_all_uses_with(get_attr_nodes[node.target])
+                    traced.graph.erase_node(node)  # type: ignore[operator, union-attr]
+
+        # avoid looking at next node by keeping track of previous pipe_split
+        prev_pipe_split_idx = -1
+        pipe_split_nodes_to_erase = set()
+        for i, node in enumerate(traced.graph.nodes):  # type: ignore[arg-type, union-attr]
+            if (node.op, node.target) == ("call_function", pipe_split):
+                if prev_pipe_split_idx == i - 1:
+                    pipe_split_nodes_to_erase.add(node)
+                prev_pipe_split_idx = i
+
+        for node in pipe_split_nodes_to_erase:
+            traced.graph.erase_node(node)  # type: ignore[operator, union-attr]
+
+        traced.recompile()  # type: ignore[operator]
+
+        part_idx = 0
+
+        def split_callback(n: fx.Node):
+            nonlocal part_idx
+            if (n.op, n.target) == (
+                "call_function",
+                aten_pipe_split_alias,
+            ):
+                logger.debug(f"Found pipe_split {part_idx}")  # noqa: G004
+                part_idx += 1
+            return part_idx
+
+        # TODO: what does split do with module invocations? does it move the modules
+        # into the submodules?
+        split = split_module(traced, mod, split_callback, partition_affix="pp")  # type: ignore[arg-type]
+        # a (custom) tracer can produce dead code like orphan get_attr nodes
+        split.graph.eliminate_dead_code()
+
+        # peephole to remove pipe_split
+        for submodule in split.modules():
+            if isinstance(submodule, fx.GraphModule):
+                for node in submodule.graph.nodes:
+                    if (node.op, node.target) == (
+                        "call_function",
+                        aten_pipe_split_alias,
+                    ):
+                        submodule.graph.erase_node(node)
+                submodule.recompile()
+
+        for name, submodule in split.named_children():
+            if isinstance(submodule, fx.GraphModule):
+                new_submod = _outline_submodules(submodule.graph)
+                # Replace old submod
+                split.register_module(name, new_submod)
+
+        # TODO: backport this into split_module
+        def delete_user_reference(node, user):
+            """
+            Delete reference of `node` from `user`'s arg list.
+            Args:
+                - node: a `get_attr` node at root.
+                - user: a submodule node that uses `node`.
+            """
+            assert len(user.kwargs) == 0
+            use_idxs = [i for i, arg in enumerate(user.args) if arg == node]
+            assert len(use_idxs) == 1
+            args_copy = list(user.args)
+            args_copy.pop(use_idxs[0])
+            user.args = tuple(args_copy)
+            logger.debug(
+                f"Deleted {node} from user {user}, arg index = {use_idxs[0]}"  # noqa: G004
+            )
+
+        # A list of param referrals for deferred deletion.
+        # To be accumulated in `move_param_to_callee`.
+        to_delete = []
+
+        def _recursive_getattr_with_parent(mod, fqn):
+            # Returns getattr call given a nested FQN, and the last parent
+            atoms = fqn.split(".")
+            for atom in atoms[:-1]:
+                if not hasattr(mod, atom):
+                    return None, None
+                mod = getattr(mod, atom)
+            if not hasattr(mod, atoms[-1]):
+                return mod, None
+            attr = getattr(mod, atoms[-1])
+            return mod, attr
+
+        def move_param_to_callee(
+            root,
+            callee_name,
+            param_fqn,
+        ):
+            """
+            Move a parameter from the root module to a submodule.
+            Args:
+                root: The root module.
+                callee_name: The name of the submodule to move the parameter to.
+                param_fqn: The fully qualified name of the parameter to move.
+            """
+            # `atoms` is a list of strings representing the path to the
+            # parameter in the original model
+            atoms = param_fqn.split(".")
+            mod_itr, param_val = _recursive_getattr_with_parent(split, param_fqn)
+            # Check whether the parameter is a buffer or a parameter
+            is_buffer = atoms[-1] in mod_itr._buffers
+
+            # Check whether the parameter is a tensor
+            assert isinstance(param_val, torch.Tensor), (
+                f"Expected '{param_fqn}' to be {torch.Tensor} but got {type(param_val)}."
+                + (
+                    f" It might happen if module '{param_fqn}' was passed to some 'leaf function'"
+                    f"(see https://pytorch.org/docs/stable/fx.html#fx.wrap). Please inspect "
+                    f"usages of '{param_fqn}' in the traced graph."
+                    if isinstance(param_val, torch.nn.Module)
+                    else ""
+                )
+            )
+
+            # Get submodule
+            callee = root.get_submodule(callee_name)
+            assert not hasattr(callee, param_fqn), (
+                f"Module {callee_name} already has a parameter named {param_fqn}"
+            )
+
+            # Assign the parameter to the submodule
+            if is_buffer:
+                _assign_attr(
+                    param_val,
+                    callee,
+                    param_fqn,
+                    attr_kind=_AttrKind.BUFFER,
+                    persistent=True,  # TODO: handle non-persistent buffer
+                )
+            else:
+                _assign_attr(
+                    param_val,
+                    callee,
+                    param_fqn,
+                    attr_kind=_AttrKind.PARAMETER,
+                )
+            logger.debug(f"Moved parameter {param_fqn} to {callee_name}")  # noqa: G004
+
+            # Next step is to replace placeholder of submodule with a get_attr.
+            # Those placeholders are created by `split_module` inside each
+            # submodule.
+            # Update: this step is now moved to `_sink_params` because
+            # `_sink_params` can do it recursively (i.e. for modules inside
+            # submodule)
+
+            to_delete.append((mod_itr, atoms[-1]))
+
+        # Get the list of all parameters in the root module
+        attr_nodes = list(filter(lambda n: n.op == "get_attr", split.graph.nodes))
+        for node in attr_nodes:
+            # Check whether the parameter is used in only one submodule
+            if len(node.users) > 1:
+                logger.info(
+                    f"Parameter {node.target} used in multiple stages: {node.users}."  # noqa: G004
+                )
+            for user in node.users:
+                assert user.op == "call_module"
+                # Move parameter into submodule
+                move_param_to_callee(
+                    split,
+                    user.target,
+                    node.target,
+                )
+
+        # [aliasing] store tensor id -> list of FQNs, built from state dict
+        # Also assign non-persistent buffers
+        id_to_fqns: dict[int, set[str]] = defaultdict(set)
+        for fqn, tensor in mod.state_dict(keep_vars=True).items():
+            id_to_fqns[id(tensor)].add(fqn)
+        for fqn, tensor in mod.named_buffers():
+            id_to_fqns[id(tensor)].add(fqn)
+
+        # After moving the params to their corresponding hierarchies, we also
+        # need to move the `get_attr` nodes from the root of the graph to those
+        # hierarchies.
+        # [aliasing] use id -> fqn mapping to list out all valid FQNs
+        inputs_to_state: dict[str, list[str]] = {}
+        for attr in attr_nodes:
+            _, tensor = _recursive_getattr_with_parent(mod, attr.target)
+            fqns = list(id_to_fqns[id(tensor)])
+            if fqns:
+                inputs_to_state[attr.name] = fqns
+            elif attr.target in exported_program.constants:  # lifted constants
+                inputs_to_state[attr.name] = [attr.target]
+
+        # [aliasing] for each submodule split, assign attributes on FQNs that may be used.
+        # We determine this based on whether or not the FQN attribute parent exists.
+        # i.e. if the last submodule exists, assign the attribute.
+        added_attributes: dict[str, list[str]] = defaultdict(list)
+        for fqn, tensor in mod.state_dict(keep_vars=True).items():
+            for name, submod in split.named_children():
+                if isinstance(submod, fx.GraphModule):
+                    parent, child = _recursive_getattr_with_parent(submod, fqn)
+                    if (
+                        parent and child is None
+                    ):  # parent exists, attribute doesn't -> assign
+                        added_attributes[name].append(fqn)
+                        setattr(parent, fqn.split(".")[-1], tensor)
+
+        # Deferral deletion: Remove the original attributes (to params) from the
+        # root GraphModule
+        for mod_itr, last_atom in to_delete:
+            try:
+                delattr(mod_itr, last_atom)
+            except AttributeError:
+                # This is expected if the parameter is used in multiple stages
+                pass
+
+        # This is done by (1) `_sink_params` at each submodule;
+        for submod in split.children():
+            if isinstance(submod, fx.GraphModule):
+                _sink_params(submod, inputs_to_state, [])
+                submod.graph.lint()
+                submod.recompile()
+
+        # [aliasing] This step is not super necessary, but helps reduce parameter usage/memory.
+        # After _sink_params() routine has run, clean up unused attributes that we previously added.
+        # Determine this based on the get_attr nodes - if not used, remove it.
+        for name, attributes in added_attributes.items():
+            submod = getattr(split, name)
+            unused_attributes = set(attributes)
+            # track used attributes in the submodule, running DFS on subgraph hierarchy
+            stack = [("", submod)]  # (scope, submodule)
+            while stack:
+                scope, _mod = stack.pop()
+                if isinstance(_mod, (fx.GraphModule, InterpreterModule)):
+                    for node in _mod.graph.nodes:
+                        if node.op == "get_attr":
+                            # get_attr might get access deeper level attribute
+                            fqn = scope + "." + node.target if scope else node.target
+                            unused_attributes.discard(fqn)
+                for _name, _submod in _mod.named_children():
+                    stack.append((scope + "." + _name if scope else _name, _submod))
+            # delete unused attributes
+            for attr in unused_attributes:
+                mod_itr, atoms = submod, attr.split(".")
+                for atom in atoms[:-1]:
+                    mod_itr = getattr(mod_itr, atom)
+                delattr(mod_itr, atoms[-1])
+
+        for node in attr_nodes:
+            # And (2): remove `get_attr` node from submod's arg list
+            for user in copy.copy(node.users):
+                assert user.op == "call_module"
+                delete_user_reference(node, user)
+            # And (3): remove the `get_attr` node from the root graph.
+            split.graph.erase_node(node)
+
+        split.delete_all_unused_submodules()
+        split.graph.lint()
+        split.recompile()
+
+        num_stages = Pipe._number_and_count_forward_stages(split)
+
+        has_loss_and_backward = False
+        generated_loss_spec = output_loss_value_spec
+
+        if output_loss_value_spec is not None:
+            loss_node, output_node, generated_loss_spec = _find_loss_output(
+                mod, split.graph, output_loss_value_spec
+            )
+            if loss_node is not None:
+                _insert_stage_symbolic_backward(
+                    split.graph,
+                    loss_node,
+                    output_node,
+                )
+                split.recompile()
+                has_loss_and_backward = True
+                logger.debug("Pipeline is in training mode, backward pass generated")
+            else:
+                raise RuntimeError(
+                    f"Did not find any loss value according to {output_loss_value_spec=}"
+                )
+        else:
+            logger.debug("Pipeline is in inference mode, backward pass not generated")
+
+        logger.debug(f"Full pipe model:\n{split}")  # noqa: G004
+
+        return Pipe(
+            split,
+            num_stages,
+            has_loss_and_backward,
+            generated_loss_spec,
+        )
+
+    def print_readable(self):
+        """
+        Print the pipe in a human-readable format.
+        This will print both the root pipe and each stage module.
+        """
+        self.split_gm.print_readable()
+
+    @staticmethod
+    def _trace_with_export(
+        mod: torch.nn.Module,
+        example_args: tuple[Any, ...],
+        example_kwargs: dict[str, Any] | None = None,
+    ) -> ExportedProgram:
+        logger.info("Tracing model ...")
+        try:
+            ep = torch.export.export(mod, example_args, example_kwargs)
+        except Exception as e:
+            raise RuntimeError(
+                "It seems that we cannot capture your model as a full graph. "
+                "Typical reasons include graph breaks, data/shape-dependent "
+                "control flow, or missing meta kernels for custom operators. "
+                "You can use our manual pipeline interfaces, or try to fix the "
+                "graph breaks, see https://pytorch.org/docs/stable/export.html"
+            ) from e
+
+        return ep
+
+    @staticmethod
+    def from_tracing(
+        mod: torch.nn.Module,
+        example_args: tuple[Any, ...],
+        example_kwargs: dict[str, Any] | None = None,
+        split_policy: Callable[[fx.GraphModule], fx.GraphModule] | None = None,
+    ):
+        # If a param will be used in multiple pipeline stages, we default the strategy to REPLICATE'ing the param across
+        # stages instead of TRANSMIT'ting it
+        multi_use_param_spec = MultiUseParameterConfig.REPLICATE
+
+        # Figure out which output is loss from output_chunk_spec
+        output_loss_value_spec: Any = None
+        # Deprecated
+        """
+        if output_chunk_spec is not None:
+            output_loss_value_spec = map_aggregate(
+                output_chunk_spec, lambda v: isinstance(v, _LossReducer)
+            )
+        """
+
+        # Trace with export
+        exported_program = Pipe._trace_with_export(
+            mod,
+            example_args,
+            example_kwargs,
+        )
+
+        pipe = Pipe._from_traced(
+            mod,
+            exported_program,
+            multi_use_param_spec,
+            output_loss_value_spec=output_loss_value_spec,
+            split_policy=split_policy,
+        )
+
+        # Users want the first pipeline stage to accept kwargs if the original
+        # program does. This is controlled by the `_codegen` field of the graph,
+        # so we make a copy here. Note: we only want the input spec and not the
+        # output spec, because the output spec is for the last stage. Maybe a
+        # TODO? Not sure yet.
+        split = pipe.split_gm
+        traced = exported_program.module()
+        submod0 = next(iter(split.children()))
+        submod0_sign = signature(submod0.forward)
+        model_sign = signature(traced.forward)
+        if len(model_sign.parameters) != len(submod0_sign.parameters):
+            # We don't change the signature of the first stage if it takes
+            # different number of args than original model
+            logger.info(
+                f"Original model takes {len(model_sign.parameters)} args but the "  # noqa: G004
+                f"first pipeline stage takes {len(submod0_sign.parameters)}. "
+                "Please provide args to respective pipeline stages."
+            )
+        else:
+            # Support kwargs for the first stage
+            submod0.graph._codegen = copy.deepcopy(traced.graph._codegen)  # type: ignore[union-attr]
+            # `_replace` is actually not "private" or internal. based on this doc:
+            # To prevent conflicts with field names, the method and attribute names
+            # start with an underscore
+            submod0.graph._codegen.pytree_info = (  # type: ignore[union-attr]
+                submod0.graph._codegen.pytree_info._replace(out_spec=None)  # type: ignore[operator, union-attr]
+            )
+            submod0.recompile()
+
+        return pipe
+
+    def __str__(self):
+        return self.split_gm.__str__()
+
+    def __repr__(self):
+        return self.split_gm.__repr__()
+
+    def info(self) -> PipeInfo:
+        """
+        Get information about the pipe.
+
+        Returns
+        -------
+        PipeInfo
+            A dataclass containing information about the pipe.
+        """
+        return PipeInfo(
+            graph=self.split_gm.graph,
+            num_stages=self.num_stages,
+            has_loss_and_backward=self.has_loss_and_backward,
+        )
+
+    def build_stage(
+        self,
+        stage_index: int,
+        device: torch.device,
+        group: ProcessGroup | None = None,
+    ) -> _PipelineStage:
+        """
+        Create a `PipelineStage` given a stage index and distributed group.
+        The `PipelineStage` can run with `PipelineSchedule`s.
+        """
+        # Find stage module
+        stage_module = self.get_stage_module(stage_index)
+
+        # Move ops argument to device
+        # Today PT2 tracer does not treat `x.device` as a symbolic device;
+        # instead, the device of tracing time got burned into the generated
+        # code.  Here we provide a workaround for users to manually modify the
+        # "device" kwarg of operations. Such operation may include:
+        # `torch.ones`, `torch.zeros`, `torch.rand`, etc.
+        if isinstance(stage_module, torch.fx.GraphModule):
+            _modify_graph_op_device(stage_module, device)
+        else:
+            logger.warning(
+                f"Expected a `torch.fx.GraphModule` but got {type(stage_module)}"  # noqa: G004
+            )
+
+        # Detach pipe info
+        # Note: be careful what's included in `pipe_info`. We don't want to keep
+        # a reference to `Pipe` or `Pipe.split_gm` which stops python from
+        # recycling them. When python recycles them, other stage modules (which
+        # are irrelevant to current rank) can be automatically freed.
+        pipe_info = self.info()
+        return _PipelineStage(stage_module, stage_index, pipe_info, device, group)
+
+
+class SplitPoint(Enum):
+    """
+    Enum representing the points at which a split can occur in the execution of a submodule.
+    Attributes:
+        BEGINNING: Represents adding a split point *before* the execution of a certain submodule in the `forward` function.
+        END: Represents adding a split point *after* the execution of a certain submodule in the `forward` function.
+    """
+
+    BEGINNING = 1
+    END = 2
+
+
+# For backward compatibility, we kept the PipeSplitWrapper class because `class
+# SplitPoint` used to be defined in this class.
+class PipeSplitWrapper:
+    # Create a class alias for BC
+    SplitPoint = SplitPoint
+
+
+def _split_before_forward(self, *args, **kwargs):
+    pipe_split()
+    return self._orig_forward(*args, **kwargs)
+
+
+def _split_after_forward(self, *args, **kwargs):
+    try:
+        return self._orig_forward(*args, **kwargs)
+    finally:
+        pipe_split()
+
+
+def annotate_split_points(mod: torch.nn.Module, spec: dict[str, SplitPoint]):
+    # TODO: make this implementation out-of-place?
+    for qualname, split_type in spec.items():
+        atoms = qualname.split(".")
+        predecessor_module = mod
+        for i, atom in enumerate(atoms[:-1]):
+            try:
+                predecessor_module = getattr(predecessor_module, atom)
+            except AttributeError as e:
+                raise AttributeError(
+                    f"Specified target {qualname} referenced "
+                    f"nonexistent module {'.'.join(atoms[: i + 1])}"
+                ) from e
+
+        mod_to_wrap = getattr(predecessor_module, atoms[-1])
+        mod_to_wrap._orig_forward = mod_to_wrap.forward
+        if split_type == SplitPoint.BEGINNING:
+            mod_to_wrap.forward = MethodType(_split_before_forward, mod_to_wrap)
+        elif split_type == SplitPoint.END:
+            mod_to_wrap.forward = MethodType(_split_after_forward, mod_to_wrap)
+        else:
+            raise ValueError("Unknown split point type.")
+
+
+def pipeline(
+    module: torch.nn.Module,
+    mb_args: tuple[Any, ...],
+    mb_kwargs: dict[str, Any] | None = None,
+    split_spec: dict[str, SplitPoint] | None = None,
+    split_policy: Callable[[fx.GraphModule], fx.GraphModule] | None = None,
+) -> Pipe:
+    """
+    Split a module based on a specification.
+
+    See `Pipe` for more details.
+
+    Arguments
+    ---------
+    module:
+        The module to be split.
+    mb_args:
+        Example positional inputs, in micro-batch form.
+    mb_kwargs:
+        Example keyword inputs, in micro-batch form. (default: `None`)
+    split_spec:
+        A dictionary using submodule names as split marker. (default: `None`)
+    split_policy:
+        The policy to use for splitting the module. (default: `None`)
+
+    Returns
+    -------
+    A pipeline representation of class `Pipe`.
+    """
+    if split_spec is not None and split_policy is not None:
+        raise ValueError(
+            "Cannot specify both `split_spec` and `split_policy`. Please use only one of them."
+        )
+
+    if split_spec is not None:
+        # Annotate split points in the module based on user spec
+        annotate_split_points(module, split_spec)
+        return Pipe.from_tracing(
+            mod=module,
+            example_args=mb_args,
+            example_kwargs=mb_kwargs,
+        )
+    else:
+        # Use split policy
+        return Pipe.from_tracing(
+            mod=module,
+            example_args=mb_args,
+            example_kwargs=mb_kwargs,
+            split_policy=split_policy,
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/pipelining/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/pipelining/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..aacaf0b7f5e4ae7f5d221906ebb5b1b6ff93dea9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/pipelining/__init__.py
@@ -0,0 +1,30 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+from ._IR import Pipe, pipe_split, pipeline, SplitPoint
+from .schedules import (
+    _ScheduleForwardOnly,
+    Schedule1F1B,
+    ScheduleDualPipeV,
+    ScheduleGPipe,
+    ScheduleInterleaved1F1B,
+    ScheduleInterleavedZeroBubble,
+    ScheduleLoopedBFS,
+    ScheduleZBVZeroBubble,
+)
+from .stage import build_stage, PipelineStage
+
+
+__all__ = [
+    "Pipe",
+    "pipe_split",
+    "SplitPoint",
+    "pipeline",
+    "PipelineStage",
+    "build_stage",
+    "Schedule1F1B",
+    "ScheduleGPipe",
+    "ScheduleInterleaved1F1B",
+    "ScheduleLoopedBFS",
+    "ScheduleInterleavedZeroBubble",
+    "ScheduleZBVZeroBubble",
+    "ScheduleDualPipeV",
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/pipelining/_backward.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/pipelining/_backward.py
new file mode 100644
index 0000000000000000000000000000000000000000..bfcf294c2946c5107c7c506b9846cd320155b27c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/pipelining/_backward.py
@@ -0,0 +1,418 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+import collections
+import logging
+from collections.abc import Iterator
+from typing import Any
+
+import torch
+from torch.autograd.graph import GradientEdge, Node
+from torch.nn import Parameter
+
+from ._debug import map_debug_info
+
+
+logger = logging.getLogger(__name__)
+
+
+def _get_grad_fn_or_grad_acc(t: torch.Tensor) -> Node | None:
+    """
+    Get the grad function or grad accumulator for a tensor.
+
+    Accumulate grad nodes are lazily created, so we need to a
+    dummy view in order to trigger its creation.
+    """
+    if t.requires_grad and t.grad_fn is None:
+        # if no grad function (leaf tensors) we use view
+        viewed_t = t.view_as(t)
+        grad_fn = viewed_t.grad_fn
+        if grad_fn is not None:
+            return grad_fn.next_functions[0][0]
+        else:
+            raise RuntimeError(
+                "Attempted to get grad_fn, but got None."
+                "Is this being created in a no-grad context?"
+            )
+    else:
+        return t.grad_fn
+
+
+def reverse_closure(
+    roots: list[Node], target_nodes: set[Node], reverse_edges_dict
+) -> tuple[set[Node], set[Node]]:
+    """
+    This function returns the reverse closure of the given roots,
+    i.e. the set of nodes that can be reached from the roots by following the
+    reverse edges of the graph. The target_nodes are the nodes that we want to
+    include in the closure.
+    """
+    # Recurse until we reach a target node
+    closure: set[Node] = set()
+    visited_target_nodes = set()
+    q: collections.deque[Node] = collections.deque()
+    for node in roots:
+        if node is not None and node not in closure:
+            closure.add(node)
+            q.append(node)
+    while q:
+        node = q.popleft()
+        reverse_edges = reverse_edges_dict[node]
+        for fn in reverse_edges:
+            if fn in closure or fn is None:
+                continue
+            if fn in target_nodes:
+                visited_target_nodes.add(fn)
+                continue
+            closure.add(fn)
+            q.append(fn)
+    return closure, visited_target_nodes
+
+
+def construct_reverse_graph(roots: list[Node]) -> dict[Node, list[Node]]:
+    q: collections.deque[Node] = collections.deque()
+    root_seen: set[Node] = set()
+    reverse_edges_dict: dict[Node, list[Node]] = collections.defaultdict(list)
+    for node in roots:
+        if node is not None and node not in root_seen:
+            q.append(node)
+            root_seen.add(node)
+    while q:
+        node = q.popleft()
+        for fn, _ in node.next_functions:
+            if fn is not None:
+                if len(reverse_edges_dict[fn]) == 0:
+                    q.append(fn)
+                reverse_edges_dict[fn].append(node)
+    return reverse_edges_dict
+
+
+def get_param_groups(
+    inputs: list[Node], params: list[Node], reverse_edges_dict
+) -> list[dict[str, Any]]:
+    """
+    Given a list of inputs and a list of parameters, return a list of parameter
+    groups, where each group contains the parameters and the intermediates that
+    are connected to the parameters.
+
+    The returned list of parameter groups is a list of dictionaries, where each
+    dictionary contains the following keys:
+    - "params": a set of parameters
+    - "intermediates": a set of intermediates
+
+    The returned list of parameter groups is a list of dictionaries,
+    """
+    # reverse graph that starts with inputs, and goes up to the dOutput or the loss,
+    # but omits weights and any subgraphs connecting weights to this closure
+    inputs_closure, _ = reverse_closure(inputs, set(), reverse_edges_dict)
+    param_groups: dict[Node, dict[str, set]] = dict()  # keyed on intermediates
+    for param in params:
+        closure, intersected = reverse_closure(
+            [param], inputs_closure, reverse_edges_dict
+        )
+        param_group: dict[str, set] = {
+            "params": {param},
+            "intermediates": intersected,
+        }
+        for input_node in intersected:
+            existing = param_groups.get(input_node)
+            if existing is not None:
+                existing["params"] = existing["params"].union(param_group["params"])
+                existing["intermediates"] = existing["intermediates"].union(
+                    param_group["intermediates"]
+                )
+                param_group = existing
+            else:
+                param_groups[input_node] = param_group
+
+    # Sanity check: union of all param_groups params should be equal to all params
+    union_params: set[Node] = set()
+    seen_ids: set[int] = set()
+    unique_param_groups = []
+    for param_group in param_groups.values():
+        if id(param_group) not in seen_ids:
+            seen_ids.add(id(param_group))
+            unique_param_groups.append(param_group)
+            union_params = union_params.union(param_group["params"])
+
+    # The assert will only be true if the input tensor requires gradients,
+    # otherwise the autograd graph will miss the first layer of inputs
+    # assert union_params == set(params)
+    return unique_param_groups
+
+
+def stage_backward_input(
+    stage_outputs_or_loss: list[torch.Tensor],
+    output_grads: list[torch.Tensor] | None,
+    input_values: list[torch.Tensor],
+    weights: Iterator[Parameter],
+) -> tuple[tuple[torch.Tensor | None, ...], list[dict[str, Any]]]:
+    """
+    Compute the gradients for only the stage inputs with
+    respect to the stage outputs (if non-last stage) or loss (if last stage)
+
+    After computing input gradients, we save the intermediate nodes in `param_groups`
+    for later use in stage_backward_weight. We don't need to save any other intermediate nodes
+    that aren't needed for dW because when we do dW calculation, we start from saved intermediates.
+    Detaching the stage_outputs_or_loss at the end of this function is important as
+    it frees up the memory that the autograd graph is anticipating to be used later (but doesn't actually need).
+    """
+    stage_output_grad_fns: list[Node] = list(
+        filter(None, map(_get_grad_fn_or_grad_acc, stage_outputs_or_loss))
+    )
+    stage_input_grad_fns: list[Node] = list(
+        filter(None, map(_get_grad_fn_or_grad_acc, input_values))
+    )
+    weight_grad_fns: list[Node] = list(
+        filter(None, map(_get_grad_fn_or_grad_acc, weights))
+    )
+
+    reverse_edges_dict = construct_reverse_graph(stage_output_grad_fns)
+    param_groups = get_param_groups(
+        stage_input_grad_fns, weight_grad_fns, reverse_edges_dict
+    )
+
+    handles = []
+    for param_group in param_groups:
+        for i, intermediate in enumerate(param_group["intermediates"]):
+
+            def get_hook(param_group, i):
+                def hook(grad_inputs):
+                    if param_group.get("grads", None) is None:
+                        param_group["grads"] = [None] * len(
+                            param_group["intermediates"]
+                        )
+                    param_group["grads"][i] = grad_inputs
+
+                return hook
+
+            # These are always "split" nodes that we need to recompute, so
+            # save their inputs.
+            handle = intermediate.register_prehook(get_hook(param_group, i))
+            handles.append(handle)
+
+    if output_grads is None:
+        # In case this is the loss and there are no output_grads, then we just use 1s
+        output_grads = [
+            torch.ones_like(stage_output) for stage_output in stage_outputs_or_loss
+        ]
+
+    # Some inputs may not be used or may not require gradients, so we filter them out
+    input_values = [inp for inp in input_values if inp.requires_grad]
+    dinputs = torch.autograd.grad(
+        stage_outputs_or_loss,
+        inputs=input_values,
+        grad_outputs=output_grads,
+        retain_graph=True,
+    )
+    # Update the gradients for inputs
+    for inp, dinput in zip(input_values, dinputs):
+        if inp.grad is None:
+            inp.grad = dinput
+        else:
+            inp.grad += dinput
+
+    # stage_outputs_or_loss are not used in backwards after this point, so we can safely remove it from the autograd graph
+    # this allows autograd to clear up the graph dedicated for this tensor and free up significant memory
+    for t in stage_outputs_or_loss:
+        t.detach_()
+
+    # hooks are no longer necessary, clean up for consistency
+    for handle in handles:
+        handle.remove()
+
+    return dinputs, param_groups
+
+
+def stage_backward_weight(
+    weights: Iterator[Parameter], param_groups: list[dict[str, Any]], retain_graph=False
+) -> tuple[torch.Tensor | None, ...]:
+    # map weights to param_group_weights
+    grad_acc_to_weight = {}
+    weight_grads: list[torch.Tensor | None] = []
+    for index, weight in enumerate(weights):
+        grad_acc = _get_grad_fn_or_grad_acc(weight)
+        grad_acc_to_weight[grad_acc] = weight, index
+        weight_grads.append(weight.grad)
+
+    for param_group in param_groups:
+        valid_edges = []
+        valid_grad_outputs: list[torch.Tensor] = []
+
+        for grads_tuple, intermediate in zip(
+            param_group["grads"], param_group["intermediates"]
+        ):
+            non_none_grads = [g for g in grads_tuple if g is not None]
+            if non_none_grads:
+                summed_grad = sum(non_none_grads)
+                valid_edges.append(GradientEdge(intermediate, 0))
+                # pyrefly: ignore [bad-argument-type]
+                valid_grad_outputs.append(summed_grad)
+
+        # Break a reference cycle caused inside stage_backward_input->get_hook->hook
+        # The summarized cycle is:
+        # `hook` -> cell -> param_group -> intermediates -> `hook`
+        # because we install the hook function onto each of the intermediate autograd nodes.
+        # We need to keep intermediates alive up until backward_weight, but we can free it now.
+        del param_group["intermediates"]
+
+        if valid_edges:  # Only call autograd.grad if we have valid gradients
+            # [NEW!] Able to pass a GradientEdge to autograd.grad as output
+            weights_edges = tuple(GradientEdge(w, 0) for w in param_group["params"])
+            dweights = torch.autograd.grad(
+                valid_edges,
+                weights_edges,
+                grad_outputs=valid_grad_outputs,
+                retain_graph=retain_graph,
+            )
+
+            # release grad memory early after use
+            del param_group["grads"]
+
+            for grad_acc, dw in zip(param_group["params"], dweights):
+                weight, index = grad_acc_to_weight[grad_acc]
+                if weight.grad is None:
+                    weight.grad = dw
+                else:
+                    weight.grad += dw
+    # return grads in the original order weights were provided in
+    return tuple(weight_grads)
+
+
+def stage_backward(
+    stage_output,
+    output_grads,
+    input_values,
+    outputs_with_grads_idxs: list[int] | None = None,  # deprecated, not used
+) -> tuple[torch.Tensor | None, ...]:
+    """
+    This is a helper function to:
+    1. compute the gradients for the stage inputs, and
+    2. accumulate gradients for the stage module's parameters.
+
+    Given the input value(s) and the corresponding gradient for the output
+    value(s), compute and accumulate gradients for all parameter values (leaves
+    in the autograd trace) as well as return a list of the gradients for the
+    input values
+    """
+    if outputs_with_grads_idxs is not None:
+        # Deprecated, not used in runtime calls, only exists in compiler
+        stage_output = [stage_output[i] for i in outputs_with_grads_idxs]
+        output_grads = [output_grads[i] for i in outputs_with_grads_idxs]
+
+    try:
+        # stage_output may be a composite datatype like dict. Extract all individual
+        # tensor values here
+        stage_output_tensors: list[torch.Tensor] = []
+        output_grad_tensors: list[torch.Tensor | None] = []
+
+        def extract_tensors_with_grads(
+            output_val,
+            grad_val,
+            # Don't delete me- see [Note: ref cycle]
+            extract_tensors_with_grads,
+        ):
+            if isinstance(output_val, torch.Tensor):
+                if not output_val.requires_grad and output_val.grad_fn is None:
+                    return
+                assert isinstance(grad_val, (torch.Tensor, type(None))), (
+                    f"Expected Tensor or None gradient but got {type(grad_val)}"
+                )
+                stage_output_tensors.append(output_val)
+                output_grad_tensors.append(grad_val)
+            elif isinstance(output_val, (tuple, list)):
+                if grad_val is None:
+                    return
+                assert isinstance(grad_val, (tuple, list)), (
+                    f"grad_value expected to have type {type(output_val)} but got {type(grad_val)}"
+                )
+                assert len(output_val) == len(grad_val)
+                for ov, gv in zip(output_val, grad_val):
+                    extract_tensors_with_grads(
+                        ov,
+                        gv,
+                        extract_tensors_with_grads,
+                    )
+            elif isinstance(output_val, dict):
+                if grad_val is None:
+                    return
+                assert isinstance(grad_val, dict)
+                assert set(output_val.keys()) == set(grad_val.keys())
+                for k in output_val:
+                    extract_tensors_with_grads(
+                        output_val[k], grad_val[k], extract_tensors_with_grads
+                    )
+            else:
+                # Output is a non-tensor type; just ignore it
+                pass
+
+        # Note: ref cycle
+        # break a ref cycle that would keep tensors alive until GC runs
+        # 1. extract_tensors_with_grads refers to a cell that holds refs to any vars defined in stage_backward
+        #    and used in extract_tensors_with_grads
+        # 2. extract_tensors_with_grads referred to both stage_output_tensors, output_grad_tensors,
+        #    and to itself (extract_tensors_with_grads) since it makes a recursive call
+        # 3. stage_output_tensors was kept alive by the above refcycle, and it holds activation tensors, which is bad
+        # fix -> explicitly pass in the ref to the fn, so there is no gc cycle anymore
+        extract_tensors_with_grads(
+            stage_output, output_grads, extract_tensors_with_grads
+        )
+
+        torch.autograd.backward(
+            stage_output_tensors,
+            grad_tensors=output_grad_tensors,  # type: ignore[arg-type]
+        )
+
+        # Extract gradients wrt the input values
+        grad_inputs: list[torch.Tensor | None] = []
+        for val in input_values:
+            if isinstance(val, torch.Tensor):
+                grad_inputs.append(val.grad)
+                # Since gradients that will pass back to previous stages do not require gradient accumulation,
+                # by decrementing the gradients' reference count at this point, the memory of gradients will be
+                # returned to the allocator as soon as the next micro batch's get_bwd_send_ops comes and current
+                # asynchronous send completes.
+                # This prevents the gradients from persisting in GPU memory for the entire duration of step_microbatches
+                # until clear_runtime_states() is called.
+                val.grad = None
+            else:
+                grad_inputs.append(None)
+
+        # Alternative impl: `torch.autograd.grad`.
+        # Note that `torch.autograd.grad` will not accumulate gradients into the
+        # model's parameters.
+        """
+        inputs_with_grad = []
+        for val in input_values:
+            if isinstance(val, torch.Tensor) and val.requires_grad:
+                inputs_with_grad.append(val)
+
+        grad_inputs = torch.autograd.grad(
+            stage_output_tensors, inputs_with_grad, output_grad_tensors,  # type: ignore[arg-type]
+        )
+        """
+
+    except Exception as e:
+        exc_msg = f"""
+        Failed to run stage backward:
+        Stage output: {map_debug_info(stage_output)}
+        Output gradient: {map_debug_info(output_grads)}
+        Input: {map_debug_info(input_values)}
+        """
+        raise RuntimeError(exc_msg) from e
+
+    return tuple(grad_inputs)
+
+
+# TODO: handling requires_grad=False dynamically. Can we analyze this during initial
+# IR emission?
+def _null_coalesce_accumulate(lhs, rhs):
+    """
+    Coalesce two values, even if one of them is null, returning the non-null
+    value.
+    """
+    if lhs is None:
+        return rhs
+    elif rhs is None:
+        return lhs
+    else:
+        return torch.add(lhs, rhs)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/pipelining/_debug.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/pipelining/_debug.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3201d2d3adf1d05921e070d14b4e544844df88f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/pipelining/_debug.py
@@ -0,0 +1,22 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+import torch
+from torch.fx.node import Argument
+
+
+def friendly_debug_info(v: object) -> Argument:
+    """
+    Helper function to print out debug info in a friendly way.
+    """
+    if isinstance(v, torch.Tensor):
+        return f"Tensor({v.shape}, grad={v.requires_grad}, dtype={v.dtype})"
+    else:
+        return str(v)
+
+
+def map_debug_info(a: Argument) -> Argument:
+    """
+    Helper function to apply `friendly_debug_info` to items in `a`.
+    `a` may be a list, tuple, or dict.
+    """
+    return torch.fx.node.map_aggregate(a, friendly_debug_info)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/pipelining/_schedule_visualizer.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/pipelining/_schedule_visualizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ecc5bf19ab17d83e0f3128290c0d5cc4b862b4d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/pipelining/_schedule_visualizer.py
@@ -0,0 +1,437 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+"""
+This visualizer requires matplotlib to be installed.
+
+Example usage:
+
+ops = get_schedule_ops("InterleavedZeroBubble", 4, 8)
+visualize_schedule(ops, "test.png")
+"""
+
+import collections
+from typing import NamedTuple
+from unittest import mock
+
+from torch.distributed.pipelining.schedules import (
+    _Action,
+    _ComputationType,
+    _PipelineSchedule,
+    _PipelineScheduleRuntime,
+    get_schedule_class,
+    PipelineScheduleMulti,
+    PipelineScheduleSingle,
+)
+from torch.distributed.pipelining.stage import PipelineStage
+
+
+class OpKey(NamedTuple):
+    stage_index: int
+    computation_type: _ComputationType
+    microbatch_index: int
+
+
+def get_schedule_ops(
+    schedule: str | type[_PipelineSchedule],
+    pp_degree: int,
+    num_microbatches: int,
+    num_stages_per_rank: int | None = None,
+    add_spacing: bool = False,
+    with_comms: bool = False,
+) -> list[list[_Action | None]]:
+    """
+    Get all actions for a given schedule, pp_degree, and num_microbatches. The actions are returned in a list of lists
+    where each inner list represents a rank and each element in the inner list represents an action.
+
+    The schedule can be specified as a string which is passed into get_schedule_class() or a _PipelineSchedule instance.
+    """
+    if add_spacing and with_comms:
+        raise ValueError("Cannot add spacing and view comms at the same time")
+
+    if isinstance(schedule, str):
+        schedule_class = get_schedule_class(schedule)
+    elif issubclass(schedule, _PipelineSchedule):
+        schedule_class = schedule
+    else:
+        raise ValueError(f"Invalid schedule: {schedule}")
+
+    # Create a mock of the PipelineStage class
+    mock_pipeline_stage = mock.create_autospec(PipelineStage, instance=True)
+    # Set the return values for group_rank and group_size methods
+    mock_pipeline_stage.group_rank = 0
+    mock_pipeline_stage.group_size = pp_degree
+    mock_pipeline_stage.submod = None
+
+    # Check num_stages_per_rank is valid
+    if issubclass(schedule_class, PipelineScheduleSingle):
+        if num_stages_per_rank is None:
+            num_stages_per_rank = 1
+        assert num_stages_per_rank == 1
+        stages = mock_pipeline_stage
+        stages.num_stages = num_stages_per_rank * pp_degree
+    elif issubclass(schedule_class, PipelineScheduleMulti):
+        if num_stages_per_rank is None:
+            num_stages_per_rank = 2
+        assert num_stages_per_rank >= 2
+        stages = [mock_pipeline_stage for _ in range(num_stages_per_rank)]
+        for stage in stages:
+            stage.num_stages = num_stages_per_rank * pp_degree
+
+    else:
+        raise ValueError(f"Invalid schedule: {schedule_class}")
+
+    # Instantiate the schedule class
+    # pyrefly: ignore [bad-instantiation, bad-argument-type]
+    schedule_instance = schedule_class(stages, num_microbatches)
+    assert schedule_instance.pipeline_order is not None
+
+    # Convert to List[List[_Action]]
+    all_actions: list[list[_Action | None]] = []
+    if with_comms:
+        runtime = _PipelineScheduleRuntime(stages, num_microbatches)
+        runtime._prepare_schedule_with_comms(schedule_instance.pipeline_order)
+        for rank in range(pp_degree):
+            all_actions.append(list(runtime.pipeline_order_with_comms[rank]))
+    else:
+        for rank in range(pp_degree):
+            all_actions.append(schedule_instance.pipeline_order[rank])
+
+    # Add spacing
+    if add_spacing:
+        # remove all Nones, then respace
+        # TODO: later we can change this at the schedule creation level to not use Nones
+        all_actions = [
+            [action for action in rank if action is not None] for rank in all_actions
+        ]
+        all_actions = add_schedule_op_spacing(all_actions)
+
+    # Return the pipeline order
+    return all_actions
+
+
+class _ComputationTypeVisual:
+    def __init__(
+        self,
+        color: str,
+        text: str = "",
+        width: int = 1,
+    ):
+        self.color = color
+        self.width = width
+        self.text = text
+
+
+# Update the mapping to use _ComputationTypeVisual instances
+action_type_to_color_mapping = {
+    _ComputationType.FORWARD: _ComputationTypeVisual("blue", "Forward"),
+    _ComputationType.BACKWARD_INPUT: _ComputationTypeVisual("teal", "Backward Input"),
+    _ComputationType.BACKWARD_WEIGHT: _ComputationTypeVisual(
+        "green", "Backward Weight"
+    ),
+    _ComputationType.FULL_BACKWARD: _ComputationTypeVisual(
+        "orange", "Full Backward", 2
+    ),
+    _ComputationType.OVERLAP_F_B: _ComputationTypeVisual("purple", "Overlap F+B", 3),
+}
+
+
+def add_schedule_op_spacing(
+    schedule: list[list[_Action | None]],
+) -> list[list[_Action | None]]:
+    """
+    Add spacing to the schedule based on dependencies between ranks.
+
+    Before adding an operation to the list, this function checks if there are
+    dependencies from other ranks. If there are dependencies (other ranks have
+    not finished processing the required microbatch), it adds None instead.
+
+    For example, Forward microbatch 0 on rank 1 depends on rank 0 processing
+    Forward microbatch 0 first.
+
+    Args:
+        schedule: The original schedule as a list of lists where each inner list
+                 represents a rank and each element represents an action.
+
+    Returns:
+        A new schedule with proper spacing based on dependencies.
+    """
+    if not schedule:
+        return schedule
+
+    num_stages = (
+        max(
+            action.stage_index
+            for rank_actions in schedule
+            for action in rank_actions
+            if action is not None
+        )
+        + 1
+    )
+
+    num_ranks = len(schedule)
+    spaced_schedule: list[list[_Action | None]] = [[] for _ in range(num_ranks)]
+    rank_ops = [collections.deque(ops) for ops in schedule]
+
+    # Track completion times: (stage_index, action_type, microbatch_index) -> completion_time
+    scheduled_ops: dict[OpKey, int] = {}
+
+    def is_dependency_ready(dependency_key: OpKey, timestep: int) -> bool:
+        """Check if a dependency operation has completed by the given timestep."""
+        return (
+            dependency_key in scheduled_ops
+            and timestep >= scheduled_ops[dependency_key]
+        )
+
+    def get_dependencies(action: _Action) -> list[OpKey]:
+        """Get the list of dependencies for an action."""
+        stage_idx = action.stage_index
+        comp_type = action.computation_type
+        mb_idx = action.microbatch_index
+
+        # Ensure mb_idx is not None for dependency tracking
+        assert mb_idx is not None, f"Action {action} has None microbatch_index"
+
+        # First stage forward has no dependencies
+        if stage_idx == 0 and comp_type == _ComputationType.FORWARD:
+            return []
+
+        # Last stage backward depends on forward from previous stage
+        if stage_idx == num_stages - 1 and comp_type in (
+            _ComputationType.FULL_BACKWARD,
+            _ComputationType.BACKWARD_INPUT,
+        ):
+            return [OpKey(stage_idx - 1, _ComputationType.FORWARD, mb_idx)]
+
+        # Forward depends on previous stage forward
+        if comp_type == _ComputationType.FORWARD:
+            return [OpKey(stage_idx - 1, _ComputationType.FORWARD, mb_idx)]
+
+        # Backward depends on next stage backward
+        if comp_type in (
+            _ComputationType.FULL_BACKWARD,
+            _ComputationType.BACKWARD_INPUT,
+        ):
+            return [
+                OpKey(stage_idx + 1, _ComputationType.FULL_BACKWARD, mb_idx),
+                OpKey(stage_idx + 1, _ComputationType.BACKWARD_INPUT, mb_idx),
+            ]
+
+        # Weight backward depends on input backward
+        if comp_type == _ComputationType.BACKWARD_WEIGHT:
+            return [OpKey(stage_idx, _ComputationType.BACKWARD_INPUT, mb_idx)]
+
+        raise RuntimeError(f"Unknown computation type: {comp_type}")
+
+    def is_action_ready(action: _Action, timestep: int) -> bool:
+        """Check if an action is ready to be scheduled at the given timestep."""
+        # For OR dependencies (like backward), check if any dependency is satisfied
+        if action.computation_type in (
+            _ComputationType.FULL_BACKWARD,
+            _ComputationType.BACKWARD_INPUT,
+            _ComputationType.BACKWARD_WEIGHT,
+        ):
+            dependencies = get_dependencies(action)
+            return any(is_dependency_ready(dep, timestep) for dep in dependencies)
+        # For AND dependencies, all must be satisfied
+        elif action.computation_type == _ComputationType.FORWARD:
+            dependencies = get_dependencies(action)
+            return all(is_dependency_ready(dep, timestep) for dep in dependencies)
+        elif action.computation_type == _ComputationType.OVERLAP_F_B:
+            assert action.sub_actions is not None, (
+                f"OVERLAP_F_B action {action} has None sub_actions"
+            )
+            dep_list: list[bool] = []
+            for sub_action in action.sub_actions:
+                dep_list.append(is_action_ready(sub_action, timestep))
+            return all(dep_list)
+        else:
+            raise RuntimeError(f"Unknown computation type: {action.computation_type}")
+
+    def schedule_action(action: _Action, rank: int, timestep: int) -> int:
+        """Schedule an action and return completion time."""
+        spaced_schedule[rank].append(action)
+        comp_type = action.computation_type
+        comp_time = action_type_to_color_mapping[comp_type].width
+        completion_time = timestep + comp_time
+
+        if comp_type == _ComputationType.OVERLAP_F_B:
+            # For overlap actions, schedule each sub-action with cumulative timing
+            assert action.sub_actions is not None, (
+                f"OVERLAP_F_B action {action} has None sub_actions"
+            )
+            cumulative_time = 0
+            for sub_action in action.sub_actions:
+                assert sub_action.microbatch_index is not None, (
+                    f"Sub-action {sub_action} has None microbatch_index"
+                )
+                sub_comp_time = action_type_to_color_mapping[
+                    sub_action.computation_type
+                ].width
+                cumulative_time += sub_comp_time
+                scheduled_ops[
+                    OpKey(
+                        sub_action.stage_index,
+                        sub_action.computation_type,
+                        sub_action.microbatch_index,
+                    )
+                ] = timestep + cumulative_time
+        else:
+            assert action.microbatch_index is not None, (
+                f"Action {action} has None microbatch_index"
+            )
+            scheduled_ops[
+                OpKey(action.stage_index, comp_type, action.microbatch_index)
+            ] = completion_time
+
+        return completion_time
+
+    # Main scheduling loop
+    current_timestep = 0
+    timesteps_without_progress = 0
+    rank_completion_times = dict.fromkeys(range(num_ranks), 0)
+    while rank_ops:
+        print(f"Current timestep: {current_timestep}")
+        # Process all operations during timestep until we run out of ready operations
+        for rank, op_queue in enumerate(rank_ops):
+            if not op_queue:
+                continue
+
+            op_queue = rank_ops[rank]
+            action = op_queue[0]
+            print(f"Rank: {rank}, {action=}")
+            if action is None:
+                spaced_schedule[rank].append(None)
+                op_queue.popleft()
+                timesteps_without_progress = 0
+            elif current_timestep >= rank_completion_times[rank] and is_action_ready(
+                action, current_timestep
+            ):
+                rank_completion_times[rank] = schedule_action(
+                    action, rank, current_timestep
+                )
+                op_queue.popleft()
+                timesteps_without_progress = 0
+
+        # Add None for ranks that are waiting
+        for rank in range(num_ranks):
+            if current_timestep >= rank_completion_times[rank]:
+                spaced_schedule[rank].append(None)
+
+        # Remove empty queues and advance timestep
+        rank_ops = [op_queue for op_queue in rank_ops if op_queue]
+        current_timestep += 1
+        timesteps_without_progress += 1
+
+        if timesteps_without_progress > max(
+            visual.width for visual in action_type_to_color_mapping.values()
+        ):
+            raise RuntimeError("No progress made in scheduling - possible deadlock")
+
+    return spaced_schedule
+
+
+def visualize_schedule(
+    schedule: list[list[_Action | None]],
+    filename: str | None = None,
+) -> None:
+    """
+    Visualize the schedule using matplotlib.
+    The schedule is a list of lists where each inner list represents a rank and each element in the inner list represents an action.
+    The actions are represented as rectangles with different colors based on their computation type.
+    The filename is optional and if provided, the plot will be saved to that file.
+
+    Args:
+        schedule: The schedule to visualize.
+        filename: The filename to save the plot to. If not provided, the plot will be displayed.
+        add_schedule_spacing: If True, add spacing to the schedule based on dependencies between ranks.
+
+    """
+
+    import matplotlib.pyplot as plt
+    from matplotlib.patches import Rectangle
+
+    plt.rcParams["font.family"] = (
+        "DejaVu Sans"  # or any other font available on your system
+    )
+    num_ranks = len(schedule)
+    max_actions = max(len(rank) for rank in schedule)
+
+    # Increase the figure size to provide more space for the legend
+    fig, ax = plt.subplots(figsize=(max_actions + 2, num_ranks + 2))
+    max_draw_position = -1
+    # Calculate dynamic font size based on figure size
+    font_size = min(max_actions, num_ranks) + 4
+    used_computation = set()
+    for rank_idx, actions in enumerate(schedule):
+        draw_position = 0  # Initialize drawing position for each rank
+        for action in actions:
+            if action is not None:
+                comp_type_color = action_type_to_color_mapping.get(
+                    action.computation_type, _ComputationTypeVisual("black")
+                )
+                used_computation.add(action.computation_type)
+                color = comp_type_color.color
+                width = comp_type_color.width
+
+                # Check if action has sub_actions to determine styling
+                if action.sub_actions is not None:
+                    linewidth = 2  # Thicker border for compound actions
+                    text_weight = "normal"  # Bold text for compound actions
+                else:
+                    linewidth = 1  # Default linewidth for regular actions
+                    text_weight = "normal"  # Default text weight
+
+                # Draw the rectangle to represent the action duration
+                rect = Rectangle(
+                    (draw_position, num_ranks - rank_idx - 1),
+                    width,
+                    1,
+                    facecolor=color,
+                    edgecolor="black",
+                    linewidth=linewidth,
+                )
+                ax.add_patch(rect)
+
+                # Draw the text centered within the rectangle
+                ax.text(
+                    draw_position + width / 2,
+                    num_ranks - rank_idx - 1 + 0.5,
+                    str(action),
+                    ha="center",
+                    va="center",
+                    fontsize=font_size,
+                    color="white",
+                    weight=text_weight,
+                )
+
+                draw_position += width
+            else:
+                draw_position += 1  # Move to the next
+            max_draw_position = max(max_draw_position, draw_position)
+    ax.set_xlim(-0.5, max_draw_position + 1)
+    ax.set_ylim(-0.5, num_ranks + 0.5)  # Add extra space at the top
+    # Set y-ticks to be in the middle of each rank's row
+    ax.set_yticks([num_ranks - rank_idx - 0.5 for rank_idx in range(num_ranks)])
+    ax.set_yticklabels([f"Rank {i}" for i in range(num_ranks)], fontsize=font_size)
+    ax.set_xticklabels([])
+
+    # Remove grid lines and ticks
+    ax.grid(False)
+    # Add legend with larger font size
+    legend_elements = [
+        Rectangle(
+            (0, 0),
+            1,
+            1,
+            facecolor=action_type_to_color_mapping[comp_type].color,
+            edgecolor="black",
+            label=action_type_to_color_mapping[comp_type].text,
+        )
+        for comp_type in used_computation
+    ]
+    ax.legend(handles=legend_elements, loc="upper right", fontsize=font_size)
+    # Save to file if filename is provided, otherwise display the plot
+    if filename:
+        plt.savefig(filename, bbox_inches="tight")
+    else:
+        plt.show()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/pipelining/_unflatten.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/pipelining/_unflatten.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ed592f2f8d832de0703fbfa296225f17698afbf
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/pipelining/_unflatten.py
@@ -0,0 +1,30 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+from collections import defaultdict
+
+import torch
+from torch.export.unflatten import _ModuleFrame, _SubmoduleEntry
+
+
+def _outline_submodules(orig_graph: torch.fx.Graph) -> torch.fx.GraphModule:
+    # Create an empty GraphModule to hold the outlined modules
+    new_module = torch.fx.GraphModule(torch.nn.Module(), torch.fx.Graph())
+    seen_nodes: dict[str, torch.fx.Node] = {}
+    seen_modules: dict[int, list[_SubmoduleEntry]] = defaultdict(list)
+    seen_attrs: dict[str, set[str]] = defaultdict(set)
+    created_modules: dict[str, torch.nn.Module] = {}
+    _ModuleFrame(
+        orig_graph,
+        tuple(orig_graph.nodes),
+        seen_nodes,
+        seen_modules,
+        seen_attrs,
+        created_modules,
+        None,
+        [("", None, 0)],
+        "",
+        {},
+        module=new_module,
+    ).run_outer()
+    new_module.graph.lint()
+    new_module.recompile()
+    return new_module
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/pipelining/_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/pipelining/_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..79b74be40681425ab4a5c97198bf0a2020d1d10e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/pipelining/_utils.py
@@ -0,0 +1,159 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+import logging
+from dataclasses import dataclass
+
+import torch
+from torch import fx
+
+
+logger = logging.getLogger(__name__)
+
+
+def flatten_args_detach(args):
+    """
+    Flatten the args into a list form and detach the tensors from computational graph.
+    """
+    flat_detached_args = []
+
+    def extract_tensor_args(a):
+        nonlocal flat_detached_args
+        if isinstance(a, torch.Tensor):
+            val = a.detach().requires_grad_(a.requires_grad)
+            flat_detached_args.append(val)
+            return val
+        else:
+            flat_detached_args.append(a)
+            return a
+
+    new_args = fx.node.map_aggregate(
+        args,
+        extract_tensor_args,
+    )
+
+    return new_args, flat_detached_args
+
+
+def flatten_args(args):
+    """
+    Flatten the args into a list form.
+    """
+    flat_args = []
+
+    def extract_tensor_args(a):
+        nonlocal flat_args
+        flat_args.append(a)
+        return a
+
+    fx.node.map_aggregate(
+        args,
+        extract_tensor_args,
+    )
+
+    return flat_args
+
+
+class PipeliningShapeError(RuntimeError):
+    """Shape mismatch between configured and runtime values."""
+
+
+def validate_tensor_metadata(desc, expected, given):
+    if not expected.shape == given.shape:
+        raise PipeliningShapeError(
+            f"{desc} has a shape mismatch: expected {expected.shape} actual {given.shape}"
+        )
+    if not expected.dtype == given.dtype:
+        raise PipeliningShapeError(
+            f"{desc} has a dtype mismatch: expected {expected.dtype} actual {given.dtype}"
+        )
+    if not expected.stride() == given.stride():
+        raise PipeliningShapeError(
+            f"{desc} has a stride mismatch: expected {expected.stride()} actual {given.stride()}"
+        )
+
+
+def validate_tensors_metadata(
+    desc,
+    expected_tensors: list[torch.Tensor] | tuple[torch.Tensor, ...],
+    actual_tensors: list[torch.Tensor] | tuple[torch.Tensor, ...],
+):
+    if len(expected_tensors) != len(actual_tensors):
+        raise PipeliningShapeError(
+            f"{desc}: Number of values ({len(actual_tensors)}) does not match expected number ({len(expected_tensors)})"
+        )
+    for i in range(len(expected_tensors)):
+        validate_tensor_metadata(
+            f"{desc}: value {i}", expected_tensors[i], actual_tensors[i]
+        )
+
+
+def generate_stage_to_rank_mapping(
+    pp_size: int, num_stages: int, style: str = "loop"
+) -> dict[int, int]:
+    """
+    Compute the stage id to rank mapping for either a looped or V-style schedule.
+
+    Most commonly num_stages == pp_size * 2, but this function can be used to
+    compute the mapping for any number of stages per rank.
+    """
+    mapping = {}
+    if style == "loop":
+        for stage_index in range(num_stages):
+            mapping[stage_index] = stage_index % pp_size
+    elif style == "v":
+        if num_stages % pp_size != 0:
+            raise ValueError(
+                f"num_stages {num_stages} must be evenly divisible by pp_size {pp_size} for V schedules"
+            )
+
+        rank_index = 0
+        for stage_index in range(num_stages):
+            mapping[stage_index] = rank_index
+            # dont change rank if we are on the border (to keep v shape)
+            if (stage_index + 1) % pp_size == 0:
+                continue
+            if (stage_index // pp_size) % 2 == 0:
+                rank_index += 1
+            else:
+                rank_index -= 1
+    else:
+        raise ValueError(f"Style {style} is not supported.")
+    return mapping
+
+
+def generate_rank_to_stage_mapping(
+    pp_size: int, num_stages: int, style: str = "loop"
+) -> dict[int, list[int]]:
+    """
+    Compute the rank to stage id mapping for either a looped or V-style schedule.
+
+    This function inverts the stage_to_rank_mapping to get which stages are assigned to each rank.
+
+    Returns a dictionary mapping rank -> list of stage indices assigned to that rank.
+    """
+    stage_to_rank = generate_stage_to_rank_mapping(pp_size, num_stages, style)
+
+    # Invert the mapping: rank -> list of stages
+    rank_to_stages: dict[int, list[int]] = {}
+    for stage_id, rank in stage_to_rank.items():
+        if rank not in rank_to_stages:
+            rank_to_stages[rank] = []
+        rank_to_stages[rank].append(stage_id)
+
+    # Sort the stage lists for each rank to ensure consistent ordering
+    for stages in rank_to_stages.values():
+        stages.sort()
+
+    return rank_to_stages
+
+
+@dataclass
+class PipeInfo:
+    """
+    Captures information for a pipeline (`Pipe` object).
+    """
+
+    graph: fx.Graph
+    num_stages: int
+    has_loss_and_backward: bool
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/pipelining/microbatch.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/pipelining/microbatch.py
new file mode 100644
index 0000000000000000000000000000000000000000..a82f83072fa1897c1738e8bf879911921720cfe5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/pipelining/microbatch.py
@@ -0,0 +1,544 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+import logging
+import operator
+from collections.abc import Sequence
+from typing import Any
+
+import torch
+from torch.fx.node import map_aggregate
+from torch.nn.attention.flex_attention import BlockMask
+from torch.utils._pytree import tree_flatten, tree_map, tree_unflatten
+
+
+__all__ = [
+    "TensorChunkSpec",
+    "split_args_kwargs_into_chunks",
+    "merge_chunks",
+]
+
+logger = logging.getLogger(__name__)
+
+"""
+_debug_mask_minibatches specifies to send masked versions of the mini-batch
+through instead of micro-batch slices--this can be used for more stable
+numerical testing (see [A Note About Correctness Testing])
+"""
+_debug_mask_minibatches = False
+
+
+class _CustomReducer:
+    """
+    Custom reducer class that can be used to specify a custom operation that
+    reduces losses of multiple microbatches into one value.
+
+    Example:
+    >>> # xdoctest: +SKIP
+    >>> sum_reducer = _CustomReducer(
+    >>>     torch.tensor(0.0),
+    >>>     lambda a, b: a + b
+    >>> )
+    """
+
+    def __init__(self, init_value, reduce_fn):
+        self.init_value = init_value
+        self.reduce_fn = reduce_fn
+
+
+class _LossReducer(_CustomReducer):
+    pass
+
+
+sum_reducer = _LossReducer(torch.tensor(0.0), operator.add)
+
+# Default chunking dimension is 0. This is used for the case where the user did
+# not specify a chunking dimension.
+DEFAULT_CHUNK_DIM = 0
+
+
+class TensorChunkSpec:
+    """
+    Class used to specify chunking of inputs
+    """
+
+    def __init__(self, split_dim):
+        self.split_dim = split_dim
+
+    split_dim: int
+
+    def __repr__(self):
+        return (
+            f"{self.__class__.__module__}.{self.__class__.__name__}({self.split_dim})"
+        )
+
+    def __str__(self):
+        return f"TensorChunkSpec({self.split_dim})"
+
+    @staticmethod
+    def from_tuple(
+        chunk_dims: tuple[int, ...],
+    ):
+        """
+        A helper for creating a tuple of `TensorChunkSpec` from a tuple of chunk
+        dimensions (int's).
+        Example:
+            >>> # xdoctest: +SKIP
+            >>> # There are three positional arguments to the model, and
+            >>> # we are chunking them along dimension 0, 0 and 1, respectively
+            >>> args_chunk_spec = TensorChunkSpec.from_tuple((0, 0, 1))
+        """
+        args_chunk_spec = map_aggregate(
+            chunk_dims,
+            lambda dim: TensorChunkSpec(dim),  # type: ignore[arg-type,return-value]
+        )
+        return args_chunk_spec
+
+    @staticmethod
+    def from_dict(
+        chunk_dims: dict[str, int],
+    ):
+        """
+        A helper for creating a dictionary of `TensorChunkSpec` from a
+        dictionary of chunk dimensions (int's).
+        Example:
+            >>> # xdoctest: +SKIP
+            >>> # Chunk dimension 0 for the "id" argument, 1 for the "mask" argument
+            >>> kwargs_chunk_spec = TensorChunkSpec.from_dict({"id": 0, "mask": 1})
+        """
+        kwargs_chunk_spec = map_aggregate(
+            chunk_dims,
+            lambda dim: TensorChunkSpec(dim),  # type: ignore[arg-type,return-value]
+        )
+        return kwargs_chunk_spec
+
+
+# Class used to specify replication of inputs
+class _Replicate:
+    pass
+
+
+def _split_block_mask(
+    block_mask: BlockMask,
+    num_chunks: int,
+) -> list[BlockMask]:
+    """Given a block mask, split the block mask along the batch dimension (dim0).
+
+    Args:
+        block_mask: Block mask to split
+        num_chunks: Number of chunks to split the block mask into
+
+    Returns:
+        chunk_block_masks: List of chunked block masks
+    """
+
+    # BlockMask will broadcast if B is 1.
+    if block_mask.kv_num_blocks.size(0) == 1:
+        return [block_mask] * num_chunks
+
+    assert block_mask.kv_num_blocks.size(0) >= num_chunks, (
+        "Block mask has fewer batch size than the number of chunks. "
+    )
+
+    batch_dim = 0
+    kv_num_blocks_chunks = torch.tensor_split(
+        block_mask.kv_num_blocks, num_chunks, batch_dim
+    )
+    kv_indices_chunks = torch.tensor_split(block_mask.kv_indices, num_chunks, batch_dim)
+    full_kv_num_blocks_chunks = (
+        torch.tensor_split(block_mask.full_kv_num_blocks, num_chunks, batch_dim)
+        if block_mask.full_kv_num_blocks is not None
+        else [None] * num_chunks
+    )
+    full_kv_indices_chunks = (
+        torch.tensor_split(block_mask.full_kv_indices, num_chunks, batch_dim)
+        if block_mask.full_kv_indices is not None
+        else [None] * num_chunks
+    )
+
+    chunk_block_masks = []
+    batch_offset = 0
+    for chunk_idx in range(num_chunks):
+
+        def create_mask_mod(idx):
+            def batch_offset_mask_mod(b, h, q_idx, kv_idx):
+                b_offset = torch.full_like(b, idx)
+                return block_mask.mask_mod(b + b_offset, h, q_idx, kv_idx)
+
+            return batch_offset_mask_mod
+
+        chunk_block_masks.append(
+            BlockMask.from_kv_blocks(
+                kv_num_blocks=kv_num_blocks_chunks[chunk_idx],
+                kv_indices=kv_indices_chunks[chunk_idx],
+                full_kv_num_blocks=full_kv_num_blocks_chunks[chunk_idx],
+                full_kv_indices=full_kv_indices_chunks[chunk_idx],
+                BLOCK_SIZE=block_mask.BLOCK_SIZE,
+                mask_mod=create_mask_mod(batch_offset),
+                seq_lengths=block_mask.seq_lengths,
+            )
+        )
+        batch_offset += kv_num_blocks_chunks[chunk_idx].size(0)
+    return chunk_block_masks
+
+
+def _split_tensor(
+    tensor: torch.Tensor,
+    spec: TensorChunkSpec,
+    num_chunks: int,
+) -> Sequence[torch.Tensor]:
+    """Given a tensor, and a chunking spec, split the tensor.
+    Args:
+
+        tensor: Tensor to split
+        spec: Chunking spec
+        num_chunks: Number of chunks to split the tensor into
+
+    Returns:
+        chunk_tensors: List of chunked tensors
+    """
+
+    assert tensor.size(spec.split_dim) >= num_chunks, (
+        f"Tensor size {tensor.size(spec.split_dim)} is smaller than num_chunks"
+    )
+    chunk_tensors = torch.tensor_split(tensor, num_chunks, spec.split_dim)
+
+    if not _debug_mask_minibatches:
+        return chunk_tensors
+
+    expanded_chunks = []
+    split_dim_idx = 0
+    for chunk_tensor in chunk_tensors:
+        new_val = torch.zeros_like(tensor)
+        upper_idx = split_dim_idx + chunk_tensor.size(spec.split_dim)
+
+        slice_indices = [slice(None, None, None)] * new_val.ndim
+        slice_indices[spec.split_dim] = slice(split_dim_idx, upper_idx)
+        new_val[slice_indices] = chunk_tensor
+
+        expanded_chunks.append(new_val)
+
+        split_dim_idx += chunk_tensor.size(spec.split_dim)
+
+    return expanded_chunks
+
+
+def _shard_dict_of_args(
+    args_dict,
+    args_chunk_spec,
+    num_chunks,
+):
+    """
+    Given a dictionary of args, and a dictionary of chunking specs, shard the
+    args according to the chunking specs.
+
+    Args:
+        args_dict: Dictionary of args
+        args_chunk_spec: Dictionary of chunking specs
+        num_chunks: Number of chunks to shard the args into
+
+    Returns:
+        args_split: List of sharded args
+    """
+
+    if not args_dict:
+        return [{} for _ in range(num_chunks)]
+
+    assert len(args_dict) == len(args_chunk_spec), (
+        f"args_dict.keys() = {list(args_dict.keys())} "
+        f"args_chunk_spec.keys() = {list(args_chunk_spec.keys())}"
+    )
+    assert args_chunk_spec is not None  # Should have been set by caller
+
+    values, tree_spec = tree_flatten(
+        args_dict, is_leaf=lambda x: isinstance(x, BlockMask)
+    )
+    chunk_specs, _ = tree_flatten(
+        args_chunk_spec, is_leaf=lambda x: isinstance(x, BlockMask)
+    )
+
+    # First check and find the actual number of chunks
+    split_sizes = []
+    for v, spec in zip(values, chunk_specs, strict=True):
+        # The original logic is "spec is _Replicate". This doesn't seem to be
+        # correct. But we keep it for backward compatibility.
+        if spec is _Replicate or isinstance(spec, _Replicate):
+            split_sizes.append(num_chunks)
+        elif isinstance(v, torch.Tensor):
+            assert isinstance(spec, TensorChunkSpec)
+            split_sizes.append(v.size(spec.split_dim))
+        elif isinstance(v, BlockMask):
+            assert isinstance(spec, TensorChunkSpec)
+            assert spec.split_dim == 0, "BlockMask only supports split_dim=0"
+            # BlockMask will broadcast if B is 1.
+            if v.kv_num_blocks.size(0) == 1:
+                split_sizes.append(num_chunks)
+            else:
+                split_sizes.append(v.kv_num_blocks.size(0))
+        else:
+            raise ValueError(
+                f"Unsupported chunk spec: {spec} and value: {v} combination."
+            )
+    result_num_chunks = min(*split_sizes, num_chunks)
+
+    flat_split_results: list[Any] = [[] for _ in range(result_num_chunks)]
+    for v, spec in zip(values, chunk_specs, strict=True):
+        v_splits: Sequence[Any] = []
+        if spec is _Replicate or isinstance(spec, _Replicate):
+            v_splits = [v] * result_num_chunks
+        elif isinstance(v, torch.Tensor):
+            v_splits = _split_tensor(v, spec, result_num_chunks)
+        elif isinstance(v, BlockMask):
+            v_splits = _split_block_mask(v, result_num_chunks)
+        else:
+            raise ValueError(
+                f"Unsupported chunk spec: {spec} and value: {v} combination."
+            )
+
+        for _flat_split_result, _v_split in zip(
+            flat_split_results, v_splits, strict=True
+        ):
+            _flat_split_result.append(_v_split)
+
+    return [
+        tree_unflatten(_flat_split_result, tree_spec)
+        for _flat_split_result in flat_split_results
+    ]
+
+
+def split_args_kwargs_into_chunks(
+    args: tuple[Any, ...],
+    kwargs: dict[str, Any] | None,
+    chunks: int,
+    args_chunk_spec: tuple[TensorChunkSpec, ...] | None = None,
+    kwargs_chunk_spec: dict[str, TensorChunkSpec] | None = None,
+) -> tuple[list[tuple], list[dict]]:
+    """
+    Given a sequence of args and kwargs, split them into a number of chunks
+    according to  their respective chunking specs.
+
+    Args:
+        args: Tuple of args
+        kwargs: Dict of kwargs
+        chunks: Number of chunks to split the args and kwargs into
+        args_chunk_spec: chunking specs for args, in same shape as args
+        kwargs_chunk_spec: chunking specs for kwargs, in same shape as kwargs
+
+    Returns:
+        args_split: List of sharded args
+        kwargs_split: List of sharded kwargs
+    """
+    # Given `args` and `kwargs`, we want to yield a set of `chunks` args and kwargs such that
+    # the constituent Tensor values have been sharded/replicated according to the `args_chunk_spec`
+    # and `kwargs_chunk_spec` specifications. The steps are as follows:
+    #
+    # 1. Use pytree.tree_flatten to flatten each arg and its spec into nto a 1d array of values.
+    #    To use a running example: suppose our inputs look like
+    #
+    #       args = ([A, [B, C]], D) args_spec = ([None, [None, TensorChunkSpec]], None)
+    #       (kwargs not shown but it's a similar process)
+    #
+    #    Then for this step we would end up with
+    #
+    #       args = ([A, B, C], D) args_spec = ([None, None, TensorChunkSpec], None)
+    #
+    # 2. Shard or replicate the arguments subject to the policy in the spec. Suppose chunks = 2
+    #
+    #       args = ([[A, A], [B, B], [C_1, C_2]], [D, D])
+    #
+    # 3. Rotate the nesting order such that chunks are the outer dimension
+    #
+    #       args_chunks = [
+    #           ([A, B, C_1], D),
+    #           ([A, B, C_2], D),
+    #       ]
+    #
+    # 4. Unflatten each chunk according to the spec
+    #
+    #       args_chunks = [
+    #           ([A, [B, C_1]], D),
+    #           ([A, [B, C_2]], D),
+    #       ]
+
+    # TODO: _debug_mask_minibatches
+    # Handle the case where kwargs is None
+    if kwargs is None:
+        kwargs = {}
+
+    # If user did not provide args_chunk_spec or kwargs_chunk_spec, we extend
+    # their format and use default chunking along dim 0
+    def default_spec(v):
+        if isinstance(v, torch.Tensor | BlockMask):
+            return TensorChunkSpec(DEFAULT_CHUNK_DIM)
+        else:
+            return _Replicate()
+
+    if args_chunk_spec is None:
+        args_chunk_spec = tree_map(
+            default_spec, args, is_leaf=lambda v: isinstance(v, BlockMask)
+        )
+
+    if kwargs_chunk_spec is None:
+        kwargs_chunk_spec = tree_map(
+            default_spec, kwargs, is_leaf=lambda v: isinstance(v, BlockMask)
+        )
+
+    args_split_dict = _shard_dict_of_args(
+        dict(enumerate(args)),
+        dict(enumerate(args_chunk_spec)),
+        chunks,
+    )
+    real_num_chunks = len(args_split_dict)
+
+    kwargs_split = _shard_dict_of_args(
+        kwargs,
+        kwargs_chunk_spec,
+        real_num_chunks,
+    )
+
+    if len(kwargs_split) < real_num_chunks:
+        # In case kwargs are sharded into less chunks
+        # e.g. when `args` has no tensor, just values
+        real_num_chunks = len(kwargs_split)
+        # Re-shard args
+        args_split_dict = _shard_dict_of_args(
+            dict(enumerate(args)),
+            dict(enumerate(args_chunk_spec)),
+            real_num_chunks,
+        )
+
+    if len(args_split_dict) != len(kwargs_split):
+        raise RuntimeError(
+            "args and kwargs are split into different number of chunks: "
+            f"{len(args_split_dict)}, {len(kwargs_split)}"
+        )
+
+    args_split = [
+        tuple(chunk_args[i] for i in range(len(chunk_args)))
+        for chunk_args in args_split_dict
+    ]
+
+    return args_split, kwargs_split
+
+
+def merge_chunks(
+    chunks: list[Any],
+    chunk_spec,
+):
+    """
+    Given a list of chunks, merge them into a single value according to
+    the chunk spec.
+
+    Args:
+        chunks: list of chunks
+        chunk_spec: Chunking spec for the chunks
+
+    Returns:
+        value: Merged value
+    """
+    # This is essentially the inverse of `split_args_kwargs_into_chunks`, so the
+    # steps are similar to the steps in that function but in reverse. Given the
+    # input values:
+    #
+    #       chunks = [
+    #           ([A, [B, C_1]], D),
+    #           ([A, [B, C_2]], D),
+    #       ]
+    #       args_spec = ([None, [None, TensorChunkSpec]], None)
+    #
+    # 1. Flatten the chunks according to the chunk_spec
+    #
+    #       chunks_flat = [
+    #           ([A, B, C_1], D),
+    #           ([A, B, C_2], D),
+    #       ]
+    #
+    # 2. Rotate the nesting order such that chunks are the inner dimension
+    #
+    #       value_inner = ([A, B, [C_1, C_2]], D)
+    #
+    # 3. Concatenate sharded arguments
+    #
+    #       value_combined = ([A, B, C], D)
+    #
+    # 4. Unflatten the combined args given the spec
+    #
+    #       value = ([A, [B, C]], D)
+
+    # Preliminary: flatten the chunk spec
+    if chunk_spec is not None:
+        spec_flattened, flatten_spec = tree_flatten(chunk_spec)
+    else:
+        # If chunk_spec is not provided, we will merge chunks along the default dimension (0), for all output fields
+        # We obtain the output structure by flattening chunk 0 and generate the chunk_spec
+        chunk0_flat, flatten_spec = tree_flatten(chunks[0])
+        spec_flattened = [TensorChunkSpec(DEFAULT_CHUNK_DIM)] * len(chunk0_flat)
+
+    # Stage 1: flatten chunks
+    # chunks_flattened : [num chunks, num args]
+    chunks_flattened = []
+
+    for chunk in chunks:
+        chunk_flattened, _ = tree_flatten(chunk)
+        if len(chunk_flattened) != len(spec_flattened):
+            raise ValueError(f"Chunk {chunk} did not match chunk spec {chunk_spec}")
+
+        chunks_flattened.append(chunk_flattened)
+
+    # Stage 2 and 3: Rotate nesting order s.t. chunks are inner dimension and
+    #                concatenate sharded operands
+    # args_flattened : [num args]
+    args_flattened = []
+    for arg_idx, arg in enumerate(spec_flattened):
+        if isinstance(arg, TensorChunkSpec):
+            partial_values = [
+                chunks_flattened[chunk_idx][arg_idx]
+                for chunk_idx in range(len(chunks_flattened))
+            ]
+
+            if _debug_mask_minibatches:
+                # Infer size of individual chunks by running `tensor_split` again
+                overall_shape = partial_values[0].shape
+                for val in partial_values[1:]:
+                    assert val.shape == overall_shape
+                meta_chunks = torch.tensor_split(
+                    torch.empty(*overall_shape, device="meta"),
+                    sections=len(partial_values),
+                    dim=arg.split_dim,
+                )
+
+                values_to_cat = []
+                chunk_start_idx = 0
+                assert len(partial_values) == len(meta_chunks)
+                for partial_value, meta_chunk in zip(
+                    partial_values, meta_chunks, strict=True
+                ):
+                    chunk_end_idx = chunk_start_idx + meta_chunk.size(arg.split_dim)
+
+                    slice_indices = [slice(None, None, None)] * partial_value.ndim
+                    slice_indices[arg.split_dim] = slice(chunk_start_idx, chunk_end_idx)
+                    sliced = partial_value[slice_indices]
+                    values_to_cat.append(sliced)
+
+                    chunk_start_idx = chunk_end_idx
+
+            else:
+                values_to_cat = partial_values
+
+            args_flattened.append(torch.cat(values_to_cat, dim=arg.split_dim))
+        elif isinstance(arg, _CustomReducer):
+            reduced_val = arg.init_value
+
+            for chunk_idx in range(len(chunks_flattened)):
+                reduced_val = arg.reduce_fn(
+                    reduced_val, chunks_flattened[chunk_idx][arg_idx]
+                )
+
+            args_flattened.append(reduced_val)
+        else:
+            value = chunks_flattened[0][arg_idx]
+            for chunk_idx in range(1, len(chunks_flattened)):
+                assert chunks_flattened[chunk_idx][arg_idx] == value
+            args_flattened.append(value)
+
+    # Stage 4: Unflatten combined args
+    return tree_unflatten(args_flattened, flatten_spec)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/pipelining/schedules.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/pipelining/schedules.py
new file mode 100644
index 0000000000000000000000000000000000000000..5657068f0bcd7008a0dc9b4a2a56e364bcc92428
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/pipelining/schedules.py
@@ -0,0 +1,3438 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+import copy
+import csv
+import itertools
+import logging
+import re
+from abc import ABC, abstractmethod
+from collections import Counter, defaultdict
+from collections.abc import Callable
+from enum import Enum
+from functools import lru_cache
+from typing import Any, cast, NamedTuple, Protocol
+
+import torch
+import torch.distributed as dist
+from torch._dynamo import OptimizedModule
+from torch.distributed.fsdp import FSDPModule, UnshardHandle
+from torch.nn.modules.loss import _Loss
+from torch.profiler import record_function
+
+from ._utils import generate_rank_to_stage_mapping, generate_stage_to_rank_mapping
+from .microbatch import merge_chunks, split_args_kwargs_into_chunks, TensorChunkSpec
+from .stage import _PipelineStageBase
+
+
+__all__ = [
+    "get_schedule_class",
+    "PipelineScheduleSingle",
+    "PipelineScheduleMulti",
+    "Schedule1F1B",
+    "ScheduleGPipe",
+    "ScheduleInterleaved1F1B",
+    "ScheduleLoopedBFS",
+    "ScheduleInterleavedZeroBubble",
+    "ScheduleZBVZeroBubble",
+    "ScheduleDualPipeV",
+]
+
+logger = logging.getLogger(__name__)
+
+
+class _ComputationType(Enum):
+    # TODO(whc) rename to _ActType?
+    FORWARD = 1
+    BACKWARD_INPUT = 2
+    BACKWARD_WEIGHT = 3
+    UNSHARD = 4
+    RESHARD = 5
+    SEND_F = 6
+    RECV_F = 7
+    SEND_B = 8
+    RECV_B = 9
+    FULL_BACKWARD = 10
+    OVERLAP_F_B = 11
+    REDUCE_GRAD = 12
+
+    def __str__(self):
+        str_map = {
+            _ComputationType.FORWARD: "F",
+            _ComputationType.BACKWARD_INPUT: "I",
+            _ComputationType.BACKWARD_WEIGHT: "W",
+            _ComputationType.UNSHARD: "UNSHARD",
+            _ComputationType.RESHARD: "RESHARD",
+            _ComputationType.SEND_F: "SEND_F",
+            _ComputationType.RECV_F: "RECV_F",
+            _ComputationType.SEND_B: "SEND_B",
+            _ComputationType.RECV_B: "RECV_B",
+            _ComputationType.FULL_BACKWARD: "B",
+            _ComputationType.OVERLAP_F_B: "OVERLAP_F_B",
+            _ComputationType.REDUCE_GRAD: "REDUCE_GRAD",
+        }
+        return str_map[self]
+
+    @staticmethod
+    def from_str(action):
+        if action == "F":
+            return _ComputationType.FORWARD
+        elif action == "I":
+            return _ComputationType.BACKWARD_INPUT
+        elif action == "W":
+            return _ComputationType.BACKWARD_WEIGHT
+        elif action == "UNSHARD":
+            return _ComputationType.UNSHARD
+        elif action == "RESHARD":
+            return _ComputationType.RESHARD
+        elif action == "SEND_F":
+            return _ComputationType.SEND_F
+        elif action == "RECV_F":
+            return _ComputationType.RECV_F
+        elif action == "SEND_B":
+            return _ComputationType.SEND_B
+        elif action == "RECV_B":
+            return _ComputationType.RECV_B
+        elif action == "B":
+            return _ComputationType.FULL_BACKWARD
+        elif action == "OVERLAP_F_B":
+            return _ComputationType.OVERLAP_F_B
+        elif action == "REDUCE_GRAD":
+            return _ComputationType.REDUCE_GRAD
+        else:
+            raise RuntimeError(f"Invalid computation type {action}")
+
+
+FORWARD = _ComputationType.FORWARD
+BACKWARD_INPUT = _ComputationType.BACKWARD_INPUT
+BACKWARD_WEIGHT = _ComputationType.BACKWARD_WEIGHT
+UNSHARD = _ComputationType.UNSHARD
+RESHARD = _ComputationType.RESHARD
+SEND_F = _ComputationType.SEND_F
+RECV_F = _ComputationType.RECV_F
+SEND_B = _ComputationType.SEND_B
+RECV_B = _ComputationType.RECV_B
+FULL_BACKWARD = _ComputationType.FULL_BACKWARD
+OVERLAP_F_B = _ComputationType.OVERLAP_F_B
+REDUCE_GRAD = _ComputationType.REDUCE_GRAD
+
+# Convenience shorthand for compute actions only since they are used in 'simple schedule format'
+F = FORWARD
+I = BACKWARD_INPUT
+W = BACKWARD_WEIGHT
+B = FULL_BACKWARD
+
+# Helper to parse an action string like 1F0 into a tuple of (stage_index, computation_type, microbatch_index)
+_action_regex = re.compile(
+    r"(\d+)(F|I|B|W|UNSHARD|RESHARD|REDUCE_GRAD|SEND_F|RECV_F|SEND_B|RECV_B)(\d*)"
+)
+
+
+class _Action(NamedTuple):
+    stage_index: int
+    computation_type: _ComputationType
+    microbatch_index: int | None = None
+    sub_actions: tuple["_Action", ...] | None = None
+
+    def __str__(self):
+        return self.__repr__()
+
+    def __repr__(self):
+        if self.sub_actions is not None:
+            # Use recursive repr for sub_actions
+            sub_action_reprs = [repr(sub_action) for sub_action in self.sub_actions]
+            return f"({';'.join(sub_action_reprs)}){self.computation_type}"
+        else:
+            repr_str = str(self.stage_index)
+            repr_str += str(self.computation_type)
+            if self.microbatch_index is not None:
+                repr_str += str(self.microbatch_index)
+            return repr_str
+
+    @property
+    def is_compute_op(self) -> bool:
+        return self.computation_type in (
+            FORWARD,
+            FULL_BACKWARD,
+            BACKWARD_INPUT,
+            BACKWARD_WEIGHT,
+            OVERLAP_F_B,
+        )
+
+    @staticmethod
+    def from_str(action_string: str):
+        """
+        Reverse of __repr__
+
+        String should be formatted as [stage][action type][(microbatch)]
+            e.g. `2F0`, `1UNSHARD`, `3SEND_F1`
+        """
+        action_string = action_string.strip()
+        if action_string == "":
+            return None
+
+        # Check for sub_actions format: [sub_action1;sub_action2;...]ComputationType
+        if action_string.startswith("(") and ")" in action_string:
+            # Find the closing bracket to separate sub_actions from computation type
+            bracket_end = action_string.find(")")
+            sub_part = action_string[
+                1:bracket_end
+            ]  # Remove '[' and get content before ']'
+            computation_type_part = action_string[
+                bracket_end + 1 :
+            ]  # Get part after ']'
+
+            # Parse sub_actions
+            sub_actions = []
+            if sub_part.strip():
+                for sub_str in sub_part.split(";"):
+                    sub_action = _Action.from_str(sub_str.strip())
+                    if sub_action is not None:
+                        sub_actions.append(sub_action)
+
+            # For sub_actions format, we create an action with just the computation type
+            # The stage_index and microbatch_index are not meaningful for the container action
+            return _Action(
+                stage_index=-1,  # Placeholder, not meaningful for sub_actions container
+                computation_type=_ComputationType.from_str(computation_type_part),
+                microbatch_index=None,
+                sub_actions=tuple(sub_actions) if sub_actions else None,
+            )
+
+        # Handle regular single action format
+        if match := _action_regex.match(action_string):
+            stage_index, computation_type, microbatch_index = match.groups()
+            return _Action(
+                int(stage_index),
+                _ComputationType.from_str(computation_type),
+                int(microbatch_index) if len(microbatch_index) else None,
+            )
+        elif action_string == "":
+            return None
+        raise RuntimeError(
+            f"Invalid action string: {action_string}, should be formatted as [stage][action type][(microbatch)] e.g. 2F0"
+        )
+
+
+@lru_cache
+def _get_profiler_function_name(action: _Action) -> str:
+    return f"PP:{str(action)}"
+
+
+def _format_pipeline_order(
+    pipeline_order: dict[int, list[_Action | None]],
+    error_step_number: int | None = None,
+) -> str:
+    """
+    Formats the pipeline order in a timestep (row) x rank (column) grid of actions
+    and returns the formatted string.
+
+    If `error_step_number` is passed in, an additional label will be added to signify which step
+    that it is erroring on.
+    """
+
+    # don't mutate the original
+    pipeline_order = copy.deepcopy(pipeline_order)
+
+    # Replace None with ""
+    for rank in pipeline_order:
+        for i in range(len(pipeline_order[rank])):
+            if pipeline_order[rank][i] is None:
+                # TODO make a real 'None action' that prints as empty string and make mypy happy
+                pipeline_order[rank][i] = ""  # type: ignore[call-overload]
+
+    # Calculate the maximum number of steps across all ranks
+    num_steps = max(len(actions) for actions in pipeline_order.values())
+    step_labels = [
+        "Step " + str(i).zfill(len(str(num_steps - 1))) for i in range(num_steps)
+    ]
+    # Sorting the dictionary by keys and retrieving values in that order
+    rank_actions = [
+        pipeline_order.get(key, [""] * num_steps) for key in sorted(pipeline_order)
+    ]
+    # Transpose the list of lists (rows to columns)
+    # pyrefly: ignore [no-matching-overload]
+    transposed_actions = list(itertools.zip_longest(*rank_actions, fillvalue=""))
+    # Generate column labels for ranks
+    num_ranks = len(pipeline_order)
+    rank_labels = ["Rank " + str(i) for i in range(num_ranks)]
+    # Calculate the maximum length of each column, considering labels
+    max_lengths = [
+        max(len(str(item)) if item is not None else 0 for item in col)
+        for col in zip(step_labels, *transposed_actions)
+    ]
+    # Format the header row with rank labels
+    header_row = " " * (len(step_labels[0]) + 2) + " ".join(
+        f"{label:<{max_lengths[i]}}" for i, label in enumerate(rank_labels)
+    )
+    # Format each row with its corresponding label
+    formatted_rows = [
+        f"{label}: "
+        + " ".join(f"{str(item):<{max_lengths[i]}}" for i, item in enumerate(row))
+        + (
+            " <-- ERROR HERE"
+            if error_step_number is not None
+            and int(label.split()[1]) == error_step_number
+            else ""
+        )
+        for label, row in zip(step_labels, transposed_actions)
+    ]
+    # Join the rows into a single string
+    formatted_table = header_row + "\n" + "\n".join(formatted_rows) + "\n"
+    return formatted_table
+
+
+class _PipelineSchedule(ABC):
+    def __init__(
+        self,
+        n_microbatches: int,
+        loss_fn: Callable[..., torch.Tensor] | None = None,
+        args_chunk_spec: tuple[TensorChunkSpec, ...] | None = None,
+        kwargs_chunk_spec: dict[str, TensorChunkSpec] | None = None,
+        output_merge_spec: dict[str, Any] | tuple[Any] | None = None,
+        scale_grads: bool = True,
+    ):
+        # From arguments
+        self._n_microbatches = n_microbatches
+        self._loss_fn = loss_fn
+
+        # See documentation in `PipelineScheduleSingle` / `PipelineScheduleMulti`
+        self.scale_grads = scale_grads
+
+        # Chunking specification for positional inputs. (default: `None`)
+        self._args_chunk_spec = args_chunk_spec
+        # Chunking specification for keyword inputs. (default: `None`)
+        self._kwargs_chunk_spec = kwargs_chunk_spec
+        self._output_merge_spec = output_merge_spec
+        """
+        # args_chunk_spec and kwargs_chunk_spec specify how to chunk inputs.
+        # They are used to convert batch to microbatches in `step(x)`.  See
+        # `TensorChunkSpec` for helper methods for creating them.
+        """
+
+        # Derived
+        self._has_backward = self._loss_fn is not None
+
+        # Holds the losses for each microbatch.
+        self._internal_losses: list[torch.Tensor] = []
+        logger.info("Using %s", self.__class__.__name__)
+
+    def _maybe_compute_loss(self, stage, output, target_mbs, mb_index):
+        if stage.is_last and self._loss_fn is not None:
+            loss = self._compute_loss(output, target_mbs[mb_index])  # type: ignore[index]
+            self._internal_losses.append(loss)
+
+    def _maybe_get_loss(self, stage, mb_index):
+        valid_index = 0 <= mb_index < len(self._internal_losses)
+        if stage.is_last and self._loss_fn is not None and valid_index:
+            return self._internal_losses[mb_index]
+        elif len(self._internal_losses) != 0 and not valid_index:
+            raise RuntimeError(
+                f"Loss for microbatch {mb_index} is not available. "
+                f"Available losses for microbatches: {self._internal_losses}"
+            )
+        else:
+            return None
+
+    def _update_losses(self, stages, losses):
+        """
+        Update the losses to those in the internal state
+        """
+        # if stages not a list turn into a list
+        if not isinstance(stages, list):
+            stages = [stages]
+        contains_last_stage = any(stage.is_last for stage in stages)
+
+        # Return losses if there is a container passed in
+        if contains_last_stage and losses is not None:
+            if len(self._internal_losses) != self._n_microbatches:
+                raise RuntimeError(
+                    f"Expecting {self._n_microbatches} losses but got {len(self._internal_losses)}"
+                )
+
+            # Clean external container first
+            losses.clear()
+            # Copy internal losses to external container
+            losses.extend(self._internal_losses)
+
+        self._internal_losses.clear()
+
+    @abstractmethod
+    def _step_microbatches(
+        self,
+        arg_mbs: list | None = None,
+        kwarg_mbs: list | None = None,
+        target_mbs: list | None = None,
+        losses: list | None = None,
+        return_outputs: bool = True,
+    ):
+        """
+        Run one iteration of the pipeline schedule with list of microbatches.
+        Will go through all the microbatches according to the schedule
+        implementation.
+
+        Args:
+            microbatches: list of microbatch args.
+            return_outputs: whether to return the outputs from the last stage.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def step(
+        self,
+        *args,
+        target=None,
+        losses: list | None = None,
+        return_outputs=True,
+        **kwargs,
+    ):
+        """
+        Run one iteration of the pipeline schedule with *whole-batch* input.
+        Will chunk the input into microbatches automatically, and go through the
+        microbatches according to the schedule implementation.
+
+        args: positional arguments to the model (as in non-pipeline case).
+        kwargs: keyword arguments to the model (as in non-pipeline case).
+        target: target for the loss function.
+        losses: a list to store the losses for each microbatch.
+        return_outputs: whether to return the outputs from the last stage.
+        """
+        raise NotImplementedError
+
+    def eval(self, *args, target=None, losses: list | None = None, **kwargs):
+        """
+        Run one iteration of the pipeline schedule with *whole-batch* input.
+        Will chunk the input into microbatches automatically, and go through the
+        microbatches, calling forward only.
+
+        args: positional arguments to the model (as in non-pipeline case).
+        kwargs: keyword arguments to the model (as in non-pipeline case).
+        target: target values for the loss function.
+        losses: a list to store the losses for each microbatch.
+        """
+        # Save the original has_backward state
+        original_has_backward = self._has_backward
+        try:
+            self._has_backward = False
+            return self.step(*args, target=target, losses=losses, **kwargs)
+        finally:
+            # Restore the original state
+            self._has_backward = original_has_backward
+
+    def _check_inputs(
+        self,
+        arg_mbs: list | None = None,
+        kwarg_mbs: list | None = None,
+        target_mbs: list | None = None,
+        losses: list | None = None,
+    ) -> tuple[list, list]:
+        """
+        Pre-process/check inputs
+        """
+
+        def check_type_and_len(mbs, name: str):
+            if not isinstance(mbs, list):
+                raise TypeError(f"{name} must be a list but got a {type(mbs)}")
+            if len(mbs) != self._n_microbatches:
+                raise ValueError(
+                    f"Expecting {self._n_microbatches} {name} but got {len(mbs)}"
+                )
+
+        if arg_mbs is not None:
+            check_type_and_len(arg_mbs, "arg_mbs")
+        else:
+            arg_mbs = [()] * self._n_microbatches
+
+        if kwarg_mbs is not None:
+            check_type_and_len(kwarg_mbs, "kwarg_mbs")
+        else:
+            kwarg_mbs = [{}] * self._n_microbatches
+
+        if target_mbs is not None:
+            check_type_and_len(target_mbs, "target_mbs")
+
+        if losses is not None:
+            if not isinstance(losses, list):
+                raise TypeError(f"losses must be a list but got a {type(losses)}")
+
+        return arg_mbs, kwarg_mbs
+
+    def _compute_loss(self, output, target):
+        return self._loss_fn(output, target)  # type: ignore[misc]
+
+    def _split_inputs(
+        self,
+        args: tuple[Any, ...],
+        kwargs: dict[str, Any] | None = None,
+    ):
+        """
+        Splits a full-batch input into chunks (i.e. microbatches) and returns
+        the chunks
+        """
+        if args or kwargs:
+            args_split, kwargs_split = split_args_kwargs_into_chunks(
+                args,
+                kwargs,
+                self._n_microbatches,
+                self._args_chunk_spec,
+                self._kwargs_chunk_spec,
+            )
+            return args_split, kwargs_split
+        else:
+            # Empty inputs (e.g. when called on middle stages)
+            # Return a list of empty tuples/dicts with matching length as chunks
+            return [()] * self._n_microbatches, [{}] * self._n_microbatches
+
+    def _merge_outputs(self, output_chunks: list[Any]) -> Any:
+        """
+        Merge output chunks back to a batch state.
+        If output_merge_spec is None, the utility will merge output chunks by dimension 0 (batch dim).
+        """
+        return merge_chunks(
+            output_chunks,
+            self._output_merge_spec,
+        )
+
+
+def _batch_p2p(p2p_ops: list[dist.P2POp], desc: str | None = None) -> list[dist.Work]:
+    """
+    Simple wrapper over batch_isend_irecv from torch.distributed, which just adds a descriptive logger on top.
+    """
+    if len(p2p_ops) == 0:
+        return []
+    desc_str = f"{desc}, " if desc else ""
+    logger.debug("batch_p2p %s%s", desc_str, p2p_ops)
+    return dist.batch_isend_irecv(p2p_ops)
+
+
+def _sorted_batch_p2p(
+    p2p_ops: list[dist.P2POp], desc: str | None = None
+) -> dict[int, list[dist.Work]]:
+    """
+    Sorts the list of P2P ops by the peer rank, and then calls
+    batch_isend_irecv. Return a dictionary of works by peer rank. This function
+    helps us avoid hangs in case of skip connections.
+    """
+    # Arrange p2p_ops by peer rank:
+    #   int is the peer rank;
+    #   List is the list of ops towards the peer
+    ops_by_peer: dict[int, list[dist.P2POp]] = defaultdict(list)
+    work_by_peer: dict[int, list[dist.Work]] = {}
+    if len(p2p_ops) == 0:
+        return work_by_peer
+
+    # Classify the ops by peer rank
+    for op in p2p_ops:
+        ops_by_peer[op.peer].append(op)
+
+    # Call batch_isend_irecv per peer, in sorted order of the peers (to avoid hangs)
+    for peer, ops in sorted(ops_by_peer.items()):
+        work_by_peer[peer] = _batch_p2p(ops, desc=desc)
+
+    return work_by_peer
+
+
+def _wait_batch_p2p(work: list[dist.Work]):
+    """
+    Waits for a list of dist.Work (typically from _batch_p2p / _sorted_batch_p2p).
+    """
+    for w in work:
+        w.wait()
+
+
+class PipelineScheduleSingle(_PipelineSchedule):
+    """
+    Base class for single-stage schedules.
+    Implements the `step` method.
+    Derived classes should implement `_step_microbatches`.
+
+    Gradients are scaled by num_microbatches depending on the `scale_grads` argument, defaulting to True.  This setting
+    should match the configuration of your loss_fn, which may either average losses (scale_grads=True)
+    or sum losses (scale_grads=False).
+    """
+
+    def __init__(
+        self,
+        stage: _PipelineStageBase,
+        n_microbatches: int,
+        loss_fn: Callable | None = None,
+        args_chunk_spec: tuple[TensorChunkSpec, ...] | None = None,
+        kwargs_chunk_spec: dict[str, TensorChunkSpec] | None = None,
+        output_merge_spec: dict[str, Any] | tuple[Any] | None = None,
+        scale_grads: bool = True,
+    ):
+        # Init parent
+        super().__init__(
+            n_microbatches=n_microbatches,
+            loss_fn=loss_fn,
+            args_chunk_spec=args_chunk_spec,
+            kwargs_chunk_spec=kwargs_chunk_spec,
+            output_merge_spec=output_merge_spec,
+            scale_grads=scale_grads,
+        )
+        # Self attributes
+        self._stage = stage
+        self._num_stages = stage.num_stages
+        self._stage_forward_initialized = False
+        self._stage_backward_initialized = False
+
+        if n_microbatches < self._num_stages:
+            raise ValueError(
+                f"Number of microbatches ({n_microbatches}) must be greater than \
+or equal to the number of stages ({self._num_stages})."
+            )
+
+        self.pipeline_order: dict[int, list[_Action | None]] | None = (
+            self._get_pipeline_order()
+        )
+
+    def _initialize_stage(self, args, kwargs):
+        if not self._stage_forward_initialized:
+            # Prepare the communication needed for the pipeline schedule execution
+            # This is needed because during execution we always perform a series of batch P2P ops
+            # The first call of the batched P2P needs to involve the global group
+            all_ops: list[dist.P2POp] = []
+            all_ops.extend(self._stage._get_init_p2p_neighbors_ops())
+            _wait_batch_p2p(_batch_p2p(all_ops))
+
+            self._stage._prepare_forward_infra(self._n_microbatches, args, kwargs)
+            self._stage_forward_initialized = True
+
+        if self._has_backward and not self._stage_backward_initialized:
+            self._stage._prepare_backward_infra(self._n_microbatches)
+            self._stage_backward_initialized = True
+
+    def step(
+        self,
+        *args,
+        target=None,
+        losses: list | None = None,
+        return_outputs: bool = True,
+        **kwargs,
+    ):
+        """
+        Run one iteration of the pipeline schedule with *whole-batch* input.
+        Will chunk the input into microbatches automatically, and go through the
+        microbatches according to the schedule implementation.
+
+        args: positional arguments to the model (as in non-pipeline case).
+        kwargs: keyword arguments to the model (as in non-pipeline case).
+        target: target for the loss function.
+        losses: a list to store the losses for each microbatch.
+        return_outputs: whether to return the outputs from the last stage.
+        """
+        if self._has_backward and not torch.is_grad_enabled():
+            raise RuntimeError(
+                "step() requires gradients to be enabled for backward computation; "
+                "it should not be used under torch.no_grad() context. "
+                "Please call eval() instead."
+            )
+
+        # Set the same has_backward flag for stage object
+        self._stage.has_backward = self._has_backward
+
+        # Clean per iteration
+        self._stage.clear_runtime_states()
+
+        # Split inputs into microbatches
+        args_split, kwargs_split = self._split_inputs(args, kwargs)
+
+        # Split target into microbatches
+        if target is not None:
+            targets_split = list(torch.tensor_split(target, self._n_microbatches))
+        else:
+            targets_split = None
+
+        # Run microbatches
+        self._step_microbatches(
+            args_split, kwargs_split, targets_split, losses, return_outputs
+        )
+
+        # Return merged results per original format
+        if self._stage.is_last and return_outputs:
+            return self._merge_outputs(self._stage.output_chunks)
+        else:
+            return None
+
+    def _get_pipeline_order(self) -> dict[int, list[_Action | None]] | None:
+        """
+        Returns the pipeline execution order as a schedule IR.
+
+        The returned IR is a dictionary mapping rank IDs to lists of actions.
+        Each action is either an _Action object representing computation to perform,
+        or None representing a deliberate idle step.
+
+        The None values are used to represent pipeline bubbles where a rank
+        must wait for dependencies from other ranks before proceeding. However
+        during execution, with  the _PipelineScheduleRuntime, these Nones are
+        skipped since the relevant communication (send/recv) will be scheduled and waited on.
+
+        Returns:
+            A dictionary mapping rank -> list of actions
+        """
+        return None
+
+
+class _ScheduleForwardOnly(PipelineScheduleSingle):
+    """
+    The forward-only schedule.
+    Will go through all the microbatches and perform only the forward pass
+    """
+
+    def _step_microbatches(
+        self,
+        arg_mbs: list | None = None,
+        kwarg_mbs: list | None = None,
+        target_mbs: list | None = None,
+        losses: list | None = None,
+        return_outputs: bool = True,
+    ):
+        """
+        Run one iteration of the pipeline schedule
+        """
+        if target_mbs is not None or losses is not None:
+            raise RuntimeError(
+                "Forward-only schedule does not support loss computation"
+            )
+
+        arg_mbs, kwarg_mbs = self._check_inputs(arg_mbs, kwarg_mbs, target_mbs, losses)
+        self._initialize_stage(arg_mbs[0], kwarg_mbs[0])
+
+        # Delay send waits
+        fwd_sends_to_wait: list[list[dist.Work]] = []
+
+        # Run microbatches
+        for i in range(self._n_microbatches):
+            with record_function(f"Forward {i}"):
+                ops = self._stage.get_fwd_recv_ops(i)
+                works = _sorted_batch_p2p(ops, desc="fwd_recv")
+                for work in works.values():
+                    _wait_batch_p2p(work)
+
+                self._stage.forward_one_chunk(i, arg_mbs[i], kwarg_mbs[i])  # type: ignore[index]
+
+                ops = self._stage.get_fwd_send_ops(i)
+                works = _sorted_batch_p2p(ops, desc="fwd_send")
+                fwd_sends_to_wait.extend(works.values())
+
+            logger.debug("[%s] Forwarded microbatch %s", self._stage.stage_index, i)
+
+        # Wait for all forward sends to finish
+        # This should not have performance impact because by the time the first
+        # backward arrives all the forward sends should have been finished.
+        for work in fwd_sends_to_wait:
+            _wait_batch_p2p(work)
+
+
+class ScheduleGPipe(PipelineScheduleSingle):
+    """
+    The GPipe schedule.
+    Will go through all the microbatches in a fill-drain manner.
+    """
+
+    def _step_microbatches(
+        self,
+        arg_mbs: list | None = None,
+        kwarg_mbs: list | None = None,
+        target_mbs: list | None = None,
+        losses: list | None = None,
+        return_outputs: bool = True,
+    ):
+        """
+        Run one iteration of the pipeline schedule with list of microbatches.
+        Will go through all the microbatches according to the GPipe schedule.
+
+        Args:
+            microbatches: list of microbatch args.
+            return_outputs: whether to return the outputs from the last stage.
+        """
+        arg_mbs, kwarg_mbs = self._check_inputs(arg_mbs, kwarg_mbs, target_mbs, losses)
+        self._initialize_stage(arg_mbs[0], kwarg_mbs[0])
+
+        # Delay send waits
+        fwd_sends_to_wait: list[list[dist.Work]] = []
+
+        # Run microbatches
+        for i in range(self._n_microbatches):
+            with record_function(f"Forward {i}"):
+                ops = self._stage.get_fwd_recv_ops(i)
+                works = _sorted_batch_p2p(ops, desc="fwd_recv")
+                for work in works.values():
+                    _wait_batch_p2p(work)
+
+                output = self._stage.forward_one_chunk(
+                    i, arg_mbs[i], kwarg_mbs[i], save_forward_output=return_outputs
+                )  # type: ignore[index]
+
+                ops = self._stage.get_fwd_send_ops(i)
+                works = _sorted_batch_p2p(ops, desc="fwd_send")
+                fwd_sends_to_wait.extend(works.values())
+
+            logger.debug("[%s] Forwarded microbatch %s", self._stage.stage_index, i)
+
+            self._maybe_compute_loss(self._stage, output, target_mbs, i)
+
+        # Wait for all forward sends to finish
+        # This should not have performance impact because by the time the first
+        # backward arrives all the forward sends should have been finished.
+        for work in fwd_sends_to_wait:
+            _wait_batch_p2p(work)
+
+        # Run backward
+        # Delay send waits
+        bwd_sends_to_wait: list[list[dist.Work]] = []
+        for i in range(self._n_microbatches):
+            with record_function(f"Backward {i}"):
+                ops = self._stage.get_bwd_recv_ops(i)
+                works = _sorted_batch_p2p(ops, desc="bwd_recv")
+                for work in works.values():
+                    _wait_batch_p2p(work)
+
+                loss = self._maybe_get_loss(self._stage, i)
+                self._stage.backward_one_chunk(
+                    i,
+                    loss=loss,
+                    last_backward=i == self._n_microbatches - 1,
+                )
+
+                ops = self._stage.get_bwd_send_ops(i)
+                works = _sorted_batch_p2p(ops, desc="bwd_send")
+                bwd_sends_to_wait.extend(works.values())
+
+            logger.debug("[%s] Backwarded microbatch %s", self._stage.stage_index, i)
+
+        # Wait for all backward sends to finish
+        for work in bwd_sends_to_wait:
+            _wait_batch_p2p(work)
+
+        # Update losses if there is a container passed in
+        self._update_losses(self._stage, losses)
+
+        self._stage.perform_reduce_grad(self._n_microbatches if self.scale_grads else 1)
+
+    def _get_pipeline_order(self) -> dict[int, list[_Action | None]] | None:
+        """
+        Returns the pipeline order for GPipe schedule.
+
+        See base method in PipelineScheduleSingle for details on the schedule IR format.
+        """
+        pipeline_order = {}
+        pp_group_size = self._num_stages
+
+        for rank in range(pp_group_size):
+            actions: list[_Action | None] = []
+
+            # 1. Initial delay based on rank position
+            warmup_delay = rank
+            actions.extend([None] * warmup_delay)
+
+            # 2. Forward passes for all microbatches
+            for mb_idx in range(self._n_microbatches):
+                actions.append(_Action(rank, _ComputationType.FORWARD, mb_idx))
+
+            # 3. Wait period before backward passes can begin
+            backward_delay = 3 * (pp_group_size - 1 - rank)
+            actions.extend([None] * backward_delay)
+
+            # 4. Backward passes for all microbatches
+            for mb_idx in range(self._n_microbatches):
+                actions.append(_Action(rank, _ComputationType.FULL_BACKWARD, mb_idx))
+
+            pipeline_order[rank] = _add_reduce_grad(actions, self._n_microbatches)
+
+        return pipeline_order  # type: ignore[return-value]
+
+
+class Schedule1F1B(PipelineScheduleSingle):
+    """
+    The 1F1B schedule.
+    Will perform one forward and one backward on the microbatches in steady state.
+    """
+
+    def _step_microbatches(
+        self,
+        arg_mbs: list | None = None,
+        kwarg_mbs: list | None = None,
+        target_mbs: list | None = None,
+        losses: list | None = None,
+        return_outputs: bool = True,
+    ):
+        """
+        Run one iteration of the pipeline schedule with list of microbatches.
+        Will go through all the microbatches according to the 1F1B schedule.
+
+        Args:
+            microbatches: list of microbatch args.
+            return_outputs: whether to return the outputs from the last stage.
+        """
+        arg_mbs, kwarg_mbs = self._check_inputs(arg_mbs, kwarg_mbs, target_mbs, losses)
+        self._initialize_stage(arg_mbs[0], kwarg_mbs[0])
+
+        # Last stage has 1 warmup, second-to-last 2 warmups, ...
+        # first stage `num_stages` warmups
+        warmup_chunks = min(
+            self._n_microbatches,
+            self._num_stages - self._stage.stage_index,
+        )
+
+        # Chunk counters
+        fwd_mb_index = 0
+        bwd_mb_index = 0
+
+        # Warmup phase
+        send_work: list[dist.Work] = []
+        fwd_sends = []
+        for _ in range(warmup_chunks):
+            # Receive activations
+            fwd_recvs = self._stage.get_fwd_recv_ops(fwd_mb_index)
+            _wait_batch_p2p(_batch_p2p(fwd_recvs, desc="fwd_recv"))
+
+            # Compute
+            output = self._stage.forward_one_chunk(
+                fwd_mb_index,
+                arg_mbs[fwd_mb_index],
+                kwarg_mbs[fwd_mb_index],
+                save_forward_output=return_outputs,
+            )  # type: ignore[index]
+
+            # Clear previous chunk's forward sends (hopefully they have well
+            # finished, otherwise, we are heavily communication bound, in which
+            # case it doesn't create a lot of benefit to compute next chunk
+            # eagerly either)
+            _wait_batch_p2p(send_work)
+
+            # Send activations
+            fwd_sends = self._stage.get_fwd_send_ops(fwd_mb_index)
+            if fwd_mb_index != warmup_chunks - 1:
+                # Safe to fire
+                send_work = _batch_p2p(fwd_sends, desc="fwd_send")
+            # otherwise:
+            #   The last forward send is left for fuse with first 1B in 1B1F below
+
+            # Compute loss
+            self._maybe_compute_loss(self._stage, output, target_mbs, fwd_mb_index)
+            fwd_mb_index += 1
+
+        # Now we should have send ops left over, to be fused with first 1B of 1B1F phase below.
+
+        # 1B1F phase
+        while True:  # Don't worry, we have a break inside
+            # We actually do 1B first as the `1B1F` name indicates, so prepare its recv ops
+            bwd_recvs = self._stage.get_bwd_recv_ops(bwd_mb_index)
+
+            # Now, we need to fire the fwd_sends and bwd_recvs together
+            _wait_batch_p2p(_batch_p2p(fwd_sends + bwd_recvs, desc="fwd_send_bwd_recv"))
+
+            # Backward one chunk
+            loss = self._maybe_get_loss(self._stage, bwd_mb_index)
+            self._stage.backward_one_chunk(
+                bwd_mb_index,
+                loss=loss,
+                last_backward=bwd_mb_index == self._n_microbatches - 1,
+            )
+
+            # Get the bwd send ops, but don't fire, to be fused with the 1F below
+            bwd_sends = self._stage.get_bwd_send_ops(bwd_mb_index)
+            bwd_mb_index += 1
+
+            if fwd_mb_index == self._n_microbatches:
+                # We are done with 1B1F, so break with some left-over bwd_sends
+                break
+
+            # We prepare 1F of the `1B1F`
+            fwd_recvs = self._stage.get_fwd_recv_ops(fwd_mb_index)
+
+            # Fuse it with bwd_sends above
+            _wait_batch_p2p(_batch_p2p(bwd_sends + fwd_recvs, desc="bwd_send_fwd_recv"))
+
+            # Now do the fwd
+            output = self._stage.forward_one_chunk(
+                fwd_mb_index,
+                arg_mbs[fwd_mb_index],
+                kwarg_mbs[fwd_mb_index],
+                save_forward_output=return_outputs,
+            )  # type: ignore[index]
+
+            # Compute loss
+            self._maybe_compute_loss(self._stage, output, target_mbs, fwd_mb_index)
+
+            # Get the fwd send ops, but don't fire, leave it for the next iter (wrap-around)
+            fwd_sends = self._stage.get_fwd_send_ops(fwd_mb_index)
+            fwd_mb_index += 1
+
+        # Remember we still have some bwd_sends left over after the break? Now it is time to fire it
+        send_work = _batch_p2p(bwd_sends, desc="bwd_send")
+
+        # Cooldown
+        while bwd_mb_index < self._n_microbatches:
+            # prepare bwd recv ops
+            bwd_recvs = self._stage.get_bwd_recv_ops(bwd_mb_index)
+            _wait_batch_p2p(_batch_p2p(bwd_recvs, desc="bwd_recv"))
+
+            # Backward one chunk
+            loss = self._maybe_get_loss(self._stage, bwd_mb_index)
+            self._stage.backward_one_chunk(
+                bwd_mb_index,
+                loss=loss,
+                last_backward=bwd_mb_index == self._n_microbatches - 1,
+            )
+
+            # Clear previous chunk's backward sends (hopefully they have well finished)
+            _wait_batch_p2p(send_work)
+
+            # Get the bwd send ops, fire it
+            bwd_sends = self._stage.get_bwd_send_ops(bwd_mb_index)
+            send_work = _batch_p2p(bwd_sends, desc="bwd_send")
+            bwd_mb_index += 1
+
+        # Wait for the last backward send to finish
+        _wait_batch_p2p(send_work)
+
+        # Return losses if there is a container passed in
+        self._update_losses(self._stage, losses)
+
+        self._stage.perform_reduce_grad(self._n_microbatches if self.scale_grads else 1)
+
+    def _get_pipeline_order(self) -> dict[int, list[_Action | None]] | None:
+        """
+        Returns the pipeline order for 1F1B schedule.
+
+        See base method in PipelineScheduleSingle for details on the schedule IR format.
+        """
+        pipeline_order = {}
+        pp_group_size = self._num_stages
+
+        for rank in range(pp_group_size):
+            actions: list[_Action | None] = []
+
+            # 1. Warmup phase: initial delay based on rank
+            actions.extend([None] * rank)
+
+            # 2. Initial forward passes before 1F1B phase
+            num_forward = (pp_group_size - 1) - rank
+            forward_mb = 0
+            for i in range(num_forward):
+                actions.append(_Action(rank, _ComputationType.FORWARD, i))
+                forward_mb = i
+
+            # 3. Wait for backward to be ready
+            wait_for_1f1b = max(0, 2 * (pp_group_size - 1 - rank))
+            actions.extend([None] * wait_for_1f1b)
+
+            # 4. 1F1B steady state phase
+            backward_mb = 0
+            remaining_forward = self._n_microbatches - num_forward
+
+            while remaining_forward > 0:
+                # One forward
+                forward_mb += 1
+                actions.append(_Action(rank, _ComputationType.FORWARD, forward_mb))
+                remaining_forward -= 1
+
+                # One backward
+                actions.append(
+                    _Action(rank, _ComputationType.FULL_BACKWARD, backward_mb)
+                )
+                backward_mb += 1
+
+            # 5. Cooldown phase: remaining backward passes
+            remaining_backward = self._n_microbatches - backward_mb
+
+            while remaining_backward > 0:
+                # Add None and backward actions in alternating pattern
+                # based on distance from the last stage
+                if (pp_group_size - rank) > 0:
+                    actions.append(None)
+                    # Decrement the wait counter only if we still have backward passes to do
+                    if remaining_backward > 0:
+                        actions.append(
+                            _Action(rank, _ComputationType.FULL_BACKWARD, backward_mb)
+                        )
+                        backward_mb += 1
+                        remaining_backward -= 1
+                else:
+                    # If we're at the last stage, just add backward actions without None
+                    actions.append(
+                        _Action(rank, _ComputationType.FULL_BACKWARD, backward_mb)
+                    )
+                    backward_mb += 1
+                    remaining_backward -= 1
+
+            pipeline_order[rank] = _add_reduce_grad(actions, self._n_microbatches)
+        return pipeline_order
+
+
+def _requires_reduce_grad(action_type: _ComputationType) -> bool:
+    return action_type in (W, B)
+
+
+def _add_reduce_grad(
+    actions: list[_Action | None], n_microbatches: int
+) -> list[_Action | None]:
+    """
+    REDUCE_GRAD refers to joint across minibatches grad reduction.
+    reduce_grad frees memory and we want to schedule it just after the last "backward"-like stage.
+    """
+    actions_with_reduce_grad: list[_Action | None] = []
+    cnt: dict[int, int] = defaultdict(int)
+
+    def _leaf_action(a, to_schedule):
+        if _requires_reduce_grad(a.computation_type):
+            stage_index = a.stage_index
+            cnt[stage_index] += 1
+            if cnt[stage_index] == n_microbatches:
+                to_schedule.append(stage_index)
+
+    for a in actions:
+        if a is None:
+            continue
+        actions_with_reduce_grad.append(a)
+        schedule_reduce_grad_stage_idxs: list[int] = []
+        if a.computation_type == OVERLAP_F_B and a.sub_actions is not None:
+            for sub_action in a.sub_actions:
+                _leaf_action(sub_action, schedule_reduce_grad_stage_idxs)
+        else:
+            _leaf_action(a, schedule_reduce_grad_stage_idxs)
+
+        for stage_idx in schedule_reduce_grad_stage_idxs:
+            actions_with_reduce_grad.append(_Action(stage_idx, REDUCE_GRAD, None))
+    return actions_with_reduce_grad
+
+
+def _add_unshard_reshard(
+    compute_actions: list[_Action | None],
+    max_active_stages: int = 3,
+) -> list[_Action]:
+    """Given a basic schedule involving only compute actions (F,B,W,OVERLAP_F_B), add UNSHARD/RESHARD actions for FSDP.
+
+    UNSHARD refers to fetching the full contents of an FSDP-sharded layer, requiring an all-gather operation.
+    RESHARD does the opposite, releasing memory (but doing no communication)
+
+    We abandon the "timestep lock"  during lowering
+
+    max_active_stages controls how many prefetches we allow. It should be measured in mb and tuneable but in practice
+    3 stages is probably the thing we want?
+    (to account for having one f and one b active, and something else prefetching?)
+    """
+
+    def next_stage_indices(count: int, next_actions: list[_Action | None]) -> list[int]:
+        """Remove duplicates (same stage, different microbatch), find next 'count' stages that will do compute."""
+        seen: set[int] = set()
+        ret: list[int] = []
+
+        for a in next_actions:
+            if a is not None:
+                # Handle OVERLAP_F_B actions by checking their sub_actions
+                if a.computation_type == OVERLAP_F_B and a.sub_actions is not None:
+                    for sub_action in a.sub_actions:
+                        if sub_action.stage_index not in seen:
+                            seen.add(sub_action.stage_index)
+                            ret.append(sub_action.stage_index)
+                    if len(ret) >= count:
+                        break
+                else:
+                    # Regular action
+                    if a.stage_index not in seen:
+                        seen.add(a.stage_index)
+                        ret.append(a.stage_index)
+                        if len(ret) == count:
+                            break
+        return ret
+
+    active_stages: set[int] = set()
+    fsdp_aware_actions: list[_Action] = []
+
+    def _unshard(stage_index: int):
+        active_stages.add(stage_index)
+        fsdp_aware_actions.append(_Action(stage_index, UNSHARD, None))
+
+    def _reshard(stage_index: int):
+        active_stages.remove(stage_index)
+        fsdp_aware_actions.append(_Action(stage_index, RESHARD, None))
+
+    for i, action in enumerate(compute_actions):
+        if action is None:
+            continue
+
+        # We prefetch the next N stages we'll see, dropping existing stages to make room
+        next_n = next_stage_indices(max_active_stages, compute_actions[i:])
+        # Fetch needs to be ordered correctly, so don't use a set
+        fetch = list(filter(lambda s: s not in active_stages, next_n))
+        # Unclear what the best policy is for eviction, but we can maintain order so we do
+        evict = list(filter(lambda s: s not in next_n, active_stages))
+
+        # logger.debug(
+        #     "_add_unshard_reshard Step %d active: %s fetch %s, evict %s",
+        #     i,
+        #     active_stages,
+        #     fetch,
+        #     evict,
+        # )
+
+        for stage in evict:
+            _reshard(stage)
+        for stage in fetch:
+            _unshard(stage)
+        fsdp_aware_actions.append(action)
+
+    # Reshard all remaining active stages after processing all operations
+    for stage in list(active_stages):
+        _reshard(stage)
+
+    return fsdp_aware_actions
+
+
+def _merge_bw(
+    compute_actions: list[_Action | None],
+) -> list[_Action]:
+    """Given a basic schedule involving only compute actions (F,I,W), merge adjacent I and W ops into B ops.
+    (note: I = BACKWARD_INPUT, W = BACKWARD_WEIGHT, B = FULL_BACKWARD)
+
+    B refers to running the whole backward (not separating grad_input and grad_weight), which can be more efficient
+    in some cases.
+    """
+    merged_actions = []
+    while compute_actions:
+        action = compute_actions.pop(0)
+        if action is None:
+            continue
+
+        # Remove any None actions and find the next non-None action
+        while len(compute_actions) and compute_actions[0] is None:
+            compute_actions.pop(0)
+
+        # Get the next action if it exists
+        next_action = compute_actions[0] if len(compute_actions) > 0 else None
+
+        if (
+            action.computation_type == BACKWARD_INPUT
+            and next_action is not None
+            and next_action.computation_type == BACKWARD_WEIGHT
+            and action.stage_index == next_action.stage_index
+            and action.microbatch_index == next_action.microbatch_index
+        ):
+            merged_actions.append(
+                _Action(action.stage_index, FULL_BACKWARD, action.microbatch_index)
+            )
+            compute_actions.pop(0)
+        else:
+            merged_actions.append(action)
+    return merged_actions
+
+
+def _add_send_recv(
+    compute_actions: dict[int, list[_Action]],
+    stage_to_rank: Callable[[int], int],
+    num_stages: int,
+) -> dict[int, list[_Action]]:
+    """
+    Transforms a compute-only schedule into a complete schedule with communication actions.
+
+    For actions with sub-actions (OVERLAP_F_B) we ensure that all the subactions have been
+    computed and the communication is ready
+    """
+    comm_actions: dict[int, list[_Action]] = {rank: [] for rank in compute_actions}
+    prev_actions: dict[int, set[_Action]] = {rank: set() for rank in compute_actions}
+
+    def _has_comms(action: _Action) -> bool:
+        if action.computation_type == F:
+            return action.stage_index != num_stages - 1 and stage_to_rank(
+                action.stage_index + 1
+            ) != stage_to_rank(action.stage_index)
+        elif action.computation_type in (BACKWARD_INPUT, FULL_BACKWARD):
+            return action.stage_index != 0 and stage_to_rank(
+                action.stage_index - 1
+            ) != stage_to_rank(action.stage_index)
+        return False
+
+    def _get_comms(action: _Action) -> tuple[_Action, _Action]:
+        assert _has_comms(action), f"{action} is not a valid comm action"
+        stage_idx = action.stage_index
+        ctype = action.computation_type
+        mb_idx = action.microbatch_index
+        send = _Action(stage_idx, SEND_F if ctype == F else SEND_B, mb_idx)
+        recv_stage_idx = stage_idx + 1 if ctype == F else stage_idx - 1
+        recv = _Action(recv_stage_idx, RECV_F if ctype == F else RECV_B, mb_idx)
+        return send, recv
+
+    def _ready_to_schedule(action: _Action | None, prev_actions: set[_Action]) -> bool:
+        """We don't put our own recv ops in the schedule, we let a sender on another rank put our recv ops in place.
+        This helps ensure a sane (non-hanging) ordering of sends and recvs.
+        But it also means we might not be able to schedule our next compute action yet.
+        """
+        if action is None:
+            return True
+        elif action.computation_type == F and action.stage_index != 0:
+            if (
+                _Action(action.stage_index, RECV_F, action.microbatch_index)
+                in prev_actions
+            ):
+                return True
+            elif (
+                _Action(action.stage_index - 1, F, action.microbatch_index)
+                in prev_actions
+            ):
+                return True
+            return False
+        elif (
+            action.computation_type in (BACKWARD_INPUT, FULL_BACKWARD)
+            and action.stage_index != num_stages - 1
+        ):
+            if (
+                _Action(action.stage_index, RECV_B, action.microbatch_index)
+                in prev_actions
+            ):
+                return True
+            elif (
+                _Action(action.stage_index + 1, BACKWARD_INPUT, action.microbatch_index)
+                in prev_actions
+            ):
+                return True
+            elif (
+                _Action(action.stage_index + 1, FULL_BACKWARD, action.microbatch_index)
+                in prev_actions
+            ):
+                return True
+            return False
+        else:
+            return True
+
+    while compute_actions:
+        progress = False
+        # go in order of ranks even if dict keys aren't ordered
+        for rank in sorted(compute_actions):
+            assert len(compute_actions[rank]) > 0, (
+                f"{rank=}, {len(compute_actions[rank])=}"
+            )
+            action = compute_actions[rank][0]
+            # handle case where parent action (e.g. OVERLAP_F_B) can be comprised of subactions
+            if action is not None and action.sub_actions is not None:
+                all_actions = action.sub_actions
+            else:
+                all_actions = (action,)
+
+            if not all(_ready_to_schedule(a, prev_actions[rank]) for a in all_actions):
+                continue
+
+            # The action's dependencies are satisfied, so add to schedule
+            if action is not None:
+                comm_actions[rank].append(action)
+                for a in all_actions:
+                    prev_actions[rank].add(a)
+                    if _has_comms(a):
+                        send, recv = _get_comms(a)
+                        # TODO we can avoid send/recv if the 2 stages are on the same rank.
+                        # should we avoid that in the runtime or here?
+                        comm_actions[rank].append(send)
+                        prev_actions[rank].add(send)
+                        comm_actions[stage_to_rank(recv.stage_index)].append(recv)
+                        prev_actions[stage_to_rank(recv.stage_index)].add(recv)
+
+            compute_actions[rank].pop(0)
+            if len(compute_actions[rank]) == 0:
+                del compute_actions[rank]
+            progress = True
+        assert progress, "Malformed compute schedule, can't schedule sends/recvs"
+    return comm_actions
+
+
+def _validate_schedule(
+    actions: dict[int, list[_Action | None]],
+    pp_group_size: int,
+    num_stages: int,
+    num_microbatches: int,
+) -> dict[int, int]:
+    assert len(actions) == pp_group_size, (
+        f"Schedule has incorrect number of ranks - expected {pp_group_size}, actual {len(actions)}"
+    )
+    for rank in range(pp_group_size):
+        assert rank in actions, f"Schedule is missing actions for rank {rank}"
+
+    # We will count all the actions per stage and ensure they happen in a valid order
+    # (e.g. F before (B, I) before W for a given microbatch)
+    stage_actions: dict[int, dict[_ComputationType, set]] = {
+        stage_id: {
+            F: set(),
+            B: set(),
+            I: set(),
+            W: set(),
+        }
+        for stage_id in range(num_stages)
+    }
+    stage_index_to_rank_mapping = {}
+
+    def _process_action(action: _Action, rank: int, step: int):
+        """Process a single action and update stage_actions and stage_index_to_rank_mapping"""
+        s_id = action.stage_index
+        ctype = action.computation_type
+        mb_id = action.microbatch_index
+
+        if ctype == F:
+            stage_actions[s_id][F].add(mb_id)
+        elif ctype == B:
+            if mb_id not in stage_actions[s_id][F]:
+                error_msg = (
+                    f"Rank {rank}, step {step}: Running Full Backward for stage {s_id}, "
+                    f"microbatch {mb_id} without first running Forward"
+                )
+                formatted_schedule = _format_pipeline_order(
+                    actions, error_step_number=step
+                )
+                full_error_msg = (
+                    f"{error_msg}\n\nFull pipeline schedule:\n{formatted_schedule}"
+                )
+                raise AssertionError(full_error_msg)
+            stage_actions[s_id][B].add(mb_id)
+        elif ctype == I:
+            if mb_id not in stage_actions[s_id][F]:
+                error_msg = (
+                    f"Rank {rank}, step {step}: Running Backward Input for stage {s_id}, "
+                    f"microbatch {mb_id} without first running Forward"
+                )
+                formatted_schedule = _format_pipeline_order(
+                    actions, error_step_number=step
+                )
+                full_error_msg = (
+                    f"{error_msg}\n\nFull pipeline schedule:\n{formatted_schedule}"
+                )
+                raise AssertionError(full_error_msg)
+            stage_actions[s_id][I].add(mb_id)
+        elif ctype == W:
+            if mb_id not in stage_actions[s_id][I]:
+                error_msg = (
+                    f"Rank {rank}, step {step}: Running Backward Weight for stage {s_id}, "
+                    f"microbatch {mb_id} without first running Backward Input"
+                )
+                formatted_schedule = _format_pipeline_order(
+                    actions, error_step_number=step
+                )
+                full_error_msg = (
+                    f"{error_msg}\n\nFull pipeline schedule:\n{formatted_schedule}"
+                )
+                raise AssertionError(full_error_msg)
+            stage_actions[s_id][W].add(mb_id)
+
+        if s_id not in stage_index_to_rank_mapping:
+            stage_index_to_rank_mapping[s_id] = rank
+        else:
+            existing_rank = stage_index_to_rank_mapping[s_id]
+            assert rank == existing_rank, (
+                f"Rank {rank}, step {step}: Stage {s_id} is assigned to both rank {rank} and rank {existing_rank}"
+            )
+
+    for rank in actions:
+        for step, action in enumerate(actions[rank]):
+            if action is None:
+                continue
+            assert isinstance(action, _Action), (
+                f"Rank {rank}, step {step}: Got an invalid action: {action}, expected instance of _Action"
+            )
+
+            # Check if action has sub_actions
+            if action.sub_actions is not None:
+                # Process each sub_action instead of the main action
+                for sub_action in action.sub_actions:
+                    _process_action(sub_action, rank, step)
+            else:
+                # Process the main action normally
+                _process_action(action, rank, step)
+
+    for s_id in stage_actions:
+        f_mb = len(stage_actions[s_id][F])
+        b_mb = len(stage_actions[s_id][B])
+        i_mb = len(stage_actions[s_id][I])
+        w_mb = len(stage_actions[s_id][W])
+
+        assert f_mb == num_microbatches, (
+            f"Got {f_mb} {F} microbatches for stage {s_id}, expected {num_microbatches}"
+        )
+
+        assert i_mb == w_mb, (
+            f"Invalid backward microbatches for stage {s_id}: I and W must have equal counts, \
+            but got I={i_mb}, W={w_mb}"
+        )
+
+        assert b_mb + (i_mb + w_mb) // 2 == num_microbatches, (
+            f"Invalid backward microbatches for stage {s_id}: expected {num_microbatches} total backwards, \
+            but got B={b_mb}, I={i_mb}, W={w_mb}"
+        )
+    return stage_index_to_rank_mapping
+
+
+class PipelineScheduleMulti(_PipelineSchedule):
+    """
+    Base class for multi-stage schedules.
+    Implements the `step` method.
+
+    Gradients are scaled by num_microbatches depending on the `scale_grads` argument, defaulting to True.  This setting
+    should match the configuration of your loss_fn, which may either average losses (scale_grads=True)
+    or sum losses (scale_grads=False).
+    """
+
+    def __init__(
+        self,
+        stages: list[_PipelineStageBase],
+        n_microbatches: int,
+        loss_fn: Callable | None = None,
+        args_chunk_spec: tuple[TensorChunkSpec, ...] | None = None,
+        kwargs_chunk_spec: dict[str, TensorChunkSpec] | None = None,
+        output_merge_spec: dict[str, Any] | tuple[Any] | None = None,
+        use_full_backward: bool | None = None,
+        scale_grads: bool = True,
+        backward_requires_autograd: bool = True,
+    ):
+        # Init parent
+        super().__init__(
+            n_microbatches=n_microbatches,
+            loss_fn=loss_fn,
+            args_chunk_spec=args_chunk_spec,
+            kwargs_chunk_spec=kwargs_chunk_spec,
+            output_merge_spec=output_merge_spec,
+            scale_grads=scale_grads,
+        )
+        # Self attributes
+        self._stages = stages
+        self._num_stages = stages[0].num_stages
+        self.pp_group_size = stages[0].group_size
+        self.rank = stages[0].group_rank
+        # Set the pipeline stage states
+        self.stage_index_to_group_rank = generate_stage_to_rank_mapping(
+            self.pp_group_size, self._num_stages
+        )
+        for stage in self._stages:
+            stage.stage_index_to_group_rank = self.stage_index_to_group_rank
+
+        self._stages_forward_initialized = False
+        self._stages_backward_initialized = False
+
+        # avoid putting a reference to 'self' inside the lambda, it creates a ref cycle
+        has_loss: bool = self._loss_fn is not None
+        self._should_compute_loss = lambda stage: stage.is_last and has_loss
+
+        # This will be set during init of derived schedules
+        self.pipeline_order: dict[int, list[_Action | None]] = {}
+
+        # When using a custom backward function, we may or may not need autograd to be used
+        # for the backward pass. This flag is used to determine whether or torch.is_grad_enabled()
+        # check should be performed before the step function.
+        self._backward_requires_autograd = backward_requires_autograd
+
+        if use_full_backward is not None:
+            logger.warning(
+                "Deprecation warning: 'use_full_backward' is no longer supported. "
+                "Simply stop passing it, and everything should still work fine."
+            )
+
+    def _initialize_stages(self, args: tuple[Any, ...], kwargs):
+        if not self._stages_forward_initialized:
+            # Prepare the communication needed for the pipeline schedule execution
+            # This is needed because during execution we always perform a series of batch P2P ops
+            # The first call of the batched P2P needs to involve the global group
+            all_ops: list[dist.P2POp] = []
+            for stage in self._stages:
+                all_ops.extend(stage._get_init_p2p_neighbors_ops())
+            _wait_batch_p2p(_batch_p2p(all_ops))
+
+            # may be 'none' value (if this stage sends its output shapes to the next stage via P2P)
+            # or real value (if this stage and next stage are on the same device)
+            next_stage_args: tuple[Any, ...] = tuple()
+            for stage in self._stages:
+                if stage.is_first:
+                    next_stage_args = stage._prepare_forward_infra(
+                        self._n_microbatches, args, kwargs
+                    )
+                else:
+                    next_stage_args = stage._prepare_forward_infra(
+                        self._n_microbatches, next_stage_args, kwargs
+                    )
+            self._stages_forward_initialized = True
+
+        if self._has_backward and not self._stages_backward_initialized:
+            for stage in self._stages:
+                stage._prepare_backward_infra(self._n_microbatches)
+            self._stages_backward_initialized = True
+
+    def _validate_and_set_stage_mapping(
+        self, actions: dict[int, list[_Action | None]]
+    ) -> None:
+        """
+        Allocates the stage index to rank mapping which is needed for communication
+        """
+        self.stage_index_to_group_rank = _validate_schedule(
+            actions,
+            self.pp_group_size,
+            self._num_stages,
+            self._n_microbatches,
+        )
+        for stage in self._stages:
+            stage.stage_index_to_group_rank = self.stage_index_to_group_rank
+
+    def _dump_csv(self, filename):
+        """Dump a CSV representation of the schedule into a file with the provided filename."""
+        with open(filename, "w", newline="") as csvfile:
+            writer = csv.writer(csvfile)
+            for rank in self.pipeline_order:
+                writer.writerow(self.pipeline_order[rank])
+
+    def _load_csv(self, filename, format="compute_only"):
+        """Load a CSV representation of the schedule from a file with the provided filename.
+        This API will most likely get renamed/refactored so is marked as internal for now.
+
+        format must be "compute_only" for PipelineScheduleMulti.
+        """
+        assert format == "compute_only"
+        with open(filename, newline="") as csvfile:
+            reader = csv.reader(csvfile)
+            for rank, row in enumerate(reader):
+                self.pipeline_order[rank] = [_Action.from_str(s) for s in row]
+
+        # Validates the order of the pipeline actions and infers the stage_to_rank_mapping.
+        # This will overwrite the default stage_to_rank_mapping created in the constructor
+        self._validate_and_set_stage_mapping(self.pipeline_order)
+
+    def step(
+        self,
+        *args,
+        target=None,
+        losses: list | None = None,
+        return_outputs: bool = True,
+        **kwargs,
+    ):
+        """
+        Run one iteration of the pipeline schedule with *whole-batch* input.
+        Will chunk the input into microbatches automatically, and go through the
+        microbatches according to the schedule implementation.
+
+        args: positional arguments to the model (as in non-pipeline case).
+        kwargs: keyword arguments to the model (as in non-pipeline case).
+        target: target for the loss function.
+        losses: a list to store the losses for each microbatch.
+        return_outputs: whether to return the outputs from the last stage.
+        """
+        if (
+            self._has_backward
+            and self._backward_requires_autograd
+            and not torch.is_grad_enabled()
+        ):
+            raise RuntimeError(
+                "step() requires gradients to be enabled for backward computation; "
+                "it should not be used under torch.no_grad() context. "
+                "Please call eval() instead."
+            )
+
+        # Set the same has_backward flag for stage object
+        for stage in self._stages:
+            stage.has_backward = self._has_backward
+
+        # Clean per iteration
+        for stage in self._stages:
+            stage.clear_runtime_states()
+
+        # Split inputs into microbatches
+        args_split, kwargs_split = self._split_inputs(args, kwargs)
+
+        # Split target into microbatches
+        if target is not None:
+            targets_split = list(torch.tensor_split(target, self._n_microbatches))
+        else:
+            targets_split = None
+
+        # Run microbatches
+        self._step_microbatches(
+            args_split, kwargs_split, targets_split, losses, return_outputs
+        )
+
+        # Return merged results per original format
+        for stage in self._stages:
+            if stage.is_last and return_outputs:
+                return self._merge_outputs(stage.output_chunks)
+        # Does not contain the last stage or we do not return output chunks
+        return None
+
+    def _step_microbatches(
+        self,
+        arg_mbs: list | None = None,
+        kwarg_mbs: list | None = None,
+        target_mbs: list | None = None,
+        losses: list | None = None,
+        return_outputs: bool = True,
+    ):
+        """
+        Operate on the microbatches for looped schedules (multiple stages on each rank).
+
+        TODO: Does not use sorted_batch_isend_irecv(). As a result, this schedule does
+        not support models with skip connections.
+        """
+        arg_mbs, kwarg_mbs = self._check_inputs(arg_mbs, kwarg_mbs, target_mbs, losses)
+
+        self._initialize_stages(arg_mbs[0], kwarg_mbs[0])
+
+        # Based on the plan in Step 1 created in __init__:
+        # 2. Perform communication based on the pipeline_order
+        stage_index_to_stage: dict[int, _PipelineStageBase] = {
+            stage.stage_index: stage for stage in self._stages
+        }
+
+        # determine prev_rank and next_rank based on which ranks are next to
+        # the stages in the pipeline_order
+        all_prev_ranks: set[int] = set()
+        all_next_ranks: set[int] = set()
+        for stage_index in stage_index_to_stage:
+            # TODO: assumption that stages only communicate from distances of +1/-1 (no skip connections)
+            if stage_index > 0:
+                all_prev_ranks.add(self.stage_index_to_group_rank[stage_index - 1])
+            if stage_index < self._num_stages - 1:
+                all_next_ranks.add(self.stage_index_to_group_rank[stage_index + 1])
+        # count either full_backward or backward_weight together, to determine when to sync DP grads
+        backward_counter: Counter[int] = Counter()
+        for time_step, action in enumerate(self.pipeline_order[self.rank]):
+            try:
+                ops: list[dist.P2POp] = []
+                if action is not None:
+                    computation_type = action.computation_type
+                    mb_index = action.microbatch_index
+                    stage_index = action.stage_index
+                    assert mb_index is not None, (
+                        "All currently supported action types require valid microbatch_index"
+                    )
+                    if computation_type == _ComputationType.FORWARD:
+                        # perform forward computation
+                        stage = stage_index_to_stage[stage_index]
+                        output = stage.forward_one_chunk(
+                            mb_index,
+                            arg_mbs[mb_index],
+                            kwarg_mbs[mb_index],
+                            save_forward_output=return_outputs,
+                        )
+                        self._maybe_compute_loss(stage, output, target_mbs, mb_index)
+                        ops.extend(stage.get_fwd_send_ops(mb_index))
+                    elif computation_type == _ComputationType.FULL_BACKWARD:
+                        # perform backward computation
+                        stage = stage_index_to_stage[stage_index]
+                        loss = self._maybe_get_loss(stage, mb_index)
+                        backward_counter[stage_index] += 1
+                        last_backward = (
+                            backward_counter[stage_index] == self._n_microbatches
+                        )
+                        grad_scale_factor = (
+                            self._n_microbatches if self.scale_grads else 1
+                        )
+                        stage.backward_one_chunk(
+                            mb_index,
+                            loss=loss,
+                            full_backward=True,
+                            last_backward=last_backward,
+                        )
+                        if last_backward:
+                            stage.scale_grads(grad_scale_factor)
+
+                        ops.extend(stage.get_bwd_send_ops(mb_index))
+                    elif computation_type == _ComputationType.BACKWARD_INPUT:
+                        # perform backward computation
+                        stage = stage_index_to_stage[stage_index]
+                        loss = self._maybe_get_loss(stage, mb_index)
+                        stage.backward_one_chunk(
+                            mb_index,
+                            loss=loss,
+                            full_backward=False,
+                            last_backward=False,
+                        )
+                        ops.extend(stage.get_bwd_send_ops(mb_index))
+                    elif computation_type == _ComputationType.BACKWARD_WEIGHT:
+                        # perform weight update
+                        stage = stage_index_to_stage[stage_index]
+                        backward_counter[stage_index] += 1
+                        last_backward = (
+                            backward_counter[stage_index] == self._n_microbatches
+                        )
+                        grad_scale_factor = (
+                            self._n_microbatches if self.scale_grads else 1
+                        )
+                        stage.backward_weight_one_chunk(
+                            mb_index,
+                            last_backward=last_backward,
+                        )
+                        if last_backward:
+                            stage.scale_grads(grad_scale_factor)
+                    else:
+                        raise ValueError(f"Unknown computation type {computation_type}")
+
+                # Look at the neighboring ranks for this current timestep and determine whether
+                # this current rank needs to do any recv communication
+                for prev_rank in all_prev_ranks:
+                    prev_rank_ops = self.pipeline_order[prev_rank]
+                    prev_rank_action = None
+                    if time_step < len(prev_rank_ops):
+                        prev_rank_action = prev_rank_ops[time_step]
+                    if prev_rank_action is not None:
+                        computation_type = prev_rank_action.computation_type
+                        mb_index = prev_rank_action.microbatch_index
+                        stage_index = prev_rank_action.stage_index
+                        assert mb_index is not None, (
+                            "All currently supported action types require valid microbatch_index"
+                        )
+                        # Only handle sends for the forward from a previous rank
+                        if computation_type == _ComputationType.FORWARD:
+                            # If not the last stage, then receive fwd activations
+                            if stage_index + 1 in stage_index_to_stage:
+                                # TODO: We are assuming that stage will always receive from stage-1
+                                # however that is not necessarily true of get_fwd_recv_ops
+                                stage = stage_index_to_stage[stage_index + 1]
+                                ops.extend(stage.get_fwd_recv_ops(mb_index))
+                        elif computation_type in (
+                            FULL_BACKWARD,
+                            BACKWARD_INPUT,
+                            BACKWARD_WEIGHT,
+                        ):
+                            # Previous rank doing backward has no influence for the current rank forward recv
+                            pass
+                        else:
+                            raise ValueError(
+                                f"Unknown computation type {computation_type}"
+                            )
+                for next_rank in all_next_ranks:
+                    next_rank_ops = self.pipeline_order[next_rank]
+                    next_rank_action = None
+                    if time_step < len(next_rank_ops):
+                        next_rank_action = next_rank_ops[time_step]
+                    if next_rank_action is not None:
+                        computation_type = next_rank_action.computation_type
+                        mb_index = next_rank_action.microbatch_index
+                        stage_index = next_rank_action.stage_index
+                        assert mb_index is not None, (
+                            "All currently supported action types require valid microbatch_index"
+                        )
+                        # Only handle receives for the backwards from a next rank
+                        if computation_type in (FORWARD, BACKWARD_WEIGHT):
+                            # Next rank doing forward or weight update has no influence for the current rank backward recv
+                            pass
+                        elif computation_type in (BACKWARD_INPUT, FULL_BACKWARD):
+                            # If not the first stage, then receive bwd gradients
+                            if stage_index - 1 in stage_index_to_stage:
+                                # TODO: We are assuming that stage will always receive from stage+1
+                                # however that is not necessarily true of get_bwd_recv_ops
+                                stage = stage_index_to_stage[stage_index - 1]
+                                ops.extend(stage.get_bwd_recv_ops(mb_index))
+                        else:
+                            raise ValueError(
+                                f"Unknown computation type {computation_type}"
+                            )
+
+                # do the communication
+                _wait_batch_p2p(_batch_p2p(ops))
+            except Exception as e:
+                logger.error(  # noqa: G200
+                    "[Rank %s] pipeline schedule %s caught the following exception '%s' \
+at time_step %s when running action %s",
+                    self.rank,
+                    self.__class__.__name__,
+                    str(e),
+                    time_step,
+                    action,
+                )
+                logger.error(
+                    "%s",
+                    _format_pipeline_order(
+                        self.pipeline_order, error_step_number=time_step
+                    ),
+                )
+                raise e
+        # Return losses if there is a container passed in
+        self._update_losses(self._stages, losses)
+
+
+class _PipelineContext:
+    def __init__(
+        self,
+        schedule_ref: _PipelineSchedule,
+        arg_mbs: list[tuple] | None = None,
+        kwarg_mbs: list[dict] | None = None,
+        target_mbs: list | None = None,
+        losses: list | None = None,
+    ):
+        self.schedule_ref = schedule_ref
+        self.arg_mbs = arg_mbs
+        self.kwarg_mbs = kwarg_mbs
+        self.target_mbs = target_mbs
+        self.losses = losses
+
+
+class _CustomFunctionProtocol(Protocol):
+    def __call__(self, action: _Action, ctx: _PipelineContext) -> None: ...
+
+
+class _PipelineScheduleRuntime(PipelineScheduleMulti):
+    """
+    Provides a simple runtime that requires a 'schedule IR' including specified communication operations.
+
+    Can be instantiated directly by creating _PipelineScheduleRuntime and calling load_csv, or can be
+    subclassed and the subclass can be responsible for creating a schedule IR.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        # Action to custom function mapping
+        self._comp_type_to_function_map: dict[_ComputationType, Callable] = {}
+        # count either full_backward or backward_weight together, to determine when to sync DP grads
+        self.backward_counter: Counter[int] = Counter()
+
+        # recv ops indexed by (stage_idx, mb_idx) need to be waited on before use
+        self.bwd_recv_ops: dict[tuple[int, int], list[dist.Work]] = {}
+        self.fwd_recv_ops: dict[tuple[int, int], list[dist.Work]] = {}
+
+        # we track which stages are 'active' when used with FSDP, and wait on unshard ops before computing on stages
+        self.unshard_ops: dict[int, list[UnshardHandle]] = defaultdict(list)
+        self.unsharded_stages = set()
+
+    def register_custom_function(
+        self,
+        computation_type: _ComputationType,
+        custom_function: _CustomFunctionProtocol,
+    ) -> None:
+        """
+        Register a custom function to be executed for a specific computation type.
+
+        Args:
+            computation_type: The computation type for which to register the custom function
+            custom_function: The function to execute when this computation type is encountered.
+                Must have signature: (action: _Action, ctx: _PipelineContext) -> None
+        """
+        # Ensure that the computation type is valid
+        if computation_type not in (
+            FORWARD,
+            FULL_BACKWARD,
+            BACKWARD_INPUT,
+            BACKWARD_WEIGHT,
+            OVERLAP_F_B,
+            UNSHARD,
+            RESHARD,
+            REDUCE_GRAD,
+        ):
+            raise ValueError(
+                f"Invalid computation type {computation_type}. Only FORWARD, FULL_BACKWARD, \
+                BACKWARD_INPUT, BACKWARD_WEIGHT, OVERLAP_F_B, UNSHARD, RESHARD and REDUCE_GRAD are supported."
+            )
+
+        # Check if computation_type is already registered
+        if computation_type in self._comp_type_to_function_map:
+            logger.warning(
+                "Computation type %s is already registered. "
+                "Overwriting the existing custom function.",
+                computation_type,
+            )
+
+        self._comp_type_to_function_map[computation_type] = custom_function
+
+    def _prepare_schedule_with_comms(
+        self,
+        actions: dict[int, list[_Action | None]],
+        format: str = "compute_only",
+    ):
+        """
+        Given an in-memory representation for a simple compute-only schedule, lower it to a complex schedule including
+        communication actions.  Stores the schedule in self, and must be called before running step_mo()
+        """
+        # validate the provided actions are valid and overrides the default stage_index_to_group_rank
+        super()._validate_and_set_stage_mapping(actions)
+
+        self.pipeline_order_with_comms: dict[int, list[_Action]] = {}
+        if format == "compute_comms":
+            for rank in actions:
+                self.pipeline_order_with_comms[rank] = []
+                for action in actions[rank]:
+                    assert action is not None
+                    self.pipeline_order_with_comms[rank].append(action)
+            # TODO what level of validation should we offer for compute+comms schedule?
+        elif format == "compute_only":
+            # Validate that the schedule does not have comms already added to it
+            for rank, action_list in actions.items():
+                for i, action in enumerate(action_list):
+                    if action is not None and not action.is_compute_op:
+                        raise ValueError(
+                            f"Expected compute-only schedule but found communication action "
+                            f"'{action}' at rank {rank}, position {i}. "
+                            f"Communication actions (e.g. SEND_F, RECV_F, etc.) "
+                            f"should not be present when format='compute_only'."
+                        )
+
+            # Perform schedule lowering
+            for rank in actions:
+                self.pipeline_order_with_comms[rank] = _add_unshard_reshard(
+                    actions[rank]
+                )
+                self.pipeline_order_with_comms[rank] = _add_reduce_grad(  # type: ignore[assignment]
+                    self.pipeline_order_with_comms[rank],  # type: ignore[arg-type]
+                    self._n_microbatches,
+                )
+
+            self.pipeline_order_with_comms = _add_send_recv(
+                self.pipeline_order_with_comms,
+                stage_to_rank=lambda s: self.stage_index_to_group_rank[s],
+                num_stages=self._num_stages,
+            )
+        else:
+            raise NotImplementedError(f"{format=} is not implemented")
+
+    def _load_csv(self, filename: str, format: str = "compute_only"):
+        """Loads a csv in simple format and then lowers it to include communication actions
+
+        format must be either "compute_only" or "compute_comms".  If compute_only, the lowering passes
+        will automatically be run to generate a compute_comms schedule.
+        """
+        if format == "compute_only":
+            # this will populate self.pipeline_order
+            super()._load_csv(filename)
+            # this will populate self.pipeline_order_with_comms
+            self._prepare_schedule_with_comms(self.pipeline_order)
+        elif format == "compute_comms":
+            actions = {}
+            with open(filename, newline="") as csvfile:
+                reader = csv.reader(csvfile)
+                for rank, row in enumerate(reader):
+                    actions[rank] = [_Action.from_str(s) for s in row]
+                self._prepare_schedule_with_comms(actions, format=format)
+        else:
+            raise NotImplementedError(f"{format=} is not implemented")
+
+    def _dump_csv(self, filename: str, format: str = "compute_comms"):
+        """Dump a CSV representation of the schedule into a file with the provided filename."""
+        if format == "compute_only":
+            assert self.pipeline_order is not None, (
+                "Compute only schedule must be available"
+            )
+            with open(filename, "w", newline="") as csvfile:
+                writer = csv.writer(csvfile)
+                for rank in self.pipeline_order:
+                    writer.writerow(self.pipeline_order[rank])
+        elif format == "compute_comms":
+            assert self.pipeline_order_with_comms is not None, (
+                "Must initialize compute_comms schedule before dump_csv"
+            )
+            with open(filename, "w", newline="") as csvfile:
+                writer = csv.writer(csvfile)
+                for rank in self.pipeline_order_with_comms:
+                    writer.writerow(self.pipeline_order_with_comms[rank])
+
+    def _simulate(self):
+        return _simulate_comms_compute(
+            self.pipeline_order_with_comms,
+            lambda s: self.stage_index_to_group_rank[s],
+            self._num_stages,
+        )
+
+    def _assert_unsharded(self, stage: _PipelineStageBase):
+        """If an unshard is active for `stage_idx`, wait() it and mark `stage_idx` unshared."""
+        stage_uses_fsdp = isinstance(stage.submod, FSDPModule)
+        if stage_uses_fsdp:
+            stage_idx = stage.stage_index
+            if stage_idx in self.unshard_ops:
+                for op in self.unshard_ops[stage_idx]:
+                    op.wait()
+                del self.unshard_ops[stage_idx]
+                self.unsharded_stages.add(stage_idx)
+            assert stage_idx in self.unsharded_stages, (
+                f"Attempted to compute on sharded {stage_idx=}"
+            )
+
+    def _step_microbatches(
+        self,
+        arg_mbs: list | None = None,
+        kwarg_mbs: list | None = None,
+        target_mbs: list | None = None,
+        losses: list | None = None,
+        return_outputs: bool = True,
+    ):
+        """
+        Operate on the microbatches for looped schedules (multiple stages on each rank).
+
+        TODO: Does not use sorted_batch_isend_irecv(). As a result, this schedule does
+        not support models with skip connections.
+        """
+        arg_mbs, kwarg_mbs = self._check_inputs(arg_mbs, kwarg_mbs, target_mbs, losses)
+        self._initialize_stages(arg_mbs[0], kwarg_mbs[0])
+
+        # Based on the plan in Step 1 created in __init__:
+        # 2. Perform communication based on the pipeline_order
+        stage_index_to_stage: dict[int, _PipelineStageBase] = {
+            stage.stage_index: stage for stage in self._stages
+        }
+
+        assert self.pipeline_order_with_comms is not None, (
+            "Must call _prepare_schedule_with_comms() before calling _step_microbatches()"
+        )
+
+        # send ops should be waited on before step() exists, mainly for hygiene
+        send_ops: list[list[dist.Work]] = []
+
+        def _perform_action(action: _Action) -> None:
+            comp_type = action.computation_type
+            mb_index: int = (
+                action.microbatch_index if action.microbatch_index is not None else -1
+            )
+            assert mb_index >= 0 or comp_type in (
+                UNSHARD,
+                RESHARD,
+                REDUCE_GRAD,
+            ), f"{action=} missing mb_index"
+            stage_idx = action.stage_index
+            stage = stage_index_to_stage[stage_idx]
+            stage_uses_fsdp = isinstance(stage.submod, FSDPModule)
+            # see [Note: V-schedule special case]
+            is_next_stage_on_this_rank = stage_idx + 1 in stage_index_to_stage
+            is_prev_stage_on_this_rank = stage_idx - 1 in stage_index_to_stage
+
+            # TODO(whc) it's not actually safe to use _batch_p2p here in the uncommon case the model has skip-connections,
+            # since we do not want to batch up ops between more than a pair of ranks.  _sorted_batch_p2p would be
+            # safe to use instead.
+            # However, I was wondering if I should avoid calling batched operators at all in the case that there is
+            # only one operator per batch.  I could iterate through the 'fwd_send_ops' one by one and run them.
+            if comp_type == SEND_F:
+                send_ops.append(_batch_p2p(stage.get_fwd_send_ops(mb_index)))
+            elif comp_type == SEND_B:
+                send_ops.append(_batch_p2p(stage.get_bwd_send_ops(mb_index)))
+            elif comp_type == RECV_F:
+                assert (
+                    stage_idx,
+                    mb_index,
+                ) not in self.fwd_recv_ops, (
+                    f"Recv twice for {stage_idx=} {mb_index=} without executing forward"
+                )
+                self.fwd_recv_ops[(stage_idx, mb_index)] = _batch_p2p(
+                    stage.get_fwd_recv_ops(mb_index)
+                )
+            elif comp_type == RECV_B:
+                assert (
+                    stage_idx,
+                    mb_index,
+                ) not in self.bwd_recv_ops, (
+                    f"Recv twice for {stage_idx=} {mb_index=} without executing backward"
+                )
+                self.bwd_recv_ops[(stage_idx, mb_index)] = _batch_p2p(
+                    stage.get_bwd_recv_ops(mb_index)
+                )
+            elif comp_type == UNSHARD:
+                if stage_uses_fsdp:
+                    assert (
+                        stage_idx not in self.unsharded_stages
+                        and stage_idx not in self.unshard_ops
+                    ), f"Unsharding the same {stage_idx=} twice"
+                    for submodule in stage.submod.modules():
+                        if not isinstance(submodule, FSDPModule):
+                            continue
+                        handle = cast(UnshardHandle, submodule.unshard(async_op=True))
+                        self.unshard_ops[stage_idx].append(handle)
+            elif comp_type == RESHARD:
+                if stage_uses_fsdp:
+                    assert stage_idx in self.unsharded_stages, (
+                        f"Resharding {stage_idx=} without unsharding"
+                    )
+                    assert stage_idx not in self.unshard_ops, (
+                        f"Resharding {stage_idx=} before finishing unshard"
+                    )
+                    for submodule in stage.submod.modules():
+                        if not isinstance(submodule, FSDPModule):
+                            continue
+                        submodule.reshard()
+                    self.unsharded_stages.remove(stage_idx)
+            elif comp_type == FORWARD:
+                self._assert_unsharded(stage)
+
+                if (
+                    not stage.is_first
+                    # no recv op expected for V-schedule special case (see [Note: V-schedule special case])
+                    and not is_prev_stage_on_this_rank
+                ):
+                    assert (
+                        stage_idx,
+                        mb_index,
+                    ) in self.fwd_recv_ops, (
+                        f"Computing {action=} before receiving input"
+                    )
+                    _wait_batch_p2p(self.fwd_recv_ops.pop((stage_idx, mb_index)))
+
+                output = stage.forward_one_chunk(
+                    mb_index,
+                    arg_mbs[mb_index],  # type: ignore[index]
+                    kwarg_mbs[mb_index],  # type: ignore[index]
+                    save_forward_output=return_outputs,
+                )
+                self._maybe_compute_loss(stage, output, target_mbs, mb_index)
+
+                # SEND/RECV op are avoided for special case with 2 adjacent stages on same rank
+                # see [Note: V-schedule special case]
+                if is_next_stage_on_this_rank:
+                    stage_index_to_stage[stage_idx + 1].set_local_fwd_input(
+                        output, mb_index
+                    )
+
+            elif comp_type == FULL_BACKWARD:
+                self._assert_unsharded(stage)
+
+                if (
+                    not stage.is_last
+                    # no recv op expected for V-schedule special case (see [Note: V-schedule special case])
+                    and not is_next_stage_on_this_rank
+                ):
+                    assert (
+                        stage_idx,
+                        mb_index,
+                    ) in self.bwd_recv_ops, (
+                        f"Attempted to run compute {action=} before receiving input"
+                    )
+                    _wait_batch_p2p(self.bwd_recv_ops.pop((stage_idx, mb_index)))
+                loss = self._maybe_get_loss(stage, mb_index)
+                self.backward_counter[stage_idx] += 1
+                last_backward = self.backward_counter[stage_idx] == self._n_microbatches
+                stage.backward_one_chunk(
+                    mb_index,
+                    loss=loss,
+                    full_backward=True,
+                    last_backward=last_backward,
+                )
+                # SEND/RECV op are avoided for special case with 2 adjacent stages on same rank
+                # see [Note: V-schedule special case]
+                if is_prev_stage_on_this_rank:
+                    stage_index_to_stage[stage_idx - 1].set_local_bwd_input(
+                        stage.get_local_bwd_output(mb_index), mb_index
+                    )
+            elif comp_type == BACKWARD_INPUT:
+                self._assert_unsharded(stage)
+
+                if not stage.is_last and not is_next_stage_on_this_rank:
+                    assert (
+                        stage_idx,
+                        mb_index,
+                    ) in self.bwd_recv_ops, (
+                        f"Attempted to run compute {action=} before receiving input"
+                    )
+                    _wait_batch_p2p(self.bwd_recv_ops.pop((stage_idx, mb_index)))
+                loss = self._maybe_get_loss(stage, mb_index)
+                stage.backward_one_chunk(
+                    mb_index,
+                    loss=loss,
+                    full_backward=False,
+                    last_backward=False,
+                )
+                # SEND/RECV op are avoided for special case with 2 adjacent stages on same rank
+                # see [Note: V-schedule special case]
+                if is_prev_stage_on_this_rank:
+                    stage_index_to_stage[stage_idx - 1].set_local_bwd_input(
+                        stage.get_local_bwd_output(mb_index), mb_index
+                    )
+            elif comp_type == BACKWARD_WEIGHT:
+                self._assert_unsharded(stage)
+                self.backward_counter[stage_idx] += 1
+                last_backward = self.backward_counter[stage_idx] == self._n_microbatches
+                stage.backward_weight_one_chunk(
+                    mb_index,
+                    last_backward=last_backward,
+                )
+            elif comp_type == REDUCE_GRAD:
+                grad_scale_factor = self._n_microbatches if self.scale_grads else 1
+                stage.perform_reduce_grad(grad_scale_factor)
+            else:
+                raise ValueError(f"{action=} is unknown or unsupported")
+
+        # count either full_backward or backward_weight together, to determine when to sync DP grads
+        self.backward_counter.clear()
+        for time_step, action in enumerate(self.pipeline_order_with_comms[self.rank]):
+            logger.debug(
+                "_PipelineScheduleRuntime running time_step %d, action %s",
+                time_step,
+                action,
+            )
+            try:
+                with record_function(_get_profiler_function_name(action)):
+                    if action.computation_type in self._comp_type_to_function_map:
+                        ctx = _PipelineContext(
+                            self,
+                            arg_mbs,
+                            kwarg_mbs,
+                            target_mbs,
+                            losses,
+                        )
+                        self._comp_type_to_function_map[action.computation_type](
+                            action, ctx
+                        )
+                    elif action.computation_type == OVERLAP_F_B:
+                        assert action.sub_actions is not None, "sub_actions must be set"
+                        for sub_a in action.sub_actions:
+                            _perform_action(sub_a)
+                    else:
+                        _perform_action(action)
+            except Exception as e:
+                logger.error(
+                    "_PipelineScheduleRuntime caught exception at step %s when running action %s.  Full Schedule:",
+                    time_step,
+                    action,
+                )
+                logger.error(
+                    _format_pipeline_order(
+                        self.pipeline_order_with_comms,  # type: ignore[arg-type]
+                        error_step_number=time_step,
+                    )
+                )
+                raise e
+
+        # Mostly these operations should have finished long ago, but there isn't an obvious time when to wait for them
+        while send_ops:
+            _wait_batch_p2p(send_ops.pop())
+
+        assert len(self.unshard_ops) == 0, "Unused unshard operations"
+
+        # Return losses if there is a container passed in
+        self._update_losses(self._stages, losses)
+
+
+class ScheduleLoopedBFS(_PipelineScheduleRuntime):
+    """
+    Breadth-First Pipeline Parallelism.
+    See https://arxiv.org/abs/2211.05953 for details.
+    Similar to Interleaved 1F1B, Looped BFS supports multiple stages per rank.
+    What is different is that when microbatches are ready for multiple local
+    stages, Loops BFS will prioritizes the earlier stage, running all available
+    microbatches at once.
+    """
+
+    def __init__(
+        self,
+        stages: list[_PipelineStageBase],
+        n_microbatches: int,
+        loss_fn: Callable | _Loss | None = None,
+        output_merge_spec: dict[str, Any] | tuple[Any] | None = None,
+        scale_grads: bool = True,
+        backward_requires_autograd: bool = True,
+    ):
+        super().__init__(
+            stages=stages,
+            n_microbatches=n_microbatches,
+            loss_fn=loss_fn,
+            output_merge_spec=output_merge_spec,
+            scale_grads=scale_grads,
+            backward_requires_autograd=backward_requires_autograd,
+        )
+
+        # 1. Create the pipeline_order (all ranks do this calculation)
+        # This will be used to keep track of the current state of the entire pipeline
+        # pipeline_order[rank] = [Action(computation_type, microbatch_index, stage_index), ...]
+        self.pipeline_order: dict[int, list[_Action | None]] = {}
+        # ========================================================================
+        for rank in range(self.pp_group_size):
+            rank_ops = self._calculate_single_rank_operations(rank)
+            self.pipeline_order[rank] = rank_ops
+
+        # Initialize the pipeline order with communication necessary to run with _PipelineScheduleRuntime
+        self._prepare_schedule_with_comms(self.pipeline_order)
+
+    def _calculate_single_rank_operations(self, rank):
+        n_local_stages = len(self._stages)
+        stage_indices = range(
+            rank, self.pp_group_size * n_local_stages, self.pp_group_size
+        )
+
+        # Store the list of operations used for that rank
+        # Pre-padding, rank starts with no-ops based on the warmup.
+        rank_ops: list[_Action | None] = [None for _ in range(rank)]
+
+        for stage_index in stage_indices:
+            rank_ops.extend(
+                _Action(stage_index, _ComputationType.FORWARD, mb_index)
+                for mb_index in range(self._n_microbatches)
+            )
+
+        # wait for the first backward to trickle up
+        # which is 2 for every hop away
+        post_warmup_ops = 2 * (self.pp_group_size - 1 - rank)
+        rank_ops.extend([None] * post_warmup_ops)
+
+        for stage_index in reversed(stage_indices):
+            rank_ops.extend(
+                _Action(stage_index, _ComputationType.FULL_BACKWARD, mb_index)
+                for mb_index in reversed(range(self._n_microbatches))
+            )
+        return rank_ops
+
+
+def _get_1f1b_rank_ops(
+    n_local_stages,
+    pp_group_size,
+    warmup_ops,
+    fwd_bwd_ops,
+    cooldown_ops,
+    rank,
+    forward_stage_index,
+    backward_stage_index,
+    num_1f1b_microbatches=0,
+    enable_zero_bubble=False,
+):
+    # All stages start with handling microbatch 0
+    fwd_stage_mb_index: dict[int, int] = defaultdict(int)
+    bwd_stage_mb_index: dict[int, int] = defaultdict(int)
+    weight_stage_mb_index: dict[int, int] = defaultdict(int)
+
+    # Store the list of operations used for that rank
+    # Pre-padding, rank starts with no-ops based on the warmup.
+    rank_ops: list[_Action | None] = [None for _ in range(rank)]
+    # These are used to calculate the number of slots to fill with no-ops, to account for the delay in warmup
+    # when we want to wait for the backward to trickle back up and start 1f1b to align all ranks.
+    # Formula:
+    # pre-padding + warmup_ops + post_warmup_ops = earliest time step of first backward
+    # post_warmup_ops = [earliest time step of first backward] - (warmup_ops + pre-padding)
+    # earliest time step of first backward = [local_stages * group_size + 2 * (group_size - 1 - rank)]
+    # warmup_ops = calculated above
+    post_warmup_ops = (
+        n_local_stages * pp_group_size + 2 * (pp_group_size - 1 - rank)
+    ) - (warmup_ops + rank)
+
+    if enable_zero_bubble:
+        post_warmup_ops = pp_group_size - rank - 1
+
+    total_ops = warmup_ops + fwd_bwd_ops + cooldown_ops
+
+    backward_op_ids = []
+    weight_op_count = 0
+
+    FULL_BACKWARD_OR_BACKWARD_INPUT = (
+        BACKWARD_INPUT if enable_zero_bubble else FULL_BACKWARD
+    )
+
+    for op in range(total_ops):
+        # Warmup phase
+        if op < warmup_ops:
+            fwd_stage_index = forward_stage_index(op)
+            # This will assign the current microbatch index and update it as well
+            fwd_stage_mb_index[fwd_stage_index] = (
+                mb_index := fwd_stage_mb_index[fwd_stage_index]
+            ) + 1
+            rank_ops.append(
+                _Action(fwd_stage_index, _ComputationType.FORWARD, mb_index)
+            )
+            if op == warmup_ops - 1:
+                # This is the last step in the warmup phase, so we need to wait for the backward to trickle back up
+                rank_ops.extend([None] * post_warmup_ops)
+        # 1F1B Phase (forward and backward)
+        elif warmup_ops <= op < warmup_ops + fwd_bwd_ops:
+            fwd_stage_index = forward_stage_index(op)
+            fwd_stage_mb_index[fwd_stage_index] = (
+                fwd_mb_index := fwd_stage_mb_index[fwd_stage_index]
+            ) + 1
+            rank_ops.append(
+                _Action(fwd_stage_index, _ComputationType.FORWARD, fwd_mb_index)
+            )
+            bwd_stage_index = backward_stage_index(op)
+            bwd_stage_mb_index[bwd_stage_index] = (
+                bwd_mb_index := bwd_stage_mb_index[bwd_stage_index]
+            ) + 1
+            rank_ops.append(
+                _Action(bwd_stage_index, FULL_BACKWARD_OR_BACKWARD_INPUT, bwd_mb_index)
+            )
+            backward_op_ids.append(op)
+
+            if enable_zero_bubble and op - warmup_ops >= num_1f1b_microbatches:
+                weight_stage_index = backward_stage_index(
+                    backward_op_ids[weight_op_count]
+                )
+                weight_stage_mb_index[weight_stage_index] = (
+                    weight_mb_index := weight_stage_mb_index[weight_stage_index]
+                ) + 1
+                rank_ops.append(
+                    _Action(
+                        weight_stage_index,
+                        _ComputationType.BACKWARD_WEIGHT,
+                        weight_mb_index,
+                    )
+                )
+                weight_op_count += 1
+        # Cooldown phase
+        else:
+            # During cooldown phase, we need steps to align with 1f1b happening in other ranks
+            # TODO: we don't need to always append, after all 1f1b are finished we can stop appending None
+            if not enable_zero_bubble:
+                rank_ops.append(None)
+
+            bwd_stage_index = backward_stage_index(op)
+            bwd_stage_mb_index[bwd_stage_index] = (
+                bwd_mb_index := bwd_stage_mb_index[bwd_stage_index]
+            ) + 1
+            rank_ops.append(
+                _Action(bwd_stage_index, FULL_BACKWARD_OR_BACKWARD_INPUT, bwd_mb_index)
+            )
+            backward_op_ids.append(op)
+
+            if enable_zero_bubble and op - warmup_ops >= num_1f1b_microbatches:
+                weight_stage_index = backward_stage_index(
+                    backward_op_ids[weight_op_count]
+                )
+                weight_stage_mb_index[weight_stage_index] = (
+                    weight_mb_index := weight_stage_mb_index[weight_stage_index]
+                ) + 1
+                rank_ops.append(
+                    _Action(
+                        weight_stage_index,
+                        _ComputationType.BACKWARD_WEIGHT,
+                        weight_mb_index,
+                    )
+                )
+                weight_op_count += 1
+
+    while enable_zero_bubble and weight_op_count < len(backward_op_ids):
+        weight_stage_index = backward_stage_index(backward_op_ids[weight_op_count])
+        weight_stage_mb_index[weight_stage_index] = (
+            weight_mb_index := weight_stage_mb_index[weight_stage_index]
+        ) + 1
+        rank_ops.append(
+            _Action(
+                weight_stage_index, _ComputationType.BACKWARD_WEIGHT, weight_mb_index
+            )
+        )
+        weight_op_count += 1
+
+    return rank_ops
+
+
+class ScheduleInterleaved1F1B(_PipelineScheduleRuntime):
+    """
+    The Interleaved 1F1B schedule.
+    See https://arxiv.org/pdf/2104.04473 for details.
+    Will perform one forward and one backward on the microbatches in steady
+    state and supports multiple stages per rank. When microbatches are ready for
+    multiple local stages, Interleaved 1F1B prioritizes the earlier microbatch
+    (also called "depth first").
+
+    This schedule is mostly similar to the original paper.
+    It differs by being relaxing the requirement of num_microbatch % pp_size == 0.
+    Using the flex_pp schedule, we will have num_rounds = max(1, n_microbatches // pp_group_size) and
+    it works as long as n_microbatches % num_rounds is 0. As a few examples, support
+
+    1. pp_group_size = 4, n_microbatches = 10. We will have num_rounds = 2 and n_microbatches % 2 is 0.
+    2. pp_group_size = 4, n_microbatches = 3. We will have num_rounds = 1 and n_microbatches % 1 is 0.
+    """
+
+    def __init__(
+        self,
+        stages: list[_PipelineStageBase],
+        n_microbatches: int,
+        loss_fn: Callable | None = None,
+        args_chunk_spec: tuple[TensorChunkSpec, ...] | None = None,
+        kwargs_chunk_spec: dict[str, TensorChunkSpec] | None = None,
+        output_merge_spec: dict[str, Any] | tuple[Any] | None = None,
+        scale_grads: bool = True,
+        backward_requires_autograd: bool = True,
+    ):
+        self.pp_group_size = stages[0].group_size
+        super().__init__(
+            stages=stages,
+            n_microbatches=n_microbatches,
+            loss_fn=loss_fn,
+            args_chunk_spec=args_chunk_spec,
+            kwargs_chunk_spec=kwargs_chunk_spec,
+            output_merge_spec=output_merge_spec,
+            scale_grads=scale_grads,
+            backward_requires_autograd=backward_requires_autograd,
+        )
+        self.n_local_stages = len(stages)
+        self.rank = stages[0].group_rank
+        self.number_of_rounds = max(1, n_microbatches // self.pp_group_size)
+        self.microbatches_per_round = n_microbatches // self.number_of_rounds
+        if n_microbatches % self.number_of_rounds != 0:
+            raise ValueError(
+                "Interleaved 1F1B requires the number of microbatches to be a "
+                f"multiple of the number of rounds ({self.number_of_rounds}), "
+                f"but got {n_microbatches}."
+            )
+        # 1. Create the pipeline_order (all ranks do this calculation)
+        # This will be used to keep track of the current state of the entire pipeline
+        # pipeline_order[rank] = [Action(computation_type, microbatch_index, stage_index), ...]
+        self.pipeline_order: dict[int, list[_Action | None]] = {}
+        for rank in range(self.pp_group_size):
+            rank_ops = self._calculate_single_rank_operations(rank)
+            self.pipeline_order[rank] = rank_ops
+
+        # Initialize the pipeline order with communication necessary to run with _PipelineScheduleRuntime
+        self._prepare_schedule_with_comms(self.pipeline_order)
+
+    def _calculate_single_rank_operations(self, rank) -> list[_Action | None]:
+        def get_rank_warmup_ops(rank):
+            # Warms up operations for last stage
+            warmups_ops_last_stage = (
+                self.n_local_stages - 1
+            ) * self.microbatches_per_round
+            # Increment warmup operations by 2 for each hop away from the last stage
+            multiply_factor = 2
+            warmup_ops = warmups_ops_last_stage + multiply_factor * (
+                (self.pp_group_size - 1) - rank
+            )
+
+            # We cannot have more warmup operations than there are number of microbatches, so cap it there
+            return min(warmup_ops, self._n_microbatches * self.n_local_stages)
+
+        warmup_ops = get_rank_warmup_ops(rank)
+        microbatch_ops = self.n_local_stages * self._n_microbatches
+        # fwd_bwd_ops should encompass the remaining forwards
+        fwd_bwd_ops = microbatch_ops - warmup_ops
+        # cooldown_ops should encompass the remaining backwards
+        cooldown_ops = microbatch_ops - fwd_bwd_ops
+        # total ops encompass both forward and backward ops
+        total_ops = warmup_ops + fwd_bwd_ops + cooldown_ops
+        # warmup_ops + fwd_bwd_ops * 2 + cooldown_ops == microbatch_ops * 2
+        logger.debug(
+            "rank %s, warmup_ops %s, 1f1b %s, cooldown_ops %s total_ops %s",
+            rank,
+            warmup_ops,
+            fwd_bwd_ops,
+            cooldown_ops,
+            total_ops,
+        )
+
+        # Calculates the stage index based on step and pp_group_size
+        def forward_stage_index(step):
+            # Get the local index from 0 to n_local_stages-1
+            local_index = (step // self.microbatches_per_round) % self.n_local_stages
+            return (local_index * self.pp_group_size) + rank
+
+        def backward_stage_index(step):
+            local_index = (
+                self.n_local_stages
+                - 1
+                - ((step - warmup_ops) // self.microbatches_per_round)
+                % self.n_local_stages
+            )
+            return (local_index * self.pp_group_size) + rank
+
+        return _get_1f1b_rank_ops(
+            self.n_local_stages,
+            self.pp_group_size,
+            warmup_ops,
+            fwd_bwd_ops,
+            cooldown_ops,
+            rank,
+            forward_stage_index,
+            backward_stage_index,
+        )
+
+
+class ScheduleInterleavedZeroBubble(_PipelineScheduleRuntime):
+    """
+    The Interleaved Zero Bubble schedule.
+    See https://arxiv.org/pdf/2401.10241 for details.
+    Will perform one forward and one backward on inputs for the microbatches in steady
+    state and supports multiple stages per rank. Uses the backward for weights to fill in
+    the pipeline bubble.
+
+    In particular this is implementing the ZB1P schedule in the paper.
+    """
+
+    def __init__(
+        self,
+        stages: list[_PipelineStageBase],
+        n_microbatches: int,
+        loss_fn: Callable | None = None,
+        args_chunk_spec: tuple[TensorChunkSpec, ...] | None = None,
+        kwargs_chunk_spec: dict[str, TensorChunkSpec] | None = None,
+        output_merge_spec: dict[str, Any] | tuple[Any] | None = None,
+        scale_grads: bool = True,
+        backward_requires_autograd: bool = True,
+    ):
+        # TODO: we dont support input/weight backward split with torch.compile
+        _check_torch_compile_compatibility(stages, self.__class__.__name__)
+        self.pp_group_size = stages[0].group_size
+        super().__init__(
+            stages=stages,
+            n_microbatches=n_microbatches,
+            loss_fn=loss_fn,
+            args_chunk_spec=args_chunk_spec,
+            kwargs_chunk_spec=kwargs_chunk_spec,
+            output_merge_spec=output_merge_spec,
+            scale_grads=scale_grads,
+            backward_requires_autograd=backward_requires_autograd,
+        )
+        self.n_local_stages = len(stages)
+        self.rank = stages[0].group_rank
+        self.number_of_rounds = max(1, n_microbatches // self.pp_group_size)
+        self.microbatches_per_round = n_microbatches // self.number_of_rounds
+        if n_microbatches % self.number_of_rounds != 0:
+            raise ValueError(
+                "Zero bubble requires the number of microbatches to be a "
+                f"multiple of the number of rounds ({self.number_of_rounds}), "
+                f"but got {n_microbatches}."
+            )
+        # 1. Create the pipeline_order (all ranks do this calculation)
+        # This will be used to keep track of the current state of the entire pipeline
+        # pipeline_order[rank] = [Action(computation_type, microbatch_index, stage_index), ...]
+        self.pipeline_order: dict[int, list[_Action | None]] = {}
+        for rank in range(self.pp_group_size):
+            rank_ops = self._calculate_single_rank_operations(rank)
+            self.pipeline_order[rank] = rank_ops
+
+        # This function add bubbles to the generated schedule based on dependencies of actions
+        # Note that the ZB1P schedule will not require bubbles to be manually added and it is
+        # only useful when n_microbatches <= microbatches_per_round
+        self.pipeline_order = self._add_bubbles_to_actions(
+            self.n_local_stages * self.pp_group_size,
+        )
+
+        # Initialize the pipeline order with communication necessary to run with _PipelineScheduleRuntime
+        self._prepare_schedule_with_comms(self.pipeline_order)
+
+    def _calculate_single_rank_operations(self, rank) -> list[_Action | None]:
+        def get_rank_warmup_ops(rank):
+            # Warms up operations for last stage
+            warmups_ops_last_stage = (
+                self.n_local_stages - 1
+            ) * self.microbatches_per_round
+            # Increment warmup operations by 2 for each hop away from the last stage
+            multiply_factor = 1
+            warmup_ops = warmups_ops_last_stage + multiply_factor * (
+                (self.pp_group_size - 1) - rank
+            )
+
+            # We cannot have more warmup operations than there are number of microbatches, so cap it there
+            return min(warmup_ops, self._n_microbatches * self.n_local_stages)
+
+        warmup_ops = get_rank_warmup_ops(rank)
+        microbatch_ops = self.n_local_stages * self._n_microbatches
+        # fwd_bwd_ops should encompass the remaining forwards
+        fwd_bwd_ops = microbatch_ops - warmup_ops
+        # cooldown_ops should encompass the remaining backwards
+        cooldown_ops = microbatch_ops - fwd_bwd_ops
+        # total ops encompass both forward and backward ops
+        total_ops = warmup_ops + fwd_bwd_ops + cooldown_ops
+        # warmup_ops + fwd_bwd_ops * 2 + cooldown_ops == microbatch_ops * 2
+        logger.debug(
+            "rank %s, warmup_ops %s, 1f1b %s, cooldown_ops %s total_ops %s",
+            rank,
+            warmup_ops,
+            fwd_bwd_ops,
+            cooldown_ops,
+            total_ops,
+        )
+
+        # Calculates the stage index based on step and pp_group_size
+
+        def forward_stage_index(step):
+            # Get the local index from 0 to n_local_stages-1
+            local_index = (step // self.microbatches_per_round) % self.n_local_stages
+            return (local_index * self.pp_group_size) + rank
+
+        def backward_stage_index(step):
+            local_index = (
+                self.n_local_stages
+                - 1
+                - ((step - warmup_ops) // self.microbatches_per_round)
+                % self.n_local_stages
+            )
+            return (local_index * self.pp_group_size) + rank
+
+        num_1f1b_microbatches = rank
+
+        return _get_1f1b_rank_ops(
+            self.n_local_stages,
+            self.pp_group_size,
+            warmup_ops,
+            fwd_bwd_ops,
+            cooldown_ops,
+            rank,
+            forward_stage_index,
+            backward_stage_index,
+            num_1f1b_microbatches,
+            enable_zero_bubble=True,
+        )
+
+    def _add_bubbles_to_actions(self, num_stages_global):
+        actions = self.pipeline_order
+
+        def need_bubble(stage, op, microbatch, num_stages_global, seen_ops):
+            if op == _ComputationType.FORWARD:
+                if stage != 0 and (stage - 1, op, microbatch) not in seen_ops:
+                    return True
+            elif op == _ComputationType.FULL_BACKWARD:
+                if stage == num_stages_global - 1:
+                    return (stage, _ComputationType.FORWARD, microbatch) not in seen_ops
+                return (stage + 1, op, microbatch) not in seen_ops
+            return False
+
+        seen_ops: set[tuple[int, _ComputationType, int]] = set()
+        result: dict[int, list[_Action | None]] = {}
+        next_pointer: dict[int, int] = {}
+        bubbles_added: dict[int, int] = {}
+        total_bubbles_added = 0
+
+        for rank in range(self.pp_group_size):
+            result[rank] = []
+            next_pointer[rank] = 0
+            bubbles_added[rank] = 0
+
+        while True:
+            should_stop = True
+
+            temp_seen_ops: set[tuple[int, _ComputationType, int]] = set()
+
+            for rank in range(self.pp_group_size):
+                timestamp = next_pointer[rank]
+                if timestamp >= len(actions[rank]):
+                    continue
+
+                should_stop = False
+
+                if actions[rank][timestamp] is not None:
+                    temp_action = actions[rank][timestamp]
+                    assert temp_action is not None
+                    stage_index, op, microbatch, _ = temp_action
+                    if not need_bubble(
+                        stage_index, op, microbatch, num_stages_global, seen_ops
+                    ):
+                        result[rank].append(actions[rank][timestamp])
+                        if microbatch is not None:
+                            temp_seen_ops.add((stage_index, op, microbatch))
+                        next_pointer[rank] += 1
+                    else:
+                        result[rank].append(None)
+                        bubbles_added[rank] += 1
+                else:
+                    next_pointer[rank] += 1
+                    result[rank].append(None)
+
+            seen_ops.update(temp_seen_ops)
+            if should_stop:
+                break
+
+        if total_bubbles_added > 0:
+            logger.warning(
+                "Non zero bubbles added: total_bubbles_added=%s bubbles_added=%s",
+                total_bubbles_added,
+                bubbles_added,
+            )
+        return result
+
+
+class ScheduleZBVZeroBubble(_PipelineScheduleRuntime):
+    """
+    The Zero Bubble schedule (ZBV variant).
+    See https://arxiv.org/pdf/2401.10241 Section 6 for details.
+
+    This schedules requires exactly two stages per rank.
+
+    This schedule will perform one forward and one backward on inputs for the microbatches in steady
+    state and supports multiple stages per rank. Uses backward with respect to weights to fill in
+    the pipeline bubble.
+
+    This ZB-V schedule would have the "zero bubble" property only if time forward == time backward input == time backward weights.
+    In practice, this is not likely true for real models so alternatively
+    a greedy scheduler could be implemented for unequal/unbalanced time.
+    """
+
+    def __init__(
+        self,
+        stages: list[_PipelineStageBase],
+        n_microbatches: int,
+        loss_fn: Callable | None = None,
+        args_chunk_spec: tuple[TensorChunkSpec, ...] | None = None,
+        kwargs_chunk_spec: dict[str, TensorChunkSpec] | None = None,
+        output_merge_spec: dict[str, Any] | tuple[Any] | None = None,
+        scale_grads: bool = True,
+        backward_requires_autograd: bool = True,
+    ):
+        # TODO: we dont support input/weight backward split with torch.compile
+        _check_torch_compile_compatibility(stages, self.__class__.__name__)
+        self.pp_group_size = stages[0].group_size
+        super().__init__(
+            stages=stages,
+            n_microbatches=n_microbatches,
+            loss_fn=loss_fn,
+            args_chunk_spec=args_chunk_spec,
+            kwargs_chunk_spec=kwargs_chunk_spec,
+            output_merge_spec=output_merge_spec,
+            scale_grads=scale_grads,
+            backward_requires_autograd=backward_requires_autograd,
+        )
+        self.stage_index_to_group_rank = generate_stage_to_rank_mapping(
+            self.pp_group_size, self._num_stages, style="v"
+        )
+        for stage in self._stages:
+            stage.stage_index_to_group_rank = self.stage_index_to_group_rank
+
+        self.n_local_stages = len(stages)
+        if self.n_local_stages != 2:
+            raise ValueError(
+                "ZBV requires exactly 2 stages per rank, but got "
+                f"{self.n_local_stages}."
+            )
+
+        self.rank = stages[0].group_rank
+        self.num_stages = stages[0].num_stages
+
+        # 1. Create the pipeline_order (all ranks do this calculation)
+        # This will be used to keep track of the current state of the entire pipeline
+        # pipeline_order[rank] = [Action(computation_type, microbatch_index, stage_index), ...]
+        self.pipeline_order: dict[int, list[_Action | None]] = {}
+        for rank in range(self.pp_group_size):
+            rank_ops = self._calculate_single_rank_operations(rank)
+            self.pipeline_order[rank] = rank_ops
+
+        # Initialize the pipeline order with communication necessary to run with _PipelineScheduleRuntime
+        self._prepare_schedule_with_comms(self.pipeline_order)
+
+    def _calculate_single_rank_operations(self, rank) -> list[_Action | None]:
+        # max(2 * self.pp_group_size - 1, ...) ensure the number of microbatches is at least
+        # as large of the number of microbatches needed to fully utilize the pipeline
+        n_micro = max(2 * self.pp_group_size - 1, self._n_microbatches)
+        rank_ops: list[_Action | None] = [None for _ in range(rank)]
+
+        # Forward and backward action counts for stage chunk 0 and chunk 1
+        f0_cnt, f1_cnt, b0_cnt, b1_cnt = 0, 0, 0, 0
+        # warm-up phase
+        warmup_n1 = 2 * (self.pp_group_size - rank) - 1
+        stage_id_chunk0 = rank
+        stage_id_chunk1 = self.num_stages - 1 - rank
+
+        for _ in range(warmup_n1):
+            rank_ops.append(
+                _Action(stage_id_chunk0, computation_type=F, microbatch_index=f0_cnt)
+            )
+            f0_cnt += 1
+        warmup_n2 = rank
+        for _ in range(warmup_n2):
+            rank_ops.append(
+                _Action(stage_id_chunk1, computation_type=F, microbatch_index=f1_cnt)
+            )
+            f1_cnt += 1
+            rank_ops.append(
+                _Action(stage_id_chunk0, computation_type=F, microbatch_index=f0_cnt)
+            )
+            f0_cnt += 1
+        warmup_n3 = self.pp_group_size - rank
+        for _ in range(warmup_n3):
+            rank_ops.append(
+                _Action(stage_id_chunk1, computation_type=F, microbatch_index=f1_cnt)
+            )
+            f1_cnt += 1
+            rank_ops.append(
+                _Action(stage_id_chunk1, computation_type=I, microbatch_index=b1_cnt)
+            )
+            rank_ops.append(
+                _Action(stage_id_chunk1, computation_type=W, microbatch_index=b1_cnt)
+            )
+            b1_cnt += 1
+        # stable phase
+        while f1_cnt < f0_cnt or f0_cnt < n_micro:
+            if f0_cnt < n_micro:
+                rank_ops.append(
+                    _Action(
+                        stage_id_chunk0, computation_type=F, microbatch_index=f0_cnt
+                    )
+                )
+                f0_cnt += 1
+            rank_ops.append(
+                _Action(stage_id_chunk0, computation_type=I, microbatch_index=b0_cnt)
+            )
+            rank_ops.append(
+                _Action(stage_id_chunk0, computation_type=W, microbatch_index=b0_cnt)
+            )
+            b0_cnt += 1
+
+            rank_ops.append(
+                _Action(stage_id_chunk1, computation_type=F, microbatch_index=f1_cnt)
+            )
+            f1_cnt += 1
+            rank_ops.append(
+                _Action(stage_id_chunk1, computation_type=I, microbatch_index=b1_cnt)
+            )
+            rank_ops.append(
+                _Action(stage_id_chunk1, computation_type=W, microbatch_index=b1_cnt)
+            )
+            b1_cnt += 1
+        # cool-down phase
+        w0_cnt, w1_cnt = b0_cnt, b1_cnt
+        cooldown_n1 = rank
+        for _ in range(cooldown_n1):
+            rank_ops.append(
+                _Action(stage_id_chunk0, computation_type=I, microbatch_index=b0_cnt)
+            )
+            b0_cnt += 1
+            rank_ops.append(
+                _Action(stage_id_chunk1, computation_type=I, microbatch_index=b1_cnt)
+            )
+            b1_cnt += 1
+        cooldown_n2 = self.pp_group_size - rank
+        for _ in range(cooldown_n2):
+            rank_ops.append(
+                _Action(stage_id_chunk0, computation_type=I, microbatch_index=b0_cnt)
+            )
+            b0_cnt += 1
+            rank_ops.append(
+                _Action(stage_id_chunk0, computation_type=W, microbatch_index=w0_cnt)
+            )
+            w0_cnt += 1
+        while w1_cnt < b1_cnt:
+            rank_ops.append(
+                _Action(stage_id_chunk1, computation_type=W, microbatch_index=w1_cnt)
+            )
+            w1_cnt += 1
+        while w0_cnt < b0_cnt:
+            rank_ops.append(
+                _Action(stage_id_chunk0, computation_type=W, microbatch_index=w0_cnt)
+            )
+            w0_cnt += 1
+
+        assert w0_cnt == b0_cnt and b0_cnt == f0_cnt
+        assert w1_cnt == b1_cnt and b1_cnt == f1_cnt
+        # We use max() in the n_micro computation above, so we may need to
+        # remove redundant microbatches
+        rank_ops = [
+            (
+                action
+                if action is not None
+                and action.microbatch_index is not None
+                and action.microbatch_index < self._n_microbatches
+                else None
+            )
+            for action in rank_ops
+        ]
+        return rank_ops
+
+
+class ScheduleDualPipeV(_PipelineScheduleRuntime):
+    """
+    The DualPipeV schedule. A more efficient schedule variant based on the
+    DualPipe schedule introduced by DeepSeek in https://arxiv.org/pdf/2412.19437
+
+    Based on the open sourced code from https://github.com/deepseek-ai/DualPipe
+    """
+
+    def __init__(
+        self,
+        stages: list[_PipelineStageBase],
+        n_microbatches: int,
+        loss_fn: Callable | None = None,
+        args_chunk_spec: tuple[TensorChunkSpec, ...] | None = None,
+        kwargs_chunk_spec: dict[str, TensorChunkSpec] | None = None,
+        output_merge_spec: dict[str, Any] | tuple[Any] | None = None,
+        scale_grads: bool = True,
+        backward_requires_autograd: bool = True,
+    ):
+        # TODO: we dont support input/weight backward split with torch.compile
+        _check_torch_compile_compatibility(stages, self.__class__.__name__)
+        self.pp_group_size = stages[0].group_size
+        super().__init__(
+            stages=stages,
+            n_microbatches=n_microbatches,
+            loss_fn=loss_fn,
+            args_chunk_spec=args_chunk_spec,
+            kwargs_chunk_spec=kwargs_chunk_spec,
+            output_merge_spec=output_merge_spec,
+            scale_grads=scale_grads,
+            backward_requires_autograd=backward_requires_autograd,
+        )
+        self.stage_index_to_group_rank = generate_stage_to_rank_mapping(
+            self.pp_group_size, self._num_stages, style="v"
+        )
+        for stage in self._stages:
+            stage.stage_index_to_group_rank = self.stage_index_to_group_rank
+
+        self.n_local_stages = len(stages)
+        if self.n_local_stages != 2:
+            raise ValueError(
+                "ZBV requires exactly 2 stages per rank, but got "
+                f"{self.n_local_stages}."
+            )
+        if n_microbatches < self._num_stages:
+            raise ValueError(
+                "DualPipeV requires at least as many microbatches as stages, but got "
+                f"{n_microbatches} microbatches and {self._num_stages} stages."
+            )
+
+        self.rank = stages[0].group_rank
+        self.num_stages = stages[0].num_stages
+
+        # 1. Create the pipeline_order (all ranks do this calculation)
+        # This will be used to keep track of the current state of the entire pipeline
+        # pipeline_order[rank] = [Action(computation_type, microbatch_index, stage_index), ...]
+        self.pipeline_order: dict[int, list[_Action | None]] = {}
+        for rank in range(self.pp_group_size):
+            rank_ops = self._calculate_single_rank_operations(rank)
+            self.pipeline_order[rank] = rank_ops
+
+        # Initialize the pipeline order with communication necessary to run with _PipelineScheduleRuntime
+        self._prepare_schedule_with_comms(self.pipeline_order)
+
+    def _calculate_single_rank_operations(self, rank) -> list[_Action | None]:
+        actions: list[_Action | None] = []
+        counters: dict[
+            tuple[int, _ComputationType], int
+        ] = {}  # (stage_index, computation_type) -> mb_index
+        weight_queue = []  # Queue of (stage_index, mb_index) for pending weight actions
+
+        num_ranks = self.pp_group_size
+        num_chunks = self._n_microbatches
+
+        rank_to_stages = generate_rank_to_stage_mapping(
+            num_ranks, num_ranks * 2, style="v"
+        )
+        stage0_index, stage1_index = rank_to_stages[rank]
+
+        def increment_backward_counts(stage_index: int):
+            """Helper method to increment BACKWARD_INPUT and BACKWARD_WEIGHT counters when FULL_BACKWARD is used."""
+            input_key = (stage_index, BACKWARD_INPUT)
+            weight_key = (stage_index, BACKWARD_WEIGHT)
+            counters[input_key] = counters.get(input_key, 0) + 1
+            counters[weight_key] = counters.get(weight_key, 0) + 1
+
+        def add_overlap_f_b(
+            actions: list,
+            forward_stage: int,
+            backward_stage: int,
+        ):
+            """Helper method to add an overlapped forward+backward action which tracks microbatch index."""
+            # Create new overlapped forward+backward action with sub_actions
+            forward_key = (forward_stage, FORWARD)
+            backward_key = (backward_stage, BACKWARD_INPUT)
+
+            forward_mb = counters.get(forward_key, 0)
+            backward_mb = counters.get(backward_key, 0)
+
+            sub_actions = (
+                _Action(forward_stage, FORWARD, forward_mb),
+                _Action(backward_stage, FULL_BACKWARD, backward_mb),
+            )
+            actions.append(_Action(-1, OVERLAP_F_B, None, sub_actions))
+
+            # Update counters for sub_actions
+            counters[forward_key] = forward_mb + 1
+            increment_backward_counts(backward_stage)
+
+        def add_action(
+            actions: list,
+            stage_index: int,
+            computation_type: _ComputationType,
+        ):
+            # Regular single action, for FULL_BACKWARD we only use the BACKWARD_INPUT counter
+            key = (
+                (stage_index, computation_type)
+                if computation_type != FULL_BACKWARD
+                else (stage_index, BACKWARD_INPUT)
+            )
+            mb_index = counters.get(key, 0)
+            actions.append(_Action(stage_index, computation_type, mb_index))
+
+            # If FULL_BACKWARD is used, just increment the separate BACKWARD_INPUT and BACKWARD_WEIGHT counters
+            if computation_type == FULL_BACKWARD:
+                increment_backward_counts(stage_index)
+            else:
+                # If BACKWARD_INPUT is updated, add corresponding weight action to queue
+                if computation_type == BACKWARD_INPUT:
+                    # Add weight action to queue for later processing
+                    weight_queue.append((stage_index, mb_index))
+                counters[key] = mb_index + 1
+
+        def add_weight_action_if_pending(actions: list):
+            """Helper method to add a weight action from the queue."""
+            if not weight_queue:
+                return  # No pending weight actions, skip
+            # Pop the oldest weight action from the queue
+            actual_stage_index, weight_mb_index = weight_queue.pop(0)
+            actions.append(
+                _Action(
+                    actual_stage_index,
+                    BACKWARD_WEIGHT,
+                    weight_mb_index,
+                )
+            )
+            # Update the counter for the actual stage that was processed
+            weight_key = (actual_stage_index, BACKWARD_WEIGHT)
+            counters[weight_key] = counters.get(weight_key, 0) + 1
+
+        # Step 1: F0
+        step_1 = (num_ranks - rank - 1) * 2
+        for _ in range(step_1):
+            add_action(actions, stage0_index, FORWARD)
+
+        # Step 2: F0F1
+        step_2 = rank + 1
+        for _ in range(step_2):
+            add_action(actions, stage0_index, FORWARD)
+            add_action(actions, stage1_index, FORWARD)
+
+        # Step 3: I1W1F1 (Use zero bubble)
+        step_3 = num_ranks - rank - 1
+        for _ in range(step_3):
+            add_action(actions, stage1_index, BACKWARD_INPUT)
+            add_weight_action_if_pending(actions)
+            add_action(actions, stage1_index, FORWARD)
+
+        # Step 4 (Main step): F0B1-F1B0 (combined, overlapped forward+backward)
+        step_4 = num_chunks - num_ranks * 2 + rank + 1
+        for i in range(step_4):
+            if i == 0 and rank == num_ranks - 1:
+                # NOTE: We don't overlap these two chunks to further reduce bubble size.
+                add_action(actions, stage0_index, FORWARD)
+                add_action(actions, stage1_index, FULL_BACKWARD)
+            else:
+                add_overlap_f_b(
+                    actions,
+                    forward_stage=stage0_index,
+                    backward_stage=stage1_index,
+                )
+            add_overlap_f_b(
+                actions,
+                forward_stage=stage1_index,
+                backward_stage=stage0_index,
+            )
+
+        # Step 5: B1-F1B0
+        step_5 = num_ranks - rank - 1
+        for _ in range(step_5):
+            add_action(actions, stage1_index, FULL_BACKWARD)
+            add_overlap_f_b(
+                actions,
+                forward_stage=stage1_index,
+                backward_stage=stage0_index,
+            )
+
+        # Step 6: B1B0 (The second half of the chunks use zero bubble)
+        step_6 = rank + 1
+        enable_zb = False
+        for i in range(step_6):
+            if i == step_6 // 2 and rank % 2 == 1:
+                enable_zb = True
+            comp_type = BACKWARD_INPUT if enable_zb else FULL_BACKWARD
+            add_action(actions, stage1_index, comp_type)
+            if i == step_6 // 2 and rank % 2 == 0:
+                enable_zb = True
+            comp_type = BACKWARD_INPUT if enable_zb else FULL_BACKWARD
+            add_action(actions, stage0_index, comp_type)
+
+        # Step 7: W0B0
+        step_7 = num_ranks - rank - 1
+        for _ in range(step_7):
+            add_weight_action_if_pending(actions)
+            comp_type = BACKWARD_INPUT if enable_zb else FULL_BACKWARD
+            add_action(actions, stage0_index, comp_type)
+
+        # Step 8: W0
+        step_8 = rank + 1
+        for _ in range(step_8):
+            add_weight_action_if_pending(actions)
+
+        return actions
+
+
+def get_schedule_class(schedule_name: str):
+    """
+    Maps a schedule name (case insensitive) to its corresponding class object.
+
+    Args:
+        schedule_name (str): The name of the schedule.
+    """
+    schedule_map = {
+        "1F1B": Schedule1F1B,
+        "Interleaved1F1B": ScheduleInterleaved1F1B,
+        "GPipe": ScheduleGPipe,
+        "LoopedBFS": ScheduleLoopedBFS,
+        "InterleavedZeroBubble": ScheduleInterleavedZeroBubble,
+        "PipelineScheduleSingle": PipelineScheduleSingle,
+        "PipelineScheduleMulti": PipelineScheduleMulti,
+        "ZBVZeroBubble": ScheduleZBVZeroBubble,
+        "DualPipeV": ScheduleDualPipeV,
+    }
+    lowercase_keys = {k.lower(): k for k in schedule_map}
+    lowercase_schedule_name = schedule_name.lower()
+    if lowercase_schedule_name not in lowercase_keys:
+        raise ValueError(
+            f"Unknown schedule name '{schedule_name}'. The valid options are {list(schedule_map.keys())}"
+        )
+    return schedule_map[lowercase_keys[lowercase_schedule_name]]
+
+
+def _simulate_comms_compute(
+    pipeline_order, stage_to_rank: Callable[[int], int], num_stages: int
+):
+    """This function dry-run simulates the actions in the schedule from the perspective of all ranks, and flags
+    any deadlocks caused by missing or misordered communications.  It also simulates any bubbles in time where a rank
+    can not execute any action due to waiting for unmet dependencies.  The total number of simulator steps can be used
+    as a metric for unit tests involving IR optimization passes as reordering and merging of IR can reduce the number
+    of simulated steps.
+
+    The simulation is not high-fidelity and does not model overlapping of compute and communication, or cuda streams.
+    Future work may be to enhance this and model the compute time, comms overlap, and even memory.
+    """
+    pipeline_order = {
+        rank: [a for a in pipeline_order[rank] if a is not None]
+        for rank in sorted(pipeline_order)
+    }
+    _schedule: dict[int, list[_Action | None]] = {
+        rank: [] for rank in sorted(pipeline_order)
+    }
+
+    _prev_ops_rank: dict[int, set[_Action]] = {rank: set() for rank in _schedule}
+
+    def add_to_schedule(rank: int, action: _Action | None):
+        _schedule[rank].append(action)
+        if action is not None:
+            _prev_ops_rank[rank].add(action)
+
+    def _ready_to_schedule(action: _Action | None) -> bool:
+        if action is None:
+            return True
+
+        stage_idx = action.stage_index
+        prev_ops = _prev_ops_rank[stage_to_rank(stage_idx)]
+        if action.computation_type == F:
+            if action.stage_index == 0:
+                return True
+            elif (
+                _Action(action.stage_index, RECV_F, action.microbatch_index) in prev_ops
+            ):
+                return True
+            elif (
+                _Action(action.stage_index - 1, F, action.microbatch_index) in prev_ops
+            ):
+                return True
+            return False
+        elif action.computation_type in (BACKWARD_INPUT, FULL_BACKWARD):
+            if action.stage_index == num_stages - 1:
+                return True
+            if _Action(action.stage_index, RECV_B, action.microbatch_index) in prev_ops:
+                return True
+            if (
+                _Action(action.stage_index + 1, BACKWARD_INPUT, action.microbatch_index)
+                in prev_ops
+            ):
+                return True
+            if (
+                _Action(action.stage_index + 1, FULL_BACKWARD, action.microbatch_index)
+                in prev_ops
+            ):
+                return True
+            return False
+        elif action.computation_type == BACKWARD_WEIGHT:
+            return True
+        elif action.computation_type == SEND_F:
+            expected_f = _Action(action.stage_index, F, action.microbatch_index)
+            return expected_f in prev_ops
+        elif action.computation_type == RECV_F:
+            peer_stage_idx = stage_idx - 1
+            expected_send = _Action(peer_stage_idx, SEND_F, action.microbatch_index)
+            return expected_send in _prev_ops_rank[stage_to_rank(peer_stage_idx)]
+        elif action.computation_type == SEND_B:
+            expected_b = _Action(
+                action.stage_index, BACKWARD_INPUT, action.microbatch_index
+            )
+            expected_bw = _Action(
+                action.stage_index, FULL_BACKWARD, action.microbatch_index
+            )
+            return expected_b in prev_ops or expected_bw in prev_ops
+        elif action.computation_type == RECV_B:
+            peer_stage_idx = stage_idx + 1
+            expected_send = _Action(peer_stage_idx, SEND_B, action.microbatch_index)
+            return expected_send in _prev_ops_rank[stage_to_rank(peer_stage_idx)]
+        else:
+            raise ValueError(f"Unsupported action type {action}")
+
+    while pipeline_order:
+        progress = False
+        for rank in sorted(pipeline_order):
+            if len(pipeline_order[rank]) == 0:
+                continue
+
+            action = pipeline_order[rank][0]
+            if _ready_to_schedule(action):
+                if action is not None:
+                    add_to_schedule(rank, action)
+                pipeline_order[rank].pop(0)
+                progress = True
+            else:
+                add_to_schedule(rank, None)
+
+        for i in sorted(pipeline_order, reverse=True):
+            if len(pipeline_order[i]) == 0:
+                del pipeline_order[i]
+
+        # hacky, but do a second pass to replace any 'none' at this timestep with a real action, if it got unblocked
+        # by one of the later ranks
+        for rank in sorted(pipeline_order):
+            if len(pipeline_order[rank]) == 0:
+                continue
+
+            if _schedule[rank][-1] is not None:
+                continue
+
+            action = pipeline_order[rank][0]
+            if _ready_to_schedule(action):
+                if action is not None:
+                    _schedule[rank][-1] = action
+                    _prev_ops_rank[rank].add(action)
+                pipeline_order[rank].pop(0)
+
+        for i in sorted(pipeline_order, reverse=True):
+            if len(pipeline_order[i]) == 0:
+                del pipeline_order[i]
+
+        if not progress:
+            print("WIP comms schedule:\n", _format_pipeline_order(_schedule))
+            for rank in pipeline_order:
+                print(f"{rank=} next action= {pipeline_order[rank][0]}")
+            raise ValueError("Schedule is not progressing")
+
+    return _schedule
+
+
+def _dump_chrometrace(schedule, filename):
+    """
+    This function dumps a schedule IR into a chrometrace format so it can be visualized.
+
+    It is currently very basic and only serves as a graphical alternative to dumping the schedule IR as text.
+
+    As future work we may extend this to include more accurate heuristics for durations, or let users input durations,
+    add 'flow events' to let the UI show the connection between sends and recvs, and model cuda streams for comm/compute
+    as separate streams on the chrometrace view.
+    """
+    events = []
+    for rank in sorted(schedule):
+        for timestep, action in enumerate(schedule[rank]):
+            if action is None:
+                continue
+            events.append(
+                {
+                    "name": str(action),
+                    "cat": (
+                        "computation"
+                        if action.computation_type in (F, B, W)
+                        else "communication"
+                    ),
+                    "ph": "X",
+                    "pid": rank,
+                    "tid": rank,
+                    "ts": timestep,
+                    "dur": 1,
+                }
+            )
+    import json
+
+    with open(filename, "w") as f:
+        json.dump({"traceEvents": events}, f)
+
+
+def _check_torch_compile_compatibility(
+    stages: list[_PipelineStageBase], schedule_name: str
+):
+    """
+    Check if the schedule is compatible with torch.compile.
+
+    Args:
+        stages: List of pipeline stages to check
+        schedule_name: Name of the schedule for error message
+
+    Raises:
+        RuntimeError: If any stage uses torch.compile
+    """
+    for stage in stages:
+        if not isinstance(stage.submod, torch.nn.Module):
+            continue
+
+        for module in stage.submod.modules():
+            if isinstance(module, OptimizedModule):
+                raise RuntimeError(
+                    f"The {schedule_name} schedule is not supported with "
+                    "stage modules that have used torch.compile. "
+                    f"Found OptimizedModule in {type(module).__name__}"
+                )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/pipelining/stage.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/pipelining/stage.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc0d51020458bcfd45cdc34c45868dc374bc2564
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/pipelining/stage.py
@@ -0,0 +1,1588 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+import logging
+import operator
+from abc import ABC, abstractmethod
+from collections.abc import Callable
+from typing import Any, cast, Union
+
+import torch
+import torch.distributed as dist
+import torch.fx as fx
+import torch.nn as nn
+from torch._subclasses.fake_tensor import FakeTensor
+from torch.distributed._composable.replicate_with_fsdp import replicate, ReplicateModule
+from torch.distributed.fsdp import FSDPModule, fully_shard
+from torch.fx.node import Argument, map_aggregate
+from torch.nn.parallel import DistributedDataParallel
+from torch.utils._pytree import tree_map_only
+
+from ._backward import stage_backward, stage_backward_input, stage_backward_weight
+from ._debug import map_debug_info
+from ._utils import flatten_args, PipeInfo, validate_tensors_metadata
+
+
+__all__ = [
+    "PipelineStage",
+    "build_stage",
+]
+
+logger = logging.getLogger(__name__)
+
+
+def _normalize_model_output_as_tuple(output: Any) -> tuple[Any]:
+    """[Note: pipeline model output type]
+
+    The output of the model passed to pipelining can be any type, controlled by the user.
+
+    However, there are 2 API surfaces that complicate this.
+    (1) the outputs of intermediate stages are passed via Send/Recv ops to subsequent stages. The implicit assumption
+    is that each element of the outputs is a tensor.  Otherwise, Send/Recv would not be supported.  The exception
+    is the last layer of the model, which can output anything any which won't be communicated via Send/Recv.
+    (2) the outputs of the last layer of the model are returned to the user, or, passed to the loss function.
+    The loss function can be written in any way, such that its inputs match the outputs of the model.
+
+    It would be convenient if we could strictly type the output signature of the pipeline stage wrapping the model,
+    but we do not want to impose an unnecessary constraint on user provided models.
+
+    Currently, we let user provided models return either a Tensor or a tuple of Tensors from each stage. Due to
+    torch.export tracing, compiled models may also return a list instead of a Tuple, which we will normalize back to a
+    tuple for consistency.
+
+    TODO: should we be stricter about asserting that stage modules (intermediate and output) all return only Tensor
+    values?
+    """
+    if type(output) is list:
+        # HACK: this is a hacky workaround for the fact that export creates
+        # output in list format
+        output = tuple(output)
+
+    # Unify output form to tuple for easy correspondence with
+    # `act_send_info`
+    output_tuple = output if type(output) is tuple else (output,)
+    return output_tuple
+
+
+class _RootArgPlaceholder:
+    """
+    Placeholder for model-level inputs.
+    """
+
+    def __init__(self, tensor):
+        self.meta = tensor.to("meta")
+
+
+class _RecvInfo:
+    """
+    Represents a stage input.
+    """
+
+    def __init__(
+        self,
+        input_name: str,
+        source: int,
+        buffer: torch.Tensor,
+    ):
+        # Name of this input
+        self.input_name = input_name
+        # Stage index of the source of this input
+        self.source = source
+        # Buffer to receive the input into.
+        self.buffer = buffer
+
+    def __repr__(self):
+        return f"_RecvInfo(input={self.input_name}, source={self.source}, shape={self.buffer.size()})"
+
+
+# An input can be either a received activation or a model input
+InputInfo = Union[_RecvInfo, _RootArgPlaceholder]
+
+
+def _make_tensor_from_meta(
+    example: torch.Tensor | FakeTensor,
+    device: torch.device,
+) -> torch.Tensor:
+    """
+    Create a real tensor from a tensor.
+    """
+    return torch.empty(
+        example.size(),
+        dtype=example.dtype,
+        layout=example.layout,
+        device=device,
+    )
+
+
+class _PipelineStageBase(ABC):
+    """
+    Base class for pipeline stages.
+    Defines or implements common methods used by the `_PipelineStage` used by
+    the tracing frontend and `PipelineStage` used by manual frontend.
+    """
+
+    def __init__(
+        self,
+        submodule: torch.nn.Module,
+        stage_index: int,
+        num_stages: int,
+        device: torch.device,
+        group: dist.ProcessGroup | None = None,
+        dw_builder: Callable[[], Callable[..., None]] | None = None,
+    ):
+        """
+        Args:
+            submodule (torch.nn.Module): The module to be executed in this stage.
+            stage_index (int): The index of this stage.
+            num_stages (int): The total number of stages in this pipeline.
+            device (torch.device): The device to run this stage on.
+            group (Optional[dist.ProcessGroup]): The process group to use for communication.
+                If `None`, the default process group will be used.
+                Default: `None`.
+            dw_builder (Optional[Callable[[], Callable[..., None]]): If provided, dw_builder is a builder function
+                that will build a new dw_runner function that will run parts of module backward that were intentionally
+                skipped during the module's actual backward pass. The builder must be invoked by stage after stage runs
+                model backwards, and stage should save the latest dw_runner to run during weight pas (W).
+                If not provided, a dw_runner will be generated automatically by traversing the autograd graph.
+                When used with schedules that only have F and B steps, the fresh dw_runner function will be called as
+                part of I (input backwards). When used with F,I,W schedules, the dw_runner function implements 'W'.
+        """
+        super().__init__()
+        if stage_index >= num_stages:
+            raise ValueError(
+                f"Stage index {stage_index} is out of range of {num_stages}"
+            )
+
+        self.submod = submodule
+        self.stage_index = stage_index
+        self.num_stages = num_stages
+        # pyrefly: ignore [read-only]
+        self.device = device
+        self.group = group
+
+        self.dw_builder = dw_builder
+
+        # backward state
+        self.backward_state: dict[int, tuple[Any, ...]] = {}
+
+        # store dw_runner per microbatch_id
+        self.dw_runner: dict[int, Callable[..., None]] = {}
+
+        # `group_rank` is rank in process group `group`.
+        self.group_rank = dist.get_rank(self.group)
+        self.group_size = dist.get_world_size(self.group)
+        if self.group_size > self.num_stages:
+            raise RuntimeError(
+                f"Pipeline group size {self.group_size} cannot be larger than number of stages {self.num_stages}"
+            )
+
+        # Run time states
+        self._outputs_meta: tuple[torch.Tensor, ...] | None = None
+        # map microbatch ID to list of forward tensor args
+        self.fwd_cache: dict[int, tuple[Any, list[torch.Tensor]]] = {}
+        # map microbatch ID to list of backward grad tensor args
+        self.bwd_cache: dict[int, tuple[torch.Tensor | None, ...]] = {}
+        # Caching chunk outputs for final output merge or reduction
+        self.output_chunks: list[Any] = []
+
+        # Initialize has_backward to false; this will be set to true if loss
+        # function is passed to pipeline schedule
+        self.has_backward = False
+        # Log prefix
+        self.log_prefix = f"[Stage {self.stage_index}]"
+
+        # Forward infra
+        self.args_recv_info: dict[int, tuple[InputInfo, ...]] = {}
+        self.act_send_info: dict[int, list] = {}
+
+        # Backward infra will created lazily
+        self.grad_recv_info: dict = {}
+        self.grad_send_info: list | None = None
+
+        # To be populated later by the Schedule
+        self.chunks: int | None = None
+        self.stage_index_to_group_rank: dict[int, int] = {
+            i: i % self.group_size for i in range(self.num_stages)
+        }
+
+    @property
+    def has_backward(self) -> bool:
+        """
+        Returns true if this stage has a backward pass.
+        """
+        return self._has_backward
+
+    @has_backward.setter
+    def has_backward(self, has_backward: bool):
+        self._has_backward = has_backward
+
+    @property
+    def is_first(self):
+        """
+        Returns true if this stage is the first stage in the pipeline.
+        """
+        return self.stage_index == 0
+
+    @property
+    def is_last(self):
+        """
+        Returns true if this stage is the last stage in the pipeline.
+        """
+        return self.stage_index == self.num_stages - 1
+
+    def _check_chunk_id(self, chunk_id: int):
+        if self.chunks is None:
+            raise RuntimeError(
+                "Attempted to access chunk_id before chunks have been configured."
+            )
+        if chunk_id >= self.chunks:
+            raise RuntimeError(
+                f"Chunk id {chunk_id} is out of range [0, {self.chunks})"
+            )
+
+    def _configure_outputs_meta(self, outputs_meta: tuple[torch.Tensor, ...]):
+        """
+        Track the output shapes/dtype of this stage since they determine the send operation(s) which must match
+        recv operations of the next stage.  The next stage _will_ be freezing its recv buffers based on its initial
+        configuration, so it's important to also freeze/validate the output side to avoid any send/recv mismatches
+        which could show up as hangs, silent corruption, or other errors.
+        """
+        assert self._outputs_meta is None, (
+            "Attempting to reconfigure output_meta, which is not supported"
+        )
+        self._outputs_meta = tuple(outputs_meta)  # type: ignore[assignment]
+
+    def get_outputs_meta(self) -> tuple[torch.Tensor, ...]:
+        """Get the output metadata (meta tensors) representing the outputs of this stage"""
+        assert self._outputs_meta is not None, (
+            "Attempted to get_outputs_meta() without configuring output meta"
+        )
+        return self._outputs_meta
+
+    def _create_grad_send_info(
+        self,
+        args_recv_info: tuple,
+    ) -> list[int | None]:
+        """
+        Create a list of stage indices to send gradients to.
+        """
+        grad_send_info: list[int | None] = []
+
+        def map_recv_to_send(a):
+            # Note: we send gradients back to previous stage as long as in
+            # forward it is a received input, regardless of whether it requires
+            # grad. It is up to the previous stage to discard this gradient.
+            if isinstance(a, _RecvInfo):
+                grad_send_info.append(a.source)
+                return a.source
+            else:
+                grad_send_info.append(None)
+                return None
+
+        map_aggregate(args_recv_info, map_recv_to_send)
+
+        logger.debug("%s Grad send info: %s", self.log_prefix, grad_send_info)
+        return grad_send_info
+
+    @abstractmethod
+    def _prepare_forward_infra(
+        self,
+        num_microbatches: int,
+        args: tuple[Any, ...],
+        kwargs: dict[str, Any] | None = None,
+    ) -> tuple[Any, ...]:
+        raise NotImplementedError
+
+    def _prepare_backward_infra(self, num_microbatches: int):
+        # TODO: this is needed for backward_maybe_with_nosync
+        self.chunks = num_microbatches
+
+        for mb_index in range(num_microbatches):
+            # `grad_recv_info` is a mirror of `act_send_info`
+            self.grad_recv_info[mb_index] = self._create_grad_recv_info(
+                self.act_send_info
+            )
+
+    @abstractmethod
+    def _create_grad_recv_info(
+        self,
+        act_send_info: dict,
+    ) -> tuple[_RecvInfo, ...]:
+        raise NotImplementedError
+
+    def _get_recv_ops(
+        self,
+        recv_infos: tuple[InputInfo, ...],
+    ) -> list[dist.P2POp]:
+        """
+        Helper function shared by `get_fwd_recv_ops` and `get_bwd_recv_ops`.
+        Returns a list of ops that correspond to the recv infos.
+        """
+        ops: list[dist.P2POp] = []
+        for info in recv_infos:
+            if not isinstance(info, _RecvInfo):
+                continue
+
+            peer_rank = self.stage_index_to_group_rank[info.source]
+            peer_global_rank = (
+                peer_rank
+                if self.group is None
+                else dist.get_global_rank(self.group, peer_rank)
+            )
+            ops.append(
+                dist.P2POp(dist.irecv, info.buffer, peer_global_rank, self.group)
+            )
+
+        return ops
+
+    """[Note: V-schedule special case]
+
+    V-Schedules have a special case where 2 stages with adjacent stage_id are on the same rank.
+
+    ex: 2 ranks, 4 stages forms a simple V:
+    rank0:  stage 0                   stage 3
+    rank1:          stage 1  stage 2
+
+    stage 0,1 and 2,3 communicate activations using send/recv as usual, but stage 1,2 do not need to
+    use communication ops.  Instead, they should pass tensor data directly via function call.
+
+    set_local_fwd_input and (get_local_bwd_output + set_local_bwd_input) facilitate this optimization, and
+    should be called at the appropriate time during the pipeline schedule (after forward or backward execution).
+    """
+
+    def set_local_fwd_input(self, prev_stage_outputs: Any, mb_index: int) -> None:
+        """
+        Moves 'prev_stage_outputs' from another stage on the same rank into place as inputs for this stage. Avoids
+        copying tensor data or using send/recv op.  Detaches original tensor and sets requires_grad so the
+        tensor can serve as a leaf for autograd and gradients can be collected from it during backward.
+        """
+        recv_infos: tuple[InputInfo, ...] = self.args_recv_info[mb_index]
+
+        # See [Note: pipeline model output type]
+        prev_stage_outputs = _normalize_model_output_as_tuple(prev_stage_outputs)
+
+        for info, tensor in zip(recv_infos, prev_stage_outputs):
+            assert isinstance(tensor, torch.Tensor), (
+                f"expected tensor values as outputs from prev stage, got {type(tensor)}"
+            )
+            assert isinstance(info, _RecvInfo), (
+                "set_local_Fwd_input should only be called on non-first stage, which should always have RecvInfo"
+            )
+
+            # We don't need to do a data copy here, since we can directly pass the activation tensor reference from
+            # one stage to the next.  However, we do need to mark the activation as a leaf tensor since it will serve
+            # as the input tensor for a fresh autograd graph, not part of the previous stage's autograd graph.
+            # TODO: confirm, do we use this activation as the root of the backward call for the previous stage? does
+            # detach have any affect on that?
+            info.buffer = tensor.detach().requires_grad_(True)
+
+    def get_local_bwd_output(self, mb_index):
+        """
+        Returns the input grad tensors for this stage, which correspond to the stage inputs during forward.
+        """
+        assert self.has_backward, (
+            "can't steal_bwd_input if this stage doesn't have backward"
+        )
+        assert not self.is_first, "can't get bwd output if this stage is first"
+
+        self._check_chunk_id(mb_index)
+        return self.bwd_cache.pop(mb_index)
+
+    def set_local_bwd_input(
+        self, next_stage_bwd_outputs: tuple[torch.Tensor | None, ...], mb_index: int
+    ) -> None:
+        """
+        Moves 'grad input' tensors from the next stage to 'grad_output' on this stage, avoiding a copy or send/recv.
+        Does not detach or set '_requires_grad'.
+        """
+        assert isinstance(next_stage_bwd_outputs, tuple), (
+            f"Expected tuple, got {type(next_stage_bwd_outputs)}"
+        )
+
+        assert self.has_backward, (
+            "can't set bwd input if this stage doesn't have backward"
+        )
+        assert not self.is_last, "can't set bwd input if this stage is last"
+        recv_infos = self.grad_recv_info[mb_index]
+        for info, tensor in zip(recv_infos, next_stage_bwd_outputs):
+            assert isinstance(tensor, torch.Tensor), (
+                f"expected tensor values as outputs from prev stage, got {type(tensor)}"
+            )
+            assert isinstance(info, _RecvInfo), (
+                f"Expected a recv info, got {type(info)}"
+            )
+            info.buffer = tensor
+
+    def get_fwd_recv_ops(self, fwd_chunk_id: int) -> list[dist.P2POp]:
+        """
+        Returns a list of ops that are needed to receive the input arguments
+        for this stage.
+        """
+        recv_infos: tuple[InputInfo, ...] = self.args_recv_info[fwd_chunk_id]
+
+        return self._get_recv_ops(recv_infos)
+
+    def get_bwd_recv_ops(self, bwd_chunk_id: int) -> list[dist.P2POp]:
+        """
+        Returns a list of ops that are needed to receive the gradients
+        for this stage.
+        """
+        if not self.has_backward or self.is_last:
+            return []
+
+        recv_infos = self.grad_recv_info[bwd_chunk_id]
+        return self._get_recv_ops(recv_infos)
+
+    def get_fwd_send_ops(self, fwd_chunk_id: int) -> list[dist.P2POp]:
+        """
+        Get the activation send ops for current stage's forward.
+        """
+        output_tuple, _ = self.fwd_cache[fwd_chunk_id]
+
+        ops: list[dist.P2POp] = []
+
+        for idx, out in enumerate(output_tuple):
+            dst_stages = self.act_send_info[idx]
+            for dst in dst_stages:
+                if dst is None:
+                    continue
+                logger.debug(
+                    "%s Sending tensor to Stage %s: %s",
+                    self.log_prefix,
+                    dst,
+                    out.size(),
+                )
+                peer_rank = self.stage_index_to_group_rank[dst]
+                peer_global_rank = (
+                    peer_rank
+                    if self.group is None
+                    else dist.get_global_rank(self.group, peer_rank)
+                )
+                ops.append(dist.P2POp(dist.isend, out, peer_global_rank, self.group))
+
+        return ops
+
+    def get_bwd_send_ops(self, bwd_chunk_id: int) -> list[dist.P2POp]:
+        """
+        Get the gradient send ops for current stage's backward.
+        """
+        if not self.has_backward or self.is_first:
+            return []
+
+        self._check_chunk_id(bwd_chunk_id)
+        # Create bwd send infra lazily
+        if self.grad_send_info is None:
+            # Send info for input grads during backward:
+            # List of destinations corresponding to input grads
+            # Can be None if an input has no grad
+            # `grad_send_info` is a mirror of `args_recv_info`
+            self.grad_send_info = self._create_grad_send_info(self.args_recv_info[0])
+
+        ops: list[dist.P2POp] = []
+        grads_input = self.bwd_cache.pop(bwd_chunk_id)
+        for grad, grad_recv_stage in zip(grads_input, self.grad_send_info):
+            if isinstance(grad, torch.Tensor) and grad_recv_stage is not None:
+                logger.debug(
+                    "%s Sending gradient to Stage %s: %s",
+                    self.log_prefix,
+                    grad_recv_stage,
+                    grad.size(),
+                )
+                peer_rank = self.stage_index_to_group_rank[grad_recv_stage]
+                peer_global_rank = (
+                    peer_rank
+                    if self.group is None
+                    else dist.get_global_rank(self.group, peer_rank)
+                )
+                ops.append(dist.P2POp(dist.isend, grad, peer_global_rank, self.group))
+            else:
+                if not (grad is None and grad_recv_stage is None):
+                    raise RuntimeError(
+                        f"[{self.stage_index}] for chunk {bwd_chunk_id} has gradients {grad} "
+                        f"and is expecting to send gradients to stage {grad_recv_stage}"
+                    )
+        return ops
+
+    def clear_runtime_states(self) -> None:
+        """
+        Clear runtime states of the stage.
+        """
+        # map microbatch ID to list of forward tensor args
+        self.fwd_cache.clear()
+        # Caching chunk outputs for final output merge or reduction
+        self.output_chunks.clear()
+
+        # Clear grad of input buffers in between schedule steps. This is because
+        # `torch.autograd.backward()` will accumulate gradients into leaf
+        # tensors by default. For gradients to pass back to previous stages, we
+        # don't want such accumulation.
+        for recv_tuple in self.args_recv_info.values():  # iterate over all chunks
+            for a in recv_tuple:  # iterate over all input args
+                if isinstance(a, _RecvInfo):
+                    # Set to None is the newer and recommended way to clear grads, compared to `zero_()`.
+                    # See https://github.com/pytorch/pytorch/pull/92731
+                    a.buffer.grad = None
+
+    def _map_tensor_from_recv_info(
+        self,
+        recv_infos: tuple[InputInfo, ...],
+    ):
+        """
+        Map tensors from recv infos to a list.
+        """
+
+        def get_recv_tensor(info):
+            if isinstance(info, _RecvInfo):
+                return info.buffer
+            else:
+                raise AssertionError(f"Expected _RecvInfo but got {type(info)}")
+
+        return map_aggregate(cast(Argument, recv_infos), get_recv_tensor)
+
+    def _retrieve_recv_activations(self, fwd_chunk_id: int):
+        """
+        Retrieve the activations received for the current stage during forward.
+        """
+        recv_infos = self.args_recv_info[fwd_chunk_id]
+        activations = self._map_tensor_from_recv_info(recv_infos)
+        return activations
+
+    def _retrieve_recv_grads(
+        self,
+        bwd_chunk_id: int,
+    ):
+        """
+        Retrieve the gradients received for the current stage during backward.
+        """
+        recv_infos = self.grad_recv_info[bwd_chunk_id]
+        grads = self._map_tensor_from_recv_info(recv_infos)
+        return grads
+
+    def forward_maybe_with_nosync(self, *args, **kwargs):
+        # If submod is wrapped with DDP, we use the `no_sync` context manager to
+        # avoid gradient all-reduce per microbatch
+        if isinstance(self.submod, DistributedDataParallel):
+            with self.submod.no_sync():  # type: ignore[operator]
+                out_val = self.submod(*args, **kwargs)
+        else:
+            out_val = self.submod(*args, **kwargs)
+        return out_val
+
+    def scale_grads(self, grad_scale_factor: int) -> None:
+        """Scale gradients model gradients by `grad_scale_factor`, which should be specified in coordination with the
+        loss function used with pipelining.  For loss functions which perform 'mean' loss reduction, `grad_scale_factor`
+        should be set to num_microbatches.  For loss functions that use `sum` reduction, `grad_scale_factor` should
+        be set to 1.
+
+        Should only be called once per pipeline schedule step, after all backwards passes have completed.
+        """
+
+        # PP scales only for its own contribution (microbatches), but relies on DP to scale further
+        # for DP degree.
+        if grad_scale_factor != 1:
+            for p in self.submod.parameters():
+                if p.grad is not None:
+                    p.grad.div_(grad_scale_factor)
+
+    def backward_maybe_with_nosync(
+        self,
+        backward_type,
+        bwd_kwargs: dict,
+        last_backward: bool = False,
+    ) -> tuple[tuple[torch.Tensor | None, ...], list[dict[str, Any]] | None]:
+        """
+        Whether using PP with FSDP, DDP, or replicate there are some runtime differences between the last backward step and the
+        other steps.  Namely, we need to accumulate gradients on previous steps and reduce them on the last step, but
+        there are additional state-variables and performance considerations depending on the data parallelism used.
+        This helper should adapt any pipeline parallel schedule to work with common/supported data parallel libraries.
+        """
+
+        def perform_backward(
+            backward_type,
+        ) -> Callable[
+            [],
+            tuple[tuple[torch.Tensor | None, ...], list[dict[str, Any]] | None],
+        ]:
+            if backward_type == "full":
+                return lambda: (
+                    stage_backward(
+                        bwd_kwargs["stage_output"],
+                        bwd_kwargs["output_grads"],
+                        bwd_kwargs["input_values"],
+                    ),
+                    None,
+                )
+            elif backward_type == "input":
+                return lambda: stage_backward_input(
+                    bwd_kwargs["stage_output"],
+                    bwd_kwargs["output_grads"],
+                    bwd_kwargs["input_values"],
+                    self.submod.parameters(),
+                )
+            elif backward_type == "weight":
+                return lambda: (
+                    stage_backward_weight(
+                        self.submod.parameters(), bwd_kwargs["param_groups"]
+                    ),
+                    None,
+                )
+            else:
+                raise RuntimeError(f"Unknown backward type: {backward_type}")
+
+        # If submod is wrapped by DDP
+        if isinstance(self.submod, DistributedDataParallel):
+            if last_backward:
+                # Last chunk, prepare for gradient reduction
+                # HACK: reaching into DDP implementation details here. Is there a better way?
+                self.submod.reducer.prepare_for_backward(  # type: ignore[union-attr, operator]
+                    list(
+                        torch.nn.parallel.distributed._find_tensors(  # type: ignore[attr-defined]
+                            bwd_kwargs["stage_output"]
+                        )
+                    )
+                )
+                result = perform_backward(backward_type)()
+            else:
+                with self.submod.no_sync():  # type: ignore[operator]
+                    result = perform_backward(backward_type)()
+
+        # If submod is a FSDP or replicate module
+        elif isinstance(self.submod, FSDPModule):
+            self.submod.set_is_last_backward(False)
+            self.submod.set_reshard_after_backward(False)
+            self.submod.set_requires_gradient_sync(False)
+            result = perform_backward(backward_type)()
+
+        else:
+            # Non-DP submodule, regular backward
+            result = perform_backward(backward_type)()
+
+        grads, param_groups = result
+        return grads, param_groups
+
+    def forward_one_chunk(
+        self,
+        fwd_chunk_id: int,
+        args: tuple[Any, ...],
+        kwargs: dict[str, Any] | None = None,
+        save_forward_output: bool = True,
+    ):
+        """
+        Perform forward pass on the stage with one microbatch.
+        `args` and `kwargs` are the inputs from *external* to this stage.
+        As of Sept 2024:
+        - `args` applies to the first stage only, other stages receives args
+          through activation transmission.
+        - `kwargs` can be passed to all stages via respective `step` calls.
+        """
+
+        if self.is_first:
+            # First stage doesn't need to receive anything
+            composite_args = args
+        else:
+            # Receive activations for this chunk
+            # Activations only come in args form
+            composite_args = self._retrieve_recv_activations(fwd_chunk_id)
+
+        composite_kwargs = kwargs or {}
+
+        self._validate_fwd_input(args, kwargs)
+
+        # Compute forward
+        try:
+            output = self.forward_maybe_with_nosync(*composite_args, **composite_kwargs)
+
+        except Exception as e:
+            exc_msg = f"""
+            {self.log_prefix} failed to run forward:
+            args: {map_debug_info(composite_args)}
+            kwargs: {map_debug_info(composite_kwargs)}
+            """
+            raise RuntimeError(exc_msg) from e
+
+        # See [Note: pipeline model output type]
+        output_tuple = _normalize_model_output_as_tuple(output)
+
+        # Prepare for final output merge or reduction
+        # Output chunks is only used for the last stage since we only merge the output of the last stage
+        if self.is_last and save_forward_output:
+            self.output_chunks.append(output)
+        # Save activations and inputs for backward
+        flat_args = flatten_args(composite_args)
+        flat_kwargs = flatten_args(composite_kwargs)
+        flatten_input_tensors = flat_args + flat_kwargs
+        self.fwd_cache[fwd_chunk_id] = (
+            output_tuple,  # stage_output
+            flatten_input_tensors,  # input_values
+        )
+
+        logger.debug(
+            "%s Forwarded chunk %s, outputs: %s",
+            self.log_prefix,
+            fwd_chunk_id,
+            map_debug_info(output),
+        )
+        self._validate_fwd_outputs(output_tuple)
+
+        # We return the original user-provided output, not normalized to tuple.
+        # See [Note: pipeline model output type]
+        return output
+
+    def backward_one_chunk(
+        self,
+        bwd_chunk_id: int,
+        loss=None,
+        full_backward: bool = True,
+        last_backward=False,
+    ):
+        """
+        Perform backward pass on the module.
+        This should only be called once per microbatch.
+
+        If full_backward is True (the default), the full backward pass including weight and input gradients will be run,
+        and it is an error to call `backward_weight_one_chunk` for this bwd_chunk_id.
+
+        If full_backward is False, it is optional that `dw_runner` was provided to the PipelineStage at __init__ time,
+        and a subsequent call to `backward_weight_one_chunk` is required to invoke dw_runner and complete the backward.
+
+        last_backward is controlled by the schedule and signals synchronization of gradients across DP groups
+        after the last backward.
+        """
+        # skip backward computation if backward is not enabled
+        if not self.has_backward:
+            return
+
+        self._check_chunk_id(bwd_chunk_id)
+
+        (
+            stage_output,
+            input_values,
+        ) = self.fwd_cache.pop(bwd_chunk_id)
+
+        # Compute backward
+        if self.is_last:
+            # Last stage computes gradients from loss and has no gradients from
+            # next stage
+            bwd_kwargs = {
+                "stage_output": loss,
+                "output_grads": None,
+                "input_values": input_values,
+            }
+        else:
+            # Otherwise, receive gradients from next stage
+            grads_output = self._retrieve_recv_grads(bwd_chunk_id)
+            # If an input to the pipeline requires gradient,
+            # `torch.autograd.backward` will accumulate the gradient into the
+            # `.grad` field of such input
+            bwd_kwargs = {
+                "stage_output": stage_output,
+                "output_grads": grads_output,
+                "input_values": input_values,
+            }
+
+        grads_input: tuple[torch.Tensor | None, ...] = ()
+
+        # Custom backward function
+        if self.dw_builder:
+            # TODO: We may want to change our semantics so we are allowed to ignore
+            # the 'dw_builder' and call full_backward directly when it is a full_backward op.
+            grads_input, _ = self.backward_maybe_with_nosync(
+                "full",
+                bwd_kwargs,
+                last_backward=last_backward,
+            )
+            if full_backward:
+                self.dw_builder()()
+            else:
+                self.dw_runner[bwd_chunk_id] = self.dw_builder()
+        else:
+            if full_backward:
+                grads_input, _ = self.backward_maybe_with_nosync(
+                    "full", bwd_kwargs, last_backward=last_backward
+                )
+            else:
+                param_groups: list[dict[str, Any]] | None = None
+                # Skip the backward for the first stage since we will perform the weight update with
+                # autograd.backward in backward_weight_one_chunk
+                if not self.is_first:
+                    if isinstance(bwd_kwargs["stage_output"], torch.Tensor):
+                        bwd_kwargs["stage_output"] = (bwd_kwargs["stage_output"],)
+
+                    # perform the partial backwards for the inputs with a custom backward function
+                    # when the "stage_ouput" is a loss, then it is a tensor, otherwise it is a tuple of tensors
+                    grads_input, param_groups = self.backward_maybe_with_nosync(
+                        "input", bwd_kwargs, last_backward=last_backward
+                    )
+
+                # TODO: we dont need to save this, add to dw_runner?
+                self.backward_state[bwd_chunk_id] = (
+                    bwd_kwargs["input_values"],
+                    param_groups,
+                    bwd_kwargs["stage_output"],
+                    bwd_kwargs["output_grads"],
+                )
+                # Save a placeholder for the dw_runner
+                self.dw_runner[bwd_chunk_id] = lambda: None
+
+        self.bwd_cache[bwd_chunk_id] = grads_input
+
+        if self.is_last and not self.is_first:
+            # Autograd dependencies:
+            #    rest_of_autograd_graph -> stage_output -> loss
+            # stage_output is no longer used in the last stage for backward and only needed
+            # to return to the user in merge_output_chunks, therefore
+            # this should be detached to release autograd graph context and free memory earlier
+            for t in stage_output:
+                if not t._is_view():  # views are not detachable in-place
+                    t.detach_()
+
+        logger.debug("%s Backwarded chunk %s", self.log_prefix, bwd_chunk_id)
+
+    def backward_weight_one_chunk(self, bwd_chunk_id: int, last_backward=False):
+        # skip backward computation if backward is not enabled
+        if not self.has_backward:
+            return
+
+        assert bwd_chunk_id in self.dw_runner, (
+            f"{self.log_prefix} Attempted to run backward_weight_one_chunk for chunk {bwd_chunk_id}"
+            " without first calling `backward_one_chunk(full_backward=False)`"
+        )
+
+        if self.dw_builder is not None:
+            self.dw_runner.pop(bwd_chunk_id)()
+        else:
+            (
+                input_values,
+                param_groups,
+                stage_output,
+                output_grads,
+            ) = self.backward_state.pop(bwd_chunk_id)
+
+            if self.stage_index != 0:
+                bwd_kwargs = {
+                    "stage_output": stage_output,
+                    "param_groups": param_groups,
+                }
+                self.backward_maybe_with_nosync(
+                    "weight", bwd_kwargs, last_backward=last_backward
+                )
+            else:
+                # TODO: figure out a better way to do this:
+                # if inputs does not require gradient,
+                # then the parameter group will not be fully captured during stage_backward_input
+                # in this case, we need call grad directly on the parameters
+                # To solve: make input fn do the intersect compute and then finish it off during W
+                bwd_kwargs = {
+                    "stage_output": stage_output,
+                    "output_grads": output_grads,
+                    "input_values": input_values,
+                }
+                self.backward_maybe_with_nosync(
+                    "full", bwd_kwargs, last_backward=last_backward
+                )
+
+    def _validate_fwd_input(self, args, kwargs):
+        """Raises a RuntimeError if shapes of input args/kwargs do not match the shapes configured for this stage."""
+
+        if self.is_first:
+            # TODO why is there a separate recv_info for each pipeline chunk?
+            # kwen2501: to avoid passing a `fwd_chunk_id` to this function, we
+            # check all chunks against args_recv_info[0]
+            expected_args = self.args_recv_info[0]
+        else:
+            # We don't check inputs for non-0 stages assuming they don't accept
+            # user inputs in canonical pipeline scenarios
+            return
+
+        if len(kwargs):
+            # TODO- need a mapping of kwarg to position in self.args_recv_info
+            # Without it, we are not 100% sure how to match the args and
+            # expected_args.
+            return
+
+        # TODO- need a mapping of kwarg to position in self.args_recv_info
+        # maybe it's impossible to tell whether the len mismatches because
+        # (a) the user passed an extra arg or missed an arg
+        # (b) the user did not pass a kwarg, which has a default value baked into expected_args
+        expected_tensors_meta = [
+            e.meta if isinstance(e, _RootArgPlaceholder) else e.buffer
+            for e in expected_args
+        ]
+        validate_tensors_metadata(
+            f"Stage {self.stage_index} forward inputs", expected_tensors_meta, args
+        )
+
+    def _validate_fwd_outputs(self, outputs: tuple[torch.Tensor, ...]):
+        """Raises a RuntimeError if this stage produces an output of unexpected shape/dtype.
+        Most likely, this could be cause either by incorrect user specification of output shapes, or because
+        shape inference was done on the original model but then at runtime the model is wrapped with something like
+        mixed precision which changes output dtype.
+        """
+        expected_tensors_meta = self.get_outputs_meta()
+        validate_tensors_metadata(
+            f"Stage {self.stage_index} forward outputs", expected_tensors_meta, outputs
+        )
+
+    def _get_init_p2p_neighbors_ops(self) -> list[dist.P2POp]:
+        """
+        Get the operations to initialize the p2p communicators between previous and next stages.
+        This is done so by creating a dummy tensor and sending it to the next stage and receiving
+        from the previous stage.
+        """
+        ops: list[dist.P2POp] = []
+        next_stage_peer_rank = self.stage_index_to_group_rank.get(self.stage_index + 1)
+        prev_stage_peer_rank = self.stage_index_to_group_rank.get(self.stage_index - 1)
+
+        recv_tensor = torch.zeros(1, device=self.device, dtype=torch.float32)
+        send_tensor = torch.tensor(
+            self.stage_index, device=self.device, dtype=torch.float32
+        )
+        # forward
+        if not self.is_first:
+            ops.append(
+                dist.P2POp(
+                    dist.irecv,
+                    recv_tensor,
+                    group_peer=prev_stage_peer_rank,
+                    group=self.group,
+                )
+            )
+        if not self.is_last:
+            ops.append(
+                dist.P2POp(
+                    dist.isend,
+                    send_tensor,
+                    group_peer=next_stage_peer_rank,
+                    group=self.group,
+                )
+            )
+
+        # backward
+        if not self.is_first:
+            ops.append(
+                dist.P2POp(
+                    dist.isend,
+                    send_tensor,
+                    group_peer=prev_stage_peer_rank,
+                    group=self.group,
+                )
+            )
+        if not self.is_last:
+            ops.append(
+                dist.P2POp(
+                    dist.irecv,
+                    recv_tensor,
+                    group_peer=next_stage_peer_rank,
+                    group=self.group,
+                )
+            )
+
+        return ops
+
+    def perform_reduce_grad(self, grad_scale_factor: int):
+        """
+        Called as a part of schedule IR.
+        REDUCE_GRAD action is scheduled after all microbatches W, B actions.
+
+        Currently contains "post_backward" functionality for FSDP.
+        We can try to extract post_backward in a separate IR action in future.
+        """
+        # Manually call post backward for FSDP
+        if isinstance(self.submod, FSDPModule):
+            fsdp_module = self.submod
+            fsdp_module.set_is_last_backward(True)
+            fsdp_module.set_reshard_after_backward(True)
+            fsdp_module.set_requires_gradient_sync(True)
+
+            if isinstance(fsdp_module, ReplicateModule):
+                distributed_state = replicate.state(fsdp_module)  # type: ignore[arg-type]
+            else:
+                distributed_state = fully_shard.state(fsdp_module)  # type: ignore[attr-defined]
+
+            for state in distributed_state._state_ctx.all_states:
+                if state._fsdp_param_group:
+                    state._fsdp_param_group.post_backward()
+
+            # it would be much better if pipelining backward invoked .backward so autograd hooks
+            # worked and modules like DDP/FSDP behaved as expected.  Working around this for the time being,
+            # we need to call this too to ensure FSDP syncs its grad reduction ops back to the default stream.
+            distributed_state._root_post_backward_final_callback()
+        # Call gradient scaling at the end of the backward pass
+        # NOTE: this must happen after FSDP post_backward is FSDP is enabled
+        if grad_scale_factor != 1:
+            self.scale_grads(grad_scale_factor)
+
+
+class _PipelineStage(_PipelineStageBase):
+    def __init__(
+        self,
+        stage_module: torch.nn.Module,
+        stage_index: int,
+        pipe_info: PipeInfo,
+        device: torch.device,
+        group: dist.ProcessGroup | None = None,
+    ):
+        """
+        Create a pipeline stage given a stage_module to be wrapped by this stage
+        and a `pipe_info` describing the stage relationship of the pipeline.
+
+        Args:
+            stage_module (torch.nn.Module): the module to be wrapped by this stage
+            stage_index (int): the index of this stage in the pipeline
+            pipe_info (PipeInfo): information about the pipeline, can be retrieved by `pipe.info()`
+            device (torch.device): the device to be used by this stage
+            group (Optional[dist.ProcessGroup]): the process group to be used by this stage
+        """
+        _PipelineStageBase.__init__(
+            self,
+            stage_module,
+            stage_index,
+            pipe_info.num_stages,
+            device,
+            group,
+        )
+        self.pipe_info = pipe_info
+
+        # Find stage nodes in graph
+        submod_nodes = [
+            node for node in pipe_info.graph.nodes if node.op == "call_module"
+        ]
+        if len(submod_nodes) != self.num_stages:
+            raise AssertionError(
+                f"Number of submodules in pipe graph {len(submod_nodes)} does not match number of stages {self.num_stages}"
+            )
+
+        # Find my stage node in graph
+        self.node = submod_nodes[self.stage_index]
+        self.name = self.node.name
+        logger.info(
+            "[%s] Creating PipelineStage %s for %s",
+            self.group_rank,
+            stage_index,
+            self.name,
+        )
+
+        # Create mapping from stage name to stage index
+        self.submod_to_stage_index: dict[str, int] = {}
+        for i, node in enumerate(submod_nodes):
+            self.submod_to_stage_index.setdefault(node.name, i)
+
+        # Cast submodule to device
+        self._move_submod_to_device()
+
+    def _move_submod_to_device(self):
+        # Move submodule to indicated device if possible
+        # Note: we cannot move meta module to real devices because meta tensors
+        # do not support to() method. One needs to do an in-place tensor swap in
+        # that case.
+        has_meta_param = any(
+            isinstance(p, FakeTensor) or p.is_meta for p in self.submod.parameters()
+        )
+        if has_meta_param:
+            logger.debug("%s Found meta parameters!", self.log_prefix)
+        else:
+            self.submod.to(self.device)
+
+    def _prepare_forward_infra(
+        self,
+        num_microbatches: int,
+        args: tuple[Any, ...],
+        kwargs: dict[str, Any] | None = None,
+    ) -> tuple[Any, ...]:
+        """
+        Create send/recv infrastructures for activations (during forward)
+        """
+        # TODO(whc)
+        # this method should be deleted once lazy buffer allocation is implemented
+        # for now, it ignores args/kwargs because it should not need to do shape inference
+        for chunk in range(num_microbatches):
+            self.args_recv_info[chunk] = self._create_act_recv_info()
+
+        # Send info during forward for each activation
+        self.act_send_info = self._create_act_send_info()
+        return tuple()
+
+    def get_stage_index_of_submod(
+        self,
+        submod_name: str,
+    ):
+        """
+        Given a submodule name, return the stage index of the submodule.
+        """
+        if submod_name not in self.submod_to_stage_index:
+            raise AssertionError(f"Stage id of {submod_name} not found")
+
+        return self.submod_to_stage_index[submod_name]
+
+    def _create_act_recv_info(
+        self,
+    ):
+        """
+        Create a tuple of `_RecvInfo` for inputs to the stage.
+        """
+
+        def create_recv_tensor(placeholder, arg_node):
+            """
+            Create a receive buffer for a placeholder.
+            """
+            example_value = placeholder.meta["val"]
+            if arg_node.op == "placeholder":
+                # This is a root level placeholder, thus an input argument to the entire model.
+                # We are likely at stage 0, hence no need to create a receive buffer.
+                return _RootArgPlaceholder(example_value)
+
+            # Figure out the source stage of this input
+            while arg_node.target is operator.getitem:
+                # If the input is a getitem, we need to go deeper
+                arg_node = arg_node.args[0]
+
+            assert arg_node.op == "call_module", (
+                f"Expecting call_module, got {arg_node.op}"
+            )
+            src_stage = self.get_stage_index_of_submod(arg_node.name)
+
+            # Create a receive buffer for this placeholder
+            logger.debug(
+                "%s Creating recv buffer for input '%s' : %s, %s",
+                self.log_prefix,
+                placeholder.name,
+                example_value.shape,
+                example_value.dtype,
+            )
+            buffer = _make_tensor_from_meta(example_value, self.device)
+            # In case there is backward pass, set requires_grad for receive buffers
+            # before first forward
+            if self.has_backward:
+                buffer.requires_grad_(True)
+
+            return _RecvInfo(
+                arg_node.name,
+                src_stage,
+                buffer,
+            )
+
+        args_recv_info: list[InputInfo] = []
+        # Filter out placeholder nodes from `self.submod` (a GraphModule)
+        placeholders = filter(  # type: ignore[var-annotated]
+            lambda node: node.op == "placeholder",  # type: ignore[arg-type]
+            self.submod.graph.nodes,  # type: ignore[arg-type,union-attr]
+        )
+        # `placeholders` are nodes internal to submod.
+        # `self.node.args` are dependency nodes in the outer graph.
+        # The two are 1:1.
+        for placeholder, arg_node in zip(placeholders, self.node.args):
+            # Create a receive buffer for this placeholder
+            recv_info = create_recv_tensor(placeholder, arg_node)
+            args_recv_info.append(recv_info)
+
+        logger.debug(
+            "%s Activation recv / args info: %s", self.log_prefix, args_recv_info
+        )
+        # `args` is a Tuple, hence we will return a Tuple[InputInfo]
+        return tuple(args_recv_info)
+
+    def find_dst_rank(
+        self,
+        user: fx.Node,
+    ) -> int | None:
+        """
+        Find the destination rank of a `user` node.
+        If the `user` is not a submod, `None` may be returned.
+        """
+        if user.op == "call_module":
+            # User is a stage (`call_module`)
+            return self.get_stage_index_of_submod(user.name)
+        else:
+            # - If user.op == "output":
+            #   No need to send back to rank 0
+            # - If user.target is stage_backward:
+            #   No need to send assuming submod output is stored locally or
+            #   should be re-calculated in case of activation checkpointing
+            return None
+
+    def _create_act_send_info(self):
+        """
+        Create a dict of send info for activations.
+        The dict is of the form:
+        {
+            output_index: [dst_rank_0, dst_rank_1, ...],
+            ...
+        }
+        where the list of `dst_rank`s covers the case where an output value may
+        be consumed by multiple stages.
+        """
+        # Output index: List of receiver ranks
+        act_send_info: dict[int, list] = {}
+        out_idx = 0
+
+        for user in self.node.users:
+            if user.target is operator.getitem:
+                # Recursively find the real destination
+                gi_dsts = act_send_info.setdefault(out_idx, [])
+                for gi_user in user.users:
+                    dst_rank = self.find_dst_rank(gi_user)
+                    if dst_rank is not None:
+                        gi_dsts.append(dst_rank)
+                # Next `getitem` will point to the next output index
+                out_idx += 1
+            else:
+                # In case of single output value, `out_idx` will not increase
+                dsts = act_send_info.setdefault(out_idx, [])
+                dst_rank = self.find_dst_rank(user)
+                if dst_rank is not None:
+                    dsts.append(dst_rank)
+
+        output_node = self._get_output_node()
+        output_vals: tuple[torch.Tensor] = tuple(
+            v.meta["val"] for v in flatten_args(output_node.args)
+        )
+        self._configure_outputs_meta(output_vals)
+
+        logger.debug("%s Send info: %s", self.log_prefix, act_send_info)
+        return act_send_info
+
+    def _get_output_node(self):
+        output_nodes = [node for node in self.submod.graph.nodes if node.op == "output"]  # type: ignore[union-attr]
+        assert len(output_nodes) == 1
+        output_node = output_nodes[0]
+        return output_node
+
+    def _create_grad_recv_info(
+        self,
+        act_send_info: dict,
+    ) -> tuple[_RecvInfo, ...]:
+        """
+        Create a tuple of `_RecvInfo` for gradients.
+        """
+        # Dict[output_index, _RecvInfo]
+        grad_recv_info: dict[int, _RecvInfo] = {}
+        output_node = self._get_output_node()
+
+        # The output node may take multiple args, meaning the submod having multiple output values.
+        output_vals = flatten_args(output_node.args)
+
+        for out_idx, dst_list in act_send_info.items():
+            if not dst_list:
+                # No actual receiver for activation so no grad coming back
+                continue
+
+            output = output_vals[out_idx]
+            example_value = output.meta["val"]
+            logger.debug(
+                f"{self.log_prefix} Creating grad recv buffer for output {output.name} "  # noqa: G004
+                f": {example_value.shape}, {example_value.dtype}"
+            )
+
+            # TODO: otherwise needs grad accumulation
+            assert len(dst_list) == 1, "Backward of skip connections not supported yet"
+            grad_src = dst_list[0]
+            grad_recv_info[out_idx] = _RecvInfo(
+                f"{grad_src}",  # noqa: G004
+                grad_src,
+                _make_tensor_from_meta(example_value, self.device),
+            )
+
+        # Convert to tuple for convenience in get_ops and retrieve tensor
+        grad_recv_info_tuple = tuple(grad_recv_info.values())
+        logger.debug("%s Grad recv info: %s", self.log_prefix, grad_recv_info_tuple)
+        return grad_recv_info_tuple
+
+
+# A helper function to create a pipeline stage based on traced pipeline information
+def build_stage(
+    stage_module: torch.nn.Module,
+    stage_index: int,
+    pipe_info: PipeInfo,
+    device: torch.device,
+    group: dist.ProcessGroup | None = None,
+) -> _PipelineStage:
+    """
+    Create a pipeline stage given a stage_module to be wrapped by this stage
+    and pipeline information.
+
+    Args:
+        stage_module (torch.nn.Module): the module to be wrapped by this stage
+        stage_index (int): the index of this stage in the pipeline
+        pipe_info (PipeInfo): information about the pipeline, can be retrieved by `pipe.info()`
+        device (torch.device): the device to be used by this stage
+        group (Optional[dist.ProcessGroup]): the process group to be used by this stage
+
+    Returns:
+        _PipelineStage: a pipeline stage that can run with `PipelineSchedules`.
+    """
+    return _PipelineStage(
+        stage_module,
+        stage_index,
+        pipe_info,
+        device,
+        group,
+    )
+
+
+class PipelineStage(_PipelineStageBase):
+    """
+    A class representing a pipeline stage in a pipeline parallelism setup.
+
+    PipelineStage assumes sequential partitioning of the model, i.e. the model is split into chunks where outputs from
+    one chunk feed into inputs of the next chunk, with no skip connections.
+
+    PipelineStage performs runtime shape/dtype inference automatically by propagating the outputs from stage0 to
+    stage1 and so forth, in linear order.  To bypass shape inference, pass the `input_args` and `output_args` to each
+    PipelineStage instance.
+
+    Args:
+        submodule (nn.Module): The PyTorch module wrapped by this stage.
+        stage_index (int): The ID of this stage.
+        num_stages (int): The total number of stages.
+        device (torch.device): The device where this stage is located.
+        input_args (Union[torch.Tensor, Tuple[torch.tensor]], optional): The input arguments for the submodule.
+        output_args (Union[torch.Tensor, Tuple[torch.tensor]], optional): The output arguments for the submodule.
+        group (dist.ProcessGroup, optional): The process group for distributed training. If None, default group.
+        dw_builder (Optional[Callable[[], Callable[..., None]]): If provided, dw_builder will build a new dw_runner function
+            that will the W action (input weights) for F, I, W (Fwd, Input, Weight) zero bubble schedules.
+    """
+
+    def __init__(
+        self,
+        submodule: nn.Module,
+        stage_index: int,
+        num_stages: int,
+        device: torch.device,
+        input_args: torch.Tensor | tuple[torch.Tensor, ...] | None = None,
+        output_args: torch.Tensor | tuple[torch.Tensor, ...] | None = None,
+        group: dist.ProcessGroup | None = None,
+        dw_builder: Callable[[], Callable[..., None]] | None = None,
+    ):
+        super().__init__(submodule, stage_index, num_stages, device, group, dw_builder)
+        self.inputs: list[torch.Tensor] | None = None
+        self.inputs_meta: tuple[torch.Tensor, ...] | None = None
+        # Note: inputs and submod should ideally be on meta device. We decided not to assert this (yet) because it
+        # might be breaking for existing users.
+        if input_args is None:
+            assert output_args is None, (
+                "If specifying output_args, input_args must also be specified. "
+                "Otherwise, shape inference will be performed at runtime"
+            )
+        else:
+            self.inputs_meta = (
+                (input_args,) if isinstance(input_args, torch.Tensor) else input_args
+            )
+            if output_args is None:
+                logger.warning(
+                    "Deprecation warning: passing input_args and performing init-time shape inference is deprecated. "
+                    "PipelineStage now supports runtime shape inference using the real inputs provided to schedule step(). "
+                    "Either delete `input_args` arg to `PipelineStage` to opt-into runtime shape inference, "
+                    "or additionally pass `output_args` to `PipelineStage` to fully override shape inference. "
+                )
+                try:
+                    with torch.no_grad():
+                        output_args = submodule(*self.inputs_meta)
+                    output_args = tree_map_only(
+                        torch.Tensor, lambda x: x.to("meta"), output_args
+                    )
+                except Exception as e:
+                    raise RuntimeError(
+                        "Failed to perform pipeline shape inference- are your inputs on the same device as your module?"
+                    ) from e
+            assert output_args is not None, (
+                "If passing input_args, also pass output_args to override shape inference"
+            )
+            self._configure_outputs_meta(
+                (output_args,) if isinstance(output_args, torch.Tensor) else output_args
+            )
+
+        # these are the buffers used in backwards send/recv, they are allocated later
+        self.outputs_grad: list[torch.Tensor] = []
+
+        dbg_str = (
+            f"Finished pipeline stage init, {self.stage_index=}, {self.is_first=}, "  # noqa: G004
+            f"{self.is_last=}, {self.num_stages=}, "
+        )
+        if self.inputs_meta is not None:
+            dbg_str += (
+                f"inputs: {[inp.shape for inp in self.inputs_meta]}, "
+                f"output: {[output.shape for output in self.get_outputs_meta()]}"
+            )
+        else:
+            dbg_str += " running shape-inference at runtime"
+
+        logger.debug(dbg_str)
+
+    def _shape_inference(
+        self,
+        args: tuple[Any, ...],
+        kwargs: dict[str, Any] | None = None,
+    ):
+        if kwargs is None:
+            kwargs = {}
+        assert args is not None, "Args may be an empty tuple but not None"
+
+        # We skip recv communication if we're the first stage, but also if the previous stage is on the same rank
+        # and can pass its output shapes in as args instead of using send/recv.
+        if (
+            self.is_first
+            # if not first stage, then check if prev stage is on the same rank
+            or self.stage_index_to_group_rank[self.stage_index - 1] == self.group_rank
+        ):
+            logger.debug(
+                "Shape inference: stage %s skipping recv, because shape info passed in via `args`",
+                self.stage_index,
+            )
+            args = tree_map_only(torch.Tensor, lambda x: x.to("meta"), args)
+        else:
+            assert len(args) == 0, (
+                "Can't supply input args for shape inference on non-first stage"
+            )
+            objects = [None]
+            logger.debug(
+                "Shape inference: stage %s receiving from stage %s",
+                self.stage_index,
+                self.stage_index - 1,
+            )
+            dist.recv_object_list(
+                objects,
+                src=dist.get_global_rank(
+                    self.group or dist.distributed_c10d._get_default_group(),
+                    self.stage_index_to_group_rank[self.stage_index - 1],
+                ),
+                group=self.group,
+                device=self.device,
+                use_batch=True,
+            )
+            recv_args = objects[0]
+            assert isinstance(recv_args, tuple), type(recv_args)
+            args = recv_args
+
+        # cache input shapes for use during recv buffer allocation
+        self.inputs_meta = args
+        args = tree_map_only(
+            torch.Tensor, lambda x: torch.zeros_like(x, device=self.device), args
+        )
+
+        # set attributes needed for forward
+        with torch.no_grad():
+            outputs = self.submod(*args, **kwargs)
+
+        # if single tensor, convert so it is always a list
+        if isinstance(outputs, torch.Tensor):
+            outputs = [outputs]
+
+        # communicate meta outputs not real outputs for two reasons
+        # 1 - its faster (esp. since obj coll pickles tensor data!)
+        # 2 - avoid activating a cuda context for the src rank when unpickling on the recv end!
+        outputs_meta = tuple(
+            tree_map_only(torch.Tensor, lambda x: x.to("meta"), outputs)
+        )
+        logger.debug(
+            "Shape inference: stage %s inputs %s, outputs %s",
+            self.stage_index,
+            self.inputs_meta,
+            outputs_meta,
+        )
+        self._configure_outputs_meta(outputs_meta)
+
+        # Passing outputs to the next stage:
+        # two cases-
+        # 1. Usually: use send/recv communication to pass the output
+        # 2. Special case: for V-schedules, 2 'adjacent' stages (e.g. stage 3, 4 in an 8-stage 4-rank V)
+        #    pass their shape info via return value and function args rather than send/recv.
+        if (
+            self.is_last
+            # if not last stage, then check if next stage is on the same rank
+            or self.stage_index_to_group_rank[self.stage_index + 1] == self.group_rank
+        ):
+            # Case (2) above: pass shape info via return value and caller passes it as args to next stage's
+            # _shape_inference call
+            logger.debug(
+                "Shape inference: stage %s skipping send to next stage",
+                self.stage_index,
+            )
+
+        else:
+            # Case (1): send shapes via send operation, and ensure not to return it to the caller
+            logger.debug(
+                "Shape inference: stage %s sending to stage %s",
+                self.stage_index,
+                self.stage_index + 1,
+            )
+            dist.send_object_list(
+                [outputs_meta],
+                dst=dist.get_global_rank(
+                    self.group or dist.distributed_c10d._get_default_group(),
+                    self.stage_index_to_group_rank[self.stage_index + 1],
+                ),
+                group=self.group,
+                device=self.device,
+                use_batch=True,
+            )
+            outputs_meta = tuple()
+
+        return outputs_meta
+
+    def _prepare_forward_infra(
+        self,
+        num_microbatches: int,
+        args: tuple[Any, ...],
+        kwargs: dict[str, Any] | None = None,
+    ) -> tuple[Any, ...]:
+        # TODO move self.device to an argument from step API (from its input tensors)?
+        assert num_microbatches is not None, "TODO fix num_microbatches"
+
+        outputs: tuple[Any, ...] = tuple()
+        if self.inputs_meta is None:
+            outputs = self._shape_inference(args, kwargs)
+
+        assert self.inputs_meta is not None
+        # Receive info during forward
+        # TODO: create args_recv_info lazily? (same needed for PipelineStage)
+        for chunk_id in range(num_microbatches):
+            if not self.is_first:
+                # We assume that we always receive from stage - 1
+                recv_infos = tuple(
+                    _RecvInfo(
+                        f"recv_for_{self.stage_index}_from_{self.stage_index - 1}",
+                        self.stage_index - 1,
+                        _make_tensor_from_meta(inp, self.device),
+                    )
+                    for inp in self.inputs_meta
+                )
+                # In case there is backward pass, set requires_grad for receive buffers
+                if self.has_backward:
+                    for r in recv_infos:
+                        r.buffer.requires_grad_(True)
+
+                self.args_recv_info[chunk_id] = recv_infos
+            else:
+                self.args_recv_info[chunk_id] = tuple(
+                    _RootArgPlaceholder(i) for i in self.inputs_meta
+                )
+
+        # Send info during forward for each activation
+        # only need the rank that is being sent to
+        self.act_send_info: dict[int, list] = {}
+
+        for idx in range(len(self.get_outputs_meta())):
+            # We assume we always send to stage + 1
+            if not self.is_last:
+                self.act_send_info[idx] = [self.stage_index + 1]
+            else:
+                self.act_send_info[idx] = []
+
+        return outputs
+
+    def _create_grad_recv_info(
+        self,
+        act_send_info: dict,
+    ) -> tuple[_RecvInfo, ...]:
+        grad_recv_info: tuple[_RecvInfo, ...] = ()
+        if not self.is_last:
+            # Receiving gradients from multiple sources is not supported
+            # hence we only take the first destination
+            grad_recv_info = tuple(
+                _RecvInfo(
+                    f"recv_grad_for_{self.stage_index}_from_{dst_list[0]}",
+                    dst_list[0],
+                    _make_tensor_from_meta(self.get_outputs_meta()[idx], self.device),
+                )
+                for idx, dst_list in act_send_info.items()
+            )
+        return grad_recv_info
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/remote_device.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/remote_device.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ad0076f5e8901644e56d14530dc36624ecf87a0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/remote_device.py
@@ -0,0 +1,122 @@
+# mypy: allow-untyped-defs
+
+import torch
+
+
+class _remote_device:
+    """
+    Represents a device on a remote worker.
+
+    Args:
+        remote_device (str or torch.device): Represents a device on a remote worker.
+            The string format should be one of the following:
+
+                1. "/", where the device field can be parsed as torch.device type.
+                   E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0".
+                   In addition, the device field can be optional and the default value is "cpu".
+                2. "rank:/", where  is the rank of the
+                   process and device can be parsed as torch.device type.
+                   E.g., "rank:0/cpu", "rank:0", "rank:0/cuda:0"
+                3.  and  are optional and formats like "cpu"
+                    and "cuda:1", just represent local devices.
+    """
+
+    def __init__(self, remote_device: str | torch.device):
+        PARSE_ERROR = (
+            f"Could not parse remote_device: {remote_device}. The valid format is "
+            "'/' or 'rank:/' or ''"
+        )
+        self._worker_name = None
+        self._rank = None
+        self._device: str | int | torch.device | None = None
+
+        if isinstance(remote_device, torch.device):
+            self._device = remote_device
+        elif isinstance(remote_device, str):
+            fields = remote_device.split("/")
+            if len(fields) == 2:
+                # pyrefly: ignore [bad-assignment]
+                self._worker_name, self._device = fields
+            elif len(fields) == 1:
+                # Check if this is a valid device.
+                if _remote_device._is_valid_local_device(fields[0]):
+                    self._device = fields[0]
+                else:
+                    # pyrefly: ignore [bad-assignment]
+                    self._worker_name = fields[0]
+                    self._device = "cpu"
+            else:
+                raise ValueError(PARSE_ERROR)
+        else:
+            raise TypeError(f"Invalid type for remote_device: {type(remote_device)}")
+
+        # Do some basic sanity check (no empty string)
+        if self._worker_name is not None and not self._worker_name:
+            raise ValueError(PARSE_ERROR)
+
+        # Validate the device.
+        self._device = torch.device(self._device)
+
+        # Check for rank based format.
+        if self._worker_name is not None:
+            fields = self._worker_name.split(":")
+            if len(fields) == 2:
+                # rank:/device format, extract rank
+                if fields[0] == "rank" and fields[1].isdigit():
+                    self._rank = int(fields[1])  # type: ignore[assignment]
+                    # pyrefly: ignore [bad-assignment]
+                    self._worker_name = None
+                else:
+                    raise ValueError(PARSE_ERROR)
+            elif len(fields) > 2:
+                raise ValueError(PARSE_ERROR)
+
+    @staticmethod
+    def _is_valid_local_device(device):
+        # Check for torch.device
+        try:
+            torch.device(device)
+            return True
+        except Exception:
+            return False
+
+    def worker_name(self) -> str | None:
+        """Return the name of remote worker representing the remote device and ``None`` if no worker name is available."""
+        return self._worker_name
+
+    def rank(self) -> int | None:
+        """
+        Returns the rank of remote worker representing the remote device.
+        Returns ``None`` if no rank is available.
+        """
+        return self._rank
+
+    def device(self) -> torch.device:
+        """Return the local device on the remote worker."""
+        return self._device  # type: ignore[return-value]
+
+    def __repr__(self):
+        if self._device is not None:
+            if self._worker_name is not None:
+                return f"{self._worker_name}/{self._device}"
+            elif self._rank is not None:
+                return f"rank:{self._rank}/{self._device}"
+            else:
+                return str(self._device)
+        else:
+            if self._worker_name is not None:
+                return f"{self._worker_name}"
+            elif self._rank is not None:
+                return f"{self._rank}"
+            else:
+                raise RuntimeError("Invalid state!")
+
+    def __eq__(self, other):
+        return isinstance(other, _remote_device) and (
+            self._worker_name == other._worker_name
+            and self._device == other._device
+            and self._rank == other._rank
+        )
+
+    def __hash__(self):
+        return hash(self._worker_name) ^ hash(self._device) ^ hash(self._rank)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rendezvous.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rendezvous.py
new file mode 100644
index 0000000000000000000000000000000000000000..f7913341175fbecd69fcd6e621aeb02f2cfc82b6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rendezvous.py
@@ -0,0 +1,293 @@
+# mypy: allow-untyped-defs
+try:
+    from urllib.parse import urlparse, urlunparse
+except ImportError as e:
+    raise ImportError(
+        "urllib cannot be found, urlparse from python2 is no longer supported."
+    ) from e
+
+import numbers
+import os
+import sys
+from collections.abc import Callable, Iterator
+from datetime import timedelta
+
+from torch.distributed import FileStore, Store, TCPStore
+
+from .constants import default_pg_timeout
+
+
+_rendezvous_handlers: dict[str, Callable[..., Iterator[tuple[Store, int, int]]]] = {}
+
+__all__ = ["register_rendezvous_handler", "rendezvous"]
+
+
+def register_rendezvous_handler(scheme, handler):
+    """
+    Register a new rendezvous handler.
+
+    Before we can run collective algorithms, participating processes
+    need to find each other and exchange information to be able to
+    communicate. We call this process rendezvous.
+
+    The outcome of the rendezvous process is a triplet containing a
+    shared key/value store, the rank of the process, and the total
+    number of participating processes.
+
+    If none of the bundled rendezvous methods apply to your execution
+    environment you can opt to register your own rendezvous handler.
+    Pick a unique name and use the URL scheme to identify it when
+    calling the `rendezvous()` function.
+
+    Args:
+        scheme (str): URL scheme to identify your rendezvous handler.
+        handler (function): Handler that is invoked when the
+            `rendezvous()` function is called with a URL that uses
+            the corresponding scheme. It must be a generator function
+            that yields the triplet.
+    """
+    global _rendezvous_handlers
+    if scheme in _rendezvous_handlers:
+        raise RuntimeError(f"Rendezvous handler for {scheme}:// already registered")
+    _rendezvous_handlers[scheme] = handler
+
+
+# Query will have format "rank=0&world_size=1" and is
+# converted into {"rank": 0, "world_size": 1}
+def _query_to_dict(query: str) -> dict[str, str]:
+    return {
+        pair[0]: pair[1]
+        for pair in (pair.split("=") for pair in filter(None, query.split("&")))
+    }
+
+
+def _get_use_libuv_from_query_dict(query_dict: dict[str, str]) -> bool:
+    # libuv is the default backend for TCPStore. To enable the non-libuv backend,
+    # user can explicitly specify ``use_libuv=0`` in the URL parameter.
+    if sys.platform == "win32":
+        #  PyTorch is built without libuv support on windows, so default to 0
+        return query_dict.get("use_libuv", os.environ.get("USE_LIBUV", "0")) == "1"
+    return query_dict.get("use_libuv", os.environ.get("USE_LIBUV", "1")) == "1"
+
+
+def _rendezvous_helper(url: str, rank: int, world_size_opt: int | None, **kwargs):
+    result = urlparse(url)
+    if world_size_opt is None:
+        world_size = -1
+        if result.scheme == "env":
+            rank = int(os.environ.get("RANK", rank))
+            # If the world_size env variable is not present then it is a dynamic group
+            world_size = int(os.environ.get("WORLD_SIZE", world_size))
+    else:
+        world_size = world_size_opt
+    if rank != -1 or world_size != -1 or world_size_opt is None:
+        query_dict = _query_to_dict(result.query)
+        if "rank" in query_dict or "world_size" in query_dict:
+            raise AssertionError(
+                f"The url: {url} has node-specific arguments(rank, world_size) already."
+            )
+        if rank != -1:
+            query_dict["rank"] = str(rank)
+        if world_size != -1 or world_size_opt is None:
+            query_dict["world_size"] = str(world_size)
+        result = result._replace(
+            query=f"{'&'.join([f'{k}={v}' for k, v in query_dict.items()])}"
+        )
+        # pyrefly: ignore [bad-assignment]
+        url = urlunparse(result)
+
+    if result.scheme not in _rendezvous_handlers:
+        raise RuntimeError(f"No rendezvous handler for {result.scheme}://")
+    return _rendezvous_handlers[result.scheme](url, **kwargs)
+
+
+def rendezvous(url: str, rank: int = -1, world_size: int = -1, **kwargs):
+    if not isinstance(url, (str, bytes)):
+        raise RuntimeError(f"`url` must be a string. {type(url)}: {url}")
+
+    if not isinstance(rank, numbers.Integral):
+        raise RuntimeError(f"`rank` must be an integer. {rank}")
+
+    if not isinstance(world_size, numbers.Integral):
+        raise RuntimeError(f"`world_size` must be an integer. {world_size}")
+
+    # pyrefly: ignore [bad-argument-type]
+    return _rendezvous_helper(url, rank, world_size, **kwargs)
+
+
+def _create_store_from_options(backend_options, rank):
+    store, _, _ = next(_rendezvous_helper(backend_options.init_method, rank, None))
+    return store
+
+
+def _rendezvous_error(msg):
+    return ValueError("Error initializing torch.distributed using " + msg)
+
+
+def _file_rendezvous_handler(url: str, **kwargs):
+    def _error(msg):
+        return _rendezvous_error("file:// rendezvous: " + msg)
+
+    result = urlparse(url)
+    path = result.path
+    if sys.platform == "win32":
+        import urllib.request
+
+        full_path = result.netloc + result.path
+        path = urllib.request.url2pathname(full_path)
+        if path:
+            # Normalizing an empty string produces ".", which is not expected.
+            path = os.path.normpath(path)
+
+    if not path:
+        raise _error("path missing")
+    query_dict = _query_to_dict(result.query)
+    if "rank" not in query_dict:
+        raise _error("rank parameter missing")
+    if "world_size" not in query_dict:
+        raise _error("world size parameter missing")
+
+    rank = int(query_dict["rank"])
+    world_size = int(query_dict["world_size"])
+    store = FileStore(path, world_size)
+    yield (store, rank, world_size)
+
+    # If this configuration is invalidated, there is nothing we can do about it
+    raise RuntimeError("Unable to perform rerendezvous using file:// method")
+
+
+def _torchelastic_use_agent_store() -> bool:
+    return os.environ.get("TORCHELASTIC_USE_AGENT_STORE", None) == str(True)
+
+
+def _create_c10d_store(
+    hostname, port, rank, world_size, timeout, use_libuv=True
+) -> Store:
+    """
+    Smartly creates a c10d Store object on ``rank`` based on whether we need to reuse agent store.
+
+    The TCPStore server is assumed to be hosted
+    on ``hostname:port``.
+
+    By default, the TCPStore server uses the asynchronous implementation
+    ``LibUVStoreDaemon`` which utilizes libuv.
+
+    If ``torchelastic_use_agent_store()`` is ``True``, then it is assumed that
+    the agent leader (node rank 0) hosts the TCPStore server (for which the
+    endpoint is specified by the given ``hostname:port``). Hence
+    ALL ranks will create and return a TCPStore client (e.g. ``start_daemon=False``).
+
+    If ``torchelastic_use_agent_store()`` is ``False``, then rank 0 will host
+    the TCPStore (with multi-tenancy) and it is assumed that rank 0's hostname
+    and port are correctly passed via ``hostname`` and ``port``. All
+    non-zero ranks will create and return a TCPStore client.
+    """
+    # check if port is uint16_t
+    if not 0 <= port < 2**16:
+        raise ValueError(f"port must have value from 0 to 65535 but was {port}.")
+
+    if _torchelastic_use_agent_store():
+        # We create a new TCPStore for every retry so no need to add prefix for each attempt.
+        return TCPStore(
+            host_name=hostname,
+            port=port,
+            world_size=world_size,
+            is_master=False,
+            timeout=timeout,
+        )
+    else:
+        start_daemon = rank == 0
+        return TCPStore(
+            host_name=hostname,
+            port=port,
+            world_size=world_size,
+            is_master=start_daemon,
+            timeout=timeout,
+            multi_tenant=True,
+            use_libuv=use_libuv,
+        )
+
+
+def _tcp_rendezvous_handler(
+    url: str, timeout: timedelta = default_pg_timeout, **kwargs
+):
+    def _error(msg):
+        return _rendezvous_error("tcp:// rendezvous: " + msg)
+
+    result = urlparse(url)
+    if result.port is None:
+        raise _error("port number missing")
+    query_dict = _query_to_dict(result.query)
+    if "rank" not in query_dict:
+        raise _error("rank parameter missing")
+    if "world_size" not in query_dict:
+        raise _error("world size parameter missing")
+
+    rank = int(query_dict["rank"])
+    world_size = int(query_dict["world_size"])
+    use_libuv = _get_use_libuv_from_query_dict(query_dict)
+
+    if result.hostname is None:
+        raise AssertionError("hostname cannot be None")
+
+    store = _create_c10d_store(
+        result.hostname, result.port, rank, world_size, timeout, use_libuv
+    )
+
+    yield (store, rank, world_size)
+
+    # If this configuration is invalidated, there is nothing we can do about it
+    raise RuntimeError("Unable to perform re-rendezvous using tcp:// method")
+
+
+def _env_rendezvous_handler(
+    url: str, timeout: timedelta = default_pg_timeout, **kwargs
+):
+    def _error(msg):
+        return _rendezvous_error("env:// rendezvous: " + msg)
+
+    def _env_error(var):
+        return _error(f"environment variable {var} expected, but not set")
+
+    def _get_env_or_raise(env_var: str) -> str:
+        env_val = os.environ.get(env_var, None)
+        if not env_val:
+            raise _env_error(env_var)
+        else:
+            return env_val
+
+    result = urlparse(url)
+    query_dict = _query_to_dict(result.query)
+
+    rank: int
+    world_size: int
+    master_port: int
+    master_addr: str
+
+    if "rank" in query_dict:
+        rank = int(query_dict["rank"])
+    else:
+        rank = int(_get_env_or_raise("RANK"))
+
+    if "world_size" in query_dict:
+        world_size = int(query_dict["world_size"])
+    else:
+        world_size = int(_get_env_or_raise("WORLD_SIZE"))
+
+    master_addr = _get_env_or_raise("MASTER_ADDR")
+    master_port = int(_get_env_or_raise("MASTER_PORT"))
+    use_libuv = _get_use_libuv_from_query_dict(query_dict)
+
+    store = _create_c10d_store(
+        master_addr, master_port, rank, world_size, timeout, use_libuv
+    )
+
+    yield (store, rank, world_size)
+
+    # If this configuration is invalidated, there is nothing we can do about it
+    raise RuntimeError("Unable to perform re-rendezvous using env:// method")
+
+
+register_rendezvous_handler("tcp", _tcp_rendezvous_handler)
+register_rendezvous_handler("env", _env_rendezvous_handler)
+register_rendezvous_handler("file", _file_rendezvous_handler)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..adf901d6b6e3e693f69464e5c64d58a857ae6014
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/__init__.py
@@ -0,0 +1,257 @@
+# mypy: allow-untyped-defs
+import logging
+import os
+import threading
+import warnings
+from collections.abc import Generator
+from datetime import timedelta
+from urllib.parse import urlparse
+
+import torch
+import torch.distributed as dist
+
+
+__all__ = ["is_available"]
+
+
+logger = logging.getLogger(__name__)
+
+
+_init_counter = 0
+_init_counter_lock = threading.Lock()
+
+
+def is_available() -> bool:
+    return hasattr(torch._C, "_rpc_init")
+
+
+if is_available() and not torch._C._rpc_init():
+    raise RuntimeError("Failed to initialize torch.distributed.rpc")
+
+
+if is_available():
+    _is_tensorpipe_available = hasattr(
+        torch._C._distributed_rpc, "_TensorPipeRpcBackendOptionsBase"
+    )
+
+    import numbers
+
+    import torch.distributed.autograd as dist_autograd
+    from torch._C._distributed_c10d import Store
+    from torch._C._distributed_rpc import (  # noqa: F401
+        _cleanup_python_rpc_handler,
+        _DEFAULT_INIT_METHOD,
+        _DEFAULT_RPC_TIMEOUT_SEC,
+        _delete_all_user_and_unforked_owner_rrefs,
+        _destroy_rref_context,
+        _disable_jit_rref_pickle,
+        _disable_server_process_global_profiler,
+        _enable_jit_rref_pickle,
+        _enable_server_process_global_profiler,
+        _get_current_rpc_agent,
+        _invoke_remote_builtin,
+        _invoke_remote_python_udf,
+        _invoke_remote_torchscript,
+        _invoke_rpc_builtin,
+        _invoke_rpc_python_udf,
+        _invoke_rpc_torchscript,
+        _is_current_rpc_agent_set,
+        _reset_current_rpc_agent,
+        _rref_context_get_debug_info,
+        _set_and_start_rpc_agent,
+        _set_profiler_node_id,
+        _set_rpc_timeout,
+        _UNSET_RPC_TIMEOUT,
+        enable_gil_profiling,
+        get_rpc_timeout,
+        PyRRef,
+        RemoteProfilerManager,
+        RpcAgent,
+        RpcBackendOptions,
+        WorkerInfo,
+    )
+
+    if _is_tensorpipe_available:
+        from torch._C._distributed_rpc import (  # noqa: F401
+            _DEFAULT_NUM_WORKER_THREADS,
+            _TensorPipeRpcBackendOptionsBase,
+            TensorPipeAgent,
+        )
+
+    from . import api, backend_registry, functions
+    from .api import *  # noqa: F401,F403
+    from .backend_registry import BackendType
+    from .options import TensorPipeRpcBackendOptions  # noqa: F401
+    from .server_process_global_profiler import _server_process_global_profile
+
+    rendezvous_iterator: Generator[tuple[Store, int, int], None, None]
+
+    __all__ += ["init_rpc", "BackendType", "TensorPipeRpcBackendOptions"]
+    __all__ = __all__ + api.__all__ + backend_registry.__all__  # noqa: PLE0605
+
+    def init_rpc(
+        name,
+        backend=None,
+        rank=-1,
+        world_size=None,
+        rpc_backend_options=None,
+    ):
+        r"""
+        Initializes RPC primitives such as the local RPC agent
+        and distributed autograd, which immediately makes the current
+        process ready to send and receive RPCs.
+
+        Args:
+            name (str): a globally unique name of this node. (e.g.,
+                ``Trainer3``, ``ParameterServer2``, ``Master``, ``Worker1``)
+                Name can only contain number, alphabet, underscore, colon,
+                and/or dash, and must be shorter than 128 characters.
+            backend (BackendType, optional): The type of RPC backend
+                implementation. Supported values is
+                ``BackendType.TENSORPIPE`` (the default).
+                See :ref:`rpc-backends` for more information.
+            rank (int): a globally unique id/rank of this node.
+            world_size (int): The number of workers in the group.
+            rpc_backend_options (RpcBackendOptions, optional): The options
+                passed to the RpcAgent constructor. It must be an agent-specific
+                subclass of :class:`~torch.distributed.rpc.RpcBackendOptions`
+                and contains agent-specific initialization configurations. By
+                default, for all agents, it sets the default timeout to 60
+                seconds and performs the rendezvous with an underlying process
+                group initialized using ``init_method = "env://"``,
+                meaning that environment variables ``MASTER_ADDR`` and
+                ``MASTER_PORT`` need to be set properly. See
+                :ref:`rpc-backends` for more information and find which options
+                are available.
+        """
+        torch._C._log_api_usage_once("torch.distributed.init_rpc")
+        if backend is not None and not isinstance(
+            backend, backend_registry.BackendType
+        ):
+            raise TypeError("Argument backend must be a member of BackendType")
+
+        if rpc_backend_options is not None and not isinstance(
+            rpc_backend_options, RpcBackendOptions
+        ):
+            raise TypeError(
+                "Argument rpc_backend_options must be an instance of RpcBackendOptions"
+            )
+
+        # Try to detect the backend from the options
+        if backend is None and rpc_backend_options is not None:
+            for candidate_backend in BackendType:
+                if isinstance(
+                    rpc_backend_options,
+                    type(
+                        backend_registry.construct_rpc_backend_options(
+                            candidate_backend
+                        )
+                    ),
+                ):
+                    backend = candidate_backend
+                    break
+            else:
+                raise TypeError(
+                    f"Could not infer backend for options {rpc_backend_options}"
+                )
+            # Ignore type error because mypy doesn't handle dynamically generated type objects (#4865)
+            if backend != BackendType.TENSORPIPE:  # type: ignore[attr-defined]
+                logger.warning(
+                    "RPC was initialized with no explicit backend but with options "  # type: ignore[attr-defined]
+                    "corresponding to %(backend)s, hence that backend will be used "
+                    "instead of the default BackendType.TENSORPIPE. To silence this "
+                    "warning pass `backend=%(backend)s` explicitly.",
+                    {"backend": backend},
+                )
+
+        if backend is None:
+            backend = BackendType.TENSORPIPE  # type: ignore[attr-defined]
+
+        if rpc_backend_options is None:
+            # default construct a set of RPC backend options.
+            rpc_backend_options = backend_registry.construct_rpc_backend_options(
+                backend
+            )
+
+        # Create store, performs rendezvous for static RPC group.
+        if not world_size:
+            # If world_size is not set in construction and also not set in environment variables
+            # The store will be created for the dynamic group setting
+            store = dist._create_store_from_options(rpc_backend_options, rank)
+        else:
+            # This rendezvous state sometimes is destroyed before all processes
+            # finishing handshaking. To avoid that issue, we make it global to
+            # keep it alive.
+            global rendezvous_iterator
+            rendezvous_iterator = dist.rendezvous(
+                rpc_backend_options.init_method, rank=rank, world_size=world_size
+            )
+            store, _, _ = next(rendezvous_iterator)
+        # Use same timeout as RPC.
+        store.set_timeout(timedelta(seconds=rpc_backend_options.rpc_timeout))
+
+        # Use a PrefixStore to distinguish multiple invocations.
+        with _init_counter_lock:
+            global _init_counter
+            store = dist.PrefixStore(str(f"rpc_prefix_{_init_counter}"), store)
+            _init_counter += 1
+
+        # Initialize autograd before RPC since _init_rpc_backend guarantees all
+        # processes sync via the store. If we initialize autograd after RPC,
+        # there could be a race where some nodes might have initialized autograd
+        # and others might not have. As a result, a node calling
+        # torch.distributed.autograd.backward() would run into errors since
+        # other nodes might not have been initialized.
+        dist_autograd._init(rank)
+
+        _set_profiler_node_id(rank)
+        # Initialize RPC.
+        _init_rpc_backend(backend, store, name, rank, world_size, rpc_backend_options)
+
+    def _validate_rpc_args(backend, store, name, rank, world_size, rpc_backend_options):
+        type_mapping = {
+            backend: backend_registry.BackendType,
+            store: dist.Store,
+            name: str,
+            rank: numbers.Integral,
+            # world_size can be None for a dynamic group
+            world_size: (numbers.Integral, type(None)),
+            rpc_backend_options: RpcBackendOptions,
+        }
+        for arg, arg_type in type_mapping.items():
+            if not isinstance(arg, arg_type):  # type: ignore[arg-type]
+                raise RuntimeError(
+                    f"Argument {arg} must be of type {arg_type} but got type {type(arg)}"
+                )
+
+    def _init_rpc_backend(
+        backend=BackendType.TENSORPIPE,  # type: ignore[attr-defined]
+        store=None,
+        name=None,
+        rank=-1,
+        world_size=None,
+        rpc_backend_options=None,
+    ):
+        _validate_rpc_args(backend, store, name, rank, world_size, rpc_backend_options)
+
+        if _is_current_rpc_agent_set():
+            raise RuntimeError("RPC is already initialized")
+
+        # Initialize RPC.
+        rpc_agent = backend_registry.init_backend(
+            backend,
+            store=store,
+            name=name,
+            rank=rank,
+            world_size=world_size,
+            rpc_backend_options=rpc_backend_options,
+        )
+
+        api._init_rpc_states(rpc_agent)
+
+    @api._require_initialized
+    def _get_debug_info():
+        info = _rref_context_get_debug_info()
+        info.update(api._get_current_rpc_agent().get_debug_info())
+        info.update(dist_autograd._get_debug_info())
+        return info
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/_testing/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/_testing/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0abd737becafbae33b0b63799c1eb43c913e1998
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/_testing/__init__.py
@@ -0,0 +1,18 @@
+import torch
+
+
+def is_available() -> bool:
+    return hasattr(torch._C, "_faulty_agent_init")
+
+
+if is_available() and not torch._C._faulty_agent_init():
+    raise RuntimeError("Failed to initialize torch.distributed.rpc._testing")
+
+if is_available():
+    # Registers FAULTY_TENSORPIPE RPC backend.
+    from torch._C._distributed_rpc_testing import (
+        FaultyTensorPipeAgent,
+        FaultyTensorPipeRpcBackendOptions,
+    )
+
+    from . import faulty_agent_backend_registry
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/_testing/faulty_agent_backend_registry.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/_testing/faulty_agent_backend_registry.py
new file mode 100644
index 0000000000000000000000000000000000000000..d04882e16e79a94f74ddc1350e94f547ef625611
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/_testing/faulty_agent_backend_registry.py
@@ -0,0 +1,62 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+import torch.distributed as dist
+import torch.distributed.rpc as rpc
+
+
+def _faulty_tensorpipe_construct_rpc_backend_options_handler(
+    rpc_timeout,
+    init_method,
+    num_worker_threads,
+    messages_to_fail,
+    messages_to_delay,
+    num_fail_sends,
+    **kwargs,
+):
+    from . import FaultyTensorPipeRpcBackendOptions
+
+    return FaultyTensorPipeRpcBackendOptions(
+        num_worker_threads=num_worker_threads,
+        rpc_timeout=rpc_timeout,
+        init_method=init_method,
+        messages_to_fail=messages_to_fail,
+        messages_to_delay=messages_to_delay,
+        num_fail_sends=num_fail_sends,
+    )
+
+
+def _faulty_tensorpipe_init_backend_handler(
+    store, name, rank, world_size, rpc_backend_options
+):
+    from torch.distributed.rpc import api
+
+    from . import FaultyTensorPipeAgent, FaultyTensorPipeRpcBackendOptions
+
+    if not isinstance(store, dist.Store):
+        raise TypeError(f"`store` must be a c10d::Store. {store}")
+
+    if not isinstance(rpc_backend_options, FaultyTensorPipeRpcBackendOptions):
+        raise TypeError(
+            f"`rpc_backend_options` must be a `FaultyTensorPipeRpcBackendOptions`. {rpc_backend_options}"
+        )
+
+    agent = FaultyTensorPipeAgent(
+        store,
+        name,
+        rank,
+        world_size,
+        rpc_backend_options,
+        {},  # reverse_device_map
+        [],  # devices
+    )
+    api._init_rpc_states(agent)
+
+    return agent
+
+
+rpc.backend_registry.register_backend(
+    "FAULTY_TENSORPIPE",
+    _faulty_tensorpipe_construct_rpc_backend_options_handler,
+    _faulty_tensorpipe_init_backend_handler,
+)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..a0021ff1e43d8653df457cb99e7ea3637a508851
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/_utils.py
@@ -0,0 +1,47 @@
+# mypy: allow-untyped-defs
+import logging
+from contextlib import contextmanager
+from typing import cast
+
+
+logger = logging.getLogger(__name__)
+
+
+@contextmanager
+def _group_membership_management(store, name, is_join):
+    token_key = "RpcGroupManagementToken"
+    join_or_leave = "join" if is_join else "leave"
+    my_token = f"Token_for_{name}_{join_or_leave}"
+    while True:
+        # Retrieve token from store to signal start of rank join/leave critical section
+        returned = store.compare_set(token_key, "", my_token).decode()
+        if returned == my_token:
+            # Yield to the function this context manager wraps
+            yield
+            # Finished, now exit and release token
+            # Update from store to signal end of rank join/leave critical section
+            store.set(token_key, "")
+            # Other will wait for this token to be set before they execute
+            store.set(my_token, "Done")
+            break
+        else:
+            # Store will wait for the token to be released
+            try:
+                store.wait([returned])
+            except RuntimeError:
+                logger.error(
+                    "Group membership token %s timed out waiting for %s to be released.",
+                    my_token,
+                    returned,
+                )
+                raise
+
+
+def _update_group_membership(worker_info, my_devices, reverse_device_map, is_join):
+    from . import api, TensorPipeAgent
+
+    agent = cast(TensorPipeAgent, api._get_current_rpc_agent())
+    ret = agent._update_group_membership(
+        worker_info, my_devices, reverse_device_map, is_join
+    )
+    return ret
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/api.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..845ce0b7faf6c4cb1390c4d7089f745a1861f335
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/api.py
@@ -0,0 +1,965 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+
+import collections
+import contextlib
+import functools
+import inspect
+import logging
+import threading
+from typing import Any, Generic, TYPE_CHECKING, TypeVar
+
+import torch
+from torch._C._distributed_rpc import (
+    _cleanup_python_rpc_handler,
+    _delete_all_user_and_unforked_owner_rrefs,
+    _destroy_rref_context,
+    _get_current_rpc_agent,
+    _invoke_remote_builtin,
+    _invoke_remote_python_udf,
+    _invoke_remote_torchscript,
+    _invoke_rpc_builtin,
+    _invoke_rpc_python_udf,
+    _invoke_rpc_torchscript,
+    _is_current_rpc_agent_set,
+    _reset_current_rpc_agent,
+    _set_and_start_rpc_agent,
+    get_rpc_timeout,
+    PyRRef,
+    RemoteProfilerManager,
+    WorkerInfo,
+)
+from torch.futures import Future
+
+from ._utils import _group_membership_management, _update_group_membership
+from .constants import DEFAULT_SHUTDOWN_TIMEOUT, UNSET_RPC_TIMEOUT
+from .internal import (
+    _build_rpc_profiling_key,
+    _internal_rpc_pickler,
+    PythonUDF,
+    RPCExecMode,
+)
+
+
+__all__ = [
+    "shutdown",
+    "get_worker_info",
+    "remote",
+    "rpc_sync",
+    "rpc_async",
+    "RRef",
+    "AllGatherStates",
+    "method_factory",
+    "new_method",
+]
+
+
+logger = logging.getLogger(__name__)
+
+# NB: Ignoring RRef leaks during shutdown. Without this, applications have to
+# make sure there is no references to any RRef in the application code and
+# Python GC has done its job to delete those RRefs. This is could result in bad
+# debugging experiences especially when for large applications. Therefore, by
+# default, we are going to ignore RRef leaks during shutdown. This is usually
+# fine as shutdown means applications have done training and no longer care
+# about states.
+#
+# To enable RRef leak checking, set this _ignore_rref_leak to False
+_ignore_rref_leak = True
+_default_pickler = _internal_rpc_pickler
+
+
+@contextlib.contextmanager
+def _use_rpc_pickler(rpc_pickler):
+    r"""
+    rpc_pickler: (.internal._InternalRPCPickler) Overrides the default RPC pickler
+    """
+    global _default_pickler
+    _default_pickler = rpc_pickler
+    try:
+        yield
+    finally:
+        _default_pickler = _internal_rpc_pickler
+
+
+def _require_initialized(func):
+    @functools.wraps(func)
+    def wrapper(*args, **kwargs):
+        if not _is_current_rpc_agent_set():
+            raise RuntimeError(
+                "RPC has not been initialized. Call "
+                "torch.distributed.rpc.init_rpc first."
+            )
+        return func(*args, **kwargs)
+
+    return wrapper
+
+
+class AllGatherStates:
+    def __init__(self):
+        # Each `gathered_objects` is an empty dict at beginning.
+        # The leader worker is elected as the first worker in a sorted worker
+        # name list. Whenever there is a worker entering `_all_gather()`, it
+        # runs `_gather_to_leader()` on the leader to add its own name and
+        # data obj to this dict. The leader also adds itself's name to the dict
+        # on calling `_all_gather()`.
+        # Once `set(gathered_objects.keys()) == _ALL_WORKER_NAMES`, the leader
+        # will broadcast the gathered dict to all follower workers and set their
+        # `gathered_objects` field and the `proceed_signal` field.
+        self.gathered_objects = {}
+        # All workers wait on this signal until it receives all gathered
+        # objects.
+        self.proceed_signal = threading.Event()
+
+
+# States used by `def _all_gather()`.
+# `_ALL_WORKER_NAMES` is initialized on initializing RPC layer.
+_ALL_WORKER_NAMES: set[Any] = set()
+_all_gather_dict_lock = threading.RLock()
+_all_gather_sequence_id: dict[str, int] = {}
+_all_gather_sequence_id_to_states: collections.defaultdict = collections.defaultdict(
+    AllGatherStates
+)
+
+
+def _init_rpc_states(agent):
+    worker_infos = agent.get_worker_infos()
+    global _ALL_WORKER_NAMES
+    _ALL_WORKER_NAMES = {worker_info.name for worker_info in worker_infos}
+
+    # NB: backend implementation might have already set the rpc_agent.
+    if not _is_current_rpc_agent_set():
+        _set_and_start_rpc_agent(agent)
+
+
+def _gather_to_leader(sequence_id, worker_name, obj, worker_names=None):
+    with _all_gather_dict_lock:
+        if not worker_names:
+            worker_names = _ALL_WORKER_NAMES
+            assert worker_name in worker_names, (
+                f"{worker_name} is not expected by leader."
+            )
+        states = _all_gather_sequence_id_to_states[sequence_id]
+        assert worker_name not in states.gathered_objects, (
+            f"{worker_name} reported intent sequence id {sequence_id} twice. "
+        )
+        states.gathered_objects[worker_name] = obj
+        if worker_names == set(states.gathered_objects.keys()):
+            states.proceed_signal.set()
+
+
+def _broadcast_to_followers(sequence_id, objects_map):
+    with _all_gather_dict_lock:
+        states = _all_gather_sequence_id_to_states[sequence_id]
+
+    assert not states.proceed_signal.is_set(), (
+        f"Termination signal sequence id {sequence_id} got set twice."
+    )
+    states.gathered_objects = objects_map
+    states.proceed_signal.set()
+
+
+_thread_local_var = threading.local()
+
+
+@contextlib.contextmanager
+def _wait_all():
+    r"""
+    A context manager that collects all futures returned by ``rpc_async`` and
+    waits them on the context manager's exit; relieving the user of needing
+    to explicitly call wait.
+
+
+    Example::
+        >>> # xdoctest: +SKIP("distributed")
+        >>> # On worker 0:
+        >>> import torch
+        >>> import torch.distributed.rpc as rpc
+        >>> rpc.init_rpc("worker0", rank=0, world_size=2)
+        >>> with rpc._wait_all():
+        >>>    fut_1 = rpc.rpc_async(dst, torch.add, (torch.ones(2, 2), 1))
+        >>>    fut_2 = rpc.rpc_async(dst, torch.add, (torch.ones(2, 2), 1))
+        >>> #fut_1 and fut_2 are waited on
+    """
+    _thread_local_var.future_list = []
+    try:
+        yield
+    finally:
+        try:
+            torch.futures.wait_all(_thread_local_var.future_list)
+        finally:
+            del _thread_local_var.future_list
+
+
+@_require_initialized
+def _all_gather(obj, worker_names=None, timeout: float = UNSET_RPC_TIMEOUT):
+    r"""
+    This is similar to torch.distributed.all_gather(), but is using RPC. It
+    picks the worker with the smallest name (alphabetic order) as the leader.
+    Then all followers send their data ``obj`` to the leader. After the leader
+    has received all, it will broadcast the results back to all followers. This
+    function blocks until all workers have received the gathered results.
+    """
+    if not worker_names:
+        assert _ALL_WORKER_NAMES is not None, (
+            "`_ALL_WORKER_NAMES` is not initialized for `def _all_gather`."
+        )
+        worker_names = _ALL_WORKER_NAMES
+    leader_name = min(worker_names)
+
+    self_name = _get_current_rpc_agent().get_worker_info().name
+
+    with _all_gather_dict_lock:
+        concat_names = "".join(sorted(worker_names))
+        sequence_num = _all_gather_sequence_id.get(concat_names, 0)
+        _all_gather_sequence_id[concat_names] = sequence_num + 1
+        sequence_id = concat_names + str(sequence_num)
+
+    is_leader = leader_name == self_name
+
+    if timeout == UNSET_RPC_TIMEOUT:
+        # Timeout is specified by agent for RPC calls
+        rpc_timeout = get_rpc_timeout()
+        # No timeout for signal
+        signal_timeout = None
+    elif timeout == DEFAULT_SHUTDOWN_TIMEOUT:
+        # No timeout for RPC
+        rpc_timeout = timeout
+        # No timeout for signal
+        signal_timeout = None
+    else:
+        # Signal and RPC timeout use the same timeout
+        signal_timeout = rpc_timeout = timeout
+
+    # Phase 1: Followers send it's object to the leader
+    if is_leader:
+        _gather_to_leader(sequence_id, self_name, obj, worker_names)
+    else:
+        rpc_sync(
+            leader_name,
+            _gather_to_leader,
+            args=(sequence_id, self_name, obj, worker_names),
+            timeout=rpc_timeout,
+        )
+
+    with _all_gather_dict_lock:
+        states = _all_gather_sequence_id_to_states[sequence_id]
+
+    # Timeout is either set by function parameter or None (which is indefinite)
+    states.proceed_signal.wait(timeout=signal_timeout)
+
+    # Phase 2: Leader broadcast gathered results to all followers
+    # Leader's signal is the first to be unblocked, after receiving all
+    # followers' data objects.
+    if is_leader:
+        worker_name_to_response_future_dict = {}
+        for follower_name in worker_names - {leader_name}:
+            fut = rpc_async(
+                follower_name,
+                _broadcast_to_followers,
+                args=(sequence_id, states.gathered_objects),
+                timeout=rpc_timeout,
+            )
+            worker_name_to_response_future_dict[follower_name] = fut
+
+        errors = []
+        for follower_name, fut in worker_name_to_response_future_dict.items():
+            try:
+                fut.wait()
+            except RuntimeError as ex:
+                errors.append((follower_name, ex))
+
+        if errors:
+            raise RuntimeError(
+                f"Followers {[e[0] for e in errors]} timed out in _all_gather "
+                f"after {rpc_timeout:.2f} seconds. The first exception is {errors[0][1]}"
+            )
+
+    # Clean up for the states using the sequence_id
+    with _all_gather_dict_lock:
+        states = _all_gather_sequence_id_to_states.pop(sequence_id)
+    return states.gathered_objects
+
+
+@_require_initialized
+def _barrier(worker_names):
+    r"""
+    Synchronizes local and remote RPC processes.
+
+    This will block until all local and remote RPC processes specified under worker_names
+    reach this method to wait for all outstanding work to complete.
+
+    Args:
+        worker_names (List[str]): The set of workers to synchronize.
+
+    """
+    try:
+        _all_gather(None, set(worker_names))
+    except RuntimeError:
+        logger.exception("Failed to complete barrier")
+
+
+@_require_initialized
+def _wait_all_workers(timeout=DEFAULT_SHUTDOWN_TIMEOUT):
+    r"""
+    Block until all local and remote RPC processes reach this method and wait
+    for all outstanding work to complete. Every RPC process must call this
+    method before exit to perform a graceful shutdown. This should be used to
+    terminate the RPC framework, and there is no guarantee that the RPC
+    framework will work after this method returns.
+    """
+    try:
+        _all_gather(None, timeout=timeout)
+    except RuntimeError as ex:
+        logger.exception("Failed to respond to 'Shutdown Proceed' in time")
+        raise ex
+
+
+@_require_initialized
+def shutdown(graceful=True, timeout=DEFAULT_SHUTDOWN_TIMEOUT):
+    r"""
+    Perform a shutdown of the RPC agent, and then destroy the RPC agent. This
+    stops the local agent from accepting outstanding requests, and shuts
+    down the RPC framework by terminating all RPC threads. If ``graceful=True``,
+    this will block until all local and remote RPC processes reach this method
+    and wait for all outstanding work to complete. Otherwise, if
+    ``graceful=False``, this is a local shutdown, and it does not wait for other
+    RPC processes to reach this method.
+
+    .. warning::
+        For :class:`~torch.futures.Future` objects returned by
+        :meth:`~torch.distributed.rpc.rpc_async`, ``future.wait()`` should not
+        be called after ``shutdown()``.
+
+    Args:
+        graceful (bool): Whether to do a graceful shutdown or not. If True,
+                         this will 1) wait until there is no pending system
+                         messages for ``UserRRefs`` and delete them; 2) block
+                         until all local and remote RPC processes have reached
+                         this method and wait for all outstanding work to
+                         complete.
+
+    Example::
+        Make sure that ``MASTER_ADDR`` and ``MASTER_PORT`` are set properly
+        on both workers. Refer to :meth:`~torch.distributed.init_process_group`
+        API for more details. For example,
+
+        export MASTER_ADDR=localhost
+        export MASTER_PORT=5678
+
+        Then run the following code in two different processes:
+
+        >>> # xdoctest: +SKIP
+        >>> # On worker 0:
+        >>> import torch
+        >>> import torch.distributed.rpc as rpc
+        >>> rpc.init_rpc("worker0", rank=0, world_size=2)
+        >>> # do some work
+        >>> result = rpc.rpc_sync("worker1", torch.add, args=(torch.ones(1), 1))
+        >>> # ready to shutdown
+        >>> rpc.shutdown()
+
+        >>> # On worker 1:
+        >>> import torch.distributed.rpc as rpc
+        >>> rpc.init_rpc("worker1", rank=1, world_size=2)
+        >>> # wait for worker 0 to finish work, and then shutdown.
+        >>> rpc.shutdown()
+    """
+    if graceful:
+        try:
+            agent = _get_current_rpc_agent()
+            from torch._C._distributed_rpc import TensorPipeAgent
+
+            if not isinstance(agent, TensorPipeAgent) or agent.is_static_group:
+                _wait_all_workers(timeout)
+                _delete_all_user_and_unforked_owner_rrefs()
+                agent.join(shutdown=True, timeout=timeout)
+            else:
+                # This is a dynamic group so we need to grab the token for the operation
+                my_worker_info = agent.get_worker_info()
+                my_name = my_worker_info.name
+                with _group_membership_management(agent.store, my_name, False):
+                    all_worker_infos = agent.get_worker_infos()
+                    for worker in all_worker_infos:
+                        if worker.name != my_name:
+                            rpc_sync(
+                                worker.name,
+                                _update_group_membership,
+                                args=(my_worker_info, [], {}, False),
+                            )
+                    agent.join(shutdown=True, timeout=timeout)
+        finally:
+            # In case of errors, continue to complete the local shutdown.
+            _finalize_shutdown()
+    else:
+        _finalize_shutdown()
+
+
+def _finalize_shutdown():
+    try:
+        # This raises a `TORCH_CHECK()` exception on RRef leak detected.
+        _destroy_rref_context(_ignore_rref_leak)
+    finally:
+        _get_current_rpc_agent().shutdown()
+        # clean up python rpc handler in shutdown(), see comments in
+        # PythonRpcHandler::cleanup(), call it in python API because the
+        # cleanup() function has python dependency, it assumes python
+        # interpreter exists.
+        # No matter if RRef leak exception is raised, this clean-up code
+        # must run to avoid destruction segfault in Python 3.5.
+        #
+        # future.wait() should not be called after shutdown().
+        # pythonRpcHandler is cleaned up in shutdown(), after
+        # shutdown(), python objects returned from rpc python call can not be
+        # resolved.
+        _cleanup_python_rpc_handler()
+        _reset_current_rpc_agent()
+
+
+@_require_initialized
+def get_worker_info(worker_name=None):
+    r"""
+    Get :class:`~torch.distributed.rpc.WorkerInfo` of a given worker name.
+    Use this :class:`~torch.distributed.rpc.WorkerInfo` to avoid passing an
+    expensive string on every invocation.
+
+    Args:
+        worker_name (str): the string name of a worker. If ``None``, return the
+                           the id of the current worker. (default ``None``)
+
+    Returns:
+        :class:`~torch.distributed.rpc.WorkerInfo` instance for the given
+        ``worker_name`` or :class:`~torch.distributed.rpc.WorkerInfo` of the
+        current worker if ``worker_name`` is ``None``.
+    """
+    if worker_name is not None:
+        return _get_current_rpc_agent().get_worker_info(worker_name)
+    else:
+        return _get_current_rpc_agent().get_worker_info()
+
+
+def _to_worker_info(to):
+    if isinstance(to, WorkerInfo):
+        return to
+    elif isinstance(to, (str, int)):
+        return get_worker_info(to)
+    else:
+        raise ValueError(f"Cannot get WorkerInfo from name {to}")
+
+
+def _rref_typeof_on_owner(rref, blocking: bool = True):
+    rref_type = type(rref.local_value())
+    if blocking:
+        return rref_type
+    else:
+        # Wrap result into a completed Future. This is so that if blocking=`False`
+        # is specified, we return a future regardless of if this call is on user
+        # or owner.
+        future = Future[type]()
+        future.set_result(rref_type)
+        return future
+
+
+def _rref_typeof_on_user(
+    rref, timeout: float = UNSET_RPC_TIMEOUT, blocking: bool = True
+):
+    fut = rpc_async(rref.owner(), _rref_typeof_on_owner, args=(rref,), timeout=timeout)
+    if blocking:
+        return fut.wait()
+    else:
+        return fut
+
+
+T = TypeVar("T")
+# pyrefly: ignore [invalid-annotation]
+GenericWithOneTypeVar = Generic[T]
+
+
+if TYPE_CHECKING:
+
+    class RRef(PyRRef[T], Generic[T]):
+        pass
+
+else:
+    try:
+        # Combine the implementation class and the type class.
+        class RRef(PyRRef, Generic[T]):
+            pass
+
+    except TypeError:
+        # TypeError: metaclass conflict: the metaclass of a derived class
+        # must be a (non-strict) subclass of the metaclasses of all its bases
+        # Mypy doesn't understand __class__ (mypy bug #4177)
+        class RRefMeta(PyRRef.__class__, GenericWithOneTypeVar.__class__):  # type: ignore[name-defined, misc, valid-type]
+            pass
+
+        # Combine the implementation class and the type class.
+        # Types for classes expecting a certain generic parameter (mypy bug #7791)
+        class RRef(PyRRef, GenericWithOneTypeVar, metaclass=RRefMeta):  # type: ignore[misc, no-redef, valid-type]
+            pass
+
+
+# Install docstrings from `PyRRef` to `RRef`.
+#
+# This is for the fact that pybind11 generates the parameter
+# `self` as type `rpc.PyRRef`, so a `:inherited-members:`
+# under `.. autoclass:: RRef` does not work.
+# we have to do the following process to replace `rpc.PyRRef` with `rpc.RRef`.
+#
+def method_factory(method_name, docstring):
+    def method(self, *args, **kwargs):
+        return getattr(super(RRef, self), method_name)(*args, **kwargs)
+
+    if method.__doc__:
+        method.__doc__ = docstring
+    return method
+
+
+for method_name, method in inspect.getmembers(PyRRef):
+    # Ignore magic methods, except "__str__".
+    if method_name.startswith("_") and method_name != "__str__":
+        continue
+
+    # Get pybind11 generated docstring.
+    # It's like,
+    """
+    to_here(self: torch.distributed.rpc.PyRRef, timeout: float=-1.0) -> object
+
+        Blocking call that copies the value of the RRef from the owner
+        to the local node and returns it. If the current node is the
+        owner, returns a reference to the local value.
+    """
+    docstring = getattr(method, "__doc__", None)
+    assert docstring is not None, "RRef user-facing methods should all have docstrings."
+
+    # Do surgery on pybind11 generated docstrings.
+    docstring = docstring.replace(
+        "torch.distributed.rpc.PyRRef", "torch.distributed.rpc.RRef"
+    )
+
+    # Attach user-facing RRef method with modified docstring.
+    new_method = method_factory(method_name, docstring)
+    setattr(RRef, method_name, new_method)
+
+
+@_require_initialized
+def remote(to, func, args=None, kwargs=None, timeout=UNSET_RPC_TIMEOUT):
+    r"""
+    Make a remote call to run ``func`` on worker ``to`` and return an
+    :class:`~torch.distributed.rpc.RRef` to the result value immediately.
+    Worker ``to`` will be the owner of the returned
+    :class:`~torch.distributed.rpc.RRef`, and the worker calling ``remote`` is
+    a user. The owner manages the global reference count of its
+    :class:`~torch.distributed.rpc.RRef`, and the owner
+    :class:`~torch.distributed.rpc.RRef` is only destructed when globally there
+    are no living references to it.
+
+    Args:
+        to (str or WorkerInfo or int): name/rank/``WorkerInfo`` of the destination worker.
+        func (Callable): a callable function, such as Python callables, builtin
+                         operators (e.g. :meth:`~torch.add`) and annotated
+                         TorchScript functions.
+        args (tuple): the argument tuple for the ``func`` invocation.
+        kwargs (dict): is a dictionary of keyword arguments for the ``func``
+                       invocation.
+
+        timeout (float, optional): timeout in seconds for this remote call. If the
+                                   creation of this
+                                   :class:`~torch.distributed.rpc.RRef` on worker
+                                   ``to`` is not successfully processed on this
+                                   worker within this timeout, then the next time
+                                   there is an attempt to use the RRef (such as
+                                   ``to_here()``), a timeout will be raised
+                                   indicating this failure. A value of 0 indicates
+                                   an infinite timeout, i.e. a timeout error will
+                                   never be raised. If not provided, the default
+                                   value set during initialization or with
+                                   ``_set_rpc_timeout`` is used.
+
+    Returns:
+        A user :class:`~torch.distributed.rpc.RRef` instance to the result
+        value. Use the blocking API :meth:`torch.distributed.rpc.RRef.to_here`
+        to retrieve the result value locally.
+
+    .. warning ::
+        The ``remote`` API does not copy storages of argument tensors until
+        sending them over the wire, which could be done by a different thread
+        depending on the RPC backend type. The caller should make sure that the
+        contents of those tensors stay intact until the returned RRef is
+        confirmed by the owner, which can be checked using the
+        :meth:`torch.distributed.rpc.RRef.confirmed_by_owner` API.
+
+    .. warning ::
+        Errors such as timeouts for the ``remote`` API are handled on a
+        best-effort basis. This means that when remote calls initiated by
+        ``remote`` fail, such as with a timeout error, we take a best-effort
+        approach to error handling. This means that errors are handled and set
+        on the resulting RRef on an asynchronous basis. If the RRef has not been
+        used by the application before this handling (such as ``to_here`` or
+        fork call), then future uses of the ``RRef`` will appropriately raise
+        errors. However, it is possible that the user application will use the
+        ``RRef`` before the errors are handled. In this case, errors may not be
+        raised as they have not yet been handled.
+
+    Example::
+
+        Make sure that ``MASTER_ADDR`` and ``MASTER_PORT`` are set properly
+        on both workers. Refer to :meth:`~torch.distributed.init_process_group`
+        API for more details. For example,
+
+        export MASTER_ADDR=localhost
+        export MASTER_PORT=5678
+
+        Then run the following code in two different processes:
+
+        >>> # xdoctest: +SKIP
+        >>> # On worker 0:
+        >>> import torch
+        >>> import torch.distributed.rpc as rpc
+        >>> rpc.init_rpc("worker0", rank=0, world_size=2)
+        >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3))
+        >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1))
+        >>> x = rref1.to_here() + rref2.to_here()
+        >>> rpc.shutdown()
+
+        >>> # On worker 1:
+        >>> import torch.distributed.rpc as rpc
+        >>> rpc.init_rpc("worker1", rank=1, world_size=2)
+        >>> rpc.shutdown()
+
+        Below is an example of running a TorchScript function using RPC.
+
+        >>> # On both workers:
+        >>> @torch.jit.script
+        >>> def my_script_add(tensor: torch.Tensor, scalar: int):
+        >>>    return torch.add(tensor, scalar)
+
+        >>> # On worker 0:
+        >>> import torch.distributed.rpc as rpc
+        >>> rpc.init_rpc("worker0", rank=0, world_size=2)
+        >>> rref = rpc.remote("worker1", my_script_add, args=(torch.ones(2), 3))
+        >>> rref.to_here()
+        >>> rpc.shutdown()
+
+        >>> # On worker 1:
+        >>> import torch.distributed.rpc as rpc
+        >>> rpc.init_rpc("worker1", rank=1, world_size=2)
+        >>> rpc.shutdown()
+    """
+    torch._C._log_api_usage_once("torch.distributed.rpc_remote")
+    qualified_name = torch.jit._builtins._find_builtin(func)
+    dst_worker_info = _to_worker_info(to)
+    should_profile = _get_should_profile()
+
+    ctx_manager = _enable_rpc_profiler(
+        should_profile, qualified_name, func, RPCExecMode.REMOTE, dst_worker_info
+    )
+
+    with ctx_manager as rf:
+        args = args if args else ()
+        kwargs = kwargs if kwargs else {}
+
+        is_async_exec = hasattr(func, "_wrapped_async_rpc_function")
+
+        if is_async_exec:
+            wrapped = func._wrapped_async_rpc_function
+            if isinstance(wrapped, torch.jit.ScriptFunction):
+                func = wrapped
+
+        if qualified_name is not None:
+            rref = _invoke_remote_builtin(
+                dst_worker_info, qualified_name, timeout, *args, **kwargs
+            )
+        elif isinstance(func, torch.jit.ScriptFunction):
+            rref = _invoke_remote_torchscript(
+                dst_worker_info.name,
+                torch._jit_internal._qualified_name(func),
+                timeout,
+                is_async_exec,
+                *args,
+                **kwargs,
+            )
+        else:
+            (pickled_python_udf, tensors) = _default_pickler.serialize(
+                PythonUDF(func, args, kwargs)
+            )
+            rref = _invoke_remote_python_udf(
+                dst_worker_info, pickled_python_udf, tensors, timeout, is_async_exec
+            )
+        # attach profiling information
+        if should_profile:
+            assert torch.autograd._profiler_enabled()
+            assert rf is not None
+            fut = rf._call_end_callbacks_on_future(rref._get_future())
+            rref._set_profiling_future(fut)
+
+    return rref
+
+
+def _invoke_rpc(
+    to, func, rpc_type, args=None, kwargs=None, rpc_timeout: float = UNSET_RPC_TIMEOUT
+):
+    if not callable(func):
+        raise TypeError("function should be callable.")
+
+    qualified_name = torch.jit._builtins._find_builtin(func)
+    dst_worker_info = _to_worker_info(to)
+
+    should_profile = _get_should_profile()
+
+    ctx_manager = _enable_rpc_profiler(
+        should_profile, qualified_name, func, rpc_type, dst_worker_info
+    )
+
+    with ctx_manager as rf:
+        args = args if args else ()
+        kwargs = kwargs if kwargs else {}
+
+        is_async_exec = hasattr(func, "_wrapped_async_rpc_function")
+
+        if is_async_exec:
+            # pyrefly: ignore [missing-attribute]
+            wrapped = func._wrapped_async_rpc_function
+            if isinstance(wrapped, torch.jit.ScriptFunction):
+                func = wrapped
+
+        if qualified_name is not None:
+            fut = _invoke_rpc_builtin(
+                dst_worker_info, qualified_name, rpc_timeout, *args, **kwargs
+            )
+        elif isinstance(func, torch.jit.ScriptFunction):
+            fut = _invoke_rpc_torchscript(
+                dst_worker_info.name,
+                torch._jit_internal._qualified_name(func),
+                args,
+                kwargs,
+                rpc_timeout,
+                is_async_exec,
+            )
+        else:
+            (pickled_python_udf, tensors) = _default_pickler.serialize(
+                PythonUDF(func, args, kwargs)
+            )
+            fut = _invoke_rpc_python_udf(
+                dst_worker_info, pickled_python_udf, tensors, rpc_timeout, is_async_exec
+            )
+        if should_profile:
+            assert torch.autograd._profiler_enabled()
+            assert rf is not None
+            # Schedule profiling callbacks to run when the future completes.
+            # This returns a future that is completed when the original future
+            # completes and the profiling callbacks have been completed as well,
+            # to guarantee that fut.wait() completes the profiling. This new
+            # future will contain the same value as the original future.
+            fut = rf._call_end_callbacks_on_future(fut)
+    return fut
+
+
+@_require_initialized
+def rpc_sync(to, func, args=None, kwargs=None, timeout: float = UNSET_RPC_TIMEOUT):
+    r"""
+    Make a blocking RPC call to run function ``func`` on worker ``to``. RPC
+    messages are sent and received in parallel to execution of Python code. This
+    method is thread-safe.
+
+    Args:
+        to (str or WorkerInfo or int): name/rank/``WorkerInfo`` of the destination worker.
+        func (Callable): a callable function, such as Python callables, builtin
+                         operators (e.g. :meth:`~torch.add`) and annotated
+                         TorchScript functions.
+        args (tuple): the argument tuple for the ``func`` invocation.
+        kwargs (dict): is a dictionary of keyword arguments for the ``func``
+                       invocation.
+        timeout (float, optional): timeout in seconds to use for this RPC. If
+                                   the RPC does not complete in this amount of
+                                   time, an exception indicating it has
+                                   timed out will be raised. A value of 0
+                                   indicates an infinite timeout, i.e. a timeout
+                                   error will never be raised. If not provided,
+                                   the default value set during initialization
+                                   or with ``_set_rpc_timeout`` is used.
+
+    Returns:
+        Returns the result of running ``func`` with ``args`` and ``kwargs``.
+
+    Example::
+        Make sure that ``MASTER_ADDR`` and ``MASTER_PORT`` are set properly
+        on both workers. Refer to :meth:`~torch.distributed.init_process_group`
+        API for more details. For example,
+
+        export MASTER_ADDR=localhost
+        export MASTER_PORT=5678
+
+        Then run the following code in two different processes:
+
+        >>> # xdoctest: +SKIP
+        >>> # On worker 0:
+        >>> import torch
+        >>> import torch.distributed.rpc as rpc
+        >>> rpc.init_rpc("worker0", rank=0, world_size=2)
+        >>> ret = rpc.rpc_sync("worker1", torch.add, args=(torch.ones(2), 3))
+        >>> rpc.shutdown()
+
+        >>> # On worker 1:
+        >>> import torch.distributed.rpc as rpc
+        >>> rpc.init_rpc("worker1", rank=1, world_size=2)
+        >>> rpc.shutdown()
+
+        Below is an example of running a TorchScript function using RPC.
+
+        >>> # On both workers:
+        >>> @torch.jit.script
+        >>> def my_script_add(tensor: torch.Tensor, scalar: int):
+        >>>    return torch.add(tensor, scalar)
+
+        >>> # On worker 0:
+        >>> import torch.distributed.rpc as rpc
+        >>> rpc.init_rpc("worker0", rank=0, world_size=2)
+        >>> ret = rpc.rpc_sync("worker1", my_script_add, args=(torch.ones(2), 3))
+        >>> rpc.shutdown()
+
+        >>> # On worker 1:
+        >>> import torch.distributed.rpc as rpc
+        >>> rpc.init_rpc("worker1", rank=1, world_size=2)
+        >>> rpc.shutdown()
+
+    """
+    torch._C._log_api_usage_once("torch.distributed.rpc_sync")
+    fut = _invoke_rpc(to, func, RPCExecMode.SYNC, args, kwargs, timeout)
+    return fut.wait()
+
+
+@_require_initialized
+def rpc_async(to, func, args=None, kwargs=None, timeout=UNSET_RPC_TIMEOUT):
+    r"""
+    Make a non-blocking RPC call to run function ``func`` on worker ``to``. RPC
+    messages are sent and received in parallel to execution of Python code. This
+    method is thread-safe. This method will immediately return a
+    :class:`~torch.futures.Future` that can be awaited on.
+
+    Args:
+        to (str or WorkerInfo or int): name/rank/``WorkerInfo`` of the destination worker.
+        func (Callable): a callable function, such as Python callables, builtin
+                         operators (e.g. :meth:`~torch.add`) and annotated
+                         TorchScript functions.
+        args (tuple): the argument tuple for the ``func`` invocation.
+        kwargs (dict): is a dictionary of keyword arguments for the ``func``
+                       invocation.
+        timeout (float, optional): timeout in seconds to use for this RPC. If
+                                   the RPC does not complete in this amount of
+                                   time, an exception indicating it has
+                                   timed out will be raised. A value of 0
+                                   indicates an infinite timeout, i.e. a timeout
+                                   error will never be raised. If not provided,
+                                   the default value set during initialization
+                                   or with ``_set_rpc_timeout`` is used.
+
+
+    Returns:
+        Returns a :class:`~torch.futures.Future` object that can be waited
+        on. When completed, the return value of ``func`` on ``args`` and
+        ``kwargs`` can be retrieved from the :class:`~torch.futures.Future`
+        object.
+
+    .. warning ::
+        Using GPU tensors as arguments or return values of ``func`` is not
+        supported since we don't support sending GPU tensors over the wire. You
+        need to explicitly copy GPU tensors to CPU before using them as
+        arguments or return values of ``func``.
+
+    .. warning ::
+        The ``rpc_async`` API does not copy storages of argument tensors until
+        sending them over the wire, which could be done by a different thread
+        depending on the RPC backend type. The caller should make sure that the
+        contents of those tensors stay intact until the returned
+        :class:`~torch.futures.Future` completes.
+
+    Example::
+        Make sure that ``MASTER_ADDR`` and ``MASTER_PORT`` are set properly
+        on both workers. Refer to :meth:`~torch.distributed.init_process_group`
+        API for more details. For example,
+
+        export MASTER_ADDR=localhost
+        export MASTER_PORT=5678
+
+        Then run the following code in two different processes:
+
+        >>> # xdoctest: +SKIP
+        >>> # On worker 0:
+        >>> import torch
+        >>> import torch.distributed.rpc as rpc
+        >>> rpc.init_rpc("worker0", rank=0, world_size=2)
+        >>> fut1 = rpc.rpc_async("worker1", torch.add, args=(torch.ones(2), 3))
+        >>> fut2 = rpc.rpc_async("worker1", min, args=(1, 2))
+        >>> result = fut1.wait() + fut2.wait()
+        >>> rpc.shutdown()
+
+        >>> # On worker 1:
+        >>> import torch.distributed.rpc as rpc
+        >>> rpc.init_rpc("worker1", rank=1, world_size=2)
+        >>> rpc.shutdown()
+
+        Below is an example of running a TorchScript function using RPC.
+
+        >>> # On both workers:
+        >>> @torch.jit.script
+        >>> def my_script_add(tensor: torch.Tensor, scalar: int):
+        >>>    return torch.add(tensor, scalar)
+
+        >>> # On worker 0:
+        >>> import torch.distributed.rpc as rpc
+        >>> rpc.init_rpc("worker0", rank=0, world_size=2)
+        >>> fut = rpc.rpc_async("worker1", my_script_add, args=(torch.ones(2), 3))
+        >>> ret = fut.wait()
+        >>> rpc.shutdown()
+
+        >>> # On worker 1:
+        >>> import torch.distributed.rpc as rpc
+        >>> rpc.init_rpc("worker1", rank=1, world_size=2)
+        >>> rpc.shutdown()
+    """
+    torch._C._log_api_usage_once("torch.distributed.rpc_async")
+    fut = _invoke_rpc(to, func, RPCExecMode.ASYNC, args, kwargs, timeout)
+    if hasattr(_thread_local_var, "future_list"):
+        _thread_local_var.future_list.append(fut)
+    return fut
+
+
+def _get_should_profile():
+    # Legacy profiler should be enabled. RPC profiling is not supported with
+    # Kineto profiler.
+    ActiveProfilerType = torch._C._profiler.ActiveProfilerType
+    return (
+        torch.autograd._profiler_enabled()
+        and torch._C._autograd._profiler_type() == ActiveProfilerType.LEGACY  # type: ignore[attr-defined]
+    )
+
+
+def _enable_rpc_profiler(
+    should_profile, qualified_name, func, rpc_type, dst_worker_info
+):
+    ctx_manager = contextlib.nullcontext()
+
+    if should_profile:
+        # Create appropriate string representation based on type of func
+        # (builtin, script, python)
+        if qualified_name is None:
+            func_name = (
+                torch._jit_internal._qualified_name(func)
+                if isinstance(func, torch.jit.ScriptFunction)
+                else func.__qualname__
+            )
+        else:
+            func_name = qualified_name
+        # Build RPC profiling key.
+        rpc_profiling_key = _build_rpc_profiling_key(
+            rpc_type,
+            func_name,
+            get_worker_info().name,
+            dst_worker_info.name,
+        )
+        RemoteProfilerManager.set_current_profiling_key(rpc_profiling_key)
+        # Mypy doesn't support re-def of a variable not in the same block (#1174)
+        ctx_manager = torch.autograd.profiler.record_function(rpc_profiling_key)  # type: ignore[assignment]
+
+    return ctx_manager
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/backend_registry.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/backend_registry.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f30252bd825665280a9b4cf96613bd6a676d190
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/backend_registry.py
@@ -0,0 +1,431 @@
+# mypy: allow-untyped-defs
+
+
+import collections
+import enum
+from typing import cast
+
+import torch
+import torch.distributed as dist
+
+from . import api, constants as rpc_constants
+from ._utils import _group_membership_management, _update_group_membership
+
+
+__all__ = [
+    "backend_registered",
+    "register_backend",
+    "construct_rpc_backend_options",
+    "init_backend",
+    "BackendValue",
+    "BackendType",
+]
+
+BackendValue = collections.namedtuple(
+    "BackendValue", ["construct_rpc_backend_options_handler", "init_backend_handler"]
+)
+
+
+def _backend_type_repr(self):
+    return "BackendType." + self.name
+
+
+_backend_type_doc = """
+    An enum class of available backends.
+
+    PyTorch ships with a builtin ``BackendType.TENSORPIPE`` backend.
+    Additional ones can be registered using the
+    :func:`~torch.distributed.rpc.backend_registry.register_backend` function.
+"""
+
+# Create an enum type, `BackendType`, with empty members.
+# Can't handle Function Enum API (mypy bug #9079)
+BackendType = enum.Enum(value="BackendType", names={})  # type: ignore[misc]
+# Unable to assign a function a method (mypy bug #2427)
+BackendType.__repr__ = _backend_type_repr  # type: ignore[assignment]
+
+if BackendType.__doc__:
+    BackendType.__doc__ = _backend_type_doc
+
+
+def backend_registered(backend_name):
+    """
+    Checks if backend_name is registered as an RPC backend.
+
+    Args:
+        backend_name (str): string to identify the RPC backend.
+    Returns:
+        True if the backend has been registered with ``register_backend``, else
+        False.
+    """
+    return backend_name in BackendType.__members__
+
+
+def register_backend(
+    backend_name, construct_rpc_backend_options_handler, init_backend_handler
+):
+    """Registers a new RPC backend.
+
+    Args:
+        backend_name (str): backend string to identify the handler.
+        construct_rpc_backend_options_handler (function):
+            Handler that is invoked when
+            rpc_backend.construct_rpc_backend_options(**dict) is called.
+        init_backend_handler (function): Handler that is invoked when the
+            `_init_rpc_backend()` function is called with a backend.
+             This returns the agent.
+    """
+    global BackendType
+    if backend_registered(backend_name):
+        raise RuntimeError(f"RPC backend {backend_name}: already registered")
+    # Create a new enum type, `BackendType`, with extended members.
+    existing_enum_dict = {member.name: member.value for member in BackendType}
+    extended_enum_dict = dict(
+        {
+            backend_name: BackendValue(
+                construct_rpc_backend_options_handler=construct_rpc_backend_options_handler,
+                init_backend_handler=init_backend_handler,
+            )
+        },
+        **existing_enum_dict,
+    )
+    # Can't handle Function Enum API (mypy bug #9079)
+    BackendType = enum.Enum(value="BackendType", names=extended_enum_dict)  # type: ignore[misc]
+    # Unable to assign a function a method (mypy bug #2427)
+    BackendType.__repr__ = _backend_type_repr  # type: ignore[assignment]
+    if BackendType.__doc__:
+        BackendType.__doc__ = _backend_type_doc
+    # pyrefly: ignore [unsupported-operation]
+    return BackendType[backend_name]
+
+
+def construct_rpc_backend_options(
+    backend,
+    rpc_timeout=rpc_constants.DEFAULT_RPC_TIMEOUT_SEC,
+    init_method=rpc_constants.DEFAULT_INIT_METHOD,
+    **kwargs,
+):
+    return backend.value.construct_rpc_backend_options_handler(
+        rpc_timeout, init_method, **kwargs
+    )
+
+
+def init_backend(backend, *args, **kwargs):
+    return backend.value.init_backend_handler(*args, **kwargs)
+
+
+def _init_process_group(store, rank, world_size):
+    # Initialize ProcessGroup.
+    process_group_timeout = rpc_constants.DEFAULT_PROCESS_GROUP_TIMEOUT
+
+    # We're using a bunch of private APIs here since `new_group` requires the
+    # default group to be initialized.
+    group = dist.ProcessGroupGloo(store, rank, world_size, process_group_timeout)
+
+    assert group is not None, "Failed to initialize default ProcessGroup."
+
+    if (rank != -1) and (rank != group.rank()):
+        raise RuntimeError(f"rank argument {rank} doesn't match pg rank {group.rank()}")
+    if (world_size != -1) and (world_size != group.size()):
+        raise RuntimeError(
+            f"world_size argument {world_size} doesn't match pg size {group.size()}"
+        )
+    return group
+
+
+def _tensorpipe_construct_rpc_backend_options_handler(
+    rpc_timeout,
+    init_method,
+    num_worker_threads=rpc_constants.DEFAULT_NUM_WORKER_THREADS,
+    _transports=None,
+    _channels=None,
+    **kwargs,
+):
+    from . import TensorPipeRpcBackendOptions
+
+    return TensorPipeRpcBackendOptions(
+        rpc_timeout=rpc_timeout,
+        init_method=init_method,
+        num_worker_threads=num_worker_threads,
+        _transports=_transports,
+        _channels=_channels,
+    )
+
+
+def _tensorpipe_validate_devices(devices, device_count):
+    return all(
+        d.type == "cpu" or (d.type == "cuda" and 0 <= d.index < device_count)
+        for d in devices
+    )
+
+
+# detect if any worker has invalid device_map configurations, and return
+# reverse device maps
+def _tensorpipe_exchange_and_check_all_device_maps(
+    my_name, my_device_count, my_device_maps, my_devices, group
+):
+    gathered: list[
+        tuple[str, int, dict[str, dict[torch.device, torch.device]], list[torch.device]]
+    ] = [("", 0, {}, []) for _ in range(group.size())]
+    dist.all_gather_object(
+        gathered, (my_name, my_device_count, my_device_maps, my_devices), group
+    )
+    all_names = [name for name, _, _, _ in gathered]
+    all_device_counts = {name: count for name, count, _, _ in gathered}
+    all_device_maps = {name: map_ for name, _, map_, _ in gathered}
+    all_devices = {name: devices for name, _, _, devices in gathered}
+
+    _validate_device_maps(all_names, all_device_counts, all_device_maps, all_devices)
+
+    # passed all checked, construct reverse mapping and get list of devices handled by this agent
+    reverse_device_maps = _create_reverse_mapping(my_name, all_names, all_device_maps)
+    my_devices = _create_device_list(my_devices, my_device_maps, reverse_device_maps)
+    return reverse_device_maps, my_devices
+
+
+def _validate_device_maps(
+    all_names, all_device_counts, all_device_maps, all_devices, is_static_group=True
+):
+    for node in all_names:
+        devices = all_devices[node]
+        if len(set(devices)) != len(devices):
+            raise ValueError(f"Node {node} has duplicated devices\ndevices = {devices}")
+        if not _tensorpipe_validate_devices(devices, all_device_counts[node]):
+            raise ValueError(
+                f"Node {node} has devices with invalid indices\n"
+                f"devices = {devices}\n"
+                f"device count = {all_device_counts[node]}"
+            )
+
+    for source_node in all_names:
+        # For dynamic group (non-static) do not check the target node name since it may not have joined yet
+        if is_static_group and not set(all_device_maps[source_node].keys()).issubset(
+            all_names
+        ):
+            raise ValueError(
+                f"Node {source_node} has invalid target node names in its device maps\n"
+                f"device maps = {all_device_maps[source_node].keys()}\n"
+                f"node names = {all_names}"
+            )
+        for target_node, map_ in all_device_maps[source_node].items():
+            if len(set(map_.values())) != len(map_):
+                raise ValueError(
+                    f"Node {source_node} has duplicated target devices "
+                    f"in its device map for {target_node}\n"
+                    f"device map = {map_}"
+                )
+            if all_devices[source_node]:
+                if not set(map_.keys()).issubset(all_devices[source_node]):
+                    raise ValueError(
+                        f"Node {source_node} has unexpected source devices "
+                        f"in its device map for {target_node}\n"
+                        f"device map = {map_}\n"
+                        f"devices = {all_devices[source_node]}"
+                    )
+            elif not _tensorpipe_validate_devices(
+                map_.keys(), all_device_counts[source_node]
+            ):
+                raise ValueError(
+                    f"Node {source_node} has source devices with invalid indices "
+                    f"in its device map for {target_node}\n"
+                    f"device map = {map_}\n"
+                    f"device count = {all_device_counts[source_node]}"
+                )
+            if all_devices.get(target_node, []):
+                if not set(map_.values()).issubset(all_devices[target_node]):
+                    raise ValueError(
+                        f"Node {source_node} has unexpected target devices "
+                        f"in its device map for {target_node}\n"
+                        f"device map = {map_}\n"
+                        f"devices = {all_devices[target_node]}"
+                    )
+            elif target_node in all_device_counts and not _tensorpipe_validate_devices(
+                map_.values(), all_device_counts[target_node]
+            ):
+                raise ValueError(
+                    f"Node {source_node} has target devices with invalid indices "
+                    f"in its device map for {target_node}\n"
+                    f"device map = {map_}\n"
+                    f"device count = {all_device_counts[target_node]}"
+                )
+
+
+def _create_device_list(my_devices, my_device_maps, reverse_device_maps):
+    if not my_devices:
+        devices_set: set[torch.device] = set()
+        for map_ in my_device_maps.values():
+            devices_set.update(map_.keys())
+        for map_ in reverse_device_maps.values():
+            devices_set.update(map_.keys())
+        devices_set.discard(torch.device("cpu"))
+        my_devices = list(devices_set)
+    my_devices = sorted(my_devices, key=lambda d: d.index)
+    return my_devices
+
+
+def _create_reverse_mapping(my_name, all_names, all_device_maps):
+    reverse_device_maps: dict[str, dict[torch.device, torch.device]] = {}
+    for node in all_names:
+        if my_name in all_device_maps[node]:
+            reverse_device_maps[node] = {
+                v: k for k, v in all_device_maps[node][my_name].items()
+            }
+    return reverse_device_maps
+
+
+def _get_device_infos():
+    from . import TensorPipeAgent
+
+    agent = cast(TensorPipeAgent, api._get_current_rpc_agent())
+    opts = agent._get_backend_options()
+    device_count = torch.cuda.device_count()
+    if torch.cuda.is_available() and opts.devices:
+        torch.cuda.init()
+    return device_count, opts.device_maps, opts.devices
+
+
+def _set_devices_and_reverse_device_map(agent):
+    from . import TensorPipeAgent
+
+    agent = cast(TensorPipeAgent, agent)
+    # Group state is retrieved from local agent
+    # On initialization, tensorpipe agent retrieves information from all existing workers, so group state is valid
+    my_worker_info = agent.get_worker_info()
+    my_name = my_worker_info.name
+    all_worker_infos = agent.get_worker_infos()
+    # One round to get device_maps of all workers and construct reverse device maps
+    all_device_counts, all_device_maps, all_devices, all_names = {}, {}, {}, []
+    for worker_info in all_worker_infos:
+        worker_name = worker_info.name
+        if worker_name != my_name:
+            # TODO: make async?
+            device_count, device_map, devices = api.rpc_sync(
+                worker_name, _get_device_infos
+            )
+        else:
+            opts = agent._get_backend_options()
+            device_count, device_map, devices = (
+                torch.cuda.device_count(),
+                opts.device_maps,
+                opts.devices,
+            )
+        all_device_counts[worker_name] = device_count
+        all_device_maps[worker_name] = device_map
+        all_devices[worker_name] = devices
+        all_names.append(worker_name)
+
+    _validate_device_maps(
+        all_names,
+        all_device_counts,
+        all_device_maps,
+        all_devices,
+        is_static_group=False,
+    )
+    reverse_device_maps = _create_reverse_mapping(my_name, all_names, all_device_maps)
+
+    # Perform RPC call to all workers, including itself, to include newly joined worker information and device maps
+    for worker_name in all_names:
+        # Set device list for each worker
+        all_devices[worker_name] = _create_device_list(
+            all_devices[worker_name], all_device_maps[worker_name], reverse_device_maps
+        )
+        api.rpc_sync(
+            worker_name,
+            _update_group_membership,
+            args=(my_worker_info, all_devices[worker_name], reverse_device_maps, True),
+        )
+
+
+def _tensorpipe_init_backend_handler(
+    store, name, rank, world_size, rpc_backend_options
+):
+    from . import TensorPipeAgent, TensorPipeRpcBackendOptions
+
+    if not isinstance(store, dist.Store):
+        raise TypeError(f"`store` must be a c10d::Store. {store}")
+
+    if not isinstance(rpc_backend_options, TensorPipeRpcBackendOptions):
+        raise TypeError(
+            f"`rpc_backend_options` must be a `TensorPipeRpcBackendOptions`. {rpc_backend_options}"
+        )
+
+    device_count = torch.cuda.device_count()
+
+    is_static_group = bool(world_size)
+    # world_size is specified so this is a static group (ranks cannot join and leave)
+    if is_static_group:
+        # The agent's join method is required to behave like a barrier and perform
+        # collective operations, for which it relies on a process group, instead of
+        # re-implementing this on top of RPCs.
+        group = _init_process_group(store, rank, world_size)
+
+        reverse_device_maps, devices = _tensorpipe_exchange_and_check_all_device_maps(
+            name,
+            device_count,
+            rpc_backend_options.device_maps,
+            rpc_backend_options.devices,
+            group,
+        )
+
+        if torch.cuda.is_available() and devices:
+            # It's necessary to initialize PyTorch CUDA states here (e.g.,
+            # CUDACachingAllocator). If this is missing, we could hit errors like
+            # "allocator not initialized", because other processes might send
+            # CUDA-related RPC request to this process before user code in this
+            # process initializes its PyTorch CUDA states.
+            torch.cuda.init()
+
+        # TODO: add try-except and destroy _agent in all processes if any fails.
+        agent = TensorPipeAgent(
+            store,
+            name,
+            rank,
+            world_size,
+            rpc_backend_options,
+            reverse_device_maps,
+            devices,
+        )
+
+        api._init_rpc_states(agent)
+
+        # Run one dummy round of RPC to initialize channels/transports. Without
+        # this, it's easy to hit timeout in rpc.shutdown() if there is no other RPC
+        # on that process before rpc.shutdown(), as the agent initialization can
+        # take longer than 5s.
+        api._all_gather(None, timeout=rpc_backend_options.rpc_timeout)
+        # Need a barrier here to make sure no peers leave before the rank0 finishes
+        # _all_gather
+        group.barrier().wait()
+
+        return agent
+    # initialization for dynamic rpc (ranks can join and leave)
+    else:
+        with _group_membership_management(store, name, True):
+            # Construct TPAgent with empty reverse_device_map and devices
+            # these properties will be updated after initialization
+            agent = TensorPipeAgent(
+                store,
+                name,
+                rank,
+                world_size,
+                rpc_backend_options,
+                {},
+                [],
+            )
+            api._init_rpc_states(agent)
+
+            try:
+                # Notify all workers in group this rank has joined and set devices and reverse_device_map
+                # This is a synchronous operation that completes once all existing ranks are updated
+                _set_devices_and_reverse_device_map(agent)
+            except Exception:
+                api.shutdown()
+                raise
+            return agent
+
+
+register_backend(
+    "TENSORPIPE",
+    _tensorpipe_construct_rpc_backend_options_handler,
+    _tensorpipe_init_backend_handler,
+)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/constants.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..f0eaf92b8aef56dc96700c1ddb42bfb988542650
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/constants.py
@@ -0,0 +1,24 @@
+from datetime import timedelta
+
+from torch._C._distributed_rpc import (
+    _DEFAULT_INIT_METHOD,
+    _DEFAULT_NUM_WORKER_THREADS,
+    _DEFAULT_RPC_TIMEOUT_SEC,
+    _UNSET_RPC_TIMEOUT,
+)
+
+
+# For any RpcAgent.
+DEFAULT_RPC_TIMEOUT_SEC: float = _DEFAULT_RPC_TIMEOUT_SEC
+DEFAULT_INIT_METHOD: str = _DEFAULT_INIT_METHOD
+DEFAULT_SHUTDOWN_TIMEOUT: float = 0
+
+# For TensorPipeAgent.
+DEFAULT_NUM_WORKER_THREADS: int = _DEFAULT_NUM_WORKER_THREADS
+# Ensure that we don't time out when there are long periods of time without
+# any operations against the underlying ProcessGroup.
+DEFAULT_PROCESS_GROUP_TIMEOUT: timedelta = timedelta(milliseconds=2**31 - 1)
+# Value indicating that timeout is not set for RPC call, and the default should be used.
+UNSET_RPC_TIMEOUT: float = _UNSET_RPC_TIMEOUT
+
+__all__: list[str] = []
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/functions.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..e48ea8cc534ab87838965c947bbd0ed76d4d64c7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/functions.py
@@ -0,0 +1,169 @@
+# mypy: allow-untyped-defs
+import functools
+
+
+def async_execution(fn):
+    r"""
+    A decorator for a function indicating that the return value of the function
+    is guaranteed to be a :class:`~torch.futures.Future` object and this
+    function can run asynchronously on the RPC callee. More specifically, the
+    callee extracts the :class:`~torch.futures.Future` returned by the wrapped
+    function and installs subsequent processing steps as a callback to that
+    :class:`~torch.futures.Future`. The installed callback will read the value
+    from the :class:`~torch.futures.Future` when completed and send the
+    value back as the RPC response. That also means the returned
+    :class:`~torch.futures.Future` only exists on the callee side and is never
+    sent through RPC. This decorator is useful when the wrapped function's
+    (``fn``) execution needs to pause and resume due to, e.g., containing
+    :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals.
+
+    .. note:: To enable asynchronous execution, applications must pass the
+        function object returned by this decorator to RPC APIs. If RPC detected
+        attributes installed by this decorator, it knows that this function
+        returns a ``Future`` object and will handle that accordingly.
+        However, this does not mean this decorator has to be outmost one when
+        defining a function. For example, when combined with ``@staticmethod``
+        or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the
+        inner decorator to allow the target function be recognized as a static
+        or class function. This target function can still execute asynchronously
+        because, when accessed, the static or class method preserves attributes
+        installed by ``@rpc.functions.async_execution``.
+
+
+    Example::
+        The returned :class:`~torch.futures.Future` object can come from
+        :meth:`~torch.distributed.rpc.rpc_async`,
+        :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future`
+        constructor. The example below shows directly using the
+        :class:`~torch.futures.Future` returned by
+        :meth:`~torch.futures.Future.then`.
+
+        >>> from torch.distributed import rpc
+        >>>
+        >>> # omitting setup and shutdown RPC
+        >>>
+        >>> # On all workers
+        >>> @rpc.functions.async_execution
+        >>> def async_add_chained(to, x, y, z):
+        >>>     # This function runs on "worker1" and returns immediately when
+        >>>     # the callback is installed through the `then(cb)` API. In the
+        >>>     # mean time, the `rpc_async` to "worker2" can run concurrently.
+        >>>     # When the return value of that `rpc_async` arrives at
+        >>>     # "worker1", "worker1" will run the lambda function accordingly
+        >>>     # and set the value for the previously returned `Future`, which
+        >>>     # will then trigger RPC to send the result back to "worker0".
+        >>>     return rpc.rpc_async(to, torch.add, args=(x, y)).then(
+        >>>         lambda fut: fut.wait() + z
+        >>>     )
+        >>>
+        >>> # On worker0
+        >>> # xdoctest: +SKIP
+        >>> ret = rpc.rpc_sync(
+        >>>     "worker1",
+        >>>     async_add_chained,
+        >>>     args=("worker2", torch.ones(2), 1, 1)
+        >>> )
+        >>> print(ret)  # prints tensor([3., 3.])
+
+        When combined with TorchScript decorators, this decorator must be the
+        outmost one.
+
+        >>> from torch import Tensor
+        >>> from torch.futures import Future
+        >>> from torch.distributed import rpc
+        >>>
+        >>> # omitting setup and shutdown RPC
+        >>>
+        >>> # On all workers
+        >>> @torch.jit.script
+        >>> def script_add(x: Tensor, y: Tensor) -> Tensor:
+        >>>     return x + y
+        >>>
+        >>> @rpc.functions.async_execution
+        >>> @torch.jit.script
+        >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]:
+        >>>     return rpc.rpc_async(to, script_add, (x, y))
+        >>>
+        >>> # On worker0
+        >>> ret = rpc.rpc_sync(
+        >>>     "worker1",
+        >>>     async_add,
+        >>>     args=("worker2", torch.ones(2), 1)
+        >>> )
+        >>> print(ret)  # prints tensor([2., 2.])
+
+        When combined with static or class method, this decorator must be the
+        inner one.
+
+        >>> from torch.distributed import rpc
+        >>>
+        >>> # omitting setup and shutdown RPC
+        >>>
+        >>> # On all workers
+        >>> class AsyncExecutionClass:
+        >>>
+        >>>     @staticmethod
+        >>>     @rpc.functions.async_execution
+        >>>     def static_async_add(to, x, y, z):
+        >>>         return rpc.rpc_async(to, torch.add, args=(x, y)).then(
+        >>>             lambda fut: fut.wait() + z
+        >>>         )
+        >>>
+        >>>     @classmethod
+        >>>     @rpc.functions.async_execution
+        >>>     def class_async_add(cls, to, x, y, z):
+        >>>         ret_fut = torch.futures.Future()
+        >>>         rpc.rpc_async(to, torch.add, args=(x, y)).then(
+        >>>             lambda fut: ret_fut.set_result(fut.wait() + z)
+        >>>         )
+        >>>         return ret_fut
+        >>>
+        >>>     @rpc.functions.async_execution
+        >>>     def bound_async_add(self, to, x, y, z):
+        >>>         return rpc.rpc_async(to, torch.add, args=(x, y)).then(
+        >>>             lambda fut: fut.wait() + z
+        >>>         )
+        >>>
+        >>> # On worker0
+        >>> ret = rpc.rpc_sync(
+        >>>     "worker1",
+        >>>     AsyncExecutionClass.static_async_add,
+        >>>     args=("worker2", torch.ones(2), 1, 2)
+        >>> )
+        >>> print(ret)  # prints tensor([4., 4.])
+        >>>
+        >>> ret = rpc.rpc_sync(
+        >>>     "worker1",
+        >>>     AsyncExecutionClass.class_async_add,
+        >>>     args=("worker2", torch.ones(2), 1, 2)
+        >>> )
+        >>> print(ret)  # prints tensor([4., 4.])
+
+        This decorator also works with RRef helpers, i.e., .
+        :meth:`torch.distributed.rpc.RRef.rpc_sync`,
+        :meth:`torch.distributed.rpc.RRef.rpc_async`, and
+        :meth:`torch.distributed.rpc.RRef.remote`.
+
+        >>> from torch.distributed import rpc
+        >>>
+        >>> # reuse the AsyncExecutionClass class above
+        >>> rref = rpc.remote("worker1", AsyncExecutionClass)
+        >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2)
+        >>> print(ret)  # prints tensor([4., 4.])
+        >>>
+        >>> rref = rpc.remote("worker1", AsyncExecutionClass)
+        >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait()
+        >>> print(ret)  # prints tensor([4., 4.])
+        >>>
+        >>> rref = rpc.remote("worker1", AsyncExecutionClass)
+        >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here()
+        >>> print(ret)  # prints tensor([4., 4.])
+    """
+
+    @functools.wraps(fn)
+    def wrapper(*args, **kwargs):
+        return fn(*args, **kwargs)
+
+    # Can't declare and use attributes of function objects (mypy#2087)
+    wrapper._wrapped_async_rpc_function = fn  # type: ignore[attr-defined]
+    return wrapper
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/internal.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/internal.py
new file mode 100644
index 0000000000000000000000000000000000000000..faef8afddfc2caac25c8360c216509aed5acf8c1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/internal.py
@@ -0,0 +1,285 @@
+# mypy: allow-untyped-defs
+import collections
+import copyreg
+import io
+import pickle
+import sys
+import threading
+import traceback
+from enum import Enum
+
+import torch
+import torch.distributed as dist
+from torch._C._distributed_rpc import _get_current_rpc_agent
+
+
+__all__ = ["RPCExecMode", "serialize", "deserialize", "PythonUDF", "RemoteException"]
+
+# Thread local tensor tables to store tensors while pickling torch.Tensor
+# objects
+_thread_local_tensor_tables = threading.local()
+_pickler = pickle.Pickler
+_unpickler = pickle.Unpickler
+
+
+class RPCExecMode(Enum):
+    SYNC = "sync"
+    ASYNC = "async"
+    ASYNC_JIT = "async_jit"
+    REMOTE = "remote"
+
+
+class _InternalRPCPickler:
+    r"""
+    This class provides serialize() and deserialize() interfaces to serialize
+    data to be "binary string + tensor table" format
+    So for RPC python UDF function and args, non tensor data will be serialized
+    into regular binary string, tensor data will be put into thread local tensor
+    tables, this serialization format is consistent with builtin operator and args
+    using JIT pickler. This format will make tensor handling in C++ much easier,
+    e.g. attach tensor to distributed autograd graph in C++
+    """
+
+    def __init__(self):
+        # Ignore type error because dispatch_table is defined in third-party package
+        self._dispatch_table = copyreg.dispatch_table.copy()  # type: ignore[attr-defined]
+        self._dispatch_table[torch.Tensor] = self._tensor_reducer
+        # Used for registering customized picklers.
+        self._class_reducer_dict = {}
+
+    def _register_reducer(self, obj_class, reducer):
+        # For the same class, only register the reducer once.
+        if obj_class not in self._class_reducer_dict:
+            self._class_reducer_dict[obj_class] = reducer
+
+    @classmethod
+    def _tensor_receiver(cls, tensor_index):
+        global _thread_local_tensor_tables
+        return _thread_local_tensor_tables.recv_tables[tensor_index]
+
+    def _tensor_reducer(self, tensor):
+        global _thread_local_tensor_tables
+        _thread_local_tensor_tables.send_tables.append(tensor)
+        tensor_index = len(_thread_local_tensor_tables.send_tables) - 1
+        return (_InternalRPCPickler._tensor_receiver, (tensor_index,))
+
+    @classmethod
+    def _py_rref_receiver(cls, rref_fork_data):
+        return dist.rpc.PyRRef._deserialize(rref_fork_data)
+
+    def _py_rref_reducer(self, py_rref):
+        rref_fork_data = py_rref._serialize()
+        return (_InternalRPCPickler._py_rref_receiver, (rref_fork_data,))
+
+    def _rref_reducer(self, rref):
+        return self._py_rref_reducer(rref)
+
+    @classmethod
+    def _script_module_receiver(cls, script_module_serialized):
+        """
+        Given a serialized representation of a ScriptModule created with torch.jit.save,
+        loads and returns the ScriptModule.
+        """
+        f = io.BytesIO(script_module_serialized)
+        m = torch.jit.load(f)
+        return m
+
+    def _script_module_reducer(self, script_module):
+        """
+        Serializes a ScriptModule.
+        """
+        f = io.BytesIO()
+        torch.jit.save(script_module, f)
+        return (_InternalRPCPickler._script_module_receiver, (f.getvalue(),))
+
+    def serialize(self, obj):
+        r"""
+        Serialize non tensor data into binary string, tensor data into
+        tensor table
+        """
+        f = io.BytesIO()
+        p = _pickler(f)
+        p.dispatch_table = self._dispatch_table
+
+        # rpc api could accept user picklers inheriting from _InternalRPCPickler to serialize rref,
+        # user picklers could have different initialization function from _InternalRPCPickler,
+        # but all the user picklers should call serialize() and use _rref_reducer to pickle rref
+        # in python. also, when _internal_rpc_pickler is imported to rpc/api.py, rpc.RRef is not
+        # compiled yet, it is not good place to access rpc.RRef inside _InternalRPCPickler constructor,
+        # so putting rref's dispatch table here
+        #
+        # The return value of a `rpc.remote(..)` call is type of `rpc.PyRRef`.
+        # The deserialized RRef object on an RPC receiver side is type of `rpc.PyRRef`.
+        # Ignore type error because dispatch_table is defined in third-party package
+        p.dispatch_table[dist.rpc.PyRRef] = self._py_rref_reducer  # type: ignore[index]
+        # An RRef created locally by RRef Python constructor is type of `rpc.RRef`.
+        # Ignore type error because dispatch_table is defined in third-party package
+        p.dispatch_table[dist.rpc.RRef] = self._rref_reducer  # type: ignore[index]
+
+        # Add dispatch pickling for ScriptModule or its subclass.
+        if isinstance(obj, torch.jit.ScriptModule):
+            # Ignore type error because dispatch_table is defined in third-party package
+            p.dispatch_table[obj.__class__] = self._script_module_reducer  # type: ignore[index]
+
+        # Install customized picklers.
+        for class_name in self._class_reducer_dict:
+            p.dispatch_table[class_name] = self._class_reducer_dict[class_name]  # type: ignore[index]
+
+        # save _thread_local_tensor_tables.send_tables if it is in nested call
+        global _thread_local_tensor_tables
+        if hasattr(_thread_local_tensor_tables, "send_tables"):
+            old_send_tables = _thread_local_tensor_tables.send_tables
+        else:
+            old_send_tables = None
+        _thread_local_tensor_tables.send_tables = []
+
+        p.dump(obj)
+
+        # restore _thread_local_tensor_tables.send_tables if return
+        # from nested call, otherwise clean up the table
+        tensors = _thread_local_tensor_tables.send_tables
+        if old_send_tables is not None:
+            _thread_local_tensor_tables.send_tables = old_send_tables
+        else:
+            del _thread_local_tensor_tables.send_tables
+
+        return (f.getvalue(), tensors)
+
+    def deserialize(self, binary_data, tensor_table):
+        r"""
+        Deserialize binary string + tensor table to original obj
+        """
+        # save _thread_local_tensor_tables.recv_tables if it is in nested call
+        global _thread_local_tensor_tables
+        if hasattr(_thread_local_tensor_tables, "recv_tables"):
+            old_recv_tables = _thread_local_tensor_tables.recv_tables
+        else:
+            old_recv_tables = None
+        _thread_local_tensor_tables.recv_tables = tensor_table
+
+        try:
+            unpickler = _unpickler(io.BytesIO(binary_data))
+            ret = unpickler.load()
+        except AttributeError as e:
+            # Occurs when function is not found on module/class during
+            # unpickling.
+            except_str = (
+                str(e)
+                + """ Default RPC pickler does not serialize
+            function code. Ensure that UDFs are defined on both caller and
+            callee modules."""
+            )
+            ret = AttributeError(except_str)
+            # Ensure the stack trace gets preserved
+            ret.__cause__ = e
+
+        # restore _thread_local_tensor_tables.recv_tables if return
+        # from nested call, otherwise clean up the table
+        if old_recv_tables is not None:
+            _thread_local_tensor_tables.recv_tables = old_recv_tables
+        else:
+            del _thread_local_tensor_tables.recv_tables
+
+        return ret
+
+
+# Create _internal_rpc_pickler only once to initialize _dispatch_table only once
+_internal_rpc_pickler = _InternalRPCPickler()
+
+
+def serialize(obj):
+    return _internal_rpc_pickler.serialize(obj)
+
+
+def deserialize(binary_data, tensor_table):
+    return _internal_rpc_pickler.deserialize(binary_data, tensor_table)
+
+
+def _run_function(python_udf):
+    r"""
+    This function is exclusively called from C++.
+    See ``torch/csrc/distributed/rpc/python_rpc_handler.cpp``.
+
+    Runs a Python UDF and returns its return value.
+    Wraps any exception in ``RemoteException`` if the function raises.
+    """
+    try:
+        if isinstance(python_udf, AttributeError):
+            raise python_udf
+        result = python_udf.func(*python_udf.args, **python_udf.kwargs)
+    except Exception as e:
+        # except str = exception info + traceback string
+        except_str = (
+            f"On {_get_current_rpc_agent().get_worker_info()}:\n"
+            f"{repr(e)}\n{traceback.format_exc()}"
+        )
+        print(except_str, file=sys.stderr)
+        result = RemoteException(except_str, type(e))
+    return result
+
+
+def _handle_exception(result):
+    if isinstance(result, RemoteException):
+        exception_msg = result.msg.encode("utf-8").decode("unicode_escape")
+        # We wrap exception re-creation here in case some exception classes
+        # cannot be constructed directly from a string.
+        exc = None
+        try:
+            exc = result.exception_type(exception_msg)
+        except BaseException as e:  # noqa: B036
+            raise RuntimeError(  # noqa: B904
+                f"Failed to create original exception type. Error msg was {str(e)}"
+                f" Original exception on remote side was {exception_msg}"
+            ) from e
+
+        if exc is not None:
+            raise exc
+
+
+def _build_rpc_profiling_key(
+    exec_type, func_name, current_worker_name, dst_worker_name
+):
+    """
+    Builds the key that RPC calls are profiled with using the autograd profiler.
+    This will be the name of the corresponding Event recorded in the profiler.
+
+    Args:
+        exec_type (RPCExecMode): Type of RPC/RRef call
+        func_name (str): Name of function being profiled.
+        current_worker_name (str): Name of current worker.
+        dst_worker_name (str): Name of the destination worker.
+
+    Returns:
+        String representing profiling key
+    """
+    profile_key = (
+        f"rpc_{exec_type.value}#{func_name}({current_worker_name} -> {dst_worker_name})"
+    )
+    return profile_key
+
+
+def _start_record_function(exec_type, func_name, current_worker_name, dest_worker_name):
+    """
+    This function should be called from RPC/RRef functions to create a
+    RecordFunction object for profiling. This function also runs the before
+    callbacks that start the profiling, though the user is responsible for
+    running the appropriate callbacks when the function to be profiled finishes.
+
+    Args:
+        exec_type (RPCExecMode): Type of RPC/RRef call
+        func_name (str): Name of function being profiled.
+        current_worker_name (str): Name of current worker.
+        dest_worker_name (str): Name of the destination worker.
+
+    Returns:
+        An instance of `torch.autograd._RecordFunction`.
+    """
+    assert torch.autograd._profiler_enabled(), "Autograd profiler should be enabled."
+    profile_key = f"rpc_{exec_type.value}#{str(func_name)}({current_worker_name} -> {dest_worker_name})"
+    rf = torch.autograd._RecordFunction()  # type: ignore[attr-defined]
+    torch.autograd._run_before_callbacks(rf, profile_key)  # type: ignore[attr-defined]
+    return rf
+
+
+PythonUDF = collections.namedtuple("PythonUDF", ["func", "args", "kwargs"])
+RemoteException = collections.namedtuple("RemoteException", ["msg", "exception_type"])
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/options.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/options.py
new file mode 100644
index 0000000000000000000000000000000000000000..c58a2bf923910039502ed98f1fd742b827800f20
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/options.py
@@ -0,0 +1,181 @@
+# mypy: allow-untyped-defs
+from typing import Union
+
+import torch
+
+from . import _is_tensorpipe_available, constants as rpc_contants
+
+
+DeviceType = Union[int, str, torch.device]
+
+__all__ = ["TensorPipeRpcBackendOptions"]
+
+
+def _to_device(device: DeviceType) -> torch.device:
+    device = torch.device(device)
+    if device.type != "cuda":
+        raise ValueError(
+            "`set_devices` expect a list of CUDA devices, but got "
+            f"device type {device.type}."
+        )
+    return device
+
+
+def _to_device_map(
+    device_map: dict[DeviceType, DeviceType],
+) -> dict[torch.device, torch.device]:
+    full_device_map: dict[torch.device, torch.device] = {}
+    reverse_map: dict[torch.device, torch.device] = {}
+    for k, v in device_map.items():
+        k, v = torch.device(k), torch.device(v)
+        if v in reverse_map:
+            raise ValueError(
+                "`device_map` only supports 1-to-1 mapping, "
+                f"trying to map {k} and {reverse_map[v]} to {v}"
+            )
+        full_device_map[k] = v
+        reverse_map[v] = k
+    return full_device_map
+
+
+def _to_device_list(devices: list[DeviceType]) -> list[torch.device]:
+    return list(map(_to_device, devices))
+
+
+if _is_tensorpipe_available:  # type: ignore[has-type]
+    from torch._C._distributed_rpc import _TensorPipeRpcBackendOptionsBase
+else:
+    _TensorPipeRpcBackendOptionsBase = object  # type: ignore[assignment, misc]
+
+
+# pyrefly: ignore [invalid-inheritance]
+class TensorPipeRpcBackendOptions(_TensorPipeRpcBackendOptionsBase):
+    r"""
+    The backend options for
+    :class:`~torch.distributed.rpc.TensorPipeAgent`, derived from
+    :class:`~torch.distributed.rpc.RpcBackendOptions`.
+
+    Args:
+        num_worker_threads (int, optional): The number of threads in the
+            thread-pool used by
+            :class:`~torch.distributed.rpc.TensorPipeAgent` to execute
+            requests (default: 16).
+        rpc_timeout (float, optional): The default timeout, in seconds,
+            for RPC requests (default: 60 seconds). If the RPC has not
+            completed in this timeframe, an exception indicating so will
+            be raised. Callers can override this timeout for individual
+            RPCs in :meth:`~torch.distributed.rpc.rpc_sync` and
+            :meth:`~torch.distributed.rpc.rpc_async` if necessary.
+        init_method (str, optional): The URL to initialize the distributed
+            store used for rendezvous. It takes any value accepted for the
+            same argument of :meth:`~torch.distributed.init_process_group`
+            (default: ``env://``).
+        device_maps (Dict[str, Dict], optional): Device placement mappings from
+            this worker to the callee. Key is the callee worker name and value
+            the dictionary (``Dict`` of ``int``, ``str``, or ``torch.device``)
+            that maps this worker's devices to the callee worker's devices.
+            (default: ``None``)
+        devices (List[int, str, or ``torch.device``], optional): all local
+            CUDA devices used by RPC agent. By Default, it will be initialized
+            to all local devices from its own ``device_maps`` and corresponding
+            devices from its peers' ``device_maps``. When processing CUDA RPC
+            requests, the agent will properly synchronize CUDA streams for
+            all devices in this ``List``.
+    """
+
+    def __init__(
+        self,
+        *,
+        num_worker_threads: int = rpc_contants.DEFAULT_NUM_WORKER_THREADS,
+        rpc_timeout: float = rpc_contants.DEFAULT_RPC_TIMEOUT_SEC,
+        init_method: str = rpc_contants.DEFAULT_INIT_METHOD,
+        device_maps: dict[str, dict[DeviceType, DeviceType]] | None = None,
+        devices: list[DeviceType] | None = None,
+        _transports: list | None = None,
+        _channels: list | None = None,
+    ):
+        full_device_maps = (
+            {}
+            if device_maps is None
+            else {k: _to_device_map(v) for k, v in device_maps.items()}
+        )
+        full_device_list = [] if devices is None else _to_device_list(devices)
+        super().__init__(
+            num_worker_threads,
+            _transports,
+            _channels,
+            rpc_timeout,
+            init_method,
+            full_device_maps,
+            full_device_list,
+        )
+
+    def set_device_map(self, to: str, device_map: dict[DeviceType, DeviceType]):
+        r"""
+        Set device mapping between each RPC caller and callee pair. This
+        function can be called multiple times to incrementally add
+        device placement configurations.
+
+        Args:
+            to (str): Callee name.
+            device_map (Dict of int, str, or torch.device): Device placement
+                mappings from this worker to the callee. This map must be
+                invertible.
+
+        Example:
+            >>> # xdoctest: +SKIP("distributed")
+            >>> # both workers
+            >>> def add(x, y):
+            >>>     print(x)  # tensor([1., 1.], device='cuda:1')
+            >>>     return x + y, (x + y).to(2)
+            >>>
+            >>> # on worker 0
+            >>> options = TensorPipeRpcBackendOptions(
+            >>>     num_worker_threads=8,
+            >>>     device_maps={"worker1": {0: 1}}
+            >>> # maps worker0's cuda:0 to worker1's cuda:1
+            >>> )
+            >>> options.set_device_map("worker1", {1: 2})
+            >>> # maps worker0's cuda:1 to worker1's cuda:2
+            >>>
+            >>> rpc.init_rpc(
+            >>>     "worker0",
+            >>>     rank=0,
+            >>>     world_size=2,
+            >>>     backend=rpc.BackendType.TENSORPIPE,
+            >>>     rpc_backend_options=options
+            >>> )
+            >>>
+            >>> x = torch.ones(2)
+            >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1))
+            >>> # The first argument will be moved to cuda:1 on worker1. When
+            >>> # sending the return value back, it will follow the invert of
+            >>> # the device map, and hence will be moved back to cuda:0 and
+            >>> # cuda:1 on worker0
+            >>> print(rets[0])  # tensor([2., 2.], device='cuda:0')
+            >>> print(rets[1])  # tensor([2., 2.], device='cuda:1')
+        """
+        full_device_map = _to_device_map(device_map)
+        curr_device_maps = super().device_maps
+
+        if to in curr_device_maps:
+            for k, v in full_device_map.items():
+                if k in curr_device_maps[to] and v != curr_device_maps[to][k]:
+                    raise ValueError(
+                        "`set_device_map` only supports 1-to-1 mapping, trying"
+                        f" to map {k} to {v} and {curr_device_maps[to][k]}"
+                    )
+
+        super()._set_device_map(to, full_device_map)
+
+    def set_devices(self, devices: list[DeviceType]):
+        r"""
+        Set local devices used by the TensorPipe RPC agent. When processing
+        CUDA RPC requests, the TensorPipe RPC agent will properly synchronize
+        CUDA streams for all devices in this ``List``.
+
+        Args:
+            devices (List of int, str, or torch.device): local devices used by
+                the TensorPipe RPC agent.
+        """
+        self.devices = _to_device_list(devices)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/rref_proxy.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/rref_proxy.py
new file mode 100644
index 0000000000000000000000000000000000000000..46eecf19e22c9bcb11a475963f9be0461261b0a4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/rref_proxy.py
@@ -0,0 +1,80 @@
+# mypy: allow-untyped-defs
+from functools import partial
+
+import torch
+from torch.futures import Future
+
+from . import functions, rpc_async
+from .constants import UNSET_RPC_TIMEOUT
+
+
+def _local_invoke(rref, func_name, args, kwargs):
+    return getattr(rref.local_value(), func_name)(*args, **kwargs)
+
+
+@functions.async_execution
+def _local_invoke_async_execution(rref, func_name, args, kwargs):
+    return getattr(rref.local_value(), func_name)(*args, **kwargs)
+
+
+def _invoke_rpc(rref, rpc_api, func_name, timeout, *args, **kwargs):
+    def _rref_type_cont(rref_fut):
+        rref_type = rref_fut.value()
+
+        _invoke_func = _local_invoke
+        # Bypass ScriptModules when checking for async function attribute.
+        bypass_type = issubclass(rref_type, torch.jit.ScriptModule) or issubclass(
+            rref_type, torch._C.ScriptModule
+        )
+        if not bypass_type:
+            func = getattr(rref_type, func_name)
+            if hasattr(func, "_wrapped_async_rpc_function"):
+                _invoke_func = _local_invoke_async_execution
+
+        return rpc_api(
+            rref.owner(),
+            _invoke_func,
+            args=(rref, func_name, args, kwargs),
+            timeout=timeout,
+        )
+
+    rref_fut = rref._get_type(timeout=timeout, blocking=False)
+
+    if rpc_api is not rpc_async:
+        rref_fut.wait()
+        return _rref_type_cont(rref_fut)
+    else:
+        # A little explanation on this.
+        # rpc_async returns a Future pointing to the return value of `func_name`, it returns a `Future[T]`
+        # Calling _rref_type_cont from the `then` lambda causes Future wrapping. IOW, `then` returns a `Future[Future[T]]`
+        # To address that, we return a Future that is completed with the result of the async call.
+        result: Future = Future()
+
+        def _wrap_rref_type_cont(fut):
+            try:
+                _rref_type_cont(fut).then(_complete_op)
+            except BaseException as ex:  # noqa: B036
+                result.set_exception(ex)
+
+        def _complete_op(fut):
+            try:
+                result.set_result(fut.value())
+            except BaseException as ex:  # noqa: B036
+                result.set_exception(ex)
+
+        rref_fut.then(_wrap_rref_type_cont)
+        return result
+
+
+# This class manages proxied RPC API calls for RRefs. It is entirely used from
+# C++ (see python_rpc_handler.cpp).
+class RRefProxy:
+    def __init__(self, rref, rpc_api, timeout=UNSET_RPC_TIMEOUT):
+        self.rref = rref
+        self.rpc_api = rpc_api
+        self.rpc_timeout = timeout
+
+    def __getattr__(self, func_name):
+        return partial(
+            _invoke_rpc, self.rref, self.rpc_api, func_name, self.rpc_timeout
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/server_process_global_profiler.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/server_process_global_profiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..29a916772d330b555673645a3e38308788b31535
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/rpc/server_process_global_profiler.py
@@ -0,0 +1,190 @@
+#!/usr/bin/python3
+# mypy: allow-untyped-defs
+
+import itertools
+
+import torch
+
+# pyrefly: ignore [deprecated]
+from torch.autograd.profiler_legacy import profile
+
+from . import (
+    _disable_server_process_global_profiler,
+    _enable_server_process_global_profiler,
+)
+
+
+__all__: list[str] = []
+
+
+class _server_process_global_profile(profile):
+    """
+    It has the same API as ``torch.autograd.profiler.profile`` class,
+    except that it enables profiling on all threads running RPC server request callbacks.
+
+    Context manager that manages autograd profiler state and holds a summary of results.
+    Under the hood it just records events of functions being executed in C++ and
+    exposes those events to Python. You can wrap any code into it and it will
+    only report runtime of PyTorch functions.
+    Note: profiler is thread local and is automatically propagated into the async tasks
+
+    Args:
+        enabled (bool, optional): Setting this to False makes this context manager a no-op.
+            Default: ``True``.
+
+        use_cuda (bool, optional): Enables timing of CUDA events as well using the cudaEvent API.
+            Adds approximately 4us of overhead to each tensor operation.
+            Default: ``False``
+
+        record_shapes (bool, optional): If shapes recording is set, information
+            about input dimensions will be collected. This allows one to see which
+            dimensions have been used under the hood and further group by them
+            using prof.key_averages(group_by_input_shape=True). Please note that
+            shape recording might skew your profiling data. It is recommended to
+            use separate runs with and without shape recording to validate the timing.
+            Most likely the skew will be negligible for bottom most events (in a case
+            of nested function calls). But for higher level functions the total
+            self cpu time might be artificially increased because of the shape
+            collection.
+
+        profile_memory (bool, optional): Whether to report memory usage, default: ``False``
+
+    .. warning::
+        Enabling memory profiling incurs additional profiler overhead
+
+    .. warning::
+        Due to some CUDA multiprocessing limitations (see :ref:`multiprocessing-cuda-note`),
+        one cannot use the profiler with ``use_cuda = True`` to benchmark
+        DataLoaders with ``num_workers > 0``. If you wish to benchmark data loading,
+        please use ``use_cuda = False`` or ``num_workers = 0``.
+
+    Example:
+        >>> # xdoctest: +SKIP
+        >>> # On worker 0:
+        >>> import torch
+        >>> import torch.distributed.rpc as rpc
+        >>> rpc.init_rpc("worker0", rank=0, world_size=2)
+        >>> x, y = torch.tensor(1), torch.tensor(2)
+        >>> outer_profile_rref = rpc.remote(
+        ...     dst_worker_name, rpc._server_process_global_profile
+        ... )
+        >>> outer_profile_rref.rpc_sync().__enter__()
+        >>> rpc.rpc_sync(dst_worker_name, torch.add, (x, y))
+        >>> inner_profile_rref = rpc.remote(
+        ...     dst_worker_name, rpc._server_process_global_profile
+        ... )
+        >>> inner_profile_rref.rpc_sync().__enter__()
+        >>> rpc.rpc_sync(dst_worker_name, torch.sub, (x, y))
+        >>> inner_profile_rref.rpc_sync().__exit__(None, None, None)
+        >>> outer_profile_rref.rpc_sync().__exit__(None, None, None)
+        >>> print(inner_profile_rref.rpc_sync().key_averages())
+        ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
+        Name       Self CPU total %  Self CPU total   CPU total %      CPU total        CPU time avg     Number of Calls
+        ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
+        sub        85.06%           76.275us         100.00%          89.667us         89.667us         1
+        empty      14.94%           13.392us         14.94%           13.392us         13.392us         1
+        ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
+        Self CPU time total: 89.667us
+        >>> print(outer_profile_rref.rpc_sync().key_averages())
+        ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
+        Name       Self CPU total %  Self CPU total   CPU total %      CPU total        CPU time avg     Number of Calls
+        ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
+        sub        35.65%           76.275us         41.91%           89.667us         89.667us         1
+        empty      12.67%           27.101us         12.67%           27.101us         13.551us         2
+        add        51.68%           110.550us        58.09%           124.259us        124.259us        1
+        ---------  ---------------  ---------------  ---------------  ---------------  ---------------  ---------------
+        Self CPU time total: 213.926us
+        >>> rpc.shutdown()
+
+        >>> # On worker 1:
+        >>> import torch.distributed.rpc as rpc
+        >>> rpc.init_rpc("worker1", rank=1, world_size=2)
+        >>> # wait for worker 0 to finish work, and then shutdown.
+        >>> rpc.shutdown()
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def __enter__(self):
+        """
+        Turn on server-side process-global profiling.
+        This enables thread-local profiler on all RPC threads running server-side request callbacks.
+        """
+        if not self.enabled:
+            return
+
+        if self.entered:  # type: ignore[has-type]
+            raise RuntimeError("autograd profiler traces are not reentrant")
+        self.entered = True
+
+        profiler_kind = (
+            torch.autograd.ProfilerState.CUDA
+            if self.use_cuda
+            else torch.autograd.ProfilerState.CPU
+        )
+        profiler_config = torch.autograd.ProfilerConfig(
+            profiler_kind,
+            self.record_shapes,
+            self.profile_memory,
+            False,
+            False,
+            False,
+            torch.profiler._ExperimentalConfig(),
+        )
+        _enable_server_process_global_profiler(profiler_config)
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        """
+        Turn off server-side process-global profiling.
+        Aggregate all profiling events recorded by RPC threads.
+
+        These attributes are assigned on exiting context.
+
+        Attributes:
+            function_events (torch.autograd.profiler.EventList).  It's a list that has helper
+            methods, like 1) show record items in a pretty-print table.
+            2) do averaging by grouping on keys. 3) and more.
+
+            process_global_function_events (List[torch.autograd.profiler.FunctionEvent]).
+            It's a list of ``FunctionEvent`` elements. Every element is a profiling result
+            of an RPC request handling within the profiling range.
+        """
+        if not self.enabled:
+            return
+
+        process_global_events = _disable_server_process_global_profiler()
+
+        # Every element in this list is a thread profiling result from an RPC request handling.
+        process_global_function_events = []
+        for thread_local_events in process_global_events:
+            # Parse from ``Event``s to ``FunctionEvent``s.
+            thread_local_function_events = (
+                torch.autograd.profiler_legacy._parse_legacy_records(
+                    thread_local_events
+                )
+            )
+            thread_local_function_events.sort(
+                key=lambda function_event: [
+                    function_event.time_range.start,
+                    -(function_event.time_range.end),
+                ]
+            )
+            process_global_function_events.append(thread_local_function_events)
+
+        flattened_function_events = list(
+            itertools.chain.from_iterable(process_global_function_events)
+        )
+        # pyrefly: ignore [bad-assignment]
+        self.function_events = torch.autograd.profiler_util.EventList(
+            flattened_function_events,
+            use_device="cuda" if self.use_cuda else None,
+            profile_memory=self.profile_memory,
+        )
+        # pyrefly: ignore [missing-attribute]
+        self.function_events._build_tree()
+
+        self.process_global_function_events = process_global_function_events
+
+        return False
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/run.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/run.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d8d0fb64276eb4ed8f53dea9a62a55d7c69f14f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/run.py
@@ -0,0 +1,995 @@
+#!/usr/bin/env python3
+# mypy: allow-untyped-defs
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Module ``torch.distributed.run``.
+
+``torch.distributed.run`` is a module that spawns up multiple distributed
+training processes on each of the training nodes.
+
+``torchrun`` is a python
+`console script `_
+to the main module
+`torch.distributed.run `_
+declared in the ``entry_points`` configuration in
+`setup.py `_.
+It is equivalent to invoking ``python -m torch.distributed.run``.
+
+``torchrun`` can be used for single-node distributed training, in which one or
+more processes per node will be spawned. It can be used for either
+CPU training or GPU training. If it is used for GPU training,
+each distributed process will be operating on a single GPU. This can achieve
+well-improved single-node training performance. ``torchrun`` can also be used in
+multi-node distributed training, by spawning up multiple processes on each node
+for well-improved multi-node distributed training performance as well.
+This will especially be beneficial for systems with multiple Infiniband
+interfaces that have direct-GPU support, since all of them can be utilized for
+aggregated communication bandwidth.
+
+In both cases of single-node distributed training or multi-node distributed
+training, ``torchrun`` will launch the given number of processes per node
+(``--nproc-per-node``). If used for GPU training, this number needs to be less
+or equal to the number of GPUs on the current system (``nproc_per_node``),
+and each process will be operating on a single GPU from *GPU 0 to
+GPU (nproc_per_node - 1)*.
+
+.. versionchanged:: 2.0.0
+
+    ``torchrun`` will pass the ``--local-rank=`` argument to your script.
+    From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the
+    previously used underscored ``--local_rank``.
+
+    For backward compatibility, it may be necessary for users to handle both
+    cases in their argument parsing code. This means including both ``"--local-rank"``
+    and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is
+    provided, ``torchrun`` will trigger an error: "error: unrecognized arguments:
+    --local-rank=". For training code that only supports PyTorch 2.0.0+,
+    including ``"--local-rank"`` should be sufficient.
+
+    ::
+
+        >>> # xdoctest: +SKIP
+        >>> import argparse
+        >>> parser = argparse.ArgumentParser()
+        >>> parser.add_argument("--local-rank", "--local_rank", type=int)
+        >>> args = parser.parse_args()
+
+Usage
+-----
+
+Single-node multi-worker
+++++++++++++++++++++++++
+
+::
+
+    torchrun
+        --standalone
+        --nnodes=1
+        --nproc-per-node=$NUM_TRAINERS
+        YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...)
+
+.. note:: ``--nproc-per-node`` may be
+          ``"gpu"`` (spawn one process per GPU),
+          ``"cpu"`` (spawn one process per CPU),
+          ``"xpu"`` (spawn one process per XPU),
+          ``"auto"`` (equivalent to ``"gpu"`` if CUDA is available,
+          else equivalent to ``"xpu"`` if XPU is available,
+          else equivalent to ``"cpu"``),
+          or an integer specifying the number of processes.
+          See `torch.distributed.run.determine_local_world_size
+          `_
+          for more details.
+
+Stacked single-node multi-worker
+++++++++++++++++++++++++++++++++
+
+To run multiple instances (separate jobs) of single-node, multi-worker on the
+same host, we need to make sure that each instance (job) is
+setup on different ports to avoid port conflicts (or worse, two jobs being merged
+as a single job). To do this you have to run with ``--rdzv-backend=c10d``
+and specify a different port by setting ``--rdzv-endpoint=localhost:$PORT_k``.
+For ``--nodes=1``, its often convenient to let ``torchrun`` pick a free random
+port automatically instead of manually assigning different ports for each run.
+
+::
+
+    torchrun
+        --rdzv-backend=c10d
+        --rdzv-endpoint=localhost:0
+        --nnodes=1
+        --nproc-per-node=$NUM_TRAINERS
+        YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...)
+
+
+Fault tolerant (fixed sized number of workers, no elasticity, tolerates 3 failures)
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+::
+
+    torchrun
+        --nnodes=$NUM_NODES
+        --nproc-per-node=$NUM_TRAINERS
+        --max-restarts=3
+        --rdzv-id=$JOB_ID
+        --rdzv-backend=c10d
+        --rdzv-endpoint=$HOST_NODE_ADDR
+        YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...)
+
+``HOST_NODE_ADDR``, in form [:] (e.g. node1.example.com:29400), specifies the node and
+the port on which the C10d rendezvous backend should be instantiated and hosted. It can be any
+node in your training cluster, but ideally you should pick a node that has a high bandwidth.
+
+.. note::
+   If no port number is specified ``HOST_NODE_ADDR`` defaults to 29400.
+
+Elastic (``min=1``, ``max=4``, tolerates up to 3 membership changes or failures)
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+::
+
+    torchrun
+        --nnodes=1:4
+        --nproc-per-node=$NUM_TRAINERS
+        --max-restarts=3
+        --rdzv-id=$JOB_ID
+        --rdzv-backend=c10d
+        --rdzv-endpoint=$HOST_NODE_ADDR
+        YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...)
+
+``HOST_NODE_ADDR``, in form [:] (e.g. node1.example.com:29400), specifies the node and
+the port on which the C10d rendezvous backend should be instantiated and hosted. It can be any
+node in your training cluster, but ideally you should pick a node that has a high bandwidth.
+
+.. note::
+   If no port number is specified ``HOST_NODE_ADDR`` defaults to 29400.
+
+Note on rendezvous backend
+--------------------------
+
+For multi-node training you need to specify:
+
+1. ``--rdzv-id``: A unique job id (shared by all nodes participating in the job)
+2. ``--rdzv-backend``: An implementation of
+   :py:class:`torch.distributed.elastic.rendezvous.RendezvousHandler`
+3. ``--rdzv-endpoint``: The endpoint where the rendezvous backend is running; usually in form
+   ``host:port``.
+
+Currently ``c10d`` (recommended), ``etcd-v2``, and ``etcd`` (legacy)  rendezvous backends are
+supported out of the box. To use ``etcd-v2`` or ``etcd``, setup an etcd server with the ``v2`` api
+enabled (e.g. ``--enable-v2``).
+
+.. warning::
+   ``etcd-v2`` and ``etcd`` rendezvous use etcd API v2. You MUST enable the v2 API on the etcd
+   server. Our tests use etcd v3.4.3.
+
+.. warning::
+   For etcd-based rendezvous we recommend using ``etcd-v2`` over ``etcd`` which is functionally
+   equivalent, but uses a revised implementation. ``etcd`` is in maintenance mode and will be
+   removed in a future version.
+
+Definitions
+-----------
+
+1. ``Node`` - A physical instance or a container; maps to the unit that the job manager works with.
+
+2. ``Worker`` - A worker in the context of distributed training.
+
+3. ``WorkerGroup`` - The set of workers that execute the same function (e.g. trainers).
+
+4. ``LocalWorkerGroup`` - A subset of the workers in the worker group running on the same node.
+
+5. ``RANK`` - The rank of the worker within a worker group.
+
+6. ``WORLD_SIZE`` - The total number of workers in a worker group.
+
+7. ``LOCAL_RANK`` - The rank of the worker within a local worker group.
+
+8. ``LOCAL_WORLD_SIZE`` - The size of the local worker group.
+
+9. ``rdzv_id`` - A user-defined id that uniquely identifies the worker group for a job. This id is
+   used by each node to join as a member of a particular worker group.
+
+9. ``rdzv_backend`` - The backend of the rendezvous (e.g. ``c10d``). This is typically a strongly
+   consistent key-value store.
+
+10. ``rdzv_endpoint`` - The rendezvous backend endpoint; usually in form ``:``.
+
+A ``Node`` runs ``LOCAL_WORLD_SIZE`` workers which comprise a ``LocalWorkerGroup``. The union of
+all ``LocalWorkerGroups`` in the nodes in the job comprise the ``WorkerGroup``.
+
+Environment Variables
+---------------------
+
+The following environment variables are made available to you in your script:
+
+1. ``LOCAL_RANK`` -  The local rank.
+
+2. ``RANK`` -  The global rank.
+
+3. ``GROUP_RANK`` - The rank of the worker group. A number between 0 and ``max_nnodes``. When
+   running a single worker group per node, this is the rank of the node.
+
+4. ``ROLE_RANK`` -  The rank of the worker across all the workers that have the same role. The role
+   of the worker is specified in the ``WorkerSpec``.
+
+5. ``LOCAL_WORLD_SIZE`` - The local world size (e.g. number of workers running locally); equals to
+   ``--nproc-per-node`` specified on ``torchrun``.
+
+6. ``WORLD_SIZE`` - The world size (total number of workers in the job).
+
+7. ``ROLE_WORLD_SIZE`` - The total number of workers that was launched with the same role specified
+   in ``WorkerSpec``.
+
+8. ``MASTER_ADDR`` - The FQDN of the host that is running worker with rank 0; used to initialize
+   the Torch Distributed backend.
+
+9. ``MASTER_PORT`` - The port on the ``MASTER_ADDR`` that can be used to host the C10d TCP store.
+
+10. ``TORCHELASTIC_RESTART_COUNT`` - The number of worker group restarts so far.
+
+11. ``TORCHELASTIC_MAX_RESTARTS`` - The configured maximum number of restarts.
+
+12. ``TORCHELASTIC_RUN_ID`` - Equal to the rendezvous ``run_id`` (e.g. unique job id).
+
+13. ``PYTHON_EXEC`` - System executable override. If provided, the python user script will
+    use the value of ``PYTHON_EXEC`` as executable. The `sys.executable` is used by default.
+
+Deployment
+----------
+
+1. (Not needed for the C10d backend) Start the rendezvous backend server and get the endpoint (to be
+   passed as ``--rdzv-endpoint`` to ``torchrun``)
+
+2. Single-node multi-worker: Start ``torchrun`` on the host to start the agent process which
+   creates and monitors a local worker group.
+
+3. Multi-node multi-worker: Start ``torchrun`` with the same arguments on all the nodes
+   participating in training.
+
+When using a job/cluster manager, the entry point command to the multi-node job should be ``torchrun``.
+
+Failure Modes
+-------------
+
+1. Worker failure: For a training job with ``n`` workers, if ``k<=n`` workers fail all workers
+   are stopped and restarted up to ``max_restarts``.
+
+2. Agent failure: An agent failure results in a local worker group failure. It is up to the job
+   manager to fail the entire job (gang semantics) or attempt to replace the node. Both behaviors
+   are supported by the agent.
+
+3. Node failure: Same as agent failure.
+
+Membership Changes
+------------------
+
+1. Node departure (scale-down): The agent is notified of the departure, all existing workers are
+   stopped, a new ``WorkerGroup`` is formed, and all workers are started with a new ``RANK`` and
+   ``WORLD_SIZE``.
+
+2. Node arrival (scale-up): The new node is admitted to the job, all existing workers are stopped,
+   a new ``WorkerGroup`` is formed, and all workers are started with a new ``RANK`` and
+   ``WORLD_SIZE``.
+
+Important Notices
+-----------------
+
+1. This utility and multi-process distributed (single-node or
+   multi-node) GPU training currently only achieves the best performance using
+   the NCCL distributed backend. Thus NCCL backend is the recommended backend to
+   use for GPU training.
+
+2. The environment variables necessary to initialize a Torch process group are provided to you by
+   this module, no need for you to pass ``RANK`` manually.  To initialize a process group in your
+   training script, simply run:
+
+::
+
+    >>> # xdoctest: +SKIP("stub")
+    >>> import torch.distributed as dist
+    >>> dist.init_process_group(backend="gloo|nccl")
+
+3. In your training program, you can either use regular distributed functions
+   or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your
+   training program uses GPUs for training and you would like to use
+   :func:`torch.nn.parallel.DistributedDataParallel` module,
+   here is how to configure it.
+
+::
+
+    local_rank = int(os.environ["LOCAL_RANK"])
+    model = torch.nn.parallel.DistributedDataParallel(
+        model, device_ids=[local_rank], output_device=local_rank
+    )
+
+Please ensure that ``device_ids`` argument is set to be the only GPU device id
+that your code will be operating on. This is generally the local rank of the
+process. In other words, the ``device_ids`` needs to be ``[int(os.environ("LOCAL_RANK"))]``,
+and ``output_device`` needs to be ``int(os.environ("LOCAL_RANK"))`` in order to use this
+utility
+
+
+4. On failures or membership changes ALL surviving workers are killed immediately. Make sure to
+   checkpoint your progress. The frequency of checkpoints should depend on your job's tolerance
+   for lost work.
+
+5. This module only supports homogeneous ``LOCAL_WORLD_SIZE``. That is, it is assumed that all
+   nodes run the same number of local workers (per role).
+
+6. ``RANK`` is NOT stable. Between restarts, the local workers on a node can be assigned a
+   different range of ranks than before. NEVER hard code any assumptions about the stable-ness of
+   ranks or some correlation between ``RANK`` and ``LOCAL_RANK``.
+
+7. When using elasticity (``min_size!=max_size``) DO NOT hard code assumptions about
+   ``WORLD_SIZE`` as the world size can change as nodes are allowed to leave and join.
+
+8. It is recommended for your script to have the following structure:
+
+::
+
+    def main():
+        load_checkpoint(checkpoint_path)
+        initialize()
+        train()
+
+
+    def train():
+        for batch in iter(dataset):
+            train_step(batch)
+
+            if should_checkpoint:
+                save_checkpoint(checkpoint_path)
+
+9. (Recommended) On worker errors, this tool will summarize the details of the error
+   (e.g. time, rank, host, pid, traceback, etc). On each node, the first error (by timestamp)
+   is heuristically reported as the "Root Cause" error. To get tracebacks as part of this
+   error summary print out, you must decorate your main entrypoint function in your
+   training script as shown in the example below. If not decorated, then the summary
+   will not include the traceback of the exception and will only contain the exitcode.
+   For details on torchelastic error handling see: https://pytorch.org/docs/stable/elastic/errors.html
+
+::
+
+    from torch.distributed.elastic.multiprocessing.errors import record
+
+
+    @record
+    def main():
+        # do train
+        pass
+
+
+    if __name__ == "__main__":
+        main()
+"""  # noqa: E501
+
+import os
+import sys
+import uuid
+from argparse import ArgumentParser, REMAINDER
+from collections.abc import Callable
+from importlib import metadata
+
+import torch
+from torch.distributed.argparse_util import check_env, env
+from torch.distributed.elastic.multiprocessing import DefaultLogsSpecs, LogsSpecs, Std
+from torch.distributed.elastic.multiprocessing.errors import record
+from torch.distributed.elastic.rendezvous.utils import _parse_rendezvous_config
+from torch.distributed.elastic.utils import macros
+from torch.distributed.elastic.utils.logging import get_logger
+from torch.distributed.launcher.api import elastic_launch, LaunchConfig
+from torch.numa.binding import (
+    AffinityMode as _AffinityMode,  # Signify as private with _
+    NumaOptions as _NumaOptions,
+)
+from torch.utils.backend_registration import _get_custom_mod_func
+
+
+logger = get_logger(__name__)
+
+
+def get_args_parser() -> ArgumentParser:
+    """Parse the command line options."""
+    parser = ArgumentParser(description="Torch Distributed Elastic Training Launcher")
+
+    def comma_separated_list(value):
+        placeholder = ""
+        value = value.replace(",,", placeholder)
+        items = value.split(",")
+        items = [item.replace(placeholder, ",") for item in items]
+        return items
+
+    #
+    # Worker/node size related arguments.
+    #
+
+    parser.add_argument(
+        "--nnodes",
+        action=env,
+        type=str,
+        default="1:1",
+        help="Number of nodes, or the range of nodes in form :.",
+    )
+    parser.add_argument(
+        "--nproc-per-node",
+        "--nproc_per_node",
+        action=env,
+        type=str,
+        default="1",
+        help="Number of workers per node; supported values: [auto, cpu, gpu, xpu, int].",
+    )
+
+    #
+    # Rendezvous related arguments
+    #
+
+    parser.add_argument(
+        "--rdzv-backend",
+        "--rdzv_backend",
+        action=env,
+        type=str,
+        default="static",
+        help="Rendezvous backend.",
+    )
+    parser.add_argument(
+        "--rdzv-endpoint",
+        "--rdzv_endpoint",
+        action=env,
+        type=str,
+        default="",
+        help="Rendezvous backend endpoint; usually in form :.",
+    )
+    parser.add_argument(
+        "--rdzv-id",
+        "--rdzv_id",
+        action=env,
+        type=str,
+        default="none",
+        help="User-defined group id.",
+    )
+    parser.add_argument(
+        "--rdzv-conf",
+        "--rdzv_conf",
+        action=env,
+        type=str,
+        default="",
+        help="Additional rendezvous configuration (=,=,...).",
+    )
+    parser.add_argument(
+        "--standalone",
+        action=check_env,
+        help="Start a local standalone rendezvous backend that is represented by a C10d TCP store "
+        "on a free port. Useful when launching single-node, multi-worker job. If specified "
+        "--rdzv-backend, --rdzv-endpoint, --rdzv-id are auto-assigned and any explicitly set values "
+        "are ignored.",
+    )
+
+    #
+    # User-code launch related arguments.
+    #
+
+    parser.add_argument(
+        "--max-restarts",
+        "--max_restarts",
+        action=env,
+        type=int,
+        default=0,
+        help="Maximum number of worker group restarts before failing.",
+    )
+    parser.add_argument(
+        "--monitor-interval",
+        "--monitor_interval",
+        action=env,
+        type=float,
+        default=0.1,
+        help="Interval, in seconds, to monitor the state of workers.",
+    )
+    parser.add_argument(
+        "--start-method",
+        "--start_method",
+        action=env,
+        type=str,
+        default="spawn",
+        choices=["spawn", "fork", "forkserver"],
+        help="Multiprocessing start method to use when creating workers.",
+    )
+    parser.add_argument(
+        "--event-log-handler",
+        "--event_log_handler",
+        action=env,
+        type=str,
+        default="null",
+        help="name of a registered event logging handler (see: https://docs.pytorch.org/docs/stable/elastic/events.html)",
+    )
+    parser.add_argument(
+        "--role",
+        action=env,
+        type=str,
+        default="default",
+        help="User-defined role for the workers.",
+    )
+    parser.add_argument(
+        "-m",
+        "--module",
+        action=check_env,
+        help="Change each process to interpret the launch script as a Python module, executing "
+        "with the same behavior as 'python -m'.",
+    )
+    parser.add_argument(
+        "--no-python",
+        "--no_python",
+        action=check_env,
+        help="Skip prepending the training script with 'python' - just execute it directly. Useful "
+        "when the script is not a Python script.",
+    )
+
+    parser.add_argument(
+        "--run-path",
+        "--run_path",
+        action=check_env,
+        help="Run the training script with runpy.run_path in the same interpreter."
+        " Script must be provided as an abs path (e.g. /abs/path/script.py)."
+        " Takes precedence over --no-python.",
+    )
+    parser.add_argument(
+        "--log-dir",
+        "--log_dir",
+        action=env,
+        type=str,
+        default=None,
+        help="Base directory to use for log files (e.g. /var/log/torch/elastic). The same "
+        "directory is reused for multiple runs (a unique job-level sub-directory is created with "
+        "rdzv_id as the prefix).",
+    )
+    parser.add_argument(
+        "-r",
+        "--redirects",
+        action=env,
+        type=str,
+        default="0",
+        help="Redirect std streams into a log file in the log directory (e.g. [-r 3] redirects "
+        "both stdout+stderr for all workers, [-r 0:1,1:2] redirects stdout for local rank 0 and "
+        "stderr for local rank 1).",
+    )
+    parser.add_argument(
+        "-t",
+        "--tee",
+        action=env,
+        type=str,
+        default="0",
+        help="Tee std streams into a log file and also to console (see --redirects for format).",
+    )
+
+    parser.add_argument(
+        "--local-ranks-filter",
+        "--local_ranks_filter",
+        action=env,
+        type=str,
+        default="",
+        help="Only show logs from specified ranks in console (e.g. [--local_ranks_filter=0,1,2] will "
+        "only show logs from rank 0, 1 and 2). This will only apply to stdout and stderr, not to"
+        "log files saved via --redirect or --tee",
+    )
+
+    parser.add_argument(
+        "--duplicate-stdout-filters",
+        "--duplicate_stdout_filters",
+        action=env,
+        type=comma_separated_list,
+        default=[],
+        help="Duplicates logs streamed to stdout to another specified file with a list of filters (e.g. "
+        "[--duplicate_stdout_filters 'apple,orange'] will duplicate log lines matching 'apple' "
+        "OR 'orange'. An empty filters list won't duplicate any lines. Use double comma to escape a comma) ",
+    )
+
+    parser.add_argument(
+        "--duplicate-stderr-filters",
+        "--duplicate_stderr_filters",
+        action=env,
+        type=comma_separated_list,
+        default=[],
+        help="Duplicates logs streamed to stderr to another specified file with a list of filters (e.g. "
+        "[--duplicate_stdout_filters 'apple,orange'] will duplicate log lines matching 'apple' "
+        "OR 'orange'. An empty filters list won't duplicate any lines. Use double comma to escape a comma) ",
+    )
+
+    #
+    # Backwards compatible parameters with caffe2.distributed.launch.
+    #
+
+    parser.add_argument(
+        "--node-rank",
+        "--node_rank",
+        type=int,
+        action=env,
+        default=0,
+        help="Rank of the node for multi-node distributed training.",
+    )
+    parser.add_argument(
+        "--master-addr",
+        "--master_addr",
+        default="127.0.0.1",
+        type=str,
+        action=env,
+        help="Address of the master node (rank 0) that only used for static rendezvous. It should "
+        "be either the IP address or the hostname of rank 0. For single node multi-proc training "
+        "the --master-addr can simply be 127.0.0.1; IPv6 should have the pattern "
+        "`[0:0:0:0:0:0:0:1]`.",
+    )
+    parser.add_argument(
+        "--master-port",
+        "--master_port",
+        default=29500,
+        type=int,
+        action=env,
+        help="Port on the master node (rank 0) to be used for communication during distributed "
+        "training. It is only used for static rendezvous.",
+    )
+    parser.add_argument(
+        "--local-addr",
+        "--local_addr",
+        default=None,
+        type=str,
+        action=env,
+        help="Address of the local node. If specified, will use the given address for connection. "
+        "Else, will look up the local node address instead. Else, it will be default to local "
+        "machine's FQDN.",
+    )
+
+    parser.add_argument(
+        "--logs-specs",
+        "--logs_specs",
+        default=None,
+        type=str,
+        help="torchrun.logs_specs group entrypoint name, value must be type of LogsSpecs. "
+        "Can be used to override custom logging behavior.",
+    )
+
+    parser.add_argument(
+        "--numa-binding",
+        "--numa_binding",
+        type=str,
+        choices=[mode.value for mode in _AffinityMode],
+        default=None,
+        help="""
+        If provided, we will affinitize the worker processes based on NUMA nodes
+        for better performance. (E.g., preferring to allocate memory locally and run on CPUs on the
+        same NUMA node.)
+
+        NOTE: This is currently only supported for GPUs, and we assume
+        that the LOCAL_RANK process corresponds to the GPU with index LOCAL_RANK. If this is not
+        accurate for your workload, this feature may be a pessimization.
+
+        Available options are:
+          - node: Processes are bound to cpu cores within a NUMA node. This is a good starting point,
+          but other options may perform even slightly better in some cases.
+          - socket: Processes are bound to cpu cores within a socket.
+          - exclusive: Processes are bound to exclusive sets of cpu cores within a NUMA node.
+          - core-complex: Processes are bound to cpu cores in a core-complex.
+          NOTE: The core-complex option might not achieve optimal performance on architectures
+          featuring a single L3 cache per socket.""",
+    )
+
+    parser.add_argument(
+        "--signals-to-handle",
+        "--signals_to_handle",
+        action=env,
+        type=str,
+        default="SIGTERM,SIGINT,SIGHUP,SIGQUIT",
+        help="Comma-separated list of signals to handle and forward to subprocesses. "
+        "Default: SIGTERM,SIGINT,SIGHUP,SIGQUIT. "
+        "Common additional signals: SIGUSR1,SIGUSR2 (used in SLURM environments).",
+    )
+
+    parser.add_argument(
+        "--virtual-local-rank",
+        "--virtual_local_rank",
+        action=check_env,
+        help="Enable virtual local rank mode for workers. When enabled, LOCAL_RANK is set to 0 "
+        "for all workers and CUDA_VISIBLE_DEVICES is adjusted so each worker accesses its "
+        "assigned GPU at device index 0.",
+    )
+
+    #
+    # Positional arguments.
+    #
+
+    parser.add_argument(
+        "training_script",
+        type=str,
+        help="Full path to the (single GPU) training program/script to be launched in parallel, "
+        "followed by all the arguments for the training script.",
+    )
+
+    # Rest from the training program.
+    parser.add_argument("training_script_args", nargs=REMAINDER)
+
+    return parser
+
+
+def parse_args(args):
+    parser = get_args_parser()
+    return parser.parse_args(args)
+
+
+def parse_min_max_nnodes(nnodes: str):
+    arr = nnodes.split(":")
+
+    if len(arr) == 1:
+        min_nodes = max_nodes = int(arr[0])
+    elif len(arr) == 2:
+        min_nodes = int(arr[0])
+        max_nodes = int(arr[1])
+    else:
+        raise RuntimeError(f'nnodes={nnodes} is not in "MIN:MAX" format')  # noqa: E231
+
+    return min_nodes, max_nodes
+
+
+def determine_local_world_size(nproc_per_node: str):
+    try:
+        logger.info("Using nproc_per_node=%s.", nproc_per_node)
+        return int(nproc_per_node)
+    except ValueError as e:
+        if nproc_per_node == "cpu":
+            num_proc = os.cpu_count()
+            device_type = "cpu"
+        elif nproc_per_node == "gpu":
+            if not torch.cuda.is_available():
+                raise ValueError("Cuda is not available.") from e
+            device_type = "gpu"
+            num_proc = torch.cuda.device_count()
+        elif nproc_per_node == "xpu":
+            if not torch.xpu.is_available():
+                raise ValueError("Xpu is not available.") from e
+            device_type = "xpu"
+            num_proc = torch.xpu.device_count()
+        elif nproc_per_node == torch._C._get_privateuse1_backend_name():
+            if not _get_custom_mod_func("is_available")():
+                raise ValueError(f"{nproc_per_node} is not available.") from e
+            device_type = nproc_per_node
+            num_proc = _get_custom_mod_func("device_count")()
+        elif nproc_per_node == "auto":
+            if torch.accelerator.is_available():
+                num_proc = torch.accelerator.device_count()
+                device_type = torch.accelerator.current_accelerator().type  # type: ignore[union-attr]
+            else:
+                num_proc = os.cpu_count()
+                device_type = "cpu"
+        else:
+            raise ValueError(
+                f"Unsupported nproc_per_node value: {nproc_per_node}"
+            ) from e
+
+        logger.info(
+            "Using nproc_per_node=%s, setting nproc_per_node to %s since the instance has %s %s",
+            nproc_per_node,
+            num_proc,
+            num_proc,
+            device_type,
+        )
+        return num_proc
+
+
+def get_rdzv_endpoint(args):
+    if args.rdzv_backend == "static" and not args.rdzv_endpoint:
+        return f"{args.master_addr}:{args.master_port}"  # noqa: E231
+    return args.rdzv_endpoint
+
+
+def get_use_env(args) -> bool:
+    """
+    Retrieve ``use_env`` from the args.
+
+    ``use_env`` is a legacy argument, if ``use_env`` is False, the
+    ``--node-rank`` argument will be transferred to all worker processes.
+    ``use_env`` is only used by the ``torch.distributed.launch`` and will
+    be deprecated in future releases.
+    """
+    if not hasattr(args, "use_env"):
+        return True
+    return args.use_env
+
+
+def _get_logs_specs_class(logs_specs_name: str | None) -> type[LogsSpecs]:
+    """
+    Attempts to load `torchrun.logs_spec` entrypoint with key of `logs_specs_name` param.
+    Provides plugin mechanism to provide custom implementation of LogsSpecs.
+
+    Returns `DefaultLogsSpecs` when logs_spec_name is None.
+    Raises ValueError when entrypoint for `logs_spec_name` can't be found in entrypoints.
+    """
+    logs_specs_cls = None
+    if logs_specs_name is not None:
+        eps = metadata.entry_points()
+        group = eps.select(group="torchrun.logs_specs")
+        if group.select(name=logs_specs_name):
+            logs_specs_cls = group[logs_specs_name].load()
+
+        if logs_specs_cls is None:
+            raise ValueError(
+                f"Could not find entrypoint under 'torchrun.logs_specs[{logs_specs_name}]' key"
+            )
+
+        logger.info(
+            "Using logs_spec '%s' mapped to %s", logs_specs_name, str(logs_specs_cls)
+        )
+    else:
+        logs_specs_cls = DefaultLogsSpecs
+
+    return logs_specs_cls
+
+
+def config_from_args(args) -> tuple[LaunchConfig, Callable | str, list[str]]:
+    # If ``args`` not passed, defaults to ``sys.argv[:1]``
+    min_nodes, max_nodes = parse_min_max_nnodes(args.nnodes)
+    if not (0 < min_nodes <= max_nodes):
+        raise AssertionError(
+            f"min_nodes must be > 0 and <= max_nodes, got min_nodes={min_nodes}, max_nodes={max_nodes}"
+        )
+    if args.max_restarts < 0:
+        raise AssertionError("max_restarts must be >= 0")
+
+    if (
+        hasattr(args, "master_addr")
+        and args.rdzv_backend != "static"
+        and not args.rdzv_endpoint
+    ):
+        logger.warning(
+            "master_addr is only used for static rdzv_backend and when rdzv_endpoint "
+            "is not specified."
+        )
+
+    nproc_per_node = determine_local_world_size(args.nproc_per_node)
+    if "OMP_NUM_THREADS" not in os.environ and nproc_per_node > 1:
+        omp_num_threads = 1
+        logger.warning(
+            "\n*****************************************\n"
+            "Setting OMP_NUM_THREADS environment variable for each process to be "
+            "%s in default, to avoid your system being overloaded, "
+            "please further tune the variable for optimal performance in "
+            "your application as needed. \n"
+            "*****************************************",
+            omp_num_threads,
+        )
+        # This env variable will be passed down to the subprocesses
+        os.environ["OMP_NUM_THREADS"] = str(omp_num_threads)
+
+    log_line_prefix_template = os.getenv("TORCHELASTIC_LOG_LINE_PREFIX_TEMPLATE")
+
+    rdzv_configs = _parse_rendezvous_config(args.rdzv_conf)
+
+    if args.rdzv_backend == "static":
+        rdzv_configs["rank"] = args.node_rank
+
+    rdzv_endpoint = get_rdzv_endpoint(args)
+
+    ranks: set[int] | None = None
+    if args.local_ranks_filter:
+        try:
+            ranks = set(map(int, args.local_ranks_filter.split(",")))
+            if not ranks:
+                raise AssertionError("ranks set cannot be empty")
+        except Exception as e:
+            raise ValueError(
+                "--local_ranks_filter must be a comma-separated list of integers e.g. --local_ranks_filter=0,1,2"
+            ) from e
+
+    logs_specs_cls: type[LogsSpecs] = _get_logs_specs_class(args.logs_specs)
+    # pyrefly: ignore [bad-instantiation]
+    logs_specs = logs_specs_cls(
+        log_dir=args.log_dir,
+        redirects=Std.from_str(args.redirects),
+        tee=Std.from_str(args.tee),
+        local_ranks_filter=ranks,
+    )
+    numa_options = (
+        None
+        if args.numa_binding is None
+        else _NumaOptions(affinity_mode=_AffinityMode(args.numa_binding))
+    )
+
+    config = LaunchConfig(
+        min_nodes=min_nodes,
+        max_nodes=max_nodes,
+        nproc_per_node=nproc_per_node,
+        run_id=args.rdzv_id,
+        role=args.role,
+        rdzv_endpoint=rdzv_endpoint,
+        rdzv_backend=args.rdzv_backend,
+        rdzv_configs=rdzv_configs,
+        max_restarts=args.max_restarts,
+        monitor_interval=args.monitor_interval,
+        start_method=args.start_method,
+        log_line_prefix_template=log_line_prefix_template,
+        local_addr=args.local_addr,
+        logs_specs=logs_specs,
+        event_log_handler=args.event_log_handler,
+        numa_options=numa_options,
+        signals_to_handle=args.signals_to_handle,
+        duplicate_stdout_filters=args.duplicate_stdout_filters,
+        duplicate_stderr_filters=args.duplicate_stderr_filters,
+        virtual_local_rank=args.virtual_local_rank,
+    )
+
+    with_python = not args.no_python
+    cmd: Callable | str
+    cmd_args = []
+    use_env = get_use_env(args)
+    if args.run_path:
+        cmd = run_script_path
+        cmd_args.append(args.training_script)
+    else:
+        if with_python:
+            cmd = os.getenv("PYTHON_EXEC", sys.executable)
+            cmd_args.append("-u")
+            if args.module:
+                cmd_args.append("-m")
+            cmd_args.append(args.training_script)
+        else:
+            if args.module:
+                raise ValueError(
+                    "Don't use both the '--no-python' flag"
+                    " and the '--module' flag at the same time."
+                )
+            cmd = args.training_script
+    if not use_env:
+        cmd_args.append(f"--local-rank={macros.local_rank}")
+    cmd_args.extend(args.training_script_args)
+
+    return config, cmd, cmd_args
+
+
+def run_script_path(training_script: str, *training_script_args: str):
+    """
+    Run the provided `training_script` from within this interpreter.
+
+    Usage: `script_as_function("/abs/path/to/script.py", "--arg1", "val1")`
+    """
+    import runpy
+    import sys
+
+    sys.argv = [training_script] + [*training_script_args]
+    runpy.run_path(sys.argv[0], run_name="__main__")
+
+
+def run(args):
+    torch.multiprocessing._set_thread_name("pt_elastic")
+
+    if args.standalone:
+        args.rdzv_backend = "c10d"
+        args.rdzv_endpoint = "localhost:0"
+        args.rdzv_id = str(uuid.uuid4())
+        logger.info(
+            "\n**************************************\n"
+            "Rendezvous info:\n"
+            "--rdzv-backend=%s "
+            "--rdzv-endpoint=%s "
+            "--rdzv-id=%s\n"
+            "**************************************\n",
+            args.rdzv_backend,
+            args.rdzv_endpoint,
+            args.rdzv_id,
+        )
+
+    config, cmd, cmd_args = config_from_args(args)
+    elastic_launch(
+        config=config,
+        entrypoint=cmd,
+    )(*cmd_args)
+
+
+@record
+def main(args=None):
+    args = parse_args(args)
+    run(args)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..067d4c0917e9de33b516c7ed47c678be2ac6c692
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/__init__.py
@@ -0,0 +1,88 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+import torch
+import torch.distributed.tensor._ops  # force import all built-in dtensor ops
+from torch.distributed.device_mesh import DeviceMesh, init_device_mesh  # noqa: F401
+from torch.distributed.tensor._api import (
+    distribute_module,
+    distribute_tensor,
+    DTensor,
+    empty,
+    full,
+    ones,
+    rand,
+    randn,
+    zeros,
+)
+from torch.distributed.tensor.placement_types import (
+    Partial,
+    Placement,
+    Replicate,
+    Shard,
+)
+from torch.optim.optimizer import (
+    _foreach_supported_types as _optim_foreach_supported_types,
+)
+from torch.utils._foreach_utils import (
+    _foreach_supported_types as _util_foreach_supported_types,
+)
+
+
+# All public APIs from dtensor package
+__all__ = [
+    "DTensor",
+    "distribute_tensor",
+    "distribute_module",
+    "Shard",
+    "Replicate",
+    "Partial",
+    "Placement",
+    "ones",
+    "empty",
+    "full",
+    "rand",
+    "randn",
+    "zeros",
+]
+
+# For weights_only torch.load
+from ._dtensor_spec import (
+    DTensorSpec as _DTensorSpec,
+    ShardOrderEntry as _ShardOrderEntry,
+    TensorMeta as _TensorMeta,
+)
+
+
+torch.serialization.add_safe_globals(
+    [
+        DeviceMesh,
+        _DTensorSpec,
+        _TensorMeta,
+        _ShardOrderEntry,
+        DTensor,
+        Partial,
+        Replicate,
+        Shard,
+    ]
+)
+
+
+# Append DTensor to the list of supported types for foreach implementation for optimizer
+# and clip_grad_norm_ so that we will try to use foreach over the for-loop implementation on CUDA.
+if DTensor not in _optim_foreach_supported_types:
+    _optim_foreach_supported_types.append(DTensor)
+
+if DTensor not in _util_foreach_supported_types:
+    _util_foreach_supported_types.append(DTensor)  # type: ignore[arg-type]
+
+
+# Set namespace for exposed private names
+DTensor.__module__ = "torch.distributed.tensor"
+distribute_tensor.__module__ = "torch.distributed.tensor"
+distribute_module.__module__ = "torch.distributed.tensor"
+ones.__module__ = "torch.distributed.tensor"
+empty.__module__ = "torch.distributed.tensor"
+full.__module__ = "torch.distributed.tensor"
+rand.__module__ = "torch.distributed.tensor"
+randn.__module__ = "torch.distributed.tensor"
+zeros.__module__ = "torch.distributed.tensor"
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/_api.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/_api.py
new file mode 100644
index 0000000000000000000000000000000000000000..78e00d5137ea075fdcda11d3e97f2ed7ed7f3f0a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/_api.py
@@ -0,0 +1,1385 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+import copy
+import inspect
+import warnings
+from collections.abc import Callable, Sequence
+from typing import Any
+from typing_extensions import deprecated
+
+import torch
+import torch.distributed.tensor._dispatch as op_dispatch
+import torch.distributed.tensor._random as random
+import torch.nn as nn
+from torch._export.wrappers import mark_subclass_constructor_exportable_experimental
+from torch.distributed.device_mesh import _mesh_resources, DeviceMesh
+from torch.distributed.tensor._collective_utils import check_tensor_meta, mesh_broadcast
+from torch.distributed.tensor._dtensor_spec import DTensorSpec, TensorMeta
+from torch.distributed.tensor._redistribute import (
+    Redistribute,
+    redistribute_local_tensor,
+)
+from torch.distributed.tensor._utils import (
+    compute_global_tensor_info,
+    compute_local_shape_and_global_offset,
+    normalize_to_torch_size,
+)
+from torch.distributed.tensor.placement_types import (
+    _StridedShard,
+    Partial,
+    Placement,
+    Replicate,
+    Shard,
+)
+
+
+__all__ = [
+    "DTensor",
+    "distribute_tensor",
+    "distribute_module",
+    "ones",
+    "empty",
+    "full",
+    "rand",
+    "randn",
+    "zeros",
+]
+
+aten = torch.ops.aten
+
+
+# NOTE [Autograd interaction between torch.Tensor]
+#
+# The autograd functions defined below are being used by the public
+# facing APIs (i.e. from_local, to_local) to ensure DTensor to work
+# together with torch.Tensor within the autograd engine. This
+# allows DTensor to only exist on part of the module hierarchy.
+#
+# As an example, we have the a module that consists of submodules
+# A, B, and C, the execution flow would be like:
+#  input(torch.Tensor) -> Module A -> Module B -> Module C -> output (torch.Tensor)
+#
+# Suppose I only want to make Module B be a sharded module with
+# DTensor params, the following forward/backward should work:
+#
+#  input(torch.Tensor) -> Module A
+#       -> DTensor input (from_local) -> Sharded Module B -> DTensor output
+#           -> torch.Tensor output (to_local) -> Module C
+#
+# So from_local/to_local must be Autograd functions.
+#
+class _ToTorchTensor(torch.autograd.Function):
+    @staticmethod
+    def forward(  # type: ignore[override]
+        ctx,
+        input: "DTensor",
+        grad_placements: Sequence[Placement] | None,
+    ):
+        ctx.dtensor_spec = input._spec
+        ctx.grad_placements = grad_placements
+        local_tensor = input._local_tensor
+
+        # We need to return a fresh Tensor object there as autograd metadata
+        # will be inplaced into it. So we don't want to pollute the Tensor
+        # object stored in the _local_tensor of this DTensor.
+        return local_tensor.view_as(local_tensor)
+
+    @staticmethod
+    def backward(ctx, grad_output: torch.Tensor):  # type: ignore[override]
+        dtensor_spec = ctx.dtensor_spec
+        mesh = dtensor_spec.mesh
+        grad_placements = ctx.grad_placements
+        dtensor_meta = dtensor_spec.tensor_meta
+
+        _, tensor_stride = compute_global_tensor_info(
+            grad_output, mesh, dtensor_spec.placements
+        )
+        tensor_stride = tuple(tensor_stride)
+        grad_placements = grad_placements or dtensor_spec.placements
+        if (
+            tensor_stride == dtensor_meta.stride
+            and grad_placements == dtensor_spec.placements
+        ):
+            # Avoid actual sharing of specs in case they're modified during (e.g.)
+            # sharding propagation.
+            grad_spec = copy.copy(dtensor_spec)
+        else:
+            grad_spec = DTensorSpec(
+                mesh,
+                grad_placements,
+                tensor_meta=TensorMeta(
+                    shape=dtensor_meta.shape,
+                    stride=tensor_stride,
+                    dtype=dtensor_meta.dtype,
+                ),
+            )
+        return (
+            # pyrefly: ignore [bad-argument-type]
+            DTensor(
+                # pyrefly: ignore [bad-argument-count]
+                grad_output,
+                grad_spec,
+                # pyrefly: ignore [unexpected-keyword]
+                requires_grad=grad_output.requires_grad,
+            ),
+            None,
+        )
+
+
+class _FromTorchTensor(torch.autograd.Function):
+    @staticmethod
+    def forward(  # type: ignore[override]
+        ctx,  # pyre-ignore[2]: Parameter must be annotated.
+        input: torch.Tensor,
+        device_mesh: DeviceMesh,
+        placements: tuple[Placement, ...],
+        run_check: bool,
+        shape: torch.Size | None = None,
+        stride: tuple[int, ...] | None = None,
+    ) -> "DTensor":
+        ctx.previous_placement = placements
+        ctx.previous_device_mesh = device_mesh
+
+        if shape and stride:
+            tensor_shape, tensor_stride = shape, stride
+        elif not shape and not stride:
+            # if it's not by default run_check, we assume user is certain that each
+            # rank has the same tensor shape, and we just use that to calculate the
+            # global shape
+            global_shape, global_stride = compute_global_tensor_info(
+                input, device_mesh, placements
+            )
+            tensor_shape, tensor_stride = torch.Size(global_shape), tuple(global_stride)
+        else:
+            raise RuntimeError(
+                f"Found shape:{shape}, stride:{stride}.",
+                "Please pass both shape and stride at the same time.",
+            )
+
+        if device_mesh.get_coordinate() is None:
+            # if the global rank is not participating in the device mesh, we
+            # simply set the local tensor to an empty tensor
+            input = input.new_empty(0, requires_grad=input.requires_grad)
+        elif run_check:
+            # TODO: support uneven sharding when global shape/stride not passed, by
+            # building the global TensorMeta during check_tensor_meta
+            check_shape_stride = not shape and not stride
+            check_tensor_meta(input, check_shape_stride=check_shape_stride)
+            # TODO: See if we need to make this run_check logic
+            # have a corresponding backward.
+            for idx, placement in enumerate(placements):
+                if placement.is_replicate():
+                    # broadcast rank 0 tensor to all ranks
+                    # only broadcast if run_check is True
+                    input = input.contiguous()
+                    mesh_broadcast(input, device_mesh, mesh_dim=idx)
+
+        dist_spec = DTensorSpec(
+            device_mesh,
+            placements,
+            tensor_meta=TensorMeta(
+                tensor_shape,
+                tensor_stride,
+                input.dtype,
+            ),
+        )
+
+        # We want a fresh Tensor object that shares memory with the input tensor
+        # pyrefly: ignore [bad-argument-type]
+        dist_tensor = DTensor(
+            # pyrefly: ignore [bad-argument-count]
+            input.view_as(input),
+            dist_spec,
+            # requires_grad of the dist tensor depends on if input
+            # requires_grad or not
+            # pyrefly: ignore [unexpected-keyword]
+            requires_grad=input.requires_grad,
+        )
+        return dist_tensor
+
+    @staticmethod
+    def backward(ctx, grad_output: "DTensor"):  # type: ignore[override]
+        previous_placement = ctx.previous_placement
+        previous_device_mesh = ctx.previous_device_mesh
+
+        # reshard to the placement when creating DistributedTensor
+        # so that the gradient layout matches, and we could return
+        # local gradients directly
+        if grad_output.placements != previous_placement:
+            current_spec = grad_output._spec
+            target_spec = DTensorSpec(
+                previous_device_mesh,
+                previous_placement,
+                tensor_meta=grad_output._spec.tensor_meta,
+            )
+            local_tensor = grad_output._local_tensor
+            output = redistribute_local_tensor(
+                local_tensor, current_spec, target_spec, is_backward=True
+            )
+            # TODO: return the redistributed local tensor directly without
+            # differentiable backward. see if this make sense for all cases.
+            return output, None, None, None, None, None
+
+        # TODO: backward is also differentiable now, add a test
+        # to test higher level gradients.
+        return grad_output.to_local(), None, None, None, None, None
+
+
+class DTensor(torch.Tensor):
+    """
+    ``DTensor`` (Distributed Tensor) is a subclass of ``torch.Tensor`` that provides single-device like
+    abstraction to program with multi-device ``torch.Tensor``. It describes the distributed tensor sharding
+    layout (DTensor Layout) through the :class:`DeviceMesh` and following types of :class:`Placement`:
+
+    * :class:`Shard`: Tensor sharded on the tensor dimension ``dim`` on the devices of the ``DeviceMesh`` dimension
+    * :class:`Replicate`: Tensor replicated on the devices of the ``DeviceMesh`` dimension
+    * :class:`Partial`: Tensor is pending reduction on the devices of the ``DeviceMesh`` dimension
+
+    When calling PyTorch operators, ``DTensor`` overrides the PyTorch operators to perform sharded computation and issue
+    communications whenever necessary. Along with the operator computation, ``DTensor`` will transform or propagate the
+    placements (DTensor Layout) properly (based on the operator semantic itself) and generate new ``DTensor`` outputs.
+
+    To ensure numerical correctness of the ``DTensor`` sharded computation when calling PyTorch operators, ``DTensor``
+    requires every Tensor argument of the operator be DTensor.
+
+    .. note:: Directly using the Tensor subclass constructor here is not the recommended way to create a ``DTensor``
+        (i.e. it does not handle autograd correctly hence is not the public API). Please refer to the `create_dtensor`_
+        section to see how to create a ``DTensor``.
+    """
+
+    _local_tensor: torch.Tensor
+    _spec: DTensorSpec
+    __slots__ = ["_local_tensor", "_spec"]
+
+    # _op_dispatcher instance as a class attribute to handle runtime dispatching logic
+    _op_dispatcher: op_dispatch.OpDispatcher = op_dispatch.OpDispatcher()
+
+    # This implementation is just to convince mypy _spec and _local_tensor are
+    # initialized; it is immediately overridden below.
+    def __new__(
+        cls,
+        local_tensor: torch.Tensor,
+        spec: DTensorSpec,
+        *,
+        requires_grad: bool,
+    ) -> "DTensor":
+        r = torch.Tensor._dtensor__new__(
+            cls, local_tensor, spec, requires_grad=requires_grad
+        )
+        r._spec = spec
+        r._local_tensor = local_tensor
+        return r
+
+    __new__ = torch.Tensor._dtensor__new__  # type: ignore[assignment] # noqa: F811
+
+    @torch._disable_dynamo
+    @mark_subclass_constructor_exportable_experimental
+    def __init__(self, *args, **kwargs):
+        """
+        Construct a DTensor from a local tensor, device mesh, and placement and
+        other tensor properties (i.e. shape, requires_grad, strides, etc).
+        .. note:: This is not a public API and it's only supposed to be used by the
+            operator implementations and internals. If you want to construct a
+            DTensor from a local tensor, consider using ``DTensor.from_local``, if
+            you want to construct a DTensor from a "global" tensor (where you
+            already have tensor initialized and want to shard this tensor),
+            consider using ``distribute_tensor``.
+        """
+        super().__init__()
+
+    # pyre-fixme[14]: `__repr__` overrides method defined in `DTensor` inconsistently.
+    # pyre-fixme[3]: Return type must be annotated.
+    def __repr__(self):  # type: ignore[override]
+        # TODO: consider all_gather the local tensors for better debugging
+        return f"DTensor(local_tensor={self._local_tensor}, device_mesh={self._spec.mesh}, placements={self._spec.placements})"
+
+    def __tensor_flatten__(self):
+        """
+        protocol to inform how to flatten a DTensor to local tensor
+        for PT2 tracing
+        """
+        return ["_local_tensor"], (self._spec, self.requires_grad)
+
+    @staticmethod
+    def __tensor_unflatten__(inner_tensors, flatten_spec, outer_size, outer_stride):
+        assert flatten_spec is not None, (
+            "Expecting spec to be not None from `__tensor_flatten__` return value!"
+        )
+        local_tensor = inner_tensors["_local_tensor"]
+        spec, requires_grad = flatten_spec
+        unflatten_tensor_meta = TensorMeta(
+            shape=outer_size,
+            stride=outer_stride,
+            dtype=spec.tensor_meta.dtype,
+        )
+        unflatten_spec = DTensorSpec(
+            spec.mesh,
+            spec.placements,
+            tensor_meta=unflatten_tensor_meta,
+        )
+        # pyrefly: ignore [bad-argument-type]
+        return DTensor(
+            # pyrefly: ignore [bad-argument-count]
+            local_tensor,
+            unflatten_spec,
+            # pyrefly: ignore [unexpected-keyword]
+            requires_grad=requires_grad,
+        )
+
+    def __coerce_tangent_metadata__(self):
+        if not any(isinstance(p, Partial) for p in self.placements):
+            return self
+        placements = [
+            Replicate() if isinstance(p, Partial) else p for p in self.placements
+        ]
+        return self.redistribute(device_mesh=self.device_mesh, placements=placements)
+
+    def __coerce_same_metadata_as_tangent__(self, flatten_spec, expected_type=None):
+        if expected_type is not None:
+            return None
+
+        (spec, _) = flatten_spec  # Result of tensor_flatten()
+        return self.redistribute(
+            device_mesh=self.device_mesh,
+            placements=spec.placements,
+        )
+
+    @classmethod
+    def __torch_dispatch__(cls, func, types, args=(), kwargs=None):  # type: ignore[override]
+        # We just need to have an implementation here; the __torch_dispatch__ machinery
+        # calls into a specific C++ fast path that doesn't call here.
+        # See #167051 for details
+        # python_arg_parser.cpp: dispatch_on_subclass()
+        # -> python_variable.cpp: dispatchDTensorOp()
+        raise NotImplementedError(
+            "DTensor.__torch_dispatch__ should not actually get called"
+        )
+
+    @staticmethod
+    def from_local(
+        local_tensor: torch.Tensor,
+        device_mesh: DeviceMesh | None = None,
+        placements: Sequence[Placement] | None = None,
+        *,
+        run_check: bool = False,
+        shape: torch.Size | None = None,
+        stride: tuple[int, ...] | None = None,
+    ) -> "DTensor":
+        """
+        Create a :class:`DTensor` from a local torch.Tensor on each rank
+        according to the ``device_mesh`` and ``placements`` specified.
+
+        Args:
+            local_tensor (torch.Tensor): local torch.Tensor on each rank.
+            device_mesh (:class:`DeviceMesh`, optional): DeviceMesh to place the
+                tensor, if not specified, must be called under a DeviceMesh
+                context manager, default: None
+            placements (List[:class:`Placement`], optional): the placements that
+                describes how to place the local torch.Tensor on DeviceMesh, must
+                have the same number of elements as ``device_mesh.ndim``.
+
+        Keyword args:
+            run_check (bool, optional): at a cost of extra communications, perform
+                sanity check across ranks to check each local tensor's meta information
+                to ensure correctness. If have :class:`Replicate` in ``placements``, the
+                data on first rank of the device mesh dimension will be broadcasted
+                to other ranks. default: False
+            shape (torch.Size, optional): A List of int which specifies the size of
+                DTensor which build on top of `local_tensor`. Note this needs to be
+                provided if the shape of ``local_tensor`` are different across the ranks.
+                If not provided, ``shape`` will be computed assuming the given distributed
+                tensor is evenly sharded across ranks. default: None
+            stride (tuple, optional): A List of int which specifies the stride of DTensor.
+                If not provided, ``stride`` will be computed assuming the given distributed
+                tensor is evenly sharded across ranks. default: None
+
+        Returns:
+            A :class:`DTensor` object
+
+        .. note:: When ``run_check=False``, it is the user's responsibility to ensure the
+            local tensor passed in is correct across ranks (i.e. the tensor is sharded for
+            the ``Shard(dim)`` placement or replicated for the ``Replicate()`` placement).
+            If not, the behavior of the created DTensor is undefined.
+
+        .. note:: ``from_local`` is differentiable, the `requires_grad` of the created
+            `DTensor` object will depend on if `local_tensor` requires_grad or not.
+        """
+        # `local_tensor` argument cannot be DTensor
+        if isinstance(local_tensor, DTensor):
+            raise RuntimeError(
+                f"the local_tensor argument only accepts torch.Tensor but got {type(local_tensor)} value."
+            )
+
+        # if same shape/dtype, no need to run_check, if not, must allgather
+        # the metadatas to check the size/dtype across ranks
+        # There should be no data communication unless there's replication
+        # strategy, where we broadcast the replication from the first rank
+        # in the mesh dimension
+        device_mesh = device_mesh or _mesh_resources.get_current_mesh()
+        device_type = device_mesh.device_type
+
+        # convert the local tensor to desired device base on device mesh's device_type
+        if device_type != local_tensor.device.type and not local_tensor.is_meta:
+            local_tensor = local_tensor.to(device_type)
+
+        # set default placements to replicated if not specified
+        if placements is None:
+            placements = [Replicate() for _ in range(device_mesh.ndim)]
+        else:
+            placements = list(placements)
+            for idx, placement in enumerate(placements):
+                # normalize shard dim to be positive
+                if isinstance(placement, Shard | _StridedShard):
+                    if placement.dim < 0:
+                        normalized_dim = placement.dim + local_tensor.ndim
+                        if type(placement) is _StridedShard:
+                            placements[idx] = _StridedShard(
+                                normalized_dim, split_factor=placement.split_factor
+                            )
+                        elif type(placement) is Shard:
+                            placements[idx] = Shard(normalized_dim)
+
+        # `from_local` is differentiable, and the gradient of the dist tensor this function
+        # created should flow back the gradients to the local_tensor, so we call an autograd
+        # function to construct the dist tensor instead.
+        return _FromTorchTensor.apply(  # pyre-ignore[16]: autograd func
+            local_tensor,
+            device_mesh,
+            tuple(placements),
+            run_check,
+            shape,
+            stride,
+        )
+
+    def to_local(
+        self, *, grad_placements: Sequence[Placement] | None = None
+    ) -> torch.Tensor:
+        """
+        Get the local tensor of this DTensor on its current rank. For sharding it returns
+        a local shard of the logical tensor view, for replication it returns the replica on
+        its current rank.
+
+        Keyword args:
+            grad_placements (List[:class:`Placement`], optional): the placements describes
+                the future layout of any gradient layout of the Tensor returned from this
+                function.
+                `to_local` converts DTensor to local tensor and the returned local tensor
+                might not be used as the original DTensor layout later in the code. This
+                argument is the hint that user can give to autograd in case the gradient
+                layout of the returned tensor does not match the original DTensor layout.
+                If not specified, we will assume the gradient layout remains the same
+                as the original DTensor and use that for gradient computation.
+
+        Returns:
+            A :class:`torch.Tensor` or ``AsyncCollectiveTensor`` object. it represents the
+            local tensor on its current rank. When an ``AsyncCollectiveTensor`` object is returned,
+            it means the local tensor is not ready yet (i.e. communication is not finished). In this
+            case, user needs to call ``wait`` to wait the local tensor to be ready.
+
+        .. note:: ``to_local`` is differentiable, the ``requires_grad`` of the local tensor returned
+            will depend on if the `DTensor` requires_grad or not.
+        """
+        if not torch.is_grad_enabled():
+            return self._local_tensor
+
+        if grad_placements is not None and not isinstance(grad_placements, tuple):
+            grad_placements = tuple(grad_placements)
+        return _ToTorchTensor.apply(
+            self, grad_placements
+        )  # pyre-ignore[16]: autograd func
+
+    def redistribute(
+        self,
+        device_mesh: DeviceMesh | None = None,
+        placements: Sequence[Placement] | None = None,
+        *,
+        async_op: bool = False,
+        forward_dtype: torch.dtype | None = None,
+        backward_dtype: torch.dtype | None = None,
+    ) -> "DTensor":
+        """
+        ``redistribute`` performs necessary collective operations that redistribute the current
+        DTensor from its current placements to a new placements, or from its current DeviceMesh
+        to a new DeviceMesh. i.e. we can turn a Sharded DTensor to a Replicated DTensor by
+        specifying a Replicate placement for each dimension of the DeviceMesh.
+
+        When redistributing from current to the new placements on one device mesh dimension, we
+        will perform the following operations including communication collective or local operation:
+
+        1. ``Shard(dim)`` -> ``Replicate()``: ``all_gather``
+        2. ``Shard(src_dim)`` -> ``Shard(dst_dim)``: ``all_to_all``
+        3. ``Replicate()`` -> ``Shard(dim)``: local chunking (i.e. ``torch.chunk``)
+        4. ``Partial()`` -> ``Replicate()``: ``all_reduce``
+        5. ``Partial()`` -> ``Shard(dim)``: ``reduce_scatter``
+
+
+        ``redistribute`` would correctly figure out the necessary redistribute steps for DTensors
+        that are created either on 1-D or N-D DeviceMesh.
+
+        Args:
+            device_mesh (:class:`DeviceMesh`, optional): DeviceMesh to place the
+                DTensor. If not specified, it would use the current DTensor's DeviceMesh.
+                default: None
+            placements (List[:class:`Placement`], optional): the new placements that
+                describes how to place the DTensor into the DeviceMesh, must
+                have the same number of elements as ``device_mesh.ndim``.
+                default: replicate on all mesh dimensions
+
+        Keyword args:
+            async_op (bool, optional): whether to perform the DTensor redistribute operation
+                asynchronously or not. Default: False
+            forward_dtype (torch.dtype, optional): the local tensor datatype can be converted to
+                ``forward_dtype`` before redistributing the local tensor in its forward.
+                The result DTensor will be in ``forward_dtype`` Default: None.
+            backward_dtype (torch.dtype, optional): the local tensor datatype can be converted to
+                ``backward_dtype`` before redistributing the local tensor in its backward.
+                The result DTensor gradient would be converted back to the current DTensor dtype. Default: None
+
+        Returns:
+            A :class:`DTensor` object
+
+        .. note:: ``redistribute`` is differentiable, which means user do not need to worry about
+            the backward formula of the redistribute operation.
+
+        .. note:: ``redistribute`` currently only supports redistributing DTensor on the same DeviceMesh,
+            Please file an issue if you need to redistribute DTensor to different DeviceMesh.
+        """
+        # NOTE: This redistribute API currently only supports out
+        # of place redistribution, i.e. it always create a new
+        # DTensor object and leave the original one unchanged.
+
+        # if device_mesh is not specified, use the current device_mesh
+        device_mesh = device_mesh or self.device_mesh
+        # raise error if new placements not specified
+        if placements is None:
+            raise RuntimeError("placements is needed for redistribute!")
+
+        placements = list(placements)
+        for i, placement in enumerate(placements):
+            if placement.is_partial() and self.placements[i] != placement:
+                raise RuntimeError(
+                    f"Can not redistribute from {self.placements[i]} to {placement}, "
+                    "redistributing to Partial is for internal use only!"
+                )
+            elif isinstance(placement, Shard) and placement.dim < 0:
+                # normalize shard dim to be positive
+                placements[i] = Shard(placement.dim + self.ndim)
+            elif isinstance(placement, _StridedShard) and placement.dim < 0:
+                placements[i] = _StridedShard(
+                    placement.dim + self.ndim, split_factor=placement.split_factor
+                )
+        placements = tuple(placements)
+
+        # pyre-fixme[16]: `Redistribute` has no attribute `apply`.
+        return Redistribute.apply(
+            self, device_mesh, placements, async_op, forward_dtype, backward_dtype
+        )
+
+    def full_tensor(
+        self, *, grad_placements: Sequence[Placement] | None = None
+    ) -> torch.Tensor:
+        """
+        Return the full tensor of this DTensor. It will perform necessary collectives
+        to gather the local tensors from other ranks in its DeviceMesh and concatenate
+        them together. It's a syntactic sugar of the following code:
+
+        ``dtensor.redistribute(placements=[Replicate()] * mesh.ndim).to_local()``
+
+        Keyword args:
+            grad_placements (List[:class:`Placement`], optional): the placements describes
+                the future layout of any gradient layout of the full Tensor returned from this
+                function.
+                `full_tensor` converts DTensor to a full torch.Tensor and the returned torch.tensor
+                might not be used as the original replicated DTensor layout later in the code. This
+                argument is the hint that user can give to autograd in case the gradient
+                layout of the returned tensor does not match the original replicated DTensor layout.
+                If not specified, we will assume the gradient layout of the full tensor be replicated.
+
+        Returns:
+            A :class:`torch.Tensor` object that represents the full tensor of this DTensor.
+
+        .. note:: ``full_tensor`` is differentiable.
+        """
+
+        redist_res = self.redistribute(
+            placements=[Replicate()] * self.device_mesh.ndim, async_op=False
+        )
+        return _ToTorchTensor.apply(redist_res, grad_placements)
+
+    @property
+    def device_mesh(self) -> DeviceMesh:
+        """
+        The :class:`DeviceMesh` attribute that associates with this DTensor object.
+
+        .. note:: ``device_mesh`` is a read-only property, it can not be set.
+        """
+        return self._spec.mesh
+
+    @property
+    def placements(self) -> tuple[Placement, ...]:
+        """
+        The placements attribute of this DTensor that describes the layout of this
+        DTensor on the its DeviceMesh.
+
+        .. note:: ``placements`` is a read-only property, it can not be set.
+        """
+        return self._spec.placements
+
+    def _raise_if_contains_partial_placements(self) -> None:
+        """
+        Raise an error if the DTensor contains partial placements.
+        """
+        for placement in self._spec.placements:
+            if not isinstance(placement, Partial):
+                continue
+
+            raise ValueError(
+                "Any checkpointing related operations are not supported for "
+                "DTensor with partial placements!"
+            )
+
+    def __create_write_items__(self, fqn: str, object: Any):
+        self._raise_if_contains_partial_placements()
+        from torch.distributed.checkpoint.planner_helpers import (
+            _create_write_items_for_dtensor,
+        )
+
+        if hasattr(self._local_tensor, "__create_write_items__"):
+            return self._local_tensor.__create_write_items__(fqn, object)  # type: ignore[attr-defined]
+        elif isinstance(self._local_tensor, torch.Tensor):
+            return [_create_write_items_for_dtensor(fqn, object)]
+        else:
+            raise RuntimeError("Unsupported tensor type!")
+
+    def __create_chunk_list__(self):
+        """
+        Return a list of ChunkStorageMetadata, which is a dataclass that describes the size/offset of the local shard/replica
+        on current rank. For DTensor, each rank will have a single local shard/replica, so the returned list usually only
+        has one element.
+
+        This dunder method is primariy used for distributed checkpoint purpose.
+
+        Returns:
+            A List[:class:`ChunkStorageMetadata`] object that represents the shard size/offset on the current rank.
+        """
+        self._raise_if_contains_partial_placements()
+        from torch.distributed.checkpoint.planner_helpers import (
+            _create_chunk_from_dtensor,
+        )
+
+        if hasattr(self._local_tensor, "__create_chunk_list__"):
+            return self._local_tensor.__create_chunk_list__()  # type: ignore[attr-defined]
+        elif isinstance(self._local_tensor, torch.Tensor):
+            return [_create_chunk_from_dtensor(self)]
+        else:
+            raise RuntimeError("Unsupported tensor type!")
+
+    def __get_tensor_shard__(self, index):
+        self._raise_if_contains_partial_placements()
+        if hasattr(self._local_tensor, "__get_tensor_shard__"):
+            return self._local_tensor.__get_tensor_shard__(index)  # type: ignore[attr-defined]
+        elif isinstance(self._local_tensor, torch.Tensor):
+            return self.to_local()
+        else:
+            raise RuntimeError("Unsupported tensor type!")
+
+    @classmethod
+    def __metadata_guard__(
+        cls, orig: tuple[DTensorSpec, bool], other: tuple[DTensorSpec, bool]
+    ) -> bool:
+        # TODO - delete this - This is now unused after the PR -
+        # https://github.com/pytorch/pytorch/pull/165824
+        orig_spec, orig_requires_grad = orig
+        other_spec, other_requires_grad = other
+        return (
+            orig_spec._check_equals(other_spec, skip_shapes=True)
+            and orig_requires_grad == other_requires_grad
+        )
+
+
+def distribute_tensor(
+    tensor: torch.Tensor,
+    device_mesh: DeviceMesh | None = None,
+    placements: Sequence[Placement] | None = None,
+    *,
+    src_data_rank: int | None = 0,
+) -> DTensor:
+    """
+    Distribute a leaf ``torch.Tensor`` (i.e. nn.Parameter/buffers) to the ``device_mesh`` according
+    to the ``placements`` specified. The rank of ``device_mesh`` and ``placements`` must be the
+    same. The ``tensor`` to distribute is the logical or "global" tensor, and the API would use
+    the ``tensor`` from first rank of the DeviceMesh dimension as the source of truth to preserve
+    the single-device semantic. If you want to construct a DTensor in the middle of the Autograd
+    computation, please use :meth:`DTensor.from_local` instead.
+
+    Args:
+        tensor (torch.Tensor): torch.Tensor to be distributed. Note that if you
+            want to shard a tensor on a dimension that is not evenly divisible by
+            the number of devices in that mesh dimension, we use ``torch.chunk``
+            semantic to shard the tensor and scatter the shards. The uneven sharding
+            behavior is experimental and subject to change.
+        device_mesh (:class:`DeviceMesh`, optional): DeviceMesh to distribute the
+            tensor, if not specified, must be called under a DeviceMesh context
+            manager, default: None
+        placements (List[:class:`Placement`], optional): the placements that
+            describes how to place the tensor on DeviceMesh, must have the same
+            number of elements as ``device_mesh.ndim``. If not specified, we will
+            by default replicate the tensor across the ``device_mesh`` from the
+            first rank of each dimension of the `device_mesh`.
+
+    Keyword args:
+        src_data_rank (int, optional): the rank of the source data for the logical/global tensor, it is
+            used by :meth:`distribute_tensor` to scatter/broadcast the shards/replicas to other ranks.
+            By default, we use ``group_rank=0`` on each DeviceMesh dimension as the source data to preserve
+            the single-device semantic. If passing ``None`` explicitly, :meth:`distribute_tensor` simply uses
+            its local data instead of trying to preserve the single-device semantic via scatter/broadcast.
+            Default: 0
+
+    Returns:
+        A :class:`DTensor` or ``XLAShardedTensor`` object.
+
+    .. note::
+        When initialize the DeviceMesh with the ``xla`` device_type, ``distribute_tensor``
+        return `XLAShardedTensor` instead. see `this issue `__
+        for more details. The XLA integration is experimental and subject to change.
+    """
+
+    torch._C._log_api_usage_once("torch.dtensor.distribute_tensor")
+
+    # get default device mesh if there's nothing specified
+    device_mesh = device_mesh or _mesh_resources.get_current_mesh()
+    device_type = device_mesh.device_type
+    if device_type == "xla":
+        try:
+            # call PyTorch/XLA SPMD for `xla` backend type device mesh.
+            # This returns XLAShardedTensor
+            from torch_xla.distributed.spmd import (  # type:ignore[import]
+                xla_distribute_tensor,
+            )
+
+            return xla_distribute_tensor(tensor, device_mesh, placements)  # type:ignore[return-value]
+        except ImportError as e:
+            msg = "To use DTensor API with xla, you must install the torch_xla package!"
+            raise ImportError(msg) from e
+
+    if not tensor.is_leaf:
+        raise RuntimeError(
+            "`distribute_tensor` should be used to distribute leaf tensors! but found non-leaf tensor!"
+        )
+
+    # convert tensor to the corresponding device type if it's not in that device type
+    if device_type != tensor.device.type and not tensor.is_meta:
+        tensor = tensor.to(device_type)
+
+    # set default placements to replicated if not specified
+    if placements is None:
+        placements = [Replicate() for _ in range(device_mesh.ndim)]
+
+    if len(placements) != device_mesh.ndim:
+        raise ValueError(
+            f"`placements` must have the same length as `device_mesh.ndim`! "
+            f"Found placements length: {len(placements)}, and device_mesh.ndim: {device_mesh.ndim}."
+        )
+    if isinstance(tensor, DTensor):
+        # if the tensor is already a DTensor, we need to check:
+        # 1. if the we can further shard this DTensor if the two device mesh belong to
+        #   the same parenet mesh and further sharding is possible.
+        # 2. check if device mesh and placements are the same
+        if tensor.device_mesh != device_mesh:
+            raise ValueError(
+                f"Cannot distribute a DTensor with device mesh {tensor.device_mesh} "
+                f"to a different device mesh {device_mesh}."
+            )
+        if tensor.placements != tuple(placements):
+            raise ValueError(
+                f"Cannot distribute a DTensor with placements {tensor.placements} "
+                f"to a different placements {placements}. do you want to call "
+                f"`redistribute` instead?"
+            )
+        return tensor
+
+    local_tensor = tensor.detach()
+
+    # TODO(xilun): address sharding order
+    # distribute the tensor according to the placements.
+    placements = list(placements)
+    for idx, placement in enumerate(placements):
+        if isinstance(placement, Shard | _StridedShard):
+            placement_dim = (
+                placement.dim + tensor.ndim if placement.dim < 0 else placement.dim
+            )
+            if isinstance(placement, Shard):
+                local_tensor = Shard._make_shard_tensor(
+                    placement_dim, local_tensor, device_mesh, idx, src_data_rank
+                )
+                placements[idx] = Shard(placement_dim)
+            else:
+                local_tensor = _StridedShard._make_shard_tensor(
+                    placement_dim,
+                    local_tensor,
+                    device_mesh,
+                    idx,
+                    src_data_rank,
+                    split_factor=placement.split_factor,
+                )
+                placements[idx] = _StridedShard(
+                    placement_dim, split_factor=placement.split_factor
+                )
+        elif isinstance(placement, Replicate):
+            local_tensor = Replicate._make_replicate_tensor(
+                local_tensor, device_mesh, idx, src_data_rank
+            )
+        elif isinstance(placement, Partial):
+            local_tensor = Replicate._make_replicate_tensor(
+                local_tensor, device_mesh, idx, src_data_rank
+            )
+            local_tensor = placement._partition_value(local_tensor, device_mesh, idx)
+        else:
+            raise RuntimeError(
+                f"Trying to distribute tensor with unsupported placements {placement} on device mesh dimension {idx}!"
+            )
+    placements = tuple(placements)
+
+    assert local_tensor is not None, "distributing a tensor should not be None"
+    # detach the local tensor passed to DTensor since after the construction
+    # of DTensor, autograd would work on top of DTensor instead of local tensor
+    spec = DTensorSpec(
+        mesh=device_mesh,
+        placements=placements,
+        tensor_meta=TensorMeta(
+            shape=tensor.size(),
+            stride=tensor.stride(),
+            dtype=tensor.dtype,
+        ),
+    )
+    # pyrefly: ignore [bad-argument-type]
+    return DTensor(
+        # pyrefly: ignore [bad-argument-count]
+        local_tensor.requires_grad_(tensor.requires_grad),
+        spec,
+        # pyrefly: ignore [unexpected-keyword]
+        requires_grad=tensor.requires_grad,
+    )
+
+
+@deprecated("Please use `distribute_tensor` with `src_data_rank=None` instead.")
+def _shard_tensor(
+    full_tensor: torch.Tensor,
+    placements: Sequence[Shard],
+    device_mesh: DeviceMesh | None = None,
+) -> "DTensor":
+    """
+    Locally shards a full tensor based on indicated sharding arrangement, and
+    returns a DTensor containing the local shard.
+
+    .. warning:: This is a private API that is subject to change. It skips the
+        communication otherwise required by `distribute_tensor`. It is only
+        applicable to cases where all ranks have the same `full_tensor`. For
+        example, in distributed inference all ranks load from the same
+        checkpoint. This API will not check for data equality between ranks, it
+        is thus user's responsibility to ensure the `full_tensor` is the same
+        across ranks.
+
+    Args:
+        full_tensor (torch.Tensor): the full tensor to be sharded.
+        placements (Sequence[:class:`Shard`]): the placements that
+            describes how to place the local tensor on DeviceMesh.
+        device_mesh (:class:`DeviceMesh`, optional): DeviceMesh to place the
+            DTensor.  Must have same dimension as the number of placements.
+            If not specified, would be retrieve from current context.
+
+    Returns:
+        A :class:`DTensor` object with the shard as its local tensor.
+
+    Examples:
+        >>> # xdoctest: +SKIP("need world_size and rank")
+        >>> device_mesh = dist.init_device_mesh("cuda", (world_size,))
+        >>> full_tensor = torch.arange(world_size, device=f"cuda:{rank}")
+        >>> dtensor = _shard_tensor(full_tensor, [Shard(1)], device_mesh)
+    """
+    return distribute_tensor(full_tensor, device_mesh, placements, src_data_rank=None)
+
+
+def distribute_module(
+    module: nn.Module,
+    device_mesh: DeviceMesh | None = None,
+    partition_fn: Callable[[str, nn.Module, DeviceMesh], None] | None = None,
+    input_fn: Callable[[nn.Module, Any, DeviceMesh], None] | None = None,
+    output_fn: Callable[[nn.Module, Any, DeviceMesh], None] | None = None,
+) -> nn.Module:
+    """
+    This function expose three functions to control the parameters/inputs/outputs of the module:
+
+    1. To perform sharding on the module before runtime execution by specifying the
+    ``partition_fn`` (i.e. allow user to convert Module parameters to :class:`DTensor`
+    parameters according to the `partition_fn` specified).
+    2. To control the inputs or outputs of the module during runtime execution by
+    specifying the ``input_fn`` and ``output_fn``. (i.e. convert the input to
+    :class:`DTensor`, convert the output back to ``torch.Tensor``)
+
+    Args:
+        module (:class:`nn.Module`): user module to be partitioned.
+        device_mesh (:class:`DeviceMesh`): the device mesh to place the module.
+        partition_fn (Callable): the function to partition parameters (i.e. shard certain
+            parameters across the ``device_mesh``). If ``partition_fn`` is not specified,
+            by default we replicate all module parameters of ``module`` across the mesh.
+        input_fn (Callable): specify the input distribution, i.e. could control how the
+            input of the module is sharded. ``input_fn`` will be installed as a module
+            ``forward_pre_hook`` (pre forward hook).
+        output_fn (Callable): specify the output distribution, i.e. could control how the
+            output is sharded, or convert it back to torch.Tensor. ``output_fn`` will be
+            installed as a module ``forward_hook`` (post forward hook).
+
+    Returns:
+        A module that contains parameters/buffers that are all ``DTensor`` s.
+
+    .. note::
+        When initialize the DeviceMesh with the ``xla`` device_type, ``distribute_module``
+        return nn.Module with PyTorch/XLA SPMD annotated parameters. See
+        `this issue `__
+        for more details. The XLA integration is experimental and subject to change.
+
+    """
+
+    torch._C._log_api_usage_once("torch.dtensor.distribute_module")
+
+    already_distributed = getattr(module, "_distribute_module_applied", False)
+    if already_distributed:
+        raise RuntimeError(
+            "distribute_module should only be called once on a module, "
+            "but it has already been called on this module!"
+        )
+
+    device_mesh = device_mesh or _mesh_resources.get_current_mesh()
+    device_type = device_mesh.device_type
+    if device_type == "xla":
+        try:
+            # This function annotates all module parameters for auto-partitioning with
+            # PyTorch/XLA SPMD or explicitly partition to :class:`XLAShardedTensor` parameters
+            # according to the `partition_fn` specified.
+            from torch_xla.distributed.spmd import (  # type:ignore[import]
+                xla_distribute_module,
+            )
+
+            return xla_distribute_module(
+                module, device_mesh, partition_fn, input_fn, output_fn
+            )  # type:ignore[return-value]
+        except ImportError as e:
+            msg = "To use DTensor API with xla, you must install the torch_xla package!"
+            raise ImportError(msg) from e
+
+    def replicate_module_params_buffers(m: nn.Module, mesh: DeviceMesh) -> None:
+        # This function loop over the immediate module parameters and
+        # buffers, replicate all non DTensor params/buffers to DTensor
+        # parameters/buffers, if they have not been partitioned in the
+        # partition_fn, we can't easily use `module._apply` here
+        # because we don't know what happened inside partition_fn as
+        # user could do anything, i.e. install hooks, and we want to
+        # preserve those.
+        full_replicate = [Replicate()] * mesh.ndim
+        for key, param in m._parameters.items():
+            if param is not None and not isinstance(param, DTensor):
+                m.register_parameter(
+                    key,
+                    nn.Parameter(distribute_tensor(param.data, mesh, full_replicate)),
+                )
+        for key, buffer in m._buffers.items():
+            if buffer is not None and not isinstance(buffer, DTensor):
+                m._buffers[key] = distribute_tensor(buffer, mesh, full_replicate)
+
+    if partition_fn is None:
+        # if partition_fn not specified, we by default replicate
+        # all module params/buffers
+        for submod in module.modules():
+            replicate_module_params_buffers(submod, device_mesh)
+    else:
+        # apply partition_fun to submodules
+        for name, submod in module.named_modules():
+            partition_fn(name, submod, device_mesh)
+            replicate_module_params_buffers(submod, device_mesh)
+
+    # register input_fn as module forward pre hook
+    if input_fn is not None:
+        # check the input_fn signature
+        num_args = len(inspect.signature(input_fn).parameters)
+        if num_args == 2:
+            # input_fn only takes in inputs and device mesh
+            warnings.warn(
+                "Deprecating input_fn that takes two arguments (inputs, device_mesh), "
+                "please use input_fn that takes in (module, inputs, device_mesh) instead!",
+                FutureWarning,
+                stacklevel=2,
+            )
+            module.register_forward_pre_hook(
+                lambda _, inputs: input_fn(inputs, device_mesh)  # type: ignore[call-arg]
+            )
+        elif num_args == 3:
+            # input_fn takes in module, inputs, device mesh
+            module.register_forward_pre_hook(
+                lambda mod, inputs: input_fn(mod, inputs, device_mesh)
+            )
+        else:
+            raise ValueError(
+                f"input_fn should take in 3 arguments, but got {num_args} arguments!"
+            )
+    # register output_fn as module forward hook
+    if output_fn is not None:
+        num_args = len(inspect.signature(output_fn).parameters)
+        if num_args == 2:
+            # output_fn only takes in outputs and device mesh
+            warnings.warn(
+                "Deprecating output_fn that takes two arguments (inputs, device_mesh), "
+                "please use output_fn that takes in (module, inputs, device_mesh) instead!",
+                FutureWarning,
+                stacklevel=2,
+            )
+            module.register_forward_hook(
+                lambda mod, inputs, outputs: output_fn(outputs, device_mesh)  # type: ignore[call-arg]
+            )
+        elif num_args == 3:
+            module.register_forward_hook(
+                lambda mod, inputs, outputs: output_fn(mod, outputs, device_mesh)
+            )
+        else:
+            raise ValueError(
+                f"output_fn should take in 3 arguments, but got {num_args} arguments!"
+            )
+
+    module._distribute_module_applied = True  # type: ignore[assignment]
+    return module
+
+
+# Below are tensor factory function APIs, which are used to create a DTensor directly. We need
+# to make separate factory function APIs because tensor subclass could not override the tensor
+# factory methods, and we need user to call the factory functions with user intended device_mesh
+# and placements to create a proper DTensor.
+
+
+def _dtensor_init_helper(  # type: ignore[no-untyped-def]
+    init_op,
+    size: torch.Size,
+    device_mesh: DeviceMesh | None = None,
+    placements: Sequence[Placement] | None = None,
+    **kwargs,
+) -> DTensor:
+    # if device_mesh is None, use the one from mesh resources
+    device_mesh = device_mesh or _mesh_resources.get_current_mesh()
+    kwargs["device"] = device_mesh.device_type
+
+    # set default placements to replicated if not specified
+    placements = placements or tuple(Replicate() for _ in range(device_mesh.ndim))
+
+    # check device_mesh against placements
+    assert device_mesh.ndim == len(placements), (
+        "mesh dimension does not match the length of placements"
+    )
+
+    assert kwargs["layout"] == torch.strided, "layout value not supported!"
+    torch_stride = torch._prims_common.make_contiguous_strides_for(size)
+
+    # get local tensor shape
+    local_shape, _ = compute_local_shape_and_global_offset(
+        size, device_mesh, placements, skip_offset=True
+    )
+
+    # initialize the local tensor
+    if init_op is torch.full:
+        fill_value = kwargs.pop("fill_value", 0)
+        local_tensor = init_op(local_shape, fill_value, **kwargs)
+    elif init_op is torch.rand or init_op is torch.randn:
+        # this tensor meta is not used except `shape`
+        dtype = kwargs.get("dtype", torch.get_default_dtype())
+
+        tensor_meta = TensorMeta(size, (0,), dtype)
+        spec = DTensorSpec(device_mesh, tuple(placements), tensor_meta=tensor_meta)
+
+        if random.is_rng_supported_mesh(device_mesh) and not random._rng_tracker:
+            random._rng_tracker = random.OffsetBasedRNGTracker(device_mesh)
+
+        assert random._rng_tracker is not None
+        with random._rng_tracker._distribute_region(spec):
+            local_tensor = init_op(local_shape, **kwargs)
+    else:
+        local_tensor = init_op(local_shape, **kwargs)
+
+    spec = DTensorSpec(
+        device_mesh,
+        tuple(placements),
+        tensor_meta=TensorMeta(
+            size,
+            torch_stride,
+            local_tensor.dtype,
+        ),
+    )
+
+    # pyrefly: ignore [bad-argument-type]
+    return DTensor(
+        # pyrefly: ignore [bad-argument-count]
+        local_tensor,
+        spec,
+        # pyrefly: ignore [unexpected-keyword]
+        requires_grad=kwargs["requires_grad"],
+    )
+
+
+def ones(  # type: ignore[no-untyped-def]
+    *size,
+    dtype: torch.dtype | None = None,
+    layout: torch.layout = torch.strided,
+    requires_grad: bool = False,
+    device_mesh: DeviceMesh | None = None,
+    placements: Sequence[Placement] | None = None,
+) -> DTensor:
+    """
+    Returns a :class:`DTensor` filled with the scalar value 1, with the shape defined
+    by the variable argument ``size``.
+
+    Args:
+        size (int...): a sequence of integers defining the shape of the output :class:`DTensor`.
+            Can be a variable number of arguments or a collection like a list or tuple.
+            E.g.: ones(1,2,3..) or ones([1,2,3..]) or ones((1,2,3..))
+
+    Keyword args:
+        dtype (:class:`torch.dtype`, optional): the desired data type of returned :class:`DTensor`.
+            Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`).
+        layout (:class:`torch.layout`, optional): the desired layout of returned DTensor.
+            Default: ``torch.strided``.
+        requires_grad (bool, optional): If autograd should record operations on the
+            returned :class:`DTensor`. Default: ``False``.
+        device_mesh: :class:`DeviceMesh` type, contains the mesh info of ranks
+        placements: a sequence of :class:`Placement` type: ``Shard``, ``Replicate``
+
+    Returns:
+        A :class:`DTensor` object on each rank
+    """
+    torch_size = normalize_to_torch_size(size)
+
+    return _dtensor_init_helper(
+        torch.ones,
+        torch_size,
+        dtype=dtype,
+        layout=layout,
+        requires_grad=requires_grad,
+        device_mesh=device_mesh,
+        placements=placements,
+    )
+
+
+def empty(  # type: ignore[no-untyped-def]
+    *size,
+    dtype: torch.dtype | None = None,
+    layout: torch.layout = torch.strided,
+    requires_grad: bool = False,
+    device_mesh: DeviceMesh | None = None,
+    placements: Sequence[Placement] | None = None,
+) -> DTensor:
+    """
+    Returns a :class:`DTensor` filled with uninitialized data. The shape of the :class:`DTensor`
+    is defined by the variable argument ``size``.
+
+    Args:
+        size (int...): a sequence of integers defining the shape of the output :class:`DTensor`.
+            Can be a variable number of arguments or a collection like a list or tuple.
+            E.g.: empty(1,2,3..) or empty([1,2,3..]) or empty((1,2,3..))
+
+    Keyword args:
+        dtype (:class:`torch.dtype`, optional): the desired data type of returned :class:`DTensor`.
+            Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`).\
+        layout (:class:`torch.layout`, optional): the desired layout of returned :class:`DTensor`.
+            Default: ``torch.strided``.
+        requires_grad (bool, optional): If autograd should record operations on the
+            returned :class:`DTensor`. Default: ``False``.
+        device_mesh: :class:`DeviceMesh` type, contains the mesh info of ranks
+        placements: a sequence of :class:`Placement` type: ``Shard``, ``Replicate``
+
+    Returns:
+        A :class:`DTensor` object on each rank
+    """
+    torch_size = normalize_to_torch_size(size)
+
+    return _dtensor_init_helper(
+        torch.empty,
+        torch_size,
+        dtype=dtype,
+        layout=layout,
+        requires_grad=requires_grad,
+        device_mesh=device_mesh,
+        placements=placements,
+    )
+
+
+def full(  # type: ignore[no-untyped-def]
+    size,
+    fill_value,
+    *,
+    dtype: torch.dtype | None = None,
+    layout: torch.layout = torch.strided,
+    requires_grad: bool = False,
+    device_mesh: DeviceMesh | None = None,
+    placements: Sequence[Placement] | None = None,
+) -> DTensor:
+    """
+    Returns a :class:`DTensor` filled with ``fill_value`` according to ``device_mesh`` and
+    ``placements``, with the shape defined by the argument ``size``.
+
+    Args:
+        size (int...): a sequence of integers defining the shape of the output :class:`DTensor`.
+            Can be a variable number of arguments or a collection like a list or tuple.
+            E.g.: ones(1,2,3..) or ones([1,2,3..]) or ones((1,2,3..))
+        fill_value(Scalar): the value to fill the output tensor with.
+
+    Keyword args:
+        dtype (:class:`torch.dtype`, optional): the desired data type of returned :class:`DTensor`.
+            Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`).
+        layout (:class:`torch.layout`, optional): the desired layout of returned DTensor.
+            Default: ``torch.strided``.
+        requires_grad (bool, optional): If autograd should record operations on the
+            returned :class:`DTensor`. Default: ``False``.
+        device_mesh: :class:`DeviceMesh` type, contains the mesh info of ranks.
+        placements: a sequence of :class:`Placement` type: ``Shard``, ``Replicate``
+
+    Returns:
+        A :class:`DTensor` object on each rank
+    """
+    torch_size = normalize_to_torch_size(size)
+
+    return _dtensor_init_helper(
+        torch.full,
+        torch_size,
+        fill_value=fill_value,
+        dtype=dtype,
+        layout=layout,
+        requires_grad=requires_grad,
+        device_mesh=device_mesh,
+        placements=placements,
+    )
+
+
+def rand(  # type: ignore[no-untyped-def]
+    *size,
+    requires_grad: bool = False,
+    dtype: torch.dtype | None = None,
+    layout: torch.layout = torch.strided,
+    device_mesh: DeviceMesh | None = None,
+    placements: Sequence[Placement] | None = None,
+) -> DTensor:
+    """
+    Returns a :class:`DTensor` filled with random numbers from a uniform distribution
+    on the interval ``[0, 1)``. The shape of the tensor is defined by the variable
+    argument ``size``.
+
+    Args:
+        size (int...): a sequence of integers defining the shape of the output :class:`DTensor`.
+            Can be a variable number of arguments or a collection like a list or tuple.
+            E.g.: ones(1,2,3..) or ones([1,2,3..]) or ones((1,2,3..))
+
+    Keyword args:
+        dtype (:class:`torch.dtype`, optional): the desired data type of returned :class:`DTensor`.
+            Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`).
+        layout (:class:`torch.layout`, optional): the desired layout of returned DTensor.
+            Default: ``torch.strided``.
+        requires_grad (bool, optional): If autograd should record operations on the
+            returned :class:`DTensor`. Default: ``False``.
+        device_mesh: :class:`DeviceMesh` type, contains the mesh info of ranks.
+        placements: a sequence of :class:`Placement` type: ``Shard``, ``Replicate``
+
+    Returns:
+        A :class:`DTensor` object on each rank
+    """
+    torch_size = normalize_to_torch_size(size)
+
+    return _dtensor_init_helper(
+        torch.rand,
+        torch_size,
+        dtype=dtype,
+        layout=layout,
+        requires_grad=requires_grad,
+        device_mesh=device_mesh,
+        placements=placements,
+    )
+
+
+def randn(  # type: ignore[no-untyped-def]
+    *size,
+    requires_grad: bool = False,
+    dtype: torch.dtype | None = None,
+    layout: torch.layout = torch.strided,
+    device_mesh: DeviceMesh | None = None,
+    placements: Sequence[Placement] | None = None,
+) -> DTensor:
+    """
+    Returns a :class:`DTensor` filled with random numbers from a normal distribution
+    with mean 0 and variance 1. The shape of the tensor is defined by the variable
+    argument ``size``.
+
+    Args:
+        size (int...): a sequence of integers defining the shape of the output :class:`DTensor`.
+            Can be a variable number of arguments or a collection like a list or tuple.
+            E.g.: ones(1,2,3..) or ones([1,2,3..]) or ones((1,2,3..))
+
+    Keyword args:
+        dtype (:class:`torch.dtype`, optional): the desired data type of returned :class:`DTensor`.
+            Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`).
+        layout (:class:`torch.layout`, optional): the desired layout of returned DTensor.
+            Default: ``torch.strided``.
+        requires_grad (bool, optional): If autograd should record operations on the
+            returned :class:`DTensor`. Default: ``False``.
+        device_mesh: :class:`DeviceMesh` type, contains the mesh info of ranks.
+        placements: a sequence of :class:`Placement` type: ``Shard``, ``Replicate``
+
+    Returns:
+        A :class:`DTensor` object on each rank
+    """
+    torch_size = normalize_to_torch_size(size)
+
+    return _dtensor_init_helper(
+        torch.randn,
+        torch_size,
+        dtype=dtype,
+        layout=layout,
+        requires_grad=requires_grad,
+        device_mesh=device_mesh,
+        placements=placements,
+    )
+
+
+def zeros(  # type: ignore[no-untyped-def]
+    *size,
+    requires_grad: bool = False,
+    dtype: torch.dtype | None = None,
+    layout: torch.layout = torch.strided,
+    device_mesh: DeviceMesh | None = None,
+    placements: Sequence[Placement] | None = None,
+) -> DTensor:
+    """
+    Returns a :class:`DTensor` filled with the scalar value 0.
+
+    Args:
+        size (int...): a sequence of integers defining the shape of the output :class:`DTensor`.
+            Can be a variable number of arguments or a collection like a list or tuple.
+            E.g.: zeros(1,2,3..) or zeros([1,2,3..]) or zeros((1,2,3..))
+    Keyword args:
+        requires_grad (bool, optional): If autograd should record operations on the
+            returned :class:`DTensor`. Default: ``False``.
+        dtype (:class:`torch.dtype`, optional): the desired data type of returned :class:`DTensor`.
+            Default: if ``None``, uses a global default (see :func:`torch.set_default_dtype`).
+        layout (:class:`torch.layout`, optional): the desired layout of returned :class:`DTensor`.
+            Default: ``torch.strided``.
+        device_mesh: :class:`DeviceMesh` type, contains the mesh info of ranks
+        placements: a sequence of :class:`Placement` type: ``Shard``, ``Replicate``
+
+    Returns:
+        A :class:`DTensor` object on each rank
+    """
+    torch_size = normalize_to_torch_size(size)
+
+    return _dtensor_init_helper(
+        torch.zeros,
+        torch_size,
+        dtype=dtype,
+        layout=layout,
+        requires_grad=requires_grad,
+        device_mesh=device_mesh,
+        placements=placements,
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/_argmin_argmax.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/_argmin_argmax.py
new file mode 100644
index 0000000000000000000000000000000000000000..730291a7926b3130e23a0d1b98b6d6170fca03c3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/_argmin_argmax.py
@@ -0,0 +1,120 @@
+import operator
+from functools import reduce
+
+import torch
+import torch.distributed._functional_collectives as funcol
+import torch.distributed.tensor._api as dtensor
+from torch.distributed.tensor._utils import compute_local_shape_and_global_offset
+from torch.distributed.tensor.placement_types import Partial, Replicate, Shard
+
+
+_REDUCTION_OPS = {
+    torch.ops.aten.argmax.default: torch.max,
+    torch.ops.aten.argmin.default: torch.min,
+}
+
+
+def argmin_argmax_handler(
+    op_call: torch._ops.OpOverload,
+    args: tuple["dtensor.DTensor", int] | tuple["dtensor.DTensor", int, bool],
+    kwargs: dict[str, object],
+):
+    """
+    Handles reduces on sharded dimensions locally to limit calls to replicate.
+    """
+    op_info = dtensor.DTensor._op_dispatcher.unwrap_to_op_info(op_call, args, kwargs)
+    dtensor.DTensor._op_dispatcher.sharding_propagator.propagate(op_info)
+    output_sharding = op_info.output_sharding
+    assert output_sharding is not None, "output sharding should not be None"
+    if op_call not in _REDUCTION_OPS:
+        raise NotImplementedError(f"Unsupported reduction op: {op_call}")
+    val_op = _REDUCTION_OPS[op_call]
+
+    input_dtensor = args[0]
+    if not isinstance(input_dtensor, dtensor.DTensor):
+        raise NotImplementedError
+
+    dim: int | None = args[1] if len(args) > 1 else None  # type: ignore[assignment]
+    keepdim = args[2] if len(args) > 2 else False
+
+    placements = input_dtensor.placements
+
+    # check for partial placements and handle it as replicate.
+    if any(isinstance(p, Partial) for p in placements):
+        target_placements = [
+            Replicate() if isinstance(p, Partial) else p for p in placements
+        ]
+        input_dtensor = input_dtensor.redistribute(
+            device_mesh=input_dtensor.device_mesh, placements=target_placements
+        )
+        placements = input_dtensor.placements
+    local_tensor = input_dtensor.to_local()
+
+    input_shape = list(local_tensor.shape)
+    if dim is None:
+        expected_shape = (
+            torch.Size([1] * len(input_shape)) if keepdim else torch.Size([])
+        )
+    elif keepdim:
+        if input_shape:
+            input_shape[dim] = 1
+        expected_shape = torch.Size(input_shape)
+    else:
+        if input_shape:
+            input_shape.pop(dim)
+        expected_shape = torch.Size(input_shape)
+
+    shard_mesh_dims = []
+    for mesh_dim, p in enumerate(placements):
+        if isinstance(p, Shard):
+            if dim is None or p.dim == (dim if dim >= 0 else local_tensor.ndim + dim):
+                shard_mesh_dims.append(mesh_dim)
+
+    device_mesh = input_dtensor.device_mesh
+
+    if dim is None:
+        local_idx = op_call(local_tensor)
+        local_max = local_tensor.flatten()[local_idx]
+    else:
+        local_max, local_idx = val_op(local_tensor, dim=dim, keepdim=True)
+
+    if not shard_mesh_dims:
+        return dtensor.DTensor._op_dispatcher.wrap(
+            local_idx.reshape(expected_shape), output_sharding.output_spec
+        )
+
+    # find the correct offset for sharded dim
+    global_shape = input_dtensor.shape
+    _, global_offset = compute_local_shape_and_global_offset(
+        global_shape, device_mesh, placements
+    )
+    gathered_maxes = local_max
+    if dim is None:
+        local_coord = torch.unravel_index(local_idx, local_tensor.shape)
+        global_coord = torch.stack(local_coord)
+        gather_dim = 0
+        for i, offset in enumerate(global_offset):
+            global_coord[i] += offset
+        # compute with proper striding
+        gathered_idxs = torch.tensor(0, device=local_tensor.device, dtype=torch.long)
+        for i, coord in enumerate(global_coord):
+            gathered_idxs += coord * reduce(operator.mul, global_shape[i + 1 :], 1)
+    else:
+        gather_dim = dim
+        gathered_idxs = local_idx + global_offset[dim]
+
+    for mesh_dim in shard_mesh_dims:
+        gathered_maxes = funcol.all_gather_tensor(
+            gathered_maxes, gather_dim=gather_dim, group=(device_mesh, mesh_dim)
+        )
+        gathered_idxs = funcol.all_gather_tensor(
+            gathered_idxs, gather_dim=gather_dim, group=(device_mesh, mesh_dim)
+        )
+
+    rank_winner = op_call(gathered_maxes, dim, True)
+
+    final_idx = torch.gather(gathered_idxs, dim=gather_dim, index=rank_winner)
+
+    return dtensor.DTensor._op_dispatcher.wrap(
+        final_idx.reshape(expected_shape), output_sharding.output_spec
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/_collective_utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/_collective_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..766b030ad9524d7c3e8dc185ac3ff3056e4bcc77
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/_collective_utils.py
@@ -0,0 +1,396 @@
+# mypy: allow-untyped-defs
+import logging
+import math
+from dataclasses import dataclass
+from functools import lru_cache
+from typing import Optional
+
+import torch
+import torch.distributed._functional_collectives as funcol
+import torch.distributed.tensor._dtensor_spec as dtensor_spec
+from torch._C._distributed_c10d import _resolve_process_group
+from torch._logging import warning_once
+from torch.distributed._local_tensor import (
+    local_tensor_mode,
+    maybe_run_for_local_tensor,
+)
+from torch.distributed.device_mesh import _mesh_resources, DeviceMesh
+from torch.distributed.distributed_c10d import (
+    _get_group_size_by_name,
+    broadcast,
+    get_group_rank,
+    get_rank,
+    ProcessGroup,
+    scatter,
+    Work,
+)
+
+
+logger = logging.getLogger(__name__)
+
+
+@torch.library.register_fake("_dtensor::shard_dim_alltoall")
+def _shard_dim_alltoall_meta(input, gather_dim, shard_dim, group_name):
+    group_size = _get_group_size_by_name(group_name)
+    stacked_list = [torch.empty_like(input) for _ in range(group_size)]
+    group = _resolve_process_group(group_name)
+    group_rank = get_group_rank(group, get_rank())
+
+    return (
+        torch.cat(stacked_list, dim=gather_dim)
+        .chunk(group_size, dim=shard_dim)[group_rank]
+        .contiguous()
+    )
+
+
+def shard_dim_alltoall(input, gather_dim, shard_dim, mesh, mesh_dim):
+    if mesh.device_type == "cpu" and local_tensor_mode() is None:
+        # Gloo does not support alltoall, so falling back to allgather + chunk
+        warning_once(
+            logger,
+            "CPU process group does not support alltoall yet, falling back with allgather + chunk!",
+        )
+        out = funcol.all_gather_tensor(input, gather_dim, (mesh, mesh_dim))
+        if isinstance(out, funcol.AsyncCollectiveTensor):
+            # stick to the same behavior for the alltoall case, remove this once we enable alltoall async
+            out = out.wait()
+        out = torch.chunk(out, mesh.size(mesh_dim), dim=shard_dim)[
+            mesh.get_local_rank(mesh_dim)
+        ]
+        return out.contiguous()
+
+    group_name = funcol._resolve_group_name((mesh, mesh_dim))
+    # TODO: enable async op for shard_dim_alltoall
+    return torch.ops._dtensor.shard_dim_alltoall(
+        input, gather_dim, shard_dim, group_name
+    )
+
+
+def mesh_scatter(
+    output: torch.Tensor,
+    scatter_list: list[torch.Tensor],
+    mesh: DeviceMesh,
+    mesh_dim: int = 0,
+    async_op: bool = False,
+    *,
+    group_src: int = 0,
+) -> Work | None:
+    """
+    scatter a list of tensors to a device mesh dimension. We by default
+    use the first rank of the mesh dimension as the source of truth, i.e
+    for a 2d mesh [[0, 1], [2, 3]], if we scatter on mesh_dim = 1, we will
+    scatter the tensor list on rank 0 to rank 0/1, and tensor list on rank
+    2 to rank 2/3.
+
+    Args:
+        output (torch.Tensor): the tensor to receive the scattered list.
+        scatter_list (List[torch.Tensor]): the tensor list to be scattered.
+        mesh_dim (int, optional): indicate which mesh dimension we want
+            to scatter on, we by default choose the first rank on the
+            mesh dimension as source of truth.
+
+    Keyword args:
+        group_src (int, optional): the group rank of the source data for the
+        logical/global tensor, on the specific mesh dimension. By default, we
+        use ``group_rank=0`` on each DeviceMesh dimension as the source data
+        to preserve the single-device semantic. If passing ``None`` explicitly,
+        this method simply uses its local data with no communication.
+
+    Returns:
+        A :class:`Work` object
+    """
+    # TODO: Ideally we should use the meta tensor way
+    # (to register a meta kernel for the collective op)
+    # so that it would avoid the communication. Need to
+    # remove the check below once that is done.
+    if output.is_meta:
+        return None
+    dim_group = mesh.get_group(mesh_dim)
+    assert isinstance(dim_group, ProcessGroup)
+
+    if group_src == get_rank(dim_group):
+        fut = scatter(
+            output,
+            scatter_list=scatter_list,
+            group=dim_group,
+            async_op=async_op,
+            group_src=group_src,
+        )
+    else:
+        fut = scatter(
+            output,
+            scatter_list=None,
+            group=dim_group,
+            async_op=async_op,
+            group_src=group_src,
+        )
+
+    return fut
+
+
+def mesh_broadcast(
+    tensor: torch.Tensor,
+    mesh: DeviceMesh,
+    mesh_dim: int = 0,
+    async_op: bool = False,
+    *,
+    group_src: int = 0,
+) -> Work | None:
+    """
+    broadcast the tensor to a device mesh dimension. We by default
+    use the first rank of the mesh dimension as the source of truth, i.e
+    for a 2d mesh [[0, 1], [2, 3]], if we broadcast on mesh_dim = 1, we will
+    broadcast the tensor on rank 0 to rank 0/1, and tensor on rank 2
+    to rank 2/3.
+
+    Args:
+        tensor (torch.Tensor): tensor to broadcast.
+        mesh_dim (int, optional): indicate which mesh dimension we want
+            to scatter on, we by default choose the first rank on the
+            mesh dimension as source of truth.
+
+    Keyword args:
+        group_src (int, optional): the group rank of the source data for the
+        logical/global tensor, on the specific mesh dimension. By default, we
+        use ``group_rank=0`` on each DeviceMesh dimension as the source data
+        to preserve the single-device semantic. If passing ``None`` explicitly,
+        this method simply uses its local data with no communication.
+
+    Returns:
+        A :class:`Work` object
+    """
+    # TODO: Ideally we should use the meta tensor way
+    # (to register a meta kernel for the collective op)
+    # so that it would avoid the communication. Need to
+    # remove the check below once that is done.
+    if tensor.is_meta:
+        return None
+    dim_group = mesh.get_group(mesh_dim)
+    assert isinstance(dim_group, ProcessGroup)
+
+    return broadcast(tensor, group=dim_group, async_op=async_op, group_src=group_src)
+
+
+@maybe_run_for_local_tensor
+def pad_tensor(tensor: torch.Tensor, pad_dim: int, pad_size: int) -> torch.Tensor:
+    if pad_size == 0:
+        return tensor
+    pad = [0, 0] * (tensor.ndim - pad_dim)
+    pad[-1] = pad_size
+    return torch.nn.functional.pad(tensor, pad)
+
+
+@maybe_run_for_local_tensor
+def unpad_tensor(tensor: torch.Tensor, pad_dim: int, pad_size: int) -> torch.Tensor:
+    if pad_size == 0:
+        return tensor
+    return tensor.narrow(
+        pad_dim,
+        start=0,
+        length=tensor.size(pad_dim) - pad_size,
+    )
+
+
+def fill_empty_tensor_to_shards(
+    shards: list[torch.Tensor], shard_dim: int, num_empty_tensors: int
+) -> list[torch.Tensor]:
+    if num_empty_tensors == 0:
+        return shards
+    tensor_size = list(shards[0].size())
+    tensor_size[shard_dim] = 0
+    tensor = shards[0].new_zeros(tensor_size)
+    shards.extend(tensor for _ in range(num_empty_tensors))
+    return shards
+
+
+def check_tensor_meta(
+    local_tensor, check_shape_stride=False
+) -> Optional["dtensor_spec.TensorMeta"]:
+    local_metadata = {
+        "dtype": local_tensor.dtype,
+        "requires_grad": local_tensor.requires_grad,
+    }
+
+    if check_shape_stride:
+        local_metadata.update(
+            {"shape": local_tensor.shape, "stride": local_tensor.stride()}
+        )
+
+    gathered_metadata = [None for _ in range(torch.distributed.get_world_size())]
+    torch.distributed.all_gather_object(gathered_metadata, local_metadata)
+
+    # Check if metadata is consistent across ranks
+    if not all(meta == local_metadata for meta in gathered_metadata):
+        raise ValueError(
+            "Inconsistent tensor metadata (including shape and stride) across ranks."
+        )
+    return None
+
+
+def spec_to_bytes(spec: "dtensor_spec.DTensorSpec") -> int:
+    assert spec.tensor_meta is not None, "spec should have tensor meta defined!"
+    return spec.tensor_meta.dtype.itemsize * math.prod(spec.shape)
+
+
+@dataclass
+class MeshTopoInfo:
+    """
+    Mesh information for collective cost estimation
+    """
+
+    mesh: DeviceMesh
+    mesh_dim_devices: list[int]
+    mesh_dim_bandwidth: list[float]
+    mesh_dim_latency: list[float]
+
+    @staticmethod
+    @lru_cache(None)
+    def build_from_mesh(mesh: DeviceMesh) -> "MeshTopoInfo":
+        # Generate mesh topology info for intra-host/inter-host communication pattern
+        # Note that we made bunch of assumptions for simplicity:
+        # 1. we assume the mesh is homogeneous, and it's gpu/nccl model
+        # 2. we assume gpu arch is Ampere or Hopper
+        # 3. we assume collectives are all ring base algo for now
+        num_devices_per_host = _mesh_resources.num_devices_per_host(mesh.device_type)
+        # the base bw number (intra-node), GB/s
+        base_bw = 87.7
+        mesh_dim_bandwidth = [base_bw] * mesh.ndim
+        # the latency in terms of us (intra-node, nv-link)
+        mesh_dim_latency = [0.6] * mesh.ndim
+        mesh_dim_devices = [1] * mesh.ndim
+
+        total_num_devices = 1
+        for mesh_dim in reversed(range(mesh.ndim)):
+            num_devices = mesh.size(mesh_dim)
+            mesh_dim_devices[mesh_dim] = num_devices
+            total_num_devices *= num_devices
+            if total_num_devices > num_devices_per_host:
+                # magic number for inter-host communication bandwidth/latency factor
+                # This number assumes latest GPU arch, i.e. Ampere or Hopper
+                # TODO: see if we need to tweak this or offer a way for user
+                # to specify the bandwidths/latency
+                mesh_dim_bandwidth[mesh_dim] *= 0.22
+                # set to ethernet latency for inter-host
+                mesh_dim_latency[mesh_dim] = 2.7
+
+        return MeshTopoInfo(
+            mesh, mesh_dim_devices, mesh_dim_bandwidth, mesh_dim_latency
+        )
+
+
+def allgather_cost(bytes_gb: float, mesh_topo: MeshTopoInfo, mesh_dim: int) -> float:
+    num_devices_on_mesh_dim = mesh_topo.mesh_dim_devices[mesh_dim]
+    mesh_dim_bandwidth = mesh_topo.mesh_dim_bandwidth[mesh_dim]
+    num_hops = num_devices_on_mesh_dim - 1
+    # base latency + comm latency
+    latency = 6.6 + num_hops * mesh_topo.mesh_dim_latency[mesh_dim]  # us
+    bw = (bytes_gb * num_hops / num_devices_on_mesh_dim) / mesh_dim_bandwidth  # s
+    return latency + bw * 1e6  # rescale to us
+
+
+def allreduce_cost(bytes_gb: float, mesh_topo: MeshTopoInfo, mesh_dim: int) -> float:
+    num_devices_on_mesh_dim = mesh_topo.mesh_dim_devices[mesh_dim]
+    mesh_dim_bandwidth = mesh_topo.mesh_dim_bandwidth[mesh_dim]
+    # allreduce have almost 2x comm bytes compare to allgather/reduce_scatter
+    num_hops = 2 * (num_devices_on_mesh_dim - 1)
+
+    latency = 6.6 + num_hops * mesh_topo.mesh_dim_latency[mesh_dim]
+    bw = (bytes_gb * num_hops / num_devices_on_mesh_dim) / mesh_dim_bandwidth
+    return latency + bw * 1e6
+
+
+def reduce_scatter_cost(
+    bytes_gb: float,
+    mesh_topo: MeshTopoInfo,
+    mesh_dim: int,
+) -> float:
+    num_devices_on_mesh_dim = mesh_topo.mesh_dim_devices[mesh_dim]
+    mesh_dim_bandwidth = mesh_topo.mesh_dim_bandwidth[mesh_dim]
+    num_hops = num_devices_on_mesh_dim - 1
+    # base latency + comm latency
+    latency = 6.6 + num_hops * mesh_topo.mesh_dim_latency[mesh_dim]
+    bw = (bytes_gb * num_hops / num_devices_on_mesh_dim) / mesh_dim_bandwidth
+    return latency + bw * 1e6
+
+
+def redistribute_cost(
+    current_spec: "dtensor_spec.DTensorSpec",
+    target_spec: "dtensor_spec.DTensorSpec",
+) -> float:
+    """
+    This function returns the cost of redistribute from current to target DTensorSpec.
+
+    NOTE:
+    1. Only consider communication cost here, since computation costs for redistribute
+       are quite trivial (i.e. we only need to narrow or simple division)
+    2. Only consider redistribute cost on same mesh, cross mesh communication cost is
+       not quite needed for operator strategy estimation/selection.
+    """
+    if current_spec.mesh != target_spec.mesh:
+        # make infinite cost if meshes are not same
+        # TODO: see if we want to support this once there's cross mesh communication
+        return float("inf")
+
+    if current_spec.is_replicated():
+        # short-cut:
+        # comm cost is 0 if current spec is already full replication
+        return 0.0
+
+    mesh_topo = MeshTopoInfo.build_from_mesh(current_spec.mesh)
+    cost = 0.0
+    comm_bytes_gb = (
+        spec_to_bytes(current_spec) / current_spec.num_shards / 1024 / 1024 / 1024
+    )
+    # Transformation that considered for redistribute cost:
+    # 1. allgather 2. alltoall
+    # 3. allreduce 4. reduce_scatter
+    from torch.distributed._functional_collectives import _are_we_tracing
+    from torch.distributed.tensor._redistribute import (
+        _gen_transform_infos,
+        _gen_transform_infos_non_cached,
+    )
+
+    # No redistribution needed when placements are already identical.
+    # This also prevents potential failures in _gen_transform_infos for certain configurations
+    # (e.g., sub-meshes) where finding a transform path between identical states may error out.
+    # TODO(zpcore): test placements with _StridedShard.
+    if current_spec.placements == target_spec.placements:
+        return cost
+    if _are_we_tracing():
+        transform_infos = _gen_transform_infos_non_cached(current_spec, target_spec)
+    else:
+        transform_infos = _gen_transform_infos(current_spec, target_spec)
+    for transform_info in transform_infos:
+        assert current_spec.tensor_meta is not None, (
+            "spec should have tensor meta defined!"
+        )
+        current = transform_info.src_dst_placements[0]
+        target = transform_info.src_dst_placements[1]
+        if current == target:
+            continue
+        mesh_dim = transform_info.mesh_dim
+        num_devices_on_mesh_dim = mesh_topo.mesh_dim_devices[mesh_dim]
+        if current.is_shard() and target.is_replicate():
+            # allgather gives larger comm bytes
+            comm_bytes_gb *= num_devices_on_mesh_dim
+            # add up allgather comm cost
+            cost += allgather_cost(comm_bytes_gb, mesh_topo, mesh_dim)
+        elif current.is_shard() and target.is_shard():
+            # should be alltoall comm, since we haven't implement it yet, add 1.0 as penalty
+            # to favor allgather instead
+            # TODO: add alltoall_cost
+            cost += allgather_cost(comm_bytes_gb, mesh_topo, mesh_dim) + 1.0
+        elif current.is_partial() and target.is_replicate():
+            # add up allreduce comm cost
+            cost += allreduce_cost(comm_bytes_gb, mesh_topo, mesh_dim)
+        elif current.is_partial() and target.is_shard():
+            # add up reduce_scatter comm cost
+            cost += reduce_scatter_cost(comm_bytes_gb, mesh_topo, mesh_dim)
+            # after reduce_scatter the comm bytes for further collectives halved.
+            comm_bytes_gb /= num_devices_on_mesh_dim
+        elif current.is_shard() and target.is_partial():
+            # ban shard -> partial as it does not make sense to perform
+            # this redistribute
+            return float("inf")
+
+    return cost
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/_dispatch.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/_dispatch.py
new file mode 100644
index 0000000000000000000000000000000000000000..54c0cf63440b947587eca96781371c98ffa58407
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/_dispatch.py
@@ -0,0 +1,653 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+import contextlib
+import logging
+import warnings
+from collections.abc import Sequence
+from typing import cast
+
+import torch
+import torch.distributed as dist
+import torch.distributed.tensor._api as dtensor
+import torch.distributed.tensor._random as random
+from torch._library.utils import fill_defaults
+from torch.distributed._functional_collectives import _are_we_tracing
+from torch.distributed.device_mesh import DeviceMesh
+from torch.distributed.tensor._argmin_argmax import argmin_argmax_handler
+from torch.distributed.tensor._dtensor_spec import DTensorSpec, TensorMeta
+from torch.distributed.tensor._op_schema import (
+    OpInfo,
+    OpSchema,
+    OutputSharding,
+    OutputSpecType,
+)
+from torch.distributed.tensor._random import is_rng_supported_mesh
+from torch.distributed.tensor._redistribute import redistribute_local_tensor
+from torch.distributed.tensor._sharding_prop import ShardingPropagator
+from torch.distributed.tensor._tp_conv import (
+    convolution_backward_handler,
+    convolution_handler,
+)
+from torch.distributed.tensor._utils import (
+    ExplicitRedistributionContext,
+    try_find_mesh_from_args,
+)
+from torch.distributed.tensor.placement_types import Partial, Placement, Replicate
+from torch.utils._debug_mode import get_active_debug_mode
+from torch.utils._python_dispatch import return_and_correct_aliasing
+
+
+try:
+    from torch.utils import _cxx_pytree as pytree
+except ImportError:
+    from torch.utils import _pytree as pytree  # type: ignore[no-redef]
+
+aten = torch.ops.aten
+logger = logging.getLogger(__name__)
+
+
+def as_strided_handler(
+    op_call: torch._ops.OpOverload,
+    args: tuple[object, ...],
+    kwargs: dict[str, object],
+):
+    args, kwargs = fill_defaults(op_call._schema, args, kwargs)
+    assert not kwargs
+    tensor, size, stride, storage_offset = args
+    if (
+        tensor.size() == tuple(size)
+        and tensor.stride() == tuple(stride)
+        and (storage_offset is None or tensor.storage_offset() == storage_offset)
+    ):
+        return torch.ops.aten.alias.default(tensor)
+    raise RuntimeError("as_strided not supported with DTensor")
+
+
+def is_same_size_handler(
+    op_call: torch._ops.OpOverload,
+    args: tuple[object, ...],
+    kwargs: dict[str, object],
+) -> bool:
+    lhs = cast(torch.Tensor, args[0])
+    rhs = cast(torch.Tensor, args[1])
+    return lhs.shape == rhs.shape
+
+
+def found_inf_reduce_handler(
+    op_call: torch._ops.OpOverload,
+    args: tuple[object, ...],
+    kwargs: dict[str, object],
+) -> None:
+    op_info = dtensor.DTensor._op_dispatcher.unwrap_to_op_info(op_call, args, kwargs)
+    local_tensor_args = pytree.tree_unflatten(
+        cast(list[object], op_info.local_args),
+        op_info.args_tree_spec,  # type: ignore[arg-type]
+    )
+    local_tensor_args = cast(tuple[object, ...], local_tensor_args)
+    op_call(*local_tensor_args, **op_info.local_kwargs)
+
+    grad_dtensor = cast(list[dtensor.DTensor], args[0])[0]
+    grad_placements = grad_dtensor.placements
+    mesh = grad_dtensor.device_mesh
+
+    found_inf_placements: list[Placement] = []
+    for placement in grad_placements:
+        if isinstance(placement, Replicate):
+            found_inf_placements.append(placement)
+        else:
+            found_inf_placements.append(Partial("max"))
+
+    target_tensor = cast(torch.Tensor, args[1])
+    spec = DTensorSpec(
+        mesh=mesh,
+        placements=tuple(found_inf_placements),
+        tensor_meta=TensorMeta(
+            shape=target_tensor.size(),
+            stride=target_tensor.stride(),
+            dtype=target_tensor.dtype,
+        ),
+    )
+    # pyrefly: ignore [bad-argument-type]
+    found_inf_dtensor = dtensor.DTensor(
+        local_tensor=target_tensor,  # pyrefly: ignore [unexpected-keyword]
+        spec=spec,  # pyrefly: ignore [unexpected-keyword]
+        requires_grad=False,  # pyrefly: ignore [unexpected-keyword]
+    )
+    found_inf = found_inf_dtensor.full_tensor()
+    target_tensor.copy_(found_inf)
+
+
+class OpDispatcher:
+    """
+    Op dispatching class instance to handle args/kwargs pre-processing (un-wrapping), sharding
+    propagation, redistribute local args, local compute, and post-processing (re-wrapping). It
+    also handles any op specific logic if necessary.
+
+    NOTE: Given the runtime overhead of Tensor subclass (__torch_dispatch__), the OpDispatcher
+    is designed to minimize the CPU overhead by using the tricks of proper unflattening, faster
+    pytree if needed, and leveraging various caching mechanisms implemented in the sharding
+    propagation and redistribute modules. The CPU overhead is critical to eager mode performance,
+    one need to carefully measure the CPU overhead when making significant changes to the
+    OpDispatcher and ShardingPropagator.
+    """
+
+    def __init__(self) -> None:
+        self.sharding_propagator = ShardingPropagator()
+        # NOTE: must stay in sync with is_random_op in
+        # torch/csrc/autograd/python_variable.cpp
+        self._random_ops = {
+            aten.native_dropout.default,
+            aten.normal_.default,
+            aten.rand.default,
+            aten.rand_like.default,
+            aten.randn.default,
+            aten.randn_like.default,
+            aten.randint_like.default,
+            aten.randint_like.low_dtype,
+            aten.randint_like.low_dtype_out,
+            aten.uniform_.default,
+            aten.bernoulli.default,
+            aten.bernoulli_.float,
+        }
+        self._custom_op_handlers = {
+            aten.is_same_size.default: is_same_size_handler,
+            aten.convolution.default: convolution_handler,
+            aten.convolution_backward.default: convolution_backward_handler,
+            aten._amp_foreach_non_finite_check_and_unscale_.default: found_inf_reduce_handler,
+            aten.as_strided.default: as_strided_handler,
+            aten.argmin.default: argmin_argmax_handler,
+            aten.argmax.default: argmin_argmax_handler,
+        }
+
+    # ********************************************************************************************
+    # def dispatch(...)
+    #
+    # NOTE: this class no longer contains the top-level dispatch entrypoint!
+    # See #167051 for details
+    #
+    # The entrypoint has been moved to C++, and it handles common cases and then calls back into
+    # OpDispatcher python to handle corner cases.
+    # See dispatchDTensorOp() defined in python_variable.cpp and called from python_arg_parser.cpp
+    # ********************************************************************************************
+
+    # This flag is used internally to control whether we treat the torch.Tensor(non-DTensor)
+    # as implicitly replicated or we throw error to user.
+    # NOTE: It is EXTREMELY UNSAFE to turn this flag on by default so we intentionally leave
+    # it as False by default.
+    @property
+    def _allow_implicit_replication(self) -> bool:
+        return torch._C._get_dtensor_allow_implicit_replication()
+
+    @_allow_implicit_replication.setter
+    def _allow_implicit_replication(self, value: bool) -> None:
+        return torch._C._set_dtensor_allow_implicit_replication(value)
+
+    def _propagate_op_sharding_dispatch_slow_path(
+        self,
+        op_call: torch._ops.OpOverload,
+        args: tuple[object, ...],
+        kwargs: dict[str, object],
+        op_info: OpInfo,
+        # The logic here is a bit messy.  There are several reasons why the
+        # C++ fastpath may have bailed out.  If we just cache missed, we will
+        # come here because we need to actually calculate the real thing.
+        # There's no need to have a SECOND Python cache lookup; the C++ native
+        # cache completely subsumes it.  But sometimes, we will have failed
+        # to compute the cache key in C++ entirely.  In this case, we DO need
+        # to do a cache lookup in Python, as the missing cache key in C++
+        # means we don't have access to it all.  Furthermore, without duping
+        # this function, we need to do the try_cache test inside of the
+        # try-except block so that either case hits the inference mode /
+        # exception rewrapping case.
+        #
+        # This should be cleaned up.  First, ensuring the C++ codepath can
+        # always compute a key will be a big help.  Second, we should properly
+        # fastpath inference mode composite implicit autograd so that you
+        # don't have to throw an exception even in "fastpath".
+        try_cache: bool,
+    ) -> object:
+        try:
+            # We have basically inlined propagate() here, but WITHOUT the
+            # output_sharding assignment
+            if try_cache and not _are_we_tracing():
+                return self.sharding_propagator.propagate_op_sharding(op_info.schema)
+            else:
+                return self.sharding_propagator.propagate_op_sharding_non_cached(
+                    op_info.schema
+                )
+        except NotImplementedError:
+            if torch._C._dispatch_has_kernel_for_dispatch_key(
+                op_call.name(), torch._C.DispatchKey.CompositeImplicitAutograd
+            ):
+                # When running under inference mode, CompositeImplicitAutograd ops show up in __torch_dispatch__,
+                # so we manually decompose them, here
+                out = op_call.decompose(*args, **kwargs)
+                assert out is not NotImplemented
+                return out
+            else:
+                raise
+        except Exception as e:
+            raise RuntimeError(
+                f"{e}\n\nSharding propagation failed for {op_info.schema}"
+            ) from e
+
+    def _dispatch_get_local_results_slow_path(
+        self,
+        op_call: torch._ops.OpOverload,
+        args: tuple[object, ...],
+        op_info: OpInfo,
+    ) -> object:
+        output_sharding = op_info.output_sharding
+        assert output_sharding is not None, "output sharding should not be None"
+
+        mesh = op_info.compute_mesh
+        participating = mesh.get_coordinate() is not None
+        local_results = None
+        if participating:
+            # computation that happens in the current rank of the mesh, normal case
+            if output_sharding.needs_redistribute:
+                # If sharding propagation decision needs redistribute, perform redistribute
+                # on args first, which could potentially modify args (i.e. allgather certain arg)
+                assert output_sharding.redistribute_schema is not None
+                self.redistribute_local_args(
+                    op_info,
+                    output_sharding.redistribute_schema,
+                    output_sharding.use_val_from_redistribute_schema,
+                )
+
+            local_tensor_args = (
+                pytree.tree_unflatten(
+                    cast(list[object], op_info.local_args),
+                    # pyrefly: ignore [bad-argument-type]
+                    op_info.args_tree_spec,
+                )
+                if op_info.args_tree_spec
+                else op_info.local_args
+            )
+
+            # run local op computation with potentially modified args/kwargs
+            local_tensor_args = cast(tuple[object, ...], local_tensor_args)
+            if op_call in self._random_ops:
+                if not random._rng_tracker and is_rng_supported_mesh(mesh):
+                    # Default to `OffsetBasedRNGTracker` if the parallelism API
+                    # did not already construct one
+                    random._rng_tracker = random.OffsetBasedRNGTracker(mesh)
+
+                first_arg, first_local_arg = (
+                    cast(dtensor.DTensor, args[0]),
+                    cast(torch.Tensor, local_tensor_args[0]),
+                )
+
+                # If the user provided a generator, we hook it up to our RNG manager, but we also pop it from kwargs
+                # so the op_call does not directly use it (we want op_call to fall back to the 'default' which is
+                # our RNG manager)
+                maybe_user_generator = op_info.local_kwargs.pop("generator", None)
+                assert maybe_user_generator is None or isinstance(
+                    maybe_user_generator, torch.Generator
+                )
+                # maybe_user_generator = None
+                rng_context = (
+                    random._rng_tracker._distribute_region(
+                        first_arg._spec, generator=maybe_user_generator
+                    )
+                    if random._rng_tracker and not first_local_arg.is_meta
+                    else contextlib.nullcontext()
+                )
+                # For DTensor random operator, run it within a RNGTracker context to
+                # ensure the random number generator is properly distributed.
+                with rng_context:
+                    local_results = op_call(*local_tensor_args, **op_info.local_kwargs)
+            else:
+                # normal case, run local sharded op computation
+                local_results = op_call(*local_tensor_args, **op_info.local_kwargs)
+
+        else:
+            # For a non-participating device (happens on rank that does not belong to
+            # the device mesh), we do:
+            #   1. if the return type is scalar, set the local result to None.
+            #   2. if the return type is Tensor or List[Tensor], return empty
+            #   tensor(s) with correct dtype.
+            spec = output_sharding.output_spec
+            ret_list = op_call._schema.returns
+
+            if spec is None:
+                # For a scalar return type, the non-participating device has None
+                # as its local result
+                local_results = None
+            else:
+
+                def default_tensor(spec: DTensorSpec) -> torch.Tensor:
+                    if spec.tensor_meta is not None:
+                        shape = spec.tensor_meta.shape
+                        dtype = spec.tensor_meta.dtype
+                        if len(shape) == 0:
+                            # scalar tensor
+                            return torch.zeros((), dtype=dtype)
+                        else:
+                            # non-scalar tensor
+                            return torch.tensor([], dtype=dtype)
+                    else:
+                        raise RuntimeError(f"{spec} has no tensor metadata.")
+
+                if isinstance(spec, DTensorSpec):
+                    # return a Tensor value
+                    local_results = default_tensor(spec)
+                elif isinstance(spec, Sequence):
+                    # return a List[Tensor] value
+                    local_results = [
+                        default_tensor(s) if s is not None else None for s in spec
+                    ]
+                    assert isinstance(local_results, list)
+                    if None in local_results:
+                        ret_type = str(ret_list[0].type)
+                        raise NotImplementedError(
+                            f"return type {ret_type} in DTensor op is not supported"
+                        )
+        return local_results
+
+    def _dispatch_fast_path_python_tail(
+        self,
+        op_call: torch._ops.OpOverload,
+        args: tuple[object, ...],
+        kwargs: dict[str, object],
+        compute_mesh: DeviceMesh,
+        output_sharding: OutputSharding,
+        local_results: object,
+        participating: bool,
+        is_inplace_op: bool,
+        is_out_variant_op: bool,
+    ) -> object:
+        """
+        Tail of main dispatching logic, called from C++ fast path.
+        """
+
+        if output_sharding.output_spec is None:
+            if op_call == aten.equal.default:
+                # The output of the equal op is a bool, by converting it into a
+                # a single value tensor, we can use all-reduce with min reduce op
+                # to simulate logical and.
+                assert local_results is None or isinstance(local_results, bool)
+                r = torch.tensor(
+                    int(local_results) if local_results is not None else 1,
+                    device=compute_mesh.device_type,
+                )
+                dist.all_reduce(r, op=dist.ReduceOp.MIN)
+                local_results = bool(r.item())
+
+        if is_inplace_op:
+            # inplace op should return self instead of re-wrapping
+            if output_sharding.output_spec is not None:
+                output_spec = output_sharding.output_spec
+                assert isinstance(output_spec, DTensorSpec)
+                assert isinstance(args[0], dtensor.DTensor)
+
+                # NOTE: aten.squeeze_.dim is an inplace op but it also may change
+                # the inplace argument's tensor meta. Here we choose to special case
+                # this op because as far as I know this is the only inplace op that
+                # has such as behavior. We can extend this special case if necessary.
+                if op_call == aten.squeeze_.dim:
+                    # update the spec to handle tensor meta changes
+                    args[0]._spec = output_spec
+                    # use return_and_correct_aliasing to match the outer and the inner
+                    # aliasing. See https://github.com/pytorch/pytorch/pull/158954
+                    return return_and_correct_aliasing(op_call, args, kwargs, args[0])
+                else:
+                    # For all other inplace ops, check if placement changes are required
+                    # Inplace operations that change placement are not supported because
+                    # they would require redistribution, which breaks aliasing semantics.
+                    # If there are views into the tensor, the views would not be updated.
+                    if args[0]._spec.placements != output_spec.placements:
+                        raise RuntimeError(
+                            f"{op_call}: in-place operations that require placement changes "
+                            f"are not supported. The operation would change placement from "
+                            f"{args[0]._spec.placements} to {output_spec.placements}, "
+                            f"which requires redistribution and breaks aliasing semantics. "
+                            f"Please use the out-of-place version of this operation instead."
+                        )
+                    # Most inplace ops don't change tensor meta, so no spec update needed
+                    return args[0]
+            else:
+                return None
+        elif is_out_variant_op:
+            # out variant could possibly have multiple out args (i.e. lu_unpack.out)
+            output_specs = (
+                (output_sharding.output_spec,)
+                if not isinstance(output_sharding.output_spec, tuple)
+                else output_sharding.output_spec
+            )
+            out_dts = []
+            spec_idx = 0
+            for argument in op_call._schema.arguments:
+                if argument.is_out:
+                    out_dt = cast(dtensor.DTensor, kwargs[argument.name])
+                    out_dt._spec = cast(DTensorSpec, output_specs[spec_idx])
+                    out_dts.append(out_dt)
+                    spec_idx += 1
+
+            assert len(out_dts) >= 1, "out variant should have at least one out arg"
+            return tuple(out_dts) if len(out_dts) > 1 else out_dts[0]
+        else:
+            assert op_call == aten.equal.default, op_call
+            ret = self.wrap(local_results, output_sharding.output_spec)  # type: ignore[possibly-undefined]
+            if participating and op_call._schema._is_view_op():
+                return return_and_correct_aliasing(op_call, args, kwargs, ret)
+            else:
+                return ret
+
+    @staticmethod
+    def redistribute_local_args(
+        op_info: OpInfo,
+        suggested_input_schema: OpSchema,
+        use_val_from_redistribute_schema: bool,
+    ) -> None:
+        debug_mode = get_active_debug_mode()
+
+        # NOTE: it's very rare that we need to reshard kwargs so we intentionally skip it
+        if op_info.args_tree_spec is not None:
+            flatten_args_schema_to_reshard = tuple(
+                pytree.tree_leaves(suggested_input_schema.args_schema)
+            )
+        else:
+            flatten_args_schema_to_reshard = suggested_input_schema.args_schema
+
+        new_local_args: list[object] = []
+        for i, arg_spec in enumerate(op_info.flat_args_schema):
+            reshard_arg_spec = flatten_args_schema_to_reshard[i]
+            if isinstance(arg_spec, DTensorSpec):
+                local_tensor = cast(torch.Tensor, op_info.local_args[i])
+                if arg_spec != reshard_arg_spec:
+                    redistribute_context = (
+                        debug_mode.record_redistribute_calls(  # type: ignore[union-attr]
+                            i, arg_spec, reshard_arg_spec
+                        )
+                        if debug_mode is not None
+                        else contextlib.nullcontext()
+                    )
+                    ExplicitRedistributionContext.observe_redistribution(
+                        arg_spec,
+                        # pyrefly: ignore [bad-argument-type]
+                        reshard_arg_spec,
+                        message=f"Implicit redistribution occurred for {op_info.schema} "
+                        "while ExplicitRedistributionContext was active",
+                    )
+                    with redistribute_context:
+                        resharded_local_tensor = redistribute_local_tensor(
+                            local_tensor,
+                            arg_spec,
+                            # pyrefly: ignore [bad-argument-type]
+                            reshard_arg_spec,
+                        )
+                    new_local_args.append(resharded_local_tensor)
+                else:
+                    new_local_args.append(local_tensor)
+            else:
+                if use_val_from_redistribute_schema:
+                    # args can be updated for view related ops, we refer to the
+                    # update in redistribute_schema.
+                    new_local_args.append(reshard_arg_spec)
+                else:
+                    new_local_args.append(arg_spec)
+
+        op_info.local_args = tuple(new_local_args)
+
+    def unwrap_to_op_info(
+        self,
+        op_call: torch._ops.OpOverload,
+        args: tuple[object, ...],
+        kwargs: dict[str, object],
+    ) -> OpInfo:
+        return self._unwrap_to_op_info_impl(op_call, args, kwargs, True)
+
+    def _unwrap_to_op_info_impl(
+        self,
+        op_call: torch._ops.OpOverload,
+        args: tuple[object, ...],
+        kwargs: dict[str, object],
+        create_schema: bool,
+    ) -> OpInfo:
+        # get runtime schema info to determine whether to use pytree to flatten inputs
+        runtime_schema_info = self.sharding_propagator.op_to_schema_info.get(
+            op_call, None
+        )
+
+        if runtime_schema_info is not None and runtime_schema_info.needs_pytree:
+            # flatten args/kwargs when op says necessary
+            tree_args, args_spec = pytree.tree_flatten(args)
+            args_list: Sequence[object] = tree_args
+        else:
+            args_list, args_spec = args, None
+
+        args_schema: list[object] = []
+        kwargs_schema: dict[str, object] = {}
+        local_args: list[object] = []
+        local_kwargs: dict[str, object] = {}
+        compute_mesh: DeviceMesh | None = None
+
+        for arg in args_list:
+            if isinstance(arg, dtensor.DTensor):
+                local_args.append(arg._local_tensor)
+                args_schema.append(arg._spec)
+                if compute_mesh is None:
+                    # record the first compute device mesh from args
+                    compute_mesh = arg.device_mesh
+            elif isinstance(arg, torch.Tensor):
+                compute_mesh = compute_mesh or try_find_mesh_from_args(
+                    op_call, args_list
+                )
+                args_schema.append(
+                    self._try_replicate_spec_for_scalar_tensor(
+                        op_call, arg, compute_mesh
+                    )
+                )
+                local_args.append(arg)
+            else:
+                # non DTensor/Tensor args (i.e. int/float/bool), just add to args_schema/local_args
+                args_schema.append(arg)
+                local_args.append(arg)
+
+        for k, v in kwargs.items():
+            if isinstance(v, dtensor.DTensor):
+                local_kwargs[k] = v._local_tensor
+                kwargs_schema[k] = v._spec
+            elif isinstance(v, torch.Tensor):
+                compute_mesh = compute_mesh or try_find_mesh_from_args(
+                    op_call, args_list
+                )
+                kwargs_schema[k] = self._try_replicate_spec_for_scalar_tensor(
+                    op_call,
+                    v,
+                    # pyrefly: ignore [bad-argument-type]
+                    compute_mesh,
+                )
+                local_kwargs[k] = v
+            else:
+                # non DTensor/Tensor args (i.e. int/float/bool), just add to args_schema/local_args
+                kwargs_schema[k] = v
+                local_kwargs[k] = v
+
+        assert compute_mesh is not None, (
+            f"found no DeviceMesh from dtensor args for {op_call}!"
+        )
+        op_info = OpInfo(
+            compute_mesh,
+            OpSchema(
+                op_call,
+                (
+                    # pyrefly: ignore [bad-argument-type]
+                    pytree.tree_unflatten(args_schema, args_spec)
+                    if args_spec
+                    else tuple(args_schema)
+                ),
+                kwargs_schema,
+                schema_info=runtime_schema_info,
+            )
+            if create_schema
+            else None,  # type: ignore[arg-type]
+            args_schema,
+            tuple(local_args),
+            local_kwargs,
+            args_spec,
+        )
+        return op_info
+
+    @staticmethod
+    def wrap(res: object, spec: OutputSpecType) -> object:
+        if isinstance(res, torch.Tensor):
+            if spec is not None:
+                assert isinstance(spec, DTensorSpec), (
+                    f"output spec does not match with output! Expected DTensorSpec, got {spec}."
+                )
+                # pyrefly: ignore [bad-argument-type, bad-argument-count, unexpected-keyword]
+                return dtensor.DTensor(res, spec, requires_grad=res.requires_grad)
+            else:
+                # if output does not have a DTensorSpec due to specific ops, it must be a scalar tensor
+                assert res.ndim == 0, "output tensor should be scalar!"
+                return res
+        elif isinstance(res, (list, tuple)):
+            assert spec is not None and isinstance(spec, (list, tuple)), (
+                f"output spec does not match with output! Expected list/tuple, got {spec}."
+            )
+            res_list = []
+            for e, s in zip(res, spec):
+                res_list.append(OpDispatcher.wrap(e, s))
+
+            return tuple(res_list) if isinstance(res, tuple) else res_list
+        else:
+            # if the res contains only non tensor values (i.e. int/float/none), we simply return it
+            # without rewrapping to DTensor.
+            return res
+
+    def _try_replicate_spec_for_scalar_tensor(
+        self,
+        op_call: torch._ops.OpOverload,
+        tensor_arg: torch.Tensor,
+        compute_mesh: DeviceMesh,
+    ) -> DTensorSpec:
+        # util function to produce a replicate spec for a scalar tensor arg/kwarg
+        if tensor_arg.numel() == 1 and tensor_arg.ndim == 1:
+            warnings.warn(
+                "Found a non-scalar tensor with numel=1 and ndim!=0, "
+                "we are implicitly creating a replicated DTensor for it. "
+                "However, please consider changing it to a scalar tensor "
+                "or explicitly create a DTensor under distributed environment.",
+                stacklevel=2,
+            )
+
+        if tensor_arg.numel() == 1 or self._allow_implicit_replication:
+            # scalar tensor can be safely treated as replicated
+            replication_spec = DTensorSpec(
+                compute_mesh,
+                (Replicate(),) * compute_mesh.ndim,
+                tensor_meta=TensorMeta(
+                    shape=tensor_arg.shape,
+                    stride=tensor_arg.stride(),
+                    dtype=tensor_arg.dtype,
+                ),
+            )
+        else:
+            raise RuntimeError(
+                f"{op_call}: got mixed torch.Tensor and DTensor, need to convert all"
+                " torch.Tensor to DTensor before calling distributed operators!"
+                " Please see https://docs.pytorch.org/docs/main/distributed.tensor.html#mixed-tensor-and-dtensor-operations"
+                " for more details."
+            )
+        return replication_spec
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/_dtensor_spec.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/_dtensor_spec.py
new file mode 100644
index 0000000000000000000000000000000000000000..629bf104e11632beee286256d6f0f77609a289eb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/_dtensor_spec.py
@@ -0,0 +1,710 @@
+import itertools
+import math
+from collections import defaultdict
+from dataclasses import dataclass
+from typing import Any, cast, NamedTuple, Optional
+
+import torch
+from torch.distributed.device_mesh import DeviceMesh
+from torch.distributed.tensor.placement_types import (
+    _StridedShard,
+    MaskPartial,
+    Partial,
+    Placement,
+    Replicate,
+    Shard,
+)
+from torch.utils._debug_mode import _stringify_shape
+from torch.utils._dtype_abbrs import dtype_abbrs
+
+
+class ShardOrderEntry(NamedTuple):
+    """
+    Represents how a single tensor dimension is sharded across mesh dimensions.
+
+    Attributes:
+        tensor_dim: The tensor dimension being sharded (e.g., 0, 1, 2 for a 3D tensor).
+        mesh_dims: Tuple of mesh dimensions across which this tensor dimension is sharded,
+                   in execution order. The first mesh dim is applied first, second is applied
+                   second, etc. This tuple is guaranteed to be non-empty.
+
+    Examples:
+        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_DISTRIBUTED)
+        >>> # Tensor dim 1 sharded across mesh dim 2, then mesh dim 0
+        >>> ShardOrderEntry(tensor_dim=1, mesh_dims=(2, 0))
+
+        >>> # Tensor dim 0 sharded only on mesh dim 1
+        >>> ShardOrderEntry(tensor_dim=0, mesh_dims=(1,))
+    """
+
+    tensor_dim: int
+    mesh_dims: tuple[int, ...]  # guaranteed to be non-empty
+
+
+# Type alias for the complete shard order specification
+# A tuple of ShardOrderEntry, one per sharded tensor dimension
+#
+# Example:
+#   shard_order = (
+#       ShardOrderEntry(tensor_dim=0, mesh_dims=(1,)),
+#       ShardOrderEntry(tensor_dim=2, mesh_dims=(0, 3)),
+#   )
+#   This means:
+#     - Tensor dimension 0 is sharded on mesh dimension 1
+#     - Tensor dimension 2 is sharded on mesh dimension 0 first, then mesh dimension 3
+ShardOrder = tuple[ShardOrderEntry, ...]
+
+
+class TensorMeta(NamedTuple):
+    # simple named tuple to represent tensor metadata
+    # intentionally to stay simple only for sharding
+    # propagation purposes.
+    shape: torch.Size
+    stride: tuple[int, ...]
+    dtype: torch.dtype
+
+
+# used internally to propagate the placements
+@dataclass
+class DTensorSpec:
+    mesh: DeviceMesh
+    placements: tuple[Placement, ...]
+
+    # tensor meta will only be set during sharding propagation
+    tensor_meta: TensorMeta | None = None
+
+    # When a tensor dimension is sharded across multiple mesh axes,
+    # `shard_order` specifies the sequence in which these shardings are applied.
+    # This order determines how tensor shards are mapped and distributed across
+    # devices.
+    #
+    # Example:
+    #   For a tensor of shape [8, 16] and a 3D device mesh, if dim 0 is sharded over
+    #   mesh dim 1, and dim 1 is sharded over mesh dim 0 and then mesh dim 2,
+    #   the shard_order would be:
+    #     shard_order = (
+    #         ShardOrderEntry(tensor_dim=0, mesh_dims=(1,)),
+    #         ShardOrderEntry(tensor_dim=1, mesh_dims=(0, 2)),
+    #     )
+    shard_order: ShardOrder = None  # type: ignore[assignment]
+
+    def __post_init__(self) -> None:
+        if not isinstance(self.placements, tuple):
+            self.placements = tuple(self.placements)
+        if self.shard_order is None:
+            # pyrefly: ignore [bad-assignment]
+
+            _, self.shard_order = self._normalize_placements_into_shard_order(
+                self.placements, self.mesh
+            )
+        self._hash: int | None = None
+
+    @staticmethod
+    def _normalize_placements_into_shard_order(
+        placements: tuple[Placement, ...], mesh: DeviceMesh
+    ) -> tuple[tuple[Placement, ...], Optional[ShardOrder]]:
+        # If the returned shard_order is None, it means the StridedShard/Shard
+        # combinations can't be interpreted as shard order.
+        # If no _StridedShard in placements, we create default order.
+        if not any(isinstance(p, _StridedShard) for p in placements):
+            return placements, DTensorSpec.compute_default_shard_order(placements)
+        # _StridedShard in placements, try check if it can be decoded as shard order
+        shard_order = DTensorSpec._maybe_convert_StridedShard_to_shard_order(
+            placements, mesh
+        )
+        if shard_order is not None:
+            normalized_placements = tuple(
+                [
+                    p if not isinstance(p, _StridedShard) else Shard(p.dim)
+                    for p in placements
+                ]
+            )
+            return normalized_placements, shard_order
+        # unable to decode placements to shard order(e.g., the _StridedShard is
+        # also used by `view` op shard propagation).
+        return placements, None
+
+    @staticmethod
+    def compute_default_shard_order(
+        placements: tuple[Placement, ...],
+    ) -> ShardOrder:
+        """
+        Compute the default shard order from placements.
+
+        Returns a ShardOrder where each ShardOrderEntry maps a tensor dimension
+        to the mesh dimensions it's sharded on, in left-to-right order.
+        """
+        # follow default left-to-right device order if shard_order is not specified
+        tensor_dim_to_mesh_dims: defaultdict[int, list[int]] = defaultdict(list)
+        mesh_ndim = len(placements)
+        for mesh_dim in range(mesh_ndim):
+            # shard_order doesn't work with _StridedShard
+            if isinstance(placements[mesh_dim], _StridedShard):
+                return ()
+            if isinstance(placements[mesh_dim], Shard):
+                placement = cast(Shard, placements[mesh_dim])
+                shard_dim = placement.dim
+                assert shard_dim >= 0, (
+                    f"Shard dim {shard_dim} in placements {placements} must be normalized"
+                )
+                tensor_dim_to_mesh_dims[shard_dim].append(mesh_dim)
+
+        # Convert dict into ShardOrderEntry tuples
+        default_shard_order = tuple(
+            ShardOrderEntry(tensor_dim=key, mesh_dims=tuple(value))
+            for key, value in sorted(tensor_dim_to_mesh_dims.items())
+            if value
+        )
+        return default_shard_order
+
+    @staticmethod
+    def _convert_shard_order_to_StridedShard(
+        shard_order: ShardOrder, placements: tuple[Placement, ...], mesh: DeviceMesh
+    ) -> tuple[Placement, ...]:
+        """
+        Convert ShardOrder to placements with _StridedShard.
+
+        This function converts a ShardOrder specification into a tuple of Placement objects,
+        using _StridedShard when a tensor dimension is sharded across multiple mesh dimensions
+        in a non-default order. The split_factor of each _StridedShard is determined by the
+        product of mesh dimension sizes that appear earlier in the shard order but later in
+        the placement tuple.
+
+        Args:
+            shard_order: ShardOrder specification indicating which tensor dimensions are
+                sharded on which mesh dimensions and in what execution order.
+            placements: Tuple of Placement objects that does not contain _StridedShard.
+            mesh: DeviceMesh containing the size information for each mesh dimension.
+
+        Returns:
+            Updated tuple of Placement objects with Shard or _StridedShard placements.
+
+        Algorithm:
+            For each ShardOrderEntry in shard_order:
+              - For each mesh dimension in the entry's mesh_dims (in order):
+                - Calculate split_factor as the product of mesh sizes for all mesh dimensions
+                  that appear:
+                  1. Earlier in the shard order (lower index in mesh_dims), and
+                  2. Later in the placement tuple (higher mesh dimension index)
+                - If split_factor == 1: use normal Shard
+                - Otherwise: use _StridedShard with the calculated split_factor
+
+        Example:
+            >>> # xdoctest: +SKIP("Requires DeviceMesh")
+            >>> # Tensor dimension 0 sharded on mesh dims [2, 0, 1] in that order
+            >>> # mesh = DeviceMesh([4, 3, 2])  # sizes: mesh[0]=4, mesh[1]=3, mesh[2]=2
+            >>> shard_order = (ShardOrderEntry(tensor_dim=0, mesh_dims=(2, 0, 1)),)
+            >>> placements = (Shard(0), Shard(0), Shard(0))
+            >>> # For mesh_dim=2 (index 0 in mesh_dims): no earlier dims, split_factor=1
+            >>> #   -> placements[2] = Shard(0)
+            >>> # For mesh_dim=0 (index 1 in mesh_dims): mesh_dim=2 is earlier and has index 2>0
+            >>> #   -> split_factor = mesh.size(2) = 2
+            >>> #   -> placements[0] = _StridedShard(0, split_factor=2)
+            >>> # For mesh_dim=1 (index 2 in mesh_dims): mesh_dim=2 is earlier and has index 2>1
+            >>> #   -> split_factor = mesh.size(2) = 2
+            >>> #   -> placements[1] = _StridedShard(0, split_factor=2)
+            >>> # Result: (_StridedShard(0, sf=2), _StridedShard(0, sf=2), Shard(0))
+        """
+        placements_list = list(placements)
+        for entry in shard_order:
+            tensor_dim = entry.tensor_dim
+            mesh_dims = entry.mesh_dims
+            for idx in range(len(mesh_dims)):
+                # TODO(zpcore): split_factor from `view` and `shard order`
+                # should be able to be multiplied into one. Need to loosen the
+                # condition here.
+                mesh_dim = mesh_dims[idx]
+                if type(placements[mesh_dim]) is not Shard:
+                    raise ValueError(
+                        f"Only Shard placement can be converted to _StridedShard, "
+                        f"found {placements[mesh_dim]} in {placements=}."
+                    )
+                split_factor = math.prod(
+                    mesh.size(i) for i in mesh_dims[:idx] if i > mesh_dim
+                )
+                if split_factor == 1:
+                    # use normal Shard
+                    placements_list[mesh_dim] = Shard(tensor_dim)
+                else:
+                    placements_list[mesh_dim] = _StridedShard(
+                        tensor_dim, split_factor=split_factor
+                    )
+        return tuple(placements_list)
+
+    @staticmethod
+    def _maybe_convert_StridedShard_to_shard_order(
+        placements: tuple[Placement, ...], mesh: DeviceMesh
+    ) -> ShardOrder | None:
+        """
+        Try to convert _StridedShard placements to ShardOrder.
+
+        This is the inverse of `_convert_shard_order_to_StridedShard`. It reconstructs the shard
+        order by examining the split_factor of each _StridedShard and determining its position
+        in the execution order. If the _StridedShard configuration cannot be represented as a
+        valid ShardOrder (i.e., there's no shard order that produces the observed split_factors),
+        this function returns None.
+
+        Args:
+            placements: Tuple of Placement objects that may contain _StridedShard.
+            mesh: DeviceMesh containing the size information for each mesh dimension.
+
+        Returns:
+            ShardOrder if conversion is possible, None otherwise. For placements without
+            _StridedShard, returns the default shard order.
+
+          Algorithm:
+              1. If no _StridedShard in placements, return default shard order
+              2. Create an empty list for each tensor dimension to represent mesh dim ordering
+              3. Iterate through placements in reverse order (right to left):
+                 - For each Shard/_StridedShard on a tensor dimension:
+                   - Extract its split_factor (1 for Shard, split_factor for _StridedShard)
+                   - Find the position in mesh_dims_order where accumulated_sf equals split_factor
+                   - accumulated_sf is the product of mesh sizes of mesh dimensions that appear
+                     earlier in mesh_dims_order (lower indices)
+                   - Insert mesh_dim at the found position
+              4. If no valid position found for any split_factor, return None (unable to convert)
+              5. Construct ShardOrderEntry for each tensor dimension from mesh_dims_order
+
+        Example:
+            >>> # xdoctest: +SKIP("Requires DeviceMesh")
+            >>> # mesh = DeviceMesh([4, 3, 2])  # sizes: mesh[0]=4, mesh[1]=3, mesh[2]=2
+            >>> # placements = (_StridedShard(0, sf=2), _StridedShard(0, sf=2), Shard(0))
+            >>> # Process tensor_dim=0 from right to left:
+            >>> #   - mesh_dim=2: Shard(0) with sf=1
+            >>> #     Try position 0: accumulated_sf=1, matches! Insert at position 0
+            >>> #     Current mesh_dims_order order: [2]
+            >>> #   - mesh_dim=1: _StridedShard(0, sf=2) with sf=2
+            >>> #     Try position 0: accumulated_sf=1, no match
+            >>> #     Try position 1: accumulated_sf=1*mesh.size(2)=2, matches! Insert at position 1
+            >>> #     Current mesh_dims_order order: [2, 1]
+            >>> #   - mesh_dim=0: _StridedShard(0, sf=2) with sf=2
+            >>> #     Try position 0: accumulated_sf=1, no match
+            >>> #     Try position 1: accumulated_sf=1*mesh.size(2)=2, matches! Insert at position 1
+            >>> #     Final mesh_dims_order order: [2, 0, 1]
+            >>> # Result: ShardOrder((ShardOrderEntry(tensor_dim=0, mesh_dims=(2, 0, 1)),))
+            >>> # This means: first shard on mesh_dim=2, then mesh_dim=0, then mesh_dim=1
+
+        Note:
+            This function validates that _StridedShard can be represented as a ShardOrder.
+            Not all _StridedShard configurations are valid - the split_factor must match
+            the product of mesh sizes in some execution order.
+        """
+        if not any(isinstance(p, _StridedShard) for p in placements):
+            return DTensorSpec.compute_default_shard_order(placements)
+        max_tensor_dim = (
+            max([i.dim for i in placements if isinstance(i, Shard | _StridedShard)]) + 1
+        )
+        shard_order = []
+
+        tensor_dim_to_mesh_dims_order: list[list[int]] = [
+            [] for i in range(max_tensor_dim)
+        ]
+        for mesh_dim in reversed(range(len(placements))):
+            cur_placement = placements[mesh_dim]
+            # _StridedShard may not be a subclass of Shard in the future, so write in this way:
+            if isinstance(cur_placement, Shard | _StridedShard):
+                tensor_dim = cur_placement.dim
+                mesh_dims_order = tensor_dim_to_mesh_dims_order[tensor_dim]
+                cur_sf = 1
+                if isinstance(cur_placement, _StridedShard):
+                    cur_sf = cur_placement.split_factor
+                accumulated_sf = 1
+                find_order = False
+                for i in range(len(mesh_dims_order) + 1):
+                    if accumulated_sf == cur_sf:
+                        mesh_dims_order.insert(i, mesh_dim)
+                        find_order = True
+                        break
+                    if i < len(mesh_dims_order):
+                        accumulated_sf *= mesh.size(mesh_dims_order[i])
+                if not find_order:
+                    # _StridedShard is not convertible to ShardOrder
+                    return None
+            else:
+                if not isinstance(cur_placement, Replicate | Partial | MaskPartial):
+                    raise ValueError(
+                        f"Unsupported placement type {type(cur_placement)} encountered in "
+                        f"{placements}; expected Replicate, Partial, or MaskPartial."
+                    )
+        for tensor_dim in range(max_tensor_dim):
+            if len(tensor_dim_to_mesh_dims_order[tensor_dim]) > 0:
+                shard_order.append(
+                    ShardOrderEntry(
+                        tensor_dim=tensor_dim,
+                        mesh_dims=tuple(tensor_dim_to_mesh_dims_order[tensor_dim]),
+                    )
+                )
+        return tuple(shard_order)
+
+    def _verify_shard_order(self, shard_order: ShardOrder) -> None:
+        """Verify that the shard_order is valid and matches the placements."""
+        total_shard = 0
+        if any(isinstance(p, _StridedShard) for p in self.placements):
+            return
+        prev_tensor_dim = -1
+        for entry in shard_order:
+            tensor_dim = entry.tensor_dim
+            mesh_dims = entry.mesh_dims
+            assert len(mesh_dims) > 0, f"shard_order {shard_order} has empty mesh dim"
+            assert tensor_dim >= 0, (
+                f"shard_order {shard_order} has invalid tensor dim {tensor_dim}"
+            )
+            assert tensor_dim > prev_tensor_dim, (
+                "tensor dim should be sorted in shard_order"
+            )
+            prev_tensor_dim = tensor_dim
+            total_shard += len(mesh_dims)
+            for mesh_dim in mesh_dims:
+                assert 0 <= mesh_dim < len(self.placements), (
+                    f"shard_order {shard_order} has invalid mesh dim {mesh_dims}"
+                )
+                assert self.placements[mesh_dim] == Shard(tensor_dim), (
+                    f"placement[{mesh_dim}] doesn't have a matching shard in shard_order"
+                )
+        assert total_shard == sum(1 for p in self.placements if isinstance(p, Shard))
+
+    def __setattr__(self, attr: str, value: Any) -> None:
+        if attr == "shard_order" and value is not None:
+            self._verify_shard_order(value)
+        super().__setattr__(attr, value)
+        # Make sure to recompute the hash in case any of the hashed attributes
+        # change (though we do not expect `mesh`, `placements` or `shard_order`
+        # to change)
+        if hasattr(self, "_hash") and attr in (
+            "mesh",
+            "placements",
+            "tensor_meta",
+            "shard_order",
+        ):
+            self._hash = None
+        # This assert was triggered by buggy handling for dict outputs in some
+        # FX passes, where you accidentally iterate over a dict and try to put
+        # keys into TensorMeta.  See https://github.com/pytorch/pytorch/issues/157919
+        if attr == "tensor_meta" and value is not None:
+            from torch.fx.passes.shape_prop import TensorMetadata
+
+            # TODO: the TensorMetadata arises from
+            # test/distributed/tensor/experimental/test_tp_transform.py::TensorParallelTest::test_tp_transform_e2e
+            # but I actually can't reproduce it, maybe it is also a bug!
+            assert isinstance(value, TensorMeta | TensorMetadata), value
+
+    def _hash_impl(self) -> int:
+        # hashing and equality check for DTensorSpec are used to cache the sharding
+        # propagation results. We only need to consider the mesh, placements, shape
+        # dtype and stride.
+        # Caveat: we need to keep this in mind and sync hash and eq if we add more
+        # fields to them.
+        if self.tensor_meta is not None:
+            return hash(
+                (
+                    self.mesh,
+                    self.placements,
+                    self.shard_order,
+                    self.tensor_meta.shape,
+                    self.tensor_meta.stride,
+                    self.tensor_meta.dtype,
+                )
+            )
+        return hash((self.mesh, self.placements, self.shard_order))
+
+    def __hash__(self) -> int:
+        # We lazily cache the spec to avoid recomputing the hash upon each
+        # use, where we make sure to update the hash when the `tensor_meta`
+        # changes by overriding `__setattr__`. This must be lazy so that Dynamo
+        # does not try to hash non-singleton `SymInt`s for the stride.
+        if self._hash is None:
+            self._hash = self._hash_impl()
+        return self._hash
+
+    def _check_equals(self, other: object, skip_shapes: bool = False) -> bool:
+        if not (
+            isinstance(other, DTensorSpec)
+            and self.mesh == other.mesh
+            and self.placements == other.placements
+            and self.shard_order == other.shard_order
+        ):
+            return False
+        if self.tensor_meta is None or other.tensor_meta is None:
+            return self.tensor_meta == other.tensor_meta
+
+        if skip_shapes:
+            return self.tensor_meta.dtype == other.tensor_meta.dtype
+        return (
+            self.tensor_meta.shape == other.tensor_meta.shape  # type: ignore[union-attr]
+            and self.tensor_meta.stride == other.tensor_meta.stride  # type: ignore[union-attr]
+            and self.tensor_meta.dtype == other.tensor_meta.dtype  # type: ignore[union-attr]
+        )
+
+    def __eq__(self, other: object, /) -> bool:
+        return self._check_equals(other)
+
+    def __str__(self) -> str:
+        """
+        human readable representation of the DTensorSpec
+        """
+        placement_str = self.format_shard_order_str(self.placements, self.shard_order)
+        if self.tensor_meta is not None:
+            tensor_shape = _stringify_shape(self.tensor_meta.shape)
+            tensor_dtype = dtype_abbrs[self.tensor_meta.dtype]
+        else:
+            tensor_shape = "unknown shape"
+            tensor_dtype = "unknown dtype"
+
+        return f"Spec({tensor_dtype}{tensor_shape}({placement_str}))"
+
+    @staticmethod
+    def is_default_device_order(shard_order: ShardOrder) -> bool:
+        """
+        Check if the device order is the default left-to-right order.
+        """
+        for entry in shard_order:
+            mesh_dims = entry.mesh_dims
+            is_increasing = all(
+                prev < nxt for prev, nxt in itertools.pairwise(mesh_dims)
+            )
+            if not is_increasing:
+                return False
+        return True
+
+    @staticmethod
+    def format_shard_order_str(
+        placements: tuple[Placement, ...],
+        shard_order: ShardOrder | None = None,
+    ) -> str:
+        """
+        Format DTensor sharding information as a human-readable string.
+
+        This method formats the sharding pattern in mesh-centric order, showing the placement
+        for each mesh dimension sequentially. When a tensor dimension is sharded across multiple
+        mesh dimensions, the order index indicates the execution sequence of the sharding operations.
+
+        Args:
+            placements: Tuple of placement objects for each mesh dimension.
+            shard_order: Optional ShardOrder specifying the sharding order.
+
+        Returns:
+            String representation of the sharding pattern in mesh-centric format.
+
+        Example:
+            For a 3D tensor on a 2x2x2x2 mesh (16 devices) with::
+
+                placements = [Partial(), Shard(1), Shard(1), Replicate()]
+                shard_order = (ShardOrderEntry(tensor_dim=1, mesh_dims=(2, 1)),)
+
+            Mesh configuration:
+                - mesh_dim_0: Partial reduction (sum)
+                - mesh_dim_1: Shard tensor dimension 1 (executed second, order index 1)
+                - mesh_dim_2: Shard tensor dimension 1 (executed first, order index 0)
+                - mesh_dim_3: Replicate
+
+            Output: ``"PS(1)[1]S(1)[0]R"``
+
+            Explanation:
+                - ``P``: mesh dimension 0 has partial reduction
+                - ``S(1)[1]``: mesh dimension 1 shards tensor dimension 1 (order index 1 means second)
+                - ``S(1)[0]``: mesh dimension 2 shards tensor dimension 1 (order index 0 means first)
+                - ``R``: mesh dimension 3 replicates
+
+            The format follows mesh dimension order (0, 1, 2, 3), and when a tensor dimension
+            is sharded across multiple mesh dimensions, the bracketed index shows the execution
+            order: ``[0]`` is executed first, ``[1]`` is executed second, etc.
+        """
+        out_str = ""
+        # native dtensor-style sharding representation: map from mesh
+        # dim to tensor dim
+        for mesh_dim, placement in enumerate(placements):
+            if isinstance(placement, Shard):
+                if shard_order is not None:
+                    for entry in shard_order:
+                        tensor_dim = entry.tensor_dim
+                        mesh_dims = entry.mesh_dims
+
+                        if placement.dim == tensor_dim:
+                            assert mesh_dim in mesh_dims
+                            if len(mesh_dims) > 1:
+                                out_str += f"{placement}[{mesh_dims.index(mesh_dim)}]"
+                            else:
+                                # no need to show device order if the tensor dim is
+                                # only sharded in one mesh dim
+                                out_str += str(placement)
+                            break
+                else:
+                    out_str += str(placement)
+            else:
+                out_str += str(placement)
+        return out_str
+
+    @property
+    def shape(self) -> torch.Size:
+        if self.tensor_meta is None:
+            raise ValueError("tensor_meta is not set")
+        return self.tensor_meta.shape
+
+    @property
+    def stride(self) -> tuple[int, ...]:
+        if self.tensor_meta is None:
+            raise ValueError("tensor_meta is not set")
+        return self.tensor_meta.stride
+
+    @property
+    def ndim(self) -> int:
+        if self.tensor_meta is None:
+            raise ValueError("tensor_meta is not set")
+        return len(self.tensor_meta.shape)
+
+    @property
+    def num_shards(self) -> int:
+        num_shards = 1
+        for i, placement in enumerate(self.placements):
+            if placement.is_shard():
+                num_shards *= self.mesh.size(i)
+        return num_shards
+
+    @property
+    def device_mesh(self) -> DeviceMesh:
+        # simple aliasing for the mesh field, make some
+        # checks that mixes DTensor/DTensorSpec easier
+        return self.mesh
+
+    @property
+    def dim_map(self) -> list[int]:
+        """
+        dim_map is a property we derive from `placements` of
+        the distributed tensor. It simply return a list of ints
+        where dim_map[i] denotes the sharding mapping to the mesh
+        dimension, and len(dim_map) == dist_tensor.ndim
+        dim_map[i] = -1: means tensor dim i replicate on mesh
+        dim_map[i] = j: means tensor dim i shard on mesh dim j
+
+        For example, we have a dist tensor that have the shape of
+        [18, 20, 30], and device_mesh([0, 1, 2, 3]), placements:
+        [Shard(1)], the dim_map of this placement would be:
+        [-1, 0, -1]. This representation is pretty helpful during
+        sharding propagation where we could know exactly each
+        tensor dimension is sharded or not.
+
+        Note that if placements contains `_Partial`, we have to
+        explicitly deal with it, so that when we create a DTensorSpec
+        with dim_map, we could properly record the pending sums.
+        """
+        # dims mapping of dist tensor sharding
+        # return size of tensor ndim, -1 represent replicate
+        # and int >=0 represent shard on that device mesh dim
+        r = [-1] * self.ndim
+        for i, placement in enumerate(self.placements):
+            if placement.is_shard():
+                shard_dim = cast(Shard, placement).dim
+                if r[shard_dim] > -1:
+                    raise ValueError(
+                        f"Tensor dim {shard_dim} is already sharded on mesh dim {r[shard_dim]},"
+                        " DTensor operator implementation does not support things like hybrid"
+                        " sharding strategies yet (i.e. [Shard(0), Shard(0)])"
+                    )
+                r[shard_dim] = i
+        return r
+
+    @property
+    def num_shards_map(self) -> list[int]:
+        """
+        dim_map is a property we derive from `placements` of
+        the distributed tensor. Unlike `dim_map`, `num_shards_map`
+        denotes how many shards each tensor dim has. Like `dim_map`:
+            len(num_shards_map) == dist_tensor.ndim
+            num_shards_map[i] = 1: means tensor dim i is not sharded
+            num_shards_map[i] = j: means tensor dim i has j shards in total
+
+        For example, we have a dist tensor of shape [18, 20, 30],
+        a device_mesh ([[0, 1, 2, 3], [4, 5, 6, 7]]), and placements
+        ([Shard(1), Shard(0)]), the num_shards_map of this distributed tensor
+        would be: [4, 2, 1].
+        """
+        r = [1] * self.ndim
+        for i, placement in enumerate(self.placements):
+            if placement.is_shard():
+                shard_dim = cast(Shard, placement).dim
+                r[shard_dim] *= self.mesh.size(i)
+
+        return r
+
+    @property
+    def sums(self) -> list[int]:
+        """
+        sums is a property we derive from `placements` of the
+        distributed tensor. It simply return a list of ints where
+        sums[i] denotes the pending sum (partial) on mesh dim i
+        """
+        return [
+            idx
+            for idx, placement in enumerate(self.placements)
+            if placement.is_partial()
+        ]
+
+    @classmethod
+    def from_dim_map(
+        cls,
+        mesh: DeviceMesh,
+        dim_map: list[int],
+        sums: list[int],
+        tensor_meta: TensorMeta | None = None,
+    ) -> "DTensorSpec":
+        """
+        Construct a DTensorSpec from dim_map list and pending sum.
+
+        Args:
+            mesh (class:`DeviceMesh`): device mesh to be used in the DTensorSpec
+            dim_map (List[int]): a list of integer that represents sharding on each
+                tensor dimension, see `dim_map` property doc for details
+            sums (List[int]): a list of integer that represents the dist tensor have
+                pending sum on which device mesh dimension.
+            tensor meta (TensorMeta): DTensor metadata
+
+        Return:
+            a class:`DTensorSpec` object
+        """
+        # by default replicate on device mesh dims
+        placements: list[Placement] = [Replicate() for _ in range(mesh.ndim)]
+
+        # find all mesh dims that need pending reductions
+        for s in sums:
+            placements[s] = Partial()
+
+        for i, m in enumerate(dim_map):
+            if m >= 0:
+                placement = placements[m]
+                if placement.is_shard():
+                    placement = cast(Shard, placement)
+                    raise RuntimeError(
+                        f"DeviceMesh dimension can't be mapped to two dimension of the same tensor: {i} and {placement.dim}"
+                    )
+                elif placement.is_partial():
+                    raise RuntimeError(
+                        f"DeviceMesh dimension {m} cannot be both shard and partial!"
+                    )
+                placements[m] = Shard(i)
+
+        return cls(mesh, tuple(placements), tensor_meta=tensor_meta)
+
+    def is_replicated(self) -> bool:
+        """
+        return True if the current DTensorSpec replicates on all mesh dims (devices)
+        """
+        return all(placement.is_replicate() for placement in self.placements)
+
+    def is_sharded(self) -> bool:
+        """
+        return True if the current DTensorSpec uses Shard() placement on any mesh dims (devices)
+        """
+        return any(placement.is_shard() for placement in self.placements)
+
+    def shallow_copy_with_tensor_meta(
+        self, tensor_meta: TensorMeta | None
+    ) -> "DTensorSpec":
+        """
+        Shallow copy the DTensorSpec with a new tensor_meta.
+        """
+        assert tensor_meta is not None, "shallow copy with no tensor_meta!"
+        return DTensorSpec(
+            self.mesh,
+            self.placements,
+            tensor_meta=tensor_meta,
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/_op_schema.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/_op_schema.py
new file mode 100644
index 0000000000000000000000000000000000000000..4fec0293554ac1c0bb4031b91953386d3dc6d541
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/_op_schema.py
@@ -0,0 +1,612 @@
+# mypy: allow-untyped-defs
+"""
+DTensor operator schema definitions and utilities.
+
+This module defines the core data structures and utilities for describing and managing
+distributed tensor operations in PyTorch's DTensor system. It provides the foundational
+schema types used for sharding propagation, operator strategy selection, and distributed
+execution planning.
+
+Key components:
+- OpSpec: Describes acceptable sharding placements for operations
+- OpStrategy: Represents the possible sharding strategies for an operator
+- TupleStrategy: Container for multiple strategies when ops have tuple/list of tensors input
+- OpSchema: Describes operator input/output schemas with DTensorSpecs
+- OutputSharding: Manages output sharding specifications and redistribution
+- RuntimeSchemaInfo: Runtime execution metadata for operators
+- OpInfo: Complete runtime operator execution information
+
+These schema definitions enable the DTensor system to:
+1. Propagate tensor sharding information to the operator outputs
+2. Greedily select sharding strategies for distributed operations
+3. Plan and execute tensor redistributions when needed
+4. Cache sharding decisions for performance optimization
+"""
+
+from collections.abc import Sequence
+from dataclasses import dataclass
+from functools import cached_property
+from typing import Any
+from typing_extensions import deprecated
+
+import torch
+from torch._C import (
+    _DTensor_OpSchema_post_init,
+    _DTensor_OpSchema_recompute_comparison_key,
+)
+from torch._ops import OpOverload
+from torch.distributed.device_mesh import DeviceMesh
+from torch.distributed.tensor._dtensor_spec import DTensorSpec
+from torch.distributed.tensor.placement_types import Placement
+
+
+try:
+    from torch.utils._cxx_pytree import (
+        register_pytree_node,
+        tree_leaves,
+        tree_map_only,
+        TreeSpec,
+    )
+except ImportError:
+    from torch.utils._pytree import (  # type: ignore[no-redef, assignment]
+        register_pytree_node,
+        tree_leaves,
+        tree_map_only,
+        TreeSpec,
+    )
+
+
+# Common type aliases
+ArgsType = tuple[object, ...]
+KwargsType = dict[str, object]
+
+PlacementList = list[Placement | None]
+
+# ATen op schemas could have Tensor, Tuple[Tensor] and List[Tensor], so output type should
+# be the same set of possibilities.
+OutputSpecType = DTensorSpec | Sequence[DTensorSpec | None] | None
+
+
+def _rebuild_tensor_from_dtensor_meta(arg) -> object:
+    """
+    This is used to propagate tensor metadata, must be under fake mode
+    """
+    assert arg.tensor_meta is not None, "DTensorSpec does not contain tensor_meta."
+    return torch.empty_strided(
+        arg.tensor_meta.shape,
+        arg.tensor_meta.stride,
+        dtype=arg.tensor_meta.dtype,
+    )
+
+
+def _pretty_print_spec(spec: object) -> str:
+    if spec is None:
+        return "None"
+    elif isinstance(spec, DTensorSpec):
+        return "".join([str(p) for p in spec.placements])
+    elif isinstance(spec, Sequence):
+        return "(" + ", ".join([_pretty_print_spec(s) for s in spec]) + ")"
+    else:
+        raise RuntimeError(f"Unknown spec type to print: spec={spec}")
+
+
+@dataclass
+class OpSpec:
+    """
+    An OpSpec describes an acceptable sharding placements of an operation, with the
+    specified DTensorSpecs for both the output and the inputs.
+
+    note: when the op return value is a single DTensor object, output_specs is
+    DTensorSpec; when the return value is a tuple of Optional[DTensor],
+    output_specs is a tuple of Optional[DTensorSpec].
+
+    note: we MUST produce an DTensorSpec for every output that is a Tensor.  None
+    entries only occur for non-Tensor outputs (e.g., operators that return Optional[Tensor],
+    or non-Tensor outputs.)
+
+    invariant: the DeviceMesh on all DTensorSpec must be the same
+    """
+
+    # output_specs and input_specs are related: for this op, given these input_specs,
+    # this is the way the output would look
+    output_specs: DTensorSpec | tuple[DTensorSpec | None, ...]
+    input_specs: Sequence[DTensorSpec] | None = None
+
+    """
+    redistribute_cost tells how expensive it is to redistribute a given input into the
+    placement specified in this OpSpec.
+
+    outer list: one entry (list) per (tensor) input in the op's arg schema
+    inner list: one entry (cost value) per possible sharding spec for that input
+
+    Example:
+    -------
+    another_op() -> tensor_a   # another_op produces the output that becomes our first input
+    my_op(tensor_a)
+
+    Let's assume this OpSpec's input_specs are [Replicate()],
+    but another_op() supports 2 strategies (OpSpecs) which produce outputs of
+       Replicate()
+       Shard(0)
+
+    In this example, redistribute_costs would look like this
+    [
+        # one row representing "my_op's first input" (tensor_a)
+        [
+            # two entries, one for each strategies supported by another_op
+            0.0,  # cost of redistributing tensor_a from 'Replicate()'
+            K,    # cost of redistributing tensor_a from 'Shard(0)'
+        ],
+    """
+    redistribute_cost: list[list[float]] | None = None
+
+    @cached_property
+    def output_spec(self) -> DTensorSpec:
+        """
+        This function requires that the strategy have exactly one DTensorSpec as the
+        output spec. If the output_specs is a tuple, we throw an exception.
+        """
+        if isinstance(self.output_specs, DTensorSpec):
+            return self.output_specs
+        else:
+            raise ValueError(
+                f"function output_spec expects a single DTensorSpec but got: {self.output_specs}"
+            )
+
+    @cached_property
+    def mesh(self):
+        if isinstance(self.output_specs, DTensorSpec):
+            return self.output_specs.mesh
+        elif isinstance(self.output_specs, tuple):
+            out_spec = self.output_specs[0]
+            assert isinstance(out_spec, DTensorSpec)
+            return out_spec.mesh
+        else:
+            raise ValueError(
+                f"function output_spec expects a single DTensorSpec or a tuple of DTensorSpec but got: {self.output_specs}"
+            )
+
+    def input_spec(self, index: int = 0) -> DTensorSpec:
+        assert self.input_specs is not None, "input_specs of OpSpec is None!"
+        assert len(self.input_specs) > index, (
+            f"Invalid index {index} for input_specs of length "
+            f"{len(self.input_specs)}: {self.input_specs}"
+        )
+        return self.input_specs[index]
+
+    def __str__(self) -> str:
+        if self.input_specs is not None:
+            input_specs_str = f"{_pretty_print_spec(self.input_specs)} -> "
+        else:
+            input_specs_str = ""
+        output_spec_str = _pretty_print_spec(self.output_specs)
+        return f"{input_specs_str}{output_spec_str}"
+
+
+class StrategyType:
+    """
+    Base class type for op strategy, We have two StrategyType:
+        OpStrategy and TupleStrategy
+    """
+
+
+class OpStrategy(StrategyType):
+    """
+    OpStrategy that consists of a list of sharding strategies associated with the op,
+    where each strategy is an OpSpec that describes the acceptable input/output sharding.
+
+    invariant: the DeviceMesh on all OpSpec must be the same
+    """
+
+    def __init__(self, strategies: list[OpSpec]) -> None:
+        super().__init__()
+        self.strategies: list[OpSpec] = strategies
+
+    def __str__(self) -> str:
+        strategy_list_str = ", ".join([str(strategy) for strategy in self.strategies])
+        mesh_shape = self.mesh_shape
+        return f"OpStrategy[{strategy_list_str}] @ mesh: {mesh_shape}"
+
+    def max_num_shards(self) -> int:
+        """
+        Returns the max number of shards across all OpSpecs
+        """
+        return max(strategy.output_spec.num_shards for strategy in self.strategies)
+
+    @property
+    def mesh(self):
+        return self.strategies[0].mesh
+
+    @property
+    def mesh_shape(self):
+        return self.strategies[0].mesh.shape
+
+    @property
+    def ndim(self):
+        return self.strategies[0].output_spec.ndim
+
+    @property
+    def shape(self):
+        return self.strategies[0].output_spec.shape
+
+
+class TupleStrategy(StrategyType):
+    """
+    TupleStrategy is a special case for operators that are fundamentally compound or batched such that some subset
+    of the inputs and outputs are completely unrelated to some other subset.
+
+    Generally, foreach_* ops are the most common use-case for TupleStrategy, because they accept lists of inputs,
+    but operate independently on each input or tuple of zipped inputs.
+
+    For example, [out_a, out_b] = torch.foreach_add([a,  b], scalar): input a's sharding only affects out_a's sharding,
+    independent of b and out_b.
+
+    An example of an operator that should NOT use TupleStrategy is torch.split.  It produces a List[Tensor]
+    as its output, but the sharding decision of one output is bound together with the decision
+    of each other output and the common input.
+    """
+
+    def __init__(
+        self,
+        children: Sequence[StrategyType],
+    ) -> None:
+        super().__init__()
+        self.children: Sequence[StrategyType] = children
+
+    @property
+    @deprecated(
+        "TupleStrategy.childs is deprecated, use TupleStrategy.children instead.",  # codespell:ignore childs
+        category=FutureWarning,
+    )
+    def childs(self) -> Sequence[StrategyType]:  # codespell:ignore childs
+        """
+        Alias for children, to maintain backward compatibility.
+        """
+        return self.children
+
+    def child_mesh(self, index: int) -> DeviceMesh:
+        op_strategy = self.children[index]
+        assert isinstance(op_strategy, OpStrategy)
+        return op_strategy.mesh
+
+    def __str__(self) -> str:
+        child_strategies_str = ", ".join(
+            [f"{str(strat)}" for idx, strat in enumerate(self.children)]
+        )
+        return f"TupleStrategy({child_strategies_str})"
+
+
+try:
+    register_pytree_node(
+        TupleStrategy,
+        lambda node: (node.children, None),
+        lambda children, _: TupleStrategy(tuple(children)),
+    )
+except ValueError:
+    # already registered TupleStrategy, skip
+    pass
+
+
+@dataclass
+class RuntimeSchemaInfo:
+    """
+    RuntimeSchemaInfo stores the operator schema related information for runtime (eager)
+    execution. This is mainly used for two ways: 1. to generate hash for args to determine
+    whether to re-run sharding prop or not 2. to determine if we need pytree
+    """
+
+    # This static_argnum records static arg "starting index" for ops that have non-tensor
+    # args/kwargs which would affect sharding propagation results. All args starting from
+    # this index would be hashed to our sharding cache.
+    # Note that only a few ops need this information, e.g. view, transpose, var.dim, etc.
+    static_argnum: int = 100
+    # This static_kwargkey records static kwarg names which would affect sharding prop
+    static_kwargkey: list[str] | None = None
+    # each op can decide if it wants to use pytree flatten/unflatten during operator
+    # eager execution, by default we don't need to do flatten/unflatten, only if the
+    # op indicate it needs to, this is to accelerate eager performance.
+    needs_pytree: bool = False
+
+
+@dataclass
+class OpSchema:
+    """
+    OpSchema is a data class that describes an operator input schemas, it includes
+    DTensorSpecs/OpStrategies (instead of DTensor) and non-tensor args/kwargs (positional
+    order preserved). It is mainly used by the DTensor's dispatching logic to perform various
+    actions (i.e. sharding propagation, caching sharding decisions, redistribute, etc.)
+
+    NOTE: this must be used as a read only data class
+    TODO: make this a frozen dataclass
+
+    Args:
+        op: the operator overload we are intercepting
+        args_schema: contains args except that the DTensor args have been replaced
+            with its DTensorSpec or OpStrategy
+        kwargs_schema: contains kwargs except that the DTensor kwargs have been replaced
+            with its DTensorSpec or OpStrategy
+    """
+
+    op: OpOverload
+    args_schema: ArgsType
+    kwargs_schema: KwargsType
+
+    schema_info: RuntimeSchemaInfo | None = None
+
+    _comparison_key: tuple[object, ...] | None = None
+
+    @property
+    def args_spec(self) -> tuple[DTensorSpec, ...]:
+        """
+        args_spec: Tuple[DTensorSpec, ...]: contains a clean list of args spec list
+            with NO non-DTensor positional arguments (i.e. int/float/tuple, etc)
+            mainly used by sharding propagation to propagate the output spec
+        """
+        args = (
+            tree_leaves(self.args_schema)
+            if self.schema_info is not None and self.schema_info.needs_pytree
+            else self.args_schema
+        )
+        return tuple(item for item in args if isinstance(item, DTensorSpec))
+
+    @property
+    def args_strategy(self) -> tuple[OpStrategy, ...]:
+        # filter out non-relevant values from args schema to get a clean OpStrategy list
+        # separate with args_spec for the ease of type annotation
+        # TODO: see if we should merge this with args_spec
+        args = (
+            tree_leaves(self.args_schema)
+            if self.schema_info is not None and self.schema_info.needs_pytree
+            else self.args_schema
+        )
+        return tuple(item for item in args if isinstance(item, OpStrategy))
+
+    @property
+    def kwargs_strategy(self) -> tuple[OpStrategy, ...]:
+        # returns OpStrategy items from kwargs_schema.
+        kwargs_vals = (
+            tree_leaves(self.kwargs_schema)
+            if self.schema_info is not None and self.schema_info.needs_pytree
+            else self.kwargs_schema.values()
+        )
+        return tuple(item for item in kwargs_vals if isinstance(item, OpStrategy))
+
+    def __repr__(self) -> str:
+        args_schema = ", ".join([str(arg_schema) for arg_schema in self.args_schema])
+        return (
+            f"OpSchema(op={self.op},"
+            f" args_schema=({args_schema}),"
+            f" kwargs_schema={self.kwargs_schema})"
+        )
+
+    def __str__(self) -> str:
+        args_schema: list[str] = []
+        device_mesh = None
+
+        for arg in self.args_schema:
+            if isinstance(arg, DTensorSpec):
+                args_schema.append(str(arg))
+                device_mesh = arg.mesh
+            elif isinstance(arg, OpStrategy):
+                assert len(arg.strategies) == 1
+                args_schema.append(_pretty_print_spec(arg.strategies[0].output_specs))
+                device_mesh = arg.mesh
+            elif isinstance(arg, TupleStrategy):
+                first_op_strategy = arg.children[0]
+                assert isinstance(first_op_strategy, OpStrategy)
+                device_mesh = first_op_strategy.mesh
+                args_schema.append(str(arg))
+            else:
+                args_schema.append(str(arg))
+
+        return f"{self.op}({', '.join(args_schema)}) on {device_mesh})"
+
+    def __post_init__(self) -> None:
+        _DTensor_OpSchema_post_init(self)
+
+    def arg_type_tensor_or_tensor_list_like(self, arg: object) -> bool:
+        is_tensor = isinstance(arg, DTensorSpec)
+        if is_tensor:
+            return True
+
+        if not isinstance(arg, list):
+            return False
+
+        return all(isinstance(e, DTensorSpec) or e is None for e in arg)
+
+    def return_type_tuple_tensor_like(self) -> bool:
+        # all dispatch ops could only return Tuple[Tensor] or have None/ints/floats
+        # in the tuple, but the first element must be a Tensor, so this check is enough
+        return_types = self.op._schema.returns
+        return len(return_types) > 1 and isinstance(
+            return_types[0].type, torch.TensorType
+        )
+
+    def return_type_list_tensor_like(self) -> bool:
+        # returns True if the return type is a List
+        return_types = self.op._schema.returns
+        return len(return_types) == 1 and isinstance(
+            return_types[0].type, torch.ListType
+        )
+
+    def return_type_tensor(self) -> bool:
+        return_types = self.op._schema.returns
+        # all dispatch ops only return Tensor or Tuple[Tensor] for tensor like
+        # return types, so this check is enough for tensor like types
+        return isinstance(return_types[0].type, torch.TensorType)
+
+    def get_mesh_from_args(self, validate: bool = True) -> DeviceMesh:
+        """
+        This util can be used to get a mesh from the OpSchema that contains multiple
+        DTensors as arguments. When `validate` is True, it will try to validate that all the
+        arguments have the same mesh to avoid unexpected cross mesh errors.
+
+        NOTE: this util currently does not handle TupleStrategy when `validate=True`,
+        this is because for TupleStrategy there could be different types of checks, i.e.:
+            - for stack and cat like op, we need to check within a TupleStrategy is every
+              input is on the same mesh
+            - for foreach like ops we need to check "zipped" inputs are on the same mesh
+              for each index.
+        """
+        first_arg = self.args_schema[0]
+        if isinstance(first_arg, (DTensorSpec, OpStrategy)):
+            mesh = first_arg.mesh
+        elif isinstance(first_arg, (list, tuple, TupleStrategy)):
+            first_elem = (
+                first_arg.children[0]
+                if isinstance(first_arg, TupleStrategy)
+                else first_arg[0]
+            )
+            assert isinstance(first_elem, (DTensorSpec, OpStrategy))
+            mesh = first_elem.mesh
+        else:
+            raise ValueError(f"Cannot find device mesh from args for op : {self.op}.")
+
+        if validate:
+            for arg in self.args_schema[1:]:
+                if isinstance(arg, (DTensorSpec, OpStrategy)) and arg.mesh != mesh:
+                    raise RuntimeError(
+                        f"DTensor does not support cross-mesh operation on {self.op}! "
+                        f"Got meshes: {mesh} {arg.mesh}. "
+                        f"Please make sure all the arguments have the same DeviceMesh."
+                    )
+
+        return mesh
+
+    def is_inplace_op(self) -> bool:
+        # simple analysis of function schema to determine
+        # if this is an inplace variant, it might not
+        # be entirely correct, but it's good enough for now.
+        return self.op._schema.name[-1] == "_"
+
+    def is_out_variant_op(self) -> bool:
+        # simple analysis of function schema to determine
+        # if this is an out variant, it might not
+        # be entirely correct, but it's good enough for now.
+        return "out" in self.op._schema.overload_name
+
+    def is_view_op(self) -> bool:
+        return self.op._schema._is_view_op()
+
+    def _recompute_comparison_key(self) -> None:
+        _DTensor_OpSchema_recompute_comparison_key(self)
+
+    def __hash__(self) -> int:
+        return hash(self._comparison_key)
+
+    def __eq__(self, other: object) -> bool:
+        # early return checks
+        if not isinstance(other, OpSchema):
+            return False
+
+        if self.op != other.op:
+            return False
+
+        if len(self.args_schema) != len(other.args_schema):
+            return False
+
+        return self._comparison_key == other._comparison_key
+
+    def gen_fake_args(self) -> ArgsType:
+        """
+        gen_fake_args: generate fake args for the operator, this is mainly used
+            by sharding propagation rules to generate fake args for the operator
+            to run the local tensor operator and get the output spec.
+        """
+        return tree_map_only(
+            DTensorSpec,
+            _rebuild_tensor_from_dtensor_meta,
+            self.args_schema,
+            is_leaf=lambda x: isinstance(x, DTensorSpec),
+        )
+
+    def gen_fake_kwargs(self) -> KwargsType:
+        """
+        gen_fake_kwargs: generate fake kwargs for the operator, this is mainly used
+            by sharding propagation rules to generate fake kwargs for the operator
+            to run the local tensor operator and get the output spec.
+        """
+        return tree_map_only(
+            DTensorSpec,
+            _rebuild_tensor_from_dtensor_meta,
+            self.kwargs_schema,
+            is_leaf=lambda x: isinstance(x, DTensorSpec),
+        )
+
+    def _inplace_rewrap_schema_suggestion(self, origin_schema: "OpSchema") -> None:
+        suggestion_args_spec = self.args_spec
+        new_arg_schema: list[object] = []
+        idx_of_args_spec = 0
+        if (
+            origin_schema.schema_info is not None
+            and origin_schema.schema_info.needs_pytree
+        ):
+            args_schema: Sequence[Any] = tree_leaves(origin_schema.args_schema)
+        else:
+            args_schema = origin_schema.args_schema
+        for arg in args_schema:
+            if isinstance(arg, DTensorSpec):
+                new_arg_schema.append(suggestion_args_spec[idx_of_args_spec])
+                idx_of_args_spec += 1
+            else:
+                new_arg_schema.append(arg)
+        self.args_schema = tuple(new_arg_schema)
+        self.kwargs_schema = origin_schema.kwargs_schema
+        self._recompute_comparison_key()
+
+
+@dataclass
+class OutputSharding:
+    """
+    OutputSharding is a data class that is used by the sharding propagation,
+    it could set the output_spec upon successful propagation. If needs_redistribute
+    is set to True, a redistribute_schema would be returned together to indicate
+    the input arguments needs to be redistributed before the op execution.
+
+    NOTE: the redistribute_schema generated by sharding propagation should be
+    exactly the same as the operator OpSchema, except the DTensorSpecs
+    """
+
+    # specifies the output sharding pattern
+    output_spec: OutputSpecType
+    # schema for redistribution if needed
+    redistribute_schema: OpSchema | None = None
+    # flag indicating if inputs need redistribution
+    needs_redistribute: bool = False
+    # flag to use values from `redistribute_schema`
+    use_val_from_redistribute_schema: bool = False
+
+    @cached_property
+    def mesh(self):
+        if isinstance(self.output_spec, DTensorSpec):
+            return self.output_spec.mesh
+        elif isinstance(self.output_spec, tuple):
+            out_spec = self.output_spec[0]
+            if isinstance(out_spec, DTensorSpec):
+                return out_spec.mesh
+            else:
+                raise ValueError(f"Unknown output spec type: {type(out_spec)}")
+        else:
+            raise ValueError(f"Unknown output spec type: {type(self.output_spec)}")
+
+
+@dataclass
+class OpInfo:
+    """
+    All Runtime Op execution info are packed here
+    """
+
+    # The first compute device mesh recorded from args
+    # NOTE: one op could have multiple meshes from its args. We just record the first
+    # mesh here to check if current rank should participate in computation or not.
+    compute_mesh: DeviceMesh
+
+    # compete runtime operator infos
+    schema: OpSchema
+    flat_args_schema: list[object]
+    local_args: Sequence[object]
+    local_kwargs: dict[str, object]
+    args_tree_spec: TreeSpec | None = None
+
+    # the output sharding info
+    output_sharding: OutputSharding | None = None
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/_random.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/_random.py
new file mode 100644
index 0000000000000000000000000000000000000000..995a057b0c7faa085ae94d67e11d7603d057e05a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/_random.py
@@ -0,0 +1,478 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+import contextlib
+import warnings
+from logging import getLogger
+from typing import Optional
+
+import torch
+from torch.distributed._local_tensor import maybe_run_for_local_tensor
+from torch.distributed.device_mesh import _get_device_handle, DeviceMesh
+from torch.distributed.tensor._dtensor_spec import DTensorSpec
+from torch.distributed.tensor.placement_types import _StridedShard, Shard
+
+
+logger = getLogger(__name__)
+
+__all__ = [
+    "is_rng_supported_mesh",
+    "manual_seed",
+    "OffsetBasedRNGTracker",
+]
+
+_rng_tracker: Optional["_RNGStateTracker"] = None
+
+
+def is_rng_supported_mesh(device_mesh: DeviceMesh) -> bool:
+    """Checks if the current device of ``device_mesh`` supports DTensor's random APIs.
+    Currently DTensor Random APIs only supports cuda/cuda-like devices. We suggest
+    users call this API to test the availability before using our random APIs.
+
+    Args:
+        device_mesh (:class:`DeviceMesh`): The device mesh on which we check if the
+            random ops APIs are supported.
+
+    Returns:
+        A bool value. True if ``device_mesh`` supports DTensor Random APIs; False otherwise.
+
+    .. warning::
+        Currently we only support correct RNG on cuda/cuda-like devices.
+    """
+    device_handle = _get_device_handle(device_mesh.device_type)
+    if device_handle and hasattr(device_handle, "set_rng_state"):
+        return True
+    else:
+        # TODO: Logs way too much
+        warnings.warn(
+            f"DTensor random operators may not have complete support on {device_mesh.device_type} device mesh",
+            stacklevel=2,
+        )
+        return False
+
+
+def manual_seed(seed: int, device_mesh: DeviceMesh) -> None:
+    """Sets the seed for generating random numbers for the calling rank.
+
+    Args:
+        seed (int): The desired seed.
+        device_mesh (:class:`DeviceMesh`): The device mesh to set the seed. It is
+            required that the ``device_mesh`` include the calling rank. This is
+            to ensure that the SPMD region maintains a synchronous RNG state, which
+            means no ranks should be initialized with values other than ``seed``.
+
+    Returns:
+        None
+
+    .. warning::
+        :func:`manual_seed` does not check the ``seed`` value correctness. Users must
+        ensure on their own that the value passed in is the desired ``seed`` for ranks
+        within ``device_mesh``.
+        If ``device_mesh`` is a sub-mesh and the calling rank is not a part of it,
+        ``manual_seed`` will throw an error.
+        Current implementation only supports a GPU device mesh.
+    """
+    if not is_rng_supported_mesh(device_mesh):
+        warnings.warn(
+            "DTensor manual_seed() may not have complete support "
+            f"on {device_mesh.device_type} device mesh",
+            stacklevel=2,
+        )
+        return
+
+    # TODO: deprecate this API, but also need to ensure we disable broadcast for PP case, and that's currently
+    # bundled together with this API.  See torchtitan/distributed/utils.py:set_determinism
+    # warnings.warn(
+    #     "DTensor manual_seed() is deprecated, since DTensor no longer maintains a separate copy of generator state. "
+    #     "Use `torch.manual_seed` instead"
+    # )
+    # Note: we still need to ensure setting `run_state_sync=False` to support the pp case
+
+    # instantiate a RNG tracker if haven't. By default DTensor uses an
+    # OffsetBasedRNGTracker to perform random operators.
+    global _rng_tracker
+    if not _rng_tracker:
+        _rng_tracker = OffsetBasedRNGTracker(device_mesh, run_state_sync=False)
+
+    if device_mesh.get_coordinate() is None:
+        raise RuntimeError(
+            "manual_seed requires the current rank to be a part of the device mesh "
+            "otherwise DTensor RNG state on the rank will not be initialized and "
+            "the behavior of DTensor random ops is undefined."
+        )
+
+    # DTensor no longer maintains a copy of rng state. manual seed on dtensor is the same thing
+    # as manual seed on torch.
+    #
+    # torch.manual_seed will handle LocalTensor mode correctly by
+    # iterating through all ranks if seed is a LocalIntNode.
+    torch.manual_seed(seed)
+
+
+class _PhiloxState:
+    """
+    Convenience accessor for interpreting the packed bits of (seed: uint64, offset: uint64) in the philox state,
+    which for some reason is actually exposed as a size-16 uint8 tensor.
+
+    The state is always moved to .cpu since it is necessary for it to be on CPU before applying it back to a generator.
+    """
+
+    def __init__(self, state: torch.Tensor):
+        self._state = state.to("cpu")
+
+    @property
+    def state(self):
+        return self._state
+
+    @property
+    def offset(self) -> int:
+        return int(self._state[8:].view(dtype=torch.int64).item())
+
+    @offset.setter
+    def offset(self, offset: int) -> None:
+        offset_tensor = torch.tensor([offset], dtype=torch.uint64, device="cpu").view(
+            torch.uint8
+        )
+        self._state[8:] = offset_tensor
+
+    @property
+    def seed(self) -> int:
+        return int(self._state[:8].view(dtype=torch.uint64).item())
+
+    @seed.setter
+    def seed(self, seed: int) -> None:
+        seed_tensor = torch.tensor([seed], dtype=torch.uint64, device="cpu").view(
+            torch.uint8
+        )
+        self._state[:8] = seed_tensor
+
+
+class _RNGStateTracker:
+    """
+    _RNGStateTracker stores Random Number Generator (RNG) state (a ByteTensor object)
+    in a dict, mapping from a corresponding tag to each state tensor. It also provides
+    a set of convenient utility methods to help access/modify the state tensors. The most
+    important interface is _distribute_region which will be used when DTensor executes
+    a random op (an operator that calls RNG).
+    """
+
+    def __init__(self, device: torch.device):
+        # pyrefly: ignore [read-only]
+        self._device = device
+        self._device_handle = _get_device_handle(self._device.type)
+        if not (self._device_handle and self._device_handle.is_available()):
+            raise RuntimeError(
+                f"{self.__class__.__name__} instantiation requires the presence of "
+                f"{device.type} device but couldn't find."
+            )
+        self._use_distribute_region = True
+
+    @property
+    def distribute_region_enabled(self) -> bool:
+        return self._use_distribute_region
+
+    @distribute_region_enabled.setter
+    def distribute_region_enabled(self, value) -> None:
+        self._use_distribute_region = value
+
+    def _distribute_region(
+        self, spec: DTensorSpec, generator: torch.Generator | None = None
+    ):
+        pass
+
+    def _manual_seed(self, parallel_seed: int) -> None:
+        pass
+
+
+class OffsetBasedRNGTracker(_RNGStateTracker):
+    """
+    This subclass of ``_RNGStateTracker`` defines the default policy of how RNG states
+    should be shared and synchronized among all ranks to respect the semantics of DTensor
+    random operators.
+
+    note: _RNGStateTracker only supports cuda/cuda-like device.
+    """
+
+    def __init__(
+        self,
+        device_mesh: DeviceMesh,
+        run_state_sync: bool = True,
+    ):
+        super().__init__(_resolve_device(device_mesh=device_mesh))
+        assert self._device_handle is not None
+        # DTensor RNG tracker so far only supports CUDA/CUDA-like devices
+        if self._device.type == "cpu":
+            raise RuntimeError(
+                f"{self.__class__.__name__} instantiation requires the presence of "
+                f"CUDA/CUDA-like/XPU device. Got {self._device.type} instead."
+            )
+
+        rng_state = self._get_device_state()
+        if run_state_sync:
+            # synchronize RNG state using rank 0's current one
+            torch.distributed.broadcast(rng_state, 0)
+            my_rng_state = self._get_device_state()
+            if not all(my_rng_state == rng_state):
+                logger.warning(
+                    "DTensor is synchronizing RNG states of every rank with the state from rank 0. "
+                    "This behavior is deprecated. "
+                    "Please call `torch.manual_seed()` on every rank that participates in SPMD DTensor Operations with "
+                    "the same seed. If using Pipeline Parallelism, each pipeling state would use a different seed, "
+                    "but all ranks belonging to one pipeline stage would use the same seed."
+                )
+            self._set_device_state(rng_state)
+
+    def _get_device_state(self) -> torch.Tensor:
+        if self._device.type == "hpu":
+            self._device_handle.set_rng_ctx("philox")
+        rng_state = self._device_handle.get_rng_state().to(self._device)
+        if self._device.type == "hpu":
+            self._device_handle.unset_rng_ctx("philox")
+        return rng_state
+
+    def _set_device_state(self, state: torch.Tensor):
+        # It seems that the underlying generator wants a cpu tensor but the dtensor code expects `_get_device_state`
+        # to convert to a 'device' tensor, probably because we may use it with our backend comms for sync/debug
+        # for now, we just convert back to cpu here to make sure it always works.
+        if self._device.type == "hpu":
+            self._device_handle.set_rng_ctx("philox")
+        self._device_handle.set_rng_state(state.to("cpu"))
+        if self._device.type == "hpu":
+            self._device_handle.unset_rng_ctx("philox")
+
+    @contextlib.contextmanager
+    def _distribute_region(
+        self, spec: DTensorSpec, generator: torch.Generator | None = None
+    ):
+        from torch.distributed._local_tensor import maybe_enable_local_tracker
+
+        if local_tracker_context := maybe_enable_local_tracker(
+            self._device.type, self.distribute_region_enabled, spec, generator
+        ):
+            with local_tracker_context:
+                yield
+            return
+
+        # regular (non-LocalTensor) mode
+        if generator is not None:
+            # This is a little hacky, but for any user-passed generator, we store its state under a unique key,
+            # not because we need to keep a copy of it but because its the easiest way to make it work with the
+            # existing set/get APIs. We also ensure we remove it from rng_states after each _distribute_region.
+            state = _PhiloxState(generator.get_state())
+        else:
+            state = _PhiloxState(self._get_device_state())
+
+        if self.distribute_region_enabled:
+            if self._device.type == "hpu":
+                self._device_handle.set_rng_ctx("philox")
+            old_offset = state.offset
+            self._set_pre_op_offset(state, spec)
+            with torch.random.fork_rng(
+                devices=[self._device], device_type=self._device.type
+            ):
+                assert self._device_handle is not None
+                self._device_handle.set_rng_state(state.state)
+                try:
+                    yield  # execute the region code
+                finally:
+                    # update offset to synchronize among ranks
+                    self._set_post_op_offset(state, spec, old_offset)
+            if self._device.type == "hpu":
+                self._device_handle.unset_rng_ctx("philox")
+        else:
+            yield
+
+        if generator is not None:
+            # ensure we (a) propagate the state advancement back to the user's RNG so its visible and impacts any future
+            # usage of that RNG (dtensor or non-dtensor), (b) drop it from our own cache so that if the user updates
+            # the seed value in their rng and uses it with DTensor again, we always use the latest value
+            generator.set_state(state.state)
+        else:
+            self._set_device_state(state.state)
+
+    def _set_pre_op_offset(self, state: _PhiloxState, spec: DTensorSpec) -> None:
+        """Set the starting RNG offset for current device's local shard before actual
+        op execution. The pre_op_offset value should start from the current RNG offset
+        and increment by the size of local shard until it reaches the size of the whole
+        DTensor. For different ranks that hold the same DTensor shard, their pre_op_offset
+        will be the same.
+
+        Args:
+            state (:class:`Tensor`): The generator state to modify
+            spec (:class:`DTensorSpec`): the spec of the DTensor object on which
+                we prepare the offset for running random ops.
+
+        Returns:
+            None
+
+        .. warning::
+            Note that, current implementation does not consider DTensor's continguity.
+
+        Example:
+            take a DTensor of shape [8, 16] as an example. Assume that the DTensor
+            is placed on a device mesh with placements ([Shard(1), Replicate(), Shard(0)]),
+            and the mesh is:
+                [[[0, 1], [2, 3]], [[4, 5], [6, 7]]]
+            ``spec.mesh.get_coordinate()`` provides the coordinate of the current rank
+            in the mesh. For example, the coordinate of rank 5 is (1, 0, 1).
+
+            Another concept to introduce besides rank coordinate is shard coordinate.
+            Each rank holds a local shard of the DTensor. In the example, the DTensor
+            is partitioned into 4 [4, 8] shards. The first shard has 2 replicas and
+            rank 0 (coord (0, 0, 0)) and rank 2 (coord (0, 1, 0)) have 1 replica each.
+            That being said, the local shard on rank 0 and rank 2 correspond to the same
+            shard of the DTensor. To denote each DTensor shard, we use a shard coordinate
+            (in the example, it will be a tuple (i, j) where shard (i, j) has the slice
+            DTensor[4 * i : 4 * (i + 1), 8 * j : 8 * (j + 1)], 0 <= i < 2, 0 <= j < 2).
+
+            Once we have rank coordinate and shard coordinate, we can calculate on each rank
+            what shard of the DTensor the rank holds, with the help of dim_map. The dim_map
+            of the above DTensor is [2, 0] so the shard coordinate of a rank with rank coord
+            (x, y, z) is simply (z, x) by taking(rank_coord[dim_map[0]],rank_coord[dim_map[1]]).
+            Following this calculation,
+            rank 0 and rank 2 holds the shard of coord (0, 0);
+            rank 1 and rank 3 holds the shard of coord (0, 1);
+            rank 4 and rank 6 holds the shard of coord (1, 0);
+            rank 5 and rank 7 holds the shard of coord (1, 1);
+
+            The last value to calculate before obtaining the starting offset is the shard linear index.
+            The starting offset for each rank will be its shard_linear_index * local_tensor_numel.
+        """
+        mesh = spec.mesh
+        mesh_coordinate = mesh.get_coordinate()
+        assert mesh_coordinate is not None
+
+        # Compute shard index and total number of shards on each tensor dim
+        shard_idx_by_dim, total_num_shards_by_dim = _calc_shard_info(
+            mesh_coordinate, spec
+        )
+
+        # compute shard linear index
+        shard_linear_idx = self._calc_shard_linear_idx(
+            shard_idx_by_dim, total_num_shards_by_dim
+        )
+
+        # compute starting offset using the first shard's size
+        local_size_on_rank_0 = _calc_first_shard_size(spec)
+
+        from torch.distributed.tensor._ops.utils import prod
+
+        local_size = prod(local_size_on_rank_0)
+
+        # get current RNG offset
+        current_offset = state.offset
+
+        # pytorch: offset must be multiple of 4
+        # source: aten/src/ATen/cuda/CUDAGeneratorImpl.cpp
+        offset_incr = (shard_linear_idx * local_size + 3) // 4 * 4
+        state.offset = current_offset + offset_incr
+
+    def _set_post_op_offset(
+        self, state: _PhiloxState, spec: DTensorSpec, old_offset: int
+    ) -> None:
+        """Sets the RNG to a synchronized state after running the local random op. Every
+        rank should set its RNG offset to `old_offset + DTensor.numel()` where old_offset is
+        the offset before calling `set_pre_op_offset` i.e. the offset before running DTensor
+        random ops.
+
+        Args:
+            state (:class:`Tensor`): The generator state to modify.
+            spec (:class:`DTensorSpec`): the spec of the DTensor object on which
+                we post-process the offset for running random ops.
+
+        Returns:
+            None
+        """
+        dtensor_shape = spec.shape
+
+        from torch.distributed.tensor._ops.utils import prod
+
+        numel = prod(dtensor_shape)
+        # pytorch: offset must be multiple of 4
+        # source: aten/src/ATen/cuda/CUDAGeneratorImpl.cpp
+        numel = (numel + 3) // 4 * 4
+        state.offset = old_offset + numel
+
+    def _calc_shard_linear_idx(
+        self, shard_coord: list[int], shard_size: list[int]
+    ) -> int:
+        return _calc_shard_linear_idx(shard_coord, shard_size)
+
+
+def _calc_first_shard_size(spec: DTensorSpec) -> list[int]:
+    local_size_on_rank_0 = list(spec.shape)
+    for idx, placement in enumerate(spec.placements):
+        if isinstance(placement, Shard | _StridedShard):
+            mesh_dim_size = spec.mesh.size(idx)
+            shard_dim = placement.dim
+            local_size_on_rank_0[shard_dim], _ = placement._local_shard_size_and_offset(
+                spec.shape[shard_dim],
+                mesh_dim_size,
+                0,
+            )
+    return local_size_on_rank_0
+
+
+def _calc_shard_info(
+    mesh_coordinate: list[int], spec: DTensorSpec
+) -> tuple[list[int], list[int]]:
+    mesh = spec.mesh
+    # note: dim_map does not allow double sharding which is the FSDP(fully_shard)+TP
+    # case. Replace the custom logic with dim_map once we support it.
+    dim_map: list[int | list[int]] = [-1] * spec.ndim
+    for i, placement in enumerate(spec.placements):
+        if isinstance(placement, Shard | _StridedShard):
+            shard_dim = placement.dim
+            if dim_map[shard_dim] == -1:
+                dim_map[shard_dim] = [i]
+            else:
+                mesh_dim_list = dim_map[shard_dim]
+                assert isinstance(mesh_dim_list, list)
+                mesh_dim_list.append(i)
+
+    # Compute shard coordinate:
+    # The coordinate on each tensor dim is a tuple (idx, range)
+    # If a DTensor is partitioned on its dim i into n shards, and the current rank
+    # holds the j-th, then its shard coordinate will be (idx=j, range=n) on dim i
+    assert mesh_coordinate is not None
+    mesh_size = mesh.shape
+    shard_idx_by_dim = []
+    total_num_shards_by_dim = []  # total number of shards on each tensor dim
+    for mesh_dim in dim_map:
+        shard_idx = 0
+        total_num_shards = 1
+        # the tensor dim is sharded on more than 1 mesh dim
+        if isinstance(mesh_dim, list):
+            rank_coord = [mesh_coordinate[d] for d in mesh_dim]
+            num_shards = [mesh_size[d] for d in mesh_dim]
+            # compute the shard idx and total number of shards
+            for idx, size in zip(rank_coord, num_shards):
+                shard_idx = shard_idx * size + idx
+                total_num_shards *= size
+
+        shard_idx_by_dim.append(shard_idx)
+        total_num_shards_by_dim.append(total_num_shards)
+    return shard_idx_by_dim, total_num_shards_by_dim
+
+
+def _calc_shard_linear_idx(shard_coord: list[int], shard_size: list[int]) -> int:
+    # compute shard linear index
+    shard_linear_idx = 0
+    shard_coord_stride = 1
+    for idx, size in zip(reversed(shard_coord), reversed(shard_size)):
+        shard_linear_idx += idx * shard_coord_stride
+        shard_coord_stride *= size
+
+    return shard_linear_idx
+
+
+def _resolve_device(device_mesh: DeviceMesh) -> torch.device:
+    device_type = device_mesh.device_type
+    device_handle = _get_device_handle(device_type)
+    assert device_handle is not None
+    device_idx = device_mesh.get_rank() % device_handle.device_count()
+
+    @maybe_run_for_local_tensor
+    def get_device(device_idx):
+        return torch.device(f"{device_type}:{device_idx:d}")
+
+    return get_device(device_idx)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/_redistribute.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/_redistribute.py
new file mode 100644
index 0000000000000000000000000000000000000000..7119fd9ae6529c174f7a34f55145434a35070e2a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/_redistribute.py
@@ -0,0 +1,1067 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+import contextlib
+import dataclasses
+import itertools
+import logging
+import weakref
+from collections import defaultdict
+from collections.abc import Sequence
+from functools import cache
+from typing import cast, NamedTuple, Optional
+
+import torch
+import torch.distributed._functional_collectives as funcol
+import torch.distributed.tensor._api as dtensor
+from torch.distributed._functional_collectives import _are_we_tracing
+from torch.distributed.tensor._dtensor_spec import (
+    DTensorSpec,
+    ShardOrder,
+    ShardOrderEntry,
+    TensorMeta,
+)
+from torch.distributed.tensor.device_mesh import DeviceMesh
+from torch.distributed.tensor.placement_types import (
+    _StridedShard,
+    Partial,
+    Placement,
+    Replicate,
+    Shard,
+)
+from torch.utils._debug_mode import get_active_debug_mode
+
+
+logger = logging.getLogger(__name__)
+
+# Global configuration flag to control the redistribution planning strategy.
+# When True, forces the graph-based algorithm using Dijkstra's shortest path.
+# When False, prefers the greedy algorithm for faster planning. Uses the graph-based algorithm
+# only when necessary to support strided-shard redistribution
+_FORCE_MIN_COST_REDISTRIBUTION_PLAN: Optional[bool] = None
+
+
+@contextlib.contextmanager
+def use_min_cost_redistribution_plan(enabled: bool = True):
+    """
+    Context manager to control the redistribution planning strategy for DTensor operations.
+
+    This context manager allows you to choose between two algorithms for computing the
+    sequence of collective operations needed to redistribute a DTensor from one placement
+    to another:
+
+    - **Graph-based**: Uses Dijkstra's algorithm to find the minimum-cost path
+      through all possible placement transformations. This approach considers the global
+      cost of all collective operations and finds the optimal sequence. Best for complex
+      redistribution patterns where reducing communication cost and memory overhead is critical.
+
+    - **Greedy**: Uses a heuristic approach that makes locally optimal choices
+      at each step. This is faster to compute but may not produce the globally optimal
+      transformation sequence. Best for simple redistribution patterns or when planning
+      speed is more important than optimal communication.
+
+    **Default Behavior (without this context manager):**
+
+    When this context manager is NOT used, the algorithm selection follows this priority:
+
+    1. **Non-default shard orders**
+       → Always use graph-based algorithm (required for correctness)
+
+    2. **Explicit `use_graph_based_transform` parameter** to `_gen_transform_infos_non_cached`
+       → Use the specified algorithm (True = graph-based, False = greedy)
+
+    3. **No explicit parameter** (default case)
+       → Use greedy algorithm for faster planning
+
+    **Behavior with this context manager:**
+
+    This context manager overrides the default selection by setting the global flag
+    `_FORCE_MIN_COST_REDISTRIBUTION_PLAN`, which takes precedence over the explicit
+    `use_graph_based_transform` parameter (but not over non-default shard order requirements).
+
+    **Cache Considerations:**
+
+    The redistribution planner caches transform info for performance via the `@cache`
+    decorator on `_gen_transform_infos`. If you need to change the algorithm selection
+    for the same input specs, clear the cache using `_gen_transform_infos.cache_clear()`
+    to ensure the new setting takes effect and doesn't reuse cached results from a
+    previous run.
+
+    Args:
+        enabled (bool): If True, forces the use of the graph-based algorithm.
+                       If False, forces the use of the greedy algorithm.
+                       Default: True
+    """
+    global _FORCE_MIN_COST_REDISTRIBUTION_PLAN
+    old_value = _FORCE_MIN_COST_REDISTRIBUTION_PLAN
+    _FORCE_MIN_COST_REDISTRIBUTION_PLAN = enabled
+    try:
+        yield
+    finally:
+        _FORCE_MIN_COST_REDISTRIBUTION_PLAN = old_value
+
+
+class _TransformInfo(NamedTuple):
+    mesh_dim: int
+    src_dst_placements: tuple[Placement, Placement]
+    # logical_shape on this mesh dimension
+    logical_shape: list[int]
+
+
+# Global cache for DTensorRedistributePlanner instances
+_planner_cache: dict[
+    tuple[weakref.ReferenceType, int], "DTensorRedistributePlanner"
+] = {}
+
+
+def get_redistribute_planner(
+    device_mesh: DeviceMesh, tensor_dimension: int
+) -> "DTensorRedistributePlanner":
+    """
+    Factory function to get or create a DTensorRedistributePlanner instance.
+    This function provides transparent caching of planner instances based on
+    device_mesh and tensor_dimension. Multiple calls with the same parameters
+    will return the same cached instance for better performance.
+    Args:
+        device_mesh: The device mesh for the planner
+        tensor_dimension: Number of tensor dimensions
+    Returns:
+        A DTensorRedistributePlanner instance (potentially cached)
+    """
+    cache_key = (weakref.ref(device_mesh), tensor_dimension)
+
+    if cache_key not in _planner_cache:
+        planner = DTensorRedistributePlanner(device_mesh, tensor_dimension)
+        _planner_cache[cache_key] = planner
+
+    return _planner_cache[cache_key]
+
+
+def clear_redistribute_planner_cache() -> None:
+    """Clear the cache of DTensorRedistributePlanner instances."""
+    _planner_cache.clear()
+
+
+class DTensorRedistributePlanner:
+    """
+    This class is used to plan the collective calls to transform the local shard
+    of the DTensor from its current spec to the target spec.
+    Suppose there are N tensor dimensions and M mesh dimensions, the total
+    possible state size will be (N+2)*M*M!.
+    Note: Use get_redistribute_planner() factory function instead of direct
+    instantiation for automatic caching.
+    """
+
+    @dataclasses.dataclass(frozen=True, slots=True)
+    class DistState:
+        placements: tuple[Placement, ...]
+        tensor_dim_to_mesh_dim: ShardOrder
+        _hash: int | None = dataclasses.field(
+            default=None, init=False, repr=False, compare=False
+        )
+
+        def __str__(self):
+            return DTensorSpec.format_shard_order_str(
+                self.placements,
+                self.tensor_dim_to_mesh_dim,
+            )
+
+        def __repr__(self):
+            return self.__str__()
+
+        def __post_init__(self):
+            # precompute hash after all attributes are set
+            object.__setattr__(
+                self,
+                "_hash",
+                self._compute_hash(),
+            )
+
+        def __hash__(self) -> int:
+            return self._hash if self._hash is not None else self._compute_hash()
+
+        def _compute_hash(self) -> int:
+            return hash(
+                (
+                    self.placements,
+                    self.tensor_dim_to_mesh_dim,
+                )
+            )
+
+        def __eq__(self, other: object) -> bool:
+            if not isinstance(other, DTensorRedistributePlanner.DistState):
+                return False
+            if self._hash != other._hash:
+                return False
+            return (
+                self.placements,
+                self.tensor_dim_to_mesh_dim,
+            ) == (
+                other.placements,
+                other.tensor_dim_to_mesh_dim,
+            )
+
+    def _to_tuple(self, x):
+        """Convert a nested list structure to a nested tuple structure."""
+        if isinstance(x, list | tuple):
+            return tuple(self._to_tuple(item) for item in x)
+        return x
+
+    @staticmethod
+    def _dict_to_ShardOrder(x: dict[int, list[int]]) -> ShardOrder:
+        """Convert dict to ShardOrder"""
+        return tuple(
+            ShardOrderEntry(tensor_dim=key, mesh_dims=tuple(value))
+            for key, value in sorted(x.items())
+            if value
+        )
+
+    @staticmethod
+    def _ShardOrder_to_dict(x: ShardOrder) -> dict[int, list[int]]:
+        """Convert ShardOrder to dict with tensor dim as key"""
+        tensor_mesh_dim_dict = defaultdict(list)
+        for entry in x:
+            tensor_mesh_dim_dict[entry.tensor_dim] = list(entry.mesh_dims)
+        return tensor_mesh_dim_dict
+
+    @staticmethod
+    def stringify_transform_infos(
+        mesh: DeviceMesh,
+        transform_infos: Sequence[_TransformInfo],
+        src_placement: tuple[Placement, ...],
+        src_shard_order: ShardOrder | None = None,
+    ) -> str:
+        """
+        Generate a string representation of the sequence of state transitions
+        (placements and shard orders) as described by the given transform_info.
+
+        Args:
+            mesh: The DeviceMesh used for the redistribution.
+            transform_infos: A sequence of _TransformInfo objects describing each
+                transformation step.
+            src_placement: The initial tuple of Placement objects.
+            src_shard_order: (Optional) The initial ShardOrder representing
+                the mapping of tensor dimensions to mesh dimensions. If None,
+                the default shard order is computed from src_placement and mesh.
+
+        Returns:
+            A string showing the sequence of DistState transitions, separated by '->'.
+        """
+        assert len(src_placement) == mesh.ndim
+        if src_shard_order is None:
+            src_shard_order = DTensorSpec.compute_default_shard_order(src_placement)
+        cur_placement = list(src_placement)
+        shard_order_dict = DTensorRedistributePlanner._ShardOrder_to_dict(
+            src_shard_order
+        )
+        cur_state = DTensorRedistributePlanner.DistState(
+            tuple(cur_placement), src_shard_order
+        )
+        state_list = [
+            cur_state,
+        ]
+        for transform_info in transform_infos:
+            src_dim_placement, dst_dim_placement = transform_info.src_dst_placements
+            if src_dim_placement.is_shard():
+                src_dim = src_dim_placement.dim  # type: ignore[attr-defined]
+                assert (
+                    src_dim in shard_order_dict and len(shard_order_dict[src_dim]) > 0
+                )
+                shard_order_dict[src_dim].pop()
+            if dst_dim_placement.is_shard():
+                dst_dim = dst_dim_placement.dim  # type: ignore[attr-defined]
+                if dst_dim not in shard_order_dict:
+                    shard_order_dict[dst_dim] = []
+                shard_order_dict[dst_dim].append(transform_info.mesh_dim)
+            cur_placement[transform_info.mesh_dim] = dst_dim_placement
+            new_state = DTensorRedistributePlanner.DistState(
+                tuple(cur_placement),
+                DTensorRedistributePlanner._dict_to_ShardOrder(shard_order_dict),
+            )
+            state_list.append(new_state)
+        return "->".join([str(s) for s in state_list])
+
+    def __init__(
+        self,
+        device_mesh: DeviceMesh,
+        tensor_dimension: int,
+    ) -> None:
+        """
+        Initialize DTensorRedistributePlanner.
+
+        Args:
+            device_mesh: The device mesh for this planner
+            tensor_dimension: Number of tensor dimensions
+        """
+        self.device_mesh = device_mesh
+        self.coordinate = device_mesh.get_coordinate()
+        assert self.coordinate is not None
+        self.tensor_dimension = tensor_dimension
+        self.setup_collective_cost()
+
+    def setup_collective_cost(
+        self,
+        all_reduce_cost: int = 4,
+        all_to_all_cost: int = 1,
+        all_gather_cost: int = 2,
+        reduce_scatter_cost: int = 2,
+        chunk_cost: int = 0,
+    ) -> None:
+        """
+        Set up the cost weights for different collective operations.
+        """
+        # those can be turned in a handler considering the tensor dim size
+        self.all_reduce_cost = all_reduce_cost
+        self.all_to_all_cost = all_to_all_cost
+        self.all_gather_cost = all_gather_cost
+        self.reduce_scatter = reduce_scatter_cost
+        self.chunk_cost = chunk_cost
+
+    def get_next_state(
+        self,
+        placements: tuple[Placement, ...],
+        tensor_mesh_dim_tuple: ShardOrder,
+    ) -> dict["DTensorRedistributePlanner.DistState", int]:
+        # We map tensor dimensions to device mesh axes, similar to JAX-style
+        # sharding representation. Notation:
+        # S()[] means tensor dimension
+        #  is sharded on the listed device mesh axes, where
+        #  is sorted by device order.
+        #
+        # To generalize to arbitrary dimensionality, we use the following notation:
+        #   S(a)[x, ...]   : tensor dimension 'a' is sharded on device mesh axes x, ... (variadic, possibly empty)
+        #   R[...]         : replicated on the listed device mesh axes (possibly empty)
+        #   P[...]         : partial on the listed device mesh axes (possibly empty)
+        # The ellipsis '...' denotes a variadic wildcard, i.e., zero or more device mesh axes.
+        #
+        # Below are possible transitions from one sharding state to another.
+        # We use `S` for Shard, `R` for Replicate, and `P` for Partial.
+        #
+        # Case 1. Shard(a) -> Shard(b), use all-to-all (a2a), applies to:
+        #   S(a)[..., x] -> S(b)[..., x]
+        #   or
+        #   S(a)[..., x, y]S(b)[..., z, k] -> S(a)[..., x]S(b)[..., z, k, y]
+        #   where device order of 'y' > device order of 'z' and 'k'
+        #
+        # Case 2. Shard() -> Replicate(), use all-gather, applies to:
+        #   S(a)[..., x, y, z] -> S(a)[..., x, y]
+        #
+        # Case 3. Partial() -> Replicate(), use all-reduce, applies to:
+        #   P[..., x, y] -> P[..., y] or P[..., x]
+        #   Note: this case can be disabled because all-reduce technically is not
+        #   a primitive since it combines a reduce-scatter + all-gather.
+        #
+        # Case 4. Replicate() -> Shard(), use chunk, applies to:
+        #   S(a)[..., z] -> S(a)[..., z, y] (`a` can be any tensor dim). Note that
+        #   'y' must be after 'z'.
+        #
+        # Case 5. Partial() -> Shard(), use reduce-scatter, applies to:
+        #  P[..., x, y] -> P[..., x]S(a)[..., y] or P[..., x, y] -> P[..., y]S(a)[..., x]
+        #
+        # Case 6. Replicate() -> Partial(), local math op, applies to:
+        #   R* -> P[..., x]
+        #
+        # NB: Device order in Partial placement doesn't take impact. We should be able
+        # to operate on any Partial mesh dim.
+
+        # list of [DistState, cost]
+        all_next_state: dict[DTensorRedistributePlanner.DistState, int] = {}
+
+        tensor_mesh_dim_dict = DTensorRedistributePlanner._ShardOrder_to_dict(
+            tensor_mesh_dim_tuple
+        )
+        ######################################################################
+        # handle case 1: Shard(a) -> Shard(b)
+        # For S(a), S(b), only the last device order of S(a) and S(b) can be a2a
+        # interchangeably.
+
+        # convert sparse tuple
+        for entry in tensor_mesh_dim_tuple:
+            src_tensor_dim = entry.tensor_dim
+            for dst_tensor_dim in range(self.tensor_dimension):
+                if src_tensor_dim == dst_tensor_dim:
+                    continue
+                # try move the last sharded device dim from
+                # Shard(src_tensor_dim) to Shard(dst_tensor_dim)
+                move_mesh_dim = tensor_mesh_dim_dict[src_tensor_dim].pop()
+                tensor_mesh_dim_dict[dst_tensor_dim].append(move_mesh_dim)
+                new_placements = list(placements)
+                new_placements[move_mesh_dim] = Shard(dst_tensor_dim)
+                dist_state = self.DistState(
+                    self._to_tuple(new_placements),
+                    DTensorRedistributePlanner._dict_to_ShardOrder(
+                        tensor_mesh_dim_dict
+                    ),
+                )
+                all_next_state[dist_state] = self.all_to_all_cost
+                # reset content for next iteration
+                tensor_mesh_dim_dict[src_tensor_dim].append(move_mesh_dim)
+                tensor_mesh_dim_dict[dst_tensor_dim].pop()
+        # TODO(zpcore): support discovering submesh to prevent padding when
+        # tensor dim is not divisible by the mesh dim.
+
+        ######################################################################
+        # handle case 2: Shard() -> Replicate()
+        for entry in tensor_mesh_dim_tuple:
+            src_tensor_dim = entry.tensor_dim
+            move_mesh_dim = tensor_mesh_dim_dict[src_tensor_dim].pop()
+            new_placements = list(placements)
+            new_placements[move_mesh_dim] = Replicate()
+            dist_state = self.DistState(
+                self._to_tuple(new_placements),
+                DTensorRedistributePlanner._dict_to_ShardOrder(tensor_mesh_dim_dict),
+            )
+            tensor_mesh_dim_dict[src_tensor_dim].append(move_mesh_dim)
+            all_next_state[dist_state] = self.all_gather_cost
+
+        ######################################################################
+        # handle case 3: Partial() -> Replicate()
+        for src_mesh_dim, placement in enumerate(placements):
+            if not isinstance(placement, Partial):
+                continue
+            new_placements = list(placements)
+            new_placements[src_mesh_dim] = Replicate()
+            dist_state = self.DistState(
+                self._to_tuple(new_placements), tensor_mesh_dim_tuple
+            )
+            all_next_state[dist_state] = self.all_reduce_cost
+
+        ######################################################################
+        # handle case 4: Replicate() -> Shard()
+        for mesh_dim, placement in enumerate(placements):
+            if not isinstance(placement, Replicate):
+                continue
+            for dst_tensor_dim in range(self.tensor_dimension):
+                # try convert placement[mesh_dim] to Shard(dst_tensor_dim)
+                new_placements = list(placements)
+                new_placements[mesh_dim] = Shard(dst_tensor_dim)
+                tensor_mesh_dim_dict[dst_tensor_dim].append(mesh_dim)
+                dist_state = self.DistState(
+                    self._to_tuple(new_placements),
+                    DTensorRedistributePlanner._dict_to_ShardOrder(
+                        tensor_mesh_dim_dict
+                    ),
+                )
+                all_next_state[dist_state] = self.chunk_cost
+                tensor_mesh_dim_dict[dst_tensor_dim].pop()
+
+        ######################################################################
+        # handle case 5: Partial() -> Shard()
+        for mesh_dim, placement in enumerate(placements):
+            if not isinstance(placement, Partial):
+                continue
+            for dst_tensor_dim in range(self.tensor_dimension):
+                # try convert placement[mesh_dim] to Shard(dst_tensor_dim)
+                new_placements = list(placements)
+                new_placements[mesh_dim] = Shard(dst_tensor_dim)
+                tensor_mesh_dim_dict[dst_tensor_dim].append(mesh_dim)
+                dist_state = self.DistState(
+                    self._to_tuple(new_placements),
+                    DTensorRedistributePlanner._dict_to_ShardOrder(
+                        tensor_mesh_dim_dict
+                    ),
+                )
+                all_next_state[dist_state] = self.reduce_scatter
+                tensor_mesh_dim_dict[dst_tensor_dim].pop()
+
+        ######################################################################
+        # handle case 6: Replicate() -> Partial(), default to partial(sum)
+        for mesh_dim, placement in enumerate(placements):
+            if not isinstance(placement, Replicate):
+                continue
+            new_placements = list(placements)
+            new_placements[mesh_dim] = Partial()
+            dist_state = self.DistState(
+                self._to_tuple(new_placements), tensor_mesh_dim_tuple
+            )
+            all_next_state[dist_state] = self.chunk_cost
+
+        return all_next_state
+
+    # TODO(zpcore): if the dst_state contains special placement like
+    # `_MaskPartial`, we will never reach that state. Need to support this case.
+    def find_min_cost_path(
+        self, src_state: DistState, dst_state: DistState
+    ) -> list["DTensorRedistributePlanner.DistState"]:
+        """
+        Find the min cost path from src_state to dst_state using Dijkstra's
+        algorithm.
+
+        Args:
+            src_state: The source state
+            dst_state: The destination state
+
+        Returns:
+            A list of states representing the min cost path from src_state to
+            dst_state
+        """
+        import heapq
+
+        # priority queue (cost, counter, state, path) for Dijkstra's algorithm
+        # use counter to break ties and avoid comparing DistState objects
+        counter = 0
+        pq: list[
+            tuple[
+                int,
+                int,
+                DTensorRedistributePlanner.DistState,
+                list[DTensorRedistributePlanner.DistState],
+            ]
+        ] = [(0, counter, src_state, [src_state])]
+        visited = set()
+        while pq:
+            cost, _, current_state, path = heapq.heappop(pq)
+            if current_state == dst_state:
+                return path
+            if current_state in visited:
+                continue
+            visited.add(current_state)
+            # get all possible next states and their costs
+            next_states = self.get_next_state(
+                current_state.placements, current_state.tensor_dim_to_mesh_dim
+            )
+            for next_state, transition_cost in next_states.items():
+                if next_state not in visited:
+                    new_cost = cost + transition_cost
+                    new_path = path + [next_state]
+                    counter += 1
+                    heapq.heappush(pq, (new_cost, counter, next_state, new_path))
+        raise AssertionError(
+            f"No path found from src_state {src_state} to dst_state {dst_state}"
+        )
+
+    def get_logical_shape(
+        self,
+        src_state: "DTensorRedistributePlanner.DistState",
+        mesh_dim: int,
+        full_tensor_shape: tuple[int, ...],
+    ) -> list[int]:
+        new_logical_shape = list(full_tensor_shape)
+        assert self.coordinate is not None
+        for entry in src_state.tensor_dim_to_mesh_dim:
+            tensor_dim = entry.tensor_dim
+            mesh_dims = entry.mesh_dims
+            assert len(mesh_dims) > 0
+            for mdim in mesh_dims:
+                if mdim == mesh_dim:
+                    continue
+                new_size = Shard.local_shard_size_and_offset(
+                    new_logical_shape[tensor_dim],
+                    self.device_mesh.size(mesh_dim=mdim),
+                    self.coordinate[mdim],
+                )[0]
+                new_logical_shape[tensor_dim] = new_size
+        return new_logical_shape
+
+    def generate_graph_based_transform_infos(
+        self,
+        src_spec: DTensorSpec,
+        dst_spec: DTensorSpec,
+        full_tensor_shape: tuple[int, ...],
+    ) -> list[_TransformInfo]:
+        # In case _StridedShard exists in placements, we let _StridedShard have
+        # higher priority to express shard_order.
+        if any(
+            isinstance(placement, _StridedShard) for placement in src_spec.placements
+        ):
+            src_placements, src_shard_order = (
+                DTensorSpec._normalize_placements_into_shard_order(
+                    src_spec.placements, src_spec.mesh
+                )
+            )
+        else:
+            src_placements = src_spec.placements
+            src_shard_order = src_spec.shard_order
+        if any(
+            isinstance(placement, _StridedShard) for placement in dst_spec.placements
+        ):
+            dst_placements, dst_shard_order = (
+                DTensorSpec._normalize_placements_into_shard_order(
+                    dst_spec.placements, dst_spec.mesh
+                )
+            )
+        else:
+            dst_placements = dst_spec.placements
+            dst_shard_order = dst_spec.shard_order
+        if src_shard_order is None or dst_shard_order is None:
+            raise NotImplementedError(
+                "Redistribution of _StridedShard placement is only supported for "
+                "_StridedShard that can be converted to ordered Shard placements. "
+                "Full _StridedShard redistribution support is not yet implemented."
+            )
+        src_state = self.DistState(src_placements, src_shard_order)
+        dst_state = self.DistState(dst_placements, dst_shard_order)
+        transform_infos: list[_TransformInfo] = []
+        state_path = self.find_min_cost_path(src_state, dst_state)
+        for cur_state, nxt_state in itertools.pairwise(state_path):
+            # find the mesh_dim that is different between cur_state and nxt_state
+            if cur_state.placements != nxt_state.placements:
+                update_mesh_dim = -1
+                for mesh_dim, (cur_placement, nxt_placement) in enumerate(
+                    zip(cur_state.placements, nxt_state.placements)
+                ):
+                    if cur_placement != nxt_placement:
+                        if update_mesh_dim != -1:
+                            raise AssertionError(
+                                "Multiple mesh_dims are different between cur_state and nxt_state"
+                            )
+                        update_mesh_dim = mesh_dim
+                        logical_shape = self.get_logical_shape(
+                            cur_state, mesh_dim, full_tensor_shape
+                        )
+                        transform_infos.append(
+                            _TransformInfo(
+                                mesh_dim=update_mesh_dim,
+                                src_dst_placements=(cur_placement, nxt_placement),
+                                logical_shape=logical_shape,
+                            )
+                        )
+
+        return transform_infos
+
+    def generate_greedy_transform_infos(
+        self,
+        src_spec: DTensorSpec,
+        dst_spec: DTensorSpec,
+    ) -> list[_TransformInfo]:
+        """
+        Generate the transform infos from the source placements to the target placements.
+
+        To transform from source to target placement it might have multiple steps, i.e. it
+        might decompose Si -> Sj into Si -> R -> Sj.
+        This would detect if there're mis-aligned/nested shardings between src/dst placements.
+        E.g. Suppose the redistribution to perform is (Shard(0), Shard(0)) -> (Replicate(), Shard(0)),
+        in this case Shard(0) -> Shard(0) for mesh dimension 1 actually needs resharding, because in
+        the former is a nested-sharding of a tensor already already sharded dimension 0, whereas
+        the latter is the first sharding on tensor dimension 0.
+        """
+        # logical shape records the logic tensor shape on the mesh dimension
+        # this is useful to ensure uneven sharding gets correct output shape
+        assert self.coordinate is not None
+        initial_logical_shape = list(src_spec.shape)
+        mesh_dims_to_logical_shape = [initial_logical_shape]
+        transform_infos: list[_TransformInfo] = []
+        if self.device_mesh.ndim == 1:
+            # if device_mesh is 1D, redistribute is a simple direct
+            # transformation
+            transform_infos.append(
+                _TransformInfo(
+                    mesh_dim=0,
+                    src_dst_placements=(src_spec.placements[0], dst_spec.placements[0]),
+                    logical_shape=initial_logical_shape,
+                )
+            )
+            return transform_infos
+
+        # Handle multi-dim device mesh placement redistribution First, we need
+        # to build the logical shape for each mesh dim for correct allgather
+        # uneven shards on each mesh dim (with dynamic padding)
+        for i, src in enumerate(src_spec.placements):
+            current_logical_shape = mesh_dims_to_logical_shape[i]
+            if isinstance(src, Shard):
+                if i < self.device_mesh.ndim - 1:
+                    # calculate and save the logical shape for this sharding
+                    mesh_dim_size = self.device_mesh.size(mesh_dim=i)
+                    local_shard_size, _ = src._local_shard_size_and_offset(
+                        current_logical_shape[src.dim],
+                        mesh_dim_size,
+                        self.coordinate[i],
+                    )
+                    new_logical_shape = list(current_logical_shape)
+                    new_logical_shape[src.dim] = local_shard_size
+                    mesh_dims_to_logical_shape.append(new_logical_shape)
+            else:
+                mesh_dims_to_logical_shape.append(current_logical_shape)
+
+        # Next, we need to derive the transform infos from src to dst
+        # placements, here we use a greedy search with step by step state
+        # transformations
+        current_placements = list(src_spec.placements)
+        target_placements = list(dst_spec.placements)
+
+        if src_spec.num_shards > 1:
+            # If src_spec have sharding, it could potentially have sharding that
+            # is misaligned with dst_spec a common case of this is nested
+            # sharding (i.e. (S(0), S(0)) -> (R, S(0))). In those cases, we
+            # first traverse from inner placement to outer placement to detect
+            # misaligned shardings and properly replicate nested sharding first.
+            for mesh_dim in reversed(range(len(current_placements))):
+                current = current_placements[mesh_dim]
+                target = target_placements[mesh_dim]
+                # If target is not Shard, we can directly redistribute since we
+                # are traversing from inner to outer placements here
+                if isinstance(target, Shard):
+                    # If target is Shard, check for nested sharding on the
+                    # tensor dim BEFORE the current mesh_dim
+                    shard_dim = target.dim
+                    current_mesh_sharding, target_mesh_sharding = [], []
+                    for i, (s, p) in enumerate(
+                        zip(current_placements, target_placements)
+                    ):
+                        if i >= mesh_dim:
+                            break
+                        if s.is_shard(shard_dim):
+                            current_mesh_sharding.append(i)
+                        if p.is_shard(shard_dim):
+                            target_mesh_sharding.append(i)
+
+                    if current_mesh_sharding != target_mesh_sharding:
+                        # if current/target_placements have misaligned sharding
+                        # on the tensor dim BEFORE the current mesh_dim, we need
+                        # to replicate the tensor on the mesh dim first to clear
+                        # the nested sharding
+                        target = Replicate()
+
+                if current != target:
+                    transform_infos.append(
+                        _TransformInfo(
+                            mesh_dim=mesh_dim,
+                            src_dst_placements=(current, target),
+                            logical_shape=mesh_dims_to_logical_shape[mesh_dim],
+                        )
+                    )
+                    current_placements[mesh_dim] = target
+
+        # We always traverse from outer placement to inner placement to collect
+        # the remaining needed transform infos (i.e. the replication from nested
+        # sharding might need to further perform resharding to Shard again)
+        for mesh_dim, (current, target) in enumerate(
+            zip(current_placements, target_placements)
+        ):
+            if current != target:
+                transform_infos.append(
+                    _TransformInfo(
+                        mesh_dim=mesh_dim,
+                        src_dst_placements=(current, target),
+                        logical_shape=mesh_dims_to_logical_shape[mesh_dim],
+                    )
+                )
+                current_placements[mesh_dim] = target
+        return transform_infos
+
+
+def _gen_transform_infos_non_cached(
+    src_spec: DTensorSpec,
+    dst_spec: DTensorSpec,
+    use_graph_based_transform: bool | None = None,
+) -> list[_TransformInfo]:
+    device_mesh = src_spec.device_mesh
+    src_shard_order = src_spec.shard_order
+    dst_shard_order = dst_spec.shard_order
+    # DTensorSpec should automatically generate shard_order, and it can be () if
+    # no shard.
+    assert src_shard_order is not None and dst_shard_order is not None
+    # Determine which transform strategy to use:
+    # 1. Non-standard device order → always use graph-based
+    # 2. Global flag or explicit parameter True → use graph-based
+    # 3. Otherwise → use greedy
+    has_non_default_order = not all(
+        DTensorSpec.is_default_device_order(order)
+        for order in (src_shard_order, dst_shard_order)
+    )
+
+    if has_non_default_order is True:
+        use_graph_based_transform = True
+    elif _FORCE_MIN_COST_REDISTRIBUTION_PLAN is not None:
+        use_graph_based_transform = _FORCE_MIN_COST_REDISTRIBUTION_PLAN
+    elif use_graph_based_transform is None:
+        use_graph_based_transform = False
+    drp = get_redistribute_planner(device_mesh, len(src_spec.shape))
+    if use_graph_based_transform:
+        transform_infos = drp.generate_graph_based_transform_infos(
+            src_spec, dst_spec, src_spec.shape
+        )
+    else:
+        transform_infos = drp.generate_greedy_transform_infos(src_spec, dst_spec)
+    return transform_infos
+
+
+@cache
+def _gen_transform_infos(
+    src_spec: DTensorSpec,
+    dst_spec: DTensorSpec,
+    use_graph_based_transform: bool | None = None,
+) -> list[_TransformInfo]:
+    return _gen_transform_infos_non_cached(
+        src_spec, dst_spec, use_graph_based_transform
+    )
+
+
+def redistribute_local_tensor(
+    local_tensor: torch.Tensor,
+    current_spec: DTensorSpec,
+    target_spec: DTensorSpec,
+    *,
+    async_op: bool = False,
+    is_backward: bool = False,
+    use_graph_based_transform: bool | None = None,
+) -> torch.Tensor:
+    """
+    This redistribute the local tensor (torch.Tensor) from the current DTensorSpec to
+    the target DTensorSpec, which involves the necessary collective calls to transform
+    the local shard of the DTensor from its current spec to the target spec.
+    """
+
+    if current_spec.mesh != target_spec.mesh:
+        # TODO: alltoall/permute reshuffling to change device_mesh if they are not the same
+        raise NotImplementedError("Cross device mesh comm not supported yet!")
+
+    new_local_tensor = local_tensor
+    device_mesh = current_spec.mesh
+
+    my_coordinate = device_mesh.get_coordinate()
+
+    if my_coordinate is None:
+        # if rank is not part of mesh, we skip redistribute and simply return local_tensor,
+        # which should be an empty tensor
+        return local_tensor
+
+    if _are_we_tracing():
+        transform_infos = _gen_transform_infos_non_cached(
+            current_spec, target_spec, use_graph_based_transform
+        )
+    else:
+        transform_infos = _gen_transform_infos(
+            current_spec, target_spec, use_graph_based_transform
+        )
+
+    debug_mode = get_active_debug_mode()
+    redistribute_context = (
+        debug_mode.record_redistribute_calls(  # type: ignore[union-attr]
+            local_tensor,
+            current_spec.placements,
+            target_spec.placements,
+            DTensorRedistributePlanner.stringify_transform_infos(
+                device_mesh,
+                transform_infos,
+                current_spec.placements,
+                current_spec.shard_order,
+            ),
+        )
+        if debug_mode is not None
+        else contextlib.nullcontext()
+    )
+
+    with redistribute_context:
+        for transform_info in transform_infos:
+            i = transform_info.mesh_dim
+            current, target = transform_info.src_dst_placements
+            num_chunks = device_mesh.size(mesh_dim=i)
+
+            if current == target:
+                # short cut, just use the original local tensor
+                new_local_tensor = local_tensor
+                continue
+
+            if num_chunks == 1:
+                # short cut, if there's only one shard, we don't need to do any collective
+                # comm, just use the original local tensor
+                new_local_tensor = local_tensor
+                continue
+
+            if target.is_replicate():
+                # Case 1: target is Replicate
+                if current.is_partial():
+                    partial_spec = cast(Partial, current)
+                    new_local_tensor = partial_spec._reduce_value(
+                        local_tensor, device_mesh, i
+                    )
+                elif current.is_shard():
+                    current_placement = cast(Shard, current)
+                    new_local_tensor = current_placement._to_replicate_tensor(
+                        local_tensor, device_mesh, i, transform_info.logical_shape
+                    )
+                else:
+                    raise RuntimeError(
+                        f"redistribute from {current} to {target} not supported yet"
+                    )
+
+            elif target.is_shard():
+                # Case 2: target is Shard
+                target_placement = cast(Shard, target)
+                if current.is_partial():
+                    partial_spec = cast(Partial, current)
+                    new_local_tensor = partial_spec._reduce_shard_value(
+                        local_tensor, device_mesh, i, target_placement
+                    )
+                elif current.is_replicate():
+                    # split the tensor and return the corresponding cloned local shard
+                    new_local_tensor = target_placement._replicate_to_shard(
+                        local_tensor, device_mesh, i, my_coordinate[i]
+                    )
+                else:
+                    assert current.is_shard(), (
+                        f"Current placement should be shard but found {current}"
+                    )
+                    shard_spec = cast(Shard, current)
+                    if shard_spec.dim != target_placement.dim:
+                        new_local_tensor = shard_spec._to_new_shard_dim(
+                            local_tensor,
+                            device_mesh,
+                            i,
+                            transform_info.logical_shape,
+                            target_placement.dim,
+                        )
+            elif target.is_partial():
+                if current.is_replicate():
+                    partial_spec = cast(Partial, target)
+                    # skip the replicate to partial transformation when we are in backward pass
+                    # In this case we keep the grad as replicate, this is because we don't
+                    # want to convert the replicated gradients back to partial, although
+                    # that's logically conform with the same layout, converting the gradients
+                    # back to partial is actually useless as you would have to do reduce later
+                    # which would be more expensive than keeping it replicate! For this reason,
+                    # we keep the replicate grad here.
+                    new_local_tensor = (
+                        partial_spec._partition_value(local_tensor, device_mesh, i)
+                        if not is_backward
+                        else local_tensor
+                    )
+                elif current.is_shard():
+                    if not is_backward:
+                        raise RuntimeError(
+                            f"redistribute from {current} to {target} not supported yet"
+                        )
+                    # for backward shard -> partial, we just need to convert the shard to replicate
+                    current_placement = cast(Shard, current)
+                    new_local_tensor = current_placement._to_replicate_tensor(
+                        local_tensor, device_mesh, i, transform_info.logical_shape
+                    )
+                else:
+                    # partial -> partial no op, should never hit
+                    new_local_tensor = local_tensor
+
+            if not async_op and isinstance(
+                new_local_tensor, funcol.AsyncCollectiveTensor
+            ):
+                new_local_tensor = new_local_tensor.wait()
+            local_tensor = new_local_tensor
+    return new_local_tensor
+
+
+class Redistribute(torch.autograd.Function):
+    @staticmethod
+    def forward(  # type: ignore[override]
+        # pyre-fixme[2]: Parameter must be annotated.
+        ctx,
+        input: "dtensor.DTensor",
+        device_mesh: DeviceMesh,
+        placements: tuple[Placement, ...],
+        async_op: bool = False,
+        forward_dtype: torch.dtype | None = None,
+        backward_dtype: torch.dtype | None = None,
+    ):
+        ctx.async_op = async_op
+        ctx.backward_dtype = backward_dtype
+        ctx.original_dtype = input._local_tensor.dtype
+
+        if forward_dtype is not None and forward_dtype != input._local_tensor.dtype:
+            local_tensor = input._local_tensor.to(dtype=forward_dtype)
+            current_spec = DTensorSpec(
+                mesh=device_mesh,
+                placements=input._spec.placements,
+                tensor_meta=TensorMeta(
+                    shape=input.shape,
+                    stride=input.stride(),
+                    dtype=forward_dtype,
+                ),
+            )
+        else:
+            local_tensor = input._local_tensor
+            current_spec = input._spec
+
+        ctx.current_spec = current_spec
+
+        if current_spec.placements != placements:
+            target_spec = DTensorSpec(
+                device_mesh, placements, tensor_meta=current_spec.tensor_meta
+            )
+
+            output = redistribute_local_tensor(
+                local_tensor, current_spec, target_spec, async_op=async_op
+            )
+        else:
+            # use the same local tensor if placements are the same.
+            output = local_tensor
+            target_spec = current_spec
+
+        # pyrefly: ignore [bad-argument-type]
+        return dtensor.DTensor(
+            # pyrefly: ignore [bad-argument-count]
+            output,
+            target_spec,
+            # pyrefly: ignore [unexpected-keyword]
+            requires_grad=input.requires_grad,
+        )
+
+    @staticmethod
+    def backward(ctx, grad_output: "dtensor.DTensor"):  # type: ignore[override]
+        previous_spec = ctx.current_spec
+        async_op = ctx.async_op
+        backward_dtype = ctx.backward_dtype or ctx.original_dtype
+
+        if backward_dtype != grad_output._local_tensor.dtype:
+            local_tensor = grad_output._local_tensor.to(dtype=backward_dtype)
+            current_spec = DTensorSpec(
+                mesh=grad_output._spec.device_mesh,
+                placements=grad_output._spec.placements,
+                tensor_meta=TensorMeta(
+                    shape=grad_output.shape,
+                    stride=grad_output.stride(),
+                    dtype=backward_dtype,
+                ),
+            )
+            previous_spec = DTensorSpec(
+                mesh=previous_spec.device_mesh,
+                placements=previous_spec.placements,
+                tensor_meta=current_spec.tensor_meta,
+            )
+        else:
+            local_tensor = grad_output._local_tensor
+            current_spec = grad_output._spec
+
+        output = redistribute_local_tensor(
+            local_tensor,
+            current_spec,
+            previous_spec,
+            async_op=async_op,
+            is_backward=True,
+        )
+
+        if output.dtype != ctx.original_dtype:
+            output = output.to(ctx.original_dtype)
+
+        # normalize the target placement to replicate if it is partial
+        normalized_placements: list[Placement] = []
+        for previous_placement in previous_spec.placements:
+            if previous_placement.is_partial():
+                # keep target placement to replicate instead of partial in this case
+                normalized_placements.append(Replicate())
+            else:
+                normalized_placements.append(previous_placement)
+
+        spec = DTensorSpec(
+            previous_spec.device_mesh,
+            tuple(normalized_placements),
+            tensor_meta=TensorMeta(
+                shape=grad_output.shape,
+                stride=grad_output.stride(),
+                dtype=output.dtype,
+            ),
+        )
+        # pyrefly: ignore [bad-argument-type]
+        output_dtensor = dtensor.DTensor(
+            # pyrefly: ignore [bad-argument-count]
+            output,
+            spec,
+            # pyrefly: ignore [unexpected-keyword]
+            requires_grad=grad_output.requires_grad,
+        )
+
+        return (
+            output_dtensor,
+            None,
+            None,
+            None,
+            None,
+            None,
+        )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/_sharding_prop.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/_sharding_prop.py
new file mode 100644
index 0000000000000000000000000000000000000000..c1fddd05c9d6e7f38e637ea10a3bf2ffe0e16fe0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/_sharding_prop.py
@@ -0,0 +1,680 @@
+# mypy: allow-untyped-defs
+import logging
+import threading
+from collections.abc import Callable, Sequence
+from functools import lru_cache
+from itertools import chain
+from typing import cast
+
+import torch
+from torch._guards import detect_fake_mode
+from torch._ops import OpOverload
+from torch._subclasses import FakeTensorMode
+from torch.distributed._functional_collectives import _are_we_tracing
+from torch.distributed.tensor._dtensor_spec import DTensorSpec, TensorMeta
+from torch.distributed.tensor._op_schema import (
+    OpInfo,
+    OpSchema,
+    OpSpec,
+    OpStrategy,
+    OutputSharding,
+    OutputSpecType,
+    RuntimeSchemaInfo,
+    StrategyType,
+    TupleStrategy,
+)
+from torch.distributed.tensor._utils import (
+    compute_local_shape_and_global_offset,
+    compute_local_stride,
+)
+from torch.distributed.tensor.placement_types import _StridedShard, Shard
+
+
+aten = torch.ops.aten
+
+log = logging.getLogger(__name__)
+
+
+def _length(obj) -> int:
+    if obj is None:
+        return 0
+    if not isinstance(obj, Sequence):
+        return 1
+    return len(obj)
+
+
+class LocalLRUCache(threading.local):
+    def __init__(self, user_function: Callable) -> None:
+        self.cache = lru_cache(None)(user_function)
+
+    def __call__(self, *args, **kwargs) -> object:
+        return self.cache(*args, **kwargs)
+
+    def cache_info(self):
+        return self.cache.cache_info()
+
+    def cache_clear(self):
+        return self.cache.cache_clear()
+
+
+class ShardingPropagator:
+    def __init__(self) -> None:
+        self.op_to_rules: dict[OpOverload, Callable[[OpSchema], OutputSharding]] = {}
+        self.op_strategy_funcs: dict[
+            OpOverload,
+            Callable[[OpSchema], StrategyType],
+        ] = {}
+        # op map to save static argnum to decide to reuse sharding prop cache or
+        # re-run sharding prop
+        self.op_to_schema_info: dict[OpOverload, RuntimeSchemaInfo] = {}
+        self.propagate_op_sharding = LocalLRUCache(
+            self.propagate_op_sharding_non_cached
+        )
+        # op map to save indices of shape (and stride) args which may need to be
+        # modified in sharding prop
+        self.op_to_shape_and_stride_idx: dict[OpOverload, int | tuple[int, int]] = {
+            # new factory ops
+            aten.new_empty.default: 1,
+            aten.new_full.default: 1,
+            aten.new_ones.default: 1,
+            aten.new_zeros.default: 1,
+            aten.new_empty_strided.default: (1, 2),
+            # view ops
+            aten.expand.default: 1,
+            aten.reshape.default: 1,
+            aten.view.default: 1,
+            aten._unsafe_view.default: 1,
+            aten.select_backward.default: 1,
+            aten.slice_backward.default: 1,
+        }
+
+    def register_sharding_prop_rule(
+        self,
+        op_overload: OpOverload,
+        rule_func: Callable[[OpSchema], OutputSharding],
+        schema_info: RuntimeSchemaInfo | None = None,
+    ):
+        """
+        Register a sharding propagation rule for an operator.
+        """
+        self.op_to_rules[op_overload] = rule_func
+        if schema_info is not None:
+            self.op_to_schema_info[op_overload] = schema_info
+
+    def register_op_strategy(
+        self,
+        op_overload: OpOverload,
+        strategy_func: Callable[[OpSchema], StrategyType],
+        schema_info: RuntimeSchemaInfo | None = None,
+    ):
+        """
+        Register a :class:`OpStrategy` generator for an operator.
+
+        During the sharding propagation, DTensor wants to enumerate all
+        acceptable sharding specs (:class:`OpSpec`) for an operator,
+        and by "acceptable" we mean that the operator can be executed on
+        the ``_local_tensor`` of DTensor args/kwargs (with ``OpSpec.input_specs``)
+        and the output(s) constitute valid DTensor(s) (with ``OpSpec.output_specs``).
+
+        ``strategy_func`` is the function that enumerates such acceptable specs
+        for the operator ``op_overload``. One general approach to write ``strategy_func``
+        is, if the operator has simple arguments structure (e.g. mm, bmm), first enumerating
+        all sharding specs for the operands, and then filtering out the ones that
+        are not valid. For example, for ``mm``, the operands are two 2D tensors, and
+        if both ``input`` and ``mat2`` have sharding placements ``[Shard(0)]``, then this
+        is not an acceptable ``input_specs``.
+
+        Once we have a way to enumerate all acceptable sharding specs, we can use each
+        of them to construct a :class:`OpSpec`. The ``OpSpec.input_specs`` directly comes
+        from the sharding spec, and the ``OpSpec.output_specs`` is therefore determined
+        (e.g. ``[Shard(1)]`` @ ``[Shard(0)]`` yields ``[Partial()]``). In addition,
+        :class:`OpSpec` also contains ``redistribute_cost`` which records the redistribution
+        cost from each :class:`OpSpec` in the source :class:`OpStrategy.strategies` to
+        the target sharding spec, for each operand.
+
+        The ``strategy_func`` should return a :class:`OpStrategy` which contains a list of
+        all the :class:`OpSpec`s generated in the above.
+
+        The optional ``schema_info`` tells which non-DTensor args/kwargs could affect the
+        cache and whether ``pytree`` is needed to flatten the nested args. ``static_argnum``
+        marks the starting index of the non-DTensor args that should be hashed into the
+        sharding propagation hash key, and ``static_kwargkey`` marks the keys of the
+        non-DTensor kwargs that should be hashed. ``needs_pytree`` should be used when
+        the input arg has :class:`list` or :class:`dict` structure.
+
+        For example, ``aten.cat.default`` op has a ``List[Tensor]`` argument ``tensors``
+        and an ``int`` argument ``dim``. Because ``dim`` affects the sharding propagation
+        result, we want to pass ``RuntimeSchemaInfo(static_argnum=1)`` because the argument
+        index of ``dim`` is 1. Besides, we also want to set ``needs_pytree=True`` because
+        ``tensors`` needs be flattened in sharding propagation. Another example is
+        ``aten.histc.default``. ``histc`` has 4 arguments (self, bins, min, max) and the
+        last two would affect sharding propagation along with the :class:`DTensor` argument
+        ``self``. Since the argument index of ``min`` is 2, the `schema_info` should be
+        `RuntimeSchemaInfo(static_argnum=2)`.
+        """
+        self.op_strategy_funcs[op_overload] = strategy_func
+        if schema_info is not None:
+            self.op_to_schema_info[op_overload] = schema_info
+
+    def _propagate_tensor_meta_non_cached(
+        self, op_schema: OpSchema
+    ) -> None | TensorMeta | Sequence[TensorMeta | None]:
+        """
+        Propagate the tensor metadata, it could either return a TensorMeta
+        or a list/tuple of TensorMetas
+        """
+        if op_schema.op == aten.equal.default:
+            # data dependent ops can't be used for fake propagation
+            return None
+
+        # NOTE: We must call the tracing in fake tensor mode so that it avoids
+        # materializing memory.
+        fake_mode = detect_fake_mode() or FakeTensorMode()
+        with fake_mode:
+            fake_args = op_schema.gen_fake_args()
+            fake_kwargs = op_schema.gen_fake_kwargs()
+            fake_out = op_schema.op(*fake_args, **fake_kwargs)
+
+        if isinstance(fake_out, torch.Tensor):
+            return TensorMeta(
+                shape=fake_out.shape, stride=fake_out.stride(), dtype=fake_out.dtype
+            )
+
+        elif isinstance(fake_out, (tuple, list)):
+            tensor_meta_list: list[TensorMeta | None] = []
+            for fake_out_item in fake_out:
+                if isinstance(fake_out_item, torch.Tensor):
+                    tensor_meta_list.append(
+                        TensorMeta(
+                            shape=fake_out_item.shape,
+                            stride=fake_out_item.stride(),
+                            dtype=fake_out_item.dtype,
+                        )
+                    )
+                else:
+                    tensor_meta_list.append(None)
+            return (
+                tuple(tensor_meta_list)
+                if isinstance(fake_out, tuple)
+                else tensor_meta_list
+            )
+        else:
+            # if fake is not a tensor or tuple of tensor, return as none
+            return None
+
+    @lru_cache  # noqa: B019
+    def _propagate_tensor_meta(
+        self, op_schema: OpSchema
+    ) -> None | TensorMeta | Sequence[TensorMeta | None]:
+        """
+        Cached version of _propagate_tensor_meta_non_cached
+        This is a private API. Use propagate_tensor_meta instead.
+        """
+        return self._propagate_tensor_meta_non_cached(op_schema)
+
+    def propagate_tensor_meta(
+        self, op_schema: OpSchema
+    ) -> None | TensorMeta | Sequence[TensorMeta | None]:
+        """
+        Propagate the tensor metadata, it could either return a TensorMeta
+        or a list/tuple of TensorMetas. This is a public API that should be
+        used if cache should be used.
+        """
+        if _are_we_tracing():
+            return self._propagate_tensor_meta_non_cached(op_schema)
+        else:
+            return self._propagate_tensor_meta(op_schema)
+
+    def _create_output_spec_with_new_tensor_meta(
+        self,
+        op: OpOverload,
+        output_specs: OutputSpecType,
+        output_tensor_meta: None | TensorMeta | Sequence[TensorMeta | None],
+    ) -> OutputSpecType:
+        """
+        Wrap the output_specs with the tensor metadata from the output.
+        """
+
+        if isinstance(output_specs, DTensorSpec):
+            if not isinstance(output_tensor_meta, TensorMeta):
+                # Either error due to ShardingPropagator or due to incorrect OutputSpec
+                if not isinstance(output_tensor_meta, (tuple, list)):
+                    raise ValueError(
+                        "ShardingPropagator error: output does not have an associated "
+                        "TensorMeta"
+                    )
+                raise ValueError(
+                    f"For the op {op.name()}, `output_specs` has 1 output which does "
+                    "not equal the "
+                    f"number of op outputs: {len(output_tensor_meta)}."
+                )
+            return output_specs.shallow_copy_with_tensor_meta(output_tensor_meta)
+        elif isinstance(output_specs, (tuple, list)):
+            new_specs: list[DTensorSpec | None] = []
+            if not isinstance(output_tensor_meta, (tuple, list)) or len(
+                output_specs
+            ) != len(output_tensor_meta):
+                raise ValueError(
+                    f"For the op {op.name()}, `output_specs` has {len(output_specs)} "
+                    "outputs which does not equal the "
+                    f"number of op outputs {_length(output_tensor_meta)}."
+                )
+
+            for i, spec in enumerate(output_specs):
+                if isinstance(spec, DTensorSpec):
+                    output_tensor_meta_i = output_tensor_meta[i]
+                    if not isinstance(output_tensor_meta_i, TensorMeta):
+                        # NOTE: aten.convolution_backward.default is an exception and it
+                        # needs extra handling because any Tensor in the output tuple
+                        # can be `None` depending on the output_mask parameter. This can
+                        # occur during double backpropagation or when certain gradients
+                        # are not needed (e.g., grad_input when input has requires_grad=False,
+                        # grad_weight/grad_bias when weight/bias have requires_grad=False,
+                        # or grad_bias when bias is None). We explicitly allow the
+                        # corresponding TensorMeta to be `None`.
+                        if (
+                            op == aten.convolution_backward.default
+                            and i in (0, 1, 2)
+                            and output_tensor_meta_i is None
+                        ):
+                            assert isinstance(output_specs, list)
+                            new_specs.append(None)
+                            continue
+                        else:
+                            raise ValueError(
+                                f"ShardingPropagator error: output {i} of {op.name()} "
+                                "does not have an associated TensorMeta"
+                            )
+
+                    new_specs.append(
+                        spec.shallow_copy_with_tensor_meta(output_tensor_meta_i)
+                    )
+                else:
+                    new_specs.append(spec)
+
+            return tuple(new_specs)
+        else:
+            assert output_specs is None
+            return output_specs
+
+    def _wrap_with_op_strategy(self, op_schema: OpSchema) -> OpSchema:
+        """
+        wrap a op_schema that contains DTensorSpec to another op_schema that contains
+        OpStrategy/TupleStrategy, the returned op_schema is then used for sharding
+        strategy propagation on pytorch operators.
+        """
+
+        def spec_to_strategy(spec: object) -> object:
+            if isinstance(spec, DTensorSpec):
+                return OpStrategy([OpSpec(spec)])
+            elif (
+                isinstance(spec, (list, tuple))
+                and len(spec) > 0
+                and isinstance(spec[0], DTensorSpec)
+            ):
+                # tensor list create tuple strategy
+                tuple_strategy = [spec_to_strategy(s) for s in spec]
+                tuple_strategy = cast(Sequence[StrategyType], tuple_strategy)
+                return TupleStrategy(
+                    tuple(tuple_strategy) if isinstance(spec, tuple) else tuple_strategy
+                )
+            else:
+                return spec
+
+        args_op_strategy = [spec_to_strategy(i) for i in op_schema.args_schema]
+
+        kwargs_op_strategy = {
+            k: spec_to_strategy(v) for k, v in op_schema.kwargs_schema.items()
+        }
+
+        return OpSchema(
+            op=op_schema.op,
+            args_schema=tuple(args_op_strategy),
+            kwargs_schema=kwargs_op_strategy,
+            schema_info=op_schema.schema_info,
+        )
+
+    def propagate(self, op_info: OpInfo) -> None:
+        # NB: The logic here is duplicated in _propagate_op_sharding_dispatch_slow_path.
+        # Ideally, this function would be deleted, but there are a handful of
+        # one off call sites here that aren't cleaned up.
+
+        # We cannot use an lru cache if we know that inputs will have dynamic shapes,
+        # because SymInts are not hashable.
+        # This is generally ok because this only happens during tracing in torch.compile,
+        # and tracing does not need to be as fast as eagermode DTensor usages.
+        if _are_we_tracing():
+            output_sharding = self.propagate_op_sharding_non_cached(op_info.schema)
+        else:
+            output_sharding = cast(
+                OutputSharding, self.propagate_op_sharding(op_info.schema)
+            )
+        op_info.output_sharding = output_sharding
+
+    def propagate_op_sharding_non_cached(self, op_schema: OpSchema) -> OutputSharding:
+        """
+        Propagate the sharding for an operator given the op_schema.
+        """
+        # no-op in OSS, logs API usage metrics in meta-internal runs
+        torch._C._log_api_usage_once(
+            "torch.distributed.tensor._sharding_prop.ShardingPropagator.propogate_op_sharding_non_cached"
+        )
+        # special case op, we don't need to propagate for local
+        # scalar. TODO: figure out a better way to handle this
+        if op_schema.op is aten._local_scalar_dense.default:
+            return OutputSharding(None, op_schema)
+
+        out_tensor_meta = self._propagate_tensor_meta_non_cached(op_schema)
+        if op_schema.op in self.op_strategy_funcs:
+            # wrap the op_schema with op strategy for sharding strategy propagation
+            strategy_schema = self._wrap_with_op_strategy(op_schema)
+
+            # run sharding strategy propagation/generation
+            op_strategy = self.op_strategy_funcs[op_schema.op](strategy_schema)
+
+            if isinstance(op_strategy, OpStrategy):
+                # single Op strategy
+                output_strategy = self._select_strategy(op_strategy, op_schema)
+
+                # check if we need to redistribute the input
+                needs_redistribute = False
+                # check if we want to use args value from redistribute_schema
+                use_val_from_redistribute_schema = False
+                expected_input_specs: list[DTensorSpec] = []
+
+                # in case where the op does not specify input_specs and output_specs
+                # is a DTensorSpec, we use output_specs as the spec for each DTensor
+                # input arg.
+                if output_strategy.input_specs is None:
+                    assert isinstance(output_strategy.output_specs, DTensorSpec)
+
+                for idx, input_spec in enumerate(op_schema.args_spec):
+                    desired_spec = (
+                        output_strategy.output_spec
+                        if output_strategy.input_specs is None
+                        else output_strategy.input_specs[idx]
+                    )
+                    expected_input_specs.append(
+                        desired_spec.shallow_copy_with_tensor_meta(
+                            input_spec.tensor_meta
+                        )
+                    )
+                    if input_spec.placements != desired_spec.placements:
+                        needs_redistribute = True
+
+                suggestion_schema = None
+                if needs_redistribute:
+                    suggestion_schema = OpSchema(
+                        op_schema.op, tuple(expected_input_specs), {}
+                    )
+                    suggestion_schema._inplace_rewrap_schema_suggestion(op_schema)
+
+                # shape and stride args need to be modified for
+                # view ops and new factory ops, potentially
+                if op_schema.op in self.op_to_shape_and_stride_idx:
+                    assert isinstance(output_strategy.output_spec, DTensorSpec)
+                    # It happens when the output has the same shape as the input
+                    # and the input placements are not all Replicate().
+                    if any(
+                        isinstance(p, Shard | _StridedShard)
+                        for p in output_strategy.output_spec.placements
+                    ):
+                        schema = suggestion_schema or op_schema
+                        assert isinstance(out_tensor_meta, TensorMeta)
+                        suggestion_schema = self._adjust_shape_and_stride_args(
+                            out_tensor_meta, schema, output_strategy.output_spec
+                        )
+                        needs_redistribute = True
+                        use_val_from_redistribute_schema = True
+
+                # construct output spec for the op
+                if op_schema.return_type_tuple_tensor_like():
+                    # for ops that return multiple tensors and the output_specs is not
+                    # a tuple, we use a tuple of that single output spec as the new
+                    # output_specs
+                    output_specs: OutputSpecType = output_strategy.output_specs
+                    if isinstance(output_specs, DTensorSpec):
+                        output_specs = tuple(
+                            # create a new DTensorSpec with the same placement as the
+                            # output_specs in output_strategy
+                            DTensorSpec(
+                                mesh=output_specs.mesh,
+                                placements=output_specs.placements,
+                                tensor_meta=output_specs.tensor_meta,
+                            )
+                            for _ in range(len(op_schema.op._schema.returns))
+                        )
+                elif (
+                    op_schema.return_type_tensor()
+                    or op_schema.return_type_list_tensor_like()
+                ):
+                    output_specs = output_strategy.output_specs
+                else:
+                    output_specs = None
+
+                output_sharding = OutputSharding(
+                    output_specs,
+                    suggestion_schema,
+                    needs_redistribute=needs_redistribute,
+                    use_val_from_redistribute_schema=use_val_from_redistribute_schema,
+                )
+            elif isinstance(op_strategy, TupleStrategy):
+                # tuple strategy output sharding processing
+                # runtime select OpSpec for each TupleStrategy input arg
+                selected_strategies: list[OpSpec] = []
+                out_spec_list: list[DTensorSpec] = []
+                for strategy in op_strategy.children:
+                    assert isinstance(strategy, OpStrategy)
+                    selected_strategy = self._select_strategy(strategy)
+                    selected_strategies.append(selected_strategy)
+                    out_spec_list.append(selected_strategy.output_spec)
+
+                needs_redistribute = False
+                suggestion_args: list[object] = []
+                tensor_or_list_tensor_arg_idx = 0
+
+                for arg in op_schema.args_schema:
+                    if (
+                        arg
+                        and isinstance(arg, (list, tuple))
+                        and isinstance(arg[0], DTensorSpec)
+                    ):
+                        expected_input_spec_list: list[DTensorSpec] = []
+                        for idx, arg_spec in enumerate(arg):
+                            expected_input_spec = selected_strategies[idx].input_spec(
+                                tensor_or_list_tensor_arg_idx
+                            )
+                            expected_input_spec = (
+                                expected_input_spec.shallow_copy_with_tensor_meta(
+                                    arg_spec.tensor_meta
+                                )
+                            )
+                            if arg_spec.placements != expected_input_spec.placements:
+                                needs_redistribute = True
+                            expected_input_spec_list.append(expected_input_spec)
+                        suggestion_args.append(
+                            tuple(expected_input_spec_list)
+                            if isinstance(arg, tuple)
+                            else expected_input_spec_list
+                        )
+                        tensor_or_list_tensor_arg_idx += 1
+
+                    elif isinstance(arg, DTensorSpec):
+                        expected_input_spec = selected_strategies[0].input_spec(
+                            tensor_or_list_tensor_arg_idx
+                        )
+                        expected_input_spec = (
+                            expected_input_spec.shallow_copy_with_tensor_meta(
+                                arg.tensor_meta
+                            )
+                        )
+                        if arg.placements != expected_input_spec.placements:
+                            needs_redistribute = True
+                        suggestion_args.append(expected_input_spec)
+                        tensor_or_list_tensor_arg_idx += 1
+                    else:
+                        suggestion_args.append(arg)
+
+                suggestion_schema = None
+                if needs_redistribute:
+                    suggestion_schema = OpSchema(
+                        op_schema.op, tuple(suggestion_args), op_schema.kwargs_schema
+                    )
+
+                output_sharding = OutputSharding(
+                    tuple(out_spec_list) if out_tensor_meta is not None else None,
+                    suggestion_schema,
+                    needs_redistribute=needs_redistribute,
+                    use_val_from_redistribute_schema=False,
+                )
+            else:
+                raise ValueError("Unsupported op strategy type")
+
+            # associate the output sharding with the output tensor metadata
+            new_output_spec = self._create_output_spec_with_new_tensor_meta(
+                op_schema.op, output_sharding.output_spec, out_tensor_meta
+            )
+            output_sharding.output_spec = new_output_spec
+            return output_sharding
+        elif op_schema.op in self.op_to_rules:
+            # propagate the sharding with rule
+            sharding_prop_func = self.op_to_rules[op_schema.op]
+
+            # step 1. there's sharding propagation rule, run
+            # sharding propagation to get the output sharding
+            try:
+                output_sharding = sharding_prop_func(op_schema)
+            except NotImplementedError as e:
+                raise e
+            except Exception as e:
+                raise RuntimeError(
+                    f"Sharding propagation failed on op {op_schema}.\nError: {e}"
+                ) from e
+
+            # step 2. if can't get output_spec from sharding
+            # propagation (i.e. no rules apply for input
+            # placements), we return the output sharding
+            # with schema suggestions, which can be used to
+            # decide how to do redistribute on inputs
+            if output_sharding.output_spec is None:
+                if output_sharding.redistribute_schema is None:
+                    raise RuntimeError(
+                        f"Sharding propagation failed on op {op_schema}!"
+                    )
+                else:
+                    # we do auto redistribute on inputs if necessary
+                    # run sharding propagation again with suggested schema
+                    propagation_res = sharding_prop_func(
+                        output_sharding.redistribute_schema
+                    )
+                    # we set the output sharding with the new propagation result
+                    # so that dispatching know both output_spec and redistribute_schema
+                    # exist, which indicates a reshard is needed
+                    output_sharding.output_spec = propagation_res.output_spec
+                    output_sharding.needs_redistribute = True
+
+            # associate the output sharding with the output tensor metadata
+            new_output_spec = self._create_output_spec_with_new_tensor_meta(
+                op_schema.op, output_sharding.output_spec, out_tensor_meta
+            )
+            output_sharding.output_spec = new_output_spec
+
+            return output_sharding
+        else:
+            raise NotImplementedError(
+                f"Operator {op_schema.op} does not have a sharding strategy registered."
+            )
+
+    def _select_strategy(
+        self, strategy: OpStrategy, op_schema: OpSchema | None = None
+    ) -> OpSpec:
+        from torch.fx.experimental.symbolic_shapes import guard_or_false
+
+        if len(strategy.strategies) == 1:
+            # short cut with only one possible OpSpec
+            return strategy.strategies[0]
+
+        op_spec_costs: list[torch.types.FloatLikeType] = []
+        no_redistribute_strategy_index: int = -1
+        negative_cost_index: int = -1
+        zero_cost_index: int = -1
+        for strategy_idx, op_spec in enumerate(strategy.strategies):
+            assert op_spec.redistribute_cost is not None, (
+                "must set redistribute cost each OpSpec!"
+            )
+            redistribute_cost = sum(chain.from_iterable(op_spec.redistribute_cost))
+            op_spec_costs.append(redistribute_cost)
+
+            # If there are strategies with negative/zero/no redistribute cost,
+            # we record those indices.
+            # TODO: Currently this only applies to OpStrategy selection. Requires extra
+            # logic to make it work for TupleStrategy, if needed.
+            if op_schema is not None:
+                if guard_or_false(redistribute_cost < 0):
+                    if (
+                        negative_cost_index == -1
+                        or redistribute_cost < op_spec_costs[negative_cost_index]
+                    ):
+                        negative_cost_index = strategy_idx
+                elif guard_or_false(redistribute_cost == 0):
+                    needs_redistribute = False
+                    for spec_idx, input_spec in enumerate(op_schema.args_spec):
+                        desired_spec = (
+                            op_spec.output_spec
+                            if op_spec.input_specs is None
+                            else op_spec.input_specs[spec_idx]
+                        )
+                        if input_spec.placements != desired_spec.placements:
+                            needs_redistribute = True
+                            break
+
+                    if not needs_redistribute:
+                        no_redistribute_strategy_index = strategy_idx
+                    elif zero_cost_index == -1:
+                        zero_cost_index = strategy_idx
+
+        # prioritize negative/zero/no redistribute cost strategies
+        if negative_cost_index != -1:
+            # If there's negative cost, we select the one with the minimal cost,
+            # even if this means we need to redistribute, e.g. via local chunking.
+            # E.g. this can happen for ops in self.op_to_shape_and_stride_idx
+            # when the inputs / outputs are sharded.
+            selected_strategy_index = negative_cost_index
+        elif no_redistribute_strategy_index != -1:
+            selected_strategy_index = no_redistribute_strategy_index
+        elif zero_cost_index != -1:
+            selected_strategy_index = zero_cost_index
+        else:
+            # default to choosing minimal redistribute cost
+            min_cost = min(op_spec_costs)
+            selected_strategy_index = op_spec_costs.index(min_cost)
+
+        return strategy.strategies[selected_strategy_index]
+
+    def _adjust_shape_and_stride_args(
+        self,
+        out_tensor_meta: TensorMeta,
+        schema: OpSchema,
+        spec: DTensorSpec,
+    ) -> OpSchema:
+        shape_stride_idx = self.op_to_shape_and_stride_idx[schema.op]
+        if isinstance(shape_stride_idx, tuple):
+            shape_idx, stride_idx = shape_stride_idx
+        else:
+            shape_idx = shape_stride_idx
+            stride_idx = None
+
+        expected_input_schema = list(schema.args_schema)
+        # adjust shape to be the same as that of the _local_tensor
+        # of the DTensor input arg at index 0, which is inferred
+        expected_input_schema[shape_idx], _ = compute_local_shape_and_global_offset(
+            out_tensor_meta.shape, spec.mesh, spec.placements, skip_offset=True
+        )
+
+        # adjust the stride arg for aten.new_empty_strided.default
+        if stride_idx:
+            expected_input_schema[stride_idx] = compute_local_stride(
+                out_tensor_meta.stride, spec.mesh, spec.placements
+            )
+
+        return OpSchema(schema.op, tuple(expected_input_schema), schema.kwargs_schema)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/_shards_wrapper.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/_shards_wrapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..1673dd7e34b994470386e1fb1a5079c302302393
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/_shards_wrapper.py
@@ -0,0 +1,359 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Any
+
+import torch
+from torch.distributed.checkpoint.metadata import (
+    ChunkStorageMetadata,
+    MetadataIndex,
+    TensorProperties,
+    TensorStorageMetadata,
+)
+from torch.distributed.checkpoint.planner import (
+    TensorWriteData,
+    WriteItem,
+    WriteItemType,
+)
+
+
+aten = torch.ops.aten
+
+
+class LocalShardsWrapper(torch.Tensor):
+    """
+    A wrapper class to hold local shards of a DTensor.
+    This class is used largely for checkpointing purposes and implicitly subtypes
+    the _Checkpointable protocol.
+    """
+
+    __slots__ = ["_local_shards", "_storage_meta"]
+    _local_shards: list[torch.Tensor]
+    _storage_meta: TensorStorageMetadata
+
+    @staticmethod
+    def __new__(
+        cls, local_shards: list[torch.Tensor], local_offsets: list[tuple[int, ...]]
+    ) -> "LocalShardsWrapper":
+        assert all(
+            tensor.device == local_shards[0].device for tensor in local_shards[1:]
+        )
+
+        # if empty shard, we create a empty tensor
+        if len(local_shards) == 0:
+            r = torch.Tensor._make_wrapper_subclass(
+                cls,
+                torch.Size([0, 0]),
+            )
+            r._local_shards = []
+            r._storage_meta = TensorStorageMetadata(
+                properties=TensorProperties(),
+                size=torch.Size([0, 0]),
+                chunks=[
+                    ChunkStorageMetadata(
+                        offsets=torch.Size([0, 0]), sizes=torch.Size([0, 0])
+                    )
+                ],
+            )
+            return r
+
+        # we calculate the total tensor size by "concat" on second tensor dimension
+        cat_tensor_shape = list(local_shards[0].size())
+        if len(local_shards) > 1 and local_shards[0].ndim == 2:  # column-wise sharding
+            for shard in local_shards[1:]:
+                cat_tensor_shape[1] += shard.size()[1]
+
+        # in cases of sharding optimizer rowwise, we calculate total tensor size by "concat" on first tensor dimension
+        if len(local_shards) > 1 and local_shards[0].ndim == 1:  # column-wise sharding
+            for shard in local_shards[1:]:
+                cat_tensor_shape[0] += shard.size()[0]
+
+        wrapper_properties = TensorProperties.create_from_tensor(local_shards[0])
+        wrapper_shape = torch.Size(cat_tensor_shape)
+        chunks_meta = [
+            ChunkStorageMetadata(
+                offsets=torch.Size(offset),
+                sizes=shard.size(),
+            )
+            for shard, offset in zip(local_shards, local_offsets)
+        ]
+
+        r = torch.Tensor._make_wrapper_subclass(
+            cls,
+            torch.Size(cat_tensor_shape),
+        )
+        r._local_shards = local_shards
+        r._storage_meta = TensorStorageMetadata(
+            properties=wrapper_properties,
+            size=wrapper_shape,
+            chunks=chunks_meta,
+        )
+
+        return r
+
+    # necessary for ops dispatching from this subclass to its local shards
+    @classmethod
+    def __torch_dispatch__(cls, func, types, args=(), kwargs=None):  # type: ignore[override]
+        kwargs = kwargs or {}
+
+        dispatcher = {
+            torch.ops._c10d_functional.all_gather_into_tensor.default: cls.handle_all_gather_into_tensor,
+            torch.ops._c10d_functional.wait_tensor.default: cls.handle_wait_tensor,
+            aten._to_copy.default: cls.handle_to_copy,
+            aten.view.default: cls.handle_view,
+            aten.equal.default: cls.handle_equal,
+            aten.detach.default: cls.handle_detach,
+            aten.clone.default: cls.handle_clone,
+            aten.new_empty.default: cls.handle_new_empty,
+        }
+
+        if func in dispatcher:
+            return dispatcher[func](args, kwargs)
+        else:
+            raise NotImplementedError(
+                f"{func} is not supported for LocalShardsWrapper!"
+            )
+
+    @staticmethod
+    def handle_all_gather_into_tensor(args, kwargs) -> torch.Tensor:
+        dim = args[0].local_sizes()[0][1]
+        cat_tensor = torch.cat(
+            [t.view(-1) for t in args[0].local_shards()], dim=0
+        ).view(-1, dim)
+        return torch.ops._c10d_functional.all_gather_into_tensor.default(
+            cat_tensor, *args[1:], **kwargs
+        )
+
+    @staticmethod
+    def handle_wait_tensor(args, kwargs) -> torch.Tensor:
+        return torch.ops._c10d_functional.wait_tensor(args[0])
+
+    @staticmethod
+    def handle_to_copy(args, kwargs) -> torch.Tensor:
+        res_shards_list = [
+            aten._to_copy.default(shard, *args[1:], **kwargs)
+            for shard in args[0].local_shards()
+        ]
+        return LocalShardsWrapper(res_shards_list, args[0].local_offsets())
+
+    @staticmethod
+    def handle_view(args, kwargs) -> "LocalShardsWrapper":
+        view_shape = args[1]
+        res_shards_list = []
+        if len(args[0].local_shards()) > 1:
+            if args[0].local_shards()[0].ndim == 2:
+                assert (
+                    args[0].storage_metadata().size[0] == view_shape[0]
+                    and args[0].storage_metadata().size[1] == view_shape[1]
+                )
+                # This accounts for a DTensor quirk, when multiple shards are present on a rank, DTensor on
+                # init calls view_as() on the global tensor shape
+                # will fail because the view shape is not applicable to individual shards.
+                res_shards_list = [
+                    aten.view.default(shard, shard.shape, **kwargs)
+                    for shard in args[0].local_shards()
+                ]
+            elif args[0].local_shards()[0].ndim == 1:
+                assert args[0].storage_metadata().size[0] == view_shape[0]
+                # This case is for optimizer sharding as regardless of sharding type, optimizer state is row wise sharded
+                res_shards_list = [
+                    aten.view.default(shard, shard.shape, **kwargs)
+                    for shard in args[0].local_shards()
+                ]
+            else:
+                raise NotImplementedError("No support for view on tensors ndim > 2")
+        else:
+            # view is called per shard
+            res_shards_list = [
+                aten.view.default(shard, args[1], **kwargs)
+                for shard in args[0].local_shards()
+            ]
+        return LocalShardsWrapper(res_shards_list, args[0].local_offsets())
+
+    @staticmethod
+    def handle_equal(args, kwargs) -> bool:
+        """
+        LocalShardsWrapper equal impl also checks for equality of storage metadata
+        and the order of shards
+        """
+        a, b = args[0], args[1]
+        if len(a.local_shards()) != len(b.local_shards()):
+            return False
+        if not all(
+            aten.equal.default(x, y) for x, y in zip(a.local_shards(), b.local_shards())
+        ):
+            return False
+        if a.storage_metadata() != b.storage_metadata():
+            return False
+        return True
+
+    @staticmethod
+    def handle_detach(args, kwargs) -> "LocalShardsWrapper":
+        self_ls = args[0]
+        deatched_local_shards = [
+            aten.detach.default(shard) for shard in self_ls.local_shards()
+        ]
+        self_ls._local_shards = deatched_local_shards
+        self_ls._storage_meta.properties.requires_grad = False
+        return self_ls
+
+    @staticmethod
+    def handle_clone(args, kwargs) -> "LocalShardsWrapper":
+        self_ls = args[0]
+        desired_memory_format = kwargs.get("memory_format", None)
+        if desired_memory_format and desired_memory_format != torch.preserve_format:
+            raise NotImplementedError(
+                f"{desired_memory_format} is not supported for LocalShardsWrapper!"
+            )
+        cloned_local_shards = [
+            shard.clone(memory_format=desired_memory_format)
+            for shard in self_ls._local_shards
+        ]
+        return LocalShardsWrapper(cloned_local_shards, self_ls.local_offsets())
+
+    @staticmethod
+    def handle_new_empty(args, kwargs) -> "LocalShardsWrapper":
+        self_ls = args[0]
+        return LocalShardsWrapper(
+            [torch.empty_like(shard) for shard in self_ls._local_shards],
+            self_ls.local_offsets(),
+        )
+
+    @property
+    def device(self) -> torch._C.device:  # type: ignore[override]
+        return (
+            self._local_shards[0].device if self._local_shards else torch.device("meta")
+        )
+
+    @property
+    def is_meta(self) -> bool:  # type: ignore[override]
+        return self._local_shards[0].is_meta if self._local_shards else True
+
+    def is_pinned(self) -> bool:  # type: ignore[override]
+        return self._storage_meta.properties.pin_memory
+
+    def requires_grad_(self, requires_grad: bool = True) -> "LocalShardsWrapper":
+        self._storage_meta.properties.requires_grad = requires_grad
+        [shard.requires_grad_(requires_grad) for shard in self._local_shards]
+        return self
+
+    def local_shards(self) -> list[torch.Tensor]:
+        """
+        Returns a list of :class:`torch.Tensor' corresponding to the
+        local shards for this rank. Returns an empty list if the current rank
+        does not host any shards for this Tensor.
+        """
+        return self._local_shards
+
+    def local_sizes(self) -> list[torch.Size]:
+        """
+        Returns a list of :class:`torch.Size' corresponding to the
+        local sizes for the shards on this rank. Returns an empty list if the current rank
+        does not host any shards for this Tensor.
+        """
+        return [chunk.sizes for chunk in self._storage_meta.chunks]
+
+    def local_offsets(self) -> list[torch.Size]:
+        """
+        Returns a list of :class:`torch.Size' corresponding to the
+        local offsets for the shards on this rank. Returns an empty list if the current rank
+        does not host any shards for this Tensor.
+        """
+        return [chunk.offsets for chunk in self._storage_meta.chunks]
+
+    @property
+    def local_chunks(self) -> list[ChunkStorageMetadata]:
+        """
+        Returns a :class:`list[ChunkStorageMetadata]` object corresponding to the
+        metadata for each tensor shard
+        """
+        return self._storage_meta.chunks
+
+    def storage_metadata(self) -> TensorStorageMetadata:
+        """
+        Returns a :class:`TensorStorageMetadata` object corresponding to the
+        metadata for the local tensor on current rank
+        """
+        return self._storage_meta
+
+    def is_empty_shard(self) -> bool:
+        """
+        Returns a :class:`bool` object indicating if the local tensor on current rank
+        is an empty tensor
+        """
+        return self._storage_meta.size[0] == 0 and self._storage_meta.size[1] == 0
+
+    def __create_write_items__(self, fqn: str, object: Any) -> list[WriteItem]:
+        """
+        For compatibility with DCP, we support creation of WriteItems
+        such that they can be saved properly.
+        """
+        return [
+            WriteItem(
+                index=MetadataIndex(fqn, chunks.offsets),
+                type=WriteItemType.SHARD,
+                tensor_data=TensorWriteData(
+                    chunk=ChunkStorageMetadata(
+                        offsets=chunks.offsets,
+                        sizes=chunks.sizes,
+                    ),
+                    properties=self._storage_meta.properties,
+                    size=object.size(),
+                ),
+            )
+            for tensor, chunks in zip(self.local_shards(), self.local_chunks)
+        ]
+
+    def __create_chunk_list__(self) -> list[ChunkStorageMetadata]:
+        """
+        For compatibility with DCP, we support creation of chunk lists
+        such that they can be saved properly.
+        """
+        return self._storage_meta.chunks
+
+    def __get_tensor_shard__(self, index: MetadataIndex) -> torch.Tensor:
+        """
+        For compatibility with DCP, we support finding shard based on index
+        Return a 'torch.Tensor' shard based on 'MetadataIndex'.
+        """
+        # Fast lookup path
+        if index.index is not None:
+            if (
+                len(self._local_shards) > index.index
+                and self._storage_meta.chunks[index.index].offsets == index.offset
+            ):
+                return self._local_shards[index.index]
+
+        if index.offset is not None:
+            for shard, chunk in zip(self._local_shards, self._storage_meta.chunks):
+                if chunk.offsets == index.offset:
+                    return shard
+
+        # Empty shard case
+        if len(self._local_shards) == 0 and self._storage_meta.chunks[
+            0
+        ].sizes == torch.Size([0, 0]):
+            return torch.empty(0)
+
+        raise ValueError(
+            f"Could not find shard at '{index.offset}' for FQN: '{index.fqn}'"
+        )
+
+    def _get_tensor_size_bytes(self) -> int:
+        object_size = 0
+        for shard in self.local_shards():
+            object_size += shard.nelement() * shard.element_size()
+        return object_size
+
+    def __hash__(self) -> int:
+        return id(self)
+
+    def __repr__(self) -> str:  # type: ignore[override]
+        return f"LocalShardsWrapper:{self._local_shards} {self._storage_meta}"
+
+    def __str__(self) -> str:
+        return f"LocalShardsWrapper:{self._local_shards} {self._storage_meta}"
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/_tp_conv.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/_tp_conv.py
new file mode 100644
index 0000000000000000000000000000000000000000..275cb07934b5030bc9cd5bc71dc66f82e98eb3b5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/tensor/_tp_conv.py
@@ -0,0 +1,293 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Meta Platforms, Inc. and affiliates
+# implement matrix related ops for distributed tensor
+from typing import cast
+
+import torch
+import torch.distributed as dist
+import torch.distributed.tensor._api as dtensor
+
+
+aten = torch.ops.aten
+
+
+def _requires_data_exchange(padding, dim_map) -> bool:
+    # Data exchange is not need if only sharded across batch dim
+    if all(x == -1 for x in dim_map[1:]):
+        return False
+    # TODO: whether there requires data exchange is currently determined by padding
+    return padding[-1] != 0
+
+
+def _is_supported(input_size, kernel_size, stride, padding, dilation):
+    if dilation[-1] != 1:
+        raise RuntimeError("Dilation must be 1 for tensor parallel convolution.")
+    if padding[-1] != 0:
+        if stride[-1] != 1:
+            raise RuntimeError(
+                "Stride must be 1 when there is padding for tensor parallel convolution."
+            )
+        if kernel_size[-1] // 2 > input_size[-1]:
+            raise RuntimeError(
+                "kernel_size[-1] // 2 should be less than or equal to input_size[-1] for tensor parallel convolution."
+            )
+    else:
+        if not (input_size[-1] % stride[-1] == 0 and stride[-1] == kernel_size[-1]):
+            raise RuntimeError(
+                "It requires that input_size[-1] is divisible by stride[-1] and stride[-1] equals kernel_size[-1] "
+                "when there is padding for tensor parallel convolution."
+            )
+    return True
+
+
+def _ring_send_recv_construct(in_tensor, d1, d2, left, right, rank, size):
+    # dist comms and reconstruct local input tensor
+    send_to_right = in_tensor[..., -d1:].contiguous()
+    send_to_left = in_tensor[..., :d2].contiguous()
+    recv_from_right = torch.zeros_like(send_to_left)
+    recv_from_left = torch.zeros_like(send_to_right)
+
+    send_op_right = dist.P2POp(dist.isend, send_to_right, right)
+    send_op_left = dist.P2POp(dist.isend, send_to_left, left)
+    recv_op_right = dist.P2POp(dist.irecv, recv_from_right, right)
+    recv_op_left = dist.P2POp(dist.irecv, recv_from_left, left)
+
+    reqs = dist.batch_isend_irecv(
+        [send_op_right, send_op_left, recv_op_left, recv_op_right]
+    )
+    for req in reqs:
+        req.wait()
+
+    if rank == 0:
+        in_tensor = torch.cat([in_tensor, recv_from_right], dim=-1)
+    elif rank == size - 1:
+        in_tensor = torch.cat([recv_from_left, in_tensor], dim=-1)
+    else:
+        in_tensor = torch.cat([recv_from_left, in_tensor, recv_from_right], dim=-1)
+
+    return in_tensor
+
+
+def _ring_send_recv_aggregate(grad_in_tensor, d1, d2, left, right, rank, size):
+    # dist comms and aggregate gradients for edge pixels
+    send_to_right = grad_in_tensor[:, :, :, -d2:].contiguous()
+    send_to_left = grad_in_tensor[:, :, :, :d1].contiguous()
+    recv_from_right = torch.zeros_like(send_to_left)
+    recv_from_left = torch.zeros_like(send_to_right)
+
+    send_op_right = dist.P2POp(dist.isend, send_to_right, right)
+    send_op_left = dist.P2POp(dist.isend, send_to_left, left)
+    recv_op_right = dist.P2POp(dist.irecv, recv_from_right, right)
+    recv_op_left = dist.P2POp(dist.irecv, recv_from_left, left)
+
+    reqs = dist.batch_isend_irecv(
+        [send_op_right, send_op_left, recv_op_left, recv_op_right]
+    )
+    for req in reqs:
+        req.wait()
+
+    if rank == 0:
+        grad_in_tensor = grad_in_tensor[:, :, :, :-d2]
+        grad_in_tensor[:, :, :, -d1:] = torch.add(
+            grad_in_tensor[:, :, :, -d1:], recv_from_right
+        )
+    elif rank == size - 1:
+        grad_in_tensor = grad_in_tensor[:, :, :, d1:]
+        grad_in_tensor[:, :, :, :d2] = torch.add(
+            grad_in_tensor[:, :, :, :d2], recv_from_left
+        )
+    else:
+        grad_in_tensor = grad_in_tensor[:, :, :, d1:-d2]
+        grad_in_tensor[:, :, :, -d1:] = torch.add(
+            grad_in_tensor[:, :, :, -d1:], recv_from_right
+        )
+        grad_in_tensor[:, :, :, :d2] = torch.add(
+            grad_in_tensor[:, :, :, :d2], recv_from_left
+        )
+
+
+def tp_convolution(
+    op_call: torch._ops.OpOverload,
+    local_tensor_args: tuple[object, ...],
+    local_tensor_kwargs: dict[str, object],
+    dim_map: list[int],
+) -> object:
+    assert op_call == aten.convolution.default
+    assert len(local_tensor_args) == 9
+
+    rank = dist.get_rank()
+    size = dist.get_world_size()
+    in_tensor = cast(torch.Tensor, local_tensor_args[0])
+    weight = cast(torch.Tensor, local_tensor_args[1])
+    stride, padding, dilation = local_tensor_args[3:6]
+
+    assert _is_supported(in_tensor.shape, weight.shape, stride, padding, dilation)
+    assert isinstance(padding, list)
+
+    if not _requires_data_exchange(padding, dim_map):
+        local_results = op_call(*local_tensor_args, **local_tensor_kwargs)
+        return local_results
+    else:
+        # step 0 compute the overlap pixels of the input tensor
+        d = weight.shape[-1] - 1
+        d1 = d // 2
+        d2 = d - d1
+        assert d1 + d2 == d
+        right = (rank + 1) % size
+        left = (rank - 1 + size) % size
+
+        # step1 reconstruct local input tensor
+        in_tensor = _ring_send_recv_construct(
+            in_tensor, d1, d2, left, right, rank, size
+        )
+
+        # step2 feed local input tensor to op_call
+        local_tensor_args_list = list(local_tensor_args)
+        local_tensor_args_list[0] = in_tensor
+        local_tensor_args = cast(tuple[object, ...], local_tensor_args_list)
+        local_results = op_call(*local_tensor_args, **local_tensor_kwargs)
+
+        # step3 remove extra outputs from the results
+        padding_w = padding[-1]
+        w = local_results.size(-1)
+        if rank == 0:
+            local_results = local_results[..., : w - padding_w]
+        elif rank == size - 1:
+            local_results = local_results[..., padding_w:]
+        else:
+            local_results = local_results[..., padding_w : w - padding_w]
+
+        return local_results
+
+
+def tp_convolution_backward(
+    op_call: torch._ops.OpOverload,
+    local_tensor_args: tuple[object, ...],
+    local_tensor_kwargs: dict[str, object],
+    dim_map: list[int],
+) -> object:
+    assert op_call == aten.convolution_backward.default
+    assert len(local_tensor_args) == 11
+
+    rank = dist.get_rank()
+    size = dist.get_world_size()
+    grad_out_tensor = cast(torch.Tensor, local_tensor_args[0])
+    in_tensor = cast(torch.Tensor, local_tensor_args[1])
+    weight = cast(torch.Tensor, local_tensor_args[2])
+    stride, padding, dilation = local_tensor_args[4:7]
+
+    assert _is_supported(in_tensor.shape, weight.shape, stride, padding, dilation)
+    assert isinstance(padding, list)
+
+    if not _requires_data_exchange(padding, dim_map):
+        local_results = op_call(*local_tensor_args, **local_tensor_kwargs)
+        return local_results
+    else:
+        # step 0 compute the overlap pixels of the input tensor
+        d = weight.shape[3] - 1
+        d1 = d // 2
+        d2 = d - d1
+        assert d1 + d2 == d
+        right = (rank + 1) % size
+        left = (rank - 1 + size) % size
+
+        # step1 reconstruct local input tensor
+        in_tensor = _ring_send_recv_construct(
+            in_tensor, d1, d2, left, right, rank, size
+        )
+
+        # step2 reconstruct local gradient output tensor
+        padding_w = padding[1]
+        if rank == 0:
+            grad_out_tensor = torch.nn.functional.pad(
+                grad_out_tensor, (0, padding_w), "constant", 0
+            )
+        elif rank == size - 1:
+            grad_out_tensor = torch.nn.functional.pad(
+                grad_out_tensor, (padding_w, 0), "constant", 0
+            )
+        else:
+            grad_out_tensor = torch.nn.functional.pad(
+                grad_out_tensor, (padding_w, padding_w), "constant", 0
+            )
+
+        # step3 feed local input tensor to op_call
+        local_tensor_args_list = list(local_tensor_args)
+        local_tensor_args_list[0] = grad_out_tensor
+        local_tensor_args_list[1] = in_tensor
+        local_tensor_args = cast(tuple[object, ...], local_tensor_args_list)
+        local_results = op_call(*local_tensor_args, **local_tensor_kwargs)
+
+        # step4 aggregate gradients for edge pixels
+        grad_in_tensor = local_results[0]
+        if grad_in_tensor is not None:
+            grad_in_tensor = _ring_send_recv_aggregate(
+                grad_in_tensor, d1, d2, left, right, rank, size
+            )
+            local_results = list(local_results)
+            local_results[0] = grad_in_tensor
+
+        local_results = cast(tuple[object, ...], local_results)
+
+        return local_results
+
+
+def convolution_handler(
+    op_call: torch._ops.OpOverload,
+    args: tuple[object, ...],
+    kwargs: dict[str, object],
+) -> object:
+    # extract local tensor and sharding infos to a OpInfo
+    op_info = dtensor.DTensor._op_dispatcher.unwrap_to_op_info(op_call, args, kwargs)
+
+    # sharding propagation
+    dtensor.DTensor._op_dispatcher.sharding_propagator.propagate(op_info)
+    output_sharding = op_info.output_sharding
+    assert output_sharding is not None, "output sharding should not be None"
+    output_spec = output_sharding.output_spec
+    assert isinstance(output_spec, dtensor.DTensorSpec)
+
+    # local propagation
+    local_results = tp_convolution(
+        op_call,
+        tuple(op_info.local_args),
+        op_info.local_kwargs,
+        output_spec.dim_map,
+    )
+
+    return dtensor.DTensor._op_dispatcher.wrap(local_results, output_spec)
+
+
+def convolution_backward_handler(
+    op_call: torch._ops.OpOverload,
+    args: tuple[object, ...],
+    kwargs: dict[str, object],
+) -> object:
+    # Redistribute grad_output tensor to the same placement as input tensor
+    # pyrefly: ignore [bad-assignment]
+    args = list(args)
+    assert isinstance(args[0], dtensor.DTensor) and isinstance(args[1], dtensor.DTensor)
+    # pyrefly: ignore [unsupported-operation]
+    args[0] = args[0].redistribute(args[1].device_mesh, args[1].placements)
+    args = tuple(args)
+
+    # extract local tensor and sharding infos to a OpInfo
+    op_info = dtensor.DTensor._op_dispatcher.unwrap_to_op_info(op_call, args, kwargs)
+
+    # sharding propagation
+    dtensor.DTensor._op_dispatcher.sharding_propagator.propagate(op_info)
+    output_sharding = op_info.output_sharding
+    assert output_sharding is not None, "output sharding should not be None"
+    assert isinstance(op_info.flat_args_schema[0], dtensor.DTensorSpec)
+
+    # local propagation
+    local_results = tp_convolution_backward(
+        op_call,
+        tuple(op_info.local_args),
+        op_info.local_kwargs,
+        op_info.flat_args_schema[0].dim_map,
+    )
+
+    return dtensor.DTensor._op_dispatcher.wrap(
+        local_results, output_sharding.output_spec
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/utils.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..9422d05bf7e7d5c10b1e9d7bdaf56c21af17019b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/torch/distributed/utils.py
@@ -0,0 +1,381 @@
+# mypy: allow-untyped-defs
+import dataclasses
+import traceback
+from collections import OrderedDict
+from collections.abc import Callable, Container
+from typing import Any, Optional, overload, TypeVar
+
+import torch
+import torch.distributed as dist
+from torch import nn
+from torch.nn.utils.rnn import PackedSequence
+
+
+__all__ = []  # type: ignore[var-annotated]
+
+
+def _pack_kwargs(*args: Any, **kwargs: Any) -> tuple[tuple[Any, ...], tuple[str, ...]]:
+    """
+    Turn argument list into separate key list and value list (unpack_kwargs does the opposite).
+
+    Inspiration: https://github.com/facebookresearch/fairscale/blob/eeb6684/fairscale/internal/containers.py#L70
+    Usage::
+
+        kwarg_keys, flat_args = pack_kwargs(1, 2, a=3, b=4)
+        assert kwarg_keys == ("a", "b")
+        assert flat_args == (1, 2, 3, 4)
+        args, kwargs = unpack_kwargs(kwarg_keys, flat_args)
+        assert args == (1, 2)
+        assert kwargs == {"a": 3, "b": 4}
+    Returns:
+        Tuple[Tuple[Any, ...], Tuple[str, ...]]: The first tuple element gives
+        gives both positional args and kwarg values, where the positional args
+        proceed kwarg values and kwarg values are ordered consistently with the
+        kwarg keys. The second tuple element gives the kwarg keys.
+        The second tuple element's length is at most the first tuple element's length.
+    """
+    kwarg_keys: list[str] = []
+    flat_args: list[Any] = list(args)
+    for k, v in kwargs.items():
+        kwarg_keys.append(k)
+        flat_args.append(v)
+
+    return tuple(flat_args), tuple(kwarg_keys)
+
+
+def _cast_forward_inputs(
+    dtype: torch.dtype | None,
+    *args: Any,
+    **kwargs: Any,
+) -> tuple[Any, Any]:
+    """
+    Cast floating point tensors in ``args`` and ``kwargs`` to ``input_dtype``.
+
+    This respects the existing ``requires_grad`` on the tensors.
+    """
+    if dtype is None:
+        return args, kwargs
+
+    def cast_fn(x: torch.Tensor) -> torch.Tensor:
+        if not torch.is_floating_point(x) or x.dtype == dtype:
+            return x
+
+        return x.to(dtype)
+
+    return (_apply_to_tensors(cast_fn, args), _apply_to_tensors(cast_fn, kwargs))
+
+
+def _unpack_kwargs(
+    flat_args: tuple[Any, ...], kwarg_keys: tuple[str, ...]
+) -> tuple[tuple[Any, ...], dict[str, Any]]:
+    """See _pack_kwargs."""
+    if len(kwarg_keys) > len(flat_args):
+        raise AssertionError(f"too many keys {len(kwarg_keys)} vs. {len(flat_args)}")
+    if len(kwarg_keys) == 0:
+        return flat_args, {}
+    args = flat_args[: -len(kwarg_keys)]
+    kwargs = dict(zip(kwarg_keys, flat_args[-len(kwarg_keys) :]))
+    return args, kwargs
+
+
+S = TypeVar("S", dict, list, tuple)
+T = TypeVar("T", torch.Tensor, PackedSequence)
+
+
+@overload
+def _recursive_to(
+    inputs: S, target_device: torch.device, use_side_stream_for_tensor_copies: bool
+) -> list[S]: ...
+
+
+@overload
+def _recursive_to(
+    inputs: T, target_device: torch.device, use_side_stream_for_tensor_copies: bool
+) -> tuple[T]: ...
+
+
+def _recursive_to(inputs, target_device, use_side_stream_for_tensor_copies):
+    r"""Recursively moves input to the target_device."""
+
+    def to_map(obj):
+        if isinstance(obj, (torch.Tensor, PackedSequence)):
+            device = obj.data.device if isinstance(obj, PackedSequence) else obj.device
+            if device == target_device:
+                return (obj,)
+            if not use_side_stream_for_tensor_copies:
+                return (obj.to(target_device),)
+            else:
+                # If the custom module is not registered to torch, stream is not used for acceleration
+                if device.type == "cpu":
+                    return (obj.to(target_device),)
+
+                from torch.nn.parallel._functions import _get_stream
+
+                # Perform CPU -> target_device copies in a background stream. This code is
+                # motivated from similar logic in torch/nn/parallel/_functions.py
+                stream = _get_stream(target_device)
+                with stream:
+                    output = obj.to(target_device)
+                # synchronize with the copy stream
+                with torch.accelerator.device_index(target_device.index):
+                    current_stream = torch.accelerator.current_stream()
+                    # Sync the current stream with the copy stream
+                    current_stream.wait_stream(stream)
+                    # Ensure tensor memory is not reused until work on
+                    # main stream is complete
+                    if isinstance(obj, PackedSequence):
+                        output.data.record_stream(current_stream)  # type: ignore[arg-type]
+                    else:
+                        if not isinstance(output, torch.Tensor):
+                            raise AssertionError("output must be a torch.Tensor")
+                        output.record_stream(current_stream)  # type: ignore[arg-type]
+                return (output,)
+
+        from torch.nn.parallel.scatter_gather import _is_namedtuple
+
+        if _is_namedtuple(obj):
+            # pyrefly: ignore [no-matching-overload]
+            return [type(obj)(*args) for args in zip(*map(to_map, obj))]
+        if isinstance(obj, tuple) and len(obj) > 0:
+            # pyrefly: ignore [no-matching-overload]
+            return list(zip(*map(to_map, obj)))
+        if isinstance(obj, list) and len(obj) > 0:
+            # pyrefly: ignore [no-matching-overload]
+            return [list(i) for i in zip(*map(to_map, obj))]
+        if isinstance(obj, dict) and len(obj) > 0:
+            # pyrefly: ignore [no-matching-overload]
+            return [type(obj)(i) for i in zip(*map(to_map, obj.items()))]
+        return [obj]
+
+    # Avoid reference cycle
+    try:
+        res = to_map(inputs)
+    finally:
+        to_map = None  # type: ignore[assignment]
+    return res
+
+
+def _p_assert(cond: Any, s: str, raise_assertion_error: bool = True) -> None:
+    """Alternate to ``assert`` when in the backward context to print the error message ``s`` since otherwise, it is swallowed."""
+    if not cond:
+        print(s)
+        traceback.print_stack()
+        if raise_assertion_error:
+            raise AssertionError(s)
+
+
+def _alloc_storage(tensor: torch.Tensor, size: torch.Size) -> None:
+    """
+    Allocate storage for ``tensor`` with the given size.
+
+    Returns:
+        bool: ``True`` if this method allocated storage and ``False`` if the
+        storage was already allocated.
+    """
+    with torch.no_grad():
+        if not torch.distributed._functional_collectives.is_torchdynamo_compiling():
+            already_allocated = tensor._typed_storage()._size() == size.numel()
+            if not already_allocated:
+                tensor_storage_size = tensor._typed_storage()._size()
+                _p_assert(
+                    tensor_storage_size == 0,
+                    "Tensor storage should have been resized to be 0 but got PLACEHOLDEr",
+                )
+                tensor._typed_storage()._resize_(size.numel())
+
+
+def _free_storage(tensor: torch.Tensor):
+    """
+    Frees the underlying storage of ``tensor``.
+
+    Returns:
+        bool: ``True`` if the method freed the storage and ``False`` if the
+        storage was already freed.
+    """
+    with torch.no_grad():
+        if not torch.distributed._functional_collectives.is_torchdynamo_compiling():
+            already_freed = tensor._typed_storage()._size() == 0
+            if not already_freed:
+                _p_assert(
+                    tensor.storage_offset() == 0,
+                    "Freeing a tensor's storage is unsafe when it is not the sole occupant\n"
+                    f"storage offset: {tensor.storage_offset()}\n"
+                    f"storage size: {tensor._typed_storage()._size()}\n"
+                    f"tensor shape: {tensor.shape}",
+                )
+                tensor._typed_storage()._resize_(0)
+
+
+Q = TypeVar("Q")
+R = TypeVar("R", dict, list, tuple, set, OrderedDict, PackedSequence, Any)
+
+
+@overload
+def _apply_to_tensors(
+    fn: Callable[[torch.Tensor], Q], container: torch.Tensor
+) -> Q: ...
+
+
+@overload
+def _apply_to_tensors(fn: Callable[[torch.Tensor], Any], container: R) -> R: ...
+
+
+def _apply_to_tensors(fn, container):
+    """Recursively apply to all tensor in different kinds of container types."""
+
+    def apply(x):
+        from torch.nn.parallel.scatter_gather import _is_namedtuple
+
+        if isinstance(x, torch.Tensor):
+            return fn(x)
+        elif hasattr(x, "__dataclass_fields__"):
+            dc = dataclasses.replace(x)
+            changes = {
+                f.name: apply(getattr(dc, f.name)) for f in dataclasses.fields(dc)
+            }
+            return dataclasses.replace(dc, **changes)
+        elif isinstance(x, OrderedDict):
+            od = x.__class__()
+            for key, value in x.items():
+                od[key] = apply(value)
+            return od
+        elif isinstance(x, PackedSequence):
+            apply(x.data)
+            return x
+        elif isinstance(x, dict):
+            return {key: apply(value) for key, value in x.items()}
+        elif _is_namedtuple(x):
+            res = (apply(el) for el in x)
+            return type(x)(*res)
+        elif isinstance(x, (list, tuple, set)):
+            return type(x)(apply(el) for el in x)
+        else:
+            return x
+
+    return apply(container)
+
+
+def _to_kwargs(
+    inputs: tuple[Any, ...],
+    kwargs: dict[str, Any] | None,
+    target_device: torch.device,
+    use_side_stream_for_tensor_copies: bool,
+) -> tuple[tuple[Any, ...], tuple[dict[str, Any], ...]]:
+    moved_inputs = (
+        _recursive_to(inputs, target_device, use_side_stream_for_tensor_copies)
+        if inputs
+        else []
+    )
+    moved_kwargs = (
+        _recursive_to(kwargs, target_device, use_side_stream_for_tensor_copies)
+        if kwargs
+        else []
+    )
+    if len(moved_inputs) < len(moved_kwargs):
+        moved_inputs.extend([() for _ in range(len(moved_kwargs) - len(inputs))])
+    elif len(moved_kwargs) < len(moved_inputs):
+        moved_kwargs.extend([{} for _ in range(len(moved_inputs) - len(moved_kwargs))])
+    return tuple(moved_inputs), tuple(moved_kwargs)
+
+
+def _verify_param_shape_across_processes(
+    process_group: dist.ProcessGroup,
+    tensors: list[torch.Tensor],
+    logger: Optional["dist.Logger"] = None,
+):
+    return dist._verify_params_across_processes(process_group, tensors, logger)
+
+
+def _sync_module_states(
+    module: nn.Module,
+    process_group: dist.ProcessGroup,
+    broadcast_bucket_size: int,
+    src: int,
+    params_and_buffers_to_ignore: Container[str],
+    broadcast_buffers: bool = True,
+) -> None:
+    """
+    Sync ``module``'s parameters and buffers state.
+
+    Syncs ``module``'s parameters and buffers state so that all ranks contain
+    the same module state across all ranks. Note that this API assumes that all
+    parameter shapes are consistent before running the synchronization. This can
+    be checked with ``_verify_param_shape_across_processes``.
+    """
+    module_states: list[torch.Tensor] = []
+    for name, param in module.named_parameters():
+        if name not in params_and_buffers_to_ignore:
+            module_states.append(param.detach())
+
+    if broadcast_buffers:
+        for name, buffer in module.named_buffers():
+            if name not in params_and_buffers_to_ignore:
+                module_states.append(buffer.detach())
+
+    _sync_params_and_buffers(process_group, module_states, broadcast_bucket_size, src)
+
+
+def _sync_params_and_buffers(
+    process_group: dist.ProcessGroup,
+    module_states: list[torch.Tensor],
+    broadcast_bucket_size: int,
+    src: int,
+) -> None:
+    """Synchronize ``module_states`` (list of tensors) across all processes by broadcasting them from rank 0."""
+    if len(module_states) > 0:
+        dist._broadcast_coalesced(
+            process_group, module_states, broadcast_bucket_size, src
+        )
+
+
+def _replace_by_prefix(
+    state_dict: dict[str, Any],
+    old_prefix: str,
+    new_prefix: str,
+) -> None:
+    """
+    Replace all keys that match a given old_prefix with a new_prefix (in-place).
+
+    Usage::
+
+        state_dict = {"layer.xyz": torch.tensor(1)}
+        replace_by_prefix_(state_dict, "layer.", "module.layer.")
+        assert state_dict == {"module.layer.xyz": torch.tensor(1)}
+    """
+    if old_prefix == new_prefix:
+        raise ValueError("old_prefix and new_prefix must be distinct")
+    for key in list(state_dict.keys()):
+        if not key.startswith(old_prefix):
+            continue
+        new_key = new_prefix + key[len(old_prefix) :]
+        state_dict[new_key] = state_dict[key]
+        del state_dict[key]
+
+
+def _data_ptr_allocated(tensor: torch.Tensor) -> bool:
+    return tensor.untyped_storage().data_ptr() > 0
+
+
+def _get_root_modules(modules: list[nn.Module]) -> list[nn.Module]:
+    """
+    Returns the modules in ``modules`` that are root modules (i.e.
+    parent-less) with respect to the set ``modules``. In other words, these
+    are the modules in ``modules`` that are the not child of any other
+    module in ``modules``.
+    """
+    root_modules: list[nn.Module] = []
+    module_to_modules: dict[nn.Module, set[nn.Module]] = {
+        module: set(module.modules()) for module in modules
+    }
+    for candidate_module in modules:
+        is_root_module = True
+        for module, _modules in module_to_modules.items():
+            is_child_module = (
+                candidate_module is not module and candidate_module in _modules
+            )
+            if is_child_module:
+                is_root_module = False
+                break
+        if is_root_module:
+            root_modules.append(candidate_module)
+    return root_modules